id	sid	tid	token	lemma	pos
cuesj-110	1	1	37	37	NUM
cuesj-110	1	2	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-110	1	3	cuesj	cuesj	NOUN
cuesj-110	1	4	2019	2019	NUM
cuesj-110	1	5	,	,	PUNCT
cuesj-110	1	6	3	3	NUM
cuesj-110	1	7	(	(	PUNCT
cuesj-110	1	8	2	2	NUM
cuesj-110	1	9	):	):	PUNCT
cuesj-110	1	10	37	37	NUM
cuesj-110	1	11	-	-	SYM
cuesj-110	1	12	41	41	NUM
cuesj-110	1	13	research	research	NOUN
cuesj-110	1	14	article	article	NOUN
cuesj-110	1	15	measuring	measure	VERB
cuesj-110	1	16	the	the	DET
cuesj-110	1	17	score	score	NOUN
cuesj-110	1	18	matching	matching	NOUN
cuesj-110	1	19	of	of	ADP
cuesj-110	1	20	the	the	DET
cuesj-110	1	21	pairwise	pairwise	NOUN
cuesj-110	1	22	deoxyribonucleic	deoxyribonucleic	VERB
cuesj-110	1	23	acid	acid	NOUN
cuesj-110	1	24	sequencing	sequencing	NOUN
cuesj-110	1	25	using	use	VERB
cuesj-110	1	26	neuro	neuro	NOUN
cuesj-110	1	27	-	-	PUNCT
cuesj-110	1	28	fuzzy	fuzzy	ADJ
cuesj-110	1	29	safa	safa	PROPN
cuesj-110	1	30	a.	a.	PROPN
cuesj-110	1	31	hameed	hameed	PROPN
cuesj-110	1	32	*	*	PROPN
cuesj-110	1	33	,	,	PUNCT
cuesj-110	1	34	raed	raed	PROPN
cuesj-110	1	35	i.	i.	PROPN
cuesj-110	1	36	hamed	hamed	PROPN
cuesj-110	1	37	department	department	PROPN
cuesj-110	1	38	of	of	ADP
cuesj-110	1	39	computer	computer	NOUN
cuesj-110	1	40	science	science	NOUN
cuesj-110	1	41	,	,	PUNCT
cuesj-110	1	42	college	college	NOUN
cuesj-110	1	43	of	of	ADP
cuesj-110	1	44	engineering	engineering	NOUN
cuesj-110	1	45	and	and	CCONJ
cuesj-110	1	46	science	science	NOUN
cuesj-110	1	47	,	,	PUNCT
cuesj-110	1	48	university	university	PROPN
cuesj-110	1	49	of	of	ADP
cuesj-110	1	50	bayan	bayan	PROPN
cuesj-110	1	51	,	,	PUNCT
cuesj-110	1	52	erbil	erbil	PROPN
cuesj-110	1	53	,	,	PUNCT
cuesj-110	1	54	iraq	iraq	PROPN
cuesj-110	1	55	abstract	abstract	NOUN
cuesj-110	1	56	the	the	DET
cuesj-110	1	57	proposed	propose	VERB
cuesj-110	1	58	model	model	NOUN
cuesj-110	1	59	for	for	ADP
cuesj-110	1	60	getting	get	VERB
cuesj-110	1	61	the	the	DET
cuesj-110	1	62	score	score	NOUN
cuesj-110	1	63	matching	matching	NOUN
cuesj-110	1	64	of	of	ADP
cuesj-110	1	65	the	the	DET
cuesj-110	1	66	deoxyribonucleic	deoxyribonucleic	ADJ
cuesj-110	1	67	acid	acid	NOUN
cuesj-110	1	68	(	(	PUNCT
cuesj-110	1	69	dna	dna	NOUN
cuesj-110	1	70	)	)	PUNCT
cuesj-110	1	71	sequence	sequence	NOUN
cuesj-110	1	72	is	be	AUX
cuesj-110	1	73	introduced	introduce	VERB
cuesj-110	1	74	;	;	PUNCT
cuesj-110	1	75	the	the	DET
cuesj-110	1	76	neuro	neuro	NOUN
cuesj-110	1	77	-	-	PUNCT
cuesj-110	1	78	fuzzy	fuzzy	ADJ
cuesj-110	1	79	procedure	procedure	NOUN
cuesj-110	1	80	is	be	AUX
cuesj-110	1	81	the	the	DET
cuesj-110	1	82	strategy	strategy	NOUN
cuesj-110	1	83	actualized	actualize	VERB
cuesj-110	1	84	in	in	ADP
cuesj-110	1	85	this	this	DET
cuesj-110	1	86	paper	paper	NOUN
cuesj-110	1	87	;	;	PUNCT
cuesj-110	1	88	it	it	PRON
cuesj-110	1	89	is	be	AUX
cuesj-110	1	90	used	use	VERB
cuesj-110	1	91	the	the	DET
cuesj-110	1	92	collection	collection	NOUN
cuesj-110	1	93	of	of	ADP
cuesj-110	1	94	biological	biological	ADJ
cuesj-110	1	95	information	information	NOUN
cuesj-110	1	96	of	of	ADP
cuesj-110	1	97	the	the	DET
cuesj-110	1	98	dna	dna	PROPN
cuesj-110	1	99	sequence	sequence	NOUN
cuesj-110	1	100	performing	perform	VERB
cuesj-110	1	101	with	with	ADP
cuesj-110	1	102	global	global	ADJ
cuesj-110	1	103	and	and	CCONJ
cuesj-110	1	104	local	local	ADJ
cuesj-110	1	105	calculations	calculation	NOUN
cuesj-110	1	106	so	so	SCONJ
cuesj-110	1	107	as	as	SCONJ
cuesj-110	1	108	to	to	PART
cuesj-110	1	109	advance	advance	VERB
cuesj-110	1	110	the	the	DET
cuesj-110	1	111	ideal	ideal	ADJ
cuesj-110	1	112	arrangement	arrangement	NOUN
cuesj-110	1	113	;	;	PUNCT
cuesj-110	1	114	we	we	PRON
cuesj-110	1	115	utilize	utilize	VERB
cuesj-110	1	116	the	the	DET
cuesj-110	1	117	pairwise	pairwise	NOUN
cuesj-110	1	118	dna	dna	NOUN
cuesj-110	1	119	sequence	sequence	NOUN
cuesj-110	1	120	alignment	alignment	NOUN
cuesj-110	1	121	to	to	PART
cuesj-110	1	122	gauge	gauge	VERB
cuesj-110	1	123	the	the	DET
cuesj-110	1	124	score	score	NOUN
cuesj-110	1	125	of	of	ADP
cuesj-110	1	126	the	the	DET
cuesj-110	1	127	likeness	likeness	NOUN
cuesj-110	1	128	,	,	PUNCT
cuesj-110	1	129	which	which	PRON
cuesj-110	1	130	depend	depend	VERB
cuesj-110	1	131	on	on	ADP
cuesj-110	1	132	information	information	NOUN
cuesj-110	1	133	gathering	gathering	NOUN
cuesj-110	1	134	from	from	ADP
cuesj-110	1	135	the	the	DET
cuesj-110	1	136	pairwise	pairwise	NOUN
cuesj-110	1	137	dna	dna	PROPN
cuesj-110	1	138	series	series	PROPN
cuesj-110	1	139	to	to	PART
cuesj-110	1	140	be	be	AUX
cuesj-110	1	141	embedded	embed	VERB
cuesj-110	1	142	into	into	ADP
cuesj-110	1	143	the	the	DET
cuesj-110	1	144	implicit	implicit	ADJ
cuesj-110	1	145	framework	framework	NOUN
cuesj-110	1	146	;	;	PUNCT
cuesj-110	1	147	an	an	DET
cuesj-110	1	148	adaptive	adaptive	ADJ
cuesj-110	1	149	neuro	neuro	NOUN
cuesj-110	1	150	-	-	PUNCT
cuesj-110	1	151	fuzzy	fuzzy	ADJ
cuesj-110	1	152	inference	inference	NOUN
cuesj-110	1	153	system	system	NOUN
cuesj-110	1	154	model	model	NOUN
cuesj-110	1	155	is	be	AUX
cuesj-110	1	156	reasonable	reasonable	ADJ
cuesj-110	1	157	for	for	ADP
cuesj-110	1	158	foreseeing	foresee	VERB
cuesj-110	1	159	the	the	DET
cuesj-110	1	160	matching	match	VERB
cuesj-110	1	161	score	score	NOUN
cuesj-110	1	162	through	through	ADP
cuesj-110	1	163	the	the	DET
cuesj-110	1	164	preparation	preparation	NOUN
cuesj-110	1	165	and	and	CCONJ
cuesj-110	1	166	testing	testing	NOUN
cuesj-110	1	167	in	in	ADP
cuesj-110	1	168	neural	neural	ADJ
cuesj-110	1	169	system	system	NOUN
cuesj-110	1	170	and	and	CCONJ
cuesj-110	1	171	the	the	DET
cuesj-110	1	172	induction	induction	NOUN
cuesj-110	1	173	fuzzy	fuzzy	ADJ
cuesj-110	1	174	system	system	NOUN
cuesj-110	1	175	in	in	ADP
cuesj-110	1	176	fuzzy	fuzzy	ADJ
cuesj-110	1	177	logic	logic	NOUN
cuesj-110	1	178	that	that	PRON
cuesj-110	1	179	accomplishes	accomplish	VERB
cuesj-110	1	180	the	the	DET
cuesj-110	1	181	outcome	outcome	NOUN
cuesj-110	1	182	in	in	ADP
cuesj-110	1	183	elite	elite	ADJ
cuesj-110	1	184	execution	execution	NOUN
cuesj-110	1	185	.	.	PUNCT
cuesj-110	2	1	keywords	keyword	NOUN
cuesj-110	2	2	:	:	PUNCT
cuesj-110	2	3	component	component	NOUN
cuesj-110	2	4	,	,	PUNCT
cuesj-110	2	5	dynamic	dynamic	ADJ
cuesj-110	2	6	programming	programming	NOUN
cuesj-110	2	7	,	,	PUNCT
cuesj-110	2	8	matching	matching	NOUN
cuesj-110	2	9	,	,	PUNCT
cuesj-110	2	10	neuro	neuro	NOUN
cuesj-110	2	11	-	-	PUNCT
cuesj-110	2	12	fuzzy	fuzzy	ADJ
cuesj-110	2	13	,	,	PUNCT
cuesj-110	2	14	sequence	sequence	NOUN
cuesj-110	2	15	alignment	alignment	NOUN
cuesj-110	2	16	introduction	introduction	NOUN
cuesj-110	2	17	deoxyribonucleic	deoxyribonucleic	ADJ
cuesj-110	2	18	acid	acid	NOUN
cuesj-110	2	19	(	(	PUNCT
cuesj-110	2	20	dna	dna	NOUN
cuesj-110	2	21	)	)	PUNCT
cuesj-110	2	22	sequence	sequence	NOUN
cuesj-110	2	23	matching	matching	NOUN
cuesj-110	2	24	is	be	AUX
cuesj-110	2	25	an	an	DET
cuesj-110	2	26	essential	essential	ADJ
cuesj-110	2	27	area	area	NOUN
cuesj-110	2	28	and	and	CCONJ
cuesj-110	2	29	more	more	ADJ
cuesj-110	2	30	approaching	approach	VERB
cuesj-110	2	31	nearby	nearby	ADV
cuesj-110	2	32	in	in	ADP
cuesj-110	2	33	computational	computational	ADJ
cuesj-110	2	34	biological	biological	ADJ
cuesj-110	2	35	data.[1	data.[1	NOUN
cuesj-110	2	36	]	]	PUNCT
cuesj-110	2	37	dna	dna	PROPN
cuesj-110	2	38	sequence	sequence	NOUN
cuesj-110	2	39	analysis	analysis	NOUN
cuesj-110	2	40	is	be	AUX
cuesj-110	2	41	an	an	DET
cuesj-110	2	42	imperative	imperative	ADJ
cuesj-110	2	43	exploration	exploration	NOUN
cuesj-110	2	44	topic	topic	NOUN
cuesj-110	2	45	in	in	ADP
cuesj-110	2	46	bioinformatics	bioinformatics	NOUN
cuesj-110	2	47	.	.	PUNCT
cuesj-110	3	1	assessing	assess	VERB
cuesj-110	3	2	the	the	DET
cuesj-110	3	3	similarity	similarity	NOUN
cuesj-110	3	4	between	between	ADP
cuesj-110	3	5	sequences	sequence	NOUN
cuesj-110	3	6	,	,	PUNCT
cuesj-110	3	7	this	this	PRON
cuesj-110	3	8	is	be	AUX
cuesj-110	3	9	important	important	ADJ
cuesj-110	3	10	for	for	ADP
cuesj-110	3	11	sequence	sequence	NOUN
cuesj-110	3	12	analysis	analysis	NOUN
cuesj-110	3	13	,	,	PUNCT
cuesj-110	3	14	because	because	SCONJ
cuesj-110	3	15	similarity	similarity	NOUN
cuesj-110	3	16	proves	prove	VERB
cuesj-110	3	17	congruence.[2	congruence.[2	NOUN
cuesj-110	3	18	]	]	X
cuesj-110	3	19	the	the	DET
cuesj-110	3	20	dna	dna	PROPN
cuesj-110	3	21	atom	atom	NOUN
cuesj-110	3	22	contains	contain	VERB
cuesj-110	3	23	biological	biological	ADJ
cuesj-110	3	24	,	,	PUNCT
cuesj-110	3	25	physical	physical	ADJ
cuesj-110	3	26	,	,	PUNCT
cuesj-110	3	27	and	and	CCONJ
cuesj-110	3	28	chemical	chemical	NOUN
cuesj-110	3	29	data	datum	NOUN
cuesj-110	3	30	;	;	PUNCT
cuesj-110	3	31	it	it	PRON
cuesj-110	3	32	has	have	AUX
cuesj-110	3	33	turned	turn	VERB
cuesj-110	3	34	out	out	ADP
cuesj-110	3	35	to	to	PART
cuesj-110	3	36	be	be	AUX
cuesj-110	3	37	essential	essential	ADJ
cuesj-110	3	38	to	to	PART
cuesj-110	3	39	examine	examine	VERB
cuesj-110	3	40	dna	dna	PROPN
cuesj-110	3	41	sequences	sequence	NOUN
cuesj-110	3	42	statistically.[3	statistically.[3	NUM
cuesj-110	3	43	]	]	PUNCT
cuesj-110	3	44	string	string	NOUN
cuesj-110	3	45	matching	matching	NOUN
cuesj-110	3	46	is	be	AUX
cuesj-110	3	47	a	a	DET
cuesj-110	3	48	strategy	strategy	NOUN
cuesj-110	3	49	to	to	PART
cuesj-110	3	50	find	find	VERB
cuesj-110	3	51	a	a	DET
cuesj-110	3	52	design	design	NOUN
cuesj-110	3	53	from	from	ADP
cuesj-110	3	54	the	the	DET
cuesj-110	3	55	predefined	predefine	VERB
cuesj-110	3	56	info	info	NOUN
cuesj-110	3	57	string.[4	string.[4	NOUN
cuesj-110	3	58	]	]	PUNCT
cuesj-110	3	59	similarities	similarity	NOUN
cuesj-110	3	60	between	between	ADP
cuesj-110	3	61	dna	dna	PROPN
cuesj-110	3	62	sequences	sequence	NOUN
cuesj-110	3	63	may	may	AUX
cuesj-110	3	64	emerge	emerge	VERB
cuesj-110	3	65	due	due	ADP
cuesj-110	3	66	to	to	ADP
cuesj-110	3	67	the	the	DET
cuesj-110	3	68	functional	functional	ADJ
cuesj-110	3	69	,	,	PUNCT
cuesj-110	3	70	structural	structural	ADJ
cuesj-110	3	71	,	,	PUNCT
cuesj-110	3	72	or	or	CCONJ
cuesj-110	3	73	transformative	transformative	ADJ
cuesj-110	3	74	relationship	relationship	NOUN
cuesj-110	3	75	among	among	ADP
cuesj-110	3	76	them.[5	them.[5	PRON
cuesj-110	3	77	]	]	PUNCT
cuesj-110	3	78	sequence	sequence	NOUN
cuesj-110	3	79	alignment	alignment	NOUN
cuesj-110	3	80	of	of	ADP
cuesj-110	3	81	two	two	NUM
cuesj-110	3	82	biological	biological	ADJ
cuesj-110	3	83	sequences	sequence	NOUN
cuesj-110	3	84	may	may	AUX
cuesj-110	3	85	be	be	AUX
cuesj-110	3	86	called	call	VERB
cuesj-110	3	87	pairwise	pairwise	NOUN
cuesj-110	3	88	sequence	sequence	NOUN
cuesj-110	3	89	alignment	alignment	NOUN
cuesj-110	3	90	,	,	PUNCT
cuesj-110	3	91	also	also	ADV
cuesj-110	3	92	in	in	ADP
cuesj-110	3	93	the	the	DET
cuesj-110	3	94	event	event	NOUN
cuesj-110	3	95	,	,	PUNCT
cuesj-110	3	96	more	more	ADJ
cuesj-110	3	97	than	than	ADP
cuesj-110	3	98	two	two	NUM
cuesj-110	3	99	sequences	sequence	NOUN
cuesj-110	3	100	are	be	AUX
cuesj-110	3	101	involved	involve	VERB
cuesj-110	3	102	;	;	PUNCT
cuesj-110	3	103	it	it	PRON
cuesj-110	3	104	may	may	AUX
cuesj-110	3	105	be	be	AUX
cuesj-110	3	106	called	call	VERB
cuesj-110	3	107	multiple	multiple	ADJ
cuesj-110	3	108	sequence	sequence	NOUN
cuesj-110	3	109	alignment.[6	alignment.[6	PRON
cuesj-110	3	110	]	]	PUNCT
cuesj-110	4	1	the	the	DET
cuesj-110	4	2	dynamic	dynamic	ADJ
cuesj-110	4	3	programming	programming	NOUN
cuesj-110	4	4	is	be	AUX
cuesj-110	4	5	the	the	DET
cuesj-110	4	6	method	method	NOUN
cuesj-110	4	7	to	to	PART
cuesj-110	4	8	implement	implement	VERB
cuesj-110	4	9	the	the	DET
cuesj-110	4	10	dna	dna	PROPN
cuesj-110	4	11	alignment	alignment	NOUN
cuesj-110	4	12	using	use	VERB
cuesj-110	4	13	the	the	DET
cuesj-110	4	14	needleman	needleman	NOUN
cuesj-110	4	15	–	–	PUNCT
cuesj-110	4	16	wunsch[7	wunsch[7	PUNCT
cuesj-110	4	17	]	]	PUNCT
cuesj-110	4	18	and	and	CCONJ
cuesj-110	4	19	smith	smith	PROPN
cuesj-110	4	20	–	–	PUNCT
cuesj-110	4	21	waterman	waterman	PROPN
cuesj-110	4	22	algorithms.[8	algorithms.[8	PROPN
cuesj-110	4	23	]	]	PUNCT
cuesj-110	4	24	here	here	ADV
cuesj-110	4	25	,	,	PUNCT
cuesj-110	4	26	in	in	ADP
cuesj-110	4	27	this	this	DET
cuesj-110	4	28	article	article	NOUN
cuesj-110	4	29	,	,	PUNCT
cuesj-110	4	30	we	we	PRON
cuesj-110	4	31	use	use	VERB
cuesj-110	4	32	the	the	DET
cuesj-110	4	33	pairwise	pairwise	NOUN
cuesj-110	4	34	sequence	sequence	NOUN
cuesj-110	4	35	alignment	alignment	NOUN
cuesj-110	4	36	in	in	ADP
cuesj-110	4	37	a	a	DET
cuesj-110	4	38	global	global	ADJ
cuesj-110	4	39	and	and	CCONJ
cuesj-110	4	40	local	local	ADJ
cuesj-110	4	41	algorithms	algorithm	NOUN
cuesj-110	4	42	and	and	CCONJ
cuesj-110	4	43	examined	examine	VERB
cuesj-110	4	44	the	the	DET
cuesj-110	4	45	measure	measure	NOUN
cuesj-110	4	46	of	of	ADP
cuesj-110	4	47	the	the	DET
cuesj-110	4	48	matching	matching	NOUN
cuesj-110	4	49	based	base	VERB
cuesj-110	4	50	on	on	ADP
cuesj-110	4	51	the	the	DET
