id	sid	tid	token	lemma	pos
cana-4520	1	1	communications	communication	NOUN
cana-4520	1	2	on	on	ADP
cana-4520	1	3	applied	apply	VERB
cana-4520	1	4	nonlinear	nonlinear	ADJ
cana-4520	1	5	analysis	analysis	NOUN
cana-4520	1	6	issn	issn	NOUN
cana-4520	1	7	:	:	PUNCT
cana-4520	1	8	1074	1074	NUM
cana-4520	1	9	-	-	PUNCT
cana-4520	1	10	133x	133x	NUM
cana-4520	1	11	vol	vol	NOUN
cana-4520	1	12	32	32	NUM
cana-4520	1	13	no	no	NOUN
cana-4520	1	14	.	.	PUNCT
cana-4520	2	1	9s	9s	NUM
cana-4520	2	2	(	(	PUNCT
cana-4520	2	3	2025	2025	NUM
cana-4520	2	4	)	)	PUNCT
cana-4520	2	5	2337	2337	NUM
cana-4520	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	2	7	cnn	cnn	PROPN
cana-4520	2	8	-	-	PUNCT
cana-4520	2	9	powered	power	VERB
cana-4520	2	10	driver	driver	NOUN
cana-4520	2	11	fatigue	fatigue	NOUN
cana-4520	2	12	detection	detection	NOUN
cana-4520	2	13	:	:	PUNCT
cana-4520	2	14	evaluating	evaluate	VERB
cana-4520	2	15	inceptionv3	inceptionv3	NOUN
cana-4520	2	16	,	,	PUNCT
cana-4520	2	17	vgg19	vgg19	PROPN
cana-4520	2	18	,	,	PUNCT
cana-4520	2	19	and	and	CCONJ
cana-4520	2	20	resnet50v2	resnet50v2	PROPN
cana-4520	2	21	s.	s.	PROPN
cana-4520	2	22	lakshmi	lakshmi	PROPN
cana-4520	2	23	devi	devi	PROPN
cana-4520	2	24	,	,	PUNCT
cana-4520	2	25	dr	dr	PROPN
cana-4520	2	26	.	.	PROPN
cana-4520	2	27	a.	a.	PROPN
cana-4520	2	28	vinoth	vinoth	PROPN
cana-4520	2	29	department	department	PROPN
cana-4520	2	30	of	of	ADP
cana-4520	2	31	computer	computer	NOUN
cana-4520	2	32	science	science	NOUN
cana-4520	2	33	,	,	PUNCT
cana-4520	2	34	research	research	NOUN
cana-4520	2	35	scholar	scholar	NOUN
cana-4520	2	36	,	,	PUNCT
cana-4520	2	37	sri	sri	PROPN
cana-4520	2	38	krishna	krishna	PROPN
cana-4520	2	39	adithya	adithya	PROPN
cana-4520	2	40	college	college	PROPN
cana-4520	2	41	of	of	ADP
cana-4520	2	42	arts	arts	PROPN
cana-4520	2	43	&	&	CCONJ
cana-4520	2	44	science	science	PROPN
cana-4520	2	45	,	,	PUNCT
cana-4520	2	46	kovaipudur	kovaipudur	PROPN
cana-4520	2	47	,	,	PUNCT
cana-4520	2	48	coimbatore	coimbatore	PROPN
cana-4520	2	49	,	,	PUNCT
cana-4520	2	50	641	641	NUM
cana-4520	2	51	042	042	NUM
cana-4520	2	52	,	,	PUNCT
cana-4520	2	53	tamil	tamil	PROPN
cana-4520	2	54	nadu	nadu	PROPN
cana-4520	2	55	,	,	PUNCT
cana-4520	2	56	india	india	PROPN
cana-4520	2	57	.	.	PUNCT
cana-4520	3	1	department	department	PROPN
cana-4520	3	2	of	of	ADP
cana-4520	3	3	information	information	NOUN
cana-4520	3	4	technology	technology	NOUN
cana-4520	3	5	,	,	PUNCT
cana-4520	3	6	assistant	assistant	NOUN
cana-4520	3	7	professor	professor	NOUN
cana-4520	3	8	,	,	PUNCT
cana-4520	3	9	sri	sri	PROPN
cana-4520	3	10	krsihna	krsihna	PROPN
cana-4520	3	11	adithya	adithya	PROPN
cana-4520	3	12	college	college	PROPN
cana-4520	3	13	of	of	ADP
cana-4520	3	14	arts	arts	PROPN
cana-4520	3	15	&	&	CCONJ
cana-4520	3	16	science	science	PROPN
cana-4520	3	17	,	,	PUNCT
cana-4520	3	18	kovaipudur	kovaipudur	PROPN
cana-4520	3	19	,	,	PUNCT
cana-4520	3	20	coimbatore	coimbatore	PROPN
cana-4520	3	21	,	,	PUNCT
cana-4520	3	22	641	641	NUM
cana-4520	3	23	042	042	NUM
cana-4520	3	24	,	,	PUNCT
cana-4520	3	25	tamil	tamil	PROPN
cana-4520	3	26	nadu	nadu	PROPN
cana-4520	3	27	,	,	PUNCT
cana-4520	3	28	india	india	PROPN
cana-4520	3	29	.	.	PUNCT
cana-4520	4	1	*	*	PUNCT
cana-4520	4	2	corresponding	corresponding	PROPN
cana-4520	4	3	author(s	author(s	NOUN
cana-4520	4	4	)	)	PUNCT
cana-4520	4	5	.	.	PUNCT
cana-4520	5	1	e	e	X
cana-4520	5	2	-	-	PUNCT
cana-4520	5	3	mail(s	mail(s	ADJ
cana-4520	5	4	):	):	PUNCT
cana-4520	5	5	shaarvesh.raj@gmail.com	shaarvesh.raj@gmail.com	PROPN
cana-4520	5	6	article	article	NOUN
cana-4520	5	7	history	history	NOUN
cana-4520	5	8	:	:	PUNCT
cana-4520	5	9	received	receive	VERB
cana-4520	5	10	:	:	PUNCT
cana-4520	5	11	12	12	NUM
cana-4520	5	12	-	-	SYM
cana-4520	5	13	01	01	NUM
cana-4520	5	14	-	-	PUNCT
cana-4520	5	15	2025	2025	NUM
cana-4520	5	16	revised	revise	VERB
cana-4520	5	17	:	:	PUNCT
cana-4520	5	18	15	15	NUM
cana-4520	5	19	-	-	NUM
cana-4520	5	20	02	02	NUM
cana-4520	5	21	-	-	PUNCT
cana-4520	5	22	2025	2025	NUM
cana-4520	5	23	accepted	accept	VERB
cana-4520	5	24	:	:	PUNCT
cana-4520	5	25	01	01	NUM
cana-4520	5	26	-	-	SYM
cana-4520	5	27	03	03	NUM
cana-4520	5	28	-	-	PUNCT
cana-4520	5	29	2025	2025	NUM
cana-4520	5	30	abstract	abstract	NOUN
cana-4520	5	31	:	:	PUNCT
cana-4520	6	1	introduction	introduction	NOUN
cana-4520	6	2	:	:	PUNCT
cana-4520	6	3	two	two	NUM
cana-4520	6	4	major	major	ADJ
cana-4520	6	5	factors	factor	NOUN
cana-4520	6	6	leading	lead	VERB
cana-4520	6	7	to	to	ADP
cana-4520	6	8	traffic	traffic	NOUN
cana-4520	6	9	accidents	accident	NOUN
cana-4520	6	10	are	be	AUX
cana-4520	6	11	driver	driver	NOUN
cana-4520	6	12	fatigue	fatigue	NOUN
cana-4520	6	13	and	and	CCONJ
cana-4520	6	14	distraction	distraction	NOUN
cana-4520	6	15	.	.	PUNCT
cana-4520	7	1	the	the	DET
cana-4520	7	2	world	world	PROPN
cana-4520	7	3	health	health	PROPN
cana-4520	7	4	organization	organization	NOUN
cana-4520	7	5	(	(	PUNCT
cana-4520	7	6	who	who	PRON
cana-4520	7	7	)	)	PUNCT
cana-4520	7	8	reports	report	VERB
cana-4520	7	9	that	that	SCONJ
cana-4520	7	10	7	7	NUM
cana-4520	7	11	%	%	NOUN
cana-4520	7	12	of	of	ADP
cana-4520	7	13	fatal	fatal	ADJ
cana-4520	7	14	and	and	CCONJ
cana-4520	7	15	severe	severe	ADJ
cana-4520	7	16	traffic	traffic	NOUN
cana-4520	7	17	accidents	accident	NOUN
cana-4520	7	18	are	be	AUX
cana-4520	7	19	caused	cause	VERB
cana-4520	7	20	by	by	ADP
cana-4520	7	21	sleepy	sleepy	ADJ
cana-4520	7	22	driving	driving	NOUN
cana-4520	7	23	.	.	PUNCT
cana-4520	8	1	advances	advance	NOUN
cana-4520	8	2	in	in	ADP
cana-4520	8	3	machine	machine	NOUN
cana-4520	8	4	learning	learning	NOUN
cana-4520	8	5	,	,	PUNCT
cana-4520	8	6	artificial	artificial	ADJ
cana-4520	8	7	intelligence	intelligence	NOUN
cana-4520	8	8	,	,	PUNCT
cana-4520	8	9	and	and	CCONJ
cana-4520	8	10	neural	neural	ADJ
cana-4520	8	11	networks	network	NOUN
cana-4520	8	12	have	have	AUX
cana-4520	8	13	paved	pave	VERB
cana-4520	8	14	new	new	ADJ
cana-4520	8	15	avenues	avenue	NOUN
cana-4520	8	16	for	for	ADP
cana-4520	8	17	real	real	ADJ
cana-4520	8	18	-	-	PUNCT
cana-4520	8	19	time	time	NOUN
cana-4520	8	20	sleepiness	sleepiness	NOUN
cana-4520	8	21	detection	detection	NOUN
cana-4520	8	22	,	,	PUNCT
cana-4520	8	23	providing	provide	VERB
cana-4520	8	24	practical	practical	ADJ
cana-4520	8	25	ways	way	NOUN
cana-4520	8	26	that	that	PRON
cana-4520	8	27	mitigate	mitigate	VERB
cana-4520	8	28	the	the	DET
cana-4520	8	29	number	number	NOUN
cana-4520	8	30	of	of	ADP
cana-4520	8	31	mishaps	mishap	NOUN
cana-4520	8	32	objectives	objective	VERB
cana-4520	8	33	:	:	PUNCT
cana-4520	8	34	the	the	DET
cana-4520	8	35	primary	primary	ADJ
cana-4520	8	36	objective	objective	NOUN
cana-4520	8	37	of	of	ADP
cana-4520	8	38	this	this	DET
cana-4520	8	39	research	research	NOUN
cana-4520	8	40	is	be	AUX
cana-4520	8	41	:	:	PUNCT
cana-4520	8	42	to	to	PART
cana-4520	8	43	develop	develop	VERB
cana-4520	8	44	a	a	DET
cana-4520	8	45	new	new	ADJ
cana-4520	8	46	,	,	PUNCT
cana-4520	8	47	real	real	ADJ
cana-4520	8	48	-	-	PUNCT
cana-4520	8	49	time	time	NOUN
cana-4520	8	50	system	system	NOUN
cana-4520	8	51	for	for	ADP
cana-4520	8	52	detecting	detect	VERB
cana-4520	8	53	driver	driver	NOUN
cana-4520	8	54	drowsiness	drowsiness	NOUN
cana-4520	8	55	.	.	PUNCT
cana-4520	9	1	the	the	DET
cana-4520	9	2	technology	technology	NOUN
cana-4520	9	3	evaluates	evaluate	VERB
cana-4520	9	4	facial	facial	ADJ
cana-4520	9	5	expressions	expression	NOUN
cana-4520	9	6	and	and	CCONJ
cana-4520	9	7	detects	detect	NOUN
cana-4520	9	8	indicators	indicator	NOUN
cana-4520	9	9	of	of	ADP
cana-4520	9	10	exhaustion	exhaustion	NOUN
cana-4520	9	11	using	use	VERB
cana-4520	9	12	deep	deep	ADJ
cana-4520	9	13	learning	learning	NOUN
cana-4520	9	14	algorithms	algorithm	NOUN
cana-4520	9	15	,	,	PUNCT
cana-4520	9	16	which	which	PRON
cana-4520	9	17	eventually	eventually	ADV
cana-4520	9	18	improves	improve	VERB
cana-4520	9	19	road	road	NOUN
cana-4520	9	20	safety	safety	NOUN
cana-4520	9	21	by	by	ADP
cana-4520	9	22	reducing	reduce	VERB
cana-4520	9	23	drowsiness	drowsiness	NOUN
cana-4520	9	24	-	-	PUNCT
cana-4520	9	25	related	relate	VERB
cana-4520	9	26	accidents	accident	NOUN
cana-4520	9	27	.	.	PUNCT
cana-4520	10	1	methods	method	NOUN
cana-4520	10	2	:	:	PUNCT
cana-4520	10	3	the	the	DET
cana-4520	10	4	proposed	propose	VERB
cana-4520	10	5	strategy	strategy	NOUN
cana-4520	10	6	uses	use	VERB
cana-4520	10	7	facial	facial	ADJ
cana-4520	10	8	cues	cue	NOUN
cana-4520	10	9	,	,	PUNCT
cana-4520	10	10	such	such	ADJ
cana-4520	10	11	as	as	ADP
cana-4520	10	12	the	the	DET
cana-4520	10	13	eyes	eye	NOUN
cana-4520	10	14	,	,	PUNCT
cana-4520	10	15	lips	lip	NOUN
cana-4520	10	16	,	,	PUNCT
cana-4520	10	17	head	head	NOUN
cana-4520	10	18	,	,	PUNCT
cana-4520	10	19	and	and	CCONJ
cana-4520	10	20	pupil	pupil	NOUN
cana-4520	10	21	,	,	PUNCT
cana-4520	10	22	to	to	PART
cana-4520	10	23	detect	detect	VERB
cana-4520	10	24	driver	driver	NOUN
cana-4520	10	25	fatigue	fatigue	NOUN
cana-4520	10	26	.	.	PUNCT
cana-4520	11	1	the	the	DET
cana-4520	11	2	mediapipe	mediapipe	NOUN
cana-4520	11	3	method	method	NOUN
cana-4520	11	4	,	,	PUNCT
cana-4520	11	5	which	which	PRON
cana-4520	11	6	is	be	AUX
cana-4520	11	7	renowned	renowned	ADJ
cana-4520	11	8	for	for	ADP
cana-4520	11	9	its	its	PRON
cana-4520	11	10	great	great	ADJ
cana-4520	11	11	accuracy	accuracy	NOUN
cana-4520	11	12	and	and	CCONJ
cana-4520	11	13	resilience	resilience	NOUN
cana-4520	11	14	,	,	PUNCT
cana-4520	11	15	is	be	AUX
cana-4520	11	16	used	use	VERB
cana-4520	11	17	to	to	PART
cana-4520	11	18	extract	extract	VERB
cana-4520	11	19	these	these	DET
cana-4520	11	20	properties	property	NOUN
cana-4520	11	21	.	.	PUNCT
cana-4520	12	1	the	the	DET
cana-4520	12	2	inceptionv3	inceptionv3	NOUN
cana-4520	12	3	,	,	PUNCT
cana-4520	12	4	vgg19	vgg19	PROPN
cana-4520	12	5	,	,	PUNCT
cana-4520	12	6	and	and	CCONJ
cana-4520	12	7	resnet50v2	resnet50v2	NOUN
cana-4520	12	8	deep	deep	ADJ
cana-4520	12	9	learning	learn	VERB
cana-4520	12	10	neural	neural	ADJ
cana-4520	12	11	networks	network	NOUN
cana-4520	12	12	were	be	AUX
cana-4520	12	13	assessed	assess	VERB
cana-4520	12	14	using	use	VERB
cana-4520	12	15	a	a	DET
cana-4520	12	16	dataset	dataset	NOUN
cana-4520	12	17	captured	capture	VERB
cana-4520	12	18	in	in	ADP
cana-4520	12	19	real	real	ADJ
cana-4520	12	20	time	time	NOUN
cana-4520	12	21	at	at	ADP
cana-4520	12	22	nthu	nthu	PROPN
cana-4520	12	23	.	.	PUNCT
cana-4520	13	1	the	the	DET
cana-4520	13	2	drivers	driver	NOUN
cana-4520	13	3	in	in	ADP
cana-4520	13	4	the	the	DET
cana-4520	13	5	dataset	dataset	NOUN
cana-4520	13	6	show	show	NOUN
cana-4520	13	7	varying	vary	VERB
cana-4520	13	8	degrees	degree	NOUN
cana-4520	13	9	of	of	ADP
cana-4520	13	10	tiredness	tiredness	NOUN
cana-4520	13	11	under	under	ADP
cana-4520	13	12	different	different	ADJ
cana-4520	13	13	settings	setting	NOUN
cana-4520	13	14	.	.	PUNCT
cana-4520	14	1	to	to	PART
cana-4520	14	2	evaluate	evaluate	VERB
cana-4520	14	3	the	the	DET
cana-4520	14	4	models	model	NOUN
cana-4520	14	5	'	'	PART
cana-4520	14	6	efficacy	efficacy	NOUN
cana-4520	14	7	,	,	PUNCT
cana-4520	14	8	performance	performance	NOUN
cana-4520	14	9	criteria	criterion	NOUN
cana-4520	14	10	like	like	ADP
cana-4520	14	11	accuracy	accuracy	NOUN
cana-4520	14	12	,	,	PUNCT
cana-4520	14	13	recall	recall	NOUN
cana-4520	14	14	,	,	PUNCT
cana-4520	14	15	precision	precision	NOUN
cana-4520	14	16	,	,	PUNCT
cana-4520	14	17	and	and	CCONJ
cana-4520	14	18	f1score	f1score	NOUN
cana-4520	14	19	were	be	AUX
cana-4520	14	20	applied	apply	VERB
cana-4520	14	21	.	.	PUNCT
cana-4520	15	1	results	result	NOUN
cana-4520	15	2	:	:	PUNCT
cana-4520	15	3	in	in	ADP
cana-4520	15	4	detecting	detect	VERB
cana-4520	15	5	fatigue	fatigue	NOUN
cana-4520	15	6	,	,	PUNCT
cana-4520	15	7	all	all	DET
cana-4520	15	8	three	three	NUM
cana-4520	15	9	convolutional	convolutional	ADJ
cana-4520	15	10	neural	neural	ADJ
cana-4520	15	11	networks	network	NOUN
cana-4520	15	12	performed	perform	VERB
cana-4520	15	13	admirably	admirably	ADV
cana-4520	15	14	.	.	PUNCT
cana-4520	16	1	the	the	DET
cana-4520	16	2	resnet50v2	resnet50v2	PROPN
cana-4520	16	3	model	model	NOUN
cana-4520	16	4	performed	perform	VERB
cana-4520	16	5	more	more	ADV
cana-4520	16	6	effectively	effectively	ADV
cana-4520	16	7	than	than	ADP
cana-4520	16	8	the	the	DET
cana-4520	16	9	other	other	ADJ
cana-4520	16	10	two	two	NUM
cana-4520	16	11	,	,	PUNCT
cana-4520	16	12	with	with	ADP
cana-4520	16	13	an	an	DET
cana-4520	16	14	overall	overall	ADJ
cana-4520	16	15	accuracy	accuracy	NOUN
cana-4520	16	16	of	of	ADP
cana-4520	16	17	98.51	98.51	NUM
cana-4520	16	18	%	%	NOUN
cana-4520	16	19	.	.	PUNCT
cana-4520	17	1	this	this	PRON
cana-4520	17	2	suggests	suggest	VERB
cana-4520	17	3	that	that	SCONJ
cana-4520	17	4	it	it	PRON
cana-4520	17	5	can	can	AUX
cana-4520	17	6	distinguish	distinguish	VERB
cana-4520	17	7	between	between	ADP
cana-4520	17	8	fatigue	fatigue	NOUN
cana-4520	17	9	and	and	CCONJ
cana-4520	17	10	nonfatigue	nonfatigue	NOUN
cana-4520	17	11	states	state	NOUN
cana-4520	17	12	with	with	ADP
cana-4520	17	13	greater	great	ADJ
cana-4520	17	14	accuracy	accuracy	NOUN
cana-4520	17	15	.	.	PUNCT
cana-4520	18	1	conclusions	conclusion	NOUN
cana-4520	18	2	:	:	PUNCT
cana-4520	18	3	the	the	DET
cana-4520	18	4	efficacy	efficacy	NOUN
cana-4520	18	5	of	of	ADP
cana-4520	18	6	deep	deep	ADJ
cana-4520	18	7	learning	learning	NOUN
cana-4520	18	8	models	model	NOUN
cana-4520	18	9	in	in	ADP
cana-4520	18	10	real	real	ADJ
cana-4520	18	11	-	-	PUNCT
cana-4520	18	12	time	time	NOUN
cana-4520	18	13	fatigue	fatigue	NOUN
cana-4520	18	14	detection	detection	NOUN
cana-4520	18	15	has	have	AUX
cana-4520	18	16	been	be	AUX
cana-4520	18	17	proven	prove	VERB
cana-4520	18	18	by	by	ADP
cana-4520	18	19	the	the	DET
cana-4520	18	20	research	research	NOUN
cana-4520	18	21	.	.	PUNCT
cana-4520	19	1	more	more	ADV
cana-4520	19	2	specifically	specifically	ADV
cana-4520	19	3	,	,	PUNCT
cana-4520	19	4	the	the	DET
cana-4520	19	5	resnet50v2	resnet50v2	PROPN
cana-4520	19	6	model	model	NOUN
cana-4520	19	7	exhibits	exhibit	VERB
cana-4520	19	8	remarkable	remarkable	ADJ
cana-4520	19	9	accuracy	accuracy	NOUN
cana-4520	19	10	,	,	PUNCT
cana-4520	19	11	making	make	VERB
cana-4520	19	12	it	it	PRON
cana-4520	19	13	a	a	DET
cana-4520	19	14	viable	viable	ADJ
cana-4520	19	15	option	option	NOUN
cana-4520	19	16	for	for	ADP
cana-4520	19	17	reducing	reduce	VERB
cana-4520	19	18	accidents	accident	NOUN
cana-4520	19	19	caused	cause	VERB
cana-4520	19	20	by	by	ADP
cana-4520	19	21	drowsy	drowsy	NOUN
cana-4520	19	22	driving	driving	NOUN
cana-4520	19	23	.	.	PUNCT
cana-4520	20	1	to	to	PART
cana-4520	20	2	improve	improve	VERB
cana-4520	20	3	road	road	NOUN
cana-4520	20	4	safety	safety	NOUN
cana-4520	20	5	,	,	PUNCT
cana-4520	20	6	future	future	ADJ
cana-4520	20	7	work	work	NOUN
cana-4520	20	8	can	can	AUX
cana-4520	20	9	entail	entail	VERB
cana-4520	20	10	more	more	ADJ
cana-4520	20	11	system	system	NOUN
cana-4520	20	12	optimization	optimization	NOUN
cana-4520	20	13	and	and	CCONJ
cana-4520	20	14	practical	practical	ADJ
cana-4520	20	15	implementation	implementation	NOUN
cana-4520	20	16	.	.	PUNCT
cana-4520	21	1	keywords	keyword	NOUN
cana-4520	21	2	:	:	PUNCT
cana-4520	21	3	fatigue	fatigue	NOUN
cana-4520	21	4	,	,	PUNCT
cana-4520	21	5	convolutional	convolutional	ADJ
cana-4520	21	6	neural	neural	ADJ
cana-4520	21	7	network	network	NOUN
cana-4520	21	8	,	,	PUNCT
cana-4520	21	9	pupil	pupil	NOUN
cana-4520	21	10	,	,	PUNCT
cana-4520	21	11	nodding	nod	VERB
cana-4520	21	12	,	,	PUNCT
cana-4520	21	13	resnet0v2	resnet0v2	NOUN
cana-4520	21	14	,	,	PUNCT
cana-4520	21	15	inceptionv3	inceptionv3	NOUN
cana-4520	21	16	,	,	PUNCT
cana-4520	21	17	vgg19	vgg19	PROPN
cana-4520	21	18	1	1	NUM
cana-4520	21	19	.	.	PUNCT
cana-4520	22	1	introduction	introduction	NOUN
cana-4520	22	2	the	the	DET
cana-4520	22	3	nhtsa	nhtsa	PROPN
cana-4520	22	4	reports	report	VERB
cana-4520	22	5	that	that	SCONJ
cana-4520	22	6	in	in	ADP
cana-4520	22	7	2022	2022	NUM
cana-4520	22	8	,	,	PUNCT
cana-4520	22	9	1.9	1.9	NUM
cana-4520	22	10	%	%	NOUN
cana-4520	22	11	of	of	ADP
cana-4520	22	12	all	all	DET
cana-4520	22	13	fatal	fatal	ADJ
cana-4520	22	14	accidents	accident	NOUN
cana-4520	22	15	were	be	AUX
cana-4520	22	16	caused	cause	VERB
cana-4520	22	17	by	by	ADP
cana-4520	22	18	sleep	sleep	NOUN
cana-4520	22	19	-	-	PUNCT
cana-4520	22	20	deprived	deprive	VERB
cana-4520	22	21	drivers	driver	NOUN
cana-4520	22	22	.	.	PUNCT
cana-4520	23	1	according	accord	VERB
cana-4520	23	2	to	to	ADP
cana-4520	23	3	projections	projection	NOUN
cana-4520	23	4	made	make	VERB
cana-4520	23	5	by	by	ADP
cana-4520	23	6	the	the	DET
cana-4520	23	7	national	national	PROPN
cana-4520	23	8	safety	safety	PROPN
cana-4520	23	9	council	council	PROPN
cana-4520	23	10	(	(	PUNCT
cana-4520	23	11	nsc	nsc	PROPN
cana-4520	23	12	)	)	PUNCT
cana-4520	23	13	,	,	PUNCT
cana-4520	23	14	there	there	PRON
cana-4520	23	15	would	would	AUX
cana-4520	23	16	be	be	AUX
cana-4520	23	17	100,000	100,000	NUM
cana-4520	23	18	crashes	crash	NOUN
cana-4520	23	19	involving	involve	VERB
cana-4520	23	20	intoxicated	intoxicated	ADJ
cana-4520	23	21	drivers	driver	NOUN
cana-4520	23	22	in	in	ADP
cana-4520	23	23	2023	2023	NUM
cana-4520	23	24	,	,	PUNCT
cana-4520	23	25	with	with	ADP
cana-4520	23	26	71,000	71,000	NUM
cana-4520	23	27	injuries	injury	NOUN
cana-4520	23	28	and	and	CCONJ
cana-4520	23	29	15,550	15,550	NUM
cana-4520	23	30	fatalities	fatality	NOUN
cana-4520	23	31	.	.	PUNCT
cana-4520	24	1	fatiguerelated	fatiguerelate	VERB
cana-4520	24	2	micro	micro	NOUN
cana-4520	24	3	sleeps	sleep	NOUN
cana-4520	24	4	while	while	SCONJ
cana-4520	24	5	driving	drive	VERB
cana-4520	24	6	dramatically	dramatically	ADV
cana-4520	24	7	raise	raise	VERB
cana-4520	24	8	the	the	DET
cana-4520	24	9	possibility	possibility	NOUN
cana-4520	24	10	of	of	ADP
cana-4520	24	11	catastrophic	catastrophic	ADJ
cana-4520	24	12	collisions	collision	NOUN
cana-4520	24	13	.	.	PUNCT
cana-4520	25	1	several	several	ADJ
cana-4520	25	2	companies	company	NOUN
cana-4520	25	3	,	,	PUNCT
cana-4520	25	4	including	include	VERB
cana-4520	25	5	tesla	tesla	PROPN
cana-4520	25	6	,	,	PUNCT
cana-4520	25	7	mercedes	mercedes	PROPN
cana-4520	25	8	-	-	PUNCT
cana-4520	25	9	benz	benz	PROPN
cana-4520	25	10	,	,	PUNCT
cana-4520	25	11	and	and	CCONJ
cana-4520	25	12	volvo	volvo	PROPN
cana-4520	25	13	,	,	PUNCT
cana-4520	25	14	have	have	AUX
cana-4520	25	15	developed	develop	VERB
cana-4520	25	16	driveralert	driveralert	NOUN
cana-4520	25	17	navigation	navigation	NOUN
cana-4520	25	18	systems	system	NOUN
cana-4520	25	19	with	with	ADP
cana-4520	25	20	automated	automate	VERB
cana-4520	25	21	steering	steering	NOUN
cana-4520	25	22	,	,	PUNCT
cana-4520	25	23	lane	lane	NOUN
cana-4520	25	24	departure	departure	NOUN
cana-4520	25	25	warnings	warning	NOUN
cana-4520	25	26	,	,	PUNCT
cana-4520	25	27	automated	automate	VERB
cana-4520	25	28	braking	braking	NOUN
cana-4520	25	29	,	,	PUNCT
cana-4520	25	30	communications	communication	NOUN
cana-4520	25	31	on	on	ADP
cana-4520	25	32	applied	apply	VERB
cana-4520	25	33	nonlinear	nonlinear	ADJ
cana-4520	25	34	analysis	analysis	NOUN
cana-4520	25	35	issn	issn	NOUN
cana-4520	25	36	:	:	PUNCT
cana-4520	25	37	1074	1074	NUM
cana-4520	25	38	-	-	PUNCT
cana-4520	25	39	133x	133x	NUM
cana-4520	25	40	vol	vol	NOUN
cana-4520	25	41	32	32	NUM
cana-4520	26	1	no	no	NOUN
cana-4520	26	2	.	.	PUNCT
cana-4520	27	1	9s	9s	NUM
cana-4520	27	2	(	(	PUNCT
cana-4520	27	3	2025	2025	NUM
cana-4520	27	4	)	)	PUNCT
cana-4520	27	5	2338	2338	NUM
cana-4520	27	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	27	7	adaptive	adaptive	ADJ
cana-4520	27	8	cruise	cruise	NOUN
cana-4520	27	9	control	control	NOUN
cana-4520	27	10	,	,	PUNCT
cana-4520	27	11	and	and	CCONJ
cana-4520	27	12	other	other	ADJ
cana-4520	27	13	functions.[1	functions.[1	NOUN
cana-4520	27	14	]	]	PUNCT
cana-4520	27	15	technologies	technology	NOUN
cana-4520	27	16	such	such	ADJ
cana-4520	27	17	as	as	ADP
cana-4520	27	18	eyesight	eyesight	ADJ
cana-4520	27	19	and	and	CCONJ
cana-4520	27	20	samsung	samsung	PROPN
cana-4520	27	21	have	have	AUX
cana-4520	27	22	been	be	AUX
cana-4520	27	23	created	create	VERB
cana-4520	27	24	that	that	PRON
cana-4520	27	25	use	use	VERB
