id	sid	tid	token	lemma	pos
cana-1737	1	1	communications	communication	NOUN
cana-1737	1	2	on	on	ADP
cana-1737	1	3	applied	apply	VERB
cana-1737	1	4	nonlinear	nonlinear	ADJ
cana-1737	1	5	analysis	analysis	NOUN
cana-1737	1	6	issn	issn	NOUN
cana-1737	1	7	:	:	PUNCT
cana-1737	1	8	1074	1074	NUM
cana-1737	1	9	-	-	PUNCT
cana-1737	1	10	133x	133x	NUM
cana-1737	1	11	vol	vol	NOUN
cana-1737	1	12	32	32	NUM
cana-1737	1	13	no	no	NOUN
cana-1737	1	14	.	.	NOUN
cana-1737	1	15	2	2	NUM
cana-1737	1	16	(	(	PUNCT
cana-1737	1	17	2025	2025	NUM
cana-1737	1	18	)	)	PUNCT
cana-1737	1	19	202	202	NUM
cana-1737	1	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	1	21	parkinson	parkinson	NOUN
cana-1737	1	22	's	's	PART
cana-1737	1	23	disease	disease	NOUN
cana-1737	1	24	detection	detection	NOUN
cana-1737	1	25	on	on	ADP
cana-1737	1	26	unbalanced	unbalanced	ADJ
cana-1737	1	27	speech	speech	NOUN
cana-1737	1	28	data	datum	NOUN
cana-1737	1	29	using	use	VERB
cana-1737	1	30	convolutional	convolutional	ADJ
cana-1737	1	31	neural	neural	ADJ
cana-1737	1	32	networks	network	NOUN
cana-1737	1	33	mrs	mrs	PROPN
cana-1737	1	34	.	.	PROPN
cana-1737	1	35	a.	a.	PROPN
cana-1737	1	36	g.	g.	PROPN
cana-1737	1	37	phakatkar	phakatkar	PROPN
cana-1737	1	38	1	1	NUM
cana-1737	1	39	,	,	PUNCT
cana-1737	1	40	vaishnavi	vaishnavi	VERB
cana-1737	1	41	taware	taware	ADJ
cana-1737	1	42	2	2	NUM
cana-1737	1	43	1,2	1,2	NUM
cana-1737	1	44	department	department	NOUN
cana-1737	1	45	of	of	ADP
cana-1737	1	46	computer	computer	NOUN
cana-1737	1	47	engineering	engineering	NOUN
cana-1737	1	48	,	,	PUNCT
cana-1737	1	49	sctr	sctr	PROPN
cana-1737	1	50	’s	’s	PART
cana-1737	1	51	pune	pune	PROPN
cana-1737	1	52	institute	institute	PROPN
cana-1737	1	53	of	of	ADP
cana-1737	1	54	computer	computer	NOUN
cana-1737	1	55	technology	technology	NOUN
cana-1737	1	56	article	article	NOUN
cana-1737	1	57	history	history	NOUN
cana-1737	1	58	:	:	PUNCT
cana-1737	1	59	received	receive	VERB
cana-1737	1	60	:	:	PUNCT
cana-1737	1	61	30	30	NUM
cana-1737	1	62	-	-	SYM
cana-1737	1	63	07	07	NUM
cana-1737	1	64	-	-	PUNCT
cana-1737	1	65	2024	2024	NUM
cana-1737	1	66	revised	revise	VERB
cana-1737	1	67	:	:	PUNCT
cana-1737	1	68	08	08	NUM
cana-1737	1	69	-	-	SYM
cana-1737	1	70	09	09	NUM
cana-1737	1	71	-	-	PUNCT
cana-1737	1	72	2024	2024	NUM
cana-1737	1	73	accepted	accept	VERB
cana-1737	1	74	:	:	PUNCT
cana-1737	1	75	17	17	NUM
cana-1737	1	76	-	-	SYM
cana-1737	1	77	09	09	NUM
cana-1737	1	78	-	-	PUNCT
cana-1737	1	79	2024	2024	NUM
cana-1737	1	80	abstract	abstract	NOUN
cana-1737	1	81	:	:	PUNCT
cana-1737	1	82	parkinson	parkinson	NOUN
cana-1737	1	83	’s	’s	PART
cana-1737	1	84	disease	disease	NOUN
cana-1737	1	85	is	be	AUX
cana-1737	1	86	a	a	DET
cana-1737	1	87	progressive	progressive	ADJ
cana-1737	1	88	condition	condition	NOUN
cana-1737	1	89	impacting	impact	VERB
cana-1737	1	90	movement	movement	NOUN
cana-1737	1	91	and	and	CCONJ
cana-1737	1	92	communication	communication	NOUN
cana-1737	1	93	.	.	PUNCT
cana-1737	2	1	initially	initially	ADV
cana-1737	2	2	,	,	PUNCT
cana-1737	2	3	symptoms	symptom	NOUN
cana-1737	2	4	manifest	manifest	VERB
cana-1737	2	5	primarily	primarily	ADV
cana-1737	2	6	in	in	ADP
cana-1737	2	7	speech	speech	NOUN
cana-1737	2	8	difficulties	difficulty	NOUN
cana-1737	2	9	,	,	PUNCT
cana-1737	2	10	which	which	PRON
cana-1737	2	11	worsen	worsen	VERB
cana-1737	2	12	over	over	ADP
cana-1737	2	13	time	time	NOUN
cana-1737	2	14	,	,	PUNCT
cana-1737	2	15	affecting	affect	VERB
cana-1737	2	16	aspects	aspect	NOUN
cana-1737	2	17	such	such	ADJ
cana-1737	2	18	as	as	ADP
cana-1737	2	19	pitch	pitch	NOUN
cana-1737	2	20	and	and	CCONJ
cana-1737	2	21	articulation	articulation	NOUN
cana-1737	2	22	.	.	PUNCT
cana-1737	3	1	detecting	detect	VERB
cana-1737	3	2	signs	sign	NOUN
cana-1737	3	3	of	of	ADP
cana-1737	3	4	parkinson	parkinson	NOUN
cana-1737	3	5	's	's	PART
cana-1737	3	6	disease	disease	NOUN
cana-1737	3	7	often	often	ADV
cana-1737	3	8	relies	rely	VERB
cana-1737	3	9	on	on	ADP
cana-1737	3	10	analysing	analyse	VERB
cana-1737	3	11	speech	speech	NOUN
cana-1737	3	12	patterns	pattern	NOUN
cana-1737	3	13	.	.	PUNCT
cana-1737	4	1	in	in	ADP
cana-1737	4	2	this	this	DET
cana-1737	4	3	study	study	NOUN
cana-1737	4	4	,	,	PUNCT
cana-1737	4	5	a	a	DET
cana-1737	4	6	convolutional	convolutional	ADJ
cana-1737	4	7	neural	neural	ADJ
cana-1737	4	8	network	network	NOUN
cana-1737	4	9	is	be	AUX
cana-1737	4	10	utilized	utilize	VERB
cana-1737	4	11	for	for	ADP
cana-1737	4	12	parkinson	parkinson	NOUN
cana-1737	4	13	's	's	PART
cana-1737	4	14	speech	speech	NOUN
cana-1737	4	15	detection	detection	NOUN
cana-1737	4	16	.	.	PUNCT
cana-1737	5	1	convolutional	convolutional	ADJ
cana-1737	5	2	neural	neural	ADJ
cana-1737	5	3	network	network	NOUN
cana-1737	5	4	excels	excel	NOUN
cana-1737	5	5	in	in	ADP
cana-1737	5	6	capturing	capture	VERB
cana-1737	5	7	subtle	subtle	ADJ
cana-1737	5	8	spatial	spatial	ADJ
cana-1737	5	9	structures	structure	NOUN
cana-1737	5	10	and	and	CCONJ
cana-1737	5	11	local	local	ADJ
cana-1737	5	12	patterns	pattern	NOUN
cana-1737	5	13	,	,	PUNCT
cana-1737	5	14	crucial	crucial	ADJ
cana-1737	5	15	for	for	ADP
cana-1737	5	16	discerning	discern	VERB
cana-1737	5	17	the	the	DET
cana-1737	5	18	nuanced	nuanced	ADJ
cana-1737	5	19	pitch	pitch	NOUN
cana-1737	5	20	,	,	PUNCT
cana-1737	5	21	rhythm	rhythm	NOUN
cana-1737	5	22	,	,	PUNCT
cana-1737	5	23	and	and	CCONJ
cana-1737	5	24	phonetic	phonetic	ADJ
cana-1737	5	25	traits	trait	NOUN
cana-1737	5	26	of	of	ADP
cana-1737	5	27	individuals	individual	NOUN
cana-1737	5	28	with	with	ADP
cana-1737	5	29	parkinson	parkinson	NOUN
cana-1737	5	30	’s	’s	PART
cana-1737	5	31	disease	disease	NOUN
cana-1737	5	32	.	.	PUNCT
cana-1737	6	1	the	the	DET
cana-1737	6	2	research	research	NOUN
cana-1737	6	3	employs	employ	VERB
cana-1737	6	4	acoustic	acoustic	ADJ
cana-1737	6	5	voice	voice	NOUN
cana-1737	6	6	measures	measure	NOUN
cana-1737	6	7	like	like	ADP
cana-1737	6	8	jitter	jitter	NOUN
cana-1737	6	9	and	and	CCONJ
cana-1737	6	10	shimmer	shimmer	ADJ
cana-1737	6	11	as	as	ADP
cana-1737	6	12	speech	speech	NOUN
cana-1737	6	13	input	input	NOUN
cana-1737	6	14	parameters	parameter	NOUN
cana-1737	6	15	,	,	PUNCT
cana-1737	6	16	utilizing	utilize	VERB
cana-1737	6	17	a	a	DET
cana-1737	6	18	dataset	dataset	NOUN
cana-1737	6	19	sourced	source	VERB
cana-1737	6	20	from	from	ADP
cana-1737	6	21	the	the	DET
cana-1737	6	22	uci	uci	PROPN
cana-1737	6	23	machine	machine	NOUN
cana-1737	6	24	learning	learn	VERB
cana-1737	6	25	repository	repository	NOUN
cana-1737	6	26	.	.	PUNCT
cana-1737	7	1	the	the	DET
cana-1737	7	2	dataset	dataset	NOUN
cana-1737	7	3	exhibits	exhibit	VERB
cana-1737	7	4	a	a	DET
cana-1737	7	5	class	class	NOUN
cana-1737	7	6	imbalance	imbalance	NOUN
cana-1737	7	7	problem	problem	NOUN
cana-1737	7	8	.	.	PUNCT
cana-1737	8	1	to	to	PART
cana-1737	8	2	address	address	VERB
cana-1737	8	3	the	the	DET
cana-1737	8	4	class	class	NOUN
cana-1737	8	5	imbalance	imbalance	NOUN
cana-1737	8	6	issue	issue	NOUN
cana-1737	8	7	synthetic	synthetic	ADJ
cana-1737	8	8	minority	minority	NOUN
cana-1737	8	9	oversampling	oversample	VERB
cana-1737	8	10	technique	technique	NOUN
cana-1737	8	11	algorithm	algorithm	NOUN
cana-1737	8	12	is	be	AUX
cana-1737	8	13	used	use	VERB
cana-1737	8	14	.	.	PUNCT
cana-1737	9	1	the	the	DET
cana-1737	9	2	convolutional	convolutional	ADJ
cana-1737	9	3	neural	neural	ADJ
cana-1737	9	4	network	network	NOUN
cana-1737	9	5	algorithm	algorithm	NOUN
cana-1737	9	6	significantly	significantly	ADV
cana-1737	9	7	enhances	enhance	VERB
cana-1737	9	8	parkinson	parkinson	NOUN
cana-1737	9	9	’s	’s	PART
cana-1737	9	10	disease	disease	NOUN
cana-1737	9	11	voice	voice	NOUN
cana-1737	9	12	detection	detection	NOUN
cana-1737	9	13	exhibiting	exhibit	VERB
cana-1737	9	14	a	a	DET
cana-1737	9	15	remarkable	remarkable	ADJ
cana-1737	9	16	accuracy	accuracy	NOUN
cana-1737	9	17	of	of	ADP
cana-1737	9	18	91.52	91.52	NUM
cana-1737	9	19	%	%	NOUN
cana-1737	9	20	outperforming	outperform	VERB
cana-1737	9	21	traditional	traditional	ADJ
cana-1737	9	22	machine	machine	NOUN
cana-1737	9	23	learning	learning	NOUN
cana-1737	9	24	approaches	approach	NOUN
cana-1737	9	25	.	.	PUNCT
cana-1737	10	1	keywords	keyword	NOUN
cana-1737	10	2	:	:	PUNCT
cana-1737	10	3	parkinson	parkinson	NOUN
cana-1737	10	4	’s	’s	PART
cana-1737	10	5	disease	disease	NOUN
cana-1737	10	6	;	;	PUNCT
cana-1737	10	7	machine	machine	NOUN
cana-1737	10	8	learning	learning	NOUN
cana-1737	10	9	;	;	PUNCT
cana-1737	10	10	deep	deep	ADJ
cana-1737	10	11	learning	learning	NOUN
cana-1737	10	12	;	;	PUNCT
cana-1737	10	13	convolutional	convolutional	ADJ
cana-1737	10	14	neural	neural	ADJ
cana-1737	10	15	networks	network	NOUN
cana-1737	10	16	;	;	PUNCT
cana-1737	10	17	smote	smote	VERB
cana-1737	10	18	1	1	NUM
cana-1737	10	19	.	.	PUNCT
cana-1737	10	20	introduction	introduction	NOUN
cana-1737	10	21	parkinson	parkinson	NOUN
cana-1737	10	22	’s	’s	PART
cana-1737	10	23	disease	disease	NOUN
cana-1737	10	24	(	(	PUNCT
cana-1737	10	25	pd	pd	NOUN
cana-1737	10	26	)	)	PUNCT
cana-1737	10	27	is	be	AUX
cana-1737	10	28	a	a	DET
cana-1737	10	29	progressive	progressive	ADJ
cana-1737	10	30	condition	condition	NOUN
cana-1737	10	31	affecting	affect	VERB
cana-1737	10	32	both	both	CCONJ
cana-1737	10	33	motor	motor	NOUN
cana-1737	10	34	and	and	CCONJ
cana-1737	10	35	non	non	ADJ
cana-1737	10	36	-	-	ADJ
cana-1737	10	37	motor	motor	ADJ
cana-1737	10	38	functions	function	NOUN
cana-1737	10	39	,	,	PUNCT
cana-1737	10	40	with	with	ADP
cana-1737	10	41	no	no	DET
cana-1737	10	42	known	know	VERB
cana-1737	10	43	cure	cure	NOUN
cana-1737	10	44	.	.	PUNCT
cana-1737	11	1	it	it	PRON
cana-1737	11	2	impacts	impact	VERB
cana-1737	11	3	the	the	DET
cana-1737	11	4	brain	brain	NOUN
cana-1737	11	5	's	's	PART
cana-1737	11	6	neurons	neuron	NOUN
cana-1737	11	7	responsible	responsible	ADJ
cana-1737	11	8	for	for	ADP
cana-1737	11	9	dopamine	dopamine	NOUN
cana-1737	11	10	production	production	NOUN
cana-1737	11	11	,	,	PUNCT
cana-1737	11	12	crucial	crucial	ADJ
cana-1737	11	13	for	for	ADP
cana-1737	11	14	coordination	coordination	NOUN
cana-1737	11	15	,	,	PUNCT
cana-1737	11	16	resulting	result	VERB
cana-1737	11	17	in	in	ADP
cana-1737	11	18	a	a	DET
cana-1737	11	19	variety	variety	NOUN
cana-1737	11	20	of	of	ADP
cana-1737	11	21	motor	motor	NOUN
cana-1737	11	22	symptoms	symptom	NOUN
cana-1737	11	23	like	like	ADP
cana-1737	11	24	tremors	tremor	NOUN
cana-1737	11	25	and	and	CCONJ
cana-1737	11	26	balance	balance	NOUN
cana-1737	11	27	issues	issue	NOUN
cana-1737	11	28	,	,	PUNCT
cana-1737	11	29	as	as	ADV
cana-1737	11	30	well	well	ADV
cana-1737	11	31	as	as	ADP
cana-1737	11	32	nonmotor	nonmotor	NOUN
cana-1737	11	33	symptoms	symptom	NOUN
cana-1737	11	34	such	such	ADJ
cana-1737	11	35	as	as	ADP
cana-1737	11	36	sleep	sleep	NOUN
cana-1737	11	37	disturbances	disturbance	NOUN
cana-1737	11	38	and	and	CCONJ
cana-1737	11	39	speech	speech	NOUN
cana-1737	11	40	difficulties	difficulty	NOUN
cana-1737	11	41	.	.	PUNCT
cana-1737	12	1	pd	pd	PROPN
cana-1737	12	2	has	have	VERB
cana-1737	12	3	complex	complex	ADJ
cana-1737	12	4	origins	origin	NOUN
cana-1737	12	5	involving	involve	VERB
cana-1737	12	6	genetic	genetic	ADJ
cana-1737	12	7	and	and	CCONJ
cana-1737	12	8	environmental	environmental	ADJ
cana-1737	12	9	factors	factor	NOUN
cana-1737	12	10	.	.	PUNCT
cana-1737	13	1	speech	speech	NOUN
cana-1737	13	2	impairments	impairment	NOUN
cana-1737	13	3	are	be	AUX
cana-1737	13	4	common	common	ADJ
cana-1737	13	5	non	non	ADJ
cana-1737	13	6	-	-	ADJ
cana-1737	13	7	motor	motor	ADJ
cana-1737	13	8	symptoms	symptom	NOUN
cana-1737	13	9	of	of	ADP
cana-1737	13	10	pd	pd	PROPN
cana-1737	13	11	,	,	PUNCT
cana-1737	13	12	stemming	stem	VERB
cana-1737	13	13	from	from	ADP
cana-1737	13	14	reduced	reduced	ADJ
cana-1737	13	15	control	control	NOUN
cana-1737	13	16	over	over	ADP
cana-1737	13	17	vocal	vocal	ADJ
cana-1737	13	18	muscles	muscle	NOUN
cana-1737	13	19	and	and	CCONJ
cana-1737	13	20	cognitive	cognitive	ADJ
cana-1737	13	21	decline	decline	NOUN
cana-1737	13	22	.	.	PUNCT
cana-1737	14	1	analysis	analysis	NOUN
cana-1737	14	2	of	of	ADP
cana-1737	14	3	pd	pd	NOUN
cana-1737	14	4	speech	speech	NOUN
cana-1737	14	5	reveals	reveal	VERB
cana-1737	14	6	shorter	short	ADJ
cana-1737	14	7	phonation	phonation	NOUN
cana-1737	14	8	time	time	NOUN
cana-1737	14	9	,	,	PUNCT
cana-1737	14	10	increased	increase	VERB
cana-1737	14	11	jitter	jitter	NOUN
cana-1737	14	12	and	and	CCONJ
cana-1737	14	13	shimmer	shimmer	ADJ
cana-1737	14	14	,	,	PUNCT
cana-1737	14	15	and	and	CCONJ
cana-1737	14	16	altered	alter	VERB
cana-1737	14	17	pitch	pitch	NOUN
cana-1737	14	18	range	range	NOUN
cana-1737	14	19	.	.	PUNCT
cana-1737	15	1	the	the	DET
cana-1737	15	2	unified	unified	ADJ
cana-1737	15	3	parkinson	parkinson	NOUN
cana-1737	15	4	's	's	PART
cana-1737	15	5	disease	disease	NOUN
cana-1737	15	6	rating	rating	NOUN
cana-1737	15	7	scale	scale	NOUN
cana-1737	15	8	evaluates	evaluate	VERB
cana-1737	15	9	these	these	DET
cana-1737	15	10	symptoms	symptom	NOUN
cana-1737	15	11	comprehensively	comprehensively	ADV
cana-1737	15	12	.	.	PUNCT
cana-1737	16	1	pd	pd	PROPN
cana-1737	16	2	progresses	progress	VERB
cana-1737	16	3	through	through	ADP
cana-1737	16	4	stages	stage	NOUN
cana-1737	16	5	,	,	PUNCT
cana-1737	16	6	with	with	ADP
cana-1737	16	7	vocal	vocal	ADJ
cana-1737	16	8	cord	cord	NOUN
cana-1737	16	9	injuries	injury	NOUN
cana-1737	16	10	often	often	ADV
cana-1737	16	11	appearing	appear	VERB
cana-1737	16	12	early	early	ADV
cana-1737	16	13	.	.	PUNCT
cana-1737	17	1	vocal	vocal	ADJ
cana-1737	17	2	impairment	impairment	NOUN
cana-1737	17	3	is	be	AUX
cana-1737	17	4	easily	easily	ADV
cana-1737	17	5	measurable	measurable	ADJ
cana-1737	17	6	and	and	CCONJ
cana-1737	17	7	can	can	AUX
cana-1737	17	8	be	be	AUX
cana-1737	17	9	assessed	assess	VERB
cana-1737	17	10	remotely	remotely	ADV
cana-1737	17	11	through	through	ADP
cana-1737	17	12	telemedicine	telemedicine	NOUN
cana-1737	17	13	,	,	PUNCT
cana-1737	17	14	enabling	enable	VERB
cana-1737	17	15	patients	patient	NOUN
cana-1737	17	16	to	to	PART
cana-1737	17	17	conduct	conduct	VERB
cana-1737	17	18	tests	test	NOUN
cana-1737	17	19	at	at	ADP
cana-1737	17	20	home	home	NOUN
cana-1737	17	21	using	use	VERB
cana-1737	17	22	their	their	PRON
cana-1737	17	23	phones	phone	NOUN
cana-1737	17	24	.	.	PUNCT
cana-1737	18	1	acoustic	acoustic	ADJ
cana-1737	18	2	measurements	measurement	NOUN
cana-1737	18	3	provide	provide	VERB
cana-1737	18	4	valuable	valuable	ADJ
cana-1737	18	5	insights	insight	NOUN
cana-1737	18	6	into	into	ADP
cana-1737	18	7	speech	speech	NOUN
cana-1737	18	8	characteristics	characteristic	NOUN
cana-1737	18	9	,	,	PUNCT
cana-1737	18	10	aiding	aid	VERB
cana-1737	18	11	diagnosis	diagnosis	NOUN
cana-1737	18	12	and	and	CCONJ
cana-1737	18	13	research	research	NOUN
cana-1737	18	14	.	.	PUNCT
cana-1737	19	1	this	this	DET
cana-1737	19	2	study	study	NOUN
cana-1737	19	3	focuses	focus	VERB
cana-1737	19	4	on	on	ADP
cana-1737	19	5	detecting	detect	VERB
cana-1737	19	6	pd	pd	NOUN
cana-1737	19	7	speech	speech	NOUN
cana-1737	19	8	disorders	disorder	NOUN
cana-1737	19	9	using	use	VERB
cana-1737	19	10	convolutional	convolutional	ADJ
cana-1737	19	11	neural	neural	ADJ
cana-1737	19	12	networks	network	NOUN
cana-1737	19	13	(	(	PUNCT
cana-1737	19	14	cnn	cnn	PROPN
cana-1737	19	15	)	)	PUNCT
cana-1737	19	16	,	,	PUNCT
cana-1737	19	17	which	which	PRON
cana-1737	19	18	excel	excel	VERB
cana-1737	19	19	at	at	ADP
cana-1737	19	20	recognizing	recognize	VERB
cana-1737	19	21	subtle	subtle	ADJ
cana-1737	19	22	variations	variation	NOUN
cana-1737	19	23	in	in	ADP
cana-1737	19	24	speech	speech	NOUN
cana-1737	19	25	patterns	pattern	NOUN
cana-1737	19	26	.	.	PUNCT
cana-1737	20	1	by	by	ADP
cana-1737	20	2	analyzing	analyze	VERB
cana-1737	20	3	pitch	pitch	NOUN
cana-1737	20	4	,	,	PUNCT
cana-1737	20	5	rhythm	rhythm	NOUN
cana-1737	20	6	,	,	PUNCT
cana-1737	20	7	and	and	CCONJ
cana-1737	20	8	phonics	phonic	NOUN
cana-1737	20	9	,	,	PUNCT
cana-1737	20	10	cnns	cnns	PROPN
cana-1737	20	11	offer	offer	VERB
cana-1737	20	12	a	a	DET
cana-1737	20	13	non	non	ADJ
cana-1737	20	14	-	-	ADJ
cana-1737	20	15	intrusive	intrusive	ADJ
cana-1737	20	16	method	method	NOUN
cana-1737	20	17	for	for	SCONJ
cana-1737	20	18	early	early	ADJ
cana-1737	20	19	pd	pd	PROPN
cana-1737	20	20	communications	communication	NOUN
cana-1737	20	21	on	on	ADP
cana-1737	20	22	applied	apply	VERB
cana-1737	20	23	nonlinear	nonlinear	ADJ
cana-1737	20	24	analysis	analysis	NOUN
cana-1737	20	25	issn	issn	NOUN
cana-1737	20	26	:	:	PUNCT
cana-1737	20	27	1074	1074	NUM
cana-1737	20	28	-	-	PUNCT
cana-1737	20	29	133x	133x	NUM
cana-1737	20	30	vol	vol	NOUN
cana-1737	20	31	32	32	NUM
cana-1737	20	32	no	no	NOUN
cana-1737	20	33	.	.	NOUN
cana-1737	20	34	2	2	NUM
cana-1737	20	35	(	(	PUNCT
cana-1737	20	36	2025	2025	NUM
cana-1737	20	37	)	)	PUNCT
cana-1737	20	38	203	203	NUM
cana-1737	20	39	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	20	40	detection	detection	NOUN
cana-1737	20	41	.	.	PUNCT
cana-1737	21	1	the	the	DET
cana-1737	21	2	paper	paper	NOUN
cana-1737	21	3	outlines	outline	VERB
cana-1737	21	4	the	the	DET
cana-1737	21	5	use	use	NOUN
cana-1737	21	6	of	of	ADP
cana-1737	21	7	synthetic	synthetic	ADJ
cana-1737	21	8	minority	minority	NOUN
cana-1737	21	9	over	over	ADP
cana-1737	21	10	-	-	PUNCT
cana-1737	21	11	sampling	sample	VERB
cana-1737	21	12	technique	technique	NOUN
cana-1737	21	13	(	(	PUNCT
cana-1737	21	14	smote	smote	NOUN
cana-1737	21	15	)	)	PUNCT
cana-1737	21	16	techniques	technique	NOUN
cana-1737	21	17	to	to	PART
cana-1737	21	18	handle	handle	VERB
cana-1737	21	19	imbalanced	imbalanced	ADJ
cana-1737	21	20	data	datum	NOUN
cana-1737	21	21	and	and	CCONJ
cana-1737	21	22	the	the	DET
cana-1737	21	23	implementation	implementation	NOUN
cana-1737	21	24	of	of	ADP
cana-1737	21	25	cnn	cnn	PROPN
cana-1737	21	26	models	model	NOUN
cana-1737	21	27	for	for	ADP
cana-1737	21	28	classification	classification	NOUN
cana-1737	21	29	.	.	PUNCT
cana-1737	22	1	the	the	DET
cana-1737	22	2	paper	paper	NOUN
cana-1737	22	3	's	's	PART
cana-1737	22	4	structure	structure	NOUN
cana-1737	22	5	includes	include	VERB
cana-1737	22	6	sections	section	NOUN
cana-1737	22	7	on	on	ADP
cana-1737	22	8	related	related	ADJ
cana-1737	22	9	work	work	NOUN
cana-1737	22	10	,	,	PUNCT
cana-1737	22	11	cnn	cnn	PROPN
cana-1737	22	12	algorithm	algorithm	PROPN
cana-1737	22	13	explanation	explanation	PROPN
cana-1737	22	14	,	,	PUNCT
cana-1737	22	15	smote	smote	ADJ
cana-1737	22	16	technique	technique	NOUN
cana-1737	22	17	presentation	presentation	NOUN
cana-1737	22	18	,	,	PUNCT
cana-1737	22	19	methodology	methodology	NOUN
cana-1737	22	20	for	for	ADP
cana-1737	22	21	pd	pd	PROPN
cana-1737	22	22	detection	detection	NOUN
cana-1737	22	23	,	,	PUNCT
cana-1737	22	24	experimental	experimental	ADJ
cana-1737	22	25	results	result	NOUN
cana-1737	22	26	,	,	PUNCT
cana-1737	22	27	and	and	CCONJ
cana-1737	22	28	concluding	conclude	VERB
cana-1737	22	29	remarks	remark	NOUN
cana-1737	22	30	with	with	ADP
cana-1737	22	31	future	future	ADJ
cana-1737	22	32	research	research	NOUN
cana-1737	22	33	directions	direction	NOUN
cana-1737	22	34	.	.	PUNCT
cana-1737	23	1	2	2	X
cana-1737	23	2	.	.	X
cana-1737	23	3	literature	literature	NOUN
cana-1737	23	4	survey	survey	VERB
cana-1737	23	5	previous	previous	ADJ
cana-1737	23	6	research	research	NOUN
cana-1737	23	7	in	in	ADP
cana-1737	23	8	pd	pd	PROPN
cana-1737	23	9	detection	detection	NOUN
cana-1737	23	10	has	have	AUX
cana-1737	23	11	explored	explore	VERB
cana-1737	23	12	various	various	ADJ
cana-1737	23	13	machine	machine	NOUN
cana-1737	23	14	learning	learn	VERB
cana-1737	23	15	techniques	technique	NOUN
cana-1737	23	16	,	,	PUNCT
cana-1737	23	17	with	with	ADP
cana-1737	23	18	an	an	DET
cana-1737	23	19	increasing	increase	VERB
cana-1737	23	20	focus	focus	NOUN
cana-1737	23	21	on	on	ADP
cana-1737	23	22	utilizing	utilize	VERB
cana-1737	23	23	voice	voice	NOUN
cana-1737	23	24	characteristics	characteristic	NOUN
cana-1737	23	25	due	due	ADP
cana-1737	23	26	to	to	ADP
cana-1737	23	27	their	their	PRON
cana-1737	23	28	early	early	ADJ
cana-1737	23	29	manifestation	manifestation	NOUN
cana-1737	23	30	in	in	ADP
cana-1737	23	31	pd	pd	PROPN
cana-1737	23	32	patients	patient	NOUN
cana-1737	23	33	.	.	PUNCT
cana-1737	24	1	in	in	ADP
cana-1737	24	2	[	[	X
cana-1737	24	3	1	1	NUM
cana-1737	24	4	]	]	X
cana-1737	24	5	k	k	ADJ
cana-1737	24	6	-	-	PUNCT
cana-1737	24	7	nearest	near	ADJ
cana-1737	24	8	neighbors	neighbor	NOUN
cana-1737	24	9	(	(	PUNCT
cana-1737	24	10	knn	knn	PROPN
cana-1737	24	11	)	)	PUNCT
cana-1737	24	12	and	and	CCONJ
cana-1737	24	13	support	support	VERB
cana-1737	24	14	vector	vector	NOUN
cana-1737	24	15	machines	machine	NOUN
cana-1737	24	16	(	(	PUNCT
cana-1737	24	17	svm	svm	ADJ
cana-1737	24	18	)	)	PUNCT
cana-1737	24	19	classifiers	classifier	NOUN
cana-1737	24	20	were	be	AUX
cana-1737	24	21	applied	apply	VERB
cana-1737	24	22	to	to	ADP
cana-1737	24	23	the	the	DET
cana-1737	24	24	uci	uci	PROPN
cana-1737	24	25	speech	speech	NOUN
cana-1737	24	26	dataset	dataset	VERB
cana-1737	24	27	for	for	ADP
cana-1737	24	28	the	the	DET
cana-1737	24	29	identification	identification	NOUN
cana-1737	24	30	of	of	ADP
cana-1737	24	31	parkinson	parkinson	NOUN
cana-1737	24	32	’s	’s	PART
cana-1737	24	33	voice	voice	NOUN
cana-1737	24	34	disorder	disorder	NOUN
cana-1737	24	35	the	the	DET
cana-1737	24	36	dataset	dataset	NOUN
cana-1737	24	37	consisted	consist	VERB
cana-1737	24	38	of	of	ADP
cana-1737	24	39	80	80	NUM
cana-1737	24	40	subjects	subject	NOUN
cana-1737	24	41	,	,	PUNCT
cana-1737	24	42	where	where	SCONJ
cana-1737	24	43	40	40	NUM
cana-1737	24	44	were	be	AUX
cana-1737	24	45	diagnosed	diagnose	VERB
cana-1737	24	46	with	with	ADP
cana-1737	24	47	pd	pd	PROPN
cana-1737	24	48	and	and	CCONJ
cana-1737	24	49	40	40	NUM
cana-1737	24	50	healthy	healthy	ADJ
cana-1737	24	51	individuals	individual	NOUN
cana-1737	24	52	.	.	PUNCT
cana-1737	25	1	each	each	DET
cana-1737	25	2	person	person	NOUN
cana-1737	25	3	contributed	contribute	VERB
cana-1737	25	4	three	three	NUM
cana-1737	25	5	sound	sound	ADJ
cana-1737	25	6	samples	sample	NOUN
cana-1737	25	7	,	,	PUNCT
cana-1737	25	8	resulting	result	VERB
cana-1737	25	9	in	in	ADP
cana-1737	25	10	a	a	DET
cana-1737	25	11	total	total	NOUN
cana-1737	25	12	of	of	ADP
cana-1737	25	13	240	240	NUM
cana-1737	25	14	samples	sample	NOUN
cana-1737	25	15	.	.	PUNCT
cana-1737	26	1	from	from	ADP
cana-1737	26	2	these	these	PRON
cana-1737	26	3	,	,	PUNCT
cana-1737	26	4	44	44	NUM
cana-1737	26	5	feature	feature	NOUN
cana-1737	26	6	vectors	vector	NOUN
cana-1737	26	7	were	be	AUX
cana-1737	26	8	created	create	VERB
cana-1737	26	9	,	,	PUNCT
cana-1737	26	10	and	and	CCONJ
cana-1737	26	11	177	177	NUM
cana-1737	26	12	features	feature	NOUN
cana-1737	26	13	were	be	AUX
cana-1737	26	14	extracted	extract	VERB
cana-1737	26	15	.	.	PUNCT
cana-1737	27	1	svm	svm	PROPN
cana-1737	27	2	achieved	achieve	VERB
cana-1737	27	3	91.25	91.25	NUM
cana-1737	27	4	%	%	NOUN
cana-1737	27	5	accuracy	accuracy	NOUN
cana-1737	27	6	and	and	CCONJ
cana-1737	27	7	knn	knn	PROPN
cana-1737	27	8	achieved	achieve	VERB
cana-1737	27	9	91.23	91.23	NUM
cana-1737	27	10	%	%	NOUN
cana-1737	27	11	accuracy	accuracy	NOUN
cana-1737	27	12	.	.	PUNCT
cana-1737	28	1	these	these	DET
cana-1737	28	2	results	result	NOUN
cana-1737	28	3	demonstrate	demonstrate	VERB
cana-1737	28	4	the	the	DET
cana-1737	28	5	effectiveness	effectiveness	NOUN
cana-1737	28	6	of	of	ADP
cana-1737	28	7	both	both	DET
cana-1737	28	8	techniques	technique	NOUN
cana-1737	28	9	in	in	ADP
cana-1737	28	10	accurately	accurately	ADV
cana-1737	28	11	identifying	identify	VERB
cana-1737	28	12	parkinson	parkinson	NOUN
cana-1737	28	13	's	's	PART
cana-1737	28	14	voice	voice	NOUN
cana-1737	28	15	disorder	disorder	NOUN
cana-1737	28	16	based	base	VERB
cana-1737	28	17	on	on	ADP
cana-1737	28	18	voice	voice	NOUN
cana-1737	28	19	characteristics	characteristic	NOUN
cana-1737	28	20	.	.	PUNCT
cana-1737	29	1	in	in	ADP
cana-1737	29	2	[	[	X
cana-1737	29	3	2	2	NUM
cana-1737	29	4	]	]	PUNCT
cana-1737	29	5	,	,	PUNCT
cana-1737	29	6	a	a	DET
cana-1737	29	7	comparative	comparative	ADJ
cana-1737	29	8	study	study	NOUN
cana-1737	29	9	of	of	ADP
cana-1737	29	10	different	different	ADJ
cana-1737	29	11	machine	machine	NOUN
cana-1737	29	12	learning	learn	VERB
cana-1737	29	13	classifiers	classifier	NOUN
cana-1737	29	14	and	and	CCONJ
cana-1737	29	15	twin	twin	ADJ
cana-1737	29	16	-	-	PUNCT
cana-1737	29	17	support	support	NOUN
cana-1737	29	18	vector	vector	NOUN
cana-1737	29	19	machine	machine	NOUN
cana-1737	29	20	(	(	PUNCT
cana-1737	29	21	tsvm	tsvm	NOUN
cana-1737	29	22	)	)	PUNCT
cana-1737	29	23	classifiers	classifier	NOUN
cana-1737	29	24	with	with	ADP
cana-1737	29	25	and	and	CCONJ
cana-1737	29	26	without	without	ADP
cana-1737	29	27	feature	feature	NOUN
cana-1737	29	28	selection	selection	NOUN
cana-1737	29	29	methods	method	NOUN
cana-1737	29	30	was	be	AUX
cana-1737	29	31	carried	carry	VERB
cana-1737	29	32	out	out	ADP
cana-1737	29	33	.	.	PUNCT
cana-1737	30	1	the	the	DET
cana-1737	30	2	dataset	dataset	NOUN
cana-1737	30	3	,	,	PUNCT
cana-1737	30	4	sourced	source	VERB
cana-1737	30	5	from	from	ADP
cana-1737	30	6	the	the	DET
cana-1737	30	7	uci	uci	PROPN
cana-1737	30	8	machine	machine	NOUN
cana-1737	30	9	learning	learn	VERB
cana-1737	30	10	repository	repository	NOUN
cana-1737	30	11	,	,	PUNCT
cana-1737	30	12	consists	consist	VERB
cana-1737	30	13	of	of	ADP
cana-1737	30	14	147	147	NUM
cana-1737	30	15	samples	sample	NOUN
cana-1737	30	16	categorized	categorize	VERB
cana-1737	30	17	as	as	ADP
cana-1737	30	18	pd	pd	PROPN
cana-1737	30	19	and	and	CCONJ
cana-1737	30	20	48	48	NUM
cana-1737	30	21	samples	sample	NOUN
cana-1737	30	22	categorized	categorize	VERB
cana-1737	30	23	as	as	ADP
cana-1737	30	24	hc	hc	PROPN
cana-1737	30	25	.	.	PUNCT
cana-1737	31	1	to	to	PART
cana-1737	31	2	address	address	VERB
cana-1737	31	3	class	class	NOUN
cana-1737	31	4	imbalance	imbalance	NOUN
cana-1737	31	5	issues	issue	NOUN
cana-1737	31	6	,	,	PUNCT
cana-1737	31	7	smote	smote	VERB
cana-1737	31	8	oversampling	oversample	VERB
cana-1737	31	9	method	method	NOUN
cana-1737	31	10	is	be	AUX
cana-1737	31	11	implemented	implement	VERB
cana-1737	31	12	.	.	PUNCT
cana-1737	32	1	correlation	correlation	NOUN
cana-1737	32	2	-	-	PUNCT
cana-1737	32	3	based	base	VERB
cana-1737	32	4	feature	feature	NOUN
cana-1737	32	5	subset	subset	NOUN
cana-1737	32	6	selection	selection	NOUN
cana-1737	32	7	method	method	NOUN
cana-1737	32	8	used	use	VERB
cana-1737	32	9	.	.	PUNCT
cana-1737	33	1	using	use	VERB
cana-1737	33	2	a	a	DET
cana-1737	33	3	forward	forward	ADJ
cana-1737	33	4	selection	selection	NOUN
cana-1737	33	5	method	method	NOUN
cana-1737	33	6	with	with	ADP
cana-1737	33	7	weka	weka	PROPN
cana-1737	33	8	’s	’s	PART
cana-1737	33	9	best	well	ADV
cana-1737	33	10	first	first	ADV
cana-1737	33	11	searching	search	VERB
cana-1737	33	12	techniques	technique	NOUN
cana-1737	33	13	13	13	NUM
cana-1737	33	14	features	feature	NOUN
cana-1737	33	15	were	be	AUX
cana-1737	33	16	selected	select	VERB
cana-1737	33	17	.	.	PUNCT
cana-1737	34	1	the	the	DET
cana-1737	34	2	different	different	ADJ
cana-1737	34	3	machine	machine	NOUN
cana-1737	34	4	learning	learn	VERB
cana-1737	34	5	classifiers	classifier	NOUN
cana-1737	34	6	such	such	ADJ
cana-1737	34	7	as	as	ADP
cana-1737	34	8	logistic	logistic	ADJ
cana-1737	34	9	regression	regression	NOUN
cana-1737	34	10	(	(	PUNCT
cana-1737	34	11	lr	lr	NOUN
cana-1737	34	12	)	)	PUNCT
cana-1737	34	13	,	,	PUNCT
cana-1737	34	14	support	support	VERB
cana-1737	34	15	vector	vector	NOUN
cana-1737	34	16	machines	machine	NOUN
cana-1737	34	17	(	(	PUNCT
cana-1737	34	18	svm	svm	PROPN
cana-1737	34	19	)	)	PUNCT
cana-1737	34	20	,	,	PUNCT
cana-1737	34	21	naive	naive	ADJ
cana-1737	34	22	bayes	bayes	NOUN
cana-1737	34	23	(	(	PUNCT
cana-1737	34	24	nb	nb	NOUN
cana-1737	34	25	)	)	PUNCT
cana-1737	34	26	,	,	PUNCT
cana-1737	34	27	decision	decision	NOUN
cana-1737	34	28	tree	tree	NOUN
cana-1737	34	29	(	(	PUNCT
cana-1737	34	30	dt	dt	NOUN
cana-1737	34	31	)	)	PUNCT
cana-1737	34	32	,	,	PUNCT
cana-1737	34	33	and	and	CCONJ
cana-1737	34	34	k	k	X
cana-1737	34	35	-	-	PUNCT
cana-1737	34	36	nearest	near	ADJ
cana-1737	34	37	neighbor	neighbor	NOUN
cana-1737	34	38	(	(	PUNCT
cana-1737	34	39	knn	knn	PROPN
cana-1737	34	40	)	)	PUNCT
cana-1737	34	41	were	be	AUX
cana-1737	34	42	used	use	VERB
cana-1737	34	43	for	for	ADP
cana-1737	34	44	comparative	comparative	ADJ
cana-1737	34	45	analysis	analysis	NOUN
cana-1737	34	46	.	.	PUNCT
cana-1737	35	1	with	with	ADP
cana-1737	35	2	feature	feature	NOUN
cana-1737	35	3	selection	selection	NOUN
cana-1737	35	4	lr	lr	NOUN
cana-1737	35	5	achieved	achieve	VERB
cana-1737	35	6	85.0	85.0	NUM
cana-1737	35	7	%	%	NOUN
cana-1737	35	8	,	,	PUNCT
cana-1737	35	9	svm	svm	PROPN
