id sid tid token lemma pos cana-574 1 1 communications communication NOUN cana-574 1 2 on on ADP cana-574 1 3 applied apply VERB cana-574 1 4 nonlinear nonlinear ADJ cana-574 1 5 analysis analysis NOUN cana-574 1 6 issn issn NOUN cana-574 1 7 : : PUNCT cana-574 1 8 1074 1074 NUM cana-574 1 9 - - PUNCT cana-574 1 10 133x 133x NUM cana-574 1 11 vol vol NOUN cana-574 1 12 31 31 NUM cana-574 1 13 no no NOUN cana-574 1 14 . . NOUN cana-574 1 15 2 2 NUM cana-574 1 16 ( ( PUNCT cana-574 1 17 2024 2024 NUM cana-574 1 18 ) ) PUNCT cana-574 1 19 370 370 NUM cana-574 1 20 https://internationalpubls.com https://internationalpubls.com X cana-574 1 21 integration integration NOUN cana-574 1 22 of of ADP cana-574 1 23 mathematical mathematical ADJ cana-574 1 24 operators operator NOUN cana-574 1 25 based base VERB cana-574 1 26 on on ADP cana-574 1 27 decision decision NOUN cana-574 1 28 boundary boundary ADJ cana-574 1 29 complexity complexity NOUN cana-574 1 30 and and CCONJ cana-574 1 31 combinatorial combinatorial ADJ cana-574 1 32 optimization optimization NOUN cana-574 1 33 for for ADP cana-574 1 34 improved improved ADJ cana-574 1 35 deep deep ADJ cana-574 1 36 learning learning NOUN cana-574 1 37 classifiers classifier NOUN cana-574 1 38 shruti shruti PROPN cana-574 1 39 thapar1 thapar1 PROPN cana-574 1 40 , , PUNCT cana-574 1 41 krati krati PROPN cana-574 1 42 sharma2 sharma2 PROPN cana-574 1 43 , , PUNCT cana-574 1 44 dharmveer dharmveer NOUN cana-574 1 45 yadav3 yadav3 NOUN cana-574 1 46 , , PUNCT cana-574 1 47 budesh budesh NOUN cana-574 1 48 kanwer4 kanwer4 NOUN cana-574 1 49 , , PUNCT cana-574 1 50 ashish ashish PROPN cana-574 1 51 raj5 raj5 PROPN cana-574 1 52 1department 1department NUM cana-574 1 53 of of ADP cana-574 1 54 electronics electronic NOUN cana-574 1 55 & & CCONJ cana-574 1 56 communication communication NOUN cana-574 1 57 , , PUNCT cana-574 1 58 poornima poornima PROPN cana-574 1 59 institute institute PROPN cana-574 1 60 of of ADP cana-574 1 61 engineering engineering PROPN cana-574 1 62 & & CCONJ cana-574 1 63 technology technology PROPN cana-574 1 64 , , PUNCT cana-574 1 65 jaipur jaipur PROPN cana-574 1 66 , , PUNCT cana-574 1 67 rajasthan rajasthan PROPN cana-574 1 68 , , PUNCT cana-574 1 69 india india PROPN cana-574 1 70 2department 2department NUM cana-574 1 71 of of ADP cana-574 1 72 english english PROPN cana-574 1 73 and and CCONJ cana-574 1 74 soft soft ADJ cana-574 1 75 skills skill NOUN cana-574 1 76 , , PUNCT cana-574 1 77 poornima poornima PROPN cana-574 1 78 institute institute PROPN cana-574 1 79 of of ADP cana-574 1 80 engineering engineering PROPN cana-574 1 81 & & CCONJ cana-574 1 82 technology technology PROPN cana-574 1 83 , , PUNCT cana-574 1 84 jaipur jaipur PROPN cana-574 1 85 , , PUNCT cana-574 1 86 rajasthan rajasthan PROPN cana-574 1 87 , , PUNCT cana-574 1 88 india india PROPN cana-574 1 89 3department 3department PROPN cana-574 1 90 of of ADP cana-574 1 91 computer computer NOUN cana-574 1 92 science science NOUN cana-574 1 93 , , PUNCT cana-574 1 94 st st PROPN cana-574 1 95 . . PROPN cana-574 1 96 xavier xavier PROPN cana-574 1 97 ’s ’s PROPN cana-574 1 98 college college PROPN cana-574 1 99 , , PUNCT cana-574 1 100 jaipur jaipur PROPN cana-574 1 101 , , PUNCT cana-574 1 102 rajasthan rajasthan PROPN cana-574 1 103 , , PUNCT cana-574 1 104 india india PROPN cana-574 1 105 4department 4department NUM cana-574 1 106 of of ADP cana-574 1 107 ai&ds ai&ds PROPN cana-574 1 108 , , PUNCT cana-574 1 109 poornima poornima PROPN cana-574 1 110 institute institute PROPN cana-574 1 111 of of ADP cana-574 1 112 engineering engineering PROPN cana-574 1 113 & & CCONJ cana-574 1 114 technology technology PROPN cana-574 1 115 , , PUNCT cana-574 1 116 jaipur jaipur PROPN cana-574 1 117 , , PUNCT cana-574 1 118 rajasthan rajasthan PROPN cana-574 1 119 , , PUNCT cana-574 1 120 india india PROPN cana-574 1 121 5department 5department NUM cana-574 1 122 of of ADP cana-574 1 123 electrical electrical ADJ cana-574 1 124 and and CCONJ cana-574 1 125 electronics electronic NOUN cana-574 1 126 engineering engineering NOUN cana-574 1 127 , , PUNCT cana-574 1 128 poornima poornima PROPN cana-574 1 129 university university PROPN cana-574 1 130 , , PUNCT cana-574 1 131 jaipur jaipur PROPN cana-574 1 132 , , PUNCT cana-574 1 133 rajasthan rajasthan PROPN cana-574 1 134 , , PUNCT cana-574 1 135 india india PROPN cana-574 1 136 emailshruti.thapar@poornima.org emailshruti.thapar@poornima.org PROPN cana-574 1 137 1 1 NUM cana-574 1 138 , , PUNCT cana-574 1 139 kratibhomia@gmail.com kratibhomia@gmail.com PROPN cana-574 1 140 2 2 NUM cana-574 1 141 , , PUNCT cana-574 1 142 dharmveerya21@gmail.com dharmveerya21@gmail.com PROPN cana-574 1 143 3 3 NUM cana-574 1 144 , , PUNCT cana-574 1 145 budesh82@gmail.com budesh82@gmail.com X cana-574 1 146 4 4 NUM cana-574 1 147 , , PUNCT cana-574 1 148 ashishraj1987@gmail.com ashishraj1987@gmail.com NOUN cana-574 1 149 5 5 NUM cana-574 1 150 article article NOUN cana-574 1 151 history history NOUN cana-574 1 152 : : PUNCT cana-574 1 153 received receive VERB cana-574 1 154 : : PUNCT cana-574 1 155 16 16 NUM cana-574 1 156 - - PUNCT cana-574 1 157 02 02 NUM cana-574 1 158 - - PUNCT cana-574 1 159 2024 2024 NUM cana-574 1 160 revised revise VERB cana-574 1 161 : : PUNCT cana-574 1 162 22 22 NUM cana-574 1 163 - - PUNCT cana-574 1 164 04 04 NUM cana-574 1 165 - - PUNCT cana-574 1 166 2024 2024 NUM cana-574 1 167 accepted accept VERB cana-574 1 168 : : PUNCT cana-574 1 169 05 05 NUM cana-574 1 170 - - PUNCT cana-574 1 171 05 05 NUM cana-574 1 172 - - PUNCT cana-574 1 173 2024 2024 NUM cana-574 1 174 abstract abstract NOUN cana-574 1 175 : : PUNCT cana-574 1 176 the the DET cana-574 1 177 proliferation proliferation NOUN cana-574 1 178 of of ADP cana-574 1 179 complex complex ADJ cana-574 1 180 diseases disease NOUN cana-574 1 181 in in ADP cana-574 1 182 livestock livestock NOUN cana-574 1 183 , , PUNCT cana-574 1 184 such such ADJ cana-574 1 185 as as ADP cana-574 1 186 lumpy lumpy ADJ cana-574 1 187 skin skin NOUN cana-574 1 188 disease disease NOUN cana-574 1 189 , , PUNCT cana-574 1 190 demands demand VERB cana-574 1 191 advanced advanced ADJ cana-574 1 192 diagnostic diagnostic ADJ cana-574 1 193 tools tool NOUN cana-574 1 194 that that PRON cana-574 1 195 can can AUX cana-574 1 196 accurately accurately ADV cana-574 1 197 classify classify VERB cana-574 1 198 and and CCONJ cana-574 1 199 predict predict VERB cana-574 1 200 outbreaks outbreak NOUN cana-574 1 201 . . PUNCT cana-574 2 1 this this DET cana-574 2 2 study study NOUN cana-574 2 3 explores explore VERB cana-574 2 4 the the DET cana-574 2 5 integration integration NOUN cana-574 2 6 of of ADP cana-574 2 7 complex complex ADJ cana-574 2 8 mathematical mathematical ADJ cana-574 2 9 operators operator NOUN cana-574 2 10 within within ADP cana-574 2 11 deep deep ADJ cana-574 2 12 learning learning NOUN cana-574 2 13 classifiers classifier NOUN cana-574 2 14 to to PART cana-574 2 15 enhance enhance VERB cana-574 2 16 their their PRON cana-574 2 17 accuracy accuracy NOUN cana-574 2 18 and and CCONJ cana-574 2 19 efficiency efficiency NOUN cana-574 2 20 in in ADP cana-574 2 21 diagnosing diagnose VERB cana-574 2 22 lumpy lumpy ADJ cana-574 2 23 skin skin NOUN cana-574 2 24 disease disease NOUN cana-574 2 25 . . PUNCT cana-574 3 1 by by ADP cana-574 3 2 focusing focus VERB cana-574 3 3 on on ADP cana-574 3 4 the the DET cana-574 3 5 decision decision NOUN cana-574 3 6 boundary boundary ADJ cana-574 3 7 complexity complexity NOUN cana-574 3 8 , , PUNCT cana-574 3 9 which which PRON cana-574 3 10 delineates delineate VERB cana-574 3 11 different different ADJ cana-574 3 12 disease disease NOUN cana-574 3 13 states state NOUN cana-574 3 14 in in ADP cana-574 3 15 high high ADJ cana-574 3 16 - - PUNCT cana-574 3 17 dimensional dimensional ADJ cana-574 3 18 spaces space NOUN cana-574 3 19 , , PUNCT cana-574 3 20 and and CCONJ cana-574 3 21 employing employ VERB cana-574 3 22 combinatorial combinatorial ADJ cana-574 3 23 optimization optimization NOUN cana-574 3 24 techniques technique NOUN cana-574 3 25 , , PUNCT cana-574 3 26 we we PRON cana-574 3 27 develop develop VERB cana-574 3 28 a a DET cana-574 3 29 novel novel ADJ cana-574 3 30 framework framework NOUN cana-574 3 31 that that PRON cana-574 3 32 significantly significantly ADV cana-574 3 33 improves improve VERB cana-574 3 34 classification classification NOUN cana-574 3 35 performance performance NOUN cana-574 3 36 . . PUNCT cana-574 4 1 the the DET cana-574 4 2 methodology methodology NOUN cana-574 4 3 hinges hinge VERB cana-574 4 4 on on ADP cana-574 4 5 optimizing optimize VERB cana-574 4 6 the the DET cana-574 4 7 configuration configuration NOUN cana-574 4 8 and and CCONJ cana-574 4 9 combination combination NOUN cana-574 4 10 of of ADP cana-574 4 11 mathematical mathematical ADJ cana-574 4 12 operators operator NOUN cana-574 4 13 , , PUNCT cana-574 4 14 such such ADJ cana-574 4 15 as as ADP cana-574 4 16 gradient gradient ADJ cana-574 4 17 operators operator NOUN cana-574 4 18 and and CCONJ cana-574 4 19 higher high ADJ cana-574 4 20 - - PUNCT cana-574 4 21 order order NOUN cana-574 4 22 derivatives derivative NOUN cana-574 4 23 , , PUNCT cana-574 4 24 to to PART cana-574 4 25 refine refine VERB cana-574 4 26 feature feature NOUN cana-574 4 27 extraction extraction NOUN cana-574 4 28 processes process NOUN cana-574 4 29 . . PUNCT cana-574 5 1 this this DET cana-574 5 2 approach approach NOUN cana-574 5 3 allows allow VERB cana-574 5 4 for for ADP cana-574 5 5 a a DET cana-574 5 6 more more ADV cana-574 5 7 nuanced nuanced ADJ cana-574 5 8 understanding understanding NOUN cana-574 5 9 of of ADP cana-574 5 10 the the DET cana-574 5 11 disease disease NOUN cana-574 5 12 features feature VERB cana-574 5 13 that that PRON cana-574 5 14 are be AUX cana-574 5 15 critical critical ADJ cana-574 5 16 for for ADP cana-574 5 17 accurate accurate ADJ cana-574 5 18 classification classification NOUN cana-574 5 19 . . PUNCT cana-574 6 1 using use VERB cana-574 6 2 a a DET cana-574 6 3 dataset dataset NOUN cana-574 6 4 comprised comprise VERB cana-574 6 5 of of ADP cana-574 6 6 clinical clinical ADJ cana-574 6 7 and and CCONJ cana-574 6 8 image image NOUN cana-574 6 9 data datum NOUN cana-574 6 10 from from ADP cana-574 6 11 infected infected ADJ cana-574 6 12 cattle cattle NOUN cana-574 6 13 , , PUNCT cana-574 6 14 our our PRON cana-574 6 15 enhanced enhance VERB cana-574 6 16 classifiers classifier NOUN cana-574 6 17 demonstrate demonstrate VERB cana-574 6 18 a a DET cana-574 6 19 marked mark VERB cana-574 6 20 improvement improvement NOUN cana-574 6 21 in in ADP cana-574 6 22 predictive predictive ADJ cana-574 6 23 accuracy accuracy NOUN cana-574 6 24 compared compare VERB cana-574 6 25 to to ADP cana-574 6 26 traditional traditional ADJ cana-574 6 27 deep deep ADJ cana-574 6 28 learning learning NOUN cana-574 6 29 models model NOUN cana-574 6 30 . . PUNCT cana-574 7 1 the the DET cana-574 7 2 case case NOUN cana-574 7 3 study study NOUN cana-574 7 4 not not PART cana-574 7 5 only only ADV cana-574 7 6 underscores underscore VERB cana-574 7 7 the the DET cana-574 7 8 potential potential NOUN cana-574 7 9 of of ADP cana-574 7 10 integrating integrate VERB cana-574 7 11 advanced advanced ADJ cana-574 7 12 mathematical mathematical ADJ cana-574 7 13 concepts concept NOUN cana-574 7 14 into into ADP cana-574 7 15 deep deep ADJ cana-574 7 16 learning learning NOUN cana-574 7 17 but but CCONJ cana-574 7 18 also also ADV cana-574 7 19 sets set VERB cana-574 7 20 a a DET cana-574 7 21 precedent precedent NOUN cana-574 7 22 for for ADP cana-574 7 23 tackling tackle VERB cana-574 7 24 similar similar ADJ cana-574 7 25 challenges challenge NOUN cana-574 7 26 in in ADP cana-574 7 27 veterinary veterinary ADJ cana-574 7 28 medicine medicine NOUN cana-574 7 29 and and CCONJ cana-574 7 30 beyond beyond ADP cana-574 7 31 . . PUNCT cana-574 8 1 keywords keyword NOUN cana-574 8 2 : : PUNCT cana-574 8 3 deep deep ADJ cana-574 8 4 learning learning NOUN cana-574 8 5 classifiers classifier NOUN cana-574 8 6 , , PUNCT cana-574 8 7 lumpy lumpy ADJ cana-574 8 8 skin skin NOUN cana-574 8 9 disease disease NOUN cana-574 8 10 , , PUNCT cana-574 8 11 decision decision NOUN cana-574 8 12 boundary boundary ADJ cana-574 8 13 complexity complexity NOUN cana-574 8 14 , , PUNCT cana-574 8 15 combinatorial combinatorial ADJ cana-574 8 16 optimization optimization NOUN cana-574 8 17 , , PUNCT cana-574 8 18 mathematical mathematical ADJ cana-574 8 19 operators operator NOUN cana-574 8 20 , , PUNCT cana-574 8 21 feature feature NOUN cana-574 8 22 extraction extraction NOUN cana-574 8 23 , , PUNCT cana-574 8 24 livestock livestock NOUN cana-574 8 25 disease disease NOUN cana-574 8 26 classification classification NOUN cana-574 8 27 , , PUNCT cana-574 8 28 predictive predictive ADJ cana-574 8 29 accuracy accuracy NOUN cana-574 8 30 , , PUNCT cana-574 8 31 veterinary veterinary ADJ cana-574 8 32 medicine medicine NOUN cana-574 8 33 , , PUNCT cana-574 8 34 highdimensional highdimensional ADJ cana-574 8 35 data datum NOUN cana-574 8 36 analysis analysis NOUN cana-574 8 37 . . PUNCT cana-574 9 1 1 1 X cana-574 9 2 . . X cana-574 9 3 mathematical mathematical ADJ cana-574 9 4 foundations foundation NOUN cana-574 9 5 and and CCONJ cana-574 9 6 optimization optimization NOUN cana-574 9 7 techniques technique NOUN cana-574 9 8 : : PUNCT cana-574 9 9 deep deep ADJ cana-574 9 10 learning learning NOUN cana-574 9 11 , , PUNCT cana-574 9 12 a a DET cana-574 9 13 subset subset NOUN cana-574 9 14 of of ADP cana-574 9 15 machine machine NOUN cana-574 9 16 learning learning NOUN cana-574 9 17 , , PUNCT cana-574 9 18 has have AUX cana-574 9 19 revolutionized revolutionize VERB cana-574 9 20 numerous numerous ADJ cana-574 9 21 fields field NOUN cana-574 9 22 from from ADP cana-574 9 23 image image NOUN cana-574 9 24 and and CCONJ cana-574 9 25 speech speech NOUN cana-574 9 26 recognition recognition NOUN cana-574 9 27 to to ADP cana-574 9 28 autonomous autonomous ADJ cana-574 9 29 driving driving NOUN cana-574 9 30 and and CCONJ cana-574 9 31 medical medical ADJ cana-574 9 32 diagnosis diagnosis NOUN cana-574 9 33 . . PUNCT cana-574 10 1 at at ADP cana-574 10 2 its its PRON cana-574 10 3 core core NOUN cana-574 10 4 , , PUNCT cana-574 10 5 deep deep ADJ cana-574 10 6 learning learning NOUN cana-574 10 7 constructs construct VERB cana-574 10 8 algorithms algorithm NOUN cana-574 10 9 , , PUNCT cana-574 10 10 known know VERB cana-574 10 11 as as ADP cana-574 10 12 neural neural ADJ cana-574 10 13 networks network NOUN cana-574 10 14 , , PUNCT cana-574 10 15 that that PRON cana-574 10 16 can can AUX cana-574 10 17 learn learn VERB cana-574 10 18 and and CCONJ cana-574 10 19 make make VERB cana-574 10 20 intelligent intelligent ADJ cana-574 10 21 decisions decision NOUN cana-574 10 22 on on ADP cana-574 10 23 their their PRON cana-574 10 24 own own ADJ cana-574 10 25 . . PUNCT cana-574 11 1 this this DET cana-574 11 2 ability ability NOUN cana-574 11 3 stems stem VERB cana-574 11 4 from from ADP cana-574 11 5 the the DET cana-574 11 6 network network NOUN cana-574 11 7 ’s ’s PART cana-574 11 8 capacity capacity NOUN cana-574 11 9 to to PART cana-574 11 10 perform perform VERB cana-574 11 11 complex complex ADJ cana-574 11 12 mathematical mathematical ADJ cana-574 11 13 operations operation NOUN cana-574 11 14 and and CCONJ cana-574 11 15 optimize optimize VERB cana-574 11 16 these these DET cana-574 11 17 operations operation NOUN cana-574 11 18 for for ADP cana-574 11 19 better well ADJ cana-574 11 20 performance performance NOUN cana-574 11 21 and and CCONJ cana-574 11 22 accuracy accuracy NOUN cana-574 11 23 . . PUNCT cana-574 12 1 understanding understand VERB cana-574 12 2 communications communication NOUN cana-574 12 3 on on ADP cana-574 12 4 applied apply VERB cana-574 12 5 nonlinear nonlinear ADJ cana-574 12 6 analysis analysis NOUN cana-574 12 7 issn issn NOUN cana-574 12 8 : : PUNCT cana-574 12 9 1074 1074 NUM cana-574 12 10 - - PUNCT cana-574 12 11 133x 133x NUM cana-574 12 12 vol vol NOUN cana-574 12 13 31 31 NUM cana-574 12 14 no no NOUN cana-574 12 15 . . NOUN cana-574 12 16 2 2 NUM cana-574 12 17 ( ( PUNCT cana-574 12 18 2024 2024 NUM cana-574 12 19 ) ) PUNCT cana-574 12 20 371 371 NUM cana-574 12 21 https://internationalpubls.com https://internationalpubls.com X cana-574 12 22 these these DET cana-574 12 23 underlying underlie VERB cana-574 12 24 mathematical mathematical ADJ cana-574 12 25 concepts concept NOUN cana-574 12 26 and and CCONJ cana-574 12 27 optimization optimization NOUN cana-574 12 28 techniques technique NOUN cana-574 12 29 is be AUX cana-574 12 30 crucial crucial ADJ cana-574 12 31 for for ADP cana-574 12 32 advancing advance VERB cana-574 12 33 deep deep ADJ cana-574 12 34 learning learning NOUN cana-574 12 35 applications application NOUN cana-574 12 36 . . PUNCT cana-574 13 1 mathematical mathematical ADJ cana-574 13 2 operators operator NOUN cana-574 13 3 in in ADP cana-574 13 4 neural neural ADJ cana-574 13 5 networks network NOUN cana-574 13 6 the the DET cana-574 13 7 foundation foundation NOUN cana-574 13 8 of of ADP cana-574 13 9 any any DET cana-574 13 10 neural neural ADJ cana-574 13 11 network network NOUN cana-574 13 12 is be AUX cana-574 13 13 its its PRON cana-574 13 14 ability ability NOUN cana-574 13 15 to to PART cana-574 13 16 perform perform VERB cana-574 13 17 mathematical mathematical ADJ cana-574 13 18 calculations calculation NOUN cana-574 13 19 on on ADP cana-574 13 20 input input NOUN cana-574 13 21 data datum NOUN cana-574 13 22 to to PART cana-574 13 23 extract extract VERB cana-574 13 24 features feature NOUN cana-574 13 25 and and CCONJ cana-574 13 26 make make VERB cana-574 13 27 predictions prediction NOUN cana-574 13 28 . . PUNCT cana-574 14 1 these these DET cana-574 14 2 calculations calculation NOUN cana-574 14 3 are be AUX cana-574 14 4 primarily primarily ADV cana-574 14 5 linear linear ADJ cana-574 14 6 algebraic algebraic ADJ cana-574 14 7 operations operation NOUN cana-574 14 8 , , PUNCT cana-574 14 9 which which PRON cana-574 14 10 involve involve VERB cana-574 14 11 vectors vector NOUN cana-574 14 12 , , PUNCT cana-574 14 13 matrices matrix NOUN cana-574 14 14 , , PUNCT cana-574 14 15 and and CCONJ cana-574 14 16 tensors tensor NOUN cana-574 14 17 . . PUNCT cana-574 15 1 at at ADP cana-574 15 2 the the DET cana-574 15 3 basic basic ADJ cana-574 15 4 level level NOUN cana-574 15 5 , , PUNCT cana-574 15 6 a a DET cana-574 15 7 neural neural ADJ cana-574 15 8 network network NOUN cana-574 15 9 transforms transform VERB cana-574 15 10 its its PRON cana-574 15 11 input input NOUN cana-574 15 12 through through ADP cana-574 15 13 a a DET cana-574 15 14 series series NOUN cana-574 15 15 of of ADP cana-574 15 16 layers layer NOUN cana-574 15 17 , , PUNCT cana-574 15 18 each each PRON cana-574 15 19 defined define VERB cana-574 15 20 by by ADP cana-574 15 21 a a DET cana-574 15 22 set set NOUN cana-574 15 23 of of ADP cana-574 15 24 weights weight NOUN cana-574 15 25 and and CCONJ cana-574 15 26 biases bias NOUN cana-574 15 27 , , PUNCT cana-574 15 28 and and CCONJ cana-574 15 29 applies apply VERB cana-574 15 30 nonlinear nonlinear ADJ cana-574 15 31 transformations transformation NOUN cana-574 15 32 to to PART cana-574 15 33 enable enable VERB cana-574 15 34 complex complex ADJ cana-574 15 35 representations representation NOUN cana-574 15 36 . . PUNCT cana-574 16 1 1 1 X cana-574 16 2 . . X cana-574 16 3 linear linear ADJ cana-574 16 4 transformations transformation NOUN cana-574 16 5 and and CCONJ cana-574 16 6 decision decision NOUN cana-574 16 7 boundaries boundary NOUN cana-574 16 8 : : PUNCT cana-574 16 9 every every DET cana-574 16 10 neural neural ADJ cana-574 16 11 network network NOUN cana-574 16 12 layer layer NOUN cana-574 16 13 essentially essentially ADV cana-574 16 14 performs perform VERB cana-574 16 15 a a DET cana-574 16 16 linear linear ADJ cana-574 16 17 transformation transformation NOUN cana-574 16 18 followed follow VERB cana-574 16 19 by by ADP cana-574 16 20 a a DET cana-574 16 21 nonlinear nonlinear ADJ cana-574 16 22 activation activation NOUN cana-574 16 23 . . PUNCT cana-574 17 1 the the DET cana-574 17 2 simplest simple ADJ cana-574 17 3 form form NOUN cana-574 17 4 of of ADP cana-574 17 5 a a DET cana-574 17 6 linear linear ADJ cana-574 17 7 transformation transformation NOUN cana-574 17 8 in in ADP cana-574 17 9 a a DET cana-574 17 10 binary binary ADJ cana-574 17 11 classifier classifier NOUN cana-574 17 12 can can AUX cana-574 17 13 be be AUX cana-574 17 14 represented represent VERB cana-574 17 15 by by ADP cana-574 17 16 the the DET cana-574 17 17 decision decision NOUN cana-574 17 18 boundary boundary ADJ cana-574 17 19 equation equation NOUN cana-574 17 20 𝑓(π‘₯)=𝑀𝑇π‘₯+𝑏=0 𝑓(π‘₯)=𝑀𝑇π‘₯+𝑏=0 PROPN cana-574 17 21 .this .this PRON cana-574 17 22 equation equation NOUN cana-574 17 23 defines define VERB cana-574 17 24 a a DET cana-574 17 25 hyperplane hyperplane NOUN cana-574 17 26 that that PRON cana-574 17 27 divides divide VERB cana-574 17 28 the the DET cana-574 17 29 input input NOUN cana-574 17 30 space space NOUN cana-574 17 31 into into ADP cana-574 17 32 two two NUM cana-574 17 33 decision decision NOUN cana-574 17 34 areas area NOUN cana-574 17 35 , , PUNCT cana-574 17 36 each each PRON cana-574 17 37 corresponding correspond VERB cana-574 17 38 to to ADP cana-574 17 39 one one NUM cana-574 17 40 of of ADP cana-574 17 41 the the DET cana-574 17 42 classes class NOUN cana-574 17 43 . . PUNCT cana-574 18 1 2 2 X cana-574 18 2 . . X cana-574 18 3 optimization optimization NOUN cana-574 18 4 techniques technique NOUN cana-574 18 5 : : PUNCT cana-574 18 6 optimization optimization NOUN cana-574 18 7 is be AUX cana-574 18 8 at at ADP cana-574 18 9 the the DET cana-574 18 10 heart heart NOUN cana-574 18 11 of of ADP cana-574 18 12 training train VERB cana-574 18 13 deep deep ADJ cana-574 18 14 learning learning NOUN cana-574 18 15 models model NOUN cana-574 18 16 . . PUNCT cana-574 19 1 it it PRON cana-574 19 2 involves involve VERB cana-574 19 3 adjusting adjust VERB cana-574 19 4 the the DET cana-574 19 5 weights weight NOUN cana-574 19 6 of of ADP cana-574 19 7 the the DET cana-574 19 8 network network NOUN cana-574 19 9 to to PART cana-574 19 10 minimize minimize VERB cana-574 19 11 a a DET cana-574 19 12 loss loss NOUN cana-574 19 13 function function NOUN cana-574 19 14 , , PUNCT cana-574 19 15 which which PRON cana-574 19 16 measures measure VERB cana-574 19 17 the the DET cana-574 19 18 difference difference NOUN cana-574 19 19 between between ADP cana-574 19 20 the the DET cana-574 19 21 predicted predict VERB cana-574 19 22 output output NOUN cana-574 19 23 and and CCONJ cana-574 19 24 the the DET cana-574 19 25 actual actual ADJ cana-574 19 26 output output NOUN cana-574 19 27 . . PUNCT cana-574 20 1 the the DET cana-574 20 2 gradient gradient ADJ cana-574 20 3 descent descent NOUN cana-574 20 4 update update NOUN cana-574 20 5 rule rule NOUN cana-574 20 6 𝑀𝑛𝑒𝑀=π‘€π‘œπ‘™π‘‘βˆ’π›Όβˆ‡π‘€πΏwnew 𝑀𝑛𝑒𝑀=π‘€π‘œπ‘™π‘‘βˆ’π›Όβˆ‡π‘€πΏwnew NOUN cana-574 20 7 = = PRON cana-574 20 8 woldβˆ’Ξ±βˆ‡wl woldβˆ’Ξ±βˆ‡wl PROPN cana-574 20 9 is be AUX cana-574 20 10 a a DET cana-574 20 11 primary primary ADJ cana-574 20 12 example example NOUN cana-574 20 13 of of ADP cana-574 20 14 how how SCONJ cana-574 20 15 weights weight NOUN cana-574 20 16 are be AUX cana-574 20 17 iteratively iteratively ADV cana-574 20 18 adjusted adjust VERB cana-574 20 19 in in ADP cana-574 20 20 the the DET cana-574 20 21 direction direction NOUN cana-574 20 22 that that PRON cana-574 20 23 most most ADV cana-574 20 24 reduces reduce VERB cana-574 20 25 the the DET cana-574 20 26 loss loss NOUN cana-574 20 27 . . PUNCT cana-574 21 1 activation activation NOUN cana-574 21 2 functions function NOUN cana-574 21 3 activation activation NOUN cana-574 21 4 functions function NOUN cana-574 21 5 introduce introduce VERB cana-574 21 6 non non ADJ cana-574 21 7 - - ADJ cana-574 21 8 linear linear ADJ cana-574 21 9 properties property NOUN cana-574 21 10 to to ADP cana-574 21 11 the the DET cana-574 21 12 network network NOUN cana-574 21 13 which which PRON cana-574 21 14 allows allow VERB cana-574 21 15 the the DET cana-574 21 16 model model NOUN cana-574 21 17 to to PART cana-574 21 18 learn learn VERB cana-574 21 19 more more ADJ cana-574 21 20 complex complex ADJ cana-574 21 21 patterns pattern NOUN cana-574 21 22 . . PUNCT cana-574 22 1 two two NUM cana-574 22 2 common common ADJ cana-574 22 3 examples example NOUN cana-574 22 4 are be AUX cana-574 22 5 : : PUNCT cana-574 22 6 3 3 X cana-574 22 7 . . NOUN cana-574 22 8 sigmoid sigmoid NOUN cana-574 22 9 activation activation NOUN cana-574 22 10 function function NOUN cana-574 22 11 : : PUNCT cana-574 22 12 𝜎(𝑧)=11+π‘’βˆ’π‘§ 𝜎(𝑧)=11+π‘’βˆ’π‘§ PUNCT cana-574 22 13 is be AUX cana-574 22 14 particularly particularly ADV cana-574 22 15 useful useful ADJ cana-574 22 16 in in ADP cana-574 22 17 binary binary ADJ cana-574 22 18 classification classification NOUN cana-574 22 19 as as SCONJ cana-574 22 20 it it PRON cana-574 22 21 maps map VERB cana-574 22 22 any any DET cana-574 22 23 real real ADV cana-574 22 24 - - PUNCT cana-574 22 25 valued value VERB cana-574 22 26 number number NOUN cana-574 22 27 into into ADP cana-574 22 28 the the DET cana-574 22 29 ( ( PUNCT cana-574 22 30 0,1)(0,1 0,1)(0,1 ADJ cana-574 22 31 ) ) PUNCT cana-574 22 32 interval interval NOUN cana-574 22 33 , , PUNCT cana-574 22 34 representing represent VERB cana-574 22 35 a a DET cana-574 22 36 probability probability NOUN cana-574 22 37 - - PUNCT cana-574 22 38 like like ADJ cana-574 22 39 output output NOUN cana-574 22 40 . . PUNCT cana-574 23 1 4 4 X cana-574 23 2 . . X cana-574 23 3 softmax softmax NOUN cana-574 23 4 function function NOUN cana-574 23 5 for for ADP cana-574 23 6 multi multi ADJ cana-574 23 7 - - ADJ cana-574 23 8 class class ADJ cana-574 23 9 classification classification NOUN cana-574 23 10 : : PUNCT cana-574 23 11 for for ADP cana-574 23 12 scenarios scenario NOUN cana-574 23 13 where where SCONJ cana-574 23 14 classifications classification NOUN cana-574 23 15 are be AUX cana-574 23 16 mutually mutually ADV cana-574 23 17 exclusive exclusive ADJ cana-574 23 18 , , PUNCT cana-574 23 19 the the DET cana-574 23 20 softmax softmax NOUN cana-574 23 21 function function NOUN cana-574 23 22 normalizes normalize VERB cana-574 23 23 the the DET cana-574 23 24 outputs output NOUN cana-574 23 25 , , PUNCT cana-574 23 26 converting convert VERB cana-574 23 27 them they PRON cana-574 23 28 into into ADP cana-574 23 29 probability probability NOUN cana-574 23 30 scores score NOUN cana-574 23 31 summing sum VERB cana-574 23 32 to to ADP cana-574 23 33 one one NUM cana-574 23 34 . . PUNCT cana-574 24 1 loss loss NOUN cana-574 24 2 functions function NOUN cana-574 24 3 to to PART cana-574 24 4 quantify quantify VERB cana-574 24 5 how how SCONJ cana-574 24 6 well well ADV cana-574 24 7 a a DET cana-574 24 8 network network NOUN cana-574 24 9 performs perform VERB cana-574 24 10 , , PUNCT cana-574 24 11 loss loss NOUN cana-574 24 12 functions function NOUN cana-574 24 13 are be AUX cana-574 24 14 crucial crucial ADJ cana-574 24 15 . . PUNCT cana-574 25 1 they they PRON cana-574 25 2 provide provide VERB cana-574 25 3 a a DET cana-574 25 4 metric metric NOUN cana-574 25 5 to to PART cana-574 25 6 evaluate evaluate VERB cana-574 25 7 the the DET cana-574 25 8 errors error NOUN cana-574 25 9 made make VERB cana-574 25 10 by by ADP cana-574 25 11 a a DET cana-574 25 12 network network NOUN cana-574 25 13 during during ADP cana-574 25 14 training training NOUN cana-574 25 15 . . PUNCT cana-574 26 1 5 5 X cana-574 26 2 . . X cana-574 26 3 cross cross ADJ cana-574 26 4 - - ADJ cana-574 26 5 entropy entropy ADJ cana-574 26 6 loss loss NOUN cana-574 26 7 : : PUNCT cana-574 26 8 in in ADP cana-574 26 9 classification classification NOUN cana-574 26 10 problems problem NOUN cana-574 26 11 , , PUNCT cana-574 26 12 cross cross ADJ cana-574 26 13 - - ADJ cana-574 26 14 entropy entropy NOUN cana-574 26 15 helps help VERB cana-574 26 16 measure measure VERB cana-574 26 17 the the DET cana-574 26 18 performance performance NOUN cana-574 26 19 by by ADP cana-574 26 20 calculating calculate VERB cana-574 26 21 the the DET cana-574 26 22 total total ADJ cana-574 26 23 entropy entropy NOUN cana-574 26 24 between between ADP cana-574 26 25 the the DET cana-574 26 26 predicted predict VERB cana-574 26 27 probabilities probability NOUN cana-574 26 28 and and CCONJ cana-574 26 29 the the DET cana-574 26 30 actual actual ADJ cana-574 26 31 distribution distribution NOUN cana-574 26 32 . . PUNCT cana-574 27 1 regularization regularization NOUN cana-574 27 2 techniques technique NOUN cana-574 27 3 overfitting overfitte VERB cana-574 27 4 is be AUX cana-574 27 5 a a DET cana-574 27 6 common common ADJ cana-574 27 7 problem problem NOUN cana-574 27 8 where where SCONJ cana-574 27 9 a a DET cana-574 27 10 model model NOUN cana-574 27 11 learns learn VERB cana-574 27 12 the the DET cana-574 27 13 detail detail NOUN cana-574 27 14 and and CCONJ cana-574 27 15 noise noise NOUN cana-574 27 16 in in ADP cana-574 27 17 the the DET cana-574 27 18 training training NOUN cana-574 27 19 data datum NOUN cana-574 27 20 to to ADP cana-574 27 21 an an DET cana-574 27 22 extent extent NOUN cana-574 27 23 that that SCONJ cana-574 27 24 it it PRON cana-574 27 25 negatively negatively ADV cana-574 27 26 impacts impact VERB cana-574 27 27 the the DET cana-574 27 28 performance performance NOUN cana-574 27 29 of of ADP cana-574 27 30 the the DET cana-574 27 31 model model NOUN cana-574 27 32 on on ADP cana-574 27 33 new new ADJ cana-574 27 34 data datum NOUN cana-574 27 35 . . PUNCT cana-574 28 1 communications communication NOUN cana-574 28 2 on on ADP cana-574 28 3 applied apply VERB cana-574 28 4 nonlinear nonlinear ADJ cana-574 28 5 analysis analysis NOUN cana-574 28 6 issn issn NOUN cana-574 28 7 : : PUNCT cana-574 28 8 1074 1074 NUM cana-574 28 9 - - PUNCT cana-574 28 10 133x 133x NUM cana-574 28 11 vol vol NOUN cana-574 28 12 31 31 NUM cana-574 28 13 no no NOUN cana-574 28 14 . . NOUN cana-574 28 15 2 2 NUM cana-574 28 16 ( ( PUNCT cana-574 28 17 2024 2024 NUM cana-574 28 18 ) ) PUNCT cana-574 29 1 372 372 NUM cana-574 29 2 https://internationalpubls.com https://internationalpubls.com X cana-574 29 3 6 6 NUM cana-574 29 4 . . PUNCT cana-574 29 5 l2 l2 NOUN cana-574 29 6 regularization regularization NOUN cana-574 29 7 : : PUNCT cana-574 29 8 also also ADV cana-574 29 9 known know VERB cana-574 29 10 as as ADP cana-574 29 11 ridge ridge NOUN cana-574 29 12 regression regression NOUN cana-574 29 13 , , PUNCT cana-574 29 14 it it PRON cana-574 29 15 adds add VERB cana-574 29 16 a a DET cana-574 29 17 penalty penalty NOUN cana-574 29 18 on on ADP cana-574 29 19 the the DET cana-574 29 20 size size NOUN cana-574 29 21 of of ADP cana-574 29 22 coefficients coefficient NOUN cana-574 29 23 . . PUNCT cana-574 30 1 𝑅(𝑀)=πœ†βˆ₯𝑀βˆ₯2r(w)=Ξ»βˆ₯wβˆ₯2 𝑅(𝑀)=πœ†βˆ₯𝑀βˆ₯2r(w)=Ξ»βˆ₯wβˆ₯2 NOUN cana-574 30 2 where where SCONJ cana-574 30 3 πœ†Ξ» πœ†Ξ» NOUN cana-574 30 4 is be AUX cana-574 30 5 a a DET cana-574 30 6 complexity complexity NOUN cana-574 30 7 parameter parameter NOUN cana-574 30 8 that that PRON cana-574 30 9 controls control VERB cana-574 30 10 the the DET cana-574 30 11 amount amount NOUN cana-574 30 12 of of ADP cana-574 30 13 shrinkage shrinkage NOUN cana-574 30 14 : : PUNCT cana-574 30 15 the the PRON cana-574 30 16 larger large ADJ cana-574 30 17 the the DET cana-574 30 18 value value NOUN cana-574 30 19 of of ADP cana-574 30 20 πœ†Ξ» πœ†Ξ» NOUN cana-574 30 21 , , PUNCT cana-574 30 22 the the PRON cana-574 30 23 greater great ADJ cana-574 30 24 the the DET cana-574 30 25 amount amount NOUN cana-574 30 26 of of ADP cana-574 30 27 shrinkage shrinkage NOUN cana-574 30 28 . . PUNCT cana-574 31 1 7 7 X cana-574 31 2 . . X cana-574 31 3 dropout dropout NOUN cana-574 31 4 : : PUNCT cana-574 31 5 a a DET cana-574 31 6 different different ADJ cana-574 31 7 type type NOUN cana-574 31 8 of of ADP cana-574 31 9 regularization regularization NOUN cana-574 31 10 where where SCONJ cana-574 31 11 randomly randomly ADV cana-574 31 12 selected select VERB cana-574 31 13 neurons neuron NOUN cana-574 31 14 are be AUX cana-574 31 15 ignored ignore VERB cana-574 31 16 during during ADP cana-574 31 17 training training NOUN cana-574 31 18 , , PUNCT cana-574 31 19 reducing reduce VERB cana-574 31 20 the the DET