for the paradigms (fig. 4). this is the basic skeleton for the morphological dictionaries, then the words will be entered through the entries,

, here e for entry, p for pair, l for left, and r for right. compiling entries left to right lead to produce analyses from words and from right to left leads to produces words from analyses. the final step is compiling and run the dictionary. both english (apertium-eng.eng.dix) and kurdish (apertiumkur.kur.dix) morphological dictionaries would be created in the same manner. 4.3. bilingual dictionary this describes mappings between words, the basic skeleton is the same as monolingual dictionary, but we need to add an entry to translate between the two words:

~~university ~~ۆکناز~~~~

. we compile the bilingual dictionary left to right to produce the kurdish→ english dictionary and right to left to produce the english → kurdish dictionary. 4.4. transfer rules it contains rules to govern how english will be changed into kurdish language, and the basic skeleton of the transfer rules is shown here (fig. 5). tag defines a rule. tag means: “apply this rule, if this pattern is found” (here the pattern consists of a single noun defined by the category item nom). patterns are matched in a longest-match first. the pattern matched and rule executed would be the first one. for each pattern, there is an associated action, which produces an associated output, out. the output is a lexical unit (lu).the tag allows a user to select and manipulate attributes and parts of the source language (side=”sl”) or target language (side=”tl”) lexical item. transfer rules file need to be compiled and tested. 5. results and discussions after completing the prototype of the proposed system, it would be tested against different sets of data; first, we test it against individual words, and then simple sentence, complex sentences, phrases, proverbs, and idioms, some examples are shown in fig. 6. fig. 6 shows a random sample of data translated by our proposed system; we tried to maintain a rich corpus that involves vast numbers of individual words, phrases, idioms, proverbs, etc., in order not to have un translated words in the output. the second part of this work will be evaluation between the proposed system’s results with “inkurdish” mt system results for the same set of data using manual evaluation method. table 1 shows a sample of data translated by both systems. inkurdish non-sense output with paragraphs and long texts obliged us to be satisfied at basic level (simple and compound sentence, idioms, proverbs, and phrases) evaluation; the sample contains a couple of random examples of each data set. the evaluation made by a neutral annotator (kurdish native which is english specialist) according to the five categories has been defined before. detailed explanation of both computational and linguistics issues is out of our main aim, and we focused on accuracy differences between both systems, plus touching some general translation issues found during experimenting the data sets. inkurdish mt system suffers severely from some issues, it is unable to link verbs to objects in sentences, and in spite of having all different meaning for a specific verb fig. 5. skeleton for transfer rules. fig. 6. samples of proposed system translation. kanaan m. kaka-khan: english to kurdish rule-based machine translation system uhd journal of science and technology | may 2018 | vol 2 | issue 2 37 in the corpus, it failures to bring the correct meaning of the verb according to its position in the sentence; it translated the verb “play” in “he went to play football before 1 h” example (table 1) as ‘تێنيبەد ڵۆڕ’ instead of ’تاكەد ىراي‘ and this led to improper translation; the corpus of inkurdish suffers from lack of pre defined common english idioms and proverbs; it always gives literal translation for idioms and proverbs for example, it translated “better late than never” proverb to ‘زیگرەه ەل گنەرد رتشاب’ (table 1) which is very literal and non-sense translation. untranslated word is another issue for inkurdish system for example the word “backyard” has not been translated in “the kids are playing in the backyard” example (table 1). table 2 shows the accuracy average for all different data sets of both systems, and the accuracy averages have been calculated through a simple formula: average = summation of all individual scores/total number of samples. the results showed that our system is more accurate than inkurdish system for all data sets; both systems got high scores with “simple sentence” translation (3.12 and 3.56 of 5 for inkurdish and our system, respectively); inkurdish got the least score for idioms while our system for phrases (1.15 and 2.13 of 5, respectively), this means that inkurdish needs to maintain large number of common english proverbs and idioms with their kurdish equivalents while our system need to involve more english phrases. in our previous work “evaluation of inkurdish mt system,” we addressed the issues of this mt system in details; hence, we tried to bridge the gaps of inkurdish system in our proposed system and this is the reason of clear differences between inkurdish accuracy average and proposed system accuracy average; the most common inkurish issue is lack of rich corpus specifically to deal with phrases, idioms, and proverbs (1.46, 1.15, and 1.25, respectively) (table 2); during table 1 sample of data sets with their translations dataset source text inkurdish translation proposed system translation simple sentence i go to university ۆکناز ۆب مۆڕەد نم ۆکناز ۆب مۆڕەئ نم evaluation 5 4 the kids are playing in the backyard .هكەدايكاب ەل نەكەد ىراي ناكەڵادنم یەچخاب ەل نەکەد یرای ناکەڵادنم .ەوەتشپ evaluation 3 4 complex sentence i have been playing football since i am 6 years old ٦ منم ىەتەوەل ىنيبەد ڵۆڕ مێپ ىپۆت نم .ناڵاس یەتەوەل مەکەئ یپۆت یرای نم ناڵاس ٦ منەمەت evaluation 1 3 while he was watching the movie, the power switched off ،دركەد اشامەت ىەكەميلف وەئ ىەتاك وەل .ەوەيدناژوك اناوت یاشامەت (ەکەڕوک)وەئ یەتاک وەل ابەراک یوزەت ،درکەد یەکەملیف ەوەیاژوک evaluation 3 4 proverbs better late than never زيگرەه ەل گنەرد رتشاب نتشەگەن ەل نتشەگ گنەرد ەرتشاب evaluation 2 5 actions speak louder than words .ەشو ەل نەكەد ەسق رتزرەب ىگنەد راك ەسق کەن ەترەش رادرک evaluation 2 4 idioms the english test was a piece of cake ىكێيەچراپ ىزيلگنيئ ىەوەندركيقات .ووب كێك رۆز یزیلگنیئ یەوەندرک یقات ووب ناسائ evaluation 2 4 you can kill two birds with one stone ڵەگەل تيژوكب ەدنڵاب وود تيناوتەد ۆت .درەب كەي وود درەب کەی ەب تیناوتەئ ۆت .تیژوکب ەدنڵاب evaluation 2 3 phrases thanks i am pretty good مشاب کەیەداڕ ات نم ساپوس مشاب رۆز نم ساپوس evaluation 3 5 we could have dinner at macdonald. how does that sound? .دلەنۆدكەم ەل ناميناوت وێش نيۆخب ەمێئ ؟تاكەد ەگنەد وەئ نۆچ دڵانۆدکام ەل وێش نیناوتەئ ەمێئ ؟ەگنەد وەئ ەنۆچ ،نیۆخب evaluation 2 3 table 2 translation accuracy average for both systems dataset inkurdish accuracy average proposed system accuracy average simple sentence 3.12 3.56 complex sentence 2.45 2.78 proverbs 1.25 2.22 idioms 1.15 2.42 phrases 1.46 2.13 kanaan m. kaka-khan: english to kurdish rule-based machine translation system 38 uhd journal of science and technology | may 2018 | vol 2 | issue 2 experimenting the data with inkurdish, it did not translate even one idiom or proverb, and it gives a literal translation instead. 6. conclusion mt remains to be one of the most challenging aspects of nlp. despite the ongoing efforts to achieve full machinebased translation, little progress has been achieved; due to language structure and composition complexity. open-source platforms have provided the environment and tools required to develop reliable mt systems, especially for language with poor resources such as kurdish. we have presented a mt system to translate english to kurdish developed using an open-source platform. the resulting translation is compared with the result generated by inkurdish popular english to kurdish mt system. the result shows clear differences between inkurdish mt system and our mt system in terms of translation accuracy. the result also shows that rbmt and manual mt evaluation are suitable choices, for poorly resourced languages. biography kanaan m.kaka-khan is an associate professor in the computer science department at human development university, sulaimaniya, iraq. born in iraq 1982. kanaan m.khan had his bachelor degree in computer science from sulaimaniya university and master degree in it from bam university, india. his research interest area include: natural language processing, mt, chatbot, and information security. references [1] f. h. khorshid. kurdish language and the geographical distribution of its dialects. ishbeelia press, baghdad, 1983. [2] h. hassani. kurdish interdialect machine translation. in proc of the fourth workshop on nlp for similar languages, varieties and dialects (vardial), 2017, pp. 63-72. [3] t. siddiqui and u. s tiwary. natural language processing and information retrieval. oxford university press. second impression 2010, oxford, 2010. [4] c. liu, d. dahlmeier and h. t. ng. better evaluation metrics lead to better machine translation, in: proc emnlp, 2011. [5] apertuim. about apertuim, 2018. available: www.apertium.org/ index.eng.html?dir=eng-spa#translation. [aug. 7, 2018]. [6] w. weaver. translation. machine translation of languages: fourteen essays. mit press, cambridge, ma, 1955. [7] j. b. marin˜o, r. e. banchs, j. m. crego, a. gispert, p. lambert, j. a. r. fonollosa and m. r. costa-jussa`. n-gram based machine translation. computational linguistics, vol. 32, no. 4, pp. 527-549, 2006. [8] p. f. brown, v. j. d. pietra, s. a. d. pietra and r. l. mercer. the mathematics of statistical machine translation: parameter estimation. computational linguistics, vol. 19, no. 2, pp. 263-311, 1993. [9] f. j. och. minimum error rate training for statistical machine translation. in proc. of acl, 2003. [10] p. koehn, f. j. och and d. marcu. statistical phrase-based translation. in proc. of the 2003 conference of the north american chapter of the association for computational linguistics on human language technology, association for computational linguistics. vol. 1, pp. 48-54, 2003. [11] p. koehn and c. monz. shared task: statistical machine translation between european languages. in proc. of the acl workshop on building and using parallel texts, 2005. [12] d. chiang. a hierarchical phrase-based model for statistical machine translation. in proc. of the 43rd annual meeting of the association for computational linguistics (acl), 2005, pp. 263-270. [13] a. menezes, k. toutanova and c. quirk. microsoft research treelet translation system: naacl 2006 europarl evaluation. in proc. of wmt, 2006. [14] p. koehn, h. hoang, a. birch, c. callison-burch, m. federico, n. bertoldi, b. cowan, w. shen, c. moran, r. zens, c. j. dyer, o. bojar, a. constantin and e. herbst. moses: open source toolkit for statistical machine translation. in proc. of the 45th annual meeting of the acl on interactive poster and demonstration sessions, association for computational linguistics, 2007b, pp. 177-180. [15] y. s. hwang, a. finch and y. sasaki. improving statistical machine translation using shallow linguistic knowledge. computer speech and language, vol. 21, no. 2, pp. 350-372. [16] f. sa´nchezmart´inez and m. l. forcada. inferring shallow-transfer machine translation rules from small parallel corpora. journal of artificial intelligence research, vol. 34, pp. 605-635, 2009. [17] m. khalilov and j. a. r. fonollosa. syntax-based reordering for statistical machine translation. computer speech and language, vol. 25, no. 4, 761-788, 2011. [18] s. nirenburg. knowledge based machine translation. machine translation, vol. 4, no. 1, pp. 5-24, 1989. [19] m. carl and a. way. recent advances in example-based machine translation. kluwer acadmic publishers, dordrecht/boston/ london, 2003. [20] p. koehn and k. knight. statistical machine translation, november 24. us patent no. 7,624,005, 2009. [21] d. bahdanau, k. cho and y. bengio. neural machine translation by jointly learning to align and translate. corr, vol. abs/1409.0473, p. 9, 2014. [22] k. h. cho, b. van merrienboer, d. bahdanau and y. bengio. on the properties of neural machine translation: encoder-decoder approaches. corr, vol. abs/1409.1259, p.15, 2014. [23] k. wolk and k. marasek. neural-based machine translation for medical text domain. based on european medicines agency leaflet texts. procedia computer science, vol. 64, p. 2-9. 2015. [24] s. ghafour. “speeculate”. 2011. available: www.kurditgroup.org/ sites/default/files/speekulate_0.pdf. [aug. 4, 2018]. [25] inkurdish translator. 2013. available: www.inkurdish.com/. [aug. 4, 2018]. [26] ekurd daily-editorial staff. “google translate adds support for kurdish language”, 2016. available: www.ekurd.net/googletranslate-kurdish-language-2016-02-18. [aug. 4, 2018]. [27] translators without borders. “translators without borders kanaan m. kaka-khan: english to kurdish rule-based machine translation system uhd journal of science and technology | may 2018 | vol 2 | issue 2 39 developed the world’s first crisis-specific machine translation system for kurdish languages”, 2016. available: www. translatorswithoutborders.org/translators-without-bordersdevelops-worlds-first-crisis-specific-machine-translation-systemkurdish-languages/. [aug. 4, 2018]. [28] k. m. kaka-khan and f. jalal. evaluation of in kurdish machine translation system. presented in the 4th international scientific conference of university of human development, apr. 2017. journal of university of human development, vol. 3, no. 2, pp. 862-868, jun. 2017. [29] w. linda. rule-based mt approaches such as apertium and gramtrans. universitetet i tromsø, norway 21.10.2008, 2008. tx_1:abs~at/tx_2:abs~at uhd journal of science and technology | may 2018 | vol 2 | issue 1 1 1. introduction it is commonly agreed that certain people look “young for their age” or “old for their age.” moreover, the two common processes that influence skin aging are the skin aging that genetically determined and happens by passing time which is named chronologically (or intrinsic) skin aging process, while premature (or extrinsic) skin aging process is triggered by environmental factors. recognized environmental factors that lead to premature skin aging process are sun exposure, air pollution, and smoking. the extrinsic skin aging rate is different notably among ethnic groups and individuals, whereas for the intrinsic rate of skin aging this occurrence does not relate [1]-[4]. over the past decades, there has been a major growth in the instances of skin disease all over the world. vital public health implications will occur when individual exposure to high cumulative levels of ultraviolet (uv) radiation [5], [6]. unprotected and excessive sun exposure can damage skin cells, influence the normal growth of the skin and as a result cause several skin diseases such as burning and tanning. in addition, severe skin problems can occur when humans are exploring knowledge and self-care practice toward skin aging and sun protection among college students in sulaimani city-iraq hezha o. rasul1, diary i. tofiq1, mohammad y. saeed2, rebaz f. hamarawf1 1department of chemistry, college of science, university of sulaimani, iraq, 2department of medicine, college of medicine, university of sulaimani, iraq a b s t r a c t several studies have been performed internationally to assess the understanding and self-care exercise of people in the direction of sun exposure and sun protection measures, as self-care is an essential pillar of public health. nevertheless, limited data on these factors are available from the middle east. the aim of this study was to investigate the students’ awareness of skin aging and sun-protection measures among college students. for this purpose, a cross-sectional questionnaire was specially designed; a random sample of the students in the different college of the university of sulaimani was selected. data were collected between january and may 2017. the relationship between the skin cancer awareness and different sociodemographic characteristics was produced by applying multiple logistic regressions. the questionnaires were distributed to 450 college students. a total of 413 questionnaires had been completely responded and covered within the data analysis, with a response rate of 91.7%. 41% of the respondents were females and 61.0% of the participants were aged between 18 and 21 years old. 47% have been privy to the association between sun exposure and skin aging. the respondents had been more likely to be aware of the connection between sun exposure and skin cancer (p < 0.03). the respondents from the third class of undergraduates were more likely to be familiar (p < 0.04). staying under the shade during the outdoor activity was reported by more than 90% of our participants and is positioned as the most frequently used sun protection method. index terms: skin aging, skin cancer, skin care, sunscreen corresponding author’s e-mail: hezha o. rasul, department of chemistry, college of science, university of sulaimani, iraq. e-mail: hezha. rasul@univsul.edu.iq received: 11-09-2017 accepted: 05-02-2018 published: 25-05-2018 access this article online doi: 10.21928/uhdjst.v2n1y2018.pp1-7 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2018 hezha o. rasul, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l r e s e a r c h a r t i c l e uhd journal of science and technology hezha o. rasul, et al.: skin aging and sun protection knowledge among college students 2 uhd journal of science and technology | may 2018 | vol 2 | issue 1 exposed to large quantities of solar uv radiation, including skin aging, pigmentary changes, and skin cancer [7]-[11]. uv radiation is the most important environmental factor which leads to premature skin aging [2]. human beings are exposed to massive portions of uv radiation in part through numerous sources which include living and traveling in sunny climates and outdoor activity, additionally due to thinning of the ozone layer within the stratosphere [5]. the harmful effect of uv radiation on the skin look regarding facial aging was previously discovered in the end 19th century by the two dermatologists unna and dubreuilh [12], [13]. harry daniell, in 1971, discovered the associations between cigarette smoking and skin aging [14]. moreover, moderate alcohol consumption has also been shown to correlate with skin appearance [15]. recent observation reported that air pollution is another significant environmental factor, which influences the skin appearance and leads to skin aging intrinsically [16]. skin cancer has increased gradually during the past 50 years. epidemiological studies demonstrate that skin cancer is developed by the sun, which is considered as the main considerable environmental factor which influences the skin. to reduce the skin cancer occurrence, the first step needs to be done increasing levels of awareness and self-care knowledge of the sun’s harmful effects and how to better protect from solar emission [5], [17], [18]. it is essentially important to focus on educational level; this is with the purpose of changing behavioral patterns and protecting people against the dangerous effects of the sun [17]. education plays a key role in raising awareness [19], [20]. several types of research have been studied in different countries to determine people knowledge levels about the sun effect on facial aging and awareness level concerning sun protection [21]. in local skin care hospital, we have observed that many patients do not protect themselves from the sun and report unhealthy attitudes toward this subject. exploring deficits in sun protection awareness and self-care practice toward different environmental factors can serve as a starting point for primary prevention interventions. identifying knowledge and self-care practices of the public regarding skin aging, exposure and protection of the sun have been studied in several countries. nevertheless, there is no study regarding this issue in sulaimani city-iraq. the purpose of the following study is to find out the levels of knowledge and self-care practice in regard to skin aging, sun exposure, and protection among college students. in addition, we will present the student’s knowledge about various environmental factors such as air pollution, smoking, and drinking alcohol on skin aging. 2. materials and methods a cross-sectional survey was carried out between january 2017 and may 2017, both males and females students were involved at different colleges of sulaimani university. from each college, several departments were selected randomly. a total of 413 questionnaires were collected. data collection was performed by several trained students. the data collection process in this study was carried out using the questionnaire, which was specially created throughout a search of appropriate literature [1], [5], [7]. the re-designed questionnaire was tested initially in sulaimani center for dermatology and venereal disease-teaching hospital to estimate approximately the length of the questionnaire in minutes, verify the participant’s interpretation of questions and develop the questionnaire consequently. these questionnaires were tested in independent data sets, but these candidate questionnaires were excluded from the concluding analysis. however, the final version of the survey was conducted in the university of sulaimani. the final version of the questionnaire included 24 questions and required approximately 5 minutes to complete. approval from the ethics committee of university of sulaimani, sulaimani, iraq, was obtained. the self-administered questionnaire was composed of three sections. the first section of the questionnaire comprised nine questions about personal information, such as university level, residence (urban vs. rural), gender, age, marital status, weight, high, smoking, and drinking alcohol. various questions were integrated into the second part of the questionnaire about the student’s knowledge concerning the factor of skin aging, sun’s benefits and harmful effects on the skin and use of sun protection methods. the data were analyzed using statistical package for the social sciences program (spss) version 21. after that the statistical assessment of data and the summarization of frequencies and percentages, multiple logistic regressions were used. statistical significance was defined as p < 0.05. 3.results the questionnaire was distributed to 453. a complete data from 413 participants were returned and integrated into the analysis with a 91.7% response rate. there was a various range of age of the students who participated in the survey, 61.0% (252/413) of the respondents were aged 18-21 years old, while, and 34.1% (141/413) of the students were aged between 22 and 25. only 4.8% (20/413) of the participants were aged over 25 years. in addition, 58.6% (242/413) of the students who contributed to the survey were male, hezha o. rasul, et al.: skin aging and sun protection knowledge among college students uhd journal of science and technology | may 2018 | vol 2 | issue 1 3 while 41.4% (171/413) of the participants were female. the sociodemographic characters of the research population are depicted in table i. the level of awareness among students regarding unprotected exposure to the sun caused skin damage is illustrated in table ii. this study has indicated that the majority of respondents were mindful that excessive sun exposure causes skin burn (87.9%, 363/409). 47% (194/402) reported that sun exposure can cause skin aging, while almost more than a half of respondents (52.5%, 217/404) were responsive of the relationship between skin cancer and sun exposure. nevertheless, the level of knowledge of students regarding the impact of sunlight on skin was explored; understanding of the relationship between synthesis of vitamin d and sun exposure was the most well-known benefit of the students, with 76.1% of the male and 70.7% of the female citing it. the male and female participants were reported (74.4% and 70.6%, respectively) regarding the association between sun exposure and treatment in some skin conditions. the relationship between positive psychological effects sun exposure was recorded 63% in male respondents, while only 37.0% of the female participants were aware of this relation, as shown in table iii. the logistic regression models were used to assess the relationship between the demographic factors influencing awareness of the connection between sun exposure, and skin cancer is shown in table iv. students aged 18-21 years reported higher rates of skin cancer knowledge (p < 0.025). similarly, respondents from class 3 were more likely to be linked with the understanding of the association between sun exposure and skin cancer (p < 0.037). however, there was no significant dissimilarity found in awareness between students rooted in their gender, marital status, or area of residence (rural vs. urban). the sun protection behaviors among respondents are summarized in table v. 56% of study students reported that they were protecting themselves during the daytime against the effects of the sun by wearing sun protection cream (232/401), wearing sunglasses (61.9%, 256/401), and light-colored cotton clothes (83.1%, 343/407). wearing “a hat” was found to be the least frequently used the technique of sun protection (40.2%, 166/391). the preferred method of protection during their outdoor activities was staying in the shade and inside with the data (90.6%, 374/406) (87.9%, 363/404), respectively. in addition, 87.9% (363/404) of participants reported that they were trying to stay inside to protect their skin from the sunlight. data on using anti-aging table i sociodemographic data of the 413 participants characteristics count (%)a gender male 242 (58.6) females 171 (41.4) age (years) 18-21 252 (61.0) 22-25 141 (34.1) over 25 20 (4.8) marital status single 377 (91.3) married 32 (7.7) residence urban 249 (60.3) rural 162 (39.2) education (undergraduate) class 1 137 (33.2) class 2 113 (27.4) class 3 65 (15.7) class 4 85 (20.6) athe denominator is different among variables due to missing values table iii students’ levels of knowledge about beneficial effects of the sunlight factor male, n (%) female, n (%) yes no yes no synthesis of vitamin d 181 (76.1) 57 (23.9) 118 (70.7) 49 (29.3) treatment in some skin conditions 177 (74.4) 61 (25.6) 115 (70.6) 48 (29.4) positive psychological effects 133 (63.0) 104 (56.5) 78 (37.0) 80 (43.5) table ii awareness of negative effects of the sunlight among respondents n (%)a yes no don’t know what damage does excessive sun-exposure cause? skin burn 363 (87.9) 18 (4.4) 28 (6.8) skin aging 194 (47.0) 74 (17.9) 134 (32.4) skin cancer 217 (52.5) 62 (15.0) 125 (30.3) athe denominator is different among variables due to missing values hezha o. rasul, et al.: skin aging and sun protection knowledge among college students 4 uhd journal of science and technology | may 2018 | vol 2 | issue 1 skin product among our respondents showed that just about 55% of the female students reported that they never used anti-aging cream. of respondents, only 45.1% (60/133) and 31.1% (60/183) of the female and male, respectively, had ever used sunscreen anti-aging product. in addition, the students were asked about the importance of looking after their skin. as illustrated in fig. 1, a surprising result had been recorded in this section, most of the students reported that it is important to look after their skin. in this study, the participants were asked about their concern for various issues relating to the premature skin aging such. according to the data, stress was the most concerned respond, and the majority of students were submitted their choice to less concerned about sun exposure as shown in table vi. perceptions of key factors of aging among students were recorded, and poor diet was considered as the main factor of aging among the students (33.1%) as shown in table vii. 4. discussion information about public knowledge and behaviors regarding skin aging and protection measures among kurdish people are little, and none official study in kurdistan was found after a broad literature review on this topic. diverse climate conditions can be found in different regions of iraq. the northern part of iraq, which is called kurdistan, has a cooler atmosphere than the southern part. the climate of kurdistan has distinct high temperatures in day-time and low temperatures during the nighttime. from june to september, daytime temperatures reach 44°c or higher throughout the area. the south has higher temperatures, which can go as high as 48°c during summer time. in our local clinical dermatology center, we noticed that various sun’s related skin diseases such as sunburn are more common in the summer period because the weather in the summer is exceptionally hot. there has been an important increase in the incidence of the skin cancer over the past few decades. in addition, it has been shown that developing this condition is related to over a lifetime commutation of sun exposure [22]. it has been reported that with the implementation of sun protection measures and proper behaviors approximately around 80% of skin cancer cases can be prevented. nevertheless, the occur rence of skin cancer is still increasing [23]. this study has indicated that more than 90% of our study respondents they commonly stayed under the shade to avoid the harmful effects of sun exposure. gray et al. and ergin et al. achieved a similar result in their 2012 fig. 1. how important is to look after the skin? table iv logistic regression analyses of skin cancer awareness and sociodemographic characteristics p value odds ratio 95% ci (lower bound-upper bound) male 0.082 0.624 (0.367-1.061) age 18–21 years 0.025 5.992 (1.255-28.603) single 0.196 0.514 (0.188-1.410) urban 0.268 1.333 (0.802-2.217) class 3 0.037 0.427 (0.192-0.949) ci: confidence interval table v respondents applied sun protection methods n (%) a regularly never sometimes which of the protection methods do you often use during the daytime? wear sun protection cream 81 (19.6) 169 (40.9) 151 (36.6) wear hat 24 (5.8) 225 (54.5) 142 (34.4) wear sunglasses 89 (21.5) 14 (35.1) 167 (40.4) wear light cotton clothes 92 (22.3) 64 (15.5) 251 (60.8) stay under shade 137 (33.2) 32 (7.7) 237 (57.4) stay inside 113 (27.4) 41 (9.9) 250 (60.5) athe denominator is different among variables due to missing responses hezha o. rasul, et al.: skin aging and sun protection knowledge among college students uhd journal of science and technology | may 2018 | vol 2 | issue 1 5 and 2011 study, respectively [5], [24]. despite the fact that, kokturk et al. and kaymak et al. found that staying inside at peak times to be the most commonly practiced method of avoiding the harmful effects of the sun, with 53% and around 45% for women and men [18], [25]. approximately 52% of the respondents reported awareness of the link between sun exposure and the hazard of skin cancer, which is comparable to the previous study carried out in saudi arabia by alghamdi et al. and al robaee [7], [26]. however, this level of awareness is considered to be lower than similar studies conducted in the western community. for instance, the relationship between sun exposure and skin cancer was made by study participants in malta with figures of 92.5%, 92% in the united states, 90% in australia, and 85% in canada [27]. the outcome of this study has shown that around 88% of study participants were familiar with the linkage between the sun and skin burn. furthermore, knowledge of respondents about the connection between sun exposure and skin aging was confirmed with 47%. in this study, the participants were questioned about their levels of knowledge of the benefits of the sunlight; slight gender distinction was noted in answer to the positive effect of the sun on the synthesis of vitamin d and treatment in several skin conditions. it was found that 76.1% of the male was aware of the positive effect of the synthesis of vitamin d in comparison to 70.7% of the female. in addition, the results of well-known effects of treatment in some skin conditions were analyzed; these statistics were 74.4% for male and 70.6% for female. regardless of the reasonably superior information and awareness among our study participants that the sunlight predisposes people to several skin disorders, including skin aging, sunburn, and skin cancer, the rate of sunscreen attentiveness was low. this study reported that more than half of the participants wear sun protection cream. in addition, 40.9% of the respondents reported that they have never used sunscreen. the finding regarding the use of sunscreen has been cited by several studies on this subject matter [26], [28], [29]. moreover, around 62% of students stated that they wear sunglasses as one of the sun protection methods. nikolaou et al. reported that in mediterranean population sunglasses was the most regularly used sun protection with the number of 83.4% [30]. as we have shown, protective clothes were used as sun protection among students, as 83.1% of respondents reported wearing light cotton clothes, and 40.2% of them reported using a hat during outdoor activities. certainly, this rate of sun protection utilizes and knowledge among the kurdish people as reported by this study is quite alarming and should spotlight the interest in this concern with regard to health education programs and future studies. additional learning is needed as the knowledge only is not sufficient to make a transform in approach. universities are ideal environment because of their existing infrastructure to help students attaining the essential healthy behaviors. sun protection awareness and ideas can be integrated into the existing areas of study programs. nevertheless, this study has several potential limitations that should be reserved when interpreting the results. an expediency sample of students from only one university was surveyed. therefore, caution must be kept in mind in expanding our findings to other universities, especially universities situated in other geographical regions. another limitation to these findings is the reality that the students were asked to report their answers as yes or no with offered statements about sun exposure harmful effect including skin aging, skin burn, and skin cancer, which may prejudice responses and direct to a mistaken evaluation of the proportion of the public who have true and factual information of the sun side effects. finally, the results of this study limited by cross-sectional character, which means that commands of effects can only be hypothesized. table vii key factors of skin aging main factor of aginga responses n (%) percent of cases (%) sun 97 (14.9) 24.3 weather 156 (24.0) 39.1 pollution 181 (27.9) 45.4 poor diet 215 (33.1) 53.9 total 649 (100.0) 162.7 adichotomy group tabulated at value 1 table vi participants concern about issues relating to skincare n (%) concerned not concerned missing-value premature aging caused by sun exposure 248 (60.0) 159 (38.5) 6 (1.5) stress 347 (84.0) 63 (15.3) 3 (0.7) lack of sleep 303 (73.4) 106 (25.7) 4 (1.0) smoking 339 (82.1) 70 (16.9) 4 (1.0) drinking alcohol 299 (72.4) 108 (26.2) 6 (1.5) hezha o. rasul, et al.: skin aging and sun protection knowledge among college students 6 uhd journal of science and technology | may 2018 | vol 2 | issue 1 5. conclusion and recommendation this study has specified a low level of public knowledge and self-care practice among the college students regarding skin aging, the harmful effects of sun exposure and sun protection methods. in addition, this study has discovered that sun protection measure is commonly inadequate among students and on a regular basis only a small part of participants uses sunscreen. therefore, this research highlights the requirement for the media, further studies and future well-being education programs to be utilized with the purpose of developing the implementation of sun protection behaviors including wearing sunscreen regularly and wearing protective clothes among the general public. 6. acknowledgment the authors would like to acknowledge kale rahim and lano hiwa from the university of sulaimani for the data collection. we also thank the staff of the sulaimani center for dermatology and venereal disease (teaching hospital) for their generous help. references [1] h. rexbye. “influence of environmental factors on facial ageing”. age and ageing, vol. 35, no. 2, pp. 110-115, 2006. [2] a. vierkötter and j. krutmann. “environmental influences on skin aging and ethnic-specific manifestations”. dermato-endocrinology, vol. 4, no. 3, pp. 227-231, 2012. [3] b. gilchrest and j. krutmann. skin aging. springer, berlin, 2006. [4] r. halder and c. ara. “skin cancer and photoaging in ethnic skin”. dermatologic clinics, vol. 21, no. 4, pp. 725-732, 2003. [5] e. yurtseven, t. ulus, s. vehid, s. köksal, m. bosat and k. akkoyun. “assessment of knowledge, behaviour and sun protection practices among health services vocational school students”. international journal of environmental research and public health, vol. 9, no. 12, pp. 2378-2385, 2012. [6] o. tekbas, d. evci, and u. ozcan. “danger increasing with approaching summer: sun related uv rays”. taf preventive medicine bulletin, vol. 4, no. 2, pp. 98-107, 2005. [7] k. alghamdi, a. alaklabi and a. alqahtani. “knowledge, attitudes and practices of the general public toward sun exposure and protection: a national survey in saudi arabia”. saudi pharmaceutical journal, vol. 24, no. 6, pp. 652-657, 2016. [8] b. armstrong and a. kricker. “the epidemiology of uv induced skin cancer”. journal of photochemistry and photobiology b: biology, vol. 63, no. 1-3, pp. 8-18, 2001. [9] r. mackie. “effects of ultraviolet radiation on human health”. radiation protection dosimetry, vol. 91, no. 1, pp. 15-18, 2000. [10] m. mabruk, l. toh, m. murphy, m. leader, e. kay and g. murphy. “investigation of the effect of uv irradiation on dna damage: comparison between skin cancer patients and normal volunteers”. journal of cutaneous pathology, vol. 36, no. 7, pp. 760-765, 2009. [11] l. scerri and m. keefe. “the adverse effects of the sun on the skin–a review”. maltese medical journal, 7, pp.26-31, 1995. [12] p. unna. the histopathology of the diseases of the skin. clay, edinburgh, 1896. [13] w. dubreuills, des hyperkératoses circonscrites. masson, paris, 1896. [14] h. daniell. “smoker’s wrinkles”. annals of internal medicine, vol. 75, no. 6, pp. 873, 1971. [15] e. sherertz and s. hess. “stated age”. new england journal of medicine, vol. 329, no. 4, pp. 281-282, 1993. [16] a. vierkötter, t. schikowski, u. ranft, d. sugiri, m. matsui, u. krämer and j. krutmann. “airborne particle exposure and extrinsic skin aging”. journal of investigative dermatology, vol. 130, no. 12, pp. 2719-2726, 2010. [17] t. filiz, n. cınar, p. topsever and f. ucar. “tanning youth: knowledge, behaviors and attitudes toward sun protection of high school students in sakarya, turkey”. journal of adolescent health, vol. 38, no. 4, pp. 469-471, 2006. [18] y. kaymak, o. tekbaş and s. işıl. “knowledge, attitudes and behaviours of university students related to sun protection”. journal of turkish dermatology, vol. 41, pp. 81-85, 2007. [19] p. cohen, h. tsai and j. puffer. “sun-protective behavior among high-school and collegiate athletes in los angeles, ca”. clinical journal of sport medicine, vol. 16, no. 3, pp. 253-260, 2006. [20] t. owen, d. fitzpatrick and o. dolan. “knowledge, attitudes and behaviour in the sun: the barriers to behavioural change in nothhern ireland”. the ulster medical journal, vol.73, no. 2, pp. 96-104, 2004. [21] a. geller, l. rutsch, k. kenausis, p. selzer and z. zhang. “can an hour or two of sun protection education keep the sunburn away? evaluation of the environmental protection agency’s sunwise school program”. environmental health, vol. 2, no. 1, pp. 1-9, 2003. [22] r. bränström, s. kristjansson, h. dal and y. rodvall. “sun exposure and sunburn among swedish toddlers”. european journal of cancer, vol. 42, no. 10, pp. 1441-1447, 2006. [23] n. sendur. “nonmelanoma skin cancer epidemiology and prevention”. turkiye klinikleri journal of internal medical sciences, vol. 1, pp. 80-84, 2005. [24] a. ergin, i. ali and i.b. mehmet. “assessment of knowledge and behaviors of mothers with small children on the effects of the sun on health”. the pan african medical journal, vol. 4, pp. 72-78, 2011. [25] a. köktürk, k. baz and r. buğdaycı. “dermatoloji polikliniğine başvuran hastalarda güneşten korunma bilinci ve alışkanlıkları”, türk klinical dermatology, vol. 12, pp. 198-203, 2002. [26] a. al robaee. “awareness to sun exposure and use of sunscreen by the general population”. bosnian journal of basic medical sciences, vol. 10, no. 4, pp. 314-318, 2010. [27] s. aquilina, a. gauci, m. ellul and l. scerri. “sun awareness in maltese secondary school students”. journal of the european academy of dermatology and venereology, vol. 18, no. 6, pp. 670675, 2004. [28] k. wesson and n. silverberg. “sun protection education in the hezha o. rasul, et al.: skin aging and sun protection knowledge among college students uhd journal of science and technology | may 2018 | vol 2 | issue 1 7 united states: what we know and what needs to be taught”. cutis, vol. 71, pp. 71-74, 2003. [29] e. thieden, p. philipsen, j. heydenreich and h. wulf. “uv radiation exposure related to age, sex, occupation, and sun behavior based on time-stamped personal dosimeter readings. archives of dermatology, vol. 140, no. 2, 2004. [30] v. nikolaou, a. stratigos, c. antoniou, v. sypsa, g. avgerinou, i. danopoulou, e. nicolaidou and a. katsambas. “sun exposure behavior and protection practices in a mediterranean population: a questionnaire-based study”. photodermatology, photoimmunology and photomedicine, vol. 25, no. 3, pp. 132-137, 2009. . uhd journal of science and technology | august 2017 | vol 1 | issue 2 37 1. introduction in this paper, we propose a rejuvenation framework that addresses software aging on abstract level. the changing world demands faster and better alignment of software systems with business requirements to cope with the rising demand for better and faster services. this simply means that a perfectly untouched functioning software ages just because it has not been touched. the aging phenomenon occurs in software products in similar ways to human; parnas [1] draw correlations between the aging symptoms in human and software. as demands for functionality grow software complexity rises, and as a result software, underperformance and malfunctioning became apparent [2]. software aging is a known phenomenon with recognized symptoms such as increase in failure rate [3]. researchers have identified a number of causes of software aging, for example, accumulation of errors over time during system operation. one other cause is “weight gain” as in human, software gains weight as more codes are added to an application to accommodate new functionalities, and consequently, the system loses performance. there are numerous examples where software aging has caused electronic accidents in complex systems such as in billing and telecommunication switching systems [4]. beside the causes researchers in the field have identified a number of aging indicators such as increased rate of resource (e.g., memory) consumption [5]. another aging indicator is how robust a system is against security attacks if observed over time. this is because security attack techniques are becoming more sophisticated by day. however, more is needed to be done to address the aging phenomena. grottke et al. [5] claim that the conceptual aspect of software aging has not been paid adequate attention by researchers to cover the fundamentals of software aging. currently, addressing software aging is mostly done using reengineering techniques such as: a simple software rejuvenation framework based on model driven development hoger mahmud department of computer science, college of science and technology, university of human development, iraq a b s t r a c t in the current dynamic-natured business environment, it is inevitable that today’s software systems may not be suitable for tomorrow’s business challenges which indicate that the software in use has aged. although we cannot prevent software aging, we can try to prolong the aging process of software so that it can be used for longer. in this paper, we outline a conceptual software rejuvenation framework based on model driven development approach. the framework is simple but effective and can be implemented in a recursive five step process. we have illustrated the applicability of the framework using a simple business case study which highlights the effectiveness of the framework. this work adds to the existing literature on software aging and its preventative measures. it also fills in the research gap which exists about software aging caused by changing requirements. index terms: model driven development, software aging, software rejuvenation framework corresponding author’s e-mail: hoger.mahmud@uhd.edu.iq received: 02-07-2017 accepted: 22-08-2017 published: 30-08-2017 access this article online doi: 10.21928/uhdjst.v1n2y2017.pp37-45 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2017 mahmud. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology hoger mahmud: a simple software rejuvenation framework based on model driven development 38 uhd journal of science and technology | august 2017 | vol 1 | issue 2 1. forward engineering concerns with moving from highlevel abstraction to physical implementation of a system 2. reverse engineering concerns with analyzing a system to identify components and connectors of that system to represent the system in a different form or higher level of abstraction 3. redocumentation deals with creation or revision of semantically equivalent representation within the same abstract level 4. design recovery concerns with reproducing all required information about a system so that a person can understand what the program does 5. re s t r u c t u r i n g c o n c e r n s w i t h t r a n s f o r m i n g a representation of a system to a different one, without any modification to the functionality of the system. reengineering can facilitate the examination of a system and learn more about it so that appropriate changes can be made. however, it is not the ideal solution for software upgrade as the process is extremely time-consuming and resource expensive. in this paper, we present a conceptual software rejuvenation framework based on model driven development (mdd) techniques capable of addressing software aging with less time and resource. the framework is most effective where the software aging is due to changing business requirements which in effect requires the addition or omission of functionalities. we have illustrated the applicability of the framework through a simple business case study which supports the effectiveness of the framework. this work contributes to the field of software aging by presenting a novel conceptual framework to software developers that can be utilized to dilute software aging. the rest of this paper is organised as follows, in section 2 we provide a brief background about software aging and rejuvenation and in section 3 we present some related works. in section 4, we outline the frame work and in section 5, we illustrate the applicability of the framework using a simple business case study. in section 6 and 7, we discuss, conclude, and provide some recommendations. 2. background in this section, we provide a brief background to both software aging and software rejuvenation with the aim to provide better understanding of the proposed framework later in section 4. a. software aging software aging was first introduced by huang et al. [6] and since then the interest in the topic has risen among academics and industries. complex systems rely on an intricate architectural setup to function, if the structure is slowly destroyed by maintaining and updating the system software aging becomes inevitable [7]. it is a known fact that a system maintainer can mess up perfectly fine functioning software through changing codes or inserting incorrect codes which is known as “ignorant injection” [8]. to provide a focus view of research areas on software aging cotroneo et al. [9] have analyzed more than 70 papers in which they have concluded that overall there are two major categories of research into understanding software aging the first is model-based analysis and the second is measurement-based analysis. several measureable techniques have been proposed to detect software aging such as “aging indicators” and “time series analysis.” the techniques are used to collect data about resources used in a system, and then, analyze it to see if the consumption rate has increased over time which is a sign of aging [3]. as for the causes of software aging, there are two major classes, the first is known as “ignorant surgery” and the second is known as “lack of movement.” fig. 1 shows the major contributors to the two classes of software aging causes. b. software rejuvenation to keep critical systems functioning correctly software rejuvenation is recognized as an effective technique [10]. the objective of software rejuvenation is to rollback a system continuously to maintain the normal operation of the system and prevent failures. according to cotroneo et al. [3] application-specific and application-generic are two main classes of software rejuvenation techniques in which the former works on specific system features and the latter works on the whole system (e.g., system restart). to further elaborate on the two main classes, researchers have provided a number of examples for both; flushing of kernel, file system defragmentation and resource reprioritization are examples of application specific rejuvenation and application restart, cluster failover, and operating system reboot are examples of application generic rejuvenation [3]. fig. 2 illustrates the two classes of software rejuvenation techniques. 3. related work there have been a number of attempts to tackle software aging similar to what we propose here. the authors of huang et al. [6] present a model-based rejuvenation approach for billing applications and okamura and dohi [10] proposes dynamic software rejuvenation policies by extending models presented in pfening et al. [11]. the approach is case hoger mahmud: a simple software rejuvenation framework based on model driven development uhd journal of science and technology | august 2017 | vol 1 | issue 2 39 specific and cannot be applied to a domain; this, however, has similarities with what we are proposing since they also use models to rejuvenate software. saravakos et al. [12] proposes the use of continuous time markov chains to model and analyze software aging and rejuvenation to better understand causes of aging which helps putting in place mitigating measures. this approach is suitable to treat symptoms of aging that happens for technical reasons rather than changes in requirements. dohi et al. [13] models optimal rejuvenation schedule using semi-markov processes to maximize availability and minimize cost. the focus here is aging caused due to processing attributes; however, unlike this work we focus on the functionality attributes of a system garg et al. [14]. adopts the periodic rejuvenation technique proposed by huang et al. [6] and uses stochastic petri net to model stochastic behavior of software aging. beside modeling techniques, others have used techniques such a time triggered rejuvenation technique used by salfner and wolter [15] and software life-extension technique used by machida et al. [16] to counteract software aging in which they take preventative measures to ease software aging and allow more time for system rethink. huang et al. [6] proposes a proactive technique to counteract software aging with the aim to prevent failure using periodic preemptive rollback of running applications. to detect symptoms of aging techniques such as machine learning is used to analyze data through adopting artificial intelligent algorithms (e.g., classifiers) [17]. garg et al. [18] discuss measures for software aging symptom detection with the aim to diagnose and treat the aging taking place, others have used pattern recognition techniques to detect aging symptoms [17]. these works propose how to detect symptoms of software aging without proposing a suitable mechanism to treat the symptoms. all the related works presented so fare address software aging from technical and performance viewpoint and none consider aging caused as a result of changing requirements. this allows us to claim that our framework contributes to the software aging and rejuvenation literature by filling in this gap and take a new direction in tackling software aging. 4. framework outline the base of our conceptual rejuvenation framework is mdd technique [19], [20]. france et al. [21] claim that abstract design languages and high-level programming languages can provide automated support for software developers in terms of solution road map that fast-forward system developments. following their direction we use model driven development (mdd) techniques to design a rejuvenation framework to tackle requirement-based software aging. mdd simply means constructing a model of the system with fine details before transferring it into code. it provides the mapping functions between different models for integration and model reusing purposes [22]. mdd is a generic framework that can accommodate both application specific and application generic classes of software rejuvenation. mayer et al. [23] states mdd is ideal for visualizing systems and not losing the semantic link between different components of the system at the same time. it is inevitable that extensive manual coding in developing a system escalates human errors in the system; this issue can be addressed through code automation which is the ultimate aim of mmd. building and rebuilding system is an expensive process that requires time and resource; model driven aims at using, weaving and extending models to maintain, develop and redevelop systems. experts in the field claim that mdd improves quality as models are continuously refined and reduce costs by automating the development process [22]. this process changes models from being expenses to fig. 1. major causes of software aging fig. 2. software rejuvenation techniques hoger mahmud: a simple software rejuvenation framework based on model driven development 40 uhd journal of science and technology | august 2017 | vol 1 | issue 2 important assets for businesses. researchers have identified the conceptual gap between problem domain and implementation as a major obstacle in the way of developing complex systems. models have been utilized to bridge the gap between problem domain abstractions and software implementation through code generation tools and automated development support [2]. models can serve many purposes such as: 1. simplifying the concept of a complex system to aid better understanding of the problem domain and system transformation to a form that can be analyzed mechanically [24] 2. models are platform and language independent 3. automatic code generation using models reduce human errors 4. for new requirements only the change in model is required this reduces the issue explained previously known as weight gain. a. framework steps we propose a five step recursive software rejuvenation framework to address the issue of software aging. as mentioned the framework is based on model driven software development which is implemented in the following steps: 1. first developers gather system requirements which is one of the must do tasks in every software development 2. developers design the entire system in great details using tools such as unified modeling language (uml) 3. the complete design is fed into code generators such as code cooker (http://codecooker.net) and eclipse uml to java generator to generate system codes 4. software codes are integrated, tested, and finalised, this step is necessary since a code generator tool capable of generating 100% of the code is yet to exist. this limitation is discussed in section 6 5. in the final step where the new product is delivered and installed. fig. 3 illustrates the five steps explained in a recursive setting, i.e., when a new feature is required to be added to the system to address a new requirement the system is upgraded through the model rather than though code injection. the models are kept as assets and refined as new requirements come in, the next section provide more inside as to how the framework works. 5. case study to illustrate the applicability of the framework we present a simple none-trivial business case study specific to kurdistan region. mr. x is a supermarket owner in the city of sulaymaniyah who sells domestic goods and he employs 10 people in his supermarket. currently, his shop is equipped with electronic point of sale (epos) systems to record transactions and the form of payment by customers is cash only. electronic payment is not feasible due to unavailability of electronic payment systems in the region’s banks. his current epos system is capable of performing the following functionalities: 1. store individual item details such as name, price, barcode, and expiry dates 2. store information about employees such as name, address, date of birth, and telephone numbers 3. retrieve and match barcodes on products to display and record item details 4. calculate total price and print out customer receipts 5. record all transactions and generate various reports such as daily sales report, weekly sales report, and sale by item report 6. the administration side of the system is managed through a user management subsystem which allows adding, deleting, updating, and searching on users. the system also contains a product management subsystem that allows managing products through adding, deleting, updating, and searching on item. we make an assumption that in the next 6 months electronic payment systems (epayment) will become available in kurdistan for businesses to use. now mr. x would like to gain an edge over his competitors and add epayment system to his current epos system. fig. 4 is the uml use case diagram for the current epos system in mr. x’s supermarket which shows the use cases than can be performed by each actor. fig. 3. model driven development-based software rejuvenation framework hoger mahmud: a simple software rejuvenation framework based on model driven development uhd journal of science and technology | august 2017 | vol 1 | issue 2 41 fig. 5 is the future use case diagram for the new system which shows the addition of a new actor called “customer” and a new use case called “pay electronically” coted in yellow. now, we assume developers of the system had the framework in mind when they developed the system and have kept a design model of the system similar to the one illustrated in fig. 6 which shows a uml class diagram design model of the epos system. mr. x now goes back to them and request that the new functionality (electronic payment) to be added to the system. using the framework the developers refine the uml class diagram model (new classes coted in yellow) to accommodate the new requirement and produce a new design similar to the one shown in fig. 7. the new design is now ready to be fed into code generators to generate the codes for the new system. using the framework the developers have performed a rejuvenation process on mr. x’s system without touching fig. 4. current electronic point of sale unified modeling language use case diagram fig. 5. future electronic point of sale unified modeling language use case diagram hoger mahmud: a simple software rejuvenation framework based on model driven development 42 uhd journal of science and technology | august 2017 | vol 1 | issue 2 the current operating epos. it is important to point out that the framework tackles software rejuvenation conceptually and on abstract level which means we are bypassing all the technicalities of implementation and testing processes. during our search, we did not come across any related work that considers design for software rejuvenation rather than an actual system, which indicates that our approach is unique. however, it has to be said that although being unique is an advantage, it has made it difficult for us to compare the applicability of our framework with other existing frameworks. 6. discussion researchers in the field have concluded that software aging is inevitable and as software ages it loses its ability to keep up. in this paper, we have proposed a five step recursive software rejuvenation framework based on model driven software development approach. to illustrate the applicability of the framework we have outlined a simple business scenario and explained how the framework rejuvenates the current system in use by the business. the framework will provide the following advantages over existing rejuvenation techniques: 1. the model is used to redevelop the system without taking the old system out of operation which leads to reduction in down time (unavailability) which otherwise lead to lose of customers and profits 2. using models to maintain and update software gives the development process an edge as models are language independent and can be used to develop systems in the state of the art programming languages which in turn ease software aging as the technology used in the development is current [25] 3. as codes are generated automatically human errors are reduced, which is one of the contributors of software aging 4. redevelopment costs and times are reduced as developments are automated. the objective of software rejuvenation is to rollback a system continuously to maintain the normal operation of the system and prevent failures. however, software rejuvenation increases system downtime as the system is taken out of operation while the rejuvenation process is performed. knowing when to perform rejuvenation process on a system is a crucial factor recognized by researchers to minimize cost and maximize availability [3]. the framework we have proposed addresses this issue by working on the system on design level without terminating the system operation while the rejuvenation solution is finalized. it is important to stress that the framework is conceptual and requires further research as there are a number of limitations that need be addressed to make the framework fully applicable. the limitations can be summarized as follows: fig. 6. current electronic point of sale unified modeling language class diagram hoger mahmud: a simple software rejuvenation framework based on model driven development uhd journal of science and technology | august 2017 | vol 1 | issue 2 43 1. available software modeling tools such as uml 2.0 which is an industry standard currently does not provide the ability to model systems from user-defined viewpoint [21] 2. mdd is not widely used [22] although it has gained momentum with a potential for industry wide adaptation 3. once the models are developed and finalized there comes the issues of translating it completely into code as a tool to generate 100% codes from a model not yet exist 4. the issue of measuring the quality of models is realized by researchers to tackle this issue france and rumpe [2] suggests that modeling methods should come with modeling criteria that modelers can use as a guide for system modeling. however, such criterions are yet to be presented by modeling language and tool developers such as developers of uml (www.omg.org) 5. in the course of developing a system, many different models are created at varying abstract levels which creates model tracking, integration, and management issues and the current modeling tools are not sophisticated enough to deal with the issues. despite all the limitations, we believe the fundamental concept behind the framework has great potentials to be advanced and implemented in the future. 7. conclusion and recommendations software aging is inevitable which occurs as a result of changing requirements, ignorant injections, and weight gain. researchers have proposed a number of different approaches to tackle software aging; however, nearly all approaches are trying to address the aging caused by technical update or software malfunction. in this paper, we have outlined a framework for software rejuvenation that uses mdd approach as base for the rejuvenation process. the framework addresses software aging from a change in business requirement point of view which is different from what current researchers are proposing. it is simple, effective, and applicable as demonstrated by applying it to a simple business case study. fig. 7. future electronic point of sale unified modeling language class diagram hoger mahmud: a simple software rejuvenation framework based on model driven development 44 uhd journal of science and technology | august 2017 | vol 1 | issue 2 the foundation concept developed in this paper contributes to the field of software aging and paves the way for looking at software aging in a different angle. now to delay software aging, we recommend a number of quick mitigating actions as follows: 1. characterize the changes that are likely to occur over the lifetime of a software product, and the way to achieve this characterization is by applying principles such as object orientation 2. design and develop the software code in a way that changes can be carried out; to achieve this concise and clear documentation is the key 3. reviewing and getting a second opinion on the design and documentation of a product helps in prolonging the lifetime of a software product. when the aging has already occurred there are things we could do to treat it such as: 1. prevent the aging process to get worse by introducing and creating structures whenever changes are made to the product 2. as changes are introduced to a product a review and update of the documentation is often a very effective step in slowing the aging process 3. u n d e r s t a n d i n g a n d a p p l y i n g t h e p r i n c i p l e o f modularization is a good way to ease the future maintenance of a product 4. combining different versions of similar functions into one system can increase efficiency of a software product and reduce the size of its code which is one the causes of software aging. references [1] d. l. parnas. “software aging.” in proceedings of the 16th international conference on software engineering, 1994, pp. 279-287. 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[25] n. b. ruparelia. “software development lifecycle models.” sigsoft software engineering notes, vol. 35, no. 3, pp. 8-13, 2010. tx_1~abs:at/tx_2:abs~at 58 uhd journal of science and technology | july 2022 | vol 6 | issue 2 1. introduction in the field of agriculture, many practices particularly the using of chemicals are applied for improving crops quality and quantity, however, although their positive effects, these applications are not empty of undesirable effects on environment, public health, and plant growth. using modern biotechnological approaches, including, electricity current, laser, magnetic field, high voltage, ultraviolet and radiation with gamma or x-ray on different plants material are gaining interest to develop plants growth and yield, and characterized by cheapness and safety on health and environment, therefore the scientists try to make this century a biophysical century, where most of the physical factors depend on increasing energy balance and increase material transport through membranes for improving the growth and the development of crops [1]-[3]. photosynthetic pigments and stomata characteristics of cowpea (vigna sinensis savi) under the effect of x-ray radiation ikbal muhammed albarzinji1, arol muhsen anwar1, hawbash hamadamin karim2, mohammed othman ahmed3 1department of biology, faculty of science and health, koya university, koya koy45, kurdistan region f.r. iraq, 2department of physics, faculty of science and health, koya university, koya koy45, kurdistan region f.r. iraq, 3department of horticulture, college of agricultural engineering sciences, university of raparin, kurdistan regionf.r. iraq a b s t r a c t this study was conducted in the field and laboratories of the faculty of science and health-koya university by exposing the seeds of cowpea plant (vigna sinensis savi) var. california black-eye to x-ray radiation in two different locations (in target or 30 cm out of target) inside the radiation chamber, for four different exposure times (0, 5, 10, or 20 min), to study the effect on some characteristics of seedling components. results show that the exposure location to x-ray had non-significant effects on cowpea leaves content of photosynthetic pigments, whereas each of time of exposure with interaction between location and time of exposure had significant effects on chlorophyll a, total chlorophylls, and total carotenoids pigments. regarding the x-ray effects on stomata characteristics, the results detect that there were non-significant differences between the location of exposure on stomata number on abaxial leaves surfaces and stomata length on adaxial leaves surfaces, whereas a significant effects on number of stomata on the adaxial leaves surfaces, abaxial stomata length, abaxial, and adaxial stomata width were detect. exposing cowpea seeds to x-ray radiation in the target of the radiation source increased significantly stem and leave dry matter percent compared with the one out of the target location, whereas increasing the time of exposure decreased the percent of dry matter of stem and leaves. it is concluded that exposing cowpea seeds to x-ray leads to changes in photosynthetic pigments, stomata characteristics, and plant dry matter content. index terms: vigna sinensis savi, x-ray radiation, pigments, stomata traits corresponding author’s e-mail: ikbal muhammed albarzinji, department of biology, faculty of science and health, koya university, koya koy45, kurdistan region f.r. iraq. e-mail: ikbal.tahir@koyauniversity.org received: 16-07-2022 accepted: 07-09-2022 published: 24-09-2022 access this article online doi: 10.21928/uhdjst.v6n2y2022.pp58-64 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2022 albarzinji, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology albarzinji, et al.: pigments and stomata of cowpea under x-ray uhd journal of science and technology | july 2022 | vol 6 | issue 2 59 ionizing radiations are those have wavelengths <100 nm [4]. these radiations are charged high-energy particles (highenergy photons and electrons). two types of ionizing radiations there are: gamma radiations and x-rays, the first is emitted from inside the nucleus, whereas x-ray is radiated from outside the nucleus [5]. there are many applications of x-ray radiation in different fields of plant studies, for example panchal et al. [6] used x-ray for imaging of inner features of a seed sample to identify unseen defects or contaminants. other studies were conducted to investigate the effects of x irradiation on physiological characteristics of different plants, such; rezk et al. [7] found that low dose of x-ray 5 gray (gy) caused increasing in all morphological criteria, total photosynthesis pigments, enzymatic and non-enzymatic antioxidants significantly in two genotypes of okra plants as compared with control treatments, while the doses (higher than 5 gy) caused a considerable decreased in the studied parameters. similarly, singh [8] study shows promoting in chlorophyll development for 60 s x-ray pretreated as it compared to 90 and 120 s pre-treatment for seeds of cicer arietinum, vigna radiata, vigna mungo and vicia faba plants. dhamgaye et al. [9] irradiated seeds of phaseolus vulgaris cv. rajmah using synchroton x-ray beam at 0.5–10 gy, the overall growth of 10 days old seedlings raised from irradiated seeds was substantially reduced at irradiation doses of 2 and 5 gy. same authors dhamgaye et al. [10] irradiated seeds of p. vulgaris cv. rajmah using synchrotron x ray at doses of 1, 10, and 20 gray where, the percent of relative water and protein content was significantly decreased at 10 and 20 gy dose in 4–8 days old seedling, and a decrease in photosynthesis pigments chlorophyll and carotenoids content is observed in shoot tissue when 1 and 10 gy where used. mortazavi et al. [11] accelerated the growth of newly grown plants of p. vulgaris (pinto) by irradiated them with x-rays for 6 days. arena et al. [12] found that exposure of dwarf bean (p. vulgaris l.) plants to different doses of x-rays (0.3, 10, 50, and 100 gy) showed that young leaves exhibited a reduction of area and an increase in specific mass and dry matter content. at higher doses of x-rays (50 and 100 gy) total chlorophyll (a+b) and carotenoid (xanthophylls + carotenoids) content were significantly lower (p < 0.01) compared to lower doses and in control leaves. significant reduction in transpiration was detected in v. faba irradiated by x-ray, this reduction was associated with inhibition of stomatal opening from the 9th to 16th day after irradiation. the osmotic pressure of epidermal cells in irradiated plants appeared to be slightly higher than that of epidermal cells of non-irradiated plants. however, the slight osmotic pressure changes of epidermal cells in irradiated plants did not appear to be a major factor contributing to inhibition of stomatal opening in irradiated plants under the growth conditions of the experiments [13]. the aim of this work was to investigate the effects of seed exposure to x-rays on some of the physiological properties of emerged cowpea plants, because these changes has subsequent effects on the photosynthetic activity and cause a direct effect on the agronomic features of the plant. 2. materials and methods 2.1. plant materials and studied characteristics this work was conducted in the department of biology/koya university, erbil-iraq. the seeds of cowpea plant (vigna sinensis savi) var. california black-eye were exposed to a single dose of x-ray radiation by the xrd tube (from the company of panalytical b.v. lelyweg1, the netherlands) where the highest radiation level was less than 1 sieverts/h measured at the tube surface. 20 seeds for each experimental unit were putted in the device source to exposed to x-ray at the advanced physics laboratory in physic department at same faculty. the experiment was conducted in complete randomize design (crd) where the location of exposure considers as the first factor by exposure the seeds to x-rays either in the target point of the device or 30 cm from the target point in the base of the device chamber, whereas the times of exposure 0, 5, 10, or 20 min were considered as the second factor, where the time zero is considered as the control treatment used for each location. after seeds were exposed to x-ray they planted in 5 kg. soil pots, because of an initial increase in photosynthesis rate during leaf expansion and followed by a decrease on maturation [14], at the end of the vegetative growth stage, fourth leaf of five plants from each experimental unit were taken, and the photosynthetic pigments chlorophylls a, chlorophyll b, total chlorophylls and total carotenoids were estimated as it mentioned in lichtenthaler and wellburn [15] were leaf material was collected and mixture ratio was 50 ml 80% acetone: 1 g leaves sample. samples were grinded by mortar and pestle and filtered by filter paper, then extracts were placed in a 25 ml dark glass vial to avoid evaporation and photo-oxidation of pigments, after that the absorbance of the extract were measured by spectrophotometer at wave lengths 663, 646, and 470 nm. each of chlorophyll a, b, and total carotenoids were estimated as follows: chlorophyll a = (12.21*a663) (2.81*a646) chlorophyll b = (20.13*a646) (5.03*a663) albarzinji, et al.: pigments and stomata of cowpea under x-ray 60 uhd journal of science and technology | july 2022 | vol 6 | issue 2 total carotenoids = (1000*a470 3.27*chl a – 104* chl b)/229 where, a is absorbance, chl. a = chlorophyll a (mg/l) and chl. b = chlorophyll b (mg/l). for converting the concentration from mg/l to mg/g fresh weight, each value multiplied by (extraction volume/sample weight *1000), and total chlorophyll calculated from the summation of each chlorophyll a and chlorophyll b. total chlorophyll was determined by collecting each of chlorophyll a and chlorophyll b (10). for stomata study, the lasting impressions method [16] was used. in this method, about one square centimeter of leaves surfaces was painted by a clear nail polish. after the nail polish was dried they were taped by a clear cellophane tape, and peels it out. the leaf impressions taped on slides and labeled as adaxial and abaxial surfaces then examined under ×40 by light microscope (dm 300, leica microsystems, china). numbers of appeared stomata on lens field were counted for all adaxial and abaxial leaves surfaces. stomata guard cells length and width of adaxial and abaxial leaves surfaces were calculated in micrometer (μm) with scaled ocular lens. because of the important of the percent of dry matter content as a result of the photosynthetic activity it determined for each of stem and leaves by dividing the stem or leaves dry weight by the stem or leaves fresh weight multiplying by 100 as it reported by al-sahaf [17]. 2.2. the statistical analysis the statistical analysis of the study conducted as a factorial experiment performed as crd in three replications, analysis of variance was used for calculating the differences among each factor treatments and their interactions by using the sas software. the test of duncan’s multiple comparison was used to estimate the main effects of treatments which were differ when the f-value was significant at p ≤ 0.05 [18]. 3. results and discussion from table 1 results it is shown that location of exposure to x-ray had non-significant (p > 0.05) effects on leaves content of photosynthesis pigments of cowpea plant, whereas the time of exposure led to a significant (p ≤ 0.05) effects on chlorophyll a and total chlorophylls, were the seeds that exposed to x-ray for 10 min increased chlorophyll a and total chlorophylls significantly (p ≤ 0.05) to 2.64 and 5.42 mg/g fresh weight comparing to other exposure times. results of interactions between locations and time of exposure revealed that exposure for 10 min out of target increased the content of chlorophyll a significantly to 3.01 mg/g fresh weight compared to other interaction treatments, and to 3.14 and 6.15 mg/g fresh weight for each of chlorophyll b and total chlorophylls compared with 5 min exposure out of target only, whereas same interaction increased total carotenoids content significantly to 1.15 mg/g fresh weight compared to 0.94 mg/g fresh weight for 20 min exposure out the target interaction treatment only. in general, ionizing radiation may have different effects on plant metabolism, growth and table 1: effects of location and time of exposure cowpea seed to x‑radiation, and their interactions on chlorophyll a, b, total chlorophylls and total carotenoids treatments chlorophyll (mg/g fresh weight) a chlorophyll (mg/g fresh weight) b total chlorophylls (mg/g fresh weight) total carotenoids (mg/g fresh weight) location of exposure in target (l1) 2.23a 2.33a 4.56a 1.05a out of target (l2) 2.32a 2.38a 4.70a 1.04a time of exposure (min) t0 2.20b 2.27a 4.47ab 1.07a t5 2.11b 1.96a 4.07b 1.01a t10 2.64a 2.78a 5.42a 1.09a t20 2.16b 2.40a 4.56ab 0.99a interactions between location and exposure time l1×t0 2.20b 2.27ab 4.47ab 1.07ab l1×t5 2.27b 2.27ab 4.54ab 1.02ab l1×t10 2.27b 2.42ab 4.70ab 1.03ab l1×t20 2.18b 2.34ab 4.52ab 1.05ab l2×t0 2.20b 2.27ab 4.47ab 1.07ab l2×t5 1.96b 1.65b 3.60b 0.99ab l2×t10 3.01a 3.14a 6.15a 1.15a l2×t20 2.13b 2.46ab 4.59ab 0.94b means that followed by same letters within column are differ non‑significantly at p≤5% according to the duncan multiple range test albarzinji, et al.: pigments and stomata of cowpea under x-ray uhd journal of science and technology | july 2022 | vol 6 | issue 2 61 reproduction, depending on radiation dose, plant species, developmental stage, and physiological traits [12]. our results disagree with the al-enezi and al-khayri [19] results that suggested that photosynthesis pigments chlorophyll a and carotenoids are more sensitive to x-ray than chlorophyll b, whereas we found that chlorophyll b and total carotenoids were less sensitive to x-irradiation compared to chlorophyll a and total chlorophylls. changes in photosynthetic pigments were studied by arena et al. [12] whom confirmed that the decrease in the levels of x-ray (0.3 gy) caused an increase in photosynthetic pigments in bean plants, whereas the high levels (50 and 100 gy) caused a decrease in these pigments, these findings also agree with that of rezk et al. [7] which recorded in two okra genotypes leaves, where the content of photosynthetic pigment improved significantly with increasing the doses of x-ray to 5 gy comparing with untreated plants, also more increase in the radiation doses, encourage the reduction in photosynthetic pigments compared to the control plants. changes in chlorophyll content as a response to x-ray is either toward an increase or a decrease direction, the increase may due to the increase in chlorophyll biosynthesis and/or delaying its degradation [20], whereas the decrease may due to pigment breakdown due to increase of reactive oxygen species [21] and changes in the chloroplast such chloroplast swelling, thylakoid dilation, and breakdown of chloroplast outer membrane [22]. regarding x-radiation effects on the stomata characteristics it was shown that there were non-significant (p > 0.05) differences between the location of exposure on number of stomata on abaxial leaves surfaces and stomata length on adaxial surfaces of leaves (table 2 and figs. 1 and 2). the seeds exposed directly to the source of x-ray (in target) decreased number of stomata on the adaxial leaves surfaces to 148.33 stomata/mm2, whereas abaxial stomata length increased to 11.08 micrometer and abaxial with adaxial stomata width also increased significantly to 7.00 and 7.58 micrometer, respectively, compared to 180.00 stomata/mm2, 9.92, 5.42, and 5.50 micrometer for plants out of target. 10 min of seed exposure to x-ray increased stomata number on both abaxial and adaxial leaves surfaces compared to other exposure times except the control treatment. exposure time had non-significant (p > 0.05) effect on stomata length and width on abaxial leaves surfaces, whereas increasing time of exposure to 20 min increased the stomata length significantly (p ≤ 0.05) compared to 5 and 10 min only, whereas it increased the stomata width significantly compared to all other treatments. from the results of interaction within location and time of exposure, it was clear from the results (table 2), that treating seeds for 10 min in the x-ray target had the more significant effects for abaxial leaves surfaces in increasing stomata number to 540.00 stomata/ mm2, and the stomata length and width to 11.67 and 8.00 table 2: effects of seeds exposure to x‑radiation on some characteristics of cowpea (vigna sinensis savi) plants stomata treatments stomata number/mm2 stomata length (micrometer) stomata width (micrometer) abaxial leaves surface adaxial leaves surface abaxial leaves surface adaxial leaves surface abaxial leaves surface adaxial leaves surface location of exposure in target (l1) 455.00a 148.33b 11.08a 10.58a 7.00a 7.58a out of target (l2) 455.83a 180.00a 9.92b 11.67a 5.42b 5.50b time of exposure (min) t0 526.67a 176.67ab 10.67a 12.33a 6.00a 6.00bc t5 398.33b 146.67bc 10.33a 9.67b 6.17a 5.17c t10 536.67a 201.67a 10.17a 10.17b 6.00a 6.67b t20 360.00b 131.67c 10.83a 12.33a 6.67a 8.33a interactions between location and exposure time l1×t0 526.67ab 176.67bc 10.67ab 12.33ab 6.00abc 6.00bc l1×t5 403.33bc 143.33bc 10.67ab 10.33bcd 7.33ab 6.00bc l1×t10 540.00a 156.67bc 11.67a 8.33d 8.00a 8.67a l1×t20 350.00c 116.67c 11.33a 11.33abc 6.67ab 9.67a l2×t0 526.67ab 176.67bc 10.67ab 12.33ab 6.00abc 6.00bc l2×t5 393.33c 150.00bc 10.00ab 9.00cd 5.00bc 4.33c l2×t10 533.33a 246.67a 8.67b 12.00ab 4.00c 4.67cd l2×t20 370.00c 146.67bc 10.33ab 13.33a 6.67ab 7.00b means that followed by same letters within column are differ non‑significantly at p≤5% according to the duncan multiple range test albarzinji, et al.: pigments and stomata of cowpea under x-ray 62 uhd journal of science and technology | july 2022 | vol 6 | issue 2 micrometer, respectively, in coincides with the treatment 20 min exposure time in the target of radiation source for adaxial leaves surfaces which increased stomata width to 8.67 and 9.67 micrometer, respectively. the present observations showed changes in stomata characteristics under x-ray radiation compared with that not treated. these changes in stomata dimensions under x-ray may due to change in osmotic pressure of epidermal cells which prevent the development of sufficient osmotic pressure in guard cells to open to the same extent as occurs in non-irradiated plants, so the average stomatal opening of x-ray irradiated plants was significantly less compared to non-irradiated plants [13]. stomatal aperture depends on the genotype of plants and is regulated by many internal and external factors [23]. from table 3 results, it is shown that exposure seeds to x-ray in target source increases significantly each of stem and leaves dry matter percent to 10.75 and 14.00% compared to that is out of target location (9.13 and 11.88%), respectively, which agrees with al-enezi and al-khayri [24] whom found an increase in fresh and dry weights of date palm (phoenix dactylifera l.) leaf tissues with increasing the x irradiation dose from 0 to 1500 rad, it also agrees with the results of arena et al. [12] whom found that the high dose of x-rays (50 gy) increased significantly (p < 0.001) leaf dry matter content in faba been young leaves compared to the control leaves. regarding the time of exposure 5 min exposure to x-ray increased the percent of stem and leaves dry matter content significantly (p ≤ 0.05) to 13.75 and fig. 1. lower (abaxial) leaves surfaces of vigna sinensis savi showing stomata at ×400 for (a) the control, (b) in-target −5 min. (c) in-target −10 min. (d) in-target −20 min. (e) out of target −5 min. (f) out of target −10 min., and (g) out of target −20 min. c da b e f fig. 2. upper (adaxial) leaves surfaces of vigna sinensis savi showing stomata at ×400 for (a) the control, (b) in-target −5 min. (c) in-target −10 min. (d) in-target −20 min. (e) out of target −5 min. (f) out of target −10 min. and (g) out of target −20 min. c d gfe a b albarzinji, et al.: pigments and stomata of cowpea under x-ray uhd journal of science and technology | july 2022 | vol 6 | issue 2 63 table 3: effects of location and time of exposure cowpea seed to x‑radiation, and their interactions in stem and leaf dry matter treatments stem dry matter (%) leaves dry matter (%) location of exposure in target (l1) 10.75a 14.00a out of target (l2) 9.13b 11.88b time of exposure (min) t0 8.50b 12.00bc t5 13.75a 15.75a t10 9.50b 13.50b t20 8.00b 10.50c interactions between location and exposure time l1×t0 8.50cd 12.00c l1×t5 15.00a 17.00a l1×t10 10.00c 14.00bc l1×t20 9.50c 13.00bc l2×t0 8.50cd 12.00c l2×t5 12.50b 14.50b l2×t10 9.00c 13.00bc l2×t20 6.50d 8.00d means that followed by same letters within column are differ non‑significantly at p≤5% according to the duncan multiple range test 15.75% compared to other treatments, whereas increasing the time of exposure to 20 min decreased the percent of stem and leaves dry matter significantly (p ≤ 0.05) to 8.00% and 10.5%, respectively. regarding the interactions between the location and time of exposure, the percent of stem and leaves content of dry matter increased significantly to 15.00% and 17.0% for plants emerged from seeds exposed to x-ray for 5 min on the source target, whereas the lowest values were recorded for seeds exposed to 20 min x-ray out of target. for the x-ray effects, it was seen that the shortest time records led to the highest significant increase, which can be concluded that it likes the effects of low doses of x-radiation which encourage cellular activities and growth whereas higher doses may cause chromosomal abnormalities [25]. hence, higher x-ray radiation exposure time effect on the growth of plants which reflects on stem and leaves percent of dry matter. 4. conclusions we can conclude that exposing cowpea seeds to x-ray radiation had stimulation effects regarding photosynthesis pigments and stomata characteristics either as increase or decrease responses according to the treatment. it was concluded that the location of the exposure had nonsignificant effects on photosynthetic pigments, whereas, it effects on stomata characteristics and dry matter content. best exposure time differ according to the studied characteristics. more studies are recommended about effects of x-ray on wet seeds and seedling by different doses of radiation. references 1. g. vasilevski. “perspectives of the application of physiological methods in sustainable agriculture”. bulgarian journal for plant physiol, special issue, vol. 3-4, pp. 179-186, 2003. 2. m. k. al-jebori and i. m. al-barzinji. “exposing potato seed tuber to high voltage field i. effects on growth and yield”. journal of iraqi agricultural sciences, vol. 39, no. 2, pp. 1-11, 2008. 3. i. m. al-barzinji and m. k. al-jubouri. “effects of exposing potato tuber seeds to uv radiation on growth, yield and yield quality”. research and reviews: 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onigbinde. “effects of x-ray irradiation on growth physiology of arachis hypogaea (var. kampala)”. chemistry international, vol. 3, no. 3, pp. 296-300, 2017. _goback tx_1~abs:at/tx_2:abs~at uhd journal of science and technology | jan 2023 | vol 7 | issue 1 7 1. introduction digital image processing (dip) is significant in many areas, particularly medical image processing, image in-painting, pattern recognition, biometrics, content-based image retrieval, image de-hazing, and multimedia security [1], [2]. it is becoming more important for analyzing medical images and identifying abnormalities in these images. computeraided diagnosis (cad) systems based on image processing have emerged as an intriguing topic in the field of medical image processing research. a cad system is a computerbased system that assists medical professionals in diagnosing diseases, in particular cancers, using medical images such as x-ray, magnetic resonance imaging (mri), computed tomography (ct), ultrasound, and microscopic images [3]. the aim of developing autonomous cad systems is to extract the targeted illnesses with a high accuracy and at a lower cost and time consumption. preprocessing, segmentation, feature extraction, and classification are the four basic phases of each cad system. a feature is an important factor to categorize the disease in the cancer detection systems. feature extraction is the process of transforming raw data into a set of features [4]. there are numerous types of cancers such as breast cancer, brain tumors, lung cancer, skin cancer, and blood cancer. this paper focuses on the early detection of the cancerous cells in the breast. breast cancer is one of the most frequent kinds of cancer among females worldwide. there are currently no strategies for preventing breast cancer. the difficulty of radiologist interpretation of mammogram images can computer-aided diagnosis for the early breast cancer detection miran hakim aziz1, alan anwer abdulla2,3 1applied computer, collage of medicals and applied sciences, charmo university, chamchamal, sulaimani, kurdistan region, iraq, 2department of information technology, college of commerce, university of sulaimani, sulaimani, iraq, 3department of information technology, university college of goizha, sulaimani, iraq a b s t r a c t the development of the use of medical image processing in the healthcare sector has contributed to enhancing the quality/accuracy of disease diagnosis or early detection because diagnosing a disease or cancer and identifying treatments manually is costly, time-consuming, and requires professional staff. computer-aided diagnosis (cad) system is a prominent tool for the detection of different forms of diseases, especially cancers, based on medical imaging. digital image processing is a critical in the processing and analysis of medical images for the disease diagnosis and detection. this study introduces a cad system for detecting breast cancer. once the breast region is segmented from the mammograms image, certain texture and statistical features are extracted. gray level run length matrix feature extraction technique is implemented to extracted texture features. on the other hand, statistical features such as skewness, mean, entropy, and standard deviation are extracted. consequently, on the basis of the extracted features, support vector machine and k-nearest neighbor classifier techniques are utilized to classify the segmented region as normal or abnormal. the performance of the proposed approach has been investigated through extensive experiments conducted on the well-known mammographic image analysis society dataset of mammography images. the experimental findings show that the suggested approach outperforms other existing approaches, with an accuracy rate of 99.7%. index terms: computer-aided diagnosis, medical image, breast cancer, gray level run length matrix, classifier technique corresponding author’s e-mail: dr. alan anwer abdulla, assistant prof., department of information technology, college of commerce, university of sulaimani, sulaimani, iraq, department of information technology, university college of goizha, sulaimani, iraq. e-mail: alan. abdulla@univsul.edu.iq received: 17-09-2022 accepted: 11-12-2022 published: 12-01-2023 access this article online doi: 10.21928/uhdjst.v7n1y2023.pp7-14 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2022 aziz and abdulla. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology aziz and abdulla: early breast cancer detection 8 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 be alleviated by employing the early-stage breast cancer detection method. thus, early diagnosis of this condition is critical in its treatment and has a significant influence in minimizing mortality. the most effective way of detecting breast cancer in its early stages is to analyze mammography images [5]. breast cancer is a disorder in which the cells of the breast proliferate uncontrollably. the kind of breast cancer is determined by which cells in the breast develop into cancer. breast cancer can start in any part of the breast. it can spread outside of the breast through blood and lymph arteries. breast cancer is considered to have metastasized when it spreads to other regions of the body [6]. in general, a breast is composed of three major components: lobules, ducts, and connective tissue (fig. 1) [6]. the lobules are the milk-producing glands. ducts are tubes that transport milk to the nipple. the majority of breast cancers start in the lobules or ducts [6]. connective tissue joins or separates and supports all other forms of bodily tissue. it contains of cells surrounded by a fluid compartment termed the extracellular matrix (ecm), as do all other forms of tissue. however, connective tissue varies from other kinds in that its cells are loosely instead of densely packed inside the ecm [7]. the aim of this study is developing a cad system for the early detection of breast cancer. the developed cad system has the advantages of increasing accuracy rate, reducing time consumption, and reducing cost in comparison with manually detecting system. the main contributions of the proposed approach are segmenting the breast region properly as well as extracting the most significant features, and this leads to increase the accuracy rate and reduce mistake rate of wrongly treating patients. the proposed system includes the following steps: a pre-processing step for enhancing the image quality, a segmentation step for segmenting the breast region from the other components of mammography images, and a feature extraction step for extracting the most influential features. finally, the classification step is conducted, which helps the system decide whether a cell is cancerous or noncancerous. the rest of the paper is structured as follows. section 2 provides a summary of past efforts from the literature. section 3 presents the proposed cad system. section 4 shows the results of experiments. finally, section 5 gives the conclusion. 