cana-1737	35	10	achieved	achieve	VERB
cana-1737	35	11	84.7	84.7	NUM
cana-1737	35	12	%	%	NOUN
cana-1737	35	13	,	,	PUNCT
cana-1737	35	14	naïve	naïve	ADJ
cana-1737	35	15	bayes	baye	NOUN
cana-1737	35	16	achieved	achieve	VERB
cana-1737	35	17	80.6	80.6	NUM
cana-1737	35	18	%	%	NOUN
cana-1737	35	19	,	,	PUNCT
cana-1737	35	20	dt	dt	PROPN
cana-1737	35	21	achieved	achieve	VERB
cana-1737	35	22	86.4	86.4	NUM
cana-1737	35	23	%	%	NOUN
cana-1737	35	24	,	,	PUNCT
cana-1737	35	25	and	and	CCONJ
cana-1737	35	26	knn	knn	PROPN
cana-1737	35	27	achieved	achieve	VERB
cana-1737	35	28	88.8	88.8	NUM
cana-1737	35	29	%	%	NOUN
cana-1737	35	30	.	.	PUNCT
cana-1737	36	1	among	among	ADP
cana-1737	36	2	these	these	DET
cana-1737	36	3	algorithms	algorithm	NOUN
cana-1737	36	4	,	,	PUNCT
cana-1737	36	5	tsvm	tsvm	PROPN
cana-1737	36	6	achieved	achieve	VERB
cana-1737	36	7	the	the	DET
cana-1737	36	8	highest	high	ADJ
cana-1737	36	9	accuracy	accuracy	NOUN
cana-1737	36	10	of	of	ADP
cana-1737	36	11	93.2	93.2	NUM
cana-1737	36	12	%	%	NOUN
cana-1737	36	13	without	without	ADP
cana-1737	36	14	feature	feature	NOUN
cana-1737	36	15	selection	selection	NOUN
cana-1737	36	16	and	and	CCONJ
cana-1737	36	17	93.9	93.9	NUM
cana-1737	36	18	%	%	NOUN
cana-1737	36	19	with	with	ADP
cana-1737	36	20	feature	feature	NOUN
cana-1737	36	21	selection	selection	NOUN
cana-1737	36	22	.	.	PUNCT
cana-1737	37	1	the	the	DET
cana-1737	37	2	comparative	comparative	ADJ
cana-1737	37	3	analysis	analysis	NOUN
cana-1737	37	4	of	of	ADP
cana-1737	37	5	ensemble	ensemble	ADJ
cana-1737	37	6	learning	learning	NOUN
cana-1737	37	7	classifiers	classifier	NOUN
cana-1737	37	8	to	to	PART
cana-1737	37	9	classify	classify	VERB
cana-1737	37	10	pd	pd	PROPN
cana-1737	37	11	is	be	AUX
cana-1737	37	12	conducted	conduct	VERB
cana-1737	37	13	in	in	ADP
cana-1737	37	14	[	[	X
cana-1737	37	15	3	3	NUM
cana-1737	37	16	]	]	PUNCT
cana-1737	37	17	.	.	PUNCT
cana-1737	38	1	the	the	DET
cana-1737	38	2	authors	author	NOUN
cana-1737	38	3	investigate	investigate	VERB
cana-1737	38	4	parkinson	parkinson	NOUN
cana-1737	38	5	's	's	PART
cana-1737	38	6	disease	disease	NOUN
cana-1737	38	7	classification	classification	NOUN
cana-1737	38	8	using	use	VERB
cana-1737	38	9	vocal	vocal	ADJ
cana-1737	38	10	datasets	dataset	NOUN
cana-1737	38	11	and	and	CCONJ
cana-1737	38	12	compare	compare	VERB
cana-1737	38	13	two	two	NUM
cana-1737	38	14	different	different	ADJ
cana-1737	38	15	ensemble	ensemble	ADJ
cana-1737	38	16	learning	learning	NOUN
cana-1737	38	17	techniques	technique	NOUN
cana-1737	38	18	.	.	PUNCT
cana-1737	39	1	they	they	PRON
cana-1737	39	2	introduce	introduce	VERB
cana-1737	39	3	the	the	DET
cana-1737	39	4	stacking	stacking	NOUN
cana-1737	39	5	classifier	classifier	NOUN
cana-1737	39	6	and	and	CCONJ
cana-1737	39	7	voting	voting	NOUN
cana-1737	39	8	classifier	classifier	NOUN
cana-1737	39	9	to	to	PART
cana-1737	39	10	distinguish	distinguish	VERB
cana-1737	39	11	between	between	ADP
cana-1737	39	12	pd	pd	NOUN
cana-1737	39	13	patients	patient	NOUN
cana-1737	39	14	and	and	CCONJ
cana-1737	39	15	non	non	ADJ
cana-1737	39	16	-	-	ADJ
cana-1737	39	17	pd	pd	ADJ
cana-1737	39	18	patients	patient	NOUN
cana-1737	39	19	.	.	PUNCT
cana-1737	40	1	the	the	DET
cana-1737	40	2	dataset	dataset	NOUN
cana-1737	40	3	comprises	comprise	NOUN
cana-1737	40	4	sustained	sustain	VERB
cana-1737	40	5	phonation	phonation	NOUN
cana-1737	40	6	recordings	recording	NOUN
cana-1737	40	7	of	of	ADP
cana-1737	40	8	the	the	DET
cana-1737	40	9	vowel	vowel	NOUN
cana-1737	40	10	/a/	/a/	PUNCT
cana-1737	40	11	from	from	ADP
cana-1737	40	12	188	188	NUM
cana-1737	40	13	pd	pd	NOUN
cana-1737	40	14	patients	patient	NOUN
cana-1737	40	15	and	and	CCONJ
cana-1737	40	16	64	64	NUM
cana-1737	40	17	non	non	ADJ
cana-1737	40	18	-	-	ADJ
cana-1737	40	19	pd	pd	ADJ
cana-1737	40	20	patients	patient	NOUN
cana-1737	40	21	,	,	PUNCT
cana-1737	40	22	sourced	source	VERB
cana-1737	40	23	from	from	ADP
cana-1737	40	24	the	the	DET
cana-1737	40	25	uci	uci	PROPN
cana-1737	40	26	machine	machine	NOUN
cana-1737	40	27	learning	learn	VERB
cana-1737	40	28	repository	repository	NOUN
cana-1737	40	29	created	create	VERB
cana-1737	40	30	by	by	ADP
cana-1737	40	31	the	the	DET
cana-1737	40	32	department	department	PROPN
cana-1737	40	33	of	of	ADP
cana-1737	40	34	neurology	neurology	NOUN
cana-1737	40	35	at	at	ADP
cana-1737	40	36	cerrahpasa	cerrahpasa	PROPN
cana-1737	40	37	faculty	faculty	NOUN
cana-1737	40	38	of	of	ADP
cana-1737	40	39	medicine	medicine	NOUN
cana-1737	40	40	,	,	PUNCT
cana-1737	40	41	istanbul	istanbul	PROPN
cana-1737	40	42	university	university	PROPN
cana-1737	40	43	.	.	PUNCT
cana-1737	41	1	the	the	DET
cana-1737	41	2	voting	voting	NOUN
cana-1737	41	3	classifier	classifier	NOUN
cana-1737	41	4	achieves	achieve	VERB
cana-1737	41	5	an	an	DET
cana-1737	41	6	accuracy	accuracy	NOUN
cana-1737	41	7	of	of	ADP
cana-1737	41	8	88.8	88.8	NUM
cana-1737	41	9	%	%	NOUN
cana-1737	41	10	,	,	PUNCT
cana-1737	41	11	while	while	SCONJ
cana-1737	41	12	the	the	DET
cana-1737	41	13	stacking	stacking	NOUN
cana-1737	41	14	classifier	classifier	NOUN
cana-1737	41	15	achieves	achieve	VERB
cana-1737	41	16	the	the	DET
cana-1737	41	17	highest	high	ADJ
cana-1737	41	18	accuracy	accuracy	NOUN
cana-1737	41	19	of	of	ADP
cana-1737	41	20	92.2	92.2	NUM
cana-1737	41	21	%	%	NOUN
cana-1737	41	22	.	.	PUNCT
cana-1737	42	1	an	an	DET
cana-1737	42	2	innovative	innovative	ADJ
cana-1737	42	3	framework	framework	NOUN
cana-1737	42	4	for	for	ADP
cana-1737	42	5	detecting	detect	VERB
cana-1737	42	6	voice	voice	NOUN
cana-1737	42	7	loss	loss	NOUN
cana-1737	42	8	in	in	ADP
cana-1737	42	9	parkinson	parkinson	NOUN
cana-1737	42	10	's	's	PART
cana-1737	42	11	disease	disease	NOUN
cana-1737	42	12	(	(	PUNCT
cana-1737	42	13	pd	pd	NOUN
cana-1737	42	14	)	)	PUNCT
cana-1737	42	15	by	by	ADP
cana-1737	42	16	employing	employ	VERB
cana-1737	42	17	a	a	DET
cana-1737	42	18	twolevel	twolevel	NOUN
cana-1737	42	19	feature	feature	NOUN
cana-1737	42	20	selection	selection	NOUN
cana-1737	42	21	process	process	NOUN
cana-1737	42	22	based	base	VERB
cana-1737	42	23	on	on	ADP
cana-1737	42	24	weight	weight	NOUN
cana-1737	42	25	was	be	AUX
cana-1737	42	26	introduced	introduce	VERB
cana-1737	42	27	in	in	ADP
cana-1737	42	28	[	[	X
cana-1737	42	29	4	4	NUM
cana-1737	42	30	]	]	PUNCT
cana-1737	42	31	.	.	PUNCT
cana-1737	43	1	principal	principal	ADJ
cana-1737	43	2	component	component	NOUN
cana-1737	43	3	analysis	analysis	NOUN
cana-1737	43	4	(	(	PUNCT
cana-1737	43	5	pca	pca	NOUN
cana-1737	43	6	)	)	PUNCT
cana-1737	43	7	and	and	CCONJ
cana-1737	43	8	the	the	DET
cana-1737	43	9	eigenvector	eigenvector	NOUN
cana-1737	43	10	centrality	centrality	NOUN
cana-1737	43	11	feature	feature	NOUN
cana-1737	43	12	selection	selection	NOUN
cana-1737	43	13	(	(	PUNCT
cana-1737	43	14	ecfs	ecfs	NOUN
cana-1737	43	15	)	)	PUNCT
cana-1737	43	16	methods	method	NOUN
cana-1737	43	17	were	be	AUX
cana-1737	43	18	employed	employ	VERB
cana-1737	43	19	for	for	ADP
cana-1737	43	20	feature	feature	NOUN
cana-1737	43	21	selection	selection	NOUN
cana-1737	43	22	.	.	PUNCT
cana-1737	44	1	features	feature	VERB
cana-1737	44	2	selection	selection	NOUN
cana-1737	44	3	sets	set	NOUN
cana-1737	44	4	generated	generate	VERB
cana-1737	44	5	from	from	ADP
cana-1737	44	6	pca	pca	PROPN
cana-1737	44	7	and	and	CCONJ
cana-1737	44	8	ecfs	ecfs	PROPN
cana-1737	44	9	were	be	AUX
cana-1737	44	10	combined	combine	VERB
cana-1737	44	11	.	.	PUNCT
cana-1737	45	1	the	the	DET
cana-1737	45	2	svm	svm	PROPN
cana-1737	45	3	classifier	classifier	NOUN
cana-1737	45	4	applied	apply	VERB
cana-1737	45	5	on	on	ADP
cana-1737	45	6	hybrid	hybrid	NOUN
cana-1737	45	7	weighted	weight	VERB
cana-1737	45	8	features	feature	NOUN
cana-1737	45	9	selected	select	VERB
cana-1737	45	10	set	set	NOUN
cana-1737	45	11	.	.	PUNCT
cana-1737	46	1	the	the	DET
cana-1737	46	2	dataset	dataset	NOUN
cana-1737	46	3	was	be	AUX
cana-1737	46	4	taken	take	VERB
cana-1737	46	5	from	from	ADP
cana-1737	46	6	the	the	DET
cana-1737	46	7	uci	uci	PROPN
cana-1737	46	8	machine	machine	NOUN
cana-1737	46	9	learning	learn	VERB
cana-1737	46	10	repository	repository	NOUN
cana-1737	46	11	which	which	PRON
cana-1737	46	12	includes	include	VERB
cana-1737	46	13	756	756	NUM
cana-1737	46	14	instances	instance	NOUN
cana-1737	46	15	and	and	CCONJ
cana-1737	46	16	753	753	NUM
cana-1737	46	17	features	feature	NOUN
cana-1737	46	18	where	where	SCONJ
cana-1737	46	19	each	each	DET
cana-1737	46	20	subject	subject	NOUN
cana-1737	46	21	has	have	VERB
cana-1737	46	22	3	3	NUM
cana-1737	46	23	records	record	NOUN
cana-1737	46	24	.	.	PUNCT
cana-1737	47	1	the	the	DET
cana-1737	47	2	communications	communication	NOUN
cana-1737	47	3	on	on	ADP
cana-1737	47	4	applied	apply	VERB
cana-1737	47	5	nonlinear	nonlinear	ADJ
cana-1737	47	6	analysis	analysis	NOUN
cana-1737	47	7	issn	issn	NOUN
cana-1737	47	8	:	:	PUNCT
cana-1737	47	9	1074	1074	NUM
cana-1737	47	10	-	-	PUNCT
cana-1737	47	11	133x	133x	NUM
cana-1737	47	12	vol	vol	NOUN
cana-1737	47	13	32	32	NUM
cana-1737	47	14	no	no	NOUN
cana-1737	47	15	.	.	NOUN
cana-1737	47	16	2	2	NUM
cana-1737	47	17	(	(	PUNCT
cana-1737	47	18	2025	2025	NUM
cana-1737	47	19	)	)	PUNCT
cana-1737	47	20	204	204	NUM
cana-1737	47	21	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1737	47	22	proposed	propose	VERB
cana-1737	47	23	cubic	cubic	ADJ
cana-1737	47	24	kernel	kernel	NOUN
cana-1737	47	25	-	-	PUNCT
cana-1737	47	26	svm	svm	ADJ
cana-1737	47	27	model	model	NOUN
cana-1737	47	28	achieved	achieve	VERB
cana-1737	47	29	94	94	NUM
cana-1737	47	30	%	%	NOUN
cana-1737	47	31	accuracy	accuracy	NOUN
cana-1737	47	32	,	,	PUNCT
cana-1737	47	33	while	while	SCONJ
cana-1737	47	34	the	the	DET
cana-1737	47	35	svm	svm	ADJ
cana-1737	47	36	classifier	classifier	NOUN
cana-1737	47	37	without	without	ADP
cana-1737	47	38	the	the	DET
cana-1737	47	39	feature	feature	NOUN
cana-1737	47	40	selection	selection	NOUN
cana-1737	47	41	method	method	NOUN
cana-1737	47	42	achieved	achieve	VERB
cana-1737	47	43	88	88	NUM
cana-1737	47	44	%	%	NOUN
cana-1737	47	45	accuracy	accuracy	NOUN
cana-1737	47	46	.	.	PUNCT
cana-1737	48	1	various	various	ADJ
cana-1737	48	2	machine	machine	NOUN
cana-1737	48	3	learning	learn	VERB
cana-1737	48	4	techniques	technique	NOUN
cana-1737	48	5	for	for	ADP
cana-1737	48	6	pd	pd	NOUN
cana-1737	48	7	speech	speech	NOUN
cana-1737	48	8	feature	feature	NOUN
cana-1737	48	9	classification	classification	NOUN
cana-1737	48	10	were	be	AUX
cana-1737	48	11	examined	examine	VERB
cana-1737	48	12	and	and	CCONJ
cana-1737	48	13	compared	compare	VERB
cana-1737	48	14	using	use	VERB
cana-1737	48	15	multilayer	multilayer	ADJ
cana-1737	48	16	perceptron	perceptron	PROPN
cana-1737	48	17	(	(	PUNCT
cana-1737	48	18	mlp	mlp	PROPN
cana-1737	48	19	)	)	PUNCT
cana-1737	48	20	,	,	PUNCT
cana-1737	48	21	knn	knn	PROPN
cana-1737	48	22	,	,	PUNCT
cana-1737	48	23	svm	svm	ADJ
cana-1737	48	24	,	,	PUNCT
cana-1737	48	25	random	random	ADJ
cana-1737	48	26	forest	forest	NOUN
cana-1737	48	27	(	(	PUNCT
cana-1737	48	28	rf	rf	NOUN
cana-1737	48	29	)	)	PUNCT
cana-1737	48	30	,	,	PUNCT
cana-1737	48	31	and	and	CCONJ
cana-1737	48	32	extreme	extreme	ADJ
cana-1737	48	33	gradient	gradient	NOUN
cana-1737	48	34	boosting	boost	VERB
cana-1737	48	35	(	(	PUNCT
cana-1737	48	36	xgbooster	xgbooster	NOUN
cana-1737	48	37	)	)	PUNCT
cana-1737	48	38	classifiers	classifier	NOUN
cana-1737	48	39	in	in	ADP
cana-1737	48	40	[	[	X
cana-1737	48	41	5	5	NUM
cana-1737	48	42	]	]	PUNCT
cana-1737	48	43	.	.	PUNCT
cana-1737	49	1	the	the	DET
cana-1737	49	2	authors	author	NOUN
cana-1737	49	3	proposed	propose	VERB
cana-1737	49	4	a	a	DET
cana-1737	49	5	principal	principal	ADJ
cana-1737	49	6	component	component	NOUN
cana-1737	49	7	analysis	analysis	NOUN
cana-1737	49	8	-	-	PUNCT
cana-1737	49	9	support	support	NOUN
cana-1737	49	10	vector	vector	NOUN
cana-1737	49	11	machine	machine	NOUN
cana-1737	49	12	(	(	PUNCT
cana-1737	49	13	pca	pca	NOUN
cana-1737	49	14	-	-	PUNCT
cana-1737	49	15	svm	svm	NOUN
cana-1737	49	16	)	)	PUNCT
cana-1737	49	17	and	and	CCONJ
cana-1737	49	18	a	a	DET
cana-1737	49	19	sparse	sparse	ADJ
cana-1737	49	20	autoencodersupport	autoencodersupport	NOUN
cana-1737	49	21	vector	vector	NOUN
cana-1737	49	22	machine	machine	NOUN
cana-1737	49	23	(	(	PUNCT
cana-1737	49	24	sae	sae	PROPN
cana-1737	49	25	-	-	ADJ
cana-1737	49	26	svm	svm	ADJ
cana-1737	49	27	)	)	PUNCT
cana-1737	49	28	two	two	NUM
cana-1737	49	29	hybrid	hybrid	ADJ
cana-1737	49	30	models	model	NOUN
cana-1737	49	31	for	for	ADP
cana-1737	49	32	analyzing	analyze	VERB
cana-1737	49	33	voice	voice	NOUN
cana-1737	49	34	disorders	disorder	NOUN
cana-1737	49	35	in	in	ADP
cana-1737	49	36	patients	patient	NOUN
cana-1737	49	37	.	.	PUNCT
cana-1737	50	1	this	this	DET
cana-1737	50	2	study	study	NOUN
cana-1737	50	3	uses	use	VERB
cana-1737	50	4	a	a	DET
cana-1737	50	5	uci	uci	NOUN
cana-1737	50	6	speech	speech	NOUN
cana-1737	50	7	dataset	dataset	NOUN
cana-1737	50	8	containing	contain	VERB
cana-1737	50	9	a	a	DET
cana-1737	50	10	total	total	NOUN
cana-1737	50	11	of	of	ADP
cana-1737	50	12	756	756	NUM
cana-1737	50	13	voice	voice	NOUN
cana-1737	50	14	samples	sample	NOUN
cana-1737	50	15	which	which	PRON
cana-1737	50	16	contains	contain	VERB
cana-1737	50	17	754	754	NUM
cana-1737	50	18	speech	speech	NOUN
cana-1737	50	19	feature	feature	NOUN
cana-1737	50	20	attributes	attribute	NOUN
cana-1737	50	21	.	.	PUNCT
cana-1737	51	1	out	out	ADP
cana-1737	51	2	of	of	ADP
cana-1737	51	3	252	252	NUM
cana-1737	51	4	subjects,188	subjects,188	PROPN
cana-1737	51	5	belonged	belong	VERB
cana-1737	51	6	to	to	ADP
cana-1737	51	7	pd	pd	NOUN
cana-1737	51	8	samples	sample	NOUN
cana-1737	51	9	and	and	CCONJ
cana-1737	51	10	the	the	DET
cana-1737	51	11	remaining	remain	VERB
cana-1737	51	12	64	64	NUM
cana-1737	51	13	belonged	belong	VERB
cana-1737	51	14	to	to	ADP
cana-1737	51	15	healthy	healthy	ADJ
cana-1737	51	16	samples	sample	NOUN
cana-1737	51	17	.	.	PUNCT
cana-1737	52	1	smote	smote	ADJ
cana-1737	52	2	techniques	technique	NOUN
cana-1737	52	3	applied	apply	VERB
cana-1737	52	4	to	to	ADP
cana-1737	52	5	the	the	DET
cana-1737	52	6	dataset	dataset	NOUN
cana-1737	52	7	.	.	PUNCT
cana-1737	53	1	rf	rf	NOUN
cana-1737	53	2	classifiers	classifier	NOUN
cana-1737	53	3	achieved	achieve	VERB
cana-1737	53	4	83.6	83.6	NUM
cana-1737	53	5	%	%	NOUN
cana-1737	53	6	accuracy	accuracy	NOUN
cana-1737	53	7	,	,	PUNCT
cana-1737	53	8	mlp	mlp	PROPN
cana-1737	53	9	classifier	classifier	NOUN
cana-1737	53	10	achieved	achieve	VERB
cana-1737	53	11	84.5	84.5	NUM
cana-1737	53	12	%	%	NOUN
cana-1737	53	13	accuracy	accuracy	NOUN
cana-1737	53	14	,	,	PUNCT
cana-1737	53	15	knn	knn	PROPN
cana-1737	53	16	classifier	classifier	PROPN
cana-1737	53	17	achieved	achieve	VERB
cana-1737	53	18	76.5	76.5	NUM
cana-1737	53	19	%	%	NOUN
cana-1737	53	20	accuracy	accuracy	NOUN
cana-1737	53	21	,	,	PUNCT
cana-1737	53	22	xgboost	xgboost	PRON
cana-1737	53	23	classifier	classifier	PROPN
cana-1737	53	24	achieved	achieve	VERB
cana-1737	53	25	88.1	88.1	NUM
cana-1737	53	26	%	%	NOUN
cana-1737	53	27	accuracy	accuracy	NOUN
cana-1737	53	28	and	and	CCONJ
cana-1737	53	29	svm	svm	ADJ
cana-1737	53	30	classifier	classifier	NOUN
cana-1737	53	31	achieved	achieve	VERB
cana-1737	53	32	85.4	85.4	NUM
cana-1737	53	33	%	%	NOUN
cana-1737	53	34	accuracy	accuracy	NOUN
cana-1737	53	35	whereas	whereas	SCONJ
cana-1737	53	36	the	the	DET
cana-1737	53	37	proposed	propose	VERB
cana-1737	53	38	model	model	NOUN
cana-1737	53	39	pca	pca	PROPN
cana-1737	53	40	-	-	PUNCT
cana-1737	53	41	svm	svm	PROPN
cana-1737	53	42	achieved	achieve	VERB
cana-1737	53	43	88.9	88.9	NUM
cana-1737	53	44	%	%	NOUN
cana-1737	53	45	accuracy	accuracy	NOUN
cana-1737	53	46	and	and	CCONJ
cana-1737	53	47	the	the	DET
cana-1737	53	48	highest	high	ADJ
cana-1737	53	49	accuracy	accuracy	NOUN
cana-1737	53	50	93.5	93.5	NUM
cana-1737	53	51	%	%	NOUN
cana-1737	53	52	achieved	achieve	VERB
cana-1737	53	53	by	by	ADP
cana-1737	53	54	sae	sae	NOUN
cana-1737	53	55	-	-	ADJ
cana-1737	53	56	svm	svm	ADJ
cana-1737	53	57	model	model	NOUN
cana-1737	53	58	.	.	PUNCT
cana-1737	54	1	in	in	ADP
cana-1737	54	2	[	[	X
cana-1737	54	3	6	6	NUM
cana-1737	54	4	]	]	PUNCT
cana-1737	54	5	,	,	PUNCT
cana-1737	54	6	a	a	DET
cana-1737	54	7	deep	deep	ADJ
cana-1737	54	8	learning	learning	NOUN
cana-1737	54	9	approach	approach	NOUN
cana-1737	54	10	,	,	PUNCT
cana-1737	54	11	cnn	cnn	PROPN
cana-1737	54	12	,	,	PUNCT
cana-1737	54	13	is	be	AUX
cana-1737	54	14	employed	employ	VERB
cana-1737	54	15	to	to	PART
cana-1737	54	16	detect	detect	VERB
cana-1737	54	17	pd	pd	NOUN
cana-1737	54	18	speech	speech	NOUN
cana-1737	54	19	signals	signal	NOUN
cana-1737	54	20	.	.	PUNCT
cana-1737	55	1	two	two	NUM
cana-1737	55	2	-	-	PUNCT
cana-1737	55	3	dimensional	dimensional	ADJ
cana-1737	55	4	convolutional	convolutional	ADJ
cana-1737	55	5	neural	neural	ADJ
cana-1737	55	6	networks	network	NOUN
cana-1737	55	7	(	(	PUNCT
cana-1737	55	8	2d	2d	NOUN
cana-1737	55	9	-	-	PUNCT
cana-1737	55	10	cnns	cnn	NOUN
cana-1737	55	11	)	)	PUNCT
cana-1737	55	12	are	be	AUX
cana-1737	55	13	used	use	VERB
cana-1737	55	14	to	to	PART
cana-1737	55	15	extract	extract	VERB
cana-1737	55	16	dynamic	dynamic	ADJ
cana-1737	55	17	features	feature	NOUN
cana-1737	55	18	from	from	ADP
cana-1737	55	19	speech	speech	NOUN
cana-1737	55	20	signals	signal	NOUN
cana-1737	55	21	.	.	PUNCT
cana-1737	56	1	these	these	DET
cana-1737	56	2	features	feature	NOUN
cana-1737	56	3	are	be	AUX
cana-1737	56	4	processed	process	VERB
cana-1737	56	5	using	use	VERB
cana-1737	56	6	time	time	NOUN
cana-1737	56	7	-	-	PUNCT
cana-1737	56	8	distributed	distribute	VERB
cana-1737	56	9	techniques	technique	NOUN
cana-1737	56	10	to	to	PART
cana-1737	56	11	capture	capture	VERB
cana-1737	56	12	temporal	temporal	ADJ
cana-1737	56	13	dynamics	dynamic	NOUN
cana-1737	56	14	.	.	PUNCT
cana-1737	57	1	further	far	ADV
cana-1737	57	2	,	,	PUNCT
cana-1737	57	3	a	a	DET
cana-1737	57	4	one	one	NUM
cana-1737	57	5	-	-	PUNCT
cana-1737	57	6	dimensional	dimensional	ADJ
cana-1737	57	7	cnn	cnn	NOUN
cana-1737	57	8	(	(	PUNCT
cana-1737	57	9	1d	1d	PROPN
cana-1737	57	10	-	-	PUNCT
cana-1737	57	11	cnn	cnn	NOUN
cana-1737	57	12	)	)	PUNCT
cana-1737	57	13	captured	capture	VERB
cana-1737	57	14	the	the	DET
cana-1737	57	15	dependencies	dependency	NOUN
cana-1737	57	16	among	among	ADP
cana-1737	57	17	these	these	DET
cana-1737	57	18	dynamic	dynamic	ADJ
cana-1737	57	19	features	feature	NOUN
cana-1737	57	20	.	.	PUNCT
cana-1737	58	1	the	the	DET
cana-1737	58	2	two	two	NUM
cana-1737	58	3	datasets	dataset	NOUN
cana-1737	58	4	used	use	VERB
cana-1737	58	5	in	in	ADP
cana-1737	58	6	the	the	DET
cana-1737	58	7	study	study	NOUN
cana-1737	58	8	were	be	AUX
cana-1737	58	9	collected	collect	VERB
cana-1737	58	10	from	from	ADP
cana-1737	58	11	the	the	DET
cana-1737	58	12	gyenno	gyenno	PROPN
cana-1737	58	13	science	science	NOUN
cana-1737	58	14	parkinson	parkinson	NOUN
cana-1737	58	15	speech	speech	NOUN
cana-1737	58	16	data	datum	NOUN
cana-1737	58	17	and	and	CCONJ
cana-1737	58	18	the	the	DET
cana-1737	58	19	pc	pc	NOUN
cana-1737	58	20	-	-	PUNCT
cana-1737	58	21	pita	pita	NOUN
cana-1737	58	22	database	database	NOUN
cana-1737	58	23	.	.	PUNCT
cana-1737	59	1	the	the	DET
cana-1737	59	2	gyenno	gyenno	PROPN
cana-1737	59	3	science	science	NOUN
cana-1737	59	4	dataset	dataset	NOUN
cana-1737	59	5	includes	include	VERB
cana-1737	59	6	45	45	NUM
cana-1737	59	7	individuals	individual	NOUN
cana-1737	59	8	where	where	SCONJ
cana-1737	59	9	30	30	NUM
cana-1737	59	10	pd	pd	NOUN
cana-1737	59	11	subjects	subject	NOUN
cana-1737	59	12	and	and	CCONJ
cana-1737	59	13	15	15	NUM
cana-1737	59	14	healthy	healthy	ADJ
cana-1737	59	15	individuals	individual	NOUN
cana-1737	59	16	.	.	PUNCT
cana-1737	60	1	this	this	DET
cana-1737	60	2	dataset	dataset	NOUN
cana-1737	60	3	has	have	VERB
cana-1737	60	4	dynamic	dynamic	ADJ
cana-1737	60	5	voice	voice	NOUN
cana-1737	60	6	features	feature	NOUN
cana-1737	60	7	.	.	PUNCT
cana-1737	61	1	the	the	DET
cana-1737	61	2	pc	pc	NOUN
cana-1737	61	3	-	-	PUNCT
cana-1737	61	4	pita	pita	NOUN
cana-1737	61	5	database	database	NOUN
cana-1737	61	6	consists	consist	VERB
cana-1737	61	7	of	of	ADP
cana-1737	61	8	100	100	NUM
cana-1737	61	9	subjects	subject	NOUN
cana-1737	61	10	,	,	PUNCT
cana-1737	61	11	where	where	SCONJ
cana-1737	61	12	50	50	NUM
cana-1737	61	13	people	people	NOUN
cana-1737	61	14	belong	belong	VERB
cana-1737	61	15	to	to	ADP
cana-1737	61	16	healthy	healthy	ADJ
cana-1737	61	17	controls	control	NOUN
cana-1737	61	18	and	and	CCONJ
cana-1737	61	19	50	50	NUM
cana-1737	61	20	people	people	NOUN
cana-1737	61	21	belong	belong	VERB
cana-1737	61	22	to	to	ADP
cana-1737	61	23	pd	pd	NOUN
cana-1737	61	24	patients	patient	NOUN
cana-1737	61	25	.	.	PUNCT
cana-1737	62	1	this	this	DET
cana-1737	62	2	dataset	dataset	NOUN
cana-1737	62	3	has	have	VERB
cana-1737	62	4	speech	speech	NOUN
cana-1737	62	5	samples	sample	NOUN
cana-1737	62	6	in	in	ADP
cana-1737	62	7	spanish	spanish	ADJ
cana-1737	62	8	.	.	PUNCT
cana-1737	63	1	the	the	DET
cana-1737	63	2	proposed	propose	VERB
cana-1737	63	3	model	model	NOUN
cana-1737	63	4	achieves	achieve	VERB
cana-1737	63	5	an	an	DET
cana-1737	63	6	accuracy	accuracy	NOUN
cana-1737	63	7	of	of	ADP
cana-1737	63	8	81.6	81.6	NUM
cana-1737	63	9	%	%	NOUN
cana-1737	63	10	for	for	ADP
cana-1737	63	11	the	the	DET
cana-1737	63	12	sustained	sustain	VERB
cana-1737	63	13	vowel	vowel	NOUN
cana-1737	63	14	/a/	/a/	NOUN
cana-1737	63	15	task	task	NOUN
cana-1737	63	16	and	and	CCONJ
cana-1737	63	17	75.3	75.3	NUM
cana-1737	63	18	%	%	NOUN
cana-1737	63	19	for	for	ADP
cana-1737	63	20	reading	read	VERB
cana-1737	63	21	short	short	ADJ
cana-1737	63	22	sentences	sentence	NOUN
cana-1737	63	23	on	on	ADP
cana-1737	63	24	the	the	DET
cana-1737	63	25	gyenno	gyenno	PROPN
cana-1737	63	26	science	science	NOUN
cana-1737	63	27	dataset	dataset	NOUN
cana-1737	63	28	.	.	PUNCT
cana-1737	64	1	for	for	ADP
cana-1737	64	2	the	the	DET
cana-1737	64	3	pc	pc	NOUN
cana-1737	64	4	-	-	PUNCT
cana-1737	64	5	pita	pita	NOUN
cana-1737	64	6	database	database	NOUN
cana-1737	64	7	,	,	PUNCT
cana-1737	64	8	the	the	DET
cana-1737	64	9	proposed	propose	VERB
cana-1737	64	10	system	system	NOUN
cana-1737	64	11	achieved	achieve	VERB
cana-1737	64	12	92	92	NUM
cana-1737	64	13	%	%	NOUN
cana-1737	64	14	accuracy	accuracy	NOUN
cana-1737	64	15	for	for	ADP
cana-1737	64	16	tasks	task	NOUN
cana-1737	64	17	involving	involve	VERB
cana-1737	64	18	reading	read	VERB
cana-1737	64	19	a	a	DET
cana-1737	64	20	sentence	sentence	NOUN
cana-1737	64	21	in	in	ADP
cana-1737	64	22	spanish	spanish	NOUN
cana-1737	64	23	.	.	PUNCT
cana-1737	65	1	in	in	ADP
cana-1737	65	2	[	[	X
cana-1737	65	3	7	7	NUM
cana-1737	65	4	]	]	PUNCT
cana-1737	65	5	authors	author	NOUN
cana-1737	65	6	examine	examine	VERB
cana-1737	65	7	the	the	DET
cana-1737	65	8	utilization	utilization	NOUN
cana-1737	65	9	of	of	ADP
cana-1737	65	10	deep	deep	ADJ
cana-1737	65	11	learning	learning	NOUN
cana-1737	65	12	and	and	CCONJ
cana-1737	65	13	artificial	artificial	ADJ
cana-1737	65	14	intelligence	intelligence	NOUN
cana-1737	65	15	methodologies	methodology	NOUN
cana-1737	65	16	for	for	ADP
cana-1737	65	17	analyzing	analyze	VERB
cana-1737	65	18	speech	speech	NOUN
cana-1737	65	19	and	and	CCONJ
cana-1737	65	20	language	language	NOUN
cana-1737	65	21	patterns	pattern	NOUN
cana-1737	65	22	in	in	ADP
cana-1737	65	23	parkinson	parkinson	NOUN
cana-1737	65	24	's	's	PART
cana-1737	65	25	disease	disease	NOUN
cana-1737	65	26	.	.	PUNCT
cana-1737	66	1	to	to	PART
cana-1737	66	2	examine	examine	VERB
cana-1737	66	3	speech	speech	NOUN
cana-1737	66	4	and	and	CCONJ
cana-1737	66	5	language	language	NOUN
cana-1737	66	6	patterns	pattern	NOUN
cana-1737	66	7	in	in	ADP
cana-1737	66	8	parkinson	parkinson	NOUN
cana-1737	66	9	's	's	PART
cana-1737	66	10	patients	patient	NOUN
cana-1737	66	11	,	,	PUNCT
cana-1737	66	12	they	they	PRON
cana-1737	66	13	applied	apply	VERB
cana-1737	66	14	1d	1d	NUM
cana-1737	66	15	and	and	CCONJ
cana-1737	66	16	2d	2d	NUM
cana-1737	66	17	cnn	cnn	PROPN
cana-1737	66	18	algorithms	algorithm	NOUN
cana-1737	66	19	,	,	PUNCT
cana-1737	66	20	as	as	ADV
cana-1737	66	21	well	well	ADV
cana-1737	66	22	as	as	ADP
cana-1737	66	23	wav2vec	wav2vec	NOUN
cana-1737	66	24	2.0	2.0	NUM
cana-1737	66	25	,	,	PUNCT
cana-1737	66	26	bidirectional	bidirectional	ADJ
cana-1737	66	27	encoder	encoder	NOUN
cana-1737	66	28	representations	representation	VERB
cana-1737	66	29	from	from	ADP
cana-1737	66	30	transformers	transformer	NOUN
cana-1737	66	31	(	(	PUNCT
cana-1737	66	32	bert	bert	PROPN
cana-1737	66	33	)	)	PUNCT
cana-1737	66	34	,	,	PUNCT
cana-1737	66	35	and	and	CCONJ
cana-1737	66	36	beto	beto	NOUN
cana-1737	66	37	models	model	NOUN
cana-1737	66	38	.	.	PUNCT
cana-1737	67	1	the	the	DET
cana-1737	67	2	dataset	dataset	NOUN
cana-1737	67	3	contains	contain	VERB
cana-1737	67	4	165	165	NUM
cana-1737	67	5	colombian	colombian	ADJ
cana-1737	67	6	spanish	spanish	ADJ
cana-1737	67	7	native	native	ADJ
cana-1737	67	8	speakers	speaker	NOUN
cana-1737	67	9	,	,	PUNCT
cana-1737	67	10	where	where	SCONJ
cana-1737	67	11	80	80	NUM
cana-1737	67	12	pd	pd	NOUN
cana-1737	67	13	cases	case	NOUN
cana-1737	67	14	and	and	CCONJ
cana-1737	67	15	85	85	NUM
cana-1737	67	16	healthy	healthy	ADJ
cana-1737	67	17	cases	case	NOUN
cana-1737	67	18	.	.	PUNCT
cana-1737	68	1	the	the	DET
cana-1737	68	2	result	result	NOUN
cana-1737	68	3	obtained	obtain	VERB
cana-1737	68	4	by	by	ADP
cana-1737	68	5	the	the	DET
cana-1737	68	6	speech	speech	NOUN
cana-1737	68	7	model	model	NOUN
cana-1737	68	8	using	use	VERB
cana-1737	68	9	wav2vec	wav2vec	PROPN
cana-1737	68	10	2.0	2.0	NUM
cana-1737	68	11	is	be	AUX
cana-1737	68	12	88	88	NUM
cana-1737	68	13	%	%	NOUN
cana-1737	68	14	and	and	CCONJ
cana-1737	68	15	by	by	ADP
cana-1737	68	16	using	use	VERB
cana-1737	68	17	cnn	cnn	PROPN
cana-1737	68	18	and	and	CCONJ
cana-1737	68	19	beto	beto	PROPN
cana-1737	68	20	result	result	NOUN
cana-1737	68	21	achieved	achieve	VERB
cana-1737	68	22	by	by	ADP
cana-1737	68	23	the	the	DET
cana-1737	68	24	language	language	NOUN
cana-1737	68	25	model	model	NOUN
cana-1737	68	26	is	be	AUX
cana-1737	68	27	77	77	NUM
cana-1737	68	28	%	%	NOUN
cana-1737	68	29	.	.	PUNCT
cana-1737	69	1	machine	machine	NOUN
cana-1737	69	2	learning	learn	VERB
cana-1737	69	3	classifiers	classifier	NOUN
cana-1737	69	4	,	,	PUNCT
cana-1737	69	5	including	include	VERB
cana-1737	69	6	svm	svm	ADJ
cana-1737	69	7	,	,	PUNCT
cana-1737	69	8	random	random	ADJ
cana-1737	69	9	forests	forest	NOUN
cana-1737	69	10	,	,	PUNCT
cana-1737	69	11	logistic	logistic	ADJ
cana-1737	69	12	regression	regression	NOUN
cana-1737	69	13	,	,	PUNCT
cana-1737	69	14	and	and	CCONJ
cana-1737	69	15	knn	knn	PROPN
cana-1737	69	16	,	,	PUNCT
cana-1737	69	17	are	be	AUX
cana-1737	69	18	applied	apply	VERB
cana-1737	69	19	and	and	CCONJ
cana-1737	69	20	compared	compare	VERB
cana-1737	69	21	to	to	PART
cana-1737	69	22	analyze	analyze	VERB
cana-1737	69	23	subtle	subtle	ADJ
cana-1737	69	24	changes	change	NOUN
cana-1737	69	25	and	and	CCONJ
cana-1737	69	26	acoustic	acoustic	ADJ
cana-1737	69	27	measures	measure	NOUN
cana-1737	69	28	in	in	ADP
cana-1737	69	29	the	the	DET
cana-1737	69	30	speech	speech	NOUN
cana-1737	69	31	of	of	ADP
cana-1737	69	32	individuals	individual	NOUN
cana-1737	69	33	with	with	ADP
cana-1737	69	34	parkinson	parkinson	NOUN
cana-1737	69	35	's	's	PART
cana-1737	69	36	disease	disease	NOUN
cana-1737	69	37	(	(	PUNCT
cana-1737	69	38	pd	pd	X
cana-1737	69	39	)	)	PUNCT
cana-1737	70	1	[	[	X
cana-1737	70	2	8	8	NUM
cana-1737	70	3	]	]	PUNCT
cana-1737	70	4	.	.	PUNCT
cana-1737	71	1	the	the	DET
cana-1737	71	2	two	two	NUM
cana-1737	71	3	datasets	dataset	NOUN
cana-1737	71	4	were	be	AUX
cana-1737	71	5	taken	take	VERB
cana-1737	71	6	from	from	ADP
cana-1737	71	7	ppmi	ppmi	NOUN
cana-1737	71	8	and	and	CCONJ
cana-1737	71	9	uci	uci	PROPN
cana-1737	71	10	.	.	PUNCT
cana-1737	72	1	the	the	DET
cana-1737	72	2	dataset	dataset	NOUN
cana-1737	72	3	contains	contain	VERB
cana-1737	72	4	acoustic	acoustic	ADJ
cana-1737	72	5	speech	speech	NOUN
cana-1737	72	6	measures	measure	NOUN
cana-1737	72	7	such	such	ADJ
cana-1737	72	8	as	as	ADP
cana-1737	72	9	shimmer	shimmer	ADJ
cana-1737	72	10	,	,	PUNCT
cana-1737	72	11	jitter	jitter	NOUN
cana-1737	72	12	,	,	PUNCT
cana-1737	72	13	etc	etc	X
cana-1737	72	14	.	.	X
cana-1737	73	1	the	the	DET
cana-1737	73	2	dataset	dataset	NOUN
cana-1737	73	3	contains	contain	VERB
cana-1737	73	4	a	a	DET
cana-1737	73	5	total	total	NOUN
cana-1737	73	6	of	of	ADP
cana-1737	73	7	31	31	NUM
cana-1737	73	8	individuals	individual	NOUN
cana-1737	73	9	where	where	SCONJ
cana-1737	73	10	23	23	NUM
cana-1737	73	11	have	have	VERB
cana-1737	73	12	pd	pd	PROPN
cana-1737	73	13	.195	.195	NUM
cana-1737	73	14	record	record	NOUN
cana-1737	73	15	attributes	attribute	NOUN
cana-1737	73	16	are	be	AUX
