id	sid	tid	token	lemma	pos
cana-659	1	1	ieee	ieee	NOUN
cana-659	1	2	paper	paper	NOUN
cana-659	1	3	template	template	NOUN
cana-659	1	4	in	in	ADP
cana-659	1	5	a4	a4	NOUN
cana-659	1	6	(	(	PUNCT
cana-659	1	7	v1	v1	NOUN
cana-659	1	8	)	)	PUNCT
cana-659	1	9	communications	communication	NOUN
cana-659	1	10	on	on	ADP
cana-659	1	11	applied	apply	VERB
cana-659	1	12	nonlinear	nonlinear	ADJ
cana-659	1	13	analysis	analysis	NOUN
cana-659	1	14	issn	issn	NOUN
cana-659	1	15	:	:	PUNCT
cana-659	1	16	1074	1074	NUM
cana-659	1	17	-	-	PUNCT
cana-659	1	18	133x	133x	NUM
cana-659	1	19	vol	vol	NOUN
cana-659	1	20	31	31	NUM
cana-659	1	21	no	no	NOUN
cana-659	1	22	.	.	PUNCT
cana-659	2	1	2s	2s	NUM
cana-659	2	2	(	(	PUNCT
cana-659	2	3	2024	2024	NUM
cana-659	2	4	)	)	PUNCT
cana-659	2	5	436	436	NUM
cana-659	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	2	7	analysis	analysis	NOUN
cana-659	2	8	of	of	ADP
cana-659	2	9	machine	machine	NOUN
cana-659	2	10	learning	learn	VERB
cana-659	2	11	approaches	approach	NOUN
cana-659	2	12	for	for	ADP
cana-659	2	13	dna	dna	NOUN
cana-659	2	14	sequencing	sequencing	NOUN
cana-659	2	15	and	and	CCONJ
cana-659	2	16	classification	classification	NOUN
cana-659	2	17	:	:	PUNCT
cana-659	2	18	an	an	DET
cana-659	2	19	optimized	optimize	VERB
cana-659	2	20	approach	approach	NOUN
cana-659	2	21	bhushan	bhushan	PROPN
cana-659	2	22	bawankar1	bawankar1	PROPN
cana-659	2	23	,	,	PUNCT
cana-659	2	24	kotadi	kotadi	PROPN
cana-659	2	25	chinnaiah2	chinnaiah2	PROPN
cana-659	2	26	,	,	PUNCT
cana-659	2	27	rajesh	rajesh	ADJ
cana-659	2	28	dharmik3	dharmik3	PROPN
cana-659	3	1	¹computer	¹computer	X
cana-659	3	2	science	science	NOUN
cana-659	3	3	and	and	CCONJ
cana-659	3	4	engineering	engineering	NOUN
cana-659	3	5	,	,	PUNCT
cana-659	3	6	ghru	ghru	PROPN
cana-659	3	7	,	,	PUNCT
cana-659	3	8	amravati	amravati	PROPN
cana-659	3	9	,	,	PUNCT
cana-659	3	10	india	india	PROPN
cana-659	3	11	²computer	²computer	PROPN
cana-659	3	12	science	science	NOUN
cana-659	3	13	and	and	CCONJ
cana-659	3	14	engineering	engineering	NOUN
cana-659	3	15	,	,	PUNCT
cana-659	3	16	ghrce	ghrce	NOUN
cana-659	3	17	,	,	PUNCT
cana-659	3	18	nagpur	nagpur	PROPN
cana-659	3	19	,	,	PUNCT
cana-659	3	20	india	india	PROPN
cana-659	3	21	³information	³information	PROPN
cana-659	3	22	technology	technology	NOUN
cana-659	3	23	,	,	PUNCT
cana-659	3	24	ycce	ycce	NOUN
cana-659	3	25	,	,	PUNCT
cana-659	3	26	nagpur	nagpur	PROPN
cana-659	3	27	,	,	PUNCT
cana-659	3	28	india	india	PROPN
cana-659	3	29	1bubawankar@gmail.com	1bubawankar@gmail.com	PROPN
cana-659	3	30	;	;	PUNCT
cana-659	3	31	2kotadi.chinnaiah@raisoni.net	2kotadi.chinnaiah@raisoni.net	NUM
cana-659	3	32	;	;	PUNCT
cana-659	3	33	3raj_dharmik@yahoo.com	3raj_dharmik@yahoo.com	NUM
cana-659	3	34	article	article	NOUN
cana-659	3	35	history	history	NOUN
cana-659	3	36	:	:	PUNCT
cana-659	3	37	received	receive	VERB
cana-659	3	38	:	:	PUNCT
cana-659	3	39	22	22	NUM
cana-659	3	40	-	-	SYM
cana-659	3	41	03	03	NUM
cana-659	3	42	-	-	PUNCT
cana-659	3	43	2024	2024	NUM
cana-659	3	44	revised	revise	VERB
cana-659	3	45	:	:	PUNCT
cana-659	3	46	10	10	NUM
cana-659	3	47	-	-	SYM
cana-659	3	48	05	05	NUM
cana-659	3	49	-	-	PUNCT
cana-659	3	50	2024	2024	NUM
cana-659	3	51	accepted	accept	VERB
cana-659	3	52	:	:	PUNCT
cana-659	3	53	20	20	NUM
cana-659	3	54	-	-	SYM
cana-659	3	55	05	05	NUM
cana-659	3	56	-	-	PUNCT
cana-659	3	57	2024	2024	NUM
cana-659	3	58	abstract	abstract	NOUN
cana-659	3	59	:	:	PUNCT
cana-659	3	60	dna	dna	PROPN
cana-659	3	61	sequencing	sequencing	NOUN
cana-659	3	62	is	be	AUX
cana-659	3	63	essential	essential	ADJ
cana-659	3	64	to	to	ADP
cana-659	3	65	contemporary	contemporary	ADJ
cana-659	3	66	research	research	NOUN
cana-659	3	67	.	.	PUNCT
cana-659	4	1	it	it	PRON
cana-659	4	2	facilitates	facilitate	VERB
cana-659	4	3	the	the	DET
cana-659	4	4	advancement	advancement	NOUN
cana-659	4	5	of	of	ADP
cana-659	4	6	many	many	ADJ
cana-659	4	7	fields	field	NOUN
cana-659	4	8	,	,	PUNCT
cana-659	4	9	including	include	VERB
cana-659	4	10	phylogenetics	phylogenetic	NOUN
cana-659	4	11	,	,	PUNCT
cana-659	4	12	genetics	genetic	NOUN
cana-659	4	13	,	,	PUNCT
cana-659	4	14	and	and	CCONJ
cana-659	4	15	meta	meta	ADJ
cana-659	4	16	-	-	PUNCT
cana-659	4	17	genetics	genetics	NOUN
cana-659	4	18	.	.	PUNCT
cana-659	5	1	dna	dna	PROPN
cana-659	5	2	strands	strand	NOUN
cana-659	5	3	must	must	AUX
cana-659	5	4	be	be	AUX
cana-659	5	5	extracted	extract	VERB
cana-659	5	6	and	and	CCONJ
cana-659	5	7	read	read	VERB
cana-659	5	8	in	in	ADP
cana-659	5	9	order	order	NOUN
cana-659	5	10	to	to	PART
cana-659	5	11	perform	perform	VERB
cana-659	5	12	dna	dna	NOUN
cana-659	5	13	sequencing	sequencing	NOUN
cana-659	5	14	.	.	PUNCT
cana-659	6	1	in	in	ADP
cana-659	6	2	order	order	NOUN
cana-659	6	3	to	to	PART
cana-659	6	4	improve	improve	VERB
cana-659	6	5	prediction	prediction	NOUN
cana-659	6	6	for	for	ADP
cana-659	6	7	dna	dna	PROPN
cana-659	6	8	research	research	NOUN
cana-659	6	9	and	and	CCONJ
cana-659	6	10	obtain	obtain	VERB
cana-659	6	11	the	the	DET
cana-659	6	12	most	most	ADV
cana-659	6	13	accurate	accurate	ADJ
cana-659	6	14	results	result	NOUN
cana-659	6	15	,	,	PUNCT
cana-659	6	16	this	this	DET
cana-659	6	17	research	research	NOUN
cana-659	6	18	paper	paper	NOUN
cana-659	6	19	compares	compare	VERB
cana-659	6	20	dna	dna	NOUN
cana-659	6	21	sequencing	sequence	VERB
cana-659	6	22	using	use	VERB
cana-659	6	23	machine	machine	NOUN
cana-659	6	24	learning	learning	NOUN
cana-659	6	25	algorithms	algorithm	NOUN
cana-659	6	26	.	.	PUNCT
cana-659	7	1	it	it	PRON
cana-659	7	2	also	also	ADV
cana-659	7	3	aims	aim	VERB
cana-659	7	4	to	to	PART
cana-659	7	5	efficiently	efficiently	ADV
cana-659	7	6	classify	classify	VERB
cana-659	7	7	dna	dna	PROPN
cana-659	7	8	sequences	sequence	NOUN
cana-659	7	9	according	accord	VERB
cana-659	7	10	to	to	ADP
cana-659	7	11	their	their	PRON
cana-659	7	12	features	feature	NOUN
cana-659	7	13	,	,	PUNCT
cana-659	7	14	improving	improve	VERB
cana-659	7	15	the	the	DET
cana-659	7	16	efficiency	efficiency	NOUN
cana-659	7	17	and	and	CCONJ
cana-659	7	18	accuracy	accuracy	NOUN
cana-659	7	19	of	of	ADP
cana-659	7	20	dna	dna	PROPN
cana-659	7	21	sequence	sequence	NOUN
cana-659	7	22	classification	classification	NOUN
cana-659	7	23	.	.	PUNCT
cana-659	8	1	the	the	DET
cana-659	8	2	efficiency	efficiency	NOUN
cana-659	8	3	of	of	ADP
cana-659	8	4	various	various	ADJ
cana-659	8	5	methods	method	NOUN
cana-659	8	6	is	be	AUX
cana-659	8	7	evaluated	evaluate	VERB
cana-659	8	8	and	and	CCONJ
cana-659	8	9	contrasted	contrast	VERB
cana-659	8	10	in	in	ADP
cana-659	8	11	the	the	DET
cana-659	8	12	study	study	NOUN
cana-659	8	13	using	use	VERB
cana-659	8	14	important	important	ADJ
cana-659	8	15	metrics	metric	NOUN
cana-659	8	16	like	like	ADP
cana-659	8	17	as	as	ADP
cana-659	8	18	f1	f1	NOUN
cana-659	8	19	-	-	PUNCT
cana-659	8	20	score	score	NOUN
cana-659	8	21	,	,	PUNCT
cana-659	8	22	accuracy	accuracy	NOUN
cana-659	8	23	,	,	PUNCT
cana-659	8	24	precision	precision	NOUN
cana-659	8	25	,	,	PUNCT
cana-659	8	26	and	and	CCONJ
cana-659	8	27	recall	recall	NOUN
cana-659	8	28	.	.	PUNCT
cana-659	9	1	the	the	DET
cana-659	9	2	results	result	NOUN
cana-659	9	3	indicated	indicate	VERB
cana-659	9	4	that	that	SCONJ
cana-659	9	5	for	for	ADP
cana-659	9	6	the	the	DET
cana-659	9	7	human	human	ADJ
cana-659	9	8	dna	dna	PROPN
cana-659	9	9	sequence	sequence	NOUN
cana-659	9	10	,	,	PUNCT
cana-659	9	11	the	the	DET
cana-659	9	12	random	random	ADJ
cana-659	9	13	forest	forest	NOUN
cana-659	9	14	approach	approach	NOUN
cana-659	9	15	provided	provide	VERB
cana-659	9	16	the	the	DET
cana-659	9	17	best	good	ADJ
cana-659	9	18	accuracy	accuracy	NOUN
cana-659	9	19	of	of	ADP
cana-659	9	20	0.9292	0.9292	NUM
cana-659	9	21	and	and	CCONJ
cana-659	9	22	f1	f1	NOUN
cana-659	9	23	-	-	PUNCT
cana-659	9	24	score	score	NOUN
cana-659	9	25	of	of	ADP
cana-659	9	26	0.930	0.930	NUM
cana-659	9	27	,	,	PUNCT
cana-659	9	28	while	while	SCONJ
cana-659	9	29	the	the	DET
cana-659	9	30	genetic	genetic	ADJ
cana-659	9	31	algorithm	algorithm	NOUN
cana-659	9	32	produced	produce	VERB
cana-659	9	33	higher	high	ADJ
cana-659	9	34	accuracy	accuracy	NOUN
cana-659	9	35	of	of	ADP
cana-659	9	36	0.91	0.91	NUM
cana-659	9	37	and	and	CCONJ
cana-659	9	38	f1	f1	NOUN
cana-659	9	39	-	-	PUNCT
cana-659	9	40	score	score	NOUN
cana-659	9	41	of	of	ADP
cana-659	9	42	0.913	0.913	NUM
cana-659	9	43	.	.	PUNCT
cana-659	10	1	the	the	DET
cana-659	10	2	potential	potential	ADJ
cana-659	10	3	advancement	advancement	NOUN
cana-659	10	4	of	of	ADP
cana-659	10	5	genomics	genomic	NOUN
cana-659	10	6	research	research	NOUN
cana-659	10	7	,	,	PUNCT
cana-659	10	8	customized	customize	VERB
cana-659	10	9	medicine	medicine	NOUN
cana-659	10	10	,	,	PUNCT
cana-659	10	11	and	and	CCONJ
cana-659	10	12	various	various	ADJ
cana-659	10	13	scientific	scientific	ADJ
cana-659	10	14	applications	application	NOUN
cana-659	10	15	that	that	PRON
cana-659	10	16	rely	rely	VERB
cana-659	10	17	on	on	ADP
cana-659	10	18	precise	precise	ADJ
cana-659	10	19	classification	classification	NOUN
cana-659	10	20	of	of	ADP
cana-659	10	21	dna	dna	PROPN
cana-659	10	22	sequences	sequence	NOUN
cana-659	10	23	is	be	AUX
cana-659	10	24	presented	present	VERB
cana-659	10	25	by	by	ADP
cana-659	10	26	the	the	DET
cana-659	10	27	combination	combination	NOUN
cana-659	10	28	of	of	ADP
cana-659	10	29	machine	machine	NOUN
cana-659	10	30	learning	learning	NOUN
cana-659	10	31	and	and	CCONJ
cana-659	10	32	dna	dna	NOUN
cana-659	10	33	sequencing	sequencing	NOUN
cana-659	10	34	.	.	PUNCT
cana-659	11	1	keywords	keyword	NOUN
cana-659	11	2	:	:	PUNCT
cana-659	11	3	feature	feature	NOUN
cana-659	11	4	extraction	extraction	NOUN
cana-659	11	5	,	,	PUNCT
cana-659	11	6	machine	machine	NOUN
cana-659	11	7	learning	learning	NOUN
cana-659	11	8	,	,	PUNCT
cana-659	11	9	dna	dna	PROPN
cana-659	11	10	sequences	sequence	NOUN
cana-659	11	11	.	.	PUNCT
cana-659	12	1	1	1	X
cana-659	12	2	.	.	X
cana-659	12	3	introduction	introduction	NOUN
cana-659	12	4	today	today	NOUN
cana-659	12	5	’s	’s	PART
cana-659	12	6	world	world	NOUN
cana-659	12	7	is	be	AUX
cana-659	12	8	an	an	DET
cana-659	12	9	era	era	NOUN
cana-659	12	10	of	of	ADP
cana-659	12	11	data	datum	NOUN
cana-659	12	12	,	,	PUNCT
cana-659	12	13	with	with	ADP
cana-659	12	14	everything	everything	PRON
cana-659	12	15	in	in	ADP
cana-659	12	16	our	our	PRON
cana-659	12	17	lives	life	NOUN
cana-659	12	18	being	be	AUX
cana-659	12	19	digitally	digitally	ADV
cana-659	12	20	recorded	record	VERB
cana-659	12	21	and	and	CCONJ
cana-659	12	22	everything	everything	PRON
cana-659	12	23	in	in	ADP
cana-659	12	24	our	our	PRON
cana-659	12	25	surroundings	surrounding	NOUN
cana-659	12	26	being	be	AUX
cana-659	12	27	connected	connect	VERB
cana-659	12	28	to	to	ADP
cana-659	12	29	a	a	DET
cana-659	12	30	data	datum	NOUN
cana-659	12	31	source	source	NOUN
cana-659	12	32	.	.	PUNCT
cana-659	13	1	the	the	DET
cana-659	13	2	data	datum	NOUN
cana-659	13	3	may	may	AUX
cana-659	13	4	be	be	AUX
cana-659	13	5	unstructured	unstructure	VERB
cana-659	13	6	,	,	PUNCT
cana-659	13	7	semistructured	semistructure	VERB
cana-659	13	8	,	,	PUNCT
cana-659	13	9	or	or	CCONJ
cana-659	13	10	structured	structure	VERB
cana-659	13	11	.	.	PUNCT
cana-659	14	1	by	by	ADP
cana-659	14	2	drawing	draw	VERB
cana-659	14	3	conclusions	conclusion	NOUN
cana-659	14	4	from	from	ADP
cana-659	14	5	these	these	DET
cana-659	14	6	data	datum	NOUN
cana-659	14	7	,	,	PUNCT
cana-659	14	8	numerous	numerous	ADJ
cana-659	14	9	intelligent	intelligent	ADJ
cana-659	14	10	applications	application	NOUN
cana-659	14	11	in	in	ADP
cana-659	14	12	pertinent	pertinent	ADJ
cana-659	14	13	fields	field	NOUN
cana-659	14	14	can	can	AUX
cana-659	14	15	be	be	AUX
cana-659	14	16	constructed	construct	VERB
cana-659	14	17	.	.	PUNCT
cana-659	15	1	therefore	therefore	ADV
cana-659	15	2	,	,	PUNCT
cana-659	15	3	real	real	ADJ
cana-659	15	4	-	-	PUNCT
cana-659	15	5	world	world	NOUN
cana-659	15	6	applications	application	NOUN
cana-659	15	7	are	be	AUX
cana-659	15	8	dependent	dependent	ADJ
cana-659	15	9	on	on	ADP
cana-659	15	10	data	datum	NOUN
cana-659	15	11	management	management	NOUN
cana-659	15	12	tools	tool	NOUN
cana-659	15	13	and	and	CCONJ
cana-659	15	14	techniques	technique	NOUN
cana-659	15	15	that	that	PRON
cana-659	15	16	can	can	AUX
cana-659	15	17	quickly	quickly	ADV
cana-659	15	18	and	and	CCONJ
cana-659	15	19	intelligently	intelligently	ADV
cana-659	15	20	extract	extract	VERB
cana-659	15	21	insights	insight	NOUN
cana-659	15	22	or	or	CCONJ
cana-659	15	23	meaningful	meaningful	ADJ
cana-659	15	24	knowledge	knowledge	NOUN
cana-659	15	25	from	from	ADP
cana-659	15	26	data	data	PROPN
cana-659	15	27	.	.	PUNCT
cana-659	16	1	these	these	DET
cana-659	16	2	tools	tool	NOUN
cana-659	16	3	and	and	CCONJ
cana-659	16	4	techniques	technique	NOUN
cana-659	16	5	are	be	AUX
cana-659	16	6	critically	critically	ADV
cana-659	16	7	needed	need	VERB
cana-659	16	8	[	[	X
cana-659	16	9	1	1	NUM
cana-659	16	10	]	]	PUNCT
cana-659	16	11	.	.	PUNCT
cana-659	17	1	the	the	DET
cana-659	17	2	genetic	genetic	ADJ
cana-659	17	3	material	material	NOUN
cana-659	17	4	of	of	ADP
cana-659	17	5	living	living	NOUN
cana-659	17	6	organisms	organism	NOUN
cana-659	17	7	is	be	AUX
cana-659	17	8	found	find	VERB
cana-659	17	9	in	in	ADP
cana-659	17	10	deoxyribonucleic	deoxyribonucleic	ADJ
cana-659	17	11	acid	acid	NOUN
cana-659	17	12	(	(	PUNCT
cana-659	17	13	dna	dna	PROPN
cana-659	17	14	)	)	PUNCT
cana-659	17	15	,	,	PUNCT
cana-659	17	16	a	a	DET
cana-659	17	17	lengthy	lengthy	ADJ
cana-659	17	18	repeating	repeat	VERB
cana-659	17	19	chain	chain	NOUN
cana-659	17	20	of	of	ADP
cana-659	17	21	nucleic	nucleic	ADJ
cana-659	17	22	acids	acid	NOUN
cana-659	17	23	.	.	PUNCT
cana-659	18	1	dna	dna	PROPN
cana-659	18	2	is	be	AUX
cana-659	18	3	the	the	DET
cana-659	18	4	blueprint	blueprint	NOUN
cana-659	18	5	for	for	ADP
cana-659	18	6	life	life	NOUN
cana-659	18	7	and	and	CCONJ
cana-659	18	8	is	be	AUX
cana-659	18	9	essential	essential	ADJ
cana-659	18	10	for	for	ADP
cana-659	18	11	reproduction	reproduction	NOUN
cana-659	18	12	because	because	SCONJ
cana-659	18	13	it	it	PRON
cana-659	18	14	transmits	transmit	VERB
cana-659	18	15	genetic	genetic	ADJ
cana-659	18	16	information	information	NOUN
cana-659	18	17	from	from	ADP
cana-659	18	18	parent	parent	NOUN
cana-659	18	19	to	to	ADP
cana-659	18	20	child	child	NOUN
cana-659	18	21	.	.	PUNCT
cana-659	19	1	the	the	DET
cana-659	19	2	primary	primary	ADJ
cana-659	19	3	purpose	purpose	NOUN
cana-659	19	4	of	of	ADP
cana-659	19	5	dna	dna	PROPN
cana-659	19	6	i.e.	i.e.	X
cana-659	19	7	as	as	ADP
cana-659	19	8	a	a	DET
cana-659	19	9	biological	biological	ADJ
cana-659	19	10	macromolecule	macromolecule	NOUN
cana-659	19	11	,	,	PUNCT
cana-659	19	12	is	be	AUX
cana-659	19	13	information	information	NOUN
cana-659	19	14	storage	storage	NOUN
cana-659	19	15	.	.	PUNCT
cana-659	20	1	dna	dna	PROPN
cana-659	20	2	sequencing	sequence	VERB
cana-659	20	3	is	be	AUX
cana-659	20	4	the	the	DET
cana-659	20	5	process	process	NOUN
cana-659	20	6	of	of	ADP
cana-659	20	7	determining	determine	VERB
cana-659	20	8	the	the	DET
cana-659	20	9	precise	precise	ADJ
cana-659	20	10	base	base	NOUN
cana-659	20	11	-	-	PUNCT
cana-659	20	12	pair	pair	NOUN
cana-659	20	13	sequences	sequence	NOUN
cana-659	20	14	of	of	ADP
cana-659	20	15	adenine	adenine	NOUN
cana-659	20	16	,	,	PUNCT
cana-659	20	17	thymine	thymine	NOUN
cana-659	20	18	,	,	PUNCT
cana-659	20	19	cytosine	cytosine	NOUN
cana-659	20	20	,	,	PUNCT
cana-659	20	21	and	and	CCONJ
cana-659	20	22	guanine	guanine	NOUN
cana-659	20	23	in	in	ADP
cana-659	20	24	a	a	DET
cana-659	20	25	dna	dna	NOUN
cana-659	20	26	molecule	molecule	NOUN
cana-659	20	27	.	.	PUNCT
cana-659	21	1	information	information	NOUN
cana-659	21	2	carried	carry	VERB
cana-659	21	3	by	by	ADP
cana-659	21	4	dna	dna	PROPN
cana-659	21	5	is	be	AUX
cana-659	21	6	stored	store	VERB
cana-659	21	7	in	in	ADP
cana-659	21	8	the	the	DET
cana-659	21	9	form	form	NOUN
cana-659	21	10	of	of	ADP
cana-659	21	11	gene	gene	NOUN
cana-659	21	12	sequences	sequence	NOUN
cana-659	21	13	.	.	PUNCT
cana-659	22	1	the	the	DET
cana-659	22	2	rapid	rapid	ADJ
cana-659	22	3	growth	growth	NOUN
cana-659	22	4	of	of	ADP
cana-659	22	5	dna	dna	PROPN
cana-659	22	6	sequence	sequence	NOUN
cana-659	22	7	data	datum	NOUN
cana-659	22	8	due	due	ADP
cana-659	22	9	to	to	ADP
cana-659	22	10	the	the	DET
cana-659	22	11	development	development	NOUN
cana-659	22	12	of	of	ADP
cana-659	22	13	sequencing	sequence	VERB
cana-659	22	14	technologies	technology	NOUN
cana-659	22	15	has	have	AUX
cana-659	22	16	propelled	propel	VERB
cana-659	22	17	the	the	DET
cana-659	22	18	study	study	NOUN
cana-659	22	19	of	of	ADP
cana-659	22	20	dna	dna	PROPN
cana-659	22	21	sequences	sequence	NOUN
cana-659	22	22	into	into	ADP
cana-659	22	23	the	the	DET
cana-659	22	24	big	big	ADJ
cana-659	22	25	data	datum	NOUN
cana-659	22	26	wave	wave	NOUN
cana-659	22	27	[	[	X
cana-659	22	28	2][3	2][3	X
cana-659	22	29	]	]	PUNCT
cana-659	22	30	.	.	PUNCT
cana-659	23	1	communications	communication	NOUN
cana-659	23	2	on	on	ADP
cana-659	23	3	applied	apply	VERB
cana-659	23	4	nonlinear	nonlinear	ADJ
cana-659	23	5	analysis	analysis	NOUN
cana-659	23	6	issn	issn	NOUN
cana-659	23	7	:	:	PUNCT
cana-659	23	8	1074	1074	NUM
cana-659	23	9	-	-	PUNCT
cana-659	23	10	133x	133x	NUM
cana-659	23	11	vol	vol	NOUN
cana-659	23	12	31	31	NUM
cana-659	23	13	no	no	NOUN
cana-659	23	14	.	.	PUNCT
cana-659	24	1	2s	2s	NUM
cana-659	24	2	(	(	PUNCT
cana-659	24	3	2024	2024	NUM
cana-659	24	4	)	)	PUNCT
cana-659	24	5	437	437	NUM
cana-659	24	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	24	7	the	the	DET
cana-659	24	8	machine	machine	NOUN
cana-659	24	9	learning	learn	VERB
cana-659	24	10	approach	approach	NOUN
cana-659	24	11	is	be	AUX
cana-659	24	12	a	a	DET
cana-659	24	13	method	method	NOUN
cana-659	24	14	that	that	PRON
cana-659	24	15	teaches	teach	VERB
cana-659	24	16	machines	machine	NOUN
cana-659	24	17	how	how	SCONJ
cana-659	24	18	to	to	PART
cana-659	24	19	handle	handle	VERB
cana-659	24	20	data	datum	NOUN
cana-659	24	21	more	more	ADV
cana-659	24	22	accurately	accurately	ADV
cana-659	24	23	and	and	CCONJ
cana-659	24	24	effectively	effectively	ADV
cana-659	24	25	.	.	PUNCT
cana-659	25	1	we	we	PRON
cana-659	25	2	are	be	AUX
cana-659	25	3	unable	unable	ADJ
cana-659	25	4	to	to	PART
cana-659	25	5	comprehend	comprehend	VERB
cana-659	25	6	the	the	DET
cana-659	25	7	information	information	NOUN
cana-659	25	8	in	in	ADP
cana-659	25	9	the	the	DET
cana-659	25	10	data	datum	NOUN
cana-659	25	11	after	after	ADP
cana-659	25	12	viewing	view	VERB
cana-659	25	13	it	it	PRON
cana-659	25	14	.	.	PUNCT
cana-659	26	1	in	in	ADP
cana-659	26	2	this	this	DET
cana-659	26	3	case	case	NOUN
cana-659	26	4	,	,	PUNCT
cana-659	26	5	we	we	PRON
cana-659	26	6	predict	predict	VERB
cana-659	26	7	the	the	DET
cana-659	26	8	data	datum	NOUN
cana-659	26	9	using	use	VERB
cana-659	26	10	machine	machine	NOUN
cana-659	26	11	learning	learning	NOUN
cana-659	26	12	approaches	approach	NOUN
cana-659	26	13	.	.	PUNCT
cana-659	27	1	machine	machine	NOUN
cana-659	27	2	learning	learning	NOUN
cana-659	27	3	is	be	AUX
cana-659	27	4	being	be	AUX
cana-659	27	5	used	use	VERB
cana-659	27	6	by	by	ADP
cana-659	27	7	many	many	ADJ
cana-659	27	8	sectors	sector	NOUN
cana-659	27	9	to	to	PART
cana-659	27	10	extract	extract	VERB
cana-659	27	11	pertinent	pertinent	ADJ
cana-659	27	12	data	datum	NOUN
cana-659	27	13	from	from	ADP
cana-659	27	14	accessible	accessible	ADJ
cana-659	27	15	datasets	dataset	NOUN
cana-659	27	16	.	.	PUNCT
cana-659	28	1	a	a	DET
cana-659	28	2	wide	wide	ADJ
cana-659	28	3	variety	variety	NOUN
cana-659	28	4	of	of	ADP
cana-659	28	5	algorithms	algorithm	NOUN
cana-659	28	6	have	have	AUX
cana-659	28	7	been	be	AUX
cana-659	28	8	developed	develop	VERB
cana-659	28	9	to	to	PART
cana-659	28	10	enable	enable	VERB
cana-659	28	11	computers	computer	NOUN
cana-659	28	12	to	to	PART
cana-659	28	13	learn	learn	VERB
cana-659	28	14	on	on	ADP
cana-659	28	15	their	their	PRON
cana-659	28	16	own	own	ADJ
cana-659	28	17	,	,	PUNCT
cana-659	28	18	with	with	ADP
cana-659	28	19	the	the	DET
cana-659	28	20	primary	primary	ADJ
cana-659	28	21	goal	goal	NOUN
cana-659	28	22	of	of	ADP
cana-659	28	23	machine	machine	NOUN
cana-659	28	24	learning	learn	VERB
cana-659	28	25	being	be	AUX
cana-659	28	26	the	the	DET
cana-659	28	27	extraction	extraction	NOUN
cana-659	28	28	of	of	ADP
cana-659	28	29	knowledge	knowledge	NOUN
cana-659	28	30	from	from	ADP
cana-659	28	31	available	available	ADJ
cana-659	28	32	data	datum	NOUN
cana-659	28	33	[	[	X
cana-659	28	34	2][3][4][5	2][3][4][5	NUM
cana-659	28	35	]	]	X
cana-659	28	36	.	.	PUNCT
cana-659	29	1	the	the	DET
cana-659	29	2	application	application	NOUN
cana-659	29	3	of	of	ADP
cana-659	29	4	machine	machine	NOUN
cana-659	29	5	learning	learn	VERB
cana-659	29	6	techniques	technique	NOUN
cana-659	29	7	to	to	PART
cana-659	29	8	dna	dna	VERB
cana-659	29	9	sequencing	sequencing	NOUN
cana-659	29	10	and	and	CCONJ
cana-659	29	11	classification	classification	NOUN
cana-659	29	12	aims	aim	VERB
cana-659	29	13	to	to	PART
cana-659	29	14	improve	improve	VERB
cana-659	29	15	our	our	PRON
cana-659	29	16	comprehension	comprehension	NOUN
cana-659	29	17	of	of	ADP
cana-659	29	18	genetic	genetic	ADJ
cana-659	29	19	data	datum	NOUN
cana-659	29	20	,	,	PUNCT
cana-659	29	21	advance	advance	NOUN
cana-659	29	22	computational	computational	ADJ
cana-659	29	23	analysis	analysis	NOUN
cana-659	29	24	techniques	technique	NOUN
cana-659	29	25	,	,	PUNCT
cana-659	29	26	and	and	CCONJ
cana-659	29	27	enable	enable	VERB
cana-659	29	28	a	a	DET
cana-659	29	29	number	number	NOUN
cana-659	29	30	of	of	ADP
cana-659	29	31	applications	application	NOUN
cana-659	29	32	in	in	ADP
cana-659	29	33	biological	biological	ADJ
cana-659	29	34	and	and	CCONJ
cana-659	29	35	medical	medical	ADJ
cana-659	29	36	research	research	NOUN
cana-659	29	37	.	.	PUNCT
cana-659	30	1	the	the	DET
cana-659	30	2	following	follow	VERB
cana-659	30	3	are	be	AUX
cana-659	30	4	some	some	DET
cana-659	30	5	main	main	ADJ
cana-659	30	6	goals	goal	NOUN
cana-659	30	7	:	:	PUNCT
cana-659	30	8	•	•	ADP
cana-659	30	9	to	to	PART
cana-659	30	10	increase	increase	VERB
cana-659	30	11	the	the	DET
cana-659	30	12	precision	precision	NOUN
cana-659	30	13	and	and	CCONJ
cana-659	30	14	effectiveness	effectiveness	NOUN
cana-659	30	15	of	of	ADP
cana-659	30	16	sequence	sequence	NOUN
cana-659	30	17	alignment	alignment	NOUN
cana-659	30	18	,	,	PUNCT
cana-659	30	19	making	make	VERB
cana-659	30	20	it	it	PRON
cana-659	30	21	possible	possible	ADJ
cana-659	30	22	to	to	PART
cana-659	30	23	identify	identify	VERB
cana-659	30	24	genetic	genetic	ADJ
cana-659	30	25	variants	variant	NOUN
cana-659	30	26	and	and	CCONJ
cana-659	30	27	compare	compare	VERB
cana-659	30	28	dna	dna	PROPN
cana-659	30	29	sequences	sequence	NOUN
cana-659	30	30	more	more	ADV
cana-659	30	31	precisely	precisely	ADV
cana-659	30	32	.	.	PUNCT
cana-659	31	1	•	•	ADP
cana-659	31	2	to	to	AUX
cana-659	31	3	precisely	precisely	ADV
cana-659	31	4	detect	detect	VERB
cana-659	31	5	and	and	CCONJ
cana-659	31	6	categorize	categorize	VERB
cana-659	31	7	genomic	genomic	ADJ
cana-659	31	8	variations	variation	NOUN
cana-659	31	9	from	from	ADP
cana-659	31	10	sequencing	sequence	VERB
cana-659	31	11	data	datum	NOUN
cana-659	31	12	,	,	PUNCT
cana-659	31	13	with	with	ADP
cana-659	31	14	high	high	ADJ
cana-659	31	15	sensitivity	sensitivity	NOUN
cana-659	31	16	and	and	CCONJ
cana-659	31	17	specificity	specificity	NOUN
cana-659	31	18	,	,	PUNCT
cana-659	31	19	differentiating	differentiate	VERB
cana-659	31	20	genuine	genuine	ADJ
cana-659	31	21	variants	variant	NOUN
cana-659	31	22	from	from	ADP
cana-659	31	23	errors	error	NOUN
cana-659	31	24	or	or	CCONJ
cana-659	31	25	artifacts	artifact	NOUN
cana-659	31	26	.	.	PUNCT
cana-659	32	1	•	•	X
cana-659	32	2	to	to	PART
cana-659	32	3	label	label	VERB
cana-659	32	4	genetic	genetic	ADJ
cana-659	32	5	variations	variation	NOUN
cana-659	32	6	and	and	CCONJ
cana-659	32	7	forecast	forecast	VERB
cana-659	32	8	the	the	DET
cana-659	32	9	functional	functional	ADJ
cana-659	32	10	ramifications	ramification	NOUN
cana-659	32	11	they	they	PRON
cana-659	32	12	will	will	AUX
cana-659	32	13	have	have	VERB
cana-659	32	14	,	,	PUNCT
cana-659	32	15	such	such	ADJ
cana-659	32	16	as	as	ADP
cana-659	32	17	how	how	SCONJ
cana-659	32	18	they	they	PRON
cana-659	32	19	would	would	AUX
cana-659	32	20	affect	affect	VERB
cana-659	32	21	gene	gene	NOUN
cana-659	32	22	regulation	regulation	NOUN
cana-659	32	23	,	,	PUNCT
cana-659	32	24	protein	protein	NOUN
cana-659	32	25	structure	structure	NOUN
cana-659	32	26	,	,	PUNCT
cana-659	32	27	or	or	CCONJ
cana-659	32	28	function	function	NOUN
cana-659	32	29	.	.	PUNCT
cana-659	33	1	•	•	INTJ
cana-659	33	2	to	to	PART
cana-659	33	3	annotate	annotate	VERB
cana-659	33	4	genetic	genetic	ADJ
cana-659	33	5	variations	variation	NOUN
cana-659	33	6	and	and	CCONJ
cana-659	33	7	explain	explain	VERB
cana-659	33	8	their	their	PRON
cana-659	33	9	relevance	relevance	NOUN
cana-659	33	10	for	for	ADP
cana-659	33	11	treatment	treatment	NOUN
cana-659	33	12	response	response	NOUN
cana-659	33	13	,	,	PUNCT
cana-659	33	14	illness	illness	NOUN
cana-659	33	15	susceptibility	susceptibility	NOUN
cana-659	33	16	,	,	PUNCT
cana-659	33	17	and	and	CCONJ
cana-659	33	18	other	other	ADJ
cana-659	33	19	biological	biological	ADJ
cana-659	33	20	processes	process	NOUN
cana-659	33	21	.	.	PUNCT
cana-659	34	1	•	•	NUM
cana-659	34	2	to	to	PART
cana-659	34	3	improve	improve	VERB
cana-659	34	4	phylogenetic	phylogenetic	ADJ
cana-659	34	5	analysis	analysis	NOUN
cana-659	34	6	and	and	CCONJ
cana-659	34	7	identify	identify	VERB
cana-659	34	8	genetic	genetic	ADJ
cana-659	34	9	signals	signal	NOUN
cana-659	34	10	underpinning	underpin	VERB
cana-659	34	11	species	specie	NOUN
cana-659	34	12	divergence	divergence	NOUN
cana-659	34	13	and	and	CCONJ
cana-659	34	14	adaption	adaption	NOUN
cana-659	34	15	,	,	PUNCT
cana-659	34	16	as	as	ADV
cana-659	34	17	well	well	ADV
cana-659	34	18	as	as	ADP
cana-659	34	19	to	to	PART
cana-659	34	20	reconstruct	reconstruct	VERB
cana-659	34	21	evolutionary	evolutionary	ADJ
cana-659	34	22	connections	connection	NOUN
cana-659	34	23	more	more	ADV
cana-659	34	24	accurately	accurately	ADV
cana-659	34	25	.	.	PUNCT
cana-659	35	1	•	•	ADP
cana-659	35	2	to	to	PART
cana-659	35	3	evaluate	evaluate	VERB
cana-659	35	4	and	and	CCONJ
cana-659	35	5	decipher	decipher	ADJ
cana-659	35	6	epigenomic	epigenomic	ADJ
cana-659	35	7	data	datum	NOUN
cana-659	35	8	,	,	PUNCT
cana-659	35	9	revealing	reveal	VERB
cana-659	35	10	chromatin	chromatin	NOUN
cana-659	35	11	accessibility	accessibility	NOUN
cana-659	35	12	,	,	PUNCT
cana-659	35	13	histone	histone	NOUN
cana-659	35	14	modification	modification	NOUN
cana-659	35	15	,	,	PUNCT
cana-659	35	16	and	and	CCONJ
cana-659	35	17	dna	dna	PROPN
cana-659	35	18	methylation	methylation	NOUN
cana-659	35	19	patterns	pattern	NOUN
cana-659	35	20	connected	connect	VERB
cana-659	35	21	to	to	ADP
cana-659	35	22	gene	gene	NOUN
cana-659	35	23	regulation	regulation	NOUN
cana-659	35	24	and	and	CCONJ
cana-659	35	25	cellular	cellular	ADJ
cana-659	35	26	development	development	NOUN
cana-659	35	27	.	.	PUNCT
cana-659	36	1	•	•	ADP
cana-659	36	2	to	to	PART
cana-659	36	3	establish	establish	VERB
cana-659	36	4	a	a	DET
cana-659	36	5	relationship	relationship	NOUN
cana-659	36	6	between	between	ADP
cana-659	36	7	genetic	genetic	ADJ
cana-659	36	8	differences	difference	NOUN
cana-659	36	9	and	and	CCONJ
cana-659	36	10	drug	drug	NOUN
cana-659	36	11	responses	response	NOUN
cana-659	36	12	,	,	PUNCT
cana-659	36	13	illness	illness	NOUN
cana-659	36	14	susceptibility	susceptibility	NOUN
cana-659	36	15	,	,	PUNCT
cana-659	36	16	and	and	CCONJ
cana-659	36	17	treatment	treatment	NOUN
cana-659	36	18	outcomes	outcome	NOUN
cana-659	36	19	.	.	PUNCT
cana-659	37	1	this	this	PRON
cana-659	37	2	will	will	AUX
cana-659	37	3	help	help	AUX
cana-659	37	4	identify	identify	VERB
cana-659	37	5	potential	potential	ADJ
cana-659	37	6	therapeutic	therapeutic	ADJ
cana-659	37	7	targets	target	NOUN
cana-659	37	8	,	,	PUNCT
cana-659	37	9	prospects	prospect	NOUN
cana-659	37	10	for	for	ADP
cana-659	37	11	drug	drug	NOUN
cana-659	37	12	repurposing	repurposing	NOUN
cana-659	37	13	,	,	PUNCT
cana-659	37	14	and	and	CCONJ
cana-659	37	15	individualized	individualized	ADJ
cana-659	37	16	treatment	treatment	NOUN
cana-659	37	17	plans	plan	NOUN
cana-659	37	18	.	.	PUNCT
cana-659	38	1	•	•	X
cana-659	38	2	to	to	PART
cana-659	38	3	provide	provide	VERB
cana-659	38	4	scalable	scalable	ADJ
cana-659	38	5	,	,	PUNCT
cana-659	38	6	interpretable	interpretable	ADJ
cana-659	38	7	,	,	PUNCT
cana-659	38	8	and	and	CCONJ
cana-659	38	9	resilient	resilient	ADJ
cana-659	38	10	machine	machine	NOUN
cana-659	38	11	learning	learn	VERB
cana-659	38	12	techniques	technique	NOUN
cana-659	38	13	so	so	SCONJ
cana-659	38	14	that	that	SCONJ
cana-659	38	15	biologists	biologist	NOUN
cana-659	38	16	and	and	CCONJ
