id	sid	tid	token	lemma	pos
ajmri-740	1	1	pa	pa	PROPN
ajmri-740	1	2	ge	ge	PROPN
ajmri-740	1	3	1	1	NUM
ajmri-740	1	4	pa	pa	PROPN
ajmri-740	1	5	ge	ge	PROPN
ajmri-740	1	6	24	24	NUM
ajmri-740	1	7	american	american	PROPN
ajmri-740	1	8	journal	journal	PROPN
ajmri-740	1	9	of	of	ADP
ajmri-740	1	10	multidisciplinary	multidisciplinary	ADJ
ajmri-740	1	11	research	research	NOUN
ajmri-740	1	12	and	and	CCONJ
ajmri-740	1	13	innovation	innovation	NOUN
ajmri-740	1	14	(	(	PUNCT
ajmri-740	1	15	ajmri	ajmri	NOUN
ajmri-740	1	16	)	)	PUNCT
ajmri-740	1	17	application	application	NOUN
ajmri-740	1	18	of	of	ADP
ajmri-740	1	19	three	three	NUM
ajmri-740	1	20	convolutional	convolutional	ADJ
ajmri-740	1	21	neural	neural	ADJ
ajmri-740	1	22	network	network	NOUN
ajmri-740	1	23	algorithms	algorithm	NOUN
ajmri-740	1	24	for	for	ADP
ajmri-740	1	25	occluded	occluded	ADJ
ajmri-740	1	26	face	face	NOUN
ajmri-740	1	27	identification	identification	NOUN
ajmri-740	1	28	and	and	CCONJ
ajmri-740	1	29	recognition	recognition	NOUN
ajmri-740	1	30	for	for	ADP
ajmri-740	1	31	system	system	NOUN
ajmri-740	1	32	security	security	NOUN
ajmri-740	1	33	mamadou	mamadou	PROPN
ajmri-740	1	34	diarra1	diarra1	PROPN
ajmri-740	1	35	*	*	PROPN
ajmri-740	1	36	,	,	PUNCT
ajmri-740	1	37	ayikpa	ayikpa	PROPN
ajmri-740	1	38	kacoutchy	kacoutchy	PROPN
ajmri-740	1	39	jean2	jean2	PROPN
ajmri-740	1	40	,	,	PUNCT
ajmri-740	1	41	ballo	ballo	PROPN
ajmri-740	1	42	abou	abou	PROPN
ajmri-740	1	43	bakary	bakary	PROPN
ajmri-740	1	44	1	1	NUM
ajmri-740	1	45	,	,	PUNCT
ajmri-740	1	46	kouassi	kouassi	PROPN
ajmri-740	1	47	brou	brou	PROPN
ajmri-740	1	48	medard1	medard1	NOUN
ajmri-740	1	49	volume	volume	NOUN
ajmri-740	1	50	1	1	NUM
ajmri-740	1	51	issue	issue	NOUN
ajmri-740	1	52	5	5	NUM
ajmri-740	1	53	,	,	PUNCT
ajmri-740	1	54	year	year	NOUN
ajmri-740	1	55	2022	2022	NUM
ajmri-740	1	56	issn	issn	VERB
ajmri-740	1	57	:	:	PUNCT
ajmri-740	1	58	2158	2158	NUM
ajmri-740	1	59	-	-	SYM
ajmri-740	1	60	8155	8155	NUM
ajmri-740	1	61	(	(	PUNCT
ajmri-740	1	62	online	online	NOUN
ajmri-740	1	63	)	)	PUNCT
ajmri-740	1	64	,	,	PUNCT
ajmri-740	1	65	2832	2832	NUM
ajmri-740	1	66	-	-	SYM
ajmri-740	1	67	4854	4854	NUM
ajmri-740	1	68	(	(	PUNCT
ajmri-740	1	69	print	print	NOUN
ajmri-740	1	70	)	)	PUNCT
ajmri-740	1	71	doi	doi	NOUN
ajmri-740	1	72	:	:	PUNCT
ajmri-740	1	73	https://doi.org/10.54536/ajmri.v1i5.740	https://doi.org/10.54536/ajmri.v1i5.740	NOUN
ajmri-740	1	74	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	1	75	article	article	NOUN
ajmri-740	1	76	information	information	NOUN
ajmri-740	1	77	abstract	abstract	ADV
ajmri-740	1	78	received	receive	VERB
ajmri-740	1	79	:	:	PUNCT
ajmri-740	1	80	october	october	PROPN
ajmri-740	1	81	10	10	NUM
ajmri-740	1	82	,	,	PUNCT
ajmri-740	1	83	2022	2022	NUM
ajmri-740	1	84	accepted	accept	VERB
ajmri-740	1	85	:	:	PUNCT
ajmri-740	1	86	october	october	PROPN
ajmri-740	1	87	21	21	NUM
ajmri-740	1	88	,	,	PUNCT
ajmri-740	1	89	2022	2022	NUM
ajmri-740	1	90	published	publish	VERB
ajmri-740	1	91	:	:	PUNCT
ajmri-740	1	92	october	october	PROPN
ajmri-740	1	93	26	26	NUM
ajmri-740	1	94	,	,	PUNCT
ajmri-740	1	95	2022	2022	NUM
ajmri-740	1	96	deep	deep	ADJ
ajmri-740	1	97	learning	learning	NOUN
ajmri-740	1	98	techniques	technique	NOUN
ajmri-740	1	99	in	in	ADP
ajmri-740	1	100	computer	computer	NOUN
ajmri-740	1	101	vision	vision	NOUN
ajmri-740	1	102	have	have	AUX
ajmri-740	1	103	become	become	VERB
ajmri-740	1	104	indispensable	indispensable	ADJ
ajmri-740	1	105	elements	element	NOUN
ajmri-740	1	106	in	in	ADP
ajmri-740	1	107	biometric	biometric	ADJ
ajmri-740	1	108	systems	system	NOUN
ajmri-740	1	109	,	,	PUNCT
ajmri-740	1	110	especially	especially	ADV
ajmri-740	1	111	face	face	VERB
ajmri-740	1	112	recognition	recognition	NOUN
ajmri-740	1	113	.	.	PUNCT
ajmri-740	2	1	facial	facial	ADJ
ajmri-740	2	2	recognition	recognition	NOUN
ajmri-740	2	3	can	can	AUX
ajmri-740	2	4	be	be	AUX
ajmri-740	2	5	reliably	reliably	ADV
ajmri-740	2	6	used	use	VERB
ajmri-740	2	7	as	as	ADP
ajmri-740	2	8	an	an	DET
ajmri-740	2	9	identification	identification	NOUN
ajmri-740	2	10	and	and	CCONJ
ajmri-740	2	11	authentication	authentication	NOUN
ajmri-740	2	12	tool	tool	NOUN
ajmri-740	2	13	for	for	ADP
ajmri-740	2	14	premises	premise	NOUN
ajmri-740	2	15	or	or	CCONJ
ajmri-740	2	16	network	network	NOUN
ajmri-740	2	17	access	access	NOUN
ajmri-740	2	18	security	security	NOUN
ajmri-740	2	19	.	.	PUNCT
ajmri-740	3	1	the	the	DET
ajmri-740	3	2	masks	mask	NOUN
ajmri-740	3	3	wearing	wear	VERB
ajmri-740	3	4	,	,	PUNCT
ajmri-740	3	5	which	which	PRON
ajmri-740	3	6	is	be	AUX
ajmri-740	3	7	one	one	NUM
ajmri-740	3	8	of	of	ADP
ajmri-740	3	9	the	the	DET
ajmri-740	3	10	problems	problem	NOUN
ajmri-740	3	11	of	of	ADP
ajmri-740	3	12	concealment	concealment	NOUN
ajmri-740	3	13	,	,	PUNCT
ajmri-740	3	14	are	be	AUX
ajmri-740	3	15	nowadays	nowadays	ADV
ajmri-740	3	16	part	part	NOUN
ajmri-740	3	17	of	of	ADP
ajmri-740	3	18	our	our	PRON
ajmri-740	3	19	habits	habit	NOUN
ajmri-740	3	20	for	for	ADP
ajmri-740	3	21	preventing	prevent	VERB
ajmri-740	3	22	covid-19	covid-19	PROPN
ajmri-740	3	23	disease	disease	NOUN
ajmri-740	3	24	,	,	PUNCT
ajmri-740	3	25	which	which	PRON
ajmri-740	3	26	leads	lead	VERB
ajmri-740	3	27	to	to	ADP
ajmri-740	3	28	an	an	DET
ajmri-740	3	29	obstruction	obstruction	NOUN
ajmri-740	3	30	of	of	ADP
ajmri-740	3	31	facial	facial	ADJ
ajmri-740	3	32	recognition	recognition	NOUN
ajmri-740	3	33	.	.	PUNCT
ajmri-740	4	1	occulted	occult	VERB
ajmri-740	4	2	face	face	NOUN
ajmri-740	4	3	recognition	recognition	NOUN
ajmri-740	4	4	is	be	AUX
ajmri-740	4	5	one	one	NUM
ajmri-740	4	6	of	of	ADP
ajmri-740	4	7	the	the	DET
ajmri-740	4	8	most	most	ADV
ajmri-740	4	9	challenging	challenging	ADJ
ajmri-740	4	10	problems	problem	NOUN
ajmri-740	4	11	biometrics	biometric	NOUN
ajmri-740	4	12	deals	deal	NOUN
ajmri-740	4	13	with	with	ADP
ajmri-740	4	14	.	.	PUNCT
ajmri-740	5	1	this	this	DET
ajmri-740	5	2	paper	paper	NOUN
ajmri-740	5	3	presents	present	VERB
ajmri-740	5	4	convolution	convolution	NOUN
ajmri-740	5	5	neural	neural	ADJ
ajmri-740	5	6	network	network	NOUN
ajmri-740	5	7	algorithms	algorithm	NOUN
ajmri-740	5	8	for	for	ADP
ajmri-740	5	9	occluded	occluded	ADJ
ajmri-740	5	10	face	face	NOUN
ajmri-740	5	11	recognition	recognition	NOUN
ajmri-740	5	12	.	.	PUNCT
ajmri-740	6	1	our	our	PRON
ajmri-740	6	2	study	study	NOUN
ajmri-740	6	3	presents	present	VERB
ajmri-740	6	4	a	a	DET
ajmri-740	6	5	robust	robust	ADJ
ajmri-740	6	6	method	method	NOUN
ajmri-740	6	7	using	use	VERB
ajmri-740	6	8	algorithms	algorithm	NOUN
ajmri-740	6	9	such	such	ADJ
ajmri-740	6	10	as	as	ADP
ajmri-740	6	11	resnet-50	resnet-50	PROPN
ajmri-740	6	12	,	,	PUNCT
ajmri-740	6	13	vgg-19	vgg-19	NOUN
ajmri-740	6	14	,	,	PUNCT
ajmri-740	6	15	and	and	CCONJ
ajmri-740	6	16	densenet-201	densenet-201	ADJ
ajmri-740	6	17	to	to	PART
ajmri-740	6	18	contribute	contribute	VERB
ajmri-740	6	19	to	to	ADP
ajmri-740	6	20	occluded	occluded	ADJ
ajmri-740	6	21	face	face	NOUN
ajmri-740	6	22	recognition	recognition	NOUN
ajmri-740	6	23	.	.	PUNCT
ajmri-740	7	1	various	various	ADJ
ajmri-740	7	2	parameters	parameter	NOUN
ajmri-740	7	3	are	be	AUX
ajmri-740	7	4	used	use	VERB
ajmri-740	7	5	for	for	ADP
ajmri-740	7	6	this	this	DET
ajmri-740	7	7	experiment	experiment	NOUN
ajmri-740	7	8	,	,	PUNCT
ajmri-740	7	9	such	such	ADJ
ajmri-740	7	10	as	as	ADP
ajmri-740	7	11	the	the	DET
ajmri-740	7	12	cross	cross	NOUN
ajmri-740	7	13	-	-	ADJ
ajmri-740	7	14	entropy	entropy	NOUN
ajmri-740	7	15	used	use	VERB
ajmri-740	7	16	as	as	ADP
ajmri-740	7	17	a	a	DET
ajmri-740	7	18	loss	loss	NOUN
ajmri-740	7	19	function	function	NOUN
ajmri-740	7	20	and	and	CCONJ
ajmri-740	7	21	optimization	optimization	NOUN
ajmri-740	7	22	algorithms	algorithm	NOUN
ajmri-740	7	23	adapted	adapt	VERB
ajmri-740	7	24	to	to	ADP
ajmri-740	7	25	deep	deep	ADJ
ajmri-740	7	26	learning	learning	NOUN
ajmri-740	7	27	.	.	PUNCT
ajmri-740	8	1	these	these	PRON
ajmri-740	8	2	include	include	VERB
ajmri-740	8	3	the	the	DET
ajmri-740	8	4	sgd	sgd	PROPN
ajmri-740	8	5	,	,	PUNCT
ajmri-740	8	6	adam	adam	PROPN
ajmri-740	8	7	,	,	PUNCT
ajmri-740	8	8	and	and	CCONJ
ajmri-740	8	9	rmsprop	rmsprop	NOUN
ajmri-740	8	10	optimizers	optimizer	NOUN
ajmri-740	8	11	.	.	PUNCT
ajmri-740	9	1	the	the	DET
ajmri-740	9	2	convolution	convolution	NOUN
ajmri-740	9	3	neural	neural	ADJ
ajmri-740	9	4	network	network	NOUN
ajmri-740	9	5	algorithms	algorithm	NOUN
ajmri-740	9	6	were	be	AUX
ajmri-740	9	7	evaluated	evaluate	VERB
ajmri-740	9	8	on	on	ADP
ajmri-740	9	9	the	the	DET
ajmri-740	9	10	ar	ar	NOUN
ajmri-740	9	11	database	database	NOUN
ajmri-740	9	12	.	.	PUNCT
ajmri-740	10	1	this	this	DET
ajmri-740	10	2	experiment	experiment	NOUN
ajmri-740	10	3	gave	give	VERB
ajmri-740	10	4	results	result	NOUN
ajmri-740	10	5	that	that	PRON
ajmri-740	10	6	ranged	range	VERB
ajmri-740	10	7	from	from	ADP
ajmri-740	10	8	94.81	94.81	NUM
ajmri-740	10	9	to	to	ADP
ajmri-740	10	10	99.81	99.81	NUM
ajmri-740	10	11	%	%	NOUN
ajmri-740	10	12	for	for	ADP
ajmri-740	10	13	sgd	sgd	PROPN
ajmri-740	10	14	,	,	PUNCT
ajmri-740	10	15	from	from	ADP
ajmri-740	10	16	0	0	NUM
ajmri-740	10	17	to	to	ADP
ajmri-740	10	18	96.92	96.92	NUM
ajmri-740	10	19	for	for	ADP
ajmri-740	10	20	adam	adam	PROPN
ajmri-740	10	21	,	,	PUNCT
ajmri-740	10	22	and	and	CCONJ
ajmri-740	10	23	finally	finally	ADV
ajmri-740	10	24	from	from	ADP
ajmri-740	10	25	0	0	NUM
ajmri-740	10	26	to	to	ADP
ajmri-740	10	27	96.92	96.92	NUM
ajmri-740	10	28	for	for	ADP
ajmri-740	10	29	rmsprop	rmsprop	NOUN
ajmri-740	10	30	.	.	PUNCT
ajmri-740	11	1	densenet-201	densenet-201	ADJ
ajmri-740	11	2	algorithm	algorithm	NOUN
ajmri-740	11	3	using	use	VERB
ajmri-740	11	4	the	the	DET
ajmri-740	11	5	sgd	sgd	PROPN
ajmri-740	11	6	optimizer	optimizer	NOUN
ajmri-740	11	7	obtained	obtain	VERB
ajmri-740	11	8	the	the	DET
ajmri-740	11	9	best	good	ADJ
ajmri-740	11	10	score	score	NOUN
ajmri-740	11	11	with	with	ADP
ajmri-740	11	12	99.81	99.81	NUM
ajmri-740	11	13	%	%	NOUN
ajmri-740	11	14	,	,	PUNCT
ajmri-740	11	15	and	and	CCONJ
ajmri-740	11	16	all	all	DET
ajmri-740	11	17	the	the	DET
ajmri-740	11	18	performance	performance	NOUN
ajmri-740	11	19	metrics	metric	NOUN
ajmri-740	11	20	used	use	VERB
ajmri-740	11	21	such	such	ADJ
ajmri-740	11	22	as	as	ADP
ajmri-740	11	23	accuracy	accuracy	NOUN
ajmri-740	11	24	,	,	PUNCT
ajmri-740	11	25	mse	mse	PROPN
ajmri-740	11	26	,	,	PUNCT
ajmri-740	11	27	f	f	PROPN
ajmri-740	11	28	-	-	PUNCT
ajmri-740	11	29	score	score	NOUN
ajmri-740	11	30	,	,	PUNCT
ajmri-740	11	31	recall	recall	NOUN
ajmri-740	11	32	,	,	PUNCT
ajmri-740	11	33	and	and	CCONJ
ajmri-740	11	34	mcc	mcc	PROPN
ajmri-740	11	35	were	be	AUX
ajmri-740	11	36	used	use	VERB
ajmri-740	11	37	to	to	PART
ajmri-740	11	38	confirm	confirm	VERB
ajmri-740	11	39	this	this	DET
ajmri-740	11	40	good	good	ADJ
ajmri-740	11	41	performance	performance	NOUN
ajmri-740	11	42	.	.	PUNCT
ajmri-740	12	1	keywords	keyword	VERB
ajmri-740	12	2	computer	computer	NOUN
ajmri-740	12	3	vision	vision	NOUN
ajmri-740	12	4	,	,	PUNCT
ajmri-740	12	5	cnn	cnn	PROPN
ajmri-740	12	6	pretrained	pretraine	VERB
ajmri-740	12	7	,	,	PUNCT
ajmri-740	12	8	deep	deep	ADJ
ajmri-740	12	9	learning	learning	NOUN
ajmri-740	12	10	,	,	PUNCT
ajmri-740	12	11	face	face	NOUN
ajmri-740	12	12	recognition	recognition	NOUN
ajmri-740	12	13	,	,	PUNCT
ajmri-740	12	14	occlusion	occlusion	NOUN
ajmri-740	12	15	introduction	introduction	NOUN
ajmri-740	12	16	the	the	DET
ajmri-740	12	17	significant	significant	ADJ
ajmri-740	12	18	increase	increase	NOUN
ajmri-740	12	19	in	in	ADP
ajmri-740	12	20	network	network	NOUN
ajmri-740	12	21	security	security	NOUN
ajmri-740	12	22	breaches	breach	NOUN
ajmri-740	12	23	,	,	PUNCT
ajmri-740	12	24	data	datum	NOUN
ajmri-740	12	25	breaches	breach	NOUN
ajmri-740	12	26	,	,	PUNCT
ajmri-740	12	27	and	and	CCONJ
ajmri-740	12	28	identity	identity	NOUN
ajmri-740	12	29	theft	theft	NOUN
ajmri-740	12	30	requires	require	VERB
ajmri-740	12	31	the	the	DET
ajmri-740	12	32	design	design	NOUN
ajmri-740	12	33	of	of	ADP
ajmri-740	12	34	robust	robust	ADJ
ajmri-740	12	35	security	security	NOUN
ajmri-740	12	36	systems	system	NOUN
ajmri-740	12	37	including	include	VERB
ajmri-740	12	38	biometrics	biometric	NOUN
ajmri-740	12	39	.	.	PUNCT
ajmri-740	13	1	to	to	PART
ajmri-740	13	2	circumvent	circumvent	VERB
ajmri-740	13	3	biometric	biometric	ADJ
ajmri-740	13	4	face	face	NOUN
ajmri-740	13	5	authentication	authentication	NOUN
ajmri-740	13	6	,	,	PUNCT
ajmri-740	13	7	some	some	DET
ajmri-740	13	8	fraudsters	fraudster	NOUN
ajmri-740	13	9	are	be	AUX
ajmri-740	13	10	turning	turn	VERB
ajmri-740	13	11	to	to	PART
ajmri-740	13	12	face	face	VERB
ajmri-740	13	13	occlusion	occlusion	NOUN
ajmri-740	13	14	.	.	PUNCT
ajmri-740	14	1	because	because	SCONJ
ajmri-740	14	2	biological	biological	ADJ
ajmri-740	14	3	and	and	CCONJ
ajmri-740	14	4	physical	physical	ADJ
ajmri-740	14	5	characteristics	characteristic	NOUN
ajmri-740	14	6	are	be	AUX
ajmri-740	14	7	unique	unique	ADJ
ajmri-740	14	8	to	to	ADP
ajmri-740	14	9	each	each	DET
ajmri-740	14	10	individual	individual	NOUN
ajmri-740	14	11	,	,	PUNCT
ajmri-740	14	12	biometric	biometric	ADJ
ajmri-740	14	13	security	security	NOUN
ajmri-740	14	14	consists	consist	VERB
ajmri-740	14	15	of	of	ADP
ajmri-740	14	16	measuring	measure	VERB
ajmri-740	14	17	these	these	DET
ajmri-740	14	18	characteristics	characteristic	NOUN
ajmri-740	14	19	before	before	ADP
ajmri-740	14	20	accessing	access	VERB
ajmri-740	14	21	an	an	DET
ajmri-740	14	22	environment	environment	NOUN
ajmri-740	14	23	or	or	CCONJ
ajmri-740	14	24	a	a	DET
ajmri-740	14	25	computer	computer	NOUN
ajmri-740	14	26	tool	tool	NOUN
ajmri-740	14	27	.	.	PUNCT
ajmri-740	15	1	face	face	NOUN
ajmri-740	15	2	recognition	recognition	NOUN
ajmri-740	15	3	is	be	AUX
ajmri-740	15	4	a	a	DET
ajmri-740	15	5	key	key	ADJ
ajmri-740	15	6	research	research	NOUN
ajmri-740	15	7	issue	issue	NOUN
ajmri-740	15	8	in	in	ADP
ajmri-740	15	9	computer	computer	NOUN
ajmri-740	15	10	vision	vision	NOUN
ajmri-740	15	11	.	.	PUNCT
ajmri-740	16	1	in	in	ADP
ajmri-740	16	2	recent	recent	ADJ
ajmri-740	16	3	years	year	NOUN
ajmri-740	16	4	,	,	PUNCT
ajmri-740	16	5	researchers	researcher	NOUN
ajmri-740	16	6	have	have	AUX
ajmri-740	16	7	proposed	propose	VERB
ajmri-740	16	8	many	many	ADJ
ajmri-740	16	9	algorithms	algorithm	NOUN
ajmri-740	16	10	;	;	PUNCT
ajmri-740	16	11	most	most	ADV
ajmri-740	16	12	previous	previous	ADJ
ajmri-740	16	13	biometric	biometric	NOUN
ajmri-740	16	14	-	-	PUNCT
ajmri-740	16	15	based	base	VERB
ajmri-740	16	16	research	research	NOUN
ajmri-740	16	17	exploiting	exploit	VERB
ajmri-740	16	18	physiological	physiological	ADJ
ajmri-740	16	19	and	and	CCONJ
ajmri-740	16	20	behavioral	behavioral	ADJ
ajmri-740	16	21	characteristics	characteristic	NOUN
ajmri-740	16	22	,	,	PUNCT
ajmri-740	16	23	including	include	VERB
ajmri-740	16	24	human	human	ADJ
ajmri-740	16	25	emotion	emotion	NOUN
ajmri-740	16	26	signals	signal	NOUN
ajmri-740	16	27	and	and	CCONJ
ajmri-740	16	28	expression	expression	NOUN
ajmri-740	16	29	,	,	PUNCT
ajmri-740	16	30	has	have	AUX
ajmri-740	16	31	achieved	achieve	VERB
ajmri-740	16	32	satisfactory	satisfactory	ADJ
ajmri-740	16	33	recognition	recognition	NOUN
ajmri-740	16	34	performance	performance	NOUN
ajmri-740	16	35	under	under	ADP
ajmri-740	16	36	uniform	uniform	ADJ
ajmri-740	16	37	lighting	lighting	NOUN
ajmri-740	16	38	conditions	condition	NOUN
ajmri-740	16	39	with	with	ADP
ajmri-740	16	40	frontal	frontal	ADJ
ajmri-740	16	41	face	face	NOUN
ajmri-740	16	42	images	image	NOUN
ajmri-740	16	43	(	(	PUNCT
ajmri-740	16	44	jiang	jiang	PROPN
ajmri-740	16	45	et	et	PROPN
ajmri-740	16	46	al	al	PROPN
ajmri-740	16	47	.	.	PROPN
ajmri-740	16	48	,	,	PUNCT
ajmri-740	16	49	2020	2020	NUM
ajmri-740	16	50	)	)	PUNCT
ajmri-740	16	51	.	.	PUNCT
ajmri-740	17	1	however	however	ADV
ajmri-740	17	2	,	,	PUNCT
ajmri-740	17	3	illumination	illumination	NOUN
ajmri-740	17	4	,	,	PUNCT
ajmri-740	17	5	facial	facial	ADJ
ajmri-740	17	6	expression	expression	NOUN
ajmri-740	17	7	,	,	PUNCT
ajmri-740	17	8	pose	pose	VERB
ajmri-740	17	9	,	,	PUNCT
ajmri-740	17	10	occlusions	occlusion	NOUN
ajmri-740	17	11	,	,	PUNCT
ajmri-740	17	12	and	and	CCONJ
ajmri-740	17	13	facial	facial	ADJ
ajmri-740	17	14	recognition	recognition	NOUN
ajmri-740	17	15	methods	method	NOUN
ajmri-740	17	16	are	be	AUX
ajmri-740	17	17	still	still	ADV
ajmri-740	17	18	affected	affect	VERB
ajmri-740	17	19	.	.	PUNCT
ajmri-740	18	1	the	the	DET
ajmri-740	18	2	development	development	NOUN
ajmri-740	18	3	of	of	ADP
ajmri-740	18	4	research	research	NOUN
ajmri-740	18	5	in	in	ADP
ajmri-740	18	6	facial	facial	ADJ
ajmri-740	18	7	recognition	recognition	NOUN
ajmri-740	18	8	has	have	AUX
ajmri-740	18	9	led	lead	VERB
ajmri-740	18	10	to	to	ADP
ajmri-740	18	11	a	a	DET
ajmri-740	18	12	high	high	ADJ
ajmri-740	18	13	level	level	NOUN
ajmri-740	18	14	of	of	ADP
ajmri-740	18	15	performance	performance	NOUN
ajmri-740	18	16	in	in	ADP
ajmri-740	18	17	many	many	ADJ
ajmri-740	18	18	applications	application	NOUN
ajmri-740	18	19	.	.	PUNCT
ajmri-740	19	1	it	it	PRON
ajmri-740	19	2	is	be	AUX
ajmri-740	19	3	a	a	DET
ajmri-740	19	4	field	field	NOUN
ajmri-740	19	5	of	of	ADP
ajmri-740	19	6	study	study	NOUN
ajmri-740	19	7	that	that	PRON
ajmri-740	19	8	remains	remain	VERB
ajmri-740	19	9	very	very	ADV
ajmri-740	19	10	challenging	challenging	ADJ
ajmri-740	19	11	because	because	SCONJ
ajmri-740	19	12	images	image	NOUN
ajmri-740	19	13	of	of	ADP
ajmri-740	19	14	the	the	DET
ajmri-740	19	15	same	same	ADJ
ajmri-740	19	16	person	person	NOUN
ajmri-740	19	17	seem	seem	VERB
ajmri-740	19	18	to	to	PART
ajmri-740	19	19	differ	differ	VERB
ajmri-740	19	20	due	due	ADP
ajmri-740	19	21	to	to	ADP
ajmri-740	19	22	several	several	ADJ
ajmri-740	19	23	phenomena	phenomenon	NOUN
ajmri-740	19	24	,	,	PUNCT
ajmri-740	19	25	including	include	VERB
ajmri-740	19	26	occlusion	occlusion	NOUN
ajmri-740	19	27	(	(	PUNCT
ajmri-740	19	28	wu	wu	PROPN
ajmri-740	19	29	et	et	PROPN
ajmri-740	19	30	al	al	PROPN
ajmri-740	19	31	.	.	PROPN
ajmri-740	19	32	,	,	PUNCT
ajmri-740	19	33	2019	2019	NUM
ajmri-740	19	34	)	)	PUNCT
ajmri-740	19	35	.	.	PUNCT
ajmri-740	20	1	in	in	ADP
ajmri-740	20	2	addition	addition	NOUN
ajmri-740	20	3	to	to	ADP
ajmri-740	20	4	existing	exist	VERB
ajmri-740	20	5	security	security	NOUN
ajmri-740	20	6	methods	method	NOUN
ajmri-740	20	7	,	,	PUNCT
ajmri-740	20	8	face	face	NOUN
ajmri-740	20	9	biometrics	biometric	NOUN
ajmri-740	20	10	,	,	PUNCT
ajmri-740	20	11	especially	especially	ADV
ajmri-740	20	12	with	with	ADP
ajmri-740	20	13	occlusion	occlusion	NOUN
ajmri-740	20	14	,	,	PUNCT
ajmri-740	20	15	can	can	AUX
ajmri-740	20	16	be	be	AUX
ajmri-740	20	17	used	use	VERB
ajmri-740	20	18	to	to	PART
ajmri-740	20	19	protect	protect	VERB
ajmri-740	20	20	cyberspace	cyberspace	NOUN
ajmri-740	20	21	from	from	ADP
ajmri-740	20	22	hackers	hacker	NOUN
ajmri-740	20	23	and	and	CCONJ
ajmri-740	20	24	malicious	malicious	ADJ
ajmri-740	20	25	people	people	NOUN
ajmri-740	20	26	among	among	ADP
ajmri-740	20	27	users	user	NOUN
ajmri-740	20	28	of	of	ADP
ajmri-740	20	29	networks	network	NOUN
ajmri-740	20	30	,	,	PUNCT
ajmri-740	20	31	the	the	DET
ajmri-740	20	32	internet	internet	NOUN
ajmri-740	20	33	,	,	PUNCT
ajmri-740	20	34	connected	connected	ADJ
ajmri-740	20	35	devices	device	NOUN
ajmri-740	20	36	,	,	PUNCT
ajmri-740	20	37	etc	etc	X
ajmri-740	20	38	.	.	X
ajmri-740	21	1	among	among	ADP
ajmri-740	21	2	the	the	DET
ajmri-740	21	3	various	various	ADJ
ajmri-740	21	4	problems	problem	NOUN
ajmri-740	21	5	associated	associate	VERB
ajmri-740	21	6	with	with	ADP
ajmri-740	21	7	a	a	DET
ajmri-740	21	8	face	face	NOUN
ajmri-740	21	9	recognition	recognition	NOUN
ajmri-740	21	10	system	system	NOUN
ajmri-740	21	11	,	,	PUNCT
ajmri-740	21	12	occlusion	occlusion	NOUN
ajmri-740	21	13	management	management	NOUN
ajmri-740	21	14	is	be	AUX
ajmri-740	21	15	one	one	NUM
ajmri-740	21	16	of	of	ADP
ajmri-740	21	17	the	the	DET
ajmri-740	21	18	most	most	ADV
ajmri-740	21	19	difficult	difficult	ADJ
ajmri-740	21	20	problems	problem	NOUN
ajmri-740	21	21	to	to	PART
ajmri-740	21	22	solve	solve	VERB
ajmri-740	21	23	.	.	PUNCT
ajmri-740	22	1	due	due	ADP
ajmri-740	22	2	to	to	ADP
ajmri-740	22	3	objects	object	NOUN
ajmri-740	22	4	or	or	CCONJ
ajmri-740	22	5	elements	element	NOUN
ajmri-740	22	6	such	such	ADJ
ajmri-740	22	7	as	as	ADP
ajmri-740	22	8	sunglasses	sunglass	NOUN
ajmri-740	22	9	,	,	PUNCT
ajmri-740	22	10	scarves	scarf	NOUN
ajmri-740	22	11	,	,	PUNCT
ajmri-740	22	12	or	or	CCONJ
ajmri-740	22	13	masks	mask	NOUN
ajmri-740	22	14	,	,	PUNCT
ajmri-740	22	15	the	the	DET
ajmri-740	22	16	occlusion	occlusion	NOUN
ajmri-740	22	17	problem	problem	NOUN
ajmri-740	22	18	becomes	become	VERB
ajmri-740	22	19	eminent	eminent	ADJ
ajmri-740	22	20	.	.	PUNCT
ajmri-740	23	1	one	one	NUM
ajmri-740	23	2	of	of	ADP
ajmri-740	23	3	the	the	DET
ajmri-740	23	4	most	most	ADV
ajmri-740	23	5	recent	recent	ADJ
ajmri-740	23	6	problems	problem	NOUN
ajmri-740	23	7	is	be	AUX
ajmri-740	23	8	the	the	DET
ajmri-740	23	9	wearing	wearing	NOUN
ajmri-740	23	10	of	of	ADP
ajmri-740	23	11	the	the	DET
ajmri-740	23	12	mask	mask	NOUN
ajmri-740	23	13	recommended	recommend	VERB
ajmri-740	23	14	by	by	ADP
ajmri-740	23	15	the	the	DET
ajmri-740	23	16	health	health	NOUN
ajmri-740	23	17	measures	measure	NOUN
ajmri-740	23	18	against	against	ADP
ajmri-740	23	19	the	the	DET
ajmri-740	23	20	coronavirus	coronavirus	NOUN
ajmri-740	23	21	disease	disease	NOUN
ajmri-740	23	22	(	(	PUNCT
ajmri-740	23	23	covid-19	covid-19	PROPN
ajmri-740	23	24	)	)	PUNCT
ajmri-740	23	25	.	.	PUNCT
ajmri-740	24	1	cloaked	cloak	VERB
ajmri-740	24	2	face	face	NOUN
ajmri-740	24	3	images	image	NOUN
ajmri-740	24	4	mainly	mainly	ADV
ajmri-740	24	5	degrade	degrade	VERB
ajmri-740	24	6	the	the	DET
ajmri-740	24	7	performance	performance	NOUN
ajmri-740	24	8	of	of	ADP
ajmri-740	24	9	face	face	NOUN
ajmri-740	24	10	recognition	recognition	NOUN
ajmri-740	24	11	systems	system	NOUN
ajmri-740	24	12	,	,	PUNCT
ajmri-740	24	13	thus	thus	ADV
ajmri-740	24	14	the	the	DET
ajmri-740	24	15	need	need	NOUN
ajmri-740	24	16	for	for	ADP
ajmri-740	24	17	a	a	DET
ajmri-740	24	18	robust	robust	ADJ
ajmri-740	24	19	cloaked	cloaked	ADJ
ajmri-740	24	20	face	face	NOUN
ajmri-740	24	21	system	system	NOUN
ajmri-740	24	22	is	be	AUX
ajmri-740	24	23	necessary	necessary	ADJ
ajmri-740	24	24	for	for	ADP
ajmri-740	24	25	realworld	realworld	PROPN
ajmri-740	24	26	applications	application	NOUN
ajmri-740	24	27	.	.	PUNCT
ajmri-740	25	1	in	in	ADP
ajmri-740	25	2	this	this	DET
ajmri-740	25	3	perspective	perspective	NOUN
ajmri-740	25	4	,	,	PUNCT
ajmri-740	25	5	we	we	PRON
ajmri-740	25	6	will	will	AUX
ajmri-740	25	7	use	use	VERB
ajmri-740	25	8	new	new	ADJ
ajmri-740	25	9	approaches	approach	NOUN
ajmri-740	25	10	based	base	VERB
ajmri-740	25	11	on	on	ADP
ajmri-740	25	12	machine	machine	NOUN
ajmri-740	25	13	learning	learning	NOUN
ajmri-740	25	14	,	,	PUNCT
ajmri-740	25	15	more	more	ADV
ajmri-740	25	16	precisely	precisely	ADV
ajmri-740	25	17	on	on	ADP
ajmri-740	25	18	deep	deep	ADJ
ajmri-740	25	19	learning	learning	NOUN
ajmri-740	25	20	,	,	PUNCT
ajmri-740	25	21	which	which	PRON
ajmri-740	25	22	extracts	extract	VERB
ajmri-740	25	23	hierarchical	hierarchical	ADJ
ajmri-740	25	24	and	and	CCONJ
ajmri-740	25	25	semantic	semantic	ADJ
ajmri-740	25	26	structures	structure	NOUN
ajmri-740	25	27	existing	exist	VERB
ajmri-740	25	28	in	in	ADP
ajmri-740	25	29	images	image	NOUN
ajmri-740	25	30	;	;	PUNCT
ajmri-740	25	31	these	these	PRON
ajmri-740	25	32	are	be	AUX
ajmri-740	25	33	convolutional	convolutional	ADJ
ajmri-740	25	34	neural	neural	ADJ
ajmri-740	25	35	networks	network	NOUN
ajmri-740	25	36	used	use	VERB
ajmri-740	25	37	.	.	PUNCT
ajmri-740	26	1	convolutional	convolutional	ADJ
ajmri-740	26	2	neural	neural	ADJ
ajmri-740	26	3	networks	network	NOUN
ajmri-740	26	4	,	,	PUNCT
ajmri-740	26	5	which	which	PRON
ajmri-740	26	6	are	be	AUX