cuesj-110	4	52	collected	collect	VERB
cuesj-110	4	53	data	datum	NOUN
cuesj-110	4	54	for	for	ADP
cuesj-110	4	55	dna	dna	PROPN
cuesj-110	4	56	alignment	alignment	NOUN
cuesj-110	4	57	.	.	PUNCT
cuesj-110	5	1	the	the	DET
cuesj-110	5	2	neuro	neuro	NOUN
cuesj-110	5	3	-	-	PUNCT
cuesj-110	5	4	fuzzy	fuzzy	ADJ
cuesj-110	5	5	model[9	model[9	NOUN
cuesj-110	5	6	]	]	PUNCT
cuesj-110	5	7	is	be	AUX
cuesj-110	5	8	used	use	VERB
cuesj-110	5	9	in	in	ADP
cuesj-110	5	10	the	the	DET
cuesj-110	5	11	matlab	matlab	PROPN
cuesj-110	5	12	tool	tool	NOUN
cuesj-110	5	13	that	that	PRON
cuesj-110	5	14	implemented	implement	VERB
cuesj-110	5	15	by	by	ADP
cuesj-110	5	16	the	the	DET
cuesj-110	5	17	data	datum	NOUN
cuesj-110	5	18	set	set	VERB
cuesj-110	5	19	files	file	NOUN
cuesj-110	5	20	of	of	ADP
cuesj-110	5	21	measure	measure	NOUN
cuesj-110	5	22	score	score	NOUN
cuesj-110	5	23	matching	matching	NOUN
cuesj-110	5	24	of	of	ADP
cuesj-110	5	25	dna	dna	PROPN
cuesj-110	5	26	sequences	sequence	NOUN
cuesj-110	5	27	that	that	PRON
cuesj-110	5	28	deal	deal	VERB
cuesj-110	5	29	with	with	ADP
cuesj-110	5	30	the	the	DET
cuesj-110	5	31	set	set	NOUN
cuesj-110	5	32	of	of	ADP
cuesj-110	5	33	biological	biological	ADJ
cuesj-110	5	34	data	datum	NOUN
cuesj-110	5	35	.	.	PUNCT
cuesj-110	6	1	this	this	DET
cuesj-110	6	2	tool	tool	NOUN
cuesj-110	6	3	is	be	AUX
cuesj-110	6	4	efficient	efficient	ADJ
cuesj-110	6	5	and	and	CCONJ
cuesj-110	6	6	fast	fast	ADJ
cuesj-110	6	7	to	to	PART
cuesj-110	6	8	evaluate	evaluate	VERB
cuesj-110	6	9	the	the	DET
cuesj-110	6	10	scoring	scoring	NOUN
cuesj-110	6	11	measure	measure	NOUN
cuesj-110	6	12	of	of	ADP
cuesj-110	6	13	matching	match	VERB
cuesj-110	6	14	the	the	DET
cuesj-110	6	15	dna	dna	PROPN
cuesj-110	6	16	sequences	sequence	NOUN
cuesj-110	6	17	.	.	PUNCT
cuesj-110	7	1	literature	literature	PROPN
cuesj-110	7	2	review	review	VERB
cuesj-110	7	3	the	the	DET
cuesj-110	7	4	study	study	NOUN
cuesj-110	7	5	of	of	ADP
cuesj-110	7	6	a	a	DET
cuesj-110	7	7	biological	biological	ADJ
cuesj-110	7	8	sequence	sequence	NOUN
cuesj-110	7	9	has	have	AUX
cuesj-110	7	10	been	be	AUX
cuesj-110	7	11	growing	grow	VERB
cuesj-110	7	12	exponentially	exponentially	ADV
cuesj-110	7	13	,	,	PUNCT
cuesj-110	7	14	while	while	SCONJ
cuesj-110	7	15	the	the	DET
cuesj-110	7	16	applications	application	NOUN
cuesj-110	7	17	of	of	ADP
cuesj-110	7	18	the	the	DET
cuesj-110	7	19	sequence	sequence	NOUN
cuesj-110	7	20	alignment	alignment	NOUN
cuesj-110	7	21	cover	cover	VERB
cuesj-110	7	22	the	the	DET
cuesj-110	7	23	wide	wide	ADJ
cuesj-110	7	24	range	range	NOUN
cuesj-110	7	25	in	in	ADP
cuesj-110	7	26	bioinformatics	bioinformatics	NOUN
cuesj-110	7	27	.	.	PUNCT
cuesj-110	8	1	the	the	DET
cuesj-110	8	2	previous	previous	ADJ
cuesj-110	8	3	research	research	NOUN
cuesj-110	8	4	work	work	NOUN
cuesj-110	8	5	has	have	AUX
cuesj-110	8	6	been	be	AUX
cuesj-110	8	7	studied	study	VERB
cuesj-110	8	8	to	to	PART
cuesj-110	8	9	provide	provide	VERB
cuesj-110	8	10	new	new	ADJ
cuesj-110	8	11	algorithms	algorithm	NOUN
cuesj-110	8	12	with	with	ADP
cuesj-110	8	13	the	the	DET
cuesj-110	8	14	main	main	ADJ
cuesj-110	8	15	purpose	purpose	NOUN
cuesj-110	8	16	for	for	ADP
cuesj-110	8	17	the	the	DET
cuesj-110	8	18	requirements	requirement	NOUN
cuesj-110	8	19	of	of	ADP
cuesj-110	8	20	matching	match	VERB
cuesj-110	8	21	sequences	sequence	NOUN
cuesj-110	8	22	;	;	PUNCT
cuesj-110	8	23	the	the	DET
cuesj-110	8	24	techniques	technique	NOUN
cuesj-110	8	25	have	have	AUX
cuesj-110	8	26	been	be	AUX
cuesj-110	8	27	used	use	VERB
cuesj-110	8	28	all	all	DET
cuesj-110	8	29	the	the	DET
cuesj-110	8	30	latest	late	ADJ
cuesj-110	8	31	with	with	ADP
cuesj-110	8	32	providing	provide	VERB
cuesj-110	8	33	fast	fast	ADJ
cuesj-110	8	34	and	and	CCONJ
cuesj-110	8	35	efficient	efficient	ADJ
cuesj-110	8	36	sequence	sequence	NOUN
cuesj-110	8	37	alignment	alignment	NOUN
cuesj-110	8	38	algorithms	algorithm	NOUN
cuesj-110	8	39	.	.	PUNCT
cuesj-110	9	1	bhukya	bhukya	NOUN
cuesj-110	9	2	and	and	CCONJ
cuesj-110	9	3	somayajulu[1	somayajulu[1	PRON
cuesj-110	9	4	]	]	PUNCT
cuesj-110	9	5	suggested	suggest	VERB
cuesj-110	9	6	a	a	DET
cuesj-110	9	7	new	new	ADJ
cuesj-110	9	8	pattern	pattern	NOUN
cuesj-110	9	9	for	for	ADP
cuesj-110	9	10	matching	matching	NOUN
cuesj-110	9	11	technique	technique	NOUN
cuesj-110	9	12	defined	define	VERB
cuesj-110	9	13	as	as	ADP
cuesj-110	9	14	exact	exact	ADJ
cuesj-110	9	15	multiple	multiple	ADJ
cuesj-110	9	16	pattern	pattern	NOUN
cuesj-110	9	17	-	-	PUNCT
cuesj-110	9	18	matching	match	VERB
cuesj-110	9	19	algorithms	algorithm	NOUN
cuesj-110	9	20	that	that	PRON
cuesj-110	9	21	utilize	utilize	VERB
cuesj-110	9	22	dna	dna	PROPN
cuesj-110	9	23	sequence	sequence	NOUN
cuesj-110	9	24	.	.	PUNCT
cuesj-110	10	1	the	the	DET
cuesj-110	10	2	current	current	ADJ
cuesj-110	10	3	method	method	NOUN
cuesj-110	10	4	is	be	AUX
cuesj-110	10	5	used	use	VERB
cuesj-110	10	6	to	to	PART
cuesj-110	10	7	avoid	avoid	VERB
cuesj-110	10	8	unneeded	unneeded	ADJ
cuesj-110	10	9	comparisons	comparison	NOUN
cuesj-110	10	10	in	in	ADP
cuesj-110	10	11	the	the	DET
cuesj-110	10	12	dna	dna	PROPN
cuesj-110	10	13	sequence	sequence	NOUN
cuesj-110	10	14	.	.	PUNCT
cuesj-110	11	1	gill	gill	NOUN
cuesj-110	11	2	and	and	CCONJ
cuesj-110	11	3	singh[6	singh[6	PRON
cuesj-110	11	4	]	]	PUNCT
cuesj-110	11	5	proposed	propose	VERB
cuesj-110	11	6	a	a	DET
cuesj-110	11	7	multiple	multiple	ADJ
cuesj-110	11	8	sequence	sequence	NOUN
cuesj-110	11	9	alignment	alignment	NOUN
cuesj-110	11	10	algorithm	algorithm	NOUN
cuesj-110	11	11	which	which	PRON
cuesj-110	11	12	performs	perform	VERB
cuesj-110	11	13	fuzzy	fuzzy	ADJ
cuesj-110	11	14	logic	logic	NOUN
cuesj-110	11	15	to	to	PART
cuesj-110	11	16	measure	measure	VERB
cuesj-110	11	17	the	the	DET
cuesj-110	11	18	similarity	similarity	NOUN
cuesj-110	11	19	of	of	ADP
cuesj-110	11	20	sequences	sequence	NOUN
cuesj-110	11	21	based	base	VERB
cuesj-110	11	22	on	on	ADP
cuesj-110	11	23	the	the	DET
cuesj-110	11	24	fuzzy	fuzzy	ADJ
cuesj-110	11	25	parameters	parameter	NOUN
cuesj-110	11	26	.	.	PUNCT
cuesj-110	12	1	nasser	nasser	PROPN
cuesj-110	12	2	et	et	PROPN
cuesj-110	12	3	al.[10	al.[10	PROPN
cuesj-110	12	4	]	]	PUNCT
cuesj-110	12	5	suggested	suggest	VERB
cuesj-110	12	6	the	the	DET
cuesj-110	12	7	fuzzy	fuzzy	ADJ
cuesj-110	12	8	logic	logic	NOUN
cuesj-110	12	9	model	model	NOUN
cuesj-110	12	10	for	for	ADP
cuesj-110	12	11	approximate	approximate	ADJ
cuesj-110	12	12	matching	matching	NOUN
cuesj-110	12	13	of	of	ADP
cuesj-110	12	14	dna	dna	PROPN
cuesj-110	12	15	subsequences	subsequence	NOUN
cuesj-110	12	16	.	.	PUNCT
cuesj-110	13	1	kim	kim	PROPN
cuesj-110	13	2	et	et	PROPN
cuesj-110	13	3	al.[11	al.[11	PROPN
cuesj-110	13	4	]	]	PUNCT
cuesj-110	13	5	suggested	suggest	VERB
cuesj-110	13	6	a	a	DET
cuesj-110	13	7	dna	dna	PROPN
cuesj-110	13	8	sequence	sequence	NOUN
cuesj-110	13	9	alignment	alignment	NOUN
cuesj-110	13	10	,	,	PUNCT
cuesj-110	13	11	which	which	PRON
cuesj-110	13	12	uses	use	VERB
cuesj-110	13	13	quality	quality	NOUN
cuesj-110	13	14	information	information	NOUN
cuesj-110	13	15	and	and	CCONJ
cuesj-110	13	16	a	a	DET
cuesj-110	13	17	fuzzy	fuzzy	ADJ
cuesj-110	13	18	inference	inference	NOUN
cuesj-110	13	19	implementation	implementation	NOUN
cuesj-110	13	20	developed	develop	VERB
cuesj-110	13	21	based	base	VERB
cuesj-110	13	22	on	on	ADP
cuesj-110	13	23	the	the	DET
cuesj-110	13	24	features	feature	NOUN
cuesj-110	13	25	of	of	ADP
cuesj-110	13	26	dna	dna	PROPN
cuesj-110	13	27	parts	part	NOUN
cuesj-110	13	28	and	and	CCONJ
cuesj-110	13	29	a	a	DET
cuesj-110	13	30	fuzzy	fuzzy	ADJ
cuesj-110	13	31	logic	logic	NOUN
cuesj-110	13	32	system	system	NOUN
cuesj-110	13	33	.	.	PUNCT
cuesj-110	14	1	chai	chai	NOUN
cuesj-110	14	2	et	et	PROPN
cuesj-110	14	3	al.[12	al.[12	PROPN
cuesj-110	14	4	]	]	PUNCT
cuesj-110	14	5	explained	explain	VERB
cuesj-110	14	6	how	how	SCONJ
cuesj-110	14	7	to	to	PART
cuesj-110	14	8	perform	perform	VERB
cuesj-110	14	9	pairwise	pairwise	NOUN
cuesj-110	14	10	sequence	sequence	NOUN
cuesj-110	14	11	alignments	alignment	NOUN
cuesj-110	14	12	utilizing	utilize	VERB
cuesj-110	14	13	the	the	DET
cuesj-110	14	14	biostrings	biostring	NOUN
cuesj-110	14	15	bundle	bundle	NOUN
cuesj-110	14	16	using	use	VERB
cuesj-110	14	17	the	the	DET
cuesj-110	14	18	pairwise	pairwise	NOUN
cuesj-110	14	19	alignment	alignment	NOUN
cuesj-110	14	20	function	function	NOUN
cuesj-110	14	21	.	.	PUNCT
cuesj-110	15	1	hameed	hameed	NOUN
cuesj-110	15	2	and	and	CCONJ
cuesj-110	15	3	hamed[13	hamed[13	PROPN
cuesj-110	15	4	]	]	PUNCT
cuesj-110	15	5	discussed	discuss	VERB
cuesj-110	15	6	how	how	SCONJ
cuesj-110	15	7	to	to	PART
cuesj-110	15	8	cihan	cihan	VERB
cuesj-110	15	9	university	university	NOUN
cuesj-110	15	10	-	-	PUNCT
cuesj-110	15	11	erbil	erbil	PROPN
cuesj-110	15	12	scientific	scientific	ADJ
cuesj-110	15	13	journal	journal	NOUN
cuesj-110	15	14	(	(	PUNCT
cuesj-110	15	15	cuesj	cuesj	PROPN
cuesj-110	15	16	)	)	PUNCT
cuesj-110	15	17	corresponding	correspond	VERB
cuesj-110	15	18	author	author	NOUN
cuesj-110	15	19	:	:	PUNCT
cuesj-110	15	20	safa	safa	PROPN
cuesj-110	15	21	a.	a.	PROPN
cuesj-110	15	22	hameed	hameed	PROPN
cuesj-110	15	23	,	,	PUNCT
cuesj-110	15	24	department	department	NOUN
cuesj-110	15	25	of	of	ADP
cuesj-110	15	26	computer	computer	NOUN
cuesj-110	15	27	science	science	NOUN
cuesj-110	15	28	,	,	PUNCT
cuesj-110	15	29	college	college	NOUN
cuesj-110	15	30	of	of	ADP
cuesj-110	15	31	engineering	engineering	NOUN
cuesj-110	15	32	and	and	CCONJ
cuesj-110	15	33	science	science	NOUN
cuesj-110	15	34	,	,	PUNCT
cuesj-110	15	35	university	university	PROPN
cuesj-110	15	36	of	of	ADP
cuesj-110	15	37	bayan	bayan	PROPN
cuesj-110	15	38	,	,	PUNCT
cuesj-110	15	39	erbil	erbil	PROPN
cuesj-110	15	40	,	,	PUNCT
cuesj-110	15	41	iraq	iraq	PROPN
cuesj-110	15	42	.	.	PUNCT
cuesj-110	16	1	e-mail:safa.hamid@bnu.edu.iq	e-mail:safa.hamid@bnu.edu.iq	PROPN
cuesj-110	16	2	received	receive	VERB
cuesj-110	16	3	:	:	PUNCT
cuesj-110	16	4	mar	mar	PROPN
cuesj-110	16	5	21	21	NUM
cuesj-110	16	6	,	,	PUNCT
cuesj-110	16	7	2019	2019	NUM
cuesj-110	16	8	accepted	accept	VERB
cuesj-110	16	9	:	:	PUNCT
cuesj-110	16	10	apr	apr	NOUN
cuesj-110	16	11	24	24	NUM
cuesj-110	16	12	,	,	PUNCT
cuesj-110	16	13	2019	2019	NUM
cuesj-110	16	14	published	publish	VERB
cuesj-110	16	15	:	:	PUNCT
cuesj-110	16	16	aug	aug	PROPN
cuesj-110	16	17	20	20	NUM
cuesj-110	16	18	,	,	PUNCT
cuesj-110	16	19	2019	2019	NUM
cuesj-110	16	20	doi	doi	NOUN
cuesj-110	16	21	:	:	PUNCT
cuesj-110	16	22	10.24086	10.24086	NUM
cuesj-110	16	23	/	/	SYM
cuesj-110	16	24	cuesj.v3n2y2019.pp37	cuesj.v3n2y2019.pp37	NOUN
cuesj-110	16	25	-	-	SYM
cuesj-110	16	26	41	41	NUM
cuesj-110	16	27	copyright	copyright	NOUN
cuesj-110	16	28	©	©	PROPN
cuesj-110	16	29	2019	2019	NUM
cuesj-110	16	30	safa	safa	PROPN
cuesj-110	16	31	a.	a.	PROPN
cuesj-110	16	32	hameed	hameed	PROPN
cuesj-110	16	33	,	,	PUNCT
cuesj-110	16	34	raed	raed	PROPN
cuesj-110	16	35	i.	i.	PROPN
cuesj-110	16	36	hamed	hamed	PROPN
cuesj-110	16	37	.	.	PUNCT
cuesj-110	17	1	this	this	PRON
cuesj-110	17	2	is	be	AUX
cuesj-110	17	3	an	an	DET
cuesj-110	17	4	open	open	ADJ
cuesj-110	17	5	-	-	PUNCT
cuesj-110	17	6	access	access	NOUN
cuesj-110	17	7	article	article	NOUN
cuesj-110	17	8	distributed	distribute	VERB
cuesj-110	17	9	under	under	ADP
cuesj-110	17	10	the	the	DET
cuesj-110	17	11	creative	creative	ADJ
cuesj-110	17	12	commons	common	NOUN
cuesj-110	17	13	attribution	attribution	NOUN
cuesj-110	17	14	license	license	NOUN
cuesj-110	17	15	.	.	PUNCT
cuesj-110	18	1	hameed	hameed	PROPN
cuesj-110	18	2	and	and	CCONJ
cuesj-110	18	3	hamed	hamed	PROPN
cuesj-110	18	4	:	:	PUNCT
cuesj-110	18	5	measuring	measure	VERB
cuesj-110	18	6	the	the	DET
cuesj-110	18	7	score	score	NOUN
cuesj-110	18	8	matching	matching	NOUN
cuesj-110	18	9	of	of	ADP
cuesj-110	18	10	dna	dna	PROPN
cuesj-110	18	11	using	use	VERB
cuesj-110	18	12	nf	nf	ADP
cuesj-110	18	13	38	38	NUM
cuesj-110	18	14	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-110	18	15	cuesj	cuesj	NOUN
cuesj-110	18	16	2019	2019	NUM
cuesj-110	18	17	,	,	PUNCT
cuesj-110	18	18	3	3	NUM
cuesj-110	18	19	(	(	PUNCT
cuesj-110	18	20	2	2	NUM
cuesj-110	18	21	):	):	PUNCT
cuesj-110	18	22	37	37	NUM
cuesj-110	18	23	-	-	SYM
cuesj-110	18	24	41	41	NUM
cuesj-110	18	25	implement	implement	VERB
cuesj-110	18	26	the	the	DET
cuesj-110	18	27	pairwise	pairwise	NOUN
cuesj-110	18	28	alignment	alignment	NOUN
cuesj-110	18	29	technique	technique	NOUN
cuesj-110	18	30	to	to	PART
cuesj-110	18	31	get	get	VERB
cuesj-110	18	32	the	the	DET
cuesj-110	18	33	score	score	NOUN
cuesj-110	18	34	of	of	ADP
cuesj-110	18	35	similarity	similarity	NOUN
cuesj-110	18	36	for	for	ADP
cuesj-110	18	37	a	a	DET
cuesj-110	18	38	pair	pair	NOUN
cuesj-110	18	39	of	of	ADP
cuesj-110	18	40	characters	character	NOUN
cuesj-110	18	41	.	.	PUNCT
cuesj-110	19	1	in	in	ADP
cuesj-110	19	2	our	our	PRON
cuesj-110	19	3	work	work	NOUN
cuesj-110	19	4	,	,	PUNCT
