id	sid	tid	token	lemma	pos
aiti-8538	1	1	3___aiti#8538___105	3___aiti#8538___105	NUM
aiti-8538	1	2	-	-	SYM
aiti-8538	1	3	117	117	NUM
aiti-8538	1	4	advances	advance	NOUN
aiti-8538	1	5	in	in	ADP
aiti-8538	1	6	technology	technology	NOUN
aiti-8538	1	7	innovation	innovation	NOUN
aiti-8538	1	8	,	,	PUNCT
aiti-8538	1	9	vol	vol	NOUN
aiti-8538	1	10	.	.	PROPN
aiti-8538	1	11	7	7	NUM
aiti-8538	1	12	,	,	PUNCT
aiti-8538	1	13	no	no	INTJ
aiti-8538	1	14	.	.	NOUN
aiti-8538	1	15	2	2	NUM
aiti-8538	1	16	,	,	PUNCT
aiti-8538	1	17	2022	2022	NUM
aiti-8538	1	18	,	,	PUNCT
aiti-8538	1	19	pp	pp	ADJ
aiti-8538	1	20	.	.	PUNCT
aiti-8538	2	1	105	105	NUM
aiti-8538	2	2	-	-	SYM
aiti-8538	2	3	117	117	NUM
aiti-8538	2	4	an	an	DET
aiti-8538	2	5	integrated	integrated	ADJ
aiti-8538	2	6	approach	approach	NOUN
aiti-8538	2	7	towards	towards	ADP
aiti-8538	2	8	efficient	efficient	ADJ
aiti-8538	2	9	image	image	NOUN
aiti-8538	2	10	classification	classification	NOUN
aiti-8538	2	11	using	use	VERB
aiti-8538	2	12	deep	deep	ADJ
aiti-8538	2	13	cnn	cnn	NOUN
aiti-8538	2	14	with	with	ADP
aiti-8538	2	15	transfer	transfer	NOUN
aiti-8538	2	16	learning	learning	NOUN
aiti-8538	2	17	and	and	CCONJ
aiti-8538	2	18	pca	pca	PROPN
aiti-8538	2	19	rahul	rahul	PROPN
aiti-8538	2	20	sharma	sharma	PROPN
aiti-8538	2	21	*	*	PUNCT
aiti-8538	2	22	,	,	PUNCT
aiti-8538	2	23	amar	amar	PROPN
aiti-8538	2	24	singh	singh	PROPN
aiti-8538	2	25	department	department	PROPN
aiti-8538	2	26	of	of	ADP
aiti-8538	2	27	computer	computer	NOUN
aiti-8538	2	28	applications	application	NOUN
aiti-8538	2	29	,	,	PUNCT
aiti-8538	2	30	lovely	lovely	ADJ
aiti-8538	2	31	professional	professional	ADJ
aiti-8538	2	32	university	university	NOUN
aiti-8538	2	33	,	,	PUNCT
aiti-8538	2	34	jalandhar	jalandhar	PROPN
aiti-8538	2	35	,	,	PUNCT
aiti-8538	2	36	india	india	PROPN
aiti-8538	2	37	received	receive	VERB
aiti-8538	2	38	22	22	NUM
aiti-8538	2	39	september	september	PROPN
aiti-8538	2	40	2021	2021	NUM
aiti-8538	2	41	;	;	PUNCT
aiti-8538	2	42	received	receive	VERB
aiti-8538	2	43	in	in	ADP
aiti-8538	2	44	revised	revise	VERB
aiti-8538	2	45	form	form	NOUN
aiti-8538	2	46	21	21	NUM
aiti-8538	2	47	october	october	PROPN
aiti-8538	2	48	2021	2021	NUM
aiti-8538	2	49	;	;	PUNCT
aiti-8538	2	50	accepted	accept	VERB
aiti-8538	2	51	22	22	NUM
aiti-8538	2	52	october	october	PROPN
aiti-8538	2	53	2021	2021	NUM
aiti-8538	2	54	doi	doi	NOUN
aiti-8538	2	55	:	:	PUNCT
aiti-8538	2	56	https://doi.org/10.46604/aiti.2022.8538	https://doi.org/10.46604/aiti.2022.8538	PROPN
aiti-8538	2	57	abstract	abstract	NOUN
aiti-8538	2	58	in	in	ADP