cana-574 31 21 chance chance NOUN cana-574 31 22 for for ADP cana-574 31 23 overfitting overfitte VERB cana-574 31 24 . . PUNCT cana-574 32 1 advanced advanced ADJ cana-574 32 2 optimization optimization NOUN cana-574 32 3 beyond beyond ADP cana-574 32 4 simple simple ADJ cana-574 32 5 gradient gradient ADJ cana-574 32 6 descent descent NOUN cana-574 32 7 , , PUNCT cana-574 32 8 more more ADV cana-574 32 9 sophisticated sophisticated ADJ cana-574 32 10 algorithms algorithm NOUN cana-574 32 11 can can AUX cana-574 32 12 improve improve VERB cana-574 32 13 convergence convergence NOUN cana-574 32 14 . . PUNCT cana-574 33 1 8 8 X cana-574 33 2 . . PUNCT cana-574 34 1 stochastic stochastic ADJ cana-574 34 2 gradient gradient ADJ cana-574 34 3 descent descent NOUN cana-574 34 4 ( ( PUNCT cana-574 34 5 sgd sgd NOUN cana-574 34 6 ) ) PUNCT cana-574 34 7 and and CCONJ cana-574 34 8 adam adam PROPN cana-574 34 9 optimizer optimizer NOUN cana-574 34 10 : : PUNCT cana-574 34 11 while while SCONJ cana-574 34 12 sgd sgd PROPN cana-574 34 13 updates update VERB cana-574 34 14 parameters parameter NOUN cana-574 34 15 with with ADP cana-574 34 16 a a DET cana-574 34 17 subset subset NOUN cana-574 34 18 of of ADP cana-574 34 19 training training NOUN cana-574 34 20 data datum NOUN cana-574 34 21 rather rather ADV cana-574 34 22 than than ADP cana-574 34 23 the the DET cana-574 34 24 full full ADJ cana-574 34 25 dataset dataset NOUN cana-574 34 26 for for ADP cana-574 34 27 faster fast ADJ cana-574 34 28 computations computation NOUN cana-574 34 29 , , PUNCT cana-574 34 30 adam adam PROPN cana-574 34 31 , , PUNCT cana-574 34 32 on on ADP cana-574 34 33 the the DET cana-574 34 34 other other ADJ cana-574 34 35 hand hand NOUN cana-574 34 36 , , PUNCT cana-574 34 37 computes compute VERB cana-574 34 38 adaptive adaptive ADJ cana-574 34 39 learning learning NOUN cana-574 34 40 rates rate NOUN cana-574 34 41 for for ADP cana-574 34 42 each each DET cana-574 34 43 parameter parameter NOUN cana-574 34 44 . . PUNCT cana-574 35 1 beyond beyond ADP cana-574 35 2 classical classical ADJ cana-574 35 3 layers layer NOUN cana-574 35 4 : : PUNCT cana-574 35 5 specialized specialized ADJ cana-574 35 6 operations operation NOUN cana-574 35 7 deep deep ADJ cana-574 35 8 learning learning NOUN cana-574 35 9 also also ADV cana-574 35 10 involves involve VERB cana-574 35 11 more more ADJ cana-574 35 12 complex complex ADJ cana-574 35 13 operations operation NOUN cana-574 35 14 which which PRON cana-574 35 15 are be AUX cana-574 35 16 suited suit VERB cana-574 35 17 for for ADP cana-574 35 18 specific specific ADJ cana-574 35 19 data datum NOUN cana-574 35 20 types type NOUN cana-574 35 21 and and CCONJ cana-574 35 22 problems problem NOUN cana-574 35 23 . . PUNCT cana-574 36 1 9 9 X cana-574 36 2 . . X cana-574 36 3 backpropagation backpropagation NOUN cana-574 36 4 through through ADP cana-574 36 5 time time NOUN cana-574 36 6 in in ADP cana-574 36 7 rnns rnns NOUN cana-574 36 8 : : PUNCT cana-574 36 9 essential essential ADJ cana-574 36 10 for for ADP cana-574 36 11 training train VERB cana-574 36 12 recurrent recurrent ADJ cana-574 36 13 neural neural ADJ cana-574 36 14 networks network NOUN cana-574 36 15 , , PUNCT cana-574 36 16 it it PRON cana-574 36 17 involves involve VERB cana-574 36 18 moving move VERB cana-574 36 19 backward backward ADV cana-574 36 20 through through ADP cana-574 36 21 the the DET cana-574 36 22 network network NOUN cana-574 36 23 to to PART cana-574 36 24 calculate calculate VERB cana-574 36 25 gradients gradient NOUN cana-574 36 26 over over ADP cana-574 36 27 time time NOUN cana-574 36 28 , , PUNCT cana-574 36 29 crucial crucial ADJ cana-574 36 30 for for ADP cana-574 36 31 sequence sequence NOUN cana-574 36 32 prediction prediction NOUN cana-574 36 33 problems problem NOUN cana-574 36 34 . . PUNCT cana-574 37 1 10 10 X cana-574 37 2 . . PUNCT cana-574 38 1 convolution convolution NOUN cana-574 38 2 operations operation NOUN cana-574 38 3 in in ADP cana-574 38 4 cnns cnns PROPN cana-574 38 5 : : PUNCT cana-574 38 6 used use VERB cana-574 38 7 predominantly predominantly ADV cana-574 38 8 in in ADP cana-574 38 9 processing process VERB cana-574 38 10 image image NOUN cana-574 38 11 data datum NOUN cana-574 38 12 , , PUNCT cana-574 38 13 this this DET cana-574 38 14 operation operation NOUN cana-574 38 15 extracts extract VERB cana-574 38 16 features feature NOUN cana-574 38 17 from from ADP cana-574 38 18 local local ADJ cana-574 38 19 image image NOUN cana-574 38 20 patches patch NOUN cana-574 38 21 through through ADP cana-574 38 22 a a DET cana-574 38 23 filter filter NOUN cana-574 38 24 . . PUNCT cana-574 39 1 decision decision NOUN cana-574 39 2 boundary boundary ADJ cana-574 39 3 equation equation NOUN cana-574 39 4 : : PUNCT cana-574 39 5 𝑓(π‘₯ 𝑓(π‘₯ NOUN cana-574 39 6 ) ) PUNCT cana-574 39 7 = = PUNCT cana-574 40 1 𝑀𝑇π‘₯ 𝑀𝑇π‘₯ X cana-574 40 2 + + NOUN cana-574 40 3 𝑏 𝑏 DET cana-574 40 4 βˆ’ βˆ’ PROPN cana-574 40 5 0 0 NUM cana-574 40 6 ( ( PUNCT cana-574 40 7 1 1 X cana-574 40 8 ) ) PUNCT cana-574 40 9 this this DET cana-574 40 10 equation equation NOUN cana-574 40 11 defines define VERB cana-574 40 12 the the DET cana-574 40 13 decision decision NOUN cana-574 40 14 boundary boundary NOUN cana-574 40 15 of of ADP cana-574 40 16 a a DET cana-574 40 17 linear linear ADJ cana-574 40 18 classifier classifier NOUN cana-574 40 19 , , PUNCT cana-574 40 20 where where SCONJ cana-574 40 21 𝑀 𝑀 X cana-574 40 22 is be AUX cana-574 40 23 the the DET cana-574 40 24 weight weight NOUN cana-574 40 25 vector vector NOUN cana-574 40 26 , , PUNCT cana-574 40 27 π‘₯ π‘₯ PROPN cana-574 40 28 is be AUX cana-574 40 29 the the DET cana-574 40 30 input input NOUN cana-574 40 31 feature feature NOUN cana-574 40 32 vector vector NOUN cana-574 40 33 , , PUNCT cana-574 40 34 and and CCONJ cana-574 40 35 𝑏 𝑏 PROPN cana-574 40 36 is be AUX cana-574 40 37 the the DET cana-574 40 38 bias bias NOUN cana-574 40 39 . . PUNCT cana-574 41 1 the the DET cana-574 41 2 decision decision NOUN cana-574 41 3 boundary boundary NOUN cana-574 41 4 separates separate VERB cana-574 41 5 different different ADJ cana-574 41 6 classes class NOUN cana-574 41 7 in in ADP cana-574 41 8 the the DET cana-574 41 9 feature feature NOUN cana-574 41 10 space space NOUN cana-574 41 11 . . PUNCT cana-574 42 1 gradient gradient ADJ cana-574 42 2 descent descent NOUN cana-574 42 3 update update NOUN cana-574 42 4 rule rule NOUN cana-574 42 5 : : PUNCT cana-574 42 6 𝑀new 𝑀new NOUN cana-574 42 7 = = NOUN cana-574 42 8 𝑀old 𝑀old NOUN cana-574 42 9 βˆ’ βˆ’ PROPN cana-574 42 10 π›Όβˆ‡π‘€πΏ π›Όβˆ‡π‘€πΏ PROPN cana-574 42 11 ( ( PUNCT cana-574 42 12 2 2 NUM cana-574 42 13 ) ) PUNCT cana-574 42 14 this this DET cana-574 42 15 equation equation NOUN cana-574 42 16 represents represent VERB cana-574 42 17 the the DET cana-574 42 18 update update NOUN cana-574 42 19 rule rule NOUN cana-574 42 20 for for ADP cana-574 42 21 the the DET cana-574 42 22 weights weight NOUN cana-574 42 23 𝑀 𝑀 X cana-574 42 24 in in ADP cana-574 42 25 gradient gradient ADJ cana-574 42 26 descent descent NOUN cana-574 42 27 optimization optimization NOUN cana-574 42 28 , , PUNCT cana-574 42 29 where where SCONJ cana-574 42 30 𝛼 𝛼 NOUN cana-574 42 31 is be AUX cana-574 42 32 the the DET cana-574 42 33 learning learning NOUN cana-574 42 34 rate rate NOUN cana-574 42 35 and and CCONJ cana-574 42 36 βˆ‡π‘€πΏ βˆ‡π‘€πΏ NOUN cana-574 42 37 is be AUX cana-574 42 38 the the DET cana-574 42 39 gradient gradient NOUN cana-574 42 40 of of ADP cana-574 42 41 the the DET cana-574 42 42 loss loss NOUN cana-574 42 43 function function NOUN cana-574 42 44 𝐿 𝐿 PROPN cana-574 42 45 with with ADP cana-574 42 46 respect respect NOUN cana-574 42 47 to to ADP cana-574 42 48 the the DET cana-574 42 49 weights weight NOUN cana-574 42 50 . . PUNCT cana-574 43 1 sigmoid sigmoid NOUN cana-574 43 2 activation activation NOUN cana-574 43 3 function function NOUN cana-574 43 4 : : PUNCT cana-574 43 5 𝜎(𝑧 𝜎(𝑧 NUM cana-574 43 6 ) ) PUNCT cana-574 43 7 = = NOUN cana-574 43 8 1 1 NUM cana-574 43 9 1+π‘’βˆ’π‘§ 1+π‘’βˆ’π‘§ NUM cana-574 43 10 ( ( PUNCT cana-574 43 11 3 3 NUM cana-574 43 12 ) ) PUNCT cana-574 43 13 the the DET cana-574 43 14 sigmoid sigmoid NOUN cana-574 43 15 function function NOUN cana-574 43 16 is be AUX cana-574 43 17 used use VERB cana-574 43 18 as as ADP cana-574 43 19 an an DET cana-574 43 20 activation activation NOUN cana-574 43 21 function function NOUN cana-574 43 22 in in ADP cana-574 43 23 neural neural ADJ cana-574 43 24 networks network NOUN cana-574 43 25 to to PART cana-574 43 26 map map VERB cana-574 43 27 real real ADV cana-574 43 28 valued value VERB cana-574 43 29 inputs input NOUN cana-574 43 30 to to ADP cana-574 43 31 the the DET cana-574 43 32 ( ( PUNCT cana-574 43 33 0,1 0,1 NUM cana-574 43 34 ) ) PUNCT cana-574 43 35 range range NOUN cana-574 43 36 , , PUNCT cana-574 43 37 facilitating facilitate VERB cana-574 43 38 binary binary ADJ cana-574 43 39 classification classification NOUN cana-574 43 40 tasks task NOUN cana-574 43 41 . . PUNCT cana-574 44 1 communications communication NOUN cana-574 44 2 on on ADP cana-574 44 3 applied apply VERB cana-574 44 4 nonlinear nonlinear ADJ cana-574 44 5 analysis analysis NOUN cana-574 44 6 issn issn NOUN cana-574 44 7 : : PUNCT cana-574 44 8 1074 1074 NUM cana-574 44 9 - - PUNCT cana-574 44 10 133x 133x NUM cana-574 44 11 vol vol NOUN cana-574 44 12 31 31 NUM cana-574 44 13 no no NOUN cana-574 44 14 . . NOUN cana-574 44 15 2 2 NUM cana-574 44 16 ( ( PUNCT cana-574 44 17 2024 2024 NUM cana-574 44 18 ) ) PUNCT cana-574 44 19 373 373 NUM cana-574 44 20 https://internationalpubls.com https://internationalpubls.com X cana-574 44 21 softmax softmax NOUN cana-574 44 22 function function NOUN cana-574 44 23 for for ADP cana-574 44 24 multi multi ADJ cana-574 44 25 - - ADJ cana-574 44 26 class class ADJ cana-574 44 27 classification classification NOUN cana-574 44 28 : : PUNCT cana-574 44 29 softmax⁑(𝑧𝑖 softmax⁑(𝑧𝑖 X cana-574 44 30 ) ) PUNCT cana-574 45 1 = = SYM cana-574 45 2 𝑒2 𝑒2 PROPN cana-574 45 3 2 2 NUM cana-574 45 4 βˆ‘ βˆ‘ PART cana-574 45 5 β€Š β€Š SPACE cana-574 45 6 𝑖 𝑖 SYM cana-574 45 7 β€Š β€Š SPACE cana-574 45 8 𝑒 𝑒 PROPN cana-574 45 9 3 3 NUM cana-574 45 10 ( ( PUNCT cana-574 45 11 4 4 NUM cana-574 45 12 ) ) PUNCT cana-574 45 13 softmax softmax NOUN cana-574 45 14 function function NOUN cana-574 45 15 is be AUX cana-574 45 16 used use VERB cana-574 45 17 in in ADP cana-574 45 18 multi multi ADJ cana-574 45 19 - - ADJ cana-574 45 20 class class ADJ cana-574 45 21 classification classification NOUN cana-574 45 22 to to PART cana-574 45 23 convert convert VERB cana-574 45 24 the the DET cana-574 45 25 output output NOUN cana-574 45 26 logits logit NOUN cana-574 45 27 𝑧 𝑧 VERB cana-574 45 28 into into ADP cana-574 45 29 probabilities probability NOUN cana-574 45 30 by by ADP cana-574 45 31 normalizing normalize VERB cana-574 45 32 them they PRON cana-574 45 33 across across ADP cana-574 45 34 all all DET cana-574 45 35 classes class NOUN cana-574 45 36 . . PUNCT cana-574 46 1 cross cross ADJ cana-574 46 2 - - ADJ cana-574 46 3 entropy entropy ADJ cana-574 46 4 loss loss NOUN cana-574 46 5 for for ADP cana-574 46 6 binary binary ADJ cana-574 46 7 classification classification NOUN cana-574 46 8 : : PUNCT cana-574 46 9 𝐿(𝑦 𝐿(𝑦 NOUN cana-574 46 10 , , PUNCT cana-574 46 11 οΏ½ οΏ½ NOUN cana-574 46 12 Μ‚ Μ‚ NOUN cana-574 46 13 οΏ½ οΏ½ NOUN cana-574 46 14 ) ) PUNCT cana-574 46 15 = = SYM cana-574 46 16 βˆ’[𝑦log⁑( βˆ’[𝑦log⁑( PROPN cana-574 46 17 οΏ½ οΏ½ PROPN cana-574 46 18 Μ‚ Μ‚ NUM cana-574 46 19 οΏ½ οΏ½ PROPN cana-574 46 20 ) ) PUNCT cana-574 46 21 + + CCONJ cana-574 46 22 ( ( PUNCT cana-574 46 23 1 1 NUM cana-574 46 24 βˆ’ βˆ’ PROPN cana-574 46 25 𝑦)log⁑(1 𝑦)log⁑(1 NUM cana-574 46 26 βˆ’ βˆ’ PROPN cana-574 46 27 οΏ½ οΏ½ PROPN cana-574 46 28 Μ‚ Μ‚ NOUN cana-574 46 29 οΏ½ οΏ½ NOUN cana-574 46 30 ) ) PUNCT cana-574 46 31 ] ] PUNCT cana-574 46 32 ( ( PUNCT cana-574 46 33 5 5 X cana-574 46 34 ) ) PUNCT cana-574 46 35 cross cross ADJ cana-574 46 36 - - ADJ cana-574 46 37 entropy entropy ADJ cana-574 46 38 loss loss NOUN cana-574 46 39 measures measure NOUN cana-574 46 40 the the DET cana-574 46 41 performance performance NOUN cana-574 46 42 of of ADP cana-574 46 43 a a DET cana-574 46 44 classification classification NOUN cana-574 46 45 model model NOUN cana-574 46 46 whose whose DET cana-574 46 47 output output NOUN cana-574 46 48 is be AUX cana-574 46 49 a a DET cana-574 46 50 probability probability NOUN cana-574 46 51 value value NOUN cana-574 46 52 between between ADP cana-574 46 53 0 0 NUM cana-574 46 54 and and CCONJ cana-574 46 55 1 1 NUM cana-574 46 56 . . PUNCT cana-574 47 1 it it PRON cana-574 47 2 penalizes penalize VERB cana-574 47 3 the the DET cana-574 47 4 deviation deviation NOUN cana-574 47 5 from from ADP cana-574 47 6 actual actual ADJ cana-574 47 7 labels label NOUN cana-574 47 8 𝑦 𝑦 PRON cana-574 47 9 and and CCONJ cana-574 47 10 predicted predict VERB cana-574 47 11 probabilities probability NOUN cana-574 47 12 οΏ½ οΏ½ PROPN cana-574 47 13 Μ‚ Μ‚ NOUN cana-574 47 14 οΏ½ οΏ½ NOUN cana-574 47 15 . . PUNCT cana-574 48 1 l2 l2 NOUN cana-574 48 2 regularization regularization NOUN cana-574 48 3 term term NOUN cana-574 48 4 : : PUNCT cana-574 48 5 𝑅(𝑀 𝑅(𝑀 X cana-574 48 6 ) ) PUNCT cana-574 48 7 = = SYM cana-574 48 8 πœ† πœ† X cana-574 48 9 βˆ₯ βˆ₯ PUNCT cana-574 48 10 𝑀 𝑀 ADP cana-574 48 11 βˆ₯2 βˆ₯2 ADV cana-574 48 12 ( ( PUNCT cana-574 48 13 6 6 NUM cana-574 48 14 ) ) PUNCT cana-574 48 15 l2 l2 NOUN cana-574 48 16 regularization regularization NOUN cana-574 48 17 is be AUX cana-574 48 18 used use VERB cana-574 48 19 to to PART cana-574 48 20 prevent prevent VERB cana-574 48 21 overfitting overfitte VERB cana-574 48 22 by by ADP cana-574 48 23 adding add VERB cana-574 48 24 a a DET cana-574 48 25 penalty penalty NOUN cana-574 48 26 term term NOUN cana-574 48 27 𝑅(𝑀 𝑅(𝑀 X cana-574 48 28 ) ) PUNCT cana-574 48 29 to to ADP cana-574 48 30 the the DET cana-574 48 31 loss loss NOUN cana-574 48 32 function function NOUN cana-574 48 33 , , PUNCT cana-574 48 34 which which PRON cana-574 48 35 is be AUX cana-574 48 36 proportional proportional ADJ cana-574 48 37 to to ADP cana-574 48 38 the the DET cana-574 48 39 square square NOUN cana-574 48 40 of of ADP cana-574 48 41 the the DET cana-574 48 42 magnitude magnitude NOUN cana-574 48 43 of of ADP cana-574 48 44 the the DET cana-574 48 45 weights weight NOUN cana-574 48 46 , , PUNCT cana-574 48 47 controlled control VERB cana-574 48 48 by by ADP cana-574 48 49 the the DET cana-574 48 50 regularization regularization NOUN cana-574 48 51 parameter parameter NOUN cana-574 48 52 πœ†. πœ†. NOUN cana-574 49 1 stochastic stochastic ADJ cana-574 49 2 gradient gradient ADJ cana-574 49 3 descent descent NOUN cana-574 49 4 ( ( PUNCT cana-574 49 5 sgd sgd PROPN cana-574 49 6 ): ): PUNCT cana-574 49 7 𝑀 𝑀 ADP cana-574 49 8 = = PUNCT cana-574 49 9 𝑀 𝑀 PROPN cana-574 49 10 βˆ’ βˆ’ PROPN cana-574 49 11 π›Όβˆ‡π‘€πΏπ‘– π›Όβˆ‡π‘€πΏπ‘– PROPN cana-574 49 12 ( ( PUNCT cana-574 49 13 7 7 NUM cana-574 49 14 ) ) PUNCT cana-574 49 15 stochastic stochastic ADJ cana-574 49 16 gradient gradient ADJ cana-574 49 17 descent descent NOUN cana-574 49 18 updates update VERB cana-574 49 19 the the DET cana-574 49 20 model model NOUN cana-574 49 21 weights weight VERB cana-574 49 22 using use VERB cana-574 49 23 the the DET cana-574 49 24 gradient gradient NOUN cana-574 49 25 of of ADP cana-574 49 26 the the DET cana-574 49 27 loss loss NOUN cana-574 49 28 function function NOUN cana-574 49 29 𝐿𝑖 𝐿𝑖 NOUN cana-574 49 30 with with ADP cana-574 49 31 respect respect NOUN cana-574 49 32 to to ADP cana-574 49 33 a a DET cana-574 49 34 randomly randomly ADV cana-574 49 35 selected select VERB cana-574 49 36 subset subset NOUN cana-574 49 37 of of ADP cana-574 49 38 data datum NOUN cana-574 49 39 , , PUNCT cana-574 49 40 improving improve VERB cana-574 49 41 optimization optimization NOUN cana-574 49 42 speed speed NOUN cana-574 49 43 . . PUNCT cana-574 50 1 backpropagation backpropagation NOUN cana-574 50 2 through through ADP cana-574 50 3 time time NOUN cana-574 50 4 ( ( PUNCT cana-574 50 5 for for ADP cana-574 50 6 rnns rnn NOUN cana-574 50 7 ): ): PUNCT cana-574 50 8 βˆ‚πΏ βˆ‚πΏ PROPN cana-574 50 9 βˆ‚π‘€ βˆ‚π‘€ PROPN cana-574 50 10 = = PUNCT cana-574 50 11 βˆ‘ βˆ‘ PUNCT cana-574 50 12 β€Š β€Š SPACE cana-574 50 13 𝑑 𝑑 PROPN cana-574 50 14 βˆ‚πΏπ‘’ βˆ‚πΏπ‘’ NOUN cana-574 50 15 βˆ‚π‘€ βˆ‚π‘€ PROPN cana-574 50 16 ( ( PUNCT cana-574 50 17 8) 8) NUM cana-574 50 18 this this DET cana-574 50 19 equation equation NOUN cana-574 50 20 is be AUX cana-574 50 21 used use VERB cana-574 50 22 to to PART cana-574 50 23 calculate calculate VERB cana-574 50 24 the the DET cana-574 50 25 gradients gradient NOUN cana-574 50 26 of of ADP cana-574 50 27 loss loss NOUN cana-574 50 28 function function NOUN cana-574 50 29 𝐿 𝐿 PROPN cana-574 50 30 with with ADP cana-574 50 31 respect respect NOUN cana-574 50 32 to to ADP cana-574 50 33 weights weight NOUN cana-574 50 34 𝑀 𝑀 ADP cana-574 50 35 in in ADP cana-574 50 36 recurrent recurrent ADJ cana-574 50 37 neural neural ADJ cana-574 50 38 networks network NOUN cana-574 50 39 ( ( PUNCT cana-574 50 40 rnns rnns PROPN cana-574 50 41 ) ) PUNCT cana-574 50 42 , , PUNCT cana-574 50 43 summed sum VERB cana-574 50 44 over over ADP cana-574 50 45 all all DET cana-574 50 46 time time NOUN cana-574 50 47 steps step NOUN cana-574 50 48 𝑑. 𝑑. NOUN cana-574 50 49 convolution convolution NOUN cana-574 50 50 operation operation NOUN cana-574 50 51 in in ADP cana-574 50 52 cnns cnns PROPN cana-574 50 53 : : PUNCT cana-574 50 54 ( ( PUNCT cana-574 50 55 𝑓 𝑓 DET cana-574 50 56 βˆ— βˆ— NOUN cana-574 50 57 𝑔)(𝑑 𝑔)(𝑑 PUNCT cana-574 50 58 ) ) PUNCT cana-574 50 59 = = SYM cana-574 51 1 ∫ ∫ PROPN cana-574 51 2 ⁑ ⁑ INTJ cana-574 51 3 𝑓(𝜏)𝑔(𝑑 𝑓(𝜏)𝑔(𝑑 VERB cana-574 51 4 βˆ’ βˆ’ PROPN cana-574 51 5 𝜏)π‘‘πœ 𝜏)π‘‘πœ PROPN cana-574 51 6 ( ( PUNCT cana-574 51 7 9 9 NUM cana-574 51 8 ) ) PUNCT cana-574 51 9 in in ADP cana-574 51 10 the the DET cana-574 51 11 context context NOUN cana-574 51 12 of of ADP cana-574 51 13 convolutional convolutional ADJ cana-574 51 14 neural neural ADJ cana-574 51 15 networks network NOUN cana-574 51 16 ( ( PUNCT cana-574 51 17 cnns cnns PROPN cana-574 51 18 ) ) PUNCT cana-574 51 19 , , PUNCT cana-574 51 20 this this DET cana-574 51 21 equation equation NOUN cana-574 51 22 describes describe VERB cana-574 51 23 the the DET cana-574 51 24 convolution convolution NOUN cana-574 51 25 operation operation NOUN cana-574 51 26 between between ADP cana-574 51 27 an an DET cana-574 51 28 input input NOUN cana-574 51 29 function function NOUN cana-574 51 30 𝑓 𝑓 ADP cana-574 51 31 and and CCONJ cana-574 51 32 a a DET cana-574 51 33 kernel kernel NOUN cana-574 51 34 𝑔 𝑔 PROPN cana-574 51 35 , , PUNCT cana-574 51 36 used use VERB cana-574 51 37 for for ADP cana-574 51 38 feature feature NOUN cana-574 51 39 extraction extraction NOUN cana-574 51 40 . . PUNCT cana-574 52 1 max max PROPN cana-574 52 2 pooling pooling PROPN cana-574 52 3 in in ADP cana-574 52 4 cnns cnn NOUN cana-574 52 5 : : PUNCT cana-574 52 6 maxpooling⁑(𝐴 maxpooling⁑(𝐴 NOUN cana-574 52 7 ) ) PUNCT cana-574 53 1 = = SYM cana-574 53 2 max max PROPN cana-574 53 3 𝑖,π‘—βˆˆπ΄ 𝑖,π‘—βˆˆπ΄ PROPN cana-574 53 4 β€Š β€Š SPACE cana-574 53 5 π‘Žπ‘–π‘— π‘Žπ‘–π‘— NOUN cana-574 53 6 ( ( PUNCT cana-574 53 7 10 10 NUM cana-574 53 8 ) ) PUNCT cana-574 53 9 max max PROPN cana-574 53 10 pooling pooling NOUN cana-574 53 11 reduces reduce VERB cana-574 53 12 the the DET cana-574 53 13 dimensionality dimensionality NOUN cana-574 53 14 of of ADP cana-574 53 15 each each DET cana-574 53 16 feature feature NOUN cana-574 53 17 map map NOUN cana-574 53 18 in in ADP cana-574 53 19 cnns cnn NOUN cana-574 53 20 by by ADP cana-574 53 21 taking take VERB cana-574 53 22 the the DET cana-574 53 23 maximum maximum ADJ cana-574 53 24 value value NOUN cana-574 53 25 over over ADP cana-574 53 26 a a DET cana-574 53 27 specified specified ADJ cana-574 53 28 window window NOUN cana-574 53 29 𝐴. 𝐴. NOUN cana-574 53 30 this this DET cana-574 53 31 selection selection NOUN cana-574 53 32 includes include VERB cana-574 53 33 equations equation NOUN cana-574 53 34 relevant relevant ADJ cana-574 53 35 to to ADP cana-574 53 36 various various ADJ cana-574 53 37 aspects aspect NOUN cana-574 53 38 of of ADP cana-574 53 39 deep deep ADJ cana-574 53 40 learning learning NOUN cana-574 53 41 , , PUNCT cana-574 53 42 from from ADP cana-574 53 43 basic basic ADJ cana-574 53 44 operations operation NOUN cana-574 53 45 to to ADP cana-574 53 46 more more ADJ cana-574 53 47 complex complex ADJ cana-574 53 48 functions function NOUN cana-574 53 49 involving involve VERB cana-574 53 50 optimization optimization NOUN cana-574 53 51 and and CCONJ cana-574 53 52 regularization regularization NOUN cana-574 53 53 . . PUNCT cana-574 54 1 communications communication NOUN cana-574 54 2 on on ADP cana-574 54 3 applied apply VERB cana-574 54 4 nonlinear nonlinear ADJ cana-574 54 5 analysis analysis NOUN cana-574 54 6 issn issn NOUN cana-574 54 7 : : PUNCT cana-574 54 8 1074 1074 NUM cana-574 54 9 - - PUNCT cana-574 54 10 133x 133x NUM cana-574 54 11 vol vol NOUN cana-574 54 12 31 31 NUM cana-574 54 13 no no NOUN cana-574 54 14 . . NOUN cana-574 54 15 2 2 NUM cana-574 54 16 ( ( PUNCT cana-574 54 17 2024 2024 NUM cana-574 54 18 ) ) PUNCT cana-574 54 19 374 374 NUM cana-574 54 20 https://internationalpubls.com https://internationalpubls.com X cana-574 54 21 euler euler NOUN cana-574 54 22 's 's PART cana-574 54 23 formula formula NOUN cana-574 54 24 for for ADP cana-574 54 25 complex complex ADJ cana-574 54 26 exponentials exponential NOUN cana-574 54 27 : : PUNCT cana-574 54 28 π‘’π‘–πœƒ π‘’π‘–πœƒ PROPN cana-574 54 29 = = SYM cana-574 54 30 cos⁑(πœƒ cos⁑(πœƒ PROPN cana-574 54 31 ) ) PUNCT cana-574 54 32 + + NUM cana-574 55 1 𝑖sin⁑(πœƒ 𝑖sin⁑(πœƒ X cana-574 55 2 ) ) PUNCT cana-574 55 3 ( ( PUNCT cana-574 55 4 11 11 NUM cana-574 55 5 ) ) PUNCT cana-574 55 6 this this DET cana-574 55 7 fundamental fundamental ADJ cana-574 55 8 equation equation NOUN cana-574 55 9 in in ADP cana-574 55 10 complex complex ADJ cana-574 55 11 analysis analysis NOUN cana-574 55 12 is be AUX cana-574 55 13 used use VERB cana-574 55 14 for for ADP cana-574 55 15 transforming transform VERB cana-574 55 16 complex complex ADJ cana-574 55 17 numbers number NOUN cana-574 55 18 into into ADP cana-574 55 19 their their PRON cana-574 55 20 exponential exponential ADJ cana-574 55 21 form form NOUN cana-574 55 22 , , PUNCT cana-574 55 23 which which PRON cana-574 55 24 can can AUX cana-574 55 25 be be AUX cana-574 55 26 useful useful ADJ cana-574 55 27 in in ADP cana-574 55 28 signal signal ADJ cana-574 55 29 processing processing NOUN cana-574 55 30 aspects aspect NOUN cana-574 55 31 of of ADP cana-574 55 32 neural neural ADJ cana-574 55 33 networks network NOUN cana-574 55 34 . . PUNCT cana-574 56 1 laplacian laplacian ADJ cana-574 56 2 operator operator NOUN cana-574 56 3 in in ADP cana-574 56 4 graph graph NOUN cana-574 56 5 theory theory NOUN cana-574 56 6 : : PUNCT cana-574 56 7 𝐿 𝐿 PROPN cana-574 56 8 = = PROPN cana-574 56 9 𝐷 𝐷 PROPN cana-574 56 10 βˆ’ βˆ’ PROPN cana-574 56 11 𝐴 𝐴 PROPN cana-574 56 12 ( ( PUNCT cana-574 56 13 12 12 NUM cana-574 56 14 ) ) PUNCT cana-574 56 15 the the DET cana-574 56 16 laplacian laplacian ADJ cana-574 56 17 matrix matrix NOUN cana-574 56 18 𝐿 𝐿 PROPN cana-574 56 19 of of ADP cana-574 56 20 a a DET cana-574 56 21 graph graph NOUN cana-574 56 22 is be AUX cana-574 56 23 used use VERB cana-574 56 24 in in ADP cana-574 56 25 graph graph NOUN cana-574 56 26 neural neural ADJ cana-574 56 27 networks network NOUN cana-574 56 28 , , PUNCT cana-574 56 29 where where SCONJ cana-574 56 30 𝐷 𝐷 PROPN cana-574 56 31 is be AUX cana-574 56 32 the the DET cana-574 56 33 degree degree NOUN cana-574 56 34 matrix matrix NOUN cana-574 56 35 and and CCONJ cana-574 56 36 𝐴 𝐴 PROPN cana-574 56 37 is be AUX cana-574 56 38 the the DET cana-574 56 39 adjacency adjacency NOUN cana-574 56 40 matrix matrix NOUN cana-574 56 41 , , PUNCT cana-574 56 42 helping help VERB cana-574 56 43 to to PART cana-574 56 44 define define VERB cana-574 56 45 the the DET cana-574 56 46 structure structure NOUN cana-574 56 47 of of ADP cana-574 56 48 the the DET cana-574 56 49 graph graph NOUN cana-574 56 50 . . PUNCT cana-574 57 1 eigenvalue eigenvalue ADJ cana-574 57 2 decomposition decomposition NOUN cana-574 57 3 for for ADP cana-574 57 4 pca pca PROPN cana-574 57 5 : : PUNCT cana-574 57 6 𝑋𝑇𝑋𝑣 𝑋𝑇𝑋𝑣 X cana-574 57 7 = = SYM cana-574 57 8 πœ†π‘£ πœ†π‘£ NOUN cana-574 57 9 ( ( PUNCT cana-574 57 10 13 13 NUM cana-574 57 11 ) ) PUNCT cana-574 57 12 principal principal ADJ cana-574 57 13 component component NOUN cana-574 57 14 analysis analysis NOUN cana-574 57 15 ( ( PUNCT cana-574 57 16 pca pca NOUN cana-574 57 17 ) ) PUNCT cana-574 57 18 involves involve VERB cana-574 57 19 finding find VERB cana-574 57 20 the the DET cana-574 57 21 eigenvalues eigenvalue NOUN cana-574 57 22 πœ† πœ† ADP cana-574 57 23 and and CCONJ cana-574 57 24 eigenvectors eigenvector NOUN cana-574 57 25 𝑣 𝑣 PRON cana-574 57 26 of of ADP cana-574 57 27 the the DET cana-574 57 28 covariance covariance NOUN cana-574 57 29 matrix matrix NOUN cana-574 57 30 𝑋𝑇𝑋 𝑋𝑇𝑋 ADJ cana-574 57 31 to to PART cana-574 57 32 reduce reduce VERB cana-574 57 33 dimensions dimension NOUN cana-574 57 34 and and CCONJ cana-574 57 35 extract extract VERB cana-574 57 36 features feature NOUN cana-574 57 37 . . PUNCT cana-574 58 1 relu relu NOUN cana-574 58 2 activation activation NOUN cana-574 58 3 function function NOUN cana-574 58 4 : : PUNCT cana-574 58 5 relu⁑(π‘₯ relu⁑(π‘₯ NUM cana-574 58 6 ) ) PUNCT cana-574 59 1 βˆ’ βˆ’ PROPN cana-574 59 2 max(0 max(0 NOUN cana-574 59 3 , , PUNCT cana-574 59 4 π‘₯ π‘₯ X cana-574 59 5 ) ) PUNCT cana-574 59 6 the the DET cana-574 59 7 rectified rectified ADJ cana-574 59 8 linear linear NOUN cana-574 59 9 unit unit NOUN cana-574 59 10 ( ( PUNCT cana-574 59 11 relu relu NOUN cana-574 59 12 ) ) PUNCT cana-574 59 13 activation activation NOUN cana-574 59 14 function function NOUN cana-574 59 15 is be AUX cana-574 59 16 widely widely ADV cana-574 59 17 used use VERB cana-574 59 18 in in ADP cana-574 59 19 deep deep ADJ cana-574 59 20 learning learning NOUN cana-574 59 21 for for ADP cana-574 59 22 introducing introduce VERB cana-574 59 23 non non ADJ cana-574 59 24 - - ADJ cana-574 59 25 linearity linearity ADJ cana-574 59 26 , , PUNCT cana-574 59 27 improving improve VERB cana-574 59 28 training training NOUN cana-574 59 29 speeds speed NOUN cana-574 59 30 and and CCONJ cana-574 59 31 convergence convergence NOUN cana-574 59 32 . . PUNCT cana-574 60 1 adam adam PROPN cana-574 60 2 optimizer optimizer NOUN cana-574 60 3 update update NOUN cana-574 60 4 rule rule NOUN cana-574 60 5 : : PUNCT cana-574 60 6 𝑀𝑑+1 𝑀𝑑+1 NOUN cana-574 60 7 = = PRON cana-574 60 8 𝑀𝑑 𝑀𝑑 VERB cana-574 60 9 βˆ’ βˆ’ PROPN cana-574 60 10 𝛼 𝛼 X cana-574 60 11 √6𝑑+𝑒 √6𝑑+𝑒 PROPN cana-574 60 12 οΏ½ οΏ½ PROPN cana-574 60 13 Μ‚ Μ‚ SYM cana-574 60 14 οΏ½ οΏ½ NOUN cana-574 60 15 𝑑 𝑑 NOUN cana-574 60 16 ( ( PUNCT cana-574 60 17 14 14 NUM cana-574 60 18 ) ) PUNCT cana-574 60 19 adam adam PROPN cana-574 60 20 optimization optimization NOUN cana-574 60 21 algorithm algorithm PROPN cana-574 60 22 uses use VERB cana-574 60 23 adaptive adaptive ADJ cana-574 60 24 learning learning NOUN cana-574 60 25 rates rate NOUN cana-574 60 26 for for ADP cana-574 60 27 each each DET cana-574 60 28 parameter parameter NOUN cana-574 60 29 by by ADP cana-574 60 30 estimating estimate VERB cana-574 60 31 first first ADV cana-574 60 32 ( ( PUNCT cana-574 60 33 π‘š π‘š NOUN cana-574 60 34 ) ) PUNCT cana-574 60 35 and and CCONJ cana-574 60 36 second second ADJ cana-574 60 37 ( ( PUNCT cana-574 60 38 v v NOUN cana-574 60 39 ) ) PUNCT cana-574 60 40 moments moment NOUN cana-574 60 41 of of ADP cana-574 60 42 the the DET cana-574 60 43 gradients gradient NOUN cana-574 60 44 , , PUNCT cana-574 60 45 improving improve VERB cana-574 60 46 the the DET cana-574 60 47 efficiency efficiency NOUN cana-574 60 48 of of ADP cana-574 60 49 network network NOUN cana-574 60 50 training training NOUN cana-574 60 51 . . PUNCT cana-574 61 1 batch batch NOUN cana-574 61 2 normalization normalization NOUN cana-574 61 3 transform transform NOUN cana-574 61 4 : : PUNCT cana-574 61 5 𝑦 𝑦 NOUN cana-574 61 6 = = SYM cana-574 61 7 𝛾 𝛾 PROPN cana-574 61 8 ( ( PUNCT cana-574 61 9 π‘₯βˆ’πœ‡ π‘₯βˆ’πœ‡ X cana-574 61 10 √𝜎2+πœ€ √𝜎2+πœ€ X cana-574 61 11 ) ) PUNCT cana-574 62 1 + + CCONJ cana-574 62 2 𝛽 𝛽 NOUN cana-574 62 3 ( ( PUNCT cana-574 62 4 15 15 NUM cana-574 62 5 ) ) PUNCT cana-574 62 6 batch batch NOUN cana-574 62 7 normalization normalization NOUN cana-574 62 8 stabilizes stabilize VERB cana-574 62 9 and and CCONJ cana-574 62 10 accelerates accelerate VERB cana-574 62 11 deep deep ADJ cana-574 62 12 network network NOUN cana-574 62 13 training training NOUN cana-574 62 14 by by ADP cana-574 62 15 normalizing normalize VERB cana-574 62 16 the the DET cana-574 62 17 inputs input NOUN cana-574 62 18 ( ( PUNCT cana-574 62 19 layer layer NOUN cana-574 62 20 activations activation NOUN cana-574 62 21 ) ) PUNCT cana-574 63 1 π‘₯ π‘₯ NOUN cana-574 63 2 using use VERB cana-574 63 3 mean mean NOUN cana-574 63 4 πœ‡ πœ‡ ADP cana-574 63 5 and and CCONJ cana-574 63 6 variance variance NOUN cana-574 63 7 𝜎2 𝜎2 NOUN cana-574 63 8 , , PUNCT cana-574 63 9 scaled scale VERB cana-574 63 10 by by ADP cana-574 63 11 𝛾 𝛾 PROPN cana-574 63 12 and and CCONJ cana-574 63 13 shifted shift VERB cana-574 63 14 by by ADP cana-574 63 15 𝛽. 