id	sid	tid	token	lemma	pos
cana-857	1	1	communications	communication	NOUN
cana-857	1	2	on	on	ADP
cana-857	1	3	applied	apply	VERB
cana-857	1	4	nonlinear	nonlinear	ADJ
cana-857	1	5	analysis	analysis	NOUN
cana-857	1	6	issn	issn	NOUN
cana-857	1	7	:	:	PUNCT
cana-857	1	8	1074	1074	NUM
cana-857	1	9	-	-	PUNCT
cana-857	1	10	133x	133x	NUM
cana-857	1	11	vol	vol	NOUN
cana-857	1	12	31	31	NUM
cana-857	1	13	no	no	NOUN
cana-857	1	14	.	.	PUNCT
cana-857	2	1	4s	4s	NUM
cana-857	2	2	(	(	PUNCT
cana-857	2	3	2024	2024	NUM
cana-857	2	4	)	)	PUNCT
cana-857	2	5	350	350	NUM
cana-857	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	2	7	a	a	DET
cana-857	2	8	novel	novel	ADJ
cana-857	2	9	hybrid	hybrid	ADJ
cana-857	2	10	sdr	sdr	PROPN
cana-857	2	11	model	model	NOUN
cana-857	2	12	for	for	ADP
cana-857	2	13	dengue	dengue	NOUN
cana-857	2	14	prediction	prediction	NOUN
cana-857	2	15	using	use	VERB
cana-857	2	16	opt_recurr	opt_recurr	NOUN
cana-857	2	17	feature	feature	NOUN
cana-857	2	18	selection	selection	NOUN
cana-857	2	19	algorithm	algorithm	NOUN
cana-857	2	20	sharlie	sharlie	PROPN
cana-857	2	21	vasanthi	vasanthi	PROPN
cana-857	2	22	.	.	PUNCT
cana-857	3	1	n1	n1	PROPN
cana-857	3	2	,	,	PUNCT
cana-857	3	3	dr	dr	PROPN
cana-857	3	4	.	.	PROPN
cana-857	3	5	s.	s.	PROPN
cana-857	3	6	nagasundaram2	nagasundaram2	PROPN
cana-857	4	1	1research	1research	NUM
cana-857	4	2	scholar	scholar	NOUN
cana-857	4	3	,	,	PUNCT
cana-857	4	4	phd	phd	NOUN
cana-857	4	5	computer	computer	NOUN
cana-857	4	6	science	science	NOUN
cana-857	4	7	,	,	PUNCT
cana-857	4	8	vels	vels	PROPN
cana-857	4	9	institute	institute	PROPN
cana-857	4	10	of	of	ADP
cana-857	4	11	science	science	PROPN
cana-857	4	12	technology	technology	NOUN
cana-857	4	13	and	and	CCONJ
cana-857	4	14	advanced	advanced	ADJ
cana-857	4	15	studies	study	NOUN
cana-857	4	16	,	,	PUNCT
cana-857	4	17	chennai	chennai	PROPN
cana-857	4	18	600117	600117	NUM
cana-857	4	19	associate	associate	NOUN
cana-857	4	20	professor	professor	NOUN
cana-857	4	21	,	,	PUNCT
cana-857	4	22	department	department	NOUN
cana-857	4	23	of	of	ADP
cana-857	4	24	computer	computer	NOUN
cana-857	4	25	science	science	NOUN
cana-857	4	26	and	and	CCONJ
cana-857	4	27	technology	technology	NOUN
cana-857	4	28	women	woman	NOUN
cana-857	4	29	‘s	‘s	PROPN
cana-857	4	30	christian	christian	PROPN
cana-857	4	31	college	college	PROPN
cana-857	4	32	chennai-6	chennai-6	PROPN
cana-857	4	33	,	,	PUNCT
cana-857	4	34	nsharlie74@gmail.com	nsharlie74@gmail.com	X
cana-857	4	35	2research	2research	NUM
cana-857	4	36	supervisor	supervisor	NOUN
cana-857	4	37	,	,	PUNCT
cana-857	4	38	assistant	assistant	NOUN
cana-857	4	39	professor	professor	NOUN
cana-857	4	40	,	,	PUNCT
cana-857	4	41	department	department	NOUN
cana-857	4	42	of	of	ADP
cana-857	4	43	computer	computer	NOUN
cana-857	4	44	applications	application	NOUN
cana-857	4	45	,	,	PUNCT
cana-857	4	46	vels	vel	NOUN
cana-857	4	47	institute	institute	PROPN
cana-857	4	48	of	of	ADP
cana-857	4	49	science	science	NOUN
cana-857	4	50	,	,	PUNCT
cana-857	4	51	technology	technology	NOUN
cana-857	4	52	and	and	CCONJ
cana-857	4	53	advanced	advanced	ADJ
cana-857	4	54	studies	study	NOUN
cana-857	4	55	,	,	PUNCT
cana-857	4	56	chennai	chennai	PROPN
cana-857	4	57	600117	600117	NUM
cana-857	4	58	,	,	PUNCT
cana-857	4	59	snagasundaram.scs@velsuniv.ac.in	snagasundaram.scs@velsuniv.ac.in	PROPN
cana-857	4	60	article	article	NOUN
cana-857	4	61	history	history	NOUN
cana-857	4	62	:	:	PUNCT
cana-857	4	63	received	receive	VERB
cana-857	4	64	:	:	PUNCT
cana-857	4	65	26	26	NUM
cana-857	4	66	-	-	PUNCT
cana-857	4	67	04	04	NUM
cana-857	4	68	-	-	PUNCT
cana-857	4	69	2024	2024	NUM
cana-857	4	70	revised	revise	VERB
cana-857	4	71	:	:	PUNCT
cana-857	4	72	12	12	NUM
cana-857	4	73	-	-	PUNCT
cana-857	4	74	06	06	NUM
cana-857	4	75	-	-	PUNCT
cana-857	4	76	2024	2024	NUM
cana-857	4	77	accepted	accept	VERB
cana-857	4	78	:	:	PUNCT
cana-857	4	79	25	25	NUM
cana-857	4	80	-	-	PUNCT
cana-857	4	81	06	06	NUM
cana-857	4	82	-	-	PUNCT
cana-857	4	83	2024	2024	NUM
cana-857	4	84	abstract	abstract	NOUN
cana-857	4	85	:	:	PUNCT
cana-857	4	86	dengue	dengue	NOUN
cana-857	4	87	is	be	AUX
cana-857	4	88	a	a	DET
cana-857	4	89	vector	vector	NOUN
cana-857	4	90	borne	borne	NOUN
cana-857	4	91	disease	disease	NOUN
cana-857	4	92	,	,	PUNCT
cana-857	4	93	which	which	PRON
cana-857	4	94	can	can	AUX
cana-857	4	95	be	be	AUX
cana-857	4	96	fatal	fatal	ADJ
cana-857	4	97	at	at	ADP
cana-857	4	98	times	time	NOUN
cana-857	4	99	.	.	PUNCT
cana-857	5	1	early	early	ADJ
cana-857	5	2	detection	detection	NOUN
cana-857	5	3	dengue	dengue	NOUN
cana-857	5	4	of	of	ADP
cana-857	5	5	dengue	dengue	NOUN
cana-857	5	6	is	be	AUX
cana-857	5	7	vital	vital	ADJ
cana-857	5	8	as	as	SCONJ
cana-857	5	9	no	no	DET
cana-857	5	10	vaccines	vaccine	NOUN
cana-857	5	11	have	have	AUX
cana-857	5	12	been	be	AUX
cana-857	5	13	developed	develop	VERB
cana-857	5	14	for	for	ADP
cana-857	5	15	dengue	dengue	NOUN
cana-857	5	16	yet	yet	ADV
cana-857	5	17	..	..	PUNCT
cana-857	5	18	the	the	DET
cana-857	5	19	process	process	NOUN
cana-857	5	20	of	of	ADP
cana-857	5	21	eliminating	eliminate	VERB
cana-857	5	22	irrelevant	irrelevant	ADJ
cana-857	5	23	and	and	CCONJ
cana-857	5	24	redundant	redundant	ADJ
cana-857	5	25	features	feature	NOUN
cana-857	5	26	from	from	ADP
cana-857	5	27	the	the	DET
cana-857	5	28	data	data	NOUN
cana-857	5	29	set	set	NOUN
cana-857	5	30	facilitates	facilitate	VERB
cana-857	5	31	the	the	DET
cana-857	5	32	optimal	optimal	ADJ
cana-857	5	33	features	feature	NOUN
cana-857	5	34	selection	selection	NOUN
cana-857	5	35	.	.	PUNCT
cana-857	6	1	this	this	DET
cana-857	6	2	study	study	NOUN
cana-857	6	3	is	be	AUX
cana-857	6	4	proposed	propose	VERB
cana-857	6	5	to	to	PART
cana-857	6	6	select	select	VERB
cana-857	6	7	the	the	DET
cana-857	6	8	optimal	optimal	ADJ
cana-857	6	9	features	feature	NOUN
cana-857	6	10	by	by	ADP
cana-857	6	11	using	use	VERB
cana-857	6	12	opt_recur	opt_recur	ADP
cana-857	6	13	algorithm	algorithm	NOUN
cana-857	6	14	from	from	ADP
cana-857	6	15	the	the	DET
cana-857	6	16	dengue	dengue	NOUN
cana-857	6	17	dataset	dataset	NOUN
cana-857	6	18	,	,	PUNCT
cana-857	6	19	a	a	DET
cana-857	6	20	hybrid	hybrid	ADJ
cana-857	6	21	sdr	sdr	PROPN
cana-857	6	22	model	model	NOUN
cana-857	6	23	which	which	PRON
cana-857	6	24	makes	make	VERB
cana-857	6	25	prediction	prediction	NOUN
cana-857	6	26	with	with	ADP
cana-857	6	27	better	well	ADJ
cana-857	6	28	accuracy	accuracy	NOUN
cana-857	6	29	when	when	SCONJ
cana-857	6	30	compared	compare	VERB
cana-857	6	31	to	to	ADP
cana-857	6	32	the	the	DET
cana-857	6	33	conventional	conventional	ADJ
cana-857	6	34	classifiers	classifier	NOUN
cana-857	6	35	like	like	ADP
cana-857	6	36	the	the	DET
cana-857	6	37	support	support	NOUN
cana-857	6	38	vector	vector	NOUN
cana-857	6	39	machine	machine	NOUN
cana-857	6	40	(	(	PUNCT
cana-857	6	41	svm	svm	PROPN
cana-857	6	42	)	)	PUNCT
cana-857	6	43	,	,	PUNCT
cana-857	6	44	decision	decision	NOUN
cana-857	6	45	tree(dt	tree(dt	NUM
cana-857	6	46	)	)	PUNCT
cana-857	6	47	and	and	CCONJ
cana-857	6	48	the	the	DET
cana-857	6	49	random	random	ADJ
cana-857	6	50	forest	forest	NOUN
cana-857	6	51	(	(	PUNCT
cana-857	6	52	rf	rf	NOUN
cana-857	6	53	)	)	PUNCT
cana-857	6	54	classifiers	classifier	NOUN
cana-857	6	55	.	.	PUNCT
cana-857	7	1	keywords	keyword	NOUN
cana-857	7	2	:	:	PUNCT
cana-857	8	1	dengue	dengue	NOUN
cana-857	8	2	,	,	PUNCT
cana-857	8	3	hybrid	hybrid	ADJ
cana-857	8	4	sdr	sdr	PROPN
cana-857	8	5	model	model	NOUN
cana-857	8	6	,	,	PUNCT
cana-857	8	7	opt_recurr	opt_recurr	NOUN
cana-857	8	8	algorithm	algorithm	NOUN
cana-857	8	9	,	,	PUNCT
cana-857	8	10	feature	feature	NOUN
cana-857	8	11	selection	selection	NOUN
cana-857	8	12	,	,	PUNCT
cana-857	8	13	svm	svm	PROPN
cana-857	8	14	.	.	PROPN
cana-857	8	15	dt	dt	PROPN
cana-857	8	16	,	,	PUNCT
cana-857	8	17	rf	rf	ADJ
cana-857	8	18	.	.	NOUN
cana-857	8	19	1	1	NUM
cana-857	8	20	.	.	X
cana-857	9	1	introduction	introduction	NOUN
cana-857	9	2	dengue	dengue	NOUN
cana-857	9	3	fever	fever	NOUN
cana-857	9	4	is	be	AUX
cana-857	9	5	a	a	DET
cana-857	9	6	viral	viral	ADJ
cana-857	9	7	disease	disease	NOUN
cana-857	9	8	that	that	PRON
cana-857	9	9	spreads	spread	VERB
cana-857	9	10	quickly	quickly	ADV
cana-857	9	11	by	by	ADP
cana-857	9	12	the	the	DET
cana-857	9	13	mosquito	mosquito	NOUN
cana-857	9	14	in	in	ADP
cana-857	9	15	warm	warm	ADJ
cana-857	9	16	weather	weather	NOUN
cana-857	9	17	.	.	PUNCT
cana-857	10	1	it	it	PRON
cana-857	10	2	is	be	AUX
cana-857	10	3	transferred	transfer	VERB
cana-857	10	4	by	by	ADP
cana-857	10	5	a	a	DET
cana-857	10	6	female	female	ADJ
cana-857	10	7	mosquito	mosquito	NOUN
cana-857	10	8	known	know	VERB
cana-857	10	9	as	as	ADP
cana-857	10	10	'	'	PUNCT
cana-857	10	11	aedes	aedes	PROPN
cana-857	10	12	aegypti	aegypti	ADJ
cana-857	10	13	.	.	PUNCT
cana-857	10	14	'	'	PUNCT
cana-857	10	15	.	.	PUNCT
cana-857	11	1	dengue	dengue	NOUN
cana-857	11	2	cases	case	NOUN
cana-857	11	3	have	have	AUX
cana-857	11	4	increased	increase	VERB
cana-857	11	5	dramatically	dramatically	ADV
cana-857	11	6	in	in	ADP
cana-857	11	7	recent	recent	ADJ
cana-857	11	8	years	year	NOUN
cana-857	11	9	over	over	ADP
cana-857	11	10	the	the	DET
cana-857	11	11	world	world	NOUN
cana-857	11	12	.	.	PUNCT
cana-857	12	1	however	however	ADV
cana-857	12	2	,	,	PUNCT
cana-857	12	3	the	the	DET
cana-857	12	4	actual	actual	ADJ
cana-857	12	5	number	number	NOUN
cana-857	12	6	of	of	ADP
cana-857	12	7	dengue	dengue	NOUN
cana-857	12	8	infections	infection	NOUN
cana-857	12	9	is	be	AUX
cana-857	12	10	either	either	PRON
cana-857	12	11	never	never	ADV
cana-857	12	12	recorded	record	VERB
cana-857	12	13	or	or	CCONJ
cana-857	12	14	is	be	AUX
cana-857	12	15	classified	classify	VERB
cana-857	12	16	incorrectly	incorrectly	ADV
cana-857	12	17	.	.	PUNCT
cana-857	13	1	dengue	dengue	NOUN
cana-857	13	2	fever	fever	NOUN
cana-857	13	3	is	be	AUX
cana-857	13	4	a	a	DET
cana-857	13	5	fatal	fatal	ADJ
cana-857	13	6	disease	disease	NOUN
cana-857	13	7	that	that	PRON
cana-857	13	8	is	be	AUX
cana-857	13	9	widespread	widespread	ADJ
cana-857	13	10	by	by	ADP
cana-857	13	11	viral	viral	ADJ
cana-857	13	12	infections	infection	NOUN
cana-857	13	13	.	.	PUNCT
cana-857	14	1	it	it	PRON
cana-857	14	2	is	be	AUX
cana-857	14	3	a	a	DET
cana-857	14	4	rapidly	rapidly	ADV
cana-857	14	5	spreading	spread	VERB
cana-857	14	6	tropical	tropical	ADJ
cana-857	14	7	virus	virus	NOUN
cana-857	14	8	infection	infection	NOUN
cana-857	14	9	with	with	ADP
cana-857	14	10	an	an	DET
cana-857	14	11	increased	increase	VERB
cana-857	14	12	death	death	NOUN
cana-857	14	13	rate[6	rate[6	NOUN
cana-857	14	14	]	]	PUNCT
cana-857	14	15	according	accord	VERB
cana-857	14	16	to	to	ADP
cana-857	14	17	the	the	DET
cana-857	14	18	world	world	PROPN
cana-857	14	19	health	health	NOUN
cana-857	14	20	organization	organization	NOUN
cana-857	14	21	,	,	PUNCT
cana-857	14	22	dengue	dengue	NOUN
cana-857	14	23	is	be	AUX
cana-857	14	24	a	a	DET
cana-857	14	25	fatal	fatal	ADJ
cana-857	14	26	disease	disease	NOUN
cana-857	14	27	so	so	SCONJ
cana-857	14	28	early	early	ADJ
cana-857	14	29	diagnosis	diagnosis	NOUN
cana-857	14	30	is	be	AUX
cana-857	14	31	a	a	DET
cana-857	14	32	must	must	NOUN
cana-857	14	33	.	.	PUNCT
cana-857	15	1	there	there	PRON
cana-857	15	2	are	be	VERB
cana-857	15	3	many	many	ADJ
cana-857	15	4	machine	machine	NOUN
cana-857	15	5	learning	learn	VERB
cana-857	15	6	methods	method	NOUN
cana-857	15	7	help	help	VERB
cana-857	15	8	physicians	physician	NOUN
cana-857	15	9	to	to	PART
cana-857	15	10	early	early	ADV
cana-857	15	11	diagnose	diagnose	VERB
cana-857	15	12	the	the	DET
cana-857	15	13	disease	disease	NOUN
cana-857	15	14	,	,	PUNCT
cana-857	15	15	which	which	PRON
cana-857	15	16	makes	make	VERB
cana-857	15	17	accurate	accurate	ADJ
cana-857	15	18	prediction	prediction	NOUN
cana-857	15	19	using	use	VERB
cana-857	15	20	the	the	DET
cana-857	15	21	classification	classification	NOUN
cana-857	15	22	techniques	technique	NOUN
cana-857	15	23	which	which	PRON
cana-857	15	24	can	can	AUX
cana-857	15	25	save	save	VERB
cana-857	15	26	up	up	ADP
cana-857	15	27	time.[13].the	time.[13].the	DET
cana-857	15	28	machine	machine	NOUN
cana-857	15	29	learning	learn	VERB
cana-857	15	30	techniques	technique	NOUN
cana-857	15	31	use	use	VERB
cana-857	15	32	statistical	statistical	ADJ
cana-857	15	33	methods	method	NOUN
cana-857	15	34	to	to	PART
cana-857	15	35	select	select	VERB
cana-857	15	36	optimal	optimal	ADJ
cana-857	15	37	features	feature	NOUN
cana-857	15	38	from	from	ADP
cana-857	15	39	the	the	DET
cana-857	15	40	dataset	dataset	NOUN
cana-857	15	41	.	.	PUNCT
cana-857	16	1	optimal	optimal	ADJ
cana-857	16	2	features	feature	NOUN
cana-857	16	3	helps	help	VERB
cana-857	16	4	not	not	PART
cana-857	16	5	only	only	ADV
cana-857	16	6	to	to	PART
cana-857	16	7	make	make	VERB
cana-857	16	8	accurate	accurate	ADJ
cana-857	16	9	prediction	prediction	NOUN
cana-857	16	10	it	it	PRON
cana-857	16	11	also	also	ADV
cana-857	16	12	speeds	speed	VERB
cana-857	16	13	up	up	ADP
cana-857	16	14	the	the	DET
cana-857	16	15	execution	execution	NOUN
cana-857	16	16	time	time	NOUN
cana-857	16	17	.	.	PUNCT
cana-857	17	1	there	there	PRON
cana-857	17	2	are	be	VERB
cana-857	17	3	many	many	ADJ
cana-857	17	4	machine	machine	NOUN
cana-857	17	5	learning	learn	VERB
cana-857	17	6	algorithms	algorithm	NOUN
cana-857	17	7	that	that	PRON
cana-857	17	8	help	help	VERB
cana-857	17	9	to	to	PART
cana-857	17	10	make	make	VERB
cana-857	17	11	predictions	prediction	NOUN
cana-857	17	12	of	of	ADP
cana-857	17	13	the	the	DET
cana-857	17	14	disease	disease	NOUN
cana-857	17	15	in	in	ADP
cana-857	17	16	this	this	DET
cana-857	17	17	paper	paper	NOUN
cana-857	17	18	it	it	PRON
cana-857	17	19	is	be	AUX
cana-857	17	20	put	put	VERB
cana-857	17	21	forth	forth	ADV
cana-857	17	22	that	that	SCONJ