cuesj-110	19	5	we	we	PRON
cuesj-110	19	6	use	use	VERB
cuesj-110	19	7	the	the	DET
cuesj-110	19	8	neuro	neuro	NOUN
cuesj-110	19	9	-	-	PUNCT
cuesj-110	19	10	fuzzy	fuzzy	ADJ
cuesj-110	19	11	model	model	NOUN
cuesj-110	19	12	that	that	PRON
cuesj-110	19	13	utilizes	utilize	VERB
cuesj-110	19	14	the	the	DET
cuesj-110	19	15	biological	biological	ADJ
cuesj-110	19	16	dataset	dataset	NOUN
cuesj-110	19	17	files	file	NOUN
cuesj-110	19	18	for	for	ADP
cuesj-110	19	19	matching	match	VERB
cuesj-110	19	20	dna	dna	NOUN
cuesj-110	19	21	and	and	CCONJ
cuesj-110	19	22	measures	measure	VERB
cuesj-110	19	23	the	the	DET
cuesj-110	19	24	score	score	NOUN
cuesj-110	19	25	of	of	ADP
cuesj-110	19	26	matching	match	VERB
cuesj-110	19	27	the	the	DET
cuesj-110	19	28	dna	dna	PROPN
cuesj-110	19	29	sequences	sequence	NOUN
cuesj-110	19	30	with	with	ADP
cuesj-110	19	31	global	global	ADJ
cuesj-110	19	32	and	and	CCONJ
cuesj-110	19	33	local	local	ADJ
cuesj-110	19	34	alignment	alignment	NOUN
cuesj-110	19	35	.	.	PUNCT
cuesj-110	20	1	sequence	sequence	NOUN
cuesj-110	20	2	alignment	alignment	NOUN
cuesj-110	20	3	dna	dna	PROPN
cuesj-110	20	4	matching	matching	NOUN
cuesj-110	20	5	is	be	AUX
cuesj-110	20	6	a	a	DET
cuesj-110	20	7	significant	significant	ADJ
cuesj-110	20	8	venture	venture	NOUN
cuesj-110	20	9	in	in	ADP
cuesj-110	20	10	the	the	DET
cuesj-110	20	11	sequence	sequence	NOUN
cuesj-110	20	12	alignment	alignment	NOUN
cuesj-110	20	13	.	.	PUNCT
cuesj-110	21	1	since	since	SCONJ
cuesj-110	21	2	sequence	sequence	NOUN
cuesj-110	21	3	alignment	alignment	NOUN
cuesj-110	21	4	is	be	AUX
cuesj-110	21	5	a	a	DET
cuesj-110	21	6	discretionary	discretionary	ADJ
cuesj-110	21	7	matching	matching	NOUN
cuesj-110	21	8	process	process	NOUN
cuesj-110	21	9	,	,	PUNCT
cuesj-110	21	10	there	there	PRON
cuesj-110	21	11	is	be	VERB
cuesj-110	21	12	a	a	DET
cuesj-110	21	13	need	need	NOUN
cuesj-110	21	14	for	for	ADP
cuesj-110	21	15	better	well	ADJ
cuesj-110	21	16	algorithms.[10	algorithms.[10	PROPN
cuesj-110	21	17	]	]	X
cuesj-110	21	18	dna	dna	PROPN
cuesj-110	21	19	sequence	sequence	NOUN
cuesj-110	21	20	alignment	alignment	NOUN
cuesj-110	21	21	algorithms	algorithm	NOUN
cuesj-110	21	22	over	over	ADP
cuesj-110	21	23	computational	computational	ADJ
cuesj-110	21	24	biological	biological	ADJ
cuesj-110	21	25	science	science	NOUN
cuesj-110	21	26	have	have	AUX
cuesj-110	21	27	been	be	AUX
cuesj-110	21	28	enhanced	enhance	VERB
cuesj-110	21	29	eventually	eventually	ADV
cuesj-110	21	30	by	by	ADP
cuesj-110	21	31	different	different	ADJ
cuesj-110	21	32	techniques:[11	techniques:[11	NOUN
cuesj-110	21	33	]	]	PUNCT
cuesj-110	21	34	the	the	DET
cuesj-110	21	35	(	(	PUNCT
cuesj-110	21	36	needleman	needleman	PROPN
cuesj-110	21	37	-	-	PUNCT
cuesj-110	21	38	wunsch	wunsch	PROPN
cuesj-110	21	39	)	)	PUNCT
cuesj-110	21	40	global	global	PROPN
cuesj-110	21	41	,	,	PUNCT
cuesj-110	21	42	the	the	DET
cuesj-110	21	43	(	(	PUNCT
cuesj-110	21	44	smithwaterman	smithwaterman	NOUN
cuesj-110	21	45	)	)	PUNCT
cuesj-110	21	46	local	local	ADJ
cuesj-110	21	47	,	,	PUNCT
cuesj-110	21	48	and	and	CCONJ
cuesj-110	21	49	(	(	PUNCT
cuesj-110	21	50	ends	end	NOUN
cuesj-110	21	51	-	-	PUNCT
cuesj-110	21	52	free	free	ADJ
cuesj-110	21	53	)	)	PUNCT
cuesj-110	21	54	cover	cover	VERB
cuesj-110	21	55	pairwise	pairwise	NOUN
cuesj-110	21	56	sequence	sequence	NOUN
cuesj-110	21	57	alignment	alignment	NOUN
cuesj-110	21	58	issues.[12	issues.[12	PROPN
cuesj-110	21	59	]	]	PUNCT
cuesj-110	21	60	pairwise	pairwise	NOUN
cuesj-110	21	61	alignment	alignment	NOUN
cuesj-110	21	62	is	be	AUX
cuesj-110	21	63	a	a	DET
cuesj-110	21	64	technique	technique	NOUN
cuesj-110	21	65	for	for	ADP
cuesj-110	21	66	scoring	score	VERB
cuesj-110	21	67	the	the	DET
cuesj-110	21	68	similarity	similarity	NOUN
cuesj-110	21	69	of	of	ADP
cuesj-110	21	70	a	a	DET
cuesj-110	21	71	pair	pair	NOUN
cuesj-110	21	72	of	of	ADP
cuesj-110	21	73	characters	character	NOUN
cuesj-110	21	74	.	.	PUNCT
cuesj-110	22	1	it	it	PRON
cuesj-110	22	2	decides	decide	VERB
cuesj-110	22	3	the	the	DET
cuesj-110	22	4	correspondences	correspondence	NOUN
cuesj-110	22	5	between	between	ADP
cuesj-110	22	6	the	the	DET
cuesj-110	22	7	substrings	substring	NOUN
cuesj-110	22	8	in	in	ADP
cuesj-110	22	9	the	the	DET
cuesj-110	22	10	sequences	sequence	NOUN
cuesj-110	22	11	like	like	ADP
cuesj-110	22	12	the	the	DET
cuesj-110	22	13	similarity	similarity	NOUN
cuesj-110	22	14	score	score	NOUN
cuesj-110	22	15	is	be	AUX
cuesj-110	22	16	amplified.[13	amplified.[13	NOUN
cuesj-110	22	17	]	]	PUNCT
cuesj-110	22	18	for	for	SCONJ
cuesj-110	22	19	it	it	PRON
cuesj-110	22	20	is	be	AUX
cuesj-110	22	21	a	a	DET
cuesj-110	22	22	large	large	ADJ
cuesj-110	22	23	portion	portion	NOUN
cuesj-110	22	24	basic	basic	ADJ
cuesj-110	22	25	form	form	NOUN
cuesj-110	22	26	,	,	PUNCT
cuesj-110	22	27	known	know	VERB
cuesj-110	22	28	as	as	ADP
cuesj-110	22	29	pairwise	pairwise	NOUN
cuesj-110	22	30	sequence	sequence	NOUN
cuesj-110	22	31	alignment	alignment	NOUN
cuesj-110	22	32	,	,	PUNCT
cuesj-110	22	33	we	we	PRON
cuesj-110	22	34	provided	provide	VERB
cuesj-110	22	35	for	for	ADP
cuesj-110	22	36	two	two	NUM
cuesj-110	22	37	sequences	sequence	NOUN
cuesj-110	22	38	a	a	PRON
cuesj-110	22	39	and	and	CCONJ
cuesj-110	22	40	b	b	NOUN
cuesj-110	22	41	and	and	CCONJ
cuesj-110	22	42	discover	discover	VERB
cuesj-110	22	43	their	their	PRON
cuesj-110	22	44	best	good	ADJ
cuesj-110	22	45	alignment	alignment	NOUN
cuesj-110	22	46	(	(	PUNCT
cuesj-110	22	47	either	either	CCONJ
cuesj-110	22	48	global	global	ADJ
cuesj-110	22	49	or	or	CCONJ
cuesj-110	22	50	local).[12	local).[12	PROPN
cuesj-110	22	51	]	]	PUNCT
cuesj-110	22	52	aligned	align	VERB
cuesj-110	22	53	sequences	sequence	NOUN
cuesj-110	22	54	represented	represent	VERB
cuesj-110	22	55	as	as	ADP
cuesj-110	22	56	rows	row	NOUN
cuesj-110	22	57	in	in	ADP
cuesj-110	22	58	a	a	DET
cuesj-110	22	59	grid	grid	NOUN
cuesj-110	22	60	.	.	PUNCT
cuesj-110	23	1	gaps	gap	NOUN
cuesj-110	23	2	(	(	PUNCT
cuesj-110	23	3	“	"	PUNCT
cuesj-110	23	4	−	−	PROPN
cuesj-110	23	5	“	"	PUNCT
cuesj-110	23	6	)	)	PUNCT
cuesj-110	23	7	need	need	VERB
cuesj-110	23	8	aid	aid	NOUN
cuesj-110	23	9	embedded	embed	VERB
cuesj-110	23	10	between	between	ADP
cuesj-110	23	11	the	the	DET
cuesj-110	23	12	characters	character	NOUN
cuesj-110	23	13	with	with	ADP
cuesj-110	23	14	the	the	DET
cuesj-110	23	15	goal.[6	goal.[6	NOUN
cuesj-110	23	16	]	]	X
cuesj-110	23	17	the	the	DET
cuesj-110	23	18	ways	way	NOUN
cuesj-110	23	19	we	we	PRON
cuesj-110	23	20	use	use	VERB
cuesj-110	23	21	it	it	PRON
cuesj-110	23	22	to	to	PART
cuesj-110	23	23	perform	perform	VERB
cuesj-110	23	24	the	the	DET
cuesj-110	23	25	alignment	alignment	NOUN
cuesj-110	23	26	are	be	AUX
cuesj-110	23	27	global	global	ADJ
cuesj-110	23	28	and	and	CCONJ
cuesj-110	23	29	local	local	ADJ
cuesj-110	23	30	alignment	alignment	NOUN
cuesj-110	23	31	,	,	PUNCT
cuesj-110	23	32	these	these	DET
cuesj-110	23	33	algorithms	algorithm	NOUN
cuesj-110	23	34	uses	use	VERB
cuesj-110	23	35	the	the	DET
cuesj-110	23	36	proposed	propose	VERB
cuesj-110	23	37	matrix	matrix	NOUN
cuesj-110	23	38	to	to	PART
cuesj-110	23	39	measure	measure	VERB
cuesj-110	23	40	the	the	DET
cuesj-110	23	41	similarity	similarity	NOUN
cuesj-110	23	42	of	of	ADP
cuesj-110	23	43	bases	basis	NOUN
cuesj-110	23	44	in	in	ADP
cuesj-110	23	45	the	the	DET
cuesj-110	23	46	two	two	NUM
cuesj-110	23	47	sequences	sequence	NOUN
cuesj-110	23	48	.	.	PUNCT
cuesj-110	24	1	for	for	ADP
cuesj-110	24	2	the	the	DET
cuesj-110	24	3	needleman	needleman	PROPN
cuesj-110	24	4	–	–	PUNCT
cuesj-110	24	5	wunsch	wunsch	PROPN
cuesj-110	24	6	algorithm	algorithm	PROPN
cuesj-110	24	7	,	,	PUNCT
cuesj-110	24	8	a	a	DET
cuesj-110	24	9	scoring	scoring	NOUN
cuesj-110	24	10	matrix	matrix	NOUN
cuesj-110	24	11	is	be	AUX
cuesj-110	24	12	ascertained	ascertain	VERB
cuesj-110	24	13	for	for	ADP
cuesj-110	24	14	those	those	DET
cuesj-110	24	15	two	two	NUM
cuesj-110	24	16	provided	provide	VERB
cuesj-110	24	17	for	for	ADP
cuesj-110	24	18	sequences	sequence	NOUN
cuesj-110	24	19	a	a	PRON
cuesj-110	24	20	and	and	CCONJ
cuesj-110	24	21	b	b	NOUN
cuesj-110	24	22	,	,	PUNCT
cuesj-110	24	23	by	by	ADP
cuesj-110	24	24	setting	set	VERB
cuesj-110	24	25	one	one	NUM
cuesj-110	24	26	sequence	sequence	NOUN
cuesj-110	24	27	along	along	ADP
cuesj-110	24	28	column	column	NOUN
cuesj-110	24	29	side	side	NOUN
cuesj-110	24	30	,	,	PUNCT
cuesj-110	24	31	furthermore	furthermore	ADV
cuesj-110	24	32	on	on	ADP
cuesj-110	24	33	the	the	DET
cuesj-110	24	34	turn	turn	NOUN
cuesj-110	24	35	sequence	sequence	NOUN
cuesj-110	24	36	side	side	NOUN
cuesj-110	24	37	.	.	PUNCT
cuesj-110	25	1	it	it	PRON
cuesj-110	25	2	is	be	AUX
cuesj-110	25	3	additionally	additionally	ADV
cuesj-110	25	4	frequently	frequently	ADV
cuesj-110	25	5	referred	refer	VERB
cuesj-110	25	6	as	as	ADP
cuesj-110	25	7	optimal	optimal	ADJ
cuesj-110	25	8	matching	matching	NOUN
cuesj-110	25	9	algorithm	algorithm	NOUN
cuesj-110	25	10	and	and	CCONJ
cuesj-110	25	11	the	the	DET
cuesj-110	25	12	global	global	ADJ
cuesj-110	25	13	alignment	alignment	NOUN
cuesj-110	25	14	technique.[7	technique.[7	PUNCT
cuesj-110	25	15	]	]	X
cuesj-110	25	16	the	the	DET
cuesj-110	25	17	smith	smith	PROPN
cuesj-110	25	18	–	–	PUNCT
cuesj-110	25	19	waterman	waterman	PROPN
cuesj-110	25	20	algorithm	algorithm	PROPN
cuesj-110	25	21	,	,	PUNCT
cuesj-110	25	22	which	which	PRON
cuesj-110	25	23	is	be	AUX
cuesj-110	25	24	the	the	DET
cuesj-110	25	25	method	method	NOUN
cuesj-110	25	26	used	use	VERB
cuesj-110	25	27	to	to	PART
cuesj-110	25	28	perform	perform	VERB
cuesj-110	25	29	the	the	DET
cuesj-110	25	30	local	local	ADJ
cuesj-110	25	31	sequence	sequence	NOUN
cuesj-110	25	32	alignment	alignment	NOUN
cuesj-110	25	33	,	,	PUNCT
cuesj-110	25	34	local	local	ADJ
cuesj-110	25	35	alignment	alignment	NOUN
cuesj-110	25	36	algorithms	algorithm	NOUN
cuesj-110	25	37	find	find	VERB
cuesj-110	25	38	the	the	DET
cuesj-110	25	39	sections	section	NOUN
cuesj-110	25	40	of	of	ADP
cuesj-110	25	41	the	the	DET
cuesj-110	25	42	highest	high	ADJ
cuesj-110	25	43	similarity	similarity	NOUN
cuesj-110	25	44	between	between	ADP
cuesj-110	25	45	two	two	NUM
cuesj-110	25	46	sequences	sequence	NOUN
cuesj-110	25	47	and	and	CCONJ
cuesj-110	25	48	create	create	VERB
cuesj-110	25	49	the	the	DET
cuesj-110	25	50	alignment	alignment	NOUN
cuesj-110	25	51	to	to	ADP
cuesj-110	25	52	abroad	abroad	ADV
cuesj-110	25	53	from	from	ADP
cuesj-110	25	54	there	there	ADV
cuesj-110	25	55	,	,	PUNCT
cuesj-110	25	56	that	that	ADV
cuesj-110	25	57	is	is	ADV
cuesj-110	25	58	,	,	PUNCT
cuesj-110	25	59	identify	identify	VERB
cuesj-110	25	60	the	the	DET
cuesj-110	25	61	most	most	ADV
cuesj-110	25	62	similar	similar	ADJ
cuesj-110	25	63	portion	portion	NOUN
cuesj-110	25	64	comparable	comparable	ADJ
cuesj-110	25	65	subregion	subregion	NOUN
cuesj-110	25	66	imparted	impart	VERB
cuesj-110	25	67	between	between	ADP
cuesj-110	25	68	two	two	NUM
cuesj-110	25	69	successions.[8	successions.[8	NOUN
cuesj-110	25	70	]	]	PUNCT
cuesj-110	25	71	the	the	DET
cuesj-110	25	72	proposed	propose	VERB
cuesj-110	25	73	method	method	NOUN
cuesj-110	25	74	we	we	PRON
cuesj-110	25	75	use	use	VERB
cuesj-110	25	76	the	the	DET
cuesj-110	25	77	nero	nero	ADJ
cuesj-110	25	78	-	-	PUNCT
cuesj-110	25	79	fuzzy	fuzzy	ADJ
cuesj-110	25	80	technique	technique	NOUN
cuesj-110	25	81	in	in	ADP
cuesj-110	25	82	matlab	matlab	PROPN
cuesj-110	25	83	tool	tool	NOUN
cuesj-110	25	84	.	.	PUNCT
cuesj-110	26	1	the	the	DET
cuesj-110	26	2	neuro	neuro	NOUN
cuesj-110	26	3	-	-	PUNCT
cuesj-110	26	4	fuzzy	fuzzy	ADJ
cuesj-110	26	5	model	model	NOUN
cuesj-110	26	6	is	be	AUX
cuesj-110	26	7	very	very	ADV
cuesj-110	26	8	well	well	ADV
cuesj-110	26	9	established	establish	VERB
cuesj-110	26	10	approach	approach	NOUN
cuesj-110	26	11	and	and	CCONJ
cuesj-110	26	12	has	have	VERB
cuesj-110	26	13	a	a	DET
cuesj-110	26	14	tremendous	tremendous	ADJ
cuesj-110	26	15	potentiality	potentiality	NOUN
cuesj-110	26	16	to	to	PART
cuesj-110	26	17	outcome	outcome	VERB
cuesj-110	26	18	results	result	NOUN
cuesj-110	26	19	with	with	ADP
cuesj-110	26	20	high	high	ADJ
cuesj-110	26	21	accuracy	accuracy	NOUN
cuesj-110	26	22	ratio	ratio	NOUN
cuesj-110	26	23	and	and	CCONJ
cuesj-110	26	24	the	the	DET
cuesj-110	26	25	efficiency	efficiency	NOUN
cuesj-110	26	26	with	with	ADP
cuesj-110	26	27	biological	biological	ADJ
cuesj-110	26	28	data	datum	NOUN
cuesj-110	26	29	to	to	PART
cuesj-110	26	30	determine	determine	VERB
cuesj-110	26	31	the	the	DET
cuesj-110	26	32	measure	measure	NOUN
cuesj-110	26	33	score	score	NOUN
cuesj-110	26	34	of	of	ADP
cuesj-110	26	35	matching	match	VERB
cuesj-110	26	36	dna	dna	NOUN
cuesj-110	26	37	sequencing	sequencing	NOUN
cuesj-110	26	38	.	.	PUNCT
cuesj-110	27	1	those	those	PRON
cuesj-110	27	2	recommended	recommend	VERB
cuesj-110	27	3	sequence	sequence	NOUN
cuesj-110	27	4	-	-	PUNCT
cuesj-110	27	5	matching	match	VERB
cuesj-110	27	6	algorithm	algorithm	NOUN