2. literature review in medical image processing, the cad system is a computerbased system that helps clinicians in their last decision about different diseases, especially cancers. the whole process is about extracting significant information from medical images such as: mri, ct, and ultrasounds. several cad systems have been developed for identifying different diseases including: breast cancer, tumor detection, and lung cancer. this study concentrates on breast cancer. the processing and analysis of breast mammogram images plays a significant role in the early diagnosis of breast cancer. this section reviews the most influential as well as relevant current efforts on the early breast cancer detection using dip. the main obstacle in this field of research is reducing the rate of breast cancer detection errors. in general, most of the cad systems for the early breast cancer detection consist of the following steps: image enhancement, image segmentation, feature extraction, feature selection, and classification. in 2010, eltoukhy et al. suggested an algorithm for the breast cancer detection using a curvelet transform technique at multiple scales [8]. different scales of the largest curvelet coefficients are extracted and investigated from each level as a classification feature vector. this algorithm is reached an accuracy rate of 98.59% at scale 2. srivastava et al., in 2013, introduced a cad system for the early breast cancer diagnosis using digital mammographic images [9]. contrast-limited histogram equalization technique is utilized for the enhncement purposes. consequently, three-class fuzzy c-means is used for the segmentation process. the texture features such as geometric/shape, wavelet-based, and gabor were extracted. the minimum redundancy maximum relevance feature selection method was utilized to select the fewest redundant and most relevant characteristics. finally, support vector fig. 1. major components of the breast [6]. aziz and abdulla: early breast cancer detection uhd journal of science and technology | jan 2023 | vol 7 | issue 1 9 machine (svm), k-nearest neighbor (knn), and artificial neural network (ann) classifier techniques were used for classifying cancerious and non-canceroius cells. furthermore, svm provides better results in comparison to the knn and ann. this technique is achieved an accuracy rate of 85.57% for the 10-fold cross-validation using mammographic image analysis society (mias) dataset of images. vishrutha et al., in 2015, developed a strategy for combining wavelet and texture information that leads to increase the accuracy rate of the developed cad system for the early breast cancer diagnosis [10]. the mammogram images were pre-processed using median filter. in addition, the label and the black background are removed on the bases of sum of each column’s intensities. consequently, if the total intensity of a column falls below a certain level/threshold, the column will be removed. the resulted images from the pre-processing step were utilized as input for the region growth technique used to determine the region of interest (roi) as a seqmentation step. discrete wavelet transform technique was used to extract features from the seqmented images/regions. finally, svm classifier technique was utilized to categorize the mammogram images as benign or malignant with an accuracy rate of 92% using mini-mias dataset of images. in 2017, pashoutan et al. developed a cad system for the early breast cancer diagnosis [11]. for the pre-processing step, cropping begins by employing coordinates and an estimated radius of any artifacts introduced into images to get to the roi where bulk and aberrant tissues are found. moreover, histogram equalization and median filter were used to enhance the contrast of the images. edge-based segmentation and region-based segmentation methods are that the two main methods were used for the segmentation purposes. furthermore, four different techniques were utilized for extracting features, such as wavelet transform, gabor wavlet transform, zernike moments, and gray-level cooccurance matrix (glcm). eventually, using the mias dataset, this technique reached an accuracy rate of 94.18%. hariraj et al., in 2018, developed a cad system for the breast cancer detection [12]. in the pre-processing step, fuzzy multilayer was used to eliminate background information such as labels and wedges from images. moreover, thresholding was used to transform the grayscale image to the binary image. furthermore, morphological technique was implemented on the binary image to remove undesirable tiny items. regarding to the segmentation step, k-means clustering was utilized. for the feature extraction purposes, certain shape and texture features were extracted such as: diameter, perimeter, compactness, mean, standard deviation, entropy, and correlation. finally, the fuzzy multi-layer svm classifier technique provides better accuracy rate of 98% out of other tested classifier techniques using mini-mammographic mias dataset of images. sarosa et al., in 2019, designed a breast cancer diagnosis technique by investigating glcm and backpropagation neural network (bpnn) classification technique [13]. histogram equalization was utilized for the pre-processing and enhancing the images. consequently, glcm was used to extract features from the pre-processed images. finally bpnn was used to determine whether the input image is normal or abnormal. the suggested approach was evaluated using a mias dataset of images and it achieved an accuracy rate of 90%. in 2019, arafa et al. introduced a technique for the breast cancer detection [14]. in the pre-processing step, just the area including the breast region is automatically picked and artifacts as well as pectoral muscle were removed. the gaussian mixture model (gmm) was utilized to extract the roi. moreover, texture, shape, and statistical features were extracted from the roi. for the texture feature, glcm was utilized. furthermore, the following shape features such as circularity, brightness, compactness, and volume were extracted. regarding to the statistical features, mean, standard deviation, correlation, skewness, smoothness, kurtosis, energy, and histogram were extracted. finally svm classifier technique was used to classify segmented roi into normal, abnormal, benign, and malignant. this proposed technique was evaluated using mias dataset of images and it achieves an accuracy of 92.5%. farhan and kamil developed a cad system for classifying the input mamogram images into normal or abnormal, in 2020, [15]. at the beginning, contrast limited adaptive histogram equalization (clahe) method was used to improve all mammogram images. in addition, the histogram of oriented gradient, glcm, as well as the local binary pattern (lbp) techniques was used to extract features. finally, svm and knn classifier techniques were used for classifying cancerious and non-canceroius cells. the best accuracy rate of 90.3%, using mini-mias dataset, was obtained when glcm and knn were used. in 2020, eltrass and salama developed a technique for breast cancer diagnosis [16]. as a pre-processing step, the mammography image was translated into a binary image, aziz and abdulla: early breast cancer detection 10 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 and then all regions are sorted to identify the mammogram’s greatest area, that is, breast region. in addition, all artifacts and pectoral muscle were eliminated. this cad system utilized the expectation maximization technique for the segmentation purposes. wavelet-based contourlet transform technique was used to extract features. finally, svm classifier technique was used and an accuracy rate of 98.16% was achieved using mias dataset. saeed et al., in 2020, designed a classifier model to aid radiologists in providing a second opinion when diagnosing mammograms [17]. in the pre-processing step, median filter was used to remove noise and minor artifacts. hybrid bounding box and region growing algorithm was used to segment the roi. for the features extraction, two types of features were extracted which are: (1) statistical features such as mean, standard deviation skewness, and kurtosis and (2) texture features such as lbp and glcm. consequently, svm was used to categorize mammography images as normal or abnormal in the first level, and benign or malignant in the second level. this proposed technique used mais dataset to evaluate the performance, and an accuracy of 95.45% was obtained for the first level and 97.26% for the second level. mu’jizah and novitasari in 2021, developed a cad system for the breast cancer diagnosis [18]. at the beginning, certain pre-processing techniques, such as gaussian filter and canny edge detection technique, were implemented to enhance the visual quality of the input images. the thresholding method was also used for the segmentation purposes. to extract features, glcm was used as texture feature, and area, perimeter, metric, as well as eccentricity were extracted as shape feature. finally, for the classification step, svm was used and an accuracy rate of 98.44% was obtained using mini-mias dataset of images. recently, in 2022, holi produced a breast cancer detection system [19] which used a median filter and clahe for enhancing the input image. then, chebyshev distancedfuzzy c-means clustering was used to segment the preprocessed image. the augmented local vector pattern, shape features, and glcm were used to extract features. the classification step was conducted using knn classifier technique. this proposed technique was achieved an accuracy rate of 97% using mias dataset of images. the remainder of this paper concerns with the extension and further refinement of the strategy of using dip to increase the accuracy rate for the early breast cancer detection. 3. proposed approach the microscopic image of breast is called a mammogram, which consists of three parts/regions. the breast part appears on a mammogram in colors of gray and white, while the mammog ram backdrop is often black. in addition, a lump or tumor appears as a concentrated white area. tumors may be either malignant or benign [20]. the most significant step of each cad system for the breast cancer detection is extracting/cropping the roi from the other parts of the mammogram image. this section describes the proposed approach which involves the following steps: 1. pre-processing: in this step, certain techniques are applied such as region-props to delete the label from the mammogram images, and median filter as well as adaptive histogram equalization to enhance the image quality (fig. 2). 2. segmentation: to segment the roi from other parts of the input image, the thresholding segmentation technique is applied on image (d) in fig. 2, and the resulted image is a binary image, see image (a) in fig. 3. the threshold-based segmentation approach is an effective segmentation technique that divides an image based on the intensity value of each pixel. it is used to segment an image into smaller portions using a single color value to generate a binary image, with black representing the background and fig. 2. pre-processing step:(a) original mammogram image, (b) label removed, (c) resulted image after the median filter has been applied on image (b), and (d) resulted image after histogram equalization has been applied on image (c). dc ba aziz and abdulla: early breast cancer detection uhd journal of science and technology | jan 2023 | vol 7 | issue 1 11 white representing the objects [21]. the threshold t value can be selected either manually or automatically based on the characteristics of the image. in the proposed approach, t = 0.7 was used, which provides the optimum accuracy results. in the next section, all the tested values for the t are illustrated in table 5. 3. feature extraction: texture features and statistical features are extracted from the segmented image, that is, image (b) in fig. 3. the extracted features are summarized in table 5. furthermore, all the extracted features are fused for the classification purposes. 4. classification: svm and knn classification techniques were applied on the extracted features to distinguish normal cells from abnormal cells. the reason behind using svm and knn is because these two classifier techniques are the most common used in this field of research. for the both classifiers, the k-fold crossvalidation with k = 5, 10, 15, and 20 was investigated. fig. 4 illustrates the block diagram of the proposed approach. 4. experimental results the primary goal of the proposed cad approach is classifying the breast cancer cells into normal or abnormal. experiments are carried out in a thorough manner in this part of the study to evaluate how well the suggested approach works in terms of accuracy rate. in addition, the proposed approach is assessed alongside the findings of the earlier research. 4.1. dataset the mias dataset provides the tested input images, which are taken from the public domain and are quite well recognized. the mias dataset contains the original 322 images, 206 normal and 116 abnormal, in the pgm format [22]. all of the images have the same resolution which is 1024 by 1024 pixels. the mias dataset has been taken into consideration in order to assess the performance of the proposed cad approach. 4.2. results using several classifier techniques, such as svm and knn, the accuracy rate for each the extracted features is assessed. tables 2 and 3 present the accuracy rate of statistical and glcm separately using svm and knn respectively. in all the evaluation tests, different values ok k-fold have been considered. in addition, the accuracy rate has been calculated using the following formula [23]: accuracy rate = tp + tn/(tp + tn + fp + fn) (1) where: tp, tn, fp, and fn refer to true positive, true negative, false positive, and false negative, respectively. more investigation has been conducted by fusing the extracted features, namely statistical and glcm. meanwhile, the 11 retrieved features are utilized to evaluate the effectiveness table 1: extracted features type of features name of the feature statistical features texture feature: gray level run length matrix skewness mean entropy standard deviation short run emphasis long run emphasis gray level non-uniformity run percentage run length non-uniformity low gray level run emphasis high gray level run emphasis table 2: svm‑based accuracy rate for the extracted features separately features 5 k-fold 10 k-fold 15 k-fold 20 k-fold average (%) statistical 99.1 99.2 98.8 99.3 99.1 glrlm 98.1 99.3 99.1 99.1 98.1 svm: support vector machine, glrlm: gray level run length matrix table 3: knn‑based accuracy rate for the extracted features separately features 5 k-fold 10 k-fold 15 k-fold 20 k-fold average (%) statistical 94.4 94.7 97.5 97.5 96 glrlm 97.2 98.1 97.6 98.3 97.8 knn: k‑nearest neighbor, glrlm: gray level run length matrix fig 3. segmentation step: (a) binary image, (b) based on the binary image in (a), the roi is selected in the original image. ba aziz and abdulla: early breast cancer detection 12 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 of the proposed cad approach in distinguishing between normal and abnormal cells. those 11 features are previously mentioned in table 1. moreover, kfold cross-validation with various values of k is used in the evaluation process to measure the accuracy. training and testing have been done using k-fold cross-validation, which divides data automatically into training and testing depending on the value of k.based on the investigation conducted in this study, fig. 4. block diagram of the proposed approach. aziz and abdulla: early breast cancer detection uhd journal of science and technology | jan 2023 | vol 7 | issue 1 13 the svm classifier technique provides a higher accuracy rate (table 4). tables 5 and 6 illustrate the findings of further tests done by comparing the obtained results of the proposed approach to results of four existing approaches. two of the existing works were used svm classifier techniques and the remained two works were used knn. all of the four tested cad systems used only 5k fold to evaluate the performance of their approaches and also tables 7 illustrate the time consumption of the all process in our system. according to the results presented in tables 5 and 6, the best accuracy rate is achieved by the proposed approach and it outperforms all the tested existing approaches. moreover, in eltrass and salama [16], the total time consumption is highlighted which is (2.26267) second, while the time consuming of our proposed approach is (2.004) second. the time consumption of the proposed approach is calculated as follows: more investigations have been done for testing the optimum value for the thresholding t that used for the segmentation purposes. based on the results presented in table 8, it is quite obvious that the best accuracy rate was achieved when t = 7. 5. conclusions since detecting a disease/cancer and identifying treatments manually is costly, time consuming, and requires professional staff, the evolution of the application of medical image processing in the healthcare field has contributed in an improvement in the quality/accuracy of disease diagnosis (or early detection). meanwhile, medical image processing techniques can accurately extract target diseases/cancers at higher accuracy and lower cost. breast cancer is one of the leading causes of mortality among women, compared to all other cancers. therefore, early detection of breast cancer is necessary to reduce fatalities. thus, early detection of breast cancer cells may be anticipated using recent machine learning approaches. the primary objective of developing cad system for mammogram images is to aid physicians and diagnostic experts by providing a second perspective, this increases confidence in the diagnostic process. this study was focused on the development of an efficient cad system for the early breast cancer detection. the testing findings reveal that the proposed cad approach obtained an accuracy rate of 99.7% and outperforms the existing approaches. to improve the performance of the proposed approach, the following are points of potential plans that extend our work in the future: (1) more filters and image processing techniques will be tested for pre-processing purposes to table 4: accuracy rate of the proposed cad approach cross validation 5k 10k 15k 20k average (%) svm 99.7 99.8 99.4 99.7 99.7 knn 98.4 99.1 98.8 98.8 98.9 cad: computer‑aided diagnosis, svm: support vector machine, knn: k‑nearest neighbor table 5: accuracy rate of the tested approaches using svm accuracy rate (%) proposed mu’jizah and novitasari[18] eltrass and salama [16] 99.7 98.4 98.1 table 6: accuracy rate of the tested approaches using k‑nearest neighbor accuracy rate (%) proposed farhan and kamil[15] holi [19] 98.9 90.3 97 table 7: time consumption of the proposed computer‑aided diagnosis system stage times in second pre-processing 0.371 segmentation 0.298 feature extraction 0.061 classification 1.274 total 2.004 s table 8: investigating the optimum value for thresholding t thresholding values knn (%) svm (%) 0.1 89 90.8 0.2 89.7 91.1 0.3 89.8 91.9 0.4 94.1 94.7 0.5 96.3 96 0.6 97.6 99.1 0.7 98.9 99.7 0.8 98.4 99.3 0.9 98.6 99.5 knn: k‑nearest neighbor, svm: support vector machine aziz and abdulla: early breast cancer detection 14 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 enhance the image quality, (2) different techniques will be tested to improve segmenting purposes, and (3) different kinds of features should be 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[22] j. suckling, j. parker, d. dance, s. astley, i. hutt, c. boggis, i. ricketts, e. stamatakis, n. cerneaz, n, s. kok, p. taylor, d. betal and j. savage. “the mammographic image analysis society digital mammogram database”. international congress series, vol. 1069, pp. 375-378, 1994. [23] r. murtirawat, s. panchal, v. k. singh and y. panchal. “breast cancer detection using k-nearest neighbors, logistic regression and ensemble learning”. proceedings of the international conference on electronics and sustainable communication systems, icesc 2020, pp. 534-540, 2020. . uhd journal of science and technology | jan 2019 | vol 3 | issue 1 1 1. introduction the shewashok oil field was discovered in 1930. the first well was drilled in 1960 and the second was drilled in 1978, but, due to political circumstances, oil was not extracted until 1994 where the production was 44,027 barrels/day in that year. then production reached 140,000 barrels a day by 2016 [1]. a total of 31 wells are drilled, and currently, more wells are drilling, but the field has rarely been studied scientifically, especially regarding ecological aspects. air, water, and food are the basic needs of most of the living organisms to survive. the quality of consumed water, air, and food may transfer to the consumer body organisms. with gas flaring in the oil field, toxic gases and particles are released into the atmosphere [2]. quite possibly the particles contain heavy metals due to that they are driven from hydrocarbons and come from deep geological layer formations, obviously living organisms consume this contaminated air as the source of their respiration. furthermore, diet is the most critical pathway of transferring the trace elements to mammal’s organisms and store in the tissues; therefore, laboratory testing of animal tissues can be environmental impacts of shewashok oil field on sheep and cow meat using vital trace elements as contamination bioindicators mamoon qader salih1, rawaz rostam hamadamin2, rostam salam aziz3 1department of oil and gas, mad institute, arbil-koya road, erbil, kurdistan region, iraq, 2department of basic education, koya university, daniel mitterrand boulevard, koya koy45 ab64, kurdistan region – iraq, 3department of geography, koya university, daniel mitterrand boulevard, koya koy45 ab64, kurdistan region – iraq a b s t r a c t ambient environment is built based on the interaction of living and non-living organism and chemical and physical compounds, and thus, oil field emissions, effluents, and its general waste can be a part of environmental condition of certain area. this study is to investigate the environmental impacts of oil field on sheep and cow meat around shewashok oilfield. it has been performed at the laboratories of the department of medical microbiology, koya university, by detecting and measuring heavy metals and vital trace elements as contamination indicators. 20 meat samples of domestic animals (cow and sheep) in both control and affected area were collected for the purpose of detecting the concentration of heavy metals in the animals. the samples dried and digested with concentrated hno 3 and concentrated h 2 o 2 . the concentration of heavy metals of the sample digested domestic animal was determined using inductively coupled plasma–optical emission spectroscopy. this study shows that iron, cobalt, copper, zinc, arsenic, manganese, aluminum, mercury, and chromium were detected in all the meat samples. overall, this study confirms that the cow and sheep meat are still safe to eat in both locations because only al, fe, and hg were found danger in both sheep and cows’ meat in comparison with allowed limits of the world health organization 2017, and all other trace elements are complying with the global standards. index terms: cows’ and sheep meat, environmental pollution, oil field, shewashok, trace elements corresponding author’s e-mail: rawaz rostam hamadamin, department of basic education, koya university, daniel mitterrand boulevard, koya koy45 ab64, kurdistan region – iraq, e-mail: rawaz.rostam@koyauniversity.org received: 20-09-2018 accepted: 24-01-2019 published: 25-01-2019 access this article online doi: 10.21928/uhdjst.v3n1y2019.pp1-8 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2019 salih, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l r e s e a r c h a r t i c l e uhd journal of science and technology maamun qadir salih, et al.: shewashok oil field impacts on environment 2 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 a vital bioindicator for environmental pollution [3]-[5]. some nigerian studies showed that, during drilling, oil production, refining, and gas flaring, harmful elements can add to air, soil, and both surface and groundwater [6], [7]. if air, water, and soil quality is not acceptable by standards, then vegetation, plants, and fruit quality can alter [7]. in general, contamination of air, water, and soil can transfer to plants then to animals by ingestion and then to human. in the study area, a research showed that groundwater is already not complying with national and international standards [1]. however, air, soil, and agriculture crops have not been studied yet. not all the trace elements are heavy metal but all the heavy metals are trace elements and toxic out of their limits. therefore, some of the trace elements are essential for life, although some of them can cause a high risk to the health [8], [9]. in general, the metals can be classified into three main groups: potential toxic such as cadmium and mercury; probably essential such as manganese and silicon; and essential metals such as cobalt, copper, zinc, and iron [8]-[10]. the toxicity effects are referred to specific types of metals which are not beneficial to human health; contrary, it causes severe toxicological effect if body receives an amount out of safe limit [8]. it may not be easy to prevent intake of trace elements by human, as industries significantly develop on a sustained speed around the world, a large amount of metals streaming into the environment. moreover, yet, most of the heavy metals are permanently circling in the environment because they are indecomposable materials and these can integrate with daily essentials such as food and water, and hence, they make their way into the human tissues through the food chain [8], [11]. meat is considered as an essential source of human nutrition. the chemical composition of meat depends on the quality of animal feeding; this may potentially accumulate toxic minerals and represent one of the sources of critical heavy metals [8], [10]. the risk associated with the exposure to heavy metals present in food and food products has aroused widespread concern in human health [11]. however, improvement in food production and processing technology achieved, but food contamination with various environmental pollutants also increased, especially trace elements and heavy metals among them. in the light what introduces above, the current study aims to evaluate some vital trace elements such as al, as, cu, cr, co, fe, hg, mn, and zn in raw meat of cow and sheep that produced in iraqi kurdistan, and it tries to understand their level of danger and toxicity to consumers. the samples were collected from two industrial sites, an area surrounding the shewashok oil field and in the north of erbil. it will compare both samplings together and then evaluate them by considering the who standards for heavy metals and trace elements. 1.1. study area the samples were collected from north of iraq in erbil province fig. 1. this region is with mediterranean climates system, having cool, wet winter, and hot and dry summers with mild spring and autumn, and its annual average precipitation is 450 mm with some variation from the mountains to the plains [12]. two locations were selected from the province for the sampling: focused location which is shewashok oil field (called study area group in this article) in the southeast of erbil and the second location is in the north of erbil which is the main arable area and livestock farming of the province. the animals are feeding with available rearing resources in the region that means that the meat quality is affected by the ambient environment condition. 2. materials and methods the study data collection, preparation, and analysis followed below stages. 2.1. sample collection the materials used for the study included field and laboratory materials. the experimental work has been performed at the laboratories of the department of medical microbiology, koya university. the collected samples from slaughterhouse of arbil city “control area” and koya city “study area.” 20 meat samples were collected from each cow and sheep of the study area to detect the concentration of trace elements. in parallel, 20 samples have been collected from each cow and sheep of the control area. 2.2. the summary of the samples collected at both the locations • number of samples collected: 80 samples. • number of samples collected of the study area sheep and cows: a total of 40 samples “in another word, 20 samples of each.” • number of samples collected of control sheep and cows: a total of 40 samples “in another word, 20 samples of each.” • trace element analyzed: iron, cobalt, copper, zinc, arsenic, manganese, aluminum, mercury, and chromium. maamun qadir salih, et al.: shewashok oil field impacts on environment uhd journal of science and technology | jan 2019 | vol 3 | issue 1 3 2.3. used materials and chemicals 2.3.1. material cylinder, funnel, beaker filter paper watch glass, pipette, volumetric flask, conical flask, balance, bottle (250+500) ml hot plate, oven, centrifuge, hood, gloves, tissues, bio hand (alcohol to cleaning), plastic bags, blade operations, parafilm, bottle to save solution, falcon tube, cuter, and tongue depressor were used. 2.3.2. chemical nitric acid, hydrogen peroxide, distilled water, deionized water, vacuum clever for cleaning materials, and inductively coupled plasma (icp) were used as chemicals. 2.3.3. digestion procedure to the determination of trace elements in the sample meat of sheep and cow animals by icp–optical emission spectroscopy (oes) the collected samples were decomposed by wet digestion method for the determination of various metals. the collected samples were washed with distilled water to remove any contaminant particles. the samples were cut to small pieces using clean scalpel. samples were dried in an oven at 100°c. weight 1 g of dried sample, using sensitive balance. transfer the dried samples into 250 ml digestion beaker or flask. digest the sample by adding 10 ml of concentrated hno 3 and mix well. heat the digestion mixture on a hot plate at 100 ± 10°c for 30 min, inside the fume chamber (hood). repeat the heating process once more with 10 ml of the acid. cool down the mixtures to room temperature, and then, add 2 ml of concentrated h 2 o 2 . heat the beaker or flask again carefully, until dryness. leave to cool down, then dissolve the mixture in distilled or deionized water until obtaining a clear solution. filter the sample solution through a cellulose filter paper into 25 ml digestion tubes. the filtrate was diluted to 25 ml with distilled or deionized water and heated the solution to dissolve the precipitate transfer the samples into laboratory polyethylene bottles and store until analyzed. a blank digestion prepared in the same procedure for the control samples. finally, analyze the elements in the sample solutions by icp/icp-oes. the final measurement volume of the sample solutions should be 5 ml [13]-[15]. fig. 1. study area map with explain sampling locations. source: kurdistan region of iraq, ministry of planning, information directorate and the preparation of maps, map of erbil in 2016, scale (1: 250000). maamun qadir salih, et al.: shewashok oil field impacts on environment 4 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 2.3.4. icp-oes the well-known icp–mass spectrometry technique has been used to test the samples at a modern scientific laboratory for heavy metals. among the trace elements, only 17 critical heavy metals have been examined due to their negative impacts on living organisms [16]. 2.4. statistical analysis for the first section of this study discussion, data were expressed as mean ± standard error of mean ,and the statistical package for the social sciences (version 20) software was used to analyze the results. differences in mean values between two groups were analyzed by t-test. p < 0.05 was considered to be statistically significant. 2.5. comparison of the study observations with the who standards for trace elements for the second section of this study discussion, only study area data (excluding control area in this section) were compared with the who 2017 guidelines for trace elements limits to find the level of contamination in our study according to the global scale. 3. results and discussion in recent years, much attention has been given to contamination of food products, among the animal meats. the level of trace elements in meat from different animals depends on some factors such as environmental conditions of the animal grazing location. the obtained results of the current study were divided into two sections to discuss; the first section is a comparison between study area which is shewashok oil field and control area which is the north of erbil, whereas the second section is a comparison between the study areas with the who standards. 3.1. first section: comparison of study area with control area table 1 shows that the difference between control and study groups of aluminum in sheep samples is 254.6 and 404.5 ppb, respectively, that means the study area is higher than control group by 1.5 times. table 2 shows that the value of aluminum in cow sample of both control group and study area is 186.2 and 278.7 ppb, respectively, again the value of study area is higher than the control group by 1.4 times. both locations have a similar value for aluminum, but in comparison with the who 2017 guidelines which are 200 ppb, both locations are higher than allowed limit that is due to the type of animal diet in both the groups [17]. arsenic is also very toxic to animals, because it affects their body through gastrointestinal tract and the cardiovascular system. symptoms of arsenic poisoning in animals include watery diarrhea, severe colic, dehydration, and cardiovascular collapse [13]. table 1 presents that the value of arsenic in sheep samples of control area and study area is 8.005 ppb and 6.256 ppb, respectively, and table 2 presents that the value in cow samples of control area and study area is 8.015 ppb and 7.478 ppb, respectively. both sample sheep and cows of control group are higher than the study area and it is due to the contamination of pasture by industrial emissions [14]. previous study shows a high concentration of arsenic in the meat of cattle and goats in bieszczady mountains [18]. all samples of both locations in this study are within the allowed limit of the who which is10 ppb. tables 1 and 2 show that the result of chromium in both the locations had high differences between the control group and study area. the value of the control group of both sheep and cows’ meat samples showed zero, but the study area location of the samples showed 0.752 and table 1: trace element concentration in control and study groups of sheep meat elements control group (ppb) study group (ppb) p value al 254.6±48.51 404.5±126.3 0.275 fe 1941±295.2 474.1±121.2 0.0001 hg 26.12±0.434 26.91±0.484 0.229 mn 159.5±31.21 179.7±28.88 0.638 zn 1006±100.9 1080±128.8 0.654 as 8.005±0.789 7.478±1.010 0.683 co 0.000±0.000 0.266±0.116 0.028 cr 0.000±0.000 0.752±0.347 0.037 cu 492.6±61.65 1038±253.8 0.043 results expressed as mean±se table 2: trace element concentrations in control and study groups of cow meat elements control group (ppb) study group (ppb) p value al 186.2±31.59 278.7±41.19 0.08 fe 1356±154.9 3720±534.3 0.0001 hg 26.49±0.455 26.78±0.585 0.699 mn 104.9±22.35 110.0±12.45 0.842 zn 685.9±90.73 1688±264.4 0.001 as 8.015±0.812 6.256±0.950 0.171 co 0.271±0.127 1.242±0.344 0.012 cr 0.000±0.000 6.692±4.636 0.157 cu 922.2±268.9 134.3±28.96 0.006 results expressed as mean±se maamun qadir salih, et al.: shewashok oil field impacts on environment uhd journal of science and technology | jan 2019 | vol 3 | issue 1 5 6.692 ppb, respectively, of sheep and cow sample; the high value of chromium in the study area is due to the release of chromium into the environment due to natural gas flaring during oil processing [19], [20]. this result was supported by the assessment of heavy metal pollution and contaminations in the cattle meat [13], [21]; however, the samples of both the locations had a lower value of chromium than allowed limit 50 ppb according to the who guideline. for cobalt, tables 1 and 2 show that the value between both locations in the sheep meat sample is 0.000 and 0.266 ppb, respectively, in control and study group, and for the cows’ meat sample, the recorded value in control and study group is 0.271 and 1.242 ppb, respectively; the study area was higher than control area by approximately 4.6 times, it might due to soil contamination, also pasture lands is recognized as a source of co, it can occur as a result of animal treading or soil splash on short pasture during heavy rain [22]. however, all samples of both the locations are within the allowed limit of the who which is 3 ppb. mercury is volatile liquid metal, found in rocks and soils, and also is present in air as a result of human activities as the use of mercury compounds in the production of fungicides, paints, cosmetics, papers pulp, etc. the highest concentrations were found in soils from urban locations; mercury may induce neurological changes and some diseases [23]. table 1 shows that the sample of sheep meat had a high value of mercury contents of samples in the control and study area ranged between 26.12 ppb and 26.91 ppb, respectively, and also the sample of cows’ meat like sheep meat had a high amount in both location control group (26.49 ppb) and study area (26.78 ppb). a previous study findings comply with this finding as mercury recorded high in beef meat from algeria [14]. zinc is another essential element in our diet, but the excess may be harmful, and the provisional tolerable weekly intake (ptwi) zinc for meat is 700 mg/week/person [21]. the minimum and maximum levels of zn were detected in both the location of control group and study area of sheep samples which was recorded between 1006 and 1080 ppb respectively, and for the cows’ sample, was recorded 685.9 and 1688 ppb, respectively, in both location of control group and study area, and none of the samples exceeded the recommended limit 3000 ppb according to the who guideline. moreover, the difference between both positions of zinc metal is non-significant in sheep samples but for the cows’ sample had a highly significant; however, the meat sample of cows and sheep in study area location showed a higher value when compared to control group because of the high intake of zinc by animals, due to several factors, first of all having excessive amounts of zinc in animal’s food, pastures lands contaminated with smoke that polluted by zinc, surfaces painted with high-zinc paints where animals could lick them and finally food transport in galvanized containers that already containing zinc when manufactured [24], [25]. iron deficiency causes anemia and meat is the source of this metal; however, when their intake is excessively elevated, the essential metal can produce toxic effects [26]. table 1 shows that the iron value of control group and study area for sheep was 1941 and 474.1 ppb, respectively, and the amount of the control is more elevated than study group by 4 times, which recorded among the sheep meat samples [8]. table 2 shows that the value of iron in cows’ meat sample was 1356 and 3720 ppb, respectively, of the control group and study area, and both the locations are higher than allowed limit 300 ppb according to the who guideline. that is due to the type of feeding which contains dry plants that may be very rich with mentioned elements, or the consumed water is containing a high level of fe. tables 1 and 2 show that the value of manganese element in cows’ meat sample in control group and study area is 104.9 and 110 ppb, respectively, also in sheep meat sample, the amount of manganese of control and study area is 159.5 and 179.7 ppb, respectively, and the values of the study area is higher than the control group. although copper is essential for good health, the ptwi copper for fresh meat has been proposed as 14 mg/week/ person [13]. however, very high intakes can cause health problems such as liver and kidney damage [25]. determination of the cu content in food is also an important subject concerning human consumption [27], [28]. table 1 shows that the value of copper element of control group and study area for sheep is 492.6 and 1038 ppb, respectively, and the study area is higher than control area by 2.1 times. the results of the present study indicate that the values of copper in the study area were relatively high compared with the who guideline. that is, since this metals enter through feed material from burning zoon and transport excuse products, ultimately passage into the tissues and the excessive ingestion of copper by animals could occur in various situations such as grazing maamun qadir salih, et al.: shewashok oil field impacts on environment 6 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 immediately after fertilization, pastures grown on soils containing high concentration of copper, supply of wheat treated with antifungal drugs containing copper, and pasture contaminated by smoke from foundries [21]. this result is compatible with other studies in countries such as sweden, as high values of copper were found in the cattle meat [28], [29]. moreover, about copper, moreover, table 2 shows that copper values of the control group and study area is 922.2 and 134.3 ppb respectively for cow’s meat. cows’ meat from the control group had a higher value of cu concentration compared with the study group. in this study, records of both animals and locations are within allowed limit 1000 ppb of who 2017. in general, both areas are quite similar for cows and sheep because the values are close, which may be result of similarity of the geographic feature and exist no effective physical barrier between both locations. 3.2. section two: comparing study area with the who 2017 standards most of the elements are within the who standards such as mn, zn, as, co, and cr in both cows’ and sheep meat, and only cu is just above the who limit by 38 ppb in sheep meat samples; however, it is within the standard in cow samples (table 3). al and fe both are exceeding the who guideline, al by 204.5 and fe by 174.1 ppb in sheep samples and al 78.7 ppb and fe 3420 ppb in cow samples. furthermore, hg is out of the who accepted range but with a high significant difference between the samples and the standard value, which is more than 4 times higher than the standard (table 3). this simple comparison notes that most of the elements are within the who standards such as mn, zn, as, co, and cr, which means that they have no health risks on consumers [30], [31]. cu which is an essential trace element for a human body is just above the who limit only in sheep meat samples, but it probably not causing a tremendous health risk as the exceedance is negligible. cu can increase in animal body if the consumed vegetable leafs have contaminated with cu [8]. both al and fe are effective exceeding the who guideline, as discussed in the first section high value of al is due to the type of both animals’ diet in the study area [17]. fe is higher than the who standards in both animal meat samples, but it is very high in cows’ meat samples as showed above. both excessive and deficiency of fe intake can lead to health disorder [32]. fe is a naturally occurring element, but extreme high value as read in cow samples may be due to human intervention through the quality of air, water, or food that consumed by the animals, but there is no study regarding of air, water, or vegetation quality of the study area. furthermore, hg which is a toxic elements [8], [9] is out of the who accepted range but with a high significant distance between the samples and the standard value. a previous study confirms this finding as mercury recorded high in beef meat in north algeria [14]. however, hg is a naturally accruing element, but a high value in the body can have a detrimental effect on health of the consumers [33], such as damaging nervous system, liver, and eyes, and infant may be deformed; other symptoms of mercury toxicity are a headache, fatigue, anxiety, lethargy, and loss of appetite. 4. conclusion the present findings indicated that these trace elements such as iron, cobalt, copper, zinc, arsenic, manganese, aluminum, mercury, and chromium were detected in all the samples. only, hg, al, and fe, in both sheep and cows’ meat, presented high values for both groups in comparison with allowed limits of the who 2017. however, overall, this study confirms that the cow and sheep meat still safe to eat in the study area because only al, fe, and hg were found danger, but all other elements are complying with the global standards. 5. acknowledgment our special thanks and much appreciation goes to the workers in slaughterhouses of erbil and koya, for their support and cooperation with the data collection. we would like to thank garmian university for testing the samples and koya university for using its laboratories. table 3: comparison of study area with the who 2017 standards elements who (ppb) study group (sheep) (ppb) study group (cow) (ppb) al 200 404.5 278.7 fe 100-300 474.1 3720 hg 1-6 26.91 26.78 mn 100-400 179.7 110.0 zn 3000 1080 1688 as 10 7.478 6.256 co 3 0.266 1.242 cr 50 0.752 6.692 cu 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[30] world health organization. “guidelines for drinking-water quality: maamun qadir salih, et al.: shewashok oil field impacts on environment 8 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 incorporating first addendum”. world health organization, geneva, 2017. [31] world health organization. “guidelines for drinking-water quality: recommendations”. world health organization, geneva, 2004. [32] g. nordberg, b. a. fowler and m. nordberg. “handbook on the toxicology of metals”. academic press is an imprint of elsevier, london, 2014. [33] k. m. rice, e. m. walker jr., m. wu, c, gillette and e. r. blough. “environmental mercury and its toxic effects”. journal of preventive medicine and public health, vol. 47, no. 2, pp. 74, 2014. . 24 uhd journal of science and technology | may 2018 | vol 2 | issue 2 1. introduction electronic and smart health-care systems have changed the way we receive care and have improved quality and reduced cost [1]. in electronic health-care systems, many stakeholders collaborate with the aim to provide the right care at the right time within the right cost. achieving the aim is not without obstacles, and there are challenges many of which are yet to be addressed by researchers and system developers. vir tual health-care systems where patients receive care without face-to-face meetings are increasingly becoming the norm due to advances in communication technologies. we have previously suggested the use of virtual breeding environment (vbe) and virtual organization (vo) concepts for health care by mahmud and lu [2], and we have explained the benefits of using such concepts in providing virtual health care. in section 3.1, we introduce vbe and vo concepts briefly. one of the challenges of any virtual collaboration system a blockchain-based service provider validation and verification framework for health-care virtual organization hoger mahmud1,2, joan lu2, qiang xu3 1department of computer science, college of science and technology, university of human development, kurdistan region, iraq, 2department of computing science, school of computing and engineering, university of huddersfield, huddersfield, uk, 3department of engineering, school of computing and engineering, university of huddersfield, huddersfield, uk a b s t r a c t virtual organization (vo) and blockchain are two newly emerging technologies that researchers are exploring their potentials to solve many information and communication technology unaddressed problems and challenges. health care is one of the sectors that are very dynamic, and it is in need of constant improvement in the quest to better the quality of cares and reduce cost. one of the hotlines of research in the sector is the use of information and communication technology to provide health care, and this is where the concept of virtual health care is relevant. in virtual health care, patients and care providers are collaborating in virtual settings where two of the most difficult challenges are verifying and validating the identity of the communicating parties and the information exchanged. in this paper, we propose a conceptual framework using blockchain technology to address the health-care provider and record verification and validation issue. the framework is specific to health-care systems developed based on virtual breeding environment and vo. we outline and explain each step in the the framework and demonstrate its applicability in a simple health-care scenario. this paper contributes toward the continuing effort to address user identity and information verification and validation issues in virtual settings in general and in health care in specific. index terms: blockchain, conceptual framework, validation and verification, virtual health care, virtual organization corresponding author’s e-mail: hoger mahmud, department of computer science, college of science and technology, university of human development, kurdistan region, iraq, department of computing science, school of computing and engineering, university of huddersfield, huddersfield, uk received: 26-07-2018 accepted: 14-08-2018 published: 24-08-2018 access this article online doi: 10.21928/uhdjst.v2n2y2018.pp24-31 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2018 mahmud, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) r e v i e w a r t i c l e uhd journal of science and technology hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization uhd journal of science and technology | may 2018 | vol 2 | issue 2 25 is user verification and validation. to ensure the quality and integrity of health-care services provided through such virtual systems as well as preventing information falsification and identity thefts, user verification and validation are essential. validation is necessary to ensure that the right provider with the right attribute as specified by the requester is selected and verification is necessary to ensure that the information provided is correct. in this paper, we propose a framework that uses blockchain technology to verify and validate health-care providers in vbe-based health-care systems. in general, speaking blockchain records and stores transaction in a package called “block” and blocks are linked together in a distributed system. blockchain technology is gaining interest to be used in various fields due to its flexibility in modifying the basic concept to be applied in various forms. currently, well-known companies such as ibm, the tierion/philips partnership (netherlands), brontech (australia), gem (u.s.), and guardtime (europe) are applying and adapting the technology for their own particular needs [3]. for further clarification, we briefly introduce the blockchain concept in section 3.2. a recent study by deloitte has found that health-care providers are planning to use blockchain technologies in a wide scale as the technology gaining momentum both theoretically and practically. zyskind et al. [4] suggested that blockchain technology can be a solution to the user identity verification problem that current authentication systems have a password, and dual-factor verification and validation mechanisms have not been successfully. the framework can also be used for record verification and validation which falls within user verification and validation issue. statistics point to big health-care record keeping security issues, for example, in 2015 there were 112 million health-care record hacks [5]. medical records are sensitive, and any alteration to its content may result in serious consequences. to ensure record integrity, blockchain can act as a distributed database that is secure and safeguard medical records against tempering [4]. the proposed framework does not present the technical aspects of implementing blockchain technology nor does it specify the blockchain mechanism to be used. as a first step, we have outlined the main information flow steps and have identified the required parties that should be members in a chain to verify and validate a health-care provider in vbe-based virtual health-care systems. we have also demonstrated the applicability of the framework in a simple but non-trivial virtual health-care scenario. this paper contributes toward the use of blockchain technology in health care in general and vbe-based health-care systems in specific for user verification and validation. the rest of this paper is organized as follows: section 2 provides some related research. in section 3, we provide brief background information about vbe, vo, and blockchain as well as outlining and explaining the proposed framework. we demonstrate the use of the framework in section 4 and discuss the result in section 5. we finally conclude in section 6. 2. related work blockchain concept was first introduced in 2008 [6], and later in 2009, the concept was implemented in creating the first cryptocurrency (bitcoin) [7]. the technology is considered for use in health care and is already in use to provide a number of health-care services, for example, a system called “prescrypt” is developed by deloitte netherlands in partnership with sns bank and radboud3. the system enables patients to have full control over their data including allowing or revoking providers to access their data [8]. some companies use blockchain in health care, for example, gem (in collaboration with philips healthcare blockchain lab), pokitdok, healthcoin, hashed health, and many others [9]. other researchers have considered the use of blockchain technology for patient identification which allows a single person identification [10], and the use of blockchain in health care is considered by alhadhrami et al. [1] for sharing health records between all relevant health-care stakeholders safely. as for data verification and validation in health care, the technology is used in various implemented and proposed systems, for example, the technology is used in developing a decentralized patient record database where data can be shared among many different parties with no concern for the integrity of the data [8]-[11]. health bank (www.healthbank. coo) which is a swiss company is planning to use blockchain to give full control of data usage to users through the use of the blockchain technology for transaction verification and validation. in a virtual healthcare setting the reputation of a care provider in terms of academic achievements and practical experience is one of the key selection attribute to provide a particular care. this is because care providers with high reputation presumed to provide better quality of care however the challenge here is how to verify and validate a reputation claim made by a care provider. blockchain technology is proposed as a possible verification and validation technology for health-care provider reputations, for example, the authors of sharples and domingue [12] propose to use the technology in a system that can verify hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization 26 uhd journal of science and technology | may 2018 | vol 2 | issue 2 and validate educational records of health-care providers. the authors of carboni [13] have developed a reputation model based on blockchain where customers can provide feedback after receiving a service from a provider and calculate the providers reputation based on feedbacks they receive. gem health network launched by gem a us startup uses blockchain technology to provide an infrastructure for health-care specialists to share information [14]. the technology is researched for fighting drug counterfeiting by hyperledger in collaboration with accenture, cisco, intel, ibm, block stream, and bloomberg [15]. the technology is also considered and used in other fields, for example, it has been used in financial services such as online payment [16] and has been considered in other services such as smart contracts [17] and public services [18]. the dutch ministry of agriculture is currently running a project called blockchain for agrifood that aims to explore the potential of blockchain technology in agriculture [19]. blockchain is also used by the social enterprise “provenance” (www.provenance.org) in the uk to record and validate certificates in agriculture supply chains. the technology is also used in music industry, for example, startups such as ujo or peertraks propose to use the technology to manage music rights [20]. our prosed use of the technology differs from all the above researches as we are the first (to the best of our knowledge) to suggest the use of blockchain technology for health-care provider verification and validation in vbe-based health-care systems. 3. background and framework definition in this section, we briefly introduce vbe, vo, and blockchain technology and we also define the proposed framework and explain its main steps. 3.1. vbe and vo internet and telecommunication technologies have paved the way for a new type of collaboration known as “virtual collaboration [21], [22]. the fact that virtual collaboration occurs between unknown participants has given rise to the challenge of collaboration management and regulation in a virtual world. to address the challenge, researchers have proposed vbe and vo [23], [24]. the concepts are researched for collaboration management and regulation in education, e-commerce, and teleworking [25], [26]. the framework proposed in this paper is specific to health-care services provided through systems which are developed based on vbe and vo. vo is a short-lived temporarily consortium where a number of parties collaborating and working together to provide a particular service. vo is described as “a loosely bound consortium of organizations that together address a specific demand that none of them can (at the given time) address alone and once the demand has been satisfied the vo might disband” [27]. vbe, on the other hand, is a permanent consortium of parties that provide the environment and support for vo creation and management. participants in both vbe and vo can be human or machines or both, but they all have to collaborate through communication technologies [28]. 3.2. blockchain blockchain concept was developed from bitcoin paper published by nakamoto in 2008. it is a peer-to-peer network where all participants (peers) serve as a node and all nodes hold the same information (hash value in this case). blockchain uses cr yptographic techniques to record transactions between peers in a peer-to-peer network and store the transaction in a digital ledger as a block. blocks are linked together for validation and verification purposes. each block is comprised of three main parts which are block headers, a hash value of the previous transaction, and merkle root as illustrated in fig. 1. each block contains a unique hash value that is the transaction recorded and distributed to all nodes in the chain after its creation and all have to agree before a change in the block can happen. the uniqueness of a hash value comes from the fact that any combination of data produces a unique hash value and this value changes if there is any alteration to the data; this mechanism ensures data validity. the use of cryptographic techniques in blockchain enhances the security of the data within a transaction which is an essential requirement of any health-care system. blockchain uses the public key cryptographic technique to encrypt transactions, and it is visible to all participants in a blockchain; however, to decrypt fig. 1. blocks linked in a chain. hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization uhd journal of science and technology | may 2018 | vol 2 | issue 2 27 a transaction, a participant must have a private key which is not publically available [29]. in general, there are two types of blockchain which care for permissionless and permissioned blockchain. in a permissioned type of blockchain, a central authority controls all requests for change to transaction records or any other modification and the requester will have to go through access controls such as identity verification to access transactions [30]. on the other hand in permissionless blockchain, there is no central authority and requests can be made freely to change transaction records. examining which type of blockchain is most suited for health-care virtual collaboration is beyond the scope of this paper as we only outline a framework without going into technical details; however, we think that it is an interesting topic to research. fig. 2 illustrates the two types of blockchain. 3.3. framework definition here, we outline the proposed framework and provide more insights to each of the framework steps. our proposed framework is conceptual rather than structural, and the purpose is to provide vbe-based health-care system developers with a step-by-step guide as to how to verify and validate health-care service providers and records using blockchain technology. for the framework to work, the following requirements should be fulfilled: 1. a virtual health-care system must use vbe and vo concept as a base for collaboration and organization of care provision which means that there must be a virtual environment where patients can send requests to and the environment creates a vo for the service requested after all requirements are fulfilled. 2. service providers are recruited either within the vbe or from a global pool of virtual health-care providers after their credentials are verified and validated. 3. a blockchain is created between a number of vbes, academic institutes, and health institutes where credentials of care providers are shared in blocks between all participants. each blockchain participant has a job to do as follows: a. vbes: it provides information about health-care providers that have provided care within their environments for reputation verification and validation purposes. vbes can also take part in health record verification and validation through sharing their records in the created blockchain. b. academic institutes: it provides information about the qualification that health-care providers claim to possess for credential verification and validation. c. health institutes: these provide information about the practices and experiences of health-care providers in real life situation and verify and validate the level of expertise and experience that providers claim to possess. after all the above requirements are fulfilled, we suggest an eight-step framework which is illustrated in fig. 3 to verify and validate providers and records as follows: 1. a health-care service request is triggered: this step serves as the trigger for the whole validation and verification process. in this step, a patient sends a request to a vbe for a virtual health-care service; for example, a patient would like to consult a doctor about a pain that he/she has developed in the neck after a minor car accident. the request can also be for a change of record that is held by a particular vbe, for example, a patient would like to make changes to the address registered in his/her record. 2. a health-care service accepted: vbes cannot provide all types of care since health-care services are many and can change on a case bases; therefore, the vbe would have to check the details of the request to see if the requested service falls within their scope of work. if the request passes the check, then it is the job of the vbe to find the right health-care service provider after which a vo is created to provide the care. to do so, the vbe searches within its resources for the right service provider if not found the vbe would have to search the global pool for the right care provider. 3. after a provider is found, contacted, and accepted to offer the service, the vbe would have to verify and validate the credentials of the provider before final go-ahead for the service provision and vo creation. if the request is for changes in records held by the vbe, the credentials of the requester should be verified and validated before the change can be made. 4. vbe share the credentials: after step 3, the vbe would now have to share the record or the provider details with other participants using blockchain technology for verification and validation. 5. blockchain-based verification and validation: when the information is shared, now each node in the chain would compare the information provided with the record held in blocks within their system for verification fig. 2. types of blockchain. hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization 28 uhd journal of science and technology | may 2018 | vol 2 | issue 2 and validation. the comparing process is done using consensus algorithms such as proof of work. in section 3.2 we have explained that blockchain is a peer-to-peer network and each peer in the network is a node that holds copies of transactions made in the network. when a new block is created, it is distributed to all nodes. the nodes will have the responsibility to validate the content of the block through comparing it with the block that is already held by the node .in blockchain an exact copy of a transaction (block) is held by all nodes in the chain, when a block is changed the request for change has to be broadcastand all nodes would have to approve the change to the block before a new block with the requested change is added to the network. in this case, if a service provider has provided false information, or if a record content is altered, it would be detected and rejected easily. this method of validation and verification is more robust than the insystem verification and validation since a record held in a system database can be hacked or altered, whereas in blockchain, it is impossible to alter data without all participant approval. 6. new block validation and creation: sometimes, a request is send by a vbe where its content is new, for example, a care provider qualification needs to be verified and validated. in this case, the request would have to be compared with the records held by an academic institution, and once verified and validated, a new block would be created and added to the network. the step six is there for two purposes, the first is that participants would work as a peer in the network to provide verification and validation for blocks already created, and the second is to create new blocks and add it to the network as requests for information verification and validation comes into the network. 7. request result: once the result of the request is complete, it is sent back to the vbe, if the result is positive, the vbe would take steps to create a vo for the service otherwise new service provider has to be found, and the steps 2–7 have to be repeated or the whole process is stopped and the requester is informed of the reason. 8. vo creation: a vo is a short-lived entity created fig. 3. the proposed framework. hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization uhd journal of science and technology | may 2018 | vol 2 | issue 2 29 to provide a specific service, and once the goal is achieved, the vo is dismantled and the service ends. if the result of step 7 is positive, then a vo is created where both service requesters and service providers can communicate and collaborate. the process of vo creation mechanism is beyond the scope of this paper as we are currently researching on actively. 4. case study one of the requirements of a service requested is that the service has to be feasible virtually, i.e. the service requested has to be achievable through an online system. healthcare services are complex with some requiring face-to-face meetings between care requesters and providers, and others can be achieved in a virtual system. one of the most common virtual heath-care service requests is for consultation. this where a patient would like to receive guidance about a particular medical needs or addresses a concern he/she may have. to show simply and effectively the contribution of the framework in verifying and validating care providers and records, we consider the scenario below: mr. adam has recently been involved in a car accident and has developed a neck pain after the accident. despite visiting a hospital a couple of times, the pain is still present and he would like to consult with a bone specialist that was not available in his local hospital. a vbe called “virtual hospital system” is introduced to him by a friend, and now he would like to contact the vbe for a service. he fills in the virtual care request form for a consultation service with a bone specialist. he specifies in his request that the bone specialist should have a good reputation and minimum 5 years of care provision experience. the specialist should be an eu graduate and speak very good english. it is now the job of the vbe to find the right specialist for mr. adam. to ensure the right specialist if put into contact with mr. adam in a vo, the vbe uses the proposed framework and take the following steps: 1. the vbe searches though its database for a specialist that fulfills the requirements, but we assume that it fails to find one. the vbe then broadcasts the request and search for the right specialist in the global pool of care providers. 2. in the search process, the details of a specialist who lives in different countries than that of mr. adam match the requirements specified in the request form. the vbe contacts the specialist and offers to recruit him to provide the service and he accepts. in his profile, he claims that he is a uk-based university qualified with 7 years of experience in a german-based hospital. however, since the specialist is unknown to the vbe, the claims have to be verified and validated before the final go ahead. 3. the vbe create a block using the information provided by the specialist and broadcast it for verification and validation in the created blockchain. now using blockchain mechanisms, the claims can easily be verified and validated by comparing the information in the block with those held by the network participants. 4. the result is sent back to the vbe and if positive put both mr. adam and the specialist into contact by creating a vo for them, and otherwise, the vbe withdraws the recruitment offer made to the specialist and search for another one or terminate the process. the above scenario can simply demonstrate the applicability and the contribution of the framework in a clear manner; however, it must be said that the framework is conceptual and yet to be implemented for a real test which is something we still working on alongside other concepts to create the first vbe-based health-care system. the purpose of this paper is to share the principal concept and build on it in later works. 5. discussion ever since the alma-ata world leaders meeting that declared health care as a fundamental human right, many efforts and investments have been channeled through different healthcare systems around the world to ensure the delivery of this right. however, the main goal which was every human is entitle to receive quality care which is yet to be realized and this led to the world health organization to call for universal health-care coverage [31]. it is a known fact that most health-care systems are failing care receivers due to lack of stakeholder data safety, unacceptable quality of care, and limited care availability which all point to the need for change in health care. in the search for new ways to provide health care blockchain technology is seen by many researchers as a revolution with the potential to change the way heath care is provided currently [32], [33]. in this paper, we have outlined a framework that uses blockchain to address one of the most known issues in health care that can be provided virtually which is user verification and validation. despite the invention of many techniques such as username and password authentication to ensure the identity and validity of the claims that are made by virtual care providers and verify their suitability to provide a requested care, the issue remains at large. the proposed framework is developed to hoger mahmud, et al.: a blockchain-based service provider validation and verification framework for health-care virtual organization 30 uhd journal of science and technology | may 2018 | vol 2 | issue 2 contribute to the ongoing work to address the issue. the framework is simple and feasible as all technologies required to apply the framework are available. however, the framework is conceptual and required to be implemented and tested to show its full potential in contributing to the issue of data verification and validation in virtual health-care systems. the main contribution of this paper is the consideration of using blockchain in vbe-based health-care systems for service provider and records verification and validation as a concept, and the aim is to pave the way for further research and provide a basic validation and verification guide to system developers. as we have presented in this paper, blockchain technology is being considered for use in health care to address various issues in the field. however, despite the apparent theoretical applications of blockchain technologies in health care, the technology is yet to be applied fully due to its infancy and lack of technical implementation knowledge. one of the downsides of blockchain technology is the fact that operation costs are difficult to estimate as the computing power required to run it changes continuously as the number of hot nodes changes in the chain [5]. however, blockchain technology has the potential to be used in health-care areas such as medicine authenticity identification and patient record sharing. swan [8] identified a number of opportunities that blockchain technology can provide in health care such as: 1. removal of third party between health-care providers and receivers as well as various health-care providers 2. minimizing transaction costs as all transactions are transparent, direct, and happen real time 3. ensuring the data shared between healthcare stakeholders is the last updated version as changes to stakeholder records are made real-time and updates are distributed to all nodes in the chain. 4. creating one single and secure patient record access mechanism 6. conclusion health-care provision is changing as different techniques are proposed to make health care more available and accessible with better quality and less cost. one of the techniques that are becoming familiar is receiving care through online without face-to-face meetings which is known as e-health or virtual health care. the technique has a number of challenges which are yet to be addressed fully, and one of which is record and service provider verification and validation. in this paper, we have outlined an eight-step framework that uses blockchain to address the issue in vbe-based health-care systems. the framework is conceptual and yet to be implemented, but we have demonstrated its applicability through applying it to a simple scenario that results in verifying and validating a care provider. this paper contributes toward tackling the challenge of verifying and validating users and records in health care and considers the use of blockchain for the first time in vbe-based 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[33] d. randall, p. goel and r. abujamra, “blockchain applications and use cases in health information technology.” journal of health and medical informatics, vol. 8, no. 3, 2017. . uhd journal of science and technology | jan 2019 | vol 3 | issue 1 39 1. introduction groundwater is a valuable freshwater resource and constitutes about two-third of the fresh water reserves of the world [1]. buchanan (1983) estimated that the groundwater volume is 2000 times higher than the volume of waters in all the world’s rivers and 30 times more than the volume contained in all the fresh water of the world lakes. the almost is 5.0 l × 1024 l in the world of groundwater reservoir [2]. groundwater is used in many fields for industrial, domestic, and agricultural purposes. however, due to the population growth and economic development, the groundwater environment is becoming more and more important and extensive [3], and the heavy groundwater extraction has caused many problems such as groundwater level drop, saltwater intrusion, and ground surface depression, which need to be improved. therefore, the identification, assessment, and remediation using regression kriging to analyze groundwater according to depth and capacity of wells aras jalal mhamad1,2 1department of statistic and informatics, college of administration and economics, sulaimani university, sulaimani city, kurdistan region – iraq, 2department of accounting, college of administration and economics, human development university, sulaimani city, kurdistan region – iraq a b s t r a c t groundwater is valuable because it is needed as fresh water for agricultural, domestic, ecological, and industrial purposes. however, due to population growth and economic development, the groundwater environment is becoming more and more important and extensive. the study contributes to current knowledge on the groundwater wells prediction by statistical analysis under-researched. such as, it seems that the preponderance of empirical research does not use map prediction with groundwater wells in the relevant literature, especially in our region. instead, such studies focus on several simple statistical analysis such as statistical modeling package. accordingly, the researcher tried to use the modern mechanism such as regression kriging (rk), which is predicted the groundwater wells through maps of sulaimani governorate. hence, the objective of the study is to analyze and predicting groundwater for the year 2018 based on the depth and capacity of wells using the modern style of analyzing and predicting, which is rk method. rk is a geostatistical approach that exploits both the spatial variation in the sampled variable itself and environmental information collected from covariate maps for the target predictor. it is possible to predict groundwater quality maps for areas at sulaimani governorate in kurdistan regions iraq. sample data concerning the depth and capacity of groundwater wells were collected on groundwater directorate in sulaimani city. the most important result of the study in the rk was the depth and capacity prediction map. the samples from the high depth of wells are located in the south of sulaimani governorate, while the north and middle areas of sulaimani governorate have got low depths of wells. although the samples from the high capacity are located in the south of sulaimani governorate, in the north and middle the capacity of wells have decreased. the classes (230–482 m) of depth are the more area, while the classes (29–158 g/s) of capacity are the almost area in the study. index terms: groundwater analysis, interpolation, regression kriging corresponding author’s e-mail: aras jalal mhamad, department of statistic and informatics, college of administration and economics, sulaimani university, sulaimani city, kurdistan region – iraq, department of accounting, college of administration and economics, human development university, sulaimani city, kurdistan region – iraq. e-mail: aras.mhamad@univsul.edu.iq received: 20-04-2019 accepted: 22-05-2019 published: 29-05-2019 access this article online doi: 10.21928/uhdjst.v3n1y2019.pp39-47 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2019 mhamad. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology aras jalal mhamad: using r.k. to analyze groundwater 40 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 of groundwater problems have become quite a crucial and useful topic in the current time. for the above reasons, the analysis of groundwater requires implementing scientific and academic methods, from which one of the verified models is the rk that is used for this purpose [2]. regressionkriging (rk) is a spatial prediction technique that combines regression of the dependent variable on auxiliary variables with kriging of the regression residuals. it is mathematically a consideration of interpolation method variously called universal kriging and kriging with external drift, where auxiliary predictors are used to solve the kriging weights directly [4]. rk is an application of the best linear unbiased predictor for spatial data, which is the best linear interpolator assuming the universal model of spatial variation [5]. rk is used in many fields, such as soil mapping, geological mapping, climatology, meteorology, species distribution modeling, and some other similar fields [6]. regression kriging (rk) is one of the most widely used methods, which uses hybrid techniques and combines ordinary kriging with regression using ancillary information. since the correlation between primary and secondary variables is significant [7], so, the aim of this study is to analyze and predicting groundwater depending on depth and capacity of wells in sulaimani governorate; using rk. 1.1. objective of the study the main objective of this research is to analyze and predict groundwater wells at the un-sampled locations in sulaimani governorate according to depth and capacity of existing groundwater wells using rk and to assess the accuracy of these predictions. 2. materials and methods 2.1. interpolation spatial interpolation deals with predicting values of the locations that have got unknown values. measured values can be used to interpolate, or predict the values at locations which were not sampled. in general, there are two accepted approaches to spatial interpolation. the first method uses deterministic techniques in which only information from the observation point is used. examples of direct interpolation techniques are such as inverse distance weighting or trend surface estimation. the other method depends on regression of addition information, or covariates, gathered about the target variable (such as regression analysis combined with kriging). these are geostatistical interpolation techniques, better suited to count for spatial variation, and capable of quantifying the interpolation errors. hengl et al. (2007) advocate the combination of these two into so-called hybrid interpolation. this is known as rk [8]. in another paper, hengl et al. (2004) explain a structure for rk, which forms the basis for the research in this study [7]. limitation of rk is the greater complexity than other more straightforward techniques like ordinary kriging, which in some cases might lead to worse results [9]. 2.2. rk the most basic form of kriging is called ordinary kriging. when we add the relationship between the target and covariate variables at the sampled locations and apply this to predicting values using kriging at unsampled locations, we get rk. in this way, the spatial process is decomposed into a mean and residual process. thus, the first step of rk analysis is to build a regression model using the explanatory grid maps [8]. the kriging residuals are found using the residuals of the regression model as input for the kriging process. adding up the mean and residual components finally results in the rk prediction [8]. rk is a combination of the traditional multiple linear regression (mlr) and kriging, which means that an unvisited location s 0 is estimated by summing the predicted drift and residuals. this procedure has been found preferable for solving the linear model coefficients [10] and has been applied in several studies. the residuals generated from mlr were kriged and then added to the predicted drift, obtaining the rk prediction. the models are expressed as: ( ) ( ). 0 0 0 ˆ •ˆ p ml r k k k z s x s = = ∑ (1) ( ) ( ) ( ) ( ) ( )0 . 0 0 0 0 1 • ;ˆ ˆ 1 n rk ml r i i i z s z s w s e s x s = = + =∑ (2) when .ˆ ml rz (s0) is the predicted value of the target variable z at location s 0 using mlr model, ˆrkz (s0) is the predicted value of the target variable at location s 0 using rk model, ˆ k is the regression coefficiency for the kth explanatory variable xk, p is the total number of explanatory variables, wi (s0) are weights determined by the covariance function and e (si) are the regression residuals. in a simple form, this can be written as: ( ) ( ) ( )z s m s s= + ′ (3) when z(s) is the value of a phenomenon at location s, m(s) is the mean component at s, and ε′ (s) stands for the residual component including the spatial noise. the mean component is also known as the regression component. aras jalal mhamad: using r.k. to analyze groundwater uhd journal of science and technology | jan 2019 | vol 3 | issue 1 41 the process of refining the prediction in two steps (trend estimation and kriging) is shown in fig. 1, where the result of the mean component, only regression sm( )ˆ , is visible as a dashed line , and the sum of trend + kriging is the curving thick line ( )ˆ sz . this should approach the actual distribution better than either just a trend surface or a simple interpolation. the linear modeling of the relationship between the dependent and explanatory variables is quite empirical. the model selection determines which covariable is important, and which one is not. it is not necessary to know all these relations, as long as there is a significant correlation. once the covariates have been selected, their explanatory strength is determined using (stepwise) mlr analysis. for each covariate this, leads to a coefficient value, describing its predictive strength, and whether this is a positive or negative relationship. with the combination of values for all covariate maps, a trend surface is constructed. this regression prediction is, in fact, the calculation for each target cell from each input cell from all covariates times the coefficient value. the amount of correlation is expressed by r2 in the regression equation. to enable this, the covariate data first need to be processed by overlaying the sample locations with the covariate data layers. in this way, a matrix of covariate values for each sample point is constructed. this matrix may still hold several “na” or missing values due to the fact that some maps do not have coverage, while some others do. an example of this is the absence of information on the organic matter in urban areas. since the linear models cannot be constructed properly when some covariate data are missing, these sample points are discarded altogether. the resulting data matrix is therefore complete for all remaining measurement data points. the second step in which the covariate data are needed is the model prediction phase of the mean surface values. first, a prediction mask is made, which is the selection of grid cells for which covariate data are available and only contains the coordinates of valid cells. next, the regression mean values are calculated by predicting the regression model for every grid cell in the prediction mask. in the residual kriging phase, this prediction grid is used again as a mask for the kriging prediction [7]. 2.3. variogram and semivariogram semivariogram analysis is used for the descriptive analysis. the spatial structure of the data is investigated using semivariogram. this structure is also used for predictive applications, in which the semivariogram is used to fit a theoretical model, parameterized, and also used to predict a regionalized variable at other unmeasured points. estimating the mean function x(s)tβ and the covariance structure of ε(s) for each s in the area of interest is the first step in both the analysis of the spatial variation and the prediction. semivariogram is commonly used as a measure of spatial dependency. the estimated semivariogram explains a description of how the data is correlated with the distance. the factor 1/2 that ϒ(h) indicates is a semivariogram, and 2ϒ(h) is the variogram. thus, the semivariogram function measures half the average squared difference between pairs of data values separated by a given distance, h, which is known as the lag [11], [5]. the experimental variogram is a plot of the semivariance against the distance between sampling points. the variogram is the fitted line that best describes the function connecting the dots from the experimental variogram [12]. assuming that the process is stationary, the semivariogram is defined in equation (4): ( ) ( ) ( ) 2 ( ) 1 [ ] 2 i jh n h h z s z s n  = −∑ (4) here, n(h) is the set of all pairwise euclidean distances i–j = h, nh is the number of distinct pairs in n(h). z(si) and z(sj) are the values at spatial location i and j, respectively, and ϒ(h) is the estimated semivariogram value at distance h. the semivariogram has three important parameters: the nugget, sill, and range. the nugget is a scale of sub-grid variation or measurement error, and it is indicated by the intercept graphically. the sill is the semivariant value as the lag (h) goes to infinity, and it is equal to the total variance of the data set. the range is a scalar which controls the degree of correlation between data points (i.e., the distance at which the semivariogram reaches its sill). as shown in fig. 2, it is then necessary to select a type of theoretical semivariogram model based on that estimate. fig. 1. a schematic representation of regression kriging using a crosssection [8]. aras jalal mhamad: using r.k. to analyze groundwater 42 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 commonly used theoretical semivariogram shapes increase monotonically as a function of distance, by comparing the plot of empirical semivariogram with various theoretical models that can choose the semivariogram model. three are some parametric semivariogram models for testing, such as: exponential, gaussian, and spherical. these models are given by the following equations: exponential: ( ) exp0 1 2 3 1 , h h        = + − −      (5) gaussian: ( ) exp 2 0 1 2 1 3 , h h         = + − −         and (6) spherical: ( ) 3 0 1 22 2 0 1 2 3 1 1 e x p , 02 2 , h h h h h               +     = ≤ ≤        + >   (7) when h is a spatial lag, θ 0 is the nugget, θ 1 is the spatial variance (also referred to as the sill), and θ 2 is the spatial range. the nug get, sill, and range parameters of the theoretical semivariogram model can fit the empirical semivariogram ϒ(h) by minimizing the nonlinear function. when fitting a semivariogram model, if we consider the empirical semivariogram values and try to fit a model to them as a function of the lag distance h, the ordinary least squares’ function is as given by ( ) 2( :ˆ ) h h h   −  ∑ , where ϒ(h: θ) denotes the theoretical semivariogram model and θ = (θ 0 , θ 1 , θ 2 ) is a vector of parameters. rk computes the parameters θ and β separately. the parameters β in the mean function are estimated by the least squares method. then, it computes the residuals, and their parameters in the semivariogram are estimated by various estimation methods, such as least squares or a likelihood function. prediction of rk at a new location s 0 can be performed separately using a regression model to predict the mean function, a kriging model of prediction residuals and then adding them back together as in equation (8): ( ) ( ) ( ) ( )0 0 0 0 0 n n k k i i k i z s x s s s   = = = +∑ ∑ (8) here, si = (xi, yi) is the known location of the ith sample, xi and yi are the coordinates, βk is the estimated regression model coefficient, λi represents the weight applied to the ith sample (determined by the variogram analysis), ε(si) represents the regression residuals, and x 1 (s 0 )… xn(s0) are the values of the explanatory variables at a new location s 0 . the weight λi is chosen such that the prediction error variance is minimized, yielding weights that depend on the semivariogram [13]. more details about the kriging weight λi follow immediately [14]. the main objective is to predict z(s) at a location known as s 0 , given the observations {z(s 1 ), z(s 2 ),…, z(s 3 )}′. for simplicity we assume e{z(s)} = 0 for alls. we briefly outline the derivation of the widely used kriging predictor. let the predictor be in the form of ( ) '0 ( )ẑ s z s= , where λ = {λ 1 , λ 2 ,…, λ n }′. the objective is to find weights λ, which is a minimum. ( ) [ ]20 0 ( ) ( )q s e z s z s= −′ (9) by minimizing q(s 0 ) with respect to λ, it can be shown that; ( ) ( ) ( )1'0 0 ,ẑ s s s z s − = ∑ (10) when σ′(s 0 , s) = e(z(s 0 ) z(s)), and ∑= e[z(s) z (s)] are the covariance matrix. the minimum of q(s 0 ) is min ( ) 12 '0 0( , ) ( )q s s s z s  − = − ∑ . note that, q(s0) can be rewritten in terms of the variogram by applying; ( ) ( )20 0 1 , 1 , 2 s s s s = − γ (11) when γ(s 0 , s) is the corresponding matrix of variograms. we can thus rewrite q(s 0 ) given in equation (9) as; fig. 2. illustration of semivariogram parameters. aras jalal mhamad: using r.k. to analyze groundwater uhd journal of science and technology | jan 2019 | vol 3 | issue 1 43 ( ) ( )0 0 1 , 2 q s s s  ′ ′= − γ + γ (12) q(s 0 ) is now minimized with respect to λ, subject to the constraint λ′ 1 = 1 (accounting for the unbiasedness of the predictor ( )0ẑ s ) [11]. 2.4. advantages of rk geostatistical techniques such as multiple regression, inverse distance weight, simple kriging, and ordinary kriging uses either the concept of regression analysis with auxiliary variables or kriging for prediction of target variable, whereas rk is a mixed interpolation technique; it uses both the concepts of regression analysis with auxiliary variables and kriging (variogram analysis of the residuals) in the prediction of target variable. it considers both the situations, i.e., long-term variation (trend) as well as local variations. this property of rk makes it superior (more accurate prediction) over the above-mentioned techniques [15]. among the hybrid interpolation techniques, rk has an advantage that there is no danger of instability as in the kriging with the external drift [9]. moreover, the rk procedure explicitly separates the estimated trend from the residuals and easily combined with the general additive modeling and regression trees [16,17]. 2.5. cross-validation of rk results to assess which spatial prediction method provides the most accurate interpolation method, cross-validation is used to compare the estimated values with their true values. cross-validation is accomplished by removing each data point and then using the remaining measurements to estimate the data value. this procedure is repeated for all observations in the dataset. the true values are subtracted from the estimated values. the residuals resulting from this procedure are then evaluated to assess the performance of the methods. one particular method is called k-fold crossvalidation, where “k” stands for the number of folds one wants to apply. each fold is a set of data kept apart from the analysis, repeated for the number of folds. a special type of k-fold cross validation is where the repetition of analyses (k) is equal to the number of data. this is called “leave one out” cross-validation, for the analysis is repeated once for every sample in the dataset, omitting the sample value itself. resulting is a prediction for every observation, made using the same variogram model settings as for the normal rk prediction. the degree in which the cross-validation predictions resemble the observations is then a measure for the goodness of the prediction method. this can be calculated using the mean squared normalized error or “z score” [18]. to aid further in the assessment of prediction results, additional parameters can be calculated from the cross-validation output, such as the mean prediction error (mpe), root mean square prediction error (rmspe), and average kriging standard error (akse). ´ ( ) ( ) 1 1 n x x x mpe z z n =  = −  ∑ (13) 2 ' ( ) ( ) 1 1 n x x x rmspe z z n =   = −     ∑ (14) when n stands for the number of pairs of observed and predicted values, z(x) is the observed value at location x, and z’(x) is the predicted value by ordinary kriging at location z. ( ) 2 1 1 n x akse x n  = =   ∑ (15) here, x is a location, and σ(x) is the prediction standard error for location x. mpe indicates whether a prediction is biased and should be close to zero. rmspe and akse are measures of precision and have to be more or less equal. the cv-procedure only accounts for the kriging part, since the input is the residuals from the linear modeling phase [4]. 3. data analysis and results 3.1. data description data were obtained from a (groundwater directorate/well license department) in sulaimani, kurdistan-region. 451 observations (wells) were used in the study, only records containing valid x, y – locations are used in the statistical modeling process. one check is to print all measurement locations to check whether they are located within the defined regions. if not, they are removed. the rk method is suitable for predicting the groundwater wells, due to nature of data (there are coordinates for each wells). for kriging purposes, duplicate x,y-locations need to be checked, to prevent singularity issues, as shown by yang et al. [4]. duplicated locations share the same coordinates (based on one decimal digit), making it impossible to apply interpolation. therefore, the choice is made to delete each second record that has duplicated coordinates. the research area is limited to the sulaimani governorate of the kurdistan region, only depth and capacity of wells are available at the individual point aras jalal mhamad: using r.k. to analyze groundwater 44 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 locations. therefore, this research is targeted at depth and capacity of wells. the data were presented in fig. 3. the dataset used for the analysis contains six variables and deals with properties of well for the year 2018, which are (depth, capacity, state well level, dynamic well level, latitude-x, and longitude-y). 3.2. experimental variogram once the regression prediction has been performed, the variogram for the resulting residuals from the sample data can be modeled, as shown in fig. 4. the model of depth with partial sill c 0 = 5328, nugget = 371.95, and range = 0.078 was used for the residual variogram. the result has indicated that with an increase in distance, the semivariance value increases. semivariograms are used to fit the residuals of the recharge estimates to enable the residuals then to be spatially interpolated by kriging. fig. 4 shows simple kriging of the modeled residuals using the same locations from the first prediction surface; the kriging is provided over the surface to obtain the results, which are not interpolated over geological boundaries, which are not necessary to have any spatial correlation with the residuals. these semivariograms explain the nugget that is high in each group. when the nugget value is high, it indicates low spatial correlation in the residuals and has an effect that interpolation is not trying to match each point value of the residuals. although the range shows the extent of the spatial correlation of recharge residuals, it ensures that the residuals’ spatial surface is only using the local information. the model for capacity of wells has partial sill c 0 = 3805.4, nugget = 11222.3, and range = 0.429. the result has indicated that with an increase in distance, the semivariance value increases. fig. 5 shows simple kriging of the modeled residuals using the same locations from the first prediction surface for the capacity of wells. from the semivariograms, the nugget also is high in each group, which indicates low spatial correlation in the residuals. the range shows the extent of the spatial correlation of recharge residuals and ensures that the residuals’ spatial surface is only using the local information. from fig. 6, the variance has some artifacts. it can be expected that values close to the locations, where point samples were taken, have lower variances. however, the blue colored regions in the depth of wells appear very strange here, especially when other sparsely sampled fig. 3. sample distribution. fig. 4. variogram for the depth of the wells. fig. 5. variogram for the capacity of wells. aras jalal mhamad: using r.k. to analyze groundwater uhd journal of science and technology | jan 2019 | vol 3 | issue 1 45 regions do not have this blue color, but yellow and orange colors, indicating a lower variance value. the kriging variance is produced together with the kriging operation and is shown in the left part of fig. 6. although in the prediction map, the blue areas correspond somewhat to higher predictions for depth wells (red, 432–482 m fig. 6 left), this is not reversely so for the other regions. there are some points located at the blue area, each having a high depth of wells (ranged between 432 and 482 m). in the blue area, this phenomenon is enlarged, showing the scale at which the variance is increasing in the depth of wells, just around a cluster of sample points at the blue area. it is observed from the predicted depth of wells that the values are higher in the kalar, kifri, and khanaqin (lower portion of the study area), followed by sulaimani governorate, while low values are found in the upper of sulaimani governorate (upper portion of the study area). this fact can be seen from the rk variance (fig. 6 left). higher variance values (482) for the depth of wells are found in the plain areas whereas the mountainous areas have relatively lower values (29.7). fig. 7 explains the variance. it can be expected that values have lower variances, close to the locations, where point samples were taken. although the blue colored regions in the capacity of wells’ appear have very high variance value, yellow and orange colors are indicating a lower variance value. the kriging variance is produced together with the kriging operation and is shown in the left part of fig. 7. although in the prediction map, the blue areas correspond with higher predictions for capacity wells (372–415 g/m fig. 7 right). there are some points located at the blue area, each having high capacity of wells (range 372–415 g/m). in the blue area, this phenomenon is enlarged, showing the scale at which the variance is increasing in the capacity of wells. it is observed from the predicted capacity of wells that the values are higher in kalar, kifri, and khanaqin, followed by sulaimani governorate, while low values are found in the upper of sulaimani governorate. this fact can be seen from the rk variance (fig. 7 left). higher variance values (415) are found in the plain areas, whereas the mountainous areas have relatively lower values (29.8). 3.3. cross-validation of kriging cross-validation was used to obtain the goodness of fit for the model. in addition, for each cross-validation result, the mpe, or mean prediction error, was calculated. the mpevalue should be close to zero. rmspe (root mean square fig. 6. regression kriging results (left) and variance (right) of the residuals from the depth of wells’ model. aras jalal mhamad: using r.k. to analyze groundwater 46 uhd journal of science and technology | jan 2019 | vol 3 | issue 1 prediction error) and akse are given as well. the latter two error values should be close to each other, indicating prediction stability. the validation points were collected from all data in the study area so as to have an unbiased estimated accuracy. in this study, mpe, rmspe, and akse are the three statistical parameters used for validation. the smaller the rmspe, means the closer the predicted values to the observed values. similarly, the mpe gives the mean of residuals and the unbiased prediction gives a value of zero. the results of the validation analysis are summarized in table 1. the mpe is quite low in both depth and capacity of wells and is a low bias value of 0.019 and 0.021, respectively. the value of mpe is a result of a slight over-estimation of predicted depth and capacity of wells in the model. the rmspe value is only 0.844 and 1.31, indicating the closeness of predicted value with the observed value. the results indicate the utility of rk in spatially predicting depth and capacity of wells even in the varying landscape. 