cana-659	38	17	medical	medical	ADJ
cana-659	38	18	professionals	professional	NOUN
cana-659	38	19	may	may	AUX
cana-659	38	20	rely	rely	VERB
cana-659	38	21	on	on	ADP
cana-659	38	22	the	the	DET
cana-659	38	23	models	model	NOUN
cana-659	38	24	'	'	PART
cana-659	38	25	predictions	prediction	NOUN
cana-659	38	26	and	and	CCONJ
cana-659	38	27	successfully	successfully	ADV
cana-659	38	28	apply	apply	VERB
cana-659	38	29	them	they	PRON
cana-659	38	30	to	to	ADP
cana-659	38	31	their	their	PRON
cana-659	38	32	research	research	NOUN
cana-659	38	33	and	and	CCONJ
cana-659	38	34	clinical	clinical	ADJ
cana-659	38	35	practices	practice	NOUN
cana-659	38	36	.	.	PUNCT
cana-659	39	1	machine	machine	NOUN
cana-659	39	2	learning	learn	VERB
cana-659	39	3	techniques	technique	NOUN
cana-659	39	4	can	can	AUX
cana-659	39	5	transform	transform	VERB
cana-659	39	6	the	the	DET
cana-659	39	7	way	way	NOUN
cana-659	39	8	we	we	PRON
cana-659	39	9	study	study	VERB
cana-659	39	10	,	,	PUNCT
cana-659	39	11	interpret	interpret	VERB
cana-659	39	12	,	,	PUNCT
cana-659	39	13	and	and	CCONJ
cana-659	39	14	use	use	VERB
cana-659	39	15	genetic	genetic	ADJ
cana-659	39	16	data	datum	NOUN
cana-659	39	17	by	by	ADP
cana-659	39	18	achieving	achieve	VERB
cana-659	39	19	these	these	DET
cana-659	39	20	goals	goal	NOUN
cana-659	39	21	.	.	PUNCT
cana-659	40	1	this	this	PRON
cana-659	40	2	will	will	AUX
cana-659	40	3	lead	lead	VERB
cana-659	40	4	to	to	ADP
cana-659	40	5	breakthroughs	breakthrough	NOUN
cana-659	40	6	in	in	ADP
cana-659	40	7	the	the	DET
cana-659	40	8	fields	field	NOUN
cana-659	40	9	of	of	ADP
cana-659	40	10	biological	biological	ADJ
cana-659	40	11	understanding	understanding	NOUN
cana-659	40	12	,	,	PUNCT
cana-659	40	13	medical	medical	ADJ
cana-659	40	14	diagnostics	diagnostic	NOUN
cana-659	40	15	,	,	PUNCT
cana-659	40	16	and	and	CCONJ
cana-659	40	17	therapeutic	therapeutic	ADJ
cana-659	40	18	treatments	treatment	NOUN
cana-659	40	19	.	.	PUNCT
cana-659	41	1	our	our	PRON
cana-659	41	2	goal	goal	NOUN
cana-659	41	3	is	be	AUX
cana-659	41	4	to	to	PART
cana-659	41	5	classify	classify	VERB
cana-659	41	6	dna	dna	PROPN
cana-659	41	7	sequences	sequence	NOUN
cana-659	41	8	using	use	VERB
cana-659	41	9	several	several	ADJ
cana-659	41	10	machine	machine	NOUN
cana-659	41	11	learning	learning	NOUN
cana-659	41	12	(	(	PUNCT
cana-659	41	13	ml	ml	NOUN
cana-659	41	14	)	)	PUNCT
cana-659	41	15	approaches	approach	NOUN
cana-659	41	16	,	,	PUNCT
cana-659	41	17	including	include	VERB
cana-659	41	18	k	k	NOUN
cana-659	41	19	-	-	PUNCT
cana-659	41	20	nearest	near	ADJ
cana-659	41	21	neighbour	neighbour	NOUN
cana-659	41	22	,	,	PUNCT
cana-659	41	23	support	support	NOUN
cana-659	41	24	vector	vector	NOUN
cana-659	41	25	machine	machine	NOUN
cana-659	41	26	,	,	PUNCT
cana-659	41	27	random	random	ADJ
cana-659	41	28	forest	forest	NOUN
cana-659	41	29	,	,	PUNCT
cana-659	41	30	decision	decision	NOUN
cana-659	41	31	tree	tree	NOUN
cana-659	41	32	,	,	PUNCT
cana-659	41	33	logistic	logistic	ADJ
cana-659	41	34	regression	regression	NOUN
cana-659	41	35	,	,	PUNCT
cana-659	41	36	genetic	genetic	ADJ
cana-659	41	37	algorithm	algorithm	NOUN
cana-659	41	38	,	,	PUNCT
cana-659	41	39	ant	ant	ADJ
cana-659	41	40	colony	colony	NOUN
cana-659	41	41	,	,	PUNCT
cana-659	41	42	grid	grid	NOUN
cana-659	41	43	search	search	NOUN
cana-659	41	44	,	,	PUNCT
cana-659	41	45	gradient	gradient	NOUN
cana-659	41	46	boosting	boosting	NOUN
cana-659	41	47	,	,	PUNCT
cana-659	41	48	and	and	CCONJ
cana-659	41	49	hill	hill	NOUN
cana-659	41	50	climbing	climbing	NOUN
cana-659	41	51	methods	method	NOUN
cana-659	41	52	,	,	PUNCT
cana-659	41	53	and	and	CCONJ
cana-659	41	54	to	to	PART
cana-659	41	55	compare	compare	VERB
cana-659	41	56	them	they	PRON
cana-659	41	57	with	with	ADP
cana-659	41	58	one	one	NUM
cana-659	41	59	another	another	DET
cana-659	41	60	.	.	PUNCT
cana-659	42	1	three	three	NUM
cana-659	42	2	publicly	publicly	ADV
cana-659	42	3	available	available	ADJ
cana-659	42	4	datasets	dataset	NOUN
cana-659	42	5	are	be	AUX
cana-659	42	6	used	use	VERB
cana-659	42	7	to	to	PART
cana-659	42	8	gauge	gauge	VERB
cana-659	42	9	this	this	DET
cana-659	42	10	study	study	NOUN
cana-659	42	11	's	's	PART
cana-659	42	12	effectiveness	effectiveness	NOUN
cana-659	42	13	.	.	PUNCT
cana-659	43	1	to	to	PART
cana-659	43	2	evaluate	evaluate	VERB
cana-659	43	3	the	the	DET
cana-659	43	4	proposed	propose	VERB
cana-659	43	5	model	model	NOUN
cana-659	43	6	's	's	PART
cana-659	43	7	performance	performance	NOUN
cana-659	43	8	in	in	ADP
cana-659	43	9	terms	term	NOUN
cana-659	43	10	of	of	ADP
cana-659	43	11	f1	f1	NOUN
cana-659	43	12	-	-	PUNCT
cana-659	43	13	score	score	NOUN
cana-659	43	14	,	,	PUNCT
cana-659	43	15	accuracy	accuracy	NOUN
cana-659	43	16	,	,	PUNCT
cana-659	43	17	precision	precision	NOUN
cana-659	43	18	,	,	PUNCT
cana-659	43	19	recall	recall	VERB
cana-659	43	20	on	on	ADP
cana-659	43	21	occurrence	occurrence	NOUN
cana-659	43	22	of	of	ADP
cana-659	43	23	dna	dna	PROPN
cana-659	43	24	sequences	sequence	NOUN
cana-659	43	25	.	.	PUNCT
cana-659	44	1	communications	communication	NOUN
cana-659	44	2	on	on	ADP
cana-659	44	3	applied	apply	VERB
cana-659	44	4	nonlinear	nonlinear	ADJ
cana-659	44	5	analysis	analysis	NOUN
cana-659	44	6	issn	issn	NOUN
cana-659	44	7	:	:	PUNCT
cana-659	44	8	1074	1074	NUM
cana-659	44	9	-	-	PUNCT
cana-659	44	10	133x	133x	NUM
cana-659	44	11	vol	vol	NOUN
cana-659	44	12	31	31	NUM
cana-659	44	13	no	no	NOUN
cana-659	44	14	.	.	PUNCT
cana-659	45	1	2s	2s	NUM
cana-659	45	2	(	(	PUNCT
cana-659	45	3	2024	2024	NUM
cana-659	45	4	)	)	PUNCT
cana-659	45	5	438	438	NUM
cana-659	45	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	45	7	md	md	PROPN
cana-659	45	8	.	.	PROPN
cana-659	46	1	ahsan	ahsan	PROPN
cana-659	47	1	habib	habib	PROPN
cana-659	47	2	et.al	et.al	PROPN
cana-659	47	3	.	.	PUNCT
cana-659	48	1	[	[	X
cana-659	48	2	4	4	X
cana-659	48	3	]	]	PUNCT
cana-659	48	4	investigates	investigate	VERB
cana-659	48	5	the	the	DET
cana-659	48	6	utilization	utilization	NOUN
cana-659	48	7	of	of	ADP
cana-659	48	8	machine	machine	NOUN
cana-659	48	9	learning	learn	VERB
cana-659	48	10	techniques	technique	NOUN
cana-659	48	11	for	for	ADP
cana-659	48	12	classifying	classify	VERB
cana-659	48	13	dna	dna	PROPN
cana-659	48	14	sequences	sequence	NOUN
cana-659	48	15	,	,	PUNCT
cana-659	48	16	employing	employ	VERB
cana-659	48	17	algorithms	algorithm	NOUN
cana-659	48	18	like	like	ADP
cana-659	48	19	support	support	NOUN
cana-659	48	20	vector	vector	NOUN
cana-659	48	21	machines	machine	NOUN
cana-659	48	22	,	,	PUNCT
cana-659	48	23	random	random	ADJ
cana-659	48	24	forests	forest	NOUN
cana-659	48	25	,	,	PUNCT
cana-659	48	26	and	and	CCONJ
cana-659	48	27	neural	neural	ADJ
cana-659	48	28	networks	network	NOUN
cana-659	48	29	on	on	ADP
cana-659	48	30	a	a	DET
cana-659	48	31	specified	specify	VERB
cana-659	48	32	dataset	dataset	NOUN
cana-659	48	33	.	.	PUNCT
cana-659	49	1	the	the	DET
cana-659	49	2	study	study	NOUN
cana-659	49	3	meticulously	meticulously	ADV
cana-659	49	4	describes	describe	VERB
cana-659	49	5	the	the	DET
cana-659	49	6	dataset	dataset	NOUN
cana-659	49	7	's	's	PART
cana-659	49	8	origin	origin	NOUN
cana-659	49	9	and	and	CCONJ
cana-659	49	10	preprocessing	preprocessing	NOUN
cana-659	49	11	steps	step	NOUN
cana-659	49	12	,	,	PUNCT
cana-659	49	13	evaluating	evaluate	VERB
cana-659	49	14	model	model	NOUN
cana-659	49	15	performance	performance	NOUN
cana-659	49	16	using	use	VERB
cana-659	49	17	key	key	ADJ
cana-659	49	18	metrics	metric	NOUN
cana-659	49	19	such	such	ADJ
cana-659	49	20	as	as	ADP
cana-659	49	21	accuracy	accuracy	NOUN
cana-659	49	22	,	,	PUNCT
cana-659	49	23	precision	precision	NOUN
cana-659	49	24	,	,	PUNCT
cana-659	49	25	recall	recall	NOUN
cana-659	49	26	,	,	PUNCT
cana-659	49	27	and	and	CCONJ
cana-659	49	28	f1	f1	NOUN
cana-659	49	29	-	-	PUNCT
cana-659	49	30	score	score	NOUN
cana-659	49	31	.	.	PUNCT
cana-659	50	1	through	through	ADP
cana-659	50	2	a	a	DET
cana-659	50	3	comparative	comparative	ADJ
cana-659	50	4	analysis	analysis	NOUN
cana-659	50	5	,	,	PUNCT
cana-659	50	6	the	the	DET
cana-659	50	7	strengths	strength	NOUN
cana-659	50	8	and	and	CCONJ
cana-659	50	9	limitations	limitation	NOUN
cana-659	50	10	of	of	ADP
cana-659	50	11	each	each	DET
cana-659	50	12	algorithm	algorithm	NOUN
cana-659	50	13	in	in	ADP
cana-659	50	14	the	the	DET
cana-659	50	15	context	context	NOUN
cana-659	50	16	of	of	ADP
cana-659	50	17	dna	dna	PROPN
cana-659	50	18	sequence	sequence	NOUN
cana-659	50	19	classification	classification	NOUN
cana-659	50	20	are	be	AUX
cana-659	50	21	elucidated	elucidate	VERB
cana-659	50	22	.	.	PUNCT
cana-659	51	1	the	the	DET
cana-659	51	2	findings	finding	NOUN
cana-659	51	3	offer	offer	VERB
cana-659	51	4	potential	potential	ADJ
cana-659	51	5	biological	biological	ADJ
cana-659	51	6	insights	insight	NOUN
cana-659	51	7	,	,	PUNCT
cana-659	51	8	and	and	CCONJ
cana-659	51	9	methodological	methodological	ADJ
cana-659	51	10	considerations	consideration	NOUN
cana-659	51	11	,	,	PUNCT
cana-659	51	12	encompassing	encompass	VERB
cana-659	51	13	feature	feature	NOUN
cana-659	51	14	extraction	extraction	NOUN
cana-659	51	15	and	and	CCONJ
cana-659	51	16	validation	validation	NOUN
cana-659	51	17	processes	process	NOUN
cana-659	51	18	,	,	PUNCT
cana-659	51	19	are	be	AUX
cana-659	51	20	highlighted	highlight	VERB
cana-659	51	21	.	.	PUNCT
cana-659	52	1	concluding	conclude	VERB
cana-659	52	2	with	with	ADP
cana-659	52	3	suggestions	suggestion	NOUN
cana-659	52	4	for	for	ADP
cana-659	52	5	future	future	ADJ
cana-659	52	6	research	research	NOUN
cana-659	52	7	,	,	PUNCT
cana-659	52	8	the	the	DET
cana-659	52	9	preprint	preprint	NOUN
cana-659	52	10	contributes	contribute	VERB
cana-659	52	11	to	to	ADP
cana-659	52	12	the	the	DET
cana-659	52	13	evolving	evolve	VERB
cana-659	52	14	landscape	landscape	NOUN
cana-659	52	15	of	of	ADP
cana-659	52	16	machine	machine	NOUN
cana-659	52	17	learning	learn	VERB
cana-659	52	18	applications	application	NOUN
cana-659	52	19	in	in	ADP
cana-659	52	20	genomics	genomic	NOUN
cana-659	52	21	.	.	PUNCT
cana-659	53	1	osisanwo	osisanwo	PROPN
cana-659	53	2	f.y	f.y	PROPN
cana-659	53	3	.	.	PROPN
cana-659	53	4	et.al	et.al	PROPN
cana-659	53	5	.	.	PUNCT
cana-659	54	1	[	[	X
cana-659	54	2	5	5	NUM
cana-659	54	3	]	]	PUNCT
cana-659	54	4	compares	compare	VERB
cana-659	54	5	various	various	ADJ
cana-659	54	6	supervised	supervised	ADJ
cana-659	54	7	machine	machine	NOUN
cana-659	54	8	learning	learn	VERB
cana-659	54	9	algorithms	algorithm	NOUN
cana-659	54	10	for	for	ADP
cana-659	54	11	classification	classification	NOUN
cana-659	54	12	.	.	PUNCT
cana-659	55	1	the	the	DET
cana-659	55	2	authors	author	NOUN
cana-659	55	3	detail	detail	VERB
cana-659	55	4	the	the	DET
cana-659	55	5	principles	principle	NOUN
cana-659	55	6	and	and	CCONJ
cana-659	55	7	application	application	NOUN
cana-659	55	8	of	of	ADP
cana-659	55	9	algorithms	algorithm	NOUN
cana-659	55	10	such	such	ADJ
cana-659	55	11	as	as	ADP
cana-659	55	12	decision	decision	NOUN
cana-659	55	13	trees	tree	NOUN
cana-659	55	14	,	,	PUNCT
cana-659	55	15	support	support	NOUN
cana-659	55	16	vector	vector	NOUN
cana-659	55	17	machines	machine	NOUN
cana-659	55	18	,	,	PUNCT
cana-659	55	19	random	random	ADJ
cana-659	55	20	forests	forest	NOUN
cana-659	55	21	,	,	PUNCT
cana-659	55	22	and	and	CCONJ
cana-659	55	23	others	other	NOUN
cana-659	55	24	.	.	PUNCT
cana-659	56	1	using	use	VERB
cana-659	56	2	a	a	DET
cana-659	56	3	specified	specified	ADJ
cana-659	56	4	dataset	dataset	NOUN
cana-659	56	5	,	,	PUNCT
cana-659	56	6	they	they	PRON
cana-659	56	7	establish	establish	VERB
cana-659	56	8	a	a	DET
cana-659	56	9	framework	framework	NOUN
cana-659	56	10	for	for	ADP
cana-659	56	11	comparing	compare	VERB
cana-659	56	12	algorithmic	algorithmic	ADJ
cana-659	56	13	performance	performance	NOUN
cana-659	56	14	based	base	VERB
cana-659	56	15	on	on	ADP
cana-659	56	16	metrics	metric	NOUN
cana-659	56	17	like	like	ADP
cana-659	56	18	accuracy	accuracy	NOUN
cana-659	56	19	,	,	PUNCT
cana-659	56	20	precision	precision	NOUN
cana-659	56	21	,	,	PUNCT
cana-659	56	22	recall	recall	NOUN
cana-659	56	23	,	,	PUNCT
cana-659	56	24	and	and	CCONJ
cana-659	56	25	f1score	f1score	NOUN
cana-659	56	26	.	.	PUNCT
cana-659	57	1	the	the	DET
cana-659	57	2	results	result	NOUN
cana-659	57	3	and	and	CCONJ
cana-659	57	4	comparative	comparative	ADJ
cana-659	57	5	analysis	analysis	NOUN
cana-659	57	6	unveil	unveil	ADJ
cana-659	57	7	the	the	DET
cana-659	57	8	strengths	strength	NOUN
cana-659	57	9	and	and	CCONJ
cana-659	57	10	weaknesses	weakness	NOUN
cana-659	57	11	of	of	ADP
cana-659	57	12	each	each	DET
cana-659	57	13	algorithm	algorithm	NOUN
cana-659	57	14	,	,	PUNCT
cana-659	57	15	providing	provide	VERB
cana-659	57	16	insights	insight	NOUN
cana-659	57	17	into	into	ADP
cana-659	57	18	their	their	PRON
cana-659	57	19	practical	practical	ADJ
cana-659	57	20	implications	implication	NOUN
cana-659	57	21	.	.	PUNCT
cana-659	58	1	the	the	DET
cana-659	58	2	paper	paper	NOUN
cana-659	58	3	concludes	conclude	VERB
cana-659	58	4	with	with	ADP
cana-659	58	5	a	a	DET
cana-659	58	6	summary	summary	NOUN
cana-659	58	7	of	of	ADP
cana-659	58	8	key	key	ADJ
cana-659	58	9	findings	finding	NOUN
cana-659	58	10	and	and	CCONJ
cana-659	58	11	potential	potential	ADJ
cana-659	58	12	avenues	avenue	NOUN
cana-659	58	13	for	for	ADP
cana-659	58	14	future	future	ADJ
cana-659	58	15	research	research	NOUN
cana-659	58	16	in	in	ADP
cana-659	58	17	the	the	DET
cana-659	58	18	domain	domain	NOUN
cana-659	58	19	of	of	ADP
cana-659	58	20	supervised	supervised	ADJ
cana-659	58	21	machine	machine	NOUN
cana-659	58	22	learning	learn	VERB
cana-659	58	23	algorithms	algorithm	NOUN
cana-659	58	24	for	for	ADP
cana-659	58	25	classification	classification	NOUN
cana-659	58	26	.	.	PUNCT
cana-659	59	1	varda	varda	PROPN
cana-659	59	2	venkata	venkata	PROPN
cana-659	59	3	sai	sai	PROPN
cana-659	59	4	dilip	dilip	PROPN
cana-659	59	5	et	et	PROPN
cana-659	59	6	.	.	PUNCT
cana-659	60	1	al	al	PROPN
cana-659	60	2	.	.	PUNCT
cana-659	61	1	[	[	X
cana-659	61	2	6	6	NUM
cana-659	61	3	]	]	PUNCT
cana-659	61	4	compares	compare	VERB
cana-659	61	5	machine	machine	NOUN
cana-659	61	6	learning	learning	NOUN
cana-659	61	7	(	(	PUNCT
cana-659	61	8	decision	decision	NOUN
cana-659	61	9	trees	tree	NOUN
cana-659	61	10	,	,	PUNCT
cana-659	61	11	random	random	ADJ
cana-659	61	12	forest	forest	NOUN
cana-659	61	13	,	,	PUNCT
cana-659	61	14	naive	naive	ADJ
cana-659	61	15	bayes	baye	NOUN
cana-659	61	16	)	)	PUNCT
cana-659	61	17	and	and	CCONJ
cana-659	61	18	deep	deep	ADJ
cana-659	61	19	learning	learning	NOUN
cana-659	61	20	(	(	PUNCT
cana-659	61	21	transfer	transfer	NOUN
cana-659	61	22	learning	learning	NOUN
cana-659	61	23	,	,	PUNCT
cana-659	61	24	cnn	cnn	PROPN
cana-659	61	25	)	)	PUNCT
cana-659	61	26	algorithms	algorithm	NOUN
cana-659	61	27	for	for	ADP
cana-659	61	28	dna	dna	NOUN
cana-659	61	29	sequencing	sequencing	NOUN
cana-659	61	30	.	.	PUNCT
cana-659	62	1	the	the	DET
cana-659	62	2	objective	objective	NOUN
cana-659	62	3	is	be	AUX
cana-659	62	4	to	to	PART
cana-659	62	5	enhance	enhance	VERB
cana-659	62	6	prediction	prediction	NOUN
cana-659	62	7	models	model	NOUN
cana-659	62	8	in	in	ADP
cana-659	62	9	dna	dna	PROPN
cana-659	62	10	research	research	NOUN
cana-659	62	11	.	.	PUNCT
cana-659	63	1	the	the	DET
cana-659	63	2	chosen	choose	VERB
cana-659	63	3	models	model	NOUN
cana-659	63	4	,	,	PUNCT
cana-659	63	5	including	include	VERB
cana-659	63	6	decision	decision	NOUN
cana-659	63	7	tree	tree	NOUN
cana-659	63	8	,	,	PUNCT
cana-659	63	9	random	random	ADJ
cana-659	63	10	forest	forest	NOUN
cana-659	63	11	,	,	PUNCT
cana-659	63	12	naive	naive	ADJ
cana-659	63	13	bayes	bayes	NOUN
cana-659	63	14	,	,	PUNCT
cana-659	63	15	cnn	cnn	PROPN
cana-659	63	16	,	,	PUNCT
cana-659	63	17	and	and	CCONJ
cana-659	63	18	transfer	transfer	NOUN
cana-659	63	19	learning	learning	NOUN
cana-659	63	20	,	,	PUNCT
cana-659	63	21	exhibit	exhibit	VERB
cana-659	63	22	high	high	ADJ
cana-659	63	23	accuracy	accuracy	NOUN
cana-659	63	24	.	.	PUNCT
cana-659	64	1	naive	naive	ADJ
cana-659	64	2	bayes	bayes	PROPN
cana-659	64	3	achieves	achieve	VERB
cana-659	64	4	98.00	98.00	NUM
cana-659	64	5	%	%	NOUN
cana-659	64	6	accuracy	accuracy	NOUN
cana-659	64	7	in	in	ADP
cana-659	64	8	machine	machine	NOUN
cana-659	64	9	learning	learning	NOUN
cana-659	64	10	,	,	PUNCT
cana-659	64	11	while	while	SCONJ
cana-659	64	12	transfer	transfer	NOUN
cana-659	64	13	learning	learning	NOUN
cana-659	64	14	attains	attain	NOUN
cana-659	64	15	94.57	94.57	NUM
cana-659	64	16	%	%	NOUN
cana-659	64	17	accuracy	accuracy	NOUN
cana-659	64	18	in	in	ADP
cana-659	64	19	deep	deep	ADJ
cana-659	64	20	learning	learning	NOUN
cana-659	64	21	.	.	PUNCT
cana-659	65	1	these	these	DET
cana-659	65	2	findings	finding	NOUN
cana-659	65	3	emphasize	emphasize	VERB
cana-659	65	4	the	the	DET
cana-659	65	5	efficiency	efficiency	NOUN
cana-659	65	6	of	of	ADP
cana-659	65	7	these	these	DET
cana-659	65	8	models	model	NOUN
cana-659	65	9	in	in	ADP
cana-659	65	10	optimizing	optimize	VERB
cana-659	65	11	dna	dna	NOUN
cana-659	65	12	research	research	NOUN
cana-659	65	13	predictions	prediction	NOUN
cana-659	65	14	across	across	ADP
cana-659	65	15	medical	medical	ADJ
cana-659	65	16	domains	domain	NOUN
cana-659	65	17	.	.	PUNCT
cana-659	66	1	ahmed	ahmed	PROPN
cana-659	66	2	el	el	PROPN
cana-659	66	3	-	-	PUNCT
cana-659	66	4	tohamy	tohamy	ADJ
cana-659	66	5	et	et	NOUN
cana-659	66	6	.	.	PUNCT
cana-659	67	1	al	al	PROPN
cana-659	67	2	.	.	PUNCT
cana-659	68	1	[	[	X
cana-659	68	2	9	9	NUM
cana-659	68	3	]	]	PUNCT
cana-659	68	4	presents	present	VERB
cana-659	68	5	a	a	DET
cana-659	68	6	novel	novel	ADJ
cana-659	68	7	approach	approach	NOUN
cana-659	68	8	for	for	ADP
cana-659	68	9	viral	viral	ADJ
cana-659	68	10	dna	dna	NOUN
cana-659	68	11	sequence	sequence	NOUN
cana-659	68	12	classification	classification	NOUN
cana-659	68	13	by	by	ADP
cana-659	68	14	integrating	integrate	VERB
cana-659	68	15	deep	deep	ADJ
cana-659	68	16	learning	learning	NOUN
cana-659	68	17	,	,	PUNCT
cana-659	68	18	possibly	possibly	ADV
cana-659	68	19	neural	neural	ADJ
cana-659	68	20	networks	network	NOUN
cana-659	68	21	,	,	PUNCT
cana-659	68	22	with	with	ADP
cana-659	68	23	a	a	DET
cana-659	68	24	genetic	genetic	ADJ
cana-659	68	25	algorithm	algorithm	NOUN
cana-659	68	26	.	.	PUNCT
cana-659	69	1	the	the	DET
cana-659	69	2	study	study	NOUN
cana-659	69	3	focuses	focus	VERB
cana-659	69	4	on	on	ADP
cana-659	69	5	the	the	DET
cana-659	69	6	classification	classification	NOUN
cana-659	69	7	of	of	ADP
cana-659	69	8	viral	viral	ADJ
cana-659	69	9	dna	dna	NOUN
cana-659	69	10	sequences	sequence	NOUN
cana-659	69	11	,	,	PUNCT
cana-659	69	12	providing	provide	VERB
cana-659	69	13	insights	insight	NOUN
cana-659	69	14	into	into	ADP
cana-659	69	15	the	the	DET
cana-659	69	16	methodology	methodology	NOUN
cana-659	69	17	,	,	PUNCT
cana-659	69	18	dataset	dataset	NOUN
cana-659	69	19	characteristics	characteristic	NOUN
cana-659	69	20	,	,	PUNCT
cana-659	69	21	and	and	CCONJ
cana-659	69	22	performance	performance	NOUN
cana-659	69	23	evaluation	evaluation	NOUN
cana-659	69	24	metrics	metric	NOUN
cana-659	69	25	.	.	PUNCT
cana-659	70	1	the	the	DET
cana-659	70	2	results	result	NOUN
cana-659	70	3	showcase	showcase	VERB
cana-659	70	4	the	the	DET
cana-659	70	5	effectiveness	effectiveness	NOUN
cana-659	70	6	of	of	ADP
cana-659	70	7	the	the	DET
cana-659	70	8	proposed	propose	VERB
cana-659	70	9	deep	deep	ADJ
cana-659	70	10	learning	learning	NOUN
cana-659	70	11	approach	approach	NOUN
cana-659	70	12	,	,	PUNCT
cana-659	70	13	potentially	potentially	ADV
cana-659	70	14	offering	offer	VERB
cana-659	70	15	advancements	advancement	NOUN
cana-659	70	16	in	in	ADP
cana-659	70	17	genomics	genomics	NOUN
cana-659	70	18	and	and	CCONJ
cana-659	70	19	virology	virology	NOUN
cana-659	70	20	research	research	NOUN
cana-659	70	21	.	.	PUNCT
cana-659	71	1	the	the	DET
cana-659	71	2	paper	paper	NOUN
cana-659	71	3	concludes	conclude	VERB
cana-659	71	4	by	by	ADP
cana-659	71	5	suggesting	suggest	VERB
cana-659	71	6	future	future	ADJ
cana-659	71	7	research	research	NOUN
cana-659	71	8	directions	direction	NOUN
cana-659	71	9	,	,	PUNCT
cana-659	71	10	emphasizing	emphasize	VERB
cana-659	71	11	the	the	DET
cana-659	71	12	significance	significance	NOUN
cana-659	71	13	of	of	ADP
cana-659	71	14	this	this	DET
cana-659	71	15	integrated	integrate	VERB
cana-659	71	16	approach	approach	NOUN
cana-659	71	17	in	in	ADP
cana-659	71	18	viral	viral	ADJ
cana-659	71	19	dna	dna	NOUN
cana-659	71	20	sequence	sequence	NOUN
cana-659	71	21	classification	classification	NOUN
cana-659	71	22	.	.	PUNCT
cana-659	72	1	belal	belal	PROPN
cana-659	72	2	a.	a.	PROPN
cana-659	72	3	hamed	hamed	PROPN
cana-659	72	4	et	et	PROPN
cana-659	72	5	.	.	PUNCT
cana-659	73	1	al	al	PROPN
cana-659	73	2	.	.	PUNCT
cana-659	74	1	[	[	X
cana-659	74	2	17	17	NUM
cana-659	74	3	]	]	PUNCT
cana-659	74	4	focuses	focus	VERB
cana-659	74	5	on	on	ADP
cana-659	74	6	enhancing	enhance	VERB
cana-659	74	7	classification	classification	NOUN
cana-659	74	8	efficiency	efficiency	NOUN
cana-659	74	9	through	through	ADP
cana-659	74	10	machine	machine	NOUN
cana-659	74	11	learning	learn	VERB
cana-659	74	12	techniques	technique	NOUN
cana-659	74	13	for	for	ADP
cana-659	74	14	pattern	pattern	NOUN
cana-659	74	15	matching	matching	NOUN
cana-659	74	16	.	.	PUNCT
cana-659	75	1	the	the	DET
cana-659	75	2	study	study	NOUN
cana-659	75	3	likely	likely	ADV
cana-659	75	4	delves	delve	VERB
cana-659	75	5	into	into	ADP
cana-659	75	6	the	the	DET
cana-659	75	7	selection	selection	NOUN
cana-659	75	8	and	and	CCONJ
cana-659	75	9	optimization	optimization	NOUN
cana-659	75	10	of	of	ADP
cana-659	75	11	specific	specific	ADJ
cana-659	75	12	algorithms	algorithm	NOUN
cana-659	75	13	,	,	PUNCT
cana-659	75	14	offering	offer	VERB
cana-659	75	15	insights	insight	NOUN
cana-659	75	16	into	into	ADP
cana-659	75	17	the	the	DET
cana-659	75	18	methodology	methodology	NOUN
cana-659	75	19	,	,	PUNCT
cana-659	75	20	dataset	dataset	NOUN
cana-659	75	21	characteristics	characteristic	NOUN
cana-659	75	22	,	,	PUNCT
cana-659	75	23	and	and	CCONJ
cana-659	75	24	performance	performance	NOUN
cana-659	75	25	metrics	metric	NOUN
cana-659	75	26	.	.	PUNCT
cana-659	76	1	results	result	NOUN
cana-659	76	2	are	be	AUX
cana-659	76	3	expected	expect	VERB
cana-659	76	4	to	to	PART
cana-659	76	5	showcase	showcase	VERB
cana-659	76	6	the	the	DET
cana-659	76	7	effectiveness	effectiveness	NOUN
cana-659	76	8	of	of	ADP
cana-659	76	9	the	the	DET
cana-659	76	10	chosen	choose	VERB
cana-659	76	11	machine	machine	NOUN
cana-659	76	12	learning	learning	NOUN
cana-659	76	13	methods	method	NOUN
cana-659	76	14	in	in	ADP
cana-659	76	15	achieving	achieve	VERB
cana-659	76	16	improved	improved	ADJ
cana-659	76	17	classification	classification	NOUN
cana-659	76	18	efficiency	efficiency	NOUN
cana-659	76	19	.	.	PUNCT
cana-659	77	1	the	the	DET
cana-659	77	2	paper	paper	NOUN
cana-659	77	3	may	may	AUX
cana-659	77	4	conclude	conclude	VERB
cana-659	77	5	with	with	ADP
cana-659	77	6	implications	implication	NOUN
cana-659	77	7	for	for	ADP
cana-659	77	8	pattern	pattern	NOUN
cana-659	77	9	matching	matching	NOUN
cana-659	77	10	applications	application	NOUN
cana-659	77	11	and	and	CCONJ
cana-659	77	12	potential	potential	ADJ
cana-659	77	13	directions	direction	NOUN
cana-659	77	14	for	for	ADP
cana-659	77	15	future	future	ADJ
cana-659	77	16	research	research	NOUN
cana-659	77	17	in	in	ADP
cana-659	77	18	the	the	DET
cana-659	77	19	domain	domain	NOUN
cana-659	77	20	of	of	ADP
cana-659	77	21	machine	machine	NOUN
cana-659	77	22	learning	learn	VERB
cana-659	77	23	optimization	optimization	NOUN
cana-659	77	24	for	for	ADP
cana-659	77	25	efficient	efficient	ADJ
cana-659	77	26	pattern	pattern	NOUN
cana-659	77	27	recognition	recognition	NOUN
cana-659	77	28	.	.	PUNCT
cana-659	78	1	communications	communication	NOUN
cana-659	78	2	on	on	ADP
cana-659	78	3	applied	apply	VERB
cana-659	78	4	nonlinear	nonlinear	ADJ
cana-659	78	5	analysis	analysis	NOUN
cana-659	78	6	issn	issn	NOUN
cana-659	78	7	:	:	PUNCT
cana-659	78	8	1074	1074	NUM
cana-659	78	9	-	-	PUNCT
cana-659	78	10	133x	133x	NUM
cana-659	78	11	vol	vol	NOUN
cana-659	78	12	31	31	NUM
cana-659	78	13	no	no	NOUN
cana-659	78	14	.	.	PUNCT
cana-659	79	1	2s	2s	NUM
cana-659	79	2	(	(	PUNCT
cana-659	79	3	2024	2024	NUM
cana-659	79	4	)	)	PUNCT
cana-659	79	5	439	439	NUM
cana-659	79	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	79	7	2.data	2.data	NUM
cana-659	79	8	and	and	CCONJ
cana-659	79	9	different	different	ADJ
cana-659	79	10	learning	learning	NOUN
cana-659	79	11	types	type	NOUN
cana-659	79	12	in	in	ADP
cana-659	79	13	ml	ml	ADP
cana-659	79	14	the	the	DET
cana-659	79	15	availability	availability	NOUN
cana-659	79	16	of	of	ADP
cana-659	79	17	data	datum	NOUN
cana-659	79	18	is	be	AUX
cana-659	79	19	seen	see	VERB
cana-659	79	20	to	to	PART
cana-659	79	21	be	be	AUX
cana-659	79	22	essential	essential	ADJ
cana-659	79	23	to	to	ADP
cana-659	79	24	building	build	VERB
cana-659	79	25	a	a	DET
cana-659	79	26	machine	machine	NOUN
cana-659	79	27	learning	learning	NOUN
cana-659	79	28	model	model	NOUN
cana-659	79	29	or	or	CCONJ
cana-659	79	30	data	data	NOUN
cana-659	79	31	-	-	PUNCT
cana-659	79	32	driven	drive	VERB
cana-659	79	33	real	real	ADJ
cana-659	79	34	-	-	PUNCT
cana-659	79	35	world	world	NOUN
cana-659	79	36	systems	system	NOUN
cana-659	79	37	.	.	PUNCT
cana-659	80	1	data	datum	NOUN
cana-659	80	2	can	can	AUX
cana-659	80	3	be	be	AUX
cana-659	80	4	in	in	ADP
cana-659	80	5	many	many	ADJ
cana-659	80	6	different	different	ADJ
cana-659	80	7	forms	form	NOUN
cana-659	80	8	,	,	PUNCT
cana-659	80	9	including	include	VERB
cana-659	80	10	structured	structure	VERB
cana-659	80	11	,	,	PUNCT
cana-659	80	12	semi	semi	ADJ
cana-659	80	13	-	-	ADJ
cana-659	80	14	structured	structured	ADJ
cana-659	80	15	,	,	PUNCT
cana-659	80	16	unstructured	unstructured	ADJ
cana-659	80	17	,	,	PUNCT
cana-659	80	18	and	and	CCONJ
cana-659	80	19	metadata	metadata	NOUN
cana-659	80	20	,	,	PUNCT
cana-659	80	21	which	which	PRON
cana-659	80	22	is	be	AUX
cana-659	80	23	another	another	DET
cana-659	80	24	type	type	NOUN
cana-659	80	25	that	that	PRON
cana-659	80	26	often	often	ADV
cana-659	80	27	includes	include	VERB
cana-659	80	28	data	datum	NOUN
cana-659	80	29	about	about	ADP
cana-659	80	30	the	the	DET
cana-659	80	31	data	datum	NOUN
cana-659	80	32	.	.	PUNCT
cana-659	81	1	we	we	PRON
cana-659	81	2	go	go	VERB
cana-659	81	3	through	through	ADP
cana-659	81	4	several	several	ADJ
cana-659	81	5	kinds	kind	NOUN
cana-659	81	6	of	of	ADP
cana-659	81	7	data	datum	NOUN
cana-659	81	8	in	in	ADP
cana-659	81	9	brief	brief	ADJ
cana-659	81	10	,	,	PUNCT
cana-659	81	11	structured	structured	ADJ
cana-659	81	12	i.e.	i.e.	X
cana-659	81	13	it	it	PRON
cana-659	81	14	is	be	AUX
cana-659	81	15	highly	highly	ADV
cana-659	81	16	ordered	order	VERB
cana-659	81	17	,	,	PUNCT
cana-659	81	18	readily	readily	ADV
cana-659	81	19	accessible	accessible	ADJ
cana-659	81	20	,	,	PUNCT
cana-659	81	21	and	and	CCONJ
cana-659	81	22	adheres	adhere	VERB
cana-659	81	23	to	to	ADP
cana-659	81	24	a	a	DET
cana-659	81	25	data	data	NOUN
cana-659	81	26	model	model	NOUN
cana-659	81	27	with	with	ADP
cana-659	81	28	a	a	DET
cana-659	81	29	clearly	clearly	ADV
cana-659	81	30	defined	define	VERB
cana-659	81	31	structure	structure	NOUN
cana-659	81	32	and	and	CCONJ
cana-659	81	33	are	be	AUX
cana-659	81	34	usually	usually	ADV
cana-659	81	35	kept	keep	VERB
cana-659	81	36	in	in	ADP
cana-659	81	37	tabular	tabular	NOUN
cana-659	81	38	form	form	NOUN
cana-659	81	39	in	in	ADP
cana-659	81	40	relational	relational	ADJ
cana-659	81	41	databases	database	NOUN
cana-659	81	42	.	.	PUNCT
cana-659	82	1	unstructured	unstructured	ADJ
cana-659	82	2	i.e.	i.e.	X
cana-659	82	3	no	no	DET
cana-659	82	4	predetermined	predetermined	ADJ
cana-659	82	5	structure	structure	NOUN
cana-659	82	6	or	or	CCONJ
cana-659	82	7	format	format	NOUN
cana-659	82	8	,	,	PUNCT
cana-659	82	9	which	which	PRON
cana-659	82	10	makes	make	VERB
cana-659	82	11	it	it	PRON
cana-659	82	12	considerably	considerably	ADV
cana-659	82	13	more	more	ADV
cana-659	82	14	challenging	challenging	ADJ
cana-659	82	15	to	to	PART
cana-659	82	16	collect	collect	VERB
cana-659	82	17	,	,	PUNCT
cana-659	82	18	handle	handle	VERB
cana-659	82	19	and	and	CCONJ
cana-659	82	20	analyze	analyze	VERB
cana-659	82	21	,	,	PUNCT
cana-659	82	22	including	include	VERB
cana-659	82	23	text	text	NOUN
cana-659	82	24	and	and	CCONJ
cana-659	82	25	multimedia	multimedia	NOUN
cana-659	82	26	content	content	NOUN
cana-659	82	27	mostly	mostly	ADV
cana-659	82	28	.	.	PUNCT
cana-659	83	1	semi	semi	ADJ
cana-659	83	2	-	-	ADJ
cana-659	83	3	structured	structured	ADJ
cana-659	83	4	i.e.	i.e.	X
cana-659	83	5	although	although	SCONJ
cana-659	83	6	data	datum	NOUN
cana-659	83	7	is	be	AUX
cana-659	83	8	not	not	PART
cana-659	83	9	kept	keep	VERB
cana-659	83	10	in	in	ADP
cana-659	83	11	a	a	DET
cana-659	83	12	relational	relational	ADJ