aiti-8538	2	59	image	image	NOUN
aiti-8538	2	60	processing	processing	NOUN
aiti-8538	2	61	,	,	PUNCT
aiti-8538	2	62	developing	develop	VERB
aiti-8538	2	63	efficient	efficient	ADJ
aiti-8538	2	64	,	,	PUNCT
aiti-8538	2	65	automated	automate	VERB
aiti-8538	2	66	,	,	PUNCT
aiti-8538	2	67	and	and	CCONJ
aiti-8538	2	68	accurate	accurate	ADJ
aiti-8538	2	69	techniques	technique	NOUN
aiti-8538	2	70	to	to	PART
aiti-8538	2	71	classify	classify	VERB
aiti-8538	2	72	images	image	NOUN
aiti-8538	2	73	with	with	ADP
aiti-8538	2	74	varying	vary	VERB
aiti-8538	2	75	intensity	intensity	NOUN
aiti-8538	2	76	level	level	NOUN
aiti-8538	2	77	,	,	PUNCT
aiti-8538	2	78	resolution	resolution	NOUN
aiti-8538	2	79	,	,	PUNCT
aiti-8538	2	80	aspect	aspect	NOUN
aiti-8538	2	81	ratio	ratio	NOUN
aiti-8538	2	82	,	,	PUNCT
aiti-8538	2	83	orientation	orientation	NOUN
aiti-8538	2	84	,	,	PUNCT
aiti-8538	2	85	contrast	contrast	NOUN
aiti-8538	2	86	,	,	PUNCT
aiti-8538	2	87	sharpness	sharpness	NOUN
aiti-8538	2	88	,	,	PUNCT
aiti-8538	2	89	etc	etc	X
aiti-8538	2	90	.	.	X
aiti-8538	2	91	is	be	AUX
aiti-8538	2	92	a	a	DET
aiti-8538	2	93	challenging	challenging	ADJ
aiti-8538	2	94	task	task	NOUN
aiti-8538	2	95	.	.	PUNCT
aiti-8538	3	1	this	this	DET
aiti-8538	3	2	study	study	NOUN
aiti-8538	3	3	presents	present	VERB
aiti-8538	3	4	an	an	DET
aiti-8538	3	5	integrated	integrated	ADJ
aiti-8538	3	6	approach	approach	NOUN
aiti-8538	3	7	for	for	ADP
aiti-8538	3	8	image	image	NOUN
aiti-8538	3	9	classification	classification	NOUN
aiti-8538	3	10	by	by	ADP
aiti-8538	3	11	employing	employ	VERB
aiti-8538	3	12	transfer	transfer	NOUN
aiti-8538	3	13	learning	learning	NOUN
aiti-8538	3	14	for	for	ADP
aiti-8538	3	15	feature	feature	NOUN
aiti-8538	3	16	selection	selection	NOUN
aiti-8538	3	17	and	and	CCONJ
aiti-8538	3	18	using	use	VERB
aiti-8538	3	19	principal	principal	ADJ
aiti-8538	3	20	component	component	NOUN
aiti-8538	3	21	analysis	analysis	NOUN
aiti-8538	3	22	(	(	PUNCT
aiti-8538	3	23	pca	pca	NOUN
aiti-8538	3	24	)	)	PUNCT
aiti-8538	3	25	for	for	ADP
aiti-8538	3	26	feature	feature	NOUN
aiti-8538	3	27	reduction	reduction	NOUN
aiti-8538	3	28	.	.	PUNCT
aiti-8538	4	1	the	the	DET
aiti-8538	4	2	pca	pca	PROPN
aiti-8538	4	3	algorithm	algorithm	NOUN
aiti-8538	4	4	is	be	AUX
aiti-8538	4	5	employed	employ	VERB
aiti-8538	4	6	for	for	ADP
aiti-8538	4	7	reducing	reduce	VERB
aiti-8538	4	8	the	the	DET
aiti-8538	4	9	dimensionality	dimensionality	NOUN
aiti-8538	4	10	of	of	ADP
aiti-8538	4	11	the	the	DET
aiti-8538	4	12	features	feature	NOUN
aiti-8538	4	13	extracted	extract	VERB