cana-1737	73	17	collected	collect	VERB
cana-1737	73	18	from	from	ADP
cana-1737	73	19	individuals	individual	NOUN
cana-1737	73	20	.	.	PUNCT
cana-1737	74	1	random	random	ADJ
cana-1737	74	2	forest	forest	NOUN
cana-1737	74	3	classifier	classifier	NOUN
cana-1737	74	4	and	and	CCONJ
cana-1737	74	5	svm	svm	PROPN
cana-1737	74	6	achieved	achieve	VERB
cana-1737	74	7	the	the	DET
cana-1737	74	8	highest	high	ADJ
cana-1737	74	9	91.83	91.83	NUM
cana-1737	74	10	%	%	NOUN
cana-1737	74	11	result	result	NOUN
cana-1737	74	12	compared	compare	VERB
cana-1737	74	13	to	to	ADP
cana-1737	74	14	other	other	ADJ
cana-1737	74	15	machine	machine	NOUN
cana-1737	74	16	learning	learn	VERB
cana-1737	74	17	techniques	technique	NOUN
cana-1737	74	18	.	.	PUNCT
cana-1737	75	1	the	the	DET
cana-1737	75	2	study	study	NOUN
cana-1737	75	3	[	[	X
cana-1737	75	4	9	9	NUM
cana-1737	75	5	]	]	PUNCT
cana-1737	75	6	focuses	focus	VERB
cana-1737	75	7	on	on	ADP
cana-1737	75	8	utilizing	utilize	VERB
cana-1737	75	9	multi	multi	ADJ
cana-1737	75	10	-	-	ADJ
cana-1737	75	11	class	class	ADJ
cana-1737	75	12	classification	classification	NOUN
cana-1737	75	13	techniques	technique	NOUN
cana-1737	75	14	to	to	PART
cana-1737	75	15	distinguish	distinguish	VERB
cana-1737	75	16	between	between	ADP
cana-1737	75	17	parkinson	parkinson	NOUN
cana-1737	75	18	’s	’s	PART
cana-1737	75	19	disease	disease	NOUN
cana-1737	75	20	(	(	PUNCT
cana-1737	75	21	pd	pd	NOUN
cana-1737	75	22	)	)	PUNCT
cana-1737	75	23	and	and	CCONJ
cana-1737	75	24	adductor	adductor	NOUN
cana-1737	75	25	spasmodic	spasmodic	ADJ
cana-1737	75	26	dysphonia	dysphonia	NOUN
cana-1737	75	27	(	(	PUNCT
cana-1737	75	28	adsd	adsd	ADJ
cana-1737	75	29	)	)	PUNCT
cana-1737	75	30	based	base	VERB
cana-1737	75	31	on	on	ADP
cana-1737	75	32	voice	voice	NOUN
cana-1737	75	33	disorder	disorder	NOUN
cana-1737	75	34	characteristics	characteristic	NOUN
cana-1737	75	35	.	.	PUNCT
cana-1737	76	1	the	the	DET
cana-1737	76	2	authors	author	NOUN
cana-1737	76	3	employ	employ	VERB
cana-1737	76	4	machine	machine	NOUN
cana-1737	76	5	learning	learn	VERB
cana-1737	76	6	classifiers	classifier	NOUN
cana-1737	76	7	such	such	ADJ
cana-1737	76	8	as	as	ADP
cana-1737	76	9	naïve	naïve	ADJ
cana-1737	76	10	bayes	bayes	NOUN
cana-1737	76	11	,	,	PUNCT
cana-1737	76	12	random	random	ADJ
cana-1737	76	13	forest	forest	NOUN
cana-1737	76	14	,	,	PUNCT
cana-1737	76	15	and	and	CCONJ
cana-1737	76	16	multi	multi	ADJ
cana-1737	76	17	-	-	ADJ
cana-1737	76	18	layer	layer	ADJ
cana-1737	76	19	perceptron	perceptron	NOUN
cana-1737	76	20	for	for	ADP
cana-1737	76	21	multiclass	multiclass	ADJ
cana-1737	76	22	voice	voice	NOUN
cana-1737	76	23	disorder	disorder	NOUN
cana-1737	76	24	classification	classification	NOUN
cana-1737	76	25	.	.	PUNCT
cana-1737	77	1	the	the	DET
cana-1737	77	2	dataset	dataset	NOUN
cana-1737	77	3	collected	collect	VERB
cana-1737	77	4	from	from	ADP
cana-1737	77	5	communications	communication	NOUN
cana-1737	77	6	on	on	ADP
cana-1737	77	7	applied	apply	VERB
cana-1737	77	8	nonlinear	nonlinear	ADJ
cana-1737	77	9	analysis	analysis	NOUN
cana-1737	77	10	issn	issn	NOUN
cana-1737	77	11	:	:	PUNCT
cana-1737	77	12	1074	1074	NUM
cana-1737	77	13	-	-	PUNCT
cana-1737	77	14	133x	133x	NUM
cana-1737	77	15	vol	vol	NOUN
cana-1737	77	16	32	32	NUM
cana-1737	77	17	no	no	NOUN
cana-1737	77	18	.	.	NOUN
cana-1737	77	19	2	2	NUM
cana-1737	77	20	(	(	PUNCT
cana-1737	77	21	2025	2025	NUM
cana-1737	77	22	)	)	PUNCT
cana-1737	77	23	205	205	NUM
cana-1737	77	24	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	77	25	pvt	pvt	PROPN
cana-1737	77	26	contains	contain	VERB
cana-1737	77	27	51	51	NUM
cana-1737	77	28	pd	pd	NOUN
cana-1737	77	29	subjects	subject	NOUN
cana-1737	77	30	,	,	PUNCT
cana-1737	77	31	60	60	NUM
cana-1737	77	32	dysphonic	dysphonic	ADJ
cana-1737	77	33	subjects	subject	NOUN
cana-1737	77	34	,	,	PUNCT
cana-1737	77	35	and	and	CCONJ
cana-1737	77	36	111	111	NUM
cana-1737	77	37	healthy	healthy	ADJ
cana-1737	77	38	subjects	subject	NOUN
cana-1737	77	39	.	.	PUNCT
cana-1737	78	1	from	from	ADP
cana-1737	78	2	the	the	DET
cana-1737	78	3	sustained	sustained	ADJ
cana-1737	78	4	vowel	vowel	NOUN
cana-1737	78	5	and	and	CCONJ
cana-1737	78	6	italian	italian	ADJ
cana-1737	78	7	sentence	sentence	NOUN
cana-1737	78	8	voice	voice	NOUN
cana-1737	78	9	dataset	dataset	NOUN
cana-1737	78	10	,	,	PUNCT
cana-1737	78	11	a	a	DET
cana-1737	78	12	total	total	NOUN
cana-1737	78	13	of	of	ADP
cana-1737	78	14	6373	6373	NUM
cana-1737	78	15	voice	voice	NOUN
cana-1737	78	16	features	feature	NOUN
cana-1737	78	17	were	be	AUX
cana-1737	78	18	extracted	extract	VERB
cana-1737	78	19	.	.	PUNCT
cana-1737	79	1	statistical	statistical	ADJ
cana-1737	79	2	methods	method	NOUN
cana-1737	79	3	and	and	CCONJ
cana-1737	79	4	genetic	genetic	ADJ
cana-1737	79	5	algorithms	algorithm	NOUN
cana-1737	79	6	were	be	AUX
cana-1737	79	7	used	use	VERB
cana-1737	79	8	for	for	ADP
cana-1737	79	9	feature	feature	NOUN
cana-1737	79	10	selection	selection	NOUN
cana-1737	79	11	.	.	PUNCT
cana-1737	80	1	the	the	DET
cana-1737	80	2	combination	combination	NOUN
cana-1737	80	3	of	of	ADP
cana-1737	80	4	the	the	DET
cana-1737	80	5	genetic	genetic	ADJ
cana-1737	80	6	algorithm	algorithm	NOUN
cana-1737	80	7	and	and	CCONJ
cana-1737	80	8	mlp	mlp	PROPN
cana-1737	80	9	achieved	achieve	VERB
cana-1737	80	10	98.39	98.39	NUM
cana-1737	80	11	%	%	NOUN
cana-1737	80	12	accuracy	accuracy	NOUN
cana-1737	80	13	,	,	PUNCT
cana-1737	80	14	the	the	DET
cana-1737	80	15	rf	rf	NOUN
cana-1737	80	16	classifier	classifier	NOUN
cana-1737	80	17	achieved	achieve	VERB
cana-1737	80	18	96.77	96.77	NUM
cana-1737	80	19	accuracy	accuracy	NOUN
cana-1737	80	20	and	and	CCONJ
cana-1737	80	21	the	the	DET
cana-1737	80	22	naive	naive	ADJ
cana-1737	80	23	bayes	bayes	NOUN
cana-1737	80	24	classifier	classifier	NOUN
cana-1737	80	25	achieved	achieve	VERB
cana-1737	80	26	the	the	DET
cana-1737	80	27	highest	high	ADJ
cana-1737	80	28	result	result	NOUN
cana-1737	80	29	99.46	99.46	NUM
cana-1737	80	30	%	%	NOUN
cana-1737	80	31	for	for	ADP
cana-1737	80	32	multiclass	multiclass	ADJ
cana-1737	80	33	classification	classification	NOUN
cana-1737	80	34	.	.	PUNCT
cana-1737	81	1	a	a	DET
cana-1737	81	2	novel	novel	ADJ
cana-1737	81	3	approach	approach	NOUN
cana-1737	81	4	for	for	ADP
cana-1737	81	5	the	the	DET
cana-1737	81	6	automatic	automatic	ADJ
cana-1737	81	7	and	and	CCONJ
cana-1737	81	8	early	early	ADJ
cana-1737	81	9	detection	detection	NOUN
cana-1737	81	10	of	of	ADP
cana-1737	81	11	parkinson	parkinson	NOUN
cana-1737	81	12	’s	’s	PART
cana-1737	81	13	disease	disease	NOUN
cana-1737	81	14	(	(	PUNCT
cana-1737	81	15	pd	pd	PROPN
cana-1737	81	16	)	)	PUNCT
cana-1737	81	17	through	through	ADP
cana-1737	81	18	the	the	DET
cana-1737	81	19	analysis	analysis	NOUN
cana-1737	81	20	of	of	ADP
cana-1737	81	21	acoustic	acoustic	ADJ
cana-1737	81	22	signals	signal	NOUN
cana-1737	81	23	using	use	VERB
cana-1737	81	24	classification	classification	NOUN
cana-1737	81	25	algorithms	algorithm	NOUN
cana-1737	81	26	proposed	propose	VERB
cana-1737	81	27	in	in	ADP
cana-1737	81	28	[	[	X
cana-1737	81	29	10	10	NUM
cana-1737	81	30	]	]	PUNCT
cana-1737	81	31	.	.	PUNCT
cana-1737	82	1	the	the	DET
cana-1737	82	2	study	study	NOUN
cana-1737	82	3	evaluates	evaluate	VERB
cana-1737	82	4	selected	select	VERB
cana-1737	82	5	features	feature	NOUN
cana-1737	82	6	and	and	CCONJ
cana-1737	82	7	performs	perform	VERB
cana-1737	82	8	hyperparameter	hyperparameter	NOUN
cana-1737	82	9	tuning	tuning	NOUN
cana-1737	82	10	of	of	ADP
cana-1737	82	11	machine	machine	NOUN
cana-1737	82	12	learning	learning	NOUN
cana-1737	82	13	(	(	PUNCT
cana-1737	82	14	ml	ml	NOUN
cana-1737	82	15	)	)	PUNCT
cana-1737	82	16	algorithms	algorithm	NOUN
cana-1737	82	17	.	.	PUNCT
cana-1737	83	1	to	to	PART
cana-1737	83	2	balance	balance	VERB
cana-1737	83	3	the	the	DET
cana-1737	83	4	dataset	dataset	NOUN
cana-1737	83	5	,	,	PUNCT
cana-1737	83	6	smote	smote	NOUN
cana-1737	83	7	is	be	AUX
cana-1737	83	8	employed	employ	VERB
cana-1737	83	9	,	,	PUNCT
cana-1737	83	10	while	while	SCONJ
cana-1737	83	11	recursive	recursive	ADJ
cana-1737	83	12	feature	feature	NOUN
cana-1737	83	13	elimination	elimination	NOUN
cana-1737	83	14	(	(	PUNCT
cana-1737	83	15	ref	ref	NOUN
cana-1737	83	16	)	)	PUNCT
cana-1737	83	17	is	be	AUX
cana-1737	83	18	utilized	utilize	VERB
cana-1737	83	19	for	for	ADP
cana-1737	83	20	feature	feature	NOUN
cana-1737	83	21	selection	selection	NOUN
cana-1737	83	22	.	.	PUNCT
cana-1737	84	1	additionally	additionally	ADV
cana-1737	84	2	,	,	PUNCT
cana-1737	84	3	feature	feature	NOUN
cana-1737	84	4	extraction	extraction	NOUN
cana-1737	84	5	is	be	AUX
cana-1737	84	6	conducted	conduct	VERB
cana-1737	84	7	using	use	VERB
cana-1737	84	8	t	t	PROPN
cana-1737	84	9	-	-	PUNCT
cana-1737	84	10	sne	sne	PROPN
cana-1737	84	11	and	and	CCONJ
cana-1737	84	12	pca	pca	PROPN
cana-1737	84	13	algorithms	algorithm	NOUN
cana-1737	84	14	.	.	PUNCT
cana-1737	85	1	the	the	DET
cana-1737	85	2	machine	machine	NOUN
cana-1737	85	3	learning	learn	VERB
cana-1737	85	4	classifiers	classifier	NOUN
cana-1737	85	5	applied	apply	VERB
cana-1737	85	6	in	in	ADP
cana-1737	85	7	the	the	DET
cana-1737	85	8	study	study	NOUN
cana-1737	85	9	include	include	VERB
cana-1737	85	10	knn	knn	PROPN
cana-1737	85	11	,	,	PUNCT
cana-1737	85	12	svm	svm	ADJ
cana-1737	85	13	,	,	PUNCT
cana-1737	85	14	decision	decision	NOUN
cana-1737	85	15	trees	tree	NOUN
cana-1737	85	16	(	(	PUNCT
cana-1737	85	17	dt	dt	PROPN
cana-1737	85	18	)	)	PUNCT
cana-1737	85	19	,	,	PUNCT
cana-1737	85	20	random	random	ADJ
cana-1737	85	21	forests	forest	NOUN
cana-1737	85	22	(	(	PUNCT
cana-1737	85	23	rf	rf	NOUN
cana-1737	85	24	)	)	PUNCT
cana-1737	85	25	,	,	PUNCT
cana-1737	85	26	and	and	CCONJ
cana-1737	85	27	multi	multi	ADJ
cana-1737	85	28	-	-	ADJ
cana-1737	85	29	layer	layer	ADJ
cana-1737	85	30	perceptron	perceptron	NOUN
cana-1737	85	31	(	(	PUNCT
cana-1737	85	32	mlp	mlp	PROPN
cana-1737	85	33	)	)	PUNCT
cana-1737	85	34	.	.	PUNCT
cana-1737	86	1	the	the	DET
cana-1737	86	2	dataset	dataset	NOUN
cana-1737	86	3	was	be	AUX
cana-1737	86	4	taken	take	VERB
cana-1737	86	5	from	from	ADP
cana-1737	86	6	the	the	DET
cana-1737	86	7	uci	uci	PROPN
cana-1737	86	8	machine	machine	NOUN
cana-1737	86	9	learning	learn	VERB
cana-1737	86	10	repository	repository	NOUN
cana-1737	86	11	and	and	CCONJ
cana-1737	86	12	contains	contain	VERB
cana-1737	86	13	195	195	NUM
cana-1737	86	14	samples	sample	NOUN
cana-1737	86	15	,	,	PUNCT
cana-1737	86	16	with	with	ADP
cana-1737	86	17	147	147	NUM
cana-1737	86	18	identified	identify	VERB
cana-1737	86	19	as	as	ADP
cana-1737	86	20	parkinson	parkinson	NOUN
cana-1737	86	21	's	's	PART
cana-1737	86	22	disease	disease	NOUN
cana-1737	86	23	(	(	PUNCT
cana-1737	86	24	pd	pd	NOUN
cana-1737	86	25	)	)	PUNCT
cana-1737	86	26	cases	case	NOUN
cana-1737	86	27	and	and	CCONJ
cana-1737	86	28	48	48	NUM
cana-1737	86	29	as	as	ADP
cana-1737	86	30	healthy	healthy	ADJ
cana-1737	86	31	individuals	individual	NOUN
cana-1737	86	32	.	.	PUNCT
cana-1737	87	1	the	the	DET
cana-1737	87	2	random	random	ADJ
cana-1737	87	3	forest	forest	NOUN
cana-1737	87	4	(	(	PUNCT
cana-1737	87	5	rf	rf	NOUN
cana-1737	87	6	)	)	PUNCT
cana-1737	87	7	classifiers	classifier	NOUN
cana-1737	87	8	with	with	ADP
cana-1737	87	9	the	the	DET
cana-1737	87	10	t	t	PROPN
cana-1737	87	11	-	-	PUNCT
cana-1737	87	12	sne	sne	NOUN
cana-1737	87	13	algorithm	algorithm	NOUN
cana-1737	87	14	,	,	PUNCT
cana-1737	87	15	obtained	obtain	VERB
cana-1737	87	16	an	an	DET
cana-1737	87	17	accuracy	accuracy	NOUN
cana-1737	87	18	of	of	ADP
cana-1737	87	19	97	97	NUM
cana-1737	87	20	%	%	NOUN
cana-1737	87	21	.	.	PUNCT
cana-1737	88	1	meanwhile	meanwhile	ADV
cana-1737	88	2	,	,	PUNCT
cana-1737	88	3	the	the	DET
cana-1737	88	4	combination	combination	NOUN
cana-1737	88	5	of	of	ADP
cana-1737	88	6	pca	pca	PROPN
cana-1737	88	7	and	and	CCONJ
cana-1737	88	8	mlp	mlp	PROPN
cana-1737	88	9	achieved	achieve	VERB
cana-1737	88	10	the	the	DET
cana-1737	88	11	highest	high	ADJ
cana-1737	88	12	accuracy	accuracy	NOUN
cana-1737	88	13	of	of	ADP
cana-1737	88	14	98	98	NUM
cana-1737	88	15	%	%	NOUN
cana-1737	88	16	.	.	PUNCT
cana-1737	89	1	3	3	X
cana-1737	89	2	.	.	X
cana-1737	89	3	convolution	convolution	NOUN
cana-1737	89	4	neural	neural	ADJ
cana-1737	89	5	network	network	NOUN
cana-1737	89	6	in	in	ADP
cana-1737	89	7	the	the	DET
cana-1737	89	8	realm	realm	NOUN
cana-1737	89	9	of	of	ADP
cana-1737	89	10	deep	deep	ADJ
cana-1737	89	11	learning	learning	NOUN
cana-1737	89	12	,	,	PUNCT
cana-1737	89	13	cnns	cnn	NOUN
cana-1737	89	14	stand	stand	VERB
cana-1737	89	15	out	out	ADP
cana-1737	89	16	as	as	ADP
cana-1737	89	17	specialized	specialized	ADJ
cana-1737	89	18	neural	neural	ADJ
cana-1737	89	19	networks	network	NOUN
cana-1737	89	20	extensively	extensively	ADV
cana-1737	89	21	utilized	utilize	VERB
cana-1737	89	22	in	in	ADP
cana-1737	89	23	various	various	ADJ
cana-1737	89	24	domains	domain	NOUN
cana-1737	89	25	such	such	ADJ
cana-1737	89	26	as	as	ADP
cana-1737	89	27	image	image	NOUN
cana-1737	89	28	processing	processing	NOUN
cana-1737	89	29	,	,	PUNCT
cana-1737	89	30	pattern	pattern	NOUN
cana-1737	89	31	recognition	recognition	NOUN
cana-1737	89	32	,	,	PUNCT
cana-1737	89	33	video	video	NOUN
cana-1737	89	34	analysis	analysis	NOUN
cana-1737	89	35	,	,	PUNCT
cana-1737	89	36	and	and	CCONJ
cana-1737	89	37	speech	speech	NOUN
cana-1737	89	38	recognition	recognition	NOUN
cana-1737	89	39	.	.	PUNCT
cana-1737	90	1	serving	serve	VERB
cana-1737	90	2	as	as	ADP
cana-1737	90	3	an	an	DET
cana-1737	90	4	advanced	advanced	ADJ
cana-1737	90	5	iteration	iteration	NOUN
cana-1737	90	6	of	of	ADP
cana-1737	90	7	artificial	artificial	ADJ
cana-1737	90	8	neural	neural	ADJ
cana-1737	90	9	networks	network	NOUN
cana-1737	90	10	(	(	PUNCT
cana-1737	90	11	anns	anns	PROPN
cana-1737	90	12	)	)	PUNCT
cana-1737	90	13	,	,	PUNCT
cana-1737	90	14	cnns	cnn	NOUN
cana-1737	90	15	are	be	AUX
cana-1737	90	16	tailored	tailor	VERB
cana-1737	90	17	to	to	PART
cana-1737	90	18	handle	handle	VERB
cana-1737	90	19	structured	structured	ADJ
cana-1737	90	20	data	datum	NOUN
cana-1737	90	21	like	like	ADP
cana-1737	90	22	images	image	NOUN
cana-1737	90	23	and	and	CCONJ
cana-1737	90	24	sequences	sequence	NOUN
cana-1737	90	25	efficiently	efficiently	ADV
cana-1737	90	26	.	.	PUNCT
cana-1737	91	1	cnns	cnns	PROPN
cana-1737	91	2	feature	feature	VERB
cana-1737	91	3	multiple	multiple	ADJ
cana-1737	91	4	layers	layer	NOUN
cana-1737	91	5	,	,	PUNCT
cana-1737	91	6	with	with	ADP
cana-1737	91	7	convolutional	convolutional	ADJ
cana-1737	91	8	layers	layer	NOUN
cana-1737	91	9	being	be	AUX
cana-1737	91	10	a	a	DET
cana-1737	91	11	cornerstone	cornerstone	NOUN
cana-1737	91	12	component	component	NOUN
cana-1737	91	13	.	.	PUNCT
cana-1737	92	1	these	these	DET
cana-1737	92	2	layers	layer	NOUN
cana-1737	92	3	apply	apply	VERB
cana-1737	92	4	filters	filter	NOUN
cana-1737	92	5	to	to	ADP
cana-1737	92	6	the	the	DET
cana-1737	92	7	input	input	NOUN
cana-1737	92	8	data	datum	NOUN
cana-1737	92	9	,	,	PUNCT
cana-1737	92	10	extracting	extract	VERB
cana-1737	92	11	essential	essential	ADJ
cana-1737	92	12	features	feature	NOUN
cana-1737	92	13	such	such	ADJ
cana-1737	92	14	as	as	ADP
cana-1737	92	15	edges	edge	NOUN
cana-1737	92	16	and	and	CCONJ
cana-1737	92	17	textures	texture	NOUN
cana-1737	92	18	.	.	PUNCT
cana-1737	93	1	subsequently	subsequently	ADV
cana-1737	93	2	,	,	PUNCT
cana-1737	93	3	pooling	pool	VERB
cana-1737	93	4	layers	layer	NOUN
cana-1737	93	5	are	be	AUX
cana-1737	93	6	employed	employ	VERB
cana-1737	93	7	to	to	ADP
cana-1737	93	8	down	down	ADP
cana-1737	93	9	-	-	PUNCT
cana-1737	93	10	sample	sample	NOUN
cana-1737	93	11	the	the	DET
cana-1737	93	12	feature	feature	NOUN
cana-1737	93	13	maps	map	NOUN
cana-1737	93	14	while	while	SCONJ
cana-1737	93	15	preserving	preserve	VERB
cana-1737	93	16	critical	critical	ADJ
cana-1737	93	17	information	information	NOUN
cana-1737	93	18	.	.	PUNCT
cana-1737	94	1	finally	finally	ADV
cana-1737	94	2	,	,	PUNCT
cana-1737	94	3	fully	fully	ADV
cana-1737	94	4	connected	connected	ADJ
cana-1737	94	5	layers	layer	NOUN
cana-1737	94	6	are	be	AUX
cana-1737	94	7	responsible	responsible	ADJ
cana-1737	94	8	for	for	ADP
cana-1737	94	9	making	make	VERB
cana-1737	94	10	predictions	prediction	NOUN
cana-1737	94	11	based	base	VERB
cana-1737	94	12	on	on	ADP
cana-1737	94	13	the	the	DET
cana-1737	94	14	pooled	pooled	ADJ
cana-1737	94	15	outputs	output	NOUN
cana-1737	94	16	.	.	PUNCT
cana-1737	95	1	the	the	DET
cana-1737	95	2	architecture	architecture	NOUN
cana-1737	95	3	of	of	ADP
cana-1737	95	4	cnns	cnns	PROPN
cana-1737	95	5	is	be	AUX
cana-1737	95	6	characterized	characterize	VERB
cana-1737	95	7	by	by	ADP
cana-1737	95	8	its	its	PRON
cana-1737	95	9	layered	layered	ADJ
cana-1737	95	10	structure	structure	NOUN
cana-1737	95	11	,	,	PUNCT
cana-1737	95	12	as	as	SCONJ
cana-1737	95	13	depicted	depict	VERB
cana-1737	95	14	in	in	ADP
cana-1737	95	15	fig	fig	NOUN
cana-1737	95	16	.	.	PUNCT
cana-1737	96	1	1	1	X
cana-1737	96	2	.	.	X
cana-1737	96	3	each	each	DET
cana-1737	96	4	layer	layer	NOUN
cana-1737	96	5	plays	play	VERB
cana-1737	96	6	a	a	DET
cana-1737	96	7	distinct	distinct	ADJ
cana-1737	96	8	role	role	NOUN
cana-1737	96	9	in	in	ADP
cana-1737	96	10	the	the	DET
cana-1737	96	11	overall	overall	ADJ
cana-1737	96	12	process	process	NOUN
cana-1737	96	13	,	,	PUNCT
cana-1737	96	14	with	with	ADP
cana-1737	96	15	convolutional	convolutional	ADJ
cana-1737	96	16	layers	layer	NOUN
cana-1737	96	17	crucial	crucial	ADJ
cana-1737	96	18	for	for	ADP
cana-1737	96	19	extracting	extract	VERB
cana-1737	96	20	informative	informative	ADJ
cana-1737	96	21	patterns	pattern	NOUN
cana-1737	96	22	via	via	ADP
cana-1737	96	23	their	their	PRON
cana-1737	96	24	filters	filter	NOUN
cana-1737	96	25	.	.	PUNCT
cana-1737	97	1	as	as	SCONJ
cana-1737	97	2	the	the	DET
cana-1737	97	3	data	data	NOUN
cana-1737	97	4	progresses	progress	VERB
cana-1737	97	5	through	through	ADP
cana-1737	97	6	subsequent	subsequent	ADJ
cana-1737	97	7	layers	layer	NOUN
cana-1737	97	8	,	,	PUNCT
cana-1737	97	9	these	these	DET
cana-1737	97	10	patterns	pattern	NOUN
cana-1737	97	11	are	be	AUX
cana-1737	97	12	further	far	ADV
cana-1737	97	13	refined	refine	VERB
cana-1737	97	14	,	,	PUNCT
cana-1737	97	15	ultimately	ultimately	ADV
cana-1737	97	16	contributing	contribute	VERB
cana-1737	97	17	to	to	ADP
cana-1737	97	18	the	the	DET
cana-1737	97	19	network	network	NOUN
cana-1737	97	20	's	's	PART
cana-1737	97	21	ability	ability	NOUN
cana-1737	97	22	to	to	PART
cana-1737	97	23	make	make	VERB
cana-1737	97	24	accurate	accurate	ADJ
cana-1737	97	25	diagnoses	diagnosis	NOUN
cana-1737	98	1	[	[	X
cana-1737	98	2	11,12	11,12	NOUN
cana-1737	98	3	]	]	PUNCT
cana-1737	98	4	.	.	PUNCT
cana-1737	99	1	fig	fig	NOUN
cana-1737	99	2	.	.	PUNCT
cana-1737	100	1	1	1	NUM
cana-1737	100	2	architecture	architecture	NOUN
cana-1737	100	3	of	of	ADP
cana-1737	100	4	cnn	cnn	PROPN
cana-1737	101	1	[	[	X
cana-1737	101	2	13	13	NUM
cana-1737	101	3	]	]	PUNCT
cana-1737	101	4	communications	communication	NOUN
cana-1737	101	5	on	on	ADP
cana-1737	101	6	applied	apply	VERB
cana-1737	101	7	nonlinear	nonlinear	ADJ
cana-1737	101	8	analysis	analysis	NOUN
cana-1737	101	9	issn	issn	NOUN
cana-1737	101	10	:	:	PUNCT
cana-1737	101	11	1074	1074	NUM
cana-1737	101	12	-	-	PUNCT
cana-1737	101	13	133x	133x	NUM
cana-1737	101	14	vol	vol	NOUN
cana-1737	101	15	32	32	NUM
cana-1737	101	16	no	no	NOUN
cana-1737	101	17	.	.	NOUN
cana-1737	101	18	2	2	NUM
cana-1737	101	19	(	(	PUNCT
cana-1737	101	20	2025	2025	NUM
cana-1737	101	21	)	)	PUNCT
cana-1737	101	22	206	206	NUM
cana-1737	101	23	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	101	24	key	key	ADJ
cana-1737	101	25	components	component	NOUN
cana-1737	101	26	of	of	ADP
cana-1737	101	27	cnn	cnn	PROPN
cana-1737	101	28	architecture	architecture	NOUN
cana-1737	101	29	•	•	NOUN
cana-1737	101	30	input	input	NOUN
cana-1737	101	31	layer	layer	NOUN
cana-1737	101	32	:	:	PUNCT
cana-1737	101	33	the	the	DET
cana-1737	101	34	input	input	NOUN
cana-1737	101	35	layer	layer	NOUN
cana-1737	101	36	receives	receive	VERB
cana-1737	101	37	raw	raw	ADJ
cana-1737	101	38	input	input	NOUN
cana-1737	101	39	data	datum	NOUN
cana-1737	101	40	.	.	PUNCT
cana-1737	102	1	here	here	ADV
cana-1737	102	2	the	the	DET
cana-1737	102	3	raw	raw	ADJ
cana-1737	102	4	input	input	NOUN
cana-1737	102	5	data	datum	NOUN
cana-1737	102	6	is	be	AUX
cana-1737	102	7	speech	speech	NOUN
cana-1737	102	8	features	feature	NOUN
cana-1737	102	9	.	.	PUNCT
cana-1737	103	1	•	•	NUM
cana-1737	103	2	convolutional	convolutional	ADJ
cana-1737	103	3	layers	layer	NOUN
cana-1737	103	4	:	:	PUNCT
cana-1737	103	5	the	the	DET
cana-1737	103	6	convolutional	convolutional	ADJ
cana-1737	103	7	layer	layer	NOUN
cana-1737	103	8	uses	use	VERB
cana-1737	103	9	a	a	DET
cana-1737	103	10	kernel	kernel	NOUN
cana-1737	103	11	which	which	PRON
cana-1737	103	12	is	be	AUX
cana-1737	103	13	also	also	ADV
cana-1737	103	14	called	call	VERB
cana-1737	103	15	a	a	DET
cana-1737	103	16	filter	filter	NOUN
cana-1737	103	17	and	and	CCONJ
cana-1737	103	18	it	it	PRON
cana-1737	103	19	captures	capture	VERB
cana-1737	103	20	hierarchical	hierarchical	ADJ
cana-1737	103	21	representation	representation	NOUN
cana-1737	103	22	and	and	CCONJ
cana-1737	103	23	detects	detect	NOUN
cana-1737	103	24	spatial	spatial	ADJ
cana-1737	103	25	patterns	pattern	NOUN
cana-1737	103	26	of	of	ADP
cana-1737	103	27	input	input	NOUN
cana-1737	103	28	.	.	PUNCT
cana-1737	104	1	•	•	NUM
cana-1737	104	2	activation	activation	NOUN
cana-1737	104	3	function	function	NOUN
cana-1737	104	4	:	:	PUNCT
cana-1737	104	5	the	the	DET
cana-1737	104	6	activation	activation	NOUN
cana-1737	104	7	function	function	NOUN
cana-1737	104	8	facilitates	facilitate	VERB
cana-1737	104	9	the	the	DET
cana-1737	104	10	network	network	NOUN
cana-1737	104	11	's	's	PART
cana-1737	104	12	capacity	capacity	NOUN
cana-1737	104	13	to	to	PART
cana-1737	104	14	grasp	grasp	VERB
cana-1737	104	15	intricate	intricate	ADJ
cana-1737	104	16	relationships	relationship	NOUN
cana-1737	104	17	by	by	ADP
cana-1737	104	18	introducing	introduce	VERB
cana-1737	104	19	non	non	ADJ
cana-1737	104	20	-	-	ADJ
cana-1737	104	21	linear	linear	ADJ
cana-1737	104	22	elements	element	NOUN
cana-1737	104	23	.	.	PUNCT
cana-1737	105	1	specifically	specifically	ADV
cana-1737	105	2	,	,	PUNCT
cana-1737	105	3	the	the	DET
cana-1737	105	4	rectified	rectified	ADJ
cana-1737	105	5	linear	linear	NOUN
cana-1737	105	6	unit	unit	NOUN
cana-1737	105	7	(	(	PUNCT
cana-1737	105	8	relu	relu	NOUN
cana-1737	105	9	)	)	PUNCT
cana-1737	105	10	activation	activation	NOUN
cana-1737	105	11	function	function	NOUN
cana-1737	105	12	substitutes	substitute	VERB
cana-1737	105	13	negative	negative	ADJ
cana-1737	105	14	values	value	NOUN
cana-1737	105	15	with	with	ADP
cana-1737	105	16	zeros	zero	NOUN
cana-1737	105	17	.	.	NOUN
cana-1737	106	1	•	•	NOUN
cana-1737	106	2	pooling	pool	VERB
cana-1737	106	3	(	(	PUNCT
cana-1737	106	4	subsampling	subsample	VERB
cana-1737	106	5	or	or	CCONJ
cana-1737	106	6	down	down	ADV
cana-1737	106	7	-	-	PUNCT
cana-1737	106	8	sampling	sample	VERB
cana-1737	106	9	)	)	PUNCT
cana-1737	106	10	layer	layer	NOUN
cana-1737	106	11	:	:	PUNCT
cana-1737	106	12	max	max	PROPN
cana-1737	106	13	pooling	pooling	NOUN
cana-1737	106	14	gives	give	VERB
cana-1737	106	15	maximum	maximum	ADJ
cana-1737	106	16	value	value	NOUN
cana-1737	106	17	from	from	ADP
cana-1737	106	18	a	a	DET
cana-1737	106	19	set	set	NOUN
cana-1737	106	20	of	of	ADP
cana-1737	106	21	values	value	NOUN
cana-1737	106	22	and	and	CCONJ
cana-1737	106	23	reduces	reduce	VERB
cana-1737	106	24	the	the	DET
cana-1737	106	25	spatial	spatial	ADJ
cana-1737	106	26	dimensions	dimension	NOUN
cana-1737	106	27	of	of	ADP
cana-1737	106	28	the	the	DET
cana-1737	106	29	input	input	NOUN
cana-1737	106	30	volume	volume	NOUN
cana-1737	106	31	by	by	ADP
cana-1737	106	32	preserving	preserve	VERB
cana-1737	106	33	the	the	DET
cana-1737	106	34	most	most	ADV
cana-1737	106	35	important	important	ADJ
cana-1737	106	36	features	feature	NOUN
cana-1737	106	37	.	.	PUNCT
cana-1737	107	1	•	•	NOUN
cana-1737	107	2	flattening	flattening	NOUN
cana-1737	107	3	:	:	PUNCT
cana-1737	107	4	flattening	flattening	NOUN
cana-1737	107	5	converts	convert	VERB
cana-1737	107	6	the	the	DET
cana-1737	107	7	high	high	ADV
cana-1737	107	8	-	-	PUNCT
cana-1737	107	9	dimensional	dimensional	ADJ
cana-1737	107	10	feature	feature	NOUN
cana-1737	107	11	maps	map	NOUN
cana-1737	107	12	into	into	ADP
cana-1737	107	13	a	a	DET
cana-1737	107	14	one	one	NUM
cana-1737	107	15	-	-	PUNCT
cana-1737	107	16	dimensional	dimensional	ADJ
cana-1737	107	17	vector	vector	NOUN
cana-1737	107	18	and	and	CCONJ
cana-1737	107	19	for	for	ADP
cana-1737	107	20	a	a	DET
cana-1737	107	21	fully	fully	ADV
cana-1737	107	22	connected	connect	VERB
cana-1737	107	23	layer	layer	NOUN
cana-1737	107	24	,	,	PUNCT
cana-1737	107	25	it	it	PRON
cana-1737	107	26	prepares	prepare	VERB
cana-1737	107	27	data	datum	NOUN
cana-1737	107	28	for	for	ADP
cana-1737	107	29	processing	processing	NOUN
cana-1737	107	30	.	.	PUNCT
cana-1737	108	1	•	•	NUM
cana-1737	108	2	fully	fully	ADV
cana-1737	108	3	connected	connected	ADJ
cana-1737	108	4	layers	layer	NOUN
cana-1737	108	5	:	:	PUNCT
cana-1737	108	6	it	it	PRON
cana-1737	108	7	is	be	AUX
cana-1737	108	8	also	also	ADV
cana-1737	108	9	called	call	VERB
cana-1737	108	10	a	a	DET
cana-1737	108	11	dense	dense	ADJ
cana-1737	108	12	layer	layer	NOUN
cana-1737	108	13	.	.	PUNCT
cana-1737	109	1	it	it	PRON
cana-1737	109	2	connects	connect	VERB
cana-1737	109	3	every	every	DET
cana-1737	109	4	one	one	NUM
cana-1737	109	5	-	-	PUNCT
cana-1737	109	6	layer	layer	NOUN
cana-1737	109	7	neuron	neuron	NOUN
cana-1737	109	8	to	to	ADP
cana-1737	109	9	every	every	DET
cana-1737	109	10	next	next	ADJ
cana-1737	109	11	-	-	PUNCT
cana-1737	109	12	layer	layer	NOUN
cana-1737	109	13	neuron	neuron	NOUN
cana-1737	109	14	.	.	PUNCT
cana-1737	110	1	this	this	PRON
cana-1737	110	2	is	be	AUX
cana-1737	110	3	the	the	DET
cana-1737	110	4	last	last	ADJ
cana-1737	110	5	stage	stage	NOUN
cana-1737	110	6	of	of	ADP
cana-1737	110	7	the	the	DET
cana-1737	110	8	network	network	NOUN
cana-1737	110	9	to	to	PART
cana-1737	110	10	perform	perform	VERB
cana-1737	110	11	various	various	ADJ
cana-1737	110	12	machine	machine	NOUN
cana-1737	110	13	learning	learning	NOUN
cana-1737	110	14	tasks	task	NOUN
cana-1737	110	15	such	such	ADJ
cana-1737	110	16	as	as	ADP
cana-1737	110	17	regression	regression	NOUN
cana-1737	110	18	and	and	CCONJ
cana-1737	110	19	classification	classification	NOUN
cana-1737	110	20	functions	function	NOUN
cana-1737	110	21	.	.	PUNCT
cana-1737	111	1	•	•	NUM
cana-1737	111	2	dropout	dropout	NOUN
cana-1737	111	3	:	:	PUNCT
cana-1737	111	4	in	in	ADP
cana-1737	111	5	most	most	ADJ
cana-1737	111	6	cases	case	NOUN
cana-1737	111	7	dropout	dropout	NOUN
cana-1737	111	8	is	be	AUX
cana-1737	111	9	optional	optional	ADJ
cana-1737	111	10	.	.	PUNCT
cana-1737	112	1	with	with	ADP
cana-1737	112	2	dense	dense	ADJ
cana-1737	112	3	layer	layer	NOUN
cana-1737	112	4	dropout	dropout	NOUN
cana-1737	112	5	(	(	PUNCT
cana-1737	112	6	0.5	0.5	NUM
cana-1737	112	7	)	)	PUNCT
cana-1737	112	8	added	add	VERB
cana-1737	112	9	prevents	prevent	NOUN
cana-1737	112	10	overfitting	overfitte	VERB
cana-1737	112	11	by	by	ADP
cana-1737	112	12	reducing	reduce	VERB
cana-1737	112	13	the	the	DET
cana-1737	112	14	dependencies	dependency	NOUN
cana-1737	112	15	on	on	ADP
cana-1737	112	16	specific	specific	ADJ
cana-1737	112	17	neurons	neuron	NOUN
cana-1737	112	18	and	and	CCONJ
cana-1737	112	19	encouraging	encourage	VERB
cana-1737	112	20	the	the	DET
cana-1737	112	21	network	network	NOUN
cana-1737	112	22	to	to	PART
cana-1737	112	23	learn	learn	VERB
cana-1737	112	24	the	the	DET
cana-1737	112	25	important	important	ADJ
cana-1737	112	26	features	feature	NOUN
cana-1737	112	27	.	.	PUNCT
cana-1737	113	1	it	it	PRON
cana-1737	113	2	is	be	AUX
cana-1737	113	3	a	a	DET
cana-1737	113	4	regularization	regularization	NOUN
cana-1737	113	5	technique	technique	NOUN
cana-1737	113	6	.	.	PUNCT
cana-1737	114	1	•	•	NUM
cana-1737	114	2	output	output	NOUN
cana-1737	114	3	layer	layer	NOUN
cana-1737	114	4	:	:	PUNCT
cana-1737	114	5	the	the	DET
cana-1737	114	6	output	output	NOUN
cana-1737	114	7	layer	layer	NOUN
cana-1737	114	8	provides	provide	VERB
cana-1737	114	9	the	the	DET
cana-1737	114	10	outcomes	outcome	NOUN
cana-1737	114	11	and	and	CCONJ
cana-1737	114	12	performs	perform	VERB
cana-1737	114	13	classification	classification	NOUN
cana-1737	114	14	tasks	task	NOUN
cana-1737	114	15	.	.	PUNCT
cana-1737	115	1	the	the	DET
cana-1737	115	2	softmax	softmax	NOUN
cana-1737	115	3	activation	activation	NOUN
cana-1737	115	4	function	function	NOUN
cana-1737	115	5	is	be	AUX
cana-1737	115	6	applied	apply	VERB