𝛽. NOUN cana-574 63 16 hinge hinge NOUN cana-574 63 17 loss loss NOUN cana-574 63 18 for for ADP cana-574 63 19 svm svm ADJ cana-574 63 20 : : PUNCT cana-574 63 21 𝐿 𝐿 PROPN cana-574 63 22 = = SYM cana-574 63 23 max(0,1 max(0,1 PROPN cana-574 63 24 βˆ’ βˆ’ PROPN cana-574 63 25 𝑦 𝑦 SYM cana-574 63 26 β‹… β‹… NUM cana-574 63 27 𝑓(π‘₯ 𝑓(π‘₯ NOUN cana-574 63 28 ) ) PUNCT cana-574 63 29 ) ) PUNCT cana-574 63 30 ( ( PUNCT cana-574 63 31 16 16 NUM cana-574 63 32 ) ) PUNCT cana-574 63 33 used use VERB cana-574 63 34 primarily primarily ADV cana-574 63 35 in in ADP cana-574 63 36 support support NOUN cana-574 63 37 vector vector NOUN cana-574 63 38 machines machine NOUN cana-574 63 39 ( ( PUNCT cana-574 63 40 svms svms NOUN cana-574 63 41 ) ) PUNCT cana-574 63 42 for for ADP cana-574 63 43 classification classification NOUN cana-574 63 44 , , PUNCT cana-574 63 45 hinge hinge NOUN cana-574 63 46 loss loss NOUN cana-574 63 47 penalizes penalize NOUN cana-574 63 48 misclassifications misclassification NOUN cana-574 63 49 and and CCONJ cana-574 63 50 instances instance NOUN cana-574 63 51 where where SCONJ cana-574 63 52 the the DET cana-574 63 53 classification classification NOUN cana-574 63 54 margin margin NOUN cana-574 63 55 is be AUX cana-574 63 56 not not PART cana-574 63 57 maintained maintain VERB cana-574 63 58 . . PUNCT cana-574 64 1 entropy entropy PROPN cana-574 64 2 in in ADP cana-574 64 3 information information NOUN cana-574 64 4 theory theory NOUN cana-574 64 5 : : PUNCT cana-574 64 6 𝐻(𝑋 𝐻(𝑋 NOUN cana-574 64 7 ) ) PUNCT cana-574 64 8 = = PUNCT cana-574 64 9 βˆ’βˆ‘ βˆ’βˆ‘ PROPN cana-574 64 10 β€Š β€Š SPACE cana-574 64 11 𝑖 𝑖 PROPN cana-574 64 12 𝑝(π‘₯𝑖)log⁑ 𝑝(π‘₯𝑖)log⁑ X cana-574 64 13 𝑝(π‘₯𝑖 𝑝(π‘₯𝑖 NUM cana-574 64 14 ) ) PUNCT cana-574 64 15 ( ( PUNCT cana-574 64 16 17 17 NUM cana-574 64 17 ) ) PUNCT cana-574 64 18 entropy entropy NOUN cana-574 64 19 measures measure VERB cana-574 64 20 the the DET cana-574 64 21 uncertainty uncertainty NOUN cana-574 64 22 or or CCONJ cana-574 64 23 impurity impurity NOUN cana-574 64 24 in in ADP cana-574 64 25 a a DET cana-574 64 26 dataset dataset NOUN cana-574 64 27 and and CCONJ cana-574 64 28 is be AUX cana-574 64 29 used use VERB cana-574 64 30 in in ADP cana-574 64 31 various various ADJ cana-574 64 32 machine machine NOUN cana-574 64 33 learning learn VERB cana-574 64 34 algorithms algorithm NOUN cana-574 64 35 to to PART cana-574 64 36 gauge gauge VERB cana-574 64 37 information information NOUN cana-574 64 38 gain gain NOUN cana-574 64 39 and and CCONJ cana-574 64 40 decision decision NOUN cana-574 64 41 making making NOUN cana-574 64 42 . . PUNCT cana-574 65 1 gaussian gaussian ADJ cana-574 65 2 kernel kernel NOUN cana-574 65 3 in in ADP cana-574 65 4 svms svms NOUN cana-574 65 5 : : PUNCT cana-574 65 6 communications communication NOUN cana-574 65 7 on on ADP cana-574 65 8 applied apply VERB cana-574 65 9 nonlinear nonlinear ADJ cana-574 65 10 analysis analysis NOUN cana-574 65 11 issn issn NOUN cana-574 65 12 : : PUNCT cana-574 65 13 1074 1074 NUM cana-574 65 14 - - PUNCT cana-574 65 15 133x 133x NUM cana-574 65 16 vol vol NOUN cana-574 65 17 31 31 NUM cana-574 65 18 no no NOUN cana-574 65 19 . . NOUN cana-574 65 20 2 2 NUM cana-574 65 21 ( ( PUNCT cana-574 65 22 2024 2024 NUM cana-574 65 23 ) ) PUNCT cana-574 65 24 375 375 NUM cana-574 65 25 https://internationalpubls.com https://internationalpubls.com X cana-574 65 26 𝐾(π‘₯ 𝐾(π‘₯ NOUN cana-574 65 27 , , PUNCT cana-574 65 28 π‘₯β€² π‘₯β€² NUM cana-574 65 29 ) ) PUNCT cana-574 66 1 = = SYM cana-574 66 2 exp⁑ exp⁑ X cana-574 66 3 ( ( PUNCT cana-574 66 4 βˆ’ βˆ’ PROPN cana-574 66 5 βˆ₯βˆ₯π‘₯βˆ’π‘₯β€²βˆ₯βˆ₯ βˆ₯βˆ₯π‘₯βˆ’π‘₯β€²βˆ₯βˆ₯ NUM cana-574 66 6 2 2 NUM cana-574 66 7 2𝜎2 2𝜎2 NUM cana-574 66 8 ) ) PUNCT cana-574 66 9 ( ( PUNCT cana-574 66 10 18 18 NUM cana-574 66 11 ) ) PUNCT cana-574 66 12 the the DET cana-574 66 13 gaussian gaussian NOUN cana-574 66 14 or or CCONJ cana-574 66 15 rbf rbf PROPN cana-574 66 16 kernel kernel PROPN cana-574 66 17 is be AUX cana-574 66 18 used use VERB cana-574 66 19 in in ADP cana-574 66 20 svms svms NOUN cana-574 66 21 for for ADP cana-574 66 22 transforming transform VERB cana-574 66 23 the the DET cana-574 66 24 input input NOUN cana-574 66 25 space space NOUN cana-574 66 26 into into ADP cana-574 66 27 a a DET cana-574 66 28 higherdimensional higherdimensional ADJ cana-574 66 29 space space NOUN cana-574 66 30 , , PUNCT cana-574 66 31 allowing allow VERB cana-574 66 32 for for ADP cana-574 66 33 the the DET cana-574 66 34 handling handling NOUN cana-574 66 35 of of ADP cana-574 66 36 nonlinear nonlinear ADJ cana-574 66 37 boundaries boundary NOUN cana-574 66 38 . . PUNCT cana-574 67 1 dropout dropout NOUN cana-574 67 2 regularization regularization NOUN cana-574 67 3 : : PUNCT cana-574 67 4 π‘Ÿπ‘— π‘Ÿπ‘— ADP cana-574 67 5 ∼ ∼ NOUN cana-574 67 6 bernoulli⁑(𝑝 bernoulli⁑(𝑝 X cana-574 67 7 ) ) PUNCT cana-574 68 1 π‘₯β€Ύ π‘₯β€Ύ X cana-574 68 2 = = PUNCT cana-574 68 3 π‘₯ π‘₯ PROPN cana-574 68 4 βˆ— βˆ— NOUN cana-574 68 5 π‘Ÿ π‘Ÿ X cana-574 68 6 ( ( PUNCT cana-574 68 7 19 19 NUM cana-574 68 8 ) ) PUNCT cana-574 68 9 dropout dropout NOUN cana-574 68 10 prevents prevent VERB cana-574 68 11 overfitting overfitte VERB cana-574 68 12 in in ADP cana-574 68 13 neural neural ADJ cana-574 68 14 networks network NOUN cana-574 68 15 by by ADP cana-574 68 16 randomly randomly ADV cana-574 68 17 setting set VERB cana-574 68 18 a a DET cana-574 68 19 fraction fraction NOUN cana-574 68 20 𝑝 𝑝 NOUN cana-574 68 21 of of ADP cana-574 68 22 input input NOUN cana-574 68 23 units unit NOUN cana-574 68 24 to to ADP cana-574 68 25 zero zero NUM cana-574 68 26 at at ADP cana-574 68 27 each each DET cana-574 68 28 update update NOUN cana-574 68 29 during during ADP cana-574 68 30 training training NOUN cana-574 68 31 . . PUNCT cana-574 69 1 attention attention NOUN cana-574 69 2 mechanism mechanism NOUN cana-574 69 3 in in ADP cana-574 69 4 transformers transformer NOUN cana-574 69 5 : : PUNCT cana-574 69 6 attention attention NOUN cana-574 69 7 ( ( PUNCT cana-574 69 8 𝑄 𝑄 PROPN cana-574 69 9 , , PUNCT cana-574 69 10 𝐾 𝐾 PROPN cana-574 69 11 , , PUNCT cana-574 69 12 𝑉 𝑉 PROPN cana-574 69 13 ) ) PUNCT cana-574 69 14 = = SYM cana-574 69 15 softmax⁑ softmax⁑ NOUN cana-574 69 16 ( ( PUNCT cana-574 69 17 𝑄𝐾𝑇 𝑄𝐾𝑇 PROPN cana-574 69 18 π‘‰π‘‘π‘˜ π‘‰π‘‘π‘˜ PROPN cana-574 69 19 ) ) PUNCT cana-574 69 20 𝑉 𝑉 PROPN cana-574 69 21 ( ( PUNCT cana-574 69 22 20 20 NUM cana-574 69 23 ) ) PUNCT cana-574 69 24 in in ADP cana-574 69 25 the the DET cana-574 69 26 context context NOUN cana-574 69 27 of of ADP cana-574 69 28 transformer transformer NOUN cana-574 69 29 models model NOUN cana-574 69 30 , , PUNCT cana-574 69 31 this this DET cana-574 69 32 equation equation NOUN cana-574 69 33 calculates calculate VERB cana-574 69 34 the the DET cana-574 69 35 attention attention NOUN cana-574 69 36 weights weight NOUN cana-574 69 37 and and CCONJ cana-574 69 38 outputs output NOUN cana-574 69 39 by by ADP cana-574 69 40 scaling scale VERB cana-574 69 41 the the DET cana-574 69 42 dot dot NOUN cana-574 69 43 products product NOUN cana-574 69 44 of of ADP cana-574 69 45 queries query NOUN cana-574 69 46 𝑄 𝑄 PRON cana-574 69 47 and and CCONJ cana-574 69 48 keys key VERB cana-574 69 49 𝐾 𝐾 PROPN cana-574 69 50 with with ADP cana-574 69 51 values value NOUN cana-574 69 52 𝑉. 𝑉. NOUN cana-574 69 53 gated gate VERB cana-574 69 54 recurrent recurrent ADJ cana-574 69 55 unit unit NOUN cana-574 69 56 ( ( PUNCT cana-574 69 57 gru gru NOUN cana-574 69 58 ) ) PUNCT cana-574 69 59 update update NOUN cana-574 69 60 gate gate NOUN cana-574 69 61 : : PUNCT cana-574 69 62 𝑧𝑑 𝑧𝑑 ADJ cana-574 69 63 = = PUNCT cana-574 69 64 𝜎(π‘Šπ‘§ 𝜎(π‘Šπ‘§ NOUN cana-574 69 65 β‹… β‹… PROPN cana-574 70 1 [ [ X cana-574 70 2 β„Žπ‘‘βˆ’1 β„Žπ‘‘βˆ’1 ADJ cana-574 70 3 , , PUNCT cana-574 70 4 π‘₯𝑑 π‘₯𝑑 ADV cana-574 70 5 ] ] PUNCT cana-574 70 6 ) ) PUNCT cana-574 70 7 ( ( PUNCT cana-574 70 8 21 21 NUM cana-574 70 9 ) ) PUNCT cana-574 70 10 the the DET cana-574 70 11 update update NOUN cana-574 70 12 gate gate NOUN cana-574 70 13 in in ADP cana-574 70 14 gru gru PROPN cana-574 70 15 helps help VERB cana-574 70 16 the the DET cana-574 70 17 model model NOUN cana-574 70 18 to to PART cana-574 70 19 determine determine VERB cana-574 70 20 how how SCONJ cana-574 70 21 much much ADJ cana-574 70 22 of of ADP cana-574 70 23 the the DET cana-574 70 24 past past ADJ cana-574 70 25 information information NOUN cana-574 70 26 ( ( PUNCT cana-574 70 27 from from ADP cana-574 70 28 previous previous ADJ cana-574 70 29 time time NOUN cana-574 70 30 steps step NOUN cana-574 70 31 ) ) PUNCT cana-574 70 32 needs need VERB cana-574 70 33 to to PART cana-574 70 34 be be AUX cana-574 70 35 passed pass VERB cana-574 70 36 along along ADP cana-574 70 37 to to ADP cana-574 70 38 the the DET cana-574 70 39 future future NOUN cana-574 70 40 . . PUNCT cana-574 71 1 gated gate VERB cana-574 71 2 recurrent recurrent ADJ cana-574 71 3 unit unit NOUN cana-574 71 4 ( ( PUNCT cana-574 71 5 gru gru NOUN cana-574 71 6 ) ) PUNCT cana-574 71 7 reset reset ADJ cana-574 71 8 gate gate NOUN cana-574 71 9 : : PUNCT cana-574 71 10 π‘Ÿπ‘‘ π‘Ÿπ‘‘ PROPN cana-574 71 11 = = PUNCT cana-574 71 12 𝜎(π‘Šπ‘Ÿ 𝜎(π‘Šπ‘Ÿ X cana-574 71 13 β‹… β‹… X cana-574 72 1 [ [ X cana-574 72 2 β„Žπ‘‘βˆ’1 β„Žπ‘‘βˆ’1 ADJ cana-574 72 3 , , PUNCT cana-574 72 4 π‘₯𝑑 π‘₯𝑑 ADV cana-574 72 5 ] ] PUNCT cana-574 72 6 ) ) PUNCT cana-574 72 7 ( ( PUNCT cana-574 72 8 22 22 NUM cana-574 72 9 ) ) PUNCT cana-574 72 10 the the DET cana-574 72 11 reset reset NOUN cana-574 72 12 gate gate NOUN cana-574 72 13 in in ADP cana-574 72 14 gru gru NOUN cana-574 72 15 controls control VERB cana-574 72 16 how how SCONJ cana-574 72 17 much much ADJ cana-574 72 18 of of ADP cana-574 72 19 the the DET cana-574 72 20 past past ADJ cana-574 72 21 information information NOUN cana-574 72 22 to to PART cana-574 72 23 forget forget VERB cana-574 72 24 , , PUNCT cana-574 72 25 which which PRON cana-574 72 26 helps help VERB cana-574 72 27 in in ADP cana-574 72 28 capturing capture VERB cana-574 72 29 dependencies dependency NOUN cana-574 72 30 and and CCONJ cana-574 72 31 managing manage VERB cana-574 72 32 vanishing vanish VERB cana-574 72 33 gradient gradient NOUN cana-574 72 34 issues issue NOUN cana-574 72 35 in in ADP cana-574 72 36 sequences sequence NOUN cana-574 72 37 . . PUNCT cana-574 73 1 gated gate VERB cana-574 73 2 recurrent recurrent ADJ cana-574 73 3 unit unit NOUN cana-574 73 4 ( ( PUNCT cana-574 73 5 gru gru NOUN cana-574 73 6 ) ) PUNCT cana-574 73 7 current current ADJ cana-574 73 8 memory memory NOUN cana-574 73 9 content content NOUN cana-574 73 10 : : PUNCT cana-574 73 11 β„Žβ€Ύπ‘‘ β„Žβ€Ύπ‘‘ PROPN cana-574 73 12 = = SYM cana-574 73 13 tanh⁑(π‘Š tanh⁑(π‘Š X cana-574 73 14 β‹… β‹… PROPN cana-574 74 1 [ [ X cana-574 74 2 π‘Ÿπ‘‘ π‘Ÿπ‘‘ NOUN cana-574 74 3 βˆ— βˆ— NOUN cana-574 74 4 β„Žπ‘‘βˆ’1 β„Žπ‘‘βˆ’1 ADJ cana-574 74 5 , , PUNCT cana-574 74 6 π‘₯𝑑 π‘₯𝑑 ADV cana-574 74 7 ] ] PUNCT cana-574 74 8 ) ) PUNCT cana-574 74 9 ( ( PUNCT cana-574 74 10 23 23 NUM cana-574 74 11 ) ) PUNCT cana-574 74 12 this this PRON cana-574 74 13 represents represent VERB cana-574 74 14 the the DET cana-574 74 15 candidate candidate NOUN cana-574 74 16 activation activation NOUN cana-574 74 17 or or CCONJ cana-574 74 18 new new ADJ cana-574 74 19 memory memory NOUN cana-574 74 20 content content NOUN cana-574 74 21 in in ADP cana-574 74 22 gru gru PROPN cana-574 74 23 , , PUNCT cana-574 74 24 combining combine VERB cana-574 74 25 the the DET cana-574 74 26 reset reset NOUN cana-574 74 27 gate gate NOUN cana-574 74 28 's 's PART cana-574 74 29 output output NOUN cana-574 74 30 with with ADP cana-574 74 31 the the DET cana-574 74 32 current current ADJ cana-574 74 33 input input NOUN cana-574 74 34 . . PUNCT cana-574 75 1 gated gate VERB cana-574 75 2 recurrent recurrent ADJ cana-574 75 3 unit unit NOUN cana-574 75 4 ( ( PUNCT cana-574 75 5 gru gru NOUN cana-574 75 6 ) ) PUNCT cana-574 75 7 final final ADJ cana-574 75 8 memory memory NOUN cana-574 75 9 at at ADP cana-574 75 10 current current ADJ cana-574 75 11 step step NOUN cana-574 75 12 : : PUNCT cana-574 75 13 β„Žπ‘‘ β„Žπ‘‘ NOUN cana-574 75 14 = = SYM cana-574 75 15 ( ( PUNCT cana-574 75 16 1 1 NUM cana-574 75 17 βˆ’ βˆ’ PROPN cana-574 75 18 𝑧𝑑 𝑧𝑑 X cana-574 75 19 ) ) PUNCT cana-574 75 20 βˆ— βˆ— NOUN cana-574 75 21 β„Žπ‘‘βˆ’1 β„Žπ‘‘βˆ’1 VERB cana-574 75 22 + + CCONJ cana-574 75 23 𝑧𝑑 𝑧𝑑 DET cana-574 75 24 βˆ— βˆ— NOUN cana-574 75 25 β„Žβ€Ύπ‘‘ β„Žβ€Ύπ‘‘ PROPN cana-574 75 26 ( ( PUNCT cana-574 75 27 25 25 NUM cana-574 75 28 ) ) PUNCT cana-574 75 29 this this DET cana-574 75 30 final final ADJ cana-574 75 31 state state NOUN cana-574 75 32 for for ADP cana-574 75 33 gru gru NOUN cana-574 75 34 at at ADP cana-574 75 35 time time NOUN cana-574 75 36 𝑑 𝑑 PROPN cana-574 75 37 blends blend VERB cana-574 75 38 the the DET cana-574 75 39 old old ADJ cana-574 75 40 state state NOUN cana-574 75 41 β„Žπ‘‘βˆ’1 β„Žπ‘‘βˆ’1 PROPN cana-574 75 42 and and CCONJ cana-574 75 43 the the DET cana-574 75 44 new new ADJ cana-574 75 45 candidate candidate NOUN cana-574 75 46 state state NOUN cana-574 75 47 β„Žβ€Ύπ‘‘ β„Žβ€Ύπ‘‘ PROPN cana-574 75 48 based base VERB cana-574 75 49 on on ADP cana-574 75 50 the the DET cana-574 75 51 update update NOUN cana-574 75 52 gate gate NOUN cana-574 75 53 𝑧𝑑. 𝑧𝑑. PROPN cana-574 75 54 l l ADJ cana-574 75 55 - - ADJ cana-574 75 56 bfgs bfgs ADJ cana-574 75 57 update update NOUN cana-574 75 58 formula formula NOUN cana-574 75 59 for for ADP cana-574 75 60 optimization optimization NOUN cana-574 75 61 : : PUNCT cana-574 75 62 π‘ π‘˜ π‘ π‘˜ NOUN cana-574 75 63 = = SYM cana-574 75 64 π‘₯π‘˜+1 π‘₯π‘˜+1 NOUN cana-574 75 65 βˆ’ βˆ’ PROPN cana-574 75 66 π‘₯π‘˜β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘(26 π‘₯π‘˜β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘β‘(26 NOUN cana-574 75 67 ) ) PUNCT cana-574 75 68 π‘¦π‘˜ π‘¦π‘˜ PROPN cana-574 75 69 = = SYM cana-574 75 70 βˆ‡π‘“(π‘₯π‘˜+1 βˆ‡π‘“(π‘₯π‘˜+1 PROPN cana-574 75 71 ) ) PUNCT cana-574 75 72 βˆ’ βˆ’ PROPN cana-574 76 1 βˆ‡π‘“(π‘₯π‘˜)⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(27 βˆ‡π‘“(π‘₯π‘˜)⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(27 NUM cana-574 76 2 ) ) PUNCT cana-574 76 3 πœŒπ‘˜ πœŒπ‘˜ PRON cana-574 76 4 = = NOUN cana-574 77 1 1 1 NUM cana-574 77 2 𝑦 𝑦 NOUN cana-574 77 3 π‘˜ π‘˜ DET cana-574 77 4 π‘‡π‘ π‘˜ π‘‡π‘ π‘˜ ADJ cana-574 77 5 ⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(28 ⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(28 NOUN cana-574 77 6 ) ) PUNCT cana-574 77 7 π»π‘˜+1 π»π‘˜+1 NOUN cana-574 77 8 = = SYM cana-574 77 9 ( ( PUNCT cana-574 77 10 𝐼 𝐼 PROPN cana-574 77 11 βˆ’ βˆ’ PROPN cana-574 77 12 πœŒπ‘˜π‘ π‘˜π‘¦π‘˜ πœŒπ‘˜π‘ π‘˜π‘¦π‘˜ NOUN cana-574 77 13 𝑇)π»π‘˜(𝐼 𝑇)π»π‘˜(𝐼 NUM cana-574 77 14 βˆ’ βˆ’ NOUN cana-574 77 15 πœŒπ‘˜π‘¦π‘˜π‘ π‘˜ πœŒπ‘˜π‘¦π‘˜π‘ π‘˜ VERB cana-574 77 16 𝑇 𝑇 PROPN cana-574 77 17 ) ) PUNCT cana-574 78 1 + + NUM cana-574 78 2 πœŒπ‘˜π‘ π‘˜π‘ π‘˜ πœŒπ‘˜π‘ π‘˜π‘ π‘˜ NOUN cana-574 78 3 𝑇⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(29 𝑇⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑⁑(29 NOUN cana-574 78 4 ) ) PUNCT cana-574 78 5 communications communication NOUN cana-574 78 6 on on ADP cana-574 78 7 applied apply VERB cana-574 78 8 nonlinear nonlinear ADJ cana-574 78 9 analysis analysis NOUN cana-574 78 10 issn issn NOUN cana-574 78 11 : : PUNCT cana-574 78 12 1074 1074 NUM cana-574 78 13 - - PUNCT cana-574 78 14 133x 133x NUM cana-574 78 15 vol vol NOUN cana-574 78 16 31 31 NUM cana-574 78 17 no no NOUN cana-574 78 18 . . NOUN cana-574 78 19 2 2 NUM cana-574 78 20 ( ( PUNCT cana-574 78 21 2024 2024 NUM cana-574 78 22 ) ) PUNCT cana-574 78 23 376 376 NUM cana-574 78 24 https://internationalpubls.com https://internationalpubls.com X cana-574 79 1 l l NOUN cana-574 79 2 - - ADJ cana-574 79 3 bfgs bfgs ADJ cana-574 79 4 is be AUX cana-574 79 5 a a DET cana-574 79 6 quasi quasi NOUN cana-574 79 7 - - ADJ cana-574 79 8 newton newton PROPN cana-574 79 9 method method NOUN cana-574 79 10 that that PRON cana-574 79 11 approximates approximate VERB cana-574 79 12 the the DET cana-574 79 13 hessian hessian ADJ cana-574 79 14 matrix matrix NOUN cana-574 79 15 involved involve VERB cana-574 79 16 in in ADP cana-574 79 17 the the DET cana-574 79 18 optimization optimization NOUN cana-574 79 19 of of ADP cana-574 79 20 nonlinear nonlinear ADJ cana-574 79 21 functions function NOUN cana-574 79 22 , , PUNCT cana-574 79 23 suitable suitable ADJ cana-574 79 24 for for ADP cana-574 79 25 large large ADJ cana-574 79 26 - - PUNCT cana-574 79 27 scale scale NOUN cana-574 79 28 problems problem NOUN cana-574 79 29 in in ADP cana-574 79 30 deep deep ADJ cana-574 79 31 learning learning NOUN cana-574 79 32 . . PUNCT cana-574 80 1 tikhonov tikhonov PROPN cana-574 80 2 regularization regularization NOUN cana-574 80 3 ( ( PUNCT cana-574 80 4 ridge ridge NOUN cana-574 80 5 regression regression NOUN cana-574 80 6 ): ): PUNCT cana-574 80 7 min min PROPN cana-574 80 8 𝑀 𝑀 PART cana-574 80 9 β€Š β€Š SPACE cana-574 80 10 βˆ₯ βˆ₯ NUM cana-574 81 1 𝑋𝑀 𝑋𝑀 ADJ cana-574 81 2 βˆ’ βˆ’ NOUN cana-574 81 3 𝑦 𝑦 NOUN cana-574 81 4 βˆ₯2 βˆ₯2 NOUN cana-574 81 5 + + CCONJ cana-574 81 6 πœ† πœ† PROPN cana-574 81 7 βˆ₯ βˆ₯ X cana-574 81 8 𝑀 𝑀 ADP cana-574 81 9 βˆ₯2 βˆ₯2 ADV cana-574 81 10 ( ( PUNCT cana-574 81 11 30 30 NUM cana-574 81 12 ) ) PUNCT cana-574 81 13 tikhonov tikhonov NOUN cana-574 81 14 regularization regularization NOUN cana-574 81 15 , , PUNCT cana-574 81 16 or or CCONJ cana-574 81 17 ridge ridge NOUN cana-574 81 18 regression regression NOUN cana-574 81 19 , , PUNCT cana-574 81 20 is be AUX cana-574 81 21 used use VERB cana-574 81 22 to to PART cana-574 81 23 solve solve VERB cana-574 81 24 regression regression NOUN cana-574 81 25 problems problem NOUN cana-574 81 26 by by ADP cana-574 81 27 imposing impose VERB cana-574 81 28 a a DET cana-574 81 29 penalty penalty NOUN cana-574 81 30 on on ADP cana-574 81 31 the the DET cana-574 81 32 size size NOUN cana-574 81 33 of of ADP cana-574 81 34 coefficients coefficient NOUN cana-574 81 35 . . PUNCT cana-574 82 1 elastic elastic ADJ cana-574 82 2 net net ADJ cana-574 82 3 regularization regularization NOUN cana-574 82 4 : : PUNCT cana-574 82 5 min min NOUN cana-574 82 6 𝑀 𝑀 PART cana-574 82 7 β€Š β€Š SPACE cana-574 82 8 βˆ₯ βˆ₯ NUM cana-574 83 1 𝑋𝑀 𝑋𝑀 ADJ cana-574 83 2 βˆ’ βˆ’ NOUN cana-574 83 3 𝑦 𝑦 NOUN cana-574 83 4 βˆ₯2 βˆ₯2 NOUN cana-574 83 5 + + X cana-574 83 6 πœ†1 πœ†1 NOUN cana-574 83 7 βˆ₯ βˆ₯ X cana-574 83 8 𝑀 𝑀 ADP cana-574 83 9 βˆ₯1 βˆ₯1 NOUN cana-574 83 10 + + NOUN cana-574 83 11 πœ†2 πœ†2 NOUN cana-574 83 12 βˆ₯ βˆ₯ X cana-574 83 13 𝑀 𝑀 PART cana-574 83 14 βˆ₯2 βˆ₯2 ADV cana-574 83 15 ( ( PUNCT cana-574 83 16 31 31 NUM cana-574 83 17 ) ) PUNCT cana-574 83 18 elastic elastic ADJ cana-574 83 19 net net NOUN cana-574 83 20 combines combine VERB cana-574 83 21 l1 l1 PROPN cana-574 83 22 and and CCONJ cana-574 83 23 l2 l2 NOUN cana-574 83 24 regularization regularization NOUN cana-574 83 25 , , PUNCT cana-574 83 26 helping help VERB cana-574 83 27 in in ADP cana-574 83 28 variable variable ADJ cana-574 83 29 selection selection NOUN cana-574 83 30 ( ( PUNCT cana-574 83 31 l1 l1 PROPN cana-574 83 32 ) ) PUNCT cana-574 83 33 and and CCONJ cana-574 83 34 shrinking shrink VERB cana-574 83 35 coefficients coefficient NOUN cana-574 83 36 ( ( PUNCT cana-574 83 37 l2 l2 NOUN cana-574 83 38 ) ) PUNCT cana-574 83 39 , , PUNCT cana-574 83 40 beneficial beneficial ADJ cana-574 83 41 in in ADP cana-574 83 42 scenarios scenario NOUN cana-574 83 43 with with ADP cana-574 83 44 highly highly ADV cana-574 83 45 correlated correlate VERB cana-574 83 46 variables variable NOUN cana-574 83 47 . . PUNCT cana-574 84 1 kullback kullback NOUN cana-574 84 2 - - PUNCT cana-574 84 3 leibler leibler NOUN cana-574 84 4 divergence divergence NOUN cana-574 84 5 for for ADP cana-574 84 6 probability probability NOUN cana-574 84 7 distributions distribution NOUN cana-574 84 8 : : PUNCT cana-574 84 9 𝐷𝐾𝐿(𝑃 𝐷𝐾𝐿(𝑃 PROPN cana-574 84 10 βˆ₯ βˆ₯ PUNCT cana-574 84 11 𝑄 𝑄 PROPN cana-574 84 12 ) ) PUNCT cana-574 84 13 = = PUNCT cana-574 85 1 βˆ‘ βˆ‘ PUNCT cana-574 85 2 β€Š β€Š SPACE cana-574 85 3 𝑖 𝑖 SYM cana-574 85 4 𝑃(𝑖)log⁑ 𝑃(𝑖)log⁑ ADJ cana-574 85 5 𝑃(𝑖 𝑃(𝑖 NUM cana-574 85 6 ) ) PUNCT cana-574 85 7 𝑄(𝑖 𝑄(𝑖 NUM cana-574 85 8 ) ) PUNCT cana-574 85 9 ( ( PUNCT cana-574 85 10 32 32 NUM cana-574 85 11 ) ) PUNCT cana-574 85 12 kullback kullback NOUN cana-574 85 13 - - PUNCT cana-574 85 14 leibler leibler NOUN cana-574 85 15 divergence divergence NOUN cana-574 85 16 measures measure NOUN cana-574 85 17 how how SCONJ cana-574 85 18 one one NUM cana-574 85 19 probability probability NOUN cana-574 85 20 distribution distribution NOUN cana-574 85 21 diverges diverge VERB cana-574 85 22 from from ADP cana-574 85 23 a a DET cana-574 85 24 second second ADJ cana-574 85 25 , , PUNCT cana-574 85 26 expected expect VERB cana-574 85 27 probability probability NOUN cana-574 85 28 distribution distribution NOUN cana-574 85 29 . . PUNCT cana-574 86 1 dynamic dynamic ADJ cana-574 86 2 programming programming NOUN cana-574 86 3 recurrence recurrence NOUN cana-574 86 4 relation relation PROPN cana-574 86 5 : : PUNCT cana-574 86 6 𝑉(𝑖 𝑉(𝑖 X cana-574 86 7 , , PUNCT cana-574 86 8 𝑀 𝑀 ADP cana-574 86 9 ) ) PUNCT cana-574 86 10 = = SYM cana-574 87 1 max(𝑉(𝑖 max(𝑉(𝑖 PROPN cana-574 87 2 βˆ’ βˆ’ NOUN cana-574 87 3 1,𝑀 1,𝑀 NUM cana-574 87 4 ) ) PUNCT cana-574 87 5 , , PUNCT cana-574 87 6 𝑉(𝑖 𝑉(𝑖 PRON cana-574 87 7 βˆ’ βˆ’ PROPN cana-574 87 8 1,𝑀 1,𝑀 NUM cana-574 87 9 βˆ’ βˆ’ NOUN cana-574 87 10 𝑀𝑖 𝑀𝑖 NOUN cana-574 87 11 ) ) PUNCT cana-574 87 12 + + CCONJ cana-574 87 13 𝑣𝑖 𝑣𝑖 NOUN cana-574 87 14 ) ) PUNCT cana-574 87 15 ( ( PUNCT cana-574 87 16 33 33 NUM cana-574 87 17 ) ) PUNCT cana-574 87 18 this this DET cana-574 87 19 equation equation NOUN cana-574 87 20 is be AUX cana-574 87 21 used use VERB cana-574 87 22 in in ADP cana-574 87 23 the the DET cana-574 87 24 knapsack knapsack NOUN cana-574 87 25 problem problem NOUN cana-574 87 26 , , PUNCT cana-574 87 27 a a DET cana-574 87 28 common common ADJ cana-574 87 29 problem problem NOUN cana-574 87 30 in in ADP cana-574 87 31 combinatorial combinatorial ADJ cana-574 87 32 optimization optimization NOUN cana-574 87 33 , , PUNCT cana-574 87 34 where where SCONJ cana-574 87 35 𝑉(𝑖 𝑉(𝑖 X cana-574 87 36 , , PUNCT cana-574 87 37 𝑀 𝑀 X cana-574 87 38 ) ) PUNCT cana-574 87 39 is be AUX cana-574 87 40 the the DET cana-574 87 41 maximum maximum ADJ cana-574 87 42 value value NOUN cana-574 87 43 that that PRON cana-574 87 44 can can AUX cana-574 87 45 be be AUX cana-574 87 46 achieved achieve VERB cana-574 87 47 with with ADP cana-574 87 48 the the DET cana-574 87 49 first first ADJ cana-574 87 50 𝑖 𝑖 ADJ cana-574 87 51 items item NOUN cana-574 87 52 and and CCONJ cana-574 87 53 a a DET cana-574 87 54 weight weight NOUN cana-574 87 55 limit limit NOUN cana-574 87 56 𝑀. 𝑀. NOUN cana-574 87 57 these these DET cana-574 87 58 equations equation NOUN cana-574 87 59 collectively collectively ADV cana-574 87 60 illustrate illustrate VERB cana-574 87 61 the the DET cana-574 87 62 mathematical mathematical ADJ cana-574 87 63 and and CCONJ cana-574 87 64 computational computational ADJ cana-574 87 65 complexity complexity NOUN cana-574 87 66 involved involve VERB cana-574 87 67 in in ADP cana-574 87 68 designing design VERB cana-574 87 69 and and CCONJ cana-574 87 70 optimizing optimize VERB cana-574 87 71 deep deep ADJ cana-574 87 72 learning learning NOUN cana-574 87 73 models model NOUN cana-574 87 74 , , PUNCT cana-574 87 75 particularly particularly ADV cana-574 87 76 when when SCONJ cana-574 87 77 dealing deal VERB cana-574 87 78 with with ADP cana-574 87 79 sophisticated sophisticated ADJ cana-574 87 80 classifiers classifier NOUN cana-574 87 81 and and CCONJ cana-574 87 82 large large ADJ cana-574 87 83 datasets dataset NOUN cana-574 87 84 . . PUNCT cana-574 88 1 the the DET cana-574 88 2 equations equation NOUN cana-574 88 3 and and CCONJ cana-574 88 4 concepts concept NOUN cana-574 88 5 discussed discuss VERB cana-574 88 6 represent represent VERB cana-574 88 7 the the DET cana-574 88 8 backbone backbone NOUN cana-574 88 9 of of ADP cana-574 88 10 deep deep ADJ cana-574 88 11 learning learning NOUN cana-574 88 12 technologies technology NOUN cana-574 88 13 . . PUNCT cana-574 89 1 by by ADP cana-574 89 2 integrating integrate VERB cana-574 89 3 these these DET cana-574 89 4 mathematical mathematical ADJ cana-574 89 5 principles principle NOUN cana-574 89 6 , , PUNCT cana-574 89 7 deep deep ADJ cana-574 89 8 learning learning NOUN cana-574 89 9 models model NOUN cana-574 89 10 can can AUX cana-574 89 11 be be AUX cana-574 89 12 optimized optimize VERB cana-574 89 13 and and CCONJ cana-574 89 14 tailored tailor VERB cana-574 89 15 to to ADP cana-574 89 16 specific specific ADJ cana-574 89 17 problems problem NOUN cana-574 89 18 more more ADV cana-574 89 19 effectively effectively ADV cana-574 89 20 . . PUNCT cana-574 90 1 as as SCONJ cana-574 90 2 we we PRON cana-574 90 3 continue continue VERB cana-574 90 4 to to PART cana-574 90 5 push push VERB cana-574 90 6 the the DET cana-574 90 7 boundaries boundary NOUN cana-574 90 8 of of ADP cana-574 90 9 what what PRON cana-574 90 10 is be AUX cana-574 90 11 possible possible ADJ cana-574 90 12 with with ADP cana-574 90 13 these these DET cana-574 90 14 models model NOUN cana-574 90 15 , , PUNCT cana-574 90 16 understanding understanding NOUN cana-574 90 17 and and CCONJ cana-574 90 18 applying apply VERB cana-574 90 19 these these DET cana-574 90 20 foundational foundational ADJ cana-574 90 21 equations equation NOUN cana-574 90 22 remain remain VERB cana-574 90 23 critical.each critical.each DET cana-574 90 24 component component NOUN cana-574 90 25 , , PUNCT cana-574 90 26 from from ADP cana-574 90 27 linear linear ADJ cana-574 90 28 transformations transformation NOUN cana-574 90 29 and and CCONJ cana-574 90 30 regularization regularization NOUN cana-574 90 31 to to ADP cana-574 90 32 loss loss NOUN cana-574 90 33 functions function NOUN cana-574 90 34 and and CCONJ cana-574 90 35 specialized specialized ADJ cana-574 90 36 neural neural ADJ cana-574 90 37 network network NOUN cana-574 90 38 operations operation NOUN cana-574 90 39 , , PUNCT cana-574 90 40 plays play VERB cana-574 90 41 a a DET cana-574 90 42 crucial crucial ADJ cana-574 90 43 role role NOUN cana-574 90 44 in in ADP cana-574 90 45 building build VERB cana-574 90 46 robust robust ADJ cana-574 90 47 , , PUNCT cana-574 90 48 efficient efficient ADJ cana-574 90 49 , , PUNCT cana-574 90 50 and and CCONJ cana-574 90 51 scalable scalable ADJ cana-574 90 52 models model NOUN cana-574 90 53 . . PUNCT cana-574 91 1 the the DET cana-574 91 2 continuous continuous ADJ cana-574 91 3 evolution evolution NOUN cana-574 91 4 of of ADP cana-574 91 5 these these DET cana-574 91 6 mathematical mathematical ADJ cana-574 91 7 frameworks framework NOUN cana-574 91 8 is be AUX cana-574 91 9 what what PRON cana-574 91 10 will will AUX cana-574 91 11 drive drive VERB cana-574 91 12 future future ADJ cana-574 91 13 innovations innovation NOUN cana-574 91 14 in in ADP cana-574 91 15 deep deep ADJ cana-574 91 16 learning learning NOUN cana-574 91 17 , , PUNCT cana-574 91 18 enabling enable VERB cana-574 91 19 smarter smart ADJ cana-574 91 20 and and CCONJ cana-574 91 21 more more ADV cana-574 91 22 intuitive intuitive ADJ cana-574 91 23 artificial artificial ADJ cana-574 91 24 intelligence intelligence NOUN cana-574 91 25 systems system NOUN cana-574 91 26 . . PUNCT cana-574 92 1 whether whether SCONJ cana-574 92 2 for for ADP cana-574 92 3 simple simple ADJ cana-574 92 4 applications application NOUN cana-574 92 5 or or CCONJ cana-574 92 6 complex complex ADJ cana-574 92 7 , , PUNCT cana-574 92 8 domain domain NOUN cana-574 92 9 - - PUNCT cana-574 92 10 specific specific ADJ cana-574 92 11 tasks task NOUN cana-574 92 12 , , PUNCT cana-574 92 13 the the DET cana-574 92 14 integration integration NOUN cana-574 92 15 of of ADP cana-574 92 16 complex complex ADJ cana-574 92 17 mathematical mathematical ADJ cana-574 92 18 operators operator NOUN cana-574 92 19 and and CCONJ cana-574 92 20 advanced advanced ADJ cana-574 92 21 optimization optimization NOUN cana-574 92 22 techniques technique NOUN cana-574 92 23 will will AUX cana-574 92 24 remain remain VERB cana-574 92 25 at at ADP cana-574 92 26 the the DET cana-574 92 27 forefront forefront NOUN cana-574 92 28 of of ADP cana-574 92 29 technological technological ADJ cana-574 92 30 advancements advancement NOUN cana-574 92 31 in in ADP cana-574 92 32 ai ai PROPN cana-574 92 33 . . PUNCT cana-574 93 1 communications communication NOUN cana-574 93 2 on on ADP cana-574 93 3 applied apply VERB cana-574 93 4 nonlinear nonlinear ADJ cana-574 93 5 analysis analysis NOUN cana-574 93 6 issn issn NOUN cana-574 93 7 : : PUNCT cana-574 93 8 1074 1074 NUM cana-574 93 9 - - PUNCT cana-574 93 10 133x 133x NUM cana-574 93 11 vol vol NOUN cana-574 93 12 31 31 NUM cana-574 93 13 no no NOUN cana-574 93 14 . . NOUN cana-574 93 15 2 2 NUM cana-574 93 16 ( ( PUNCT cana-574 93 17 2024 2024 NUM cana-574 93 18 ) ) PUNCT cana-574 93 19 377 377 NUM cana-574 93 20 https://internationalpubls.com https://internationalpubls.com X cana-574 93 21 figure figure NOUN cana-574 93 22 1 1 NUM cana-574 93 23 . . PUNCT cana-574 94 1 different different ADJ cana-574 94 2 samples sample NOUN cana-574 94 3 of of ADP cana-574 94 4 foot foot NOUN cana-574 94 5 - - PUNCT cana-574 94 6 mouth mouth NOUN cana-574 94 7 disease disease NOUN cana-574 94 8 and and CCONJ cana-574 94 9 skin skin NOUN cana-574 94 10 disease disease NOUN cana-574 94 11 the the DET cana-574 94 12 introduction introduction NOUN cana-574 94 13 of of ADP cana-574 94 14 these these DET cana-574 94 15 technologies technology NOUN cana-574 94 16 underscores underscore VERB cana-574 94 17 a a DET cana-574 94 18 larger large ADJ cana-574 94 19 trend trend NOUN cana-574 94 20 toward toward ADP cana-574 94 21 the the DET cana-574 94 22 automation automation NOUN cana-574 94 23 and and CCONJ cana-574 94 24 enhancement enhancement NOUN cana-574 94 25 of of ADP cana-574 94 26 disease disease NOUN cana-574 94 27 detection detection NOUN cana-574 94 28 methods method NOUN cana-574 94 29 in in ADP cana-574 94 30 agriculture agriculture NOUN cana-574 94 31 . . PUNCT cana-574 95 1 deep deep ADJ cana-574 95 2 learning learning NOUN cana-574 95 3 , , PUNCT cana-574 95 4 through through ADP cana-574 95 5 the the DET cana-574 95 6 use use NOUN cana-574 95 7 of of ADP cana-574 95 8 convolutional convolutional ADJ cana-574 95 9 neural neural ADJ cana-574 95 10 networks network NOUN cana-574 95 11 ( ( PUNCT cana-574 95 12 cnns cnns PROPN cana-574 95 13 ) ) PUNCT cana-574 95 14 , , PUNCT cana-574 95 15 enables enable VERB cana-574 95 16 the the DET cana-574 95 17 analysis analysis NOUN cana-574 95 18 of of ADP cana-574 95 19 cattle cattle NOUN cana-574 95 20 images image NOUN cana-574 95 21 to to PART cana-574 95 22 identify identify VERB cana-574 95 23 and and CCONJ cana-574 95 24 classify classify VERB cana-574 95 25 diseases disease NOUN cana-574 95 26 accurately accurately ADV cana-574 95 27 . . PUNCT cana-574 96 1 this this PRON cana-574 96 2 not not PART cana-574 96 3 only only ADV cana-574 96 4 aids aid NOUN cana-574 96 5 in in ADP cana-574 96 6 early early ADJ cana-574 96 7 detection detection NOUN cana-574 96 8 but but CCONJ cana-574 96 9 also also ADV cana-574 96 10 enhances enhance VERB cana-574 96 11 the the DET cana-574 96 12 overall overall ADJ cana-574 96 13 management management NOUN cana-574 96 14 of of ADP cana-574 96 15 cattle cattle NOUN cana-574 96 16 health health NOUN cana-574 96 17 , , PUNCT cana-574 96 18 contributing contribute VERB cana-574 96 19 to to ADP cana-574 96 20 more more ADV cana-574 96 21 sustainable sustainable ADJ cana-574 96 22 agricultural agricultural ADJ cana-574 96 23 practices practice NOUN cana-574 96 24 . . PUNCT cana-574 97 1 our our PRON cana-574 97 2 research research NOUN cana-574 97 3 aims aim VERB cana-574 97 4 to to PART cana-574 97 5 further further VERB cana-574 97 6 these these DET cana-574 97 7 advancements advancement NOUN cana-574 97 8 by by ADP cana-574 97 9 focusing focus VERB cana-574 97 10 on on ADP cana-574 97 11 the the DET cana-574 97 12 development development NOUN cana-574 97 13 of of ADP cana-574 97 14 an an DET cana-574 97 15 efficient efficient ADJ cana-574 97 16 cattle cattle NOUN cana-574 97 17 disease disease NOUN cana-574 97 18 detection detection NOUN cana-574 97 19 system system NOUN cana-574 97 20 that that PRON cana-574 97 21 is be AUX cana-574 97 22 accessible accessible ADJ cana-574 97 23 and and CCONJ cana-574 97 24 user user NOUN cana-574 97 25 - - PUNCT cana-574 97 26 friendly friendly ADJ cana-574 97 27 . . PUNCT cana-574 98 1 by by ADP cana-574 98 2 leveraging leverage VERB cana-574 98 3 deep deep ADJ cana-574 98 4 learning learning NOUN cana-574 98 5 and and CCONJ cana-574 98 6 smartphone smartphone NOUN cana-574 98 7 technology technology NOUN cana-574 98 8 , , PUNCT cana-574 98 9 our our PRON cana-574 98 10 proposed propose VERB cana-574 98 11 system system NOUN cana-574 98 12 aims aim VERB cana-574 98 13 to to PART cana-574 98 14 empower empower VERB cana-574 98 15 farmers farmer NOUN cana-574 98 16 and and CCONJ cana-574 98 17 veterinarians veterinarian NOUN cana-574 98 18 to to PART cana-574 98 19 diagnose diagnose VERB cana-574 98 20 diseases disease NOUN cana-574 98 21 accurately accurately ADV cana-574 98 22 and and CCONJ cana-574 98 23 efficiently efficiently ADV cana-574 98 24 , , PUNCT cana-574 98 25 even even ADV cana-574 98 26 in in ADP cana-574 98 27 resource resource NOUN cana-574 98 28 - - PUNCT cana-574 98 29 constrained constrain VERB cana-574 98 30 environments environment NOUN cana-574 98 31 . . PUNCT cana-574 99 1 this this DET cana-574 99 2 approach approach NOUN cana-574 99 3 addresses address VERB cana-574 99 4 the the DET cana-574 99 5 critical critical ADJ cana-574 99 6 problem problem NOUN cana-574 99 7 of of ADP cana-574 99 8 delayed delayed ADJ cana-574 99 9 or or CCONJ cana-574 99 10 inaccurate inaccurate ADJ cana-574 99 11 disease disease NOUN cana-574 99 12 diagnosis diagnosis NOUN cana-574 99 13 , , PUNCT cana-574 99 14 which which PRON cana-574 99 15 can can AUX cana-574 99 16 lead lead VERB cana-574 99 17 to to ADP cana-574 99 18 prolonged prolonged ADJ cana-574 99 19 animal animal NOUN cana-574 99 20 suffering suffering NOUN cana-574 99 21 , , PUNCT cana-574 99 22 heightened heighten VERB cana-574 99 23 economic economic ADJ cana-574 99 24 losses loss NOUN cana-574 99 25 , , PUNCT cana-574 99 26 and and CCONJ cana-574 99 27 increased increase VERB cana-574 99 28 risk risk NOUN cana-574 99 29 of of ADP cana-574 99 30 disease disease NOUN cana-574 99 31 spread spread VERB cana-574 99 32 . . PUNCT cana-574 100 1 in in ADP cana-574 100 2 essence essence NOUN cana-574 100 3 , , PUNCT cana-574 100 4 the the DET cana-574 100 5 problem problem NOUN cana-574 100 6 our our PRON cana-574 100 7 research research NOUN cana-574 100 8 seeks seek VERB cana-574 100 9 to to PART cana-574 100 10 solve solve VERB cana-574 100 11 is be AUX cana-574 100 12 the the DET cana-574 100 13 significant significant ADJ cana-574 100 14 gap gap NOUN cana-574 100 15 in in ADP cana-574 100 16 timely timely ADJ cana-574 100 17 and and CCONJ cana-574 100 18 accurate accurate ADJ cana-574 100 19 disease disease NOUN cana-574 100 20 detection detection NOUN cana-574 100 21 in in