cana-857	17	23	traditional	traditional	ADJ
cana-857	17	24	classifiers	classifier	NOUN
cana-857	17	25	like	like	ADP
cana-857	17	26	the	the	DET
cana-857	17	27	svm	svm	PROPN
cana-857	17	28	,	,	PUNCT
cana-857	17	29	decision	decision	NOUN
cana-857	17	30	tree	tree	NOUN
cana-857	17	31	and	and	CCONJ
cana-857	17	32	random	random	ADJ
cana-857	17	33	forest	forest	NOUN
cana-857	17	34	has	have	AUX
cana-857	17	35	applied	apply	VERB
cana-857	17	36	on	on	ADP
cana-857	17	37	the	the	DET
cana-857	17	38	optimal	optimal	ADJ
cana-857	17	39	features	feature	NOUN
cana-857	17	40	were	be	AUX
cana-857	17	41	selected	select	VERB
cana-857	17	42	by	by	ADP
cana-857	17	43	the	the	DET
cana-857	17	44	opt	opt	NOUN
cana-857	17	45	-	-	PUNCT
cana-857	17	46	recur	recur	NOUN
cana-857	17	47	algorithm	algorithm	NOUN
cana-857	17	48	.	.	PUNCT
cana-857	18	1	this	this	DET
cana-857	18	2	study	study	NOUN
cana-857	18	3	makes	make	VERB
cana-857	18	4	comparison	comparison	NOUN
cana-857	18	5	among	among	ADP
cana-857	18	6	the	the	DET
cana-857	18	7	prominent	prominent	ADJ
cana-857	18	8	classifier	classifier	NOUN
cana-857	18	9	like	like	ADP
cana-857	18	10	svm	svm	PROPN
cana-857	18	11	,	,	PUNCT
cana-857	18	12	decision	decision	NOUN
cana-857	18	13	tree	tree	NOUN
cana-857	18	14	and	and	CCONJ
cana-857	18	15	random	random	ADJ
cana-857	18	16	forest	forest	NOUN
cana-857	18	17	,	,	PUNCT
cana-857	18	18	and	and	CCONJ
cana-857	18	19	also	also	ADV
cana-857	18	20	communications	communication	NOUN
cana-857	18	21	on	on	ADP
cana-857	18	22	applied	apply	VERB
cana-857	18	23	nonlinear	nonlinear	ADJ
cana-857	18	24	analysis	analysis	NOUN
cana-857	18	25	issn	issn	NOUN
cana-857	18	26	:	:	PUNCT
cana-857	18	27	1074	1074	NUM
cana-857	18	28	-	-	PUNCT
cana-857	18	29	133x	133x	NUM
cana-857	18	30	vol	vol	NOUN
cana-857	18	31	31	31	NUM
cana-857	18	32	no	no	NOUN
cana-857	18	33	.	.	PUNCT
cana-857	19	1	4s	4s	NUM
cana-857	19	2	(	(	PUNCT
cana-857	19	3	2024	2024	NUM
cana-857	19	4	)	)	PUNCT
cana-857	19	5	351	351	NUM
cana-857	19	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	19	7	with	with	ADP
cana-857	19	8	proposed	propose	VERB
cana-857	19	9	hybrid	hybrid	NOUN
cana-857	19	10	sdf	sdf	PROPN
cana-857	19	11	model	model	NOUN
cana-857	19	12	in	in	ADP
cana-857	19	13	terms	term	NOUN
cana-857	19	14	of	of	ADP
cana-857	19	15	their	their	PRON
cana-857	19	16	accuracy	accuracy	NOUN
cana-857	19	17	rate	rate	NOUN
cana-857	19	18	,	,	PUNCT
cana-857	19	19	precision	precision	NOUN
cana-857	19	20	,	,	PUNCT
cana-857	19	21	recall	recall	NOUN
cana-857	19	22	and	and	CCONJ
cana-857	19	23	f1	f1	NOUN
cana-857	19	24	-	-	PUNCT
cana-857	19	25	score	score	NOUN
cana-857	19	26	.	.	PUNCT
cana-857	20	1	the	the	DET
cana-857	20	2	roc	roc	PROPN
cana-857	20	3	curve	curve	NOUN
cana-857	20	4	is	be	AUX
cana-857	20	5	finally	finally	ADV
cana-857	20	6	used	use	VERB
cana-857	20	7	to	to	PART
cana-857	20	8	evaluate	evaluate	VERB
cana-857	20	9	the	the	DET
cana-857	20	10	performance	performance	NOUN
cana-857	20	11	measurement.[14	measurement.[14	PROPN
cana-857	20	12	]	]	PUNCT
cana-857	20	13	.	.	PUNCT
cana-857	21	1	2	2	X
cana-857	21	2	.	.	X
cana-857	21	3	related	relate	VERB
cana-857	21	4	work	work	NOUN
cana-857	21	5	the	the	DET
cana-857	21	6	weighted	weight	VERB
cana-857	21	7	score	score	NOUN
cana-857	21	8	evaluation	evaluation	NOUN
cana-857	21	9	values	value	NOUN
cana-857	21	10	were	be	AUX
cana-857	21	11	first	first	ADV
cana-857	21	12	combined	combine	VERB
cana-857	21	13	using	use	VERB
cana-857	21	14	the	the	DET
cana-857	21	15	weighted	weight	VERB
cana-857	21	16	score	score	NOUN
cana-857	21	17	arithmetic	arithmetic	ADJ
cana-857	21	18	averaging	averaging	NOUN
cana-857	21	19	(	(	PUNCT
cana-857	21	20	wsaa	wsaa	ADJ
cana-857	21	21	)	)	PUNCT
cana-857	21	22	operator	operator	NOUN
cana-857	21	23	.	.	PUNCT
cana-857	22	1	secondly	secondly	ADV
cana-857	22	2	,	,	PUNCT
cana-857	22	3	the	the	DET
cana-857	22	4	functions	function	NOUN
cana-857	22	5	for	for	ADP
cana-857	22	6	scoring	scoring	NOUN
cana-857	22	7	were	be	AUX
cana-857	22	8	computed	compute	VERB
cana-857	22	9	.	.	PUNCT
cana-857	23	1	third	third	ADJ
cana-857	23	2	,	,	PUNCT
cana-857	23	3	based	base	VERB
cana-857	23	4	on	on	ADP
cana-857	23	5	the	the	DET
cana-857	23	6	score	score	NOUN
cana-857	23	7	values	value	NOUN
cana-857	23	8	,	,	PUNCT
cana-857	23	9	the	the	DET
cana-857	23	10	best	good	ADJ
cana-857	23	11	option	option	NOUN
cana-857	23	12	is	be	AUX
cana-857	23	13	chosen	choose	VERB
cana-857	23	14	.	.	PUNCT
cana-857	24	1	ultimately	ultimately	ADV
cana-857	24	2	,	,	PUNCT
cana-857	24	3	it	it	PRON
cana-857	24	4	is	be	AUX
cana-857	24	5	possible	possible	ADJ
cana-857	24	6	to	to	PART
cana-857	24	7	compute	compute	VERB
cana-857	24	8	the	the	DET
cana-857	24	9	score	score	NOUN
cana-857	24	10	values	value	NOUN
cana-857	24	11	and	and	CCONJ
cana-857	24	12	get	get	VERB
cana-857	24	13	the	the	DET
cana-857	24	14	ranking	ranking	ADJ
cana-857	24	15	outcomes	outcome	NOUN
cana-857	24	16	.	.	PUNCT
cana-857	25	1	then	then	ADV
cana-857	25	2	,	,	PUNCT
cana-857	25	3	the	the	DET
cana-857	25	4	j48	j48	PROPN
cana-857	25	5	appears	appear	VERB
cana-857	25	6	to	to	PART
cana-857	25	7	be	be	AUX
cana-857	25	8	the	the	DET
cana-857	25	9	best	good	ADJ
cana-857	25	10	classifier	classifier	NOUN
cana-857	25	11	out	out	ADP
cana-857	25	12	of	of	ADP
cana-857	25	13	the	the	DET
cana-857	25	14	four	four	NUM
cana-857	25	15	traditional	traditional	ADJ
cana-857	25	16	classifiers	classifier	NOUN
cana-857	25	17	:	:	PUNCT
cana-857	25	18	naïve	naïve	ADJ
cana-857	25	19	bayes	bayes	NOUN
cana-857	25	20	(	(	PUNCT
cana-857	25	21	nb	nb	INTJ
cana-857	25	22	)	)	PUNCT
cana-857	25	23	,	,	PUNCT
cana-857	25	24	decision	decision	NOUN
cana-857	25	25	tree	tree	NOUN
cana-857	25	26	(	(	PUNCT
cana-857	25	27	j48	j48	PROPN
cana-857	25	28	)	)	PUNCT
cana-857	25	29	,	,	PUNCT
cana-857	25	30	multi	multi	ADJ
cana-857	25	31	layer	layer	NOUN
cana-857	25	32	perceptron	perceptron	PROPN
cana-857	25	33	(	(	PUNCT
cana-857	25	34	mlp	mlp	PROPN
cana-857	25	35	)	)	PUNCT
cana-857	25	36	,	,	PUNCT
cana-857	25	37	and	and	CCONJ
cana-857	25	38	support	support	VERB
cana-857	25	39	vector	vector	NOUN
cana-857	25	40	machine	machine	NOUN
cana-857	25	41	(	(	PUNCT
cana-857	25	42	svm	svm	PROPN
cana-857	25	43	)	)	PUNCT
cana-857	25	44	[	[	X
cana-857	25	45	2	2	NUM
cana-857	25	46	]	]	PUNCT
cana-857	25	47	has	have	AUX
cana-857	25	48	concluded	conclude	VERB
cana-857	25	49	from	from	ADP
cana-857	25	50	the	the	DET
cana-857	25	51	study	study	NOUN
cana-857	25	52	.	.	PUNCT
cana-857	26	1	naïve	naïve	ADJ
cana-857	26	2	bayes	bayes	PROPN
cana-857	26	3	,	,	PUNCT
cana-857	26	4	knn	knn	PROPN
cana-857	26	5	,	,	PUNCT
cana-857	26	6	and	and	CCONJ
cana-857	26	7	j48	j48	ADJ
cana-857	26	8	feature	feature	NOUN
cana-857	26	9	selection	selection	NOUN
cana-857	26	10	techniques	technique	NOUN
cana-857	26	11	are	be	AUX
cana-857	26	12	used	use	VERB
cana-857	26	13	in	in	ADP
cana-857	26	14	pca	pca	PROPN
cana-857	26	15	and	and	CCONJ
cana-857	26	16	wrapper	wrapper	NOUN
cana-857	26	17	.	.	PUNCT
cana-857	27	1	different	different	ADJ
cana-857	27	2	ann	ann	PROPN
cana-857	27	3	models	model	NOUN
cana-857	27	4	have	have	AUX
cana-857	27	5	been	be	AUX
cana-857	27	6	created	create	VERB
cana-857	27	7	for	for	ADP
cana-857	27	8	every	every	DET
cana-857	27	9	method	method	NOUN
cana-857	27	10	of	of	ADP
cana-857	27	11	feature	feature	NOUN
cana-857	27	12	selection	selection	NOUN
cana-857	27	13	.	.	PUNCT
cana-857	28	1	of	of	ADP
cana-857	28	2	the	the	DET
cana-857	28	3	four	four	NUM
cana-857	28	4	feature	feature	NOUN
cana-857	28	5	selection	selection	NOUN
cana-857	28	6	techniques	technique	NOUN
cana-857	28	7	,	,	PUNCT
cana-857	28	8	pca	pca	PROPN
cana-857	28	9	produces	produce	VERB
cana-857	28	10	an	an	DET
cana-857	28	11	ann	ann	NOUN
cana-857	28	12	with	with	ADP
cana-857	28	13	a	a	DET
cana-857	28	14	greater	great	ADJ
cana-857	28	15	accuracy	accuracy	NOUN
cana-857	28	16	.	.	PUNCT
cana-857	29	1	in	in	ADP
cana-857	29	2	summary	summary	NOUN
cana-857	29	3	,	,	PUNCT
cana-857	29	4	pca	pca	PROPN
cana-857	29	5	works	work	VERB
cana-857	29	6	better	well	ADV
cana-857	29	7	with	with	ADP
cana-857	29	8	the	the	DET
cana-857	29	9	provided	provide	VERB
cana-857	29	10	dataset	dataset	NOUN
cana-857	29	11	.	.	PUNCT
cana-857	30	1	the	the	DET
cana-857	30	2	two	two	NUM
cana-857	30	3	most	most	ADV
cana-857	30	4	expressive	expressive	ADJ
cana-857	30	5	characteristics	characteristic	NOUN
cana-857	30	6	selected	select	VERB
cana-857	30	7	by	by	ADP
cana-857	30	8	all	all	DET
cana-857	30	9	wrapper	wrapper	NOUN
cana-857	30	10	feature	feature	NOUN
cana-857	30	11	selection	selection	NOUN
cana-857	30	12	methods	method	NOUN
cana-857	30	13	are	be	AUX
cana-857	30	14	myalgia	myalgia	ADJ
cana-857	30	15	and	and	CCONJ
cana-857	30	16	retro	retro	ADJ
cana-857	30	17	-	-	PUNCT
cana-857	30	18	ocular	ocular	ADJ
cana-857	30	19	pain	pain	NOUN
cana-857	30	20	[	[	X
cana-857	30	21	4	4	X
cana-857	30	22	]	]	PUNCT
cana-857	30	23	has	have	AUX
cana-857	30	24	been	be	AUX
cana-857	30	25	proposed	propose	VERB
cana-857	30	26	in	in	ADP
cana-857	30	27	the	the	DET
cana-857	30	28	study	study	NOUN
cana-857	30	29	..	..	PUNCT
cana-857	31	1	the	the	DET
cana-857	31	2	important	important	ADJ
cana-857	31	3	features	feature	NOUN
cana-857	31	4	were	be	AUX
cana-857	31	5	chosen	choose	VERB
cana-857	31	6	using	use	VERB
cana-857	31	7	the	the	DET
cana-857	31	8	features	feature	NOUN
cana-857	31	9	selection	selection	NOUN
cana-857	31	10	technique	technique	NOUN
cana-857	31	11	.	.	PUNCT
cana-857	32	1	z	z	X
cana-857	32	2	-	-	PUNCT
cana-857	32	3	score	score	NOUN
cana-857	32	4	was	be	AUX
cana-857	32	5	used	use	VERB
cana-857	32	6	to	to	PART
cana-857	32	7	standardize	standardize	VERB
cana-857	32	8	the	the	DET
cana-857	32	9	data	datum	NOUN
cana-857	32	10	.	.	PUNCT
cana-857	33	1	to	to	PART
cana-857	33	2	address	address	VERB
cana-857	33	3	the	the	DET
cana-857	33	4	imbalance	imbalance	NOUN
cana-857	33	5	issue	issue	NOUN
cana-857	33	6	in	in	ADP
cana-857	33	7	the	the	DET
cana-857	33	8	dataset	dataset	NOUN
cana-857	33	9	,	,	PUNCT
cana-857	33	10	the	the	DET
cana-857	33	11	smote+enn	smote+enn	PROPN
cana-857	33	12	hybrid	hybrid	NOUN
cana-857	33	13	technique	technique	NOUN
cana-857	33	14	was	be	AUX
cana-857	33	15	utilized	utilize	VERB
cana-857	33	16	to	to	PART
cana-857	33	17	divide	divide	VERB
cana-857	33	18	the	the	DET
cana-857	33	19	dataset	dataset	NOUN
cana-857	33	20	into	into	ADP
cana-857	33	21	training	training	NOUN
cana-857	33	22	and	and	CCONJ
cana-857	33	23	testing	testing	NOUN
cana-857	33	24	sets	set	NOUN
cana-857	33	25	using	use	VERB
cana-857	33	26	cross	cross	ADJ
cana-857	33	27	-	-	ADJ
cana-857	33	28	validation	validation	ADJ
cana-857	33	29	techniques	technique	NOUN
cana-857	33	30	such	such	ADJ
cana-857	33	31	10	10	NUM
cana-857	33	32	-	-	ADJ
cana-857	33	33	fold	fold	ADJ
cana-857	33	34	and	and	CCONJ
cana-857	33	35	holdout	holdout	VERB
cana-857	33	36	cross	cross	NOUN
cana-857	33	37	-	-	NOUN
cana-857	33	38	validation	validation	NOUN
cana-857	33	39	.	.	PUNCT
cana-857	34	1	following	follow	VERB
cana-857	34	2	that	that	PRON
cana-857	34	3	,	,	PUNCT
cana-857	34	4	machine	machine	NOUN
cana-857	34	5	learning	learning	NOUN
cana-857	34	6	models	model	NOUN
cana-857	34	7	were	be	AUX
cana-857	34	8	created	create	VERB
cana-857	34	9	,	,	PUNCT
cana-857	34	10	and	and	CCONJ
cana-857	34	11	the	the	DET
cana-857	34	12	accuracy	accuracy	NOUN
cana-857	34	13	,	,	PUNCT
cana-857	34	14	f1	f1	NOUN
cana-857	34	15	-	-	PUNCT
cana-857	34	16	score	score	NOUN
cana-857	34	17	,	,	PUNCT
cana-857	34	18	precision	precision	NOUN
cana-857	34	19	,	,	PUNCT
cana-857	34	20	recall	recall	NOUN
cana-857	34	21	,	,	PUNCT
cana-857	34	22	and	and	CCONJ
cana-857	34	23	auc	auc	NOUN
cana-857	34	24	scores	score	NOUN
cana-857	34	25	were	be	AUX
cana-857	34	26	used	use	VERB
cana-857	34	27	to	to	PART
cana-857	34	28	assess	assess	VERB
cana-857	34	29	how	how	SCONJ
cana-857	34	30	well	well	ADV
cana-857	34	31	they	they	PRON
cana-857	34	32	performed	perform	VERB
cana-857	34	33	[	[	PUNCT
cana-857	34	34	6	6	NUM
cana-857	34	35	]	]	PUNCT
cana-857	34	36	.	.	PUNCT
cana-857	35	1	in	in	ADP
cana-857	35	2	[	[	X
cana-857	35	3	10	10	NUM
cana-857	35	4	]	]	PUNCT
cana-857	35	5	concluded	conclude	VERB
cana-857	35	6	that	that	SCONJ
cana-857	35	7	when	when	SCONJ
cana-857	35	8	compared	compare	VERB
cana-857	35	9	to	to	ADP
cana-857	35	10	lda	lda	PROPN
cana-857	35	11	and	and	CCONJ
cana-857	35	12	knn	knn	PROPN
cana-857	35	13	,	,	PUNCT
cana-857	35	14	the	the	DET
cana-857	35	15	chosen	choose	VERB
cana-857	35	16	models	model	NOUN
cana-857	35	17	dt	dt	PROPN
cana-857	35	18	,	,	PUNCT
cana-857	35	19	cart	cart	NOUN
cana-857	35	20	,	,	PUNCT
cana-857	35	21	and	and	CCONJ
cana-857	35	22	nb	nb	PROPN
cana-857	35	23	demonstrated	demonstrate	VERB
cana-857	35	24	great	great	ADJ
cana-857	35	25	accuracy	accuracy	NOUN
cana-857	35	26	.	.	PUNCT
cana-857	36	1	but	but	CCONJ
cana-857	36	2	when	when	SCONJ
cana-857	36	3	it	it	PRON
cana-857	36	4	comes	come	VERB
cana-857	36	5	to	to	ADP
cana-857	36	6	accuracy	accuracy	NOUN
cana-857	36	7	,	,	PUNCT
cana-857	36	8	recall	recall	NOUN
cana-857	36	9	,	,	PUNCT
cana-857	36	10	sensitivity	sensitivity	NOUN
cana-857	36	11	,	,	PUNCT
cana-857	36	12	and	and	CCONJ
cana-857	36	13	specificity	specificity	NOUN
cana-857	36	14	—	—	PUNCT
cana-857	36	15	all	all	PRON
cana-857	36	16	of	of	ADP
cana-857	36	17	which	which	PRON
cana-857	36	18	are	be	AUX
cana-857	36	19	determined	determine	VERB
cana-857	36	20	by	by	ADP
cana-857	36	21	looking	look	VERB
cana-857	36	22	at	at	ADP
cana-857	36	23	the	the	DET
cana-857	36	24	classification	classification	NOUN
cana-857	36	25	matrix	matrix	NOUN
cana-857	36	26	—	—	PUNCT
cana-857	36	27	dt	dt	PUNCT
cana-857	36	28	was	be	AUX