ajmri-740	26	7	multilayer	multilayer	ADJ
ajmri-740	26	8	perceptrons	perceptron	NOUN
ajmri-740	26	9	coupled	couple	VERB
ajmri-740	26	10	with	with	ADP
ajmri-740	26	11	convolutional	convolutional	ADJ
ajmri-740	26	12	layers	layer	NOUN
ajmri-740	26	13	,	,	PUNCT
ajmri-740	26	14	are	be	AUX
ajmri-740	26	15	part	part	NOUN
ajmri-740	26	16	of	of	ADP
ajmri-740	26	17	deep	deep	ADJ
ajmri-740	26	18	learning	learning	NOUN
ajmri-740	26	19	approaches	approach	NOUN
ajmri-740	26	20	and	and	CCONJ
ajmri-740	26	21	have	have	AUX
ajmri-740	26	22	become	become	VERB
ajmri-740	26	23	indispensable	indispensable	ADJ
ajmri-740	26	24	for	for	ADP
ajmri-740	26	25	detection	detection	NOUN
ajmri-740	26	26	and	and	CCONJ
ajmri-740	26	27	recognition	recognition	NOUN
ajmri-740	26	28	in	in	ADP
ajmri-740	26	29	computer	computer	NOUN
ajmri-740	26	30	vision	vision	NOUN
ajmri-740	26	31	(	(	PUNCT
ajmri-740	26	32	siegmund	siegmund	PROPN
ajmri-740	26	33	et	et	PROPN
ajmri-740	26	34	al	al	PROPN
ajmri-740	26	35	.	.	PROPN
ajmri-740	26	36	,	,	PUNCT
ajmri-740	26	37	2021	2021	NUM
ajmri-740	26	38	)	)	PUNCT
ajmri-740	26	39	.	.	PUNCT
ajmri-740	27	1	they	they	PRON
ajmri-740	27	2	can	can	AUX
ajmri-740	27	3	extract	extract	VERB
ajmri-740	27	4	landmarks	landmark	NOUN
ajmri-740	27	5	by	by	ADP
ajmri-740	27	6	themselves	themselves	PRON
ajmri-740	27	7	(	(	PUNCT
ajmri-740	27	8	wang	wang	PROPN
ajmri-740	27	9	et	et	PROPN
ajmri-740	27	10	al	al	PROPN
ajmri-740	27	11	.	.	PROPN
ajmri-740	27	12	,	,	PUNCT
ajmri-740	27	13	2020	2020	NUM
ajmri-740	27	14	)	)	PUNCT
ajmri-740	27	15	.	.	PUNCT
ajmri-740	28	1	pre	pre	VERB
ajmri-740	28	2	-	-	ADJ
ajmri-740	28	3	trained	train	VERB
ajmri-740	28	4	convolutional	convolutional	ADJ
ajmri-740	28	5	neural	neural	ADJ
ajmri-740	28	6	network	network	NOUN
ajmri-740	28	7	algorithms	algorithm	NOUN
ajmri-740	28	8	allow	allow	VERB
ajmri-740	28	9	for	for	ADP
ajmri-740	28	10	transfer	transfer	NOUN
ajmri-740	28	11	learning	learning	NOUN
ajmri-740	28	12	,	,	PUNCT
ajmri-740	28	13	which	which	PRON
ajmri-740	28	14	transfers	transfer	VERB
ajmri-740	28	15	the	the	DET
ajmri-740	28	16	skill	skill	NOUN
ajmri-740	28	17	learned	learn	VERB
ajmri-740	28	18	on	on	ADP
ajmri-740	28	19	one	one	NUM
ajmri-740	28	20	dataset	dataset	NOUN
ajmri-740	28	21	to	to	PART
ajmri-740	28	22	adapt	adapt	VERB
ajmri-740	28	23	it	it	PRON
ajmri-740	28	24	to	to	ADP
ajmri-740	28	25	a	a	DET
ajmri-740	28	26	new	new	ADJ
ajmri-740	28	27	dataset	dataset	NOUN
ajmri-740	28	28	it	it	PRON
ajmri-740	28	29	will	will	AUX
ajmri-740	28	30	be	be	AUX
ajmri-740	28	31	faced	face	VERB
ajmri-740	28	32	with	with	ADP
ajmri-740	28	33	(	(	PUNCT
ajmri-740	28	34	arnia	arnia	PROPN
ajmri-740	28	35	et	et	PROPN
ajmri-740	28	36	al	al	PROPN
ajmri-740	28	37	.	.	PROPN
ajmri-740	28	38	,	,	PUNCT
ajmri-740	28	39	2021	2021	NUM
ajmri-740	28	40	)	)	PUNCT
ajmri-740	28	41	.	.	PUNCT
ajmri-740	29	1	the	the	DET
ajmri-740	29	2	algorithms	algorithm	NOUN
ajmri-740	29	3	used	use	VERB
ajmri-740	29	4	were	be	AUX
ajmri-740	29	5	trained	train	VERB
ajmri-740	29	6	on	on	ADP
ajmri-740	29	7	the	the	DET
ajmri-740	29	8	image	image	NOUN
ajmri-740	29	9	database	database	NOUN
ajmri-740	29	10	,	,	PUNCT
ajmri-740	29	11	namely	namely	ADV
ajmri-740	29	12	imagenet	imagenet	NOUN
ajmri-740	29	13	.	.	PUNCT
ajmri-740	30	1	the	the	DET
ajmri-740	30	2	study	study	NOUN
ajmri-740	30	3	contributes	contribute	VERB
ajmri-740	30	4	to	to	ADP
ajmri-740	30	5	recognizing	recognize	VERB
ajmri-740	30	6	hidden	hide	VERB
ajmri-740	30	7	faces	face	NOUN
ajmri-740	30	8	by	by	ADP
ajmri-740	30	9	comparing	compare	VERB
ajmri-740	30	10	three	three	NUM
ajmri-740	30	11	deep	deep	ADJ
ajmri-740	30	12	learning	learning	NOUN
ajmri-740	30	13	algorithms	algorithm	NOUN
ajmri-740	30	14	,	,	PUNCT
ajmri-740	30	15	resnet-50	resnet-50	PROPN
ajmri-740	30	16	,	,	PUNCT
ajmri-740	30	17	vgg-19	vgg-19	NOUN
ajmri-740	30	18	,	,	PUNCT
ajmri-740	30	19	and	and	CCONJ
ajmri-740	30	20	densenet-201	densenet-201	ADJ
ajmri-740	30	21	,	,	PUNCT
ajmri-740	30	22	based	base	VERB
ajmri-740	30	23	on	on	ADP
ajmri-740	30	24	pre	pre	ADJ
ajmri-740	30	25	-	-	ADJ
ajmri-740	30	26	trained	train	VERB
ajmri-740	30	27	convolutional	convolutional	ADJ
ajmri-740	30	28	neural	neural	ADJ
ajmri-740	30	29	network	network	NOUN
ajmri-740	30	30	algorithms	algorithm	NOUN
ajmri-740	30	31	.	.	PUNCT
ajmri-740	31	1	these	these	DET
ajmri-740	31	2	algorithms	algorithm	NOUN
ajmri-740	31	3	will	will	AUX
ajmri-740	31	4	be	be	AUX
ajmri-740	31	5	used	use	VERB
ajmri-740	31	6	for	for	ADP
ajmri-740	31	7	the	the	DET
ajmri-740	31	8	face	face	NOUN
ajmri-740	31	9	recognition	recognition	NOUN
ajmri-740	31	10	of	of	ADP
ajmri-740	31	11	occluded	occluded	ADJ
ajmri-740	31	12	faces	face	NOUN
ajmri-740	31	13	from	from	ADP
ajmri-740	31	14	the	the	DET
ajmri-740	31	15	reference	reference	NOUN
ajmri-740	31	16	ar	ar	PROPN
ajmri-740	31	17	face	face	NOUN
ajmri-740	31	18	database	database	NOUN
ajmri-740	31	19	,	,	PUNCT
ajmri-740	31	20	and	and	CCONJ
ajmri-740	31	21	it	it	PRON
ajmri-740	31	22	will	will	AUX
ajmri-740	31	23	be	be	AUX
ajmri-740	31	24	a	a	DET
ajmri-740	31	25	question	question	NOUN
ajmri-740	31	26	of	of	ADP
ajmri-740	31	27	comparing	compare	VERB
ajmri-740	31	28	the	the	DET
ajmri-740	31	29	performances	performance	NOUN
ajmri-740	31	30	by	by	ADP
ajmri-740	31	31	playing	play	VERB
ajmri-740	31	32	on	on	ADP
ajmri-740	31	33	the	the	DET
ajmri-740	31	34	following	follow	VERB
ajmri-740	31	35	parameters	parameter	NOUN
ajmri-740	31	36	:	:	PUNCT
ajmri-740	31	37	•	•	NUM
ajmri-740	31	38	epochs	epoch	NOUN
ajmri-740	31	39	•	•	NOUN
ajmri-740	31	40	batch	batch	NOUN
ajmri-740	31	41	size	size	NOUN
ajmri-740	31	42	•	•	NOUN
ajmri-740	31	43	optimizers	optimizer	NOUN
ajmri-740	31	44	1	1	NUM
ajmri-740	31	45	laboratoire	laboratoire	PROPN
ajmri-740	31	46	mécanique	mécanique	X
ajmri-740	31	47	et	et	PROPN
ajmri-740	31	48	informatique	informatique	PROPN
ajmri-740	31	49	,	,	PUNCT
ajmri-740	31	50	université	université	PROPN
ajmri-740	31	51	felix	felix	PROPN
ajmri-740	31	52	houphouët	houphouët	PROPN
ajmri-740	31	53	-	-	PUNCT
ajmri-740	31	54	boigny	boigny	PROPN
ajmri-740	31	55	,	,	PUNCT
ajmri-740	31	56	côte	côte	NOUN
ajmri-740	31	57	d’ivoire	d’ivoire	NOUN
ajmri-740	31	58	2	2	NUM
ajmri-740	31	59	unité	unité	NOUN
ajmri-740	31	60	de	de	X
ajmri-740	31	61	recherche	recherche	X
ajmri-740	31	62	et	et	PROPN
ajmri-740	31	63	d’expertise	d’expertise	PROPN
ajmri-740	31	64	numérique	numérique	PROPN
ajmri-740	31	65	,	,	PUNCT
ajmri-740	31	66	université	université	PROPN
ajmri-740	31	67	virtuelle	virtuelle	X
ajmri-740	31	68	de	de	X
ajmri-740	31	69	côte	côte	PROPN
ajmri-740	31	70	d’ivoire	d’ivoire	PROPN
ajmri-740	31	71	,	,	PUNCT
ajmri-740	31	72	côte	côte	PROPN
ajmri-740	31	73	d’ivoire	d’ivoire	NOUN
ajmri-740	31	74	*	*	PUNCT
ajmri-740	31	75	corresponding	correspond	VERB
ajmri-740	31	76	author	author	NOUN
ajmri-740	31	77	’s	’s	PART
ajmri-740	31	78	e	e	NOUN
ajmri-740	31	79	-	-	NOUN
ajmri-740	31	80	mail	mail	NOUN
ajmri-740	31	81	:	:	PUNCT
ajmri-740	32	1	patoudiarra@gmail.com	patoudiarra@gmail.com	X
ajmri-740	33	1	https://doi.org/10.54536/ajmri.v1i5.740	https://doi.org/10.54536/ajmri.v1i5.740	NOUN
ajmri-740	33	2	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	33	3	mailto	mailto	NOUN
ajmri-740	33	4	:	:	PUNCT
ajmri-740	33	5	patoudiarra%40gmail.com?subject=	patoudiarra%40gmail.com?subject=	ADJ
ajmri-740	33	6	pa	pa	PROPN
ajmri-740	33	7	ge	ge	PROPN
ajmri-740	33	8	25	25	NUM
ajmri-740	33	9	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	33	10	am	am	NOUN
ajmri-740	33	11	.	.	PUNCT
ajmri-740	34	1	j.	j.	PROPN
ajmri-740	34	2	multidis	multidis	PROPN
ajmri-740	34	3	.	.	PUNCT
ajmri-740	35	1	res	re	NOUN
ajmri-740	35	2	.	.	PUNCT
ajmri-740	36	1	innov	innov	PROPN
ajmri-740	36	2	.	.	PUNCT
ajmri-740	37	1	1(5	1(5	NUM
ajmri-740	37	2	)	)	PUNCT
ajmri-740	37	3	24	24	NUM
ajmri-740	37	4	-	-	SYM
ajmri-740	37	5	32	32	NUM
ajmri-740	37	6	,	,	PUNCT
ajmri-740	37	7	2022	2022	NUM
ajmri-740	37	8	all	all	DET
ajmri-740	37	9	parameters	parameter	NOUN
ajmri-740	37	10	will	will	AUX
ajmri-740	37	11	evaluate	evaluate	VERB
ajmri-740	37	12	the	the	DET
ajmri-740	37	13	performance	performance	NOUN
ajmri-740	37	14	of	of	ADP
ajmri-740	37	15	the	the	DET
ajmri-740	37	16	previous	previous	ADJ
ajmri-740	37	17	algorithm	algorithm	NOUN
ajmri-740	37	18	and	and	CCONJ
ajmri-740	37	19	select	select	VERB
ajmri-740	37	20	the	the	DET
ajmri-740	37	21	one	one	NOUN
ajmri-740	37	22	with	with	ADP
ajmri-740	37	23	the	the	DET
ajmri-740	37	24	best	good	ADJ
ajmri-740	37	25	accuracy	accuracy	NOUN
ajmri-740	37	26	on	on	ADP
ajmri-740	37	27	the	the	DET
ajmri-740	37	28	reference	reference	NOUN
ajmri-740	37	29	data	datum	NOUN
ajmri-740	37	30	used	use	VERB
ajmri-740	37	31	.	.	PUNCT
ajmri-740	38	1	the	the	DET
ajmri-740	38	2	challenges	challenge	NOUN
ajmri-740	38	3	of	of	ADP
ajmri-740	38	4	face	face	NOUN
ajmri-740	38	5	occlusion	occlusion	NOUN
ajmri-740	38	6	and	and	CCONJ
ajmri-740	38	7	deep	deep	ADJ
ajmri-740	38	8	learning	learning	NOUN
ajmri-740	38	9	could	could	AUX
ajmri-740	38	10	bring	bring	VERB
ajmri-740	38	11	innovation	innovation	NOUN
ajmri-740	38	12	to	to	ADP
ajmri-740	38	13	the	the	DET
ajmri-740	38	14	security	security	NOUN
ajmri-740	38	15	of	of	ADP
ajmri-740	38	16	computer	computer	NOUN
ajmri-740	38	17	networks	network	NOUN
ajmri-740	38	18	and	and	CCONJ
ajmri-740	38	19	cybersecurity	cybersecurity	NOUN
ajmri-740	38	20	.	.	PUNCT
ajmri-740	39	1	this	this	DET
ajmri-740	39	2	article	article	NOUN
ajmri-740	39	3	is	be	AUX
ajmri-740	39	4	organized	organize	VERB
ajmri-740	39	5	as	as	SCONJ
ajmri-740	39	6	follows	follow	VERB
ajmri-740	39	7	:	:	PUNCT
ajmri-740	39	8	section	section	NOUN
ajmri-740	39	9	2	2	NUM
ajmri-740	39	10	presents	present	VERB
ajmri-740	39	11	some	some	DET
ajmri-740	39	12	previous	previous	ADJ
ajmri-740	39	13	work	work	NOUN
ajmri-740	39	14	.	.	PUNCT
ajmri-740	40	1	section	section	NOUN
ajmri-740	40	2	3	3	NUM
ajmri-740	40	3	offers	offer	VERB
ajmri-740	40	4	convolution	convolution	NOUN
ajmri-740	40	5	neural	neural	ADJ
ajmri-740	40	6	networks	network	NOUN
ajmri-740	40	7	and	and	CCONJ
ajmri-740	40	8	details	detail	NOUN
ajmri-740	40	9	our	our	PRON
ajmri-740	40	10	proposed	propose	VERB
ajmri-740	40	11	methodology	methodology	NOUN
ajmri-740	40	12	and	and	CCONJ
ajmri-740	40	13	hardware	hardware	NOUN
ajmri-740	40	14	.	.	PUNCT
ajmri-740	41	1	section	section	NOUN
ajmri-740	41	2	4	4	NUM
ajmri-740	41	3	presents	present	VERB
ajmri-740	41	4	the	the	DET
ajmri-740	41	5	analysis	analysis	NOUN
ajmri-740	41	6	of	of	ADP
ajmri-740	41	7	our	our	PRON
ajmri-740	41	8	experiments	experiment	NOUN
ajmri-740	41	9	and	and	CCONJ
ajmri-740	41	10	results	result	NOUN
ajmri-740	41	11	,	,	PUNCT
ajmri-740	41	12	evaluates	evaluate	NOUN
ajmri-740	41	13	and	and	CCONJ
ajmri-740	41	14	compares	compare	VERB
ajmri-740	41	15	our	our	PRON
ajmri-740	41	16	used	used	ADJ
ajmri-740	41	17	algorithms	algorithm	NOUN
ajmri-740	41	18	,	,	PUNCT
ajmri-740	41	19	also	also	ADV
ajmri-740	41	20	discusses	discuss	VERB
ajmri-740	41	21	the	the	DET
ajmri-740	41	22	obtained	obtain	VERB
ajmri-740	41	23	results	result	NOUN
ajmri-740	41	24	with	with	ADP
ajmri-740	41	25	other	other	ADJ
ajmri-740	41	26	works	work	NOUN
ajmri-740	41	27	,	,	PUNCT
ajmri-740	41	28	and	and	CCONJ
ajmri-740	41	29	finally	finally	ADV
ajmri-740	41	30	,	,	PUNCT
ajmri-740	41	31	we	we	PRON
ajmri-740	41	32	will	will	AUX
ajmri-740	41	33	conclude	conclude	VERB
ajmri-740	41	34	.	.	PUNCT
ajmri-740	42	1	literature	literature	NOUN
ajmri-740	42	2	review	review	NOUN
ajmri-740	42	3	in	in	ADP
ajmri-740	42	4	this	this	DET
ajmri-740	42	5	section	section	NOUN
ajmri-740	42	6	,	,	PUNCT
ajmri-740	42	7	we	we	PRON
ajmri-740	42	8	will	will	AUX
ajmri-740	42	9	review	review	VERB
ajmri-740	42	10	some	some	DET
ajmri-740	42	11	face	face	NOUN
ajmri-740	42	12	recognition	recognition	NOUN
ajmri-740	42	13	work	work	NOUN
ajmri-740	42	14	with	with	ADP
ajmri-740	42	15	occlusion	occlusion	NOUN
ajmri-740	42	16	.	.	PUNCT
ajmri-740	43	1	methods	method	NOUN
ajmri-740	43	2	addressing	address	VERB
ajmri-740	43	3	face	face	NOUN
ajmri-740	43	4	recognition	recognition	NOUN
ajmri-740	43	5	with	with	ADP
ajmri-740	43	6	occlusion	occlusion	NOUN
ajmri-740	43	7	include	include	VERB
ajmri-740	43	8	finding	find	VERB
ajmri-740	43	9	features	feature	NOUN
ajmri-740	43	10	or	or	CCONJ
ajmri-740	43	11	classifiers	classifier	NOUN
ajmri-740	43	12	that	that	PRON
ajmri-740	43	13	tolerate	tolerate	VERB
ajmri-740	43	14	corruption	corruption	NOUN
ajmri-740	43	15	.	.	PUNCT
ajmri-740	44	1	for	for	ADP
ajmri-740	44	2	example	example	NOUN
ajmri-740	44	3	,	,	PUNCT
ajmri-740	44	4	aleix	aleix	VERB
ajmri-740	44	5	m.	m.	PROPN
ajmri-740	44	6	martinez	martinez	PROPN
ajmri-740	44	7	proposed	propose	VERB
ajmri-740	44	8	a	a	DET
ajmri-740	44	9	probabilistic	probabilistic	ADJ
ajmri-740	44	10	approach	approach	NOUN
ajmri-740	44	11	that	that	PRON
ajmri-740	44	12	can	can	AUX
ajmri-740	44	13	compensate	compensate	VERB
ajmri-740	44	14	partially	partially	ADV
ajmri-740	44	15	occluded	occlude	VERB
ajmri-740	44	16	faces	face	NOUN
ajmri-740	44	17	(	(	PUNCT
ajmri-740	44	18	martinez	martinez	PROPN
ajmri-740	44	19	,	,	PUNCT
ajmri-740	44	20	2002	2002	NUM
ajmri-740	44	21	)	)	PUNCT
ajmri-740	44	22	.	.	PUNCT
ajmri-740	45	1	park	park	NOUN
ajmri-740	45	2	et	et	PROPN
ajmri-740	45	3	al	al	PROPN
ajmri-740	45	4	.	.	PROPN
ajmri-740	45	5	proposed	propose	VERB
ajmri-740	45	6	a	a	DET
ajmri-740	45	7	new	new	ADJ
ajmri-740	45	8	feature	feature	NOUN
ajmri-740	45	9	-	-	PUNCT
ajmri-740	45	10	based	base	VERB
ajmri-740	45	11	face	face	NOUN
ajmri-740	45	12	recognition	recognition	NOUN
ajmri-740	45	13	algorithm	algorithm	NOUN
ajmri-740	45	14	where	where	SCONJ
ajmri-740	45	15	the	the	DET
ajmri-740	45	16	face	face	NOUN
ajmri-740	45	17	-	-	PUNCT
ajmri-740	45	18	arg	arg	NOUN
ajmri-740	45	19	model	model	NOUN
ajmri-740	45	20	represents	represent	VERB
ajmri-740	45	21	a	a	DET
ajmri-740	45	22	face	face	NOUN
ajmri-740	45	23	.	.	PUNCT
ajmri-740	46	1	all	all	DET
ajmri-740	46	2	geometric	geometric	ADJ
ajmri-740	46	3	quantities	quantity	NOUN
ajmri-740	46	4	and	and	CCONJ
ajmri-740	46	5	structural	structural	ADJ
ajmri-740	46	6	information	information	NOUN
ajmri-740	46	7	are	be	AUX
ajmri-740	46	8	encoded	encode	VERB
ajmri-740	46	9	into	into	ADP
ajmri-740	46	10	an	an	DET
ajmri-740	46	11	attributed	attribute	VERB
ajmri-740	46	12	relational	relational	ADJ
ajmri-740	46	13	graph	graph	NOUN
ajmri-740	46	14	(	(	PUNCT
ajmri-740	46	15	arg	arg	NOUN
ajmri-740	46	16	)	)	PUNCT
ajmri-740	46	17	structure	structure	NOUN
ajmri-740	46	18	.	.	PUNCT
ajmri-740	47	1	then	then	ADV
ajmri-740	47	2	partial	partial	ADJ
ajmri-740	47	3	arg	arg	NOUN
ajmri-740	47	4	matching	matching	NOUN
ajmri-740	47	5	is	be	AUX
ajmri-740	47	6	performed	perform	VERB
ajmri-740	47	7	to	to	PART
ajmri-740	47	8	match	match	VERB
ajmri-740	47	9	the	the	DET
ajmri-740	47	10	face	face	NOUN
ajmri-740	47	11	args	args	NOUN
ajmri-740	47	12	(	(	PUNCT
ajmri-740	47	13	bo	bo	NOUN
ajmri-740	47	14	-	-	PUNCT
ajmri-740	47	15	gun	gun	NOUN
ajmri-740	47	16	park	park	NOUN
ajmri-740	47	17	et	et	PROPN
ajmri-740	47	18	al	al	PROPN
ajmri-740	47	19	.	.	PROPN
ajmri-740	47	20	,	,	PUNCT
ajmri-740	47	21	2005	2005	NUM
ajmri-740	47	22	)	)	PUNCT
ajmri-740	47	23	;	;	PUNCT
ajmri-740	47	24	min	min	PROPN
ajmri-740	47	25	et	et	PROPN
ajmri-740	47	26	al	al	PROPN
ajmri-740	47	27	.	.	PUNCT
ajmri-740	48	1	propose	propose	VERB
ajmri-740	48	2	an	an	DET
ajmri-740	48	3	efficient	efficient	ADJ
ajmri-740	48	4	approach	approach	NOUN
ajmri-740	48	5	that	that	PRON
ajmri-740	48	6	first	first	ADV
ajmri-740	48	7	analyzes	analyze	VERB
ajmri-740	48	8	the	the	DET
ajmri-740	48	9	presence	presence	NOUN
ajmri-740	48	10	of	of	ADP
ajmri-740	48	11	a	a	DET
ajmri-740	48	12	potential	potential	ADJ
ajmri-740	48	13	occlusion	occlusion	NOUN
ajmri-740	48	14	on	on	ADP
ajmri-740	48	15	a	a	DET
ajmri-740	48	16	face	face	NOUN
ajmri-740	48	17	and	and	CCONJ
ajmri-740	48	18	then	then	ADV
ajmri-740	48	19	performs	perform	VERB
ajmri-740	48	20	face	face	NOUN
ajmri-740	48	21	recognition	recognition	NOUN
ajmri-740	48	22	on	on	ADP
ajmri-740	48	23	the	the	DET
ajmri-740	48	24	unoccluded	unoccluded	ADJ
ajmri-740	48	25	facial	facial	ADJ
ajmri-740	48	26	regions	region	NOUN
ajmri-740	48	27	based	base	VERB
ajmri-740	48	28	on	on	ADP
ajmri-740	48	29	selective	selective	ADJ
ajmri-740	48	30	local	local	ADJ
ajmri-740	48	31	gabor	gabor	PROPN
ajmri-740	48	32	binary	binary	NOUN
ajmri-740	48	33	models	model	NOUN
ajmri-740	48	34	(	(	PUNCT
ajmri-740	48	35	min	min	NOUN
ajmri-740	48	36	et	et	PROPN
ajmri-740	48	37	al	al	PROPN
ajmri-740	48	38	.	.	PROPN
ajmri-740	48	39	,	,	PUNCT
ajmri-740	48	40	2014	2014	NUM
ajmri-740	48	41	)	)	PUNCT
ajmri-740	48	42	,	,	PUNCT
ajmri-740	48	43	tsai	tsai	PROPN
ajmri-740	48	44	et	et	PROPN
ajmri-740	48	45	al	al	PROPN
ajmri-740	48	46	.	.	PROPN
ajmri-740	48	47	proposed	propose	VERB
ajmri-740	48	48	a	a	DET
ajmri-740	48	49	deep	deep	ADJ
ajmri-740	48	50	convolution	convolution	NOUN
ajmri-740	48	51	neural	neural	ADJ
ajmri-740	48	52	network	network	NOUN
ajmri-740	48	53	architecture	architecture	NOUN
ajmri-740	48	54	for	for	ADP
ajmri-740	48	55	efficient	efficient	ADJ
ajmri-740	48	56	multi	multi	ADJ
ajmri-740	48	57	-	-	ADJ
ajmri-740	48	58	person	person	NOUN
ajmri-740	48	59	and	and	CCONJ
ajmri-740	48	60	multi	multi	ADJ
ajmri-740	48	61	-	-	ADJ
ajmri-740	48	62	angle	angle	ADJ
ajmri-740	48	63	face	face	NOUN
ajmri-740	48	64	recognition	recognition	NOUN
ajmri-740	48	65	,	,	PUNCT
ajmri-740	48	66	this	this	PRON
ajmri-740	48	67	achieved	achieve	VERB
ajmri-740	48	68	the	the	DET
ajmri-740	48	69	identity	identity	NOUN
ajmri-740	48	70	confidence	confidence	NOUN
ajmri-740	48	71	by	by	ADP
ajmri-740	48	72	using	use	VERB
ajmri-740	48	73	a	a	DET
ajmri-740	48	74	classifier	classifier	NOUN
ajmri-740	48	75	for	for	ADP
ajmri-740	48	76	these	these	DET
ajmri-740	48	77	features	feature	NOUN
ajmri-740	48	78	.	.	PUNCT
ajmri-740	49	1	the	the	DET
ajmri-740	49	2	experimental	experimental	ADJ
ajmri-740	49	3	results	result	NOUN
ajmri-740	49	4	showed	show	VERB
ajmri-740	49	5	that	that	SCONJ
ajmri-740	49	6	the	the	DET
ajmri-740	49	7	accuracy	accuracy	NOUN
ajmri-740	49	8	of	of	ADP
ajmri-740	49	9	identity	identity	NOUN
ajmri-740	49	10	recognition	recognition	NOUN
ajmri-740	49	11	could	could	AUX
ajmri-740	49	12	reach	reach	VERB
ajmri-740	49	13	90.61	90.61	NUM
ajmri-740	49	14	%	%	NOUN
ajmri-740	49	15	(	(	PUNCT
ajmri-740	49	16	tsai	tsai	PROPN
ajmri-740	49	17	et	et	PROPN
ajmri-740	49	18	al	al	PROPN
ajmri-740	49	19	.	.	PROPN
ajmri-740	49	20	,	,	PUNCT
ajmri-740	49	21	2018	2018	NUM
ajmri-740	49	22	)	)	PUNCT
ajmri-740	49	23	.	.	PUNCT
ajmri-740	50	1	montera	montera	NOUN
ajmri-740	50	2	et	et	PROPN
ajmri-740	50	3	al	al	PROPN
ajmri-740	50	4	.	.	PROPN
ajmri-740	50	5	proposed	propose	VERB
ajmri-740	50	6	a	a	DET
ajmri-740	50	7	face	face	NOUN
ajmri-740	50	8	recognition	recognition	NOUN
ajmri-740	50	9	method	method	NOUN
ajmri-740	50	10	performed	perform	VERB
ajmri-740	50	11	using	use	VERB
ajmri-740	50	12	a	a	DET
ajmri-740	50	13	hybrid	hybrid	ADJ
ajmri-740	50	14	process	process	NOUN
ajmri-740	50	15	that	that	PRON
ajmri-740	50	16	combines	combine	VERB
ajmri-740	50	17	haar	haar	NOUN
ajmri-740	50	18	cascades	cascade	NOUN
ajmri-740	50	19	and	and	CCONJ
ajmri-740	50	20	eigenface	eigenface	NOUN
ajmri-740	50	21	methods	method	NOUN
ajmri-740	50	22	,	,	PUNCT
ajmri-740	50	23	which	which	PRON
ajmri-740	50	24	can	can	AUX
ajmri-740	50	25	detect	detect	VERB
ajmri-740	50	26	multiple	multiple	ADJ
ajmri-740	50	27	faces	face	NOUN
ajmri-740	50	28	(	(	PUNCT
ajmri-740	50	29	55	55	NUM
ajmri-740	50	30	faces	face	NOUN
ajmri-740	50	31	)	)	PUNCT
ajmri-740	50	32	in	in	ADP
ajmri-740	50	33	a	a	DET
ajmri-740	50	34	single	single	ADJ
ajmri-740	50	35	detection	detection	NOUN
ajmri-740	50	36	process	process	NOUN
ajmri-740	50	37	with	with	ADP
ajmri-740	50	38	an	an	DET
ajmri-740	50	39	accuracy	accuracy	NOUN
ajmri-740	50	40	level	level	NOUN
ajmri-740	50	41	of	of	ADP
ajmri-740	50	42	91.67	91.67	NUM
ajmri-740	50	43	%	%	NOUN
ajmri-740	50	44	(	(	PUNCT
ajmri-740	50	45	mantoro	mantoro	NOUN
ajmri-740	50	46	et	et	PROPN
ajmri-740	50	47	al	al	PROPN
ajmri-740	50	48	.	.	PROPN
ajmri-740	50	49	,	,	PUNCT
ajmri-740	50	50	2018	2018	NUM
ajmri-740	50	51	)	)	PUNCT
ajmri-740	50	52	;	;	PUNCT
ajmri-740	50	53	lu	lu	PROPN
ajmri-740	50	54	et	et	PROPN
ajmri-740	50	55	al	al	PROPN
ajmri-740	50	56	.	.	PROPN
ajmri-740	50	57	proposed	propose	VERB
ajmri-740	50	58	a	a	DET
ajmri-740	50	59	partial	partial	ADJ
ajmri-740	50	60	occlusion	occlusion	NOUN
ajmri-740	50	61	face	face	NOUN
ajmri-740	50	62	recognition	recognition	NOUN
ajmri-740	50	63	algorithm	algorithm	NOUN
ajmri-740	50	64	based	base	VERB
ajmri-740	50	65	on	on	ADP
ajmri-740	50	66	a	a	DET
ajmri-740	50	67	recurrent	recurrent	ADJ
ajmri-740	50	68	neural	neural	ADJ
ajmri-740	50	69	network	network	NOUN
ajmri-740	50	70	that	that	PRON
ajmri-740	50	71	yields	yield	VERB
ajmri-740	50	72	a	a	DET
ajmri-740	50	73	result	result	NOUN
ajmri-740	50	74	that	that	PRON
ajmri-740	50	75	ranges	range	VERB
ajmri-740	50	76	from	from	ADP
ajmri-740	50	77	88.49	88.49	NUM
ajmri-740	50	78	to	to	ADP
ajmri-740	50	79	98.45	98.45	NUM
ajmri-740	50	80	%	%	NOUN
ajmri-740	50	81	(	(	PUNCT
ajmri-740	50	82	zhang	zhang	PROPN
ajmri-740	50	83	et	et	PROPN
ajmri-740	50	84	al	al	PROPN
ajmri-740	50	85	.	.	PROPN
ajmri-740	50	86	,	,	PUNCT
ajmri-740	50	87	2020	2020	NUM
ajmri-740	50	88	)	)	PUNCT
ajmri-740	50	89	,	,	PUNCT
ajmri-740	50	90	wu	wu	PROPN
ajmri-740	50	91	et	et	PROPN
ajmri-740	50	92	al	al	PROPN
ajmri-740	50	93	.	.	PROPN
ajmri-740	50	94	proposed	propose	VERB
ajmri-740	50	95	a	a	DET
ajmri-740	50	96	method	method	NOUN
ajmri-740	50	97	based	base	VERB
ajmri-740	50	98	on	on	ADP
ajmri-740	50	99	deep	deep	ADJ
ajmri-740	50	100	learning	learning	NOUN
ajmri-740	50	101	for	for	ADP
ajmri-740	50	102	occluded	occluded	ADJ
ajmri-740	50	103	face	face	NOUN
ajmri-740	50	104	recognition	recognition	NOUN
ajmri-740	50	105	with	with	ADP
ajmri-740	50	106	the	the	DET
ajmri-740	50	107	result	result	NOUN
ajmri-740	50	108	that	that	PRON
ajmri-740	50	109	reaches	reach	VERB
ajmri-740	50	110	98.6	98.6	NUM
ajmri-740	50	111	%	%	NOUN
ajmri-740	50	112	(	(	PUNCT
ajmri-740	50	113	wu	wu	PROPN
ajmri-740	50	114	et	et	PROPN
ajmri-740	50	115	al	al	PROPN
ajmri-740	50	116	.	.	PROPN
ajmri-740	50	117	,	,	PUNCT
ajmri-740	50	118	2019	2019	NUM
ajmri-740	50	119	)	)	PUNCT
ajmri-740	50	120	.	.	PUNCT
ajmri-740	51	1	materials	material	NOUN
ajmri-740	51	2	and	and	CCONJ
ajmri-740	51	3	method	method	NOUN
ajmri-740	51	4	machine	machine	NOUN
ajmri-740	51	5	learning	learning	NOUN
ajmri-740	51	6	is	be	AUX
ajmri-740	51	7	a	a	DET
ajmri-740	51	8	branch	branch	NOUN
ajmri-740	51	9	of	of	ADP
ajmri-740	51	10	artificial	artificial	ADJ
ajmri-740	51	11	intelligence	intelligence	NOUN
ajmri-740	51	12	(	(	PUNCT
ajmri-740	51	13	ai	ai	NOUN
ajmri-740	51	14	)	)	PUNCT
ajmri-740	51	15	that	that	PRON
ajmri-740	51	16	uses	use	VERB
ajmri-740	51	17	algorithms	algorithm	NOUN
ajmri-740	51	18	to	to	PART
ajmri-740	51	19	enable	enable	VERB
ajmri-740	51	20	computer	computer	NOUN
ajmri-740	51	21	systems	system	NOUN
ajmri-740	51	22	to	to	PART
ajmri-740	51	23	infer	infer	VERB
ajmri-740	51	24	patterns	pattern	NOUN
ajmri-740	51	25	from	from	ADP
ajmri-740	51	26	data	datum	NOUN
ajmri-740	51	27	.	.	PUNCT
ajmri-740	52	1	it	it	PRON
ajmri-740	52	2	has	have	VERB
ajmri-740	52	3	many	many	ADJ
ajmri-740	52	4	applications	application	NOUN
ajmri-740	52	5	,	,	PUNCT
ajmri-740	52	6	including	include	VERB
ajmri-740	52	7	bioinformatics	bioinformatics	NOUN
ajmri-740	52	8	,	,	PUNCT
ajmri-740	52	9	fraud	fraud	NOUN
ajmri-740	52	10	detection	detection	NOUN
ajmri-740	52	11	,	,	PUNCT
ajmri-740	52	12	finance	finance	NOUN
ajmri-740	52	13	,	,	PUNCT
ajmri-740	52	14	human	human	ADJ
ajmri-740	52	15	resource	resource	NOUN
ajmri-740	52	16	and	and	CCONJ