ADP cana-574 100 22 cattle cattle NOUN cana-574 100 23 , , PUNCT cana-574 100 24 which which PRON cana-574 100 25 can can AUX cana-574 100 26 be be AUX cana-574 100 27 transformative transformative ADJ cana-574 100 28 for for ADP cana-574 100 29 cattle cattle NOUN cana-574 100 30 farming farming NOUN cana-574 100 31 . . PUNCT cana-574 101 1 by by ADP cana-574 101 2 employing employ VERB cana-574 101 3 advanced advanced ADJ cana-574 101 4 ai ai NOUN cana-574 101 5 technologies technology NOUN cana-574 101 6 and and CCONJ cana-574 101 7 making make VERB cana-574 101 8 these these DET cana-574 101 9 tools tool NOUN cana-574 101 10 accessible accessible ADJ cana-574 101 11 through through ADP cana-574 101 12 smartphones smartphone NOUN cana-574 101 13 , , PUNCT cana-574 101 14 we we PRON cana-574 101 15 can can AUX cana-574 101 16 revolutionize revolutionize VERB cana-574 101 17 how how SCONJ cana-574 101 18 diseases disease NOUN cana-574 101 19 are be AUX cana-574 101 20 managed manage VERB cana-574 101 21 in in ADP cana-574 101 22 the the DET cana-574 101 23 livestock livestock NOUN cana-574 101 24 industry industry NOUN cana-574 101 25 . . PUNCT cana-574 102 1 the the DET cana-574 102 2 following follow VERB cana-574 102 3 sections section NOUN cana-574 102 4 of of ADP cana-574 102 5 this this DET cana-574 102 6 paper paper NOUN cana-574 102 7 will will AUX cana-574 102 8 detail detail VERB cana-574 102 9 the the DET cana-574 102 10 methodology methodology NOUN cana-574 102 11 , , PUNCT cana-574 102 12 implementation implementation NOUN cana-574 102 13 , , PUNCT cana-574 102 14 evaluation evaluation NOUN cana-574 102 15 results result NOUN cana-574 102 16 , , PUNCT cana-574 102 17 and and CCONJ cana-574 102 18 potential potential ADJ cana-574 102 19 impact impact NOUN cana-574 102 20 of of ADP cana-574 102 21 our our PRON cana-574 102 22 system system NOUN cana-574 102 23 , , PUNCT cana-574 102 24 aiming aim VERB cana-574 102 25 to to PART cana-574 102 26 provide provide VERB cana-574 102 27 a a DET cana-574 102 28 comprehensive comprehensive ADJ cana-574 102 29 solution solution NOUN cana-574 102 30 to to ADP cana-574 102 31 a a DET cana-574 102 32 pressing press VERB cana-574 102 33 issue issue NOUN cana-574 102 34 in in ADP cana-574 102 35 global global ADJ cana-574 102 36 agriculture agriculture NOUN cana-574 102 37 . . PUNCT cana-574 103 1 2 2 X cana-574 103 2 . . NUM cana-574 103 3 related relate VERB cana-574 103 4 study study NOUN cana-574 103 5 cattle cattle NOUN cana-574 103 6 farming farming NOUN cana-574 103 7 , , PUNCT cana-574 103 8 a a DET cana-574 103 9 cornerstone cornerstone NOUN cana-574 103 10 of of ADP cana-574 103 11 global global ADJ cana-574 103 12 agriculture agriculture NOUN cana-574 103 13 , , PUNCT cana-574 103 14 faces face VERB cana-574 103 15 significant significant ADJ cana-574 103 16 challenges challenge NOUN cana-574 103 17 due due ADP cana-574 103 18 to to ADP cana-574 103 19 diseases disease NOUN cana-574 103 20 that that PRON cana-574 103 21 can can AUX cana-574 103 22 severely severely ADV cana-574 103 23 impact impact VERB cana-574 103 24 livestock livestock NOUN cana-574 103 25 health health NOUN cana-574 103 26 and and CCONJ cana-574 103 27 productivity productivity NOUN cana-574 103 28 . . PUNCT cana-574 104 1 this this DET cana-574 104 2 literature literature NOUN cana-574 104 3 review review NOUN cana-574 104 4 explores explore VERB cana-574 104 5 the the DET cana-574 104 6 recent recent ADJ cana-574 104 7 advancements advancement NOUN cana-574 104 8 in in ADP cana-574 104 9 machine machine NOUN cana-574 104 10 learning learning NOUN cana-574 104 11 and and CCONJ cana-574 104 12 deep deep ADJ cana-574 104 13 learning learning NOUN cana-574 104 14 techniques technique NOUN cana-574 104 15 applied apply VERB cana-574 104 16 to to ADP cana-574 104 17 cattle cattle NOUN cana-574 104 18 disease disease NOUN cana-574 104 19 detection detection NOUN cana-574 104 20 , , PUNCT cana-574 104 21 classification classification NOUN cana-574 104 22 , , PUNCT cana-574 104 23 and and CCONJ cana-574 104 24 management management NOUN cana-574 104 25 . . PUNCT cana-574 105 1 it it PRON cana-574 105 2 draws draw VERB cana-574 105 3 on on ADP cana-574 105 4 various various ADJ cana-574 105 5 studies study NOUN cana-574 105 6 to to PART cana-574 105 7 highlight highlight VERB cana-574 105 8 the the DET cana-574 105 9 current current ADJ cana-574 105 10 technologies technology NOUN cana-574 105 11 , , PUNCT cana-574 105 12 methodologies methodology NOUN cana-574 105 13 , , PUNCT cana-574 105 14 and and CCONJ cana-574 105 15 the the DET cana-574 105 16 effectiveness effectiveness NOUN cana-574 105 17 of of ADP cana-574 105 18 these these DET cana-574 105 19 approaches approach NOUN cana-574 105 20 , , PUNCT cana-574 105 21 as as ADV cana-574 105 22 well well ADV cana-574 105 23 as as ADP cana-574 105 24 the the DET cana-574 105 25 existing exist VERB cana-574 105 26 gaps gap NOUN cana-574 105 27 and and CCONJ cana-574 105 28 future future ADJ cana-574 105 29 directions direction NOUN cana-574 105 30 in in ADP cana-574 105 31 research research NOUN cana-574 105 32 . . PUNCT cana-574 106 1 communications communication NOUN cana-574 106 2 on on ADP cana-574 106 3 applied apply VERB cana-574 106 4 nonlinear nonlinear ADJ cana-574 106 5 analysis analysis NOUN cana-574 106 6 issn issn NOUN cana-574 106 7 : : PUNCT cana-574 106 8 1074 1074 NUM cana-574 106 9 - - PUNCT cana-574 106 10 133x 133x NUM cana-574 106 11 vol vol NOUN cana-574 106 12 31 31 NUM cana-574 106 13 no no NOUN cana-574 106 14 . . NOUN cana-574 106 15 2 2 NUM cana-574 106 16 ( ( PUNCT cana-574 106 17 2024 2024 NUM cana-574 106 18 ) ) PUNCT cana-574 106 19 378 378 NUM cana-574 106 20 https://internationalpubls.com https://internationalpubls.com X cana-574 106 21 table table NOUN cana-574 106 22 1 1 NUM cana-574 106 23 . . PUNCT cana-574 106 24 analysis analysis NOUN cana-574 106 25 of of ADP cana-574 106 26 methodologies methodology NOUN cana-574 106 27 reference reference NOUN cana-574 106 28 research research NOUN cana-574 106 29 purpose purpose NOUN cana-574 106 30 methodologies methodology NOUN cana-574 106 31 used use VERB cana-574 106 32 key key ADJ cana-574 106 33 findings finding NOUN cana-574 106 34 challenges challenge NOUN cana-574 106 35 / / SYM cana-574 106 36 research research NOUN cana-574 106 37 gaps gap NOUN cana-574 106 38 y. y. PROPN cana-574 106 39 li li PROPN cana-574 106 40 et et PROPN cana-574 106 41 al al PROPN cana-574 106 42 . . PUNCT cana-574 107 1 classification classification NOUN cana-574 107 2 of of ADP cana-574 107 3 cattle cattle NOUN cana-574 107 4 behaviors behavior NOUN cana-574 107 5 knn knn PROPN cana-574 107 6 , , PUNCT cana-574 107 7 random random ADJ cana-574 107 8 forest forest NOUN cana-574 107 9 , , PUNCT cana-574 107 10 extreme extreme ADJ cana-574 107 11 boosting boost VERB cana-574 107 12 high high ADJ cana-574 107 13 accuracy accuracy NOUN cana-574 107 14 in in ADP cana-574 107 15 behavior behavior NOUN cana-574 107 16 classification classification NOUN cana-574 107 17 limited limited ADJ cana-574 107 18 temporal temporal ADJ cana-574 107 19 information information NOUN cana-574 107 20 affects affect VERB cana-574 107 21 accuracy accuracy NOUN cana-574 108 1 k. k. PROPN cana-574 108 2 liakos liakos PROPN cana-574 108 3 et et PROPN cana-574 108 4 al al PROPN cana-574 108 5 . . PUNCT cana-574 109 1 real real ADJ cana-574 109 2 - - PUNCT cana-574 109 3 time time NOUN cana-574 109 4 lameness lameness NOUN cana-574 109 5 detection detection PROPN cana-574 109 6 ann ann PROPN cana-574 109 7 , , PUNCT cana-574 109 8 svm svm PROPN cana-574 109 9 , , PUNCT cana-574 109 10 random random ADJ cana-574 109 11 forest forest NOUN cana-574 109 12 effective effective ADJ cana-574 109 13 in in ADP cana-574 109 14 lameness lameness ADJ cana-574 109 15 detection detection NOUN cana-574 109 16 needs need VERB cana-574 109 17 more more ADV cana-574 109 18 reliable reliable ADJ cana-574 109 19 and and CCONJ cana-574 109 20 practical practical ADJ cana-574 109 21 systems system NOUN cana-574 110 1 d. d. PROPN cana-574 110 2 wu wu PROPN cana-574 110 3 et et PROPN cana-574 110 4 al al PROPN cana-574 110 5 . . PUNCT cana-574 110 6 detection detection NOUN cana-574 110 7 of of ADP cana-574 110 8 cattle cattle NOUN cana-574 110 9 lameness lameness PROPN cana-574 110 10 yolov3 yolov3 PROPN cana-574 110 11 , , PUNCT cana-574 110 12 svm svm PROPN cana-574 110 13 , , PUNCT cana-574 110 14 knn knn PROPN cana-574 110 15 , , PUNCT cana-574 110 16 dtc dtc VERB cana-574 110 17 high high ADJ cana-574 110 18 accuracy accuracy NOUN cana-574 110 19 in in ADP cana-574 110 20 lameness lameness NOUN cana-574 110 21 detection detection NOUN cana-574 110 22 limited limit VERB cana-574 110 23 datasets dataset NOUN cana-574 110 24 m. m. NOUN cana-574 110 25 e. e. PROPN cana-574 110 26 pastell pastell PROPN cana-574 110 27 et et PROPN cana-574 110 28 al al PROPN cana-574 110 29 . . PROPN cana-574 110 30 lameness lameness PROPN cana-574 110 31 detection detection NOUN cana-574 110 32 using use VERB cana-574 110 33 pnn pnn PROPN cana-574 110 34 probabilistic probabilistic ADJ cana-574 110 35 neural neural ADJ cana-574 110 36 network network NOUN cana-574 110 37 ( ( PUNCT cana-574 110 38 pnn pnn PROPN cana-574 110 39 ) ) PUNCT cana-574 110 40 , , PUNCT cana-574 110 41 4balance 4balance NUM cana-574 110 42 system system NOUN cana-574 110 43 data data VERB cana-574 110 44 high high ADJ cana-574 110 45 accuracy accuracy NOUN cana-574 110 46 in in ADP cana-574 110 47 identifying identify VERB cana-574 110 48 lameness lameness NOUN cana-574 110 49 system system NOUN cana-574 110 50 may may AUX cana-574 110 51 not not PART cana-574 110 52 be be AUX cana-574 110 53 suitable suitable ADJ cana-574 110 54 for for SCONJ cana-574 110 55 all all DET cana-574 110 56 farms farm NOUN cana-574 110 57 n. n. VERB cana-574 110 58 v. v. ADP cana-574 110 59 kishan kishan PROPN cana-574 111 1 et et PROPN cana-574 111 2 al al PROPN cana-574 111 3 . . PROPN cana-574 111 4 health health NOUN cana-574 111 5 monitoring monitoring NOUN cana-574 111 6 and and CCONJ cana-574 111 7 disease disease NOUN cana-574 111 8 prediction prediction NOUN cana-574 111 9 data datum NOUN cana-574 111 10 mining mining NOUN cana-574 111 11 , , PUNCT cana-574 111 12 k k PROPN cana-574 111 13 - - PUNCT cana-574 111 14 nn nn ADJ cana-574 111 15 , , PUNCT cana-574 111 16 naive naive ADJ cana-574 111 17 bayes bayes NOUN cana-574 111 18 , , PUNCT cana-574 111 19 svm svm PROPN cana-574 111 20 , , PUNCT cana-574 111 21 iot iot ADJ cana-574 111 22 focus focus NOUN cana-574 111 23 on on ADP cana-574 111 24 rare rare ADJ cana-574 111 25 diseases disease NOUN cana-574 111 26 and and CCONJ cana-574 111 27 veterinary veterinary ADJ cana-574 111 28 care care NOUN cana-574 111 29 integration integration NOUN cana-574 111 30 with with ADP cana-574 111 31 existing exist VERB cana-574 111 32 practices practice NOUN cana-574 111 33 g. g. PROPN cana-574 111 34 rai rai PROPN cana-574 111 35 et et PROPN cana-574 111 36 al al PROPN cana-574 111 37 . . PROPN cana-574 111 38 prediction prediction NOUN cana-574 111 39 of of ADP cana-574 111 40 lumpy lumpy ADJ cana-574 111 41 skin skin NOUN cana-574 111 42 disease disease NOUN cana-574 111 43 cnn cnn PROPN cana-574 111 44 , , PUNCT cana-574 111 45 ann ann PROPN cana-574 111 46 , , PUNCT cana-574 111 47 vgg-16 vgg-16 PROPN cana-574 111 48 , , PUNCT cana-574 111 49 vgg-19 vgg-19 NUM cana-574 111 50 , , PUNCT cana-574 111 51 inception inception NOUN cana-574 111 52 - - PUNCT cana-574 111 53 v3 v3 NOUN cana-574 111 54 high high ADJ cana-574 111 55 accuracy accuracy NOUN cana-574 111 56 in in ADP cana-574 111 57 disease disease NOUN cana-574 111 58 prediction prediction NOUN cana-574 111 59 no no DET cana-574 111 60 standard standard ADJ cana-574 111 61 dataset dataset VERB cana-574 111 62 available available PROPN cana-574 111 63 y. y. PROPN cana-574 111 64 qiao qiao PROPN cana-574 111 65 et et PROPN cana-574 111 66 al al PROPN cana-574 111 67 . . PUNCT cana-574 112 1 multi multi ADJ cana-574 112 2 - - ADJ cana-574 112 3 cattle cattle ADJ cana-574 112 4 segmentation segmentation NOUN cana-574 112 5 mask mask NOUN cana-574 112 6 r r PROPN cana-574 112 7 - - PUNCT cana-574 112 8 cnn cnn PROPN cana-574 112 9 high high ADJ cana-574 112 10 accuracy accuracy NOUN cana-574 112 11 in in ADP cana-574 112 12 segmentation segmentation NOUN cana-574 112 13 superior superior ADJ cana-574 112 14 to to ADP cana-574 112 15 traditional traditional ADJ cana-574 112 16 segmentation segmentation NOUN cana-574 112 17 methods method NOUN cana-574 113 1 r. r. PROPN cana-574 113 2 dulal dulal PROPN cana-574 113 3 et et PROPN cana-574 113 4 al al PROPN cana-574 113 5 . . PROPN cana-574 114 1 cattle cattle NOUN cana-574 114 2 identification identification NOUN cana-574 114 3 using use VERB cana-574 114 4 yolov5 yolov5 NOUN cana-574 114 5 yolov5 yolov5 NOUN cana-574 114 6 effective effective ADJ cana-574 114 7 in in ADP cana-574 114 8 overcoming overcome VERB cana-574 114 9 rfid rfid NOUN cana-574 114 10 limitations limitation NOUN cana-574 114 11 cost cost NOUN cana-574 114 12 and and CCONJ cana-574 114 13 scalability scalability NOUN cana-574 114 14 analysis analysis NOUN cana-574 114 15 needed need VERB cana-574 114 16 y. y. PROPN cana-574 114 17 li li PROPN cana-574 114 18 et et PROPN cana-574 114 19 al al PROPN cana-574 114 20 . . PROPN cana-574 114 21 developed develop VERB cana-574 114 22 a a DET cana-574 114 23 method method NOUN cana-574 114 24 for for ADP cana-574 114 25 classifying classify VERB cana-574 114 26 multiple multiple ADJ cana-574 114 27 cattle cattle NOUN cana-574 114 28 behaviors behavior NOUN cana-574 114 29 using use VERB cana-574 114 30 machine machine NOUN cana-574 114 31 learning learn VERB cana-574 114 32 algorithms algorithm NOUN cana-574 114 33 like like ADP cana-574 114 34 k k NOUN cana-574 114 35 - - PUNCT cana-574 114 36 nearest near ADJ cana-574 114 37 neighbors neighbor NOUN cana-574 114 38 and and CCONJ cana-574 114 39 random random ADJ cana-574 114 40 forests forest NOUN cana-574 114 41 , , PUNCT cana-574 114 42 achieving achieve VERB cana-574 114 43 a a DET cana-574 114 44 high high ADJ cana-574 114 45 f1 f1 NOUN cana-574 114 46 score score NOUN cana-574 114 47 accuracy accuracy NOUN cana-574 114 48 . . PUNCT cana-574 115 1 however however ADV cana-574 115 2 , , PUNCT cana-574 115 3 their their PRON cana-574 115 4 study study NOUN cana-574 115 5 noted note VERB cana-574 115 6 limitations limitation NOUN cana-574 115 7 in in ADP cana-574 115 8 capturing capture VERB cana-574 115 9 temporal temporal ADJ cana-574 115 10 information information NOUN cana-574 115 11 which which PRON cana-574 115 12 affected affect VERB cana-574 115 13 the the DET cana-574 115 14 accuracy accuracy NOUN cana-574 115 15 of of ADP cana-574 115 16 movement movement NOUN cana-574 115 17 classifications classification NOUN cana-574 115 18 such such ADJ cana-574 115 19 as as ADP cana-574 115 20 feed feed NOUN cana-574 115 21 tossing toss VERB cana-574 115 22 and and CCONJ cana-574 115 23 rolling roll VERB cana-574 115 24 biting bite VERB cana-574 115 25 . . PUNCT cana-574 116 1 several several ADJ cana-574 116 2 researchers researcher NOUN cana-574 116 3 have have AUX cana-574 116 4 focused focus VERB cana-574 116 5 on on ADP cana-574 116 6 detecting detect VERB cana-574 116 7 lameness lameness NOUN cana-574 116 8 , , PUNCT cana-574 116 9 a a DET cana-574 116 10 prevalent prevalent ADJ cana-574 116 11 issue issue NOUN cana-574 116 12 in in ADP cana-574 116 13 cattle cattle NOUN cana-574 116 14 which which PRON cana-574 116 15 can can AUX cana-574 116 16 significantly significantly ADV cana-574 116 17 affect affect VERB cana-574 116 18 their their PRON cana-574 116 19 welfare welfare NOUN cana-574 116 20 and and CCONJ cana-574 116 21 farm farm NOUN cana-574 116 22 productivity productivity NOUN cana-574 116 23 . . PUNCT cana-574 117 1 k. k. PROPN cana-574 118 1 liakos liakos PROPN cana-574 118 2 et et PROPN cana-574 118 3 al al PROPN cana-574 118 4 . . PROPN cana-574 119 1 trained train VERB cana-574 119 2 artificial artificial ADJ cana-574 119 3 neural neural ADJ cana-574 119 4 networks network NOUN cana-574 119 5 ( ( PUNCT cana-574 119 6 ann ann PROPN cana-574 119 7 ) ) PUNCT cana-574 119 8 and and CCONJ cana-574 119 9 used use VERB cana-574 119 10 support support NOUN cana-574 119 11 vector vector NOUN cana-574 119 12 machines machine NOUN cana-574 119 13 ( ( PUNCT cana-574 119 14 svm svm PROPN cana-574 119 15 ) ) PUNCT cana-574 119 16 for for ADP cana-574 119 17 real real ADJ cana-574 119 18 - - PUNCT cana-574 119 19 time time NOUN cana-574 119 20 lameness lameness NOUN cana-574 119 21 detection detection NOUN cana-574 119 22 . . PUNCT cana-574 120 1 this this DET cana-574 120 2 approach approach NOUN cana-574 120 3 highlighted highlight VERB cana-574 120 4 the the DET cana-574 120 5 potential potential NOUN cana-574 120 6 of of ADP cana-574 120 7 machine machine NOUN cana-574 120 8 learning learn VERB cana-574 120 9 to to PART cana-574 120 10 improve improve VERB cana-574 120 11 the the DET cana-574 120 12 accuracy accuracy NOUN cana-574 120 13 and and CCONJ cana-574 120 14 reliability reliability NOUN cana-574 120 15 of of ADP cana-574 120 16 automated automate VERB cana-574 120 17 lameness lameness NOUN cana-574 120 18 detection detection NOUN cana-574 120 19 systems system NOUN cana-574 120 20 . . PUNCT cana-574 121 1 d. d. PROPN cana-574 121 2 wu wu PROPN cana-574 121 3 et et PROPN cana-574 121 4 al al PROPN cana-574 121 5 . . PROPN cana-574 121 6 also also ADV cana-574 121 7 targeted target VERB cana-574 121 8 lameness lameness NOUN cana-574 121 9 detection detection NOUN cana-574 121 10 using use VERB cana-574 121 11 a a DET cana-574 121 12 combination combination NOUN cana-574 121 13 of of ADP cana-574 121 14 yolov3 yolov3 PROPN cana-574 121 15 , , PUNCT cana-574 121 16 svm svm PROPN cana-574 121 17 , , PUNCT cana-574 121 18 k k ADJ cana-574 121 19 - - PUNCT cana-574 121 20 nearest near ADJ cana-574 121 21 neighbor neighbor NOUN cana-574 121 22 , , PUNCT cana-574 121 23 and and CCONJ cana-574 121 24 decision decision NOUN cana-574 121 25 tree tree NOUN cana-574 121 26 classifiers classifier NOUN cana-574 121 27 . . PUNCT cana-574 122 1 their their PRON cana-574 122 2 approach approach NOUN cana-574 122 3 achieved achieve VERB cana-574 122 4 a a DET cana-574 122 5 notable notable ADJ cana-574 122 6 accuracy accuracy NOUN cana-574 122 7 of of ADP cana-574 122 8 98.57 98.57 NUM cana-574 122 9 % % NOUN cana-574 122 10 , , PUNCT cana-574 122 11 demonstrating demonstrate VERB cana-574 122 12 the the DET cana-574 122 13 efficacy efficacy NOUN cana-574 122 14 of of ADP cana-574 122 15 deep deep ADJ cana-574 122 16 learning learning NOUN cana-574 122 17 algorithms algorithm NOUN cana-574 122 18 in in ADP cana-574 122 19 diagnosing diagnose VERB cana-574 122 20 complex complex ADJ cana-574 122 21 conditions condition NOUN cana-574 122 22 like like ADP cana-574 122 23 lameness lameness NOUN cana-574 122 24 .m .m PROPN cana-574 122 25 . . PUNCT cana-574 123 1 e. e. PROPN cana-574 123 2 pastell pastell PROPN cana-574 123 3 et et PROPN cana-574 123 4 al al PROPN cana-574 123 5 . . PROPN cana-574 123 6 utilized utilize VERB cana-574 123 7 a a DET cana-574 123 8 probabilistic probabilistic ADJ cana-574 123 9 neural neural ADJ cana-574 123 10 network network NOUN cana-574 123 11 ( ( PUNCT cana-574 123 12 pnn pnn PROPN cana-574 123 13 ) ) PUNCT cana-574 123 14 model model NOUN cana-574 123 15 using use VERB cana-574 123 16 data datum NOUN cana-574 123 17 from from ADP cana-574 123 18 a a DET cana-574 123 19 4 4 NUM cana-574 123 20 - - PUNCT cana-574 123 21 balance balance NOUN cana-574 123 22 system system NOUN cana-574 123 23 to to PART cana-574 123 24 detect detect VERB cana-574 123 25 lameness lameness NOUN cana-574 123 26 with with ADP cana-574 123 27 high high ADJ cana-574 123 28 accuracy accuracy NOUN cana-574 123 29 . . PUNCT cana-574 124 1 this this DET cana-574 124 2 system system NOUN cana-574 124 3 , , PUNCT cana-574 124 4 while while SCONJ cana-574 124 5 effective effective ADJ cana-574 124 6 , , PUNCT cana-574 124 7 was be AUX cana-574 124 8 noted note VERB cana-574 124 9 to to PART cana-574 124 10 be be AUX cana-574 124 11 potentially potentially ADV cana-574 124 12 unsuitable unsuitable ADJ cana-574 124 13 for for ADP cana-574 124 14 all all DET cana-574 124 15 farm farm NOUN cana-574 124 16 types type NOUN cana-574 124 17 due due ADP cana-574 124 18 to to ADP cana-574 124 19 its its PRON cana-574 124 20 design design NOUN cana-574 124 21 and and CCONJ cana-574 124 22 implementation implementation NOUN cana-574 124 23 challenges challenge NOUN cana-574 124 24 .n .n ADV cana-574 124 25 . . PUNCT cana-574 125 1 v. v. ADP cana-574 125 2 kishan kishan PROPN cana-574 125 3 et et PROPN cana-574 125 4 al al PROPN cana-574 125 5 . . PROPN cana-574 125 6 explored explore VERB cana-574 125 7 the the DET cana-574 125 8 use use NOUN cana-574 125 9 of of ADP cana-574 125 10 data datum NOUN cana-574 125 11 mining mining NOUN cana-574 125 12 techniques technique NOUN cana-574 125 13 alongside alongside ADP cana-574 125 14 iot iot NOUN cana-574 125 15 - - PUNCT cana-574 125 16 based base VERB cana-574 125 17 systems system NOUN cana-574 125 18 for for ADP cana-574 125 19 cattle cattle NOUN cana-574 125 20 health health NOUN cana-574 125 21 monitoring monitoring NOUN cana-574 125 22 and and CCONJ cana-574 125 23 disease disease NOUN cana-574 125 24 prediction prediction NOUN cana-574 125 25 . . PUNCT cana-574 126 1 their their PRON cana-574 126 2 approach approach NOUN cana-574 126 3 used use VERB cana-574 126 4 classification classification NOUN cana-574 126 5 methods method NOUN cana-574 126 6 such such ADJ cana-574 126 7 as as ADP cana-574 126 8 knearest knearest NOUN cana-574 126 9 neighbors neighbor NOUN cana-574 126 10 , , PUNCT cana-574 126 11 naive naive ADJ cana-574 126 12 bayes bayes NOUN cana-574 126 13 , , PUNCT cana-574 126 14 and and CCONJ cana-574 126 15 svm svm VERB cana-574 126 16 to to PART cana-574 126 17 focus focus VERB cana-574 126 18 on on ADP cana-574 126 19 rare rare ADJ cana-574 126 20 diseases disease NOUN cana-574 126 21 and and CCONJ cana-574 126 22 improve improve VERB cana-574 126 23 veterinary veterinary ADJ cana-574 126 24 practices practice NOUN cana-574 126 25 . . PUNCT cana-574 127 1 g. g. PROPN cana-574 127 2 rai rai PROPN cana-574 127 3 et et PROPN cana-574 127 4 al al PROPN cana-574 127 5 . . PROPN cana-574 127 6 applied apply VERB cana-574 127 7 convolutional convolutional ADJ cana-574 127 8 neural neural ADJ cana-574 127 9 networks network NOUN cana-574 127 10 ( ( PUNCT cana-574 127 11 cnn cnn PROPN cana-574 127 12 ) ) PUNCT cana-574 127 13 like like ADP cana-574 127 14 vgg-16 vgg-16 NOUN cana-574 127 15 and and CCONJ cana-574 127 16 communications communication NOUN cana-574 127 17 on on ADP cana-574 127 18 applied apply VERB cana-574 127 19 nonlinear nonlinear ADJ cana-574 127 20 analysis analysis NOUN cana-574 127 21 issn issn NOUN cana-574 127 22 : : PUNCT cana-574 127 23 1074 1074 NUM cana-574 127 24 - - PUNCT cana-574 127 25 133x 133x NUM cana-574 127 26 vol vol NOUN cana-574 127 27 31 31 NUM cana-574 127 28 no no NOUN cana-574 127 29 . . NOUN cana-574 127 30 2 2 NUM cana-574 127 31 ( ( PUNCT cana-574 127 32 2024 2024 NUM cana-574 127 33 ) ) PUNCT cana-574 127 34 379 379 NUM cana-574 127 35 https://internationalpubls.com https://internationalpubls.com X cana-574 128 1 inception inception PROPN cana-574 128 2 - - PUNCT cana-574 128 3 v3 v3 PROPN cana-574 128 4 for for ADP cana-574 128 5 predicting predict VERB cana-574 128 6 lumpy lumpy ADJ cana-574 128 7 skin skin NOUN cana-574 128 8 disease disease NOUN cana-574 128 9 , , PUNCT cana-574 128 10 achieving achieve VERB cana-574 128 11 a a DET cana-574 128 12 high high ADJ cana-574 128 13 accuracy accuracy NOUN cana-574 128 14 of of ADP cana-574 128 15 92.5 92.5 NUM cana-574 128 16 % % NOUN cana-574 128 17 . . PUNCT cana-574 129 1 however however ADV cana-574 129 2 , , PUNCT cana-574 129 3 the the DET cana-574 129 4 absence absence NOUN cana-574 129 5 of of ADP cana-574 129 6 a a DET cana-574 129 7 standard standard ADJ cana-574 129 8 dataset dataset NOUN cana-574 129 9 posed pose VERB cana-574 129 10 a a DET cana-574 129 11 significant significant ADJ cana-574 129 12 challenge challenge NOUN cana-574 129 13 . . PUNCT cana-574 130 1 y. y. PROPN cana-574 130 2 qiao qiao PROPN cana-574 130 3 et et PROPN cana-574 130 4 al al PROPN cana-574 130 5 . . PROPN cana-574 130 6 introduced introduce VERB cana-574 130 7 a a DET cana-574 130 8 method method NOUN cana-574 130 9 for for ADP cana-574 130 10 multi multi ADJ cana-574 130 11 - - ADJ cana-574 130 12 cattle cattle NOUN cana-574 130 13 segmentation segmentation NOUN cana-574 130 14 using use VERB cana-574 130 15 mask mask NOUN cana-574 130 16 r r PROPN cana-574 130 17 - - PUNCT cana-574 130 18 cnn cnn PROPN cana-574 130 19 , , PUNCT cana-574 130 20 achieving achieve VERB cana-574 130 21 a a DET cana-574 130 22 mean mean ADJ cana-574 130 23 pixel pixel NOUN cana-574 130 24 accuracy accuracy NOUN cana-574 130 25 of of ADP cana-574 130 26 92 92 NUM cana-574 130 27 % % NOUN cana-574 130 28 . . PUNCT cana-574 131 1 this this DET cana-574 131 2 method method NOUN cana-574 131 3 surpassed surpass VERB cana-574 131 4 traditional traditional ADJ cana-574 131 5 instance instance NOUN cana-574 131 6 segmentation segmentation NOUN cana-574 131 7 methods method NOUN cana-574 131 8 like like ADP cana-574 131 9 sharpmask sharpmask VERB cana-574 131 10 and and CCONJ cana-574 131 11 deepmask deepmask PROPN cana-574 131 12 , , PUNCT cana-574 131 13 highlighting highlight VERB cana-574 131 14 the the DET cana-574 131 15 potential potential NOUN cana-574 131 16 of of ADP cana-574 131 17 deep deep ADJ cana-574 131 18 learning learning NOUN cana-574 131 19 in in ADP cana-574 131 20 precise precise ADJ cana-574 131 21 livestock livestock NOUN cana-574 131 22 management management NOUN cana-574 131 23 . . PUNCT cana-574 132 1 r. r. PROPN cana-574 132 2 dulal dulal PROPN cana-574 132 3 et et PROPN cana-574 132 4 al al PROPN cana-574 132 5 . . PROPN cana-574 132 6 used use VERB cana-574 132 7 the the DET cana-574 132 8 yolov5 yolov5 NOUN cana-574 132 9 model model NOUN cana-574 132 10 for for ADP cana-574 132 11 cattle cattle NOUN cana-574 132 12 identification identification NOUN cana-574 132 13 , , PUNCT cana-574 132 14 focusing focus VERB cana-574 132 15 on on ADP cana-574 132 16 overcoming overcome VERB cana-574 132 17 the the DET cana-574 132 18 limitations limitation NOUN cana-574 132 19 of of ADP cana-574 132 20 rfid rfid NOUN cana-574 132 21 systems system NOUN cana-574 132 22 through through ADP cana-574 132 23 deep deep ADJ cana-574 132 24 learning learning NOUN cana-574 132 25 techniques technique NOUN cana-574 132 26 . . PUNCT cana-574 133 1 this this DET cana-574 133 2 study study NOUN cana-574 133 3 provided provide VERB cana-574 133 4 insights insight NOUN cana-574 133 5 into into ADP cana-574 133 6 cost cost NOUN cana-574 133 7 - - PUNCT cana-574 133 8 effective effective ADJ cana-574 133 9 and and CCONJ cana-574 133 10 scalable scalable ADJ cana-574 133 11 solutions solution NOUN cana-574 133 12 for for ADP cana-574 133 13 cattle cattle NOUN cana-574 133 14 management management NOUN cana-574 133 15 . . PUNCT cana-574 134 1 the the DET cana-574 134 2 integration integration NOUN cana-574 134 3 of of ADP cana-574 134 4 advanced advanced ADJ cana-574 134 5 technologies technology NOUN cana-574 134 6 in in ADP cana-574 134 7 real real ADJ cana-574 134 8 - - PUNCT cana-574 134 9 world world NOUN cana-574 134 10 farming farming NOUN cana-574 134 11 operations operation NOUN cana-574 134 12 remains remain VERB cana-574 134 13 a a DET cana-574 134 14 significant significant ADJ cana-574 134 15 challenge challenge NOUN cana-574 134 16 . . PUNCT cana-574 135 1 the the DET cana-574 135 2 need need NOUN cana-574 135 3 for for ADP cana-574 135 4 systems system NOUN cana-574 135 5 that that PRON cana-574 135 6 are be AUX cana-574 135 7 not not PART cana-574 135 8 only only ADV cana-574 135 9 accurate accurate ADJ cana-574 135 10 but but CCONJ cana-574 135 11 also also ADV cana-574 135 12 user user NOUN cana-574 135 13 - - PUNCT cana-574 135 14 friendly friendly ADJ cana-574 135 15 and and CCONJ cana-574 135 16 adaptable adaptable ADJ cana-574 135 17 to to ADP cana-574 135 18 different different ADJ cana-574 135 19 farming farming NOUN cana-574 135 20 conditions condition NOUN cana-574 135 21 is be AUX cana-574 135 22 critical critical ADJ cana-574 135 23 for for ADP cana-574 135 24 widespread widespread ADJ cana-574 135 25 adoption adoption NOUN cana-574 135 26 [ [ X cana-574 135 27 10 10 NUM cana-574 135 28 - - SYM cana-574 135 29 19 19 NUM cana-574 135 30 ] ] PUNCT cana-574 135 31 . . PUNCT cana-574 136 1 3 3 X cana-574 136 2 . . X cana-574 136 3 materials material NOUN cana-574 136 4 and and CCONJ cana-574 136 5 methods method NOUN cana-574 136 6 this this DET cana-574 136 7 research research NOUN cana-574 136 8 focuses focus VERB cana-574 136 9 on on ADP cana-574 136 10 creating create VERB cana-574 136 11 an an DET cana-574 136 12 advanced advanced ADJ cana-574 136 13 system system NOUN cana-574 136 14 for for ADP cana-574 136 15 detecting detect VERB cana-574 136 16 cattle cattle NOUN cana-574 136 17 diseases disease NOUN cana-574 136 18 using use VERB cana-574 136 19 deep deep ADJ cana-574 136 20 learning learning NOUN cana-574 136 21 techniques technique NOUN cana-574 136 22 , , PUNCT cana-574 136 23 particularly particularly ADV cana-574 136 24 convolutional convolutional ADJ cana-574 136 25 neural neural ADJ cana-574 136 26 networks network NOUN cana-574 136 27 ( ( PUNCT cana-574 136 28 cnns cnns PROPN cana-574 136 29 ) ) PUNCT cana-574 136 30 and and CCONJ cana-574 136 31 transfer transfer NOUN cana-574 136 32 learning learning NOUN cana-574 136 33 . . PUNCT cana-574 137 1 the the DET cana-574 137 2 primary primary ADJ cana-574 137 3 goal goal NOUN cana-574 137 4 is be AUX cana-574 137 5 to to PART cana-574 137 6 assist assist VERB cana-574 137 7 farmers farmer NOUN cana-574 137 8 and and CCONJ cana-574 137 9 veterinarians veterinarian NOUN cana-574 137 10 in in ADP cana-574 137 11 the the DET cana-574 137 12 early early ADJ cana-574 137 13 detection detection NOUN cana-574 137 14 and and CCONJ cana-574 137 15 diagnosis diagnosis NOUN cana-574 137 16 of of ADP cana-574 137 17 cattle cattle NOUN cana-574 137 18 diseases disease NOUN cana-574 137 19 , , PUNCT cana-574 137 20 thereby thereby ADV cana-574 137 21 enhancing enhance VERB cana-574 137 22 animal animal NOUN cana-574 137 23 health health NOUN cana-574 137 24 and and CCONJ cana-574 137 25 productivity productivity NOUN cana-574 137 26 . . PUNCT cana-574 138 1 the the DET cana-574 138 2 methodology methodology NOUN cana-574 138 3 utilizes utilize VERB cana-574 138 4 state state NOUN cana-574 138 5 - - PUNCT cana-574 138 6 of of ADP cana-574 138 7 - - PUNCT cana-574 138 8 the the DET cana-574 138 9 - - PUNCT cana-574 138 10 art art NOUN cana-574 138 11 deep deep ADJ cana-574 138 12 learning learning NOUN cana-574 138 13 models model NOUN cana-574 138 14 , , PUNCT cana-574 138 15 known know VERB cana-574 138 16 for for ADP cana-574 138 17 their their PRON cana-574 138 18 effectiveness effectiveness NOUN cana-574 138 19 in in ADP cana-574 138 20 image image NOUN cana-574 138 21 classification classification NOUN cana-574 138 22 , , PUNCT cana-574 138 23 tailored tailor VERB cana-574 138 24 specifically specifically ADV cana-574 138 25 for for ADP cana-574 138 26 identifying identify VERB cana-574 138 27 various various ADJ cana-574 138 28 cattle cattle NOUN cana-574 138 29 diseases disease NOUN cana-574 138 30 from from ADP cana-574 138 31 visual visual ADJ cana-574 138 32 cues[20 cues[20 PROPN cana-574 138 33 ] ] PUNCT cana-574 138 34 . . PUNCT cana-574 139 1 figure figure NOUN cana-574 139 2 2 2 NUM cana-574 139 3 . . PUNCT cana-574 139 4 process process NOUN cana-574 139 5 flow flow NOUN cana-574 139 6 diagram diagram NOUN cana-574 139 7 figure figure NOUN cana-574 139 8 2 2 NUM cana-574 139 9 displays display VERB cana-574 139 10 the the DET cana-574 139 11 workflow workflow NOUN cana-574 139 12 from from ADP cana-574 139 13 data datum NOUN cana-574 139 14 acquisition acquisition NOUN cana-574 139 15 to to ADP cana-574 139 16 the the DET cana-574 139 17 output output NOUN cana-574 139 18 of of ADP cana-574 139 19 