cana-659	83	13	database	database	NOUN
cana-659	83	14	in	in	ADP
cana-659	83	15	the	the	DET
cana-659	83	16	same	same	ADJ
cana-659	83	17	way	way	NOUN
cana-659	83	18	as	as	ADP
cana-659	83	19	structured	structured	ADJ
cana-659	83	20	data	datum	NOUN
cana-659	83	21	,	,	PUNCT
cana-659	83	22	it	it	PRON
cana-659	83	23	does	do	AUX
cana-659	83	24	have	have	VERB
cana-659	83	25	some	some	DET
cana-659	83	26	organizing	organize	VERB
cana-659	83	27	characteristics	characteristic	NOUN
cana-659	83	28	that	that	PRON
cana-659	83	29	facilitate	facilitate	VERB
cana-659	83	30	analysis	analysis	NOUN
cana-659	83	31	.	.	PUNCT
cana-659	84	1	metadata	metadata	PROPN
cana-659	84	2	i.e.	i.e.	X
cana-659	84	3	data	datum	NOUN
cana-659	84	4	are	be	AUX
cana-659	84	5	the	the	DET
cana-659	84	6	materials	material	NOUN
cana-659	84	7	that	that	PRON
cana-659	84	8	can	can	AUX
cana-659	84	9	be	be	AUX
cana-659	84	10	used	use	VERB
cana-659	84	11	to	to	PART
cana-659	84	12	classify	classify	VERB
cana-659	84	13	,	,	PUNCT
cana-659	84	14	quantify	quantify	ADJ
cana-659	84	15	,	,	PUNCT
cana-659	84	16	or	or	CCONJ
cana-659	84	17	even	even	ADV
cana-659	84	18	document	document	VERB
cana-659	84	19	something	something	PRON
cana-659	84	20	in	in	ADP
cana-659	84	21	relation	relation	NOUN
cana-659	84	22	to	to	ADP
cana-659	84	23	an	an	DET
cana-659	84	24	organization	organization	NOUN
cana-659	84	25	's	's	PART
cana-659	84	26	data	datum	NOUN
cana-659	84	27	attributes	attribute	VERB
cana-659	84	28	.	.	PUNCT
cana-659	85	1	however	however	ADV
cana-659	85	2	,	,	PUNCT
cana-659	85	3	metadata	metadata	PROPN
cana-659	85	4	gives	give	VERB
cana-659	85	5	data	datum	NOUN
cana-659	85	6	users	user	NOUN
cana-659	85	7	greater	great	ADJ
cana-659	85	8	context	context	NOUN
cana-659	85	9	for	for	ADP
cana-659	85	10	the	the	DET
cana-659	85	11	pertinent	pertinent	ADJ
cana-659	85	12	data	data	NOUN
cana-659	85	13	material	material	NOUN
cana-659	85	14	by	by	ADP
cana-659	85	15	describing	describe	VERB
cana-659	85	16	it	it	PRON
cana-659	85	17	[	[	X
cana-659	85	18	1	1	NUM
cana-659	85	19	]	]	PUNCT
cana-659	85	20	.	.	PUNCT
cana-659	86	1	2.1	2.1	NUM
cana-659	86	2	.	.	PUNCT
cana-659	87	1	learning	learn	VERB
cana-659	87	2	types	type	NOUN
cana-659	87	3	in	in	ADP
cana-659	87	4	ml	ml	NOUN
cana-659	87	5	learning	learn	VERB
cana-659	87	6	is	be	AUX
cana-659	87	7	the	the	DET
cana-659	87	8	process	process	NOUN
cana-659	87	9	of	of	ADP
cana-659	87	10	acquiring	acquire	VERB
cana-659	87	11	knowledge	knowledge	NOUN
cana-659	87	12	;	;	PUNCT
cana-659	87	13	individuals	individual	NOUN
cana-659	87	14	naturally	naturally	ADV
cana-659	87	15	pick	pick	VERB
cana-659	87	16	up	up	ADP
cana-659	87	17	knowledge	knowledge	NOUN
cana-659	87	18	from	from	ADP
cana-659	87	19	their	their	PRON
cana-659	87	20	experiences	experience	NOUN
cana-659	87	21	due	due	ADP
cana-659	87	22	to	to	ADP
cana-659	87	23	their	their	PRON
cana-659	87	24	capacity	capacity	NOUN
cana-659	87	25	for	for	ADP
cana-659	87	26	reason	reason	NOUN
cana-659	87	27	.	.	PUNCT
cana-659	88	1	conversely	conversely	ADV
cana-659	88	2	,	,	PUNCT
cana-659	88	3	conventional	conventional	ADJ
cana-659	88	4	computers	computer	NOUN
cana-659	88	5	rely	rely	VERB
cana-659	88	6	on	on	ADP
cana-659	88	7	algorithms	algorithm	NOUN
cana-659	88	8	for	for	ADP
cana-659	88	9	learning	learn	VERB
cana-659	88	10	.	.	PUNCT
cana-659	89	1	based	base	VERB
cana-659	89	2	on	on	ADP
cana-659	89	3	how	how	SCONJ
cana-659	89	4	they	they	PRON
cana-659	89	5	approach	approach	VERB
cana-659	89	6	the	the	DET
cana-659	89	7	learning	learning	NOUN
cana-659	89	8	process	process	NOUN
cana-659	89	9	in	in	ADP
cana-659	89	10	fig.1	fig.1	PROPN
cana-659	89	11	,	,	PUNCT
cana-659	89	12	many	many	ADJ
cana-659	89	13	machine	machine	NOUN
cana-659	89	14	learning	learn	VERB
cana-659	89	15	algorithms	algorithm	NOUN
cana-659	89	16	that	that	PRON
cana-659	89	17	are	be	AUX
cana-659	89	18	provided	provide	VERB
cana-659	89	19	can	can	AUX
cana-659	89	20	be	be	AUX
cana-659	89	21	categorized	categorize	VERB
cana-659	89	22	into	into	ADP
cana-659	89	23	four	four	NUM
cana-659	89	24	groups	group	NOUN
cana-659	89	25	:	:	PUNCT
cana-659	89	26	supervised	supervised	ADJ
cana-659	89	27	,	,	PUNCT
cana-659	89	28	unsupervised	unsupervised	ADJ
cana-659	89	29	,	,	PUNCT
cana-659	89	30	semisupervised	semisupervise	VERB
cana-659	89	31	,	,	PUNCT
cana-659	89	32	and	and	CCONJ
cana-659	89	33	reinforcement	reinforcement	NOUN
cana-659	89	34	[	[	X
cana-659	89	35	2	2	NUM
cana-659	89	36	]	]	PUNCT
cana-659	89	37	.	.	PUNCT
cana-659	90	1	fig	fig	NOUN
cana-659	90	2	.	.	PUNCT
cana-659	91	1	1	1	NUM
cana-659	91	2	different	different	ADJ
cana-659	91	3	learning	learning	NOUN
cana-659	91	4	and	and	CCONJ
cana-659	91	5	classifiers	classifier	NOUN
cana-659	91	6	based	base	VERB
cana-659	91	7	on	on	ADP
cana-659	91	8	groups	group	NOUN
cana-659	91	9	communications	communication	NOUN
cana-659	91	10	on	on	ADP
cana-659	91	11	applied	apply	VERB
cana-659	91	12	nonlinear	nonlinear	ADJ
cana-659	91	13	analysis	analysis	NOUN
cana-659	91	14	issn	issn	NOUN
cana-659	91	15	:	:	PUNCT
cana-659	91	16	1074	1074	NUM
cana-659	91	17	-	-	PUNCT
cana-659	91	18	133x	133x	NUM
cana-659	91	19	vol	vol	NOUN
cana-659	91	20	31	31	NUM
cana-659	91	21	no	no	NOUN
cana-659	91	22	.	.	PUNCT
cana-659	92	1	2s	2s	NUM
cana-659	92	2	(	(	PUNCT
cana-659	92	3	2024	2024	NUM
cana-659	92	4	)	)	PUNCT
cana-659	92	5	440	440	NUM
cana-659	92	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	92	7	supervisedthe	supervisedthe	PROPN
cana-659	92	8	learning	learning	NOUN
cana-659	92	9	model	model	NOUN
cana-659	92	10	that	that	PRON
cana-659	92	11	efficiently	efficiently	ADV
cana-659	92	12	learns	learn	VERB
cana-659	92	13	how	how	SCONJ
cana-659	92	14	to	to	PART
cana-659	92	15	estimate	estimate	VERB
cana-659	92	16	from	from	ADP
cana-659	92	17	training	training	NOUN
cana-659	92	18	data	datum	NOUN
cana-659	92	19	is	be	AUX
cana-659	92	20	constructed	construct	VERB
cana-659	92	21	through	through	ADP
cana-659	92	22	supervised	supervised	ADJ
cana-659	92	23	learning	learning	NOUN
cana-659	92	24	.	.	PUNCT
cana-659	93	1	in	in	ADP
cana-659	93	2	supervised	supervised	ADJ
cana-659	93	3	learning	learning	NOUN
cana-659	93	4	,	,	PUNCT
cana-659	93	5	the	the	DET
cana-659	93	6	complete	complete	ADJ
cana-659	93	7	dataset	dataset	NOUN
cana-659	93	8	is	be	AUX
cana-659	93	9	split	split	VERB
cana-659	93	10	into	into	ADP
cana-659	93	11	two	two	NUM
cana-659	93	12	portions	portion	NOUN
cana-659	93	13	:	:	PUNCT
cana-659	93	14	the	the	DET
cana-659	93	15	training	training	NOUN
cana-659	93	16	portion	portion	NOUN
cana-659	93	17	is	be	AUX
cana-659	93	18	used	use	VERB
cana-659	93	19	to	to	PART
cana-659	93	20	train	train	VERB
cana-659	93	21	the	the	DET
cana-659	93	22	classifier	classifier	NOUN
cana-659	93	23	,	,	PUNCT
cana-659	93	24	and	and	CCONJ
cana-659	93	25	the	the	DET
cana-659	93	26	remaining	remain	VERB
cana-659	93	27	portion	portion	NOUN
cana-659	93	28	is	be	AUX
cana-659	93	29	used	use	VERB
cana-659	93	30	to	to	PART
cana-659	93	31	assess	assess	VERB
cana-659	93	32	the	the	DET
cana-659	93	33	classifier	classifier	NOUN
cana-659	93	34	's	's	PART
cana-659	93	35	accuracy	accuracy	NOUN
cana-659	93	36	.	.	PUNCT
cana-659	94	1	after	after	SCONJ
cana-659	94	2	it	it	PRON
cana-659	94	3	is	be	AUX
cana-659	94	4	finished	finish	VERB
cana-659	94	5	,	,	PUNCT
cana-659	94	6	we	we	PRON
cana-659	94	7	can	can	AUX
cana-659	94	8	utilize	utilize	VERB
cana-659	94	9	it	it	PRON
cana-659	94	10	to	to	PART
cana-659	94	11	test	test	VERB
cana-659	94	12	fresh	fresh	ADJ
cana-659	94	13	data	datum	NOUN
cana-659	94	14	that	that	PRON
cana-659	94	15	will	will	AUX
cana-659	94	16	serve	serve	VERB
cana-659	94	17	as	as	ADP
cana-659	94	18	a	a	DET
cana-659	94	19	basis	basis	NOUN
cana-659	94	20	for	for	ADP
cana-659	94	21	upcoming	upcoming	ADJ
cana-659	94	22	information	information	NOUN
cana-659	94	23	.	.	PUNCT
cana-659	95	1	the	the	DET
cana-659	95	2	two	two	NUM
cana-659	95	3	most	most	ADV
cana-659	95	4	popular	popular	ADJ
cana-659	95	5	supervised	supervised	ADJ
cana-659	95	6	tasks	task	NOUN
cana-659	95	7	are	be	AUX
cana-659	95	8	first	first	ADJ
cana-659	95	9	regression	regression	NOUN
cana-659	95	10	which	which	PRON
cana-659	95	11	fits	fit	VERB
cana-659	95	12	the	the	DET
cana-659	95	13	data	datum	NOUN
cana-659	95	14	and	and	CCONJ
cana-659	95	15	second	second	ADJ
cana-659	95	16	classification	classification	NOUN
cana-659	95	17	which	which	PRON
cana-659	95	18	divides	divide	VERB
cana-659	95	19	the	the	DET
cana-659	95	20	data	datum	NOUN
cana-659	95	21	[	[	X
cana-659	95	22	1][3	1][3	X
cana-659	95	23	]	]	PUNCT
cana-659	95	24	.	.	PUNCT
cana-659	96	1	unsupervisedunsupervised	unsupervisedunsupervised	ADJ
cana-659	96	2	learning	learning	NOUN
cana-659	96	3	allows	allow	VERB
cana-659	96	4	for	for	ADP
cana-659	96	5	the	the	DET
cana-659	96	6	analysis	analysis	NOUN
cana-659	96	7	of	of	ADP
cana-659	96	8	unlabelled	unlabelled	ADJ
cana-659	96	9	datasets	dataset	NOUN
cana-659	96	10	without	without	ADP
cana-659	96	11	requiring	require	VERB
cana-659	96	12	human	human	ADJ
cana-659	96	13	intervention	intervention	NOUN
cana-659	96	14	.	.	PUNCT
cana-659	97	1	this	this	PRON
cana-659	97	2	is	be	AUX
cana-659	97	3	frequently	frequently	ADV
cana-659	97	4	used	use	VERB
cana-659	97	5	for	for	ADP
cana-659	97	6	exploratory	exploratory	ADJ
cana-659	97	7	reasons	reason	NOUN
cana-659	97	8	,	,	PUNCT
cana-659	97	9	groupings	grouping	NOUN
cana-659	97	10	in	in	ADP
cana-659	97	11	findings	finding	NOUN
cana-659	97	12	,	,	PUNCT
cana-659	97	13	generative	generative	ADJ
cana-659	97	14	feature	feature	NOUN
cana-659	97	15	extraction	extraction	NOUN
cana-659	97	16	,	,	PUNCT
cana-659	97	17	and	and	CCONJ
cana-659	97	18	the	the	DET
cana-659	97	19	identification	identification	NOUN
cana-659	97	20	of	of	ADP
cana-659	97	21	significant	significant	ADJ
cana-659	97	22	patterns	pattern	NOUN
cana-659	97	23	and	and	CCONJ
cana-659	97	24	structures	structure	NOUN
cana-659	97	25	.	.	PUNCT
cana-659	98	1	clustering	cluster	VERB
cana-659	98	2	approach	approach	NOUN
cana-659	98	3	is	be	AUX
cana-659	98	4	one	one	NUM
cana-659	98	5	of	of	ADP
cana-659	98	6	the	the	DET
cana-659	98	7	most	most	ADV
cana-659	98	8	popular	popular	ADJ
cana-659	98	9	unsupervised	unsupervised	ADJ
cana-659	98	10	learning	learning	NOUN
cana-659	99	1	[	[	X
cana-659	99	2	1	1	NUM
cana-659	99	3	]	]	PUNCT
cana-659	99	4	.	.	PUNCT
cana-659	100	1	semi	semi	ADJ
cana-659	100	2	-	-	VERB
cana-659	100	3	supervisedworking	supervisedworke	VERB
cana-659	100	4	with	with	ADP
cana-659	100	5	both	both	PRON
cana-659	100	6	labelled	label	VERB
cana-659	100	7	and	and	CCONJ
cana-659	100	8	unlabelled	unlabelled	ADJ
cana-659	100	9	data	datum	NOUN
cana-659	100	10	,	,	PUNCT
cana-659	100	11	semi	semi	ADJ
cana-659	100	12	-	-	ADJ
cana-659	100	13	supervised	supervised	ADJ
cana-659	100	14	learning	learning	NOUN
cana-659	100	15	is	be	AUX
cana-659	100	16	sometimes	sometimes	ADV
cana-659	100	17	referred	refer	VERB
cana-659	100	18	to	to	ADP
cana-659	100	19	as	as	ADP
cana-659	100	20	a	a	DET
cana-659	100	21	hybridization	hybridization	NOUN
cana-659	100	22	of	of	ADP
cana-659	100	23	supervised	supervised	ADJ
cana-659	100	24	and	and	CCONJ
cana-659	100	25	unsupervised	unsupervised	ADJ
cana-659	100	26	.	.	PUNCT
cana-659	101	1	in	in	ADP
cana-659	101	2	real	real	ADJ
cana-659	101	3	,	,	PUNCT
cana-659	101	4	semi	semi	ADJ
cana-659	101	5	-	-	ADJ
cana-659	101	6	supervised	supervised	ADJ
cana-659	101	7	learning	learning	NOUN
cana-659	101	8	is	be	AUX
cana-659	101	9	helpful	helpful	ADJ
cana-659	101	10	since	since	SCONJ
cana-659	101	11	unlabelled	unlabelled	ADJ
cana-659	101	12	data	datum	NOUN
cana-659	101	13	are	be	AUX
cana-659	101	14	common	common	ADJ
cana-659	101	15	and	and	CCONJ
cana-659	101	16	labelled	label	VERB
cana-659	101	17	data	datum	NOUN
cana-659	101	18	may	may	AUX
cana-659	101	19	be	be	AUX
cana-659	101	20	scarce	scarce	ADJ
cana-659	101	21	under	under	ADP
cana-659	101	22	various	various	ADJ
cana-659	101	23	circumstances	circumstance	NOUN
cana-659	101	24	.	.	PUNCT
cana-659	102	1	a	a	DET
cana-659	102	2	semi	semi	ADJ
cana-659	102	3	-	-	ADJ
cana-659	102	4	supervised	supervised	ADJ
cana-659	102	5	learning	learning	NOUN
cana-659	102	6	model	model	NOUN
cana-659	102	7	's	's	PART
cana-659	102	8	ultimate	ultimate	ADJ
cana-659	102	9	objective	objective	NOUN
cana-659	102	10	is	be	AUX
cana-659	102	11	to	to	PART
cana-659	102	12	produce	produce	VERB
cana-659	102	13	a	a	DET
cana-659	102	14	better	well	ADJ
cana-659	102	15	prediction	prediction	NOUN
cana-659	102	16	result	result	NOUN
cana-659	102	17	than	than	SCONJ
cana-659	102	18	one	one	PRON
cana-659	102	19	might	might	AUX
cana-659	102	20	obtain	obtain	VERB
cana-659	102	21	from	from	ADP
cana-659	102	22	the	the	DET
cana-659	102	23	model	model	NOUN
cana-659	102	24	utilizing	utilize	VERB
cana-659	102	25	just	just	ADV
cana-659	102	26	the	the	DET
cana-659	102	27	labelled	label	VERB
cana-659	102	28	data	datum	NOUN
cana-659	102	29	[	[	X
cana-659	102	30	1	1	NUM
cana-659	102	31	]	]	PUNCT
cana-659	102	32	.	.	PUNCT
cana-659	103	1	reinforcementreinforcement	reinforcementreinforcement	NOUN
cana-659	103	2	learning	learning	NOUN
cana-659	103	3	is	be	AUX
cana-659	103	4	an	an	DET
cana-659	103	5	environment	environment	NOUN
cana-659	103	6	-	-	PUNCT
cana-659	103	7	driven	drive	VERB
cana-659	103	8	methodology	methodology	NOUN
cana-659	103	9	that	that	PRON
cana-659	103	10	makes	make	VERB
cana-659	103	11	it	it	PRON
cana-659	103	12	possible	possible	ADJ
cana-659	103	13	for	for	SCONJ
cana-659	103	14	machines	machine	NOUN
cana-659	103	15	and	and	CCONJ
cana-659	103	16	software	software	NOUN
cana-659	103	17	agents	agent	NOUN
cana-659	103	18	to	to	PART
cana-659	103	19	automatically	automatically	ADV
cana-659	103	20	assess	assess	VERB
cana-659	103	21	the	the	DET
cana-659	103	22	most	most	ADV
cana-659	103	23	effective	effective	ADJ
cana-659	103	24	behaviour	behaviour	NOUN
cana-659	103	25	in	in	ADP
cana-659	103	26	a	a	DET
cana-659	103	27	given	give	VERB
cana-659	103	28	situation	situation	NOUN
cana-659	103	29	in	in	ADP
cana-659	103	30	order	order	NOUN
cana-659	103	31	to	to	PART
cana-659	103	32	increase	increase	VERB
cana-659	103	33	productivity	productivity	NOUN
cana-659	103	34	.	.	PUNCT
cana-659	104	1	the	the	DET
cana-659	104	2	purpose	purpose	NOUN
cana-659	104	3	of	of	ADP
cana-659	104	4	this	this	DET
cana-659	104	5	reward	reward	NOUN
cana-659	104	6	-	-	PUNCT
cana-659	104	7	or	or	CCONJ
cana-659	104	8	penalty	penalty	NOUN
cana-659	104	9	-	-	PUNCT
cana-659	104	10	based	base	VERB
cana-659	104	11	learning	learning	NOUN
cana-659	104	12	approach	approach	NOUN
cana-659	104	13	is	be	AUX
cana-659	104	14	to	to	PART
cana-659	104	15	use	use	VERB
cana-659	104	16	the	the	DET
cana-659	104	17	knowledge	knowledge	NOUN
cana-659	104	18	activists	activist	NOUN
cana-659	104	19	provide	provide	VERB
cana-659	104	20	to	to	PART
cana-659	104	21	take	take	VERB
cana-659	104	22	actions	action	NOUN
cana-659	104	23	that	that	PRON
cana-659	104	24	will	will	AUX
cana-659	104	25	maximize	maximize	VERB
cana-659	104	26	reward	reward	NOUN
cana-659	104	27	or	or	CCONJ
cana-659	104	28	reduce	reduce	VERB
cana-659	104	29	danger	danger	NOUN
cana-659	104	30	.	.	PUNCT
cana-659	105	1	it	it	PRON
cana-659	105	2	's	be	AUX
cana-659	105	3	an	an	DET
cana-659	105	4	effective	effective	ADJ
cana-659	105	5	tool	tool	NOUN
cana-659	105	6	that	that	PRON
cana-659	105	7	can	can	AUX
cana-659	105	8	help	help	VERB
cana-659	105	9	automate	automate	VERB
cana-659	105	10	more	more	ADJ
cana-659	105	11	jobs	job	NOUN
cana-659	105	12	or	or	CCONJ
cana-659	105	13	improve	improve	VERB
cana-659	105	14	the	the	DET
cana-659	105	15	operational	operational	ADJ
cana-659	105	16	efficiency	efficiency	NOUN
cana-659	105	17	of	of	ADP
cana-659	105	18	complex	complex	ADJ
cana-659	105	19	systems	system	NOUN
cana-659	105	20	like	like	ADP
cana-659	105	21	robotics	robotic	NOUN
cana-659	105	22	and	and	CCONJ
cana-659	105	23	autonomous	autonomous	ADJ
cana-659	105	24	driving	driving	NOUN
cana-659	105	25	,	,	PUNCT
cana-659	105	26	among	among	ADP
cana-659	105	27	others	other	NOUN
cana-659	105	28	[	[	X
cana-659	105	29	1	1	NUM
cana-659	105	30	]	]	PUNCT
cana-659	105	31	.	.	PUNCT
cana-659	106	1	depending	depend	VERB
cana-659	106	2	on	on	ADP
cana-659	106	3	the	the	DET
cana-659	106	4	type	type	NOUN
cana-659	106	5	of	of	ADP
cana-659	106	6	data	datum	NOUN
cana-659	106	7	and	and	CCONJ
cana-659	106	8	the	the	DET
cana-659	106	9	desired	desire	VERB
cana-659	106	10	result	result	NOUN
cana-659	106	11	,	,	PUNCT
cana-659	106	12	several	several	ADJ
cana-659	106	13	kinds	kind	NOUN
cana-659	106	14	of	of	ADP
cana-659	106	15	machine	machine	NOUN
cana-659	106	16	learning	learning	NOUN
cana-659	106	17	approaches	approach	NOUN
cana-659	106	18	can	can	AUX
cana-659	106	19	be	be	AUX
cana-659	106	20	very	very	ADV
cana-659	106	21	helpful	helpful	ADJ
cana-659	106	22	in	in	ADP
cana-659	106	23	building	build	VERB
cana-659	106	24	efficient	efficient	ADJ
cana-659	106	25	models	model	NOUN
cana-659	106	26	in	in	ADP
cana-659	106	27	different	different	ADJ
cana-659	106	28	application	application	NOUN
cana-659	106	29	areas	area	NOUN
cana-659	106	30	.	.	PUNCT
cana-659	107	1	these	these	DET
cana-659	107	2	techniques	technique	NOUN
cana-659	107	3	can	can	AUX
cana-659	107	4	be	be	AUX
cana-659	107	5	distinguished	distinguish	VERB
cana-659	107	6	by	by	ADP
cana-659	107	7	their	their	PRON
cana-659	107	8	learning	learning	NOUN
cana-659	107	9	capacities	capacity	NOUN
cana-659	107	10	.	.	PUNCT
cana-659	108	1	below	below	ADV
cana-659	108	2	,	,	PUNCT
cana-659	108	3	we	we	PRON
cana-659	108	4	present	present	VERB
cana-659	108	5	an	an	DET
cana-659	108	6	overview	overview	NOUN
cana-659	108	7	of	of	ADP
cana-659	108	8	supervised	supervised	ADJ
cana-659	108	9	machine	machine	NOUN
cana-659	108	10	learning	learn	VERB
cana-659	108	11	techniques	technique	NOUN
cana-659	108	12	that	that	PRON
cana-659	108	13	can	can	AUX
cana-659	108	14	be	be	AUX
cana-659	108	15	used	use	VERB
cana-659	108	16	to	to	PART
cana-659	108	17	improve	improve	VERB
cana-659	108	18	the	the	DET
cana-659	108	19	functionality	functionality	NOUN
cana-659	108	20	and	and	CCONJ
cana-659	108	21	intelligence	intelligence	NOUN
cana-659	108	22	of	of	ADP
cana-659	108	23	a	a	DET
cana-659	108	24	data	data	NOUN
cana-659	108	25	-	-	PUNCT
cana-659	108	26	driven	drive	VERB
cana-659	108	27	application	application	NOUN
cana-659	108	28	[	[	X
cana-659	108	29	1	1	NUM
cana-659	108	30	-	-	SYM
cana-659	108	31	2	2	NUM
cana-659	108	32	]	]	PUNCT
cana-659	108	33	.	.	PUNCT
cana-659	109	1	3	3	X
cana-659	109	2	.	.	X
cana-659	109	3	supervised	supervise	VERB
cana-659	109	4	machine	machine	NOUN
cana-659	109	5	learning	learn	VERB
cana-659	109	6	algorithms	algorithm	NOUN
cana-659	109	7	researchers	researcher	NOUN
cana-659	109	8	'	'	PART
cana-659	109	9	attention	attention	NOUN
cana-659	109	10	has	have	AUX
cana-659	109	11	been	be	AUX
cana-659	109	12	drawn	draw	VERB
cana-659	109	13	to	to	ADP
cana-659	109	14	machine	machine	NOUN
cana-659	109	15	learning	learning	NOUN
cana-659	109	16	approaches	approach	NOUN
cana-659	109	17	due	due	ADJ
cana-659	109	18	to	to	ADP
cana-659	109	19	technological	technological	ADJ
cana-659	109	20	advancements	advancement	NOUN
cana-659	109	21	.	.	PUNCT
cana-659	110	1	in	in	ADP
cana-659	110	2	table	table	NOUN
cana-659	110	3	1	1	NUM
cana-659	110	4	,	,	PUNCT
cana-659	110	5	shows	show	VERB
cana-659	110	6	the	the	DET
cana-659	110	7	comparative	comparative	ADJ
cana-659	110	8	analysis	analysis	NOUN
cana-659	110	9	of	of	ADP
cana-659	110	10	classifiers	classifier	NOUN
cana-659	110	11	.	.	PUNCT
cana-659	111	1	among	among	ADP
cana-659	111	2	the	the	DET
cana-659	111	3	supervised	supervised	ADJ
cana-659	111	4	machine	machine	NOUN
cana-659	111	5	learning	learning	NOUN
cana-659	111	6	algorithms	algorithm	NOUN
cana-659	111	7	that	that	PRON
cana-659	111	8	focus	focus	VERB
cana-659	111	9	more	more	ADV
cana-659	111	10	on	on	ADP
cana-659	111	11	classification	classification	NOUN
cana-659	111	12	are	be	AUX
cana-659	111	13	naïve	naïve	ADJ
cana-659	111	14	bayes	bayes	NOUN
cana-659	111	15	classifier	classifier	NOUN
cana-659	111	16	,	,	PUNCT
cana-659	111	17	support	support	VERB
cana-659	111	18	vector	vector	NOUN
cana-659	111	19	machine	machine	NOUN
cana-659	111	20	,	,	PUNCT
cana-659	111	21	k	k	ADJ
cana-659	111	22	-	-	PUNCT
cana-659	111	23	means	mean	VERB
cana-659	111	24	clustering	clustering	NOUN
cana-659	111	25	,	,	PUNCT
cana-659	111	26	random	random	ADJ
cana-659	111	27	forest	forest	NOUN
cana-659	111	28	,	,	PUNCT
cana-659	111	29	decision	decision	NOUN
cana-659	111	30	tree	tree	NOUN
cana-659	111	31	,	,	PUNCT
cana-659	111	32	and	and	CCONJ
cana-659	111	33	logistic	logistic	ADJ
cana-659	111	34	regression	regression	NOUN
cana-659	111	35	.	.	PUNCT
cana-659	112	1	here	here	ADV
cana-659	112	2	is	be	AUX
cana-659	112	3	a	a	DET
cana-659	112	4	recap	recap	NOUN
cana-659	112	5	of	of	ADP
cana-659	112	6	every	every	DET
cana-659	112	7	machine	machine	NOUN
cana-659	112	8	learning	learn	VERB
cana-659	112	9	approach	approach	NOUN
cana-659	112	10	.	.	PUNCT
cana-659	113	1	[	[	X
cana-659	113	2	4][5	4][5	X
cana-659	113	3	]	]	X
cana-659	113	4	3.1	3.1	NUM
cana-659	113	5	k	k	NOUN
cana-659	113	6	-	-	PUNCT
cana-659	113	7	nearest	near	ADJ
cana-659	113	8	neighbour	neighbour	NOUN
cana-659	113	9	a	a	DET
cana-659	113	10	straightforward	straightforward	ADJ
cana-659	113	11	supervised	supervised	ADJ
cana-659	113	12	machine	machine	NOUN
cana-659	113	13	learning	learning	NOUN
cana-659	113	14	technique	technique	NOUN
cana-659	113	15	,	,	PUNCT
cana-659	113	16	k	k	X
cana-659	113	17	-	-	PUNCT
cana-659	113	18	nearest	near	ADJ
cana-659	113	19	neighbour	neighbour	NOUN
cana-659	113	20	categorizes	categorize	VERB
cana-659	113	21	incoming	incoming	ADJ
cana-659	113	22	instances	instance	NOUN
cana-659	113	23	according	accord	VERB
cana-659	113	24	to	to	ADP
cana-659	113	25	a	a	DET
cana-659	113	26	similarity	similarity	NOUN
cana-659	113	27	metric	metric	ADJ
cana-659	113	28	and	and	CCONJ
cana-659	113	29	may	may	AUX
cana-659	113	30	be	be	AUX
cana-659	113	31	applied	apply	VERB
cana-659	113	32	to	to	ADP
cana-659	113	33	regression	regression	NOUN
cana-659	113	34	and	and	CCONJ
cana-659	113	35	classification	classification	NOUN
cana-659	113	36	tasks	task	NOUN
cana-659	113	37	alike	alike	ADV
cana-659	113	38	.	.	PUNCT
cana-659	114	1	numerous	numerous	ADJ
cana-659	114	2	distance	distance	NOUN
cana-659	114	3	measuring	measure	VERB
cana-659	114	4	approaches	approach	NOUN
cana-659	114	5	,	,	PUNCT
cana-659	114	6	such	such	ADJ
cana-659	114	7	as	as	ADP
cana-659	114	8	manhattan	manhattan	PROPN
cana-659	114	9	,	,	PUNCT
cana-659	114	10	euclidean	euclidean	ADJ
cana-659	114	11	,	,	PUNCT
cana-659	114	12	and	and	CCONJ
cana-659	114	13	others	other	NOUN
cana-659	114	14	,	,	PUNCT
cana-659	114	15	can	can	AUX
cana-659	114	16	be	be	AUX
cana-659	114	17	communications	communication	NOUN
cana-659	114	18	on	on	ADP
cana-659	114	19	applied	apply	VERB
cana-659	114	20	nonlinear	nonlinear	ADJ
cana-659	114	21	analysis	analysis	NOUN
cana-659	114	22	issn	issn	NOUN
cana-659	114	23	:	:	PUNCT
cana-659	114	24	1074	1074	NUM
cana-659	114	25	-	-	PUNCT
cana-659	114	26	133x	133x	NUM
cana-659	114	27	vol	vol	NOUN
cana-659	114	28	31	31	NUM
cana-659	114	29	no	no	NOUN
cana-659	114	30	.	.	PUNCT
cana-659	115	1	2s	2s	NUM
cana-659	115	2	(	(	PUNCT
cana-659	115	3	2024	2024	NUM
cana-659	115	4	)	)	PUNCT
cana-659	115	5	441	441	NUM
cana-659	115	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	115	7	table	table	NOUN
cana-659	115	8	1	1	NUM
cana-659	115	9	:	:	PUNCT
cana-659	115	10	comparative	comparative	ADJ
cana-659	115	11	analysis	analysis	NOUN
cana-659	115	12	of	of	ADP
cana-659	115	13	classifiers	classifier	NOUN
cana-659	115	14	decision	decision	VERB
cana-659	115	15	tree	tree	NOUN
cana-659	115	16	naïve	naïve	ADJ
cana-659	115	17	bayes	bayes	PROPN
cana-659	115	18	svm	svm	VERB
cana-659	115	19	knn	knn	PROPN
cana-659	115	20	accuracy	accuracy	PROPN
cana-659	115	21	in	in	ADP
cana-659	115	22	overall	overall	ADJ
cana-659	115	23	avg	avg	PROPN
cana-659	115	24	avg	avg	PROPN
cana-659	115	25	best	good	ADJ
cana-659	115	26	avg	avg	NOUN
cana-659	115	27	speed	speed	NOUN
cana-659	115	28	(	(	PUNCT
cana-659	115	29	learning	learn	VERB
cana-659	115	30	+	+	CCONJ
cana-659	115	31	classification	classification	NOUN
cana-659	115	32	)	)	PUNCT
cana-659	115	33	best	well	ADV
cana-659	115	34	best	good	ADJ
cana-659	115	35	avg	avg	PROPN
cana-659	115	36	avg	avg	PROPN
cana-659	115	37	tolerance	tolerance	NOUN
cana-659	115	38	(	(	PUNCT
cana-659	115	39	missing	miss	VERB
cana-659	115	40	+	+	CCONJ
cana-659	115	41	irrelevant	irrelevant	ADJ
cana-659	115	42	+	+	ADJ
cana-659	115	43	redundant	redundant	ADJ
cana-659	115	44	)	)	PUNCT
cana-659	115	45	attributes	attribute	VERB
cana-659	115	46	better	well	ADV
cana-659	115	47	better	well	ADV
cana-659	115	48	better	well	ADJ
cana-659	115	49	avg	avg	NOUN
cana-659	115	50	noise	noise	NOUN
cana-659	115	51	tolerance	tolerance	NOUN
cana-659	115	52	avg	avg	NOUN
cana-659	115	53	better	well	ADJ
cana-659	115	54	avg	avg	PROPN
cana-659	115	55	poor	poor	ADJ
cana-659	115	56	time	time	NOUN
cana-659	115	57	complexity	complexity	NOUN
cana-659	115	58	(	(	PUNCT
cana-659	115	59	upper	upper	ADJ
cana-659	115	60	bound	bind	VERB
cana-659	115	61	)	)	PUNCT
cana-659	115	62	o(l	o(l	PROPN
cana-659	115	63	)	)	PUNCT
cana-659	115	64	for	for	ADP
cana-659	115	65	level	level	NOUN
cana-659	115	66	or	or	CCONJ
cana-659	115	67	depth	depth	NOUN
cana-659	115	68	of	of	ADP
cana-659	115	69	tree	tree	NOUN
cana-659	115	70	o(n*d	o(n*d	NOUN
cana-659	115	71	)	)	PUNCT
cana-659	115	72	for	for	ADP
cana-659	115	73	n	n	NUM
cana-659	115	74	instances	instance	NOUN
cana-659	115	75	o(n*d	o(n*d	ADV
cana-659	115	76	)	)	PUNCT
cana-659	115	77	for	for	ADP
cana-659	115	78	n	n	NUM
cana-659	115	79	support	support	NOUN
cana-659	115	80	vectors	vector	NOUN
cana-659	115	81	o(k*d	o(k*d	VERB
cana-659	115	82	)	)	PUNCT
cana-659	115	83	for	for	ADP
cana-659	115	84	k	k	PROPN
cana-659	115	85	neighbours	neighbour	NOUN
cana-659	115	86	used	use	VERB
cana-659	115	87	to	to	PART
cana-659	115	88	detect	detect	VERB
cana-659	115	89	data	datum	NOUN
cana-659	115	90	similarity	similarity	NOUN
cana-659	115	91	.	.	PUNCT
cana-659	116	1	knn	knn	PROPN
cana-659	116	2	has	have	AUX
cana-659	116	3	been	be	AUX
cana-659	116	4	utilized	utilize	VERB
cana-659	116	5	in	in	ADP
cana-659	116	6	statistical	statistical	ADJ
cana-659	116	7	estimate	estimate	NOUN
cana-659	116	8	and	and	CCONJ
cana-659	116	9	pattern	pattern	NOUN
cana-659	116	10	identification	identification	NOUN
cana-659	116	11	based	base	VERB
cana-659	116	12	on	on	ADP
cana-659	116	13	their	their	PRON
cana-659	116	14	nearest	near	ADJ
cana-659	116	15	neighbours	neighbour	NOUN
cana-659	116	16	.	.	PUNCT
cana-659	117	1	because	because	SCONJ
cana-659	117	2	the	the	DET
cana-659	117	3	knn	knn	PROPN
cana-659	117	4	algorithm	algorithm	PROPN
cana-659	117	5	does	do	AUX
cana-659	117	6	n't	not	PART
cana-659	117	7	require	require	VERB
cana-659	117	8	training	training	NOUN
cana-659	117	9	in	in	ADP
cana-659	117	10	order	order	NOUN
cana-659	117	11	to	to	PART
cana-659	117	12	generate	generate	VERB
cana-659	117	13	predictions	prediction	NOUN
cana-659	117	14	,	,	PUNCT
cana-659	117	15	it	it	PRON
cana-659	117	16	is	be	AUX
cana-659	117	17	faster	fast	ADJ
cana-659	117	18	than	than	ADP
cana-659	117	19	other	other	ADJ
cana-659	117	20	approaches	approach	NOUN
cana-659	117	21	.	.	PUNCT
cana-659	118	1	[	[	X
cana-659	118	2	3][6	3][6	NOUN
cana-659	118	3	]	]	SYM
cana-659	118	4	3.2	3.2	NUM
cana-659	118	5	decision	decision	NOUN
cana-659	118	6	tree	tree	NOUN
cana-659	118	7	one	one	NUM
cana-659	118	8	supervised	supervised	ADJ
cana-659	118	9	machine	machine	NOUN
cana-659	118	10	learning	learning	NOUN
cana-659	118	11	technique	technique	NOUN
cana-659	118	12	that	that	PRON
cana-659	118	13	can	can	AUX
cana-659	118	14	be	be	AUX
cana-659	118	15	used	use	VERB
cana-659	118	16	for	for	ADP
cana-659	118	17	both	both	CCONJ
cana-659	118	18	regression	regression	NOUN
cana-659	118	19	and	and	CCONJ
cana-659	118	20	classification	classification	NOUN
cana-659	118	21	issues	issue	NOUN
cana-659	118	22	is	be	AUX
cana-659	118	23	the	the	DET
cana-659	118	24	decision	decision	NOUN
cana-659	118	25	tree.[4	tree.[4	NOUN
cana-659	118	26	]	]	X
cana-659	118	27	decision	decision	NOUN
cana-659	118	28	trees	tree	NOUN
cana-659	118	29	are	be	AUX
cana-659	118	30	used	use	VERB
cana-659	118	31	to	to	PART
cana-659	118	32	create	create	VERB
cana-659	118	33	hierarchical	hierarchical	ADJ
cana-659	118	34	categorization	categorization	NOUN
cana-659	118	35	models	model	NOUN
cana-659	118	36	.	.	PUNCT
cana-659	119	1	the	the	DET
cana-659	119	2	process	process	NOUN
cana-659	119	3	of	of	ADP
cana-659	119	4	gradually	gradually	ADV
cana-659	119	5	dividing	divide	VERB
cana-659	119	6	the	the	DET