ajmri-740	52	17	risk	risk	NOUN
ajmri-740	52	18	management	management	NOUN
ajmri-740	52	19	,	,	PUNCT
ajmri-740	52	20	market	market	NOUN
ajmri-740	52	21	analysis	analysis	NOUN
ajmri-740	52	22	,	,	PUNCT
ajmri-740	52	23	image	image	NOUN
ajmri-740	52	24	recognition	recognition	NOUN
ajmri-740	52	25	,	,	PUNCT
ajmri-740	52	26	and	and	CCONJ
ajmri-740	52	27	natural	natural	ADJ
ajmri-740	52	28	language	language	NOUN
ajmri-740	52	29	processing	processing	NOUN
ajmri-740	52	30	(	(	PUNCT
ajmri-740	52	31	praseetha	praseetha	NOUN
ajmri-740	52	32	et	et	PROPN
ajmri-740	52	33	al	al	PROPN
ajmri-740	52	34	.	.	PROPN
ajmri-740	52	35	,	,	PUNCT
ajmri-740	52	36	2019	2019	NUM
ajmri-740	52	37	)	)	PUNCT
ajmri-740	52	38	.	.	PUNCT
ajmri-740	53	1	new	new	ADJ
ajmri-740	53	2	face	face	NOUN
ajmri-740	53	3	recognition	recognition	NOUN
ajmri-740	53	4	methods	method	NOUN
ajmri-740	53	5	extract	extract	VERB
ajmri-740	53	6	the	the	DET
ajmri-740	53	7	best	good	ADJ
ajmri-740	53	8	features	feature	NOUN
ajmri-740	53	9	from	from	ADP
ajmri-740	53	10	images	image	NOUN
ajmri-740	53	11	and	and	CCONJ
ajmri-740	53	12	tend	tend	VERB
ajmri-740	53	13	to	to	PART
ajmri-740	53	14	learn	learn	VERB
ajmri-740	53	15	these	these	DET
ajmri-740	53	16	features	feature	NOUN
ajmri-740	53	17	using	use	VERB
ajmri-740	53	18	deep	deep	ADJ
ajmri-740	53	19	convolution	convolution	NOUN
ajmri-740	53	20	neural	neural	ADJ
ajmri-740	53	21	network	network	NOUN
ajmri-740	53	22	architectures	architecture	NOUN
ajmri-740	53	23	(	(	PUNCT
ajmri-740	53	24	idelette	idelette	PROPN
ajmri-740	53	25	kambi	kambi	PROPN
ajmri-740	53	26	beli	beli	PROPN
ajmri-740	53	27	&	&	CCONJ
ajmri-740	53	28	guo	guo	PROPN
ajmri-740	53	29	,	,	PUNCT
ajmri-740	53	30	2017	2017	NUM
ajmri-740	53	31	)	)	PUNCT
ajmri-740	53	32	.	.	PUNCT
ajmri-740	54	1	this	this	PRON
ajmri-740	54	2	has	have	AUX
ajmri-740	54	3	led	lead	VERB
ajmri-740	54	4	to	to	ADP
ajmri-740	54	5	the	the	DET
ajmri-740	54	6	extraordinary	extraordinary	ADJ
ajmri-740	54	7	success	success	NOUN
ajmri-740	54	8	of	of	ADP
ajmri-740	54	9	famous	famous	ADJ
ajmri-740	54	10	convolutional	convolutional	ADJ
ajmri-740	54	11	architectures	architecture	NOUN
ajmri-740	54	12	such	such	ADJ
ajmri-740	54	13	as	as	ADP
ajmri-740	54	14	vggnet	vggnet	NOUN
ajmri-740	54	15	,	,	PUNCT
ajmri-740	54	16	googlenet	googlenet	NOUN
ajmri-740	54	17	,	,	PUNCT
ajmri-740	54	18	resnet	resnet	NOUN
ajmri-740	54	19	,	,	PUNCT
ajmri-740	54	20	etc.(zhou	etc.(zhou	X
ajmri-740	54	21	et	et	PROPN
ajmri-740	54	22	al	al	PROPN
ajmri-740	54	23	.	.	PROPN
ajmri-740	54	24	,	,	PUNCT
ajmri-740	54	25	2018	2018	NUM
ajmri-740	54	26	)	)	PUNCT
ajmri-740	54	27	.	.	PUNCT
ajmri-740	55	1	deep	deep	ADJ
ajmri-740	55	2	learning	learning	NOUN
ajmri-740	55	3	is	be	AUX
ajmri-740	55	4	one	one	NUM
ajmri-740	55	5	of	of	ADP
ajmri-740	55	6	the	the	DET
ajmri-740	55	7	most	most	ADV
ajmri-740	55	8	widely	widely	ADV
ajmri-740	55	9	used	use	VERB
ajmri-740	55	10	machine	machine	NOUN
ajmri-740	55	11	learning	learn	VERB
ajmri-740	55	12	techniques	technique	NOUN
ajmri-740	55	13	that	that	PRON
ajmri-740	55	14	has	have	AUX
ajmri-740	55	15	been	be	AUX
ajmri-740	55	16	hugely	hugely	ADV
ajmri-740	55	17	successful	successful	ADJ
ajmri-740	55	18	in	in	ADP
ajmri-740	55	19	applications	application	NOUN
ajmri-740	55	20	such	such	ADJ
ajmri-740	55	21	as	as	ADP
ajmri-740	55	22	anomaly	anomaly	NOUN
ajmri-740	55	23	detection	detection	NOUN
ajmri-740	55	24	,	,	PUNCT
ajmri-740	55	25	image	image	NOUN
ajmri-740	55	26	detection	detection	NOUN
ajmri-740	55	27	,	,	PUNCT
ajmri-740	55	28	pattern	pattern	NOUN
ajmri-740	55	29	recognition	recognition	NOUN
ajmri-740	55	30	,	,	PUNCT
ajmri-740	55	31	and	and	CCONJ
ajmri-740	55	32	natural	natural	ADJ
ajmri-740	55	33	language	language	NOUN
ajmri-740	55	34	processing	processing	NOUN
ajmri-740	55	35	(	(	PUNCT
ajmri-740	55	36	praseetha	praseetha	NOUN
ajmri-740	55	37	et	et	PROPN
ajmri-740	55	38	al	al	PROPN
ajmri-740	55	39	.	.	PROPN
ajmri-740	55	40	,	,	PUNCT
ajmri-740	55	41	2019	2019	NUM
ajmri-740	55	42	)	)	PUNCT
ajmri-740	55	43	.	.	PUNCT
ajmri-740	56	1	but	but	CCONJ
ajmri-740	56	2	training	train	VERB
ajmri-740	56	3	deeper	deep	ADJ
ajmri-740	56	4	neural	neural	ADJ
ajmri-740	56	5	networks	network	NOUN
ajmri-740	56	6	is	be	AUX
ajmri-740	56	7	challenging	challenge	VERB
ajmri-740	56	8	due	due	ADJ
ajmri-740	56	9	to	to	ADP
ajmri-740	56	10	the	the	DET
ajmri-740	56	11	vanishing	vanish	VERB
ajmri-740	56	12	gradient	gradient	NOUN
ajmri-740	56	13	and	and	CCONJ
ajmri-740	56	14	degradation	degradation	NOUN
ajmri-740	56	15	problems	problem	NOUN
ajmri-740	56	16	(	(	PUNCT
ajmri-740	56	17	reddy	reddy	PROPN
ajmri-740	56	18	&	&	CCONJ
ajmri-740	56	19	juliet	juliet	PROPN
ajmri-740	56	20	,	,	PUNCT
ajmri-740	56	21	2019	2019	NUM
ajmri-740	56	22	)	)	PUNCT
ajmri-740	56	23	.	.	PUNCT
ajmri-740	57	1	however	however	ADV
ajmri-740	57	2	,	,	PUNCT
ajmri-740	57	3	there	there	PRON
ajmri-740	57	4	are	be	VERB
ajmri-740	57	5	four	four	NUM
ajmri-740	57	6	(	(	PUNCT
ajmri-740	57	7	4	4	NUM
ajmri-740	57	8	)	)	PUNCT
ajmri-740	57	9	significant	significant	ADJ
ajmri-740	57	10	families	family	NOUN
ajmri-740	57	11	of	of	ADP
ajmri-740	57	12	deep	deep	ADJ
ajmri-740	57	13	learning	learning	NOUN
ajmri-740	57	14	algorithms	algorithm	NOUN
ajmri-740	57	15	,	,	PUNCT
ajmri-740	57	16	deep	deep	ADJ
ajmri-740	57	17	neural	neural	ADJ
ajmri-740	57	18	network	network	NOUN
ajmri-740	57	19	,	,	PUNCT
ajmri-740	57	20	convolutional	convolutional	ADJ
ajmri-740	57	21	neural	neural	ADJ
ajmri-740	57	22	network	network	NOUN
ajmri-740	57	23	,	,	PUNCT
ajmri-740	57	24	recurrent	recurrent	ADJ
ajmri-740	57	25	neural	neural	ADJ
ajmri-740	57	26	network	network	NOUN
ajmri-740	57	27	,	,	PUNCT
ajmri-740	57	28	and	and	CCONJ
ajmri-740	57	29	deep	deep	ADJ
ajmri-740	57	30	belief	belief	NOUN
ajmri-740	57	31	network	network	NOUN
ajmri-740	57	32	(	(	PUNCT
ajmri-740	57	33	singh	singh	PROPN
ajmri-740	57	34	et	et	PROPN
ajmri-740	57	35	al	al	PROPN
ajmri-740	57	36	.	.	PROPN
ajmri-740	57	37	,	,	PUNCT
ajmri-740	57	38	2020	2020	NUM
ajmri-740	57	39	)	)	PUNCT
ajmri-740	57	40	.	.	PUNCT
ajmri-740	58	1	our	our	PRON
ajmri-740	58	2	study	study	NOUN
ajmri-740	58	3	will	will	AUX
ajmri-740	58	4	use	use	VERB
ajmri-740	58	5	convolutional	convolutional	ADJ
ajmri-740	58	6	neural	neural	ADJ
ajmri-740	58	7	networks	network	NOUN
ajmri-740	58	8	such	such	ADJ
ajmri-740	58	9	as	as	ADP
ajmri-740	58	10	resnet-50	resnet-50	PROPN
ajmri-740	58	11	,	,	PUNCT
ajmri-740	58	12	vgg19	vgg19	NOUN
ajmri-740	58	13	,	,	PUNCT
ajmri-740	58	14	and	and	CCONJ
ajmri-740	58	15	dense	dense	ADJ
ajmri-740	58	16	-	-	PUNCT
ajmri-740	58	17	net-201	net-201	ADJ
ajmri-740	58	18	.	.	PUNCT
ajmri-740	59	1	resnet-50	resnet-50	NOUN
ajmri-740	59	2	model	model	NOUN
ajmri-740	59	3	resnet-50	resnet-50	PROPN
ajmri-740	59	4	model	model	NOUN
ajmri-740	59	5	is	be	AUX
ajmri-740	59	6	a	a	DET
ajmri-740	59	7	convolutional	convolutional	ADJ
ajmri-740	59	8	neural	neural	ADJ
ajmri-740	59	9	network	network	NOUN
ajmri-740	59	10	50	50	NUM
ajmri-740	59	11	layers	layer	NOUN
ajmri-740	59	12	deep	deep	ADV
ajmri-740	59	13	;	;	PUNCT
ajmri-740	59	14	microsoft	microsoft	PROPN
ajmri-740	59	15	built	build	VERB
ajmri-740	59	16	and	and	CCONJ
ajmri-740	59	17	trained	train	VERB
ajmri-740	59	18	it	it	PRON
ajmri-740	59	19	in	in	ADP
ajmri-740	59	20	2015	2015	NUM
ajmri-740	59	21	(	(	PUNCT
ajmri-740	59	22	he	he	PRON
ajmri-740	59	23	et	et	PROPN
ajmri-740	59	24	al	al	PROPN
ajmri-740	59	25	.	.	PROPN
ajmri-740	59	26	,	,	PUNCT
ajmri-740	59	27	2015	2015	NUM
ajmri-740	59	28	)	)	PUNCT
ajmri-740	59	29	.	.	PUNCT
ajmri-740	60	1	this	this	DET
ajmri-740	60	2	model	model	NOUN
ajmri-740	60	3	was	be	AUX
ajmri-740	60	4	trained	train	VERB
ajmri-740	60	5	on	on	ADP
ajmri-740	60	6	more	more	ADJ
ajmri-740	60	7	than	than	ADP
ajmri-740	60	8	one	one	NUM
ajmri-740	60	9	million	million	NUM
ajmri-740	60	10	images	image	NOUN
ajmri-740	60	11	from	from	ADP
ajmri-740	60	12	the	the	DET
ajmri-740	60	13	imagenet	imagenet	NOUN
ajmri-740	60	14	database	database	NOUN
ajmri-740	60	15	,	,	PUNCT
ajmri-740	60	16	it	it	PRON
ajmri-740	60	17	can	can	AUX
ajmri-740	60	18	classify	classify	VERB
ajmri-740	60	19	up	up	ADP
ajmri-740	60	20	to	to	ADP
ajmri-740	60	21	1000	1000	NUM
ajmri-740	60	22	objects	object	NOUN
ajmri-740	60	23	,	,	PUNCT
ajmri-740	60	24	and	and	CCONJ
ajmri-740	60	25	the	the	DET
ajmri-740	60	26	network	network	NOUN
ajmri-740	60	27	was	be	AUX
ajmri-740	60	28	trained	train	VERB
ajmri-740	60	29	on	on	ADP
ajmri-740	60	30	224x224	224x224	NUM
ajmri-740	60	31	pixel	pixel	ADJ
ajmri-740	60	32	-	-	PUNCT
ajmri-740	60	33	colored	colored	ADJ
ajmri-740	60	34	images	image	NOUN
ajmri-740	60	35	.	.	PUNCT
ajmri-740	61	1	it	it	PRON
ajmri-740	61	2	contains	contain	VERB
ajmri-740	61	3	33	33	NUM
ajmri-740	61	4	623	623	NUM
ajmri-740	61	5	012	012	NUM
ajmri-740	61	6	parameters	parameter	NOUN
ajmri-740	61	7	.	.	PUNCT
ajmri-740	62	1	vgg-19	vgg-19	NUM
ajmri-740	62	2	model	model	NOUN
ajmri-740	62	3	vgg-19	vgg-19	PROPN
ajmri-740	62	4	model	model	NOUN
ajmri-740	62	5	is	be	AUX
ajmri-740	62	6	a	a	DET
ajmri-740	62	7	convolutional	convolutional	ADJ
ajmri-740	62	8	neural	neural	ADJ
ajmri-740	62	9	network	network	NOUN
ajmri-740	62	10	19	19	NUM
ajmri-740	62	11	layers	layer	NOUN
ajmri-740	62	12	deep	deep	ADV
ajmri-740	62	13	;	;	PUNCT
ajmri-740	62	14	it	it	PRON
ajmri-740	62	15	was	be	AUX
ajmri-740	62	16	developed	develop	VERB
ajmri-740	62	17	by	by	ADP
ajmri-740	62	18	the	the	DET
ajmri-740	62	19	visual	visual	ADJ
ajmri-740	62	20	geometry	geometry	NOUN
ajmri-740	62	21	group	group	NOUN
ajmri-740	62	22	of	of	ADP
ajmri-740	62	23	the	the	DET
ajmri-740	62	24	department	department	PROPN
ajmri-740	62	25	of	of	ADP
ajmri-740	62	26	engineering	engineering	NOUN
ajmri-740	62	27	sciences	science	NOUN
ajmri-740	62	28	at	at	ADP
ajmri-740	62	29	oxford	oxford	PROPN
ajmri-740	62	30	university	university	PROPN
ajmri-740	62	31	.	.	PUNCT
ajmri-740	63	1	this	this	DET
ajmri-740	63	2	model	model	NOUN
ajmri-740	63	3	has	have	AUX
ajmri-740	63	4	been	be	AUX
ajmri-740	63	5	trained	train	VERB
ajmri-740	63	6	on	on	ADP
ajmri-740	63	7	over	over	ADP
ajmri-740	63	8	a	a	DET
ajmri-740	63	9	million	million	NUM
ajmri-740	63	10	images	image	NOUN
ajmri-740	63	11	from	from	ADP
ajmri-740	63	12	the	the	DET
ajmri-740	63	13	imagenet	imagenet	NOUN
ajmri-740	63	14	database	database	NOUN
ajmri-740	63	15	,	,	PUNCT
ajmri-740	63	16	it	it	PRON
ajmri-740	63	17	can	can	AUX
ajmri-740	63	18	classify	classify	VERB
ajmri-740	63	19	up	up	ADP
ajmri-740	63	20	to	to	ADP
ajmri-740	63	21	1000	1000	NUM
ajmri-740	63	22	objects	object	NOUN
ajmri-740	63	23	,	,	PUNCT
ajmri-740	63	24	and	and	CCONJ
ajmri-740	63	25	the	the	DET
ajmri-740	63	26	network	network	NOUN
ajmri-740	63	27	was	be	AUX
ajmri-740	63	28	trained	train	VERB
ajmri-740	63	29	on	on	ADP
ajmri-740	63	30	224x224	224x224	NUM
ajmri-740	63	31	pixel	pixel	ADJ
ajmri-740	63	32	-	-	PUNCT
ajmri-740	63	33	colored	colored	ADJ
ajmri-740	63	34	images	image	NOUN
ajmri-740	63	35	.	.	PUNCT
ajmri-740	64	1	it	it	PRON
ajmri-740	64	2	contains	contain	VERB
ajmri-740	64	3	21	21	NUM
ajmri-740	64	4	560	560	NUM
ajmri-740	64	5	484	484	NUM
ajmri-740	64	6	parameters	parameter	NOUN
ajmri-740	64	7	.	.	PUNCT
ajmri-740	65	1	densenet-201	densenet-201	ADJ
ajmri-740	65	2	model	model	PROPN
ajmri-740	65	3	densenet-201	densenet-201	PROPN
ajmri-740	65	4	model	model	NOUN
ajmri-740	65	5	is	be	AUX
ajmri-740	65	6	a	a	DET
ajmri-740	65	7	convolutional	convolutional	ADJ
ajmri-740	65	8	neural	neural	ADJ
ajmri-740	65	9	network	network	NOUN
ajmri-740	65	10	of	of	ADP
ajmri-740	65	11	201	201	NUM
ajmri-740	65	12	layers	layer	NOUN
ajmri-740	65	13	of	of	ADP
ajmri-740	65	14	depth	depth	NOUN
ajmri-740	65	15	.	.	PUNCT
ajmri-740	66	1	it	it	PRON
ajmri-740	66	2	was	be	AUX
ajmri-740	66	3	implemented	implement	VERB
ajmri-740	66	4	by	by	ADP
ajmri-740	66	5	huang	huang	PROPN
ajmri-740	66	6	et	et	PROPN
ajmri-740	66	7	al	al	PROPN
ajmri-740	66	8	.	.	PROPN
ajmri-740	67	1	(	(	PUNCT
ajmri-740	67	2	siegmund	siegmund	PROPN
ajmri-740	67	3	et	et	PROPN
ajmri-740	67	4	al	al	PROPN
ajmri-740	67	5	.	.	PROPN
ajmri-740	67	6	,	,	PUNCT
ajmri-740	67	7	2021	2021	NUM
ajmri-740	67	8	)	)	PUNCT
ajmri-740	67	9	.	.	PUNCT
ajmri-740	68	1	this	this	DET
ajmri-740	68	2	model	model	NOUN
ajmri-740	68	3	was	be	AUX
ajmri-740	68	4	trained	train	VERB
ajmri-740	68	5	on	on	ADP
ajmri-740	68	6	more	more	ADJ
ajmri-740	68	7	than	than	ADP
ajmri-740	68	8	one	one	NUM
ajmri-740	68	9	million	million	NUM
ajmri-740	68	10	images	image	NOUN
ajmri-740	68	11	from	from	ADP
ajmri-740	68	12	the	the	DET
ajmri-740	68	13	imagenet	imagenet	NOUN
ajmri-740	68	14	database	database	NOUN
ajmri-740	68	15	,	,	PUNCT
ajmri-740	68	16	it	it	PRON
ajmri-740	68	17	can	can	AUX
ajmri-740	68	18	classify	classify	VERB
ajmri-740	68	19	up	up	ADP
ajmri-740	68	20	to	to	ADP
ajmri-740	68	21	1000	1000	NUM
ajmri-740	68	22	objects	object	NOUN
ajmri-740	68	23	,	,	PUNCT
ajmri-740	68	24	and	and	CCONJ
ajmri-740	68	25	the	the	DET
ajmri-740	68	26	network	network	NOUN
ajmri-740	68	27	was	be	AUX
ajmri-740	68	28	trained	train	VERB
ajmri-740	68	29	on	on	ADP
ajmri-740	68	30	224x224	224x224	NUM
ajmri-740	68	31	pixel	pixel	ADJ
ajmri-740	68	32	-	-	PUNCT
ajmri-740	68	33	colored	colored	ADJ
ajmri-740	68	34	images	image	NOUN
ajmri-740	68	35	.	.	PUNCT
ajmri-740	69	1	it	it	PRON
ajmri-740	69	2	contains	contain	VERB
ajmri-740	69	3	21	21	NUM
ajmri-740	69	4	202	202	NUM
ajmri-740	69	5	084	084	NUM
ajmri-740	69	6	parameters	parameter	NOUN
ajmri-740	69	7	.	.	PUNCT
ajmri-740	70	1	ar	ar	PROPN
ajmri-740	70	2	faces	face	VERB
ajmri-740	70	3	database	database	VERB
ajmri-740	70	4	the	the	DET
ajmri-740	70	5	database	database	NOUN
ajmri-740	70	6	used	use	VERB
ajmri-740	70	7	is	be	AUX
ajmri-740	70	8	the	the	DET
ajmri-740	70	9	ar	ar	PROPN
ajmri-740	70	10	face	face	NOUN
ajmri-740	70	11	database	database	NOUN
ajmri-740	70	12	(	(	PUNCT
ajmri-740	70	13	ar	ar	NOUN
ajmri-740	70	14	face	face	NOUN
ajmri-740	70	15	database	database	NOUN
ajmri-740	70	16	webpage	webpage	NOUN
ajmri-740	70	17	,	,	PUNCT
ajmri-740	70	18	s.	s.	PROPN
ajmri-740	70	19	d.	d.	PROPN
ajmri-740	70	20	)	)	PUNCT
ajmri-740	70	21	.	.	PUNCT
ajmri-740	71	1	it	it	PRON
ajmri-740	71	2	contains	contain	VERB
ajmri-740	71	3	more	more	ADJ
ajmri-740	71	4	than	than	ADP
ajmri-740	71	5	4,000	4,000	NUM
ajmri-740	71	6	colored	color	VERB
ajmri-740	71	7	faces	face	NOUN
ajmri-740	71	8	of	of	ADP
ajmri-740	71	9	126	126	NUM
ajmri-740	71	10	persons	person	NOUN
ajmri-740	71	11	,	,	PUNCT
ajmri-740	71	12	namely	namely	ADV
ajmri-740	71	13	70	70	NUM
ajmri-740	71	14	men	man	NOUN
ajmri-740	71	15	and	and	CCONJ
ajmri-740	71	16	56	56	NUM
ajmri-740	71	17	women	woman	NOUN
ajmri-740	71	18	.	.	PUNCT
ajmri-740	72	1	frontal	frontal	ADJ
ajmri-740	72	2	faces	face	NOUN
ajmri-740	72	3	are	be	AUX
ajmri-740	72	4	characterized	characterize	VERB
ajmri-740	72	5	by	by	ADP
ajmri-740	72	6	different	different	ADJ
ajmri-740	72	7	facial	facial	ADJ
ajmri-740	72	8	expressions	expression	NOUN
ajmri-740	72	9	,	,	PUNCT
ajmri-740	72	10	lighting	lighting	NOUN
ajmri-740	72	11	conditions	condition	NOUN
ajmri-740	72	12	,	,	PUNCT
ajmri-740	72	13	and	and	CCONJ
ajmri-740	72	14	occlusions	occlusion	NOUN
ajmri-740	72	15	like	like	ADP
ajmri-740	72	16	sunglasses	sunglass	NOUN
ajmri-740	72	17	and	and	CCONJ
ajmri-740	72	18	scarf	scarf	NOUN
ajmri-740	72	19	.	.	PUNCT
ajmri-740	73	1	there	there	PRON
ajmri-740	73	2	are	be	VERB
ajmri-740	73	3	26	26	NUM
ajmri-740	73	4	other	other	ADJ
ajmri-740	73	5	images	image	NOUN
ajmri-740	73	6	per	per	ADP
ajmri-740	73	7	person	person	NOUN
ajmri-740	73	8	,	,	PUNCT
ajmri-740	73	9	taken	take	VERB
ajmri-740	73	10	in	in	ADP
ajmri-740	73	11	two	two	NUM
ajmri-740	73	12	sessions	session	NOUN
ajmri-740	73	13	separated	separate	VERB
ajmri-740	73	14	by	by	ADP
ajmri-740	73	15	two	two	NUM
ajmri-740	73	16	weeks	week	NOUN
ajmri-740	73	17	,	,	PUNCT
ajmri-740	73	18	each	each	DET
ajmri-740	73	19	consisting	consist	VERB
ajmri-740	73	20	of	of	ADP
ajmri-740	73	21	13	13	NUM
ajmri-740	73	22	images	image	NOUN
ajmri-740	73	23	.	.	PUNCT
ajmri-740	74	1	a	a	DET
ajmri-740	74	2	dataset	dataset	NOUN
ajmri-740	74	3	of	of	ADP
ajmri-740	74	4	2600	2600	NUM
ajmri-740	74	5	images	image	NOUN
ajmri-740	74	6	of	of	ADP
ajmri-740	74	7	100	100	NUM
ajmri-740	74	8	different	different	ADJ
ajmri-740	74	9	subjects	subject	NOUN
ajmri-740	74	10	(	(	PUNCT
ajmri-740	74	11	50	50	NUM
ajmri-740	74	12	males	male	NOUN
ajmri-740	74	13	and	and	CCONJ
ajmri-740	74	14	50	50	NUM
ajmri-740	74	15	females	female	NOUN
ajmri-740	74	16	)	)	PUNCT
ajmri-740	74	17	were	be	AUX
ajmri-740	74	18	used	use	VERB
ajmri-740	74	19	in	in	ADP
ajmri-740	74	20	our	our	PRON
ajmri-740	74	21	experiment	experiment	NOUN
ajmri-740	74	22	;	;	PUNCT
ajmri-740	74	23	martinez	martinez	PROPN
ajmri-740	74	24	and	and	CCONJ
ajmri-740	74	25	kak	kak	PROPN
ajmri-740	74	26	(	(	PUNCT
ajmri-740	74	27	martinez	martinez	PROPN
ajmri-740	74	28	&	&	CCONJ
ajmri-740	74	29	kak	kak	PROPN
ajmri-740	74	30	,	,	PUNCT
ajmri-740	74	31	2001	2001	NUM
ajmri-740	74	32	)	)	PUNCT
ajmri-740	74	33	used	use	VERB
ajmri-740	74	34	the	the	DET
ajmri-740	74	35	same	same	ADJ
ajmri-740	74	36	data	datum	NOUN
ajmri-740	74	37	set	set	VERB
ajmri-740	74	38	.	.	PUNCT
ajmri-740	75	1	each	each	PRON
ajmri-740	75	2	of	of	ADP
ajmri-740	75	3	the	the	DET
ajmri-740	75	4	images	image	NOUN
ajmri-740	75	5	in	in	ADP
ajmri-740	75	6	this	this	DET
ajmri-740	75	7	dataset	dataset	NOUN
ajmri-740	75	8	is	be	AUX
ajmri-740	75	9	165x120x3	165x120x3	NUM
ajmri-740	75	10	pixels	pixel	NOUN
ajmri-740	75	11	in	in	ADP
ajmri-740	75	12	size	size	NOUN
ajmri-740	75	13	.	.	PUNCT
ajmri-740	76	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	76	2	pa	pa	PROPN
ajmri-740	76	3	ge	ge	PROPN
ajmri-740	76	4	26	26	NUM
ajmri-740	76	5	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	76	6	am	am	NOUN
ajmri-740	76	7	.	.	PUNCT
ajmri-740	77	1	j.	j.	PROPN
ajmri-740	77	2	multidis	multidis	PROPN
ajmri-740	77	3	.	.	PUNCT
ajmri-740	78	1	res	re	NOUN
ajmri-740	78	2	.	.	PUNCT
ajmri-740	79	1	innov	innov	PROPN
ajmri-740	79	2	.	.	PUNCT
ajmri-740	80	1	1(5	1(5	NUM
ajmri-740	80	2	)	)	PUNCT
ajmri-740	80	3	24	24	NUM
ajmri-740	80	4	-	-	SYM
ajmri-740	80	5	32	32	NUM
ajmri-740	80	6	,	,	PUNCT
ajmri-740	80	7	2022	2022	NUM
ajmri-740	80	8	•	•	NUM
ajmri-740	80	9	faces	face	NOUN
ajmri-740	80	10	with	with	ADP
ajmri-740	80	11	frontal	frontal	ADJ
ajmri-740	80	12	view	view	NOUN
ajmri-740	80	13	and	and	CCONJ
ajmri-740	80	14	lighting	lighting	NOUN
ajmri-740	80	15	conditions	condition	NOUN
ajmri-740	80	16	•	•	VERB
ajmri-740	80	17	faces	face	NOUN
ajmri-740	80	18	with	with	ADP
ajmri-740	80	19	facial	facial	ADJ
ajmri-740	80	20	expression	expression	NOUN
ajmri-740	80	21	•	•	ADP
ajmri-740	80	22	faces	face	NOUN
ajmri-740	80	23	occluded	occlude	VERB
ajmri-740	80	24	with	with	ADP
ajmri-740	80	25	a	a	DET
ajmri-740	80	26	foxhole	foxhole	NOUN
ajmri-740	80	27	•	•	ADP
ajmri-740	80	28	faces	face	NOUN
ajmri-740	80	29	occulted	occult	VERB
ajmri-740	80	30	with	with	ADP
ajmri-740	80	31	a	a	DET
ajmri-740	80	32	lens	lens	NOUN
ajmri-740	80	33	and	and	CCONJ
ajmri-740	80	34	a	a	DET
ajmri-740	80	35	pair	pair	NOUN
ajmri-740	80	36	of	of	ADP
ajmri-740	80	37	glasses	glass	NOUN
ajmri-740	80	38	•	•	NUM
ajmri-740	80	39	faces	face	NOUN
ajmri-740	80	40	occulted	occult	VERB
ajmri-740	80	41	with	with	ADP
ajmri-740	80	42	a	a	DET
ajmri-740	80	43	pair	pair	NOUN
ajmri-740	80	44	of	of	ADP
ajmri-740	80	45	glasses	glass	NOUN
ajmri-740	81	1	•	•	NOUN
ajmri-740	81	2	faces	face	NOUN
ajmri-740	81	3	with	with	ADP
ajmri-740	81	4	a	a	DET
ajmri-740	81	5	facial	facial	ADJ
ajmri-740	81	6	expression	expression	NOUN
ajmri-740	81	7	and	and	CCONJ
ajmri-740	81	8	a	a	DET
ajmri-740	81	9	pair	pair	NOUN
ajmri-740	81	10	of	of	ADP
ajmri-740	81	11	glasses	glass	NOUN
ajmri-740	81	12	•	•	NUM
ajmri-740	81	13	faces	face	NOUN
ajmri-740	81	14	occulted	occult	VERB
ajmri-740	81	15	with	with	ADP
ajmri-740	81	16	a	a	DET
ajmri-740	81	17	lens	lens	NOUN
ajmri-740	81	18	and	and	CCONJ
ajmri-740	81	19	a	a	DET
ajmri-740	81	20	pair	pair	NOUN
ajmri-740	81	21	of	of	ADP
ajmri-740	81	22	glasses	glass	NOUN
ajmri-740	81	23	•	•	NUM
ajmri-740	81	24	faces	face	NOUN
ajmri-740	81	25	occulted	occult	VERB
ajmri-740	81	26	with	with	ADP
ajmri-740	81	27	a	a	DET
ajmri-740	81	28	pair	pair	NOUN
ajmri-740	81	29	of	of	ADP
ajmri-740	81	30	glasses	glass	NOUN
ajmri-740	81	31	figure	figure	NOUN
ajmri-740	81	32	1	1	NUM
ajmri-740	81	33	:	:	PUNCT
ajmri-740	81	34	sample	sample	NOUN
ajmri-740	81	35	from	from	ADP
ajmri-740	81	36	the	the	DET
ajmri-740	81	37	ar	ar	NOUN
ajmri-740	81	38	database	database	NOUN
ajmri-740	81	39	the	the	DET
ajmri-740	81	40	architecture	architecture	NOUN
ajmri-740	81	41	of	of	ADP
ajmri-740	81	42	our	our	PRON
ajmri-740	81	43	method	method	NOUN
ajmri-740	81	44	figure	figure	NOUN
ajmri-740	81	45	2	2	NUM
ajmri-740	81	46	:	:	PUNCT
ajmri-740	81	47	the	the	DET
ajmri-740	81	48	architecture	architecture	NOUN
ajmri-740	81	49	of	of	ADP
ajmri-740	81	50	our	our	PRON
ajmri-740	81	51	occluded	occluded	ADJ
ajmri-740	81	52	face	face	NOUN
ajmri-740	81	53	recognition	recognition	NOUN
ajmri-740	81	54	method	method	NOUN
ajmri-740	81	55	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	81	56	pa	pa	PROPN
ajmri-740	81	57	ge	ge	PROPN
ajmri-740	81	58	27	27	NUM
ajmri-740	81	59	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	81	60	am	am	NOUN
ajmri-740	81	61	.	.	PUNCT
ajmri-740	82	1	j.	j.	PROPN
ajmri-740	82	2	multidis	multidis	PROPN
ajmri-740	82	3	.	.	PUNCT
ajmri-740	83	1	res	re	NOUN
ajmri-740	83	2	.	.	PUNCT
ajmri-740	84	1	innov	innov	PROPN
ajmri-740	84	2	.	.	PUNCT
ajmri-740	85	1	1(5	1(5	NUM
ajmri-740	85	2	)	)	PUNCT
ajmri-740	85	3	24	24	NUM
ajmri-740	85	4	-	-	SYM
ajmri-740	85	5	32	32	NUM
ajmri-740	85	6	,	,	PUNCT
ajmri-740	85	7	2022	2022	NUM
ajmri-740	85	8	our	our	PRON
ajmri-740	85	9	method	method	NOUN
ajmri-740	85	10	is	be	AUX
ajmri-740	85	11	as	as	SCONJ
ajmri-740	85	12	follows	follow	VERB
ajmri-740	85	13	:	:	PUNCT
ajmri-740	85	14	step	step	NOUN
ajmri-740	85	15	1	1	NUM
ajmri-740	85	16	:	:	SYM
ajmri-740	85	17	•	•	ADV
ajmri-740	85	18	preprocessing	preprocesse	VERB
ajmri-740	85	19	•	•	NOUN
ajmri-740	85	20	splitting	splitting	NOUN
ajmri-740	85	21	data	datum	NOUN
ajmri-740	85	22	step	step	NOUN
ajmri-740	85	23	2	2	NUM
ajmri-740	85	24	:	:	SYM
ajmri-740	85	25	•	•	NUM
ajmri-740	85	26	loading	loading	NOUN
ajmri-740	85	27	of	of	ADP
ajmri-740	85	28	models	model	NOUN
ajmri-740	85	29	•	•	ADP
ajmri-740	85	30	collecting	collect	VERB
ajmri-740	85	31	extraction	extraction	NOUN
ajmri-740	85	32	features	feature	NOUN
ajmri-740	85	33	•	•	NUM
ajmri-740	85	34	flatten	flatten	VERB
ajmri-740	85	35	data	data	NOUN
ajmri-740	85	36	•	•	NOUN
ajmri-740	85	37	activation	activation	NOUN
ajmri-740	85	38	function	function	NOUN
ajmri-740	85	39	•	•	NOUN
ajmri-740	85	40	model	model	NOUN
ajmri-740	85	41	training	training	NOUN
ajmri-740	85	42	step	step	NOUN
ajmri-740	85	43	3	3	NUM
ajmri-740	85	44	:	:	SYM
ajmri-740	85	45	•	•	NUM
ajmri-740	85	46	classification	classification	NOUN
ajmri-740	85	47	•	•	NOUN
ajmri-740	85	48	face	face	NOUN
ajmri-740	85	49	recognition	recognition	NOUN
ajmri-740	85	50	preprocessing	preprocesse	VERB
ajmri-740	85	51	•	•	NOUN
ajmri-740	85	52	in	in	ADP
ajmri-740	85	53	this	this	DET
ajmri-740	85	54	step	step	NOUN
ajmri-740	85	55	,	,	PUNCT
ajmri-740	85	56	we	we	PRON
ajmri-740	85	57	will	will	AUX
ajmri-740	85	58	retrieve	retrieve	VERB
ajmri-740	85	59	each	each	DET
ajmri-740	85	60	image	image	NOUN
ajmri-740	85	61	from	from	ADP
ajmri-740	85	62	our	our	PRON
ajmri-740	85	63	database	database	NOUN
ajmri-740	85	64	to	to	PART
ajmri-740	85	65	add	add	VERB
ajmri-740	85	66	it	it	PRON
ajmri-740	85	67	to	to	ADP
ajmri-740	85	68	a	a	DET
ajmri-740	85	69	list	list	NOUN
ajmri-740	85	70	and	and	CCONJ
ajmri-740	85	71	each	each	DET
ajmri-740	85	72	label	label	NOUN
ajmri-740	85	73	to	to	ADP
