id	sid	tid	token	lemma	pos
cana-3454	1	1	communications	communication	NOUN
cana-3454	1	2	on	on	ADP
cana-3454	1	3	applied	apply	VERB
cana-3454	1	4	nonlinear	nonlinear	ADJ
cana-3454	1	5	analysis	analysis	NOUN
cana-3454	1	6	issn	issn	NOUN
cana-3454	1	7	:	:	PUNCT
cana-3454	1	8	1074	1074	NUM
cana-3454	1	9	-	-	PUNCT
cana-3454	1	10	133x	133x	NUM
cana-3454	1	11	vol	vol	NOUN
cana-3454	1	12	32	32	NUM
cana-3454	1	13	no	no	NOUN
cana-3454	1	14	.	.	PUNCT
cana-3454	2	1	7s	7	NOUN
cana-3454	2	2	(	(	PUNCT
cana-3454	2	3	2025	2025	NUM
cana-3454	2	4	)	)	PUNCT
cana-3454	2	5	432	432	NUM
cana-3454	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	2	7	applications	application	NOUN
cana-3454	2	8	of	of	ADP
cana-3454	2	9	machine	machine	NOUN
cana-3454	2	10	learning	learn	VERB
cana-3454	2	11	in	in	ADP
cana-3454	2	12	computer	computer	NOUN
cana-3454	2	13	vision	vision	NOUN
cana-3454	2	14	:	:	PUNCT
cana-3454	2	15	a	a	DET
cana-3454	2	16	review	review	NOUN
cana-3454	2	17	r.sugunthakunthalambigai1	r.sugunthakunthalambigai1	NOUN
cana-3454	2	18	,	,	PUNCT
cana-3454	2	19	dr	dr	PROPN
cana-3454	2	20	ranadheer	ranadheer	PROPN
cana-3454	2	21	donthi2	donthi2	NOUN
cana-3454	2	22	,	,	PUNCT
cana-3454	2	23	pratik	pratik	NOUN
cana-3454	2	24	agrawal3	agrawal3	NOUN
cana-3454	2	25	,	,	PUNCT
cana-3454	2	26	dr	dr	PROPN
cana-3454	2	27	p	p	PROPN
cana-3454	2	28	kiran	kiran	PROPN
cana-3454	2	29	kumar	kumar	PROPN
cana-3454	2	30	reddy4	reddy4	PROPN
cana-3454	2	31	,	,	PUNCT
cana-3454	2	32	revathi	revathi	PROPN
cana-3454	2	33	v5	v5	PROPN
cana-3454	2	34	,	,	PUNCT
cana-3454	2	35	akansh	akansh	PROPN
cana-3454	2	36	garg5	garg5	PROPN
cana-3454	3	1	1assistant	1assistant	NUM
cana-3454	3	2	professor	professor	NOUN
cana-3454	3	3	of	of	ADP
cana-3454	3	4	mathematics	mathematic	NOUN
cana-3454	3	5	,	,	PUNCT
cana-3454	3	6	basic	basic	ADJ
cana-3454	3	7	engineering	engineering	NOUN
cana-3454	3	8	and	and	CCONJ
cana-3454	3	9	applied	apply	VERB
cana-3454	3	10	science	science	NOUN
cana-3454	3	11	,	,	PUNCT
cana-3454	3	12	agricultural	agricultural	ADJ
cana-3454	3	13	engineering	engineering	NOUN
cana-3454	3	14	college	college	PROPN
cana-3454	3	15	and	and	CCONJ
cana-3454	3	16	research	research	PROPN
cana-3454	3	17	institute(tamil	institute(tamil	PROPN
cana-3454	3	18	nadu	nadu	ADJ
cana-3454	3	19	agricultural	agricultural	ADJ
cana-3454	3	20	university),tiruchirapalli	university),tiruchirapalli	PROPN
cana-3454	3	21	,	,	PUNCT
cana-3454	3	22	tamilnadu	tamilnadu	NOUN
cana-3454	3	23	.	.	PUNCT
cana-3454	4	1	email	email	NOUN
cana-3454	4	2	i	i	PROPN
cana-3454	4	3	d	d	PROPN
cana-3454	4	4	suguntha	suguntha	PROPN
cana-3454	4	5	@tnau.ac.in	@tnau.ac.in	PROPN
cana-3454	4	6	2assistant	2assistant	PROPN
cana-3454	4	7	professor	professor	NOUN
cana-3454	4	8	,	,	PUNCT
cana-3454	4	9	department	department	NOUN
cana-3454	4	10	of	of	ADP
cana-3454	4	11	mathematics	mathematics	PROPN
cana-3454	4	12	,	,	PUNCT
cana-3454	4	13	st.martin	st.martin	NOUN
cana-3454	4	14	’s	’s	NOUN
cana-3454	4	15	engineering	engineering	NOUN
cana-3454	4	16	college	college	PROPN
cana-3454	4	17	,	,	PUNCT
cana-3454	4	18	secunderabad	secunderabad	PROPN
cana-3454	4	19	,	,	PUNCT
cana-3454	4	20	telangana	telangana	PROPN
cana-3454	4	21	,	,	PUNCT
cana-3454	4	22	pin500100	pin500100	NOUN
cana-3454	4	23	.	.	PUNCT
cana-3454	5	1	email	email	NOUN
cana-3454	6	1	i	i	PROPN
cana-3454	6	2	d	d	PROPN
cana-3454	6	3	:	:	PUNCT
cana-3454	6	4	ranadheer.phdku@gmail.com	ranadheer.phdku@gmail.com	X
cana-3454	6	5	3assistant	3assistant	PROPN
cana-3454	6	6	professor	professor	NOUN
cana-3454	6	7	,	,	PUNCT
cana-3454	6	8	computer	computer	NOUN
cana-3454	6	9	science	science	NOUN
cana-3454	6	10	and	and	CCONJ
cana-3454	6	11	engineering	engineering	NOUN
cana-3454	6	12	,	,	PUNCT
cana-3454	6	13	symbiosis	symbiosis	NOUN
cana-3454	6	14	institute	institute	PROPN
cana-3454	6	15	of	of	ADP
cana-3454	6	16	technology	technology	PROPN
cana-3454	6	17	,	,	PUNCT
cana-3454	6	18	nagpur	nagpur	PROPN
cana-3454	6	19	campus	campus	PROPN
cana-3454	6	20	,	,	PUNCT
cana-3454	6	21	symbiosis	symbiosis	NOUN
cana-3454	6	22	international(deemed	international(deemed	PROPN
cana-3454	6	23	university	university	NOUN
cana-3454	6	24	)	)	PUNCT
cana-3454	6	25	,	,	PUNCT
cana-3454	6	26	pune	pune	NOUN
cana-3454	6	27	,	,	PUNCT
cana-3454	6	28	india.nagpur	india.nagpur	PROPN
cana-3454	6	29	,	,	PUNCT
cana-3454	6	30	maharashtra	maharashtra	PROPN
cana-3454	6	31	.	.	PUNCT
cana-3454	7	1	email:pratik.agrawaal@gmail.com	email:pratik.agrawaal@gmail.com	PROPN
cana-3454	7	2	4	4	NUM
cana-3454	7	3	professor	professor	NOUN
cana-3454	7	4	,	,	PUNCT
cana-3454	7	5	aiml	aiml	NOUN
cana-3454	7	6	,	,	PUNCT
cana-3454	7	7	mlr	mlr	PROPN
cana-3454	7	8	institute	institute	PROPN
cana-3454	7	9	of	of	ADP
cana-3454	7	10	technology	technology	PROPN
cana-3454	7	11	,	,	PUNCT
cana-3454	7	12	medchal	medchal	ADJ
cana-3454	7	13	,	,	PUNCT
cana-3454	7	14	hyderabad	hyderabad	PROPN
cana-3454	7	15	,	,	PUNCT
cana-3454	7	16	telangana	telangana	PROPN
cana-3454	7	17	.	.	PUNCT
cana-3454	8	1	email	email	NOUN
cana-3454	9	1	i	i	PROPN
cana-3454	9	2	d	d	PROPN
cana-3454	9	3	kiran.penubaka@gmail.com	kiran.penubaka@gmail.com	PROPN
cana-3454	9	4	5professor	5professor	PROPN
cana-3454	9	5	&	&	CCONJ
cana-3454	9	6	dean	dean	PROPN
cana-3454	9	7	r	r	PROPN
cana-3454	9	8	&	&	CCONJ
cana-3454	9	9	d	d	PROPN
cana-3454	9	10	,	,	PUNCT
cana-3454	9	11	new	new	ADJ
cana-3454	9	12	horizon	horizon	PROPN
cana-3454	9	13	college	college	PROPN
cana-3454	9	14	of	of	ADP
cana-3454	9	15	engineering	engineering	PROPN
cana-3454	9	16	,	,	PUNCT
cana-3454	9	17	bangalore.jaishriresch@gmail.com	bangalore.jaishriresch@gmail.com	X
cana-3454	9	18	article	article	NOUN
cana-3454	9	19	history	history	NOUN
cana-3454	9	20	:	:	PUNCT
cana-3454	9	21	received	receive	VERB
cana-3454	9	22	:	:	PUNCT
cana-3454	9	23	26	26	NUM
cana-3454	9	24	-	-	SYM
cana-3454	9	25	10	10	NUM
cana-3454	9	26	-	-	PUNCT
cana-3454	9	27	2024	2024	NUM
cana-3454	9	28	revised:10	revised:10	NOUN
cana-3454	9	29	-	-	PUNCT
cana-3454	9	30	11	11	NUM
cana-3454	9	31	-	-	PUNCT
cana-3454	9	32	2024	2024	NUM
cana-3454	9	33	accepted:18	accepted:18	PROPN
cana-3454	9	34	-	-	PUNCT
cana-3454	9	35	12	12	NUM
cana-3454	9	36	-	-	PUNCT
cana-3454	9	37	2024	2024	NUM
cana-3454	9	38	abstract	abstract	NOUN
cana-3454	9	39	:	:	PUNCT
cana-3454	9	40	machine	machine	NOUN
cana-3454	9	41	learning	learning	NOUN
cana-3454	9	42	(	(	PUNCT
cana-3454	9	43	ml	ml	NOUN
cana-3454	9	44	)	)	PUNCT
cana-3454	9	45	has	have	AUX
cana-3454	9	46	become	become	VERB
cana-3454	9	47	an	an	DET
cana-3454	9	48	important	important	ADJ
cana-3454	9	49	aspect	aspect	NOUN
cana-3454	9	50	of	of	ADP
cana-3454	9	51	computer	computer	NOUN
cana-3454	9	52	vision	vision	NOUN
cana-3454	9	53	due	due	ADP
cana-3454	9	54	to	to	ADP
cana-3454	9	55	the	the	DET
cana-3454	9	56	increased	increase	VERB
cana-3454	9	57	efficiency	efficiency	NOUN
cana-3454	9	58	,	,	PUNCT
cana-3454	9	59	scalability	scalability	NOUN
cana-3454	9	60	and	and	CCONJ
cana-3454	9	61	high	high	ADJ
cana-3454	9	62	accuracy	accuracy	NOUN
cana-3454	9	63	in	in	ADP
cana-3454	9	64	image	image	NOUN
cana-3454	9	65	recognition	recognition	NOUN
cana-3454	9	66	,	,	PUNCT
cana-3454	9	67	object	object	VERB
cana-3454	9	68	detection	detection	NOUN
cana-3454	9	69	and	and	CCONJ
cana-3454	9	70	classification	classification	NOUN
cana-3454	9	71	.	.	PUNCT
cana-3454	10	1	this	this	DET
cana-3454	10	2	work	work	NOUN
cana-3454	10	3	examines	examine	VERB
cana-3454	10	4	the	the	DET
cana-3454	10	5	performance	performance	NOUN
cana-3454	10	6	of	of	ADP
cana-3454	10	7	four	four	NUM
cana-3454	10	8	chosen	choose	VERB
cana-3454	10	9	ml	ml	PROPN
cana-3454	10	10	techniques	technique	NOUN
cana-3454	10	11	cnns	cnns	PROPN
cana-3454	10	12	,	,	PUNCT
cana-3454	10	13	svms	svms	NOUN
cana-3454	10	14	,	,	PUNCT
cana-3454	10	15	rfs	rfs	PROPN
cana-3454	10	16	,	,	PUNCT
cana-3454	10	17	and	and	CCONJ
cana-3454	10	18	knns	knn	NOUN
cana-3454	10	19	to	to	ADP
cana-3454	10	20	a	a	DET
cana-3454	10	21	variety	variety	NOUN
cana-3454	10	22	of	of	ADP
cana-3454	10	23	visual	visual	ADJ
cana-3454	10	24	tasks	task	NOUN
cana-3454	10	25	in	in	ADP
cana-3454	10	26	healthcare	healthcare	PROPN
cana-3454	10	27	,	,	PUNCT
cana-3454	10	28	manufacturing	manufacturing	NOUN
cana-3454	10	29	,	,	PUNCT
cana-3454	10	30	agriculture	agriculture	NOUN
cana-3454	10	31	and	and	CCONJ
cana-3454	10	32	environment	environment	NOUN
cana-3454	10	33	surveillance	surveillance	NOUN
cana-3454	10	34	.	.	PUNCT
cana-3454	11	1	to	to	PART
cana-3454	11	2	assess	assess	VERB
cana-3454	11	3	the	the	DET
cana-3454	11	4	performance	performance	NOUN
cana-3454	11	5	of	of	ADP
cana-3454	11	6	these	these	DET
cana-3454	11	7	algorithms	algorithm	NOUN
cana-3454	11	8	,	,	PUNCT
cana-3454	11	9	accuracy	accuracy	NOUN
cana-3454	11	10	,	,	PUNCT
cana-3454	11	11	precision	precision	NOUN
cana-3454	11	12	,	,	PUNCT
cana-3454	11	13	recall	recall	NOUN
cana-3454	11	14	,	,	PUNCT
cana-3454	11	15	and	and	CCONJ
cana-3454	11	16	the	the	DET
cana-3454	11	17	f1	f1	NOUN
cana-3454	11	18	-	-	PUNCT
cana-3454	11	19	score	score	NOUN
cana-3454	11	20	were	be	AUX
cana-3454	11	21	conducted	conduct	VERB
cana-3454	11	22	on	on	ADP
cana-3454	11	23	the	the	DET
cana-3454	11	24	foundation	foundation	NOUN
cana-3454	11	25	of	of	ADP
cana-3454	11	26	a	a	DET
cana-3454	11	27	strong	strong	ADJ
cana-3454	11	28	data	datum	NOUN
cana-3454	11	29	set	set	VERB
cana-3454	11	30	of	of	ADP
cana-3454	11	31	50,000	50,000	NUM
cana-3454	11	32	annotated	annotate	VERB
cana-3454	11	33	images	image	NOUN
cana-3454	11	34	.	.	PUNCT
cana-3454	12	1	as	as	SCONJ
cana-3454	12	2	it	it	PRON
cana-3454	12	3	is	be	AUX
cana-3454	12	4	shown	show	VERB
cana-3454	12	5	the	the	DET
cana-3454	12	6	highest	high	ADJ
cana-3454	12	7	accuracy	accuracy	NOUN
cana-3454	12	8	of	of	ADP
cana-3454	12	9	the	the	DET
cana-3454	12	10	algorithm	algorithm	NOUN
cana-3454	12	11	is	be	AUX
cana-3454	12	12	97.8	97.8	NUM
cana-3454	12	13	%	%	NOUN
cana-3454	12	14	,	,	PUNCT
cana-3454	12	15	which	which	PRON
cana-3454	12	16	belongs	belong	VERB
cana-3454	12	17	to	to	ADP
cana-3454	12	18	cnn	cnn	PROPN
cana-3454	12	19	while	while	SCONJ
cana-3454	12	20	the	the	DET
cana-3454	12	21	lowest	low	ADJ
cana-3454	12	22	accuracy	accuracy	NOUN
cana-3454	12	23	is	be	AUX
cana-3454	12	24	88.9	88.9	NUM
cana-3454	12	25	%	%	NOUN
cana-3454	12	26	which	which	PRON
cana-3454	12	27	is	be	AUX
cana-3454	12	28	associated	associate	VERB
cana-3454	12	29	with	with	ADP
cana-3454	12	30	knn	knn	PROPN
cana-3454	12	31	,	,	PUNCT
cana-3454	12	32	while	while	SCONJ
cana-3454	12	33	svm	svm	ADJ
cana-3454	12	34	and	and	CCONJ
cana-3454	12	35	rf	rf	PRON
cana-3454	12	36	showed	show	VERB
cana-3454	12	37	the	the	DET
cana-3454	12	38	accuracy	accuracy	NOUN
cana-3454	12	39	of	of	ADP
cana-3454	12	40	92.4	92.4	NUM
cana-3454	12	41	%	%	NOUN
cana-3454	12	42	and	and	CCONJ
cana-3454	12	43	90.6%%	90.6%%	NUM
cana-3454	12	44	correspondingly	correspondingly	ADV
cana-3454	12	45	.	.	PUNCT
cana-3454	13	1	another	another	DET
cana-3454	13	2	outstanding	outstanding	ADJ
cana-3454	13	3	feature	feature	NOUN
cana-3454	13	4	was	be	AUX
cana-3454	13	5	higher	high	ADJ
cana-3454	13	6	accuracy	accuracy	NOUN
cana-3454	13	7	of	of	ADP
cana-3454	13	8	cnn	cnn	PROPN
cana-3454	13	9	;	;	PUNCT
cana-3454	13	10	98.2	98.2	NUM
cana-3454	13	11	%	%	NOUN
cana-3454	13	12	,	,	PUNCT
cana-3454	13	13	and	and	CCONJ
cana-3454	13	14	quality	quality	NOUN
cana-3454	13	15	,	,	PUNCT
cana-3454	13	16	or	or	CCONJ
cana-3454	13	17	recall	recall	NOUN
cana-3454	13	18	;	;	PUNCT
cana-3454	13	19	96.9	96.9	NUM
cana-3454	13	20	%	%	NOUN
cana-3454	13	21	;	;	PUNCT
cana-3454	13	22	thus	thus	ADV
cana-3454	13	23	cnn	cnn	PROPN
cana-3454	13	24	can	can	AUX
cana-3454	13	25	be	be	AUX
cana-3454	13	26	used	use	VERB
cana-3454	13	27	in	in	ADP
cana-3454	13	28	medical	medical	ADJ
cana-3454	13	29	imaging	imaging	NOUN
cana-3454	13	30	,	,	PUNCT
cana-3454	13	31	robotic	robotic	ADJ
cana-3454	13	32	quality	quality	NOUN
cana-3454	13	33	control	control	NOUN
cana-3454	13	34	,	,	PUNCT
cana-3454	13	35	etc	etc	X
cana-3454	13	36	.	.	X
cana-3454	14	1	the	the	DET
cana-3454	14	2	comparative	comparative	ADJ
cana-3454	14	3	analysis	analysis	NOUN
cana-3454	14	4	with	with	ADP
cana-3454	14	5	the	the	DET
cana-3454	14	6	related	relate	VERB
cana-3454	14	7	work	work	NOUN
cana-3454	14	8	showed	show	VERB
cana-3454	14	9	noticeable	noticeable	ADJ
cana-3454	14	10	enhancement	enhancement	NOUN
cana-3454	14	11	in	in	ADP
cana-3454	14	12	the	the	DET
cana-3454	14	13	efficiency	efficiency	NOUN
cana-3454	14	14	and	and	CCONJ
cana-3454	14	15	reliability	reliability	NOUN
cana-3454	14	16	;	;	PUNCT
cana-3454	14	17	hence	hence	ADV
cana-3454	14	18	,	,	PUNCT
cana-3454	14	19	the	the	DET
cana-3454	14	20	practicality	practicality	NOUN
cana-3454	14	21	of	of	ADP
cana-3454	14	22	using	use	VERB
cana-3454	14	23	ml	ml	NOUN
cana-3454	14	24	in	in	ADP
cana-3454	14	25	the	the	DET
cana-3454	14	26	real	real	ADJ
cana-3454	14	27	-	-	PUNCT
cana-3454	14	28	life	life	NOUN
cana-3454	14	29	applications	application	NOUN
cana-3454	14	30	was	be	AUX
cana-3454	14	31	also	also	ADV
cana-3454	14	32	verified	verify	VERB
cana-3454	14	33	.	.	PUNCT
cana-3454	15	1	however	however	ADV
cana-3454	15	2	,	,	PUNCT
cana-3454	15	3	there	there	PRON
cana-3454	15	4	were	be	VERB
cana-3454	15	5	challenges	challenge	NOUN
cana-3454	15	6	highlighted	highlight	VERB
cana-3454	15	7	for	for	ADP
cana-3454	15	8	future	future	ADJ
cana-3454	15	9	work	work	NOUN
cana-3454	15	10	including	include	VERB
cana-3454	15	11	the	the	DET
cana-3454	15	12	computational	computational	ADJ
cana-3454	15	13	requirement	requirement	NOUN
cana-3454	15	14	for	for	ADP
cana-3454	15	15	such	such	ADJ
cana-3454	15	16	analyses	analysis	NOUN
cana-3454	15	17	,	,	PUNCT
cana-3454	15	18	as	as	ADV
cana-3454	15	19	well	well	ADV
cana-3454	15	20	as	as	ADP
cana-3454	15	21	ethical	ethical	ADJ
cana-3454	15	22	issues	issue	NOUN
cana-3454	15	23	.	.	PUNCT
cana-3454	16	1	this	this	DET
cana-3454	16	2	work	work	NOUN
cana-3454	16	3	also	also	ADV
cana-3454	16	4	underscores	underscore	VERB
cana-3454	16	5	the	the	DET
cana-3454	16	6	possibilities	possibility	NOUN
cana-3454	16	7	of	of	ADP
cana-3454	16	8	ml	ml	NOUN
cana-3454	16	9	for	for	ADP
cana-3454	16	10	change	change	NOUN
cana-3454	16	11	in	in	ADP
cana-3454	16	12	computer	computer	NOUN
cana-3454	16	13	vision	vision	NOUN
cana-3454	16	14	and	and	CCONJ
cana-3454	16	15	opens	open	VERB
cana-3454	16	16	new	new	ADJ
cana-3454	16	17	avenues	avenue	NOUN
cana-3454	16	18	in	in	ADP
cana-3454	16	19	critical	critical	ADJ
cana-3454	16	20	fields	field	NOUN
cana-3454	16	21	.	.	PUNCT
cana-3454	17	1	keywords	keyword	NOUN
cana-3454	17	2	:	:	PUNCT
cana-3454	17	3	machine	machine	NOUN
cana-3454	17	4	learning	learning	NOUN
cana-3454	17	5	,	,	PUNCT
cana-3454	17	6	computer	computer	NOUN
cana-3454	17	7	vision	vision	NOUN
cana-3454	17	8	,	,	PUNCT
cana-3454	17	9	convolutional	convolutional	ADJ
cana-3454	17	10	neural	neural	ADJ
cana-3454	17	11	networks	network	NOUN
cana-3454	17	12	,	,	PUNCT
cana-3454	17	13	object	object	VERB
cana-3454	17	14	detection	detection	NOUN
cana-3454	17	15	,	,	PUNCT
cana-3454	17	16	image	image	NOUN
cana-3454	17	17	classification	classification	NOUN
cana-3454	17	18	i.	i.	NOUN
cana-3454	17	19	introduction	introduction	NOUN
cana-3454	17	20	computer	computer	NOUN
cana-3454	17	21	vision	vision	NOUN
cana-3454	17	22	(	(	PUNCT
cana-3454	17	23	cv	cv	PROPN
cana-3454	17	24	)	)	PUNCT
cana-3454	17	25	is	be	AUX
cana-3454	17	26	an	an	DET
cana-3454	17	27	emergent	emergent	ADJ
cana-3454	17	28	research	research	NOUN
cana-3454	17	29	area	area	NOUN
cana-3454	17	30	of	of	ADP
cana-3454	17	31	artificial	artificial	ADJ
cana-3454	17	32	intelligence	intelligence	NOUN
cana-3454	17	33	(	(	PUNCT
cana-3454	17	34	ai	ai	NOUN
cana-3454	17	35	)	)	PUNCT
cana-3454	17	36	that	that	PRON
cana-3454	17	37	allows	allow	VERB
cana-3454	17	38	the	the	DET
cana-3454	17	39	computer	computer	NOUN
cana-3454	17	40	to	to	PART
cana-3454	17	41	understand	understand	VERB
cana-3454	17	42	and	and	CCONJ
cana-3454	17	43	process	process	VERB
cana-3454	17	44	image	image	NOUN
cana-3454	17	45	and	and	CCONJ
cana-3454	17	46	video	video	NOUN
cana-3454	17	47	data	datum	NOUN
cana-3454	17	48	.	.	PUNCT
cana-3454	18	1	gaining	gain	VERB
cana-3454	18	2	its	its	PRON
cana-3454	18	3	origins	origin	NOUN
cana-3454	18	4	in	in	ADP
cana-3454	18	5	emulating	emulate	VERB
cana-3454	18	6	human	human	ADJ
cana-3454	18	7	vision	vision	NOUN
cana-3454	18	8	,	,	PUNCT
cana-3454	18	9	computer	computer	NOUN
cana-3454	18	10	vision	vision	NOUN
cana-3454	18	11	has	have	AUX
cana-3454	18	12	come	come	VERB
cana-3454	18	13	a	a	DET
cana-3454	18	14	long	long	ADJ
cana-3454	18	15	way	way	NOUN
cana-3454	18	16	and	and	CCONJ
cana-3454	18	17	mainly	mainly	ADV
cana-3454	18	18	owes	owe	VERB
cana-3454	18	19	it	it	PRON
cana-3454	18	20	to	to	ADP
cana-3454	18	21	the	the	DET
cana-3454	18	22	implementation	implementation	NOUN
cana-3454	18	23	of	of	ADP
cana-3454	18	24	ml	ml	NOUN
cana-3454	18	25	algorithms	algorithm	NOUN
cana-3454	18	26	.	.	PUNCT
cana-3454	19	1	deep	deep	ADJ
cana-3454	19	2	learning	learning	NOUN
cana-3454	19	3	–	–	PUNCT
cana-3454	19	4	the	the	DET
cana-3454	19	5	most	most	ADV
cana-3454	19	6	significant	significant	ADJ
cana-3454	19	7	branch	branch	NOUN
cana-3454	19	8	of	of	ADP
cana-3454	19	9	machine	machine	NOUN
cana-3454	19	10	learning	learning	NOUN
cana-3454	19	11	–	–	PUNCT
cana-3454	19	12	has	have	AUX
cana-3454	19	13	lately	lately	ADV
cana-3454	19	14	brought	bring	VERB
cana-3454	19	15	highly	highly	ADV
cana-3454	19	16	efficient	efficient	ADJ
cana-3454	19	17	tools	tool	NOUN
cana-3454	19	18	for	for	ADP
cana-3454	19	19	analyzing	analyze	VERB
cana-3454	19	20	,	,	PUNCT
cana-3454	19	21	classifying	classify	VERB
cana-3454	19	22	,	,	PUNCT
cana-3454	19	23	and	and	CCONJ
cana-3454	19	24	,	,	PUNCT
cana-3454	19	25	most	most	ADV
cana-3454	19	26	importantly	importantly	ADV
cana-3454	19	27	,	,	PUNCT
cana-3454	19	28	recognizing	recognize	VERB
cana-3454	19	29	objects	object	NOUN
cana-3454	19	30	and	and	CCONJ
cana-3454	19	31	making	make	VERB
cana-3454	19	32	decisions	decision	NOUN
cana-3454	19	33	based	base	VERB
cana-3454	19	34	on	on	ADP
cana-3454	19	35	the	the	DET
cana-3454	19	36	seen	see	VERB
cana-3454	19	37	data	datum	NOUN
cana-3454	19	38	to	to	PART
cana-3454	19	39	computer	computer	NOUN
cana-3454	19	40	vision	vision	NOUN
cana-3454	19	41	[	[	X
cana-3454	19	42	1	1	NUM
cana-3454	19	43	]	]	PUNCT
cana-3454	19	44	.	.	PUNCT
cana-3454	20	1	this	this	DET
cana-3454	20	2	compatibility	compatibility	NOUN
cana-3454	20	3	has	have	AUX
cana-3454	20	4	expanded	expand	VERB
cana-3454	20	5	the	the	DET
cana-3454	20	6	spheres	sphere	NOUN
cana-3454	20	7	of	of	ADP
cana-3454	20	8	computer	computer	NOUN
cana-3454	20	9	vision	vision	NOUN
cana-3454	20	10	use	use	NOUN
cana-3454	20	11	from	from	ADP
cana-3454	20	12	healthcare	healthcare	NOUN
cana-3454	20	13	and	and	CCONJ
cana-3454	20	14	automotive	automotive	ADJ
cana-3454	20	15	to	to	ADP
cana-3454	20	16	agriculture	agriculture	NOUN
cana-3454	20	17	,	,	PUNCT
cana-3454	20	18	retail	retail	NOUN
cana-3454	20	19	and	and	CCONJ
cana-3454	20	20	others	other	NOUN
cana-3454	20	21	.	.	PUNCT
cana-3454	21	1	traditional	traditional	ADJ
cana-3454	21	2	mailto:kiran.penubaka@gmail.com	mailto:kiran.penubaka@gmail.com	PROPN
cana-3454	21	3	mailto:jaishriresch@gmail.com	mailto:jaishriresch@gmail.com	X
cana-3454	22	1	communications	communication	NOUN
cana-3454	22	2	on	on	ADP
cana-3454	22	3	applied	apply	VERB
cana-3454	22	4	nonlinear	nonlinear	ADJ
cana-3454	22	5	analysis	analysis	NOUN
cana-3454	22	6	issn	issn	NOUN
cana-3454	22	7	:	:	PUNCT
cana-3454	22	8	1074	1074	NUM
cana-3454	22	9	-	-	PUNCT
cana-3454	22	10	133x	133x	NUM
cana-3454	22	11	vol	vol	NOUN
cana-3454	22	12	32	32	NUM
cana-3454	22	13	no	no	NOUN
cana-3454	22	14	.	.	PUNCT
cana-3454	23	1	7s	7	NOUN
cana-3454	23	2	(	(	PUNCT
cana-3454	23	3	2025	2025	NUM
cana-3454	23	4	)	)	PUNCT
cana-3454	23	5	433	433	NUM
cana-3454	24	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	24	2	ml	ml	NOUN
cana-3454	24	3	algorithms	algorithm	NOUN
cana-3454	24	4	that	that	PRON
cana-3454	24	5	are	be	AUX
cana-3454	24	6	dominant	dominant	ADJ
cana-3454	24	7	in	in	ADP
cana-3454	24	8	computer	computer	NOUN
cana-3454	24	9	vision	vision	NOUN
cana-3454	24	10	tasks	task	NOUN
cana-3454	24	11	are	be	AUX
cana-3454	24	12	now	now	ADV
cana-3454	24	13	supplemented	supplement	VERB
cana-3454	24	14	or	or	CCONJ
cana-3454	24	15	replaced	replace	VERB
cana-3454	24	16	by	by	ADP
cana-3454	24	17	deep	deep	ADJ
cana-3454	24	18	learning	learning	NOUN
cana-3454	24	19	frameworks	framework	NOUN
cana-3454	24	20	like	like	ADP
cana-3454	24	21	cnns	cnn	NOUN
cana-3454	24	22	,	,	PUNCT
cana-3454	24	23	rnns	rnn	NOUN
cana-3454	24	24	and	and	CCONJ
cana-3454	24	25	transformers	transformer	NOUN
cana-3454	24	26	[	[	X
cana-3454	24	27	2	2	NUM
cana-3454	24	28	]	]	PUNCT
cana-3454	24	29	.	.	PUNCT
cana-3454	25	1	these	these	DET
cana-3454	25	2	algorithms	algorithm	NOUN
cana-3454	25	3	make	make	VERB
cana-3454	25	4	realizations	realization	NOUN
cana-3454	25	5	of	of	ADP
cana-3454	25	6	applications	application	NOUN
cana-3454	25	7	such	such	ADJ
cana-3454	25	8	as	as	ADP
cana-3454	25	9	image	image	NOUN
cana-3454	25	10	or	or	CCONJ
cana-3454	25	11	object	object	VERB
cana-3454	25	12	categorization	categorization	NOUN
cana-3454	25	13	,	,	PUNCT
cana-3454	25	14	detection	detection	NOUN
cana-3454	25	15	,	,	PUNCT
cana-3454	25	16	segmentation	segmentation	NOUN
cana-3454	25	17	,	,	PUNCT
cana-3454	25	18	or	or	CCONJ
cana-3454	25	19	face	face	VERB
cana-3454	25	20	identification	identification	NOUN
cana-3454	25	21	possible	possible	ADJ
cana-3454	25	22	with	with	ADP
cana-3454	25	23	unfathomable	unfathomable	ADJ
cana-3454	25	24	precision	precision	NOUN
cana-3454	25	25	and	and	CCONJ
cana-3454	25	26	speed	speed	NOUN
cana-3454	25	27	.	.	PUNCT
cana-3454	26	1	for	for	ADP
cana-3454	26	2	instance	instance	NOUN
cana-3454	26	3	,	,	PUNCT
cana-3454	26	4	the	the	DET
cana-3454	26	5	latest	late	ADJ
cana-3454	26	6	ml	ml	VERB
cana-3454	26	7	-	-	PUNCT
cana-3454	26	8	cv	cv	NOUN
cana-3454	26	9	systems	system	NOUN
cana-3454	26	10	are	be	AUX
cana-3454	26	11	now	now	ADV
cana-3454	26	12	used	use	VERB
cana-3454	26	13	in	in	ADP
cana-3454	26	14	diagnosis	diagnosis	NOUN
cana-3454	26	15	of	of	ADP
cana-3454	26	16	diseases	disease	NOUN
cana-3454	26	17	from	from	ADP
cana-3454	26	18	medical	medical	ADJ
cana-3454	26	19	images	image	NOUN
cana-3454	26	20	,	,	PUNCT
cana-3454	26	21	identification	identification	NOUN
cana-3454	26	22	of	of	ADP
cana-3454	26	23	objects	object	NOUN
cana-3454	26	24	in	in	ADP
cana-3454	26	25	auto	auto	NOUN
cana-3454	26	26	mobiles	mobile	NOUN
cana-3454	26	27	for	for	ADP
cana-3454	26	28	self	self	NOUN
cana-3454	26	29	-	-	PUNCT
cana-3454	26	30	driving	driving	NOUN
cana-3454	26	31	,	,	PUNCT
cana-3454	26	32	or	or	CCONJ
cana-3454	26	33	in	in	ADP
cana-3454	26	34	surveillance	surveillance	NOUN
cana-3454	26	35	through	through	ADP
cana-3454	26	36	facial	facial	ADJ
cana-3454	26	37	and	and	CCONJ
cana-3454	26	38	behavioral	behavioral	ADJ
cana-3454	26	39	analysis	analysis	NOUN
cana-3454	26	40	[	[	X
cana-3454	26	41	3	3	NUM
cana-3454	26	42	]	]	PUNCT
cana-3454	26	43	.	.	PUNCT
cana-3454	27	1	new	new	ADJ
cana-3454	27	2	opportunities	opportunity	NOUN
cana-3454	27	3	include	include	VERB
cana-3454	27	4	application	application	NOUN
cana-3454	27	5	of	of	ADP
cana-3454	27	6	machine	machine	NOUN
cana-3454	27	7	learning	learn	VERB
cana-3454	27	8	for	for	ADP
cana-3454	27	9	real	real	ADJ
cana-3454	27	10	-	-	PUNCT
cana-3454	27	11	time	time	NOUN
cana-3454	27	12	video	video	NOUN
cana-3454	27	13	analysis	analysis	NOUN
cana-3454	27	14	,	,	PUNCT
cana-3454	27	15	ar	ar	NOUN
cana-3454	27	16	and	and	CCONJ
cana-3454	27	17	simplify	simplify	VERB
cana-3454	27	18	i	i	PROPN
cana-3454	27	19	/	/	SYM
cana-3454	27	20	o	o	NOUN
cana-3454	27	21	operations	operation	NOUN
cana-3454	27	22	of	of	ADP
cana-3454	27	23	the	the	DET
cana-3454	27	24	computer	computer	NOUN
cana-3454	27	25	.	.	PUNCT
cana-3454	28	1	in	in	ADP
cana-3454	28	2	addition	addition	NOUN
cana-3454	28	3	,	,	PUNCT
cana-3454	28	4	there	there	PRON
cana-3454	28	5	are	be	VERB
cana-3454	28	6	large	large	ADJ
cana-3454	28	7	scale	scale	NOUN
cana-3454	28	8	databases	database	NOUN
cana-3454	28	9	for	for	ADP
cana-3454	28	10	data	datum	NOUN
cana-3454	28	11	storage	storage	NOUN
cana-3454	28	12	and	and	CCONJ
cana-3454	28	13	improved	improve	VERB
cana-3454	28	14	computational	computational	ADJ
cana-3454	28	15	capabilities	capability	NOUN
cana-3454	28	16	that	that	PRON
cana-3454	28	17	have	have	AUX
cana-3454	28	18	boosted	boost	VERB
cana-3454	28	19	the	the	DET
cana-3454	28	20	research	research	NOUN
cana-3454	28	21	and	and	CCONJ
cana-3454	28	22	utility	utility	NOUN
cana-3454	28	23	of	of	ADP
cana-3454	28	24	this	this	DET
cana-3454	28	25	domain	domain	NOUN
cana-3454	28	26	more	more	ADV
cana-3454	28	27	rapidly	rapidly	ADV
cana-3454	28	28	.	.	PUNCT
cana-3454	29	1	this	this	DET
cana-3454	29	2	paper	paper	NOUN
cana-3454	29	3	seeks	seek	VERB
cana-3454	29	4	to	to	PART
cana-3454	29	5	discuss	discuss	VERB
cana-3454	29	6	in	in	ADP
cana-3454	29	7	detail	detail	NOUN
cana-3454	29	8	,	,	PUNCT
cana-3454	29	9	the	the	DET
cana-3454	29	10	usefulness	usefulness	NOUN
cana-3454	29	11	of	of	ADP
cana-3454	29	12	machine	machine	NOUN
cana-3454	29	13	learning	learn	VERB
cana-3454	29	14	in	in	ADP
cana-3454	29	15	computer	computer	NOUN
cana-3454	29	16	vision	vision	NOUN
cana-3454	29	17	and	and	CCONJ
cana-3454	29	18	how	how	SCONJ
cana-3454	29	19	it	it	PRON
cana-3454	29	20	has	have	AUX
cana-3454	29	21	affected	affect	VERB
cana-3454	29	22	different	different	ADJ
cana-3454	29	23	departments	department	NOUN
cana-3454	29	24	.	.	PUNCT
cana-3454	30	1	based	base	VERB
cana-3454	30	2	on	on	ADP
cana-3454	30	3	the	the	DET
cana-3454	30	4	exploration	exploration	NOUN
cana-3454	30	5	of	of	ADP
cana-3454	30	6	current	current	ADJ
cana-3454	30	7	approaches	approach	NOUN
cana-3454	30	8	,	,	PUNCT
cana-3454	30	9	major	major	ADJ
cana-3454	30	10	issues	issue	NOUN
cana-3454	30	11	,	,	PUNCT
cana-3454	30	12	and	and	CCONJ
cana-3454	30	13	future	future	ADJ
cana-3454	30	14	directions	direction	NOUN
cana-3454	30	15	of	of	ADP
cana-3454	30	16	ml	ml	ADP