cana-659	119	7	dataset	dataset	NOUN
cana-659	119	8	into	into	ADP
cana-659	119	9	smaller	small	ADJ
cana-659	119	10	and	and	CCONJ
cana-659	119	11	smaller	small	ADJ
cana-659	119	12	results	result	NOUN
cana-659	119	13	in	in	ADP
cana-659	119	14	the	the	DET
cana-659	119	15	development	development	NOUN
cana-659	119	16	of	of	ADP
cana-659	119	17	decision	decision	NOUN
cana-659	119	18	trees.[3	trees.[3	NOUN
cana-659	119	19	]	]	PUNCT
cana-659	119	20	there	there	PRON
cana-659	119	21	are	be	VERB
cana-659	119	22	various	various	ADJ
cana-659	119	23	nodes	node	NOUN
cana-659	119	24	.	.	PUNCT
cana-659	120	1	the	the	DET
cana-659	120	2	nodes	node	NOUN
cana-659	120	3	that	that	PRON
cana-659	120	4	store	store	VERB
cana-659	120	5	the	the	DET
cana-659	120	6	data	datum	NOUN
cana-659	120	7	in	in	ADP
cana-659	120	8	the	the	DET
cana-659	120	9	middle	middle	NOUN
cana-659	120	10	are	be	AUX
cana-659	120	11	referred	refer	VERB
cana-659	120	12	to	to	ADP
cana-659	120	13	as	as	ADP
cana-659	120	14	internal	internal	ADJ
cana-659	120	15	nodes	node	NOUN
cana-659	120	16	;	;	PUNCT
cana-659	120	17	the	the	DET
cana-659	120	18	nodes	node	NOUN
cana-659	120	19	that	that	PRON
cana-659	120	20	are	be	AUX
cana-659	120	21	at	at	ADP
cana-659	120	22	the	the	DET
cana-659	120	23	end	end	NOUN
cana-659	120	24	are	be	AUX
cana-659	120	25	referred	refer	VERB
cana-659	120	26	to	to	ADP
cana-659	120	27	as	as	ADP
cana-659	120	28	leaf	leaf	NOUN
cana-659	120	29	nodes	node	NOUN
cana-659	120	30	.	.	PUNCT
cana-659	121	1	the	the	DET
cana-659	121	2	root	root	NOUN
cana-659	121	3	node	node	NOUN
cana-659	121	4	is	be	AUX
cana-659	121	5	the	the	DET
cana-659	121	6	first	first	ADJ
cana-659	121	7	node.[6	node.[6	PRON
cana-659	121	8	]	]	X
cana-659	122	1	a	a	DET
cana-659	122	2	choice	choice	NOUN
cana-659	122	3	is	be	AUX
cana-659	122	4	represented	represent	VERB
cana-659	122	5	as	as	ADP
cana-659	122	6	a	a	DET
cana-659	122	7	leaf	leaf	NOUN
cana-659	122	8	node	node	NOUN
cana-659	122	9	,	,	PUNCT
cana-659	122	10	and	and	CCONJ
cana-659	122	11	the	the	DET
cana-659	122	12	root	root	NOUN
cana-659	122	13	node	node	NOUN
cana-659	122	14	of	of	ADP
cana-659	122	15	a	a	DET
cana-659	122	16	tree	tree	NOUN
cana-659	122	17	corresponds	correspond	VERB
cana-659	122	18	to	to	ADP
cana-659	122	19	the	the	DET
cana-659	122	20	best	good	ADJ
cana-659	122	21	predictor	predictor	NOUN
cana-659	122	22	found	find	VERB
cana-659	122	23	in	in	ADP
cana-659	122	24	the	the	DET
cana-659	122	25	datasets	dataset	NOUN
cana-659	122	26	provided.[3	provided.[3	NOUN
cana-659	122	27	]	]	X
cana-659	122	28	the	the	DET
cana-659	122	29	algorithm	algorithm	NOUN
cana-659	122	30	uses	use	VERB
cana-659	122	31	basic	basic	ADJ
cana-659	122	32	decision	decision	NOUN
cana-659	122	33	rules	rule	NOUN
cana-659	122	34	established	establish	VERB
cana-659	122	35	from	from	ADP
cana-659	122	36	training	train	VERB
cana-659	122	37	data	datum	NOUN
cana-659	122	38	to	to	PART
cana-659	122	39	develop	develop	VERB
cana-659	122	40	a	a	DET
cana-659	122	41	training	training	NOUN
cana-659	122	42	model	model	NOUN
cana-659	122	43	that	that	PRON
cana-659	122	44	can	can	AUX
cana-659	122	45	predict	predict	VERB
cana-659	122	46	the	the	DET
cana-659	122	47	class	class	NOUN
cana-659	122	48	or	or	CCONJ
cana-659	122	49	value	value	NOUN
cana-659	122	50	of	of	ADP
cana-659	122	51	a	a	DET
cana-659	122	52	target	target	NOUN
cana-659	122	53	variable	variable	NOUN
cana-659	122	54	.	.	PUNCT
cana-659	123	1	the	the	DET
cana-659	123	2	prediction	prediction	NOUN
cana-659	123	3	begins	begin	VERB
cana-659	123	4	comparing	compare	VERB
cana-659	123	5	values	value	NOUN
cana-659	123	6	from	from	ADP
cana-659	123	7	the	the	DET
cana-659	123	8	tree	tree	NOUN
cana-659	123	9	's	's	PART
cana-659	123	10	root	root	NOUN
cana-659	123	11	node	node	NOUN
cana-659	123	12	and	and	CCONJ
cana-659	123	13	works	work	VERB
cana-659	123	14	its	its	PRON
cana-659	123	15	way	way	NOUN
cana-659	123	16	up	up	ADP
cana-659	123	17	to	to	ADP
cana-659	123	18	the	the	DET
cana-659	123	19	terminal	terminal	ADJ
cana-659	123	20	node	node	NOUN
cana-659	123	21	.	.	PUNCT
cana-659	124	1	[	[	X
cana-659	124	2	4	4	NUM
cana-659	124	3	]	]	PUNCT
cana-659	124	4	algorithm	algorithm	NOUN
cana-659	124	5	:	:	PUNCT
cana-659	124	6	1	1	X
cana-659	124	7	.	.	PUNCT
cana-659	124	8	gini	gini	NOUN
cana-659	124	9	impurity	impurity	NOUN
cana-659	124	10	(	(	PUNCT
cana-659	124	11	for	for	ADP
cana-659	124	12	classification	classification	NOUN
cana-659	124	13	)	)	PUNCT
cana-659	124	14	the	the	DET
cana-659	124	15	gini	gini	PROPN
cana-659	124	16	impurity	impurity	NOUN
cana-659	124	17	measures	measure	VERB
cana-659	124	18	the	the	DET
cana-659	124	19	probability	probability	NOUN
cana-659	124	20	of	of	ADP
cana-659	124	21	a	a	DET
cana-659	124	22	randomly	randomly	ADV
cana-659	124	23	chosen	choose	VERB
cana-659	124	24	element	element	NOUN
cana-659	124	25	being	be	AUX
cana-659	124	26	misclassified	misclassifie	VERB
cana-659	124	27	if	if	SCONJ
cana-659	124	28	it	it	PRON
cana-659	124	29	was	be	AUX
cana-659	124	30	randomly	randomly	ADV
cana-659	124	31	labeled	label	VERB
cana-659	124	32	according	accord	VERB
cana-659	124	33	to	to	ADP
cana-659	124	34	the	the	DET
cana-659	124	35	distribution	distribution	NOUN
cana-659	124	36	of	of	ADP
cana-659	124	37	labels	label	NOUN
cana-659	124	38	in	in	ADP
cana-659	124	39	the	the	DET
cana-659	124	40	subset	subset	NOUN
cana-659	124	41	.	.	PUNCT
cana-659	125	1	𝐺𝑖𝑛𝑖(𝐷	𝐺𝑖𝑛𝑖(𝐷	PROPN
cana-659	125	2	)	)	PUNCT
cana-659	125	3	=	=	NOUN
cana-659	126	1	1	1	NUM
cana-659	126	2	−	−	NOUN
cana-659	126	3	∑(𝑖	∑(𝑖	NUM
cana-659	126	4	=	=	SYM
cana-659	126	5	1	1	NUM
cana-659	126	6	𝑡𝑜	𝑡𝑜	NOUN
cana-659	126	7	𝐶)(𝑝𝑖2	𝐶)(𝑝𝑖2	NOUN
cana-659	126	8	)	)	PUNCT
cana-659	126	9	•	•	NOUN
cana-659	126	10	where	where	SCONJ
cana-659	126	11	pi	pi	NOUN
cana-659	126	12	is	be	AUX
cana-659	126	13	the	the	DET
cana-659	126	14	probability	probability	NOUN
cana-659	126	15	of	of	ADP
cana-659	126	16	class	class	NOUN
cana-659	126	17	i	i	PROPN
cana-659	126	18	in	in	ADP
cana-659	126	19	dataset	dataset	ADJ
cana-659	126	20	d	d	NOUN
cana-659	126	21	,	,	PUNCT
cana-659	126	22	and	and	CCONJ
cana-659	126	23	c	c	NOUN
cana-659	126	24	is	be	AUX
cana-659	126	25	the	the	DET
cana-659	126	26	number	number	NOUN
cana-659	126	27	of	of	ADP
cana-659	126	28	classes	class	NOUN
cana-659	126	29	.	.	PUNCT
cana-659	127	1	2	2	X
cana-659	127	2	.	.	X
cana-659	127	3	entropy	entropy	PROPN
cana-659	127	4	(	(	PUNCT
cana-659	127	5	for	for	ADP
cana-659	127	6	classification	classification	NOUN
cana-659	127	7	)	)	PUNCT
cana-659	127	8	entropy	entropy	NOUN
cana-659	127	9	measures	measure	VERB
cana-659	127	10	the	the	DET
cana-659	127	11	impurity	impurity	NOUN
cana-659	127	12	or	or	CCONJ
cana-659	127	13	randomness	randomness	NOUN
cana-659	127	14	in	in	ADP
cana-659	127	15	the	the	DET
cana-659	127	16	data	datum	NOUN
cana-659	127	17	.	.	PUNCT
cana-659	128	1	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	NOUN
cana-659	128	2	)	)	PUNCT
cana-659	128	3	=	=	PUNCT
cana-659	129	1	−	−	PUNCT
cana-659	129	2	∑(𝑖	∑(𝑖	NUM
cana-659	129	3	=	=	SYM
cana-659	129	4	1	1	NUM
cana-659	129	5	𝑡𝑜	𝑡𝑜	PROPN
cana-659	129	6	𝐶)(𝑝𝑖	𝐶)(𝑝𝑖	ADV
cana-659	129	7	∗	∗	NOUN
cana-659	129	8	𝑙𝑜𝑔2(𝑝𝑖	𝑙𝑜𝑔2(𝑝𝑖	NOUN
cana-659	129	9	)	)	PUNCT
cana-659	129	10	)	)	PUNCT
cana-659	130	1	•	•	ADP
cana-659	130	2	where	where	SCONJ
cana-659	130	3	pi	pi	NOUN
cana-659	130	4	is	be	AUX
cana-659	130	5	the	the	DET
cana-659	130	6	probability	probability	NOUN
cana-659	130	7	of	of	ADP
cana-659	130	8	class	class	NOUN
cana-659	130	9	i	i	PROPN
cana-659	130	10	in	in	ADP
cana-659	130	11	dataset	dataset	ADJ
cana-659	130	12	d	d	NOUN
cana-659	130	13	,	,	PUNCT
cana-659	130	14	and	and	CCONJ
cana-659	130	15	c	c	NOUN
cana-659	130	16	is	be	AUX
cana-659	130	17	the	the	DET
cana-659	130	18	number	number	NOUN
cana-659	130	19	of	of	ADP
cana-659	130	20	classes	class	NOUN
cana-659	130	21	.	.	PUNCT
cana-659	131	1	communications	communication	NOUN
cana-659	131	2	on	on	ADP
cana-659	131	3	applied	apply	VERB
cana-659	131	4	nonlinear	nonlinear	ADJ
cana-659	131	5	analysis	analysis	NOUN
cana-659	131	6	issn	issn	NOUN
cana-659	131	7	:	:	PUNCT
cana-659	131	8	1074	1074	NUM
cana-659	131	9	-	-	PUNCT
cana-659	131	10	133x	133x	NUM
cana-659	131	11	vol	vol	NOUN
cana-659	131	12	31	31	NUM
cana-659	131	13	no	no	NOUN
cana-659	131	14	.	.	PUNCT
cana-659	132	1	2s	2s	NUM
cana-659	132	2	(	(	PUNCT
cana-659	132	3	2024	2024	NUM
cana-659	132	4	)	)	PUNCT
cana-659	132	5	442	442	NUM
cana-659	132	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	132	7	3	3	X
cana-659	132	8	.	.	X
cana-659	132	9	information	information	NOUN
cana-659	132	10	gain	gain	VERB
cana-659	132	11	information	information	NOUN
cana-659	132	12	gain	gain	NOUN
cana-659	132	13	measures	measure	VERB
cana-659	132	14	the	the	DET
cana-659	132	15	reduction	reduction	NOUN
cana-659	132	16	in	in	ADP
cana-659	132	17	entropy	entropy	NOUN
cana-659	132	18	after	after	SCONJ
cana-659	132	19	a	a	DET
cana-659	132	20	dataset	dataset	NOUN
cana-659	132	21	is	be	AUX
cana-659	132	22	split	split	VERB
cana-659	132	23	on	on	ADP
cana-659	132	24	a	a	DET
cana-659	132	25	feature	feature	NOUN
cana-659	132	26	.	.	PUNCT
cana-659	133	1	𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛	𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛	PROPN
cana-659	133	2	𝐺𝑎𝑖𝑛(𝐷	𝐺𝑎𝑖𝑛(𝐷	PROPN
cana-659	133	3	,	,	PUNCT
cana-659	133	4	𝐴	𝐴	PROPN
cana-659	133	5	)	)	PUNCT
cana-659	133	6	=	=	SYM
cana-659	133	7	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	PROPN
cana-659	133	8	)	)	PUNCT
cana-659	134	1	−	−	PROPN
cana-659	134	2	∑(𝑣	∑(𝑣	PROPN
cana-659	134	3	𝑖𝑛	𝑖𝑛	NUM
cana-659	134	4	𝑉𝑎𝑙𝑢𝑒𝑠(𝐴	𝑉𝑎𝑙𝑢𝑒𝑠(𝐴	NOUN
cana-659	134	5	)	)	PUNCT
cana-659	134	6	)	)	PUNCT
cana-659	134	7	(	(	PUNCT
cana-659	134	8	|𝐷𝑣|	|𝐷𝑣|	PROPN
cana-659	134	9	|𝐷|	|𝐷|	NOUN
cana-659	134	10	)	)	PUNCT
cana-659	134	11	∗	∗	NOUN
cana-659	134	12	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷𝑣	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷𝑣	NOUN
cana-659	134	13	)	)	PUNCT
cana-659	134	14	•	•	NUM
cana-659	134	15	where	where	SCONJ
cana-659	134	16	d	d	NOUN
cana-659	134	17	is	be	AUX
cana-659	134	18	the	the	DET
cana-659	134	19	dataset	dataset	NOUN
cana-659	134	20	,	,	PUNCT
cana-659	134	21	a	a	PRON
cana-659	134	22	is	be	AUX
cana-659	134	23	the	the	DET
cana-659	134	24	feature	feature	NOUN
cana-659	134	25	,	,	PUNCT
cana-659	134	26	values(a	values(a	NOUN
cana-659	134	27	)	)	PUNCT
cana-659	134	28	are	be	AUX
cana-659	134	29	the	the	DET
cana-659	134	30	possible	possible	ADJ
cana-659	134	31	values	value	NOUN
cana-659	134	32	of	of	ADP
cana-659	134	33	feature	feature	NOUN
cana-659	134	34	a	a	PRON
cana-659	134	35	,	,	PUNCT
cana-659	134	36	dv	dv	PROPN
cana-659	134	37	is	be	AUX
cana-659	134	38	the	the	DET
cana-659	134	39	subset	subset	NOUN
cana-659	134	40	of	of	ADP
cana-659	134	41	d	d	PROPN
cana-659	134	42	where	where	SCONJ
cana-659	134	43	feature	feature	NOUN
cana-659	134	44	a	a	DET
cana-659	134	45	has	have	AUX
cana-659	134	46	value	value	NOUN
cana-659	134	47	v.	v.	ADP
cana-659	134	48	4	4	NUM
cana-659	134	49	.	.	NOUN
cana-659	134	50	variance	variance	NOUN
cana-659	134	51	reduction	reduction	NOUN
cana-659	134	52	(	(	PUNCT
cana-659	134	53	for	for	ADP
cana-659	134	54	regression	regression	NOUN
cana-659	134	55	)	)	PUNCT
cana-659	134	56	variance	variance	NOUN
cana-659	134	57	reduction	reduction	NOUN
cana-659	134	58	measures	measure	VERB
cana-659	134	59	the	the	DET
cana-659	134	60	reduction	reduction	NOUN
cana-659	134	61	in	in	ADP
cana-659	134	62	variance	variance	NOUN
cana-659	134	63	after	after	SCONJ
cana-659	134	64	a	a	DET
cana-659	134	65	dataset	dataset	NOUN
cana-659	134	66	is	be	AUX
cana-659	134	67	split	split	VERB
cana-659	134	68	on	on	ADP
cana-659	134	69	a	a	DET
cana-659	134	70	feature	feature	NOUN
cana-659	134	71	.	.	PUNCT
cana-659	135	1	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷	NOUN
cana-659	135	2	)	)	PUNCT
cana-659	135	3	=	=	PUNCT
cana-659	135	4	(	(	PUNCT
cana-659	135	5	1	1	NUM
cana-659	135	6	|𝐷|	|𝐷|	NOUN
cana-659	135	7	)	)	PUNCT
cana-659	136	1	∗	∗	NOUN
cana-659	136	2	∑(𝑖	∑(𝑖	PUNCT
cana-659	137	1	=	=	SYM
cana-659	137	2	1	1	NUM
cana-659	137	3	𝑡𝑜	𝑡𝑜	NOUN
cana-659	137	4	|𝐷|)(𝑦𝑖	|𝐷|)(𝑦𝑖	ADV
cana-659	137	5	−	−	ADV
cana-659	137	6	ȳ)2	ȳ)2	VERB
cana-659	137	7	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒	PROPN
cana-659	137	8	𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛(𝐷	𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛(𝐷	PROPN
cana-659	137	9	,	,	PUNCT
cana-659	137	10	𝐴	𝐴	PROPN
cana-659	137	11	)	)	PUNCT
cana-659	137	12	=	=	SYM
cana-659	137	13	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷	PROPN
cana-659	137	14	)	)	PUNCT
cana-659	137	15	−	−	PROPN
cana-659	138	1	∑(𝑣	∑(𝑣	PROPN
cana-659	138	2	𝑖𝑛	𝑖𝑛	NUM
cana-659	138	3	𝑉𝑎𝑙𝑢𝑒𝑠(𝐴	𝑉𝑎𝑙𝑢𝑒𝑠(𝐴	NOUN
cana-659	138	4	)	)	PUNCT
cana-659	138	5	)	)	PUNCT
cana-659	139	1	(	(	PUNCT
cana-659	139	2	|𝐷𝑣|	|𝐷𝑣|	PROPN
cana-659	139	3	|𝐷|	|𝐷|	NOUN
cana-659	139	4	)	)	PUNCT
cana-659	139	5	∗	∗	NOUN
cana-659	139	6	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷𝑣	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐷𝑣	NOUN
cana-659	139	7	)	)	PUNCT
cana-659	139	8	•	•	NUM
cana-659	139	9	where	where	SCONJ
cana-659	139	10	yi	yi	PROPN
cana-659	139	11	is	be	AUX
cana-659	139	12	the	the	DET
cana-659	139	13	target	target	NOUN
cana-659	139	14	value	value	NOUN
cana-659	139	15	of	of	ADP
cana-659	139	16	the	the	DET
cana-659	139	17	i	i	PROPN
cana-659	139	18	-	-	PUNCT
cana-659	139	19	th	th	X
cana-659	139	20	instance	instance	NOUN
cana-659	139	21	,	,	PUNCT
cana-659	139	22	ȳ	ȳ	PROPN
cana-659	139	23	is	be	AUX
cana-659	139	24	the	the	DET
cana-659	139	25	mean	mean	NOUN
cana-659	139	26	of	of	ADP
cana-659	139	27	target	target	NOUN
cana-659	139	28	values	value	NOUN
cana-659	139	29	in	in	ADP
cana-659	139	30	d	d	PROPN
cana-659	139	31	,	,	PUNCT
cana-659	139	32	dv	dv	PROPN
cana-659	139	33	is	be	AUX
cana-659	139	34	the	the	DET
cana-659	139	35	subset	subset	NOUN
cana-659	139	36	of	of	ADP
cana-659	139	37	d	d	PROPN
cana-659	139	38	where	where	SCONJ
cana-659	139	39	feature	feature	NOUN
cana-659	139	40	a	a	DET
cana-659	139	41	has	have	AUX
cana-659	139	42	value	value	NOUN
cana-659	139	43	v.	v.	ADP
cana-659	139	44	3.3	3.3	NUM
cana-659	139	45	random	random	ADJ
cana-659	139	46	forest	forest	NOUN
cana-659	139	47	due	due	ADP
cana-659	139	48	to	to	ADP
cana-659	139	49	adaptability	adaptability	NOUN
cana-659	139	50	and	and	CCONJ
cana-659	139	51	simplicity	simplicity	NOUN
cana-659	139	52	,	,	PUNCT
cana-659	139	53	random	random	ADJ
cana-659	139	54	forest	forest	NOUN
cana-659	139	55	is	be	AUX
cana-659	139	56	a	a	DET
cana-659	139	57	popular	popular	ADJ
cana-659	139	58	supervised	supervised	ADJ
cana-659	139	59	machine	machine	NOUN
cana-659	139	60	learning	learning	NOUN
cana-659	139	61	technique	technique	NOUN
cana-659	139	62	that	that	PRON
cana-659	139	63	may	may	AUX
cana-659	139	64	be	be	AUX
cana-659	139	65	used	use	VERB
cana-659	139	66	for	for	ADP
cana-659	139	67	both	both	CCONJ
cana-659	139	68	regression	regression	NOUN
cana-659	139	69	and	and	CCONJ
cana-659	139	70	classification	classification	NOUN
cana-659	139	71	issues	issue	NOUN
cana-659	139	72	.	.	PUNCT
cana-659	140	1	[	[	X
cana-659	140	2	4	4	X
cana-659	140	3	]	]	PUNCT
cana-659	140	4	as	as	SCONJ
cana-659	140	5	the	the	DET
cana-659	140	6	name	name	NOUN
cana-659	140	7	suggests	suggest	VERB
cana-659	140	8	,	,	PUNCT
cana-659	140	9	a	a	DET
cana-659	140	10	forest	forest	NOUN
cana-659	140	11	is	be	AUX
cana-659	140	12	constructed	construct	VERB
cana-659	140	13	from	from	ADP
cana-659	140	14	numerous	numerous	ADJ
cana-659	140	15	trees	tree	NOUN
cana-659	140	16	.	.	PUNCT
cana-659	141	1	the	the	DET
cana-659	141	2	model	model	NOUN
cana-659	141	3	trains	train	VERB
cana-659	141	4	several	several	ADJ
cana-659	141	5	distinct	distinct	ADJ
cana-659	141	6	decision	decision	NOUN
cana-659	141	7	trees	tree	NOUN
cana-659	141	8	on	on	ADP
cana-659	141	9	various	various	ADJ
cana-659	141	10	sets	set	NOUN
cana-659	141	11	of	of	ADP
cana-659	141	12	data	datum	NOUN
cana-659	141	13	,	,	PUNCT
cana-659	141	14	and	and	CCONJ
cana-659	141	15	the	the	DET
cana-659	141	16	average	average	ADJ
cana-659	141	17	result	result	NOUN
cana-659	141	18	from	from	ADP
cana-659	141	19	these	these	DET
cana-659	141	20	trees	tree	NOUN
cana-659	141	21	is	be	AUX
cana-659	141	22	used	use	VERB
cana-659	141	23	as	as	ADP
cana-659	141	24	the	the	DET
cana-659	141	25	output	output	NOUN
cana-659	141	26	in	in	ADP
cana-659	141	27	the	the	DET
cana-659	141	28	end	end	NOUN
cana-659	141	29	.	.	PUNCT
cana-659	142	1	[	[	X
cana-659	142	2	7	7	X
cana-659	142	3	]	]	X
cana-659	142	4	the	the	DET
cana-659	142	5	random	random	ADJ
cana-659	142	6	forest	forest	NOUN
cana-659	142	7	classification	classification	NOUN
cana-659	142	8	algorithm	algorithm	NOUN
cana-659	142	9	will	will	AUX
cana-659	142	10	be	be	AUX
cana-659	142	11	used	use	VERB
cana-659	142	12	in	in	ADP
cana-659	142	13	the	the	DET
cana-659	142	14	same	same	ADJ
cana-659	142	15	way	way	NOUN
cana-659	142	16	as	as	SCONJ
cana-659	142	17	the	the	DET
cana-659	142	18	decision	decision	NOUN
cana-659	142	19	tree	tree	NOUN
cana-659	142	20	algorithm.[6	algorithm.[6	PRON
cana-659	142	21	]	]	PUNCT
cana-659	143	1	the	the	DET
cana-659	143	2	technique	technique	NOUN
cana-659	143	3	builds	build	VERB
cana-659	143	4	numerous	numerous	ADJ
cana-659	143	5	decision	decision	NOUN
cana-659	143	6	trees	tree	NOUN
cana-659	143	7	,	,	PUNCT
cana-659	143	8	which	which	PRON
cana-659	143	9	are	be	AUX
cana-659	143	10	typically	typically	ADV
cana-659	143	11	trained	train	VERB
cana-659	143	12	using	use	VERB
cana-659	143	13	the	the	DET
cana-659	143	14	bagging	bagging	NOUN
cana-659	143	15	approach	approach	NOUN
cana-659	143	16	,	,	PUNCT
cana-659	143	17	then	then	ADV
cana-659	143	18	combines	combine	VERB
cana-659	143	19	them	they	PRON
cana-659	143	20	to	to	PART
cana-659	143	21	provide	provide	VERB
cana-659	143	22	a	a	DET
cana-659	143	23	more	more	ADV
cana-659	143	24	stable	stable	ADJ
cana-659	143	25	and	and	CCONJ
cana-659	143	26	precise	precise	ADJ
cana-659	143	27	evaluation	evaluation	NOUN
cana-659	143	28	.	.	PUNCT
cana-659	144	1	[	[	X
cana-659	144	2	4	4	X
cana-659	144	3	]	]	X
cana-659	144	4	a	a	DET
cana-659	144	5	straightforward	straightforward	ADJ
cana-659	144	6	,	,	PUNCT
cana-659	144	7	understandable	understandable	ADJ
cana-659	144	8	technique	technique	NOUN
cana-659	144	9	that	that	PRON
cana-659	144	10	can	can	AUX
cana-659	144	11	handle	handle	VERB
cana-659	144	12	challenging	challenge	VERB
cana-659	144	13	nonlinear	nonlinear	ADJ
cana-659	144	14	classification	classification	NOUN
cana-659	144	15	tasks	task	NOUN
cana-659	144	16	is	be	AUX
cana-659	144	17	the	the	DET
cana-659	144	18	random	random	ADJ
cana-659	144	19	forest.[7	forest.[7	ADV
cana-659	144	20	]	]	PUNCT
cana-659	144	21	algorithm	algorithm	NOUN
cana-659	144	22	:	:	PUNCT
cana-659	144	23	a.	a.	NOUN
cana-659	144	24	bootstrap	bootstrap	NOUN
cana-659	144	25	sampling	sampling	NOUN
cana-659	144	26	:	:	PUNCT
cana-659	144	27	•	•	PUNCT
cana-659	144	28	generate	generate	VERB
cana-659	144	29	multiple	multiple	ADJ
cana-659	144	30	bootstrap	bootstrap	NOUN
cana-659	144	31	samples	sample	NOUN
cana-659	144	32	from	from	ADP
cana-659	144	33	the	the	DET
cana-659	144	34	original	original	ADJ
cana-659	144	35	dataset	dataset	NOUN
cana-659	144	36	.	.	PUNCT
cana-659	145	1	each	each	DET
cana-659	145	2	sample	sample	NOUN
cana-659	145	3	is	be	AUX
cana-659	145	4	created	create	VERB
cana-659	145	5	by	by	ADP
cana-659	145	6	randomly	randomly	ADV
cana-659	145	7	selecting	select	VERB
cana-659	145	8	data	datum	NOUN
cana-659	145	9	points	point	NOUN
cana-659	145	10	with	with	ADP
cana-659	145	11	replacement	replacement	NOUN
cana-659	145	12	.	.	PUNCT
cana-659	146	1	•	•	NUM
cana-659	146	2	let	let	VERB
cana-659	146	3	{	{	PUNCT
cana-659	146	4	𝐷1	𝐷1	PROPN
cana-659	146	5	,	,	PUNCT
cana-659	146	6	𝐷2	𝐷2	NOUN
cana-659	146	7	,	,	PUNCT
cana-659	146	8	…	…	PUNCT
cana-659	146	9	,	,	PUNCT
cana-659	146	10	𝐷𝐵	𝐷𝐵	PROPN
cana-659	146	11	}	}	PUNCT
cana-659	146	12	be	be	AUX
cana-659	146	13	the	the	DET
cana-659	146	14	b	b	PROPN
cana-659	146	15	bootstrap	bootstrap	NOUN
cana-659	146	16	samples	sample	NOUN
cana-659	146	17	.	.	PUNCT
cana-659	147	1	b.	b.	PROPN
cana-659	147	2	build	build	VERB
cana-659	147	3	decision	decision	NOUN
cana-659	147	4	trees	tree	NOUN
cana-659	147	5	:	:	PUNCT
cana-659	147	6	•	•	NOUN
cana-659	147	7	for	for	ADP
cana-659	147	8	each	each	DET
cana-659	147	9	bootstrap	bootstrap	NOUN
cana-659	147	10	sample	sample	NOUN
cana-659	147	11	di	di	NOUN
cana-659	147	12	,	,	PUNCT
cana-659	147	13	construct	construct	VERB
cana-659	147	14	a	a	DET
cana-659	147	15	decision	decision	NOUN
cana-659	147	16	tree	tree	NOUN
cana-659	147	17	ti	ti	NOUN
cana-659	147	18	using	use	VERB
cana-659	147	19	a	a	DET
cana-659	147	20	subset	subset	NOUN
cana-659	147	21	of	of	ADP
cana-659	147	22	features	feature	NOUN
cana-659	147	23	.	.	PUNCT
cana-659	148	1	•	•	X
cana-659	148	2	at	at	ADP
cana-659	148	3	each	each	DET
cana-659	148	4	node	node	NOUN
cana-659	148	5	in	in	ADP
cana-659	148	6	the	the	DET
cana-659	148	7	tree	tree	NOUN
cana-659	148	8	,	,	PUNCT
cana-659	148	9	select	select	VERB
cana-659	148	10	the	the	DET
cana-659	148	11	best	good	ADJ
cana-659	148	12	feature	feature	NOUN
cana-659	148	13	from	from	ADP
cana-659	148	14	a	a	DET
cana-659	148	15	randomly	randomly	ADV
cana-659	148	16	chosen	choose	VERB
cana-659	148	17	subset	subset	NOUN
cana-659	148	18	of	of	ADP
cana-659	148	19	m	m	PROPN
cana-659	148	20	features	feature	NOUN
cana-659	148	21	(	(	PUNCT
cana-659	148	22	where	where	SCONJ
cana-659	148	23	m	m	VERB
cana-659	148	24	<	<	X
cana-659	148	25	total	total	ADJ
cana-659	148	26	number	number	NOUN
cana-659	148	27	of	of	ADP
cana-659	148	28	features	feature	NOUN
cana-659	148	29	)	)	PUNCT
cana-659	148	30	.	.	PUNCT
cana-659	149	1	•	•	NUM
cana-659	149	2	use	use	VERB
cana-659	149	3	the	the	DET
cana-659	149	4	chosen	choose	VERB
cana-659	149	5	feature	feature	NOUN
cana-659	149	6	to	to	PART
cana-659	149	7	split	split	VERB
cana-659	149	8	the	the	DET
cana-659	149	9	data	datum	NOUN
cana-659	149	10	and	and	CCONJ
cana-659	149	11	repeat	repeat	VERB
cana-659	149	12	this	this	DET
cana-659	149	13	process	process	NOUN
cana-659	149	14	recursively	recursively	ADV
cana-659	149	15	to	to	PART
cana-659	149	16	grow	grow	VERB
cana-659	149	17	the	the	DET
cana-659	149	18	tree	tree	NOUN
cana-659	149	19	.	.	PUNCT
cana-659	150	1	communications	communication	NOUN
cana-659	150	2	on	on	ADP
cana-659	150	3	applied	apply	VERB
cana-659	150	4	nonlinear	nonlinear	ADJ
cana-659	150	5	analysis	analysis	NOUN
cana-659	150	6	issn	issn	NOUN
cana-659	150	7	:	:	PUNCT
cana-659	150	8	1074	1074	NUM
cana-659	150	9	-	-	PUNCT
cana-659	150	10	133x	133x	NUM
cana-659	150	11	vol	vol	NOUN
cana-659	150	12	31	31	NUM
cana-659	150	13	no	no	NOUN
cana-659	150	14	.	.	PUNCT
cana-659	151	1	2s	2s	NUM
cana-659	151	2	(	(	PUNCT
cana-659	151	3	2024	2024	NUM
cana-659	151	4	)	)	PUNCT
cana-659	151	5	443	443	NUM
cana-659	151	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	151	7	c.	c.	NOUN
cana-659	151	8	aggregate	aggregate	ADJ
cana-659	151	9	predictions	prediction	NOUN
cana-659	151	10	(	(	PUNCT
cana-659	151	11	ensemble	ensemble	ADJ
cana-659	151	12	method	method	NOUN
cana-659	151	13	):	):	PUNCT
cana-659	151	14	•	•	ADP
cana-659	151	15	for	for	ADP
cana-659	151	16	classification	classification	NOUN
cana-659	151	17	:	:	PUNCT
cana-659	151	18	•	•	NOUN
cana-659	151	19	let	let	VERB
cana-659	151	20	ci(x	ci(x	PUNCT
cana-659	151	21	)	)	PUNCT
cana-659	151	22	be	be	AUX
cana-659	151	23	the	the	DET
cana-659	151	24	predicted	predict	VERB
cana-659	151	25	class	class	NOUN
cana-659	151	26	by	by	ADP
cana-659	151	27	the	the	DET
cana-659	151	28	i	i	PROPN
cana-659	151	29	-	-	PUNCT
cana-659	151	30	th	th	X
cana-659	151	31	tree	tree	NOUN
cana-659	151	32	for	for	ADP
cana-659	151	33	input	input	NOUN
cana-659	151	34	x.	x.	NOUN
cana-659	151	35	•	•	ADP
cana-659	152	1	the	the	DET
cana-659	152	2	final	final	ADJ
cana-659	152	3	prediction	prediction	NOUN
cana-659	152	4	c^(x	c^(x	NOUN
cana-659	152	5	)	)	PUNCT
cana-659	152	6	is	be	AUX
cana-659	152	7	obtained	obtain	VERB
cana-659	152	8	by	by	ADP
cana-659	152	9	majority	majority	NOUN
cana-659	152	10	voting	voting	NOUN
cana-659	152	11	:	:	PUNCT
cana-659	152	12	𝐶^(𝑥	𝐶^(𝑥	X
cana-659	152	13	)	)	PUNCT
cana-659	152	14	=	=	SYM
cana-659	152	15	𝑚𝑜𝑑𝑒	𝑚𝑜𝑑𝑒	NOUN
cana-659	152	16	{	{	PUNCT
cana-659	152	17	𝐶1(𝑥	𝐶1(𝑥	NOUN
cana-659	152	18	)	)	PUNCT
cana-659	152	19	,	,	PUNCT
cana-659	152	20	𝐶2(𝑥	𝐶2(𝑥	PROPN
cana-659	152	21	)	)	PUNCT
cana-659	152	22	,	,	PUNCT
cana-659	152	23	…	…	PUNCT
cana-659	152	24	,	,	PUNCT
cana-659	152	25	𝐶𝐵(𝑥	𝐶𝐵(𝑥	PROPN
cana-659	152	26	)	)	PUNCT
cana-659	152	27	}	}	PUNCT
cana-659	152	28	d.	d.	NOUN
cana-659	152	29	for	for	ADP
cana-659	152	30	regression	regression	NOUN
cana-659	152	31	:	:	PUNCT
cana-659	152	32	•	•	ADV
cana-659	152	33	let	let	VERB
cana-659	152	34	ri(x	ri(x	NUM
cana-659	152	35	)	)	PUNCT
cana-659	152	36	be	be	VERB
cana-659	152	37	the	the	DET
cana-659	152	38	predicted	predict	VERB
cana-659	152	39	value	value	NOUN
cana-659	152	40	by	by	ADP
cana-659	152	41	the	the	DET
cana-659	152	42	i	i	PROPN
cana-659	152	43	-	-	PUNCT
cana-659	152	44	th	th	X
cana-659	152	45	tree	tree	NOUN
cana-659	152	46	for	for	ADP
cana-659	152	47	input	input	NOUN
cana-659	152	48	x.	x.	NOUN
cana-659	152	49	•	•	ADP
cana-659	153	1	the	the	DET
cana-659	153	2	final	final	ADJ
cana-659	153	3	prediction	prediction	NOUN
cana-659	153	4	r^(x	r^(x	NOUN
cana-659	153	5	)	)	PUNCT
cana-659	153	6	is	be	AUX
cana-659	153	7	obtained	obtain	VERB
cana-659	153	8	by	by	ADP
cana-659	153	9	averaging	average	VERB
cana-659	153	10	:	:	PUNCT
cana-659	153	11	𝑅𝑥	𝑅𝑥	ADP
cana-659	153	12	=	=	PUNCT
cana-659	153	13	(	(	PUNCT
cana-659	153	14	1	1	NUM
cana-659	153	15	𝐵	𝐵	NOUN
cana-659	153	16	)	)	PUNCT
cana-659	153	17	∑(𝑖	∑(𝑖	PUNCT
cana-659	154	1	=	=	SYM
cana-659	154	2	1	1	NUM
cana-659	154	3	𝑡𝑜	𝑡𝑜	PROPN
cana-659	154	4	𝐵)𝑅𝑖(𝑥	𝐵)𝑅𝑖(𝑥	PROPN
cana-659	154	5	)	)	PUNCT
cana-659	154	6	e.	e.	PROPN
cana-659	154	7	out	out	ADP
cana-659	154	8	-	-	PUNCT
cana-659	154	9	of	of	ADP
cana-659	154	10	-	-	PUNCT
cana-659	154	11	bag	bag	NOUN
cana-659	154	12	(	(	PUNCT
cana-659	154	13	oob	oob	NOUN
cana-659	154	14	)	)	PUNCT
cana-659	154	15	error	error	NOUN
cana-659	154	16	estimation	estimation	NOUN
cana-659	154	17	:	:	PUNCT
cana-659	154	18	•	•	NOUN
cana-659	154	19	for	for	ADP
cana-659	154	20	each	each	DET
cana-659	154	21	data	datum	NOUN
cana-659	154	22	point	point	NOUN
cana-659	154	23	,	,	PUNCT
cana-659	154	24	predict	predict	VERB
cana-659	154	25	its	its	PRON
cana-659	154	26	value	value	NOUN
cana-659	154	27	using	use	VERB
cana-659	154	28	only	only	ADV
cana-659	154	29	the	the	DET
cana-659	154	30	trees	tree	NOUN
cana-659	154	31	that	that	PRON
cana-659	154	32	did	do	AUX
cana-659	154	33	not	not	PART
cana-659	154	34	include	include	VERB
cana-659	154	35	this	this	DET
cana-659	154	36	point	point	NOUN
cana-659	154	37	in	in	ADP
cana-659	154	38	their	their	PRON
cana-659	154	39	bootstrap	bootstrap	NOUN
cana-659	154	40	sample	sample	NOUN
cana-659	154	41	(	(	PUNCT
cana-659	154	42	oob	oob	NOUN
cana-659	154	43	prediction	prediction	NOUN
cana-659	154	44	)	)	PUNCT
cana-659	154	45	.	.	PUNCT
cana-659	155	1	•	•	NUM
cana-659	155	2	calculate	calculate	VERB
cana-659	155	3	the	the	DET
cana-659	155	4	oob	oob	NOUN
cana-659	155	5	error	error	NOUN
cana-659	155	6	by	by	ADP
cana-659	155	7	comparing	compare	VERB
cana-659	155	8	the	the	DET
cana-659	155	9	oob	oob	NOUN
cana-659	155	10	predictions	prediction	NOUN
cana-659	155	11	with	with	ADP
cana-659	155	12	the	the	DET
cana-659	155	13	actual	actual	ADJ
cana-659	155	14	values	value	NOUN
cana-659	155	15	.	.	PUNCT
cana-659	156	1	f.	f.	PROPN
cana-659	156	2	for	for	ADP
cana-659	156	3	classification	classification	NOUN
cana-659	156	4	:	:	PUNCT
cana-659	156	5	𝑂𝑂𝐵	𝑂𝑂𝐵	PROPN
cana-659	156	6	𝐸𝑟𝑟𝑜𝑟	𝐸𝑟𝑟𝑜𝑟	PROPN
cana-659	156	7	=	=	PUNCT
cana-659	156	8	(	(	PUNCT
cana-659	156	9	1	1	NUM
cana-659	156	10	𝑁	𝑁	PROPN
cana-659	156	11	)	)	PUNCT
cana-659	156	12	∑(𝑖	∑(𝑖	PUNCT
cana-659	156	13	=	=	SYM
cana-659	156	14	1	1	NUM
cana-659	156	15	𝑡𝑜	𝑡𝑜	PROPN
cana-659	156	16	𝑁)𝐼(𝐶𝑂𝑂𝐵(𝑥𝑖	𝑁)𝐼(𝐶𝑂𝑂𝐵(𝑥𝑖	VERB
cana-659	156	17	)	)	PUNCT
cana-659	156	18	≠	≠	PROPN
cana-659	156	19	𝑦𝑖	𝑦𝑖	PROPN
cana-659	156	20	)	)	PUNCT
cana-659	156	21	•	•	NOUN
cana-659	156	22	where	where	SCONJ
cana-659	156	23	n	n	PRON
cana-659	156	24	is	be	AUX
cana-659	156	25	the	the	DET
cana-659	156	26	number	number	NOUN
cana-659	156	27	of	of	ADP
cana-659	156	28	data	datum	NOUN
cana-659	156	29	points	point	NOUN
cana-659	156	30	,	,	PUNCT
cana-659	156	31	c^oob(xi	c^oob(xi	VERB
cana-659	156	32	)	)	PUNCT
cana-659	156	33	is	be	AUX
cana-659	156	34	the	the	DET
cana-659	156	35	oob	oob	NOUN
cana-659	156	36	predicted	predict	VERB
cana-659	156	37	class	class	NOUN
cana-659	156	38	for	for	ADP
cana-659	156	39	xi	xi	PROPN
cana-659	156	40	,	,	PUNCT