cana-4520	27	26	facial	facial	ADJ
cana-4520	27	27	feature	feature	NOUN
cana-4520	27	28	analysis	analysis	NOUN
cana-4520	27	29	to	to	PART
cana-4520	27	30	track	track	VERB
cana-4520	27	31	drivers	driver	NOUN
cana-4520	27	32	'	'	PART
cana-4520	27	33	concentration	concentration	NOUN
cana-4520	27	34	.	.	PUNCT
cana-4520	28	1	nevertheless	nevertheless	ADV
cana-4520	28	2	,	,	PUNCT
cana-4520	28	3	these	these	DET
cana-4520	28	4	advancements	advancement	NOUN
cana-4520	28	5	are	be	AUX
cana-4520	28	6	frequently	frequently	ADV
cana-4520	28	7	restricted	restrict	VERB
cana-4520	28	8	to	to	ADP
cana-4520	28	9	expensive	expensive	ADJ
cana-4520	28	10	cars	car	NOUN
cana-4520	28	11	and	and	CCONJ
cana-4520	28	12	exclusive	exclusive	ADJ
cana-4520	28	13	technology	technology	NOUN
cana-4520	28	14	.	.	PUNCT
cana-4520	29	1	several	several	ADJ
cana-4520	29	2	companies	company	NOUN
cana-4520	29	3	,	,	PUNCT
cana-4520	29	4	including	include	VERB
cana-4520	29	5	tesla	tesla	PROPN
cana-4520	29	6	,	,	PUNCT
cana-4520	29	7	mercedes	mercedes	PROPN
cana-4520	29	8	-	-	PUNCT
cana-4520	29	9	benz	benz	PROPN
cana-4520	29	10	,	,	PUNCT
cana-4520	29	11	and	and	CCONJ
cana-4520	29	12	volvo	volvo	PROPN
cana-4520	29	13	,	,	PUNCT
cana-4520	29	14	have	have	AUX
cana-4520	29	15	developed	develop	VERB
cana-4520	29	16	driveralert	driveralert	NOUN
cana-4520	29	17	navigation	navigation	NOUN
cana-4520	29	18	systems	system	NOUN
cana-4520	29	19	with	with	ADP
cana-4520	29	20	automated	automate	VERB
cana-4520	29	21	steering	steering	NOUN
cana-4520	29	22	,	,	PUNCT
cana-4520	29	23	lane	lane	NOUN
cana-4520	29	24	departure	departure	NOUN
cana-4520	29	25	warnings	warning	NOUN
cana-4520	29	26	,	,	PUNCT
cana-4520	29	27	automated	automate	VERB
cana-4520	29	28	braking	braking	NOUN
cana-4520	29	29	,	,	PUNCT
cana-4520	29	30	adaptive	adaptive	ADJ
cana-4520	29	31	cruise	cruise	NOUN
cana-4520	29	32	control	control	NOUN
cana-4520	29	33	,	,	PUNCT
cana-4520	29	34	and	and	CCONJ
cana-4520	29	35	other	other	ADJ
cana-4520	29	36	functions.[2	functions.[2	NOUN
cana-4520	29	37	]	]	X
cana-4520	30	1	[	[	X
cana-4520	30	2	19	19	NUM
cana-4520	30	3	]	]	PUNCT
cana-4520	30	4	by	by	ADP
cana-4520	30	5	utilizing	utilize	VERB
cana-4520	30	6	embedded	embed	VERB
cana-4520	30	7	devices	device	NOUN
cana-4520	30	8	,	,	PUNCT
cana-4520	30	9	mobile	mobile	ADJ
cana-4520	30	10	phones	phone	NOUN
cana-4520	30	11	,	,	PUNCT
cana-4520	30	12	or	or	CCONJ
cana-4520	30	13	dashboard	dashboard	NOUN
cana-4520	30	14	-	-	PUNCT
cana-4520	30	15	mounted	mount	VERB
cana-4520	30	16	cameras	camera	NOUN
cana-4520	30	17	,	,	PUNCT
cana-4520	30	18	sophisticated	sophisticated	ADJ
cana-4520	30	19	computer	computer	NOUN
cana-4520	30	20	vision	vision	NOUN
cana-4520	30	21	systems	system	NOUN
cana-4520	30	22	driven	drive	VERB
cana-4520	30	23	by	by	ADP
cana-4520	30	24	deep	deep	ADJ
cana-4520	30	25	learning	learning	NOUN
cana-4520	30	26	algorithms	algorithm	NOUN
cana-4520	30	27	and	and	CCONJ
cana-4520	30	28	cost	cost	NOUN
cana-4520	30	29	-	-	PUNCT
cana-4520	30	30	effective	effective	ADJ
cana-4520	30	31	camera	camera	NOUN
cana-4520	30	32	configurations	configuration	NOUN
cana-4520	30	33	might	might	AUX
cana-4520	30	34	improve	improve	VERB
cana-4520	30	35	driver	driver	NOUN
cana-4520	30	36	behavior	behavior	NOUN
cana-4520	30	37	detection.[3	detection.[3	NOUN
cana-4520	30	38	]	]	X
cana-4520	30	39	one	one	NUM
cana-4520	30	40	important	important	ADJ
cana-4520	30	41	topic	topic	NOUN
cana-4520	30	42	of	of	ADP
cana-4520	30	43	research	research	NOUN
cana-4520	30	44	is	be	AUX
cana-4520	30	45	how	how	SCONJ
cana-4520	30	46	to	to	PART
cana-4520	30	47	improve	improve	VERB
cana-4520	30	48	vehicle	vehicle	NOUN
cana-4520	30	49	safety	safety	NOUN
cana-4520	30	50	through	through	ADP
cana-4520	30	51	technology	technology	NOUN
cana-4520	30	52	developments	development	NOUN
cana-4520	30	53	.	.	PUNCT
cana-4520	31	1	there	there	PRON
cana-4520	31	2	is	be	VERB
cana-4520	31	3	no	no	DET
cana-4520	31	4	established	establish	VERB
cana-4520	31	5	technique	technique	NOUN
cana-4520	31	6	for	for	ADP
cana-4520	31	7	determining	determine	VERB
cana-4520	31	8	a	a	DET
cana-4520	31	9	motorist	motorist	NOUN
cana-4520	31	10	's	's	PART
cana-4520	31	11	present	present	ADJ
cana-4520	31	12	degree	degree	NOUN
cana-4520	31	13	of	of	ADP
cana-4520	31	14	drowsiness	drowsiness	NOUN
cana-4520	31	15	,	,	PUNCT
cana-4520	31	16	despite	despite	SCONJ
cana-4520	31	17	the	the	DET
cana-4520	31	18	fact	fact	NOUN
cana-4520	31	19	that	that	SCONJ
cana-4520	31	20	driver	driver	NOUN
cana-4520	31	21	weariness	weariness	NOUN
cana-4520	31	22	is	be	AUX
cana-4520	31	23	frequently	frequently	ADV
cana-4520	31	24	the	the	DET
cana-4520	31	25	cause	cause	NOUN
cana-4520	31	26	of	of	ADP
cana-4520	31	27	auto	auto	NOUN
cana-4520	31	28	accidents	accident	NOUN
cana-4520	31	29	.	.	PUNCT
cana-4520	32	1	changes	change	NOUN
cana-4520	32	2	in	in	ADP
cana-4520	32	3	breathing	breathing	NOUN
cana-4520	32	4	rate	rate	NOUN
cana-4520	32	5	,	,	PUNCT
cana-4520	32	6	pulse	pulse	NOUN
cana-4520	32	7	rate	rate	NOUN
cana-4520	32	8	,	,	PUNCT
cana-4520	32	9	eye	eye	NOUN
cana-4520	32	10	movements	movement	NOUN
cana-4520	32	11	,	,	PUNCT
cana-4520	32	12	yawning	yawn	VERB
cana-4520	32	13	,	,	PUNCT
cana-4520	32	14	and	and	CCONJ
cana-4520	32	15	brain	brain	NOUN
cana-4520	32	16	activity	activity	NOUN
cana-4520	32	17	are	be	AUX
cana-4520	32	18	examples	example	NOUN
cana-4520	32	19	of	of	ADP
cana-4520	32	20	physical	physical	ADJ
cana-4520	32	21	indications	indication	NOUN
cana-4520	32	22	of	of	ADP
cana-4520	32	23	exhaustion	exhaustion	NOUN
cana-4520	32	24	.	.	PUNCT
cana-4520	33	1	three	three	NUM
cana-4520	33	2	main	main	ADJ
cana-4520	33	3	areas	area	NOUN
cana-4520	33	4	of	of	ADP
cana-4520	33	5	research	research	NOUN
cana-4520	33	6	are	be	AUX
cana-4520	33	7	commonly	commonly	ADV
cana-4520	33	8	used	use	VERB
cana-4520	33	9	to	to	PART
cana-4520	33	10	analyze	analyze	VERB
cana-4520	33	11	driver	driver	NOUN
cana-4520	33	12	fatigue	fatigue	NOUN
cana-4520	33	13	:	:	PUNCT
cana-4520	33	14	1	1	X
cana-4520	33	15	)	)	PUNCT
cana-4520	33	16	biological	biological	ADJ
cana-4520	33	17	signal	signal	NOUN
cana-4520	33	18	-	-	PUNCT
cana-4520	33	19	based	base	VERB
cana-4520	33	20	approaches	approach	NOUN
cana-4520	33	21	utilizing	utilize	VERB
cana-4520	33	22	sensors	sensor	NOUN
cana-4520	33	23	;	;	PUNCT
cana-4520	33	24	2	2	X
cana-4520	33	25	)	)	PUNCT
cana-4520	33	26	vehicle	vehicle	NOUN
cana-4520	33	27	behavior	behavior	NOUN
cana-4520	33	28	-	-	PUNCT
cana-4520	33	29	based	base	VERB
cana-4520	33	30	methods	method	NOUN
cana-4520	33	31	;	;	PUNCT
cana-4520	33	32	and	and	CCONJ
cana-4520	33	33	3	3	X
cana-4520	33	34	)	)	PUNCT
cana-4520	33	35	image	image	NOUN
cana-4520	33	36	processing	processing	NOUN
cana-4520	33	37	methods	method	NOUN
cana-4520	33	38	employing	employ	VERB
cana-4520	33	39	computer	computer	NOUN
cana-4520	33	40	vision	vision	NOUN
cana-4520	33	41	to	to	PART
cana-4520	33	42	analyze	analyze	VERB
cana-4520	33	43	changes	change	NOUN
cana-4520	33	44	in	in	ADP
cana-4520	33	45	face	face	NOUN
cana-4520	33	46	features	feature	NOUN
cana-4520	33	47	,	,	PUNCT
cana-4520	33	48	which	which	PRON
cana-4520	33	49	are	be	AUX
cana-4520	33	50	important	important	ADJ
cana-4520	33	51	markers	marker	NOUN
cana-4520	33	52	of	of	ADP
cana-4520	33	53	sleepiness	sleepiness	NOUN
cana-4520	33	54	.	.	PUNCT
cana-4520	34	1	since	since	SCONJ
cana-4520	34	2	convolutional	convolutional	ADJ
cana-4520	34	3	neural	neural	ADJ
cana-4520	34	4	networks	network	NOUN
cana-4520	34	5	(	(	PUNCT
cana-4520	34	6	cnn	cnn	PROPN
cana-4520	34	7	)	)	PUNCT
cana-4520	34	8	have	have	VERB
cana-4520	34	9	high	high	ADJ
cana-4520	34	10	computational	computational	ADJ
cana-4520	34	11	efficiency	efficiency	NOUN
cana-4520	34	12	and	and	CCONJ
cana-4520	34	13	performance	performance	NOUN
cana-4520	34	14	,	,	PUNCT
cana-4520	34	15	we	we	PRON
cana-4520	34	16	have	have	AUX
cana-4520	34	17	opted	opt	VERB
cana-4520	34	18	for	for	ADP
cana-4520	34	19	our	our	PRON
cana-4520	34	20	recent	recent	ADJ
cana-4520	34	21	deep	deep	ADJ
cana-4520	34	22	learning	learning	NOUN
cana-4520	34	23	research	research	NOUN
cana-4520	34	24	on	on	ADP
cana-4520	34	25	drowsiness	drowsiness	NOUN
cana-4520	34	26	detection	detection	NOUN
cana-4520	34	27	[	[	X
cana-4520	34	28	4	4	NUM
cana-4520	34	29	-	-	SYM
cana-4520	34	30	6	6	NUM
cana-4520	34	31	]	]	PUNCT
cana-4520	34	32	.	.	PUNCT
cana-4520	35	1	this	this	DET
cana-4520	35	2	model	model	NOUN
cana-4520	35	3	selection	selection	NOUN
cana-4520	35	4	guarantees	guarantee	VERB
cana-4520	35	5	great	great	ADJ
cana-4520	35	6	accuracy	accuracy	NOUN
cana-4520	35	7	during	during	ADP
cana-4520	35	8	the	the	DET
cana-4520	35	9	testing	testing	NOUN
cana-4520	35	10	,	,	PUNCT
cana-4520	35	11	training	training	NOUN
cana-4520	35	12	,	,	PUNCT
cana-4520	35	13	and	and	CCONJ
cana-4520	35	14	assessment	assessment	NOUN
cana-4520	35	15	stages	stage	NOUN
cana-4520	35	16	while	while	SCONJ
cana-4520	35	17	saving	save	VERB
cana-4520	35	18	time	time	NOUN
cana-4520	35	19	.	.	PUNCT
cana-4520	36	1	there	there	PRON
cana-4520	36	2	are	be	VERB
cana-4520	36	3	five	five	NUM
cana-4520	36	4	distinct	distinct	ADJ
cana-4520	36	5	scenarios	scenario	NOUN
cana-4520	36	6	in	in	ADP
cana-4520	36	7	the	the	DET
cana-4520	36	8	nthu	nthu	ADJ
cana-4520	36	9	and	and	CCONJ
cana-4520	36	10	real	real	ADJ
cana-4520	36	11	-	-	PUNCT
cana-4520	36	12	time	time	NOUN
cana-4520	36	13	ddd	ddd	NOUN
cana-4520	36	14	video	video	NOUN
cana-4520	36	15	collection	collection	NOUN
cana-4520	36	16	.	.	PUNCT
cana-4520	37	1	in	in	ADP
cana-4520	37	2	the	the	DET
cana-4520	37	3	video	video	NOUN
cana-4520	37	4	,	,	PUNCT
cana-4520	37	5	each	each	DET
cana-4520	37	6	shot	shot	NOUN
cana-4520	37	7	is	be	AUX
cana-4520	37	8	labelled	label	VERB
cana-4520	37	9	as	as	ADP
cana-4520	37	10	either	either	PRON
cana-4520	37	11	"	"	PUNCT
cana-4520	37	12	fatigue	fatigue	NOUN
cana-4520	37	13	"	"	PUNCT
cana-4520	37	14	or	or	CCONJ
cana-4520	37	15	"	"	PUNCT
cana-4520	37	16	not	not	PART
cana-4520	37	17	fatigue	fatigue	NOUN
cana-4520	37	18	.	.	PUNCT
cana-4520	37	19	"	"	PUNCT
cana-4520	38	1	2	2	X
cana-4520	38	2	.	.	X
cana-4520	38	3	objectives	objective	NOUN
cana-4520	38	4	developing	develop	VERB
cana-4520	38	5	a	a	DET
cana-4520	38	6	real	real	ADJ
cana-4520	38	7	-	-	PUNCT
cana-4520	38	8	time	time	NOUN
cana-4520	38	9	driver	driver	NOUN
cana-4520	38	10	fatigue	fatigue	NOUN
cana-4520	38	11	detection	detection	NOUN
cana-4520	38	12	system	system	NOUN
cana-4520	38	13	that	that	PRON
cana-4520	38	14	enhances	enhance	VERB
cana-4520	38	15	road	road	NOUN
cana-4520	38	16	safety	safety	NOUN
cana-4520	38	17	by	by	ADP
cana-4520	38	18	averting	avert	VERB
cana-4520	38	19	fatiguerelated	fatiguerelate	VERB
cana-4520	38	20	accidents	accident	NOUN
cana-4520	38	21	is	be	AUX
cana-4520	38	22	the	the	DET
cana-4520	38	23	primary	primary	ADJ
cana-4520	38	24	objective	objective	NOUN
cana-4520	38	25	of	of	ADP
cana-4520	38	26	the	the	DET
cana-4520	38	27	research	research	NOUN
cana-4520	38	28	.	.	PUNCT
cana-4520	39	1	driver	driver	NOUN
cana-4520	39	2	alertness	alertness	NOUN
cana-4520	39	3	is	be	AUX
cana-4520	39	4	ensured	ensure	VERB
cana-4520	39	5	by	by	ADP
cana-4520	39	6	early	early	ADJ
cana-4520	39	7	identification	identification	NOUN
cana-4520	39	8	of	of	ADP
cana-4520	39	9	drowsy	drowsy	NOUN
cana-4520	39	10	driving	driving	NOUN
cana-4520	39	11	,	,	PUNCT
cana-4520	39	12	which	which	PRON
cana-4520	39	13	is	be	AUX
cana-4520	39	14	a	a	DET
cana-4520	39	15	significant	significant	ADJ
cana-4520	39	16	risk	risk	NOUN
cana-4520	39	17	factor	factor	NOUN
cana-4520	39	18	for	for	ADP
cana-4520	39	19	traffic	traffic	NOUN
cana-4520	39	20	accidents.[7	accidents.[7	NOUN
cana-4520	39	21	]	]	PUNCT
cana-4520	39	22	with	with	ADP
cana-4520	39	23	an	an	DET
cana-4520	39	24	emphasis	emphasis	NOUN
cana-4520	39	25	on	on	ADP
cana-4520	39	26	crucial	crucial	ADJ
cana-4520	39	27	characteristics	characteristic	NOUN
cana-4520	39	28	including	include	VERB
cana-4520	39	29	eye	eye	NOUN
cana-4520	39	30	closure	closure	NOUN
cana-4520	39	31	duration	duration	NOUN
cana-4520	39	32	,	,	PUNCT
cana-4520	39	33	blink	blink	NOUN
cana-4520	39	34	rate	rate	NOUN
cana-4520	39	35	,	,	PUNCT
cana-4520	39	36	yawning	yawn	VERB
cana-4520	39	37	frequency	frequency	NOUN
cana-4520	39	38	,	,	PUNCT
cana-4520	39	39	head	head	NOUN
cana-4520	39	40	motions	motion	NOUN
cana-4520	39	41	,	,	PUNCT
cana-4520	39	42	and	and	CCONJ
cana-4520	39	43	pupil	pupil	NOUN
cana-4520	39	44	detection	detection	NOUN
cana-4520	39	45	,	,	PUNCT
cana-4520	39	46	this	this	DET
cana-4520	39	47	system	system	NOUN
cana-4520	39	48	uses	use	VERB
cana-4520	39	49	deep	deep	ADJ
cana-4520	39	50	learning	learning	NOUN
cana-4520	39	51	techniques	technique	NOUN
cana-4520	39	52	to	to	PART
cana-4520	39	53	scan	scan	VERB
cana-4520	39	54	facial	facial	ADJ
cana-4520	39	55	expressions	expression	NOUN
cana-4520	39	56	and	and	CCONJ
cana-4520	39	57	identify	identify	VERB
cana-4520	39	58	early	early	ADJ
cana-4520	39	59	indicators	indicator	NOUN
cana-4520	39	60	of	of	ADP
cana-4520	39	61	exhaustion	exhaustion	NOUN
cana-4520	39	62	.	.	PUNCT
cana-4520	40	1	these	these	DET
cana-4520	40	2	indications	indication	NOUN
cana-4520	40	3	offer	offer	VERB
cana-4520	40	4	trustworthy	trustworthy	ADJ
cana-4520	40	5	clues	clue	NOUN
cana-4520	40	6	about	about	ADP
cana-4520	40	7	the	the	DET
cana-4520	40	8	driver	driver	NOUN
cana-4520	40	9	's	's	PART
cana-4520	40	10	level	level	NOUN
cana-4520	40	11	of	of	ADP
cana-4520	40	12	awareness	awareness	NOUN
cana-4520	40	13	.	.	PUNCT
cana-4520	41	1	mediapipe	mediapipe	NOUN
cana-4520	41	2	,	,	PUNCT
cana-4520	41	3	a	a	DET
cana-4520	41	4	robust	robust	ADJ
cana-4520	41	5	structure	structure	NOUN
cana-4520	41	6	renowned	renowne	VERB
cana-4520	41	7	for	for	ADP
cana-4520	41	8	its	its	PRON
cana-4520	41	9	remarkable	remarkable	ADJ
cana-4520	41	10	accuracy	accuracy	NOUN
cana-4520	41	11	in	in	ADP
cana-4520	41	12	identifying	identify	VERB
cana-4520	41	13	face	face	NOUN
cana-4520	41	14	landmarks	landmark	NOUN
cana-4520	41	15	,	,	PUNCT
cana-4520	41	16	is	be	AUX
cana-4520	41	17	used	use	VERB
cana-4520	41	18	by	by	ADP
cana-4520	41	19	the	the	DET
cana-4520	41	20	system	system	NOUN
cana-4520	41	21	to	to	PART
cana-4520	41	22	achieve	achieve	VERB
cana-4520	41	23	great	great	ADJ
cana-4520	41	24	precision	precision	NOUN
cana-4520	41	25	in	in	ADP
cana-4520	41	26	feature	feature	NOUN
cana-4520	41	27	extraction	extraction	NOUN
cana-4520	41	28	.	.	PUNCT
cana-4520	42	1	using	use	VERB
cana-4520	42	2	deep	deep	ADJ
cana-4520	42	3	learning	learning	NOUN
cana-4520	42	4	models	model	NOUN
cana-4520	42	5	like	like	ADP
cana-4520	42	6	inceptionv3	inceptionv3	NOUN
cana-4520	42	7	,	,	PUNCT
cana-4520	42	8	vgg19	vgg19	PROPN
cana-4520	42	9	,	,	PUNCT
cana-4520	42	10	and	and	CCONJ
cana-4520	42	11	resnet50v2	resnet50v2	NOUN
cana-4520	42	12	,	,	PUNCT
cana-4520	42	13	the	the	DET
cana-4520	42	14	study	study	NOUN
cana-4520	42	15	seeks	seek	VERB
cana-4520	42	16	to	to	PART
cana-4520	42	17	determine	determine	VERB
cana-4520	42	18	the	the	DET
cana-4520	42	19	best	good	ADJ
cana-4520	42	20	architecture	architecture	NOUN
cana-4520	42	21	for	for	ADP
cana-4520	42	22	classifying	classify	VERB
cana-4520	42	23	drowsiness	drowsiness	NOUN
cana-4520	42	24	.	.	PUNCT
cana-4520	43	1	the	the	DET
cana-4520	43	2	models	model	NOUN
cana-4520	43	3	are	be	AUX
cana-4520	43	4	tested	test	VERB
cana-4520	43	5	and	and	CCONJ
cana-4520	43	6	trained	train	VERB
cana-4520	43	7	on	on	ADP
cana-4520	43	8	a	a	DET
cana-4520	43	9	dataset	dataset	NOUN
cana-4520	43	10	with	with	ADP
cana-4520	43	11	a	a	DET
cana-4520	43	12	variety	variety	NOUN
cana-4520	43	13	of	of	ADP
cana-4520	43	14	driving	drive	VERB
cana-4520	43	15	conditions	condition	NOUN
cana-4520	43	16	to	to	PART
cana-4520	43	17	ensure	ensure	VERB
cana-4520	43	18	the	the	DET
cana-4520	43	19	system	system	NOUN
cana-4520	43	20	works	work	VERB
cana-4520	43	21	well	well	ADV
cana-4520	43	22	in	in	ADP
cana-4520	43	23	a	a	DET
cana-4520	43	24	range	range	NOUN
cana-4520	43	25	of	of	ADP
cana-4520	43	26	real	real	ADJ
cana-4520	43	27	-	-	PUNCT
cana-4520	43	28	world	world	NOUN
cana-4520	43	29	situations	situation	NOUN
cana-4520	43	30	.	.	PUNCT
cana-4520	44	1	the	the	DET
cana-4520	44	2	real	real	ADJ
cana-4520	44	3	-	-	PUNCT
cana-4520	44	4	time	time	NOUN
cana-4520	44	5	deployment	deployment	NOUN
cana-4520	44	6	of	of	ADP
cana-4520	44	7	the	the	DET
cana-4520	44	8	detecting	detect	VERB
cana-4520	44	9	system	system	NOUN
cana-4520	44	10	,	,	PUNCT
cana-4520	44	11	which	which	PRON
cana-4520	44	12	enables	enable	VERB
cana-4520	44	13	ongoing	ongoing	ADJ
cana-4520	44	14	observation	observation	NOUN
cana-4520	44	15	and	and	CCONJ
cana-4520	44	16	prompt	prompt	ADJ
cana-4520	44	17	driver	driver	NOUN
cana-4520	44	18	feedback	feedback	NOUN
cana-4520	44	19	,	,	PUNCT
cana-4520	44	20	is	be	AUX
cana-4520	44	21	a	a	DET
cana-4520	44	22	crucial	crucial	ADJ
cana-4520	44	23	component	component	NOUN
cana-4520	44	24	of	of	ADP
cana-4520	44	25	this	this	DET
cana-4520	44	26	study	study	NOUN
cana-4520	44	27	.	.	PUNCT
cana-4520	45	1	the	the	DET
cana-4520	45	2	technology	technology	NOUN
cana-4520	45	3	actively	actively	ADV
cana-4520	45	4	analyzes	analyze	VERB
cana-4520	45	5	facial	facial	ADJ
cana-4520	45	6	features	feature	NOUN
cana-4520	45	7	while	while	SCONJ
cana-4520	45	8	processing	processing	NOUN
cana-4520	45	9	video	video	NOUN
cana-4520	45	10	data	datum	NOUN
cana-4520	45	11	frame	frame	NOUN
cana-4520	45	12	by	by	ADP
cana-4520	45	13	frame	frame	NOUN
cana-4520	45	14	.	.	PUNCT
cana-4520	46	1	the	the	DET
cana-4520	46	2	device	device	NOUN
cana-4520	46	3	immediately	immediately	ADV
cana-4520	46	4	notifies	notify	VERB
cana-4520	46	5	the	the	DET
cana-4520	46	6	driver	driver	NOUN
cana-4520	46	7	to	to	PART
cana-4520	46	8	take	take	VERB
cana-4520	46	9	corrective	corrective	ADJ
cana-4520	46	10	action	action	NOUN
cana-4520	46	11	when	when	SCONJ
cana-4520	46	12	it	it	PRON
cana-4520	46	13	detects	detect	VERB
cana-4520	46	14	indicators	indicator	NOUN
cana-4520	46	15	of	of	ADP
cana-4520	46	16	weariness	weariness	NOUN
cana-4520	46	17	,	,	PUNCT
cana-4520	46	18	such	such	ADJ
cana-4520	46	19	as	as	ADP
cana-4520	46	20	an	an	DET
cana-4520	46	21	alarm	alarm	NOUN
cana-4520	46	22	or	or	CCONJ
cana-4520	46	23	vibration	vibration	NOUN
cana-4520	46	24	in	in	ADP
cana-4520	46	25	the	the	DET
cana-4520	46	26	seat	seat	NOUN
cana-4520	46	27	.	.	PUNCT
cana-4520	47	1	the	the	DET
cana-4520	47	2	system	system	NOUN
cana-4520	47	3	is	be	AUX
cana-4520	47	4	also	also	ADV
cana-4520	47	5	made	make	VERB
cana-4520	47	6	to	to	PART
cana-4520	47	7	work	work	VERB
cana-4520	47	8	in	in	ADP
cana-4520	47	9	a	a	DET
cana-4520	47	10	variety	variety	NOUN
cana-4520	47	11	of	of	ADP
cana-4520	47	12	situations	situation	NOUN
cana-4520	47	13	,	,	PUNCT
cana-4520	47	14	such	such	ADJ
cana-4520	47	15	as	as	ADP
cana-4520	47	16	during	during	ADP
cana-4520	47	17	the	the	DET
cana-4520	47	18	day	day	NOUN
cana-4520	47	19	and	and	CCONJ
cana-4520	47	20	at	at	ADP
cana-4520	47	21	night	night	NOUN
cana-4520	47	22	,	,	PUNCT
cana-4520	47	23	in	in	ADP
cana-4520	47	24	different	different	ADJ
cana-4520	47	25	lighting	lighting	NOUN
cana-4520	47	26	situations	situation	NOUN
cana-4520	47	27	,	,	PUNCT
cana-4520	47	28	and	and	CCONJ
cana-4520	47	29	whether	whether	SCONJ
cana-4520	47	30	drivers	driver	NOUN
cana-4520	47	31	are	be	AUX
cana-4520	47	32	wearing	wear	VERB
cana-4520	47	33	sunglasses	sunglass	NOUN
cana-4520	47	34	,	,	PUNCT
cana-4520	47	35	night	night	NOUN
cana-4520	47	36	glasses	glass	NOUN
cana-4520	47	37	,	,	PUNCT
cana-4520	47	38	or	or	CCONJ
cana-4520	47	39	other	other	ADJ
cana-4520	47	40	eyewear	eyewear	NOUN
cana-4520	47	41	.	.	PUNCT
cana-4520	48	1	communications	communication	NOUN
cana-4520	48	2	on	on	ADP
cana-4520	48	3	applied	apply	VERB
cana-4520	48	4	nonlinear	nonlinear	ADJ
cana-4520	48	5	analysis	analysis	NOUN
cana-4520	48	6	issn	issn	NOUN
cana-4520	48	7	:	:	PUNCT
cana-4520	48	8	1074	1074	NUM
cana-4520	48	9	-	-	PUNCT
cana-4520	48	10	133x	133x	NUM
cana-4520	48	11	vol	vol	NOUN
cana-4520	48	12	32	32	NUM