cana-3454	30	17	application	application	NOUN
cana-3454	30	18	in	in	ADP
cana-3454	30	19	cv	cv	PROPN
cana-3454	30	20	,	,	PUNCT
cana-3454	30	21	this	this	DET
cana-3454	30	22	study	study	NOUN
cana-3454	30	23	discusses	discuss	VERB
cana-3454	30	24	possibilities	possibility	NOUN
cana-3454	30	25	and	and	CCONJ
cana-3454	30	26	potential	potential	ADJ
cana-3454	30	27	directions	direction	NOUN
cana-3454	30	28	for	for	ADP
cana-3454	30	29	further	further	ADJ
cana-3454	30	30	development	development	NOUN
cana-3454	30	31	.	.	PUNCT
cana-3454	31	1	by	by	ADP
cana-3454	31	2	doing	do	VERB
cana-3454	31	3	so	so	ADV
cana-3454	31	4	it	it	PRON
cana-3454	31	5	aims	aim	VERB
cana-3454	31	6	at	at	ADP
cana-3454	31	7	being	be	AUX
cana-3454	31	8	a	a	DET
cana-3454	31	9	reference	reference	NOUN
cana-3454	31	10	source	source	NOUN
cana-3454	31	11	for	for	ADP
cana-3454	31	12	academicians	academician	NOUN
cana-3454	31	13	and	and	CCONJ
cana-3454	31	14	practitioners	practitioner	NOUN
cana-3454	31	15	along	along	ADP
cana-3454	31	16	with	with	ADP
cana-3454	31	17	industry	industry	NOUN
cana-3454	31	18	professionals	professional	NOUN
cana-3454	31	19	who	who	PRON
cana-3454	31	20	are	be	AUX
cana-3454	31	21	interested	interested	ADJ
cana-3454	31	22	in	in	ADP
cana-3454	31	23	and	and	CCONJ
cana-3454	31	24	aspire	aspire	VERB
cana-3454	31	25	to	to	PART
cana-3454	31	26	advance	advance	VERB
cana-3454	31	27	this	this	DET
cana-3454	31	28	burgeoning	burgeon	VERB
cana-3454	31	29	field	field	NOUN
cana-3454	31	30	of	of	ADP
cana-3454	31	31	study	study	NOUN
cana-3454	31	32	.	.	PUNCT
cana-3454	32	1	ii	ii	PROPN
cana-3454	32	2	.	.	PROPN
cana-3454	32	3	related	relate	VERB
cana-3454	32	4	works	work	VERB
cana-3454	32	5	the	the	DET
cana-3454	32	6	use	use	NOUN
cana-3454	32	7	of	of	ADP
cana-3454	32	8	ai	ai	NOUN
cana-3454	32	9	with	with	ADP
cana-3454	32	10	integrated	integrated	ADJ
cana-3454	32	11	ml	ml	NOUN
cana-3454	32	12	has	have	AUX
cana-3454	32	13	significantly	significantly	ADV
cana-3454	32	14	increased	increase	VERB
cana-3454	32	15	in	in	ADP
cana-3454	32	16	nearly	nearly	ADV
cana-3454	32	17	all	all	PRON
cana-3454	32	18	fields	field	NOUN
cana-3454	32	19	and	and	CCONJ
cana-3454	32	20	is	be	AUX
cana-3454	32	21	primarily	primarily	ADV
cana-3454	32	22	associated	associate	VERB
cana-3454	32	23	with	with	ADP
cana-3454	32	24	the	the	DET
cana-3454	32	25	development	development	NOUN
cana-3454	32	26	of	of	ADP
cana-3454	32	27	smart	smart	ADJ
cana-3454	32	28	autonomous	autonomous	ADJ
cana-3454	32	29	devices	device	NOUN
cana-3454	32	30	,	,	PUNCT
cana-3454	32	31	technologies	technology	NOUN
cana-3454	32	32	in	in	ADP
cana-3454	32	33	the	the	DET
cana-3454	32	34	medical	medical	ADJ
cana-3454	32	35	industry	industry	NOUN
cana-3454	32	36	,	,	PUNCT
cana-3454	32	37	production	production	NOUN
cana-3454	32	38	,	,	PUNCT
cana-3454	32	39	farming	farming	NOUN
cana-3454	32	40	,	,	PUNCT
cana-3454	32	41	and	and	CCONJ
cana-3454	32	42	meteorology	meteorology	NOUN
cana-3454	32	43	.	.	PUNCT
cana-3454	33	1	this	this	DET
cana-3454	33	2	section	section	NOUN
cana-3454	33	3	presents	present	VERB
cana-3454	33	4	a	a	DET
cana-3454	33	5	review	review	NOUN
cana-3454	33	6	of	of	ADP
cana-3454	33	7	related	related	ADJ
cana-3454	33	8	work	work	NOUN
cana-3454	33	9	,	,	PUNCT
cana-3454	33	10	presenting	present	VERB
cana-3454	33	11	major	major	ADJ
cana-3454	33	12	findings	finding	NOUN
cana-3454	33	13	,	,	PUNCT
cana-3454	33	14	and	and	CCONJ
cana-3454	33	15	discussion	discussion	NOUN
cana-3454	33	16	of	of	ADP
cana-3454	33	17	their	their	PRON
cana-3454	33	18	significance	significance	NOUN
cana-3454	33	19	.	.	PUNCT
cana-3454	34	1	automated	automate	VERB
cana-3454	34	2	systems	system	NOUN
cana-3454	34	3	and	and	CCONJ
cana-3454	34	4	artificial	artificial	ADJ
cana-3454	34	5	intelligence	intelligence	NOUN
cana-3454	34	6	the	the	DET
cana-3454	34	7	various	various	ADJ
cana-3454	34	8	developments	development	NOUN
cana-3454	34	9	of	of	ADP
cana-3454	34	10	path	path	NOUN
cana-3454	34	11	planning	planning	NOUN
cana-3454	34	12	strategies	strategy	NOUN
cana-3454	34	13	for	for	ADP
cana-3454	34	14	amrs	amrs	PROPN
cana-3454	34	15	presented	present	VERB
cana-3454	34	16	in	in	ADP
cana-3454	34	17	the	the	DET
cana-3454	34	18	analyzing	analyze	VERB
cana-3454	34	19	part	part	NOUN
cana-3454	34	20	show	show	VERB
cana-3454	34	21	how	how	SCONJ
cana-3454	34	22	intelligent	intelligent	ADJ
cana-3454	34	23	approaches	approach	NOUN
cana-3454	34	24	can	can	AUX
cana-3454	34	25	address	address	VERB
cana-3454	34	26	the	the	DET
cana-3454	34	27	navigation	navigation	NOUN
cana-3454	34	28	issues	issue	NOUN
cana-3454	34	29	and	and	CCONJ
cana-3454	34	30	improve	improve	VERB
cana-3454	34	31	the	the	DET
cana-3454	34	32	performance	performance	NOUN
cana-3454	34	33	of	of	ADP
cana-3454	34	34	the	the	DET
cana-3454	34	35	existing	exist	VERB
cana-3454	34	36	systems	system	NOUN
cana-3454	34	37	[	[	X
cana-3454	34	38	15	15	NUM
cana-3454	34	39	]	]	PUNCT
cana-3454	34	40	.	.	PUNCT
cana-3454	35	1	the	the	DET
cana-3454	35	2	paper	paper	NOUN
cana-3454	35	3	by	by	ADP
cana-3454	35	4	galarza	galarza	NOUN
cana-3454	35	5	-	-	PUNCT
cana-3454	35	6	falfan	falfan	NOUN
cana-3454	35	7	et	et	PROPN
cana-3454	35	8	al	al	PROPN
cana-3454	35	9	.	.	PROPN
cana-3454	36	1	(	(	PUNCT
cana-3454	36	2	2024	2024	NUM
cana-3454	36	3	)	)	PUNCT
cana-3454	36	4	aims	aim	VERB
cana-3454	36	5	to	to	PART
cana-3454	36	6	review	review	VERB
cana-3454	36	7	novel	novel	ADJ
cana-3454	36	8	approaches	approach	NOUN
cana-3454	36	9	for	for	ADP
cana-3454	36	10	solving	solve	VERB
cana-3454	36	11	such	such	ADJ
cana-3454	36	12	difficult	difficult	ADJ
cana-3454	36	13	stamps	stamp	NOUN
cana-3454	36	14	for	for	ADP
cana-3454	36	15	path	path	NOUN
cana-3454	36	16	planning	planning	NOUN
cana-3454	36	17	in	in	ADP
cana-3454	36	18	singular	singular	ADJ
cana-3454	36	19	settings	setting	NOUN
cana-3454	36	20	.	.	PUNCT
cana-3454	37	1	the	the	DET
cana-3454	37	2	use	use	NOUN
cana-3454	37	3	of	of	ADP
cana-3454	37	4	intelligent	intelligent	ADJ
cana-3454	37	5	algorithms	algorithm	NOUN
cana-3454	37	6	has	have	AUX
cana-3454	37	7	been	be	AUX
cana-3454	37	8	instrumental	instrumental	ADJ
cana-3454	37	9	in	in	ADP
cana-3454	37	10	enhancing	enhance	VERB
cana-3454	37	11	the	the	DET
cana-3454	37	12	course	course	NOUN
cana-3454	37	13	of	of	ADP
cana-3454	37	14	autonomous	autonomous	ADJ
cana-3454	37	15	systems	system	NOUN
cana-3454	37	16	especially	especially	ADV
cana-3454	37	17	in	in	ADP
cana-3454	37	18	providing	provide	VERB
cana-3454	37	19	timely	timely	ADJ
cana-3454	37	20	decision	decision	NOUN
cana-3454	37	21	and	and	CCONJ
cana-3454	37	22	flexibility	flexibility	NOUN
cana-3454	37	23	.	.	PUNCT
cana-3454	38	1	agricultural	agricultural	ADJ
cana-3454	38	2	and	and	CCONJ
cana-3454	38	3	services	service	NOUN
cana-3454	38	4	and	and	CCONJ
cana-3454	38	5	integrated	integrate	VERB
cana-3454	38	6	systems	system	NOUN
cana-3454	38	7	several	several	ADJ
cana-3454	38	8	cases	case	NOUN
cana-3454	38	9	,	,	PUNCT
cana-3454	38	10	applying	apply	VERB
cana-3454	38	11	ml	ml	ADP
cana-3454	38	12	algorithms	algorithm	NOUN
cana-3454	38	13	in	in	ADP
cana-3454	38	14	agricultural	agricultural	ADJ
cana-3454	38	15	practices	practice	NOUN
cana-3454	38	16	,	,	PUNCT
cana-3454	38	17	indicate	indicate	VERB
cana-3454	38	18	that	that	SCONJ
cana-3454	38	19	the	the	DET
cana-3454	38	20	approach	approach	NOUN
cana-3454	38	21	can	can	AUX
cana-3454	38	22	help	help	VERB
cana-3454	38	23	increase	increase	VERB
cana-3454	38	24	yields	yield	NOUN
cana-3454	38	25	,	,	PUNCT
cana-3454	38	26	as	as	ADV
cana-3454	38	27	well	well	ADV
cana-3454	38	28	as	as	ADP
cana-3454	38	29	make	make	VERB
cana-3454	38	30	farming	farming	NOUN
cana-3454	38	31	practices	practice	NOUN
cana-3454	38	32	more	more	ADV
cana-3454	38	33	sustainable	sustainable	ADJ
cana-3454	38	34	.	.	PUNCT
cana-3454	39	1	gomes	gomes	PROPN
cana-3454	39	2	ana	ana	PROPN
cana-3454	39	3	et	et	PROPN
cana-3454	39	4	al	al	PROPN
cana-3454	39	5	.	.	PROPN
cana-3454	39	6	,	,	PUNCT
cana-3454	39	7	in	in	ADP
cana-3454	39	8	a	a	DET
cana-3454	39	9	systematic	systematic	ADJ
cana-3454	39	10	literature	literature	NOUN
cana-3454	39	11	review	review	NOUN
cana-3454	39	12	addressed	address	VERB
cana-3454	39	13	the	the	DET
cana-3454	39	14	topic	topic	NOUN
cana-3454	39	15	of	of	ADP
cana-3454	39	16	using	use	VERB
cana-3454	39	17	ml	ml	NOUN
cana-3454	39	18	algorithms	algorithm	NOUN
cana-3454	39	19	in	in	ADP
cana-3454	39	20	weed	weed	NOUN
cana-3454	39	21	management	management	NOUN
cana-3454	39	22	for	for	ADP
cana-3454	39	23	integrated	integrate	VERB
cana-3454	39	24	croplivestock	croplivestock	NOUN
cana-3454	39	25	farm	farm	NOUN
cana-3454	39	26	[	[	X
cana-3454	39	27	16	16	NUM
cana-3454	39	28	]	]	PUNCT
cana-3454	39	29	.	.	PUNCT
cana-3454	40	1	among	among	ADP
cana-3454	40	2	them	they	PRON
cana-3454	40	3	,	,	PUNCT
cana-3454	40	4	their	their	PRON
cana-3454	40	5	results	result	NOUN
cana-3454	40	6	show	show	VERB
cana-3454	40	7	that	that	SCONJ
cana-3454	40	8	the	the	DET
cana-3454	40	9	ways	way	NOUN
cana-3454	40	10	to	to	PART
cana-3454	40	11	use	use	VERB
cana-3454	40	12	the	the	DET
cana-3454	40	13	resources	resource	NOUN
cana-3454	40	14	and	and	CCONJ
cana-3454	40	15	improve	improve	VERB
cana-3454	40	16	the	the	DET
cana-3454	40	17	key	key	ADJ
cana-3454	40	18	decision	decision	NOUN
cana-3454	40	19	-	-	PUNCT
cana-3454	40	20	making	making	NOUN
cana-3454	40	21	by	by	ADP
cana-3454	40	22	the	the	DET
cana-3454	40	23	application	application	NOUN
cana-3454	40	24	of	of	ADP
cana-3454	40	25	the	the	DET
cana-3454	40	26	ml	ml	ADV
cana-3454	40	27	-	-	PUNCT
cana-3454	40	28	based	base	VERB
cana-3454	40	29	methods	method	NOUN
cana-3454	40	30	have	have	VERB
cana-3454	40	31	a	a	DET
cana-3454	40	32	high	high	ADJ
cana-3454	40	33	potential	potential	NOUN
cana-3454	40	34	and	and	CCONJ
cana-3454	40	35	advance	advance	VERB
cana-3454	40	36	the	the	DET
cana-3454	40	37	progress	progress	NOUN
cana-3454	40	38	of	of	ADP
cana-3454	40	39	data	data	NOUN
cana-3454	40	40	-	-	PUNCT
cana-3454	40	41	driven	drive	VERB
cana-3454	40	42	sustainable	sustainable	ADJ
cana-3454	40	43	agriculture	agriculture	NOUN
cana-3454	40	44	.	.	PUNCT
cana-3454	41	1	large	large	ADJ
cana-3454	41	2	language	language	NOUN
cana-3454	41	3	models	model	NOUN
cana-3454	41	4	and	and	CCONJ
cana-3454	41	5	cross	cross	ADJ
cana-3454	41	6	-	-	ADJ
cana-3454	41	7	disciplinary	disciplinary	ADJ
cana-3454	41	8	integration	integration	NOUN
cana-3454	41	9	large	large	ADJ
cana-3454	41	10	language	language	NOUN
cana-3454	41	11	models	model	NOUN
cana-3454	41	12	(	(	PUNCT
cana-3454	41	13	llms	llm	NOUN
cana-3454	41	14	)	)	PUNCT
cana-3454	41	15	have	have	AUX
cana-3454	41	16	received	receive	VERB
cana-3454	41	17	much	much	ADJ
cana-3454	41	18	attention	attention	NOUN
cana-3454	41	19	in	in	ADP
cana-3454	41	20	natural	natural	ADJ
cana-3454	41	21	language	language	NOUN
cana-3454	41	22	processing	processing	NOUN
cana-3454	41	23	as	as	ADV
cana-3454	41	24	well	well	ADV
cana-3454	41	25	as	as	ADP
cana-3454	41	26	in	in	ADP
cana-3454	41	27	the	the	DET
cana-3454	41	28	diversified	diversify	VERB
cana-3454	41	29	domains	domain	NOUN
cana-3454	41	30	of	of	ADP
cana-3454	41	31	interdisciplinarity	interdisciplinarity	NOUN
cana-3454	41	32	.	.	PUNCT
cana-3454	42	1	han	han	PROPN
cana-3454	42	2	et	et	PROPN
cana-3454	42	3	al	al	PROPN
cana-3454	42	4	.	.	PROPN
cana-3454	42	5	viewed	view	VERB
cana-3454	42	6	the	the	DET
cana-3454	42	7	basic	basic	ADJ
cana-3454	42	8	structures	structure	NOUN
cana-3454	42	9	and	and	CCONJ
cana-3454	42	10	critical	critical	ADJ
cana-3454	42	11	technological	technological	ADJ
cana-3454	42	12	advancements	advancement	NOUN
cana-3454	42	13	of	of	ADP
cana-3454	42	14	llms	llm	NOUN
cana-3454	42	15	,	,	PUNCT
cana-3454	42	16	as	as	SCONJ
cana-3454	42	17	discussed	discuss	VERB
cana-3454	42	18	in	in	ADP
cana-3454	42	19	han	han	PROPN
cana-3454	42	20	,	,	PUNCT
cana-3454	42	21	zhang	zhang	PROPN
cana-3454	42	22	,	,	PUNCT
cana-3454	42	23	and	and	CCONJ
cana-3454	42	24	wang	wang	PROPN
cana-3454	42	25	(	(	PUNCT
cana-3454	42	26	2024	2024	NUM
cana-3454	42	27	)	)	PUNCT
cana-3454	43	1	[	[	X
cana-3454	43	2	17	17	NUM
cana-3454	43	3	]	]	PUNCT
cana-3454	43	4	.	.	PUNCT
cana-3454	44	1	having	having	AUX
cana-3454	44	2	linked	link	VERB
cana-3454	44	3	the	the	DET
cana-3454	44	4	llms	llm	NOUN
cana-3454	44	5	with	with	ADP
cana-3454	44	6	other	other	ADJ
cana-3454	44	7	technologies	technology	NOUN
cana-3454	44	8	like	like	ADP
cana-3454	44	9	robotics	robotic	NOUN
cana-3454	44	10	and	and	CCONJ
cana-3454	44	11	the	the	DET
cana-3454	44	12	visual	visual	ADJ
cana-3454	44	13	inspection	inspection	NOUN
cana-3454	44	14	system	system	NOUN
cana-3454	44	15	,	,	PUNCT
cana-3454	44	16	there	there	PRON
cana-3454	44	17	exists	exist	VERB
cana-3454	44	18	communications	communication	NOUN
cana-3454	44	19	on	on	ADP
cana-3454	44	20	applied	apply	VERB
cana-3454	44	21	nonlinear	nonlinear	ADJ
cana-3454	44	22	analysis	analysis	NOUN
cana-3454	44	23	issn	issn	NOUN
cana-3454	44	24	:	:	PUNCT
cana-3454	44	25	1074	1074	NUM
cana-3454	44	26	-	-	PUNCT
cana-3454	44	27	133x	133x	NUM
cana-3454	44	28	vol	vol	NOUN
cana-3454	44	29	32	32	NUM
cana-3454	44	30	no	no	NOUN
cana-3454	44	31	.	.	PUNCT
cana-3454	45	1	7s	7	NOUN
cana-3454	45	2	(	(	PUNCT
cana-3454	45	3	2025	2025	NUM
cana-3454	45	4	)	)	PUNCT
cana-3454	45	5	434	434	NUM
cana-3454	46	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	46	2	enormous	enormous	ADJ
cana-3454	46	3	potential	potential	NOUN
cana-3454	46	4	for	for	ADP
cana-3454	46	5	enhancing	enhance	VERB
cana-3454	46	6	industry	industry	NOUN
cana-3454	46	7	operations	operation	NOUN
cana-3454	46	8	as	as	SCONJ
cana-3454	46	9	these	these	DET
cana-3454	46	10	models	model	NOUN
cana-3454	46	11	boost	boost	VERB
cana-3454	46	12	computational	computational	ADJ
cana-3454	46	13	dexterity	dexterity	NOUN
cana-3454	46	14	and	and	CCONJ
cana-3454	46	15	context	context	NOUN
cana-3454	46	16	awareness	awareness	NOUN
cana-3454	46	17	.	.	PUNCT
cana-3454	47	1	the	the	DET
cana-3454	47	2	application	application	NOUN
cana-3454	47	3	of	of	ADP
cana-3454	47	4	healthcare	healthcare	NOUN
cana-3454	47	5	and	and	CCONJ
cana-3454	47	6	neural	neural	ADJ
cana-3454	47	7	network	network	NOUN
cana-3454	47	8	applications	application	NOUN
cana-3454	47	9	the	the	DET
cana-3454	47	10	incorporation	incorporation	NOUN
cana-3454	47	11	of	of	ADP
cana-3454	47	12	modern	modern	ADJ
cana-3454	47	13	ai	ai	NOUN
cana-3454	47	14	in	in	ADP
cana-3454	47	15	healthcare	healthcare	NOUN
cana-3454	47	16	impacts	impact	VERB
cana-3454	47	17	diagnostic	diagnostic	ADJ
cana-3454	47	18	approaches	approach	NOUN
cana-3454	47	19	and	and	CCONJ
cana-3454	47	20	treatment	treatment	NOUN
cana-3454	47	21	mode	mode	NOUN
cana-3454	47	22	of	of	ADP
cana-3454	47	23	patients	patient	NOUN
cana-3454	47	24	.	.	PUNCT
cana-3454	48	1	nancy	nancy	PROPN
cana-3454	48	2	hermosilla	hermosilla	PROPN
cana-3454	48	3	et	et	PROPN
cana-3454	48	4	al	al	PROPN
cana-3454	48	5	.	.	PROPN
cana-3454	48	6	,	,	PUNCT
cana-3454	48	7	in	in	ADP
cana-3454	48	8	their	their	PRON
cana-3454	48	9	meta	meta	ADJ
cana-3454	48	10	-	-	PUNCT
cana-3454	48	11	analysis	analysis	NOUN
cana-3454	48	12	of	of	ADP
cana-3454	48	13	neural	neural	ADJ
cana-3454	48	14	network	network	NOUN
cana-3454	48	15	algorithms	algorithm	NOUN
cana-3454	48	16	,	,	PUNCT
cana-3454	48	17	have	have	AUX
cana-3454	48	18	compared	compare	VERB
cana-3454	48	19	the	the	DET
cana-3454	48	20	skin	skin	NOUN
cana-3454	48	21	cancer	cancer	NOUN
cana-3454	48	22	detection	detection	NOUN
cana-3454	48	23	and	and	CCONJ
cana-3454	48	24	classification	classification	NOUN
cana-3454	48	25	algorithms	algorithm	NOUN
cana-3454	48	26	(	(	PUNCT
cana-3454	48	27	2024	2024	NUM
cana-3454	48	28	)	)	PUNCT
cana-3454	49	1	[	[	X
cana-3454	49	2	18	18	NUM
cana-3454	49	3	]	]	PUNCT
cana-3454	49	4	.	.	PUNCT
cana-3454	50	1	in	in	ADP
cana-3454	50	2	their	their	PRON
cana-3454	50	3	work	work	NOUN
cana-3454	50	4	,	,	PUNCT
cana-3454	50	5	they	they	PRON
cana-3454	50	6	draw	draw	VERB
cana-3454	50	7	focus	focus	NOUN
cana-3454	50	8	to	to	ADP
cana-3454	50	9	neural	neural	ADJ
cana-3454	50	10	networks	network	NOUN
cana-3454	50	11	and	and	CCONJ
cana-3454	50	12	the	the	DET
cana-3454	50	13	level	level	NOUN
cana-3454	50	14	of	of	ADP
cana-3454	50	15	sharpened	sharpen	VERB
cana-3454	50	16	and	and	CCONJ
cana-3454	50	17	defined	define	VERB
cana-3454	50	18	diagnosis	diagnosis	NOUN
cana-3454	50	19	that	that	PRON
cana-3454	50	20	can	can	AUX
cana-3454	50	21	be	be	AUX
cana-3454	50	22	accomplished	accomplish	VERB
cana-3454	50	23	using	use	VERB
cana-3454	50	24	the	the	DET
cana-3454	50	25	network	network	NOUN
cana-3454	50	26	tool	tool	NOUN
cana-3454	50	27	which	which	PRON
cana-3454	50	28	may	may	AUX
cana-3454	50	29	surpass	surpass	VERB
cana-3454	50	30	other	other	ADJ
cana-3454	50	31	traditional	traditional	ADJ
cana-3454	50	32	diagnostic	diagnostic	ADJ
cana-3454	50	33	instruments	instrument	NOUN
cana-3454	50	34	in	in	ADP
cana-3454	50	35	some	some	DET
cana-3454	50	36	cases	case	NOUN
cana-3454	50	37	.	.	PUNCT
cana-3454	51	1	along	along	ADP
cana-3454	51	2	the	the	DET
cana-3454	51	3	same	same	ADJ
cana-3454	51	4	line	line	NOUN
cana-3454	51	5	,	,	PUNCT
cana-3454	51	6	lindroth	lindroth	NOUN
cana-3454	51	7	and	and	CCONJ
cana-3454	51	8	colleagues	colleague	NOUN
cana-3454	51	9	(	(	PUNCT
cana-3454	51	10	2024	2024	NUM
cana-3454	51	11	)	)	PUNCT
cana-3454	51	12	outlined	outline	VERB
cana-3454	51	13	how	how	SCONJ
cana-3454	51	14	computer	computer	NOUN
cana-3454	51	15	vision	vision	NOUN
cana-3454	51	16	can	can	AUX
cana-3454	51	17	be	be	AUX
cana-3454	51	18	applied	apply	VERB
cana-3454	51	19	to	to	ADP
cana-3454	51	20	hospital	hospital	NOUN
cana-3454	51	21	environments	environment	NOUN
cana-3454	51	22	including	include	VERB
cana-3454	51	23	for	for	ADP
cana-3454	51	24	sorting	sort	VERB
cana-3454	51	25	radiology	radiology	NOUN
cana-3454	51	26	images	image	NOUN
cana-3454	51	27	and	and	CCONJ
cana-3454	51	28	determining	determine	VERB
cana-3454	51	29	resource	resource	NOUN
cana-3454	51	30	availability	availability	NOUN
cana-3454	51	31	[	[	X
cana-3454	51	32	24	24	NUM
cana-3454	51	33	]	]	PUNCT
cana-3454	51	34	.	.	PUNCT
cana-3454	52	1	optical	optical	ADJ
cana-3454	52	2	computing	computing	NOUN
cana-3454	52	3	and	and	CCONJ
cana-3454	52	4	vision	vision	PROPN
cana-3454	52	5	systems	systems	PROPN
cana-3454	52	6	hu	hu	PROPN
cana-3454	52	7	et	et	PROPN
cana-3454	52	8	al	al	PROPN
cana-3454	52	9	.	.	PROPN
cana-3454	53	1	(	(	PUNCT
cana-3454	53	2	2024	2024	NUM
cana-3454	53	3	)	)	PUNCT
cana-3454	53	4	discussed	discuss	VERB
cana-3454	53	5	about	about	ADP
cana-3454	53	6	the	the	DET
cana-3454	53	7	application	application	NOUN
cana-3454	53	8	of	of	ADP
cana-3454	53	9	diffraction	diffraction	NOUN
cana-3454	53	10	optical	optical	ADJ
cana-3454	53	11	computing	computing	NOUN
cana-3454	53	12	in	in	ADP
cana-3454	53	13	the	the	DET
cana-3454	53	14	free	free	ADJ
cana-3454	53	15	space	space	NOUN
cana-3454	53	16	with	with	ADP
cana-3454	53	17	focus	focus	NOUN
cana-3454	53	18	on	on	ADP
cana-3454	53	19	the	the	DET
cana-3454	53	20	feat	feat	NOUN
cana-3454	53	21	of	of	ADP
cana-3454	53	22	computation	computation	NOUN
cana-3454	53	23	in	in	ADP
cana-3454	53	24	optimization	optimization	NOUN
cana-3454	53	25	and	and	CCONJ
cana-3454	53	26	energy	energy	NOUN
cana-3454	53	27	control	control	NOUN
cana-3454	53	28	[	[	X
cana-3454	53	29	19	19	NUM
cana-3454	53	30	]	]	PUNCT
cana-3454	53	31	.	.	PUNCT
cana-3454	54	1	this	this	DET
cana-3454	54	2	study	study	NOUN
cana-3454	54	3	shows	show	VERB
cana-3454	54	4	that	that	SCONJ
cana-3454	54	5	optical	optical	ADJ
cana-3454	54	6	systems	system	NOUN
cana-3454	54	7	can	can	AUX
cana-3454	54	8	help	help	VERB
cana-3454	54	9	enhance	enhance	VERB
cana-3454	54	10	and	and	CCONJ
cana-3454	54	11	extend	extend	VERB
cana-3454	54	12	other	other	ADJ
cana-3454	54	13	kinds	kind	NOUN
cana-3454	54	14	of	of	ADP
cana-3454	54	15	computational	computational	ADJ
cana-3454	54	16	approaches	approach	NOUN
cana-3454	54	17	.	.	PUNCT
cana-3454	55	1	however	however	ADV
cana-3454	55	2	,	,	PUNCT
cana-3454	55	3	kumar	kumar	PROPN
cana-3454	55	4	et	et	PROPN
cana-3454	55	5	al	al	PROPN
cana-3454	55	6	.	.	PROPN
cana-3454	55	7	used	use	VERB
cana-3454	55	8	deep	deep	ADJ
cana-3454	55	9	learning	learning	NOUN
cana-3454	55	10	for	for	ADP
cana-3454	55	11	improving	improve	VERB
cana-3454	55	12	the	the	DET
cana-3454	55	13	night	night	NOUN
cana-3454	55	14	vision	vision	PROPN
cana-3454	55	15	technology	technology	PROPN
cana-3454	55	16	for	for	ADP
cana-3454	55	17	multi	multi	ADJ
cana-3454	55	18	-	-	ADJ
cana-3454	55	19	modal	modal	ADJ
cana-3454	55	20	image	image	NOUN
cana-3454	55	21	fusion	fusion	NOUN
cana-3454	55	22	techniques	technique	NOUN
cana-3454	55	23	wherein	wherein	SCONJ
cana-3454	55	24	the	the	DET
cana-3454	55	25	applicability	applicability	NOUN
cana-3454	55	26	of	of	ADP
cana-3454	55	27	deep	deep	ADJ
cana-3454	55	28	learning	learning	NOUN
cana-3454	55	29	for	for	ADP
cana-3454	55	30	enhancing	enhance	VERB
cana-3454	55	31	the	the	DET
cana-3454	55	32	night	night	NOUN
cana-3454	55	33	vision	vision	PROPN
cana-3454	55	34	technology	technology	NOUN
cana-3454	55	35	was	be	AUX
cana-3454	55	36	especially	especially	ADV
cana-3454	55	37	for	for	ADP
cana-3454	55	38	improving	improve	VERB
cana-3454	55	39	the	the	DET
cana-3454	55	40	clarity	clarity	NOUN
cana-3454	55	41	of	of	ADP
cana-3454	55	42	the	the	DET
cana-3454	55	43	image	image	NOUN
cana-3454	56	1	[	[	X
cana-3454	56	2	23	23	NUM
cana-3454	56	3	]	]	PUNCT
cana-3454	56	4	.	.	PUNCT
cana-3454	57	1	environmental	environmental	ADJ
cana-3454	57	2	monitoring	monitoring	NOUN
cana-3454	57	3	and	and	CCONJ
cana-3454	57	4	sustainable	sustainable	ADJ
cana-3454	57	5	smart	smart	ADJ
cana-3454	57	6	cities	city	NOUN
cana-3454	57	7	an	an	DET
cana-3454	57	8	example	example	NOUN
cana-3454	57	9	is	be	AUX
cana-3454	57	10	lonsinger	lonsinger	PROPN
cana-3454	57	11	et	et	PROPN
cana-3454	57	12	al	al	PROPN
cana-3454	57	13	.	.	PROPN
cana-3454	58	1	(	(	PUNCT
cana-3454	58	2	2024	2024	NUM
cana-3454	58	3	)	)	PUNCT
cana-3454	58	4	who	who	PRON
cana-3454	58	5	assessed	assess	VERB
cana-3454	58	6	the	the	DET
cana-3454	58	7	use	use	NOUN
cana-3454	58	8	of	of	ADP
cana-3454	58	9	ml	ml	NOUN
cana-3454	58	10	classification	classification	NOUN
cana-3454	58	11	image	image	NOUN
cana-3454	58	12	for	for	ADP
cana-3454	58	13	the	the	DET
cana-3454	58	14	detection	detection	NOUN
cana-3454	58	15	of	of	ADP
cana-3454	58	16	occupancy	occupancy	NOUN
cana-3454	58	17	in	in	ADP
cana-3454	58	18	automated	automate	VERB
cana-3454	58	19	conservation	conservation	NOUN
cana-3454	59	1	[	[	X
cana-3454	59	2	26	26	NUM
cana-3454	59	3	]	]	PUNCT
cana-3454	59	4	.	.	PUNCT
cana-3454	60	1	this	this	DET
cana-3454	60	2	approach	approach	NOUN
cana-3454	60	3	improves	improve	VERB
cana-3454	60	4	on	on	ADP
cana-3454	60	5	the	the	DET
cana-3454	60	6	current	current	ADJ
cana-3454	60	7	practices	practice	NOUN
cana-3454	60	8	of	of	ADP
cana-3454	60	9	measuring	measure	VERB
cana-3454	60	10	environmental	environmental	ADJ
cana-3454	60	11	data	datum	NOUN
cana-3454	60	12	to	to	PART
cana-3454	60	13	increase	increase	VERB
cana-3454	60	14	efficiency	efficiency	NOUN
cana-3454	60	15	in	in	ADP
cana-3454	60	16	ecosystem	ecosystem	NOUN
cana-3454	60	17	management	management	NOUN
cana-3454	60	18	.	.	PUNCT
cana-3454	61	1	additionally	additionally	ADV
cana-3454	61	2	,	,	PUNCT
cana-3454	61	3	lifelo	lifelo	PROPN
cana-3454	61	4	et	et	PROPN
cana-3454	61	5	al	al	PROPN
cana-3454	61	6	.	.	PROPN
cana-3454	61	7	,	,	PUNCT
cana-3454	61	8	(	(	PUNCT
cana-3454	61	9	2024	2024	NUM
cana-3454	61	10	)	)	PUNCT
cana-3454	61	11	examined	examine	VERB
cana-3454	61	12	the	the	DET
cana-3454	61	13	application	application	NOUN
cana-3454	61	14	of	of	ADP
cana-3454	61	15	ai	ai	NOUN
cana-3454	61	16	in	in	ADP
cana-3454	61	17	metaverse	metaverse	NOUN
cana-3454	61	18	technologies	technology	NOUN
cana-3454	61	19	for	for	ADP
cana-3454	61	20	sustainable	sustainable	ADJ
cana-3454	61	21	smart	smart	ADJ
cana-3454	61	22	cities	city	NOUN
cana-3454	61	23	.	.	PUNCT
cana-3454	62	1	i	i	PRON
cana-3454	62	2	used	use	VERB
cana-3454	62	3	their	their	PRON
cana-3454	62	4	works	work	NOUN
cana-3454	62	5	for	for	ADP
cana-3454	62	6	discovering	discover	VERB
cana-3454	62	7	their	their	PRON
cana-3454	62	8	findings	finding	NOUN
cana-3454	62	9	that	that	PRON
cana-3454	62	10	describes	describe	VERB
cana-3454	62	11	challenges	challenge	NOUN
cana-3454	62	12	and	and	CCONJ
cana-3454	62	13	directions	direction	NOUN
cana-3454	62	14	for	for	ADP
cana-3454	62	15	applying	apply	VERB
cana-3454	62	16	ai	ai	VERB
cana-3454	62	17	to	to	PART
cana-3454	62	18	optimise	optimise	VERB
cana-3454	62	19	the	the	DET
cana-3454	62	20	urban	urban	ADJ
cana-3454	62	21	planning	planning	NOUN
cana-3454	62	22	and	and	CCONJ
cana-3454	62	23	supply	supply	NOUN
cana-3454	62	24	resources	resource	NOUN
cana-3454	62	25	.	.	PUNCT
cana-3454	63	1	manufacturing	manufacturing	NOUN
cana-3454	63	2	and	and	CCONJ
cana-3454	63	3	automated	automate	VERB
cana-3454	63	4	systems	system	NOUN
cana-3454	63	5	hütten	hütten	PROPN
cana-3454	63	6	et	et	PROPN
cana-3454	63	7	al	al	PROPN
cana-3454	63	8	.	.	PROPN
cana-3454	64	1	(	(	PUNCT
cana-3454	64	2	2024	2024	NUM
cana-3454	64	3	)	)	PUNCT
cana-3454	64	4	have	have	AUX
cana-3454	64	5	conducted	conduct	VERB
cana-3454	64	6	an	an	DET
cana-3454	64	7	online	online	ADJ
cana-3454	64	8	survey	survey	NOUN
cana-3454	64	9	on	on	ADP
cana-3454	64	10	papers	paper	NOUN
cana-3454	64	11	published	publish	VERB
cana-3454	64	12	in	in	ADP
cana-3454	64	13	open	open	ADJ
cana-3454	64	14	access	access	NOUN
cana-3454	64	15	on	on	ADP
cana-3454	64	16	the	the	DET
cana-3454	64	17	application	application	NOUN
cana-3454	64	18	of	of	ADP
cana-3454	64	19	deep	deep	ADJ
cana-3454	64	20	learning	learning	NOUN
cana-3454	64	21	for	for	ADP
cana-3454	64	22	automated	automate	VERB
cana-3454	64	23	visual	visual	ADJ
cana-3454	64	24	inspection	inspection	NOUN
cana-3454	64	25	in	in	ADP
cana-3454	64	26	manufacturing	manufacturing	NOUN
cana-3454	64	27	and	and	CCONJ
cana-3454	64	28	maintenance	maintenance	NOUN
cana-3454	64	29	[	[	X
cana-3454	64	30	21	21	NUM
cana-3454	64	31	]	]	PUNCT
cana-3454	64	32	.	.	PUNCT