aiti-8538	4	14	by	by	ADP
aiti-8538	4	15	the	the	DET
aiti-8538	4	16	vgg16	vgg16	NOUN
aiti-8538	4	17	model	model	NOUN
aiti-8538	4	18	to	to	PART
aiti-8538	4	19	obtain	obtain	VERB
aiti-8538	4	20	a	a	DET
aiti-8538	4	21	handful	handful	NOUN
aiti-8538	4	22	of	of	ADP
aiti-8538	4	23	features	feature	NOUN
aiti-8538	4	24	for	for	ADP
aiti-8538	4	25	speeding	speed	VERB
aiti-8538	4	26	up	up	ADP
aiti-8538	4	27	image	image	NOUN
aiti-8538	4	28	reorganization	reorganization	NOUN
aiti-8538	4	29	.	.	PUNCT
aiti-8538	5	1	for	for	ADP
aiti-8538	5	2	multilayer	multilayer	ADJ
aiti-8538	5	3	perceptron	perceptron	PROPN
aiti-8538	5	4	classifiers	classifier	NOUN
aiti-8538	5	5	,	,	PUNCT
aiti-8538	5	6	support	support	VERB
aiti-8538	5	7	vector	vector	NOUN
aiti-8538	5	8	machine	machine	NOUN
aiti-8538	5	9	(	(	PUNCT
aiti-8538	5	10	svm	svm	PROPN
aiti-8538	5	11	)	)	PUNCT
aiti-8538	5	12	and	and	CCONJ
aiti-8538	5	13	random	random	ADJ
aiti-8538	5	14	forest	forest	NOUN
aiti-8538	5	15	(	(	PUNCT
aiti-8538	5	16	rf	rf	NOUN
aiti-8538	5	17	)	)	PUNCT
aiti-8538	5	18	algorithms	algorithm	NOUN
aiti-8538	5	19	are	be	AUX
aiti-8538	5	20	used	use	VERB
aiti-8538	5	21	.	.	PUNCT
aiti-8538	6	1	the	the	DET
aiti-8538	6	2	performance	performance	NOUN
aiti-8538	6	3	of	of	ADP
aiti-8538	6	4	the	the	DET
aiti-8538	6	5	proposed	propose	VERB
aiti-8538	6	6	approach	approach	NOUN
aiti-8538	6	7	is	be	AUX
aiti-8538	6	8	compared	compare	VERB
aiti-8538	6	9	with	with	ADP
aiti-8538	6	10	other	other	ADJ
aiti-8538	6	11	classifiers	classifier	NOUN
aiti-8538	6	12	.	.	PUNCT
aiti-8538	7	1	the	the	DET
aiti-8538	7	2	experimental	experimental	ADJ
aiti-8538	7	3	results	result	NOUN
aiti-8538	7	4	establish	establish	VERB
aiti-8538	7	5	the	the	DET
aiti-8538	7	6	supremacy	supremacy	NOUN
aiti-8538	7	7	of	of	ADP
aiti-8538	7	8	the	the	DET
aiti-8538	7	9	vgg16	vgg16	PROPN
aiti-8538	7	10	-	-	PUNCT
aiti-8538	7	11	pca	pca	NOUN
aiti-8538	7	12	-	-	PUNCT
aiti-8538	7	13	multilayer	multilayer	ADJ
aiti-8538	7	14	perceptron	perceptron	PROPN
aiti-8538	7	15	model	model	PROPN
aiti-8538	7	16	integrated	integrate	VERB
aiti-8538	7	17	approach	approach	NOUN
aiti-8538	7	18	and	and	CCONJ
aiti-8538	7	19	achieve	achieve	VERB
aiti-8538	7	20	a	a	DET
aiti-8538	7	21	reorganization	reorganization	NOUN
aiti-8538	7	22	accuracy	accuracy	NOUN
aiti-8538	7	23	of	of	ADP
aiti-8538	7	24	91.145	91.145	NUM
aiti-8538	7	25	%	%	NOUN
aiti-8538	7	26	,	,	PUNCT
aiti-8538	7	27	95.0	95.0	NUM
aiti-8538	7	28	%	%	NOUN
aiti-8538	7	29	,	,	PUNCT
aiti-8538	7	30	92.33	92.33	NUM
aiti-8538	7	31	%	%	NOUN