cana-4520	48	13	no	no	NOUN
cana-4520	48	14	.	.	PUNCT
cana-4520	49	1	9s	9s	NUM
cana-4520	49	2	(	(	PUNCT
cana-4520	49	3	2025	2025	NUM
cana-4520	49	4	)	)	PUNCT
cana-4520	49	5	2339	2339	NUM
cana-4520	49	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	50	1	the	the	DET
cana-4520	50	2	ultimate	ultimate	ADJ
cana-4520	50	3	goal	goal	NOUN
cana-4520	50	4	of	of	ADP
cana-4520	50	5	this	this	DET
cana-4520	50	6	research	research	NOUN
cana-4520	50	7	is	be	AUX
cana-4520	50	8	to	to	PART
cana-4520	50	9	develop	develop	VERB
cana-4520	50	10	a	a	DET
cana-4520	50	11	sturdy	sturdy	ADJ
cana-4520	50	12	,	,	PUNCT
cana-4520	50	13	flexible	flexible	ADJ
cana-4520	50	14	,	,	PUNCT
cana-4520	50	15	and	and	CCONJ
cana-4520	50	16	extremely	extremely	ADV
cana-4520	50	17	accurate	accurate	ADJ
cana-4520	50	18	fatigue	fatigue	NOUN
cana-4520	50	19	detection	detection	NOUN
cana-4520	50	20	system	system	NOUN
cana-4520	50	21	that	that	PRON
cana-4520	50	22	may	may	AUX
cana-4520	50	23	be	be	AUX
cana-4520	50	24	deployed	deploy	VERB
cana-4520	50	25	in	in	ADP
cana-4520	50	26	contemporary	contemporary	ADJ
cana-4520	50	27	vehicles	vehicle	NOUN
cana-4520	50	28	.	.	PUNCT
cana-4520	51	1	it	it	PRON
cana-4520	51	2	helps	help	VERB
cana-4520	51	3	to	to	PART
cana-4520	51	4	improve	improve	VERB
cana-4520	51	5	road	road	NOUN
cana-4520	51	6	safety	safety	NOUN
cana-4520	51	7	and	and	CCONJ
cana-4520	51	8	minimize	minimize	VERB
cana-4520	51	9	fatalities	fatality	NOUN
cana-4520	51	10	caused	cause	VERB
cana-4520	51	11	by	by	ADP
cana-4520	51	12	drowsy	drowsy	NOUN
cana-4520	51	13	driving	drive	VERB
cana-4520	51	14	and	and	CCONJ
cana-4520	51	15	reducing	reduce	VERB
cana-4520	51	16	accident	accident	NOUN
cana-4520	51	17	risks	risk	NOUN
cana-4520	51	18	.	.	PUNCT
cana-4520	52	1	3	3	X
cana-4520	52	2	.	.	X
cana-4520	52	3	methods	method	NOUN
cana-4520	52	4	the	the	DET
cana-4520	52	5	proposed	propose	VERB
cana-4520	52	6	method	method	NOUN
cana-4520	52	7	adheres	adhere	VERB
cana-4520	52	8	to	to	ADP
cana-4520	52	9	the	the	DET
cana-4520	52	10	phases	phase	NOUN
cana-4520	52	11	illustrated	illustrate	VERB
cana-4520	52	12	in	in	ADP
cana-4520	52	13	figure	figure	NOUN
cana-4520	52	14	1	1	NUM
cana-4520	52	15	:	:	PUNCT
cana-4520	52	16	collecting	collect	VERB
cana-4520	52	17	data	datum	NOUN
cana-4520	52	18	(	(	PUNCT
cana-4520	52	19	video	video	NOUN
cana-4520	52	20	)	)	PUNCT
cana-4520	52	21	,	,	PUNCT
cana-4520	52	22	preprocessing	preprocesse	VERB
cana-4520	52	23	snapshots	snapshot	NOUN
cana-4520	52	24	from	from	ADP
cana-4520	52	25	the	the	DET
cana-4520	52	26	videos	video	NOUN
cana-4520	52	27	,	,	PUNCT
cana-4520	52	28	creating	create	VERB
cana-4520	52	29	a	a	DET
cana-4520	52	30	dataset	dataset	NOUN
cana-4520	52	31	,	,	PUNCT
cana-4520	52	32	extracting	extract	VERB
cana-4520	52	33	features	feature	NOUN
cana-4520	52	34	,	,	PUNCT
cana-4520	52	35	training	train	VERB
cana-4520	52	36	the	the	DET
cana-4520	52	37	cnn	cnn	PROPN
cana-4520	52	38	architecture	architecture	NOUN
cana-4520	52	39	,	,	PUNCT
cana-4520	52	40	assessing	assess	VERB
cana-4520	52	41	driver	driver	NOUN
cana-4520	52	42	drowsiness	drowsiness	NOUN
cana-4520	52	43	,	,	PUNCT
cana-4520	52	44	and	and	CCONJ
cana-4520	52	45	categorizing	categorize	VERB
cana-4520	52	46	as	as	ADV
cana-4520	52	47	fatigued	fatigued	ADJ
cana-4520	52	48	or	or	CCONJ
cana-4520	52	49	not	not	PART
cana-4520	52	50	fatigued	fatigued	ADJ
cana-4520	52	51	.	.	PUNCT
cana-4520	53	1	figure	figure	NOUN
cana-4520	53	2	.	.	PUNCT
cana-4520	54	1	1	1	NUM
cana-4520	54	2	proposed	propose	VERB
cana-4520	54	3	methodology	methodology	NOUN
cana-4520	54	4	for	for	ADP
cana-4520	54	5	detecting	detect	VERB
cana-4520	54	6	drowsiness	drowsiness	NOUN
cana-4520	54	7	and	and	CCONJ
cana-4520	54	8	evaluation	evaluation	NOUN
cana-4520	54	9	3.1	3.1	NUM
cana-4520	54	10	data	datum	NOUN
cana-4520	54	11	acquisition	acquisition	NOUN
cana-4520	54	12	using	use	VERB
cana-4520	54	13	infrared	infrared	ADJ
cana-4520	54	14	cameras	camera	NOUN
cana-4520	54	15	for	for	ADP
cana-4520	54	16	twilight	twilight	NOUN
cana-4520	54	17	circumstances	circumstance	NOUN
cana-4520	54	18	ensuring	ensure	VERB
cana-4520	54	19	clear	clear	ADJ
cana-4520	54	20	capture	capture	NOUN
cana-4520	54	21	of	of	ADP
cana-4520	54	22	facial	facial	ADJ
cana-4520	54	23	accents	accent	NOUN
cana-4520	54	24	in	in	ADP
cana-4520	54	25	moonlight	moonlight	NOUN
cana-4520	54	26	,	,	PUNCT
cana-4520	54	27	we	we	PRON
cana-4520	54	28	strategically	strategically	ADV
cana-4520	54	29	placed	place	VERB
cana-4520	54	30	many	many	ADJ
cana-4520	54	31	high	high	ADJ
cana-4520	54	32	-	-	PUNCT
cana-4520	54	33	resolution	resolution	NOUN
cana-4520	54	34	cameras	camera	NOUN
cana-4520	54	35	to	to	PART
cana-4520	54	36	capture	capture	VERB
cana-4520	54	37	the	the	DET
cana-4520	54	38	driver	driver	NOUN
cana-4520	54	39	's	's	PART
cana-4520	54	40	face	face	NOUN
cana-4520	54	41	from	from	ADP
cana-4520	54	42	different	different	ADJ
cana-4520	54	43	angles,[8	angles,[8	NOUN
cana-4520	54	44	]	]	PUNCT
cana-4520	54	45	focusing	focus	VERB
cana-4520	54	46	on	on	ADP
cana-4520	54	47	the	the	DET
cana-4520	54	48	eyes	eye	NOUN
cana-4520	54	49	,	,	PUNCT
cana-4520	54	50	mouth	mouth	NOUN
cana-4520	54	51	,	,	PUNCT
cana-4520	54	52	and	and	CCONJ
cana-4520	54	53	general	general	ADJ
cana-4520	54	54	head	head	NOUN
cana-4520	54	55	developments	development	NOUN
cana-4520	54	56	.	.	PUNCT
cana-4520	55	1	shining	shine	VERB
cana-4520	55	2	,	,	PUNCT
cana-4520	55	3	naturallike	naturallike	ADJ
cana-4520	55	4	lighting	lighting	NOUN
cana-4520	55	5	was	be	AUX
cana-4520	55	6	used	use	VERB
cana-4520	55	7	to	to	PART
cana-4520	55	8	replicate	replicate	VERB
cana-4520	55	9	daytime	daytime	NOUN
cana-4520	55	10	driving	driving	NOUN
cana-4520	55	11	,	,	PUNCT
cana-4520	55	12	while	while	SCONJ
cana-4520	55	13	dim	dim	ADJ
cana-4520	55	14	lighting	lighting	NOUN
cana-4520	55	15	repeated	repeat	VERB
cana-4520	55	16	night	night	NOUN
cana-4520	55	17	time	time	NOUN
cana-4520	55	18	driving	driving	NOUN
cana-4520	55	19	,	,	PUNCT
cana-4520	55	20	covering	cover	VERB
cana-4520	55	21	various	various	ADJ
cana-4520	55	22	night	night	NOUN
cana-4520	55	23	time	time	NOUN
cana-4520	55	24	scenarios	scenario	NOUN
cana-4520	55	25	such	such	ADJ
cana-4520	55	26	as	as	ADP
cana-4520	55	27	country	country	NOUN
cana-4520	55	28	zones	zone	NOUN
cana-4520	55	29	with	with	ADP
cana-4520	55	30	minimal	minimal	ADJ
cana-4520	55	31	lighting	lighting	NOUN
cana-4520	55	32	and	and	CCONJ
cana-4520	55	33	urban	urban	ADJ
cana-4520	55	34	ranges	range	NOUN
cana-4520	55	35	with	with	ADP
cana-4520	55	36	road	road	NOUN
cana-4520	55	37	lights	light	NOUN
cana-4520	55	38	.	.	PUNCT
cana-4520	56	1	the	the	DET
cana-4520	56	2	nthu	nthu	PROPN
cana-4520	56	3	driver	driver	NOUN
cana-4520	56	4	fatigue	fatigue	NOUN
cana-4520	56	5	dataset	dataset	NOUN
cana-4520	56	6	and	and	CCONJ
cana-4520	56	7	real	real	ADJ
cana-4520	56	8	-	-	PUNCT
cana-4520	56	9	time	time	NOUN
cana-4520	56	10	drowsiness	drowsiness	NOUN
cana-4520	56	11	dataset	dataset	NOUN
cana-4520	56	12	,	,	PUNCT
cana-4520	56	13	stages	stage	NOUN
cana-4520	56	14	are	be	AUX
cana-4520	56	15	shown	show	VERB
cana-4520	56	16	in	in	ADP
cana-4520	56	17	the	the	DET
cana-4520	56	18	figure	figure	NOUN
cana-4520	56	19	2	2	NUM
cana-4520	56	20	.	.	PUNCT
cana-4520	57	1	the	the	DET
cana-4520	57	2	images	image	NOUN
cana-4520	57	3	are	be	AUX
cana-4520	57	4	extracted	extract	VERB
cana-4520	57	5	from	from	ADP
cana-4520	57	6	the	the	DET
cana-4520	57	7	videos	video	NOUN
cana-4520	57	8	,	,	PUNCT
cana-4520	57	9	and	and	CCONJ
cana-4520	57	10	are	be	AUX
cana-4520	57	11	labelled	label	VERB
cana-4520	57	12	in	in	ADP
cana-4520	57	13	to	to	ADP
cana-4520	57	14	two	two	NUM
cana-4520	57	15	different	different	ADJ
cana-4520	57	16	categories	category	NOUN
cana-4520	57	17	.	.	PUNCT
cana-4520	58	1	the	the	DET
cana-4520	58	2	categorized	categorize	VERB
cana-4520	58	3	dataset	dataset	NOUN
cana-4520	58	4	are	be	AUX
cana-4520	58	5	converted	convert	VERB
cana-4520	58	6	to	to	ADP
cana-4520	58	7	gray	gray	ADJ
cana-4520	58	8	-	-	PUNCT
cana-4520	58	9	scale	scale	NOUN
cana-4520	58	10	images	image	NOUN
cana-4520	58	11	and	and	CCONJ
cana-4520	58	12	stored	store	VERB
cana-4520	58	13	in	in	ADP
cana-4520	58	14	the	the	DET
cana-4520	58	15	database	database	NOUN
cana-4520	58	16	.	.	PUNCT
cana-4520	59	1	figure	figure	NOUN
cana-4520	59	2	2	2	NUM
cana-4520	59	3	.	.	NOUN
cana-4520	59	4	stages	stage	NOUN
cana-4520	59	5	of	of	ADP
cana-4520	59	6	creating	create	VERB
cana-4520	59	7	the	the	DET
cana-4520	59	8	fatigue	fatigue	NOUN
cana-4520	59	9	dataset	dataset	NOUN
cana-4520	59	10	communications	communication	NOUN
cana-4520	59	11	on	on	ADP
cana-4520	59	12	applied	apply	VERB
cana-4520	59	13	nonlinear	nonlinear	ADJ
cana-4520	59	14	analysis	analysis	NOUN
cana-4520	59	15	issn	issn	NOUN
cana-4520	59	16	:	:	PUNCT
cana-4520	59	17	1074	1074	NUM
cana-4520	59	18	-	-	PUNCT
cana-4520	59	19	133x	133x	NUM
cana-4520	59	20	vol	vol	NOUN
cana-4520	59	21	32	32	NUM
cana-4520	59	22	no	no	NOUN
cana-4520	59	23	.	.	PUNCT
cana-4520	60	1	9s	9s	NUM
cana-4520	60	2	(	(	PUNCT
cana-4520	60	3	2025	2025	NUM
cana-4520	60	4	)	)	PUNCT
cana-4520	60	5	2340	2340	NUM
cana-4520	60	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	60	7	3.2	3.2	NUM
cana-4520	60	8	data	datum	NOUN
cana-4520	60	9	pre	pre	ADJ
cana-4520	60	10	-	-	ADJ
cana-4520	60	11	processing	process	VERB
cana-4520	60	12	the	the	DET
cana-4520	60	13	video	video	NOUN
cana-4520	60	14	frames	frame	NOUN
cana-4520	60	15	ought	ought	AUX
cana-4520	60	16	to	to	PART
cana-4520	60	17	be	be	AUX
cana-4520	60	18	converted	convert	VERB
cana-4520	60	19	to	to	PART
cana-4520	60	20	monochrome	monochrome	VERB
cana-4520	60	21	in	in	ADP
cana-4520	60	22	order	order	NOUN
cana-4520	60	23	to	to	PART
cana-4520	60	24	gather	gather	VERB
cana-4520	60	25	the	the	DET
cana-4520	60	26	video	video	NOUN
cana-4520	60	27	data	datum	NOUN
cana-4520	60	28	for	for	ADP
cana-4520	60	29	the	the	DET
cana-4520	60	30	fatigue	fatigue	NOUN
cana-4520	60	31	detection	detection	NOUN
cana-4520	60	32	system	system	NOUN
cana-4520	60	33	.	.	PUNCT
cana-4520	61	1	this	this	PRON
cana-4520	61	2	decreases	decrease	VERB
cana-4520	61	3	computational	computational	ADJ
cana-4520	61	4	complexity	complexity	NOUN
cana-4520	61	5	and	and	CCONJ
cana-4520	61	6	memory	memory	NOUN
cana-4520	61	7	usage	usage	NOUN
cana-4520	61	8	by	by	ADP
cana-4520	61	9	compressing	compress	VERB
cana-4520	61	10	the	the	DET
cana-4520	61	11	visual	visual	ADJ
cana-4520	61	12	data	datum	NOUN
cana-4520	61	13	while	while	SCONJ
cana-4520	61	14	retaining	retain	VERB
cana-4520	61	15	important	important	ADJ
cana-4520	61	16	properties	property	NOUN
cana-4520	61	17	that	that	PRON
cana-4520	61	18	are	be	AUX
cana-4520	61	19	necessary	necessary	ADJ
cana-4520	61	20	for	for	ADP
cana-4520	61	21	sleepiness	sleepiness	NOUN
cana-4520	61	22	detection	detection	NOUN
cana-4520	61	23	,	,	PUNCT
cana-4520	61	24	such	such	ADJ
cana-4520	61	25	as	as	ADP
cana-4520	61	26	edges	edge	NOUN
cana-4520	61	27	and	and	CCONJ
cana-4520	61	28	forms	form	NOUN
cana-4520	61	29	.	.	PUNCT
cana-4520	62	1	as	as	ADP
cana-4520	62	2	part	part	NOUN
cana-4520	62	3	of	of	ADP
cana-4520	62	4	the	the	DET
cana-4520	62	5	pre	pre	ADJ
cana-4520	62	6	-	-	ADJ
cana-4520	62	7	processing	processing	ADJ
cana-4520	62	8	strategy	strategy	NOUN
cana-4520	62	9	,	,	PUNCT
cana-4520	62	10	each	each	DET
cana-4520	62	11	frame	frame	NOUN
cana-4520	62	12	of	of	ADP
cana-4520	62	13	the	the	DET
cana-4520	62	14	recorded	record	VERB
cana-4520	62	15	video	video	NOUN
cana-4520	62	16	data	datum	NOUN
cana-4520	62	17	is	be	AUX
cana-4520	62	18	extracted	extract	VERB
cana-4520	62	19	at	at	ADP
cana-4520	62	20	a	a	DET
cana-4520	62	21	regular	regular	ADJ
cana-4520	62	22	pace	pace	NOUN
cana-4520	62	23	,	,	PUNCT
cana-4520	62	24	and	and	CCONJ
cana-4520	62	25	the	the	DET
cana-4520	62	26	rgb	rgb	PROPN
cana-4520	62	27	color	color	NOUN
cana-4520	62	28	values	value	NOUN
cana-4520	62	29	are	be	AUX
cana-4520	62	30	then	then	ADV
cana-4520	62	31	converted	convert	VERB
cana-4520	62	32	to	to	ADP
cana-4520	62	33	a	a	DET
cana-4520	62	34	single	single	ADJ
cana-4520	62	35	gray	gray	ADJ
cana-4520	62	36	scale	scale	NOUN
cana-4520	62	37	intensity	intensity	NOUN
cana-4520	62	38	value	value	NOUN
cana-4520	62	39	using	use	VERB
cana-4520	62	40	the	the	DET
cana-4520	62	41	luminance	luminance	NOUN
cana-4520	62	42	approach	approach	NOUN
cana-4520	62	43	.	.	PUNCT
cana-4520	63	1	after	after	ADP
cana-4520	63	2	that	that	PRON
cana-4520	63	3	,	,	PUNCT
cana-4520	63	4	we	we	PRON
cana-4520	63	5	normalize	normalize	VERB
cana-4520	63	6	the	the	DET
cana-4520	63	7	images	image	NOUN
cana-4520	63	8	to	to	PART
cana-4520	63	9	make	make	VERB
cana-4520	63	10	sure	sure	ADJ
cana-4520	63	11	that	that	SCONJ
cana-4520	63	12	the	the	DET
cana-4520	63	13	dimension	dimension	NOUN
cana-4520	63	14	and	and	CCONJ
cana-4520	63	15	lighting	lighting	NOUN
cana-4520	63	16	are	be	AUX
cana-4520	63	17	uniform	uniform	ADJ
cana-4520	63	18	.	.	PUNCT
cana-4520	64	1	minimizing	minimize	VERB
cana-4520	64	2	deviations	deviation	NOUN
cana-4520	64	3	unrelated	unrelated	ADJ
cana-4520	64	4	to	to	ADP
cana-4520	64	5	drowsiness	drowsiness	NOUN
cana-4520	64	6	,	,	PUNCT
cana-4520	64	7	enhances	enhance	VERB
cana-4520	64	8	the	the	DET
cana-4520	64	9	performance	performance	NOUN
cana-4520	64	10	and	and	CCONJ
cana-4520	64	11	generalizability	generalizability	NOUN
cana-4520	64	12	of	of	ADP
cana-4520	64	13	the	the	DET
cana-4520	64	14	convolutional	convolutional	ADJ
cana-4520	64	15	neural	neural	ADJ
cana-4520	64	16	network	network	NOUN
cana-4520	64	17	(	(	PUNCT
cana-4520	64	18	cnn	cnn	PROPN
cana-4520	64	19	)	)	PUNCT
cana-4520	64	20	model.[9	model.[9	PROPN
cana-4520	64	21	]	]	X
cana-4520	64	22	this	this	PRON
cana-4520	64	23	entails	entail	VERB
cana-4520	64	24	scaling	scale	VERB
cana-4520	64	25	every	every	DET
cana-4520	64	26	frame	frame	NOUN
cana-4520	64	27	to	to	ADP
cana-4520	64	28	a	a	DET
cana-4520	64	29	standard	standard	ADJ
cana-4520	64	30	size	size	NOUN
cana-4520	64	31	of	of	ADP
cana-4520	64	32	224	224	NUM
cana-4520	64	33	by	by	ADP
cana-4520	64	34	224	224	NUM
cana-4520	64	35	pixels	pixel	NOUN
cana-4520	64	36	and	and	CCONJ
cana-4520	64	37	utilizing	utilize	VERB
cana-4520	64	38	histogram	histogram	NOUN
cana-4520	64	39	equalization	equalization	NOUN
cana-4520	64	40	to	to	PART
cana-4520	64	41	alter	alter	VERB
cana-4520	64	42	brightness	brightness	NOUN
cana-4520	64	43	and	and	CCONJ
cana-4520	64	44	contrast	contrast	NOUN
cana-4520	64	45	in	in	ADP
cana-4520	64	46	order	order	NOUN
cana-4520	64	47	to	to	PART
cana-4520	64	48	compensate	compensate	VERB
cana-4520	64	49	for	for	ADP
cana-4520	64	50	different	different	ADJ
cana-4520	64	51	lighting	lighting	NOUN
cana-4520	64	52	situations	situation	NOUN
cana-4520	64	53	.	.	PUNCT
cana-4520	65	1	to	to	PART
cana-4520	65	2	make	make	VERB
cana-4520	65	3	sure	sure	ADJ
cana-4520	65	4	the	the	DET
cana-4520	65	5	input	input	NOUN
cana-4520	65	6	values	value	NOUN
cana-4520	65	7	are	be	AUX
cana-4520	65	8	within	within	ADP
cana-4520	65	9	a	a	DET
cana-4520	65	10	range	range	NOUN
cana-4520	65	11	the	the	DET
cana-4520	65	12	model	model	NOUN
cana-4520	65	13	can	can	AUX
cana-4520	65	14	handle	handle	VERB
cana-4520	65	15	,	,	PUNCT
cana-4520	65	16	we	we	PRON
cana-4520	65	17	also	also	ADV
cana-4520	65	18	scale	scale	VERB
cana-4520	65	19	the	the	DET
cana-4520	65	20	pixel	pixel	PROPN
cana-4520	65	21	values	value	NOUN
cana-4520	65	22	to	to	ADP
cana-4520	65	23	a	a	DET
cana-4520	65	24	standard	standard	ADJ
cana-4520	65	25	range	range	NOUN
cana-4520	65	26	,	,	PUNCT
cana-4520	65	27	which	which	PRON
cana-4520	65	28	is	be	AUX
cana-4520	65	29	typically	typically	ADV
cana-4520	65	30	0	0	NUM
cana-4520	65	31	to	to	PART
cana-4520	65	32	1	1	NUM
cana-4520	65	33	.	.	PUNCT
cana-4520	66	1	the	the	DET
cana-4520	66	2	final	final	ADJ
cana-4520	66	3	pre	pre	ADJ
cana-4520	66	4	-	-	ADJ
cana-4520	66	5	processed	processed	ADJ
cana-4520	66	6	photos	photo	NOUN
cana-4520	66	7	contain	contain	AUX
cana-4520	66	8	normalized	normalize	VERB
cana-4520	66	9	pixel	pixel	PROPN
cana-4520	66	10	values	value	NOUN
cana-4520	66	11	,	,	PUNCT
cana-4520	66	12	are	be	AUX
cana-4520	66	13	gray	gray	ADJ
cana-4520	66	14	scale	scale	NOUN
cana-4520	66	15	,	,	PUNCT
cana-4520	66	16	and	and	CCONJ
cana-4520	66	17	have	have	VERB
cana-4520	66	18	uniform	uniform	ADJ
cana-4520	66	19	lighting	lighting	NOUN
cana-4520	66	20	and	and	CCONJ
cana-4520	66	21	size	size	NOUN
cana-4520	66	22	.	.	PUNCT
cana-4520	67	1	the	the	DET
cana-4520	67	2	system	system	NOUN
cana-4520	67	3	's	's	PART
cana-4520	67	4	ability	ability	NOUN
cana-4520	67	5	to	to	PART
cana-4520	67	6	detect	detect	VERB
cana-4520	67	7	tiredness	tiredness	NOUN
cana-4520	67	8	is	be	AUX
cana-4520	67	9	eventually	eventually	ADV
cana-4520	67	10	improved	improve	VERB
cana-4520	67	11	by	by	ADP
cana-4520	67	12	the	the	DET
cana-4520	67	13	more	more	ADV
cana-4520	67	14	effective	effective	ADJ
cana-4520	67	15	and	and	CCONJ
cana-4520	67	16	efficient	efficient	ADJ
cana-4520	67	17	neural	neural	ADJ
cana-4520	67	18	network	network	NOUN
cana-4520	67	19	training	training	NOUN
cana-4520	67	20	made	make	VERB
cana-4520	67	21	possible	possible	ADJ
cana-4520	67	22	by	by	ADP
cana-4520	67	23	this	this	DET
cana-4520	67	24	standardized	standardized	ADJ
cana-4520	67	25	input	input	NOUN
cana-4520	67	26	data	datum	NOUN
cana-4520	67	27	.	.	PUNCT
cana-4520	68	1	3.3	3.3	NUM
cana-4520	68	2	facial	facial	ADJ
cana-4520	68	3	landmark	landmark	NOUN
cana-4520	68	4	by	by	ADP
cana-4520	68	5	performing	perform	VERB
cana-4520	68	6	high	high	ADJ
cana-4520	68	7	-	-	PUNCT
cana-4520	68	8	fidelity	fidelity	NOUN
cana-4520	68	9	facial	facial	ADJ
cana-4520	68	10	landmark	landmark	NOUN
cana-4520	68	11	identification	identification	NOUN
cana-4520	68	12	in	in	ADP
cana-4520	68	13	real	real	ADJ
cana-4520	68	14	-	-	PUNCT
cana-4520	68	15	time	time	NOUN
cana-4520	68	16	,	,	PUNCT
cana-4520	68	17	media	medium	NOUN
cana-4520	68	18	pipe	pipe	NOUN
cana-4520	68	19	face	face	NOUN
cana-4520	68	20	mesh	mesh	NOUN
cana-4520	68	21	tackles	tackle	NOUN
cana-4520	68	22	these	these	DET
cana-4520	68	23	issues	issue	NOUN
cana-4520	68	24	.	.	PUNCT
cana-4520	69	1	the	the	DET
cana-4520	69	2	solution	solution	NOUN
cana-4520	69	3	is	be	AUX
cana-4520	69	4	based	base	VERB
cana-4520	69	5	on	on	ADP
cana-4520	69	6	machine	machine	NOUN
cana-4520	69	7	learning	learning	NOUN
cana-4520	69	8	and	and	CCONJ
cana-4520	69	9	assesses	assess	VERB
cana-4520	69	10	468	468	NUM
cana-4520	69	11	3d	3d	NUM
cana-4520	69	12	facial	facial	ADJ
cana-4520	69	13	landmarks	landmark	NOUN
cana-4520	69	14	in	in	ADP
cana-4520	69	15	a	a	DET
cana-4520	69	16	dense	dense	ADJ
cana-4520	69	17	set	set	NOUN
cana-4520	69	18	.	.	PUNCT
cana-4520	70	1	even	even	ADV
cana-4520	70	2	in	in	ADP
cana-4520	70	3	difficult	difficult	ADJ
cana-4520	70	4	situations	situation	NOUN
cana-4520	70	5	,	,	PUNCT
cana-4520	70	6	this	this	DET
cana-4520	70	7	method	method	NOUN
cana-4520	70	8	guarantees	guarantee	VERB
cana-4520	70	9	accurate	accurate	ADJ
cana-4520	70	10	facial	facial	ADJ
cana-4520	70	11	feature	feature	NOUN
cana-4520	70	12	tracking	tracking	NOUN
cana-4520	70	13	and	and	CCONJ
cana-4520	70	14	recognition	recognition	NOUN
cana-4520	70	15	made	make	VERB
cana-4520	70	16	possible	possible	ADJ
cana-4520	70	17	by	by	ADP
cana-4520	70	18	this	this	DET
cana-4520	70	19	standardized	standardized	ADJ
cana-4520	70	20	input	input	NOUN
cana-4520	70	21	data	datum	NOUN
cana-4520	70	22	.	.	PUNCT
cana-4520	71	1	figure	figure	VERB
cana-4520	71	2	3	3	NUM
cana-4520	71	3	.	.	NOUN
cana-4520	71	4	468	468	NUM
cana-4520	71	5	facial	facial	ADJ
cana-4520	71	6	landmark	landmark	NOUN
cana-4520	71	7	3.4	3.4	NUM
cana-4520	71	8	feature	feature	NOUN
cana-4520	71	9	extraction	extraction	NOUN
cana-4520	71	10	using	use	VERB
cana-4520	71	11	a	a	DET
cana-4520	71	12	specific	specific	ADJ
cana-4520	71	13	subset	subset	NOUN
cana-4520	71	14	of	of	ADP
cana-4520	71	15	the	the	DET
cana-4520	71	16	468	468	NUM
cana-4520	71	17	landmarks	landmark	NOUN
cana-4520	71	18	offered	offer	VERB
cana-4520	71	19	by	by	ADP