cana-857	36	29	discovered	discover	VERB
cana-857	36	30	to	to	PART
cana-857	36	31	be	be	AUX
cana-857	36	32	the	the	DET
cana-857	36	33	best	good	ADJ
cana-857	36	34	.	.	PUNCT
cana-857	37	1	patients	patient	NOUN
cana-857	37	2	with	with	ADP
cana-857	37	3	dengue	dengue	NOUN
cana-857	37	4	fever	fever	NOUN
cana-857	37	5	:	:	PUNCT
cana-857	37	6	this	this	PRON
cana-857	37	7	is	be	AUX
cana-857	37	8	the	the	DET
cana-857	37	9	group	group	NOUN
cana-857	37	10	from	from	ADP
cana-857	37	11	which	which	PRON
cana-857	37	12	we	we	PRON
cana-857	37	13	can	can	AUX
cana-857	37	14	also	also	ADV
cana-857	37	15	highlight	highlight	VERB
cana-857	37	16	the	the	DET
cana-857	37	17	importance	importance	NOUN
cana-857	37	18	of	of	ADP
cana-857	37	19	prompt	prompt	ADJ
cana-857	37	20	medical	medical	ADJ
cana-857	37	21	attention	attention	NOUN
cana-857	37	22	for	for	ADP
cana-857	37	23	those	those	PRON
cana-857	37	24	exhibiting	exhibit	VERB
cana-857	37	25	warning	warning	NOUN
cana-857	37	26	indicators	indicator	NOUN
cana-857	37	27	.	.	PUNCT
cana-857	38	1	out	out	ADP
cana-857	38	2	of	of	ADP
cana-857	38	3	the	the	DET
cana-857	38	4	pool	pool	NOUN
cana-857	38	5	of	of	ADP
cana-857	38	6	classifiers	classifier	NOUN
cana-857	38	7	that	that	PRON
cana-857	38	8	were	be	AUX
cana-857	38	9	chosen	choose	VERB
cana-857	38	10	,	,	PUNCT
cana-857	38	11	the	the	DET
cana-857	38	12	suggested	suggest	VERB
cana-857	38	13	model	model	NOUN
cana-857	38	14	produced	produce	VERB
cana-857	38	15	good	good	ADJ
cana-857	38	16	classification	classification	NOUN
cana-857	38	17	characteristics	characteristic	NOUN
cana-857	38	18	including	include	VERB
cana-857	38	19	99.8	99.8	NUM
cana-857	38	20	%	%	NOUN
cana-857	38	21	accuracy	accuracy	NOUN
cana-857	38	22	,	,	PUNCT
cana-857	38	23	0.86	0.86	NUM
cana-857	38	24	precision	precision	NOUN
cana-857	38	25	,	,	PUNCT
cana-857	38	26	and	and	CCONJ
cana-857	38	27	1.0	1.0	NUM
cana-857	38	28	recall	recall	NOUN
cana-857	38	29	,	,	PUNCT
cana-857	38	30	which	which	PRON
cana-857	38	31	indicated	indicate	VERB
cana-857	38	32	promise	promise	NOUN
cana-857	38	33	.	.	PUNCT
cana-857	39	1	3	3	X
cana-857	39	2	.	.	X
cana-857	39	3	methodology	methodology	NOUN
cana-857	39	4	feature	feature	NOUN
cana-857	39	5	selection	selection	NOUN
cana-857	39	6	a	a	DET
cana-857	39	7	crucial	crucial	ADJ
cana-857	39	8	stage	stage	NOUN
cana-857	39	9	in	in	ADP
cana-857	39	10	data	datum	NOUN
cana-857	39	11	processing	processing	NOUN
cana-857	39	12	before	before	ADP
cana-857	39	13	putting	put	VERB
cana-857	39	14	the	the	DET
cana-857	39	15	information	information	NOUN
cana-857	39	16	into	into	ADP
cana-857	39	17	a	a	DET
cana-857	39	18	learning	learning	NOUN
cana-857	39	19	system	system	NOUN
cana-857	39	20	is	be	AUX
cana-857	39	21	feature	feature	NOUN
cana-857	39	22	selection	selection	NOUN
cana-857	39	23	.	.	PUNCT
cana-857	40	1	removing	remove	VERB
cana-857	40	2	redundant	redundant	ADJ
cana-857	40	3	and	and	CCONJ
cana-857	40	4	irrelevant	irrelevant	ADJ
cana-857	40	5	input	input	NOUN
cana-857	40	6	improves	improve	VERB
cana-857	40	7	the	the	DET
cana-857	40	8	machine	machine	NOUN
cana-857	40	9	learning	learn	VERB
cana-857	40	10	algorithm	algorithm	NOUN
cana-857	40	11	's	's	PART
cana-857	40	12	performance	performance	NOUN
cana-857	40	13	.	.	PUNCT
cana-857	41	1	the	the	DET
cana-857	41	2	efficiency	efficiency	NOUN
cana-857	41	3	and	and	CCONJ
cana-857	41	4	efficacy	efficacy	NOUN
cana-857	41	5	of	of	ADP
cana-857	41	6	many	many	ADJ
cana-857	41	7	current	current	ADJ
cana-857	41	8	feature	feature	NOUN
cana-857	41	9	selection	selection	NOUN
cana-857	41	10	techniques	technique	NOUN
cana-857	41	11	are	be	AUX
cana-857	41	12	severely	severely	ADV
cana-857	41	13	hampered	hamper	VERB
cana-857	41	14	by	by	ADP
cana-857	41	15	the	the	DET
cana-857	41	16	growth	growth	NOUN
cana-857	41	17	in	in	ADP
cana-857	41	18	the	the	DET
cana-857	41	19	dimensionality	dimensionality	NOUN
cana-857	41	20	of	of	ADP
cana-857	41	21	data	datum	NOUN
cana-857	41	22	.	.	PUNCT
cana-857	42	1	this	this	DET
cana-857	42	2	study	study	NOUN
cana-857	42	3	examines	examine	VERB
cana-857	42	4	several	several	ADJ
cana-857	42	5	popular	popular	ADJ
cana-857	42	6	feature	feature	NOUN
cana-857	42	7	selection	selection	NOUN
cana-857	42	8	strategies	strategy	NOUN
cana-857	42	9	and	and	CCONJ
cana-857	42	10	examines	examine	VERB
cana-857	42	11	how	how	SCONJ
cana-857	42	12	well	well	ADV
cana-857	42	13	they	they	PRON
cana-857	42	14	may	may	AUX
cana-857	42	15	be	be	AUX
cana-857	42	16	applied	apply	VERB
cana-857	42	17	to	to	PART
cana-857	42	18	attain	attain	VERB
cana-857	42	19	high	high	ADJ
cana-857	42	20	machine	machine	NOUN
cana-857	42	21	learning	learn	VERB
cana-857	42	22	algorithm	algorithm	NOUN
cana-857	42	23	performance	performance	NOUN
cana-857	42	24	,	,	PUNCT
cana-857	42	25	which	which	PRON
cana-857	42	26	in	in	ADP
cana-857	42	27	turn	turn	NOUN
cana-857	42	28	enhances	enhance	VERB
cana-857	42	29	the	the	DET
cana-857	42	30	classifier	classifier	NOUN
cana-857	42	31	's	's	PART
cana-857	42	32	predicted	predict	VERB
cana-857	42	33	accuracy	accuracy	NOUN
cana-857	42	34	.	.	PUNCT
cana-857	43	1	as	as	ADP
cana-857	43	2	a	a	DET
cana-857	43	3	result	result	NOUN
cana-857	43	4	,	,	PUNCT
cana-857	43	5	the	the	DET
cana-857	43	6	classifiers	classifier	NOUN
cana-857	43	7	processes	process	VERB
cana-857	43	8	the	the	DET
cana-857	43	9	data	datum	NOUN
cana-857	43	10	more	more	ADV
cana-857	43	11	quickly	quickly	ADV
cana-857	43	12	and	and	CCONJ
cana-857	43	13	accurately	accurately	ADV
cana-857	43	14	,	,	PUNCT
cana-857	43	15	which	which	PRON
cana-857	43	16	also	also	ADV
cana-857	43	17	improves	improve	VERB
cana-857	43	18	accuracy	accuracy	NOUN
cana-857	43	19	.	.	PUNCT
cana-857	44	1	the	the	DET
cana-857	44	2	classification	classification	NOUN
cana-857	44	3	accuracy	accuracy	NOUN
cana-857	44	4	can	can	AUX
cana-857	44	5	be	be	AUX
cana-857	44	6	significantly	significantly	ADV
cana-857	44	7	impacted	impact	VERB
cana-857	44	8	by	by	ADP
cana-857	44	9	irrelevant	irrelevant	ADJ
cana-857	44	10	information	information	NOUN
cana-857	44	11	,	,	PUNCT
cana-857	44	12	such	such	ADJ
cana-857	44	13	as	as	ADP
cana-857	44	14	noisy	noisy	ADJ
cana-857	44	15	data	datum	NOUN
cana-857	44	16	.	.	PUNCT
cana-857	45	1	by	by	ADP
cana-857	45	2	using	use	VERB
cana-857	45	3	feature	feature	NOUN
cana-857	45	4	selection	selection	NOUN
cana-857	45	5	approaches	approach	NOUN
cana-857	45	6	,	,	PUNCT
cana-857	45	7	data	datum	NOUN
cana-857	45	8	handling	handling	NOUN
cana-857	45	9	can	can	AUX
cana-857	45	10	be	be	AUX
cana-857	45	11	enhanced	enhance	VERB
cana-857	45	12	while	while	SCONJ
cana-857	45	13	communications	communication	NOUN
cana-857	45	14	on	on	ADP
cana-857	45	15	applied	apply	VERB
cana-857	45	16	nonlinear	nonlinear	ADJ
cana-857	45	17	analysis	analysis	NOUN
cana-857	45	18	issn	issn	NOUN
cana-857	45	19	:	:	PUNCT
cana-857	45	20	1074	1074	NUM
cana-857	45	21	-	-	PUNCT
cana-857	45	22	133x	133x	NUM
cana-857	45	23	vol	vol	NOUN
cana-857	45	24	31	31	NUM
cana-857	45	25	no	no	NOUN
cana-857	45	26	.	.	PUNCT
cana-857	46	1	4s	4s	NUM
cana-857	46	2	(	(	PUNCT
cana-857	46	3	2024	2024	NUM
cana-857	46	4	)	)	PUNCT
cana-857	46	5	352	352	NUM
cana-857	46	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	46	7	successfully	successfully	ADV
cana-857	46	8	lowering	lower	VERB
cana-857	46	9	costs	cost	NOUN
cana-857	46	10	.	.	PUNCT
cana-857	47	1	feature	feature	NOUN
cana-857	47	2	selection	selection	NOUN
cana-857	47	3	techniques	technique	NOUN
cana-857	47	4	are	be	AUX
cana-857	47	5	frequently	frequently	ADV
cana-857	47	6	employed	employ	VERB
cana-857	47	7	to	to	PART
cana-857	47	8	boost	boost	VERB
cana-857	47	9	a	a	DET
cana-857	47	10	classifier	classifier	NOUN
cana-857	47	11	's	's	PART
cana-857	47	12	capacity	capacity	NOUN
cana-857	47	13	for	for	ADP
cana-857	47	14	generalization	generalization	NOUN
cana-857	47	15	.	.	PUNCT
cana-857	48	1	to	to	PART
cana-857	48	2	obtain	obtain	VERB
cana-857	48	3	the	the	DET
cana-857	48	4	best	good	ADJ
cana-857	48	5	accuracy	accuracy	NOUN
cana-857	48	6	,	,	PUNCT
cana-857	48	7	we	we	PRON
cana-857	48	8	compare	compare	VERB
cana-857	48	9	the	the	DET
cana-857	48	10	dataset	dataset	NOUN
cana-857	48	11	's	's	PART
cana-857	48	12	result	result	NOUN
cana-857	48	13	with	with	ADP
cana-857	48	14	significant	significant	ADJ
cana-857	48	15	attributes	attribute	NOUN
cana-857	48	16	.	.	PUNCT
cana-857	49	1	filter	filter	NOUN
cana-857	49	2	,	,	PUNCT
cana-857	49	3	wrapper	wrapper	NOUN
cana-857	49	4	,	,	PUNCT
cana-857	49	5	and	and	CCONJ
cana-857	49	6	embedding	embed	VERB
cana-857	49	7	methods	method	NOUN
cana-857	49	8	are	be	AUX
cana-857	49	9	the	the	DET
cana-857	49	10	three	three	NUM
cana-857	49	11	categories	category	NOUN
cana-857	49	12	into	into	ADP
cana-857	49	13	which	which	PRON
cana-857	49	14	feature	feature	NOUN
cana-857	49	15	selection	selection	NOUN
cana-857	49	16	techniques	technique	NOUN
cana-857	49	17	fall	fall	VERB
cana-857	49	18	.	.	PUNCT
cana-857	50	1	the	the	DET
cana-857	50	2	data	datum	NOUN
cana-857	50	3	is	be	AUX
cana-857	50	4	preprocessed	preprocesse	VERB
cana-857	50	5	using	use	VERB
cana-857	50	6	filter	filter	NOUN
cana-857	50	7	algorithms	algorithm	NOUN
cana-857	50	8	.	.	PUNCT
cana-857	51	1	in	in	ADP
cana-857	51	2	order	order	NOUN
cana-857	51	3	to	to	PART
cana-857	51	4	compute	compute	VERB
cana-857	51	5	and	and	CCONJ
cana-857	51	6	forecast	forecast	VERB
cana-857	51	7	the	the	DET
cana-857	51	8	target	target	NOUN
cana-857	51	9	feature	feature	NOUN
cana-857	51	10	,	,	PUNCT
cana-857	51	11	these	these	PRON
cana-857	51	12	take	take	VERB
cana-857	51	13	into	into	ADP
cana-857	51	14	account	account	NOUN
cana-857	51	15	the	the	DET
cana-857	51	16	correlation	correlation	NOUN
cana-857	51	17	between	between	ADP
cana-857	51	18	features	feature	NOUN
cana-857	51	19	.	.	PUNCT
cana-857	52	1	to	to	PART
cana-857	52	2	find	find	VERB
cana-857	52	3	the	the	DET
cana-857	52	4	high	high	ADJ
cana-857	52	5	rank	rank	NOUN
cana-857	52	6	feature	feature	NOUN
cana-857	52	7	,	,	PUNCT
cana-857	52	8	a	a	DET
cana-857	52	9	variety	variety	NOUN
cana-857	52	10	of	of	ADP
cana-857	52	11	statistical	statistical	ADJ
cana-857	52	12	tests	test	NOUN
cana-857	52	13	are	be	AUX
cana-857	52	14	run	run	VERB
cana-857	52	15	on	on	ADP
cana-857	52	16	the	the	DET
cana-857	52	17	characteristics	characteristic	NOUN
cana-857	52	18	[	[	X
cana-857	52	19	3	3	NUM
cana-857	52	20	]	]	PUNCT
cana-857	52	21	.	.	PUNCT
cana-857	53	1	the	the	DET
cana-857	53	2	proposed	propose	VERB
cana-857	53	3	opt_recur	opt_recur	NOUN
cana-857	53	4	algorithm	algorithm	PROPN
cana-857	53	5	runs	run	VERB
cana-857	53	6	through	through	ADP
cana-857	53	7	a	a	DET
cana-857	53	8	series	series	NOUN
cana-857	53	9	of	of	ADP
cana-857	53	10	steps	step	NOUN
cana-857	53	11	which	which	PRON
cana-857	53	12	makes	make	VERB
cana-857	53	13	accurate	accurate	ADJ
cana-857	53	14	prediction	prediction	NOUN
cana-857	53	15	of	of	ADP
cana-857	53	16	desired	desire	VERB
cana-857	53	17	features	feature	NOUN
cana-857	53	18	which	which	PRON
cana-857	53	19	are	be	AUX
cana-857	53	20	needed	need	VERB
cana-857	53	21	to	to	PART
cana-857	53	22	make	make	VERB
cana-857	53	23	accurate	accurate	ADJ
cana-857	53	24	prediction	prediction	NOUN
cana-857	53	25	of	of	ADP
cana-857	53	26	dengue	dengue	NOUN
cana-857	53	27	.	.	PUNCT
cana-857	54	1	opt_recur	opt_recur	PROPN
cana-857	54	2	algorithm	algorithm	PROPN
cana-857	54	3	:	:	PUNCT
cana-857	54	4	1	1	X
cana-857	54	5	.	.	X
cana-857	54	6	input	input	NOUN
cana-857	54	7	all	all	DET
cana-857	54	8	the	the	DET
cana-857	54	9	attributes	attribute	NOUN
cana-857	54	10	from	from	ADP
cana-857	54	11	the	the	DET
cana-857	54	12	dataset	dataset	NOUN
cana-857	54	13	.	.	PUNCT
cana-857	55	1	2	2	X
cana-857	55	2	.	.	X
cana-857	55	3	find	find	VERB
cana-857	55	4	the	the	DET
cana-857	55	5	correlation	correlation	NOUN
cana-857	55	6	coefficient	coefficient	NOUN
cana-857	55	7	of	of	ADP
cana-857	55	8	all	all	DET
cana-857	55	9	the	the	DET
cana-857	55	10	attributes	attribute	NOUN
cana-857	55	11	.	.	PUNCT
cana-857	56	1	3	3	X
cana-857	56	2	.	.	X
cana-857	56	3	select	select	VERB
cana-857	56	4	the	the	DET
cana-857	56	5	attributes	attribute	NOUN
cana-857	56	6	which	which	PRON
cana-857	56	7	are	be	AUX
cana-857	56	8	highly	highly	ADV
cana-857	56	9	correlated	correlate	VERB
cana-857	56	10	by	by	ADP
cana-857	56	11	ranking	rank	VERB
cana-857	56	12	them	they	PRON
cana-857	56	13	and	and	CCONJ
cana-857	56	14	filtering	filter	VERB
cana-857	56	15	the	the	DET
cana-857	56	16	attributes	attribute	NOUN
cana-857	56	17	above	above	ADP
cana-857	56	18	the	the	DET
cana-857	56	19	threshold	threshold	NOUN
cana-857	56	20	.	.	PUNCT
cana-857	57	1	4	4	X
cana-857	57	2	.	.	X
cana-857	57	3	the	the	DET
cana-857	57	4	selected	select	VERB
cana-857	57	5	attributes	attribute	NOUN
cana-857	57	6	are	be	AUX
cana-857	57	7	passed	pass	VERB
cana-857	57	8	into	into	ADP
cana-857	57	9	rfe	rfe	PROPN
cana-857	57	10	algorithm	algorithm	NOUN
cana-857	57	11	which	which	PRON
cana-857	57	12	further	far	ADV
cana-857	57	13	eliminates	eliminate	VERB
cana-857	57	14	the	the	DET
cana-857	57	15	undesired	undesired	ADJ
cana-857	57	16	attributes	attribute	NOUN
cana-857	57	17	and	and	CCONJ
cana-857	57	18	iterates	iterate	NOUN
cana-857	57	19	until	until	SCONJ
cana-857	57	20	the	the	DET
cana-857	57	21	desired	desire	VERB
cana-857	57	22	attributes	attribute	NOUN
cana-857	57	23	are	be	AUX
cana-857	57	24	reached	reach	VERB
cana-857	57	25	.	.	PUNCT
cana-857	58	1	the	the	DET
cana-857	58	2	opt_recur	opt_recur	PROPN
cana-857	58	3	algorithm	algorithm	NOUN
cana-857	58	4	selects	select	VERB
cana-857	58	5	optimal	optimal	ADJ
cana-857	58	6	features	feature	NOUN
cana-857	58	7	which	which	PRON
cana-857	58	8	are	be	AUX
cana-857	58	9	need	need	NOUN