cuesj-110	27	7	utilize	utilize	VERB
cuesj-110	27	8	the	the	DET
cuesj-110	27	9	three	three	NUM
cuesj-110	27	10	input	input	NOUN
cuesj-110	27	11	variables	variable	NOUN
cuesj-110	27	12	–	–	PUNCT
cuesj-110	27	13	match	match	NOUN
cuesj-110	27	14	score	score	NOUN
cuesj-110	27	15	(	(	PUNCT
cuesj-110	27	16	match	match	NOUN
cuesj-110	27	17	)	)	PUNCT
cuesj-110	27	18	,	,	PUNCT
cuesj-110	27	19	mismatch	mismatch	ADJ
cuesj-110	27	20	score	score	NOUN
cuesj-110	27	21	(	(	PUNCT
cuesj-110	27	22	mismatch	mismatch	NOUN
cuesj-110	27	23	)	)	PUNCT
cuesj-110	27	24	,	,	PUNCT
cuesj-110	27	25	and	and	CCONJ
cuesj-110	27	26	gaps	gap	NOUN
cuesj-110	27	27	,	,	PUNCT
cuesj-110	27	28	as	as	SCONJ
cuesj-110	27	29	shown	show	VERB
cuesj-110	27	30	in	in	ADP
cuesj-110	27	31	“	"	PUNCT
cuesj-110	27	32	figure	figure	NOUN
cuesj-110	27	33	1	1	NUM
cuesj-110	27	34	.	.	PUNCT
cuesj-110	27	35	”	"	PUNCT
cuesj-110	28	1	these	these	DET
cuesj-110	28	2	three	three	NUM
cuesj-110	28	3	inputs	input	NOUN
cuesj-110	28	4	would	would	AUX
cuesj-110	28	5	then	then	ADV
cuesj-110	28	6	fuzzified	fuzzifie	VERB
cuesj-110	28	7	utilizing	utilize	VERB
cuesj-110	28	8	the	the	DET
cuesj-110	28	9	following	follow	VERB
cuesj-110	28	10	membership	membership	NOUN
cuesj-110	28	11	functions	function	NOUN
cuesj-110	28	12	(	(	PUNCT
cuesj-110	28	13	mfs	mfs	PROPN
cuesj-110	28	14	)	)	PUNCT
cuesj-110	28	15	equations	equation	NOUN
cuesj-110	28	16	and	and	CCONJ
cuesj-110	28	17	giving	give	VERB
cuesj-110	28	18	the	the	DET
cuesj-110	28	19	calculated	calculated	ADJ
cuesj-110	28	20	resulting	result	VERB
cuesj-110	28	21	score	score	NOUN
cuesj-110	28	22	:	:	PUNCT
cuesj-110	28	23	matching	match	VERB
cuesj-110	28	24	=	=	NOUN
cuesj-110	28	25	0	0	PUNCT
cuesj-110	29	1	if	if	SCONJ
cuesj-110	29	2	there	there	PRON
cuesj-110	29	3	is	be	VERB
cuesj-110	29	4	no	no	DET
cuesj-110	29	5	similarity	similarity	NOUN
cuesj-110	29	6	1	1	NUM
cuesj-110	29	7	if	if	SCONJ
cuesj-110	29	8	there	there	PRON
cuesj-110	29	9	is	be	VERB
cuesj-110	29	10	highest	high	ADJ
cuesj-110	29	11	simmilarity	simmilarity	NOUN
cuesj-110	29	12	100	100	NUM
cuesj-110	29	13	%	%	NOUN
cuesj-110	29	14	1,0	1,0	NUM
cuesj-110	29	15	matching	matching	NOUN
cuesj-110	29	16	score	score	NOUN
cuesj-110	29	17	/	/	SYM
cuesj-110	29	18	lenseq	lenseq	NUM
cuesj-110	30	1			NOUN
cuesj-110	30	2			NOUN
cuesj-110	30	3			PUNCT
cuesj-110	30	4			ADP
cuesj-110	31	1			NUM
cuesj-110	31	2			NUM
cuesj-110	32	1			PROPN
cuesj-110	32	2			NUM
cuesj-110	32	3			NUM
cuesj-110	32	4	(	(	PUNCT
cuesj-110	32	5	1	1	NUM
cuesj-110	32	6	)	)	PUNCT
cuesj-110	32	7	mismatch	mismatch	NOUN
cuesj-110	32	8	=	=	SYM
cuesj-110	32	9	0	0	PUNCT
cuesj-110	33	1	if	if	SCONJ
cuesj-110	33	2	there	there	PRON
cuesj-110	33	3	is	be	VERB
cuesj-110	33	4	no	no	DET
cuesj-110	33	5	mismatch	mismatch	NOUN
cuesj-110	33	6	1	1	NUM
cuesj-110	33	7	if	if	SCONJ
cuesj-110	33	8	there	there	PRON
cuesj-110	33	9	is	be	VERB
cuesj-110	33	10	no	no	DET
cuesj-110	33	11	similarityy	similarityy	ADJ
cuesj-110	33	12	1,0	1,0	NUM
cuesj-110	33	13	mismatching	mismatch	VERB
cuesj-110	33	14	score	score	NOUN
cuesj-110	33	15	/	/	SYM
cuesj-110	33	16	lenseq	lenseq	NUM
cuesj-110	33	17			NOUN
cuesj-110	33	18			NOUN
cuesj-110	33	19			PUNCT
cuesj-110	33	20			ADP
cuesj-110	33	21			NUM
cuesj-110	33	22			NUM
cuesj-110	34	1			PROPN
cuesj-110	34	2			NUM
cuesj-110	34	3			NUM
cuesj-110	34	4	(	(	PUNCT
cuesj-110	34	5	2	2	NUM
cuesj-110	34	6	)	)	PUNCT
cuesj-110	34	7	gap=	gap=	PROPN
cuesj-110	34	8	0	0	PUNCT
cuesj-110	35	1	if	if	SCONJ
cuesj-110	35	2	there	there	PRON
cuesj-110	35	3	is	be	VERB
cuesj-110	35	4	no	no	DET
cuesj-110	35	5	need	need	NOUN
cuesj-110	35	6	to	to	PART
cuesj-110	35	7	put	put	VERB
cuesj-110	35	8	a	a	DET
cuesj-110	35	9	gap	gap	NOUN
cuesj-110	35	10	1	1	NUM
cuesj-110	35	11	,	,	PUNCT
cuesj-110	35	12	0	0	NUM
cuesj-110	35	13	gaps	gap	VERB
cuesj-110	35	14	score	score	NOUN
cuesj-110	35	15	/	/	SYM
cuesj-110	35	16	lens	lens	NOUN
cuesj-110	35	17			ADP
cuesj-110	35	18	eeq	eeq	ADV
cuesj-110	35	19			PROPN
cuesj-110	35	20			ADP
cuesj-110	35	21			NUM
cuesj-110	35	22			NUM
cuesj-110	35	23			PROPN
cuesj-110	35	24	(	(	PUNCT
cuesj-110	35	25	3	3	X
cuesj-110	35	26	)	)	PUNCT
cuesj-110	35	27	figure	figure	NOUN
cuesj-110	35	28	1	1	NUM
cuesj-110	35	29	:	:	PUNCT
cuesj-110	35	30	the	the	DET
cuesj-110	35	31	three	three	NUM
cuesj-110	35	32	input	input	NOUN
cuesj-110	35	33	variables	variable	NOUN
cuesj-110	35	34	and	and	CCONJ
cuesj-110	35	35	the	the	DET
cuesj-110	35	36	output	output	NOUN
cuesj-110	35	37	figure	figure	NOUN
cuesj-110	35	38	2	2	NUM
cuesj-110	35	39	:	:	PUNCT
cuesj-110	35	40	the	the	DET
cuesj-110	35	41	membership	membership	NOUN
cuesj-110	35	42	function	function	NOUN
cuesj-110	35	43	and	and	CCONJ
cuesj-110	35	44	the	the	DET
cuesj-110	35	45	training	training	NOUN
cuesj-110	35	46	testing	testing	NOUN
cuesj-110	35	47	phase	phase	NOUN
cuesj-110	35	48	for	for	ADP
cuesj-110	35	49	the	the	DET
cuesj-110	35	50	lowest	low	ADJ
cuesj-110	35	51	possible	possible	ADJ
cuesj-110	35	52	error	error	NOUN
cuesj-110	35	53	.	.	PUNCT
cuesj-110	36	1	(	(	PUNCT
cuesj-110	36	2	a	a	X
cuesj-110	36	3	)	)	PUNCT
cuesj-110	36	4	input	input	NOUN
cuesj-110	36	5	membership	membership	NOUN
cuesj-110	36	6	function	function	NOUN
cuesj-110	36	7	.	.	PUNCT
cuesj-110	37	1	(	(	PUNCT
cuesj-110	37	2	b	b	X
cuesj-110	37	3	)	)	PUNCT
cuesj-110	37	4	the	the	DET
cuesj-110	37	5	testing	testing	NOUN
cuesj-110	37	6	data	datum	NOUN
cuesj-110	37	7	ba	ba	PROPN
cuesj-110	37	8	hameed	hameed	PROPN
cuesj-110	37	9	and	and	CCONJ
cuesj-110	37	10	hamed	hamed	PROPN
cuesj-110	37	11	:	:	PUNCT
cuesj-110	37	12	measuring	measure	VERB
cuesj-110	37	13	the	the	DET
cuesj-110	37	14	score	score	NOUN
cuesj-110	37	15	matching	matching	NOUN
cuesj-110	37	16	of	of	ADP
cuesj-110	37	17	dna	dna	PROPN
cuesj-110	37	18	using	use	VERB
cuesj-110	37	19	nf	nf	ADP
cuesj-110	37	20	39	39	NUM
cuesj-110	37	21	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-110	37	22	cuesj	cuesj	NOUN
cuesj-110	37	23	2019	2019	NUM
cuesj-110	37	24	,	,	PUNCT
cuesj-110	37	25	3	3	NUM
cuesj-110	37	26	(	(	PUNCT
cuesj-110	37	27	2	2	NUM
cuesj-110	37	28	):	):	PUNCT
cuesj-110	37	29	37	37	NUM
cuesj-110	37	30	-	-	SYM
cuesj-110	37	31	41	41	NUM
cuesj-110	37	32	score=	score=	NUM
cuesj-110	37	33	0	0	NUM
cuesj-110	38	1	if	if	SCONJ
cuesj-110	38	2	the	the	DET
cuesj-110	38	3	resulting	result	VERB
cuesj-110	38	4	score	score	NOUN
cuesj-110	38	5	£	£	SYM
cuesj-110	38	6	0	0	NUM
cuesj-110	38	7	1	1	NUM
cuesj-110	38	8	if	if	SCONJ
cuesj-110	38	9	the	the	DET
cuesj-110	38	10	resulting	result	VERB
cuesj-110	38	11	perfect	perfect	ADJ
cuesj-110	38	12	11	11	NUM
cuesj-110	38	13	,	,	PUNCT
cuesj-110	38	14	0	0	NUM
cuesj-110	38	15	resulting	result	VERB
cuesj-110	38	16	score	score	NOUN
cuesj-110	38	17	/	/	SYM
cuesj-110	38	18	perfect	perfect	ADJ
cuesj-110	38	19	score	score	NOUN
cuesj-110	38	20			ADP
cuesj-110	38	21			NOUN
cuesj-110	38	22			PUNCT
cuesj-110	38	23			ADP
cuesj-110	38	24			NUM
cuesj-110	38	25			NUM
cuesj-110	39	1			PROPN
cuesj-110	39	2			NUM
cuesj-110	39	3			NUM
cuesj-110	39	4	(	(	PUNCT
cuesj-110	39	5	4	4	NUM
cuesj-110	39	6	)	)	PUNCT
cuesj-110	39	7	the	the	DET
cuesj-110	39	8	variable	variable	ADJ
cuesj-110	39	9	“	"	PUNCT
cuesj-110	39	10	lenseq	lenseq	NOUN
cuesj-110	39	11	”	"	PUNCT
cuesj-110	39	12	mean	mean	VERB
cuesj-110	39	13	the	the	DET
cuesj-110	39	14	entire	entire	ADJ
cuesj-110	39	15	length	length	NOUN
cuesj-110	39	16	of	of	ADP
cuesj-110	39	17	the	the	DET
cuesj-110	39	18	sequence	sequence	NOUN
cuesj-110	39	19	.	.	PUNCT
cuesj-110	40	1	score	score	NOUN
cuesj-110	40	2	=	=	PUNCT
cuesj-110	40	3	match	match	NOUN
cuesj-110	40	4	+	+	CCONJ
cuesj-110	40	5	(	(	PUNCT
cuesj-110	40	6	−	−	NOUN
cuesj-110	40	7	mismatch	mismatch	NOUN
cuesj-110	40	8	)	)	PUNCT
cuesj-110	41	1	+	+	CCONJ
cuesj-110	41	2	(	(	PUNCT
cuesj-110	41	3	−	−	PROPN
cuesj-110	41	4	gabs	gab	NOUN
cuesj-110	41	5	)	)	PUNCT
cuesj-110	41	6	(	(	PUNCT
cuesj-110	41	7	5	5	X
cuesj-110	41	8	)	)	PUNCT
cuesj-110	41	9	the	the	DET
cuesj-110	41	10	simulation	simulation	NOUN
cuesj-110	41	11	results	result	VERB
cuesj-110	41	12	in	in	ADP
cuesj-110	41	13	our	our	PRON
cuesj-110	41	14	work	work	NOUN
cuesj-110	41	15	,	,	PUNCT
cuesj-110	41	16	we	we	PRON
cuesj-110	41	17	perform	perform	VERB
cuesj-110	41	18	the	the	DET
cuesj-110	41	19	neuro	neuro	NOUN
cuesj-110	41	20	-	-	PUNCT
cuesj-110	41	21	fuzzy	fuzzy	ADJ
cuesj-110	41	22	model	model	NOUN
cuesj-110	41	23	by	by	ADP
cuesj-110	41	24	an	an	DET
cuesj-110	41	25	adaptive	adaptive	ADJ
cuesj-110	41	26	neuro	neuro	NOUN
cuesj-110	41	27	-	-	PUNCT
cuesj-110	41	28	fuzzy	fuzzy	ADJ
cuesj-110	41	29	inference	inference	NOUN
cuesj-110	41	30	system	system	NOUN
cuesj-110	41	31	(	(	PUNCT
cuesj-110	41	32	anfis	anfis	ADJ
cuesj-110	41	33	)	)	PUNCT
cuesj-110	41	34	tool	tool	NOUN
cuesj-110	41	35	in	in	ADP
cuesj-110	41	36	matlab	matlab	PROPN
cuesj-110	41	37	,	,	PUNCT
cuesj-110	41	38	using	use	VERB
cuesj-110	41	39	the	the	DET
cuesj-110	41	40	data	datum	NOUN
cuesj-110	41	41	set	set	VERB
cuesj-110	41	42	about	about	ADV
cuesj-110	41	43	600	600	NUM
cuesj-110	41	44	samples	sample	NOUN
cuesj-110	41	45	for	for	ADP
cuesj-110	41	46	a	a	DET
cuesj-110	41	47	matching	match	VERB
cuesj-110	41	48	measure	measure	NOUN
cuesj-110	41	49	score	score	NOUN
cuesj-110	41	50	of	of	ADP
cuesj-110	41	51	dna	dna	NOUN
cuesj-110	41	52	sequencing	sequencing	NOUN
cuesj-110	41	53	;	;	PUNCT
cuesj-110	41	54	these	these	DET
cuesj-110	41	55	data	datum	NOUN
cuesj-110	41	56	divide	divide	VERB
cuesj-110	41	57	into	into	ADP
cuesj-110	41	58	two	two	NUM
cuesj-110	41	59	data	data	NOUN
cuesj-110	41	60	files	file	NOUN
cuesj-110	41	61	for	for	ADP
cuesj-110	41	62	the	the	DET
cuesj-110	41	63	training	training	NOUN
cuesj-110	41	64	and	and	CCONJ
cuesj-110	41	65	testing	testing	NOUN
cuesj-110	41	66	,	,	PUNCT
cuesj-110	41	67	for	for	ADP
cuesj-110	41	68	the	the	DET
cuesj-110	41	69	training	training	NOUN
cuesj-110	41	70	step	step	NOUN
cuesj-110	41	71	,	,	PUNCT
cuesj-110	41	72	we	we	PRON
cuesj-110	41	73	use	use	VERB
cuesj-110	41	74	the	the	DET
cuesj-110	41	75	data	datum	NOUN
cuesj-110	41	76	set	set	VERB
cuesj-110	41	77	about	about	ADV
cuesj-110	41	78	450	450	NUM
cuesj-110	41	79	samples	sample	NOUN
cuesj-110	41	80	,	,	PUNCT
cuesj-110	41	81	and	and	CCONJ
cuesj-110	41	82	for	for	ADP
cuesj-110	41	83	the	the	DET
cuesj-110	41	84	testing	testing	NOUN
cuesj-110	41	85	step	step	NOUN
cuesj-110	41	86	,	,	PUNCT
cuesj-110	41	87	we	we	PRON
cuesj-110	41	88	use	use	VERB
cuesj-110	41	89	the	the	DET
cuesj-110	41	90	data	datum	NOUN
cuesj-110	41	91	about	about	ADV
cuesj-110	41	92	150	150	NUM
cuesj-110	41	93	samples.[14	samples.[14	PROPN
cuesj-110	41	94	]	]	X
cuesj-110	41	95	we	we	PRON
cuesj-110	41	96	use	use	VERB
cuesj-110	41	97	these	these	DET
cuesj-110	41	98	dataset	dataset	ADJ
cuesj-110	41	99	files	file	NOUN
cuesj-110	41	100	in	in	ADP
cuesj-110	41	101	anfis	anfis	ADJ
cuesj-110	41	102	system	system	NOUN
cuesj-110	41	103	in	in	ADP
cuesj-110	41	104	the	the	DET
cuesj-110	41	105	range	range	NOUN
cuesj-110	41	106	value	value	NOUN
cuesj-110	41	107	in	in	ADP
cuesj-110	41	108	equation	equation	NOUN
cuesj-110	41	109	1	1	NUM
cuesj-110	41	110	,	,	PUNCT
cuesj-110	41	111	2	2	NUM
cuesj-110	41	112	,	,	PUNCT
cuesj-110	41	113	3	3	NUM
cuesj-110	41	114	,	,	PUNCT
cuesj-110	41	115	and	and	CCONJ
cuesj-110	41	116	4	4	NUM
cuesj-110	41	117	and	and	CCONJ
cuesj-110	41	118	output	output	VERB
cuesj-110	41	119	the	the	DET
cuesj-110	41	120	result	result	NOUN
cuesj-110	41	121	according	accord	VERB
cuesj-110	41	122	to	to	ADP
cuesj-110	41	123	the	the	DET
cuesj-110	41	124	equation	equation	NOUN
cuesj-110	41	125	5	5	NUM
cuesj-110	41	126	.	.	PUNCT
cuesj-110	42	1	we	we	PRON
cuesj-110	42	2	use	use	VERB
cuesj-110	42	3	different	different	ADJ
cuesj-110	42	4	processing	processing	NOUN
cuesj-110	42	5	systems	system	NOUN
cuesj-110	42	6	to	to	PART
cuesj-110	42	7	implement	implement	VERB
cuesj-110	42	8	the	the	DET
cuesj-110	42	9	matching	matching	NOUN
cuesj-110	42	10	process	process	NOUN
cuesj-110	42	11	,	,	PUNCT
cuesj-110	42	12	and	and	CCONJ
cuesj-110	42	13	each	each	DET
cuesj-110	42	14	system	system	NOUN
cuesj-110	42	15	has	have	VERB
cuesj-110	42	16	different	different	ADJ
cuesj-110	42	17	results	result	NOUN
cuesj-110	42	18	with	with	ADP
cuesj-110	42	19	convergent	convergent	NOUN
cuesj-110	42	20	values	value	NOUN
cuesj-110	42	21	,	,	PUNCT
cuesj-110	42	22	and	and	CCONJ
cuesj-110	42	23	that	that	SCONJ
cuesj-110	42	24	for	for	ADP
cuesj-110	42	25	choosing	choose	VERB
cuesj-110	42	26	the	the	DET
cuesj-110	42	27	most	most	ADV
cuesj-110	42	28	suitable	suitable	ADJ
cuesj-110	42	29	one	one	NUM
cuesj-110	42	30	with	with	ADP
cuesj-110	42	31	less	less	ADJ
cuesj-110	42	32	error	error	NOUN
cuesj-110	42	33	percentage	percentage	NOUN
cuesj-110	42	34	and	and	CCONJ
cuesj-110	42	35	depends	depend	VERB
cuesj-110	42	36	on	on	ADP
cuesj-110	42	37	it	it	PRON