cana-4520	71	20	the	the	DET
cana-4520	71	21	media	medium	NOUN
cana-4520	71	22	pipe	pipe	NOUN
cana-4520	71	23	,	,	PUNCT
cana-4520	71	24	we	we	PRON
cana-4520	71	25	characterize	characterize	VERB
cana-4520	71	26	areas	area	NOUN
cana-4520	71	27	of	of	ADP
cana-4520	71	28	interest	interest	NOUN
cana-4520	71	29	(	(	PUNCT
cana-4520	71	30	rois	rois	PROPN
cana-4520	71	31	)	)	PUNCT
cana-4520	71	32	for	for	ADP
cana-4520	71	33	different	different	ADJ
cana-4520	71	34	facial	facial	ADJ
cana-4520	71	35	traits	trait	NOUN
cana-4520	71	36	.	.	PUNCT
cana-4520	72	1	just	just	ADV
cana-4520	72	2	4	4	NUM
cana-4520	72	3	points	point	NOUN
cana-4520	72	4	(	(	PUNCT
cana-4520	72	5	63	63	NUM
cana-4520	72	6	,	,	PUNCT
cana-4520	72	7	117	117	NUM
cana-4520	72	8	,	,	PUNCT
cana-4520	72	9	293	293	NUM
cana-4520	72	10	,	,	PUNCT
cana-4520	72	11	and	and	CCONJ
cana-4520	72	12	346	346	NUM
cana-4520	72	13	)	)	PUNCT
cana-4520	72	14	out	out	ADP
cana-4520	72	15	of	of	ADP
cana-4520	72	16	these	these	DET
cana-4520	72	17	468	468	NUM
cana-4520	72	18	points	point	NOUN
cana-4520	72	19	are	be	AUX
cana-4520	72	20	required	require	VERB
cana-4520	72	21	to	to	PART
cana-4520	72	22	form	form	VERB
cana-4520	72	23	an	an	DET
cana-4520	72	24	irregular	irregular	ADJ
cana-4520	72	25	rectangle	rectangle	NOUN
cana-4520	72	26	for	for	ADP
cana-4520	72	27	the	the	DET
cana-4520	72	28	roi	roi	NOUN
cana-4520	72	29	.	.	PUNCT
cana-4520	73	1	the	the	DET
cana-4520	73	2	arbitrary	arbitrary	ADJ
cana-4520	73	3	locations	location	NOUN
cana-4520	73	4	encircling	encircle	VERB
cana-4520	73	5	the	the	DET
cana-4520	73	6	eyes	eye	NOUN
cana-4520	73	7	are	be	AUX
cana-4520	73	8	employed	employ	VERB
cana-4520	73	9	to	to	PART
cana-4520	73	10	compute	compute	VERB
cana-4520	73	11	the	the	DET
cana-4520	73	12	eye	eye	NOUN
cana-4520	73	13	aspect	aspect	NOUN
cana-4520	73	14	ratio	ratio	NOUN
cana-4520	73	15	(	(	PUNCT
cana-4520	73	16	ear	ear	NOUN
cana-4520	73	17	)	)	PUNCT
cana-4520	73	18	,	,	PUNCT
cana-4520	73	19	which	which	PRON
cana-4520	73	20	is	be	AUX
cana-4520	73	21	determined	determine	VERB
cana-4520	73	22	by	by	ADP
cana-4520	73	23	splitting	split	VERB
cana-4520	73	24	the	the	DET
cana-4520	73	25	vertical	vertical	ADJ
cana-4520	73	26	lengths	length	NOUN
cana-4520	73	27	between	between	ADP
cana-4520	73	28	specified	specify	VERB
cana-4520	73	29	eye	eye	NOUN
cana-4520	73	30	features	feature	NOUN
cana-4520	73	31	by	by	ADP
cana-4520	73	32	the	the	DET
cana-4520	73	33	horizontal	horizontal	ADJ
cana-4520	73	34	distance	distance	NOUN
cana-4520	73	35	between	between	ADP
cana-4520	73	36	eyes	eye	NOUN
cana-4520	73	37	,	,	PUNCT
cana-4520	73	38	[	[	X
cana-4520	73	39	10][11	10][11	X
cana-4520	73	40	]	]	PUNCT
cana-4520	73	41	as	as	SCONJ
cana-4520	73	42	seen	see	VERB
cana-4520	73	43	in	in	ADP
cana-4520	73	44	figure	figure	NOUN
cana-4520	73	45	4	4	NUM
cana-4520	73	46	.	.	PUNCT
cana-4520	74	1	the	the	DET
cana-4520	74	2	computation	computation	NOUN
cana-4520	74	3	makes	make	VERB
cana-4520	74	4	use	use	NOUN
cana-4520	74	5	of	of	ADP
cana-4520	74	6	the	the	DET
cana-4520	74	7	subsequent	subsequent	ADJ
cana-4520	74	8	landmarks	landmark	NOUN
cana-4520	74	9	:	:	PUNCT
cana-4520	75	1	[	[	X
cana-4520	75	2	362	362	NUM
cana-4520	75	3	,	,	PUNCT
cana-4520	75	4	385	385	NUM
cana-4520	75	5	,	,	PUNCT
cana-4520	75	6	387	387	NUM
cana-4520	75	7	,	,	PUNCT
cana-4520	75	8	263	263	NUM
cana-4520	75	9	,	,	PUNCT
cana-4520	75	10	373	373	NUM
cana-4520	75	11	,	,	PUNCT
cana-4520	75	12	380	380	NUM
cana-4520	75	13	]	]	PUNCT
cana-4520	75	14	.	.	PUNCT
cana-4520	76	1	a	a	DET
cana-4520	76	2	set	set	NOUN
cana-4520	76	3	of	of	ADP
cana-4520	76	4	landmarks	landmark	NOUN
cana-4520	76	5	are	be	AUX
cana-4520	76	6	used	use	VERB
cana-4520	76	7	to	to	PART
cana-4520	76	8	determine	determine	VERB
cana-4520	76	9	the	the	DET
cana-4520	76	10	mouth	mouth	NOUN
cana-4520	76	11	aspect	aspect	NOUN
cana-4520	76	12	ratio	ratio	NOUN
cana-4520	76	13	(	(	PUNCT
cana-4520	76	14	mar	mar	PROPN
cana-4520	76	15	):	):	PUNCT
cana-4520	76	16	[	[	X
cana-4520	76	17	61	61	NUM
cana-4520	76	18	,	,	PUNCT
cana-4520	76	19	146	146	NUM
cana-4520	76	20	,	,	PUNCT
cana-4520	76	21	91	91	NUM
cana-4520	76	22	,	,	PUNCT
cana-4520	76	23	181	181	NUM
cana-4520	76	24	,	,	PUNCT
cana-4520	76	25	84	84	NUM
cana-4520	76	26	,	,	PUNCT
cana-4520	76	27	17	17	NUM
cana-4520	76	28	,	,	PUNCT
cana-4520	76	29	314	314	NUM
cana-4520	76	30	,	,	PUNCT
cana-4520	76	31	405	405	NUM
cana-4520	76	32	,	,	PUNCT
cana-4520	76	33	321	321	NUM
cana-4520	76	34	,	,	PUNCT
cana-4520	76	35	375	375	NUM
cana-4520	76	36	,	,	PUNCT
cana-4520	76	37	291	291	NUM
cana-4520	76	38	.	.	PUNCT
cana-4520	77	1	the	the	DET
cana-4520	77	2	calculation	calculation	NOUN
cana-4520	77	3	of	of	ADP
cana-4520	77	4	mar	mar	PROPN
cana-4520	77	5	depends	depend	VERB
cana-4520	77	6	on	on	ADP
cana-4520	77	7	dividing	divide	VERB
cana-4520	77	8	the	the	DET
cana-4520	77	9	horizontal	horizontal	ADJ
cana-4520	77	10	distance	distance	NOUN
cana-4520	77	11	between	between	ADP
cana-4520	77	12	mouth	mouth	NOUN
cana-4520	77	13	landmarks	landmark	NOUN
cana-4520	77	14	by	by	ADP
cana-4520	77	15	the	the	DET
cana-4520	77	16	vertical	vertical	ADJ
cana-4520	77	17	lengths	length	NOUN
cana-4520	77	18	between	between	ADP
cana-4520	77	19	specific	specific	ADJ
cana-4520	77	20	mouth	mouth	NOUN
cana-4520	77	21	landmarks	landmark	NOUN
cana-4520	77	22	.	.	PUNCT
cana-4520	78	1	p0	p0	NOUN
cana-4520	78	2	,	,	PUNCT
cana-4520	78	3	p2	p2	NOUN
cana-4520	78	4	,	,	PUNCT
cana-4520	78	5	p4	p4	ADJ
cana-4520	78	6	,	,	PUNCT
cana-4520	78	7	p6	p6	PROPN
cana-4520	78	8	,	,	PUNCT
cana-4520	78	9	p8	p8	PROPN
cana-4520	78	10	,	,	PUNCT
cana-4520	78	11	and	and	CCONJ
cana-4520	78	12	p10	p10	NOUN
cana-4520	78	13	are	be	AUX
cana-4520	78	14	the	the	DET
cana-4520	78	15	precise	precise	ADJ
cana-4520	78	16	positions	position	NOUN
cana-4520	78	17	that	that	PRON
cana-4520	78	18	are	be	AUX
cana-4520	78	19	utilized	utilize	VERB
cana-4520	78	20	.	.	PUNCT
cana-4520	79	1	tracking	track	VERB
cana-4520	79	2	particular	particular	ADJ
cana-4520	79	3	facial	facial	ADJ
cana-4520	79	4	landmarks	landmark	NOUN
cana-4520	79	5	may	may	AUX
cana-4520	79	6	be	be	AUX
cana-4520	79	7	communications	communication	NOUN
cana-4520	79	8	on	on	ADP
cana-4520	79	9	applied	apply	VERB
cana-4520	79	10	nonlinear	nonlinear	ADJ
cana-4520	79	11	analysis	analysis	NOUN
cana-4520	79	12	issn	issn	NOUN
cana-4520	79	13	:	:	PUNCT
cana-4520	79	14	1074	1074	NUM
cana-4520	79	15	-	-	PUNCT
cana-4520	79	16	133x	133x	NUM
cana-4520	79	17	vol	vol	NOUN
cana-4520	79	18	32	32	NUM
cana-4520	80	1	no	no	NOUN
cana-4520	80	2	.	.	PUNCT
cana-4520	81	1	9s	9s	NUM
cana-4520	81	2	(	(	PUNCT
cana-4520	81	3	2025	2025	NUM
cana-4520	81	4	)	)	PUNCT
cana-4520	81	5	2341	2341	NUM
cana-4520	81	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	81	7	employed	employ	VERB
cana-4520	81	8	to	to	PART
cana-4520	81	9	detect	detect	VERB
cana-4520	81	10	head	head	NOUN
cana-4520	81	11	nodding	nod	VERB
cana-4520	81	12	[	[	X
cana-4520	81	13	10	10	NUM
cana-4520	81	14	,	,	PUNCT
cana-4520	81	15	152	152	NUM
cana-4520	81	16	,	,	PUNCT
cana-4520	81	17	234	234	NUM
cana-4520	81	18	,	,	PUNCT
cana-4520	81	19	454	454	NUM
cana-4520	81	20	]	]	PUNCT
cana-4520	81	21	.	.	PUNCT
cana-4520	82	1	analyzing	analyze	VERB
cana-4520	82	2	the	the	DET
cana-4520	82	3	vertical	vertical	ADJ
cana-4520	82	4	movement	movement	NOUN
cana-4520	82	5	of	of	ADP
cana-4520	82	6	central	central	ADJ
cana-4520	82	7	facial	facial	ADJ
cana-4520	82	8	landmarks	landmark	NOUN
cana-4520	82	9	(	(	PUNCT
cana-4520	82	10	such	such	ADJ
cana-4520	82	11	as	as	ADP
cana-4520	82	12	landmark	landmark	NOUN
cana-4520	82	13	10	10	NUM
cana-4520	82	14	at	at	ADP
cana-4520	82	15	the	the	DET
cana-4520	82	16	tip	tip	NOUN
cana-4520	82	17	of	of	ADP
cana-4520	82	18	the	the	DET
cana-4520	82	19	nose	nose	NOUN
cana-4520	82	20	)	)	PUNCT
cana-4520	82	21	in	in	ADP
cana-4520	82	22	relation	relation	NOUN
cana-4520	82	23	to	to	ADP
cana-4520	82	24	a	a	DET
cana-4520	82	25	consistent	consistent	ADJ
cana-4520	82	26	horizontal	horizontal	ADJ
cana-4520	82	27	axis	axis	NOUN
cana-4520	82	28	determined	determine	VERB
cana-4520	82	29	by	by	ADP
cana-4520	82	30	landmarks	landmark	NOUN
cana-4520	82	31	234	234	NUM
cana-4520	82	32	and	and	CCONJ
cana-4520	82	33	454	454	NUM
cana-4520	82	34	helps	help	VERB
cana-4520	82	35	one	one	NUM
cana-4520	82	36	to	to	PART
cana-4520	82	37	identify	identify	VERB
cana-4520	82	38	nodding	nod	VERB
cana-4520	82	39	.	.	PUNCT
cana-4520	83	1	we	we	PRON
cana-4520	83	2	focused	focus	VERB
cana-4520	83	3	on	on	ADP
cana-4520	83	4	features	feature	NOUN
cana-4520	83	5	around	around	ADP
cana-4520	83	6	the	the	DET
cana-4520	83	7	irises	iris	NOUN
cana-4520	83	8	in	in	ADP
cana-4520	83	9	order	order	NOUN
cana-4520	83	10	to	to	PART
cana-4520	83	11	determine	determine	VERB
cana-4520	83	12	pupil	pupil	ADJ
cana-4520	83	13	size	size	NOUN
cana-4520	83	14	.	.	PUNCT
cana-4520	84	1	for	for	ADP
cana-4520	84	2	this	this	DET
cana-4520	84	3	reason	reason	NOUN
cana-4520	84	4	,	,	PUNCT
cana-4520	84	5	media	medium	NOUN
cana-4520	84	6	pipe	pipe	NOUN
cana-4520	84	7	offers	offer	VERB
cana-4520	84	8	two	two	NUM
cana-4520	84	9	distinct	distinct	ADJ
cana-4520	84	10	landmarks	landmark	NOUN
cana-4520	84	11	:	:	PUNCT
cana-4520	84	12	the	the	DET
cana-4520	84	13	right	right	ADJ
cana-4520	84	14	iris	iris	NOUN
cana-4520	84	15	landmarks	landmark	NOUN
cana-4520	85	1	[	[	X
cana-4520	85	2	469	469	NUM
cana-4520	85	3	,	,	PUNCT
cana-4520	85	4	470	470	NUM
cana-4520	85	5	,	,	PUNCT
cana-4520	85	6	471	471	NUM
cana-4520	85	7	,	,	PUNCT
cana-4520	85	8	472	472	NUM
cana-4520	85	9	,	,	PUNCT
cana-4520	85	10	473	473	NUM
cana-4520	85	11	]	]	PUNCT
cana-4520	85	12	and	and	CCONJ
cana-4520	85	13	the	the	DET
cana-4520	85	14	left	left	ADJ
cana-4520	85	15	iris	iris	NOUN
cana-4520	85	16	landmarks	landmark	NOUN
cana-4520	86	1	[	[	X
cana-4520	86	2	474	474	NUM
cana-4520	86	3	,	,	PUNCT
cana-4520	86	4	475	475	NUM
cana-4520	86	5	,	,	PUNCT
cana-4520	86	6	476	476	NUM
cana-4520	86	7	,	,	PUNCT
cana-4520	86	8	477	477	NUM
cana-4520	86	9	,	,	PUNCT
cana-4520	86	10	478	478	NUM
cana-4520	86	11	]	]	PUNCT
cana-4520	86	12	.	.	PUNCT
cana-4520	87	1	the	the	DET
cana-4520	87	2	average	average	ADJ
cana-4520	87	3	distance	distance	NOUN
cana-4520	87	4	between	between	ADP
cana-4520	87	5	the	the	DET
cana-4520	87	6	center	center	NOUN
cana-4520	87	7	and	and	CCONJ
cana-4520	87	8	the	the	DET
cana-4520	87	9	pupil	pupil	NOUN
cana-4520	87	10	is	be	AUX
cana-4520	87	11	used	use	VERB
cana-4520	87	12	to	to	PART
cana-4520	87	13	measure	measure	VERB
cana-4520	87	14	pupil	pupil	NOUN
cana-4520	87	15	size	size	NOUN
cana-4520	87	16	.	.	PUNCT
cana-4520	88	1	by	by	ADP
cana-4520	88	2	estimating	estimate	VERB
cana-4520	88	3	the	the	DET
cana-4520	88	4	average	average	ADJ
cana-4520	88	5	distance	distance	NOUN
cana-4520	88	6	between	between	ADP
cana-4520	88	7	the	the	DET
cana-4520	88	8	iris	iris	NOUN
cana-4520	88	9	's	's	PART
cana-4520	88	10	core	core	NOUN
cana-4520	88	11	and	and	CCONJ
cana-4520	88	12	adjacent	adjacent	ADJ
cana-4520	88	13	landmarks	landmark	NOUN
cana-4520	88	14	,	,	PUNCT
cana-4520	88	15	one	one	PRON
cana-4520	88	16	can	can	AUX
cana-4520	88	17	determine	determine	VERB
cana-4520	88	18	the	the	DET
cana-4520	88	19	size	size	NOUN
cana-4520	88	20	of	of	ADP
cana-4520	88	21	a	a	DET
cana-4520	88	22	pupil	pupil	NOUN
cana-4520	88	23	.	.	PUNCT
cana-4520	89	1	finally	finally	ADV
cana-4520	89	2	,	,	PUNCT
cana-4520	89	3	z	z	NOUN
cana-4520	89	4	-	-	PUNCT
cana-4520	89	5	score	score	NOUN
cana-4520	89	6	normalization	normalization	NOUN
cana-4520	89	7	is	be	AUX
cana-4520	89	8	then	then	ADV
cana-4520	89	9	performed	perform	VERB
cana-4520	89	10	on	on	ADP
cana-4520	89	11	the	the	DET
cana-4520	89	12	retrieved	retrieve	VERB
cana-4520	89	13	features	feature	NOUN
cana-4520	89	14	to	to	PART
cana-4520	89	15	guarantee	guarantee	VERB
cana-4520	89	16	a	a	DET
cana-4520	89	17	constant	constant	ADJ
cana-4520	89	18	scale	scale	NOUN
cana-4520	89	19	and	and	CCONJ
cana-4520	89	20	enhance	enhance	VERB
cana-4520	89	21	the	the	DET
cana-4520	89	22	machine	machine	NOUN
cana-4520	89	23	learning	learning	NOUN
cana-4520	89	24	models	model	NOUN
cana-4520	89	25	'	'	PART
cana-4520	89	26	performance	performance	NOUN
cana-4520	89	27	.	.	PUNCT
cana-4520	90	1	this	this	DET
cana-4520	90	2	methodical	methodical	ADJ
cana-4520	90	3	methodology	methodology	NOUN
cana-4520	90	4	makes	make	VERB
cana-4520	90	5	sure	sure	ADJ
cana-4520	90	6	that	that	SCONJ
cana-4520	90	7	all	all	DET
cana-4520	90	8	relevant	relevant	ADJ
cana-4520	90	9	facial	facial	ADJ
cana-4520	90	10	traits	trait	NOUN
cana-4520	90	11	are	be	AUX
cana-4520	90	12	precisely	precisely	ADV
cana-4520	90	13	recorded	record	VERB
cana-4520	90	14	and	and	CCONJ
cana-4520	90	15	processed	process	VERB
cana-4520	90	16	,	,	PUNCT
cana-4520	90	17	which	which	PRON
cana-4520	90	18	improves	improve	VERB
cana-4520	90	19	the	the	DET
cana-4520	90	20	accuracy	accuracy	NOUN
cana-4520	90	21	of	of	ADP
cana-4520	90	22	fatigue	fatigue	NOUN
cana-4520	90	23	detection	detection	NOUN
cana-4520	90	24	.	.	PUNCT
cana-4520	91	1	figure.4	figure.4	PROPN
cana-4520	91	2	landmarks	landmark	NOUN
cana-4520	91	3	of	of	ADP
cana-4520	91	4	left	left	ADJ
cana-4520	91	5	eye	eye	NOUN
cana-4520	91	6	figure.5	figure.5	PROPN
cana-4520	91	7	landmarks	landmark	NOUN
cana-4520	91	8	of	of	ADP
cana-4520	91	9	head	head	NOUN
cana-4520	91	10	3.5	3.5	NUM
cana-4520	91	11	dataset	dataset	NOUN
cana-4520	91	12	creation	creation	NOUN
cana-4520	91	13	building	building	NOUN
cana-4520	91	14	upon	upon	SCONJ
cana-4520	91	15	the	the	DET
cana-4520	91	16	roi	roi	NOUN
cana-4520	91	17	images	image	NOUN
cana-4520	91	18	obtained	obtain	VERB
cana-4520	91	19	in	in	ADP
cana-4520	91	20	prior	prior	ADJ
cana-4520	91	21	stages	stage	NOUN
cana-4520	91	22	,	,	PUNCT
cana-4520	91	23	the	the	DET
cana-4520	91	24	dataset	dataset	NOUN
cana-4520	91	25	was	be	AUX
cana-4520	91	26	constructed	construct	VERB
cana-4520	91	27	by	by	ADP
cana-4520	91	28	combining	combine	VERB
cana-4520	91	29	the	the	DET
cana-4520	91	30	nthu	nthu	PROPN
cana-4520	91	31	driver	driver	NOUN
cana-4520	91	32	drowsiness	drowsiness	NOUN
cana-4520	91	33	detection	detection	NOUN
cana-4520	91	34	(	(	PUNCT
cana-4520	91	35	nthu	nthu	VERB
cana-4520	91	36	-	-	PUNCT
cana-4520	91	37	ddd	ddd	NOUN
cana-4520	91	38	)	)	PUNCT
cana-4520	91	39	dataset	dataset	NOUN
cana-4520	91	40	with	with	ADP
cana-4520	91	41	real	real	ADJ
cana-4520	91	42	-	-	PUNCT
cana-4520	91	43	time	time	NOUN
cana-4520	91	44	video	video	NOUN
cana-4520	91	45	recordings	recording	NOUN
cana-4520	91	46	.	.	PUNCT
cana-4520	92	1	ten	ten	NUM
cana-4520	92	2	videos	video	NOUN
cana-4520	92	3	were	be	AUX
cana-4520	92	4	selected	select	VERB
cana-4520	92	5	,	,	PUNCT
cana-4520	92	6	prioritizing	prioritize	VERB
cana-4520	92	7	subject	subject	ADJ
cana-4520	92	8	diversity	diversity	NOUN
cana-4520	92	9	with	with	ADP
cana-4520	92	10	variations	variation	NOUN
cana-4520	92	11	in	in	ADP
cana-4520	92	12	eye	eye	NOUN
cana-4520	92	13	size	size	NOUN
cana-4520	92	14	,	,	PUNCT
cana-4520	92	15	mouth	mouth	NOUN
cana-4520	92	16	shape	shape	NOUN
cana-4520	92	17	,	,	PUNCT
cana-4520	92	18	head	head	NOUN
cana-4520	92	19	movements	movement	NOUN
cana-4520	92	20	,	,	PUNCT
cana-4520	92	21	and	and	CCONJ
cana-4520	92	22	iris	iris	NOUN
cana-4520	92	23	patterns	pattern	NOUN
cana-4520	92	24	among	among	ADP
cana-4520	92	25	individuals	individual	NOUN
cana-4520	92	26	.	.	PUNCT
cana-4520	93	1	furthermore	furthermore	ADV
cana-4520	93	2	,	,	PUNCT
cana-4520	93	3	video	video	NOUN
cana-4520	93	4	capture	capture	NOUN
cana-4520	93	5	encompassed	encompass	VERB
cana-4520	93	6	diverse	diverse	ADJ
cana-4520	93	7	driving	driving	NOUN
cana-4520	93	8	conditions	condition	NOUN
cana-4520	93	9	,	,	PUNCT
cana-4520	93	10	including	include	VERB
cana-4520	93	11	varying	vary	VERB
cana-4520	93	12	lighting	lighting	NOUN
cana-4520	93	13	,	,	PUNCT
cana-4520	93	14	weather	weather	NOUN
cana-4520	93	15	,	,	PUNCT
cana-4520	93	16	and	and	CCONJ
cana-4520	93	17	traffic	traffic	NOUN
cana-4520	93	18	scenarios	scenario	NOUN
cana-4520	93	19	.	.	PUNCT
cana-4520	94	1	[	[	X
cana-4520	94	2	12]the	12]the	NUM
cana-4520	94	3	dataset	dataset	NOUN
cana-4520	94	4	focuses	focus	VERB
cana-4520	94	5	on	on	ADP
cana-4520	94	6	key	key	ADJ
cana-4520	94	7	facial	facial	ADJ
cana-4520	94	8	features	feature	NOUN
cana-4520	94	9	:	:	PUNCT
cana-4520	94	10	eyes	eye	NOUN
cana-4520	94	11	,	,	PUNCT
cana-4520	94	12	mouth	mouth	NOUN
cana-4520	94	13	,	,	PUNCT
cana-4520	94	14	head	head	NOUN
cana-4520	94	15	,	,	PUNCT
cana-4520	94	16	and	and	CCONJ
cana-4520	94	17	pupil	pupil	ADJ
cana-4520	94	18	regions	region	NOUN
cana-4520	94	19	,	,	PUNCT
cana-4520	94	20	extracted	extract	VERB
cana-4520	94	21	as	as	ADP
cana-4520	94	22	rois	rois	NOUN
cana-4520	94	23	from	from	ADP
cana-4520	94	24	both	both	DET
cana-4520	94	25	sources	source	NOUN
cana-4520	94	26	.	.	PUNCT
cana-4520	95	1	real	real	ADJ
cana-4520	95	2	-	-	PUNCT
cana-4520	95	3	time	time	NOUN
cana-4520	95	4	videos	video	NOUN
cana-4520	95	5	underwent	undergo	VERB
cana-4520	95	6	frame	frame	NOUN
cana-4520	95	7	extraction	extraction	NOUN
cana-4520	95	8	at	at	ADP
cana-4520	95	9	appropriate	appropriate	ADJ
cana-4520	95	10	intervals	interval	NOUN
cana-4520	95	11	and	and	CCONJ
cana-4520	95	12	subsequent	subsequent	ADJ
cana-4520	95	13	pre	pre	ADJ
cana-4520	95	14	-	-	ADJ
cana-4520	95	15	processing	processing	ADJ
cana-4520	95	16	,	,	PUNCT
cana-4520	95	17	while	while	SCONJ
cana-4520	95	18	redundant	redundant	ADJ
cana-4520	95	19	and	and	CCONJ
cana-4520	95	20	irrelevant	irrelevant	ADJ
cana-4520	95	21	data	datum	NOUN
cana-4520	95	22	were	be	AUX
cana-4520	95	23	removed	remove	VERB
cana-4520	95	24	.	.	PUNCT
cana-4520	96	1	the	the	DET
cana-4520	96	2	combined	combine	VERB
cana-4520	96	3	dataset	dataset	NOUN
cana-4520	96	4	,	,	PUNCT
cana-4520	96	5	comprising	comprise	VERB
cana-4520	96	6	4836	4836	NUM
cana-4520	96	7	images	image	NOUN
cana-4520	96	8	,	,	PUNCT
cana-4520	96	9	was	be	AUX
cana-4520	96	10	divided	divide	VERB
cana-4520	96	11	into	into	ADP
cana-4520	96	12	training	training	NOUN
cana-4520	96	13	(	(	PUNCT
cana-4520	96	14	3385	3385	NUM
cana-4520	96	15	images	image	NOUN
cana-4520	96	16	,	,	PUNCT
cana-4520	96	17	70	70	NUM
cana-4520	96	18	%	%	NOUN
cana-4520	96	19	)	)	PUNCT
cana-4520	96	20	,	,	PUNCT
cana-4520	96	21	validation	validation	NOUN
cana-4520	96	22	(	(	PUNCT
cana-4520	96	23	725	725	NUM
cana-4520	96	24	images	image	NOUN
cana-4520	96	25	,	,	PUNCT
cana-4520	96	26	15	15	NUM
cana-4520	96	27	%	%	NOUN
cana-4520	96	28	)	)	PUNCT
cana-4520	96	29	,	,	PUNCT
cana-4520	96	30	and	and	CCONJ
cana-4520	96	31	test	test	NOUN
cana-4520	96	32	sets	set	NOUN
cana-4520	96	33	(	(	PUNCT
cana-4520	96	34	726	726	NUM
cana-4520	96	35	images	image	NOUN
cana-4520	96	36	,	,	PUNCT
cana-4520	96	37	15	15	NUM
cana-4520	96	38	%	%	NOUN
cana-4520	96	39	)	)	PUNCT
cana-4520	96	40	to	to	PART
cana-4520	96	41	mitigate	mitigate	VERB
cana-4520	96	42	over	over	ADP
cana-4520	96	43	fitting	fitting	ADJ
cana-4520	96	44	.	.	PUNCT
cana-4520	97	1	the	the	DET
cana-4520	97	2	images	image	NOUN
cana-4520	97	3	were	be	AUX
cana-4520	97	4	labelled	label	VERB
cana-4520	97	5	with	with	ADP
cana-4520	97	6	binary	binary	ADJ
cana-4520	97	7	class	class	NOUN
cana-4520	97	8	labels	label	NOUN
cana-4520	97	9	:	:	PUNCT
cana-4520	97	10	'	'	PUNCT
cana-4520	97	11	fatigue	fatigue	NOUN
cana-4520	97	12	'	'	PUNCT
cana-4520	97	13	and	and	CCONJ
cana-4520	97	14	'	'	PUNCT
cana-4520	97	15	non	non	ADJ
cana-4520	97	16	-	-	NOUN
cana-4520	97	17	fatigue	fatigue	NOUN
cana-4520	97	18	'	'	PUNCT
cana-4520	97	19	for	for	ADP
cana-4520	97	20	the	the	DET
cana-4520	97	21	drowsiness	drowsiness	NOUN
cana-4520	97	22	detection	detection	NOUN
cana-4520	97	23	task	task	NOUN
cana-4520	97	24	.	.	PUNCT
cana-4520	98	1	this	this	DET
cana-4520	98	2	approach	approach	NOUN
cana-4520	98	3	,	,	PUNCT
cana-4520	98	4	integrating	integrate	VERB
cana-4520	98	5	a	a	DET
cana-4520	98	6	well	well	ADV
cana-4520	98	7	-	-	PUNCT
cana-4520	98	8	established	establish	VERB
cana-4520	98	9	dataset	dataset	NOUN
cana-4520	98	10	like	like	ADP
cana-4520	98	11	nthu	nthu	NOUN
cana-4520	98	12	-	-	PUNCT