cana-857	58	10	,	,	PUNCT
cana-857	58	11	it	it	PRON
cana-857	58	12	ranks	rank	VERB
cana-857	58	13	all	all	DET
cana-857	58	14	the	the	DET
cana-857	58	15	attributes	attribute	NOUN
cana-857	58	16	according	accord	VERB
cana-857	58	17	to	to	ADP
cana-857	58	18	the	the	DET
cana-857	58	19	scores	score	NOUN
cana-857	58	20	of	of	ADP
cana-857	58	21	the	the	DET
cana-857	58	22	correlation	correlation	NOUN
cana-857	58	23	coefficient	coefficient	NOUN
cana-857	58	24	and	and	CCONJ
cana-857	58	25	the	the	DET
cana-857	58	26	heat	heat	NOUN
cana-857	58	27	map	map	NOUN
cana-857	58	28	which	which	PRON
cana-857	58	29	visualizes	visualize	VERB
cana-857	58	30	the	the	DET
cana-857	58	31	correlation	correlation	NOUN
cana-857	58	32	scores	score	NOUN
cana-857	58	33	of	of	ADP
cana-857	58	34	these	these	DET
cana-857	58	35	variables	variable	NOUN
cana-857	58	36	is	be	AUX
cana-857	58	37	shown	show	VERB
cana-857	58	38	in	in	ADP
cana-857	58	39	fig	fig	NOUN
cana-857	58	40	1	1	NUM
cana-857	58	41	.	.	PUNCT
cana-857	59	1	the	the	DET
cana-857	59	2	pearson	pearson	PROPN
cana-857	59	3	correlation	correlation	NOUN
cana-857	59	4	coefficient	coefficient	NOUN
cana-857	59	5	is	be	AUX
cana-857	59	6	used	use	VERB
cana-857	59	7	for	for	ADP
cana-857	59	8	correlation	correlation	NOUN
cana-857	59	9	analysis	analysis	NOUN
cana-857	59	10	in	in	ADP
cana-857	59	11	appropriate	appropriate	ADJ
cana-857	59	12	feature	feature	NOUN
cana-857	59	13	selection	selection	NOUN
cana-857	59	14	.	.	PUNCT
cana-857	60	1	values	value	NOUN
cana-857	60	2	range	range	VERB
cana-857	60	3	from	from	ADP
cana-857	60	4	-1	-1	INTJ
cana-857	60	5	to	to	ADP
cana-857	60	6	1	1	NUM
cana-857	60	7	,	,	PUNCT
cana-857	60	8	and	and	CCONJ
cana-857	60	9	it	it	PRON
cana-857	60	10	can	can	AUX
cana-857	60	11	be	be	AUX
cana-857	60	12	used	use	VERB
cana-857	60	13	to	to	PART
cana-857	60	14	calculate	calculate	VERB
cana-857	60	15	the	the	DET
cana-857	60	16	correlation	correlation	NOUN
cana-857	60	17	between	between	ADP
cana-857	60	18	two	two	NUM
cana-857	60	19	variables	variable	NOUN
cana-857	60	20	.	.	PUNCT
cana-857	61	1	features	feature	NOUN
cana-857	61	2	are	be	AUX
cana-857	61	3	generally	generally	ADV
cana-857	61	4	regarded	regard	VERB
cana-857	61	5	as	as	ADP
cana-857	61	6	associated	associate	VERB
cana-857	61	7	if	if	SCONJ
cana-857	61	8	their	their	PRON
cana-857	61	9	pearson	pearson	NOUN
cana-857	61	10	correlation	correlation	NOUN
cana-857	61	11	coefficient	coefficient	NOUN
cana-857	61	12	is	be	AUX
cana-857	61	13	higher	high	ADJ
cana-857	61	14	than	than	ADP
cana-857	61	15	0.3	0.3	NUM
cana-857	61	16	.	.	PUNCT
cana-857	62	1	[	[	X
cana-857	62	2	19	19	NUM
cana-857	62	3	]	]	PUNCT
cana-857	62	4	.	.	PUNCT
cana-857	63	1	in	in	ADP
cana-857	63	2	this	this	DET
cana-857	63	3	study	study	NOUN
cana-857	63	4	those	those	DET
cana-857	63	5	features	feature	NOUN
cana-857	63	6	with	with	ADP
cana-857	63	7	the	the	DET
cana-857	63	8	correlation	correlation	NOUN
cana-857	63	9	coefficient	coefficient	NOUN
cana-857	63	10	less	less	ADJ
cana-857	63	11	than	than	ADP
cana-857	63	12	0.3	0.3	NUM
cana-857	63	13	,	,	PUNCT
cana-857	63	14	have	have	AUX
cana-857	63	15	been	be	AUX
cana-857	63	16	filtered	filter	VERB
cana-857	63	17	out	out	ADP
cana-857	63	18	.those	.those	DET
cana-857	63	19	attributes	attribute	NOUN
cana-857	63	20	which	which	PRON
cana-857	63	21	are	be	AUX
cana-857	63	22	above	above	ADP
cana-857	63	23	the	the	DET
cana-857	63	24	threshold	threshold	NOUN
cana-857	63	25	is	be	AUX
cana-857	63	26	further	far	ADV
cana-857	63	27	passed	pass	VERB
cana-857	63	28	on	on	ADP
cana-857	63	29	to	to	ADP
cana-857	63	30	the	the	DET
cana-857	63	31	recursive	recursive	ADJ
cana-857	63	32	feature	feature	NOUN
cana-857	63	33	elimination	elimination	NOUN
cana-857	63	34	algorithm	algorithm	NOUN
cana-857	63	35	which	which	PRON
cana-857	63	36	selects	select	VERB
cana-857	63	37	the	the	DET
cana-857	63	38	optimal	optimal	ADJ
cana-857	63	39	features	feature	NOUN
cana-857	63	40	which	which	PRON
cana-857	63	41	are	be	AUX
cana-857	63	42	needed	need	VERB
cana-857	63	43	for	for	ADP
cana-857	63	44	making	make	VERB
cana-857	63	45	accurate	accurate	ADJ
cana-857	63	46	prediction	prediction	NOUN
cana-857	63	47	.	.	PUNCT
cana-857	64	1	fig	fig	NOUN
cana-857	64	2	1	1	NUM
cana-857	64	3	.	.	PUNCT
cana-857	65	1	heat	heat	NOUN
cana-857	65	2	map	map	NOUN
cana-857	65	3	of	of	ADP
cana-857	65	4	the	the	DET
cana-857	65	5	ranked	rank	VERB
cana-857	65	6	attributes	attribute	NOUN
cana-857	65	7	for	for	ADP
cana-857	65	8	feature	feature	NOUN
cana-857	65	9	selection	selection	NOUN
cana-857	65	10	communications	communication	NOUN
cana-857	65	11	on	on	ADP
cana-857	65	12	applied	apply	VERB
cana-857	65	13	nonlinear	nonlinear	ADJ
cana-857	65	14	analysis	analysis	NOUN
cana-857	65	15	issn	issn	NOUN
cana-857	65	16	:	:	PUNCT
cana-857	65	17	1074	1074	NUM
cana-857	65	18	-	-	PUNCT
cana-857	65	19	133x	133x	NUM
cana-857	65	20	vol	vol	NOUN
cana-857	65	21	31	31	NUM
cana-857	65	22	no	no	NOUN
cana-857	65	23	.	.	PUNCT
cana-857	66	1	4s	4s	NUM
cana-857	66	2	(	(	PUNCT
cana-857	66	3	2024	2024	NUM
cana-857	66	4	)	)	PUNCT
cana-857	66	5	353	353	NUM
cana-857	66	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	66	7	hybrid	hybrid	PROPN
cana-857	66	8	sdr	sdr	PROPN
cana-857	66	9	model	model	NOUN
cana-857	66	10	after	after	SCONJ
cana-857	66	11	the	the	DET
cana-857	66	12	optimal	optimal	ADJ
cana-857	66	13	features	feature	NOUN
cana-857	66	14	are	be	AUX
cana-857	66	15	selected	select	VERB
cana-857	66	16	the	the	DET
cana-857	66	17	dataset	dataset	NOUN
cana-857	66	18	is	be	AUX
cana-857	66	19	split	split	VERB
cana-857	66	20	into	into	ADP
cana-857	66	21	testing	testing	NOUN
cana-857	66	22	data	datum	NOUN
cana-857	66	23	and	and	CCONJ
cana-857	66	24	the	the	DET
cana-857	66	25	training	training	NOUN
cana-857	66	26	data	datum	NOUN
cana-857	66	27	.	.	PUNCT
cana-857	67	1	the	the	DET
cana-857	67	2	base	base	NOUN
cana-857	67	3	model	model	NOUN
cana-857	67	4	is	be	AUX
cana-857	67	5	chosen	choose	VERB
cana-857	67	6	which	which	PRON
cana-857	67	7	makes	make	VERB
cana-857	67	8	accurate	accurate	ADJ
cana-857	67	9	prediction	prediction	NOUN
cana-857	67	10	on	on	ADP
cana-857	67	11	the	the	DET
cana-857	67	12	test	test	NOUN
cana-857	67	13	and	and	CCONJ
cana-857	67	14	train	train	NOUN
cana-857	67	15	data	datum	NOUN
cana-857	67	16	.	.	PUNCT
cana-857	68	1	in	in	ADP
cana-857	68	2	the	the	DET
cana-857	68	3	proposed	propose	VERB
cana-857	68	4	sdr	sdr	PROPN
cana-857	68	5	hybrid	hybrid	NOUN
cana-857	68	6	model	model	NOUN
cana-857	68	7	we	we	PRON
cana-857	68	8	use	use	VERB
cana-857	68	9	have	have	AUX
cana-857	68	10	used	use	VERB
cana-857	68	11	support	support	NOUN
cana-857	68	12	vector	vector	NOUN
cana-857	68	13	machine	machine	NOUN
cana-857	68	14	,	,	PUNCT
cana-857	68	15	decision	decision	NOUN
cana-857	68	16	tree	tree	NOUN
cana-857	68	17	and	and	CCONJ
cana-857	68	18	random	random	ADJ
cana-857	68	19	forest	forest	NOUN
cana-857	68	20	classifiers	classifier	NOUN
cana-857	68	21	as	as	ADP
cana-857	68	22	the	the	DET
cana-857	68	23	base	base	NOUN
cana-857	68	24	model	model	NOUN
cana-857	68	25	,	,	PUNCT
cana-857	68	26	each	each	PRON
cana-857	68	27	of	of	ADP
cana-857	68	28	these	these	DET
cana-857	68	29	base	base	NOUN
cana-857	68	30	model	model	NOUN
cana-857	68	31	make	make	VERB
cana-857	68	32	accurate	accurate	ADJ
cana-857	68	33	prediction	prediction	NOUN
cana-857	68	34	on	on	ADP
cana-857	68	35	data	datum	NOUN
cana-857	68	36	.	.	PUNCT
cana-857	69	1	the	the	DET
cana-857	69	2	predictions	prediction	NOUN
cana-857	69	3	which	which	PRON
cana-857	69	4	have	have	AUX
cana-857	69	5	been	be	AUX
cana-857	69	6	made	make	VERB
cana-857	69	7	are	be	AUX
cana-857	69	8	fed	feed	VERB
cana-857	69	9	into	into	ADP
cana-857	69	10	the	the	DET
cana-857	69	11	meta	meta	ADJ
cana-857	69	12	model	model	NOUN
cana-857	69	13	.	.	PUNCT
cana-857	70	1	the	the	DET
cana-857	70	2	meta	meta	PROPN
cana-857	70	3	model	model	NOUN
cana-857	70	4	is	be	AUX
cana-857	70	5	the	the	DET
cana-857	70	6	logistic	logistic	ADJ
cana-857	70	7	regression	regression	NOUN
cana-857	70	8	which	which	PRON
cana-857	70	9	combines	combine	VERB
cana-857	70	10	all	all	DET
cana-857	70	11	the	the	DET
cana-857	70	12	predictions	prediction	NOUN
cana-857	70	13	made	make	VERB
cana-857	70	14	from	from	ADP
cana-857	70	15	traditional	traditional	ADJ
cana-857	70	16	classifiers	classifier	NOUN
cana-857	70	17	.	.	PUNCT
cana-857	71	1	the	the	DET
cana-857	71	2	architecture	architecture	NOUN
cana-857	71	3	of	of	ADP
cana-857	71	4	the	the	DET
cana-857	71	5	proposed	propose	VERB
cana-857	71	6	model	model	NOUN
cana-857	71	7	is	be	AUX
cana-857	71	8	shown	show	VERB
cana-857	71	9	.	.	PUNCT
cana-857	72	1	the	the	DET
cana-857	72	2	sdr	sdr	PROPN
cana-857	72	3	hybrid	hybrid	NOUN
cana-857	72	4	model	model	NOUN
cana-857	72	5	can	can	AUX
cana-857	72	6	be	be	AUX
cana-857	72	7	represented	represent	VERB
cana-857	72	8	mathematically	mathematically	ADV
cana-857	72	9	as	as	ADP
cana-857	72	10	y_hybrid	y_hybrid	PROPN
cana-857	72	11	.	.	PUNCT
cana-857	73	1	let	let	VERB
cana-857	73	2	(	(	PUNCT
cana-857	73	3	y1,y2,y3	y1,y2,y3	NOUN
cana-857	73	4	…	…	PUNCT
cana-857	73	5	.yn	.yn	NUM
cana-857	73	6	)	)	PUNCT
cana-857	73	7	be	be	AUX
cana-857	73	8	represented	represent	VERB
cana-857	73	9	as	as	ADP
cana-857	73	10	the	the	DET
cana-857	73	11	predictions	prediction	NOUN
cana-857	73	12	of	of	ADP
cana-857	73	13	the	the	DET
cana-857	73	14	base	base	NOUN
cana-857	73	15	model	model	NOUN
cana-857	73	16	.	.	PUNCT
cana-857	74	1	y1	y1	NOUN
cana-857	74	2	represents	represent	VERB
cana-857	74	3	prediction	prediction	NOUN
cana-857	74	4	of	of	ADP
cana-857	74	5	support	support	NOUN
cana-857	74	6	vector	vector	NOUN
cana-857	74	7	machine	machine	NOUN
cana-857	74	8	,	,	PUNCT
cana-857	74	9	y2	y2	PROPN
cana-857	74	10	represents	represent	VERB
cana-857	74	11	prediction	prediction	NOUN
cana-857	74	12	of	of	ADP
cana-857	74	13	decision	decision	NOUN
cana-857	74	14	tree	tree	NOUN
cana-857	74	15	,	,	PUNCT
cana-857	74	16	y3	y3	NOUN
cana-857	74	17	represents	represent	VERB
cana-857	74	18	prediction	prediction	NOUN
cana-857	74	19	of	of	ADP
cana-857	74	20	random	random	ADJ
cana-857	74	21	forest	forest	NOUN
cana-857	74	22	are	be	AUX
cana-857	74	23	the	the	DET
cana-857	74	24	base	base	NOUN
cana-857	74	25	models	model	NOUN
cana-857	74	26	.	.	PUNCT
cana-857	75	1	the	the	DET
cana-857	75	2	meta	meta	PROPN
cana-857	75	3	model	model	NOUN
cana-857	75	4	which	which	PRON
cana-857	75	5	is	be	AUX
cana-857	75	6	the	the	DET
cana-857	75	7	logistic	logistic	ADJ
cana-857	75	8	regression	regression	NOUN
cana-857	75	9	which	which	PRON
cana-857	75	10	combines	combine	VERB
cana-857	75	11	all	all	DET
cana-857	75	12	the	the	DET
cana-857	75	13	base	base	NOUN
cana-857	75	14	models	model	NOUN
cana-857	75	15	are	be	AUX
cana-857	75	16	represented	represent	VERB
cana-857	75	17	as	as	ADP
cana-857	75	18	y_meta	y_meta	NOUN
cana-857	75	19	.	.	PUNCT
cana-857	76	1	y_hybrid=	y_hybrid=	X
cana-857	76	2	y_meta(y1,y2,y3	y_meta(y1,y2,y3	PROPN
cana-857	76	3	…	…	PUNCT
cana-857	76	4	.yn	.yn	NUM
cana-857	76	5	)	)	PUNCT
cana-857	76	6	(	(	PUNCT
cana-857	76	7	1	1	X
cana-857	76	8	)	)	PUNCT
cana-857	76	9	the	the	DET
cana-857	76	10	proposed	propose	VERB
cana-857	76	11	sdr	sdr	PROPN
cana-857	76	12	hybrid	hybrid	NOUN
cana-857	76	13	model	model	NOUN
cana-857	76	14	are	be	AUX
cana-857	76	15	finally	finally	ADV
cana-857	76	16	compared	compare	VERB
cana-857	76	17	with	with	ADP
cana-857	76	18	other	other	ADJ
cana-857	76	19	conventional	conventional	ADJ
cana-857	76	20	classifiers	classifier	NOUN
cana-857	76	21	like	like	VERB
cana-857	76	22	as	as	ADP
cana-857	76	23	support	support	NOUN
cana-857	76	24	vector	vector	NOUN
cana-857	76	25	machine	machine	NOUN
cana-857	76	26	,	,	PUNCT
cana-857	76	27	decision	decision	NOUN
cana-857	76	28	tree	tree	NOUN
cana-857	76	29	and	and	CCONJ
cana-857	76	30	random	random	ADJ
cana-857	76	31	forest	forest	NOUN
cana-857	76	32	in	in	ADP
cana-857	76	33	terms	term	NOUN
cana-857	76	34	of	of	ADP
cana-857	76	35	accuracy	accuracy	NOUN
cana-857	76	36	rate	rate	NOUN
cana-857	76	37	,	,	PUNCT
cana-857	76	38	precision	precision	NOUN
cana-857	76	39	rate	rate	NOUN
cana-857	76	40	,	,	PUNCT
cana-857	76	41	recall	recall	NOUN
cana-857	76	42	rate	rate	NOUN
cana-857	76	43	and	and	CCONJ
cana-857	76	44	f1	f1	NOUN
cana-857	76	45	-	-	PUNCT
cana-857	76	46	score	score	NOUN
cana-857	76	47	.	.	PUNCT
cana-857	77	1	the	the	DET
cana-857	77	2	roc	roc	PROPN
cana-857	77	3	curve	curve	NOUN
cana-857	77	4	which	which	PRON
cana-857	77	5	makes	make	VERB
cana-857	77	6	additional	additional	ADJ
cana-857	77	7	measurement	measurement	NOUN
cana-857	77	8	of	of	ADP
cana-857	77	9	proposed	propose	VERB
cana-857	77	10	model	model	NOUN
cana-857	77	11	.	.	PUNCT
cana-857	78	1	fig	fig	NOUN
cana-857	78	2	2	2	NUM
cana-857	78	3	.	.	PUNCT
cana-857	79	1	the	the	DET
cana-857	79	2	proposed	propose	VERB
cana-857	79	3	architecture	architecture	NOUN
cana-857	79	4	of	of	ADP
cana-857	79	5	the	the	DET
cana-857	79	6	hybrid	hybrid	ADJ
cana-857	79	7	model	model	NOUN
cana-857	79	8	communications	communication	NOUN
cana-857	79	9	on	on	ADP
cana-857	79	10	applied	apply	VERB
cana-857	79	11	nonlinear	nonlinear	ADJ
cana-857	79	12	analysis	analysis	NOUN
cana-857	79	13	issn	issn	NOUN
cana-857	79	14	:	:	PUNCT
cana-857	79	15	1074	1074	NUM
cana-857	79	16	-	-	PUNCT
cana-857	79	17	133x	133x	NUM
cana-857	79	18	vol	vol	NOUN
cana-857	79	19	31	31	NUM
cana-857	79	20	no	no	NOUN
cana-857	79	21	.	.	PUNCT
cana-857	80	1	4s	4s	NUM
cana-857	80	2	(	(	PUNCT
cana-857	80	3	2024	2024	NUM
cana-857	80	4	)	)	PUNCT
cana-857	80	5	354	354	NUM
cana-857	80	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-857	80	7	4	4	NUM
cana-857	80	8	.	.	NOUN