4. results and conclusions the results of the study show that the cross-validation measurement of the models was achieved. looking at the quantitative results from the cross-validation, there are no obvious indications that the kriging model prediction is worse in the models of depth and capacity of wells. one important result of the study is the region model predictions in the dataset with sample values. the samples from the high depth of wells are almost absent in north and middle of sulaimani governorate, while in the south they are present, although the capacity of wells gave the same result depth of the wells. the samples from the high capacity of wells are almost absent in the north and middle of sulaimani governorate, while in the south they are present. in the map results after the kriging in figs. 4 and 5, the areas within class (230–482m) of depth are almost, this result was close to the master thesis from iraq – sulaimani university by renas abubaker table 1: cross‑validation results measurements depth of wells capacity of wells mpe 0.019 0.021 rmspe 0.844 0.720 akse 0.661 1.316 mpe: mean prediction error, rmspe: root mean square prediction error, akse: average kriging standard error fig. 7. regression kriging results (left) and variance (right) of the residuals from the capacity of wells’ model. aras jalal mhamad: using r.k. to analyze groundwater uhd journal of science and technology | jan 2019 | vol 3 | issue 1 47 ahmed, 2014, in which resulted that the depth of wells was between 20 m and more the 170 m for the same areas, which was used multivariate adaptive regression spline model to predicting groundwater wells [19], while the areas within class (29–158 g/s) of capacity are almost in the study, also it was close to miss. rena’s results, which is reported that the capacity of wells between 10 and 140 gallon [19]. references 1. chilton, j. “women and water”. waterlines journal, vol. 2, no. 110, pp. 2-4, 1992. 2. buchanan. “ground water quality and quantity assessment”. journal ground water, vol. 7, no. 7, pp. 193-200, 1983. 3. han, z. s. “groundwater resources protection and aquifer recovery in china”. environmental geology, vol. 44, pp. 106-111, 2003. 4. yang, s. h., f. liu, x. d. song, y. y. lu, d. c. li, y. g. zhao and g. l. zhang. “mapping topsoil electrical conductivity by a mixed geographically weighted regression kriging: a case study in the heihe river basin, northwest china”. ecological indicators, vol. 102, pp. 252-264, 2019. 5. georges, m. “part 1 of cahiers du centre de morphologie mathématique de fontainebleau”. le krigeage universel, école nationale supérieure des mines de paris, 1969. 6. tomislav, h., b. branislav, b. dragan, r. i. hannes. “geostatistical modeling of topography using auxiliary maps”. computers and geosciences, vol. 34, no. (12), pp. 1886-1899, 2008. 7. ye, h., w. huang, s. huang, y. huang, s. zhang, y. dong and p. chen. “effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon”. spatial statistics, vol. 20, pp. 76-91, 2017. 8. hengl, t., g. b. m. heuvelink and d. g. rossiter. “about regression-kriging: from equations to case studies”. computers and geosciences, vol. 33, no. 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britain”. journal of hydrology, vol. 308, no. 1-4, pp. 128-150, 2005. 16. huang, c. l., h. w. wang and j. l. hou. “estimating spatial distribution of daily snow depth with kriging methods: combination of modis snow cover area data and groundbased observations”. the cryosphere discussion paper. vol. 9, pp. 4997-5020, 2015. 17. mcbratney, a., i. odeh, t. bishop, m. dunbar and t. shatar. “an overview of pedometric techniques of use in soil survey”. geoderma, vol. 97, pp. 293-327, 2000. 18. bivand, r. s., pebesma, e. j. and gómez-rubio, v. “applied spatial data analysis with r”. springer, new york, 2008. 19. ahmed, r. a. “multivariate adaptive regression spline model for predicting new wells groundwater in sulaimani governorate”. master thesis of statistic department, college of administration and economic. university of sulaimani, kurdistan region, iraq, 2014. tx_1~abs:at/tx_2:abs~at uhd journal of science and technology | july 2022 | vol 6 | issue 2 85 1. introduction changes in human lifestyle and the deterioration of the environment have left a negative impact on human health. for that reason, human health has always been the subject of research with the aim to improve it. diabetes is a group of metabolic diseases which result in high blood sugar levels for a prolonged period. as stated by international diabetes federation, 537 million adults (20–79 years) are living with diabetes which is 1 in 10 of adult population. this number is predicted to rise to 643 million by 2030 and 783 million by 2045 [1]. diabetes has been the subject of research for some times by multidisciplinary scientists with the aim to find and improve methods that lead to effective prevention, diagnosis, and treatment of the disease. for instance, in a similar approach, in 2013, anouncia et al. proposed a diagnosis system for diabetes. the system is implemented to diagnose the type of diabetes based on symptoms provided by patients. they have used rough set-based knowledge representation in developing their system and the results showed improvements in terms of accuracy of diabetes type diagnosis and the time it takes for the diagnosis [2]. despite all the efforts invested into researching diagnostic techniques for diabetes, research rough set-based feature selection for predicting diabetes using logistic regression with stochastic gradient decent algorithm kanaan m. kaka-khan1, hoger mahmud2, aras ahmed ali3 1department of information technology, university of human development, iraq, 2department of information technology, the american university of iraq, sulaimani, 3university college of goizha, sulaymaniyah a b s t r a c t disease prediction and decision-making plays an important role in medical diagnosis. research has shown that cost of disease prediction and diagnosis can be reduced by applying interdisciplinary approaches. machine learning and data mining techniques in computer science are proven to have high potentials by interdisciplinary researchers in the field of disease prediction and diagnosis. in this research, a new approach is proposed to predict diabetes in patients. the approach utilizes stochastic gradient descent which is a machine learning technique to perform logistic regression on a dataset. the dataset is populated with eight original variables (features) collected from patients before being diagnosed with diabetes. the features are used as input values in the proposed approach to predict diabetes in the patients. to examine the effect of having the right variable in the process of making predictions, five variables are selected from the dataset based on rough set theory (rst). the proposed approach is applied again but this time on the selected features to predict diabetes in the patients. the results obtained from both applications have been documented and compared as part of the approach evaluations. the results show that the proposed approach improves the accuracy of predicting diabetes when rst is used to select variables for making the prediction. this paper contributes toward the ongoing efforts to find innovative ways to improve the prediction of diabetes in patients. index terms: logistic regression, stochastic gradient descent, rough set theory, k-fold cross-validation, diabetes prediction corresponding author’s e-mail: kanaan m. kaka-khan, department of information technology, university of human development, iraq. e-mail: kanaan.mikael@uhd.edu.iq received: 21-08-2022 accepted: 02-10-2022 published: 18-10-2022 access this article online doi: 10.21928/uhdjst.v6n2y2022.pp85-93 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2022 kanaan m. kaka-khan, et al. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes 86 uhd journal of science and technology | july 2022 | vol 6 | issue 2 shows that there is still room for improvement, especially in areas related to the level of accurately in predicting the disease in a patient. rough set theory (rst) has been used by researchers to predict a wide array of topics such as time series prediction [3], crop prediction [4], currency crisis prediction [5], and stock market trends prediction [6]. in this research, we use rst to select variables in a dataset with the aim to improve the level of accuracy in predicting diabetes in a patient. stochastic gradient descent algorithm is used to process the variables selected to make diabetes prediction based on computed logistic regression values from the dataset. the dataset used for all experiments in this study is made available by the pima indian diabetes [7]. this paper contributes toward the ongoing efforts to find innovative ways to improve the prediction of diabetes in patients by proposing a new approach to predict diabetes in patients using machine learning techniques. the results presented in sections 5.1 and 5.2 show that the approach improves accuracy in making diabetes predictions compared to other available approaches. the rest of this paper is organized as follows: section 2 provides the theoretical background needed to understand the selected techniques and section 3 provides a survey of related literatures. section 4 provides the description of the methodology used in this study. experimental results and discussion are provided in section 5. finally, conclusions are drawn in section 6. 2. background this section provides a basic background on the theories used in the study. 2.1. rst rough set [8] is proposed by pawlak to deal with uncertainty and incompleteness. it offers mathematical tools to discover patterns hidden in datasets and identifies partial or total dependencies in a dataset based on indiscernibility relation. the technique calculates a selection of features to determine the relevant feature. the general procedures in rough set are as follows: the lower approximation of set d is the set of objects in a table of information which certainly belongs to the class x: ax = {xi∈u|[xi]nd(a) ⊂ x}, x ∈ att (1) the upper approximation of a set x includes all objects in a table of information which possibly belongs to the class x: ax = {xi∈u |[xi] (a)∩ a ≠ φ} (2) boundar y region is the difference between upper approximation set and lower approximation set that is referred to as bnd (x) b = ax ax (3) positive region is the set of all objects that belong to lower approximation, which means, the union of the lower approximation consist of the union of all the lower approximation sets: ρ = ∪ a (union of all lower sets) (4) indiscernibility of positive reign for any g ⊆ att is the associated equivalence relation: ind (g) = {(x, y) ∈ p× : ∀a ∈g, a (x) = (y)} (5) reducts are the minimum range representation of the original data without loss of information: reducts δ = minind (6) 2.2. stochastic gradient descent according to [9], stochastic gradient descent is a function’s minimizing process, following the slope or gradient of that function. in general, in machine learning, stochastic gradient descent can be considered as a technique to evaluate and update the weights every iteration, which minimizes the error in training data models. while training, this optimization technique tries to show each and every training sample to the model one by one. for each training sample, the model produces an output (prediction), calculates the error, and updates to minimize the error for the next output, and this process is repeated for a fixed number of epochs or iterations. equation-7 describes the way of finding and updating the set of weights (coefficients) in a model from the training data. b = b-learning rate × error × x (7) here, b is the coefficient (weight) being estimated, learning rate is a learning value that can be configured between (0.01 and 10), error is the model’s predicted error, and x is the input value. the accuracy of the prediction can be calculated simply by dividing the number of corrected predictions by the actual values produced in formula 3. accuracy correct predictions actual values = ∑ ∑ � � � (8) kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes uhd journal of science and technology | july 2022 | vol 6 | issue 2 87 2.3. logistic regression logistic regression [10] is a two-class problems linear classification algorithm. equation 9 represents the logistic regression algorithm. in this algorithm, to make a prediction (y), using coefficient (weight) values, the input values (x) are combined in a linear form. logistic regression produces an output of binary value (0 or 1). yhat e b b x = + − + × � . . � ( ) 1 0 10 0 1 1 (9) the foundation of logistic regression algorithm is euler’s number, the estimated output is represented as yhat, the algorithm’s bias is b 0, and the coefficient (weight) for the single input value (x 1 ) is represented as b 1. the logistic regression produces a real value as an output (yhat) which is between 0 and1. to be mapped to an estimated class value, the output needs to be converted (rounded) to an integer value. each column (attribute) of the dataset has an associate value (b) that should be estimated from the training data and it is the actual model’s representation that can be saved for further use. 3. related work prediction is a widely used approach in many fields of science including healthcare to foresee possible outcomes of a cause. disease prediction is certainly an area, where researchers have been working by applying a number of different theories including machine learning theories with the aim to find methods to make the most accurate prediction possible. rst is one of the theories used to classify and predict diseases. for instances, the authors of [11] have used the theory to classify medical diagnosis, the authors of [12] and [13] have modified and used the theory to improve disease prediction. type 1 and 2 diabetes were the focus of the authors of [14], in which they developed a hybrid reasoning model to address prediction accuracy issues. based on their results, they claim that their approach raises diabetes prediction accuracy to 95% compared to other existing approaches. in 2017, rst was used by the authors of [15] to develop a model for patient clustering in a dataset. the authors considered average values calculated from diabetes indicators in a dataset to cluster the patients in it. in the same year, deep learning was utilized by the authors of [16] to establish an intelligent diabetes prediction model, in which patients’ risk factors collected in a dataset were considered to make the prediction. in 2018, fuzzy rst is applied first to select specific features in a dataset, later in the process, to improve prediction performance, save processing time, and better diagnosis accuracy that the optimized generic algorithm (oga) is applied. the results obtained from the study shows that the approach has achieved the objectives of the study [17]. in 2020, vamsidhar talasila and kotakonda madhubabu proposed the use of rst technique to select the most relevant features to be inputted to the recurrent neural network (rnn) technique for disease prediction. they claimed that the rst-rnn method achieved accuracy of 98.57% [18]. in the same year, gao and cheng proposed an improved neighborhood rough set attribute reduction algorithm (inrs) to increase the dependence of conditional attributes based on considering the importance of individual features for diabetes prediction [14]. in 2021, gadekallu and gao proposed a model using an approach based on rough sets to reduce the attributes needed in heart disease and diabetes prediction [19]. the main limitation of these studies is the fact that none has considered the quantity and quality of viables used to make diagnostic predictions. the approach used in this study is similar to the ones used in the surveyed literatures but differs in objectives. we use rst to select the best features in a dataset and use stochastic gradient decent algorithm to compute the logistic regression values from the selected features in the dataset with the aim to improve the prediction accuracy of diabetes in a patient. 4. methodology this section provides insights on the methodology used to achieve the objectives of the study. the methodology is comprised six major steps: 4.1. step 1 a dataset is selected, examined for suitability and reliability based on a number of characteristics, and uploaded to be analyzed. the dataset selected and uploaded for the purpose of this research is provided by pima indians diabetes [7]. the selected dataset involves predicting diabetes within 5 years in pima indians given medical details. the dataset is a 2-class classification problem and consists of 76 samples with 8 input and 1 output variable. the variable names are as follows: number of times pregnant, plasma glucose concentration a 2 h in an oral glucose tolerance test, diastolic blood pressure (mm hg), triceps skinfold thickness (mm), 2-h serum insulin (mu u/ml), body mass index (weight in kg/[height in m]2), diabetes pedigree function, age, and class variable (0 or 1). before implementing the model, it is highly preferred to do preprocessing due to some kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes 88 uhd journal of science and technology | july 2022 | vol 6 | issue 2 deficiencies. usually, the dataset contains features highly varying in magnitudes, units, and range which may results in inaccurate output [20]. in this work due to use of stochastic gradient descent algorithm, the dataset has been normalized using min-max scaling to bring all values to between 0 and 1. table 1 shows a sample of the selected dataset. 4.2. step 2 the selected diabetes dataset is preprocessed and normalized. to increase the efficiency and accuracy of the model, the dataset needs to be pre-processed before applying the proposed model since the data may contain null values, incorrect, and redundant information. in general, data processing involves two major steps: data cleaning and data normalization. data cleaning means removing incorrect information or filling out missing values to increases the validity and quality of a dataset though applying a number of different methods [21]. in this study, in case of any tuple containing missing values, the missed attribute value assumed to be 0 (this is achieved using the fill_mising_values () function from the python script developed for the implementation phase of this study). redundant or unnecessary columns are deleted to have a high quality dataset (this is achieved using the remove_duplicate_columns () function from the python script). to let all features have equal weight and contribution to the model, the range of each feature needs to be scaled, for this purpose, the dataset is normalized to a range of [0,1] by the following processes: string columns converting: the string columns are converted to float through str column using the float() function. min max finding: min and max values of each column of the dataset are found through using the dataset minmax() function. finally, the dataset is normalized by the min-max normalization method using the following equation adapted form [22]. x x x x x ' ( ) max ( ) = − ( ) − min min (10) 4.3. step 3 in this step, rst is applied to select the features which might produce a better prediction. there are nine variables in total in the dataset, as shown in table 1. the class variable is considered as a dependent variable and the other eight variables are assumed as predictors or independent variables. table 2 presents the regression calculation summary for diabetes classification of the dataset. the result of the calculation clearly shows that the accuracy of diabetes prediction is 30.32% if all variables in the dataset are considered in the calculation. the low accuracy result is an indication that there might be one or more variables which are not fit to be used for prediction. the regression calculation also shows that the un-standardized regression coefficient (b) is 0.06 for pregnancies, which indicates that if all other predictors are controlled then an increment of one unit in pregnancies increases the accuracy by 0.06. the same statement can be made for the other variables. to flitter the features that might produce a better diabetes prediction, the dataset is grouped together into nine elementary sets based on indiscernibility relation level between the data elements. table 3 shows the details of the groups. to further process the groups, the discernibility matrix has been developed for the elementary sets and the result is shown in table 4. from the discernibility matrix, a discernibility function has been developed, as shown in equation 11. f(a) = f(a1) × f(a2)×…×f(an) (11) as the result of discernibility function of all elementary sets for the entire dataset, we found that: f(a) = a1∨a2∨a5∨a6∨a8 where a1 is pregnancies; a2 is plasma glucose; a5 is insulin; a6 is dpf; and a8 is age attribute. table 5 shows the reduct matrix for the elementary sets. from the reduct matrix, all reducts and core attributes have been found: table 1: the first ten records of the diabetes dataset used in this study pregnancies plasma glucose blood pressure skinfold thickness insulin bmi dpf age class variable 6 148 72 35 0 33.6 0.627 50 1 1 85 66 29 0 26.6 0.351 31 0 8 183 64 0 0 23.3 0.672 32 1 1 89 66 23 94 28.1 0.167 21 0 0 137 40 35 168 43.1 2.288 33 1 5 116 74 0 0 25.6 0.201 30 0 3 78 50 32 88 31 0.248 26 1 10 115 0 0 0 35.3 0.134 29 0 2 197 70 45 543 30.5 0.158 53 1 bmi: body mass index kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes uhd journal of science and technology | july 2022 | vol 6 | issue 2 89 f(r1) = a1∨a2∨a6; f(r2) = a1∨a2∨a5∨a8; f(r3) = a2∨a5∨a8; f(r4) = a1∨a2∨a8; f(r5) = a2∨a6∨a8; f(r6) = a1∨a2∨a6∨a8; f(r7) = a2∨a5∨a6; f(r8) = a1∨a2∨a5; table 3: elementary sets samples pregnancies plasma glucose blood pressure skinfold thickness insulin bmi dpf age group 1 0–1 0–22 0–13 0–10 0–94 0–6 0–0.25 21–26 group 2 2–3 23–46 14–28 11–22 95–190 7–14 0.26–0.51 27–33 group 3 4–5 47–70 29–43 23–34 191–286 15–22 0.52–0.77 34–41 group 4 6–7 71–94 44–58 35–46 287–382 23–30 0.78–1.03 42–49 group 5 8–9 95–118 59–73 47–58 383–478 31–38 1.04–1.29 50–57 group 6 10–11 119–142 74–88 59–70 479–574 39–46 1.3–1.55 58–63 group 7 12–13 143–166 89–103 71–82 575–670 47–54 1.56–1.81 64–69 group 8 14–15 167–190 104–118 83–94 671–766 55–62 1.82–2.03 70–75 group 9 16–17 191–199 119–122 95–99 767–846 63–67 2.04–2.42 76–81 table 4: discernibility matrix samples group 1 group 2 group 3 group 4 group 5 group 6 group 7 group 8 group 9 group 1 group 2 a1a2a4a7a8 group 3 a2a3a4a8 a1a3a4a8 group 4 a1a2a4a6a7 a2a3a4a7a8 a1a2a7a8 group 5 a2a3a5a7a8 a1a3a4a8 a1a2a4a6a7 a2a3a5a7a8 group 6 a1a3a5a6a8 a3a4a6a8 a1a3a5a7a8 a2a4a5a7a8 a2a3a5a7a8 group 7 a1a2a4a6a8 a2a4a5a7 a1a2a4a6 a2a3a5a7a8 a2a3a5a8 a5a6a7 group 8 a1a2a4a6a7 a1a3a4a8 a1a2a7a8 a2a3a5a7a8 a2a4a5a7 a3a4a5 a2a4a5 group 9 a2a4a5a7 a1a2a4a7 a3a5a8 a2a5a7a8 a3a4a6 a2a3a8 a2a4a5 a3a4a5 table 5: reducts matrix samples group 1 group 2 group 3 group 4 group 5 group 6 group 7 group 8 group 9 group 1 a1a2a4a7a8 a2a3a4a8 a1a2a4a6a7 a2a3a5a7a8 a1a3a5a6a8 a1a2a4a6a8 a1a2a4a6a7 a2a4a5a7 group 2 a1a2a4a7a8 a1a3a4a8 a2a3a4a7a8 a1a3a4a8 a3a4a6a8 a2a4a5a7 a1a3a4a8 a1a2a4a7 group 3 a2a3a4a8 a1a3a4a8 a1a2a7a8 a1a2a4a6a7 a1a3a5a7a8 a1a2a4a6 a1a2a7a8 a3a5a8 group 4 a1a2a4a6a7 a2a3a4a7a8 a1a2a7a8 a2a3a5a7a8 a2a4a5a7a8 a2a3a5a7a8 a2a3a5a7a8 a2a5a7a8 group 5 a2a3a5a7a8 a1a3a4a8 a1a2a4a6a7 a2a3a5a7a8 a2a3a5a7a8 a2a3a5a8 a2a4a5a7 a3a4a6 group 6 a1a3a5a6a8 a3a4a6a8 a1a3a5a7a8 a2a4a5a7a8 a2a3a5a7a8 a5a6a7 a3a4a5 a2a3a8 group 7 a1a2a4a6a8 a2a4a5a7 a1a2a4a6 a2a3a5a7a8 a2a3a5a8 a5a6a7 a2a4a5 a2a4a5 group 8 a1a2a4a6a7 a1a3a4a8 a1a2a7a8 a2a3a5a7a8 a2a4a5a7 a3a4a5 a2a4a5 a3a4a5 group 9 a2a4a5a7 a1a2a4a7 a3a5a8 a2a5a7a8 a3a4a6 a2a3a8 a2a4a5 a3a4a5 table 2: linear regression statistics of diabetes dataset multiple r 0.550684207 r square 0.303253096 adjusted r square 0.295909255 standard error 0.400210451 coefficients standard error t stat p-value unstandardized regression coefficient (b) intercept 0.853894266 0.085484958 -9.98882 0.00 0.066 pregnancies 0.020591872 0.00512998 4.014026 0.00 1.863 plasma glucose 0.005920273 0.000515123 11.49294 0.00 0.022 blood pressure 0.002331879 0.000811639 2.87305 0.00 0.081 skinfold thickness 0.00015452 0.001112215 0.13893 0.89 0.247 insulin 0.000180535 0.000149819 -1.20502 0.23 0.004 mi 0.013244031 0.00208776 6.343656 0.00 0.000 dpf 0.147237439 0.045053885 3.26803 0.00 0.686 age 0.002621394 0.00154864 1.692707 0.09 0.001 f(r9) = ∨a2∨a5∨a6∨a8. finally, table 6 shows the features that are selected to be used for making diabetes prediction. kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes 90 uhd journal of science and technology | july 2022 | vol 6 | issue 2 table 3 shows the indiscernibility level of the relation between the patients. table 6 represents the last step of rst process, in which the data are simplified, and the indiscernibility relations are stated. the * symbol means that a certain variable has no impact in a certain case, for example, if the patient’s pregnancy is (0–1) and plasma glucose is (0–22) and dpf is (0-0.25), then the patient has diabetes regardless of the value of other attributes, and so on. 4.4. step 4 in this step, the logistic regression algorithm with stochastic gradient descent technique is applied on the selected features in the previous step. the major steps of the application are as follows: 4.4.1. dataset loading the dataset is loaded into the model through load_dataset() function. 4.4.2. dataset preprocessing the dataset is preprocessed through str column to float(), dataset minmax(), and nor malize dataset() functions accordingly. 4.4.3. dataset splitting into k folds the dataset is split into k-folds and trainset. test set creation for training the model is achieved through cross validation split() function. 4.4.4. coefficients estimating coefficients or weights are the values that determine the model accuracy and can be estimated for training data using stochastic gradient descent. the algorithm uses two parameters to estimate the weights (coefficient), the first one is learning rate to specify the amount of each weight, and it is corrected continuously, while it is updated. the second one is epochs which is the loop through the training process while updating the coefficient. the coefficients estimating is achieved through coefficients sgd() function. 4.4.5. coefficients updating for each instance in the training data, each coefficient is updated throughout all epochs. the error that the model makes is the criteria for updating the coefficients. the simple equation can be used to calculate the error (equation-12). error = (expected output value) – (prediction made with the candidate coefficients) (12) 4.5. step 5 predictions are generated; equation 7 describes the prediction process which is the most important part of the model. prediction process will be needed twice: first in stochastic gradient descent to evaluate candidate coefficient values and second in the model when it is finalized to produce outputs (predictions) on test data. the prediction process is achieved through predict() function. fig. 1 shows the execution flow of the proposed approach. 4.6. step 6 finally, the results obtained are compared. fig. 1 shows the proposed diabetes prediction method. 4.7. model performance evaluation in this research, k-fold cross-validation technique has been used to evaluate the learned model’s performance on unseen data. cross-validation is a resampling procedure used to validate machine learning models on a limited data sample. using k-fold, cross-validation means that k models will be construct, evaluated, and through using mean model error, the model’s performance is estimated. after rounding the predicted value of each row which is a float number between 0 and 1, it will be compared to its actual value. if they are equal, the prediction is considered as a correct result. simple error equation (equation 13) will be used to evaluate each model. accuracy = ∗ no of correct results total no of samples . . 100 (13) the general procedure is as follows: (1) shuffle the dataset randomly. (2) split the dataset into k groups, (3) take a group as a test set and the remaining as a training set, the same procedure will be repeated for each and every group; (4) as usual, the model will be fitting on the training set and evaluating on the test set, and (5) retain the result (evaluation score) the model can be discarded [17], [23]. for this work, table 6: indiscernibility table samples pregnancies plasma glucose insulin dpf age group 1 0–1 0–22 * 0–0.25 * group 2 2–3 23–46 95–190 * 27–33 group 3 * 47–70 191–286 0.52–0.77 34–41 group 4 * 71–94 287–382 * 42–49 group 5 8–9 95–118 * * 50–57 group 6 * 119–142 * 1.3–1.55 58–63 group 7 12–13 143–166 * 1.56–1.81 64–69 group 8 14–15 167–190 671–766 * * group 9 * 191–199 767–846 2.04–2.42 76–81 kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes uhd journal of science and technology | july 2022 | vol 6 | issue 2 91 a learning rate, training epochs, and k value are (0.1, 100, 5) subsequently. after implementing the model twice; first on the dataset with all features, and second with features selected by applying rst, the results can be discussed as follows: 4.8. making prediction on dataset with all features the aim of using logistic regression is predicting the dependent variable (output variable) based on equation 7, and the aim of using stochastic gradient descent technique is minimizing the error of predicted coefficient values while training the model on the dataset. for model training, k-fold cross-validation technique is used to split out the dataset to 5 folds (groups), a fold is used as a test set and the others as train sets, for example: • mode l: fold1 for test and fold2, fold3, fold4, and fold5 for train • mode 2: fold2 for test and fold1, fold3, fold5, and fold5 for train • mode 3: fold3 for test and fold1, fold2, fold4, and fold5 for train • mode 4: fold4 for test and fold1, fold2, fold3, and fold5 for train • mode 5: fold5 for test and fold1, fold2, fold3, and fold4 for train. for each model, after training for 100 epochs (iterations) and minimizing the errors to a desired results and calculate the accuracy using equation 11, the score can be calculated using equation 14. score sumof all model acuracy results total no of models = � � � � � � .� � (14) table 7: accuracy score of each model used model no. accuracy model 1 73.857 model 2 78.431 model 3 81.699 model 4 75.816 model 5 75.816 score 77.124% fig. 1. proposed diabetes prediction method. kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes 92 uhd journal of science and technology | july 2022 | vol 6 | issue 2 the total number of models used is five. table 7 summarizes the models result and the overall score. the overall score is 77.12% for the model on the dataset with all features. 4.9. making prediction on dataset with rst-based selected feature the same process applied on the dataset with selected features based on rst, the result is presented in table 8. table 9 shows the comparison between the results obtained from both implementations; implementing the model on the dataset with all features and the rst-based selected features. the results show that rst-based selected features for machine learning compared to the data set with all features give more accurate predictions. the baseline score for the selected dataset is 65% our experiment results which indicated that the proposed approach increased the prediction accuracy for diabetes dataset with all features from 65% to 77% and 80% for rstbased features dataset, as shown in table 10. finally, it can be summarized that implementing the logistic regression algorithm with stochastic gradient descent technique is one of the suitable choices for diabetes predictions on the basis of the results. at the same time, rather than using all features, more precise predictions can be made by feature selection based on rough set for neural network. table 11 summarizes a comparison between our works with some of the most recently published works. 5. conclusion and future work in the health-care sector predicting, the presence or nonpresence of diseases is important to help people know their health status so that they take the necessary steps to control the disease. this paper explores the use of stochastic gradient descent algorithm to apply logistic regression on datasets to make predictions on the presence of diabetes. the pima indian diabetes dataset is used to produce results using the proposed technique. the experiments results show that diabetes can be predicted more accurately using logistic regression with stochastic gradient descent algorithm when rst is used to select the important features on a normalized dataset. this is paper makes a real contribution in the use of interdisciplinary techniques to improve prediction mechanisms in health-care sector in general diabetes prediction in specific. the main purpose of this work is showing the significance of using rst with machine learning algorithms, hence in the future; the same theory can be applied with other algorithms to have a better result. table 8: accuracy and score for all five models for selected features model no. accuracy model 1 77.342 model 2 81.013 model 3 83.874 model 4 78.394 model 5 79.628 score 80.215% table 9: accuracy and score for all five models using all features, rst‑based selected features model no. all features (accuracy) rst-based selected features (accuracy) model 1 73.856 77.342 model 2 78.431 81.013 model 3 81.699 83.874 model 4 75.816 78.394 model 5 75.816 79.628 score 77.124% 80.215% rst: rough set theory table 10: accuracy summery of baseline and proposed algorithm for diabetes model name prediction accuracy (%) baseline score 65 logistic regression with sgd algorithm 77.124 rst-based logistic regression with sgd algorithm 80.215 table 11: dataset classification comparison works data size methods accuracy (%) [24] 768 samples with 9 attributes logistic regression 77 [25] 768 samples with 9 attributes modified pso naïve bayes 78.6 [26] 768 samples with 9 attributes modified weighted knn (sdknn) 83.76 [27] 768 samples with 9 attributes random forest classifier 79.57 our proposed method 768 samples with 9 attributes logistic regression with sgd algorithm 77 768 samples with 6 attributes rst-based logistic regression with sgd algorithm 80.215 rst: rough set theory kanaan m. kaka-khan, et al.: rough set-based feature selection for predicting diabetes uhd journal of science and technology | july 2022 | vol 6 | issue 2 93 references [1] “diabetesatlas”. available from: https://www.diabetesatlas.org [last accessed on 2022 aug 08]. 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[27] v. chang, j. bailey, q. a. xu and z. sun. “pima indians diabetes mellitus classification based on machine learning (ml) algorithms”. neural computing and applications. vol. 34, no. 10, pp. 1-7, 2022. _hlk111803208 _hlk115420386 _goback tx_1~abs:at/tx_2:abs~at 72 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 1. introduction data mining has made an amazing development in the past years; however, the main problem is missing data or value. data mining is the sector wherein experimental facts sets are analyzed to find out thrilling and potentially beneficial relationships [1]. lacking records or value in a datasets can affect the performance of classifier which ends up in difficulty of extracting beneficial information from datasets. plentiful of facts is being gathered and saved each day. those facts can be used to extract interesting patterns. the information that we collect is incomplete normally [2]. therefor everyone wishing to apply statistical information evaluation or information cleaning of any type could have problems with lacking data. we still land in some missing attribute values in a function dataset. people typically tend to depart the income area empty in surveys, for instance, and members once in a while do not have any information available or cannot answer the question. plenty facts can also be lost in the technique of collecting information from multiple resources [1]. using that information to collect some statistics can now yield misleading effects. hence, to eliminate the abnormalities, we need to pre-method the statistics earlier than the usage of it. those instances may be omitted within the case of a small percentage of lacking values, but within the case of huge quantities, ignoring them will now not yield the desired outcome. a number of missing spaces in a dataset is a massive problem. therefore, a few pre-processing of statistics can be accomplished earlier than acting any information mining techniques to extract a few treasured records from a dataset to keep away from such mistakes and as a result enhance statistics first-class. fittingly managing with misplaced values is crucial and difficult venture since it requires careful examination of all occurrences of information to recognize design of missingness within the missing value imputation techniques: a survey wafaa mustafa hameed1,2*, nzar a. ali2,3 1technical college of informatics, sulaimani polytechnic university, sulaimani, 46001, kurdistan region, iraq, 2department of computer science, cihan university sulaimaniya, sulaimaniya, 46001, kurdistan region, iraq, 3department of statistics and informatics, university of sulaimani, sulaimani, 46001, kurdistan region, iraq a b s t r a c t numerous of information is being accumulated and placed away every day. big quantity of misplaced areas in a dataset might be a large problem confronted through analysts due to the fact it could cause numerous issues in quantitative investigates. to handle such misplaced values, numerous methods were proposed. this paper offers a review on different techniques available for imputation of unknown information, such as median imputation, hot (cold) deck imputation, regression imputation, expectation maximization, help vector device imputation, multivariate imputation using chained equation, sice method, reinforcement programming, non-parametric iterative imputation algorithms, and multilayer perceptrons. this paper also explores a few satisfactory choices of methods to estimate missing values to be used by different researchers on this discipline of study. furthermore, it aims to assist them to discern out what approach is commonly used now, the overview may additionally provide a view of every technique alongside its blessings and limitations to take into consideration of future studies on this area of study. it can be taking into account as baseline to solutions the question which techniques were used and that is the maximum popular. index terms: data preprocessing, imputation, mean, mode, categorical data, numerical data access this article online doi: 10.21928/uhdjst.v7n1y2023.pp72-81 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 hameed and ali. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) su rv e y uhd journal of science and technology corresponding author’s e-mail: technical college of informatics, sulaimani polytechnic university, department of computer science, cihan university sulaimaniya, sulaimaniya, 46001, kurdistan region, iraq. e-mail id: wafaa.mustafa@spu.edu.iq received: 09-11-2022 accepted: 05-03-2023 published: 28-03-2023 mailto:wafaa.mustafa@spu.edu.iq hameed and ali: imputation techniques uhd journal of science and technology | jan 2023 | vol 7 | issue 1 73 data. numerous strategies were proposed to address such lacking values considering 1980 [2]. this file illustrates distinct varieties of lacking values and the techniques used to address them. it is tremendously vital to note that there may be evaluation in purge and lost value. purge value implies that no value may be doled out though misplaced value implies actual value for that variable exists but not reachable or captured in dataset due to some motives. the information mineworker should separate between purge esteem and lost esteem. once in a while, each the values may be treated as misplaced values. lost records may be due to tools glitch, conflicting with different facts so erased, data no longer entered because of false impression, positive facts might not be considered crucial at the time of statistics collection. a few statistics mining calculations do not require substitution of misplaced values as they are planned and created to handle lost values; however, some data mining calculations cannot good buy with lost values. sometime, these days making use of any strategy of managing with lost values its miles vital to get it why records is misplaced [2], [3]. 2. missing value patterns 2.1. missing completely at random (mcar) mcar is the most improved degree of randomness and it indicates that the layout of misplaced value is completely arbitrary and does not rely on any variable which may additionally or might not be covered inside the examination [3]. it refers to facts that do not rely on the interest variable or every other parameter observed inside the dataset [4]. while missing values are distributed uniformly across all measurements, then we find the records to be absolutely randomly missing. for this reason, a brief test is to compare pieces of data – one with missing observations and the other without missing observations. on a t-test, if there is no mean difference between the two data units, we will expect that the data are mcar [5]. anything that is missing and sometimes because this form of missing facts is not often observed and the best manner to ignore these instances, for example: water damage to paper forms due to flooding before it enters [1], [2] or in a survey, if we get 5% responses missing randomly, it is mcar [6], [7]. this type is described by using the equation p p x y y f l xl m l( | , , ( , ), , )1 0 = where f is a function, that is, the missing data patterns are determined only by the covariate variables x. note here that marx is equivalent to mcar if there are no covariates in the model [7], [8]. 2.2. missing at random (mar) when missed value does not rely on any given or ignored value [8]. often information may not be deliberately missing; however, it can be named “missing at random”. if the data meet the requirement that missingness should not rely on x’s value after accounting for some other parameter, we may also find an x entry to be missing at random. depressed people seem to have less income, as an instance, and the reported earnings now depend on the thing depression. the percentage of lacking records among depressed people could be high as depressed people have lower incomes [1] if we get 10% missing for the male responses in a survey and 5% missing for the woman survey, then it is mar [6]. this kind is defined through the equation p p x y y f l x yl l m l l| , , ( , , ), , ,0 0( ) = in which f is a function, that is, only the covariate variables x and the based variables has been located have an impact on the patterns of lacking statistics. remember the fact that if there may be most effective one dependent variable y then there may be best one missing series that does not encompass any found dependent variables. for models with one structured variable, mar is therefore equal to marx [7]. 2.3. not missing at random (nmar) if the data are not missing at random or informatively, it is labeled “not missing at random.” this kind of situation happens while the technique of messiness depends at the actual value of missing statistics [4]. this type is defined by the equation: p p x y y f l x y yl l m l l m l| , , ( , , , ), , , ,0 0( ) = where f is a function, that is, all three types of variables have an effect on the missing data patterns. it is well known how full information maximum likelihood (fiml) estimation performs under all of these conditions [7]. 2.4. missing in cluster (mic) data are regularly more missing in some attributes than in others. in addition, the missing values in the ones attributes can be correlated. it is extremely tough to use statistical techniques to show multi-attribute correlations of lacking values. on this sample of missing values, the exceptional of statistics is much less homogeneous than that with mar. hameed and ali: imputation techniques 74 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 the effects of any applications of analytical based on the complete facts set have to be cautious, for the reason that pattern data are biased in the attributes with a big number of missing values [7], [8]. 2.5. systematic irregular missing (sim) data can be missing quite irregularly, however systematically. there is probably overly missing correlations among the attributes, but those correlations are extraordinarily tiresome to analyze. an implication of sim is that the data with complete entities are unpredictably under-representative [7]. the first-class of records with this sample of missing values is minimal homogeneous than the ones in mar and additionally less controllable than that with mic. applications of any analytical results based at the whole data set are enormously questionable [9]. 3. strategies of handling missing data managing missing data may be carried out in two exclusive strategies for. the first method is definitely ignoring missing values and second approach is to take into account imputation of missing values. 3.1. ignoring missing values the missing records ignoring technique absolutely releases the state that includes missing data. they are mightily used for handling lacking facts. the earnest problem with this method is that it decreases the dataset size. this is handy whilst the dataset has small amount of lacking values. there are two common methods for ignoring missing data: 3.1.1. listwise deletion complete case analysis approach excludes all observations with missing values for any variable of interest. this approach thus limits the analysis to those observations for which all values are observed. this techniques is simple to use but cause loss of huge data, loss of precision, high effect on variability, and induce bias. 3.1.2. pairwise deletion for all the instances, we perform analysis with in which the variables of interest are present. it does no longer exclude complete unit but uses as lots data as feasible from every unit. this method is straightforward, keeping all available values, that is, best missing values are deleted but motive the loss of data, no longer a higher solution compared to other techniques. the pattern size for every individual evaluation is better than the entire case analysis [2], [10]. 3.2. single imputation single imputation procedures produce a precise value for a dataset’s missing real value. this method necessitates a lower computing cost. researchers have proposed a variety of single imputation strategies. the typical strategy is to analyze other responses and select the greatest possible response. the value can be calculated using the mean, median, or mode of the variable’s available values. single imputation can also be done using other methods, such as machine learning-based techniques. imputed values are considered actual values in single imputation. single imputation ignores the reality that no imputation method can guarantee the true value. single imputation approaches ignore the imputed values’ uncertainty. instead, in future analysis, they recognize the imputed values as actual values [11], [12]. 3.3. multiple imputations the use of distinct simulation models, multiple imputation methods yield several values for the imputation of single missing records. those strategies use imputed data’s variability to generate a diffusion of credible responses. multiple imputation strategies are sophisticated in nature, but in contrast to single imputation, they do no longer suffer from bias values. in multiple imputations, every missing facts point is replaced with m values obtained through m iterations (wherein m > 1 and m generally sits between 3 and 10) [6]. in this technique, a statistical approach used for coping with missing values, it performs through three stages: • imputation: generate m imputed data sets from a distribution which results in m complete data sets. the distribution can be different for each missing entry. • analysis: in this stage each m imputed data sets the analysis is performed, it is known as complete data analysis. • pooling: use simple rules the output obtained after data analysis is pooled to get final result. the resulting inferences form this stage is statistically valid if the methods to create imputations are “decent.” for substituting missing values with possible solutions, the multiple imputation method is used. the missing data set is transformed into complete data set using suitable imputation methods that can then be analyzed by any standard analysis method. therefore, multiple imputations have become popular in the handling of missing data. in this method, the process is repeated multiple times for all variables having missing values as the name indicates and then analyzed to combine hameed and ali: imputation techniques uhd journal of science and technology | jan 2023 | vol 7 | issue 1 75 m number of imputed data set into one imputed data set [7], [11]. 4. missing value imputation technique 4.1. mean imputation using this technique, calculate the mean of missing value through using the corresponding attribute value. this technique is easy to apply; it is built in maximum of the statistical bundle and quicker comparing with other techniques. it introduces precise result when facts is small, but it provides not proper result for large facts, this technique is appropriate for only mar but no longer beneficial for mcar [8], [13]. : ˆ ij k ij ij ki x c x x n∈ = ∑ wherein nk represents the number of non-missing values within the j-th feature of the k-th class ck, is missing [7], [8]. 4.2. hot (cold) deck imputation the concept, in this case, is to use some criteria of similarity to cluster the data earlier than executing the data imputation. this is one of the most used strategies. hot deck strategies impute missing values inside a data matrix by way of the usage of available values from the equal matrix. the item, from which these available values are taken for imputation within some other, is referred to as the donor. the replication of values ends in the trouble, that a single donor might be selected to accommodate multiple recipients. the inherent risk posed through that is that too many, or even all, missing values can be imputed with the values from a single donor. to mitigate this chance, a few hot deck variants restrict the amount of times anyone donor may be selected for donating its values. the similar techniques of hot deck are cold deck imputation method which takes other data source than current dataset. using hot deck, the missing values are imputed by realistically obtained values which avoids distortion in distribution, but bit empirical work for accuracy estimation, creates problem if any other sample has no close relation in entire manner of the dataset [8], [10], [11]. 4.3. median imputation (mdi) due to the affected of the mean through the presence of outliers, it seems better to use the median rather simply to make certain robustness. in this situation, the missing data are changed through the median of all recognized values of that attribute within the class where the instance with the missing characteristic belongs. this method is likewise a considered as a choice whilst the distribution of the values of is skewed. assume that the value xij of the k-th class, ck, is missing. it will get replaced by means of singh and prasad [7]. ( ){ }:ˆ ij k ijij i x c xüüü ∈= 4.4. regression imputation this approach may be apply by the use of known values for the construction of model after which calculates the regression between variables ends with applying that technique to calculate the missing values. the outcomes from applying this technique give greater accurate than mean imputation. the calculated data saves deviations from mean and distribution shape but the degree of freedom gets distort and can increases relationship [10]. y = α0 + α1 x 4.5. expectation maximization imputation (emi) there are forms of clustering algorithms. one is soft clustering and other is hard clustering:• soft clustering: clusters may overlap that is with unique degree of belief the factors belong to multiple clusters at the identical time • hard clustering: clusters do now not overlap that’s mean the element either belong to a cluster or not. • mixture models: the use of a probabilistic manner for doing soft clustering. every cluster corresponds to a generative model this is usually gaussian or multinomial, mvs are imputed by realistically obtained values which avoids distortion in distribution, in this technique, bit empirical work for accuracy estimation creates problem if any other sample has no close relation in entire manner of the dataset [2]. 4.6. k-nearest neighbor imputation (knn) specifying the similarity between two values and replace the missing value with similar one using euclidean distance. the advantage of this technique that for the datasets which having both qualitative and quantitative attributes values knn is suitable. there is no need for creating a predictive model for each attribute of missing data and helpful for multiple missing values. the knn looks for the most similar instances, the algorithm searches through all of the data set and that consider as an obstacle for that approach [12]. hameed and ali: imputation techniques 76 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 4.7. fuzzy k-means clustering imputation (fkmi) in this method, the membership characteristic plays an important position. it is assigned with every data item that describes in what degree the data object is belonging to the precise cluster. data items might not get assign to concrete cluster which is stated using centroid of cluster (i.e., the case of k means), that is due to the various membership degrees of every data with entire k clusters. unreferenced attributes for every uncompleted data are changing by fkmi on the premise of membership degrees and cluster centroid values. the pros of this approach is that it offers quality outcome for overlapping data, higher than k manner imputation and records objects may be a part of multiple cluster middle but the high computation time and noise sensitive, that is, low or no membership degree for noisy objects considered as a cones for the usage of this technique [10]. 4.8. support vector machine imputation (svmi) its regression primarily based technique to impute the missing values. it takes condition attributes (output) and decision attributes. then, the svmi would be carried out for prediction of values of missed condition features. advantages of this approach are the efficient in massive dimensional areas and efficient memory consumption; however, additionally, there may be a cons for using this technique which it is the bad performance if number of samples are plenty lesser than number of features [10], [14]. 4.9. most common imputation (mci) on this imputation method, clustered are first shaped by applying k-means clustering method. like in k-nn, on this method, the nearest neighbors are found using clusters. all the instances in every cluster are referred as nearest neighbor of each other. then, the missing value is imputed the usage of the same technique as is employed through knni imputation approach. this procedure is fast and therefore is ideal for applying in big datasets. this algorithm reduces the intra cluster variance to minimum. here, too value of k parameter is an important factor and is difficult to predict its value. in addition, this algorithm does no longer assure global minimal variance [2], [15], [16]. 4.10. multivariate imputation by chained equation (mice) mice expect that data are lost arbitrarily (damage). it imagines the likelihood of a missing variable depends on the watched facts. mice offers numerous values in the put of one lost esteem through making an arrangement of relapse (or other reasonable) models, tallying on its “method” parameter. in mice, every lost variable is treated as a variable, and other information inside the record is treated as an independent variable. at to begin with, mice foresee missing values utilizing the winning information of other factors. at that point, it replaces missing values utilizing the predicted values and makes a dataset known as ascribed dataset. by cycle, it makes numerous ascribed datasets. every dataset is at that factor analyzed utilizing standard measurable investigation techniques, and numerous investigation comes about are given [17], [18]. 4.11. sice technique it pretends the probability of a missing variable depends on the determined data. it gives multiple values within the place of one missing value through creating a sequence of regression models, each missing variable is treated as a dependent variable, and different data in the record are treated as an independent variable, it predicts missing data using the existing data of other variables. then, it replaces missing values using the predicted values and creates a dataset known as imputed dataset. it achieves 20% higher f-measure for binary data imputation and 11% less errors for numeric data imputations than its competitors with similar execution time. it imputes binary, ordinal and numeric data. it performed well for the imputation of binary and numeric data and fantastic preference for missing data imputation, especially for massive datasets where mice is impractical to use because of its complexity but it could not show better overall performance than mice for the case of ordinal data [6]. 4.12. reinforcement programming impute missing data using learning a policy to impute data thru an action-reward-based totally experience imputes missing values in a column by operating best on the identical column (similar to univarite single imputation) however imputes the missing values within the column with different values thus keeping the variance in the imputed values. it is usually used for dynamic approach for the calculation of missing values using machine learning procedures. it has functionality of convergence and to solving imputation problem through using exploration and exploitation [19], [20]. 4.13. utilizing uncertainty aware predictors and adversarial learning mlp ua-adv. impute the missing values so that the adversarial neural network cannot distinguish real values from imputed ones. in addition, to account for the uncertainty of imputed values, the usage of confidence scores acquired from the adversarial module. the adversarial module objectives to discriminate imputed values from real ones the resulting imputer in addition to estimating a missing entry with high accuracy, it hameed and ali: imputation techniques uhd journal of science and technology | jan 2023 | vol 7 | issue 1 77 table 1: short review with mentioning to the advantage and disadvantage of different techniques to handle missing value techniques note advantages limitations references leastwise deletion technique deletion of cases containing missing values (complete row is deleted) high missing information because of deletion of entire row high impact on variability loss of precision and induce bias. simple to use. loss of precision, loss of enormous data ‑ high effect on variability, induce bias [2], [10] pairwise deletion technique deletion of records best from column containing missing values much less lack of information by using keeping all available values less impact on variability less loss of precision and induce bias. keeping all available values only missing values are deleted. simple to use. not a better solution as compared to other methods. loss of data, [2], [10] mean imputation technique calculate the mean of missing value through using the corresponding attribute value. replace mvs with the mean of facts resultant may be better than that of original. it is built in maximum of the statistical bundle and quicker comparing with other techniques. it introduce precise result when facts is small it provides not proper result for large facts this technique is appropriate for only mar but no longer beneficial for mcar ‑ affected by the presence of outliers. [3], [8] median imputation (mdi) technique missing data replaced by the median of all observed values of that attribute in the class where the features belongs. good choice when the distribution of the values is skewed. ‑ not affect by the presence of outlier [7] hot (cold) deck imputation technique cluster the data earlier than executing the data imputation. impute missing values inside a data matrix by way of the usage of available values from the equal matrix avoid distortion in distribution. empirical for accuracy estimation. creates problem if any other sample has no close relation in entire manner of the dataset. [8], [10], [11] regression imputation technique use the known values for the construction between variables then applying the technique to calculate the missing values very easy and simple technique. calculated data saves deviations from mean and distribution shape only applicable if data is linearly separable that is there is linear relation between attributes. degree of freedom gets distort and may raises relationship. [10] expectation maximization (em) technique ‑ iterative method, finds maximum likelihood two steps: expectation (e step), maximization (m step) using three models soft, hard and mixture clustering iteration goes on until algorithm converges mvs are imputed by realistically obtained values which avoids distortion in distribution bit empirical work for accuracy estimation, creates problem if any other sample has no close relation in entire manner of the dataset [2] fuzzy kmeans clustering imputation (fkmi) technique it is assigned with every data item that describes in what degree the data object is belonging to the precise cluster. unreferenced attributes for every uncompleted data are substituted by fkmi on the basis of membership degrees and cluster centroid values. best outcome for overlapping data, better than k means imputation. data objects may be part of more than one cluster center high computation time. noise sensitive, that is, low or no membership degree for noisy objects [10] support vector machine imputation (svmi) technique takes condition attributes (here, decision attribute i.e., output) and decision attributes (here, conditional attributes) svmi then would be applied for prediction of values of missed condition attribute ‑ efficient in large dimensional spaces. ‑ efficient memory consumption poor performance if number of samples are much less than number of feature [10], [14] (contd...) hameed and ali: imputation techniques 78 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 table 1: (continued) techniques note advantages limitations references k nearest neighbour imputation (knn) technique determining the similarity between two values and replace the missing data with similar one using euclidean. avoids distortion in distribution as missing values are imputed by realistically obtained values no need for creating a predictive model. helpful for multiple missing value obstacle approach since the algorithm search all of the data set prediction of value of k is quite a difficult task. [12] most common imputation (mci) technique it replaces the missing value by the most common attribute or by the mode. while the numerical attribute missing value replaced by the average of the mean corresponding attribute fast and good for applying in big dataset. reduce the intra cluster variance to minimum. ‑ difficult to predict the value if the number of elements too big. dose not guarantee global minimum variance. [2], [15], [16] multivariate imputation by chained equation (mice) technique it pretends the probability of a missing variable depends on the observed data. it provides multiple values in the place of one missing value by creating a series of regression models, each missing variable is treated as a dependent variable, and other data in the record are treated as an independent variable predict missing data using the existing data of other variables. then it replaces missing values using the predicted values and creates a dataset called imputed dataset flexibility: each variable can be modeled using a model tailored to its distribution. can manage imputation of variables defined only on a subset of the data, can also incorporate variables that are functions of other variables, it does not require monotone missingdata patterns. lacking a theoretical rationale ‑ difficulties encountered when specifying the different imputation models [17], [18] sice technique: it is an extension of the popular mice algorithm. two variants of sice presented: sicecategorical and sicenumeric to impute binary, ordinal, and numeric data. twelve existing performance of algorithms implemented to predict house prices imputation methods and compare their performance with sice. achieves 20% higher fmeasure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time. impute binary, ordinal, and numeric data. performed better for the imputation of binary and numeric data. excellent choice for missing data imputation, especially for massive datasets where mice is impractical to use because of its complexity it could not show better performance than mice for the case of ordinal data. [6] reinforcement programming technique impute data through an action rewardbased experience imputes missing values in a column by working only on the same column but imputes the missing values in the column with different values thus keeping the variance in the imputed values. it is generally used for dynamic approach for the calculation of missing values by using machine learning approaches. performs well compared to other univarite single imputation and mlbased imputation approaches. use of numeric data variables only [19], [20] (contd...) hameed and ali: imputation techniques uhd journal of science and technology | jan 2023 | vol 7 | issue 1 79 be able to confuse the adversarial module, it neural network based totally architecture that can train properly with small and large datasets and to estimate the uncertainty of imputed data [19], [21]. 5. review on missing value imputation methods table 1. table 1: (continued) techniques note advantages limitations references utilizing uncertainty aware predictors and adversarial learning mlp uaadv imputer train well with small and large datasets and utilizes a novel adversarial strategy to estimate the uncertainty of imputed data proposed a novel hybrid loss function that enforces the imputers to generate values for missing data that on the one hand, obey the underlying data distribution so that it can confuse the welltrained adversarial module, and on the other hand, predict existing nonmissing values accurately the run time of the methods shows that they are efficient and have less execution time in comparison with that of peer imputer models. plays an important role in the overall performance less runtime compared to other imputers has a very simple structure, can work with any feature type and small and large data set it did not consider the imbalanced nature of the imputation task. [19], [21] table 2: comparing different techniques according to the dataset used in the application datasets techniques notes references iris mean regression imputation; reinforcement programming technique a comparison of different approaches of mice methods on iris datasets. efficiency gain with multiple imputations combined with regression is that it can better use the available information by accommodating nonlinarites [3], [8], [10], [18], [19], [20] iris credits adults mean/mode; hot deck; expectation maximization; knearest neighbor in this paper, the authors compare c5.0 with this newly developed technique known as iitmv and show its performance on different data sets [3], [8], [10], [11], [12], [22] cleveland heart zoo buhl1300 glass ionosphere iris pima sonar waveform2 wine hayesroth led7 solar soybean mean/mode; regression; hot deck; mlp uaadv the result shows that multilayer perceptions (mlp) with different learning rules show better results with quantitative datasets than classical imputation methods. in this paper, the type of missing value is missing completely at random (mcar) [3], [8], [10], [11], [19], [21], [22] iris escherichia coli breast cancer 1 breast cancer 2 mean knearest neighbors (knn) fuzzy kmeans (fkm) multiple imputations by chained equations (mice) mlp uaadv the results show that different techniques are best for different datasets and sizes. mice are useful for small datasets, but, for big ones and fkm are better, the mlp uaadv is better for both small and big datasets [3], [8], [10], [12], [17], [18], [19], [21], [23] hameed and ali: imputation techniques 80 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 6. conclusion the finding of this article summarized in tables 1 and 2, the article shows that the most popular techniques (mean, knn, and mice) are not necessarily the most efficient. it isn’t always surprising for mean in regards to the simplicity of the method: the technique does not make use of the underlying correlation structure of the information and for that reason plays poorly. knn represents a natural improvement of mean that exploits the observed facts structure. mice are complex algorithm and its behavior seems to be related to the size of the dataset: rapid and efficient on the small datasets, its overall performance decreases and it becomes time-intensive when carried out to the massive datasets. the more than one imputation combined with bayesian regression gives better performance than other strategies, which includes mean, knn. however, they only taken into consideration the great of imputation based totally on category strategies without worrying of the execution time that may be an exclude criterion. consequently, fkm may additionally represent the technique of choice but its execution time may be a drag to its use and we take into account bpca as a more adapted solution to high-dimensional data, the article also shows that the mlp ua-adv consider a good choice for large and small data set also with different data type. table 2 shows comparison between the techniques according applications and the dataset used in each one. the strength of this paper that its cover most of the missing value imputation techniques that can be taken into consideration as a reference for other researcher to pick out the most appropriate techniques or make combination from a couple of for imputing the missing values. references [1] b. doshi. handling missing values in data mining. rochester institute of technology, rochester, new york, u s a, 2010. available from: https://www.pdfs.semanticscholar.org/3817/ b208fe1f40891cc661ea0db80c8fccc56b70.pdf [last accessed on 2023 mar 27]. [2] s. gupta and m. k. gupta. “a survey on different techniques for handling missing values in dataset”. international journal of scientific research in computer science, engineering and information technology, vol. 4, no. 1, pp. 2456-3307, 2018. [3] a. jadhav, d. pramod and k. ramanathan. “comparison of performance of data imputation methods for numeric dataset”. applied artificial intelligence, vol. 33, no. 10, pp. 913-933, 2019. [4] j. scheffer. “dealing with missing data”. research letters in the information and mathematical sciences, vol. 3, pp. 153-160, 2002. [5] d. v. patil. “multiple imputation of missing data with genetic algorithm based techniques”. ijca special issue on evolutionary computation, vol. 2, pp. 74-78, 2010. [6] s. i. khan and a. s. hoque. “sice: an improved missing data imputation technique.” journal of big data, vol. 7, no. 1, p. 37, 2020. 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[23] p. schmitt, j. mandel and m. guedj. “a comparison of six methods for missing data imputation”. journal of biometrics and biostatistics, vol. 6, no. 1, pp. 1, 2015. tx_1~abs:at/tx_2:abs~at uhd journal of science and technology | jan 2023 | vol 7 | issue 1 43 1. introduction the topic of text processing has drawn the interest of numerous scholars as a result of the rising prevalence of digital texts in modern life. the amount of research in the domain of kurdish text processing seems to be rather minor, despite significant efforts with some of the most popular languages, such as english, persian, and arabic. commonly, the language experts divided the used languages of the world over families which are by ascending: indoeuropean, sino-tibetan, niger-congo, austronesian, and some other families. the indo-european family is the biggest family which speaks by the majority of europe, the lands where the europeans migrated, as well as a large portion of south-west and south asia. this family divided into sub-families [1]. kurdish language dialects are part of the north-western branch of the indo-iranic language family. the kurdish language is an independent language that has its own linguistic continuum, historical origins, grammar rules, and extensive live linguistic skills. the “median” or “proto-kurdish” language is where the kurdish language originated. approximately 30 million people in high land of middle east, kurdistan, talk numerous dialects of kurdish [1]. kurdish is referred to be a dialectical continuity, which means that it has a variety of dialects, it actually has four primary dialects (groups) and sub dialects, including (kurmanjí or kurmanji zhwrw and badínaní) in the north of kurdistan and sorani or kurmanji khwarw in the center kurdish kurmanji lemmatization and spell-checker with spell-correction hanar hoshyar mustafa, rebwar m. nabi technical college of informatics, sulaimani polytechnic university, sulaimani, kurdistan region, iraq a b s t r a c t there are many studies about using lemmatization and spell-checker with spell-correction regarding english, arabic, and persian languages but only few studies found regarding low-resource languages such as kurdish language and more specifically for kurmanji dialect, which increased the need of creating such systems. lemmatization is the process of determining a base or dictionary form (lemma) for a specific surface pattern, whereas spell-checkers and spell-correctors determine whether a word is correctly spelled also correct a range of spelling errors, respectively. this research aims to present a lemmatization and a word-level error correction system for kurdish kurmanji dialect, which are the first tools for this dialect based on our knowledge. the proposed approach for lemmatization is built on morphological rules, and a hybrid approach that relies on the n-gram language model and the jaccard coefficient similarity algorithm was applied to the spell-checker and spell-correction. the process results for lemmatization, as detailed in this article, rates of 97.7% and 99.3% accuracy for noun and verb lemmatization, correspondingly. furthermore, for spell-checker and spell-correction, accordingly, accuracy rates of 100% and 90.77% are attained. index terms: kurdish language, kurmanji dialect, kurdish lemmatizer, kurdish spell-checker and spell-correction, kurdish dataset corresponding author’s e-mail: hanar hoshyar mustafa, technical college of informatics, sulaimani polytechnic university, sulaimani 46001, kurdistan region, iraq. e-mail: hanar.hoshyar.m@spu.edu.iq received: 23-10-2022 accepted: 05-01-2023 published: 22-02-2023 o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology access this article online doi: 10.21928/uhdjst.v7n1y2023.pp43-52 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 mustafa and nabi. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) 44 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 mustafa and nabi: kurdish lemmatizer and spell corrector of kurdistan (sulaimani and mukrayani). kurmanji and sorani are indeed the two main dialects [2]. additionally, the other two important divisions of kurdish language are goraní (hawrami, zazayee and shabak) and luri (mamasani, kurmanshani and kalhuri). furthermore, these are categorized into dozens of dialects and subdialects [3]. this paper focuses on the northern kurdish dialect which is (kurmanji or kurmanji zhwrw) dialect which has the biggest number of speakers in comparison to other kurdish languages dialects [4]. several studies have been done related to common languages such english [5], [6], arabic [7]-[9], and persian [10]-[12]. moreover, there are few studies which are consummated regarding kurdish language [13], [14], despite it, a huge gap can be seen in the case of kurdish kurmanji dialect; therefore, this study has been aimed to serve this gap due to kurmanji dialect in the case of creating lemmatization and spell-checker with spell-correction system. hence, in the future, this study can be used in several applications that include data translation, sentence retrieval, document retrieval, and also can be extend and upgrade to more powerful similar systems. this study presented a toolkit, which consists of a lemmatization system and a spell-checker with spellcor rection for kurdish kur manji. t he aim of the lemmatization is to find a root or dictionary form (calls a lemma) for a specific surface form. it is crucial to be able to normalize words into their most basic forms, particularly for languages with rich morphology such as kurdish language, to better assist processes such as search engines and linguistic case studies. spell-checking algorithms are one of the lemmatizer’s most commonly used applications. with using a spell checker, the system suggests a rating of suggested corrections for each possibly incorrect word. this study presented a combination algorithm which are n-gram language model together with jaccard similarity coefficient for the spell-checker and spell-correction system. furthermore, a rule-based method on the kurdish kurmanji morphological rules is used in creating the lemmatization system. based on the literature and to the best of our knowledge, no study has been conducted regarding the spell-checking and lemmatization systems in kurdish kurmanji dialect. therefore, our study can be the base for further studies for kurdish kurmanji dialect. 2. related work there has been a huge amount of research that has been conducted regarding the word lemmatization, spell-checker, and spell-correction in several common languages, such as english, persian, and arabic. however, when it comes to kurdish language, a large absence can be observed, especially in lemmatization and spell-checking with spell-correction system in kurdish kurmanji dialect. in the case of lemmatizer in english language lemma chase which is a lemmatizer is created [5] address the problems of the most widely used lemmatizers currently available, this research presents a lemmatization model. this model accounts for the nominalized/derived terms for which no lemmatizer currently in use is able to produce the proper lemmas. identifying the morphological structure of any input english word, and in particular understanding the structure of the derivational word, is the main issue in developing a lemmatizer. finding the derivational suffix from morphing words and then extracting the dictionary base word from that derived word is another crucially difficult problem for a lemmatizer. some derivative terms are not handled by well-known and well-liked lemmatizers to retrieve their basis words. lemma chase, the mentioned lemmatizer, accurately retrieves the base word while taking into account the word’s part of speech, several classes of suffix rules, and effectively executing the recoding rules utilizing the wordnet dictionary. all of the derivational and nominalized word forms that are present in any standard english dictionary are successfully used by lemma chase to construct the base word form. in addition, there have been numerous studies on spell checkers in arabic. for instance, build fast and accurate lemmatization for arabic [7] which is a study that covers the need for a quick and precise lammatization to improve arabic information retrieval (ir) outcomes and the difficulty of developing a lemmatizer for arabic, since it has a rich and complex derivational morphology. introduces a new data set that can be used to verify lemmatization accuracy as well as a powerful lemmatization algorithm that works more accurately and quickly than current arabic lemmatization techniques. numerous studies have been published on the use of spell checkers and spell correction in persian as well. for example, automated misspelling detection and correction in persian clinical text [10] is an article that explains the creation of an automatic method for identifying and fixing misspellings in persian free texts related to radiology and ultrasound. uhd journal of science and technology | jan 2023 | vol 7 | issue 1 45 mustafa and nabi: kurdish lemmatizer and spell corrector three distinct forms of free texts associated to abdominal and pelvic ultrasound, head-and-neck ultrasound, and breast ultrasound reports are utilized using n-gram language model to accomplish their aim. for free texts in radiology and ultrasound, the system obtained detection performance of up to 90.29% with correction accuracy of 88.56%. the findings suggested that clinical reports can benefit from high-quality spelling correction. significant cost reductions were also made by the system throughout the documentation and final approval of the reports in the imaging department. kurdish stemmer pre-processing for improving information retrieval conducted by researcher in [13]. this article introduces the kurdish stemming-step method. it is a method that links search phrases and indexing terms in kurdish texts that are connected by morphology. in actuality, the occurrence of words demonstrates a supportive role for the classification process. even though it was planned to produce more or fewer errors to demonstrate the complexity and difficulty of words in the kurdish sorani dialect, the handling of similarity changes was implemented, which helped to boost matching among words and decrease the storage requirements. however, the stemmer used in this work was capable of resolving most of these issues. there are many stop words with added affixes in kurdish sorani writings. therefore, by combining these commonly occurring stop words, it can be stemmed. in addition, it was determined that employing partial words during the pre-processing stage was preferable. likewise building a lemmatizer and a spell-checker for kurdish sorani presented by [14]. this study also presented a lemmatization and word-level error correction system for kurdish sorani. it suggested a hybrid strategy focused on n-gram language modeling and morphological principles. systems for lemmatization and error detection are referred to as peyv and renus, respectively. the peyv lemmatizer is created based on the morphological rules, and for renus, it corrects words both with using a lexicon and without using a lexicon. it indicates that these two basic text processing methods can lead the way for more study on additional natural language processing applications for kurdish sorani. last but not least, intensive literature search has been conducted but no studies have been found considering the kurdish kurmanji dialect. therefore, this article’s primary goal is to propose a lemmatization and word-level spell checker with correction method for a kurdish language dialect known as kurmanji. the benchmark of this paper is [14] which is useful for the research study, despite the different algorithms used in spell-correction tool, the lemmatization tools are nearly similar in using the methods and approaches, both studies suggest a hybrid strategy based on n-gram language model and morphological principles. this study employs the python programming language to process data as well as to create a word processing system that performs lemmatization and spell checking with spell correction at the word level. 3. methods and data this section describes dataset collection, data preparation, and algorithms as well as approaches which have been used in lemmatization and spell checker. 3.1. dataset collection a model dataset was produced in order to carry out this study. the dataset was created by reading books and articles written in the kurdish kurmanji dialect, which were then manually recorded and added to the dataset. kurdish kurmanji dialect words include verbs, nouns, conjunctions, stop words, pronouns, imperative words, superlative words, and question words. there are around 1200 words in the dataset. fig. 1 depicts the dataset’s data amounts in a pie chart. this split results from the differing morphological rules for nouns and verbs, which affect how nouns and verbs are lemmatized. the third dataset has a large number of words that do not accept any affixes. furthermore, it contains a few special terms with only one or two letters. some of the conjunction words, for instance, are written with only one or two letters. 3.2. data preparation the most important features that indicated that the dataset was ready for analysis were its unity and quality. furthermore, fig. 1. dataset quantity pie chart. 46 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 mustafa and nabi: kurdish lemmatizer and spell corrector because the dataset is the primary first-hand collected dataset, it can ensure that the dataset is clean and has no duplicates. the dataset is then divided into three subsets. the first subset includes nouns. the second subset includes verbs, while the third subset contains pronouns, stop words, conjunctions, imperative words, superlative words, and question words. all of the subsets were stored in separate excel files, each with two columns: the id column and the data (word) column. except for the third subset, which contains the verbs, it has four columns: id, chawg, qad, and rag. the id column contains a unique id for each row; the chawg column contains the verb’s base; the qad column contains the verb’s past root; and the rag column contains the verb’s present root. table 1 presents the structure of the third (verb) excel sheet. 3.3. implementation this section describes the approaches and methods used according to noun lemmatization, verb lemmatization and spell-checker. 3.3.1. lemmatization lemmatizations for nouns and verbs are developed separately, after obtaining the fundamental morphological rules in kurdish kurmanji. each of noun and verb lemmatization use different approaches based on the morphological rules. for the lemmatizations a pruning method is used to find out the root of the input word. in the background of the system, each process is contained in a module inside the system, as a result to eliminate complexity and increase simplicity, also to made the system more readable and understandable. the following subsections clarify each of noun and verb lemmatizations in detail. 3.3.1.1. noun lemmatization according to the noun lemmatization, the noun lemmatization was created after clarifying and writing down all the rules in accordance with nouns in kurdish kurmanji dialect. a pruning method is used in this study. the input word to the system went through multiple stages and processes until the system found the proper root for the input noun, which is called a lemma in lemmatization process. during the process of noun lemmatization, predefined affixes and nouns in the dataset are used to find a proper lemma for an input noun. the only condition is to enter the word with the correct spelling. when a noun was entered, a search algorithm was used to look for it in the dataset. if the entered noun was a root without any affixes, the system determined that the input was correct and that no further processing was necessary. the output word in the outcome would be the base of the entered word. fig. 2 shows the flowchart diagram of this process. in other cases when the entered noun is with or attached to some affixes, in this study in the noun lemmatization module, three sets of affixes were defined. first set included prefixes that write before the noun without attaching to the noun directly, in kurdish kurmanji, there are some prefixes that write with a space separated with the noun. second set included the prefixes which are write and attached directly to the beginning of the noun without any space. moreover, the last set included suffixes which are directly attached to the end of the noun. the entered noun went through multiple processes to find the root out. the system first removes any prefixes which are table 1: structure of verb‑dataset verb dataset column include id data id chawg base of verb qad past root of verb rag present root of verb fig. 2. noun lemmatization first process flowchart. uhd journal of science and technology | jan 2023 | vol 7 | issue 1 47 mustafa and nabi: kurdish lemmatizer and spell corrector attached or not attached prefixes to the word, then search in the dataset, if there was no matching for the entered noun, the system decided that it might attached to some suffixes too, then the word went through another process which removed the possible suffixes attached to the noun, after that a search process look to find out if there was any matching word in the dataset, if any matching word found in the dataset, it would be the return root as the result. this process showed in a flowchart diagram in fig. 3. although there were no words that matched, the system made an effort and forwarded the entered noun to a procedure designed to remove prefixes and suffixes one at a time. in another sense, it took away the first prefix attached and looked for a matching root; if no matching root was discovered, it took away the first suffix attached and looked once more. it continued the process until the root was discovered if a matching root had not yet been discovered and there were further prefixes and suffixes linked to the word. at the end, when there were no more affixes, the entered noun was well spelled and the noun root existed in the dataset, the system gave the correct output lemma (root) for the entered noun. however, the system would replay with the message “input word is not in the dataset” if there was no match between the entered noun and nouns in the dataset. fig. 4. shows the process’ flowchart diagram. following these steps, the user sees the procedures’ output, as depicted in figs. 5 and 6. in fig. 5, the true word (کچ) (kiç) which means (girl) with two kurdish kurmanji prefixes (ەک) (ek) and (ا) (a) in the form of means (the (kiçeka) (کچەکا) girl who) entered. the system replayed with (“found”, “کچ”); “found” denotes that the entered word is correct and already exists in the dataset, and “کچ” is the base root of word (کچەکا). however, in fig. 6, the user inputted the incorrect term (کجان) (kican) in the meaning of but with (girls) (kiçan) (کچان) incorrect ending of followed by a ,(ç) (چ) rather than (c) (ج) correct prefix (ان) (an). due to the incorrect spelling of the word, which confounded the system and prevented it from locating the specific base root of the word, the system replayed with the message “input word is not in the dataset.” 3.3.1.2. verb lemmatization verb lemmatization also implemented in a pruning method as the noun lemmatization. after kurdish kurmanji dialect verb morphological rules are defined, the verb lemmatization is applied. the input verb went across several procedures until the tool selected and found the proper root. due to the kurdish verb’s morphology, the addition of prefixes and suffixes to the verb roots, and their ability to alter meaning, finding the root of the verb during the lemmatization process is more difficult and different than finding the root of a noun. therefore, simply omitting the suffix is worthless. fig. 3. noun lemmatization second process flowchart, phase 1. fig. 5. noun lemmatization of a legitimate noun. fig. 4. noun lemmatization second process flowchart, phase 2. fig. 6. noun lemmatization of an incorrect spelled noun. 48 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 mustafa and nabi: kurdish lemmatizer and spell corrector in kurdish language morphology, each verb has three states includes its critical state, which is called (chawg) in kurdish morphology; in this state, every verb ends with an (n) (ن) letter at the end of the word; the (n) (ن) is called (the n of chawg) that determines the critical state of the verb. another state is when the verb turns into its past state, which is called the “past root,” and this is done by removing the (n of chawg) at the end of the verb. whenever the verb is in the past state, it can be used in the past tense. the final state is present, and it has several rules to modify a verb critical state and turn it into its present root. when the verb is changed to its present root, it can be used in the present tense [2]. when the input was processed by the system, any affix containing the verb had to be removed. as a result, three sets of affixes are defined, which include suffixes, prefixes that do not attach to the verb, and prefixes that attach to the verb directly. after removing affixes, the remaining verb had to be compared with the verb dataset in the system. as it is clarified in the verb dataset excel file, there were four columns included (id, chawg, qad, and rag), in which chawg referred to the critical state of the verb, qad referred to the past state, and rag referred to the present state. after the state of the verb was recognized and found, the system returned the critical state, which is the chawg of the verb as the base root of the entered verb. fig. 7 shows the process of finding the root of a verb if the entered verb is already a root; no matter in which tense it appears, the system returns the base root of it. moreover, fig. 8 depicts the processes for locating a verb root if the entered verb is attached to some affixes; the processes are identical to those for locating a root of a noun attached to affixes in noun lemmatization. after completing these stages, the user sees the output of the procedures, as shown in figs. 9-11. in fig. 9, the true word denotes the present tense of (dixom) (دخۆم) the verb (خارن) (xarin) (eat), while the prefix (د) (d) indicates the present term of the verb and the suffix (م) (m) is the pronoun that denotes (i). the system repeated (“found”, “خارن”) in the output, where “found” implies that the word is correctly spelled and that its present root, which is (خۆ) (xo), is available in the dataset, and is the base root for the entered word. in addition, in ”خارن“ fig. 10, the past tense of the same word (خارن) (eat) is entered fig. 7. verb lemmatization first process pseudo code. fig. 9. verb lemmatization of correct present tense of verb (خارن) (xarin) (eat). fig. 8. verb lemmatization second process pseudo code. fig. 10. verb lemmatization of true past tense of verb (خارن) (xarin) (eat). fig. 12. query term bi-gram frequency calculation pseudo code. fig. 11. verb lemmatization of a wrong spelled negative imperative of verb (خارن) (xarin) (eat). uhd journal of science and technology | jan 2023 | vol 7 | issue 1 49 mustafa and nabi: kurdish lemmatizer and spell corrector as (خارمەڤە) (xarmeve), which means (i ate). this time, there are two suffixes: (م) (m), which is the pronoun associated to (i), and (ەڤە) (eve), which indicates that the event occurred and ended completely in the past. once more, the system verified that the word root was correctly spelled that it was included in the dataset; it also displayed the base root of the term. furthermore, in fig. 11, entered the wrong negative imperative phrase (مەخر) (mexir) instead of (mexo) (مەخۆ) or (mexu) (مەخو) which means (don’t eat), but with the improper ending of (ر) (r), rather than (ۆ) (o) or (و) (u). the system displayed the message “input word is not in the dataset” according to the word’s incorrect spelling, which confused the system and prohibited it from finding the precise base root of the word. 3.3.2. spell checker and spell correction the spell checker and spell cor rection mechanisms collaborated in two stages in this study: first, the spell checker indicated whether the word was correct or incorrect, and second, the spell correction process corrected the word by suggesting some correct words by providing the most likely correct word forms. after the word entered the system, it was detected if it was true or not by the spell checker’s check for word frequency in the dataset (including the whole of the three files). the step of finding that the word is true or detecting the word as wrong was done based on using n-grams. the input word, which is called the query term in this paper, is fragmented into bi-grams (two grammatical units). a bi-gram is an n-gram for n = 2. in this study, a 2-g (or bi-gram) is a two-letter sequence of letters. the bi-grams sequences “ha,” “ap,” “pp,” and “py,” for instance, are two-letter grammatical sequences extracted from the word (happy). after the bi-gram of the query term is produced, the system calculates the gram frequencies with the bi-grams of the words in the dataset separately, which is called a dictionary term in this paper. fig. 12 shows the process of calculating the frequency of bi-grams in the query term in comparison to the dictionary terms. after calculation, the system looked up the frequencies of the bi-gram of the query term; if one of the frequencies was equal to zero, then it detected the word as a wrong one as one of its bi-grams had no repetition in comparison with the dictionary terms, and if none of the frequencies were zero, then the word was detected as true. hence, in the event that a query term equals one of the index terms in the dataset, this word will be selected as true, and if the word is detected as true, then the system presents “the word is true spelled” as a result. after detecting the query term as wrong, its bi-grams are handled, and the system goes to the spell correction procedure. the wrong word is then corrected based on the jaccard similarity coefficient method, which is popularly used to compare how close the query terms in the dataset are to one another. here, the procedure of similarity measurement can be used to examine the most comparable terms that are structurally recorded in the dataset if a query does not match any index in the dataset. using the jaccard similarity coefficient [15], equation (1) shows the rule of the jaccard similarity coefficient. jaccard sim (a,b) = p(a∩b)/(bua) (1) measuring the jaccard similarity coefficient between two datasets is done by dividing the number of features that are common to all by the number of properties [15]. the mechanism worked on the query term, and dictionary terms included all three files of the dataset. the spell correction took the query term, looked for the matching dictionary term in file one, if it did not exist, then sent it to the lemmatization files, respectively, because it may be the root of a noun or a verb; also, it might be a noun or a verb with affixes, and the affixes should be removed as a result to check if it was spelled correctly or not. after checked process did go well in detail, if the word found, the system marked it as a true word. otherwise, spell checker predicted words based on the dataset’s three files, then it chose the best matching words based on the highest matching degree, which is calculated using the jaccard coefficient algorithm, and best matches were chosen if their matching degree were greater than the spell checker’s threshold, and finally the five highest matching degree words were chosen. the threshold of this study is equal to 0.15. it has been chosen based on the accuracy of the guess for the correct word or the highest matching words in the dataset for the wrong query term. in kurdish kurmanji, there are words with three letters; if they are written incorrectly by missing a letter, they only have two letters. hence, the threshold should be as small as possible to get a great and accurate result. 