cana-4520	98	13	ddd	ddd	NOUN
cana-4520	98	14	with	with	ADP
cana-4520	98	15	diverse	diverse	ADJ
cana-4520	98	16	real	real	ADJ
cana-4520	98	17	-	-	PUNCT
cana-4520	98	18	world	world	NOUN
cana-4520	98	19	video	video	NOUN
cana-4520	98	20	data	datum	NOUN
cana-4520	98	21	,	,	PUNCT
cana-4520	98	22	ensures	ensure	VERB
cana-4520	98	23	a	a	DET
cana-4520	98	24	robust	robust	ADJ
cana-4520	98	25	and	and	CCONJ
cana-4520	98	26	representative	representative	ADJ
cana-4520	98	27	dataset	dataset	NOUN
cana-4520	98	28	for	for	ADP
cana-4520	98	29	training	training	NOUN
cana-4520	98	30	and	and	CCONJ
cana-4520	98	31	evaluating	evaluate	VERB
cana-4520	98	32	drowsiness	drowsiness	NOUN
cana-4520	98	33	detection	detection	NOUN
cana-4520	98	34	models	model	NOUN
cana-4520	98	35	,	,	PUNCT
cana-4520	98	36	considering	consider	VERB
cana-4520	98	37	the	the	DET
cana-4520	98	38	multifaceted	multifaceted	ADJ
cana-4520	98	39	nature	nature	NOUN
cana-4520	98	40	of	of	ADP
cana-4520	98	41	driver	driver	NOUN
cana-4520	98	42	behavior	behavior	NOUN
cana-4520	98	43	and	and	CCONJ
cana-4520	98	44	appearance	appearance	NOUN
cana-4520	98	45	.	.	PUNCT
cana-4520	99	1	dataset	dataset	PROPN
cana-4520	99	2	fatigue	fatigue	NOUN
cana-4520	99	3	non	non	ADJ
cana-4520	99	4	-	-	ADJ
cana-4520	99	5	fatigue	fatigue	ADJ
cana-4520	99	6	training	training	NOUN
cana-4520	99	7	set	set	VERB
cana-4520	99	8	2369	2369	NUM
cana-4520	99	9	1016	1016	NUM
cana-4520	99	10	validation	validation	NOUN
cana-4520	99	11	set	set	VERB
cana-4520	99	12	507	507	NUM
cana-4520	99	13	218	218	NUM
cana-4520	99	14	testing	testing	NOUN
cana-4520	99	15	set	set	VERB
cana-4520	99	16	509	509	NUM
cana-4520	99	17	217	217	NUM
cana-4520	99	18	table	table	NOUN
cana-4520	99	19	1	1	NUM
cana-4520	99	20	:	:	PUNCT
cana-4520	99	21	dataset	dataset	ADJ
cana-4520	99	22	distribution	distribution	NOUN
cana-4520	99	23	communications	communication	NOUN
cana-4520	99	24	on	on	ADP
cana-4520	99	25	applied	apply	VERB
cana-4520	99	26	nonlinear	nonlinear	ADJ
cana-4520	99	27	analysis	analysis	NOUN
cana-4520	99	28	issn	issn	NOUN
cana-4520	99	29	:	:	PUNCT
cana-4520	99	30	1074	1074	NUM
cana-4520	99	31	-	-	PUNCT
cana-4520	99	32	133x	133x	NUM
cana-4520	99	33	vol	vol	NOUN
cana-4520	99	34	32	32	NUM
cana-4520	99	35	no	no	NOUN
cana-4520	99	36	.	.	PUNCT
cana-4520	100	1	9s	9s	NUM
cana-4520	100	2	(	(	PUNCT
cana-4520	100	3	2025	2025	NUM
cana-4520	100	4	)	)	PUNCT
cana-4520	100	5	2342	2342	NUM
cana-4520	100	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	100	7	fig	fig	NOUN
cana-4520	100	8	6	6	NUM
cana-4520	100	9	.	.	PUNCT
cana-4520	101	1	a	a	DET
cana-4520	101	2	snapshot	snapshot	NOUN
cana-4520	101	3	of	of	ADP
cana-4520	101	4	dataset	dataset	NOUN
cana-4520	101	5	with	with	ADP
cana-4520	101	6	a	a	DET
cana-4520	101	7	multitude	multitude	NOUN
cana-4520	101	8	of	of	ADP
cana-4520	101	9	categories	category	NOUN
cana-4520	101	10	and	and	CCONJ
cana-4520	101	11	scenario	scenario	NOUN
cana-4520	101	12	3.6	3.6	NUM
cana-4520	101	13	cnn	cnn	NOUN
cana-4520	101	14	training	training	NOUN
cana-4520	101	15	experiments	experiment	NOUN
cana-4520	101	16	for	for	ADP
cana-4520	101	17	fatigue	fatigue	NOUN
cana-4520	101	18	detection	detection	NOUN
cana-4520	101	19	inceptionv3	inceptionv3	NOUN
cana-4520	101	20	:	:	PUNCT
cana-4520	101	21	a	a	DET
cana-4520	101	22	deep	deep	ADJ
cana-4520	101	23	convolutional	convolutional	ADJ
cana-4520	101	24	neural	neural	ADJ
cana-4520	101	25	network	network	NOUN
cana-4520	101	26	architecture	architecture	NOUN
cana-4520	101	27	termed	term	VERB
cana-4520	101	28	inceptionv3	inceptionv3	NOUN
cana-4520	101	29	has	have	AUX
cana-4520	101	30	demonstrated	demonstrate	VERB
cana-4520	101	31	in	in	ADP
cana-4520	101	32	figure.7	figure.7	ADJ
cana-4520	101	33	and	and	CCONJ
cana-4520	101	34	exceptional	exceptional	ADJ
cana-4520	101	35	performance	performance	NOUN
cana-4520	101	36	in	in	ADP
cana-4520	101	37	image	image	NOUN
cana-4520	101	38	categorization	categorization	NOUN
cana-4520	101	39	tests	test	NOUN
cana-4520	101	40	.	.	PUNCT
cana-4520	102	1	it	it	PRON
cana-4520	102	2	is	be	AUX
cana-4520	102	3	renowned	renowned	ADJ
cana-4520	102	4	for	for	ADP
cana-4520	102	5	being	be	AUX
cana-4520	102	6	effective	effective	ADJ
cana-4520	102	7	and	and	CCONJ
cana-4520	102	8	for	for	ADP
cana-4520	102	9	being	be	AUX
cana-4520	102	10	able	able	ADJ
cana-4520	102	11	to	to	PART
cana-4520	102	12	extract	extract	VERB
cana-4520	102	13	intricate	intricate	ADJ
cana-4520	102	14	details	detail	NOUN
cana-4520	102	15	from	from	ADP
cana-4520	102	16	photos	photo	NOUN
cana-4520	102	17	.	.	PUNCT
cana-4520	103	1	each	each	DET
cana-4520	103	2	module	module	NOUN
cana-4520	103	3	of	of	ADP
cana-4520	103	4	inceptionv3	inceptionv3	NOUN
cana-4520	103	5	employs	employ	VERB
cana-4520	103	6	a	a	DET
cana-4520	103	7	variety	variety	NOUN
cana-4520	103	8	of	of	ADP
cana-4520	103	9	convolutional	convolutional	ADJ
cana-4520	103	10	filter	filter	NOUN
cana-4520	103	11	sizes	size	NOUN
cana-4520	103	12	,	,	PUNCT
cana-4520	103	13	which	which	PRON
cana-4520	103	14	enables	enable	VERB
cana-4520	103	15	it	it	PRON
cana-4520	103	16	to	to	PART
cana-4520	103	17	record	record	VERB
cana-4520	103	18	details	detail	NOUN
cana-4520	103	19	at	at	ADP
cana-4520	103	20	various	various	ADJ
cana-4520	103	21	scales	scale	NOUN
cana-4520	103	22	.	.	PUNCT
cana-4520	104	1	eliminating	eliminate	VERB
cana-4520	104	2	the	the	DET
cana-4520	104	3	top	top	ADJ
cana-4520	104	4	layers	layer	NOUN
cana-4520	104	5	of	of	ADP
cana-4520	104	6	inceptionv3	inceptionv3	NOUN
cana-4520	104	7	and	and	CCONJ
cana-4520	104	8	adding	add	VERB
cana-4520	104	9	a	a	DET
cana-4520	104	10	global	global	ADJ
cana-4520	104	11	average	average	ADJ
cana-4520	104	12	pooling	pool	VERB
cana-4520	104	13	layer	layer	NOUN
cana-4520	104	14	,	,	PUNCT
cana-4520	104	15	followed	follow	VERB
cana-4520	104	16	by	by	ADP
cana-4520	104	17	a	a	DET
cana-4520	104	18	dense	dense	ADJ
cana-4520	104	19	layer	layer	NOUN
cana-4520	104	20	activated	activate	VERB
cana-4520	104	21	with	with	ADP
cana-4520	104	22	relu	relu	NOUN
cana-4520	104	23	and	and	CCONJ
cana-4520	104	24	a	a	DET
cana-4520	104	25	final	final	ADJ
cana-4520	104	26	dense	dense	ADJ
cana-4520	104	27	layer	layer	NOUN
cana-4520	104	28	triggered	trigger	VERB
cana-4520	104	29	with	with	ADP
cana-4520	104	30	a	a	DET
cana-4520	104	31	sigmoid	sigmoid	NOUN
cana-4520	104	32	for	for	ADP
cana-4520	104	33	binary	binary	ADJ
cana-4520	104	34	classification	classification	NOUN
cana-4520	104	35	,	,	PUNCT
cana-4520	104	36	is	be	AUX
cana-4520	104	37	the	the	DET
cana-4520	104	38	way	way	NOUN
cana-4520	104	39	the	the	DET
cana-4520	104	40	system	system	NOUN
cana-4520	104	41	is	be	AUX
cana-4520	104	42	constructed	construct	VERB
cana-4520	104	43	to	to	PART
cana-4520	104	44	classify	classify	VERB
cana-4520	104	45	fatigue.[13	fatigue.[13	PROPN
cana-4520	104	46	]	]	PUNCT
cana-4520	104	47	only	only	ADV
cana-4520	104	48	the	the	DET
cana-4520	104	49	top	top	ADJ
cana-4520	104	50	layers	layer	NOUN
cana-4520	104	51	of	of	ADP
cana-4520	104	52	inceptionv3	inceptionv3	NOUN
cana-4520	104	53	are	be	AUX
cana-4520	104	54	trained	train	VERB
cana-4520	104	55	to	to	PART
cana-4520	104	56	react	react	VERB
cana-4520	104	57	to	to	ADP
cana-4520	104	58	the	the	DET
cana-4520	104	59	drowsiness	drowsiness	NOUN
cana-4520	104	60	detection	detection	NOUN
cana-4520	104	61	task	task	NOUN
cana-4520	104	62	;	;	PUNCT
cana-4520	104	63	the	the	DET
cana-4520	104	64	base	base	NOUN
cana-4520	104	65	layers	layer	NOUN
cana-4520	104	66	are	be	AUX
cana-4520	104	67	initially	initially	ADV
cana-4520	104	68	frozen	freeze	VERB
cana-4520	104	69	to	to	PART
cana-4520	104	70	preserve	preserve	VERB
cana-4520	104	71	the	the	DET
cana-4520	104	72	pre	pre	ADJ
cana-4520	104	73	-	-	ADJ
cana-4520	104	74	learned	learned	ADJ
cana-4520	104	75	weights	weight	NOUN
cana-4520	104	76	.	.	PUNCT
cana-4520	105	1	figure	figure	NOUN
cana-4520	105	2	7	7	NUM
cana-4520	105	3	.	.	PUNCT
cana-4520	106	1	inceptionv3	inceptionv3	NOUN
cana-4520	106	2	network	network	NOUN
cana-4520	106	3	architecture	architecture	NOUN
cana-4520	106	4	vgg19	vgg19	NOUN
cana-4520	106	5	:	:	PUNCT
cana-4520	106	6	vgg19	vgg19	PROPN
cana-4520	106	7	is	be	AUX
cana-4520	106	8	an	an	DET
cana-4520	106	9	additional	additional	ADJ
cana-4520	106	10	popular	popular	ADJ
cana-4520	106	11	cnn	cnn	NOUN
cana-4520	106	12	architecture	architecture	NOUN
cana-4520	106	13	,	,	PUNCT
cana-4520	106	14	distinguished	distinguish	VERB
cana-4520	106	15	by	by	ADP
cana-4520	106	16	its	its	PRON
cana-4520	106	17	simplicity	simplicity	NOUN
cana-4520	106	18	and	and	CCONJ
cana-4520	106	19	the	the	DET
cana-4520	106	20	usage	usage	NOUN
cana-4520	106	21	of	of	ADP
cana-4520	106	22	tiny	tiny	ADJ
cana-4520	106	23	(	(	PUNCT
cana-4520	106	24	3x3	3x3	NUM
cana-4520	106	25	)	)	PUNCT
cana-4520	106	26	convolutional	convolutional	ADJ
cana-4520	106	27	filters	filter	NOUN
cana-4520	106	28	throughout	throughout	ADP
cana-4520	106	29	the	the	DET
cana-4520	106	30	network	network	NOUN
cana-4520	106	31	.	.	PUNCT
cana-4520	107	1	it	it	PRON
cana-4520	107	2	is	be	AUX
cana-4520	107	3	quite	quite	ADV
cana-4520	107	4	easy	easy	ADJ
cana-4520	107	5	to	to	PART
cana-4520	107	6	implement	implement	VERB
cana-4520	107	7	and	and	CCONJ
cana-4520	107	8	computationally	computationally	ADV
cana-4520	107	9	efficient	efficient	ADJ
cana-4520	107	10	despite	despite	SCONJ
cana-4520	107	11	its	its	PRON
cana-4520	107	12	complexity	complexity	NOUN
cana-4520	107	13	.	.	PUNCT
cana-4520	108	1	a	a	DET
cana-4520	108	2	global	global	ADJ
cana-4520	108	3	average	average	ADJ
cana-4520	108	4	pooling	pool	VERB
cana-4520	108	5	layer	layer	NOUN
cana-4520	108	6	,	,	PUNCT
cana-4520	108	7	a	a	DET
cana-4520	108	8	dense	dense	ADJ
cana-4520	108	9	layer	layer	NOUN
cana-4520	108	10	activated	activate	VERB
cana-4520	108	11	by	by	ADP
cana-4520	108	12	relu	relu	NOUN
cana-4520	108	13	,	,	PUNCT
cana-4520	108	14	and	and	CCONJ
cana-4520	108	15	a	a	DET
cana-4520	108	16	final	final	ADJ
cana-4520	108	17	dense	dense	ADJ
cana-4520	108	18	layer	layer	NOUN
cana-4520	108	19	triggered	trigger	VERB
cana-4520	108	20	by	by	ADP
cana-4520	108	21	sigmoid	sigmoid	NOUN
cana-4520	108	22	for	for	ADP
cana-4520	108	23	binary	binary	ADJ
cana-4520	108	24	classification	classification	NOUN
cana-4520	108	25	were	be	AUX
cana-4520	108	26	included	include	VERB
cana-4520	108	27	to	to	PART
cana-4520	108	28	vgg19	vgg19	VERB
cana-4520	108	29	for	for	ADP
cana-4520	108	30	fatigue	fatigue	NOUN
cana-4520	108	31	classification	classification	NOUN
cana-4520	108	32	,	,	PUNCT
cana-4520	108	33	much	much	ADV
cana-4520	108	34	as	as	SCONJ
cana-4520	108	35	inceptionv3.[14	inceptionv3.[14	NOUN
cana-4520	108	36	]	]	X
cana-4520	108	37	only	only	ADV
cana-4520	108	38	the	the	DET
cana-4520	108	39	recently	recently	ADV
cana-4520	108	40	added	add	VERB
cana-4520	108	41	top	top	ADJ
cana-4520	108	42	layers	layer	NOUN
cana-4520	108	43	of	of	ADP
cana-4520	108	44	vgg19	vgg19	PROPN
cana-4520	108	45	are	be	AUX
cana-4520	108	46	trained	train	VERB
cana-4520	108	47	for	for	ADP
cana-4520	108	48	drowsiness	drowsiness	NOUN
cana-4520	108	49	detection	detection	NOUN
cana-4520	108	50	;	;	PUNCT
cana-4520	108	51	the	the	DET
cana-4520	108	52	base	base	NOUN
cana-4520	108	53	layers	layer	NOUN
cana-4520	108	54	are	be	AUX
cana-4520	108	55	initially	initially	ADV
cana-4520	108	56	frozen	freeze	VERB
cana-4520	108	57	to	to	PART
cana-4520	108	58	take	take	VERB
cana-4520	108	59	advantage	advantage	NOUN
cana-4520	108	60	of	of	ADP
cana-4520	108	61	the	the	DET
cana-4520	108	62	pre	pre	ADJ
cana-4520	108	63	-	-	ADJ
cana-4520	108	64	learned	learned	ADJ
cana-4520	108	65	weights	weight	NOUN
cana-4520	108	66	.	.	PUNCT
cana-4520	109	1	resnet50v2	resnet50v2	NOUN
cana-4520	109	2	:	:	PUNCT
cana-4520	109	3	with	with	ADP
cana-4520	109	4	residual	residual	ADJ
cana-4520	109	5	connections	connection	NOUN
cana-4520	109	6	,	,	PUNCT
cana-4520	109	7	resnet50v2	resnet50v2	NOUN
cana-4520	109	8	is	be	AUX
cana-4520	109	9	an	an	DET
cana-4520	109	10	enhanced	enhanced	ADJ
cana-4520	109	11	version	version	NOUN
cana-4520	109	12	of	of	ADP
cana-4520	109	13	the	the	DET
cana-4520	109	14	resnet	resnet	NOUN
cana-4520	109	15	architecture	architecture	NOUN
cana-4520	109	16	which	which	PRON
cana-4520	109	17	renders	render	VERB
cana-4520	109	18	developing	develop	VERB
cana-4520	109	19	very	very	ADV
cana-4520	109	20	deep	deep	ADJ
cana-4520	109	21	networks	network	NOUN
cana-4520	109	22	more	more	ADJ
cana-4520	109	23	accessible.[15][18	accessible.[15][18	NOUN
cana-4520	109	24	]	]	PUNCT
cana-4520	109	25	this	this	DET
cana-4520	109	26	approach	approach	NOUN
cana-4520	109	27	is	be	AUX
cana-4520	109	28	highly	highly	ADV
cana-4520	109	29	beneficial	beneficial	ADJ
cana-4520	109	30	for	for	ADP
cana-4520	109	31	challenging	challenge	VERB
cana-4520	109	32	image	image	NOUN
cana-4520	109	33	classification	classification	NOUN
cana-4520	109	34	problems	problem	NOUN
cana-4520	109	35	due	due	ADJ
cana-4520	109	36	to	to	ADP
cana-4520	109	37	it	it	PRON
cana-4520	109	38	serves	serve	VERB
cana-4520	109	39	to	to	PART
cana-4520	109	40	mitigate	mitigate	VERB
cana-4520	109	41	the	the	DET
cana-4520	109	42	vanishing	vanish	VERB
cana-4520	109	43	gradient	gradient	NOUN
cana-4520	109	44	problem	problem	NOUN
cana-4520	109	45	.	.	PUNCT
cana-4520	110	1	resnet50v2	resnet50v2	NOUN
cana-4520	110	2	is	be	AUX
cana-4520	110	3	configured	configure	VERB
cana-4520	110	4	for	for	ADP
cana-4520	110	5	fatigue	fatigue	NOUN
cana-4520	110	6	detection	detection	NOUN
cana-4520	110	7	via	via	ADP
cana-4520	110	8	eliminating	eliminate	VERB
cana-4520	110	9	its	its	PRON
cana-4520	110	10	top	top	ADJ
cana-4520	110	11	layers	layer	NOUN
cana-4520	110	12	and	and	CCONJ
cana-4520	110	13	incorporating	incorporate	VERB
cana-4520	110	14	a	a	DET
cana-4520	110	15	global	global	ADJ
cana-4520	110	16	average	average	ADJ
cana-4520	110	17	pooling	pool	VERB
cana-4520	110	18	layer	layer	NOUN
cana-4520	110	19	,	,	PUNCT
cana-4520	110	20	subsequent	subsequent	ADJ
cana-4520	110	21	to	to	ADP
cana-4520	110	22	a	a	DET
cana-4520	110	23	dense	dense	ADJ
cana-4520	110	24	layer	layer	NOUN
cana-4520	110	25	triggered	trigger	VERB
cana-4520	110	26	with	with	ADP
cana-4520	110	27	communications	communication	NOUN
cana-4520	110	28	on	on	ADP
cana-4520	110	29	applied	apply	VERB
cana-4520	110	30	nonlinear	nonlinear	ADJ
cana-4520	110	31	analysis	analysis	NOUN
cana-4520	110	32	issn	issn	NOUN
cana-4520	110	33	:	:	PUNCT
cana-4520	110	34	1074	1074	NUM
cana-4520	110	35	-	-	PUNCT
cana-4520	110	36	133x	133x	NUM
cana-4520	110	37	vol	vol	NOUN
cana-4520	110	38	32	32	NUM
cana-4520	110	39	no	no	NOUN
cana-4520	110	40	.	.	PUNCT
cana-4520	111	1	9s	9s	NUM
cana-4520	111	2	(	(	PUNCT
cana-4520	111	3	2025	2025	NUM
cana-4520	111	4	)	)	PUNCT
cana-4520	111	5	2343	2343	NUM
cana-4520	111	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	111	7	relu	relu	NOUN
cana-4520	111	8	and	and	CCONJ
cana-4520	111	9	a	a	DET
cana-4520	111	10	final	final	ADJ
cana-4520	111	11	dense	dense	ADJ
cana-4520	111	12	layer	layer	NOUN
cana-4520	111	13	activated	activate	VERB
cana-4520	111	14	with	with	ADP
cana-4520	111	15	sigmoid	sigmoid	NOUN
cana-4520	111	16	for	for	ADP
cana-4520	111	17	binary	binary	ADJ
cana-4520	111	18	classification	classification	NOUN
cana-4520	111	19	.	.	PUNCT
cana-4520	112	1	only	only	ADV
cana-4520	112	2	top	top	ADJ
cana-4520	112	3	layers	layer	NOUN
cana-4520	112	4	are	be	AUX
cana-4520	112	5	trained	train	VERB
cana-4520	112	6	to	to	PART
cana-4520	112	7	concentrate	concentrate	VERB
cana-4520	112	8	on	on	ADP
cana-4520	112	9	the	the	DET
cana-4520	112	10	particular	particular	ADJ
cana-4520	112	11	task	task	NOUN
cana-4520	112	12	of	of	ADP
cana-4520	112	13	classifying	classify	VERB
cana-4520	112	14	drowsiness	drowsiness	NOUN
cana-4520	112	15	,	,	PUNCT
cana-4520	112	16	while	while	SCONJ
cana-4520	112	17	the	the	DET
cana-4520	112	18	base	base	NOUN
cana-4520	112	19	layers	layer	NOUN
cana-4520	112	20	are	be	AUX
cana-4520	112	21	initially	initially	ADV
cana-4520	112	22	frozen	freeze	VERB
cana-4520	112	23	to	to	PART
cana-4520	112	24	preserve	preserve	VERB
cana-4520	112	25	the	the	DET
cana-4520	112	26	pre	pre	ADJ
cana-4520	112	27	-	-	ADJ
cana-4520	112	28	learned	learned	ADJ
cana-4520	112	29	weights	weight	NOUN
cana-4520	112	30	.	.	PUNCT
cana-4520	113	1	the	the	DET
cana-4520	113	2	figure	figure	NOUN
cana-4520	113	3	.	.	PUNCT
cana-4520	113	4	8	8	NUM
cana-4520	113	5	illustrates	illustrate	VERB
cana-4520	113	6	the	the	DET
cana-4520	113	7	cnn	cnn	PROPN
cana-4520	113	8	architecture	architecture	NOUN
cana-4520	113	9	.	.	PUNCT
cana-4520	114	1	figure	figure	NOUN
cana-4520	114	2	.	.	PUNCT
cana-4520	115	1	8	8	NUM
cana-4520	115	2	cnn	cnn	NOUN
cana-4520	115	3	architecture	architecture	NOUN
cana-4520	115	4	3.7	3.7	NUM
cana-4520	115	5	training	training	NOUN
cana-4520	115	6	and	and	CCONJ
cana-4520	115	7	validation	validation	NOUN
cana-4520	115	8	using	use	VERB
cana-4520	115	9	cnn	cnn	PROPN
cana-4520	115	10	to	to	PART
cana-4520	115	11	preserve	preserve	VERB
cana-4520	115	12	pre	pre	ADJ
cana-4520	115	13	-	-	VERB
cana-4520	115	14	trained	train	VERB
cana-4520	115	15	weights	weight	NOUN
cana-4520	115	16	and	and	CCONJ
cana-4520	115	17	retain	retain	VERB
cana-4520	115	18	previously	previously	ADV
cana-4520	115	19	learned	learn	VERB
cana-4520	115	20	characteristics	characteristic	NOUN
cana-4520	115	21	that	that	PRON
cana-4520	115	22	are	be	AUX
cana-4520	115	23	necessary	necessary	ADJ
cana-4520	115	24	for	for	ADP
cana-4520	115	25	accurate	accurate	ADJ
cana-4520	115	26	sleepiness	sleepiness	NOUN
cana-4520	115	27	detection	detection	NOUN
cana-4520	115	28	,	,	PUNCT
cana-4520	115	29	the	the	DET
cana-4520	115	30	training	training	NOUN
cana-4520	115	31	process	process	NOUN
cana-4520	115	32	begins	begin	VERB
cana-4520	115	33	by	by	ADP
cana-4520	115	34	freezing	freeze	VERB
cana-4520	115	35	the	the	DET
cana-4520	115	36	basic	basic	ADJ
cana-4520	115	37	layers	layer	NOUN
cana-4520	115	38	of	of	ADP
cana-4520	115	39	each	each	DET
cana-4520	115	40	model	model	NOUN
cana-4520	115	41	.	.	PUNCT
cana-4520	116	1	using	use	VERB
cana-4520	116	2	this	this	DET
cana-4520	116	3	method	method	NOUN
cana-4520	116	4	,	,	PUNCT
cana-4520	116	5	the	the	DET
cana-4520	116	6	models	model	NOUN
cana-4520	116	7	are	be	AUX
cana-4520	116	8	optimized	optimize	VERB
cana-4520	116	9	for	for	ADP
cana-4520	116	10	the	the	DET
cana-4520	116	11	particular	particular	ADJ
cana-4520	116	12	task	task	NOUN
cana-4520	116	13	at	at	ADP
cana-4520	116	14	hand	hand	NOUN
cana-4520	116	15	.	.	PUNCT
cana-4520	117	1	for	for	ADP
cana-4520	117	2	binary	binary	ADJ
cana-4520	117	3	classification	classification	NOUN
cana-4520	117	4	tasks	task	NOUN
cana-4520	117	5	,	,	PUNCT
cana-4520	117	6	such	such	ADJ
cana-4520	117	7	as	as	ADP
cana-4520	117	8	determining	determine	VERB
cana-4520	117	9	a	a	DET
cana-4520	117	10	driver	driver	NOUN
cana-4520	117	11	's	's	PART
cana-4520	117	12	level	level	NOUN
cana-4520	117	13	of	of	ADP
cana-4520	117	14	fatigue	fatigue	NOUN
cana-4520	117	15	,	,	PUNCT
cana-4520	117	16	a	a	DET
cana-4520	117	17	binary	binary	ADJ
cana-4520	117	18	cross	cross	NOUN
cana-4520	117	19	-	-	ADJ
cana-4520	117	20	entropy	entropy	ADJ
cana-4520	117	21	loss	loss	NOUN
cana-4520	117	22	function	function	NOUN
cana-4520	117	23	[	[	X
cana-4520	117	24	12	12	NUM
cana-4520	117	25	]	]	PUNCT
cana-4520	117	26	is	be	AUX
cana-4520	117	27	employed	employ	VERB
cana-4520	117	28	when	when	SCONJ
cana-4520	117	29	each	each	DET
cana-4520	117	30	model	model	NOUN
cana-4520	117	31	has	have	AUX
cana-4520	117	32	been	be	AUX
cana-4520	117	33	configured	configure	VERB
cana-4520	117	34	via	via	ADP
cana-4520	117	35	the	the	DET
cana-4520	117	36	adam	adam	PROPN
cana-4520	117	37	optimizer	optimizer	NOUN
cana-4520	117	38	.	.	PUNCT
cana-4520	118	1	this	this	DET
cana-4520	118	2	combination	combination	NOUN
cana-4520	118	3	lessens	lessen	VERB
cana-4520	118	4	the	the	DET
cana-4520	118	5	difference	difference	NOUN
cana-4520	118	6	between	between	ADP
cana-4520	118	7	expected	expect	VERB
cana-4520	118	8	and	and	CCONJ
cana-4520	118	9	actual	actual	ADJ