aiti-8538	7	32	,	,	PUNCT
aiti-8538	7	33	and	and	CCONJ
aiti-8538	7	34	98.59	98.59	NUM
aiti-8538	7	35	%	%	NOUN
aiti-8538	7	36	on	on	ADP
aiti-8538	7	37	fashion	fashion	NOUN
aiti-8538	7	38	-	-	PUNCT
aiti-8538	7	39	mnist	mnist	NOUN
aiti-8538	7	40	dataset	dataset	NOUN
aiti-8538	7	41	,	,	PUNCT
aiti-8538	7	42	orl	orl	PROPN
aiti-8538	7	43	dataset	dataset	NOUN
aiti-8538	7	44	of	of	ADP
aiti-8538	7	45	faces	face	NOUN
aiti-8538	7	46	,	,	PUNCT
aiti-8538	7	47	corn	corn	NOUN
aiti-8538	7	48	leaf	leaf	NOUN
aiti-8538	7	49	disease	disease	NOUN
aiti-8538	7	50	dataset	dataset	NOUN
aiti-8538	7	51	,	,	PUNCT
aiti-8538	7	52	and	and	CCONJ
aiti-8538	7	53	rice	rice	NOUN
aiti-8538	7	54	leaf	leaf	NOUN
aiti-8538	7	55	disease	disease	NOUN
aiti-8538	7	56	datasets	dataset	NOUN
aiti-8538	7	57	,	,	PUNCT
aiti-8538	7	58	respectively	respectively	ADV
aiti-8538	7	59	.	.	PUNCT
aiti-8538	8	1	keywords	keyword	NOUN
aiti-8538	8	2	:	:	PUNCT
aiti-8538	8	3	dimensionality	dimensionality	NOUN
aiti-8538	8	4	reduction	reduction	NOUN
aiti-8538	8	5	,	,	PUNCT
aiti-8538	8	6	feature	feature	NOUN
aiti-8538	8	7	extraction	extraction	NOUN
aiti-8538	8	8	,	,	PUNCT
aiti-8538	8	9	image	image	NOUN
aiti-8538	8	10	recognition	recognition	NOUN
aiti-8538	8	11	,	,	PUNCT
aiti-8538	8	12	pca	pca	PROPN
aiti-8538	8	13	,	,	PUNCT
aiti-8538	8	14	transfer	transfer	NOUN
aiti-8538	8	15	learning	learn	VERB
aiti-8538	8	16	1	1	NUM
aiti-8538	8	17	.	.	PUNCT
aiti-8538	9	1	introduction	introduction	NOUN
aiti-8538	9	2	manual	manual	ADJ
aiti-8538	9	3	image	image	NOUN
aiti-8538	9	4	recognition	recognition	NOUN
aiti-8538	9	5	by	by	ADP
aiti-8538	9	6	experts	expert	NOUN
aiti-8538	9	7	suffers	suffer	VERB
aiti-8538	9	8	from	from	ADP
aiti-8538	9	9	different	different	ADJ
aiti-8538	9	10	limitations	limitation	NOUN
aiti-8538	9	11	,	,	PUNCT
aiti-8538	9	12	e.g.	e.g.	ADV
aiti-8538	9	13	,	,	PUNCT
aiti-8538	9	14	time	time	NOUN
aiti-8538	9	15	-	-	PUNCT
aiti-8538	9	16	consuming	consume	VERB
aiti-8538	9	17	,	,	PUNCT
aiti-8538	9	18	human	human	ADJ
aiti-8538	9	19	error	error	NOUN
aiti-8538	9	20	,	,	PUNCT
aiti-8538	9	21	biased	biased	ADJ
aiti-8538	9	22	opinion	opinion	NOUN
aiti-8538	9	23	,	,	PUNCT
aiti-8538	9	24	non	non	ADJ
aiti-8538	9	25	-	-	NOUN
aiti-8538	9	26	availability	availability	NOUN
aiti-8538	9	27	of	of	ADP
aiti-8538	9	28	experts	expert	NOUN
aiti-8538	9	29	,	,	PUNCT
aiti-8538	9	30	etc	etc	X
aiti-8538	9	31	.	.	X
aiti-8538	9	32	nowadays	nowadays	ADV
aiti-8538	9	33	,	,	PUNCT
aiti-8538	9	34	automated	automate	VERB
aiti-8538	9	35	image	image	NOUN
aiti-8538	9	36	recognition	recognition	NOUN