cuesj-110	42	38	to	to	PART
cuesj-110	42	39	calculate	calculate	VERB
cuesj-110	42	40	the	the	DET
cuesj-110	42	41	matching	matching	NOUN
cuesj-110	42	42	score	score	NOUN
cuesj-110	42	43	.	.	PUNCT
cuesj-110	43	1	“	"	PUNCT
cuesj-110	43	2	figure	figure	NOUN
cuesj-110	43	3	2	2	NUM
cuesj-110	43	4	”	"	PUNCT
cuesj-110	43	5	shows	show	VERB
cuesj-110	43	6	the	the	DET
cuesj-110	43	7	chosen	choose	VERB
cuesj-110	43	8	attempt	attempt	NOUN
cuesj-110	43	9	that	that	PRON
cuesj-110	43	10	gives	give	VERB
cuesj-110	43	11	the	the	DET
cuesj-110	43	12	results	result	NOUN
cuesj-110	43	13	with	with	ADP
cuesj-110	43	14	high	high	ADJ
cuesj-110	43	15	accuracy	accuracy	NOUN
cuesj-110	43	16	.	.	PUNCT
cuesj-110	44	1	“	"	PUNCT
cuesj-110	44	2	figure	figure	NOUN
cuesj-110	44	3	2	2	NUM
cuesj-110	44	4	”	"	PUNCT
cuesj-110	44	5	explains	explain	VERB
cuesj-110	44	6	the	the	DET
cuesj-110	44	7	most	most	ADV
cuesj-110	44	8	suitable	suitable	ADJ
cuesj-110	44	9	anfis	anfis	ADJ
cuesj-110	44	10	system	system	NOUN
cuesj-110	44	11	with	with	ADP
cuesj-110	44	12	the	the	DET
cuesj-110	44	13	lowest	low	ADJ
cuesj-110	44	14	average	average	ADJ
cuesj-110	44	15	testing	testing	NOUN
cuesj-110	44	16	error	error	NOUN
cuesj-110	44	17	;	;	PUNCT
cuesj-110	44	18	table	table	NOUN
cuesj-110	44	19	1	1	NUM
cuesj-110	44	20	shows	show	VERB
cuesj-110	44	21	the	the	DET
cuesj-110	44	22	different	different	ADJ
cuesj-110	44	23	processing	processing	NOUN
cuesj-110	44	24	systems	system	NOUN
cuesj-110	44	25	;	;	PUNCT
cuesj-110	44	26	we	we	PRON
cuesj-110	44	27	implement	implement	VERB
cuesj-110	44	28	it	it	PRON
cuesj-110	44	29	with	with	ADP
cuesj-110	44	30	the	the	DET
cuesj-110	44	31	details	detail	NOUN
cuesj-110	44	32	.	.	PUNCT
cuesj-110	45	1	in	in	ADP
cuesj-110	45	2	this	this	DET
cuesj-110	45	3	table	table	NOUN
cuesj-110	45	4	the	the	DET
cuesj-110	45	5	most	most	ADV
cuesj-110	45	6	suitable	suitable	ADJ
cuesj-110	45	7	system	system	NOUN
cuesj-110	45	8	which	which	PRON
cuesj-110	45	9	has	have	AUX
cuesj-110	45	10	chosen	choose	VERB
cuesj-110	45	11	is	be	AUX
cuesj-110	45	12	the	the	DET
cuesj-110	45	13	system	system	NOUN
cuesj-110	45	14	that	that	PRON
cuesj-110	45	15	has	have	VERB
cuesj-110	45	16	the	the	DET
cuesj-110	45	17	following	following	NOUN
cuesj-110	45	18	:	:	PUNCT
cuesj-110	45	19	trapezoidal	trapezoidal	ADJ
cuesj-110	45	20	mfs	mfs	NOUN
cuesj-110	45	21	that	that	PRON
cuesj-110	45	22	has	have	VERB
cuesj-110	45	23	three	three	NUM
cuesj-110	45	24	mfs	mfs	NOUN
cuesj-110	45	25	,	,	PUNCT
cuesj-110	45	26	constant	constant	ADJ
cuesj-110	45	27	mfs	mfs	NOUN
cuesj-110	45	28	output	output	NOUN
cuesj-110	45	29	,	,	PUNCT
cuesj-110	45	30	backpropegation	backpropegation	NOUN
cuesj-110	45	31	train	train	VERB
cuesj-110	45	32	fuzzy	fuzzy	ADJ
cuesj-110	45	33	inference	inference	NOUN
cuesj-110	45	34	system	system	NOUN
cuesj-110	45	35	method	method	NOUN
cuesj-110	45	36	and	and	CCONJ
cuesj-110	45	37	the	the	DET
cuesj-110	45	38	number	number	NOUN
cuesj-110	45	39	of	of	ADP
cuesj-110	45	40	epochs	epoch	NOUN
cuesj-110	45	41	which	which	PRON
cuesj-110	45	42	are	be	AUX
cuesj-110	45	43	500	500	NUM
cuesj-110	45	44	,	,	PUNCT
cuesj-110	45	45	this	this	DET
cuesj-110	45	46	system	system	NOUN
cuesj-110	45	47	has	have	VERB
cuesj-110	45	48	the	the	DET
cuesj-110	45	49	lowest	low	ADJ
cuesj-110	45	50	average	average	ADJ
cuesj-110	45	51	training	training	NOUN
cuesj-110	45	52	and	and	CCONJ
cuesj-110	45	53	testing	testing	NOUN
cuesj-110	45	54	error	error	NOUN
cuesj-110	45	55	which	which	PRON
cuesj-110	45	56	are	be	AUX
cuesj-110	45	57	0.016572	0.016572	NUM
cuesj-110	45	58	and	and	CCONJ
cuesj-110	45	59	0.01657	0.01657	NUM
cuesj-110	45	60	respectively	respectively	ADV
cuesj-110	45	61	,	,	PUNCT
cuesj-110	45	62	and	and	CCONJ
cuesj-110	45	63	gives	give	VERB
cuesj-110	45	64	the	the	DET
cuesj-110	45	65	result	result	NOUN
cuesj-110	45	66	with	with	ADP
cuesj-110	45	67	high	high	ADJ
cuesj-110	45	68	performance	performance	NOUN
cuesj-110	45	69	,	,	PUNCT
cuesj-110	45	70	thus	thus	ADV
cuesj-110	45	71	we	we	PRON
cuesj-110	45	72	use	use	VERB
cuesj-110	45	73	it	it	PRON
cuesj-110	45	74	to	to	PART
cuesj-110	45	75	get	get	VERB
cuesj-110	45	76	the	the	DET
cuesj-110	45	77	score	score	NOUN
cuesj-110	45	78	matching	matching	NOUN
cuesj-110	45	79	of	of	ADP
cuesj-110	45	80	the	the	DET
cuesj-110	45	81	numeric	numeric	ADJ
cuesj-110	45	82	data	datum	NOUN
cuesj-110	45	83	from	from	ADP
cuesj-110	45	84	the	the	DET
cuesj-110	45	85	dna	dna	PROPN
cuesj-110	45	86	sequence	sequence	NOUN
cuesj-110	45	87	alignment	alignment	NOUN
cuesj-110	45	88	,	,	PUNCT
cuesj-110	45	89	show	show	VERB
cuesj-110	45	90	tables	table	NOUN
cuesj-110	45	91	2	2	NUM
cuesj-110	45	92	and	and	CCONJ
cuesj-110	45	93	3	3	NUM
cuesj-110	45	94	.	.	NOUN
cuesj-110	46	1	in	in	ADP
cuesj-110	46	2	table	table	NOUN
cuesj-110	46	3	3	3	NUM
cuesj-110	46	4	,	,	PUNCT
cuesj-110	46	5	we	we	PRON
cuesj-110	46	6	aligned	align	VERB
cuesj-110	46	7	the	the	DET
cuesj-110	46	8	sequence	sequence	NOUN
cuesj-110	46	9	using	use	VERB
cuesj-110	46	10	the	the	DET
cuesj-110	46	11	local	local	ADJ
cuesj-110	46	12	algorithm	algorithm	NOUN
cuesj-110	46	13	;	;	PUNCT
cuesj-110	46	14	in	in	ADP
cuesj-110	46	15	this	this	DET
cuesj-110	46	16	method	method	NOUN
cuesj-110	46	17	,	,	PUNCT
cuesj-110	46	18	the	the	DET
cuesj-110	46	19	algorithm	algorithm	NOUN
cuesj-110	46	20	takes	take	VERB
cuesj-110	46	21	the	the	DET
cuesj-110	46	22	most	most	ADV
cuesj-110	46	23	similar	similar	ADJ
cuesj-110	46	24	part	part	NOUN
cuesj-110	46	25	of	of	ADP
cuesj-110	46	26	the	the	DET
cuesj-110	46	27	pair	pair	NOUN
cuesj-110	46	28	sequence	sequence	NOUN
cuesj-110	46	29	,	,	PUNCT
cuesj-110	46	30	not	not	PART
cuesj-110	46	31	must	must	AUX
cuesj-110	46	32	in	in	ADP
cuesj-110	46	33	the	the	DET
cuesj-110	46	34	order	order	NOUN
cuesj-110	46	35	and	and	CCONJ
cuesj-110	46	36	not	not	PART
cuesj-110	46	37	need	need	VERB
cuesj-110	46	38	to	to	PART
cuesj-110	46	39	input	input	VERB
cuesj-110	46	40	the	the	DET
cuesj-110	46	41	gap	gap	NOUN
cuesj-110	46	42	;	;	PUNCT
cuesj-110	46	43	the	the	DET
cuesj-110	46	44	resulting	result	VERB
cuesj-110	46	45	score	score	NOUN
cuesj-110	46	46	is	be	AUX
cuesj-110	46	47	the	the	DET
cuesj-110	46	48	perfect	perfect	ADJ
cuesj-110	46	49	,	,	PUNCT
cuesj-110	46	50	there	there	PRON
cuesj-110	46	51	are	be	VERB
cuesj-110	46	52	no	no	DET
cuesj-110	46	53	mismatch	mismatch	NOUN
cuesj-110	46	54	and	and	CCONJ
cuesj-110	46	55	no	no	DET
cuesj-110	46	56	gaps	gap	NOUN
cuesj-110	46	57	,	,	PUNCT
cuesj-110	46	58	and	and	CCONJ
cuesj-110	46	59	is	be	AUX
cuesj-110	46	60	100	100	NUM
cuesj-110	46	61	%	%	NOUN
cuesj-110	46	62	identical	identical	ADJ
cuesj-110	46	63	.	.	PUNCT
cuesj-110	47	1	discussion	discussion	NOUN
cuesj-110	47	2	sequence	sequence	NOUN
cuesj-110	47	3	alignment	alignment	NOUN
cuesj-110	47	4	is	be	AUX
cuesj-110	47	5	a	a	DET
cuesj-110	47	6	necessary	necessary	ADJ
cuesj-110	47	7	condition	condition	NOUN
cuesj-110	47	8	for	for	ADP
cuesj-110	47	9	analyzing	analyze	VERB
cuesj-110	47	10	dna	dna	NOUN
cuesj-110	47	11	sequencing	sequencing	NOUN
cuesj-110	47	12	;	;	PUNCT
cuesj-110	47	13	in	in	ADP
cuesj-110	47	14	our	our	PRON
cuesj-110	47	15	method	method	NOUN
cuesj-110	47	16	,	,	PUNCT
cuesj-110	47	17	we	we	PRON
cuesj-110	47	18	use	use	VERB
cuesj-110	47	19	the	the	DET
cuesj-110	47	20	pairwise	pairwise	NOUN
cuesj-110	47	21	sequence	sequence	NOUN
cuesj-110	47	22	alignment	alignment	NOUN
cuesj-110	47	23	;	;	PUNCT
cuesj-110	47	24	it	it	PRON
cuesj-110	47	25	is	be	AUX
cuesj-110	47	26	applied	apply	VERB
cuesj-110	47	27	using	use	VERB
cuesj-110	47	28	the	the	DET
cuesj-110	47	29	global	global	ADJ
cuesj-110	47	30	and	and	CCONJ
cuesj-110	47	31	local	local	ADJ
cuesj-110	47	32	alignment	alignment	NOUN
cuesj-110	47	33	algorithm	algorithm	NOUN
cuesj-110	47	34	method	method	NOUN
cuesj-110	47	35	.	.	PUNCT
cuesj-110	48	1	in	in	ADP
cuesj-110	48	2	this	this	DET
cuesj-110	48	3	method	method	NOUN
cuesj-110	48	4	,	,	PUNCT
cuesj-110	48	5	we	we	PRON
cuesj-110	48	6	use	use	VERB
cuesj-110	48	7	the	the	DET
cuesj-110	48	8	numeric	numeric	ADJ
cuesj-110	48	9	biological	biological	ADJ
cuesj-110	48	10	data	datum	NOUN
cuesj-110	48	11	for	for	ADP
cuesj-110	48	12	sequence	sequence	NOUN
cuesj-110	48	13	alignment	alignment	NOUN
cuesj-110	48	14	using	use	VERB
cuesj-110	48	15	anfis	anfis	ADJ
cuesj-110	48	16	system	system	NOUN
cuesj-110	48	17	in	in	ADP
cuesj-110	48	18	matlab	matlab	PROPN
cuesj-110	48	19	;	;	PUNCT
cuesj-110	48	20	this	this	DET
cuesj-110	48	21	system	system	NOUN
cuesj-110	48	22	implements	implement	VERB
cuesj-110	48	23	several	several	ADJ
cuesj-110	48	24	processing	processing	NOUN
cuesj-110	48	25	systems	system	NOUN
cuesj-110	48	26	to	to	PART
cuesj-110	48	27	get	get	VERB
cuesj-110	48	28	the	the	DET
cuesj-110	48	29	most	most	ADV
cuesj-110	48	30	suitable	suitable	ADJ
cuesj-110	48	31	results	result	NOUN
cuesj-110	48	32	as	as	SCONJ
cuesj-110	48	33	shown	show	VERB
cuesj-110	48	34	in	in	ADP
cuesj-110	48	35	table	table	NOUN
cuesj-110	48	36	1	1	NUM
cuesj-110	48	37	;	;	PUNCT
cuesj-110	48	38	the	the	DET
cuesj-110	48	39	most	most	ADV
cuesj-110	48	40	suitable	suitable	ADJ
cuesj-110	48	41	system	system	NOUN
cuesj-110	48	42	is	be	AUX
cuesj-110	48	43	the	the	DET
cuesj-110	48	44	trapezoidal	trapezoidal	NOUN
cuesj-110	48	45	with	with	ADP
cuesj-110	48	46	three	three	NUM
cuesj-110	48	47	mf	mf	NOUN
cuesj-110	48	48	for	for	ADP
cuesj-110	48	49	each	each	DET
cuesj-110	48	50	input	input	NOUN
cuesj-110	48	51	and	and	CCONJ
cuesj-110	48	52	500	500	NUM
cuesj-110	48	53	epochs	epoch	NOUN
cuesj-110	48	54	;	;	PUNCT
cuesj-110	48	55	it	it	PRON
cuesj-110	48	56	has	have	VERB
cuesj-110	48	57	the	the	DET
cuesj-110	48	58	lowest	low	ADJ
cuesj-110	48	59	average	average	ADJ
cuesj-110	48	60	testing	testing	NOUN
cuesj-110	48	61	error	error	NOUN
cuesj-110	48	62	.	.	PUNCT
cuesj-110	49	1	we	we	PRON
cuesj-110	49	2	use	use	VERB
cuesj-110	49	3	several	several	ADJ
cuesj-110	49	4	different	different	ADJ
cuesj-110	49	5	sequences	sequence	NOUN
cuesj-110	49	6	to	to	PART
cuesj-110	49	7	be	be	AUX
cuesj-110	49	8	aligned	align	VERB
cuesj-110	49	9	,	,	PUNCT
cuesj-110	49	10	as	as	SCONJ
cuesj-110	49	11	shown	show	VERB
cuesj-110	49	12	in	in	ADP
cuesj-110	49	13	table	table	NOUN
cuesj-110	49	14	2	2	NUM
cuesj-110	49	15	;	;	PUNCT
cuesj-110	49	16	we	we	PRON
cuesj-110	49	17	aligned	align	VERB
cuesj-110	49	18	the	the	DET
cuesj-110	49	19	sequences	sequence	NOUN
cuesj-110	49	20	in	in	ADP
cuesj-110	49	21	the	the	DET
cuesj-110	49	22	global	global	ADJ
cuesj-110	49	23	alignment	alignment	NOUN
cuesj-110	49	24	algorithm	algorithm	NOUN
cuesj-110	49	25	.	.	PUNCT
cuesj-110	50	1	in	in	ADP
cuesj-110	50	2	this	this	DET
cuesj-110	50	3	method	method	NOUN
cuesj-110	50	4	,	,	PUNCT
cuesj-110	50	5	we	we	PRON
cuesj-110	50	6	insert	insert	VERB
cuesj-110	50	7	the	the	DET
cuesj-110	50	8	gaps	gap	NOUN
cuesj-110	50	9	when	when	SCONJ
cuesj-110	50	10	the	the	DET
cuesj-110	50	11	base	base	NOUN
cuesj-110	50	12	in	in	ADP
cuesj-110	50	13	the	the	DET
cuesj-110	50	14	sequence	sequence	NOUN
cuesj-110	50	15	is	be	AUX
cuesj-110	50	16	not	not	PART
cuesj-110	50	17	similar	similar	ADJ
cuesj-110	50	18	with	with	ADP
cuesj-110	50	19	the	the	DET
cuesj-110	50	20	other	other	ADJ
cuesj-110	50	21	in	in	ADP
cuesj-110	50	22	the	the	DET
cuesj-110	50	23	same	same	ADJ
cuesj-110	50	24	order	order	NOUN
cuesj-110	50	25	in	in	ADP
cuesj-110	50	26	this	this	DET
cuesj-110	50	27	pair	pair	NOUN
cuesj-110	50	28	,	,	PUNCT
cuesj-110	50	29	and	and	CCONJ
cuesj-110	50	30	shifted	shift	VERB
cuesj-110	50	31	the	the	DET
cuesj-110	50	32	character	character	NOUN
cuesj-110	50	33	and	and	CCONJ
cuesj-110	50	34	input	input	VERB
cuesj-110	50	35	the	the	DET
cuesj-110	50	36	gap	gap	NOUN
cuesj-110	50	37	in	in	ADP
cuesj-110	50	38	order	order	NOUN
cuesj-110	50	39	to	to	PART
cuesj-110	50	40	be	be	AUX
cuesj-110	50	41	identical	identical	ADJ
cuesj-110	50	42	;	;	PUNCT
cuesj-110	50	43	we	we	PRON
cuesj-110	50	44	use	use	VERB
cuesj-110	50	45	the	the	DET
cuesj-110	50	46	number	number	NOUN
cuesj-110	50	47	of	of	ADP
cuesj-110	50	48	times	time	NOUN
cuesj-110	50	49	for	for	ADP
cuesj-110	50	50	(	(	PUNCT
cuesj-110	50	51	matching	matching	NOUN
cuesj-110	50	52	)	)	PUNCT
cuesj-110	50	53	,	,	PUNCT
cuesj-110	50	54	mismatching	mismatching	NOUN
cuesj-110	50	55	and	and	CCONJ
cuesj-110	50	56	gaps	gap	NOUN
cuesj-110	50	57	as	as	ADP
cuesj-110	50	58	the	the	DET
cuesj-110	50	59	input	input	NOUN
cuesj-110	50	60	in	in	ADP
cuesj-110	50	61	the	the	DET
cuesj-110	50	62	anfis	anfis	PROPN
cuesj-110	50	63	view	view	NOUN
cuesj-110	50	64	tool	tool	NOUN
cuesj-110	50	65	,	,	PUNCT
cuesj-110	50	66	and	and	CCONJ
cuesj-110	50	67	output	output	VERB
cuesj-110	50	68	the	the	DET
cuesj-110	50	69	score	score	NOUN
cuesj-110	50	70	matching	matching	NOUN
cuesj-110	50	71	,	,	PUNCT
cuesj-110	50	72	as	as	SCONJ
cuesj-110	50	73	shown	show	VERB
cuesj-110	50	74	in	in	ADP
cuesj-110	50	75	“	"	PUNCT