cana-1737	115	7	to	to	PART
cana-1737	115	8	transform	transform	VERB
cana-1737	115	9	the	the	DET
cana-1737	115	10	output	output	NOUN
cana-1737	115	11	into	into	ADP
cana-1737	115	12	probabilities	probability	NOUN
cana-1737	115	13	.	.	PUNCT
cana-1737	116	1	at	at	ADP
cana-1737	116	2	compilation	compilation	NOUN
cana-1737	116	3	,	,	PUNCT
cana-1737	116	4	adam	adam	PROPN
cana-1737	116	5	optimizer	optimizer	NOUN
cana-1737	116	6	is	be	AUX
cana-1737	116	7	used	use	VERB
cana-1737	116	8	to	to	PART
cana-1737	116	9	optimize	optimize	VERB
cana-1737	116	10	a	a	DET
cana-1737	116	11	neural	neural	ADJ
cana-1737	116	12	network	network	NOUN
cana-1737	116	13	model	model	NOUN
cana-1737	116	14	.	.	PUNCT
cana-1737	117	1	the	the	DET
cana-1737	117	2	obtained	obtain	VERB
cana-1737	117	3	data	datum	NOUN
cana-1737	117	4	is	be	AUX
cana-1737	117	5	stored	store	VERB
cana-1737	117	6	in	in	ADP
cana-1737	117	7	a	a	DET
cana-1737	117	8	.h5	.h5	NOUN
cana-1737	117	9	format	format	NOUN
cana-1737	117	10	of	of	ADP
cana-1737	117	11	file	file	NOUN
cana-1737	117	12	for	for	ADP
cana-1737	117	13	future	future	ADJ
cana-1737	117	14	use	use	NOUN
cana-1737	117	15	during	during	ADP
cana-1737	117	16	the	the	DET
cana-1737	117	17	testing	testing	NOUN
cana-1737	117	18	.	.	PUNCT
cana-1737	118	1	4	4	X
cana-1737	118	2	.	.	X
cana-1737	118	3	synthetic	synthetic	ADJ
cana-1737	118	4	minority	minority	NOUN
cana-1737	118	5	over	over	ADP
cana-1737	118	6	-	-	PUNCT
cana-1737	118	7	sampling	sample	VERB
cana-1737	118	8	technique	technique	NOUN
cana-1737	118	9	smote	smote	NOUN
cana-1737	118	10	,	,	PUNCT
cana-1737	118	11	which	which	PRON
cana-1737	118	12	stands	stand	VERB
cana-1737	118	13	for	for	ADP
cana-1737	118	14	synthetic	synthetic	ADJ
cana-1737	118	15	minority	minority	NOUN
cana-1737	118	16	over	over	ADP
cana-1737	118	17	-	-	PUNCT
cana-1737	118	18	sampling	sample	VERB
cana-1737	118	19	technique	technique	NOUN
cana-1737	118	20	,	,	PUNCT
cana-1737	118	21	represents	represent	VERB
cana-1737	118	22	a	a	DET
cana-1737	118	23	cutting	cut	VERB
cana-1737	118	24	-	-	PUNCT
cana-1737	118	25	edge	edge	NOUN
cana-1737	118	26	statistical	statistical	ADJ
cana-1737	118	27	method	method	NOUN
cana-1737	118	28	devised	devise	VERB
cana-1737	118	29	to	to	PART
cana-1737	118	30	address	address	VERB
cana-1737	118	31	the	the	DET
cana-1737	118	32	challenge	challenge	NOUN
cana-1737	118	33	of	of	ADP
cana-1737	118	34	imbalanced	imbalanced	ADJ
cana-1737	118	35	datasets	dataset	NOUN
cana-1737	118	36	.	.	PUNCT
cana-1737	119	1	imbalance	imbalance	NOUN
cana-1737	119	2	occurs	occur	VERB
cana-1737	119	3	when	when	SCONJ
cana-1737	119	4	one	one	NUM
cana-1737	119	5	class	class	NOUN
cana-1737	119	6	within	within	ADP
cana-1737	119	7	a	a	DET
cana-1737	119	8	dataset	dataset	NOUN
cana-1737	119	9	possesses	possesse	NOUN
cana-1737	119	10	significantly	significantly	ADV
cana-1737	119	11	fewer	few	ADJ
cana-1737	119	12	instances	instance	NOUN
cana-1737	119	13	than	than	ADP
cana-1737	119	14	another	another	PRON
cana-1737	119	15	.	.	PUNCT
cana-1737	120	1	this	this	DET
cana-1737	120	2	issue	issue	NOUN
cana-1737	120	3	can	can	AUX
cana-1737	120	4	distort	distort	VERB
cana-1737	120	5	the	the	DET
cana-1737	120	6	performance	performance	NOUN
cana-1737	120	7	of	of	ADP
cana-1737	120	8	machine	machine	NOUN
cana-1737	120	9	learning	learn	VERB
cana-1737	120	10	algorithms	algorithm	NOUN
cana-1737	120	11	.	.	PUNCT
cana-1737	121	1	smote	smote	VERB
cana-1737	121	2	effectively	effectively	ADV
cana-1737	121	3	tackles	tackle	VERB
cana-1737	121	4	this	this	DET
cana-1737	121	5	problem	problem	NOUN
cana-1737	121	6	by	by	ADP
cana-1737	121	7	generating	generate	VERB
cana-1737	121	8	synthetic	synthetic	ADJ
cana-1737	121	9	samples	sample	NOUN
cana-1737	121	10	for	for	ADP
cana-1737	121	11	the	the	DET
cana-1737	121	12	minority	minority	NOUN
cana-1737	121	13	class	class	NOUN
cana-1737	121	14	,	,	PUNCT
cana-1737	121	15	thus	thus	ADV
cana-1737	121	16	rebalancing	rebalance	VERB
cana-1737	121	17	the	the	DET
cana-1737	121	18	class	class	NOUN
cana-1737	121	19	distribution	distribution	NOUN
cana-1737	121	20	.	.	PUNCT
cana-1737	122	1	the	the	DET
cana-1737	122	2	technique	technique	NOUN
cana-1737	122	3	achieves	achieve	VERB
cana-1737	122	4	this	this	PRON
cana-1737	122	5	by	by	ADP
cana-1737	122	6	creating	create	VERB
cana-1737	122	7	synthetic	synthetic	ADJ
cana-1737	122	8	instances	instance	NOUN
cana-1737	122	9	along	along	ADP
cana-1737	122	10	line	line	NOUN
cana-1737	122	11	segments	segment	NOUN
cana-1737	122	12	connecting	connect	VERB
cana-1737	122	13	existing	exist	VERB
cana-1737	122	14	minority	minority	NOUN
cana-1737	122	15	class	class	NOUN
cana-1737	122	16	instances	instance	NOUN
cana-1737	122	17	.	.	PUNCT
cana-1737	123	1	by	by	ADP
cana-1737	123	2	doing	do	VERB
cana-1737	123	3	so	so	ADV
cana-1737	123	4	,	,	PUNCT
cana-1737	123	5	smote	smote	VERB
cana-1737	123	6	ensures	ensure	VERB
cana-1737	123	7	a	a	DET
cana-1737	123	8	more	more	ADV
cana-1737	123	9	equitable	equitable	ADJ
cana-1737	123	10	distribution	distribution	NOUN
cana-1737	123	11	across	across	ADP
cana-1737	123	12	classes	class	NOUN
cana-1737	123	13	within	within	ADP
cana-1737	123	14	the	the	DET
cana-1737	123	15	dataset	dataset	NOUN
cana-1737	123	16	.	.	PUNCT
cana-1737	124	1	this	this	DET
cana-1737	124	2	balanced	balanced	ADJ
cana-1737	124	3	representation	representation	NOUN
cana-1737	124	4	enhances	enhance	VERB
cana-1737	124	5	the	the	DET
cana-1737	124	6	effectiveness	effectiveness	NOUN
cana-1737	124	7	of	of	ADP
cana-1737	124	8	various	various	ADJ
cana-1737	124	9	machine	machine	NOUN
cana-1737	124	10	learning	learning	NOUN
cana-1737	124	11	tasks	task	NOUN
cana-1737	124	12	,	,	PUNCT
cana-1737	124	13	particularly	particularly	ADV
cana-1737	124	14	classification	classification	NOUN
cana-1737	124	15	[	[	X
cana-1737	124	16	14,15	14,15	NUM
cana-1737	124	17	]	]	PUNCT
cana-1737	124	18	.	.	PUNCT
cana-1737	125	1	5	5	X
cana-1737	125	2	.	.	X
cana-1737	125	3	methodolgy	methodolgy	NOUN
cana-1737	125	4	in	in	ADP
cana-1737	125	5	this	this	DET
cana-1737	125	6	work	work	NOUN
cana-1737	125	7	,	,	PUNCT
cana-1737	125	8	the	the	DET
cana-1737	125	9	speech	speech	NOUN
cana-1737	125	10	dataset	dataset	NOUN
cana-1737	125	11	is	be	AUX
cana-1737	125	12	sourced	source	VERB
cana-1737	125	13	from	from	ADP
cana-1737	125	14	the	the	DET
cana-1737	125	15	uci	uci	PROPN
cana-1737	125	16	machine	machine	NOUN
cana-1737	125	17	learning	learn	VERB
cana-1737	125	18	repository	repository	NOUN
cana-1737	125	19	.	.	PUNCT
cana-1737	126	1	subsequently	subsequently	ADV
cana-1737	126	2	,	,	PUNCT
cana-1737	126	3	preprocessing	preprocesse	VERB
cana-1737	126	4	techniques	technique	NOUN
cana-1737	126	5	are	be	AUX
cana-1737	126	6	applied	apply	VERB
cana-1737	126	7	,	,	PUNCT
cana-1737	126	8	followed	follow	VERB
cana-1737	126	9	by	by	ADP
cana-1737	126	10	the	the	DET
cana-1737	126	11	resolution	resolution	NOUN
cana-1737	126	12	of	of	ADP
cana-1737	126	13	class	class	NOUN
cana-1737	126	14	imbalance	imbalance	NOUN
cana-1737	126	15	through	through	ADP
cana-1737	126	16	the	the	DET
cana-1737	126	17	utilization	utilization	NOUN
cana-1737	126	18	of	of	ADP
cana-1737	126	19	the	the	DET
cana-1737	126	20	smote	smote	ADJ
cana-1737	126	21	algorithm	algorithm	NOUN
cana-1737	126	22	.	.	PUNCT
cana-1737	127	1	feature	feature	NOUN
cana-1737	127	2	extraction	extraction	NOUN
cana-1737	127	3	and	and	CCONJ
cana-1737	127	4	selection	selection	NOUN
cana-1737	127	5	processes	process	NOUN
cana-1737	127	6	are	be	AUX
cana-1737	127	7	then	then	ADV
cana-1737	127	8	carried	carry	VERB
cana-1737	127	9	out	out	ADP
cana-1737	127	10	.	.	PUNCT
cana-1737	128	1	the	the	DET
cana-1737	128	2	model	model	NOUN
cana-1737	128	3	undergoes	undergo	VERB
cana-1737	128	4	training	training	NOUN
cana-1737	128	5	using	use	VERB
cana-1737	128	6	the	the	DET
cana-1737	128	7	cnn	cnn	PROPN
cana-1737	128	8	algorithm	algorithm	NOUN
cana-1737	128	9	.	.	PUNCT
cana-1737	129	1	the	the	DET
cana-1737	129	2	architectural	architectural	ADJ
cana-1737	129	3	layout	layout	NOUN
cana-1737	129	4	of	of	ADP
cana-1737	129	5	the	the	DET
cana-1737	129	6	methodology	methodology	NOUN
cana-1737	129	7	is	be	AUX
cana-1737	129	8	illustrated	illustrate	VERB
cana-1737	129	9	in	in	ADP
cana-1737	129	10	fig	fig	NOUN
cana-1737	129	11	.	.	PUNCT
cana-1737	130	1	2	2	X
cana-1737	130	2	.	.	X
cana-1737	130	3	communications	communication	NOUN
cana-1737	130	4	on	on	ADP
cana-1737	130	5	applied	apply	VERB
cana-1737	130	6	nonlinear	nonlinear	ADJ
cana-1737	130	7	analysis	analysis	NOUN
cana-1737	130	8	issn	issn	NOUN
cana-1737	130	9	:	:	PUNCT
cana-1737	130	10	1074	1074	NUM
cana-1737	130	11	-	-	PUNCT
cana-1737	130	12	133x	133x	NUM
cana-1737	130	13	vol	vol	NOUN
cana-1737	130	14	32	32	NUM
cana-1737	130	15	no	no	NOUN
cana-1737	130	16	.	.	NOUN
cana-1737	130	17	2	2	NUM
cana-1737	130	18	(	(	PUNCT
cana-1737	130	19	2025	2025	NUM
cana-1737	130	20	)	)	PUNCT
cana-1737	130	21	207	207	NUM
cana-1737	130	22	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	130	23	5.1	5.1	NUM
cana-1737	130	24	data	datum	NOUN
cana-1737	130	25	pre	pre	ADJ
cana-1737	130	26	-	-	NOUN
cana-1737	130	27	processing	processing	NOUN
cana-1737	130	28	during	during	ADP
cana-1737	130	29	the	the	DET
cana-1737	130	30	data	datum	NOUN
cana-1737	130	31	pre	pre	ADJ
cana-1737	130	32	-	-	ADJ
cana-1737	130	33	processing	processing	ADJ
cana-1737	130	34	phase	phase	NOUN
cana-1737	130	35	,	,	PUNCT
cana-1737	130	36	various	various	ADJ
cana-1737	130	37	checks	check	NOUN
cana-1737	130	38	are	be	AUX
cana-1737	130	39	conducted	conduct	VERB
cana-1737	130	40	,	,	PUNCT
cana-1737	130	41	encompassing	encompass	VERB
cana-1737	130	42	an	an	DET
cana-1737	130	43	assessment	assessment	NOUN
cana-1737	130	44	of	of	ADP
cana-1737	130	45	information	information	NOUN
cana-1737	130	46	,	,	PUNCT
cana-1737	130	47	identification	identification	NOUN
cana-1737	130	48	of	of	ADP
cana-1737	130	49	null	null	ADJ
cana-1737	130	50	values	value	NOUN
cana-1737	130	51	,	,	PUNCT
cana-1737	130	52	handling	handle	VERB
cana-1737	130	53	of	of	ADP
cana-1737	130	54	missing	miss	VERB
cana-1737	130	55	values	value	NOUN
cana-1737	130	56	,	,	PUNCT
cana-1737	130	57	detection	detection	NOUN
cana-1737	130	58	of	of	ADP
cana-1737	130	59	duplicate	duplicate	ADJ
cana-1737	130	60	values	value	NOUN
cana-1737	130	61	,	,	PUNCT
cana-1737	130	62	outlier	outlier	NOUN
cana-1737	130	63	analysis	analysis	NOUN
cana-1737	130	64	and	and	CCONJ
cana-1737	130	65	handling	handle	VERB
cana-1737	130	66	unbalanced	unbalanced	ADJ
cana-1737	130	67	speech	speech	NOUN
cana-1737	130	68	data	datum	NOUN
cana-1737	130	69	.	.	PUNCT
cana-1737	131	1	additionally	additionally	ADV
cana-1737	131	2	,	,	PUNCT
cana-1737	131	3	categorical	categorical	ADJ
cana-1737	131	4	data	datum	NOUN
cana-1737	131	5	is	be	AUX
cana-1737	131	6	transformed	transform	VERB
cana-1737	131	7	into	into	ADP
cana-1737	131	8	numerical	numerical	ADJ
cana-1737	131	9	format	format	NOUN
cana-1737	131	10	,	,	PUNCT
cana-1737	131	11	and	and	CCONJ
cana-1737	131	12	min	min	NOUN
cana-1737	131	13	-	-	ADJ
cana-1737	131	14	max	max	ADJ
cana-1737	131	15	scaling	scaling	NOUN
cana-1737	131	16	is	be	AUX
cana-1737	131	17	employed	employ	VERB
cana-1737	131	18	to	to	PART
cana-1737	131	19	standardize	standardize	VERB
cana-1737	131	20	the	the	DET
cana-1737	131	21	numerical	numerical	ADJ
cana-1737	131	22	features	feature	NOUN
cana-1737	131	23	,	,	PUNCT
cana-1737	131	24	ensuring	ensure	VERB
cana-1737	131	25	consistency	consistency	NOUN
cana-1737	131	26	within	within	ADP
cana-1737	131	27	a	a	DET
cana-1737	131	28	range	range	NOUN
cana-1737	131	29	of	of	ADP
cana-1737	131	30	-1	-1	INTJ
cana-1737	131	31	to	to	ADP
cana-1737	131	32	1	1	NUM
cana-1737	131	33	.	.	PUNCT
cana-1737	132	1	the	the	DET
cana-1737	132	2	smote	smote	ADJ
cana-1737	132	3	algorithm	algorithm	NOUN
cana-1737	132	4	is	be	AUX
cana-1737	132	5	applied	apply	VERB
cana-1737	132	6	to	to	PART
cana-1737	132	7	address	address	VERB
cana-1737	132	8	class	class	NOUN
cana-1737	132	9	imbalance	imbalance	NOUN
cana-1737	132	10	problems	problem	NOUN
cana-1737	132	11	.	.	PUNCT
cana-1737	133	1	fig	fig	NOUN
cana-1737	133	2	.	.	PUNCT
cana-1737	134	1	2	2	NUM
cana-1737	134	2	system	system	NOUN
cana-1737	134	3	architecture	architecture	NOUN
cana-1737	134	4	5.2	5.2	NUM
cana-1737	134	5	feature	feature	NOUN
cana-1737	134	6	extraction	extraction	NOUN
cana-1737	134	7	and	and	CCONJ
cana-1737	134	8	selection	selection	NOUN
cana-1737	134	9	feature	feature	NOUN
cana-1737	134	10	extraction	extraction	NOUN
cana-1737	134	11	plays	play	VERB
cana-1737	134	12	a	a	DET
cana-1737	134	13	crucial	crucial	ADJ
cana-1737	134	14	role	role	NOUN
cana-1737	134	15	in	in	ADP
cana-1737	134	16	developing	develop	VERB
cana-1737	134	17	an	an	DET
cana-1737	134	18	effective	effective	ADJ
cana-1737	134	19	parkinson	parkinson	NOUN
cana-1737	134	20	’s	’s	PART
cana-1737	134	21	detection	detection	NOUN
cana-1737	134	22	system	system	NOUN
cana-1737	134	23	from	from	ADP
cana-1737	134	24	speech	speech	NOUN
cana-1737	134	25	samples	sample	NOUN
cana-1737	134	26	.	.	PUNCT
cana-1737	135	1	it	it	PRON
cana-1737	135	2	involves	involve	VERB
cana-1737	135	3	reducing	reduce	VERB
cana-1737	135	4	the	the	DET
cana-1737	135	5	dimensionality	dimensionality	NOUN
cana-1737	135	6	of	of	ADP
cana-1737	135	7	data	datum	NOUN
cana-1737	135	8	while	while	SCONJ
cana-1737	135	9	retaining	retain	VERB
cana-1737	135	10	relevant	relevant	ADJ
cana-1737	135	11	information	information	NOUN
cana-1737	135	12	.	.	PUNCT
cana-1737	136	1	in	in	ADP
cana-1737	136	2	this	this	DET
cana-1737	136	3	study	study	NOUN
cana-1737	136	4	,	,	PUNCT
cana-1737	136	5	speech	speech	NOUN
cana-1737	136	6	parameters	parameter	NOUN
cana-1737	136	7	such	such	ADJ
cana-1737	136	8	as	as	ADP
cana-1737	136	9	jitter	jitter	NOUN
cana-1737	136	10	,	,	PUNCT
cana-1737	136	11	amplitude	amplitude	NOUN
cana-1737	136	12	,	,	PUNCT
cana-1737	136	13	shimmer	shimmer	ADJ
cana-1737	136	14	,	,	PUNCT
cana-1737	136	15	mdvp	mdvp	NOUN
cana-1737	136	16	,	,	PUNCT
cana-1737	136	17	and	and	CCONJ
cana-1737	136	18	pitch	pitch	NOUN
cana-1737	136	19	,	,	PUNCT
cana-1737	136	20	along	along	ADP
cana-1737	136	21	with	with	ADP
cana-1737	136	22	24	24	NUM
cana-1737	136	23	features	feature	NOUN
cana-1737	136	24	,	,	PUNCT
cana-1737	136	25	are	be	AUX
cana-1737	136	26	considered	consider	VERB
cana-1737	136	27	.	.	PUNCT
cana-1737	137	1	out	out	ADP
cana-1737	137	2	of	of	ADP
cana-1737	137	3	the	the	DET
cana-1737	137	4	24	24	NUM
cana-1737	137	5	feature	feature	NOUN
cana-1737	137	6	attributes	attribute	NOUN
cana-1737	137	7	,	,	PUNCT
cana-1737	137	8	all	all	PRON
cana-1737	137	9	are	be	AUX
cana-1737	137	10	chosen	choose	VERB
cana-1737	137	11	except	except	SCONJ
cana-1737	137	12	for	for	ADP
cana-1737	137	13	the	the	DET
cana-1737	137	14	attribute	attribute	NOUN
cana-1737	137	15	containing	contain	VERB
cana-1737	137	16	the	the	DET
cana-1737	137	17	names	name	NOUN
cana-1737	137	18	.	.	PUNCT
cana-1737	138	1	5.3	5.3	NUM
cana-1737	138	2	model	model	NOUN
cana-1737	138	3	training	training	NOUN
cana-1737	138	4	to	to	PART
cana-1737	138	5	train	train	VERB
cana-1737	138	6	the	the	DET
cana-1737	138	7	cnn	cnn	PROPN
cana-1737	138	8	classifier	classifier	NOUN
cana-1737	138	9	for	for	ADP
cana-1737	138	10	distinguishing	distinguish	VERB
cana-1737	138	11	parkinson	parkinson	NOUN
cana-1737	138	12	’s	’s	PART
cana-1737	138	13	patients	patient	NOUN
cana-1737	138	14	from	from	ADP
cana-1737	138	15	healthy	healthy	ADJ
cana-1737	138	16	individuals	individual	NOUN
cana-1737	138	17	using	use	VERB
cana-1737	138	18	the	the	DET
cana-1737	138	19	speech	speech	NOUN
cana-1737	138	20	dataset	dataset	NOUN
cana-1737	138	21	,	,	PUNCT
cana-1737	138	22	the	the	DET
cana-1737	138	23	raw	raw	ADJ
cana-1737	138	24	input	input	NOUN
cana-1737	138	25	data	data	NOUN
cana-1737	138	26	comprises	comprise	VERB
cana-1737	138	27	speech	speech	NOUN
cana-1737	138	28	features	feature	NOUN
cana-1737	138	29	.	.	PUNCT
cana-1737	139	1	the	the	DET
cana-1737	139	2	convolutional	convolutional	ADJ
cana-1737	139	3	layer	layer	NOUN
cana-1737	139	4	was	be	AUX
cana-1737	139	5	added	add	VERB
cana-1737	139	6	as	as	ADP
cana-1737	139	7	a	a	DET
cana-1737	139	8	first	first	ADJ
cana-1737	139	9	layer	layer	NOUN
cana-1737	139	10	with	with	ADP
cana-1737	139	11	filter	filter	NOUN
cana-1737	139	12	32	32	NUM
cana-1737	139	13	and	and	CCONJ
cana-1737	139	14	kernel	kernel	PROPN
cana-1737	139	15	size	size	NOUN
cana-1737	139	16	3	3	NUM
cana-1737	139	17	x	x	SYM
cana-1737	139	18	3	3	X
cana-1737	139	19	.	.	X
cana-1737	140	1	for	for	ADP
cana-1737	140	2	the	the	DET
cana-1737	140	3	second	second	ADJ
cana-1737	140	4	layer	layer	NOUN
cana-1737	140	5	of	of	ADP
cana-1737	140	6	the	the	DET
cana-1737	140	7	convolution	convolution	NOUN
cana-1737	140	8	neural	neural	ADJ
cana-1737	140	9	network,64	network,64	NOUN
cana-1737	140	10	kernels	kernel	NOUN
cana-1737	140	11	of	of	ADP
cana-1737	140	12	size	size	NOUN
cana-1737	140	13	3	3	NUM
cana-1737	140	14	x	x	SYM
cana-1737	140	15	3	3	NUM
cana-1737	140	16	are	be	AUX
cana-1737	140	17	used	use	VERB
cana-1737	140	18	.	.	PUNCT
cana-1737	141	1	the	the	DET
cana-1737	141	2	convolutional	convolutional	ADJ
cana-1737	141	3	neural	neural	ADJ
cana-1737	141	4	network	network	NOUN
cana-1737	141	5	added	add	VERB
cana-1737	141	6	the	the	DET
cana-1737	141	7	third	third	ADJ
cana-1737	141	8	layer	layer	NOUN
cana-1737	141	9	with	with	ADP
cana-1737	141	10	filter	filter	NOUN
cana-1737	141	11	128	128	NUM
cana-1737	141	12	of	of	ADP
cana-1737	141	13	size	size	NOUN
cana-1737	141	14	3	3	NUM
cana-1737	141	15	x	x	SYM
cana-1737	141	16	3	3	NUM
cana-1737	141	17	.	.	X
cana-1737	141	18	1d	1d	NUM
cana-1737	141	19	cnn	cnn	NOUN
cana-1737	141	20	is	be	AUX
cana-1737	141	21	used	use	VERB
cana-1737	141	22	with	with	ADP
cana-1737	141	23	filter	filter	NOUN
cana-1737	141	24	size	size	NOUN
cana-1737	141	25	(	(	PUNCT
cana-1737	141	26	32,64,128	32,64,128	NUM
cana-1737	141	27	)	)	PUNCT
cana-1737	141	28	and	and	CCONJ
cana-1737	141	29	kernel	kernel	PROPN
cana-1737	141	30	size	size	NOUN
cana-1737	141	31	(	(	PUNCT
cana-1737	141	32	3	3	NUM
cana-1737	141	33	)	)	PUNCT
cana-1737	141	34	along	along	ADP
cana-1737	141	35	with	with	ADP
cana-1737	141	36	relu	relu	NOUN
cana-1737	141	37	activation	activation	NOUN
cana-1737	141	38	function	function	NOUN
cana-1737	141	39	.	.	PUNCT
cana-1737	142	1	following	follow	VERB
cana-1737	142	2	the	the	DET
cana-1737	142	3	convolutional	convolutional	ADJ
cana-1737	142	4	layer	layer	NOUN
cana-1737	142	5	and	and	CCONJ
cana-1737	142	6	activation	activation	NOUN
cana-1737	142	7	function	function	VERB
cana-1737	142	8	a	a	DET
cana-1737	142	9	max	max	PROPN
cana-1737	142	10	pooling	pool	VERB
cana-1737	142	11	1d	1d	NUM
cana-1737	142	12	layer	layer	NOUN
cana-1737	142	13	is	be	AUX
cana-1737	142	14	added	add	VERB
cana-1737	142	15	with	with	ADP
cana-1737	142	16	kernel	kernel	PROPN
cana-1737	142	17	size	size	NOUN
cana-1737	142	18	2	2	NUM
cana-1737	142	19	x	x	SYM
cana-1737	142	20	2	2	X
cana-1737	142	21	.	.	X
cana-1737	142	22	flattening	flattening	NOUN
cana-1737	142	23	is	be	AUX
cana-1737	142	24	added	add	VERB
cana-1737	142	25	.	.	PUNCT
cana-1737	143	1	with	with	ADP
cana-1737	143	2	batch	batch	NOUN
cana-1737	143	3	normalization	normalization	NOUN
cana-1737	143	4	,	,	PUNCT
cana-1737	143	5	the	the	DET
cana-1737	143	6	first	first	ADJ
cana-1737	143	7	dense	dense	ADJ
cana-1737	143	8	layer	layer	NOUN
cana-1737	143	9	of	of	ADP
cana-1737	143	10	size	size	NOUN
cana-1737	143	11	256	256	NUM
cana-1737	143	12	is	be	AUX
cana-1737	143	13	added	add	VERB
cana-1737	143	14	with	with	ADP
cana-1737	143	15	the	the	DET
cana-1737	143	16	relu	relu	NOUN
cana-1737	143	17	activation	activation	NOUN
cana-1737	143	18	function	function	NOUN
cana-1737	143	19	.	.	PUNCT
cana-1737	144	1	the	the	DET
cana-1737	144	2	second	second	ADJ
cana-1737	144	3	dense	dense	ADJ
cana-1737	144	4	layer	layer	NOUN
cana-1737	144	5	is	be	AUX
cana-1737	144	6	added	add	VERB
cana-1737	144	7	of	of	ADP
cana-1737	144	8	size	size	NOUN
cana-1737	144	9	512	512	NUM
cana-1737	144	10	.	.	PUNCT
cana-1737	145	1	these	these	DET
cana-1737	145	2	two	two	NUM
cana-1737	145	3	layers	layer	NOUN
cana-1737	145	4	are	be	AUX
cana-1737	145	5	added	add	VERB
cana-1737	145	6	with	with	ADP
cana-1737	145	7	the	the	DET
cana-1737	145	8	relu	relu	NOUN
cana-1737	145	9	activation	activation	NOUN
cana-1737	145	10	function	function	NOUN
cana-1737	145	11	.	.	PUNCT
cana-1737	146	1	the	the	DET
cana-1737	146	2	third	third	ADJ
cana-1737	146	3	dense	dense	ADJ
cana-1737	146	4	layer	layer	NOUN
cana-1737	146	5	is	be	AUX
cana-1737	146	6	added	add	VERB
cana-1737	146	7	of	of	ADP
cana-1737	146	8	size	size	NOUN
cana-1737	146	9	10	10	NUM
cana-1737	146	10	with	with	ADP
cana-1737	146	11	softmax	softmax	ADJ
cana-1737	146	12	activation	activation	NOUN
cana-1737	146	13	function	function	NOUN
cana-1737	146	14	.	.	PUNCT
cana-1737	147	1	with	with	ADP
cana-1737	147	2	dense	dense	ADJ
cana-1737	147	3	layer	layer	NOUN
cana-1737	147	4	dropout	dropout	NOUN
cana-1737	147	5	(	(	PUNCT
cana-1737	147	6	0.5	0.5	NUM
cana-1737	147	7	)	)	PUNCT
cana-1737	147	8	added	add	VERB
cana-1737	147	9	.	.	PUNCT
cana-1737	148	1	the	the	DET
cana-1737	148	2	softmax	softmax	NOUN
cana-1737	148	3	activation	activation	NOUN
cana-1737	148	4	communications	communication	NOUN
cana-1737	148	5	on	on	ADP
cana-1737	148	6	applied	apply	VERB
cana-1737	148	7	nonlinear	nonlinear	ADJ
cana-1737	148	8	analysis	analysis	NOUN
cana-1737	148	9	issn	issn	NOUN
cana-1737	148	10	:	:	PUNCT
cana-1737	148	11	1074	1074	NUM
cana-1737	148	12	-	-	PUNCT
cana-1737	148	13	133x	133x	NUM
cana-1737	148	14	vol	vol	NOUN
cana-1737	148	15	32	32	NUM
cana-1737	148	16	no	no	NOUN
cana-1737	148	17	.	.	NOUN
cana-1737	148	18	2	2	NUM
cana-1737	148	19	(	(	PUNCT
cana-1737	148	20	2025	2025	NUM
cana-1737	148	21	)	)	PUNCT
cana-1737	148	22	208	208	NUM
cana-1737	148	23	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	148	24	function	function	NOUN
cana-1737	148	25	is	be	AUX
cana-1737	148	26	applied	apply	VERB
cana-1737	148	27	in	in	ADP
cana-1737	148	28	the	the	DET
cana-1737	148	29	output	output	NOUN
cana-1737	148	30	layer	layer	NOUN
cana-1737	148	31	.	.	PUNCT
cana-1737	149	1	at	at	ADP
cana-1737	149	2	compilation	compilation	NOUN
cana-1737	149	3	,	,	PUNCT
cana-1737	149	4	adam	adam	PROPN
cana-1737	149	5	optimizer	optimizer	NOUN
cana-1737	149	6	is	be	AUX
cana-1737	149	7	used	use	VERB
cana-1737	149	8	to	to	PART
cana-1737	149	9	optimize	optimize	VERB
cana-1737	149	10	a	a	DET
cana-1737	149	11	neural	neural	ADJ
cana-1737	149	12	network	network	NOUN
cana-1737	149	13	model	model	NOUN
cana-1737	149	14	with	with	ADP
cana-1737	149	15	epochs	epoch	NOUN
cana-1737	149	16	20	20	NUM
cana-1737	149	17	and	and	CCONJ
cana-1737	149	18	batch	batch	NOUN
cana-1737	149	19	size	size	NOUN
cana-1737	149	20	32	32	NUM
cana-1737	149	21	.	.	PUNCT
cana-1737	150	1	algorithm	algorithm	PROPN
cana-1737	150	2	:	:	PUNCT
cana-1737	150	3	cnn	cnn	PROPN
cana-1737	150	4	model	model	NOUN
cana-1737	150	5	for	for	ADP
cana-1737	150	6	parkinson	parkinson	NOUN
cana-1737	150	7	diseases	disease	NOUN
cana-1737	150	8	detection	detection	NOUN
cana-1737	150	9	input	input	NOUN
cana-1737	150	10	:	:	PUNCT
cana-1737	150	11	speech	speech	NOUN
cana-1737	150	12	dataset	dataset	VERB
cana-1737	150	13	from	from	ADP
cana-1737	150	14	uci	uci	PROPN
cana-1737	150	15	machine	machine	NOUN
cana-1737	150	16	learning	learn	VERB
cana-1737	150	17	repository	repository	NOUN
cana-1737	150	18	output	output	NOUN
cana-1737	150	19	:	:	PUNCT
cana-1737	150	20	trained	train	VERB
cana-1737	150	21	model	model	NOUN
cana-1737	150	22	,	,	PUNCT
cana-1737	150	23	evaluation	evaluation	NOUN
cana-1737	150	24	results	result	NOUN
cana-1737	150	25	start	start	VERB
cana-1737	150	26	step	step	NOUN
cana-1737	150	27	1	1	NUM
cana-1737	150	28	:	:	PUNCT
cana-1737	150	29	load	load	NOUN
cana-1737	150	30	speech	speech	NOUN
cana-1737	150	31	dataset	dataset	NOUN
cana-1737	150	32	step	step	NOUN
cana-1737	150	33	2	2	NUM
cana-1737	150	34	:	:	PUNCT
cana-1737	150	35	splitting	split	VERB
cana-1737	150	36	data	datum	NOUN
cana-1737	150	37	and	and	CCONJ
cana-1737	150	38	oversampling	oversample	VERB
cana-1737	150	39	train	train	NOUN
cana-1737	150	40	set	set	VERB
cana-1737	150	41	with	with	ADP
cana-1737	150	42	smote	smote	ADJ
cana-1737	150	43	sampling	sampling	NOUN
cana-1737	150	44	technique	technique	NOUN
cana-1737	150	45	step	step	NOUN
cana-1737	150	46	3	3	NUM
cana-1737	150	47	:	:	PUNCT
cana-1737	150	48	model	model	NOUN
cana-1737	150	49	definition	definition	NOUN
cana-1737	150	50	3.1	3.1	NUM
cana-1737	150	51	add	add	VERB
cana-1737	150	52	convolutional	convolutional	ADJ
cana-1737	150	53	,	,	PUNCT
cana-1737	150	54	pooling	pooling	NOUN
cana-1737	150	55	,	,	PUNCT
cana-1737	150	56	dropout	dropout	NOUN
cana-1737	150	57	,	,	PUNCT
cana-1737	150	58	and	and	CCONJ
cana-1737	150	59	dense	dense	ADJ
cana-1737	150	60	layers	layer	NOUN
cana-1737	150	61	to	to	ADP
cana-1737	150	62	the	the	DET
cana-1737	150	63	model	model	NOUN
cana-1737	150	64	step	step	NOUN
cana-1737	150	65	4	4	NUM
cana-1737	150	66	:	:	PUNCT
cana-1737	150	67	model	model	NOUN
cana-1737	150	68	training	train	VERB
cana-1737	150	69	4.1	4.1	NUM
cana-1737	150	70	compile	compile	NOUN
cana-1737	150	71	the	the	DET
cana-1737	150	72	model	model	NOUN
cana-1737	150	73	with	with	ADP
cana-1737	150	74	adam	adam	PROPN
cana-1737	150	75	optimizer	optimizer	NOUN
cana-1737	150	76	4.2	4.2	NUM
cana-1737	150	77	define	define	NOUN
cana-1737	150	78	epochs	epoch	NOUN
cana-1737	150	79	(	(	PUNCT
cana-1737	150	80	20	20	NUM
cana-1737	150	81	)	)	PUNCT
cana-1737	150	82	,	,	PUNCT
cana-1737	150	83	batch_size(32	batch_size(32	PROPN
cana-1737	150	84	)	)	PUNCT
cana-1737	150	85	and	and	CCONJ
cana-1737	150	86	early	early	ADV
cana-1737	150	87	stopping	stop	VERB
cana-1737	150	88	4.3	4.3	NUM
cana-1737	150	89	train	train	NOUN
cana-1737	150	90	the	the	DET
cana-1737	150	91	model	model	NOUN
cana-1737	150	92	on	on	ADP
cana-1737	150	93	the	the	DET
cana-1737	150	94	oversampled	oversample	VERB
cana-1737	150	95	data	datum	NOUN
cana-1737	150	96	using	use	VERB
cana-1737	150	97	early	early	ADJ
cana-1737	150	98	stopping	stop	VERB
cana-1737	150	99	for	for	ADP
cana-1737	150	100	regularization	regularization	NOUN
cana-1737	150	101	step	step	NOUN
cana-1737	150	102	5	5	NUM
cana-1737	150	103	:	:	PUNCT
cana-1737	150	104	evaluate	evaluate	VERB
cana-1737	150	105	the	the	DET
cana-1737	150	106	trained	train	VERB
cana-1737	150	107	model	model	NOUN
cana-1737	150	108	on	on	ADP
cana-1737	150	109	the	the	DET
cana-1737	150	110	testing	testing	NOUN
cana-1737	150	111	data	datum	NOUN
cana-1737	150	112	and	and	CCONJ
cana-1737	150	113	compute	compute	VERB
cana-1737	150	114	the	the	DET
cana-1737	150	115	accuracy	accuracy	NOUN
cana-1737	150	116	6	6	NUM
cana-1737	150	117	.	.	PUNCT
cana-1737	151	1	experimental	experimental	ADJ
cana-1737	151	2	result	result	NOUN
cana-1737	151	3	this	this	DET
cana-1737	151	4	section	section	NOUN
cana-1737	151	5	describes	describe	VERB
cana-1737	151	6	the	the	DET
cana-1737	151	7	dataset	dataset	NOUN
cana-1737	151	8	particulars	particular	NOUN
cana-1737	151	9	and	and	CCONJ
cana-1737	151	10	performance	performance	NOUN
cana-1737	151	11	criteria	criterion	NOUN
cana-1737	151	12	employed	employ	VERB
cana-1737	151	13	to	to	PART
cana-1737	151	14	assess	assess	VERB
cana-1737	151	15	the	the	DET
cana-1737	151	16	model	model	NOUN
cana-1737	151	17	's	's	PART
cana-1737	151	18	capability	capability	NOUN
cana-1737	151	19	in	in	ADP
cana-1737	151	20	detecting	detect	VERB
cana-1737	151	21	pd	pd	NOUN
cana-1737	151	22	speech	speech	NOUN
cana-1737	151	23	disorders	disorder	NOUN
cana-1737	151	24	.	.	PUNCT
cana-1737	152	1	the	the	DET
cana-1737	152	2	study	study	NOUN
cana-1737	152	3	's	's	PART
cana-1737	152	4	experiments	experiment	NOUN
cana-1737	152	5	illustrate	illustrate	VERB
cana-1737	152	6	the	the	DET
cana-1737	152	7	efficacy	efficacy	NOUN
cana-1737	152	8	of	of	ADP
cana-1737	152	9	tackling	tackle	VERB
cana-1737	152	10	an	an	DET
cana-1737	152	11	imbalanced	imbalanced	ADJ
cana-1737	152	12	dataset	dataset	NOUN
cana-1737	152	13	through	through	ADP
cana-1737	152	14	the	the	DET
cana-1737	152	15	utilization	utilization	NOUN
cana-1737	152	16	of	of	ADP
cana-1737	152	17	the	the	DET
cana-1737	152	18	smote	smote	ADJ
cana-1737	152	19	algorithm	algorithm	NOUN
cana-1737	152	20	,	,	PUNCT
cana-1737	152	21	resulting	result	VERB
cana-1737	152	22	in	in	ADP
cana-1737	152	23	superior	superior	ADJ
cana-1737	152	24	pd	pd	PROPN
cana-1737	152	25	detection	detection	NOUN
cana-1737	152	26	performance	performance	NOUN
cana-1737	152	27	.	.	PUNCT