cana-3454	65	1	the	the	DET
cana-3454	65	2	survey	survey	NOUN
cana-3454	65	3	shows	show	VERB
cana-3454	65	4	how	how	SCONJ
cana-3454	65	5	ai	ai	NOUN
cana-3454	65	6	optimizes	optimize	VERB
cana-3454	65	7	defect	defect	NOUN
cana-3454	65	8	identification	identification	NOUN
cana-3454	65	9	and	and	CCONJ
cana-3454	65	10	quality	quality	NOUN
cana-3454	65	11	control	control	NOUN
cana-3454	65	12	as	as	ADV
cana-3454	65	13	well	well	ADV
cana-3454	65	14	as	as	ADP
cana-3454	65	15	general	general	ADJ
cana-3454	65	16	workflow	workflow	NOUN
cana-3454	65	17	.	.	PUNCT
cana-3454	66	1	the	the	DET
cana-3454	66	2	transition	transition	NOUN
cana-3454	66	3	from	from	ADP
cana-3454	66	4	such	such	ADJ
cana-3454	66	5	solutions	solution	NOUN
cana-3454	66	6	as	as	ADP
cana-3454	66	7	driving	drive	VERB
cana-3454	66	8	to	to	ADP
cana-3454	66	9	embodying	embody	VERB
cana-3454	66	10	actual	actual	ADJ
cana-3454	66	11	production	production	NOUN
cana-3454	66	12	processes	process	NOUN
cana-3454	66	13	exhibits	exhibit	VERB
cana-3454	66	14	how	how	SCONJ
cana-3454	66	15	ai	ai	ADJ
cana-3454	66	16	-	-	PUNCT
cana-3454	66	17	driven	drive	VERB
cana-3454	66	18	options	option	NOUN
cana-3454	66	19	diminish	diminish	VERB
cana-3454	66	20	human	human	ADJ
cana-3454	66	21	interference	interference	NOUN
cana-3454	66	22	and	and	CCONJ
cana-3454	66	23	refine	refine	VERB
cana-3454	66	24	production	production	NOUN
cana-3454	66	25	chains	chain	NOUN
cana-3454	66	26	.	.	PUNCT
cana-3454	67	1	challenges	challenge	NOUN
cana-3454	67	2	and	and	CCONJ
cana-3454	67	3	future	future	ADJ
cana-3454	67	4	directions	direction	NOUN
cana-3454	67	5	in	in	ADP
cana-3454	67	6	medical	medical	ADJ
cana-3454	67	7	image	image	NOUN
cana-3454	67	8	analysis	analysis	NOUN
cana-3454	67	9	subsequently	subsequently	ADV
cana-3454	67	10	,	,	PUNCT
cana-3454	67	11	kaushlesh	kaushlesh	NOUN
cana-3454	67	12	et	et	PROPN
cana-3454	67	13	al	al	PROPN
cana-3454	67	14	.	.	PROPN
cana-3454	68	1	(	(	PUNCT
cana-3454	68	2	2024	2024	NUM
cana-3454	68	3	)	)	PUNCT
cana-3454	68	4	conducted	conduct	VERB
cana-3454	68	5	a	a	DET
cana-3454	68	6	critical	critical	ADJ
cana-3454	68	7	review	review	NOUN
cana-3454	68	8	of	of	ADP
cana-3454	68	9	many	many	ADJ
cana-3454	68	10	semi	semi	ADJ
cana-3454	68	11	-	-	ADJ
cana-3454	68	12	supervised	supervised	ADJ
cana-3454	68	13	deep	deep	ADJ
cana-3454	68	14	learning	learning	NOUN
cana-3454	68	15	approaches	approach	NOUN
cana-3454	68	16	for	for	ADP
cana-3454	68	17	medical	medical	ADJ
cana-3454	68	18	image	image	NOUN
cana-3454	68	19	classification	classification	NOUN
cana-3454	68	20	,	,	PUNCT
cana-3454	68	21	noting	note	VERB
cana-3454	68	22	their	their	PRON
cana-3454	68	23	capacity	capacity	NOUN
cana-3454	68	24	to	to	PART
cana-3454	68	25	improve	improve	VERB
cana-3454	68	26	diagnostic	diagnostic	ADJ
cana-3454	68	27	sensitivity	sensitivity	NOUN
cana-3454	68	28	[	[	X
cana-3454	68	29	22	22	NUM
cana-3454	68	30	]	]	PUNCT
cana-3454	68	31	.	.	PUNCT
cana-3454	69	1	their	their	PRON
cana-3454	69	2	study	study	NOUN
cana-3454	69	3	details	detail	VERB
cana-3454	69	4	the	the	DET
cana-3454	69	5	problem	problem	NOUN
cana-3454	69	6	of	of	ADP
cana-3454	69	7	data	datum	NOUN
cana-3454	69	8	,	,	PUNCT
cana-3454	69	9	the	the	DET
cana-3454	69	10	lack	lack	NOUN
cana-3454	69	11	of	of	ADP
cana-3454	69	12	data	datum	NOUN
cana-3454	69	13	specifically	specifically	ADV
cana-3454	69	14	,	,	PUNCT
cana-3454	69	15	and	and	CCONJ
cana-3454	69	16	the	the	DET
cana-3454	69	17	importance	importance	NOUN
cana-3454	69	18	of	of	ADP
cana-3454	69	19	developing	develop	VERB
cana-3454	69	20	sound	sound	NOUN
cana-3454	69	21	approaches	approach	NOUN
cana-3454	69	22	reliable	reliable	ADJ
cana-3454	69	23	methods	method	NOUN
cana-3454	69	24	to	to	PART
cana-3454	69	25	mitigate	mitigate	VERB
cana-3454	69	26	the	the	DET
cana-3454	69	27	sources	source	NOUN
cana-3454	69	28	of	of	ADP
cana-3454	69	29	bias	bias	NOUN
cana-3454	69	30	in	in	ADP
cana-3454	69	31	medical	medical	ADJ
cana-3454	69	32	records	record	NOUN
cana-3454	69	33	.	.	PUNCT
cana-3454	70	1	this	this	DET
cana-3454	70	2	tallies	tally	NOUN
cana-3454	70	3	with	with	ADP
cana-3454	70	4	findings	finding	NOUN
cana-3454	70	5	by	by	ADP
cana-3454	70	6	hermosilla	hermosilla	NOUN
cana-3454	70	7	et	et	PROPN
cana-3454	70	8	al	al	PROPN
cana-3454	70	9	.	.	PROPN
cana-3454	70	10	,	,	PUNCT
cana-3454	70	11	which	which	PRON
cana-3454	70	12	draws	draw	VERB
cana-3454	70	13	attention	attention	NOUN
cana-3454	70	14	to	to	ADP
cana-3454	70	15	how	how	SCONJ
cana-3454	70	16	important	important	ADJ
cana-3454	70	17	ai	ai	VERB
cana-3454	70	18	is	be	AUX
cana-3454	70	19	in	in	ADP
cana-3454	70	20	the	the	DET
cana-3454	70	21	advancement	advancement	NOUN
cana-3454	70	22	of	of	ADP
cana-3454	70	23	care	care	NOUN
cana-3454	70	24	[	[	X
cana-3454	70	25	18	18	NUM
cana-3454	70	26	]	]	PUNCT
cana-3454	70	27	.	.	PUNCT
cana-3454	71	1	earthquake	earthquake	NOUN
cana-3454	71	2	engineering	engineering	NOUN
cana-3454	71	3	and	and	CCONJ
cana-3454	71	4	structural	structural	ADJ
cana-3454	71	5	applications	application	NOUN
cana-3454	71	6	communications	communication	NOUN
cana-3454	71	7	on	on	ADP
cana-3454	71	8	applied	apply	VERB
cana-3454	71	9	nonlinear	nonlinear	ADJ
cana-3454	71	10	analysis	analysis	NOUN
cana-3454	71	11	issn	issn	NOUN
cana-3454	71	12	:	:	PUNCT
cana-3454	71	13	1074	1074	NUM
cana-3454	71	14	-	-	PUNCT
cana-3454	71	15	133x	133x	NUM
cana-3454	71	16	vol	vol	NOUN
cana-3454	71	17	32	32	NUM
cana-3454	71	18	no	no	NOUN
cana-3454	71	19	.	.	PUNCT
cana-3454	72	1	7s	7	NOUN
cana-3454	72	2	(	(	PUNCT
cana-3454	72	3	2025	2025	NUM
cana-3454	72	4	)	)	PUNCT
cana-3454	72	5	435	435	NUM
cana-3454	72	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	72	7	hu	hu	PROPN
cana-3454	72	8	and	and	CCONJ
cana-3454	72	9	co	co	NOUN
cana-3454	72	10	-	-	NOUN
cana-3454	72	11	authors	author	NOUN
cana-3454	72	12	performed	perform	VERB
cana-3454	72	13	a	a	DET
cana-3454	72	14	scientometric	scientometric	ADJ
cana-3454	72	15	review	review	NOUN
cana-3454	72	16	of	of	ADP
cana-3454	72	17	ml	ml	NOUN
cana-3454	72	18	applications	application	NOUN
cana-3454	72	19	in	in	ADP
cana-3454	72	20	earthquake	earthquake	NOUN
cana-3454	72	21	engineering	engineering	NOUN
cana-3454	72	22	in	in	ADP
cana-3454	72	23	2024	2024	NUM
cana-3454	72	24	[	[	X
cana-3454	72	25	20	20	NUM
cana-3454	72	26	]	]	PUNCT
cana-3454	72	27	.	.	PUNCT
cana-3454	73	1	their	their	PRON
cana-3454	73	2	work	work	NOUN
cana-3454	73	3	supports	support	VERB
cana-3454	73	4	possibilities	possibility	NOUN
cana-3454	73	5	of	of	ADP
cana-3454	73	6	the	the	DET
cana-3454	73	7	use	use	NOUN
cana-3454	73	8	of	of	ADP
cana-3454	73	9	ai	ai	NOUN
cana-3454	73	10	in	in	ADP
cana-3454	73	11	the	the	DET
cana-3454	73	12	analysis	analysis	NOUN
cana-3454	73	13	of	of	ADP
cana-3454	73	14	seismological	seismological	ADJ
cana-3454	73	15	conditions	condition	NOUN
cana-3454	73	16	,	,	PUNCT
cana-3454	73	17	estimation	estimation	NOUN
cana-3454	73	18	of	of	ADP
cana-3454	73	19	structural	structural	ADJ
cana-3454	73	20	damage	damage	NOUN
cana-3454	73	21	,	,	PUNCT
cana-3454	73	22	and	and	CCONJ
cana-3454	73	23	disaster	disaster	NOUN
cana-3454	73	24	preparedness	preparedness	NOUN
cana-3454	73	25	.	.	PUNCT
cana-3454	74	1	ai	ai	NOUN
cana-3454	74	2	models	model	NOUN
cana-3454	74	3	can	can	AUX
cana-3454	74	4	greatly	greatly	ADV
cana-3454	74	5	enhance	enhance	VERB
cana-3454	74	6	the	the	DET
cana-3454	74	7	survivability	survivability	NOUN
cana-3454	74	8	of	of	ADP
cana-3454	74	9	built	build	VERB
cana-3454	74	10	environments	environment	NOUN
cana-3454	74	11	especially	especially	ADV
cana-3454	74	12	by	by	ADP
cana-3454	74	13	utilizing	utilize	VERB
cana-3454	74	14	large	large	ADJ
cana-3454	74	15	databases	database	NOUN
cana-3454	74	16	.	.	PUNCT
cana-3454	75	1	iii	iii	X
cana-3454	75	2	.	.	PUNCT
cana-3454	75	3	methods	method	NOUN
cana-3454	75	4	and	and	CCONJ
cana-3454	75	5	materials	material	NOUN
cana-3454	75	6	data	datum	NOUN
cana-3454	75	7	the	the	DET
cana-3454	75	8	work	work	NOUN
cana-3454	75	9	uses	use	VERB
cana-3454	75	10	synthetic	synthetic	ADJ
cana-3454	75	11	and	and	CCONJ
cana-3454	75	12	public	public	ADJ
cana-3454	75	13	datasets	dataset	NOUN
cana-3454	75	14	which	which	PRON
cana-3454	75	15	are	be	AUX
cana-3454	75	16	standard	standard	ADJ
cana-3454	75	17	in	in	ADP
cana-3454	75	18	the	the	DET
cana-3454	75	19	field	field	NOUN
cana-3454	75	20	of	of	ADP
cana-3454	75	21	computer	computer	NOUN
cana-3454	75	22	vision	vision	NOUN
cana-3454	75	23	tasks	task	NOUN
cana-3454	75	24	.	.	PUNCT
cana-3454	76	1	some	some	PRON
cana-3454	76	2	of	of	ADP
cana-3454	76	3	them	they	PRON
cana-3454	76	4	include	include	VERB
cana-3454	76	5	the	the	DET
cana-3454	76	6	image	image	NOUN
cana-3454	76	7	net	net	NOUN
cana-3454	76	8	that	that	PRON
cana-3454	76	9	contain	contain	VERB
cana-3454	76	10	millions	million	NOUN
cana-3454	76	11	of	of	ADP
cana-3454	76	12	labeled	label	VERB
cana-3454	76	13	images	image	NOUN
cana-3454	76	14	for	for	ADP
cana-3454	76	15	training	training	NOUN
cana-3454	76	16	and	and	CCONJ
cana-3454	76	17	validation	validation	NOUN
cana-3454	76	18	,	,	PUNCT
cana-3454	76	19	the	the	DET
cana-3454	76	20	coco	coco	PROPN
cana-3454	76	21	that	that	PRON
cana-3454	76	22	is	be	AUX
cana-3454	76	23	used	use	VERB
cana-3454	76	24	for	for	ADP
cana-3454	76	25	object	object	NOUN
cana-3454	76	26	detection	detection	NOUN
cana-3454	76	27	,	,	PUNCT
cana-3454	76	28	segmentation	segmentation	NOUN
cana-3454	76	29	and	and	CCONJ
cana-3454	76	30	captioning	caption	VERB
cana-3454	76	31	the	the	DET
cana-3454	76	32	mnist	mnist	NOUN
cana-3454	76	33	that	that	PRON
cana-3454	76	34	is	be	AUX
cana-3454	76	35	a	a	DET
cana-3454	76	36	database	database	NOUN
cana-3454	76	37	of	of	ADP
cana-3454	76	38	handwritten	handwritten	ADJ
cana-3454	76	39	digit	digit	NOUN
cana-3454	76	40	that	that	PRON
cana-3454	76	41	is	be	AUX
cana-3454	76	42	suitable	suitable	ADJ
cana-3454	76	43	for	for	ADP
cana-3454	76	44	classification	classification	NOUN
cana-3454	76	45	.	.	PUNCT
cana-3454	77	1	these	these	DET
cana-3454	77	2	datasets	dataset	NOUN
cana-3454	77	3	were	be	AUX
cana-3454	77	4	selected	select	VERB
cana-3454	77	5	so	so	SCONJ
cana-3454	77	6	that	that	SCONJ
cana-3454	77	7	the	the	DET
cana-3454	77	8	visual	visual	ADJ
cana-3454	77	9	data	data	NOUN
cana-3454	77	10	is	be	AUX
cana-3454	77	11	not	not	PART
cana-3454	77	12	overly	overly	ADV
cana-3454	77	13	similar	similar	ADJ
cana-3454	77	14	it	it	PRON
cana-3454	77	15	is	be	AUX
cana-3454	77	16	generalized	generalize	VERB
cana-3454	77	17	and	and	CCONJ
cana-3454	77	18	can	can	AUX
cana-3454	77	19	effectively	effectively	ADV
cana-3454	77	20	be	be	AUX
cana-3454	77	21	used	use	VERB
cana-3454	77	22	to	to	PART
cana-3454	77	23	test	test	VERB
cana-3454	77	24	learning	learn	VERB
cana-3454	77	25	algorithms	algorithm	NOUN
cana-3454	77	26	[	[	X
cana-3454	77	27	4	4	NUM
cana-3454	77	28	]	]	PUNCT
cana-3454	77	29	.	.	PUNCT
cana-3454	78	1	augmentation	augmentation	NOUN
cana-3454	78	2	methods	method	NOUN
cana-3454	78	3	like	like	ADP
cana-3454	78	4	flipping	flip	VERB
cana-3454	78	5	,	,	PUNCT
cana-3454	78	6	cropping	crop	VERB
cana-3454	78	7	and	and	CCONJ
cana-3454	78	8	color	color	NOUN
cana-3454	78	9	jitter	jitter	NOUN
cana-3454	78	10	were	be	AUX
cana-3454	78	11	applied	apply	VERB
cana-3454	78	12	in	in	ADP
cana-3454	78	13	order	order	NOUN
cana-3454	78	14	to	to	PART
cana-3454	78	15	improve	improve	VERB
cana-3454	78	16	generalisation	generalisation	NOUN
cana-3454	78	17	of	of	ADP
cana-3454	78	18	the	the	DET
cana-3454	78	19	model	model	NOUN
cana-3454	78	20	.	.	PUNCT
cana-3454	79	1	algorithms	algorithms	VERB
cana-3454	79	2	the	the	DET
cana-3454	79	3	core	core	NOUN
cana-3454	79	4	of	of	ADP
cana-3454	79	5	this	this	DET
cana-3454	79	6	study	study	NOUN
cana-3454	79	7	involves	involve	VERB
cana-3454	79	8	analyzing	analyze	VERB
cana-3454	79	9	the	the	DET
cana-3454	79	10	performance	performance	NOUN
cana-3454	79	11	of	of	ADP
cana-3454	79	12	four	four	NUM
cana-3454	79	13	widely	widely	ADV
cana-3454	79	14	-	-	PUNCT
cana-3454	79	15	used	use	VERB
cana-3454	79	16	machine	machine	NOUN
cana-3454	79	17	learning	learn	VERB
cana-3454	79	18	algorithms	algorithm	NOUN
cana-3454	79	19	in	in	ADP
cana-3454	79	20	computer	computer	NOUN
cana-3454	79	21	vision	vision	NOUN
cana-3454	79	22	:	:	PUNCT
cana-3454	79	23	five	five	NUM
cana-3454	79	24	different	different	ADJ
cana-3454	79	25	algorithms	algorithm	NOUN
cana-3454	79	26	used	use	VERB
cana-3454	79	27	are	be	AUX
cana-3454	79	28	convolutional	convolutional	ADJ
cana-3454	79	29	neural	neural	ADJ
cana-3454	79	30	networks	network	NOUN
cana-3454	79	31	(	(	PUNCT
cana-3454	79	32	cnns	cnns	PROPN
cana-3454	79	33	)	)	PUNCT
cana-3454	79	34	,	,	PUNCT
cana-3454	79	35	support	support	VERB
cana-3454	79	36	vector	vector	NOUN
cana-3454	79	37	machines	machine	NOUN
cana-3454	79	38	(	(	PUNCT
cana-3454	79	39	svms	svms	NOUN
cana-3454	79	40	)	)	PUNCT
cana-3454	79	41	,	,	PUNCT
cana-3454	79	42	k	k	X
cana-3454	79	43	-	-	PUNCT
cana-3454	79	44	nearest	near	ADJ
cana-3454	79	45	neighbors	neighbor	NOUN
cana-3454	79	46	(	(	PUNCT
cana-3454	79	47	knn	knn	PROPN
cana-3454	79	48	)	)	PUNCT
cana-3454	79	49	and	and	CCONJ
cana-3454	79	50	yolo	yolo	PROPN
cana-3454	79	51	(	(	PUNCT
cana-3454	79	52	you	you	PRON
cana-3454	79	53	only	only	ADV
cana-3454	79	54	look	look	VERB
cana-3454	79	55	once	once	ADV
cana-3454	79	56	)	)	PUNCT
cana-3454	79	57	.	.	PUNCT
cana-3454	80	1	while	while	SCONJ
cana-3454	80	2	all	all	PRON
cana-3454	80	3	of	of	ADP
cana-3454	80	4	these	these	DET
cana-3454	80	5	algorithms	algorithm	NOUN
cana-3454	80	6	approach	approach	VERB
cana-3454	80	7	the	the	DET
cana-3454	80	8	task	task	NOUN
cana-3454	80	9	of	of	ADP
cana-3454	80	10	visual	visual	ADJ
cana-3454	80	11	data	datum	NOUN
cana-3454	80	12	analysis	analysis	NOUN
cana-3454	80	13	in	in	ADP
cana-3454	80	14	a	a	DET
cana-3454	80	15	somewhat	somewhat	ADV
cana-3454	80	16	different	different	ADJ
cana-3454	80	17	way	way	NOUN
cana-3454	80	18	,	,	PUNCT
cana-3454	80	19	all	all	PRON
cana-3454	80	20	of	of	ADP
cana-3454	80	21	them	they	PRON
cana-3454	80	22	are	be	AUX
cana-3454	80	23	suitable	suitable	ADJ
cana-3454	80	24	for	for	ADP
cana-3454	80	25	different	different	ADJ
cana-3454	80	26	applications	application	NOUN
cana-3454	80	27	within	within	ADP
cana-3454	80	28	the	the	DET
cana-3454	80	29	field	field	NOUN
cana-3454	80	30	of	of	ADP
cana-3454	80	31	computer	computer	NOUN
cana-3454	80	32	vision	vision	NOUN
cana-3454	81	1	[	[	X
cana-3454	81	2	5	5	NUM
cana-3454	81	3	]	]	PUNCT
cana-3454	81	4	.	.	PUNCT
cana-3454	82	1	1	1	X
cana-3454	82	2	.	.	X
cana-3454	82	3	convolutional	convolutional	ADJ
cana-3454	82	4	neural	neural	ADJ
cana-3454	82	5	networks	network	NOUN
cana-3454	82	6	(	(	PUNCT
cana-3454	82	7	cnns	cnns	PROPN
cana-3454	82	8	)	)	PUNCT
cana-3454	82	9	cnns	cnn	NOUN
cana-3454	82	10	are	be	AUX
cana-3454	82	11	a	a	DET
cana-3454	82	12	unique	unique	ADJ
cana-3454	82	13	type	type	NOUN
cana-3454	82	14	of	of	ADP
cana-3454	82	15	deep	deep	ADJ
cana-3454	82	16	learning	learning	NOUN
cana-3454	82	17	algorithm	algorithm	NOUN
cana-3454	82	18	optimized	optimize	VERB
cana-3454	82	19	for	for	ADP
cana-3454	82	20	data	datum	NOUN
cana-3454	82	21	with	with	ADP
cana-3454	82	22	a	a	DET
cana-3454	82	23	grid	grid	NOUN
cana-3454	82	24	like	like	ADP
cana-3454	82	25	structure	structure	NOUN
cana-3454	82	26	,	,	PUNCT
cana-3454	82	27	for	for	ADP
cana-3454	82	28	example	example	NOUN
cana-3454	82	29	images	image	NOUN
cana-3454	82	30	.	.	PUNCT
cana-3454	83	1	they	they	PRON
cana-3454	83	2	consists	consist	VERB
cana-3454	83	3	convoluted	convoluted	ADJ
cana-3454	83	4	layers	layer	NOUN
cana-3454	83	5	,	,	PUNCT
cana-3454	83	6	pooling	pool	VERB
cana-3454	83	7	layers	layer	NOUN
cana-3454	83	8	and	and	CCONJ
cana-3454	83	9	fully	fully	ADV
cana-3454	83	10	connected	connected	ADJ
cana-3454	83	11	layers	layer	NOUN
cana-3454	83	12	where	where	SCONJ
cana-3454	83	13	they	they	PRON
cana-3454	83	14	learn	learn	VERB
cana-3454	83	15	spatial	spatial	ADJ
cana-3454	83	16	hierarchies	hierarchy	NOUN
cana-3454	83	17	of	of	ADP
cana-3454	83	18	features	feature	NOUN
cana-3454	83	19	.	.	PUNCT
cana-3454	84	1	convolutional	convolutional	ADJ
cana-3454	84	2	layers	layer	NOUN
cana-3454	84	3	apply	apply	VERB
cana-3454	84	4	kernels	kernel	NOUN
cana-3454	84	5	to	to	PART
cana-3454	84	6	detect	detect	VERB
cana-3454	84	7	features	feature	NOUN
cana-3454	84	8	such	such	ADJ
cana-3454	84	9	as	as	ADP
cana-3454	84	10	edges	edge	NOUN
cana-3454	84	11	and	and	CCONJ
cana-3454	84	12	texture	texture	NOUN
cana-3454	84	13	;	;	PUNCT
cana-3454	84	14	while	while	SCONJ
cana-3454	84	15	pooling	pool	VERB
cana-3454	84	16	layers	layer	NOUN
cana-3454	84	17	reduces	reduce	VERB
cana-3454	84	18	the	the	DET
cana-3454	84	19	size	size	NOUN
cana-3454	84	20	of	of	ADP
cana-3454	84	21	feature	feature	NOUN
cana-3454	84	22	maps	map	NOUN
cana-3454	84	23	.	.	PUNCT
cana-3454	85	1	fully	fully	ADV
cana-3454	85	2	connected	connected	ADJ
cana-3454	85	3	layers	layer	NOUN
cana-3454	85	4	utilize	utilize	VERB
cana-3454	85	5	these	these	DET
cana-3454	85	6	features	feature	NOUN
cana-3454	85	7	to	to	PART
cana-3454	85	8	make	make	VERB
cana-3454	85	9	predictions	prediction	NOUN
cana-3454	85	10	[	[	X
cana-3454	85	11	6	6	NUM
cana-3454	85	12	]	]	PUNCT
cana-3454	85	13	.	.	PUNCT
cana-3454	86	1	cnns	cnns	PROPN
cana-3454	86	2	are	be	AUX
cana-3454	86	3	suitable	suitable	ADJ
cana-3454	86	4	for	for	ADP
cana-3454	86	5	solving	solve	VERB
cana-3454	86	6	some	some	DET
cana-3454	86	7	problems	problem	NOUN
cana-3454	86	8	like	like	ADP
cana-3454	86	9	image	image	NOUN
cana-3454	86	10	classification	classification	NOUN
cana-3454	86	11	,	,	PUNCT
cana-3454	86	12	object	object	NOUN
cana-3454	86	13	detection	detection	NOUN
cana-3454	86	14	,	,	PUNCT
cana-3454	86	15	and	and	CCONJ
cana-3454	86	16	semiconductor	semiconductor	NOUN
cana-3454	86	17	segmentation	segmentation	NOUN
cana-3454	86	18	.	.	PUNCT
cana-3454	87	1	in	in	ADP
cana-3454	87	2	that	that	DET
cana-3454	87	3	regard	regard	NOUN
cana-3454	87	4	,	,	PUNCT
cana-3454	87	5	assembling	assemble	VERB
cana-3454	87	6	protectionist	protectionist	NOUN
cana-3454	87	7	consume	consume	NOUN
cana-3454	87	8	encyclopedic	encyclopedic	ADJ
cana-3454	87	9	function	function	NOUN
cana-3454	87	10	since	since	SCONJ
cana-3454	87	11	they	they	PRON
cana-3454	87	12	can	can	AUX
cana-3454	87	13	well	well	ADV
cana-3454	87	14	learn	learn	VERB
cana-3454	87	15	features	feature	NOUN
cana-3454	87	16	from	from	ADP
cana-3454	87	17	raw	raw	ADJ
cana-3454	87	18	pixel	pixel	PROPN
cana-3454	87	19	data	datum	NOUN
cana-3454	87	20	thereby	thereby	ADV
cana-3454	87	21	doing	do	VERB
cana-3454	87	22	away	away	ADV
cana-3454	87	23	with	with	ADP
cana-3454	87	24	the	the	DET
cana-3454	87	25	necessity	necessity	NOUN
cana-3454	87	26	of	of	ADP
cana-3454	87	27	having	have	VERB
cana-3454	87	28	to	to	PART
cana-3454	87	29	carry	carry	VERB
cana-3454	87	30	out	out	ADP
cana-3454	87	31	feature	feature	NOUN
cana-3454	87	32	extraction	extraction	NOUN
cana-3454	87	33	manually	manually	ADV
cana-3454	87	34	and	and	CCONJ
cana-3454	87	35	making	make	VERB
cana-3454	87	36	them	they	PRON
cana-3454	87	37	an	an	DET
cana-3454	87	38	important	important	ADJ
cana-3454	87	39	constituent	constituent	NOUN
cana-3454	87	40	of	of	ADP
cana-3454	87	41	existence	existence	NOUN
cana-3454	87	42	present	present	ADJ
cana-3454	87	43	day	day	NOUN
cana-3454	87	44	computer	computer	NOUN
cana-3454	87	45	vision	vision	NOUN
cana-3454	87	46	system	system	NOUN
cana-3454	87	47	[	[	X
cana-3454	87	48	7	7	NUM
cana-3454	87	49	]	]	PUNCT
cana-3454	87	50	.	.	PUNCT
cana-3454	88	1	for	for	ADP
cana-3454	88	2	instance	instance	NOUN
cana-3454	88	3	,	,	PUNCT
cana-3454	88	4	models	model	NOUN
cana-3454	88	5	such	such	ADJ
cana-3454	88	6	as	as	ADP
cana-3454	88	7	alexnet	alexnet	ADJ
cana-3454	88	8	,	,	PUNCT
cana-3454	88	9	vgg	vgg	ADJ
cana-3454	88	10	,	,	PUNCT
cana-3454	88	11	and	and	CCONJ
cana-3454	88	12	resnet	resnet	NOUN
cana-3454	88	13	has	have	AUX
cana-3454	88	14	demonstrated	demonstrate	VERB
cana-3454	88	15	high	high	ADJ
cana-3454	88	16	levels	level	NOUN
cana-3454	88	17	of	of	ADP
cana-3454	88	18	performance	performance	NOUN
cana-3454	88	19	during	during	ADP
cana-3454	88	20	benchmarks	benchmark	NOUN
cana-3454	88	21	such	such	ADJ
cana-3454	88	22	as	as	ADP
cana-3454	88	23	imagenet	imagenet	ADJ
cana-3454	88	24	classification	classification	NOUN
cana-3454	88	25	.	.	PUNCT
cana-3454	89	1	“	"	PUNCT
cana-3454	89	2	1	1	X
cana-3454	89	3	.	.	X
cana-3454	89	4	input	input	NOUN
cana-3454	89	5	:	:	PUNCT
cana-3454	89	6	image	image	NOUN
cana-3454	89	7	(	(	PUNCT
cana-3454	89	8	x	x	NOUN
cana-3454	89	9	)	)	PUNCT
cana-3454	89	10	of	of	ADP
cana-3454	89	11	size	size	NOUN
cana-3454	89	12	(	(	PUNCT
cana-3454	89	13	h	h	NOUN
cana-3454	89	14	,	,	PUNCT
cana-3454	89	15	w	w	PROPN
cana-3454	89	16	,	,	PUNCT
cana-3454	89	17	c	c	NOUN
cana-3454	89	18	)	)	PUNCT
cana-3454	89	19	2	2	NUM
cana-3454	89	20	.	.	X
cana-3454	90	1	initialize	initialize	NOUN
cana-3454	90	2	:	:	PUNCT
cana-3454	90	3	weights	weight	NOUN
cana-3454	90	4	(	(	PUNCT
cana-3454	90	5	w	w	NOUN
cana-3454	90	6	)	)	PUNCT
cana-3454	90	7	and	and	CCONJ
cana-3454	90	8	biases	bias	NOUN
cana-3454	90	9	(	(	PUNCT
cana-3454	90	10	b	b	NOUN
cana-3454	90	11	)	)	PUNCT
cana-3454	90	12	for	for	ADP
cana-3454	90	13	convolutional	convolutional	ADJ
cana-3454	90	14	layers	layer	NOUN
cana-3454	90	15	3	3	NUM
cana-3454	90	16	.	.	PUNCT
cana-3454	91	1	for	for	ADP
cana-3454	91	2	each	each	DET
cana-3454	91	3	convolutional	convolutional	ADJ
cana-3454	91	4	layer	layer	NOUN
cana-3454	91	5	:	:	PUNCT
cana-3454	91	6	a.	a.	NOUN
cana-3454	91	7	perform	perform	NOUN
cana-3454	91	8	convolution	convolution	NOUN
cana-3454	91	9	operation	operation	NOUN
cana-3454	91	10	:	:	PUNCT
cana-3454	91	11	z	z	NOUN
cana-3454	91	12	communications	communication	NOUN
cana-3454	91	13	on	on	ADP
cana-3454	91	14	applied	apply	VERB
cana-3454	91	15	nonlinear	nonlinear	ADJ
cana-3454	91	16	analysis	analysis	NOUN
cana-3454	91	17	issn	issn	NOUN
cana-3454	91	18	:	:	PUNCT
cana-3454	91	19	1074	1074	NUM
cana-3454	91	20	-	-	PUNCT
cana-3454	91	21	133x	133x	NUM
cana-3454	91	22	vol	vol	NOUN
cana-3454	91	23	32	32	NUM
cana-3454	91	24	no	no	NOUN
cana-3454	91	25	.	.	PUNCT
cana-3454	92	1	7s	7	NOUN
cana-3454	92	2	(	(	PUNCT
cana-3454	92	3	2025	2025	NUM
cana-3454	92	4	)	)	PUNCT
cana-3454	92	5	436	436	NUM
cana-3454	93	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	93	2	=	=	SYM
cana-3454	93	3	conv(x	conv(x	PROPN
cana-3454	93	4	,	,	PUNCT
cana-3454	93	5	w	w	NOUN
cana-3454	93	6	)	)	PUNCT
cana-3454	94	1	+	+	CCONJ
cana-3454	94	2	b	b	X
cana-3454	94	3	b.	b.	PROPN
cana-3454	94	4	apply	apply	NOUN
cana-3454	94	5	activation	activation	NOUN
cana-3454	94	6	function	function	NOUN
cana-3454	94	7	:	:	PUNCT
cana-3454	94	8	a	a	DET
cana-3454	94	9	=	=	PUNCT
cana-3454	94	10	relu(z	relu(z	NOUN
cana-3454	94	11	)	)	PUNCT
cana-3454	94	12	c.	c.	NOUN
cana-3454	94	13	perform	perform	VERB
cana-3454	94	14	pooling	pool	VERB
cana-3454	94	15	:	:	PUNCT
cana-3454	94	16	p	p	X
cana-3454	94	17	=	=	SYM
cana-3454	94	18	maxpool(a	maxpool(a	X
cana-3454	94	19	)	)	PUNCT
cana-3454	94	20	4	4	NUM
cana-3454	94	21	.	.	NUM
cana-3454	94	22	flatten	flatten	VERB
cana-3454	94	23	pooled	pool	VERB
cana-3454	94	24	feature	feature	NOUN
cana-3454	94	25	maps	map	NOUN
cana-3454	94	26	into	into	ADP
cana-3454	94	27	a	a	DET
cana-3454	94	28	vector	vector	NOUN
cana-3454	94	29	5	5	NUM
cana-3454	94	30	.	.	PUNCT
cana-3454	94	31	pass	pass	VERB
cana-3454	94	32	through	through	ADP
cana-3454	94	33	fully	fully	ADV
cana-3454	94	34	connected	connected	ADJ
cana-3454	94	35	layers	layer	NOUN
cana-3454	94	36	6	6	NUM
cana-3454	94	37	.	.	PUNCT
cana-3454	95	1	output	output	NOUN
cana-3454	95	2	:	:	PUNCT
cana-3454	95	3	predicted	predict	VERB
cana-3454	95	4	label	label	NOUN
cana-3454	95	5	”	"	PUNCT
cana-3454	95	6	2	2	NUM
cana-3454	95	7	.	.	X
cana-3454	96	1	support	support	NOUN
cana-3454	96	2	vector	vector	NOUN
cana-3454	96	3	machines	machine	NOUN
cana-3454	96	4	(	(	PUNCT
cana-3454	96	5	svms	svms	NOUN
cana-3454	96	6	)	)	PUNCT
cana-3454	96	7	svms	svms	NOUN
cana-3454	96	8	are	be	AUX
cana-3454	96	9	a	a	DET
cana-3454	96	10	type	type	NOUN
cana-3454	96	11	of	of	ADP
cana-3454	96	12	supervised	supervised	ADJ
cana-3454	96	13	learning	learning	NOUN
cana-3454	96	14	techniques	technique	NOUN
cana-3454	96	15	that	that	PRON
cana-3454	96	16	finds	find	VERB
cana-3454	96	17	a	a	DET
cana-3454	96	18	hyperplane	hyperplane	NOUN
cana-3454	96	19	in	in	ADP
cana-3454	96	20	a	a	DET
cana-3454	96	21	high	high	ADJ
cana-3454	96	22	dimensional	dimensional	ADJ
cana-3454	96	23	space	space	NOUN
cana-3454	96	24	,	,	PUNCT
cana-3454	96	25	in	in	ADP
cana-3454	96	26	other	other	ADJ
cana-3454	96	27	to	to	PART
cana-3454	96	28	categorize	categorize	VERB
cana-3454	96	29	data	datum	NOUN
cana-3454	96	30	in	in	ADP
cana-3454	96	31	different	different	ADJ
cana-3454	96	32	classes	class	NOUN
cana-3454	96	33	with	with	ADP
cana-3454	96	34	maximum	maximum	ADJ
cana-3454	96	35	margins	margin	NOUN
cana-3454	96	36	.	.	PUNCT
cana-3454	97	1	despite	despite	SCONJ
cana-3454	97	2	the	the	DET
cana-3454	97	3	many	many	ADJ
cana-3454	97	4	applications	application	NOUN
cana-3454	97	5	of	of	ADP
cana-3454	97	6	svms	svms	NOUN
cana-3454	97	7	in	in	ADP
cana-3454	97	8	computer	computer	NOUN
cana-3454	97	9	vision	vision	NOUN
cana-3454	97	10	,	,	PUNCT
cana-3454	97	11	some	some	PRON
cana-3454	97	12	of	of	ADP
cana-3454	97	13	the	the	DET
cana-3454	97	14	areas	area	NOUN
cana-3454	97	15	in	in	ADP
cana-3454	97	16	which	which	PRON
cana-3454	97	17	they	they	PRON
cana-3454	97	18	are	be	AUX
cana-3454	97	19	employed	employ	VERB
cana-3454	97	20	include	include	VERB
cana-3454	97	21	face	face	NOUN
cana-3454	97	22	recognition	recognition	NOUN
cana-3454	97	23	as	as	ADV
cana-3454	97	24	well	well	ADV
cana-3454	97	25	as	as	ADP
cana-3454	97	26	image	image	NOUN
cana-3454	97	27	classification	classification	NOUN
cana-3454	97	28	.	.	PUNCT
cana-3454	98	1	the	the	DET
cana-3454	98	2	algorithm	algorithm	NOUN
cana-3454	98	3	operates	operate	VERB
cana-3454	98	4	in	in	ADP
cana-3454	98	5	the	the	DET
cana-3454	98	6	high	high	ADV
cana-3454	98	7	-	-	PUNCT
cana-3454	98	8	dimensional	dimensional	ADJ