cana-857	80	9	results	result	NOUN
cana-857	80	10	and	and	CCONJ
cana-857	80	11	discussions	discussion	NOUN
cana-857	80	12	in	in	ADP
cana-857	80	13	this	this	DET
cana-857	80	14	proposed	propose	VERB
cana-857	80	15	study	study	NOUN
cana-857	80	16	,	,	PUNCT
cana-857	80	17	the	the	DET
cana-857	80	18	data	datum	NOUN
cana-857	80	19	set	set	VERB
cana-857	80	20	is	be	AUX
cana-857	80	21	first	first	ADV
cana-857	80	22	preprocessed	preprocesse	VERB
cana-857	80	23	then	then	ADV
cana-857	80	24	the	the	DET
cana-857	80	25	dengue	dengue	NOUN
cana-857	80	26	dataset	dataset	NOUN
cana-857	80	27	is	be	AUX
cana-857	80	28	validated	validate	VERB
cana-857	80	29	by	by	ADP
cana-857	80	30	splitting	split	VERB
cana-857	80	31	the	the	DET
cana-857	80	32	input	input	NOUN
cana-857	80	33	data	datum	NOUN
cana-857	80	34	set	set	VERB
cana-857	80	35	into	into	ADP
cana-857	80	36	test	test	NOUN
cana-857	80	37	and	and	CCONJ
cana-857	80	38	train	train	NOUN
cana-857	80	39	data	datum	NOUN
cana-857	80	40	.	.	PUNCT
cana-857	81	1	before	before	ADP
cana-857	81	2	applying	apply	VERB
cana-857	81	3	the	the	DET
cana-857	81	4	any	any	DET
cana-857	81	5	feature	feature	NOUN
cana-857	81	6	selection	selection	NOUN
cana-857	81	7	techniques	technique	NOUN
cana-857	81	8	the	the	DET
cana-857	81	9	dengue	dengue	NOUN
cana-857	81	10	data	datum	NOUN
cana-857	81	11	set	set	VERB
cana-857	81	12	is	be	AUX
cana-857	81	13	used	use	VERB
cana-857	81	14	to	to	PART
cana-857	81	15	make	make	VERB
cana-857	81	16	prediction	prediction	NOUN
cana-857	81	17	using	use	VERB
cana-857	81	18	conventional	conventional	ADJ
cana-857	81	19	classifiers	classifier	NOUN
cana-857	81	20	such	such	ADJ
cana-857	81	21	as	as	ADP
cana-857	81	22	svm	svm	NOUN
cana-857	81	23	,	,	PUNCT
cana-857	81	24	dt	dt	PUNCT
cana-857	82	1	and	and	CCONJ
cana-857	82	2	rf	rf	NOUN
cana-857	82	3	.	.	PUNCT
cana-857	83	1	the	the	DET
cana-857	83	2	measurement	measurement	NOUN
cana-857	83	3	of	of	ADP
cana-857	83	4	the	the	DET
cana-857	83	5	prediction	prediction	NOUN
cana-857	83	6	is	be	AUX
cana-857	83	7	made	make	VERB
cana-857	83	8	in	in	ADP
cana-857	83	9	terms	term	NOUN
cana-857	83	10	of	of	ADP
cana-857	83	11	the	the	DET
cana-857	83	12	accuracy	accuracy	NOUN
cana-857	83	13	rate	rate	NOUN
cana-857	83	14	,	,	PUNCT
cana-857	83	15	precision	precision	NOUN
cana-857	83	16	rate	rate	NOUN
cana-857	83	17	,	,	PUNCT
cana-857	83	18	recall	recall	NOUN
cana-857	83	19	rate	rate	NOUN
cana-857	83	20	and	and	CCONJ
cana-857	83	21	f1	f1	NOUN
cana-857	83	22	-	-	PUNCT
cana-857	83	23	score	score	NOUN
cana-857	83	24	.	.	PUNCT
cana-857	84	1	with	with	ADP
cana-857	84	2	the	the	DET
cana-857	84	3	exception	exception	NOUN
cana-857	84	4	of	of	ADP
cana-857	84	5	time	time	NOUN
cana-857	84	6	,	,	PUNCT
cana-857	84	7	the	the	DET
cana-857	84	8	confusion	confusion	NOUN
cana-857	84	9	matrix	matrix	NOUN
cana-857	84	10	aids	aid	NOUN
cana-857	84	11	in	in	ADP
cana-857	84	12	the	the	DET
cana-857	84	13	calculation	calculation	NOUN
cana-857	84	14	of	of	ADP
cana-857	84	15	all	all	DET
cana-857	84	16	metrics	metric	NOUN
cana-857	84	17	.	.	PUNCT
cana-857	85	1	the	the	DET
cana-857	85	2	components	component	NOUN
cana-857	85	3	of	of	ADP
cana-857	85	4	the	the	DET
cana-857	85	5	confusion	confusion	NOUN
cana-857	85	6	matrix	matrix	NOUN
cana-857	85	7	are	be	AUX
cana-857	85	8	false	false	ADV
cana-857	85	9	positive	positive	ADJ
cana-857	85	10	(	(	PUNCT
cana-857	85	11	fp	fp	NOUN
cana-857	85	12	)	)	PUNCT
cana-857	85	13	,	,	PUNCT
cana-857	85	14	false	false	ADJ
cana-857	85	15	negative	negative	ADJ
cana-857	85	16	(	(	PUNCT
cana-857	85	17	tn	tn	NOUN
cana-857	85	18	)	)	PUNCT
cana-857	85	19	,	,	PUNCT
cana-857	85	20	false	false	ADJ
cana-857	85	21	positive	positive	ADJ
cana-857	85	22	(	(	PUNCT
cana-857	85	23	tp	tp	NOUN
cana-857	85	24	)	)	PUNCT
cana-857	85	25	,	,	PUNCT
cana-857	85	26	and	and	CCONJ
cana-857	85	27	false	false	ADJ
cana-857	85	28	-	-	PUNCT
cana-857	85	29	negative	negative	ADJ
cana-857	85	30	(	(	PUNCT
cana-857	85	31	fn	fn	NOUN
cana-857	85	32	)	)	PUNCT
cana-857	85	33	.	.	PUNCT
cana-857	86	1	the	the	DET
cana-857	86	2	most	most	ADV
cana-857	86	3	significant	significant	ADJ
cana-857	86	4	prediction	prediction	NOUN
cana-857	86	5	in	in	ADP
cana-857	86	6	health	health	NOUN
cana-857	86	7	care	care	NOUN
cana-857	86	8	statistics	statistic	NOUN
cana-857	86	9	is	be	AUX
cana-857	86	10	a	a	DET
cana-857	86	11	false	false	ADJ
cana-857	86	12	negative	negative	ADJ
cana-857	86	13	[	[	X
cana-857	86	14	6	6	NUM
cana-857	86	15	]	]	PUNCT
cana-857	86	16	.	.	PUNCT
cana-857	87	1	the	the	DET
cana-857	87	2	performance	performance	NOUN
cana-857	87	3	measures	measure	NOUN
cana-857	87	4	that	that	PRON
cana-857	87	5	are	be	AUX
cana-857	87	6	used	use	VERB
cana-857	87	7	to	to	PART
cana-857	87	8	evaluate	evaluate	VERB
cana-857	87	9	can	can	AUX
cana-857	87	10	be	be	AUX
cana-857	87	11	represented	represent	VERB
cana-857	87	12	mathematically	mathematically	ADV
cana-857	87	13	.	.	PUNCT
cana-857	88	1	accuracy	accuracy	NOUN
cana-857	88	2	=	=	PRON
cana-857	88	3	{	{	PUNCT
cana-857	88	4	(	(	PUNCT
cana-857	88	5	𝑇𝑃	𝑇𝑃	PROPN
cana-857	88	6	+	+	CCONJ
cana-857	88	7	𝑇𝑁	𝑇𝑁	PROPN
cana-857	88	8	)	)	PUNCT
cana-857	88	9	(	(	PUNCT
cana-857	88	10	tp+fp+tn+fn	tp+fp+tn+fn	NOUN
cana-857	88	11	)	)	PUNCT
cana-857	88	12	⁄	⁄	ADJ
cana-857	88	13	}	}	PUNCT
cana-857	88	14	𝑋	𝑋	PROPN
cana-857	88	15	100	100	NUM
cana-857	88	16	(	(	PUNCT
cana-857	88	17	2	2	NUM
cana-857	88	18	)	)	PUNCT
cana-857	88	19	precision	precision	NOUN
cana-857	88	20	=	=	SYM
cana-857	88	21	{	{	PUNCT
cana-857	88	22	𝑇𝑃	𝑇𝑃	PROPN
cana-857	88	23	(	(	PUNCT
cana-857	88	24	tp+fp	tp+fp	NOUN
cana-857	88	25	)	)	PUNCT
cana-857	88	26	⁄	⁄	ADJ
cana-857	88	27	}	}	PUNCT
cana-857	88	28	𝑋	𝑋	PROPN
cana-857	88	29	100	100	NUM
cana-857	88	30	(	(	PUNCT
cana-857	88	31	3	3	NUM
cana-857	88	32	)	)	PUNCT
cana-857	88	33	recall	recall	NOUN
cana-857	88	34	=	=	SYM
cana-857	88	35	{	{	PUNCT
cana-857	88	36	𝑇𝑃	𝑇𝑃	PROPN
cana-857	88	37	(	(	PUNCT
cana-857	88	38	tp+fp	tp+fp	NOUN
cana-857	88	39	)	)	PUNCT
cana-857	88	40	⁄	⁄	ADJ
cana-857	88	41	}	}	PUNCT
cana-857	88	42	𝑋	𝑋	PROPN
cana-857	88	43	100	100	NUM
cana-857	88	44	(	(	PUNCT
cana-857	88	45	4	4	NUM
cana-857	88	46	)	)	PUNCT
cana-857	88	47	f1	f1	NOUN
cana-857	88	48	score	score	NOUN
cana-857	88	49	=	=	PUNCT
cana-857	88	50	{	{	PUNCT
cana-857	88	51	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-857	88	52	𝑥	𝑥	PROPN
cana-857	88	53	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-857	88	54	(	(	PUNCT
cana-857	88	55	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-857	88	56	+	+	CCONJ
cana-857	88	57	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-857	88	58	⁄	⁄	PROPN
cana-857	88	59	}	}	PUNCT
cana-857	88	60	𝑋	𝑋	PROPN
cana-857	88	61	100	100	NUM
cana-857	88	62	(	(	PUNCT
cana-857	88	63	5	5	NUM
cana-857	88	64	)	)	PUNCT
cana-857	88	65	the	the	DET
cana-857	88	66	performance	performance	NOUN
cana-857	88	67	evaluation	evaluation	NOUN
cana-857	88	68	of	of	ADP
cana-857	88	69	the	the	DET
cana-857	88	70	conventional	conventional	ADJ
cana-857	88	71	classifiers	classifier	NOUN
cana-857	88	72	like	like	ADP
cana-857	88	73	the	the	DET
cana-857	88	74	svm	svm	NOUN
cana-857	88	75	,	,	PUNCT
cana-857	88	76	decision	decision	NOUN
cana-857	88	77	tree	tree	NOUN
cana-857	88	78	and	and	CCONJ
cana-857	88	79	the	the	DET
cana-857	88	80	random	random	ADJ
cana-857	88	81	forest	forest	NOUN
cana-857	88	82	,	,	PUNCT
cana-857	88	83	then	then	ADV
cana-857	88	84	in	in	ADP
cana-857	88	85	the	the	DET
cana-857	88	86	hybrid	hybrid	ADJ
cana-857	88	87	sdr	sdr	PROPN
cana-857	88	88	model	model	NOUN
cana-857	88	89	without	without	ADP
cana-857	88	90	applying	apply	VERB
cana-857	88	91	feature	feature	NOUN
cana-857	88	92	selection	selection	NOUN
cana-857	88	93	opt_recur	opt_recur	NOUN
cana-857	88	94	algorithm	algorithm	PROPN
cana-857	88	95	is	be	AUX
cana-857	88	96	shown	show	VERB
cana-857	88	97	in	in	ADP
cana-857	88	98	table	table	NOUN
cana-857	88	99	1	1	NUM
cana-857	88	100	.	.	PUNCT
cana-857	89	1	the	the	DET
cana-857	89	2	proposed	propose	VERB
cana-857	89	3	opt_recur	opt_recur	NOUN
cana-857	89	4	feature	feature	NOUN
cana-857	89	5	selection	selection	NOUN
cana-857	89	6	approach	approach	NOUN
cana-857	89	7	is	be	AUX
cana-857	89	8	applied	apply	VERB
cana-857	89	9	to	to	ADP
cana-857	89	10	standard	standard	ADJ
cana-857	89	11	classifiers	classifier	NOUN
cana-857	89	12	such	such	ADJ
cana-857	89	13	as	as	ADP
cana-857	89	14	support	support	NOUN
cana-857	89	15	vector	vector	NOUN
cana-857	89	16	machine	machine	NOUN
cana-857	89	17	(	(	PUNCT
cana-857	89	18	svm	svm	PROPN
cana-857	89	19	)	)	PUNCT
cana-857	89	20	,	,	PUNCT
cana-857	89	21	decision	decision	NOUN
cana-857	89	22	tree	tree	NOUN
cana-857	89	23	(	(	PUNCT
cana-857	89	24	dt	dt	NOUN
cana-857	89	25	)	)	PUNCT
cana-857	89	26	,	,	PUNCT
cana-857	89	27	and	and	CCONJ
cana-857	89	28	random	random	ADJ
cana-857	89	29	forest	forest	NOUN
cana-857	89	30	(	(	PUNCT
cana-857	89	31	rf	rf	NOUN
cana-857	89	32	)	)	PUNCT
cana-857	89	33	,	,	PUNCT
cana-857	89	34	and	and	CCONJ
cana-857	89	35	the	the	DET
cana-857	89	36	results	result	NOUN
cana-857	89	37	are	be	AUX
cana-857	89	38	compared	compare	VERB
cana-857	89	39	in	in	ADP
cana-857	89	40	terms	term	NOUN
cana-857	89	41	of	of	ADP
cana-857	89	42	accuracy	accuracy	NOUN
cana-857	89	43	,	,	PUNCT
cana-857	89	44	precision	precision	NOUN
cana-857	89	45	,	,	PUNCT
cana-857	89	46	recall	recall	NOUN
cana-857	89	47	,	,	PUNCT
cana-857	89	48	and	and	CCONJ
cana-857	89	49	f1	f1	PROPN
cana-857	89	50	score	score	NOUN
cana-857	89	51	.	.	PUNCT
cana-857	90	1	with	with	ADP
cana-857	90	2	the	the	DET
cana-857	90	3	proposed	propose	VERB
cana-857	90	4	hybrid	hybrid	PROPN
cana-857	90	5	sdr	sdr	PROPN
cana-857	90	6	model	model	NOUN
cana-857	90	7	after	after	SCONJ
cana-857	90	8	that	that	PRON
cana-857	90	9	is	be	AUX
cana-857	90	10	shown	show	VERB
cana-857	90	11	in	in	ADP
cana-857	90	12	table	table	NOUN
cana-857	90	13	2	2	NUM
cana-857	90	14	table	table	NOUN
cana-857	90	15	1	1	NUM
cana-857	90	16	.	.	PUNCT
cana-857	90	17	performance	performance	NOUN
cana-857	90	18	evaluation	evaluation	NOUN
cana-857	90	19	of	of	ADP
cana-857	90	20	svm	svm	PROPN
cana-857	90	21	,	,	PUNCT
cana-857	90	22	dt	dt	X
cana-857	90	23	,	,	PUNCT
cana-857	90	24	rf	rf	VERB
cana-857	90	25	without	without	ADP
cana-857	90	26	applying	apply	VERB
cana-857	90	27	opt_recur	opt_recur	ADP
cana-857	90	28	feature	feature	NOUN
cana-857	90	29	selection	selection	NOUN
cana-857	90	30	algorithm	algorithm	NOUN
cana-857	90	31	classifier	classifier	NOUN
cana-857	90	32	accuracy	accuracy	PROPN
cana-857	90	33	precision	precision	NOUN
cana-857	90	34	recall	recall	VERB
cana-857	90	35	f1	f1	NOUN
cana-857	90	36	-	-	PUNCT
cana-857	90	37	score	score	NOUN
cana-857	90	38	svm	svm	NOUN
cana-857	90	39	77	77	NUM
cana-857	90	40	%	%	NOUN
cana-857	90	41	92	92	NUM
cana-857	90	42	%	%	NOUN
cana-857	90	43	72	72	NUM
cana-857	90	44	%	%	NOUN
cana-857	90	45	81	81	NUM
cana-857	90	46	%	%	NOUN
cana-857	90	47	dt	dt	NOUN
cana-857	90	48	80	80	NUM
cana-857	90	49	%	%	NOUN
cana-857	90	50	86	86	NUM
cana-857	90	51	%	%	NOUN
cana-857	90	52	92	92	NUM
cana-857	90	53	%	%	NOUN
cana-857	90	54	89	89	NUM
cana-857	90	55	%	%	NOUN
cana-857	90	56	rf	rf	NUM
cana-857	90	57	94	94	NUM
cana-857	90	58	%	%	NOUN
cana-857	90	59	88	88	NUM
cana-857	90	60	%	%	NOUN
cana-857	90	61	85	85	NUM
cana-857	90	62	%	%	NOUN
cana-857	90	63	87	87	NUM
cana-857	90	64	%	%	NOUN
cana-857	90	65	hybrid	hybrid	ADJ
cana-857	90	66	sdr	sdr	PROPN
cana-857	90	67	96	96	NUM
cana-857	90	68	%	%	NOUN
cana-857	90	69	91	91	NUM
cana-857	90	70	%	%	NOUN
cana-857	90	71	92	92	NUM
cana-857	90	72	%	%	NOUN
cana-857	90	73	96	96	NUM
cana-857	90	74	%	%	NOUN
cana-857	90	75	table	table	NOUN
cana-857	90	76	2	2	NUM
cana-857	90	77	.	.	PUNCT
cana-857	90	78	performance	performance	NOUN
cana-857	90	79	evaluation	evaluation	NOUN
cana-857	90	80	of	of	ADP
cana-857	90	81	svm	svm	PROPN
cana-857	90	82	,	,	PUNCT
cana-857	90	83	dt	dt	X
cana-857	90	84	,	,	PUNCT
cana-857	90	85	rf	rf	VERB
cana-857	90	86	after	after	ADP
cana-857	90	87	applying	apply	VERB
cana-857	90	88	opt_recur	opt_recur	ADP
cana-857	90	89	feature	feature	NOUN
cana-857	90	90	selection	selection	NOUN
cana-857	90	91	algorithm	algorithm	NOUN
cana-857	90	92	classifier	classifier	NOUN
cana-857	90	93	accuracy	accuracy	PROPN
cana-857	90	94	precision	precision	NOUN
cana-857	90	95	recall	recall	VERB
cana-857	90	96	f1	f1	NOUN
cana-857	90	97	-	-	PUNCT
cana-857	90	98	score	score	NOUN
cana-857	90	99	svm	svm	NOUN
cana-857	90	100	85	85	NUM
cana-857	90	101	%	%	NOUN
cana-857	90	102	100	100	NUM
cana-857	90	103	%	%	NOUN
cana-857	90	104	77	77	NUM
cana-857	90	105	%	%	NOUN
cana-857	90	106	87	87	NUM
cana-857	90	107	%	%	NOUN
cana-857	90	108	dt	dt	NOUN
cana-857	90	109	93	93	NUM
cana-857	90	110	%	%	NOUN
cana-857	90	111	90	90	NUM
cana-857	90	112	100	100	NUM
cana-857	90	113	%	%	NOUN
cana-857	90	114	95	95	NUM
cana-857	90	115	%	%	NOUN
cana-857	90	116	rf	rf	NUM
cana-857	90	117	96	96	NUM
cana-857	90	118	%	%	NOUN
cana-857	90	119	96	96	NUM
cana-857	90	120	%	%	NOUN
cana-857	90	121	92	92	NUM
cana-857	90	122	%	%	NOUN
cana-857	90	123	96	96	NUM
cana-857	90	124	%	%	NOUN
cana-857	90	125	hybrid	hybrid	ADJ
cana-857	90	126	sdr	sdr	PROPN
cana-857	90	127	96	96	NUM
cana-857	90	128	%	%	NOUN
cana-857	90	129	100	100	NUM
cana-857	90	130	%	%	NOUN
cana-857	90	131	93	93	NUM
cana-857	90	132	%	%	NOUN
cana-857	90	133	97	97	NUM
cana-857	90	134	%	%	NOUN
cana-857	90	135	communications	communication	NOUN
cana-857	90	136	on	on	ADP
cana-857	90	137	applied	apply	VERB
cana-857	90	138	nonlinear	nonlinear	ADJ
cana-857	90	139	analysis	analysis	NOUN
cana-857	90	140	issn	issn	NOUN
cana-857	90	141	:	:	PUNCT
cana-857	90	142	1074	1074	NUM
cana-857	90	143	-	-	PUNCT
cana-857	90	144	133x	133x	NUM