results result NOUN cana-574 139 20 , , PUNCT cana-574 139 21 outlining outline VERB cana-574 139 22 the the DET cana-574 139 23 steps step NOUN cana-574 139 24 involved involve VERB cana-574 139 25 in in ADP cana-574 139 26 processing processing NOUN cana-574 139 27 and and CCONJ cana-574 139 28 analyzing analyze VERB cana-574 139 29 the the DET cana-574 139 30 images image NOUN cana-574 139 31 . . PUNCT cana-574 140 1 figure figure NOUN cana-574 140 2 3 3 NUM cana-574 140 3 provide provide VERB cana-574 140 4 insights insight NOUN cana-574 140 5 into into ADP cana-574 140 6 the the DET cana-574 140 7 distribution distribution NOUN cana-574 140 8 of of ADP cana-574 140 9 images image NOUN cana-574 140 10 across across ADP cana-574 140 11 different different ADJ cana-574 140 12 classes class NOUN cana-574 140 13 , , PUNCT cana-574 140 14 highlighting highlight VERB cana-574 140 15 the the DET cana-574 140 16 diversity diversity NOUN cana-574 140 17 and and CCONJ cana-574 140 18 balance balance NOUN cana-574 140 19 of of ADP cana-574 140 20 the the DET cana-574 140 21 dataset dataset NOUN cana-574 140 22 which which PRON cana-574 140 23 is be AUX cana-574 140 24 crucial crucial ADJ cana-574 140 25 for for ADP cana-574 140 26 training train VERB cana-574 140 27 unbiased unbiased ADJ cana-574 140 28 models.figure models.figure NOUN cana-574 140 29 4 4 NUM cana-574 140 30 shows show VERB cana-574 140 31 a a DET cana-574 140 32 pie pie NOUN cana-574 140 33 chart chart NOUN cana-574 140 34 that that PRON cana-574 140 35 represents represent VERB cana-574 140 36 the the DET cana-574 140 37 proportion proportion NOUN cana-574 140 38 of of ADP cana-574 140 39 images image NOUN cana-574 140 40 per per ADP cana-574 140 41 disease disease NOUN cana-574 140 42 category category NOUN cana-574 140 43 , , PUNCT cana-574 140 44 emphasizing emphasize VERB cana-574 140 45 the the DET cana-574 140 46 dataset dataset NOUN cana-574 140 47 's 's PART cana-574 140 48 variety variety NOUN cana-574 140 49 and and CCONJ cana-574 140 50 the the DET cana-574 140 51 need need NOUN cana-574 140 52 for for ADP cana-574 140 53 a a DET cana-574 140 54 model model NOUN cana-574 140 55 capable capable ADJ cana-574 140 56 of of ADP cana-574 140 57 recognizing recognize VERB cana-574 140 58 a a DET cana-574 140 59 broad broad ADJ cana-574 140 60 spectrum spectrum NOUN cana-574 140 61 of of ADP cana-574 140 62 disease disease NOUN cana-574 140 63 indicators indicator NOUN cana-574 140 64 . . PUNCT cana-574 141 1 dataset dataset ADJ cana-574 141 2 visualization visualization NOUN cana-574 141 3 and and CCONJ cana-574 141 4 acquisition acquisition NOUN cana-574 141 5 the the DET cana-574 141 6 foundation foundation NOUN cana-574 141 7 of of ADP cana-574 141 8 any any DET cana-574 141 9 deep deep ADJ cana-574 141 10 learning learning NOUN cana-574 141 11 application application NOUN cana-574 141 12 lies lie VERB cana-574 141 13 in in ADP cana-574 141 14 the the DET cana-574 141 15 quality quality NOUN cana-574 141 16 and and CCONJ cana-574 141 17 structure structure NOUN cana-574 141 18 of of ADP cana-574 141 19 the the DET cana-574 141 20 dataset dataset NOUN cana-574 141 21 used use VERB cana-574 141 22 . . PUNCT cana-574 142 1 for for ADP cana-574 142 2 this this DET cana-574 142 3 study study NOUN cana-574 142 4 , , PUNCT cana-574 142 5 a a DET cana-574 142 6 comprehensive comprehensive ADJ cana-574 142 7 dataset dataset NOUN cana-574 142 8 consisting consist VERB cana-574 142 9 of of ADP cana-574 142 10 455 455 NUM cana-574 142 11 images image NOUN cana-574 142 12 was be AUX cana-574 142 13 compiled compile VERB cana-574 142 14 . . PUNCT cana-574 143 1 these these DET cana-574 143 2 images image NOUN cana-574 143 3 include include VERB cana-574 143 4 various various ADJ cana-574 143 5 cattle cattle NOUN cana-574 143 6 diseases disease NOUN cana-574 143 7 and and CCONJ cana-574 143 8 healthy healthy ADJ cana-574 143 9 specimens specimen NOUN cana-574 143 10 , , PUNCT cana-574 143 11 categorized categorize VERB cana-574 143 12 into into ADP cana-574 143 13 different different ADJ cana-574 143 14 classes class NOUN cana-574 143 15 based base VERB cana-574 143 16 on on ADP cana-574 143 17 the the DET cana-574 143 18 disease disease NOUN cana-574 143 19 type type NOUN cana-574 143 20 . . PUNCT cana-574 144 1 each each DET cana-574 144 2 class class NOUN cana-574 144 3 is be AUX cana-574 144 4 distinctly distinctly ADV cana-574 144 5 labeled label VERB cana-574 144 6 to to PART cana-574 144 7 facilitate facilitate VERB cana-574 144 8 supervised supervised ADJ cana-574 144 9 learning learning NOUN cana-574 144 10 , , PUNCT cana-574 144 11 where where SCONJ cana-574 144 12 the the DET cana-574 144 13 model model NOUN cana-574 144 14 learns learn VERB cana-574 144 15 to to PART cana-574 144 16 associate associate VERB cana-574 144 17 specific specific ADJ cana-574 144 18 images image NOUN cana-574 144 19 with with ADP cana-574 144 20 their their PRON cana-574 144 21 corresponding correspond VERB cana-574 144 22 disease disease NOUN cana-574 144 23 labels label NOUN cana-574 144 24 . . PUNCT cana-574 145 1 communications communication NOUN cana-574 145 2 on on ADP cana-574 145 3 applied apply VERB cana-574 145 4 nonlinear nonlinear ADJ cana-574 145 5 analysis analysis NOUN cana-574 145 6 issn issn NOUN cana-574 145 7 : : PUNCT cana-574 145 8 1074 1074 NUM cana-574 145 9 - - PUNCT cana-574 145 10 133x 133x NUM cana-574 145 11 vol vol NOUN cana-574 145 12 31 31 NUM cana-574 145 13 no no NOUN cana-574 145 14 . . NOUN cana-574 145 15 2 2 NUM cana-574 145 16 ( ( PUNCT cana-574 145 17 2024 2024 NUM cana-574 145 18 ) ) PUNCT cana-574 145 19 380 380 NUM cana-574 145 20 https://internationalpubls.com https://internationalpubls.com X cana-574 145 21 data datum NOUN cana-574 145 22 pre pre ADJ cana-574 145 23 - - ADJ cana-574 145 24 processing processing ADJ cana-574 145 25 data datum NOUN cana-574 145 26 pre pre ADJ cana-574 145 27 - - ADJ cana-574 145 28 processing processing NOUN cana-574 145 29 is be AUX cana-574 145 30 critical critical ADJ cana-574 145 31 to to PART cana-574 145 32 enhance enhance VERB cana-574 145 33 the the DET cana-574 145 34 data datum NOUN cana-574 145 35 quality quality NOUN cana-574 145 36 , , PUNCT cana-574 145 37 which which PRON cana-574 145 38 in in ADP cana-574 145 39 turn turn NOUN cana-574 145 40 , , PUNCT cana-574 145 41 improves improve VERB cana-574 145 42 the the DET cana-574 145 43 learning learning NOUN cana-574 145 44 efficiency efficiency NOUN cana-574 145 45 and and CCONJ cana-574 145 46 performance performance NOUN cana-574 145 47 of of ADP cana-574 145 48 the the DET cana-574 145 49 model model NOUN cana-574 145 50 . . PUNCT cana-574 146 1 data datum NOUN cana-574 146 2 noise noise NOUN cana-574 146 3 removal removal NOUN cana-574 146 4 : : PUNCT cana-574 146 5 noise noise NOUN cana-574 146 6 reduction reduction NOUN cana-574 146 7 is be AUX cana-574 146 8 pivotal pivotal ADJ cana-574 146 9 in in ADP cana-574 146 10 image image NOUN cana-574 146 11 processing processing NOUN cana-574 146 12 , , PUNCT cana-574 146 13 especially especially ADV cana-574 146 14 for for ADP cana-574 146 15 medical medical ADJ cana-574 146 16 and and CCONJ cana-574 146 17 biological biological ADJ cana-574 146 18 imaging imaging NOUN cana-574 146 19 where where SCONJ cana-574 146 20 clarity clarity NOUN cana-574 146 21 and and CCONJ cana-574 146 22 detail detail NOUN cana-574 146 23 are be AUX cana-574 146 24 essential essential ADJ cana-574 146 25 for for ADP cana-574 146 26 accurate accurate ADJ cana-574 146 27 diagnosis diagnosis NOUN cana-574 146 28 . . PUNCT cana-574 147 1 techniques technique NOUN cana-574 147 2 were be AUX cana-574 147 3 implemented implement VERB cana-574 147 4 to to PART cana-574 147 5 reduce reduce VERB cana-574 147 6 noise noise NOUN cana-574 147 7 and and CCONJ cana-574 147 8 remove remove VERB cana-574 147 9 irrelevant irrelevant ADJ cana-574 147 10 background background NOUN cana-574 147 11 elements element NOUN cana-574 147 12 , , PUNCT cana-574 147 13 enhancing enhance VERB cana-574 147 14 the the DET cana-574 147 15 contrast contrast NOUN cana-574 147 16 and and CCONJ cana-574 147 17 focus focus VERB cana-574 147 18 on on ADP cana-574 147 19 key key ADJ cana-574 147 20 features feature NOUN cana-574 147 21 essential essential ADJ cana-574 147 22 for for ADP cana-574 147 23 disease disease NOUN cana-574 147 24 classification[22 classification[22 NOUN cana-574 147 25 ] ] PUNCT cana-574 147 26 . . PUNCT cana-574 148 1 figure figure NOUN cana-574 148 2 3 3 NUM cana-574 148 3 . . PUNCT cana-574 148 4 sample sample NOUN cana-574 148 5 distribution distribution NOUN cana-574 148 6 of of ADP cana-574 148 7 database database NOUN cana-574 148 8 data datum NOUN cana-574 148 9 augmentation augmentation NOUN cana-574 148 10 : : PUNCT cana-574 148 11 to to PART cana-574 148 12 bolster bolster VERB cana-574 148 13 the the DET cana-574 148 14 model model NOUN cana-574 148 15 's 's PART cana-574 148 16 robustness robustness NOUN cana-574 148 17 and and CCONJ cana-574 148 18 its its PRON cana-574 148 19 ability ability NOUN cana-574 148 20 to to PART cana-574 148 21 generalize generalize VERB cana-574 148 22 across across ADP cana-574 148 23 different different ADJ cana-574 148 24 visual visual ADJ cana-574 148 25 representations representation NOUN cana-574 148 26 of of ADP cana-574 148 27 cattle cattle NOUN cana-574 148 28 diseases disease NOUN cana-574 148 29 , , PUNCT cana-574 148 30 various various ADJ cana-574 148 31 data datum NOUN cana-574 148 32 augmentation augmentation NOUN cana-574 148 33 techniques technique NOUN cana-574 148 34 were be AUX cana-574 148 35 applied apply VERB cana-574 148 36 . . PUNCT cana-574 149 1 these these PRON cana-574 149 2 included include VERB cana-574 149 3 geometric geometric ADJ cana-574 149 4 transformations transformation NOUN cana-574 149 5 like like ADP cana-574 149 6 rotations rotation NOUN cana-574 149 7 , , PUNCT cana-574 149 8 flips flip NOUN cana-574 149 9 , , PUNCT cana-574 149 10 and and CCONJ cana-574 149 11 translations translation NOUN cana-574 149 12 ; ; PUNCT cana-574 149 13 color color NOUN cana-574 149 14 adjustments adjustment NOUN cana-574 149 15 to to PART cana-574 149 16 mimic mimic VERB cana-574 149 17 different different ADJ cana-574 149 18 lighting lighting NOUN cana-574 149 19 conditions condition NOUN cana-574 149 20 ; ; PUNCT cana-574 149 21 and and CCONJ cana-574 149 22 zooms zoom NOUN cana-574 149 23 and and CCONJ cana-574 149 24 crops crop NOUN cana-574 149 25 to to PART cana-574 149 26 vary vary VERB cana-574 149 27 the the DET cana-574 149 28 image image NOUN cana-574 149 29 focus focus NOUN cana-574 149 30 and and CCONJ cana-574 149 31 perspective perspective NOUN cana-574 149 32 . . PUNCT cana-574 150 1 additionally additionally ADV cana-574 150 2 , , PUNCT cana-574 150 3 artificial artificial ADJ cana-574 150 4 noise noise NOUN cana-574 150 5 and and CCONJ cana-574 150 6 blur blur NOUN cana-574 150 7 were be AUX cana-574 150 8 introduced introduce VERB cana-574 150 9 to to PART cana-574 150 10 ensure ensure VERB cana-574 150 11 the the DET cana-574 150 12 model model NOUN cana-574 150 13 's 's PART cana-574 150 14 effectiveness effectiveness NOUN cana-574 150 15 under under ADP cana-574 150 16 varied varied ADJ cana-574 150 17 photographic photographic ADJ cana-574 150 18 conditions condition NOUN cana-574 150 19 , , PUNCT cana-574 150 20 simulating simulate VERB cana-574 150 21 real real ADJ cana-574 150 22 - - PUNCT cana-574 150 23 world world NOUN cana-574 150 24 scenarios scenario NOUN cana-574 150 25 where where SCONJ cana-574 150 26 images image NOUN cana-574 150 27 may may AUX cana-574 150 28 not not PART cana-574 150 29 always always ADV cana-574 150 30 be be AUX cana-574 150 31 perfect perfect ADJ cana-574 150 32 . . PUNCT cana-574 151 1 deep deep ADJ cana-574 151 2 learning learning NOUN cana-574 151 3 models model NOUN cana-574 151 4 the the DET cana-574 151 5 project project NOUN cana-574 151 6 utilizes utilize VERB cana-574 151 7 several several ADJ cana-574 151 8 advanced advanced ADJ cana-574 151 9 deep deep ADJ cana-574 151 10 learning learning NOUN cana-574 151 11 models model NOUN cana-574 151 12 , , PUNCT cana-574 151 13 adapting adapt VERB cana-574 151 14 their their PRON cana-574 151 15 architecture architecture NOUN cana-574 151 16 through through ADP cana-574 151 17 the the DET cana-574 151 18 process process NOUN cana-574 151 19 of of ADP cana-574 151 20 transfer transfer NOUN cana-574 151 21 learning learn VERB cana-574 151 22 to to PART cana-574 151 23 meet meet VERB cana-574 151 24 the the DET cana-574 151 25 specific specific ADJ cana-574 151 26 requirements requirement NOUN cana-574 151 27 of of ADP cana-574 151 28 cattle cattle NOUN cana-574 151 29 disease disease NOUN cana-574 151 30 detection detection NOUN cana-574 151 31 . . PUNCT cana-574 152 1 communications communication NOUN cana-574 152 2 on on ADP cana-574 152 3 applied apply VERB cana-574 152 4 nonlinear nonlinear ADJ cana-574 152 5 analysis analysis NOUN cana-574 152 6 issn issn NOUN cana-574 152 7 : : PUNCT cana-574 152 8 1074 1074 NUM cana-574 152 9 - - PUNCT cana-574 152 10 133x 133x NUM cana-574 152 11 vol vol NOUN cana-574 152 12 31 31 NUM cana-574 152 13 no no NOUN cana-574 152 14 . . NOUN cana-574 152 15 2 2 NUM cana-574 152 16 ( ( PUNCT cana-574 152 17 2024 2024 NUM cana-574 152 18 ) ) PUNCT cana-574 152 19 381 381 NUM cana-574 152 20 https://internationalpubls.com https://internationalpubls.com X cana-574 152 21 figure figure NOUN cana-574 152 22 4 4 NUM cana-574 152 23 . . PUNCT cana-574 153 1 design design NOUN cana-574 153 2 of of ADP cana-574 153 3 vgg vgg PROPN cana-574 153 4 16 16 NUM cana-574 153 5 based base VERB cana-574 153 6 classifier classifier PROPN cana-574 153 7 vgg16 vgg16 PROPN cana-574 153 8 : : PUNCT cana-574 153 9 vgg16 vgg16 PROPN cana-574 153 10 , , PUNCT cana-574 153 11 known know VERB cana-574 153 12 for for ADP cana-574 153 13 its its PRON cana-574 153 14 depth depth NOUN cana-574 153 15 and and CCONJ cana-574 153 16 simplicity simplicity NOUN cana-574 153 17 , , PUNCT cana-574 153 18 was be AUX cana-574 153 19 selected select VERB cana-574 153 20 for for ADP cana-574 153 21 its its PRON cana-574 153 22 proven prove VERB cana-574 153 23 track track NOUN cana-574 153 24 record record NOUN cana-574 153 25 in in ADP cana-574 153 26 image image NOUN cana-574 153 27 classification classification NOUN cana-574 153 28 tasks task NOUN cana-574 153 29 . . PUNCT cana-574 154 1 the the DET cana-574 154 2 model model NOUN cana-574 154 3 , , PUNCT cana-574 154 4 pre pre VERB cana-574 154 5 - - VERB cana-574 154 6 trained train VERB cana-574 154 7 on on ADP cana-574 154 8 the the DET cana-574 154 9 imagenet imagenet NOUN cana-574 154 10 database database NOUN cana-574 154 11 , , PUNCT cana-574 154 12 was be AUX cana-574 154 13 fine fine ADV cana-574 154 14 - - PUNCT cana-574 154 15 tuned tune VERB cana-574 154 16 on on ADP cana-574 154 17 the the DET cana-574 154 18 cattle cattle NOUN cana-574 154 19 disease disease NOUN cana-574 154 20 dataset dataset VERB cana-574 154 21 . . PUNCT cana-574 155 1 the the DET cana-574 155 2 last last ADJ cana-574 155 3 few few ADJ cana-574 155 4 layers layer NOUN cana-574 155 5 were be AUX cana-574 155 6 specifically specifically ADV cana-574 155 7 modified modify VERB cana-574 155 8 to to PART cana-574 155 9 align align VERB cana-574 155 10 with with ADP cana-574 155 11 the the DET cana-574 155 12 number number NOUN cana-574 155 13 of of ADP cana-574 155 14 disease disease NOUN cana-574 155 15 classes class NOUN cana-574 155 16 identified identify VERB cana-574 155 17 in in ADP cana-574 155 18 the the DET cana-574 155 19 dataset dataset NOUN cana-574 155 20 . . PUNCT cana-574 156 1 this this DET cana-574 156 2 fine fine ADV cana-574 156 3 - - PUNCT cana-574 156 4 tuning tuning NOUN cana-574 156 5 allows allow VERB cana-574 156 6 the the DET cana-574 156 7 pre pre ADJ cana-574 156 8 - - ADJ cana-574 156 9 trained trained ADJ cana-574 156 10 network network NOUN cana-574 156 11 to to PART cana-574 156 12 adapt adapt VERB cana-574 156 13 its its PRON cana-574 156 14 learned learn VERB cana-574 156 15 features feature NOUN cana-574 156 16 to to ADP cana-574 156 17 new new ADJ cana-574 156 18 , , PUNCT cana-574 156 19 specific specific ADJ cana-574 156 20 tasks task NOUN cana-574 156 21 of of ADP cana-574 156 22 recognizing recognize VERB cana-574 156 23 cattle cattle NOUN cana-574 156 24 diseases[2325 diseases[2325 PROPN cana-574 156 25 ] ] PUNCT cana-574 156 26 . . PUNCT cana-574 157 1 figure figure NOUN cana-574 157 2 5 5 NUM cana-574 157 3 . . PUNCT cana-574 158 1 design design NOUN cana-574 158 2 of of ADP cana-574 158 3 inception inception PROPN cana-574 158 4 v3 v3 PROPN cana-574 158 5 based base VERB cana-574 158 6 classifier classifier PROPN cana-574 158 7 inception inception PROPN cana-574 158 8 v3 v3 PROPN cana-574 158 9 : : PUNCT cana-574 158 10 inception inception PROPN cana-574 158 11 v3 v3 PROPN cana-574 158 12 is be AUX cana-574 158 13 another another DET cana-574 158 14 model model NOUN cana-574 158 15 chosen choose VERB cana-574 158 16 for for ADP cana-574 158 17 its its PRON cana-574 158 18 sophisticated sophisticated ADJ cana-574 158 19 architecture architecture NOUN cana-574 158 20 designed design VERB cana-574 158 21 to to PART cana-574 158 22 capture capture VERB cana-574 158 23 information information NOUN cana-574 158 24 at at ADP cana-574 158 25 multiple multiple ADJ cana-574 158 26 scales scale NOUN cana-574 158 27 using use VERB cana-574 158 28 various various ADJ cana-574 158 29 convolutions convolution NOUN cana-574 158 30 and and CCONJ cana-574 158 31 pooling pool VERB cana-574 158 32 strategies strategy NOUN cana-574 158 33 . . PUNCT cana-574 159 1 the the DET cana-574 159 2 model model NOUN cana-574 159 3 was be AUX cana-574 159 4 fine fine ADV cana-574 159 5 - - PUNCT cana-574 159 6 tuned tune VERB cana-574 159 7 with with ADP cana-574 159 8 additional additional ADJ cana-574 159 9 layers layer NOUN cana-574 159 10 like like ADP cana-574 159 11 batch batch NOUN cana-574 159 12 normalization normalization NOUN cana-574 159 13 and and CCONJ cana-574 159 14 dropout dropout NOUN cana-574 159 15 to to PART cana-574 159 16 enhance enhance VERB cana-574 159 17 training training NOUN cana-574 159 18 stability stability NOUN cana-574 159 19 and and CCONJ cana-574 159 20 minimize minimize VERB cana-574 159 21 overfitting overfitte VERB cana-574 159 22 , , PUNCT cana-574 159 23 adapting adapt VERB cana-574 159 24 it it PRON cana-574 159 25 to to ADP cana-574 159 26 the the DET cana-574 159 27 nuances nuance NOUN cana-574 159 28 of of ADP cana-574 159 29 cattle cattle NOUN cana-574 159 30 disease disease NOUN cana-574 159 31 imaging imaging NOUN cana-574 159 32 . . PUNCT cana-574 160 1 resnet50 resnet50 PROPN cana-574 160 2 : : PUNCT cana-574 160 3 resnet50 resnet50 NOUN cana-574 160 4 employs employ VERB cana-574 160 5 residual residual ADJ cana-574 160 6 learning learning NOUN cana-574 160 7 to to PART cana-574 160 8 facilitate facilitate VERB cana-574 160 9 the the DET cana-574 160 10 training training NOUN cana-574 160 11 of of ADP cana-574 160 12 very very ADV cana-574 160 13 deep deep ADJ cana-574 160 14 networks network NOUN cana-574 160 15 by by ADP cana-574 160 16 incorporating incorporate VERB cana-574 160 17 skip skip ADJ cana-574 160 18 connections connection NOUN cana-574 160 19 that that PRON cana-574 160 20 jump jump VERB cana-574 160 21 over over ADP cana-574 160 22 some some DET cana-574 160 23 layers layer NOUN cana-574 160 24 . . PUNCT cana-574 161 1 these these DET cana-574 161 2 connections connection NOUN cana-574 161 3 help help VERB cana-574 161 4 mitigate mitigate VERB cana-574 161 5 the the DET cana-574 161 6 vanishing vanish VERB cana-574 161 7 gradient gradient NOUN cana-574 161 8 problem problem NOUN cana-574 161 9 , , PUNCT cana-574 161 10 allowing allow VERB cana-574 161 11 for for ADP cana-574 161 12 deeper deep ADJ cana-574 161 13 network network NOUN cana-574 161 14 architectures architecture NOUN cana-574 161 15 without without ADP cana-574 161 16 a a DET cana-574 161 17 loss loss NOUN cana-574 161 18 in in ADP cana-574 161 19 performance performance NOUN cana-574 161 20 . . PUNCT cana-574 162 1 resnet50 resnet50 NOUN cana-574 162 2 was be AUX cana-574 162 3 tailored tailor VERB cana-574 162 4 for for ADP cana-574 162 5 deeper deep ADJ cana-574 162 6 and and CCONJ cana-574 162 7 more more ADV cana-574 162 8 precise precise ADJ cana-574 162 9 feature feature NOUN cana-574 162 10 extraction extraction NOUN cana-574 162 11 , , PUNCT cana-574 162 12 offering offer VERB cana-574 162 13 substantial substantial ADJ cana-574 162 14 computational computational ADJ cana-574 162 15 efficiency efficiency NOUN cana-574 162 16 over over ADP cana-574 162 17 other other ADJ cana-574 162 18 deep deep ADJ cana-574 162 19 networks network NOUN cana-574 162 20 without without ADP cana-574 162 21 residual residual ADJ cana-574 162 22 connections connection NOUN cana-574 162 23 [ [ X cana-574 162 24 27 27 NUM cana-574 162 25 ] ] PUNCT cana-574 162 26 . . PUNCT cana-574 163 1 communications communication NOUN cana-574 163 2 on on ADP cana-574 163 3 applied apply VERB cana-574 163 4 nonlinear nonlinear ADJ cana-574 163 5 analysis analysis NOUN cana-574 163 6 issn issn NOUN cana-574 163 7 : : PUNCT cana-574 163 8 1074 1074 NUM cana-574 163 9 - - PUNCT cana-574 163 10 133x 133x NUM cana-574 163 11 vol vol NOUN cana-574 163 12 31 31 NUM cana-574 163 13 no no NOUN cana-574 163 14 . . NOUN cana-574 163 15 2 2 NUM cana-574 163 16 ( ( PUNCT cana-574 163 17 2024 2024 NUM cana-574 163 18 ) ) PUNCT cana-574 164 1 382 382 NUM cana-574 164 2 https://internationalpubls.com https://internationalpubls.com X cana-574 164 3 figure figure NOUN cana-574 164 4 6 6 NUM cana-574 164 5 . . PUNCT cana-574 165 1 distribution distribution NOUN cana-574 165 2 of of ADP cana-574 165 3 diseases disease NOUN cana-574 165 4 in in ADP cana-574 165 5 database database NOUN cana-574 165 6 training training NOUN cana-574 165 7 details detail NOUN cana-574 165 8 : : PUNCT cana-574 165 9 β€’ β€’ NUM cana-574 165 10 optimization optimization NOUN cana-574 165 11 algorithms algorithm NOUN cana-574 165 12 : : PUNCT cana-574 165 13 the the DET cana-574 165 14 adam adam PROPN cana-574 165 15 optimizer optimizer NOUN cana-574 165 16 was be AUX cana-574 165 17 used use VERB cana-574 165 18 due due ADP cana-574 165 19 to to ADP cana-574 165 20 its its PRON cana-574 165 21 adaptive adaptive ADJ cana-574 165 22 learning learning NOUN cana-574 165 23 rate rate NOUN cana-574 165 24 features feature NOUN cana-574 165 25 , , PUNCT cana-574 165 26 enhancing enhance VERB cana-574 165 27 the the DET cana-574 165 28 convergence convergence NOUN cana-574 165 29 speed speed NOUN cana-574 165 30 . . PUNCT cana-574 166 1 β€’ β€’ NUM cana-574 166 2 loss loss NOUN cana-574 166 3 functions function NOUN cana-574 166 4 : : PUNCT cana-574 166 5 cross cross ADJ cana-574 166 6 - - ADJ cana-574 166 7 entropy entropy ADJ cana-574 166 8 loss loss NOUN cana-574 166 9 , , PUNCT cana-574 166 10 a a DET cana-574 166 11 standard standard NOUN cana-574 166 12 for for ADP cana-574 166 13 multiclass multiclass ADJ cana-574 166 14 classification classification NOUN cana-574 166 15 tasks task NOUN cana-574 166 16 , , PUNCT cana-574 166 17 was be AUX cana-574 166 18 employed employ VERB cana-574 166 19 to to PART cana-574 166 20 compute compute VERB cana-574 166 21 the the DET cana-574 166 22 model model NOUN cana-574 166 23 's 's PART cana-574 166 24 loss loss NOUN cana-574 166 25 . . PUNCT cana-574 167 1 β€’ β€’ NUM cana-574 167 2 performance performance NOUN cana-574 167 3 metrics metric NOUN cana-574 167 4 : : PUNCT cana-574 167 5 metrics metric NOUN cana-574 167 6 such such ADJ cana-574 167 7 as as ADP cana-574 167 8 accuracy accuracy NOUN cana-574 167 9 , , PUNCT cana-574 167 10 precision precision NOUN cana-574 167 11 , , PUNCT cana-574 167 12 recall recall NOUN cana-574 167 13 , , PUNCT cana-574 167 14 and and CCONJ cana-574 167 15 f1 f1 NOUN cana-574 167 16 - - PUNCT cana-574 167 17 score score NOUN cana-574 167 18 were be AUX cana-574 167 19 meticulously meticulously ADV cana-574 167 20 recorded record VERB cana-574 167 21 to to PART cana-574 167 22 evaluate evaluate VERB cana-574 167 23 the the DET cana-574 167 24 model model NOUN cana-574 167 25 's 's PART cana-574 167 26 performance performance NOUN cana-574 167 27 and and CCONJ cana-574 167 28 ensure ensure VERB cana-574 167 29 reliability reliability NOUN cana-574 167 30 . . PUNCT cana-574 168 1 figure figure VERB cana-574 168 2 7 7 NUM cana-574 168 3 . . PUNCT cana-574 168 4 analysis analysis NOUN cana-574 168 5 of of ADP cana-574 168 6 distribution distribution NOUN cana-574 168 7 of of ADP cana-574 168 8 diseases disease NOUN cana-574 168 9 in in ADP cana-574 168 10 database database NOUN cana-574 168 11 evaluation evaluation NOUN cana-574 168 12 : : PUNCT cana-574 168 13 β€’ β€’ NUM cana-574 168 14 validation validation NOUN cana-574 168 15 strategy strategy NOUN cana-574 168 16 : : PUNCT cana-574 168 17 the the DET cana-574 168 18 dataset dataset NOUN cana-574 168 19 was be AUX cana-574 168 20 strategically strategically ADV cana-574 168 21 divided divide VERB cana-574 168 22 into into ADP cana-574 168 23 training training NOUN cana-574 168 24 , , PUNCT cana-574 168 25 validation validation NOUN cana-574 168 26 , , PUNCT cana-574 168 27 and and CCONJ cana-574 168 28 test test NOUN cana-574 168 29 sets set NOUN cana-574 168 30 , , PUNCT cana-574 168 31 ensuring ensure VERB cana-574 168 32 that that SCONJ cana-574 168 33 the the DET cana-574 168 34 model model NOUN cana-574 168 35 's 's PART cana-574 168 36 performance performance NOUN cana-574 168 37 is be AUX cana-574 168 38 rigorously rigorously ADV cana-574 168 39 evaluated evaluate VERB cana-574 168 40 on on ADP cana-574 168 41 unseen unseen ADJ cana-574 168 42 data datum NOUN cana-574 168 43 . . PUNCT cana-574 169 1 β€’ β€’ NUM cana-574 169 2 cross cross NOUN cana-574 169 3 - - NOUN cana-574 169 4 validation validation NOUN cana-574 169 5 : : PUNCT cana-574 169 6 used use VERB cana-574 169 7 to to PART cana-574 169 8 ascertain ascertain VERB cana-574 169 9 the the DET cana-574 169 10 consistency consistency NOUN cana-574 169 11 and and CCONJ cana-574 169 12 reliability reliability NOUN cana-574 169 13 of of ADP cana-574 169 14 the the DET cana-574 169 15 model model NOUN cana-574 169 16 across across ADP cana-574 169 17 different different ADJ cana-574 169 18 data datum NOUN cana-574 169 19 subsets subset NOUN cana-574 169 20 , , PUNCT cana-574 169 21 ensuring ensure VERB cana-574 169 22 that that SCONJ cana-574 169 23 the the DET cana-574 169 24 model model NOUN cana-574 169 25 performs perform VERB cana-574 169 26 well well ADV cana-574 169 27 in in ADP cana-574 169 28 various various ADJ cana-574 169 29 scenarios scenario NOUN cana-574 169 30 and and CCONJ cana-574 169 31 setups setup NOUN cana-574 169 32 . . PUNCT cana-574 170 1 this this DET cana-574 170 2 research research NOUN cana-574 170 3 methodology methodology NOUN cana-574 170 4 leverages leverage VERB cana-574 170 5 the the DET cana-574 170 6 powerful powerful ADJ cana-574 170 7 capabilities capability NOUN cana-574 170 8 of of ADP cana-574 170 9 cnns cnn NOUN cana-574 170 10 and and CCONJ cana-574 170 11 transfer transfer NOUN cana-574 170 12 learning learn VERB cana-574 170 13 to to PART cana-574 170 14 develop develop VERB cana-574 170 15 a a DET cana-574 170 16 robust robust ADJ cana-574 170 17 , , PUNCT cana-574 170 18 accurate accurate ADJ cana-574 170 19 , , PUNCT cana-574 170 20 and and CCONJ cana-574 170 21 efficient efficient ADJ cana-574 170 22 system system NOUN cana-574 170 23 for for ADP cana-574 170 24 cattle cattle NOUN cana-574 170 25 disease disease NOUN cana-574 170 26 detection detection NOUN cana-574 170 27 . . PUNCT cana-574 171 1 by by ADP cana-574 171 2 combining combine VERB cana-574 171 3 sophisticated sophisticated ADJ cana-574 171 4 image image NOUN cana-574 171 5 processing processing NOUN cana-574 171 6 techniques technique NOUN cana-574 171 7 with with ADP cana-574 171 8 cutting cut VERB cana-574 171 9 - - PUNCT cana-574 171 10 edge edge NOUN cana-574 171 11 deep deep ADJ cana-574 171 12 learning learning NOUN cana-574 171 13 technologies technology NOUN cana-574 171 14 , , PUNCT cana-574 171 15 this this DET cana-574 171 16 system system NOUN cana-574 171 17 is be AUX cana-574 171 18 poised poise VERB cana-574 171 19 to to PART cana-574 171 20 make make VERB cana-574 171 21 significant significant ADJ cana-574 171 22 contributions contribution NOUN cana-574 171 23 to to ADP cana-574 171 24 veterinary veterinary ADJ cana-574 171 25 medicine medicine NOUN cana-574 171 26 . . PUNCT cana-574 172 1 it it PRON cana-574 172 2 promises promise VERB cana-574 172 3 to to PART cana-574 172 4 enhance enhance VERB cana-574 172 5 the the DET cana-574 172 6 capabilities capability NOUN cana-574 172 7 for for ADP cana-574 172 8 early early ADJ cana-574 172 9 disease disease NOUN cana-574 172 10 detection detection NOUN cana-574 172 11 and and CCONJ cana-574 172 12 management management NOUN cana-574 172 13 in in ADP cana-574 172 14 cattle cattle NOUN cana-574 172 15 , , PUNCT cana-574 172 16 thereby thereby ADV cana-574 172 17 communications communication NOUN cana-574 172 18 on on ADP cana-574 172 19 applied apply VERB cana-574 172 20 nonlinear nonlinear ADJ cana-574 172 21 analysis analysis NOUN cana-574 172 22 issn issn NOUN cana-574 172 23 : : PUNCT cana-574 172 24 1074 1074 NUM cana-574 172 25 - - PUNCT cana-574 172 26 133x 133x NUM cana-574 172 27 vol vol NOUN cana-574 172 28 31 31 NUM cana-574 172 29 no no NOUN cana-574 172 30 . . NOUN cana-574 172 31 2 2 NUM cana-574 172 32 ( ( PUNCT cana-574 172 33 2024 2024 NUM cana-574 172 34 ) ) PUNCT cana-574 172 35 383 383 NUM cana-574 172 36 https://internationalpubls.com https://internationalpubls.com X cana-574 172 37 improving improve VERB cana-574 172 38 animal animal NOUN cana-574 172 39 health health NOUN cana-574 172 40 , , PUNCT cana-574 172 41 increasing increase VERB cana-574 172 42 farm farm NOUN cana-574 172 43 productivity productivity NOUN cana-574 172 44 , , PUNCT cana-574 172 45 and and CCONJ cana-574 172 46 reducing reduce VERB cana-574 172 47 economic economic ADJ cana-574 172 48 losses loss NOUN cana-574 172 49 associated associate VERB cana-574 172 50 with with ADP cana-574 172 51 cattle cattle NOUN cana-574 172 52 diseases disease NOUN cana-574 172 53 [ [ X cana-574 172 54 28 28 NUM cana-574 172 55 - - SYM cana-574 172 56 32 32 NUM cana-574 172 57 ] ] PUNCT cana-574 172 58 . . PUNCT cana-574 173 1 4 4 X cana-574 173 2 . . X cana-574 173 3 results result NOUN cana-574 173 4 & & CCONJ cana-574 173 5 discussion discussion NOUN cana-574 173 6 recent recent ADJ cana-574 173 7 advances advance NOUN cana-574 173 8 in in ADP cana-574 173 9 ai ai NOUN cana-574 173 10 , , PUNCT cana-574 173 11 particularly particularly ADV cana-574 173 12 the the DET cana-574 173 13 development development NOUN cana-574 173 14 of of ADP cana-574 173 15 deep deep ADJ cana-574 173 16 learning learning NOUN cana-574 173 17 technologies technology NOUN cana-574 173 18 , , PUNCT cana-574 173 19 have have AUX cana-574 173 20 opened open VERB cana-574 173 21 up up ADP cana-574 173 22 new new ADJ cana-574 173 23 avenues avenue NOUN cana-574 173 24 for for ADP cana-574 173 25 innovation innovation NOUN cana-574 173 26 in in ADP cana-574 173 27 disease disease NOUN cana-574 173 28 detection detection NOUN cana-574 173 29 . . PUNCT cana-574 174 1 cnns cnns PROPN cana-574 174 2 , , PUNCT cana-574 174 3 a a DET cana-574 174 4 class class NOUN cana-574 174 5 of of ADP cana-574 174 6 deep deep ADJ cana-574 174 7 neural neural ADJ cana-574 174 8 networks network NOUN cana-574 174 9 , , PUNCT cana-574 174 10 are be AUX cana-574 174 11 particularly particularly ADV cana-574 174 12 suited suit VERB cana-574 174 13 for for ADP cana-574 174 14 image image NOUN cana-574 174 15 recognition recognition NOUN cana-574 174 16 tasks task NOUN cana-574 174 17 and and CCONJ cana-574 174 18 have have AUX cana-574 174 19 been be AUX cana-574 174 20 successfully successfully ADV cana-574 174 21 applied apply VERB cana-574 174 22 in in ADP cana-574 174 23 various various ADJ cana-574 174 24 fields field NOUN cana-574 174 25 including include VERB cana-574 174 26 medical medical ADJ cana-574 174 27 imaging imaging NOUN cana-574 174 28 and and