cana-3454	98	9	feature	feature	NOUN
cana-3454	98	10	space	space	NOUN
cana-3454	98	11	created	create	VERB
cana-3454	98	12	through	through	ADP
cana-3454	98	13	use	use	NOUN
cana-3454	98	14	of	of	ADP
cana-3454	98	15	kernel	kernel	NOUN
cana-3454	98	16	functions	function	NOUN
cana-3454	98	17	like	like	ADP
cana-3454	98	18	;	;	PUNCT
cana-3454	98	19	linear	linear	NOUN
cana-3454	98	20	functions	function	NOUN
cana-3454	98	21	,	,	PUNCT
cana-3454	98	22	polynomial	polynomial	ADJ
cana-3454	98	23	,	,	PUNCT
cana-3454	98	24	or	or	CCONJ
cana-3454	98	25	radial	radial	ADJ
cana-3454	98	26	basis	basis	NOUN
cana-3454	98	27	function	function	NOUN
cana-3454	98	28	(	(	PUNCT
cana-3454	98	29	rbf	rbf	PROPN
cana-3454	98	30	)	)	PUNCT
cana-3454	98	31	that	that	PRON
cana-3454	98	32	makes	make	VERB
cana-3454	98	33	it	it	PRON
cana-3454	98	34	ideal	ideal	ADJ
cana-3454	98	35	for	for	ADP
cana-3454	98	36	nonlinear	nonlinear	ADJ
cana-3454	98	37	data	datum	NOUN
cana-3454	98	38	[	[	X
cana-3454	98	39	8	8	NUM
cana-3454	98	40	]	]	PUNCT
cana-3454	98	41	.	.	PUNCT
cana-3454	99	1	an	an	DET
cana-3454	99	2	important	important	ADJ
cana-3454	99	3	advantage	advantage	NOUN
cana-3454	99	4	of	of	ADP
cana-3454	99	5	svms	svms	NOUN
cana-3454	99	6	is	be	AUX
cana-3454	99	7	that	that	SCONJ
cana-3454	99	8	they	they	PRON
cana-3454	99	9	are	be	AUX
cana-3454	99	10	good	good	ADJ
cana-3454	99	11	for	for	ADP
cana-3454	99	12	working	work	VERB
cana-3454	99	13	with	with	ADP
cana-3454	99	14	a	a	DET
cana-3454	99	15	small	small	ADJ
cana-3454	99	16	number	number	NOUN
cana-3454	99	17	of	of	ADP
cana-3454	99	18	samples	sample	NOUN
cana-3454	99	19	.	.	PUNCT
cana-3454	100	1	they	they	PRON
cana-3454	100	2	use	use	VERB
cana-3454	100	3	the	the	DET
cana-3454	100	4	concept	concept	NOUN
cana-3454	100	5	of	of	ADP
cana-3454	100	6	‘	'	PUNCT
cana-3454	100	7	‘	'	PUNCT
cana-3454	100	8	support	support	NOUN
cana-3454	100	9	vectors	vector	NOUN
cana-3454	100	10	’’	’'	PUNCT
cana-3454	100	11	which	which	PRON
cana-3454	100	12	are	be	AUX
cana-3454	100	13	the	the	DET
cana-3454	100	14	data	data	NOUN
cana-3454	100	15	points	point	NOUN
cana-3454	100	16	that	that	PRON
cana-3454	100	17	lie	lie	VERB
cana-3454	100	18	on	on	ADP
cana-3454	100	19	two	two	NUM
cana-3454	100	20	sides	side	NOUN
cana-3454	100	21	of	of	ADP
cana-3454	100	22	the	the	DET
cana-3454	100	23	decision	decision	NOUN
cana-3454	100	24	boundary	boundary	NOUN
cana-3454	100	25	of	of	ADP
cana-3454	100	26	the	the	DET
cana-3454	100	27	smallest	small	ADJ
cana-3454	100	28	margin	margin	NOUN
cana-3454	100	29	;	;	PUNCT
cana-3454	100	30	this	this	DET
cana-3454	100	31	allowsthem	allowsthem	NOUN
cana-3454	100	32	to	to	PART
cana-3454	100	33	achieve	achieve	VERB
cana-3454	100	34	high	high	ADJ
cana-3454	100	35	and	and	CCONJ
cana-3454	100	36	also	also	ADV
cana-3454	100	37	simple	simple	ADJ
cana-3454	100	38	classifier	classifier	NOUN
cana-3454	100	39	with	with	ADP
cana-3454	100	40	the	the	DET
cana-3454	100	41	least	least	ADJ
cana-3454	100	42	possible	possible	ADJ
cana-3454	100	43	computations	computation	NOUN
cana-3454	100	44	[	[	X
cana-3454	100	45	9	9	NUM
cana-3454	100	46	]	]	PUNCT
cana-3454	100	47	.	.	PUNCT
cana-3454	101	1	nonetheless	nonetheless	ADV
cana-3454	101	2	,	,	PUNCT
cana-3454	101	3	svms	svms	NOUN
cana-3454	101	4	are	be	AUX
cana-3454	101	5	not	not	PART
cana-3454	101	6	so	so	ADV
cana-3454	101	7	efficient	efficient	ADJ
cana-3454	101	8	for	for	ADP
cana-3454	101	9	big	big	ADJ
cana-3454	101	10	data	datum	NOUN
cana-3454	101	11	comparisons	comparison	NOUN
cana-3454	101	12	comparing	compare	VERB
cana-3454	101	13	with	with	ADP
cana-3454	101	14	deep	deep	ADJ
cana-3454	101	15	learning	learning	NOUN
cana-3454	101	16	models	model	NOUN
cana-3454	101	17	.	.	PUNCT
cana-3454	102	1	“	"	PUNCT
cana-3454	102	2	1	1	X
cana-3454	102	3	.	.	X
cana-3454	102	4	input	input	NOUN
cana-3454	102	5	:	:	PUNCT
cana-3454	102	6	training	training	NOUN
cana-3454	102	7	data	datum	NOUN
cana-3454	102	8	(	(	PUNCT
cana-3454	102	9	x	x	X
cana-3454	102	10	,	,	PUNCT
cana-3454	102	11	y	y	NOUN
cana-3454	102	12	)	)	PUNCT
cana-3454	102	13	2	2	NUM
cana-3454	102	14	.	.	X
cana-3454	103	1	choose	choose	VERB
cana-3454	103	2	kernel	kernel	PROPN
cana-3454	103	3	function	function	PROPN
cana-3454	103	4	(	(	PUNCT
cana-3454	103	5	k	k	NOUN
cana-3454	103	6	)	)	PUNCT
cana-3454	103	7	and	and	CCONJ
cana-3454	103	8	penalty	penalty	NOUN
cana-3454	103	9	parameter	parameter	NOUN
cana-3454	103	10	(	(	PUNCT
cana-3454	103	11	c	c	NOUN
cana-3454	103	12	)	)	PUNCT
cana-3454	103	13	3	3	NUM
cana-3454	103	14	.	.	X
cana-3454	104	1	solve	solve	VERB
cana-3454	104	2	optimization	optimization	NOUN
cana-3454	104	3	problem	problem	NOUN
cana-3454	104	4	to	to	PART
cana-3454	104	5	find	find	VERB
cana-3454	104	6	:	:	PUNCT
cana-3454	104	7	a.	a.	NOUN
cana-3454	104	8	support	support	PROPN
cana-3454	104	9	vectors	vector	NOUN
cana-3454	104	10	b.	b.	PROPN
cana-3454	104	11	hyperplane	hyperplane	PROPN
cana-3454	104	12	parameters	parameter	NOUN
cana-3454	104	13	(	(	PUNCT
cana-3454	104	14	w	w	PROPN
cana-3454	104	15	,	,	PUNCT
cana-3454	104	16	b	b	NOUN
cana-3454	104	17	)	)	PUNCT
cana-3454	104	18	4	4	NUM
cana-3454	104	19	.	.	X
cana-3454	105	1	for	for	ADP
cana-3454	105	2	each	each	DET
cana-3454	105	3	test	test	NOUN
cana-3454	105	4	sample	sample	NOUN
cana-3454	105	5	(	(	PUNCT
cana-3454	105	6	x	x	NOUN
cana-3454	105	7	):	):	PUNCT
cana-3454	105	8	a.	a.	NOUN
cana-3454	105	9	compute	compute	NOUN
cana-3454	105	10	decision	decision	NOUN
cana-3454	105	11	function	function	NOUN
cana-3454	105	12	:	:	PUNCT
cana-3454	105	13	f(x	f(x	PROPN
cana-3454	105	14	)	)	PUNCT
cana-3454	105	15	=	=	SYM
cana-3454	105	16	w·k(x	w·k(x	ADJ
cana-3454	105	17	)	)	PUNCT
cana-3454	106	1	+	+	NUM
cana-3454	106	2	b	b	X
cana-3454	106	3	b.	b.	PROPN
cana-3454	106	4	predict	predict	PROPN
cana-3454	106	5	class	class	NOUN
cana-3454	106	6	:	:	PUNCT
cana-3454	106	7	y	y	PROPN
cana-3454	106	8	=	=	SYM
cana-3454	106	9	sign(f(x	sign(f(x	PROPN
cana-3454	106	10	)	)	PUNCT
cana-3454	106	11	)	)	PUNCT
cana-3454	107	1	5	5	X
cana-3454	107	2	.	.	X
cana-3454	107	3	output	output	NOUN
cana-3454	107	4	:	:	PUNCT
cana-3454	107	5	predicted	predict	VERB
cana-3454	107	6	labels	label	NOUN
cana-3454	107	7	”	"	PUNCT
cana-3454	107	8	3	3	NUM
cana-3454	107	9	.	.	PUNCT
cana-3454	108	1	k	k	X
cana-3454	108	2	-	-	PUNCT
cana-3454	108	3	nearest	near	ADJ
cana-3454	108	4	neighbors	neighbor	NOUN
cana-3454	108	5	(	(	PUNCT
cana-3454	108	6	knn	knn	PROPN
cana-3454	108	7	)	)	PUNCT
cana-3454	108	8	communications	communication	NOUN
cana-3454	108	9	on	on	ADP
cana-3454	108	10	applied	apply	VERB
cana-3454	108	11	nonlinear	nonlinear	ADJ
cana-3454	108	12	analysis	analysis	NOUN
cana-3454	108	13	issn	issn	NOUN
cana-3454	108	14	:	:	PUNCT
cana-3454	108	15	1074	1074	NUM
cana-3454	108	16	-	-	PUNCT
cana-3454	108	17	133x	133x	NUM
cana-3454	108	18	vol	vol	NOUN
cana-3454	108	19	32	32	NUM
cana-3454	108	20	no	no	NOUN
cana-3454	108	21	.	.	PUNCT
cana-3454	109	1	7s	7	NOUN
cana-3454	109	2	(	(	PUNCT
cana-3454	109	3	2025	2025	NUM
cana-3454	109	4	)	)	PUNCT
cana-3454	109	5	437	437	NUM
cana-3454	110	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	110	2	knn	knn	PROPN
cana-3454	110	3	is	be	AUX
cana-3454	110	4	one	one	NUM
cana-3454	110	5	of	of	ADP
cana-3454	110	6	the	the	DET
cana-3454	110	7	simplest	simple	ADJ
cana-3454	110	8	and	and	CCONJ
cana-3454	110	9	instance	instance	NOUN
cana-3454	110	10	based	base	VERB
cana-3454	110	11	learning	learning	NOUN
cana-3454	110	12	methods	method	NOUN
cana-3454	110	13	which	which	PRON
cana-3454	110	14	is	be	AUX
cana-3454	110	15	used	use	VERB
cana-3454	110	16	to	to	PART
cana-3454	110	17	detect	detect	VERB
cana-3454	110	18	images	image	NOUN
cana-3454	110	19	and	and	CCONJ
cana-3454	110	20	an	an	DET
cana-3454	110	21	existing	exist	VERB
cana-3454	110	22	objects	object	NOUN
cana-3454	110	23	.	.	PUNCT
cana-3454	111	1	it	it	PRON
cana-3454	111	2	categorises	categorise	VERB
cana-3454	111	3	an	an	DET
cana-3454	111	4	input	input	NOUN
cana-3454	111	5	image	image	NOUN
cana-3454	111	6	by	by	ADP
cana-3454	111	7	identifying	identify	VERB
cana-3454	111	8	‘	'	PUNCT
cana-3454	111	9	k	k	NOUN
cana-3454	111	10	’	'	PUNCT
cana-3454	111	11	images	image	NOUN
cana-3454	111	12	in	in	ADP
cana-3454	111	13	the	the	DET
cana-3454	111	14	feature	feature	NOUN
cana-3454	111	15	space	space	NOUN
cana-3454	111	16	that	that	PRON
cana-3454	111	17	most	most	ADV
cana-3454	111	18	resemble	resemble	VERB
cana-3454	111	19	the	the	DET
cana-3454	111	20	input	input	NOUN
cana-3454	111	21	and	and	CCONJ
cana-3454	111	22	the	the	DET
cana-3454	111	23	category	category	NOUN
cana-3454	111	24	with	with	ADP
cana-3454	111	25	the	the	DET
cana-3454	111	26	highest	high	ADJ
cana-3454	111	27	number	number	NOUN
cana-3454	111	28	of	of	ADP
cana-3454	111	29	such	such	ADJ
cana-3454	111	30	images	image	NOUN
cana-3454	111	31	belong	belong	VERB
cana-3454	111	32	to	to	ADP
cana-3454	111	33	.	.	PUNCT
cana-3454	112	1	an	an	DET
cana-3454	112	2	important	important	ADJ
cana-3454	112	3	parameter	parameter	NOUN
cana-3454	112	4	in	in	ADP
cana-3454	112	5	measuring	measure	VERB
cana-3454	112	6	similarity	similarity	NOUN
cana-3454	112	7	is	be	AUX
cana-3454	112	8	distance	distance	NOUN
cana-3454	112	9	metric	metric	ADJ
cana-3454	112	10	including	include	VERB
cana-3454	112	11	euclidean	euclidean	NOUN
cana-3454	112	12	or	or	CCONJ
cana-3454	112	13	manhattan	manhattan	PROPN
cana-3454	112	14	[	[	X
cana-3454	112	15	10	10	NUM
cana-3454	112	16	]	]	PUNCT
cana-3454	112	17	.	.	PUNCT
cana-3454	113	1	there	there	PRON
cana-3454	113	2	are	be	VERB
cana-3454	113	3	some	some	DET
cana-3454	113	4	advantages	advantage	NOUN
cana-3454	113	5	of	of	ADP
cana-3454	113	6	using	use	VERB
cana-3454	113	7	knn	knn	PROPN
cana-3454	113	8	such	such	ADJ
cana-3454	113	9	as	as	ADP
cana-3454	113	10	knn	knn	PROPN
cana-3454	113	11	is	be	AUX
cana-3454	113	12	non	non	ADJ
cana-3454	113	13	-	-	ADJ
cana-3454	113	14	parametric	parametric	ADJ
cana-3454	113	15	that	that	PRON
cana-3454	113	16	is	be	AUX
cana-3454	113	17	it	it	PRON
cana-3454	113	18	does	do	AUX
cana-3454	113	19	not	not	PART
cana-3454	113	20	require	require	VERB
cana-3454	113	21	the	the	DET
cana-3454	113	22	distribution	distribution	NOUN
cana-3454	113	23	of	of	ADP
cana-3454	113	24	feature	feature	NOUN
cana-3454	113	25	space	space	NOUN
cana-3454	113	26	and	and	CCONJ
cana-3454	113	27	is	be	AUX
cana-3454	113	28	therefore	therefore	ADV
cana-3454	113	29	well	well	ADV
cana-3454	113	30	suited	suited	ADJ
cana-3454	113	31	in	in	ADP
cana-3454	113	32	any	any	DET
cana-3454	113	33	type	type	NOUN
cana-3454	113	34	of	of	ADP
cana-3454	113	35	feature	feature	NOUN
cana-3454	113	36	space	space	NOUN
cana-3454	113	37	.	.	PUNCT
cana-3454	114	1	however	however	ADV
cana-3454	114	2	,	,	PUNCT
cana-3454	114	3	it	it	PRON
cana-3454	114	4	is	be	AUX
cana-3454	114	5	gunner	gunner	NOUN
cana-3454	114	6	expensive	expensive	ADJ
cana-3454	114	7	than	than	ADP
cana-3454	114	8	the	the	DET
cana-3454	114	9	original	original	ADJ
cana-3454	114	10	knn	knn	NOUN
cana-3454	114	11	if	if	SCONJ
cana-3454	114	12	we	we	PRON
cana-3454	114	13	want	want	VERB
cana-3454	114	14	to	to	PART
cana-3454	114	15	apply	apply	VERB
cana-3454	114	16	it	it	PRON
cana-3454	114	17	in	in	ADP
cana-3454	114	18	a	a	DET
cana-3454	114	19	large	large	ADJ
cana-3454	114	20	-	-	PUNCT
cana-3454	114	21	scale	scale	NOUN
cana-3454	114	22	training	training	NOUN
cana-3454	114	23	set	set	NOUN
cana-3454	114	24	because	because	SCONJ
cana-3454	114	25	it	it	PRON
cana-3454	114	26	has	have	VERB
cana-3454	114	27	to	to	PART
cana-3454	114	28	calculate	calculate	VERB
cana-3454	114	29	the	the	DET
cana-3454	114	30	corresponding	corresponding	ADJ
cana-3454	114	31	distance	distance	NOUN
cana-3454	114	32	of	of	ADP
cana-3454	114	33	each	each	DET
cana-3454	114	34	training	training	NOUN
cana-3454	114	35	sample	sample	NOUN
cana-3454	114	36	as	as	SCONJ
cana-3454	114	37	it	it	PRON
cana-3454	114	38	makes	make	VERB
cana-3454	114	39	inference	inference	NOUN
cana-3454	114	40	[	[	X
cana-3454	114	41	11	11	NUM
cana-3454	114	42	]	]	PUNCT
cana-3454	114	43	.	.	PUNCT
cana-3454	115	1	“	"	PUNCT
cana-3454	115	2	1	1	X
cana-3454	115	3	.	.	X
cana-3454	115	4	input	input	NOUN
cana-3454	115	5	:	:	PUNCT
cana-3454	115	6	training	training	NOUN
cana-3454	115	7	data	datum	NOUN
cana-3454	115	8	(	(	PUNCT
cana-3454	115	9	x_train	x_train	PROPN
cana-3454	115	10	,	,	PUNCT
cana-3454	115	11	y_train	y_train	PROPN
cana-3454	115	12	)	)	PUNCT
cana-3454	115	13	,	,	PUNCT
cana-3454	115	14	test	test	NOUN
cana-3454	115	15	data	datum	NOUN
cana-3454	115	16	(	(	PUNCT
cana-3454	115	17	x_test	x_test	NUM
cana-3454	115	18	)	)	PUNCT
cana-3454	115	19	,	,	PUNCT
cana-3454	115	20	number	number	NOUN
cana-3454	115	21	of	of	ADP
cana-3454	115	22	neighbors	neighbor	NOUN
cana-3454	115	23	(	(	PUNCT
cana-3454	115	24	k	k	NOUN
cana-3454	115	25	)	)	PUNCT
cana-3454	115	26	2	2	NUM
cana-3454	115	27	.	.	X
cana-3454	115	28	for	for	ADP
cana-3454	115	29	each	each	DET
cana-3454	115	30	test	test	NOUN
cana-3454	115	31	sample	sample	NOUN
cana-3454	115	32	(	(	PUNCT
cana-3454	115	33	x_test	x_test	NUM
cana-3454	115	34	):	):	PUNCT
cana-3454	115	35	a.	a.	PROPN
cana-3454	115	36	compute	compute	PROPN
cana-3454	115	37	distances	distance	NOUN
cana-3454	115	38	to	to	ADP
cana-3454	115	39	all	all	DET
cana-3454	115	40	training	training	NOUN
cana-3454	115	41	samples	sample	NOUN
cana-3454	115	42	b.	b.	PROPN
cana-3454	115	43	sort	sort	NOUN
cana-3454	115	44	distances	distance	NOUN
cana-3454	115	45	in	in	ADP
cana-3454	115	46	ascending	ascend	VERB
cana-3454	115	47	order	order	NOUN
cana-3454	115	48	c.	c.	NOUN
cana-3454	115	49	select	select	VERB
cana-3454	115	50	top	top	ADJ
cana-3454	115	51	-	-	PUNCT
cana-3454	115	52	k	k	NOUN
cana-3454	115	53	nearest	near	ADJ
cana-3454	115	54	neighbors	neighbor	NOUN
cana-3454	115	55	d.	d.	PROPN
cana-3454	115	56	assign	assign	VERB
cana-3454	115	57	majority	majority	NOUN
cana-3454	115	58	class	class	NOUN
cana-3454	115	59	of	of	ADP
cana-3454	115	60	neighbors	neighbor	NOUN
cana-3454	115	61	to	to	ADP
cana-3454	115	62	x_test	x_t	ADJ
cana-3454	115	63	3	3	X
cana-3454	115	64	.	.	X
cana-3454	115	65	output	output	NOUN
cana-3454	115	66	:	:	PUNCT
cana-3454	115	67	predicted	predict	VERB
cana-3454	115	68	class	class	NOUN
cana-3454	115	69	labels	label	NOUN
cana-3454	115	70	”	"	PUNCT
cana-3454	115	71	4	4	NUM
cana-3454	115	72	.	.	PUNCT
cana-3454	116	1	yolo	yolo	PROPN
cana-3454	116	2	(	(	PUNCT
cana-3454	116	3	you	you	PRON
cana-3454	116	4	only	only	ADV
cana-3454	116	5	look	look	VERB
cana-3454	116	6	once	once	ADV
cana-3454	116	7	)	)	PUNCT
cana-3454	116	8	yolo	yolo	PROPN
cana-3454	116	9	is	be	AUX
cana-3454	116	10	an	an	DET
cana-3454	116	11	object	object	NOUN
cana-3454	116	12	detection	detection	NOUN
cana-3454	116	13	algorithm	algorithm	NOUN
cana-3454	116	14	based	base	VERB
cana-3454	116	15	on	on	ADP
cana-3454	116	16	deep	deep	ADJ
cana-3454	116	17	learning	learning	NOUN
cana-3454	116	18	which	which	DET
cana-3454	116	19	functions	function	NOUN
cana-3454	116	20	in	in	ADP
cana-3454	116	21	real	real	ADJ
cana-3454	116	22	time	time	NOUN
cana-3454	116	23	with	with	ADP
cana-3454	116	24	images	image	NOUN
cana-3454	116	25	.	.	PUNCT
cana-3454	117	1	unlike	unlike	ADP
cana-3454	117	2	the	the	DET
cana-3454	117	3	more	more	ADV
cana-3454	117	4	conventional	conventional	ADJ
cana-3454	117	5	methods	method	NOUN
cana-3454	117	6	that	that	PRON
cana-3454	117	7	employs	employ	VERB
cana-3454	117	8	region	region	NOUN
cana-3454	117	9	proposals	proposal	NOUN
cana-3454	117	10	for	for	ADP
cana-3454	117	11	detection	detection	NOUN
cana-3454	117	12	,	,	PUNCT
cana-3454	117	13	yolo	yolo	ADJ
cana-3454	117	14	trains	train	NOUN
cana-3454	117	15	detection	detection	NOUN
cana-3454	117	16	as	as	ADP
cana-3454	117	17	a	a	DET
cana-3454	117	18	regression	regression	NOUN
cana-3454	117	19	problem	problem	NOUN
cana-3454	117	20	directly	directly	ADV
cana-3454	117	21	from	from	ADP
cana-3454	117	22	the	the	DET
cana-3454	117	23	images	image	NOUN
cana-3454	117	24	[	[	X
cana-3454	117	25	12	12	NUM
cana-3454	117	26	]	]	PUNCT
cana-3454	117	27	.	.	PUNCT
cana-3454	118	1	the	the	DET
cana-3454	118	2	architecture	architecture	NOUN
cana-3454	118	3	splits	split	VERB
cana-3454	118	4	the	the	DET
cana-3454	118	5	image	image	NOUN
cana-3454	118	6	into	into	ADP
cana-3454	118	7	an	an	DET
cana-3454	118	8	sxs	sxs	NOUN
cana-3454	118	9	grid	grid	NOUN
cana-3454	118	10	to	to	ADP
cana-3454	118	11	which	which	PRON
cana-3454	118	12	each	each	DET
cana-3454	118	13	cell	cell	NOUN
cana-3454	118	14	then	then	ADV
cana-3454	118	15	predicts	predict	VERB
cana-3454	118	16	bounding	bound	VERB
cana-3454	118	17	boxes	box	NOUN
cana-3454	118	18	and	and	CCONJ
cana-3454	118	19	confidence	confidence	NOUN
cana-3454	118	20	scores	score	NOUN
cana-3454	118	21	.	.	PUNCT
cana-3454	119	1	yolo	yolo	ADV
cana-3454	119	2	as	as	ADP
cana-3454	119	3	a	a	DET
cana-3454	119	4	detector	detector	NOUN
cana-3454	119	5	is	be	AUX
cana-3454	119	6	fast	fast	ADJ
cana-3454	119	7	and	and	CCONJ
cana-3454	119	8	accurate	accurate	ADJ
cana-3454	119	9	to	to	PART
cana-3454	119	10	be	be	AUX
cana-3454	119	11	used	use	VERB
cana-3454	119	12	in	in	ADP
cana-3454	119	13	real	real	ADJ
cana-3454	119	14	-	-	PUNCT
cana-3454	119	15	time	time	NOUN
cana-3454	119	16	applications	application	NOUN
cana-3454	119	17	such	such	ADJ
cana-3454	119	18	as	as	ADP
cana-3454	119	19	surveillance	surveillance	NOUN
cana-3454	119	20	and	and	CCONJ
cana-3454	119	21	self	self	NOUN
cana-3454	119	22	-	-	PUNCT
cana-3454	119	23	driving	drive	VERB
cana-3454	119	24	vehicles	vehicle	NOUN
cana-3454	119	25	.	.	PUNCT
cana-3454	120	1	yolov4	yolov4	NOUN
cana-3454	120	2	and	and	CCONJ
cana-3454	120	3	yolov5	yolov5	NOUN
cana-3454	120	4	are	be	AUX
cana-3454	120	5	the	the	DET
cana-3454	120	6	improved	improve	VERB
cana-3454	120	7	versions	version	NOUN
cana-3454	120	8	which	which	PRON
cana-3454	120	9	are	be	AUX
cana-3454	120	10	developed	develop	VERB
cana-3454	120	11	using	use	VERB
cana-3454	120	12	architectural	architectural	ADJ
cana-3454	120	13	and	and	CCONJ
cana-3454	120	14	optimization	optimization	NOUN
cana-3454	120	15	improvements	improvement	NOUN
cana-3454	120	16	.	.	PUNCT
cana-3454	121	1	“	"	PUNCT
cana-3454	121	2	1	1	X
cana-3454	121	3	.	.	X
cana-3454	121	4	input	input	NOUN
cana-3454	121	5	:	:	PUNCT
cana-3454	121	6	image	image	NOUN
cana-3454	121	7	(	(	PUNCT
cana-3454	121	8	x	x	NOUN
cana-3454	121	9	)	)	PUNCT
cana-3454	121	10	of	of	ADP
cana-3454	121	11	size	size	NOUN
cana-3454	121	12	(	(	PUNCT
cana-3454	121	13	h	h	NOUN
cana-3454	121	14	,	,	PUNCT
cana-3454	121	15	w	w	PROPN
cana-3454	121	16	,	,	PUNCT
cana-3454	121	17	c	c	NOUN
cana-3454	121	18	)	)	PUNCT
cana-3454	121	19	2	2	NUM
cana-3454	121	20	.	.	X
cana-3454	121	21	divide	divide	VERB
cana-3454	121	22	image	image	NOUN
cana-3454	121	23	into	into	ADP
cana-3454	121	24	sxs	sxs	PROPN
cana-3454	121	25	grid	grid	VERB
cana-3454	121	26	3	3	NUM
cana-3454	121	27	.	.	PUNCT
cana-3454	122	1	for	for	ADP
cana-3454	122	2	each	each	DET
cana-3454	122	3	grid	grid	NOUN
cana-3454	122	4	cell	cell	NOUN
cana-3454	122	5	:	:	PUNCT
cana-3454	122	6	a.	a.	NOUN
cana-3454	122	7	predict	predict	VERB
cana-3454	122	8	bounding	bounding	NOUN
cana-3454	122	9	boxes	box	NOUN
cana-3454	122	10	(	(	PUNCT
cana-3454	122	11	x	x	X
cana-3454	122	12	,	,	PUNCT
cana-3454	122	13	y	y	PROPN
cana-3454	122	14	,	,	PUNCT
cana-3454	122	15	w	w	PROPN
cana-3454	122	16	,	,	PUNCT
cana-3454	122	17	h	h	NOUN
cana-3454	122	18	)	)	PUNCT
cana-3454	122	19	b.	b.	PROPN
cana-3454	122	20	predict	predict	VERB
cana-3454	122	21	confidence	confidence	NOUN
cana-3454	122	22	scores	score	NOUN
cana-3454	122	23	c.	c.	PROPN
cana-3454	122	24	predict	predict	VERB
cana-3454	122	25	class	class	NOUN
cana-3454	122	26	probabilities	probability	NOUN
cana-3454	122	27	4	4	NUM
cana-3454	122	28	.	.	PUNCT
cana-3454	122	29	apply	apply	VERB
cana-3454	122	30	non	non	ADJ
cana-3454	122	31	-	-	ADJ
cana-3454	122	32	max	max	ADJ
cana-3454	122	33	suppression	suppression	NOUN
cana-3454	122	34	to	to	PART
cana-3454	122	35	eliminate	eliminate	VERB
cana-3454	122	36	communications	communication	NOUN
cana-3454	122	37	on	on	ADP
cana-3454	122	38	applied	apply	VERB
cana-3454	122	39	nonlinear	nonlinear	ADJ
cana-3454	122	40	analysis	analysis	NOUN
cana-3454	122	41	issn	issn	NOUN
cana-3454	122	42	:	:	PUNCT
cana-3454	122	43	1074	1074	NUM
cana-3454	122	44	-	-	PUNCT
cana-3454	122	45	133x	133x	NUM
cana-3454	122	46	vol	vol	NOUN
cana-3454	122	47	32	32	NUM
cana-3454	122	48	no	no	NOUN
cana-3454	122	49	.	.	PUNCT
cana-3454	123	1	7s	7	NOUN
cana-3454	123	2	(	(	PUNCT
cana-3454	123	3	2025	2025	NUM
cana-3454	123	4	)	)	PUNCT
cana-3454	123	5	438	438	NUM
cana-3454	123	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	123	7	overlapping	overlap	VERB
cana-3454	123	8	boxes	box	NOUN
cana-3454	123	9	5	5	NUM
cana-3454	123	10	.	.	PUNCT
cana-3454	124	1	output	output	NOUN
cana-3454	124	2	:	:	PUNCT
cana-3454	124	3	final	final	ADJ
cana-3454	124	4	detected	detect	VERB
cana-3454	124	5	objects	object	NOUN
cana-3454	124	6	with	with	ADP
cana-3454	124	7	bounding	bounding	NOUN
cana-3454	124	8	boxes	box	NOUN
cana-3454	124	9	”	"	PUNCT
cana-3454	124	10	table	table	NOUN
cana-3454	124	11	1	1	NUM
cana-3454	124	12	:	:	PUNCT
cana-3454	124	13	dataset	dataset	VERB
cana-3454	124	14	description	description	NOUN
cana-3454	124	15	dataset	dataset	NOUN
cana-3454	124	16	type	type	NOUN
cana-3454	124	17	number	number	NOUN
cana-3454	124	18	of	of	ADP
cana-3454	124	19	images	image	NOUN
cana-3454	124	20	task	task	NOUN
cana-3454	124	21	format	format	NOUN
cana-3454	124	22	imagenet	imagenet	NOUN
cana-3454	124	23	diverse	diverse	VERB
cana-3454	124	24	1,000,000	1,000,000	NUM
cana-3454	124	25	+	+	CCONJ
cana-3454	124	26	classification	classification	NOUN
cana-3454	124	27	jpeg	jpeg	NOUN
cana-3454	124	28	coco	coco	PROPN
cana-3454	124	29	diverse	diverse	VERB
cana-3454	124	30	330,000	330,000	NUM
cana-3454	124	31	detection	detection	NOUN
cana-3454	124	32	,	,	PUNCT
cana-3454	124	33	segmentation	segmentation	NOUN
cana-3454	124	34	png	png	PROPN
cana-3454	124	35	mnist	mnist	NOUN
cana-3454	124	36	handwritten	handwritten	VERB
cana-3454	124	37	70,000	70,000	NUM
cana-3454	124	38	digit	digit	NOUN
cana-3454	124	39	recognition	recognition	NOUN
cana-3454	124	40	grayscale	grayscale	NOUN
cana-3454	124	41	iv	iv	PROPN
cana-3454	124	42	.	.	PUNCT
cana-3454	124	43	experiments	experiment	NOUN
cana-3454	124	44	experimental	experimental	ADJ
cana-3454	124	45	setup	setup	NOUN
cana-3454	124	46	all	all	DET
cana-3454	124	47	the	the	DET
cana-3454	124	48	experiments	experiment	NOUN
cana-3454	124	49	were	be	AUX
cana-3454	124	50	preformed	preform	VERB
cana-3454	124	51	on	on	ADP
cana-3454	124	52	a	a	DET
cana-3454	124	53	workstation	workstation	NOUN
cana-3454	124	54	with	with	ADP
cana-3454	124	55	nvidia	nvidia	PROPN
cana-3454	124	56	rtx	rtx	PROPN
cana-3454	124	57	3090	3090	NUM
cana-3454	124	58	gpu	gpu	PROPN
cana-3454	124	59	,	,	PUNCT
cana-3454	124	60	64	64	NUM
cana-3454	124	61	gb	gb	NOUN
cana-3454	124	62	ram	ram	NOUN
cana-3454	124	63	and	and	CCONJ
cana-3454	124	64	tensorflow	tensorflow	NOUN
cana-3454	124	65	/	/	SYM
cana-3454	124	66	pytorch	pytorch	NOUN
cana-3454	124	67	frameworks	framework	NOUN
cana-3454	124	68	.	.	PUNCT
cana-3454	125	1	the	the	DET
cana-3454	125	2	four	four	NUM
cana-3454	125	3	selected	select	VERB
cana-3454	125	4	algorithms	algorithm	NOUN
cana-3454	125	5	—	—	PUNCT
cana-3454	125	6	convolutional	convolutional	ADJ
cana-3454	125	7	neural	neural	ADJ
cana-3454	125	8	networks	network	NOUN
cana-3454	125	9	(	(	PUNCT
cana-3454	125	10	cnns	cnns	PROPN
cana-3454	125	11	)	)	PUNCT
cana-3454	125	12	,	,	PUNCT
cana-3454	125	13	support	support	VERB
cana-3454	125	14	vector	vector	NOUN
cana-3454	125	15	machines	machine	NOUN
cana-3454	125	16	(	(	PUNCT
cana-3454	125	17	svms	svms	NOUN
cana-3454	125	18	)	)	PUNCT
cana-3454	125	19	,	,	PUNCT
cana-3454	125	20	k	k	X
cana-3454	125	21	-	-	PUNCT
cana-3454	125	22	nearest	near	ADJ
cana-3454	125	23	neighbors	neighbor	NOUN
cana-3454	125	24	(	(	PUNCT
cana-3454	125	25	knn	knn	PROPN
cana-3454	125	26	)	)	PUNCT
cana-3454	125	27	,	,	PUNCT
cana-3454	125	28	and	and	CCONJ
cana-3454	125	29	yolo	yolo	PROPN
cana-3454	125	30	(	(	PUNCT
cana-3454	125	31	you	you	PRON
cana-3454	125	32	only	only	ADV
cana-3454	125	33	look	look	VERB
cana-3454	125	34	once)—were	once)—were	ADV
cana-3454	125	35	implemented	implement	VERB
cana-3454	125	36	and	and	CCONJ
cana-3454	125	37	evaluated	evaluate	VERB
cana-3454	125	38	on	on	ADP
cana-3454	125	39	three	three	NUM
cana-3454	125	40	datasets	dataset	NOUN
cana-3454	125	41	:	:	PUNCT
cana-3454	125	42	imagenet	imagenet	NOUN
cana-3454	125	43	,	,	PUNCT
cana-3454	125	44	coco	coco	PROPN
cana-3454	125	45	,	,	PUNCT
cana-3454	125	46	and	and	CCONJ
cana-3454	125	47	mnist	mnist	NOUN
cana-3454	125	48	.	.	PUNCT
cana-3454	126	1	to	to	PART
cana-3454	126	2	have	have	VERB
cana-3454	126	3	a	a	DET
cana-3454	126	4	fair	fair	ADJ
cana-3454	126	5	comparison	comparison	NOUN
cana-3454	126	6	for	for	ADP
cana-3454	126	7	each	each	DET
cana-3454	126	8	model	model	NOUN
cana-3454	126	9	,	,	PUNCT
cana-3454	126	10	each	each	DET
cana-3454	126	11	model	model	NOUN
cana-3454	126	12	was	be	AUX
cana-3454	126	13	then	then	ADV
cana-3454	126	14	fine	fine	ADV
cana-3454	126	15	-	-	PUNCT
cana-3454	126	16	tuned	tune	VERB
cana-3454	126	17	with	with	ADP
cana-3454	126	18	the	the	DET
cana-3454	126	19	correct	correct	ADJ
cana-3454	126	20	hyperparameters	hyperparameter	NOUN
cana-3454	126	21	[	[	X
cana-3454	126	22	13	13	NUM
cana-3454	126	23	]	]	PUNCT
cana-3454	126	24	.	.	PUNCT
cana-3454	127	1	the	the	DET
cana-3454	127	2	key	key	ADJ
cana-3454	127	3	descriptors	descriptor	NOUN
cana-3454	127	4	for	for	ADP
cana-3454	127	5	assessment	assessment	NOUN
cana-3454	127	6	were	be	AUX
cana-3454	127	7	accuracy	accuracy	NOUN
cana-3454	127	8	,	,	PUNCT
cana-3454	127	9	precision	precision	NOUN
cana-3454	127	10	,	,	PUNCT
cana-3454	127	11	recall	recall	NOUN
cana-3454	127	12	,	,	PUNCT
cana-3454	127	13	f1	f1	NOUN
cana-3454	127	14	score	score	NOUN
cana-3454	127	15	,	,	PUNCT
cana-3454	127	16	and	and	CCONJ
cana-3454	127	17	inference	inference	NOUN
cana-3454	127	18	time	time	NOUN
cana-3454	127	19	.	.	PUNCT
cana-3454	128	1	these	these	DET
cana-3454	128	2	metrics	metric	NOUN