cana-659	156	41	and	and	CCONJ
cana-659	156	42	yi	yi	PROPN
cana-659	156	43	is	be	AUX
cana-659	156	44	the	the	DET
cana-659	156	45	true	true	ADJ
cana-659	156	46	class	class	NOUN
cana-659	156	47	.	.	PUNCT
cana-659	157	1	g.	g.	NOUN
cana-659	157	2	for	for	ADP
cana-659	157	3	regression	regression	NOUN
cana-659	157	4	:	:	PUNCT
cana-659	157	5	𝑂𝑂𝐵	𝑂𝑂𝐵	PROPN
cana-659	157	6	𝐸𝑟𝑟𝑜𝑟	𝐸𝑟𝑟𝑜𝑟	PROPN
cana-659	157	7	=	=	PUNCT
cana-659	157	8	(	(	PUNCT
cana-659	157	9	1	1	NUM
cana-659	157	10	𝑁	𝑁	PROPN
cana-659	157	11	)	)	PUNCT
cana-659	157	12	∑(𝑖	∑(𝑖	PUNCT
cana-659	158	1	=	=	SYM
cana-659	158	2	1	1	NUM
cana-659	158	3	𝑡𝑜	𝑡𝑜	PROPN
cana-659	158	4	𝑁)(𝑅𝑂𝑂𝐵(𝑥𝑖	𝑁)(𝑅𝑂𝑂𝐵(𝑥𝑖	VERB
cana-659	158	5	)	)	PUNCT
cana-659	158	6	−	−	PROPN
cana-659	158	7	𝑦𝑖	𝑦𝑖	PROPN
cana-659	158	8	)	)	PUNCT
cana-659	158	9	2	2	NUM
cana-659	158	10	•	•	NOUN
cana-659	158	11	where	where	SCONJ
cana-659	158	12	n	n	PRON
cana-659	158	13	is	be	AUX
cana-659	158	14	the	the	DET
cana-659	158	15	number	number	NOUN
cana-659	158	16	of	of	ADP
cana-659	158	17	data	datum	NOUN
cana-659	158	18	points	point	NOUN
cana-659	158	19	,	,	PUNCT
cana-659	158	20	r^oob(xi	r^oob(xi	NUM
cana-659	158	21	)	)	PUNCT
cana-659	158	22	is	be	AUX
cana-659	158	23	the	the	DET
cana-659	158	24	oob	oob	NOUN
cana-659	158	25	predicted	predict	VERB
cana-659	158	26	value	value	NOUN
cana-659	158	27	for	for	ADP
cana-659	158	28	xi	xi	PROPN
cana-659	158	29	,	,	PUNCT
cana-659	158	30	and	and	CCONJ
cana-659	158	31	yi	yi	PROPN
cana-659	158	32	is	be	AUX
cana-659	158	33	the	the	DET
cana-659	158	34	true	true	ADJ
cana-659	158	35	value	value	NOUN
cana-659	158	36	.	.	PUNCT
cana-659	159	1	3.4	3.4	NUM
cana-659	159	2	naive	naive	ADJ
cana-659	159	3	bayes	baye	NOUN
cana-659	159	4	based	base	VERB
cana-659	159	5	on	on	ADP
cana-659	159	6	the	the	DET
cana-659	159	7	independence	independence	NOUN
cana-659	159	8	assumptions	assumption	NOUN
cana-659	159	9	between	between	ADP
cana-659	159	10	predictors	predictor	NOUN
cana-659	159	11	and	and	CCONJ
cana-659	159	12	the	the	DET
cana-659	159	13	bayes	bayes	NOUN
cana-659	159	14	theorem	theorem	VERB
cana-659	159	15	,	,	PUNCT
cana-659	159	16	the	the	DET
cana-659	159	17	naïve	naïve	ADJ
cana-659	159	18	bayes	bayes	NOUN
cana-659	159	19	algorithm	algorithm	PROPN
cana-659	159	20	is	be	AUX
cana-659	159	21	a	a	DET
cana-659	159	22	classification	classification	NOUN
cana-659	159	23	method	method	NOUN
cana-659	159	24	.	.	PUNCT
cana-659	160	1	large	large	ADJ
cana-659	160	2	datasets	dataset	NOUN
cana-659	160	3	benefit	benefit	VERB
cana-659	160	4	greatly	greatly	ADV
cana-659	160	5	from	from	ADP
cana-659	160	6	the	the	DET
cana-659	160	7	simplicity	simplicity	NOUN
cana-659	160	8	and	and	CCONJ
cana-659	160	9	lack	lack	NOUN
cana-659	160	10	of	of	ADP
cana-659	160	11	complex	complex	ADJ
cana-659	160	12	iterative	iterative	NOUN
cana-659	160	13	parameter	parameter	NOUN
cana-659	160	14	estimation	estimation	NOUN
cana-659	160	15	that	that	PRON
cana-659	160	16	characterizes	characterize	VERB
cana-659	160	17	naive	naive	ADJ
cana-659	160	18	bayesian	bayesian	NOUN
cana-659	160	19	models.[3][4	models.[3][4	PROPN
cana-659	160	20	]	]	PUNCT
cana-659	160	21	with	with	ADP
cana-659	160	22	the	the	DET
cana-659	160	23	algorithm	algorithm	NOUN
cana-659	160	24	:	:	PUNCT
cana-659	160	25	1	1	X
cana-659	160	26	.	.	X
cana-659	160	27	calculate	calculate	VERB
cana-659	160	28	prior	prior	ADJ
cana-659	160	29	probabilities	probability	NOUN
cana-659	160	30	:	:	PUNCT
cana-659	160	31	•	•	NOUN
cana-659	160	32	compute	compute	VERB
cana-659	160	33	the	the	DET
cana-659	160	34	prior	prior	ADJ
cana-659	160	35	probability	probability	NOUN
cana-659	160	36	for	for	ADP
cana-659	160	37	each	each	DET
cana-659	160	38	class	class	NOUN
cana-659	160	39	based	base	VERB
cana-659	160	40	on	on	ADP
cana-659	160	41	the	the	DET
cana-659	160	42	frequency	frequency	NOUN
cana-659	160	43	of	of	ADP
cana-659	160	44	each	each	DET
cana-659	160	45	class	class	NOUN
cana-659	160	46	in	in	ADP
cana-659	160	47	the	the	DET
cana-659	160	48	training	training	NOUN
cana-659	160	49	dataset	dataset	NOUN
cana-659	160	50	.	.	PUNCT
cana-659	161	1	𝑃(𝐶𝑘	𝑃(𝐶𝑘	NUM
cana-659	161	2	)	)	PUNCT
cana-659	162	1	=	=	PRON
cana-659	163	1	(	(	PUNCT
cana-659	163	2	𝑁𝑢𝑚𝑏𝑒𝑟	𝑁𝑢𝑚𝑏𝑒𝑟	PROPN
cana-659	163	3	𝑜𝑓	𝑜𝑓	ADP
cana-659	163	4	𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠	𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠	PROPN
cana-659	163	5	𝑖𝑛	𝑖𝑛	PRON
cana-659	163	6	𝑐𝑙𝑎𝑠𝑠	𝑐𝑙𝑎𝑠𝑠	PROPN
cana-659	163	7	𝐶𝑘	𝐶𝑘	PROPN
cana-659	163	8	)	)	PUNCT
cana-659	163	9	/	/	PUNCT
cana-659	164	1	(	(	PUNCT
cana-659	164	2	𝑇𝑜𝑡𝑎𝑙	𝑇𝑜𝑡𝑎𝑙	PRON
cana-659	164	3	𝑛𝑢𝑚𝑏𝑒𝑟	𝑛𝑢𝑚𝑏𝑒𝑟	NOUN
cana-659	164	4	𝑜𝑓	𝑜𝑓	ADP
cana-659	164	5	𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠	𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠	NOUN
cana-659	164	6	)	)	PUNCT
cana-659	164	7	communications	communication	NOUN
cana-659	164	8	on	on	ADP
cana-659	164	9	applied	apply	VERB
cana-659	164	10	nonlinear	nonlinear	ADJ
cana-659	164	11	analysis	analysis	NOUN
cana-659	164	12	issn	issn	NOUN
cana-659	164	13	:	:	PUNCT
cana-659	164	14	1074	1074	NUM
cana-659	164	15	-	-	PUNCT
cana-659	164	16	133x	133x	NUM
cana-659	164	17	vol	vol	NOUN
cana-659	164	18	31	31	NUM
cana-659	164	19	no	no	NOUN
cana-659	164	20	.	.	PUNCT
cana-659	165	1	2s	2s	NUM
cana-659	165	2	(	(	PUNCT
cana-659	165	3	2024	2024	NUM
cana-659	165	4	)	)	PUNCT
cana-659	165	5	444	444	NUM
cana-659	165	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	165	7	•	•	NUM
cana-659	165	8	where	where	SCONJ
cana-659	165	9	p(ck	p(ck	NOUN
cana-659	165	10	)	)	PUNCT
cana-659	165	11	is	be	AUX
cana-659	165	12	the	the	DET
cana-659	165	13	prior	prior	ADJ
cana-659	165	14	probability	probability	NOUN
cana-659	165	15	of	of	ADP
cana-659	165	16	class	class	NOUN
cana-659	165	17	ck	ck	NOUN
cana-659	165	18	.	.	PROPN
cana-659	166	1	2	2	NUM
cana-659	166	2	.	.	NOUN
cana-659	166	3	calculate	calculate	NOUN
cana-659	166	4	likelihood	likelihood	NOUN
cana-659	166	5	:	:	PUNCT
cana-659	166	6	•	•	NUM
cana-659	166	7	calculate	calculate	VERB
cana-659	166	8	the	the	DET
cana-659	166	9	likelihood	likelihood	NOUN
cana-659	166	10	of	of	ADP
cana-659	166	11	each	each	DET
cana-659	166	12	feature	feature	NOUN
cana-659	166	13	given	give	VERB
cana-659	166	14	each	each	DET
cana-659	166	15	class	class	NOUN
cana-659	166	16	.	.	PUNCT
cana-659	167	1	for	for	ADP
cana-659	167	2	a	a	DET
cana-659	167	3	feature	feature	NOUN
cana-659	167	4	xi	xi	NOUN
cana-659	167	5	and	and	CCONJ
cana-659	167	6	class	class	NOUN
cana-659	167	7	ck	ck	PROPN
cana-659	167	8	,	,	PUNCT
cana-659	167	9	this	this	PRON
cana-659	167	10	involves	involve	VERB
cana-659	167	11	determining	determine	VERB
cana-659	167	12	the	the	DET
cana-659	167	13	probability	probability	NOUN
cana-659	167	14	of	of	ADP
cana-659	167	15	xi	xi	X
cana-659	167	16	occurring	occur	VERB
cana-659	167	17	in	in	ADP
cana-659	167	18	ck	ck	PROPN
cana-659	167	19	.	.	PROPN
cana-659	167	20	•	•	NUM
cana-659	167	21	for	for	ADP
cana-659	167	22	continuous	continuous	ADJ
cana-659	167	23	features	feature	NOUN
cana-659	167	24	,	,	PUNCT
cana-659	167	25	typically	typically	ADV
cana-659	167	26	assume	assume	VERB
cana-659	167	27	a	a	DET
cana-659	167	28	gaussian	gaussian	ADJ
cana-659	167	29	distribution	distribution	NOUN
cana-659	167	30	:	:	PUNCT
cana-659	167	31	𝑃(𝑥𝑖	𝑃(𝑥𝑖	VERB
cana-659	167	32	|	|	ADV
cana-659	167	33	𝐶𝑘	𝐶𝑘	VERB
cana-659	167	34	)	)	PUNCT
cana-659	167	35	=	=	PUNCT
cana-659	167	36	(	(	PUNCT
cana-659	167	37	1	1	NUM
cana-659	167	38	/	/	SYM
cana-659	167	39	√(2𝜋𝜎𝑘^2	√(2𝜋𝜎𝑘^2	NOUN
cana-659	167	40	)	)	PUNCT
cana-659	167	41	)	)	PUNCT
cana-659	168	1	exp	exp	NOUN
cana-659	168	2	(	(	PUNCT
cana-659	168	3	−((𝑥𝑖	−((𝑥𝑖	PROPN
cana-659	168	4	−	−	PROPN
cana-659	168	5	𝜇𝑘)^2	𝜇𝑘)^2	PROPN
cana-659	168	6	)	)	PUNCT
cana-659	168	7	/	/	SYM
cana-659	168	8	(	(	PUNCT
cana-659	168	9	2𝜎𝑘^2	2𝜎𝑘^2	NUM
cana-659	168	10	)	)	PUNCT
cana-659	168	11	)	)	PUNCT
cana-659	168	12	•	•	NUM
cana-659	168	13	where	where	SCONJ
cana-659	168	14	μk	μk	NOUN
cana-659	168	15	and	and	CCONJ
cana-659	168	16	σk	σk	PROPN
cana-659	168	17	are	be	AUX
cana-659	168	18	the	the	DET
cana-659	168	19	mean	mean	ADJ
cana-659	168	20	and	and	CCONJ
cana-659	168	21	standard	standard	ADJ
cana-659	168	22	deviation	deviation	NOUN
cana-659	168	23	of	of	ADP
cana-659	168	24	feature	feature	NOUN
cana-659	168	25	xi	xi	ADP
cana-659	168	26	in	in	ADP
cana-659	168	27	class	class	NOUN
cana-659	168	28	ck	ck	PROPN
cana-659	168	29	.	.	PROPN
cana-659	169	1	3	3	X
cana-659	169	2	.	.	NOUN
cana-659	169	3	calculate	calculate	ADJ
cana-659	169	4	posterior	posterior	ADJ
cana-659	169	5	probabilities	probability	NOUN
cana-659	169	6	:	:	PUNCT
cana-659	169	7	•	•	NUM
cana-659	169	8	use	use	VERB
cana-659	169	9	bayes	baye	NOUN
cana-659	169	10	'	'	PART
cana-659	169	11	theorem	theorem	NOUN
cana-659	169	12	to	to	PART
cana-659	169	13	calculate	calculate	VERB
cana-659	169	14	the	the	DET
cana-659	169	15	posterior	posterior	ADJ
cana-659	169	16	probability	probability	NOUN
cana-659	169	17	for	for	ADP
cana-659	169	18	each	each	DET
cana-659	169	19	class	class	NOUN
cana-659	169	20	given	give	VERB
cana-659	169	21	the	the	DET
cana-659	169	22	feature	feature	NOUN
cana-659	169	23	vector	vector	NOUN
cana-659	169	24	x.	x.	NOUN
cana-659	170	1	the	the	DET
cana-659	170	2	posterior	posterior	ADJ
cana-659	170	3	probability	probability	NOUN
cana-659	170	4	is	be	AUX
cana-659	170	5	proportional	proportional	ADJ
cana-659	170	6	to	to	ADP
cana-659	170	7	the	the	DET
cana-659	170	8	product	product	NOUN
cana-659	170	9	of	of	ADP
cana-659	170	10	the	the	DET
cana-659	170	11	prior	prior	ADJ
cana-659	170	12	probability	probability	NOUN
cana-659	170	13	and	and	CCONJ
cana-659	170	14	the	the	DET
cana-659	170	15	likelihood	likelihood	NOUN
cana-659	170	16	of	of	ADP
cana-659	170	17	the	the	DET
cana-659	170	18	observed	observed	ADJ
cana-659	170	19	features	feature	NOUN
cana-659	170	20	:	:	PUNCT
cana-659	170	21	𝑃(𝐶𝑘	𝑃(𝐶𝑘	PROPN
cana-659	170	22	|	|	ADV
cana-659	170	23	𝑥	𝑥	NOUN
cana-659	170	24	)	)	PUNCT
cana-659	170	25	=	=	SYM
cana-659	170	26	(	(	PUNCT
cana-659	170	27	𝑃(𝐶𝑘	𝑃(𝐶𝑘	PROPN
cana-659	170	28	)	)	PUNCT
cana-659	170	29	∏(𝑖	∏(𝑖	NOUN
cana-659	170	30	=	=	SYM
cana-659	170	31	1	1	NUM
cana-659	170	32	𝑡𝑜	𝑡𝑜	NUM
cana-659	170	33	𝑛	𝑛	NOUN
cana-659	170	34	)	)	PUNCT
cana-659	170	35	𝑃(𝑥𝑖	𝑃(𝑥𝑖	VERB
cana-659	170	36	|	|	ADV
cana-659	170	37	𝐶𝑘	𝐶𝑘	NOUN
cana-659	170	38	)	)	PUNCT
cana-659	170	39	)	)	PUNCT
cana-659	170	40	/	/	SYM
cana-659	171	1	𝑃(𝑥	𝑃(𝑥	NUM
cana-659	171	2	)	)	PUNCT
cana-659	171	3	•	•	NUM
cana-659	171	4	where	where	SCONJ
cana-659	171	5	p(x	p(x	NOUN
cana-659	171	6	)	)	PUNCT
cana-659	171	7	is	be	AUX
cana-659	171	8	the	the	DET
cana-659	171	9	evidence	evidence	NOUN
cana-659	171	10	(	(	PUNCT
cana-659	171	11	normalizing	normalize	VERB
cana-659	171	12	constant	constant	ADJ
cana-659	171	13	)	)	PUNCT
cana-659	171	14	,	,	PUNCT
cana-659	171	15	often	often	ADV
cana-659	171	16	not	not	PART
cana-659	171	17	computed	compute	VERB
cana-659	171	18	directly	directly	ADV
cana-659	171	19	since	since	SCONJ
cana-659	171	20	it	it	PRON
cana-659	171	21	is	be	AUX
cana-659	171	22	the	the	DET
cana-659	171	23	same	same	ADJ
cana-659	171	24	for	for	ADP
cana-659	171	25	all	all	DET
cana-659	171	26	classes	class	NOUN
cana-659	171	27	.	.	PUNCT
cana-659	172	1	4	4	X
cana-659	172	2	.	.	X
cana-659	172	3	class	class	NOUN
cana-659	172	4	prediction	prediction	NOUN
cana-659	172	5	:	:	PUNCT
cana-659	172	6	•	•	NUM
cana-659	172	7	predict	predict	VERB
cana-659	172	8	the	the	DET
cana-659	172	9	class	class	NOUN
cana-659	172	10	with	with	ADP
cana-659	172	11	the	the	DET
cana-659	172	12	highest	high	ADJ
cana-659	172	13	posterior	posterior	ADJ
cana-659	172	14	probability	probability	NOUN
cana-659	172	15	.	.	PUNCT
cana-659	173	1	this	this	PRON
cana-659	173	2	is	be	AUX
cana-659	173	3	known	know	VERB
cana-659	173	4	as	as	ADP
cana-659	173	5	the	the	DET
cana-659	173	6	maximum	maximum	NOUN
cana-659	173	7	a	a	DET
cana-659	173	8	posteriori	posteriori	NOUN
cana-659	173	9	(	(	PUNCT
cana-659	173	10	map	map	NOUN
cana-659	173	11	)	)	PUNCT
cana-659	173	12	decision	decision	NOUN
cana-659	173	13	rule	rule	NOUN
cana-659	173	14	:	:	PUNCT
cana-659	173	15	ĉ	ĉ	PROPN
cana-659	173	16	=	=	PRON
cana-659	173	17	𝑎𝑟𝑔	𝑎𝑟𝑔	PROPN
cana-659	173	18	𝑚𝑎𝑥𝐶𝑘	𝑚𝑎𝑥𝐶𝑘	DET
cana-659	173	19	𝑃(𝐶𝑘	𝑃(𝐶𝑘	PROPN
cana-659	173	20	|	|	ADV
cana-659	173	21	𝑥	𝑥	NOUN
cana-659	173	22	)	)	PUNCT
cana-659	173	23	•	•	NOUN
cana-659	173	24	choose	choose	VERB
cana-659	173	25	the	the	DET
cana-659	173	26	class	class	NOUN
cana-659	173	27	ck	ck	INTJ
cana-659	173	28	that	that	PRON
cana-659	173	29	maximizes	maximize	VERB
cana-659	173	30	p(ck	p(ck	PROPN
cana-659	173	31	|	|	NOUN
cana-659	173	32	x	x	NOUN
cana-659	173	33	)	)	PUNCT
cana-659	173	34	.	.	PUNCT
cana-659	174	1	help	help	NOUN
cana-659	174	2	of	of	ADP
cana-659	174	3	this	this	DET
cana-659	174	4	theorem	theorem	NOUN
cana-659	174	5	,	,	PUNCT
cana-659	174	6	we	we	PRON
cana-659	174	7	may	may	AUX
cana-659	174	8	estimate	estimate	VERB
cana-659	174	9	the	the	DET
cana-659	174	10	likelihood	likelihood	NOUN
cana-659	174	11	that	that	SCONJ
cana-659	174	12	an	an	DET
cana-659	174	13	event	event	NOUN
cana-659	174	14	will	will	AUX
cana-659	174	15	occur	occur	VERB
cana-659	174	16	and	and	CCONJ
cana-659	174	17	create	create	VERB
cana-659	174	18	pairs	pair	NOUN
cana-659	174	19	of	of	ADP
cana-659	174	20	probabilities	probability	NOUN
cana-659	174	21	.	.	PUNCT
cana-659	175	1	compared	compare	VERB
cana-659	175	2	to	to	ADP
cana-659	175	3	other	other	ADJ
cana-659	175	4	prediction	prediction	NOUN
cana-659	175	5	models	model	NOUN
cana-659	175	6	,	,	PUNCT
cana-659	175	7	a	a	DET
cana-659	175	8	classifier	classifier	NOUN
cana-659	175	9	makes	make	VERB
cana-659	175	10	predictions	prediction	NOUN
cana-659	175	11	easily.[6	easily.[6	VERB
cana-659	175	12	]	]	PUNCT
cana-659	175	13	despite	despite	SCONJ
cana-659	175	14	its	its	PRON
cana-659	175	15	simplicity	simplicity	NOUN
cana-659	175	16	,	,	PUNCT
cana-659	175	17	the	the	DET
cana-659	175	18	naive	naive	ADJ
cana-659	175	19	bayesian	bayesian	NOUN
cana-659	175	20	classifier	classifier	NOUN
cana-659	175	21	is	be	AUX
cana-659	175	22	widely	widely	ADV
cana-659	175	23	used	use	VERB
cana-659	175	24	,	,	PUNCT
cana-659	175	25	frequently	frequently	ADV
cana-659	175	26	performs	perform	VERB
cana-659	175	27	surprisingly	surprisingly	ADV
cana-659	175	28	well	well	ADV
cana-659	175	29	,	,	PUNCT
cana-659	175	30	and	and	CCONJ
cana-659	175	31	outperforms	outperform	VERB
cana-659	175	32	more	more	ADV
cana-659	175	33	complex	complex	ADJ
cana-659	175	34	classification	classification	NOUN
cana-659	175	35	techniques.[3	techniques.[3	NOUN
cana-659	175	36	]	]	PUNCT
cana-659	175	37	3.5	3.5	NUM
cana-659	175	38	support	support	NOUN
cana-659	175	39	vector	vector	NOUN
cana-659	175	40	machine	machine	NOUN
cana-659	175	41	one	one	NUM
cana-659	175	42	supervised	supervised	ADJ
cana-659	175	43	machine	machine	NOUN
cana-659	175	44	learning	learn	VERB
cana-659	175	45	technology	technology	NOUN
cana-659	175	46	that	that	PRON
cana-659	175	47	can	can	AUX
cana-659	175	48	be	be	AUX
cana-659	175	49	used	use	VERB
cana-659	175	50	for	for	ADP
cana-659	175	51	regression	regression	NOUN
cana-659	175	52	and	and	CCONJ
cana-659	175	53	classification	classification	NOUN
cana-659	175	54	analysis	analysis	NOUN
cana-659	175	55	is	be	AUX
cana-659	175	56	the	the	DET
cana-659	175	57	support	support	NOUN
cana-659	175	58	vector	vector	NOUN
cana-659	175	59	machine.[4	machine.[4	X
cana-659	175	60	]	]	PUNCT
cana-659	175	61	a	a	DET
cana-659	175	62	svm	svm	NOUN
cana-659	175	63	has	have	VERB
cana-659	175	64	a	a	DET
cana-659	175	65	great	great	ADJ
cana-659	175	66	ability	ability	NOUN
cana-659	175	67	to	to	PART
cana-659	175	68	generalize	generalize	VERB
cana-659	175	69	since	since	SCONJ
cana-659	175	70	it	it	PRON
cana-659	175	71	can	can	AUX
cana-659	175	72	handle	handle	VERB
cana-659	175	73	small	small	ADJ
cana-659	175	74	amounts	amount	NOUN
cana-659	175	75	of	of	ADP
cana-659	175	76	data	datum	NOUN
cana-659	175	77	and	and	CCONJ
cana-659	175	78	is	be	AUX
cana-659	175	79	less	less	ADV
cana-659	175	80	sensitive	sensitive	ADJ
cana-659	175	81	to	to	PART
cana-659	175	82	noise	noise	VERB
cana-659	175	83	in	in	ADP
cana-659	175	84	a	a	DET
cana-659	175	85	dataset	dataset	NOUN
cana-659	175	86	.	.	PUNCT
cana-659	176	1	the	the	DET
cana-659	176	2	hyperplane	hyperplane	NOUN
cana-659	176	3	that	that	PRON
cana-659	176	4	maximizes	maximize	VERB
cana-659	176	5	the	the	DET
cana-659	176	6	margin	margin	NOUN
cana-659	176	7	between	between	ADP
cana-659	176	8	the	the	DET
cana-659	176	9	two	two	NUM
cana-659	176	10	classes	class	NOUN
cana-659	176	11	is	be	AUX
cana-659	176	12	what	what	PRON
cana-659	176	13	the	the	DET
cana-659	176	14	svm	svm	PROPN
cana-659	176	15	seeks	seek	VERB
cana-659	176	16	to	to	PART
cana-659	176	17	identify	identify	VERB
cana-659	176	18	.	.	PUNCT
cana-659	177	1	training	train	VERB
cana-659	177	2	non	non	ADJ
cana-659	177	3	-	-	ADJ
cana-659	177	4	linear	linear	ADJ
cana-659	177	5	svm	svm	ADJ
cana-659	177	6	models	model	NOUN
cana-659	177	7	is	be	AUX
cana-659	177	8	possible	possible	ADJ
cana-659	177	9	provided	provide	VERB
cana-659	177	10	that	that	SCONJ
cana-659	177	11	the	the	DET
cana-659	177	12	right	right	ADJ
cana-659	177	13	kernel	kernel	PROPN
cana-659	177	14	functions	function	NOUN
cana-659	177	15	are	be	AUX
cana-659	177	16	applied	apply	VERB
cana-659	177	17	.	.	PUNCT
cana-659	178	1	new	new	ADJ
cana-659	178	2	feature	feature	NOUN
cana-659	178	3	vectors	vector	NOUN
cana-659	178	4	produced	produce	VERB
cana-659	178	5	by	by	ADP
cana-659	178	6	kernel	kernel	PROPN
cana-659	178	7	functions	function	NOUN
cana-659	178	8	typically	typically	ADV
cana-659	178	9	have	have	VERB
cana-659	178	10	more	more	ADJ
cana-659	178	11	dimensions	dimension	NOUN
cana-659	178	12	than	than	ADP
cana-659	178	13	the	the	DET
cana-659	178	14	original	original	ADJ
cana-659	178	15	input	input	NOUN
cana-659	178	16	.	.	PUNCT
cana-659	179	1	in	in	ADP
cana-659	179	2	the	the	DET
cana-659	179	3	new	new	ADJ
cana-659	179	4	feature	feature	NOUN
cana-659	179	5	space	space	NOUN
cana-659	179	6	,	,	PUNCT
cana-659	179	7	the	the	DET
cana-659	179	8	svm	svm	PROPN
cana-659	179	9	locates	locate	VERB
cana-659	179	10	the	the	DET
cana-659	179	11	new	new	ADJ
cana-659	179	12	hyperplane	hyperplane	NOUN
cana-659	179	13	,	,	PUNCT
cana-659	179	14	which	which	PRON
cana-659	179	15	is	be	AUX
cana-659	179	16	linear	linear	ADJ
cana-659	179	17	.	.	PUNCT
cana-659	180	1	[	[	X
cana-659	180	2	6	6	NUM
cana-659	180	3	]	]	SYM
cana-659	180	4	3.6	3.6	NUM
cana-659	180	5	logistic	logistic	ADJ
cana-659	180	6	regression	regression	NOUN
cana-659	180	7	a	a	DET
cana-659	180	8	supervised	supervised	ADJ
cana-659	180	9	machine	machine	NOUN
cana-659	180	10	learning	learning	NOUN
cana-659	180	11	technique	technique	NOUN
cana-659	180	12	called	call	VERB
cana-659	180	13	logistic	logistic	ADJ
cana-659	180	14	regression	regression	NOUN
cana-659	180	15	is	be	AUX
cana-659	180	16	typically	typically	ADV
cana-659	180	17	used	use	VERB
cana-659	180	18	to	to	PART
cana-659	180	19	address	address	VERB
cana-659	180	20	classification	classification	NOUN
cana-659	180	21	issues	issue	NOUN
cana-659	180	22	,	,	PUNCT
cana-659	180	23	particularly	particularly	ADV
cana-659	180	24	those	those	PRON
cana-659	180	25	involving	involve	VERB
cana-659	180	26	binary	binary	ADJ
cana-659	180	27	classification	classification	NOUN
cana-659	180	28	.	.	PUNCT
cana-659	181	1	the	the	DET
cana-659	181	2	basic	basic	ADJ
cana-659	181	3	operation	operation	NOUN
cana-659	181	4	of	of	ADP
cana-659	181	5	this	this	DET
cana-659	181	6	method	method	NOUN
cana-659	181	7	,	,	PUNCT
cana-659	181	8	which	which	PRON
cana-659	181	9	is	be	AUX
cana-659	181	10	often	often	ADV
cana-659	181	11	referred	refer	VERB
cana-659	181	12	to	to	ADP
cana-659	181	13	as	as	ADP
cana-659	181	14	the	the	DET
cana-659	181	15	sigmoid	sigmoid	NOUN
cana-659	181	16	function	function	NOUN
cana-659	181	17	,	,	PUNCT
cana-659	181	18	is	be	AUX
cana-659	181	19	the	the	DET
cana-659	181	20	logistic	logistic	ADJ
cana-659	181	21	function	function	NOUN
cana-659	181	22	.	.	PUNCT
cana-659	182	1	any	any	DET
cana-659	182	2	actual	actual	ADJ
cana-659	182	3	communications	communication	NOUN
cana-659	182	4	on	on	ADP
cana-659	182	5	applied	apply	VERB
cana-659	182	6	nonlinear	nonlinear	ADJ
cana-659	182	7	analysis	analysis	NOUN
cana-659	182	8	issn	issn	NOUN
cana-659	182	9	:	:	PUNCT
cana-659	182	10	1074	1074	NUM
cana-659	182	11	-	-	PUNCT
cana-659	182	12	133x	133x	NUM
cana-659	182	13	vol	vol	NOUN
cana-659	182	14	31	31	NUM
cana-659	182	15	no	no	NOUN
cana-659	182	16	.	.	PUNCT
cana-659	183	1	2s	2s	NUM
cana-659	183	2	(	(	PUNCT
cana-659	183	3	2024	2024	NUM
cana-659	183	4	)	)	PUNCT
cana-659	183	5	445	445	NUM
cana-659	183	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	183	7	number	number	NOUN
cana-659	183	8	can	can	AUX
cana-659	183	9	be	be	AUX
cana-659	183	10	entered	enter	VERB
cana-659	183	11	,	,	PUNCT
cana-659	183	12	and	and	CCONJ
cana-659	183	13	it	it	PRON
cana-659	183	14	will	will	AUX
cana-659	183	15	convert	convert	VERB
cana-659	183	16	it	it	PRON
cana-659	183	17	to	to	ADP
cana-659	183	18	a	a	DET
cana-659	183	19	value	value	NOUN
cana-659	183	20	between	between	ADP
cana-659	183	21	0	0	NUM
cana-659	183	22	and	and	CCONJ
cana-659	183	23	1.[4	1.[4	NUM
cana-659	183	24	]	]	X
cana-659	183	25	similar	similar	ADJ
cana-659	183	26	to	to	ADP
cana-659	183	27	a	a	DET
cana-659	183	28	linear	linear	ADJ
cana-659	183	29	regression	regression	NOUN
cana-659	183	30	,	,	PUNCT
cana-659	183	31	a	a	DET
cana-659	183	32	logistic	logistic	ADJ
cana-659	183	33	regression	regression	NOUN
cana-659	183	34	builds	build	VERB
cana-659	183	35	the	the	DET
cana-659	183	36	curve	curve	NOUN
cana-659	183	37	using	use	VERB
cana-659	183	38	the	the	DET
cana-659	183	39	natural	natural	ADJ
cana-659	183	40	logarithm	logarithm	NOUN
cana-659	183	41	rather	rather	ADV
cana-659	183	42	than	than	ADP
cana-659	183	43	the	the	DET
cana-659	183	44	probability.[3	probability.[3	NOUN
cana-659	183	45	]	]	PUNCT
cana-659	183	46	this	this	DET
cana-659	183	47	approach	approach	NOUN
cana-659	183	48	works	work	VERB
cana-659	183	49	well	well	ADV
cana-659	183	50	for	for	ADP
cana-659	183	51	linearly	linearly	ADV
cana-659	183	52	separable	separable	ADJ
cana-659	183	53	data	datum	NOUN
cana-659	183	54	and	and	CCONJ
cana-659	183	55	is	be	AUX
cana-659	183	56	considerably	considerably	ADV
cana-659	183	57	simpler	simple	ADJ
cana-659	183	58	to	to	PART
cana-659	183	59	use	use	VERB
cana-659	183	60	,	,	PUNCT
cana-659	183	61	understand	understand	ADJ
cana-659	183	62	,	,	PUNCT
cana-659	183	63	and	and	CCONJ
cana-659	183	64	train	train	NOUN
cana-659	183	65	.	.	PUNCT
cana-659	184	1	[	[	X
cana-659	184	2	4	4	NUM
cana-659	184	3	]	]	SYM
cana-659	184	4	4	4	NUM
cana-659	184	5	.	.	PUNCT
cana-659	184	6	optimization	optimization	NOUN
cana-659	184	7	algorithms	algorithm	VERB
cana-659	184	8	4.1	4.1	NUM
cana-659	184	9	genetic	genetic	ADJ
cana-659	184	10	algorithm	algorithm	NOUN
cana-659	184	11	a	a	DET
cana-659	184	12	search	search	NOUN
cana-659	184	13	-	-	PUNCT
cana-659	184	14	based	base	VERB
cana-659	184	15	optimization	optimization	NOUN
cana-659	184	16	method	method	NOUN
cana-659	184	17	founded	found	VERB
cana-659	184	18	on	on	ADP
cana-659	184	19	the	the	DET
cana-659	184	20	ideas	idea	NOUN
cana-659	184	21	of	of	ADP
cana-659	184	22	natural	natural	ADJ
cana-659	184	23	selection	selection	NOUN
cana-659	184	24	and	and	CCONJ
cana-659	184	25	genetics	genetic	NOUN
cana-659	184	26	is	be	AUX
cana-659	184	27	known	know	VERB
cana-659	184	28	as	as	ADP
cana-659	184	29	the	the	DET
cana-659	184	30	genetic	genetic	ADJ
cana-659	184	31	algorithm	algorithm	NOUN
cana-659	184	32	.	.	PUNCT
cana-659	185	1	in	in	ADP
cana-659	185	2	numerous	numerous	ADJ
cana-659	185	3	domains	domain	NOUN
cana-659	185	4	,	,	PUNCT
cana-659	185	5	including	include	VERB
cana-659	185	6	scheduling	scheduling	NOUN
cana-659	185	7	,	,	PUNCT
cana-659	185	8	fuzzy	fuzzy	ADJ
cana-659	185	9	logic	logic	NOUN
cana-659	185	10	control	control	NOUN
cana-659	185	11	,	,	PUNCT
cana-659	185	12	neural	neural	ADJ
cana-659	185	13	networks	network	NOUN
cana-659	185	14	,	,	PUNCT
cana-659	185	15	expert	expert	NOUN
cana-659	185	16	systems	system	NOUN
cana-659	185	17	,	,	PUNCT
cana-659	185	18	optimization	optimization	NOUN
cana-659	185	19	design	design	NOUN
cana-659	185	20	etc	etc	X
cana-659	185	21	.	.	X
cana-659	185	22	widely	widely	ADV
cana-659	185	23	used	use	VERB
cana-659	185	24	to	to	PART
cana-659	185	25	identify	identify	VERB
cana-659	185	26	the	the	DET
cana-659	185	27	best	good	ADJ
cana-659	185	28	answers	answer	NOUN
cana-659	185	29	to	to	ADP
cana-659	185	30	challenging	challenging	ADJ
cana-659	185	31	issues	issue	NOUN
cana-659	185	32	.	.	PUNCT
cana-659	186	1	population	population	NOUN
cana-659	186	2	,	,	PUNCT
cana-659	186	3	assessment	assessment	NOUN
cana-659	186	4	,	,	PUNCT
cana-659	186	5	fitness	fitness	NOUN
cana-659	186	6	function	function	NOUN
cana-659	186	7	,	,	PUNCT
cana-659	186	8	selection	selection	NOUN
cana-659	186	9	,	,	PUNCT
cana-659	186	10	crossover	crossover	NOUN
cana-659	186	11	,	,	PUNCT
cana-659	186	12	and	and	CCONJ
cana-659	186	13	mutation	mutation	NOUN
cana-659	186	14	are	be	AUX
cana-659	186	15	some	some	DET
cana-659	186	16	basic	basic	ADJ
cana-659	186	17	terms	term	NOUN
cana-659	186	18	used	use	VERB
cana-659	186	19	in	in	ADP
cana-659	186	20	relation	relation	NOUN
cana-659	186	21	to	to	ADP
cana-659	186	22	genetic	genetic	ADJ
cana-659	186	23	algorithms	algorithm	NOUN
cana-659	186	24	.	.	PUNCT
cana-659	187	1	the	the	DET
cana-659	187	2	ga	ga	PROPN
cana-659	187	3	codes	code	VERB
cana-659	187	4	a	a	DET
cana-659	187	5	chromosome	chromosome	NOUN
cana-659	187	6	as	as	ADP
cana-659	187	7	a	a	DET
cana-659	187	8	solution	solution	NOUN
cana-659	187	9	to	to	ADP
cana-659	187	10	a	a	DET
cana-659	187	11	particular	particular	ADJ
cana-659	187	12	issue	issue	NOUN
cana-659	187	13	.	.	PUNCT
cana-659	188	1	after	after	ADP
cana-659	188	2	that	that	PRON
cana-659	188	3	,	,	PUNCT
cana-659	188	4	it	it	PRON
cana-659	188	5	specifies	specify	VERB
cana-659	188	6	a	a	DET
cana-659	188	7	starting	start	VERB
cana-659	188	8	population	population	NOUN
cana-659	188	9	made	make	VERB
cana-659	188	10	up	up	ADP
cana-659	188	11	of	of	ADP
cana-659	188	12	people	people	NOUN
cana-659	188	13	who	who	PRON
cana-659	188	14	belong	belong	VERB
cana-659	188	15	to	to	ADP
cana-659	188	16	a	a	DET
cana-659	188	17	portion	portion	NOUN
cana-659	188	18	of	of	ADP
cana-659	188	19	the	the	DET
cana-659	188	20	problem	problem	NOUN
cana-659	188	21	's	's	PART
cana-659	188	22	solution	solution	NOUN
cana-659	188	23	space	space	NOUN
cana-659	188	24	.	.	PUNCT
cana-659	189	1	thus	thus	ADV
cana-659	189	2	,	,	PUNCT
cana-659	189	3	the	the	DET
cana-659	189	4	solution	solution	NOUN
cana-659	189	5	space	space	NOUN
cana-659	189	6	in	in	ADP
cana-659	189	7	which	which	PRON
cana-659	189	8	each	each	DET
cana-659	189	9	workable	workable	ADJ
cana-659	189	10	solution	solution	NOUN
cana-659	189	11	is	be	AUX
cana-659	189	12	represented	represent	VERB
cana-659	189	13	by	by	ADP
cana-659	189	14	a	a	DET
cana-659	189	15	unique	unique	ADJ
cana-659	189	16	chromosome	chromosome	NOUN
cana-659	189	17	is	be	AUX
cana-659	189	18	known	know	VERB
cana-659	189	19	as	as	ADP
cana-659	189	20	the	the	DET
cana-659	189	21	search	search	NOUN
cana-659	189	22	space	space	NOUN
cana-659	189	23	.	.	PUNCT
cana-659	190	1	to	to	PART
cana-659	190	2	create	create	VERB
cana-659	190	3	the	the	DET
cana-659	190	4	initial	initial	ADJ
cana-659	190	5	population	population	NOUN
cana-659	190	6	,	,	PUNCT
cana-659	190	7	a	a	DET
cana-659	190	8	random	random	ADJ
cana-659	190	9	selection	selection	NOUN
cana-659	190	10	of	of	ADP
cana-659	190	11	chromosomes	chromosome	NOUN
cana-659	190	12	is	be	AUX
cana-659	190	13	made	make	VERB
cana-659	190	14	from	from	ADP
cana-659	190	15	the	the	DET
cana-659	190	16	search	search	NOUN
cana-659	190	17	space	space	NOUN
cana-659	190	18	prior	prior	ADV
cana-659	190	19	to	to	ADP
cana-659	190	20	the	the	DET
cana-659	190	21	search	search	NOUN
cana-659	190	22	commencing	commencing	NOUN
cana-659	190	23	.	.	PUNCT
cana-659	191	1	subsequently	subsequently	ADV
cana-659	191	2	,	,	PUNCT
cana-659	191	3	the	the	DET
cana-659	191	4	candidates	candidate	NOUN
cana-659	191	5	are	be	AUX
cana-659	191	6	computationally	computationally	ADV
cana-659	191	7	chosen	choose	VERB
cana-659	191	8	in	in	ADP
cana-659	191	9	a	a	DET
cana-659	191	10	competitive	competitive	ADJ
cana-659	191	11	fashion	fashion	NOUN
cana-659	191	12	according	accord	VERB
cana-659	191	13	to	to	ADP
cana-659	191	14	their	their	PRON
cana-659	191	15	fitness	fitness	NOUN
cana-659	191	16	as	as	SCONJ
cana-659	191	17	determined	determine	VERB
cana-659	191	18	by	by	ADP
cana-659	191	19	a	a	DET
cana-659	191	20	certain	certain	ADJ
cana-659	191	21	objective	objective	ADJ