cana-1737	153	1	employing	employ	VERB
cana-1737	153	2	a	a	DET
cana-1737	153	3	cnn	cnn	PROPN
cana-1737	153	4	model	model	NOUN
cana-1737	153	5	,	,	PUNCT
cana-1737	153	6	the	the	DET
cana-1737	153	7	system	system	NOUN
cana-1737	153	8	achieves	achieve	VERB
cana-1737	153	9	pd	pd	PROPN
cana-1737	153	10	patient	patient	PROPN
cana-1737	153	11	classification	classification	NOUN
cana-1737	153	12	.	.	PUNCT
cana-1737	154	1	performance	performance	NOUN
cana-1737	154	2	measures	measure	NOUN
cana-1737	154	3	including	include	VERB
cana-1737	154	4	precision	precision	NOUN
cana-1737	154	5	,	,	PUNCT
cana-1737	154	6	recall	recall	NOUN
cana-1737	154	7	,	,	PUNCT
cana-1737	154	8	and	and	CCONJ
cana-1737	154	9	f1	f1	NOUN
cana-1737	154	10	-	-	PUNCT
cana-1737	154	11	score	score	NOUN
cana-1737	154	12	are	be	AUX
cana-1737	154	13	calculated	calculate	VERB
cana-1737	154	14	to	to	PART
cana-1737	154	15	gauge	gauge	VERB
cana-1737	154	16	the	the	DET
cana-1737	154	17	model	model	NOUN
cana-1737	154	18	's	's	PART
cana-1737	154	19	efficacy	efficacy	NOUN
cana-1737	154	20	.	.	PUNCT
cana-1737	155	1	the	the	DET
cana-1737	155	2	experimental	experimental	ADJ
cana-1737	155	3	investigation	investigation	NOUN
cana-1737	155	4	was	be	AUX
cana-1737	155	5	conducted	conduct	VERB
cana-1737	155	6	utilizing	utilize	VERB
cana-1737	155	7	python	python	NOUN
cana-1737	155	8	programming	programming	NOUN
cana-1737	155	9	within	within	ADP
cana-1737	155	10	a	a	DET
cana-1737	155	11	google	google	NOUN
cana-1737	155	12	colaboratory	colaboratory	NOUN
cana-1737	155	13	(	(	PUNCT
cana-1737	155	14	google	google	PROPN
cana-1737	155	15	colab	colab	PROPN
cana-1737	155	16	)	)	PUNCT
cana-1737	155	17	environment	environment	NOUN
cana-1737	155	18	.	.	PUNCT
cana-1737	156	1	6.1	6.1	NUM
cana-1737	156	2	dataset	dataset	ADJ
cana-1737	156	3	description	description	NOUN
cana-1737	156	4	the	the	DET
cana-1737	156	5	speech	speech	NOUN
cana-1737	156	6	dataset	dataset	VERB
cana-1737	156	7	originates	originate	NOUN
cana-1737	156	8	from	from	ADP
cana-1737	156	9	the	the	DET
cana-1737	156	10	uci	uci	PROPN
cana-1737	156	11	machine	machine	NOUN
cana-1737	156	12	learning	learn	VERB
cana-1737	156	13	repository	repository	NOUN
cana-1737	156	14	,	,	PUNCT
cana-1737	156	15	developed	develop	VERB
cana-1737	156	16	by	by	ADP
cana-1737	156	17	max	max	PROPN
cana-1737	156	18	little	little	ADJ
cana-1737	156	19	in	in	ADP
cana-1737	156	20	collaboration	collaboration	NOUN
cana-1737	156	21	with	with	ADP
cana-1737	156	22	the	the	DET
cana-1737	156	23	national	national	ADJ
cana-1737	156	24	centre	centre	NOUN
cana-1737	156	25	for	for	ADP
cana-1737	156	26	voice	voice	NOUN
cana-1737	156	27	and	and	CCONJ
cana-1737	156	28	speech	speech	NOUN
cana-1737	156	29	in	in	ADP
cana-1737	156	30	colorado	colorado	NOUN
cana-1737	156	31	.	.	PUNCT
cana-1737	157	1	it	it	PRON
cana-1737	157	2	comprises	comprise	VERB
cana-1737	157	3	195	195	NUM
cana-1737	157	4	voice	voice	NOUN
cana-1737	157	5	samples	sample	NOUN
cana-1737	157	6	collected	collect	VERB
cana-1737	157	7	from	from	ADP
cana-1737	157	8	31	31	NUM
cana-1737	157	9	individuals	individual	NOUN
cana-1737	157	10	,	,	PUNCT
cana-1737	157	11	including	include	VERB
cana-1737	157	12	23	23	NUM
cana-1737	157	13	with	with	ADP
cana-1737	157	14	parkinson	parkinson	NOUN
cana-1737	157	15	's	's	PART
cana-1737	157	16	disease	disease	NOUN
cana-1737	157	17	and	and	CCONJ
cana-1737	157	18	8	8	NUM
cana-1737	157	19	healthy	healthy	ADJ
cana-1737	157	20	controls	control	NOUN
cana-1737	157	21	.	.	PUNCT
cana-1737	158	1	each	each	DET
cana-1737	158	2	subject	subject	NOUN
cana-1737	158	3	contributed	contribute	VERB
cana-1737	158	4	six	six	NUM
cana-1737	158	5	voice	voice	NOUN
cana-1737	158	6	samples	sample	NOUN
cana-1737	158	7	,	,	PUNCT
cana-1737	158	8	yielding	yield	VERB
cana-1737	158	9	a	a	DET
cana-1737	158	10	total	total	NOUN
cana-1737	158	11	of	of	ADP
cana-1737	158	12	24	24	NUM
cana-1737	158	13	attributes	attribute	NOUN
cana-1737	158	14	per	per	ADP
cana-1737	158	15	sample	sample	NOUN
cana-1737	158	16	.	.	PUNCT
cana-1737	159	1	the	the	DET
cana-1737	159	2	dataset	dataset	NOUN
cana-1737	159	3	captures	capture	VERB
cana-1737	159	4	the	the	DET
cana-1737	159	5	averages	average	NOUN
cana-1737	159	6	of	of	ADP
cana-1737	159	7	six	six	NUM
cana-1737	159	8	phonation	phonation	NOUN
cana-1737	159	9	’s	’s	X
cana-1737	159	10	per	per	ADP
cana-1737	159	11	subject	subject	NOUN
cana-1737	159	12	,	,	PUNCT
cana-1737	159	13	ranging	range	VERB
cana-1737	159	14	from	from	ADP
cana-1737	159	15	1	1	NUM
cana-1737	159	16	to	to	PART
cana-1737	159	17	36	36	NUM
cana-1737	159	18	seconds	second	NOUN
cana-1737	159	19	in	in	ADP
cana-1737	159	20	duration	duration	NOUN
cana-1737	159	21	.	.	PUNCT
cana-1737	160	1	6.2	6.2	NUM
cana-1737	160	2	performance	performance	NOUN
cana-1737	160	3	parameter	parameter	NOUN
cana-1737	160	4	for	for	ADP
cana-1737	160	5	model	model	NOUN
cana-1737	160	6	evaluation	evaluation	NOUN
cana-1737	160	7	to	to	PART
cana-1737	160	8	classify	classify	VERB
cana-1737	160	9	parkinson	parkinson	NOUN
cana-1737	160	10	’s	’s	PART
cana-1737	160	11	disease	disease	NOUN
cana-1737	160	12	,	,	PUNCT
cana-1737	160	13	we	we	PRON
cana-1737	160	14	divided	divide	VERB
cana-1737	160	15	the	the	DET
cana-1737	160	16	dataset	dataset	NOUN
cana-1737	160	17	into	into	ADP
cana-1737	160	18	three	three	NUM
cana-1737	160	19	sections	section	NOUN
cana-1737	160	20	:	:	PUNCT
cana-1737	160	21	the	the	DET
cana-1737	160	22	training	training	NOUN
cana-1737	160	23	set	set	NOUN
cana-1737	160	24	,	,	PUNCT
cana-1737	160	25	comprising	comprise	VERB
cana-1737	160	26	70	70	NUM
cana-1737	160	27	%	%	NOUN
cana-1737	160	28	of	of	ADP
cana-1737	160	29	the	the	DET
cana-1737	160	30	dataset	dataset	NOUN
cana-1737	160	31	;	;	PUNCT
cana-1737	160	32	and	and	CCONJ
cana-1737	160	33	the	the	DET
cana-1737	160	34	testing	testing	NOUN
cana-1737	160	35	set	set	NOUN
cana-1737	160	36	,	,	PUNCT
cana-1737	160	37	comprising	comprise	VERB
cana-1737	160	38	30	30	NUM
cana-1737	160	39	%	%	NOUN
cana-1737	160	40	.	.	PUNCT
cana-1737	161	1	during	during	ADP
cana-1737	161	2	the	the	DET
cana-1737	161	3	testing	testing	NOUN
cana-1737	161	4	phase	phase	NOUN
cana-1737	161	5	,	,	PUNCT
cana-1737	161	6	performance	performance	NOUN
cana-1737	161	7	evaluation	evaluation	NOUN
cana-1737	161	8	metrics	metric	NOUN
cana-1737	161	9	such	such	ADJ
cana-1737	161	10	as	as	ADP
cana-1737	161	11	precision	precision	NOUN
cana-1737	161	12	,	,	PUNCT
cana-1737	161	13	sensitivity	sensitivity	NOUN
cana-1737	161	14	,	,	PUNCT
cana-1737	161	15	specificity	specificity	NOUN
cana-1737	161	16	,	,	PUNCT
cana-1737	161	17	and	and	CCONJ
cana-1737	161	18	accuracy	accuracy	NOUN
cana-1737	161	19	were	be	AUX
cana-1737	161	20	employed	employ	VERB
cana-1737	161	21	to	to	PART
cana-1737	161	22	assess	assess	VERB
cana-1737	161	23	the	the	DET
cana-1737	161	24	model	model	NOUN
cana-1737	161	25	's	's	PART
cana-1737	161	26	effectiveness	effectiveness	NOUN
cana-1737	161	27	.	.	PUNCT
cana-1737	162	1	communications	communication	NOUN
cana-1737	162	2	on	on	ADP
cana-1737	162	3	applied	apply	VERB
cana-1737	162	4	nonlinear	nonlinear	ADJ
cana-1737	162	5	analysis	analysis	NOUN
cana-1737	162	6	issn	issn	NOUN
cana-1737	162	7	:	:	PUNCT
cana-1737	162	8	1074	1074	NUM
cana-1737	162	9	-	-	PUNCT
cana-1737	162	10	133x	133x	NUM
cana-1737	162	11	vol	vol	NOUN
cana-1737	162	12	32	32	NUM
cana-1737	162	13	no	no	NOUN
cana-1737	162	14	.	.	NOUN
cana-1737	162	15	2	2	NUM
cana-1737	162	16	(	(	PUNCT
cana-1737	162	17	2025	2025	NUM
cana-1737	162	18	)	)	PUNCT
cana-1737	162	19	209	209	NUM
cana-1737	162	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	162	21	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-1737	162	22	=	=	SYM
cana-1737	162	23	𝑇𝑃	𝑇𝑃	PROPN
cana-1737	162	24	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NOUN
cana-1737	162	25	.	.	PUNCT
cana-1737	163	1	(	(	PUNCT
cana-1737	163	2	1	1	X
cana-1737	163	3	)	)	PUNCT
cana-1737	163	4	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-1737	163	5	=	=	SYM
cana-1737	163	6	𝑇𝑃	𝑇𝑃	NOUN
cana-1737	163	7	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
cana-1737	163	8	.	.	PUNCT
cana-1737	164	1	(	(	PUNCT
cana-1737	164	2	2	2	X
cana-1737	164	3	)	)	PUNCT
cana-1737	164	4	𝐹1	𝐹1	NOUN
cana-1737	164	5	=	=	SYM
cana-1737	164	6	2	2	NUM
cana-1737	164	7	∗	∗	NOUN
cana-1737	164	8	precision	precision	NOUN
cana-1737	164	9	+	+	CCONJ
cana-1737	164	10	recal	recal	ADJ
cana-1737	164	11	precision×recall	precision×recall	NOUN
cana-1737	164	12	.	.	PUNCT
cana-1737	165	1	(	(	PUNCT
cana-1737	165	2	3	3	X
cana-1737	165	3	)	)	PUNCT
cana-1737	165	4	accuracy	accuracy	NOUN
cana-1737	165	5	:	:	PUNCT
cana-1737	165	6	𝑇𝑃+𝑇𝑁	𝑇𝑃+𝑇𝑁	X
cana-1737	166	1	𝑇𝑃+tn+fp+𝐹𝑁	𝑇𝑃+tn+fp+𝐹𝑁	X
cana-1737	166	2	.	.	PUNCT
cana-1737	167	1	(	(	PUNCT
cana-1737	167	2	4	4	X
cana-1737	167	3	)	)	PUNCT
cana-1737	167	4	6.3	6.3	NUM
cana-1737	167	5	class	class	NOUN
cana-1737	167	6	imbalance	imbalance	NOUN
cana-1737	167	7	problem	problem	NOUN
cana-1737	167	8	the	the	DET
cana-1737	167	9	speech	speech	NOUN
cana-1737	167	10	dataset	dataset	VERB
cana-1737	167	11	provided	provide	VERB
cana-1737	167	12	presents	present	NOUN
cana-1737	167	13	a	a	DET
cana-1737	167	14	class	class	NOUN
cana-1737	167	15	imbalance	imbalance	NOUN
cana-1737	167	16	challenge	challenge	NOUN
cana-1737	167	17	,	,	PUNCT
cana-1737	167	18	with	with	ADP
cana-1737	167	19	147	147	NUM
cana-1737	167	20	samples	sample	NOUN
cana-1737	167	21	attributed	attribute	VERB
cana-1737	167	22	to	to	ADP
cana-1737	167	23	the	the	DET
cana-1737	167	24	pd	pd	PROPN
cana-1737	167	25	group	group	NOUN
cana-1737	167	26	and	and	CCONJ
cana-1737	167	27	only	only	ADV
cana-1737	167	28	48	48	NUM
cana-1737	167	29	samples	sample	NOUN
cana-1737	167	30	representing	represent	VERB
cana-1737	167	31	healthy	healthy	ADJ
cana-1737	167	32	individuals	individual	NOUN
cana-1737	167	33	.	.	PUNCT
cana-1737	168	1	to	to	PART
cana-1737	168	2	rectify	rectify	VERB
cana-1737	168	3	this	this	DET
cana-1737	168	4	imbalance	imbalance	NOUN
cana-1737	168	5	,	,	PUNCT
cana-1737	168	6	the	the	DET
cana-1737	168	7	smote	smote	ADJ
cana-1737	168	8	technique	technique	NOUN
cana-1737	168	9	is	be	AUX
cana-1737	168	10	utilized	utilize	VERB
cana-1737	168	11	.	.	PUNCT
cana-1737	169	1	smote	smote	PROPN
cana-1737	169	2	uses	use	VERB
cana-1737	169	3	only	only	ADV
cana-1737	169	4	the	the	DET
cana-1737	169	5	training	training	NOUN
cana-1737	169	6	data	datum	NOUN
cana-1737	169	7	to	to	PART
cana-1737	169	8	generate	generate	VERB
cana-1737	169	9	synthetic	synthetic	ADJ
cana-1737	169	10	samples	sample	NOUN
cana-1737	169	11	for	for	ADP
cana-1737	169	12	the	the	DET
cana-1737	169	13	minority	minority	NOUN
cana-1737	169	14	class	class	NOUN
cana-1737	169	15	.	.	PUNCT
cana-1737	170	1	the	the	DET
cana-1737	170	2	testing	testing	NOUN
cana-1737	170	3	data	datum	NOUN
cana-1737	170	4	remains	remain	VERB
cana-1737	170	5	completely	completely	ADV
cana-1737	170	6	separate	separate	ADJ
cana-1737	170	7	and	and	CCONJ
cana-1737	170	8	untouched	untouched	ADJ
cana-1737	170	9	during	during	ADP
cana-1737	170	10	this	this	DET
cana-1737	170	11	process	process	NOUN
cana-1737	170	12	.	.	PUNCT
cana-1737	171	1	by	by	ADP
cana-1737	171	2	applying	apply	VERB
cana-1737	171	3	the	the	DET
cana-1737	171	4	smote	smote	ADJ
cana-1737	171	5	algorithm	algorithm	NOUN
cana-1737	171	6	,	,	PUNCT
cana-1737	171	7	the	the	DET
cana-1737	171	8	number	number	NOUN
cana-1737	171	9	of	of	ADP
cana-1737	171	10	samples	sample	NOUN
cana-1737	171	11	in	in	ADP
cana-1737	171	12	the	the	DET
cana-1737	171	13	healthy	healthy	ADJ
cana-1737	171	14	class	class	NOUN
cana-1737	171	15	(	(	PUNCT
cana-1737	171	16	minority	minority	NOUN
cana-1737	171	17	)	)	PUNCT
cana-1737	171	18	is	be	AUX
cana-1737	171	19	augmented	augment	VERB
cana-1737	171	20	from	from	ADP
cana-1737	171	21	48	48	NUM
cana-1737	171	22	to	to	ADP
cana-1737	171	23	147	147	NUM
cana-1737	171	24	through	through	ADP
cana-1737	171	25	the	the	DET
cana-1737	171	26	generation	generation	NOUN
cana-1737	171	27	of	of	ADP
cana-1737	171	28	new	new	ADJ
cana-1737	171	29	synthetic	synthetic	ADJ
cana-1737	171	30	samples	sample	NOUN
cana-1737	171	31	.	.	PUNCT
cana-1737	172	1	consequently	consequently	ADV
cana-1737	172	2	,	,	PUNCT
cana-1737	172	3	the	the	DET
cana-1737	172	4	class	class	NOUN
cana-1737	172	5	imbalance	imbalance	NOUN
cana-1737	172	6	issue	issue	NOUN
cana-1737	172	7	is	be	AUX
cana-1737	172	8	effectively	effectively	ADV
cana-1737	172	9	addressed	address	VERB
cana-1737	172	10	.	.	PUNCT
cana-1737	173	1	the	the	DET
cana-1737	173	2	smote	smote	ADJ
cana-1737	173	3	technique	technique	NOUN
cana-1737	173	4	showcases	showcase	NOUN
cana-1737	173	5	promising	promise	VERB
cana-1737	173	6	outcomes	outcome	NOUN
cana-1737	173	7	in	in	ADP
cana-1737	173	8	this	this	DET
cana-1737	173	9	context	context	NOUN
cana-1737	173	10	.	.	PUNCT
cana-1737	174	1	figure	figure	NOUN
cana-1737	174	2	3	3	NUM
cana-1737	174	3	depicts	depict	VERB
cana-1737	174	4	the	the	DET
cana-1737	174	5	class	class	NOUN
cana-1737	174	6	distribution	distribution	NOUN
cana-1737	174	7	before	before	ADP
cana-1737	174	8	the	the	DET
cana-1737	174	9	application	application	NOUN
cana-1737	174	10	of	of	ADP
cana-1737	174	11	the	the	DET
cana-1737	174	12	smote	smote	ADJ
cana-1737	174	13	algorithm	algorithm	NOUN
cana-1737	174	14	,	,	PUNCT
cana-1737	174	15	highlighting	highlight	VERB
cana-1737	174	16	a	a	DET
cana-1737	174	17	class	class	NOUN
cana-1737	174	18	imbalance	imbalance	NOUN
cana-1737	174	19	scenario	scenario	NOUN
cana-1737	174	20	where	where	SCONJ
cana-1737	174	21	147	147	NUM
cana-1737	174	22	samples	sample	NOUN
cana-1737	174	23	are	be	AUX
cana-1737	174	24	associated	associate	VERB
cana-1737	174	25	with	with	ADP
cana-1737	174	26	status	status	NOUN
cana-1737	174	27	1	1	NUM
cana-1737	174	28	(	(	PUNCT
cana-1737	174	29	unhealthy	unhealthy	ADJ
cana-1737	174	30	)	)	PUNCT
cana-1737	174	31	,	,	PUNCT
cana-1737	174	32	and	and	CCONJ
cana-1737	174	33	48	48	NUM
cana-1737	174	34	samples	sample	NOUN
cana-1737	174	35	are	be	AUX
cana-1737	174	36	associated	associate	VERB
cana-1737	174	37	with	with	ADP
cana-1737	174	38	status	status	NOUN
cana-1737	174	39	0	0	NUM
cana-1737	174	40	(	(	PUNCT
cana-1737	174	41	healthy	healthy	ADJ
cana-1737	174	42	)	)	PUNCT
cana-1737	174	43	.	.	PUNCT
cana-1737	175	1	fig	fig	NOUN
cana-1737	175	2	.	.	PUNCT
cana-1737	176	1	3	3	NUM
cana-1737	176	2	class	class	NOUN
cana-1737	176	3	distribution	distribution	NOUN
cana-1737	176	4	of	of	ADP
cana-1737	176	5	pd	pd	PROPN
cana-1737	176	6	dataset	dataset	VERB
cana-1737	176	7	before	before	ADP
cana-1737	176	8	applying	apply	VERB
cana-1737	176	9	smote	smote	ADJ
cana-1737	176	10	algorithm	algorithm	NOUN
cana-1737	176	11	.	.	PUNCT
cana-1737	177	1	after	after	ADP
cana-1737	177	2	applying	apply	VERB
cana-1737	177	3	the	the	DET
cana-1737	177	4	smote	smote	ADJ
cana-1737	177	5	algorithm	algorithm	NOUN
cana-1737	177	6	to	to	ADP
cana-1737	177	7	the	the	DET
cana-1737	177	8	pd	pd	PROPN
cana-1737	177	9	dataset	dataset	NOUN
cana-1737	177	10	,	,	PUNCT
cana-1737	177	11	the	the	DET
cana-1737	177	12	count	count	NOUN
cana-1737	177	13	of	of	ADP
cana-1737	177	14	the	the	DET
cana-1737	177	15	healthy	healthy	ADJ
cana-1737	177	16	group	group	NOUN
cana-1737	177	17	(	(	PUNCT
cana-1737	177	18	status	status	NOUN
cana-1737	177	19	0	0	NUM
cana-1737	177	20	)	)	PUNCT
cana-1737	177	21	has	have	AUX
cana-1737	177	22	been	be	AUX
cana-1737	177	23	increased	increase	VERB
cana-1737	177	24	to	to	PART
cana-1737	177	25	match	match	VERB
cana-1737	177	26	the	the	DET
cana-1737	177	27	count	count	NOUN
cana-1737	177	28	of	of	ADP
cana-1737	177	29	the	the	DET
cana-1737	177	30	unhealthy	unhealthy	ADJ
cana-1737	177	31	group	group	NOUN
cana-1737	177	32	(	(	PUNCT
cana-1737	177	33	status	status	NOUN
cana-1737	177	34	1	1	NUM
cana-1737	177	35	)	)	PUNCT
cana-1737	177	36	,	,	PUNCT
cana-1737	177	37	effectively	effectively	ADV
cana-1737	177	38	managing	manage	VERB
cana-1737	177	39	the	the	DET
cana-1737	177	40	class	class	NOUN
cana-1737	177	41	distribution	distribution	NOUN
cana-1737	177	42	,	,	PUNCT
cana-1737	177	43	as	as	SCONJ
cana-1737	177	44	depicted	depict	VERB
cana-1737	177	45	in	in	ADP
cana-1737	177	46	the	the	DET
cana-1737	177	47	following	follow	VERB
cana-1737	177	48	fig	fig	NOUN
cana-1737	177	49	.	.	PUNCT
cana-1737	178	1	4	4	X
cana-1737	178	2	.	.	X
cana-1737	178	3	communications	communication	NOUN
cana-1737	178	4	on	on	ADP
cana-1737	178	5	applied	apply	VERB
cana-1737	178	6	nonlinear	nonlinear	ADJ
cana-1737	178	7	analysis	analysis	NOUN
cana-1737	178	8	issn	issn	NOUN
cana-1737	178	9	:	:	PUNCT
cana-1737	178	10	1074	1074	NUM
cana-1737	178	11	-	-	PUNCT
cana-1737	178	12	133x	133x	NUM
cana-1737	178	13	vol	vol	NOUN
cana-1737	178	14	32	32	NUM
cana-1737	178	15	no	no	NOUN
cana-1737	178	16	.	.	NOUN
cana-1737	178	17	2	2	NUM
cana-1737	178	18	(	(	PUNCT
cana-1737	178	19	2025	2025	NUM
cana-1737	178	20	)	)	PUNCT
cana-1737	178	21	210	210	NUM
cana-1737	178	22	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	178	23	fig	fig	NOUN
cana-1737	178	24	.	.	PUNCT
cana-1737	179	1	4	4	NUM
cana-1737	179	2	class	class	NOUN
cana-1737	179	3	distribution	distribution	NOUN
cana-1737	179	4	of	of	ADP
cana-1737	179	5	pd	pd	PROPN
cana-1737	179	6	dataset	dataset	PROPN
cana-1737	179	7	after	after	ADP
cana-1737	179	8	applying	apply	VERB
cana-1737	179	9	smote	smote	ADJ
cana-1737	179	10	algorithm	algorithm	NOUN
cana-1737	179	11	.	.	PUNCT
cana-1737	180	1	6.3	6.3	NUM
cana-1737	180	2	results	result	NOUN
cana-1737	180	3	and	and	CCONJ
cana-1737	180	4	discussions	discussion	NOUN
cana-1737	180	5	the	the	DET
cana-1737	180	6	study	study	NOUN
cana-1737	180	7	utilizes	utilize	VERB
cana-1737	180	8	the	the	DET
cana-1737	180	9	cnn	cnn	PROPN
cana-1737	180	10	algorithm	algorithm	NOUN
cana-1737	180	11	with	with	ADP
cana-1737	180	12	the	the	DET
cana-1737	180	13	adam	adam	PROPN
cana-1737	180	14	optimizer	optimizer	NOUN
cana-1737	180	15	to	to	PART
cana-1737	180	16	classify	classify	VERB
cana-1737	180	17	pd	pd	NOUN
cana-1737	180	18	patients	patient	NOUN
cana-1737	180	19	,	,	PUNCT
cana-1737	180	20	employing	employ	VERB
cana-1737	180	21	a	a	DET
cana-1737	180	22	dataset	dataset	NOUN
cana-1737	180	23	collected	collect	VERB
cana-1737	180	24	from	from	ADP
cana-1737	180	25	the	the	DET
cana-1737	180	26	uci	uci	PROPN
cana-1737	180	27	machine	machine	NOUN
cana-1737	180	28	learning	learn	VERB
cana-1737	180	29	repository	repository	NOUN
cana-1737	180	30	.	.	PUNCT
cana-1737	181	1	confusion	confusion	NOUN
cana-1737	181	2	matrices	matrix	NOUN
cana-1737	181	3	are	be	AUX
cana-1737	181	4	generated	generate	VERB
cana-1737	181	5	to	to	PART
cana-1737	181	6	evaluate	evaluate	VERB
cana-1737	181	7	the	the	DET
cana-1737	181	8	model	model	NOUN
cana-1737	181	9	's	's	PART
cana-1737	181	10	performance	performance	NOUN
cana-1737	181	11	,	,	PUNCT
cana-1737	181	12	alongside	alongside	ADP
cana-1737	181	13	precision	precision	NOUN
cana-1737	181	14	,	,	PUNCT
cana-1737	181	15	recall	recall	NOUN
cana-1737	181	16	,	,	PUNCT
cana-1737	181	17	and	and	CCONJ
cana-1737	181	18	f1	f1	ADJ
cana-1737	181	19	-	-	PUNCT
cana-1737	181	20	score	score	NOUN
cana-1737	181	21	calculations	calculation	NOUN
cana-1737	181	22	.	.	PUNCT
cana-1737	182	1	additionally	additionally	ADV
cana-1737	182	2	,	,	PUNCT
cana-1737	182	3	the	the	DET
cana-1737	182	4	study	study	NOUN
cana-1737	182	5	visualizes	visualize	VERB
cana-1737	182	6	accuracy	accuracy	NOUN
cana-1737	182	7	and	and	CCONJ
cana-1737	182	8	loss	loss	NOUN
cana-1737	182	9	graphs	graph	NOUN
cana-1737	182	10	of	of	ADP
cana-1737	182	11	the	the	DET
cana-1737	182	12	model	model	NOUN
cana-1737	182	13	.	.	PUNCT
cana-1737	183	1	experimental	experimental	ADJ
cana-1737	183	2	findings	finding	NOUN
cana-1737	183	3	reveal	reveal	VERB
cana-1737	183	4	that	that	SCONJ
cana-1737	183	5	employing	employ	VERB
cana-1737	183	6	the	the	DET
cana-1737	183	7	smote	smote	ADJ
cana-1737	183	8	algorithm	algorithm	NOUN
cana-1737	183	9	with	with	ADP
cana-1737	183	10	cnn	cnn	PROPN
cana-1737	183	11	,	,	PUNCT
cana-1737	183	12	using	use	VERB
cana-1737	183	13	20	20	NUM
cana-1737	183	14	epochs	epoch	NOUN
cana-1737	183	15	and	and	CCONJ
cana-1737	183	16	a	a	DET
cana-1737	183	17	batch	batch	NOUN
cana-1737	183	18	size	size	NOUN
cana-1737	183	19	of	of	ADP
cana-1737	183	20	32	32	NUM
cana-1737	183	21	,	,	PUNCT
cana-1737	183	22	achieves	achieve	VERB
cana-1737	183	23	an	an	DET
cana-1737	183	24	accuracy	accuracy	NOUN
cana-1737	183	25	of	of	ADP
cana-1737	183	26	91.52	91.52	NUM
cana-1737	183	27	%	%	NOUN
cana-1737	183	28	for	for	ADP
cana-1737	183	29	acoustic	acoustic	ADJ
cana-1737	183	30	speech	speech	NOUN
cana-1737	183	31	features	feature	NOUN
cana-1737	183	32	.	.	PUNCT
cana-1737	184	1	a	a	DET
cana-1737	184	2	comparison	comparison	NOUN
cana-1737	184	3	is	be	AUX
cana-1737	184	4	conducted	conduct	VERB
cana-1737	184	5	before	before	ADV
cana-1737	184	6	and	and	CCONJ
cana-1737	184	7	after	after	ADP
cana-1737	184	8	applying	apply	VERB
cana-1737	184	9	the	the	DET
cana-1737	184	10	smote	smote	ADJ
cana-1737	184	11	algorithm	algorithm	NOUN
cana-1737	184	12	to	to	PART
cana-1737	184	13	address	address	VERB
cana-1737	184	14	the	the	DET
cana-1737	184	15	class	class	NOUN
cana-1737	184	16	imbalance	imbalance	NOUN
cana-1737	184	17	issue	issue	NOUN
cana-1737	184	18	in	in	ADP
cana-1737	184	19	the	the	DET
cana-1737	184	20	pd	pd	PROPN
cana-1737	184	21	dataset	dataset	NOUN
cana-1737	184	22	.	.	PUNCT
cana-1737	185	1	initially	initially	ADV
cana-1737	185	2	,	,	PUNCT
cana-1737	185	3	with	with	ADP
cana-1737	185	4	48	48	NUM
cana-1737	185	5	samples	sample	NOUN
cana-1737	185	6	in	in	ADP
cana-1737	185	7	the	the	DET
cana-1737	185	8	healthy	healthy	ADJ
cana-1737	185	9	group	group	NOUN
cana-1737	185	10	(	(	PUNCT
cana-1737	185	11	class	class	NOUN
cana-1737	185	12	0	0	NUM
cana-1737	185	13	)	)	PUNCT
cana-1737	185	14	and	and	CCONJ
cana-1737	185	15	147	147	NUM
cana-1737	185	16	in	in	ADP
cana-1737	185	17	the	the	DET
cana-1737	185	18	unhealthy	unhealthy	ADJ
cana-1737	185	19	group	group	NOUN
cana-1737	185	20	(	(	PUNCT
cana-1737	185	21	class	class	NOUN
cana-1737	185	22	1	1	NUM
cana-1737	185	23	)	)	PUNCT
cana-1737	185	24	,	,	PUNCT
cana-1737	185	25	the	the	DET
cana-1737	185	26	dataset	dataset	NOUN
cana-1737	185	27	exhibits	exhibit	VERB
cana-1737	185	28	skewness	skewness	NOUN
cana-1737	185	29	towards	towards	ADP
cana-1737	185	30	pd	pd	PROPN
cana-1737	185	31	patients	patient	NOUN
cana-1737	185	32	.	.	PUNCT
cana-1737	186	1	the	the	DET
cana-1737	186	2	cnn	cnn	PROPN
cana-1737	186	3	model	model	NOUN
cana-1737	186	4	applied	apply	VERB
cana-1737	186	5	to	to	ADP
cana-1737	186	6	the	the	DET
cana-1737	186	7	unbalanced	unbalanced	ADJ
cana-1737	186	8	dataset	dataset	NOUN
cana-1737	186	9	yields	yield	NOUN
cana-1737	186	10	an	an	DET
cana-1737	186	11	accuracy	accuracy	NOUN
cana-1737	186	12	of	of	ADP
cana-1737	186	13	74.35	74.35	NUM
cana-1737	186	14	%	%	NOUN
cana-1737	186	15	.	.	PUNCT
cana-1737	187	1	however	however	ADV
cana-1737	187	2	,	,	PUNCT
cana-1737	187	3	performance	performance	NOUN
cana-1737	187	4	metrics	metric	NOUN
cana-1737	187	5	such	such	ADJ
cana-1737	187	6	as	as	ADP
cana-1737	187	7	precision	precision	NOUN
cana-1737	187	8	,	,	PUNCT
cana-1737	187	9	recall	recall	NOUN
cana-1737	187	10	,	,	PUNCT
cana-1737	187	11	and	and	CCONJ
cana-1737	187	12	f1	f1	NOUN
cana-1737	187	13	-	-	PUNCT
cana-1737	187	14	score	score	NOUN
cana-1737	187	15	are	be	AUX
cana-1737	187	16	relatively	relatively	ADV
cana-1737	187	17	low	low	ADJ
cana-1737	187	18	.	.	PUNCT
cana-1737	188	1	to	to	PART
cana-1737	188	2	improve	improve	VERB
cana-1737	188	3	model	model	NOUN
cana-1737	188	4	performance	performance	NOUN
cana-1737	188	5	,	,	PUNCT
cana-1737	188	6	the	the	DET
cana-1737	188	7	smote	smote	ADJ
cana-1737	188	8	algorithm	algorithm	NOUN
cana-1737	188	9	is	be	AUX
cana-1737	188	10	utilized	utilize	VERB
cana-1737	188	11	to	to	PART
cana-1737	188	12	balance	balance	VERB
cana-1737	188	13	the	the	DET
cana-1737	188	14	dataset	dataset	NOUN
cana-1737	188	15	.	.	PUNCT
cana-1737	189	1	subsequently	subsequently	ADV
cana-1737	189	2	,	,	PUNCT
cana-1737	189	3	the	the	DET
cana-1737	189	4	cnn	cnn	PROPN
cana-1737	189	5	model	model	NOUN
cana-1737	189	6	is	be	AUX
cana-1737	189	7	applied	apply	VERB
cana-1737	189	8	to	to	ADP
cana-1737	189	9	the	the	DET
cana-1737	189	10	balanced	balanced	ADJ
cana-1737	189	11	dataset	dataset	NOUN
cana-1737	189	12	,	,	PUNCT
cana-1737	189	13	resulting	result	VERB
cana-1737	189	14	in	in	ADP
cana-1737	189	15	an	an	DET
cana-1737	189	16	accuracy	accuracy	NOUN
cana-1737	189	17	of	of	ADP
cana-1737	189	18	91.52	91.52	NUM
cana-1737	189	19	%	%	NOUN
cana-1737	189	20	.	.	PUNCT
cana-1737	190	1	notably	notably	ADV
cana-1737	190	2	,	,	PUNCT
cana-1737	190	3	precision	precision	NOUN
cana-1737	190	4	metrics	metric	NOUN
cana-1737	190	5	demonstrate	demonstrate	VERB
cana-1737	190	6	superior	superior	ADJ
cana-1737	190	7	performance	performance	NOUN
cana-1737	190	8	compared	compare	VERB
cana-1737	190	9	to	to	ADP
cana-1737	190	10	the	the	DET
cana-1737	190	11	pre	pre	ADJ
cana-1737	190	12	-	-	ADJ
cana-1737	190	13	smote	smote	ADJ
cana-1737	190	14	application	application	NOUN
cana-1737	190	15	.	.	PUNCT
cana-1737	191	1	table	table	NOUN
cana-1737	191	2	3	3	NUM
cana-1737	191	3	depicts	depict	VERB
cana-1737	191	4	the	the	DET
cana-1737	191	5	performance	performance	NOUN
cana-1737	191	6	evaluation	evaluation	NOUN
cana-1737	191	7	metrics	metric	NOUN
cana-1737	191	8	before	before	ADV
cana-1737	191	9	and	and	CCONJ
cana-1737	191	10	after	after	ADP
cana-1737	191	11	employing	employ	VERB
cana-1737	191	12	the	the	DET
cana-1737	191	13	smote	smote	ADJ
cana-1737	191	14	algorithm	algorithm	NOUN
cana-1737	191	15	.	.	PUNCT
cana-1737	192	1	prior	prior	ADV
cana-1737	192	2	to	to	ADP
cana-1737	192	3	smote	smote	VERB
cana-1737	192	4	,	,	PUNCT
cana-1737	192	5	the	the	DET
cana-1737	192	6	cnn	cnn	PROPN
cana-1737	192	7	model	model	NOUN
cana-1737	192	8	exhibits	exhibit	VERB
cana-1737	192	9	lower	low	ADJ
cana-1737	192	10	performance	performance	NOUN
cana-1737	192	11	metrics	metric	NOUN
cana-1737	192	12	.	.	PUNCT
cana-1737	193	1	however	however	ADV
cana-1737	193	2	,	,	PUNCT
cana-1737	193	3	post	post	ADJ
cana-1737	193	4	-	-	ADJ
cana-1737	193	5	smote	smote	ADJ
cana-1737	193	6	application	application	NOUN
cana-1737	193	7	,	,	PUNCT
cana-1737	193	8	there	there	PRON
cana-1737	193	9	is	be	VERB
cana-1737	193	10	a	a	DET
cana-1737	193	11	significant	significant	ADJ
cana-1737	193	12	enhancement	enhancement	NOUN
cana-1737	193	13	in	in	ADP
cana-1737	193	14	the	the	DET
cana-1737	193	15	cnn	cnn	PROPN
cana-1737	193	16	model	model	NOUN
cana-1737	193	17	's	's	PART
cana-1737	193	18	performance	performance	NOUN
cana-1737	193	19	,	,	PUNCT
cana-1737	193	20	leading	lead	VERB
cana-1737	193	21	to	to	ADP
cana-1737	193	22	overall	overall	ADJ
cana-1737	193	23	higher	high	ADJ
cana-1737	193	24	performance	performance	NOUN
cana-1737	193	25	metrics	metric	NOUN
cana-1737	193	26	.	.	PUNCT
cana-1737	194	1	table	table	NOUN
cana-1737	194	2	3	3	NUM
cana-1737	194	3	performance	performance	NOUN
cana-1737	194	4	evaluation	evaluation	NOUN
cana-1737	194	5	metrics	metric	NOUN
cana-1737	194	6	before	before	ADV
cana-1737	194	7	and	and	CCONJ
cana-1737	194	8	after	after	ADP
cana-1737	194	9	applying	apply	VERB
cana-1737	194	10	smote	smote	NOUN
cana-1737	194	11	before	before	ADP
cana-1737	194	12	smote	smote	NOUN
cana-1737	194	13	after	after	ADP
cana-1737	194	14	smote	smote	ADJ
cana-1737	194	15	precision	precision	NOUN
cana-1737	194	16	60	60	NUM
cana-1737	194	17	%	%	NOUN
cana-1737	194	18	92.01	92.01	NUM
cana-1737	194	19	%	%	NOUN
cana-1737	194	20	recall	recall	NOUN
cana-1737	194	21	62.05	62.05	NUM
cana-1737	194	22	%	%	NOUN
cana-1737	194	23	91.43	91.43	NUM
cana-1737	194	24	%	%	NOUN
cana-1737	194	25	f1	f1	NOUN
cana-1737	194	26	-	-	PUNCT
cana-1737	194	27	score	score	NOUN
cana-1737	194	28	60.68	60.68	NUM
cana-1737	194	29	%	%	NOUN
cana-1737	194	30	91.48	91.48	NUM
cana-1737	194	31	%	%	NOUN
cana-1737	194	32	accuracy	accuracy	NOUN
cana-1737	194	33	71.54	71.54	NUM
cana-1737	194	34	%	%	NOUN
cana-1737	194	35	91.52	91.52	NUM
cana-1737	194	36	%	%	NOUN
cana-1737	194	37	the	the	DET
cana-1737	194	38	confusion	confusion	NOUN
cana-1737	194	39	matrix	matrix	NOUN
cana-1737	194	40	provides	provide	VERB
cana-1737	194	41	a	a	DET
cana-1737	194	42	tabular	tabular	ADJ
cana-1737	194	43	summary	summary	NOUN
cana-1737	194	44	of	of	ADP
cana-1737	194	45	the	the	DET
cana-1737	194	46	proposed	propose	VERB
cana-1737	194	47	model	model	NOUN
cana-1737	194	48	's	's	PART
cana-1737	194	49	performance	performance	NOUN
cana-1737	194	50	,	,	PUNCT
cana-1737	194	51	detailing	detail	VERB