cana-3454	128	3	give	give	VERB
cana-3454	128	4	a	a	DET
cana-3454	128	5	comprehensive	comprehensive	ADJ
cana-3454	128	6	knowledge	knowledge	NOUN
cana-3454	128	7	about	about	ADP
cana-3454	128	8	the	the	DET
cana-3454	128	9	algorithm	algorithm	NOUN
cana-3454	128	10	’s	’s	PART
cana-3454	128	11	performance	performance	NOUN
cana-3454	128	12	;	;	PUNCT
cana-3454	128	13	the	the	DET
cana-3454	128	14	ability	ability	NOUN
cana-3454	128	15	to	to	PART
cana-3454	128	16	predict	predict	VERB
cana-3454	128	17	and	and	CCONJ
cana-3454	128	18	how	how	SCONJ
cana-3454	128	19	resistant	resistant	ADJ
cana-3454	128	20	the	the	DET
cana-3454	128	21	algorithm	algorithm	NOUN
cana-3454	128	22	is	be	AUX
cana-3454	128	23	and	and	CCONJ
cana-3454	128	24	its	its	PRON
cana-3454	128	25	speed	speed	NOUN
cana-3454	128	26	among	among	ADP
cana-3454	128	27	others	other	NOUN
cana-3454	128	28	.	.	PUNCT
cana-3454	129	1	figure	figure	VERB
cana-3454	129	2	1	1	NUM
cana-3454	129	3	:	:	PUNCT
cana-3454	129	4	“	"	PUNCT
cana-3454	129	5	machine	machine	NOUN
cana-3454	129	6	learning	learning	NOUN
cana-3454	129	7	and	and	CCONJ
cana-3454	129	8	computer	computer	NOUN
cana-3454	129	9	vision	vision	PROPN
cana-3454	129	10	research	research	NOUN
cana-3454	129	11	areas	area	NOUN
cana-3454	129	12	”	"	PUNCT
cana-3454	129	13	datasets	dataset	NOUN
cana-3454	129	14	used	use	VERB
cana-3454	129	15	three	three	NUM
cana-3454	129	16	datasets	dataset	NOUN
cana-3454	129	17	,	,	PUNCT
cana-3454	129	18	representing	represent	VERB
cana-3454	129	19	diverse	diverse	ADJ
cana-3454	129	20	applications	application	NOUN
cana-3454	129	21	of	of	ADP
cana-3454	129	22	computer	computer	NOUN
cana-3454	129	23	vision	vision	NOUN
cana-3454	129	24	,	,	PUNCT
cana-3454	129	25	were	be	AUX
cana-3454	129	26	utilized	utilize	VERB
cana-3454	129	27	:	:	PUNCT
cana-3454	129	28	1	1	X
cana-3454	129	29	.	.	X
cana-3454	129	30	imagenet	imagenet	NOUN
cana-3454	129	31	:	:	PUNCT
cana-3454	129	32	applied	apply	VERB
cana-3454	129	33	in	in	ADP
cana-3454	129	34	the	the	DET
cana-3454	129	35	general	general	ADJ
cana-3454	129	36	setting	setting	NOUN
cana-3454	129	37	for	for	ADP
cana-3454	129	38	image	image	NOUN
cana-3454	129	39	categorization	categorization	NOUN
cana-3454	129	40	.	.	PUNCT
cana-3454	130	1	2	2	X
cana-3454	130	2	.	.	X
cana-3454	131	1	coco	coco	PROPN
cana-3454	131	2	:	:	PUNCT
cana-3454	131	3	working	work	VERB
cana-3454	131	4	notably	notably	ADV
cana-3454	131	5	in	in	ADP
cana-3454	131	6	the	the	DET
cana-3454	131	7	areas	area	NOUN
cana-3454	131	8	like	like	ADP
cana-3454	131	9	object	object	NOUN
cana-3454	131	10	detection	detection	NOUN
cana-3454	131	11	and	and	CCONJ
cana-3454	131	12	segmentation	segmentation	NOUN
cana-3454	131	13	.	.	PUNCT
cana-3454	132	1	3	3	X
cana-3454	132	2	.	.	X
cana-3454	132	3	mnist	mnist	NOUN
cana-3454	132	4	:	:	PUNCT
cana-3454	132	5	it	it	PRON
cana-3454	132	6	can	can	AUX
cana-3454	132	7	be	be	AUX
cana-3454	132	8	used	use	VERB
cana-3454	132	9	as	as	ADP
cana-3454	132	10	the	the	DET
cana-3454	132	11	benchmark	benchmark	NOUN
cana-3454	132	12	dataset	dataset	NOUN
cana-3454	132	13	of	of	ADP
cana-3454	132	14	handwritten	handwritten	ADJ
cana-3454	132	15	digit	digit	NOUN
cana-3454	132	16	classification	classification	NOUN
cana-3454	132	17	.	.	PUNCT
cana-3454	133	1	communications	communication	NOUN
cana-3454	133	2	on	on	ADP
cana-3454	133	3	applied	apply	VERB
cana-3454	133	4	nonlinear	nonlinear	ADJ
cana-3454	133	5	analysis	analysis	NOUN
cana-3454	133	6	issn	issn	NOUN
cana-3454	133	7	:	:	PUNCT
cana-3454	133	8	1074	1074	NUM
cana-3454	133	9	-	-	PUNCT
cana-3454	133	10	133x	133x	NUM
cana-3454	133	11	vol	vol	NOUN
cana-3454	133	12	32	32	NUM
cana-3454	133	13	no	no	NOUN
cana-3454	133	14	.	.	PUNCT
cana-3454	134	1	7s	7	NOUN
cana-3454	134	2	(	(	PUNCT
cana-3454	134	3	2025	2025	NUM
cana-3454	134	4	)	)	PUNCT
cana-3454	134	5	439	439	NUM
cana-3454	134	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	134	7	results	result	VERB
cana-3454	134	8	model	model	NOUN
cana-3454	134	9	accuracy	accuracy	NOUN
cana-3454	134	10	the	the	DET
cana-3454	134	11	accuracy	accuracy	NOUN
cana-3454	134	12	of	of	ADP
cana-3454	134	13	each	each	DET
cana-3454	134	14	algorithm	algorithm	NOUN
cana-3454	134	15	was	be	AUX
cana-3454	134	16	then	then	ADV
cana-3454	134	17	computed	compute	VERB
cana-3454	134	18	over	over	ADP
cana-3454	134	19	the	the	DET
cana-3454	134	20	three	three	NUM
cana-3454	134	21	different	different	ADJ
cana-3454	134	22	datasets	dataset	NOUN
cana-3454	134	23	.	.	PUNCT
cana-3454	135	1	based	base	VERB
cana-3454	135	2	on	on	ADP
cana-3454	135	3	the	the	DET
cana-3454	135	4	results	result	NOUN
cana-3454	135	5	,	,	PUNCT
cana-3454	135	6	it	it	PRON
cana-3454	135	7	is	be	AUX
cana-3454	135	8	clear	clear	ADJ
cana-3454	135	9	that	that	SCONJ
cana-3454	135	10	cnn	cnn	PROPN
cana-3454	135	11	produced	produce	VERB
cana-3454	135	12	the	the	DET
cana-3454	135	13	optimum	optimum	ADJ
cana-3454	135	14	results	result	NOUN
cana-3454	135	15	because	because	SCONJ
cana-3454	135	16	of	of	ADP
cana-3454	135	17	the	the	DET
cana-3454	135	18	accuracy	accuracy	NOUN
cana-3454	135	19	in	in	ADP
cana-3454	135	20	learning	learn	VERB
cana-3454	135	21	hierarchical	hierarchical	ADJ
cana-3454	135	22	features	feature	NOUN
cana-3454	135	23	.	.	PUNCT
cana-3454	136	1	in	in	ADP
cana-3454	136	2	real	real	ADJ
cana-3454	136	3	-	-	PUNCT
cana-3454	136	4	time	time	NOUN
cana-3454	136	5	object	object	NOUN
cana-3454	136	6	detection	detection	NOUN
cana-3454	136	7	,	,	PUNCT
cana-3454	136	8	yolo	yolo	PROPN
cana-3454	136	9	delivered	deliver	VERB
cana-3454	136	10	high	high	ADJ
cana-3454	136	11	accuracy	accuracy	NOUN
cana-3454	136	12	in	in	ADP
cana-3454	136	13	recognition	recognition	NOUN
cana-3454	136	14	tasks	task	NOUN
cana-3454	136	15	on	on	ADP
cana-3454	136	16	the	the	DET
cana-3454	136	17	coco	coco	PROPN
cana-3454	136	18	dataset	dataset	PROPN
cana-3454	136	19	whereas	whereas	SCONJ
cana-3454	136	20	,	,	PUNCT
cana-3454	136	21	for	for	ADP
cana-3454	136	22	small	small	ADJ
cana-3454	136	23	datasets	dataset	NOUN
cana-3454	136	24	such	such	ADJ
cana-3454	136	25	as	as	ADP
cana-3454	136	26	mnist	mnist	NOUN
cana-3454	136	27	,	,	PUNCT
cana-3454	136	28	svm	svm	PROPN
cana-3454	136	29	and	and	CCONJ
cana-3454	136	30	knn	knn	PROPN
cana-3454	136	31	performed	perform	VERB
cana-3454	136	32	well	well	ADV
cana-3454	136	33	[	[	X
cana-3454	136	34	14	14	NUM
cana-3454	136	35	]	]	PUNCT
cana-3454	136	36	.	.	PUNCT
cana-3454	137	1	table	table	NOUN
cana-3454	137	2	1	1	NUM
cana-3454	137	3	:	:	PUNCT
cana-3454	137	4	accuracy	accuracy	NOUN
cana-3454	137	5	of	of	ADP
cana-3454	137	6	algorithms	algorithm	NOUN
cana-3454	137	7	across	across	ADP
cana-3454	137	8	datasets	dataset	NOUN
cana-3454	137	9	algorithm	algorithm	NOUN
cana-3454	137	10	imagenet	imagenet	NOUN
cana-3454	137	11	accuracy	accuracy	NOUN
cana-3454	137	12	(	(	PUNCT
cana-3454	137	13	%	%	INTJ
cana-3454	137	14	)	)	PUNCT
cana-3454	137	15	coco	coco	PROPN
cana-3454	137	16	accuracy	accuracy	NOUN
cana-3454	137	17	(	(	PUNCT
cana-3454	137	18	%	%	INTJ
cana-3454	137	19	)	)	PUNCT
cana-3454	137	20	mnist	mnist	NOUN
cana-3454	137	21	accuracy	accuracy	NOUN
cana-3454	137	22	(	(	PUNCT
cana-3454	137	23	%	%	INTJ
cana-3454	137	24	)	)	PUNCT
cana-3454	137	25	cnn	cnn	PROPN
cana-3454	137	26	93.2	93.2	NUM
cana-3454	137	27	89.4	89.4	NUM
cana-3454	137	28	98.6	98.6	NUM
cana-3454	137	29	svm	svm	NOUN
cana-3454	137	30	82.1	82.1	NUM
cana-3454	137	31	75.3	75.3	NUM
cana-3454	137	32	96.2	96.2	NUM
cana-3454	137	33	knn	knn	NOUN
cana-3454	137	34	78.4	78.4	NUM
cana-3454	137	35	72.5	72.5	NUM
cana-3454	137	36	95.0	95.0	NUM
cana-3454	137	37	yolo	yolo	ADJ
cana-3454	137	38	90.8	90.8	NUM
cana-3454	137	39	92.1	92.1	NUM
cana-3454	137	40	not	not	PART
cana-3454	137	41	applicable	applicable	ADJ
cana-3454	137	42	inference	inference	NOUN
cana-3454	137	43	speed	speed	VERB
cana-3454	137	44	another	another	DET
cana-3454	137	45	important	important	ADJ
cana-3454	137	46	aspect	aspect	NOUN
cana-3454	137	47	of	of	ADP
cana-3454	137	48	concern	concern	NOUN
cana-3454	137	49	for	for	ADP
cana-3454	137	50	the	the	DET
cana-3454	137	51	real	real	ADJ
cana-3454	137	52	-	-	PUNCT
cana-3454	137	53	time	time	NOUN
cana-3454	137	54	behaviors	behavior	NOUN
cana-3454	137	55	is	be	AUX
cana-3454	137	56	a	a	DET
cana-3454	137	57	speed	speed	NOUN
cana-3454	137	58	of	of	ADP
cana-3454	137	59	the	the	DET
cana-3454	137	60	inference	inference	NOUN
cana-3454	137	61	procedures	procedure	NOUN
cana-3454	137	62	.	.	PUNCT
cana-3454	138	1	however	however	ADV
cana-3454	138	2	,	,	PUNCT
cana-3454	138	3	in	in	ADP
cana-3454	138	4	terms	term	NOUN
cana-3454	138	5	of	of	ADP
cana-3454	138	6	the	the	DET
cana-3454	138	7	speed	speed	NOUN
cana-3454	138	8	capabilities	capability	NOUN
cana-3454	138	9	,	,	PUNCT
cana-3454	138	10	the	the	DET
cana-3454	138	11	yolo	yolo	ADJ
cana-3454	138	12	algorithm	algorithm	NOUN
cana-3454	138	13	was	be	AUX
cana-3454	138	14	faster	fast	ADJ
cana-3454	138	15	than	than	SCONJ
cana-3454	138	16	all	all	DET
cana-3454	138	17	the	the	DET
cana-3454	138	18	rest	rest	NOUN
cana-3454	138	19	to	to	PART
cana-3454	138	20	be	be	AUX
cana-3454	138	21	used	use	VERB
cana-3454	138	22	for	for	ADP
cana-3454	138	23	real	real	ADJ
cana-3454	138	24	-	-	PUNCT
cana-3454	138	25	time	time	NOUN
cana-3454	138	26	object	object	NOUN
cana-3454	138	27	detection	detection	NOUN
cana-3454	138	28	[	[	X
cana-3454	138	29	27	27	NUM
cana-3454	138	30	]	]	PUNCT
cana-3454	138	31	.	.	PUNCT
cana-3454	139	1	knn	knn	PROPN
cana-3454	139	2	was	be	AUX
cana-3454	139	3	slow	slow	ADJ
cana-3454	139	4	being	be	AUX
cana-3454	139	5	a	a	DET
cana-3454	139	6	lazy	lazy	ADJ
cana-3454	139	7	learner	learner	NOUN
cana-3454	139	8	because	because	SCONJ
cana-3454	139	9	it	it	PRON
cana-3454	139	10	was	be	AUX
cana-3454	139	11	heavily	heavily	ADV
cana-3454	139	12	depending	depend	VERB
cana-3454	139	13	on	on	ADP
cana-3454	139	14	the	the	DET
cana-3454	139	15	calculation	calculation	NOUN
cana-3454	139	16	of	of	ADP
cana-3454	139	17	distance	distance	NOUN
cana-3454	139	18	at	at	ADP
cana-3454	139	19	runtime	runtime	NOUN
cana-3454	139	20	.	.	PUNCT
cana-3454	140	1	table	table	NOUN
cana-3454	140	2	2	2	NUM
cana-3454	140	3	:	:	PUNCT
cana-3454	140	4	inference	inference	NOUN
cana-3454	140	5	speed	speed	NOUN
cana-3454	140	6	(	(	PUNCT
cana-3454	140	7	ms	ms	NOUN
cana-3454	140	8	/	/	SYM
cana-3454	140	9	frame	frame	NOUN
cana-3454	140	10	)	)	PUNCT
cana-3454	140	11	algorithm	algorithm	NOUN
cana-3454	140	12	imagenet	imagenet	NOUN
cana-3454	140	13	(	(	PUNCT
cana-3454	140	14	ms	ms	PROPN
cana-3454	140	15	)	)	PUNCT
cana-3454	140	16	coco	coco	PROPN
cana-3454	140	17	(	(	PUNCT
cana-3454	140	18	ms	ms	PROPN
cana-3454	140	19	)	)	PUNCT
cana-3454	140	20	mnist	mnist	NOUN
cana-3454	140	21	(	(	PUNCT
cana-3454	140	22	ms	ms	PROPN
cana-3454	140	23	)	)	PUNCT
cana-3454	140	24	cnn	cnn	PROPN
cana-3454	140	25	32	32	NUM
cana-3454	140	26	35	35	NUM
cana-3454	140	27	15	15	NUM
cana-3454	140	28	svm	svm	NOUN
cana-3454	140	29	45	45	NUM
cana-3454	140	30	48	48	NUM
cana-3454	140	31	20	20	NUM
cana-3454	140	32	knn	knn	NOUN
cana-3454	140	33	100	100	NUM
cana-3454	140	34	120	120	NUM
cana-3454	140	35	60	60	NUM
cana-3454	140	36	yolo	yolo	ADV
cana-3454	140	37	10	10	NUM
cana-3454	140	38	12	12	NUM
cana-3454	140	39	not	not	PART
cana-3454	140	40	applicable	applicable	ADJ
cana-3454	140	41	comparison	comparison	NOUN
cana-3454	140	42	of	of	ADP
cana-3454	140	43	metrics	metric	NOUN
cana-3454	140	44	accuracy	accuracy	NOUN
cana-3454	140	45	,	,	PUNCT
cana-3454	140	46	sensitivity	sensitivity	NOUN
cana-3454	140	47	,	,	PUNCT
cana-3454	140	48	specificity	specificity	NOUN
cana-3454	140	49	,	,	PUNCT
cana-3454	140	50	and	and	CCONJ
cana-3454	140	51	f1	f1	NOUN
cana-3454	140	52	measure	measure	NOUN
cana-3454	140	53	were	be	AUX
cana-3454	140	54	employed	employ	VERB
cana-3454	140	55	in	in	ADP
cana-3454	140	56	order	order	NOUN
cana-3454	140	57	to	to	PART
cana-3454	140	58	examine	examine	VERB
cana-3454	140	59	the	the	DET
cana-3454	140	60	quality	quality	NOUN
cana-3454	140	61	of	of	ADP
cana-3454	140	62	classification	classification	NOUN
cana-3454	140	63	and	and	CCONJ
cana-3454	140	64	stability	stability	NOUN
cana-3454	140	65	of	of	ADP
cana-3454	140	66	detection	detection	NOUN
cana-3454	140	67	.	.	PUNCT
cana-3454	141	1	from	from	ADP
cana-3454	141	2	the	the	DET
cana-3454	141	3	results	result	NOUN
cana-3454	141	4	obtained	obtain	VERB
cana-3454	141	5	with	with	ADP
cana-3454	141	6	reference	reference	NOUN
cana-3454	141	7	to	to	ADP
cana-3454	141	8	coco	coco	PROPN
cana-3454	141	9	,	,	PUNCT
cana-3454	141	10	it	it	PRON
cana-3454	141	11	was	be	AUX
cana-3454	141	12	apparent	apparent	ADJ
cana-3454	141	13	that	that	SCONJ
cana-3454	141	14	yolo	yolo	PROPN
cana-3454	141	15	delivered	deliver	VERB
cana-3454	141	16	better	well	ADJ
cana-3454	141	17	precision	precision	NOUN
cana-3454	141	18	and	and	CCONJ
cana-3454	141	19	recall	recall	NOUN
cana-3454	141	20	for	for	ADP
cana-3454	141	21	the	the	DET
cana-3454	141	22	object	object	NOUN
cana-3454	141	23	detection	detection	NOUN
cana-3454	141	24	task	task	NOUN
cana-3454	141	25	[	[	X
cana-3454	141	26	28	28	NUM
cana-3454	141	27	]	]	PUNCT
cana-3454	141	28	.	.	PUNCT
cana-3454	142	1	on	on	ADP
cana-3454	142	2	the	the	DET
cana-3454	142	3	similar	similar	ADJ
cana-3454	142	4	line	line	NOUN
cana-3454	142	5	,	,	PUNCT
cana-3454	142	6	cnn	cnn	PROPN
cana-3454	142	7	garners	garner	VERB
cana-3454	142	8	the	the	DET
cana-3454	142	9	best	good	ADJ
cana-3454	142	10	f1	f1	NOUN
cana-3454	142	11	score	score	NOUN
cana-3454	142	12	in	in	ADP
cana-3454	142	13	all	all	DET
cana-3454	142	14	the	the	DET
cana-3454	142	15	datasets	dataset	NOUN
cana-3454	142	16	.	.	PUNCT
cana-3454	143	1	communications	communication	NOUN
cana-3454	143	2	on	on	ADP
cana-3454	143	3	applied	apply	VERB
cana-3454	143	4	nonlinear	nonlinear	ADJ
cana-3454	143	5	analysis	analysis	NOUN
cana-3454	143	6	issn	issn	NOUN
cana-3454	143	7	:	:	PUNCT
cana-3454	143	8	1074	1074	NUM
cana-3454	143	9	-	-	PUNCT
cana-3454	143	10	133x	133x	NUM
cana-3454	143	11	vol	vol	NOUN
cana-3454	143	12	32	32	NUM
cana-3454	143	13	no	no	NOUN
cana-3454	143	14	.	.	PUNCT
cana-3454	144	1	7s	7	NOUN
cana-3454	144	2	(	(	PUNCT
cana-3454	144	3	2025	2025	NUM
cana-3454	144	4	)	)	PUNCT
cana-3454	144	5	440	440	NUM
cana-3454	145	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	145	2	figure	figure	NOUN
cana-3454	145	3	2	2	NUM
cana-3454	145	4	:	:	PUNCT
cana-3454	145	5	“	"	PUNCT
cana-3454	145	6	an	an	DET
cana-3454	145	7	illustration	illustration	NOUN
cana-3454	145	8	of	of	ADP
cana-3454	145	9	graph	graph	NOUN
cana-3454	145	10	machine	machine	NOUN
cana-3454	145	11	learning	learning	NOUN
cana-3454	145	12	”	"	PUNCT
cana-3454	145	13	table	table	NOUN
cana-3454	145	14	3	3	NUM
cana-3454	145	15	:	:	PUNCT
cana-3454	145	16	precision	precision	NOUN
cana-3454	145	17	,	,	PUNCT
cana-3454	145	18	recall	recall	NOUN
cana-3454	145	19	,	,	PUNCT
cana-3454	145	20	and	and	CCONJ
cana-3454	145	21	f1	f1	NOUN
cana-3454	145	22	score	score	NOUN
cana-3454	145	23	algorithm	algorithm	NOUN
cana-3454	145	24	dataset	dataset	NOUN
cana-3454	145	25	precision	precision	NOUN
cana-3454	145	26	(	(	PUNCT
cana-3454	145	27	%	%	INTJ
cana-3454	145	28	)	)	PUNCT
cana-3454	145	29	recall	recall	NOUN
cana-3454	145	30	(	(	PUNCT
cana-3454	145	31	%	%	NOUN
cana-3454	145	32	)	)	PUNCT
cana-3454	145	33	f1	f1	NOUN
cana-3454	145	34	score	score	NOUN
cana-3454	145	35	(	(	PUNCT
cana-3454	145	36	%	%	INTJ
cana-3454	145	37	)	)	PUNCT
cana-3454	145	38	cnn	cnn	PROPN
cana-3454	145	39	imagenet	imagenet	NOUN
cana-3454	145	40	94.0	94.0	NUM
cana-3454	145	41	92.1	92.1	NUM
cana-3454	145	42	93.0	93.0	NUM
cana-3454	145	43	svm	svm	NOUN
cana-3454	145	44	imagenet	imagenet	NOUN
cana-3454	145	45	85.5	85.5	NUM
cana-3454	145	46	81.3	81.3	NUM
cana-3454	145	47	83.3	83.3	NUM
cana-3454	145	48	knn	knn	NOUN
cana-3454	145	49	mnist	mnist	PROPN
cana-3454	145	50	90.2	90.2	NUM
cana-3454	145	51	88.7	88.7	NUM
cana-3454	145	52	89.4	89.4	NUM
cana-3454	145	53	yolo	yolo	ADJ
cana-3454	145	54	coco	coco	PROPN
cana-3454	145	55	91.8	91.8	NUM
cana-3454	145	56	93.0	93.0	NUM
cana-3454	145	57	92.4	92.4	NUM
cana-3454	145	58	comparative	comparative	ADJ
cana-3454	145	59	analysis	analysis	NOUN
cana-3454	145	60	with	with	ADP
cana-3454	145	61	related	related	ADJ
cana-3454	145	62	work	work	NOUN
cana-3454	145	63	image	image	NOUN
cana-3454	145	64	classification	classification	NOUN
cana-3454	145	65	the	the	DET
cana-3454	145	66	proposed	propose	VERB
cana-3454	145	67	cnns	cnn	NOUN
cana-3454	145	68	,	,	PUNCT
cana-3454	145	69	therefore	therefore	ADV
cana-3454	145	70	,	,	PUNCT
cana-3454	145	71	outperformed	outperform	VERB
cana-3454	145	72	traditional	traditional	ADJ
cana-3454	145	73	techniques	technique	NOUN
cana-3454	145	74	,	,	PUNCT
cana-3454	145	75	including	include	VERB
cana-3454	145	76	histogram	histogram	NOUN
cana-3454	145	77	-	-	PUNCT
cana-3454	145	78	based	base	VERB
cana-3454	145	79	classifiers	classifier	NOUN
cana-3454	145	80	used	use	VERB
cana-3454	145	81	in	in	ADP
cana-3454	145	82	[	[	X
cana-3454	145	83	author	author	NOUN
cana-3454	145	84	a	a	X
cana-3454	145	85	et	et	NOUN
cana-3454	145	86	al	al	PROPN
cana-3454	145	87	.	.	PROPN
cana-3454	145	88	,	,	PUNCT
cana-3454	145	89	2022	2022	NUM
cana-3454	145	90	]	]	PUNCT
cana-3454	145	91	.	.	PUNCT
cana-3454	146	1	our	our	PRON
cana-3454	146	2	trained	train	VERB
cana-3454	146	3	cnn	cnn	PROPN
cana-3454	146	4	model	model	NOUN
cana-3454	146	5	outperformed	outperform	VERB
cana-3454	146	6	the	the	DET
cana-3454	146	7	expected	expect	VERB
cana-3454	146	8	results	result	NOUN
cana-3454	146	9	of	of	ADP
cana-3454	146	10	tiny	tiny	ADJ
cana-3454	146	11	cnn	cnn	PROPN
cana-3454	146	12	models	model	NOUN
cana-3454	146	13	provided	provide	VERB
cana-3454	146	14	in	in	ADP
cana-3454	146	15	the	the	DET
cana-3454	146	16	literature	literature	NOUN
cana-3454	146	17	by	by	ADP
cana-3454	146	18	3.5	3.5	NUM
cana-3454	146	19	%	%	NOUN
cana-3454	146	20	on	on	ADP
cana-3454	146	21	imagenet	imagenet	PROPN
cana-3454	146	22	.	.	PUNCT
cana-3454	147	1	object	object	NOUN
cana-3454	147	2	detection	detection	NOUN
cana-3454	147	3	compared	compare	VERB
cana-3454	147	4	to	to	ADP
cana-3454	147	5	the	the	DET
cana-3454	147	6	faster	fast	ADJ
cana-3454	147	7	r	r	NOUN
cana-3454	147	8	-	-	PUNCT
cana-3454	147	9	cnn	cnn	PROPN
cana-3454	147	10	model	model	NOUN
cana-3454	147	11	mentioned	mention	VERB
cana-3454	147	12	in	in	ADP
cana-3454	147	13	[	[	X
cana-3454	147	14	author	author	NOUN
cana-3454	147	15	b	b	PROPN
cana-3454	147	16	et	et	PROPN
cana-3454	147	17	al	al	PROPN
cana-3454	147	18	.	.	PROPN
cana-3454	147	19	,	,	PUNCT
cana-3454	147	20	2021	2021	NUM
cana-3454	147	21	]	]	PUNCT
cana-3454	147	22	,	,	PUNCT
cana-3454	147	23	yolo	yolo	PROPN
cana-3454	147	24	achieved	achieve	VERB
cana-3454	147	25	higher	high	ADJ
cana-3454	147	26	precision	precision	NOUN
cana-3454	147	27	and	and	CCONJ
cana-3454	147	28	recall	recall	NOUN
cana-3454	147	29	while	while	SCONJ
cana-3454	147	30	being	be	AUX
cana-3454	147	31	12	12	NUM
cana-3454	147	32	%	%	NOUN
cana-3454	147	33	faster	fast	ADV
cana-3454	147	34	in	in	ADP
cana-3454	147	35	inference	inference	NOUN
cana-3454	147	36	speed	speed	NOUN
cana-3454	147	37	within	within	ADP
cana-3454	147	38	the	the	DET
cana-3454	147	39	coco	coco	PROPN
cana-3454	147	40	.	.	PUNCT
cana-3454	148	1	this	this	PRON
cana-3454	148	2	is	be	AUX
cana-3454	148	3	particularly	particularly	ADV
cana-3454	148	4	in	in	ADP
cana-3454	148	5	line	line	NOUN
cana-3454	148	6	with	with	ADP
cana-3454	148	7	the	the	DET
cana-3454	148	8	work	work	NOUN
cana-3454	148	9	of	of	ADP
cana-3454	148	10	[	[	X
cana-3454	148	11	author	author	NOUN
cana-3454	148	12	c	c	PROPN
cana-3454	148	13	et	et	PROPN
cana-3454	148	14	al	al	PROPN
cana-3454	148	15	.	.	PROPN
cana-3454	148	16	,	,	PUNCT
cana-3454	148	17	2023	2023	NUM
cana-3454	148	18	]	]	PUNCT
cana-3454	148	19	,	,	PUNCT
cana-3454	148	20	who	who	PRON
cana-3454	148	21	underscore	underscore	VERB
cana-3454	148	22	the	the	DET
cana-3454	148	23	real	real	ADJ
cana-3454	148	24	-	-	PUNCT
cana-3454	148	25	time	time	NOUN
cana-3454	148	26	capability	capability	NOUN
cana-3454	148	27	of	of	ADP
cana-3454	148	28	yolo	yolo	NOUN
cana-3454	148	29	for	for	ADP
cana-3454	148	30	detection	detection	NOUN
cana-3454	148	31	[	[	X
cana-3454	148	32	29	29	NUM
cana-3454	148	33	]	]	PUNCT
cana-3454	148	34	.	.	PUNCT
cana-3454	149	1	small	small	ADJ
cana-3454	149	2	dataset	dataset	NOUN
cana-3454	149	3	applications	application	NOUN
cana-3454	149	4	svm	svm	VERB
cana-3454	149	5	and	and	CCONJ
cana-3454	149	6	knn	knn	PROPN
cana-3454	149	7	performed	perform	VERB
cana-3454	149	8	equally	equally	ADV
cana-3454	149	9	well	well	ADV
cana-3454	149	10	on	on	ADP
cana-3454	149	11	mnist	mnist	NOUN
cana-3454	149	12	as	as	SCONJ
cana-3454	149	13	was	be	AUX
cana-3454	149	14	the	the	DET
cana-3454	149	15	performance	performance	NOUN
cana-3454	149	16	reported	report	VERB
cana-3454	149	17	by	by	ADP
cana-3454	149	18	[	[	X
cana-3454	149	19	author	author	NOUN
cana-3454	149	20	d	d	X
cana-3454	149	21	et	et	PROPN
cana-3454	149	22	al	al	PROPN
cana-3454	149	23	.	.	PROPN
cana-3454	149	24	,	,	PUNCT
cana-3454	149	25	2020	2020	NUM
cana-3454	149	26	]	]	PUNCT
cana-3454	149	27	.	.	PUNCT
cana-3454	150	1	however	however	ADV
cana-3454	150	2	,	,	PUNCT
cana-3454	150	3	cnn	cnn	PROPN
cana-3454	150	4	outperforms	outperform	VERB
cana-3454	150	5	both	both	CCONJ
cana-3454	150	6	in	in	ADP
cana-3454	150	7	accuracy	accuracy	NOUN
cana-3454	150	8	by	by	ADP
cana-3454	150	9	2.4	2.4	NUM
cana-3454	150	10	%	%	NOUN
cana-3454	150	11	,	,	PUNCT
cana-3454	150	12	which	which	PRON
cana-3454	150	13	proves	prove	VERB
cana-3454	150	14	the	the	DET
cana-3454	150	15	position	position	NOUN
cana-3454	150	16	of	of	ADP
cana-3454	150	17	this	this	DET
cana-3454	150	18	model	model	NOUN
cana-3454	150	19	even	even	ADV
cana-3454	150	20	at	at	ADP
cana-3454	150	21	a	a	DET
cana-3454	150	22	small	small	ADJ
cana-3454	150	23	number	number	NOUN
cana-3454	150	24	of	of	ADP
cana-3454	150	25	training	training	NOUN
cana-3454	150	26	samples	sample	NOUN
cana-3454	150	27	.	.	PUNCT
cana-3454	151	1	communications	communication	NOUN
cana-3454	151	2	on	on	ADP
cana-3454	151	3	applied	apply	VERB
cana-3454	151	4	nonlinear	nonlinear	ADJ
cana-3454	151	5	analysis	analysis	NOUN
cana-3454	151	6	issn	issn	NOUN
cana-3454	151	7	:	:	PUNCT
cana-3454	151	8	1074	1074	NUM
cana-3454	151	9	-	-	PUNCT
cana-3454	151	10	133x	133x	NUM
cana-3454	151	11	vol	vol	NOUN
cana-3454	151	12	32	32	NUM
cana-3454	151	13	no	no	NOUN
cana-3454	151	14	.	.	PUNCT
cana-3454	152	1	7s	7	NOUN
cana-3454	152	2	(	(	PUNCT
cana-3454	152	3	2025	2025	NUM
cana-3454	152	4	)	)	PUNCT
cana-3454	152	5	441	441	NUM
cana-3454	152	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	152	7	figure	figure	NOUN
cana-3454	152	8	3	3	NUM
cana-3454	152	9	:	:	PUNCT
cana-3454	152	10	“	"	PUNCT
cana-3454	152	11	machine	machine	NOUN
cana-3454	152	12	learning	learn	VERB
cana-3454	152	13	market	market	NOUN
cana-3454	152	14	size	size	NOUN
cana-3454	152	15	,	,	PUNCT
cana-3454	152	16	share	share	NOUN
cana-3454	152	17	&	&	CCONJ
cana-3454	152	18	growth	growth	PROPN
cana-3454	152	19	report	report	NOUN
cana-3454	152	20	,	,	PUNCT
cana-3454	152	21	2030	2030	NUM
cana-3454	152	22	”	"	PUNCT
cana-3454	152	23	algorithm	algorithm	NOUN
cana-3454	152	24	performance	performance	NOUN
cana-3454	152	25	breakdown	breakdown	NOUN
cana-3454	152	26	cnn	cnn	PROPN
cana-3454	152	27	performance	performance	NOUN
cana-3454	152	28	cnns	cnn	NOUN
cana-3454	152	29	were	be	AUX
cana-3454	152	30	beneficial	beneficial	ADJ
cana-3454	152	31	in	in	ADP
cana-3454	152	32	learning	learn	VERB
cana-3454	152	33	spatial	spatial	ADJ
cana-3454	152	34	hierarchies	hierarchy	NOUN
cana-3454	152	35	of	of	ADP
cana-3454	152	36	features	feature	NOUN
cana-3454	152	37	making	make	VERB
cana-3454	152	38	cnn	cnn	PROPN
cana-3454	152	39	excel	excel	VERB
cana-3454	152	40	in	in	ADP
cana-3454	152	41	the	the	DET
cana-3454	152	42	process	process	NOUN
cana-3454	152	43	.	.	PUNCT
cana-3454	153	1	it	it	PRON
cana-3454	153	2	was	be	AUX
cana-3454	153	3	very	very	ADV
cana-3454	153	4	resilient	resilient	ADJ
cana-3454	153	5	on	on	ADP
cana-3454	153	6	imagenet	imagenet	NOUN
cana-3454	153	7	,	,	PUNCT
cana-3454	153	8	achieving	achieve	VERB
cana-3454	153	9	a	a	DET
cana-3454	153	10	93.2	93.2	NUM
cana-3454	153	11	%	%	NOUN
cana-3454	153	12	of	of	ADP
cana-3454	153	13	accuracy	accuracy	NOUN
cana-3454	153	14	.	.	PUNCT
cana-3454	154	1	coco	coco	PROPN
cana-3454	154	2	,	,	PUNCT
cana-3454	154	3	it	it	PRON
cana-3454	154	4	was	be	AUX
cana-3454	154	5	slightly	slightly	ADV
cana-3454	154	6	lower	low	ADJ
cana-3454	154	7	because	because	SCONJ
cana-3454	154	8	of	of	ADP
cana-3454	154	9	the	the	DET
cana-3454	154	10	increased	increase	VERB
cana-3454	154	11	difficulty	difficulty	NOUN
cana-3454	154	12	of	of	ADP
cana-3454	154	13	the	the	DET
cana-3454	154	14	detection	detection	NOUN
cana-3454	154	15	tasks	task	NOUN
cana-3454	154	16	performed	perform	VERB
cana-3454	154	17	for	for	ADP
cana-3454	154	18	it	it	PRON
cana-3454	154	19	.	.	PUNCT
cana-3454	155	1	table	table	NOUN
cana-3454	155	2	4	4	NUM
cana-3454	155	3	:	:	PUNCT
cana-3454	155	4	cnn	cnn	PROPN
cana-3454	155	5	performance	performance	NOUN
cana-3454	155	6	on	on	ADP
cana-3454	155	7	tasks	task	NOUN
cana-3454	155	8	dataset	dataset	VERB
cana-3454	155	9	accuracy	accuracy	NOUN
cana-3454	155	10	(	(	PUNCT
cana-3454	155	11	%	%	INTJ
cana-3454	155	12	)	)	PUNCT
cana-3454	155	13	training	training	NOUN
cana-3454	155	14	time	time	NOUN
cana-3454	155	15	(	(	PUNCT
cana-3454	155	16	hrs	hrs	NOUN
cana-3454	155	17	)	)	PUNCT
cana-3454	155	18	application	application	NOUN
cana-3454	155	19	example	example	NOUN
cana-3454	155	20	imagenet	imagenet	NOUN
cana-3454	155	21	93.2	93.2	NUM
cana-3454	155	22	12	12	NUM
cana-3454	155	23	image	image	NOUN
cana-3454	155	24	classification	classification	NOUN
cana-3454	155	25	coco	coco	NOUN
cana-3454	155	26	89.4	89.4	NUM
cana-3454	155	27	15	15	NUM
cana-3454	155	28	object	object	NOUN
cana-3454	155	29	detection	detection	NOUN
cana-3454	155	30	mnist	mnist	NOUN
cana-3454	155	31	98.6	98.6	NUM
cana-3454	155	32	5	5	NUM
cana-3454	155	33	digit	digit	NOUN
cana-3454	155	34	recognition	recognition	NOUN
cana-3454	155	35	svm	svm	ADJ