cana-659	191	22	function	function	NOUN
cana-659	191	23	.	.	PUNCT
cana-659	192	1	the	the	DET
cana-659	192	2	next	next	ADJ
cana-659	192	3	step	step	NOUN
cana-659	192	4	is	be	AUX
cana-659	192	5	to	to	PART
cana-659	192	6	apply	apply	VERB
cana-659	192	7	the	the	DET
cana-659	192	8	genetic	genetic	ADJ
cana-659	192	9	search	search	NOUN
cana-659	192	10	operator	operator	NOUN
cana-659	192	11	’s	’s	PART
cana-659	192	12	crossover	crossover	NOUN
cana-659	192	13	,	,	PUNCT
cana-659	192	14	mutation	mutation	NOUN
cana-659	192	15	,	,	PUNCT
cana-659	192	16	and	and	CCONJ
cana-659	192	17	selection	selection	NOUN
cana-659	192	18	one	one	NUM
cana-659	192	19	after	after	ADP
cana-659	192	20	the	the	DET
cana-659	192	21	other	other	ADJ
cana-659	192	22	to	to	PART
cana-659	192	23	create	create	VERB
cana-659	192	24	a	a	DET
cana-659	192	25	new	new	ADJ
cana-659	192	26	generation	generation	NOUN
cana-659	192	27	of	of	ADP
cana-659	192	28	chromosomes	chromosome	NOUN
cana-659	192	29	whose	whose	DET
cana-659	192	30	predicted	predict	VERB
cana-659	192	31	quality	quality	NOUN
cana-659	192	32	across	across	ADP
cana-659	192	33	the	the	DET
cana-659	192	34	board	board	NOUN
cana-659	192	35	is	be	AUX
cana-659	192	36	higher	high	ADJ
cana-659	192	37	than	than	ADP
cana-659	192	38	that	that	PRON
cana-659	192	39	of	of	ADP
cana-659	192	40	the	the	DET
cana-659	192	41	previous	previous	ADJ
cana-659	192	42	generation	generation	NOUN
cana-659	192	43	.	.	PUNCT
cana-659	193	1	the	the	DET
cana-659	193	2	best	good	ADJ
cana-659	193	3	chromosome	chromosome	NOUN
cana-659	193	4	from	from	ADP
cana-659	193	5	the	the	DET
cana-659	193	6	most	most	ADV
cana-659	193	7	recent	recent	ADJ
cana-659	193	8	generation	generation	NOUN
cana-659	193	9	is	be	AUX
cana-659	193	10	declared	declare	VERB
cana-659	193	11	as	as	ADP
cana-659	193	12	the	the	DET
cana-659	193	13	final	final	ADJ
cana-659	193	14	solution	solution	NOUN
cana-659	193	15	after	after	SCONJ
cana-659	193	16	this	this	DET
cana-659	193	17	process	process	NOUN
cana-659	193	18	is	be	AUX
cana-659	193	19	repeated	repeat	VERB
cana-659	193	20	until	until	SCONJ
cana-659	193	21	the	the	DET
cana-659	193	22	termination	termination	NOUN
cana-659	193	23	requirement	requirement	NOUN
cana-659	193	24	is	be	AUX
cana-659	193	25	satisfied	satisfied	ADJ
cana-659	193	26	.	.	PUNCT
cana-659	194	1	the	the	DET
cana-659	194	2	benefits	benefit	NOUN
cana-659	194	3	of	of	ADP
cana-659	194	4	using	use	VERB
cana-659	194	5	a	a	DET
cana-659	194	6	genetic	genetic	ADJ
cana-659	194	7	algorithm	algorithm	NOUN
cana-659	194	8	include	include	VERB
cana-659	194	9	its	its	PRON
cana-659	194	10	ability	ability	NOUN
cana-659	194	11	to	to	PART
cana-659	194	12	optimize	optimize	VERB
cana-659	194	13	a	a	DET
cana-659	194	14	wide	wide	ADJ
cana-659	194	15	range	range	NOUN
cana-659	194	16	of	of	ADP
cana-659	194	17	problems	problem	NOUN
cana-659	194	18	,	,	PUNCT
cana-659	194	19	including	include	VERB
cana-659	194	20	continuous	continuous	ADJ
cana-659	194	21	functions	function	NOUN
cana-659	194	22	,	,	PUNCT
cana-659	194	23	discrete	discrete	ADJ
cana-659	194	24	functions	function	NOUN
cana-659	194	25	,	,	PUNCT
cana-659	194	26	and	and	CCONJ
cana-659	194	27	multi	multi	ADJ
cana-659	194	28	-	-	ADJ
cana-659	194	29	objective	objective	ADJ
cana-659	194	30	issues	issue	NOUN
cana-659	194	31	.	.	PUNCT
cana-659	195	1	it	it	PRON
cana-659	195	2	also	also	ADV
cana-659	195	3	offers	offer	VERB
cana-659	195	4	a	a	DET
cana-659	195	5	solution	solution	NOUN
cana-659	195	6	that	that	PRON
cana-659	195	7	becomes	become	VERB
cana-659	195	8	better	well	ADJ
cana-659	195	9	with	with	ADP
cana-659	195	10	time	time	NOUN
cana-659	195	11	.	.	PUNCT
cana-659	196	1	[	[	X
cana-659	196	2	8][9	8][9	X
cana-659	196	3	]	]	X
cana-659	196	4	4.2	4.2	NUM
cana-659	196	5	ant	ant	ADJ
cana-659	196	6	colony	colony	NOUN
cana-659	196	7	a	a	DET
cana-659	196	8	population	population	NOUN
cana-659	196	9	-	-	PUNCT
cana-659	196	10	based	base	VERB
cana-659	196	11	metaheuristic	metaheuristic	NOUN
cana-659	196	12	called	call	VERB
cana-659	196	13	"	"	PUNCT
cana-659	196	14	ant	ant	ADJ
cana-659	196	15	colony	colony	NOUN
cana-659	196	16	optimization	optimization	NOUN
cana-659	196	17	"	"	PUNCT
cana-659	196	18	can	can	AUX
cana-659	196	19	be	be	AUX
cana-659	196	20	used	use	VERB
cana-659	196	21	to	to	PART
cana-659	196	22	roughly	roughly	ADV
cana-659	196	23	solve	solve	VERB
cana-659	196	24	challenging	challenging	ADJ
cana-659	196	25	optimization	optimization	NOUN
cana-659	196	26	issues	issue	NOUN
cana-659	196	27	.	.	PUNCT
cana-659	197	1	artificial	artificial	ADJ
cana-659	197	2	ants	ant	NOUN
cana-659	197	3	,	,	PUNCT
cana-659	197	4	a	a	DET
cana-659	197	5	group	group	NOUN
cana-659	197	6	of	of	ADP
cana-659	197	7	software	software	NOUN
cana-659	197	8	agents	agent	NOUN
cana-659	197	9	,	,	PUNCT
cana-659	197	10	look	look	VERB
cana-659	197	11	for	for	ADP
cana-659	197	12	sensible	sensible	ADJ
cana-659	197	13	answers	answer	NOUN
cana-659	197	14	given	give	VERB
cana-659	197	15	to	to	ADP
cana-659	197	16	optimization	optimization	NOUN
cana-659	197	17	problems	problem	NOUN
cana-659	197	18	in	in	ADP
cana-659	197	19	aco	aco	PROPN
cana-659	197	20	.	.	PUNCT
cana-659	198	1	the	the	DET
cana-659	198	2	optimization	optimization	NOUN
cana-659	198	3	issue	issue	NOUN
cana-659	198	4	is	be	AUX
cana-659	198	5	changed	change	VERB
cana-659	198	6	to	to	PART
cana-659	198	7	become	become	VERB
cana-659	198	8	the	the	DET
cana-659	198	9	optimal	optimal	ADJ
cana-659	198	10	path	path	NOUN
cana-659	198	11	on	on	ADP
cana-659	198	12	a	a	DET
cana-659	198	13	weighted	weight	VERB
cana-659	198	14	graph	graph	NOUN
cana-659	198	15	in	in	ADP
cana-659	198	16	order	order	NOUN
cana-659	198	17	to	to	PART
cana-659	198	18	apply	apply	VERB
cana-659	198	19	aco	aco	PROPN
cana-659	198	20	.	.	PUNCT
cana-659	199	1	the	the	DET
cana-659	199	2	artificial	artificial	ADJ
cana-659	199	3	ants	ant	NOUN
cana-659	199	4	move	move	VERB
cana-659	199	5	on	on	ADP
cana-659	199	6	the	the	DET
cana-659	199	7	graph	graph	NOUN
cana-659	199	8	,	,	PUNCT
cana-659	199	9	piece	piece	NOUN
cana-659	199	10	by	by	ADP
cana-659	199	11	piece	piece	NOUN
cana-659	199	12	,	,	PUNCT
cana-659	199	13	building	building	NOUN
cana-659	199	14	solutions	solution	NOUN
cana-659	199	15	.	.	PUNCT
cana-659	200	1	the	the	DET
cana-659	200	2	process	process	NOUN
cana-659	200	3	of	of	ADP
cana-659	200	4	building	build	VERB
cana-659	200	5	the	the	DET
cana-659	200	6	solution	solution	NOUN
cana-659	200	7	is	be	AUX
cana-659	200	8	random	random	ADJ
cana-659	200	9	and	and	CCONJ
cana-659	200	10	biased	bias	VERB
cana-659	200	11	by	by	ADP
cana-659	200	12	a	a	DET
cana-659	200	13	pheromone	pheromone	NOUN
cana-659	200	14	model	model	NOUN
cana-659	200	15	,	,	PUNCT
cana-659	200	16	which	which	PRON
cana-659	200	17	is	be	AUX
cana-659	200	18	a	a	DET
cana-659	200	19	collection	collection	NOUN
cana-659	200	20	of	of	ADP
cana-659	200	21	parameters	parameter	NOUN
cana-659	200	22	related	relate	VERB
cana-659	200	23	to	to	ADP
cana-659	200	24	nodes	node	NOUN
cana-659	200	25	or	or	CCONJ
cana-659	200	26	edges	edge	NOUN
cana-659	200	27	in	in	ADP
cana-659	200	28	the	the	DET
cana-659	200	29	graph	graph	NOUN
cana-659	200	30	,	,	PUNCT
cana-659	200	31	the	the	DET
cana-659	200	32	values	value	NOUN
cana-659	200	33	of	of	ADP
cana-659	200	34	which	which	PRON
cana-659	200	35	are	be	AUX
cana-659	200	36	changed	change	VERB
cana-659	200	37	by	by	ADP
cana-659	200	38	the	the	DET
cana-659	200	39	ants	ant	NOUN
cana-659	200	40	during	during	ADP
cana-659	200	41	runtime	runtime	NOUN
cana-659	200	42	.	.	PUNCT
cana-659	201	1	aco	aco	PROPN
cana-659	201	2	can	can	AUX
cana-659	201	3	be	be	AUX
cana-659	201	4	used	use	VERB
cana-659	201	5	to	to	PART
cana-659	201	6	identify	identify	VERB
cana-659	201	7	the	the	DET
cana-659	201	8	best	good	ADJ
cana-659	201	9	answers	answer	NOUN
cana-659	201	10	to	to	ADP
cana-659	201	11	a	a	DET
cana-659	201	12	variety	variety	NOUN
cana-659	201	13	of	of	ADP
cana-659	201	14	optimization	optimization	NOUN
cana-659	201	15	problems	problem	NOUN
cana-659	201	16	,	,	PUNCT
cana-659	201	17	including	include	VERB
cana-659	201	18	the	the	DET
cana-659	201	19	group	group	NOUN
cana-659	201	20	shop	shop	NOUN
cana-659	201	21	scheduling	scheduling	NOUN
cana-659	201	22	problem	problem	NOUN
cana-659	201	23	,	,	PUNCT
cana-659	201	24	frequency	frequency	NOUN
cana-659	201	25	assignment	assignment	NOUN
cana-659	201	26	problem	problem	NOUN
cana-659	201	27	,	,	PUNCT
cana-659	201	28	redundancy	redundancy	NOUN
cana-659	201	29	allocation	allocation	NOUN
cana-659	201	30	problem	problem	NOUN
cana-659	201	31	,	,	PUNCT
cana-659	201	32	traveling	travel	VERB
cana-659	201	33	salesman	salesman	ADJ
cana-659	201	34	problem	problem	NOUN
cana-659	201	35	,	,	PUNCT
cana-659	201	36	and	and	CCONJ
cana-659	201	37	nursing	nursing	NOUN
cana-659	201	38	time	time	NOUN
cana-659	201	39	distribution	distribution	NOUN
cana-659	201	40	scheduling	scheduling	NOUN
cana-659	201	41	.	.	PUNCT
cana-659	202	1	ant	ant	ADJ
cana-659	202	2	colony	colony	NOUN
cana-659	202	3	benefits	benefit	NOUN
cana-659	202	4	include	include	VERB
cana-659	202	5	their	their	PRON
cana-659	202	6	ability	ability	NOUN
cana-659	202	7	to	to	PART
cana-659	202	8	adapt	adapt	VERB
cana-659	202	9	changes	change	NOUN
cana-659	202	10	in	in	ADP
cana-659	202	11	circumstances	circumstance	NOUN
cana-659	202	12	,	,	PUNCT
cana-659	202	13	such	such	ADJ
cana-659	202	14	as	as	ADP
cana-659	202	15	new	new	ADJ
cana-659	202	16	distances	distance	NOUN
cana-659	202	17	,	,	PUNCT
cana-659	202	18	and	and	CCONJ
cana-659	202	19	their	their	PRON
cana-659	202	20	efficiency	efficiency	NOUN
cana-659	202	21	in	in	ADP
cana-659	202	22	solving	solve	VERB
cana-659	202	23	challenges	challenge	NOUN
cana-659	202	24	akin	akin	ADJ
cana-659	202	25	to	to	ADP
cana-659	202	26	the	the	DET
cana-659	202	27	traveling	travel	VERB
cana-659	202	28	salesman	salesman	NOUN
cana-659	202	29	dilemma	dilemma	NOUN
cana-659	202	30	[	[	X
cana-659	202	31	10][11	10][11	PROPN
cana-659	202	32	]	]	PUNCT
cana-659	202	33	.	.	PUNCT
cana-659	203	1	4.3	4.3	NUM
cana-659	203	2	gradient	gradient	NOUN
cana-659	203	3	boosting	boost	VERB
cana-659	203	4	one	one	NUM
cana-659	203	5	technique	technique	NOUN
cana-659	203	6	that	that	PRON
cana-659	203	7	stands	stand	VERB
cana-659	203	8	out	out	ADP
cana-659	203	9	for	for	ADP
cana-659	203	10	its	its	PRON
cana-659	203	11	accuracy	accuracy	NOUN
cana-659	203	12	and	and	CCONJ
cana-659	203	13	speed	speed	NOUN
cana-659	203	14	of	of	ADP
cana-659	203	15	prediction	prediction	NOUN
cana-659	203	16	,	,	PUNCT
cana-659	203	17	especially	especially	ADV
cana-659	203	18	when	when	SCONJ
cana-659	203	19	working	work	VERB
cana-659	203	20	with	with	ADP
cana-659	203	21	big	big	ADJ
cana-659	203	22	and	and	CCONJ
cana-659	203	23	complicated	complicated	ADJ
cana-659	203	24	datasets	dataset	NOUN
cana-659	203	25	,	,	PUNCT
cana-659	203	26	is	be	AUX
cana-659	203	27	gradient	gradient	ADJ
cana-659	203	28	boosting	boosting	NOUN
cana-659	203	29	.	.	PUNCT
cana-659	204	1	this	this	DET
cana-659	204	2	algorithm	algorithm	NOUN
cana-659	204	3	's	's	PART
cana-659	204	4	primary	primary	ADJ
cana-659	204	5	concept	concept	NOUN
cana-659	204	6	is	be	AUX
cana-659	204	7	to	to	PART
cana-659	204	8	build	build	VERB
cana-659	204	9	models	model	NOUN
cana-659	204	10	one	one	NUM
cana-659	204	11	after	after	ADP
cana-659	204	12	the	the	DET
cana-659	204	13	other	other	ADJ
cana-659	204	14	,	,	PUNCT
cana-659	204	15	with	with	ADP
cana-659	204	16	each	each	DET
cana-659	204	17	new	new	ADJ
cana-659	204	18	model	model	NOUN
cana-659	204	19	attempting	attempt	VERB
cana-659	204	20	to	to	PART
cana-659	204	21	minimize	minimize	VERB
cana-659	204	22	the	the	DET
cana-659	204	23	mistakes	mistake	NOUN
cana-659	204	24	of	of	ADP
cana-659	204	25	the	the	DET
cana-659	204	26	communications	communication	NOUN
cana-659	204	27	on	on	ADP
cana-659	204	28	applied	apply	VERB
cana-659	204	29	nonlinear	nonlinear	ADJ
cana-659	204	30	analysis	analysis	NOUN
cana-659	204	31	issn	issn	NOUN
cana-659	204	32	:	:	PUNCT
cana-659	204	33	1074	1074	NUM
cana-659	204	34	-	-	PUNCT
cana-659	204	35	133x	133x	NUM
cana-659	204	36	vol	vol	NOUN
cana-659	204	37	31	31	NUM
cana-659	204	38	no	no	NOUN
cana-659	204	39	.	.	PUNCT
cana-659	205	1	2s	2s	NUM
cana-659	205	2	(	(	PUNCT
cana-659	205	3	2024	2024	NUM
cana-659	205	4	)	)	PUNCT
cana-659	205	5	446	446	NUM
cana-659	205	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	205	7	preceding	precede	VERB
cana-659	205	8	model	model	NOUN
cana-659	205	9	.	.	PUNCT
cana-659	206	1	to	to	PART
cana-659	206	2	accomplish	accomplish	VERB
cana-659	206	3	this	this	PRON
cana-659	206	4	,	,	PUNCT
cana-659	206	5	a	a	DET
cana-659	206	6	new	new	ADJ
cana-659	206	7	model	model	NOUN
cana-659	206	8	is	be	AUX
cana-659	206	9	constructed	construct	VERB
cana-659	206	10	using	use	VERB
cana-659	206	11	the	the	DET
cana-659	206	12	residuals	residual	NOUN
cana-659	206	13	or	or	CCONJ
cana-659	206	14	errors	error	NOUN
cana-659	206	15	of	of	ADP
cana-659	206	16	the	the	DET
cana-659	206	17	old	old	ADJ
cana-659	206	18	model	model	NOUN
cana-659	206	19	.	.	PUNCT
cana-659	207	1	typically	typically	ADV
cana-659	207	2	,	,	PUNCT
cana-659	207	3	a	a	DET
cana-659	207	4	gradient	gradient	NOUN
cana-659	207	5	boosting	boost	VERB
cana-659	207	6	machine	machine	NOUN
cana-659	207	7	builds	build	VERB
cana-659	207	8	an	an	DET
cana-659	207	9	additive	additive	ADJ
cana-659	207	10	model	model	NOUN
cana-659	207	11	of	of	ADP
cana-659	207	12	poorly	poorly	ADV
cana-659	207	13	optimized	optimize	VERB
cana-659	207	14	simple	simple	ADJ
cana-659	207	15	decision	decision	NOUN
cana-659	207	16	trees	tree	NOUN
cana-659	207	17	,	,	PUNCT
cana-659	207	18	which	which	PRON
cana-659	207	19	it	it	PRON
cana-659	207	20	then	then	ADV
cana-659	207	21	generalizes	generalize	VERB
cana-659	207	22	by	by	ADP
cana-659	207	23	optimizing	optimize	VERB
cana-659	207	24	an	an	DET
cana-659	207	25	arbitrarily	arbitrarily	ADV
cana-659	207	26	defined	define	VERB
cana-659	207	27	loss	loss	NOUN
cana-659	207	28	function	function	NOUN
cana-659	207	29	to	to	PART
cana-659	207	30	provide	provide	VERB
cana-659	207	31	more	more	ADV
cana-659	207	32	accurate	accurate	ADJ
cana-659	207	33	predictions	prediction	NOUN
cana-659	207	34	[	[	X
cana-659	207	35	12][13	12][13	X
cana-659	207	36	]	]	PUNCT
cana-659	207	37	.	.	PUNCT
cana-659	208	1	4.4	4.4	NUM
cana-659	208	2	grid	grid	NOUN
cana-659	208	3	search	search	NOUN
cana-659	208	4	grid	grid	NOUN
cana-659	208	5	search	search	NOUN
cana-659	208	6	is	be	AUX
cana-659	208	7	an	an	DET
cana-659	208	8	optimization	optimization	NOUN
cana-659	208	9	technique	technique	NOUN
cana-659	208	10	that	that	PRON
cana-659	208	11	allows	allow	VERB
cana-659	208	12	you	you	PRON
cana-659	208	13	to	to	PART
cana-659	208	14	choose	choose	VERB
cana-659	208	15	from	from	ADP
cana-659	208	16	a	a	DET
cana-659	208	17	list	list	NOUN
cana-659	208	18	of	of	ADP
cana-659	208	19	parameter	parameter	NOUN
cana-659	208	20	alternatives	alternative	NOUN
cana-659	208	21	the	the	DET
cana-659	208	22	optimal	optimal	ADJ
cana-659	208	23	parameters	parameter	NOUN
cana-659	208	24	for	for	ADP
cana-659	208	25	your	your	PRON
cana-659	208	26	optimization	optimization	NOUN
cana-659	208	27	problem	problem	NOUN
cana-659	208	28	.	.	PUNCT
cana-659	209	1	one	one	NUM
cana-659	209	2	fundamental	fundamental	ADJ
cana-659	209	3	tool	tool	NOUN
cana-659	209	4	for	for	ADP
cana-659	209	5	hyperparameter	hyperparameter	NOUN
cana-659	209	6	optimization	optimization	NOUN
cana-659	209	7	is	be	AUX
cana-659	209	8	the	the	DET
cana-659	209	9	grid	grid	NOUN
cana-659	209	10	search	search	NOUN
cana-659	209	11	technique	technique	NOUN
cana-659	209	12	.	.	PUNCT
cana-659	210	1	the	the	DET
cana-659	210	2	grid	grid	NOUN
cana-659	210	3	search	search	NOUN
cana-659	210	4	method	method	NOUN
cana-659	210	5	selects	select	VERB
cana-659	210	6	the	the	DET
cana-659	210	7	hyperparameter	hyperparameter	NOUN
cana-659	210	8	combination	combination	NOUN
cana-659	210	9	that	that	PRON
cana-659	210	10	yields	yield	VERB
cana-659	210	11	the	the	DET
cana-659	210	12	lowest	low	ADJ
cana-659	210	13	error	error	NOUN
cana-659	210	14	score	score	NOUN
cana-659	210	15	after	after	ADP
cana-659	210	16	taking	take	VERB
cana-659	210	17	into	into	ADP
cana-659	210	18	account	account	NOUN
cana-659	210	19	several	several	ADJ
cana-659	210	20	combinations	combination	NOUN
cana-659	210	21	.	.	PUNCT
cana-659	211	1	a	a	DET
cana-659	211	2	hold	hold	VERB
cana-659	211	3	-	-	PUNCT
cana-659	211	4	out	out	ADP
cana-659	211	5	validation	validation	NOUN
cana-659	211	6	set	set	VERB
cana-659	211	7	on	on	ADP
cana-659	211	8	the	the	DET
cana-659	211	9	training	training	NOUN
cana-659	211	10	set	set	NOUN
cana-659	211	11	is	be	AUX
cana-659	211	12	used	use	VERB
cana-659	211	13	to	to	PART
cana-659	211	14	evaluate	evaluate	VERB
cana-659	211	15	the	the	DET
cana-659	211	16	performance	performance	NOUN
cana-659	211	17	of	of	ADP
cana-659	211	18	each	each	DET
cana-659	211	19	combination	combination	NOUN
cana-659	211	20	.	.	PUNCT
cana-659	212	1	the	the	DET
cana-659	212	2	configurations	configuration	NOUN
cana-659	212	3	that	that	PRON
cana-659	212	4	offer	offer	VERB
cana-659	212	5	the	the	DET
cana-659	212	6	best	good	ADJ
cana-659	212	7	performance	performance	NOUN
cana-659	212	8	during	during	ADP
cana-659	212	9	the	the	DET
cana-659	212	10	validation	validation	NOUN
cana-659	212	11	process	process	NOUN
cana-659	212	12	are	be	AUX
cana-659	212	13	then	then	ADV
cana-659	212	14	produced	produce	VERB
cana-659	212	15	by	by	ADP
cana-659	212	16	the	the	DET
cana-659	212	17	gs	gs	PROPN
cana-659	212	18	algorithm	algorithm	NOUN
cana-659	212	19	.	.	PUNCT
cana-659	213	1	the	the	DET
cana-659	213	2	grid	grid	NOUN
cana-659	213	3	search	search	NOUN
cana-659	213	4	's	's	PART
cana-659	213	5	highest	high	ADJ
cana-659	213	6	hyperparameter	hyperparameter	NOUN
cana-659	213	7	values	value	NOUN
cana-659	213	8	are	be	AUX
cana-659	213	9	then	then	ADV
cana-659	213	10	used	use	VERB
cana-659	213	11	by	by	ADP
cana-659	213	12	the	the	DET
cana-659	213	13	model	model	NOUN
cana-659	213	14	.	.	PUNCT
cana-659	214	1	but	but	CCONJ
cana-659	214	2	when	when	SCONJ
cana-659	214	3	the	the	DET
cana-659	214	4	frequency	frequency	NOUN
cana-659	214	5	of	of	ADP
cana-659	214	6	the	the	DET
cana-659	214	7	hyperparameters	hyperparameter	NOUN
cana-659	214	8	increases	increase	NOUN
cana-659	214	9	,	,	PUNCT
cana-659	214	10	the	the	DET
cana-659	214	11	number	number	NOUN
cana-659	214	12	of	of	ADP
cana-659	214	13	evaluations	evaluation	NOUN
cana-659	214	14	increases	increase	VERB
cana-659	214	15	exponentially	exponentially	ADV
cana-659	214	16	,	,	PUNCT
cana-659	214	17	and	and	CCONJ
cana-659	214	18	gs	gs	PROPN
cana-659	214	19	becomes	become	VERB
cana-659	214	20	useless	useless	ADJ
cana-659	214	21	in	in	ADP
cana-659	214	22	the	the	DET
cana-659	214	23	configuration	configuration	NOUN
cana-659	214	24	space	space	NOUN
cana-659	214	25	of	of	ADP
cana-659	214	26	highdimensionality	highdimensionality	NOUN
cana-659	214	27	hyperparameters	hyperparameter	NOUN
cana-659	214	28	.	.	PUNCT
cana-659	215	1	therefore	therefore	ADV
cana-659	215	2	,	,	PUNCT
cana-659	215	3	in	in	ADP
cana-659	215	4	order	order	NOUN
cana-659	215	5	to	to	PART
cana-659	215	6	make	make	VERB
cana-659	215	7	gs	gs	PART
cana-659	215	8	an	an	DET
cana-659	215	9	effective	effective	ADJ
cana-659	215	10	optimization	optimization	NOUN
cana-659	215	11	strategy	strategy	NOUN
cana-659	215	12	,	,	PUNCT
cana-659	215	13	the	the	DET
cana-659	215	14	hyperparameter	hyperparameter	NOUN
cana-659	215	15	setting	set	VERB
cana-659	215	16	needs	need	NOUN
cana-659	215	17	to	to	PART
cana-659	215	18	be	be	AUX
cana-659	215	19	limited	limit	VERB
cana-659	215	20	[	[	X
cana-659	215	21	14][15	14][15	X
cana-659	215	22	]	]	X
cana-659	215	23	.	.	PUNCT
cana-659	216	1	4.5	4.5	NUM
cana-659	216	2	hill	hill	NOUN
cana-659	216	3	climbing	climbing	NOUN
cana-659	216	4	in	in	ADP
cana-659	216	5	order	order	NOUN
cana-659	216	6	to	to	PART
cana-659	216	7	find	find	VERB
cana-659	216	8	the	the	DET
cana-659	216	9	mountain	mountain	NOUN
cana-659	216	10	's	's	PART
cana-659	216	11	top	top	NOUN
cana-659	216	12	,	,	PUNCT
cana-659	216	13	the	the	DET
cana-659	216	14	greedy	greedy	ADJ
cana-659	216	15	local	local	ADJ
cana-659	216	16	search	search	NOUN
cana-659	216	17	algorithm	algorithm	NOUN
cana-659	216	18	known	know	VERB
cana-659	216	19	as	as	ADP
cana-659	216	20	the	the	DET
cana-659	216	21	hill	hill	NOUN
cana-659	216	22	climbing	climbing	NOUN
cana-659	216	23	algorithm	algorithm	NOUN
cana-659	216	24	keeps	keep	VERB
cana-659	216	25	going	go	VERB
cana-659	216	26	in	in	ADP
cana-659	216	27	the	the	DET
cana-659	216	28	direction	direction	NOUN
cana-659	216	29	of	of	ADP
cana-659	216	30	rising	rise	VERB
cana-659	216	31	value	value	NOUN
cana-659	216	32	.	.	PUNCT
cana-659	217	1	it	it	PRON
cana-659	217	2	just	just	ADV
cana-659	217	3	looks	look	VERB
cana-659	217	4	at	at	ADP
cana-659	217	5	its	its	PRON
cana-659	217	6	good	good	ADJ
cana-659	217	7	local	local	ADJ
cana-659	217	8	neighbour	neighbour	NOUN
cana-659	217	9	state	state	NOUN
cana-659	217	10	and	and	CCONJ
cana-659	217	11	not	not	PART
cana-659	217	12	beyond	beyond	ADP
cana-659	217	13	that	that	PRON
cana-659	217	14	,	,	PUNCT
cana-659	217	15	hence	hence	ADV
cana-659	217	16	it	it	PRON
cana-659	217	17	ends	end	VERB
cana-659	217	18	when	when	SCONJ
cana-659	217	19	it	it	PRON
cana-659	217	20	reaches	reach	VERB
cana-659	217	21	a	a	DET
cana-659	217	22	peak	peak	NOUN
cana-659	217	23	value	value	NOUN
cana-659	217	24	when	when	SCONJ
cana-659	217	25	no	no	DET
cana-659	217	26	neighbour	neighbour	NOUN
cana-659	217	27	has	have	VERB
cana-659	217	28	a	a	DET
cana-659	217	29	higher	high	ADJ
cana-659	217	30	value	value	NOUN
cana-659	217	31	.	.	PUNCT
cana-659	218	1	the	the	DET
cana-659	218	2	two	two	NUM
cana-659	218	3	parts	part	NOUN
cana-659	218	4	of	of	ADP
cana-659	218	5	a	a	DET
cana-659	218	6	node	node	NOUN
cana-659	218	7	of	of	ADP
cana-659	218	8	a	a	DET
cana-659	218	9	hill	hill	NOUN
cana-659	218	10	climbing	climbing	NOUN
cana-659	218	11	algorithm	algorithm	NOUN
cana-659	218	12	are	be	AUX
cana-659	218	13	value	value	NOUN
cana-659	218	14	and	and	CCONJ
cana-659	218	15	state	state	NOUN
cana-659	218	16	.	.	PUNCT
cana-659	219	1	when	when	SCONJ
cana-659	219	2	an	an	DET
cana-659	219	3	effective	effective	ADJ
cana-659	219	4	heuristic	heuristic	NOUN
cana-659	219	5	is	be	AUX
cana-659	219	6	available	available	ADJ
cana-659	219	7	,	,	PUNCT
cana-659	219	8	the	the	DET
cana-659	219	9	hill	hill	NOUN
cana-659	219	10	climbing	climbing	NOUN
cana-659	219	11	algorithm	algorithm	NOUN
cana-659	219	12	is	be	AUX
cana-659	219	13	a	a	DET
cana-659	219	14	strategy	strategy	NOUN
cana-659	219	15	used	use	VERB
cana-659	219	16	to	to	PART
cana-659	219	17	optimize	optimize	VERB
cana-659	219	18	mathematical	mathematical	ADJ
cana-659	219	19	problems	problem	NOUN
cana-659	219	20	.	.	PUNCT
cana-659	220	1	a	a	DET
cana-659	220	2	well	well	ADV
cana-659	220	3	-	-	PUNCT
cana-659	220	4	known	know	VERB
cana-659	220	5	illustration	illustration	NOUN
cana-659	220	6	of	of	ADP
cana-659	220	7	a	a	DET
cana-659	220	8	hill	hill	NOUN
cana-659	220	9	climbing	climbing	NOUN
cana-659	220	10	algorithm	algorithm	NOUN
cana-659	220	11	is	be	AUX
cana-659	220	12	the	the	DET
cana-659	220	13	travelingsalesman	travelingsalesman	PROPN
cana-659	220	14	problem	problem	NOUN
cana-659	220	15	,	,	PUNCT
cana-659	220	16	in	in	ADP
cana-659	220	17	which	which	PRON
cana-659	220	18	the	the	DET
cana-659	220	19	salesman	salesman	NOUN
cana-659	220	20	's	's	PART
cana-659	220	21	journey	journey	NOUN
cana-659	220	22	distance	distance	NOUN
cana-659	220	23	must	must	AUX
cana-659	220	24	be	be	AUX
cana-659	220	25	minimized	minimize	VERB
cana-659	220	26	[	[	X
cana-659	220	27	16	16	NUM
cana-659	220	28	]	]	PUNCT
cana-659	220	29	.	.	PUNCT
cana-659	221	1	5	5	X
cana-659	221	2	.	.	NUM
cana-659	221	3	proposed	propose	VERB
cana-659	221	4	methodology	methodology	NOUN
cana-659	221	5	in	in	ADP
cana-659	221	6	order	order	NOUN
cana-659	221	7	to	to	PART
cana-659	221	8	investigate	investigate	VERB
cana-659	221	9	the	the	DET
cana-659	221	10	machine	machine	NOUN
cana-659	221	11	learning	learn	VERB
cana-659	221	12	methods	method	NOUN
cana-659	221	13	discussed	discuss	VERB
cana-659	221	14	earlier	early	ADV
cana-659	221	15	,	,	PUNCT
cana-659	221	16	we	we	PRON
cana-659	221	17	decided	decide	VERB
cana-659	221	18	to	to	PART
cana-659	221	19	construct	construct	VERB
cana-659	221	20	databases	database	NOUN
cana-659	221	21	on	on	ADP
cana-659	221	22	dna	dna	PROPN
cana-659	221	23	.	.	PUNCT
cana-659	222	1	the	the	DET
cana-659	222	2	user	user	NOUN
cana-659	222	3	selects	select	VERB
cana-659	222	4	the	the	DET
cana-659	222	5	dataset	dataset	NOUN
cana-659	222	6	for	for	ADP
cana-659	222	7	design	design	NOUN
cana-659	222	8	and	and	CCONJ
cana-659	222	9	implementation	implementation	NOUN
cana-659	222	10	,	,	PUNCT
cana-659	222	11	after	after	ADP
cana-659	222	12	which	which	PRON
cana-659	222	13	a	a	DET
cana-659	222	14	few	few	ADJ
cana-659	222	15	classification	classification	NOUN
cana-659	222	16	and	and	CCONJ
cana-659	222	17	optimization	optimization	NOUN
cana-659	222	18	algorithms	algorithm	NOUN
cana-659	222	19	are	be	AUX
cana-659	222	20	run	run	VERB
cana-659	222	21	to	to	PART
cana-659	222	22	obtain	obtain	VERB
cana-659	222	23	a	a	DET
cana-659	222	24	comparative	comparative	ADJ
cana-659	222	25	study	study	NOUN
cana-659	222	26	of	of	ADP
cana-659	222	27	the	the	DET
cana-659	222	28	methods	method	NOUN
cana-659	222	29	.	.	PUNCT
cana-659	223	1	to	to	PART
cana-659	223	2	train	train	VERB
cana-659	223	3	and	and	CCONJ
cana-659	223	4	deploy	deploy	VERB
cana-659	223	5	the	the	DET
cana-659	223	6	model	model	NOUN
cana-659	223	7	with	with	ADP
cana-659	223	8	the	the	DET
cana-659	223	9	maximum	maximum	ADJ
cana-659	223	10	level	level	NOUN
cana-659	223	11	of	of	ADP
cana-659	223	12	accuracy	accuracy	NOUN
cana-659	223	13	and	and	CCONJ
cana-659	223	14	efficiency	efficiency	NOUN
cana-659	223	15	,	,	PUNCT
cana-659	223	16	our	our	PRON
cana-659	223	17	goal	goal	NOUN
cana-659	223	18	is	be	AUX
cana-659	223	19	to	to	PART
cana-659	223	20	determine	determine	VERB
cana-659	223	21	which	which	DET
cana-659	223	22	optimizer	optimizer	NOUN
cana-659	223	23	and	and	CCONJ
cana-659	223	24	classifier	classifier	NOUN
cana-659	223	25	combination	combination	NOUN
cana-659	223	26	performs	perform	VERB
cana-659	223	27	best	good	ADJ
cana-659	223	28	for	for	ADP
cana-659	223	29	a	a	DET
cana-659	223	30	given	give	VERB
cana-659	223	31	task	task	NOUN
cana-659	223	32	.	.	PUNCT
cana-659	224	1	the	the	DET
cana-659	224	2	analysis	analysis	NOUN
cana-659	224	3	may	may	AUX
cana-659	224	4	have	have	AUX
cana-659	224	5	employed	employ	VERB
cana-659	224	6	accuracy	accuracy	NOUN
cana-659	224	7	,	,	PUNCT
cana-659	224	8	precision	precision	NOUN
cana-659	224	9	,	,	PUNCT
cana-659	224	10	recall	recall	NOUN
cana-659	224	11	,	,	PUNCT
cana-659	224	12	f1	f1	NOUN
cana-659	224	13	-	-	PUNCT
cana-659	224	14	score	score	NOUN
cana-659	224	15	,	,	PUNCT
cana-659	224	16	and	and	CCONJ
cana-659	224	17	other	other	ADJ
cana-659	224	18	pertinent	pertinent	ADJ
cana-659	224	19	performance	performance	NOUN
cana-659	224	20	measures	measure	NOUN
cana-659	224	21	as	as	ADP
cana-659	224	22	evaluation	evaluation	NOUN
cana-659	224	23	metrics	metric	NOUN
cana-659	224	24	.	.	PUNCT
cana-659	225	1	the	the	DET
cana-659	225	2	user	user	NOUN
cana-659	225	3	has	have	VERB
cana-659	225	4	the	the	DET
cana-659	225	5	option	option	NOUN
cana-659	225	6	to	to	PART
cana-659	225	7	upload	upload	VERB
cana-659	225	8	or	or	CCONJ
cana-659	225	9	drag	drag	VERB
cana-659	225	10	and	and	CCONJ
cana-659	225	11	drop	drop	VERB
cana-659	225	12	the	the	DET
cana-659	225	13	files	file	NOUN
cana-659	225	14	that	that	PRON
cana-659	225	15	they	they	PRON
cana-659	225	16	want	want	VERB
cana-659	225	17	to	to	PART
cana-659	225	18	compare	compare	VERB
cana-659	225	19	at	at	ADP
cana-659	225	20	the	the	DET
cana-659	225	21	moment	moment	NOUN
cana-659	225	22	of	of	ADP
cana-659	225	23	execution	execution	NOUN
cana-659	225	24	.	.	PUNCT
cana-659	226	1	the	the	DET
cana-659	226	2	chosen	choose	VERB
cana-659	226	3	file	file	NOUN
cana-659	226	4	needs	need	VERB
cana-659	226	5	to	to	PART
cana-659	226	6	be	be	AUX
cana-659	226	7	in	in	ADP
cana-659	226	8	the	the	DET
cana-659	226	9	*	*	ADJ
cana-659	226	10	.txt	.txt	ADJ
cana-659	226	11	or	or	CCONJ
cana-659	226	12	*	*	ADJ
cana-659	226	13	.csv	.csv	ADJ
cana-659	226	14	extension	extension	NOUN
cana-659	226	15	.	.	PUNCT
cana-659	227	1	three	three	NUM
cana-659	227	2	datasets—human_data.txt	datasets—human_data.txt	NOUN
cana-659	227	3	,	,	PUNCT
cana-659	227	4	dog.txt	dog.txt	PROPN
cana-659	227	5	,	,	PUNCT
cana-659	227	6	and	and	CCONJ
cana-659	227	7	chimp_data.txt	chimp_data.txt	NOUN
cana-659	227	8	—	—	PUNCT
cana-659	227	9	have	have	AUX