cana-857	90	145	vol	vol	NOUN
cana-857	90	146	31	31	NUM
cana-857	90	147	no	no	NOUN
cana-857	90	148	.	.	PUNCT
cana-857	91	1	4s	4s	NUM
cana-857	91	2	(	(	PUNCT
cana-857	91	3	2024	2024	NUM
cana-857	91	4	)	)	PUNCT
cana-857	91	5	355	355	NUM
cana-857	91	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	91	7	the	the	DET
cana-857	91	8	proposed	propose	VERB
cana-857	91	9	study	study	NOUN
cana-857	91	10	depicts	depict	VERB
cana-857	91	11	that	that	SCONJ
cana-857	91	12	hybrid	hybrid	ADJ
cana-857	91	13	sdr	sdr	PROPN
cana-857	91	14	model	model	NOUN
cana-857	91	15	performs	perform	VERB
cana-857	91	16	well	well	ADV
cana-857	91	17	when	when	SCONJ
cana-857	91	18	compared	compare	VERB
cana-857	91	19	to	to	ADP
cana-857	91	20	other	other	ADJ
cana-857	91	21	conventional	conventional	ADJ
cana-857	91	22	classifiers	classifier	NOUN
cana-857	91	23	like	like	ADP
cana-857	91	24	the	the	DET
cana-857	91	25	svm	svm	NOUN
cana-857	91	26	,	,	PUNCT
cana-857	91	27	dt	dt	PUNCT
cana-857	91	28	and	and	CCONJ
cana-857	91	29	the	the	DET
cana-857	91	30	rf	rf	NOUN
cana-857	91	31	before	before	ADP
cana-857	91	32	applying	apply	VERB
cana-857	91	33	the	the	DET
cana-857	91	34	opt_recur	opt_recur	NOUN
cana-857	91	35	feature	feature	NOUN
cana-857	91	36	selection	selection	NOUN
cana-857	91	37	algorithm	algorithm	NOUN
cana-857	91	38	with	with	ADP
cana-857	91	39	an	an	DET
cana-857	91	40	accuracy	accuracy	NOUN
cana-857	91	41	rate	rate	NOUN
cana-857	91	42	of	of	ADP
cana-857	91	43	96	96	NUM
cana-857	91	44	%	%	NOUN
cana-857	91	45	,	,	PUNCT
cana-857	91	46	precision	precision	NOUN
cana-857	91	47	rate	rate	NOUN
cana-857	91	48	of	of	ADP
cana-857	91	49	91	91	NUM
cana-857	91	50	%	%	NOUN
cana-857	91	51	,	,	PUNCT
cana-857	91	52	recall	recall	NOUN
cana-857	91	53	rate	rate	NOUN
cana-857	91	54	of	of	ADP
cana-857	91	55	92	92	NUM
cana-857	91	56	%	%	NOUN
cana-857	91	57	and	and	CCONJ
cana-857	91	58	f1	f1	NOUN
cana-857	91	59	score	score	NOUN
cana-857	91	60	96	96	NUM
cana-857	91	61	%	%	NOUN
cana-857	91	62	.	.	PUNCT
cana-857	92	1	the	the	DET
cana-857	92	2	evaluation	evaluation	NOUN
cana-857	92	3	metrics	metric	NOUN
cana-857	92	4	after	after	ADP
cana-857	92	5	applying	apply	VERB
cana-857	92	6	the	the	DET
cana-857	92	7	opt_recur	opt_recur	NOUN
cana-857	92	8	feature	feature	NOUN
cana-857	92	9	selection	selection	NOUN
cana-857	92	10	algorithm	algorithm	NOUN
cana-857	92	11	on	on	ADP
cana-857	92	12	the	the	DET
cana-857	92	13	conventional	conventional	ADJ
cana-857	92	14	classifiers	classifier	NOUN
cana-857	92	15	like	like	ADP
cana-857	92	16	the	the	DET
cana-857	92	17	svm	svm	NOUN
cana-857	92	18	,	,	PUNCT
cana-857	92	19	dt	dt	PUNCT
cana-857	92	20	and	and	CCONJ
cana-857	92	21	the	the	DET
cana-857	92	22	rf	rf	NOUN
cana-857	92	23	.the	.the	DET
cana-857	92	24	hybrid	hybrid	PROPN
cana-857	92	25	sdr	sdr	PROPN
cana-857	92	26	model	model	NOUN
cana-857	92	27	showed	show	VERB
cana-857	92	28	better	well	ADJ
cana-857	92	29	performance	performance	NOUN
cana-857	92	30	than	than	ADP
cana-857	92	31	others	other	NOUN
cana-857	92	32	classifiers	classifier	NOUN
cana-857	92	33	with	with	ADP
cana-857	92	34	accuracy	accuracy	NOUN
cana-857	92	35	of	of	ADP
cana-857	92	36	96	96	NUM
cana-857	92	37	%	%	NOUN
cana-857	92	38	,	,	PUNCT
cana-857	92	39	precision	precision	NOUN
cana-857	92	40	of	of	ADP
cana-857	92	41	100	100	NUM
cana-857	92	42	%	%	NOUN
cana-857	92	43	,	,	PUNCT
cana-857	92	44	recall	recall	NOUN
cana-857	92	45	of	of	ADP
cana-857	92	46	93	93	NUM
cana-857	92	47	%	%	NOUN
cana-857	92	48	and	and	CCONJ
cana-857	92	49	f1	f1	NOUN
cana-857	92	50	score	score	NOUN
cana-857	92	51	97	97	NUM
cana-857	92	52	%	%	NOUN
cana-857	92	53	.	.	PUNCT
cana-857	93	1	fig	fig	NOUN
cana-857	93	2	3	3	NUM
cana-857	93	3	.	.	PUNCT
cana-857	94	1	roc	roc	PROPN
cana-857	94	2	curve	curve	NOUN
cana-857	94	3	for	for	ADP
cana-857	94	4	the	the	DET
cana-857	94	5	hybrid	hybrid	PROPN
cana-857	94	6	sdr	sdr	PROPN
cana-857	94	7	model	model	NOUN
cana-857	94	8	the	the	DET
cana-857	94	9	performance	performance	NOUN
cana-857	94	10	of	of	ADP
cana-857	94	11	the	the	DET
cana-857	94	12	classification	classification	NOUN
cana-857	94	13	models	model	NOUN
cana-857	94	14	is	be	AUX
cana-857	94	15	typically	typically	ADV
cana-857	94	16	estimated	estimate	VERB
cana-857	94	17	diagrammatically	diagrammatically	ADV
cana-857	94	18	using	use	VERB
cana-857	94	19	the	the	DET
cana-857	94	20	receiver	receiver	NOUN
cana-857	94	21	operating	operate	VERB
cana-857	94	22	characteristic	characteristic	NOUN
cana-857	94	23	(	(	PUNCT
cana-857	94	24	roc	roc	PROPN
cana-857	94	25	)	)	PUNCT
cana-857	94	26	curve	curve	NOUN
cana-857	94	27	,	,	PUNCT
cana-857	94	28	which	which	PRON
cana-857	94	29	spans	span	VERB
cana-857	94	30	all	all	DET
cana-857	94	31	practical	practical	ADJ
cana-857	94	32	thresholds	threshold	NOUN
cana-857	94	33	.	.	PUNCT
cana-857	95	1	by	by	ADP
cana-857	95	2	tracing	trace	VERB
cana-857	95	3	the	the	DET
cana-857	95	4	fpr	fpr	NOUN
cana-857	95	5	on	on	ADP
cana-857	95	6	the	the	DET
cana-857	95	7	x	x	NOUN
cana-857	95	8	-	-	NOUN
cana-857	95	9	axis	axis	NOUN
cana-857	95	10	against	against	ADP
cana-857	95	11	the	the	DET
cana-857	95	12	tpr	tpr	NOUN
cana-857	95	13	on	on	ADP
cana-857	95	14	the	the	DET
cana-857	95	15	y	y	NOUN
cana-857	95	16	-	-	PUNCT
cana-857	95	17	axis	axis	NOUN
cana-857	95	18	,	,	PUNCT
cana-857	95	19	a	a	DET
cana-857	95	20	roc	roc	PROPN
cana-857	95	21	curve	curve	NOUN
cana-857	95	22	is	be	AUX
cana-857	95	23	produced	produce	VERB
cana-857	95	24	.	.	PUNCT
cana-857	96	1	since	since	SCONJ
cana-857	96	2	roc	roc	PROPN
cana-857	96	3	is	be	AUX
cana-857	96	4	unbiased	unbiased	ADJ
cana-857	96	5	toward	toward	ADP
cana-857	96	6	both	both	DET
cana-857	96	7	classes	class	NOUN
cana-857	96	8	,	,	PUNCT
cana-857	96	9	it	it	PRON
cana-857	96	10	is	be	AUX
cana-857	96	11	significant	significant	ADJ
cana-857	96	12	when	when	SCONJ
cana-857	96	13	training	training	NOUN
cana-857	96	14	results	result	NOUN
cana-857	96	15	show	show	VERB
cana-857	96	16	a	a	DET
cana-857	96	17	change	change	NOUN
cana-857	96	18	in	in	ADP
cana-857	96	19	the	the	DET
cana-857	96	20	number	number	NOUN
cana-857	96	21	of	of	ADP
cana-857	96	22	instances	instance	NOUN
cana-857	96	23	of	of	ADP
cana-857	96	24	either	either	DET
cana-857	96	25	class	class	NOUN
cana-857	96	26	.	.	PUNCT
cana-857	97	1	the	the	DET
cana-857	97	2	optimal	optimal	ADJ
cana-857	97	3	classifier	classifier	NOUN
cana-857	97	4	has	have	VERB
cana-857	97	5	a	a	DET
cana-857	97	6	range	range	NOUN
cana-857	97	7	under	under	ADP
cana-857	97	8	the	the	DET
cana-857	97	9	roc	roc	NOUN
cana-857	97	10	that	that	PRON
cana-857	97	11	is	be	AUX
cana-857	97	12	near	near	ADJ
cana-857	97	13	to	to	ADP
cana-857	97	14	1	1	NUM
cana-857	97	15	.	.	PUNCT
cana-857	98	1	[	[	X
cana-857	98	2	16	16	NUM
cana-857	98	3	]	]	PUNCT
cana-857	98	4	.	.	PUNCT
cana-857	99	1	roc	roc	PROPN
cana-857	99	2	curve	curve	NOUN
cana-857	99	3	of	of	ADP
cana-857	99	4	the	the	DET
cana-857	99	5	proposed	propose	VERB
cana-857	99	6	hybrid	hybrid	PROPN
cana-857	99	7	sdr	sdr	PROPN
cana-857	99	8	model	model	NOUN
cana-857	99	9	shows	show	VERB
cana-857	99	10	the	the	DET
cana-857	99	11	value	value	NOUN
cana-857	99	12	of	of	ADP
cana-857	99	13	.99	.99	NUM
cana-857	99	14	is	be	AUX
cana-857	99	15	proved	prove	VERB
cana-857	99	16	to	to	PART
cana-857	99	17	be	be	AUX
cana-857	99	18	the	the	DET
cana-857	99	19	best	good	ADJ
cana-857	99	20	classifier	classifier	NOUN
cana-857	99	21	.	.	PUNCT
cana-857	100	1	5	5	X
cana-857	100	2	.	.	X
cana-857	100	3	conclusion	conclusion	NOUN
cana-857	100	4	dengue	dengue	NOUN
cana-857	100	5	is	be	AUX
cana-857	100	6	one	one	NUM
cana-857	100	7	deadliest	deadly	ADJ
cana-857	100	8	disease	disease	NOUN
cana-857	100	9	in	in	ADP
cana-857	100	10	the	the	DET
cana-857	100	11	world	world	NOUN
cana-857	100	12	,	,	PUNCT
cana-857	100	13	the	the	DET
cana-857	100	14	proposed	propose	VERB
cana-857	100	15	study	study	NOUN
cana-857	100	16	helps	help	VERB
cana-857	100	17	in	in	ADP
cana-857	100	18	the	the	DET
cana-857	100	19	accurate	accurate	ADJ
cana-857	100	20	prediction	prediction	NOUN
cana-857	100	21	at	at	ADP
cana-857	100	22	the	the	DET
cana-857	100	23	early	early	ADJ
cana-857	100	24	stage	stage	NOUN
cana-857	100	25	will	will	AUX
cana-857	100	26	save	save	VERB
cana-857	100	27	human	human	ADJ
cana-857	100	28	life	life	NOUN
cana-857	100	29	.	.	PUNCT
cana-857	101	1	the	the	DET
cana-857	101	2	proposed	propose	VERB
cana-857	101	3	opt_recur	opt_recur	NOUN
cana-857	101	4	algorithm	algorithm	PROPN
cana-857	101	5	is	be	AUX
cana-857	101	6	feature	feature	NOUN
cana-857	101	7	selection	selection	NOUN
cana-857	101	8	algorithm	algorithm	NOUN
cana-857	101	9	which	which	PRON
cana-857	101	10	make	make	VERB
cana-857	101	11	optimal	optimal	ADJ
cana-857	101	12	selection	selection	NOUN
cana-857	101	13	feature	feature	NOUN
cana-857	101	14	which	which	PRON
cana-857	101	15	are	be	AUX
cana-857	101	16	needed	need	VERB
cana-857	101	17	for	for	ADP
cana-857	101	18	making	make	VERB
cana-857	101	19	accurate	accurate	ADJ
cana-857	101	20	prediction	prediction	NOUN
cana-857	101	21	.	.	PUNCT
cana-857	102	1	the	the	DET
cana-857	102	2	hybrid	hybrid	PROPN
cana-857	102	3	sdr	sdr	PROPN
cana-857	102	4	algorithm	algorithm	NOUN
cana-857	102	5	which	which	PRON
cana-857	102	6	is	be	AUX
cana-857	102	7	found	find	VERB
cana-857	102	8	to	to	PART
cana-857	102	9	produce	produce	VERB
cana-857	102	10	better	well	ADJ
cana-857	102	11	efficiency	efficiency	NOUN
cana-857	102	12	even	even	ADV
cana-857	102	13	when	when	SCONJ
cana-857	102	14	compared	compare	VERB
cana-857	102	15	to	to	ADP
cana-857	102	16	traditional	traditional	ADJ
cana-857	102	17	classifiers	classifier	NOUN
cana-857	102	18	like	like	ADP
cana-857	102	19	the	the	DET
cana-857	102	20	support	support	NOUN
cana-857	102	21	vector	vector	NOUN
cana-857	102	22	machine	machine	NOUN
cana-857	102	23	(	(	PUNCT
cana-857	102	24	svm	svm	PROPN
cana-857	102	25	)	)	PUNCT
cana-857	102	26	,	,	PUNCT
cana-857	102	27	decision	decision	NOUN
cana-857	102	28	tree(dt	tree(dt	NUM
cana-857	102	29	)	)	PUNCT
cana-857	102	30	and	and	CCONJ
cana-857	102	31	the	the	DET
cana-857	102	32	random	random	ADJ
cana-857	102	33	forest(rf	forest(rf	NOUN
cana-857	102	34	)	)	PUNCT
cana-857	102	35	.	.	PUNCT
cana-857	103	1	the	the	DET
cana-857	103	2	roc	roc	PROPN
cana-857	103	3	curve	curve	NOUN
cana-857	103	4	also	also	ADV
cana-857	103	5	provided	provide	VERB
cana-857	103	6	the	the	DET
cana-857	103	7	hybrid	hybrid	NOUN
cana-857	103	8	model	model	NOUN
cana-857	103	9	had	have	AUX
cana-857	103	10	out	out	ADP
cana-857	103	11	performed	perform	VERB
cana-857	103	12	the	the	DET
cana-857	103	13	traditional	traditional	ADJ
cana-857	103	14	classifiers	classifier	NOUN
cana-857	103	15	.	.	PUNCT
cana-857	104	1	communications	communication	NOUN
cana-857	104	2	on	on	ADP
cana-857	104	3	applied	apply	VERB
cana-857	104	4	nonlinear	nonlinear	ADJ
cana-857	104	5	analysis	analysis	NOUN
cana-857	104	6	issn	issn	NOUN
cana-857	104	7	:	:	PUNCT
cana-857	104	8	1074	1074	NUM
cana-857	104	9	-	-	PUNCT
cana-857	104	10	133x	133x	NUM
cana-857	104	11	vol	vol	NOUN
cana-857	104	12	31	31	NUM
cana-857	104	13	no	no	NOUN
cana-857	104	14	.	.	PUNCT
cana-857	105	1	4s	4s	NUM
cana-857	105	2	(	(	PUNCT
cana-857	105	3	2024	2024	NUM
cana-857	105	4	)	)	PUNCT
cana-857	105	5	356	356	NUM
cana-857	105	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	105	7	references	reference	NOUN
cana-857	105	8	[	[	X
cana-857	105	9	1	1	NUM
cana-857	105	10	]	]	X
cana-857	105	11	rian	rian	PROPN
cana-857	105	12	budi	budi	PROPN
cana-857	105	13	lukmantoa	lukmantoa	PROPN
cana-857	105	14	,	,	PUNCT
cana-857	105	15	suharjitoa	suharjitoa	ADJ
cana-857	105	16	,	,	PUNCT
cana-857	105	17	ariadi	ariadi	NOUN
cana-857	105	18	nugrohoa	nugrohoa	NOUN
cana-857	105	19	,	,	PUNCT
cana-857	105	20	habibullah	habibullah	PROPN
cana-857	105	21	akbara	akbara	PROPN
cana-857	105	22	,	,	PUNCT
cana-857	105	23	‘	'	PUNCT
cana-857	105	24	early	early	ADJ
cana-857	105	25	detection	detection	NOUN
cana-857	105	26	of	of	ADP
cana-857	105	27	diabetes	diabetes	NOUN
cana-857	105	28	mellitus	mellitus	NOUN
cana-857	105	29	using	use	VERB
cana-857	105	30	feature	feature	NOUN
cana-857	105	31	selection	selection	NOUN
cana-857	105	32	and	and	CCONJ
cana-857	105	33	fuzzy	fuzzy	ADJ
cana-857	105	34	support	support	NOUN
cana-857	105	35	vector	vector	NOUN
cana-857	105	36	machine	machine	NOUN
cana-857	105	37	’	'	PUNCT
cana-857	105	38	,	,	PUNCT
cana-857	105	39	4th	4th	ADJ
cana-857	105	40	international	international	ADJ
cana-857	105	41	conference	conference	NOUN
cana-857	105	42	on	on	ADP
cana-857	105	43	computer	computer	NOUN
cana-857	105	44	science	science	NOUN
cana-857	105	45	and	and	CCONJ
cana-857	105	46	computational	computational	ADJ
cana-857	105	47	intelligence	intelligence	NOUN
cana-857	105	48	2019	2019	NUM
cana-857	105	49	(	(	PUNCT
cana-857	105	50	iccsci	iccsci	PROPN
cana-857	105	51	)	)	PUNCT
cana-857	105	52	,	,	PUNCT
cana-857	105	53	12–13	12–13	NUM
cana-857	105	54	september	september	PROPN
cana-857	105	55	2019	2019	NUM
cana-857	105	56	,	,	PUNCT
cana-857	105	57	pp	pp	ADJ
cana-857	105	58	.	.	PUNCT
cana-857	106	1	48–54	48–54	NUM
cana-857	106	2	,	,	PUNCT
cana-857	106	3	2019	2019	NUM
cana-857	106	4	.	.	PUNCT
cana-857	107	1	[	[	X
cana-857	107	2	2	2	X
cana-857	107	3	]	]	PUNCT
cana-857	107	4	s.	s.	PROPN
cana-857	107	5	appavu	appavu	PROPN
cana-857	107	6	alias	alias	PROPN
cana-857	107	7	balam	balam	PROPN
cana-857	107	8	,	,	PUNCT
cana-857	107	9	,	,	PUNCT
cana-857	107	10	g.	g.	PROPN
cana-857	107	11	chinthana	chinthana	PROPN
cana-857	107	12	urugan	urugan	PROPN
cana-857	107	13	,	,	PUNCT
cana-857	107	14	m.s	m.s	PROPN
cana-857	107	15	.	.	PROPN
cana-857	107	16	mohamed	mohamed	PROPN
cana-857	107	17	mallick	mallick	PROPN
cana-857	107	18	,	,	PUNCT