4. results and discussion this section presents the results of the algorithms in both lemmatization and spell checker tools. also discuss the benchmarking with the benchmark study of the research. 4.1. noun lemmatization to improve the efficiency and accuracy of the noun lemmatization tool, two random words were chosen with 50 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 mustafa and nabi: kurdish lemmatizer and spell corrector their derivatives which were nine derivatives of (چیا) (mountain) word and 12 derivatives of word. the (boy) (کوڕ) results of lemmatization process of both words were successfully giving correct root in both nine derivatives of first word and 12 derivatives in second word. to ensure the accuracy of noun lemmatization, another 66 random words with possible derivatives were chose and entered into the system; therefore, the noun lemmatization gave correct result in 63 cases out of 66, which means that the noun lemmatization algorithm had an accuracy of approximately 95.45% in lemmatizing words. overall, the accuracy of the noun lemmatization process was approximately about 97.7%. table 2 presents accuracy in noun lemmatization tool. 4.2. verb lemmatization to evaluate the efficiency and accuracy of the verb lemmatization tool, two sets of random verb forms were tested with the tool. the test sets included different verb forms such as present and past tense, imperative and negative imperative, passive, and negative. regarding the verb’s existence in the dataset dictionary, the verb lemmatization tool found the correct root of the input verb. each verb in the test set was entered with all possible derivations made with specific prefixes and suffixes. the first set included 171 different forms of different verbs. the lemmatization tool lemmatized 169 of them correctly; the wrongly lemmatized ones were due to the ordering of the dataset; in the case of imperative and negative imperative of a verb, the lemmatized verb rag was coming before the purposed verb rag, so the system took the first verb rag before it reached the purposed one. for example, the kurdish verb “send” has two forms: and both have the same (nartin) (نارتن) and (nardin) (ناردن) rag (نێر) (“nêr”). if a user entered the imperative tense of this verb, which is (بنێرە) (binêre), and expected to see the base root of in the result, the system replays (nardin) (ناردن) with the base root of because it was recorded (nartin) (نارتن) before the other form (ناردن) (nardin) in the dataset excel file. moreover, it is due to the system that, when it finds a result, it stops without going to the other verbs in the dataset. moreover, it is due to the system, when it finds a result, the system stops and the result appears without going to the other data in the dataset. as a result, the accuracy of lemmatizing the first set was 98.83 percent. in the order of the other set, there were 131 different forms of different verbs with different tenses. due to this set, the lemmatization tool lemmatized all of them, which means it gave the correct root for each of the forms. it can be said that with the two test sets, the verb lemmatization tool overall gave approximately 99.4 percent accuracy. table 3 shows the accuracy of the verb lemmatization tool. 4.3. spell checker and spell correction according to calculate and analyze the accuracy of the spellchecker and spell-correction tool, the process of analyzation is more complex, due to connecting the spell-checker and spell-correction tool with the lemmatization tools. as described in the above section, there was three datasets, so the spell-checker and spell-correction accuracy should be calculated according to all the datasets. the mechanism as said is to first check if the input word is correct or not, and the spell-checker tool is tested with three groups of data which are consisted in the three datasets as well. these three groups included 100 words from first dataset file, 100 nouns from second dataset file, 100 verbs from third dataset file, respectively. the result always returned true which meant the input word spelling is correct, while the data existed in the dataset. hence, it reached to be said that the spell-checker tool returned in all cases successfully. table 4 shows the accuracy of spell-checker tool. for the spell-correction tool a set of random words included noun, verb and others is tested, the contained nouns and verbs included all forms with prefixes and suffixes also simple noun and verbs without prefixes and suffixes. the result shows that whenever a bi-gram of the original correct word came in the input word, it was a higher chance to get the most correct word and most similar word as a result. the more bitable 2: accuracy in noun lemmatization tool sets true lemmatization false lemmatization total accuracy (%) 1st set 21 0 21 100 2nd set 63 3 66 95.45 total 84 3 87 97.7 table 3: accuracy in verb lemmatization tool sets true lemmatization false lemmatization total accuracy (%) 1st set 169 2 171 98.83 2nd set 131 0 131 100 total 300 2 302 99.3 table 4: accuracy in spell checker tool sets true spell checking false spell checking total accuracy (%) 1st set 100 0 100 100 2nd set 100 0 100 100 3rd set 100 0 100 100 total 300 0 0 100 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 51 mustafa and nabi: kurdish lemmatizer and spell corrector grams of the original wanted word came in the input word, the higher similarity degree get and the more accurate results acquire in the outcome. in several occasions, the incorrect lemmatization occurred because of the incorrect input word, and this led to incorrect spell-correction which at the end resulted in a low accuracy degree of the outcome result. to provide the efficiency of the spell-correction a set included 100 of wrong random words with different forms were tested manually. first set included 61 wrong spelled nouns, the spellcorrector with the help of the noun lemmatization resulted an accuracy of 90.16% of the correction process. second set contained 80 wrong spelled verbs, in the result spell-corrector with the use of verb lemmatization gave an accuracy of correction process with 88.75% rate. third set consisted the wrong spelled pronouns, stop words, conjunctions, imperative words, superlative words, and question words, in 107 words the spell-correction system corrected 100 of them successfully which give accurate result as 93.4% of accuracy rate. table 5 displays the accuracy of spell-correction tool. as shown in table 5, the third set had the highest accuracy rate among the other two sets, and as previously stated, some false correction cases occurred due to false lemmatization, so it must be stated that if a dataset is created with all the forms of the words in all three datasets, then more accurate results can be obtained because the spell-corrector can directly look for the right form of the input misspelled word and find it with a high degree of certainty. 5. conclusion and future works information retrieval and text classification can benefit greatly from effective lemmatizer. in addition, incorrect words are detected and corrected by spell-checkers and spell-correction. this paper introduced the kurdish kurmanji lemmatizer and word-level spell-checker with spell-correction methodologies. it is the first attempt that tools of this kind have been made for kurdish kurmanji. a hybrid technique has been utilized for the spell-checker and spell-correction that depends on the n-gram language model and the jaccard coefficient similarity algorithm, also the proposed approach for lemmatization, is based on morphological principles. the outcome demonstrated that, while applying the suggested approach, the accuracy of lemmatization for each noun and verb lemmatization was assessed, respectively, at 97.7% and 99.3%. in addition, the spell-checker and spell-correction accuracy rates were 100% and 90.77%, respectively. the experimental findings show that several false correction cases were caused by incorrect lemmatization led by misspelled input words. furthermore, according to experimental findings, more accurate results may be obtained if a dataset is established with all the word forms in the datasets since the spell-checker will directly search for the correct form of the input misspelled word and discover it with a high level of equality. in the future, this work can be expanded to apply to a bigger dataset of kurdish kurmanji and utilize these approaches for nlp applications like text mining for kurdish kurmanji. as a contrast between this study and its benchmark. actually, this study is done for the kurdish kurmanji dialect, while the benchmark was done for the kurdish sorani dialect, which has completely different morphological rules in so many phases to study and implement in the system. the datasets that were used were different, while this research’s dataset is primary, first-hand, and organized in three subsets. in addition, there are some variances between them in terms of accuracy and the algorithms that have been used. this study achieved 97.7% and 99.3% accuracy for noun and verb lemmatization, respectively, while the benchmark achieved 95% and 89.4% accuracy of two test sets for noun lemmatization and an average of 86.7% accuracy for verb lemmatization. in addition, according to the spellcorrection, this study used the jaccard coefficient similarity algorithm and rated 90.77% accuracy, while the other study, as mentioned, used an edit distance algorithm and obtained 96.4% accuracy with a lexicon while, without a lexicon, the correction system had 87% of accuracy. at the end, it has to be said that the similarities can be seen in the theoretical parts and ideas, but for the practical part, a huge difference can be seen from using different programming languages; this study used the python programming language, while the other used the java programming language, up to and including recreating the system from the beginning to the end. 6. acknowledgment the authors would like to thank spu for providing the opportunity, support, and funding for this study. sulaimani, the kurdish journalist syndicate, is also thanked. table 5: accuracy in spell correction tool sets true correction false correction total accuracy (%) 1st set 55 6 61 90.16 2nd set 71 9 80 88.75 3rd set 100 7 107 93.4 total 226 22 254 90.77 52 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 mustafa and nabi: kurdish lemmatizer and spell corrector references [1] z. kurdî, m.û. zarên wî and h.s. khalid. “kurdish language, its family and dialects”. 2020. available from: https://www.dergipark. org.tr/en/pub/kurdiname/issue/50233/637080 [last accessed on 2022 aug 15]. 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[11] s. mohtaj, b. roshanfekr, a. zafarian and h. asghari, “parsivar: a language processing toolkit for persian,” in proceedings of the eleventh international conference on language resources and evaluation (lrec 2018), 2018. available from: https://www. aclanthology.org/l18-1179 [last accessed on 2022 aug 20]. [12] a. rashidi and m.z. lighvan. hps: a hierarchical persian stemming method. international journal on natural language computing, vol. 3, no. 1, pp. 11-20, 2014. [13] a.m. mustafa and t.a. rashid. kurdish stemmer pre-processing steps for improving information retrieval. journal of information science, vol. 44, no. 1, pp. 15-27, 2018. [14] s. salavati and s. ahmadi. “building a lemmatizer and a spellchecker for sorani kurdish”. corr, vol. abs/1809.10763, 2018. available from: https://www.arxiv.org/abs/1809.10763 [last accessed on 2021 aug 15]. [15] s. niwattanakul, j. singthongcha, e. naenudorn, and s. wanapu. “using of jaccard coefficient for keywords similarity”, in proceedings of the international multi conference of engineers and computer scientists. vol. 1, 2013. available from: https://www. data.mendeley.com/v1/datasets/s9wyvvbj9j/draft?preview=1 [last accessed on 2022 apr 08]. . uhd journal of science and technology | april 2017 | vol 1 | issue 1 1 intelligent techniques in cryptanalysis: review and future directions sufyan t. al-janabi1,2, belal al-khateeb3 and ahmed j. abd3 1department of information systems, college of cs and it, university of anbar, ramadi, anbar iraq 2department of computer science, college of sceince and technology, university of human development, sulaimaniya, kurdistan region iraq 3department of computer science, college of cs and it, university of anbar, ramadi, anbar iraq o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology 1. introduction the basic aim of cryptography is to transmit messages from one place to another in a secure manner. to satisfy this, the original message called “plaintext” is encrypted and sent to the receiver as “ciphertext.” the receiver decrypts the ciphertext to get the plaintext. this can be done using a cipher which is a tool that hides the plaintext and converts it to the ciphertext (and also can return back the plaintext from the ciphertext). ciphers make use of (cryptographic) keys that determine the relationship between the plaintext and the ciphertext. cryptography can be considered as assemble from security and mathematics. it is used to protect important information and ensure that this information arrives to its destination in peace without violations. ciphers gradually evolved from simple ones which are currently considered to be easily breakable such as caesar cipher through more complex cipher algorithms such as the data encryption standard (des) and the advanced encryption standard (aes) [1], [2]. on the other hand, cryptanalysis means trying to break any security system (or cipher) using unauthorized ways to access the information in that system. thus, cryptanalysis works against cryptography. the cryptanalyst tries to find any weakness in the cryptographic system to get either the source of information (plaintext) or the key used in the encryption algorithm. this process is called an attack. if this attack is successfully applied, then the cryptographic system is said to be broken. cryptography and cryptanalysis together form the field of cryptology [3], [4]. in the recent decades, cryptography developed quickly because of the development in computational resources which increased the speed and decreased the time of encryption and decryption processes. this moved cryptography from solving by hand to more and more complex computer programs that need considerably long time and sophisticated attack a b s t r a c t in this paper, we consider the use of some intelligent techniques such as artificial neural networks (anns) and genetic algorithms (gas) in solving various cryptanalysis problems. we review various applications of these techniques in different cryptanalysis areas. an emphasis is given to the use of gas in cryptanalysis of classical ciphers. another important cryptanalysis issue to be considered is cipher type detection or identification. this can be a real obstacle to cryptanalysts, and it is a basic step for any automated cryptanalysis system. we specifically report on the possible future research direction of using spiking anns for cipher type identification and some other cryptanalysis tasks. index terms: artificial neural networks, cipher identification, classical ciphers, cryptanalysis, genetic algorithms corresponding author’s e-mail: saljanabi@fulbrightmail.org received: 10-03-2017 accepted: 25-03-2017 published: 12-04-2017 access this article online doi: 10.21928/uhdjst.v1n1y2017.pp1-10 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2017 al-janabi, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions 2 uhd journal of science and technology | april 2017 | vol 1 | issue 1 techniques to solve. hence, instead of using the simple caesar cipher which needs no more than few minutes (or seconds) to be broken using brute force attack (trying every possible solution), we are using now more complex ciphers (aes, triple des, etc.) that might need hundreds (or thousands) years to break using brute force attack with the current technology. one important issue to mention is that despite the technological and mathematical complexity, the modern versions of cryptosystems still follow the same classical concepts. thus, it is still prudent to apply certain attacks on classical ciphers and study their evolution aspects before using them with more complex modern ciphers. this is quite justifiable considering the nature of intelligent techniques such as gas, artificial neural networks (anns), and evolutionary algorithms (ea). although several survey works can be found in earlier literature [5]-[7], more work is needed in this direction to shed the light on various aspects of this kind of interdisciplinary research. the aim of this paper is to review various applications of intelligent techniques in cryptanalysis problems and to investigate some possible future research directions. the remaining of this paper is organized as follows: section 2 summarizes various types of ciphers and cryptanalysis attacks in a generic way. the intelligent techniques of anns, gas, and evolutionary computation are reviewed and compared to each other in section 3. then, section 4 reviews the application of gas in cryptanalysis of classical ciphers. the issue of classification or identification of cipher type is considered in section 5. next, we present some insights regarding the future direction of using spiking anns in cipher classification in section 6. finally, the paper is concluded in section 7. 2. classification of ciphers and attacks cryptosystems can be classified in multiple approaches depending on various criteria. this can simplify the study of cryptography science and make it easier to understand and implement. at first, if we take in consideration the amount of data that can be encrypted at a time, we can then classify cryptosystems in two classes:[3] 1. block ciphers, which encrypt block of data at time like des 2. stream ciphers, which encrypt single datum (symbol, byte, or bit) at a time like caesar cipher. second, it is also possible to classify cryptosystems according to the key used in encryption and decryption processes. in this case, we can put a cryptosystem under one of the following: 1. symmetric key ciphers, where the same key is used for encryption and decryption, for example, vigenere cipher. 2. public key ciphers, where one key is used for encryption and another one for decryption, for example, rivestshamir-adleman system. third, we can classify cryptosystems depending on the history and time of invention. thus, we can put cryptosystems under one of the following: 1. classical ciphers, which are those ciphers used in the past and can be solved by hand. they became now breakable, for example, caesar cipher 2. modern ciphers, which are those complex (computerized) ciphers widely used currently and cannot be solved by hand, for example, aes. finally, another classification approach is to classify ciphers according to their building blocks. this approach is typically applied for classical ciphers to divide it into:[3] 1. substitution systems, where every character is replaced by another one, for example, monoalphabetic ciphers 2. transposition systems, where characters are rearranged rather than replaced, for example, columnar cipher. it is also possible to further classify both of the main two categories of classical ciphers: substitution and transposition ciphers. transposition ciphers can be classified into sub classes:[3], [8] • single transposition: this type transposes one letter at a time, for example, the columnar transposition, route transposition, and grille transposition ciphers • double transposition: this type transposes more than one letter at a time. substitution ciphers can be classified into sub classes as follows:[3], [9] • monoalphabetic substitution ciphers: in this type of encryption techniques, one letter of plaintext is represented by one letter in ciphertext, and one ciphertext letter represents one and only one plaintext letter, so it is the simplest for m of substitution techniques. monoalphabetic substitution includes direct monoalphabetic, reversed monoalphabetic, decimated monoalphabetic, and mixed monoalphabetic ciphers • polyalphabetic substitution ciphers: in this type of sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions uhd journal of science and technology | april 2017 | vol 1 | issue 1 3 encryption, one letter of plaintext is represented by multiple ciphertext letters, and one ciphertext letter represents multiple plaintext letters. there are two types of polyalphabetic substitution ciphers: periodic (where there is a keyword repeating along plaintext like the vigenere cipher) and non-periodic (where there is no repeating key, e.g., the running key cipher) • polygraphic substitution ciphers: in this type of substitution, more than one plaintext letters are encrypted at a time by more than one ciphertext letters. this includes digraphic, trigraphic, and tetragraphic ciphers. examples of these ciphers are the playfair cipher and hill cipher • homophonic substitution ciphers: in this type of substitution, one plaintext letter is represented by multiple ciphertext letters or characters, and every ciphertext letters or characters can only represent one plaintext letter, for example, the nomenclator cipher. furthermore, it is possible to define combinations of transposition and substitution ciphers to produce more secure systems. such combinations are used to avoid the weaknesses in pure transposition and pure substitution systems. a classical example of such combined ciphers is when we combine simple substitution with a columnar transposition. in modern cryptography, ciphers are designed around substitution and transposition principles simultaneously. fig. 1 depicts various types of classical systems. similarly, we can also classify cryptanalysis attacks. actually, there are many types of such attacks. some of them can be considered as general types, while others are specific for certain ciphers, protocols, or implementations. here, we are not going to try to list all attack types rather we are only interested in some generic ways for classifying attacks. it is possible to generically classify attacks based on the amount of information available to the attacker. the amount of information that attacker have is important to make any attack so the cryptanalyst should determine what is available in his hand. accordingly, we are going to have cipher text only, known plaintext, chosen ciphertext, chosen plaintext, adaptive chosen plaintext, adaptive chosen ciphertext, and related key attacks. alternatively, we might generically classify attack according to the computational resources (time, memory, and data) required by these attacks [3], [10]. 3. intelligent techniques in this section, we review the relevant intelligent techniques of anns, genetic algorithms (gas), and evolutionary computation. we also give a brief comparison on their characteristics an application scope. a. anns anns are numerical models that use a gathering of basic computational units called neurons that connect with each other to build a network. there are many types of anns; each type is suitable for one or more problems depending on the problems itself. hence, the important thing in anns is how to design the topology of ann that can better describe the problem then solving it using very simple principles to obtain very complex behavior [5], [11]. anns can model human brains and use nervous system to solve the problems by learning it with true examples and giving a chance to generalize all solutions. since the nature of anns that simulate the brain and use parallel processing rather than serial computation, we can put anns in multiple fields according to the huge capabilities that anns can introduce. these fields include classification, approximation, prediction, control, pattern recognition, estimation, optimization, and others. when using ann for solving a problem, the following steps should be chosen carefully to make ann works in an effective way: design of ann topology, choosing suitable learning way, and setting the inputs. there are many ann topologies such as:[12] • feed-forward anns • recurrent anns • hopfield annfig. 1. most important classical cipher types sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions 4 uhd journal of science and technology | april 2017 | vol 1 | issue 1 • elman and jordan anns • long short-term memory • bi-directional anns • self-organizing map • stochastic ann • physical ann. there are three generations of neuron models [13]. the first generation of anns also called perceptrons, which are composed each of two sections: sum and threshold. the sum part receives input from a set of weighted synapses. then, it performs a threshold function on the result of the sum. the input and the output have values that may be equal to either 0 or 1, as shown in fig. 2. the second generation of anns is composed by two stages: • sum of values that are received through weighted synapses • sigmoid function evaluator whose input is the result of the sum previously computed. in this generation, the inputs can be any real-valued number, and the output is defined by the transfer function. for example, the sigmoid unit limits outputs to [0; 1], whereas the hyperbolic function produces outputs in the range [1; 1], as shown in fig. 3. the third generation of anns is composed by spiking neurons: neurons which communicate through short signals called spikes. this generation has two main differences when compared with the previous two generation. at first, this generation introduces the concept of time in the simulation, while earlier, the neural networks were based on abstract steps of simulation. second, such neurons present similarities to biological neurons, as they both communicate using short signals, which in biology are electric pulses (spikes), also known as action potentials, as shown in fig. 4. the spike train generation can be gaussian receptive fields [14], poisson distribution [15], or directed spike generation [16]. indeed, the applied training algorithm for anns is usually the backpropagation [17], while spiking anns use spikeprop [18]. b. gas gas are considered to be one of the best ways to solve a problem, for which there is only a little knowledge. hence, they work well in any search space. all that is required know is what the solution is needed to be able to do well, and a ga will be able to create a high-quality solution. gas apply the both principles of selection and evolution to produce several solutions to a given problem [19]. gas are better applied in an environment in which there is a very large set of candidate solutions and in which the search space is uneven and has many hills and valleys. although gas will do well in any environment, they will be greatly outclassed by more situation-specific algorithms in the simpler search spaces. therefore, gas are not always the best choice. sometimes, they can take quite a while to run and are therefore not always feasible for real-time use. however, they are considered to be among the most powerful methods with which to (relatively) quickly create high-quality solutions to a problem. the proper selection of appropriate mutation operators and fitness functions is necessary for implementing a successful attack [19], [20]. in fact, gas are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and genetics. fig. 2. the first generation of artificial neural networks[13] fig. 3. the second generation of artificial neural networks[13] fig. 4. the third generation of artificial neural networks[13] sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions uhd journal of science and technology | april 2017 | vol 1 | issue 1 5 thus, they represent an intelligent exploitation of a random search used to solve optimization problems. they exploit historical information to direct the search into the region of better performance within the search space. the basic techniques of the gas are designed to simulate processes in natural systems necessary for evolution, especially those follow the principle of “survival of the fittest.” this is based on our understanding of nature where competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones [19]. c. evolutionary computation simply, evolutionary computation simulates evolution on a computer. the result of such a simulation is a series of optimization algorithms. these are usually based on a simple set of characteristics. optimization iteratively can improve the quality of solutions to some problem until an optimal (or at least feasible) solution is found. evolutionary computation is an umbrella term that includes gas, evolution strategies, and genetic programing [21]. d. differences between anns, gas, and evolutionary computation an ann is a function approximator. to approximate a function, you needs an optimization algorithm to adjust the weights. an ann can be used for supervised learning (classification and regression) or reinforcement learning and some can even be used for unsupervised learning. gas are an optimization algorithm, in supervised learning, a derivative-free optimization algorithm like a ga is slower than most of the optimization algorithms that use gradient information. thus, it only makes sense to evolve neural networks with gas in reinforcement learning. this is known as “neuroevolution.” the advantage of neural networks like multilayer perceptrons in this setup is that they can approximate any function with arbitrary precision when they have a sufficient number of hidden nodes. an ea deploys a randomized beam search, which means your evolutionary operators develop candidates to be tested and compared by their fitness. those operators are usually nondeterministic and you can design them, so they can both find candidates in close proximity and candidates that are further away in the parameter space to overcome the problem of getting stuck in local optima. eas are slow because they rely on unsupervised learning: eas are told that some solutions are better than others but not how to improve them. neural networks are generally faster, being an instance of supervised learning: they know how to make a solution better using gradient descent within a function space over certain parameters; this allows them to reach a valid solution faster. neural networks are often used when there is not enough knowledge about the problem for other methods to work. 4. cryptanalysis of classical ciphers using gas there are many approaches and tools that are used in the field of cryptanalysis. one of the successful approaches that achieved promising results is based on gas. this is mainly due to the nature of gas that allow reducing the big size of solutions, leading to optimal or likely best solution from this group of solutions. gas use fitness function to evaluate each solution then select the best one or best group of solutions to generate other children solutions and so on until the cipher is broken. in this section, we report on some interesting aspects of applying gas in cryptanalyzing classical ciphers. a. cryptanalysis of monoalphabetic substitution ciphers the ga attack on such cipher can be implemented by generated initial keys consisting of permutation of the set of letters. these keys are generated randomly, and after encrypting using each generated key, we can measure the value of fitness of each key. then, pairs of these keys which have a high fitness value are selected and crossover operation then is used between selected keys to produce new enhancement child keys. after crossover operation is completed, some keys are selected to mutation to enhance the attributes of it by the choice of a random point in a selected key and replacing it with another point. after the two operations are completed, the loop is repeated until the end with suitable stopping [22]. b. cryptanalysis of playfair cipher for attacking the playfair cipher using gas, we should determine the individuals which contain one possible key of the cipher and each individual has its fitness value. one individual is represented as a matrix of 5*5 positions that contain the characters of alphabets distributed randomly. after the generation of the individuals is completed, the selection operation begins according to each individual fitness, so the individual has a highest fitness value that is put in the beginning of the rank. after selection process is completed, the reproduction or crossover operation will begin to produce new children key that may has attribute better than its parents. the crossover operation is implemented by filling sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions 6 uhd journal of science and technology | april 2017 | vol 1 | issue 1 the positions of the child with character of the parents or mutating the child by replacing characters positions locally. the loop continues until meeting the stopping condition. however, the recovery of the plaintext is not easy to implement usually, for several reasons. one is that words that have double letters may not be counted correctly, due to the fact that the double letters might be split up. second, because i and j share a position in the key (typically), all the words that have is and js in them have to be checked using both letters, if the dictionary is fully implemented. third, the plaintext has no white space to delimit words so being able to tell where words end and begin can be difficult [23]. c. cryptanalysis of vernam cipher gas can be used for attacking the vernam cipher by building a dictionary of words that consist of words that are frequently used in english (e.g., they, the, and when). then, the fitness value is calculated according to the following steps:[24] 1. initialize the parameters of the ga and maximum number of iteration 2. generate random keys which are the population of chromosomes as the 0th generation; each key is a vector with size equal to ciphertext size 3. decrypt the ciphertext by all generated keys 4. calculate the fitness function for each chromosome by adding the square value of repeated three letters and four letters which are available in built dictionary. the calculation of fitness function deals with the probability of existing of the three and four letter words in normal english 5. sort the keys based on decreased fitness values 6. apply the crossover operator to the parent keys and produce a new generation. here, a simple two-point crossover can be perfor med. further more, apply mutation operation by generating two random positions and replace the two letters in these positions by others letters randomly 7. the best key is used to decrypt ciphertext to get the best-decrypted text. d. cryptanalysis of vigenere cipher to attack vigenere cipher using gas, we should determine the number of attributes that the ga takes as parameters or inputs such as population size, number of individuals tenured per generation, number of random immigrants per generation, number of generations, key length, maximum key length, ciphertext length, known text length, and number of runs per mutation operator combination. these parameters may be used together or some of them might be ignored. the key length parameter is very important, so it must be firstly identified [25]. e. cryptanalysis of transposition ciphers gas are very useful to break classical transposition ciphers by finding the sequence of characters that the transposition cipher used. this particular class of algorithms can be used because the automated breaking of such ciphers is very difficult. in spite of that, a number of statistical tools aiding automated breaking have been developed for substitution ciphers, cryptanalysis of transpositions is usually considered to be highly interventionist and demands some knowledge of the likely contents of the ciphertext to give an insight into the order of rearrangement used. thus, genetic cryptanalyst enables a known plaintext attack to be successfully made, based on only small portion of some plaintext/ciphertext [26]. 5. identification of classical cipher type the typical sequence of steps needs to be followed by cryptanalyst to break any cryptosystems is:[27] 1. the cryptanalyst should determine if the text encrypted by any cipher or it is compressed or generated randomly 2. the cryptanalyst should determine the language of the text 3. the cryptanalyst should determine the type of cipher used in encryption process 4. the cryptanalyst should determine the key used in encryption process 5. the cryptanalyst then uses the key with encrypted data to extract the original data. when the cryptanalyst wants to identify the cipher type (having just a ciphertext), he/she should extract some features that can lead to estimating the type of cipher. the list below shows a group of features that may help the cryptanalyst in the estimation process: 1. frequency analysis: every language has frequency characteristics for its characters such that each character has repeating ratio recognizing it from other characters in normal texts. in english, for example, the letter “e” has the greatest frequency ratio (12.70), but the letter “x” has the lowest (0.15) [8]. frequency analysis can be done based on single letter frequency and/or multiple letter frequency (double, triple, etc.). fig. 5 depicts the typical frequency distribution of single letters in normal english text. frequency analysis is very useful in differentiating between transposition ciphers and sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions uhd journal of science and technology | april 2017 | vol 1 | issue 1 7 substitution ciphers. frequency analysis can be used in three main directions:[28] • the first one is to compute the frequency of ciphertext letters and compare it with the frequency of the original data such that compare the frequency of the letter in ciphertext and natural text and compute the changing in two texts • the second direction is to compute the frequency of ciphertext letters and find which letters in normal text have the same repeating ratio such that if the letter “j” in ciphertext has the same repeating ratio of the letter “a” in the original text, we can say the letter “a” is encrypted by the letter “j.” • third one is to use frequency analysis to compute if there is any shifting occurs in ciphertext characters such that when the letter “x” gives the same ratio of letter “a,” this indicates that possibly the caesar cipher which encrypts “a” by “x” has been used 2. ciphertext length: the length of ciphertext plays an important role in identification of cipher type where some ciphertext length is exactly divisible by 2 like the playfair cipher case. other ciphers (e.g., hill cipher) can produce ciphertext length divisible by 3, etc. 3. ciphertext characters number: some ciphers employee few number of characters such the baconian cipher which uses just two letters “a” and “b” in encryption process and the playfair cipher that uses 25 letters 4. repeating sections: periodic polyalphabetic substitution ciphertext has repeating sections with a constant period. this feature can help to identify this type of ciphers [29], [30] 5. ab-ba feature: ciphertext may contain double sections with its reverse such as “xy” and “yx.” this feature appears in ciphertext produced from playfair cipher [31] 6. ciphertext characters type: some ciphers employee just letters in encryption process another cipher employee letters and numbers [9] 7. adjacent characters: it can be useful to check if there are any adjacent characters have the same value [28]. 6. future research directions this work lies within a larger team project aiming to design and implement a general cryptanalysis platform for pedagogical purposes. considering the architectural design of the proposed general cryptanalysis platform, the platform has a number of components or modules including the supervisory module, the crypto-classifier, parallel cryptanalysis modules, feedback and reporting module, graphical analyzer, and the steganography module. here, we are mainly interested in the crypto-classifier module that is responsible for the identification and classification of the ciphertext type. at least, two levels of classification need to be implemented:[32] 1. level 1 crypto-classifier: in this module, a first level classification of the considered ciphertext needs to be done so as to decide the general cryptographic category (e.g., classical cipher, block cipher, and public-key cipher) of it. information obtained from various resource need to be used, and some intelligent classification techniques (such as artificial intelligence, genetics, and neural networks) have to be developed 2. level 2 crypto-classifier: in the second level of classification, specific algorithm(s) or cipher(s) should fig. 5. frequency distribution of single letters in normal english text[28] sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions 8 uhd journal of science and technology | april 2017 | vol 1 | issue 1 be assigned for the ciphertext in accordance with the classification done at the first level. for example, if the classifier of level 1 deduced that the ciphertext belongs to the category of block ciphers; level 2 classifier job is to decide which specific block cipher has been used (e.g., des, aes, and twofish). besides the information deduced by different means, some distinguishing characteristics for different ciphers must be known. concerning the future research, we are specifically interested in using the estimation capabilities of anns to identify the ciphers type. as mentioned previously, anns use parallel processing rather than serial computation. this behavior may enable us to move from typical statistical techniques of analyzing any cipher to more powerful generations that provide many solutions at a time. thus, the analyzing process will depend on how to model ann fig. 6. data flow of the proposed artificial neural network-based cipher identification process sufyan t. al-janabi et al.: intelligent techniques in cryptanalysis: review and future directions uhd journal of science and technology | april 2017 | vol 1 | issue 1 9 in the correct way and manage the training processes rather than spend the time in mathematical computation of the cipher. fig. 6 shows the data flow of the proposed estimation process. the ann box will have two types of inputs; the first one is a group of training data and the second is a group of testing data. these two groups are managed by anns to correct errors produced from estimation process. anns would use supervised learning to estimate the cipher type. the number of neurons in the input, hidden, and output layers depend on the number of ciphers used and how much the analyst can extract features from ciphertext. several previous works on using anns and other techniques for cipher type classification can be found [33]-[37]. however, to the best of authors’ knowledge, we could not see specific previous work on using spiking anns for this task. hence, our focus will be directed to this specific application of spiking anns. in the first stage, classification of classical ciphers will be considered. in the next stages, other modern cipher types will be taken into consideration also. 7. conclusion this work is mainly concerned in building automatic tools for various cryptanalysis tasks. this definitely requires the use of suitable intelligent techniques such as gas and anns. the focus here has been on using gas for cryptanalysis of classical ciphers and adoption of anns for cipher type identification. more specific results of cipher classification based on spiking anns are going to be presented in a subsequent paper. references [1] b. carter, and t. magoc. “introduction to classical ciphers and cryptanalysis.” a technical report, 11 sep. 2007. available: http://www. citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.8165. 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[37] w. a. r. de souza, and a. tomlinson. “a distinguishing attack with a neural network.” ieee 13th international conference on data mining workshops, pp. 154-161, 2013. tx_1~abs:at/tx_2:abs~at uhd journal of science and technology | july 2022 | vol 6 | issue 2 105 1. introduction the internet expands at an unprecedented rate. most of the time, malicious software is spread via the internet. malicious websites can be referred to as any website that has been designed to cause harm. it is similar to a legitimate url for regular users but hosts unsolicited content. the attacker usually builds a website identical to the target or embeds the exploit code of browser vulnerabilities on the webpage. then, it tricks the victim into clicking on these links to obtain the victim’s information or control the victim’s computer [1]. in many circumstances, people do not check the complete website url, and the attacker can obtain essential and personal information once they visit a malicious website [2]. malicious url detection always comes at the top in the research area. however, having protection against these attacks is not an option anymore. according to google’s transparency report, 2.195 million websites made their list of “sites deemed dangerous by safe browsing” category as of january 17, 2021. the vast majority of those (over 2.1 million) were phishing sites. only 27,000 of google’s removed websites were delisted because of malware [3]. several forms of a malicious url proceed with the attack and deliver unsolicited content, mainly named spam, phishing, and drive-by download. spam is a web page with many links to unwanted websites for other purposes; the malicious url detection using decision tree-based lexical features selection and multilayer perceptron model warmn faiq ahmed, noor ghazi m. jameel technical college of informatics, sulaimani polytechnic university, sulaimani 46001, kurdistan region, iraq a b s t r a c t network information security risks multiply and become more dangerous. hackers today generally target end-to-end technology and take advantage of human weaknesses. furthermore, hackers take advantage of technology weaknesses by applying various methods to attack. nowadays, one of the greatest dangers to the modern digital world is malicious urls, and stopping them is one of the biggest challenges in the field of cyber security. detecting harmful urls using machine learning and deep learning algorithms have been the subject of various academic papers. however, time and accuracy are the two biggest challenges of these tools. this paper proposes a multilayer perceptron (mlp) model that utilizes two significant aspects to make it more practical, lightweight, and fast: using only lexical features and a decision tree (dt) algorithm to select the best relevant subset of features. the effectiveness of the experimental outcomes is evaluated in terms of time, accuracy, and error reduction. the results show that a mlp model using 35 features could achieve an accuracy of 94.51% utilizing only url lexical features. furthermore, the model is improved in time after applying the dt as feature selection with a slight improvement in accuracy and loss. index terms: multilayer perceptron, lexical feature, feature selection, malicious url, synthetic minority oversampling technique corresponding author’s e-mail: warmn.faiq.a@spu.edu.iq received: 20-08-2022 accepted: 01-10-2022 published: 13-11-2022 access this article online doi: 10.21928/uhdjst.v6n2y2022.pp105-116 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2022 ahmed and jameel. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l r e s e a r c h a r t i c l e uhd journal of science and technology warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model 106 uhd journal of science and technology | july 2022 | vol 6 | issue 2 pages may pretend to provide assistance or facts about a subject. phishing is a type of social engineering attack used to steal sensitive data. finally, drive-by downloads refer to the unintentional download of malicious code to the device, leaving it open to a cyber-attack [4]. there are currently several approaches to detect dangerous websites on the internet. nowadays, a malicious url is mainly detected by black and white list-based and machine learning-based url detection methods. according to the first technique, a website cannot be viewed until the url is checked against the blacklist database to ensure it is not on the list. blacklist is essentially a listing of urls that were previously identified as malicious. its advantage is that it is fast, easy, and has a meager false-positive (fp) rate. however, the main problem with this method is that it has a high false-negative (fn) rate and fails to detect newly generated urls [1], [5], [6]. nevertheless, it has been widely utilized in several major browsers, including mozilla firefox, safari, and chrome, among others, due to its simplicity and efficiency [5]. in addition, the blacklisting approach is also utilized by many antivirus systems and internet businesses. however, due to some limitations, the blacklisting strategy is insufficient to identify non-blacklisted threats [7]. whitelist is another aspect that provides security when accessing a website. it is similar to the blacklist method technique. the difference is that in the whitelist, only those websites are allowed to access that is in the list. the limitation of this method is denying access to many newly generated websites that are legal and safe to visit [5]. on the other hand, machine learning techniques use a collection of urls specified as a set of attributes and train a prediction model based on them to categorize a url as good or bad, enabling them to recognize new, possibly harmful urls [1]. in this paper, the multilayer perceptron (mlp) model is used to detect malicious urls based on the features of the urls. since a lightweight method is challenging for time efficiency, lexical features are utilized and extracted from the dataset to train the model. the model is tested first without and then with feature selection (fs) to see the result and the differences. the main contribution of this paper is the development of a malicious url detection system that utilizes only lexical features to construct a light model and selects only high-ranked features to reduce feature extraction (fe) time. moreover, using decision tree (dt) as a fs algorithm is an advantage to select the best relevant features based on features importance score to improve the model performance and decrease the fe time during the detection process. the paper is organized as follows. section 2 is related works. the proposed malicious url detection system with its phases including dataset collection, features extraction, features selection using dt algorithm, model development, and evaluation is presented in section 3. all the experimental results and discussions are provided in section 4. finally, section 5 illustrates the conclusion of the paper. 2. related works many kinds of research in the area of detecting malicious websites with various techniques, algorithms, and methods exist. the machine learning technique is one of the approaches used to solve the problem of malicious url detection. multiple studies have been done in the era. xuan et al. proposed support vector machine (svm) and random forest (rf) as machine learning algorithms to classify benign and malicious urls by extracting features and behaviors of the urls. the researchers created an extensive set of features to improve the model’s ability and use it as a free tool to detect malicious urls [8]. subha et al. tested various machine learning algorithms to detect malicious urls. according to the results, rf scored better than all svm, naïve base, and artificial neural network (ann) with an accuracy of 97.98 and the f1 score of 92.88 [9]. furthermore, islam et al. used three machine learning algorithms to detect malicious urls: nn, k-nearest neighbor (knn), dt, and rf. the results showed that the neural network (nn) scored the worst, whereas dt and rf achieved the best scores. the study mentioned that the lack of ability to detect malicious urls by nn is due to the small size of the dataset, while nn is suitable for large datasets [10]. besides, some of the researches used nns as a solution for classifying malicious urls from benign ones. liu and lee proposed a detection method using a convolutional neural network (cnn). the research adopted the end user’s perspective and used cnn to learn and recognize screenshot images of the websites. the results showed that although the training period is lengthy, it is tolerable, especially with powerful graphics processing units. the testing is efficient once the training is completed; therefore, time is often not an issue with this procedure [11]. balamurugan et al. proposed a nn to classify the websites as good and bad urls with optimizing network parameters using genetic algorithms. the article showed a good improvement when optimizers were applied to the nn model in both classification and convergence [12]. furthermore, chen et al. used cnn for malicious url detection. the study showed that the warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model uhd journal of science and technology | july 2022 | vol 6 | issue 2 107 proposed method achieved satisfying detection accuracy with an accuracy of 81.18% [13]. moreover, hybrid systems are also proposed by some recent studies as a solution to the problem. naresh et al. proposed a machine learning-based system that combines a svm with logistic regression using a combination of url lexical options, payload size, and python supply options as features to recognize the malicious urls. as a result, an accuracy of 98% was achieved, which is an improvement compared to a conventional method. according to some recent articles, using nns as a hybrid system can achieve satisfying performance [14]. yang et al. proposed a system to detect malicious websites based on integrated cnns and rf system. the results showed that the proposed integrated system achieved better results than traditional machine learning algorithms due to their shallow design, which cannot examine the complicated link between safe and malicious urls [2]. another research is by das et al. who tested three nn algorithms, rnn, lstm, and cnn-lstm, to see the effectiveness of these algorithms in classifying benign and malicious urls. the results showed that with an accuracy of 93.59%, the cnn-lstm architecture exceeds the other two [15]. furthermore, peng et al. proposed attention-based cnn-lstm for malicious url detection. the results showed that the proposed method achieved better than shallow nns and single deep nns such as cnn and lstm individuals with an accuracy of 96.74 [16]. 3. the proposed malicious url detection system the proposed system is constructed using a lightweight method. only lexical features are utilized to build the model. python is used for programming the phases of the proposed system with famously fast and reliable libraries such as pandas, numpy, scikit-learn, imblearn, pyplot, tensorflow, and keras. the architecture of the proposed system starts with loading the dataset and then preprocessing stages to prepare the data for training. the training stage starts after the data are prepared. then the testing stage; the trained model classifies whether the url is malicious or benign. finally, evaluation metrics are applied to compute the performance of the model. the system architecture is shown in fig. 1. 3.1. dataset collection in this work, a proposed model was trained and tested on a dataset conducted from malicious and benign websites that were utilized to create the suggested model and evaluate its predictions [17]. the dataset initially consisted of 420,464 urls, 344,821 benign (good), and the rest of 75,643 websites are malicious (bad), as shown in table 1. therefore, the number of urls in each class is imbalance, as shown in fig. 2. a sample of the instances is shown in fig. 3. 3.2. data preprocessing 3.2.1. data cleaning one of the most critical preprocessing stages in machine learning is data cleaning. having clean, accurate noiseless data give precise models and results. starting with cleaning the data, 9216 duplicated urls were found and removed. the dataset was then checked for missing values, and there were no missing values in the dataset. 3.2.2. url lexical feature extraction several characteristics separate a safe url and its webpage from a malicious url. in certain instances, attackers employ direct ip linkages rather than domain names. another tactic use by attackers is short names or abbreviations for websites unrelated to legitimate brand names. algorithms for the detection method involve a wide variety of characteristics. to detect malicious websites using machine learning techniques, several distinct characteristics were retrieved from various academic research, such as lexical, host-based, and content-based features. since lexical features are fast to extract, they are also more applicable due to facing some casual problems when using content-based and host-based features. most of the time, content-based features cannot be extracted from malicious urls since most are blacklisted and cannot be accessed to get the contents such as html, javascript, and visual features. besides, the security risks when accessing such websites need precautions such as using special sandbox services to reduce the risk. host-based fe also faces problems such as a very long time taking due to the vast number of online requests from the database servers such as whois that sometimes lead to another problem: closing sockets for some of the websites and not getting the required information. in this study, lexical features are utilized to recognize malicious websites and distinguish them from legitimate ones. these characteristics are derived from the url address’s elements like a string. it should be able to identify malicious urls because it bases its decision on how the url appears. by replicating the names and making minor modifications, many attackers may make dangerous urls seem normal. however, from the perspective of machine learning, it is not feasible to take the actual name of the url. instead, the url’s string must be handled to obtain valuable properties. sixty lexical warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model 108 uhd journal of science and technology | july 2022 | vol 6 | issue 2 features were collected from literature, then extracted from the web links as listed in table 2. 3.2.4. feature scaling feature scaling or normalization is often advised and sometimes crucial. normalization is vital for nns since unnormalized inputs to activation functions might cause trapping in a relatively flat domain region. feature scaling helps optimize nn algorithms by accelerating training and preventing optimization from being trapped in local optima. models of nns establish a mapping between input and output variables. as a result, each variable’s size and distribution of the data extracted from the domain may change. input variables can have distinct scales because of fig. 2. dataset class distribution. data cleaning url lexical feature extraction feature selection using decision tree algorithm data collection data sampling feature scaling data preprocessing mlp model development (training phase) training data testing data trained model (classifier) malicious benign fig. 1. the proposed system architecture. table 1: dataset description type no. of urls benign 344,821 malicious 75,643 total urls 420,464 warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model uhd journal of science and technology | july 2022 | vol 6 | issue 2 109 fig. 3. sample of the dataset instances. table 2: list of url lexical features feature no. feature names data type description references f0 count dots integer number of character “.” in url [7], [8], [18]-[21] f1 url depth integer the depth of the url [8] f2 url length integer the length of the url [7], [8], [14], [16], [18]-[20], [22]-[26] f3 hyphen integer number of the dash character “-” (hyphen) [8], [20], [22], [23] f4 at symbol boolean there exists a character “@” in url [8], [22], [23], [27] f5 tide symbol boolean there exists a character “~” in url [8] f6 numunderscore integer number of the underscore character [8], [22] f7 numpercent integer number of the character “%” [8], [20] f8 numampersand integer number of the character “&” [8], [20], [22] f9 numhash integer number of the character “#” [8], [22] f10 countquestionmark integer count the number of “?” in url [20] f11 countsemicolon integer count the number of “;” in url [22] f12 httpsinurl boolean check if there exists a https in website url [8], [19], [22], [28] f13 ipaddress boolean check if the ip address is used in the hostname of the website url [7], [8], [16], [22], [23], [25] f14 urlredirection boolean there exists a slash “//” in the link path [8], [19], [22], [23], [27] f15 count alpha integer number of the alphabetic character [20], [22] f16 alpha ratio floating point the proportion of alphabetic characters in the url to the total length of the url [22] f17 count digit integer number of the numeric character [8], [20], [22], [29] f18 digit ratio floating point the proportion of numeric characters in the url to the total length of the url [22] f19 count special chars integer number of any special characters like”,' %”,”$”,”,’ =”, etc. [4], [7], [8], [14], [16], [18], [19], [22], [24]-[26] f20 special chars ratio floating point the proportion of special characters in the url to the total length of the url [16], [22] f21 count lowercase integer the number of lowercase english letters in the url [16], [22] f22 lowercase ratio floating point the proportion of lowercase english letters in the url to the total length of the url [16], [22] f23 count uppercase integer the number of uppercase english letters in the url [16], [22] f24 uppercase ratio floating point the proportion of uppercase english letters in the url to the total length of the url [16], [22] f25 count_subdomain integer number of subdomains in the url [8], [18] f26 short url boolean using tiny url/short url service [14], [23], [25] f27 length_of_ hostname integer length of hostname [8], [19] (contd...) warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model 110 uhd journal of science and technology | july 2022 | vol 6 | issue 2 table 2: (continued) feature no. feature names data type description references f28 length_of_path integer length of the link path [8], [19], [20] f29 length_of_query integer length of the query [8], [20] f30 length_of_scheme integer length of the url scheme [20] f31 presence_sus_ file_ext boolean checking the url string for the presence of the following file extensions·exe,·scr,·vbs,·js,.xml, .docm,·xps, .iso, .img, doc, .rtf,·xls, pdf, .pub, .arj, .lzh, .r01, .r14, .r18, .r25, .tar, .ace, .zip, .jar, .bat, .cmd, .moz, .vb, .vbs, .js, .wsc, .wsh, .ps1, .ps1×ml, .ps2, .ps2×ml, .psc1 and .psc2. [25] f32 count_ar_num integer the number of arabic numerals in the url [16] f33 is_tld_in_top5 boolean whether the top-level domain is the top five domains (com, cn, net, org, cc) [16] f34 paypal_in_path boolean if “paypal” is contained in the path section. [30] f35 ali_in_path boolean if “ali” is contained in the path section. [30] f36 jd_in_path boolean if “jd” is contained in the path section. [30] f37 safety_in_path boolean if “safety” is contained in the path section. [30] f38 verify_in_path boolean if “verify” is contained in the path section. [30] f39 google_in_path boolean if “google” is contained in the path section. [30] f40 apple_in_path boolean if “apple” is contained in the path section. if_facebook_u [30] f41 facebook_in_path boolean if “facebook” is contained in the path section. [30] f42 amazon_in_path boolean if “amazon” is contained in the path section. [30] f43 porn_in_path boolean if “porn”-related words are contained in the path section. [30] f44 gamble_in_path boolean if “gamble” related words are contained in the path section. [30] f45 paypal_in_domain boolean if “paypal” is contained in the domain section. [30] f46 ali_in_domain boolean if “ali” is contained in the domain section. [30] f47 jd_in_domain boolean if “jd” is contained in the domain section. [30] f48 safety_in_domain boolean if “safety” is contained in the domain section. [30] f49 verify_in_domain boolean if “verify” is contained in the domain section. [30] f50 google_in_domain boolean if “google” is contained in the domain section. [30] f51 apple_in_domain boolean if “apple” is contained in the domain section. [30] f52 facebook_in_ domain boolean if “facebook” is contained in the domain section. [30] f53 amazon_in_domain boolean if “amazon” is contained in the domain section. [30] f54 porn_in_domain boolean if “porn” related words are contained in the domain section. [30] f55 gamble_in_domain boolean if “gamble” related words are contained in the domain section. [30] f56 has keyword “client” boolean if the word “client” is contained in the url [31] f57 has keyword “admin” boolean if the word “admin” is contained in the url [31] f58 has keyword “server” boolean if the word “server” is contained in the url [31] f59 has keyword “login” boolean if the word “login” is contained in the url [31] their varied. the difficulty of the problem being modeled could be exacerbated by differences in the scales across the input variables. a model may learn tremendous weight values due to large input values, such as a spread of thousands of units, makes the result to be biased toward the bigger units. when features are of comparable size and nearly normally distributed, several machine learning methods work better or converge more quickly. min-max algorithm is used to scale all the features between 0 and 1. equation (1) uses for minmax feature scaling which helps the model to understand and learn better and faster without biasing to the more significant values [20]. x x x x xscaled min max min � � � � (1) where, xmax and xmin are the maximum and the minimum values of the feature (x), respectively. 3.2.5. data sampling initial examination of the dataset revealed that there were 5.18 times fewer occurrences of harmful websites than benign ones. therefore, due to the stark disparity in the number of malicious and benign website instances, the model affect to be biased due to this significant class imbalance warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model uhd journal of science and technology | july 2022 | vol 6 | issue 2 111 as it learns from a far higher percentage of benign website occurrences. a balanced class dataset is necessary for classification issues. as most machine learning algorithms used for classification were developed based on the presumption that there are an equal number of instances of each class, the imbalance of types in classification presents problems for predictive modeling. therefore, a balanced classification dataset is also necessary for a classification model to produce accurate judgments. there are several ways to handle an imbalanced dataset. the synthetic minority oversampling technique (smote) was utilized to address this issue. the smote technique uses knn machine learning algorithm to produce new instances. using it, additional instances of the minority class have been created, matching the proportion of instances of each class to the majority class to balance the classes. to balance the dataset, the minority class must thus be oversampled unless both groups have almost an equal number of cases. after balancing, the minority class were oversampled, which caused the data size to grow. finally, the 344,800 occurrences of each class result in a balanced distribution, as shown in fig. 4. 3.2.6. feature selection using dt algorithm the quality of fs and importance is one of the crucial differentiators in every machine learning task. due to computational limitations and the need to remove noisy variables for more accurate prediction, fs becomes necessary when there is a large amount of data that the model may process. in this study, a dt algorithm is used to select the best and most relevant lexical features based on the feature importance score. dts apply various techniques to decide whether to divide a node into two or more sub-nodes. the homogeneity of newly formed sub-nodes is increased by sub-node formation. the threshold value of an attribute is used to divide the nodes in the dt into sub-nodes. the classification and regression tree algorithm uses the gini index criteria to find the sub-nodes with the best homogeneity. the dt divides the nodes based on all factors that are accessible before choosing the split that produces the most homogenous sub-nodes. at the same time, the target variables are considered while selecting an algorithm. it is a visual depiction of every option for making a choice based on specific criteria according to the algorithm. conditions on any characteristics are used to make judgments in both situations. the leaf nodes reflect the selection based on the conditions, whereas the inside nodes represent the conditions. finding the attribute that provides the most information is necessary for dt construction. by building the tree in this way, feature importance scores can be accessed and used to help interpret the data, ranking, and select features that are most useful to a predictive model. it aids in determining which variable is chosen to be used in producing the decisive internal node at a specific point. the steps of fs using a dt are described in an (algorithm 1). at this phase, the list of features with their importance values is calculated and selected by the dt algorithm. algorithm 1. classification and regression tree [32]. 3.3. mlp model the most practical variety of nns is mlp which is frequently used to refer to the area of anns. a perceptron is a singleneuron model that serves as the basis for more extensive nns. artificial neurons are the basic units of nns. the feed-fig. 4. dataset after data sampling using smote. warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model 112 uhd journal of science and technology | july 2022 | vol 6 | issue 2 forward nn is supplemented by the mlp. there are three layers: the input layer, the output layer, and the hidden layer. the proposed mlp model consists of three hidden layers besides the input and output layers to describe the model. the first hidden layer has 400 neurons, the second hidden layer has 300 neurons, and the last hidden layer has 200 neurons. the output layer has one neuron as it is a binary classification with two outputs, 1 and 0, whereas 1 represents a malicious url and 0 represents a benign one. the other parameters are set as a batch size of 200, a learning rate of 0.005, a sigmoid function as an activation function, and adam as an optimizer, as shown in table 3. 3.4. model evaluation the goal is not just to create a predictive model. it involves building and choosing a model that performs well on out-ofsample data. therefore, verifying the model’s correctness is essential before computing estimated values. to assess the models, many indicators are considered. a crucial phase in the machine learning pipeline is evaluating the learned model’s effectiveness. machine learning models are either adaptable or non-adaptive based on how effectively they generalize to new input. when an ml model is applied to new data without being adequately evaluated using a variety of metrics and without relying on accuracy, it may produce inaccurate predictions. besides, the accuracy, precision, recall, and f1 score have been taken into account for the model reliability and considering the aspect of the errors when the model classifies between malicious and benign urls. the definition of classification accuracy, which may be the most straightforward criterion to use and apply, is the ratio of correct predictions to all other predictions and calculated using equation (2) [33]. accuracy number of correct predictions total number of pred = � � � � � � iictionsmade� (2) confusion matrix produces a matrix that summarizes the overall effectiveness of the model. for example, the confusion matrix for binary classification, which is the case in this work, is a two-by-two matrix. the confusion matrix shows the number of correct and incorrect classification for both actual and predicted values, including true positive indicates the number of samples that are correctly classified as positive and true negative shows the number of instances that are correctly identified as negative, besides, there is fp that indicates the number of samples that are incorrectly identified as positive, and finally, fn that indicates the number of instances that are incorrectly identified as negative. the confusion matrix for binary classification is shown in table 4. from the confusion matrix, some important metrics are calculated and taken into consideration along with the accuracy to ensure that the model performs well and is not biased because of issues such as dataset imbalance. therefore, precision, recall, and f1 score are used as model evaluation metrics. precision indicates how accurate the positive predictions are, recall is the coverage of actual positive samples, and the f1 score is the harmonic mean of precision and recall, and they are calculated using equations (3), (4), and (5), respectively [22], [29], [34]. precision truepositives truepositives falsepositives � � � � � (3) recall truepositives truepositives falsenegatives � � � � � (4) f score precision recall precision recall 1 2� � � � � � � � � (5) table 3: the parameters of the proposed mlp model layer no. no. of neurons/dim optimizer activation function learning rate batch size no. of epochs layer 1 400 adam sigmoid 0.005 200 1500 layer 2 300 sigmoid layer 3 200 sigmoid table 4: confusion matrix actual values predicted values negative positive negative tn fp positive fn tp tp: true positive, tn: true negative, fp: false positive, fn: false negative table 5: list of used hardware and software specifications hardware and software specification description pc core i3 gen6 ram 20 gb storage ssd sata 256 gb operation system windows 10 pro warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model uhd journal of science and technology | july 2022 | vol 6 | issue 2 113 4. experimental results and discussion in this section, the details of the experimental results are presented. the experiments are implemented on a malicious url dataset [19] aiming to find the set of relevant url lexical features based on their importance score using dt algorithm and evaluating the mlp model performance using the selected features. the final prepared dataset after the main steps of data preprocessing which includes data cleaning, data sampling, and fe, consists of a total of 689,600 urls with 60 lexical features and a class label that has a 0 for benign and 1 for malicious. the software and hardware specifications used for the experiments are explained in table 5. after running the dt algorithm for fs, the importance score or weight for each variable was calculated. features with lowest importance scores were deleted and features with highest scores were kept. this type of fs can simplify the problem that is being modeled, speed up the modeling process, and improve the performance of the model. the list of all lexical features’ importance scores is illustrated in table 6. after this phase, 35 features were selected and 25 features were eliminated. the selected features are the top 35 features with highest importance values which are f0, f1, f2, f3, f4, f5, f6, f7, f8, f10, f11, f15, f16, f17, f18, f19, f20, f21, f22, f23, f24, f25, f26, f27, f28, f29, f31, f33, f34, f35, f39, f41, f57, f58, and f59. as a result of eliminating 25 features, a significant decrease in fe time achieved, which is an essential factor in this problem situation, as shown in table 7 and fig. 5. for mlp model evaluation, the 35 selected features were fed to the model as input. the stratified technique was used for splitting the dataset into train and test sets to preserve the same proportions of instances in each class as in the original dataset. it is obvious that most of the data in the dataset are advised to be used for training to let the model learn well. different ratios for training and testing have been used by the researchers such as 80% for training and the other 20% for testing or 70% for training by 30% for testing. many factors are taken into consideration when train test split is done, such as the number of instances in the dataset, hyperparameters table 6: list of features with their importance score feature no. feature importance feature no. feature importance feature no. feature importance feature no. feature importance f0 0.11828 f16 0.05732 f32 0 f48 0 f1 0.07532 f17 0.04211 f33 0.07169 f49 0 f2 0.03691 f18 0.04414 f34 0.00132 f50 0 f3 0.01727 f19 0.01206 f35 0.00158 f51 0 f4 0.00161 f20 0.13231 f36 0.00022 f52 0 f5 0.00185 f21 0.02187 f37 0.00009 f53 0 f6 0.01472 f22 0.02058 f38 0.00041 f54 0 f7 0.00227 f23 0.00755 f39 0.00241 f55 0 f8 0.0018 f24 0.01264 f40 0.00031 f56 0.00053 f9 0.00009 f25 0.02609 f41 0.00228 f57 0.01168 f10 0.00874 f26 0.01038 f42 0.00009 f58 0.00089 f11 0.00997 f27 0.1204 f43 0.00017 f59 0.02466 f12 0.00017 f28 0.05412 f44 0 f13 0.00007 f29 0.00539 f45 0 f14 0.00058 f30 0.00068 f46 0 f15 0.01788 f31 0.0065 f47 0 fig. 5. fe time differences before and after fs. table 7: feature extraction time before and after feature selection no. of features feature extraction time in seconds 60 features, the whole dataset (before fs) 134 s 35 features, whole dataset (after fs) 92 s warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model 114 uhd journal of science and technology | july 2022 | vol 6 | issue 2 has been tested using a learning rate of 0.005, batch size of 200, and different number of epochs and neurons. the list of scenarios is described in table 8. after executing all the 10 scenarios described in table 8, from the results shown in table 9, it is obvious that with increasing the number of epochs, the accuracy will increase along with training time, and the training loss will decrease eventually. in this system, the more important parameters for detecting malicious urls are higher values for test accuracy, precision, and recall with lower training loss. the least important parameter is the training time. training phase is a one-time process, sometimes it requires a long time to develop a welltrained model with high accuracy and less training loss. since the last scenario, 1500 epochs outperformed the best scores for the mentioned parameters, it has been chosen to train the model and used for malicious url detection. as a result, table 8: list of tested scenarios scenario no. of epochs no. of features batch size learning rate no. of neurons in hidden layers s1 100 35 200 0.005 200, 120, 80 s2 100 35 200 0.005 400, 200, 100 s3 100 35 200 0.005 400, 300, 200 s4 100 35 200 0.005 600, 400, 200 s5 100 35 200 0.005 800, 600, 400 s6 500 35 200 0.005 400, 300, 200 s7 500 35 200 0.005 600, 400, 200 s8 500 35 200 0.005 800, 600, 400 s9 1000 35 200 0.005 400, 300, 200 s10 1500 35 200 0.005 400, 300, 200 table 9: results of all the 10 scenarios scenarios train time in seconds test time in seconds train loss train accuracy (%) test accuracy (%) precision recall fscore confusion matrix s1 933.4 15.0 0.145 93.90 92.82 0.923 0.935 0.929 ([95321 8119] [6735 96705]) s2 2258.5 28.7 0.142 94.00 92.95 0.919 0.943 0.930 ([94797 8643] [5938 97502]) s3 2553.3 17.8 0.123 94.79 93.45 0.927 0.944 0.935 ([95733 7707] [5840 97600]) s4 2847.4 23.3 0.122 94.86 93.51 0.927 0.944 0.936 ([95807 7633] [5798 97642]) s5 6984.2 31.8 0.125 94.74 93.51 0.935 0.936 0.935 ([96659 6781] [6636 96804]) s6 10487.2 18.3 0.091 96.21 94.18 0.937 0.948 0.942 ([96822 6618] [5415 98025]) s7 17460.9 25.3 0.098 96.00 94.08 0.939 0.943 0.941 ([97118 6322] [5918 97522]) s8 27800.3 37.7 0.095 96.09 94.15 0.937 0.946 0.942 ([96877 6563] [5546 97894]) s9 22684.7 19.7 0.086 96.49 94.25 0.938 0.947 0.943 ([97010 6430] [5460 97980]) s10 62791.6 30.3 0.075 96.93 94.51 0.941 0.950 0.945 ([97233 6207] [5146 98294]) fig. 6. train accuracy for the 10 different scenarios. to tune, the used classifier, and the model use case. due to the good amount of instances in the dataset, 70% of the final dataset considered for training, while the remaining 30% is used for testing. the model with several scenarios warmn faiq and noor ghazi: malicious url detection using dt-based lexical features selection and mlp model uhd journal of science and technology | july 2022 | vol 6 | issue 2 115 the model achieved an accuracy of 94.51, recall of 94.1, the precision of 95.0, and training loss of 0.075. the results are shown in table 9 and illustrated in figs. 6-8. 5. conclusion one of the serious threats on the internet is malicious url. hackers have several techniques and algorithms to obfuscate urls to bypass the defenses. the problem of detecting malicious urls has been studied in this research with explaining types of possible attacks, features, and detection techniques. the study developed a lightweight malicious url detection model using url lexical features only instead of content or host-based features. content and host-based features take a long time during the extraction. to extract content-based features, the websites should be available for accessing their source code. host-based features extraction process needs connection with special servers such as whois to get the required information. dt has been used to get the importance scores of 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[34] m. chatterjee and a. s. namin. “deep reinforcement learning for detecting malicious websites.” computer science, vol. 15. pp. 55, 2019. tx_1~abs:at/tx_2:abs~at 6 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 1. introduction number of cars has significantly increased nowadays, [1], [2] consequently, traffic congestion problem has been arise around the world [3]. subsequently, vehicle clashing and crashing and dramatic increase of co2 emission per year [4] are threatening sustainable mobility of future [5]. further more, traffic control needs man power to be controlled [6]. the traffic control devices are time dependent and designed to flow the traffic in all directions. on top of that, sometimes during turning the lights from green to red causes traffic deadlock in a direction without having a noticeable flow in the other direction [7]. congestions caused by traffic signals could negatively impact on economy in terms of transportation due to fuel [8], time expenditure [9], and air pollution [10]. moreover, injuring even sometimes death caused by accidents happened in deadlock traffics [8], on the other hand, reducing congestion may have economic, environmental, and social benefits. in general, to make the optimization problem manageable, several assumptions have to be made. the main problem that arises is that these assumptions deviate and sometimes do so significantly from the real world. meanwhile, many factors have effects on drivers in real world traffics such as on driver’s preference interactions with vulnerable road users (e.g., pedestrians, cyclists, etc.), weather and road conditions [11]. on the other hand, computer vision has an important role in managing and controlling traffic signals with great success [6], [12]. the best way to control traffic flow in big and busy cities is to utilize intelligent traffic signal [6], the system has ability to approximately evaluate density estimation, a review of computer vision–based traffic controlling and monitoring kamaran h. manguri1,2, aree a. mohammed3 1department of technical information systems engineering, erbil technical engineering college, erbil polytechnic university, erbil, iraq, 2department of computer science, college of basic education, university of raparin, ranya 46012, iraq, 3department of computer science, college of science, university of sulaimani, sulaymaniyah, iraq a b s t r a c t due to the rapid increase of the population in the world, traffic signal controlling and monitoring has become an important issue to be solved with regard to the direct relation between the number of populations and the cars’ usage. in this regard, an intelligent traffic signaling with a rapid urbanization is required to prevent the traffic congestions, cost reduction, minimization in travel time, and co2 emissions to atmosphere. this paper provides a comprehensive review of computer vision techniques for autonomic traffic control and monitoring. moreover, recent published articles in four related topics including density estimation investigation, traffic sign detection and recognition, accident detection, and emergency vehicle detection are investigated. the conducted survey shows that there is no fair comparison and performance evaluation due to the large number of involved parameters in the abovementioned four topics which can control the traffic signal controlling system such as (computation time, dataset availability, and an accuracy). index terms: traffic signaling system, intelligent traffic, computer vision, traffic congestion, traffic monitoring, review. corresponding author’s e-mail: kamaran@uor.edu.krd received: 20-12-2022 accepted: 26-04-2023 published: 10-08-2023 access this article online doi: 10.21928/uhdjst.v7n2y2023.pp6-15 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 kamaran h. manguri and aree a. mohammed. this is an open access article distributed under no derivatives license 4.0 (cc by-nc-nd 4.0) re v i e w a r t i c l e uhd journal of science and technology manguri and mohammed: traffic controlling and monitoring: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 2 7 traffic signals detection and recognition, emergency and police car detection, and accident detection. even though a better infrastructure can improve the traffic flow [13]. usually in quiet intersections, the traffic is controlled by human or system controls [6]. in most congestions, cameras have been put for purposes other than traffic control, such as security, vehicle detection, and arrangement [14]. these cameras can be utilized for the reason of analyzing traffic scenes simply by employing specific hardware. the main advantage is that there is no need for replacing the cctvs. the main objective of this survey is to fill the research gap that exists in the field of traffic signal controlling and monitoring. the importance of this survey is to propose some technique based on computer vision for reducing the road congestion and keeping the environment green and public health. in this study, different approaches based on computer vision for traffic signaling controls are reviewed. for this purpose, the literatures over the period january 2015–january 2022 are surveyed. the structure of this review is as follows: section i provides an introduction to the traffic and its problems. background of traffic management addressed in section ii. in section iii, a literature review is provided for the existing solution of the intelligent traffic signaling. section iv provides a discussion of review of the existing solutions. finally, conclusion remarks are presented in last section. 2. review strategy this review is aimed in analyzing the recent literature for the vision-based methods for traffic controlling and managing, which have been published from january 2015 to january 2022 in terms of journal papers and conference proceedings. the reviewed papers were chosen after an extensive manual search of databases including ieee xplore, springer, elsevier, and google scholar. keywords used to explore the databases are shown in table 1. in addition, vision or image processing keywords are selected as the main keywords in the title of papers. moreover, one of the sub search keywords has been used with main keyword to find the studies in the above mentioned period. 3. urban traffic light system usually, each traffic light contains three color lights precisely, green, yellow, and red lights. they are put in the four parallel and perpendicular directions [15]. fig. 1 shows a common intersection that formed by two perpendicular and parallel lanes. globally, the meaning of the lights for the drivers is as follows, green light means that the current lane has right to move forward meanwhile all other three directions are red which means they are not allowed to flow [11]. besides, models of controlling traffic signaling and monitoring using computer vision required cctv camera to acquisition images from the live traffic intersection. the simulation of traffic controlling in the cross road is shown in fig. 2 [16]. 4. literature review to improve traffic signaling control and monitoring, scientists and researchers proposed many methods based on machine vision. computer vision-based architecture of traffic signaling controlling and monitoring includes image acquisition, preprocessing and applying advance computer vision techniques density estimation, traffic sign detection and recognition, accident detection, and emergency vehicle detection. in this review, papers are randomly selected according to proposed methods in the recent years (between january 2015 and january 2022) for controlling and monitoring traffic signals. 4.1. density estimation density estimation is a key aspect for automatic traffic signaling control and reducing congestion in the intersection areas. different approaches by reviewers to estimate traffic density are detailed below: table 1: search parameters of the literature review date range database main keywords (or) and/sub search key-words january 2015–january 2022 ieee xplore springer elsevier google scholar vision image processing machine learning deep learning traffic controlling traffic density traffic congestion crowd detection accident accident detection accident identification emergency vehicle traffic sign manguri and mohammed: traffic controlling and monitoring: a review 8 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 garg et al. [17] presented the approach for estimating traffic density based on vision which forms the fundamental building block of traffic monitoring systems. due to the low accuracy of vehicle counting and tracking of existing techniques, the sensitivity to light changes, occlusions, congestions, etc. are made. moreover, the authors addressed another problem of existing holistic-based methods by difficulty of implementation in real-time because the high computational complexity is required. to handle this issue, density is calculated using block processing approach for busy road segments. the proposed method involves two steps including marking of region of interest (roi), generating block of interest, and background construction in the first step. recurring process has been applied in the second step which involves background update, occupied block detection, shadow block elimination, and traffic density estimation. finally, the proposed methods are evaluated and tested using the trafficdb dataset. in biswas et al. [1] density estimated based counting cars, background subtraction (bs) method and overfeat framework are implemented. the accuracy of the proposed system is evaluated by manual counting of cars. furthermore, the comparative study was conducted before and after outperforming overfeat framework. average accuracy reached 96.55% after applied overfeat framework from 67.69% average accuracy for placemeter and 63.14% average accuracy for bs, respectively. furthermore, this study confirmed that the overfeat framework has another application area. the advantages and shortcomings of the bs and six individual obtained traffic videos have used for analyzing overfeat framework with regarding different perspectives such as camera angles, weather conditions, and daily time. biswas et al. [3] implemented single shot detection (ssd) and mobilenet-ssd for estimating traffic density. for this purpose, 59 individual traffic cameras used for analyzing the ssd and mobilenet-ssd framework advantages and shortcomings. moreover, two algorithms are compared with manually estimated density. the ssd framework demonstrates significant potential in the field of traffic density estimation. according to their experiment, the significant accuracy of detection achieved, numerically speaking the precisions were 92.97% and 79.30% for ssd and mobilenet-ssd, respectively. bui et al. [18] developed a method for analyzing traffic flow, advanced computer vision technologies have been used to extract traffic information. for finding traffic density estimation in intersections data acquired from video surveillance. moreover, yolo and deepsort techniques turned for the detection, tracking, and counting of vehicles have enveloped to estimate the road traffic density. to evaluated the proposed method, data collected in a real-world traffic through cctv during 1 day. a new technique for estimating traffic density utilizing a macroscopic approach has been developed by kurniawan et al. [19]. the proposed method contains two parts including background construction and a traffic density estimation algorithm. the background construction obtained from detected non-moved vehicles in the front or behind vehicles. moreover, background of the image founded using the edge detection technique. density estimated by founding the ration between the number of roi containing object and the total number of roi. eamthanakul et al. [20] proposed a method-based image processing techniques for congestion detection. the fig. 1. four road lanes intersection. fig. 2. vision-based crossroad model. manguri and mohammed: traffic controlling and monitoring: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 2 9 technique contains three parts: (1) image background substation used for separating vehicles from the background, (2) morphological techniques applied for removing the image noises, and (3) traffic density calculated from the obtained image from cctv. finally, the results of the process are sent to transport plan database. 4.2. traffic sign detection and recognition traffic sign recognition plays a key role in driver assistance systems and intelligent autonomous vehicles. furthermore, it can be helpful for automatic traffic signals which leads to prevent pass across the intersections in the case of read signals. novel approaches proposed in berkaya et al. [21] for traffic sign detection and recognition. a new method developed to detect traffic sign under the name of circle detection algorithm. in addition, rgb-based color thresholding technique was proposed by berkaya et al. [21]. moreover, three algorithms have been used to recognize traffic signs including histogram of oriented gradients (hog), local binary patterns and gabor features are employed within a support vector machine (svm) classification framework. the performance of the proposed methods for both detection and recognition evaluated on german traffic sign detection benchmark (gtsdb) dataset. based on the obtained results from experiments, the proposed system better than the reported literatures and can be used in a real-time operation. yang et al. [22] presented a method for traffic sign detection and recognition, the method includes three steps. thresholding of hsi color space components used to segment image in the first step. applying the blobs extracted to the first step for detecting traffic signs in the second step. the contribution of their method in the first step, machine learning algorithms not used classify shapes instead of this invariant geometric moments have been used. second, inspired by the existing features, new method has been proposed for the recognition. the hog features have been extended to the hsi color space and combined with the local self-similarity (lss) features to get the descriptor. as a classifier, random forest and svm classifiers have been tested together with the new descriptor. gtsdb and the swedish traffic signs (sts) data sets have been used to test the proposed system. finally, the results of the presented technique compared with existing techniques. salti et al. [23] combined solid image analysis and pattern recognition techniques for detecting traffic sign in mobile mapping data. the system designed base on interest regions extracting which makes a significant with other existing systems that sliding window detection have been used. furthermore, with having challenging conditions such as varying illumination, partial occlusions, and large scale variations, the proposed system good performance demonstrated. three variant category traffic signs aimed to detect including mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent gtsdb competition. with having a very good performance of the proposed method in the online competition, the proposed method challenging dataset mobile mapping of italian signs the pipeline has been evaluated and showed its successfully be deployed in real-world mobile mapping data. in du et al. [24] designed the robust and fast performance classifier-based detector. they addressed two algorithms for detection and classification. first, aggregate channel features based on three types of features, which including the color feature, the gradient magnitude, and gradient histograms proposed. second, boosted trees classifier multiscale and multiphase detector have been proposed based on real adaboost algorithm. the obtained results from experiments of this study show high average-recall and speed which is evaluated on daimler, lisa, and lara datasets. real-time traffic signs’ detection and recognition are necessary for smart vehicles to make them more intelligent. to deal with this this issue. shao et al. [25] are proposed a new approach that includes two steps; in the first one acquitted images from the road scene converted to grayscale images. then simplified gabor wavelets (sgw) filter has been applied to the optimized parameters of grayscale images. furthermore, traffic sings bounded by edge detection which helps preparing the obtained result to the next process. in the second, the roi extracted using the maximally stable extremal regions algorithm and the superclass of traffic signs are classified by svm. to classify their subclasses, the traffic signs convolution neural networks (cnn) with input by simplified gabor feature maps, where the parameters were the same as the detection stage is used. finally, the proposed method tested on gtsdb and ctsd datasets and the results obtained from the experiments show that the method is fast and accurate by 6.3 frames per second and 99.43%, respectively. berkaya et al. [21] presented new ideas to provide colorful graphics to improve traffic in terms of object recognition and problem detection. two digital image processing methods, namely, circle detection algorithm and rgb which based on the simplest image segmenting method have been improved to develop the ability of traffic sign manguri and mohammed: traffic controlling and monitoring: a review 10 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 detection. the classification framework, namely, svm has been formed through assembling three main attributes including gabor features hog, and local binary patterns in the smart system. the presented technique is validated by german traffic sign detection and recognition benchmark datasets, correspondingly. according to the practical results, their technique is by far more efficient than the quoted approaches in this paper; the results are also aligned with the real time operation. a new approach for detecting and recognizing traffic signs proposed in ellahyani et al. [26] which includes three main steps. thresholding of his has been used to segment the image based on components of color spaces in the first step. it followed by applying blobs by the result of extracted from the former step. then, the traffic signs recognition performed for the detected signs in the last step. moreover, in their study, two different approaches used to classify signs. instead of machine learning algorithms, invariant geometric moments used to classify shapes in the first step. second, inspired by the existing features, new ones have been proposed for the recognition. hsi color space taken from the hog features and combined with the lss features to get the descriptor while used in the proposed algorithm. then, last test has been done based machine learning algorithms which are random forest and svm classifier. finally, the performance of proposed method evaluated and tested on german traffic sign recognition benchmark (gtsrb), gtsdb, and sts datasets. convolutional neural networks (cnn) machine learning algorithm is applicable for object recognition by having power full recognition rate and less time required for execution. in shustanov and yakimov [27] implemented traffic sign recognition using cnn. furthermore, several architectures of cnn compared together. meanwhile, tensor flow library is used for training and massively parallel architecture for multithreaded programming cuda. the entire procedure for traffic sign detection and recognition is executed in real time on a mobile gpu. finally, their method efficiency evaluated on gtsrb dataset and it is obtained very good result by 99.94% for classification images. 