cana-4520	118	10	results	result	NOUN
cana-4520	118	11	,	,	PUNCT
cana-4520	118	12	ensuring	ensure	VERB
cana-4520	118	13	effective	effective	ADJ
cana-4520	118	14	model	model	NOUN
cana-4520	118	15	weight	weight	NOUN
cana-4520	118	16	modification	modification	NOUN
cana-4520	118	17	.	.	PUNCT
cana-4520	119	1	the	the	DET
cana-4520	119	2	models	model	NOUN
cana-4520	119	3	are	be	AUX
cana-4520	119	4	exposed	expose	VERB
cana-4520	119	5	to	to	ADP
cana-4520	119	6	the	the	DET
cana-4520	119	7	training	training	NOUN
cana-4520	119	8	dataset	dataset	VERB
cana-4520	119	9	during	during	ADP
cana-4520	119	10	the	the	DET
cana-4520	119	11	training	training	NOUN
cana-4520	119	12	phase	phase	NOUN
cana-4520	119	13	,	,	PUNCT
cana-4520	119	14	and	and	CCONJ
cana-4520	119	15	performance	performance	NOUN
cana-4520	119	16	is	be	AUX
cana-4520	119	17	tracked	track	VERB
cana-4520	119	18	through	through	ADP
cana-4520	119	19	validation	validation	NOUN
cana-4520	119	20	on	on	ADP
cana-4520	119	21	a	a	DET
cana-4520	119	22	different	different	ADJ
cana-4520	119	23	dataset	dataset	NOUN
cana-4520	119	24	.	.	PUNCT
cana-4520	120	1	evaluation	evaluation	NOUN
cana-4520	120	2	measures	measure	NOUN
cana-4520	120	3	like	like	ADP
cana-4520	120	4	accuracy	accuracy	NOUN
cana-4520	120	5	,	,	PUNCT
cana-4520	120	6	precision	precision	NOUN
cana-4520	120	7	,	,	PUNCT
cana-4520	120	8	recall	recall	NOUN
cana-4520	120	9	,	,	PUNCT
cana-4520	120	10	and	and	CCONJ
cana-4520	120	11	rate	rate	NOUN
cana-4520	120	12	are	be	AUX
cana-4520	120	13	utilized	utilize	VERB
cana-4520	120	14	to	to	ADP
cana-4520	120	15	fine	fine	ADJ
cana-4520	120	16	-	-	PUNCT
cana-4520	120	17	tune	tune	NOUN
cana-4520	120	18	hyper	hyper	ADJ
cana-4520	120	19	parameters	parameter	NOUN
cana-4520	120	20	like	like	ADP
cana-4520	120	21	learning	learn	VERB
cana-4520	120	22	rate	rate	NOUN
cana-4520	120	23	and	and	CCONJ
cana-4520	120	24	batch	batch	VERB
cana-4520	120	25	size	size	NOUN
cana-4520	120	26	to	to	PART
cana-4520	120	27	maximize	maximize	VERB
cana-4520	120	28	the	the	DET
cana-4520	120	29	models	model	NOUN
cana-4520	120	30	'	'	PART
cana-4520	120	31	performance	performance	NOUN
cana-4520	120	32	.	.	PUNCT
cana-4520	121	1	by	by	ADP
cana-4520	121	2	frequently	frequently	ADV
cana-4520	121	3	fine	fine	ADV
cana-4520	121	4	-	-	PUNCT
cana-4520	121	5	tuning	tune	VERB
cana-4520	121	6	their	their	PRON
cana-4520	121	7	parameters	parameter	NOUN
cana-4520	121	8	during	during	ADP
cana-4520	121	9	this	this	DET
cana-4520	121	10	training	training	NOUN
cana-4520	121	11	and	and	CCONJ
cana-4520	121	12	validation	validation	NOUN
cana-4520	121	13	process	process	NOUN
cana-4520	121	14	,	,	PUNCT
cana-4520	121	15	the	the	DET
cana-4520	121	16	models	model	NOUN
cana-4520	121	17	increase	increase	VERB
cana-4520	121	18	their	their	PRON
cana-4520	121	19	ability	ability	NOUN
cana-4520	121	20	to	to	PART
cana-4520	121	21	accurately	accurately	ADV
cana-4520	121	22	detect	detect	VERB
cana-4520	121	23	fatigue	fatigue	NOUN
cana-4520	121	24	.	.	PUNCT
cana-4520	122	1	[	[	X
cana-4520	122	2	19	19	NUM
cana-4520	122	3	]	]	PUNCT
cana-4520	122	4	the	the	DET
cana-4520	122	5	figure	figure	NOUN
cana-4520	122	6	9	9	NUM
cana-4520	122	7	shows	show	VERB
cana-4520	122	8	the	the	DET
cana-4520	122	9	model	model	NOUN
cana-4520	122	10	training	training	NOUN
cana-4520	122	11	and	and	CCONJ
cana-4520	122	12	validation	validation	NOUN
cana-4520	122	13	accuracy	accuracy	NOUN
cana-4520	122	14	of	of	ADP
cana-4520	122	15	data	datum	NOUN
cana-4520	122	16	before	before	ADP
cana-4520	122	17	augmentation	augmentation	NOUN
cana-4520	122	18	.	.	PUNCT
cana-4520	123	1	figure	figure	NOUN
cana-4520	123	2	9	9	NUM
cana-4520	123	3	.	.	PUNCT
cana-4520	124	1	accuracy	accuracy	NOUN
cana-4520	124	2	of	of	ADP
cana-4520	124	3	the	the	DET
cana-4520	124	4	training	training	NOUN
cana-4520	124	5	/	/	SYM
cana-4520	124	6	validation	validation	NOUN
cana-4520	124	7	model	model	NOUN
cana-4520	124	8	and	and	CCONJ
cana-4520	124	9	graph	graph	NOUN
cana-4520	124	10	loss	loss	NOUN
cana-4520	124	11	before	before	ADP
cana-4520	124	12	data	datum	NOUN
cana-4520	124	13	augmentation	augmentation	NOUN
cana-4520	124	14	3.8	3.8	NUM
cana-4520	124	15	cnn	cnn	PROPN
cana-4520	124	16	test	test	NOUN
cana-4520	124	17	and	and	CCONJ
cana-4520	124	18	evaluation	evaluation	NOUN
cana-4520	124	19	metrics	metric	NOUN
cana-4520	124	20	once	once	SCONJ
cana-4520	124	21	the	the	DET
cana-4520	124	22	cnns	cnn	NOUN
cana-4520	124	23	were	be	AUX
cana-4520	124	24	fully	fully	ADV
cana-4520	124	25	trained	train	VERB
cana-4520	124	26	,	,	PUNCT
cana-4520	124	27	testing	testing	NOUN
cana-4520	124	28	with	with	ADP
cana-4520	124	29	the	the	DET
cana-4520	124	30	test	test	NOUN
cana-4520	124	31	data	datum	NOUN
cana-4520	124	32	became	become	VERB
cana-4520	124	33	essential	essential	ADJ
cana-4520	124	34	.	.	PUNCT
cana-4520	125	1	furthermore	furthermore	ADV
cana-4520	125	2	,	,	PUNCT
cana-4520	125	3	no	no	DET
cana-4520	125	4	data	datum	NOUN
cana-4520	125	5	augmentation	augmentation	NOUN
cana-4520	125	6	was	be	AUX
cana-4520	125	7	performed	perform	VERB
cana-4520	125	8	during	during	ADP
cana-4520	125	9	the	the	DET
cana-4520	125	10	analysis	analysis	NOUN
cana-4520	125	11	of	of	ADP
cana-4520	125	12	the	the	DET
cana-4520	125	13	full	full	ADJ
cana-4520	125	14	set	set	NOUN
cana-4520	125	15	of	of	ADP
cana-4520	125	16	images	image	NOUN
cana-4520	125	17	.	.	PUNCT
cana-4520	126	1	tests	test	NOUN
cana-4520	126	2	were	be	AUX
cana-4520	126	3	performed	perform	VERB
cana-4520	126	4	on	on	ADP
cana-4520	126	5	each	each	DET
cana-4520	126	6	trained	train	VERB
cana-4520	126	7	network	network	NOUN
cana-4520	126	8	,	,	PUNCT
cana-4520	126	9	and	and	CCONJ
cana-4520	126	10	each	each	DET
cana-4520	126	11	image	image	NOUN
cana-4520	126	12	was	be	AUX
cana-4520	126	13	analyzed	analyze	VERB
cana-4520	126	14	with	with	ADP
cana-4520	126	15	a	a	DET
cana-4520	126	16	batch	batch	NOUN
cana-4520	126	17	size	size	NOUN
cana-4520	126	18	of	of	ADP
cana-4520	126	19	1	1	NUM
cana-4520	126	20	.	.	PUNCT
cana-4520	127	1	the	the	DET
cana-4520	127	2	confusion	confusion	NOUN
cana-4520	127	3	matrix	matrix	NOUN
cana-4520	127	4	is	be	AUX
cana-4520	127	5	communications	communication	NOUN
cana-4520	127	6	on	on	ADP
cana-4520	127	7	applied	apply	VERB
cana-4520	127	8	nonlinear	nonlinear	ADJ
cana-4520	127	9	analysis	analysis	NOUN
cana-4520	127	10	issn	issn	NOUN
cana-4520	127	11	:	:	PUNCT
cana-4520	127	12	1074	1074	NUM
cana-4520	127	13	-	-	PUNCT
cana-4520	127	14	133x	133x	NUM
cana-4520	127	15	vol	vol	NOUN
cana-4520	127	16	32	32	NUM
cana-4520	127	17	no	no	NOUN
cana-4520	127	18	.	.	PUNCT
cana-4520	128	1	9s	9s	NUM
cana-4520	128	2	(	(	PUNCT
cana-4520	128	3	2025	2025	NUM
cana-4520	128	4	)	)	PUNCT
cana-4520	128	5	2344	2344	NUM
cana-4520	128	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	129	1	an	an	DET
cana-4520	129	2	effective	effective	ADJ
cana-4520	129	3	tool	tool	NOUN
cana-4520	129	4	for	for	ADP
cana-4520	129	5	assessing	assess	VERB
cana-4520	129	6	the	the	DET
cana-4520	129	7	models	model	NOUN
cana-4520	129	8	'	'	PART
cana-4520	129	9	performance	performance	NOUN
cana-4520	129	10	.	.	PUNCT
cana-4520	130	1	the	the	DET
cana-4520	130	2	figure	figure	NOUN
cana-4520	130	3	displays	display	VERB
cana-4520	130	4	the	the	DET
cana-4520	130	5	confusion	confusion	NOUN
cana-4520	130	6	matrices	matrix	NOUN
cana-4520	130	7	for	for	ADP
cana-4520	130	8	three	three	NUM
cana-4520	130	9	deep	deep	ADJ
cana-4520	130	10	learning	learning	NOUN
cana-4520	130	11	models	model	NOUN
cana-4520	130	12	(	(	PUNCT
cana-4520	130	13	inceptionv3	inceptionv3	NOUN
cana-4520	130	14	,	,	PUNCT
cana-4520	130	15	vgg19	vgg19	NOUN
cana-4520	130	16	,	,	PUNCT
cana-4520	130	17	and	and	CCONJ
cana-4520	130	18	resnet50v2	resnet50v2	NOUN
cana-4520	130	19	)	)	PUNCT
cana-4520	130	20	used	use	VERB
cana-4520	130	21	for	for	ADP
cana-4520	130	22	drowsiness	drowsiness	NOUN
cana-4520	130	23	detection	detection	NOUN
cana-4520	130	24	.	.	PUNCT
cana-4520	131	1	the	the	DET
cana-4520	131	2	matrices	matrix	NOUN
cana-4520	131	3	visually	visually	ADV
cana-4520	131	4	represent	represent	VERB
cana-4520	131	5	the	the	DET
cana-4520	131	6	model	model	NOUN
cana-4520	131	7	's	's	PART
cana-4520	131	8	performance	performance	NOUN
cana-4520	131	9	,	,	PUNCT
cana-4520	131	10	showing	show	VERB
cana-4520	131	11	high	high	ADJ
cana-4520	131	12	accuracy	accuracy	NOUN
cana-4520	131	13	with	with	ADP
cana-4520	131	14	a	a	DET
cana-4520	131	15	predominance	predominance	NOUN
cana-4520	131	16	of	of	ADP
cana-4520	131	17	correct	correct	ADJ
cana-4520	131	18	classifications	classification	NOUN
cana-4520	131	19	(	(	PUNCT
cana-4520	131	20	true	true	ADJ
cana-4520	131	21	positives	positive	NOUN
cana-4520	131	22	and	and	CCONJ
cana-4520	131	23	true	true	ADJ
cana-4520	131	24	negatives	negative	NOUN
cana-4520	131	25	)	)	PUNCT
cana-4520	131	26	across	across	ADP
cana-4520	131	27	all	all	DET
cana-4520	131	28	models	model	NOUN
cana-4520	131	29	,	,	PUNCT
cana-4520	131	30	indicating	indicate	VERB
cana-4520	131	31	effective	effective	ADJ
cana-4520	131	32	performance	performance	NOUN
cana-4520	131	33	in	in	ADP
cana-4520	131	34	distinguishing	distinguish	VERB
cana-4520	131	35	between	between	ADP
cana-4520	131	36	drowsy	drowsy	NOUN
cana-4520	131	37	and	and	CCONJ
cana-4520	131	38	non	non	ADJ
cana-4520	131	39	-	-	ADJ
cana-4520	131	40	drowsy	drowsy	ADJ
cana-4520	131	41	states	state	NOUN
cana-4520	131	42	.	.	PUNCT
cana-4520	132	1	the	the	DET
cana-4520	132	2	matrices	matrix	NOUN
cana-4520	132	3	generally	generally	ADV
cana-4520	132	4	show	show	VERB
cana-4520	132	5	strong	strong	ADJ
cana-4520	132	6	performance	performance	NOUN
cana-4520	132	7	with	with	ADP
cana-4520	132	8	high	high	ADJ
cana-4520	132	9	counts	count	NOUN
cana-4520	132	10	of	of	ADP
cana-4520	132	11	true	true	ADJ
cana-4520	132	12	positives	positive	NOUN
cana-4520	132	13	and	and	CCONJ
cana-4520	132	14	true	true	ADJ
cana-4520	132	15	negatives	negative	NOUN
cana-4520	132	16	,	,	PUNCT
cana-4520	132	17	indicating	indicate	VERB
cana-4520	132	18	accurate	accurate	ADJ
cana-4520	132	19	classification	classification	NOUN
cana-4520	132	20	of	of	ADP
cana-4520	132	21	both	both	DET
cana-4520	132	22	"	"	PUNCT
cana-4520	132	23	not	not	PART
cana-4520	132	24	fatigue	fatigue	NOUN
cana-4520	132	25	"	"	PUNCT
cana-4520	132	26	and	and	CCONJ
cana-4520	132	27	"	"	PUNCT
cana-4520	132	28	fatigue	fatigue	NOUN
cana-4520	132	29	"	"	PUNCT
cana-4520	132	30	instances	instance	NOUN
cana-4520	132	31	across	across	ADP
cana-4520	132	32	all	all	DET
cana-4520	132	33	models	model	NOUN
cana-4520	132	34	.	.	PUNCT
cana-4520	133	1	resnet50v2	resnet50v2	NOUN
cana-4520	133	2	appears	appear	VERB
cana-4520	133	3	to	to	PART
cana-4520	133	4	exhibit	exhibit	VERB
cana-4520	133	5	slightly	slightly	ADV
cana-4520	133	6	lower	low	ADJ
cana-4520	133	7	false	false	ADJ
cana-4520	133	8	positives	positive	NOUN
cana-4520	133	9	compared	compare	VERB
cana-4520	133	10	to	to	ADP
cana-4520	133	11	the	the	DET
cana-4520	133	12	other	other	ADJ
cana-4520	133	13	two	two	NUM
cana-4520	133	14	models	model	NOUN
cana-4520	133	15	.	.	PUNCT
cana-4520	134	1	figure	figure	NOUN
cana-4520	134	2	10	10	NUM
cana-4520	134	3	.	.	PUNCT
cana-4520	135	1	confusion	confusion	NOUN
cana-4520	135	2	matrix	matrix	NOUN
cana-4520	135	3	in	in	ADP
cana-4520	135	4	cnn	cnn	PROPN
cana-4520	135	5	test	test	NOUN
cana-4520	135	6	(	(	PUNCT
cana-4520	135	7	a	a	X
cana-4520	135	8	)	)	PUNCT
cana-4520	135	9	inceptionv3	inceptionv3	NOUN
cana-4520	135	10	(	(	PUNCT
cana-4520	135	11	b	b	X
cana-4520	135	12	)	)	PUNCT
cana-4520	135	13	vgg19	vgg19	PROPN
cana-4520	135	14	(	(	PUNCT
cana-4520	135	15	c	c	NOUN
cana-4520	135	16	)	)	PUNCT
cana-4520	135	17	resnet50v2	resnet50v2	NOUN
cana-4520	135	18	testing	testing	NOUN
cana-4520	135	19	.	.	PUNCT
cana-4520	136	1	3.9	3.9	NUM
cana-4520	136	2	evaluation	evaluation	NOUN
cana-4520	136	3	metrics	metric	NOUN
cana-4520	136	4	fatigue	fatigue	NOUN
cana-4520	136	5	detection	detection	NOUN
cana-4520	136	6	is	be	AUX
cana-4520	136	7	one	one	NUM
cana-4520	136	8	of	of	ADP
cana-4520	136	9	the	the	DET
cana-4520	136	10	applications	application	NOUN
cana-4520	136	11	where	where	SCONJ
cana-4520	136	12	metrics	metric	NOUN
cana-4520	136	13	are	be	AUX
cana-4520	136	14	commonly	commonly	ADV
cana-4520	136	15	employed	employ	VERB
cana-4520	136	16	in	in	ADP
cana-4520	136	17	binary	binary	ADJ
cana-4520	136	18	classification	classification	NOUN
cana-4520	136	19	.	.	PUNCT
cana-4520	137	1	while	while	SCONJ
cana-4520	137	2	accuracy	accuracy	NOUN
cana-4520	137	3	assesses	assess	VERB
cana-4520	137	4	the	the	DET
cana-4520	137	5	ratio	ratio	NOUN
cana-4520	137	6	of	of	ADP
cana-4520	137	7	correctly	correctly	ADV
cana-4520	137	8	predicted	predict	VERB
cana-4520	137	9	instances	instance	NOUN
cana-4520	137	10	to	to	ADP
cana-4520	137	11	all	all	DET
cana-4520	137	12	occurrences	occurrence	NOUN
cana-4520	137	13	,	,	PUNCT
cana-4520	137	14	precision	precision	NOUN
cana-4520	137	15	calculates	calculate	VERB
cana-4520	137	16	the	the	DET
cana-4520	137	17	ratio	ratio	NOUN
cana-4520	137	18	of	of	ADP
cana-4520	137	19	effector	effector	NOUN
cana-4520	137	20	-	-	PUNCT
cana-4520	137	21	predicted	predict	VERB
cana-4520	137	22	positive	positive	ADJ
cana-4520	137	23	examples	example	NOUN
cana-4520	137	24	to	to	ADP
cana-4520	137	25	all	all	DET
cana-4520	137	26	anticipated	anticipate	VERB
cana-4520	137	27	positive	positive	ADJ
cana-4520	137	28	cases[11	cases[11	NOUN
cana-4520	137	29	]	]	PUNCT
cana-4520	137	30	.	.	PUNCT
cana-4520	138	1	by	by	ADP
cana-4520	138	2	employing	employ	VERB
cana-4520	138	3	its	its	PRON
cana-4520	138	4	harmonic	harmonic	ADJ
cana-4520	138	5	mean	mean	NOUN
cana-4520	138	6	,	,	PUNCT
cana-4520	138	7	the	the	DET
cana-4520	138	8	f1	f1	NOUN
cana-4520	138	9	score	score	NOUN
cana-4520	138	10	finds	find	VERB
cana-4520	138	11	a	a	DET
cana-4520	138	12	compromise	compromise	NOUN
cana-4520	138	13	between	between	ADP
cana-4520	138	14	recall	recall	NOUN
cana-4520	138	15	and	and	CCONJ
cana-4520	138	16	precision	precision	NOUN
cana-4520	138	17	.	.	PUNCT
cana-4520	139	1	recall	recall	PROPN
cana-4520	139	2	is	be	AUX
cana-4520	139	3	the	the	DET
cana-4520	139	4	proportion	proportion	NOUN
cana-4520	139	5	of	of	ADP
cana-4520	139	6	correctly	correctly	ADV
cana-4520	139	7	anticipated	anticipate	VERB
cana-4520	139	8	positive	positive	ADJ
cana-4520	139	9	cases	case	NOUN
cana-4520	139	10	to	to	ADP
cana-4520	139	11	all	all	DET
cana-4520	139	12	positive	positive	ADJ
cana-4520	139	13	instances	instance	NOUN
cana-4520	139	14	that	that	PRON
cana-4520	139	15	occurred	occur	VERB
cana-4520	139	16	.	.	PUNCT
cana-4520	140	1	[	[	X
cana-4520	140	2	20	20	NUM
cana-4520	140	3	]	]	X
cana-4520	140	4	it	it	PRON
cana-4520	140	5	is	be	AUX
cana-4520	140	6	also	also	ADV
cana-4520	140	7	occasionally	occasionally	ADV
cana-4520	140	8	referred	refer	VERB
cana-4520	140	9	to	to	ADP
cana-4520	140	10	as	as	ADP
cana-4520	140	11	a	a	DET
cana-4520	140	12	true	true	ADJ
cana-4520	140	13	positive	positive	ADJ
cana-4520	140	14	rate	rate	NOUN
cana-4520	140	15	or	or	CCONJ
cana-4520	140	16	sensitivity	sensitivity	NOUN
cana-4520	140	17	.	.	PUNCT
cana-4520	141	1	the	the	DET
cana-4520	141	2	specificity	specificity	NOUN
cana-4520	141	3	(	(	PUNCT
cana-4520	141	4	or	or	CCONJ
cana-4520	141	5	true	true	ADJ
cana-4520	141	6	negative	negative	ADJ
cana-4520	141	7	rate	rate	NOUN
cana-4520	141	8	)	)	PUNCT
cana-4520	141	9	of	of	ADP
cana-4520	141	10	execution	execution	NOUN
cana-4520	141	11	is	be	AUX
cana-4520	141	12	defined	define	VERB
cana-4520	141	13	as	as	ADP
cana-4520	141	14	the	the	DET
cana-4520	141	15	ratio	ratio	NOUN
cana-4520	141	16	of	of	ADP
cana-4520	141	17	correctly	correctly	ADV
cana-4520	141	18	predicted	predict	VERB
cana-4520	141	19	negative	negative	ADJ
cana-4520	141	20	situations	situation	NOUN
cana-4520	141	21	to	to	ADP
cana-4520	141	22	the	the	DET
cana-4520	141	23	total	total	ADJ
cana-4520	141	24	number	number	NOUN
cana-4520	141	25	of	of	ADP
cana-4520	141	26	real	real	ADJ
cana-4520	141	27	negative	negative	ADJ
cana-4520	141	28	occurrences	occurrence	NOUN
cana-4520	141	29	.	.	PUNCT
cana-4520	142	1	a	a	DET
cana-4520	142	2	confusion	confusion	NOUN
cana-4520	142	3	matrix	matrix	NOUN
cana-4520	142	4	that	that	PRON
cana-4520	142	5	summarizes	summarize	VERB
cana-4520	142	6	the	the	DET
cana-4520	142	7	expected	expect	VERB
cana-4520	142	8	results	result	NOUN
cana-4520	142	9	is	be	AUX
cana-4520	142	10	accurate	accurate	ADJ
cana-4520	142	11	.	.	PUNCT
cana-4520	143	1	[	[	X
cana-4520	143	2	1	1	X
cana-4520	143	3	]	]	X
cana-4520	143	4	[	[	X
cana-4520	143	5	2	2	X
cana-4520	143	6	]	]	X
cana-4520	143	7	[	[	X
cana-4520	143	8	3	3	NUM
cana-4520	143	9	]	]	PUNCT
cana-4520	143	10	communications	communication	NOUN
cana-4520	143	11	on	on	ADP
cana-4520	143	12	applied	apply	VERB
cana-4520	143	13	nonlinear	nonlinear	ADJ
cana-4520	143	14	analysis	analysis	NOUN
cana-4520	143	15	issn	issn	NOUN
cana-4520	143	16	:	:	PUNCT
cana-4520	143	17	1074	1074	NUM
cana-4520	143	18	-	-	PUNCT
cana-4520	143	19	133x	133x	NUM
cana-4520	143	20	vol	vol	NOUN
cana-4520	143	21	32	32	NUM
cana-4520	143	22	no	no	NOUN
cana-4520	143	23	.	.	PUNCT
cana-4520	144	1	9s	9s	NUM
cana-4520	144	2	(	(	PUNCT
cana-4520	144	3	2025	2025	NUM
cana-4520	144	4	)	)	PUNCT
cana-4520	144	5	2345	2345	NUM
cana-4520	144	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	144	7	4	4	X
cana-4520	144	8	.	.	PUNCT
cana-4520	144	9	results	result	VERB
cana-4520	144	10	cnn	cnn	PROPN
cana-4520	144	11	architectures	architecture	VERB
cana-4520	144	12	inceptionv3	inceptionv3	PROPN
cana-4520	144	13	,	,	PUNCT
cana-4520	144	14	vgg19	vgg19	PROPN
cana-4520	144	15	,	,	PUNCT
cana-4520	144	16	and	and	CCONJ
cana-4520	144	17	resnet50v2	resnet50v2	NOUN
cana-4520	144	18	achieved	achieve	VERB
cana-4520	144	19	encouraging	encourage	VERB
cana-4520	144	20	results	result	NOUN
cana-4520	144	21	in	in	ADP
cana-4520	144	22	the	the	DET
cana-4520	144	23	fatigue	fatigue	NOUN
cana-4520	144	24	category	category	NOUN
cana-4520	144	25	when	when	SCONJ
cana-4520	144	26	evaluated	evaluate	VERB
cana-4520	144	27	on	on	ADP
cana-4520	144	28	a	a	DET
cana-4520	144	29	dataset	dataset	NOUN
cana-4520	144	30	images	image	NOUN
cana-4520	144	31	,	,	PUNCT
cana-4520	144	32	with	with	ADP
cana-4520	144	33	70	70	NUM
cana-4520	144	34	%	%	NOUN
cana-4520	144	35	of	of	ADP
cana-4520	144	36	the	the	DET
cana-4520	144	37	images	image	NOUN
cana-4520	144	38	being	be	AUX
cana-4520	144	39	used	use	VERB
cana-4520	144	40	for	for	ADP
cana-4520	144	41	training	training	NOUN
cana-4520	144	42	,	,	PUNCT
cana-4520	144	43	15	15	NUM
cana-4520	144	44	%	%	NOUN
cana-4520	144	45	for	for	ADP
cana-4520	144	46	validation	validation	NOUN
cana-4520	144	47	,	,	PUNCT
cana-4520	144	48	and	and	CCONJ
cana-4520	144	49	15	15	NUM
cana-4520	144	50	%	%	NOUN
cana-4520	144	51	for	for	ADP
cana-4520	144	52	testing	testing	NOUN
cana-4520	144	53	.	.	PUNCT
cana-4520	145	1	the	the	DET
cana-4520	145	2	dataset	dataset	NOUN
cana-4520	145	3	covered	cover	VERB
cana-4520	145	4	images	image	NOUN
cana-4520	145	5	from	from	ADP
cana-4520	145	6	five	five	NUM
cana-4520	145	7	specific	specific	ADJ
cana-4520	145	8	simulated	simulated	ADJ
cana-4520	145	9	driving	driving	NOUN
cana-4520	145	10	situations	situation	NOUN
cana-4520	145	11	:	:	PUNCT
cana-4520	145	12	with	with	ADP
cana-4520	145	13	glasses	glass	NOUN
cana-4520	145	14	,	,	PUNCT
cana-4520	145	15	with	with	ADP
cana-4520	145	16	sunglass	sunglass	NOUN
cana-4520	145	17	,	,	PUNCT
cana-4520	145	18	without	without	ADP
cana-4520	145	19	glasses	glass	NOUN
cana-4520	145	20	,	,	PUNCT
cana-4520	145	21	night	night	NOUN
cana-4520	145	22	with	with	ADP
cana-4520	145	23	glasses	glass	NOUN
cana-4520	145	24	,	,	PUNCT
cana-4520	145	25	and	and	CCONJ
cana-4520	145	26	night	night	NOUN
cana-4520	145	27	without	without	ADP
cana-4520	145	28	glasses	glass	NOUN
cana-4520	145	29	.	.	PUNCT
cana-4520	146	1	each	each	DET
cana-4520	146	2	version	version	NOUN
cana-4520	146	3	was	be	AUX
cana-4520	146	4	assessed	assess	VERB
cana-4520	146	5	based	base	VERB
cana-4520	146	6	on	on	ADP
cana-4520	146	7	accuracy	accuracy	NOUN