aiti-8538	9	37	technology	technology	NOUN
aiti-8538	9	38	appears	appear	VERB
aiti-8538	9	39	in	in	ADP
aiti-8538	9	40	many	many	ADJ
aiti-8538	9	41	aspects	aspect	NOUN
aiti-8538	9	42	of	of	ADP
aiti-8538	9	43	day	day	NOUN
aiti-8538	9	44	-	-	PUNCT
aiti-8538	9	45	to	to	ADP
aiti-8538	9	46	-	-	PUNCT
aiti-8538	9	47	day	day	NOUN
aiti-8538	9	48	tasks	task	NOUN
aiti-8538	9	49	and	and	CCONJ
aiti-8538	9	50	plays	play	VERB
aiti-8538	9	51	a	a	DET
aiti-8538	9	52	very	very	ADV
aiti-8538	9	53	important	important	ADJ
aiti-8538	9	54	role	role	NOUN
aiti-8538	9	55	in	in	ADP
aiti-8538	9	56	education	education	NOUN
aiti-8538	9	57	,	,	PUNCT
aiti-8538	9	58	face	face	NOUN
aiti-8538	9	59	recognition	recognition	NOUN
aiti-8538	9	60	,	,	PUNCT
aiti-8538	9	61	medical	medical	ADJ
aiti-8538	9	62	disease	disease	NOUN
aiti-8538	9	63	diagnosis	diagnosis	NOUN
aiti-8538	9	64	,	,	PUNCT
aiti-8538	9	65	agricultural	agricultural	ADJ
aiti-8538	9	66	activities	activity	NOUN
aiti-8538	9	67	,	,	PUNCT
aiti-8538	9	68	driverless	driverless	NOUN
aiti-8538	9	69	cars	car	NOUN
aiti-8538	9	70	,	,	PUNCT
aiti-8538	9	71	advertising	advertising	NOUN
aiti-8538	9	72	,	,	PUNCT
aiti-8538	9	73	image	image	NOUN
aiti-8538	9	74	restoration	restoration	NOUN
aiti-8538	9	75	,	,	PUNCT
aiti-8538	9	76	etc	etc	X
aiti-8538	9	77	.	.	X
aiti-8538	9	78	automated	automate	VERB
aiti-8538	9	79	solutions	solution	NOUN
aiti-8538	9	80	are	be	AUX
aiti-8538	9	81	very	very	ADV
aiti-8538	9	82	useful	useful	ADJ
aiti-8538	9	83	for	for	ADP
aiti-8538	9	84	amateurs	amateur	NOUN
aiti-8538	9	85	as	as	ADV
aiti-8538	9	86	well	well	ADV
aiti-8538	9	87	as	as	ADP
aiti-8538	9	88	field	field	NOUN
aiti-8538	9	89	experts	expert	NOUN
aiti-8538	9	90	.	.	PUNCT
aiti-8538	10	1	with	with	ADP
aiti-8538	10	2	the	the	DET
aiti-8538	10	3	advances	advance	NOUN
aiti-8538	10	4	in	in	ADP
aiti-8538	10	5	technology	technology	NOUN
aiti-8538	10	6	,	,	PUNCT
aiti-8538	10	7	the	the	DET
aiti-8538	10	8	widespread	widespread	ADJ
aiti-8538	10	9	use	use	NOUN
aiti-8538	10	10	of	of	ADP
aiti-8538	10	11	image	image	NOUN
aiti-8538	10	12	capturing	capture	VERB
aiti-8538	10	13	and	and	CCONJ
aiti-8538	10	14	processing	processing	NOUN
aiti-8538	10	15	devices	device	NOUN
aiti-8538	10	16	enabled	enable	VERB
aiti-8538	10	17	people	people	NOUN
aiti-8538	10	18	to	to	PART
aiti-8538	10	19	capture	capture	VERB
aiti-8538	10	20	,	,	PUNCT
aiti-8538	10	21	share	share	NOUN
aiti-8538	10	22	,	,	PUNCT
aiti-8538	10	23	search	search	NOUN
aiti-8538	10	24	,	,	PUNCT
aiti-8538	10	25	and	and	CCONJ
aiti-8538	10	26	retrieve	retrieve	VERB