CCONJ cana-574 174 29 autonomous autonomous ADJ cana-574 174 30 driving driving NOUN cana-574 174 31 . . PUNCT cana-574 175 1 these these DET cana-574 175 2 networks network NOUN cana-574 175 3 automatically automatically ADV cana-574 175 4 learn learn VERB cana-574 175 5 to to PART cana-574 175 6 detect detect VERB cana-574 175 7 complex complex ADJ cana-574 175 8 patterns pattern NOUN cana-574 175 9 in in ADP cana-574 175 10 data datum NOUN cana-574 175 11 , , PUNCT cana-574 175 12 making make VERB cana-574 175 13 them they PRON cana-574 175 14 ideal ideal ADJ cana-574 175 15 for for ADP cana-574 175 16 recognizing recognize VERB cana-574 175 17 signs sign NOUN cana-574 175 18 of of ADP cana-574 175 19 disease disease NOUN cana-574 175 20 in in ADP cana-574 175 21 images image NOUN cana-574 175 22 of of ADP cana-574 175 23 cattle cattle NOUN cana-574 175 24 . . PUNCT cana-574 176 1 the the DET cana-574 176 2 primary primary ADJ cana-574 176 3 objective objective NOUN cana-574 176 4 of of ADP cana-574 176 5 this this DET cana-574 176 6 study study NOUN cana-574 176 7 was be AUX cana-574 176 8 to to PART cana-574 176 9 develop develop VERB cana-574 176 10 a a DET cana-574 176 11 robust robust ADJ cana-574 176 12 automated automate VERB cana-574 176 13 system system NOUN cana-574 176 14 capable capable ADJ cana-574 176 15 of of ADP cana-574 176 16 detecting detect VERB cana-574 176 17 specific specific ADJ cana-574 176 18 cattle cattle NOUN cana-574 176 19 diseases disease NOUN cana-574 176 20 from from ADP cana-574 176 21 images image NOUN cana-574 176 22 using use VERB cana-574 176 23 cnn cnn PROPN cana-574 176 24 architectures architecture NOUN cana-574 176 25 such such ADJ cana-574 176 26 as as ADP cana-574 176 27 inceptionv3 inceptionv3 NOUN cana-574 176 28 , , PUNCT cana-574 176 29 mobilenetv2 mobilenetv2 NOUN cana-574 176 30 , , PUNCT cana-574 176 31 and and CCONJ cana-574 176 32 densenet169 densenet169 PROPN cana-574 176 33 . . PUNCT cana-574 177 1 this this DET cana-574 177 2 system system NOUN cana-574 177 3 aimed aim VERB cana-574 177 4 to to PART cana-574 177 5 classify classify VERB cana-574 177 6 images image NOUN cana-574 177 7 into into ADP cana-574 177 8 distinct distinct ADJ cana-574 177 9 categories category NOUN cana-574 177 10 based base VERB cana-574 177 11 on on ADP cana-574 177 12 the the DET cana-574 177 13 presence presence NOUN cana-574 177 14 of of ADP cana-574 177 15 foot foot NOUN cana-574 177 16 , , PUNCT cana-574 177 17 mouth mouth NOUN cana-574 177 18 , , PUNCT cana-574 177 19 and and CCONJ cana-574 177 20 lumpy lumpy VERB cana-574 177 21 skin skin NOUN cana-574 177 22 diseases disease NOUN cana-574 177 23 , , PUNCT cana-574 177 24 leveraging leverage VERB cana-574 177 25 a a DET cana-574 177 26 dataset dataset NOUN cana-574 177 27 compiled compile VERB cana-574 177 28 from from ADP cana-574 177 29 various various ADJ cana-574 177 30 online online ADJ cana-574 177 31 sources source NOUN cana-574 177 32 . . PUNCT cana-574 178 1 a a DET cana-574 178 2 comprehensive comprehensive ADJ cana-574 178 3 dataset dataset NOUN cana-574 178 4 was be AUX cana-574 178 5 essential essential ADJ cana-574 178 6 for for ADP cana-574 178 7 training training NOUN cana-574 178 8 and and CCONJ cana-574 178 9 testing test VERB cana-574 178 10 the the DET cana-574 178 11 deep deep ADJ cana-574 178 12 learning learning NOUN cana-574 178 13 models model NOUN cana-574 178 14 . . PUNCT cana-574 179 1 the the DET cana-574 179 2 dataset dataset NOUN cana-574 179 3 consisted consist VERB cana-574 179 4 of of ADP cana-574 179 5 images image NOUN cana-574 179 6 classified classify VERB cana-574 179 7 into into ADP cana-574 179 8 different different ADJ cana-574 179 9 disease disease NOUN cana-574 179 10 categories category NOUN cana-574 179 11 , , PUNCT cana-574 179 12 providing provide VERB cana-574 179 13 a a DET cana-574 179 14 varied varied ADJ cana-574 179 15 pool pool NOUN cana-574 179 16 of of ADP cana-574 179 17 data datum NOUN cana-574 179 18 points point NOUN cana-574 179 19 for for ADP cana-574 179 20 the the DET cana-574 179 21 models model NOUN cana-574 179 22 to to PART cana-574 179 23 learn learn VERB cana-574 179 24 from from ADP cana-574 179 25 . . PUNCT cana-574 180 1 the the DET cana-574 180 2 images image NOUN cana-574 180 3 were be AUX cana-574 180 4 collected collect VERB cana-574 180 5 from from ADP cana-574 180 6 openly openly ADV cana-574 180 7 available available ADJ cana-574 180 8 internet internet NOUN cana-574 180 9 sources source NOUN cana-574 180 10 , , PUNCT cana-574 180 11 ensuring ensure VERB cana-574 180 12 a a DET cana-574 180 13 diverse diverse ADJ cana-574 180 14 representation representation NOUN cana-574 180 15 of of ADP cana-574 180 16 disease disease NOUN cana-574 180 17 manifestations manifestation NOUN cana-574 180 18 . . PUNCT cana-574 181 1 this this DET cana-574 181 2 diversity diversity NOUN cana-574 181 3 is be AUX cana-574 181 4 critical critical ADJ cana-574 181 5 in in ADP cana-574 181 6 training train VERB cana-574 181 7 robust robust ADJ cana-574 181 8 models model NOUN cana-574 181 9 that that PRON cana-574 181 10 can can AUX cana-574 181 11 generalize generalize VERB cana-574 181 12 well well ADV cana-574 181 13 across across ADP cana-574 181 14 different different ADJ cana-574 181 15 real real ADJ cana-574 181 16 - - PUNCT cana-574 181 17 world world NOUN cana-574 181 18 scenarios scenario NOUN cana-574 181 19 . . PUNCT cana-574 182 1 the the DET cana-574 182 2 study study NOUN cana-574 182 3 utilized utilize VERB cana-574 182 4 the the DET cana-574 182 5 python python NOUN cana-574 182 6 programming programming NOUN cana-574 182 7 language language NOUN cana-574 182 8 , , PUNCT cana-574 182 9 known know VERB cana-574 182 10 for for ADP cana-574 182 11 its its PRON cana-574 182 12 extensive extensive ADJ cana-574 182 13 libraries library NOUN cana-574 182 14 and and CCONJ cana-574 182 15 frameworks framework NOUN cana-574 182 16 that that PRON cana-574 182 17 facilitate facilitate VERB cana-574 182 18 data datum NOUN cana-574 182 19 handling handling NOUN cana-574 182 20 , , PUNCT cana-574 182 21 processing processing NOUN cana-574 182 22 , , PUNCT cana-574 182 23 and and CCONJ cana-574 182 24 model model NOUN cana-574 182 25 development development PROPN cana-574 182 26 . . PUNCT cana-574 183 1 key key ADJ cana-574 183 2 technologies technology NOUN cana-574 183 3 used use VERB cana-574 183 4 included include VERB cana-574 183 5 tensorflow tensorflow NOUN cana-574 183 6 and and CCONJ cana-574 183 7 keras keras PROPN cana-574 183 8 for for ADP cana-574 183 9 building building NOUN cana-574 183 10 and and CCONJ cana-574 183 11 training train VERB cana-574 183 12 the the DET cana-574 183 13 cnn cnn PROPN cana-574 183 14 models model NOUN cana-574 183 15 , , PUNCT cana-574 183 16 and and CCONJ cana-574 183 17 numpy numpy VERB cana-574 183 18 for for ADP cana-574 183 19 performing perform VERB cana-574 183 20 mathematical mathematical ADJ cana-574 183 21 operations operation NOUN cana-574 183 22 essential essential ADJ cana-574 183 23 for for ADP cana-574 183 24 data data NOUN cana-574 183 25 preprocessing preprocessing NOUN cana-574 183 26 and and CCONJ cana-574 183 27 analysis analysis NOUN cana-574 183 28 . . PUNCT cana-574 184 1 the the DET cana-574 184 2 performance performance NOUN cana-574 184 3 of of ADP cana-574 184 4 each each DET cana-574 184 5 model model NOUN cana-574 184 6 was be AUX cana-574 184 7 evaluated evaluate VERB cana-574 184 8 using use VERB cana-574 184 9 standard standard ADJ cana-574 184 10 metrics metric NOUN cana-574 184 11 derived derive VERB cana-574 184 12 from from ADP cana-574 184 13 the the DET cana-574 184 14 confusion confusion NOUN cana-574 184 15 matrix matrix NOUN cana-574 184 16 , , PUNCT cana-574 184 17 including include VERB cana-574 184 18 accuracy accuracy NOUN cana-574 184 19 , , PUNCT cana-574 184 20 precision precision NOUN cana-574 184 21 , , PUNCT cana-574 184 22 recall recall NOUN cana-574 184 23 , , PUNCT cana-574 184 24 and and CCONJ cana-574 184 25 the the DET cana-574 184 26 f1 f1 NOUN cana-574 184 27 - - PUNCT cana-574 184 28 score score NOUN cana-574 184 29 . . PUNCT cana-574 185 1 these these DET cana-574 185 2 metrics metric NOUN cana-574 185 3 provide provide VERB cana-574 185 4 a a DET cana-574 185 5 comprehensive comprehensive ADJ cana-574 185 6 understanding understanding NOUN cana-574 185 7 of of ADP cana-574 185 8 model model NOUN cana-574 185 9 performance performance NOUN cana-574 185 10 , , PUNCT cana-574 185 11 highlighting highlight VERB cana-574 185 12 strengths strength NOUN cana-574 185 13 and and CCONJ cana-574 185 14 weaknesses weakness NOUN cana-574 185 15 in in ADP cana-574 185 16 specific specific ADJ cana-574 185 17 areas area NOUN cana-574 185 18 such such ADJ cana-574 185 19 as as ADP cana-574 185 20 the the DET cana-574 185 21 model model NOUN cana-574 185 22 ’s ’s PART cana-574 185 23 ability ability NOUN cana-574 185 24 to to PART cana-574 185 25 correctly correctly ADV cana-574 185 26 identify identify VERB cana-574 185 27 disease disease NOUN cana-574 185 28 - - PUNCT cana-574 185 29 positive positive ADJ cana-574 185 30 cases case NOUN cana-574 185 31 ( ( PUNCT cana-574 185 32 precision precision NOUN cana-574 185 33 ) ) PUNCT cana-574 185 34 and and CCONJ cana-574 185 35 its its PRON cana-574 185 36 sensitivity sensitivity NOUN cana-574 185 37 in in ADP cana-574 185 38 detecting detect VERB cana-574 185 39 these these DET cana-574 185 40 cases case NOUN cana-574 185 41 among among ADP cana-574 185 42 all all DET cana-574 185 43 positive positive ADJ cana-574 185 44 instances instance NOUN cana-574 185 45 ( ( PUNCT cana-574 185 46 recall recall NOUN cana-574 185 47 ) ) PUNCT cana-574 185 48 . . PUNCT cana-574 186 1 classification classification NOUN cana-574 186 2 accuracy accuracy NOUN cana-574 186 3 : : PUNCT cana-574 186 4 the the DET cana-574 186 5 accuracy accuracy NOUN cana-574 186 6 of of ADP cana-574 186 7 classification classification NOUN cana-574 186 8 is be AUX cana-574 186 9 determined determine VERB cana-574 186 10 by by ADP cana-574 186 11 determining determine VERB cana-574 186 12 the the DET cana-574 186 13 percentage percentage NOUN cana-574 186 14 of of ADP cana-574 186 15 data datum NOUN cana-574 186 16 points point NOUN cana-574 186 17 that that PRON cana-574 186 18 have have AUX cana-574 186 19 been be AUX cana-574 186 20 successfully successfully ADV cana-574 186 21 classified classify VERB cana-574 186 22 out out ADP cana-574 186 23 of of ADP cana-574 186 24 the the DET cana-574 186 25 total total ADJ cana-574 186 26 number number NOUN cana-574 186 27 of of ADP cana-574 186 28 data datum NOUN cana-574 186 29 points point NOUN cana-574 186 30 . . PUNCT cana-574 187 1 calculated calculate VERB cana-574 187 2 by by ADP cana-574 187 3 taking take VERB cana-574 187 4 the the DET cana-574 187 5 total total ADJ cana-574 187 6 number number NOUN cana-574 187 7 of of ADP cana-574 187 8 data datum NOUN cana-574 187 9 points point NOUN cana-574 187 10 and and CCONJ cana-574 187 11 dividing divide VERB cana-574 187 12 it it PRON cana-574 187 13 by by ADP cana-574 187 14 the the DET cana-574 187 15 sum sum NOUN cana-574 187 16 of of ADP cana-574 187 17 true true ADJ cana-574 187 18 positives positive NOUN cana-574 187 19 ( ( PUNCT cana-574 187 20 tp tp NOUN cana-574 187 21 ) ) PUNCT cana-574 187 22 and and CCONJ cana-574 187 23 true true ADJ cana-574 187 24 negatives negative NOUN cana-574 187 25 ( ( PUNCT cana-574 187 26 tn tn NOUN cana-574 187 27 ) ) PUNCT cana-574 187 28 , , PUNCT cana-574 187 29 it it PRON cana-574 187 30 includes include VERB cana-574 187 31 the the DET cana-574 187 32 following following NOUN cana-574 187 33 : : PUNCT cana-574 187 34 accuracy= accuracy= PROPN cana-574 187 35 𝐓𝐏+𝐓𝐍+𝐅𝐏+𝐅𝐍 𝐓𝐏+𝐓𝐍+𝐅𝐏+𝐅𝐍 PROPN cana-574 187 36 𝐓𝐏+𝐓𝐍 𝐓𝐏+𝐓𝐍 ADJ cana-574 187 37 ( ( PUNCT cana-574 187 38 34 34 NUM cana-574 187 39 ) ) PUNCT cana-574 187 40 precision precision NOUN cana-574 187 41 : : PUNCT cana-574 187 42 accuracy accuracy NOUN cana-574 187 43 can can AUX cana-574 187 44 be be AUX cana-574 187 45 defined define VERB cana-574 187 46 as as ADP cana-574 187 47 the the DET cana-574 187 48 ratio ratio NOUN cana-574 187 49 of of ADP cana-574 187 50 the the DET cana-574 187 51 actual actual ADJ cana-574 187 52 positive positive ADJ cana-574 187 53 to to ADP cana-574 187 54 the the DET cana-574 187 55 total total ADJ cana-574 187 56 number number NOUN cana-574 187 57 of of ADP cana-574 187 58 positives positive NOUN cana-574 187 59 anticipated anticipate VERB cana-574 187 60 . . PUNCT cana-574 188 1 a a DET cana-574 188 2 model model NOUN cana-574 188 3 with with ADP cana-574 188 4 a a DET cana-574 188 5 high high ADJ cana-574 188 6 precision precision NOUN cana-574 188 7 has have VERB cana-574 188 8 a a DET cana-574 188 9 low low ADJ cana-574 188 10 false false ADJ cana-574 188 11 positive positive ADJ cana-574 188 12 rate rate NOUN cana-574 188 13 , , PUNCT cana-574 188 14 which which PRON cana-574 188 15 means mean VERB cana-574 188 16 that that SCONJ cana-574 188 17 it it PRON cana-574 188 18 only only ADV cana-574 188 19 sometimes sometimes ADV cana-574 188 20 incorrectly incorrectly ADV cana-574 188 21 identifies identify VERB cana-574 188 22 negative negative ADJ cana-574 188 23 occurrences occurrence NOUN cana-574 188 24 as as ADP cana-574 188 25 positive positive ADJ cana-574 188 26 . . PUNCT cana-574 189 1 conversely conversely ADV cana-574 189 2 , , PUNCT cana-574 189 3 a a DET cana-574 189 4 low low ADJ cana-574 189 5 precision precision NOUN cana-574 189 6 suggests suggest VERB cana-574 189 7 that that SCONJ cana-574 189 8 the the DET cana-574 189 9 model model NOUN cana-574 189 10 tends tend VERB cana-574 189 11 to to PART cana-574 189 12 make make VERB cana-574 189 13 a a DET cana-574 189 14 significant significant ADJ cana-574 189 15 number number NOUN cana-574 189 16 of of ADP cana-574 189 17 false false ADJ cana-574 189 18 positive positive ADJ cana-574 189 19 predictions prediction NOUN cana-574 189 20 , , PUNCT cana-574 189 21 which which PRON cana-574 189 22 can can AUX cana-574 189 23 be be AUX cana-574 189 24 problematic problematic ADJ cana-574 189 25 in in ADP cana-574 189 26 scenarios scenario NOUN cana-574 189 27 where where SCONJ cana-574 189 28 false false ADJ cana-574 189 29 positives positive NOUN cana-574 189 30 are be AUX cana-574 189 31 costly costly ADJ cana-574 189 32 or or CCONJ cana-574 189 33 undesirable undesirable ADJ cana-574 189 34 as as ADP cana-574 189 35 in in ADP cana-574 189 36 fraud fraud NOUN cana-574 189 37 detection detection NOUN cana-574 189 38 , , PUNCT cana-574 189 39 and and CCONJ cana-574 189 40 spam spam NOUN cana-574 189 41 filtering filtering NOUN cana-574 189 42 . . PUNCT cana-574 190 1 communications communication NOUN cana-574 190 2 on on ADP cana-574 190 3 applied apply VERB cana-574 190 4 nonlinear nonlinear ADJ cana-574 190 5 analysis analysis NOUN cana-574 190 6 issn issn NOUN cana-574 190 7 : : PUNCT cana-574 190 8 1074 1074 NUM cana-574 190 9 - - PUNCT cana-574 190 10 133x 133x NUM cana-574 190 11 vol vol NOUN cana-574 190 12 31 31 NUM cana-574 190 13 no no NOUN cana-574 190 14 . . NOUN cana-574 190 15 2 2 NUM cana-574 190 16 ( ( PUNCT cana-574 190 17 2024 2024 NUM cana-574 190 18 ) ) PUNCT cana-574 190 19 384 384 NUM cana-574 190 20 https://internationalpubls.com https://internationalpubls.com X cana-574 190 21 precision precision NOUN cana-574 190 22 = = SYM cana-574 190 23 𝐓𝐏 𝐓𝐏 PROPN cana-574 190 24 𝐓𝐏+𝐅𝐏 𝐓𝐏+𝐅𝐏 NOUN cana-574 190 25 ( ( PUNCT cana-574 190 26 35 35 NUM cana-574 190 27 ) ) PUNCT cana-574 190 28 recall recall NOUN cana-574 190 29 , , PUNCT cana-574 190 30 in in ADP cana-574 190 31 classification classification NOUN cana-574 190 32 tasks task NOUN cana-574 190 33 , , PUNCT cana-574 190 34 a a DET cana-574 190 35 performance performance NOUN cana-574 190 36 indicator indicator NOUN cana-574 190 37 that that PRON cana-574 190 38 is be AUX cana-574 190 39 also also ADV cana-574 190 40 known know VERB cana-574 190 41 as as ADP cana-574 190 42 sensitivity sensitivity NOUN cana-574 190 43 or or CCONJ cana-574 190 44 true true ADJ cana-574 190 45 positive positive ADJ cana-574 190 46 rate rate NOUN cana-574 190 47 is be AUX cana-574 190 48 utilized utilize VERB cana-574 190 49 to to PART cana-574 190 50 evaluate evaluate VERB cana-574 190 51 the the DET cana-574 190 52 capability capability NOUN cana-574 190 53 of of ADP cana-574 190 54 a a DET cana-574 190 55 model model NOUN cana-574 190 56 to to PART cana-574 190 57 accurately accurately ADV cana-574 190 58 identify identify VERB cana-574 190 59 all all DET cana-574 190 60 positive positive ADJ cana-574 190 61 cases case NOUN cana-574 190 62 from from ADP cana-574 190 63 the the DET cana-574 190 64 entire entire ADJ cana-574 190 65 number number NOUN cana-574 190 66 of of ADP cana-574 190 67 actual actual ADJ cana-574 190 68 positive positive ADJ cana-574 190 69 examples example NOUN cana-574 190 70 that that PRON cana-574 190 71 are be AUX cana-574 190 72 contained contain VERB cana-574 190 73 inside inside ADP cana-574 190 74 the the DET cana-574 190 75 dataset dataset NOUN cana-574 190 76 . . PUNCT cana-574 191 1 a a DET cana-574 191 2 formula formula NOUN cana-574 191 3 that that PRON cana-574 191 4 is be AUX cana-574 191 5 used use VERB cana-574 191 6 to to PART cana-574 191 7 compute compute VERB cana-574 191 8 it it PRON cana-574 191 9 is be AUX cana-574 191 10 as as SCONJ cana-574 191 11 follows follow VERB cana-574 191 12 : : PUNCT cana-574 191 13 recall-= recall-= PROPN cana-574 191 14 𝐓𝐏 𝐓𝐏 PROPN cana-574 191 15 𝐓𝐏+𝐅𝐍 𝐓𝐏+𝐅𝐍 PROPN cana-574 191 16 ( ( PUNCT cana-574 191 17 36 36 NUM cana-574 191 18 ) ) PUNCT cana-574 191 19 figure figure NOUN cana-574 191 20 8 8 NUM cana-574 191 21 . . PUNCT cana-574 192 1 performance performance NOUN cana-574 192 2 parameters parameter NOUN cana-574 192 3 of of ADP cana-574 192 4 classifier classifier ADJ cana-574 192 5 figure figure NOUN cana-574 192 6 9 9 NUM cana-574 192 7 . . PUNCT cana-574 192 8 comparative comparative ADJ cana-574 192 9 analysis analysis NOUN cana-574 192 10 of of ADP cana-574 192 11 performance performance NOUN cana-574 192 12 parameters parameter NOUN cana-574 192 13 of of ADP cana-574 192 14 classifier classifier NOUN cana-574 192 15 communications communication NOUN cana-574 192 16 on on ADP cana-574 192 17 applied apply VERB cana-574 192 18 nonlinear nonlinear ADJ cana-574 192 19 analysis analysis NOUN cana-574 192 20 issn issn NOUN cana-574 192 21 : : PUNCT cana-574 192 22 1074 1074 NUM cana-574 192 23 - - PUNCT cana-574 192 24 133x 133x NUM cana-574 192 25 vol vol NOUN cana-574 192 26 31 31 NUM cana-574 192 27 no no NOUN cana-574 192 28 . . NOUN cana-574 192 29 2 2 NUM cana-574 192 30 ( ( PUNCT cana-574 192 31 2024 2024 NUM cana-574 192 32 ) ) PUNCT cana-574 192 33 385 385 NUM cana-574 192 34 https://internationalpubls.com https://internationalpubls.com X cana-574 192 35 figure figure NOUN cana-574 192 36 10 10 NUM cana-574 192 37 . . PUNCT cana-574 193 1 comparative comparative ADJ cana-574 193 2 analysis analysis NOUN cana-574 193 3 of of ADP cana-574 193 4 confusion confusion NOUN cana-574 193 5 matrix matrix NOUN cana-574 193 6 for for ADP cana-574 193 7 vgg-16 vgg-16 NOUN cana-574 193 8 and and CCONJ cana-574 193 9 combined combine VERB cana-574 193 10 classifier classifier NOUN cana-574 193 11 in in ADP cana-574 193 12 the the DET cana-574 193 13 contemporary contemporary ADJ cana-574 193 14 landscape landscape NOUN cana-574 193 15 of of ADP cana-574 193 16 agricultural agricultural ADJ cana-574 193 17 technology technology NOUN cana-574 193 18 , , PUNCT cana-574 193 19 the the DET cana-574 193 20 implementation implementation NOUN cana-574 193 21 of of ADP cana-574 193 22 artificial artificial ADJ cana-574 193 23 intelligence intelligence NOUN cana-574 193 24 ( ( PUNCT cana-574 193 25 ai ai NOUN cana-574 193 26 ) ) PUNCT cana-574 193 27 , , PUNCT cana-574 193 28 particularly particularly ADV cana-574 193 29 deep deep ADJ cana-574 193 30 learning learning NOUN cana-574 193 31 models model NOUN cana-574 193 32 , , PUNCT cana-574 193 33 offers offer VERB cana-574 193 34 a a DET cana-574 193 35 promising promising ADJ cana-574 193 36 frontier frontier NOUN cana-574 193 37 for for ADP cana-574 193 38 enhancing enhance VERB cana-574 193 39 livestock livestock NOUN cana-574 193 40 disease disease NOUN cana-574 193 41 detection detection NOUN cana-574 193 42 . . PUNCT cana-574 194 1 this this DET cana-574 194 2 analysis analysis NOUN cana-574 194 3 focuses focus VERB cana-574 194 4 on on ADP cana-574 194 5 evaluating evaluate VERB cana-574 194 6 the the DET cana-574 194 7 performance performance NOUN cana-574 194 8 of of ADP cana-574 194 9 various various ADJ cana-574 194 10 convolutional convolutional ADJ cana-574 194 11 neural neural ADJ cana-574 194 12 networks network NOUN cana-574 194 13 ( ( PUNCT cana-574 194 14 cnns cnns PROPN cana-574 194 15 ) ) PUNCT cana-574 194 16 employed employ VERB cana-574 194 17 in in ADP cana-574 194 18 the the DET cana-574 194 19 detection detection NOUN cana-574 194 20 of of ADP cana-574 194 21 cattle cattle NOUN cana-574 194 22 diseases disease NOUN cana-574 194 23 , , PUNCT cana-574 194 24 including include VERB cana-574 194 25 inception inception PROPN cana-574 194 26 v3 v3 PROPN cana-574 194 27 , , PUNCT cana-574 194 28 mobilenetv2 mobilenetv2 PROPN cana-574 194 29 , , PUNCT cana-574 194 30 densenet169 densenet169 PROPN cana-574 194 31 , , PUNCT cana-574 194 32 vgg-16 vgg-16 NOUN cana-574 194 33 , , PUNCT cana-574 194 34 and and CCONJ cana-574 194 35 resnet-50 resnet-50 PROPN cana-574 194 36 . . PUNCT cana-574 195 1 the the DET cana-574 195 2 study study NOUN cana-574 195 3 uses use VERB cana-574 195 4 a a DET cana-574 195 5 tailored tailor VERB cana-574 195 6 dataset dataset NOUN cana-574 195 7 that that PRON cana-574 195 8 reflects reflect VERB cana-574 195 9 common common ADJ cana-574 195 10 cattle cattle NOUN cana-574 195 11 ailments ailment NOUN cana-574 195 12 , , PUNCT cana-574 195 13 such such ADJ cana-574 195 14 as as ADP cana-574 195 15 foot foot NOUN cana-574 195 16 , , PUNCT cana-574 195 17 mouth mouth NOUN cana-574 195 18 , , PUNCT cana-574 195 19 and and CCONJ cana-574 195 20 lumpy lumpy VERB cana-574 195 21 skin skin NOUN cana-574 195 22 diseases disease NOUN cana-574 195 23 . . PUNCT cana-574 196 1 the the DET cana-574 196 2 dataset dataset NOUN cana-574 196 3 comprises comprise VERB cana-574 196 4 images image NOUN cana-574 196 5 sourced source VERB cana-574 196 6 from from ADP cana-574 196 7 various various ADJ cana-574 196 8 online online ADJ cana-574 196 9 repositories repository NOUN cana-574 196 10 , , PUNCT cana-574 196 11 classified classify VERB cana-574 196 12 into into ADP cana-574 196 13 specific specific ADJ cana-574 196 14 disease disease NOUN cana-574 196 15 categories category NOUN cana-574 196 16 to to PART cana-574 196 17 train train VERB cana-574 196 18 the the DET cana-574 196 19 cnn cnn PROPN cana-574 196 20 models model NOUN cana-574 196 21 . . PUNCT cana-574 197 1 this this DET cana-574 197 2 approach approach NOUN cana-574 197 3 is be AUX cana-574 197 4 designed design VERB cana-574 197 5 to to PART cana-574 197 6 simulate simulate VERB cana-574 197 7 a a DET cana-574 197 8 real real ADJ cana-574 197 9 - - PUNCT cana-574 197 10 world world NOUN cana-574 197 11 application application NOUN cana-574 197 12 where where SCONJ cana-574 197 13 visual visual ADJ cana-574 197 14 data datum NOUN cana-574 197 15 from from ADP cana-574 197 16 livestock livestock NOUN cana-574 197 17 would would AUX cana-574 197 18 be be AUX cana-574 197 19 analyzed analyze VERB cana-574 197 20 to to PART cana-574 197 21 predict predict VERB cana-574 197 22 disease disease NOUN cana-574 197 23 presence presence NOUN cana-574 197 24 . . PUNCT cana-574 198 1 the the DET cana-574 198 2 models model NOUN cana-574 198 3 were be AUX cana-574 198 4 evaluated evaluate VERB cana-574 198 5 based base VERB cana-574 198 6 on on ADP cana-574 198 7 several several ADJ cana-574 198 8 metrics metric NOUN cana-574 198 9 , , PUNCT cana-574 198 10 including include VERB cana-574 198 11 training training NOUN cana-574 198 12 and and CCONJ cana-574 198 13 validation validation NOUN cana-574 198 14 accuracy accuracy NOUN cana-574 198 15 , , PUNCT cana-574 198 16 precision precision NOUN cana-574 198 17 , , PUNCT cana-574 198 18 recall recall NOUN cana-574 198 19 , , PUNCT cana-574 198 20 and and CCONJ cana-574 198 21 f1 f1 NOUN cana-574 198 22 - - PUNCT cana-574 198 23 score score NOUN cana-574 198 24 , , PUNCT cana-574 198 25 which which PRON cana-574 198 26 provide provide VERB cana-574 198 27 a a DET cana-574 198 28 comprehensive comprehensive ADJ cana-574 198 29 view view NOUN cana-574 198 30 of of ADP cana-574 198 31 model model NOUN cana-574 198 32 performance.the performance.the DET cana-574 198 33 results result NOUN cana-574 198 34 presented present VERB cana-574 198 35 in in ADP cana-574 198 36 the the DET cana-574 198 37 study study NOUN cana-574 198 38 are be AUX cana-574 198 39 as as SCONJ cana-574 198 40 follows follow VERB cana-574 198 41 : : PUNCT cana-574 198 42 β€’ β€’ NUM cana-574 198 43 inception inception PROPN cana-574 198 44 v3 v3 PROPN cana-574 198 45 : : PUNCT cana-574 198 46 demonstrated demonstrate VERB cana-574 198 47 high high ADJ cana-574 198 48 reliability reliability NOUN cana-574 198 49 with with ADP cana-574 198 50 a a DET cana-574 198 51 training training NOUN cana-574 198 52 accuracy accuracy NOUN cana-574 198 53 of of ADP cana-574 198 54 97.60 97.60 NUM cana-574 198 55 % % NOUN cana-574 198 56 and and CCONJ cana-574 198 57 validation validation NOUN cana-574 198 58 accuracy accuracy NOUN cana-574 198 59 of of ADP cana-574 198 60 97.98 97.98 NUM cana-574 198 61 % % NOUN cana-574 198 62 . . PUNCT cana-574 199 1 the the DET cana-574 199 2 model model NOUN cana-574 199 3 showed show VERB cana-574 199 4 robust robust ADJ cana-574 199 5 precision precision NOUN cana-574 199 6 and and CCONJ cana-574 199 7 recall recall NOUN cana-574 199 8 , , PUNCT cana-574 199 9 making make VERB cana-574 199 10 it it PRON cana-574 199 11 suitable suitable ADJ cana-574 199 12 for for ADP cana-574 199 13 scenarios scenario NOUN cana-574 199 14 where where SCONJ cana-574 199 15 both both DET cana-574 199 16 false false ADJ cana-574 199 17 positives positive NOUN cana-574 199 18 and and CCONJ cana-574 199 19 false false ADJ cana-574 199 20 negatives negative NOUN cana-574 199 21 carry carry VERB cana-574 199 22 significant significant ADJ cana-574 199 23 consequences consequence NOUN cana-574 199 24 . . PUNCT cana-574 200 1 β€’ β€’ NUM cana-574 200 2 mobilenetv2 mobilenetv2 NOUN cana-574 200 3 : : PUNCT cana-574 200 4 featured feature VERB cana-574 200 5 slightly slightly ADV cana-574 200 6 lower low ADJ cana-574 200 7 validation validation NOUN cana-574 200 8 accuracy accuracy NOUN cana-574 200 9 than than ADP cana-574 200 10 inception inception PROPN cana-574 200 11 v3 v3 PROPN cana-574 200 12 , , PUNCT cana-574 200 13 indicating indicate VERB cana-574 200 14 potential potential ADJ cana-574 200 15 overfitting overfitting NOUN cana-574 200 16 or or CCONJ cana-574 200 17 sensitivity sensitivity NOUN cana-574 200 18 to to ADP cana-574 200 19 the the DET cana-574 200 20 dataset dataset NOUN cana-574 200 21 's 's PART cana-574 200 22 variability variability NOUN cana-574 200 23 . . PUNCT cana-574 201 1 nevertheless nevertheless ADV cana-574 201 2 , , PUNCT cana-574 201 3 its its PRON cana-574 201 4 high high ADJ cana-574 201 5 precision precision NOUN cana-574 201 6 and and CCONJ cana-574 201 7 recall recall NOUN cana-574 201 8 suggest suggest VERB cana-574 201 9 it it PRON cana-574 201 10 remains remain VERB cana-574 201 11 an an DET cana-574 201 12 effective effective ADJ cana-574 201 13 tool tool NOUN cana-574 201 14 for for ADP cana-574 201 15 disease disease NOUN cana-574 201 16 detection detection NOUN cana-574 201 17 . . PUNCT cana-574 202 1 β€’ β€’ NUM cana-574 202 2 densenet169 densenet169 PROPN cana-574 202 3 : : PUNCT cana-574 202 4 emerged emerge VERB cana-574 202 5 as as ADP cana-574 202 6 the the DET cana-574 202 7 top top ADJ cana-574 202 8 performer performer NOUN cana-574 202 9 with with ADP cana-574 202 10 the the DET cana-574 202 11 highest high ADJ cana-574 202 12 validation validation NOUN cana-574 202 13 accuracy accuracy NOUN cana-574 202 14 ( ( PUNCT cana-574 202 15 99.37 99.37 NUM cana-574 202 16 % % NOUN cana-574 202 17 ) ) PUNCT cana-574 202 18 and and CCONJ cana-574 202 19 excellent excellent ADJ cana-574 202 20 precision precision NOUN cana-574 202 21 and and CCONJ cana-574 202 22 recall recall NOUN cana-574 202 23 . . PUNCT cana-574 203 1 its its PRON cana-574 203 2 architecture architecture NOUN cana-574 203 3 possibly possibly ADV cana-574 203 4 offers offer VERB cana-574 203 5 better well ADJ cana-574 203 6 feature feature NOUN cana-574 203 7 extraction extraction NOUN cana-574 203 8 capabilities capability NOUN cana-574 203 9 , , PUNCT cana-574 203 10 which which PRON cana-574 203 11 is be AUX cana-574 203 12 crucial crucial ADJ cana-574 203 13 for for ADP cana-574 203 14 detailed detailed ADJ cana-574 203 15 image image NOUN cana-574 203 16 analysis analysis NOUN cana-574 203 17 in in ADP cana-574 203 18 disease disease NOUN cana-574 203 19 identification identification NOUN cana-574 203 20 . . PUNCT cana-574 204 1 β€’ β€’ NUM cana-574 204 2 vgg-16 vgg-16 NOUN cana-574 204 3 : : PUNCT cana-574 204 4 while while SCONJ cana-574 204 5 it it PRON cana-574 204 6 had have VERB cana-574 204 7 the the DET cana-574 204 8 lowest low ADJ cana-574 204 9 validation validation NOUN cana-574 204 10 accuracy accuracy NOUN cana-574 204 11 among among ADP cana-574 204 12 the the DET cana-574 204 13 top top ADJ cana-574 204 14 performers performer NOUN cana-574 204 15 , , PUNCT cana-574 204 16 its its PRON cana-574 204 17 precision precision NOUN cana-574 204 18 and and CCONJ cana-574 204 19 recall recall NOUN cana-574 204 20 were be AUX cana-574 204 21 high high ADJ cana-574 204 22 . . PUNCT cana-574 205 1 this this DET cana-574 205 2 model model NOUN cana-574 205 3 might might AUX cana-574 205 4 be be AUX cana-574 205 5 more more ADV cana-574 205 6 sensitive sensitive ADJ cana-574 205 7 to to ADP cana-574 205 8 the the DET cana-574 205 9 training training NOUN cana-574 205 10 dataset dataset NOUN cana-574 205 11 's 's PART cana-574 205 12 nuances nuance NOUN cana-574 205 13 and and CCONJ cana-574 205 14 could could AUX cana-574 205 15 benefit benefit VERB cana-574 205 16 from from ADP cana-574 205 17 further further ADJ cana-574 205 18 tuning tuning NOUN cana-574 205 19 . . PUNCT cana-574 206 1 communications communication NOUN cana-574 206 2 on on ADP cana-574 206 3 applied apply VERB cana-574 206 4 nonlinear nonlinear ADJ cana-574 206 5 analysis analysis NOUN cana-574 206 6 issn issn NOUN cana-574 206 7 : : PUNCT cana-574 206 8 1074 1074 NUM cana-574 206 9 - - PUNCT cana-574 206 10 133x 133x NUM cana-574 206 11 vol vol NOUN cana-574 206 12 31 31 NUM cana-574 206 13 no no NOUN cana-574 206 14 . . NOUN cana-574 206 15 2 2 NUM cana-574 206 16 ( ( PUNCT cana-574 206 17 2024 2024 NUM cana-574 206 18 ) ) PUNCT cana-574 206 19 386 386 NUM cana-574 206 20 https://internationalpubls.com https://internationalpubls.com X cana-574 206 21 β€’ β€’ NUM cana-574 206 22 resnet-50 resnet-50 NUM cana-574 206 23 : : PUNCT cana-574 206 24 although although SCONJ cana-574 206 25 it it PRON cana-574 206 26 had have VERB cana-574 206 27 the the DET cana-574 206 28 highest high ADJ cana-574 206 29 training training NOUN cana-574 206 30 accuracy accuracy NOUN cana-574 206 31 ( ( PUNCT cana-574 206 32 99.40 99.40 NUM cana-574 206 33 % % NOUN cana-574 206 34 ) ) PUNCT cana-574 206 35 , , PUNCT cana-574 206 36 its its PRON cana-574 206 37 lower low ADJ cana-574 206 38 validation validation NOUN cana-574 206 39 accuracy accuracy NOUN cana-574 206 40 and and CCONJ cana-574 206 41 significantly significantly ADV cana-574 206 42 lower low ADJ cana-574 206 43 precision precision NOUN cana-574 206 44 and and CCONJ cana-574 206 45 recall recall NOUN cana-574 206 46 indicate indicate VERB cana-574 206 47 issues issue NOUN cana-574 206 48 with with ADP cana-574 206 49 generalizability generalizability NOUN cana-574 206 50 and and CCONJ cana-574 206 51 potential potential ADJ cana-574 206 52 overfitting overfitting NOUN cana-574 206 53 . . PUNCT cana-574 207 1 β€’ β€’ NUM cana-574 207 2 combined combine VERB cana-574 207 3 : : PUNCT cana-574 207 