cana-3454	155	36	performance	performance	NOUN
cana-3454	155	37	this	this	PRON
cana-3454	155	38	was	be	AUX
cana-3454	155	39	because	because	SCONJ
cana-3454	155	40	svm	svm	PROPN
cana-3454	155	41	is	be	AUX
cana-3454	155	42	suitable	suitable	ADJ
cana-3454	155	43	with	with	ADP
cana-3454	155	44	small	small	ADJ
cana-3454	155	45	datasets	dataset	NOUN
cana-3454	155	46	and	and	CCONJ
cana-3454	155	47	still	still	ADV
cana-3454	155	48	had	have	VERB
cana-3454	155	49	excellent	excellent	ADJ
cana-3454	155	50	performance	performance	NOUN
cana-3454	155	51	on	on	ADP
cana-3454	155	52	mnist	mnist	NOUN
cana-3454	155	53	.	.	PUNCT
cana-3454	156	1	but	but	CCONJ
cana-3454	156	2	it	it	PRON
cana-3454	156	3	succeeded	succeed	VERB
cana-3454	156	4	on	on	ADP
cana-3454	156	5	the	the	DET
cana-3454	156	6	mnist	mnist	NOUN
cana-3454	156	7	dataset	dataset	NOUN
cana-3454	156	8	and	and	CCONJ
cana-3454	156	9	failed	fail	VERB
cana-3454	156	10	on	on	ADP
cana-3454	156	11	the	the	DET
cana-3454	156	12	coco	coco	PROPN
cana-3454	156	13	dataset	dataset	PROPN
cana-3454	156	14	because	because	SCONJ
cana-3454	156	15	the	the	DET
cana-3454	156	16	computational	computational	ADJ
cana-3454	156	17	complexity	complexity	NOUN
cana-3454	156	18	of	of	ADP
cana-3454	156	19	kernel	kernel	NOUN
cana-3454	156	20	functions	function	NOUN
cana-3454	156	21	in	in	ADP
cana-3454	156	22	high	high	ADJ
cana-3454	156	23	dimensions	dimension	NOUN
cana-3454	156	24	of	of	ADP
cana-3454	156	25	data	data	PROPN
cana-3454	156	26	.	.	PUNCT
cana-3454	157	1	table	table	NOUN
cana-3454	157	2	5	5	NUM
cana-3454	157	3	:	:	PUNCT
cana-3454	157	4	svm	svm	VERB
cana-3454	157	5	challenges	challenge	NOUN
cana-3454	157	6	on	on	ADP
cana-3454	157	7	coco	coco	PROPN
cana-3454	157	8	metric	metric	PROPN
cana-3454	157	9	value	value	NOUN
cana-3454	157	10	observation	observation	NOUN
cana-3454	157	11	accuracy	accuracy	NOUN
cana-3454	157	12	(	(	PUNCT
cana-3454	157	13	%	%	INTJ
cana-3454	157	14	)	)	PUNCT
cana-3454	157	15	75.3	75.3	NUM
cana-3454	157	16	lower	low	ADJ
cana-3454	157	17	due	due	ADJ
cana-3454	157	18	to	to	ADP
cana-3454	157	19	kernel	kernel	NOUN
cana-3454	157	20	inefficiency	inefficiency	NOUN
cana-3454	157	21	training	training	NOUN
cana-3454	157	22	time	time	NOUN
cana-3454	157	23	20	20	NUM
cana-3454	157	24	hrs	hrs	NOUN
cana-3454	157	25	high	high	ADJ
cana-3454	157	26	computational	computational	ADJ
cana-3454	157	27	overhead	overhead	ADJ
cana-3454	157	28	precision	precision	NOUN
cana-3454	157	29	(	(	PUNCT
cana-3454	157	30	%	%	INTJ
cana-3454	157	31	)	)	PUNCT
cana-3454	157	32	70.5	70.5	NUM
cana-3454	157	33	suboptimal	suboptimal	NOUN
cana-3454	157	34	for	for	ADP
cana-3454	157	35	complex	complex	ADJ
cana-3454	157	36	object	object	NOUN
cana-3454	157	37	detection	detection	NOUN
cana-3454	157	38	knn	knn	PROPN
cana-3454	157	39	performance	performance	PROPN
cana-3454	157	40	knn	knn	PROPN
cana-3454	157	41	was	be	AUX
cana-3454	157	42	more	more	ADV
cana-3454	157	43	convenient	convenient	ADJ
cana-3454	157	44	and	and	CCONJ
cana-3454	157	45	ran	run	VERB
cana-3454	157	46	faster	fast	ADV
cana-3454	157	47	on	on	ADP
cana-3454	157	48	mnist	mnist	NOUN
cana-3454	157	49	,	,	PUNCT
cana-3454	157	50	however	however	ADV
cana-3454	157	51	,	,	PUNCT
cana-3454	157	52	demonstrated	demonstrate	VERB
cana-3454	157	53	poor	poor	ADJ
cana-3454	157	54	ability	ability	NOUN
cana-3454	157	55	to	to	PART
cana-3454	157	56	scale	scale	VERB
cana-3454	157	57	on	on	ADP
cana-3454	157	58	larger	large	ADJ
cana-3454	157	59	datasets	dataset	NOUN
cana-3454	157	60	.	.	PUNCT
cana-3454	158	1	despite	despite	SCONJ
cana-3454	158	2	that	that	PRON
cana-3454	158	3	,	,	PUNCT
cana-3454	158	4	its	its	PRON
cana-3454	158	5	inference	inference	NOUN
cana-3454	158	6	speed	speed	NOUN
cana-3454	158	7	was	be	AUX
cana-3454	158	8	slow	slow	ADJ
cana-3454	158	9	,	,	PUNCT
cana-3454	158	10	mainly	mainly	ADV
cana-3454	158	11	due	due	ADP
cana-3454	158	12	to	to	ADP
cana-3454	158	13	the	the	DET
cana-3454	158	14	calculations	calculation	NOUN
cana-3454	158	15	of	of	ADP
cana-3454	158	16	distance	distance	NOUN
cana-3454	158	17	at	at	ADP
cana-3454	158	18	runtime	runtime	NOUN
cana-3454	158	19	on	on	ADP
cana-3454	158	20	coco	coco	PROPN
cana-3454	158	21	.	.	PUNCT
cana-3454	159	1	table	table	NOUN
cana-3454	159	2	6	6	NUM
cana-3454	159	3	:	:	PUNCT
cana-3454	159	4	knn	knn	VERB
cana-3454	159	5	efficiency	efficiency	NOUN
cana-3454	159	6	across	across	ADP
cana-3454	159	7	datasets	dataset	NOUN
cana-3454	159	8	communications	communication	NOUN
cana-3454	159	9	on	on	ADP
cana-3454	159	10	applied	apply	VERB
cana-3454	159	11	nonlinear	nonlinear	ADJ
cana-3454	159	12	analysis	analysis	NOUN
cana-3454	159	13	issn	issn	NOUN
cana-3454	159	14	:	:	PUNCT
cana-3454	159	15	1074	1074	NUM
cana-3454	159	16	-	-	PUNCT
cana-3454	159	17	133x	133x	NUM
cana-3454	159	18	vol	vol	NOUN
cana-3454	159	19	32	32	NUM
cana-3454	159	20	no	no	NOUN
cana-3454	159	21	.	.	PUNCT
cana-3454	160	1	7s	7	NOUN
cana-3454	160	2	(	(	PUNCT
cana-3454	160	3	2025	2025	NUM
cana-3454	160	4	)	)	PUNCT
cana-3454	160	5	442	442	NUM
cana-3454	160	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-3454	160	7	dataset	dataset	NOUN
cana-3454	160	8	accuracy	accuracy	NOUN
cana-3454	160	9	(	(	PUNCT
cana-3454	160	10	%	%	INTJ
cana-3454	160	11	)	)	PUNCT
cana-3454	160	12	inference	inference	NOUN
cana-3454	160	13	time	time	NOUN
cana-3454	160	14	(	(	PUNCT
cana-3454	160	15	ms	ms	NOUN
cana-3454	160	16	)	)	PUNCT
cana-3454	160	17	limitation	limitation	NOUN
cana-3454	160	18	imagenet	imagenet	NOUN
cana-3454	160	19	78.4	78.4	NUM
cana-3454	160	20	100	100	NUM
cana-3454	160	21	high	high	ADJ
cana-3454	160	22	computational	computational	ADJ
cana-3454	160	23	cost	cost	NOUN
cana-3454	160	24	mnist	mnist	NOUN
cana-3454	160	25	95.0	95.0	NUM
cana-3454	160	26	60	60	NUM
cana-3454	160	27	effective	effective	ADJ
cana-3454	160	28	for	for	ADP
cana-3454	160	29	small	small	ADJ
cana-3454	160	30	data	datum	NOUN
cana-3454	160	31	yolo	yolo	ADJ
cana-3454	160	32	performance	performance	NOUN
cana-3454	160	33	in	in	ADP
cana-3454	160	34	terms	term	NOUN
cana-3454	160	35	of	of	ADP
cana-3454	160	36	real	real	ADJ
cana-3454	160	37	-	-	PUNCT
cana-3454	160	38	time	time	NOUN
cana-3454	160	39	usage	usage	NOUN
cana-3454	160	40	,	,	PUNCT
cana-3454	160	41	yolo	yolo	PROPN
cana-3454	160	42	was	be	AUX
cana-3454	160	43	the	the	DET
cana-3454	160	44	most	most	ADV
cana-3454	160	45	accurate	accurate	ADJ
cana-3454	160	46	with	with	ADP
cana-3454	160	47	a	a	DET
cana-3454	160	48	92.1	92.1	NUM
cana-3454	160	49	%	%	NOUN
cana-3454	160	50	on	on	ADP
cana-3454	160	51	coco	coco	PROPN
cana-3454	160	52	algorithms	algorithms	PROPN
cana-3454	160	53	.	.	PUNCT
cana-3454	161	1	due	due	ADP
cana-3454	161	2	to	to	ADP
cana-3454	161	3	its	its	PRON
cana-3454	161	4	applicability	applicability	NOUN
cana-3454	161	5	in	in	ADP
cana-3454	161	6	high	high	ADJ
cana-3454	161	7	speed	speed	NOUN
cana-3454	161	8	real	real	ADJ
cana-3454	161	9	-	-	PUNCT
cana-3454	161	10	time	time	NOUN
cana-3454	161	11	applications	application	NOUN
cana-3454	161	12	such	such	ADJ
cana-3454	161	13	as	as	ADP
cana-3454	161	14	autonomous	autonomous	ADJ
cana-3454	161	15	vehicles	vehicle	NOUN
cana-3454	161	16	and	and	CCONJ
cana-3454	161	17	surveillance	surveillance	NOUN
cana-3454	161	18	systems	system	NOUN
cana-3454	161	19	,	,	PUNCT
cana-3454	161	20	it	it	PRON
cana-3454	161	21	had	have	VERB
cana-3454	161	22	a	a	DET
cana-3454	161	23	frame	frame	NOUN
cana-3454	161	24	rate	rate	NOUN
cana-3454	161	25	of	of	ADP
cana-3454	161	26	10	10	NUM
cana-3454	161	27	ms	ms	PROPN
cana-3454	161	28	.	.	PROPN
cana-3454	161	29	figure	figure	NOUN
cana-3454	161	30	4	4	NUM
cana-3454	161	31	:	:	PUNCT
cana-3454	161	32	“	"	PUNCT
cana-3454	161	33	machine	machine	NOUN
cana-3454	161	34	vision	vision	NOUN
cana-3454	161	35	vs	vs	ADP
cana-3454	161	36	computer	computer	NOUN
cana-3454	161	37	vision	vision	NOUN
cana-3454	161	38	vs	vs	ADP
cana-3454	161	39	image	image	NOUN
cana-3454	161	40	processing	processing	NOUN
cana-3454	161	41	”	"	PUNCT
cana-3454	161	42	table	table	NOUN
cana-3454	161	43	7	7	NUM
cana-3454	161	44	:	:	PUNCT
cana-3454	161	45	yolo	yolo	ADJ
cana-3454	161	46	application	application	NOUN
cana-3454	161	47	examples	example	NOUN
cana-3454	161	48	task	task	NOUN
cana-3454	161	49	dataset	dataset	NOUN
cana-3454	161	50	accuracy	accuracy	NOUN
cana-3454	161	51	(	(	PUNCT
cana-3454	161	52	%	%	INTJ
cana-3454	161	53	)	)	PUNCT
cana-3454	161	54	speed	speed	NOUN
cana-3454	161	55	(	(	PUNCT
cana-3454	161	56	ms	ms	NOUN
cana-3454	161	57	/	/	SYM
cana-3454	161	58	frame	frame	NOUN
cana-3454	161	59	)	)	PUNCT
cana-3454	161	60	real	real	ADJ
cana-3454	161	61	-	-	PUNCT
cana-3454	161	62	time	time	NOUN
cana-3454	161	63	detection	detection	NOUN
cana-3454	161	64	coco	coco	PROPN
cana-3454	161	65	92.1	92.1	NUM
cana-3454	161	66	10	10	NUM
cana-3454	161	67	vehicle	vehicle	NOUN
cana-3454	161	68	detection	detection	NOUN
cana-3454	161	69	custom	custom	NOUN
cana-3454	161	70	91.5	91.5	NUM
cana-3454	161	71	12	12	NUM
cana-3454	161	72	conclusion	conclusion	NOUN
cana-3454	161	73	from	from	ADP
cana-3454	161	74	experiments	experiment	NOUN
cana-3454	161	75	●	●	NUM
cana-3454	161	76	cnns	cnn	NOUN
cana-3454	161	77	are	be	AUX
cana-3454	161	78	the	the	DET
cana-3454	161	79	most	most	ADV
cana-3454	161	80	universal	universal	ADJ
cana-3454	161	81	,	,	PUNCT
cana-3454	161	82	as	as	SCONJ
cana-3454	161	83	they	they	PRON
cana-3454	161	84	produce	produce	VERB
cana-3454	161	85	the	the	DET
cana-3454	161	86	highest	high	ADJ
cana-3454	161	87	accuracy	accuracy	NOUN
cana-3454	161	88	across	across	ADP
cana-3454	161	89	the	the	DET
cana-3454	161	90	datasets	dataset	NOUN
cana-3454	161	91	for	for	ADP
cana-3454	161	92	various	various	ADJ
cana-3454	161	93	types	type	NOUN
cana-3454	161	94	of	of	ADP
cana-3454	161	95	applications	application	NOUN
cana-3454	161	96	which	which	PRON
cana-3454	161	97	make	make	VERB
cana-3454	161	98	them	they	PRON
cana-3454	161	99	ideal	ideal	ADJ
cana-3454	161	100	for	for	ADP
cana-3454	161	101	use	use	NOUN
cana-3454	161	102	in	in	ADP
cana-3454	161	103	areas	area	NOUN
cana-3454	161	104	such	such	ADJ
cana-3454	161	105	as	as	ADP
cana-3454	161	106	medical	medical	ADJ
cana-3454	161	107	area	area	NOUN
cana-3454	161	108	or	or	CCONJ
cana-3454	161	109	image	image	NOUN
cana-3454	161	110	classification	classification	NOUN
cana-3454	161	111	.	.	PUNCT
cana-3454	162	1	●	●	PUNCT
cana-3454	162	2	this	this	DET
cana-3454	162	3	while	while	NOUN
cana-3454	162	4	,	,	PUNCT
cana-3454	162	5	makes	make	VERB
cana-3454	162	6	svms	svms	ADJ
cana-3454	162	7	efficient	efficient	ADJ
cana-3454	162	8	for	for	ADP
cana-3454	162	9	small	small	ADJ
cana-3454	162	10	data	datum	NOUN
cana-3454	162	11	and	and	CCONJ
cana-3454	162	12	not	not	PART
cana-3454	162	13	so	so	ADV
cana-3454	162	14	efficient	efficient	ADJ
cana-3454	162	15	for	for	ADP
cana-3454	162	16	large	large	ADJ
cana-3454	162	17	datasets	dataset	NOUN
cana-3454	162	18	.	.	PUNCT
cana-3454	163	1	●	●	PUNCT
cana-3454	163	2	pros	pro	NOUN
cana-3454	163	3	:	:	PUNCT
cana-3454	163	4	easier	easy	ADJ
cana-3454	163	5	to	to	PART
cana-3454	163	6	compute	compute	VERB
cana-3454	163	7	and	and	CCONJ
cana-3454	163	8	implements	implement	NOUN
cana-3454	163	9	and	and	CCONJ
cana-3454	163	10	cons	con	NOUN
cana-3454	163	11	:	:	PUNCT
cana-3454	163	12	inappropriate	inappropriate	ADJ
cana-3454	163	13	for	for	ADP
cana-3454	163	14	large	large	ADJ
cana-3454	163	15	sets	set	NOUN
cana-3454	163	16	of	of	ADP
cana-3454	163	17	data	datum	NOUN
cana-3454	163	18	because	because	SCONJ
cana-3454	163	19	it	it	PRON
cana-3454	163	20	is	be	AUX
cana-3454	163	21	time	time	NOUN
cana-3454	163	22	-	-	PUNCT
cana-3454	163	23	consuming	consume	VERB
cana-3454	163	24	[	[	X
cana-3454	163	25	30	30	NUM
cana-3454	163	26	]	]	SYM
cana-3454	163	27	.	.	PUNCT
cana-3454	164	1	●	●	PUNCT
cana-3454	164	2	for	for	ADP
cana-3454	164	3	real	real	ADJ
cana-3454	164	4	-	-	PUNCT
cana-3454	164	5	time	time	NOUN
cana-3454	164	6	detection	detection	NOUN
cana-3454	164	7	yolo	yolo	ADV
cana-3454	164	8	is	be	VERB
cana-3454	164	9	the	the	DET
cana-3454	164	10	most	most	ADV
cana-3454	164	11	effective	effective	ADJ
cana-3454	164	12	tool	tool	NOUN
cana-3454	164	13	that	that	PRON
cana-3454	164	14	can	can	AUX
cana-3454	164	15	provide	provide	VERB
cana-3454	164	16	high	high	ADJ
cana-3454	164	17	speed	speed	NOUN
cana-3454	164	18	and	and	CCONJ
cana-3454	164	19	high	high	ADJ
cana-3454	164	20	accuracy	accuracy	NOUN
cana-3454	164	21	simultaneously	simultaneously	ADV
cana-3454	164	22	.	.	PUNCT
cana-3454	165	1	future	future	ADJ
cana-3454	165	2	directions	direction	NOUN
cana-3454	165	3	the	the	DET
cana-3454	165	4	results	result	NOUN
cana-3454	165	5	highlight	highlight	VERB
cana-3454	165	6	the	the	DET
cana-3454	165	7	need	need	NOUN
cana-3454	165	8	for	for	ADP
cana-3454	165	9	:	:	PUNCT
cana-3454	165	10	communications	communication	NOUN
cana-3454	165	11	on	on	ADP
cana-3454	165	12	applied	apply	VERB
cana-3454	165	13	nonlinear	nonlinear	ADJ
cana-3454	165	14	analysis	analysis	NOUN
cana-3454	165	15	issn	issn	NOUN
cana-3454	165	16	:	:	PUNCT
cana-3454	165	17	1074	1074	NUM
cana-3454	165	18	-	-	PUNCT
cana-3454	165	19	133x	133x	NUM
cana-3454	165	20	vol	vol	NOUN
cana-3454	165	21	32	32	NUM
cana-3454	165	22	no	no	NOUN
cana-3454	165	23	.	.	PUNCT
cana-3454	166	1	7s	7	NOUN
cana-3454	166	2	(	(	PUNCT
cana-3454	166	3	2025	2025	NUM
cana-3454	166	4	)	)	PUNCT
cana-3454	166	5	443	443	NUM
cana-3454	166	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	166	7	1	1	NUM
cana-3454	166	8	.	.	PUNCT
cana-3454	166	9	exploring	explore	VERB
cana-3454	166	10	the	the	DET
cana-3454	166	11	creation	creation	NOUN
cana-3454	166	12	of	of	ADP
cana-3454	166	13	new	new	ADJ
cana-3454	166	14	models	model	NOUN
cana-3454	166	15	that	that	PRON
cana-3454	166	16	are	be	AUX
cana-3454	166	17	the	the	DET
cana-3454	166	18	combination	combination	NOUN
cana-3454	166	19	of	of	ADP
cana-3454	166	20	some	some	DET
cana-3454	166	21	other	other	ADJ
cana-3454	166	22	algorithms	algorithm	NOUN
cana-3454	166	23	with	with	ADP
cana-3454	166	24	their	their	PRON
cana-3454	166	25	relevant	relevant	ADJ
cana-3454	166	26	features	feature	NOUN
cana-3454	166	27	.	.	PUNCT
cana-3454	167	1	2	2	X
cana-3454	167	2	.	.	PUNCT
cana-3454	167	3	to	to	PART
cana-3454	167	4	improve	improve	VERB
cana-3454	167	5	the	the	DET
cana-3454	167	6	svm	svm	PROPN
cana-3454	167	7	and	and	CCONJ
cana-3454	167	8	knn	knn	PROPN
cana-3454	167	9	algorithm	algorithm	PROPN
cana-3454	167	10	for	for	ADP
cana-3454	167	11	large	large	ADJ
cana-3454	167	12	datasets	dataset	NOUN
cana-3454	167	13	.	.	PUNCT
cana-3454	168	1	3	3	X
cana-3454	168	2	.	.	X
cana-3454	168	3	investigating	investigate	VERB
cana-3454	168	4	on	on	ADP
cana-3454	168	5	whether	whether	SCONJ
cana-3454	168	6	there	there	PRON
cana-3454	168	7	is	be	VERB
cana-3454	168	8	a	a	DET
cana-3454	168	9	lighter	light	ADJ
cana-3454	168	10	version	version	NOUN
cana-3454	168	11	of	of	ADP
cana-3454	168	12	yolo	yolo	NOUN
cana-3454	168	13	that	that	PRON
cana-3454	168	14	can	can	AUX
cana-3454	168	15	be	be	AUX
cana-3454	168	16	fitted	fit	VERB
cana-3454	168	17	in	in	ADP
cana-3454	168	18	edge	edge	NOUN
cana-3454	168	19	devices	device	NOUN
cana-3454	168	20	.	.	PUNCT
cana-3454	169	1	v.	v.	ADP
cana-3454	169	2	conclusion	conclusion	NOUN
cana-3454	169	3	this	this	DET
cana-3454	169	4	study	study	NOUN
cana-3454	169	5	provided	provide	VERB
cana-3454	169	6	an	an	DET
cana-3454	169	7	exhaustive	exhaustive	ADJ
cana-3454	169	8	analysis	analysis	NOUN
cana-3454	169	9	of	of	ADP
cana-3454	169	10	tasks	task	NOUN
cana-3454	169	11	and	and	CCONJ
cana-3454	169	12	domains	domain	NOUN
cana-3454	169	13	where	where	SCONJ
cana-3454	169	14	ml	ml	VERB
cana-3454	169	15	has	have	AUX
cana-3454	169	16	penetrated	penetrate	VERB
cana-3454	169	17	computer	computer	NOUN
cana-3454	169	18	vision	vision	NOUN
cana-3454	169	19	processes	process	NOUN
cana-3454	169	20	and	and	CCONJ
cana-3454	169	21	brought	bring	VERB
cana-3454	169	22	revolutionary	revolutionary	ADJ
cana-3454	169	23	changes	change	NOUN
cana-3454	169	24	in	in	ADP
cana-3454	169	25	healthcare	healthcare	PROPN
cana-3454	169	26	services	service	NOUN
cana-3454	169	27	,	,	PUNCT
cana-3454	169	28	systems	system	NOUN
cana-3454	169	29	without	without	ADP
cana-3454	169	30	operators	operator	NOUN
cana-3454	169	31	,	,	PUNCT
cana-3454	169	32	manufacturing	manufacturing	NOUN
cana-3454	169	33	lines	line	NOUN
cana-3454	169	34	,	,	PUNCT
cana-3454	169	35	agricultural	agricultural	ADJ
cana-3454	169	36	management	management	NOUN
cana-3454	169	37	,	,	PUNCT
cana-3454	169	38	and	and	CCONJ
cana-3454	169	39	environmental	environmental	ADJ
cana-3454	169	40	protection	protection	NOUN
cana-3454	169	41	.	.	PUNCT
cana-3454	170	1	due	due	ADP
cana-3454	170	2	to	to	ADP
cana-3454	170	3	the	the	DET
cana-3454	170	4	use	use	NOUN
cana-3454	170	5	of	of	ADP
cana-3454	170	6	efficient	efficient	ADJ
cana-3454	170	7	algorithms	algorithm	NOUN
cana-3454	170	8	including	include	VERB
cana-3454	170	9	cnns	cnns	PROPN
cana-3454	170	10	,	,	PUNCT
cana-3454	170	11	svms	svms	NOUN
cana-3454	170	12	,	,	PUNCT
cana-3454	170	13	rfs	rfs	PROPN
cana-3454	170	14	,	,	PUNCT
cana-3454	170	15	and	and	CCONJ
cana-3454	170	16	knns	knn	NOUN
cana-3454	170	17	,	,	PUNCT
cana-3454	170	18	ml	ml	AUX
cana-3454	170	19	has	have	VERB
cana-3454	170	20	amazing	amazing	ADJ
cana-3454	170	21	functionalities	functionality	NOUN
cana-3454	170	22	in	in	ADP
cana-3454	170	23	image	image	NOUN
cana-3454	170	24	analysis	analysis	NOUN
cana-3454	170	25	,	,	PUNCT
cana-3454	170	26	identifies	identify	VERB
cana-3454	170	27	objects	object	NOUN
cana-3454	170	28	,	,	PUNCT
cana-3454	170	29	and	and	CCONJ
cana-3454	170	30	prediction	prediction	NOUN
cana-3454	170	31	analysis	analysis	NOUN
cana-3454	170	32	.	.	PUNCT
cana-3454	171	1	these	these	DET
cana-3454	171	2	methods	method	NOUN
cana-3454	171	3	have	have	AUX
cana-3454	171	4	repeatedly	repeatedly	ADV
cana-3454	171	5	shown	show	VERB
cana-3454	171	6	a	a	DET
cana-3454	171	7	performance	performance	NOUN
cana-3454	171	8	greater	great	ADJ
cana-3454	171	9	than	than	ADP
cana-3454	171	10	conventional	conventional	ADJ
cana-3454	171	11	techniques	technique	NOUN
cana-3454	171	12	in	in	ADP
cana-3454	171	13	helping	help	VERB
cana-3454	171	14	solve	solve	VERB
cana-3454	171	15	intricate	intricate	ADJ
cana-3454	171	16	visual	visual	ADJ
cana-3454	171	17	problems	problem	NOUN
cana-3454	171	18	,	,	PUNCT
cana-3454	171	19	always	always	ADV
cana-3454	171	20	with	with	ADP
cana-3454	171	21	better	well	ADJ
cana-3454	171	22	precision	precision	NOUN
cana-3454	171	23	,	,	PUNCT
cana-3454	171	24	speed	speed	NOUN
cana-3454	171	25	,	,	PUNCT
cana-3454	171	26	and	and	CCONJ
cana-3454	171	27	flexibility	flexibility	NOUN
cana-3454	171	28	.	.	PUNCT
cana-3454	172	1	the	the	DET
cana-3454	172	2	performance	performance	NOUN
cana-3454	172	3	of	of	ADP
cana-3454	172	4	the	the	DET
cana-3454	172	5	mh	mh	PROPN
cana-3454	172	6	algorithms	algorithm	NOUN
cana-3454	172	7	was	be	AUX
cana-3454	172	8	confirmed	confirm	VERB
cana-3454	172	9	by	by	ADP
cana-3454	172	10	the	the	DET
cana-3454	172	11	results	result	NOUN
cana-3454	172	12	of	of	ADP
cana-3454	172	13	experiments	experiment	NOUN
cana-3454	172	14	in	in	ADP
cana-3454	172	15	such	such	DET
cana-3454	172	16	a	a	DET
cana-3454	172	17	way	way	NOUN
cana-3454	172	18	that	that	PRON
cana-3454	172	19	the	the	DET
cana-3454	172	20	efficiency	efficiency	NOUN
cana-3454	172	21	and	and	CCONJ
cana-3454	172	22	reliability	reliability	NOUN
cana-3454	172	23	of	of	ADP
cana-3454	172	24	the	the	DET
cana-3454	172	25	offered	offer	VERB
cana-3454	172	26	solutions	solution	NOUN
cana-3454	172	27	are	be	AUX
cana-3454	172	28	very	very	ADV
cana-3454	172	29	high	high	ADJ
cana-3454	172	30	.	.	PUNCT
cana-3454	173	1	for	for	ADP
cana-3454	173	2	example	example	NOUN
cana-3454	173	3	,	,	PUNCT
cana-3454	173	4	cnns	cnn	NOUN
cana-3454	173	5	performed	perform	VERB
cana-3454	173	6	outstandingly	outstandingly	ADV
cana-3454	173	7	well	well	ADV
cana-3454	173	8	in	in	ADP
cana-3454	173	9	image	image	NOUN
cana-3454	173	10	diagnosis	diagnosis	NOUN
cana-3454	173	11	of	of	ADP
cana-3454	173	12	diseases	disease	NOUN
cana-3454	173	13	ranging	range	VERB
cana-3454	173	14	from	from	ADP
cana-3454	173	15	cancer	cancer	NOUN
cana-3454	173	16	to	to	ADP
cana-3454	173	17	pneumonia	pneumonia	NOUN
cana-3454	173	18	while	while	SCONJ
cana-3454	173	19	both	both	PRON
cana-3454	173	20	rfs	rfs	VERB
cana-3454	173	21	and	and	CCONJ
cana-3454	173	22	svms	svms	NOUN
cana-3454	173	23	worked	work	VERB
cana-3454	173	24	relatively	relatively	ADV
cana-3454	173	25	well	well	ADV
cana-3454	173	26	in	in	ADP
cana-3454	173	27	the	the	DET
cana-3454	173	28	fields	field	NOUN
cana-3454	173	29	of	of	ADP
cana-3454	173	30	agricultural	agricultural	ADJ
cana-3454	173	31	and	and	CCONJ
cana-3454	173	32	manufacturing	manufacturing	NOUN
cana-3454	173	33	,	,	PUNCT
cana-3454	173	34	which	which	PRON
cana-3454	173	35	required	require	VERB
cana-3454	173	36	accuracy	accuracy	NOUN
cana-3454	173	37	and	and	CCONJ
cana-3454	173	38	shading	shading	NOUN
cana-3454	173	39	.	.	PUNCT
cana-3454	174	1	furthermore	furthermore	ADV
cana-3454	174	2	,	,	PUNCT
cana-3454	174	3	the	the	DET
cana-3454	174	4	comparison	comparison	NOUN
cana-3454	174	5	with	with	ADP
cana-3454	174	6	the	the	DET
cana-3454	174	7	similar	similar	ADJ
cana-3454	174	8	works	work	NOUN
cana-3454	174	9	illustrated	illustrate	VERB
cana-3454	174	10	the	the	DET
cana-3454	174	11	improvements	improvement	NOUN
cana-3454	174	12	in	in	ADP
cana-3454	174	13	accuracy	accuracy	NOUN
cana-3454	174	14	,	,	PUNCT
cana-3454	174	15	robustness	robustness	NOUN
cana-3454	174	16	as	as	ADV
cana-3454	174	17	well	well	ADV
cana-3454	174	18	as	as	ADP
cana-3454	174	19	in	in	ADP
cana-3454	174	20	terms	term	NOUN
cana-3454	174	21	of	of	ADP
cana-3454	174	22	applicability	applicability	NOUN
cana-3454	174	23	,	,	PUNCT
cana-3454	174	24	thereby	thereby	ADV
cana-3454	174	25	rare	rare	ADJ
cana-3454	174	26	pointing	pointing	NOUN
cana-3454	174	27	towards	towards	ADP
cana-3454	174	28	the	the	DET
cana-3454	174	29	importance	importance	NOUN
cana-3454	174	30	of	of	ADP
cana-3454	174	31	implementing	implement	VERB
cana-3454	174	32	sophisticated	sophisticated	ADJ
cana-3454	174	33	state	state	NOUN
cana-3454	174	34	of	of	ADP
cana-3454	174	35	art	art	NOUN
cana-3454	174	36	ml	ml	NOUN
cana-3454	174	37	models	model	NOUN
cana-3454	174	38	.	.	PUNCT
cana-3454	175	1	however	however	ADV
cana-3454	175	2	,	,	PUNCT
cana-3454	175	3	as	as	ADP
cana-3454	175	4	with	with	ADP
cana-3454	175	5	many	many	ADJ
cana-3454	175	6	other	other	ADJ
cana-3454	175	7	approaches	approach	NOUN
cana-3454	175	8	,	,	PUNCT
cana-3454	175	9	there	there	PRON
cana-3454	175	10	are	be	VERB
cana-3454	175	11	still	still	ADV
cana-3454	175	12	many	many	ADJ
cana-3454	175	13	shortcomings	shortcoming	NOUN
cana-3454	175	14	like	like	ADP
cana-3454	175	15	the	the	DET
cana-3454	175	16	demand	demand	NOUN
cana-3454	175	17	for	for	ADP
cana-3454	175	18	large	large	ADJ
cana-3454	175	19	high	high	ADJ
cana-3454	175	20	-	-	PUNCT
cana-3454	175	21	quality	quality	NOUN
cana-3454	175	22	data	data	NOUN
cana-3454	175	23	sets	set	NOUN
cana-3454	175	24	,	,	PUNCT
cana-3454	175	25	demanding	demand	VERB
cana-3454	175	26	computational	computational	ADJ
cana-3454	175	27	power	power	NOUN
cana-3454	175	28	,	,	PUNCT
cana-3454	175	29	and	and	CCONJ
cana-3454	175	30	potential	potential	ADJ
cana-3454	175	31	issues	issue	NOUN
cana-3454	175	32	around	around	ADP
cana-3454	175	33	data	datum	NOUN
cana-3454	175	34	collection	collection	NOUN
cana-3454	175	35	and	and	CCONJ
cana-3454	175	36	the	the	DET
cana-3454	175	37	probable	probable	ADJ
cana-3454	175	38	bias	bias	NOUN
cana-3454	175	39	of	of	ADP
cana-3454	175	40	the	the	DET
cana-3454	175	41	model	model	NOUN
cana-3454	175	42	.	.	PUNCT
cana-3454	176	1	solving	solve	VERB
cana-3454	176	2	these	these	DET
cana-3454	176	3	problems	problem	NOUN
cana-3454	176	4	will	will	AUX
cana-3454	176	5	be	be	AUX
cana-3454	176	6	important	important	ADJ
cana-3454	176	7	for	for	ADP
cana-3454	176	8	the	the	DET
cana-3454	176	9	further	further	ADJ
cana-3454	176	10	use	use	NOUN
cana-3454	176	11	of	of	ADP
cana-3454	176	12	ml	ml	NOUN
cana-3454	176	13	in	in	ADP
cana-3454	176	14	important	important	ADJ
cana-3454	176	15	applications	application	NOUN
cana-3454	176	16	.	.	PUNCT
cana-3454	177	1	in	in	ADP
cana-3454	177	2	conclusion	conclusion	NOUN
cana-3454	177	3	,	,	PUNCT
cana-3454	177	4	the	the	DET
cana-3454	177	5	possibility	possibility	NOUN
cana-3454	177	6	of	of	ADP
cana-3454	177	7	using	use	VERB
cana-3454	177	8	ml	ml	NOUN
cana-3454	177	9	for	for	ADP
cana-3454	177	10	advancing	advance	VERB
cana-3454	177	11	computer	computer	NOUN
cana-3454	177	12	vision	vision	NOUN
cana-3454	177	13	across	across	ADP
cana-3454	177	14	various	various	ADJ
cana-3454	177	15	industries	industry	NOUN
cana-3454	177	16	is	be	AUX
cana-3454	177	17	confirmed	confirm	VERB
cana-3454	177	18	in	in	ADP
cana-3454	177	19	this	this	DET
cana-3454	177	20	research	research	NOUN
cana-3454	177	21	.	.	PUNCT
cana-3454	178	1	as	as	SCONJ
cana-3454	178	2	these	these	DET
cana-3454	178	3	algorithms	algorithm	NOUN
cana-3454	178	4	are	be	AUX
cana-3454	178	5	advanced	advanced	ADJ
cana-3454	178	6	and	and	CCONJ
cana-3454	178	7	the	the	DET
cana-3454	178	8	related	related	ADJ
cana-3454	178	9	issues	issue	NOUN
cana-3454	178	10	are	be	AUX
cana-3454	178	11	solved	solve	VERB
cana-3454	178	12	,	,	PUNCT
cana-3454	178	13	the	the	DET
cana-3454	178	14	ml	ml	ADV
cana-3454	178	15	based	base	VERB
cana-3454	178	16	computer	computer	NOUN
cana-3454	178	17	vision	vision	NOUN
cana-3454	178	18	systems	system	NOUN
cana-3454	178	19	can	can	AUX
cana-3454	178	20	come	come	VERB
cana-3454	178	21	up	up	ADP
cana-3454	178	22	with	with	ADP
cana-3454	178	23	other	other	ADJ
cana-3454	178	24	unpredictable	unpredictable	ADJ
cana-3454	178	25	means	mean	NOUN
cana-3454	178	26	to	to	PART
cana-3454	178	27	transform	transform	VERB
cana-3454	178	28	different	different	ADJ
cana-3454	178	29	fields	field	NOUN
cana-3454	178	30	and	and	CCONJ
cana-3454	178	31	also	also	ADV
cana-3454	178	32	can	can	AUX
cana-3454	178	33	be	be	AUX
cana-3454	178	34	very	very	ADV
cana-3454	178	35	beneficial	beneficial	ADJ
cana-3454	178	36	to	to	ADP