cuesj-110	50	76	figure	figure	NOUN
cuesj-110	50	77	3	3	NUM
cuesj-110	50	78	”	"	PUNCT
cuesj-110	50	79	we	we	PRON
cuesj-110	50	80	can	can	AUX
cuesj-110	50	81	compute	compute	VERB
cuesj-110	50	82	the	the	DET
cuesj-110	50	83	percentage	percentage	NOUN
cuesj-110	50	84	similarity	similarity	NOUN
cuesj-110	50	85	of	of	ADP
cuesj-110	50	86	the	the	DET
cuesj-110	50	87	alignment	alignment	NOUN
cuesj-110	50	88	by	by	ADP
cuesj-110	50	89	using	use	VERB
cuesj-110	50	90	this	this	DET
cuesj-110	50	91	code	code	NOUN
cuesj-110	50	92	in	in	ADP
cuesj-110	50	93	matlab	matlab	PROPN
cuesj-110	50	94	[	[	X
cuesj-110	50	95	score	score	NOUN
cuesj-110	50	96	,	,	PUNCT
cuesj-110	50	97	alignment	alignment	NOUN
cuesj-110	50	98	]	]	X
cuesj-110	50	99	=	=	SYM
cuesj-110	50	100	nwalign(‘s1’,’s2	nwalign(‘s1’,’s2	X
cuesj-110	50	101	’	'	PUNCT
cuesj-110	50	102	)	)	PUNCT
cuesj-110	50	103	;	;	PUNCT
cuesj-110	50	104	showalignment(alignment	showalignment(alignment	PROPN
cuesj-110	50	105	)	)	PUNCT
cuesj-110	50	106	;	;	PUNCT
cuesj-110	50	107	in	in	ADP
cuesj-110	50	108	order	order	NOUN
cuesj-110	50	109	to	to	PART
cuesj-110	50	110	display	display	VERB
cuesj-110	50	111	a	a	DET
cuesj-110	50	112	pairwise	pairwise	NOUN
cuesj-110	50	113	sequence	sequence	NOUN
cuesj-110	50	114	alignment	alignment	NOUN
cuesj-110	50	115	,	,	PUNCT
cuesj-110	50	116	as	as	SCONJ
cuesj-110	50	117	shown	show	VERB
cuesj-110	50	118	in	in	ADP
cuesj-110	50	119	“	"	PUNCT
cuesj-110	50	120	figure	figure	NOUN
cuesj-110	50	121	4	4	NUM
cuesj-110	50	122	.	.	PUNCT
cuesj-110	50	123	”	"	PUNCT
cuesj-110	51	1	this	this	PRON
cuesj-110	51	2	use	use	VERB
cuesj-110	51	3	the	the	DET
cuesj-110	51	4	table	table	NOUN
cuesj-110	51	5	1	1	NUM
cuesj-110	51	6	:	:	PUNCT
cuesj-110	51	7	the	the	DET
cuesj-110	51	8	various	various	ADJ
cuesj-110	51	9	anfis	anfis	ADJ
cuesj-110	51	10	testing	testing	NOUN
cuesj-110	51	11	results	result	VERB
cuesj-110	51	12	sequences	sequence	NOUN
cuesj-110	51	13	global	global	ADJ
cuesj-110	51	14	alignment	alignment	NOUN
cuesj-110	51	15	identities	identity	NOUN
cuesj-110	51	16	(	(	PUNCT
cuesj-110	51	17	%	%	INTJ
cuesj-110	51	18	)	)	PUNCT
cuesj-110	51	19	score	score	NOUN
cuesj-110	51	20	aggttgc	aggttgc	PROPN
cuesj-110	51	21	aggttgc	aggttgc	PROPN
cuesj-110	51	22	7	7	NUM
cuesj-110	51	23	-	-	PUNCT
cuesj-110	51	24	may	may	AUX
cuesj-110	51	25	0.149	0.149	NUM
cuesj-110	51	26	aggtc	aggtc	NOUN
cuesj-110	51	27	aggt	aggt	NOUN
cuesj-110	51	28	--	--	PUNCT
cuesj-110	51	29	c	c	NOUN
cuesj-110	51	30	−71	−71	NOUN
cuesj-110	51	31	%	%	NOUN
cuesj-110	51	32	gtaggcttaaggtta	gtaggcttaaggtta	NOUN
cuesj-110	51	33	gtaggcttaaggtta	gtaggcttaaggtta	NOUN
cuesj-110	51	34	15	15	NUM
cuesj-110	51	35	-	-	PUNCT
cuesj-110	51	36	may	may	PROPN
cuesj-110	51	37	0	0	NUM
cuesj-110	51	38	tagatc	tagatc	NOUN
cuesj-110	51	39	at	at	ADP
cuesj-110	51	40	-	-	PUNCT
cuesj-110	51	41	ctag	ctag	NOUN
cuesj-110	51	42	−33	−33	NOUN
cuesj-110	51	43	%	%	NOUN
cuesj-110	51	44	agtcca	agtcca	NOUN
cuesj-110	51	45	a	a	DET
cuesj-110	51	46	–	–	PUNCT
cuesj-110	51	47	gtcca	gtcca	NOUN
cuesj-110	51	48	7	7	PROPN
cuesj-110	51	49	-	-	PUNCT
cuesj-110	51	50	may	may	NOUN
cuesj-110	51	51	0.149	0.149	NUM
cuesj-110	51	52	atgtcc	atgtcc	ADJ
cuesj-110	51	53	atgtcc−71	atgtcc−71	PROPN
cuesj-110	51	54	%	%	NOUN
cuesj-110	51	55	cggga	cggga	NOUN
cuesj-110	51	56	cggga6	cggga6	PROPN
cuesj-110	51	57	-	-	PUNCT
cuesj-110	51	58	feb	feb	PROPN
cuesj-110	51	59	0	0	NUM
cuesj-110	51	60	attgac	attgac	PROPN
cuesj-110	51	61	attgac	attgac	PROPN
cuesj-110	51	62	33	33	NUM
cuesj-110	51	63	%	%	NOUN
cuesj-110	51	64	ctatccg	ctatccg	NOUN
cuesj-110	51	65	ctatc	ctatc	NOUN
cuesj-110	51	66	cg	cg	NOUN
cuesj-110	51	67	7	7	NUM
cuesj-110	51	68	-	-	PUNCT
cuesj-110	51	69	may	may	PROPN
cuesj-110	51	70	0.425	0.425	NUM
cuesj-110	51	71	ctagtcg	ctagtcg	VERB
cuesj-110	51	72	ctagtcg	ctagtcg	VERB
cuesj-110	51	73	−71	−71	NUM
cuesj-110	51	74	%	%	NOUN
cuesj-110	51	75	anfis	anfis	PROPN
cuesj-110	51	76	:	:	PUNCT
cuesj-110	51	77	adaptive	adaptive	ADJ
cuesj-110	51	78	neuro	neuro	NOUN
cuesj-110	51	79	-	-	PUNCT
cuesj-110	51	80	fuzzy	fuzzy	ADJ
cuesj-110	51	81	inference	inference	NOUN
cuesj-110	51	82	system	system	NOUN
cuesj-110	51	83	table	table	NOUN
cuesj-110	51	84	2	2	NUM
cuesj-110	51	85	:	:	PUNCT
cuesj-110	51	86	the	the	DET
cuesj-110	51	87	score	score	NOUN
cuesj-110	51	88	matching	match	VERB
cuesj-110	51	89	for	for	ADP
cuesj-110	51	90	global	global	ADJ
cuesj-110	51	91	alignment	alignment	NOUN
cuesj-110	51	92	of	of	ADP
cuesj-110	51	93	anfis	anfis	ADJ
cuesj-110	51	94	tool	tool	NOUN
cuesj-110	51	95	sequences	sequence	NOUN
cuesj-110	51	96	local	local	ADJ
cuesj-110	51	97	alignment	alignment	NOUN
cuesj-110	51	98	identities	identity	NOUN
cuesj-110	51	99	(	(	PUNCT
cuesj-110	51	100	%	%	INTJ
cuesj-110	51	101	)	)	PUNCT
cuesj-110	51	102	score	score	NOUN
cuesj-110	51	103	aggttgc	aggttgc	PROPN
cuesj-110	51	104	aggt	aggt	VERB
cuesj-110	51	105	100	100	NUM
cuesj-110	51	106	1	1	NUM
cuesj-110	51	107	aggtc	aggtc	NOUN
cuesj-110	51	108	aggt	aggt	NOUN
cuesj-110	51	109	gtaggcttaaggtta	gtaggcttaaggtta	NOUN
cuesj-110	51	110	tag	tag	NOUN
cuesj-110	51	111	100	100	NUM
cuesj-110	51	112	1	1	NUM
cuesj-110	51	113	tagatc	tagatc	NOUN
cuesj-110	51	114	tag	tag	NOUN
cuesj-110	51	115	agtcca	agtcca	ADV
cuesj-110	51	116	gtcc	gtcc	VERB
cuesj-110	51	117	100	100	NUM
cuesj-110	51	118	1	1	NUM
cuesj-110	51	119	atgtcc	atgtcc	ADP
cuesj-110	51	120	gtcc	gtcc	PROPN
cuesj-110	51	121	cggga	cggga	PROPN
cuesj-110	51	122	ga	ga	PROPN
cuesj-110	51	123	100	100	NUM
cuesj-110	51	124	1	1	NUM
cuesj-110	51	125	attgac	attgac	PROPN
cuesj-110	51	126	ga	ga	PROPN
cuesj-110	51	127	ctatccg	ctatccg	PROPN
cuesj-110	51	128	cta	cta	PROPN
cuesj-110	51	129	100	100	NUM
cuesj-110	51	130	1	1	NUM
cuesj-110	51	131	ctagtcg	ctagtcg	NOUN
cuesj-110	51	132	cta	cta	PROPN
cuesj-110	51	133	anfis	anfis	PROPN
cuesj-110	51	134	:	:	PUNCT
cuesj-110	51	135	adaptive	adaptive	ADJ
cuesj-110	51	136	neuro	neuro	NOUN
cuesj-110	51	137	-	-	PUNCT
cuesj-110	51	138	fuzzy	fuzzy	ADJ
cuesj-110	51	139	inference	inference	NOUN
cuesj-110	51	140	system	system	NOUN
cuesj-110	51	141	hameed	hameed	NOUN
cuesj-110	51	142	and	and	CCONJ
cuesj-110	51	143	hamed	hamed	PROPN
cuesj-110	51	144	:	:	PUNCT
cuesj-110	51	145	measuring	measure	VERB
cuesj-110	51	146	the	the	DET
cuesj-110	51	147	score	score	NOUN
cuesj-110	51	148	matching	matching	NOUN
cuesj-110	51	149	of	of	ADP
cuesj-110	51	150	dna	dna	PROPN
cuesj-110	51	151	using	use	VERB
cuesj-110	51	152	nf	nf	NUM
cuesj-110	51	153	40	40	NUM
cuesj-110	51	154	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-110	51	155	cuesj	cuesj	NOUN
cuesj-110	51	156	2019	2019	NUM
cuesj-110	51	157	,	,	PUNCT
cuesj-110	51	158	3	3	NUM
cuesj-110	51	159	(	(	PUNCT
cuesj-110	51	160	2	2	NUM
cuesj-110	51	161	):	):	PUNCT
cuesj-110	51	162	37	37	NUM
cuesj-110	51	163	-	-	SYM
cuesj-110	51	164	41	41	NUM
cuesj-110	51	165	number	number	NOUN
cuesj-110	51	166	of	of	ADP
cuesj-110	51	167	times	time	NOUN
cuesj-110	51	168	similar	similar	ADJ
cuesj-110	51	169	in	in	ADP
cuesj-110	51	170	the	the	DET
cuesj-110	51	171	sequence	sequence	NOUN
cuesj-110	51	172	alignment	alignment	NOUN
cuesj-110	51	173	divided	divide	VERB
cuesj-110	51	174	on	on	ADP
cuesj-110	51	175	the	the	DET
cuesj-110	51	176	entire	entire	ADJ
cuesj-110	51	177	length	length	NOUN
cuesj-110	51	178	of	of	ADP
cuesj-110	51	179	the	the	DET
cuesj-110	51	180	alignment	alignment	NOUN
cuesj-110	51	181	sequence	sequence	NOUN
cuesj-110	51	182	.	.	PUNCT
cuesj-110	52	1	in	in	ADP
cuesj-110	52	2	table	table	NOUN
cuesj-110	52	3	3	3	NUM
cuesj-110	52	4	,	,	PUNCT
cuesj-110	52	5	we	we	PRON
cuesj-110	52	6	aligned	align	VERB
cuesj-110	52	7	the	the	DET
cuesj-110	52	8	sequence	sequence	NOUN
cuesj-110	52	9	using	use	VERB
cuesj-110	52	10	the	the	DET
cuesj-110	52	11	local	local	ADJ
cuesj-110	52	12	algorithm	algorithm	NOUN
cuesj-110	52	13	,	,	PUNCT
cuesj-110	52	14	in	in	ADP
cuesj-110	52	15	this	this	DET
cuesj-110	52	16	method	method	NOUN
cuesj-110	52	17	the	the	DET
cuesj-110	52	18	algorithm	algorithm	NOUN
cuesj-110	52	19	take	take	VERB
cuesj-110	52	20	the	the	DET
cuesj-110	52	21	most	most	ADV
cuesj-110	52	22	similar	similar	ADJ
cuesj-110	52	23	part	part	NOUN
cuesj-110	52	24	of	of	ADP
cuesj-110	52	25	the	the	DET
cuesj-110	52	26	pair	pair	NOUN
cuesj-110	52	27	sequence	sequence	NOUN
cuesj-110	52	28	,	,	PUNCT
cuesj-110	52	29	not	not	PART
cuesj-110	52	30	must	must	AUX
cuesj-110	52	31	in	in	ADP
cuesj-110	52	32	the	the	DET
cuesj-110	52	33	order	order	NOUN
cuesj-110	52	34	and	and	CCONJ
cuesj-110	52	35	not	not	PART
cuesj-110	52	36	need	need	VERB
cuesj-110	52	37	to	to	PART
cuesj-110	52	38	input	input	VERB
cuesj-110	52	39	the	the	DET
cuesj-110	52	40	gap	gap	NOUN
cuesj-110	52	41	,	,	PUNCT
cuesj-110	52	42	the	the	DET
cuesj-110	52	43	resulting	result	VERB
cuesj-110	52	44	score	score	NOUN
cuesj-110	52	45	is	be	AUX
cuesj-110	52	46	the	the	DET
cuesj-110	52	47	perfect	perfect	ADJ
cuesj-110	52	48	there	there	PRON
cuesj-110	52	49	is	be	VERB
cuesj-110	52	50	no	no	DET
cuesj-110	52	51	mismatch	mismatch	NOUN
cuesj-110	52	52	and	and	CCONJ
cuesj-110	52	53	no	no	DET
cuesj-110	52	54	gaps	gap	NOUN
cuesj-110	52	55	,	,	PUNCT
cuesj-110	52	56	and	and	CCONJ
cuesj-110	52	57	have	have	VERB
cuesj-110	52	58	100	100	NUM
cuesj-110	52	59	%	%	NOUN
cuesj-110	52	60	identical	identical	ADJ
cuesj-110	52	61	.	.	PUNCT
cuesj-110	53	1	conclusion	conclusion	NOUN
cuesj-110	53	2	the	the	DET
cuesj-110	53	3	proposed	propose	VERB
cuesj-110	53	4	technique	technique	NOUN
cuesj-110	53	5	,	,	PUNCT
cuesj-110	53	6	here	here	ADV
cuesj-110	53	7	,	,	PUNCT
cuesj-110	53	8	is	be	AUX
cuesj-110	53	9	used	use	VERB
cuesj-110	53	10	to	to	PART
cuesj-110	53	11	obtain	obtain	VERB
cuesj-110	53	12	the	the	DET
cuesj-110	53	13	similarity	similarity	NOUN
cuesj-110	53	14	measure	measure	NOUN
cuesj-110	53	15	of	of	ADP
cuesj-110	53	16	the	the	DET
cuesj-110	53	17	pairwise	pairwise	NOUN
cuesj-110	53	18	dna	dna	PROPN
cuesj-110	53	19	sequence	sequence	NOUN
cuesj-110	53	20	alignment	alignment	NOUN
cuesj-110	53	21	;	;	PUNCT
cuesj-110	53	22	the	the	DET
cuesj-110	53	23	pattern	pattern	NOUN
cuesj-110	53	24	matching	matching	NOUN
cuesj-110	53	25	is	be	AUX
cuesj-110	53	26	an	an	DET
cuesj-110	53	27	essential	essential	ADJ
cuesj-110	53	28	task	task	NOUN
cuesj-110	53	29	of	of	ADP
cuesj-110	53	30	example	example	NOUN
cuesj-110	53	31	the	the	DET
cuesj-110	53	32	disclosure	disclosure	NOUN
cuesj-110	53	33	process	process	NOUN
cuesj-110	53	34	in	in	ADP
cuesj-110	53	35	this	this	DET
cuesj-110	53	36	day	day	NOUN
cuesj-110	53	37	and	and	CCONJ
cuesj-110	53	38	age	age	NOUN
cuesj-110	53	39	for	for	ADP
cuesj-110	53	40	finding	find	VERB
cuesj-110	53	41	the	the	DET
cuesj-110	53	42	basic	basic	ADJ
cuesj-110	53	43	and	and	CCONJ
cuesj-110	53	44	utilitarian	utilitarian	ADJ
cuesj-110	53	45	conduct	conduct	NOUN
cuesj-110	53	46	in	in	ADP
cuesj-110	53	47	the	the	DET
cuesj-110	53	48	dna	dna	NOUN
cuesj-110	53	49	sequencing	sequencing	NOUN
cuesj-110	53	50	.	.	PUNCT
cuesj-110	54	1	in	in	ADP
cuesj-110	54	2	spite	spite	NOUN
cuesj-110	54	3	of	of	ADP
cuesj-110	54	4	the	the	DET
cuesj-110	54	5	fact	fact	NOUN
cuesj-110	54	6	that	that	SCONJ
cuesj-110	54	7	,	,	PUNCT
cuesj-110	54	8	the	the	DET
cuesj-110	54	9	example	example	NOUN
cuesj-110	54	10	of	of	ADP
cuesj-110	54	11	matching	matching	NOUN
cuesj-110	54	12	is	be	AUX
cuesj-110	54	13	commonly	commonly	ADV
cuesj-110	54	14	utilized	utilize	VERB
cuesj-110	54	15	as	as	ADP
cuesj-110	54	16	a	a	DET
cuesj-110	54	17	part	part	NOUN
cuesj-110	54	18	of	of	ADP
cuesj-110	54	19	computer	computer	NOUN
cuesj-110	54	20	science	science	NOUN
cuesj-110	54	21	and	and	CCONJ
cuesj-110	54	22	information	information	NOUN
cuesj-110	54	23	processing	processing	NOUN
cuesj-110	54	24	.	.	PUNCT
cuesj-110	55	1	in	in	ADP
cuesj-110	55	2	this	this	DET
cuesj-110	55	3	paper	paper	NOUN
cuesj-110	55	4	the	the	DET
cuesj-110	55	5	proposed	propose	VERB
cuesj-110	55	6	algorithms	algorithm	NOUN
cuesj-110	55	7	are	be	AUX
cuesj-110	55	8	used	use	VERB
cuesj-110	55	9	that	that	PRON
cuesj-110	55	10	are	be	AUX
cuesj-110	55	11	global	global	ADJ
cuesj-110	55	12	and	and	CCONJ
cuesj-110	55	13	local	local	ADJ
cuesj-110	55	14	alignment	alignment	NOUN
cuesj-110	55	15	to	to	PART
cuesj-110	55	16	measure	measure	VERB
cuesj-110	55	17	the	the	DET
cuesj-110	55	18	score	score	NOUN
cuesj-110	55	19	matching	matching	NOUN
cuesj-110	55	20	,	,	PUNCT
cuesj-110	55	21	which	which	PRON
cuesj-110	55	22	are	be	AUX
cuesj-110	55	23	utilized	utilize	VERB
cuesj-110	55	24	;	;	PUNCT
cuesj-110	55	25	it	it	PRON
cuesj-110	55	26	as	as	ADP
cuesj-110	55	27	a	a	DET
cuesj-110	55	28	method	method	NOUN
cuesj-110	55	29	to	to	PART
cuesj-110	55	30	be	be	AUX
cuesj-110	55	31	align	align	VERB
cuesj-110	55	32	the	the	DET
cuesj-110	55	33	two	two	NUM
cuesj-110	55	34	dna	dna	PROPN
cuesj-110	55	35	sequences	sequence	NOUN
cuesj-110	55	36	.	.	PUNCT
cuesj-110	56	1	the	the	DET