ajmri-740	85	74	another	another	PRON
ajmri-740	85	75	.	.	PUNCT
ajmri-740	86	1	•	•	NUM
ajmri-740	86	2	then	then	ADV
ajmri-740	86	3	,	,	PUNCT
ajmri-740	86	4	we	we	PRON
ajmri-740	86	5	will	will	AUX
ajmri-740	86	6	get	get	VERB
ajmri-740	86	7	the	the	DET
ajmri-740	86	8	number	number	NOUN
ajmri-740	86	9	of	of	ADP
ajmri-740	86	10	categories	category	NOUN
ajmri-740	86	11	in	in	ADP
ajmri-740	86	12	our	our	PRON
ajmri-740	86	13	database	database	NOUN
ajmri-740	86	14	to	to	PART
ajmri-740	86	15	transform	transform	VERB
ajmri-740	86	16	our	our	PRON
ajmri-740	86	17	list	list	NOUN
ajmri-740	86	18	of	of	ADP
ajmri-740	86	19	labels	label	NOUN
ajmri-740	86	20	into	into	ADP
ajmri-740	86	21	a	a	DET
ajmri-740	86	22	matrix	matrix	NOUN
ajmri-740	86	23	of	of	ADP
ajmri-740	86	24	size	size	NOUN
ajmri-740	86	25	corresponding	correspond	VERB
ajmri-740	86	26	to	to	ADP
ajmri-740	86	27	the	the	DET
ajmri-740	86	28	number	number	NOUN
ajmri-740	86	29	of	of	ADP
ajmri-740	86	30	classes	class	NOUN
ajmri-740	86	31	.	.	PUNCT
ajmri-740	87	1	•	•	NUM
ajmri-740	87	2	finally	finally	ADV
ajmri-740	87	3	,	,	PUNCT
ajmri-740	87	4	we	we	PRON
ajmri-740	87	5	will	will	AUX
ajmri-740	87	6	normalize	normalize	VERB
ajmri-740	87	7	our	our	PRON
ajmri-740	87	8	images	image	NOUN
ajmri-740	87	9	in	in	ADP
ajmri-740	87	10	the	the	DET
ajmri-740	87	11	value	value	NOUN
ajmri-740	87	12	interval	interval	NOUN
ajmri-740	87	13	[	[	X
ajmri-740	87	14	0;1	0;1	NOUN
ajmri-740	87	15	]	]	PUNCT
ajmri-740	87	16	.	.	PUNCT
ajmri-740	88	1	splitting	split	VERB
ajmri-740	88	2	data	datum	NOUN
ajmri-740	88	3	•	•	ADP
ajmri-740	88	4	we	we	PRON
ajmri-740	88	5	will	will	AUX
ajmri-740	88	6	divide	divide	VERB
ajmri-740	88	7	our	our	PRON
ajmri-740	88	8	dataset	dataset	NOUN
ajmri-740	88	9	into	into	ADP
ajmri-740	88	10	two	two	NUM
ajmri-740	88	11	subsets	subset	NOUN
ajmri-740	88	12	:	:	PUNCT
ajmri-740	88	13	•	•	ADP
ajmri-740	88	14	the	the	DET
ajmri-740	88	15	first	first	ADJ
ajmri-740	88	16	subset	subset	NOUN
ajmri-740	88	17	will	will	AUX
ajmri-740	88	18	be	be	AUX
ajmri-740	88	19	called	call	VERB
ajmri-740	88	20	the	the	DET
ajmri-740	88	21	training	training	NOUN
ajmri-740	88	22	dataset	dataset	NOUN
ajmri-740	88	23	,	,	PUNCT
ajmri-740	88	24	which	which	PRON
ajmri-740	88	25	will	will	AUX
ajmri-740	88	26	be	be	AUX
ajmri-740	88	27	used	use	VERB
ajmri-740	88	28	to	to	PART
ajmri-740	88	29	allow	allow	VERB
ajmri-740	88	30	our	our	PRON
ajmri-740	88	31	model	model	NOUN
ajmri-740	88	32	to	to	PART
ajmri-740	88	33	do	do	VERB
ajmri-740	88	34	its	its	PRON
ajmri-740	88	35	learning	learning	NOUN
ajmri-740	88	36	.	.	PUNCT
ajmri-740	89	1	•	•	NUM
ajmri-740	89	2	the	the	DET
ajmri-740	89	3	second	second	ADJ
ajmri-740	89	4	subset	subset	NOUN
ajmri-740	89	5	will	will	AUX
ajmri-740	89	6	be	be	AUX
ajmri-740	89	7	called	call	VERB
ajmri-740	89	8	the	the	DET
ajmri-740	89	9	test	test	NOUN
ajmri-740	89	10	dataset	dataset	NOUN
ajmri-740	89	11	,	,	PUNCT
ajmri-740	89	12	which	which	PRON
ajmri-740	89	13	will	will	AUX
ajmri-740	89	14	be	be	AUX
ajmri-740	89	15	used	use	VERB
ajmri-740	89	16	to	to	PART
ajmri-740	89	17	evaluate	evaluate	VERB
ajmri-740	89	18	the	the	DET
ajmri-740	89	19	learning	learning	NOUN
ajmri-740	89	20	of	of	ADP
ajmri-740	89	21	our	our	PRON
ajmri-740	89	22	model	model	NOUN
ajmri-740	89	23	by	by	ADP
ajmri-740	89	24	testing	test	VERB
ajmri-740	89	25	the	the	DET
ajmri-740	89	26	results	result	NOUN
ajmri-740	89	27	obtained	obtain	VERB
ajmri-740	89	28	with	with	ADP
ajmri-740	89	29	the	the	DET
ajmri-740	89	30	expected	expect	VERB
ajmri-740	89	31	results	result	NOUN
ajmri-740	89	32	loading	loading	NOUN
ajmri-740	89	33	models	model	NOUN
ajmri-740	89	34	we	we	PRON
ajmri-740	89	35	proceed	proceed	VERB
ajmri-740	89	36	to	to	ADP
ajmri-740	89	37	the	the	DET
ajmri-740	89	38	loading	loading	NOUN
ajmri-740	89	39	of	of	ADP
ajmri-740	89	40	our	our	PRON
ajmri-740	89	41	model	model	NOUN
ajmri-740	89	42	by	by	ADP
ajmri-740	89	43	passing	pass	VERB
ajmri-740	89	44	the	the	DET
ajmri-740	89	45	size	size	NOUN
ajmri-740	89	46	of	of	ADP
ajmri-740	89	47	our	our	PRON
ajmri-740	89	48	images	image	NOUN
ajmri-740	89	49	as	as	ADP
ajmri-740	89	50	a	a	DET
ajmri-740	89	51	parameter	parameter	NOUN
ajmri-740	89	52	without	without	ADP
ajmri-740	89	53	the	the	DET
ajmri-740	89	54	fully	fully	ADV
ajmri-740	89	55	connected	connected	ADJ
ajmri-740	89	56	layers	layer	NOUN
ajmri-740	89	57	of	of	ADP
ajmri-740	89	58	the	the	DET
ajmri-740	89	59	model	model	NOUN
ajmri-740	89	60	.	.	PUNCT
ajmri-740	90	1	collection	collection	NOUN
ajmri-740	90	2	of	of	ADP
ajmri-740	90	3	extraction	extraction	NOUN
ajmri-740	90	4	features	feature	NOUN
ajmri-740	90	5	we	we	PRON
ajmri-740	90	6	will	will	AUX
ajmri-740	90	7	use	use	VERB
ajmri-740	90	8	the	the	DET
ajmri-740	90	9	convolution	convolution	NOUN
ajmri-740	90	10	and	and	CCONJ
ajmri-740	90	11	pooling	pool	VERB
ajmri-740	90	12	already	already	ADV
ajmri-740	90	13	trained	train	VERB
ajmri-740	90	14	in	in	ADP
ajmri-740	90	15	our	our	PRON
ajmri-740	90	16	model	model	NOUN
ajmri-740	90	17	for	for	ADP
ajmri-740	90	18	the	the	DET
ajmri-740	90	19	feature	feature	NOUN
ajmri-740	90	20	extraction	extraction	NOUN
ajmri-740	90	21	of	of	ADP
ajmri-740	90	22	our	our	PRON
ajmri-740	90	23	images	image	NOUN
ajmri-740	90	24	.	.	PUNCT
ajmri-740	91	1	flatten	flatten	ADJ
ajmri-740	91	2	data	datum	NOUN
ajmri-740	91	3	we	we	PRON
ajmri-740	91	4	will	will	AUX
ajmri-740	91	5	reduce	reduce	VERB
ajmri-740	91	6	the	the	DET
ajmri-740	91	7	input	input	NOUN
ajmri-740	91	8	dimensions	dimension	NOUN
ajmri-740	91	9	of	of	ADP
ajmri-740	91	10	our	our	PRON
ajmri-740	91	11	data	datum	NOUN
ajmri-740	91	12	by	by	ADP
ajmri-740	91	13	adapting	adapt	VERB
ajmri-740	91	14	it	it	PRON
ajmri-740	91	15	to	to	ADP
ajmri-740	91	16	the	the	DET
ajmri-740	91	17	input	input	NOUN
ajmri-740	91	18	dimensions	dimension	NOUN
ajmri-740	91	19	of	of	ADP
ajmri-740	91	20	the	the	DET
ajmri-740	91	21	model	model	NOUN
ajmri-740	91	22	.	.	PUNCT
ajmri-740	92	1	activation	activation	NOUN
ajmri-740	92	2	function	function	NOUN
ajmri-740	92	3	we	we	PRON
ajmri-740	92	4	used	use	VERB
ajmri-740	92	5	the	the	DET
ajmri-740	92	6	softmax	softmax	NOUN
ajmri-740	92	7	activation	activation	NOUN
ajmri-740	92	8	function	function	NOUN
ajmri-740	92	9	because	because	SCONJ
ajmri-740	92	10	we	we	PRON
ajmri-740	92	11	have	have	VERB
ajmri-740	92	12	multiple	multiple	ADJ
ajmri-740	92	13	categories	category	NOUN
ajmri-740	92	14	,	,	PUNCT
ajmri-740	92	15	and	and	CCONJ
ajmri-740	92	16	softmax	softmax	NOUN
ajmri-740	92	17	is	be	AUX
ajmri-740	92	18	efficient	efficient	ADJ
ajmri-740	92	19	for	for	ADP
ajmri-740	92	20	multiclass	multiclass	ADJ
ajmri-740	92	21	classification	classification	NOUN
ajmri-740	92	22	.	.	PUNCT
ajmri-740	93	1	the	the	DET
ajmri-740	93	2	mathematical	mathematical	ADJ
ajmri-740	93	3	representation	representation	NOUN
ajmri-740	93	4	of	of	ADP
ajmri-740	93	5	the	the	DET
ajmri-740	93	6	softmax	softmax	NOUN
ajmri-740	93	7	activation	activation	NOUN
ajmri-740	93	8	is	be	AUX
ajmri-740	93	9	:	:	PUNCT
ajmri-740	93	10	z	z	NOUN
ajmri-740	93	11	is	be	AUX
ajmri-740	93	12	a	a	DET
ajmri-740	93	13	vector	vector	NOUN
ajmri-740	93	14	such	such	ADJ
ajmri-740	93	15	as	as	ADP
ajmri-740	93	16	z	z	PROPN
ajmri-740	93	17	=(	=(	NOUN
ajmri-740	93	18	z1	z1	PROPN
ajmri-740	93	19	…	…	SYM
ajmri-740	93	20	,zk	,zk	NUM
ajmri-740	93	21	)	)	PUNCT
ajmri-740	93	22	.	.	PUNCT
ajmri-740	94	1	........	........	PUNCT
ajmri-740	94	2	(	(	PUNCT
ajmri-740	94	3	2	2	X
ajmri-740	94	4	)	)	PUNCT
ajmri-740	94	5	k	k	PROPN
ajmri-740	94	6	∈	∈	PROPN
ajmri-740	94	7	r+	r+	NOUN
ajmri-740	94	8	and	and	CCONJ
ajmri-740	94	9	j	j	PROPN
ajmri-740	94	10	∈	∈	PROPN
ajmri-740	94	11	{	{	PUNCT
ajmri-740	94	12	1	1	NUM
ajmri-740	94	13	…	…	NUM
ajmri-740	94	14	,	,	PUNCT
ajmri-740	94	15	k	k	NOUN
ajmri-740	94	16	}	}	PUNCT
ajmri-740	94	17	..................	..................	PUNCT
ajmri-740	94	18	(	(	PUNCT
ajmri-740	94	19	3	3	X
ajmri-740	94	20	)	)	PUNCT
ajmri-740	94	21	model	model	NOUN
ajmri-740	94	22	training	training	NOUN
ajmri-740	94	23	we	we	PRON
ajmri-740	94	24	proceed	proceed	VERB
ajmri-740	94	25	to	to	ADP
ajmri-740	94	26	the	the	DET
ajmri-740	94	27	training	training	NOUN
ajmri-740	94	28	phase	phase	NOUN
ajmri-740	94	29	of	of	ADP
ajmri-740	94	30	the	the	DET
ajmri-740	94	31	model	model	NOUN
ajmri-740	94	32	in	in	ADP
ajmri-740	94	33	15	15	NUM
ajmri-740	94	34	epochs	epoch	NOUN
ajmri-740	94	35	with	with	ADP
ajmri-740	94	36	different	different	ADJ
ajmri-740	94	37	optimizers	optimizer	NOUN
ajmri-740	94	38	and	and	CCONJ
ajmri-740	94	39	batch	batch	VERB
ajmri-740	94	40	sizes	size	NOUN
ajmri-740	94	41	of	of	ADP
ajmri-740	94	42	4,8	4,8	NUM
ajmri-740	94	43	,	,	PUNCT
ajmri-740	94	44	and	and	CCONJ
ajmri-740	94	45	16	16	NUM
ajmri-740	94	46	.	.	PUNCT
ajmri-740	95	1	classification	classification	NOUN
ajmri-740	95	2	we	we	PRON
ajmri-740	95	3	classify	classify	VERB
ajmri-740	95	4	our	our	PRON
ajmri-740	95	5	test	test	NOUN
ajmri-740	95	6	data	datum	NOUN
ajmri-740	95	7	according	accord	VERB
ajmri-740	95	8	to	to	ADP
ajmri-740	95	9	the	the	DET
ajmri-740	95	10	categories	category	NOUN
ajmri-740	95	11	.	.	PUNCT
ajmri-740	96	1	our	our	PRON
ajmri-740	96	2	data	datum	NOUN
ajmri-740	96	3	contains	contain	VERB
ajmri-740	96	4	100	100	NUM
ajmri-740	96	5	categories	category	NOUN
ajmri-740	96	6	that	that	PRON
ajmri-740	96	7	range	range	VERB
ajmri-740	96	8	from	from	ADP
ajmri-740	96	9	0	0	NUM
ajmri-740	96	10	to	to	ADP
ajmri-740	96	11	99	99	NUM
ajmri-740	96	12	.	.	PUNCT
ajmri-740	97	1	the	the	DET
ajmri-740	97	2	loss	loss	NOUN
ajmri-740	97	3	function	function	NOUN
ajmri-740	97	4	used	use	VERB
ajmri-740	97	5	for	for	ADP
ajmri-740	97	6	our	our	PRON
ajmri-740	97	7	work	work	NOUN
ajmri-740	97	8	is	be	AUX
ajmri-740	97	9	the	the	DET
ajmri-740	97	10	cross	cross	NOUN
ajmri-740	97	11	-	-	NOUN
ajmri-740	97	12	entropy	entropy	NOUN
ajmri-740	97	13	to	to	PART
ajmri-740	97	14	evaluate	evaluate	VERB
ajmri-740	97	15	the	the	DET
ajmri-740	97	16	loss	loss	NOUN
ajmri-740	97	17	during	during	ADP
ajmri-740	97	18	classification	classification	NOUN
ajmri-740	97	19	,	,	PUNCT
ajmri-740	97	20	its	its	PRON
ajmri-740	97	21	equation	equation	NOUN
ajmri-740	97	22	is	be	AUX
ajmri-740	97	23	as	as	SCONJ
ajmri-740	97	24	follows	follow	VERB
ajmri-740	97	25	:	:	PUNCT
ajmri-740	97	26	with	with	ADP
ajmri-740	97	27	test(x	test(x	NOUN
ajmri-740	97	28	):	):	PUNCT
ajmri-740	97	29	vector	vector	NOUN
ajmri-740	97	30	containing	contain	VERB
ajmri-740	97	31	the	the	DET
ajmri-740	97	32	values	value	NOUN
ajmri-740	97	33	of	of	ADP
ajmri-740	97	34	labels	label	NOUN
ajmri-740	97	35	to	to	PART
ajmri-740	97	36	be	be	AUX
ajmri-740	97	37	predicted	predict	VERB
ajmri-740	97	38	and	and	CCONJ
ajmri-740	97	39	pred(x	pred(x	NOUN
ajmri-740	97	40	)	)	PUNCT
ajmri-740	97	41	is	be	AUX
ajmri-740	97	42	the	the	DET
ajmri-740	97	43	vector	vector	NOUN
ajmri-740	97	44	containing	contain	VERB
ajmri-740	97	45	values	value	NOUN
ajmri-740	97	46	of	of	ADP
ajmri-740	97	47	labels	label	NOUN
ajmri-740	97	48	provided	provide	VERB
ajmri-740	97	49	by	by	ADP
ajmri-740	97	50	our	our	PRON
ajmri-740	97	51	softmax	softmax	ADJ
ajmri-740	97	52	activation	activation	NOUN
ajmri-740	97	53	function	function	NOUN
ajmri-740	97	54	.	.	PUNCT
ajmri-740	98	1	we	we	PRON
ajmri-740	98	2	will	will	AUX
ajmri-740	98	3	use	use	VERB
ajmri-740	98	4	the	the	DET
ajmri-740	98	5	accuracy	accuracy	NOUN
ajmri-740	98	6	to	to	PART
ajmri-740	98	7	evaluate	evaluate	VERB
ajmri-740	98	8	the	the	DET
ajmri-740	98	9	model	model	NOUN
ajmri-740	98	10	.	.	PUNCT
ajmri-740	99	1	its	its	PRON
ajmri-740	99	2	formula	formula	NOUN
ajmri-740	99	3	is	be	AUX
ajmri-740	99	4	as	as	SCONJ
ajmri-740	99	5	follows	follow	VERB
ajmri-740	99	6	:	:	PUNCT
ajmri-740	99	7	face	face	NOUN
ajmri-740	99	8	recognition	recognition	NOUN
ajmri-740	99	9	we	we	PRON
ajmri-740	99	10	proceed	proceed	VERB
ajmri-740	99	11	to	to	PART
ajmri-740	99	12	recognize	recognize	VERB
ajmri-740	99	13	each	each	DET
ajmri-740	99	14	image	image	NOUN
ajmri-740	99	15	according	accord	VERB
ajmri-740	99	16	to	to	ADP
ajmri-740	99	17	its	its	PRON
ajmri-740	99	18	classification	classification	NOUN
ajmri-740	99	19	in	in	ADP
ajmri-740	99	20	a	a	DET
ajmri-740	99	21	category	category	NOUN
ajmri-740	99	22	.	.	PUNCT
ajmri-740	100	1	experimental	experimental	ADJ
ajmri-740	100	2	and	and	CCONJ
ajmri-740	100	3	results	result	NOUN
ajmri-740	100	4	we	we	PRON
ajmri-740	100	5	trained	train	VERB
ajmri-740	100	6	the	the	DET
ajmri-740	100	7	models	model	NOUN
ajmri-740	100	8	on	on	ADP
ajmri-740	100	9	a	a	DET
ajmri-740	100	10	windows	window	NOUN
ajmri-740	100	11	10	10	NUM
ajmri-740	100	12	system	system	NOUN
ajmri-740	100	13	with	with	ADP
ajmri-740	100	14	an	an	DET
ajmri-740	100	15	intel(r	intel(r	NOUN
ajmri-740	100	16	)	)	PUNCT
ajmri-740	100	17	core	core	NOUN
ajmri-740	100	18	™	™	ADJ
ajmri-740	100	19	i7	i7	NOUN
ajmri-740	100	20	-	-	PUNCT
ajmri-740	100	21	8650u	8650u	NOUN
ajmri-740	100	22	processor	processor	NOUN
ajmri-740	100	23	,	,	PUNCT
ajmri-740	100	24	16	16	NUM
ajmri-740	100	25	gb	gb	ADP
ajmri-740	100	26	of	of	ADP
ajmri-740	100	27	randomaccess	randomaccess	NOUN
ajmri-740	100	28	memory	memory	NOUN
ajmri-740	100	29	(	(	PUNCT
ajmri-740	100	30	ram	ram	NOUN
ajmri-740	100	31	)	)	PUNCT
ajmri-740	100	32	,	,	PUNCT
ajmri-740	100	33	and	and	CCONJ
ajmri-740	100	34	an	an	DET
ajmri-740	100	35	nvidia	nvidia	PROPN
ajmri-740	100	36	geforce	geforce	NOUN
ajmri-740	100	37	mx150	mx150	PROPN
ajmri-740	100	38	graphics	graphic	NOUN
ajmri-740	100	39	processing	processing	NOUN
ajmri-740	100	40	unit	unit	NOUN
ajmri-740	100	41	(	(	PUNCT
ajmri-740	100	42	gpu	gpu	PROPN
ajmri-740	100	43	)	)	PUNCT
ajmri-740	100	44	.	.	PUNCT
ajmri-740	101	1	the	the	DET
ajmri-740	101	2	models	model	NOUN
ajmri-740	101	3	are	be	AUX
ajmri-740	101	4	configured	configure	VERB
ajmri-740	101	5	in	in	ADP
ajmri-740	101	6	python	python	NOUN
ajmri-740	101	7	using	use	VERB
ajmri-740	101	8	the	the	DET
ajmri-740	101	9	keras	keras	PROPN
ajmri-740	101	10	version	version	PROPN
ajmri-740	101	11	2.4	2.4	NUM
ajmri-740	101	12	api	api	NOUN
ajmri-740	101	13	with	with	ADP
ajmri-740	101	14	the	the	DET
ajmri-740	101	15	tensorflow	tensorflow	NOUN
ajmri-740	101	16	version	version	NOUN
ajmri-740	101	17	2.4	2.4	NUM
ajmri-740	101	18	backend	backend	NOUN
ajmri-740	101	19	and	and	CCONJ
ajmri-740	101	20	cuda/	cuda/	NUM
ajmri-740	101	21	cudnn	cudnn	NOUN
ajmri-740	101	22	dependencies	dependency	NOUN
ajmri-740	101	23	for	for	ADP
ajmri-740	101	24	gpu	gpu	NOUN
ajmri-740	101	25	acceleration	acceleration	NOUN
ajmri-740	101	26	(	(	PUNCT
ajmri-740	101	27	artificial	artificial	ADJ
ajmri-740	101	28	neural	neural	ADJ
ajmri-740	101	29	networks	network	NOUN
ajmri-740	101	30	.	.	PUNCT
ajmri-740	102	1	pt	pt	X
ajmri-740	102	2	.	.	PROPN
ajmri-740	102	3	3	3	NUM
ajmri-740	102	4	,	,	PUNCT
ajmri-740	102	5	2010	2010	NUM
ajmri-740	102	6	)	)	PUNCT
ajmri-740	102	7	.	.	PUNCT
ajmri-740	103	1	setting	set	VERB
ajmri-740	103	2	we	we	PRON
ajmri-740	103	3	used	use	VERB
ajmri-740	103	4	a	a	DET
ajmri-740	103	5	batch	batch	NOUN
ajmri-740	103	6	size	size	NOUN
ajmri-740	103	7	of	of	ADP
ajmri-740	103	8	4,8	4,8	NUM
ajmri-740	103	9	,	,	PUNCT
ajmri-740	103	10	and	and	CCONJ
ajmri-740	103	11	16	16	NUM
ajmri-740	103	12	for	for	ADP
ajmri-740	103	13	15	15	NUM
ajmri-740	103	14	epochs	epoch	NOUN
ajmri-740	103	15	for	for	ADP
ajmri-740	103	16	each	each	DET
ajmri-740	103	17	method	method	NOUN
ajmri-740	103	18	.	.	PUNCT
ajmri-740	104	1	our	our	PRON
ajmri-740	104	2	study	study	NOUN
ajmri-740	104	3	will	will	AUX
ajmri-740	104	4	use	use	VERB
ajmri-740	104	5	cross	cross	NOUN
ajmri-740	104	6	-	-	NOUN
ajmri-740	104	7	entropy	entropy	NOUN
ajmri-740	104	8	as	as	ADP
ajmri-740	104	9	a	a	DET
ajmri-740	104	10	loss	loss	NOUN
ajmri-740	104	11	function	function	NOUN
ajmri-740	104	12	and	and	CCONJ
ajmri-740	104	13	optimization	optimization	NOUN
ajmri-740	104	14	algorithms	algorithm	NOUN
ajmri-740	104	15	suitable	suitable	ADJ
ajmri-740	104	16	for	for	ADP
ajmri-740	104	17	deep	deep	ADJ
ajmri-740	104	18	learning	learning	NOUN
ajmri-740	104	19	to	to	PART
ajmri-740	104	20	train	train	VERB
ajmri-740	104	21	the	the	DET
ajmri-740	104	22	chosen	choose	VERB
ajmri-740	104	23	models	model	NOUN
ajmri-740	104	24	.	.	PUNCT
ajmri-740	105	1	these	these	DET
ajmri-740	105	2	algorithms	algorithm	NOUN
ajmri-740	105	3	will	will	AUX
ajmri-740	105	4	directly	directly	ADV
ajmri-740	105	5	affect	affect	VERB
ajmri-740	105	6	the	the	DET
ajmri-740	105	7	efficiency	efficiency	NOUN
ajmri-740	105	8	of	of	ADP
ajmri-740	105	9	the	the	DET
ajmri-740	105	10	models	model	NOUN
ajmri-740	105	11	in	in	ADP
ajmri-740	105	12	our	our	PRON
ajmri-740	105	13	study	study	NOUN
ajmri-740	105	14	.	.	PUNCT
ajmri-740	106	1	the	the	DET
ajmri-740	106	2	optimizers	optimizer	NOUN
ajmri-740	106	3	we	we	PRON
ajmri-740	106	4	will	will	AUX
ajmri-740	106	5	use	use	VERB
ajmri-740	106	6	are	be	AUX
ajmri-740	106	7	:	:	PUNCT
ajmri-740	106	8	•	•	ADJ
ajmri-740	106	9	sgd	sgd	PROPN
ajmri-740	106	10	•	•	PROPN
ajmri-740	106	11	adam	adam	PROPN
ajmri-740	106	12	•	•	ADP
ajmri-740	106	13	rmsprop	rmsprop	NOUN
ajmri-740	106	14	sgd	sgd	PROPN
ajmri-740	106	15	sgd	sgd	PROPN
ajmri-740	106	16	implements	implement	VERB
ajmri-740	106	17	the	the	DET
ajmri-740	106	18	stochastic	stochastic	ADJ
ajmri-740	106	19	gradient	gradient	ADJ
ajmri-740	106	20	descent	descent	NOUN
ajmri-740	106	21	optimizer	optimizer	NOUN
ajmri-740	106	22	with	with	ADP
ajmri-740	106	23	a	a	DET
ajmri-740	106	24	learning	learning	NOUN
ajmri-740	106	25	rate	rate	NOUN
ajmri-740	106	26	and	and	CCONJ
ajmri-740	106	27	momentum	momentum	NOUN
ajmri-740	106	28	.	.	PUNCT
ajmri-740	107	1	the	the	DET
ajmri-740	107	2	stochastic	stochastic	ADJ
ajmri-740	107	3	gradient	gradient	NOUN
ajmri-740	107	4	algorithm	algorithm	NOUN
ajmri-740	107	5	is	be	AUX
ajmri-740	107	6	a	a	DET
ajmri-740	107	7	gradient	gradient	ADJ
ajmri-740	107	8	descent	descent	NOUN
ajmri-740	107	9	method	method	NOUN
ajmri-740	107	10	that	that	PRON
ajmri-740	107	11	minimizes	minimize	VERB
ajmri-740	107	12	an	an	DET
ajmri-740	107	13	objective	objective	ADJ
ajmri-740	107	14	function	function	NOUN
ajmri-740	107	15	written	write	VERB
ajmri-740	107	16	as	as	ADP
ajmri-740	107	17	a	a	DET
ajmri-740	107	18	sum	sum	NOUN
ajmri-740	107	19	of	of	ADP
ajmri-740	107	20	differentiable	differentiable	ADJ
ajmri-740	107	21	functions	function	NOUN
ajmri-740	107	22	.	.	PUNCT
ajmri-740	108	1	the	the	DET
ajmri-740	108	2	learning	learning	NOUN
ajmri-740	108	3	rate	rate	NOUN
ajmri-740	108	4	was	be	AUX
ajmri-740	108	5	set	set	VERB
ajmri-740	108	6	to	to	ADP
ajmri-740	108	7	0.0001	0.0001	NUM
ajmri-740	108	8	with	with	ADP
ajmri-740	108	9	a	a	DET
ajmri-740	108	10	momentum	momentum	NOUN
ajmri-740	108	11	of	of	ADP
ajmri-740	108	12	0.9	0.9	NUM
ajmri-740	108	13	(	(	PUNCT
ajmri-740	108	14	team	team	NOUN
ajmri-740	108	15	,	,	PUNCT
ajmri-740	108	16	s.	s.	PROPN
ajmri-740	108	17	d.	d.	PROPN
ajmri-740	108	18	)	)	PUNCT
ajmri-740	108	19	.	.	PUNCT
ajmri-740	109	1	adam	adam	PROPN
ajmri-740	109	2	adam	adam	PROPN
ajmri-740	109	3	is	be	AUX
ajmri-740	109	4	a	a	DET
ajmri-740	109	5	stochastic	stochastic	ADJ
ajmri-740	109	6	gradient	gradient	ADJ
ajmri-740	109	7	descent	descent	NOUN
ajmri-740	109	8	method	method	NOUN
ajmri-740	109	9	based	base	VERB
ajmri-740	109	10	on	on	ADP
ajmri-740	109	11	adaptive	adaptive	ADJ
ajmri-740	109	12	estimation	estimation	NOUN
ajmri-740	109	13	of	of	ADP
ajmri-740	109	14	first	first	ADJ
ajmri-740	109	15	and	and	CCONJ
ajmri-740	109	16	second	second	ADJ
ajmri-740	109	17	-	-	PUNCT
ajmri-740	109	18	order	order	NOUN
ajmri-740	109	19	moments	moment	NOUN
ajmri-740	109	20	.	.	PUNCT
ajmri-740	110	1	its	its	PRON
ajmri-740	110	2	implementation	implementation	NOUN
ajmri-740	110	3	is	be	AUX
ajmri-740	110	4	quite	quite	ADV
ajmri-740	110	5	simple	simple	ADJ
ajmri-740	110	6	and	and	CCONJ
ajmri-740	110	7	computationally	computationally	ADV
ajmri-740	110	8	efficient	efficient	ADJ
ajmri-740	110	9	,	,	PUNCT
ajmri-740	110	10	and	and	CCONJ
ajmri-740	110	11	its	its	PRON
ajmri-740	110	12	memory	memory	NOUN
ajmri-740	110	13	usage	usage	NOUN
ajmri-740	110	14	is	be	AUX
ajmri-740	110	15	optimized	optimize	VERB
ajmri-740	110	16	and	and	CCONJ
ajmri-740	110	17	well	well	ADV
ajmri-740	110	18	adapted	adapt	VERB
ajmri-740	110	19	to	to	ADP
ajmri-740	110	20	significant	significant	ADJ
ajmri-740	110	21	data	datum	NOUN
ajmri-740	110	22	volume	volume	NOUN
ajmri-740	110	23	problems	problem	NOUN
ajmri-740	110	24	(	(	PUNCT
ajmri-740	110	25	kingma	kingma	PROPN
ajmri-740	110	26	&	&	CCONJ
ajmri-740	110	27	ba	ba	PROPN
ajmri-740	110	28	,	,	PUNCT
ajmri-740	110	29	2014	2014	NUM
ajmri-740	110	30	)	)	PUNCT
ajmri-740	110	31	.	.	PUNCT
ajmri-740	111	1	rmsprop	rmsprop	NOUN
ajmri-740	111	2	root	root	NOUN
ajmri-740	111	3	mean	mean	VERB
ajmri-740	111	4	squared	square	VERB
ajmri-740	111	5	propagation	propagation	NOUN
ajmri-740	111	6	,	,	PUNCT
ajmri-740	111	7	or	or	CCONJ
ajmri-740	111	8	rmsprop	rmsprop	NOUN
ajmri-740	111	9	,	,	PUNCT
ajmri-740	111	10	is	be	AUX
ajmri-740	111	11	an	an	DET
ajmri-740	111	12	extension	extension	NOUN
ajmri-740	111	13	of	of	ADP
ajmri-740	111	14	gradient	gradient	ADJ
ajmri-740	111	15	descent	descent	NOUN
ajmri-740	111	16	using	use	VERB
ajmri-740	111	17	a	a	DET
ajmri-740	111	18	decreasing	decrease	VERB
ajmri-740	111	19	average	average	NOUN
ajmri-740	111	20	of	of	ADP
ajmri-740	111	21	partial	partial	ADJ
ajmri-740	111	22	gradients	gradient	NOUN
ajmri-740	111	23	to	to	PART
ajmri-740	111	24	adapt	adapt	VERB
ajmri-740	111	25	the	the	DET
ajmri-740	111	26	step	step	NOUN
ajmri-740	111	27	size	size	NOUN
ajmri-740	111	28	for	for	ADP
ajmri-740	111	29	each	each	DET
ajmri-740	111	30	parameter	parameter	NOUN
ajmri-740	111	31	.	.	PUNCT
ajmri-740	112	1	using	use	VERB
ajmri-740	112	2	a	a	DET
ajmri-740	112	3	decreasing	decrease	VERB
ajmri-740	112	4	moving	move	VERB
ajmri-740	112	5	average	average	NOUN
ajmri-740	112	6	allows	allow	VERB
ajmri-740	112	7	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	112	8	pa	pa	PROPN
ajmri-740	112	9	ge	ge	PROPN
ajmri-740	112	10	28	28	NUM
ajmri-740	112	11	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	112	12	am	am	NOUN
ajmri-740	112	13	.	.	PUNCT
ajmri-740	113	1	j.	j.	PROPN
ajmri-740	113	2	multidis	multidis	PROPN
ajmri-740	113	3	.	.	PUNCT
ajmri-740	114	1	res	re	NOUN
ajmri-740	114	2	.	.	PUNCT
ajmri-740	115	1	innov	innov	PROPN
ajmri-740	115	2	.	.	PUNCT
ajmri-740	116	1	1(5	1(5	NUM
ajmri-740	116	2	)	)	PUNCT
ajmri-740	116	3	24	24	NUM
ajmri-740	116	4	-	-	SYM
ajmri-740	116	5	32	32	NUM
ajmri-740	116	6	,	,	PUNCT
ajmri-740	116	7	2022	2022	NUM
ajmri-740	116	8	us	we	PRON
ajmri-740	116	9	to	to	PART
ajmri-740	116	10	eliminate	eliminate	VERB
ajmri-740	116	11	unnecessary	unnecessary	ADJ
ajmri-740	116	12	gradients	gradient	NOUN
ajmri-740	116	13	and	and	CCONJ
ajmri-740	116	14	keep	keep	VERB
ajmri-740	116	15	the	the	DET
ajmri-740	116	16	best	good	ADJ
ajmri-740	116	17	partial	partial	ADJ
ajmri-740	116	18	gradients	gradient	NOUN
ajmri-740	116	19	observed	observe	VERB
ajmri-740	116	20	during	during	ADP
ajmri-740	116	21	the	the	DET
ajmri-740	116	22	search	search	NOUN
ajmri-740	116	23	progress	progress	NOUN
ajmri-740	116	24	,	,	PUNCT
ajmri-740	116	25	thus	thus	ADV
ajmri-740	116	26	exceeding	exceed	VERB
ajmri-740	116	27	the	the	DET
ajmri-740	116	28	limitations	limitation	NOUN
ajmri-740	116	29	of	of	ADP
ajmri-740	116	30	adagrad	adagrad	ADJ
ajmri-740	116	31	(	(	PUNCT
ajmri-740	116	32	papers	paper	NOUN
ajmri-740	116	33	with	with	ADP
ajmri-740	116	34	code	code	NOUN
ajmri-740	116	35	rmsprop	rmsprop	NOUN
ajmri-740	116	36	explained	explain	VERB
ajmri-740	116	37	,	,	PUNCT
ajmri-740	116	38	s.	s.	PROPN
ajmri-740	116	39	d.	d.	PROPN
ajmri-740	116	40	)	)	PUNCT
ajmri-740	116	41	.	.	PUNCT
ajmri-740	117	1	summary	summary	NOUN
ajmri-740	117	2	of	of	ADP
ajmri-740	117	3	results	result	NOUN