cana-1737	194	52	the	the	DET
cana-1737	194	53	count	count	NOUN
cana-1737	194	54	of	of	ADP
cana-1737	194	55	accurate	accurate	ADJ
cana-1737	194	56	and	and	CCONJ
cana-1737	194	57	inaccurate	inaccurate	ADJ
cana-1737	194	58	predictions	prediction	NOUN
cana-1737	194	59	of	of	ADP
cana-1737	194	60	parkinson	parkinson	NOUN
cana-1737	194	61	’s	’s	PART
cana-1737	194	62	disease	disease	NOUN
cana-1737	194	63	.	.	PUNCT
cana-1737	195	1	it	it	PRON
cana-1737	195	2	serves	serve	VERB
cana-1737	195	3	as	as	ADP
cana-1737	195	4	a	a	DET
cana-1737	195	5	visual	visual	ADJ
cana-1737	195	6	aid	aid	NOUN
cana-1737	195	7	in	in	ADP
cana-1737	195	8	communications	communication	NOUN
cana-1737	195	9	on	on	ADP
cana-1737	195	10	applied	apply	VERB
cana-1737	195	11	nonlinear	nonlinear	ADJ
cana-1737	195	12	analysis	analysis	NOUN
cana-1737	195	13	issn	issn	NOUN
cana-1737	195	14	:	:	PUNCT
cana-1737	195	15	1074	1074	NUM
cana-1737	195	16	-	-	PUNCT
cana-1737	195	17	133x	133x	NUM
cana-1737	195	18	vol	vol	NOUN
cana-1737	195	19	32	32	NUM
cana-1737	195	20	no	no	NOUN
cana-1737	195	21	.	.	NOUN
cana-1737	195	22	2	2	NUM
cana-1737	195	23	(	(	PUNCT
cana-1737	195	24	2025	2025	NUM
cana-1737	195	25	)	)	PUNCT
cana-1737	195	26	211	211	NUM
cana-1737	195	27	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	195	28	evaluating	evaluate	VERB
cana-1737	195	29	the	the	DET
cana-1737	195	30	model	model	NOUN
cana-1737	195	31	's	's	PART
cana-1737	195	32	effectiveness	effectiveness	NOUN
cana-1737	195	33	.	.	PUNCT
cana-1737	196	1	in	in	ADP
cana-1737	196	2	this	this	DET
cana-1737	196	3	representation	representation	NOUN
cana-1737	196	4	,	,	PUNCT
cana-1737	196	5	individuals	individual	NOUN
cana-1737	196	6	with	with	ADP
cana-1737	196	7	parkinson	parkinson	NOUN
cana-1737	196	8	's	's	PART
cana-1737	196	9	disease	disease	NOUN
cana-1737	196	10	are	be	AUX
cana-1737	196	11	represented	represent	VERB
cana-1737	196	12	by	by	ADP
cana-1737	196	13	1	1	NUM
cana-1737	196	14	,	,	PUNCT
cana-1737	196	15	while	while	SCONJ
cana-1737	196	16	healthy	healthy	ADJ
cana-1737	196	17	individuals	individual	NOUN
cana-1737	196	18	are	be	AUX
cana-1737	196	19	denoted	denote	VERB
cana-1737	196	20	by	by	ADP
cana-1737	196	21	0	0	PROPN
cana-1737	196	22	.	.	PUNCT
cana-1737	196	23	fig	fig	NOUN
cana-1737	196	24	.	.	PUNCT
cana-1737	197	1	5	5	NUM
cana-1737	197	2	confusion	confusion	NOUN
cana-1737	197	3	matrix	matrix	NOUN
cana-1737	197	4	figure	figure	NOUN
cana-1737	197	5	5	5	NUM
cana-1737	197	6	displays	display	VERB
cana-1737	197	7	the	the	DET
cana-1737	197	8	confusion	confusion	NOUN
cana-1737	197	9	matrix	matrix	NOUN
cana-1737	197	10	illustrating	illustrate	VERB
cana-1737	197	11	the	the	DET
cana-1737	197	12	classification	classification	NOUN
cana-1737	197	13	results	result	NOUN
cana-1737	197	14	of	of	ADP
cana-1737	197	15	parkinson	parkinson	NOUN
cana-1737	197	16	’s	’s	PART
cana-1737	197	17	disease	disease	NOUN
cana-1737	197	18	.	.	PUNCT
cana-1737	198	1	within	within	ADP
cana-1737	198	2	the	the	DET
cana-1737	198	3	confusion	confusion	NOUN
cana-1737	198	4	matrix	matrix	NOUN
cana-1737	198	5	,	,	PUNCT
cana-1737	198	6	the	the	DET
cana-1737	198	7	true	true	ADJ
cana-1737	198	8	label	label	NOUN
cana-1737	198	9	indicates	indicate	VERB
cana-1737	198	10	the	the	DET
cana-1737	198	11	actual	actual	ADJ
cana-1737	198	12	presence	presence	NOUN
cana-1737	198	13	or	or	CCONJ
cana-1737	198	14	absence	absence	NOUN
cana-1737	198	15	of	of	ADP
cana-1737	198	16	pd	pd	PROPN
cana-1737	198	17	in	in	ADP
cana-1737	198	18	a	a	DET
cana-1737	198	19	given	give	VERB
cana-1737	198	20	sample	sample	NOUN
cana-1737	198	21	,	,	PUNCT
cana-1737	198	22	reflecting	reflect	VERB
cana-1737	198	23	the	the	DET
cana-1737	198	24	correct	correct	ADJ
cana-1737	198	25	classification	classification	NOUN
cana-1737	198	26	of	of	ADP
cana-1737	198	27	whether	whether	SCONJ
cana-1737	198	28	pd	pd	PROPN
cana-1737	198	29	is	be	AUX
cana-1737	198	30	present	present	ADJ
cana-1737	198	31	or	or	CCONJ
cana-1737	198	32	absent	absent	ADJ
cana-1737	198	33	.	.	PUNCT
cana-1737	199	1	the	the	DET
cana-1737	199	2	predicted	predict	VERB
cana-1737	199	3	label	label	NOUN
cana-1737	199	4	signifies	signify	VERB
cana-1737	199	5	the	the	DET
cana-1737	199	6	model	model	NOUN
cana-1737	199	7	's	's	PART
cana-1737	199	8	prediction	prediction	NOUN
cana-1737	199	9	regarding	regard	VERB
cana-1737	199	10	the	the	DET
cana-1737	199	11	presence	presence	NOUN
cana-1737	199	12	or	or	CCONJ
cana-1737	199	13	absence	absence	NOUN
cana-1737	199	14	of	of	ADP
cana-1737	199	15	pd	pd	PROPN
cana-1737	199	16	in	in	ADP
cana-1737	199	17	the	the	DET
cana-1737	199	18	sample	sample	NOUN
cana-1737	199	19	,	,	PUNCT
cana-1737	199	20	based	base	VERB
cana-1737	199	21	on	on	ADP
cana-1737	199	22	learned	learn	VERB
cana-1737	199	23	patterns	pattern	NOUN
cana-1737	199	24	from	from	ADP
cana-1737	199	25	the	the	DET
cana-1737	199	26	training	training	NOUN
cana-1737	199	27	data	datum	NOUN
cana-1737	199	28	.	.	PUNCT
cana-1737	200	1	the	the	DET
cana-1737	200	2	cnn	cnn	PROPN
cana-1737	200	3	model	model	NOUN
cana-1737	200	4	identifies	identify	VERB
cana-1737	200	5	29	29	NUM
cana-1737	200	6	true	true	ADJ
cana-1737	200	7	negatives	negative	NOUN
cana-1737	200	8	(	(	PUNCT
cana-1737	200	9	tn	tn	NOUN
cana-1737	200	10	)	)	PUNCT
cana-1737	200	11	,	,	PUNCT
cana-1737	200	12	25	25	NUM
cana-1737	200	13	true	true	ADJ
cana-1737	200	14	positives	positive	NOUN
cana-1737	200	15	(	(	PUNCT
cana-1737	200	16	tp	tp	NOUN
cana-1737	200	17	)	)	PUNCT
cana-1737	200	18	,	,	PUNCT
cana-1737	200	19	1	1	NUM
cana-1737	200	20	false	false	ADJ
cana-1737	200	21	positive	positive	ADJ
cana-1737	200	22	(	(	PUNCT
cana-1737	200	23	fp	fp	NOUN
cana-1737	200	24	)	)	PUNCT
cana-1737	200	25	,	,	PUNCT
cana-1737	200	26	and	and	CCONJ
cana-1737	200	27	4	4	NUM
cana-1737	200	28	false	false	ADJ
cana-1737	200	29	negatives	negative	NOUN
cana-1737	200	30	(	(	PUNCT
cana-1737	200	31	fn	fn	NOUN
cana-1737	200	32	)	)	PUNCT
cana-1737	200	33	.	.	PUNCT
cana-1737	201	1	where	where	SCONJ
cana-1737	201	2	:	:	PUNCT
cana-1737	201	3	•	•	X
cana-1737	201	4	tp	tp	X
cana-1737	201	5	(	(	PUNCT
cana-1737	201	6	true	true	ADJ
cana-1737	201	7	positive	positive	ADJ
cana-1737	201	8	):	):	PUNCT
cana-1737	201	9	the	the	DET
cana-1737	201	10	system	system	NOUN
cana-1737	201	11	correctly	correctly	ADV
cana-1737	201	12	identifies	identify	VERB
cana-1737	201	13	the	the	DET
cana-1737	201	14	presence	presence	NOUN
cana-1737	201	15	of	of	ADP
cana-1737	201	16	pd	pd	PROPN
cana-1737	201	17	,	,	PUNCT
cana-1737	201	18	where	where	SCONJ
cana-1737	201	19	both	both	CCONJ
cana-1737	201	20	the	the	DET
cana-1737	201	21	actual	actual	ADJ
cana-1737	201	22	and	and	CCONJ
cana-1737	201	23	predicted	predict	VERB
cana-1737	201	24	labels	label	NOUN
cana-1737	201	25	indicate	indicate	VERB
cana-1737	201	26	the	the	DET
cana-1737	201	27	disease	disease	NOUN
cana-1737	201	28	is	be	AUX
cana-1737	201	29	present	present	ADJ
cana-1737	201	30	.	.	PUNCT
cana-1737	202	1	•	•	NUM
cana-1737	202	2	tn	tn	PROPN
cana-1737	202	3	(	(	PUNCT
cana-1737	202	4	true	true	ADJ
cana-1737	202	5	negative	negative	ADJ
cana-1737	202	6	):	):	PUNCT
cana-1737	202	7	it	it	PRON
cana-1737	202	8	accurately	accurately	ADV
cana-1737	202	9	identifies	identify	VERB
cana-1737	202	10	the	the	DET
cana-1737	202	11	absence	absence	NOUN
cana-1737	202	12	of	of	ADP
cana-1737	202	13	pd	pd	PROPN
cana-1737	202	14	,	,	PUNCT
cana-1737	202	15	where	where	SCONJ
cana-1737	202	16	both	both	CCONJ
cana-1737	202	17	the	the	DET
cana-1737	202	18	actual	actual	ADJ
cana-1737	202	19	and	and	CCONJ
cana-1737	202	20	predicted	predict	VERB
cana-1737	202	21	labels	label	NOUN
cana-1737	202	22	indicate	indicate	VERB
cana-1737	202	23	the	the	DET
cana-1737	202	24	absence	absence	NOUN
cana-1737	202	25	of	of	ADP
cana-1737	202	26	pd	pd	PROPN
cana-1737	202	27	.	.	PROPN
cana-1737	202	28	•	•	NUM
cana-1737	202	29	fp	fp	INTJ
cana-1737	202	30	(	(	PUNCT
cana-1737	202	31	false	false	ADJ
cana-1737	202	32	positive	positive	ADJ
cana-1737	202	33	):	):	PUNCT
cana-1737	202	34	the	the	DET
cana-1737	202	35	system	system	NOUN
cana-1737	202	36	incorrectly	incorrectly	ADV
cana-1737	202	37	suggests	suggest	VERB
cana-1737	202	38	the	the	DET
cana-1737	202	39	presence	presence	NOUN
cana-1737	202	40	of	of	ADP
cana-1737	202	41	pd	pd	PROPN
cana-1737	202	42	when	when	SCONJ
cana-1737	202	43	it	it	PRON
cana-1737	202	44	is	be	AUX
cana-1737	202	45	absent	absent	ADJ
cana-1737	202	46	.	.	PUNCT
cana-1737	203	1	the	the	DET
cana-1737	203	2	actual	actual	ADJ
cana-1737	203	3	label	label	NOUN
cana-1737	203	4	indicates	indicate	VERB
cana-1737	203	5	no	no	DET
cana-1737	203	6	disease	disease	NOUN
cana-1737	203	7	,	,	PUNCT
cana-1737	203	8	but	but	CCONJ
cana-1737	203	9	the	the	DET
cana-1737	203	10	prediction	prediction	NOUN
cana-1737	203	11	suggests	suggest	VERB
cana-1737	203	12	otherwise	otherwise	ADV
cana-1737	203	13	.	.	PUNCT
cana-1737	204	1	•	•	NUM
cana-1737	204	2	fn	fn	INTJ
cana-1737	204	3	(	(	PUNCT
cana-1737	204	4	false	false	ADJ
cana-1737	204	5	negative	negative	ADJ
cana-1737	204	6	):	):	PUNCT
cana-1737	204	7	the	the	DET
cana-1737	204	8	system	system	NOUN
cana-1737	204	9	fails	fail	VERB
cana-1737	204	10	to	to	PART
cana-1737	204	11	detect	detect	VERB
cana-1737	204	12	pd	pd	PROPN
cana-1737	204	13	despite	despite	SCONJ
cana-1737	204	14	its	its	PRON
cana-1737	204	15	actual	actual	ADJ
cana-1737	204	16	presence	presence	NOUN
cana-1737	204	17	.	.	PUNCT
cana-1737	205	1	the	the	DET
cana-1737	205	2	actual	actual	ADJ
cana-1737	205	3	label	label	NOUN
cana-1737	205	4	indicates	indicate	VERB
cana-1737	205	5	the	the	DET
cana-1737	205	6	disease	disease	NOUN
cana-1737	205	7	,	,	PUNCT
cana-1737	205	8	but	but	CCONJ
cana-1737	205	9	the	the	DET
cana-1737	205	10	prediction	prediction	NOUN
cana-1737	205	11	fails	fail	VERB
cana-1737	205	12	to	to	PART
cana-1737	205	13	identify	identify	VERB
cana-1737	205	14	it	it	PRON
cana-1737	205	15	.	.	PUNCT
cana-1737	206	1	the	the	DET
cana-1737	206	2	cnn	cnn	PROPN
cana-1737	206	3	model	model	NOUN
cana-1737	206	4	is	be	AUX
cana-1737	206	5	trained	train	VERB
cana-1737	206	6	using	use	VERB
cana-1737	206	7	the	the	DET
cana-1737	206	8	adam	adam	PROPN
cana-1737	206	9	optimizer	optimizer	NOUN
cana-1737	206	10	for	for	ADP
cana-1737	206	11	20	20	NUM
cana-1737	206	12	epochs	epoch	NOUN
cana-1737	206	13	with	with	ADP
cana-1737	206	14	a	a	DET
cana-1737	206	15	batch	batch	NOUN
cana-1737	206	16	size	size	NOUN
cana-1737	206	17	of	of	ADP
cana-1737	206	18	32	32	NUM
cana-1737	206	19	.	.	PUNCT
cana-1737	207	1	following	follow	VERB
cana-1737	207	2	this	this	DET
cana-1737	207	3	training	training	NOUN
cana-1737	207	4	,	,	PUNCT
cana-1737	207	5	graphs	graph	NOUN
cana-1737	207	6	are	be	AUX
cana-1737	207	7	generated	generate	VERB
cana-1737	207	8	to	to	PART
cana-1737	207	9	visualize	visualize	VERB
cana-1737	207	10	the	the	DET
cana-1737	207	11	model	model	NOUN
cana-1737	207	12	's	's	PART
cana-1737	207	13	accuracy	accuracy	NOUN
cana-1737	207	14	and	and	CCONJ
cana-1737	207	15	loss	loss	NOUN
cana-1737	207	16	.	.	PUNCT
cana-1737	208	1	these	these	DET
cana-1737	208	2	graphs	graph	NOUN
cana-1737	208	3	offer	offer	VERB
cana-1737	208	4	insights	insight	NOUN
cana-1737	208	5	into	into	ADP
cana-1737	208	6	the	the	DET
cana-1737	208	7	accuracy	accuracy	NOUN
cana-1737	208	8	achieved	achieve	VERB
cana-1737	208	9	by	by	ADP
cana-1737	208	10	both	both	CCONJ
cana-1737	208	11	the	the	DET
cana-1737	208	12	training	training	NOUN
cana-1737	208	13	and	and	CCONJ
cana-1737	208	14	validation	validation	NOUN
cana-1737	208	15	datasets	dataset	NOUN
cana-1737	208	16	.	.	PUNCT
cana-1737	209	1	higher	high	ADJ
cana-1737	209	2	accuracy	accuracy	NOUN
cana-1737	209	3	values	value	NOUN
cana-1737	209	4	suggest	suggest	VERB
cana-1737	209	5	better	well	ADJ
cana-1737	209	6	model	model	NOUN
cana-1737	209	7	performance	performance	NOUN
cana-1737	209	8	.	.	PUNCT
cana-1737	210	1	the	the	DET
cana-1737	210	2	loss	loss	NOUN
cana-1737	210	3	graph	graph	NOUN
cana-1737	210	4	illustrates	illustrate	VERB
cana-1737	210	5	the	the	DET
cana-1737	210	6	model	model	NOUN
cana-1737	210	7	's	's	PART
cana-1737	210	8	performance	performance	NOUN
cana-1737	210	9	after	after	ADP
cana-1737	210	10	each	each	DET
cana-1737	210	11	iteration	iteration	NOUN
cana-1737	210	12	,	,	PUNCT
cana-1737	210	13	with	with	ADP
cana-1737	210	14	lower	low	ADJ
cana-1737	210	15	loss	loss	NOUN
cana-1737	210	16	values	value	NOUN
cana-1737	210	17	indicating	indicate	VERB
cana-1737	210	18	improved	improved	ADJ
cana-1737	210	19	performance	performance	NOUN
cana-1737	210	20	,	,	PUNCT
cana-1737	210	21	whereas	whereas	SCONJ
cana-1737	210	22	higher	high	ADJ
cana-1737	210	23	loss	loss	NOUN
cana-1737	210	24	values	value	NOUN
cana-1737	210	25	suggest	suggest	VERB
cana-1737	210	26	suboptimal	suboptimal	ADJ
cana-1737	210	27	performance	performance	NOUN
cana-1737	210	28	.	.	PUNCT
cana-1737	211	1	figure	figure	NOUN
cana-1737	211	2	6	6	NUM
cana-1737	211	3	displays	display	VERB
cana-1737	211	4	the	the	DET
cana-1737	211	5	model	model	NOUN
cana-1737	211	6	accuracy	accuracy	NOUN
cana-1737	211	7	and	and	CCONJ
cana-1737	211	8	model	model	NOUN
cana-1737	211	9	loss	loss	NOUN
cana-1737	211	10	graphs	graph	NOUN
cana-1737	211	11	.	.	PUNCT
cana-1737	212	1	communications	communication	NOUN
cana-1737	212	2	on	on	ADP
cana-1737	212	3	applied	apply	VERB
cana-1737	212	4	nonlinear	nonlinear	ADJ
cana-1737	212	5	analysis	analysis	NOUN
cana-1737	212	6	issn	issn	NOUN
cana-1737	212	7	:	:	PUNCT
cana-1737	212	8	1074	1074	NUM
cana-1737	212	9	-	-	PUNCT
cana-1737	212	10	133x	133x	NUM
cana-1737	212	11	vol	vol	NOUN
cana-1737	212	12	32	32	NUM
cana-1737	212	13	no	no	NOUN
cana-1737	212	14	.	.	NOUN
cana-1737	212	15	2	2	NUM
cana-1737	212	16	(	(	PUNCT
cana-1737	212	17	2025	2025	NUM
cana-1737	212	18	)	)	PUNCT
cana-1737	212	19	212	212	NUM
cana-1737	212	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	212	21	fig	fig	NOUN
cana-1737	212	22	.	.	PUNCT
cana-1737	213	1	6	6	NUM
cana-1737	213	2	the	the	DET
cana-1737	213	3	graph	graph	NOUN
cana-1737	213	4	of	of	ADP
cana-1737	213	5	model	model	NOUN
cana-1737	213	6	accuracy	accuracy	NOUN
cana-1737	213	7	and	and	CCONJ
cana-1737	213	8	model	model	NOUN
cana-1737	213	9	loss	loss	NOUN
cana-1737	213	10	7	7	NUM
cana-1737	213	11	.	.	PUNCT
cana-1737	213	12	conclusion	conclusion	NOUN
cana-1737	213	13	and	and	CCONJ
cana-1737	213	14	future	future	ADJ
cana-1737	213	15	work	work	NOUN
cana-1737	213	16	speech	speech	NOUN
cana-1737	213	17	presents	present	VERB
cana-1737	213	18	itself	itself	PRON
cana-1737	213	19	as	as	ADP
cana-1737	213	20	a	a	DET
cana-1737	213	21	promising	promising	ADJ
cana-1737	213	22	biomarker	biomarker	NOUN
cana-1737	213	23	for	for	ADP
cana-1737	213	24	monitoring	monitor	VERB
cana-1737	213	25	health	health	NOUN
cana-1737	213	26	conditions	condition	NOUN
cana-1737	213	27	,	,	PUNCT
cana-1737	213	28	including	include	VERB
cana-1737	213	29	the	the	DET
cana-1737	213	30	detection	detection	NOUN
cana-1737	213	31	of	of	ADP
cana-1737	213	32	parkinson	parkinson	NOUN
cana-1737	213	33	’s	’s	PART
cana-1737	213	34	disease	disease	NOUN
cana-1737	213	35	.	.	PUNCT
cana-1737	214	1	deep	deep	ADJ
cana-1737	214	2	learning	learning	NOUN
cana-1737	214	3	methodologies	methodology	NOUN
cana-1737	214	4	have	have	AUX
cana-1737	214	5	demonstrated	demonstrate	VERB
cana-1737	214	6	superior	superior	ADJ
cana-1737	214	7	efficacy	efficacy	NOUN
cana-1737	214	8	in	in	ADP
cana-1737	214	9	identifying	identify	VERB
cana-1737	214	10	parkinson	parkinson	NOUN
cana-1737	214	11	's	's	PART
cana-1737	214	12	speech	speech	NOUN
cana-1737	214	13	disorders	disorder	NOUN
cana-1737	214	14	.	.	PUNCT
cana-1737	215	1	in	in	ADP
cana-1737	215	2	this	this	DET
cana-1737	215	3	investigation	investigation	NOUN
cana-1737	215	4	,	,	PUNCT
cana-1737	215	5	we	we	PRON
cana-1737	215	6	utilized	utilize	VERB
cana-1737	215	7	a	a	DET
cana-1737	215	8	cnn	cnn	PROPN
cana-1737	215	9	model	model	NOUN
cana-1737	215	10	to	to	PART
cana-1737	215	11	discern	discern	VERB
cana-1737	215	12	parkinson	parkinson	NOUN
cana-1737	215	13	’s	’s	PART
cana-1737	215	14	disease	disease	NOUN
cana-1737	215	15	through	through	ADP
cana-1737	215	16	speech	speech	NOUN
cana-1737	215	17	analysis	analysis	NOUN
cana-1737	215	18	.	.	PUNCT
cana-1737	216	1	the	the	DET
cana-1737	216	2	cnn	cnn	PROPN
cana-1737	216	3	model	model	NOUN
cana-1737	216	4	underwent	underwent	NOUN
cana-1737	216	5	training	training	NOUN
cana-1737	216	6	using	use	VERB
cana-1737	216	7	a	a	DET
cana-1737	216	8	dataset	dataset	VERB
cana-1737	216	9	comprising	comprise	VERB
cana-1737	216	10	acoustic	acoustic	ADJ
cana-1737	216	11	speech	speech	NOUN
cana-1737	216	12	metrics	metric	NOUN
cana-1737	216	13	like	like	ADP
cana-1737	216	14	shimmer	shimmer	ADJ
cana-1737	216	15	,	,	PUNCT
cana-1737	216	16	jitter	jitter	NOUN
cana-1737	216	17	,	,	PUNCT
cana-1737	216	18	and	and	CCONJ
cana-1737	216	19	mdvp	mdvp	NOUN
cana-1737	216	20	.	.	PUNCT
cana-1737	217	1	to	to	PART
cana-1737	217	2	address	address	VERB
cana-1737	217	3	class	class	NOUN
cana-1737	217	4	imbalance	imbalance	NOUN
cana-1737	217	5	challenges	challenge	NOUN
cana-1737	217	6	,	,	PUNCT
cana-1737	217	7	the	the	DET
cana-1737	217	8	smote	smote	ADJ
cana-1737	217	9	algorithm	algorithm	NOUN
cana-1737	217	10	was	be	AUX
cana-1737	217	11	employed	employ	VERB
cana-1737	217	12	.	.	PUNCT
cana-1737	218	1	the	the	DET
cana-1737	218	2	cnn	cnn	PROPN
cana-1737	218	3	model	model	NOUN
cana-1737	218	4	showcased	showcase	VERB
cana-1737	218	5	exceptional	exceptional	ADJ
cana-1737	218	6	accuracy	accuracy	NOUN
cana-1737	218	7	,	,	PUNCT
cana-1737	218	8	reaching	reach	VERB
cana-1737	218	9	91.52	91.52	NUM
cana-1737	218	10	%	%	NOUN
cana-1737	218	11	in	in	ADP
cana-1737	218	12	distinguishing	distinguish	VERB
cana-1737	218	13	individuals	individual	NOUN
cana-1737	218	14	with	with	ADP
cana-1737	218	15	parkinson	parkinson	NOUN
cana-1737	218	16	's	's	PART
cana-1737	218	17	disease	disease	NOUN
cana-1737	218	18	from	from	ADP
cana-1737	218	19	those	those	PRON
cana-1737	218	20	without	without	ADP
cana-1737	218	21	,	,	PUNCT
cana-1737	218	22	based	base	VERB
cana-1737	218	23	on	on	ADP
cana-1737	218	24	speech	speech	NOUN
cana-1737	218	25	data	datum	NOUN
cana-1737	218	26	.	.	PUNCT
cana-1737	219	1	furthermore	furthermore	ADV
cana-1737	219	2	,	,	PUNCT
cana-1737	219	3	the	the	DET
cana-1737	219	4	model	model	NOUN
cana-1737	219	5	achieved	achieve	VERB
cana-1737	219	6	a	a	DET
cana-1737	219	7	precision	precision	NOUN
cana-1737	219	8	of	of	ADP
cana-1737	219	9	92.01	92.01	NUM
cana-1737	219	10	%	%	NOUN
cana-1737	219	11	,	,	PUNCT
cana-1737	219	12	recall	recall	NOUN
cana-1737	219	13	of	of	ADP
cana-1737	219	14	91.43	91.43	NUM
cana-1737	219	15	%	%	NOUN
cana-1737	219	16	,	,	PUNCT
cana-1737	219	17	and	and	CCONJ
cana-1737	219	18	f1	f1	NOUN
cana-1737	219	19	-	-	PUNCT
cana-1737	219	20	score	score	NOUN
cana-1737	219	21	of	of	ADP
cana-1737	219	22	91.48	91.48	NUM
cana-1737	219	23	%	%	NOUN
cana-1737	219	24	for	for	ADP
cana-1737	219	25	speech	speech	NOUN
cana-1737	219	26	attributes	attribute	NOUN
cana-1737	219	27	.	.	PUNCT
cana-1737	220	1	future	future	ADJ
cana-1737	220	2	research	research	NOUN
cana-1737	220	3	endeavors	endeavor	NOUN
cana-1737	220	4	will	will	AUX
cana-1737	220	5	delve	delve	VERB
cana-1737	220	6	into	into	ADP
cana-1737	220	7	refining	refine	VERB
cana-1737	220	8	the	the	DET
cana-1737	220	9	cnn	cnn	PROPN
cana-1737	220	10	model	model	NOUN
cana-1737	220	11	,	,	PUNCT
cana-1737	220	12	potentially	potentially	ADV
cana-1737	220	13	by	by	ADP
cana-1737	220	14	augmenting	augment	VERB
cana-1737	220	15	layers	layer	NOUN
cana-1737	220	16	and	and	CCONJ
cana-1737	220	17	integrating	integrate	VERB
cana-1737	220	18	various	various	ADJ
cana-1737	220	19	deep	deep	ADJ
cana-1737	220	20	learning	learning	NOUN
cana-1737	220	21	techniques	technique	NOUN
cana-1737	220	22	.	.	PUNCT
cana-1737	221	1	additionally	additionally	ADV
cana-1737	221	2	,	,	PUNCT
cana-1737	221	3	the	the	DET
cana-1737	221	4	inclusion	inclusion	NOUN
cana-1737	221	5	of	of	ADP
cana-1737	221	6	novel	novel	ADJ
cana-1737	221	7	features	feature	NOUN
cana-1737	221	8	alongside	alongside	ADP
cana-1737	221	9	existing	exist	VERB
cana-1737	221	10	ones	one	NOUN
cana-1737	221	11	may	may	AUX
cana-1737	221	12	further	far	ADV
cana-1737	221	13	enhance	enhance	VERB
cana-1737	221	14	the	the	DET
cana-1737	221	15	model	model	NOUN
cana-1737	221	16	's	's	PART
cana-1737	221	17	efficacy	efficacy	NOUN
cana-1737	221	18	.	.	PUNCT
cana-1737	222	1	furthermore	furthermore	ADV
cana-1737	222	2	,	,	PUNCT
cana-1737	222	3	exploring	explore	VERB
cana-1737	222	4	hybrid	hybrid	ADJ
cana-1737	222	5	algorithmic	algorithmic	ADJ
cana-1737	222	6	combinations	combination	NOUN
cana-1737	222	7	such	such	ADJ
cana-1737	222	8	as	as	ADP
cana-1737	222	9	cnn	cnn	PROPN
cana-1737	222	10	-	-	PUNCT
cana-1737	222	11	lstm	lstm	PROPN
cana-1737	222	12	and	and	CCONJ
cana-1737	222	13	cnn	cnn	PROPN
cana-1737	222	14	-	-	PUNCT
cana-1737	222	15	rnn	rnn	PROPN
cana-1737	222	16	holds	hold	VERB
cana-1737	222	17	promise	promise	NOUN
cana-1737	222	18	in	in	ADP
cana-1737	222	19	potentially	potentially	ADV
cana-1737	222	20	augmenting	augment	VERB
cana-1737	222	21	pd	pd	NOUN
cana-1737	222	22	speech	speech	NOUN
cana-1737	222	23	detection	detection	NOUN
cana-1737	222	24	capabilities	capability	NOUN
cana-1737	222	25	.	.	PUNCT
cana-1737	223	1	references	reference	NOUN
cana-1737	223	2	[	[	X
cana-1737	223	3	1	1	NUM
cana-1737	223	4	]	]	X
cana-1737	223	5	yaman	yaman	PROPN
cana-1737	223	6	,	,	PUNCT
cana-1737	223	7	orhan	orhan	PROPN
cana-1737	223	8	,	,	PUNCT
cana-1737	223	9	fatih	fatih	PROPN
cana-1737	223	10	ertam	ertam	NOUN
cana-1737	223	11	,	,	PUNCT
cana-1737	223	12	and	and	CCONJ
cana-1737	223	13	turker	turker	ADJ
cana-1737	223	14	tuncer	tuncer	NOUN
cana-1737	223	15	.	.	PUNCT
cana-1737	224	1	"	"	PUNCT
cana-1737	224	2	automated	automate	VERB
cana-1737	224	3	parkinson	parkinson	NOUN
cana-1737	224	4	’s	’s	PART
cana-1737	224	5	disease	disease	NOUN
cana-1737	224	6	recognition	recognition	NOUN
cana-1737	224	7	based	base	VERB
cana-1737	224	8	on	on	ADP
cana-1737	224	9	statistical	statistical	ADJ
cana-1737	224	10	pooling	pooling	NOUN
cana-1737	224	11	method	method	NOUN
cana-1737	224	12	using	use	VERB
cana-1737	224	13	acoustic	acoustic	ADJ
cana-1737	224	14	features	feature	NOUN
cana-1737	224	15	.	.	PUNCT
cana-1737	224	16	"	"	PUNCT
cana-1737	225	1	medical	medical	ADJ
cana-1737	225	2	hypotheses	hypothesis	NOUN
cana-1737	225	3	135	135	NUM
cana-1737	225	4	(	(	PUNCT
cana-1737	225	5	2020	2020	NUM
cana-1737	225	6	):	):	PUNCT
cana-1737	225	7	109483	109483	NUM
cana-1737	225	8	.	.	PUNCT
cana-1737	226	1	[	[	X
cana-1737	226	2	2	2	NUM
cana-1737	226	3	]	]	X
cana-1737	226	4	thapa	thapa	PROPN
cana-1737	226	5	,	,	PUNCT
cana-1737	226	6	surendrabikram	surendrabikram	PROPN
cana-1737	226	7	,	,	PUNCT
cana-1737	226	8	surabhi	surabhi	PROPN
cana-1737	226	9	adhikari	adhikari	PROPN
cana-1737	226	10	,	,	PUNCT
cana-1737	226	11	awishkar	awishkar	NOUN
cana-1737	226	12	ghimire	ghimire	NOUN
cana-1737	226	13	,	,	PUNCT
cana-1737	226	14	and	and	CCONJ
cana-1737	226	15	anshuman	anshuman	PROPN
cana-1737	226	16	aditya	aditya	PROPN
cana-1737	226	17	.	.	PUNCT
cana-1737	227	1	"	"	PUNCT
cana-1737	227	2	feature	feature	NOUN
cana-1737	227	3	selection	selection	NOUN
cana-1737	227	4	based	base	VERB
cana-1737	227	5	twinsupport	twinsupport	NOUN
cana-1737	227	6	vector	vector	NOUN
cana-1737	227	7	machine	machine	NOUN
cana-1737	227	8	for	for	ADP
cana-1737	227	9	the	the	DET
cana-1737	227	10	diagnosis	diagnosis	NOUN
cana-1737	227	11	of	of	ADP
cana-1737	227	12	parkinson	parkinson	NOUN
cana-1737	227	13	’s	’s	PART
cana-1737	227	14	disease	disease	NOUN
cana-1737	227	15	.	.	PUNCT
cana-1737	227	16	"	"	PUNCT
cana-1737	228	1	in	in	ADP
cana-1737	228	2	2020	2020	NUM
cana-1737	228	3	ieee	ieee	NOUN
cana-1737	228	4	8th	8th	NOUN
cana-1737	228	5	r10	r10	NOUN
cana-1737	228	6	humanitarian	humanitarian	ADJ
cana-1737	228	7	technology	technology	NOUN
cana-1737	228	8	conference	conference	NOUN
cana-1737	228	9	(	(	PUNCT
cana-1737	228	10	r10	r10	NOUN
cana-1737	228	11	-	-	PUNCT
cana-1737	228	12	htc	htc	NOUN
cana-1737	228	13	)	)	PUNCT
cana-1737	228	14	,	,	PUNCT
cana-1737	228	15	pp	pp	PROPN
cana-1737	228	16	.	.	PUNCT
cana-1737	229	1	1	1	NUM
cana-1737	229	2	-	-	SYM
cana-1737	229	3	6	6	NUM
cana-1737	229	4	.	.	PUNCT
cana-1737	229	5	ieee	ieee	NOUN
cana-1737	229	6	,	,	PUNCT
cana-1737	229	7	2020	2020	NUM
cana-1737	229	8	.	.	PUNCT
cana-1737	230	1	[	[	X
cana-1737	230	2	3	3	NUM
cana-1737	230	3	]	]	X
cana-1737	230	4	younis	younis	NOUN
cana-1737	230	5	thanoun	thanoun	NOUN
cana-1737	230	6	,	,	PUNCT
cana-1737	230	7	mohammed	mohammed	PROPN
cana-1737	230	8	,	,	PUNCT
cana-1737	230	9	and	and	CCONJ
cana-1737	230	10	m.	m.	NOUN
cana-1737	230	11	o.	o.	PROPN
cana-1737	230	12	h.	h.	PROPN
cana-1737	230	13	a.	a.	PROPN
cana-1737	230	14	m.	m.	PROPN
cana-1737	230	15	m.	m.	PROPN
cana-1737	230	16	a.	a.	PROPN
cana-1737	230	17	d.	d.	PROPN
cana-1737	230	18	t.	t.	PROPN
cana-1737	230	19	yaseen	yaseen	PROPN
cana-1737	230	20	.	.	PUNCT
cana-1737	231	1	"	"	PUNCT
cana-1737	231	2	a	a	DET
cana-1737	231	3	comparative	comparative	ADJ
cana-1737	231	4	study	study	NOUN
cana-1737	231	5	of	of	ADP
cana-1737	231	6	parkinson	parkinson	NOUN
cana-1737	231	7	disease	disease	NOUN
cana-1737	231	8	diagnosis	diagnosis	NOUN
cana-1737	231	9	in	in	ADP
cana-1737	231	10	machine	machine	NOUN
cana-1737	231	11	learning	learning	NOUN
cana-1737	231	12	.	.	PUNCT
cana-1737	231	13	"	"	PUNCT
cana-1737	232	1	in	in	ADP
cana-1737	232	2	proceedings	proceeding	NOUN
cana-1737	232	3	of	of	ADP
cana-1737	232	4	the	the	DET
cana-1737	232	5	4th	4th	ADJ
cana-1737	232	6	international	international	ADJ
cana-1737	232	7	conference	conference	NOUN
cana-1737	232	8	on	on	ADP
cana-1737	232	9	advances	advance	NOUN
cana-1737	232	10	in	in	ADP
cana-1737	232	11	artificial	artificial	ADJ
cana-1737	232	12	intelligence	intelligence	NOUN
cana-1737	232	13	,	,	PUNCT
cana-1737	232	14	pp	pp	ADJ
cana-1737	232	15	.	.	PUNCT
cana-1737	233	1	23	23	NUM
cana-1737	233	2	-	-	SYM
cana-1737	233	3	28	28	NUM
cana-1737	233	4	.	.	PUNCT
cana-1737	234	1	2020	2020	NUM
cana-1737	234	2	.	.	PUNCT
cana-1737	235	1	[	[	X
cana-1737	235	2	4	4	NUM
cana-1737	235	3	]	]	SYM
cana-1737	235	4	ashour	ashour	NOUN
cana-1737	235	5	,	,	PUNCT
cana-1737	235	6	amira	amira	PROPN
cana-1737	235	7	s.	s.	PROPN
cana-1737	235	8	,	,	PUNCT
cana-1737	235	9	majid	majid	PROPN
cana-1737	235	10	kamal	kamal	PROPN
cana-1737	235	11	a.	a.	PROPN
cana-1737	235	12	nour	nour	PROPN
cana-1737	235	13	,	,	PUNCT
cana-1737	235	14	kemal	kemal	PROPN
cana-1737	235	15	polat	polat	PROPN
cana-1737	235	16	,	,	PUNCT
cana-1737	235	17	yanhui	yanhui	PROPN
cana-1737	235	18	guo	guo	PROPN
cana-1737	235	19	,	,	PUNCT
cana-1737	235	20	wafaa	wafaa	PROPN
cana-1737	235	21	alsaggaf	alsaggaf	PROPN
cana-1737	235	22	,	,	PUNCT
cana-1737	235	23	and	and	CCONJ
cana-1737	235	24	amira	amira	PROPN
cana-1737	235	25	el	el	PROPN
cana-1737	235	26	-	-	PROPN
cana-1737	235	27	attar	attar	PROPN
cana-1737	235	28	.	.	PUNCT
cana-1737	236	1	"	"	PUNCT
cana-1737	236	2	a	a	DET
cana-1737	236	3	novel	novel	ADJ
cana-1737	236	4	framework	framework	NOUN
cana-1737	236	5	of	of	ADP
cana-1737	236	6	two	two	NUM
cana-1737	236	7	successive	successive	ADJ
cana-1737	236	8	feature	feature	NOUN
cana-1737	236	9	selection	selection	NOUN
cana-1737	236	10	levels	level	NOUN
cana-1737	236	11	using	use	VERB
cana-1737	236	12	weight	weight	NOUN
cana-1737	236	13	-	-	PUNCT
cana-1737	236	14	based	base	VERB
cana-1737	236	15	procedure	procedure	NOUN
cana-1737	236	16	for	for	ADP
cana-1737	236	17	voice	voice	NOUN
cana-1737	236	18	-	-	PUNCT
cana-1737	236	19	loss	loss	NOUN
cana-1737	236	20	detection	detection	NOUN
cana-1737	236	21	in	in	ADP
cana-1737	236	22	parkinson	parkinson	NOUN
cana-1737	236	23	’s	’s	PART
cana-1737	236	24	disease	disease	NOUN
cana-1737	236	25	.	.	PUNCT
cana-1737	236	26	"	"	PUNCT
cana-1737	237	1	ieee	ieee	NOUN
cana-1737	237	2	access	access	NOUN
cana-1737	237	3	8	8	NUM
cana-1737	237	4	(	(	PUNCT
cana-1737	237	5	2020	2020	NUM
cana-1737	237	6	):	):	PUNCT
cana-1737	237	7	76193	76193	NUM
cana-1737	237	8	-	-	SYM
cana-1737	237	9	76203	76203	NUM
cana-1737	237	10	.	.	PUNCT
cana-1737	238	1	[	[	X
cana-1737	238	2	5	5	NUM
cana-1737	238	3	]	]	PUNCT
cana-1737	238	4	hoq	hoq	NOUN
cana-1737	238	5	,	,	PUNCT
cana-1737	238	6	muntasir	muntasir	NOUN
cana-1737	238	7	,	,	PUNCT
cana-1737	238	8	mohammed	mohammed	PROPN
cana-1737	238	9	nazim	nazim	PROPN
cana-1737	238	10	uddin	uddin	PROPN
cana-1737	238	11	,	,	PUNCT
cana-1737	238	12	and	and	CCONJ
cana-1737	238	13	seung	seung	PROPN
cana-1737	238	14	-	-	PUNCT
cana-1737	238	15	bo	bo	PROPN
cana-1737	238	16	park	park	NOUN