aiti-8538	10	27	images	image	NOUN
aiti-8538	10	28	.	.	PUNCT
aiti-8538	11	1	image	image	NOUN
aiti-8538	11	2	recognition	recognition	NOUN
aiti-8538	11	3	helps	helps	AUX
aiti-8538	11	4	identify	identify	VERB
aiti-8538	11	5	and	and	CCONJ
aiti-8538	11	6	analyze	analyze	VERB
aiti-8538	11	7	the	the	DET
aiti-8538	11	8	objects	object	NOUN
aiti-8538	11	9	and	and	CCONJ
aiti-8538	11	10	use	use	VERB
aiti-8538	11	11	the	the	DET
aiti-8538	11	12	learned	learn	VERB
aiti-8538	11	13	knowledge	knowledge	NOUN
aiti-8538	11	14	for	for	ADP
aiti-8538	11	15	decision	decision	NOUN
aiti-8538	11	16	making	making	NOUN
aiti-8538	11	17	.	.	PUNCT
aiti-8538	12	1	the	the	DET
aiti-8538	12	2	objective	objective	NOUN
aiti-8538	12	3	of	of	ADP
aiti-8538	12	4	image	image	NOUN
aiti-8538	12	5	recognition	recognition	NOUN
aiti-8538	12	6	is	be	AUX
aiti-8538	12	7	to	to	PART
aiti-8538	12	8	label	label	VERB
aiti-8538	12	9	a	a	DET
aiti-8538	12	10	given	give	VERB
aiti-8538	12	11	image	image	NOUN
aiti-8538	12	12	into	into	ADP
aiti-8538	12	13	class	class	NOUN
aiti-8538	12	14	among	among	ADP
aiti-8538	12	15	a	a	DET
aiti-8538	12	16	set	set	NOUN
aiti-8538	12	17	of	of	ADP
aiti-8538	12	18	pre	pre	ADJ
aiti-8538	12	19	-	-	ADJ
aiti-8538	12	20	defined	define	VERB
aiti-8538	12	21	classes	class	NOUN
aiti-8538	12	22	.	.	PUNCT
aiti-8538	13	1	to	to	PART
aiti-8538	13	2	accurately	accurately	ADV
aiti-8538	13	3	label	label	VERB
aiti-8538	13	4	an	an	DET
aiti-8538	13	5	image	image	NOUN
aiti-8538	13	6	,	,	PUNCT
aiti-8538	13	7	the	the	DET
aiti-8538	13	8	extraction	extraction	NOUN
aiti-8538	13	9	of	of	ADP
aiti-8538	13	10	useful	useful	ADJ
aiti-8538	13	11	image	image	NOUN
aiti-8538	13	12	features	feature	NOUN
aiti-8538	13	13	is	be	AUX
aiti-8538	13	14	very	very	ADV
aiti-8538	13	15	important	important	ADJ
aiti-8538	13	16	.	.	PUNCT
aiti-8538	14	1	features	feature	NOUN
aiti-8538	14	2	represent	represent	VERB
aiti-8538	14	3	the	the	DET
aiti-8538	14	4	patterns	pattern	NOUN
aiti-8538	14	5	of	of	ADP
aiti-8538	14	6	the	the	DET
aiti-8538	14	7	object	object	NOUN
aiti-8538	14	8	in	in	ADP
aiti-8538	14	9	the	the	DET
aiti-8538	14	10	image	image	NOUN
aiti-8538	14	11	and	and	CCONJ
aiti-8538	14	12	are	be	AUX
aiti-8538	14	13	used	use	VERB
aiti-8538	14	14	for	for	ADP
aiti-8538	14	15	recognition	recognition	NOUN
aiti-8538	14	16	.	.	PUNCT
aiti-8538	15	1	there	there	PRON
aiti-8538	15	2	are	be	VERB
aiti-8538	15	3	different	different	ADJ
aiti-8538	15	4	methods	method	NOUN
aiti-8538	15	5	for	for	ADP
aiti-8538	15	6	feature	feature	NOUN
aiti-8538	15	7	extraction	extraction	NOUN