ajmri-740	117	4	the	the	DET
ajmri-740	117	5	results	result	NOUN
ajmri-740	117	6	of	of	ADP
ajmri-740	117	7	the	the	DET
ajmri-740	117	8	different	different	ADJ
ajmri-740	117	9	models	model	NOUN
ajmri-740	117	10	are	be	AUX
ajmri-740	117	11	shown	show	VERB
ajmri-740	117	12	in	in	ADP
ajmri-740	117	13	the	the	DET
ajmri-740	117	14	table	table	NOUN
ajmri-740	117	15	below	below	ADV
ajmri-740	117	16	:	:	PUNCT
ajmri-740	117	17	sgd	sgd	NOUN
ajmri-740	117	18	table	table	NOUN
ajmri-740	117	19	1	1	NUM
ajmri-740	117	20	:	:	PUNCT
ajmri-740	117	21	results	result	NOUN
ajmri-740	117	22	of	of	ADP
ajmri-740	117	23	the	the	DET
ajmri-740	117	24	accuracy	accuracy	NOUN
ajmri-740	117	25	in	in	ADP
ajmri-740	117	26	%	%	NOUN
ajmri-740	117	27	of	of	ADP
ajmri-740	117	28	our	our	PRON
ajmri-740	117	29	models	model	NOUN
ajmri-740	117	30	with	with	ADP
ajmri-740	117	31	sgd	sgd	PROPN
ajmri-740	117	32	.	.	PROPN
ajmri-740	117	33	optimizer	optimizer	NOUN
ajmri-740	117	34	:	:	PUNCT
ajmri-740	117	35	sgd	sgd	NOUN
ajmri-740	117	36	batch	batch	NOUN
ajmri-740	117	37	size	size	NOUN
ajmri-740	117	38	:	:	PUNCT
ajmri-740	117	39	4	4	NUM
ajmri-740	117	40	batch	batch	NOUN
ajmri-740	117	41	size	size	NOUN
ajmri-740	117	42	:	:	PUNCT
ajmri-740	117	43	8	8	NUM
ajmri-740	117	44	batch	batch	NOUN
ajmri-740	117	45	size	size	NOUN
ajmri-740	117	46	:	:	PUNCT
ajmri-740	117	47	16	16	NUM
ajmri-740	117	48	models	model	NOUN
ajmri-740	117	49	accuracy	accuracy	NOUN
ajmri-740	117	50	(	(	PUNCT
ajmri-740	117	51	%	%	NOUN
ajmri-740	117	52	)	)	PUNCT
ajmri-740	117	53	accuracy	accuracy	NOUN
ajmri-740	117	54	(	(	PUNCT
ajmri-740	117	55	%	%	NOUN
ajmri-740	117	56	)	)	PUNCT
ajmri-740	117	57	accuracy	accuracy	NOUN
ajmri-740	117	58	(	(	PUNCT
ajmri-740	117	59	%	%	INTJ
ajmri-740	117	60	)	)	PUNCT
ajmri-740	118	1	vgg-19	vgg-19	CCONJ
ajmri-740	118	2	98.65	98.65	NUM
ajmri-740	118	3	98.27	98.27	NUM
ajmri-740	118	4	94.81	94.81	NUM
ajmri-740	118	5	resnet-50	resnet-50	PROPN
ajmri-740	118	6	99.23	99.23	NUM
ajmri-740	118	7	98.27	98.27	NUM
ajmri-740	118	8	94.81	94.81	NUM
ajmri-740	118	9	densenet-201	densenet-201	VERB
ajmri-740	118	10	99.81	99.81	NUM
ajmri-740	118	11	98.65	98.65	NUM
ajmri-740	118	12	98.46	98.46	NUM
ajmri-740	118	13	the	the	DET
ajmri-740	118	14	results	result	NOUN
ajmri-740	118	15	in	in	ADP
ajmri-740	118	16	table	table	NOUN
ajmri-740	118	17	2	2	NUM
ajmri-740	118	18	show	show	VERB
ajmri-740	118	19	the	the	DET
ajmri-740	118	20	results	result	NOUN
ajmri-740	118	21	obtained	obtain	VERB
ajmri-740	118	22	from	from	ADP
ajmri-740	118	23	our	our	PRON
ajmri-740	118	24	models	model	NOUN
ajmri-740	118	25	on	on	ADP
ajmri-740	118	26	the	the	DET
ajmri-740	118	27	different	different	ADJ
ajmri-740	118	28	parameters	parameter	NOUN
ajmri-740	118	29	;	;	PUNCT
ajmri-740	118	30	the	the	DET
ajmri-740	118	31	densenet-201	densenet-201	ADJ
ajmri-740	118	32	model	model	NOUN
ajmri-740	118	33	got	get	VERB
ajmri-740	118	34	the	the	DET
ajmri-740	118	35	best	good	ADJ
ajmri-740	118	36	results	result	NOUN
ajmri-740	118	37	for	for	ADP
ajmri-740	118	38	optimizer	optimizer	NOUN
ajmri-740	118	39	sgd	sgd	NOUN
ajmri-740	118	40	table	table	NOUN
ajmri-740	118	41	2	2	NUM
ajmri-740	118	42	:	:	PUNCT
ajmri-740	118	43	results	result	NOUN
ajmri-740	118	44	of	of	ADP
ajmri-740	118	45	the	the	DET
ajmri-740	118	46	accuracy	accuracy	NOUN
ajmri-740	118	47	in	in	ADP
ajmri-740	118	48	%	%	NOUN
ajmri-740	118	49	of	of	ADP
ajmri-740	118	50	our	our	PRON
ajmri-740	118	51	models	model	NOUN
ajmri-740	118	52	with	with	ADP
ajmri-740	118	53	adam	adam	PROPN
ajmri-740	118	54	optimizer	optimizer	NOUN
ajmri-740	118	55	:	:	PUNCT
ajmri-740	118	56	adam	adam	NOUN
ajmri-740	118	57	batch	batch	NOUN
ajmri-740	118	58	size	size	NOUN
ajmri-740	118	59	:	:	PUNCT
ajmri-740	118	60	4	4	NUM
ajmri-740	118	61	batch	batch	NOUN
ajmri-740	118	62	size	size	NOUN
ajmri-740	118	63	:	:	PUNCT
ajmri-740	118	64	8	8	NUM
ajmri-740	118	65	batch	batch	NOUN
ajmri-740	118	66	size	size	NOUN
ajmri-740	118	67	:	:	PUNCT
ajmri-740	118	68	16	16	NUM
ajmri-740	118	69	models	model	NOUN
ajmri-740	118	70	accuracy	accuracy	NOUN
ajmri-740	118	71	(	(	PUNCT
ajmri-740	118	72	%	%	NOUN
ajmri-740	118	73	)	)	PUNCT
ajmri-740	118	74	accuracy	accuracy	NOUN
ajmri-740	118	75	(	(	PUNCT
ajmri-740	118	76	%	%	NOUN
ajmri-740	118	77	)	)	PUNCT
ajmri-740	118	78	accuracy	accuracy	NOUN
ajmri-740	118	79	(	(	PUNCT
ajmri-740	118	80	%	%	INTJ
ajmri-740	118	81	)	)	PUNCT
ajmri-740	119	1	vgg-19	vgg-19	CCONJ
ajmri-740	119	2	0.0	0.0	NUM
ajmri-740	119	3	0.0	0.0	NUM
ajmri-740	119	4	0.0	0.0	NUM
ajmri-740	119	5	resnet-50	resnet-50	PROPN
ajmri-740	119	6	96.92	96.92	NUM
ajmri-740	119	7	95.00	95.00	NUM
ajmri-740	119	8	81.35	81.35	NUM
ajmri-740	119	9	densenet-201	densenet-201	VERB
ajmri-740	119	10	92.50	92.50	NUM
ajmri-740	119	11	92.50	92.50	NUM
ajmri-740	119	12	91.35	91.35	NUM
ajmri-740	119	13	the	the	DET
ajmri-740	119	14	results	result	NOUN
ajmri-740	119	15	in	in	ADP
ajmri-740	119	16	table	table	NOUN
ajmri-740	119	17	2	2	NUM
ajmri-740	119	18	show	show	VERB
ajmri-740	119	19	that	that	SCONJ
ajmri-740	119	20	the	the	DET
ajmri-740	119	21	res	re	NOUN
ajmri-740	119	22	net-50	net-50	ADJ
ajmri-740	119	23	model	model	NOUN
ajmri-740	119	24	has	have	VERB
ajmri-740	119	25	a	a	DET
ajmri-740	119	26	better	well	ADJ
ajmri-740	119	27	score	score	NOUN
ajmri-740	119	28	on	on	ADP
ajmri-740	119	29	batch	batch	NOUN
ajmri-740	119	30	sizes	size	NOUN
ajmri-740	119	31	4	4	NUM
ajmri-740	119	32	and	and	CCONJ
ajmri-740	119	33	8	8	NUM
ajmri-740	119	34	,	,	PUNCT
ajmri-740	119	35	while	while	SCONJ
ajmri-740	119	36	dense	dense	ADJ
ajmri-740	119	37	net-201	net-201	NOUN
ajmri-740	119	38	has	have	VERB
ajmri-740	119	39	the	the	DET
ajmri-740	119	40	best	good	ADJ
ajmri-740	119	41	result	result	NOUN
ajmri-740	119	42	on	on	ADP
ajmri-740	119	43	batch	batch	NOUN
ajmri-740	119	44	size	size	NOUN
ajmri-740	119	45	16	16	NUM
ajmri-740	119	46	for	for	ADP
ajmri-740	119	47	optimizer	optimizer	NOUN
ajmri-740	119	48	adam	adam	PROPN
ajmri-740	119	49	table	table	NOUN
ajmri-740	119	50	3	3	NUM
ajmri-740	119	51	:	:	PUNCT
ajmri-740	119	52	results	result	NOUN
ajmri-740	119	53	of	of	ADP
ajmri-740	119	54	the	the	DET
ajmri-740	119	55	accuracy	accuracy	NOUN
ajmri-740	119	56	in	in	ADP
ajmri-740	119	57	%	%	NOUN
ajmri-740	119	58	of	of	ADP
ajmri-740	119	59	our	our	PRON
ajmri-740	119	60	models	model	NOUN
ajmri-740	119	61	with	with	ADP
ajmri-740	119	62	rmsprop	rmsprop	NOUN
ajmri-740	119	63	.	.	PUNCT
ajmri-740	120	1	optimizer	optimizer	NOUN
ajmri-740	120	2	:	:	PUNCT
ajmri-740	120	3	rmsprop	rmsprop	NOUN
ajmri-740	120	4	batch	batch	NOUN
ajmri-740	120	5	size	size	NOUN
ajmri-740	120	6	:	:	PUNCT
ajmri-740	120	7	4	4	NUM
ajmri-740	120	8	batch	batch	NOUN
ajmri-740	120	9	size	size	NOUN
ajmri-740	120	10	:	:	PUNCT
ajmri-740	120	11	8	8	NUM
ajmri-740	120	12	batch	batch	NOUN
ajmri-740	120	13	size	size	NOUN
ajmri-740	120	14	:	:	PUNCT
ajmri-740	120	15	16	16	NUM
ajmri-740	120	16	models	model	NOUN
ajmri-740	120	17	accuracy	accuracy	NOUN
ajmri-740	120	18	(	(	PUNCT
ajmri-740	120	19	%	%	NOUN
ajmri-740	120	20	)	)	PUNCT
ajmri-740	120	21	accuracy	accuracy	NOUN
ajmri-740	120	22	(	(	PUNCT
ajmri-740	120	23	%	%	NOUN
ajmri-740	120	24	)	)	PUNCT
ajmri-740	120	25	accuracy	accuracy	NOUN
ajmri-740	120	26	(	(	PUNCT
ajmri-740	120	27	%	%	INTJ
ajmri-740	120	28	)	)	PUNCT
ajmri-740	121	1	vgg-19	vgg-19	CCONJ
ajmri-740	121	2	0.0019	0.0019	NUM
ajmri-740	121	3	0.0	0.0	NUM
ajmri-740	121	4	0.0	0.0	NUM
ajmri-740	121	5	resnet-50	resnet-50	PROPN
ajmri-740	121	6	96.92	96.92	NUM
ajmri-740	121	7	96.92	96.92	NUM
ajmri-740	121	8	96.92	96.92	NUM
ajmri-740	121	9	densenet-201	densenet-201	VERB
ajmri-740	121	10	89.62	89.62	NUM
ajmri-740	121	11	95.19	95.19	NUM
ajmri-740	121	12	82.69	82.69	NUM
ajmri-740	121	13	the	the	DET
ajmri-740	121	14	results	result	NOUN
ajmri-740	121	15	in	in	ADP
ajmri-740	121	16	table	table	NOUN
ajmri-740	121	17	3	3	NUM
ajmri-740	121	18	show	show	VERB
ajmri-740	121	19	the	the	DET
ajmri-740	121	20	stability	stability	NOUN
ajmri-740	121	21	of	of	ADP
ajmri-740	121	22	the	the	DET
ajmri-740	121	23	resnet-50	resnet-50	PROPN
ajmri-740	121	24	model	model	NOUN
ajmri-740	121	25	with	with	ADP
ajmri-740	121	26	a	a	DET
ajmri-740	121	27	better	well	ADJ
ajmri-740	121	28	score	score	NOUN
ajmri-740	121	29	on	on	ADP
ajmri-740	121	30	all	all	DET
ajmri-740	121	31	parameters	parameter	NOUN
ajmri-740	121	32	used	use	VERB
ajmri-740	121	33	.	.	PUNCT
ajmri-740	122	1	evaluation	evaluation	NOUN
ajmri-740	122	2	metrics	metric	NOUN
ajmri-740	122	3	to	to	PART
ajmri-740	122	4	validate	validate	VERB
ajmri-740	122	5	the	the	DET
ajmri-740	122	6	performance	performance	NOUN
ajmri-740	122	7	of	of	ADP
ajmri-740	122	8	the	the	DET
ajmri-740	122	9	pre	pre	ADJ
ajmri-740	122	10	-	-	ADJ
ajmri-740	122	11	trained	train	VERB
ajmri-740	122	12	models	model	NOUN
ajmri-740	122	13	in	in	ADP
ajmri-740	122	14	our	our	PRON
ajmri-740	122	15	study	study	NOUN
ajmri-740	122	16	,	,	PUNCT
ajmri-740	122	17	we	we	PRON
ajmri-740	122	18	will	will	AUX
ajmri-740	122	19	use	use	VERB
ajmri-740	122	20	the	the	DET
ajmri-740	122	21	following	follow	VERB
ajmri-740	122	22	metrics	metric	NOUN
ajmri-740	122	23	:	:	PUNCT
ajmri-740	122	24	precision	precision	NOUN
ajmri-740	122	25	is	be	AUX
ajmri-740	122	26	intuitively	intuitively	ADV
ajmri-740	122	27	the	the	DET
ajmri-740	122	28	ability	ability	NOUN
ajmri-740	122	29	of	of	ADP
ajmri-740	122	30	the	the	DET
ajmri-740	122	31	classifier	classifier	NOUN
ajmri-740	122	32	not	not	PART
ajmri-740	122	33	to	to	PART
ajmri-740	122	34	label	label	VERB
ajmri-740	122	35	as	as	ADP
ajmri-740	122	36	positive	positive	ADJ
ajmri-740	122	37	a	a	DET
ajmri-740	122	38	sample	sample	NOUN
ajmri-740	122	39	that	that	PRON
ajmri-740	122	40	is	be	AUX
ajmri-740	122	41	negative	negative	ADJ
ajmri-740	122	42	an	an	DET
ajmri-740	122	43	estimator	estimator	NOUN
ajmri-740	122	44	’s	’s	PART
ajmri-740	122	45	mean	mean	ADJ
ajmri-740	122	46	square	square	ADJ
ajmri-740	122	47	error	error	NOUN
ajmri-740	122	48	(	(	PUNCT
ajmri-740	122	49	mse	mse	NOUN
ajmri-740	122	50	)	)	PUNCT
ajmri-740	122	51	measures	measure	VERB
ajmri-740	122	52	the	the	DET
ajmri-740	122	53	average	average	NOUN
ajmri-740	122	54	of	of	ADP
ajmri-740	122	55	the	the	DET
ajmri-740	122	56	squared	square	VERB
ajmri-740	122	57	errors	error	NOUN
ajmri-740	122	58	,	,	PUNCT
ajmri-740	122	59	i.e.	i.e.	X
ajmri-740	122	60	,	,	PUNCT
ajmri-740	122	61	the	the	DET
ajmri-740	122	62	mean	mean	ADJ
ajmri-740	122	63	square	square	ADJ
ajmri-740	122	64	difference	difference	NOUN
ajmri-740	122	65	between	between	ADP
ajmri-740	122	66	the	the	DET
ajmri-740	122	67	estimated	estimate	VERB
ajmri-740	122	68	and	and	CCONJ
ajmri-740	122	69	actual	actual	ADJ
ajmri-740	122	70	values	value	NOUN
ajmri-740	122	71	.	.	PUNCT
ajmri-740	123	1	it	it	PRON
ajmri-740	123	2	is	be	AUX
ajmri-740	123	3	a	a	DET
ajmri-740	123	4	risk	risk	NOUN
ajmri-740	123	5	function	function	NOUN
ajmri-740	123	6	corresponding	correspond	VERB
ajmri-740	123	7	to	to	ADP
ajmri-740	123	8	the	the	DET
ajmri-740	123	9	expected	expect	VERB
ajmri-740	123	10	value	value	NOUN
ajmri-740	123	11	of	of	ADP
ajmri-740	123	12	the	the	DET
ajmri-740	123	13	squared	square	VERB
ajmri-740	123	14	error	error	NOUN
ajmri-740	123	15	loss	loss	NOUN
ajmri-740	123	16	.	.	PUNCT
ajmri-740	124	1	it	it	PRON
ajmri-740	124	2	is	be	AUX
ajmri-740	124	3	always	always	ADV
ajmri-740	124	4	non	non	ADJ
ajmri-740	124	5	-	-	ADJ
ajmri-740	124	6	negative	negative	ADJ
ajmri-740	124	7	,	,	PUNCT
ajmri-740	124	8	and	and	CCONJ
ajmri-740	124	9	values	value	NOUN
ajmri-740	124	10	close	close	ADJ
ajmri-740	124	11	to	to	ADP
ajmri-740	124	12	zero	zero	NUM
ajmri-740	124	13	are	be	AUX
ajmri-740	124	14	better	well	ADJ
ajmri-740	124	15	.	.	PUNCT
ajmri-740	125	1	the	the	DET
ajmri-740	125	2	following	follow	VERB
ajmri-740	125	3	equation	equation	NOUN
ajmri-740	125	4	defines	define	VERB
ajmri-740	125	5	it	it	PRON
ajmri-740	125	6	with	with	ADP
ajmri-740	125	7	yi	yi	NOUN
ajmri-740	125	8	:	:	PUNCT
ajmri-740	125	9	the	the	DET
ajmri-740	125	10	observed	observe	VERB
ajmri-740	125	11	data	datum	NOUN
ajmri-740	125	12	and	and	CCONJ
ajmri-740	125	13	:	:	PUNCT
ajmri-740	125	14	the	the	DET
ajmri-740	125	15	predicted	predict	VERB
ajmri-740	125	16	values	value	NOUN
ajmri-740	125	17	recall	recall	VERB
ajmri-740	125	18	is	be	AUX
ajmri-740	125	19	the	the	DET
ajmri-740	125	20	ability	ability	NOUN
ajmri-740	125	21	of	of	ADP
ajmri-740	125	22	a	a	DET
ajmri-740	125	23	classifier	classifier	NOUN
ajmri-740	125	24	to	to	PART
ajmri-740	125	25	determine	determine	VERB
ajmri-740	125	26	actual	actual	ADJ
ajmri-740	125	27	positive	positive	ADJ
ajmri-740	125	28	results	result	NOUN
ajmri-740	125	29	f1	f1	NOUN
ajmri-740	125	30	score	score	NOUN
ajmri-740	125	31	can	can	AUX
ajmri-740	125	32	be	be	AUX
ajmri-740	125	33	interpreted	interpret	VERB
ajmri-740	125	34	as	as	ADP
ajmri-740	125	35	a	a	DET
ajmri-740	125	36	weighted	weighted	ADJ
ajmri-740	125	37	average	average	NOUN
ajmri-740	125	38	of	of	ADP
ajmri-740	125	39	precision	precision	NOUN
ajmri-740	125	40	and	and	CCONJ
ajmri-740	125	41	recall	recall	NOUN
ajmri-740	125	42	,	,	PUNCT
ajmri-740	125	43	where	where	SCONJ
ajmri-740	125	44	an	an	DET
ajmri-740	125	45	f1	f1	ADJ
ajmri-740	125	46	score	score	NOUN
ajmri-740	125	47	reaches	reach	VERB
ajmri-740	125	48	its	its	PRON
ajmri-740	125	49	best	good	ADJ
ajmri-740	125	50	value	value	NOUN
ajmri-740	125	51	at	at	ADP
ajmri-740	125	52	one	one	NUM
ajmri-740	125	53	and	and	CCONJ
ajmri-740	125	54	its	its	PRON
ajmri-740	125	55	worst	bad	ADJ
ajmri-740	125	56	score	score	NOUN
ajmri-740	125	57	at	at	ADP
ajmri-740	125	58	0	0	NUM
ajmri-740	125	59	.	.	PUNCT
ajmri-740	126	1	matthews	matthews	PROPN
ajmri-740	126	2	correlation	correlation	NOUN
ajmri-740	126	3	coefficient	coefficient	NOUN
ajmri-740	126	4	(	(	PUNCT
ajmri-740	126	5	mcc	mcc	NOUN
ajmri-740	126	6	)	)	PUNCT
ajmri-740	126	7	is	be	AUX
ajmri-740	126	8	used	use	VERB
ajmri-740	126	9	in	in	ADP
ajmri-740	126	10	machine	machine	NOUN
ajmri-740	126	11	learning	learn	VERB
ajmri-740	126	12	to	to	PART
ajmri-740	126	13	measure	measure	VERB
ajmri-740	126	14	the	the	DET
ajmri-740	126	15	quality	quality	NOUN
ajmri-740	126	16	of	of	ADP
ajmri-740	126	17	classifications	classification	NOUN
ajmri-740	126	18	.	.	PUNCT
ajmri-740	127	1	its	its	PRON
ajmri-740	127	2	value	value	NOUN
ajmri-740	127	3	is	be	AUX
ajmri-740	127	4	essentially	essentially	ADV
ajmri-740	127	5	between	between	ADP
ajmri-740	127	6	-1	-1	PUNCT
ajmri-740	127	7	and	and	CCONJ
ajmri-740	127	8	+1	+1	PROPN
ajmri-740	127	9	.	.	PUNCT
ajmri-740	128	1	a	a	DET
ajmri-740	128	2	coefficient	coefficient	NOUN
ajmri-740	128	3	of	of	ADP
ajmri-740	128	4	(	(	PUNCT
ajmri-740	128	5	+1	+1	PROPN
ajmri-740	128	6	)	)	PUNCT
ajmri-740	128	7	represents	represent	VERB
ajmri-740	128	8	a	a	DET
ajmri-740	128	9	perfect	perfect	ADJ
ajmri-740	128	10	prediction	prediction	NOUN
ajmri-740	128	11	,	,	PUNCT
ajmri-740	128	12	0	0	NUM
ajmri-740	128	13	represents	represent	VERB
ajmri-740	128	14	a	a	DET
ajmri-740	128	15	random	random	ADJ
ajmri-740	128	16	prediction	prediction	NOUN
ajmri-740	128	17	,	,	PUNCT
ajmri-740	128	18	and	and	CCONJ
ajmri-740	128	19	(	(	PUNCT
ajmri-740	128	20	-1	-1	PUNCT
ajmri-740	128	21	)	)	PUNCT
ajmri-740	128	22	represents	represent	VERB
ajmri-740	128	23	an	an	DET
ajmri-740	128	24	inverse	inverse	NOUN
ajmri-740	128	25	prediction	prediction	NOUN
ajmri-740	128	26	.	.	PUNCT
ajmri-740	129	1	the	the	DET
ajmri-740	129	2	statistic	statistic	NOUN
ajmri-740	129	3	is	be	AUX
ajmri-740	129	4	also	also	ADV
ajmri-740	129	5	known	know	VERB
ajmri-740	129	6	as	as	ADP
ajmri-740	129	7	the	the	DET
ajmri-740	129	8	phi	phi	NOUN
ajmri-740	129	9	coefficient	coefficient	NOUN
ajmri-740	129	10	.	.	PUNCT
ajmri-740	130	1	with	with	ADP
ajmri-740	130	2	:	:	PUNCT
ajmri-740	130	3	•	•	NUM
ajmri-740	130	4	tp	tp	NOUN
ajmri-740	130	5	:	:	PUNCT
ajmri-740	130	6	true	true	ADJ
ajmri-740	130	7	positive	positive	ADJ
ajmri-740	130	8	•	•	ADJ
ajmri-740	130	9	tn	tn	NOUN
ajmri-740	130	10	:	:	PUNCT
ajmri-740	130	11	true	true	ADJ
ajmri-740	130	12	negative	negative	ADJ
ajmri-740	130	13	•	•	ADP
ajmri-740	130	14	fp	fp	ADJ
ajmri-740	130	15	:	:	PUNCT
ajmri-740	130	16	false	false	ADJ
ajmri-740	130	17	positive	positive	ADJ
ajmri-740	130	18	•	•	ADJ
ajmri-740	130	19	fn	fn	NOUN
ajmri-740	130	20	:	:	PUNCT
ajmri-740	130	21	false	false	ADJ
ajmri-740	130	22	negative	negative	ADJ
ajmri-740	130	23	•	•	NOUN
ajmri-740	130	24	results	result	NOUN
ajmri-740	130	25	of	of	ADP
ajmri-740	130	26	metrics	metric	NOUN
ajmri-740	130	27	evaluation	evaluation	NOUN
ajmri-740	130	28	metrics	metric	NOUN
ajmri-740	130	29	used	use	VERB
ajmri-740	130	30	to	to	PART
ajmri-740	130	31	consolidate	consolidate	VERB
ajmri-740	130	32	obtained	obtain	VERB
ajmri-740	130	33	results	result	NOUN
ajmri-740	130	34	confirm	confirm	VERB
ajmri-740	130	35	that	that	SCONJ
ajmri-740	130	36	the	the	DET
ajmri-740	130	37	resnet-50	resnet-50	PROPN
ajmri-740	130	38	model	model	NOUN
ajmri-740	130	39	had	have	VERB
ajmri-740	130	40	the	the	DET
ajmri-740	130	41	best	good	ADJ
ajmri-740	130	42	impact	impact	NOUN
ajmri-740	130	43	on	on	ADP
ajmri-740	130	44	batch	batch	NOUN
ajmri-740	130	45	size	size	NOUN
ajmri-740	130	46	4	4	NUM
ajmri-740	130	47	,	,	PUNCT
ajmri-740	130	48	while	while	SCONJ
ajmri-740	130	49	densenet-201	densenet-201	ADJ
ajmri-740	130	50	had	have	VERB
ajmri-740	130	51	the	the	DET
ajmri-740	130	52	best	good	ADJ
ajmri-740	130	53	effect	effect	NOUN
ajmri-740	130	54	on	on	ADP
ajmri-740	130	55	batch	batch	NOUN
ajmri-740	130	56	sizes	size	NOUN
ajmri-740	130	57	8	8	NUM
ajmri-740	130	58	and	and	CCONJ
ajmri-740	130	59	16	16	NUM
ajmri-740	130	60	using	use	VERB
ajmri-740	130	61	rmsprop	rmsprop	NOUN
ajmri-740	130	62	optimizer	optimizer	NOUN
ajmri-740	130	63	.	.	PUNCT
ajmri-740	131	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	131	2	pa	pa	PROPN
ajmri-740	131	3	ge	ge	PROPN
ajmri-740	131	4	29	29	NUM
ajmri-740	131	5	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	131	6	am	am	NOUN
ajmri-740	131	7	.	.	PUNCT
ajmri-740	132	1	j.	j.	PROPN
ajmri-740	132	2	multidis	multidis	PROPN
ajmri-740	132	3	.	.	PUNCT
ajmri-740	133	1	res	re	NOUN
ajmri-740	133	2	.	.	PUNCT
ajmri-740	134	1	innov	innov	PROPN
ajmri-740	134	2	.	.	PUNCT
ajmri-740	135	1	1(5	1(5	NUM
ajmri-740	135	2	)	)	PUNCT
ajmri-740	135	3	24	24	NUM
ajmri-740	135	4	-	-	SYM
ajmri-740	135	5	32	32	NUM
ajmri-740	135	6	,	,	PUNCT
ajmri-740	135	7	2022	2022	NUM
ajmri-740	135	8	table	table	NOUN
ajmri-740	135	9	4	4	NUM
ajmri-740	135	10	:	:	PUNCT
ajmri-740	135	11	performance	performance	NOUN
ajmri-740	135	12	metrics	metric	NOUN
ajmri-740	135	13	of	of	ADP
ajmri-740	135	14	sgd	sgd	NOUN
ajmri-740	135	15	optimizer	optimizer	NOUN
ajmri-740	135	16	models	model	NOUN
ajmri-740	135	17	.	.	PUNCT
ajmri-740	136	1	setting	set	VERB
ajmri-740	136	2	–	–	PUNCT
ajmri-740	136	3	optimizer	optimizer	NOUN
ajmri-740	136	4	:	:	PUNCT
ajmri-740	136	5	sgd	sgd	NOUN
ajmri-740	136	6	batch	batch	NOUN
ajmri-740	136	7	size	size	NOUN
ajmri-740	136	8	:	:	PUNCT
ajmri-740	136	9	4	4	NUM
ajmri-740	136	10	models	model	NOUN
ajmri-740	136	11	precision	precision	NOUN
ajmri-740	136	12	(	(	PUNCT
ajmri-740	136	13	%	%	INTJ
ajmri-740	136	14	)	)	PUNCT
ajmri-740	136	15	mse	mse	NOUN
ajmri-740	136	16	(	(	PUNCT
ajmri-740	136	17	%	%	INTJ
ajmri-740	136	18	)	)	PUNCT
ajmri-740	137	1	f	f	X
ajmri-740	137	2	-	-	PUNCT
ajmri-740	137	3	score	score	NOUN
ajmri-740	137	4	(	(	PUNCT
ajmri-740	137	5	%	%	INTJ
ajmri-740	137	6	)	)	PUNCT
ajmri-740	137	7	recall	recall	NOUN
ajmri-740	137	8	(	(	PUNCT
ajmri-740	137	9	%	%	INTJ
ajmri-740	137	10	)	)	PUNCT
ajmri-740	137	11	mcc	mcc	NOUN
ajmri-740	137	12	(	(	PUNCT
ajmri-740	137	13	%	%	INTJ
ajmri-740	137	14	)	)	PUNCT
ajmri-740	138	1	vgg-19	vgg-19	CCONJ
ajmri-740	138	2	99.02	99.02	NUM
ajmri-740	138	3	0.019	0.019	NUM
ajmri-740	138	4	98.70	98.70	NUM
ajmri-740	138	5	98.64	98.64	NUM
ajmri-740	138	6	98.64	98.64	NUM
ajmri-740	138	7	resnet-50	resnet-50	PROPN
ajmri-740	138	8	99.37	99.37	NUM
ajmri-740	138	9	0.014	0.014	NUM
ajmri-740	138	10	99.24	99.24	NUM
ajmri-740	138	11	99.23	99.23	NUM
ajmri-740	138	12	99.22	99.22	NUM
ajmri-740	138	13	densenet-201	densenet-201	VERB
ajmri-740	138	14	99.87	99.87	NUM
ajmri-740	138	15	0.004	0.004	NUM
ajmri-740	138	16	99.81	99.81	NUM
ajmri-740	138	17	99.80	99.80	NUM
ajmri-740	138	18	99.80	99.80	NUM
ajmri-740	138	19	setting	setting	NOUN
ajmri-740	138	20	–	–	PUNCT
ajmri-740	138	21	optimizer	optimizer	NOUN
ajmri-740	138	22	:	:	PUNCT
ajmri-740	138	23	sgd	sgd	NOUN
ajmri-740	138	24	batch	batch	NOUN
ajmri-740	138	25	size	size	NOUN
ajmri-740	138	26	:	:	PUNCT
ajmri-740	138	27	8	8	NUM
ajmri-740	138	28	models	model	NOUN
ajmri-740	138	29	precision	precision	NOUN
ajmri-740	138	30	(	(	PUNCT
ajmri-740	138	31	%	%	INTJ
ajmri-740	138	32	)	)	PUNCT
ajmri-740	138	33	mse	mse	NOUN
ajmri-740	138	34	(	(	PUNCT
ajmri-740	138	35	%	%	INTJ
ajmri-740	138	36	)	)	PUNCT
ajmri-740	139	1	f	f	X
ajmri-740	139	2	-	-	PUNCT
ajmri-740	139	3	score	score	NOUN
ajmri-740	139	4	(	(	PUNCT
ajmri-740	139	5	%	%	INTJ
ajmri-740	139	6	)	)	PUNCT
ajmri-740	139	7	recall	recall	NOUN
ajmri-740	139	8	(	(	PUNCT
ajmri-740	139	9	%	%	INTJ
ajmri-740	139	10	)	)	PUNCT
ajmri-740	139	11	mcc	mcc	NOUN
ajmri-740	139	12	(	(	PUNCT
ajmri-740	139	13	%	%	INTJ
ajmri-740	139	14	)	)	PUNCT
ajmri-740	140	1	vgg-19	vgg-19	CCONJ
ajmri-740	140	2	98.58	98.58	NUM
ajmri-740	140	3	0.025	0.025	NUM
ajmri-740	140	4	98.28	98.28	NUM
ajmri-740	140	5	98.26	98.26	NUM
ajmri-740	140	6	98.25	98.25	NUM
ajmri-740	140	7	resnet-50	resnet-50	PROPN
ajmri-740	140	8	98.62	98.62	NUM
ajmri-740	140	9	0.029	0.029	NUM
ajmri-740	140	10	98.24	98.24	NUM
ajmri-740	140	11	98.26	98.26	NUM
ajmri-740	140	12	98.25	98.25	NUM
ajmri-740	140	13	densenet-201	densenet-201	VERB
ajmri-740	140	14	98.92	98.92	NUM
ajmri-740	140	15	0.032	0.032	NUM
ajmri-740	140	16	98.67	98.67	NUM
ajmri-740	140	17	98.65	98.65	NUM
ajmri-740	140	18	98.64	98.64	NUM
ajmri-740	140	19	setting	setting	NOUN
ajmri-740	140	20	–	–	PUNCT
ajmri-740	140	21	optimizer	optimizer	NOUN
ajmri-740	140	22	:	:	PUNCT
ajmri-740	140	23	sgd	sgd	NOUN
ajmri-740	140	24	batch	batch	NOUN
ajmri-740	140	25	size	size	NOUN
ajmri-740	140	26	:	:	PUNCT
ajmri-740	140	27	16	16	NUM
ajmri-740	140	28	models	model	NOUN
ajmri-740	140	29	precision	precision	NOUN
ajmri-740	140	30	(	(	PUNCT
ajmri-740	140	31	%	%	INTJ
ajmri-740	140	32	)	)	PUNCT
ajmri-740	140	33	mse	mse	NOUN
ajmri-740	140	34	(	(	PUNCT
ajmri-740	140	35	%	%	INTJ
ajmri-740	140	36	)	)	PUNCT
ajmri-740	141	1	f	f	X
ajmri-740	141	2	-	-	PUNCT
ajmri-740	141	3	score	score	NOUN
ajmri-740	141	4	(	(	PUNCT
ajmri-740	141	5	%	%	INTJ
ajmri-740	141	6	)	)	PUNCT
ajmri-740	141	7	recall	recall	NOUN
ajmri-740	141	8	(	(	PUNCT
ajmri-740	141	9	%	%	INTJ
ajmri-740	141	10	)	)	PUNCT
ajmri-740	141	11	mcc	mcc	NOUN
ajmri-740	141	12	(	(	PUNCT
ajmri-740	141	13	%	%	INTJ
ajmri-740	141	14	)	)	PUNCT
ajmri-740	142	1	vgg-19	vgg-19	CCONJ
ajmri-740	142	2	96.01	96.01	NUM
ajmri-740	142	3	0.070	0.070	NUM
ajmri-740	142	4	94.88	94.88	NUM
ajmri-740	142	5	94.80	94.80	NUM
ajmri-740	142	6	94.75	94.75	NUM
ajmri-740	142	7	resnet-50	resnet-50	NOUN
ajmri-740	142	8	95.57	95.57	NUM
ajmri-740	142	9	0.089	0.089	NUM
ajmri-740	142	10	94.71	94.71	NUM
ajmri-740	142	11	94.80	94.80	NUM
ajmri-740	142	12	94.75	94.75	NUM
ajmri-740	142	13	densenet-201	densenet-201	NOUN
ajmri-740	142	14	98.71	98.71	NUM
ajmri-740	142	15	0.070	0.070	NUM
ajmri-740	142	16	98.43	98.43	NUM
ajmri-740	142	17	98.46	98.46	NUM
ajmri-740	142	18	98.44	98.44	NUM
ajmri-740	142	19	metrics	metric	NOUN
ajmri-740	142	20	used	use	VERB
ajmri-740	142	21	to	to	PART
ajmri-740	142	22	consolidate	consolidate	VERB
ajmri-740	142	23	the	the	DET
ajmri-740	142	24	obtained	obtain	VERB
ajmri-740	142	25	results	result	NOUN
ajmri-740	142	26	confirm	confirm	VERB
ajmri-740	142	27	that	that	SCONJ
ajmri-740	142	28	the	the	DET
ajmri-740	142	29	densenet-201	densenet-201	ADJ