cana-857	107	19	‘	'	PUNCT
cana-857	107	20	improved	improved	ADJ
cana-857	107	21	prediction	prediction	NOUN
cana-857	107	22	of	of	ADP
cana-857	107	23	dengue	dengue	NOUN
cana-857	107	24	outbreak	outbreak	NOUN
cana-857	107	25	using	use	VERB
cana-857	107	26	combinatorial	combinatorial	ADJ
cana-857	107	27	feature	feature	NOUN
cana-857	107	28	selector	selector	NOUN
cana-857	107	29	and	and	CCONJ
cana-857	107	30	classifier	classifier	NOUN
cana-857	107	31	based	base	VERB
cana-857	107	32	on	on	ADP
cana-857	107	33	entropy	entropy	PROPN
cana-857	107	34	weighted	weight	VERB
cana-857	107	35	score	score	NOUN
cana-857	107	36	based	base	VERB
cana-857	107	37	optimal	optimal	ADJ
cana-857	107	38	ranking	ranking	NOUN
cana-857	107	39	’	'	PUNCT
cana-857	107	40	,	,	PUNCT
cana-857	107	41	informatics	informatic	NOUN
cana-857	107	42	in	in	ADP
cana-857	107	43	medicine	medicine	NOUN
cana-857	107	44	unlocked	unlock	VERB
cana-857	107	45	,	,	PUNCT
cana-857	107	46	july2020	july2020	PROPN
cana-857	107	47	,	,	PUNCT
cana-857	107	48	doi	doi	NOUN
cana-857	107	49	:	:	PUNCT
cana-857	107	50	https://doi.org/10.1016/j.imu.2020.100400	https://doi.org/10.1016/j.imu.2020.100400	PROPN
cana-857	107	51	.	.	PUNCT
cana-857	108	1	[	[	X
cana-857	108	2	3	3	NUM
cana-857	108	3	]	]	PUNCT
cana-857	108	4	.	.	PUNCT
cana-857	109	1	gopika	gopika	NOUN
cana-857	109	2	,	,	PUNCT
cana-857	109	3	a.meena	a.meena	VERB
cana-857	109	4	kowshalaya	kowshalaya	NOUN
cana-857	109	5	,	,	PUNCT
cana-857	109	6	‘	'	PUNCT
cana-857	109	7	correlation	correlation	NOUN
cana-857	109	8	based	base	VERB
cana-857	109	9	feature	feature	NOUN
cana-857	109	10	selection	selection	NOUN
cana-857	109	11	algorithm	algorithm	NOUN
cana-857	109	12	for	for	ADP
cana-857	109	13	machine	machine	NOUN
cana-857	109	14	learning	learn	VERB
cana-857	109	15	’	'	PUNCT
cana-857	109	16	,	,	PUNCT
cana-857	109	17	proceedings	proceeding	NOUN
cana-857	109	18	of	of	ADP
cana-857	109	19	the	the	DET
cana-857	109	20	international	international	ADJ
cana-857	109	21	conference	conference	NOUN
cana-857	109	22	on	on	ADP
cana-857	109	23	communication	communication	NOUN
cana-857	109	24	and	and	CCONJ
cana-857	109	25	electronics	electronic	NOUN
cana-857	109	26	systems	system	NOUN
cana-857	109	27	(	(	PUNCT
cana-857	109	28	icces	icce	NOUN
cana-857	109	29	2018	2018	NUM
cana-857	109	30	)	)	PUNCT
cana-857	109	31	,	,	PUNCT
cana-857	109	32	pp	pp	PROPN
cana-857	109	33	.	.	PUNCT
cana-857	110	1	692	692	NUM
cana-857	110	2	–	–	PUNCT
cana-857	110	3	695	695	NUM
cana-857	110	4	.	.	PUNCT
cana-857	111	1	[	[	X
cana-857	111	2	4	4	NUM
cana-857	111	3	]	]	X
cana-857	111	4	sruthi	sruthi	PROPN
cana-857	111	5	nair	nair	PROPN
cana-857	111	6	,	,	PUNCT
cana-857	111	7	abhishek	abhishek	PROPN
cana-857	111	8	gupta	gupta	PROPN
cana-857	111	9	,	,	PUNCT
cana-857	111	10	dr	dr	PROPN
cana-857	111	11	.	.	PROPN
cana-857	111	12	vidya	vidya	PROPN
cana-857	111	13	chitre	chitre	PROPN
cana-857	111	14	,	,	PUNCT
cana-857	111	15	raunak	raunak	PROPN
cana-857	111	16	joshi	joshi	PROPN
cana-857	111	17	,	,	PUNCT
cana-857	111	18	‘	'	PUNCT
cana-857	111	19	combining	combine	VERB
cana-857	111	20	varied	varied	ADJ
cana-857	111	21	learners	learner	NOUN
cana-857	111	22	for	for	ADP
cana-857	111	23	binary	binary	ADJ
cana-857	111	24	classification	classification	NOUN
cana-857	111	25	using	use	VERB
cana-857	111	26	stacked	stack	VERB
cana-857	111	27	generalization	generalization	NOUN
cana-857	111	28	’	'	PUNCT
cana-857	111	29	,	,	PUNCT
cana-857	111	30	:	:	PUNCT
cana-857	111	31	2202.08910v1	2202.08910v1	NUM
cana-857	112	1	[	[	X
cana-857	112	2	cs.lg	cs.lg	X
cana-857	112	3	]	]	X
cana-857	112	4	17	17	NUM
cana-857	112	5	feb	feb	NOUN
cana-857	112	6	2022	2022	NUM
cana-857	112	7	,	,	PUNCT
cana-857	112	8	feb2022	feb2022	NOUN
cana-857	112	9	.	.	PUNCT
cana-857	113	1	[	[	X
cana-857	113	2	5	5	NUM
cana-857	113	3	]	]	X
cana-857	113	4	r	r	NOUN
cana-857	113	5	lathesparan	lathesparan	NOUN
cana-857	113	6	,	,	PUNCT
cana-857	113	7	rmkt	rmkt	NOUN
cana-857	113	8	rathnayaka	rathnayaka	NOUN
cana-857	113	9	,	,	PUNCT
cana-857	113	10	wu	wu	PROPN
cana-857	113	11	wickramaarachchi	wickramaarachchi	PROPN
cana-857	113	12	,	,	PUNCT
cana-857	113	13	‘	'	PUNCT
cana-857	113	14	finding	find	VERB
cana-857	113	15	the	the	DET
cana-857	113	16	best	good	ADJ
cana-857	113	17	feature	feature	NOUN
cana-857	113	18	selection	selection	NOUN
cana-857	113	19	method	method	NOUN
cana-857	113	20	for	for	ADP
cana-857	113	21	dengue	dengue	NOUN
cana-857	113	22	diagnosis	diagnosis	NOUN
cana-857	113	23	predictions	prediction	NOUN
cana-857	113	24	’	'	PUNCT
cana-857	113	25	,	,	PUNCT
cana-857	113	26	https://www.researchgate.net/publication/360587139	https://www.researchgate.net/publication/360587139	PROPN
cana-857	113	27	,	,	PUNCT
cana-857	113	28	pp	pp	ADJ
cana-857	113	29	.	.	PUNCT
cana-857	114	1	123–129	123–129	NUM
cana-857	114	2	,	,	PUNCT
cana-857	114	3	may2022	may2022	NOUN
cana-857	114	4	.	.	PUNCT
cana-857	115	1	[	[	X
cana-857	115	2	6	6	NUM
cana-857	115	3	]	]	PUNCT
cana-857	115	4	bilal	bilal	PROPN
cana-857	115	5	abdualgalil	abdualgalil	NOUN
cana-857	115	6	,	,	PUNCT
cana-857	115	7	sajimon	sajimon	ADJ
cana-857	115	8	abraham	abraham	PROPN
cana-857	115	9	,	,	PUNCT
cana-857	115	10	waleed	waleed	PROPN
cana-857	115	11	m.	m.	PROPN
cana-857	115	12	ismael	ismael	PROPN
cana-857	115	13	,	,	PUNCT
cana-857	115	14	‘	'	PUNCT
cana-857	115	15	early	early	ADJ
cana-857	115	16	diagnosis	diagnosis	NOUN
cana-857	115	17	for	for	ADP
cana-857	115	18	dengue	dengue	NOUN
cana-857	115	19	disease	disease	NOUN
cana-857	115	20	prediction	prediction	NOUN
cana-857	115	21	using	use	VERB
cana-857	115	22	efficient	efficient	ADJ
cana-857	115	23	machine	machine	NOUN
cana-857	115	24	learning	learn	VERB
cana-857	115	25	techniques	technique	NOUN
cana-857	115	26	based	base	VERB
cana-857	115	27	on	on	ADP
cana-857	115	28	clinical	clinical	ADJ
cana-857	115	29	data	datum	NOUN
cana-857	115	30	’	'	PUNCT
cana-857	115	31	,	,	PUNCT
cana-857	115	32	journal	journal	NOUN
cana-857	115	33	of	of	ADP
cana-857	115	34	robotics	robotic	NOUN
cana-857	115	35	and	and	CCONJ
cana-857	115	36	control	control	PROPN
cana-857	115	37	(	(	PUNCT
cana-857	115	38	jrc	jrc	PROPN
cana-857	115	39	)	)	PUNCT
cana-857	115	40	,	,	PUNCT
cana-857	115	41	vol	vol	NOUN
cana-857	115	42	.	.	PROPN
cana-857	116	1	3	3	NUM
cana-857	116	2	,	,	PUNCT
cana-857	116	3	no	no	INTJ
cana-857	116	4	.	.	NOUN
cana-857	116	5	3	3	NUM
cana-857	116	6	,	,	PUNCT
cana-857	116	7	pp	pp	ADJ
cana-857	116	8	.	.	PUNCT
cana-857	117	1	257–268	257–268	NUM
cana-857	117	2	,	,	PUNCT
cana-857	117	3	may	may	AUX
cana-857	117	4	2022	2022	NUM
cana-857	117	5	,	,	PUNCT
cana-857	117	6	doi	doi	NOUN
cana-857	117	7	:	:	PUNCT
cana-857	117	8	10.18196	10.18196	NUM
cana-857	117	9	/	/	SYM
cana-857	117	10	jrc.v3i3.14387	jrc.v3i3.14387	PROPN
cana-857	117	11	.	.	PUNCT
cana-857	118	1	[	[	X
cana-857	118	2	7	7	NUM
cana-857	118	3	]	]	PUNCT
cana-857	118	4	xi	xi	X
cana-857	118	5	li	li	PROPN
cana-857	118	6	,	,	PUNCT
cana-857	118	7	michèle	michèle	ADJ
cana-857	118	8	,	,	PUNCT
cana-857	118	9	curiger	curiger	NOUN
cana-857	118	10	thomas	thomas	PROPN
cana-857	118	11	,	,	PUNCT
cana-857	118	12	hannerolf	hannerolf	PROPN
cana-857	118	13	dornberger	dornberger	NOUN
cana-857	118	14	,	,	PUNCT
cana-857	118	15	‘	'	PUNCT
cana-857	118	16	optimized	optimize	VERB
cana-857	118	17	computational	computational	ADJ
cana-857	118	18	diabetes	diabetes	NOUN
cana-857	118	19	prediction	prediction	NOUN
cana-857	118	20	with	with	ADP
cana-857	118	21	feature	feature	NOUN
cana-857	118	22	selection	selection	NOUN
cana-857	118	23	algorithms	algorithm	NOUN
cana-857	118	24	’	'	PUNCT
cana-857	118	25	,	,	PUNCT
cana-857	118	26	ismsi	ismsi	VERB
cana-857	118	27	2023	2023	NUM
cana-857	118	28	,	,	PUNCT
cana-857	118	29	april	april	PROPN
cana-857	118	30	23	23	NUM
cana-857	118	31	,	,	PUNCT
cana-857	118	32	24	24	NUM
cana-857	118	33	,	,	PUNCT
cana-857	118	34	2023	2023	NUM
cana-857	118	35	,	,	PUNCT
cana-857	118	36	virtual	virtual	ADJ
cana-857	118	37	event	event	NOUN
cana-857	118	38	,	,	PUNCT
cana-857	118	39	malaysia	malaysia	PROPN
cana-857	118	40	,	,	PUNCT
cana-857	118	41	pp	pp	ADJ
cana-857	118	42	.	.	PUNCT
cana-857	118	43	36–43	36–43	NUM
cana-857	118	44	,	,	PUNCT
cana-857	118	45	jul	jul	PROPN
cana-857	118	46	.	.	PROPN
cana-857	118	47	2023	2023	NUM
cana-857	118	48	,	,	PUNCT
cana-857	118	49	doi	doi	NOUN
cana-857	118	50	:	:	PUNCT
cana-857	118	51	https://doi.org/10.1145/3596947.3596948	https://doi.org/10.1145/3596947.3596948	NOUN
cana-857	118	52	.	.	PUNCT
cana-857	119	1	[	[	X
cana-857	119	2	8	8	X
cana-857	119	3	]	]	X
cana-857	119	4	robson	robson	NOUN
cana-857	119	5	aleixo	aleixo	PROPN
cana-857	119	6	,	,	PUNCT
cana-857	119	7	fabio	fabio	PROPN
cana-857	119	8	,	,	PUNCT
cana-857	119	9	kon	kon	PROPN
cana-857	119	10	,	,	PUNCT
cana-857	119	11	rudi	rudi	PROPN
cana-857	119	12	rocha	rocha	PROPN
cana-857	119	13	,	,	PUNCT
cana-857	119	14	marcela	marcela	PROPN
cana-857	119	15	santos	santos	PROPN
cana-857	119	16	camargo	camargo	PROPN
cana-857	119	17	,	,	PUNCT
cana-857	119	18	raphael	raphael	PROPN
cana-857	119	19	y.	y.	PROPN
cana-857	119	20	de	de	PROPN
cana-857	119	21	camargo	camargo	PROPN
cana-857	119	22	,	,	PUNCT
cana-857	119	23	‘	'	PUNCT
cana-857	119	24	predicting	predict	VERB
cana-857	119	25	dengue	dengue	NOUN
cana-857	119	26	outbreaks	outbreak	NOUN
cana-857	119	27	with	with	ADP
cana-857	119	28	explainable	explainable	ADJ
cana-857	119	29	machine	machine	NOUN
cana-857	119	30	learning	learning	NOUN
cana-857	119	31	’	'	PUNCT
cana-857	119	32	,	,	PUNCT
cana-857	119	33	http://www.riocomsaude.rj.gov.br/publico/mostrararquivo.aspx?c=nqvipkhblju%3d	http://www.riocomsaude.rj.gov.br/publico/mostrararquivo.aspx?c=nqvipkhblju%3d	X
cana-857	119	34	.	.	PUNCT
cana-857	120	1	[	[	X
cana-857	120	2	9	9	NUM
cana-857	120	3	]	]	SYM
cana-857	120	4	r	r	NOUN
cana-857	120	5	arafiyah	arafiyah	NOUN
cana-857	120	6	,	,	PUNCT
cana-857	120	7	f	f	PROPN
cana-857	120	8	hermin	hermin	PROPN
cana-857	120	9	,	,	PUNCT
cana-857	120	10	i	i	PROPN
cana-857	120	11	r	r	PROPN
cana-857	120	12	kartika	kartika	PROPN
cana-857	120	13	,	,	PUNCT
cana-857	120	14	a	a	DET
cana-857	120	15	alimuddin	alimuddin	NOUN
cana-857	120	16	and	and	CCONJ
cana-857	120	17	i	i	PRON
cana-857	120	18	saraswati	saraswati	NOUN
cana-857	120	19	,	,	PUNCT
cana-857	120	20	‘	'	PUNCT
cana-857	120	21	classification	classification	NOUN
cana-857	120	22	of	of	ADP
cana-857	120	23	dengue	dengue	NOUN
cana-857	120	24	haemorrhagic	haemorrhagic	ADJ
cana-857	120	25	fever	fever	NOUN
cana-857	120	26	(	(	PUNCT
cana-857	120	27	dhf	dhf	NOUN
cana-857	120	28	)	)	PUNCT
cana-857	120	29	using	use	VERB
cana-857	120	30	svm	svm	PROPN
cana-857	120	31	,	,	PUNCT
cana-857	120	32	naive	naive	ADJ
cana-857	120	33	bayes	bayes	NOUN
cana-857	120	34	and	and	CCONJ
cana-857	120	35	random	random	ADJ
cana-857	120	36	forest	forest	NOUN
cana-857	120	37	’	'	PUNCT
cana-857	120	38	,	,	PUNCT
cana-857	120	39	iop	iop	PROPN
cana-857	120	40	conf	conf	NOUN
cana-857	120	41	.	.	PUNCT
cana-857	121	1	series	series	NOUN
cana-857	121	2	:	:	PUNCT
cana-857	121	3	materials	material	NOUN
cana-857	121	4	science	science	NOUN
cana-857	121	5	and	and	CCONJ
cana-857	121	6	engineering	engineering	NOUN
cana-857	121	7	434	434	NUM
cana-857	121	8	,	,	PUNCT
cana-857	121	9	2018	2018	NUM
cana-857	121	10	,	,	PUNCT
cana-857	121	11	doi	doi	NOUN
cana-857	121	12	:	:	PUNCT
cana-857	121	13	10.1088/1757	10.1088/1757	NUM
cana-857	121	14	-	-	PUNCT
cana-857	121	15	899x/434/1/012070	899x/434/1/012070	NUM
cana-857	121	16	.	.	PUNCT
cana-857	122	1	[	[	X
cana-857	122	2	10	10	NUM
cana-857	122	3	]	]	X
cana-857	122	4	supreet	supreet	NOUN
cana-857	122	5	kaur	kaur	PROPN
cana-857	122	6	,	,	PUNCT
cana-857	122	7	dr	dr	PROPN
cana-857	122	8	sandeep	sandeep	PROPN
cana-857	122	9	sharma	sharma	PROPN
cana-857	122	10	,	,	PUNCT
cana-857	122	11	‘	'	PUNCT
cana-857	122	12	comparative	comparative	ADJ
cana-857	122	13	analysis	analysis	NOUN
cana-857	122	14	of	of	ADP
cana-857	122	15	machine	machine	NOUN
cana-857	122	16	learning	learn	VERB
cana-857	122	17	classifiers	classifier	NOUN
cana-857	122	18	on	on	ADP
cana-857	122	19	forecasting	forecast	VERB
cana-857	122	20	dengue	dengue	NOUN
cana-857	122	21	fever	fever	NOUN
cana-857	122	22	infection	infection	NOUN
cana-857	122	23	’	'	PUNCT
cana-857	122	24	,	,	PUNCT
cana-857	122	25	recent	recent	ADJ
cana-857	122	26	developments	development	NOUN
cana-857	122	27	in	in	ADP
cana-857	122	28	electronics	electronic	NOUN
cana-857	122	29	and	and	CCONJ
cana-857	122	30	communication	communication	NOUN
cana-857	122	31	systems	system	NOUN
cana-857	122	32	,	,	PUNCT
cana-857	122	33	pp	pp	ADP
cana-857	122	34	.	.	PUNCT
cana-857	123	1	492–497	492–497	NUM
cana-857	123	2	,	,	PUNCT
cana-857	123	3	2023	2023	NUM
cana-857	123	4	,	,	PUNCT
cana-857	123	5	doi	doi	NOUN
cana-857	123	6	:	:	PUNCT
cana-857	123	7	doi:10.3233	doi:10.3233	NOUN
cana-857	123	8	/	/	SYM
cana-857	123	9	atde221302	atde221302	PROPN
cana-857	123	10	.	.	PUNCT
cana-857	124	1	[	[	X
cana-857	124	2	11	11	NUM
cana-857	124	3	]	]	X
cana-857	124	4	m	m	VERB
cana-857	124	5	g	g	PROPN
cana-857	124	6	dinesh	dinesh	PROPN
cana-857	124	7	,	,	PUNCT
cana-857	124	8	d.prabha	d.prabha	NOUN
cana-857	124	9	,	,	PUNCT
cana-857	124	10	‘	'	PUNCT
cana-857	124	11	diabetes	diabetes	NOUN
cana-857	124	12	mellitus	mellitus	NOUN
cana-857	124	13	prediction	prediction	NOUN
cana-857	124	14	system	system	NOUN
cana-857	124	15	using	use	VERB
cana-857	124	16	hybrid	hybrid	ADJ
cana-857	124	17	kpca	kpca	PROPN
cana-857	124	18	ga	ga	PROPN
cana-857	124	19	-	-	PUNCT
cana-857	124	20	svm	svm	PROPN
cana-857	124	21	feature	feature	NOUN
cana-857	124	22	selection	selection	NOUN
cana-857	124	23	techniques	technique	NOUN
cana-857	124	24	’	'	PUNCT
cana-857	124	25	,	,	PUNCT