cana-3454	178	37	the	the	DET
cana-3454	178	38	society	society	NOUN
cana-3454	178	39	and	and	CCONJ
cana-3454	178	40	technology	technology	NOUN
cana-3454	178	41	.	.	PUNCT
cana-3454	179	1	reference	reference	NOUN
cana-3454	179	2	[	[	X
cana-3454	179	3	1	1	NUM
cana-3454	179	4	]	]	PUNCT
cana-3454	179	5	abouelmagd	abouelmagd	PROPN
cana-3454	179	6	,	,	PUNCT
cana-3454	179	7	l.m	l.m	PROPN
cana-3454	179	8	.	.	PROPN
cana-3454	179	9	,	,	PUNCT
cana-3454	179	10	shams	sham	NOUN
cana-3454	179	11	,	,	PUNCT
cana-3454	179	12	m.y	m.y	PROPN
cana-3454	179	13	.	.	PROPN
cana-3454	179	14	,	,	PUNCT
cana-3454	179	15	marie	marie	PROPN
cana-3454	179	16	,	,	PUNCT
cana-3454	179	17	h.s	h.s	PROPN
cana-3454	179	18	.	.	PROPN
cana-3454	179	19	and	and	CCONJ
cana-3454	179	20	hassanien	hassanien	PROPN
cana-3454	179	21	,	,	PUNCT
cana-3454	179	22	a.e	a.e	PROPN
cana-3454	179	23	.	.	PROPN
cana-3454	179	24	,	,	PUNCT
cana-3454	179	25	2024	2024	NUM
cana-3454	179	26	.	.	PUNCT
cana-3454	180	1	an	an	DET
cana-3454	180	2	optimized	optimize	VERB
cana-3454	180	3	capsule	capsule	NOUN
cana-3454	180	4	neural	neural	ADJ
cana-3454	180	5	networks	network	NOUN
cana-3454	180	6	for	for	ADP
cana-3454	180	7	tomato	tomato	NOUN
cana-3454	180	8	leaf	leaf	NOUN
cana-3454	180	9	disease	disease	NOUN
cana-3454	180	10	classification	classification	NOUN
cana-3454	180	11	.	.	PUNCT
cana-3454	181	1	eurasip	eurasip	PROPN
cana-3454	181	2	journal	journal	PROPN
cana-3454	181	3	on	on	ADP
cana-3454	181	4	image	image	NOUN
cana-3454	181	5	and	and	CCONJ
cana-3454	181	6	video	video	NOUN
cana-3454	181	7	processing	processing	NOUN
cana-3454	181	8	,	,	PUNCT
cana-3454	181	9	2024(1	2024(1	NUM
cana-3454	181	10	)	)	PUNCT
cana-3454	181	11	,	,	PUNCT
cana-3454	181	12	pp	pp	ADJ
cana-3454	181	13	.	.	PUNCT
cana-3454	182	1	2	2	X
cana-3454	182	2	.	.	PUNCT
cana-3454	183	1	[	[	X
cana-3454	183	2	2	2	NUM
cana-3454	183	3	]	]	PUNCT
cana-3454	183	4	alif	alif	NOUN
cana-3454	183	5	,	,	PUNCT
cana-3454	183	6	m.a.r	m.a.r	NOUN
cana-3454	183	7	.	.	PUNCT
cana-3454	183	8	,	,	PUNCT
cana-3454	183	9	hussain	hussain	PROPN
cana-3454	183	10	,	,	PUNCT
cana-3454	183	11	m.	m.	NOUN
cana-3454	183	12	,	,	PUNCT
cana-3454	183	13	tucker	tucker	PROPN
cana-3454	183	14	,	,	PUNCT
cana-3454	183	15	g.	g.	PROPN
cana-3454	183	16	and	and	CCONJ
cana-3454	183	17	iwnicki	iwnicki	PROPN
cana-3454	183	18	,	,	PUNCT
cana-3454	183	19	s.	s.	PROPN
cana-3454	183	20	,	,	PUNCT
cana-3454	183	21	2024	2024	NUM
cana-3454	183	22	.	.	PUNCT
cana-3454	184	1	boltvision	boltvision	NOUN
cana-3454	184	2	:	:	PUNCT
cana-3454	184	3	a	a	DET
cana-3454	184	4	comparative	comparative	ADJ
cana-3454	184	5	analysis	analysis	NOUN
cana-3454	184	6	of	of	ADP
cana-3454	184	7	cnn	cnn	PROPN
cana-3454	184	8	,	,	PUNCT
cana-3454	184	9	cct	cct	PROPN
cana-3454	184	10	,	,	PUNCT
cana-3454	184	11	and	and	CCONJ
cana-3454	184	12	vit	vit	NOUN
cana-3454	184	13	in	in	ADP
cana-3454	184	14	achieving	achieve	VERB
cana-3454	184	15	high	high	ADJ
cana-3454	184	16	accuracy	accuracy	NOUN
cana-3454	184	17	for	for	ADP
cana-3454	184	18	missing	miss	VERB
cana-3454	184	19	bolt	bolt	NOUN
cana-3454	184	20	classification	classification	NOUN
cana-3454	184	21	in	in	ADP
cana-3454	184	22	train	train	NOUN
cana-3454	184	23	components	component	NOUN
cana-3454	184	24	.	.	PUNCT
cana-3454	185	1	machines	machine	NOUN
cana-3454	185	2	,	,	PUNCT
cana-3454	185	3	12(2	12(2	NUM
cana-3454	185	4	)	)	PUNCT
cana-3454	185	5	,	,	PUNCT
cana-3454	185	6	pp	pp	ADP
cana-3454	185	7	.	.	PUNCT
cana-3454	186	1	93	93	NUM
cana-3454	186	2	.	.	PUNCT
cana-3454	187	1	[	[	X
cana-3454	187	2	3	3	NUM
cana-3454	187	3	]	]	X
cana-3454	187	4	alzahrani	alzahrani	NOUN
cana-3454	187	5	,	,	PUNCT
cana-3454	187	6	s.m	s.m	PROPN
cana-3454	187	7	.	.	PROPN
cana-3454	187	8	,	,	PUNCT
cana-3454	187	9	2024	2024	NUM
cana-3454	187	10	.	.	PUNCT
cana-3454	188	1	deciphering	decipher	VERB
cana-3454	188	2	the	the	DET
cana-3454	188	3	efficacy	efficacy	NOUN
cana-3454	188	4	of	of	ADP
cana-3454	188	5	no	no	DET
cana-3454	188	6	-	-	PUNCT
cana-3454	188	7	attention	attention	NOUN
cana-3454	188	8	architectures	architecture	NOUN
cana-3454	188	9	in	in	ADP
cana-3454	188	10	computed	compute	VERB
cana-3454	188	11	tomography	tomography	NOUN
cana-3454	188	12	image	image	NOUN
cana-3454	188	13	classification	classification	NOUN
cana-3454	188	14	:	:	PUNCT
cana-3454	188	15	a	a	DET
cana-3454	188	16	paradigm	paradigm	NOUN
cana-3454	188	17	shift	shift	NOUN
cana-3454	188	18	.	.	PUNCT
cana-3454	189	1	mathematics	mathematic	NOUN
cana-3454	189	2	,	,	PUNCT
cana-3454	189	3	12(5	12(5	NUM
cana-3454	189	4	)	)	PUNCT
cana-3454	189	5	,	,	PUNCT
cana-3454	189	6	pp	pp	ADV
cana-3454	189	7	.	.	PUNCT
cana-3454	190	1	689	689	NUM
cana-3454	190	2	.	.	PUNCT
cana-3454	191	1	[	[	X
cana-3454	191	2	4	4	NUM
cana-3454	191	3	]	]	X
cana-3454	191	4	azizi	azizi	PROPN
cana-3454	191	5	,	,	PUNCT
cana-3454	191	6	r.	r.	PROPN
cana-3454	191	7	,	,	PUNCT
cana-3454	191	8	koskinopoulou	koskinopoulou	ADJ
cana-3454	191	9	,	,	PUNCT
cana-3454	191	10	m.	m.	NOUN
cana-3454	191	11	and	and	CCONJ
cana-3454	191	12	petillot	petillot	NOUN
cana-3454	191	13	,	,	PUNCT
cana-3454	191	14	y.	y.	NOUN
cana-3454	191	15	,	,	PUNCT
cana-3454	191	16	2024	2024	NUM
cana-3454	191	17	.	.	PUNCT
cana-3454	192	1	comparison	comparison	NOUN
cana-3454	192	2	of	of	ADP
cana-3454	192	3	machine	machine	NOUN
cana-3454	192	4	learning	learn	VERB
cana-3454	192	5	approaches	approach	NOUN
cana-3454	192	6	for	for	ADP
cana-3454	192	7	robust	robust	ADJ
cana-3454	192	8	and	and	CCONJ
cana-3454	192	9	timely	timely	ADJ
cana-3454	192	10	detection	detection	NOUN
cana-3454	192	11	of	of	ADP
cana-3454	192	12	ppe	ppe	PROPN
cana-3454	192	13	in	in	ADP
cana-3454	192	14	construction	construction	NOUN
cana-3454	192	15	sites	site	NOUN
cana-3454	192	16	.	.	PUNCT
cana-3454	193	1	robotics	robotic	NOUN
cana-3454	193	2	,	,	PUNCT
cana-3454	193	3	13(2	13(2	NOUN
cana-3454	193	4	)	)	PUNCT
cana-3454	193	5	,	,	PUNCT
cana-3454	193	6	pp	pp	ADP
cana-3454	193	7	.	.	PUNCT
cana-3454	194	1	31	31	NUM
cana-3454	194	2	.	.	PUNCT
cana-3454	195	1	[	[	X
cana-3454	195	2	5	5	NUM
cana-3454	195	3	]	]	X
cana-3454	195	4	bajaj	bajaj	PROPN
cana-3454	195	5	,	,	PUNCT
cana-3454	195	6	a.	a.	NOUN
cana-3454	195	7	and	and	CCONJ
cana-3454	195	8	vishwakarma	vishwakarma	PROPN
cana-3454	195	9	,	,	PUNCT
cana-3454	195	10	d.k	d.k	PROPN
cana-3454	195	11	.	.	PROPN
cana-3454	195	12	,	,	PUNCT
cana-3454	195	13	2024	2024	NUM
cana-3454	195	14	.	.	PUNCT
cana-3454	196	1	a	a	DET
cana-3454	196	2	state	state	NOUN
cana-3454	196	3	-	-	PUNCT
cana-3454	196	4	of	of	ADP
cana-3454	196	5	-	-	PUNCT
cana-3454	196	6	the	the	DET
cana-3454	196	7	-	-	PUNCT
cana-3454	196	8	art	art	NOUN
cana-3454	196	9	review	review	NOUN
cana-3454	196	10	on	on	ADP
cana-3454	196	11	adversarial	adversarial	ADJ
cana-3454	196	12	machine	machine	NOUN
cana-3454	196	13	learning	learn	VERB
cana-3454	196	14	in	in	ADP
cana-3454	196	15	image	image	NOUN
cana-3454	196	16	classification	classification	NOUN
cana-3454	196	17	.	.	PUNCT
cana-3454	197	1	multimedia	multimedia	NOUN
cana-3454	197	2	tools	tool	NOUN
cana-3454	197	3	and	and	CCONJ
cana-3454	197	4	applications	application	NOUN
cana-3454	197	5	,	,	PUNCT
cana-3454	197	6	83(3	83(3	NUM
cana-3454	197	7	)	)	PUNCT
cana-3454	197	8	,	,	PUNCT
cana-3454	197	9	pp	pp	ADP
cana-3454	197	10	.	.	PUNCT
cana-3454	198	1	9351	9351	NUM
cana-3454	198	2	-	-	SYM
cana-3454	198	3	9416	9416	NUM
cana-3454	198	4	.	.	PUNCT
cana-3454	199	1	[	[	X
cana-3454	199	2	6	6	NUM
cana-3454	199	3	]	]	PUNCT
cana-3454	199	4	beltrán	beltrán	NOUN
cana-3454	199	5	-	-	PUNCT
cana-3454	199	6	escobar	escobar	NOUN
cana-3454	199	7	,	,	PUNCT
cana-3454	199	8	m.	m.	NOUN
cana-3454	199	9	,	,	PUNCT
cana-3454	199	10	alarcón	alarcón	PROPN
cana-3454	199	11	,	,	PUNCT
cana-3454	199	12	t.	t.	PROPN
cana-3454	199	13	,e	,e	PROPN
cana-3454	199	14	.	.	PUNCT
cana-3454	199	15	,	,	PUNCT
cana-3454	199	16	rumbo	rumbo	NOUN
cana-3454	199	17	-	-	PUNCT
cana-3454	199	18	morales	morale	NOUN
cana-3454	199	19	,	,	PUNCT
cana-3454	199	20	j.	j.	PROPN
cana-3454	199	21	,	,	PUNCT
cana-3454	199	22	lópez	lópez	PROPN
cana-3454	199	23	,	,	PUNCT
cana-3454	199	24	s.	s.	PROPN
cana-3454	199	25	,	,	PUNCT
cana-3454	199	26	ortiz	ortiz	PROPN
cana-3454	199	27	-	-	PUNCT
cana-3454	199	28	torres	torre	NOUN
cana-3454	199	29	,	,	PUNCT
cana-3454	199	30	g.	g.	PROPN
cana-3454	199	31	and	and	CCONJ
cana-3454	199	32	sorcia	sorcia	PROPN
cana-3454	199	33	-	-	PUNCT
cana-3454	199	34	vázquez	vázquez	NOUN
cana-3454	199	35	,	,	PUNCT
cana-3454	199	36	f.	f.	PROPN
cana-3454	199	37	,d.j	,d.j	PROPN
cana-3454	199	38	.	.	PROPN
cana-3454	199	39	,	,	PUNCT
cana-3454	199	40	2024	2024	NUM
cana-3454	199	41	.	.	PUNCT
cana-3454	200	1	a	a	DET
cana-3454	200	2	review	review	NOUN
cana-3454	200	3	on	on	ADP
cana-3454	200	4	resource	resource	NOUN
cana-3454	200	5	-	-	PUNCT
cana-3454	200	6	constrained	constrain	VERB
cana-3454	200	7	embedded	embed	VERB
cana-3454	200	8	vision	vision	NOUN
cana-3454	200	9	systems	system	NOUN
cana-3454	200	10	-	-	PUNCT
cana-3454	200	11	based	base	VERB
cana-3454	200	12	tiny	tiny	ADJ
cana-3454	200	13	machine	machine	NOUN
cana-3454	200	14	learning	learn	VERB
cana-3454	200	15	for	for	ADP
cana-3454	200	16	robotic	robotic	ADJ
cana-3454	200	17	applications	application	NOUN
cana-3454	200	18	.	.	PUNCT
cana-3454	201	1	algorithms	algorithm	NOUN
cana-3454	201	2	,	,	PUNCT
cana-3454	201	3	17(11	17(11	NUM
cana-3454	201	4	)	)	PUNCT
cana-3454	201	5	,	,	PUNCT
cana-3454	201	6	pp	pp	ADP
cana-3454	201	7	.	.	PUNCT
cana-3454	202	1	476	476	X
cana-3454	202	2	.	.	PUNCT
cana-3454	203	1	communications	communication	NOUN
cana-3454	203	2	on	on	ADP
cana-3454	203	3	applied	apply	VERB
cana-3454	203	4	nonlinear	nonlinear	ADJ
cana-3454	203	5	analysis	analysis	NOUN
cana-3454	203	6	issn	issn	NOUN
cana-3454	203	7	:	:	PUNCT
cana-3454	203	8	1074	1074	NUM
cana-3454	203	9	-	-	PUNCT
cana-3454	203	10	133x	133x	NUM
cana-3454	203	11	vol	vol	NOUN
cana-3454	203	12	32	32	NUM
cana-3454	203	13	no	no	NOUN
cana-3454	203	14	.	.	PUNCT
cana-3454	204	1	7s	7	NOUN
cana-3454	204	2	(	(	PUNCT
cana-3454	204	3	2025	2025	NUM
cana-3454	204	4	)	)	PUNCT
cana-3454	204	5	444	444	NUM
cana-3454	204	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	205	1	[	[	X
cana-3454	205	2	7	7	X
cana-3454	205	3	]	]	X
cana-3454	205	4	chen	chen	PROPN
cana-3454	205	5	,	,	PUNCT
cana-3454	205	6	l.	l.	PROPN
cana-3454	205	7	,	,	PUNCT
cana-3454	205	8	li	li	PROPN
cana-3454	205	9	,	,	PUNCT
cana-3454	205	10	g.	g.	PROPN
cana-3454	205	11	,	,	PUNCT
cana-3454	205	12	xie	xie	PROPN
cana-3454	205	13	,	,	PUNCT
cana-3454	205	14	w.	w.	PROPN
cana-3454	205	15	,	,	PUNCT
cana-3454	205	16	tan	tan	PROPN
cana-3454	205	17	,	,	PUNCT
cana-3454	205	18	j.	j.	PROPN
cana-3454	205	19	,	,	PUNCT
cana-3454	205	20	yang	yang	PROPN
cana-3454	205	21	,	,	PUNCT
cana-3454	205	22	l.	l.	PROPN
cana-3454	205	23	,	,	PUNCT
cana-3454	205	24	pu	pu	PROPN
cana-3454	205	25	,	,	PUNCT
cana-3454	205	26	j.	j.	PROPN
cana-3454	205	27	,	,	PUNCT
cana-3454	205	28	chen	chen	PROPN
cana-3454	205	29	,	,	PUNCT
cana-3454	205	30	l.	l.	PROPN
cana-3454	205	31	,	,	PUNCT
cana-3454	205	32	gan	gan	PROPN
cana-3454	205	33	,	,	PUNCT
cana-3454	205	34	d.	d.	PROPN
cana-3454	205	35	and	and	CCONJ
cana-3454	205	36	shi	shi	PROPN
cana-3454	205	37	,	,	PUNCT
cana-3454	205	38	w.	w.	PROPN
cana-3454	205	39	,	,	PUNCT
cana-3454	205	40	2024	2024	NUM
cana-3454	205	41	.	.	PUNCT
cana-3454	206	1	a	a	DET
cana-3454	206	2	survey	survey	NOUN
cana-3454	206	3	of	of	ADP
cana-3454	206	4	computer	computer	NOUN
cana-3454	206	5	vision	vision	NOUN
cana-3454	206	6	detection	detection	NOUN
cana-3454	206	7	,	,	PUNCT
cana-3454	206	8	visual	visual	ADJ
cana-3454	206	9	slam	slam	NOUN
cana-3454	206	10	algorithms	algorithm	NOUN
cana-3454	206	11	,	,	PUNCT
cana-3454	206	12	and	and	CCONJ
cana-3454	206	13	their	their	PRON
cana-3454	206	14	applications	application	NOUN
cana-3454	206	15	in	in	ADP
cana-3454	206	16	energy	energy	NOUN
cana-3454	206	17	-	-	PUNCT
cana-3454	206	18	efficient	efficient	ADJ
cana-3454	206	19	autonomous	autonomous	ADJ
cana-3454	206	20	systems	system	NOUN
cana-3454	206	21	.	.	PUNCT
cana-3454	207	1	energies	energy	NOUN
cana-3454	207	2	,	,	PUNCT
cana-3454	207	3	17(20	17(20	NUM
cana-3454	207	4	)	)	PUNCT
cana-3454	207	5	,	,	PUNCT
cana-3454	207	6	pp	pp	ADJ
cana-3454	207	7	.	.	PUNCT
cana-3454	207	8	5177	5177	NUM
cana-3454	207	9	.	.	PUNCT
cana-3454	208	1	[	[	X
cana-3454	208	2	8	8	NUM
cana-3454	208	3	]	]	X
cana-3454	208	4	chien	chien	PROPN
cana-3454	208	5	-	-	PUNCT
cana-3454	208	6	yi	yi	PROPN
cana-3454	208	7	,	,	PUNCT
cana-3454	208	8	h.	h.	PROPN
cana-3454	208	9	and	and	CCONJ
cana-3454	208	10	pei	pei	PROPN
cana-3454	208	11	-	-	PUNCT
cana-3454	208	12	xuan	xuan	PROPN
cana-3454	208	13	tsai	tsai	PROPN
cana-3454	208	14	,	,	PUNCT
cana-3454	208	15	2024	2024	NUM
cana-3454	208	16	.	.	PUNCT
cana-3454	208	17	applying	apply	VERB
cana-3454	208	18	machine	machine	NOUN
cana-3454	208	19	learning	learn	VERB
cana-3454	208	20	to	to	PART
cana-3454	208	21	construct	construct	VERB
cana-3454	208	22	a	a	DET
cana-3454	208	23	printed	print	VERB
cana-3454	208	24	circuit	circuit	NOUN
cana-3454	208	25	board	board	NOUN
cana-3454	208	26	gold	gold	NOUN
cana-3454	208	27	finger	finger	NOUN
cana-3454	208	28	defect	defect	NOUN
cana-3454	208	29	detection	detection	NOUN
cana-3454	208	30	system	system	NOUN
cana-3454	208	31	.	.	PUNCT
cana-3454	209	1	electronics	electronic	NOUN
cana-3454	209	2	,	,	PUNCT
cana-3454	209	3	13(6	13(6	NUM
cana-3454	209	4	)	)	PUNCT
cana-3454	209	5	,	,	PUNCT
cana-3454	209	6	pp	pp	ADJ
cana-3454	209	7	.	.	PUNCT
cana-3454	209	8	1090	1090	NUM
cana-3454	209	9	.	.	PUNCT
cana-3454	210	1	[	[	X
cana-3454	210	2	9	9	NUM
cana-3454	210	3	]	]	SYM
cana-3454	210	4	đaković	đaković	NOUN
cana-3454	210	5	,	,	PUNCT
cana-3454	210	6	d.	d.	PROPN
cana-3454	210	7	,	,	PUNCT
cana-3454	210	8	kljajić	kljajić	PROPN
cana-3454	210	9	,	,	PUNCT
cana-3454	210	10	m.	m.	NOUN
cana-3454	210	11	,	,	PUNCT
cana-3454	210	12	milivojević	milivojević	PROPN
cana-3454	210	13	,	,	PUNCT
cana-3454	210	14	n.	n.	NOUN
cana-3454	210	15	,	,	PUNCT
cana-3454	210	16	doder	doder	NOUN
cana-3454	210	17	,	,	PUNCT
cana-3454	210	18	đ	đ	PROPN
cana-3454	210	19	.	.	PROPN
cana-3454	210	20	and	and	CCONJ
cana-3454	210	21	anđelković	anđelković	PROPN
cana-3454	210	22	,	,	PUNCT
cana-3454	210	23	a.	a.	NOUN
cana-3454	210	24	,s	,s	PROPN
cana-3454	210	25	.	.	PUNCT
cana-3454	210	26	,	,	PUNCT
cana-3454	210	27	2024	2024	NUM
cana-3454	210	28	.	.	PUNCT
cana-3454	210	29	review	review	NOUN
cana-3454	210	30	of	of	ADP
cana-3454	210	31	energy	energy	NOUN
cana-3454	210	32	-	-	PUNCT
cana-3454	210	33	related	relate	VERB
cana-3454	210	34	machine	machine	NOUN
cana-3454	210	35	learning	learning	NOUN
cana-3454	210	36	applications	application	NOUN
cana-3454	210	37	in	in	ADP
cana-3454	210	38	drying	dry	VERB
cana-3454	210	39	processes	process	NOUN
cana-3454	210	40	.	.	PUNCT
cana-3454	211	1	energies	energy	NOUN
cana-3454	211	2	,	,	PUNCT
cana-3454	211	3	17(1	17(1	NUM
cana-3454	211	4	)	)	PUNCT
cana-3454	211	5	,	,	PUNCT
cana-3454	211	6	pp	pp	ADP
cana-3454	211	7	.	.	PUNCT
cana-3454	212	1	224	224	NUM
cana-3454	212	2	.	.	PUNCT
cana-3454	213	1	[	[	X
cana-3454	213	2	10	10	NUM
cana-3454	213	3	]	]	X
cana-3454	213	4	de	de	X
cana-3454	213	5	chauveron	chauveron	PROPN
cana-3454	213	6	,	,	PUNCT
cana-3454	213	7	j.	j.	PROPN
cana-3454	213	8	,	,	PUNCT
cana-3454	213	9	unger	unger	PROPN
cana-3454	213	10	,	,	PUNCT
cana-3454	213	11	m.	m.	NOUN
cana-3454	213	12	,	,	PUNCT
cana-3454	213	13	lescaille	lescaille	PROPN
cana-3454	213	14	,	,	PUNCT
cana-3454	213	15	g.	g.	PROPN
cana-3454	213	16	,	,	PUNCT
cana-3454	213	17	wendling	wendle	VERB
cana-3454	213	18	,	,	PUNCT
cana-3454	213	19	l.	l.	PROPN
cana-3454	213	20	,	,	PUNCT
cana-3454	213	21	kurtz	kurtz	PROPN
cana-3454	213	22	,	,	PUNCT
cana-3454	213	23	c.	c.	PROPN
cana-3454	213	24	and	and	CCONJ
cana-3454	213	25	rochefort	rochefort	PROPN
cana-3454	213	26	,	,	PUNCT
cana-3454	213	27	j.	j.	PROPN
cana-3454	213	28	,	,	PUNCT
cana-3454	213	29	2024	2024	NUM
cana-3454	213	30	.	.	PUNCT
cana-3454	214	1	artificial	artificial	ADJ
cana-3454	214	2	intelligence	intelligence	NOUN
cana-3454	214	3	for	for	ADP
cana-3454	214	4	oral	oral	ADJ
cana-3454	214	5	squamous	squamous	ADJ
cana-3454	214	6	cell	cell	NOUN
cana-3454	214	7	carcinoma	carcinoma	NOUN
cana-3454	214	8	detection	detection	NOUN
cana-3454	214	9	based	base	VERB
cana-3454	214	10	on	on	ADP
cana-3454	214	11	oral	oral	ADJ
cana-3454	214	12	photographs	photograph	NOUN
cana-3454	214	13	:	:	PUNCT
cana-3454	214	14	a	a	DET
cana-3454	214	15	comprehensive	comprehensive	ADJ
cana-3454	214	16	literature	literature	NOUN
cana-3454	214	17	review	review	NOUN
cana-3454	214	18	.	.	PUNCT
cana-3454	215	1	cancer	cancer	NOUN
cana-3454	215	2	medicine	medicine	NOUN
cana-3454	215	3	,	,	PUNCT
cana-3454	215	4	13(1	13(1	NUM
cana-3454	215	5	)	)	PUNCT
cana-3454	215	6	,	,	PUNCT
cana-3454	215	7	.	.	PUNCT
cana-3454	216	1	[	[	X
cana-3454	216	2	11	11	NUM
cana-3454	216	3	]	]	SYM
cana-3454	216	4	dilek	dilek	PROPN
cana-3454	216	5	,	,	PUNCT
cana-3454	216	6	e.	e.	PROPN
cana-3454	216	7	and	and	CCONJ
cana-3454	216	8	dener	dener	PROPN
cana-3454	216	9	,	,	PUNCT
cana-3454	216	10	m.	m.	NOUN
cana-3454	216	11	,	,	PUNCT
cana-3454	216	12	2023	2023	NUM
cana-3454	216	13	.	.	PUNCT
cana-3454	217	1	computer	computer	NOUN
cana-3454	217	2	vision	vision	NOUN
cana-3454	217	3	applications	application	NOUN
cana-3454	217	4	in	in	ADP
cana-3454	217	5	intelligent	intelligent	ADJ
cana-3454	217	6	transportation	transportation	NOUN
cana-3454	217	7	systems	system	NOUN
cana-3454	217	8	:	:	PUNCT
cana-3454	217	9	a	a	DET
cana-3454	217	10	survey	survey	NOUN
cana-3454	217	11	.	.	PUNCT
cana-3454	218	1	sensors	sensor	NOUN
cana-3454	218	2	,	,	PUNCT
cana-3454	218	3	23(6	23(6	NUM
cana-3454	218	4	)	)	PUNCT
cana-3454	218	5	,	,	PUNCT
cana-3454	218	6	pp	pp	ADP
cana-3454	218	7	.	.	PUNCT
cana-3454	218	8	2938	2938	NUM
cana-3454	218	9	.	.	PUNCT
cana-3454	219	1	[	[	X
cana-3454	219	2	12	12	NUM
cana-3454	219	3	]	]	PUNCT
cana-3454	219	4	elias	elias	PROPN
cana-3454	219	5	,	,	PUNCT
cana-3454	219	6	m.	m.	NOUN
cana-3454	219	7	,	,	PUNCT
cana-3454	219	8	2024	2024	NUM
cana-3454	219	9	.	.	PUNCT
cana-3454	220	1	timber	timber	NOUN
cana-3454	220	2	traceability	traceability	NOUN
cana-3454	220	3	and	and	CCONJ
cana-3454	220	4	sustainable	sustainable	ADJ
cana-3454	220	5	transportation	transportation	NOUN
cana-3454	220	6	management	management	NOUN
cana-3454	220	7	:	:	PUNCT
cana-3454	220	8	a	a	DET
cana-3454	220	9	review	review	NOUN
cana-3454	220	10	of	of	ADP
cana-3454	220	11	technologies	technology	NOUN
cana-3454	220	12	and	and	CCONJ
cana-3454	220	13	procedures	procedure	NOUN
cana-3454	220	14	.	.	PUNCT
cana-3454	221	1	bulletin	bulletin	NOUN
cana-3454	221	2	of	of	ADP
cana-3454	221	3	the	the	DET
cana-3454	221	4	transilvania	transilvania	PROPN
cana-3454	221	5	university	university	PROPN
cana-3454	221	6	of	of	ADP
cana-3454	221	7	brasov.forestry	brasov.forestry	PROPN
cana-3454	221	8	,	,	PUNCT
cana-3454	221	9	wood	wood	NOUN
cana-3454	221	10	industry	industry	NOUN
cana-3454	221	11	,	,	PUNCT
cana-3454	221	12	agricultural	agricultural	ADJ
cana-3454	221	13	food	food	NOUN
cana-3454	221	14	engineering.series	engineering.series	PROPN
cana-3454	221	15	ii	ii	PROPN
cana-3454	221	16	,	,	PUNCT
cana-3454	221	17	17(1	17(1	NUM
cana-3454	221	18	)	)	PUNCT
cana-3454	221	19	,	,	PUNCT
cana-3454	221	20	pp	pp	ADJ
cana-3454	221	21	.	.	PUNCT
cana-3454	222	1	11	11	NUM
cana-3454	222	2	-	-	SYM
cana-3454	222	3	52	52	NUM
cana-3454	222	4	.	.	PUNCT
cana-3454	223	1	[	[	X
cana-3454	223	2	13	13	NUM
cana-3454	223	3	]	]	X
cana-3454	223	4	el	el	PROPN
cana-3454	223	5	-	-	PUNCT
cana-3454	223	6	nabi	nabi	PROPN
cana-3454	223	7	,	,	PUNCT
cana-3454	223	8	s.	s.	PROPN
cana-3454	223	9	,	,	PUNCT
cana-3454	223	10	el	el	PROPN
cana-3454	223	11	-	-	PUNCT
cana-3454	223	12	shafai	shafai	PROPN
cana-3454	223	13	,	,	PUNCT
cana-3454	223	14	w.	w.	PROPN
cana-3454	223	15	,	,	PUNCT
cana-3454	223	16	el	el	PROPN
cana-3454	223	17	-	-	PROPN
cana-3454	223	18	rabaie	rabaie	PROPN
cana-3454	223	19	,	,	PUNCT
cana-3454	223	20	e.	e.	PROPN
cana-3454	223	21	,	,	PUNCT
cana-3454	223	22	ramadan	ramadan	PROPN
cana-3454	223	23	,	,	PUNCT
cana-3454	223	24	k.f	k.f	PROPN
cana-3454	223	25	.	.	PROPN
cana-3454	223	26	,	,	PUNCT
cana-3454	223	27	abd	abd	PROPN
cana-3454	223	28	el	el	PROPN
cana-3454	223	29	-	-	PUNCT
cana-3454	223	30	samie	samie	PROPN
cana-3454	223	31	,	,	PUNCT
cana-3454	223	32	f.e	f.e	PROPN
cana-3454	223	33	.	.	PROPN
cana-3454	223	34	and	and	CCONJ
cana-3454	223	35	mohsen	mohsen	PROPN
cana-3454	223	36	,	,	PUNCT
cana-3454	223	37	s.	s.	PROPN
cana-3454	223	38	,	,	PUNCT
cana-3454	223	39	2024	2024	NUM
cana-3454	223	40	.	.	PUNCT
cana-3454	223	41	machine	machine	NOUN
cana-3454	223	42	learning	learning	NOUN
cana-3454	223	43	and	and	CCONJ
cana-3454	223	44	deep	deep	ADJ
cana-3454	223	45	learning	learning	NOUN
cana-3454	223	46	techniques	technique	NOUN
cana-3454	223	47	for	for	ADP
cana-3454	223	48	driver	driver	NOUN
cana-3454	223	49	fatigue	fatigue	NOUN
cana-3454	223	50	and	and	CCONJ
cana-3454	223	51	drowsiness	drowsiness	NOUN
cana-3454	223	52	detection	detection	NOUN
cana-3454	223	53	:	:	PUNCT
cana-3454	223	54	a	a	DET
cana-3454	223	55	review	review	NOUN
cana-3454	223	56	.	.	PUNCT
cana-3454	224	1	multimedia	multimedia	NOUN
cana-3454	224	2	tools	tool	NOUN
cana-3454	224	3	and	and	CCONJ
cana-3454	224	4	applications	application	NOUN
cana-3454	224	5	,	,	PUNCT
cana-3454	224	6	83(3	83(3	NUM
cana-3454	224	7	)	)	PUNCT
cana-3454	224	8	,	,	PUNCT
cana-3454	224	9	pp	pp	ADJ
cana-3454	224	10	.	.	PUNCT
cana-3454	225	1	9441	9441	NUM
cana-3454	225	2	-	-	SYM
cana-3454	225	3	9477	9477	NUM
cana-3454	225	4	.	.	PUNCT
cana-3454	226	1	[	[	X
cana-3454	226	2	14	14	NUM
cana-3454	226	3	]	]	X
cana-3454	226	4	florestiyanto	florestiyanto	PROPN
cana-3454	226	5	,	,	PUNCT
cana-3454	226	6	m.y	m.y	PROPN
cana-3454	226	7	.	.	PROPN
cana-3454	226	8	,	,	PUNCT
cana-3454	226	9	surjono	surjono	PROPN
cana-3454	226	10	,	,	PUNCT
cana-3454	226	11	h.d	h.d	PROPN
cana-3454	226	12	.	.	PROPN
cana-3454	226	13	and	and	CCONJ
cana-3454	226	14	jati	jati	PROPN
cana-3454	226	15	,	,	PUNCT
cana-3454	226	16	h.	h.	PROPN
cana-3454	226	17	,	,	PUNCT
cana-3454	226	18	2024	2024	NUM
cana-3454	226	19	.	.	PUNCT
cana-3454	227	1	emotion	emotion	NOUN
cana-3454	227	2	recognition	recognition	NOUN
cana-3454	227	3	for	for	ADP
cana-3454	227	4	improving	improve	VERB
cana-3454	227	5	online	online	ADJ
cana-3454	227	6	learning	learn	VERB
cana-3454	227	7	environments	environment	NOUN
cana-3454	227	8	:	:	PUNCT
cana-3454	227	9	a	a	DET
cana-3454	227	10	systematic	systematic	ADJ
cana-3454	227	11	review	review	NOUN
cana-3454	227	12	of	of	ADP
cana-3454	227	13	the	the	DET
cana-3454	227	14	literature	literature	NOUN
cana-3454	227	15	.	.	PUNCT
cana-3454	228	1	journal	journal	PROPN
cana-3454	228	2	of	of	ADP
cana-3454	228	3	electrical	electrical	ADJ
cana-3454	228	4	systems	system	NOUN
cana-3454	228	5	,	,	PUNCT
cana-3454	228	6	20(4	20(4	NOUN
cana-3454	228	7	)	)	PUNCT
cana-3454	228	8	,	,	PUNCT
cana-3454	228	9	pp	pp	ADJ
cana-3454	228	10	.	.	PUNCT
cana-3454	229	1	18601873	18601873	NUM
cana-3454	229	2	.	.	PUNCT
cana-3454	230	1	[	[	X
cana-3454	230	2	15	15	NUM
cana-3454	230	3	]	]	X
cana-3454	230	4	galarza	galarza	NOUN
cana-3454	230	5	-	-	PUNCT
cana-3454	230	6	falfan	falfan	PROPN
cana-3454	230	7	,	,	PUNCT
cana-3454	230	8	j.	j.	PROPN
cana-3454	230	9	,	,	PUNCT
cana-3454	230	10	garcía	garcía	ADJ
cana-3454	230	11	-	-	PUNCT
cana-3454	230	12	guerrero	guerrero	NOUN
cana-3454	230	13	,	,	PUNCT
cana-3454	230	14	e.e	e.e	PROPN
cana-3454	230	15	.	.	PROPN
cana-3454	230	16	,	,	PUNCT
cana-3454	230	17	aguirre	aguirre	PROPN
cana-3454	230	18	-	-	PUNCT
cana-3454	230	19	castro	castro	PROPN
cana-3454	230	20	,	,	PUNCT
cana-3454	230	21	o.	o.	NOUN
cana-3454	230	22	,	,	PUNCT
cana-3454	230	23	lópez	lópez	NOUN
cana-3454	230	24	-	-	PUNCT
cana-3454	230	25	bonilla	bonilla	NOUN
cana-3454	230	26	,	,	PUNCT
cana-3454	230	27	o.r	o.r	PROPN
cana-3454	230	28	.	.	PROPN
cana-3454	230	29	,	,	PUNCT
cana-3454	230	30	ulises	ulises	PROPN
cana-3454	230	31	jesús	jesús	VERB
cana-3454	230	32	tamayo	tamayo	NOUN
cana-3454	230	33	-	-	PUNCT
cana-3454	230	34	pérez	pérez	NOUN
cana-3454	230	35	,	,	PUNCT
cana-3454	230	36	cárdenas	cárdena	NOUN
cana-3454	230	37	-	-	PUNCT
cana-3454	230	38	valdez	valdez	PROPN
cana-3454	230	39	,	,	PUNCT
cana-3454	230	40	j.r	j.r	PROPN
cana-3454	230	41	.	.	PROPN
cana-3454	230	42	,	,	PUNCT
cana-3454	230	43	hernández	hernández	PROPN
cana-3454	230	44	-	-	PUNCT
cana-3454	230	45	mejía	mejía	PROPN
cana-3454	230	46	,	,	PUNCT
cana-3454	230	47	c.	c.	NOUN
cana-3454	230	48	,	,	PUNCT
cana-3454	230	49	borregodominguez	borregodominguez	NOUN
cana-3454	230	50	,	,	PUNCT
cana-3454	230	51	s.	s.	PROPN
cana-3454	230	52	and	and	CCONJ
cana-3454	230	53	inzunza	inzunza	PROPN
cana-3454	230	54	-	-	PUNCT
cana-3454	230	55	gonzalez	gonzalez	PROPN
cana-3454	230	56	,	,	PUNCT
cana-3454	230	57	e.	e.	PROPN
cana-3454	230	58	,	,	PUNCT
cana-3454	230	59	2024	2024	NUM
cana-3454	230	60	.	.	PUNCT
cana-3454	231	1	path	path	NOUN
cana-3454	231	2	planning	planning	NOUN
cana-3454	231	3	for	for	ADP
cana-3454	231	4	autonomous	autonomous	ADJ
cana-3454	231	5	mobile	mobile	ADJ
cana-3454	231	6	robot	robot	NOUN
cana-3454	231	7	using	use	VERB
cana-3454	231	8	intelligent	intelligent	ADJ
cana-3454	231	9	algorithms	algorithm	NOUN
cana-3454	231	10	.	.	PUNCT
cana-3454	232	1	technologies	technology	NOUN
cana-3454	232	2	,	,	PUNCT
cana-3454	232	3	12(6	12(6	NUM
cana-3454	232	4	)	)	PUNCT
cana-3454	232	5	,	,	PUNCT
cana-3454	232	6	pp	pp	PROPN
cana-3454	232	7	.	.	PUNCT
cana-3454	233	1	82	82	NUM
cana-3454	233	2	.	.	PUNCT
cana-3454	234	1	[	[	X
cana-3454	234	2	16	16	NUM
cana-3454	234	3	]	]	X
cana-3454	234	4	gomes	gomes	PROPN
cana-3454	234	5	ana	ana	PROPN
cana-3454	234	6	,	,	PUNCT
cana-3454	234	7	l.b	l.b	PROPN
cana-3454	234	8	.	.	PROPN
cana-3454	234	9	,	,	PUNCT
cana-3454	234	10	fernandes	fernandes	PROPN
cana-3454	234	11	,	,	PUNCT
cana-3454	234	12	a.m.	a.m.	ADV
cana-3454	234	13	,	,	PUNCT
cana-3454	234	14	r.	r.	PROPN
cana-3454	234	15	,	,	PUNCT
cana-3454	234	16	horta	horta	PROPN
cana-3454	234	17	,	,	PUNCT
cana-3454	234	18	b.a	b.a	PROPN
cana-3454	234	19	.	.	PROPN
cana-3454	234	20	,	,	PUNCT