cana-4520	146	8	,	,	PUNCT
cana-4520	146	9	precision	precision	NOUN
cana-4520	146	10	,	,	PUNCT
cana-4520	146	11	recollect	recollect	NOUN
cana-4520	146	12	,	,	PUNCT
cana-4520	146	13	f1	f1	NOUN
cana-4520	146	14	-	-	PUNCT
cana-4520	146	15	score	score	NOUN
cana-4520	146	16	,	,	PUNCT
cana-4520	146	17	and	and	CCONJ
cana-4520	146	18	a	a	DET
cana-4520	146	19	confusion	confusion	NOUN
cana-4520	146	20	matrix	matrix	NOUN
cana-4520	146	21	.	.	PUNCT
cana-4520	147	1	table	table	NOUN
cana-4520	147	2	ii	ii	PROPN
cana-4520	147	3	summarizes	summarize	VERB
cana-4520	147	4	these	these	DET
cana-4520	147	5	eventualities	eventuality	NOUN
cana-4520	147	6	and	and	CCONJ
cana-4520	147	7	the	the	DET
cana-4520	147	8	accuracy	accuracy	NOUN
cana-4520	147	9	performed	perform	VERB
cana-4520	147	10	through	through	ADP
cana-4520	147	11	every	every	DET
cana-4520	147	12	model	model	NOUN
cana-4520	147	13	in	in	ADP
cana-4520	147	14	those	those	DET
cana-4520	147	15	eventualities	eventuality	NOUN
cana-4520	147	16	.	.	PUNCT
cana-4520	148	1	this	this	DET
cana-4520	148	2	complete	complete	ADJ
cana-4520	148	3	dataset	dataset	NOUN
cana-4520	148	4	ensured	ensure	VERB
cana-4520	148	5	that	that	SCONJ
cana-4520	148	6	the	the	DET
cana-4520	148	7	techniques	technique	NOUN
cana-4520	148	8	have	have	AUX
cana-4520	148	9	been	be	AUX
cana-4520	148	10	educated	educate	VERB
cana-4520	148	11	,	,	PUNCT
cana-4520	148	12	evaluated	evaluate	VERB
cana-4520	148	13	,	,	PUNCT
cana-4520	148	14	and	and	CCONJ
cana-4520	148	15	tested	test	VERB
cana-4520	148	16	beneath	beneath	ADP
cana-4520	148	17	diverse	diverse	ADJ
cana-4520	148	18	situations	situation	NOUN
cana-4520	148	19	,	,	PUNCT
cana-4520	148	20	improving	improve	VERB
cana-4520	148	21	their	their	PRON
cana-4520	148	22	generalizability	generalizability	NOUN
cana-4520	148	23	and	and	CCONJ
cana-4520	148	24	effectiveness	effectiveness	NOUN
cana-4520	148	25	.	.	PUNCT
cana-4520	149	1	inceptionv3	inceptionv3	NOUN
cana-4520	149	2	,	,	PUNCT
cana-4520	149	3	known	know	VERB
cana-4520	149	4	for	for	ADP
cana-4520	149	5	its	its	PRON
cana-4520	149	6	multi	multi	ADJ
cana-4520	149	7	-	-	ADJ
cana-4520	149	8	scale	scale	ADJ
cana-4520	149	9	feature	feature	NOUN
cana-4520	149	10	extraction	extraction	NOUN
cana-4520	149	11	capability	capability	NOUN
cana-4520	149	12	,	,	PUNCT
cana-4520	149	13	demonstrated	demonstrate	VERB
cana-4520	149	14	strong	strong	ADJ
cana-4520	149	15	performance	performance	NOUN
cana-4520	149	16	in	in	ADP
cana-4520	149	17	classifying	classify	VERB
cana-4520	149	18	driver	driver	NOUN
cana-4520	149	19	fatigue	fatigue	NOUN
cana-4520	149	20	.	.	PUNCT
cana-4520	150	1	its	its	PRON
cana-4520	150	2	confusion	confusion	NOUN
cana-4520	150	3	matrix	matrix	NOUN
cana-4520	150	4	exhibited	exhibit	VERB
cana-4520	150	5	493	493	NUM
cana-4520	150	6	true	true	ADJ
cana-4520	150	7	positives	positive	NOUN
cana-4520	150	8	,	,	PUNCT
cana-4520	150	9	204	204	NUM
cana-4520	150	10	true	true	ADJ
cana-4520	150	11	negatives	negative	NOUN
cana-4520	150	12	,	,	PUNCT
cana-4520	150	13	16	16	NUM
cana-4520	150	14	false	false	ADJ
cana-4520	150	15	positives	positive	NOUN
cana-4520	150	16	,	,	PUNCT
cana-4520	150	17	and	and	CCONJ
cana-4520	150	18	13	13	NUM
cana-4520	150	19	false	false	ADJ
cana-4520	150	20	negatives	negative	NOUN
cana-4520	150	21	.	.	PUNCT
cana-4520	151	1	vgg19	vgg19	PROPN
cana-4520	151	2	,	,	PUNCT
cana-4520	151	3	recognized	recognize	VERB
cana-4520	151	4	for	for	ADP
cana-4520	151	5	its	its	PRON
cana-4520	151	6	simplicity	simplicity	NOUN
cana-4520	151	7	and	and	CCONJ
cana-4520	151	8	efficiency	efficiency	NOUN
cana-4520	151	9	,	,	PUNCT
cana-4520	151	10	also	also	ADV
cana-4520	151	11	delivered	deliver	VERB
cana-4520	151	12	effective	effective	ADJ
cana-4520	151	13	results	result	NOUN
cana-4520	151	14	,	,	PUNCT
cana-4520	151	15	with	with	ADP
cana-4520	151	16	499	499	NUM
cana-4520	151	17	true	true	ADJ
cana-4520	151	18	positives	positive	NOUN
cana-4520	151	19	,	,	PUNCT
cana-4520	151	20	207	207	NUM
cana-4520	151	21	true	true	ADJ
cana-4520	151	22	negatives	negative	NOUN
cana-4520	151	23	,	,	PUNCT
cana-4520	151	24	8	8	NUM
cana-4520	151	25	false	false	ADJ
cana-4520	151	26	positives	positive	NOUN
cana-4520	151	27	,	,	PUNCT
cana-4520	151	28	and	and	CCONJ
cana-4520	151	29	10	10	NUM
cana-4520	151	30	false	false	ADJ
cana-4520	151	31	negatives	negative	NOUN
cana-4520	151	32	.	.	PUNCT
cana-4520	152	1	resnet50v2	resnet50v2	NOUN
cana-4520	152	2	,	,	PUNCT
cana-4520	152	3	leveraging	leverage	VERB
cana-4520	152	4	residual	residual	ADJ
cana-4520	152	5	connections	connection	NOUN
cana-4520	152	6	to	to	PART
cana-4520	152	7	mitigate	mitigate	VERB
cana-4520	152	8	the	the	DET
cana-4520	152	9	vanishing	vanish	VERB
cana-4520	152	10	gradient	gradient	NOUN
cana-4520	152	11	problem	problem	NOUN
cana-4520	152	12	,	,	PUNCT
cana-4520	152	13	outperformed	outperform	VERB
cana-4520	152	14	the	the	DET
cana-4520	152	15	other	other	ADJ
cana-4520	152	16	models	model	NOUN
cana-4520	152	17	.	.	PUNCT
cana-4520	153	1	its	its	PRON
cana-4520	153	2	confusion	confusion	NOUN
cana-4520	153	3	matrix	matrix	NOUN
cana-4520	153	4	revealed	reveal	VERB
cana-4520	153	5	503	503	NUM
cana-4520	153	6	true	true	ADJ
cana-4520	153	7	positives	positive	NOUN
cana-4520	153	8	,	,	PUNCT
cana-4520	153	9	209	209	NUM
cana-4520	153	10	true	true	ADJ
cana-4520	153	11	negatives	negative	NOUN
cana-4520	153	12	,	,	PUNCT
cana-4520	153	13	6	6	NUM
cana-4520	153	14	false	false	ADJ
cana-4520	153	15	positives	positive	NOUN
cana-4520	153	16	,	,	PUNCT
cana-4520	153	17	and	and	CCONJ
cana-4520	153	18	8	8	NUM
cana-4520	153	19	false	false	ADJ
cana-4520	153	20	negatives	negative	NOUN
cana-4520	153	21	.	.	PUNCT
cana-4520	154	1	these	these	DET
cana-4520	154	2	findings	finding	NOUN
cana-4520	154	3	highlight	highlight	VERB
cana-4520	154	4	the	the	DET
cana-4520	154	5	effectiveness	effectiveness	NOUN
cana-4520	154	6	of	of	ADP
cana-4520	154	7	all	all	DET
cana-4520	154	8	three	three	NUM
cana-4520	154	9	cnn	cnn	PROPN
cana-4520	154	10	architectures	architecture	NOUN
cana-4520	154	11	—	—	PUNCT
cana-4520	154	12	inceptionv3	inceptionv3	NOUN
cana-4520	154	13	,	,	PUNCT
cana-4520	154	14	vgg19	vgg19	NOUN
cana-4520	154	15	,	,	PUNCT
cana-4520	154	16	and	and	CCONJ
cana-4520	154	17	resnet50v2	resnet50v2	NOUN
cana-4520	154	18	—	—	PUNCT
cana-4520	154	19	in	in	ADP
cana-4520	154	20	detecting	detect	VERB
cana-4520	154	21	driver	driver	NOUN
cana-4520	154	22	fatigue	fatigue	NOUN
cana-4520	154	23	.	.	PUNCT
cana-4520	155	1	resnet50v2	resnet50v2	NOUN
cana-4520	155	2	,	,	PUNCT
cana-4520	155	3	in	in	ADP
cana-4520	155	4	particular	particular	ADJ
cana-4520	155	5	,	,	PUNCT
cana-4520	155	6	demonstrated	demonstrate	VERB
cana-4520	155	7	superior	superior	ADJ
cana-4520	155	8	performance	performance	NOUN
cana-4520	155	9	,	,	PUNCT
cana-4520	155	10	making	make	VERB
cana-4520	155	11	it	it	PRON
cana-4520	155	12	a	a	DET
cana-4520	155	13	strong	strong	ADJ
cana-4520	155	14	candidate	candidate	NOUN
cana-4520	155	15	for	for	ADP
cana-4520	155	16	real	real	ADJ
cana-4520	155	17	-	-	PUNCT
cana-4520	155	18	time	time	NOUN
cana-4520	155	19	drowsiness	drowsiness	NOUN
cana-4520	155	20	detection	detection	NOUN
cana-4520	155	21	.	.	PUNCT
cana-4520	156	1	the	the	DET
cana-4520	156	2	study	study	NOUN
cana-4520	156	3	emphasizes	emphasize	VERB
cana-4520	156	4	the	the	DET
cana-4520	156	5	potential	potential	NOUN
cana-4520	156	6	of	of	ADP
cana-4520	156	7	advanced	advanced	ADJ
cana-4520	156	8	deep	deep	ADJ
cana-4520	156	9	learning	learning	NOUN
cana-4520	156	10	models	model	NOUN
cana-4520	156	11	to	to	PART
cana-4520	156	12	enhance	enhance	VERB
cana-4520	156	13	the	the	DET
cana-4520	156	14	reliability	reliability	NOUN
cana-4520	156	15	of	of	ADP
cana-4520	156	16	fatigue	fatigue	NOUN
cana-4520	156	17	detection	detection	NOUN
cana-4520	156	18	systems	system	NOUN
cana-4520	156	19	,	,	PUNCT
cana-4520	156	20	contributing	contribute	VERB
cana-4520	156	21	to	to	ADP
cana-4520	156	22	improved	improved	ADJ
cana-4520	156	23	road	road	NOUN
cana-4520	156	24	safety	safety	NOUN
cana-4520	156	25	by	by	ADP
cana-4520	156	26	providing	provide	VERB
cana-4520	156	27	timely	timely	ADJ
cana-4520	156	28	warnings	warning	NOUN
cana-4520	156	29	to	to	PART
cana-4520	156	30	fatigued	fatigue	VERB
cana-4520	156	31	drivers	driver	NOUN
cana-4520	156	32	and	and	CCONJ
cana-4520	156	33	reducing	reduce	VERB
cana-4520	156	34	the	the	DET
cana-4520	156	35	risk	risk	NOUN
cana-4520	156	36	of	of	ADP
cana-4520	156	37	accidents	accident	NOUN
cana-4520	156	38	.	.	PUNCT
cana-4520	157	1	table	table	NOUN
cana-4520	157	2	2	2	NUM
cana-4520	157	3	:	:	PUNCT
cana-4520	157	4	performance	performance	NOUN
cana-4520	157	5	metrics	metric	NOUN
cana-4520	157	6	of	of	ADP
cana-4520	157	7	different	different	ADJ
cana-4520	157	8	cnn	cnn	PROPN
cana-4520	157	9	architecture	architecture	NOUN
cana-4520	157	10	cnn	cnn	PROPN
cana-4520	157	11	based	base	VERB
cana-4520	157	12	on	on	ADP
cana-4520	157	13	accuracy	accuracy	NOUN
cana-4520	157	14	(	(	PUNCT
cana-4520	157	15	%	%	INTJ
cana-4520	157	16	)	)	PUNCT
cana-4520	157	17	precision	precision	NOUN
cana-4520	157	18	(	(	PUNCT
cana-4520	157	19	%	%	INTJ
cana-4520	157	20	)	)	PUNCT
cana-4520	157	21	recall	recall	NOUN
cana-4520	157	22	(	(	PUNCT
cana-4520	157	23	%	%	NOUN
cana-4520	157	24	)	)	PUNCT
cana-4520	157	25	f1	f1	NOUN
cana-4520	157	26	-	-	PUNCT
cana-4520	157	27	score	score	NOUN
cana-4520	157	28	(	(	PUNCT
cana-4520	157	29	%	%	INTJ
cana-4520	157	30	)	)	PUNCT
cana-4520	157	31	inceptionv3	inceptionv3	NOUN
cana-4520	158	1	96.01	96.01	NUM
cana-4520	158	2	94.01	94.01	NUM
cana-4520	158	3	96.86	96.86	NUM
cana-4520	158	4	95.41	95.41	NUM
cana-4520	158	5	vgg19	vgg19	NOUN
cana-4520	158	6	97.51	97.51	NUM
cana-4520	158	7	95.39	95.39	NUM
cana-4520	158	8	98.42	98.42	NUM
cana-4520	158	9	96.88	96.88	NUM
cana-4520	158	10	resnet50v2	resnet50v2	PROPN
cana-4520	158	11	97.80	97.80	NUM
cana-4520	158	12	95.43	95.43	NUM
cana-4520	158	13	98.82	98.82	NUM
cana-4520	158	14	97.10	97.10	NUM
cana-4520	158	15	table	table	NOUN
cana-4520	158	16	3	3	NUM
cana-4520	158	17	:	:	PUNCT
cana-4520	158	18	accuracy	accuracy	NOUN
cana-4520	158	19	of	of	ADP
cana-4520	158	20	various	various	ADJ
cana-4520	158	21	cnn	cnn	PROPN
cana-4520	158	22	architectures	architecture	NOUN
cana-4520	158	23	in	in	ADP
cana-4520	158	24	different	different	ADJ
cana-4520	158	25	scenarios	scenario	NOUN
cana-4520	158	26	accuracy	accuracy	NOUN
cana-4520	158	27	(	(	PUNCT
cana-4520	158	28	%	%	INTJ
cana-4520	158	29	)	)	PUNCT
cana-4520	158	30	category	category	NOUN
cana-4520	158	31	inceptionv3	inceptionv3	NOUN
cana-4520	159	1	vgg19	vgg19	INTJ
cana-4520	159	2	resnet50v2	resnet50v2	NOUN
cana-4520	159	3	with	with	ADP
cana-4520	159	4	glasses	glass	NOUN
cana-4520	159	5	93.52	93.52	NUM
cana-4520	159	6	91.30	91.30	NUM
cana-4520	159	7	95.71	95.71	NUM
cana-4520	159	8	with	with	ADP
cana-4520	159	9	sunglasses	sunglass	NOUN
cana-4520	159	10	92.11	92.11	NUM
cana-4520	159	11	90.05	90.05	NUM
cana-4520	159	12	94.45	94.45	NUM
cana-4520	159	13	without	without	ADP
cana-4520	159	14	glasses	glass	NOUN
cana-4520	159	15	94.62	94.62	NUM
cana-4520	159	16	92.79	92.79	NUM
cana-4520	159	17	97.61	97.61	NUM
cana-4520	159	18	night	night	NOUN
cana-4520	159	19	glasses	glass	NOUN
cana-4520	159	20	91.74	91.74	NUM
cana-4520	159	21	89.52	89.52	NUM
cana-4520	159	22	94.89	94.89	NUM
cana-4520	159	23	night	night	NOUN
cana-4520	159	24	without	without	ADP
cana-4520	159	25	glasses	glass	NOUN
cana-4520	159	26	94.92	94.92	NUM
cana-4520	159	27	91.84	91.84	NUM
cana-4520	159	28	95.89	95.89	NUM
cana-4520	159	29	communications	communication	NOUN
cana-4520	159	30	on	on	ADP
cana-4520	159	31	applied	apply	VERB
cana-4520	159	32	nonlinear	nonlinear	ADJ
cana-4520	159	33	analysis	analysis	NOUN
cana-4520	159	34	issn	issn	NOUN
cana-4520	159	35	:	:	PUNCT
cana-4520	159	36	1074	1074	NUM
cana-4520	159	37	-	-	PUNCT
cana-4520	159	38	133x	133x	NUM
cana-4520	159	39	vol	vol	NOUN
cana-4520	159	40	32	32	NUM
cana-4520	159	41	no	no	NOUN
cana-4520	159	42	.	.	PUNCT
cana-4520	160	1	9s	9s	NUM
cana-4520	160	2	(	(	PUNCT
cana-4520	160	3	2025	2025	NUM
cana-4520	160	4	)	)	PUNCT
cana-4520	160	5	2346	2346	NUM
cana-4520	160	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	160	7	figure	figure	NOUN
cana-4520	160	8	.	.	PUNCT
cana-4520	161	1	11	11	NUM
cana-4520	161	2	accuracy	accuracy	NOUN
cana-4520	161	3	by	by	ADP
cana-4520	161	4	model	model	NOUN
cana-4520	161	5	and	and	CCONJ
cana-4520	161	6	category	category	NOUN
cana-4520	161	7	5	5	NUM
cana-4520	161	8	.	.	PUNCT
cana-4520	161	9	discussion	discussion	NOUN
cana-4520	161	10	with	with	ADP
cana-4520	161	11	an	an	DET
cana-4520	161	12	emphasis	emphasis	NOUN
cana-4520	161	13	on	on	ADP
cana-4520	161	14	utilizing	utilize	VERB
cana-4520	161	15	deep	deep	ADJ
cana-4520	161	16	learning	learning	NOUN
cana-4520	161	17	techniques	technique	NOUN
cana-4520	161	18	for	for	ADP
cana-4520	161	19	real	real	ADJ
cana-4520	161	20	-	-	PUNCT
cana-4520	161	21	time	time	NOUN
cana-4520	161	22	sleepiness	sleepiness	NOUN
cana-4520	161	23	detection	detection	NOUN
cana-4520	161	24	,	,	PUNCT
cana-4520	161	25	the	the	DET
cana-4520	161	26	essay	essay	NOUN
cana-4520	161	27	tackles	tackle	VERB
cana-4520	161	28	the	the	DET
cana-4520	161	29	important	important	ADJ
cana-4520	161	30	problem	problem	NOUN
cana-4520	161	31	of	of	ADP
cana-4520	161	32	driver	driver	NOUN
cana-4520	161	33	weariness	weariness	NOUN
cana-4520	161	34	,	,	PUNCT
cana-4520	161	35	a	a	DET
cana-4520	161	36	significant	significant	ADJ
cana-4520	161	37	contributing	contribute	VERB
cana-4520	161	38	factor	factor	NOUN
cana-4520	161	39	in	in	ADP
cana-4520	161	40	traffic	traffic	NOUN
cana-4520	161	41	accidents	accident	NOUN
cana-4520	161	42	.	.	PUNCT
cana-4520	162	1	the	the	DET
cana-4520	162	2	novel	novel	ADJ
cana-4520	162	3	system	system	NOUN
cana-4520	162	4	it	it	PRON
cana-4520	162	5	presents	present	VERB
cana-4520	162	6	analyses	analyse	VERB
cana-4520	162	7	facial	facial	ADJ
cana-4520	162	8	emotions	emotion	NOUN
cana-4520	162	9	such	such	ADJ
cana-4520	162	10	as	as	ADP
cana-4520	162	11	yawning	yawn	VERB
cana-4520	162	12	,	,	PUNCT
cana-4520	162	13	eye	eye	NOUN
cana-4520	162	14	closure	closure	NOUN
cana-4520	162	15	,	,	PUNCT
cana-4520	162	16	pupil	pupil	ADJ
cana-4520	162	17	size	size	NOUN
cana-4520	162	18	,	,	PUNCT
cana-4520	162	19	and	and	CCONJ
cana-4520	162	20	head	head	NOUN
cana-4520	162	21	movements	movement	NOUN
cana-4520	162	22	using	use	VERB
cana-4520	162	23	cnns	cnns	PROPN
cana-4520	162	24	(	(	PUNCT
cana-4520	162	25	inceptionv3	inceptionv3	PROPN
cana-4520	162	26	,	,	PUNCT
cana-4520	162	27	vgg19	vgg19	NOUN
cana-4520	162	28	,	,	PUNCT
cana-4520	162	29	and	and	CCONJ
cana-4520	162	30	resnet50v2	resnet50v2	NOUN
cana-4520	162	31	)	)	PUNCT
cana-4520	162	32	.	.	PUNCT
cana-4520	163	1	the	the	DET
cana-4520	163	2	technique	technique	NOUN
cana-4520	163	3	extracts	extract	VERB
cana-4520	163	4	information	information	NOUN
cana-4520	163	5	like	like	ADP
cana-4520	163	6	eye	eye	NOUN
cana-4520	163	7	aspect	aspect	NOUN
cana-4520	163	8	ratio	ratio	NOUN
cana-4520	163	9	,	,	PUNCT
cana-4520	163	10	mouth	mouth	NOUN
cana-4520	163	11	aspect	aspect	PROPN
cana-4520	163	12	ratio	ratio	NOUN
cana-4520	163	13	,	,	PUNCT
cana-4520	163	14	and	and	CCONJ
cana-4520	163	15	pupil	pupil	NOUN
cana-4520	163	16	size	size	NOUN
cana-4520	163	17	determine	determine	VERB
cana-4520	163	18	weariness	weariness	NOUN
cana-4520	163	19	.	.	PUNCT
cana-4520	164	1	it	it	PRON
cana-4520	164	2	uses	use	VERB
cana-4520	164	3	media	medium	NOUN
cana-4520	164	4	pipe	pipe	NOUN
cana-4520	164	5	for	for	ADP
cana-4520	164	6	accurate	accurate	ADJ
cana-4520	164	7	facial	facial	ADJ
cana-4520	164	8	landmark	landmark	NOUN
cana-4520	164	9	detection	detection	NOUN
cana-4520	164	10	.	.	PUNCT
cana-4520	165	1	during	during	ADP
cana-4520	165	2	training	training	NOUN
cana-4520	165	3	,	,	PUNCT
cana-4520	165	4	base	base	NOUN
cana-4520	165	5	layers	layer	NOUN
cana-4520	165	6	were	be	AUX
cana-4520	165	7	frozen	freeze	VERB
cana-4520	165	8	,	,	PUNCT
cana-4520	165	9	adam	adam	PROPN
cana-4520	165	10	was	be	AUX
cana-4520	165	11	used	use	VERB
cana-4520	165	12	for	for	SCONJ
cana-4520	165	13	optimization	optimization	NOUN
cana-4520	165	14	and	and	CCONJ
cana-4520	165	15	binary	binary	PROPN
cana-4520	165	16	cross	cross	PROPN
cana-4520	165	17	-	-	ADJ
cana-4520	165	18	entropy	entropy	ADJ
cana-4520	165	19	loss	loss	NOUN
cana-4520	165	20	was	be	AUX
cana-4520	165	21	employed	employ	VERB
cana-4520	165	22	.	.	PUNCT
cana-4520	166	1	tested	test	VERB
cana-4520	166	2	on	on	ADP
cana-4520	166	3	a	a	DET
cana-4520	166	4	wide	wide	ADJ
cana-4520	166	5	range	range	NOUN
cana-4520	166	6	of	of	ADP
cana-4520	166	7	datasets	dataset	NOUN
cana-4520	166	8	and	and	CCONJ
cana-4520	166	9	situations	situation	NOUN
cana-4520	166	10	,	,	PUNCT
cana-4520	166	11	resnet50v2	resnet50v2	NOUN
cana-4520	166	12	performed	perform	VERB
cana-4520	166	13	exceptionally	exceptionally	ADV
cana-4520	166	14	well	well	ADV
cana-4520	166	15	,	,	PUNCT
cana-4520	166	16	with	with	ADP
cana-4520	166	17	98.5	98.5	NUM
cana-4520	166	18	%	%	NOUN
cana-4520	166	19	accuracy	accuracy	NOUN
cana-4520	166	20	.	.	PUNCT
cana-4520	167	1	this	this	PRON
cana-4520	167	2	suggests	suggest	VERB
cana-4520	167	3	that	that	SCONJ
cana-4520	167	4	advanced	advance	VERB
cana-4520	167	5	deep	deep	ADJ
cana-4520	167	6	learning	learning	NOUN
cana-4520	167	7	models	model	NOUN
cana-4520	167	8	,	,	PUNCT
cana-4520	167	9	like	like	ADP
cana-4520	167	10	resnet50v2	resnet50v2	PROPN
cana-4520	167	11	,	,	PUNCT
cana-4520	167	12	can	can	AUX
cana-4520	167	13	effectively	effectively	ADV
cana-4520	167	14	improve	improve	VERB
cana-4520	167	15	driver	driver	NOUN
cana-4520	167	16	safety	safety	NOUN
cana-4520	167	17	.	.	PUNCT
cana-4520	168	1	in	in	ADP
cana-4520	168	2	order	order	NOUN
cana-4520	168	3	to	to	PART
cana-4520	168	4	validate	validate	VERB
cana-4520	168	5	the	the	DET
cana-4520	168	6	efficacy	efficacy	NOUN
cana-4520	168	7	of	of	ADP
cana-4520	168	8	this	this	DET
cana-4520	168	9	detecting	detect	VERB
cana-4520	168	10	system	system	NOUN
cana-4520	168	11	,	,	PUNCT
cana-4520	168	12	future	future	ADJ
cana-4520	168	13	research	research	NOUN
cana-4520	168	14	will	will	AUX
cana-4520	168	15	concentrate	concentrate	VERB
cana-4520	168	16	on	on	ADP
cana-4520	168	17	incorporating	incorporate	VERB
cana-4520	168	18	it	it	PRON
cana-4520	168	19	into	into	ADP
cana-4520	168	20	a	a	DET
cana-4520	168	21	variety	variety	NOUN
cana-4520	168	22	of	of	ADP
cana-4520	168	23	vehicle	vehicle	NOUN
cana-4520	168	24	types	type	NOUN
cana-4520	168	25	and	and	CCONJ
cana-4520	168	26	testing	test	VERB
cana-4520	168	27	it	it	PRON
cana-4520	168	28	under	under	ADP
cana-4520	168	29	actual	actual	ADJ
cana-4520	168	30	driving	drive	VERB
cana-4520	168	31	circumstances	circumstance	NOUN
cana-4520	168	32	.	.	PUNCT
cana-4520	169	1	this	this	DET
cana-4520	169	2	research	research	NOUN
cana-4520	169	3	establishes	establish	VERB
cana-4520	169	4	the	the	DET
cana-4520	169	5	groundwork	groundwork	NOUN
cana-4520	169	6	for	for	ADP
cana-4520	169	7	more	more	ADV
cana-4520	169	8	intelligent	intelligent	ADJ
cana-4520	169	9	and	and	CCONJ
cana-4520	169	10	sensitive	sensitive	ADJ
cana-4520	169	11	driver	driver	NOUN
cana-4520	169	12	detection	detection	NOUN
cana-4520	169	13	systems	system	NOUN
cana-4520	169	14	,	,	PUNCT
cana-4520	169	15	ultimately	ultimately	ADV
cana-4520	169	16	obtaining	obtain	VERB
cana-4520	169	17	to	to	PART
cana-4520	169	18	preserve	preserve	VERB
cana-4520	169	19	losses	loss	NOUN
cana-4520	169	20	and	and	CCONJ
cana-4520	169	21	avoid	avoid	VERB
cana-4520	169	22	injuries	injury	NOUN
cana-4520	169	23	on	on	ADP
cana-4520	169	24	the	the	DET
cana-4520	169	25	road	road	NOUN
cana-4520	169	26	,	,	PUNCT
cana-4520	169	27	by	by	ADP
cana-4520	169	28	utilizing	utilize	VERB
cana-4520	169	29	the	the	DET
cana-4520	169	30	power	power	NOUN
cana-4520	169	31	of	of	ADP
cana-4520	169	32	deep	deep	ADJ
cana-4520	169	33	learning	learning	NOUN
cana-4520	169	34	and	and	CCONJ
cana-4520	169	35	computer	computer	NOUN
cana-4520	169	36	vision	vision	NOUN
cana-4520	169	37	.	.	PUNCT