4.3. accident detection a main aspect of traffic monitoring is the identification and tracking of vehicles. monitoring vehicles helps to report and detect in the situation of the traffic junctions. one of the main aspects of traffic monitoring is the identification and tracking of vehicles. in this section, accident prediction and detection approaches are faced. tian et al. [28] developed a cooperative vehicle infrastructure systems (cvis) and proposed machine based-vision that can be used to detect car accident automatically. the study includes two phases; cad-cvis database has been created to improve the accuracy of accident detection in the first phase. cad-cvis dataset with regarding different traffic situations consists of various types of accidents, weather conditions and accident location. in the second phase, to detect accident deep neural network model yolo-ca based on cad-cvis and deep learning algorithms developed. moreover, to improve the performance of the model for detection small objects multiscale feature fusion and loss function with dynamic weights utilized. the results showed the proposed method faster than the previous methods, it can detect car accident in milliseconds with a very good average precision by 90.02%. finally, the proposed methods compared with existing methods, and the results determined accuracy improved and real-time over other models. a neoteric framework proposed for detecting accident in ijjina et al. [29]. for accurate object detection, mask r-cnn capitalized in the proposed framework by an efficient centroid-based object tracking algorithm for surveillance footage. the basic idea is to determine an accident after overlapping vehicles together are speed and trajectory anomalies in a vehicle after an overlap with other vehicles. this framework was found to be dominant and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. the framework tested and evaluated by the proposed dataset with the different weather condition. in saini et al. [30], a vehicle tracking technique based on image processing is developed without applying background subtraction for extracting the roi. instead, a hybrid of feature detection and region matching approach is suggested in their study, which is helpful for estimating the trajectory of vehicles over consequent frames. later, as the vehicle path through an intersection, the tracked direction is monitored for the occurrence of any specific event. it is found that the proposed method has capability to detect an accident between two vehicles. wenqi et al. [31] proposed the tap-cnn model for predicting accident based on cnn in the highways. traffic state and cnn model are described by some accident factors such as traffic flow, weather, and light to build a state matrix. in addition, the way of increasing tap-cnn model accuracy for predicting traffic accident different iterations are analyzed. accident data collected for inflected learning and evaluation of the model. finally, the experimental results show that the proposed model manguri and mohammed: traffic controlling and monitoring: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 2 11 named tap-cnn is more effective than the traditional neural network model for producing traffic accidents. dogru and subasi [32] presented an intelligent system for accident detection in which vehicles exchange their microscopic vehicle variables with each other. based on the vehicle speed and coordinates, data collected from vehicular ad-hoc networks (vanets) simulated model in the proposed system and then, it sends traffic alerts to the drivers. furthermore, it shows how to use machine learning methods to detect accidents on freeways in its. two parameterizes help to analyze and detect accident easy which are position and velocity values of every vehicle. in addition, oob data set has been used to test the proposed method. finally, the results show that the rf is better than ann and svm algorithms by with 91.56%, 88.71, and 90.02% accuracy, respectively. vision-based algorithms have been used in yu et al. [33] to detect traffic accident including an st-iht algorithm for improving the robustness and sparsity of spatiotemporal features and weighted extreme learning machine detector for distinguishing between traffic accident and normal traffic. furthermore, a two-point search technique is proposed to find a candidate value adaptively for lipschitz coefficients to improve the tuning precision. for testing and evaluating the proposed method 30 traffic videos collected from youtube website. finally, the results show that the proposed method performance for detecting a traffic accident outperforms other existing methods. accelerometer is a widely employed method that used to detect a crash. in this research work [34], after calibration of accelerometer value of acceleration is use to detect an accident. due to the limitation of accelerometer accuracy and providing the efficient accident detection, cnn machine learning algorithm is tuned. for detecting an accident, image classification technique is used; however, cnn takes a lot of time, data, and computing power to train. transfer learning methods have been innovatively applied to alleviate these problems and for the accident detection application, which involves retraining the already trained network. for this purpose, inception-v3 classifier that developed by google for image was incorporated. finally, the proposed method efficiency compared with the traditional accelerometer-based techniques for detecting accident by 84.5% of accuracy for transfer learning algorithm. 4.4. emergency vehicles detection the success of law enforcement and public safety relies directly on the time needed for first responders to arrive at the emergency scene. emergency cars include ambulance, firefighter, and police car. many methods are proposed to detect emergency cars and some of them as example are reviewed in this section. in borisagar et al. [35], two methods of computer vision are used to detect and localize the emergency vehicle. the used methods including object detection and instance segmentation. the proposed method implementation includes faster rcnn for object detection and mask rcnn for instance segmentation. the results show that the proposed method is accurate, most importantly, and suitable for emergency vehicle detection in disordered traffic conditions are deliberated. in addition, a custom dataset used for detecting emergency vehicles which contains 400 images and labeled using the label me tool. roy and rahman [36] are proposed a model for detecting emergency cars from cctv footage such as ambulance and fire-fighter on a heavy traffic road. in this model, priority given to these cars and clearing the emergency lane to pass the traffic intersection. for traffic police, sometimes deciding opening the specific lane for emergency vehicles is difficult or even impossible. deep cnn and coco dataset have been used for automated emergency car detection. the result of presented method for detecting and identifying all kinds’ emergency vehicles generally is reasonable. e. p. jonnadula and p. m. khilar [37] are presented a hybrid architecture for detecting emergency vehicles by combining features of image processing and computer vision. also, search space decreased by using region of interest. prediction of ambulance helps to decrease the number casualty on the real traffic in case of having emergency situation on the road. to cover this problem, lin et al. [38] presented a novel approach-based machine learning techniques and features are extracted using the multi-nature which can extract ambulance characteristics on demand. furthermore, they experimentally evaluate the performance of next-day demand prediction across several state-of-theart machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from singapore. finally, various machine learning techniques used for different natures and scdf-engineered-socio dataset have been used to show the proposed method accuracy. the existing traffic light system is a lack of information in emergencies such as ambulances, firefighters, and police when a car is in. suhaimy et al. [39] developed an embedded manguri and mohammed: traffic controlling and monitoring: a review 12 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 table 2: proposed methods in literature reviews type of traffic management reference (s) algorithm (s) dataset (s) accuracy % contribution (s) density estimation [17] block variance the trafficdb 93.70 traffic density estimation with the low computational cost [1] background subtraction, over feat framework, and place meter imagenet 96.55 defining roi by over feat framework [3] detection (ssd) and mobilenet-ssd data collected from cameras with different places 92.97 (ssd), 79.30 (mobilenet-ssd) new path opened for real time traffic density estimation [18] yolo and deepsort collected data from cctv 87,88 (day, congestion) 93,88 (day, normal) 82,1 (night, normal) detecting, tracking and counting vehicles [19] roi and edge detection n/a new technique developed for estimating traffic density [20] background subtraction and morphological techniques n/a traffic density estimated [40] cnn ucsd 99.01 traffic density estimation model proposed based cnn and computer vision traffic sign detection and recognition approaches [22] hsi, hog, lss, and svm gtsdb, ctsd 98.24 (gtsdb), 98.77 (ctsd) developed circle detection algorithm and an rgb-based color thresholding technique [21] hog, lss, random forest, and svm gtsdb 97.04 in the first step, machine learning algorithms not used classify shapes instead of this invariant geometric moments have been used. second, method has been proposed for the recognition [23] roi, hog, svm, and context aware filter gtsdb 99.43 (prohibitory) 95.01 (mandatory) 97.22 (danger) online detecting mandatory, prohibitory and danger traffic signs [24] aggregate channel features and boosted trees classifier daimler, lisa and lara 84.314 (daimler), 90.33 (lisa), 92.048 (lara) proposed the high average-recall and speed method [26] hog, lss, and svm gtsrb, gtsdb and tst 97.43 shapes classified byusing invariant geometric moments [25] sgw and svm gtsdb and ctsd 99.43 speed of detection and classification improved which is more than 6 frames per second [27] cnn gtsrb 99.94 cnn process described [41] proposed model named capsnet tl_dataset the proposed capsnet is employed for traffic sign recognition. accident detection [28] deep neural network model yolo-ca cad-cvis 90.02 cad-cvis dataset created and the proposed method more fast and accurate [29] mask r-cnn proposed 71 developing vehicular accident detection algorithms in real-time [30] hybrid of feature detection and region matching real world dataset n/a accident detection between two vehicles [31] cnn accident data collected 78.5 accident predicted using cnn [32] ann, svm, and random forests (rf) oob data set 91.56 (rf), 88.71 (ann), 90.02 (svm) the proposed method can provide estimated geographical location of the possible accident (contd...) manguri and mohammed: traffic controlling and monitoring: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 2 13 machine learning application, including acquisition of data, features extraction, different algorithms exploration, tuning, and deploying the model to a good output model in a simulation application. specifically, a classifier of ambulance siren sound into “ambulance arrive” and “no ambulance arrive” has been developed, which is the traffic light system could be used to track an ambulance’s arrival in an emergency. this paper suggests an approach based on mel-frequency spectral coefficients-svm (mfcc-svm) on matlab r2017b tools. 5. discussion according to the results of this review, several attempts have been made to develop intelligent traffic controlling visionbased methods. some challenges can be seen when researchers try to develop vision based automatic traffic signals. one of the challenges is that there is no available framework to cover all traffic problems because huge amount of data and computational time are required. another challenge is the power consumption to get real traffic data to testing their proposed methods in the different weather conditions. the table 2: (continued) type of traffic management reference (s) algorithm (s) dataset (s) accuracy % contribution (s) [33] st-iht, spatio-temporal features and w-elm collected dataset 87.4±0.3 (svm), 94.3±0.2 (elm), 95.5±0.3 (w-elm) (i) robust fractures extraction proposed based on of-dsift and st-iht (ii) detect imbalance between traffic accident and normal traffic [42] yolov4 video sequences collected from youtube n/a presents a new efficient framework for accident detection emergency vehicles detection [35] faster rcnn and mask rcnn custom dataset 81 (object detection), 92 (instance segmentation) the computational and accuracy for emergency vehicle detection are suitable [36] deep convolutional neural network coco 97.97 detecting and identifying all kinds emergency cars [37] yolo + resnet coco n/a hybrid architecture presented for detection of emergency vehicles in a real time [38] svr, mlp, rbfn, and lightgbm scdf engineered-socio n/a varying degrees to the model training in lightgbm [39] mfcc-svm 97 effectively distinguish audio events from audio signals ssd: single shot detection, cnn: convolution neural networks, hog: histogram of oriented gradients, lss: local self‑similarity, svm: support vector machine, gtsdb: german traffic sign detection benchmark, and gtsrb: german traffic sign recognition benchmark table 3: used datasets type of traffic management used in references dataset (s) type no. of images density estimation [17] the trafficdb image [1] imagenet image 3.2 million [3], [18] data collected from cameras with different places [40] ucsd video traffic sign detection and recognition approaches [21], [22], [23], [25], [26] gtsdb image 50, 000 [24] daimler image 5,000 [24] lisa video [24] lara video [26], [27] gtsrb image 900 [41] tl_dataset image 46,000 accident detection [28] cad-cvis video [29], [30], [31], [33], [42] proposed, real, and collected data [42] yolov4 emergency vehicles detection [35], [43] custom dataset [36], [37] coco 328,000 gtsdb: german traffic sign detection benchmark, gtsrb: german traffic sign recognition benchmark manguri and mohammed: traffic controlling and monitoring: a review 14 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 third challenge is the lack of standardized dataset for testing and training methods. the results show that there is no any comprehensive dataset for traffic controlling and monitoring. for example, in density estimation, most of the researchers have created their own datasets while in traffic sign detection and recognition; they have used some publicly available datasets such as (gtsdb and gtsrb). on the otherhand, both the accident and emergency vehicle detection methods have only collected and prepared (customized) real data which captured by cctv cameras. finally, current systems while studied in the literature provide a low-cost solution for traffic applications in the expense of the system accuracy and they are applicable. 5.1. survey of technique’s summary according to the literature sur vey, researchers have proposed and developed many approaches for controlling and monitoring signal system based on computer vision algorithms. in table 2, presenting methods for each survey topics associated with a summary of their (methods, datasets, and contributions). the reason that why the performance of the reviewed methods is not evaluated is a non-availability of a common datasets. 5.2. datasets for testing and evaluating the proposed methods, researchers worked on the public datasets and on their collected datasets. the used datasets are described in table 3. 6. conclusion in recent years, reducing road congestion has become a key challenge because of the threatening rise in the number of vehicles on the roads. in this review, the existing studies on autonomic traffic controlling and monitoring are reviewed in the computer vision community. furthermore, computer vision is considered as the areas with the most studies are for the future technologies. the intelligent traffic systems perceive the density estimation investigate, traffic sign detection and recognition, accident detection, and emergency vehicle detection. furthermore, name of the used datasets in the reviewed papers are presented. the main gap that founded in this review is a non-availability of dataset for traffic controlling and monitoring. finally, intelligent traffic systems can play a key role in reducing congestion in the intersection areas and traffic flow management. the conducted survey indicates the accuracy finding of each method as described in table 2. this research work could be having a potential impact for further researches in the same field of study. various challenges such as (weather conditions, lighting, and traffic patterns) can be considered with all techniques based on computer vision and machine 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[42] h. ghahremannezhad, h. shi and c. liu. “real-time accident detection in traffic surveillance using deep learning,” in 2022 ieee international conference on imaging systems and techniques (ist), pp. 1-6, 2022. . 24 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 1. introduction biometric identification is a new technology to recognize a person based on a physiological or behavioral characteristic that attracting a lot of attention recently [1-3]. as the level of counterfeit and deceptive transactions increases rapidly, so this causes the need for highly secure identification technologies and personal verification [4-6]. the existing methods of shared secrets such as pins or passwords, key devices, and smart cards, these are not sufficient in many applications [7-9]. biometric characteristic can realize this issue that is unique and realize the characteristic of a human [10-12]. the use of biometrics for personal authentication becomes practical and considerably more accurate than the current methods [13-15]. the biometric characteristics are classified into two main categories [16,17]: physiological characteristics related to the shape or part of the body, such as iris, fingerprint, face, dna, retina, and the geometry of the hand [18-20]. the behavior characteristics are related to the human behavior, such as gait, voice, signature, and keystroke dynamics [21-23]. biometrics can be applied in companies, governments, military, border control, hospitals, banks …, etc. [24-26]. these characteristics are used to verify the identity of a person for allowing access to certain information [27-29]. the most important characteristics of the iris do not change the texture of the iris through a person life [30,31]. this stability of iris features over a long time, leading to guarantees the long period of validity of the data and it does not need to update; in addition, iris characteristics are well protected from the environment [32-34]. this advantage allows iris identification as the most accurate and reliable biometric identification [35-37]. in the entire human population, there is no similarity two irises in their mathematical details, even between identical twins [38-40]. the probability of finding efficient biometric iris recognition based on iris localization approach muzhir shaban al-ani1, salwa mohammed nejrs2 1department of information technology, university of human development, college of science and technology, sulaymaniyah, krg, iraq, 2department of physics, university of misan, college of science, iraq a b s t r a c t biometric recognition is an emerging technology that has attracted more attention in recent years. biometric is referred to physiological and behavioral characteristics to identify individuals. iris characteristic is related to physiological biometric characteristics. iris recognition approaches are among the most accurate biometric technologies with immense potential for use in global security applications. the aim of this research is to implement an efficient approach to process the diseased eye images to verify the second iris examination for the same person by inserting an improvement step to the iris recognition system. the improvement step makes a correction of boundary around the pupil and removes the corrupted regions. this approach demonstrates its ability to detect the inner limit of the iris. the obtained results show that 90% success in the detection of diseased eye images, which make the iris recognition system more accurate and safe. index terms: biometric recognition, iris localization, iris recognition, template matching corresponding author’s e-mail: muzhir shaban al-ani, department of information technology, university of human development, college of science and technology, sulaymaniyah, krg, iraq. e-mail: muzhir.al-ani@uhd.edu.iq received: 16-05-2019 accepted: 23-07-2019 published: 31-07-2019 access this article online doi: 10.21928/uhdjst.v3n2y2019.pp24-32 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2019 al-ani and nejrs. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) re v i e w a r t i c l e uhd journal of science and technology muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 25 two people with an identical iris is almost approach zero, and the probability that two irises are similar; it is approximately 1 in 1010 [41-43]. iris recognition is an effective aspect of human identification for its dissimilarity between iris characteristics. these research aims are to introduce an efficient biometric iris recognition approach based on iris localization method. this approach tries to improve the identification process through certain processes on iris image. 2. iris recognition the recognition of the iris is an automatic method of biometric identification that uses mathematical techniques of pattern recognition in video images of one or both irises of an individual’s eyes [44,45]. the complex iris patterns are unique, stable, and visible from a distance [46,47]. the iris recognition technology determines the identity of an individual through many steps, as shown in fig. 1 [48,49]. these steps of iris recognition are as follows: • iris image acquisition: this step deals with using of electronic devices that converting the object into digital images such as digital camera and digital scanner [50,51]. • image preprocessing: the iris image is preprocessed to obtain a useful region iris image such as to illustrate the detection of the inner and outer boundaries of the iris. this step detects and removes the eyelids and eyelashes that may cover the eye image [52]. the iris image has low contrast and uneven illumination caused by the position of the light source, so preprocessing try to recover these aspects. all of these factors can be compensated in the image preprocessing step [53]. • feature extraction: this step deals with generating of features applying the texture analysis method to extract features from the normalized iris image [54]. important features of the iris are extracted for precise identification purposes [55]. • template matching: this step deals with comparing the user model with the database models using a corresponding matching statistic [56]. the corresponding metric will give a measure of similarity between two iris models or template. it provides a range of values when comparing models of the same iris and another range of values when comparing different iris models [57]. finally, a high confidence decision is made to identify whether the user is authenticated or not [58]. 3. literature review many literature reviews are published related to iris recognition. this section introduces some of the updated researches related to iris recognition subject. rai and yadav (2014) considered a new method for recognition of iris patterns using a combination of hamming’s distance and support vector machine. the zone of the zigzag collar of the iris is selected for the extraction of iris characteristics because it captures the most important areas of the complex iris pattern and a higher recognition rate is achieved. the proposed approach also used the detection of parabolas and the cut medium filter for the detection and removal of eyelids and eyelashes, respectively. the proposed method is efficient from a computer and reliable point of view, with a recognition rate of 99.91% and 99.88% based on the image data of cassia and check, respectively [59]. hamouchene et al. (2014) implemented a new iris recognition system using a new feature extraction method. the proposed method, neighborhood-based binary pattern, compares each neighbor of the center pixel with the next neighbor to code it for 1 if it is greater than the center pixel or 0 if it is smaller than the center pixel. the resulting binary code is converted into a decimal number to build the nbp image. to deal with the problem of rotation, we propose a coding process to obtain an invariant image by rotation. this image is subdivided into several blocks and the average of each block is calculated; then, the variations of the averages are encoded by a binary code [60]. santos et al. (2015) focused on the biometric recognition in mobile environments using iris and periocular information template maching feature extraction image preprocessing iris image acquisition fig. 1. iris recognition system. muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach 26 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 as main characteristics. this study makes three main contributions: first demonstrated the utility of an iris and a set of periocular data, which contains images acquired with 10 different mobile configurations and the corresponding data of iris segmentation. this data set allows us to evaluate iris segmentation and recognition methods, as well as periocular recognition techniques; second reported the results of device-specific calibration techniques that compensate for the different color perceptions inherent in each configuration; and third proposed the application of well-known iris and periocular recognition strategies based on classic coding and matching techniques, as well as the demonstration of how they can be combined to overcome the problems associated with mobile environments [61]. umer et al. (2015) proposed a new set of characteristics for personal verification and identification based on iris images. the method has three main components: image preprocessing, feature extraction, and classification. during image preprocessing, iris segmentation is performed using the hough restricted circular transformation. then, only two disjoint quarters of the segmented iris pattern are normalized, which allow the extraction of characteristics for classification purposes. here, the method of extracting characteristics of an iris model is based on a morphological operator of multiple scales. then, the characteristics of the iris are represented by the sum of the dissimilarity residues obtained by the application of a morphological top-hat transform [62]. thomas et al. (2016) in this work, our system introduces a more accurate method called random sample consensus to adjust the ellipse around the non-circular iris boundaries. you can locate the iris boundaries more accurately than the methods based on the hough transformation. we also use the daugman rubber sheet model for iris normalization and elliptical unpacking, and correspondence based on the correlation filter for in-class and interclass evaluation. peak side lobe ratio is the measure of similarity used for the corresponding models. through these, the recognition process improves with the daugman method. the wvu database is used to perform experiments and promising results are obtained [63]. hajari et al. (2016) showed that iris recognition is a difficult problem in a noisy environment. their main objective is to develop a reliable iris recognition system that can operate in a noisy image environment and increase the rate of iris recognition in the casia and mmu iris datasets. they proposed two algorithms: first, a new method to eliminate the noise of the iris image and, second, a method to extract the characteristics of the texture through a combined approach of the local binary model and the gray level cooccurrence matrix. the proposed approach provided the highest recognition rate of 96.5% and low error rate and required less uptime [64]. soliman et al. (2017) introduced a rough algorithm to solve the computational cost problem while achieving an acceptable precision. the gray image of the iris is transformed into a binary image using an adaptive threshold obtained from the analysis of the intensity histogram of the image. the morphological treatment is used to extract an initial central point, which is considered the initial center of the iris and pupil boundaries. finally, a refinement step is performed using an integrodifferential operator to obtain the centers and the final rays of the iris and the pupil. this system is robust against occlusions and intensity variations [65]. naseem et al. (2017) proposed an algorithm to compare the vanguard spatial representation classification with bayesian fusion for several sectors. the proposed approach has shown that it overall performs the implemented algorithm in standard databases. the complexity analysis of the proposed algorithm shows a decisive superiority of the proposed approach. in this research, the concept of class-specific dictionaries for iris recognition is proposed. essentially, the query image is represented as a linear combination of learning images of each class. the well-conditioned inverse problem is solved using the least squares regression and the decision is judged in favor of the class with the most accurate estimate [66]. llano et al. (2018) presented a robust and optimized multisensor scheme with a strategy that combines the evaluation of video frame quality with robust segmentation fusion methods for image recognition and simultaneous image iris recognition. as part of the proposed scheme, they presented a fusion method based on the modified laplacian pyramid in the segmentation stage. the experimental results in the casia-v3-interval, casia-v4-mile, ubiris-v1, and mbgc-v2 databases show that the robust optimized scheme increases recognition accuracy and is robust for different types of iris sensors [67]. zhang et al. (2018) implemented a generalized stimulation framework to solve some problems of practical recognition of the iris at a distance, namely, the detection of the iris, the detection of the poor location of the iris, the detection of iris, and iris recognition. this solution takes advantage of a set muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 27 of carefully designed features and well-adjusted stimulation algorithms. basically, there are two main contributions. the first is an exploration of the intrinsic properties of remote iris recognition, as well as robust features carefully designed for specific problems. the second important contribution is the methodology on how to adapt adaboost’s learning to specific problems [68]. 4. research methodology 4.1. iris image dataset the construction of iris image dataset is a difficult job due to many reasons such as distance, lighting, and the resolution of the used device. this research needs to collect iris images of real patients those have some diseases on their iris. the captured iris images are of 8-bit gray images with a resolution of 480*640. in general, the iris is approximately form a circular shape. the diameter of the iris in the captured image in this dataset is about 200 pixels. twenty eye images of 10 patients infected with anterior uveitis are applied in this research. 4.2. implemented system during a brief reviewing in this field, you can find many systems and algorithms are implemented for biometric recognition including iris recognition. the proposed implemented approach for iris recognition contains the following components (fig. 2): • eye image acquisition: in this step, the eye object is captured using sensitive device to convert the real iris object into digital image contains of number of effective pixels. • eye image preprocessing: in this step, the acquired digital image is converted into standard image that can be adapted for the next step of processing. this step passed into many processes such as converting the image into gray scale image, image filtering, and image resizing. • iris image localization: in this step, the diseased eye image is enhanced to track the iris region to detect and localize the iris region. • iris image normalization: in this step, the iris image is normalized and then converted into gray scale to generate a standard iris image to be adequate for the next step for processing. • feature extraction: this step deals with the generation of features or characteristics related to the indicated iris image. feature is extracted using two-dimensional discrete wavelet transform (2d dwt). 2d dwt is performed through passing low-pass filter and high-pass filter for both rows and columns of the image as shown in the following two equations: x x k g n klow k n n = [ ] − =− ∑ � [ ]2 (1) x x k h n khigh k n n = [ ] − =− ∑ � [ ]2 (2) where, x represents the input array and both g and h represent low-pass and high-pass filters, respectively. • template matching: in this step, the template matching is generated that can be used to decide the personal authentication based on the selected threshold. 4.3. detection of diseased eye there are many differences between diseased eye and normal eye as shown in fig. 3. one important issue is to identify the diseased eye, in which the pupil of the diseased eye with anterior uveitis is not circular and may cause changes in iris architecture or atrophy. in this study, two factors are considered to separate between diseased and normal eyes: • to localize the pupil boundary or iris boundary as a circle, its radius must fall within the specific range. in the specified database, the range of iris radius value is within 90–150 pixels, while the pupil radius ranges are within 28–75 pixels. • the pupil is always within the iris region; hence, the pupil boundary must be within the iris boundary for normal eye image acquisition eye image preprocessing iris image localization iris image normalization feature extraction template matching fig. 2. implemented approach of iris recognition. muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach 28 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 eyes, while in the diseased eyes, the pupil boundary is localized away from iris region. this gives an evidence that the eye is diseased and an enhancement must be introduced before iris localization step. 4.4. enhancement of iris image when decision is taken that the eye is infected or diseased, then the procedure goes directly to the enhancement process. the enhancement process helps to localize the pupil boundary. the enhancement process is implemented through the following steps: 1. determine the upper boundary of iris, which leads to cover the area within the pupil (fig. 4). at this process, three parameters are stored: the upper radius (rupper), xupper boundary of the iris (xupper), and yupper boundary of the iris (yupper). 2. resizing the eye image to isolate the iris image as shown in fig. 5. the pupil boundary will be localized within the iris region instead of the whole eye region. hence, the new iris image will be determined as below: p 1 = (xupper−rupper, yupper−rupper) (3) p 2 = (xupper+rupper, yupper+rupper) (4) 3. adjusting the intensity of iris image according to the incident light as shown in fig. 6. 4. adjusting the threshold value to create the binary image as shown in fig. 7. 5. discriminate the irregular pupil by determining the minimum and maximum points on each axis as shown in fig. 8. four points must be calculated in this step: px min refers to the minimum point on x-axis found in pupil pixels (5) py min refers to the minimum point on y-axis found in pupil pixels (6) fig. 3. diseased and normal eyes. fig. 4. iris boundary localization. fig. 5. iris resizing. fig. 6. adjusting iris image. muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 29 px max refers to the maximum point on x-axis found in pupil pixels (7) py max refers to the maximum point on y-axis found in pupil pixels (8) 6. round the irregular pupil area by a rectangular shape as shown in fig. 9. this rectangular shape contains all the pixels of the irregular pupil. two points must be calculated in this step: p 1 min = (xpx min, ypy min) (9) p 1 max = (xpx max, ypy max) (10) 7. calculate the center of the rectangular according to the previous step: pcenter = (xcenter, ycenter) (11) 8. draw a circle around the pupil to complete the circular form of the pupil as shown in fig. 10. 9. update the iris image to the same position on the original image. 10. compare the processed image with the images stored in the database to identify the person. 5. results and discussion hamming distance measures the fraction of disagreeing bits resulting from bit-by-bit comparison of the two regions of interest. the obtained result indicated that the criterion is chosen to be 0.40, which means that a matching decision is never declared between two iris codes if it is exceed 40% of the disagreed bits. fig. 11 illustrates that the change in hamming distance before and after applying the proposed approach; in addition, it is clear that applying this approach causes significant decreasing in the hamming distance value. fig. 12 illustrates a comparison between the hamming distance of the diseased eye images before and after treatment fig. 8. determine rectangular dimensions. fig. 9. round the irregular pupil area by a rectangular shape. fig. 10. drawing a circle around the pupil. fig. 7. thresholding of iris image. muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach 30 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 and the hamming distance for the images of the same diseased eye after treatment. this figure indicated that the hamming distance values for the two diseased eye images after treatment are less than the hamming distance values for the images of the diseased eye before and after treatment. these results are caused by the pupil of the treated eyes seem normal unlike diseased eyes whose pupil becomes a little larger after applying the proposed enhancement method. fig. 13 indicated the differences in pupil radius values between the images of the diseased eyes after applying the enhancement and the images of the same diseased eyes after receiving the processed image. according to pupil distortion in the diseased eyes, the size of the pupil will enlarge affecting the size of iris region which should be considered when calculating hamming distance in iris recognition algorithm; this caused increasing the hamming distance values. the implemented approach is evaluated through performance evaluation in terms of false rejection rate (frr) and false acceptance rate (far). far and frr are shown in fig. 14 based on the hamming distance. perfect recognition is not possible due to the overlapping distributions. an accurate recognition rate is achieved through threshold of 0.40, in which a false accept rate and false reject rate of 0.000% and 0.100%, respectively, are obtained. 6. conclusions iris recognition is an effective method for biometric human identification. the implemented iris recognition approach is passed into many steps to achieve good system performance. this research studied the effects of infected eye on the recognition process through introducing different types of eye images. in addition, treating and enhancing processes are inserted in the overall approach to prepare an adequate iris image for processing. the obtained results indicated that the recognition performance of the implemented approach is 90%. the experimental results show that the proposed method is an effective approach in iris recognition. fig. 11. change in hamming distance before and after applying the proposed approach. fig. 12. the hamming distance outcomes of the diseased eye images before and after treatment and the hamming distance outcomes for the images of the same diseased eye after treatment. fig. 13. pupil radius values of diseased and treated eyes. fig. 14. performance evaluation in terms of false rejection rate and false acceptance rate. muzhir shaban al-ani and salwa mohammed nejrs: iris localization approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 31 references [1] m. s. al-ani. biometric security, source title: handbook of research on threat detection and countermeasures in network security. igi global, pennsylvania (usa), 2015. 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[68] m. zhang, z. he, h. zhang, t. tan and z. sun. towards practical remote iris recognition: a boosting based framework. neurocomputing, vol. 330, in press, 2018. tx_1~abs:at/tx_2:abs~at 82 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 1. introduction diabetes is a major public health issue, projected to be the seventh leading cause of death by 2030 [3]. patients with t2dm patients with suboptimal glycemic control and hba1c levels are more likely to develop microvascular problems and cardiovascular disease [1,13]. hba1c levels have been shown to be affected by modifiable psychosocial variables such self-care habits and attitude [2,5]. without good self-care practices, it might be difficult to keep hba1c levels in check [3,6] frequency, population distribution. the authors express concern that diabetes might develop into a regional public health problem and suggest measures to combat the disease [4,5]. developing healthy self-care habits is essential for managing hba1c levels, which can increase without proper exploring the relationship between attitudes and blood glucose control among patients with type 2 diabetes mellitus in chamchamal town, kurdistan, iraq hawar mardan mohammed1*, samir yonis lafi1 1department of nursing, university of raparin, kurdistan regional government, iraq, 2department of nursing, college of nursing, university of human development, kurdistan regional government, iraq a b s t r a c t background: diabetes mellitus type 2 is an endocrine disorder characterized by a progressive elevation in blood glucose levels. it is a persistent and incapacitating illness that may result in mortality if not properly managed. objectives: the objective of this research is to explore the relationship between the attitudes of individuals with type 2 diabetes mellitus and their ability to regulate blood glucose levels. in particular, the study aims to investigate the potential correlation between participants’ attitudes and their capacity to manage blood glucose levels following their participation in an educational program. moreover, the research seeks to analyze the association between individuals’ attitudes and diabetes control. ultimately, the study intends to evaluate the levels of participants’ attitudes through appropriate measures. materials and methods: the study is designed as a cross-sectional investigation and utilizes data from a diabetic outpatient center in chamchamal. the study population consists of outpatients from the evening public clinic and chronic disease control center. participants are required to complete questionnaires on their diabetes attitude. the study was conducted between august 11, 2019, and january 5, 2022. to explore the efficacy of the attitude with diabetes control, we used a correlation coefficient test and a t-test with p-value of 0.05 as our alpha level of significance. results and conclusion: the study found that the majority of patients with type 2 diabetes mellitus had low levels of educational attainment, were married and had insufficient monthly income. in addition, 85% of the patients reported not smoking, and 48.3% were classified as overweight. these findings highlight the need for health-care providers to consider sociodemographic factors in the management of diabetes mellitus. index terms: attitude, type 2 diabetes mellitus, blood glucose control, diabetic complications, self-care management corresponding author’s e-mail: hawar mardan mohammed, department of nursing, university of raparin, kurdistan regional government, iraq. e-mail: hawar.mardan84@gmail.com received: 28-11-2022 accepted: 11-03-2023 published: 12-04-2023 access this article online doi: 10.21928/uhdjst.v7n1y2023.pp82-91 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 mohammed and lafi. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l r e s e a r c h a r t i c l e uhd journal of science and technology mohammed and lafi: attitude and glucose control uhd journal of science and technology | jan 2023 | vol 7 | issue 1 83 self-care [6]. attitude is the degree to which a person believes he or she is capable of doing a job, and attitudes precede actions [7,8]. the amount of self-assurance that individual possesses regarding their capacity to carry out a task [15]. is referred to as their attitude, and it is normal for an individual’s attitudes to come before their actions. patients with diabetes need to make lifestyle changes to manage their blood glucose levels [9]. this study aims to investigate the potential correlation between attitudes of individuals with type 2 diabetes mellitus and their ability to regulate blood glucose levels [10]. patients with diabetes can also improve their health and prevent further complications by losing weight and lowering their body mass index (bmi) [11]. diabetes attitude is a patient’s attitude toward managing the disease, controlling blood sugar, reducing complications, and preventing short-term problems [12]. effective patient attitude management strategies can reduce the risk of chronic complications and prevent acute complications in type 2 diabetes [10]. individuals who maintain optimal glycemic control are at a reduced risk of developing microvascular complications, such as those that affect the kidneys, nerves, and eyes. these complications can manifest in the form of cataracts, glaucoma, renal failure, and lower limb amputations. conversely, when blood glucose levels are maintained at appropriate levels, macrovascular complications, including heart attacks and strokes, appear to be averted [14]. this study aims to investigate the relationship between attitude and ability to manage blood glucose. 2. methodology 2.1. study design sixty patients were studied in this cross-sectional study from the diabetes and chronic disease control center in the chamchamal district of sulaimaniyah, iraq, between july 7, 2020, and november 7, 2020. 2.2. sample size raosoft’s sample size calculator was used to determine the appropriate sample size. only 60 patients out of a possible 2000 at the diabetes and chronic disease control center were included in this research. 2.3. inclusion criteria the research study exclusively included adult patients who had been diagnosed with type 2 diabetes and met the rigorous eligibility criteria set forth by the trial. to be included in the study, participants were required to provide informed consent and meet all the necessary prerequisites for research participation. the eligible individuals who met the inclusion criteria are described in detail below. 2.4. exclusion criteria patients with t1dm, pregnant women with t2dm, liver failure, impairments or special requirements, and gestational diabetes were excluded from the study. 2.5. ethical approval the university approved the moral viewpoints expressed by the ethics committee of the college of nursing at raparin. in addition, participants were informed of the purpose and nature of the research. 2.6. patient informed consent before data were collected, participants were asked to sign informed consent forms and give their verbal and written informed consent in kurdish. they were also what might come out of the study. furthermore, a lot of thought goes into patients’ rights, privacy, and the safety of their information. 2.7. questionnaire a questionnaire to evaluate a patient’s attitude and behavior was designed and composed of 3 parts that covered sociodemographic factors, clinical parameters, and attitude behaviors evaluation. each section uses a likert scale to rate the respondent’s degree of agreement with each statement. the participants’ total replies were computed on a scale from 1 to 30, with always = 1, sometimes = 2, and never = 3. then, the attitude score was determined for each participant based on their responses to sets of 30 questions. the likert questionnaire had a reliability of 0.92 based on the results of the cronbach’s alpha test; then the items were presented to all patients in the same order. after taking the patient’s height and weight, the bmi (bmi; kg/m2) was determined. bmi stands for body mass index, and it is a measure of a person’s body fat based on their height and weight. it is calculated by dividing a person’s weight in kilograms by their height in meters squared (kg/m²). bmi is a commonly used metric for determining whether an individual’s weight is within a healthy range or if they are overweight or obese. it is often used in both clinical and research settings as a quick and easy screening tool for assessing a person’s weight status and associated health risks. however, it should be noted that bmi is not a perfect measure and has certain limitations, such as not taking into account body composition or distribution of body fat, a researcher used a targeted sampling technique to obtain data. mohammed and lafi: attitude and glucose control 84 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 2.8. measure of the clinical parameter the bmi was classified according to the who criteria in which <18.5 kg/m2 = underweight, 18.5–24.9 kg/m2 = normal weight, 25.0–29.9 kg/m2 = pre-obesity, 30.0–34.9 kg/m2 = obesity class i, 35.0–39.9 kg/m2 = obesity class ii, and <40 kg/m2 = obesity class iii world health organization (2021). 2.9. statistical analysis spss version 25 was utilized for conducting data analysis. 3. results 3 . 1 . p a r t i c i p a n t s ’ d e m o g r a p h i c a n d c l i n i c a l characteristics patients in this research had a mean age of 58.07 ± 0.309 years, a median age of 57.5 years, and an age range of 39–81 years. regarding educational attainment, the majority of patients (55%) were illiterate, followed by elementary school graduates (28.3%), and only 1.7% were college graduates or postgraduates. in contrast, 90% of patients were married, compared to 1.7% who were single or separated (not living together). regarding the patients’ employment, the majority (40%) were housewives, whereas the minority (10%) were retirees. the majority of patients have insufficient monthly income (65%), reside in urban areas (73.3%), do not smoke (85%), and are overweight (48.3%). the majority of patients had t2dm for 10 years and took antihyperglycemic therapy orally (98.3%) (table 1). 3.2. changes in attitudes and practices before and after the intervention table 2 shows the terms of some of the differences between the preand post-test attitudes toward controlling disease, the distribution of the mean scores of the preand post-test attitudes and practices toward the daily care of patients, and the associated constructs. the table also shows the attitudes and actions that have the most to do with stopping diseases. for example, the highest mean score for the total number of possible points in the pre-attitude group was 2.98 (i eat or drink regularly every day), while the lowest was 1.3 (i try to learn how to control my diabetes by going to different diabetes education programs) (table 2a). the highest mean score for the total number of possible points in the postattitude group was also 2.98. the point with the lowest mean score was 1.77, which said that herbal medicines have fewer side effects than medical ones (table 2b). 3.3. correlation between attitudes and sociodemographic characteristics table 3 presents a correlation matrix that facilitates the examination of the relationship between attitudes (before table 1: the t2dm patients’ (no.=60) sociodemographic and clinical information variable frequency percent level of education illiterate 33 55.0 primary school graduate 17 28.3 secondary school graduate 7 11.7 institute graduate 2 3.3 collage and post graduate 1 1.7 marital status single 1 1.7 married 54 90.0 widow 2 3.3 divorced 2 3.3 separated (not living together) 1 1.7 occupation government employed 9 15.0 self employed 12 20.0 retired 6 10.0 house wife 24 40.0 jobless 9 15.0 monthly income sufficient 4 6.7 barely sufficient 17 28.3 insufficient 39 65.0 residential area urban 44 73.3 rural 16 26.7 duration of diabetes mellitus ≤10 years 45 75.0 ≤20 years 12 20.0 >20 years 3 5.0 treatment method oral antihyperglycemic agents 59 98.3 insulin 1 1.7 do you smoke? yes 9 15.0 no 51 85.0 how many cigarettes per day? 11–20 1 10 21–30 9 90 body mass index underweight 1 1.7 normal weight 10 16.7 over weight 29 48.3 obesity ι 14 23.3 obesity ii 5 8.3 obesity iii 1 1.7 for how many years have you smoked? 10 2 15.38 15 5 38.46 20 2 15.38 25 3 23.08 40 1 7.69 source of information about disease physician 40 66.7 nurse 13 21.7 books and magazines 1 1.7 television 6 10.0 mohammed and lafi: attitude and glucose control uhd journal of science and technology | jan 2023 | vol 7 | issue 1 85 table 2a: the participants pre‑attitude behaviors evaluation variable always=3 sometime=2 never=1 mean score rank % i visit hospital regularly according to doctor’s appointment for examination or treatment of diabetes. 26 25 9 2.28 2 64 i take meals or refreshment regularly every day. 14 40 6 2.13 8 56.5 i eat as well-balance diet using a list of food exchanges 15 42 3 2.2 5 60 i take foods containing dietary fiber like grain, vegetable and fruit every day. 14 41 5 2.15 7 57.5 i set a limit of taking salt and processed foods. 29 20 11 2.3 1 65 i do a self-blood sugar test according doctors’ recommendations. 14 30 16 1.97 10 48.5 i do a self-blood sugar test more frequently, when i feel symptoms of hypoglycemia such as tremor, pallor, and headache. 14 24 22 1.87 12 43.5 i try to maintain the optimal blood sugar level. 9 34 17 1.87 13 43.5 i control the size of meals or exercise according to a blood sugar level. 6 32 22 1.73 17 36.5 i am carrying food likes sweet drink, candy or chocolate just in case of hypoglycemia. 3 20 37 1.43 25 21.5 i try to maintain optimal weight by measuring my weight regularly. 5 32 23 1.7 18 35 i carry insulin, injection and blood sugar tester whenever i go to trip. 5 10 45 1.33 27 16.5 i try to get information on diabetes control by attending various diabetes educational programs. 4 10 46 1.3 30 15 i take my diabetes medication like insulin injection as prescribe observing dosage and time regularly. 6 14 40 1.43 26 21.5 i keep in touch with my physician. 16 41 3 2.22 4 61 herbal medications have less complications than medical medications 3 27 30 1.55 20 27.5 regular exercise helps me to control diabetes. 5 23 32 1.55 21 27.5 reading handouts on proper footwear is necessary for me. 3 13 44 1.32 29 16 blood pressure control helps me to control my diabetes mellitus. 8 38 14 1.9 11 45 annual eyes examination is necessary for me. 9 34 17 1.87 14 43.5 always i be relaxing and avoid stress and bad mood because its effects diabetes negatively. 10 43 7 2.05 9 52.5 i did not miss doses of my diabetic medication. 20 31 9 2.18 6 59 i inspect my feet during and after my shower/bath. 14 21 25 1.82 15 41 i use talcum powder to keep my inter-digital spaces dry. 6 20 34 1.53 22 26.5 i check the temperature of water before use. 2 16 42 1.33 28 16.5 i examine my feet daily. 6 17 37 1.48 23 24 i used to check my blood glucose level. 5 27 28 1.62 19 31 i used to check fasting blood glucose and 2 h after meal by glucometer. 6 37 17 1.82 16 41 i take my medication according of physician recommendation. 24 27 9 2.25 3 62.5 i did not wear tide shoes. 8 13 39 1.48 24 24 and after) and sociodemographic characteristics. the matrix displays only those variables that exhibit a statistically significant correlation (p < 0.05) as determined by pearson’s r, with regard to satisfaction levels of the simulation experience. the matrix allows for an assessment of the degree of association between sociodemographic factors and participant satisfaction with the simulation, providing valuable insights into the factors that may impact user experience. the mann–whitney u-test is used in. 3.4. gender differences in attitudes before and after the intervention table 4 to compare the means of attitudes (before and after) by gender. before the test, the mean attitude score for males mohammed and lafi: attitude and glucose control 86 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 table 2b: the participant’s post‑attitude behaviors evaluation variable always=3 sometime=2 never=1 mean score rank % i take foods containing dietary fiber like grain, vegetable and fruit every day. 58 2 0 2.97 4 98.5 i set a limit of taking salt and processed foods. 59 1 0 2.98 2 99 i do a self-blood sugar test according doctors’ recommendations. 55 5 0 2.92 9 96 i do a self-blood sugar test more frequently, when i feel symptoms of hypoglycemia like tremor, pallor and headache. 47 13 0 2.78 13 89 i try to maintain the optimal blood sugar level. 41 19 0 2.68 15 84 i control the size of meals or exercise according to a blood sugar level. 37 23 0 2.62 21 81 i am carrying food likes sweet drink, candy or chocolate just in case of hypoglycemia. 10 46 4 2.1 29 55 i try to maintain optimal weight by measuring my weight regularly. 29 31 0 2.48 23 74 i carry insulin, injection and blood sugar tester whenever i go to trip. 14 43 3 2.18 28 59 i try to get information on diabetes control by attending various diabetes educational programs. 48 12 0 2.8 12 90 i take my diabetes medication like insulin injection as prescribe observing dosage and time regularly. 43 15 2 2.68 16 84 i keep in touch with my physician. 56 4 0 2.93 8 96.5 herbal medications have less complications than medical medications. 12 22 26 1.77 30 38.5 regular exercise helps me to control diabetes. 47 11 2 2.75 14 87.5 reading handouts on proper footwear is necessary for me. 41 18 1 2.67 18 83.5 blood pressure control helps me to control my diabetes mellitus. 55 5 0 2.92 10 96 annual eyes examination is necessary for me. 58 2 0 2.97 5 98.5 always i be relaxing and avoid stress and bad mood because its effects diabetes negatively. 36 24 0 2.6 22 80 i did not miss doses of my diabetic medication. 38 22 0 2.63 19 81.5 i inspect my feet during and after my shower/bath. 28 32 0 2.47 24 73.5 i use talcum powder to keep my inter-digital spaces dry. 17 43 0 2.28 27 64 i check the temperature of water before use. 28 32 0 2.47 25 73.5 i examine my feet daily. 19 40 1 2.3 26 65 i used to check my blood glucose level. 41 19 0 2.68 17 84 i used to check fasting blood glucose and 2 h after meal by glucometer. 38 22 0 2.63 20 81.5 i take my medication according of physician recommendation. 52 8 0 2.87 11 93.5 i did not wear tide shoes. 58 2 0 2.97 6 98.5 fig. 1. compare means of attitude (pre and post) by level of education using kruskal–wallis h-test. was 2.219 and for females it was 2.201. after the test, the mean attitude score for males was 1.353 and for females it was 1.308. neither score changed significantly from the pre-test. the results of the post-test showed that there wasn’t a big difference between men and women using the kruskal–wallis h-tes. 3.5. impact of education level on attitudes before and after the intervention fig. 1 shows the study compared the mean attitudes of participants before and after the intervention with respect to their level of education. the results indicated that there was no statistically significant difference between the mean pre-test and post-test attitudes of the participants. mohammed and lafi: attitude and glucose control uhd journal of science and technology | jan 2023 | vol 7 | issue 1 87 table 3: correlation matrix of attitude (pre and post) with the socio‑demographic data variable no. pre-attitude post-attitude p-value spearman rank correlation no. p-value spearman rank correlation age 60 0.003 0.378** 60 0.716 −0.048 level of education 60 0.039 −0.268* 61 0.598 0.069 family member has diabetes mellitus 60 0.598 −0.069 62 0.009 0.334** monthly income 60 0.002 0.384** 63 0.753 0.041 duration of diabetes mellitus 60 0.712 0.049 64 0.795 0.034 body mass index (bmi) 60 0.587 −0.072 65 0.739 −0.044 how many cigars per day? 10 0.415 0.291 66 0.107 0.541 for how many years have you smoked? 13 0.601 0.16 67 0.424 0.243 how long ago did you quit smoking? 5 0.581 0.335 68 0.581 0.335 *significant at 0.001 level, **significant at 0.05 level. table 4: compare means of attitude (pre and post) by gender using mann‑whitney u‑test attitude gender no. mean sd mann-whitney u p-value mean pre male 34 2.219 0.356 424.5 0.794 female 26 2.201 0.357 mean post male 34 1.353 0.143 370.0 0.280 female 26 1.308 0.112 these findings sug gest that the inter vention did not have a significant impact on the attitudes of participants toward the research topic, regardless of their level of education. 3.6. impact of income level on attitudes before and after the intervention fig. 2 contrasts the perspectives of patients who participated in the pattern-making training before and after which training. it indicates that each group’s post-test results for each attitude component differ significantly. 3.7. statistical analysis of income levels before and after intervention in fig. 3, to assess whether there was a statistically significant difference in the mean rank of income levels, the kruskal– wallis test was employed. the results indicated a highly significant difference between the pre-test and post-test means for each income level, with a p < 0.05. specifically, the pre-test mean values ranged from 2.305 to 1.63, while the post-test means ranged from 1.339 to 0.316. the highest pre-test mean was observed as 2.305, while the lowest was 1.63. these findings suggest that the intervention had a fig. 2. compare means of attitude (pre and post) by occupation using kruskal–wallis h-test. fig. 3. compare means of attitude (pre and post) by monthly income using kruskal–wallis h-test. mohammed and lafi: attitude and glucose control 88 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 significant impact on the attitudes of participants towards the research topic across all income levels, and support the need for continued efforts to improve attitudes and perceptions. however, to increase the p-value further, it may be necessary to adjust the alpha level or consider a larger sample size. 3.8. impact of residential area on attitudes before and after the intervention in fig. 4, mann–whitney test was conducted to compare the preand post-test attitude averages of participants by their residential area. the analysis revealed no significant differences between the preand post-test attitudes. the mean attitude score before the intervention was 0.451, while the mean score after the intervention was 0.608. 3.9. relationship between source of knowledge and attitudes before and after the intervention fig. 5 shows the analysis shows that there is no correlation between the mean attitudes (preand post-intervention) and the source of knowledge about the illness. the mean score for the pre-test was 0.529, and for the posttest, it was 0.704, with p = 0.05. the average attitude score before the intervention was 0.529, while after the intervention, it was 0.704. the findings indicate that there is no significant relationship between the attitudes of participants before and after receiving information about the disease (p > 0.05). 4. discussion the median age of t2dm patients in this research was 57.50 years. consequently, the research population consisted of adults and the elderly. after 50 years of age, the prevalence of (dm) grows progressively, according to the findings of various research conducted in various nations [20]. the majority of our participants were also male. however, national and regional investigations have revealed no substantial gender disparity in the frequency of dm [21]. therefore, the fact that the majority of our patients were males might be attributed to the fact that men had easier access to hospitals and clinics and more flexible work hours than women. before the initiation of the education program, the mean attitude score was found to be similar between the case and control groups. however, after the program was implemented, significant differences were observed between the two groups on multiple attitude-related questions. these findings are consistent with earlier research that employed alternative intervention methodologies, as reported by [23]. in addition, substantial patients completed diabetes selfmanagement education in the current research (dsme). consequently, they were more likely to comply with the recommended diabetic care standards and their pharmaceutical treatment regimens. this result is consistent with earlier research demonstrating that the hba1c levels of patients fell considerably following diabetes education program treatments [16]. in addition, another study comparing the opinions of 252 health professionals and 279 individuals with diabetes revealed major disparities in their perspectives. both groups agreed on the severity of t2dm, the necessity for strict glycemic control, and the psychosocial effect of the condition, but they disagreed on the importance of patient autonomy. this study found no significant differences in the severity of the illness between t1dm and t2dm individuals. in addition, people with diabetes who had previously received diabetes education had elevated rates of disease [17]. fig. 5. compare means of attitude (pre and post) by source of your information about disease using kruskal–wallis h-test. fig. 4. compare means of attitude (pre and post) by residential area using kruskal-wallis h-test. mohammed and lafi: attitude and glucose control uhd journal of science and technology | jan 2023 | vol 7 | issue 1 89 there is no significant correlation between gender attitude and program intervention, according to the current study. in contrast, the majority of married couples displayed a level of self-care that fell somewhere in the center. according to iranian study, married participants in diabetes selfmanagement programs had a more optimistic outlook than their single counterparts [18]. for instance, a separate study found that t2dm patients with stronger marital connections and mutual support have better self-care attitudes and self-management [19]. in addition, there was a substantial correlation between self-care and social support in iran cross-sectional research [23]. we also demonstrated a substantial relationship between education level and attitude. patients with higher levels of education, for example, demonstrated more positive attitudes when engaging in diabetic self-management programs. this was especially the case when the programs were implemented. moreover, illiterates were shown to have a much poorer level of self-care. in addition, it was shown that those with greater levels of education engage in more beneficial kinds of self-care than those with lower levels of education. one study including 125 individuals of diverse racial origins with t2dm found a substantial favorable connection between education and diabetes management [20]. at the outset of the study, the diabetes education program (dep) evaluated the perspectives of patients with type 2 diabetes on various aspects of the disease, including its physiopathological and nutritional components, treatments, physical activity, patient education, self-monitoring, chronic complications, special situations, and family support. the initial phase of the dep’s development involved assessing the patients’ attitude needs toward their illness, followed by an evaluation of their attitudes following the program’s implementation. this approach is consistent with the previous studies that have recommended preand post-intervention data collection to accurately evaluate the effectiveness of diabetes education programs [24]. the study results suggest that there was a significant change in diabetic patients’ perceptions of their illness after participating in the investigation. however, it is difficult to make a conclusive statement about the direct impact of this newfound knowledge on the patients’ behavior and lifestyle. while the study revealed that the dep had a positive effect on the patients’ attitudes and behavioral abilities, it was found that the improvements in diet-related attitudes were less significant than those observed for general diabetes knowledge. these findings provide concrete support for the notion that patient education programs can have a positive impact on patients’ perceptions of their illness and their ability to manage it effectively. however, further research is needed to determine the specific factors that contribute to behavior change in diabetic patients. changing the attitudes of diabetes patients is impacted by a number of factors, including their knowledge of their illness, risk factors, and treatment alternatives. the study investigated the efficiency of group education and determined that it successfully improved and altered attitudes toward selfmonitoring of capillary glucose. this was discovered by comparing the attitudes of participants prior to and following the instructional program [25]. this study found a significant association between patient attitude and glycemic control, with patients with more optimistic attitudes exhibiting better glycemic control. this result was supported by a substantial body of literature and contemporary research conducted in several cultural settings [26]. consequently, according to the american diabetes association (ada), individuals diagnosed with type 2 diabetes mellitus (t2dm) should possess a positive attitude towards their condition to effectively manage the illness and mitigate potential complications. inadequate glycemic control was associated with a low self-care score, whereas better disease management was associated with a higher self-care score [28]. in the present study, health literacy is shown to significantly influence glycemic control, while higher education levels are associated with favorable health behaviors in patients. literature supports the notion that health education and literacy can considerably influence illness outcomes, disease management, and prevention of complications. moreover, patients with higher education levels exhibit more optimistic attitudes compared to those with the lower education levels [27]. moreover, existing research has established a correlation between health literacy and the mitigation of diabetes complications through the adoption of a positive mindset (mukanoheli et al., 2020). furthermore, we observed a notable enhancement in the educational intervention concerning the identification of hypoglycemic symptoms, as inpatient care has been shown to yield better results, a finding that was reinforced during the program’s implementation. mohammed and lafi: attitude and glucose control 90 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 5. conclusion the willingness of patients with type 2 diabetes to adopt a positive attitude and participate in positive behaviors is a significant factor in the effective control of their blood glucose levels in patients with type 2 diabetes. in addition to receiving medical treatment, patients have the additional responsibility of prioritizing healthy daily routines and habits. these may include monitoring their blood glucose levels, making alterations to their food, engaging in physical activity, and caring for their feet. these healthy practices have the potential to have a major influence on the patients’ general health and to enhance their capacity to keep their blood glucose levels under control. 6. acknowledgments the scientific working group of the university of raparin deserves special gratitude for their assistance. the participants and employees at the center for diabetes and chronic disease control in the chamchamal district of sulaimaniyah, iraq, also deserve our sincere gratitude. 7. conflict of interest there is no conflict 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[28] k. hawthorne, y. robles and r. cannings-john. “glycemic control and self-care behaviors in hispanic patients with type 2 diabetes: a pilot intervention study”. journal of transcultural nursing, vol. 23, no. 3, pp. 289-296, 2012. tx_1~abs:at/tx_2:abs~at 22 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 1. introduction patients suffering from heart diseases need continuous healthcare especially for ecg monitoring and recognition to avoid dangerous of heart failure [1]. the reduction of heart attack depends on the fast identification of abnormal cardiac rhythms [2]. ecg is an effective diagnostic technique which is widely used by cardiologists [3]. ecg are electrical signals of the heart recorded by electrodes fixed on patient body [4]. ecg signals provide useful information about the rhythm and the operation of the heart. heart beats extracted from ecg signals can be categorized into classes that are: normal, atrial premature, and ventricular escape beats [5]. electrocardiographs are recorded by electrocardiograms that are very important for healthcare diseases [6]. these devices record electrical signal picked up by electrodes attached to certain parts of the patient body [7]. the signals recorded by the electrocardiograms at any moment are the sum of the all signals passing in cells throughout the heart [8]. electrocardiogram consists of 12 leads which indicated 12 electrical views of the heart [9]. the first six leads represent the frontal plane leads; i, ii, iii, v r , v l and v f . leads i, ii, and iii are the standard leads and are find by [10]: i v vl r= − (1) ii v vf r� � (2) iii v vf l= − (3) the other six leads are in the front of the heart; v 1 , v 2 , v 3 , v 4 , v 5 , and v 6 , these are recorded by the six electrodes placed on the chest of the patient [11]. ecg signal recognition based on lookup table and neural networks muzhir shaban al-ani university of human development, college of science and technology, department of information technology, sulaymaniyah, krg, iraq a b s t r a c t electrocardiograph (ecg) signals are very important part in diagnosis healthcare the heart diseases. the implemented ecg signals recognition system consists hardware devices, software algorithm and network connection. an ecg is a non-invasive way to help diagnose many common heart problems. a health-care provider can use an ecg to recognize irregular heartbeats, blocked or narrowed arteries in the heart, whether you have ever had a heart attack, and the quality of certain heart disease treatments. the main part of the software algorithm including the recognition of ecg signals parameters such as p-qrst. since the voltages at which handheld ecg equipment operate are shrinking, signal processing has become an important challenge. the implemented ecg signal recognition approach based on both lookup table and neural networks techniques. in this approach, the extracted ecg features are compared with the stored features to recognize the heart diseases of the received ecg features. the introduction of neural network technology added new benefits to the system implementing the learning and training process. index terms: electrocardiograph signals, p-qrs, healthcare, heart diseases. corresponding author’s e-mail: muzhir.al-ani@uhd.edu.iq received: 22-10-2022 accepted: 08-01-2023 published: 21-01-2023 access this article online doi: 10.21928/uhdjst.v7n1y2023.pp22-31 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 muzhir shaban al-ani. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology al-ani: ecg signal recognition uhd journal of science and technology | jan 2023 | vol 7 | issue 1 23 electrocardiograms are concentrated on all issues associated with diseases of heart attack patients that used directly with clinical [12]. recently, a reliable and automatic analysis and segmentation of ecg signals are required for health-care environments [13]. computer based methods are suitable for processing and analyzing of ecg signals [14]. artificial neural network techniques are used for analyzing different types of signals and tasks related to heart diseases [15]. most of these tasks are associated to the detection of irregular heartbeats and irregular in recording process [16]. a back propagation neural network may apply in training stage to give a powerful pattern recognition algorithm [17]. electrocardiograph (ecg) signals are very important part in diagnosis healthcare the heart diseases. the implemented ecg signals recognition system consists hardware devices, software algorithm and network connection. an ecg is a non-invasive way to help diagnose many common heart problems. a health-care provider can use an ecg to recognize irregular heartbeats, blocked or narrowed arteries in the heart, whether you have ever had a heart attack, and the quality of certain heart disease treatments. the main part of the software algorithm including the recognition of ecg signals parameters such as p-qrst. since the voltages at which handheld ecg equipment operate are shrinking, signal processing has become an important challenge. the implemented ecg signal recognition approach based on both lookup table and neural networks techniques. in this approach, the extracted ecg features are compared with the stored features to recognize the heart diseases of the received ecg features. 2. ecg signals heart diseases are the well-known disease that affects humans worldwide [18]. yearly millions of people die or suffered from heart attacks [19]. early detection and treatment of heart diseases can prevent such events [20]. this would improve the quality of life and slow the events of heart failure [21]. the main benefit of the diagnosis is to record the ecg of the patient [22]. an ecg record is a non-invasive diagnostic tool used for the assessment of a patient heart condition [23]. the extraction of ecg features and combined that with the heart rate, these can lead to a fairly accurate and fast diagnosis [24]. bioelectrical signals represent human different organs electrical activities and ecg signals are the important signals among bioelectrical signals that represent heart electrical activity [25]. deviation or distortion in any part of ecg that is called arrhythmia can illustrate a specific heart disease [26]. the investigation of the ecg has been extensively used for diagnosing many cardiac diseases [27]. the ecg is a realistic record of the direction and magnitude of the electrical commotion that is generated by depolarization and repolarization of the atria and ventricles [28]. one cardiac cycle in an ecg signal consists of the p-qrs-t waves as shown in fig. 1 [29,30]. the majority of the clinically useful information in the ecg is originated in the intervals and amplitudes defined by its features (characteristic wave peaks and time durations) [31,32]. ecg is essentially responsible for patient monitoring and diagnosis [33]. normal rhythm produces four entities; p wave, qrs complex, t wave, and u wave in which each have a fairly unique pattern as shown in fig. 1 [34,35]: • p-wave represents the movement of an electric wave from the sino atrial (sa) node and causes depolarization of the left and right atria. • p-r segment represents the pause in electrical activity caused by a delay in conduction of electrical current in the atrioventricular (av) node to allow blood to flow from the atria to the ventricles before ventricular contraction happen. • qrs complex represents the electrical activity from the beginning of the q wave to the end of the s wave and the complete depolarization of the ventricles, resulting to ventricular contraction and ejection of blood into the aorta and pulmonary arteries. • s-t segment represents the pause in electrical activity after complete depolarization of the ventricles to allow blood to flow out of the ventricles before ventricular relaxation begins and the heart to fill the next contraction. • wave t represents the repolarization of the ventricles. fig. 1. representation of electrocardiograph signals. al-ani: ecg signal recognition 24 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 • wave u represents the repolarization of the papillary muscle. 3. literature reviews mohammed et al., presented an ecg compression algorithm based on the optimal selection of wavelet filters and threshold levels in different sub bands that allow a maximum reduction of the volume of data guaranteeing the quality of the reconstruction. the proposed algorithm begins by segmenting the ecg signal into frames; where each image is decomposed into sub bands m by optimized wavelet filters. the resulting wavelet coefficients are limited and those having absolute values below the thresholds specified in all sub bands are eliminated and the remaining coefficients are properly encoded with a modified version of the run coding scheme [36]. reza et al., proposed compressed detection procedure and the collaboration detection matrix approach that used to provide a robust ultra-light energy focus for normal and abnormal ecg signals. the simulation results based on two proposed algorithms illustrate a 15% increase in signal to noise ratio and a good quality level for the degree of inconsistency between random and scatter matrices. the results of the simulation also confirmed that the toeplitz binary matrix offered the best snr performance and compression with the highest energy efficiency for the random array detection [37]. ann and andrés implemented an approach to classify multivariate ecg signals as a function of analyzing discriminant and wavelets. they used variants of multiscale wavelets and wave correlations to distinguish multivariate ecg signal models based on the variability of the individual components of each ecg signal and the relationships between each pair of these components. using the results from other ecg classification studies in the literature as references that demonstrated this approach to 12-lead ecg signals from a particular database compares favorably [38]. vafaie, et al., presented a new classification method to classify ecg signals more precisely based on the dynamic model of the ecg signal. the proposed method is constructed a diffuse classifier and its simulation results indicate that this classifier can separate the ecg with an accuracy of 93.34%. to further improve the performance of this classifier, the genetic algorithm is applied when the accuracy of the prediction increases to 98.67%. this method increased the precision of the ecg classification for a more accurate detection of the arrhythmia [39]. kamal and nader, realized a practical means to synthesize and filter of ecg signal in the presence of four types of interference signals: first, from electrical networks with a fundamental frequency of 50 hz, second, those resulting from breathing, with a frequency range 0.05–0.5 hz, third musical signals with a frequency of 25 hz and fourth white noise presented in the ecg signal band. this was accomplished by implementing a multiband digital filter (seven bands) of the finite impulse response multiband least square type using a programmable digital apparatus, which was placed on an education and development board [40]. farideh et al., explored combined discriminative ability of ecg/r signals in automatic staging. basically, this approach classified that the wakefulness of slow wave sleep and rem sleep was classified using a vector support machine fed with a set of functions extracted from characteristics of 34 features and characteristics of 45 features. first part has produced a reasonable discriminatory capacity, while the second part has considerably improved the rating and the best results were obtained using third approach. we then improved the support vector machine classifier with the recursive feature elimination method. the results of the classification were improved with 35 of the 45 features [41]. shirin and behbood classified a patient’s ecg cardiac beats into five types of cardiac beats as recommended by aami using an artificial neural network. this approach used block based on the neural network as a classifier. this approach created from a set of two dimensional blocks that are connected to each other. the internal structure of each block depends on the number of incoming and outgoing signals. the overall construction of the network was determined by the movement of signals through the network blocks. the network structure and weights are optimized using the particle swarm optimization approach [42]. prakash and shashwati, proposed an approach that attempts to reduce unwanted signals using a minorization-maximization method to optimize total signal variation. the unsuccessful signal is then segmented using the bottom-up approach. the obtained results show a significant improvement in the signal-to-noise ratio and the successful segmentation of the ecg signal sections. the extension of the heel depends on the smoothing parameter of lamda. as this approach was implemented for complete signal, then only 18 db of signal to noise ratio was achieved [43]. aleksandar and marjan, focused on a new algorithm for the digital filtering of an electrocardiogram signal received by stationary and al-ani: ecg signal recognition uhd journal of science and technology | jan 2023 | vol 7 | issue 1 25 non-stationary sensors. the basic idea of digital processing of the electrocardiogram signal is to extract the heartbeat frequencies that are normal in the range between 50 and 200 beats/min. the frequency of the extracted heart rate is irregular if the rate increases or decreases and serves as evidence for the diagnosis of a complex physiological state. the environment can generate a lot of noise, including the supply of electrical energy, breathing, physical movements, and muscles [44]. kumar et al., proposed an automated diagnosis of coronary artery disease using electrocardiogram signals. flexible analytical wavelet transform technology is used to break down electrocardiogram effects. the cross information potential parameter is calculated from the actual values of the flexible analytical wavelet transform decomposition detail coefficients. for diagnosis of coronary artery disease subjects, the mean value of the cross information potential parameter is higher in the comparison toner subjects. the statistical test is applied to check the discrimination capacity of the extracted functionalities. in addition, the functionality is fed to the least squares support vector machine for sorting. the classification accuracy is calculated at each decomposition level from the first decomposition level [45]. al-ani, explained that ecg waveform is an important process for determining the function of the heart, so it is useful to know the types of heart disease. the ecg chart gives a lot of information that is converted into an electrical signal containing the basic values in terms of amplitude and duration. the main problem that arises in this measurement is the confusion between normal and abnormal layout, in addition to certain cases where the p-qrs-t waveform overlaps. the purpose of this research is to provide an effective approach to measure all parts of the p-qrs-t waveform to give the right decision for heart function. the proposed approach depends on the classifier operation that based mainly on the features extracted from electrocardiograph waveform that achieved from exact baseline detection [46]. nallikuzhy and dandapat, explored an efficient technique to improve a low resolution ecg by merging fragmented coding and the learning model of the common dictionary. an enhance model is applied on low resolution ecg using previously learned model in order to obtain a high resolution full estimate of 12-lead ecg. this approach was applied based on the dictionary in which the common dictionary contains high and low resolution dictionaries regarding to the high and low resolution ecg and is learned simultaneously. similar fragmented representation for high and low resolution ecgs was generated using joint dictionary learning. mapping between the scattered coefficients of the high and low resolution ecgs was also learned [47]. han and shi presented an efficient method of detection and localization of myocardial infarction that combines a multilead residual neural network structure (ml-resnet) with three residual blocks and a function fused by 12-lead ecg recordings. a single network of characteristic branches was formed to automatically learn representative characteristics of different levels between different layers, which exploit the local characteristics of the ecg to characterize the representation of spatial information. then, all the main features are merged as global features. to evaluate the generalization of the proposed method and clinical utility, two schemes are used that include the intra-patient scheme and the inter-patient scheme. the obtained results indicated a high performance of accuracy and sensitivity [48]. abdulla and al-ani, implemented a review study classification for ecg signal. this work aimed to investigate and review the use of classification methods that have been used recently, such as the artificial neural network, the convolutional neural network, discrete wavelets transform, support vector machine and k-nearest neighbor. effective comparisons are presented in the result in terms of classification methods, feature extraction technique, data set, contribution, and some other aspects. the result also shows that convolutional neural network has been used more widely for ecg classification as it can achieve higher accuracy compared to other approaches [49]. abdulla and al-ani, explained an automatic ecg classification system which is difficult to detect, especially in manual analysis. an accurate classification and monitoring ecg system was proposed using the implementation of convolutional neural networks and long-short term memory. learned features are captured from the cnn model and passed to the lstm model. the output of the cnn-lstm model demonstrated superior performance compared to several of the more advanced ones cited in the results section. the proposed models are evaluated on the mit-bih arrhythmia and ptb diagnostics datasets. a high accuracy rate of 98.66% in the classification of myocardial infarction was obtained [50]. 4. methodology the methodology of this approach is divided into three parts: ecg signals recognition approach, ecg feature extraction al-ani: ecg signal recognition 26 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 fig. 2. electrocardiograph recognition approach. fig. 3. electrocardiograph feature extraction. fig. 4. design of neural network architecture. and neural network architecture. in addition, the used data are images selected with different heart diseases. the main objective of introducing forward propagation neural networks in this work is to determine the main component values of the ecg signal, which are seven values (qrs complex, qt interval, qtcb wave, pr interval, p wave, rr interval and pp interval) and to compare that with the table that carries the standard values. depend on this comparison, it is possible to make an accurate decision about whether the ecg signal is normal or abnormal. 4.1. ecg signals recognition approach the ecg signals recognition approach is implemented through the following stages (fig. 2): • feature extraction stage in which ecg signals parameters (amplitude and time interval) will be extracted from the electrodes. • ecg recognition stage in which the extracted parameters of ecg are applied through neural network that specified the diseases associated with these parameters. • lookup table stage in which the constructed lookup table is associated with the list of specified heart diseases. • decision making stage in which take the decision of which type of heart diseases are related. 4.2. ecg feature extraction tracing of ecg signal on the special recognition is very important to extract the values of the direct parameters. the main advantage of ecg feature extraction operation is to generate a small set of features that achieve the ecg signal. ecg feature extraction operation is implemented through many steps as shown in fig. 3. the first step is preprocessing in which ecg graph will be cleaned and resized. the second step focusing on thinning filter in which the ecg signal will be better quality, in addition this step will eliminate the scattering pixels around the original signal. the third step concentrates on edge detection that detects the original ecg signal. in addition, this indicates the duration and amplitude of each ecg signal part. fourth step is to calculate the required parameters of ecg signal that related on duration and amplitude of each part of ecg signal. 4.3. neural network architecture proper neural network architecture is used to be efficient and work in a wide range of conditions. it is necessary to choose network parameters such that the obtained ecg system is acceptable for theoretical and practical settings. furthermore, this neural network is an application-oriented system and the design is done with the selection of the network architecture. in this case, many parameters are selected as below (fig. 4): • choice of initial weight and biases: the choice of initial weight will influence how quickly the system coverage? the values of the initial weights must not be too large or too small to avoid out of region condition. the weights and biases of ecg network in learning phase are initialized randomly between -0.5 and 0.5. • choice of activation function: the ecg used neural network of sigmoid function which has simple derivative and nonlinear property. the sigmoid range of output lies between zero and one. • choice of learning and momentum rate: for low learning rate, the neural network will adjust their weights gradually, but the convergence may be slow, while for high learning al-ani: ecg signal recognition uhd journal of science and technology | jan 2023 | vol 7 | issue 1 27 rate the neural network has big changes that are not desirable in a trained network. the network consists of 5 nodes in input layer, 80 nodes in hidden layer, and 4 nodes in output layer. • choice of the number of hidden nodes: the number of hidden nodes in the hidden layers is varied from 5 nodes to 125 nodes, while keeping the learning rate and momentum rate constant at nominal values (learning rate = 0.7 and momentum rate = 0.9). backpropagation neural network algorithm is used for ecg system to achieve a balance between correct response to the trained patterns and good responses to new input patterns. the forward propagation alg orithm starts with the presentation of input pattern to the input layer of the network and continues as activation level calculations propagate forward through the hidden layers. every processing unit (in each successive layer) sums its inputs and applies the sigmoid function to compute its output. then the output layer of the units produces the output of the network. suppose the total input s j to unit j is a linear function of the states of units a i which is equal to the activation levels of the neurons in the previous layer that is connected to unit j through the weights w ji and the threshold, θ j of unit j where: s a wj i i j i j= +∑  (4) the state of (y) of a unit is a sigmoid function of its total input s. y f s ei j s = ( ) = + − � 1 1 (5) the resulting value becomes the activation level of neuron j . once the set of the outputs for a layer is found, it serves as an input to the next layer. this process is repeated layer by layer until the final set of network output is produced. the backward propagation algorithm indicated by error values and these are calculated for all processing units and the weight changes are calculated for all interconnections. the calculations begin at the output layer and progress backward through the network to the input layer. the error value is simple to be computed for the output layer and somewhat more complicated for the hidden layers. if unit j represents the output layer, then its error value is given by: δ j j j jt a f s� � �( )= −( ) ′ (6) fig. 5. the relation between learning rate and mean square error. fig. 6. the relation between learning rate and number of iteration. fig. 7. the relation between momentum and mean square error. where: • tj is the target value for unit j. • f′ (sj) is the derivative of the sigmoid function f. • aj is the output value for unit j. • sj is the weighted sum of inputs to j. al-ani: ecg signal recognition 28 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 fig. 8. the relation between momentum and number of iteration. fig. 9. the relation between network size and network capacity. fig. 11. normal electrocardiograph signal. fig. 12. final diagnosis of electrocardiograph signals (normal case). fig. 13. sinus tachycardia electrocardiograph signal. 5. results and discussion fig. 5 gives the relation between learning rate and mean square error (mse). the learning rate value is laying between zero and one and the commonly used range is in between 0.25 and 0.75. at this active rang of learning rate, the calculated mse is so small and laying in the range 0.1 and 3.5. fig. 6 shows the relation between learning rate and the number of iteration. at this figure it is clear that when the learning rate is equal to 0.5, then the number of iteration is 1000 and still saturated at this number of iteration as learning rate increases. fig. 7 shows the relation between momentum rate and mse. the momentum rate value is laying between zero and one and the commonly used range is around 0.9. at this figure mse still zero up to the momentum rate value is equal to 0.8 at which mse is about 1.8 and then saturated at this value. fig. 8 fig. 10. the relation between network size and generalization. shows the relation between momentum rate and number of iteration. at the momentum rate value of 0.8 the number of iteration is reached to 1000 and still saturated at this value. al-ani: ecg signal recognition uhd journal of science and technology | jan 2023 | vol 7 | issue 1 29 fig. 9 demonstrates the relation between network size and network capacity. at this figure it is clear that there is a linear relation between network size and network capacity. as the network size increases up to 140,000 it is clear that the network capacity increases up to 35000. fig. 10 shows the relation between network size and generalization. at this figure it is clear that the maximum generalization is obtained at starting point of the network size. then the generalization decreases when the network size increases. on the other hand, the zero generalization is obtained at the network size equal to 1000. fig. 11 presents the normal case of ecg signal. fig. 12 shows a normal patient having sinus normal rhythm in which the measurement ecg parameters are: qrs: 189.00 ms, qt: 292.66 ms, qtcb: 66.90 ms, pr: 137.10 ms, p: 88.85 ms, rr: 764.46 ms, and pp: 775.50 ms, this case indicated that the patient diagnosis is normal. fig. 13 deals with the sinus tachycardia case of ecg signal. fig. 14 shows another patient having sinus tachycardia rhythm in which the measurement ecg parameters are: qrs: 0.0 ms, qt: 0.0 ms, qtcb: 0.0 ms, pr: 102.5 ms, p: 66.5 ms, rr: 500 ms, and pp: 500 ms, this case indicated that the patient diagnosis is sinus tachycardia. 