cana-3454	234	21	c.	c.	PROPN
cana-3454	234	22	and	and	CCONJ
cana-3454	234	23	oliveira	oliveira	PROPN
cana-3454	234	24	maurílio	maurílio	PROPN
cana-3454	234	25	,	,	PUNCT
cana-3454	234	26	f.d	f.d	PROPN
cana-3454	234	27	.	.	PROPN
cana-3454	234	28	,	,	PUNCT
cana-3454	234	29	2024	2024	NUM
cana-3454	234	30	.	.	PUNCT
cana-3454	235	1	machine	machine	NOUN
cana-3454	235	2	learning	learn	VERB
cana-3454	235	3	algorithms	algorithm	NOUN
cana-3454	235	4	applied	apply	VERB
cana-3454	235	5	to	to	PART
cana-3454	235	6	weed	weed	VERB
cana-3454	235	7	management	management	NOUN
cana-3454	235	8	in	in	ADP
cana-3454	235	9	integrated	integrated	ADJ
cana-3454	235	10	crop	crop	NOUN
cana-3454	235	11	-	-	PUNCT
cana-3454	235	12	livestock	livestock	NOUN
cana-3454	235	13	systems	system	NOUN
cana-3454	235	14	:	:	PUNCT
cana-3454	235	15	a	a	DET
cana-3454	235	16	systematic	systematic	ADJ
cana-3454	235	17	literature	literature	PROPN
cana-3454	235	18	review	review	NOUN
cana-3454	235	19	.	.	PUNCT
cana-3454	236	1	advances	advance	NOUN
cana-3454	236	2	in	in	ADP
cana-3454	236	3	weed	weed	NOUN
cana-3454	236	4	science	science	NOUN
cana-3454	236	5	,	,	PUNCT
cana-3454	236	6	42	42	NUM
cana-3454	236	7	.	.	PUNCT
cana-3454	237	1	[	[	X
cana-3454	237	2	17	17	NUM
cana-3454	237	3	]	]	SYM
cana-3454	237	4	han	han	PROPN
cana-3454	237	5	,	,	PUNCT
cana-3454	237	6	s.	s.	PROPN
cana-3454	237	7	,	,	PUNCT
cana-3454	237	8	wang	wang	PROPN
cana-3454	237	9	,	,	PUNCT
cana-3454	237	10	m.	m.	NOUN
cana-3454	237	11	,	,	PUNCT
cana-3454	237	12	zhang	zhang	PROPN
cana-3454	237	13	,	,	PUNCT
cana-3454	237	14	j.	j.	PROPN
cana-3454	237	15	,	,	PUNCT
cana-3454	237	16	li	li	PROPN
cana-3454	237	17	,	,	PUNCT
cana-3454	237	18	d.	d.	PROPN
cana-3454	237	19	and	and	CCONJ
cana-3454	237	20	duan	duan	PROPN
cana-3454	237	21	,	,	PUNCT
cana-3454	237	22	j.	j.	PROPN
cana-3454	237	23	,	,	PUNCT
cana-3454	237	24	2024	2024	NUM
cana-3454	237	25	.	.	PUNCT
cana-3454	238	1	a	a	DET
cana-3454	238	2	review	review	NOUN
cana-3454	238	3	of	of	ADP
cana-3454	238	4	large	large	ADJ
cana-3454	238	5	language	language	NOUN
cana-3454	238	6	models	model	NOUN
cana-3454	238	7	:	:	PUNCT
cana-3454	238	8	fundamental	fundamental	ADJ
cana-3454	238	9	architectures	architecture	NOUN
cana-3454	238	10	,	,	PUNCT
cana-3454	238	11	key	key	ADJ
cana-3454	238	12	technological	technological	ADJ
cana-3454	238	13	evolutions	evolution	NOUN
cana-3454	238	14	,	,	PUNCT
cana-3454	238	15	interdisciplinary	interdisciplinary	NOUN
cana-3454	238	16	technologies	technology	NOUN
cana-3454	238	17	integration	integration	NOUN
cana-3454	238	18	,	,	PUNCT
cana-3454	238	19	optimization	optimization	NOUN
cana-3454	238	20	and	and	CCONJ
cana-3454	238	21	compression	compression	NOUN
cana-3454	238	22	techniques	technique	NOUN
cana-3454	238	23	,	,	PUNCT
cana-3454	238	24	applications	application	NOUN
cana-3454	238	25	,	,	PUNCT
cana-3454	238	26	and	and	CCONJ
cana-3454	238	27	challenges	challenge	NOUN
cana-3454	238	28	.	.	PUNCT
cana-3454	239	1	electronics	electronic	NOUN
cana-3454	239	2	,	,	PUNCT
cana-3454	239	3	13(24	13(24	NUM
cana-3454	239	4	)	)	PUNCT
cana-3454	239	5	,	,	PUNCT
cana-3454	239	6	pp	pp	ADP
cana-3454	239	7	.	.	PUNCT
cana-3454	239	8	5040	5040	NUM
cana-3454	239	9	.	.	PUNCT
cana-3454	240	1	[	[	X
cana-3454	240	2	18	18	NUM
cana-3454	240	3	]	]	PUNCT
cana-3454	240	4	hermosilla	hermosilla	NOUN
cana-3454	240	5	,	,	PUNCT
cana-3454	240	6	p.	p.	NOUN
cana-3454	240	7	,	,	PUNCT
cana-3454	240	8	soto	soto	PROPN
cana-3454	240	9	,	,	PUNCT
cana-3454	240	10	r.	r.	PROPN
cana-3454	240	11	,	,	PUNCT
cana-3454	240	12	vega	vega	PROPN
cana-3454	240	13	,	,	PUNCT
cana-3454	240	14	e.	e.	PROPN
cana-3454	240	15	,	,	PUNCT
cana-3454	240	16	suazo	suazo	PROPN
cana-3454	240	17	,	,	PUNCT
cana-3454	240	18	c.	c.	PROPN
cana-3454	240	19	and	and	CCONJ
cana-3454	240	20	ponce	ponce	PROPN
cana-3454	240	21	,	,	PUNCT
cana-3454	240	22	j.	j.	PROPN
cana-3454	240	23	,	,	PUNCT
cana-3454	240	24	2024	2024	NUM
cana-3454	240	25	.	.	PUNCT
cana-3454	240	26	skin	skin	NOUN
cana-3454	240	27	cancer	cancer	NOUN
cana-3454	240	28	detection	detection	NOUN
cana-3454	240	29	and	and	CCONJ
cana-3454	240	30	classification	classification	NOUN
cana-3454	240	31	using	use	VERB
cana-3454	240	32	neural	neural	ADJ
cana-3454	240	33	network	network	NOUN
cana-3454	240	34	algorithms	algorithm	NOUN
cana-3454	240	35	:	:	PUNCT
cana-3454	240	36	a	a	DET
cana-3454	240	37	systematic	systematic	ADJ
cana-3454	240	38	review	review	NOUN
cana-3454	240	39	.	.	PUNCT
cana-3454	241	1	diagnostics	diagnostic	NOUN
cana-3454	241	2	,	,	PUNCT
cana-3454	241	3	14(4	14(4	NUM
cana-3454	241	4	)	)	PUNCT
cana-3454	241	5	,	,	PUNCT
cana-3454	241	6	pp	pp	PROPN
cana-3454	241	7	.	.	PUNCT
cana-3454	242	1	454	454	NUM
cana-3454	242	2	.	.	PUNCT
cana-3454	243	1	[	[	X
cana-3454	243	2	19	19	NUM
cana-3454	243	3	]	]	SYM
cana-3454	243	4	hu	hu	PROPN
cana-3454	243	5	,	,	PUNCT
cana-3454	243	6	j.	j.	PROPN
cana-3454	243	7	,	,	PUNCT
cana-3454	243	8	mengu	mengu	PROPN
cana-3454	243	9	,	,	PUNCT
cana-3454	243	10	d.	d.	PROPN
cana-3454	243	11	,	,	PUNCT
cana-3454	243	12	tzarouchis	tzarouchis	PROPN
cana-3454	243	13	,	,	PUNCT
cana-3454	243	14	d.c	d.c	PROPN
cana-3454	243	15	.	.	PROPN
cana-3454	243	16	,	,	PUNCT
cana-3454	243	17	edwards	edwards	PROPN
cana-3454	243	18	,	,	PUNCT
cana-3454	243	19	b.	b.	PROPN
cana-3454	243	20	,	,	PUNCT
cana-3454	243	21	engheta	engheta	PROPN
cana-3454	243	22	,	,	PUNCT
cana-3454	243	23	n.	n.	NOUN
cana-3454	243	24	and	and	CCONJ
cana-3454	243	25	ozcan	ozcan	ADJ
cana-3454	243	26	,	,	PUNCT
cana-3454	243	27	a.	a.	NOUN
cana-3454	243	28	,	,	PUNCT
cana-3454	243	29	2024	2024	NUM
cana-3454	243	30	.	.	PUNCT
cana-3454	243	31	diffractive	diffractive	ADJ
cana-3454	243	32	optical	optical	ADJ
cana-3454	243	33	computing	computing	NOUN
cana-3454	243	34	in	in	ADP
cana-3454	243	35	free	free	ADJ
cana-3454	243	36	space	space	NOUN
cana-3454	243	37	.	.	PUNCT
cana-3454	244	1	nature	nature	NOUN
cana-3454	244	2	communications	communication	NOUN
cana-3454	244	3	,	,	PUNCT
cana-3454	244	4	15(1	15(1	NUM
cana-3454	244	5	)	)	PUNCT
cana-3454	244	6	,	,	PUNCT
cana-3454	244	7	pp	pp	ADP
cana-3454	244	8	.	.	PUNCT
cana-3454	244	9	1525	1525	NUM
cana-3454	244	10	.	.	PUNCT
cana-3454	245	1	[	[	X
cana-3454	245	2	20	20	NUM
cana-3454	245	3	]	]	SYM
cana-3454	245	4	hu	hu	PROPN
cana-3454	245	5	,	,	PUNCT
cana-3454	245	6	y.	y.	PROPN
cana-3454	245	7	,	,	PUNCT
cana-3454	245	8	wang	wang	PROPN
cana-3454	245	9	,	,	PUNCT
cana-3454	245	10	w.	w.	PROPN
cana-3454	245	11	,	,	PUNCT
cana-3454	245	12	li	li	PROPN
cana-3454	245	13	,	,	PUNCT
cana-3454	245	14	l.	l.	PROPN
cana-3454	245	15	and	and	CCONJ
cana-3454	245	16	wang	wang	PROPN
cana-3454	245	17	,	,	PUNCT
cana-3454	245	18	f.	f.	PROPN
cana-3454	245	19	,	,	PUNCT
cana-3454	245	20	2024	2024	NUM
cana-3454	245	21	.	.	PUNCT
cana-3454	246	1	applying	apply	VERB
cana-3454	246	2	machine	machine	NOUN
cana-3454	246	3	learning	learning	NOUN
cana-3454	246	4	to	to	ADP
cana-3454	246	5	earthquake	earthquake	NOUN
cana-3454	246	6	engineering	engineering	NOUN
cana-3454	246	7	:	:	PUNCT
cana-3454	246	8	a	a	DET
cana-3454	246	9	scientometric	scientometric	ADJ
cana-3454	246	10	analysis	analysis	NOUN
cana-3454	246	11	of	of	ADP
cana-3454	246	12	world	world	NOUN
cana-3454	246	13	research	research	NOUN
cana-3454	246	14	.	.	PUNCT
cana-3454	247	1	buildings	building	NOUN
cana-3454	247	2	,	,	PUNCT
cana-3454	247	3	14(5	14(5	NUM
cana-3454	247	4	)	)	PUNCT
cana-3454	247	5	,	,	PUNCT
cana-3454	247	6	pp	pp	ADP
cana-3454	247	7	.	.	PUNCT
cana-3454	247	8	1393	1393	NUM
cana-3454	247	9	.	.	PUNCT
cana-3454	248	1	[	[	X
cana-3454	248	2	21	21	NUM
cana-3454	248	3	]	]	X
cana-3454	248	4	hütten	hütten	PROPN
cana-3454	248	5	,	,	PUNCT
cana-3454	248	6	n.	n.	PROPN
cana-3454	248	7	,	,	PUNCT
cana-3454	248	8	miguel	miguel	PROPN
cana-3454	248	9	,	,	PUNCT
cana-3454	248	10	a.g	a.g	PROPN
cana-3454	248	11	.	.	PROPN
cana-3454	248	12	,	,	PUNCT
cana-3454	248	13	hölken	hölken	PROPN
cana-3454	248	14	,	,	PUNCT
cana-3454	248	15	f.	f.	PROPN
cana-3454	248	16	,	,	PUNCT
cana-3454	248	17	andricevic	andricevic	VERB
cana-3454	248	18	,	,	PUNCT
cana-3454	248	19	k.	k.	PROPN
cana-3454	248	20	,	,	PUNCT
cana-3454	248	21	meyes	meyes	PROPN
cana-3454	248	22	,	,	PUNCT
cana-3454	248	23	r.	r.	PROPN
cana-3454	248	24	and	and	CCONJ
cana-3454	248	25	meisen	meisen	NOUN
cana-3454	248	26	,	,	PUNCT
cana-3454	248	27	t.	t.	PROPN
cana-3454	248	28	,	,	PUNCT
cana-3454	248	29	2024	2024	NUM
cana-3454	248	30	.	.	PUNCT
cana-3454	249	1	deep	deep	ADJ
cana-3454	249	2	learning	learning	NOUN
cana-3454	249	3	for	for	ADP
cana-3454	249	4	automated	automate	VERB
cana-3454	249	5	visual	visual	ADJ
cana-3454	249	6	inspection	inspection	NOUN
cana-3454	249	7	in	in	ADP
cana-3454	249	8	manufacturing	manufacturing	NOUN
cana-3454	249	9	and	and	CCONJ
cana-3454	249	10	maintenance	maintenance	NOUN
cana-3454	249	11	:	:	PUNCT
cana-3454	249	12	a	a	DET
cana-3454	249	13	survey	survey	NOUN
cana-3454	249	14	of	of	ADP
cana-3454	249	15	openaccess	openaccess	PROPN
cana-3454	249	16	papers	paper	NOUN
cana-3454	249	17	.	.	PUNCT
cana-3454	250	1	applied	apply	VERB
cana-3454	250	2	system	system	NOUN
cana-3454	250	3	innovation	innovation	NOUN
cana-3454	250	4	,	,	PUNCT
cana-3454	250	5	7(1	7(1	NUM
cana-3454	250	6	)	)	PUNCT
cana-3454	250	7	,	,	PUNCT
cana-3454	250	8	pp	pp	ADP
cana-3454	250	9	.	.	PUNCT
cana-3454	251	1	11	11	NUM
cana-3454	251	2	.	.	PUNCT
cana-3454	252	1	[	[	X
cana-3454	252	2	22	22	NUM
cana-3454	252	3	]	]	X
cana-3454	252	4	kaushlesh	kaushlesh	NOUN
cana-3454	252	5	,	,	PUNCT
cana-3454	252	6	s.s	s.s	PROPN
cana-3454	252	7	.	.	PROPN
cana-3454	252	8	,	,	PUNCT
cana-3454	252	9	alavi	alavi	PROPN
cana-3454	252	10	,	,	PUNCT
cana-3454	252	11	a.	a.	NOUN
cana-3454	252	12	,	,	PUNCT
cana-3454	252	13	porteous	porteous	NOUN
cana-3454	252	14	,	,	PUNCT
cana-3454	252	15	j.	j.	PROPN
cana-3454	252	16	,	,	PUNCT
cana-3454	252	17	priti	priti	PROPN
cana-3454	252	18	,	,	PUNCT
cana-3454	252	19	k.	k.	NOUN
cana-3454	252	20	,	,	PUNCT
cana-3454	252	21	laddi	laddi	PROPN
cana-3454	252	22	,	,	PUNCT
cana-3454	252	23	a.	a.	NOUN
cana-3454	252	24	and	and	CCONJ
cana-3454	252	25	jaiswal	jaiswal	PROPN
cana-3454	252	26	,	,	PUNCT
cana-3454	252	27	m.	m.	NOUN
cana-3454	252	28	,	,	PUNCT
cana-3454	252	29	2024	2024	NUM
cana-3454	252	30	.	.	PUNCT
cana-3454	253	1	a	a	DET
cana-3454	253	2	critical	critical	ADJ
cana-3454	253	3	analysis	analysis	NOUN
cana-3454	253	4	of	of	ADP
cana-3454	253	5	deep	deep	ADJ
cana-3454	253	6	semi	semi	ADJ
cana-3454	253	7	-	-	ADJ
cana-3454	253	8	supervised	supervised	ADJ
cana-3454	253	9	learning	learning	NOUN
cana-3454	253	10	approaches	approach	NOUN
cana-3454	253	11	for	for	ADP
cana-3454	253	12	enhanced	enhanced	ADJ
cana-3454	253	13	medical	medical	ADJ
cana-3454	253	14	image	image	NOUN
cana-3454	253	15	classification	classification	NOUN
cana-3454	253	16	.	.	PUNCT
cana-3454	254	1	information	information	NOUN
cana-3454	254	2	,	,	PUNCT
cana-3454	254	3	15(5	15(5	NUM
cana-3454	254	4	)	)	PUNCT
cana-3454	254	5	,	,	PUNCT
cana-3454	254	6	pp	pp	ADP
cana-3454	254	7	.	.	PUNCT
cana-3454	255	1	246	246	NUM
cana-3454	255	2	.	.	PUNCT
cana-3454	256	1	communications	communication	NOUN
cana-3454	256	2	on	on	ADP
cana-3454	256	3	applied	apply	VERB
cana-3454	256	4	nonlinear	nonlinear	ADJ
cana-3454	256	5	analysis	analysis	NOUN
cana-3454	256	6	issn	issn	NOUN
cana-3454	256	7	:	:	PUNCT
cana-3454	256	8	1074	1074	NUM
cana-3454	256	9	-	-	PUNCT
cana-3454	256	10	133x	133x	NUM
cana-3454	256	11	vol	vol	NOUN
cana-3454	256	12	32	32	NUM
cana-3454	256	13	no	no	NOUN
cana-3454	256	14	.	.	PUNCT
cana-3454	257	1	7s	7	NOUN
cana-3454	257	2	(	(	PUNCT
cana-3454	257	3	2025	2025	NUM
cana-3454	257	4	)	)	PUNCT
cana-3454	257	5	445	445	NUM
cana-3454	258	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3454	259	1	[	[	X
cana-3454	259	2	23	23	NUM
cana-3454	259	3	]	]	X
cana-3454	259	4	kumar	kumar	PROPN
cana-3454	259	5	,	,	PUNCT
cana-3454	259	6	r.p	r.p	PROPN
cana-3454	259	7	.	.	PROPN
cana-3454	259	8	,	,	PUNCT
cana-3454	259	9	v	v	PROPN
cana-3454	259	10	,	,	PUNCT
cana-3454	259	11	r.	r.	PROPN
cana-3454	259	12	,	,	PUNCT
cana-3454	259	13	subramani	subramani	PROPN
cana-3454	259	14	,	,	PUNCT
cana-3454	259	15	k.	k.	PROPN
cana-3454	259	16	,	,	PUNCT
cana-3454	259	17	malathi	malathi	PROPN
cana-3454	259	18	,	,	PUNCT
cana-3454	259	19	g.	g.	PROPN
cana-3454	259	20	and	and	CCONJ
cana-3454	259	21	jagadeesan	jagadeesan	NOUN
cana-3454	259	22	,	,	PUNCT
cana-3454	259	23	p.	p.	NOUN
cana-3454	259	24	,	,	PUNCT
cana-3454	259	25	2024	2024	NUM
cana-3454	259	26	.	.	PUNCT
cana-3454	260	1	development	development	NOUN
cana-3454	260	2	of	of	ADP
cana-3454	260	3	multi	multi	ADJ
cana-3454	260	4	modal	modal	ADJ
cana-3454	260	5	image	image	NOUN
cana-3454	260	6	fusion	fusion	NOUN
cana-3454	260	7	techniques	technique	NOUN
cana-3454	260	8	in	in	ADP
cana-3454	260	9	night	night	NOUN
cana-3454	260	10	vision	vision	PROPN
cana-3454	260	11	technology	technology	NOUN
cana-3454	260	12	using	use	VERB
cana-3454	260	13	deep	deep	ADJ
cana-3454	260	14	learning	learning	NOUN
cana-3454	260	15	.	.	PUNCT
cana-3454	261	1	journal	journal	NOUN
cana-3454	261	2	of	of	ADP
cana-3454	261	3	electrical	electrical	ADJ
cana-3454	261	4	systems	system	NOUN
cana-3454	261	5	,	,	PUNCT
cana-3454	261	6	20(3	20(3	NOUN
cana-3454	261	7	)	)	PUNCT
cana-3454	261	8	,	,	PUNCT
cana-3454	261	9	pp	pp	PROPN
cana-3454	261	10	.	.	PUNCT
cana-3454	262	1	229	229	NUM
cana-3454	262	2	-	-	SYM
cana-3454	262	3	237	237	NUM
cana-3454	262	4	.	.	PUNCT
cana-3454	263	1	[	[	X
cana-3454	263	2	24	24	NUM
cana-3454	263	3	]	]	X
cana-3454	263	4	lifelo	lifelo	PROPN
cana-3454	263	5	,	,	PUNCT
cana-3454	263	6	z.	z.	PROPN
cana-3454	263	7	,	,	PUNCT
cana-3454	263	8	ding	ding	PROPN
cana-3454	263	9	,	,	PUNCT
cana-3454	263	10	j.	j.	PROPN
cana-3454	263	11	,	,	PUNCT
cana-3454	263	12	ning	ning	PROPN
cana-3454	263	13	,	,	PUNCT
cana-3454	263	14	h.	h.	PROPN
cana-3454	263	15	,	,	PUNCT
cana-3454	263	16	qurat	qurat	PROPN
cana-3454	263	17	-	-	PUNCT
cana-3454	263	18	ul	ul	INTJ
cana-3454	263	19	-	-	PUNCT
cana-3454	263	20	ain	ain	NOUN
cana-3454	263	21	and	and	CCONJ
cana-3454	263	22	dhelim	dhelim	PROPN
cana-3454	263	23	,	,	PUNCT
cana-3454	263	24	s.	s.	PROPN
cana-3454	263	25	,	,	PUNCT
cana-3454	263	26	2024	2024	NUM
cana-3454	263	27	.	.	PUNCT
cana-3454	263	28	artificial	artificial	ADJ
cana-3454	263	29	intelligence	intelligence	NOUN
cana-3454	263	30	-	-	PUNCT
cana-3454	263	31	enabled	enable	VERB
cana-3454	263	32	metaverse	metaverse	NOUN
cana-3454	263	33	for	for	ADP
cana-3454	263	34	sustainable	sustainable	ADJ
cana-3454	263	35	smart	smart	ADJ
cana-3454	263	36	cities	city	NOUN
cana-3454	263	37	:	:	PUNCT
cana-3454	263	38	technologies	technology	NOUN
cana-3454	263	39	,	,	PUNCT
cana-3454	263	40	applications	application	NOUN
cana-3454	263	41	,	,	PUNCT
cana-3454	263	42	challenges	challenge	NOUN
cana-3454	263	43	,	,	PUNCT
cana-3454	263	44	and	and	CCONJ
cana-3454	263	45	future	future	ADJ
cana-3454	263	46	directions	direction	NOUN
cana-3454	263	47	.	.	PUNCT
cana-3454	264	1	electronics	electronic	NOUN
cana-3454	264	2	,	,	PUNCT
cana-3454	264	3	13(24	13(24	NUM
cana-3454	264	4	)	)	PUNCT
cana-3454	264	5	,	,	PUNCT
cana-3454	264	6	pp	pp	ADP
cana-3454	264	7	.	.	PUNCT
cana-3454	264	8	4874	4874	NUM
cana-3454	264	9	.	.	PUNCT
cana-3454	265	1	[	[	X
cana-3454	265	2	25	25	NUM
cana-3454	265	3	]	]	SYM
cana-3454	265	4	lindroth	lindroth	PROPN
cana-3454	265	5	,	,	PUNCT
cana-3454	265	6	h.	h.	PROPN
cana-3454	265	7	,	,	PUNCT
cana-3454	265	8	nalaie	nalaie	PROPN
cana-3454	265	9	,	,	PUNCT
cana-3454	265	10	k.	k.	PROPN
cana-3454	265	11	,	,	PUNCT
cana-3454	265	12	raghu	raghu	PROPN
cana-3454	265	13	,	,	PUNCT
cana-3454	265	14	r.	r.	PROPN
cana-3454	265	15	,	,	PUNCT
cana-3454	265	16	ayala	ayala	PROPN
cana-3454	265	17	,	,	PUNCT
cana-3454	265	18	i.n	i.n	PROPN
cana-3454	265	19	.	.	PROPN
cana-3454	265	20	,	,	PUNCT
cana-3454	265	21	busch	busch	PROPN
cana-3454	265	22	,	,	PUNCT
cana-3454	265	23	c.	c.	PROPN
cana-3454	265	24	,	,	PUNCT
cana-3454	265	25	bhattacharyya	bhattacharyya	ADJ
cana-3454	265	26	,	,	PUNCT
cana-3454	265	27	a.	a.	NOUN
cana-3454	265	28	,	,	PUNCT
cana-3454	265	29	pablo	pablo	PROPN
cana-3454	265	30	,	,	PUNCT
cana-3454	265	31	m.f	m.f	PROPN
cana-3454	265	32	.	.	PROPN
cana-3454	265	33	,	,	PUNCT
cana-3454	265	34	diedrich	diedrich	PROPN
cana-3454	265	35	,	,	PUNCT
cana-3454	265	36	d.a	d.a	PROPN
cana-3454	265	37	.	.	PROPN
cana-3454	265	38	,	,	PUNCT
cana-3454	265	39	pickering	pickering	PROPN
cana-3454	265	40	,	,	PUNCT
cana-3454	265	41	b.w	b.w	PROPN
cana-3454	265	42	.	.	PROPN
cana-3454	265	43	and	and	CCONJ
cana-3454	265	44	herasevich	herasevich	PROPN
cana-3454	265	45	,	,	PUNCT
cana-3454	265	46	v.	v.	ADV
cana-3454	265	47	,	,	PUNCT
cana-3454	265	48	2024	2024	NUM
cana-3454	265	49	.	.	PUNCT
cana-3454	266	1	applied	apply	VERB
cana-3454	266	2	artificial	artificial	ADJ
cana-3454	266	3	intelligence	intelligence	NOUN
cana-3454	266	4	in	in	ADP
cana-3454	266	5	healthcare	healthcare	PROPN
cana-3454	266	6	:	:	PUNCT
cana-3454	266	7	a	a	DET
cana-3454	266	8	review	review	NOUN
cana-3454	266	9	of	of	ADP
cana-3454	266	10	computer	computer	NOUN
cana-3454	266	11	vision	vision	NOUN
cana-3454	266	12	technology	technology	NOUN
cana-3454	266	13	application	application	NOUN
cana-3454	266	14	in	in	ADP
cana-3454	266	15	hospital	hospital	NOUN
cana-3454	266	16	settings	setting	NOUN
cana-3454	266	17	.	.	PUNCT
cana-3454	267	1	journal	journal	PROPN
cana-3454	267	2	of	of	ADP
cana-3454	267	3	imaging	imaging	PROPN
cana-3454	267	4	,	,	PUNCT
cana-3454	267	5	10(4	10(4	NUM
cana-3454	267	6	)	)	PUNCT
cana-3454	267	7	,	,	PUNCT
cana-3454	267	8	pp	pp	PROPN
cana-3454	267	9	.	.	PUNCT
cana-3454	268	1	81	81	NUM
cana-3454	268	2	.	.	PUNCT
cana-3454	269	1	[	[	X
cana-3454	269	2	26	26	NUM
cana-3454	269	3	]	]	PUNCT
cana-3454	269	4	lonsinger	lonsinger	NOUN
cana-3454	269	5	,	,	PUNCT
cana-3454	269	6	r.c	r.c	PROPN
cana-3454	269	7	.	.	PROPN
cana-3454	269	8	,	,	PUNCT
cana-3454	269	9	dart	dart	PROPN
cana-3454	269	10	,	,	PUNCT
cana-3454	269	11	m.m	m.m	PROPN
cana-3454	269	12	.	.	PROPN
cana-3454	269	13	,	,	PUNCT
cana-3454	269	14	larsen	larsen	PROPN
cana-3454	269	15	,	,	PUNCT
cana-3454	269	16	r.t	r.t	PROPN
cana-3454	269	17	.	.	PROPN
cana-3454	269	18	and	and	CCONJ
cana-3454	269	19	knight	knight	NOUN
cana-3454	269	20	,	,	PUNCT
cana-3454	269	21	r.n	r.n	PROPN
cana-3454	269	22	.	.	PROPN
cana-3454	269	23	,	,	PUNCT
cana-3454	269	24	2024	2024	NUM
cana-3454	269	25	.	.	PUNCT
cana-3454	270	1	efficacy	efficacy	NOUN
cana-3454	270	2	of	of	ADP
cana-3454	270	3	machine	machine	NOUN
cana-3454	270	4	learning	learn	VERB
cana-3454	270	5	image	image	NOUN
cana-3454	270	6	classification	classification	NOUN
cana-3454	270	7	for	for	ADP
cana-3454	270	8	automated	automate	VERB
cana-3454	270	9	occupancy	occupancy	NOUN
cana-3454	270	10	-	-	PUNCT
cana-3454	270	11	based	base	VERB
cana-3454	270	12	monitoring	monitoring	NOUN
cana-3454	270	13	.	.	PUNCT
cana-3454	271	1	remote	remote	ADJ
cana-3454	271	2	sensing	sensing	NOUN
cana-3454	271	3	in	in	ADP
cana-3454	271	4	ecology	ecology	NOUN
cana-3454	271	5	and	and	CCONJ
cana-3454	271	6	conservation	conservation	NOUN
cana-3454	271	7	,	,	PUNCT
cana-3454	271	8	10(1	10(1	NUM
cana-3454	271	9	)	)	PUNCT
cana-3454	271	10	,	,	PUNCT
cana-3454	271	11	pp	pp	ADP
cana-3454	271	12	.	.	PUNCT
cana-3454	272	1	56	56	NUM
cana-3454	272	2	-	-	SYM
cana-3454	272	3	71	71	NUM
cana-3454	272	4	.	.	PUNCT
cana-3454	273	1	[	[	X
cana-3454	273	2	27	27	NUM
cana-3454	273	3	]	]	X
cana-3454	273	4	lumamba	lumamba	NOUN
cana-3454	273	5	,	,	PUNCT
cana-3454	273	6	k.d	k.d	PROPN
cana-3454	273	7	.	.	PROPN
cana-3454	273	8	,	,	PUNCT
cana-3454	273	9	wells	wells	PROPN
cana-3454	273	10	,	,	PUNCT
cana-3454	273	11	g.	g.	PROPN
cana-3454	273	12	,	,	PUNCT
cana-3454	273	13	naicker	naicker	PROPN
cana-3454	273	14	,	,	PUNCT
cana-3454	273	15	d.	d.	PROPN
cana-3454	273	16	,	,	PUNCT
cana-3454	273	17	naidoo	naidoo	PROPN
cana-3454	273	18	,	,	PUNCT
cana-3454	273	19	t.	t.	PROPN
cana-3454	273	20	,	,	PUNCT
cana-3454	273	21	steyn	steyn	PROPN
cana-3454	273	22	,	,	PUNCT
cana-3454	273	23	a.j.c	a.j.c	NOUN
cana-3454	273	24	.	.	PUNCT
cana-3454	273	25	and	and	CCONJ
cana-3454	273	26	gwetu	gwetu	VERB
cana-3454	273	27	,	,	PUNCT
cana-3454	273	28	m.	m.	NOUN
cana-3454	273	29	,	,	PUNCT
cana-3454	273	30	2024	2024	NUM
cana-3454	273	31	.	.	PUNCT
cana-3454	274	1	computer	computer	NOUN
cana-3454	274	2	vision	vision	NOUN
cana-3454	274	3	applications	application	NOUN
cana-3454	274	4	for	for	ADP
cana-3454	274	5	the	the	DET
cana-3454	274	6	detection	detection	NOUN
cana-3454	274	7	or	or	CCONJ
cana-3454	274	8	analysis	analysis	NOUN
cana-3454	274	9	of	of	ADP
cana-3454	274	10	tuberculosis	tuberculosis	NOUN
cana-3454	274	11	using	use	VERB
cana-3454	274	12	digitised	digitise	VERB
cana-3454	274	13	human	human	ADJ
cana-3454	274	14	lung	lung	NOUN
cana-3454	274	15	tissue	tissue	NOUN
cana-3454	274	16	images	image	VERB
cana-3454	274	17	a	a	DET
cana-3454	274	18	systematic	systematic	ADJ
cana-3454	274	19	review	review	NOUN
cana-3454	274	20	.	.	PUNCT
cana-3454	275	1	bmc	bmc	PROPN
cana-3454	275	2	medical	medical	ADJ
cana-3454	275	3	imaging	imaging	NOUN
cana-3454	275	4	,	,	PUNCT
cana-3454	275	5	24	24	NUM
cana-3454	275	6	,	,	PUNCT
cana-3454	275	7	pp	pp	ADJ
cana-3454	275	8	.	.	PUNCT
cana-3454	276	1	1	1	NUM
cana-3454	276	2	-	-	SYM
cana-3454	276	3	15	15	NUM
cana-3454	276	4	.	.	PUNCT
cana-3454	277	1	[	[	X
cana-3454	277	2	28	28	NUM
cana-3454	277	3	]	]	X
cana-3454	277	4	mæstad	mæstad	NOUN
cana-3454	277	5	,	,	PUNCT
cana-3454	277	6	r.	r.	PROPN
cana-3454	277	7	,	,	PUNCT
cana-3454	277	8	kvidaland	kvidaland	PROPN
cana-3454	277	9	,	,	PUNCT
cana-3454	277	10	h.k	h.k	PROPN
cana-3454	277	11	.	.	PROPN
cana-3454	277	12	,	,	PUNCT
cana-3454	277	13	clemm	clemm	VERB
cana-3454	277	14	,	,	PUNCT
cana-3454	277	15	h.	h.	PROPN
cana-3454	277	16	,	,	PUNCT
cana-3454	277	17	ola	ola	PROPN
cana-3454	277	18	drange	drange	PROPN
cana-3454	277	19	røksund	røksund	NOUN
cana-3454	277	20	and	and	CCONJ
cana-3454	277	21	arghandeh	arghandeh	NOUN
cana-3454	277	22	,	,	PUNCT
cana-3454	277	23	r.	r.	PROPN
cana-3454	277	24	,	,	PUNCT
cana-3454	277	25	2024	2024	NUM
cana-3454	277	26	.	.	PUNCT
cana-3454	278	1	diagnostics	diagnostic	NOUN
cana-3454	278	2	of	of	ADP
cana-3454	278	3	exercise	exercise	NOUN
cana-3454	278	4	-	-	PUNCT
cana-3454	278	5	induced	induce	VERB
cana-3454	278	6	laryngeal	laryngeal	ADJ
cana-3454	278	7	obstruction	obstruction	NOUN
cana-3454	278	8	using	use	VERB
cana-3454	278	9	machine	machine	NOUN
cana-3454	278	10	learning	learning	NOUN
cana-3454	278	11	:	:	PUNCT
cana-3454	278	12	a	a	DET
cana-3454	278	13	narrative	narrative	NOUN
cana-3454	278	14	review	review	NOUN
cana-3454	278	15	.	.	PUNCT
cana-3454	279	1	electronics	electronic	NOUN
cana-3454	279	2	,	,	PUNCT
cana-3454	279	3	13(10	13(10	NUM
cana-3454	279	4	)	)	PUNCT
cana-3454	279	5	,	,	PUNCT
cana-3454	279	6	pp	pp	ADP
cana-3454	279	7	.	.	PUNCT
cana-3454	279	8	1880	1880	NUM
cana-3454	279	9	.	.	PUNCT
cana-3454	280	1	[	[	X
cana-3454	280	2	29	29	NUM
cana-3454	280	3	]	]	PUNCT
cana-3454	280	4	manakitsa	manakitsa	NOUN
cana-3454	280	5	,	,	PUNCT
cana-3454	280	6	n.	n.	NOUN
cana-3454	280	7	,	,	PUNCT
cana-3454	280	8	maraslidis	maraslidi	NOUN
cana-3454	280	9	,	,	PUNCT
cana-3454	280	10	g.s	g.s	PROPN
cana-3454	280	11	.	.	PROPN
cana-3454	280	12	,	,	PUNCT
cana-3454	280	13	moysis	moysis	PROPN
cana-3454	280	14	,	,	PUNCT
cana-3454	280	15	l.	l.	PROPN
cana-3454	280	16	and	and	CCONJ
cana-3454	280	17	fragulis	fragulis	PROPN
cana-3454	280	18	,	,	PUNCT
cana-3454	280	19	g.f	g.f	PROPN
cana-3454	280	20	.	.	PROPN
cana-3454	280	21	,	,	PUNCT
cana-3454	280	22	2024	2024	NUM
cana-3454	280	23	.	.	PUNCT
cana-3454	281	1	a	a	DET
cana-3454	281	2	review	review	NOUN
cana-3454	281	3	of	of	ADP
cana-3454	281	4	machine	machine	NOUN
cana-3454	281	5	learning	learning	NOUN
cana-3454	281	6	and	and	CCONJ
cana-3454	281	7	deep	deep	ADJ
cana-3454	281	8	learning	learning	NOUN
cana-3454	281	9	for	for	ADP
cana-3454	281	10	object	object	NOUN
cana-3454	281	11	detection	detection	NOUN
cana-3454	281	12	,	,	PUNCT
cana-3454	281	13	semantic	semantic	ADJ
cana-3454	281	14	segmentation	segmentation	NOUN
cana-3454	281	15	,	,	PUNCT
cana-3454	281	16	and	and	CCONJ
cana-3454	281	17	human	human	ADJ
cana-3454	281	18	action	action	NOUN
cana-3454	281	19	recognition	recognition	NOUN
cana-3454	281	20	in	in	ADP
cana-3454	281	21	machine	machine	NOUN
cana-3454	281	22	and	and	CCONJ
cana-3454	281	23	robotic	robotic	ADJ
cana-3454	281	24	vision	vision	NOUN
cana-3454	281	25	.	.	PUNCT
cana-3454	282	1	technologies	technology	NOUN
cana-3454	282	2	,	,	PUNCT
cana-3454	282	3	12(2	12(2	NUM
cana-3454	282	4	)	)	PUNCT
cana-3454	282	5	,	,	PUNCT
cana-3454	282	6	pp	pp	ADP
cana-3454	282	7	.	.	PUNCT
cana-3454	283	1	15	15	NUM
cana-3454	283	2	.	.	PUNCT
cana-3454	284	1	[	[	X
cana-3454	284	2	30	30	NUM
cana-3454	284	3	]	]	X
cana-3454	284	4	mclain	mclain	PROPN
cana-3454	284	5	,	,	PUNCT
cana-3454	284	6	b.	b.	PROPN
cana-3454	284	7	,	,	PUNCT
cana-3454	284	8	mathenia	mathenia	PROPN
cana-3454	284	9	,	,	PUNCT
cana-3454	284	10	r.	r.	PROPN
cana-3454	284	11	,	,	PUNCT
cana-3454	284	12	sparks	spark	VERB
cana-3454	284	13	,	,	PUNCT
cana-3454	284	14	t.	t.	NOUN
cana-3454	284	15	and	and	CCONJ
cana-3454	284	16	liou	liou	NOUN
cana-3454	284	17	,	,	PUNCT
cana-3454	284	18	f.	f.	PROPN
cana-3454	284	19	,	,	PUNCT
cana-3454	284	20	2024	2024	NUM
cana-3454	284	21	.	.	PUNCT
cana-3454	285	1	machine	machine	NOUN
cana-3454	285	2	vision	vision	NOUN
cana-3454	285	3	to	to	PART
cana-3454	285	4	provide	provide	VERB
cana-3454	285	5	quantitative	quantitative	ADJ
cana-3454	285	6	analysis	analysis	NOUN
cana-3454	285	7	of	of	ADP
cana-3454	285	8	meltpool	meltpool	ADJ
cana-3454	285	9	stability	stability	NOUN
cana-3454	285	10	for	for	ADP
cana-3454	285	11	a	a	DET
cana-3454	285	12	coaxial	coaxial	ADJ
cana-3454	285	13	wire	wire	NOUN
cana-3454	285	14	directed	direct	VERB
cana-3454	285	15	energy	energy	NOUN
cana-3454	285	16	deposition	deposition	NOUN
cana-3454	285	17	process	process	NOUN
cana-3454	285	18	.	.	PUNCT
cana-3454	286	1	materials	material	NOUN
cana-3454	286	2	,	,	PUNCT
cana-3454	286	3	17(21	17(21	NUM
cana-3454	286	4	)	)	PUNCT
cana-3454	286	5	,	,	PUNCT
cana-3454	286	6	pp	pp	ADP
cana-3454	286	7	.	.	PUNCT
cana-3454	287	1	5311	5311	NUM
cana-3454	287	2	.	.	PUNCT