cana-659	227	10	been	be	AUX
cana-659	227	11	selected	select	VERB
cana-659	227	12	for	for	ADP
cana-659	227	13	this	this	DET
cana-659	227	14	purpose	purpose	NOUN
cana-659	227	15	.	.	PUNCT
cana-659	228	1	data	datum	NOUN
cana-659	228	2	extraction	extraction	PROPN
cana-659	228	3	,	,	PUNCT
cana-659	228	4	dna	dna	NOUN
cana-659	228	5	sequencing	sequencing	NOUN
cana-659	228	6	,	,	PUNCT
cana-659	228	7	dna	dna	NOUN
cana-659	228	8	sequencing	sequence	VERB
cana-659	228	9	utilizing	utilize	VERB
cana-659	228	10	optimization	optimization	NOUN
cana-659	228	11	algorithms	algorithm	NOUN
cana-659	228	12	,	,	PUNCT
cana-659	228	13	optimizing	optimize	VERB
cana-659	228	14	and	and	CCONJ
cana-659	228	15	parallelizing	parallelize	VERB
cana-659	228	16	the	the	DET
cana-659	228	17	first	first	ADJ
cana-659	228	18	two	two	NUM
cana-659	228	19	,	,	PUNCT
cana-659	228	20	cross	cross	NOUN
cana-659	228	21	-	-	NOUN
cana-659	228	22	validation	validation	ADJ
cana-659	228	23	,	,	PUNCT
cana-659	228	24	and	and	CCONJ
cana-659	228	25	hybrid	hybrid	ADJ
cana-659	228	26	algorithms	algorithm	NOUN
cana-659	228	27	are	be	AUX
cana-659	228	28	among	among	ADP
cana-659	228	29	the	the	DET
cana-659	228	30	options	option	NOUN
cana-659	228	31	available	available	ADJ
cana-659	228	32	to	to	ADP
cana-659	228	33	the	the	DET
cana-659	228	34	user	user	NOUN
cana-659	228	35	.	.	PUNCT
cana-659	229	1	communications	communication	NOUN
cana-659	229	2	on	on	ADP
cana-659	229	3	applied	apply	VERB
cana-659	229	4	nonlinear	nonlinear	ADJ
cana-659	229	5	analysis	analysis	NOUN
cana-659	229	6	issn	issn	NOUN
cana-659	229	7	:	:	PUNCT
cana-659	229	8	1074	1074	NUM
cana-659	229	9	-	-	PUNCT
cana-659	229	10	133x	133x	NUM
cana-659	229	11	vol	vol	NOUN
cana-659	229	12	31	31	NUM
cana-659	229	13	no	no	NOUN
cana-659	229	14	.	.	PUNCT
cana-659	230	1	2s	2s	NUM
cana-659	230	2	(	(	PUNCT
cana-659	230	3	2024	2024	NUM
cana-659	230	4	)	)	PUNCT
cana-659	230	5	447	447	NUM
cana-659	230	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	230	7	fig	fig	NOUN
cana-659	230	8	.	.	PUNCT
cana-659	231	1	3	3	NUM
cana-659	231	2	proposed	propose	VERB
cana-659	231	3	methodology	methodology	NOUN
cana-659	231	4	figure	figure	NOUN
cana-659	231	5	3	3	NUM
cana-659	231	6	shows	show	VERB
cana-659	231	7	the	the	DET
cana-659	231	8	overall	overall	ADJ
cana-659	231	9	architecture	architecture	NOUN
cana-659	231	10	flow	flow	NOUN
cana-659	231	11	and	and	CCONJ
cana-659	231	12	gives	give	VERB
cana-659	231	13	a	a	DET
cana-659	231	14	summary	summary	NOUN
cana-659	231	15	of	of	ADP
cana-659	231	16	the	the	DET
cana-659	231	17	many	many	ADJ
cana-659	231	18	stages	stage	NOUN
cana-659	231	19	in	in	ADP
cana-659	231	20	our	our	PRON
cana-659	231	21	ml	ml	NOUN
cana-659	231	22	technique	technique	NOUN
cana-659	231	23	for	for	ADP
cana-659	231	24	dna	dna	PROPN
cana-659	231	25	sequences	sequence	NOUN
cana-659	231	26	.	.	PUNCT
cana-659	232	1	our	our	PRON
cana-659	232	2	approach	approach	NOUN
cana-659	232	3	is	be	AUX
cana-659	232	4	divided	divide	VERB
cana-659	232	5	into	into	ADP
cana-659	232	6	multiple	multiple	ADJ
cana-659	232	7	stages	stage	NOUN
cana-659	232	8	,	,	PUNCT
cana-659	232	9	each	each	PRON
cana-659	232	10	of	of	ADP
cana-659	232	11	which	which	PRON
cana-659	232	12	contributes	contribute	VERB
cana-659	232	13	differently	differently	ADV
cana-659	232	14	to	to	ADP
cana-659	232	15	the	the	DET
cana-659	232	16	final	final	ADJ
cana-659	232	17	result	result	NOUN
cana-659	232	18	.	.	PUNCT
cana-659	233	1	pre	pre	ADJ
cana-659	233	2	-	-	ADJ
cana-659	233	3	processing	process	VERB
cana-659	233	4	the	the	DET
cana-659	233	5	dna	dna	PROPN
cana-659	233	6	sequence	sequence	NOUN
cana-659	233	7	data	datum	NOUN
cana-659	233	8	,	,	PUNCT
cana-659	233	9	which	which	PRON
cana-659	233	10	includes	include	VERB
cana-659	233	11	cleaning	cleaning	NOUN
cana-659	233	12	and	and	CCONJ
cana-659	233	13	filtering	filter	VERB
cana-659	233	14	the	the	DET
cana-659	233	15	data	datum	NOUN
cana-659	233	16	to	to	PART
cana-659	233	17	eliminate	eliminate	VERB
cana-659	233	18	noise	noise	NOUN
cana-659	233	19	and	and	CCONJ
cana-659	233	20	unnecessary	unnecessary	ADJ
cana-659	233	21	information	information	NOUN
cana-659	233	22	,	,	PUNCT
cana-659	233	23	is	be	AUX
cana-659	233	24	the	the	DET
cana-659	233	25	first	first	ADJ
cana-659	233	26	step	step	NOUN
cana-659	233	27	in	in	ADP
cana-659	233	28	the	the	DET
cana-659	233	29	process	process	NOUN
cana-659	233	30	.	.	PUNCT
cana-659	234	1	next	next	ADV
cana-659	234	2	,	,	PUNCT
cana-659	234	3	the	the	DET
cana-659	234	4	pre	pre	ADJ
cana-659	234	5	-	-	ADJ
cana-659	234	6	processed	processed	ADJ
cana-659	234	7	data	data	NOUN
cana-659	234	8	is	be	AUX
cana-659	234	9	put	put	VERB
cana-659	234	10	through	through	ADP
cana-659	234	11	a	a	DET
cana-659	234	12	process	process	NOUN
cana-659	234	13	called	call	VERB
cana-659	234	14	feature	feature	NOUN
cana-659	234	15	extraction	extraction	NOUN
cana-659	234	16	,	,	PUNCT
cana-659	234	17	which	which	PRON
cana-659	234	18	entails	entail	VERB
cana-659	234	19	finding	find	VERB
cana-659	234	20	and	and	CCONJ
cana-659	234	21	removing	remove	VERB
cana-659	234	22	pertinent	pertinent	ADJ
cana-659	234	23	features	feature	NOUN
cana-659	234	24	from	from	ADP
cana-659	234	25	the	the	DET
cana-659	234	26	data	datum	NOUN
cana-659	234	27	.	.	PUNCT
cana-659	235	1	the	the	DET
cana-659	235	2	next	next	ADJ
cana-659	235	3	step	step	NOUN
cana-659	235	4	involves	involve	VERB
cana-659	235	5	building	build	VERB
cana-659	235	6	a	a	DET
cana-659	235	7	model	model	NOUN
cana-659	235	8	that	that	PRON
cana-659	235	9	can	can	AUX
cana-659	235	10	categorize	categorize	VERB
cana-659	235	11	the	the	DET
cana-659	235	12	dna	dna	PROPN
cana-659	235	13	sequences	sequence	NOUN
cana-659	235	14	according	accord	VERB
cana-659	235	15	to	to	ADP
cana-659	235	16	how	how	SCONJ
cana-659	235	17	similar	similar	ADJ
cana-659	235	18	they	they	PRON
cana-659	235	19	are	be	AUX
cana-659	235	20	using	use	VERB
cana-659	235	21	the	the	DET
cana-659	235	22	attributes	attribute	NOUN
cana-659	235	23	that	that	PRON
cana-659	235	24	were	be	AUX
cana-659	235	25	retrieved	retrieve	VERB
cana-659	235	26	.	.	PUNCT
cana-659	236	1	several	several	ADJ
cana-659	236	2	machine	machine	NOUN
cana-659	236	3	learning	learn	VERB
cana-659	236	4	techniques	technique	NOUN
cana-659	236	5	,	,	PUNCT
cana-659	236	6	such	such	ADJ
cana-659	236	7	as	as	ADP
cana-659	236	8	classifiers	classifier	NOUN
cana-659	236	9	and	and	CCONJ
cana-659	236	10	optimizers	optimizer	NOUN
cana-659	236	11	,	,	PUNCT
cana-659	236	12	are	be	AUX
cana-659	236	13	used	use	VERB
cana-659	236	14	in	in	ADP
cana-659	236	15	this	this	DET
cana-659	236	16	paper	paper	NOUN
cana-659	236	17	to	to	PART
cana-659	236	18	create	create	VERB
cana-659	236	19	an	an	DET
cana-659	236	20	accurate	accurate	ADJ
cana-659	236	21	and	and	CCONJ
cana-659	236	22	effective	effective	ADJ
cana-659	236	23	model	model	NOUN
cana-659	236	24	[	[	X
cana-659	236	25	17	17	NUM
cana-659	236	26	]	]	SYM
cana-659	236	27	.	.	PUNCT
cana-659	237	1	6	6	X
cana-659	237	2	.	.	X
cana-659	237	3	experimental	experimental	ADJ
cana-659	237	4	setup	setup	NOUN
cana-659	237	5	fundamentally	fundamentally	ADV
cana-659	237	6	,	,	PUNCT
cana-659	237	7	our	our	PRON
cana-659	237	8	goal	goal	NOUN
cana-659	237	9	is	be	AUX
cana-659	237	10	to	to	PART
cana-659	237	11	comprehend	comprehend	VERB
cana-659	237	12	the	the	DET
cana-659	237	13	process	process	NOUN
cana-659	237	14	of	of	ADP
cana-659	237	15	employing	employ	VERB
cana-659	237	16	ml	ml	ADP
cana-659	237	17	algorithms	algorithm	NOUN
cana-659	237	18	for	for	ADP
cana-659	237	19	dna	dna	NOUN
cana-659	237	20	sequencing	sequencing	NOUN
cana-659	237	21	.	.	PUNCT
cana-659	238	1	we	we	PRON
cana-659	238	2	are	be	AUX
cana-659	238	3	aware	aware	ADJ
cana-659	238	4	that	that	SCONJ
cana-659	238	5	the	the	DET
cana-659	238	6	dna	dna	NOUN
cana-659	238	7	of	of	ADP
cana-659	238	8	humans	human	NOUN
cana-659	238	9	and	and	CCONJ
cana-659	238	10	other	other	ADJ
cana-659	238	11	living	live	VERB
cana-659	238	12	things	thing	NOUN
cana-659	238	13	is	be	AUX
cana-659	238	14	made	make	VERB
cana-659	238	15	up	up	ADP
cana-659	238	16	of	of	ADP
cana-659	238	17	either	either	CCONJ
cana-659	238	18	a	a	DET
cana-659	238	19	distinct	distinct	ADJ
cana-659	238	20	sort	sort	NOUN
cana-659	238	21	of	of	ADP
cana-659	238	22	sequence	sequence	NOUN
cana-659	238	23	or	or	CCONJ
cana-659	238	24	a	a	DET
cana-659	238	25	sequence	sequence	NOUN
cana-659	238	26	similar	similar	ADJ
cana-659	238	27	to	to	ADP
cana-659	238	28	atgc	atgc	NOUN
cana-659	238	29	.	.	PUNCT
cana-659	239	1	here	here	ADV
cana-659	239	2	,	,	PUNCT
cana-659	239	3	we	we	PRON
cana-659	239	4	downloaded	download	VERB
cana-659	239	5	a	a	DET
cana-659	239	6	kaggle	kaggle	ADJ
cana-659	239	7	data	data	NOUN
cana-659	239	8	collection	collection	NOUN
cana-659	239	9	containing	contain	VERB
cana-659	239	10	chimpanzee	chimpanzee	NOUN
cana-659	239	11	,	,	PUNCT
cana-659	239	12	dog	dog	NOUN
cana-659	239	13	,	,	PUNCT
cana-659	239	14	and	and	CCONJ
cana-659	239	15	human	human	ADJ
cana-659	239	16	data	datum	NOUN
cana-659	239	17	.	.	PUNCT
cana-659	240	1	we	we	PRON
cana-659	240	2	intend	intend	VERB
cana-659	240	3	to	to	PART
cana-659	240	4	utilize	utilize	VERB
cana-659	240	5	a	a	DET
cana-659	240	6	classification	classification	NOUN
cana-659	240	7	technique	technique	NOUN
cana-659	240	8	that	that	PRON
cana-659	240	9	can	can	AUX
cana-659	240	10	effectively	effectively	ADV
cana-659	240	11	classify	classify	VERB
cana-659	240	12	these	these	DET
cana-659	240	13	specific	specific	ADJ
cana-659	240	14	sequences	sequence	NOUN
cana-659	240	15	.	.	PUNCT
cana-659	241	1	we	we	PRON
cana-659	241	2	obtained	obtain	VERB
cana-659	241	3	their	their	PRON
cana-659	241	4	class	class	NOUN
cana-659	241	5	and	and	CCONJ
cana-659	241	6	the	the	DET
cana-659	241	7	dna	dna	PROPN
cana-659	241	8	sequence	sequence	NOUN
cana-659	241	9	after	after	ADP
cana-659	241	10	processing	process	VERB
cana-659	241	11	this	this	DET
cana-659	241	12	data	data	NOUN
cana-659	241	13	set	set	VERB
cana-659	241	14	.	.	PUNCT
cana-659	242	1	based	base	VERB
cana-659	242	2	on	on	ADP
cana-659	242	3	sequence	sequence	NOUN
cana-659	242	4	,	,	PUNCT
cana-659	242	5	this	this	DET
cana-659	242	6	specific	specific	ADJ
cana-659	242	7	dataset	dataset	NOUN
cana-659	242	8	should	should	AUX
cana-659	242	9	be	be	AUX
cana-659	242	10	able	able	ADJ
cana-659	242	11	to	to	PART
cana-659	242	12	determine	determine	VERB
cana-659	242	13	which	which	DET
cana-659	242	14	class	class	NOUN
cana-659	242	15	a	a	DET
cana-659	242	16	given	give	VERB
cana-659	242	17	sequence	sequence	NOUN
cana-659	242	18	belongs	belong	VERB
cana-659	242	19	to	to	ADP
cana-659	242	20	.	.	PUNCT
cana-659	243	1	these	these	DET
cana-659	243	2	sequences	sequence	NOUN
cana-659	243	3	,	,	PUNCT
cana-659	243	4	which	which	PRON
cana-659	243	5	are	be	AUX
cana-659	243	6	essentially	essentially	ADV
cana-659	243	7	divided	divide	VERB
cana-659	243	8	into	into	ADP
cana-659	243	9	different	different	ADJ
cana-659	243	10	types	type	NOUN
cana-659	243	11	,	,	PUNCT
cana-659	243	12	could	could	AUX
cana-659	243	13	represent	represent	VERB
cana-659	243	14	dna	dna	NOUN
cana-659	243	15	or	or	CCONJ
cana-659	243	16	gene	gene	NOUN
cana-659	243	17	sequences	sequence	NOUN
cana-659	243	18	.	.	PUNCT
cana-659	244	1	communications	communication	NOUN
cana-659	244	2	on	on	ADP
cana-659	244	3	applied	apply	VERB
cana-659	244	4	nonlinear	nonlinear	ADJ
cana-659	244	5	analysis	analysis	NOUN
cana-659	244	6	issn	issn	NOUN
cana-659	244	7	:	:	PUNCT
cana-659	244	8	1074	1074	NUM
cana-659	244	9	-	-	PUNCT
cana-659	244	10	133x	133x	NUM
cana-659	244	11	vol	vol	NOUN
cana-659	244	12	31	31	NUM
cana-659	244	13	no	no	NOUN
cana-659	244	14	.	.	PUNCT
cana-659	245	1	2s	2s	NUM
cana-659	245	2	(	(	PUNCT
cana-659	245	3	2024	2024	NUM
cana-659	245	4	)	)	PUNCT
cana-659	245	5	448	448	NUM
cana-659	245	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	245	7	see	see	VERB
cana-659	245	8	figure	figure	NOUN
cana-659	245	9	4	4	NUM
cana-659	245	10	,	,	PUNCT
cana-659	245	11	we	we	PRON
cana-659	245	12	used	use	VERB
cana-659	245	13	vscode	vscode	NOUN
cana-659	245	14	to	to	PART
cana-659	245	15	develop	develop	VERB
cana-659	245	16	an	an	DET
cana-659	245	17	experimental	experimental	ADJ
cana-659	245	18	python	python	NOUN
cana-659	245	19	project	project	NOUN
cana-659	245	20	and	and	CCONJ
cana-659	245	21	streamlit	streamlit	NOUN
cana-659	245	22	to	to	PART
cana-659	245	23	host	host	VERB
cana-659	245	24	it	it	PRON
cana-659	245	25	on	on	ADP
cana-659	245	26	a	a	DET
cana-659	245	27	local	local	ADJ
cana-659	245	28	web	web	NOUN
cana-659	245	29	server	server	NOUN
cana-659	245	30	.	.	PUNCT
cana-659	246	1	therefore	therefore	ADV
cana-659	246	2	,	,	PUNCT
cana-659	246	3	the	the	DET
cana-659	246	4	user	user	NOUN
cana-659	246	5	has	have	VERB
cana-659	246	6	the	the	DET
cana-659	246	7	option	option	NOUN
cana-659	246	8	to	to	PART
cana-659	246	9	upload	upload	VERB
cana-659	246	10	or	or	CCONJ
cana-659	246	11	drag	drag	VERB
cana-659	246	12	and	and	CCONJ
cana-659	246	13	drop	drop	VERB
cana-659	246	14	the	the	DET
cana-659	246	15	files	file	NOUN
cana-659	246	16	that	that	PRON
cana-659	246	17	they	they	PRON
cana-659	246	18	want	want	VERB
cana-659	246	19	to	to	PART
cana-659	246	20	compare	compare	VERB
cana-659	246	21	at	at	ADP
cana-659	246	22	the	the	DET
cana-659	246	23	moment	moment	NOUN
cana-659	246	24	of	of	ADP
cana-659	246	25	execution	execution	NOUN
cana-659	246	26	.	.	PUNCT
cana-659	247	1	the	the	DET
cana-659	247	2	chosen	choose	VERB
cana-659	247	3	file	file	NOUN
cana-659	247	4	needs	need	VERB
cana-659	247	5	to	to	PART
cana-659	247	6	be	be	AUX
cana-659	247	7	in	in	ADP
cana-659	247	8	the	the	DET
cana-659	247	9	*	*	ADJ
cana-659	247	10	.txt	.txt	ADJ
cana-659	247	11	or	or	CCONJ
cana-659	247	12	*	*	ADJ
cana-659	247	13	.csv	.csv	ADJ
cana-659	247	14	extension	extension	NOUN
cana-659	247	15	.	.	PUNCT
cana-659	248	1	three	three	NUM
cana-659	248	2	datasets—human_data.txt	datasets—human_data.txt	NOUN
cana-659	248	3	,	,	PUNCT
cana-659	248	4	dog_data.txt	dog_data.txt	PROPN
cana-659	248	5	,	,	PUNCT
cana-659	248	6	and	and	CCONJ
cana-659	248	7	chimp_data.txt	chimp_data.txt	NOUN
cana-659	248	8	—	—	PUNCT
cana-659	248	9	have	have	AUX
cana-659	248	10	been	be	AUX
cana-659	248	11	selected	select	VERB
cana-659	248	12	for	for	ADP
cana-659	248	13	this	this	DET
cana-659	248	14	purpose	purpose	NOUN
cana-659	248	15	.	.	PUNCT
cana-659	249	1	fig	fig	NOUN
cana-659	249	2	.	.	PUNCT
cana-659	250	1	4	4	NUM
cana-659	250	2	experimental	experimental	ADJ
cana-659	250	3	setup	setup	NOUN
cana-659	250	4	using	use	VERB
cana-659	250	5	streamlit	streamlit	NOUN
cana-659	250	6	we	we	PRON
cana-659	250	7	made	make	VERB
cana-659	250	8	sure	sure	ADJ
cana-659	250	9	the	the	DET
cana-659	250	10	final	final	ADJ
cana-659	250	11	dataset	dataset	NOUN
cana-659	250	12	was	be	AUX
cana-659	250	13	legitimate	legitimate	ADJ
cana-659	250	14	and	and	CCONJ
cana-659	250	15	that	that	SCONJ
cana-659	250	16	each	each	DET
cana-659	250	17	sample	sample	NOUN
cana-659	250	18	had	have	VERB
cana-659	250	19	a	a	DET
cana-659	250	20	matching	matching	ADJ
cana-659	250	21	class	class	NOUN
cana-659	250	22	label	label	NOUN
cana-659	250	23	,	,	PUNCT
cana-659	250	24	either	either	CCONJ
cana-659	250	25	1	1	NUM
cana-659	250	26	or	or	CCONJ
cana-659	250	27	0	0	NUM
cana-659	250	28	.	.	PUNCT
cana-659	251	1	this	this	PRON
cana-659	251	2	was	be	AUX
cana-659	251	3	done	do	VERB
cana-659	251	4	after	after	ADP
cana-659	251	5	choosing	choose	VERB
cana-659	251	6	the	the	DET
cana-659	251	7	dataset	dataset	NOUN
cana-659	251	8	and	and	CCONJ
cana-659	251	9	options	option	NOUN
cana-659	251	10	.	.	PUNCT
cana-659	252	1	a	a	DET
cana-659	252	2	sample	sample	NOUN
cana-659	252	3	of	of	ADP
cana-659	252	4	the	the	DET
cana-659	252	5	cleaned	clean	VERB
cana-659	252	6	dataset	dataset	NOUN
cana-659	252	7	is	be	AUX
cana-659	252	8	shown	show	VERB
cana-659	252	9	in	in	ADP
cana-659	252	10	figure	figure	NOUN
cana-659	252	11	5	5	NUM
cana-659	252	12	,	,	PUNCT
cana-659	252	13	which	which	PRON
cana-659	252	14	shows	show	VERB
cana-659	252	15	that	that	SCONJ
cana-659	252	16	the	the	DET
cana-659	252	17	data	datum	NOUN
cana-659	252	18	pre	pre	ADJ
cana-659	252	19	-	-	ADJ
cana-659	252	20	processing	processing	ADJ
cana-659	252	21	step	step	NOUN
cana-659	252	22	was	be	AUX
cana-659	252	23	successfully	successfully	ADV
cana-659	252	24	finished	finish	VERB
cana-659	252	25	.	.	PUNCT
cana-659	253	1	fig	fig	NOUN
cana-659	253	2	.	.	PUNCT
cana-659	254	1	5	5	NUM
cana-659	254	2	class	class	NOUN
cana-659	254	3	sequence	sequence	NOUN
cana-659	254	4	and	and	CCONJ
cana-659	254	5	distribution	distribution	NOUN
cana-659	254	6	communications	communication	NOUN
cana-659	254	7	on	on	ADP
cana-659	254	8	applied	apply	VERB
cana-659	254	9	nonlinear	nonlinear	ADJ
cana-659	254	10	analysis	analysis	NOUN
cana-659	254	11	issn	issn	NOUN
cana-659	254	12	:	:	PUNCT
cana-659	254	13	1074	1074	NUM
cana-659	254	14	-	-	PUNCT
cana-659	254	15	133x	133x	NUM
cana-659	254	16	vol	vol	NOUN
cana-659	254	17	31	31	NUM
cana-659	254	18	no	no	NOUN
cana-659	254	19	.	.	PUNCT
cana-659	255	1	2s	2s	NUM
cana-659	255	2	(	(	PUNCT
cana-659	255	3	2024	2024	NUM
cana-659	255	4	)	)	PUNCT
cana-659	255	5	449	449	NUM
cana-659	255	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	255	7	essentially	essentially	ADV
cana-659	255	8	,	,	PUNCT
cana-659	255	9	when	when	SCONJ
cana-659	255	10	working	work	VERB
cana-659	255	11	with	with	ADP
cana-659	255	12	dna	dna	NOUN
cana-659	255	13	sequencing	sequencing	NOUN
cana-659	255	14	,	,	PUNCT
cana-659	255	15	we	we	PRON
cana-659	255	16	translate	translate	VERB
cana-659	255	17	dna	dna	PROPN
cana-659	255	18	sequences	sequence	NOUN
cana-659	255	19	into	into	ADP
cana-659	255	20	languages	language	NOUN
cana-659	255	21	.	.	PUNCT
cana-659	256	1	to	to	PART
cana-659	256	2	do	do	VERB
cana-659	256	3	this	this	PRON
cana-659	256	4	,	,	PUNCT
cana-659	256	5	we	we	PRON
cana-659	256	6	apply	apply	VERB
cana-659	256	7	k	k	PROPN
cana-659	256	8	-	-	PUNCT
cana-659	256	9	mer	mer	NOUN
cana-659	256	10	counting	counting	NOUN
cana-659	256	11	,	,	PUNCT
cana-659	256	12	one	one	NUM
cana-659	256	13	hot	hot	ADJ
cana-659	256	14	encoding	encoding	NOUN
cana-659	256	15	,	,	PUNCT
cana-659	256	16	and	and	CCONJ
cana-659	256	17	ordinal	ordinal	ADJ
cana-659	256	18	encoding	encoding	NOUN
cana-659	256	19	.	.	PUNCT
cana-659	257	1	here	here	ADV
cana-659	257	2	,	,	PUNCT
cana-659	257	3	we	we	PRON
cana-659	257	4	take	take	VERB
cana-659	257	5	the	the	DET
cana-659	257	6	k	k	PROPN
cana-659	257	7	-	-	PUNCT
cana-659	257	8	mer	mer	NOUN
cana-659	257	9	counting	counting	NOUN
cana-659	257	10	result	result	VERB
cana-659	257	11	into	into	ADP
cana-659	257	12	consideration	consideration	NOUN
cana-659	257	13	for	for	ADP
cana-659	257	14	future	future	ADJ
cana-659	257	15	implementation	implementation	NOUN
cana-659	257	16	.	.	PUNCT
cana-659	258	1	the	the	DET
cana-659	258	2	outcomes	outcome	NOUN
cana-659	258	3	of	of	ADP
cana-659	258	4	k	k	NOUN
cana-659	258	5	-	-	PUNCT
cana-659	258	6	mers	mer	NOUN
cana-659	258	7	,	,	PUNCT
cana-659	258	8	one	one	NUM
cana-659	258	9	hot	hot	ADJ
cana-659	258	10	,	,	PUNCT
cana-659	258	11	and	and	CCONJ
cana-659	258	12	ordinal	ordinal	ADJ
cana-659	258	13	encoding	encoding	NOUN
cana-659	258	14	is	be	AUX
cana-659	258	15	displayed	display	VERB
cana-659	258	16	in	in	ADP
cana-659	258	17	figure	figure	NOUN
cana-659	258	18	6	6	NUM
cana-659	258	19	.	.	PUNCT
cana-659	259	1	the	the	DET
cana-659	259	2	list	list	NOUN
cana-659	259	3	of	of	ADP
cana-659	259	4	k	k	NOUN
cana-659	259	5	-	-	PUNCT
cana-659	259	6	mers	mer	NOUN
cana-659	259	7	for	for	ADP
cana-659	259	8	each	each	DET
cana-659	259	9	gene	gene	NOUN
cana-659	259	10	must	must	AUX
cana-659	259	11	then	then	ADV
cana-659	259	12	be	be	AUX
cana-659	259	13	transformed	transform	VERB
cana-659	259	14	into	into	ADP
cana-659	259	15	string	string	NOUN
cana-659	259	16	sentences	sentence	NOUN
cana-659	259	17	.	.	PUNCT
cana-659	260	1	all	all	PRON
cana-659	260	2	of	of	ADP
cana-659	260	3	the	the	DET
cana-659	260	4	sequences	sequence	NOUN
cana-659	260	5	must	must	AUX
cana-659	260	6	now	now	ADV
cana-659	260	7	be	be	AUX
cana-659	260	8	combined	combine	VERB
cana-659	260	9	because	because	SCONJ
cana-659	260	10	doing	do	VERB
cana-659	260	11	so	so	ADV
cana-659	260	12	makes	make	VERB
cana-659	260	13	it	it	PRON
cana-659	260	14	simple	simple	ADJ
cana-659	260	15	to	to	PART
cana-659	260	16	turn	turn	VERB
cana-659	260	17	them	they	PRON
cana-659	260	18	into	into	ADP
cana-659	260	19	a	a	DET
cana-659	260	20	bag	bag	NOUN
cana-659	260	21	of	of	ADP
cana-659	260	22	words	word	NOUN
cana-659	260	23	.	.	PUNCT
cana-659	261	1	next	next	ADJ
cana-659	261	2	,	,	PUNCT
cana-659	261	3	using	use	VERB
cana-659	261	4	the	the	DET
cana-659	261	5	count	count	NOUN
cana-659	261	6	vectorizer	vectorizer	NOUN
cana-659	261	7	,	,	PUNCT
cana-659	261	8	we	we	PRON
cana-659	261	9	will	will	AUX
cana-659	261	10	attempt	attempt	VERB
cana-659	261	11	to	to	PART
cana-659	261	12	transform	transform	VERB
cana-659	261	13	the	the	DET
cana-659	261	14	strings	string	NOUN
cana-659	261	15	by	by	ADP
cana-659	261	16	applying	apply	VERB
cana-659	261	17	a	a	DET
cana-659	261	18	bag	bag	NOUN
cana-659	261	19	of	of	ADP
cana-659	261	20	words	word	NOUN
cana-659	261	21	.	.	PUNCT
cana-659	262	1	we	we	PRON
cana-659	262	2	accomplished	accomplish	VERB
cana-659	262	3	this	this	PRON
cana-659	262	4	in	in	ADP
cana-659	262	5	order	order	NOUN
cana-659	262	6	to	to	PART
cana-659	262	7	have	have	AUX
cana-659	262	8	our	our	PRON
cana-659	262	9	independent	independent	ADJ
cana-659	262	10	feature	feature	NOUN
cana-659	262	11	in	in	ADP
cana-659	262	12	the	the	DET
cana-659	262	13	form	form	NOUN
cana-659	262	14	of	of	ADP
cana-659	262	15	strings	string	NOUN
cana-659	262	16	.	.	PUNCT
cana-659	263	1	since	since	SCONJ
cana-659	263	2	we	we	PRON
cana-659	263	3	are	be	AUX
cana-659	263	4	unable	unable	ADJ
cana-659	263	5	to	to	PART
cana-659	263	6	use	use	VERB
cana-659	263	7	data	datum	NOUN
cana-659	263	8	key	key	ADJ
cana-659	263	9	strings	string	NOUN
cana-659	263	10	straight	straight	ADV
cana-659	263	11	into	into	ADP
cana-659	263	12	our	our	PRON
cana-659	263	13	model	model	NOUN
cana-659	263	14	,	,	PUNCT
cana-659	263	15	we	we	PRON
cana-659	263	16	used	use	VERB
cana-659	263	17	the	the	DET
cana-659	263	18	count	count	NOUN
cana-659	263	19	vectorizer	vectorizer	NOUN
cana-659	263	20	to	to	PART
cana-659	263	21	turn	turn	VERB
cana-659	263	22	the	the	DET
cana-659	263	23	strings	string	NOUN
cana-659	263	24	into	into	ADP
cana-659	263	25	a	a	DET
cana-659	263	26	bag	bag	NOUN
cana-659	263	27	of	of	ADP
cana-659	263	28	words	word	NOUN
cana-659	263	29	.	.	PUNCT
cana-659	264	1	we	we	PRON
cana-659	264	2	now	now	ADV
cana-659	264	3	verify	verify	VERB
cana-659	264	4	whether	whether	SCONJ
cana-659	264	5	or	or	CCONJ
cana-659	264	6	not	not	PART
cana-659	264	7	the	the	DET
cana-659	264	8	data	datum	NOUN
cana-659	264	9	set	set	VERB
cana-659	264	10	is	be	AUX
cana-659	264	11	balanced	balanced	ADJ
cana-659	264	12	.	.	PUNCT
cana-659	265	1	fig	fig	NOUN
cana-659	265	2	.	.	PUNCT
cana-659	266	1	6	6	NUM
cana-659	266	2	feature	feature	NOUN
cana-659	266	3	selection	selection	NOUN
cana-659	266	4	and	and	CCONJ
cana-659	266	5	extraction	extraction	NOUN
cana-659	266	6	using	use	VERB
cana-659	266	7	ordinal	ordinal	ADJ
cana-659	266	8	encoding	encoding	NOUN
cana-659	266	9	,	,	PUNCT
cana-659	266	10	one	one	NUM
cana-659	266	11	-	-	PUNCT
cana-659	266	12	hot	hot	ADJ
cana-659	266	13	encoding	encoding	NOUN
cana-659	266	14	and	and	CCONJ
cana-659	266	15	k	k	ADJ
cana-659	266	16	-	-	PUNCT
cana-659	266	17	mers	mer	NOUN
cana-659	266	18	counting	count	VERB
cana-659	266	19	7	7	NUM
cana-659	266	20	.	.	PUNCT
cana-659	266	21	model	model	NOUN
cana-659	266	22	comparison	comparison	NOUN
cana-659	266	23	and	and	CCONJ
cana-659	266	24	analysis	analysis	NOUN
cana-659	266	25	the	the	DET
cana-659	266	26	goal	goal	NOUN
cana-659	266	27	of	of	ADP
cana-659	266	28	this	this	DET
cana-659	266	29	paper	paper	NOUN
cana-659	266	30	is	be	AUX
cana-659	266	31	to	to	PART
cana-659	266	32	demonstrate	demonstrate	VERB
cana-659	266	33	the	the	DET
cana-659	266	34	accuracy	accuracy	NOUN
cana-659	266	35	of	of	ADP
cana-659	266	36	the	the	DET
cana-659	266	37	proposed	propose	VERB
cana-659	266	38	system	system	NOUN
cana-659	266	39	through	through	ADP
cana-659	266	40	a	a	DET
cana-659	266	41	comparison	comparison	NOUN
cana-659	266	42	and	and	CCONJ
cana-659	266	43	analysis	analysis	NOUN
cana-659	266	44	of	of	ADP
cana-659	266	45	various	various	ADJ
cana-659	266	46	machine	machine	NOUN
cana-659	266	47	learning	learning	NOUN
cana-659	266	48	and	and	CCONJ
cana-659	266	49	optimization	optimization	NOUN
cana-659	266	50	techniques	technique	NOUN
cana-659	266	51	.	.	PUNCT
cana-659	267	1	the	the	DET
cana-659	267	2	models	model	NOUN
cana-659	267	3	that	that	PRON
cana-659	267	4	are	be	AUX
cana-659	267	5	being	be	AUX
cana-659	267	6	proposed	propose	VERB
cana-659	267	7	are	be	AUX
cana-659	267	8	decision	decision	NOUN
cana-659	267	9	tree	tree	NOUN
cana-659	267	10	,	,	PUNCT
cana-659	267	11	random	random	ADJ
cana-659	267	12	forest	forest	NOUN
cana-659	267	13	,	,	PUNCT
cana-659	267	14	logistic	logistic	ADJ
cana-659	267	15	regression	regression	NOUN
cana-659	267	16	,	,	PUNCT
cana-659	267	17	svm	svm	PROPN
cana-659	267	18	,	,	PUNCT
cana-659	267	19	knn	knn	PROPN
cana-659	267	20	,	,	PUNCT
cana-659	267	21	ga	ga	PROPN
cana-659	267	22	,	,	PUNCT
cana-659	267	23	ant	ant	ADJ
cana-659	267	24	colony	colony	NOUN
cana-659	267	25	,	,	PUNCT
cana-659	267	26	hill	hill	NOUN
cana-659	267	27	climbing	climbing	NOUN
cana-659	267	28	,	,	PUNCT
cana-659	267	29	grid	grid	NOUN
cana-659	267	30	search	search	NOUN
cana-659	267	31	,	,	PUNCT
cana-659	267	32	and	and	CCONJ
cana-659	267	33	gradient	gradient	ADJ
cana-659	267	34	boosting	boosting	NOUN
cana-659	267	35	.	.	PUNCT
cana-659	268	1	the	the	DET
cana-659	268	2	random	random	ADJ
cana-659	268	3	forest	forest	NOUN
cana-659	268	4	method	method	NOUN
cana-659	268	5	produced	produce	VERB
cana-659	268	6	better	well	ADJ
cana-659	268	7	accuracy	accuracy	NOUN
cana-659	268	8	of	of	ADP
cana-659	268	9	92.92	92.92	NUM
cana-659	268	10	percent	percent	NOUN
cana-659	268	11	in	in	ADP
cana-659	268	12	machine	machine	NOUN
cana-659	268	13	learning	learning	NOUN
cana-659	268	14	as	as	ADP
cana-659	268	15	a	a	DET
cana-659	268	16	classifier	classifier	NOUN
cana-659	268	17	,	,	PUNCT
cana-659	268	18	and	and	CCONJ
cana-659	268	19	the	the	DET
cana-659	268	20	genetic	genetic	ADJ
cana-659	268	21	algorithm	algorithm	NOUN
cana-659	268	22	produced	produce	VERB
cana-659	268	23	better	well	ADJ
cana-659	268	24	accuracy	accuracy	NOUN
cana-659	268	25	of	of	ADP
cana-659	268	26	91	91	NUM
cana-659	268	27	percent	percent	NOUN
cana-659	268	28	as	as	ADP
cana-659	268	29	an	an	DET
cana-659	268	30	optimizer	optimizer	NOUN
cana-659	268	31	in	in	ADP
cana-659	268	32	human	human	ADJ
cana-659	268	33	dna	dna	PROPN
cana-659	268	34	.	.	PUNCT
cana-659	269	1	table	table	NOUN
cana-659	269	2	2	2	NUM
cana-659	269	3	,	,	PUNCT
cana-659	269	4	3	3	NUM
cana-659	269	5	and	and	CCONJ
cana-659	269	6	4	4	NUM
cana-659	269	7	show	show	VERB
cana-659	269	8	the	the	DET
cana-659	269	9	comparison	comparison	NOUN
cana-659	269	10	communications	communication	NOUN
cana-659	269	11	on	on	ADP
cana-659	269	12	applied	apply	VERB
cana-659	269	13	nonlinear	nonlinear	ADJ
cana-659	269	14	analysis	analysis	NOUN
cana-659	269	15	issn	issn	NOUN
cana-659	269	16	:	:	PUNCT
cana-659	269	17	1074	1074	NUM
cana-659	269	18	-	-	PUNCT
cana-659	269	19	133x	133x	NUM
cana-659	269	20	vol	vol	NOUN
cana-659	269	21	31	31	NUM
cana-659	269	22	no	no	NOUN
cana-659	269	23	.	.	PUNCT
cana-659	270	1	2s	2s	NUM
cana-659	270	2	(	(	PUNCT
cana-659	270	3	2024	2024	NUM
cana-659	270	4	)	)	PUNCT
cana-659	270	5	450	450	NUM
cana-659	270	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	270	7	analysis	analysis	NOUN
cana-659	270	8	for	for	ADP
cana-659	270	9	three	three	NUM
cana-659	270	10	datasets	dataset	NOUN
cana-659	270	11	.	.	PUNCT
cana-659	271	1	similarly	similarly	ADV
cana-659	271	2	,	,	PUNCT
cana-659	271	3	for	for	ADP
cana-659	271	4	other	other	ADJ
cana-659	271	5	datasets	dataset	NOUN
cana-659	271	6	,	,	PUNCT
cana-659	271	7	various	various	ADJ
cana-659	271	8	classifiers	classifier	NOUN
cana-659	271	9	and	and	CCONJ
cana-659	271	10	optimizers	optimizer	NOUN
cana-659	271	11	performed	perform	VERB
cana-659	271	12	best	well	ADV
cana-659	271	13	.	.	PUNCT
cana-659	272	1	table	table	NOUN
cana-659	272	2	2	2	NUM
cana-659	272	3	:	:	PUNCT
cana-659	272	4	result	result	NOUN
cana-659	272	5	for	for	SCONJ
cana-659	272	6	human	human	ADJ
cana-659	272	7	dataset	dataset	NOUN
cana-659	272	8	algorithm	algorithm	NOUN
cana-659	272	9	accuracy	accuracy	NOUN
cana-659	272	10	precision	precision	NOUN
cana-659	272	11	recall	recall	PROPN
cana-659	272	12	f1	f1	PROPN