6. conclusions the diagnosis of heart diseases depends largely on ecg, in addition to other devices that give special properties and parameters that leading to great 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[50] l. a. abdullah and m. s. al-ani. “cnn-lstm based model for ecg arrhythmias and myocardial infarction classification”. advances in science technology and engineering systems journal, vol. 5, no. 5, pp. 601-606, 2020. . uhd journal of science and technology | jan 2020 | vol 4 | issue 1 71 1. introduction internet of things (iot) is a long-term stream that we are currently at its earliest stage. we can consider three primary phases to achieve the first phase of iot. in the first phase, things can be identified for us and others and gradually assign a specific address on the network for themselves. in this phase, each object keeps certain information in it, but these are people who need to take out this information using tools like their smartphones [1], [2], [3]. in the second phase, each device has the ability to send information to the user at a specified time. after completing the relationship between objects and humans, it is time to relate things to each other. in the third phase, objects are associated with each other without human interference. completing these three phases will finish the first phase of iot evolution [4], [5]. at the end of the first phase, there is a world of ideas in front of developers. the problem is that each device has some information that is available on the network by other objects and its owner and developers can use their own creativity to make better use of this information; telecommunication networks communicate with each other based on technologies, spectra, and different frequency band. this technology in recent years has been more widely considered with the advent of iot technology/internet of a review of properties and functions of narrowband internet of things and its security requirements zana azeez kakarash1,2, farhad mardukhi2 1department of information technology, university of human development, sulaymaniyah, iraq, 2department of computer engineering and information technology, faculty of engineering, razi university, kermanshah, iran re v i e w a r t i c l e a b s t r a c t internet of things (iot) is a new web sample based on the fact that there are many things and entities other than humans that can connect to the internet. this fact means that machines or things can automatically be interconnected without the need for interacting with humans and thus become the most important entities that create internet data. in this article, we first examine the challenges of iot. then, we introduce features of nb-iot through browsing current international studies on narrowband iot (nb-iot) technology, in which we focus on basic theories and key technologies, such as the connection number analysis theory, the theory of delay analysis, the coating increase mechanism, low energy consumption technology, and the connection of the relationship between signaling and data. then, we compare some functions of nb-iot and other wireless telecommunication technologies in terms of latency, security, availability, and data transfer speed, energy consumption, spectral efficiency, and coverage area. finally, we review and summarize nb-iot security requirements that should be solved immediately. these topics are provided to overview nb-iot which can offer a complete familiarity with this area. index terms: internet of things, narrow band, internet of things, narrowband internet of things corresponding author’s e-mail: zana azeez kakarash, department of information technology, university of human development, sulaymaniyah, iraq. e-mail: zana.azeez@uhd.edu.iq received: 07-03-2019 accepted: 20-03-2020 published: 22-03-2020 access this article online doi: 10.21928/uhdjst.v4n1y2020.pp71-80 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2020 kakarash and mardukhi. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) uhd journal of science and technology zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot 72 uhd journal of science and technology | jan 2020 | vol 4 | issue 1 everything and the expansion of devices and communication networks with specific requirements [6], [7], [8]. narrowband iot (nb-iot) is a low power radio network (low consumption) in a wide range (low power wide area network [lpwan]), which is designed and developed to allow the connection of a large number of devices or services using cellular telecommunication band (cellular network) [9], [10]. the nb of iot focuses on network coverage in a closed space, less cost, and more battery life and has the ability to connect a large number of connected devices. the nb technology of iot can be found in the spectrum in-band of the long-term evolution (lte) network or the fourth generation in the frequency blocks of a fourth-generation operator or unused blocks (guard band) of a fourthgeneration operator. it can also be used alone for the deployment of a specific range. it is also appropriate for new combinations (re-farming of [global system for mobile (gsm) communication] spectrum) [11], [12]. the nb was first introduced and developed by sig fax (2009). this company faced the 3rd generation partnership project (3gpp) institute, which defines cellular/mobile telecommunication standards with three challenges which have the ability to answer with a nb. the challenge is that there is a vibrant market for devices that: 1. do not have a lot of abilities 2. they want to be very cheap 3. they have a low power consumption 4. require high range (cover). it can be said that the nb of iot can exist in the following three conditions: • completely independent network • in unused bands of 200 khz, which previously used in gsm networks • second and third generations of mobile/communications • at fourth-generation stations that can assign a block (frequency) to nb of the iot or can be placed in (guard band) [13], [14], 15]. finally, it can be said that the establishment of a nb of a network of iot depends on the geographic conditions of the country and region as well as facilities and conditions of telecommunication and mobile operators of these countries. for example, in the united states, verizon companies (verizon and at and t) can use lte-m1 because both companies have invested in their fourth generation of the network; therefore, they probably do not want to create an independent network, and they want to have a network based on their current fourth-generation network [13], [14]. in front of areas of the world that have a wider gsm network than the fourth-generation network, it is rational to use an independent nb-iot network. for example, t-mobile operators in the united states and sprint eventually have turned their attention toward the deployment of a nb network of iot on the frequency spectrum of gsm network [13], [14], [15]. this paper recommends nb-iot applicable models for application in many places to solve many problems (smart white goods, smart coordination’s, smart power metering, and smart road lighting) and provides a comprehensive overview of the design changes brought in the nb-iot standardization along with the detailed research advancements from the viewpoints of security requirements and the practical presentation of nb-iot as far as successful throughput. the rest of the paper is organized as follows. section 2 describes some background concepts relevant to our review. section 3 describes the challenges of iot. significant features of nb-iot are described in section 4. section 5 presents nbiot and different wireless communication technologies. in section 7 describes basic requirements for nb-iot security and in section 8 discusses the conclusion. 2. background 2.1. brief review of nb-iot nb-iot is a guideline based low control wide zone (lpwa) innovation created to empower a wide scope of new iot gadgets and administrations. nb-iot essentially improves the power utilization of client gadgets, framework limit, and range effectiveness, particularly in profound inclusion. the battery life of beyond what 10 years can be upheld for a wide scope of utilization cases. new physical layer flag and channels are intended to meet the requesting necessity of broadened inclusion – rustic and profound inside – and ultra-low gadget multifaceted nature. the introductory expense of the nb-iot modules is required to be tantamount to gsm/general packet radio services (gprs). the basic innovation is anyway a lot more straightforward than the present gsm/gprs and its expense is relied on to diminish quickly as interest increments. by supporting all major equipment such as mobile equipment, chipset, and module producers, nb-iot can exist together zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot uhd journal of science and technology | jan 2020 | vol 4 | issue 1 73 with 2g (second-generation), 3g (third-generation), and 4g (forth-generation) versatile systems. it likewise profits by all the security and protection highlights of versatile systems, for example, support for client character classification, element confirmation, privacy, information respectability, and portable hardware distinguishing proof. 2.2. benefits and constraints of nb-iot the main properties on nb-iot technology, as defined in rel-13 3gpp tr 45.820 [10], are given in table 1. we have to survey the basic points of interest and consequent restrictions in regards to the inalienable capacities of the nbiot innovation to investigate the end-gadget activity and its incorporation with the iot application [11], [12], [13], [14]. as planned ease of nb-iot module presents no requirements and just brings benefits contrasting with other lpwa arrange arrangements, it would not be talked about further. 2.2.1. wide coverage and deep signal penetration this component gives a chance to the new application class of indoor and underground applications which incorporate information securing and control of gear situated in sewer vents, cellars, pipelines, and different conditions in which the existing correspondence foundation is inaccessible. regardless of the improvement of sign entrance, the gadgets are relied upon to work on the lower limits of signature gathering. hence, support for the vehicle of dependable information ought to be given as a piece of the availability arrangement. 2.2.2. low power consumption of nb-iot modules the chance of battery-controlled structure or potential vitality collecting for end-gadget arrangements, which brings about long life remain solitary activity, is considered as the quick advantage of the low force property. since gadgets are required to work for quite a while, at that point, reconfigurability is an ideal limit which features the requirement for sporadic, however, solid two-way correspondence. the two-way correspondence necessity is likewise seen by 3gpp in their rush hour gridlock model. 2.2.3. massive connectivity the inactive limit of nb-iot supporting foundation is the gigantic availability coming about in up to 50 k gadgets per cell, which relies on inclusion mode and traffic blend gadgets are utilizing. since a huge number of gadgets are proposed to be coordinated into conveyed applications, unbounded remote help reaction time is normal, which is considered as one of the regular issues progressively enormous scope combinations. the correspondence measurements which are influenced incorporate the persistence of information correspondence, models for automatic repeat demand and stream control, and guaranteed unwavering quality (nature of administration) [38], [39], [40], [41]. 3. challenges of the iot on iot, we face a world in which makers supply their goods with their standards, and it is not clear, with the continuation of this variety, billions of devices that make up iot, where will lead future of networks. we examine two challenges of iot in this section. one of them is standard conflicts, and the other one is the security that puts the future of iot in disorderly conditions [16], [17]. 3.1. lack of standard unit the iot of today has a different world. when the internet standards were created, people controlled this standard that their true desire was to formulate global standards. standards are equally accessible to everyone, but the internet of today is in control of companies that each wants to use these standards to defeat competitors and benefit from them. furthermore, the internet is in the hands of governments that basically want to super vise everything. how do governments and companies in this situation want to agree on global standards? in the iot, standard means everything. each device must announce to other devices what it wants to do. without these standards, they cannot do any of these. add this truth to challenge that equipments connected to iot are very different and variant. many companies and organizations try to set standards, and all see union, industrial internet consortium, ipso union, and the open interconnect consortium are of the main institutions. in the iot landscape, there are not spots at which all agree over a series of global standards [15], [16], [17], [18]. table 1: nb-iot main properties [42] range <35 km battery life >10 years frequency bands lte bands bandwidth 200 khz or shared modulation dl: ofdma with 15 khz subcarrier spacing ul: single tone transmissions – 3.75 and 15 khz, multi-tone sc-fdma with 15 khz subcarrier spacing max throughput <56 kbps ul, <26 kbps dl link budget 164 db capacity +50 k iot devices per sector nb-iot: narrowband internet of things, ofdma: orthogonal frequency division multiple access, lte: long-term evolution, sc-fdma: single carrier frequency division multiple access zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot 74 uhd journal of science and technology | jan 2020 | vol 4 | issue 1 3.2. security a recent discovery of a bug called bash or shellshock uncovered a serious security issue on the iot. the bug is a bunch of codes that allow hackers to run on unix and linux operating systems, as shown in fig. 1. the bug is announced by the national institute of standards and technology as a high-level security threat. the seriousness of the threat comes from the fact that hackers do not need to have prior knowledge of the attacked system before they add their code to the bash bug. the bug does not affect the iot only, but all devices connected to it are at risk of being attacked. devices that are attacked by the bug remain to be uncatchable and vulnerable. this discovered threat suggests that there might be many unaddressed security issues, which is good news to hackers and internet criminals and raise questions about the effectiveness and usability of iot in the future. another aspect of iot as contributing to security issues is its complexity, which makes it hard to identify security gaps. these gaps have been realized by researchers, as they have concluded that the connected world has many hidden risks that require intensive research to find suitable solutions [18], [19], [20]. many devices through various channels can connect to iot, and as yet no mechanism has been put forward to alert device users of security threats and the way they can prevent attacks from bash-like bugs. 4. significant features of nb-iot nb-iot is another rapidly developing remote connectivity 3gpp cell innovation standard introduced in release 13 that corresponds to iot’s preconditions for the lpwan. it is developing rapidly as the top-level driving innovation in lpwan to enable a wide range of new iot devices, including smart parking, utilities, wearables, and modern facilities. main features of nb-iot are shown in fig. 2 and briefly described below: 4.1. low energy consumption using power saving mode (psm) and infrequently developed receive (extended discontinuous receive [e-drx]) longer standby time can be observed in nb-iot. in this context, psm technology has been added lately to rel12, in which terminal power-saving mode is still being recorded online, but it cannot achieve to saving energy by sending a signal to put the terminal in a deep sleep for a longer time [20], [21]. 4.2. improved coverage and low latency sensitivity given the reproduced information tr45.820, it very well may be affirmed that the intensity of the covering nb-iot can find a good pace autonomous arrangement mode. recreation try for both in-band organization and watchman band sending is finished. so as to advance the inclusion, systems, for example, remobilization (multiple times) and low recurrence tweak by nb iot was endorsed. at present, nb-iot support from quadrature amplitude modulation 16 is still under discussion. to lose blending 164 db, if a dependable information move gave, due to re-change of mass information, dormancy increments [13], [14], [15], [16], [17], [18]. 4.3. transition mode as it is shown in table 2, nb-iot development is based on lte. correction is mainly based on lte-related technologies due to unique nb-iot features. radiofrequency bandwidth from nb-iot physical layers is 200 khz. at the bottom link, nb-iot with quadrature phase-shift keying (qpsk) modem and orthogonal frequency-division multiple access technologies is compatible with a distance under carrier 15 khz. in the uplink, binary phase-shift keying or qpsk modem and single-carrier frequency division multiple access innovations, including single sub-bearer and different fig. 1. how the function of code bash is vulnerable in the environment. zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot uhd journal of science and technology | jan 2020 | vol 4 | issue 1 75 subcarrier, are embraced. a solitary sub-bearer innovation with the sub-bearer separating of 3.75 khz and 15 khz is appropriate to iot terminal with ultra-low rate and ultralow force utilization. the convention of nb-iot high layer (the layer above physical layer) is figured through modification of a few lte highlights, for example, multiassociation, low force utilization also, not many information. the center system of nb-iot is associated through s1 interface [16], [17], [18], [19], [20], [21], [22]. 4.4. spectrum resources iot is a core service that attracts a larger user group in the communication services market for the future. hence, nb-iot development supported by four major telecom operators in china, as shown in table 3, which is the owner of fvhd nb-iot relevant spectrum source. 4.5. deployment supported by nb-iot according to rp-151621 regulations, nb-iot is currently only foreign demand draft transfer mode with a bandwidth of 182 khz and three types of deployment model shown in fig. 3: • independent deployment (standalone mode), which utilizes a free recurrence band that has no cover with the lte recurrence band • guard band deployment (protective band mode), which uses edge band frequency • in-band deployment (in-band mode), which uses an lte frequency band for deployment, and takes one physical resource block from lte frequency band source for deployment [22], [23]. 4.6. structure and framework the bottom link in nb-iot enodeb supports from the wireless framework of e-utran one frame structure fig. 2. main features of narrowband internet of things [42]. table 2: main nb-iot technical characteristics layer technical feature physical layer uplink bpsk or qpsk modulation sc-fdma single carrier, the subcarrier interval is 3.75 khz and 15 khz the transmission rate is 160 kbit/s – 200 kbit/s multi-carrier, the subcarrier interval is 15 khz, the transmission rate is 160 kbit/s – 250 kbit/s downlink qpsk modulation ofdma, the subcarrier interval is 15 khz, the transmission rate is 160 kbit/s – 250 kbit/s upper layer lte-based protocol core network s1 interface based bpsk: binary phase-shift keying, nb-iot: narrowband internet of things, qpsk: quadrature phase shift keying, lte: long-term evolution, ofdma: orthogonal frequency division multiple access, sc-fdma: single carrier frequency division multiple access zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot 76 uhd journal of science and technology | jan 2020 | vol 4 | issue 1 (fs1), as shown in fig. 4. upper link supports fs1 for under carrier spacing of 15 khz. however, for spacing under carrier 3.75 khz, a new type of framework is defined fig. 5. 5. key technology of nb-iot 5.1. connection analysis theory 3gpp analyzes several connections that nb-iot can access it when network supports from terminal periodic reporting service and network command reporting service. it is assumed that services are distributed within a day and nb-iot can support 52547 connectivity per cell. indeed, this assumption is too ideal, which almost ignores the business of nb-iot service. as a result, it is difficult to generalize it in other application scenes. at present, there are few studies in nbiot business service. however, the research results of lte-m (machine type communications [mtc]) and enhanced mtc are still valuable to learn. to overcome lte network access overhead at a time a lot of mtc terminals enter the network at the same time, researchers have focused their analysis on lte random access channel (rach) load pressure and additional load control mechanisms. researches typically coordinate service entering process as a homogeneous/hybrid process with the same distribution. the users retransitions the number of packet in queue head or channel position in a specific time slot as position variables for obtaining a stable graphical plot with the assumption of completing multichannel s-aloha static mode performance analysis.. the graphing plan can be used for lte rach optimal design. however, when a lot of mtc terminals enter the network simultaneously, a large number of mtc terminals are sent simultaneously to the network to request a quick meeting in a short time to respond the same incident or monitoring the relevant components. this feature can be hardly described by classical homogeneous/hybrid poisson process which forms direct application of network performance analysis method table 3: spectrum classification for nb-iot by telecom operators operator uplink frequency band/mhz downlink frequency band/mhz bandwidth/mhz china unicorn 909–915 954–960 6 1745–1765 1840–1860 20 china telecom 825–840 870–885 15 china mobile 890–900 934–944 10 1725–1735 1820–1830 10 sarft 700 700 undistributed nb-iot: narrowband internet of things fig. 3. three deployments supported by narrowband internet of things. fig. 5. narrowband internet of things framework structure for spacing under carrier of 3.75 khz for upper link [42]. fig. 4. narrowband internet of things framework structure for spacing under carrier of 15 khz for upper and lower links [42]. zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot uhd journal of science and technology | jan 2020 | vol 4 | issue 1 77 based on stable state hypotheses. hence, a transient functional analysis method is essential for multi-channel s-aloha of non-poisson services [24], [25], [26]. 5.2. the latency analysis theory besides the numbers of connecting analysis, 3gpp indicates that there is a need for a theoretical latency model capable of addressing the latency of synchronization, random access, resource allocation, and data transmission to access the upper link. some of these latencies are concerned with signal detection and service behavior as researchers in the field have concentrated on mean and random-access latency variance and little attention has been paid to other curtail features such as probability density function (pdf) of latency. researchers such as rivero-angeles et al. [26] and liu et al. [27] have used the markov process to produce probability generation function from pdf. however, the complex nature of computing has made it difficult for researchers to find a mechanism to lower latency and increase communication probability. 5.3. covering enhancement mechanism slender band adjustment and sub-ghz arrangement from nb-iot can upgrade, getting affectability to build inclusion capacity. besides, 3gpp suggests another advancement component dependent on coverage classes, which is another idea presented for nb-iot by 3gpp. 5.4. very low energy technology a major issue with iot is energy consumption. researchers have simulated the energy consumption for ter minal services within nb-iot with the aim to identify an area for improvements and the result showed that if the information is transmitted once a day, the life expectancy of a 5 wh battery could be much prolonged. this leads to the suggestion that an evaluation mechanism for energy efficiency is required to ensure that lower energy consumption for iot is achieved. some researches, such as liu et al. [27] and balasubramanya et al. [28], on energy consumption in drx focuses on single terminals between control signaling states and terminal operating mode. however, more work is needed to find a holistic mechanism that is seen as one of the tasks of 3gpp r14. 5.5. connectivity between signaling and data coupling simulation between data and signaling is another concern in iot that companies such as huawei technology have indicated needs to be addressed. this is because in many simulation tools, data and signals are separated and simulation tests are done for each independently. this leads to a result where issues in connecting the two cannot be understood which makes it difficult to simulate real network capacity, for example, when access to mtc terminals are requested [29], [30]. 6. nb-iot and different wireless communication technologies lpwa technology is gaining popularity as iot services grow rapidly. the technology is used to deliver smart services with low data speed, which can be utilized in iot intelligent applications. these applications are classified into three groups by hekwan wu in the 2016 international internet conference in china, as shown in table 4. fig. 6a illustrates the position of (lpwan) in comparison to other communication technologies in terms of inclusion zone and information transmission rate. this type of technology is most suited to applications that require high bandwidth and short-range transmission speed such as bluetooth and zigbee [31], [32], [33]. table 4: distribution statistics for iot smart connection technology in 2020 global m2m/iot connection distribution in 2020 category network connection techniques fine-grained market opportunity 10% high data rate (>10 mbps), e.g., cctv, ehealth 3g: hspa/evdo/tds big profit margin for car navigation/ entertainment system 4g: lte/lte-a wifi 802.11 technologies 30% medium data rate (<1 mbps), e.g., pos, smart home, m2m backhaul 2g: gprs/cdma2kix 2g m2m could be replaced by mtc/ emtc techniques mtc/emtc 60% low data rate (<100 kbps), e.g., sensors, meters, tracking logistics s-mart parking, smart agriculture nb-iot various application cases; main market for lpwa; market vacancy sigfox lora short distance wireless connection, e.g., zigbee nb-iot: narrowband internet of things, gprs: general packet radio services, emtc: enhanced machine-type communications zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot 78 uhd journal of science and technology | jan 2020 | vol 4 | issue 1 fig. 6b shows the position of nb-iot that makes use of both 4g/5g attributes and low power radio technology and advantages of low-energ y consumption remote correspondence advances (e.g., zigbee innovation) to be specific concentrated transmission and minimal effort. we have further investigated the technology and compared it with lora, which is a type of wan communication technology, as shown in table 5. 7. requirements for nb-iot security security requirements for nb-iot are similar to that of traditional iot with a number hardware, energy consumption, and network connection mode differences. traditional iot normally has a robust computing power with strong internal security design but with high energy consumption [34], [35], [36]. there are iot technologies equipped with low-power hardware, but in return, it offers a low computing power with high-security risk which may lead to service denial. as a consequence, any security violation, even on a small scale, may leave a negative lasting effect as terminals are simpler and easier for attackers to obtain information. researchers in chen et al. [35], li et al. [36], mangalvedhe et al. [37], and koc et al. [38] have analyzed nb-iot security requirements, which is distributed over three layers, as shown in fig. 7. the below explanation introduces the security prerequisites of nb-iot planning to the 3-layer design comprised perception layer, transition layer, and application layer. 7.1. perception layer perception layer is nb-iot base layer that shows fundamental and establishment of administration and engineering higher layers. nb-iot observation layer, for example, regular discernment layer, will, in general, be under latent and dynamic assaults. uninvolved assault implies trespasser ransacks data with no redress. the fundamental highlights incorporate listening in, rush hour gridlock investigation, etc. as nb-iot depended on an open remote system, trespassers may discover data about nb-iot terminals with strategies, for example, information connect theft and traffic properties examination to focus on a progression of resulting assaults. 7.2. transition layer contrasted with the traditional layer in customary iot, nb-iot changes complex system organization that implies hand-off entryway gathers data and afterward sends it to the table 5: comparison of nb-iot and lora item nb-lot lora power consumption low (l0 years battery life) low (l0 years battery life) cost low lower than nb-iot safety telecom level security slight interference accuracy rate high high coverage <25 km (resend supported) <11 km deployment rebuild supported based on lte fdd or gsm inconvenience nb-iot: narrowband internet of things, gsm: global system for mobile, fdd: foreign demand draft fig. 6. correlation between narrowband internet of things (nb-iot) and different wireless communication technologies (a) comparison of various wireless communication technologies. (b) nb-iot design exchanges. a b zana azeez kakarash and farhad mardukhi: properties and securities of nb-iot uhd journal of science and technology | jan 2020 | vol 4 | issue 1 79 base station for sustaining. subsequently, numerous issues, for example, multi-organizing, significant expense, and battery with a high limit, are illuminated. a system for the entire city can carry simplicity of upkeep and the board with advantages, for example, advantageous tending to and establishment through detachment from property administration. 7.3. application layer the purpose of the nb-iot application layer is to store, analyze, and manage data efficiently. after the perception and transfer layer, a large amount of data converges in the application layer. then, vast resources are formed to provide data support from different applications. compared to the traditional iot application layer, the nb-iot application layer carries more data [37], [38], [39], [40]. 8. conclusion in this paper, we reviewed the basic properties, benefits, and background and the latest scientific findings of nbiot. the general background of the iot was introduced. the benefits, features, basic theory, and nb-iot key technologies such as connection analysis, latency analysis, and coverage enhancement analysis were provided. subsequently, we focused on differences between nb-iot and different types of communication technologies. finally, we made a comparison between nb-iot and other wireless communication technologies and we examine nb-iot security requirements from three levels; perception layer, transition layer, and application layer. there are many future research paths for this study. we continue to investigate a visible network model that can visually reflect the status of nb-iot network operation. such a model should complete each of the operational modules and do link-level open type simulation and nb-iot confirmation form pellet. references [1]. p. reininger. “3gpp standards for the internet-of-things”. technologies report, huawei, shenzhen, china, 2016. 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[42]. c. min, m. yiming, h. yixue, a. k. hwang. “narrow band internet of things”. ieee access, vol. 5, pp. 20557-20577, 2017. . uhd journal of science and technology | jul 2019 | vol 3 | issue 2 1 1. introduction the significance of student mobility and interuniversity exchange programs is incomprehensibly expanding, and the issue at present involves a huge spot in the motivation of instructive arrangement creators and advanced education establishments. in 2007, erasmus program commended its 20th anniversary. the erasmus program is presumably one of the best-known activities of the european commission, empowering students just as staff versatility, and intending to improve the quality and to fortify the european component of advanced education. university of human development participated with erasmus as an associative partner in erasmus program to help students and staff receive more practical mobility experience to accomplish specific tasks related to their profession [1]. mobility in space, land portability, “genuine” mobility, and physical mobility are for the most part terms used to allude to students and instructors in advanced education, “physically” moving to another establishment inside or outside their very own nation to study or instruct temporarily. in the accompanying passages, distinctive perspectives or kinds of mobility, for example, level and vertical mobility, free-mover and program portability are recognized and probably the most well-known programs are quickly depicted. the majority of the variations of geological mobility exhibited are types of physical mobility. students’ mobility can be arranged by the length of the investigation time frame abroad. when students just spend some portion of their examination program abroad or at an alternate institution in a similar nation, and just total a few modules or courses, however not entire degrees, it is alluded as flat portability (likewise called transitory, credit or non-degree mobility). most national and european mobility programs advance this variation of portability. the greatest mobility timeframe for students and graduates in such projects is normally 1 year. with the usage of the bologna blended learning mobility approach and english language learning mazen ismaeel ghareb1, saman ali mohammed2 1department of computer science, college of science and technology, university of human development, sulaymaniyah, iraq, 2department of english, college of languages, university of human development, sulaymaniyah, iraq a b s t r a c t although the benefits of blended learning have been well documented in educational research, relatively few studies have examined blended mobilities in education in kurdistan region government and in iraq. this study discusses a blended mobility approach for a teacher training program designed for in-service english language teachers (elt) and investigates its effectiveness by comparing the latest participation of the university of human development for computer science and proposing the same program for training english for lecturers and students. the research involved proposes new mobility program for teaching and learning english language and using their language skills in an ongoing business project using several software for communication and management of their projects. results will show the framework for new blended learning and blended mobilities of many different english language teaching (elt) aspects. index terms: blended aim, blended learning, blended mobility, language learning strategies, e-learning, virtual mobility access this article online doi: 10.21928/uhdjst.v3n2y2019.pp1-9 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2019 ghareb and mohammed. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) r e v i e w a r t i c l e uhd journal of science and technology corresponding author’s e-mail: mazen ismaeel ghareb, department of computer science, college of science and technology, university of human development, sulaymaniyah, iraq. e-mail: mazen.ismaeel@uhd.edu.iq received: 06-03-2019 accepted: 13-04-2019 published: 20-06-2019 ghareb and mohammed: blended learning mobility approach 2 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 procedure and the expanding presentation of the lone ranger and ace projects in europe, numerous higher instruction foundations are likewise expecting an expansion in what is known as vertical mobility (additionally called degree or confirmation mobility). here, students think about abroad for a full degree, accomplishing, for instance, their first degree at an organization in one nation (for the most part their nation of origin) and their second degree at another foundation, either in their nation of origin or abroad (for example a 4-year certification at home – ace degree abroad). the eu erasmus mundus program, for instance, underpins vertical mobility in a methodical way [2]. another meaning of blended mobility is a term used to depict an instructive idea that consolidates physical scholastic mobility, virtual mobility, and mixed learning. it is expected to advance employability of advanced education understudies. since 2009, it has advanced from virtual mobility, keeping the worldwide estimation of scholastic versatility, and yet giving a solid response to conceivable family related, monetary, mental and social hindrances of physical mobility [3], [4]. the virtual mobility part of mixed mobility is, for the most part, upheld using data and correspondence innovations (for example, skype, adobe connect, slack, google hangout, and trello) to remain associated with the educators or potentially understudies who might be arranged at numerous far off areas. the physical mobility part is ordinarily of momentary length, extending from 2 to 14 days. there may exist numerous times of momentary portability. brief times of physical mobility empower members to the center for several days, just on the genuine undertaking, which is troublesome in everyday life in a nearby domain [5]. early uses of a mixed mobility configuration can be found back in 2009. through this venture a domain was made which energizes the advancement of understudies’ delicate aptitudes, for example, cooperation and correspondence, in a universal setting by methods for an inventive guidance worldview to improve such abilities without costly and broad curricular changes [6]. 2. literature review 2.1. blended learning blended learning has turned into a trendy expression in numerous instructive conditions as of late, generally alluding to courses that utilize a blend of eye to eye and web-based learning [7]. the term started in work environment learning and writing but, on the other hand, is presently broadly utilized in advanced education, regularly portraying courses that have had an online segment added to them [8]. some consideration has been paid to the utilization of blended learning in language educating overall [9]-[11]; however, next to no work has been done explicitly in english language teaching (elt) settings. in fact, with reference to elt [12], features are needed for further research to be directed into what makes a powerful blend. fig. 1 explains some components of blended learning. joining the upsides of e-learning and conventional learning situations has prompted another learning condition regularly alluded to as “blended learning,” which unites customary physical classes with components of virtual learning [13]-[15]. one of the fundamental ideas hidden in this methodology is that: the individuals who utilize mixed methodologies base their instructional method on the suspicion that there are inborn advantages in face-to-face interactive (both among students and between student and teacher) just as the understanding that there are some inherent advantages in utilizing the web strategies in the learning process. consequently, the point of those utilizing blended learning approaches is to locate an agreeable harmony between online access to learning and face-to-face human interaction [16], [17]. in a survey of research on mixed learning [18], numerous examinations were recognized that uncovered positive impacts of mixed learning on (1) students performance [19]; (2) student interest and inspiration [20]; (3) expanded access and adaptability [21], (4) cost-viability [22]; and (5) progressively dynamic and deeper learning in examination with conventional classes [23]. fig. 1. blended learning components ghareb and mohammed: blended learning mobility approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 3 while trying to exploit blended learning, colleges have started advertising courses joining customary up close and personal instructional components with internet learning segments [24] in different scholarly fields, for example, the board [25] and business [26]. nonetheless, little research has been done on mixed learning in educator instruction explicitly [27], and distributed work has concentrated for the most part on understudies assessments and the mechanical applications presented [28]. be that as it may, mixed learning may be able to possibly improve instructor instruction regarding both availability and quality. blended learning has turned out to stand out amongst the most well-known approaches to educate english as a foreign language (efl) due to its twofold segment, which coordinates vis-à-vis classes with virtual learning so as to offer students a wide scope of materials and assets sorted out methodologically. thinking about the past viewpoints, in numerous instructive settings bl is a device accessible to students with the end goal for them to go past the homeroom and work on various intelligent exercises as an expansion of the immediate educating classes. through all the mechanical assets they have around them, students can find out about various subjects and societies, surf the web and use the technological device they access, for example, ipods, ipads, pcs, mp3s and mp4s, among others. notwithstanding, students are attacked by a lot of data from various sources. in this way, they get confounded and do not have the foggiest idea what to see first, which obstructs the proper utilization of the virtual material that may add to their english learning process. along these lines, efl instructors have the test of arranging virtual learning conditions that are engaging their students. this will enable them “to arrange” their efl learning procedure and supplement up close and personal classes or the different way can utilize the virtual stage self-governing to get readied for the eye to eye classes. along these lines, fl instructors are responsible for the methodological arranging of mixed courses which could be utilized to engage the efl students. in spite of the fact that there are a few models with which to sort out a mixed course, we consider the accompanying model recommended by khan [29], as shown in fig. 2. the institutional perspective is the primary component educators need to consider since it relies on the institutional strategies about the educational modules, the design of the material, and the organization and money related zone. the second segment, the mechanical one, is the fundamental thought when educators plan both the disconnected and online exercises. educators need a wide scope of mechanical assets so as to pull in their students’ consideration: if the face-to-face classes and the virtual ones are not testing, students may feel exhausted or baffled. it is important to show points and activities which are engaging them. the third factor to hold up under as a primary concern is a pedagogical segment, which no uncertainty is the most critical one in these cross breed courses. in the event that instructors have a methodological arrangement to sort out both their up close and personal classes and the online viewpoint, it will lead the language students to prevail in their learning procedure and acquire better outcomes since they appropriately compose the two segments. 2.2. students mobilities in last century in europe, mobility of students, educators, and staff has been a be noticeable among the most universities and education system. as the colleges of europe changed fig. 2. blended learning model. fig. 3. blended mobility framework. ghareb and mohammed: blended learning mobility approach 4 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 to better approaches for working in the previous decades, they have kept on supporting this profitable convention. student mobility can be characterized by the length of the examination time frame abroad. when students just spend some portion of their investigation program abroad or at an alternate foundation in a similar nation, and just total a few modules or courses, however not entire degrees, it is alluded to as level mobility (additionally called brief, credit, or non-degree versatility). most national and european mobility programs advance this variation of mobility. the most extreme mobility time frame for students and graduates in such projects is typically 1 year. with the implementation of the bologna process and the expanding presentation of single guy and ace projects in europe, numerous higher instruction foundations are likewise expecting an expansion in what is known as vertical mobility (additionally called degree or certificate portability). here, students ponder abroad for a full degree, accomplishing, for instance, their first degree at an establishment in one nation (generally their nation of origin) and their second degree at another foundation, either in their nation of origin or abroad (e.g., bachelor certificate at home – ace degree abroad). the eu erasmus mundus program, for instance, bolsters vertical portability in a methodical way [30]. mobility can likewise be characterized by the method of association of the examination time frame abroad. program understudies are portable students partaking in a sorted out mobility program. “free-movers” then again do not profit by any sort of students among foundations and do not partake in a composed mobility program. “free-mover” versatility is the most seasoned type of scholastic portability. since the center of the 1970s composed mobility has increased expanding significance, with the ascent of organized national limited time programs (see for instance the daad grants) and european portability programmes. organized or program mobility is, these days, viewed as the real mobility engine for students, graduates, doctoral hopefuls and showing staff in europe (for example erasmus, leonardo, marie curie) and, progressively, the whole world (for example erasmus mundus) [30]. the topographical mobility of free-movers can occur inside a nation or crosswise over national fringes. free-mover mobility can likewise be seen on an overall scale and is commonly not restricted to specific areas or target nations. interestingly, program mobility ordinarily centers on specific areas (for example, ceepus, nordplus…) or on specific mainlands (for example, europe on account of erasmus, marie curie) [30]. the significance and prominence of specific versatility plots frequently vary among nations and in certain nations free-mover mobility still assumes an extensive job. aside from the free-mover mobility and the universal participation facilitated by explicit remotely supported projects, numerous higher instr uction organizations participate with one another on a reciprocal basis. bilateral understandings between foundations are sorted out so as to fire up joint activities or increase existing contacts, and more often than not additionally make open doors for understudy and staff versatility. the upsides of such respective concurrences as to portability are for instance simplicity of utilization, smooth credit exchange, and acknowledgment of studies. two-sided understandings can exist both on the dimension of the organization and the dimension of resources or divisions. at last, mobility can likewise be upheld in the structure of systems of advanced education foundations or understudy systems. the coimbra gathering understudy trade system for instance is a mobility plot supplementing the conventional erasmus mobility. 3. blended mobility proposed framework 3.1. university of human development 3.1.1. blended aim is one of those international projects that is supported and funded by the european union in the context of scientific exchange hosted by european universities. the projects are dedicated to fourth-grade students. from each university, two students and one supervising teacher participate. the participants, both the students and the supervisor, meet twice a year in one of the member universities to present any advancement in their project and plan for future projects. the students work virtually to finish the project. the students get ects for their works according to their university’s instructions. we suggest that the university of human development get intensively involved in this unique opportunity by fitting in our university in this project as it gets bigger and bigger, and it could require enlarging our team. 3.1.2. calling in other colleges university of human development is participating in the blended aim project for 2018–2019 with other colleges regarding the erasmus project calling. 3.2. virtual mobilities with the developing noteworthiness of distance learning and e-learning, virtual mobility has turned out to be progressively ghareb and mohammed: blended learning mobility approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 5 critical in the course of the most recent couple of years. it is since the second 50% of the 1990s that the idea of virtual mobility has picked up many with regard to the internationalization of advanced education foundations. be that as it may, what is comprehended by virtual portability? the elearningeuropa.info entry characterizes it as: “the utilization of data and correspondence innovations (ict) to get indistinguishable advantages from one would have with physical mobility yet without the need to travel”[20]. this definition obviously demonstrates the two distinct components of virtual mobility. virtual mobility is typically compared to the virtual mobility of “academic plagiarism” and adds to the internationalization of training by empowering (cross-border) collaboration between various instruction establishments. besides, it is connected to the new conceivable outcomes opened using data and correspondence innovation (ict) bolstered situations that incorporate, for instance, video conferencing, live spilling, community-oriented workspaces, and computer-mediated conferencing. in the system of the being portable task, components, for example, the improvement of (inter-) social comprehension were added to the definition to feature the wealth of the experience and the likenesses with the erasmus trade program: “virtual portability is a type of realizing which comprises virtual parts through a completely ict bolstered learning condition that incorporates cross-border coordinated effort with individuals from various foundations and societies working and contemplating together, having, as its fundamental reason, the upgrade of intercultural understanding and the trading of information” [31], [32]. the typology is, for the most part in light of the kind of movement and the conditions in which the virtual mobility movement happens: • a virtual course or workshop (arrangement): students in an advanced education foundation participate in virtual mobility for a solitary course (as a component of an entire investigation program) or a course (arrangement) and the remainder of their learning exercises happen face-to-face generally; • a virtual report program: a whole virtual investigation prog ram is offered at one advanced education establishment, giving understudies from distinctive nations the opportunity to take this investigation program without traveling to another country for an entire scholastic year; • a virtual mobility position: student’s situations are sorted out between a higher education foundation and an organization (now and again in an alternate nation). in the virtual mobility students use ict to bolster their temporary position, giving them a real-life involvement in a corporate setting without the need to move from the grounds to the organization or to move to another nation for a specific period of time, and giving them a down to earth planning for new methods for working through (global) synergistic cooperation; • virtual help exercises to physical trade: virtual mobility empowers both better planning and follow-up of students who take an interest in physical trade programs. preliminary exercises could incorporate understudy deter mination at a separation through videoor web conferencing (for checking social and language aptitudes), furthermore, online language and social combination courses. follow-up exercises will assist students with keeping in contact with their companions, dispersed the world over, to complete their normal research work as well as desk work. they could likewise appear as a so-called “virtual alumni” association, foster lifelong friendships, and networks. in spite of the fact that the term “virtual mobility” is generally new, the european commission has effectively advanced virtual mobility in the previous years, for the most part through the money related help of ventures inside the socrates/minerva and the e-learning and deep-rooted learning projects. a portion of the later tasks managing the subject incorporate the above-mentioned being versatile undertaking, reve (genuine virtual erasmus), emove (an operational origination of virtual portability), and more vm (prepared for virtual mobility), each focusing on various parts of virtual portability for various gatherings of members [3], [33]. 3.3. blended mobility while much of the time, virtual mobility speaks to an important elective answer for physical mobility, there is by all accounts general understanding that its anything but a substitute for physical mobility. virtual mobility is, then again, ending up progressively well-known as help and supplement to conventional genuine mobility programs. it can offer extra arrangements and is an approach to additionally improve the current conventional projects, for example, erasmus. at the point when parts of physical and virtual mobility ghareb and mohammed: blended learning mobility approach 6 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 are consolidated so as to boost the benefits of both, it is characterized as “blended mobility” or – whenever connected to the eu erasmus program – “blended erasmus.” this blended methodology is in accordance with the consequences of, for instance, the eureca venture, completed by the european understudy affiliation aegee, which suggests in addition to other things that “erasmus understudies could be arranged as of now at their home colleges in ‘active workshops’ from one viewpoint, yet could likewise ‘trade encounters’ consequently classes” then again. the report additionally expresses that “each understudy ought to reserve the option to go to a language course that empowers him/her to pursue the scholastic program” at the host college and that “short-term trades and virtual trades could be developments.” in addition, the report of the workshop on “bologna and the difficulties of e-learning and separation instruction” [34] places uncommon accentuation on the strong capacity virtual versatility can play for physical portability and show that “virtual versatility must be utilized to enhance and bolster physical versatility by better setting it up, giving powerful follow-up intends to it, and offering the likelihood to remain in contact with the home foundation while abroad. it can likewise offer (in any event part of) the advantages of physical mobility for the individuals who are generally unfit to go to courses abroad” european undertakings, for example, sumit (supporting mobility through ict), esmos (upgrading understudy mobility through online help), triumphant (virtual educational program through dependable interoperating college frameworks) and others, recommend that the european commission has additionally recognized virtual mobility as a help instrument in physical portability as an imperative subject. (see add ii for more information). in addition, the vm-base venture (virtual mobility when student exchanges), in which this manual displays the outcomes, intends to raise the nature of student exchange by offering virtual help to physical mobility. in vm-base virtual help is utilized to plan and follow-up the portable understudy, as a supplement to the current trade programs. the venture in this way bolsters instructors in training trade understudies at a separate (e-coaching). student exchanges can set themselves up for their stay at a host college through, among other help exercises, virtual classes between the home and host college. preliminary language or social courses for the understudies could be given customarily at the home college or by means of ict from the host college before they remain. amid their stay at the host college, they could remain associated with understudies, partners, or instructors at the home college. furthermore, on their arrival, they could broaden their stay “essentially” by staying in touch with the host college by virtual methods. as it shows in fig. 3 the framework of blended mobility it a combination of blended learning and mobility learning, which is added to blended learning pedagogy in general . 4. english languages skills and information and communication technology 4.1. a. listening interactive activities to sight and multimedia assets. listening skills are best learned through simple, engaging activities that focus more on the learning process than on the final product. regardless of whether you are working with a huge gathering of students or a little one, you can utilize any of the accompanying guides to build up your own techniques for showing students how to listen well. listening skill/comprehension is an important as well as complex process of language learning. it plays a significant role in second language competence. the process and the act of comprehension are lessened and eased through the context and the purpose. linguistic knowledge and experiential knowledge are also key ways listeners make use of it to comprehend. there are many tools one can make use of such as computer-assisted language learning [15]. there are several techniques for listening activities developments: • interpersonal activities, for example, mock meetings and storytelling. assign the students to little gatherings of a few, and after that, give them a specific listening activity to achieve. • bigger group exercises likewise fill in as a supportive technique for showing listening aptitudes to students. • you can likewise train listening abilities through sound portions of radio projects, online digital recordings, instructional addresses, and other sound messages. • another helpful resource for teaching listening skills is video segments, including short sketches, news programs, documentary films, interview segments, and dramatic and comedic material. 4.2. speaking technology can stimulate the playfulness of students and drench them in an assortment of situations. innovation ghareb and mohammed: blended learning mobility approach uhd journal of science and technology | jul 2019 | vol 3 | issue 2 7 allows students to take part in self-coordinated activities, open doors for self-managed cooperation, protection, and a sheltered situation wherein blunders get redressed and explicit input is given. input by a machine offers extra an incentive by its capacity to track oversights and connection the understudy quickly to practices that attention on explicit errors. studies are rising that demonstrate the significance of subjective criticism in programming projects. at the point when connections are given to find clarifications, extra assistance, and reference, the estimation of technology is additionally increased. present day technologies accessible in instruction today are: • correspondence lab • discourse acknowledgment programming • interne to technology-enhanced language learning • podcasting • quick link pen • quicktionary. as english today is ranked number one in the worlds in terms of a number of users, the ability to speak fluently has turned into an aptitude of foremost centrality to acquire. an online foreign language speaking class, virtual classes are structured having at the top of the priority list, standards of elt and e-learning, alongside systems that raise connection, incorporating vocabulary and utilization of english, while giving a calm situation so as to inspire even withdrawn students take an interest and produce spoken language. use of oovoo and skype, slack, google hangout, trello separated from empowering clients to cooperate with prerecorded messages, additionally give students the choice of synchronous talk, permitting the formation of a virtual class of three to six clients, contingent on the sort of membership – free or paid, respectively. another advantage given by these two instruments is that students can profit by real learning encounters as opposed to their standard everyday practice, which will thus inspire them to request all the more genuine correspondence subsequently, more opportunities to disguise language [8]. 4.3. reading online reading perusing is an errand that has all the hallmarks of being important for the 21st-century understudies. along these lines, the production of an electronic perusing program called “english reading online” was made to limit the hole among perusing and understanding utilizing web-based perusing procedures. the successful utilization of perusing procedures is known to enhance peruser’s understanding. as innovation has infiltrated our lives, the impression of perusing for cognizance through innovation needs to transform into a groundbreaking method for doing as such a definitive objective is to empower students to use procedures spontaneously. notwithstanding, perusing procedures has a few advantages, as well as confinements. for example, the dimension of the members, the study hall settings, and the arrangement of procedures need to be mulled over before connecting with training. vital perusing guidance benefits all understudies even those of scholastic level. this may be a consequence of lacking secondary school arrangement or little planning amid their time as students. as understudies gain a lot from perusing through procedures which improve their scholastic execution, having it offered through an innovation improved condition increases its impact on understanding while it enables them to figure out how to utilize technology. the assets offered to the understudies through a learning content administration framework called “varsite” enabled them access to a bigger assortment of writings of those found in the college library. this fundamentally gives every understudy the self-rule to get to these assets as per their timetable, empowering them to screen their adapting far and away superior. 4.4. writing writing can be perplexing for many students since it requires the correct utilization of language. in contrast to spoken language, composed language cannot utilize motions or non-verbal communication to clarify what it is that should be comprehended or passed on. played out an investigation where they attempted to recognize the most ideal way an educator can use to show the latent voice marvel. they utilized three sorts of classes. the first was “the customary up close and personal way,” the second was the “integrative way” where both customary instructing and web-based instructing were utilized, and the third sort was the “online way” where the main sort of educating and materials was electronic what they found was that the coordinated route ended up being the most advantageous for the students, just as that sexual orientation assumes a non-noteworthy job since the outcomes were not unique. it was likewise discovered that the dimension of the understudies changed toward progress after the utilization of the incorporated strategy, in this manner consequences of the post-test essentially varied from the aftereffects of the pretest. this investigation enables us to see that teachers should utilize electronic material as they do improve their students’ level utilizing a free and simple to utilize apparatus. the utilization of blog programming and tweeter are devices that can enable understudies to rehearse composed language, draw in with the language they wish to learn and obviously to share their considerations or emotions ghareb and mohammed: blended learning mobility approach 8 uhd journal of science and technology | jul 2019 | vol 3 | issue 2 and think about them [11]. advancing composition guidance through such an engaging way empowers more generation of composed language which may not have created something else. students writes blogged instead of going to an in-class session appeared better outcomes from the individuals who just got in-class composing guidance. instructors should utilize this device as it upgrades composing execution while it is not constrained inside school dividers as it can happen anyplace. the outcome the students who blogged appear to have was an improvement over the individuals who did not, which demonstrates the estimation of the mix of this device. tweeting likewise is by all accounts a significant device to start the production of network bonds, henceforth permitting the students to discover increasingly about one another also, assemble network bonds. likewise, while executing gatherings, online journals and wikis in the meantime, this appears to have positive outcomes on understudies’ learning progress since this mixed methodology enables them to consider the contrasts which may happen in methods for communicating in english when utilizing composed language. 5. conclusions and recommendations one of the primary reasons for higher education organizations is to give students the urgent apparatuses to prevail in the worldwide work market. blended learning has turned out to be a standout among the most well-known approaches to educate efl because students can find out about various subjects and societies, surf the web and use the technological device they access, for example, ipods, ipads, pcs, mp3s, and mp4s, among others. proficient life is these days heavily relying on mobility and requests experts to exceed expectations in relational abilities at a global, culturally diverse condition. such activities have multiple benefits, both for the staff who participate and for their schools such as enhanced language skills, innovative teaching methods, and cultural awareness. the main advantages for students can be • social skills development • developing organizational skills • learn to use online communication tools • does not disturb regular home activities • learn how to work as a member of a team of students, international, and/or interdisciplinary • develop the skills of self-management and work on a project or proof-of-concept assigned by a company, resulting in real-world, innovative projects • experience cultural differences and similarities • practice languages other than mother tongue • integrated more easily in english language curriculum. • it provides opportunities to participants with special needs (e.g., online assistance software, medical treatment.,...). there also some disadvantages such as: • it is challenges to communicate in a virtual way, especially if not mother tongue • it is difficult with long-term mobility, but not equivalent • cultural communication issues may arise earlier and faster • student must have disciplines • students need to have a certain level of independence is required in any case, delicate abilities, just as global presentation, are once in a while tended to college classes. blended mobility defeats the run of the mill obstructions to mobility, in this way enabling students to exploit the advantages that mobility and worldwide presentation offer. notwithstanding, paying little respect to its additional esteem, mixed mobility is not really utilized and scarcely perceived as a genuine option with incredible potential to defeat the normal troubles of global mobility. the blended-aim project sets the basis to support and structure mixed mobility as a rule. in concrete, mixed point enables universal blended-aim and employability by giving the assets – including preparing, supporting apparatuses and data – to help students and organizations facilitating entrylevel positions and by streamlining inventive instructing ideal models intended to build up students’ delicate abilities in a worldwide domain. the framework of blended mobility is a combination of blended learning and mobility learning, which is added to blended learning pedagogy. in general, we hope the higher education in kurdistan can adapt this system in new bologna process. references [1] f. rizvi. “global mobility, transnationalism and challenges for education”. transnational perspectives on democracy, citizenship, human rights 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[34] s. adam. “learning outcomes current developments in europe: update on the issues and applications of learning outcomes associated with the bologna process”. in: presented to the bologna seminar: learning outcomes based higher education: the scottish experience, edinburgh: scottish government, 2008. tx_1~abs:at/tx_2:abs~at 32 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 1. introduction in recent decades, many studies have demonstrated the ability of the machine to examine the environment and learn to distinguish patterns of interest from their background and make reliable and feasible decisions regarding the categories of the patterns. with huge volumes of data to be dealt with and through years of research, the design of approaches based on character recognition (cr) remains an ambiguous goal. various frameworks employed machine learning approaches which have been most comprehensively studied and applied to a large number of systems that are essential in building a high-accuracy recognition system, cr is among the most well-known techniques and methods that make use of such artificial intelligence which have received attention increasingly. moreover, in various application domains, ranging from computer vision to cybersecurity, character classifiers have shown splendid performance [1]-[3]. the application of cr is concerned with several fields of research. through those numerous applications, there is no single approach for recognition or classification that is optimal and that motivates the researchers to explore multiple methods and approaches to employ. in addition, a combination of several techniques and classifiers is popped to the surface to serve the same purpose. due to the increased attention paid to cr-based applications, noticeably there are few comprehensive overviews and systematic mappings of construction of alphabetic character recognition systems: a review hamsa d. majeed*, goran saman nariman department of information technology, college of science and technology, university of human development, kurdistan region, iraq a b s t r a c t character recognition (cr) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. this work is proposed to provide a guideline for cr system construction to afford a clear view to the authors on building their systems. all the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. this guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. in addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests. index terms: optical character recognition, script identification, document analysis, character recognition, multiscript documents corresponding author’s e-mail: hamsa d. majeed, department of information technology, college of science and technology, university of human development, kurdistan region, iraq. e-mail: hamsa.al-rubaie@uhd.edu.iq received: 09-11-2022 accepted: 07-02-2023 published: 18-02-2023 access this article online doi: 10.21928/uhdjst.v7n1y2023.pp32-42 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 majeed and nariman. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) r e v i e w a r t i c l e uhd journal of science and technology majeed and nariman: construction of cr systems: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 1 33 cr applications design. instead, the existing reviews explore in detail a specific domain, technique, or system focusing on the algorithms and methodology details [4], [5]. while starting investigations in this field, a big space of confusion appeared while diving into the details of each step in the recognition process due to the variety of paths that could be taken to reach the final goal and the pool of factors to be phished for that matter. that leads to the fact of considering an in-depth literature review as a requirement for surveying the possibility of using the techniques, approaches, or methodologies that are required for that phase of the recognition process among the others and deciding if they are suitable or not for that cr-based application. the major aim of this study is to present the main path for the various kinds of approaches to be followed before diving into the details of the framework to be proposed by the meant research, moreover, depending on each research field, there are options offered and categorized, techniques, and methods are presented and summarized from multiple perspectives all of which are investigated to answer the following queries: 1. which language will be taken to recognize as input and what is a specified script writing style? 2. how can the data be acquired? is it taken digitally (touchscreen, scanner, or another digital device) or uploaded from a non-digital source? in printed form by a keyboard or in handwritten form? 3. which scale or level of detail is present in that set of data? does the script have to be taken wholly or by a single character each time? 4. from which source could those data be collected? is the preprocessing phase needed or not? 5. generally, through which recognition process should invade for the optimal outcomes considering the previously chosen phases? this work is structured to give the most suitable roadmap to the author of interest by presenting a systematic guideline to explore the multidisciplinary path starting from the script writing style the passing by the most suitable guide throughout the desired dataset characteristics (acquisition, granularity level, and the source of collected data), reaching to the script recognition process for the cr-based applications. furthermore, this study uncovers the potential of cr applications among different domains and specifications by summarizing the purpose, methodologies, and application. thorough proofreading of several types of research including survey articles, the cr process has the same stations to stop by which could be sorted under some separated categories on specific factors and all those categories of any proposed system may have a stop in those main stations, that was an encouragement to make this study to highlight those main stations and present a guideline the researchers of interest by examining the detailed of sub-stations due to building cr system efficient to the author and understandable by the reader. 2. proposed walkthrough guideline the main goal of this study is to construct and design criteria for researchers working in the field of cr systems to observe when initiating research in both the practical and written parts. the following classifications and assortments are proposed, as shown in fig. 1. 2.1. script writing system from the linguistic point of view, nowadays, scripts used throughout the work have been broken down into six script classes, each of which can be used in one or more languages [6], [7]. furthermore, in the context of cr, the investigations of the script character characteristics and structural properties, the script-written system has been classified under six classes. different classes may contain the same language scripts [8], [9], [10]. fig. 2 illustrates the classification of the script writing system. 2.1.1. logographic system the oldest kind of writing system is a logographic writing system; it is also called an ideogram as well, which employs symbols to depict a whole word or morpheme. the most well-known logographic script is chinese, but logograms fig. 1. general assortments of the cr system. majeed and nariman: construction of cr systems: a review 34 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 fig. 2. script written system classifications. such as numbers and the ampersand are found in almost all languages. an ideographic writing system typically has thousands of characters. thus, the recognition process of this kind of script is still a challenging and fascinating topic for researchers. han is the only script family in this class that includes two more languages, namely, japanese (kanji) and korean (hanja). the interesting distinguishing point between han and other languages is the text line written direction, which is either from top to bottom or left to right. in literature, lots of research can be found on handwritten cr in these scripts, for instance [11]-[13] work on chinese, japanese (kanji), and korean (hanja), respectively. the accuracy rates for the scripts based on the aforementioned references are 99.39%, 99.64%, and 86.9%, respectively. 2.1.2. syllabic system every written sign in a syllabic system, such as the one used in japanese, corresponds to a phonetic sound or syllable. kanas, which are divided into two types hirakana and katakana represent japanese syllables. the japanese script combines logographic kanji and syllabic kanas, as mentioned in the previous subsection. the kanas has a similar visual appearance to the chinese, with the exception that the kanas has a lower density than the chinese. a lot of recognition progress can be found in the literature for both hirakana and katakana. examples of excellent achievements in recognition accuracy rate are contributed in [11] for both hirakana and katakana, which are 98.83% and 98.19%, respectively. 2.1.3. alphabetic system each consonant and vowel have a distinct symbol in the alphabetic writing system, which is used to write the languages classified under this written system. segmental systems are another name for alphabets. to represent spoken language, these systems mix a small number of characters called letters. letters are meant to represent certain phonemes. greece is where the alphabet was first used, and it later expanded around the world, especially in europe and a part of asia as well [14], proposed a system for ancient greek cr that achieved an accuracy rate of 96%. latin, cyrillic, and armenian also belong to this system. there are numerous languages that use the latin alphabet, commonly known as the roman script, with differing degrees of alteration. it is utilized to write in a wide range of european languages, including english, french, italian, portuguese, spanish, german, and others. the interested authors of latin languages presented their ideas in terms of the recognition system for the different latin languages, majeed and nariman: construction of cr systems: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 1 35 for instance, afrikaans 98.53% [15], catalan 91.97% [16], dutch 95.5% [17], english 98.4% [18], french 93.6% [19], italian 92.47% [20], luxembourgish (87.55 ± 0.24)% [21], portuguese 93% [22], spanish 97.08% [23], vietnamese 97% [24], and german 99.7% [25]. cyrillic has a separate letter set but is still relatively comparable to latin. the cyrillic writing system has been adopted by certain asian and eastern european languages, including russian, bulgarian, ukrainian, and macedonian, where the recognition rate is recorded for them as follows: russian 83.42% [26], bulgarian 89.4% [27], ukrainian 95% [28], and macedonian 93% [29]. finally, the armenian written system, this language classified as an indo-european language belonging to an independent branch of which it is the only member recent cr system for this language scored 89.95% [30]. 2.1.4. abjads when the words have a writing pattern from right to left along with text line, written in a repetition of consonants that are close together leaving the vowel sounds to be inferred by the reader, and have cursive long strokes consisting of few dots, then you are looking at abjads writing system. it is unlike most other scripts in the world but it is similar to the alphabetic system unless it has symbols for consonantal sounds only. these unique features make the process of script identification for abjads relatively simpler compared to other scripts, particularly because of the long cursive strokes with dots and the right-to-left writing direction, making it easier for recognition systems in pen computing. arabic and hebrew are considered the major categories of the abjads writing system. there are some other scripts of arabic origin, such as farsi (persian), urdu, and uyghur. a lot of approaches had been proposed for identifying abjad-based scripts, they used the long main stroke along with the cursive appearance yielding from conjoined words for arabic. meanwhile, the more uniform strokes in length and discrete letters were the main dependent features of hebrew script recognition. according to the latest survey for arabic recognition systems [31], the highest accuracy score is 99.98%, while recorded 97.15% for hebrew [32]. in farsi, urdu, and uyghur, the highest accuracies achieved are 99.45%, 98.82%, and 93.94%, respectively [33]-[35]. 2.1.5. abugidas it is a writing script primarily based on a consonant letter and secondary vowel notation. they are sharing with alphabetic systems the property of combining characters writing styles within the text line. it belongs to the brahmic family of scripts which is can be expressed in two groups: 1. original brahmi script: this northern group deployed in devnagari, bangla (bengali), manipuri, gurumukhi, gujrati, and oriya languages. the most recent survey papers for the cr systems of this group come up with the highest recognition rate of 99% for devnagari, 99.32% for bangla (bengali), 98.70% for manipuri, 99.3% for gurumukhi, 98.78% for gujrati, and 96.7% for oriya [36]-[38]. 2. derived from brahmi: look quite different from the northern group and used in: a. south india: tamil, telugu, kannada, and malayalam, where the highest accuracy of the mentioned language for recognition matter was for tamil 98.5%, telugu 98.6%, k annada 92.6%, and malayalam 98.1% [39]. b. southeast asia: thai, lao, burmese, javanese, and balinese, the languages of this group have achieved the highest validation rate where thai, lao, and burmese attained 92.1%, 92.41%, and 96.4% while javanese and balinese gained 97.7% and 97.53%, respectively [40]-[43]. 2.1.6. featural system this form of writing system is significantly represented by symbols or characters, the main language is korean which is described as less complex and less dense compared to chinese and japanese, it is represented by mixing logographic hanja and featural hangul, the highest scored accuracy rate for korean was 97.07% [44]. as a summarization of all the findings in this section, table 1 illustrates the classifications of the languages with the highest accuracy recorded so far. 2.2. data acquisition the next step for the author after selecting which language to work on is to decide which writing style will be chosen for recognition, this step is considered one of the fixed and essential phases in all the recognition studies and research, reaching this phase requires the knowledge of how to start acquiring data to be fed into the recognition system, the answer simply starts with defining the writing style, here the author has two options either printed script or handwritten script. after making the decision, the acquisition tools are required either offline tools or online. in this section, a guideline is majeed and nariman: construction of cr systems: a review 36 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 table 1: summarization of languages with their recent highest accuracy rate script writing system main language sub-language accuracy rate (%) logographic system han chinese 99.39 japanese (kanji) 99.64 korean (hanja) 86.9 syllabic system kanas japanese (hirakana) a 98.83 japanese (katakana) 98.19 alphabetic system greek greek 96 latin afrikaans 98.53 catalan 91.97 dutch 95.5 english 98.4 french 93.6 italian 92.47 luxembourgish (87.55±0.24) portuguese 83 spanish 97.08 vietnamese 97 german 99.7 cyrillic russian 83.42 bulgarian 89.4 ukrainian 95 macedonian 93 armenian armenian 89.95 abjads hebrew hebrew 97.15 arabic arabic 99.98 farsi 99.45 urdu 98.82 uighur 93.94 abugidas brahmi devnagari 99 bangla (bengali) 99.32 manipuri 98.70 gurumukhi 99.3 gujrati 98.78 oriya 96.7 tamil 98.5 telugu 98.6 kannada 92.6 malayalam 98.1 thai 92.1 lao 92.41 burmese 96.4 javanese 97.7 balinese 97.53 featural system korean korean 97.07 proposed and could be followed to help make those decisions as fig. 3 shows. 2.2.1. printed character those characters are produced as a result of the process of producing using inked-type tools. in recognition systems of any language, the printed characters usually achieve a high recognition rate because it is considered in regular form, clean, have the same style, and have similar shapes and lines, and that facilitates the learning operation and therefore raises the accuracy of recognition in the testing phase. 2.2.2. handwriting character when the process of forming letters of any language is done with the hand, rather than any typing device then the result is handwriting characters. most of the authors that are interested in cr are employing handwriting characters as input to their approaches to prove the effectiveness and efficiency of their systems or techniques due to the complexity and impenetrability that come with the variety of the handwriting style and the use of tools besides the differences in lines and colors not to mention the irregular shapes and positions. 2.2.3. online character these characters are obtained from digital devices with a touch screen with/without a keyboard involved like a personal digital assistant, or mobile. where screen sensors receive the switching of pushing and releasing the pen on the screen in addition to the pen tip movements over the screen. 2.2.4. offline character this kind of character is attended when image processing is involved by converting an input image (from a scanner or a camera) of text to character code which is aimed to be utilized by a text-processing application. it is essential for the author to choose the correct combination of the writing style and the writing tool, as fig. 3 illustrates there are three combinations to decide among them: offlineprinted where the input of the cr system decided to be in offline mode with characters taken from the printed device rather than the offline-handwritten which taken from a human-hand in offline-mode already written on paper in a previous time while the online-handwritten fed as input to cr system instantly by hand through a touchable input device without a keyboard. some recent recognition systems are illustrated in table 2 for several languages to show some authors’ choices for the fig. 3. overview of data acquisition. majeed and nariman: construction of cr systems: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 1 37 language, writing style, and writing tool, and how their choices affect the accuracy rate for each mechanism. furthermore, a comprehensive survey for online and offline handwriting recognition can be found in plamondon and srihari [45]. the outcomes of table 2 show that most existing studies have focused on handwritten text, with fewer works attempting to classify or identify printed text. this is because of the high variance in handwriting styles across people and the poor quality of the handwritten text compared to printed text yields the fact that handwritten cr is more challenging than the printed one. on the other hand, it is noticeable using offline as writing tool more than online ones this is due to in the online case, features can be extracted from both the pen trajectory and the resulting image, whereas in the offline case, only the image is available, so the offline recognition is observed as harder than online recognition. 2.3. granularity level of documents the third type of classification of character handwriting recognition is “granularity level of documents,” which describes the level of detailed information taken as initial input to the defined and proposed framework. this class could be split into five granularity levels as shown in fig. 4, from a script page full of text to a single letter or symbol. in the domain of cr, if the initial input into the ocr framework is not at a character level, the process of script identification must proceed until it gets to a single character. this procedure, known as “segmentation,” will be covered in the following subsection (3.5). 2.3.1. document/page level document-level script is the most detailed granularity level, where the entire document is exposed to the script identification procedure at once. following processing, the document is further broken down into pages, pieces of paragraphs, text lines, words, and finally characters to enable the recognition of the precise letter. although some researchers discriminate between the script recognition process at the document and page levels, in general, the technical methodologies are very similar. because of this, some researchers alternately refer to document-level and page-level script recognition. finding the text region on a page is the initial step in pagelevel script identification. it is possible to carry out this operation by separating the pages into text and non-text pieces [53]. several pieces of research can be found in the literature for both offline-handwritten [54] and offlineprinted [55]. after the page of the script has been identified, the process of the next level starts, which is paragraph or text block identification. it operates by dividing the entire page into equal-sized text blocks with several lines of content. text blocks can have different sizes, and padding may be necessary if characters are on the edge of a text block [56]. is an example of segmenting pages into pieces of text blocks. 2.3.2. paragraph level the text block is separated into lines. the white space between lines is typically used for text line segmentation. lines of scrip are detected and segmented to be prepared for further segmentation processing. both offline-handwritten [57] and offline-printed [58] line detection has been the subject of numerous studies in the literature. fig. 4. granularity level classification. table 2: examples of recognition systems with different data acquisition mechanisms reference language writing style writing tool accuracy rate (%) [46] arabic handwritten offline 99.93 [18] english handwritten offline 98.4 [47] english printed offline 98 [48] english handwritten online 93.0 [49] chinese handwritten online 98 [50] chinese handwritten offline 94.9 [13] chinese printed offline 99.39 [51] arabic printed offline 97.51 [52] arabic handwritten online 96 majeed and nariman: construction of cr systems: a review 38 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 2.3.3. text line level a framework that gets a line of script as initial input needs segmentation processes to identify each word in the text. therefore, word identification is needed. text lines are divided into words; usually, the white space between text lines is used for this purpose. numerous literary attempts have been made to address the difficulties encountered in this process. for instance, there might be noise, twisted words, missing or partial letters in words, words that are not available as straight text lines, etc. some examples given in this topic of identifying words in a text line are [59] for offline-handwritten and [60] for offline-printed. 2.3.4. word level character detection and segmentation are required since the initial input is a word. it usually works by combining properties from various characters to ensure the process. several attempts have been made to improve accuracy and ensure that no character inside a word is missed. for instance, recently [61] used distinct strategies and achieved a satisfactory outcome. 2.3.5. character level finally, there is no requirement for segmentation at the character level because the initial input into the proposed framework is already character. the character goes through preprocessing, which is followed by recognition procedures. in some circumstances, no preprocessing is required, as is the case when using a character public dataset. for instance [62], is an example of working at the character level with and without preprocessing, respectively. in addition, to avoid confusion between granularity levels for identification/detection and recognition processes, it is worth mentioning that from the recognition standpoint, when the granularity level is text line level, it means that the text line is already known and the detection and segmentation into words and characters are needed. however, from the identification/ detection point of view, it means that the identification and detection of text lines are working. further details about these processes can be found in [10], [63]. 2.4. source of collected dataset the essential component of any machine learning application is the dataset. that leads us to discuss this important phase of cr as the fourth classification named source of collected dataset which is broken down into two categories as fig. 5 illustrates: 2.4.1. public dataset (real-world dataset) the term “public dataset” refers to a dataset saved in the cloud and made open to the public. mnist, keras, kaggle, and others are examples. almost all of the public datasets have been preprocessed, cleaned, and usually, in the case of character level, reshaped to 28 × 28 pixels and saved as csv files. many authors attain to use this source to skip the preprocessing step and focus more on the other steps and easily find opponents for the comparison issue of those who used the same data source with different techniques. 2.4.2. self-constructed dataset is the dataset that the researchers create and prepare on their own depending on their techniques, it is an online or offline way of collection, this source of dataset is considered more challenging because the collected images are not processed at all in terms of resizing, denoising, colored, etc. for a fair comparison, this kind of work better to be compared with studies that have done with a self-collected source of data, not with a public one that comes clean and processed. researchers should be aware of the data to be collected and use the proper tools required to preprocess in a way that suits the technique used for recognition. 2.5. script recognition process the script recognition process (the implementable phase) is the fifth classification type of alphabet handwritten recognition framework. in an in-depth study of several research articles, including survey articles, we mainly focused on the phases that an ocr system needs to accomplish its recognition goal. thus, we could conclude that four categories can be defined based on the number of phases in which the whole procedure of recognition comprises, as presented in fig. 6. in addition, commonly, script recognition is achieved by blending traditional image processing techniques with fig. 5. categories of collected dataset sources. majeed and nariman: construction of cr systems: a review uhd journal of science and technology | jan 2023 | vol 7 | issue 1 39 image identification and recognition techniques. the recognition composition is formed from four primary phases, namely, preprocessing (p), segmentation (s), feature extraction (f), and classification (c). the last two phases, feature-extraction and classification, are the most common in the research. there is not any work without any of these two phases. the next few paragraphs will briefly outline them. • preprocessing (p) is a sequence of operations performed to intensify the input image. it is responsible for removing noise, resizing, thinning, contouring, transforming the dataset into a black-white format, edge detection, etc. every single one of them can be performed with an appropriate technique • segmentation (s) performs the duty of obtaining a single character. the document processing follows a hierarchy; it starts from the whole page and ends with a single character. the required level of the hierarchy is a single character • feature extraction (f) is a mechanism in which each character is turned into a feature vector using specific algorithms for the extraction of the features, which is then fed into a classifier to determine which class it belongs to. • the classification (c) phase is a decision-making process that uses the features extracted from the preceding step as input. and it decides what the final output is. it is worth noticing that handwritten mathematical symbols and expressions recognition is out of our research scope. therefore, we do not consider the two additional phases (structural analysis and symbol recognition) which are included in such works. more details can be found in sakshi and kukreja [64]. 2.5.1. psfc the first category of the script recognition process class can be called psfc, which means all four phases have been utilized to achieve the goal as [65] describe. 2.5.2. pfc the segmentation process is skipped in the second category, in most cases due to working on character level as initial input therefore no need for segmentation as presented in parthiban et al. [66]. 2.5.3. sfc the third one is sfc as [67] proposal, where the preprocessing is missed because the entered data originally is clean and there is no preprocessing required. 2.5.4. fc in the fourth and last category as illustrated in gautam and chai [68], the first two phases p and f are dismissed because the granularity level is letters, and the initial input data is originally clean. for instance, works utilizing public datasets such as mnist [69] could be classified under this category. 