cuesj-110	56	2	neuro	neuro	NOUN
cuesj-110	56	3	-	-	PUNCT
cuesj-110	56	4	fuzzy	fuzzy	ADJ
cuesj-110	56	5	model	model	NOUN
cuesj-110	56	6	is	be	AUX
cuesj-110	56	7	used	use	VERB
cuesj-110	56	8	to	to	PART
cuesj-110	56	9	evaluate	evaluate	VERB
cuesj-110	56	10	the	the	DET
cuesj-110	56	11	score	score	NOUN
cuesj-110	56	12	similarity	similarity	NOUN
cuesj-110	56	13	by	by	ADP
cuesj-110	56	14	the	the	DET
cuesj-110	56	15	anfis	anfis	ADJ
cuesj-110	56	16	tool	tool	NOUN
cuesj-110	56	17	in	in	ADP
cuesj-110	56	18	table	table	NOUN
cuesj-110	56	19	3	3	NUM
cuesj-110	56	20	:	:	PUNCT
cuesj-110	56	21	the	the	DET
cuesj-110	56	22	score	score	NOUN
cuesj-110	56	23	matching	match	VERB
cuesj-110	56	24	for	for	ADP
cuesj-110	56	25	local	local	ADJ
cuesj-110	56	26	alignment	alignment	NOUN
cuesj-110	56	27	of	of	ADP
cuesj-110	56	28	anfis	anfis	ADJ
cuesj-110	56	29	tool	tool	NOUN
cuesj-110	56	30	mf	mf	NOUN
cuesj-110	56	31	type	type	NOUN
cuesj-110	56	32	input	input	NOUN
cuesj-110	56	33	the	the	DET
cuesj-110	56	34	number	number	NOUN
cuesj-110	56	35	of	of	ADP
cuesj-110	56	36	mf	mf	NOUN
cuesj-110	56	37	for	for	ADP
cuesj-110	56	38	the	the	DET
cuesj-110	56	39	inputs	input	NOUN
cuesj-110	56	40	the	the	DET
cuesj-110	56	41	mf	mf	NOUN
cuesj-110	56	42	output	output	NOUN
cuesj-110	56	43	train	train	NOUN
cuesj-110	56	44	fis	fis	PROPN
cuesj-110	56	45	method	method	VERB
cuesj-110	56	46	the	the	DET
cuesj-110	56	47	number	number	NOUN
cuesj-110	56	48	of	of	ADP
cuesj-110	56	49	epochs	epoch	NOUN
cuesj-110	56	50	the	the	DET
cuesj-110	56	51	average	average	ADJ
cuesj-110	56	52	training	training	NOUN
cuesj-110	56	53	error	error	NOUN
cuesj-110	56	54	the	the	DET
cuesj-110	56	55	average	average	ADJ
cuesj-110	56	56	testing	testing	NOUN
cuesj-110	56	57	error	error	NOUN
cuesj-110	56	58	triangular	triangular	NOUN
cuesj-110	56	59	mf	mf	VERB
cuesj-110	56	60	3	3	NUM
cuesj-110	56	61	3	3	NUM
cuesj-110	56	62	3	3	NUM
cuesj-110	56	63	constant	constant	ADJ
cuesj-110	56	64	backpropagation	backpropagation	NOUN
cuesj-110	56	65	50	50	NUM
cuesj-110	56	66	0.04657	0.04657	NUM
cuesj-110	56	67	0.06078	0.06078	NUM
cuesj-110	56	68	triangular	triangular	NOUN
cuesj-110	56	69	mf	mf	VERB
cuesj-110	56	70	5	5	NUM
cuesj-110	56	71	5	5	NUM
cuesj-110	56	72	5	5	NUM
cuesj-110	56	73	constant	constant	ADJ
cuesj-110	56	74	backpropagation	backpropagation	NOUN
cuesj-110	56	75	250	250	NUM
cuesj-110	56	76	0.01109	0.01109	NUM
cuesj-110	56	77	0.04306	0.04306	NUM
cuesj-110	56	78	trapezoidal	trapezoidal	ADJ
cuesj-110	56	79	mf	mf	VERB
cuesj-110	56	80	3	3	NUM
cuesj-110	56	81	3	3	NUM
cuesj-110	56	82	3	3	NUM
cuesj-110	56	83	constant	constant	ADJ
cuesj-110	56	84	backpropagation	backpropagation	NOUN
cuesj-110	56	85	100	100	NUM
cuesj-110	56	86	0.01898	0.01898	NUM
cuesj-110	56	87	0.0453	0.0453	NUM
cuesj-110	56	88	trapezoidal	trapezoidal	ADJ
cuesj-110	56	89	mf	mf	VERB
cuesj-110	56	90	3	3	NUM
cuesj-110	56	91	3	3	NUM
cuesj-110	56	92	3	3	NUM
cuesj-110	56	93	constant	constant	ADJ
cuesj-110	56	94	backpropagation	backpropagation	NOUN
cuesj-110	56	95	500	500	NUM
cuesj-110	56	96	0.01657	0.01657	NUM
cuesj-110	56	97	0.01657	0.01657	NUM
cuesj-110	56	98	gaussian	gaussian	NOUN
cuesj-110	56	99	mf	mf	VERB
cuesj-110	56	100	5	5	NUM
cuesj-110	56	101	5	5	NUM
cuesj-110	56	102	5	5	NUM
cuesj-110	56	103	constant	constant	ADJ
cuesj-110	56	104	backpropagation	backpropagation	NOUN
cuesj-110	56	105	350	350	NUM
cuesj-110	56	106	0.01436	0.01436	NUM
cuesj-110	56	107	0.04436	0.04436	NUM
cuesj-110	56	108	gaussian2	gaussian2	NOUN
cuesj-110	56	109	mf	mf	VERB
cuesj-110	56	110	5	5	NUM
cuesj-110	56	111	5	5	NUM
cuesj-110	56	112	5	5	NUM
cuesj-110	56	113	constant	constant	ADJ
cuesj-110	56	114	backpropagation	backpropagation	NOUN
cuesj-110	56	115	500	500	NUM
cuesj-110	56	116	0.02804	0.02804	NUM
cuesj-110	56	117	0.05415	0.05415	NUM
cuesj-110	56	118	mf	mf	NOUN
cuesj-110	56	119	:	:	PUNCT
cuesj-110	56	120	membership	membership	NOUN
cuesj-110	56	121	functions	function	NOUN
cuesj-110	56	122	,	,	PUNCT
cuesj-110	56	123	anfis	anfi	VERB
cuesj-110	56	124	:	:	PUNCT
cuesj-110	56	125	adaptive	adaptive	ADJ
cuesj-110	56	126	neuro	neuro	NOUN
cuesj-110	56	127	-	-	PUNCT
cuesj-110	56	128	fuzzy	fuzzy	ADJ
cuesj-110	56	129	inference	inference	NOUN
cuesj-110	56	130	system	system	NOUN
cuesj-110	56	131	,	,	PUNCT
cuesj-110	56	132	fis	fis	PROPN
cuesj-110	56	133	:	:	PUNCT
cuesj-110	56	134	fuzzy	fuzzy	ADJ
cuesj-110	56	135	inference	inference	NOUN
cuesj-110	56	136	system	system	NOUN
cuesj-110	56	137	figure	figure	NOUN
cuesj-110	56	138	3	3	NUM
cuesj-110	56	139	:	:	PUNCT
cuesj-110	56	140	view	view	NOUN
cuesj-110	56	141	rules	rule	NOUN
cuesj-110	56	142	adaptive	adaptive	ADJ
cuesj-110	56	143	neuro	neuro	NOUN
cuesj-110	56	144	-	-	PUNCT
cuesj-110	56	145	fuzzy	fuzzy	ADJ
cuesj-110	56	146	inference	inference	NOUN
cuesj-110	56	147	system	system	NOUN
cuesj-110	56	148	figure	figure	NOUN
cuesj-110	56	149	4	4	NUM
cuesj-110	56	150	:	:	PUNCT
cuesj-110	56	151	the	the	DET
cuesj-110	56	152	identical	identical	ADJ
cuesj-110	56	153	alignment	alignment	NOUN
cuesj-110	56	154	hameed	hameed	NOUN
cuesj-110	56	155	and	and	CCONJ
cuesj-110	56	156	hamed	hamed	PROPN
cuesj-110	56	157	:	:	PUNCT
cuesj-110	56	158	measuring	measure	VERB
cuesj-110	56	159	the	the	DET
cuesj-110	56	160	score	score	NOUN
cuesj-110	56	161	matching	matching	NOUN
cuesj-110	56	162	of	of	ADP
cuesj-110	56	163	dna	dna	PROPN
cuesj-110	56	164	using	use	VERB
cuesj-110	56	165	nf	nf	NOUN
cuesj-110	56	166	41	41	NUM
cuesj-110	56	167	http://journals.cihanuniversity.edu.iq/index.php/cuesj	http://journals.cihanuniversity.edu.iq/index.php/cuesj	ADJ
cuesj-110	56	168	cuesj	cuesj	NOUN
cuesj-110	56	169	2019	2019	NUM
cuesj-110	56	170	,	,	PUNCT
cuesj-110	56	171	3	3	NUM
cuesj-110	56	172	(	(	PUNCT
cuesj-110	56	173	2	2	NUM
cuesj-110	56	174	):	):	PUNCT
cuesj-110	56	175	37	37	NUM
cuesj-110	56	176	-	-	SYM
cuesj-110	56	177	41	41	NUM
cuesj-110	56	178	matlab	matlab	NOUN
cuesj-110	56	179	;	;	PUNCT
cuesj-110	56	180	we	we	PRON
cuesj-110	56	181	implement	implement	VERB
cuesj-110	56	182	the	the	DET
cuesj-110	56	183	method	method	NOUN
cuesj-110	56	184	in	in	ADP
cuesj-110	56	185	several	several	ADJ
cuesj-110	56	186	processing	processing	NOUN
cuesj-110	56	187	systems	system	NOUN
cuesj-110	56	188	and	and	CCONJ
cuesj-110	56	189	depend	depend	VERB
cuesj-110	56	190	on	on	ADP
cuesj-110	56	191	the	the	DET
cuesj-110	56	192	most	most	ADV
cuesj-110	56	193	suitable	suitable	ADJ
cuesj-110	56	194	system	system	NOUN
cuesj-110	56	195	with	with	ADP
cuesj-110	56	196	the	the	DET
cuesj-110	56	197	lowest	low	ADJ
cuesj-110	56	198	average	average	ADJ
cuesj-110	56	199	testing	testing	NOUN
cuesj-110	56	200	error	error	NOUN
cuesj-110	56	201	;	;	PUNCT
cuesj-110	56	202	we	we	PRON
cuesj-110	56	203	obtain	obtain	VERB
cuesj-110	56	204	the	the	DET
cuesj-110	56	205	score	score	NOUN
cuesj-110	56	206	matching	matching	NOUN
cuesj-110	56	207	result	result	NOUN
cuesj-110	56	208	for	for	ADP
cuesj-110	56	209	several	several	ADJ
cuesj-110	56	210	patterns	pattern	NOUN
cuesj-110	56	211	of	of	ADP
cuesj-110	56	212	dna	dna	PROPN
cuesj-110	56	213	sequencing	sequencing	NOUN
cuesj-110	56	214	;	;	PUNCT
cuesj-110	56	215	this	this	DET
cuesj-110	56	216	model	model	NOUN
cuesj-110	56	217	presented	present	VERB
cuesj-110	56	218	the	the	DET
cuesj-110	56	219	matching	matching	NOUN
cuesj-110	56	220	implementation	implementation	NOUN
cuesj-110	56	221	in	in	ADP
cuesj-110	56	222	fast	fast	ADJ
cuesj-110	56	223	and	and	CCONJ
cuesj-110	56	224	efficient	efficient	ADJ
cuesj-110	56	225	.	.	PUNCT
cuesj-110	57	1	references	reference	NOUN
cuesj-110	57	2	1	1	NUM
cuesj-110	57	3	.	.	PUNCT
cuesj-110	57	4	r.	r.	PROPN
cuesj-110	57	5	bhukya	bhukya	PROPN
cuesj-110	57	6	and	and	CCONJ
cuesj-110	57	7	d.	d.	PROPN
cuesj-110	57	8	v.	v.	PROPN
cuesj-110	57	9	l.	l.	PROPN
cuesj-110	57	10	somayajulu	somayajulu	VERB
cuesj-110	57	11	.	.	PUNCT
cuesj-110	58	1	“	"	PUNCT
cuesj-110	58	2	exact	exact	ADJ
cuesj-110	58	3	multiple	multiple	ADJ
cuesj-110	58	4	pattern	pattern	NOUN
cuesj-110	58	5	matching	match	VERB
cuesj-110	58	6	algorithm	algorithm	NOUN
cuesj-110	58	7	using	use	VERB
cuesj-110	58	8	dna	dna	PROPN
cuesj-110	58	9	sequence	sequence	NOUN
cuesj-110	58	10	and	and	CCONJ
cuesj-110	58	11	pattern	pattern	NOUN
cuesj-110	58	12	pair	pair	NOUN
cuesj-110	58	13	”	"	PUNCT
cuesj-110	58	14	.	.	PUNCT
cuesj-110	59	1	international	international	ADJ
cuesj-110	59	2	journal	journal	PROPN
cuesj-110	59	3	of	of	ADP
cuesj-110	59	4	computer	computer	NOUN
cuesj-110	59	5	applications	application	NOUN
cuesj-110	59	6	,	,	PUNCT
cuesj-110	59	7	vol	vol	NOUN
cuesj-110	59	8	.	.	PROPN
cuesj-110	59	9	17	17	NUM
cuesj-110	59	10	,	,	PUNCT
cuesj-110	59	11	no	no	INTJ
cuesj-110	59	12	.	.	NOUN
cuesj-110	59	13	8	8	NUM
cuesj-110	59	14	,	,	PUNCT
cuesj-110	59	15	pp	pp	ADJ
cuesj-110	59	16	.	.	PUNCT
cuesj-110	60	1	32	32	NUM
cuesj-110	60	2	-	-	SYM
cuesj-110	60	3	38	38	NUM
cuesj-110	60	4	,	,	PUNCT
cuesj-110	60	5	2011	2011	NUM
cuesj-110	60	6	.	.	PUNCT
cuesj-110	61	1	2	2	NUM
cuesj-110	61	2	.	.	X
cuesj-110	61	3	x.	x.	PROPN
cuesj-110	61	4	xie	xie	PROPN
cuesj-110	61	5	,	,	PUNCT
cuesj-110	61	6	j.	j.	PROPN
cuesj-110	61	7	guan	guan	PROPN
cuesj-110	61	8	and	and	CCONJ
cuesj-110	61	9	s.	s.	PROPN
cuesj-110	61	10	zhou	zhou	PROPN
cuesj-110	61	11	.	.	PUNCT
cuesj-110	62	1	“	"	PUNCT
cuesj-110	62	2	similarity	similarity	NOUN
cuesj-110	62	3	evaluation	evaluation	NOUN
cuesj-110	62	4	of	of	ADP
cuesj-110	62	5	dna	dna	PROPN
cuesj-110	62	6	sequences	sequence	NOUN
cuesj-110	62	7	based	base	VERB
cuesj-110	62	8	on	on	ADP
cuesj-110	62	9	frequent	frequent	ADJ
cuesj-110	62	10	patterns	pattern	NOUN
cuesj-110	62	11	and	and	CCONJ
cuesj-110	62	12	entropy	entropy	PROPN
cuesj-110	62	13	”	"	PUNCT
cuesj-110	62	14	.	.	PUNCT
cuesj-110	63	1	bmc	bmc	ADJ
cuesj-110	63	2	genomics	genomics	PROPN
cuesj-110	63	3	,	,	PUNCT
cuesj-110	63	4	vol	vol	NOUN
cuesj-110	63	5	.	.	PROPN
cuesj-110	63	6	16	16	NUM
cuesj-110	63	7	,	,	PUNCT
cuesj-110	63	8	no	no	INTJ
cuesj-110	63	9	.	.	NOUN
cuesj-110	63	10	3	3	NUM
cuesj-110	63	11	,	,	PUNCT
cuesj-110	63	12	p.	p.	PROPN
cuesj-110	63	13	s5	s5	PROPN
cuesj-110	63	14	,	,	PUNCT
cuesj-110	63	15	2015	2015	NUM
cuesj-110	63	16	.	.	PUNCT
cuesj-110	64	1	3	3	X
cuesj-110	64	2	.	.	X
cuesj-110	64	3	w.	w.	PROPN
cuesj-110	64	4	deng	deng	PROPN
cuesj-110	64	5	and	and	CCONJ
cuesj-110	64	6	y.	y.	PROPN
cuesj-110	64	7	luan	luan	PROPN
cuesj-110	64	8	.	.	PUNCT
cuesj-110	65	1	“	"	PUNCT
cuesj-110	65	2	analysis	analysis	NOUN
cuesj-110	65	3	of	of	ADP
cuesj-110	65	4	similarity	similarity	NOUN
cuesj-110	65	5	/	/	SYM
cuesj-110	65	6	dissimilarity	dissimilarity	NOUN
cuesj-110	65	7	of	of	ADP
cuesj-110	65	8	dna	dna	PROPN
cuesj-110	65	9	sequences	sequence	NOUN
cuesj-110	65	10	based	base	VERB
cuesj-110	65	11	on	on	ADP
cuesj-110	65	12	chaos	chaos	NOUN
cuesj-110	65	13	game	game	NOUN
cuesj-110	65	14	representation	representation	NOUN
cuesj-110	65	15	”	"	PUNCT
cuesj-110	65	16	.	.	PUNCT
cuesj-110	66	1	abstract	abstract	ADJ
cuesj-110	66	2	and	and	CCONJ
cuesj-110	66	3	applied	apply	VERB
cuesj-110	66	4	analysis	analysis	NOUN
cuesj-110	66	5	,	,	PUNCT
cuesj-110	66	6	vol	vol	NOUN
cuesj-110	66	7	.	.	PROPN
cuesj-110	66	8	2013	2013	NUM
cuesj-110	66	9	,	,	PUNCT
cuesj-110	66	10	p.	p.	NOUN
cuesj-110	66	11	926519	926519	NUM
cuesj-110	66	12	,	,	PUNCT
cuesj-110	66	13	2013	2013	NUM
cuesj-110	66	14	.	.	PUNCT
cuesj-110	67	1	4	4	X
cuesj-110	67	2	.	.	PUNCT
cuesj-110	68	1	p.	p.	NOUN
cuesj-110	68	2	pandiselvam	pandiselvam	NOUN
cuesj-110	68	3	,	,	PUNCT
cuesj-110	68	4	t.	t.	PROPN
cuesj-110	68	5	marimuthu	marimuthu	PROPN
cuesj-110	68	6	and	and	CCONJ
cuesj-110	68	7	r.	r.	PROPN
cuesj-110	68	8	lawrance	lawrance	PROPN
cuesj-110	68	9	.	.	PUNCT
cuesj-110	69	1	“	"	PUNCT
cuesj-110	69	2	a	a	DET
cuesj-110	69	3	comparative	comparative	ADJ
cuesj-110	69	4	study	study	NOUN
cuesj-110	69	5	on	on	ADP
cuesj-110	69	6	string	string	NOUN
cuesj-110	69	7	matching	matching	NOUN
cuesj-110	69	8	algorithms	algorithm	NOUN
cuesj-110	69	9	of	of	ADP
cuesj-110	69	10	biological	biological	ADJ
cuesj-110	69	11	sequences	sequence	NOUN
cuesj-110	69	12	”	"	PUNCT
cuesj-110	69	13	.	.	PUNCT
cuesj-110	70	1	in	in	ADP
cuesj-110	70	2	:	:	PUNCT
cuesj-110	70	3	international	international	ADJ
cuesj-110	70	4	conference	conference	NOUN
cuesj-110	70	5	on	on	ADP
cuesj-110	70	6	intelligent	intelligent	ADJ
cuesj-110	70	7	computing	computing	NOUN
cuesj-110	70	8	,	,	PUNCT
cuesj-110	70	9	pp	pp	ADJ
cuesj-110	70	10	.	.	PUNCT
cuesj-110	71	1	1	1	NUM
cuesj-110	71	2	-	-	SYM
cuesj-110	71	3	5	5	NUM
cuesj-110	71	4	,	,	PUNCT
cuesj-110	71	5	2014	2014	NUM
cuesj-110	71	6	.	.	PUNCT
cuesj-110	72	1	5	5	NUM
cuesj-110	72	2	.	.	PUNCT
cuesj-110	72	3	t.	t.	PROPN
cuesj-110	72	4	chakrabarti	chakrabarti	PROPN
cuesj-110	72	5	,	,	PUNCT
cuesj-110	72	6	s.	s.	PROPN
cuesj-110	72	7	saha	saha	PROPN
cuesj-110	72	8	and	and	CCONJ
cuesj-110	72	9	d.	d.	PROPN
cuesj-110	72	10	sinha	sinha	PROPN