cana-659	272	13	classifiers	classifier	NOUN
cana-659	272	14	svm	svm	VERB
cana-659	272	15	0.8151	0.8151	NUM
cana-659	272	16	0.882	0.882	NUM
cana-659	272	17	0.815	0.815	NUM
cana-659	272	18	0.820	0.820	NUM
cana-659	272	19	random	random	ADJ
cana-659	272	20	forest	forest	NOUN
cana-659	272	21	0.9292	0.9292	NUM
cana-659	272	22	0.937	0.937	NUM
cana-659	272	23	0.929	0.929	NUM
cana-659	272	24	0.930	0.930	NUM
cana-659	272	25	knn	knn	PROPN
cana-659	272	26	0.8607	0.8607	NUM
cana-659	272	27	0.898	0.898	NUM
cana-659	272	28	0.861	0.861	NUM
cana-659	272	29	0.863	0.863	NUM
cana-659	272	30	decision	decision	NOUN
cana-659	272	31	tree	tree	NOUN
cana-659	272	32	0.8402	0.8402	NUM
cana-659	272	33	0.903	0.903	NUM
cana-659	272	34	0.840	0.840	NUM
cana-659	272	35	0.857	0.857	NUM
cana-659	272	36	logistic	logistic	ADJ
cana-659	272	37	regression	regression	NOUN
cana-659	272	38	0.9258	0.9258	NUM
cana-659	272	39	0.940	0.940	NUM
cana-659	272	40	0.926	0.926	NUM
cana-659	272	41	0.927	0.927	NUM
cana-659	272	42	optimizers	optimizer	NOUN
cana-659	272	43	genetic	genetic	ADJ
cana-659	272	44	algorithm	algorithm	NOUN
cana-659	272	45	0.91	0.91	NUM
cana-659	272	46	0.930	0.930	NUM
cana-659	272	47	0.911	0.911	NUM
cana-659	272	48	0.913	0.913	NUM
cana-659	272	49	ant	ant	ADJ
cana-659	272	50	colony	colony	NOUN
cana-659	272	51	0.86	0.86	NUM
cana-659	272	52	0.744	0.744	NUM
cana-659	272	53	0.749	0.749	NUM
cana-659	272	54	0.81	0.81	NUM
cana-659	272	55	gradient	gradient	NOUN
cana-659	272	56	boosting	boost	VERB
cana-659	272	57	0.84	0.84	NUM
cana-659	272	58	0.890	0.890	NUM
cana-659	272	59	0.873	0.873	NUM
cana-659	272	60	0.842	0.842	NUM
cana-659	272	61	grid	grid	NOUN
cana-659	272	62	search	search	NOUN
cana-659	272	63	0.90	0.90	NUM
cana-659	272	64	0.925	0.925	NUM
cana-659	272	65	0.902	0.902	NUM
cana-659	272	66	0.904	0.904	NUM
cana-659	272	67	hill	hill	NOUN
cana-659	272	68	climbing	climb	VERB
cana-659	272	69	0.41	0.41	NUM
cana-659	272	70	0.741	0.741	NUM
cana-659	272	71	0.405	0.405	NUM
cana-659	272	72	0.312	0.312	NUM
cana-659	272	73	table	table	NOUN
cana-659	272	74	3	3	NUM
cana-659	272	75	:	:	PUNCT
cana-659	272	76	result	result	NOUN
cana-659	272	77	for	for	SCONJ
cana-659	272	78	dog	dog	NOUN
cana-659	272	79	dataset	dataset	NOUN
cana-659	272	80	algorithm	algorithm	NOUN
cana-659	272	81	accuracy	accuracy	NOUN
cana-659	272	82	precision	precision	NOUN
cana-659	272	83	recall	recall	PROPN
cana-659	272	84	f1	f1	PROPN
cana-659	272	85	classifiers	classifier	NOUN
cana-659	272	86	svm	svm	VERB
cana-659	272	87	0.4512	0.4512	NUM
cana-659	272	88	0.822	0.822	NUM
cana-659	272	89	0.451	0.451	NUM
cana-659	272	90	0.414	0.414	NUM
cana-659	272	91	random	random	ADJ
cana-659	272	92	forest	forest	NOUN
cana-659	272	93	0.4939	0.4939	NUM
cana-659	272	94	0.833	0.833	NUM
cana-659	272	95	0.494	0.494	NUM
cana-659	272	96	0.480	0.480	NUM
cana-659	272	97	knn	knn	NOUN
cana-659	272	98	0.4390	0.4390	NUM
cana-659	272	99	0.827	0.827	NUM
cana-659	272	100	0.439	0.439	NUM
cana-659	272	101	0.403	0.403	NUM
cana-659	272	102	decision	decision	NOUN
cana-659	272	103	tree	tree	NOUN
cana-659	272	104	0.5549	0.5549	NUM
cana-659	272	105	0.699	0.699	NUM
cana-659	272	106	0.555	0.555	NUM
cana-659	272	107	0.546	0.546	NUM
cana-659	272	108	logistic	logistic	ADJ
cana-659	272	109	regression	regression	NOUN
cana-659	272	110	0.5183	0.5183	NUM
cana-659	272	111	0.835	0.835	NUM
cana-659	272	112	0.518	0.518	NUM
cana-659	272	113	0.510	0.510	NUM
cana-659	272	114	optimizers	optimizer	NOUN
cana-659	272	115	genetic	genetic	ADJ
cana-659	272	116	algorithm	algorithm	NOUN
cana-659	272	117	0.05	0.05	NUM
cana-659	272	118	0.833	0.833	NUM
cana-659	272	119	0.500	0.500	NUM
cana-659	272	120	0.486	0.486	NUM
cana-659	272	121	ant	ant	ADJ
cana-659	272	122	colony	colony	NOUN
cana-659	272	123	0.50	0.50	NUM
cana-659	272	124	0.758	0.758	NUM
cana-659	272	125	0.389	0.389	NUM
cana-659	272	126	0.525	0.525	NUM
cana-659	272	127	gradient	gradient	NOUN
cana-659	272	128	boosting	boost	VERB
cana-659	272	129	0.56	0.56	NUM
cana-659	272	130	0.713	0.713	NUM
cana-659	272	131	0.561	0.561	NUM
cana-659	272	132	0.551	0.551	NUM
cana-659	272	133	grid	grid	NOUN
cana-659	272	134	search	search	NOUN
cana-659	272	135	0.51	0.51	NUM
cana-659	272	136	0.835	0.835	NUM
cana-659	272	137	0.512	0.512	NUM
cana-659	272	138	0.501	0.501	NUM
cana-659	272	139	hill	hill	NOUN
cana-659	272	140	climbing	climb	VERB
cana-659	272	141	0.25	0.25	NUM
cana-659	272	142	0.062	0.062	NUM
cana-659	272	143	0.250	0.250	NUM
cana-659	272	144	0.100	0.100	NUM
cana-659	272	145	table	table	NOUN
cana-659	272	146	4	4	NUM
cana-659	272	147	:	:	PUNCT
cana-659	272	148	result	result	NOUN
cana-659	272	149	for	for	SCONJ
cana-659	272	150	chimpanzee	chimpanzee	PROPN
cana-659	272	151	dataset	dataset	VERB
cana-659	272	152	algorithm	algorithm	PROPN
cana-659	272	153	accuracy	accuracy	NOUN
cana-659	272	154	precision	precision	NOUN
cana-659	272	155	recall	recall	PROPN
cana-659	272	156	f1	f1	PROPN
cana-659	272	157	classifiers	classifier	NOUN
cana-659	272	158	svm	svm	VERB
cana-659	272	159	0.7567	0.7567	NUM
cana-659	272	160	0.860	0.860	NUM
cana-659	272	161	0.757	0.757	NUM
cana-659	272	162	0.762	0.762	NUM
cana-659	272	163	random	random	ADJ
cana-659	272	164	forest	forest	NOUN
cana-659	272	165	0.7923	0.7923	NUM
cana-659	272	166	0.877	0.877	NUM
cana-659	272	167	0.792	0.792	NUM
cana-659	272	168	0.796	0.796	NUM
cana-659	272	169	knn	knn	X
cana-659	272	170	0.7507	0.7507	NUM
cana-659	272	171	0.904	0.904	NUM
cana-659	272	172	0.751	0.751	NUM
cana-659	272	173	0.780	0.780	NUM
cana-659	272	174	decision	decision	NOUN
cana-659	272	175	tree	tree	NOUN
cana-659	272	176	0.7834	0.7834	NUM
cana-659	272	177	0.826	0.826	NUM
cana-659	272	178	0.783	0.783	NUM
cana-659	272	179	0.782	0.782	NUM
cana-659	272	180	logistic	logistic	ADJ
cana-659	272	181	regression	regression	NOUN
cana-659	272	182	0.8160	0.8160	NUM
cana-659	272	183	0.886	0.886	NUM
cana-659	272	184	0.816	0.816	NUM
cana-659	272	185	0.819	0.819	NUM
cana-659	272	186	optimizers	optimizer	NOUN
cana-659	272	187	genetic	genetic	ADJ
cana-659	272	188	algorithm	algorithm	NOUN
cana-659	272	189	0.80	0.80	NUM
cana-659	272	190	0.879	0.879	NUM
cana-659	272	191	0.798	0.798	NUM
cana-659	272	192	0.802	0.802	NUM
cana-659	272	193	ant	ant	ADJ
cana-659	272	194	colony	colony	NOUN
cana-659	272	195	0.849	0.849	NUM
cana-659	272	196	0.797	0.797	NUM
cana-659	272	197	0.738	0.738	NUM
cana-659	272	198	0.816	0.816	NUM
cana-659	272	199	gradient	gradient	NOUN
cana-659	272	200	boosting	boost	VERB
cana-659	272	201	0.78	0.78	NUM
cana-659	272	202	0.854	0.854	NUM
cana-659	272	203	0.783	0.783	NUM
cana-659	272	204	0.787	0.787	NUM
cana-659	272	205	grid	grid	NOUN
cana-659	272	206	search	search	NOUN
cana-659	272	207	0.81	0.81	NUM
cana-659	272	208	0.883	0.883	NUM
cana-659	272	209	0.810	0.810	NUM
cana-659	272	210	0.814	0.814	NUM
cana-659	272	211	hill	hill	NOUN
cana-659	272	212	climbing	climb	VERB
cana-659	272	213	0.37	0.37	NUM
cana-659	272	214	0.463	0.463	NUM
cana-659	272	215	0.368	0.368	NUM
cana-659	272	216	0.256	0.256	NUM
cana-659	272	217	communications	communication	NOUN
cana-659	272	218	on	on	ADP
cana-659	272	219	applied	apply	VERB
cana-659	272	220	nonlinear	nonlinear	ADJ
cana-659	272	221	analysis	analysis	NOUN
cana-659	272	222	issn	issn	NOUN
cana-659	272	223	:	:	PUNCT
cana-659	272	224	1074	1074	NUM
cana-659	272	225	-	-	PUNCT
cana-659	272	226	133x	133x	NUM
cana-659	272	227	vol	vol	NOUN
cana-659	272	228	31	31	NUM
cana-659	272	229	no	no	NOUN
cana-659	272	230	.	.	PUNCT
cana-659	273	1	2s	2s	NUM
cana-659	273	2	(	(	PUNCT
cana-659	273	3	2024	2024	NUM
cana-659	273	4	)	)	PUNCT
cana-659	273	5	451	451	NUM
cana-659	273	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	273	7	8	8	NUM
cana-659	273	8	.	.	PUNCT
cana-659	273	9	results	result	NOUN
cana-659	273	10	and	and	CCONJ
cana-659	273	11	discussions	discussion	NOUN
cana-659	273	12	the	the	DET
cana-659	273	13	main	main	ADJ
cana-659	273	14	goal	goal	NOUN
cana-659	273	15	of	of	ADP
cana-659	273	16	this	this	DET
cana-659	273	17	study	study	NOUN
cana-659	273	18	is	be	AUX
cana-659	273	19	to	to	PART
cana-659	273	20	gain	gain	VERB
cana-659	273	21	a	a	DET
cana-659	273	22	knowledge	knowledge	NOUN
cana-659	273	23	of	of	ADP
cana-659	273	24	how	how	SCONJ
cana-659	273	25	machine	machine	NOUN
cana-659	273	26	learning	learn	VERB
cana-659	273	27	algorithms	algorithm	NOUN
cana-659	273	28	can	can	AUX
cana-659	273	29	be	be	AUX
cana-659	273	30	used	use	VERB
cana-659	273	31	to	to	PART
cana-659	273	32	do	do	VERB
cana-659	273	33	dna	dna	NOUN
cana-659	273	34	sequencing	sequence	VERB
cana-659	273	35	.	.	PUNCT
cana-659	274	1	three	three	NUM
cana-659	274	2	datasets	dataset	NOUN
cana-659	274	3	,	,	PUNCT
cana-659	274	4	humans	human	NOUN
cana-659	274	5	,	,	PUNCT
cana-659	274	6	chimpanzees	chimpanzee	NOUN
cana-659	274	7	,	,	PUNCT
cana-659	274	8	and	and	CCONJ
cana-659	274	9	dogs	dog	NOUN
cana-659	274	10	,	,	PUNCT
cana-659	274	11	were	be	AUX
cana-659	274	12	obtained	obtain	VERB
cana-659	274	13	via	via	ADP
cana-659	274	14	kaggle	kaggle	PROPN
cana-659	274	15	.	.	PUNCT
cana-659	275	1	sequences	sequence	NOUN
cana-659	275	2	and	and	CCONJ
cana-659	275	3	labels	label	NOUN
cana-659	275	4	make	make	VERB
cana-659	275	5	up	up	ADP
cana-659	275	6	each	each	PRON
cana-659	275	7	of	of	ADP
cana-659	275	8	our	our	PRON
cana-659	275	9	datasets	dataset	NOUN
cana-659	275	10	,	,	PUNCT
cana-659	275	11	which	which	PRON
cana-659	275	12	are	be	AUX
cana-659	275	13	split	split	VERB
cana-659	275	14	into	into	ADP
cana-659	275	15	training	training	NOUN
cana-659	275	16	and	and	CCONJ
cana-659	275	17	testing	testing	NOUN
cana-659	275	18	ratios	ratio	NOUN
cana-659	275	19	of	of	ADP
cana-659	275	20	75	75	NUM
cana-659	275	21	%	%	NOUN
cana-659	275	22	and	and	CCONJ
cana-659	275	23	25	25	NUM
cana-659	275	24	%	%	NOUN
cana-659	275	25	for	for	ADP
cana-659	275	26	machine	machine	NOUN
cana-659	275	27	learning	learning	NOUN
cana-659	275	28	algorithms	algorithm	NOUN
cana-659	275	29	,	,	PUNCT
cana-659	275	30	respectively	respectively	ADV
cana-659	275	31	.	.	PUNCT
cana-659	276	1	for	for	ADP
cana-659	276	2	these	these	DET
cana-659	276	3	datasets	dataset	NOUN
cana-659	276	4	,	,	PUNCT
cana-659	276	5	we	we	PRON
cana-659	276	6	have	have	AUX
cana-659	276	7	performed	perform	VERB
cana-659	276	8	dna	dna	NOUN
cana-659	276	9	sequencing	sequence	VERB
cana-659	276	10	and	and	CCONJ
cana-659	276	11	have	have	AUX
cana-659	276	12	used	use	VERB
cana-659	276	13	various	various	ADJ
cana-659	276	14	classification	classification	NOUN
cana-659	276	15	methods	method	NOUN
cana-659	276	16	and	and	CCONJ
cana-659	276	17	optimization	optimization	NOUN
cana-659	276	18	strategies	strategy	NOUN
cana-659	276	19	.	.	PUNCT
cana-659	277	1	for	for	ADP
cana-659	277	2	sequence	sequence	NOUN
cana-659	277	3	encoding	encoding	NOUN
cana-659	277	4	,	,	PUNCT
cana-659	277	5	we	we	PRON
cana-659	277	6	have	have	AUX
cana-659	277	7	employed	employ	VERB
cana-659	277	8	one	one	NUM
cana-659	277	9	-	-	PUNCT
cana-659	277	10	hot	hot	ADJ
cana-659	277	11	,	,	PUNCT
cana-659	277	12	k	k	PROPN
cana-659	277	13	-	-	PUNCT
cana-659	277	14	mer	mer	NOUN
cana-659	277	15	,	,	PUNCT
cana-659	277	16	and	and	CCONJ
cana-659	277	17	ordinal	ordinal	ADJ
cana-659	277	18	encoding	encoding	NOUN
cana-659	277	19	.	.	PUNCT
cana-659	278	1	here	here	ADV
cana-659	278	2	,	,	PUNCT
cana-659	278	3	kmer	kmer	NOUN
cana-659	278	4	encoding	encoding	NOUN
cana-659	278	5	has	have	AUX
cana-659	278	6	been	be	AUX
cana-659	278	7	employed	employ	VERB
cana-659	278	8	for	for	ADP
cana-659	278	9	processing	processing	NOUN
cana-659	278	10	.	.	PUNCT
cana-659	279	1	we	we	PRON
cana-659	279	2	transformed	transform	VERB
cana-659	279	3	the	the	DET
cana-659	279	4	dna	dna	PROPN
cana-659	279	5	sequences	sequence	NOUN
cana-659	279	6	into	into	ADP
cana-659	279	7	languages	language	NOUN
cana-659	279	8	for	for	ADP
cana-659	279	9	each	each	DET
cana-659	279	10	machine	machine	NOUN
cana-659	279	11	learning	learn	VERB
cana-659	279	12	data	datum	NOUN
cana-659	279	13	set	set	VERB
cana-659	279	14	by	by	ADP
cana-659	279	15	using	use	VERB
cana-659	279	16	the	the	DET
cana-659	279	17	k	k	PROPN
cana-659	279	18	-	-	PUNCT
cana-659	279	19	mer	mer	NOUN
cana-659	279	20	size	size	NOUN
cana-659	279	21	of	of	ADP
cana-659	279	22	six	six	NUM
cana-659	279	23	and	and	CCONJ
cana-659	279	24	the	the	DET
cana-659	279	25	k	k	PROPN
cana-659	279	26	-	-	PUNCT
cana-659	279	27	mer	mer	ADJ
cana-659	279	28	counting	counting	NOUN
cana-659	279	29	technique	technique	NOUN
cana-659	279	30	.	.	PUNCT
cana-659	280	1	next	next	ADV
cana-659	280	2	,	,	PUNCT
cana-659	280	3	we	we	PRON
cana-659	280	4	used	use	VERB
cana-659	280	5	the	the	DET
cana-659	280	6	count	count	NOUN
cana-659	280	7	vectorizer	vectorizer	NOUN
cana-659	280	8	to	to	PART
cana-659	280	9	convert	convert	VERB
cana-659	280	10	the	the	DET
cana-659	280	11	strings	string	NOUN
cana-659	280	12	by	by	ADP
cana-659	280	13	applying	apply	VERB
cana-659	280	14	a	a	DET
cana-659	280	15	bag	bag	NOUN
cana-659	280	16	of	of	ADP
cana-659	280	17	words	word	NOUN
cana-659	280	18	and	and	CCONJ
cana-659	280	19	the	the	DET
cana-659	280	20	list	list	NOUN
cana-659	280	21	of	of	ADP
cana-659	280	22	kmers	kmer	NOUN
cana-659	280	23	for	for	ADP
cana-659	280	24	each	each	DET
cana-659	280	25	gene	gene	NOUN
cana-659	280	26	into	into	ADP
cana-659	280	27	string	string	NOUN
cana-659	280	28	phrases	phrase	NOUN
cana-659	280	29	.	.	PUNCT
cana-659	281	1	several	several	ADJ
cana-659	281	2	classification	classification	NOUN
cana-659	281	3	metrics	metric	NOUN
cana-659	281	4	,	,	PUNCT
cana-659	281	5	such	such	ADJ
cana-659	281	6	as	as	ADP
cana-659	281	7	the	the	DET
cana-659	281	8	f1	f1	PROPN
cana-659	281	9	score	score	NOUN
cana-659	281	10	,	,	PUNCT
cana-659	281	11	accuracy	accuracy	NOUN
cana-659	281	12	,	,	PUNCT
cana-659	281	13	recall	recall	NOUN
cana-659	281	14	,	,	PUNCT
cana-659	281	15	and	and	CCONJ
cana-659	281	16	precision	precision	NOUN
cana-659	281	17	,	,	PUNCT
cana-659	281	18	are	be	AUX
cana-659	281	19	used	use	VERB
cana-659	281	20	to	to	PART
cana-659	281	21	estimate	estimate	VERB
cana-659	281	22	these	these	DET
cana-659	281	23	classification	classification	NOUN
cana-659	281	24	algorithms	algorithm	NOUN
cana-659	281	25	.	.	PUNCT
cana-659	282	1	the	the	DET
cana-659	282	2	confusion	confusion	NOUN
cana-659	282	3	matrix	matrix	NOUN
cana-659	282	4	is	be	AUX
cana-659	282	5	used	use	VERB
cana-659	282	6	to	to	PART
cana-659	282	7	estimate	estimate	VERB
cana-659	282	8	each	each	PRON
cana-659	282	9	of	of	ADP
cana-659	282	10	the	the	DET
cana-659	282	11	aforementioned	aforementioned	ADJ
cana-659	282	12	characteristics	characteristic	NOUN
cana-659	282	13	.	.	PUNCT
cana-659	283	1	figures	figure	NOUN
cana-659	283	2	7	7	NUM
cana-659	283	3	,	,	PUNCT
cana-659	283	4	8	8	NUM
cana-659	283	5	and	and	CCONJ
cana-659	283	6	9	9	NUM
cana-659	283	7	below	below	ADP
cana-659	283	8	display	display	VERB
cana-659	283	9	the	the	DET
cana-659	283	10	confusion	confusion	NOUN
cana-659	283	11	matrix	matrix	NOUN
cana-659	283	12	and	and	CCONJ
cana-659	283	13	accuracy	accuracy	NOUN
cana-659	283	14	curve	curve	NOUN
cana-659	283	15	for	for	ADP
cana-659	283	16	human	human	ADJ
cana-659	283	17	,	,	PUNCT
cana-659	283	18	dog	dog	NOUN
cana-659	283	19	,	,	PUNCT
cana-659	283	20	and	and	CCONJ
cana-659	283	21	chimpanzee	chimpanzee	PROPN
cana-659	283	22	classifiers	classifier	NOUN
cana-659	283	23	and	and	CCONJ
cana-659	283	24	optimizers	optimizer	NOUN
cana-659	283	25	for	for	ADP
cana-659	283	26	dna	dna	NOUN
cana-659	283	27	sequencing	sequencing	NOUN
cana-659	283	28	.	.	PUNCT
cana-659	284	1	human	human	ADJ
cana-659	284	2	dna	dna	PROPN
cana-659	284	3	dog	dog	NOUN
cana-659	284	4	dna	dna	PROPN
cana-659	284	5	chimpanzee	chimpanzee	PROPN
cana-659	284	6	dna	dna	PROPN
cana-659	284	7	fig	fig	PROPN
cana-659	284	8	.	.	PUNCT
cana-659	285	1	7	7	NUM
cana-659	285	2	confusion	confusion	NOUN
cana-659	285	3	matrix	matrix	NOUN
cana-659	285	4	human	human	ADJ
cana-659	285	5	dna	dna	PROPN
cana-659	285	6	dog	dog	NOUN
cana-659	285	7	dna	dna	PROPN
cana-659	285	8	chimpanzee	chimpanzee	PROPN
cana-659	285	9	dna	dna	PROPN
cana-659	285	10	fig	fig	PROPN
cana-659	285	11	.	.	PUNCT
cana-659	286	1	8	8	NUM
cana-659	286	2	accuracy	accuracy	NOUN
cana-659	286	3	curve	curve	NOUN
cana-659	286	4	for	for	ADP
cana-659	286	5	classifiers	classifier	NOUN
cana-659	286	6	communications	communication	NOUN
cana-659	286	7	on	on	ADP
cana-659	286	8	applied	apply	VERB
cana-659	286	9	nonlinear	nonlinear	ADJ
cana-659	286	10	analysis	analysis	NOUN
cana-659	286	11	issn	issn	NOUN
cana-659	286	12	:	:	PUNCT
cana-659	286	13	1074	1074	NUM
cana-659	286	14	-	-	PUNCT
cana-659	286	15	133x	133x	NUM
cana-659	286	16	vol	vol	NOUN
cana-659	286	17	31	31	NUM
cana-659	286	18	no	no	NOUN
cana-659	286	19	.	.	PUNCT
cana-659	287	1	2s	2s	NUM
cana-659	287	2	(	(	PUNCT
cana-659	287	3	2024	2024	NUM
cana-659	287	4	)	)	PUNCT
cana-659	287	5	452	452	NUM
cana-659	287	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-659	287	7	human	human	ADJ
cana-659	287	8	dna	dna	PROPN
cana-659	287	9	dog	dog	NOUN
cana-659	287	10	dna	dna	PROPN
cana-659	287	11	chimpanzee	chimpanzee	PROPN
cana-659	287	12	dna	dna	PROPN
cana-659	287	13	fig	fig	PROPN
cana-659	287	14	.	.	PUNCT
cana-659	288	1	9	9	NUM
cana-659	288	2	accuracy	accuracy	NOUN
cana-659	288	3	curve	curve	NOUN
cana-659	288	4	for	for	ADP
cana-659	288	5	optimizers	optimizer	NOUN
cana-659	288	6	9	9	NUM
cana-659	288	7	.	.	PUNCT
cana-659	288	8	conclusion	conclusion	NOUN
cana-659	288	9	the	the	DET
cana-659	288	10	study	study	NOUN
cana-659	288	11	includes	include	VERB
cana-659	288	12	topics	topic	NOUN
cana-659	288	13	like	like	ADP
cana-659	288	14	creating	create	VERB
cana-659	288	15	and	and	CCONJ
cana-659	288	16	training	training	NOUN
cana-659	288	17	machine	machine	NOUN
cana-659	288	18	learning	learning	NOUN
cana-659	288	19	algorithms	algorithm	NOUN
cana-659	288	20	,	,	PUNCT
cana-659	288	21	transforming	transform	VERB
cana-659	288	22	text	text	NOUN
cana-659	288	23	inputs	input	NOUN
cana-659	288	24	to	to	ADP
cana-659	288	25	numerical	numerical	ADJ
cana-659	288	26	data	datum	NOUN
cana-659	288	27	,	,	PUNCT
cana-659	288	28	and	and	CCONJ
cana-659	288	29	contrasting	contrast	VERB
cana-659	288	30	machine	machine	NOUN
cana-659	288	31	learning	learning	NOUN
cana-659	288	32	methods	method	NOUN
cana-659	288	33	according	accord	VERB
cana-659	288	34	to	to	ADP
cana-659	288	35	f1	f1	NOUN
cana-659	288	36	-	-	PUNCT
cana-659	288	37	score	score	NOUN
cana-659	288	38	,	,	PUNCT
cana-659	288	39	accuracy	accuracy	NOUN
cana-659	288	40	,	,	PUNCT
cana-659	288	41	recall	recall	NOUN
cana-659	288	42	,	,	PUNCT
cana-659	288	43	and	and	CCONJ
cana-659	288	44	precision	precision	NOUN
cana-659	288	45	.	.	PUNCT
cana-659	289	1	in	in	ADP
cana-659	289	2	this	this	PRON
cana-659	289	3	,	,	PUNCT
cana-659	289	4	all	all	DET
cana-659	289	5	these	these	DET
cana-659	289	6	dna	dna	PROPN
cana-659	289	7	sequence	sequence	NOUN
cana-659	289	8	string	string	NOUN
cana-659	289	9	encoding	encode	VERB
cana-659	289	10	namely	namely	ADV
cana-659	289	11	k	k	PROPN
cana-659	289	12	-	-	PUNCT
cana-659	289	13	mer	mer	NOUN
cana-659	289	14	counting	counting	NOUN
cana-659	289	15	,	,	PUNCT
cana-659	289	16	onehot	onehot	ADJ
cana-659	289	17	encoding	encoding	NOUN
cana-659	289	18	and	and	CCONJ
cana-659	289	19	ordinal	ordinal	ADJ
cana-659	289	20	encoding	encoding	NOUN
cana-659	289	21	were	be	AUX
cana-659	289	22	presented	present	VERB
cana-659	289	23	and	and	CCONJ
cana-659	289	24	compared	compare	VERB
cana-659	289	25	different	different	ADJ
cana-659	289	26	machine	machine	NOUN
cana-659	289	27	learning	learn	VERB
cana-659	289	28	algorithms	algorithm	NOUN
cana-659	289	29	namely	namely	ADV
cana-659	289	30	decision	decision	NOUN
cana-659	289	31	tree	tree	NOUN
cana-659	289	32	,	,	PUNCT
cana-659	289	33	random	random	ADJ
cana-659	289	34	forest	forest	NOUN
cana-659	289	35	,	,	PUNCT
cana-659	289	36	svm	svm	PROPN
cana-659	289	37	,	,	PUNCT
cana-659	289	38	dt	dt	PROPN
cana-659	289	39	,	,	PUNCT
cana-659	289	40	knn	knn	PROPN
cana-659	289	41	,	,	PUNCT
cana-659	289	42	ga	ga	PROPN
cana-659	289	43	,	,	PUNCT
cana-659	289	44	aco	aco	PROPN
cana-659	289	45	,	,	PUNCT
cana-659	289	46	gradient	gradient	NOUN
cana-659	289	47	boosting	boosting	NOUN
cana-659	289	48	,	,	PUNCT
cana-659	289	49	hill	hill	NOUN
cana-659	289	50	climbing	climbing	NOUN
cana-659	289	51	and	and	CCONJ
cana-659	289	52	grid	grid	NOUN
cana-659	289	53	search	search	NOUN
cana-659	289	54	with	with	ADP
cana-659	289	55	k	k	ADJ
cana-659	289	56	-	-	PUNCT
cana-659	289	57	mer	mer	NOUN
cana-659	289	58	counting	counting	NOUN
cana-659	289	59	.	.	PUNCT
cana-659	290	1	we	we	PRON
cana-659	290	2	found	find	VERB
cana-659	290	3	that	that	SCONJ
cana-659	290	4	machine	machine	NOUN
cana-659	290	5	learning	learn	VERB
cana-659	290	6	algorithms	algorithm	VERB
cana-659	290	7	random	random	ADJ
cana-659	290	8	forest	forest	NOUN
cana-659	290	9	,	,	PUNCT
cana-659	290	10	decision	decision	NOUN
cana-659	290	11	tree	tree	NOUN
cana-659	290	12	and	and	CCONJ
cana-659	290	13	logistic	logistic	ADJ
cana-659	290	14	regression	regression	NOUN
cana-659	290	15	have	have	AUX
cana-659	290	16	performed	perform	VERB
cana-659	290	17	well	well	ADV
cana-659	290	18	with	with	ADP
cana-659	290	19	a	a	DET
cana-659	290	20	highest	high	ADJ
cana-659	290	21	accuracy	accuracy	NOUN
cana-659	290	22	of	of	ADP
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cana-659	290	24	%	%	NOUN
cana-659	290	25	,	,	PUNCT
cana-659	290	26	55.49	55.49	NUM
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cana-659	290	28	and	and	CCONJ
cana-659	290	29	81.60	81.60	NUM
cana-659	290	30	%	%	NOUN
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cana-659	290	33	and	and	CCONJ
cana-659	290	34	genetic	genetic	ADJ
cana-659	290	35	algorithm	algorithm	NOUN
cana-659	290	36	,	,	PUNCT
cana-659	290	37	gradient	gradient	NOUN
cana-659	290	38	boosting	boosting	NOUN
cana-659	290	39	and	and	CCONJ
cana-659	290	40	ant	ant	ADJ
cana-659	290	41	colony	colony	NOUN
cana-659	290	42	have	have	AUX
cana-659	290	43	performed	perform	VERB
cana-659	290	44	well	well	ADV
cana-659	290	45	with	with	ADP
cana-659	290	46	a	a	DET
cana-659	290	47	highest	high	ADJ
cana-659	290	48	accuracy	accuracy	NOUN
cana-659	290	49	of	of	ADP
cana-659	290	50	91	91	NUM
cana-659	290	51	%	%	NOUN
cana-659	290	52	,	,	PUNCT
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cana-659	290	54	%	%	NOUN
cana-659	290	55	and	and	CCONJ
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cana-659	290	57	%	%	NOUN
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cana-659	290	59	optimizer	optimizer	NOUN
cana-659	290	60	for	for	ADP
cana-659	290	61	human	human	ADJ
cana-659	290	62	,	,	PUNCT
cana-659	290	63	dog	dog	NOUN
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cana-659	290	68	.	.	PUNCT
cana-659	291	1	this	this	DET
cana-659	291	2	dataset	dataset	NOUN
cana-659	291	3	is	be	AUX
cana-659	291	4	evaluated	evaluate	VERB
cana-659	291	5	with	with	ADP
cana-659	291	6	different	different	ADJ
cana-659	291	7	metrics	metric	NOUN
cana-659	291	8	like	like	ADP
cana-659	291	9	accuracy	accuracy	NOUN
cana-659	291	10	,	,	PUNCT
cana-659	291	11	precision	precision	NOUN
cana-659	291	12	,	,	PUNCT
cana-659	291	13	recall	recall	NOUN
cana-659	291	14	and	and	CCONJ
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cana-659	291	16	-	-	PUNCT
cana-659	291	17	score	score	NOUN
cana-659	291	18	.	.	PUNCT
cana-659	292	1	in	in	ADP
cana-659	292	2	future	future	NOUN
cana-659	292	3	,	,	PUNCT
cana-659	292	4	the	the	DET
cana-659	292	5	study	study	NOUN
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cana-659	292	7	towards	towards	ADP
cana-659	292	8	the	the	DET
cana-659	292	9	implementation	implementation	NOUN
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cana-659	292	14	and	and	CCONJ
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cana-659	292	17	of	of	ADP
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cana-659	292	19	sequencing	sequence	VERB
cana-659	292	20	with	with	ADP
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cana-659	292	23	and	and	CCONJ
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cana-659	292	25	score	score	NOUN
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cana-659	293	2	[	[	X
cana-659	293	3	1	1	NUM
cana-659	293	4	]	]	PUNCT
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cana-659	298	23	and	and	CCONJ
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cana-659	298	49	4	4	NUM
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cana-659	298	58	/	/	SYM
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cana-659	299	2	5	5	X
cana-659	299	3	]	]	PUNCT
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cana-659	299	46	.	.	PROPN
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cana-659	299	50	:	:	PUNCT
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cana-659	299	52	/	/	SYM
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cana-659	299	54	-	-	PUNCT
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cana-659	299	56	-	-	PUNCT
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cana-659	300	2	6	6	NUM
cana-659	300	3	]	]	PUNCT
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cana-659	300	7	f.	f.	PROPN
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cana-659	300	10	“	"	PUNCT
cana-659	300	11	a	a	DET
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cana-659	301	1	[	[	X
cana-659	301	2	7	7	X
cana-659	301	3	]	]	X
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cana-659	301	11	and	and	CCONJ
cana-659	301	12	m.	m.	PROPN
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cana-659	301	16	“	"	PUNCT
cana-659	301	17	an	an	DET
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cana-659	301	22	for	for	ADP
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cana-659	301	42	.	.	PUNCT
cana-659	301	43	3620–3635	3620–3635	NUM
cana-659	301	44	,	,	PUNCT
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cana-659	301	46	.	.	PROPN
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cana-659	301	48	,	,	PUNCT
cana-659	301	49	doi	doi	NOUN
cana-659	301	50	:	:	PUNCT
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cana-659	301	52	/	/	SYM
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cana-659	302	2	(	(	PUNCT
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cana-659	302	4	)	)	PUNCT
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cana-659	303	1	[	[	X
cana-659	303	2	8	8	X
cana-659	303	3	]	]	X
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cana-659	303	9	-	-	PUNCT
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cana-659	303	11	,	,	PUNCT
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cana-659	303	22	,	,	PUNCT
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cana-659	303	52	.	.	PUNCT
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cana-659	304	2	,	,	PUNCT
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cana-659	304	10	/	/	SYM
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cana-659	305	1	[	[	X
cana-659	305	2	9	9	NUM
cana-659	305	3	]	]	PUNCT
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cana-659	306	1	doi	doi	NOUN
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cana-659	306	6	-	-	PUNCT
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cana-659	306	8	-	-	PUNCT
cana-659	306	9	00592	00592	NUM
cana-659	306	10	-	-	PUNCT
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cana-659	307	1	[	[	X
cana-659	307	2	10	10	NUM
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cana-659	309	2	,	,	PUNCT
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cana-659	309	8	:	:	PUNCT
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cana-659	309	10	/	/	SYM
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cana-659	309	14	-	-	PUNCT
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cana-659	311	1	[	[	X
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cana-659	311	3	]	]	X
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cana-659	313	1	[	[	X
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cana-659	313	3	]	]	X
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cana-659	324	41	”	"	PUNCT
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cana-659	325	13	,	,	PUNCT
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cana-659	325	15	.	.	PUNCT
cana-659	326	1	doi	doi	NOUN
cana-659	326	2	:	:	PUNCT
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cana-659	326	4	/	/	SYM
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cana-659	327	14	,	,	PUNCT
cana-659	327	15	“	"	PUNCT
cana-659	327	16	a	a	DET
cana-659	327	17	review	review	NOUN
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cana-659	327	19	classification	classification	NOUN
cana-659	327	20	techniques	technique	NOUN
cana-659	327	21	in	in	ADP
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cana-659	327	24	.	.	PUNCT
cana-659	327	25	”	"	PUNCT