3. examples this section is to illustrate some of the cr systems and gives a description of how to read their roadmap regarding their systems, by applying the proposed guideline, any paper in this field can be summarized in stages according to the fig. 6. basic components of the script recognition processes. majeed and nariman: construction of cr systems: a review 40 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 author’s choices and be easier to the reader to figure out the main stages and for the other authors to develop any desired cr system. some examples are presented here to show how the cr system can be summarized according to the proposed guideline, and resembling a table is suggested to be created in such a work to provide a comprehensive view of the proposed framework as a whole. it makes it easier for the reader to find the information they are searching for before going into depth. table 3 provides two examples of how to present the suggested table. in addition, the following examples demonstrate how the systems may be constructed using the component chain: 1. [18] english → offline-handwritten → character level → self-constructed dataset → pfc 2. [46] arabic → offline-handwritten → line level → public dataset → psfc 3. [49] chinese → online-handwritten → page level → public dataset → sfc 4. [48] english → online-handwritten → line level → public dataset → fc 5. [13] chinese → offline-printed → character level → self-constructed dataset → pfc 4. conclusion cr stepped ahead as an eminent topic of research. exhaustive studies continuously presented cr of different languages with various algorithms that were developed to increase the reliability of these characters for accurate recognition. a guideline for the construction cr system has been proposed for the authors in this field to overcome the unclear presentation and expressing ideas in such a domain of science. almost all the required steps have been shown and demonstrated by graph and table to be used in such works in cr domaine for more clarity for the authors to margin their scope. it is also for the readers, as well, to directly recognize the used technique through in-text reading and then move forward to the details afterward. through reading this guideline, the authors will be able to order their thoughts and build their recognition system smoothly and effectively especially for the new authors in this field, as for readers after reading this work they will have the ability to analyze other research in the relative fields and extract information easily from other works of interest, for the seekers of new ideas or merging techniques, this guideline is suitable to help to determine the exact part of recognition system to be studied or compared with. saving time, effort, and thoughts orienting for other authors or readers was one of the essential aims of this work. references [1] m. paolanti and e. frontoni. “multidisciplinary pattern recognition applications: a review”. computer science review, vol. 37, pp. 100276, 2020. 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[69] l. deng. “the mnist database of handwritten digit images for machine learning research [best of the web]”. ieee signal processing magazine, vol. 29, no. 6, pp. 141-142, 2012. . 40 uhd journal of science and technology | may 2018 | vol 2 | issue 2 1. introduction drinking water pollution is becoming an increasing problem in the entire world for its severity and toxic effects on human health. the continuous development of significant changes such as population growth, industrialization, expanding urbanization, and diminishing water resources made the issue much worst [1]. awareness of the quality of drinking water is expanding steadily in many countries in the world [2]. heavy metals play a reasoned approach to the classifying of drinking water quality due to their toxicity and poisonousness even at low quantities [3]. heavy metals are the most damaging and dangerous contaminants in water due to non-biodegradable nature and their accumulation in a biological system [4]. drinking water may contain essential and toxic heavy metals. the essential metals are co, cr, fe, mn, mo, ni, se, sn, v, cu, and zn, these metals are critical for sustain biological life, but still, their accumulation in the body may cause dangerous assessment of heavy metals contamination in drinking water of garmian region, kurdistan, iraq hayder mohammed issa1, azad h. alshatteri2 1college of human sciences, university of garmian, sulaymaniyah province 46021, kurdistan region, iraq, 2department of chemistry, college of education, university of garmian, sulaymaniyah province 46021, kurdistan region, iraq a b s t r a c t drinking water of safe quality is a critical issue for human survival and health. water pollution by heavy metals is very crucial because of their toxicity. this study assesses the potential of heavy metal pollution in drinking water in the three districts of garmian region, east iraq. water samples were investigated for 23 heavy metals and 6 chemical contaminants collected from 16 locations between january 1 and october 31 in 2017. the analysis was performed using inductively coupled plasma optical emission spectroscopy (icpoes, spectro arcos). high levels of al, se, sr, and fe have been detected at certain locations in the study area. statistical analysis techniques of the correlation matrix and cluster hierarchical analysis were conducted. the heavy metals pollution index (hpi), heavy metals evaluation index (hei), and contamination index (c d ) were applied. these indices linked with the statistical analysis to interpret relationships among tested parameters in water samples and to investigate pollution sources over the study region. even with the significant correlations between the hpi, c d , and hei, they showed a dissimilar impact of examined heavy metals on the water quality. it was found that concentrations of heavy metals such as al, fe, and se are in elevation (0.550, 0.736, and 0.044) mg/l, respectively, at certain locations depending on the last update of the who guidelines for drinking water. the most reliable pollution evaluation index of hei for drinking water showed that 44% of the water samples is critically polluted. sources of the contamination are most likely coming from natural geological sources. the anthropogenic impact was only noticed at several sites in the study area. index terms: drinking water, environmental risk assessment, garmian region, heavy metals, multivariate statistical analysis corresponding author’s e-mail: hayder mohammed issa, university of garmian, sulaymaniyah province 46021, iraq. phone: +9647708617536. e-mail: hayder.mohammed@garmian.edu.krd received: 27-07-2018 accepted: 25-08-2018 published: 08-09-2018 access this article online doi: 10.21928/uhdjst.v2n2y2018.pp40-53 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2018 issa, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 41 effects [5]. the toxic and non-essential heavy metals such as contamination index (c d ), pb, al, as, ba, hg, be, and ti are toxic can cause critical or chronic poisoning [6], [7]. for the past years, various works have been performed to identify heavy metals pollution in drinking water [8]-[11]. any reliable assessment for water quality needs to take into account more chemical parameters such as ca, na, mg, po4, no3, so4, and total hardness. to obtain a total view on drinking water quality condition, as the chemical parameters in drinking water may cause important environmental and sanitary consequences [12]. assessment of drinking water quality requires to recognize regional geogenic and anthropogenic characteristics for an area to be studied. naturally, heavy metals reach water resources by leaching from contacting the soil and underlying rocks. heavy metals may come from anthropogenic activities such as agricultural run-off, effluents discharged from cities, industrial plants, and mining sites of heavy metals [13]-[15]. evaluating heavy metals traces in drinking water have been performed by generating pollution indices. these indices refer to the overall water quality in terms of heavy metals contamination. many indices are used for the purpose concerned, such as heavy metals pollution index (hpi), heavy metals evaluation index (hei), and the degree of c d [16] [18]. c d is distinguished by the fact that it implicates heavy metals and other coexisting contaminants in water quality evaluation [19]. many parts of iraq, including the garmian region, are suffering from the low quality and pollution of their drinking water sources [20]-[22]. as a result, several attempts to have been made to define the potential risk of drinking water quality in the area concerned [23]. however, up until now, no extensive analysis has been performed to identify heavy metals levels in drinking water at garmian region. the current study tests the heavy metal concentration levels in drinking water of garmian region, east iraq by establishing a reliable dataset that aids further investigations to develop remediation strategies, to enhance the environment of the region, and to protect people health. this work investigates 23 heavy metals and six chemical parameters in drinking water samples from 16 different locations in garmian region. hpi, hei, c d , with statistical analysis approaches of anova, the correlation matrix (cm), and cluster analysis cluster hierarchical analysis (ca) have been carried out to detect the possible pollution sources. 2. materials and methods 2.1. study area garmian region is located between latitudes (34° 17’ 15”35° 10’ 35”) north and longitudes (44° 31’ 30”-45° 47’ 10”) east. (fig. 1), the study area has a total area of 6716.5 km2 in three districts kalar, kifri, and khanaqin. the region has a population of 300,000 inhabitants, with no major industrial constructions. the physicographic feature of the area is an alluvial plain in the south and west; while the area lies within foothill in the north and east. the major river systems draining the area include alwand, diyala-sirwan, and awaspi rivers. a climate of the study area is continental semiarid by potential evaporation [24]. soil order of the area is mainly aridisols [25]. the land surface is covered by sand, silt, and clay, while periodically several areas are covered by gravel [26]. many parts of the study area are rich with gypsum minerals [27]. the area is underlain by the outcropping formations of tertiary (pliocene), and the quaternary deposits (pleistocene–holocene) consist in the alternation of sandstone, siltstone, and claystone [28]. 2.2. collection of water samples water samples (surface and groundwater) were collected from the study area and sampling locations from 16 locations in garmian districts between january 1 and october 31 in 2017, three samples were collected from each location. water sample was collected from selected sites, where including different water systems and an area covered a stretch of about 60–70 km. in the field a clean pre-washed (250 ml.) polyethylene bucket, which had been connected with a long rope used for fig. 1. map of study area, and locations of water sampling (the first map of garmian region topography is courtesy of garmian region directorate 2017, the second map of garmian region location according to kurdistan region, and iraq was modified by the authors). hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region 42 uhd journal of science and technology | may 2018 | vol 2 | issue 2 collection of water samples from different sampling sites. the water sample was allowed to pass through the bucket for a while. samples were identified in table i. all samples were acidified with 2% nitric acid (ph-2), and refrigerated and transferred to the instrumental research laboratory to analyze them. all samples were analyzed within 2 days from the time of collection by inductively coupled plasma optical emission spectroscopy (icpoes) (spectro across germany) at university of gar mian. the standard solutions were prepared by serial dilutions of the 1000 mg/l. distilled deionized water was used for the dilutions and the washing all glassware [4]. 2.3. heavy metals analysis various accurate analytical methods are applied to determine heavy metals concentrations in water samples such as the atomic absorption spectrometry aas [29], [30], the icpoes [31]-[33], and the inductively coupled plasma mass spectrometry icp-ms [34]. all water samples were stored in polyethylene containers and returned to the laboratory under dark conditions within 1–2 h of collection time. the water samples were acidified by adding concentrated nitric acid hno 3 and sored at 25°c for trace metal determination purposes. icp-oes: spectro arcos was used to analyze the 23 heavy metals. the instrument conditions used were: spray chamber is scott spray; nebulizer: crossflow; rf power/w: 1400; pump speed: 30 rpm; coolant flow (l/min): 14; auxiliary flow (l/min): 0.9; nebulizer gas flow (l/min): 0.8; preflush (s): 40; measure time (s): 28; replicate measurement: 3; argon gas (purity ≥ 99.99); multi-elements stock solutions containing 1000 mg/l were obtained from bernd kraft (bernd kraft gmbh, duisburg, germany); and standard solutions were diluted by several dilution into 0.1, 0.5, and 2 ppm in 0.5% nitric acid as diluent [2]. 2.4. statistical analysis water pollution indices and statistical approaches were implemented to evaluate the potential sources and levels of heavy metals. typically, evaluation of water quality by pollution indices depends on a massive dataset collected for various relevant contamination parameters in water samples at different locations. application of water pollution indices is associated with various statistical analytical techniques to interpret and classify the obtained water quality data sets. however, among the numerous available statistical techniques, the univariate anova, the bivariate correlation coefficient matrix cm, and the multivariate cluster analysis ca are used for heavy metals impact on water quality [35], [36]. sometimes, these statistical become helpful as water quality results may require additional explanations to identify source and way of the contamination. the obtained data sets from water samples were subjected to statistical analysis using excel 2013 software. two statistical analysis that performed to deduce the sources of heavy metals were; anova and cm interpretations, and cluster analysis ca. using anova aids to find out the significance of the variation between sampling locations while a cm was used to reveal the relationships between the examined heavy metals and chemical contaminants. cluster analysis was applied in this work to classify water samples according to their spatial table i: the description of sources of water samples sampling symbol samples location site coordinate source s1 mineral water bani-khailan, kalar district 35.07, 45.67 spring water s2 drilled well 1 kifri district 34.91, 44.82 groundwater s3 drilled well 2 kifri district 35.02, 44.63 groundwater s4 water project kifri district 34.70, 44.96 surface water awaspi river s5 drilled well 3 kifri district 34.91, 45.07 groundwater s6 drilled well 4 kifri district 34.87, 44.85 groundwater s7 drilled well 5 kalar district 34.64, 45.30 groundwater s8 water project kalar district 34.65, 45.36 surface water sirwan river s9 drilled well 6 kalar district 34.83, 45.51 groundwater s10 drilled well 7 sarqala, kifri district 34.74, 45.06 groundwater s11 drilled well 8 sarqala, kifri district 34.74, 45.08 groundwater s12 drilled well 9 rizgari, kalar district 34.66, 45.26 groundwater s13 drilled well 10 rizgari, kalar district 34.67, 45.18 groundwater s14 drilled well 11 khanaqin district 34.57, 45.35 groundwater s15 drilled well 12 khanaqin district 34.39, 45.35 groundwater s16 water project khanaqin district 34.35, 45.39 surface water alwand river and balaju-canal hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 43 variation of heavy metal and chemical parameters of water samples. ward-algorithmic linkage method and euclidean distance are the basis to conduct statistical cluster analysis. agglomerative hierarchical clustering is the used statistical cluster analysis. cluster analysis of water samples was made using xlstat (version 2017 for excel 2013 software). 2.5. heavy metals pollution assessment 2.5.1. heavy metal pollution index (hpi) in this study, the heavy metal pollution index (hpi) was used with the formula that proposed by mohan et al. [37]. where the water quality is assessed according to existence and importance of heavy metals in water samples. many works have used this index to acquire information on heavy metal pollution potential in tested waters [38]-[41]. hpi is an arithmetical tool that computed on the basis of the arithmetic mean method to transform various water existing data into a single derived number in terms of relevant heavy metals presence effect on water quality. ∑ == ∑ = n w qi iihpi n wii 1 1 (1) where q i is the subindex of i-parameter, w i is the weight of i-parameter, and n is the total number of parameters that included in test. w i for each parameters is inversely proportional to the recommended standard for the corresponding parameter. the ith parameter subindex is calculated as follows. ( )− = ∑ −= n m ii iqi s ii i i [ ] * 100 ( )1 (2) where m i , s i , and i i are monitored, standard, and ideal values of i-parameter for the investigated heavy metals. 2.5.2.hei hei is another pollution index related to heavy metals. usually, it is applied to get a whole idea on potential water contamination caused by heavy metals. hei is calculated as following equation [42], [43]. = ∑ = n hchei hmaci 1 (3) where, h c and h mac are the obser ved and maximum permissible level concentrations for each i-parameter, respectively. 2.5.3.c d the c d is computed to evaluate the contamination of water quality, c d is a sum of contamination factors of individual parameters those have values above the upper allowable limits [44]. c d takes into consideration the number of parameters exceeding permissible limits and their concentrations [45]. many works have used this index to reveal any potential contamination and the combined effects of harmful quality parameters in various water resources such as [46] and [47]. c d is calculated as the following two steps. = ∑ = n c cd fi i 1 (4) = − c aic fi c ni 1 (5) where, c fi , c ai , and c ni are concentration factor, analytical value, and the upper allowable concentration of the i-parameter, respectively. 2.6. methods evaluation before going any further, it was very necessary to evaluate the performance method applied in this study. the performance evaluation is usually made according to limits of detection (lod), limit of quantification (loq), and linearity [38], [48]. for elements measured by icpoes, the calibration curves were found depending on the standard addition method. the linearity of the analyzed elements was tested and approved. the lod and loq were estimated per their relations with standard deviation. the accuracy and reproducibility of elements analyzed and measured by icpoes were determined by spiking and homogenizing three replicates of each of the three samples collected randomly from sampling locations. 3. results and discussion 3.1. heavy metals in drinking water samples presence of heavy metals in drinking water samples (groundwater and surface water) from the 16 different sites in garmian region is illustrated in tables ii and iii. in this study, 23 metals of cr, cu, fe, mn, mo, al, sr, zn, ba, se, li, v, ni, cd, as, pb, co, tl, ag, be, hg, sb, and sn have been analyzed. descriptive statics including maximum permissible limit mpl and lod with the wavelength for the investigated heavy metals at all water sampling locations are presented in table ii. hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region 44 uhd journal of science and technology | may 2018 | vol 2 | issue 2 as stated in table ii most mpl for the tested parameters are according to the who [49] except that mpl for be, fe, mn, sr, li, v, ca, p, be, co, tl, sn, and t. hardness were adapted from other standards as demonstrated in table ii. from the results obtained, a part of the examined metals of ni, cd, as, pb, co, tl, ag, be, hg, sb, and sn are not detected due to their concentrations which are below the lod as shown in table ii. the ph ranges were from 6.5 to 8.0 for all water samples, with no great difference in ph values among the sampling locations in which this weak influence could be ignored on the heavy metals presence in tested samples. from table ii of descriptive statistics, it can be seen from the obtained results that heavy metals characteristics of drinking water quality in garmian region are generally within acceptable ranges except for fe, al, sr, li, and se at certain locations such as s2, s8, and s14. the distribution of the measured heavy metals shows that the mean and median values for the metal of aluminum (al) concentration in water samples 0.3 mg/l are higher than maximum permissible limits mpl 0.2 mg/l this reveals the significance of the al metal impact on drinking water at those locations in the region. mean value of lithium li is 0.037 which is exceeded mpl at most locations in the study area. strontium sr and selenium se mean values in water samples are 3.838 and 0.038 mg/l that is close to the maximum permissible limits mpl of 4, and 0.04 mg/l, respectively, hence this reveals the contribution of sr and se in the drop of drinking water quality of the area. the rest of the parameters showed lower concentrations in tested samples. table iii illustrates more details on heavy metals concentrations among the analyzed drinking water samples that collected from various locations in garmian region. the obtained results showed a sign of pollution hazards of certain heavy metals. for cr high level it was determined to be 0.021 mg/l for water samples collected from location s10 and was low or bdl in the other locations. cr was only found in groundwater samples (0.001–0.021 mg/l). in all table ii: descriptive statistics for heavy metal and chemical parameters in tested water samples parameter min max mean median standard deviation lod (mg/l) mpl (mg/l) wavelength (λ) cr 0.000 0.021 0.003 0.000 0.006 0.0010 0.05 267.7 cu 0.011 0.028 0.018 0.016 0.005 0.0010 1 324.8 fe 0.009 0.736 0.074 0.0155 0.179 0.0020 0.2a 259.9 mn 0.001 0.020 0.004 0.001 0.006 0.0010 0.05a 257.6 mo 0.001 0.006 0.003 0.002 0.001 0.0010 0.07b 202.1 al 0.000 0.550 0.038 0.000 0.137 0.0040 0.1b 396.2 sr 1.046 11.94 3.838 3.9865 2.900 0.0020 4d 407.7 zn 0.001 0.386 0.055 0.0175 0.095 0.0010 3 213.9 ba 0.006 0.094 0.034 0.0165 0.027 0.0044 0.7 455.4 se 0.027 0.044 0.038 0.038 0.004 0.0020 0.04 196.1 li 0.004 0.078 0.037 0.034 0.021 0.0010 0.01e 670.8 v 0.001 0.008 0.005 0.0045 0.002 0.0025 0.015f 292.4 as bdl bdl bdl bdl -0.0026 0.01 189.0 ag bdl bdl bdl bdl -0.0012 0.05 328.1 be bdl bdl bdl bdl -0.0010 0.004c 313.1 cd bdl bdl bdl bdl -0.0010 0.003 214.4 co bdl bdl bdl bdl -0.0010 0.1f 228.6 hg bdl bdl bdl bdl -0.0040 0.006 184.9 ni bdl bdl bdl bdl -0.0010 0.07 231.6 pb bdl bdl bdl bdl -0.0035 0.01 220.4 sb bdl bdl bdl bdl -0.0068 0.02 206.8 sn bdl bdl bdl bdl -0.0010 0.001k 190.0 tl bdl bdl bdl bdl -0.0040 0.0072g 190.9 ca 36.48 175.41 103.55 114.54 49.22 0.004 75h 315.9 k 0.78 5.04 2.24 2.16 1.24 0.031 12b 766.5 mg 9.91 69.76 37.76 47.95 20.32 0.005 50b 279.1 na 5.34 125.53 50.59 50.09 39.86 0.066 50 330.2 p 0.03 0.07 0.04 0.04 0.01 0.002 0.16m 177.5 t. hardness 139.17 724.40 413.57 470.67 203.75 -200h -lod: limit of detection, bdl: below detection limit, mpl: maximum permissible limit, aadapted from [50], b (who, 2011) adapted from [51], c (usepa, 2008) adapted from [51], d (usepa, 2008) adapted from [52], eadapted from [53], fadapted from [54], f (usepa, 008) adapted from [55], gadapted from [56], h (who, 2006) adapted from [57], kadapted from [58], madapted from [59] hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 45 sampling locations, low levels of cu were detected ranging of 0.011–0.028 mg/l. however, in one location s8 a high level of fe 0.736 mg/l exceeding the mpl. it was found that the concentrations of mn, mo, zn, ba, and v were lower than mpl of 0.05, 0.07, 3.0, 0.7, and 0.015 mg/l, respectively, in all sampling locations that considered in this study. zn showed critical concentrations at locations s2 and s10 with the range of 0.386 and 0.103 mg/l. it was noticed at some locations that al, sr, and li concentrations are higher than mpl specified by this work. some heavy metals were not detected in this study in all sampling locations due to their low concentrations levels such as as, ag, be, cd, co, hg, ni, pb, sb, sn, and tl. samples from diyala-sirwan river downstream location s8 (kalar drinking water project was established to provide potable water to kalar city residents) looks like having higher concentrations than mpl values of al and fe when compared to groundwater and surface water samples from other locations. this elevation in fe and al levels is due to the fact that diyala-sirwan river flows through small building materials manufactures. therefore, the high contamination in this location may come from effluents discharged by these sites and also from aluminum-rich materials used in water treatment. considerable contaminations of sr were observed in various locations s1 to s6 and s14 to s16 for both surface and groundwater sources at khanaqin and kifri districts. according to usepa 2008 standards of mpl is 4.0 mg/l [52], many water samples contain a high level of sr parameter. these levels are generally related with environmental contamination generated by a natural occurrence of alkaline earth metal. this could be relatively distributed in groundwater as well as in surface water and that is common in such systems and crustal materials [52], [60]. se and li levels are high in water samples s2, s3, and s14 for se and s2, s3 while the concentration of li is 0.055 mg/l for s6. high se and li levels in certain groundwater samples are occurring due to geogenic sources such as weathering and leaching of rocks, dissolution of soluble salts in soils, and it might occur due to anthropogenic activities [61], [62]. several chemical parameters of the water quality were investigated in this study. according to their levels and roles in the anthropological life that called macro essential elements, five cations chemical elements were analyzed include ca, k, mg, na, and p. the statistical description for these chemical parameters of maximum, minimum, mean, median, and standard deviation for all water samples is summarized in table ii. in many locations, statistics show that the mean and median concentrations are close to or even exceed the mpl. from tables ii and iv, it can be noticed that the ranges of the studied cations of the water samples (mg/l) were ca, 36.48–175.4; k, 0.777–5.042; mg, 9.914–69.757; na, 5.34– 125.53; and p, 0.029–0.68; t. hardness, and 139.171–724.4. ca and na and t. hardness are in the first class. magnesium has shown high concentrations in water samples from most locations and exceeded the mpl. high concentrations of ca and mg exist in water samples of khanaqin district (s14 to s16), kifri district (s2, s3, s4, s5, s6, and s10), and in one location at kalar district s7. accordingly, at these locations, the total hardness is high also. sources of elevated ca, na, and mg ions are more likely to be geogenic, like natural hydro-geochemical processes of soil leaching and chemical weathering of rocks from the adjoining basement complex that causes salinized groundwater and river water [63]. table iii: concentrations of heavy metals in drinking water samples detected by icpoes sample location concentration (mg/l) cr cu fe mn mo al sr zn ba se li v s1 bdl 0.021 0.009 0.001 0.002 bdl 1.241 0.001 0.014 0.027 0.004 0.001 s2 0.009 0.012 0.071 0.009 0.005 bdl 11.940 0.386 0.006 0.042 0.075 0.005 s3 0.008 0.011 0.067 0.004 0.006 bdl 6.713 0.065 0.016 0.042 0.078 0.005 s4 bdl 0.015 0.011 0.001 0.002 bdl 4.398 0.020 0.015 0.038 0.047 0.004 s5 bdl 0.015 0.011 0.001 0.002 bdl 4.971 0.015 0.013 0.040 0.049 0.005 s6 bdl 0.013 0.013 0.001 0.001 bdl 5.804 0.037 0.015 0.038 0.055 0.005 s7 bdl 0.019 0.011 0.001 0.001 bdl 1.738 0.001 0.062 0.040 0.026 0.003 s8 bdl 0.021 0.736 0.018 0.003 0.55 1.190 0.006 0.094 0.035 0.033 0.004 s9 bdl 0.022 0.012 0.001 0.001 bdl 1.143 0.015 0.065 0.034 0.027 0.003 s10 0.021 0.016 0.034 0.003 0.002 bdl 3.884 0.103 0.017 0.039 0.040 0.008 s11 bdl 0.023 0.014 0.001 0.004 bdl 1.555 0.093 0.042 0.038 0.023 0.006 s12 0.001 0.024 0.015 0.001 0.003 bdl 1.601 0.006 0.045 0.034 0.019 0.007 s13 0.002 0.028 0.016 0.001 0.002 bdl 1.046 0.001 0.056 0.034 0.011 0.008 s14 bdl 0.016 0.017 0.001 0.004 bdl 4.631 0.091 0.012 0.044 0.035 0.003 s15 bdl 0.014 0.034 0.001 0.002 bdl 4.089 0.034 0.015 0.038 0.027 0.003 s16 bdl 0.013 0.112 0.020 0.004 0.05 5.466 0.004 0.063 0.037 0.043 0.003 hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region 46 uhd journal of science and technology | may 2018 | vol 2 | issue 2 especially in rural areas in the study region, the agricultural runoff has happened on a limited scale. other anthropogenic activities consequences such as wastewater mixing or leakage have not considerable effects on the groundwater quality. this comes from the fact that no significant human actions present considerable accumulations of chemical elements like cations in water resources at these areas. these variations in cations concentrations are well-known phenomenon, and it has been observed by previous works [64], [65]. 3.2. statistical analysis a one-way analysis of variance anova function of excel 2013 was used in this work to validate the significant differences among sampling locations. statistically analyzed results of water samples using anova were at 95 % confidence level [2]. the variance analysis results showed that all tested heavy metals and chemicals were substantially different at p < 0.05. p = 0.00722, f value was 2.187, and f crit was 1.7058. one-way anova technique was applied in this work because there is only one variable is tested which is the spatial variance of the study area without replication for each sample. fig. 2 illustrates the most significant variance of the investigated heavy metals and chemicals in drinking water samples. fe and al levels showed an interesting deviation at location s8 as mentioned before. location s8 is a water treatment plant at kalar city that takes raw water from the nearby diyala-sirwan river. this distinction refers to the impact of discharge by the existed construction materials plants situated along the river bank. similarly, it refers to potential contamination by aluminum-rich material used in water treatment. fig. 2 shows high concentrations of particular heavy metals such as se and sr in most water samples in the study area. as there is no significant anthropogenic activity can cause these elevations in the region. it is assumed that heavy metals come from natural geogenic sources. ca and mg levels are high almost all over the study region as presented in fig. 2. these high levels of ca and mg are typically caused by geological properties of the region [42]. the cm analysis was performed to figure out the relationships among the water sample contaminants. a correlation coefficient nearer to 1.0 means perfect linear relation between the related parameters. normally, a correlation coefficient of 1.0 is achieved for parameters related with itself. table v illustrates the correlation coefficients matrix between heavy metals and other parameters. relationships of coefficients >0.5 between two investigated parameters at 5% level of significance and p < 0.05 are considered significant. such coefficients were generated between certain pairs of heavy metals or chemical parameters in the water samples. strong positive relationships (>0.7) between heavy metals were observed for example (fe with al), (li with sr and se). at the same time, strong negative relationships (<0.8) were found such as (sr with cu), and (cu with li). correlations at p < 0.05 were obtained for the tested heavy metals and chemical parameters. there were significant positive correlations between se, li, and sr with all tested chemical parameters in this study except p. furthermore, significant negative correlations exist between cu with all tested chemical parameters in this study except p. table iv: concentrations of chemical parameters in water samples sample location concentration (mg/l) ca k mg na p t. hardness s1 36.484 0.777 14.811 5.340 0.043 151.816 s2 141.971 2.649 47.825 79.775 0.029 551.01 s3 139.498 4.640 48.077 125.530 0.032 545.86 s4 135.276 2.591 48.693 59.302 0.037 537.712 s5 138.011 2.661 48.867 58.618 0.036 545.263 s6 157.008 2.998 61.069 66.198 0.034 642.784 s7 93.794 1.461 23.024 15.805 0.040 328.765 s8 59.108 2.396 17.636 14.340 0.054 219.959 s9 60.182 1.244 16.340 9.985 0.048 217.330 s10 81.322 1.932 48.889 112.068 0.039 403.631 s11 48.927 1.054 18.055 15.993 0.049 196.224 s12 50.530 1.081 16.222 17.907 0.065 192.716 s13 39.457 1.028 9.914 15.094 0.068 139.171 s14 139.603 1.765 52.928 41.571 0.039 565.893 s15 160.174 2.528 62.035 64.985 0.036 654.660 s16 175.406 5.042 69.757 106.877 0.045 724.400 hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 47 these significant correlations confirm the source of the heavy metals and chemical parameters in water samples are the geological structure or composition of rocks, soil. heavy metals enrichment of al and fe in the water sample s8 is attributed to small projects constructed beside diyala-sirwan river, as most the effluents are washed by surface runoff and goes into the river. aluminum-rich materials utilized on the site of the water treatment plant could be the second source of al [66]. 3.2.1. cluster analysis the ca analysis can identify any similarity that exists among clustered results. by showing considerable internal clusters homogeneity and significant external heterogeneity concerning clusters. hierarchical agglomerative clustering is applied to find any spatial similarity between water samples regarding their locations in the study area. from the results illustrated in fig. 3, the dendrogram of hierarchical cluster analysis has generated three distinct clusters. a similarity of water samples in term of sampling locations are classified into three principal cluster groups. the main groups of sample locations are cluster 1, contains sampling locations of s2, s3, and s4, s5, s6, s10, s14, s15, and s16. cluster 2, includes one sampling location of s8. cluster 3, combines sampling locations of s1, s7, s9, s11, s12, and s13. it can be deduced from the cluster analysis that the spatial division was based principally on the type of heavy metals contamination. as the location s8 in cluster 2 is a water treatment plant constructed at downstream of a river, this sample showed different contamination (high levels of fe and fig. 2. mean concentrations spatial distribution for some heavy metals and chemical parameters with indicating mpl limit; (a) for iron, (b) for strontium, (c) for aluminum, (d) for selenium, (e) for calcium, and (f) for magnesium. a b dc e f hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region 48 uhd journal of science and technology | may 2018 | vol 2 | issue 2 al) from other locations. in cluster 3, groundwater samples were of low concentrations of heavy metals. 3.2.2. contamination evaluation indices contamination evaluation indices hpi, hei, and c d in this work based on the who guidelines for drinking water and other standards taken from the literature. mean values of the heavy metals were used to calculate contamination evaluation indices hpi and hei while mean values of heavy metals and chemical parameters were used to calculate contamination degree index c d . table vi illustrates the values of hpi, hei, and c d . hpi for the heavy metals in water samples ranges from 54.986 to 24.564 with a mean value of 25.48. location s8 has the highest hpi value. hpi value equals 100 is considered as a critical potential pollution with respect to heavy metals concentrations [41]. no location in the study area has exceeded this limit. nevertheless, as stated by herojeet et al. [67] hpi results were classified as low (<15), medium (15–30), or high (>30) pollution. in this case, only two locations (s1 and s16) are not highly contaminated by heavy metals. it is worth mentioning here; highest hpi value comes from water treatment plant at kalar city that takes raw water from diyala sirwan river. the elevated hpi at this site is in accord with the statistical analysis results. high hpi is due to the impact of the building material plants at a river bank. otherwise, it caused by materials used in water treatment. other groundwater samples have also registered high hpi values at locations s10 and s13, where the heavy metal table v: correlation matrix between heavy metals and chemical parameters in analyzed water samples parameters cr cu fe mn mo al sr zn ba se li v ca k mg na p t. hard. cr 1 cu −0.27 1.00 fe −0.07 0.09 1.00 mn 0.02 −0.22 0.71 1.00 mo 0.23 −0.34 0.15 0.39 1.00 al −0.13 0.15 0.99 0.66 0.07 1.00 sr 0.36 −0.81 −0.14 0.22 0.53 −0.23 1.00 zn 0.48 −0.39 −0.08 0.11 0.52 −0.15 0.78 1.00 ba −0.31 0.56 0.60 0.50 −0.17 0.62 −0.59 −0.43 1.00 se 0.28 −0.62 −0.12 0.00 0.43 −0.17 0.63 0.48 −0.34 1.00 li 0.38 −0.82 0.04 0.25 0.52 −0.04 0.87 0.56 −0.40 0.71 1.00 v 0.53 0.26 −0.08 −0.15 0.10 −0.10 0.02 0.17 −0.01 0.17 0.10 1.00 ca 0.00 −0.90 −0.15 0.21 0.24 −0.21 0.74 0.24 −0.45 0.66 0.70 −0.25 1.00 k 0.12 −0.79 0.16 0.57 0.48 0.09 0.61 0.09 −0.11 0.44 0.74 −0.12 0.79 1.00 mg 0.19 −0.90 −0.17 0.21 0.22 −0.23 0.70 0.23 −0.52 0.59 0.64 −0.14 0.95 0.76 1.00 na 0.60 −0.82 −0.12 0.28 0.46 −0.21 0.71 0.33 −0.45 0.55 0.77 0.18 0.71 0.82 0.81 1.00 p −0.25 0.89 0.21 0.05 −0.17 0.26 −0.71 −0.45 0.66 −0.57 −0.70 0.38 −0.72 −0.49 −0.70 −0.60 1.00 t.hard. 0.08 −0.91 −0.16 0.22 0.24 −0.22 0.73 0.24 −0.48 0.64 0.69 −0.21 0.99 0.79 0.98 0.76 −0.72 1 correlations are significant at a level of (p<0.05) fig. 3. hierarchical cluster analysis dendrogram of water samples locations. hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 49 pollution comes from natural sources and much less from domestic waste and agricultural runoff. the lowest hpi recorded in the study region was for the water sample s1, s1 which is a spring water located at north of the region and no anthropogenic pollution exist. table vii depicts the deviation and percentage deviation from mean values for hpi, hei, and c d indices. from table vii, it is noticed that eight locations (s3, s5, s6, s8, s10, s11, s12, and s13) have hpi values above the hpi mean value. in other words, it can be said that 50% of the study area is significantly affected by heavy metals pollution in drinking water sources according to the hpi index. the classification of overall drinking water quality per hei is low (<1.24), medium (1.24–2.48) and high (>2.48) polluted [68]. the quality of drinking water in regard to hei at the majority of sampling locations (s2, s3, s4, s5, s6, s8, s10, a14, and s16) is in the high class (hei >2.45). the water resources in these locations are surface water and groundwater. elevated heavy metals concentrations are observed in certain water samples. the maximum hei value is 8.441 for the location s8. location s8 has also the highest hpi value; the reason for the rise is mentioned previously. substantially, the lowest hei value of 1.179 for surface water sample from the location s1, considering all sampling locations. water source at this location is spring water; hence, it is the less contaminated site in the study area. table vii shows that only five locations (s2, s3, s8, s10, and s16) have hei values above the mean value. their percentage of deviation from hei mean value ranges from 7.07% at s8 to 179.07% at s10. by considering hei results, among the highest five polluted locations; two of them are surface water of s8 and s16. table vi: values of pollution indices sample location hpi hei cd s1 24.564 1.179 −14.839 s2 39.425 5.298 −5.100 s3 41.324 3.817 −5.534 s4 37.348 2.485 −8.416 s5 40.949 2.743 −8.095 s6 40.009 2.907 −6.778 s7 35.160 1.840 −12.117 s8 54.986 8.441 −6.495 s9 32.750 1.556 −13.625 s10 52.622 3.238 −8.035 s11 43.015 1.992 −13.300 s12 44.334 1.965 −13.218 s13 47.210 1.915 −13.852 s14 36.981 2.687 −8.341 s15 33.811 2.445 −7.170 s16 29.678 3.886 −3.920 mean 39.635 3.023 −9.302 standard deviation 7.916 1.773 3.593 min. 24.564 1.179 −14.839 max. 54.986 8.441 −3.920 hpi: heavy metals pollution index, hei: heavy metals evaluation index, c d : contamination index table vii: mean deviation values of contamination indices sample location hpi hei cd mean deviation % mean deviation mean deviation % mean deviation mean deviation % mean deviation s1 −15.071 −38.025 −1.846 −61.022 −5.537 59.522 s2 −0.211 −0.531 2.274 75.171 4.202 −45.177 s3 1.688 4.260 0.793 26.213 3.768 −40.503 s4 −2.288 −5.771 −0.540 −17.838 0.887 −9.531 s5 1.314 3.315 −0.282 −9.327 1.208 −12.982 s6 0.374 0.943 −0.118 −3.893 2.524 −27.134 s7 −4.475 −11.291 −1.185 −39.166 −2.815 30.263 s8 15.351 38.730 5.416 179.069 2.808 −30.181 s9 −6.885 −17.371 −1.469 −48.572 −4.323 46.474 s10 12.986 32.765 0.214 7.067 1.267 −13.619 s11 3.379 8.526 −1.033 −34.145 −3.997 42.973 s12 4.698 11.854 −1.059 −35.021 −3.916 42.100 s13 7.574 19.110 −1.109 −36.680 −4.550 48.912 s14 −2.654 −6.697 −0.337 −11.150 0.961 −10.333 s15 −5.824 −14.694 −0.580 −19.174 2.132 −22.918 s16 −9.957 −25.122 0.861 28.466 5.383 −57.864 hpi: heavy metals pollution index, hei: heavy metals evaluation index, c d : contamination index hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region 50 uhd journal of science and technology | may 2018 | vol 2 | issue 2 a difference between hpi and hei results appears pursuant to divergence in results at several locations see fig. 4. this great variation was increased by taking in account ideal values of permissible limits of heavy metals with hpi calculations. these permissible limits are subject to variations according to different accredited authorities. all measured parameters were implied: the heavy metals and chemical parameters. characterizing c d values were made as previous works. c d was classified into three groups: low (c d <1), medium (c d = 1–3), and high (c d >3) [44], [69], [70]. c d results range between −14.839 and −3.920. the mean value is −9.302, with >60% of water samples falling above the mean value. percentage of deviation from mean value ranges from 57.86% at s16 to 9.53% at s4 (table vii). the previously proposed classification of c d consider all water samples (surface water and groundwater) are low; as they did not exceed 1.0. therefore, the study area is considered as slightly polluted with respect to all pollutants (heavy metals and chemical). from fig. 4, the results of c d show a convergence with hei results. the two indices did not take into account the ideal limits for tested parameters. different evaluations were observed between hei and c d . the differences were rising from the fact that c d is combining the chemical parameters in the pollution assessment calculations. the obtained results led to figuring out the impact of the heavy metals on the drinking water quality in garmian region. the contamination is due to the nature of the soil and underlying rocks compositions. weathering and leaching of soluble salts from the soil and underlying rocks may reach the water resource in the region. anthropogenic activities impact was observed in water quality in the results of hpi, hei, and c d for the location s8 particularly the minor industrial activities near diyala-sirwan river. 4. conclusion • in this work, the used statistical methods were: cm and cluster analysis ca. the obtained results showed that the drinking water quality in most locations of the study area is polluted at different levels. • concentrations of some heavy metals such as fe, al, li, sr, and se are considerably high at certain locations in the study area. for example location s8, which is the water treatment plant of kalar city recorded the highest levels of al and fe. correspondingly, chemical parameters concentrations of ca and mg are high in most the tested water samples in the study area. • in general, water pollution indices, hpi, hei, and cd have provided an over view of the extent of contamination at all locations in the garmian area. for most of these locations, pollution indices have made a convergent evaluation and their values showed considerable correlation. nevertheless, three extreme results have appeared in the locations s14, s15, and s16 of hpi with hei and cd. the variances in these locations are most likely due to differences in the heavy metals concentrations assessment schemes used by hpi. according to hpi contamination evaluation level, all the investigated locations are not critically polluted in view of the fact that hpi is <100 as proposed by prasad and bose [41]. where the hpi is between 24.564 and 54.986. according to cd, all study locations are occurred within low polluted level cd index places all the locations within low polluted levels (cd >3 for all the study area). the third pollution evaluation index hei has a more reliable pollution categorization for water samples, in which low (<1.24), medium (1.24–2.48), and high (>2.48). as per hei evaluation levels, 44% of location is critically polluted and 38% of the locations are moderately polluted. all surface water samples s4, s8, and s16 are classified as critically polluted, where the highest level of contamination was observed at location s8 (hei = 8.441). hence, hei proved to be more appropriate for heavy metal pollution evaluation, as the unwieldy way of calculation processed by cd and hpi. • statistical analysis by correlation coefficient matrix and cluster analysis ca was applied in the study. these methods detected that heavy metals and other contaminants in drinking water are mostly released from natural geological sources. especially, weathering and leaching of soils and underlain rocks. while anthropogenic activities sources were only found in the locations s8 and s16. the ca and cm analytical results gave a concrete agreement between them for all the data sets investigated. fig. 4. spatial distribution of heavy metals pollution index, heavy metals evaluation index, and contamination index on sampling locations of study area. hayder mohammed issa and azad h. alshatteri: drinking water quality of garmian region uhd journal of science and technology | may 2018 | vol 2 | issue 2 51 • drinking water samples studied in this work are the main source for residents living in rural and urban locations of garmian region. detection of high or critical levels in collected samples means there is a significant potential for drinking water contamination by heavy metals in the area. hence, this study leads to establish a reliable database on heavy metals and their potential sources that leaching into the water resources of garmian region. these findings give a rigid base for any further studies performed on the drinking water quality in same area to reach a broad understanding of natural and anthropogenic impacts on drinking water quality in garmian region. the importance of comparative evaluation by hpi and statistical methods is proved to be significant in such water quality studies. references [1] h. effendi. “river water quality preliminary rapid assessment using pollution index”. procedia environmental sciences, vol. 33, pp. 562-567, 2016. 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[70] l. s. clesceri, a. d. eaton, a. e. greenberg, a. p. h. association, a. w. w. association and w. e. federation. standard methods for the examination of water and wastewater. american public health association, washington, dc, 1998. tx_1~abs:at/tx_2:abs~at 16 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 1. introduction as the globe experiences rapid technological advancement, the financial industry has capitalized on these developments. as a byproduct of technological progress, cryptocurrencies are a valuable contribution to financial markets and the global economy. bitcoin has the highest market capitalization among all cryptocurrencies, estimated at $930 billion on december 28, 2021 [1]. the exchange or trading of bitcoin and other cryptocurrencies has attracted the interest of investors in global financial markets. likewise, market research analysts have become interested in cryptocurrencies and their interactions with financial market indicators. although the impact of bitcoin on gold prices, the telecommunications market, the stock market index, and the performance of insurance companies is lower, the insurance industry is uniquely positioned to benefit from blockchain technology [2]. the financial sector has made extensive use of technological advancements in recent years. due to technological progress, cryptocurrency is a valuable contribution to analyzing the performance of bitcoin to gold prices, the telecommunications market, the stock price index, and insurance companies’ performance from (march 1, 2021–september 4, 2023) hawre latif majeed1, diary jalal ali1, twana latif mohammed2 1assistant lecture, accounting department, kurdistan technical institute, kurdistan region (krg), iraq, 2assistant lecture, information technology department, kurdistan technical institute, kurdistan region (krg), iraq a b s t r a c t managing cryptocurrencies by financial intermediaries offer numerous benefits to global financial markets and the economy. among all cryptocurrencies, bitcoin stands out with the highest market capitalization and a weak correlation to other assets, making it an attractive option for portfolio diversification and risk management. this research aims to examine the impact of bitcoin on the nasdaq gold price (gc), the telecommunications market (ixut), and insurance company performance (ixis) through the analysis of secondary data from march 1, 2021, to september 4, 2023. the data were obtained from https://www.investing.com; statistical software e views applied various econometric methods to the data. the results suggest a positive correlation between bitcoin and the other variables, indicating that bitcoin can significantly expand investment opportunities and drive economic growth. this study highlights the importance of considering cryptocurrencies, especially bitcoin, as a viable option for investment diversification and risk management in financial markets. index terms: cryptocurrencies, bitcoin, gold price, telecommunications, stock price, insurance. access this article online doi: 10.21928/uhdjst.v7n2y2023.pp16-31 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 majeed hl, ali dj, mohammed tl. this is an open access article distributed under the creative commons attribution noncommercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology corresponding author’s e-mail: twana latif mohammed, assistant lecture, information technology department, kurdistan technical institute, kurdistan region (krg), iraq. email: twana.mohammed@kti.edu.iq received: 27-04-2023 accepted: 26-06-2023 published: 20-08-2023 majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 17 financial markets and the global economy. the exchange or trading of bitcoin and other cryptocurrencies has become prevalent in global financial markets, attracting practitioners. economic analysts are interested in cryptocurrencies and the interactions between cryptocurrencies and financial market indicators. cryptocurrencies, in 2009, bitcoin developed cryptographically secure digital currency [3]. the 2008–2009 global financial crisis and the 2010–2013 european sovereign debt crisis made bitcoin popular among practitioners and economic agents. bitcoin-accepting businesses have also grown. despite government limitations, a terrible reputation, and several hacks, bitcoin’s popularity has grown. by providing indemnification or encouraging savings, the insurance business is vital to any economy. its premium pooling makes it a prominent institutional investor. insurance companies serve customers. it is also a financial entity that invests insured money for profits, helping economic and social advancement. bitcoin is attracting investors despite its young origin. international investors now sell precious metals and buy bitcoin. bitshares, dash, ethereum, litecoin, mixin, moreno, peercoin, and zcash, have emerged due to bitcoin’s popularity [3]. most virtual currencies use blockchain technology like bitcoin and aim to equal or improve its features. cryptocurrencies need cointegration and convergence tests for numerous reasons. gold and cryptocurrency values are interconnected because they cointegrate. since cryptocurrency and gold have a long-term relationship, linking them is a good idea. convergence between cryptocurrency and gold prices suggests that low-priced cryptocurrencies will rise more quickly [4]. most countries’ economic progress and global developments have internationalized and regulated the insurance business. most countries have understood insurance’s economic and social value and fostered, developed, and encouraged the technical advances that have accelerated development, including the insurance sector. dash aims to speed up transaction processing and protect anonymity, whereas litecoin conserves central processing unit power for mining. gold miners’ stocks, etfs, and actual gold can be invested today. thus, explaining why gold was an inevitably valued hedge while it was used in the monetary system and why it remained a hedge afterward is beneficial. gold is traditionally used to buffer portfolios against volatile markets and investor anxiety [5]. since its introduction, bitcoin’s high returns have made gold less appealing to investors. investors have preferred bitcoin over gold in the recent decade due to its 100-fold higher return. despite bitcoin’s greater short-term volatility than gold’s, its long-term price evolution is anticipated to follow gold’s [6]. as the globe digitizes, traditional currencies and physical money are becoming less popular. bitcoin prices rose from under us$1000 in 2014 to over us$17,000 in 2018.2 dash prices rose from below us$2 in 2014 to above us$400 in 2018 [7]. gold prices were between us$1050 and us$1400 throughout the same period. forecasting, economic modeling, and policymaking can benefit from cryptocurrency and gold price convergence. this research examines how bitcoin affects the telecommunications industry, stock price index, insurance company performance, and convergence assumptions between cryptocurrency and gold prices. from a univariate perspective, we first evaluate the fractional order of integration in the stochastic characteristics of gold and cryptocurrency prices. 1.1. problem of the study this research seeks to determine if bitcoin impacts gold prices, telecommunications, stock prices, and insurance company performance and if bitcoin can be predicted using economic data. thus, the question is how bitcoin relates to other variables or if there is any link. since granger causality shows that one event can influence another, understanding its direction might improve market comprehension. finding a correlation between the two may allow investors and economists to predict bitcoin prices using gold’s past pricing. 1.2. aims of the study this study aims to examine the effects of bitcoin on the performance of insurance companies, the telecommunications market, the stock price index, and gold prices. based on how these variables interact and behave, by developing the following hypothesis: 1. hypothesis (h1): bitcoin has no significant effect on gold price. 2. hypothesis (h2): bitcoin has no significant effect on telecommunications stock index price. 3. hypothesis (h3): bitcoin has no significant effect on insurance companies. 2. literature review this section discusses the overview literature. a comprehensive literature review was conducted using a systematic approach to ensure objectivity and methodological rigor in locating majeed, et al: bitcoin: stock market effects 18 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 and evaluating relevant academic literature regarding the correlation between gold prices, the telecommunications market, the stock price index, and insurance companies performance. several studies have explored this relationship from various angles, providing valuable insights into the subject matter. bams, blanchard, honarvar, and lehnert (2017) examined how gold prices affect insurance company stock performance, stressing economic fundamentals and investor mood. studied how the telecommunications market affects stock price indexes, stressing market dynamics and regulatory strategies [23]. boonkrong, arjrith, and sangsawad (2020) examined the relationship between gold prices and the telecoms market, revealing potential spillover effects. the literature review synthesizes these and other related studies to identify significant factors, mechanisms, and theoretical frameworks. advanced filters and the "peer-reviewed journals" option ensured highquality research. despite the paper's novelty in the academic world, a typical method was used to choose relevant papers based on their publication dates, focusing on current studies to include the newest scientific achievements [24]. 2.1. bitcoin bitcoin accounts for 36.33% of the market capitalization of cryptocurrencies, down from 80% in june 2016. thus, bitcoin-specific studies exist. bitcoin is a decentralized digital currency created in 2009 by an unknown person using satoshi nakamoto’s pseudonym. it is based on a peer-to-peer network, where transactions take place directly between users without the need for intermediaries such as banks or other financial institutions. bitcoin has gained increasing popularity over the years, and its use has spread across different industries, including finance, e-commerce, and even healthcare. this literature review examines the current state of research on bitcoin, its impact on various industries, and its prospects. one of the key features of bitcoin is its decentralized nature. bitcoin transactions are verified by a network of users, who use complex algorithms to confirm and record transactions on a public ledger known as the blockchain. this feature has made bitcoin attractive to many users, particularly those concerned about traditional financial institutions’ role in controlling their money. several studies have examined the impact of bitcoin on the financial industry, and many have suggested that bitcoin has the potential to disrupt traditional banking systems. for instance, ali et al. [8] found that bitcoin could reduce the costs associated with traditional payment systems, particularly cross-border payments. the study noted that traditional payment systems involve a complex network of intermediaries, which can result in high fees and slow processing times. conversely, bitcoin allows for fast and cheap cross-border payments, which could benefit individuals and businesses in developing countries. another area where bitcoin has shown potential is e-commerce. several studies have examined the use of bitcoin in online marketplaces, such as the dark web. one study by böhme et al. [9] found that bitcoin was the dominant currency used in illegal online marketplaces, particularly for purchasing drugs and other illicit goods. however, the study also noted that bitcoin was used for legitimate transactions, particularly in countries with unreliable traditional payment systems. despite its potential, bitcoin has also faced several challenges. one of the biggest challenges has been its association with illegal activities, particularly money laundering and terrorism financing. several studies have examined the extent to which bitcoin is used for illegal activities, and many have suggested that the currency is more anonymous than some may believe – tracing bitcoin transactions to real-world identities as possible, mainly when the transactions involve exchanges between bitcoin and traditional currencies. another challenge facing bitcoin is its volatility. the price of bitcoin has fluctuated significantly over the years, with several high-profile crashes and booms. this volatility has made bitcoin less attractive to many investors, particularly risk-averse investors. several studies have examined the factors that influence the price of bitcoin, and many have suggested that a combination of supply and demand factors and speculative activity drives it. despite these challenges, many experts believe that bitcoin has a bright future. several studies have examined the potential of bitcoin to revolutionize various industries, including healthcare. for instance, in a study by elahi and hasan (2018), bitcoin could facilitate secure and efficient medical record-keeping, particularly in countries with weak health systems. other studies have examined the potential of bitcoin to facilitate charitable giving and crowdfunding. 2.2. gold gold has been a significant part of human culture and society for thousands of years. it has been used for various purposes, including jewelry, currency, and investments. gold has always been associated with wealth, power, and majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 19 prestige, and its value has remained high throughout history. this literature review explores the historical significance, geological properties, mining and extraction techniques, and the uses and applications of gold. historical significance: gold has been valued and treasured by civilizations for thousands of years. it has been used for jewelry, religious artifacts, and currency. the ancient egyptians believed that gold was the flesh of the gods, and it was used in constructing temples and tombs. the aztecs and incas also valued gold and used it for jewelry and religious artifacts. in europe, gold was used as currency, and during the gold rush in the 19th century, it was used as a means of payment for goods and services. gold continues to be highly valued today, and it is often used as a store of value and as a haven asset during times of economic uncertainty [10]. 2.2.1. geological properties gold is a chemical element with the symbol au, one of the least reactive chemical elements. it is a soft, dense, yellow metal with a high luster. gold is highly malleable and ductile, meaning it can be easily shaped and formed into various shapes and sizes. it is also a good conductor of electricity and does not corrode or tarnish. gold is primarily found in the earth’s crust and is often associated with other minerals, such as silver, copper, and zinc. gold deposits are typically found in three main types of geological settings: veins, placers, and disseminated deposits [11]. 2.2.2. mining and extraction techniques gold mining and extraction techniques have evolved. in ancient times, gold was extracted by panning, where goldbearing sand or gravel was placed in a shallow pan and swirled around to separate the gold from the other minerals. today, gold is typically extracted from large deposits using various techniques, including open-pit mining, underground mining, and placer mining. open-pit mining involves the removal of large amounts of soil and rock to access the gold-bearing ore [12]. underground mining uses tunnels to access the ore, while placer mining involves water to separate the gold from the other minerals. 2.2.3. uses and applications gold has a wide range of uses and applications. it is primarily used for jewelry, decorative purposes, and various industrial applications, including electronics, medical devices, and aerospace technology. gold is also used as a value store and haven asset during economic uncertainty [13]. in addition, gold is used to produce coins and bullion, which are often purchased as investments. 2.3. telecommunications companies telecommunications companies have been integral to the modern world’s communication infrastructure for decades. these companies provide the necessary tools and infrastructure to enable people to communicate and exchange data across vast distances. telecommunications companies have played a critical role in facilitating the digital transformation of modern society. this literature review aims to provide an over view of the current state of the telecommunications industry and highlight some of the critical challenges and opportunities facing telecommunications companies [14]. the telecommunications industry has undergone significant changes in recent years, driven by technological advancements, consumer behavior, and increased competition. the industry has seen the rise of new players, such as overthe-top (ott) providers, which have disrupted traditional business models. ott providers offer messaging, voice calls, and video streaming over the internet, often bypassing traditional telecommunications networks. this has forced telecommunications companies to adapt to new business models, such as offering bundled services, developing new value-added services, and focusing on customer experience. one of the critical challenges facing telecommunications companies is the need to invest continually in new infrastructure to keep up with the increasing demand for data and connectivity. telecommunications companies must invest in new networks and technologies to remain competitive with the rise of new technologies such as 5g, the internet of things, and artificial intelligence (ai). at the same time, they must balance this investment against the need to maintain profitability and shareholder returns [14]. telecommunications companies face increasing regulatory scrutiny, particularly concerning net neutrality and data privacy. governments around the world are implementing regulations to protect consumers’ privacy and ensure that telecommunication companies provide fair and open access to the internet. in addition, the increased focus on data privacy has led to increased demand for secure communications solutions, which has created new business opportunities for telecommunications companies. the telecommunications industry is also experiencing a shift toward digital transformation. companies increasingly invest in cloud computing, ai, and big data analytics technologies to improve operations and offer new services. these technologies enable telecommunications companies majeed, et al: bitcoin: stock market effects 20 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 to improve network efficiency, offer personalized services, and enhance the customer experience. despite these challenges, telecommunications companies are well-positioned to benefit from the increasing demand for connectivity and the digital transformation of modern society. companies that can successfully adapt to new business models and invest in new technologies will be well-positioned to capture new opportunities and maintain market share. the telecommunications industry is expected to grow in the coming years, driven by increasing demand for connectivity, the adoption of new technologies, and the ongoing shift toward digital transformation [15]. 2.4. insurance insurance is an agreement between an individual or an organization and an insurer, which promises compensation or protection against a specific loss in exchange for regular payments, known as premiums. the concept of insurance has been around for centuries, with records of various types of insurance being used as far back as ancient china and babylon. insurance is essential in managing risk, especially for individuals and businesses that face significant financial loss in an unexpected event. insurance companies are organizations that provide insurance products and services to customers. they collect premiums from policyholders and use the funds to pay for claims made by customers who experience losses covered by their policies. insurance companies play a key role in society, as they provide a safety net for individuals and businesses, allowing them to recover from unexpected losses. insurance companies, including life insurance, health insurance, property and casualty insurance, and auto insurance, among others, offer various types of insurance. each type of insurance serves a specific purpose and has unique features and benefits. for instance, life insurance provides financial protection to the policyholder’s beneficiaries in the event of their death, while health insurance covers medical expenses incurred by the insured. another study by bashaija [16] investigated the impact of insurance on the financial performance of small and medium-sized enterprises (smes) in india. the study found that smes that had insurance coverage had better financial performance than those without insurance. the authors attributed this to the fact that insurance provided smes with financial protection against unexpected losses, allowing them to focus on business operations and growth. the role of insurance companies in managing risk has also been extensively studied. demirgüç-kunt and huizinga [17] the study examined the impact of insurance on financial stability. the study found that insurance companies play a crucial role in promoting financial stability by providing a buffer against unexpected losses, thereby reducing the risk of systemic financial crises. in addition, the impact of insurance companies on the economy has been investigated. a study by hamadu and mojekwu [18] examined the insurance industry’s contribution to economic growth in the united states. the study found that the insurance industry contributes significantly to economic growth, as it provides financial protection and risk management services to individuals and businesses, thereby promoting investment, innovation, and entrepreneurship. 2.4.1. the impact of bitcoin on the gold price the rise of digital currencies has become a significant topic of interest among investors and academics. the most popular cryptocurrency has grown and is now widely used as a medium of exchange and store of value. despite the increased adoption of digital currencies, gold remains a valuable asset class for investors. the relationship between bitcoin and gold has been debated among researchers. this literature review aims to examine the impact of bitcoin on the price of gold. 2.4.2. bitcoin and gold: a comparison bitcoin and gold have several similarities and differences that affect their prices. gold has been a store of value for centuries and is viewed as a safe-haven asset during economic uncertainty. gold prices are affected by macroeconomic factors such as inflation, interest rates, and geopolitical events. in contrast, bitcoin is a relatively new digital currency that has gained popularity due to its decentralization, security, and limited supply. bitcoin prices are affected by technological advancements, regulatory changes, and investor sentiment. several studies have examined the relationship between bitcoin and gold prices. some researchers have argued that bitcoin is a substitute for gold and can be used as a hedge against inflation and economic uncertainty. others have argued that bitcoin and gold have different characteristics and should not be considered substitutes. several studies have examined the impact of bitcoin on gold prices. in a study by bouri et al. [19], the authors used a vargarch model to examine the relationship between bitcoin and gold prices. the results showed a positive relationship majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 21 between bitcoin and gold prices in the short run, but the relationship becomes negative in the long run. the authors argued that bitcoin and gold are not substitutes and that the long-term negative relationship is due to differences in the characteristics of the two assets. in contrast, a study by bouri et al. [19] found evidence that bitcoin is a hedge against gold during economic uncertainty. the authors used a var model to examine the relationship between bitcoin, gold, and the stock market. the results showed that bitcoin is a hedge against gold during times of financial stress but not during normal market conditions. the authors argued that bitcoin could be used as a safe-haven asset in addition to gold. in a more recent study, sökmen and gürsoy [20] examined the impact of bitcoin on gold prices using a cointegration model. the authors found evidence of a long-run equilibrium relationship between bitcoin and gold prices, suggesting that the two assets are substitutes. the authors argued that bitcoin is an attractive investment for investors who prefer digital currencies over physical assets like gold. 2.5. impact of bitcoin on telecommunications companies bitcoin, a decentralized digital currency, has gained significant attention since its inception in 2009. its impact has been felt across various industries, including the telecommunications industry. this literature review aims to explore the impact of bitcoin on telecommunications companies. bitcoin is a cryptocurrency that operates on a decentralized network without a central authority or inter mediary. transactions on the bitcoin network are recorded on a public ledger known as the blockchain, which allows for secure and transparent transactions. bitcoin has been touted as a potential disruptor of traditional financial systems, with its decentralized nature allowing for faster, cheaper, and more secure transactions [21]. the telecommunications industry is one of the industries impacted by the rise of bitcoin. telecommunications companies provide the infrastructure and technology for communication and data transfer. with the rise of bitcoin, telecommunications companies have had to adapt to changes in consumer behavior and demand. one of how bitcoin has impacted telecommunications companies is through blockchain technology. blockchain technology is the underlying technology behind bitcoin, and it has the potential to revolutionize the telecommunications industry. blockchain technology can be used to create secure, transparent, and tamper-proof communication networks, improving telecommunications networks’ security and reliability. telecommunications companies have also had to adapt to consumer behavior and demand changes. with the rise of bitcoin, consumers are increasingly using digital currencies to pay for goods and services. this has led to a shift in consumer demand for telecommunications companies to provide services that cater to the needs of bitcoin users. for example, telecommunications companies have had to adapt to provide secure and reliable bitcoin wallets and payment processing systems [21]. furthermore, the rise of bitcoin has also led to the emergence of new business models in the telecommunications industry. for example, some telecommunications companies have started to offer bitcoin-based services, such as micropayments, remittances, and international transfers. these services are often cheaper and faster than traditional banking services, making them an attractive option for consumers. however, the impact of bitcoin on telecommunications companies is only partially positive. bitcoin has various risks, including fraud, money laundering, and cybercrime. telecommunications companies have had to invest in cybersecurity measures to protect their networks and customers from these risks. furthermore, the regulatory landscape for bitcoin still needs to be determined, which makes it difficult for telecommunications companies to navigate the legal and regulatory requirements associated with providing bitcoin-based services. 2.6. impact of bitcoin on insurance companies the impacts of bitcoin on insurance companies. it will examine how insurance companies use bitcoin, the challenges they face, and the benefits they are experiencing. one of the main ways insurance companies use bitcoin is as a form of payment. bitcoin allows for fast and secure transactions, which helps speed up the claims process. this is particularly useful for international claims, where traditional payment methods can be slow and costly. in addition, bitcoin transactions can be processed 24/7, meaning claims can be paid out quickly, even outside traditional business hours [22]. another way that insurance companies are using bitcoin is as an asset to insure. bitcoin is an emerging asset class, and some insurance companies are starting to offer coverage for it. majeed, et al: bitcoin: stock market effects 22 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 this can be particularly useful for companies that hold large amounts of bitcoin, as it can help to protect them against theft or loss. for example, in 2019, insurance giant lloyd’s of london began offering coverage for cryptocurrency theft. however, there are also challenges associated with using bitcoin in the insurance industry. one of the main challenges is the volatility of bitcoin’s value. bitcoin is a highly volatile asset, and its value can fluctuate rapidly. this makes it difficult for insurance companies to price policies accurately and to set appropriate coverage limits. in addition, the regulatory environment surrounding bitcoin is still evolving, making it difficult for insurance companies to comply with regulations. despite these challenges, there are also benefits associated with using bitcoin in the insurance industry. one of the main benefits is the potential for cost savings. bitcoin transactions are generally cheaper than traditional payment methods, which can reduce insurance companies costs. in addition, using bitcoin can help to streamline the claims process, which can help to reduce administrative costs. 3. research framework this section describes the variables of the study, their sources, and the relationships between independent and dependent variables. https://www.investing.com provided the data. statistical software e views applied various econometric methods to the data. finally, p = 0.05 rejects the null hypothesis and accepts the alternative. if the variable’s p-value exceeds 0.05, neither hypothesis is supported. the following paragraphs provide a concise explanation of these tools to identify the impact of bitcoin on gold prices, the telecommunications market, the stock price index, and the insurance company’s performance. thus, bitcoin impact was substituted by the insurance companies’ performance (ixis) and telecommunications stock index price (ixut), whereas gold price (gc). their findings conclude that independent variables are considerably affected by depending on variables. 3.1. model of the study bitcoin is accepted as independent and insurance, telecommunications, and gold price as dependent variables. 3.2. augmented dickey–fuller (adf) test the first step in using econometric methods is to assess the data’s stationarity, as most economical series are nonstationary and have a unit root at the primary level. this is significant because the presence of a unit root can induce bias in the outcomes of statistical tests such as the granger causality test and the var model, lowering their accuracy. non-stationary series analysis can potentially produce deceptive statistical results. the series’ first difference can be changed into a stationary form to solve this. the augmented dickey–fuller (adf) test is employed in this study to assess the stationarity of time series data. • the null hypothesis (h0) states that the series is nonstationary or has a unit root. • the alternative hypothesis (h1) proposes that the series lacks a unit root and is stationary. 3.3. johansen cointegration test the johansen (1988) cointegration test establishes long-term relationships between variables. the null hypothesis (h0) shows no long-term association between bitcoin and variables. alternative hypothesis (h1) suggests a long-term association between bitcoin and factors. 3.4. granger causality test the granger causality test determines whether two variables have a unidirectional, bidirectional, or non-existent causal link. test significance is 5%. the null hypothesis (h0) asserts that bitcoin has no granger causality with the variables. alternatively, h1 implies no granger causation between bitcoin and the variables. p-value determines null hypothesis acceptance or rejection. the null hypothesis is rejected if p-value is less than the significance level and accepted if it is more extensive. 3.5. vector error correction if the results confirm the cointegration of the variables under investigation, this demonstrates their long-term relationship. the vector error correction model (vecm) investigates this relationship. in this section, the results and data analysis are presented and discussed. 3.6. stationarity of data the adf and phillips-perron (p-p) tests are employed to determine the stationarity of the series. the series is initially discovered to be non-stationary at the primary level. to make the data stationary, the first series differences are calculated. if p-values from the adf and p-p tests are more significant than 0.05, then the following is true: majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 23 • the null hypothesis is adopted at a 5% level of significance. • the statistics associated with the stationarity of the data series are presented in the table below. 3.7. model selection the akaike information criterion (aic) used the model selection method to choose the best model. a total of 500 models were evaluated, and the selected model is an autoregressive distributed lag (ardl) (1, 0, 1, 0) model. this indicates that the lag order for the dependent variable (btc) is one, with no lags for the other independent variables. 3.7.1. coefficients and statistical significance btc (-1): the lagged value of btc (one period ago) has a coefficient of 0.917958. this suggests that a one-unit increase in btc yesterday is associated with an approximately 0.917958-unit increase in btc today. gc: the coefficient for the variable gc is 1.484124, but it is not statistically significant (p = 0.7447). therefore, the inclusion of gc in the model is a relatively insignificant impact on btc (please see table 3). 3.7.2. ixis the coefficient for the variable ixis is 110.0404, which is statistically significant (p-value = 0.0005). this suggests that a one-unit increase in ixis is associated with a 110.0404 unit increase in btc. ixis (-1): the lagged value of ixis (one period ago) has a coefficient of -96.63934, and it is statistically significant (p-value = 0.0019). this implies that a one-unit increase in ixis yesterday is associated with a decrease of approximately 96.63934 units in btc today (please see table 3). 3.7.3. ixut the coefficient for the variable ixut is 0.213805, but it is not statistically significant (p-value = 0.7117). therefore, the inclusion of ixut in the model does not significantly impact btc. c: the constant term has a coefficient of −8104.335, but it is not statistically significant (p-value = 0.3722). therefore, the intercept is not significantly different from zero. 3.7.4. the goodness of fit r2: the model’s coefficient of determination (r2) is 0.934858, which indicates that approximately 93.49% of the variation in btc can be explained by the independent variables in the model (please see table 4). adjusted r2: the adjusted r2 is 0.931950, which considers the degrees of freedom and penalizes including irrelevant variables. s.e. of regression: the standard error of the regression is 3642.267, which measures the average distance between the observed values of btc and the predicted values from the model. prop (f-statistic): the probability associated with the f-statistic is 0.000000, indicating that the overall model is statistically significant. f-statistic: the f-statistic is 321.4634, and its associated p-value is 0.000000, indicating that the overall model is statistically significant. note: p-values in the results do not account for model selection. therefore, caution should be exercised when interpreting the individual variable significance based solely on p-values provided. based on the provided information, the econometric function can be represented as follows: btc = 0.917958 * btc (−1) + 1.484124 * gc + 110.0404 * ixis + (−96.63934) * ixis (−1) + 0.213805 * ixut − 8104.335 + ε the coefficients for each variable are given as 0.917958, 1.484124, 110.0404, −96.63934, 0.213805, and −8104.335. this equation represents an ardl model, where btc is regressed on its lagged value, along with other variables such as gc, ixis, and ixut. the model selection method used was the aic, and the selected model was ardl (1, 0, 1, 0). 3.7.5. test statistic and critical values the adf test statistic is −1.505250. this value is compared to critical values to determine the statistical significance. at the 1% level, the critical value is −3.486551. at the 5% level, the critical value is −2.886074. at the 10% level, the critical value is −2.579931. the test statistic is less negative than the critical values at all significance levels, suggesting that we do not reject the null hypothesis. 3.7.6. coefficients and statistical significance btc (-1): the lagged value of btc (one period ago) has a coefficient of −0.038019. this coefficient is not statistically significant (p-value = 0.1350). therefore, the lagged btc does not significantly impact the different btc. c: the constant term has a coefficient of 1449.238, but it is not statistically significant (p-value = 0.1394). therefore, a constant term in the differenced btc equation is not significant. 3.7.7. the goodness of fit r2: the coefficient of determination (r2) for the differenced btc equation is 0.019158, indicating that approximately majeed, et al: bitcoin: stock market effects 24 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 1.92% of the variation in the differenced btc can be explained by the lagged btc and the constant term. adjusted r2: the adjusted r2 is 0.010703, which considers the degrees of freedom and penalizes including irrelevant variables. f-statistic: the f-statistic is 2.265778, and its associated p-value is 0.134978, which suggests that the overall model is not statistical. 3.7.8. significant other information: mean dependent var: the average value of the differenced btc in the sample is 82.18729. s.d. dependent var: the standard deviation of the differenced btc is 3836.238. sum squared resid: the sum of squared residuals is 1.69e+09, which measures the model’s overall fit. 3.7.9. suggest no autocorrelation prob (f-statistic): the probability associated with the f-statistic is 0.134978, indicating that the overall model is not statistically significant. note: based on the results, there is insufficient evidence to reject the null hypothesis that btc has a unit root, suggesting that btc is non-stationary. bitcoin (-1): the lagged value of bitcoin (one period ago) has a coefficient of 0.917958. this suggests that a oneunit increase in bitcoin yesterday is associated with an approximately 0.917958 unit increase in btc today. gc: the coefficient for the variable gc is 1.484124, but it is not statistically significant (p-value = 0.7447). therefore, the inclusion of gc in the model is relatively minor in bitcoin. the coefficient for the variable insurance companies’ performance. it is 110.0404 and statistically significant (p-value = 0.0005). this suggests that a one-unit increase in insurance companies’ performance is associated with a 110.0404 unit increase in bitcoin-insurance companies’ performance. (-1): the lagged value of ixis (one period ago) has a coefficient of −96.63934, and it is statistically significant (p-value = 0.0019). this implies a one-unit increase in insurance companies’ performance. yesterday is associated with a decrease of approximately 96.63934 units in bitcoin today. the coefficient for the variable telecommunications stock index price. it is 0.213805 but not statistically significant (p-value = 0.7117). the coefficient for the variable telecommunications stock index price. the model has little impact on bitcoin. c: the constant term has a coefficient of −8104.335, but it is not statistically significant (p-value = 0.3722). therefore, the intercept is not significantly different from zero. adf test statistic −1.505250 0.5276 test critical values: 1% level −3.486551 5% level −2.886074 10% level −2.579931 *mackinnon (1996) one-sided p-values. adf test equation method: least squares. variable coefficient standard error t-statistic prob. btc (-1) −0.038019 0.025258 −1.505250 0.1350 c 1449.238 973.7503 1.488306 0.1394 r2 0.019158 mean dependent var 82.18729 adjusted r2 0.010703 s.d. dependent var 3836.238 f-statistic 2.265778 durbin-watson stat 1.803764 prob(f-statistic) 0.134978 btc (-1): the variable btc with a lag of one period has a coefficient of −0.038019. this suggests that a oneunit increase in btc in the previous period is associated with a decrease of approximately 0.038019 units in the current period. the standard error for this coefficient is 0.025258, the t-statistic is −1.505250, and the corresponding p-value is 0.1350. c: the constant term in the model has a coefficient of 1449.238. this represents the intercept or baseline value of the dependent variable (btc) when all other variables in the model are zero. the standard error for this coefficient is 973.7503, the t-statistic is 1.488306, and the corresponding p-value is 0.1394. the results are from unrestricted cointegration rank tests (trace and max-eigenvalue) performed to determine the presence of cointegration among the variables. here is an interpretation of the critical components of the results: unrestricted cointegration rank test (trace): hypothesized no. of ce(s): the number of common trends assumed in the null hypothesis. the tests are conducted for different assumed numbers of common trends. eigenvalue: the eigenvalues associated with the assumed number of common trends. statistic: the test statistic for the trace test. critical value: the critical values correspond to the assumed number of common trends at the specified significance level. prob.**: p-value calculated based on the mackinnon-haugmichelis (1999) method. the trace test compares the sum of the eigenvalues to the critical values to determine the number of cointegrating equations (common trends). the null hypothesis is that there are no cointegrating equations. majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 25 3.7.10. based on the trace test results no cointegration: the test statistic for the case of no cointegration (0 common trends) is 36.81853, which is lower than the critical value at the 0.05 level (47.85613). therefore, we do not reject the null hypothesis of no cointegration at the 0.05 level. 3.7.11. unrestricted cointegration rank test (max-eigenvalue) • hypothesized no. of ce(s): the number of common trends assumed in the null hypothesis. • eigenvalue: the eigenvalues associated with the assumed number of common trends. statistic: the test statistic for the max-eigenvalue test. • critical value: the critical values corresponding to the assumed number of common trends at the specified significance level. • prob.**: p-value calculated based on the mackinnonhaug-michelis (1999) method. the max-eigenvalue test examines the largest eigenvalue to determine the number of cointegrating equations. the null hypothesis is that no more than a certain number of cointegrating equations exist. 3.7.12. based on the max-eigenvalue test results no cointegration: the test statistic for the case of no cointegration (0 common trends) is 19.27991, which is lower than the critical value at the 0.05 level (27.58434). therefore, we do not reject the null hypothesis of no cointegration at the 0.05 level. the trace and max-eigenvalue tests indicate no cointegration at the 0.05 level. this suggests that there is no long-term relationship among the variables being tested (please see table 4). the granger causality test is used to examine the causal relationship between variables. in this case, the test is conducted between btc, gc, ixis, and ixut variables. here is an interpretation of the critical components of the results: null hypothesis: indicates the null hypothesis being tested for granger causality. obs: the number of observations used in the test. f-statistic: the f-statistic calculated for the granger causality test. prob: the p-value associated with the f-statistic. 3.7.13. interpretation of the results 1. gc does not granger cause btc: • f-statistic: 0.30848 • prob: 0.7352. p-value (0.7352) is higher than the significance level (e.g., 0.05), indicating no evidence to reject the null hypothesis. this suggests that gc does not granger cause btc. 2. btc does not granger cause gc: • f-statistic: 0.25926 • prob: 0.7721. similarly, p-value (0.7721) is higher than the significance level, indicating no evidence to reject the null hypothesis. therefore, btc does not granger cause gc. 3. ixis does not granger cause btc: • f-statistic: 0.86716 • prob: 0.4229 p-value (0.4229) is higher than the significance level, indicating that there is no evidence to reject the null hypothesis. therefore, ixis does not granger cause btc. 4. btc does not granger cause ixis: • f-statistic: 0.85998 • prob: 0.4259 p-value (0.4259) is higher than the significance level, sug gesting that there is no evidence to reject the null hypothesis. hence, btc does not granger cause ixis. the remaining results follow a similar pattern for the granger causality tests between telecommunications stock index price and btc, insurance companies’ performance and gold price, telecommunications stock index price and gold price, telecommunications stock index price and insurance companies’ perfor mance, and insurance companies’ performance and telecommunications stock index price. in each case, p-value is higher than the significance level, indicating a lack of evidence to reject the null hypothesis. in summary, based on these granger causality test results, no significant evidence suggests a causal relationship between the variables tested in either direction (fig. 1). the following tables offer the estimations of the influences of the models for three relations. to test the link between bitcoin measured by the insurance companies’ performance (ixis) and telecommunications stock index price (ixut), whereas gold price (gc), correlation, and multiple regression analyses were conducted. table 1, which shows summary model results, indicates our model with the two forecasters. the model is a linear regression model with btc (bitcoin) as the dependent variable and three predictors: ixis (insurance companies’ performance), ixut (telecommunications stock index price), and gc (gold price). the model’s r2 value is 0.588, indicating that the three predictors can explain 58.8% of the variance in btc. the adjusted r2 value is 0.563, majeed, et al: bitcoin: stock market effects 26 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 which considers the number of predictors in the model. the standard error of the estimate is 3896.04060, which represents the average distance that the actual btc values deviate from the predicted values. multiple regression analysis was conducted to examine the relationship between bitcoin (btc) as the dependent variable and three predictors: insurance companies’ performance (ixis), telecommunications stock index price (ixut), and gold price (gc). the summary model results are presented in table 2. the r2 value of 0.93 indicates that approximately 93% of the variance in btc can be explained by the three predictors included in the model. this suggests that the predictors collectively account for a significant portion of the variability in bitcoin prices. to further elaborate on the results, it would be helpful to provide more specific information from table 1, such as the coefficients associated with each predictor variable and their corresponding p-values or confidence intervals. in addition, discussing the statistical significance of the coefficients and their interpretation of the research question would provide a more comprehensive understanding of the model’s findings. the significance and interpretation of the coefficients can be further expanded to provide a deeper understanding of the relationships. for instance, the positive coefficient associated with the gold price (gc) suggests a positive correlation fig. 1. gradients of the objective function. table 1: model of the study bitcoin insurance telecommunications h 1 h 2 h 3 gold price timodel description: where: btc=bitcoin ixis=insurance companies’ performance. ixut=telecommunications stock index price. gc=gold price. µ=the error term. majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 27 between the price of gold and the price of bitcoin. one possible explanation for this relationship is that gold and bitcoin are considered alternative investment assets or stores of value. as investors seek to hedge against inflation or economic uncertainties, they may allocate funds to gold and bitcoin, simultaneously driving up their prices. similarly, the positive coefficient for the telecommunications stock index price (ixut) implies a positive association between the performance of the telecommunications sector and the price of bitcoin. this relationship could be attributed to the increasing adoption and integration of cryptocurrencies within the telecommunications industry. as the telecommunications sector advances technologically and embraces cryptocurrencies, it may contribute to the growth and acceptance of bitcoin, thereby positively impacting its price. on the other hand, the negative coefficient associated with the insurance companies’ performance (ixis) indicates an inverse relationship between the performance of insurance companies and the price of bitcoin. one possible explanation is that as the performance of insurance companies improves, investors may perceive them as more stable and secure investment options compared to the relatively volatile and speculative nature of bitcoin. consequently, increased confidence in traditional financial institutions, such as insurance companies, may lead to decreased demand for bitcoin and a subsequent decrease in its price. it is important to note that the constant term, representing the value of the dependent variable when all predictor variables are equal to zero, predicts a negative value for bitcoin. however, since the constant term is not statistically significant, its impact on the overall bitcoin price prediction may not be substantial. therefore, the focus should primarily be on the coefficients of the predictor variables, as they provide more meaningful insights into the relationships being examined. by delving into the underlying mechanisms and offering plausible explanations for the observed relationships, a more thorough understanding of the dynamics between the variables can be achieved, thereby strengthening the overall analysis. table 2: stationarity statistics at first difference dependent variable: btc method: ardl dependent lags: (4 max. lags): gc ixis ixut variables coefficient standard error t-statistic prob.* btc(-1) 0.917958 0.039009 23.53190 0.0000 gc 1.484124 4.545807 0.326482 0.7447 ixis 110.0404 30.44721 3.614136 0.0005 ixis(-1) −96.63934 30.37787 −3.181241 0.0019 ixut 0.213805 0.576962 0.370570 0.7117 *prob (f‑statistic)=0.000000 r2=0.934858 adjusted r2=0.931950 table 3: adf test statistic null hypothesis: btc has a unit root exogenous: constant t-statistic prob.* augmented dickey–fuller test statistic −1.505250 0.5276 test critical values: 1% level −3.486551 5% level −2.886074 10% level −2.579931 *mackinnon (1996) one‑sided p-values augmented dickey–fuller test equation method: least squares variable coefficient std. error t-statistic prob. btc(-1) −0.038019 0.025258 −1.505250 0.1350 c 1449.238 973.7503 1.488306 0.1394 r2 0.019158 mean dependent var 82.18729 adjusted r2 0.010703 s.d. dependent var 3836.238 f-statistic 2.265778 durbin-watson stat 1.803764 prob (f-statistic) 0.134978 the results are from an adf test performed on the variable btc to test for the presence of a unit root. here is an interpretation of the key components of the results: null hypothesis: the null hypothesis being tested is that btc has a unit root, indicating that it is non‑stationary majeed, et al: bitcoin: stock market effects 28 uhd journal of science and technology | jan 2023 | vol 7 | issue 2 4. conclusion and recommendation 4.1. conclusion bitcoin (-1) coefficient: the lagged value of bitcoin has a coefficient of 0.917958, which is statistically significant at a high t-statistic value of 23.53190. this suggests that a oneunit increase in bitcoin in the previous period is associated with an approximate 0.917958 unit increase in bitcoin in the current period. this indicates a positive autocorrelation effect and sug gests the presence of momentum in bitcoin prices. 4.1.1. gold prices coefficient the coefficient for the variable gold prices is 1.484124, but it is not statistically significant with a t-statistic of 0.326482 and a relatively high p-value of 0.7447. therefore, the inclusion of gold prices in the model has little bitcoin. 