4 this this DET cana-574 207 5 model model NOUN cana-574 207 6 , , PUNCT cana-574 207 7 likely likely ADV cana-574 207 8 an an DET cana-574 207 9 ensemble ensemble NOUN cana-574 207 10 of of ADP cana-574 207 11 different different ADJ cana-574 207 12 architectures architecture NOUN cana-574 207 13 , , PUNCT cana-574 207 14 showed show VERB cana-574 207 15 balanced balanced ADJ cana-574 207 16 performance performance NOUN cana-574 207 17 across across ADP cana-574 207 18 all all DET cana-574 207 19 metrics metric NOUN cana-574 207 20 , , PUNCT cana-574 207 21 demonstrating demonstrate VERB cana-574 207 22 the the DET cana-574 207 23 utility utility NOUN cana-574 207 24 of of ADP cana-574 207 25 model model NOUN cana-574 207 26 ensembling ensemble VERB cana-574 207 27 to to PART cana-574 207 28 stabilize stabilize VERB cana-574 207 29 detection detection NOUN cana-574 207 30 performance performance NOUN cana-574 207 31 . . PUNCT cana-574 208 1 comparing compare VERB cana-574 208 2 these these DET cana-574 208 3 results result NOUN cana-574 208 4 with with ADP cana-574 208 5 historical historical ADJ cana-574 208 6 data datum NOUN cana-574 208 7 from from ADP cana-574 208 8 other other ADJ cana-574 208 9 studies study NOUN cana-574 208 10 underscores underscore VERB cana-574 208 11 the the DET cana-574 208 12 advancement advancement NOUN cana-574 208 13 in in ADP cana-574 208 14 deep deep ADJ cana-574 208 15 learning learning NOUN cana-574 208 16 applications application NOUN cana-574 208 17 for for ADP cana-574 208 18 agricultural agricultural ADJ cana-574 208 19 uses use NOUN cana-574 208 20 . . PUNCT cana-574 209 1 the the DET cana-574 209 2 densenet169 densenet169 PROPN cana-574 209 3 model model NOUN cana-574 209 4 , , PUNCT cana-574 209 5 in in ADP cana-574 209 6 particular particular ADJ cana-574 209 7 , , PUNCT cana-574 209 8 stands stand VERB cana-574 209 9 out out ADP cana-574 209 10 by by ADP cana-574 209 11 setting set VERB cana-574 209 12 a a DET cana-574 209 13 high high ADJ cana-574 209 14 benchmark benchmark NOUN cana-574 209 15 in in ADP cana-574 209 16 validation validation NOUN cana-574 209 17 accuracy accuracy NOUN cana-574 209 18 and and CCONJ cana-574 209 19 f1 f1 NOUN cana-574 209 20 - - PUNCT cana-574 209 21 score score NOUN cana-574 209 22 , , PUNCT cana-574 209 23 indicating indicate VERB cana-574 209 24 superior superior ADJ cana-574 209 25 generalization generalization NOUN cana-574 209 26 over over ADP cana-574 209 27 unseen unseen ADJ cana-574 209 28 data datum NOUN cana-574 209 29 , , PUNCT cana-574 209 30 a a DET cana-574 209 31 critical critical ADJ cana-574 209 32 factor factor NOUN cana-574 209 33 for for ADP cana-574 209 34 practical practical ADJ cana-574 209 35 deployment deployment NOUN cana-574 209 36 . . PUNCT cana-574 210 1 the the DET cana-574 210 2 high high ADJ cana-574 210 3 performance performance NOUN cana-574 210 4 of of ADP cana-574 210 5 these these DET cana-574 210 6 cnn cnn PROPN cana-574 210 7 models model NOUN cana-574 210 8 suggests suggest VERB cana-574 210 9 they they PRON cana-574 210 10 could could AUX cana-574 210 11 be be AUX cana-574 210 12 integrated integrate VERB cana-574 210 13 into into ADP cana-574 210 14 mobile mobile ADJ cana-574 210 15 applications application NOUN cana-574 210 16 for for ADP cana-574 210 17 real real ADJ cana-574 210 18 - - PUNCT cana-574 210 19 time time NOUN cana-574 210 20 disease disease NOUN cana-574 210 21 detection detection NOUN cana-574 210 22 , , PUNCT cana-574 210 23 providing provide VERB cana-574 210 24 farmers farmer NOUN cana-574 210 25 with with ADP cana-574 210 26 timely timely ADJ cana-574 210 27 , , PUNCT cana-574 210 28 accurate accurate ADJ cana-574 210 29 diagnostics diagnostic NOUN cana-574 210 30 , , PUNCT cana-574 210 31 thus thus ADV cana-574 210 32 allowing allow VERB cana-574 210 33 for for ADP cana-574 210 34 quicker quick ADJ cana-574 210 35 response response NOUN cana-574 210 36 and and CCONJ cana-574 210 37 treatment treatment NOUN cana-574 210 38 . . PUNCT cana-574 211 1 future future ADJ cana-574 211 2 research research NOUN cana-574 211 3 could could AUX cana-574 211 4 explore explore VERB cana-574 211 5 incorporating incorporate VERB cana-574 211 6 additional additional ADJ cana-574 211 7 disease disease NOUN cana-574 211 8 categories category NOUN cana-574 211 9 , , PUNCT cana-574 211 10 enhancing enhance VERB cana-574 211 11 data datum NOUN cana-574 211 12 diversity diversity NOUN cana-574 211 13 , , PUNCT cana-574 211 14 and and CCONJ cana-574 211 15 deploying deploy VERB cana-574 211 16 these these DET cana-574 211 17 models model NOUN cana-574 211 18 in in ADP cana-574 211 19 field field NOUN cana-574 211 20 trials trial NOUN cana-574 211 21 to to PART cana-574 211 22 further far ADV cana-574 211 23 validate validate VERB cana-574 211 24 their their PRON cana-574 211 25 effectiveness effectiveness NOUN cana-574 211 26 in in ADP cana-574 211 27 practical practical ADJ cana-574 211 28 setting setting NOUN cana-574 211 29 . . PUNCT cana-574 212 1 5 5 X cana-574 212 2 . . X cana-574 212 3 conclusion conclusion NOUN cana-574 212 4 in in ADP cana-574 212 5 conclusion conclusion NOUN cana-574 212 6 , , PUNCT cana-574 212 7 the the DET cana-574 212 8 application application NOUN cana-574 212 9 of of ADP cana-574 212 10 cnns cnn NOUN cana-574 212 11 in in ADP cana-574 212 12 detecting detect VERB cana-574 212 13 cattle cattle NOUN cana-574 212 14 diseases disease NOUN cana-574 212 15 has have AUX cana-574 212 16 demonstrated demonstrate VERB cana-574 212 17 significant significant ADJ cana-574 212 18 potential potential NOUN cana-574 212 19 to to PART cana-574 212 20 revolutionize revolutionize VERB cana-574 212 21 disease disease NOUN cana-574 212 22 management management NOUN cana-574 212 23 in in ADP cana-574 212 24 livestock livestock NOUN cana-574 212 25 . . PUNCT cana-574 213 1 by by ADP cana-574 213 2 continuing continue VERB cana-574 213 3 to to PART cana-574 213 4 refine refine VERB cana-574 213 5 these these DET cana-574 213 6 models model NOUN cana-574 213 7 and and CCONJ cana-574 213 8 expand expand VERB cana-574 213 9 their their PRON cana-574 213 10 capabilities capability NOUN cana-574 213 11 , , PUNCT cana-574 213 12 the the DET cana-574 213 13 agricultural agricultural ADJ cana-574 213 14 sector sector NOUN cana-574 213 15 can can AUX cana-574 213 16 look look VERB cana-574 213 17 forward forward ADV cana-574 213 18 to to ADP cana-574 213 19 greater great ADJ cana-574 213 20 efficiency efficiency NOUN cana-574 213 21 and and CCONJ cana-574 213 22 accuracy accuracy NOUN cana-574 213 23 in in ADP cana-574 213 24 livestock livestock NOUN cana-574 213 25 healthcare healthcare NOUN cana-574 213 26 , , PUNCT cana-574 213 27 ultimately ultimately ADV cana-574 213 28 leading lead VERB cana-574 213 29 to to ADP cana-574 213 30 improved improve VERB cana-574 213 31 productivity productivity NOUN cana-574 213 32 and and CCONJ cana-574 213 33 reduced reduce VERB cana-574 213 34 losses loss NOUN cana-574 213 35 due due ADJ cana-574 213 36 to to ADP cana-574 213 37 diseases disease NOUN cana-574 213 38 . . PUNCT cana-574 214 1 this this DET cana-574 214 2 study study NOUN cana-574 214 3 not not PART cana-574 214 4 only only ADV cana-574 214 5 contributes contribute VERB cana-574 214 6 to to ADP cana-574 214 7 the the DET cana-574 214 8 field field NOUN cana-574 214 9 of of ADP cana-574 214 10 agricultural agricultural ADJ cana-574 214 11 ai ai NOUN cana-574 214 12 but but CCONJ cana-574 214 13 also also ADV cana-574 214 14 sets set VERB cana-574 214 15 the the DET cana-574 214 16 stage stage NOUN cana-574 214 17 for for ADP cana-574 214 18 future future ADJ cana-574 214 19 innovations innovation NOUN cana-574 214 20 that that PRON cana-574 214 21 could could AUX cana-574 214 22 further far ADV cana-574 214 23 enhance enhance VERB cana-574 214 24 the the DET cana-574 214 25 capabilities capability NOUN cana-574 214 26 of of ADP cana-574 214 27 ai ai VERB cana-574 214 28 in in ADP cana-574 214 29 managing manage VERB cana-574 214 30 livestock livestock NOUN cana-574 214 31 health health NOUN cana-574 214 32 . . PUNCT cana-574 215 1 the the DET cana-574 215 2 potential potential NOUN cana-574 215 3 of of ADP cana-574 215 4 ai ai VERB cana-574 215 5 to to PART cana-574 215 6 impact impact VERB cana-574 215 7 livestock livestock NOUN cana-574 215 8 disease disease NOUN cana-574 215 9 management management NOUN cana-574 215 10 positively positively ADV cana-574 215 11 is be AUX cana-574 215 12 immense immense ADJ cana-574 215 13 and and CCONJ cana-574 215 14 largely largely ADV cana-574 215 15 untapped untapped ADJ cana-574 215 16 . . PUNCT cana-574 216 1 as as SCONJ cana-574 216 2 demonstrated demonstrate VERB cana-574 216 3 by by ADP cana-574 216 4 the the DET cana-574 216 5 success success NOUN cana-574 216 6 of of ADP cana-574 216 7 cnns cnn NOUN cana-574 216 8 in in ADP cana-574 216 9 this this DET cana-574 216 10 study study NOUN cana-574 216 11 , , PUNCT cana-574 216 12 there there PRON cana-574 216 13 is be VERB cana-574 216 14 a a DET cana-574 216 15 clear clear ADJ cana-574 216 16 path path NOUN cana-574 216 17 forward forward ADV cana-574 216 18 that that PRON cana-574 216 19 involves involve VERB cana-574 216 20 technological technological ADJ cana-574 216 21 innovation innovation NOUN cana-574 216 22 , , PUNCT cana-574 216 23 careful careful ADJ cana-574 216 24 consideration consideration NOUN cana-574 216 25 of of ADP cana-574 216 26 ethical ethical ADJ cana-574 216 27 and and CCONJ cana-574 216 28 practical practical ADJ cana-574 216 29 concerns concern NOUN cana-574 216 30 , , PUNCT cana-574 216 31 and and CCONJ cana-574 216 32 close close ADJ cana-574 216 33 collaboration collaboration NOUN cana-574 216 34 between between ADP cana-574 216 35 diverse diverse ADJ cana-574 216 36 stakeholders stakeholder NOUN cana-574 216 37 . . PUNCT cana-574 217 1 by by ADP cana-574 217 2 continuing continue VERB cana-574 217 3 to to PART cana-574 217 4 pursue pursue VERB cana-574 217 5 these these DET cana-574 217 6 avenues avenue NOUN cana-574 217 7 , , PUNCT cana-574 217 8 we we PRON cana-574 217 9 can can AUX cana-574 217 10 look look VERB cana-574 217 11 forward forward ADV cana-574 217 12 to to ADP cana-574 217 13 a a DET cana-574 217 14 future future NOUN cana-574 217 15 where where SCONJ cana-574 217 16 livestock livestock NOUN cana-574 217 17 management management NOUN cana-574 217 18 is be AUX cana-574 217 19 more more ADJ cana-574 217 20 data data NOUN cana-574 217 21 - - PUNCT cana-574 217 22 driven drive VERB cana-574 217 23 , , PUNCT cana-574 217 24 efficient efficient ADJ cana-574 217 25 , , PUNCT cana-574 217 26 and and CCONJ cana-574 217 27 responsive responsive ADJ cana-574 217 28 to to ADP cana-574 217 29 the the DET cana-574 217 30 challenges challenge NOUN cana-574 217 31 posed pose VERB cana-574 217 32 by by ADP cana-574 217 33 diseases disease NOUN cana-574 217 34 . . PUNCT cana-574 218 1 this this PRON cana-574 218 2 will will AUX cana-574 218 3 not not PART cana-574 218 4 only only ADV cana-574 218 5 improve improve VERB cana-574 218 6 the the DET cana-574 218 7 health health NOUN cana-574 218 8 and and CCONJ cana-574 218 9 productivity productivity NOUN cana-574 218 10 of of ADP cana-574 218 11 livestock livestock NOUN cana-574 218 12 but but CCONJ cana-574 218 13 also also ADV cana-574 218 14 the the DET cana-574 218 15 economic economic ADJ cana-574 218 16 viability viability NOUN cana-574 218 17 of of ADP cana-574 218 18 farms farm NOUN cana-574 218 19 around around ADP cana-574 218 20 the the DET cana-574 218 21 globe globe NOUN cana-574 218 22 , , PUNCT cana-574 218 23 securing secure VERB cana-574 218 24 food food NOUN cana-574 218 25 supply supply NOUN cana-574 218 26 chains chain NOUN cana-574 218 27 and and CCONJ cana-574 218 28 enhancing enhance VERB cana-574 218 29 the the DET cana-574 218 30 sustainability sustainability NOUN cana-574 218 31 of of ADP cana-574 218 32 agricultural agricultural ADJ cana-574 218 33 practice practice NOUN cana-574 218 34 . . PUNCT cana-574 219 1 references reference NOUN cana-574 219 2 [ [ X cana-574 219 3 1 1 NUM cana-574 219 4 ] ] PUNCT cana-574 219 5 g. g. PROPN cana-574 219 6 rai rai PROPN cana-574 219 7 , , PUNCT cana-574 219 8 naveen naveen PROPN cana-574 219 9 , , PUNCT cana-574 219 10 a. a. NOUN cana-574 219 11 hussain hussain PROPN cana-574 219 12 , , PUNCT cana-574 219 13 a. a. PROPN cana-574 219 14 kumar kumar PROPN cana-574 219 15 , , PUNCT cana-574 219 16 a. a. NOUN cana-574 219 17 ansari ansari PROPN cana-574 219 18 , , PUNCT cana-574 219 19 and and CCONJ cana-574 219 20 n. n. PROPN cana-574 219 21 khanduja khanduja PROPN cana-574 219 22 , , PUNCT cana-574 219 23 β€œ " PUNCT cana-574 219 24 a a DET cana-574 219 25 deep deep ADJ cana-574 219 26 learning learning NOUN cana-574 219 27 approach approach NOUN cana-574 219 28 to to PART cana-574 219 29 detect detect VERB cana-574 219 30 lumpy lumpy ADJ cana-574 219 31 skin skin NOUN cana-574 219 32 disease disease NOUN cana-574 219 33 in in ADP cana-574 219 34 cows cow NOUN cana-574 219 35 , , PUNCT cana-574 219 36 ” " PUNCT cana-574 219 37 in in ADP cana-574 219 38 computer computer NOUN cana-574 219 39 networks network NOUN cana-574 219 40 , , PUNCT cana-574 219 41 big big ADJ cana-574 219 42 data datum NOUN cana-574 219 43 and and CCONJ cana-574 219 44 iot iot NOUN cana-574 219 45 : : PUNCT cana-574 219 46 proceedings proceeding NOUN cana-574 219 47 of of ADP cana-574 219 48 iccbi iccbi PROPN cana-574 219 49 2020 2020 NUM cana-574 219 50 . . PUNCT cana-574 220 1 springer springer NOUN cana-574 220 2 , , PUNCT cana-574 220 3 2021 2021 NUM cana-574 220 4 , , PUNCT cana-574 220 5 pp pp ADV cana-574 220 6 . . PUNCT cana-574 221 1 369–377 369–377 NUM cana-574 221 2 . . PUNCT cana-574 222 1 [ [ X cana-574 222 2 2 2 NUM cana-574 222 3 ] ] PUNCT cana-574 222 4 m. m. NOUN cana-574 222 5 pastell pastell NOUN cana-574 222 6 and and CCONJ cana-574 222 7 m. m. NOUN cana-574 222 8 kujala kujala NOUN cana-574 222 9 , , PUNCT cana-574 222 10 β€œ " PUNCT cana-574 222 11 a a DET cana-574 222 12 probabilistic probabilistic ADJ cana-574 222 13 neural neural ADJ cana-574 222 14 network network NOUN cana-574 222 15 model model NOUN cana-574 222 16 for for ADP cana-574 222 17 lameness lameness NOUN cana-574 222 18 detection detection NOUN cana-574 222 19 , , PUNCT cana-574 222 20 ” " PUNCT cana-574 222 21 journal journal NOUN cana-574 222 22 of of ADP cana-574 222 23 dairy dairy NOUN cana-574 222 24 science science NOUN cana-574 222 25 , , PUNCT cana-574 222 26 vol vol NOUN cana-574 222 27 . . PROPN cana-574 222 28 90 90 NUM cana-574 222 29 , , PUNCT cana-574 222 30 no no INTJ cana-574 222 31 . . NOUN cana-574 222 32 5 5 NUM cana-574 222 33 , , PUNCT cana-574 222 34 pp pp ADJ cana-574 222 35 . . PUNCT cana-574 223 1 2283–2292 2283–2292 NUM cana-574 223 2 , , PUNCT cana-574 223 3 2007 2007 NUM cana-574 223 4 . . PUNCT cana-574 224 1 [ [ X cana-574 224 2 3 3 X cana-574 224 3 ] ] X cana-574 224 4 g. g. PROPN cana-574 224 5 li li PROPN cana-574 224 6 , , PUNCT cana-574 224 7 g. g. PROPN cana-574 224 8 e. e. PROPN cana-574 224 9 erickson erickson PROPN cana-574 224 10 , , PUNCT cana-574 224 11 and and CCONJ cana-574 224 12 y. y. PROPN cana-574 224 13 xiong xiong PROPN cana-574 224 14 , , PUNCT cana-574 224 15 β€œ " PUNCT cana-574 224 16 individual individual ADJ cana-574 224 17 beef beef NOUN cana-574 224 18 cattle cattle NOUN cana-574 224 19 identification identification NOUN cana-574 224 20 using use VERB cana-574 224 21 muzzle muzzle NOUN cana-574 224 22 images image NOUN cana-574 224 23 and and CCONJ cana-574 224 24 deep deep ADJ cana-574 224 25 learning learning NOUN cana-574 224 26 techniques technique NOUN cana-574 224 27 , , PUNCT cana-574 224 28 ” " PUNCT cana-574 224 29 animals animal NOUN cana-574 224 30 , , PUNCT cana-574 224 31 vol vol NOUN cana-574 224 32 . . PROPN cana-574 224 33 12 12 NUM cana-574 224 34 , , PUNCT cana-574 224 35 no no INTJ cana-574 224 36 . . NOUN cana-574 224 37 11 11 NUM cana-574 224 38 , , PUNCT cana-574 224 39 p. p. NOUN cana-574 224 40 1453 1453 NUM cana-574 224 41 , , PUNCT cana-574 224 42 2022 2022 NUM cana-574 224 43 . . PUNCT cana-574 225 1 [ [ X cana-574 225 2 4 4 NUM cana-574 225 3 ] ] X cana-574 225 4 n. n. PROPN cana-574 225 5 kreΛ‡siΒ΄c kreΛ‡siΒ΄c PROPN cana-574 225 6 , , PUNCT cana-574 225 7 i. i. PROPN cana-574 225 8 sΛ‡imiΒ΄c sΛ‡imiΒ΄c PROPN cana-574 225 9 , , PUNCT cana-574 225 10 t. t. PROPN cana-574 225 11 bedekoviΒ΄c bedekoviΒ΄c PROPN cana-574 225 12 , , PUNCT cana-574 225 13 zΛ‡. zΛ‡. NOUN cana-574 225 14 acinger acinger NOUN cana-574 225 15 - - PUNCT cana-574 225 16 rogiΒ΄c rogiΒ΄c NOUN cana-574 225 17 , , PUNCT cana-574 225 18 and and CCONJ cana-574 225 19 i. i. PROPN cana-574 225 20 lojkiΒ΄c lojkiΒ΄c PROPN cana-574 225 21 , , PUNCT cana-574 225 22 β€œ " PUNCT cana-574 225 23 evaluation evaluation NOUN cana-574 225 24 of of ADP cana-574 225 25 serological serological ADJ cana-574 225 26 tests test NOUN cana-574 225 27 for for ADP cana-574 225 28 detection detection NOUN cana-574 225 29 of of ADP cana-574 225 30 antibodies antibody NOUN cana-574 225 31 against against ADP cana-574 225 32 lumpy lumpy ADJ cana-574 225 33 skin skin NOUN cana-574 225 34 disease disease NOUN cana-574 225 35 virus virus NOUN cana-574 225 36 , , PUNCT cana-574 225 37 ” " PUNCT cana-574 225 38 journal journal NOUN cana-574 225 39 of of ADP cana-574 225 40 clinical clinical ADJ cana-574 225 41 microbiology microbiology NOUN cana-574 225 42 , , PUNCT cana-574 225 43 vol vol NOUN cana-574 225 44 . . PROPN cana-574 226 1 58 58 NUM cana-574 226 2 , , PUNCT cana-574 226 3 no no INTJ cana-574 226 4 . . PUNCT cana-574 227 1 communications communication NOUN cana-574 227 2 on on ADP cana-574 227 3 applied apply VERB cana-574 227 4 nonlinear nonlinear ADJ cana-574 227 5 analysis analysis NOUN cana-574 227 6 issn issn NOUN cana-574 227 7 : : PUNCT cana-574 227 8 1074 1074 NUM cana-574 227 9 - - PUNCT cana-574 227 10 133x 133x NUM cana-574 227 11 vol vol NOUN cana-574 227 12 31 31 NUM cana-574 227 13 no no NOUN cana-574 227 14 . . NOUN cana-574 227 15 2 2 NUM cana-574 227 16 ( ( PUNCT cana-574 227 17 2024 2024 NUM cana-574 227 18 ) ) PUNCT cana-574 227 19 387 387 NUM cana-574 227 20 https://internationalpubls.com https://internationalpubls.com X cana-574 227 21 9 9 NUM cana-574 227 22 , , PUNCT cana-574 227 23 pp pp ADJ cana-574 227 24 . . PUNCT cana-574 228 1 10–1128 10–1128 NUM cana-574 228 2 , , PUNCT cana-574 228 3 2020 2020 NUM cana-574 228 4 . . PUNCT cana-574 229 1 [ [ X cana-574 229 2 5 5 NUM cana-574 229 3 ] ] X cana-574 229 4 b. b. PROPN cana-574 229 5 lake lake PROPN cana-574 229 6 , , PUNCT cana-574 229 7 f. f. PROPN cana-574 229 8 getahun getahun PROPN cana-574 229 9 , , PUNCT cana-574 229 10 and and CCONJ cana-574 229 11 f. f. PROPN cana-574 229 12 t. t. PROPN cana-574 229 13 teshome teshome PROPN cana-574 229 14 , , PUNCT cana-574 229 15 β€œ " PUNCT cana-574 229 16 application application NOUN cana-574 229 17 of of ADP cana-574 229 18 artificial artificial ADJ cana-574 229 19 intelligence intelligence NOUN cana-574 229 20 algorithm algorithm NOUN cana-574 229 21 in in ADP cana-574 229 22 image image NOUN cana-574 229 23 processing processing NOUN cana-574 229 24 for for ADP cana-574 229 25 cattle cattle NOUN cana-574 229 26 disease disease NOUN cana-574 229 27 diagnosis diagnosis NOUN cana-574 229 28 , , PUNCT cana-574 229 29 ” " PUNCT cana-574 229 30 journal journal NOUN cana-574 229 31 of of ADP cana-574 229 32 intelligent intelligent ADJ cana-574 229 33 learning learning NOUN cana-574 229 34 systems system NOUN cana-574 229 35 and and CCONJ cana-574 229 36 applications application NOUN cana-574 229 37 , , PUNCT cana-574 229 38 vol vol NOUN cana-574 229 39 . . PROPN cana-574 229 40 14 14 NUM cana-574 229 41 , , PUNCT cana-574 229 42 no no INTJ cana-574 229 43 . . NOUN cana-574 229 44 04 04 NUM cana-574 229 45 , , PUNCT cana-574 229 46 pp pp ADJ cana-574 229 47 . . PUNCT cana-574 230 1 71–88 71–88 NUM cana-574 230 2 , , PUNCT cana-574 230 3 2022 2022 NUM cana-574 230 4 . . PUNCT cana-574 231 1 [ [ X cana-574 231 2 6 6 NUM cana-574 231 3 ] ] PUNCT cana-574 231 4 b. b. PROPN cana-574 231 5 jiang jiang PROPN cana-574 231 6 , , PUNCT cana-574 231 7 q. q. PROPN cana-574 231 8 wu wu PROPN cana-574 231 9 , , PUNCT cana-574 231 10 x. x. PROPN cana-574 231 11 yin yin PROPN cana-574 231 12 , , PUNCT cana-574 231 13 d. d. PROPN cana-574 231 14 wu wu PROPN cana-574 231 15 , , PUNCT cana-574 231 16 h. h. PROPN cana-574 231 17 song song PROPN cana-574 231 18 , , PUNCT cana-574 231 19 and and CCONJ cana-574 231 20 d. d. PROPN cana-574 231 21 he he PRON cana-574 231 22 , , PUNCT cana-574 231 23 β€œ " PUNCT cana-574 231 24 flyolov3 flyolov3 X cana-574 231 25 deep deep ADJ cana-574 231 26 learning learning NOUN cana-574 231 27 for for ADP cana-574 231 28 key key ADJ cana-574 231 29 parts part NOUN cana-574 231 30 of of ADP cana-574 231 31 dairy dairy NOUN cana-574 231 32 cow cow NOUN cana-574 231 33 body body NOUN cana-574 231 34 detection detection NOUN cana-574 231 35 , , PUNCT cana-574 231 36 ” " PUNCT cana-574 231 37 computers computer NOUN cana-574 231 38 and and CCONJ cana-574 231 39 electronics electronic NOUN cana-574 231 40 in in ADP cana-574 231 41 agriculture agriculture NOUN cana-574 231 42 , , PUNCT cana-574 231 43 vol vol NOUN cana-574 231 44 . . PROPN cana-574 231 45 166 166 NUM cana-574 231 46 , , PUNCT cana-574 231 47 p. p. NOUN cana-574 231 48 104982 104982 NUM cana-574 231 49 , , PUNCT cana-574 231 50 2019 2019 NUM cana-574 231 51 . . PUNCT cana-574 232 1 [ [ X cana-574 232 2 7 7 X cana-574 232 3 ] ] X cana-574 232 4 v. v. PROPN cana-574 232 5 r. r. PROPN cana-574 232 6 allugunti allugunti PROPN cana-574 232 7 , , PUNCT cana-574 232 8 β€œ " PUNCT cana-574 232 9 a a DET cana-574 232 10 machine machine NOUN cana-574 232 11 learning learn VERB cana-574 232 12 model model NOUN cana-574 232 13 for for ADP cana-574 232 14 skin skin NOUN cana-574 232 15 disease disease NOUN cana-574 232 16 classification classification NOUN cana-574 232 17 using use VERB cana-574 232 18 convolution convolution NOUN cana-574 232 19 neural neural ADJ cana-574 232 20 network network NOUN cana-574 232 21 , , PUNCT cana-574 232 22 ” " PUNCT cana-574 232 23 international international ADJ cana-574 232 24 journal journal NOUN cana-574 232 25 of of ADP cana-574 232 26 computing computing NOUN cana-574 232 27 , , PUNCT cana-574 232 28 programming programming NOUN cana-574 232 29 and and CCONJ cana-574 232 30 database database NOUN cana-574 232 31 management management NOUN cana-574 232 32 , , PUNCT cana-574 232 33 vol vol NOUN cana-574 232 34 . . PROPN cana-574 233 1 3 3 NUM cana-574 233 2 , , PUNCT cana-574 233 3 no no INTJ cana-574 233 4 . . NOUN cana-574 233 5 1 1 NUM cana-574 233 6 , , PUNCT cana-574 233 7 pp pp ADJ cana-574 233 8 . . PUNCT cana-574 234 1 141–147 141–147 NUM cana-574 234 2 , , PUNCT cana-574 234 3 2022 2022 NUM cana-574 234 4 . . PUNCT cana-574 235 1 [ [ X cana-574 235 2 8 8 NUM cana-574 235 3 ] ] PUNCT cana-574 235 4 a. a. NOUN cana-574 235 5 poursaberi poursaberi PROPN cana-574 235 6 , , PUNCT cana-574 235 7 c. c. PROPN cana-574 235 8 bahr bahr PROPN cana-574 235 9 , , PUNCT cana-574 235 10 a. a. NOUN cana-574 235 11 pluk pluk PROPN cana-574 235 12 , , PUNCT cana-574 235 13 a. a. PROPN cana-574 235 14 van van PROPN cana-574 235 15 nuffel nuffel PROPN cana-574 235 16 , , PUNCT cana-574 235 17 and and CCONJ cana-574 235 18 d. d. PROPN cana-574 235 19 berckmans berckman NOUN cana-574 235 20 , , PUNCT cana-574 235 21 β€œ " PUNCT cana-574 235 22 real real ADJ cana-574 235 23 - - PUNCT cana-574 235 24 time time NOUN cana-574 235 25 automatic automatic ADJ cana-574 235 26 lameness lameness NOUN cana-574 235 27 detection detection NOUN cana-574 235 28 based base VERB cana-574 235 29 on on ADP cana-574 235 30 back back ADJ cana-574 235 31 posture posture NOUN cana-574 235 32 extraction extraction NOUN cana-574 235 33 in in ADP cana-574 235 34 dairy dairy NOUN cana-574 235 35 cattle cattle NOUN cana-574 235 36 : : PUNCT cana-574 235 37 shape shape VERB cana-574 235 38 analysis analysis NOUN cana-574 235 39 of of ADP cana-574 235 40 cow cow NOUN cana-574 235 41 with with ADP cana-574 235 42 image image NOUN cana-574 235 43 processing processing NOUN cana-574 235 44 techniques technique NOUN cana-574 235 45 , , PUNCT cana-574 235 46 ” " PUNCT cana-574 235 47 computers computer NOUN cana-574 235 48 and and CCONJ cana-574 235 49 electronics electronic NOUN cana-574 235 50 in in ADP cana-574 235 51 agriculture agriculture NOUN cana-574 235 52 , , PUNCT cana-574 235 53 vol vol NOUN cana-574 235 54 . . PROPN cana-574 236 1 74 74 NUM cana-574 236 2 , , PUNCT cana-574 236 3 no no INTJ cana-574 236 4 . . NOUN cana-574 236 5 1 1 NUM cana-574 236 6 , , PUNCT cana-574 236 7 pp pp ADJ cana-574 236 8 . . PUNCT cana-574 237 1 110–119 110–119 NUM cana-574 237 2 , , PUNCT cana-574 237 3 2010 2010 NUM cana-574 237 4 . . PUNCT cana-574 238 1 [ [ X cana-574 238 2 9 9 NUM cana-574 238 3 ] ] X cana-574 238 4 e. e. PROPN cana-574 238 5 afshari afshari PROPN cana-574 238 6 safavi safavi VERB cana-574 238 7 , , PUNCT cana-574 238 8 β€œ " PUNCT cana-574 238 9 assessing assess VERB cana-574 238 10 machine machine NOUN cana-574 238 11 learning learn VERB cana-574 238 12 techniques technique NOUN cana-574 238 13 in in ADP cana-574 238 14 forecasting forecasting NOUN cana-574 238 15 lumpy lumpy ADJ cana-574 238 16 skin skin NOUN cana-574 238 17 disease disease NOUN cana-574 238 18 occurrence occurrence NOUN cana-574 238 19 based base VERB cana-574 238 20 on on ADP cana-574 238 21 meteorological meteorological ADJ cana-574 238 22 and and CCONJ cana-574 238 23 geospatial geospatial ADJ cana-574 238 24 features feature NOUN cana-574 238 25 , , PUNCT cana-574 238 26 ” " PUNCT cana-574 238 27 tropical tropical ADJ cana-574 238 28 animal animal NOUN cana-574 238 29 health health NOUN cana-574 238 30 and and CCONJ cana-574 238 31 production production NOUN cana-574 238 32 , , PUNCT cana-574 238 33 vol vol NOUN cana-574 238 34 . . PROPN cana-574 239 1 54 54 NUM cana-574 239 2 , , PUNCT cana-574 239 3 no no INTJ cana-574 239 4 . . NOUN cana-574 239 5 1 1 NUM cana-574 239 6 , , PUNCT cana-574 239 7 p. p. NOUN cana-574 239 8 55 55 NUM cana-574 239 9 , , PUNCT cana-574 239 10 2022 2022 NUM cana-574 239 11 . . PUNCT cana-574 240 1 [ [ X cana-574 240 2 10 10 NUM cana-574 240 3 ] ] X cana-574 240 4 e. e. PROPN cana-574 240 5 arazo arazo PROPN cana-574 240 6 , , PUNCT cana-574 240 7 r. r. PROPN cana-574 240 8 aly aly PROPN cana-574 240 9 , , PUNCT cana-574 240 10 and and CCONJ cana-574 240 11 k. k. PROPN cana-574 240 12 mcguinness mcguinness PROPN cana-574 240 13 , , PUNCT cana-574 240 14 β€œ " PUNCT cana-574 240 15 segmentation segmentation NOUN cana-574 240 16 enhanced enhance VERB cana-574 240 17 lameness lameness NOUN cana-574 240 18 detection detection NOUN cana-574 240 19 in in ADP cana-574 240 20 dairy dairy NOUN cana-574 240 21 cows cow NOUN cana-574 240 22 from from ADP cana-574 240 23 rgb rgb PROPN cana-574 240 24 and and CCONJ cana-574 240 25 depth depth NOUN cana-574 240 26 video video NOUN cana-574 240 27 , , PUNCT cana-574 240 28 ” " PUNCT cana-574 240 29 arxiv arxiv PROPN cana-574 240 30 preprint preprint NOUN cana-574 240 31 arxiv:2206.04449 arxiv:2206.04449 NOUN cana-574 240 32 , , PUNCT cana-574 240 33 2022 2022 NUM cana-574 240 34 . . PUNCT cana-574 241 1 [ [ X cana-574 241 2 11 11 NUM cana-574 241 3 ] ] PUNCT cana-574 241 4 s. s. PROPN cana-574 241 5 shahinfar shahinfar PROPN cana-574 241 6 , , PUNCT cana-574 241 7 d. d. PROPN cana-574 241 8 page page NOUN cana-574 241 9 , , PUNCT cana-574 241 10 j. j. PROPN cana-574 241 11 guenther guenther PROPN cana-574 241 12 , , PUNCT cana-574 241 13 v. v. PROPN cana-574 241 14 cabrera cabrera PROPN cana-574 241 15 , , PUNCT cana-574 241 16 p. p. NOUN cana-574 241 17 fricke fricke PROPN cana-574 241 18 , , PUNCT cana-574 241 19 and and CCONJ cana-574 241 20 k. k. PROPN cana-574 241 21 weigel weigel PROPN cana-574 241 22 , , PUNCT cana-574 241 23 β€œ " PUNCT cana-574 241 24 prediction prediction NOUN cana-574 241 25 of of ADP cana-574 241 26 insemination insemination NOUN cana-574 241 27 outcomes outcome NOUN cana-574 241 28 in in ADP cana-574 241 29 holstein holstein NOUN cana-574 241 30 dairy dairy NOUN cana-574 241 31 cattle cattle NOUN cana-574 241 32 using use VERB cana-574 241 33 alternative alternative ADJ cana-574 241 34 machine machine NOUN cana-574 241 35 learning learning NOUN cana-574 241 36 algorithms algorithm NOUN cana-574 241 37 , , PUNCT cana-574 241 38 ” " PUNCT cana-574 241 39 journal journal NOUN cana-574 241 40 of of ADP cana-574 241 41 dairy dairy NOUN cana-574 241 42 science science NOUN cana-574 241 43 , , PUNCT cana-574 241 44 vol vol NOUN cana-574 241 45 . . PROPN cana-574 242 1 97 97 NUM cana-574 242 2 , , PUNCT cana-574 242 3 no no INTJ cana-574 242 4 . . NOUN cana-574 242 5 2 2 NUM cana-574 242 6 , , PUNCT cana-574 242 7 pp pp ADJ cana-574 242 8 . . PUNCT cana-574 243 1 731–742 731–742 NUM cana-574 243 2 , , PUNCT cana-574 243 3 2014 2014 NUM cana-574 243 4 . . PUNCT cana-574 244 1 [ [ X cana-574 244 2 12 12 NUM cana-574 244 3 ] ] X cana-574 244 4 r. r. PROPN cana-574 244 5 chandrarathna chandrarathna PROPN cana-574 244 6 , , PUNCT cana-574 244 7 t. t. PROPN cana-574 244 8 weerasinghe weerasinghe NOUN cana-574 244 9 , , PUNCT cana-574 244 10 n. n. PROPN cana-574 244 11 madhuranga madhuranga PROPN cana-574 244 12 , , PUNCT cana-574 244 13 t. t. NOUN cana-574 244 14 thennakoon thennakoon NOUN cana-574 244 15 , , PUNCT cana-574 244 16 a. a. NOUN cana-574 244 17 gamage gamage NOUN cana-574 244 18 , , PUNCT cana-574 244 19 and and CCONJ cana-574 244 20 e. e. PROPN cana-574 244 21 lakmali lakmali PROPN cana-574 244 22 , , PUNCT cana-574 244 23 β€œ " PUNCT cana-574 244 24 ’ ' PUNCT cana-574 244 25 the the DET cana-574 244 26 taurus taurus NOUN cana-574 244 27 ’ ' PUNCT cana-574 244 28 : : PUNCT cana-574 244 29 cattle cattle NOUN cana-574 244 30 breeds breed NOUN cana-574 244 31 & & CCONJ cana-574 244 32 diseases disease NOUN cana-574 244 33 identification identification NOUN cana-574 244 34 mobile mobile NOUN cana-574 244 35 application application NOUN cana-574 244 36 using use VERB cana-574 244 37 machine machine NOUN cana-574 244 38 learning learning NOUN cana-574 244 39 , , PUNCT cana-574 244 40 ” " PUNCT cana-574 244 41 arxiv arxiv PROPN cana-574 244 42 preprint preprint NOUN cana-574 244 43 arxiv:2302.10920 arxiv:2302.10920 NOUN cana-574 244 44 , , PUNCT cana-574 244 45 2023 2023 NUM cana-574 244 46 . . PUNCT cana-574 245 1 [ [ X cana-574 245 2 13 13 NUM cana-574 245 3 ] ] X cana-574 245 4 y. y. PROPN cana-574 245 5 qiao qiao PROPN cana-574 245 6 , , PUNCT cana-574 245 7 d. d. PROPN cana-574 245 8 su su PROPN cana-574 245 9 , , PUNCT cana-574 245 10 h. h. PROPN cana-574 245 11 kong kong PROPN cana-574 245 12 , , PUNCT cana-574 245 13 s. s. PROPN cana-574 245 14 sukkarieh sukkarieh PROPN cana-574 245 15 , , PUNCT cana-574 245 16 s. s. PROPN cana-574 245 17 lomax lomax PROPN cana-574 245 18 , , PUNCT cana-574 245 19 and and CCONJ cana-574 245 20 c. c. PROPN cana-574 245 21 clark clark PROPN cana-574 245 22 , , PUNCT cana-574 245 23 β€œ " PUNCT cana-574 245 24 individual individual ADJ cana-574 245 25 cattle cattle NOUN cana-574 245 26 identification identification NOUN cana-574 245 27 using use VERB cana-574 245 28 a a DET cana-574 245 29 deep deep ADJ cana-574 245 30 learning learning NOUN cana-574 245 31 based base VERB cana-574 245 32 framework framework NOUN cana-574 245 33 , , PUNCT cana-574 245 34 ” " PUNCT cana-574 245 35 ifac ifac NOUN cana-574 245 36 - - PUNCT cana-574 245 37 papersonline papersonline NOUN cana-574 245 38 , , PUNCT cana-574 245 39 vol vol NOUN cana-574 245 40 . . PROPN cana-574 245 41 52 52 NUM cana-574 245 42 , , PUNCT cana-574 245 43 no no INTJ cana-574 245 44 . . NOUN cana-574 245 45 30 30 NUM cana-574 245 46 , , PUNCT cana-574 245 47 pp pp ADJ cana-574 245 48 . . PUNCT cana-574 246 1 318–323 318–323 NUM cana-574 246 2 , , PUNCT cana-574 246 3 2019 2019 NUM cana-574 246 4 . . PUNCT cana-574 247 1 [ [ X cana-574 247 2 14 14 NUM cana-574 247 3 ] ] X cana-574 247 4 m. m. NOUN cana-574 247 5 williams williams PROPN cana-574 247 6 , , PUNCT cana-574 247 7 n. n. PROPN cana-574 247 8 mac mac PROPN cana-574 247 9 parthalΒ΄ain parthalΒ΄ain NOUN cana-574 247 10 , , PUNCT cana-574 247 11 p. p. NOUN cana-574 247 12 brewer brewer NOUN cana-574 247 13 , , PUNCT cana-574 247 14 w. w. PROPN cana-574 247 15 james james PROPN cana-574 