cana-1737	238	17	.	.	PUNCT
cana-1737	239	1	"	"	PUNCT
cana-1737	239	2	vocal	vocal	ADJ
cana-1737	239	3	feature	feature	NOUN
cana-1737	239	4	extraction	extraction	NOUN
cana-1737	239	5	-	-	PUNCT
cana-1737	239	6	based	base	VERB
cana-1737	239	7	artificial	artificial	ADJ
cana-1737	239	8	intelligent	intelligent	ADJ
cana-1737	239	9	model	model	NOUN
cana-1737	239	10	for	for	ADP
cana-1737	239	11	parkinson	parkinson	NOUN
cana-1737	239	12	’s	’s	PART
cana-1737	239	13	disease	disease	NOUN
cana-1737	239	14	detection	detection	NOUN
cana-1737	239	15	.	.	PUNCT
cana-1737	239	16	"	"	PUNCT
cana-1737	240	1	diagnostics	diagnostic	NOUN
cana-1737	240	2	11	11	NUM
cana-1737	240	3	,	,	PUNCT
cana-1737	240	4	no	no	INTJ
cana-1737	240	5	.	.	NOUN
cana-1737	240	6	6	6	NUM
cana-1737	240	7	(	(	PUNCT
cana-1737	240	8	2021	2021	NUM
cana-1737	240	9	):	):	PUNCT
cana-1737	240	10	1076	1076	NUM
cana-1737	240	11	.	.	PUNCT
cana-1737	241	1	communications	communication	NOUN
cana-1737	241	2	on	on	ADP
cana-1737	241	3	applied	apply	VERB
cana-1737	241	4	nonlinear	nonlinear	ADJ
cana-1737	241	5	analysis	analysis	NOUN
cana-1737	241	6	issn	issn	NOUN
cana-1737	241	7	:	:	PUNCT
cana-1737	241	8	1074	1074	NUM
cana-1737	241	9	-	-	PUNCT
cana-1737	241	10	133x	133x	NUM
cana-1737	241	11	vol	vol	NOUN
cana-1737	241	12	32	32	NUM
cana-1737	241	13	no	no	NOUN
cana-1737	241	14	.	.	NOUN
cana-1737	241	15	2	2	NUM
cana-1737	241	16	(	(	PUNCT
cana-1737	241	17	2025	2025	NUM
cana-1737	241	18	)	)	PUNCT
cana-1737	241	19	213	213	NUM
cana-1737	241	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	242	1	[	[	X
cana-1737	242	2	6	6	NUM
cana-1737	242	3	]	]	X
cana-1737	242	4	quan	quan	PROPN
cana-1737	242	5	,	,	PUNCT
cana-1737	242	6	changqin	changqin	PROPN
cana-1737	242	7	,	,	PUNCT
cana-1737	242	8	kang	kang	PROPN
cana-1737	242	9	ren	ren	PROPN
cana-1737	242	10	,	,	PUNCT
cana-1737	242	11	zhiwei	zhiwei	PROPN
cana-1737	242	12	luo	luo	PROPN
cana-1737	242	13	,	,	PUNCT
cana-1737	242	14	zhonglue	zhonglue	PROPN
cana-1737	242	15	chen	chen	PROPN
cana-1737	242	16	,	,	PUNCT
cana-1737	242	17	and	and	CCONJ
cana-1737	242	18	yun	yun	PROPN
cana-1737	242	19	ling	ling	PROPN
cana-1737	242	20	.	.	PUNCT
cana-1737	243	1	"	"	PUNCT
cana-1737	243	2	end	end	VERB
cana-1737	243	3	-	-	PUNCT
cana-1737	243	4	to	to	ADP
cana-1737	243	5	-	-	PUNCT
cana-1737	243	6	end	end	NOUN
cana-1737	243	7	deep	deep	ADJ
cana-1737	243	8	learning	learning	NOUN
cana-1737	243	9	approach	approach	NOUN
cana-1737	243	10	for	for	ADP
cana-1737	243	11	parkinson	parkinson	NOUN
cana-1737	243	12	’s	’s	PART
cana-1737	243	13	disease	disease	NOUN
cana-1737	243	14	detection	detection	NOUN
cana-1737	243	15	from	from	ADP
cana-1737	243	16	speech	speech	NOUN
cana-1737	243	17	signals	signal	NOUN
cana-1737	243	18	.	.	PUNCT
cana-1737	243	19	"	"	PUNCT
cana-1737	244	1	biocybernetics	biocybernetic	NOUN
cana-1737	244	2	and	and	CCONJ
cana-1737	244	3	biomedical	biomedical	ADJ
cana-1737	244	4	engineering	engineering	NOUN
cana-1737	244	5	42	42	NUM
cana-1737	244	6	,	,	PUNCT
cana-1737	244	7	no	no	INTJ
cana-1737	244	8	.	.	NOUN
cana-1737	244	9	2	2	NUM
cana-1737	244	10	(	(	PUNCT
cana-1737	244	11	2022	2022	NUM
cana-1737	244	12	):	):	PUNCT
cana-1737	244	13	556	556	NUM
cana-1737	244	14	-	-	SYM
cana-1737	244	15	574	574	NUM
cana-1737	244	16	.	.	PUNCT
cana-1737	245	1	[	[	X
cana-1737	245	2	7	7	X
cana-1737	245	3	]	]	X
cana-1737	245	4	escobar	escobar	NOUN
cana-1737	245	5	-	-	PUNCT
cana-1737	245	6	grisales	grisale	NOUN
cana-1737	245	7	,	,	PUNCT
cana-1737	245	8	daniel	daniel	PROPN
cana-1737	245	9	,	,	PUNCT
cana-1737	245	10	cristian	cristian	PROPN
cana-1737	245	11	david	david	PROPN
cana-1737	245	12	ríos	ríos	PROPN
cana-1737	245	13	-	-	PUNCT
cana-1737	245	14	urrego	urrego	NOUN
cana-1737	245	15	,	,	PUNCT
cana-1737	245	16	and	and	CCONJ
cana-1737	245	17	juan	juan	PROPN
cana-1737	245	18	rafael	rafael	PROPN
cana-1737	245	19	orozco	orozco	PROPN
cana-1737	245	20	-	-	PUNCT
cana-1737	245	21	arroyave	arroyave	PROPN
cana-1737	245	22	.	.	PUNCT
cana-1737	246	1	"	"	PUNCT
cana-1737	246	2	deep	deep	ADJ
cana-1737	246	3	learning	learning	NOUN
cana-1737	246	4	and	and	CCONJ
cana-1737	246	5	artificial	artificial	ADJ
cana-1737	246	6	intelligence	intelligence	NOUN
cana-1737	246	7	applied	apply	VERB
cana-1737	246	8	to	to	ADP
cana-1737	246	9	model	model	NOUN
cana-1737	246	10	speech	speech	NOUN
cana-1737	246	11	and	and	CCONJ
cana-1737	246	12	language	language	NOUN
cana-1737	246	13	in	in	ADP
cana-1737	246	14	parkinson	parkinson	NOUN
cana-1737	246	15	’s	’s	PART
cana-1737	246	16	disease	disease	NOUN
cana-1737	246	17	.	.	PUNCT
cana-1737	246	18	"	"	PUNCT
cana-1737	247	1	diagnostics	diagnostic	NOUN
cana-1737	247	2	13	13	NUM
cana-1737	247	3	,	,	PUNCT
cana-1737	247	4	no	no	INTJ
cana-1737	247	5	.	.	NOUN
cana-1737	247	6	13	13	NUM
cana-1737	247	7	(	(	PUNCT
cana-1737	247	8	2023	2023	NUM
cana-1737	247	9	):	):	PUNCT
cana-1737	247	10	2163	2163	NUM
cana-1737	247	11	.	.	PUNCT
cana-1737	248	1	[	[	X
cana-1737	248	2	8	8	NUM
cana-1737	248	3	]	]	X
cana-1737	248	4	govindu	govindu	NOUN
cana-1737	248	5	,	,	PUNCT
cana-1737	248	6	aditi	aditi	PROPN
cana-1737	248	7	,	,	PUNCT
cana-1737	248	8	and	and	CCONJ
cana-1737	248	9	sushila	sushila	VERB
cana-1737	248	10	palwe	palwe	NOUN
cana-1737	248	11	.	.	PUNCT
cana-1737	249	1	"	"	PUNCT
cana-1737	249	2	early	early	ADJ
cana-1737	249	3	detection	detection	NOUN
cana-1737	249	4	of	of	ADP
cana-1737	249	5	parkinson	parkinson	NOUN
cana-1737	249	6	's	's	PART
cana-1737	249	7	disease	disease	NOUN
cana-1737	249	8	using	use	VERB
cana-1737	249	9	machine	machine	NOUN
cana-1737	249	10	learning	learning	NOUN
cana-1737	249	11	.	.	PUNCT
cana-1737	249	12	"	"	PUNCT
cana-1737	250	1	procedia	procedia	NOUN
cana-1737	250	2	computer	computer	NOUN
cana-1737	250	3	science	science	NOUN
cana-1737	250	4	218	218	NUM
cana-1737	250	5	(	(	PUNCT
cana-1737	250	6	2023	2023	NUM
cana-1737	250	7	):	):	PUNCT
cana-1737	250	8	249	249	NUM
cana-1737	250	9	-	-	SYM
cana-1737	250	10	261	261	NUM
cana-1737	250	11	.	.	PUNCT
cana-1737	251	1	[	[	X
cana-1737	251	2	9	9	NUM
cana-1737	251	3	]	]	SYM
cana-1737	251	4	cesarini	cesarini	NOUN
cana-1737	251	5	,	,	PUNCT
cana-1737	251	6	valerio	valerio	PROPN
cana-1737	251	7	,	,	PUNCT
cana-1737	251	8	giovanni	giovanni	PROPN
cana-1737	251	9	saggio	saggio	PROPN
cana-1737	251	10	,	,	PUNCT
cana-1737	251	11	antonio	antonio	PROPN
cana-1737	251	12	suppa	suppa	PROPN
cana-1737	251	13	,	,	PUNCT
cana-1737	251	14	francesco	francesco	PROPN
cana-1737	251	15	asci	asci	PROPN
cana-1737	251	16	,	,	PUNCT
cana-1737	251	17	antonio	antonio	PROPN
cana-1737	251	18	pisani	pisani	PROPN
cana-1737	251	19	,	,	PUNCT
cana-1737	251	20	alessandra	alessandra	PROPN
cana-1737	251	21	calculli	calculli	PROPN
cana-1737	251	22	,	,	PUNCT
cana-1737	251	23	rayan	rayan	PROPN
cana-1737	251	24	fayad	fayad	PROPN
cana-1737	251	25	,	,	PUNCT
cana-1737	251	26	mohamad	mohamad	PROPN
cana-1737	251	27	hajj	hajj	PROPN
cana-1737	251	28	-	-	PUNCT
cana-1737	251	29	hassan	hassan	PROPN
cana-1737	251	30	,	,	PUNCT
cana-1737	251	31	and	and	CCONJ
cana-1737	251	32	giovanni	giovanni	PROPN
cana-1737	251	33	costantini	costantini	PROPN
cana-1737	251	34	.	.	PUNCT
cana-1737	252	1	"	"	PUNCT
cana-1737	252	2	voice	voice	NOUN
cana-1737	252	3	disorder	disorder	NOUN
cana-1737	252	4	multi	multi	ADJ
cana-1737	252	5	-	-	ADJ
cana-1737	252	6	class	class	ADJ
cana-1737	252	7	classification	classification	NOUN
cana-1737	252	8	for	for	ADP
cana-1737	252	9	the	the	DET
cana-1737	252	10	distinction	distinction	NOUN
cana-1737	252	11	of	of	ADP
cana-1737	252	12	parkinson	parkinson	NOUN
cana-1737	252	13	’s	’s	PART
cana-1737	252	14	disease	disease	NOUN
cana-1737	252	15	and	and	CCONJ
cana-1737	252	16	adductor	adductor	NOUN
cana-1737	252	17	spasmodic	spasmodic	ADJ
cana-1737	252	18	dysphonia	dysphonia	NOUN
cana-1737	252	19	.	.	PUNCT
cana-1737	252	20	"	"	PUNCT
cana-1737	252	21	applied	apply	VERB
cana-1737	252	22	sciences	science	NOUN
cana-1737	252	23	13	13	NUM
cana-1737	252	24	,	,	PUNCT
cana-1737	252	25	no	no	INTJ
cana-1737	252	26	.	.	NOUN
cana-1737	252	27	15	15	NUM
cana-1737	252	28	(	(	PUNCT
cana-1737	252	29	2023	2023	NUM
cana-1737	252	30	):	):	PUNCT
cana-1737	252	31	8562	8562	NUM
cana-1737	252	32	.	.	PUNCT
cana-1737	253	1	[	[	X
cana-1737	253	2	10	10	NUM
cana-1737	253	3	]	]	X
cana-1737	253	4	alalayah	alalayah	ADJ
cana-1737	253	5	,	,	PUNCT
cana-1737	253	6	khaled	khaled	ADJ
cana-1737	253	7	m.	m.	NOUN
cana-1737	253	8	,	,	PUNCT
cana-1737	253	9	ebrahim	ebrahim	PROPN
cana-1737	253	10	mohammed	mohammed	PROPN
cana-1737	253	11	senan	senan	PROPN
cana-1737	253	12	,	,	PUNCT
cana-1737	253	13	hany	hany	PROPN
cana-1737	253	14	f.	f.	PROPN
cana-1737	253	15	atlam	atlam	PROPN
cana-1737	253	16	,	,	PUNCT
cana-1737	253	17	ibrahim	ibrahim	PROPN
cana-1737	253	18	abdulrab	abdulrab	PROPN
cana-1737	253	19	ahmed	ahmed	PROPN
cana-1737	253	20	,	,	PUNCT
cana-1737	253	21	and	and	CCONJ
cana-1737	253	22	hamzeh	hamzeh	NOUN
cana-1737	253	23	salameh	salameh	PROPN
cana-1737	253	24	ahmad	ahmad	PROPN
cana-1737	253	25	shatnawi	shatnawi	PROPN
cana-1737	253	26	.	.	PUNCT
cana-1737	254	1	"	"	PUNCT
cana-1737	254	2	automatic	automatic	ADJ
cana-1737	254	3	and	and	CCONJ
cana-1737	254	4	early	early	ADJ
cana-1737	254	5	detection	detection	NOUN
cana-1737	254	6	of	of	ADP
cana-1737	254	7	parkinson	parkinson	NOUN
cana-1737	254	8	’s	’s	PART
cana-1737	254	9	disease	disease	NOUN
cana-1737	254	10	by	by	ADP
cana-1737	254	11	analyzing	analyze	VERB
cana-1737	254	12	acoustic	acoustic	ADJ
cana-1737	254	13	signals	signal	NOUN
cana-1737	254	14	using	use	VERB
cana-1737	254	15	classification	classification	NOUN
cana-1737	254	16	algorithms	algorithm	NOUN
cana-1737	254	17	based	base	VERB
cana-1737	254	18	on	on	ADP
cana-1737	254	19	recursive	recursive	ADJ
cana-1737	254	20	feature	feature	NOUN
cana-1737	254	21	elimination	elimination	NOUN
cana-1737	254	22	method	method	NOUN
cana-1737	254	23	.	.	PUNCT
cana-1737	254	24	"	"	PUNCT
cana-1737	255	1	diagnostics	diagnostic	NOUN
cana-1737	255	2	13	13	NUM
cana-1737	255	3	,	,	PUNCT
cana-1737	255	4	no	no	INTJ
cana-1737	255	5	.	.	NOUN
cana-1737	255	6	11	11	NUM
cana-1737	255	7	(	(	PUNCT
cana-1737	255	8	2023	2023	NUM
cana-1737	255	9	):	):	PUNCT
cana-1737	255	10	1924	1924	NUM
cana-1737	255	11	.	.	PUNCT
cana-1737	256	1	[	[	X
cana-1737	256	2	11	11	NUM
cana-1737	256	3	]	]	SYM
cana-1737	256	4	montesinos	montesino	NOUN
cana-1737	256	5	-	-	PUNCT
cana-1737	256	6	lópez	lópez	ADJ
cana-1737	256	7	,	,	PUNCT
cana-1737	256	8	osval	osval	PROPN
cana-1737	256	9	&	&	CCONJ
cana-1737	256	10	montesinos	montesinos	PROPN
cana-1737	256	11	,	,	PUNCT
cana-1737	256	12	abelardo	abelardo	PROPN
cana-1737	256	13	&	&	CCONJ
cana-1737	256	14	crossa	crossa	PROPN
cana-1737	256	15	,	,	PUNCT
cana-1737	256	16	jose	jose	PROPN
cana-1737	256	17	.	.	PUNCT
cana-1737	257	1	(	(	PUNCT
cana-1737	257	2	2022	2022	NUM
cana-1737	257	3	)	)	PUNCT
cana-1737	257	4	.	.	PUNCT
cana-1737	258	1	convolutional	convolutional	ADJ
cana-1737	258	2	neural	neural	ADJ
cana-1737	258	3	networks	network	NOUN
cana-1737	258	4	.	.	PUNCT
cana-1737	259	1	10.1007/978	10.1007/978	NUM
cana-1737	259	2	-	-	SYM
cana-1737	259	3	3	3	NUM
cana-1737	259	4	-	-	PUNCT
cana-1737	259	5	030	030	NUM
cana-1737	259	6	-	-	PUNCT
cana-1737	259	7	89010	89010	NUM
cana-1737	259	8	-	-	PUNCT
cana-1737	259	9	0_13	0_13	NOUN
cana-1737	259	10	.	.	PUNCT
cana-1737	260	1	[	[	X
cana-1737	260	2	12	12	NUM
cana-1737	260	3	]	]	SYM
cana-1737	260	4	vaz	vaz	PROPN
cana-1737	260	5	,	,	PUNCT
cana-1737	260	6	joel	joel	PROPN
cana-1737	260	7	markus	markus	PROPN
cana-1737	260	8	,	,	PUNCT
cana-1737	260	9	and	and	CCONJ
cana-1737	260	10	s.	s.	PROPN
cana-1737	260	11	balaji	balaji	PROPN
cana-1737	260	12	.	.	PUNCT
cana-1737	261	1	"	"	PUNCT
cana-1737	261	2	convolutional	convolutional	ADJ
cana-1737	261	3	neural	neural	ADJ
cana-1737	261	4	networks	network	NOUN
cana-1737	261	5	(	(	PUNCT
cana-1737	261	6	cnns	cnns	PROPN
cana-1737	261	7	):	):	PUNCT
cana-1737	261	8	concepts	concept	NOUN
cana-1737	261	9	and	and	CCONJ
cana-1737	261	10	applications	application	NOUN
cana-1737	261	11	in	in	ADP
cana-1737	261	12	pharmacogenomics	pharmacogenomic	NOUN
cana-1737	261	13	.	.	PUNCT
cana-1737	261	14	"	"	PUNCT
cana-1737	262	1	molecular	molecular	ADJ
cana-1737	262	2	diversity	diversity	NOUN
cana-1737	262	3	25	25	NUM
cana-1737	262	4	,	,	PUNCT
cana-1737	262	5	no	no	INTJ
cana-1737	262	6	.	.	NOUN
cana-1737	262	7	3	3	NUM
cana-1737	262	8	(	(	PUNCT
cana-1737	262	9	2021	2021	NUM
cana-1737	262	10	):	):	PUNCT
cana-1737	262	11	1569	1569	NUM
cana-1737	262	12	-	-	SYM
cana-1737	262	13	1584	1584	NUM
cana-1737	262	14	.	.	PUNCT
cana-1737	263	1	[	[	X
cana-1737	263	2	13	13	NUM
cana-1737	263	3	]	]	X
cana-1737	263	4	hadi	hadi	PROPN
cana-1737	263	5	,	,	PUNCT
cana-1737	263	6	muhammad	muhammad	PROPN
cana-1737	263	7	usman	usman	PROPN
cana-1737	263	8	,	,	PUNCT
cana-1737	263	9	rizwan	rizwan	PROPN
cana-1737	263	10	qureshi	qureshi	PROPN
cana-1737	263	11	,	,	PUNCT
cana-1737	263	12	ayesha	ayesha	PROPN
cana-1737	263	13	ahmed	ahmed	PROPN
cana-1737	263	14	,	,	PUNCT
cana-1737	263	15	and	and	CCONJ
cana-1737	263	16	nadeem	nadeem	PROPN
cana-1737	263	17	iftikhar	iftikhar	PROPN
cana-1737	263	18	.	.	PUNCT
cana-1737	264	1	"	"	PUNCT
cana-1737	264	2	a	a	DET
cana-1737	264	3	lightweight	lightweight	ADJ
cana-1737	264	4	corona	corona	NOUN
cana-1737	264	5	-	-	PUNCT
cana-1737	264	6	net	net	NOUN
cana-1737	264	7	for	for	ADP
cana-1737	264	8	covid-19	covid-19	PROPN
cana-1737	264	9	detection	detection	NOUN
cana-1737	264	10	in	in	ADP
cana-1737	264	11	x	x	NOUN
cana-1737	264	12	-	-	NOUN
cana-1737	264	13	ray	ray	NOUN
cana-1737	264	14	images	image	NOUN
cana-1737	264	15	.	.	PUNCT
cana-1737	264	16	"	"	PUNCT
cana-1737	265	1	expert	expert	ADJ
cana-1737	265	2	systems	system	NOUN
cana-1737	265	3	with	with	ADP
cana-1737	265	4	applications	application	NOUN
cana-1737	265	5	225	225	NUM
cana-1737	265	6	(	(	PUNCT
cana-1737	265	7	2023	2023	NUM
cana-1737	265	8	):	):	PUNCT
cana-1737	265	9	120023	120023	NUM
cana-1737	265	10	.	.	PUNCT
cana-1737	266	1	[	[	X
cana-1737	266	2	14	14	NUM
cana-1737	266	3	]	]	SYM
cana-1737	266	4	polat	polat	NOUN
cana-1737	266	5	,	,	PUNCT
cana-1737	266	6	kemal	kemal	PROPN
cana-1737	266	7	.	.	PUNCT
cana-1737	267	1	"	"	PUNCT
cana-1737	267	2	a	a	DET
cana-1737	267	3	hybrid	hybrid	ADJ
cana-1737	267	4	approach	approach	NOUN
cana-1737	267	5	to	to	ADP
cana-1737	267	6	parkinson	parkinson	NOUN
cana-1737	267	7	disease	disease	NOUN
cana-1737	267	8	classification	classification	NOUN
cana-1737	267	9	using	use	VERB
cana-1737	267	10	speech	speech	NOUN
cana-1737	267	11	signal	signal	NOUN
cana-1737	267	12	:	:	PUNCT
cana-1737	267	13	the	the	DET
cana-1737	267	14	combination	combination	NOUN
cana-1737	267	15	of	of	ADP
cana-1737	267	16	smote	smote	ADJ
cana-1737	267	17	and	and	CCONJ
cana-1737	267	18	random	random	ADJ
cana-1737	267	19	forests	forest	NOUN
cana-1737	267	20	.	.	PUNCT
cana-1737	267	21	"	"	PUNCT
cana-1737	268	1	in	in	ADP
cana-1737	268	2	2019	2019	NUM
cana-1737	268	3	scientific	scientific	ADJ
cana-1737	268	4	meeting	meeting	NOUN
cana-1737	268	5	on	on	ADP
cana-1737	268	6	electrical	electrical	ADJ
cana-1737	268	7	-	-	PUNCT
cana-1737	268	8	electronics	electronic	NOUN
cana-1737	268	9	&	&	CCONJ
cana-1737	268	10	biomedical	biomedical	ADJ
cana-1737	268	11	engineering	engineering	NOUN
cana-1737	268	12	and	and	CCONJ
cana-1737	268	13	computer	computer	NOUN
cana-1737	268	14	science	science	NOUN
cana-1737	268	15	(	(	PUNCT
cana-1737	268	16	ebbt	ebbt	PROPN
cana-1737	268	17	)	)	PUNCT
cana-1737	268	18	,	,	PUNCT
cana-1737	268	19	pp	pp	ADJ
cana-1737	268	20	.	.	PUNCT
cana-1737	268	21	1	1	NUM
cana-1737	268	22	-	-	SYM
cana-1737	268	23	3	3	NUM
cana-1737	268	24	.	.	X
cana-1737	268	25	ieee	ieee	NOUN
cana-1737	268	26	,	,	PUNCT
cana-1737	268	27	2019	2019	NUM
cana-1737	268	28	.	.	PUNCT
cana-1737	269	1	[	[	X
cana-1737	269	2	15	15	NUM
cana-1737	269	3	]	]	X
cana-1737	269	4	chawla	chawla	PROPN
cana-1737	269	5	,	,	PUNCT
cana-1737	269	6	nitesh	nitesh	ADV
cana-1737	269	7	v.	v.	PROPN
cana-1737	269	8	,	,	PUNCT
cana-1737	269	9	kevin	kevin	PROPN
cana-1737	269	10	w.	w.	PROPN
cana-1737	269	11	bowyer	bowyer	PROPN
cana-1737	269	12	,	,	PUNCT
cana-1737	269	13	lawrence	lawrence	PROPN
cana-1737	269	14	o.	o.	PROPN
cana-1737	269	15	hall	hall	PROPN
cana-1737	269	16	,	,	PUNCT
cana-1737	269	17	and	and	CCONJ
cana-1737	269	18	w.	w.	PROPN
cana-1737	269	19	philip	philip	PROPN
cana-1737	269	20	kegelmeyer	kegelmeyer	PROPN
cana-1737	269	21	.	.	PUNCT
cana-1737	270	1	"	"	PUNCT
cana-1737	270	2	smote	smote	VERB
cana-1737	270	3	:	:	PUNCT
cana-1737	270	4	synthetic	synthetic	ADJ
cana-1737	270	5	minority	minority	NOUN
cana-1737	270	6	over	over	ADP
cana-1737	270	7	-	-	PUNCT
cana-1737	270	8	sampling	sample	VERB
cana-1737	270	9	technique	technique	NOUN
cana-1737	270	10	.	.	PUNCT
cana-1737	270	11	"	"	PUNCT
cana-1737	270	12	journal	journal	NOUN
cana-1737	270	13	of	of	ADP
cana-1737	270	14	artificial	artificial	ADJ
cana-1737	270	15	intelligence	intelligence	NOUN
cana-1737	270	16	research	research	NOUN
cana-1737	270	17	16	16	NUM
cana-1737	270	18	(	(	PUNCT
cana-1737	270	19	2002	2002	NUM
cana-1737	270	20	):	):	PUNCT
cana-1737	270	21	321	321	NUM
cana-1737	270	22	-	-	SYM
cana-1737	270	23	357	357	NUM
cana-1737	270	24	.	.	PUNCT
cana-1737	271	1	[	[	X
cana-1737	271	2	16	16	NUM
cana-1737	271	3	]	]	X
cana-1737	271	4	little	little	ADJ
cana-1737	271	5	,	,	PUNCT
cana-1737	271	6	max	max	PROPN
cana-1737	271	7	.	.	PUNCT
cana-1737	272	1	(	(	PUNCT
cana-1737	272	2	2008	2008	NUM
cana-1737	272	3	)	)	PUNCT
cana-1737	272	4	.	.	PUNCT
cana-1737	273	1	parkinsons	parkinson	NOUN
cana-1737	273	2	.	.	PUNCT
cana-1737	274	1	uci	uci	PROPN
cana-1737	274	2	machine	machine	NOUN
cana-1737	274	3	learning	learn	VERB
cana-1737	274	4	repository	repository	NOUN
cana-1737	274	5	..	..	PUNCT
cana-1737	275	1	[	[	X
cana-1737	275	2	17	17	NUM
cana-1737	275	3	]	]	X
cana-1737	275	4	ouhmida	ouhmida	PROPN
cana-1737	275	5	,	,	PUNCT
cana-1737	275	6	asmae	asmae	ADJ
cana-1737	275	7	,	,	PUNCT
cana-1737	275	8	abdelhadi	abdelhadi	NOUN
cana-1737	275	9	raihani	raihani	NOUN
cana-1737	275	10	,	,	PUNCT
cana-1737	275	11	bouchaib	bouchaib	NOUN
cana-1737	275	12	cherradi	cherradi	NOUN
cana-1737	275	13	,	,	PUNCT
cana-1737	275	14	and	and	CCONJ
cana-1737	275	15	sara	sara	PROPN
cana-1737	275	16	sandabad	sandabad	PROPN
cana-1737	275	17	.	.	PUNCT
cana-1737	276	1	"	"	PUNCT
cana-1737	276	2	parkinson	parkinson	NOUN
cana-1737	276	3	’s	’s	PART
cana-1737	276	4	diagnosis	diagnosis	NOUN
cana-1737	276	5	hybrid	hybrid	NOUN
cana-1737	276	6	system	system	NOUN
cana-1737	276	7	based	base	VERB
cana-1737	276	8	on	on	ADP
cana-1737	276	9	deep	deep	ADJ
cana-1737	276	10	learning	learn	VERB
cana-1737	276	11	classification	classification	NOUN
cana-1737	276	12	with	with	ADP
cana-1737	276	13	imbalanced	imbalanced	ADJ
cana-1737	276	14	dataset	dataset	NOUN
cana-1737	276	15	.	.	PUNCT
cana-1737	276	16	"	"	PUNCT
cana-1737	277	1	international	international	ADJ
cana-1737	277	2	journal	journal	NOUN
cana-1737	277	3	of	of	ADP
cana-1737	277	4	electrical	electrical	ADJ
cana-1737	277	5	and	and	CCONJ
cana-1737	277	6	computer	computer	NOUN
cana-1737	277	7	engineering	engineering	NOUN
cana-1737	277	8	(	(	PUNCT
cana-1737	277	9	ijece	ijece	PROPN
cana-1737	277	10	)	)	PUNCT
cana-1737	277	11	13	13	NUM
cana-1737	277	12	,	,	PUNCT
cana-1737	277	13	no	no	INTJ
cana-1737	277	14	.	.	NOUN
cana-1737	277	15	3	3	NUM
cana-1737	277	16	(	(	PUNCT
cana-1737	277	17	2023	2023	NUM
cana-1737	277	18	):	):	PUNCT
cana-1737	277	19	3204	3204	NUM
cana-1737	277	20	-	-	SYM
cana-1737	277	21	3216	3216	NUM
cana-1737	277	22	.	.	PUNCT
cana-1737	278	1	[	[	X
cana-1737	278	2	18	18	NUM
cana-1737	278	3	]	]	X
cana-1737	278	4	reddy	reddy	NOUN
cana-1737	278	5	,	,	PUNCT
cana-1737	278	6	mittapalle	mittapalle	VERB
cana-1737	278	7	kiran	kiran	NOUN
cana-1737	278	8	,	,	PUNCT
cana-1737	278	9	and	and	CCONJ
cana-1737	278	10	paavo	paavo	PROPN
cana-1737	278	11	alku	alku	PROPN
cana-1737	278	12	.	.	PUNCT
cana-1737	279	1	"	"	PUNCT
cana-1737	279	2	exemplar	exemplar	NOUN
cana-1737	279	3	-	-	PUNCT
cana-1737	279	4	based	base	VERB
cana-1737	279	5	sparse	sparse	ADJ
cana-1737	279	6	representations	representation	NOUN
cana-1737	279	7	for	for	ADP
cana-1737	279	8	detection	detection	NOUN
cana-1737	279	9	of	of	ADP
cana-1737	279	10	parkinson	parkinson	NOUN
cana-1737	279	11	's	's	PART
cana-1737	279	12	disease	disease	NOUN
cana-1737	279	13	from	from	ADP
cana-1737	279	14	speech	speech	NOUN
cana-1737	279	15	.	.	PUNCT
cana-1737	279	16	"	"	PUNCT
cana-1737	279	17	ieee	ieee	PROPN
cana-1737	279	18	/	/	SYM
cana-1737	279	19	acm	acm	NOUN
cana-1737	279	20	transactions	transaction	NOUN
cana-1737	279	21	on	on	ADP
cana-1737	279	22	audio	audio	NOUN
cana-1737	279	23	,	,	PUNCT
cana-1737	279	24	speech	speech	NOUN
cana-1737	279	25	,	,	PUNCT
cana-1737	279	26	and	and	CCONJ
cana-1737	279	27	language	language	NOUN
cana-1737	279	28	processing	processing	NOUN
cana-1737	279	29	31	31	NUM
cana-1737	279	30	(	(	PUNCT
cana-1737	279	31	2023	2023	NUM
cana-1737	279	32	):	):	PUNCT
cana-1737	279	33	1386	1386	NUM
cana-1737	279	34	-	-	SYM
cana-1737	279	35	1396	1396	NUM
cana-1737	279	36	.	.	PUNCT
cana-1737	280	1	[	[	X
cana-1737	280	2	19	19	NUM
cana-1737	280	3	]	]	X
cana-1737	280	4	quan	quan	PROPN
cana-1737	280	5	,	,	PUNCT
cana-1737	280	6	changqin	changqin	PROPN
cana-1737	280	7	,	,	PUNCT
cana-1737	280	8	kang	kang	PROPN
cana-1737	280	9	ren	ren	PROPN
cana-1737	280	10	,	,	PUNCT
cana-1737	280	11	and	and	CCONJ
cana-1737	280	12	zhiwei	zhiwei	PROPN
cana-1737	280	13	luo	luo	PROPN
cana-1737	280	14	.	.	PUNCT
cana-1737	281	1	"	"	PUNCT
cana-1737	281	2	a	a	DET
cana-1737	281	3	deep	deep	ADJ
cana-1737	281	4	learning	learning	NOUN
cana-1737	281	5	based	base	VERB
cana-1737	281	6	method	method	NOUN
cana-1737	281	7	for	for	ADP
cana-1737	281	8	parkinson	parkinson	NOUN
cana-1737	281	9	’s	’s	PART
cana-1737	281	10	disease	disease	NOUN
cana-1737	281	11	detection	detection	NOUN
cana-1737	281	12	using	use	VERB
cana-1737	281	13	dynamic	dynamic	ADJ
cana-1737	281	14	features	feature	NOUN
cana-1737	281	15	of	of	ADP
cana-1737	281	16	speech	speech	NOUN
cana-1737	281	17	.	.	PUNCT
cana-1737	281	18	"	"	PUNCT
cana-1737	282	1	ieee	ieee	NOUN
cana-1737	282	2	access	access	NOUN
cana-1737	282	3	9	9	NUM
cana-1737	282	4	(	(	PUNCT
cana-1737	282	5	2021	2021	NUM
cana-1737	282	6	):	):	PUNCT
cana-1737	282	7	10239	10239	NUM
cana-1737	282	8	-	-	SYM
cana-1737	282	9	10252	10252	NUM
cana-1737	282	10	.	.	PUNCT
cana-1737	283	1	[	[	X
cana-1737	283	2	20	20	NUM
cana-1737	283	3	]	]	X
cana-1737	283	4	ogawa	ogawa	PROPN
cana-1737	283	5	,	,	PUNCT
cana-1737	283	6	mitsuhiro	mitsuhiro	VERB
cana-1737	283	7	,	,	PUNCT
cana-1737	283	8	and	and	CCONJ
cana-1737	283	9	yiran	yiran	PROPN
cana-1737	283	10	yang	yang	PROPN
cana-1737	283	11	.	.	PUNCT
cana-1737	284	1	"	"	PUNCT
cana-1737	284	2	residual	residual	ADJ
cana-1737	284	3	-	-	PUNCT
cana-1737	284	4	network	network	NOUN
cana-1737	284	5	-	-	PUNCT
cana-1737	284	6	based	base	VERB
cana-1737	284	7	deep	deep	ADJ
cana-1737	284	8	learning	learning	NOUN
cana-1737	284	9	for	for	ADP
cana-1737	284	10	parkinson	parkinson	NOUN
cana-1737	284	11	's	's	PART
cana-1737	284	12	disease	disease	NOUN
cana-1737	284	13	classification	classification	NOUN
cana-1737	284	14	using	use	VERB
cana-1737	284	15	vocal	vocal	ADJ
cana-1737	284	16	datasets	dataset	NOUN
cana-1737	284	17	.	.	PUNCT
cana-1737	284	18	"	"	PUNCT
cana-1737	285	1	in	in	ADP
cana-1737	285	2	2021	2021	NUM
cana-1737	285	3	ieee	ieee	NOUN
cana-1737	285	4	3rd	3rd	PROPN
cana-1737	285	5	global	global	ADJ
cana-1737	285	6	conference	conference	NOUN
cana-1737	285	7	on	on	ADP
cana-1737	285	8	life	life	NOUN
cana-1737	285	9	sciences	science	NOUN
cana-1737	285	10	and	and	CCONJ
cana-1737	285	11	technologies	technology	NOUN
cana-1737	285	12	(	(	PUNCT
cana-1737	285	13	lifetech	lifetech	NOUN
cana-1737	285	14	)	)	PUNCT
cana-1737	285	15	,	,	PUNCT
cana-1737	285	16	pp	pp	ADP
cana-1737	285	17	.	.	PUNCT
cana-1737	285	18	275277	275277	NUM
cana-1737	285	19	.	.	PUNCT
cana-1737	285	20	ieee	ieee	NOUN
cana-1737	285	21	,	,	PUNCT
cana-1737	285	22	2021	2021	NUM
cana-1737	285	23	.	.	PUNCT
cana-1737	286	1	[	[	X
cana-1737	286	2	21	21	NUM
cana-1737	286	3	]	]	PUNCT
cana-1737	286	4	asmae	asmae	PROPN
cana-1737	286	5	,	,	PUNCT
cana-1737	286	6	ouhmida	ouhmida	PROPN
cana-1737	286	7	,	,	PUNCT
cana-1737	286	8	raihani	raihani	PROPN
cana-1737	286	9	abdelhadi	abdelhadi	PROPN
cana-1737	286	10	,	,	PUNCT
cana-1737	286	11	cherradi	cherradi	PROPN
cana-1737	286	12	bouchaib	bouchaib	PROPN
cana-1737	286	13	,	,	PUNCT
cana-1737	286	14	sandabad	sandabad	PROPN
cana-1737	286	15	sara	sara	PROPN
cana-1737	286	16	,	,	PUNCT
cana-1737	286	17	and	and	CCONJ
cana-1737	286	18	khalili	khalili	PROPN
cana-1737	286	19	tajeddine	tajeddine	NOUN
cana-1737	286	20	.	.	PUNCT
cana-1737	287	1	"	"	PUNCT
cana-1737	287	2	parkinson	parkinson	NOUN
cana-1737	287	3	’s	’s	PART
cana-1737	287	4	disease	disease	NOUN
cana-1737	287	5	identification	identification	NOUN
cana-1737	287	6	using	use	VERB
cana-1737	287	7	knn	knn	PROPN
cana-1737	287	8	and	and	CCONJ
cana-1737	287	9	ann	ann	PROPN
cana-1737	287	10	algorithms	algorithm	NOUN
cana-1737	287	11	based	base	VERB
cana-1737	287	12	on	on	ADP
cana-1737	287	13	voice	voice	NOUN
cana-1737	287	14	disorder	disorder	NOUN
cana-1737	287	15	.	.	PUNCT
cana-1737	287	16	"	"	PUNCT
cana-1737	288	1	in	in	ADP
cana-1737	288	2	2020	2020	NUM
cana-1737	288	3	1st	1st	ADJ
cana-1737	288	4	international	international	ADJ
cana-1737	288	5	conference	conference	NOUN
cana-1737	288	6	on	on	ADP
cana-1737	288	7	innovative	innovative	ADJ
cana-1737	288	8	research	research	NOUN
cana-1737	288	9	in	in	ADP
cana-1737	288	10	applied	apply	VERB
cana-1737	288	11	science	science	NOUN
cana-1737	288	12	,	,	PUNCT
cana-1737	288	13	engineering	engineering	NOUN
cana-1737	288	14	and	and	CCONJ
cana-1737	288	15	technology	technology	NOUN
cana-1737	288	16	(	(	PUNCT
cana-1737	288	17	iraset	iraset	NOUN
cana-1737	288	18	)	)	PUNCT
cana-1737	288	19	,	,	PUNCT
cana-1737	288	20	pp	pp	PROPN
cana-1737	288	21	.	.	PUNCT
cana-1737	289	1	1	1	NUM
cana-1737	289	2	-	-	SYM
cana-1737	289	3	6	6	NUM
cana-1737	289	4	.	.	PUNCT
cana-1737	289	5	ieee	ieee	NOUN
cana-1737	289	6	,	,	PUNCT
cana-1737	289	7	2020	2020	NUM
cana-1737	289	8	.	.	PUNCT
cana-1737	290	1	[	[	X
cana-1737	290	2	22	22	NUM
cana-1737	290	3	]	]	X
cana-1737	290	4	demir	demir	PROPN
cana-1737	290	5	,	,	PUNCT
cana-1737	290	6	fatih	fatih	PROPN
cana-1737	290	7	,	,	PUNCT
cana-1737	290	8	abdulkadir	abdulkadir	ADJ
cana-1737	290	9	sengur	sengur	PROPN
cana-1737	290	10	,	,	PUNCT
cana-1737	290	11	ali	ali	PROPN
cana-1737	290	12	ari	ari	PROPN
cana-1737	290	13	,	,	PUNCT
cana-1737	290	14	kamran	kamran	PROPN
cana-1737	290	15	siddique	siddique	PROPN
cana-1737	290	16	,	,	PUNCT
cana-1737	290	17	and	and	CCONJ
cana-1737	290	18	mohammed	mohammed	PROPN
cana-1737	290	19	alswaitti	alswaitti	PROPN
cana-1737	290	20	.	.	PUNCT
cana-1737	291	1	"	"	PUNCT
cana-1737	291	2	feature	feature	VERB
cana-1737	291	3	mapping	mapping	NOUN
cana-1737	291	4	and	and	CCONJ
cana-1737	291	5	deep	deep	ADJ
cana-1737	291	6	long	long	ADJ
cana-1737	291	7	short	short	ADJ
cana-1737	291	8	term	term	NOUN
cana-1737	291	9	memory	memory	NOUN
cana-1737	291	10	network	network	NOUN
cana-1737	291	11	-	-	PUNCT
cana-1737	291	12	based	base	VERB
cana-1737	291	13	efficient	efficient	ADJ
cana-1737	291	14	approach	approach	NOUN
cana-1737	291	15	for	for	ADP
cana-1737	291	16	parkinson	parkinson	NOUN
cana-1737	291	17	’s	’s	PART
cana-1737	291	18	disease	disease	NOUN
cana-1737	291	19	diagnosis	diagnosis	NOUN
cana-1737	291	20	.	.	PUNCT
cana-1737	291	21	"	"	PUNCT
cana-1737	292	1	ieee	ieee	NOUN
cana-1737	292	2	access	access	NOUN
cana-1737	292	3	9	9	NUM
cana-1737	292	4	(	(	PUNCT
cana-1737	292	5	2021	2021	NUM
cana-1737	292	6	):	):	PUNCT
cana-1737	292	7	149456	149456	NUM
cana-1737	292	8	-	-	SYM
cana-1737	292	9	149464	149464	NUM
cana-1737	292	10	.	.	PUNCT
cana-1737	293	1	[	[	X
cana-1737	293	2	23	23	NUM
cana-1737	293	3	]	]	X
cana-1737	293	4	sharanyaa	sharanyaa	PROPN
cana-1737	293	5	,	,	PUNCT
cana-1737	293	6	s.	s.	PROPN
cana-1737	293	7	,	,	PUNCT
cana-1737	293	8	p.	p.	PROPN
cana-1737	293	9	n.	n.	PROPN
cana-1737	293	10	renjith	renjith	PROPN
cana-1737	293	11	,	,	PUNCT
cana-1737	293	12	and	and	CCONJ
cana-1737	293	13	k.	k.	PROPN
cana-1737	293	14	ramesh	ramesh	PROPN
cana-1737	293	15	.	.	PUNCT
cana-1737	294	1	"	"	PUNCT
cana-1737	294	2	classification	classification	NOUN
cana-1737	294	3	of	of	ADP
cana-1737	294	4	parkinson	parkinson	NOUN
cana-1737	294	5	's	's	PART
cana-1737	294	6	disease	disease	NOUN
cana-1737	294	7	using	use	VERB
cana-1737	294	8	speech	speech	NOUN
cana-1737	294	9	attributes	attribute	NOUN
cana-1737	294	10	with	with	ADP
cana-1737	294	11	parametric	parametric	ADJ