cana-857	124	26	icdiis	icdiis	ADJ
cana-857	124	27	2020	2020	NUM
cana-857	124	28	journal	journal	NOUN
cana-857	124	29	of	of	ADP
cana-857	124	30	physics	physics	PROPN
cana-857	124	31	:	:	PUNCT
cana-857	125	1	conference	conference	NOUN
cana-857	125	2	series	series	NOUN
cana-857	125	3	,	,	PUNCT
cana-857	125	4	2021	2021	NUM
cana-857	125	5	,	,	PUNCT
cana-857	125	6	doi	doi	NOUN
cana-857	125	7	:	:	PUNCT
cana-857	125	8	0.1088/1742	0.1088/1742	NUM
cana-857	125	9	-	-	PUNCT
cana-857	125	10	6596/1767/1/012001	6596/1767/1/012001	NUM
cana-857	125	11	.	.	PUNCT
cana-857	126	1	[	[	X
cana-857	126	2	12	12	NUM
cana-857	126	3	]	]	PUNCT
cana-857	126	4	salim	salim	PROPN
cana-857	126	5	g.	g.	PROPN
cana-857	126	6	shaikh	shaikh	PROPN
cana-857	126	7	,	,	PUNCT
cana-857	126	8	dr	dr	PROPN
cana-857	126	9	.	.	PROPN
cana-857	126	10	b.	b.	PROPN
cana-857	126	11	suresh	suresh	PROPN
cana-857	126	12	kumar	kumar	PROPN
cana-857	126	13	,	,	PUNCT
cana-857	126	14	dr	dr	PROPN
cana-857	126	15	.	.	PROPN
cana-857	126	16	geetika	geetika	PROPN
cana-857	126	17	narang	narang	PROPN
cana-857	126	18	prof	prof	PROPN
cana-857	126	19	.	.	PUNCT
cana-857	127	1	n.n.pachpor	n.n.pachpor	PROPN
cana-857	127	2	,	,	PUNCT
cana-857	127	3	‘	'	PUNCT
cana-857	127	4	diagnosis	diagnosis	NOUN
cana-857	127	5	of	of	ADP
cana-857	127	6	vector	vector	NOUN
cana-857	127	7	borne	borne	NOUN
cana-857	127	8	disease	disease	NOUN
cana-857	127	9	using	use	VERB
cana-857	127	10	various	various	ADJ
cana-857	127	11	machine	machine	NOUN
cana-857	127	12	learning	learn	VERB
cana-857	127	13	techniques	technique	NOUN
cana-857	127	14	’	'	PUNCT
cana-857	127	15	,	,	PUNCT
cana-857	127	16	intelligent	intelligent	ADJ
cana-857	127	17	systems	system	NOUN
cana-857	127	18	and	and	CCONJ
cana-857	127	19	applications	application	NOUN
cana-857	127	20	in	in	ADP
cana-857	127	21	engineering	engineering	NOUN
cana-857	127	22	,	,	PUNCT
cana-857	127	23	pp	pp	ADP
cana-857	127	24	.	.	PUNCT
cana-857	128	1	517–526	517–526	NUM
cana-857	128	2	,	,	PUNCT
cana-857	128	3	feb	feb	PROPN
cana-857	128	4	.	.	PROPN
cana-857	128	5	2023	2023	NUM
cana-857	128	6	.	.	PUNCT
cana-857	129	1	[	[	X
cana-857	129	2	13	13	NUM
cana-857	129	3	]	]	X
cana-857	129	4	p.k	p.k	PROPN
cana-857	129	5	.	.	PROPN
cana-857	129	6	swaraj	swaraj	PROPN
cana-857	129	7	,	,	PUNCT
cana-857	129	8	g.	g.	PROPN
cana-857	129	9	kiruthiga	kiruthiga	PROPN
cana-857	129	10	,	,	PUNCT
cana-857	129	11	‘	'	PUNCT
cana-857	129	12	design	design	NOUN
cana-857	129	13	and	and	CCONJ
cana-857	129	14	analysis	analysis	NOUN
cana-857	129	15	on	on	ADP
cana-857	129	16	medical	medical	ADJ
cana-857	129	17	image	image	NOUN
cana-857	129	18	classification	classification	NOUN
cana-857	129	19	for	for	ADP
cana-857	129	20	dengue	dengue	NOUN
cana-857	129	21	detection	detection	NOUN
cana-857	129	22	using	use	VERB
cana-857	129	23	artificial	artificial	ADJ
cana-857	129	24	neural	neural	ADJ
cana-857	129	25	network	network	NOUN
cana-857	129	26	classifier	classifier	NOUN
cana-857	129	27	’	'	PUNCT
cana-857	129	28	,	,	PUNCT
cana-857	129	29	ictact	ictact	ADJ
cana-857	129	30	journal	journal	NOUN
cana-857	129	31	on	on	ADP
cana-857	129	32	image	image	NOUN
cana-857	129	33	and	and	CCONJ
cana-857	129	34	video	video	NOUN
cana-857	129	35	processing	processing	NOUN
cana-857	129	36	,	,	PUNCT
cana-857	129	37	vol	vol	NOUN
cana-857	129	38	.	.	PROPN
cana-857	129	39	11	11	NUM
cana-857	129	40	,	,	PUNCT
cana-857	129	41	no	no	INTJ
cana-857	129	42	.	.	NOUN
cana-857	129	43	3	3	NUM
cana-857	129	44	,	,	PUNCT
cana-857	129	45	pp	pp	PROPN
cana-857	129	46	.	.	PUNCT
cana-857	129	47	2407–2411	2407–2411	NUM
cana-857	129	48	,	,	PUNCT
cana-857	129	49	feb	feb	PROPN
cana-857	129	50	.	.	PROPN
cana-857	129	51	2021	2021	NUM
cana-857	129	52	,	,	PUNCT
cana-857	129	53	doi	doi	NOUN
cana-857	129	54	:	:	PUNCT
cana-857	129	55	10.21917	10.21917	NUM
cana-857	129	56	/	/	SYM
cana-857	129	57	ijivp.2021.0343	ijivp.2021.0343	PROPN
cana-857	129	58	.	.	PUNCT
cana-857	130	1	[	[	X
cana-857	130	2	14	14	NUM
cana-857	130	3	]	]	X
cana-857	130	4	yosef	yosef	PROPN
cana-857	130	5	masoudi	masoudi	ADJ
cana-857	130	6	-	-	PUNCT
cana-857	130	7	sobhanzadeh	sobhanzadeh	NOUN
cana-857	130	8	,	,	PUNCT
cana-857	130	9	habib	habib	PROPN
cana-857	130	10	motieghader	motieghader	PROPN
cana-857	130	11	,	,	PUNCT
cana-857	130	12	ali	ali	PROPN
cana-857	130	13	masoudi	masoudi	PROPN
cana-857	130	14	-	-	PUNCT
cana-857	130	15	nejad	nejad	PROPN
cana-857	130	16	,	,	PUNCT
cana-857	130	17	‘	'	PUNCT
cana-857	130	18	featureselect	featureselect	VERB
cana-857	130	19	:	:	PUNCT
cana-857	130	20	a	a	DET
cana-857	130	21	software	software	NOUN
cana-857	130	22	for	for	ADP
cana-857	130	23	feature	feature	NOUN
cana-857	130	24	selection	selection	NOUN
cana-857	130	25	based	base	VERB
cana-857	130	26	on	on	ADP
cana-857	130	27	machine	machine	NOUN
cana-857	130	28	learning	learn	VERB
cana-857	130	29	approaches	approach	NOUN
cana-857	130	30	’	'	PUNCT
cana-857	130	31	,	,	PUNCT
cana-857	130	32	mc	mc	PROPN
cana-857	130	33	bioinformatics	bioinformatics	PROPN
cana-857	130	34	,	,	PUNCT
cana-857	130	35	2019	2019	NUM
cana-857	130	36	,	,	PUNCT
cana-857	130	37	doi	doi	NOUN
cana-857	130	38	:	:	PUNCT
cana-857	130	39	https://doi.org/10.1186/s12859019-2754-0	https://doi.org/10.1186/s12859019-2754-0	NOUN
cana-857	130	40	.	.	PUNCT
cana-857	131	1	[	[	X
cana-857	131	2	15	15	NUM
cana-857	131	3	]	]	PUNCT
cana-857	131	4	t.sajana	t.sajana	NUM
cana-857	131	5	,	,	PUNCT
cana-857	131	6	m.navya	m.navya	NOUN
cana-857	131	7	,	,	PUNCT
cana-857	131	8	yvssv.gayathri	yvssv.gayathri	PROPN
cana-857	131	9	,	,	PUNCT
cana-857	131	10	n.reshma	n.reshma	NOUN
cana-857	131	11	,	,	PUNCT
cana-857	131	12	‘	'	PUNCT
cana-857	131	13	classification	classification	NOUN
cana-857	131	14	of	of	ADP
cana-857	131	15	dengue	dengue	NOUN
cana-857	131	16	using	use	VERB
cana-857	131	17	machine	machine	NOUN
cana-857	131	18	learning	learn	VERB
cana-857	131	19	techniques	technique	NOUN
cana-857	131	20	’	'	PUNCT
cana-857	131	21	,	,	PUNCT
cana-857	131	22	international	international	ADJ
cana-857	131	23	journal	journal	NOUN
cana-857	131	24	of	of	ADP
cana-857	131	25	engineering	engineering	PROPN
cana-857	131	26	&	&	CCONJ
cana-857	131	27	technology	technology	PROPN
cana-857	131	28	,	,	PUNCT
cana-857	131	29	pp	pp	ADJ
cana-857	131	30	.	.	PUNCT
cana-857	132	1	212–218	212–218	NUM
cana-857	132	2	,	,	PUNCT
cana-857	132	3	2018	2018	NUM
cana-857	132	4	.	.	PUNCT
cana-857	133	1	[	[	X
cana-857	133	2	16	16	NUM
cana-857	133	3	]	]	X
cana-857	133	4	dhiman	dhiman	NOUN
cana-857	133	5	sarma	sarma	PROPN
cana-857	133	6	,	,	PUNCT
cana-857	133	7	sohrab	sohrab	PROPN
cana-857	133	8	hossain	hossain	PROPN
cana-857	133	9	,	,	PUNCT
cana-857	133	10	tanni	tanni	PROPN
cana-857	133	11	mittra	mittra	PROPN
cana-857	133	12	,	,	PUNCT
cana-857	133	13	md	md	PROPN
cana-857	133	14	.	.	PUNCT
cana-857	133	15	abdul	abdul	PROPN
cana-857	133	16	motaleb	motaleb	PROPN
cana-857	133	17	bhuiya	bhuiya	PROPN
cana-857	133	18	,	,	PUNCT
cana-857	133	19	ishita	ishita	PROPN
cana-857	133	20	saha	saha	PROPN
cana-857	133	21	,	,	PUNCT
cana-857	133	22	ravina	ravina	VERB
cana-857	133	23	chakma	chakma	NOUN
cana-857	133	24	,	,	PUNCT
cana-857	133	25	‘	'	PUNCT
cana-857	133	26	dengue	dengue	NOUN
cana-857	133	27	prediction	prediction	NOUN
cana-857	133	28	using	use	VERB
cana-857	133	29	machine	machine	NOUN
cana-857	133	30	learning	learn	VERB
cana-857	133	31	algorithms	algorithm	NOUN
cana-857	133	32	’	'	PUNCT
cana-857	133	33	,	,	PUNCT
cana-857	133	34	2020	2020	NUM
cana-857	133	35	ieee	ieee	NOUN
cana-857	133	36	xplore(r10	xplore(r10	PROPN
cana-857	133	37	-	-	PUNCT
cana-857	133	38	htc	htc	NOUN
cana-857	133	39	)	)	PUNCT
cana-857	133	40	|	|	ADV
cana-857	133	41	978	978	NUM
cana-857	133	42	,	,	PUNCT
cana-857	133	43	2021	2021	NUM
cana-857	133	44	,	,	PUNCT
cana-857	133	45	doi	doi	NOUN
cana-857	133	46	:	:	PUNCT
cana-857	133	47	10.1109	10.1109	NUM
cana-857	133	48	/	/	SYM
cana-857	133	49	r10	r10	NOUN
cana-857	133	50	-	-	PUNCT
cana-857	133	51	htc49770.2020.9357035	htc49770.2020.9357035	NOUN
cana-857	133	52	.	.	PUNCT
cana-857	134	1	[	[	X
cana-857	134	2	17	17	NUM
cana-857	134	3	]	]	X
cana-857	134	4	rajeev	rajeev	PROPN
cana-857	134	5	kapoora	kapoora	PROPN
cana-857	134	6	,	,	PUNCT
cana-857	134	7	varinder	varinder	NOUN
cana-857	134	8	kadyanb	kadyanb	PROPN
cana-857	134	9	,	,	PUNCT
cana-857	134	10	sachin	sachin	PROPN
cana-857	134	11	ahuja	ahuja	PROPN
cana-857	134	12	,	,	PUNCT
cana-857	134	13	‘	'	PUNCT
cana-857	134	14	identification	identification	NOUN
cana-857	134	15	of	of	ADP
cana-857	134	16	influential	influential	ADJ
cana-857	134	17	parameter	parameter	NOUN
cana-857	134	18	for	for	ADP
cana-857	134	19	early	early	ADJ
cana-857	134	20	detection	detection	NOUN
cana-857	134	21	of	of	ADP
cana-857	134	22	dengue	dengue	NOUN
cana-857	134	23	using	use	VERB
cana-857	134	24	machine	machine	NOUN
cana-857	134	25	learning	learning	NOUN
cana-857	134	26	approach	approach	NOUN
cana-857	134	27	.	.	PUNCT
cana-857	134	28	’	'	PUNCT
cana-857	135	1	,	,	PUNCT
cana-857	135	2	proceedings	proceeding	NOUN
cana-857	135	3	of	of	ADP
cana-857	135	4	5th	5th	ADJ
cana-857	135	5	international	international	ADJ
cana-857	135	6	conference	conference	NOUN
cana-857	135	7	on	on	ADP
cana-857	135	8	cyber	cyber	PROPN
cana-857	135	9	security	security	PROPN
cana-857	135	10	&	&	CCONJ
cana-857	135	11	privacy	privacy	PROPN
cana-857	135	12	(	(	PUNCT
cana-857	135	13	iccs	iccs	PROPN
cana-857	135	14	)	)	PUNCT
cana-857	135	15	2019	2019	NUM
cana-857	135	16	,	,	PUNCT
cana-857	135	17	pp	pp	ADJ
cana-857	135	18	.	.	PUNCT
cana-857	136	1	129–135	129–135	NUM
cana-857	136	2	.	.	PUNCT
cana-857	137	1	communications	communication	NOUN
cana-857	137	2	on	on	ADP
cana-857	137	3	applied	apply	VERB
cana-857	137	4	nonlinear	nonlinear	ADJ
cana-857	137	5	analysis	analysis	NOUN
cana-857	137	6	issn	issn	NOUN
cana-857	137	7	:	:	PUNCT
cana-857	137	8	1074	1074	NUM
cana-857	137	9	-	-	PUNCT
cana-857	137	10	133x	133x	NUM
cana-857	137	11	vol	vol	NOUN
cana-857	137	12	31	31	NUM
cana-857	137	13	no	no	NOUN
cana-857	137	14	.	.	PUNCT
cana-857	138	1	4s	4s	NUM
cana-857	138	2	(	(	PUNCT
cana-857	138	3	2024	2024	NUM
cana-857	138	4	)	)	PUNCT
cana-857	138	5	357	357	NUM
cana-857	138	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-857	139	1	[	[	X
cana-857	139	2	18	18	NUM
cana-857	139	3	]	]	X
cana-857	139	4	rajeev	rajeev	PROPN
cana-857	139	5	kapoor	kapoor	PROPN
cana-857	139	6	,	,	PUNCT
cana-857	139	7	variender	variender	PROPN
cana-857	139	8	kadyan	kadyan	PROPN
cana-857	139	9	,	,	PUNCT
cana-857	139	10	sachin	sachin	PROPN
cana-857	139	11	ahuja	ahuja	PROPN
cana-857	139	12	,	,	PUNCT
cana-857	139	13	‘	'	PUNCT
cana-857	139	14	weight	weight	NOUN
cana-857	139	15	basedartificial	basedartificial	ADJ
cana-857	139	16	neural	neural	ADJ
cana-857	139	17	network	network	NOUN
cana-857	139	18	(	(	PUNCT
cana-857	139	19	w	w	NOUN
cana-857	139	20	-	-	PUNCT
cana-857	139	21	ann	ann	NOUN
cana-857	139	22	)	)	PUNCT
cana-857	139	23	for	for	ADP
cana-857	139	24	predicting	predict	VERB
cana-857	139	25	dengue	dengue	NOUN
cana-857	139	26	using	use	VERB
cana-857	139	27	machine	machine	NOUN
cana-857	139	28	learning	learn	VERB
cana-857	139	29	approach	approach	NOUN
cana-857	139	30	with	with	ADP
cana-857	139	31	indian	indian	PROPN
cana-857	139	32	perspective	perspective	NOUN
cana-857	139	33	’	'	PUNCT
cana-857	139	34	,	,	PUNCT
cana-857	139	35	international	international	ADJ
cana-857	139	36	journal	journal	NOUN
cana-857	139	37	of	of	ADP
cana-857	139	38	scientific	scientific	PROPN
cana-857	139	39	&	&	CCONJ
cana-857	139	40	technology	technology	PROPN
cana-857	139	41	research	research	NOUN
cana-857	139	42	volume	volume	NOUN
cana-857	139	43	9	9	NUM
cana-857	139	44	,	,	PUNCT
cana-857	139	45	issue	issue	NOUN
cana-857	139	46	02	02	NUM
cana-857	139	47	,	,	PUNCT
cana-857	139	48	february	february	PROPN
cana-857	139	49	2020	2020	NUM
cana-857	139	50	,	,	PUNCT
cana-857	139	51	vol	vol	NOUN
cana-857	139	52	.	.	NOUN
cana-857	139	53	9	9	NUM
cana-857	139	54	,	,	PUNCT
cana-857	139	55	no	no	INTJ
cana-857	139	56	.	.	NOUN
cana-857	139	57	2	2	NUM
cana-857	139	58	,	,	PUNCT
cana-857	139	59	pp	pp	ADJ
cana-857	139	60	.	.	PUNCT
cana-857	140	1	3290–3298	3290–3298	NUM
cana-857	140	2	,	,	PUNCT
cana-857	140	3	feb	feb	PROPN
cana-857	140	4	.	.	PROPN
cana-857	140	5	2020	2020	NUM
cana-857	140	6	.	.	PUNCT
cana-857	141	1	[	[	X
cana-857	141	2	19	19	NUM
cana-857	141	3	]	]	X
cana-857	141	4	fei	fei	PROPN
cana-857	141	5	zhou	zhou	PROPN
cana-857	141	6	,	,	PUNCT
cana-857	141	7	honghaifan	honghaifan	PROPN
cana-857	141	8	,	,	PUNCT
cana-857	141	9	yuhan	yuhan	PROPN
cana-857	141	10	liu	liu	PROPN
cana-857	141	11	,	,	PUNCT
cana-857	141	12	hongbao	hongbao	PROPN
cana-857	141	13	zhang	zhang	PROPN
cana-857	141	14	,	,	PUNCT
cana-857	141	15	rongyi	rongyi	VERB
cana-857	141	16	ji	ji	PROPN
cana-857	141	17	“	"	PUNCT
cana-857	141	18	hybrid	hybrid	ADJ
cana-857	141	19	model	model	NOUN
cana-857	141	20	of	of	ADP
cana-857	141	21	machine	machine	NOUN
cana-857	141	22	learning	learning	NOUN
cana-857	141	23	method	method	NOUN
cana-857	141	24	and	and	CCONJ
cana-857	141	25	empirical	empirical	ADJ
cana-857	141	26	method	method	NOUN
cana-857	141	27	for	for	ADP
cana-857	141	28	rate	rate	NOUN
cana-857	141	29	of	of	ADP
cana-857	141	30	penetration	penetration	NOUN
cana-857	141	31	prediction	prediction	NOUN
cana-857	141	32	based	base	VERB
cana-857	141	33	on	on	ADP
cana-857	141	34	data	datum	NOUN
cana-857	141	35	similarity	similarity	NOUN
cana-857	141	36	”	"	PUNCT
cana-857	141	37	,	,	PUNCT
cana-857	141	38	applied	apply	VERB
cana-857	141	39	science	science	NOUN
cana-857	141	40	2023	2023	NUM
cana-857	141	41	,	,	PUNCT
cana-857	141	42	https://doi.org/10.3390/app13105870	https://doi.org/10.3390/app13105870	NOUN
cana-857	141	43	.	.	PUNCT