ajmri-740	142	30	model	model	NOUN
ajmri-740	142	31	had	have	VERB
ajmri-740	142	32	the	the	DET
ajmri-740	142	33	best	good	ADJ
ajmri-740	142	34	effects	effect	NOUN
ajmri-740	142	35	on	on	ADP
ajmri-740	142	36	all	all	DET
ajmri-740	142	37	parameters	parameter	NOUN
ajmri-740	142	38	using	use	VERB
ajmri-740	142	39	the	the	DET
ajmri-740	142	40	sgd	sgd	PROPN
ajmri-740	142	41	optimizer	optimizer	NOUN
ajmri-740	142	42	.	.	PUNCT
ajmri-740	143	1	table	table	NOUN
ajmri-740	143	2	5	5	NUM
ajmri-740	143	3	:	:	PUNCT
ajmri-740	143	4	performance	performance	NOUN
ajmri-740	143	5	metrics	metric	NOUN
ajmri-740	143	6	of	of	ADP
ajmri-740	143	7	adam	adam	PROPN
ajmri-740	143	8	optimizer	optimizer	NOUN
ajmri-740	143	9	models	model	NOUN
ajmri-740	143	10	.	.	PUNCT
ajmri-740	144	1	setting	set	VERB
ajmri-740	144	2	–	–	PUNCT
ajmri-740	144	3	optimizer	optimizer	NOUN
ajmri-740	144	4	:	:	PUNCT
ajmri-740	144	5	adam	adam	NOUN
ajmri-740	144	6	batch	batch	NOUN
ajmri-740	144	7	size	size	NOUN
ajmri-740	144	8	:	:	PUNCT
ajmri-740	144	9	4	4	NUM
ajmri-740	144	10	models	model	NOUN
ajmri-740	144	11	precision	precision	NOUN
ajmri-740	144	12	(	(	PUNCT
ajmri-740	144	13	%	%	INTJ
ajmri-740	144	14	)	)	PUNCT
ajmri-740	144	15	mse	mse	NOUN
ajmri-740	144	16	(	(	PUNCT
ajmri-740	144	17	%	%	INTJ
ajmri-740	144	18	)	)	PUNCT
ajmri-740	145	1	f	f	X
ajmri-740	145	2	-	-	PUNCT
ajmri-740	145	3	score	score	NOUN
ajmri-740	145	4	(	(	PUNCT
ajmri-740	145	5	%	%	INTJ
ajmri-740	145	6	)	)	PUNCT
ajmri-740	145	7	recall	recall	NOUN
ajmri-740	145	8	(	(	PUNCT
ajmri-740	145	9	%	%	INTJ
ajmri-740	145	10	)	)	PUNCT
ajmri-740	145	11	mcc	mcc	NOUN
ajmri-740	145	12	(	(	PUNCT
ajmri-740	145	13	%	%	INTJ
ajmri-740	145	14	)	)	PUNCT
ajmri-740	146	1	vgg-19	vgg-19	CCONJ
ajmri-740	146	2	0.0	0.0	NUM
ajmri-740	146	3	0.99	0.99	NUM
ajmri-740	146	4	0.0	0.0	NUM
ajmri-740	146	5	0.0	0.0	NUM
ajmri-740	146	6	0.0	0.0	NUM
ajmri-740	146	7	resnet-50	resnet-50	PROPN
ajmri-740	146	8	97.92	97.92	NUM
ajmri-740	146	9	0.049	0.049	NUM
ajmri-740	146	10	97.04	97.04	NUM
ajmri-740	146	11	96.92	96.92	NUM
ajmri-740	146	12	96.89	96.89	NUM
ajmri-740	146	13	densenet-201	densenet-201	VERB
ajmri-740	146	14	94.07	94.07	NUM
ajmri-740	146	15	0.1268	0.1268	NUM
ajmri-740	146	16	92.39	92.39	NUM
ajmri-740	146	17	92.50	92.50	NUM
ajmri-740	146	18	92.43	92.43	NUM
ajmri-740	146	19	setting	setting	NOUN
ajmri-740	146	20	–	–	PUNCT
ajmri-740	146	21	optimizer	optimizer	NOUN
ajmri-740	146	22	:	:	PUNCT
ajmri-740	146	23	adam	adam	NOUN
ajmri-740	146	24	batch	batch	NOUN
ajmri-740	146	25	size	size	NOUN
ajmri-740	146	26	:	:	PUNCT
ajmri-740	146	27	8	8	NUM
ajmri-740	146	28	models	model	NOUN
ajmri-740	146	29	precision	precision	NOUN
ajmri-740	146	30	(	(	PUNCT
ajmri-740	146	31	%	%	INTJ
ajmri-740	146	32	)	)	PUNCT
ajmri-740	146	33	mse	mse	NOUN
ajmri-740	146	34	(	(	PUNCT
ajmri-740	146	35	%	%	INTJ
ajmri-740	146	36	)	)	PUNCT
ajmri-740	147	1	f	f	X
ajmri-740	147	2	-	-	PUNCT
ajmri-740	147	3	score	score	NOUN
ajmri-740	147	4	(	(	PUNCT
ajmri-740	147	5	%	%	INTJ
ajmri-740	147	6	)	)	PUNCT
ajmri-740	147	7	recall	recall	NOUN
ajmri-740	147	8	(	(	PUNCT
ajmri-740	147	9	%	%	INTJ
ajmri-740	147	10	)	)	PUNCT
ajmri-740	147	11	mcc	mcc	NOUN
ajmri-740	147	12	(	(	PUNCT
ajmri-740	147	13	%	%	INTJ
ajmri-740	147	14	)	)	PUNCT
ajmri-740	148	1	vgg-19	vgg-19	CCONJ
ajmri-740	148	2	0.0	0.0	NUM
ajmri-740	148	3	0.99	0.99	NUM
ajmri-740	148	4	0.0	0.0	NUM
ajmri-740	148	5	0.0	0.0	NUM
ajmri-740	148	6	0.0	0.0	NUM
ajmri-740	148	7	resnet-50	resnet-50	PROPN
ajmri-740	148	8	96.12	96.12	NUM
ajmri-740	148	9	0.078	0.078	NUM
ajmri-740	148	10	94.92	94.92	NUM
ajmri-740	148	11	95.00	95.00	NUM
ajmri-740	148	12	94.95	94.95	NUM
ajmri-740	148	13	densenet-201	densenet-201	VERB
ajmri-740	148	14	94.30	94.30	NUM
ajmri-740	148	15	0.1185	0.1185	NUM
ajmri-740	148	16	92.50	92.50	NUM
ajmri-740	148	17	92.50	92.50	NUM
ajmri-740	148	18	92.43	92.43	NUM
ajmri-740	148	19	setting	setting	NOUN
ajmri-740	148	20	–	–	PUNCT
ajmri-740	148	21	optimizer	optimizer	NOUN
ajmri-740	148	22	:	:	PUNCT
ajmri-740	148	23	adam	adam	NOUN
ajmri-740	148	24	batch	batch	NOUN
ajmri-740	148	25	size	size	NOUN
ajmri-740	148	26	:	:	PUNCT
ajmri-740	148	27	16	16	NUM
ajmri-740	148	28	models	model	NOUN
ajmri-740	148	29	precision	precision	NOUN
ajmri-740	148	30	(	(	PUNCT
ajmri-740	148	31	%	%	INTJ
ajmri-740	148	32	)	)	PUNCT
ajmri-740	148	33	mse	mse	NOUN
ajmri-740	148	34	(	(	PUNCT
ajmri-740	148	35	%	%	INTJ
ajmri-740	148	36	)	)	PUNCT
ajmri-740	148	37	f	f	X
ajmri-740	148	38	-	-	PUNCT
ajmri-740	148	39	score	score	NOUN
ajmri-740	148	40	(	(	PUNCT
ajmri-740	148	41	%	%	INTJ
ajmri-740	148	42	)	)	PUNCT
ajmri-740	148	43	recall	recall	NOUN
ajmri-740	148	44	(	(	PUNCT
ajmri-740	148	45	%	%	INTJ
ajmri-740	148	46	)	)	PUNCT
ajmri-740	148	47	mcc	mcc	NOUN
ajmri-740	148	48	(	(	PUNCT
ajmri-740	148	49	%	%	INTJ
ajmri-740	148	50	)	)	PUNCT
ajmri-740	149	1	vgg-19	vgg-19	CCONJ
ajmri-740	149	2	0.0	0.0	NUM
ajmri-740	149	3	0.99	0.99	NUM
ajmri-740	149	4	0.0	0.0	NUM
ajmri-740	149	5	0.0	0.0	NUM
ajmri-740	149	6	0.0	0.0	NUM
ajmri-740	149	7	resnet-50	resnet-50	PROPN
ajmri-740	149	8	87.95	87.95	NUM
ajmri-740	149	9	0.312	0.312	NUM
ajmri-740	149	10	81.80	81.80	NUM
ajmri-740	149	11	81.34	81.34	NUM
ajmri-740	149	12	81.25	81.25	NUM
ajmri-740	149	13	densenet-201	densenet-201	VERB
ajmri-740	149	14	94.73	94.73	NUM
ajmri-740	149	15	0.137	0.137	NUM
ajmri-740	149	16	91.45	91.45	NUM
ajmri-740	149	17	91.34	91.34	NUM
ajmri-740	149	18	91.28	91.28	NUM
ajmri-740	149	19	metrics	metric	NOUN
ajmri-740	149	20	used	use	VERB
ajmri-740	149	21	to	to	PART
ajmri-740	149	22	consolidate	consolidate	VERB
ajmri-740	149	23	results	result	NOUN
ajmri-740	149	24	confirm	confirm	VERB
ajmri-740	149	25	that	that	SCONJ
ajmri-740	149	26	the	the	DET
ajmri-740	149	27	resnet-50	resnet-50	PROPN
ajmri-740	149	28	model	model	NOUN
ajmri-740	149	29	had	have	VERB
ajmri-740	149	30	the	the	DET
ajmri-740	149	31	best	good	ADJ
ajmri-740	149	32	results	result	NOUN
ajmri-740	149	33	on	on	ADP
ajmri-740	149	34	batch	batch	NOUN
ajmri-740	149	35	sizes	size	NOUN
ajmri-740	149	36	4	4	NUM
ajmri-740	149	37	and	and	CCONJ
ajmri-740	149	38	8	8	NUM
ajmri-740	149	39	,	,	PUNCT
ajmri-740	149	40	while	while	SCONJ
ajmri-740	149	41	densenet-201	densenet-201	ADJ
ajmri-740	149	42	had	have	VERB
ajmri-740	149	43	the	the	DET
ajmri-740	149	44	best	good	ADJ
ajmri-740	149	45	effect	effect	NOUN
ajmri-740	149	46	at	at	ADP
ajmri-740	149	47	batch	batch	NOUN
ajmri-740	149	48	size	size	NOUN
ajmri-740	149	49	16	16	NUM
ajmri-740	149	50	using	use	VERB
ajmri-740	149	51	the	the	DET
ajmri-740	149	52	adam	adam	PROPN
ajmri-740	149	53	optimizer	optimizer	NOUN
ajmri-740	149	54	table	table	NOUN
ajmri-740	149	55	6	6	NUM
ajmri-740	149	56	:	:	PUNCT
ajmri-740	149	57	performance	performance	NOUN
ajmri-740	149	58	metrics	metric	NOUN
ajmri-740	149	59	of	of	ADP
ajmri-740	149	60	rmsprop	rmsprop	NOUN
ajmri-740	149	61	optimizer	optimizer	NOUN
ajmri-740	149	62	models	model	NOUN
ajmri-740	149	63	setting	set	VERB
ajmri-740	149	64	–	–	PUNCT
ajmri-740	149	65	optimizer	optimizer	NOUN
ajmri-740	149	66	:	:	PUNCT
ajmri-740	149	67	rmsprop	rmsprop	NOUN
ajmri-740	149	68	batch	batch	NOUN
ajmri-740	149	69	size	size	NOUN
ajmri-740	149	70	:	:	PUNCT
ajmri-740	149	71	4	4	NUM
ajmri-740	149	72	models	model	NOUN
ajmri-740	149	73	precision	precision	NOUN
ajmri-740	149	74	(	(	PUNCT
ajmri-740	149	75	%	%	INTJ
ajmri-740	149	76	)	)	PUNCT
ajmri-740	149	77	mse	mse	NOUN
ajmri-740	149	78	(	(	PUNCT
ajmri-740	149	79	%	%	INTJ
ajmri-740	149	80	)	)	PUNCT
ajmri-740	149	81	f	f	X
ajmri-740	149	82	-	-	PUNCT
ajmri-740	149	83	score	score	NOUN
ajmri-740	149	84	(	(	PUNCT
ajmri-740	149	85	%	%	INTJ
ajmri-740	149	86	)	)	PUNCT
ajmri-740	149	87	recall	recall	NOUN
ajmri-740	149	88	(	(	PUNCT
ajmri-740	149	89	%	%	INTJ
ajmri-740	149	90	)	)	PUNCT
ajmri-740	149	91	mcc	mcc	NOUN
ajmri-740	149	92	(	(	PUNCT
ajmri-740	149	93	%	%	INTJ
ajmri-740	149	94	)	)	PUNCT
ajmri-740	150	1	vgg-19	vgg-19	CCONJ
ajmri-740	150	2	0.0	0.0	NUM
ajmri-740	150	3	0.99	0.99	NUM
ajmri-740	150	4	0.0	0.0	NUM
ajmri-740	150	5	0.0	0.0	NUM
ajmri-740	150	6	0.0	0.0	NUM
ajmri-740	150	7	resnet-50	resnet-50	PROPN
ajmri-740	150	8	97.92	97.92	NUM
ajmri-740	150	9	0.049	0.049	NUM
ajmri-740	150	10	97.04	97.04	NUM
ajmri-740	150	11	96.92	96.92	NUM
ajmri-740	150	12	96.89	96.89	NUM
ajmri-740	150	13	densenet-201	densenet-201	VERB
ajmri-740	150	14	94.07	94.07	NUM
ajmri-740	150	15	0.1268	0.1268	NUM
ajmri-740	150	16	92.39	92.39	NUM
ajmri-740	150	17	92.50	92.50	NUM
ajmri-740	150	18	92.43	92.43	NUM
ajmri-740	150	19	setting	setting	NOUN
ajmri-740	150	20	–	–	PUNCT
ajmri-740	150	21	optimizer	optimizer	NOUN
ajmri-740	150	22	:	:	PUNCT
ajmri-740	150	23	rmsprop	rmsprop	NOUN
ajmri-740	150	24	batch	batch	NOUN
ajmri-740	150	25	size	size	NOUN
ajmri-740	150	26	:	:	PUNCT
ajmri-740	150	27	8	8	NUM
ajmri-740	150	28	models	model	NOUN
ajmri-740	150	29	precision	precision	NOUN
ajmri-740	150	30	(	(	PUNCT
ajmri-740	150	31	%	%	INTJ
ajmri-740	150	32	)	)	PUNCT
ajmri-740	150	33	mse	mse	NOUN
ajmri-740	150	34	(	(	PUNCT
ajmri-740	150	35	%	%	INTJ
ajmri-740	150	36	)	)	PUNCT
ajmri-740	150	37	f	f	X
ajmri-740	150	38	-	-	PUNCT
ajmri-740	150	39	score	score	NOUN
ajmri-740	150	40	(	(	PUNCT
ajmri-740	150	41	%	%	INTJ
ajmri-740	150	42	)	)	PUNCT
ajmri-740	150	43	recall	recall	NOUN
ajmri-740	150	44	(	(	PUNCT
ajmri-740	150	45	%	%	INTJ
ajmri-740	150	46	)	)	PUNCT
ajmri-740	150	47	mcc	mcc	NOUN
ajmri-740	150	48	(	(	PUNCT
ajmri-740	150	49	%	%	INTJ
ajmri-740	150	50	)	)	PUNCT
ajmri-740	151	1	vgg-19	vgg-19	CCONJ
ajmri-740	151	2	0.0	0.0	NUM
ajmri-740	151	3	0.99	0.99	NUM
ajmri-740	151	4	0.0	0.0	NUM
ajmri-740	151	5	0.0	0.0	NUM
ajmri-740	151	6	0.0	0.0	NUM
ajmri-740	151	7	resnet-50	resnet-50	PROPN
ajmri-740	151	8	96.12	96.12	NUM
ajmri-740	151	9	0.078	0.078	NUM
ajmri-740	151	10	94.92	94.92	NUM
ajmri-740	151	11	95.00	95.00	NUM
ajmri-740	151	12	94.95	94.95	NUM
ajmri-740	151	13	densenet-201	densenet-201	VERB
ajmri-740	151	14	94.30	94.30	NUM
ajmri-740	151	15	0.1185	0.1185	NUM
ajmri-740	151	16	92.50	92.50	NUM
ajmri-740	151	17	92.50	92.50	NUM
ajmri-740	151	18	92.43	92.43	NUM
ajmri-740	152	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	152	2	pa	pa	PROPN
ajmri-740	152	3	ge	ge	PROPN
ajmri-740	152	4	30	30	NUM
ajmri-740	153	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	153	2	am	am	NOUN
ajmri-740	153	3	.	.	PUNCT
ajmri-740	154	1	j.	j.	PROPN
ajmri-740	154	2	multidis	multidis	PROPN
ajmri-740	154	3	.	.	PUNCT
ajmri-740	155	1	res	re	NOUN
ajmri-740	155	2	.	.	PUNCT
ajmri-740	156	1	innov	innov	PROPN
ajmri-740	156	2	.	.	PUNCT
ajmri-740	157	1	1(5	1(5	NUM
ajmri-740	157	2	)	)	PUNCT
ajmri-740	157	3	24	24	NUM
ajmri-740	157	4	-	-	SYM
ajmri-740	157	5	32	32	NUM
ajmri-740	157	6	,	,	PUNCT
ajmri-740	157	7	2022	2022	NUM
ajmri-740	157	8	setting	setting	NOUN
ajmri-740	157	9	–	–	PUNCT
ajmri-740	157	10	optimizer	optimizer	NOUN
ajmri-740	157	11	:	:	PUNCT
ajmri-740	157	12	rmsprop	rmsprop	NOUN
ajmri-740	157	13	batch	batch	NOUN
ajmri-740	157	14	size	size	NOUN
ajmri-740	157	15	:	:	PUNCT
ajmri-740	157	16	16	16	NUM
ajmri-740	157	17	models	model	NOUN
ajmri-740	157	18	precision	precision	NOUN
ajmri-740	157	19	(	(	PUNCT
ajmri-740	157	20	%	%	INTJ
ajmri-740	157	21	)	)	PUNCT
ajmri-740	157	22	mse	mse	NOUN
ajmri-740	157	23	(	(	PUNCT
ajmri-740	157	24	%	%	INTJ
ajmri-740	157	25	)	)	PUNCT
ajmri-740	158	1	f	f	X
ajmri-740	158	2	-	-	PUNCT
ajmri-740	158	3	score	score	NOUN
ajmri-740	158	4	(	(	PUNCT
ajmri-740	158	5	%	%	INTJ
ajmri-740	158	6	)	)	PUNCT
ajmri-740	158	7	recall	recall	NOUN
ajmri-740	158	8	(	(	PUNCT
ajmri-740	158	9	%	%	INTJ
ajmri-740	158	10	)	)	PUNCT
ajmri-740	158	11	mcc	mcc	NOUN
ajmri-740	158	12	(	(	PUNCT
ajmri-740	158	13	%	%	INTJ
ajmri-740	158	14	)	)	PUNCT
ajmri-740	159	1	vgg-19	vgg-19	CCONJ
ajmri-740	159	2	0.0	0.0	NUM
ajmri-740	159	3	0.99	0.99	NUM
ajmri-740	159	4	0.0	0.0	NUM
ajmri-740	159	5	0.0	0.0	NUM
ajmri-740	159	6	0.0	0.0	NUM
ajmri-740	159	7	resnet-50	resnet-50	PROPN
ajmri-740	159	8	87.95	87.95	NUM
ajmri-740	159	9	0.312	0.312	NUM
ajmri-740	159	10	81.80	81.80	NUM
ajmri-740	159	11	81.34	81.34	NUM
ajmri-740	159	12	81.25	81.25	NUM
ajmri-740	159	13	densenet-201	densenet-201	VERB
ajmri-740	159	14	94.73	94.73	NUM
ajmri-740	159	15	0.137	0.137	NUM
ajmri-740	159	16	91.45	91.45	NUM
ajmri-740	159	17	91.34	91.34	NUM
ajmri-740	159	18	91.28	91.28	NUM
ajmri-740	159	19	metrics	metric	NOUN
ajmri-740	159	20	used	use	VERB
ajmri-740	159	21	to	to	PART
ajmri-740	159	22	consolidate	consolidate	VERB
ajmri-740	159	23	obtained	obtain	VERB
ajmri-740	159	24	results	result	NOUN
ajmri-740	159	25	confirm	confirm	VERB
ajmri-740	159	26	that	that	SCONJ
ajmri-740	159	27	the	the	DET
ajmri-740	159	28	resnet-50	resnet-50	PROPN
ajmri-740	159	29	model	model	NOUN
ajmri-740	159	30	had	have	VERB
ajmri-740	159	31	the	the	DET
ajmri-740	159	32	best	good	ADJ
ajmri-740	159	33	impact	impact	NOUN
ajmri-740	159	34	on	on	ADP
ajmri-740	159	35	batch	batch	NOUN
ajmri-740	159	36	size	size	NOUN
ajmri-740	159	37	4	4	NUM
ajmri-740	159	38	,	,	PUNCT
ajmri-740	159	39	while	while	SCONJ
ajmri-740	159	40	densenet-201	densenet-201	ADJ
ajmri-740	159	41	had	have	VERB
ajmri-740	159	42	the	the	DET
ajmri-740	159	43	best	good	ADJ
ajmri-740	159	44	effect	effect	NOUN
ajmri-740	159	45	on	on	ADP
ajmri-740	159	46	batch	batch	NOUN
ajmri-740	159	47	sizes	size	NOUN
ajmri-740	159	48	8	8	NUM
ajmri-740	159	49	and	and	CCONJ
ajmri-740	159	50	16	16	NUM
ajmri-740	159	51	using	use	VERB
ajmri-740	159	52	rmsprop	rmsprop	NOUN
ajmri-740	159	53	optimizer	optimizer	NOUN
ajmri-740	159	54	comparative	comparative	ADJ
ajmri-740	159	55	results	result	NOUN
ajmri-740	159	56	of	of	ADP
ajmri-740	159	57	occluded	occluded	NOUN
ajmri-740	159	58	and	and	CCONJ
ajmri-740	159	59	not	not	PART
ajmri-740	159	60	occluded	occlude	VERB
ajmri-740	159	61	faces	face	NOUN
ajmri-740	159	62	with	with	ADP
ajmri-740	159	63	the	the	DET
ajmri-740	159	64	best	good	ADJ
ajmri-740	159	65	model	model	NOUN
ajmri-740	159	66	we	we	PRON
ajmri-740	159	67	will	will	AUX
ajmri-740	159	68	use	use	VERB
ajmri-740	159	69	the	the	DET
ajmri-740	159	70	model	model	NOUN
ajmri-740	159	71	that	that	PRON
ajmri-740	159	72	provides	provide	VERB
ajmri-740	159	73	the	the	DET
ajmri-740	159	74	best	good	ADJ
ajmri-740	159	75	results	result	NOUN
ajmri-740	159	76	to	to	PART
ajmri-740	159	77	observe	observe	VERB
ajmri-740	159	78	the	the	DET
ajmri-740	159	79	effect	effect	NOUN
ajmri-740	159	80	of	of	ADP
ajmri-740	159	81	occultation	occultation	NOUN
ajmri-740	159	82	on	on	ADP
ajmri-740	159	83	the	the	DET
ajmri-740	159	84	dataset	dataset	NOUN
ajmri-740	159	85	images	image	NOUN
ajmri-740	159	86	.	.	PUNCT
ajmri-740	160	1	table	table	NOUN
ajmri-740	160	2	7	7	NUM
ajmri-740	160	3	:	:	PUNCT
ajmri-740	160	4	comparison	comparison	NOUN
ajmri-740	160	5	of	of	ADP
ajmri-740	160	6	the	the	DET
ajmri-740	160	7	best	good	ADJ
ajmri-740	160	8	model	model	NOUN
ajmri-740	160	9	on	on	ADP
ajmri-740	160	10	occluded	occluded	ADJ
ajmri-740	160	11	and	and	CCONJ
ajmri-740	160	12	non	non	ADJ
ajmri-740	160	13	-	-	ADJ
ajmri-740	160	14	occluded	occluded	ADJ
ajmri-740	160	15	images	image	NOUN
ajmri-740	160	16	.	.	PUNCT
ajmri-740	161	1	types	type	NOUN
ajmri-740	161	2	not	not	PART
ajmri-740	161	3	occluded	occlude	VERB
ajmri-740	161	4	occluded	occluded	ADJ
ajmri-740	161	5	accuracy	accuracy	NOUN
ajmri-740	161	6	(	(	PUNCT
ajmri-740	161	7	%	%	NOUN
ajmri-740	161	8	)	)	PUNCT
ajmri-740	161	9	99.62	99.62	NUM
ajmri-740	161	10	99.58	99.58	NUM
ajmri-740	161	11	we	we	PRON
ajmri-740	161	12	contact	contact	VERB
ajmri-740	161	13	here	here	ADV
ajmri-740	161	14	that	that	SCONJ
ajmri-740	161	15	the	the	DET
ajmri-740	161	16	occultation	occultation	NOUN
ajmri-740	161	17	has	have	AUX
ajmri-740	161	18	impacted	impact	VERB
ajmri-740	161	19	the	the	DET
ajmri-740	161	20	result	result	NOUN
ajmri-740	161	21	.	.	PUNCT
ajmri-740	162	1	state	state	NOUN
ajmri-740	162	2	-	-	PUNCT
ajmri-740	162	3	of	of	ADP
ajmri-740	162	4	-	-	PUNCT
ajmri-740	162	5	the	the	DET
ajmri-740	162	6	-	-	PUNCT
ajmri-740	162	7	art	art	NOUN
ajmri-740	162	8	comparison	comparison	NOUN
ajmri-740	162	9	the	the	DET
ajmri-740	162	10	comparison	comparison	NOUN
ajmri-740	162	11	of	of	ADP
ajmri-740	162	12	our	our	PRON
ajmri-740	162	13	study	study	NOUN
ajmri-740	162	14	with	with	ADP
ajmri-740	162	15	the	the	DET
ajmri-740	162	16	literature	literature	NOUN
ajmri-740	162	17	methods	method	NOUN
ajmri-740	162	18	shows	show	VERB
ajmri-740	162	19	that	that	SCONJ
ajmri-740	162	20	we	we	PRON
ajmri-740	162	21	perform	perform	VERB
ajmri-740	162	22	better	well	ADV
ajmri-740	162	23	using	use	VERB
ajmri-740	162	24	the	the	DET
ajmri-740	162	25	sgd	sgd	NOUN
ajmri-740	162	26	optimizer	optimizer	NOUN
ajmri-740	162	27	with	with	ADP
ajmri-740	162	28	epochs	epoch	NOUN
ajmri-740	162	29	of	of	ADP
ajmri-740	162	30	4	4	NUM
ajmri-740	162	31	.	.	PUNCT
ajmri-740	163	1	the	the	DET
ajmri-740	163	2	table	table	NOUN
ajmri-740	163	3	below	below	ADP
ajmri-740	163	4	displays	display	NOUN
ajmri-740	163	5	this	this	DET
ajmri-740	163	6	comparison	comparison	NOUN
ajmri-740	163	7	.	.	PUNCT
ajmri-740	164	1	table	table	NOUN
ajmri-740	164	2	8	8	NUM
ajmri-740	164	3	:	:	PUNCT
ajmri-740	164	4	comparison	comparison	NOUN
ajmri-740	164	5	of	of	ADP
ajmri-740	164	6	literature	literature	NOUN
ajmri-740	164	7	methods	method	NOUN
ajmri-740	164	8	with	with	ADP
ajmri-740	164	9	the	the	DET
ajmri-740	164	10	best	good	ADJ
ajmri-740	164	11	results	result	NOUN
ajmri-740	164	12	of	of	ADP
ajmri-740	164	13	our	our	PRON
ajmri-740	164	14	study	study	NOUN
ajmri-740	164	15	.	.	PUNCT
ajmri-740	165	1	methods	method	NOUN
ajmri-740	165	2	accuracy	accuracy	NOUN
ajmri-740	165	3	(	(	PUNCT
ajmri-740	165	4	%	%	INTJ
ajmri-740	165	5	)	)	PUNCT
ajmri-740	165	6	wu	wu	PROPN
ajmri-740	165	7	et	et	PROPN
ajmri-740	165	8	al	al	PROPN
ajmri-740	165	9	.	.	PUNCT
ajmri-740	166	1	(	(	PUNCT
ajmri-740	166	2	wu	wu	PROPN
ajmri-740	166	3	et	et	PROPN
ajmri-740	166	4	al	al	PROPN
ajmri-740	166	5	.	.	PROPN
ajmri-740	166	6	,	,	PUNCT
ajmri-740	166	7	2019	2019	NUM
ajmri-740	166	8	)	)	PUNCT
ajmri-740	166	9	98.60	98.60	NUM
ajmri-740	166	10	wan	wan	PROPN
ajmri-740	166	11	and	and	CCONJ
ajmri-740	166	12	chen	chen	PROPN
ajmri-740	166	13	(	(	PUNCT
ajmri-740	166	14	wang	wang	PROPN
ajmri-740	166	15	et	et	PROPN
ajmri-740	166	16	al	al	PROPN
ajmri-740	166	17	.	.	PROPN
ajmri-740	166	18	,	,	PUNCT
ajmri-740	166	19	2020	2020	NUM
ajmri-740	166	20	)	)	PUNCT
ajmri-740	166	21	93.80	93.80	NUM
ajmri-740	167	1	vgg-19	vgg-19	CCONJ
ajmri-740	167	2	with	with	ADP
ajmri-740	167	3	sgd	sgd	PROPN
ajmri-740	167	4	98.65	98.65	NUM
ajmri-740	167	5	resnet-50	resnet-50	PROPN
ajmri-740	167	6	with	with	ADP
ajmri-740	167	7	sgd	sgd	PROPN
ajmri-740	167	8	99.23	99.23	PROPN
ajmri-740	167	9	densenet-201	densenet-201	NOUN
ajmri-740	167	10	with	with	ADP
ajmri-740	167	11	sgd	sgd	PROPN
ajmri-740	167	12	99.81	99.81	NUM
ajmri-740	167	13	discussion	discussion	NOUN
ajmri-740	167	14	regarding	regard	VERB
ajmri-740	167	15	results	result	NOUN
ajmri-740	167	16	obtained	obtain	VERB
ajmri-740	167	17	in	in	ADP
ajmri-740	167	18	different	different	ADJ
ajmri-740	167	19	experiments	experiment	NOUN
ajmri-740	167	20	,	,	PUNCT
ajmri-740	167	21	we	we	PRON
ajmri-740	167	22	got	get	VERB
ajmri-740	167	23	:	:	PUNCT
ajmri-740	167	24	for	for	ADP
ajmri-740	167	25	the	the	DET
ajmri-740	167	26	rmsprop	rmsprop	NOUN
ajmri-740	167	27	optimizer	optimizer	NOUN
ajmri-740	167	28	,	,	PUNCT
ajmri-740	167	29	we	we	PRON
ajmri-740	167	30	obtained	obtain	VERB
ajmri-740	167	31	the	the	DET
ajmri-740	167	32	best	good	ADJ
ajmri-740	167	33	results	result	NOUN
ajmri-740	167	34	with	with	ADP
ajmri-740	167	35	the	the	DET
ajmri-740	167	36	resnet-50	resnet-50	PROPN
ajmri-740	167	37	model	model	NOUN
ajmri-740	167	38	,	,	PUNCT
ajmri-740	167	39	which	which	PRON
ajmri-740	167	40	was	be	AUX
ajmri-740	167	41	able	able	ADJ
ajmri-740	167	42	to	to	PART
ajmri-740	167	43	stabilize	stabilize	VERB
ajmri-740	167	44	at	at	ADP
ajmri-740	167	45	a	a	DET
ajmri-740	167	46	value	value	NOUN
ajmri-740	167	47	of	of	ADP
ajmri-740	167	48	96.92	96.92	NUM
ajmri-740	167	49	%	%	NOUN
ajmri-740	167	50	at	at	ADP
ajmri-740	167	51	all	all	DET
ajmri-740	167	52	batch	batch	NOUN
ajmri-740	167	53	sizes	size	NOUN
ajmri-740	167	54	,	,	PUNCT
ajmri-740	167	55	while	while	SCONJ
ajmri-740	167	56	the	the	DET
ajmri-740	167	57	densenet-201	densenet-201	ADJ
ajmri-740	167	58	model	model	NOUN
ajmri-740	167	59	saw	see	VERB
ajmri-740	167	60	its	its	PRON
ajmri-740	167	61	results	result	NOUN
ajmri-740	167	62	vary	vary	VERB
ajmri-740	167	63	between	between	ADP
ajmri-740	167	64	82.69	82.69	NUM
ajmri-740	167	65	to	to	ADP
ajmri-740	167	66	95.19	95.19	NUM
ajmri-740	167	67	%	%	NOUN
ajmri-740	167	68	;	;	PUNCT
ajmri-740	167	69	finally	finally	ADV
ajmri-740	167	70	,	,	PUNCT
ajmri-740	167	71	vgg-19	vgg-19	PROPN
ajmri-740	167	72	could	could	AUX
ajmri-740	167	73	not	not	PART
ajmri-740	167	74	fit	fit	VERB
ajmri-740	167	75	in	in	ADP
ajmri-740	167	76	our	our	PRON
ajmri-740	167	77	experiment	experiment	NOUN
ajmri-740	167	78	with	with	ADP
ajmri-740	167	79	rmsprop	rmsprop	NOUN
ajmri-740	167	80	optimizer	optimizer	NOUN
ajmri-740	167	81	and	and	CCONJ
ajmri-740	167	82	had	have	VERB
ajmri-740	167	83	a	a	DET
ajmri-740	167	84	null	null	ADJ
ajmri-740	167	85	result	result	NOUN
ajmri-740	167	86	.	.	PUNCT
ajmri-740	168	1	these	these	DET
ajmri-740	168	2	results	result	NOUN
ajmri-740	168	3	are	be	AUX
ajmri-740	168	4	shown	show	VERB
ajmri-740	168	5	in	in	ADP
ajmri-740	168	6	the	the	DET
ajmri-740	168	7	histogram	histogram	NOUN
ajmri-740	168	8	.	.	PUNCT
ajmri-740	169	1	for	for	ADP
ajmri-740	169	2	the	the	DET
ajmri-740	169	3	adam	adam	PROPN
ajmri-740	169	4	optimizer	optimizer	NOUN
ajmri-740	169	5	,	,	PUNCT
ajmri-740	169	6	the	the	DET
ajmri-740	169	7	resnet-50	resnet-50	PROPN
ajmri-740	169	8	model	model	NOUN
ajmri-740	169	9	got	get	VERB
ajmri-740	169	10	the	the	DET
ajmri-740	169	11	best	good	ADJ
ajmri-740	169	12	scores	score	NOUN
ajmri-740	169	13	with	with	ADP
ajmri-740	169	14	batch	batch	NOUN
ajmri-740	169	15	sizes	size	NOUN
ajmri-740	169	16	4	4	NUM
ajmri-740	169	17	and	and	CCONJ
ajmri-740	169	18	8	8	NUM
ajmri-740	169	19	with	with	ADP
ajmri-740	169	20	respective	respective	ADJ
ajmri-740	169	21	values	value	NOUN
ajmri-740	169	22	of	of	ADP
ajmri-740	169	23	96.92	96.92	NUM
ajmri-740	169	24	and	and	CCONJ
ajmri-740	169	25	95	95	NUM
ajmri-740	169	26	%	%	NOUN
ajmri-740	169	27	,	,	PUNCT
ajmri-740	169	28	and	and	CCONJ
ajmri-740	169	29	with	with	ADP
ajmri-740	169	30	batch	batch	NOUN
ajmri-740	169	31	size	size	NOUN
ajmri-740	169	32	16	16	NUM
ajmri-740	169	33	,	,	PUNCT
ajmri-740	169	34	it	it	PRON
ajmri-740	169	35	had	have	VERB
ajmri-740	169	36	a	a	DET
ajmri-740	169	37	bad	bad	ADJ
ajmri-740	169	38	performance	performance	NOUN
ajmri-740	169	39	of	of	ADP
ajmri-740	169	40	81.35	81.35	NUM
ajmri-740	169	41	%	%	NOUN
ajmri-740	169	42	,	,	PUNCT
ajmri-740	169	43	while	while	SCONJ
ajmri-740	169	44	the	the	DET
ajmri-740	169	45	densenet-201	densenet-201	ADJ
ajmri-740	169	46	model	model	NOUN
ajmri-740	169	47	made	make	VERB
ajmri-740	169	48	a	a	DET
ajmri-740	169	49	result	result	NOUN
ajmri-740	169	50	of	of	ADP
ajmri-740	169	51	92	92	NUM
ajmri-740	169	52	.	.	PUNCT
ajmri-740	170	1	50	50	NUM
ajmri-740	170	2	%	%	NOUN
ajmri-740	170	3	at	at	ADP
ajmri-740	170	4	batch	batch	NOUN
ajmri-740	170	5	sizes	size	NOUN
ajmri-740	170	6	4	4	NUM
ajmri-740	170	7	and	and	CCONJ
ajmri-740	170	8	8	8	NUM
ajmri-740	170	9	,	,	PUNCT
ajmri-740	170	10	which	which	PRON
ajmri-740	170	11	is	be	AUX
ajmri-740	170	12	less	less	ADJ
ajmri-740	170	13	than	than	ADP
ajmri-740	170	14	the	the	DET
ajmri-740	170	15	performance	performance	NOUN
ajmri-740	170	16	of	of	ADP
ajmri-740	170	17	resnet-50	resnet-50	PROPN
ajmri-740	170	18	and	and	CCONJ
ajmri-740	170	19	got	get	VERB
ajmri-740	170	20	a	a	DET
ajmri-740	170	21	score	score	NOUN
ajmri-740	170	22	of	of	ADP