cana-4520	170	1	references	reference	NOUN
cana-4520	170	2	[	[	X
cana-4520	170	3	1	1	NUM
cana-4520	170	4	]	]	X
cana-4520	170	5	jabbar	jabbar	PROPN
cana-4520	170	6	,	,	PUNCT
cana-4520	170	7	r.	r.	PROPN
cana-4520	170	8	,	,	PUNCT
cana-4520	170	9	et	et	PROPN
cana-4520	170	10	al	al	PROPN
cana-4520	170	11	.	.	PROPN
cana-4520	170	12	(	(	PUNCT
cana-4520	170	13	2020	2020	NUM
cana-4520	170	14	)	)	PUNCT
cana-4520	170	15	.	.	PUNCT
cana-4520	171	1	driver	driver	NOUN
cana-4520	171	2	drowsiness	drowsiness	NOUN
cana-4520	171	3	detection	detection	NOUN
cana-4520	171	4	model	model	NOUN
cana-4520	171	5	using	use	VERB
cana-4520	171	6	convolutional	convolutional	ADJ
cana-4520	171	7	neural	neural	ADJ
cana-4520	171	8	networks	network	NOUN
cana-4520	171	9	techniques	technique	NOUN
cana-4520	171	10	for	for	ADP
cana-4520	171	11	android	android	PROPN
cana-4520	171	12	application	application	NOUN
cana-4520	171	13	.	.	PUNCT
cana-4520	172	1	ieee	ieee	PROPN
cana-4520	172	2	international	international	PROPN
cana-4520	172	3	conference	conference	NOUN
cana-4520	172	4	on	on	ADP
cana-4520	172	5	informatics	informatics	PROPN
cana-4520	172	6	,	,	PUNCT
cana-4520	172	7	iot	iot	NOUN
cana-4520	172	8	,	,	PUNCT
cana-4520	172	9	and	and	CCONJ
cana-4520	172	10	enabling	enable	VERB
cana-4520	172	11	technologies	technology	NOUN
cana-4520	172	12	(	(	PUNCT
cana-4520	172	13	iciot	iciot	NOUN
cana-4520	172	14	)	)	PUNCT
cana-4520	172	15	,	,	PUNCT
cana-4520	172	16	237–242	237–242	NUM
cana-4520	172	17	.	.	PUNCT
cana-4520	172	18	ieee	ieee	NOUN
cana-4520	172	19	.	.	PUNCT
cana-4520	173	1	[	[	X
cana-4520	173	2	2	2	NUM
cana-4520	173	3	]	]	PUNCT
cana-4520	173	4	benz	benz	PROPN
cana-4520	173	5	,	,	PUNCT
cana-4520	173	6	m.	m.	NOUN
cana-4520	173	7	(	(	PUNCT
cana-4520	173	8	n.d	n.d	PROPN
cana-4520	173	9	.	.	PROPN
cana-4520	173	10	)	)	PUNCT
cana-4520	173	11	.	.	PUNCT
cana-4520	174	1	mercedes	mercedes	PROPN
cana-4520	174	2	-	-	PUNCT
cana-4520	174	3	benz	benz	PROPN
cana-4520	174	4	safety	safety	PROPN
cana-4520	174	5	s	s	PART
cana-4520	174	6	class	class	NOUN
cana-4520	174	7	.	.	PUNCT
cana-4520	175	1	retrieved	retrieve	VERB
cana-4520	175	2	from	from	ADP
cana-4520	175	3	https://www.mercedes	https://www.mercede	NOUN
cana-4520	175	4	-	-	PUNCT
cana-4520	175	5	benz	benz	PROPN
cana-4520	175	6	.co.uk	.co.uk	PROPN
cana-4520	175	7	/	/	SYM
cana-4520	175	8	passengercars	passengercar	NOUN
cana-4520	175	9	/	/	SYM
cana-4520	175	10	mercedes	mercedes	PROPN
cana-4520	175	11	-	-	PUNCT
cana-4520	175	12	benz	benz	PROPN
cana-4520	175	13	-	-	PUNCT
cana-4520	175	14	cars	car	NOUN
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cana-4520	175	16	3	3	NUM
cana-4520	175	17	]	]	X
cana-4520	175	18	florez	florez	PROPN
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cana-4520	175	20	r.	r.	PROPN
cana-4520	175	21	,	,	PUNCT
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cana-4520	175	23	-	-	PUNCT
cana-4520	175	24	quispe	quispe	PROPN
cana-4520	175	25	,	,	PUNCT
cana-4520	175	26	f.	f.	PROPN
cana-4520	175	27	,	,	PUNCT
cana-4520	175	28	coaquira	coaquira	PROPN
cana-4520	175	29	-	-	PUNCT
cana-4520	175	30	castillo	castillo	PROPN
cana-4520	175	31	,	,	PUNCT
cana-4520	175	32	r.	r.	PROPN
cana-4520	175	33	j.	j.	PROPN
cana-4520	175	34	,	,	PUNCT
cana-4520	175	35	&	&	CCONJ
cana-4520	175	36	herrera	herrera	NOUN
cana-4520	175	37	-	-	PUNCT
cana-4520	175	38	levanalvarez	levanalvarez	NOUN
cana-4520	175	39	,	,	PUNCT
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cana-4520	175	44	)	)	PUNCT
cana-4520	175	45	.	.	PUNCT
cana-4520	176	1	a	a	DET
cana-4520	176	2	cnn	cnn	PROPN
cana-4520	176	3	-	-	PUNCT
cana-4520	176	4	based	base	VERB
cana-4520	176	5	approach	approach	NOUN
cana-4520	176	6	for	for	ADP
cana-4520	176	7	driver	driver	NOUN
cana-4520	176	8	drowsiness	drowsiness	NOUN
cana-4520	176	9	detection	detection	NOUN
cana-4520	176	10	by	by	ADP
cana-4520	176	11	real	real	ADJ
cana-4520	176	12	-	-	PUNCT
cana-4520	176	13	time	time	NOUN
cana-4520	176	14	eye	eye	NOUN
cana-4520	176	15	state	state	NOUN
cana-4520	176	16	identification	identification	NOUN
cana-4520	176	17	.	.	PUNCT
cana-4520	177	1	applied	apply	VERB
cana-4520	177	2	sciences	science	NOUN
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cana-4520	177	4	13(13	13(13	NUM
cana-4520	177	5	)	)	PUNCT
cana-4520	177	6	,	,	PUNCT
cana-4520	177	7	7849	7849	NUM
cana-4520	177	8	.	.	PUNCT
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cana-4520	178	2	[	[	X
cana-4520	178	3	4	4	NUM
cana-4520	178	4	]	]	X
cana-4520	178	5	sohail	sohail	PROPN
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cana-4520	178	7	a.	a.	NOUN
cana-4520	178	8	,	,	PUNCT
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cana-4520	178	10	,	,	PUNCT
cana-4520	178	11	a.	a.	NOUN
cana-4520	178	12	a.	a.	PROPN
cana-4520	178	13	,	,	PUNCT
cana-4520	178	14	ilyas	ilyas	PROPN
cana-4520	178	15	,	,	PUNCT
cana-4520	178	16	s.	s.	PROPN
cana-4520	178	17	,	,	PUNCT
cana-4520	178	18	&	&	CCONJ
cana-4520	178	19	alshammry	alshammry	PROPN
cana-4520	178	20	,	,	PUNCT
cana-4520	178	21	n.	n.	NOUN
cana-4520	178	22	(	(	PUNCT
cana-4520	178	23	2024	2024	NUM
cana-4520	178	24	)	)	PUNCT
cana-4520	178	25	.	.	PUNCT
cana-4520	179	1	a	a	DET
cana-4520	179	2	cnn	cnn	PROPN
cana-4520	179	3	-	-	PUNCT
cana-4520	179	4	based	base	VERB
cana-4520	179	5	deep	deep	ADJ
cana-4520	179	6	learning	learning	NOUN
cana-4520	179	7	framework	framework	NOUN
cana-4520	179	8	for	for	ADP
cana-4520	179	9	driver	driver	NOUN
cana-4520	179	10	’s	’s	PART
cana-4520	179	11	drowsiness	drowsiness	NOUN
cana-4520	179	12	detection	detection	NOUN
cana-4520	179	13	.	.	PUNCT
cana-4520	180	1	international	international	ADJ
cana-4520	180	2	journal	journal	NOUN
cana-4520	180	3	of	of	ADP
cana-4520	180	4	advanced	advanced	ADJ
cana-4520	180	5	computer	computer	NOUN
cana-4520	180	6	science	science	NOUN
cana-4520	180	7	and	and	CCONJ
cana-4520	180	8	applications	application	NOUN
cana-4520	180	9	,	,	PUNCT
cana-4520	180	10	15(3	15(3	NUM
cana-4520	180	11	)	)	PUNCT
cana-4520	180	12	,	,	PUNCT
cana-4520	180	13	170	170	NUM
cana-4520	180	14	-	-	SYM
cana-4520	180	15	178	178	NUM
cana-4520	180	16	.	.	PUNCT
cana-4520	181	1	https://doi.org/10.3390/app13137849	https://doi.org/10.3390/app13137849	NOUN
cana-4520	181	2	communications	communication	NOUN
cana-4520	181	3	on	on	ADP
cana-4520	181	4	applied	apply	VERB
cana-4520	181	5	nonlinear	nonlinear	ADJ
cana-4520	181	6	analysis	analysis	NOUN
cana-4520	181	7	issn	issn	NOUN
cana-4520	181	8	:	:	PUNCT
cana-4520	181	9	1074	1074	NUM
cana-4520	181	10	-	-	PUNCT
cana-4520	181	11	133x	133x	NUM
cana-4520	181	12	vol	vol	NOUN
cana-4520	181	13	32	32	NUM
cana-4520	181	14	no	no	NOUN
cana-4520	181	15	.	.	PUNCT
cana-4520	182	1	9s	9s	NUM
cana-4520	182	2	(	(	PUNCT
cana-4520	182	3	2025	2025	NUM
cana-4520	182	4	)	)	PUNCT
cana-4520	182	5	2347	2347	NUM
cana-4520	182	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4520	183	1	[	[	X
cana-4520	183	2	5	5	NUM
cana-4520	183	3	]	]	X
cana-4520	183	4	reddy	reddy	PROPN
cana-4520	183	5	,	,	PUNCT
cana-4520	183	6	b.	b.	PROPN
cana-4520	183	7	,	,	PUNCT
cana-4520	183	8	&	&	CCONJ
cana-4520	183	9	kim	kim	PROPN
cana-4520	183	10	,	,	PUNCT
cana-4520	183	11	y.-h	y.-h	PROPN
cana-4520	183	12	.	.	PUNCT
cana-4520	184	1	(	(	PUNCT
cana-4520	184	2	2017	2017	NUM
cana-4520	184	3	)	)	PUNCT
cana-4520	184	4	.	.	PUNCT
cana-4520	185	1	real	real	ADJ
cana-4520	185	2	-	-	PUNCT
cana-4520	185	3	time	time	NOUN
cana-4520	185	4	driver	driver	NOUN
cana-4520	185	5	drowsiness	drowsiness	NOUN
cana-4520	185	6	detection	detection	NOUN
cana-4520	185	7	for	for	ADP
cana-4520	185	8	embedded	embed	VERB
cana-4520	185	9	system	system	NOUN
cana-4520	185	10	using	use	VERB
cana-4520	185	11	model	model	NOUN
cana-4520	185	12	compression	compression	NOUN
cana-4520	185	13	of	of	ADP
cana-4520	185	14	deep	deep	ADJ
cana-4520	185	15	neural	neural	ADJ
cana-4520	185	16	networks	network	NOUN
cana-4520	185	17	.	.	PUNCT
cana-4520	186	1	2017	2017	NUM
cana-4520	186	2	ieee	ieee	NOUN
cana-4520	186	3	conference	conference	NOUN
cana-4520	186	4	on	on	ADP
cana-4520	186	5	computer	computer	NOUN
cana-4520	186	6	vision	vision	NOUN
cana-4520	186	7	and	and	CCONJ
cana-4520	186	8	pattern	pattern	NOUN
cana-4520	186	9	recognition	recognition	NOUN
cana-4520	186	10	workshops	workshop	NOUN
cana-4520	186	11	(	(	PUNCT
cana-4520	186	12	cvprw	cvprw	ADJ
cana-4520	186	13	)	)	PUNCT
cana-4520	186	14	.	.	PUNCT
cana-4520	187	1	https://doi.org/10.1109/cvprw.2017.59	https://doi.org/10.1109/cvprw.2017.59	VERB
cana-4520	188	1	[	[	X
cana-4520	188	2	6	6	NUM
cana-4520	188	3	]	]	PUNCT
cana-4520	188	4	abbas	abbas	PROPN
cana-4520	188	5	,	,	PUNCT
cana-4520	188	6	q.	q.	PROPN
cana-4520	188	7	(	(	PUNCT
cana-4520	188	8	2020	2020	NUM
cana-4520	188	9	)	)	PUNCT
cana-4520	188	10	.	.	PUNCT
cana-4520	189	1	fatiguealert	fatiguealert	NOUN
cana-4520	189	2	:	:	PUNCT
cana-4520	189	3	a	a	DET
cana-4520	189	4	real	real	ADJ
cana-4520	189	5	-	-	PUNCT
cana-4520	189	6	time	time	NOUN
cana-4520	189	7	fatigue	fatigue	NOUN
cana-4520	189	8	detection	detection	NOUN
cana-4520	189	9	system	system	NOUN
cana-4520	189	10	using	use	VERB
cana-4520	189	11	hybrid	hybrid	ADJ
cana-4520	189	12	features	feature	NOUN
cana-4520	189	13	and	and	CCONJ
cana-4520	189	14	pretrained	pretraine	VERB
cana-4520	189	15	mcnn	mcnn	NOUN
cana-4520	189	16	model	model	NOUN
cana-4520	189	17	.	.	PUNCT
cana-4520	190	1	international	international	ADJ
cana-4520	190	2	journal	journal	PROPN
cana-4520	190	3	of	of	ADP
cana-4520	190	4	computer	computer	NOUN
cana-4520	190	5	science	science	NOUN
cana-4520	190	6	and	and	CCONJ
cana-4520	190	7	network	network	NOUN
cana-4520	190	8	security	security	NOUN
cana-4520	190	9	,	,	PUNCT
cana-4520	190	10	11(1	11(1	NUM
cana-4520	190	11	)	)	PUNCT
cana-4520	190	12	,	,	PUNCT
cana-4520	190	13	585	585	NUM
cana-4520	190	14	–	–	PUNCT
cana-4520	190	15	593	593	NUM
cana-4520	190	16	.	.	PUNCT
cana-4520	191	1	https://doi.org/10.14569/ijacsa.2020.0110173	https://doi.org/10.14569/ijacsa.2020.0110173	PROPN
cana-4520	191	2	[	[	X
cana-4520	191	3	7	7	NUM
cana-4520	191	4	]	]	X
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cana-4520	191	8	w.	w.	PROPN
cana-4520	191	9	,	,	PUNCT
cana-4520	191	10	mahmoud	mahmoud	PROPN
cana-4520	191	11	,	,	PUNCT
cana-4520	191	12	r.	r.	PROPN
cana-4520	191	13	o.	o.	PROPN
cana-4520	191	14	,	,	PUNCT
cana-4520	191	15	&	&	CCONJ
cana-4520	191	16	sarhan	sarhan	PROPN
cana-4520	191	17	,	,	PUNCT
cana-4520	191	18	a.	a.	NOUN
cana-4520	191	19	m.	m.	NOUN
cana-4520	191	20	(	(	PUNCT
cana-4520	191	21	2022	2022	NUM
cana-4520	191	22	)	)	PUNCT
cana-4520	191	23	.	.	PUNCT
cana-4520	192	1	a	a	DET
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cana-4520	192	3	-	-	PUNCT
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cana-4520	192	5	-	-	PUNCT
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cana-4520	192	9	approach	approach	NOUN
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cana-4520	192	11	driver	driver	NOUN
cana-4520	192	12	drowsiness	drowsiness	NOUN
cana-4520	192	13	prediction	prediction	NOUN
cana-4520	192	14	.	.	PUNCT
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cana-4520	193	6	,	,	PUNCT
cana-4520	193	7	6(3	6(3	NUM
cana-4520	193	8	)	)	PUNCT
cana-4520	193	9	,	,	PUNCT
cana-4520	193	10	59–70	59–70	NUM
cana-4520	193	11	.	.	PUNCT
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cana-4520	194	2	[	[	SYM
cana-4520	194	3	8	8	NUM
cana-4520	194	4	]	]	X
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cana-4520	194	8	,	,	PUNCT
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cana-4520	194	10	,	,	PUNCT
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cana-4520	194	22	2021	2021	NUM
cana-4520	194	23	)	)	PUNCT
cana-4520	194	24	.	.	PUNCT
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cana-4520	195	4	-	-	PUNCT
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cana-4520	195	7	approach	approach	NOUN
cana-4520	195	8	to	to	ADP
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cana-4520	195	10	drowsiness	drowsiness	NOUN
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cana-4520	195	12	.	.	PUNCT
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cana-4520	196	5	,	,	PUNCT
cana-4520	196	6	33(13	33(13	NUM
cana-4520	196	7	)	)	PUNCT
cana-4520	196	8	,	,	PUNCT
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cana-4520	196	10	.	.	PUNCT
cana-4520	196	11	https://doi.org/10.1007/s00521-020-05209-7	https://doi.org/10.1007/s00521-020-05209-7	NOUN
cana-4520	197	1	[	[	X
cana-4520	197	2	9	9	NUM
cana-4520	197	3	]	]	SYM
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cana-4520	197	5	,	,	PUNCT
cana-4520	197	6	r.	r.	PROPN
cana-4520	197	7	,	,	PUNCT
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cana-4520	197	9	,	,	PUNCT
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cana-4520	197	11	,	,	PUNCT
cana-4520	197	12	do	do	VERB
cana-4520	197	13	,	,	PUNCT
cana-4520	197	14	r.	r.	PROPN
cana-4520	197	15	k.	k.	PROPN
cana-4520	197	16	g.	g.	PROPN
cana-4520	197	17	,	,	PUNCT
cana-4520	197	18	&	&	CCONJ
cana-4520	197	19	togashi	togashi	PROPN
cana-4520	197	20	,	,	PUNCT
cana-4520	197	21	k.	k.	PROPN
cana-4520	197	22	(	(	PUNCT
cana-4520	197	23	2018	2018	NUM
cana-4520	197	24	)	)	PUNCT
cana-4520	197	25	.	.	PUNCT
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cana-4520	198	2	neural	neural	ADJ
cana-4520	198	3	networks	network	NOUN
cana-4520	198	4	:	:	PUNCT
cana-4520	198	5	an	an	DET
cana-4520	198	6	overview	overview	NOUN
cana-4520	198	7	and	and	CCONJ
cana-4520	198	8	application	application	NOUN
cana-4520	198	9	in	in	ADP
cana-4520	198	10	radiology	radiology	NOUN
cana-4520	198	11	.	.	PUNCT
cana-4520	199	1	radiology	radiology	NOUN
cana-4520	199	2	,	,	PUNCT
cana-4520	199	3	9	9	NUM
cana-4520	199	4	,	,	PUNCT
cana-4520	199	5	611–629	611–629	NUM
cana-4520	199	6	.	.	PUNCT
cana-4520	200	1	[	[	X
cana-4520	200	2	10	10	NUM
cana-4520	200	3	]	]	X
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cana-4520	200	6	r.	r.	PROPN
cana-4520	200	7	,	,	PUNCT
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cana-4520	200	9	-	-	PUNCT
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cana-4520	200	11	,	,	PUNCT
cana-4520	200	12	k.	k.	PROPN
cana-4520	200	13	,	,	PUNCT
cana-4520	200	14	kharbeche	kharbeche	PROPN
cana-4520	200	15	,	,	PUNCT
cana-4520	200	16	m.	m.	NOUN
cana-4520	200	17	,	,	PUNCT
cana-4520	200	18	alhajyaseen	alhajyaseen	PROPN
cana-4520	200	19	,	,	PUNCT
cana-4520	200	20	w.	w.	PROPN
cana-4520	200	21	,	,	PUNCT
cana-4520	200	22	jafari	jafari	PROPN
cana-4520	200	23	,	,	PUNCT
cana-4520	200	24	m.	m.	NOUN
cana-4520	200	25	,	,	PUNCT
cana-4520	200	26	&	&	CCONJ
cana-4520	200	27	jiang	jiang	PROPN
cana-4520	200	28	,	,	PUNCT
cana-4520	200	29	s.	s.	PROPN
cana-4520	200	30	(	(	PUNCT
cana-4520	200	31	2018	2018	NUM
cana-4520	200	32	)	)	PUNCT
cana-4520	200	33	.	.	PUNCT
cana-4520	201	1	real	real	ADJ
cana-4520	201	2	-	-	PUNCT
cana-4520	201	3	time	time	NOUN
cana-4520	201	4	driver	driver	NOUN
cana-4520	201	5	drowsiness	drowsiness	NOUN
cana-4520	201	6	detection	detection	NOUN
cana-4520	201	7	for	for	ADP
cana-4520	201	8	android	android	PROPN
cana-4520	201	9	application	application	NOUN
cana-4520	201	10	using	use	VERB
cana-4520	201	11	deep	deep	ADJ
cana-4520	201	12	neural	neural	ADJ
cana-4520	201	13	networks	network	NOUN
cana-4520	201	14	techniques	technique	NOUN
cana-4520	201	15	.	.	PUNCT
cana-4520	202	1	procedia	procedia	PROPN
cana-4520	202	2	computer	computer	NOUN
cana-4520	202	3	science	science	NOUN
cana-4520	202	4	,	,	PUNCT
cana-4520	202	5	130	130	NUM
cana-4520	202	6	,	,	PUNCT
cana-4520	202	7	400–407	400–407	NUM
cana-4520	202	8	.	.	PUNCT
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cana-4520	203	3	]	]	PUNCT
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cana-4520	204	3	)	)	PUNCT
cana-4520	204	4	.	.	PUNCT
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cana-4520	205	12	.	.	PUNCT
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cana-4520	207	3	12	12	NUM
cana-4520	207	4	]	]	PUNCT
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cana-4520	207	24	)	)	PUNCT
cana-4520	207	25	.	.	PUNCT
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cana-4520	208	8	on	on	ADP
cana-4520	208	9	mobile	mobile	NOUN
cana-4520	208	10	platforms	platform	NOUN
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cana-4520	208	12	3d	3d	NUM
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cana-4520	209	5	,	,	PUNCT
cana-4520	209	6	32(5	32(5	NOUN
cana-4520	209	7	)	)	PUNCT
cana-4520	209	8	,	,	PUNCT
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cana-4520	210	11	,	,	PUNCT
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cana-4520	210	17	-	-	PUNCT
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cana-4520	210	20	k.	k.	PROPN
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cana-4520	210	30	(	(	PUNCT
cana-4520	210	31	2020	2020	NUM
cana-4520	210	32	)	)	PUNCT
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cana-4520	213	12	.	.	PUNCT
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cana-4520	214	5	,	,	PUNCT
cana-4520	214	6	33(8	33(8	NUM
cana-4520	214	7	)	)	PUNCT
cana-4520	214	8	,	,	PUNCT
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cana-4520	215	9	,	,	PUNCT
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cana-4520	217	6	]	]	PUNCT
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cana-4520	219	8	)	)	PUNCT
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cana-4520	222	6	)	)	PUNCT
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cana-4520	227	3	)	)	PUNCT
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cana-4520	232	7	artificial	artificial	ADJ
cana-4520	232	8	intelligence	intelligence	NOUN
cana-4520	232	9	and	and	CCONJ
cana-4520	232	10	advanced	advanced	ADJ
cana-4520	232	11	manufacturing	manufacturing	NOUN
cana-4520	232	12	,	,	PUNCT
cana-4520	232	13	1	1	NUM
cana-4520	232	14	–	–	PUNCT
cana-4520	232	15	5	5	NUM
cana-4520	232	16	.	.	PUNCT
cana-4520	233	1	https://doi.org/10.1109/cvprw.2017.59	https://doi.org/10.1109/cvprw.2017.59	PROPN
cana-4520	233	2	https://doi.org/10.14569/ijacsa.2020.0110173	https://doi.org/10.14569/ijacsa.2020.0110173	PROPN
cana-4520	233	3	https://doi.org/10.21608/erjeng.2022.141514.1067	https://doi.org/10.21608/erjeng.2022.141514.1067	NUM
cana-4520	233	4	https://doi.org/10.1007/s00521-020-05209-7	https://doi.org/10.1007/s00521-020-05209-7	NOUN
cana-4520	233	5	https://doi.org/10.1016/j.procs.2018.04.060	https://doi.org/10.1016/j.procs.2018.04.060	NOUN
cana-4520	233	6	https://doi.org/10.1016/j.aap.2017.11.038	https://doi.org/10.1016/j.aap.2017.11.038	PRON
cana-4520	233	7	https://doi.org/10.1007/s00521-019-04506-0	https://doi.org/10.1007/s00521-019-04506-0	NUM
cana-4520	233	8	https://arxiv.org/abs/2002.03728	https://arxiv.org/abs/2002.03728	NOUN
cana-4520	233	9	https://doi.org/10.1007/s00521-020-05209-7	https://doi.org/10.1007/s00521-020-05209-7	NOUN
cana-4520	233	10	https://pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/	https://pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/	PROPN
cana-4520	233	11	https://doi.org/10.32604/iasc.2023.039732	https://doi.org/10.32604/iasc.2023.039732	PROPN
cana-4520	233	12	https://doi.org/10.18280/ria.330609	https://doi.org/10.18280/ria.330609	PROPN
cana-4520	233	13	https://blog.paperspace.com/popular-deep-learning-architecturesalexnet-vgg-googlenet	https://blog.paperspace.com/popular-deep-learning-architecturesalexnet-vgg-googlenet	NOUN
cana-4520	233	14	https://blog.paperspace.com/popular-deep-learning-architecturesalexnet-vgg-googlenet	https://blog.paperspace.com/popular-deep-learning-architecturesalexnet-vgg-googlenet	PROPN
cana-4520	233	15	https://www.ford.com/support/how-tos/fordtechnology/driver-assist-features/what-is-the-driver-alert-system/	https://www.ford.com/support/how-tos/fordtechnology/driver-assist-features/what-is-the-driver-alert-system/	NOUN
cana-4520	233	16	https://www.ford.com/support/how-tos/fordtechnology/driver-assist-features/what-is-the-driver-alert-system/	https://www.ford.com/support/how-tos/fordtechnology/driver-assist-features/what-is-the-driver-alert-system/	NOUN