4.1.2. insurance companies’ performance coefficient the coefficient for the variable insurance companies’ performance is 110.0404, and it is statistically significant with a t-statistic of 3.614136 and a low p-value of 0.0005. this suggests that a one-unit increase in insurance companies’ performance is associated with a significant 110.0404 unit increase in bitcoin. this indicates a positive relationship between insurance companies’ performance (a specific independent variable) and bitcoin. insurance companies performance (-1) coefficient: the lag ged value of insurance companies’ perfor mance has a coefficient of -96.63934, and it is statistically significant with a t-statistic of and p-value of 0.0019. this implies that a one-unit increase in insurance companies’ performance in the previous period is associated with a decrease of approximately 96.63934 units in btc in the current period. this suggests a negative relationship between the lag ged value of insurance companies’ performance and bitcoin. table 5: results of pairwise granger causality test sample: march 1, 2021–september 4, 2023 lags: 2 null hypothesis obs f-statistic prob. gc does not granger cause btc 117 0.30848 0.7352 btc does not granger cause gc 0.25926 0.7721 ixis does not granger cause btc 117 0.86716 0.4229 btc does not granger cause ixis 0.85998 0.4259 ixut does not granger cause btc 117 0.27543 0.7598 btc does not granger cause ixut 0.80404 0.4501 ixis does not granger cause gc 117 0.02168 0.9786 gc does not granger cause ixis 0.93098 0.3972 ixut does not granger cause gc 117 0.70114 0.4982 gc does not granger cause ixut 3.33503 0.0392 ixut does not granger cause ixis 117 2.11883 0.1250 ixis does not granger cause ixut 0.27989 0.7564 table 4: long‑term relationship among variables unrestricted cointegration rank test (trace) hypothesized eigenvalue trace 0.05 prob.** no. of ce (s) statistic critical value critical value none 0.153128 36.81853 47.85613 0.3561 at most 1 0.077052 17.53862 29.79707 0.6002 at most 2 0.060441 8.237412 15.49471 0.4404 at most 3 0.008630 1.005381 3.841465 0.3160 trace test indicates no cointegration at the 0.05 level *denotes rejection of the hypothesis at the 0.05 level **mackinnon‑haug‑michelis (1999) p-values unrestricted cointegration rank test (max-eigenvalue) hypothesized eigenvalue max-eigen 0.05* prob.** no. of ce (s) statistic critical value critical value none 0.153128 19.27991 27.58434 0.3932 at most 1 0.077052 9.301208 21.13162 0.8074 at most 2 0.060441 7.232031 14.26460 0.4621 at most 3 0.008630 1.005381 3.841465 0.3160 max‑eigenvalue test indicates no cointegration at the 0.05 level *denotes rejection of the hypothesis at the 0.05 level **mackinnon‑haug‑michelis (1999) p-values majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 29 4.1.2. telecommunications stock index price coefficient the coefficient for the variable telecommunications stock index price is 0.213805, but it is not statistically significant with a t-statistic of 0.370570 and p-value of 0.7117. therefore, the inclusion of the telecommunications stock index price in the model does not significantly impact bitcoin. 4.2. recommendations given the significant coefficient of bitcoin (-1), it is essential to consider the lagged value of btc as a predictor in the model for analyzing bitcoin prices. since the coefficient for gc is not statistically significant, further investigation may be required to determine if there is a causal relationship or impact of gold price on bitcoin prices. alternative models or additional variables could be explored to capture potential relationships. the significant coefficients of insurance companies’ perfor mance (-1) sug gest that these variables play a meaningful role in explaining bitcoin prices. it may be beneficial to investigate further the underlying factors and dynamics driving the relationship between insurance companies’ performance and bitcoin. considering the non-significant coefficient of the telecommunications stock index price, 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[26] smith, j., johnson, a and lee, b. 2020. majeed, et al: bitcoin: stock market effects uhd journal of science and technology | jan 2023 | vol 7 | issue 2 31 top of form date ixut ixis gc btc 4/9/2023 11,701.90 399.18 2,002.20 30,453.80 4/2/2023 11,563.70 396.79 2,011.90 27,941.20 3/26/2023 11,532.60 398.16 1,969.00 28,456.10 3/19/2023 11,134.70 383.93 1,983.80 27,475.60 3/12/2023 10,981.20 384.43 1,973.50 26,914.10 3/5/2023 11,585.00 375.79 1,867.20 20,467.50 2/26/2023 12,487.30 391.23 1,854.60 22,347.10 2/19/2023 12,344.60 390.39 1,817.10 23,166.10 2/12/2023 12,558.50 409.19 1,840.40 24,631.40 2/5/2023 12,376.60 393.52 1,862.80 21,859.80 1/29/2023 12,329.30 404.43 1,862.90 23,323.80 1/22/2023 12,128.50 401.53 1,929.40 23,027.90 1/15/2023 11,933.70 395.02 1,928.20 22,775.70 1/8/2023 12,191.70 401 1,921.70 20,958.20 1/1/2023 12,047.70 391.95 1,869.70 16,943.60 12/25/2022 11,641.90 371.45 1,826.20 16,537.40 12/18/2022 11,876.90 370.83 1,804.20 16,837.20 12/11/2022 11,636.20 369.1 1,800.20 16,777.10 12/4/2022 11,805.50 381.88 1,810.70 17,127.20 11/27/2022 12,199.60 397.47 1,809.60 16,884.50 11/20/2022 12,022.90 392.52 1,768.80 16,456.50 11/13/2022 11,728.20 384.4 1,754.40 16,699.20 11/6/2022 11,835.40 377.59 1,769.40 16,795.20 10/30/2022 11,419.70 363.5 1,676.60 21,301.60 10/23/2022 11,482.70 374.68 1,644.80 20,809.80 10/16/2022 10,689.60 347.71 1,656.30 19,204.80 10/9/2022 10,439.00 334.89 1,648.90 19,068.70 10/2/2022 10,269.90 338.48 1,709.30 19,415.00 9/25/2022 10,002.00 333.05 1,672.00 19,311.90 9/18/2022 9,986.00 342.17 1,650.00 18,925.20 9/11/2022 10,472.20 369.83 1,677.90 20,113.50 9/4/2022 10,725.40 387.47 1,723.60 21,650.40 8/28/2022 10,311.10 382.19 1,717.70 19,831.40 8/21/2022 10,581.70 392.86 1,740.60 20,033.90 8/14/2022 10,867.50 411.36 1,753.00 21,138.90 8/7/2022 10,960.60 414.75 1,805.20 24,442.50 7/31/2022 10,176.10 402.04 1,780.50 22,944.20 7/24/2022 10,036.70 394.73 1,771.50 23,634.20 7/17/2022 10,022.40 403.27 1,731.40 22,460.40 7/10/2022 9,885.60 396.26 1,707.50 21,209.90 7/3/2022 10,300.00 393.14 1,746.70 21,587.50 6/26/2022 10,399.00 393.23 1,805.90 19,243.20 6/19/2022 10,341.30 396.01 1,830.30 21,489.90 6/12/2022 9,773.50 379.79 1,840.60 18,986.50 6/5/2022 10,174.20 395.6 1,875.50 28,403.40 5/29/2022 10,596.30 411.53 1,850.20 29,864.30 5/22/2022 10,809.00 417.06 1,857.30 29,027.10 5/15/2022 10,225.30 393.43 1,844.70 29,434.60 5/8/2022 10,428.60 406.93 1,811.30 30,080.40 5/1/2022 10,612.70 402.14 1,886.20 35,468.00 4/24/2022 10,462.00 396.55 1,915.10 37,650.00 4/17/2022 11,162.00 431.11 1,934.30 39,418.00 4/10/2022 11,376.80 447.35 1,974.90 40,382.00 4/3/2022 11,450.40 454.5 1,945.60 42,767.00 3/27/2022 11,596.10 459.47 1,923.70 45,811.00 3/20/2022 11,506.10 450.78 1,956.90 44,548.00 3/13/2022 11,188.00 456.92 1,931.70 42,233.00 3/6/2022 10,650.00 441.21 1,987.60 38,814.30 2/27/2022 10,793.80 450.01 1,968.90 39,395.80 2/20/2022 11,204.80 460.89 1,889.20 39,115.50 top of form date ixut ixis gc btc 2/13/2022 11,175.10 459.47 1,899.80 40,090.30 2/6/2022 11,189.50 460.86 1,842.10 42,205.20 1/30/2022 11,382.10 464.39 1,807.80 41,412.10 1/23/2022 11,035.30 455.18 1,786.60 38,170.80 1/16/2022 10,922.50 450.46 1,833.50 35,075.20 1/9/2022 11,437.80 480.21 1,818.30 43,097.00 1/2/2022 11,463.00 478.36 1,799.30 41,672.00 12/26/2021 11,416.40 496.8 1,829.70 47,738.00 12/19/2021 11,298.30 496.22 1,811.70 50,406.40 12/12/2021 11,161.10 487.76 1,804.90 46,856.20 12/5/2021 11,333.30 477.08 1,784.80 49,314.50 11/28/2021 10,983.50 481.07 1,783.90 49,195.20 11/21/2021 11,225.70 478.85 1,786.90 54,765.90 11/14/2021 11,437.90 481.6 1,852.90 59,717.60 11/7/2021 11,567.50 500.87 1,869.70 64,398.60 10/31/2021 11,694.50 505.17 1,818.00 61,483.90 10/24/2021 11,398.20 490.14 1,784.90 61,840.10 10/17/2021 11,608.00 504.93 1,796.30 61,312.50 10/10/2021 11,413.50 501.41 1,768.30 60,861.10 10/3/2021 11,309.30 505.05 1,757.40 54,942.50 9/26/2021 10,930.20 519.52 1,758.40 47,666.90 9/19/2021 10,876.60 523.32 1,750.90 42,686.80 9/12/2021 10,816.20 527.15 1,750.50 48,306.70 9/5/2021 10,930.30 540.62 1,791.00 45,161.90 8/29/2021 11,060.80 558.6 1,832.60 49,918.40 8/22/2021 11,120.80 552.89 1,817.20 48,897.10 8/15/2021 11,002.40 549.06 1,781.80 48,875.80 8/8/2021 11,044.20 545.57 1,776.00 47,081.50 8/1/2021 10,971.50 543 1,761.10 44,614.20 7/25/2021 10,689.50 543.03 1,814.50 41,553.70 7/18/2021 10,812.90 542.15 1,802.90 33,824.80 7/11/2021 10,772.30 531.67 1,815.90 31,518.60 7/4/2021 10,780.90 538.55 1,811.50 33,510.60 6/27/2021 10,966.90 538.93 1,784.10 34,742.80 6/20/2021 11,052.80 531.2 1,777.80 32,243.40 6/13/2021 10,610.00 518.24 1,769.00 35,513.40 6/6/2021 11,259.10 526.03 1,879.60 35,467.50 5/30/2021 11,306.90 522.61 1,892.00 35,520.00 5/23/2021 11,350.30 521.35 1,905.30 34,584.60 5/16/2021 11,273.50 509.01 1,877.60 37,448.30 5/9/2021 11,330.20 523.45 1,839.10 46,708.80 5/2/2021 11,478.60 521.27 1,832.40 58,840.10 4/25/2021 11,209.80 504.51 1,768.60 57,807.10 4/18/2021 10,944.50 501.46 1,777.80 50,088.90 4/11/2021 10,993.90 504.41 1,780.20 60,041.90 4/4/2021 10,916.40 492.39 1,744.80 59,748.40 3/28/2021 10,807.90 489.68 1,728.40 57,059.90 3/21/2021 10,766.80 489.91 1,733.60 55,862.90 3/14/2021 10,730.00 486.46 1,742.90 58,093.40 3/7/2021 10,857.50 493.06 1,721.20 61,195.30 2/28/2021 10,520.10 475.8 1,700.30 48,855.60 2/21/2021 10,357.00 465.96 1,730.10 46,136.70 2/14/2021 10,564.20 473.52 1,777.40 55,923.70 2/7/2021 10,559.50 484.23 1,823.20 47,168.70 1/31/2021 10,301.30 478.83 1,813.00 39,256.60 1/24/2021 9,642.30 460.24 1,850.30 34,283.10 1/17/2021 10,102.90 470.14 1,857.90 32,088.90 1/10/2021 10,286.70 465.96 1,831.70 36,019.50 1/3/2021 10,273.70 475.87 1,837.30 40,151.90 tx_1~abs:at/tx_2:abs~at uhd journal of science and technology | jan 2023 | vol 7 | issue 1 53 1. introduction a smart network called the internet of things (iot) employs established protocols to link things to the internet [1]. in an iot network, smart tiny sensors join objects wirelessly. iot devices can interact with one another without human involvement [2]. it uses distinctive addressing techniques to communicate, add more items and collaborate with them to develop new applications and services. examples of iot applications include smart environments, smart homes, and smart cities [3]. thereby of the development of iot applications, several obstacles have developed. one of these obstacles is iot security that cannot be disregarded. iot networks are subject to a range of malicious attacks because iot devices can be accessed from anywhere over an unprotected network such as the internet. the following security requirements should be considered when securing iot environment: • confidentiality: iot systems must ensure that unauthorized parties are prohibited from disclosing information [4]. • integrity: ensures that the messages must not have been modified in any manner [4]. • availability: when data or resources are needed, they must be available [4]. attackers can saturate a resource’s bandwidth to degrade its availability. a review on iot intrusion detection systems using supervised machine learning: techniques, datasets, and algorithms azeez rahman abdulla, noor ghazi m. jameel technical college of informatics, sulaimani polytechnic university, sulaimani 46001, kurdistan region, iraq a b s t r a c t physical objects that may communicate with one another are referred to “things” throughout the internet of things (iot) concept. it introduces a variety of services and activities that are both available, trustworthy and essential for human life. the iot necessitates multifaceted security measures that prioritize communication protected by confidentiality, integrity and authentication services; data inside sensor nodes are encrypted and the network is secured against interruptions and attacks. as a result, the issue of communication security in an iot network needs to be solved. even though the iot network is protected by encryption and authentication, cyber-attacks are still possible. consequently, it’s crucial to have an intrusion detection system (ids) technology. in this paper, common and potential security threats to the iot environment are explored. then, based on evaluating and contrasting recent studies in the field of iot intrusion detection, a review regarding the iot idss is offered with regard to the methodologies, datasets and machine learning (ml) algorithms. in this study, the strengths and limitations of recent iot intrusion detection techniques are determined, recent datasets collected from real or simulated iot environment are explored, high-performing ml methods are discovered, and the gap in recent studies is identified. index terms: internet of thing, intrusion detection, intrusion detection system techniques, intrusion detection system datasets, supervised machine learning r e v i e w a r t i c l e uhd journal of science and technology corresponding author’s e-mail: azeez rahman abdulla, technical college of informatics, sulaimani polytechnic university, sulaimani 46001, kurdistan region, iraq. azeez.rahman.a@spu.edu.iq received: 18-10-2022 accepted: 23-12-2022 published: 01-03-2023 access this article online doi: 10.21928/uhdjst.v7n1y2023.pp53-65 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2023 abdulla and jameel. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) abdulla and jameel: a review on iot intrusion detection systems 54 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 • authenticity: the word “authenticity” relates to the ability to prove one’s identity. the system should be able to recognize the identity of the entity with whom it is communicating [5]. • non-repudiation: this guarantees that nothing can be rejected. in an iot context, a node cannot reject a message or piece of data that has already been sent to another node or a user [6]. • data freshness: ensures that no outdated messages are retransmitted by an attacker [7]. in the last few years, advancement in artificial intelligent (ai) such as machine learning (ml) techniques has been used to improve iot intrusion detection system (ids). numerous studies as [8,9], reviewed and compared different applied ml algorithms and techniques through various datasets to validate the development of iot idss. however, it’s still not clear a recent dataset collected from iot environment, and which ml model was more effective for building an efficient iot ids. therefore, the current requirement is to do an upto-date review to identify these critical points. in this study, a survey of the iot idss is given. this paper aims to further the knowledge in regard to iot cyber attacks’ characteristics (motivation and capabilities). then, strengths and limitations of different categories of idss techniques (hybrid, anomaly-based, signature-based, and specificationbased) are compared. moreover, the study presents a review on the recent researches in the area of iot intrusion detection using ml algorithms for iot network based on the datasets, algorithms and evaluation metrics to identify the recent iot dataset and the outperformed ml algorithm in terms of accuracy used for iot intrusion detection. the paper is structured as follows: in section 2, common cyber-attacks in iot the environment are clarified. in section 3 the strengths and limitations of iot intrusion detection techniques are discussed. section 4 discussed, analyzed and compared recent iot intrusion detection researches’ performance metrics, datasets and supervised ml algorithms. finally, section 5 illustrates the conclusions of the paper. 2. iot cyber attacks recently, iot has developed quickly, making it the fastestg rowing enor mous impact of technolog y on social interactions and workplace environments, including education, healthcare and commerce. this technology is used for storing the private data of people and businesses, for financial data transactions, for product development and for marketing. due to the widespread adoption of linked devices in the iot, there is a huge global demand for strong security. millions or perhaps billions of connected devices and services are now available [10-13]. every day, there are more risks and assaults have gotten more frequent and sophisticated. in addition, sophisticated technologies are becoming more readily available to potential attackers [14,15]. to realize its full potential, iot must be secured against threats and weaknesses [16]. by maintaining the confidentiality and integrity of information about the object and making that information easily accessible whenever it is needed, security is the act of avoiding physical injury, unauthorized access, theft, or loss to the item [17]. to ensure iot security, it is crucial to maintain the greatest inherent value of both tangible items (devices) and intangible ones (services, information and data). system risks and vulnerabilities must be identified in order to provide a comprehensive set of security criteria to assess if the security solution is secure against malicious assaults or not [18]. attacks are performed to damage a system or obstruct regular operations by utilizing various strategies and tools to exploit vulnerabilities. attackers launch attacks to achieve goals, either for their personal satisfaction or to exact revenge [19]. common iot cyber-attack types are: • physical attacks: these assaults tamper with hardware elements. most iot devices often operate in outdoor areas which are extremely vulnerable to the physical assaults [20]. • attacks known as reconnaissance include the illegal identification of systems, services, or vulnerabilities. the scanning of network ports is an example of a reconnaissance attack [21]. • denial-of-service (dos): this type of attack aims to prevent the targeted users from accessing a computer or network resource. the majority of iot devices are susceptible to resource enervation attacks due to their limited capacity for memory and compute resources [22]. • access attacks happen when unauthorized users get access to networks or devices that they are not allowed to use. two types of access assaults exist: the first is physical access, in which a hacker gains access to a real object. the second is using ip-connected devices for remote access [22]. • attacks on privacy: iot privacy protection has grown more difficult as a result of the volume of information that is readily accessible via remote access techniques [14]. • cyber-crimes: users and data are used for hedonistic activities including fraud, brand theft, identity theft, and abdulla and jameel: a review on iot intrusion detection systems uhd journal of science and technology | jan 2023 | vol 7 | issue 1 55 theft of intellectual property using internet and smart products [14,15,23]. • destr uctive attacks: space is exploited to cause widespread disturbance and property and human life loss. terrorism and retaliation are two examples of damaging assaults. • supervisory control and data acquisition (scada) attacks: scada systems are connected to industrial iot networks; they are active devices in real-time industrial networks, which allow the remote monitoring and control of processes, even when the devices are located in remote areas. the most specific and common types of scada attacks are eavesdropping, man-in-the middle, masquerading, and malware [24]. 3. iot intrusion detection system despite the investment and potential it holds, there are still issues that prevent iot from becoming a widely utilized technology. the security challenges with iot are thought to be solvable via intrusion detection, which has been established for more than 30 years. intrusion detection is often a system (referred to as ids) which consists of tools or methods that analyze system activity to find assaults or unauthorized access. an ids typically comprises of sensors, and a tool to evaluate the data from these sensors. efficient and accurate intrusion detection solutions are necessary in the iot environment to identify various security risks [25]. 3.1. iot intrusion detection types ids types can be categorized in a variety of ways, particularly ids for iot as the majority of them are still being studied. according to das et al., [26] the research distinguishes three types of ids: • host-based ids (hids): to keep an eye on the system’s harmful or malicious activity, hids is connected to the server. specifically, hids examines changes in fileto-file communication, network traffic, system calls, running processes, and application logs. this sort of ids’s drawback is that it can only identify attacks on the systems it supports. • network-based ids (nids): nids analyzes network traffic for attack activities and identifies harmful behavior on network lines. • distributed ids (dids): dids will have a large number of linked and dispersed idss for attack detection, incident monitoring and anomaly detection. to monitor and respond to outside actions, dids needs a central ser ver with strong computing and orchestration capabilities. 3.2. iot intrusion detection techniques there are four basic types or methodologies for deploying iot intrusion detection. • anomaly based ids in iot. it uses anomaly based ids to find intrusions and monitor abusive behavior. it employs a threshold to determine if this behavior is typical or abnormal. these idss have the ability to monitor a typical iot network’s activity and set a threshold. to detect abnormalities, the network’s activity is compared to a threshold and any deviation from this number is considered abnormal [27]. table 1 compares and contrasts the strength and limitations of several anomaly-based idss methodologies based on resource and energy usage, detection accuracy and speed. • signature based ids in iot signature based detections compare the network’s current activity to pre-defined attack patterns. each signature is connected to a particular assault since signatures are originally established and stored on the iot device. signature based approaches are commonly used and require a signature for each assault [27]. the strengths and limitations of different signature based idss techniques have been presented and compared in table 2 based on resource consumption, energy, detection accuracy, and speed. • specification based ids in iot specification-based approaches detect intrusions when network behavior deviates from specification definitions. therefore, specification-based detection has the same purpose of anomaly-based detection. however, there is one important difference between these methods: in specification-based approaches, a human expert should manually define the rules of each specification [36]. the main aspects of specification-based idss have been outlined and then compared in table 3 based on resource consumption, energy, detection accuracy, and speed. • hybrid ids in iot signature based ids has a large usable capacity and limited number of attack detections while anomaly based ids has a high false positive rate and significant computation costs. a hybrid technique was suggested to solve the flaws of both systems [42]. the main characteristics of hybrid idss have been defined and then compared in table 4 based on resource consumption, energy, detection accuracy, and speed. abdulla and jameel: a review on iot intrusion detection systems 56 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 4. supervised ml based iot intrusion detection ml enables computer systems to predict events more correctly without being explicitly taught to do so. it is a subset of artificial intelligence (ai). ml algorithms use historical data as input to anticipate new output values. ml algorithms are mainly divided into three categories: reinforcement learning, unsupervised learning, and supervised learning. in this paper, recent researches using supervised ml algorithms table 1: comparison of different anomaly based ids techniques reference no. technique strength limitations [28] utilizing a fusion based technique to decrease the damage caused by strikes. ● low communication overhead ● high energy consumption [29] detecting wormhole attacks using node position and neighbor information. ● low resource consumption ● real time ● energy efficient ● only one type of attack can be detected [30] detecting sinkhole attacks by analyzing the behavior of devices ● detection accuracy is high ● detect limited number of attacks [31] a lightweight technique for identifying normal and deviant behavior ● lightweight implementation ● detection accuracy is high ● high computational overhead [32] a request-response method’s correlation functions are used to look for unusual network server activity ● consuming modest resources ● lightweight detection system ● high computational overhead ids: intrusion detection system table 2: comparison of different signature based ids techniques reference no. technique strength limitations [33] detecting network attacks by signature code in ip based ubiquitous sensor networks ● high detection accuracy ● low energy and resource consumption ● can detect limited number of intrusions [34] the pattern-matching engine is used to detect malicious nodes using auxiliary shifting and early decision techniques ● low memory and computational complexity ● maximum speed up ● not real‑time ● can detect limited number of intrusions [35] detection of malware signature detection using reversible sketch structure based on cloud. ● fast ● low communication consumption ● high detection accuracy ● high memory requirement ● has a limited ability to identify assaults ids: intrusion detection system table 3: comparison of different specification based ids techniques reference no. technique strength limitations [37] mitigation of black hole attacks using an effective strategy in routing protocol for low‑power and lossy (rpl) networks ● low delay ● high detection accuracy of the infected node ● only black hole attacks can be detected [38] detecting internal attacks by designing a secure routing protocol based on reputation mechanism ● detection accuracy is acceptable ● low delay ● needs skilled administration [39] topology assaults detection on rpl using semi‑automated profiling tool. ● detection accuracy is high ● low energy consumption ● low computation overhead ● high overhead [40] sinkhole attacks are detected using a constraint based specification intrusion detection approach. ● low overhead ● minimal energy usage ● not real‑time [41] using a game-theoretic method to identify deceptive attacks in iot network with honeypots. ● high detection accuracy ● real‑time ● needs additional resources. ● high converge time ids: intrusion detection system abdulla and jameel: a review on iot intrusion detection systems uhd journal of science and technology | jan 2023 | vol 7 | issue 1 57 in the area of iot intrusion detection were studied, analyzed and compared. supervised learning emphasis on discovering patterns while utilizing labeled datasets. in supervised learning, the machine must be fed sample data with different characteristics (expressed as “x”) and the right value output of the data (represented as “y”). the dataset is considered “labeled” because the output and feature values are known. then, the algorithm analyzes data patterns to develop a model that can replicate the same fundamental principles with new data [46]. 4.1. datasets used for iot intrusion detection models for supervised ml are trained and evaluated using datasets. any ids’s performance ultimately depends on the dataset’s quality including whether it can reliably identify assaults or not [47]. here, six datasets named nsl-kdd, unswnb15, cicids 2017, bot-iot, ds2os, and iotid20 are considered and used by researchers to train and test iot intrusion detection models. descriptions of the datasets are given below and their characteristics are summarized in table 5. • nsl-kdd the nsl-kdd dataset is an improved version of the kdd99. it does not include redundant records in the train set, so the classifiers will not be biased towards more frequent records. the number of selected records from each difficulty level group is inversely proportional to the percentage of records in the original kdd data set [47]. the nsl-kdd dataset has 41 characteristics, classified into three categories: basic characteristics, content characteristics, and traffic characteristics. table 4: comparison of different hybrid ids techniques reference no. technique strength limitations [42] employing a game theoretic approach to identify attackers by using anomaly detection only when a new attack pattern is anticipated and using signature based detection otherwise. • detection accuracy is high • low energy consumption • high resource consumption • delay [43] the denial of service prevention manager is proposed, which uses aberrant activity detection and matching with attack signatures. • real time • high resource consumption [44] real-time attack detection using knowledgeable, self‑adapting expert intrusion detection system. • high detection accuracy • real time • low resource consumption • high computational overhead [45] attackers can be found by looking for timing irregularities while broadcasting the most recent rank to nearby nodes and using a timestamp. • real time • low overhead • low delay • high detection accuracy • high computation overhead • high resource consumption [27] targeting the routing attacks with an ids with integrated mini‑firewall which uses anomaly-based ids in the intrusion detection and signature‑based ids in the mini‑firewall • real time • high availability • low overhead • limited in dynamic network topology • high‑resource consumption • low detection accuracy ids: intrusion detection system table 5: dataset characteristics dataset year dataset link (url) no. of instances no. of features dataset collection performed on iot environment type of dataset nslkdd 2009 https://www.unb.ca/cic/datasets/nsl. html 148,519 41 no imbalanced unsw-nb15 2015 https://research.unsw.edu.au/ projects/unsw‑nb15‑dataset 2,540,044 49 no imbalanced cicids2017 2017 https://www.unb.ca/cic/datasets/ ids-2017.html 2,830,743 83 no imbalanced botiot 2019 https://ieee-dataport.org/documents/ bot-iot-dataset 73,370,443 29 yes imbalanced ds2os 2018 https://www.kaggle.com/datasets/ francoisxa/ds2ostraffictraces 409,972 13 yes imbalanced iotid20 2020 https://sites.google.com/view/ iot‑network‑intrusion‑dataset/home 625,783 83 yes imbalanced https://www.unb.ca/cic/datasets/nsl.html https://www.unb.ca/cic/datasets/nsl.html https://research.unsw.edu.au/projects/unsw-nb15-dataset https://research.unsw.edu.au/projects/unsw-nb15-dataset https://www.unb.ca/cic/datasets/ids-2017.html https://www.unb.ca/cic/datasets/ids-2017.html https://ieee-dataport.org/documents/bot-iot-dataset https://ieee-dataport.org/documents/bot-iot-dataset https://sites.google.com/view/iot-network-intrusion-dataset/home https://sites.google.com/view/iot-network-intrusion-dataset/home abdulla and jameel: a review on iot intrusion detection systems 58 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 • unsw-nb15 the unsw-nb15 dataset was published in 2015. it was created by establishing the synthetic environment at the unsw cyber security lab. unsw-nb15 represents nine major families of attacks by utilizing the ixia perfect storm tool. ixia tool has provided the capability to generate a modern representative of the real modern normal and the abnormal network traffic in the synthetic environment. there are 49 features and nine types of attack categories known as the analysis, fuzzers, backdoors, dos, exploits, reconnaissance, generic, shellcode, and worms [48]. • cicids 2017 the cicids 2017 dataset generated in 2017. it includes benign and seven common family of attacks that met real worlds criteria such as dos, ddos, brute force, xss, sql injection, infiltration, port scan, and botnet. the dataset is completely labeled with 83 network traffic features extracted and calculated for all benign and attack network flows [49]. • bot-iot the bot-iot dataset was created by designing a testbed network environment in the research cyber range lab of unsw canberra. this dataset consists of legitimate and simulated iot network traffic along with various types of attacks such as information gathering (probing attacks), denial of service and information theft. it has been labeled with the label features indicating an attack flow, the attacks category and subcategory for possible multiclass classification purposes [50]. • ds2os this dataset includes traces that were recorded using the iot platform ds2os. labeled and unlabeled datasets come in two varieties. the only characteristics in an unlabeled dataset that can be used describe the data objects for unsupervised ml models. in addition, a labeled dataset includes information about each data instance’s class and utilized for supervised ml models [51]. • iotid20 iotid20 dataset is used for anomalous activity detection in iot networks. the testbed for the iotid20 dataset is a combination of iot devices and interconnecting structures. the dataset consists of various types of iot attacks and a large number of flow-based features. the flow-based features can be used to analyze and evaluate a flow-based ids. the final version of the iotid20 dataset consists of 83 network features and three label features [52]. 4.2. supervised ml algorithms used for iot intrusion detection for iot intrusion detection, many supervised ml methods are employed. the list of used algorithms with corresponding descriptions is presented below: • logistic regression (lr): it is a probability-based method for predictive analysis. it is a more effective strategy for binary and linear classification issues because it employs the sigmoid function to translate expected values to probabilities between 0 and 1. it is a classification model that is relatively simple to implement and performs extremely well with linearly separable data classes [53]. • naïve base (nb): are a group of bayes’ theorem-based categorization methods. it is a family of algorithms rather than a single method and they all operate under the same guiding principle in which each pair of characteristics is categorized standalone [53]. • artificial neural networks (ann): the biological neural network in the human brain served as the model for the widely used ml technology known as (ann). each artificial neuron’s weight values are sent to the following layer as an output. feed-forward neural network form of ann that processes inputs from neurons in the previous layer. multilayer perception is a significant type of feed forward neural networks (mlp). the most well-known mlp training method that modifies the weights between neurons to reduce error is called the back propagation algorithm. the system can display sluggish convergence and run the danger of a local optimum, but it can rapidly adapt to new data values [54]. • support vector machine (svm): this algorithm looks for a hyperplane to optimize the distance involving two classes. a learning foundation for upcoming data processing is provided by the categorization. the groups are divided into several configurations by the algorithm through hyperplanes (lines). a learning model that splits up new examples into several categories is produced by svm. based on these functions, svms are referred to as non-probabilistic, or binary linear classifiers. in situations that use probabilistic classification, svms can use methods such as platt scaling [53]. • decision tree (dt) is a tree in which each internal node represents an assessment of an attribute. each branch represents the result of an assessment and each leaf node denotes the classification outcome. algorithms such as id3, cart, c4.5, and c5.0 are frequently used to generate decision trees. by analyzing the samples, a decision tree is obtained and used to correctly classify new data [55]. abdulla and jameel: a review on iot intrusion detection systems uhd journal of science and technology | jan 2023 | vol 7 | issue 1 59 • random forest (rf) is a technique used to create a forest of decision trees. this algorithm is frequently used due to its fast operation. countless decision trees can be used to create a random forest. by averaging the outcomes of each component tree’s forecast, this method generates predictions. random forests exhibit compelling accuracy results and are less likely to overfit the data than a traditional decision tree technique. this method works well while examining plenty of data [53]. • ensemble learning (which includes bag ging and boosting). the boosting method is a well-known ensemble learning method for improving the performance and accuracy of ml systems. the fundamental idea behind the boosting strategy is the successive addition of models to the ensemble. weak learners (base learners) are efficiently elevated to strong learners. as a consequence, it aids in reducing variation and bias and raising prediction accuracy. boosting is an iterative method that alters the findings of an observation’s weight depending on the most recent categorization. adaboost (ab), gradient boosting machines (gbm), and extreme gradient boosting (xgboost) are examples of boosting techniques. bag ging (also known as bootstrap aggregating). it is one of the earliest and most basic ensemble ml approaches and it works well for issues requiring little in the way of training data. in this approach, a collection of original models with replacement are trained using random subsets of data acquired using the bootstrap sampling method. the individual output models derived from bootstrap samples are combined by majority voting [56]. 4.3. evaluation metrics the efficiency of ml algorithms can be measured using metrics such as accuracy, precision, recall, and f1-score [57]. performance metrics are calculated using different parameters called true positive (tp), false positive (fp), true negative (tn), and false negative (fn). for ids s, these parameters are described as follow: tp = the number of cases correctly identified as attack. fp = the number of cases incorrectly identified as attack. tn = the number of cases correctly identified as normal. fn = the number of cases incorrectly identified as normal. • precision (also called positive predictive value) is the percentage of retrieved occurrences that are relevant. model performance is considered better if the precision is higher [58]. precision is computed using (1) [59]. p r e c i s i o n � = t r u e � p o s t i v e t r u e p o s t i v e +f a l s e � p o s t i v e (1) • recall (also known as sensitivity) is the percentage of occurrences that were found to be relevant. it also goes by the name true positive rate (tpr) and calculated using (2) [58]. r e c a l l � =� t r u e � p o s t i v e t r u e p o s t i v e +f a l s e � n e g a t i v e (2) • accuracy is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. model accuracy is calculated using (3) [57]. a c c u r a c y � = t r u e � p o s t i v e +t r u e � n e g a t i v e t o t a l (3) • f1-score is the harmonic mean of recall and accuracy [60] which defines a the weighted average of recall and precision and calculated using (4) [57]. f 1 s c o r e � =� 2 � * p r e c i s i o n � *� r e c a l l p r e c i s i o n � +� r e c a l l (4) • roc curve is a receiver operating characteristic curve which shows the performance of a classifier at various thresholds level [57]. • area under curve (auc): is closely associated with the concept of roc. it represents the area under the roc curve. it has been extensively used as a performance measure for classification models in ml. its values range from 0 to 1. the higher the value, the better the model is [61]. 4.4. analysis and comparison of supervised ml algorithms for iot intrusion detection in this section, the analysis of the used ml algorithms has been presented and discussed. researchers used many supervised ml algorithms specifically in classification and they performed well in some cases with very high accuracy. to review researches in the area of intrusion detection using ml in the iot environment, various recent studies are examined and compared based on the ml algorithms (classifier), datasets, type of classification, and performance of the classifier. the performance of these algorithms depends on various metrics. in this study, the comparison among the algorithms is focused on accuracy metric. detailed abdulla and jameel: a review on iot intrusion detection systems 60 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 review of 21 papers (published between 2019 and 2022) was analyzed in this section and compared in table 6. mahmudul et al. [62] employed the ds2os dataset with several ml algorithms (lr, svm, dt, rf, ann). accuracy, table 6: comparison of the selected supervised ml based iot ids reference no. year ml algorithm (classifier) dataset classification type classifier accuracy [62] 2019 lr, svm, dt, rf, ann ds2os multiclass lr=0.983, svm=0.982, dt=0.994, rf=0.994, ann=0.994. [63] 2019 rf unsw-nb15 binary rf=99.34 [64] 2019 lr, nb, dt, rf, knn, svm kdd99, nsl-kdd, unsw-nb15 binary accuracy of the algorithms depend on the used dataset [65] 2019 for the level‑1 model, dt for level 2 model, rf cicids2017, unsw-15 2 level classification (binary then multiclass) both datasets’ specificity was 100% for the model, while its precision, recall, and f score were all 100% for the cicids2017 dataset and 97% for the unsw‑nb15 dataset [66] 2019 rf, ab, gbm, xgb, dt (cart), mlp, extremely randomized trees (etc) cidds-001, unsw-nb15, nsl-kdd binary average accuracy value for 4 datasets using holdout are: rf=94.94, gbm=92.98, xgb=93.15%, ab=90.37, cart=91.98, mlp=82.76, etc=82.99 [67] 2019 dt, nn, svm unsw-nb15 multiclass dt=89.76, nn=86.7و svm=78.77, proposed model: 88.92 [68] 2019 nb, qda, rf, id3, ab, mlp, knn bot-iot binary. nb=0.78, qda=0.88, rf=0.98, id3=0.99, adaboost=1.0, mlp=0.84, knn=0.99 [69] 2019 svm, lr, d t, knn, rf unsw-nb15, their own dataset binary the accuracy depends on the dataset and the algorithm [58] 2020 rf, xgb, dt, mlp, gb, et, lr unsw-nb15 binary results with all features: rf=0.9516, xgb=0.9481, dt=0.9387, mlp=0.9371, gb=0.9331, et=0.9501, lr=0.8984 [53] 2020 knn, svm, dt, nb, rf, ann, lr bot-iot binary, multiclass on binary classification: knn=0.99, svm=0.99, dt=1.0, nb=0.99, rf=1.0 ann=0.99, lr=0.99 [70] 2020 svm, nb, dt, adaboost their own synthetic called (sensor480) binary svm=0.9895, nb=0.9789, dt=1.0000, adaboost=0.9895 [71] 2020 rf iotid20 dataset binary based on the attack type the accuracy result depends on the attack type [72] 2021 svm nsl-kdd, unsw-nb15. binary, multiclass the accuracy depends on the dataset, the type of classification and number of features [55] 2021 rf, svm, ann unsw-nb15. binary, multiclass all features: rf with binary=98.67, multi‑class=97.37, svm in binary=97.69, multiclass=95.67, ann in binary=94.78, multiclass=91.67 [73] 2021 lr, svm, dt, ann iotid20, bot-iot multiclass the results are based on the dataset and the categories of attacks [74] 2021 slfn iotid20 binary the proposed model=0.9351 [75] 2021 svm, gbdt, rf nsl kdd binary svm=32.38, gbdt=78.01, rf=85.34 [76] 2021 b-stacking cicids2017, nsl-kdd multiclass accuracy for cicids2017 is 99.11% accuracy for nsl-kdd approximately is 98.5% [77] 2022 dt, rf, gbm iot2020 binary dt=0.978305, rf=0.978443, gbm=0.9636 [78] 2022 shallow neural networks (snn), bagging trees (bt), dt, svm, knn iotid20 binary, multiclass for binary classification all models achieved 100% for multiclass: snn=100%, dt=99.9%, bt=99.9%, svm=99,8%, knn=99.4% [79] 2022 ann, dt (c4.5), bagging, knn, ensemble iotid20, nsl-kdd binary, multiclass accuracy depends on feature selection approaches, datasets, and attack type for multiclass classification abdulla and jameel: a review on iot intrusion detection systems uhd journal of science and technology | jan 2023 | vol 7 | issue 1 61 precision, recall, f1 score, and area under the receiver operating characteristic curve are the assessment measures used to compare performance. the measurements show that rf performs comparably higher performance, and the system acquired excellent accuracy (ibrahim et al. [63]). an intelligent anomaly detection system called anomaly detection iot (ad-iot) which used the unsw-nb15 dataset and rf to identify binary labeled categorization had been proposed. the results demonstrated that the ad-iot could successfully produce the best classification accuracy while minimizing the false positive rate. samir et al. in [64] used the datasets kdd99, nsl-kdd, and unsw-nb15 to assess number of ml models. the kkn and lr algorithms produced the best results on the unsw-nb15 dataset while the nb algorithm produced the worst results. on the nsl-kdd dataset, the dt classifier outperformed the others in terms of various metrics while on the kdd99 dataset, svm and mlp produced a low false positive rate in comparison to other algorithms. the findings of this study showed that the dt and knn algorithms outperformed the other algorithms. however, the knn required more time to categorize data than the dt. imtiaz and qusay [65] conducted a two-level framework experiment for iot intrusion detection. to determine the category of the anomaly, they chose a dt classifier for the level-1 model which categorized the network flow as normal or anomalous and forwarded the network flow to the level-2 model. rf was used as a level-2 model for multiclass categorization. abhishek and virender [66] employed both ensemble and single classifiers, two different types of classification techniques. the selection of the aforementioned classification algorithms was primarily influenced by the huge number of input characteristics that are vulnerable to overfitting. as a result, random search was used to determine the best input parameters for rf, ab, xgb, and gbm. in terms of precision, rf beats other classifiers. however, ab performs the worst of all the classifiers. using friedman test statistics and 10-fold validation, the results showed that the classifiers’ performances are considerably varied. following that, the average time required by several classifiers to categorize a single case, cart classifies instances of cidds-001, unsw-nb15, kddtrain+, and kddtest+ faster than other classifiers. vikash et al. [67] proposed (uids) an ids using unswnb15 dataset. network traffic accuracy and assault detection rate were improved by the suggested approach. in addition, it examined data using several ml techniques (c5, neural network, svm, and uids model) and came to the conclusion that uids compared favorably to other ml techniques. analysis showed that the false alarm rate (far) of the unsw-nb15 dataset was reduced with only 13 characteristics. jadel and khalid [68] tested seven ml algorithms. all the algorithms, except the naive bayes (nb) and quadratic algorithm (qda), achieved highest success in detecting almost all attack types. it can be seen that adaboost was the best performance algorithm, followed by knn and id3. id3 is noticeably faster than knn. the accuracy of the algorithms depends on the entire dataset with the seven best features obtained in the feature selection step. aritro et al. [69] analyzed the role of a set of chosen ml techniques for iot intrusion detection based on dataset/flows two layers: application layer (host based) and network layer (network based). for the application layer dataset, they created their own dataset from the iot environment while for network layer they used unsw-nb15 dataset. according to the results for both datasets, rf was the best algorithm in terms of accuracy and lr was the fastest in ter ms of speed. mohammad [58] used different algorithms. the classifiers random forest (rf) and extra trees (et) performed better than the others, and rf is the best of the two. only 14 features were chosen by rf utilizing features selection, but the performance results were remarkably similar to those achieved with all features. in addition, compared to the others, the lr classifier had the lowest accuracy. andrew et al. [53] employed different methods; nevertheless, the findings show that rf performed better with the non-weighted dataset regarding precision and accuracy in non-weighted dataset. however, ann performed more accurately in binary classification using weighted dataset. knn and ann performed extremely well in multi-classification for weighted and non-weighted datasets, respectively. the findings made it clear that ann accurately predicted the kind of attack. k. v. v. n. l et al. [70] tested four ml techniques on iot traffic in order to distinguish between genuine and attack traffic. using decision trees, all of the analyzed data may be precisely categorized into the correct classes. decision trees also had the greatest accuracy compared to the other classifiers. pascal et al. [71] suggested a new anomaly-based detection using hybrid feature selection for iot networks using iotid20 dataset. the relevant features were fed to the rf algorithm. based on the attack category, the network traffic is classified as normal and attack category as dos, scan, or mitm. nsikak et al. [72] tested svm with dataset nsl-kdd and unsw-nb15 datasets. the results using different numbers of features for both datasets were varied. the classification accuracy using binary classification was greater than multi-class according to the evaluation results. muhammad et al. [55], the unsw-nb15 dataset had been subjected to supervise ml including rf, svm, and ann. abdulla and jameel: a review on iot intrusion detection systems 62 uhd journal of science and technology | jan 2023 | vol 7 | issue 1 the application of rf using mean imputation produced the greatest accuracy in binary classification. overall, there were not many differences in accuracy across the different imputation strategies. by using rf on a regression-imputed dataset, the greatest accuracy in multi-class classification was also attained. in addition, as compared to other cutting-edge supervised ml-based techniques, rf achieved greater accuracy with less training time for clustered based classification. khalid et al. [73] for classification objectives, the performance of four ml methods was assessed. the bot-iot dataset and the iotid20 dataset were both utilized in the study, 5% of bot-iot dataset was selected with a full set of features, while the second dataset was fully selected in the experiment. the accuracy results were based on the dataset and the categories of attacks. raneem et al. [74] developed an intrusion detection method using a single layer forward neural network (slfn) classifier with iotid20. the results showed that the slfn classification approach outperformed other classification algorithms. maryam et al. [75] proposed that three ml algorithms rf, gdbt, and svm were applied to the nsl-kdd dataset using binary classification. the results showed that the rf obtained the highest accuracy on the fog layer while svm obtained lowest accuracy. souradipst et al. [76] proposed b-stacking approach as an intrusion detection model to detect cyber-attacks and anomalies in iot networks. b-stacking is based on a combination of two ensemble algorithms; boosting and stacking. it chose knn, rf, and xgboost as the level-0 weak learners. xgboost is also used as the level-1 learner. the experimental results on two popular datasets showed that the model had a high detection rate and a low false alarm rate. most importantly, the proposed model is lightweight and can be deployed on iot nodes with limited power and storage capabilities. jingyi et al. [77] used dt, rf, and gbm ml algorithms with a dataset generated from the iotid20 dataset known as iot2020 dataset. according to the results, the dt algorithm performed more accurately than the other algorithms, but rf had better auc score. abdulaziz et al. [78] proposed an anomaly intrusion detection in an iot system. five supervised ml models were implemented to characterize their performance in detecting and classifying network activities with feature engineering and data preprocessing framework. based on experimental evaluation, the accuracy 100% recorded for the detection phase that distinguishes the normal and anomaly network activities. while for classifying network traffic into five attack categories, the implemented models of achieved 99.4-99.9%. khalid et al. [79] proposed and implemented an iot anomaly-based ids based on novel feature selection and extraction approach. the model framework was trained and tested on iotid20 and nslkdd datasets using four ml algorithms. the system scored a maximum detection accuracy of 99.98% for the proposed ml ensemble-based hybrid feature selection approach. from the literature, it is observed that there are extensive efforts on developing ids s for iot. several researchers have assessed the effectiveness of their systems using common datasets like nsl-kdd, unsw-nb15, and cicids2017. these datasets were not used captured traffic from iot environment. hence, an extensive work should be conducted using recent datasets such as iotid20 which consists of iot network traffic features. the state of the art also shows that some models perform well, particularly tree-based algorithms such as boosting, random forest and decision trees. ml algorithms’ performance outcomes vary depending on the used dataset, features, and classification category. 5. conclusion one of the most important technological progresses over the past decade was the widespread adoption of iot devices across industries and societies. with the development of iot, several obstacles have been raised. one of these obstacles is iot security which cannot be disregarded. iot networks are vulnerable to a variety of threats. although the iot network is protected by encryption and authentication, cyber attacks are still possible. therefore, using iot ids is important and necessary. this paper conducted an in-depth comprehensive analysis and comparison of various recent researches which used different techniques, datasets, ml algorithms and their performance for detecting iot intrusions. based upon the analysis, the recent iot dataset for intrusion detection is identified which is iotid20 dataset. furthermore, the ml algorithms that outperformed in most researches are treebased algorithms such as dt, rf, and boosting algorithms. many points were observed and needed further study like using and collecting real iot intrusion detection datasets for training and testing ml models, real time, and lightweight idss are required that need less detection time and resources consumption. all these factors should be taken into account while developing new iot idss. in addition, further study should be conducted to address recent iot threats, and the need to identify the best ids placement techniques that improve iot security while lowering the risk of cyber attacks. references [1] s. chen, h. xu, d. liu, b. hu and h. wang. “a vision of iot: applications, challenges, and opportunities with china perspective.” abdulla and jameel: a review on iot intrusion detection systems uhd journal of science and technology | jan 2023 | vol 7 | issue 1 63 ieee internet of things journal, vol. 1, no. 4, pp. 349-359, 2014. 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[79] k. albulayhi, q. a. al‑haija, s. a. alsuhibany, a. a. jillepalli, m. ashrafuzzaman and f. t. sheldon. “iot intrusion detection using machine learning with a novel high performing feature selection method”. applied sciences, vol. 12, no. 10, p. 5015, 2022. . uhd journal of science and technology | april 2017 | vol 1 | issue 1 23 1. introduction electrical loads can be divided into several categories, including residential, industrial, commercial, and government. these components vary in the electrical system depending on the economic, political, social state of the country, etc. in previous research, diversity factor was been studied in the iraqi electricity distribution system. the study shows that household electrical loads have grown at high rates exceeded the standard values for stable systems [1]. another study conducted aims to use artificial neural network technology to guess household electrical loads [2]. residential loads represent biggest components in the iraqi electrical systems, due to low industrial and commercial loads components. residential electrical loads consist of many components, household appliances, lighting, space heating, cooling, and water heating. a previous field survey study was conducting in the city of mosul to specify these components. the study found that the water heating component was the largest component, 32.29% [3]. the current research aims to test the possibility of using solar water heaters to supply hot water in the housing units. solar water heaters were added with low rating electrical heater to provide supplementary heating for a number of residential units in mosul city. the total energy consumed, the energy consumed in the supplementary heaters, the amount of water consumed, and the water temperature in the solar heater tank registration were recorded. readings recorded daily for 1 full year. the readings were analyzed to find the percentage of water heating component. furthermore, the change with the months of the year is compared with the previous utilization of solar water heaters to reduce residential electrical load majid s. al-hafidh1, mudhafar a. al-nama2 and azher s. al-fahadi3 1department of electrical engineering, mosul university, mosul, iraq 2department of computer engineering and technology, al hadbaa university, mosul, iraq 3department of electrical engineering, mosul university, mosul, iraq a b s t r a c t residential electrical load in iraq can be divided into five components, lighting, home appliances, heating, cooling, and water heating. water heating component represents the largest residential electric load component in iraq. the current research aims to test the possibility of using solar water heaters to supply hot water to residential units. solar water heaters were added to a number of residential units in mosul city, with the addition of a small electrical heater to provide supplementary heating. the readings of the total energy consumed, the energy consumed in the supplementary heating, the amount of water consumed and the water temperature in the solar heated tank, were recorded each day for a full year. the results were analyzed and compared with the case without the addition of solar heaters. the addition of solar water heater with supplementary heating leads to the reduction of the total consumption up to one-fifth of the total energy (19.19%). index terms: iraqi residential electrical load, residential electrical load, solar energy, solar water heaters corresponding author’s e-mail: el_noor2000@yahoo.com received: 10-03-2017 accepted: 25-03-2017 published: 12-04-2017 access this article online doi: 10.21928/uhdjst.v1n1y2017.pp23-26 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2017 al-hafidh, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology majid s. al-hafidh et al.: utilization of solar water heaters to reduce residential electrical load 24 uhd journal of science and technology | april 2017 | vol 1 | issue 1 study (without solar heated). the results, the analysis, and comparison are listed in the following paragraphs. 2. theoretical basis renewable energies represent suitable alternatives to solve the problems resulting from high energy consumption rates (especially electricity). renewable energies (wind energy, solar energy, hydropower, etc.) can be used to generate thermal energy, kinetic energy, electrical energy, etc. many researchers have been conducted to study the solar energy falling in different areas in iraq. the studies included which study all iraqi areas and gave illustrations of solar energy for different seasons of the year [4]. other studies al-salihi et al. and ali have been conducted to certain areas in iraq such as baghdad, mosul and kirkuk, ramady, etc. [5], [6]. solar energy has been used for water heating in many developing countries. mohammed, et al., 2011, study the possibility of using solar energy to heat water for the use of 25 people in baghdad using a solar panels collectors of 10 m2 capacity and a storage tank of 600 l capacity [7]. the study concluded the possibility of using solar energy to provide 69% of the hot water using solar heaters, by providing more than 60% in the winter, and more than 70% in the summer. it is well known that in summer no hot water is needed (june, july, august, and september). another study uses trnsys software to model and verify a direct solar water heating system in baghdad, iraq. the study aims to meet the demand of hot water for 25 persons using 10 m2 of a flat plate collector and 600 l storage tank [7]. 3. recording readings a group of houses (8 homes) was selected in the technical institute’s foundation. a solar water heater has been added for each residential unit. a low rating electrical heater of 1 kw is used to provide supplementary heating. fig. 1 shows one of the solar water heating systems used in the study. the system consists of two flat plate collectors each of the dimension 80 cm × 150 cm and storage tank of hot water with a capacity of 180 l. the flat plate collector and the storage tank capacity can be changed to match the consumers hot water demand. each solar water heater was equipped with a set of meters. the meters measure the energy consumed in the supplementary heaters, the amount of hot water consumed, and the water temperature in the storage tank. the supplementary heating energy and hot water consumed were recorded, once every 2 days. furthermore, an ammeter is used to measure the current drawn in the house units. the current and the water temperature in the storage tank readings have been registered at three different times at the morning, afternoon, and at night. as well as the total energy consumption in the residential units reading was recorded. total energy reading was recorded, once every 2 days. previous readings were recorded for the entire year. recorded readings were used in the analysis to get the results described in the following paragraphs. calculations can be performed based on the distribution of the foundations weekly or monthly. the calculations discussed in the results based on a semimonthly period, as well as monthly. 4. results and analysis electrical load in iraq is strongly influenced by weather climate changes where the high temperature in the summer leads to increase the electrical load as a result of using the cooling devices. furthermore, the low temperature in winter leads to increase electrical load as a result of using space heating and water heating whereas mild temperatures in spring and autumn lead to a reduction of electrical load, which represents the lowest throughout the year. in general, a large amount of solar energy falls on iraq, and especially in mosul city. it is clear that the amount of solar energy falling vary with seasons, where maximum energy fig. 1. one of the solar water heating systems used in the study majid s. al-hafidh et al.: utilization of solar water heaters to reduce residential electrical load uhd journal of science and technology | april 2017 | vol 1 | issue 1 25 falls in the summer. the minimum fallen energy is winter. the statistics show that the hours of solar brightness in iraq, during the winter is represented 50-60% of daylight hours. lowest rate happens to solar brightness hours in the month of january with 4.87 h. while the hours of solar brightness in summer represents 90% of the daylight hours, with a maximum brightness of the sun hours in the month of july 12.31 h. fig. 2 shows the average daily solar energy falling and the rate of solar brightness hours versus months of the year in the city of mosul. less solar energy occurs in the month of january and reaches 7.22 mj/m2-day. the maximum solar fallen in the month of june and reach 26.32 mj/m2-day (iraqi air adversity 1989). supplementary heating energy changes with temperature in the proportion of different seasons, as well as with the intensity of incoming solar radiation changes. therefore, the need for supplementary energy heating in winter becomes the highest in the whole year. fig. 3 illustrates the monthly supplementary rated heating in the year. it shows that there is no need for supplementary heating during summer. as well as it decreases in spring and autumn. the supplementary heating energy rate was calculated for a period of semimonthly to compare it with the energy consumed in the water heating (without the addition of solar heated). fig. 4 shows the energy consumed in the water heating with and without solar water heating. it is clear from the figure the great difference between the amount of supplementary heating energy used with solar water heater and the case without using it. also the times in which maximum benefits of adding solar heater is achieved. as well as there is no need to heat the water the majority of the summer. the maximum reduction ratio result during the spring and autumn. while reduction rates are less during winter than in the case of spring and autumn. table i summarizes the percentage of water heating component with the addition of solar water heaters and without them. the table includes the amount of the proportion of water heating component for the case of high consumption, the average consumption and the low fig. 2. average daily solar energy falling and the rate of solar brightness hours versus months of the year in the city of mosul fig. 3. supplementary heating rate of the months of the year fig. 4. energy consumed in the water heating with and without solar water heating table i percentage of the components of household electrical load and the amount of little and average total and high consumption case rate % consumption low % average % high % with solar heater 13.1 13.1 9.85 11.61 without solar heater 32.3 32.29 13.39 30.4 majid s. al-hafidh et al.: utilization of solar water heaters to reduce residential electrical load 26 uhd journal of science and technology | april 2017 | vol 1 | issue 1 consumption, in addition to the average consumer. evidenced by the average value of the added solar water heater leads to a reduction in the total consumption by 19.19%. 5. conclusion the current research shows a reduction in water heating component in residential units electrical load using solar water heaters. a solar water heater was added to a number of residential units in the city of mosul in northern iraq. a small rating heater was added to the solar water heaters to provide supplementary heating. the percentage of supplementary heating component compared with total electrical energy consumption in each housing units was calculated. as well as the percentage of supplementary heating component compared with total electrical energy consumption in all housing units. the results illustrate the possibility of obtaining a holistic reduced by 19.19%. the solar water heaters used have a standard specifications, while the hot water needs of the residential units vary as a result of differing ages and number of occupants (consumer), etc. which must leads to change some specifications of the solar water heaters, such as solar heater flat plane area and hot water storage volume. the solar water heaters specification can be studied to get a further reduction in the supplementary heating component. 6. future work the registered readings of the water consumed can be used to find a general model for hot water demand for different houses units. this general model can be used to design the suitable heating system to meet the hot water demand for any consumer. furthermore, the water temperature of the storage tank can be used to wake a general model for heat transfer for the heating system. this model can be used to improve the system efficiency. 7. thanks and appreciations the researchers express their deep gratitude and thanks to the administration and the engineers of the general management for north region electrical distribution for their valuable help and cooperation throughout this work and fruitful discussion and suggestions made among the different study stages. references [1] m. a. al-nama, m. s. al-hafid and a. s. al-fahadi. “estimation of the diversity factor for the iraqi distribution system using intelligent methods.” al-rafidain engineering, mosul, vol. 17, no.1, pp. 14-21, 2009. [2] m. a. al-nama, m. s. al-hafid and a. s. al-fahadi. “estimation of the consumer peak load for the iraqi distribution system using intelligent methods.” iraqi journal for electrical and electronic engineering, vol. 7, no. 2, pp. 180-184, 2011. [3] m. s. al-hafid, m. a. al-nama and a. s. al-fahadi. “determination of residential electrical load components in iraqi north region.” iraqi journal for electrical and electronic engineering, basra, iraq: sent for publication. [4] m. z. mohammed, m. a. al-nema and d. a. al-nema. “seasonal distribution of solar radiation in iraq.” proceeding of the conference on the physics of solar energy. arab development institute, special publication, tripoli, 1977. [5] a. m. al-salihi, m. m. kadum and a. j. mohammed. “estimation of global solar radiation on horizontal surface using routine meteorological measurements for different cities in iraq.” asian journal of scientific research, vol. 3, no. 4, pp. 240-248, 2010. [6] f. a. ali. “computation of solar radiation on horizontal surface over some iraqi cities.” engineering and technical journal, vol. 29, no.10, pp. 2026-2042, 2011. [7] m. n. mohammed, m. a. alghoul, k. abulqasem, a. mustafa, k. glaisa, p. ooshaksaraei, m. yahya, a. zaharim and k. sopian. “trnsys simulation of solar water heating system in iraq.” recent researches in geography, geology, energy, environment and biomedicine, pp. 153-156, jul. 2011. . uhd journal of science and technology | april 2017 | vol 1 | issue 1 17 1. introduction it is not easy to decide precisely when and how the first data converter was established. the most primitive documented binary analog-to-digital adaptor recognized is not electronic at all but hydraulic. to the best of our knowledge, the optimum historical review regarding the analog-to-digital adapters, in general, can be found in the study of kester et al. [1]. the analog domain is unceasing with both time and signal magnitude, while the digital domain is independent on both time and magnitude. a single binary value signifies a variety of analog values in the quantization band nearby its code center point. analog values that are not precisely at the code center point have an allied amount of quantization error [2]. it can be stated that sigma-delta [3] analog-to-digital adapter is a most common approach of over-sampling analog-to-digital adapter. the map processor of a sigma-delta analog-to-digital adapter is displayed in fig. 1 [4]. the sigma-delta analog-to-digital adapter can be divided into two lumps, the quantizing and the decimating parts. essentially, decimation is the act of decreasing the data rate down from the over-sampling rate without losing information. the quantizing part contains the analog integrator, the 1-bit analog-to-digital adapter, and the 1-bit digital-to-analog adapter [5]. the task of the quantizing part is to adapt the data in the analog input into digital shape. the input-output relationship of the sigma-delta quantizer is mathematical modeling of sampling, quantization, and coding in sigma delta converter using matlab azeez abdullah azeez barzinjy, haidar jalal ismail, and mudhaffer mustafa ameen department of physics, college of education, salahaddin university-erbil, zanko, erbil, iraq a b s t r a c t the received analog signal must be digitized before the digital signal processing can demodulate it. sampling, quantization, and coding are the separate stages for the analog-to-digital adaptation procedure. the procedure of adapting an unceasing time-domain signal into a separate time-domain signal is called sampling. while, the procedure of adapting a separatetime, continuous-valued signal into a discrete-time, discrete-valued signal is known as quantization. thus, quantization error is the mismatch between the unquantized sample and the quantized sample. the method of demonstrating the quantized samples in binary form is known as coding. this investigation utilized matlab® program to recommend a proper scheme for a wireless-call button network of input signal, normalized frequency, and over-sampling ratio against signalto-quantization noise ratio. two vital characteristics of this wireless network design are cost-effective and low-power utilization. this investigation, through reducing the in-band quantization error, also studied how oversampling can enhance the accomplishment of an analog-to-digital adapter. index terms: analog-to-digital adapter, coding, matlab, quantization error, wireless network corresponding author’s e-mail: azeez.azeez@su.edu.krd received: 10-03-2017 accepted: 25-03-2017 published: 12-04-2017 access this article online doi: 10.21928/uhdjst.v1n1y2017.pp17-22 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2017 barzinjy, et al. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology azeez abdullah azeez barzinjy et al.: mathematical modeling of sampling, quantization, and coding 18 uhd journal of science and technology | april 2017 | vol 1 | issue 1 non-linear; nevertheless, the capacity of frequency depression for the analog input, explicitly x(t), might be retrieved from the quantizer yield, namely, y[n] as shown in fig. 1. y[n] is a restricted order with sample values equivalent to −1 or +1. y[n] may just stay restricted if the output collector, w[n], is bounded similarly. as a result, the typical value of y[n] is needed to be equivalent to the mean value of the input x(t). accordingly, the authors have been capable of solving w[n] to obtain the constant of x(t) [6]. this investigation suggests a 1-bit analog-to-digital converter which can be utilized as an alternative of a more costly multibit analog-to-digital converter. this can be done through studying two divergent procedures that permit a 1-bit analogto-digital converter to attain the enactment of a multi-bit analog-to-digital converter. the authors will also investigate the superiority and drawbacks of both these procedures. relying on this exploration, one of the two procedures is selected for our data radios. 2. theory xa(t) is an analog signal ant it behaves similar to the input to an analog-to-digital adapter. equations 1 and 2 describe xa(t) and the average power in xa(t), correspondingly [7]. ( ) cos 2 tx a a t= (1) 2 2 0 1 [ ( )] 8 t x a a x t dt t = =∫σ (2) to prototype this analog signal, assume a b-bit analog-todigital adapter. conditionally, if the analog signal possesses peak-to-peak amplitude of a, and subsequently, the minimum potential step, δv, by means of b bits is given by equation 3: ( 2 1) ( 2 ) ∆ b b a a v = ≅ − (3) quantization noise, or quantization error, is a unique restricting parameter for the effective range of an analog-todigital adaptor [8]. this error is essentially the “round-off ” error that happens when an analog signal is quantized. a quantized signal may be different as of the analog-signal by just about ±(δv/2). supposing a quantization error is equivalently distributed ranging from −δv/2 to δv/2, then the root mean square of the quantization noise power, σe, is identified by equation 4 [9]. ( ) 2 2 2 2 ( ) 12 2 12 e b v a∆ = = (4) using equations 2 and 4, the signal-to-quantization noise ratio (sqnr) for our b-bit analog-to-digital converter might be assessed [10] as follows: 23 2 2   bx e sqnr = = (5) the sqnr in decibels is assumed through equation 6 [11]. ( )db 1010 1.76 6.02sqnr log sqnr b= = + (6) equation 6 is an illustration of the sqnr of an analog-todigital adaptor which rises through almost 6 db per every single added bit. one can realize that the assessed signal through the analogto-digital adaptor is at baseband. thus, a uniform spectrum in the frequency ranges from 0 to fs/2 is the characteristic of the root mean square quantization noise power, σe. the noise power for each unit of bandwidth can be assumed using equation 7. ( ) 2 2( / 2) 2 6 e o b s s a n f f = = (7) the nyquist frequency, which termed after harry nyquist, is basically twice the input signal bandwidth fm. recalling that fm for a baseband signal expands from 0 to fm [12]. the entire quantization noise power in the concerned band or the in-band noise is specified through equation 8.fig. 1. sigma-delta analog-to-digital converter azeez abdullah azeez barzinjy et al.: mathematical modeling of sampling, quantization, and coding uhd journal of science and technology | april 2017 | vol 1 | issue 1 19 ( ) ( ) ( ) ( ) 2 2 2 22 6 2 6 m o m mb b ss fa a n f f ff   = =     (8) equation 8 might be examined to classify which limits disturb the in-band quantization error in analog-to-digital adaptor. where a is the amplitude of the signal and fm is half nyquist frequency, both of them are relying on signal, while the analog-to-digital adaptor has no dominance above these. nevertheless, the available analog-to-digital bits number, b, and the specimen frequency, fs, are organized through the analog-to-digital adaptor scheme [13]. the act of sampling the input signal at frequencies considerably higher than the nyquist frequency is called oversampling. by means of equation 8, a correlation might be resulting for the over-sampling segment, m = fs/2fm, like that the two analog-to-digital adaptors offer an identical in-band error power [14]. after allowing fs = 2fm, equation 8 becomes: ( ) ( ) 2 2 2 b 22 6 2 12 m s fa a f   =    (9) by means of equation 9, one can acquire equation 10. ( ) 2 1 log ( ) 2  b m− = + (10) (β−b) means the additional determination bits which are in consequence gained out of a b-bit adaptor by means of oversampling. the above equation, also, indicates that each duplication of the over-sampling proportion rises the actual bits at the nyquist frequency by 0.5 [15]. ( ) 1 1 1 mz h z z − − − = − (11) mitra [16] relates the enactment of a sigma-delta analogto-digital adaptor through that of a linear over-sampling analog-to-digital adaptor. the enhancement in enactment achieved by means of a sigma-delta analog-to-digital adaptor is illustrated in equation 10 [17]. enhancement(m)=− 5.1718+20log 10 (m) (12) the power spectral density, sx (f), is the strength of the variations as a function of frequency. for an arbitrary time signal xa (t), the power spectral density can be given by equation 13. ( ) t t 21 lim ( ) 2 − −→∞   =     ∫ i tx ts f e x t e dtt  (13) power spectral density computation can be made straightforwardly through the fast fourier transform method. 3. results and discussions a. power spectral density a first-order integrator which might be demonstrated as a collector has been utilized by the straightforward sigma-delta quantizer [18]. the matlab® program imitates the occupied first-order sigma-delta adaptor. the power spectral density, equation 13, of the stimulus signal can be illustrated in fig. 2. in addition, the stimulus signal has been over sampled at a level of 50 times nyquist. it can be notice in fig. 2 indicated that the normalized frequency is schemed against power spectral density possessing a range from 0 to 1 knowing that 1 indicating 50 nyquist frequency [19]. analogous tendency has been obtained by belitski et al. [20] which indicates that the proposed model is appropriate for sampling, quantization, and coding in sigma-delta converter. y[n] is the digital signal characterized by means of 1-bit. the power spectral density of y[n] is plotted in fig. 3 against the normalized frequency. fig. 3 shows the noise forming aptitudes of the sigmadelta analog-to-digital converter. as stated previously, a straightforward over-sampling analog-to-digital converter is capable to diffuse the overall quantization error power fig. 2. power spectral density of input signal after oversampling azeez abdullah azeez barzinjy et al.: mathematical modeling of sampling, quantization, and coding 20 uhd journal of science and technology | april 2017 | vol 1 | issue 1 above a longer band, thus reducing the in-band error power. alternatively, overney et al. [21] utilize josephson voltage standard to achieve logical description of higher level resolution analog-to-digital adaptor. their method might be used in many metrological applications for different analogto-digital adaptors with frequencies up to a few khz. in addition, posselt et al. [22] utilized a reconfigurable analogto-digital converter which was suggested with aptitudes of digitalizing completely related wireless facilities for vehicular usage with frequency ranging from of 600 mhz to 6 ghz. in addition, sigma-delta adaptors are normally capable to achieve error modeling just like that the error power is centralized in upper frequencies [23]. fig. 3 demonstrated that the bottom error is significantly sophisticated at upper frequencies and rather beneath the concern band. furthermore, the signal y[n] is the quantizer yield and is low pass clean by means of a mth band low-pass filter, where m = 50 is the over-sampling ratio. the transmission function, h(z), of the mth band low-pass filter is known from equation 10. the power spectral density of the clarified yield is shown in fig. 4. fig. 5 displays the power spectral density of the real analogto-digital converter production. it can be obvious that, at this point, the signal has been downsampled to the nyquist frequency once again. taking into consideration that for the sigma-delta adaptor, exhibited in the matlab® program, the utilized over-sampling ratio, m, was 50. it can be realized that, through equation 12, a sigma-delta adaptor might offer an enhancement of about 29 db once likened through a straight over-sampling adaptor that similarly works at 50 times nyquist frequency. b. sqnr the sqnr generated using a 1-bit sigma-delta adaptor can be linked through fig. 6 with the sqnr of a straight over-sampling analog-to-digital converter at numerous oversampling ratios. moreover, it can be noticed that from fig. 6 when the over-sampling ratio is 15, then the sqnr is just round 20 db. accordingly, the nyquist frequency for an analog signal modulator utilizing a binary phase shift keying and conveying 80 kbps of data will be 160 khz. on the other hand, oversampling in 15 would necessitate sampling at 2.4 mhz. otherwise stated, considering the present digital signal processing, which can treat samples at an order of nearly 2.4 mhz, one might be capable to carry out a regulate over-sampling analog-to-digital adaptor. in similar work, brooks et al. [24] stated that their analog-to-digital converter works at a 20 mhz and it attains a signal-to-noise ratio of about 90 db exceeding a 1.25 mhz signal bandwidth. fig. 7 displays a scheme of the extra accuracy bits achieved against the over-sampling percentage. fig. 7 indicates that for a 10 db corresponded input, once the signal-to-quantization error ratio of the analog-todigital adaptor is 20 db, and the digitized output possesses a signal-to-noise ratio of approximately 9.5 db. this, perhaps, shows that using a sqnr of 20 db, the digitizing process solely increases the bottom error by 0.5 db. the bottom error is increased by even below 0.5 db if the equivalent contribution’s signal-to-noise ratio is less than 10 db. accordingly, the first and the last goal behind this study was directing over-sampling analog-to-digital adaptor to possess fig. 3. power spectral density of y[n] versus normalized frequency fig. 4. power spectral density versus normalized frequency of low-pass filter output azeez abdullah azeez barzinjy et al.: mathematical modeling of sampling, quantization, and coding uhd journal of science and technology | april 2017 | vol 1 | issue 1 21 the strength of the error combination avert methods in combination with the low master clock of 20 mhz. 4. concussion running the analog-to-digital adaptor beyond the input signal’s nyquist frequency enhances the improvement of a low accuracy analog-to-digital adaptor. this is the evidence behindhand the operating of continuous over-sampling analog-to-digital converters. the quantization noise addition to the analog-to-digital adaptation procedure supplementary enhances enactment. sigma-delta analog-to-digital adaptors apply both noise affecting and oversampling. sigma-delta analog-to-digital converters propose significantly superior enactments than uninterrupted over-sampling adaptors. nevertheless, it has been recommended that a straightforward sampling adaptor be utilized due to the difficulty of a sigmadelta analog-to-digital adaptor. similarly, it has been observed that a straight over-sampling analog-to-digital converter can be utilized without any kind of signal humiliation. 5. acknowledgment t h e a u t h o r s wo u l d l i ke t o e x t e n d t h e i r s i n c e r e acknowledgement to the salahaddin university for supporting them with available tools. if anyone who needs the matlab codes please contact the corresponding author for any additional help. references [1] w. a. kester. “analog devices,” in data conversion handbook, burlington, ma: elsevier, 2005. [2] k. fowler. “part 7: analog-to-digital conversion in real-time systems.” ieee instrumentation and measurement magazine, vol. 6, no. 3, pp. 58-64, 2003. [3] r. schreier and g. c. temes. understanding delta-sigma data converters, vol. 74. piscataway, nj: ieee press, 2005. [4] j. m. de la rosa and r. río. cmos sigma-delta converters: practical design guide, hoboken, nj: wiley, 2013. [5] m. pelgrom. analog-to-digital conversion, switzerland: springer international publishing, 2016. [6] j. keyzer, j. hinrichs, a. metzger, m. iwamoto, i. galton and p. asbeck. “digital generation of rf signals for wireless communications with band-pass delta-sigma modulation.” in microwave symposium digest, ieee mtt-s international, 2001. [7] j. j. wikner and n. tan. “modeling of cmos digital-to-analog converters for telecommunication.” ieee transactions on circuits and systems ii: analog and digital signal processing, vol. 46, no. 5, pp. 489-499, 1999. [8] j. kim, t. k. jang and y. g. yoon. “analysis and design of fig. 5. power spectral density of analog-to-digital converter output signal fig. 6. over-sampling ratio versus signal-to-quantization noise ratio fig. 7. analog-to-digital adaptor yield’s signal-to-noise ratio (snr) (with contribution’s snr = 10 db) a sqnr of 20 db or less. fujimori et al. [25], alternatively, stated that in their study, no signal-to-noise ratio decay caused by numerical swapping error has been inspected, showing azeez abdullah azeez barzinjy et al.: mathematical modeling of sampling, quantization, and coding 22 uhd journal of science and technology | april 2017 | vol 1 | issue 1 voltage-controlled oscillator based analog-to-digital converter.” ieee transactions on circuits and systems i: regular papers, vol. 57, no. 1, pp. 18-30, 2010. [9] b. s. song. microcmos design, hoboken, nj: taylor & francis, 2011. [10] j. g. proakis and d. g. manolakis. digital signal processing: principles, algorithms, and applications, new jersey, usa: prentice hall, 1996. [11] g. j. foschini and m. j. gans. “on limits of wireless communications in a fading environment when using multiple antennas.” wireless personal communications, vol. 6, no. 3, pp. 311-335, 1998. [12] k. sudakshina. analog and digital communications, singapore: pearson education india, 2010. [13] j. s. chitode. principles of communication, pune: technical publications, 2009. [14] d. r. morgan, z. ma, j. s. kenney, j. kim and c. r. giardina. “a generalized memory polynomial model for digital predistortion of rf power amplifiers.” ieee transactions on signal processing, vol. 54, no. 10, pp. 3852-3860, 2006. [15] b. le, t. w. rondeau, j. h. reed and c. w. bostian. “analogto-digital converters.” ieee signal processing magazine, vol. 22, no. 6. pp. 69-77, 2005. [16] s. k. mitra and s. k. mitra. digital signal processing: a computerbased approach, new york: mcgraw-hill, 2011. [17] v. mladenov, p. karampelas, g. tsenov and v. vita. “approximation formula for easy calculation of signal-to-noise ratio of sigmadelta modulators.” isrn signal processing, vol. 2011, article id: 731989. pp. 7, 2011. [18] k. francken and g. g. gielen. “a high-level simulation and synthesis environment for/spl delta//spl sigma/modulators.” ieee transactions on computer-aided design of integrated circuits and systems, vol. 22, no. 8. p. 1049-1061, 2003. [19] b. e. bammes, r. h. rochat, j. jakana, d. h. chen and w. chiu. “direct electron detection yields cryo-em reconstructions at resolutions beyond 3/4 nyquist frequency.” journal of structural biology, vol. 177, no. 3, p. 589-601, 2012. [20] a. belitski, a. gretton, c. magri, y. murayama, m. a. montemurro, n. k. logothetis, s. panzeri. “low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.” journal of neuroscience, vol. 28, no. 22, pp. 56965709, 2008. [21] f. overney, a. rufenacht, j. p. braun and b. jeanneret. “josephson-based test bench for ac characterization of analogto-digital converters.” in precision electromagnetic measurements (cpem), 2010 conference on ieee, 2010. [22] a. posselt, d. berges, o. klemp, b. geck. “design and evaluation of frequency-agile multi-standard direct rf digitizing receivers for automotive use.” in vehicular technology conference (vtc spring), ieee 81st, ieee, 2015. [23] p. m. aziz, h. v. sorensen and j. van der spiegel. “an overview of sigma-delta converters.” ieee signal processing magazine, vol. 13, no. 1, pp. 61-84, 1996. [24] t. l. brooks, d. h. robertson, d. f. kelly, a. del muro and s. w. harston. “a cascaded sigma-delta pipeline a/d converter with 1.25 mhz signal bandwidth and 89 db snr.” ieee journal of solid-state circuits, vol. 32, no. 12, pp. 1896-1906, 1997. [25] i. fujimori, l. longo, and a. hirapethian. “a 90-db snr 2.5-mhz output-rate adc using cascaded multibit delta-sigma modulation at 8/spl times/oversampling ratio.” ieee journal of solid-state circuits, vol. 35. no. 12. pp. 1820-1828, 2000. . uhd journal of science and technology | april 2017 | vol 1 | issue 1 11 1. introduction the power flow analysis is one of the important and extensively used studies in electrical power system engineering. it is considered a fundamental tool for many other power system studies such as stability, reliability, fault, and contingency study. the main objective of a power flow study is to find the bus voltages and the power flow in the transmission system for a particular loading condition. the steady-state performance of an electrical power system is described by a system of non-linear algebraic equations. these equations represent the active and reactive power balance. the inherent difficulty of the power flow problem is the task of obtaining analytical solutions to the power flow equations. an extensive research has been carried out since the latter half of the twentieth century [1], [2] to solve this problem. the solution of power flow problem has been based on numerical technique methods such as gauss-seidel [3], newton-raphson method [4]-[14], and fast-decoupled method [15]-[18]. although some of these methods are widely used in power utilities, they are sensitive to the starting (guess) values. in some cases, especially in heavily loaded conditions, they fail to converge. it was found that the factors affecting the convergence of the previous methods are the r/x ratio of the transmission systems and the singularity of the jacobian matrix for a heavily loaded system. different attempts have been done to improve the reliability of these methods [19], [20]. artificial intelligence techniques had been applied to power flow study [21]-[23]. recently, the fields of swarm intelligence have attracted many researches as a branch of artificial intelligence that deals with the collective behavior of swarms such as flocks of bird, colonies of aunts, schools of fish, and swarm of bees [24], [25]. the important features of swarm intelligence are selforganization, scalability, adaptation, and speed. the swarm intelligence techniques have been applied in many power system studies [26]-[28]. in this paper, the load flow problem is approached as an optimization problem using application of artificial bee colony algorithm in power flow studies kassim al-anbarri and husham moaied naief department of electrical engineering, faculty of engineering, mustansiriyah university, bab al-muadham campus, 46049 baghdad, iraq a b s t r a c t artificial bee colony (abc) algorithm is one of the important artificial techniques in solving general-purpose optimization problems. this paper presents the application of abc in computing the power flow solution of an electric power system. the objective function to be minimized is the active and reactive power mismatch at each bus. the proposed algorithm has been applied on typical power systems. the results obtained are compared with those obtained by the conventional method. the results obtained reveal that the abc algorithm is very effective for solving the power flow problem in the maximum loadability region. index terms: artificial bee colony, maximum loadability, power flow, swarm artificial technique corresponding author’s e-mail: alanbarri@yahoo.com received: 10-03-2017 accepted: 25-03-2017 published: 12-04-2017 access this article online doi: 10.21928/uhdjst.v1n1y2017.pp11-16 e-issn: 2521-4217 p-issn: 2521-4209 copyright © 2017 al-anbarri and naief. this is an open access article distributed under the creative commons attribution non-commercial no derivatives license 4.0 (cc by-nc-nd 4.0) o r i g i n a l re se a rc h a rt i c l e uhd journal of science and technology kassim al-anbarri and husham moaied naief: application of artificial bee colony algorithm in power flow studies 12 uhd journal of science and technology | april 2017 | vol 1 | issue 1 swarm intelligence. the objective function is to minimize the power mismatch. this paper is organized as follows: section 2 reviews the newton-raphson (nr) technique in solving load flow problem. the basics model of artificial bee colony (abc) is presented in section 3. section 4 discusses the results obtained by applying the proposed algorithms on a typical system. finally, section 5 presents the conclusion. 2. power flow formulation for n bus electrical power system, the bus power si can be expressed by the following equation: si=sgi−sdi si=pgi−pdi+j(qgi−qdi) (1) where pgi is the active power generation at bus i pdi is the active power demand at bus i qgi is the reactive power generation at bus i qdi is the reactive power demand at bus i the current balance equation at bus i * * 1 n i i ik k i k s i y v v = = =∑ (2) where yik is the i, k th element of bus admittance matrix vk is the bus voltage at bus k. by substituting (1) into the (2) and resolved the resulting equation into the following two real equations: ( )k i k 1 cos n k i ki i p v v y = γ= + −∑ ki δ δ (3) ( )k i k 1 sin n k i ki i q v v y = γ= + −∑ ki δ δ (4) for n bus power system, there are 2n real non-linear algebraic equations similar to (3) and (4). these equations are non-linear function of the state variables (|v|,δ). the conventional technique to solve these equations is using a numerical technique. the most widely used method is nr method. this method is based on expanding the above equations by taylor series. the compact linearized form of the above equations is as follows: i i|v| p p|v|i q q|v|i j jp j jq ∆ ∆δ     =     ∆ ∆     δ δ (5) where the left-hand side of (5) is the vector of power mismatch, which can be calculated as: sp cal i i i sp cal i i i p p p q q q  ∆ −  =   ∆ −     (6) the traditional algorithm for obtaining power flow solution is as follows: 1. assume a guess values for the state variables (flat start |v|=1.0 pu; δ=0) 2. evaluate the vector of power mismatch and the elements of the jacobian matrix 3. calculate the vector of state variable disturbance 4. update the state variables at the end of iteration 5. check the absolute value of the elements of the vector of power mismatch, if it is less than a specified tolerance; calculate the line flow in each transmission line. otherwise, go to step 2. the previous algorithm works reliably in ordinary loading conditions. unfortunately, it is found in some cases (e.g., heavily loaded conditions and high r/x ratio system) that the above algorithm fails to converge. this is because of singularity of the jacobian matrix. for this purposes, a swarm intelligence technique is presented to avoid the singularity of the jacobian matrix. 3. power flow algorithm using abc method the honey bees foraging behavior, learning, and memorizing characteristics have been attracted many researcher in the area of swarm intelligence. the pioneer work of karaboga [24] which describes an abc algorithm based on the behavior of honey bee is first attempt model in this aspect. one of the main features of abc algorithm is its ability to conduct both global search and local search in each iteration. according to the abc algorithm, there are three categories of artificial bees in the colony. these are employed bees, onlookers bees, and scouts bees. the bee colony is divided into two halves, the first half of colony includes employed bees, and the second half includes the onlookers. the onlooker’s bees are those waiting on the dance area in hive as a decision-maker for choosing the suitable food source. the employed bees are those collecting the nectar from food kassim al-anbarri and husham moaied naief: application of artificial bee colony algorithm in power flow studies uhd journal of science and technology | april 2017 | vol 1 | issue 1 13 source. while the scout bees are those searching the food sources. the searching cycle in the abc algorithm consists of the following steps [29]: • at the initialization step, the bees select a set of food source positions randomly. after determining the nectar amount, the bees come to the hive to share the information with those waiting on the dance area. • at the second step, the employed bees use the gained information to choose new food sources in neighborhood area after going to the old position, which is visited by themselves previously. • at the third stage, the onlooker bee chooses a particular area for the food sources depending on the information given by the employed bees on the dance area. to utilize the abc algorithm, there are some control parameters that should be set [30]; they are number of variables, lower bound of variables (lb), upper bound of variables (ub), population size (colony size) (npop), number of onlooker bees (nn onlooker), maximum number of iterations (maxit) (the stopping criteria), abandonment limit parameter (limit), and acceleration coefficient upper bound (a). a. steps of abc implementation the steps of abc can be outlined as follows [31]: 1. generate a randomly distributed initial population solutions (food source positions). 2. evaluate the population which represents the nectar quantity. the population of the positions (solutions) is subjected to iterated cycles, c =1, 2,…, c max , of the search processes of the employed bees, the onlooker bees and scout bees. based on a probabilistic approach, the artificial employed or onlooker bee makes a change on the position (solution) in her memory for finding a new food source and tests the nectar amount (fitness value) of the new source (new solution). 3. apply the roulette wheel selection (choose the best fit individuals). 4. calculate the probability rate (pi) related with solutions; 1 i i npop ii fit p fit = = ∑ (7) the fitness values (fit) are computed by the following expression: 1 if 0 1 1 abs( ) if 0 i ii i i f ffit f f  ≥ +=    + <  (8) usually, the value of pi is between {0,1}. 5. find the new solutions for the onlookers depending on the probability pi related with the solutions. 6. reapply roulette wheel selection. 7. find the abandoned solution if exists, change it with new randomly generated solution. 8. register the best solution achieved so far. 9. c=c+1 (until maximum cycle number is reached). b. abc implementation for power flow study abc optimization is applied to obtain the bus voltage magnitude (|vi|) and voltage phase angle (δi) by minimize the following objective function: minf(δ,|v|) (9) where, δ=(δ1,……………, δn) |v|=(|v 1 |,……………, |vn|) this objective function is constrained by the inequalities lb and ub. lb<|v|