ajmri-740	170	23	91.35	91.35	NUM
ajmri-740	170	24	,	,	PUNCT
ajmri-740	170	25	which	which	PRON
ajmri-740	170	26	is	be	AUX
ajmri-740	170	27	the	the	DET
ajmri-740	170	28	best	good	ADJ
ajmri-740	170	29	performance	performance	NOUN
ajmri-740	170	30	at	at	ADP
ajmri-740	170	31	batch	batch	NOUN
ajmri-740	170	32	size	size	NOUN
ajmri-740	170	33	16	16	NUM
ajmri-740	170	34	;	;	PUNCT
ajmri-740	170	35	finally	finally	ADV
ajmri-740	170	36	,	,	PUNCT
ajmri-740	170	37	the	the	DET
ajmri-740	170	38	vgg-19	vgg-19	NOUN
ajmri-740	170	39	did	do	AUX
ajmri-740	170	40	not	not	PART
ajmri-740	170	41	fit	fit	VERB
ajmri-740	170	42	yet	yet	ADV
ajmri-740	170	43	in	in	ADP
ajmri-740	170	44	our	our	PRON
ajmri-740	170	45	experiment	experiment	NOUN
ajmri-740	170	46	with	with	ADP
ajmri-740	170	47	the	the	DET
ajmri-740	170	48	adam	adam	PROPN
ajmri-740	170	49	optimizer	optimizer	NOUN
ajmri-740	170	50	and	and	CCONJ
ajmri-740	170	51	got	get	VERB
ajmri-740	170	52	a	a	DET
ajmri-740	170	53	null	null	ADJ
ajmri-740	170	54	result	result	NOUN
ajmri-740	170	55	.	.	PUNCT
ajmri-740	171	1	the	the	DET
ajmri-740	171	2	histogram	histogram	NOUN
ajmri-740	171	3	figure	figure	NOUN
ajmri-740	171	4	3	3	NUM
ajmri-740	171	5	illustrates	illustrate	VERB
ajmri-740	171	6	our	our	PRON
ajmri-740	171	7	analysis	analysis	NOUN
ajmri-740	171	8	for	for	ADP
ajmri-740	171	9	the	the	DET
ajmri-740	171	10	sgd	sgd	PROPN
ajmri-740	171	11	optimizer	optimizer	NOUN
ajmri-740	171	12	,	,	PUNCT
ajmri-740	171	13	we	we	PRON
ajmri-740	171	14	observe	observe	VERB
ajmri-740	171	15	results	result	NOUN
ajmri-740	171	16	of	of	ADP
ajmri-740	171	17	more	more	ADJ
ajmri-740	171	18	than	than	ADP
ajmri-740	171	19	94	94	NUM
ajmri-740	171	20	%	%	NOUN
ajmri-740	171	21	for	for	ADP
ajmri-740	171	22	each	each	DET
ajmri-740	171	23	model	model	NOUN
ajmri-740	171	24	,	,	PUNCT
ajmri-740	171	25	with	with	ADP
ajmri-740	171	26	resnet-50	resnet-50	NOUN
ajmri-740	171	27	obtaining	obtain	VERB
ajmri-740	171	28	the	the	DET
ajmri-740	171	29	second	second	ADV
ajmri-740	171	30	-	-	PUNCT
ajmri-740	171	31	best	good	ADJ
ajmri-740	171	32	score	score	NOUN
ajmri-740	171	33	with	with	ADP
ajmri-740	171	34	the	the	DET
ajmri-740	171	35	values	value	NOUN
ajmri-740	171	36	99.23	99.23	NUM
ajmri-740	171	37	,	,	PUNCT
ajmri-740	171	38	98.27	98.27	NUM
ajmri-740	171	39	,	,	PUNCT
ajmri-740	171	40	and	and	CCONJ
ajmri-740	171	41	94.81	94.81	NUM
ajmri-740	171	42	for	for	ADP
ajmri-740	171	43	the	the	DET
ajmri-740	171	44	respective	respective	ADJ
ajmri-740	171	45	batch	batch	NOUN
ajmri-740	171	46	size	size	NOUN
ajmri-740	171	47	of	of	ADP
ajmri-740	171	48	4	4	NUM
ajmri-740	171	49	,	,	PUNCT
ajmri-740	171	50	8	8	NUM
ajmri-740	171	51	,	,	PUNCT
ajmri-740	171	52	and	and	CCONJ
ajmri-740	171	53	16	16	NUM
ajmri-740	171	54	;	;	PUNCT
ajmri-740	171	55	while	while	SCONJ
ajmri-740	171	56	the	the	DET
ajmri-740	171	57	densenet-201	densenet-201	ADJ
ajmri-740	171	58	model	model	NOUN
ajmri-740	171	59	outperformed	outperform	VERB
ajmri-740	171	60	all	all	DET
ajmri-740	171	61	other	other	ADJ
ajmri-740	171	62	models	model	NOUN
ajmri-740	171	63	with	with	ADP
ajmri-740	171	64	scores	score	NOUN
ajmri-740	171	65	of	of	ADP
ajmri-740	171	66	99.61	99.61	NUM
ajmri-740	171	67	,	,	PUNCT
ajmri-740	171	68	98.65	98.65	NUM
ajmri-740	171	69	,	,	PUNCT
ajmri-740	171	70	and	and	CCONJ
ajmri-740	171	71	98.46	98.46	NUM
ajmri-740	171	72	for	for	ADP
ajmri-740	171	73	the	the	DET
ajmri-740	171	74	respective	respective	ADJ
ajmri-740	171	75	batch	batch	NOUN
ajmri-740	171	76	sizes	size	NOUN
ajmri-740	171	77	of	of	ADP
ajmri-740	171	78	4	4	NUM
ajmri-740	171	79	,	,	PUNCT
ajmri-740	171	80	8	8	NUM
ajmri-740	171	81	,	,	PUNCT
ajmri-740	171	82	and	and	CCONJ
ajmri-740	171	83	16	16	NUM
ajmri-740	171	84	;	;	PUNCT
ajmri-740	171	85	finally	finally	ADV
ajmri-740	171	86	,	,	PUNCT
ajmri-740	171	87	the	the	DET
ajmri-740	171	88	vgg-19	vgg-19	NUM
ajmri-740	171	89	model	model	NOUN
ajmri-740	171	90	could	could	AUX
ajmri-740	171	91	have	have	VERB
ajmri-740	171	92	good	good	ADJ
ajmri-740	171	93	scores	score	NOUN
ajmri-740	171	94	with	with	ADP
ajmri-740	171	95	sgd	sgd	NOUN
ajmri-740	171	96	optimizer	optimizer	NOUN
ajmri-740	171	97	for	for	ADP
ajmri-740	171	98	the	the	DET
ajmri-740	171	99	scores	score	NOUN
ajmri-740	171	100	of	of	ADP
ajmri-740	171	101	98.65	98.65	NUM
ajmri-740	171	102	,	,	PUNCT
ajmri-740	171	103	98.27	98.27	NUM
ajmri-740	171	104	,	,	PUNCT
ajmri-740	171	105	and	and	CCONJ
ajmri-740	171	106	94.81	94.81	NUM
ajmri-740	171	107	for	for	ADP
ajmri-740	171	108	the	the	DET
ajmri-740	171	109	batch	batch	NOUN
ajmri-740	171	110	sizes	size	NOUN
ajmri-740	171	111	of	of	ADP
ajmri-740	171	112	4	4	NUM
ajmri-740	171	113	,	,	PUNCT
ajmri-740	171	114	8	8	NUM
ajmri-740	171	115	,	,	PUNCT
ajmri-740	171	116	and	and	CCONJ
ajmri-740	171	117	16	16	NUM
ajmri-740	171	118	.	.	PUNCT
ajmri-740	172	1	also	also	ADV
ajmri-740	172	2	,	,	PUNCT
ajmri-740	172	3	it	it	PRON
ajmri-740	172	4	could	could	AUX
ajmri-740	172	5	obtain	obtain	VERB
ajmri-740	172	6	the	the	DET
ajmri-740	172	7	same	same	ADJ
ajmri-740	172	8	results	result	NOUN
ajmri-740	172	9	as	as	ADP
ajmri-740	172	10	resnet-50	resnet-50	PROPN
ajmri-740	172	11	at	at	ADP
ajmri-740	172	12	batch	batch	NOUN
ajmri-740	172	13	sizes	size	NOUN
ajmri-740	172	14	8	8	NUM
ajmri-740	172	15	and	and	CCONJ
ajmri-740	172	16	16	16	NUM
ajmri-740	172	17	,	,	PUNCT
ajmri-740	172	18	as	as	SCONJ
ajmri-740	172	19	shown	show	VERB
ajmri-740	172	20	in	in	ADP
ajmri-740	172	21	the	the	DET
ajmri-740	172	22	histogram	histogram	NOUN
ajmri-740	172	23	figure	figure	NOUN
ajmri-740	172	24	4	4	NUM
ajmri-740	172	25	.	.	PUNCT
ajmri-740	172	26	sgd	sgd	PROPN
ajmri-740	172	27	optimizer	optimizer	NOUN
ajmri-740	172	28	is	be	AUX
ajmri-740	172	29	the	the	DET
ajmri-740	172	30	most	most	ADV
ajmri-740	172	31	optimal	optimal	ADJ
ajmri-740	172	32	for	for	ADP
ajmri-740	172	33	our	our	PRON
ajmri-740	172	34	study	study	NOUN
ajmri-740	172	35	.	.	PUNCT
ajmri-740	173	1	in	in	ADP
ajmri-740	173	2	the	the	DET
ajmri-740	173	3	paper	paper	NOUN
ajmri-740	173	4	,	,	PUNCT
ajmri-740	173	5	wu	wu	PROPN
ajmri-740	173	6	et	et	PROPN
ajmri-740	173	7	al	al	PROPN
ajmri-740	173	8	.	.	PROPN
ajmri-740	173	9	proposed	propose	VERB
ajmri-740	173	10	a	a	DET
ajmri-740	173	11	pooa	pooa	PROPN
ajmri-740	173	12	(	(	PUNCT
ajmri-740	173	13	positioning	position	VERB
ajmri-740	173	14	the	the	DET
ajmri-740	173	15	optimal	optimal	ADJ
ajmri-740	173	16	occlusion	occlusion	NOUN
ajmri-740	173	17	area	area	NOUN
ajmri-740	173	18	)	)	PUNCT
ajmri-740	173	19	algorithm	algorithm	NOUN
ajmri-740	173	20	for	for	ADP
ajmri-740	173	21	solving	solve	VERB
ajmri-740	173	22	the	the	DET
ajmri-740	173	23	occluded	occluded	ADJ
ajmri-740	173	24	face	face	NOUN
ajmri-740	173	25	detection	detection	NOUN
ajmri-740	173	26	problem	problem	NOUN
ajmri-740	173	27	with	with	ADP
ajmri-740	173	28	the	the	DET
ajmri-740	173	29	use	use	NOUN
ajmri-740	173	30	of	of	ADP
ajmri-740	173	31	a	a	DET
ajmri-740	173	32	robust	robust	ADJ
ajmri-740	173	33	principal	principal	ADJ
ajmri-740	173	34	component	component	NOUN
ajmri-740	173	35	analysis	analysis	NOUN
ajmri-740	173	36	method	method	NOUN
ajmri-740	173	37	to	to	PART
ajmri-740	173	38	obtain	obtain	VERB
ajmri-740	173	39	a	a	DET
ajmri-740	173	40	result	result	NOUN
ajmri-740	173	41	of	of	ADP
ajmri-740	173	42	98.60	98.60	NUM
ajmri-740	173	43	%	%	NOUN
ajmri-740	174	1	[	[	X
ajmri-740	174	2	2	2	NUM
ajmri-740	174	3	]	]	PUNCT
ajmri-740	174	4	,	,	PUNCT
ajmri-740	174	5	while	while	SCONJ
ajmri-740	174	6	wan	wan	PROPN
ajmri-740	174	7	and	and	CCONJ
ajmri-740	174	8	chen	chen	PROPN
ajmri-740	174	9	proposed	propose	VERB
ajmri-740	174	10	a	a	DET
ajmri-740	174	11	masknet	masknet	NOUN
ajmri-740	174	12	plan	plan	NOUN
ajmri-740	174	13	coupled	couple	VERB
ajmri-740	174	14	with	with	ADP
ajmri-740	174	15	convolutional	convolutional	ADJ
ajmri-740	174	16	neural	neural	ADJ
ajmri-740	174	17	network	network	NOUN
ajmri-740	174	18	to	to	PART
ajmri-740	174	19	get	get	VERB
ajmri-740	174	20	a	a	DET
ajmri-740	174	21	result	result	NOUN
ajmri-740	174	22	of	of	ADP
ajmri-740	174	23	93.8	93.8	NUM
ajmri-740	174	24	%	%	NOUN
ajmri-740	174	25	on	on	ADP
ajmri-740	174	26	ar	ar	PROPN
ajmri-740	174	27	face	face	NOUN
ajmri-740	174	28	database	database	NOUN
ajmri-740	175	1	[	[	X
ajmri-740	175	2	29	29	NUM
ajmri-740	175	3	]	]	PUNCT
ajmri-740	175	4	used	use	VERB
ajmri-740	175	5	in	in	ADP
ajmri-740	175	6	our	our	PRON
ajmri-740	175	7	study	study	NOUN
ajmri-740	175	8	.	.	PUNCT
ajmri-740	176	1	our	our	PRON
ajmri-740	176	2	study	study	NOUN
ajmri-740	176	3	used	use	VERB
ajmri-740	176	4	three	three	NUM
ajmri-740	176	5	convolutional	convolutional	ADJ
ajmri-740	176	6	neural	neural	ADJ
ajmri-740	176	7	network	network	NOUN
ajmri-740	176	8	methods	method	NOUN
ajmri-740	176	9	for	for	ADP
ajmri-740	176	10	face	face	NOUN
ajmri-740	176	11	recognition	recognition	NOUN
ajmri-740	176	12	with	with	ADP
ajmri-740	176	13	occluded	occluded	NOUN
ajmri-740	176	14	.	.	PUNCT
ajmri-740	177	1	we	we	PRON
ajmri-740	177	2	found	find	VERB
ajmri-740	177	3	figure	figure	NOUN
ajmri-740	177	4	3	3	NUM
ajmri-740	177	5	:	:	PUNCT
ajmri-740	177	6	face	face	NOUN
ajmri-740	177	7	recognition	recognition	NOUN
ajmri-740	177	8	accuracy	accuracy	NOUN
ajmri-740	177	9	used	use	VERB
ajmri-740	177	10	by	by	ADP
ajmri-740	177	11	rmsprop	rmsprop	NOUN
ajmri-740	177	12	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	177	13	pa	pa	PROPN
ajmri-740	177	14	ge	ge	PROPN
ajmri-740	177	15	31	31	NUM
ajmri-740	178	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	178	2	am	am	NOUN
ajmri-740	178	3	.	.	PUNCT
ajmri-740	179	1	j.	j.	PROPN
ajmri-740	179	2	multidis	multidis	PROPN
ajmri-740	179	3	.	.	PUNCT
ajmri-740	180	1	res	re	NOUN
ajmri-740	180	2	.	.	PUNCT
ajmri-740	181	1	innov	innov	PROPN
ajmri-740	181	2	.	.	PUNCT
ajmri-740	182	1	1(5	1(5	NUM
ajmri-740	182	2	)	)	PUNCT
ajmri-740	182	3	24	24	NUM
ajmri-740	182	4	-	-	SYM
ajmri-740	182	5	32	32	NUM
ajmri-740	182	6	,	,	PUNCT
ajmri-740	182	7	2022	2022	NUM
ajmri-740	182	8	figure	figure	NOUN
ajmri-740	182	9	4	4	NUM
ajmri-740	182	10	:	:	PUNCT
ajmri-740	182	11	face	face	NOUN
ajmri-740	182	12	recognition	recognition	NOUN
ajmri-740	182	13	accuracy	accuracy	NOUN
ajmri-740	182	14	used	use	VERB
ajmri-740	182	15	by	by	ADP
ajmri-740	182	16	adam	adam	PROPN
ajmri-740	182	17	figure	figure	NOUN
ajmri-740	182	18	5	5	NUM
ajmri-740	182	19	:	:	PUNCT
ajmri-740	182	20	face	face	NOUN
ajmri-740	182	21	recognition	recognition	NOUN
ajmri-740	182	22	accuracy	accuracy	NOUN
ajmri-740	182	23	used	use	VERB
ajmri-740	182	24	by	by	ADP
ajmri-740	182	25	adam	adam	PROPN
ajmri-740	182	26	the	the	DET
ajmri-740	182	27	resnet-50	resnet-50	PROPN
ajmri-740	182	28	and	and	CCONJ
ajmri-740	182	29	densenet-201	densenet-201	ADJ
ajmri-740	182	30	models	model	NOUN
ajmri-740	182	31	using	use	VERB
ajmri-740	182	32	sgd	sgd	NOUN
ajmri-740	182	33	optimizer	optimizer	NOUN
ajmri-740	182	34	and	and	CCONJ
ajmri-740	182	35	batch	batch	NOUN
ajmri-740	182	36	size	size	NOUN
ajmri-740	182	37	4	4	NUM
ajmri-740	182	38	obtained	obtain	VERB
ajmri-740	182	39	very	very	ADV
ajmri-740	182	40	close	close	ADJ
ajmri-740	182	41	results	result	NOUN
ajmri-740	182	42	on	on	ADP
ajmri-740	182	43	the	the	DET
ajmri-740	182	44	test	test	NOUN
ajmri-740	182	45	datasets	dataset	NOUN
ajmri-740	182	46	reaching	reach	VERB
ajmri-740	182	47	99.23	99.23	NUM
ajmri-740	182	48	%	%	NOUN
ajmri-740	182	49	and	and	CCONJ
ajmri-740	182	50	99.81	99.81	NUM
ajmri-740	182	51	%	%	NOUN
ajmri-740	182	52	,	,	PUNCT
ajmri-740	182	53	respectively	respectively	ADV
ajmri-740	182	54	,	,	PUNCT
ajmri-740	182	55	outperforming	outperform	VERB
ajmri-740	182	56	the	the	DET
ajmri-740	182	57	vgg-19	vgg-19	PROPN
ajmri-740	182	58	model	model	NOUN
ajmri-740	182	59	.	.	PUNCT
ajmri-740	183	1	based	base	VERB
ajmri-740	183	2	on	on	ADP
ajmri-740	183	3	the	the	DET
ajmri-740	183	4	current	current	ADJ
ajmri-740	183	5	literature	literature	NOUN
ajmri-740	183	6	survey	survey	NOUN
ajmri-740	183	7	results	result	NOUN
ajmri-740	183	8	,	,	PUNCT
ajmri-740	183	9	our	our	PRON
ajmri-740	183	10	study	study	NOUN
ajmri-740	183	11	proposes	propose	VERB
ajmri-740	183	12	methods	method	NOUN
ajmri-740	183	13	to	to	PART
ajmri-740	183	14	improve	improve	VERB
ajmri-740	183	15	the	the	DET
ajmri-740	183	16	performance	performance	NOUN
ajmri-740	183	17	of	of	ADP
ajmri-740	183	18	cloaked	cloaked	ADJ
ajmri-740	183	19	face	face	NOUN
ajmri-740	183	20	identification	identification	NOUN
ajmri-740	183	21	and	and	CCONJ
ajmri-740	183	22	recognition	recognition	NOUN
ajmri-740	183	23	with	with	ADP
ajmri-740	183	24	a	a	DET
ajmri-740	183	25	satisfactory	satisfactory	ADJ
ajmri-740	183	26	result	result	NOUN
ajmri-740	183	27	of	of	ADP
ajmri-740	183	28	99.81	99.81	NUM
ajmri-740	183	29	%	%	NOUN
ajmri-740	183	30	.	.	PUNCT
ajmri-740	184	1	conclusion	conclusion	NOUN
ajmri-740	184	2	from	from	ADP
ajmri-740	184	3	the	the	DET
ajmri-740	184	4	results	result	NOUN
ajmri-740	184	5	obtained	obtain	VERB
ajmri-740	184	6	in	in	ADP
ajmri-740	184	7	this	this	DET
ajmri-740	184	8	study	study	NOUN
ajmri-740	184	9	,	,	PUNCT
ajmri-740	184	10	we	we	PRON
ajmri-740	184	11	can	can	AUX
ajmri-740	184	12	conclude	conclude	VERB
ajmri-740	184	13	:	:	PUNCT
ajmri-740	184	14	first	first	ADV
ajmri-740	184	15	,	,	PUNCT
ajmri-740	184	16	a	a	DET
ajmri-740	184	17	comparison	comparison	NOUN
ajmri-740	184	18	between	between	ADP
ajmri-740	184	19	different	different	ADJ
ajmri-740	184	20	models	model	NOUN
ajmri-740	184	21	was	be	AUX
ajmri-740	184	22	used	use	VERB
ajmri-740	184	23	to	to	PART
ajmri-740	184	24	show	show	VERB
ajmri-740	184	25	that	that	SCONJ
ajmri-740	184	26	the	the	DET
ajmri-740	184	27	most	most	ADV
ajmri-740	184	28	optimal	optimal	ADJ
ajmri-740	184	29	result	result	NOUN
ajmri-740	184	30	was	be	AUX
ajmri-740	184	31	obtained	obtain	VERB
ajmri-740	184	32	with	with	ADP
ajmri-740	184	33	densenet-201	densenet-201	NOUN
ajmri-740	184	34	using	use	VERB
ajmri-740	184	35	sgd	sgd	NOUN
ajmri-740	184	36	optimizer	optimizer	NOUN
ajmri-740	184	37	with	with	ADP
ajmri-740	184	38	a	a	DET
ajmri-740	184	39	batch	batch	NOUN
ajmri-740	184	40	size	size	NOUN
ajmri-740	184	41	of	of	ADP
ajmri-740	184	42	4	4	NUM
ajmri-740	184	43	.	.	PUNCT
ajmri-740	185	1	we	we	PRON
ajmri-740	185	2	find	find	VERB
ajmri-740	185	3	that	that	SCONJ
ajmri-740	185	4	vgg-19	vgg-19	PROPN
ajmri-740	185	5	failed	fail	VERB
ajmri-740	185	6	to	to	PART
ajmri-740	185	7	adapt	adapt	VERB
ajmri-740	185	8	to	to	ADP
ajmri-740	185	9	adam	adam	NOUN
ajmri-740	185	10	and	and	CCONJ
ajmri-740	185	11	rmsprop	rmsprop	VERB
ajmri-740	185	12	optimizers	optimizer	NOUN
ajmri-740	185	13	with	with	ADP
ajmri-740	185	14	its	its	PRON
ajmri-740	185	15	poor	poor	ADJ
ajmri-740	185	16	results	result	NOUN
ajmri-740	185	17	obtained	obtain	VERB
ajmri-740	185	18	during	during	ADP
ajmri-740	185	19	experiments	experiment	NOUN
ajmri-740	185	20	.	.	PUNCT
ajmri-740	186	1	finally	finally	ADV
ajmri-740	186	2	,	,	PUNCT
ajmri-740	186	3	as	as	ADP
ajmri-740	186	4	a	a	DET
ajmri-740	186	5	robust	robust	ADJ
ajmri-740	186	6	security	security	NOUN
ajmri-740	186	7	tool	tool	NOUN
ajmri-740	186	8	,	,	PUNCT
ajmri-740	186	9	occlusion	occlusion	NOUN
ajmri-740	186	10	face	face	NOUN
ajmri-740	186	11	recognition	recognition	NOUN
ajmri-740	186	12	can	can	AUX
ajmri-740	186	13	be	be	AUX
ajmri-740	186	14	improved	improve	VERB
ajmri-740	186	15	by	by	ADP
ajmri-740	186	16	using	use	VERB
ajmri-740	186	17	pre	pre	ADJ
ajmri-740	186	18	-	-	ADJ
ajmri-740	186	19	trained	train	VERB
ajmri-740	186	20	convolutional	convolutional	ADJ
ajmri-740	186	21	neural	neural	ADJ
ajmri-740	186	22	network	network	NOUN
ajmri-740	186	23	models	model	NOUN
ajmri-740	186	24	.	.	PUNCT
ajmri-740	187	1	we	we	PRON
ajmri-740	187	2	can	can	AUX
ajmri-740	187	3	see	see	VERB
ajmri-740	187	4	that	that	SCONJ
ajmri-740	187	5	results	result	NOUN
ajmri-740	187	6	from	from	ADP
ajmri-740	187	7	our	our	PRON
ajmri-740	187	8	study	study	NOUN
ajmri-740	187	9	produce	produce	VERB
ajmri-740	187	10	better	well	ADJ
ajmri-740	187	11	results	result	NOUN
ajmri-740	187	12	than	than	ADP
ajmri-740	187	13	studies	study	NOUN
ajmri-740	187	14	in	in	ADP
ajmri-740	187	15	the	the	DET
ajmri-740	187	16	discussion	discussion	NOUN
ajmri-740	187	17	.	.	PUNCT
ajmri-740	188	1	thus	thus	ADV
ajmri-740	188	2	,	,	PUNCT
ajmri-740	188	3	in	in	ADP
ajmri-740	188	4	future	future	ADJ
ajmri-740	188	5	studies	study	NOUN
ajmri-740	188	6	,	,	PUNCT
ajmri-740	188	7	we	we	PRON
ajmri-740	188	8	can	can	AUX
ajmri-740	188	9	use	use	VERB
ajmri-740	188	10	other	other	ADJ
ajmri-740	188	11	convolutional	convolutional	ADJ
ajmri-740	188	12	neural	neural	ADJ
ajmri-740	188	13	network	network	NOUN
ajmri-740	188	14	models	model	NOUN
ajmri-740	188	15	using	use	VERB
ajmri-740	188	16	techniques	technique	NOUN
ajmri-740	188	17	that	that	PRON
ajmri-740	188	18	will	will	AUX
ajmri-740	188	19	allow	allow	VERB
ajmri-740	188	20	us	we	PRON
ajmri-740	188	21	to	to	PART
ajmri-740	188	22	increase	increase	VERB
ajmri-740	188	23	data	datum	NOUN
ajmri-740	188	24	for	for	ADP
ajmri-740	188	25	more	more	ADV
ajmri-740	188	26	accurate	accurate	ADJ
ajmri-740	188	27	results	result	NOUN
ajmri-740	188	28	.	.	PUNCT
ajmri-740	189	1	references	reference	NOUN
ajmri-740	189	2	arnia	arnia	PROPN
ajmri-740	189	3	,	,	PUNCT
ajmri-740	189	4	f.	f.	PROPN
ajmri-740	189	5	,	,	PUNCT
ajmri-740	189	6	saddami	saddami	PROPN
ajmri-740	189	7	,	,	PUNCT
ajmri-740	189	8	k.	k.	PROPN
ajmri-740	189	9	,	,	PUNCT
ajmri-740	189	10	&	&	CCONJ
ajmri-740	189	11	munadi	munadi	PROPN
ajmri-740	189	12	,	,	PUNCT
ajmri-740	189	13	k.	k.	PROPN
ajmri-740	189	14	(	(	PUNCT
ajmri-740	189	15	2021	2021	NUM
ajmri-740	189	16	)	)	PUNCT
ajmri-740	189	17	.	.	PUNCT
ajmri-740	190	1	dcnet	dcnet	NOUN
ajmri-740	190	2	:	:	PUNCT
ajmri-740	190	3	noise	noise	NOUN
ajmri-740	190	4	-	-	PUNCT
ajmri-740	190	5	robust	robust	ADJ
ajmri-740	190	6	convolutional	convolutional	ADJ
ajmri-740	190	7	neural	neural	ADJ
ajmri-740	190	8	networks	network	NOUN
ajmri-740	190	9	for	for	ADP
ajmri-740	190	10	degradation	degradation	NOUN
ajmri-740	190	11	classification	classification	NOUN
ajmri-740	190	12	on	on	ADP
ajmri-740	190	13	ancient	ancient	ADJ
ajmri-740	190	14	documents	document	NOUN
ajmri-740	190	15	.	.	PUNCT
ajmri-740	191	1	journal	journal	NOUN
ajmri-740	191	2	of	of	ADP
ajmri-740	191	3	imaging	imaging	NOUN
ajmri-740	191	4	,	,	PUNCT
ajmri-740	191	5	7(7	7(7	NUM
ajmri-740	191	6	)	)	PUNCT
ajmri-740	191	7	,	,	PUNCT
ajmri-740	191	8	114	114	NUM
ajmri-740	191	9	.	.	PUNCT
ajmri-740	192	1	https://doi.org/10.3390/	https://doi.org/10.3390/	PROPN
ajmri-740	192	2	jimaging7070114	jimaging7070114	PROPN
ajmri-740	192	3	ar	ar	PROPN
ajmri-740	192	4	face	face	NOUN
ajmri-740	192	5	database	database	NOUN
ajmri-740	192	6	webpage	webpage	NOUN
ajmri-740	192	7	.	.	PUNCT
ajmri-740	193	1	(	(	PUNCT
ajmri-740	193	2	2022	2022	NUM
ajmri-740	193	3	)	)	PUNCT
ajmri-740	193	4	.	.	PUNCT
ajmri-740	194	1	consulté	consulté	PROPN
ajmri-740	194	2	10	10	NUM
ajmri-740	194	3	octobre	octobre	PROPN
ajmri-740	194	4	2022	2022	NUM
ajmri-740	194	5	,	,	PUNCT
ajmri-740	194	6	à	à	X
ajmri-740	194	7	l’adresse	l’adresse	PROPN
ajmri-740	194	8	.	.	PUNCT
ajmri-740	195	1	https://www2.ece.ohio	https://www2.ece.ohio	ADJ
ajmri-740	195	2	-	-	PUNCT
ajmri-740	195	3	state	state	NOUN
ajmri-740	195	4	.	.	PUNCT
ajmri-740	196	1	edu/~aleix	edu/~aleix	VERB
ajmri-740	196	2	/	/	SYM
ajmri-740	196	3	ardatabase.html	ardatabase.html	PROPN
ajmri-740	196	4	artificial	artificial	ADJ
ajmri-740	196	5	neural	neural	ADJ
ajmri-740	196	6	networks	network	NOUN
ajmri-740	196	7	.	.	PUNCT
ajmri-740	197	1	pt	pt	X
ajmri-740	197	2	.	.	PROPN
ajmri-740	197	3	3	3	NUM
ajmri-740	197	4	.	.	PUNCT
ajmri-740	197	5	(	(	PUNCT
ajmri-740	197	6	2010	2010	NUM
ajmri-740	197	7	)	)	PUNCT
ajmri-740	197	8	.	.	PUNCT
ajmri-740	198	1	springer	springer	NOUN
ajmri-740	198	2	.	.	PUNCT
ajmri-740	199	1	bo	bo	NOUN
ajmri-740	199	2	-	-	PUNCT
ajmri-740	199	3	gun	gun	NOUN
ajmri-740	199	4	park	park	NOUN
ajmri-740	199	5	,	,	PUNCT
ajmri-740	199	6	kyoung	kyoung	PROPN
ajmri-740	199	7	-	-	PUNCT
ajmri-740	199	8	mu	mu	PROPN
ajmri-740	199	9	lee	lee	PROPN
ajmri-740	199	10	,	,	PUNCT
ajmri-740	199	11	&	&	CCONJ
ajmri-740	199	12	sang	sing	VERB
ajmri-740	199	13	-	-	PUNCT
ajmri-740	199	14	uk	uk	PROPN
ajmri-740	199	15	lee	lee	PROPN
ajmri-740	199	16	.	.	PUNCT
ajmri-740	200	1	(	(	PUNCT
ajmri-740	200	2	2005	2005	NUM
ajmri-740	200	3	)	)	PUNCT
ajmri-740	200	4	.	.	PUNCT
ajmri-740	201	1	face	face	NOUN
ajmri-740	201	2	recognition	recognition	NOUN
ajmri-740	201	3	using	use	VERB
ajmri-740	201	4	face	face	NOUN
ajmri-740	201	5	-	-	PUNCT
ajmri-740	201	6	arg	arg	NOUN
ajmri-740	201	7	matching	matching	NOUN
ajmri-740	201	8	.	.	PUNCT
ajmri-740	202	1	ieee	ieee	NOUN
ajmri-740	202	2	transactions	transaction	NOUN
ajmri-740	202	3	on	on	ADP
ajmri-740	202	4	pattern	pattern	NOUN
ajmri-740	202	5	analysis	analysis	NOUN
ajmri-740	202	6	and	and	CCONJ
ajmri-740	202	7	machine	machine	NOUN
ajmri-740	202	8	intelligence	intelligence	NOUN
ajmri-740	202	9	,	,	PUNCT
ajmri-740	202	10	27(12	27(12	NUM
ajmri-740	202	11	)	)	PUNCT
ajmri-740	202	12	,	,	PUNCT
ajmri-740	202	13	1982	1982	NUM
ajmri-740	202	14	-	-	SYM
ajmri-740	202	15	1988	1988	NUM
ajmri-740	202	16	.	.	PUNCT
ajmri-740	203	1	https://doi.org/10.1109/	https://doi.org/10.1109/	PROPN
ajmri-740	203	2	tpami.2005.243	tpami.2005.243	VERB
ajmri-740	203	3	he	he	PRON
ajmri-740	203	4	,	,	PUNCT
ajmri-740	203	5	k.	k.	PROPN
ajmri-740	203	6	,	,	PUNCT
ajmri-740	203	7	zhang	zhang	PROPN
ajmri-740	203	8	,	,	PUNCT
ajmri-740	203	9	x.	x.	PROPN
ajmri-740	203	10	,	,	PUNCT
ajmri-740	203	11	ren	ren	PROPN
ajmri-740	203	12	,	,	PUNCT
ajmri-740	203	13	s.	s.	PROPN
ajmri-740	203	14	,	,	PUNCT
ajmri-740	203	15	&	&	CCONJ
ajmri-740	203	16	sun	sun	PROPN
ajmri-740	203	17	,	,	PUNCT
ajmri-740	203	18	j.	j.	PROPN
ajmri-740	203	19	(	(	PUNCT
ajmri-740	203	20	2015	2015	NUM
ajmri-740	203	21	)	)	PUNCT
ajmri-740	203	22	.	.	PUNCT
ajmri-740	204	1	deep	deep	ADJ
ajmri-740	204	2	residual	residual	ADJ
ajmri-740	204	3	learning	learning	NOUN
ajmri-740	204	4	for	for	ADP
ajmri-740	204	5	image	image	NOUN
ajmri-740	204	6	recognition	recognition	NOUN
ajmri-740	204	7	.	.	PUNCT
ajmri-740	205	1	https://doi	https://doi	PROPN
ajmri-740	205	2	.	.	PUNCT
ajmri-740	205	3	org/10.48550	org/10.48550	PROPN
ajmri-740	205	4	/	/	SYM
ajmri-740	205	5	arxiv.1512.03385	arxiv.1512.03385	PROPN
ajmri-740	205	6	idelette	idelette	PROPN
ajmri-740	205	7	kambi	kambi	PROPN
ajmri-740	205	8	beli	beli	PROPN
ajmri-740	205	9	,	,	PUNCT
ajmri-740	205	10	&	&	CCONJ
ajmri-740	205	11	guo	guo	PROPN
ajmri-740	205	12	,	,	PUNCT
ajmri-740	205	13	c.	c.	PROPN
ajmri-740	205	14	(	(	PUNCT
ajmri-740	205	15	2017	2017	NUM
ajmri-740	205	16	)	)	PUNCT
ajmri-740	205	17	.	.	PUNCT
ajmri-740	206	1	enhancing	enhance	VERB
ajmri-740	206	2	face	face	NOUN
ajmri-740	206	3	identification	identification	NOUN
ajmri-740	206	4	using	use	VERB
ajmri-740	206	5	local	local	ADJ
ajmri-740	206	6	binary	binary	ADJ
ajmri-740	206	7	patterns	pattern	NOUN
ajmri-740	206	8	and	and	CCONJ
ajmri-740	206	9	k	k	NOUN
ajmri-740	206	10	-	-	PUNCT
ajmri-740	206	11	nearest	near	ADJ
ajmri-740	206	12	neighbors	neighbor	NOUN
ajmri-740	206	13	.	.	PUNCT
ajmri-740	207	1	journal	journal	PROPN
ajmri-740	207	2	of	of	ADP
ajmri-740	207	3	imaging	imaging	PROPN
ajmri-740	207	4	,	,	PUNCT
ajmri-740	207	5	3(3	3(3	NUM
ajmri-740	207	6	)	)	PUNCT
ajmri-740	207	7	,	,	PUNCT
ajmri-740	207	8	37	37	NUM
ajmri-740	207	9	.	.	PUNCT
ajmri-740	207	10	https://doi.org/10.3390/jimaging3030037	https://doi.org/10.3390/jimaging3030037	PROPN
ajmri-740	207	11	jiang	jiang	PROPN
ajmri-740	207	12	,	,	PUNCT
ajmri-740	207	13	r.	r.	PROPN
ajmri-740	207	14	,	,	PUNCT
ajmri-740	207	15	li	li	PROPN
ajmri-740	207	16	,	,	PUNCT
ajmri-740	207	17	c.-t	c.-t	PROPN
ajmri-740	207	18	.	.	PUNCT
ajmri-740	207	19	,	,	PUNCT
ajmri-740	207	20	crookes	crookes	PROPN
ajmri-740	207	21	,	,	PUNCT