247 16 , , PUNCT cana-574 247 17 and and CCONJ cana-574 247 18 m. m. NOUN cana-574 247 19 rose rise VERB cana-574 247 20 , , PUNCT cana-574 247 21 β€œ " PUNCT cana-574 247 22 a a DET cana-574 247 23 novel novel ADJ cana-574 247 24 behavioral behavioral ADJ cana-574 247 25 model model NOUN cana-574 247 26 of of ADP cana-574 247 27 the the DET cana-574 247 28 pasture pasture NOUN cana-574 247 29 - - PUNCT cana-574 247 30 based base VERB cana-574 247 31 dairy dairy NOUN cana-574 247 32 cow cow NOUN cana-574 247 33 from from ADP cana-574 247 34 gps gps PROPN cana-574 247 35 data datum NOUN cana-574 247 36 using use VERB cana-574 247 37 data datum NOUN cana-574 247 38 mining mining NOUN cana-574 247 39 and and CCONJ cana-574 247 40 machine machine NOUN cana-574 247 41 learning learn VERB cana-574 247 42 techniques technique NOUN cana-574 247 43 , , PUNCT cana-574 247 44 ” " PUNCT cana-574 247 45 journal journal NOUN cana-574 247 46 of of ADP cana-574 247 47 dairy dairy NOUN cana-574 247 48 science science NOUN cana-574 247 49 , , PUNCT cana-574 247 50 vol vol NOUN cana-574 247 51 . . PROPN cana-574 248 1 99 99 NUM cana-574 248 2 , , PUNCT cana-574 248 3 no no INTJ cana-574 248 4 . . NOUN cana-574 248 5 3 3 NUM cana-574 248 6 , , PUNCT cana-574 248 7 pp pp PROPN cana-574 248 8 . . PUNCT cana-574 249 1 2063–2075 2063–2075 NUM cana-574 249 2 , , PUNCT cana-574 249 3 2016 2016 NUM cana-574 249 4 . . PUNCT cana-574 250 1 [ [ X cana-574 250 2 15 15 NUM cana-574 250 3 ] ] X cana-574 250 4 y. y. PROPN cana-574 250 5 li li PROPN cana-574 250 6 , , PUNCT cana-574 250 7 h. h. PROPN cana-574 250 8 shu shu PROPN cana-574 250 9 , , PUNCT cana-574 250 10 j. j. PROPN cana-574 250 11 bindelle bindelle PROPN cana-574 250 12 , , PUNCT cana-574 250 13 b. b. PROPN cana-574 250 14 xu xu PROPN cana-574 250 15 , , PUNCT cana-574 250 16 w. w. PROPN cana-574 250 17 zhang zhang PROPN cana-574 250 18 , , PUNCT cana-574 250 19 z. z. PROPN cana-574 250 20 jin jin PROPN cana-574 250 21 , , PUNCT cana-574 250 22 l. l. PROPN cana-574 250 23 guo guo PROPN cana-574 250 24 , , PUNCT cana-574 250 25 and and CCONJ cana-574 250 26 w. w. PROPN cana-574 250 27 wang wang PROPN cana-574 250 28 , , PUNCT cana-574 250 29 β€œ " PUNCT cana-574 250 30 classification classification NOUN cana-574 250 31 and and CCONJ cana-574 250 32 analysis analysis NOUN cana-574 250 33 of of ADP cana-574 250 34 multiple multiple ADJ cana-574 250 35 cattle cattle NOUN cana-574 250 36 unitary unitary ADJ cana-574 250 37 behaviors behavior NOUN cana-574 250 38 and and CCONJ cana-574 250 39 movements movement NOUN cana-574 250 40 based base VERB cana-574 250 41 on on ADP cana-574 250 42 machine machine NOUN cana-574 250 43 learning learning NOUN cana-574 250 44 methods method NOUN cana-574 250 45 , , PUNCT cana-574 250 46 ” " PUNCT cana-574 250 47 animals animal NOUN cana-574 250 48 , , PUNCT cana-574 250 49 vol vol NOUN cana-574 250 50 . . PROPN cana-574 250 51 12 12 NUM cana-574 250 52 , , PUNCT cana-574 250 53 no no INTJ cana-574 250 54 . . NOUN cana-574 250 55 9 9 NUM cana-574 250 56 , , PUNCT cana-574 250 57 p. p. NOUN cana-574 250 58 1060 1060 NUM cana-574 250 59 , , PUNCT cana-574 250 60 2022 2022 NUM cana-574 250 61 . . PUNCT cana-574 251 1 [ [ X cana-574 251 2 16 16 NUM cana-574 251 3 ] ] X cana-574 251 4 y. y. PROPN cana-574 251 5 qiao qiao PROPN cana-574 251 6 , , PUNCT cana-574 251 7 m. m. NOUN cana-574 251 8 truman truman PROPN cana-574 251 9 , , PUNCT cana-574 251 10 and and CCONJ cana-574 251 11 s. s. PROPN cana-574 251 12 sukkarieh sukkarieh PROPN cana-574 251 13 , , PUNCT cana-574 251 14 β€œ " PUNCT cana-574 251 15 cattle cattle NOUN cana-574 251 16 segmentation segmentation NOUN cana-574 251 17 and and CCONJ cana-574 251 18 contour contour NOUN cana-574 251 19 extraction extraction NOUN cana-574 251 20 based base VERB cana-574 251 21 on on ADP cana-574 251 22 mask mask NOUN cana-574 251 23 r r NOUN cana-574 251 24 - - PUNCT cana-574 251 25 cnn cnn PROPN cana-574 251 26 for for ADP cana-574 251 27 precision precision NOUN cana-574 251 28 livestock livestock NOUN cana-574 251 29 farming farming NOUN cana-574 251 30 , , PUNCT cana-574 251 31 ” " PUNCT cana-574 251 32 computers computer NOUN cana-574 251 33 and and CCONJ cana-574 251 34 electronics electronic NOUN cana-574 251 35 in in ADP cana-574 251 36 agriculture agriculture NOUN cana-574 251 37 , , PUNCT cana-574 251 38 vol vol NOUN cana-574 251 39 . . PROPN cana-574 251 40 165 165 NUM cana-574 251 41 , , PUNCT cana-574 251 42 p. p. NOUN cana-574 251 43 104958 104958 NUM cana-574 251 44 , , PUNCT cana-574 251 45 2019 2019 NUM cana-574 251 46 . . PUNCT cana-574 252 1 [ [ X cana-574 252 2 17 17 NUM cana-574 252 3 ] ] PUNCT cana-574 252 4 k. k. PROPN cana-574 252 5 liakos liakos PROPN cana-574 252 6 , , PUNCT cana-574 252 7 s. s. PROPN cana-574 252 8 p. p. PROPN cana-574 252 9 moustakidis moustakidis PROPN cana-574 252 10 , , PUNCT cana-574 252 11 g. g. PROPN cana-574 252 12 tsiotra tsiotra PROPN cana-574 252 13 , , PUNCT cana-574 252 14 t. t. PROPN cana-574 252 15 bartzanas bartzanas PROPN cana-574 252 16 , , PUNCT cana-574 252 17 d. d. PROPN cana-574 252 18 bochtis bochtis PROPN cana-574 252 19 , , PUNCT cana-574 252 20 and and CCONJ cana-574 252 21 c. c. PROPN cana-574 252 22 parisses parisse NOUN cana-574 252 23 , , PUNCT cana-574 252 24 β€œ " PUNCT cana-574 252 25 machine machine NOUN cana-574 252 26 learning learning NOUN cana-574 252 27 based base VERB cana-574 252 28 computational computational ADJ cana-574 252 29 analysis analysis NOUN cana-574 252 30 method method NOUN cana-574 252 31 for for ADP cana-574 252 32 cattle cattle NOUN cana-574 252 33 lameness lameness NOUN cana-574 252 34 prediction prediction NOUN cana-574 252 35 . . PUNCT cana-574 252 36 ” " PUNCT cana-574 253 1 haicta haicta PROPN cana-574 253 2 , , PUNCT cana-574 253 3 vol vol NOUN cana-574 253 4 . . PROPN cana-574 253 5 128 128 NUM cana-574 253 6 , , PUNCT cana-574 253 7 p. p. NOUN cana-574 253 8 139 139 NUM cana-574 253 9 , , PUNCT cana-574 253 10 2017 2017 NUM cana-574 253 11 . . PUNCT cana-574 254 1 [ [ X cana-574 254 2 18 18 NUM cana-574 254 3 ] ] X cana-574 254 4 d. d. PROPN cana-574 254 5 wu wu PROPN cana-574 254 6 , , PUNCT cana-574 254 7 q. q. PROPN cana-574 254 8 wu wu PROPN cana-574 254 9 , , PUNCT cana-574 254 10 x. x. PROPN cana-574 254 11 yin yin PROPN cana-574 254 12 , , PUNCT cana-574 254 13 b. b. PROPN cana-574 254 14 jiang jiang PROPN cana-574 254 15 , , PUNCT cana-574 254 16 h. h. PROPN cana-574 254 17 wang wang PROPN cana-574 254 18 , , PUNCT cana-574 254 19 d. d. PROPN cana-574 254 20 he he PRON cana-574 254 21 , , PUNCT cana-574 254 22 and and CCONJ cana-574 254 23 h. h. PROPN cana-574 254 24 song song PROPN cana-574 254 25 , , PUNCT cana-574 254 26 β€œ " PUNCT cana-574 254 27 lameness lameness NOUN cana-574 254 28 detection detection NOUN cana-574 254 29 of of ADP cana-574 254 30 dairy dairy NOUN cana-574 254 31 cows cow NOUN cana-574 254 32 based base VERB cana-574 254 33 on on ADP cana-574 254 34 the the DET cana-574 254 35 yolov3 yolov3 PROPN cana-574 254 36 deep deep ADJ cana-574 254 37 learning learn VERB cana-574 254 38 algorithm algorithm NOUN cana-574 254 39 and and CCONJ cana-574 254 40 a a DET cana-574 254 41 relative relative ADJ cana-574 254 42 step step NOUN cana-574 254 43 size size NOUN cana-574 254 44 characteristic characteristic ADJ cana-574 254 45 vector vector NOUN cana-574 254 46 , , PUNCT cana-574 254 47 ” " PUNCT cana-574 254 48 biosystems biosystems PROPN cana-574 254 49 engineering engineering NOUN cana-574 254 50 , , PUNCT cana-574 254 51 vol vol NOUN cana-574 254 52 . . PROPN cana-574 254 53 189 189 NUM cana-574 254 54 , , PUNCT cana-574 254 55 pp pp ADJ cana-574 254 56 . . PUNCT cana-574 255 1 150–163 150–163 NUM cana-574 255 2 , , PUNCT cana-574 255 3 2020 2020 NUM cana-574 255 4 . . PUNCT cana-574 256 1 [ [ X cana-574 256 2 19 19 NUM cana-574 256 3 ] ] X cana-574 256 4 s. s. PROPN cana-574 256 5 grampurohit grampurohit PROPN cana-574 256 6 and and CCONJ cana-574 256 7 c. c. PROPN cana-574 256 8 sagarnal sagarnal NOUN cana-574 256 9 , , PUNCT cana-574 256 10 β€œ " PUNCT cana-574 256 11 disease disease NOUN cana-574 256 12 prediction prediction NOUN cana-574 256 13 using use VERB cana-574 256 14 machine machine NOUN cana-574 256 15 learning learning NOUN cana-574 256 16 algorithms algorithm NOUN cana-574 256 17 , , PUNCT cana-574 256 18 ” " PUNCT cana-574 256 19 in in ADP cana-574 256 20 2020 2020 NUM cana-574 256 21 international international ADJ cana-574 256 22 conference conference NOUN cana-574 256 23 for for ADP cana-574 256 24 emerging emerge VERB cana-574 256 25 technology technology NOUN cana-574 256 26 ( ( PUNCT cana-574 256 27 incet incet PROPN cana-574 256 28 ) ) PUNCT cana-574 256 29 . . PUNCT cana-574 257 1 ieee ieee PROPN cana-574 257 2 , , PUNCT cana-574 257 3 2020 2020 NUM cana-574 257 4 , , PUNCT cana-574 257 5 pp pp ADV cana-574 257 6 . . PUNCT cana-574 258 1 1–7 1–7 X cana-574 258 2 . . PUNCT cana-574 259 1 [ [ X cana-574 259 2 20 20 NUM cana-574 259 3 ] ] X cana-574 259 4 n. n. PROPN cana-574 259 5 v. v. PROPN cana-574 259 6 kishan kishan PROPN cana-574 259 7 , , PUNCT cana-574 259 8 y. y. PROPN cana-574 259 9 s. s. PROPN cana-574 259 10 trinath trinath PROPN cana-574 259 11 , , PUNCT cana-574 259 12 s. s. PROPN cana-574 259 13 kavalur kavalur PROPN cana-574 259 14 , , PUNCT cana-574 259 15 v. v. ADP cana-574 259 16 sangamesha sangamesha PROPN cana-574 259 17 , , PUNCT cana-574 259 18 and and CCONJ cana-574 259 19 s. s. PROPN cana-574 259 20 reddy reddy PROPN cana-574 259 21 , , PUNCT cana-574 259 22 β€œ " PUNCT cana-574 259 23 cattle cattle NOUN cana-574 259 24 disease disease NOUN cana-574 259 25 identification identification NOUN cana-574 259 26 using use VERB cana-574 259 27 prediction prediction NOUN cana-574 259 28 techniques technique NOUN cana-574 259 29 , , PUNCT cana-574 259 30 ” " PUNCT cana-574 259 31 ijariie ijariie NOUN cana-574 259 32 - - PUNCT cana-574 259 33 issn issn PROPN cana-574 259 34 ( ( PUNCT cana-574 259 35 o o NOUN cana-574 259 36 ) ) PUNCT cana-574 259 37 , , PUNCT cana-574 259 38 pp pp ADJ cana-574 259 39 . . PUNCT cana-574 260 1 2395 2395 NUM cana-574 260 2 – – PUNCT cana-574 260 3 4396 4396 NUM cana-574 260 4 , , PUNCT cana-574 260 5 2021 2021 NUM cana-574 260 6 . . PUNCT cana-574 261 1 [ [ X cana-574 261 2 21 21 NUM cana-574 261 3 ] ] X cana-574 261 4 r. r. PROPN cana-574 261 5 dulal dulal PROPN cana-574 261 6 , , PUNCT cana-574 261 7 l. l. PROPN cana-574 261 8 zheng zheng PROPN cana-574 261 9 , , PUNCT cana-574 261 10 m. m. NOUN cana-574 261 11 a. a. PROPN cana-574 261 12 kabir kabir PROPN cana-574 261 13 , , PUNCT cana-574 261 14 s. s. PROPN cana-574 261 15 mcgrath mcgrath PROPN cana-574 261 16 , , PUNCT cana-574 261 17 j. j. PROPN cana-574 261 18 medway medway PROPN cana-574 261 19 , , PUNCT cana-574 261 20 d. d. PROPN cana-574 261 21 swain swain PROPN cana-574 261 22 , , PUNCT cana-574 261 23 and and CCONJ cana-574 261 24 w. w. PROPN cana-574 261 25 swain swain PROPN cana-574 261 26 , , PUNCT cana-574 261 27 β€œ " PUNCT cana-574 261 28 automatic automatic ADJ cana-574 261 29 cattle cattle NOUN cana-574 261 30 identification identification NOUN cana-574 261 31 using use VERB cana-574 261 32 yolov5 yolov5 NOUN cana-574 261 33 and and CCONJ cana-574 261 34 mosaic mosaic ADJ cana-574 261 35 augmentation augmentation NOUN cana-574 261 36 : : PUNCT cana-574 261 37 a a DET cana-574 261 38 comparative comparative ADJ cana-574 261 39 analysis analysis NOUN cana-574 261 40 , , PUNCT cana-574 261 41 ” " PUNCT cana-574 261 42 in in ADP cana-574 261 43 2022 2022 NUM cana-574 261 44 international international ADJ cana-574 261 45 conference conference NOUN cana-574 261 46 on on ADP cana-574 261 47 digital digital ADJ cana-574 261 48 image image NOUN cana-574 261 49 computing computing NOUN cana-574 261 50 : : PUNCT cana-574 261 51 techniques technique NOUN cana-574 261 52 and and CCONJ cana-574 261 53 applications application NOUN cana-574 261 54 ( ( PUNCT cana-574 261 55 dicta dicta ADJ cana-574 261 56 ) ) PUNCT cana-574 261 57 . . PUNCT cana-574 262 1 ieee ieee NOUN cana-574 262 2 , , PUNCT cana-574 262 3 2022 2022 NUM cana-574 262 4 , , PUNCT cana-574 262 5 pp pp ADV cana-574 262 6 . . PUNCT cana-574 263 1 1–8 1–8 X cana-574 263 2 . . PUNCT cana-574 264 1 [ [ X cana-574 264 2 22 22 NUM cana-574 264 3 ] ] X cana-574 264 4 n. n. PROPN cana-574 264 5 abdul abdul PROPN cana-574 264 6 ghafoor ghafoor PROPN cana-574 264 7 and and CCONJ cana-574 264 8 b. b. PROPN cana-574 264 9 sitkowska sitkowska PROPN cana-574 264 10 , , PUNCT cana-574 264 11 β€œ " PUNCT cana-574 264 12 a a DET cana-574 264 13 machine machine NOUN cana-574 264 14 learning learn VERB cana-574 264 15 application application NOUN cana-574 264 16 to to PART cana-574 264 17 predict predict VERB cana-574 264 18 risk risk NOUN cana-574 264 19 of of ADP cana-574 264 20 mastitis mastitis NOUN cana-574 264 21 in in ADP cana-574 264 22 cattle cattle NOUN cana-574 264 23 communications communication NOUN cana-574 264 24 on on ADP cana-574 264 25 applied apply VERB cana-574 264 26 nonlinear nonlinear ADJ cana-574 264 27 analysis analysis NOUN cana-574 264 28 issn issn NOUN cana-574 264 29 : : PUNCT cana-574 264 30 1074 1074 NUM cana-574 264 31 - - PUNCT cana-574 264 32 133x 133x NUM cana-574 264 33 vol vol NOUN cana-574 264 34 31 31 NUM cana-574 264 35 no no NOUN cana-574 264 36 . . NOUN cana-574 264 37 2 2 NUM cana-574 264 38 ( ( PUNCT cana-574 264 39 2024 2024 NUM cana-574 264 40 ) ) PUNCT cana-574 264 41 388 388 NUM cana-574 264 42 https://internationalpubls.com https://internationalpubls.com X cana-574 264 43 from from ADP cana-574 264 44 ams ams NOUN cana-574 264 45 sensor sensor NOUN cana-574 264 46 data datum NOUN cana-574 264 47 , , PUNCT cana-574 264 48 ” " PUNCT cana-574 264 49 agriengineering agriengineering NOUN cana-574 264 50 , , PUNCT cana-574 264 51 vol vol NOUN cana-574 264 52 . . PROPN cana-574 265 1 3 3 NUM cana-574 265 2 , , PUNCT cana-574 265 3 no no INTJ cana-574 265 4 . . NOUN cana-574 265 5 3 3 NUM cana-574 265 6 , , PUNCT cana-574 265 7 pp pp ADJ cana-574 265 8 . . PUNCT cana-574 266 1 575–583 575–583 NUM cana-574 266 2 , , PUNCT cana-574 266 3 2021 2021 NUM cana-574 266 4 . . PUNCT cana-574 267 1 [ [ X cana-574 267 2 23 23 NUM cana-574 267 3 ] ] X cana-574 267 4 i. i. PROPN cana-574 267 5 ramirez ramirez PROPN cana-574 267 6 - - PUNCT cana-574 267 7 morales morales PROPN cana-574 267 8 , , PUNCT cana-574 267 9 l. l. PROPN cana-574 267 10 aguilar aguilar PROPN cana-574 267 11 , , PUNCT cana-574 267 12 e. e. PROPN cana-574 267 13 fernandez fernandez PROPN cana-574 267 14 - - PUNCT cana-574 267 15 blanco blanco PROPN cana-574 267 16 , , PUNCT cana-574 267 17 d. d. PROPN cana-574 267 18 rivero rivero PROPN cana-574 267 19 , , PUNCT cana-574 267 20 j. j. PROPN cana-574 267 21 perez perez PROPN cana-574 267 22 , , PUNCT cana-574 267 23 and and CCONJ cana-574 267 24 a. a. NOUN cana-574 267 25 pazos pazos PROPN cana-574 267 26 , , PUNCT cana-574 267 27 β€œ " PUNCT cana-574 267 28 detection detection NOUN cana-574 267 29 of of ADP cana-574 267 30 bovine bovine ADJ cana-574 267 31 mastitis mastitis NOUN cana-574 267 32 in in ADP cana-574 267 33 raw raw ADJ cana-574 267 34 milk milk NOUN cana-574 267 35 , , PUNCT cana-574 267 36 using use VERB cana-574 267 37 a a DET cana-574 267 38 low low ADJ cana-574 267 39 - - PUNCT cana-574 267 40 cost cost NOUN cana-574 267 41 nir nir ADJ cana-574 267 42 spectrometer spectrometer NOUN cana-574 267 43 and and CCONJ cana-574 267 44 k k PROPN cana-574 267 45 - - PUNCT cana-574 267 46 nn nn ADJ cana-574 267 47 algorithm algorithm NOUN cana-574 267 48 , , PUNCT cana-574 267 49 ” " PUNCT cana-574 267 50 applied apply VERB cana-574 267 51 sciences science NOUN cana-574 267 52 , , PUNCT cana-574 267 53 vol vol NOUN cana-574 267 54 . . PROPN cana-574 267 55 11 11 NUM cana-574 267 56 , , PUNCT cana-574 267 57 no no INTJ cana-574 267 58 . . NOUN cana-574 267 59 22 22 NUM cana-574 267 60 , , PUNCT cana-574 267 61 p. p. NOUN cana-574 267 62 10751 10751 NUM cana-574 267 63 , , PUNCT cana-574 267 64 2021 2021 NUM cana-574 267 65 . . PUNCT cana-574 268 1 [ [ X cana-574 268 2 24 24 NUM cana-574 268 3 ] ] X cana-574 268 4 d. d. PROPN cana-574 268 5 m. m. PROPN cana-574 268 6 amin amin PROPN cana-574 268 7 , , PUNCT cana-574 268 8 g. g. PROPN cana-574 268 9 shehab shehab PROPN cana-574 268 10 , , PUNCT cana-574 268 11 r. r. PROPN cana-574 268 12 emran emran PROPN cana-574 268 13 , , PUNCT cana-574 268 14 r. r. PROPN cana-574 268 15 t. t. PROPN cana-574 268 16 hassanien hassanien PROPN cana-574 268 17 , , PUNCT cana-574 268 18 g. g. PROPN cana-574 268 19 n. n. PROPN cana-574 268 20 alagmy alagmy PROPN cana-574 268 21 , , PUNCT cana-574 268 22 n. n. NOUN cana-574 268 23 m. m. NOUN cana-574 268 24 hagag hagag PROPN cana-574 268 25 , , PUNCT cana-574 268 26 m. m. NOUN cana-574 268 27 i. i. PROPN cana-574 268 28 abd abd PROPN cana-574 268 29 - - PROPN cana-574 268 30 el el PROPN cana-574 268 31 - - PUNCT cana-574 268 32 moniem moniem PROPN cana-574 268 33 , , PUNCT cana-574 268 34 a. a. PROPN cana-574 268 35 r. r. PROPN cana-574 268 36 habashi habashi PROPN cana-574 268 37 , , PUNCT cana-574 268 38 e. e. PROPN cana-574 268 39 m. m. PROPN cana-574 268 40 ibraheem ibraheem PROPN cana-574 268 41 , , PUNCT cana-574 268 42 and and CCONJ cana-574 268 43 m. m. PROPN cana-574 268 44 a. a. PROPN cana-574 268 45 shahein shahein PROPN cana-574 268 46 , , PUNCT cana-574 268 47 β€œ " PUNCT cana-574 268 48 diagnosis diagnosis NOUN cana-574 268 49 of of ADP cana-574 268 50 naturally naturally ADV cana-574 268 51 occurring occur VERB cana-574 268 52 lumpy lumpy ADJ cana-574 268 53 skin skin NOUN cana-574 268 54 disease disease NOUN cana-574 268 55 virus virus NOUN cana-574 268 56 infection infection NOUN cana-574 268 57 in in ADP cana-574 268 58 cattle cattle NOUN cana-574 268 59 using use VERB cana-574 268 60 virological virological ADJ cana-574 268 61 , , PUNCT cana-574 268 62 molecular molecular ADJ cana-574 268 63 , , PUNCT cana-574 268 64 and and CCONJ cana-574 268 65 immunohistopathological immunohistopathological ADJ cana-574 268 66 assays assay NOUN cana-574 268 67 , , PUNCT cana-574 268 68 ” " PUNCT cana-574 268 69 veterinary veterinary ADJ cana-574 268 70 world world NOUN cana-574 268 71 , , PUNCT cana-574 268 72 vol vol NOUN cana-574 268 73 . . PROPN cana-574 268 74 14 14 NUM cana-574 268 75 , , PUNCT cana-574 268 76 no no INTJ cana-574 268 77 . . NOUN cana-574 268 78 8 8 NUM cana-574 268 79 , , PUNCT cana-574 268 80 p. p. NOUN cana-574 268 81 2230 2230 NUM cana-574 268 82 , , PUNCT cana-574 268 83 2021 2021 NUM cana-574 268 84 . . PUNCT cana-574 269 1 [ [ X cana-574 269 2 25 25 NUM cana-574 269 3 ] ] X cana-574 269 4 g. g. PROPN cana-574 269 5 limon limon PROPN cana-574 269 6 , , PUNCT cana-574 269 7 a. a. NOUN cana-574 269 8 a. a. NOUN cana-574 269 9 gamawa gamawa PROPN cana-574 269 10 , , PUNCT cana-574 269 11 a. a. PROPN cana-574 269 12 i. i. PROPN cana-574 269 13 ahmed ahmed PROPN cana-574 269 14 , , PUNCT cana-574 269 15 n. n. PROPN cana-574 269 16 a. a. NOUN cana-574 269 17 lyons lyons PROPN cana-574 269 18 , , PUNCT cana-574 269 19 and and CCONJ cana-574 269 20 p. p. NOUN cana-574 269 21 m. m. NOUN cana-574 269 22 beard beard PROPN cana-574 269 23 , , PUNCT cana-574 269 24 β€œ " PUNCT cana-574 269 25 epidemiological epidemiological ADJ cana-574 269 26 characteristics characteristic NOUN cana-574 269 27 and and CCONJ cana-574 269 28 economic economic ADJ cana-574 269 29 impact impact NOUN cana-574 269 30 of of ADP cana-574 269 31 lumpy lumpy ADJ cana-574 269 32 skin skin NOUN cana-574 269 33 disease disease NOUN cana-574 269 34 , , PUNCT cana-574 269 35 sheeppox sheeppox NOUN cana-574 269 36 and and CCONJ cana-574 269 37 goatpox goatpox NOUN cana-574 269 38 among among ADP cana-574 269 39 subsistence subsistence NOUN cana-574 269 40 farmers farmer NOUN cana-574 269 41 in in ADP cana-574 269 42 northeast northeast PROPN cana-574 269 43 nigeria nigeria PROPN cana-574 269 44 , , PUNCT cana-574 269 45 ” " PUNCT cana-574 269 46 frontiers frontier NOUN cana-574 269 47 in in ADP cana-574 269 48 veterinary veterinary ADJ cana-574 269 49 science science NOUN cana-574 269 50 , , PUNCT cana-574 269 51 vol vol NOUN cana-574 269 52 . . PROPN cana-574 269 53 7 7 NUM cana-574 269 54 , , PUNCT cana-574 269 55 p. p. NOUN cana-574 269 56 8 8 NUM cana-574 269 57 , , PUNCT cana-574 269 58 2020 2020 NUM cana-574 269 59 . . PUNCT cana-574 270 1 [ [ X cana-574 270 2 26 26 NUM cana-574 270 3 ] ] PUNCT cana-574 270 4 l. l. PROPN cana-574 270 5 aerts aerts PROPN cana-574 270 6 , , PUNCT cana-574 270 7 a. a. NOUN cana-574 270 8 haegeman haegeman NOUN cana-574 270 9 , , PUNCT cana-574 270 10 i. i. PROPN cana-574 270 11 de de PROPN cana-574 270 12 leeuw leeuw PROPN cana-574 270 13 , , PUNCT cana-574 270 14 w. w. PROPN cana-574 270 15 philips philips PROPN cana-574 270 16 , , PUNCT cana-574 270 17 w. w. PROPN cana-574 270 18 van van PROPN cana-574 270 19 campe campe PROPN cana-574 270 20 , , PUNCT cana-574 270 21 i. i. PROPN cana-574 270 22 behaeghel behaeghel PROPN cana-574 270 23 , , PUNCT cana-574 270 24 l. l. PROPN cana-574 270 25 mostin mostin PROPN cana-574 270 26 , , PUNCT cana-574 270 27 and and CCONJ cana-574 270 28 k. k. PROPN cana-574 270 29 de de PROPN cana-574 270 30 clercq clercq PROPN cana-574 270 31 , , PUNCT cana-574 270 32 β€œ " PUNCT cana-574 270 33 detection detection NOUN cana-574 270 34 of of ADP cana-574 270 35 clinical clinical ADJ cana-574 270 36 and and CCONJ cana-574 270 37 subclinical subclinical ADJ cana-574 270 38 lumpy lumpy ADJ cana-574 270 39 skin skin NOUN cana-574 270 40 disease disease NOUN cana-574 270 41 using use VERB cana-574 270 42 ear ear NOUN cana-574 270 43 notch notch NOUN cana-574 270 44 testing testing NOUN cana-574 270 45 and and CCONJ cana-574 270 46 skin skin NOUN cana-574 270 47 biopsies biopsy NOUN cana-574 270 48 , , PUNCT cana-574 270 49 ” " PUNCT cana-574 270 50 microorganisms microorganism NOUN cana-574 270 51 , , PUNCT cana-574 270 52 vol vol NOUN cana-574 270 53 . . NOUN cana-574 270 54 9 9 NUM cana-574 270 55 , , PUNCT cana-574 270 56 no no INTJ cana-574 270 57 . . NOUN cana-574 270 58 10 10 NUM cana-574 270 59 , , PUNCT cana-574 270 60 p. p. NOUN cana-574 270 61 2171 2171 NUM cana-574 270 62 , , PUNCT cana-574 270 63 2021 2021 NUM cana-574 270 64 . . PUNCT cana-574 271 1 [ [ X cana-574 271 2 27 27 NUM cana-574 271 3 ] ] X cana-574 271 4 v. v. ADP cana-574 271 5 punyapornwithaya punyapornwithaya PROPN cana-574 271 6 , , PUNCT cana-574 271 7 o. o. PROPN cana-574 271 8 arjkumpa arjkumpa PROPN cana-574 271 9 , , PUNCT cana-574 271 10 n. n. PROPN cana-574 271 11 buamithup buamithup PROPN cana-574 271 12 , , PUNCT cana-574 271 13 c. c. PROPN cana-574 271 14 jainonthee jainonthee PROPN cana-574 271 15 , , PUNCT cana-574 271 16 r. r. PROPN cana-574 271 17 salvador salvador PROPN cana-574 271 18 , , PUNCT cana-574 271 19 and and CCONJ cana-574 271 20 k. k. PROPN cana-574 271 21 jampachaisri jampachaisri PROPN cana-574 271 22 , , PUNCT cana-574 271 23 β€œ " PUNCT cana-574 271 24 the the DET cana-574 271 25 impact impact NOUN cana-574 271 26 of of ADP cana-574 271 27 mass mass NOUN cana-574 271 28 vaccination vaccination NOUN cana-574 271 29 policy policy NOUN cana-574 271 30 and and CCONJ cana-574 271 31 control control NOUN cana-574 271 32 measures measure NOUN cana-574 271 33 on on ADP cana-574 271 34 lumpy lumpy ADJ cana-574 271 35 skin skin NOUN cana-574 271 36 disease disease NOUN cana-574 271 37 cases case NOUN cana-574 271 38 in in ADP cana-574 271 39 thailand thailand PROPN cana-574 271 40 : : PUNCT cana-574 271 41 insights insight NOUN cana-574 271 42 from from ADP cana-574 271 43 a a DET cana-574 271 44 bayesian bayesian NOUN cana-574 271 45 structural structural ADJ cana-574 271 46 time time NOUN cana-574 271 47 series series PROPN cana-574 271 48 analysis analysis NOUN cana-574 271 49 , , PUNCT cana-574 271 50 ” " PUNCT cana-574 271 51 frontiers frontier NOUN cana-574 271 52 in in ADP cana-574 271 53 veterinary veterinary ADJ cana-574 271 54 science science NOUN cana-574 271 55 , , PUNCT cana-574 271 56 vol vol NOUN cana-574 271 57 . . PROPN cana-574 271 58 10 10 NUM cana-574 271 59 , , PUNCT cana-574 271 60 p. p. NOUN cana-574 271 61 1301546 1301546 NUM cana-574 271 62 , , PUNCT cana-574 271 63 2024 2024 NUM cana-574 271 64 . . PUNCT cana-574 272 1 [ [ X cana-574 272 2 28 28 NUM cana-574 272 3 ] ] X cana-574 272 4 n. n. PROPN cana-574 272 5 murti murti PROPN cana-574 272 6 , , PUNCT cana-574 272 7 e. e. PROPN cana-574 272 8 safi safi PROPN cana-574 272 9 p. p. PROPN cana-574 272 10 srianto srianto PROPN cana-574 272 11 , , PUNCT cana-574 272 12 s. s. PROPN cana-574 272 13 madyawati madyawati PROPN cana-574 272 14 , , PUNCT cana-574 272 15 and and CCONJ cana-574 272 16 a. a. NOUN cana-574 272 17 rofikoh rofikoh NOUN cana-574 272 18 , , PUNCT cana-574 272 19 β€œ " PUNCT cana-574 272 20 prevalence prevalence NOUN cana-574 272 21 and and CCONJ cana-574 272 22 progression progression NOUN cana-574 272 23 of of ADP cana-574 272 24 lumpy lumpy ADJ cana-574 272 25 skin skin NOUN cana-574 272 26 disease disease NOUN cana-574 272 27 cases case NOUN cana-574 272 28 in in ADP cana-574 272 29 cattle cattle NOUN cana-574 272 30 over over ADP cana-574 272 31 the the DET cana-574 272 32 six six NUM cana-574 272 33 months month NOUN cana-574 272 34 leading lead VERB cana-574 272 35 up up ADP cana-574 272 36 to to ADP cana-574 272 37 eid eid PROPN cana-574 272 38 al al PROPN cana-574 272 39 - - PUNCT cana-574 272 40 adha adha PROPN cana-574 272 41 in in ADP cana-574 272 42 2023 2023 NUM cana-574 272 43 in in ADP cana-574 272 44 the the DET cana-574 272 45 cirebon cirebon NOUN cana-574 272 46 district district NOUN cana-574 272 47 of of ADP cana-574 272 48 west west PROPN cana-574 272 49 java java PROPN cana-574 272 50 province province PROPN cana-574 272 51 , , PUNCT cana-574 272 52 indonesia indonesia PROPN cana-574 272 53 , , PUNCT cana-574 272 54 ” " PUNCT cana-574 272 55 in in ADP cana-574 272 56 iop iop PROPN cana-574 272 57 conference conference NOUN cana-574 272 58 series series NOUN cana-574 272 59 : : PUNCT cana-574 272 60 earth earth NOUN cana-574 272 61 and and CCONJ cana-574 272 62 environmental environmental ADJ cana-574 272 63 science science NOUN cana-574 272 64 , , PUNCT cana-574 272 65 vol vol NOUN cana-574 272 66 . . PROPN cana-574 272 67 1292 1292 NUM cana-574 272 68 , , PUNCT cana-574 272 69 no no INTJ cana-574 272 70 . . NOUN cana-574 272 71 1 1 X cana-574 272 72 . . X cana-574 272 73 iop iop NOUN cana-574 272 74 publishing publishing NOUN cana-574 272 75 , , PUNCT cana-574 272 76 2024 2024 NUM cana-574 272 77 , , PUNCT cana-574 272 78 p. p. NOUN cana-574 272 79 012037 012037 NUM cana-574 272 80 . . PUNCT cana-574 273 1 [ [ X cana-574 273 2 29 29 NUM cana-574 273 3 ] ] X cana-574 273 4 g. g. PROPN cana-574 273 5 mackereth mackereth PROPN cana-574 273 6 , , PUNCT cana-574 273 7 k. k. PROPN cana-574 273 8 rayner rayner PROPN cana-574 273 9 , , PUNCT cana-574 273 10 a. a. PROPN cana-574 273 11 larkins larkins PROPN cana-574 273 12 , , PUNCT cana-574 273 13 d. d. PROPN cana-574 273 14 morrell morrell PROPN cana-574 273 15 , , PUNCT cana-574 273 16 e. e. PROPN cana-574 273 17 pierce pierce PROPN cana-574 273 18 , , PUNCT cana-574 273 19 and and CCONJ cana-574 273 20 p. p. PROPN cana-574 273 21 letchford letchford NOUN cana-574 273 22 , , PUNCT cana-574 273 23 β€œ " PUNCT cana-574 273 24 surveillance surveillance NOUN cana-574 273 25 for for ADP cana-574 273 26 lumpy lumpy ADJ cana-574 273 27 skin skin NOUN cana-574 273 28 disease disease NOUN cana-574 273 29 and and CCONJ cana-574 273 30 foot foot NOUN cana-574 273 31 and and CCONJ cana-574 273 32 mouth mouth NOUN cana-574 273 33 disease disease NOUN cana-574 273 34 in in ADP cana-574 273 35 the the DET cana-574 273 36 kimberley kimberley PROPN cana-574 273 37 , , PUNCT cana-574 273 38 western western ADJ cana-574 273 39 australia australia PROPN cana-574 273 40 , , PUNCT cana-574 273 41 ” " PUNCT cana-574 273 42 australian australian ADJ cana-574 273 43 veterinary veterinary ADJ cana-574 273 44 journal journal NOUN cana-574 273 45 , , PUNCT cana-574 273 46 2024 2024 NUM cana-574 273 47 . . PUNCT cana-574 274 1 [ [ X cana-574 274 2 30 30 NUM cana-574 274 3 ] ] PUNCT cana-574 274 4 a. a. NOUN cana-574 274 5 elsonbaty elsonbaty NOUN cana-574 274 6 , , PUNCT cana-574 274 7 m. m. NOUN cana-574 274 8 alharbi alharbi PROPN cana-574 274 9 , , PUNCT cana-574 274 10 a. a. PROPN cana-574 274 11 el el PROPN cana-574 274 12 - - PROPN cana-574 274 13 mesady mesady PROPN cana-574 274 14 , , PUNCT cana-574 274 15 and and CCONJ cana-574 274 16 w. w. PROPN cana-574 274 17 adel adel PROPN cana-574 274 18 , , PUNCT cana-574 274 19 β€œ " PUNCT cana-574 274 20 dynamical dynamical ADJ cana-574 274 21 analysis analysis NOUN cana-574 274 22 of of ADP cana-574 274 23 a a DET cana-574 274 24 novel novel ADJ cana-574 274 25 discrete discrete ADJ cana-574 274 26 fractional fractional ADJ cana-574 274 27 lumpy lumpy ADJ cana-574 274 28 skin skin NOUN cana-574 274 29 disease disease NOUN cana-574 274 30 model model NOUN cana-574 274 31 , , PUNCT cana-574 274 32 ” " PUNCT cana-574 274 33 partial partial ADJ cana-574 274 34 differential differential NOUN cana-574 274 35 equations equation NOUN cana-574 274 36 in in ADP cana-574 274 37 applied applied ADJ cana-574 274 38 mathematics mathematic NOUN cana-574 274 39 , , PUNCT cana-574 274 40 vol vol NOUN cana-574 274 41 . . NOUN cana-574 274 42 9 9 NUM cana-574 274 43 , , PUNCT cana-574 274 44 p. p. NOUN cana-574 274 45 100604 100604 NUM cana-574 274 46 , , PUNCT cana-574 274 47 2024 2024 NUM cana-574 274 48 . . PUNCT cana-574 275 1 [ [ X cana-574 275 2 31 31 NUM cana-574 275 3 ] ] PUNCT cana-574 275 4 alaria alaria PROPN cana-574 275 5 , , PUNCT cana-574 275 6 s. s. PROPN cana-574 275 7 k. k. PROPN cana-574 276 1 " " PUNCT cana-574 276 2 a a PROPN cana-574 276 3 .. .. PUNCT cana-574 276 4 raj raj PROPN cana-574 276 5 , , PUNCT cana-574 276 6 v. v. PROPN cana-574 276 7 sharma sharma PROPN cana-574 276 8 , , PUNCT cana-574 276 9 and and CCONJ cana-574 276 10 v. v. ADP cana-574 276 11 kumar kumar PROPN cana-574 276 12 . . PUNCT cana-574 277 1 β€œsimulation β€œsimulation NOUN cana-574 277 2 and and CCONJ cana-574 277 3 analysis analysis NOUN cana-574 277 4 of of ADP cana-574 277 5 hand hand NOUN cana-574 277 6 gesture gesture NOUN cana-574 277 7 recognition recognition NOUN cana-574 277 8 for for ADP cana-574 277 9 indian indian ADJ cana-574 277 10 sign sign NOUN cana-574 277 11 language language NOUN cana-574 277 12 using use VERB cana-574 277 13 cnn cnn PROPN cana-574 277 14 ” " PUNCT cana-574 277 15 . . PUNCT cana-574 277 16 " " PUNCT cana-574 278 1 international international ADJ cana-574 278 2 journal journal NOUN cana-574 278 3 on on ADP cana-574 278 4 recent recent ADJ cana-574 278 5 and and CCONJ cana-574 278 6 innovation innovation NOUN cana-574 278 7 trends trend NOUN cana-574 278 8 in in ADP cana-574 278 9 computing computing NOUN cana-574 278 10 and and CCONJ cana-574 278 11 communication communication NOUN cana-574 278 12 10 10 NUM cana-574 278 13 , , PUNCT cana-574 278 14 no no INTJ cana-574 278 15 . . NOUN cana-574 278 16 4 4 NUM cana-574 278 17 ( ( PUNCT cana-574 278 18 2022 2022 NUM cana-574 278 19 ): ): PUNCT cana-574 278 20 10 10 NUM cana-574 278 21 - - SYM cana-574 278 22 14 14 NUM cana-574 278 23 . . PUNCT cana-574 279 1 [ [ X cana-574 279 2 32 32 NUM cana-574 279 3 ] ] X cana-574 279 4 ashish ashish PROPN cana-574 279 5 raj raj PROPN cana-574 279 6 , , PUNCT cana-574 279 7 vijay vijay PROPN cana-574 279 8 kumar kumar PROPN cana-574 279 9 , , PUNCT cana-574 280 1 s. s. PROPN cana-574 280 2 k. k. PROPN cana-574 280 3 a. a. PROPN cana-574 281 1 v. v. PROPN cana-574 281 2 s. s. PROPN cana-574 281 3 design design PROPN cana-574 281 4 simulation simulation NOUN cana-574 281 5 and and CCONJ cana-574 281 6 assessment assessment NOUN cana-574 281 7 of of ADP cana-574 281 8 prediction prediction NOUN cana-574 281 9 of of ADP cana-574 281 10 mortality mortality NOUN cana-574 281 11 in in ADP cana-574 281 12 intensive intensive ADJ cana-574 281 13 care care NOUN cana-574 281 14 unit unit NOUN cana-574 281 15 using use VERB cana-574 281 16 intelligent intelligent ADJ cana-574 281 17 algorithms algorithm NOUN cana-574 281 18 . . PUNCT cana-574 282 1 msea msea NOUN cana-574 282 2 2022 2022 NUM cana-574 282 3 , , PUNCT cana-574 282 4 71 71 NUM cana-574 282 5 , , PUNCT cana-574 282 6 355 355 NUM cana-574 282 7 - - SYM cana-574 282 8 367 367 NUM cana-574 282 9 . . PUNCT