cana-1737	294	12	and	and	CCONJ
cana-1737	294	13	nonparametric	nonparametric	NOUN
cana-1737	294	14	machine	machine	NOUN
cana-1737	294	15	learning	learn	VERB
cana-1737	294	16	techniques	technique	NOUN
cana-1737	294	17	.	.	PUNCT
cana-1737	294	18	"	"	PUNCT
cana-1737	295	1	in	in	ADP
cana-1737	295	2	2020	2020	NUM
cana-1737	295	3	3rd	3rd	ADJ
cana-1737	295	4	international	international	ADJ
cana-1737	295	5	conference	conference	NOUN
cana-1737	295	6	on	on	ADP
cana-1737	295	7	intelligent	intelligent	ADJ
cana-1737	295	8	sustainable	sustainable	ADJ
cana-1737	295	9	systems	system	NOUN
cana-1737	295	10	(	(	PUNCT
cana-1737	295	11	iciss	iciss	ADJ
cana-1737	295	12	)	)	PUNCT
cana-1737	295	13	,	,	PUNCT
cana-1737	295	14	pp	pp	PROPN
cana-1737	295	15	.	.	PUNCT
cana-1737	296	1	437	437	NUM
cana-1737	296	2	-	-	SYM
cana-1737	296	3	442	442	NUM
cana-1737	296	4	.	.	PUNCT
cana-1737	297	1	ieee	ieee	NOUN
cana-1737	297	2	,	,	PUNCT
cana-1737	297	3	2020	2020	NUM
cana-1737	297	4	.	.	PUNCT
cana-1737	298	1	[	[	X
cana-1737	298	2	24	24	NUM
cana-1737	298	3	]	]	X
cana-1737	298	4	zahid	zahid	PROPN
cana-1737	298	5	,	,	PUNCT
cana-1737	298	6	laiba	laiba	PROPN
cana-1737	298	7	,	,	PUNCT
cana-1737	298	8	muazzam	muazzam	PROPN
cana-1737	298	9	maqsood	maqsood	PROPN
cana-1737	298	10	,	,	PUNCT
cana-1737	298	11	mehr	mehr	NOUN
cana-1737	298	12	yahya	yahya	PROPN
cana-1737	298	13	durrani	durrani	PROPN
cana-1737	298	14	,	,	PUNCT
cana-1737	298	15	maheen	maheen	PROPN
cana-1737	298	16	bakhtyar	bakhtyar	PROPN
cana-1737	298	17	,	,	PUNCT
cana-1737	298	18	junaid	junaid	PROPN
cana-1737	298	19	baber	baber	PROPN
cana-1737	298	20	,	,	PUNCT
cana-1737	298	21	habibullah	habibullah	PROPN
cana-1737	298	22	jamal	jamal	PROPN
cana-1737	298	23	,	,	PUNCT
cana-1737	298	24	irfan	irfan	PROPN
cana-1737	298	25	mehmood	mehmood	PROPN
cana-1737	298	26	,	,	PUNCT
cana-1737	298	27	and	and	CCONJ
cana-1737	298	28	oh	oh	NUM
cana-1737	298	29	-	-	PUNCT
cana-1737	298	30	young	young	ADJ
cana-1737	298	31	song	song	NOUN
cana-1737	298	32	.	.	PUNCT
cana-1737	299	1	"	"	PUNCT
cana-1737	299	2	a	a	DET
cana-1737	299	3	spectrogram	spectrogram	NOUN
cana-1737	299	4	-	-	PUNCT
cana-1737	299	5	based	base	VERB
cana-1737	299	6	deep	deep	ADJ
cana-1737	299	7	feature	feature	NOUN
cana-1737	299	8	assisted	assist	VERB
cana-1737	299	9	computer	computer	NOUN
cana-1737	299	10	-	-	PUNCT
cana-1737	299	11	aided	aid	VERB
cana-1737	299	12	diagnostic	diagnostic	ADJ
cana-1737	299	13	system	system	NOUN
cana-1737	299	14	for	for	ADP
cana-1737	299	15	parkinson	parkinson	NOUN
cana-1737	299	16	’s	’s	PART
cana-1737	299	17	disease	disease	NOUN
cana-1737	299	18	.	.	PUNCT
cana-1737	299	19	"	"	PUNCT
cana-1737	300	1	ieee	ieee	NOUN
cana-1737	300	2	access	access	NOUN
cana-1737	300	3	8	8	NUM
cana-1737	300	4	(	(	PUNCT
cana-1737	300	5	2020	2020	NUM
cana-1737	300	6	):	):	PUNCT
cana-1737	300	7	35482	35482	NUM
cana-1737	300	8	-	-	SYM
cana-1737	300	9	35495	35495	NUM
cana-1737	300	10	.	.	PUNCT
cana-1737	301	1	[	[	X
cana-1737	301	2	25	25	NUM
cana-1737	301	3	]	]	PUNCT
cana-1737	301	4	bocklet	bocklet	NOUN
cana-1737	301	5	,	,	PUNCT
cana-1737	301	6	tobias	tobias	PROPN
cana-1737	301	7	,	,	PUNCT
cana-1737	301	8	elmar	elmar	PROPN
cana-1737	301	9	nöth	nöth	PROPN
cana-1737	301	10	,	,	PUNCT
cana-1737	301	11	georg	georg	NOUN
cana-1737	301	12	stemmer	stemmer	NOUN
cana-1737	301	13	,	,	PUNCT
cana-1737	301	14	hana	hana	PROPN
cana-1737	301	15	ruzickova	ruzickova	PROPN
cana-1737	301	16	,	,	PUNCT
cana-1737	301	17	and	and	CCONJ
cana-1737	301	18	jan	jan	PROPN
cana-1737	301	19	rusz	rusz	PROPN
cana-1737	301	20	.	.	PUNCT
cana-1737	302	1	"	"	PUNCT
cana-1737	302	2	detection	detection	NOUN
cana-1737	302	3	of	of	ADP
cana-1737	302	4	persons	person	NOUN
cana-1737	302	5	with	with	ADP
cana-1737	302	6	parkinson	parkinson	NOUN
cana-1737	302	7	's	's	PART
cana-1737	302	8	disease	disease	NOUN
cana-1737	302	9	by	by	ADP
cana-1737	302	10	acoustic	acoustic	ADJ
cana-1737	302	11	,	,	PUNCT
cana-1737	302	12	vocal	vocal	ADJ
cana-1737	302	13	,	,	PUNCT
cana-1737	302	14	and	and	CCONJ
cana-1737	302	15	prosodic	prosodic	ADJ
cana-1737	302	16	analysis	analysis	NOUN
cana-1737	302	17	.	.	PUNCT
cana-1737	302	18	"	"	PUNCT
cana-1737	303	1	in	in	ADP
cana-1737	303	2	2011	2011	NUM
cana-1737	303	3	ieee	ieee	NOUN
cana-1737	303	4	workshop	workshop	NOUN
cana-1737	303	5	on	on	ADP
cana-1737	303	6	automatic	automatic	ADJ
cana-1737	303	7	speech	speech	NOUN
cana-1737	303	8	recognition	recognition	NOUN
cana-1737	303	9	&	&	CCONJ
cana-1737	303	10	understanding	understanding	NOUN
cana-1737	303	11	,	,	PUNCT
cana-1737	303	12	pp	pp	ADJ
cana-1737	303	13	.	.	PUNCT
cana-1737	303	14	478	478	NUM
cana-1737	303	15	-	-	SYM
cana-1737	303	16	483	483	NUM
cana-1737	303	17	.	.	PUNCT
cana-1737	303	18	ieee	ieee	PROPN
cana-1737	303	19	,	,	PUNCT
cana-1737	303	20	2011	2011	NUM
cana-1737	303	21	.	.	PUNCT
cana-1737	304	1	communications	communication	NOUN
cana-1737	304	2	on	on	ADP
cana-1737	304	3	applied	apply	VERB
cana-1737	304	4	nonlinear	nonlinear	ADJ
cana-1737	304	5	analysis	analysis	NOUN
cana-1737	304	6	issn	issn	NOUN
cana-1737	304	7	:	:	PUNCT
cana-1737	304	8	1074	1074	NUM
cana-1737	304	9	-	-	PUNCT
cana-1737	304	10	133x	133x	NUM
cana-1737	304	11	vol	vol	NOUN
cana-1737	304	12	32	32	NUM
cana-1737	304	13	no	no	NOUN
cana-1737	304	14	.	.	NOUN
cana-1737	304	15	2	2	NUM
cana-1737	304	16	(	(	PUNCT
cana-1737	304	17	2025	2025	NUM
cana-1737	304	18	)	)	PUNCT
cana-1737	304	19	214	214	NUM
cana-1737	304	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1737	305	1	[	[	X
cana-1737	305	2	26	26	NUM
cana-1737	305	3	]	]	X
cana-1737	305	4	agarwal	agarwal	PROPN
cana-1737	305	5	,	,	PUNCT
cana-1737	305	6	aarushi	aarushi	PROPN
cana-1737	305	7	,	,	PUNCT
cana-1737	305	8	spriha	spriha	NOUN
cana-1737	305	9	chandrayan	chandrayan	NOUN
cana-1737	305	10	,	,	PUNCT
cana-1737	305	11	and	and	CCONJ
cana-1737	305	12	sitanshu	sitanshu	PROPN
cana-1737	305	13	s.	s.	PROPN
cana-1737	305	14	sahu	sahu	PROPN
cana-1737	305	15	.	.	PUNCT
cana-1737	306	1	"	"	PUNCT
cana-1737	306	2	prediction	prediction	NOUN
cana-1737	306	3	of	of	ADP
cana-1737	306	4	parkinson	parkinson	NOUN
cana-1737	306	5	's	's	PART
cana-1737	306	6	disease	disease	NOUN
cana-1737	306	7	using	use	VERB
cana-1737	306	8	speech	speech	NOUN
cana-1737	306	9	signal	signal	NOUN
cana-1737	306	10	with	with	ADP
cana-1737	306	11	extreme	extreme	ADJ
cana-1737	306	12	learning	learning	NOUN
cana-1737	306	13	machine	machine	NOUN
cana-1737	306	14	.	.	PUNCT
cana-1737	306	15	"	"	PUNCT
cana-1737	307	1	in	in	ADP
cana-1737	307	2	2016	2016	NUM
cana-1737	307	3	international	international	ADJ
cana-1737	307	4	conference	conference	NOUN
cana-1737	307	5	on	on	ADP
cana-1737	307	6	electrical	electrical	ADJ
cana-1737	307	7	,	,	PUNCT
cana-1737	307	8	electronics	electronic	NOUN
cana-1737	307	9	,	,	PUNCT
cana-1737	307	10	and	and	CCONJ
cana-1737	307	11	optimization	optimization	NOUN
cana-1737	307	12	techniques	technique	NOUN
cana-1737	307	13	(	(	PUNCT
cana-1737	307	14	iceeot	iceeot	ADJ
cana-1737	307	15	)	)	PUNCT
cana-1737	307	16	,	,	PUNCT
cana-1737	307	17	pp	pp	ADJ
cana-1737	307	18	.	.	PUNCT
cana-1737	307	19	3776	3776	NUM
cana-1737	307	20	-	-	SYM
cana-1737	307	21	3779	3779	NUM
cana-1737	307	22	.	.	PUNCT
cana-1737	307	23	ieee	ieee	PROPN
cana-1737	307	24	,	,	PUNCT
cana-1737	307	25	2016	2016	NUM
cana-1737	307	26	..	..	PUNCT
cana-1737	308	1	[	[	X
cana-1737	308	2	27	27	NUM
cana-1737	308	3	]	]	X
cana-1737	308	4	rueda	rueda	PROPN
cana-1737	308	5	,	,	PUNCT
cana-1737	308	6	alice	alice	PROPN
cana-1737	308	7	,	,	PUNCT
cana-1737	308	8	and	and	CCONJ
cana-1737	308	9	sridhar	sridhar	PROPN
cana-1737	308	10	krishnan	krishnan	PROPN
cana-1737	308	11	.	.	PUNCT
cana-1737	309	1	"	"	PUNCT
cana-1737	309	2	feature	feature	VERB
cana-1737	309	3	analysis	analysis	NOUN
cana-1737	309	4	of	of	ADP
cana-1737	309	5	dysphonia	dysphonia	NOUN
cana-1737	309	6	speech	speech	NOUN
cana-1737	309	7	for	for	ADP
cana-1737	309	8	monitoring	monitor	VERB
cana-1737	309	9	parkinson	parkinson	NOUN
cana-1737	309	10	's	's	PART
cana-1737	309	11	disease	disease	NOUN
cana-1737	309	12	.	.	PUNCT
cana-1737	309	13	"	"	PUNCT
cana-1737	310	1	in	in	ADP
cana-1737	310	2	2017	2017	NUM
cana-1737	310	3	39th	39th	ADJ
cana-1737	310	4	annual	annual	ADJ
cana-1737	310	5	international	international	ADJ
cana-1737	310	6	conference	conference	NOUN
cana-1737	310	7	of	of	ADP
cana-1737	310	8	the	the	DET
cana-1737	310	9	ieee	ieee	NOUN
cana-1737	310	10	engineering	engineering	NOUN
cana-1737	310	11	in	in	ADP
cana-1737	310	12	medicine	medicine	NOUN
cana-1737	310	13	and	and	CCONJ
cana-1737	310	14	biology	biology	NOUN
cana-1737	310	15	society	society	NOUN
cana-1737	310	16	(	(	PUNCT
cana-1737	310	17	embc	embc	PROPN
cana-1737	310	18	)	)	PUNCT
cana-1737	310	19	,	,	PUNCT
cana-1737	310	20	pp	pp	PROPN
cana-1737	310	21	.	.	PUNCT
cana-1737	310	22	2308	2308	NUM
cana-1737	310	23	-	-	SYM
cana-1737	310	24	2311	2311	NUM
cana-1737	310	25	.	.	PUNCT
cana-1737	310	26	ieee	ieee	NOUN
cana-1737	310	27	,	,	PUNCT
cana-1737	310	28	2017	2017	NUM
cana-1737	310	29	.	.	PUNCT
cana-1737	311	1	[	[	X
cana-1737	311	2	28	28	NUM
cana-1737	311	3	]	]	X
cana-1737	311	4	wan	wan	PROPN
cana-1737	311	5	,	,	PUNCT
cana-1737	311	6	shaohua	shaohua	PROPN
cana-1737	311	7	,	,	PUNCT
cana-1737	311	8	yan	yan	PROPN
cana-1737	311	9	liang	liang	PROPN
cana-1737	311	10	,	,	PUNCT
cana-1737	311	11	yin	yin	PROPN
cana-1737	311	12	zhang	zhang	PROPN
cana-1737	311	13	,	,	PUNCT
cana-1737	311	14	and	and	CCONJ
cana-1737	311	15	mohsen	mohsen	PROPN
cana-1737	311	16	guizani	guizani	PROPN
cana-1737	311	17	.	.	PUNCT
cana-1737	312	1	"	"	PUNCT
cana-1737	312	2	deep	deep	ADJ
cana-1737	312	3	multi	multi	ADJ
cana-1737	312	4	-	-	ADJ
cana-1737	312	5	layer	layer	ADJ
cana-1737	312	6	perceptron	perceptron	PROPN
cana-1737	312	7	classifier	classifier	NOUN
cana-1737	312	8	for	for	ADP
cana-1737	312	9	behavior	behavior	NOUN
cana-1737	312	10	analysis	analysis	NOUN
cana-1737	312	11	to	to	PART
cana-1737	312	12	estimate	estimate	VERB
cana-1737	312	13	parkinson	parkinson	NOUN
cana-1737	312	14	’s	’s	PART
cana-1737	312	15	disease	disease	NOUN
cana-1737	312	16	severity	severity	NOUN
cana-1737	312	17	using	use	VERB
cana-1737	312	18	smartphones	smartphone	NOUN
cana-1737	312	19	.	.	PUNCT
cana-1737	312	20	"	"	PUNCT
cana-1737	313	1	ieee	ieee	NOUN
cana-1737	313	2	access	access	NOUN
cana-1737	313	3	6	6	NUM
cana-1737	313	4	(	(	PUNCT
cana-1737	313	5	2018	2018	NUM
cana-1737	313	6	):	):	PUNCT
cana-1737	313	7	36825	36825	NUM
cana-1737	313	8	-	-	SYM
cana-1737	313	9	36833	36833	NUM
cana-1737	313	10	.	.	PUNCT
cana-1737	314	1	[	[	X
cana-1737	314	2	29	29	NUM
cana-1737	314	3	]	]	X
cana-1737	314	4	moro	moro	PROPN
cana-1737	314	5	-	-	PUNCT
cana-1737	314	6	velazquez	velazquez	PROPN
cana-1737	314	7	,	,	PUNCT
cana-1737	314	8	laureano	laureano	ADJ
cana-1737	314	9	,	,	PUNCT
cana-1737	314	10	jaejin	jaejin	PROPN
cana-1737	314	11	cho	cho	PROPN
cana-1737	314	12	,	,	PUNCT
cana-1737	314	13	shinji	shinji	PROPN
cana-1737	314	14	watanabe	watanabe	PROPN
cana-1737	314	15	,	,	PUNCT
cana-1737	314	16	mark	mark	PROPN
cana-1737	314	17	a.	a.	PROPN
cana-1737	314	18	hasegawa	hasegawa	PROPN
cana-1737	314	19	-	-	PUNCT
cana-1737	314	20	johnson	johnson	PROPN
cana-1737	314	21	,	,	PUNCT
cana-1737	314	22	odette	odette	NOUN
cana-1737	314	23	scharenborg	scharenborg	NOUN
cana-1737	314	24	,	,	PUNCT
cana-1737	314	25	heejin	heejin	ADJ
cana-1737	314	26	kim	kim	PROPN
cana-1737	314	27	,	,	PUNCT
cana-1737	314	28	and	and	CCONJ
cana-1737	314	29	najim	najim	ADJ
cana-1737	314	30	dehak	dehak	NOUN
cana-1737	314	31	.	.	PUNCT
cana-1737	315	1	"	"	PUNCT
cana-1737	315	2	study	study	NOUN
cana-1737	315	3	of	of	ADP
cana-1737	315	4	the	the	DET
cana-1737	315	5	performance	performance	NOUN
cana-1737	315	6	of	of	ADP
cana-1737	315	7	automatic	automatic	ADJ
cana-1737	315	8	speech	speech	NOUN
cana-1737	315	9	recognition	recognition	NOUN
cana-1737	315	10	systems	system	NOUN
cana-1737	315	11	in	in	ADP
cana-1737	315	12	speakers	speaker	NOUN
cana-1737	315	13	with	with	ADP
cana-1737	315	14	parkinson	parkinson	NOUN
cana-1737	315	15	's	's	PART
cana-1737	315	16	disease	disease	NOUN
cana-1737	315	17	.	.	PUNCT
cana-1737	315	18	"	"	PUNCT
cana-1737	316	1	in	in	ADP
cana-1737	316	2	interspeech	interspeech	NOUN
cana-1737	316	3	,	,	PUNCT
cana-1737	316	4	vol	vol	NOUN
cana-1737	316	5	.	.	PROPN
cana-1737	316	6	9	9	NUM
cana-1737	316	7	,	,	PUNCT
cana-1737	316	8	pp	pp	ADJ
cana-1737	316	9	.	.	PUNCT
cana-1737	316	10	3875	3875	NUM
cana-1737	316	11	-	-	SYM
cana-1737	316	12	3879	3879	NUM
cana-1737	316	13	.	.	PUNCT
cana-1737	316	14	2019	2019	NUM
cana-1737	316	15	.	.	PUNCT
cana-1737	317	1	[	[	X
cana-1737	317	2	30	30	NUM
cana-1737	317	3	]	]	X
cana-1737	317	4	ali	ali	PROPN
cana-1737	317	5	,	,	PUNCT
cana-1737	317	6	liaqat	liaqat	PROPN
cana-1737	317	7	,	,	PUNCT
cana-1737	317	8	ce	ce	PROPN
cana-1737	317	9	zhu	zhu	PROPN
cana-1737	317	10	,	,	PUNCT
cana-1737	317	11	zhonghao	zhonghao	PROPN
cana-1737	317	12	zhang	zhang	PROPN
cana-1737	317	13	,	,	PUNCT
cana-1737	317	14	and	and	CCONJ
cana-1737	317	15	yipeng	yipeng	PROPN
cana-1737	317	16	liu	liu	PROPN
cana-1737	317	17	.	.	PUNCT
cana-1737	318	1	"	"	PUNCT
cana-1737	318	2	automated	automate	VERB
cana-1737	318	3	detection	detection	NOUN
cana-1737	318	4	of	of	ADP
cana-1737	318	5	parkinson	parkinson	NOUN
cana-1737	318	6	’s	’s	PART
cana-1737	318	7	disease	disease	NOUN
cana-1737	318	8	based	base	VERB
cana-1737	318	9	on	on	ADP
cana-1737	318	10	multiple	multiple	ADJ
cana-1737	318	11	types	type	NOUN
cana-1737	318	12	of	of	ADP
cana-1737	318	13	sustained	sustained	ADJ
cana-1737	318	14	phonations	phonation	NOUN
cana-1737	318	15	using	use	VERB
cana-1737	318	16	linear	linear	PROPN
cana-1737	318	17	discriminant	discriminant	ADJ
cana-1737	318	18	analysis	analysis	NOUN
cana-1737	318	19	and	and	CCONJ
cana-1737	318	20	genetically	genetically	ADV
cana-1737	318	21	optimized	optimize	VERB
cana-1737	318	22	neural	neural	ADJ
cana-1737	318	23	network	network	NOUN
cana-1737	318	24	.	.	PUNCT
cana-1737	318	25	"	"	PUNCT
cana-1737	319	1	ieee	ieee	PROPN
cana-1737	319	2	journal	journal	NOUN
cana-1737	319	3	of	of	ADP
cana-1737	319	4	translational	translational	ADJ
cana-1737	319	5	engineering	engineering	NOUN
cana-1737	319	6	in	in	ADP
cana-1737	319	7	health	health	NOUN
cana-1737	319	8	and	and	CCONJ
cana-1737	319	9	medicine	medicine	NOUN
cana-1737	319	10	7	7	NUM
cana-1737	319	11	(	(	PUNCT
cana-1737	319	12	2019	2019	NUM
cana-1737	319	13	):	):	PUNCT
cana-1737	319	14	1	1	NUM
cana-1737	319	15	-	-	SYM
cana-1737	319	16	10	10	NUM
cana-1737	319	17	.	.	PUNCT
cana-1737	320	1	[	[	X
cana-1737	320	2	31	31	NUM
cana-1737	320	3	]	]	PUNCT
cana-1737	320	4	gunduz	gunduz	NOUN
cana-1737	320	5	,	,	PUNCT
cana-1737	320	6	hakan	hakan	PROPN
cana-1737	320	7	.	.	PUNCT
cana-1737	321	1	"	"	PUNCT
cana-1737	321	2	deep	deep	ADJ
cana-1737	321	3	learning	learning	NOUN
cana-1737	321	4	-	-	PUNCT
cana-1737	321	5	based	base	VERB
cana-1737	321	6	parkinson	parkinson	NOUN
cana-1737	321	7	’s	’s	PART
cana-1737	321	8	disease	disease	NOUN
cana-1737	321	9	classification	classification	NOUN
cana-1737	321	10	using	use	VERB
cana-1737	321	11	vocal	vocal	ADJ
cana-1737	321	12	feature	feature	NOUN
cana-1737	321	13	sets	set	NOUN
cana-1737	321	14	.	.	PUNCT
cana-1737	321	15	"	"	PUNCT
cana-1737	322	1	ieee	ieee	NOUN
cana-1737	322	2	access	access	NOUN
cana-1737	322	3	7	7	NUM
cana-1737	322	4	(	(	PUNCT
cana-1737	322	5	2019	2019	NUM
cana-1737	322	6	):	):	PUNCT
cana-1737	322	7	115540	115540	NUM
cana-1737	322	8	-	-	SYM
cana-1737	322	9	115551	115551	NUM
cana-1737	322	10	.	.	PUNCT
cana-1737	323	1	[	[	X
cana-1737	323	2	32	32	NUM
cana-1737	323	3	]	]	PUNCT
cana-1737	323	4	appakaya	appakaya	PROPN
cana-1737	323	5	,	,	PUNCT
cana-1737	323	6	sai	sai	PROPN
cana-1737	323	7	bharadwaj	bharadwaj	PROPN
cana-1737	323	8	,	,	PUNCT
cana-1737	323	9	and	and	CCONJ
cana-1737	323	10	ravi	ravi	PROPN
cana-1737	323	11	sankar	sankar	NOUN
cana-1737	323	12	.	.	PUNCT
cana-1737	324	1	"	"	PUNCT
cana-1737	324	2	parkinson	parkinson	NOUN
cana-1737	324	3	’s	’s	PART
cana-1737	324	4	disease	disease	NOUN
cana-1737	324	5	classification	classification	NOUN
cana-1737	324	6	using	use	VERB
cana-1737	324	7	pitch	pitch	NOUN
cana-1737	324	8	synchronous	synchronous	ADJ
cana-1737	324	9	speech	speech	NOUN
cana-1737	324	10	segments	segment	NOUN
cana-1737	324	11	and	and	CCONJ
cana-1737	324	12	fine	fine	ADJ
cana-1737	324	13	gaussian	gaussian	ADJ
cana-1737	324	14	kernels	kernel	NOUN
cana-1737	324	15	based	base	VERB
cana-1737	324	16	svm	svm	PROPN
cana-1737	324	17	.	.	PUNCT
cana-1737	324	18	"	"	PUNCT
cana-1737	325	1	in	in	ADP
cana-1737	325	2	2020	2020	NUM
cana-1737	325	3	42nd	42nd	X
cana-1737	325	4	annual	annual	ADJ
cana-1737	325	5	international	international	ADJ
cana-1737	325	6	conference	conference	NOUN
cana-1737	325	7	of	of	ADP
cana-1737	325	8	the	the	DET
cana-1737	325	9	ieee	ieee	NOUN
cana-1737	325	10	engineering	engineering	NOUN
cana-1737	325	11	in	in	ADP
cana-1737	325	12	medicine	medicine	PROPN
cana-1737	325	13	&	&	CCONJ
cana-1737	325	14	biology	biology	NOUN
cana-1737	325	15	society	society	NOUN
cana-1737	325	16	(	(	PUNCT
cana-1737	325	17	embc	embc	PROPN
cana-1737	325	18	)	)	PUNCT
cana-1737	325	19	,	,	PUNCT
cana-1737	325	20	pp	pp	PROPN
cana-1737	325	21	.	.	PUNCT
cana-1737	325	22	236	236	NUM
cana-1737	325	23	-	-	SYM
cana-1737	325	24	239	239	NUM
cana-1737	325	25	.	.	PUNCT
cana-1737	325	26	ieee	ieee	NOUN
cana-1737	325	27	,	,	PUNCT
cana-1737	325	28	2020	2020	NUM
cana-1737	325	29	.	.	PUNCT
cana-1737	326	1	[	[	X
cana-1737	326	2	33	33	NUM
cana-1737	326	3	]	]	X
cana-1737	326	4	moro	moro	PROPN
cana-1737	326	5	-	-	PUNCT
cana-1737	326	6	velazquez	velazquez	PROPN
cana-1737	326	7	,	,	PUNCT
cana-1737	326	8	laureano	laureano	PROPN
cana-1737	326	9	,	,	PUNCT
cana-1737	326	10	jesus	jesus	PROPN
cana-1737	326	11	villalba	villalba	PROPN
cana-1737	326	12	,	,	PUNCT
cana-1737	326	13	and	and	CCONJ
cana-1737	326	14	najim	najim	ADJ
cana-1737	326	15	dehak	dehak	NOUN
cana-1737	326	16	.	.	PUNCT
cana-1737	327	1	"	"	PUNCT
cana-1737	327	2	using	use	VERB
cana-1737	327	3	x	x	NOUN
cana-1737	327	4	-	-	NOUN
cana-1737	327	5	vectors	vector	NOUN
cana-1737	327	6	to	to	PART
cana-1737	327	7	automatically	automatically	ADV
cana-1737	327	8	detect	detect	VERB
cana-1737	327	9	parkinson	parkinson	NOUN
cana-1737	327	10	’s	’s	PART
cana-1737	327	11	disease	disease	NOUN
cana-1737	327	12	from	from	ADP
cana-1737	327	13	speech	speech	NOUN
cana-1737	327	14	.	.	PUNCT
cana-1737	327	15	"	"	PUNCT
cana-1737	328	1	in	in	ADP
cana-1737	328	2	icassp	icassp	PROPN
cana-1737	328	3	2020	2020	NUM
cana-1737	328	4	-	-	SYM
cana-1737	328	5	2020	2020	NUM
cana-1737	328	6	ieee	ieee	NOUN
cana-1737	328	7	international	international	ADJ
cana-1737	328	8	conference	conference	NOUN
cana-1737	328	9	on	on	ADP
cana-1737	328	10	acoustics	acoustic	NOUN
cana-1737	328	11	,	,	PUNCT
cana-1737	328	12	speech	speech	NOUN
cana-1737	328	13	and	and	CCONJ
cana-1737	328	14	signal	signal	NOUN
cana-1737	328	15	processing	processing	NOUN
cana-1737	328	16	(	(	PUNCT
cana-1737	328	17	icassp	icassp	PROPN
cana-1737	328	18	)	)	PUNCT
cana-1737	328	19	,	,	PUNCT
cana-1737	328	20	pp	pp	PROPN
cana-1737	328	21	.	.	PUNCT
cana-1737	329	1	1155	1155	NUM
cana-1737	329	2	-	-	SYM
cana-1737	329	3	1159	1159	NUM
cana-1737	329	4	.	.	PUNCT
cana-1737	330	1	ieee	ieee	NOUN
cana-1737	330	2	,	,	PUNCT
cana-1737	330	3	2020	2020	NUM
cana-1737	330	4	.	.	PUNCT
cana-1737	331	1	[	[	X
cana-1737	331	2	34	34	NUM
cana-1737	331	3	]	]	SYM
cana-1737	331	4	amato	amato	PROPN
cana-1737	331	5	,	,	PUNCT
cana-1737	331	6	federica	federica	PROPN
cana-1737	331	7	,	,	PUNCT
cana-1737	331	8	luigi	luigi	PROPN
cana-1737	331	9	borzì	borzì	PROPN
cana-1737	331	10	,	,	PUNCT
cana-1737	331	11	gabriella	gabriella	PROPN
cana-1737	331	12	olmo	olmo	PROPN
cana-1737	331	13	,	,	PUNCT
cana-1737	331	14	carlo	carlo	PROPN
cana-1737	331	15	alberto	alberto	PROPN
cana-1737	331	16	artusi	artusi	PROPN
cana-1737	331	17	,	,	PUNCT
cana-1737	331	18	gabriele	gabriele	PROPN
cana-1737	331	19	imbalzano	imbalzano	PROPN
cana-1737	331	20	,	,	PUNCT
cana-1737	331	21	and	and	CCONJ
cana-1737	331	22	leonardo	leonardo	PROPN
cana-1737	331	23	lopiano	lopiano	PROPN
cana-1737	331	24	.	.	PUNCT
cana-1737	332	1	"	"	PUNCT
cana-1737	332	2	speech	speech	NOUN
cana-1737	332	3	impairment	impairment	NOUN
cana-1737	332	4	in	in	ADP
cana-1737	332	5	parkinson	parkinson	NOUN
cana-1737	332	6	’s	’s	PART
cana-1737	332	7	disease	disease	NOUN
cana-1737	332	8	:	:	PUNCT
cana-1737	332	9	acoustic	acoustic	ADJ
cana-1737	332	10	analysis	analysis	NOUN
cana-1737	332	11	of	of	ADP
cana-1737	332	12	unvoiced	unvoiced	ADJ
cana-1737	332	13	consonants	consonant	NOUN
cana-1737	332	14	in	in	ADP
cana-1737	332	15	italian	italian	ADJ
cana-1737	332	16	native	native	ADJ
cana-1737	332	17	speakers	speaker	NOUN
cana-1737	332	18	.	.	PUNCT
cana-1737	332	19	"	"	PUNCT
cana-1737	333	1	ieee	ieee	NOUN
cana-1737	333	2	access	access	NOUN
cana-1737	333	3	9	9	NUM
cana-1737	333	4	(	(	PUNCT
cana-1737	333	5	2021	2021	NUM
cana-1737	333	6	):	):	PUNCT
cana-1737	333	7	166370	166370	NUM
cana-1737	333	8	-	-	SYM
cana-1737	333	9	166381	166381	NUM
cana-1737	333	10	.	.	PUNCT
cana-1737	334	1	[	[	X
cana-1737	334	2	35	35	NUM
cana-1737	334	3	]	]	X
cana-1737	334	4	liu	liu	PROPN
cana-1737	334	5	,	,	PUNCT
cana-1737	334	6	yuanyuan	yuanyuan	PROPN
cana-1737	334	7	,	,	PUNCT
cana-1737	334	8	nelly	nelly	ADV
cana-1737	334	9	penttilä	penttilä	NOUN
cana-1737	334	10	,	,	PUNCT
cana-1737	334	11	tiina	tiina	PROPN
cana-1737	334	12	ihalainen	ihalainen	PROPN
cana-1737	334	13	,	,	PUNCT
cana-1737	334	14	juulia	juulia	ADJ
cana-1737	334	15	lintula	lintula	NOUN
cana-1737	334	16	,	,	PUNCT
cana-1737	334	17	rachel	rachel	PROPN
cana-1737	334	18	convey	convey	VERB
cana-1737	334	19	,	,	PUNCT
cana-1737	334	20	and	and	CCONJ
cana-1737	334	21	okko	okko	X
cana-1737	334	22	räsänen	räsänen	NOUN
cana-1737	334	23	.	.	PUNCT
cana-1737	335	1	"	"	PUNCT
cana-1737	335	2	languageindependent	languageindependent	VERB
cana-1737	335	3	approach	approach	NOUN
cana-1737	335	4	for	for	ADP
cana-1737	335	5	automatic	automatic	ADJ
cana-1737	335	6	computation	computation	NOUN
cana-1737	335	7	of	of	ADP
cana-1737	335	8	vowel	vowel	NOUN
cana-1737	335	9	articulation	articulation	NOUN
cana-1737	335	10	features	feature	VERB
cana-1737	335	11	in	in	ADP
cana-1737	335	12	dysarthric	dysarthric	ADJ
cana-1737	335	13	speech	speech	NOUN
cana-1737	335	14	assessment	assessment	NOUN
cana-1737	335	15	.	.	PUNCT
cana-1737	335	16	"	"	PUNCT
cana-1737	335	17	ieee	ieee	PROPN
cana-1737	335	18	/	/	SYM
cana-1737	335	19	acm	acm	NOUN
cana-1737	335	20	transactions	transaction	NOUN
cana-1737	335	21	on	on	ADP
cana-1737	335	22	audio	audio	NOUN
cana-1737	335	23	,	,	PUNCT
cana-1737	335	24	speech	speech	NOUN
cana-1737	335	25	,	,	PUNCT
cana-1737	335	26	and	and	CCONJ
cana-1737	335	27	language	language	NOUN
cana-1737	335	28	processing	processing	NOUN
cana-1737	335	29	29	29	NUM
cana-1737	335	30	(	(	PUNCT
cana-1737	335	31	2021	2021	NUM
cana-1737	335	32	):	):	PUNCT
cana-1737	335	33	2228	2228	NUM
cana-1737	335	34	-	-	SYM
cana-1737	335	35	2243	2243	NUM
cana-1737	335	36	.	.	PUNCT
cana-1737	336	1	[	[	X
cana-1737	336	2	36	36	NUM
cana-1737	336	3	]	]	X
cana-1737	336	4	demir	demir	PROPN
cana-1737	336	5	,	,	PUNCT
cana-1737	336	6	fatih	fatih	PROPN
cana-1737	336	7	,	,	PUNCT
cana-1737	336	8	abdulkadir	abdulkadir	ADJ
cana-1737	336	9	sengur	sengur	PROPN
cana-1737	336	10	,	,	PUNCT
cana-1737	336	11	ali	ali	PROPN
cana-1737	336	12	ari	ari	PROPN
cana-1737	336	13	,	,	PUNCT
cana-1737	336	14	kamran	kamran	PROPN
cana-1737	336	15	siddique	siddique	PROPN
cana-1737	336	16	,	,	PUNCT
cana-1737	336	17	and	and	CCONJ
cana-1737	336	18	mohammed	mohammed	PROPN
cana-1737	336	19	alswaitti	alswaitti	PROPN
cana-1737	336	20	.	.	PUNCT
cana-1737	337	1	"	"	PUNCT
cana-1737	337	2	feature	feature	VERB
cana-1737	337	3	mapping	mapping	NOUN
cana-1737	337	4	and	and	CCONJ
cana-1737	337	5	deep	deep	ADJ
cana-1737	337	6	long	long	ADJ
cana-1737	337	7	short	short	ADJ
cana-1737	337	8	term	term	NOUN
cana-1737	337	9	memory	memory	NOUN
cana-1737	337	10	network	network	NOUN
cana-1737	337	11	-	-	PUNCT
cana-1737	337	12	based	base	VERB
cana-1737	337	13	efficient	efficient	ADJ
cana-1737	337	14	approach	approach	NOUN
cana-1737	337	15	for	for	ADP
cana-1737	337	16	parkinson	parkinson	NOUN
cana-1737	337	17	’s	’s	PART
cana-1737	337	18	disease	disease	NOUN
cana-1737	337	19	diagnosis	diagnosis	NOUN
cana-1737	337	20	.	.	PUNCT
cana-1737	337	21	"	"	PUNCT
cana-1737	338	1	ieee	ieee	NOUN
cana-1737	338	2	access	access	NOUN
cana-1737	338	3	9	9	NUM
cana-1737	338	4	(	(	PUNCT
cana-1737	338	5	2021	2021	NUM
cana-1737	338	6	):	):	PUNCT
cana-1737	338	7	149456	149456	NUM
cana-1737	338	8	-	-	SYM
cana-1737	338	9	149464	149464	NUM
cana-1737	338	10	.	.	PUNCT
cana-1737	339	1	[	[	X
cana-1737	339	2	37	37	NUM
cana-1737	339	3	]	]	X
cana-1737	339	4	laganas	lagana	NOUN
cana-1737	339	5	,	,	PUNCT
cana-1737	339	6	christos	christo	NOUN
cana-1737	339	7	,	,	PUNCT
cana-1737	339	8	dimitrios	dimitrio	NOUN
cana-1737	339	9	iakovakis	iakovaki	NOUN
cana-1737	339	10	,	,	PUNCT
cana-1737	339	11	stelios	stelio	NOUN
cana-1737	339	12	hadjidimitriou	hadjidimitriou	NOUN
cana-1737	339	13	,	,	PUNCT
cana-1737	339	14	vasileios	vasileio	NOUN
cana-1737	339	15	charisis	charisis	NOUN
cana-1737	339	16	,	,	PUNCT
cana-1737	339	17	sofia	sofia	PROPN
cana-1737	339	18	b.	b.	PROPN
cana-1737	339	19	dias	dias	PROPN
cana-1737	339	20	,	,	PUNCT
cana-1737	339	21	sevasti	sevasti	PROPN
cana-1737	339	22	bostantzopoulou	bostantzopoulou	NOUN
cana-1737	339	23	,	,	PUNCT
cana-1737	339	24	zoe	zoe	PROPN
cana-1737	339	25	katsarou	katsarou	PROPN
cana-1737	339	26	et	et	PROPN
cana-1737	339	27	al	al	PROPN
cana-1737	339	28	.	.	PUNCT
cana-1737	340	1	"	"	PUNCT
cana-1737	340	2	parkinson	parkinson	NOUN
cana-1737	340	3	’s	’s	PART
cana-1737	340	4	disease	disease	NOUN
cana-1737	340	5	detection	detection	NOUN
cana-1737	340	6	based	base	VERB
cana-1737	340	7	on	on	ADP
cana-1737	340	8	running	run	VERB
cana-1737	340	9	speech	speech	NOUN
cana-1737	340	10	data	datum	NOUN
cana-1737	340	11	from	from	ADP
cana-1737	340	12	phone	phone	NOUN
cana-1737	340	13	calls	call	NOUN
cana-1737	340	14	.	.	PUNCT
cana-1737	340	15	"	"	PUNCT
cana-1737	341	1	ieee	ieee	NOUN
cana-1737	341	2	transactions	transaction	NOUN
cana-1737	341	3	on	on	ADP
cana-1737	341	4	biomedical	biomedical	ADJ
cana-1737	341	5	engineering	engineering	NOUN
cana-1737	341	6	69	69	NUM
cana-1737	341	7	,	,	PUNCT
cana-1737	341	8	no	no	INTJ
cana-1737	341	9	.	.	NOUN
cana-1737	341	10	5	5	NUM
cana-1737	341	11	(	(	PUNCT
cana-1737	341	12	2021	2021	NUM
cana-1737	341	13	):	):	PUNCT
cana-1737	341	14	1573	1573	NUM
cana-1737	341	15	-	-	SYM
cana-1737	341	16	1584	1584	NUM
cana-1737	341	17	.	.	PUNCT
cana-1737	342	1	[	[	X
cana-1737	342	2	38	38	NUM
cana-1737	342	3	]	]	X
cana-1737	342	4	liu	liu	PROPN
cana-1737	342	5	,	,	PUNCT
cana-1737	342	6	yuanyuan	yuanyuan	PROPN
cana-1737	342	7	,	,	PUNCT
cana-1737	342	8	mittapalle	mittapalle	VERB
cana-1737	342	9	kiran	kiran	PROPN
cana-1737	342	10	reddy	reddy	PROPN
cana-1737	342	11	,	,	PUNCT
cana-1737	342	12	nelly	nelly	ADV
cana-1737	342	13	penttilä	penttilä	NOUN
cana-1737	342	14	,	,	PUNCT
cana-1737	342	15	tiina	tiina	PROPN
cana-1737	342	16	ihalainen	ihalainen	PROPN
cana-1737	342	17	,	,	PUNCT
cana-1737	342	18	paavo	paavo	PROPN
cana-1737	342	19	alku	alku	PROPN
cana-1737	342	20	,	,	PUNCT
cana-1737	342	21	and	and	CCONJ
cana-1737	342	22	okko	okko	X
cana-1737	342	23	räsänen	räsänen	NOUN
cana-1737	342	24	.	.	PUNCT
cana-1737	343	1	"	"	PUNCT
cana-1737	343	2	automatic	automatic	ADJ
cana-1737	343	3	assessment	assessment	NOUN
cana-1737	343	4	of	of	ADP
cana-1737	343	5	parkinson	parkinson	NOUN
cana-1737	343	6	's	's	PART
cana-1737	343	7	disease	disease	NOUN
cana-1737	343	8	using	use	VERB
cana-1737	343	9	speech	speech	NOUN
cana-1737	343	10	representations	representation	NOUN
cana-1737	343	11	of	of	ADP
cana-1737	343	12	phonation	phonation	NOUN
cana-1737	343	13	and	and	CCONJ
cana-1737	343	14	articulation	articulation	NOUN
cana-1737	343	15	.	.	PUNCT
cana-1737	343	16	"	"	PUNCT
cana-1737	343	17	ieee	ieee	PROPN
cana-1737	343	18	/	/	SYM
cana-1737	343	19	acm	acm	NOUN
cana-1737	343	20	transactions	transaction	NOUN
cana-1737	343	21	on	on	ADP
cana-1737	343	22	audio	audio	NOUN
cana-1737	343	23	,	,	PUNCT
cana-1737	343	24	speech	speech	NOUN
cana-1737	343	25	,	,	PUNCT
cana-1737	343	26	and	and	CCONJ
cana-1737	343	27	language	language	NOUN
cana-1737	343	28	processing	processing	NOUN
cana-1737	343	29	31	31	NUM
cana-1737	343	30	(	(	PUNCT
cana-1737	343	31	2022	2022	NUM
cana-1737	343	32	):	):	PUNCT
cana-1737	343	33	242	242	NUM
cana-1737	343	34	-	-	SYM
cana-1737	343	35	255	255	NUM
cana-1737	343	36	.	.	PUNCT