ajmri-740	207	22	d.	d.	PROPN
ajmri-740	207	23	,	,	PUNCT
ajmri-740	207	24	meng	meng	PROPN
ajmri-740	207	25	,	,	PUNCT
ajmri-740	207	26	w.	w.	PROPN
ajmri-740	207	27	,	,	PUNCT
ajmri-740	207	28	&	&	CCONJ
ajmri-740	207	29	rosenberger	rosenberger	PROPN
ajmri-740	207	30	,	,	PUNCT
ajmri-740	207	31	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	207	32	pa	pa	PROPN
ajmri-740	207	33	ge	ge	PROPN
ajmri-740	207	34	32	32	NUM
ajmri-740	208	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	PROPN
ajmri-740	208	2	am	am	NOUN
ajmri-740	208	3	.	.	PUNCT
ajmri-740	209	1	j.	j.	PROPN
ajmri-740	209	2	multidis	multidis	PROPN
ajmri-740	209	3	.	.	PUNCT
ajmri-740	210	1	res	re	NOUN
ajmri-740	210	2	.	.	PUNCT
ajmri-740	211	1	innov	innov	PROPN
ajmri-740	211	2	.	.	PUNCT
ajmri-740	212	1	1(5	1(5	NUM
ajmri-740	212	2	)	)	PUNCT
ajmri-740	212	3	24	24	NUM
ajmri-740	212	4	-	-	SYM
ajmri-740	212	5	32	32	NUM
ajmri-740	212	6	,	,	PUNCT
ajmri-740	212	7	2022	2022	NUM
ajmri-740	212	8	c.	c.	NOUN
ajmri-740	212	9	(	(	PUNCT
ajmri-740	212	10	2020	2020	NUM
ajmri-740	212	11	)	)	PUNCT
ajmri-740	212	12	.	.	PUNCT
ajmri-740	213	1	deep	deep	ADJ
ajmri-740	213	2	biometrics	biometric	NOUN
ajmri-740	213	3	.	.	PUNCT
ajmri-740	214	1	springer	springer	NOUN
ajmri-740	214	2	.	.	PUNCT
ajmri-740	215	1	kingma	kingma	PROPN
ajmri-740	215	2	,	,	PUNCT
ajmri-740	215	3	d.	d.	PROPN
ajmri-740	215	4	p.	p.	PROPN
ajmri-740	215	5	,	,	PUNCT
ajmri-740	215	6	&	&	CCONJ
ajmri-740	215	7	ba	ba	PROPN
ajmri-740	215	8	,	,	PUNCT
ajmri-740	215	9	j.	j.	PROPN
ajmri-740	215	10	(	(	PUNCT
ajmri-740	215	11	2014	2014	NUM
ajmri-740	215	12	)	)	PUNCT
ajmri-740	215	13	.	.	PUNCT
ajmri-740	216	1	adam	adam	NOUN
ajmri-740	216	2	:	:	PUNCT
ajmri-740	216	3	a	a	DET
ajmri-740	216	4	method	method	NOUN
ajmri-740	216	5	for	for	ADP
ajmri-740	216	6	stochastic	stochastic	ADJ
ajmri-740	216	7	optimization	optimization	NOUN
ajmri-740	216	8	.	.	PUNCT
ajmri-740	217	1	https://doi.org/10.48550/	https://doi.org/10.48550/	PROPN
ajmri-740	217	2	arxiv.1412.6980	arxiv.1412.6980	PROPN
ajmri-740	217	3	mantoro	mantoro	NOUN
ajmri-740	217	4	,	,	PUNCT
ajmri-740	217	5	t.	t.	PROPN
ajmri-740	217	6	,	,	PUNCT
ajmri-740	217	7	ayu	ayu	PROPN
ajmri-740	217	8	,	,	PUNCT
ajmri-740	217	9	m.	m.	NOUN
ajmri-740	217	10	a.	a.	PROPN
ajmri-740	217	11	,	,	PUNCT
ajmri-740	217	12	&	&	CCONJ
ajmri-740	217	13	suhendi	suhendi	PROPN
ajmri-740	217	14	.	.	PUNCT
ajmri-740	218	1	(	(	PUNCT
ajmri-740	218	2	2018	2018	NUM
ajmri-740	218	3	)	)	PUNCT
ajmri-740	218	4	.	.	PUNCT
ajmri-740	219	1	multifaces	multiface	NOUN
ajmri-740	219	2	recognition	recognition	NOUN
ajmri-740	219	3	process	process	NOUN
ajmri-740	219	4	using	use	VERB
ajmri-740	219	5	haar	haar	X
ajmri-740	219	6	cascades	cascade	NOUN
ajmri-740	219	7	and	and	CCONJ
ajmri-740	219	8	eigenface	eigenface	NOUN
ajmri-740	219	9	methods	method	NOUN
ajmri-740	219	10	.	.	PUNCT
ajmri-740	220	1	2018	2018	NUM
ajmri-740	220	2	6th	6th	ADJ
ajmri-740	220	3	international	international	ADJ
ajmri-740	220	4	conference	conference	NOUN
ajmri-740	220	5	on	on	ADP
ajmri-740	220	6	multimedia	multimedia	NOUN
ajmri-740	220	7	computing	computing	NOUN
ajmri-740	220	8	and	and	CCONJ
ajmri-740	220	9	systems	system	NOUN
ajmri-740	220	10	(	(	PUNCT
ajmri-740	220	11	icmcs	icmcs	NOUN
ajmri-740	220	12	)	)	PUNCT
ajmri-740	220	13	,	,	PUNCT
ajmri-740	220	14	1	1	NUM
ajmri-740	220	15	-	-	SYM
ajmri-740	220	16	5	5	NUM
ajmri-740	220	17	.	.	PUNCT
ajmri-740	221	1	https://doi.org/10.1109/	https://doi.org/10.1109/	PROPN
ajmri-740	221	2	icmcs.2018.8525935	icmcs.2018.8525935	PROPN
ajmri-740	221	3	martinez	martinez	PROPN
ajmri-740	221	4	,	,	PUNCT
ajmri-740	221	5	a.	a.	NOUN
ajmri-740	221	6	m.	m.	NOUN
ajmri-740	221	7	(	(	PUNCT
ajmri-740	221	8	2002	2002	NUM
ajmri-740	221	9	)	)	PUNCT
ajmri-740	221	10	.	.	PUNCT
ajmri-740	222	1	recognizing	recognize	VERB
ajmri-740	222	2	imprecisely	imprecisely	ADV
ajmri-740	222	3	localized	localize	VERB
ajmri-740	222	4	,	,	PUNCT
ajmri-740	222	5	partially	partially	ADV
ajmri-740	222	6	occluded	occlude	VERB
ajmri-740	222	7	,	,	PUNCT
ajmri-740	222	8	and	and	CCONJ
ajmri-740	222	9	expression	expression	NOUN
ajmri-740	222	10	variant	variant	NOUN
ajmri-740	222	11	faces	face	NOUN
ajmri-740	222	12	from	from	ADP
ajmri-740	222	13	a	a	DET
ajmri-740	222	14	single	single	ADJ
ajmri-740	222	15	sample	sample	NOUN
ajmri-740	222	16	per	per	ADP
ajmri-740	222	17	class	class	NOUN
ajmri-740	222	18	.	.	PUNCT
ajmri-740	223	1	ieee	ieee	NOUN
ajmri-740	223	2	transactions	transaction	NOUN
ajmri-740	223	3	on	on	ADP
ajmri-740	223	4	pattern	pattern	NOUN
ajmri-740	223	5	analysis	analysis	NOUN
ajmri-740	223	6	and	and	CCONJ
ajmri-740	223	7	machine	machine	NOUN
ajmri-740	223	8	intelligence	intelligence	NOUN
ajmri-740	223	9	,	,	PUNCT
ajmri-740	223	10	24(6	24(6	NUM
ajmri-740	223	11	)	)	PUNCT
ajmri-740	223	12	,	,	PUNCT
ajmri-740	223	13	748	748	NUM
ajmri-740	223	14	-	-	SYM
ajmri-740	223	15	763	763	NUM
ajmri-740	223	16	.	.	PUNCT
ajmri-740	224	1	https://doi.org/10.1109/tpami.2002.1008382	https://doi.org/10.1109/tpami.2002.1008382	PROPN
ajmri-740	224	2	martinez	martinez	PROPN
ajmri-740	224	3	,	,	PUNCT
ajmri-740	224	4	a.	a.	NOUN
ajmri-740	224	5	m.	m.	NOUN
ajmri-740	224	6	,	,	PUNCT
ajmri-740	224	7	&	&	CCONJ
ajmri-740	224	8	kak	kak	PROPN
ajmri-740	224	9	,	,	PUNCT
ajmri-740	224	10	a.	a.	PROPN
ajmri-740	224	11	c.	c.	PROPN
ajmri-740	224	12	(	(	PUNCT
ajmri-740	224	13	2001	2001	NUM
ajmri-740	224	14	)	)	PUNCT
ajmri-740	224	15	.	.	PUNCT
ajmri-740	225	1	pca	pca	PROPN
ajmri-740	225	2	versus	versus	ADP
ajmri-740	225	3	lda	lda	PROPN
ajmri-740	225	4	.	.	PUNCT
ajmri-740	226	1	ieee	ieee	NOUN
ajmri-740	226	2	transactions	transaction	NOUN
ajmri-740	226	3	on	on	ADP
ajmri-740	226	4	pattern	pattern	NOUN
ajmri-740	226	5	analysis	analysis	NOUN
ajmri-740	226	6	and	and	CCONJ
ajmri-740	226	7	machine	machine	NOUN
ajmri-740	226	8	intelligence	intelligence	NOUN
ajmri-740	226	9	,	,	PUNCT
ajmri-740	226	10	23(2	23(2	NOUN
ajmri-740	226	11	)	)	PUNCT
ajmri-740	226	12	,	,	PUNCT
ajmri-740	226	13	228	228	NUM
ajmri-740	226	14	-	-	SYM
ajmri-740	226	15	233	233	NUM
ajmri-740	226	16	.	.	PUNCT
ajmri-740	227	1	https://doi	https://doi	PROPN
ajmri-740	227	2	.	.	PUNCT
ajmri-740	227	3	org/10.1109/34.908974	org/10.1109/34.908974	PROPN
ajmri-740	227	4	min	min	PROPN
ajmri-740	227	5	,	,	PUNCT
ajmri-740	227	6	r.	r.	PROPN
ajmri-740	227	7	,	,	PUNCT
ajmri-740	227	8	hadid	hadid	PROPN
ajmri-740	227	9	,	,	PUNCT
ajmri-740	227	10	a.	a.	NOUN
ajmri-740	227	11	,	,	PUNCT
ajmri-740	227	12	&	&	CCONJ
ajmri-740	227	13	dugelay	dugelay	NOUN
ajmri-740	227	14	,	,	PUNCT
ajmri-740	227	15	j.-l	j.-l	PROPN
ajmri-740	227	16	.	.	PUNCT
ajmri-740	228	1	(	(	PUNCT
ajmri-740	228	2	2014	2014	NUM
ajmri-740	228	3	)	)	PUNCT
ajmri-740	228	4	.	.	PUNCT
ajmri-740	229	1	efficient	efficient	ADJ
ajmri-740	229	2	detection	detection	NOUN
ajmri-740	229	3	of	of	ADP
ajmri-740	229	4	occlusion	occlusion	NOUN
ajmri-740	229	5	prior	prior	ADV
ajmri-740	229	6	to	to	ADP
ajmri-740	229	7	robust	robust	ADJ
ajmri-740	229	8	face	face	NOUN
ajmri-740	229	9	recognition	recognition	NOUN
ajmri-740	229	10	.	.	PUNCT
ajmri-740	230	1	the	the	DET
ajmri-740	230	2	scientific	scientific	ADJ
ajmri-740	230	3	world	world	NOUN
ajmri-740	230	4	journal	journal	NOUN
ajmri-740	230	5	,	,	PUNCT
ajmri-740	230	6	1	1	NUM
ajmri-740	230	7	-	-	SYM
ajmri-740	230	8	10	10	NUM
ajmri-740	230	9	.	.	PUNCT
ajmri-740	231	1	https://	https://	PROPN
ajmri-740	231	2	doi.org/10.1155/2014/519158	doi.org/10.1155/2014/519158	NOUN
ajmri-740	231	3	papers	paper	NOUN
ajmri-740	231	4	with	with	ADP
ajmri-740	231	5	code	code	NOUN
ajmri-740	231	6	—	—	PUNCT
ajmri-740	231	7	rmsprop	rmsprop	NOUN
ajmri-740	231	8	explained	explain	VERB
ajmri-740	231	9	.	.	PUNCT
ajmri-740	232	1	(	(	PUNCT
ajmri-740	232	2	s.	s.	PROPN
ajmri-740	232	3	d.	d.	PROPN
ajmri-740	232	4	)	)	PUNCT
ajmri-740	232	5	.	.	PUNCT
ajmri-740	233	1	retrieved	retrieve	VERB
ajmri-740	233	2	10	10	NUM
ajmri-740	233	3	octobre	octobre	PROPN
ajmri-740	233	4	2022	2022	NUM
ajmri-740	233	5	,	,	PUNCT
ajmri-740	233	6	à	à	X
ajmri-740	233	7	l’adresse	l’adresse	PROPN
ajmri-740	233	8	https://paperswithcode	https://paperswithcode	PROPN
ajmri-740	233	9	.	.	PUNCT
ajmri-740	233	10	com	com	NOUN
ajmri-740	233	11	/	/	SYM
ajmri-740	233	12	method	method	NOUN
ajmri-740	233	13	/	/	SYM
ajmri-740	233	14	rmsprop	rmsprop	NOUN
ajmri-740	233	15	praseetha	praseetha	NOUN
ajmri-740	233	16	,	,	PUNCT
ajmri-740	233	17	v.	v.	ADP
ajmri-740	233	18	m.	m.	NOUN
ajmri-740	233	19	,	,	PUNCT
ajmri-740	233	20	bayezeed	bayezeed	VERB
ajmri-740	233	21	,	,	PUNCT
ajmri-740	233	22	s.	s.	PROPN
ajmri-740	233	23	,	,	PUNCT
ajmri-740	233	24	&	&	CCONJ
ajmri-740	233	25	vadivel	vadivel	PROPN
ajmri-740	233	26	,	,	PUNCT
ajmri-740	233	27	s.	s.	PROPN
ajmri-740	233	28	(	(	PUNCT
ajmri-740	233	29	2019	2019	NUM
ajmri-740	233	30	)	)	PUNCT
ajmri-740	233	31	.	.	PUNCT
ajmri-740	234	1	secure	secure	ADJ
ajmri-740	234	2	fingerprint	fingerprint	NOUN
ajmri-740	234	3	authentication	authentication	NOUN
ajmri-740	234	4	using	use	VERB
ajmri-740	234	5	deep	deep	ADJ
ajmri-740	234	6	learning	learning	NOUN
ajmri-740	234	7	and	and	CCONJ
ajmri-740	234	8	minutiae	minutia	NOUN
ajmri-740	234	9	verification	verification	NOUN
ajmri-740	234	10	.	.	PUNCT
ajmri-740	235	1	journal	journal	NOUN
ajmri-740	235	2	of	of	ADP
ajmri-740	235	3	intelligent	intelligent	ADJ
ajmri-740	235	4	systems	system	NOUN
ajmri-740	235	5	,	,	PUNCT
ajmri-740	235	6	29(1	29(1	NUM
ajmri-740	235	7	)	)	PUNCT
ajmri-740	235	8	,	,	PUNCT
ajmri-740	235	9	1379	1379	NUM
ajmri-740	235	10	-	-	SYM
ajmri-740	235	11	1387	1387	NUM
ajmri-740	235	12	.	.	PUNCT
ajmri-740	236	1	https://doi.org/10.1515/jisys-2018-0289	https://doi.org/10.1515/jisys-2018-0289	ADJ
ajmri-740	236	2	reddy	reddy	NOUN
ajmri-740	236	3	,	,	PUNCT
ajmri-740	236	4	a.	a.	PROPN
ajmri-740	236	5	s.	s.	PROPN
ajmri-740	236	6	b.	b.	PROPN
ajmri-740	236	7	,	,	PUNCT
ajmri-740	236	8	&	&	CCONJ
ajmri-740	236	9	juliet	juliet	PROPN
ajmri-740	236	10	,	,	PUNCT
ajmri-740	236	11	d.	d.	PROPN
ajmri-740	236	12	s.	s.	PROPN
ajmri-740	236	13	(	(	PUNCT
ajmri-740	236	14	2019	2019	NUM
ajmri-740	236	15	)	)	PUNCT
ajmri-740	236	16	.	.	PUNCT
ajmri-740	237	1	transfer	transfer	NOUN
ajmri-740	237	2	learning	learn	VERB
ajmri-740	237	3	with	with	ADP
ajmri-740	237	4	resnet-50	resnet-50	PROPN
ajmri-740	237	5	for	for	ADP
ajmri-740	237	6	malaria	malaria	NOUN
ajmri-740	237	7	cell	cell	NOUN
ajmri-740	237	8	-	-	PUNCT
ajmri-740	237	9	image	image	NOUN
ajmri-740	237	10	classification	classification	NOUN
ajmri-740	237	11	.	.	PUNCT
ajmri-740	238	1	2019	2019	NUM
ajmri-740	238	2	international	international	ADJ
ajmri-740	238	3	conference	conference	NOUN
ajmri-740	238	4	on	on	ADP
ajmri-740	238	5	communication	communication	NOUN
ajmri-740	238	6	and	and	CCONJ
ajmri-740	238	7	signal	signal	NOUN
ajmri-740	238	8	processing	processing	NOUN
ajmri-740	238	9	(	(	PUNCT
ajmri-740	238	10	iccsp	iccsp	PROPN
ajmri-740	238	11	)	)	PUNCT
ajmri-740	238	12	,	,	PUNCT
ajmri-740	238	13	0945	0945	NUM
ajmri-740	238	14	-	-	SYM
ajmri-740	238	15	0949	0949	NUM
ajmri-740	238	16	.	.	PUNCT
ajmri-740	239	1	https://doi	https://doi	PROPN
ajmri-740	239	2	.	.	PUNCT
ajmri-740	239	3	org/10.1109	org/10.1109	PROPN
ajmri-740	239	4	/	/	SYM
ajmri-740	239	5	iccsp.2019.8697909	iccsp.2019.8697909	PROPN
ajmri-740	239	6	siegmund	siegmund	PROPN
ajmri-740	239	7	,	,	PUNCT
ajmri-740	239	8	d.	d.	PROPN
ajmri-740	239	9	,	,	PUNCT
ajmri-740	239	10	fu	fu	PROPN
ajmri-740	239	11	,	,	PUNCT
ajmri-740	239	12	b.	b.	PROPN
ajmri-740	239	13	,	,	PUNCT
ajmri-740	239	14	josé	josé	PROPN
ajmri-740	239	15	-	-	PUNCT
ajmri-740	239	16	garcía	garcía	ADJ
ajmri-740	239	17	,	,	PUNCT
ajmri-740	239	18	a.	a.	NOUN
ajmri-740	239	19	,	,	PUNCT
ajmri-740	239	20	salahuddin	salahuddin	NOUN
ajmri-740	239	21	,	,	PUNCT
ajmri-740	239	22	a.	a.	NOUN
ajmri-740	239	23	,	,	PUNCT
ajmri-740	239	24	&	&	CCONJ
ajmri-740	239	25	kuijper	kuijper	PROPN
ajmri-740	239	26	,	,	PUNCT
ajmri-740	239	27	a.	a.	NOUN
ajmri-740	239	28	(	(	PUNCT
ajmri-740	239	29	2021	2021	NUM
ajmri-740	239	30	)	)	PUNCT
ajmri-740	239	31	.	.	PUNCT
ajmri-740	240	1	detection	detection	NOUN
ajmri-740	240	2	of	of	ADP
ajmri-740	240	3	fiber	fiber	NOUN
ajmri-740	240	4	defects	defect	NOUN
ajmri-740	240	5	using	use	VERB
ajmri-740	240	6	keypoints	keypoint	NOUN
ajmri-740	240	7	and	and	CCONJ
ajmri-740	240	8	deep	deep	ADJ
ajmri-740	240	9	learning	learning	NOUN
ajmri-740	240	10	.	.	PUNCT
ajmri-740	241	1	international	international	ADJ
ajmri-740	241	2	journal	journal	NOUN
ajmri-740	241	3	of	of	ADP
ajmri-740	241	4	pattern	pattern	NOUN
ajmri-740	241	5	recognition	recognition	NOUN
ajmri-740	241	6	and	and	CCONJ
ajmri-740	241	7	artificial	artificial	ADJ
ajmri-740	241	8	intelligence	intelligence	NOUN
ajmri-740	241	9	,	,	PUNCT
ajmri-740	241	10	35(05	35(05	NUM
ajmri-740	241	11	)	)	PUNCT
ajmri-740	241	12	,	,	PUNCT
ajmri-740	241	13	2150016	2150016	NUM
ajmri-740	241	14	.	.	PUNCT
ajmri-740	242	1	https://doi.org/10.1142/s0218001421500166	https://doi.org/10.1142/s0218001421500166	NUM
ajmri-740	242	2	singh	singh	NOUN
ajmri-740	242	3	,	,	PUNCT
ajmri-740	242	4	p.	p.	PROPN
ajmri-740	242	5	k.	k.	PROPN
ajmri-740	242	6	,	,	PUNCT
ajmri-740	242	7	kar	kar	PROPN
ajmri-740	242	8	,	,	PUNCT
ajmri-740	242	9	a.	a.	PROPN
ajmri-740	242	10	k.	k.	PROPN
ajmri-740	242	11	,	,	PUNCT
ajmri-740	242	12	singh	singh	PROPN
ajmri-740	242	13	,	,	PUNCT
ajmri-740	242	14	y.	y.	PROPN
ajmri-740	242	15	,	,	PUNCT
ajmri-740	242	16	kolekar	kolekar	PROPN
ajmri-740	242	17	,	,	PUNCT
ajmri-740	242	18	m.	m.	PROPN
ajmri-740	242	19	h.	h.	PROPN
ajmri-740	242	20	,	,	PUNCT
ajmri-740	242	21	&	&	CCONJ
ajmri-740	242	22	tanwar	tanwar	PROPN
ajmri-740	242	23	,	,	PUNCT
ajmri-740	242	24	s.	s.	PROPN
ajmri-740	242	25	(	(	PUNCT
ajmri-740	242	26	éds	éds	PROPN
ajmri-740	242	27	.	.	PUNCT
ajmri-740	242	28	)	)	PUNCT
ajmri-740	242	29	.	.	PUNCT
ajmri-740	243	1	(	(	PUNCT
ajmri-740	243	2	2020	2020	NUM
ajmri-740	243	3	)	)	PUNCT
ajmri-740	243	4	.	.	PUNCT
ajmri-740	244	1	proceedings	proceeding	NOUN
ajmri-740	244	2	of	of	ADP
ajmri-740	244	3	icric	icric	ADJ
ajmri-740	244	4	2019	2019	NUM
ajmri-740	244	5	:	:	PUNCT
ajmri-740	244	6	recent	recent	ADJ
ajmri-740	244	7	innovations	innovation	NOUN
ajmri-740	244	8	in	in	ADP
ajmri-740	244	9	computing	computing	NOUN
ajmri-740	244	10	.	.	PUNCT
ajmri-740	245	1	springer	springer	NOUN
ajmri-740	245	2	.	.	PUNCT
ajmri-740	246	1	team	team	PROPN
ajmri-740	246	2	,	,	PUNCT
ajmri-740	246	3	k.	k.	PROPN
ajmri-740	246	4	(	(	PUNCT
ajmri-740	246	5	s.	s.	PROPN
ajmri-740	246	6	d.	d.	PROPN
ajmri-740	246	7	)	)	PUNCT
ajmri-740	246	8	.	.	PUNCT
ajmri-740	247	1	keras	keras	PROPN
ajmri-740	247	2	documentation	documentation	NOUN
ajmri-740	247	3	:	:	PUNCT
ajmri-740	247	4	sgd	sgd	PROPN
ajmri-740	247	5	.	.	PROPN
ajmri-740	247	6	consulté	consulté	PROPN
ajmri-740	247	7	10	10	NUM
ajmri-740	247	8	octobre	octobre	PROPN
ajmri-740	247	9	2022	2022	NUM
ajmri-740	247	10	,	,	PUNCT
ajmri-740	247	11	à	à	X
ajmri-740	247	12	l’adresse	l’adresse	PROPN
ajmri-740	247	13	https://keras.io/api/	https://keras.io/api/	VERB
ajmri-740	247	14	optimizers	optimizer	NOUN
ajmri-740	247	15	/	/	SYM
ajmri-740	247	16	sgd/	sgd/	PROPN
ajmri-740	247	17	tsai	tsai	PROPN
ajmri-740	247	18	,	,	PUNCT
ajmri-740	247	19	a.-c	a.-c	PROPN
ajmri-740	247	20	.	.	PROPN
ajmri-740	247	21	,	,	PUNCT
ajmri-740	247	22	ou	ou	PROPN
ajmri-740	247	23	,	,	PUNCT
ajmri-740	247	24	y.-y	y.-y	PROPN
ajmri-740	247	25	.	.	PROPN
ajmri-740	247	26	,	,	PUNCT
ajmri-740	247	27	hsu	hsu	PROPN
ajmri-740	247	28	,	,	PUNCT
ajmri-740	247	29	l.-y.-c	l.-y.-c	PROPN
ajmri-740	247	30	.	.	PROPN
ajmri-740	247	31	,	,	PUNCT
ajmri-740	247	32	&	&	CCONJ
ajmri-740	247	33	wang	wang	PROPN
ajmri-740	247	34	,	,	PUNCT
ajmri-740	247	35	j.-f	j.-f	PROPN
ajmri-740	247	36	.	.	PUNCT
ajmri-740	248	1	(	(	PUNCT
ajmri-740	248	2	2018	2018	NUM
ajmri-740	248	3	)	)	PUNCT
ajmri-740	248	4	.	.	PUNCT
ajmri-740	249	1	efficient	efficient	ADJ
ajmri-740	249	2	and	and	CCONJ
ajmri-740	249	3	effective	effective	ADJ
ajmri-740	249	4	multi	multi	ADJ
ajmri-740	249	5	-	-	ADJ
ajmri-740	249	6	person	person	NOUN
ajmri-740	249	7	and	and	CCONJ
ajmri-740	249	8	multi	multi	ADJ
ajmri-740	249	9	-	-	ADJ
ajmri-740	249	10	angle	angle	ADJ
ajmri-740	249	11	face	face	NOUN
ajmri-740	249	12	recognition	recognition	NOUN
ajmri-740	249	13	based	base	VERB
ajmri-740	249	14	on	on	ADP
ajmri-740	249	15	deep	deep	ADJ
ajmri-740	249	16	cnn	cnn	PROPN
ajmri-740	249	17	architecture	architecture	NOUN
ajmri-740	249	18	.	.	PUNCT
ajmri-740	250	1	2018	2018	NUM
ajmri-740	250	2	international	international	ADJ
ajmri-740	250	3	conference	conference	NOUN
ajmri-740	250	4	on	on	ADP
ajmri-740	250	5	orange	orange	PROPN
ajmri-740	250	6	technologies	technology	NOUN
ajmri-740	250	7	(	(	PUNCT
ajmri-740	250	8	icot	icot	NOUN
ajmri-740	250	9	)	)	PUNCT
ajmri-740	250	10	,	,	PUNCT
ajmri-740	250	11	1	1	NUM
ajmri-740	250	12	-	-	SYM
ajmri-740	250	13	4	4	NUM
ajmri-740	250	14	.	.	PUNCT
ajmri-740	251	1	https://doi.org/10.1109/	https://doi.org/10.1109/	PROPN
ajmri-740	251	2	icot.2018.8705876	icot.2018.8705876	PROPN
ajmri-740	251	3	wang	wang	PROPN
ajmri-740	251	4	,	,	PUNCT
ajmri-740	251	5	d.	d.	PROPN
ajmri-740	251	6	,	,	PUNCT
ajmri-740	251	7	wang	wang	PROPN
ajmri-740	251	8	,	,	PUNCT
ajmri-740	251	9	h.	h.	PROPN
ajmri-740	251	10	,	,	PUNCT
ajmri-740	251	11	sun	sun	PROPN
ajmri-740	251	12	,	,	PUNCT
ajmri-740	251	13	j.	j.	PROPN
ajmri-740	251	14	,	,	PUNCT
ajmri-740	251	15	xin	xin	PROPN
ajmri-740	251	16	,	,	PUNCT
ajmri-740	251	17	j.	j.	PROPN
ajmri-740	251	18	,	,	PUNCT
ajmri-740	251	19	&	&	CCONJ
ajmri-740	251	20	luo	luo	PROPN
ajmri-740	251	21	,	,	PUNCT
ajmri-740	251	22	y.	y.	PROPN
ajmri-740	251	23	(	(	PUNCT
ajmri-740	251	24	2020	2020	NUM
ajmri-740	251	25	)	)	PUNCT
ajmri-740	251	26	.	.	PUNCT
ajmri-740	252	1	face	face	NOUN
ajmri-740	252	2	recognition	recognition	NOUN
ajmri-740	252	3	in	in	ADP
ajmri-740	252	4	complex	complex	ADJ
ajmri-740	252	5	unconstrained	unconstrained	ADJ
ajmri-740	252	6	environment	environment	NOUN
ajmri-740	252	7	with	with	ADP
ajmri-740	252	8	an	an	DET
ajmri-740	252	9	enhanced	enhanced	ADJ
ajmri-740	252	10	wwn	wwn	NOUN
ajmri-740	252	11	algorithm	algorithm	NOUN
ajmri-740	252	12	.	.	PUNCT
ajmri-740	253	1	journal	journal	NOUN
ajmri-740	253	2	of	of	ADP
ajmri-740	253	3	intelligent	intelligent	ADJ
ajmri-740	253	4	systems	system	NOUN
ajmri-740	253	5	,	,	PUNCT
ajmri-740	253	6	30(1	30(1	NUM
ajmri-740	253	7	)	)	PUNCT
ajmri-740	253	8	,	,	PUNCT
ajmri-740	253	9	18	18	NUM
ajmri-740	253	10	-	-	SYM
ajmri-740	253	11	39	39	NUM
ajmri-740	253	12	.	.	PUNCT
ajmri-740	254	1	https://doi	https://doi	NOUN
ajmri-740	254	2	.	.	PUNCT
ajmri-740	254	3	org/10.1515	org/10.1515	PROPN
ajmri-740	254	4	/	/	SYM
ajmri-740	254	5	jisys-2019	jisys-2019	PROPN
ajmri-740	254	6	-	-	PUNCT
ajmri-740	254	7	0114	0114	NUM
ajmri-740	254	8	wu	wu	PROPN
ajmri-740	254	9	,	,	PUNCT
ajmri-740	254	10	g.	g.	PROPN
ajmri-740	254	11	,	,	PUNCT
ajmri-740	254	12	tao	tao	PROPN
ajmri-740	254	13	,	,	PUNCT
ajmri-740	254	14	j.	j.	PROPN
ajmri-740	254	15	,	,	PUNCT
ajmri-740	254	16	&	&	CCONJ
ajmri-740	254	17	xu	xu	PROPN
ajmri-740	254	18	,	,	PUNCT
ajmri-740	254	19	x.	x.	NOUN
ajmri-740	254	20	(	(	PUNCT
ajmri-740	254	21	2019	2019	NUM
ajmri-740	254	22	)	)	PUNCT
ajmri-740	254	23	.	.	PUNCT
ajmri-740	255	1	occluded	occlude	VERB
ajmri-740	255	2	face	face	NOUN
ajmri-740	255	3	recognition	recognition	NOUN
ajmri-740	255	4	based	base	VERB
ajmri-740	255	5	on	on	ADP
ajmri-740	255	6	the	the	DET
ajmri-740	255	7	deep	deep	ADJ
ajmri-740	255	8	learning	learning	NOUN
ajmri-740	255	9	.	.	PUNCT
ajmri-740	256	1	2019	2019	NUM
ajmri-740	256	2	chinese	chinese	ADJ
ajmri-740	256	3	control	control	NOUN
ajmri-740	256	4	and	and	CCONJ
ajmri-740	256	5	decision	decision	NOUN
ajmri-740	256	6	conference	conference	NOUN
ajmri-740	256	7	(	(	PUNCT
ajmri-740	256	8	ccdc	ccdc	PROPN
ajmri-740	256	9	)	)	PUNCT
ajmri-740	256	10	,	,	PUNCT
ajmri-740	256	11	793	793	NUM
ajmri-740	256	12	-	-	SYM
ajmri-740	256	13	797	797	NUM
ajmri-740	256	14	.	.	PUNCT
ajmri-740	257	1	https://doi.org/10.1109/ccdc.2019.8832330	https://doi.org/10.1109/ccdc.2019.8832330	PROPN
ajmri-740	257	2	zhang	zhang	PROPN
ajmri-740	257	3	,	,	PUNCT
ajmri-740	257	4	y.-d	y.-d	PROPN
ajmri-740	257	5	.	.	PUNCT
ajmri-740	257	6	,	,	PUNCT
ajmri-740	257	7	wang	wang	PROPN
ajmri-740	257	8	,	,	PUNCT
ajmri-740	257	9	s.-h	s.-h	PROPN
ajmri-740	257	10	.	.	PROPN
ajmri-740	257	11	,	,	PUNCT
ajmri-740	257	12	&	&	CCONJ
ajmri-740	257	13	liu	liu	PROPN
ajmri-740	257	14	,	,	PUNCT
ajmri-740	257	15	s.	s.	PROPN
ajmri-740	257	16	(	(	PUNCT
ajmri-740	257	17	éds	éds	PROPN
ajmri-740	257	18	.	.	PUNCT
ajmri-740	257	19	)	)	PUNCT
ajmri-740	257	20	.	.	PUNCT
ajmri-740	258	1	(	(	PUNCT
ajmri-740	258	2	2020	2020	NUM
ajmri-740	258	3	)	)	PUNCT
ajmri-740	258	4	.	.	PUNCT
ajmri-740	259	1	multimedia	multimedia	NOUN
ajmri-740	259	2	technology	technology	NOUN
ajmri-740	259	3	and	and	CCONJ
ajmri-740	259	4	enhanced	enhanced	ADJ
ajmri-740	259	5	learning	learning	NOUN
ajmri-740	259	6	:	:	PUNCT
ajmri-740	259	7	second	second	ADJ
ajmri-740	259	8	eai	eai	PROPN
ajmri-740	259	9	international	international	ADJ
ajmri-740	259	10	conference	conference	NOUN
ajmri-740	259	11	,	,	PUNCT
ajmri-740	259	12	icmtel	icmtel	VERB
ajmri-740	259	13	2020	2020	NUM
ajmri-740	259	14	,	,	PUNCT
ajmri-740	259	15	leicester	leicester	NOUN
ajmri-740	259	16	,	,	PUNCT
ajmri-740	259	17	uk	uk	PROPN
ajmri-740	259	18	,	,	PUNCT
ajmri-740	259	19	april	april	PROPN
ajmri-740	259	20	10	10	NUM
ajmri-740	259	21	-	-	SYM
ajmri-740	259	22	11	11	NUM
ajmri-740	259	23	,	,	PUNCT
ajmri-740	259	24	2020	2020	NUM
ajmri-740	259	25	,	,	PUNCT
ajmri-740	259	26	proceedings	proceeding	NOUN
ajmri-740	259	27	.	.	PUNCT
ajmri-740	260	1	part	part	NOUN
ajmri-740	260	2	i.	i.	PROPN
ajmri-740	260	3	springer	springer	PROPN
ajmri-740	260	4	.	.	PUNCT
ajmri-740	261	1	zhou	zhou	PROPN
ajmri-740	261	2	,	,	PUNCT
ajmri-740	261	3	j.	j.	PROPN
ajmri-740	261	4	,	,	PUNCT
ajmri-740	261	5	wang	wang	PROPN
ajmri-740	261	6	,	,	PUNCT
ajmri-740	261	7	y.	y.	PROPN
ajmri-740	261	8	,	,	PUNCT
ajmri-740	261	9	sun	sun	PROPN
ajmri-740	261	10	,	,	PUNCT
ajmri-740	261	11	z.	z.	PROPN
ajmri-740	261	12	,	,	PUNCT
ajmri-740	261	13	jia	jia	PROPN
ajmri-740	261	14	,	,	PUNCT
ajmri-740	261	15	z.	z.	PROPN
ajmri-740	261	16	,	,	PUNCT
ajmri-740	261	17	feng	feng	PROPN
ajmri-740	261	18	,	,	PUNCT
ajmri-740	261	19	j.	j.	PROPN
ajmri-740	261	20	,	,	PUNCT
ajmri-740	261	21	shan	shan	PROPN
ajmri-740	261	22	,	,	PUNCT
ajmri-740	261	23	s.	s.	PROPN
ajmri-740	261	24	,	,	PUNCT
ajmri-740	261	25	ubul	ubul	PROPN
ajmri-740	261	26	,	,	PUNCT
ajmri-740	261	27	k.	k.	PROPN
ajmri-740	261	28	,	,	PUNCT
ajmri-740	261	29	&	&	CCONJ
ajmri-740	261	30	guo	guo	PROPN
ajmri-740	261	31	,	,	PUNCT
ajmri-740	261	32	z.	z.	PROPN
ajmri-740	261	33	(	(	PUNCT
ajmri-740	261	34	éds	éds	PROPN
ajmri-740	261	35	.	.	PUNCT
ajmri-740	261	36	)	)	PUNCT
ajmri-740	261	37	.	.	PUNCT
ajmri-740	262	1	(	(	PUNCT
ajmri-740	262	2	2018	2018	NUM
ajmri-740	262	3	)	)	PUNCT
ajmri-740	262	4	.	.	PUNCT
ajmri-740	263	1	biometric	biometric	ADJ
ajmri-740	263	2	recognition	recognition	NOUN
ajmri-740	263	3	:	:	PUNCT
ajmri-740	263	4	13th	13th	ADJ
ajmri-740	263	5	chinese	chinese	ADJ
ajmri-740	263	6	conference	conference	NOUN
ajmri-740	263	7	,	,	PUNCT
ajmri-740	263	8	ccbr	ccbr	NOUN
ajmri-740	263	9	2018	2018	NUM
ajmri-740	263	10	,	,	PUNCT
ajmri-740	263	11	urumchi	urumchi	NOUN
ajmri-740	263	12	,	,	PUNCT
ajmri-740	263	13	china	china	PROPN
ajmri-740	263	14	,	,	PUNCT
ajmri-740	263	15	august	august	PROPN
ajmri-740	263	16	11	11	NUM
ajmri-740	263	17	-	-	SYM
ajmri-740	263	18	12	12	NUM
ajmri-740	263	19	,	,	PUNCT
ajmri-740	263	20	2018	2018	NUM
ajmri-740	263	21	:	:	PUNCT
ajmri-740	263	22	proceedings	proceeding	NOUN
ajmri-740	263	23	.	.	PUNCT
ajmri-740	264	1	springer	springer	NOUN
ajmri-740	264	2	.	.	PUNCT
ajmri-740	265	1	https://journals.e-palli.com/home/index.php/ajmri	https://journals.e-palli.com/home/index.php/ajmri	ADJ