cuesj-110	72	11	.	.	PUNCT
cuesj-110	73	1	“	"	PUNCT
cuesj-110	73	2	dna	dna	PROPN
cuesj-110	73	3	multiple	multiple	ADJ
cuesj-110	73	4	sequence	sequence	NOUN
cuesj-110	73	5	alignment	alignment	NOUN
cuesj-110	73	6	by	by	ADP
cuesj-110	73	7	a	a	DET
cuesj-110	73	8	hidden	hide	VERB
cuesj-110	73	9	markov	markov	NOUN
cuesj-110	73	10	model	model	NOUN
cuesj-110	73	11	and	and	CCONJ
cuesj-110	73	12	fuzzy	fuzzy	ADJ
cuesj-110	73	13	levenshtein	levenshtein	PROPN
cuesj-110	73	14	distance	distance	NOUN
cuesj-110	73	15	based	base	VERB
cuesj-110	73	16	genetic	genetic	ADJ
cuesj-110	73	17	algorithm	algorithm	NOUN
cuesj-110	73	18	”	"	PUNCT
cuesj-110	73	19	.	.	PUNCT
cuesj-110	74	1	international	international	ADJ
cuesj-110	74	2	journal	journal	PROPN
cuesj-110	74	3	of	of	ADP
cuesj-110	74	4	computer	computer	NOUN
cuesj-110	74	5	applications	application	NOUN
cuesj-110	74	6	,	,	PUNCT
cuesj-110	74	7	vol	vol	NOUN
cuesj-110	74	8	.	.	PROPN
cuesj-110	74	9	73	73	NUM
cuesj-110	74	10	,	,	PUNCT
cuesj-110	74	11	no	no	INTJ
cuesj-110	74	12	.	.	NOUN
cuesj-110	74	13	16	16	NUM
cuesj-110	74	14	,	,	PUNCT
cuesj-110	74	15	pp	pp	ADJ
cuesj-110	74	16	.	.	PUNCT
cuesj-110	75	1	26	26	NUM
cuesj-110	75	2	-	-	SYM
cuesj-110	75	3	30	30	NUM
cuesj-110	75	4	,	,	PUNCT
cuesj-110	75	5	2013	2013	NUM
cuesj-110	75	6	.	.	PUNCT
cuesj-110	76	1	6	6	X
cuesj-110	76	2	.	.	PUNCT
cuesj-110	76	3	n.	n.	PROPN
cuesj-110	76	4	gill	gill	PROPN
cuesj-110	76	5	and	and	CCONJ
cuesj-110	76	6	s.	s.	PROPN
cuesj-110	76	7	singh	singh	PROPN
cuesj-110	76	8	.	.	PUNCT
cuesj-110	77	1	“	"	PUNCT
cuesj-110	77	2	biological	biological	ADJ
cuesj-110	77	3	sequence	sequence	NOUN
cuesj-110	77	4	matching	matching	NOUN
cuesj-110	77	5	using	use	VERB
cuesj-110	77	6	fuzzy	fuzzy	ADJ
cuesj-110	77	7	logic	logic	NOUN
cuesj-110	77	8	”	"	PUNCT
cuesj-110	77	9	.	.	PUNCT
cuesj-110	78	1	international	international	ADJ
cuesj-110	78	2	journal	journal	PROPN
cuesj-110	78	3	of	of	ADP
cuesj-110	78	4	scientific	scientific	ADJ
cuesj-110	78	5	and	and	CCONJ
cuesj-110	78	6	engineering	engineering	NOUN
cuesj-110	78	7	research	research	NOUN
cuesj-110	78	8	,	,	PUNCT
cuesj-110	78	9	vol	vol	NOUN
cuesj-110	78	10	.	.	PROPN
cuesj-110	78	11	2	2	NUM
cuesj-110	78	12	,	,	PUNCT
cuesj-110	78	13	no	no	INTJ
cuesj-110	78	14	.	.	NOUN
cuesj-110	78	15	7	7	NUM
cuesj-110	78	16	,	,	PUNCT
cuesj-110	78	17	pp	pp	ADJ
cuesj-110	78	18	.	.	PUNCT
cuesj-110	79	1	1	1	NUM
cuesj-110	79	2	-	-	SYM
cuesj-110	79	3	5	5	NUM
cuesj-110	79	4	,	,	PUNCT
cuesj-110	79	5	2011	2011	NUM
cuesj-110	79	6	.	.	PUNCT
cuesj-110	80	1	7	7	X
cuesj-110	80	2	.	.	X
cuesj-110	80	3	s.	s.	PROPN
cuesj-110	80	4	b.	b.	PROPN
cuesj-110	80	5	needleman	needleman	PROPN
cuesj-110	80	6	and	and	CCONJ
cuesj-110	80	7	c.	c.	PROPN
cuesj-110	80	8	d.	d.	PROPN
cuesj-110	80	9	wunsch	wunsch	PROPN
cuesj-110	80	10	.	.	PUNCT
cuesj-110	81	1	“	"	PUNCT
cuesj-110	81	2	a	a	DET
cuesj-110	81	3	general	general	ADJ
cuesj-110	81	4	method	method	NOUN
cuesj-110	81	5	applicable	applicable	ADJ
cuesj-110	81	6	to	to	ADP
cuesj-110	81	7	the	the	DET
cuesj-110	81	8	search	search	NOUN
cuesj-110	81	9	for	for	ADP
cuesj-110	81	10	similarities	similarity	NOUN
cuesj-110	81	11	in	in	ADP
cuesj-110	81	12	the	the	DET
cuesj-110	81	13	amino	amino	NOUN
cuesj-110	81	14	acid	acid	NOUN
cuesj-110	81	15	sequence	sequence	NOUN
cuesj-110	81	16	of	of	ADP
cuesj-110	81	17	two	two	NUM
cuesj-110	81	18	proteins	protein	NOUN
cuesj-110	81	19	”	"	PUNCT
cuesj-110	81	20	.	.	PUNCT
cuesj-110	82	1	journal	journal	NOUN
cuesj-110	82	2	of	of	ADP
cuesj-110	82	3	molecular	molecular	ADJ
cuesj-110	82	4	biology	biology	NOUN
cuesj-110	82	5	,	,	PUNCT
cuesj-110	82	6	vol	vol	NOUN
cuesj-110	82	7	.	.	PROPN
cuesj-110	82	8	48	48	NUM
cuesj-110	82	9	,	,	PUNCT
cuesj-110	82	10	no	no	INTJ
cuesj-110	82	11	.	.	NOUN
cuesj-110	82	12	3	3	NUM
cuesj-110	82	13	,	,	PUNCT
cuesj-110	82	14	pp	pp	ADJ
cuesj-110	82	15	.	.	PUNCT
cuesj-110	82	16	443453	443453	NUM
cuesj-110	82	17	,	,	PUNCT
cuesj-110	82	18	1970	1970	NUM
cuesj-110	82	19	.	.	PUNCT
cuesj-110	83	1	8	8	NUM
cuesj-110	83	2	.	.	PUNCT
cuesj-110	83	3	t.	t.	PROPN
cuesj-110	83	4	f.	f.	PROPN
cuesj-110	83	5	smith	smith	PROPN
cuesj-110	83	6	and	and	CCONJ
cuesj-110	83	7	m.	m.	PROPN
cuesj-110	83	8	s.	s.	PROPN
cuesj-110	83	9	waterman	waterman	PROPN
cuesj-110	83	10	.	.	PUNCT
cuesj-110	84	1	“	"	PUNCT
cuesj-110	84	2	identification	identification	NOUN
cuesj-110	84	3	of	of	ADP
cuesj-110	84	4	common	common	ADJ
cuesj-110	84	5	molecular	molecular	ADJ
cuesj-110	84	6	subsequences	subsequence	NOUN
cuesj-110	84	7	”	"	PUNCT
cuesj-110	84	8	.	.	PUNCT
cuesj-110	85	1	journal	journal	NOUN
cuesj-110	85	2	of	of	ADP
cuesj-110	85	3	molecular	molecular	ADJ
cuesj-110	85	4	biology	biology	NOUN
cuesj-110	85	5	,	,	PUNCT
cuesj-110	85	6	vol	vol	NOUN
cuesj-110	85	7	.	.	PROPN
cuesj-110	85	8	147	147	NUM
cuesj-110	85	9	,	,	PUNCT
cuesj-110	85	10	no	no	INTJ
cuesj-110	85	11	.	.	NOUN
cuesj-110	85	12	1	1	NUM
cuesj-110	85	13	,	,	PUNCT
cuesj-110	85	14	pp	pp	ADJ
cuesj-110	85	15	.	.	PUNCT
cuesj-110	86	1	195	195	NUM
cuesj-110	86	2	-	-	SYM
cuesj-110	86	3	197	197	NUM
cuesj-110	86	4	,	,	PUNCT
cuesj-110	86	5	1981	1981	NUM
cuesj-110	86	6	.	.	PUNCT
cuesj-110	87	1	9	9	X
cuesj-110	87	2	.	.	X
cuesj-110	87	3	d.	d.	PROPN
cuesj-110	87	4	nauck	nauck	PROPN
cuesj-110	87	5	,	,	PUNCT
cuesj-110	87	6	f.	f.	PROPN
cuesj-110	87	7	klawonn	klawonn	PROPN
cuesj-110	87	8	and	and	CCONJ
cuesj-110	87	9	r.	r.	PROPN
cuesj-110	87	10	kruse	kruse	PROPN
cuesj-110	87	11	.	.	PUNCT
cuesj-110	88	1	“	"	PUNCT
cuesj-110	88	2	foundations	foundation	NOUN
cuesj-110	88	3	of	of	ADP
cuesj-110	88	4	neuro	neuro	NOUN
cuesj-110	88	5	-	-	PUNCT
cuesj-110	88	6	fuzzy	fuzzy	ADJ
cuesj-110	88	7	systems	system	NOUN
cuesj-110	88	8	”	"	PUNCT
cuesj-110	88	9	.	.	PUNCT
cuesj-110	89	1	john	john	PROPN
cuesj-110	89	2	wiley	wiley	PROPN
cuesj-110	89	3	and	and	CCONJ
cuesj-110	89	4	sons	son	NOUN
cuesj-110	89	5	,	,	PUNCT
cuesj-110	89	6	inc	inc	PROPN
cuesj-110	89	7	.	.	PROPN
cuesj-110	89	8	,	,	PUNCT
cuesj-110	89	9	new	new	PROPN
cuesj-110	89	10	york	york	PROPN
cuesj-110	89	11	,	,	PUNCT
cuesj-110	89	12	1997	1997	NUM
cuesj-110	89	13	.	.	PUNCT
cuesj-110	90	1	10	10	NUM
cuesj-110	90	2	.	.	PUNCT
cuesj-110	90	3	s.	s.	PROPN
cuesj-110	90	4	nasser	nasser	PROPN
cuesj-110	90	5	,	,	PUNCT
cuesj-110	90	6	g.	g.	PROPN
cuesj-110	90	7	l.	l.	PROPN
cuesj-110	90	8	vert	vert	PROPN
cuesj-110	90	9	,	,	PUNCT
cuesj-110	90	10	m.	m.	NOUN
cuesj-110	90	11	nicolescu	nicolescu	PROPN
cuesj-110	90	12	and	and	CCONJ
cuesj-110	90	13	a.	a.	NOUN
cuesj-110	90	14	murray	murray	PROPN
cuesj-110	90	15	.	.	PUNCT
cuesj-110	91	1	“	"	PUNCT
cuesj-110	91	2	multiple	multiple	ADJ
cuesj-110	91	3	sequence	sequence	NOUN
cuesj-110	91	4	alignment	alignment	NOUN
cuesj-110	91	5	using	use	VERB
cuesj-110	91	6	fuzzy	fuzzy	ADJ
cuesj-110	91	7	logic	logic	NOUN
cuesj-110	91	8	.	.	PUNCT
cuesj-110	92	1	in	in	ADP
cuesj-110	92	2	:	:	PUNCT
cuesj-110	92	3	2007	2007	NUM
cuesj-110	92	4	ieee	ieee	NOUN
cuesj-110	92	5	symposium	symposium	NOUN
cuesj-110	92	6	on	on	ADP
cuesj-110	92	7	computational	computational	ADJ
cuesj-110	92	8	intelligence	intelligence	NOUN
cuesj-110	92	9	and	and	CCONJ
cuesj-110	92	10	bioinformatics	bioinformatics	NOUN
cuesj-110	92	11	and	and	CCONJ
cuesj-110	92	12	computational	computational	ADJ
cuesj-110	92	13	biology	biology	NOUN
cuesj-110	92	14	,	,	PUNCT
cuesj-110	92	15	ieee	ieee	NOUN
cuesj-110	92	16	,	,	PUNCT
cuesj-110	92	17	pp	pp	ADJ
cuesj-110	92	18	.	.	PUNCT
cuesj-110	92	19	304	304	NUM
cuesj-110	92	20	-	-	SYM
cuesj-110	92	21	311	311	NUM
cuesj-110	92	22	,	,	PUNCT
cuesj-110	92	23	2007	2007	NUM
cuesj-110	92	24	.	.	PUNCT
cuesj-110	93	1	11	11	NUM
cuesj-110	93	2	.	.	PUNCT
cuesj-110	94	1	k.	k.	PROPN
cuesj-110	94	2	kim	kim	PROPN
cuesj-110	94	3	,	,	PUNCT
cuesj-110	94	4	m.	m.	PROPN
cuesj-110	94	5	kim	kim	PROPN
cuesj-110	94	6	and	and	CCONJ
cuesj-110	94	7	y.	y.	PROPN
cuesj-110	94	8	woo	woo	PROPN
cuesj-110	94	9	.	.	PUNCT
cuesj-110	95	1	“	"	PUNCT
cuesj-110	95	2	a	a	DET
cuesj-110	95	3	dna	dna	PROPN
cuesj-110	95	4	sequence	sequence	NOUN
cuesj-110	95	5	alignment	alignment	NOUN
cuesj-110	95	6	algorithm	algorithm	NOUN
cuesj-110	95	7	using	use	VERB
cuesj-110	95	8	quality	quality	NOUN
cuesj-110	95	9	information	information	NOUN
cuesj-110	95	10	and	and	CCONJ
cuesj-110	95	11	a	a	DET
cuesj-110	95	12	fuzzy	fuzzy	ADJ
cuesj-110	95	13	inference	inference	NOUN
cuesj-110	95	14	method	method	NOUN
cuesj-110	95	15	”	"	PUNCT
cuesj-110	95	16	.	.	PUNCT
cuesj-110	96	1	progress	progress	NOUN
cuesj-110	96	2	in	in	ADP
cuesj-110	96	3	natural	natural	ADJ
cuesj-110	96	4	science	science	NOUN
cuesj-110	96	5	,	,	PUNCT
cuesj-110	96	6	vol	vol	NOUN
cuesj-110	96	7	.	.	PROPN
cuesj-110	96	8	18	18	NUM
cuesj-110	96	9	,	,	PUNCT
cuesj-110	96	10	no	no	INTJ
cuesj-110	96	11	.	.	NOUN
cuesj-110	96	12	5	5	NUM
cuesj-110	96	13	,	,	PUNCT
cuesj-110	96	14	pp	pp	ADJ
cuesj-110	96	15	.	.	PUNCT
cuesj-110	97	1	595	595	NUM
cuesj-110	97	2	-	-	SYM
cuesj-110	97	3	602	602	NUM
cuesj-110	97	4	,	,	PUNCT
cuesj-110	97	5	2008	2008	NUM
cuesj-110	97	6	.	.	PUNCT
cuesj-110	98	1	12	12	NUM
cuesj-110	98	2	.	.	PUNCT
cuesj-110	98	3	n.	n.	NOUN
cuesj-110	98	4	chai	chai	NOUN
cuesj-110	98	5	,	,	PUNCT
cuesj-110	98	6	l.	l.	PROPN
cuesj-110	98	7	r.	r.	PROPN
cuesj-110	98	8	swem	swem	PROPN
cuesj-110	98	9	,	,	PUNCT
cuesj-110	98	10	m.	m.	PROPN
cuesj-110	98	11	reichelt	reichelt	PROPN
cuesj-110	98	12	,	,	PUNCT
cuesj-110	98	13	h.	h.	PROPN
cuesj-110	98	14	chen	chen	PROPN
cuesj-110	98	15	-	-	PUNCT
cuesj-110	98	16	harris	harris	PROPN
cuesj-110	98	17	,	,	PUNCT
cuesj-110	98	18	e.	e.	PROPN
cuesj-110	98	19	luis	luis	PROPN
cuesj-110	98	20	,	,	PUNCT
cuesj-110	98	21	s.	s.	PROPN
cuesj-110	98	22	park	park	PROPN
cuesj-110	98	23	and	and	CCONJ
cuesj-110	98	24	j.	j.	PROPN
cuesj-110	98	25	mcbride	mcbride	PROPN
cuesj-110	98	26	.	.	PUNCT
cuesj-110	99	1	“	"	PUNCT
cuesj-110	99	2	two	two	NUM
cuesj-110	99	3	escape	escape	NOUN
cuesj-110	99	4	mechanisms	mechanism	NOUN
cuesj-110	99	5	of	of	ADP
cuesj-110	99	6	influenza	influenza	NOUN
cuesj-110	99	7	a	a	DET
cuesj-110	99	8	virus	virus	NOUN
cuesj-110	99	9	to	to	ADP
cuesj-110	99	10	a	a	DET
cuesj-110	99	11	broadly	broadly	ADV
cuesj-110	99	12	neutralizing	neutralizing	ADJ
cuesj-110	99	13	stalk	stalk	NOUN
cuesj-110	99	14	-	-	PUNCT
cuesj-110	99	15	binding	bind	VERB
cuesj-110	99	16	antibody	antibody	NOUN
cuesj-110	99	17	”	"	PUNCT
cuesj-110	99	18	.	.	PUNCT
cuesj-110	100	1	plos	plos	PROPN
cuesj-110	100	2	pathogens	pathogen	NOUN
cuesj-110	100	3	,	,	PUNCT
cuesj-110	100	4	vol	vol	NOUN
cuesj-110	100	5	.	.	PROPN
cuesj-110	100	6	12	12	NUM
cuesj-110	100	7	,	,	PUNCT
cuesj-110	100	8	no	no	INTJ
cuesj-110	100	9	.	.	NOUN
cuesj-110	100	10	6	6	NUM
cuesj-110	100	11	,	,	PUNCT
cuesj-110	100	12	p.	p.	NOUN
cuesj-110	100	13	e1005702	e1005702	PROPN
cuesj-110	100	14	,	,	PUNCT
cuesj-110	100	15	2016	2016	NUM
cuesj-110	100	16	.	.	PUNCT
cuesj-110	101	1	13	13	NUM
cuesj-110	101	2	.	.	PUNCT
cuesj-110	101	3	s.	s.	PROPN
cuesj-110	101	4	a.	a.	PROPN
cuesj-110	101	5	hameed	hameed	PROPN
cuesj-110	101	6	and	and	CCONJ
cuesj-110	101	7	r.	r.	PROPN
cuesj-110	101	8	i.	i.	PROPN
cuesj-110	101	9	hamed	hamed	PROPN
cuesj-110	101	10	.	.	PUNCT
cuesj-110	102	1	“	"	PUNCT
cuesj-110	102	2	analysing	analyse	VERB
cuesj-110	102	3	the	the	DET
cuesj-110	102	4	score	score	NOUN
cuesj-110	102	5	matching	matching	NOUN
cuesj-110	102	6	of	of	ADP
cuesj-110	102	7	dna	dna	NOUN
cuesj-110	102	8	sequencing	sequence	VERB
cuesj-110	102	9	using	use	VERB
cuesj-110	102	10	an	an	DET
cuesj-110	102	11	expert	expert	ADJ
cuesj-110	102	12	system	system	NOUN
cuesj-110	102	13	of	of	ADP
cuesj-110	102	14	neurofuzzy	neurofuzzy	NOUN
cuesj-110	102	15	”	"	PUNCT
cuesj-110	102	16	.	.	PUNCT
cuesj-110	103	1	journal	journal	PROPN
cuesj-110	103	2	of	of	ADP
cuesj-110	103	3	theoretical	theoretical	ADJ
cuesj-110	103	4	and	and	CCONJ
cuesj-110	103	5	applied	apply	VERB
cuesj-110	103	6	information	information	NOUN
cuesj-110	103	7	technology	technology	NOUN
cuesj-110	103	8	,	,	PUNCT
cuesj-110	103	9	vol	vol	NOUN
cuesj-110	103	10	.	.	PROPN
cuesj-110	103	11	95	95	NUM
cuesj-110	103	12	,	,	PUNCT
cuesj-110	103	13	no	no	INTJ
cuesj-110	103	14	.	.	NOUN
cuesj-110	103	15	6	6	NUM
cuesj-110	103	16	,	,	PUNCT
cuesj-110	103	17	pp	pp	ADJ
cuesj-110	103	18	.	.	PUNCT
cuesj-110	104	1	1255	1255	NUM
cuesj-110	104	2	-	-	SYM
cuesj-110	104	3	1262	1262	NUM
cuesj-110	104	4	,	,	PUNCT
cuesj-110	104	5	2017	2017	NUM
cuesj-110	104	6	.	.	PUNCT
cuesj-110	105	1	14	14	NUM
cuesj-110	105	2	.	.	X
cuesj-110	105	3	dna	dna	PROPN
cuesj-110	105	4	matching	match	VERB
cuesj-110	105	5	data	datum	NOUN
cuesj-110	105	6	base	base	NOUN
cuesj-110	105	7	-	-	PUNCT
cuesj-110	105	8	ncbi	ncbi	NOUN
cuesj-110	105	9	”	"	PUNCT
cuesj-110	105	10	.	.	PUNCT
cuesj-110	106	1	https://www.ncbi.nlm.nih	https://www.ncbi.nlm.nih	PROPN
cuesj-110	106	2	.	.	PUNCT
cuesj-110	107	1	gov	gov	NOUN
cuesj-110	107	2	/	/	SYM
cuesj-110	107	3	nucleotide	nucleotide	NOUN
cuesj-110	107	4	.	.	PUNCT