aiti-8538	15	8	from	from	ADP
aiti-8538	15	9	images	image	NOUN
aiti-8538	15	10	such	such	ADJ
aiti-8538	15	11	as	as	ADP
aiti-8538	15	12	grayscale	grayscale	NOUN
aiti-8538	15	13	feature	feature	NOUN
aiti-8538	15	14	extraction	extraction	NOUN
aiti-8538	15	15	,	,	PUNCT
aiti-8538	15	16	the	the	DET
aiti-8538	15	17	use	use	NOUN
aiti-8538	15	18	of	of	ADP
aiti-8538	15	19	mean	mean	ADJ
aiti-8538	15	20	pixel	pixel	ADJ
aiti-8538	15	21	values	value	NOUN
aiti-8538	15	22	of	of	ADP
aiti-8538	15	23	channels	channel	NOUN
aiti-8538	15	24	,	,	PUNCT
aiti-8538	15	25	and	and	CCONJ
aiti-8538	15	26	edge	edge	VERB
aiti-8538	15	27	feature	feature	NOUN
aiti-8538	15	28	extraction	extraction	NOUN
aiti-8538	15	29	.	.	PUNCT
aiti-8538	16	1	the	the	DET
aiti-8538	16	2	grayscale	grayscale	NOUN
aiti-8538	16	3	feature	feature	NOUN
aiti-8538	16	4	extraction	extraction	NOUN
aiti-8538	16	5	uses	use	VERB
aiti-8538	16	6	a	a	DET
aiti-8538	16	7	single	single	ADJ
aiti-8538	16	8	channel	channel	NOUN
aiti-8538	16	9	as	as	ADP
aiti-8538	16	10	an	an	DET
aiti-8538	16	11	input	input	NOUN
aiti-8538	16	12	,	,	PUNCT
aiti-8538	16	13	and	and	CCONJ
aiti-8538	16	14	the	the	DET
aiti-8538	16	15	number	number	NOUN
aiti-8538	16	16	of	of	ADP
aiti-8538	16	17	features	feature	NOUN
aiti-8538	16	18	will	will	AUX
aiti-8538	16	19	be	be	AUX
aiti-8538	16	20	the	the	DET
aiti-8538	16	21	same	same	ADJ
aiti-8538	16	22	as	as	ADP
aiti-8538	16	23	the	the	DET
aiti-8538	16	24	number	number	NOUN
aiti-8538	16	25	of	of	ADP
aiti-8538	16	26	pixels	pixel	NOUN
aiti-8538	16	27	.	.	PUNCT
aiti-8538	17	1	mean	mean	VERB
aiti-8538	17	2	pixel	pixel	ADJ
aiti-8538	17	3	values	value	NOUN
aiti-8538	17	4	of	of	ADP
aiti-8538	17	5	channels	channel	NOUN
aiti-8538	17	6	generate	generate	VERB
aiti-8538	17	7	a	a	DET
aiti-8538	17	8	matrix	matrix	NOUN
aiti-8538	17	9	using	use	VERB
aiti-8538	17	10	the	the	DET
aiti-8538	17	11	pixel	pixel	ADJ
aiti-8538	17	12	values	value	NOUN
aiti-8538	17	13	from	from	ADP
aiti-8538	17	14	three	three	NUM
aiti-8538	17	15	channels	channel	NOUN
aiti-8538	17	16	.	.	PUNCT
aiti-8538	18	1	thus	thus	ADV
aiti-8538	18	2	,	,	PUNCT
aiti-8538	18	3	the	the	DET
aiti-8538	18	4	number	number	NOUN
aiti-8538	18	5	of	of	ADP
aiti-8538	18	6	features	feature	NOUN
aiti-8538	18	7	remains	remain	VERB
aiti-8538	18	8	the	the	DET
aiti-8538	18	9	same	same	ADJ
aiti-8538	18	10	and	and	CCONJ
aiti-8538	18	11	the	the	DET
aiti-8538	18	12	pixel	pixel	ADJ
aiti-8538	18	13	values	value	NOUN
aiti-8538	18	14	of	of	ADP
aiti-8538	18	15	all	all	DET
aiti-8538	18	16	three	three	NUM
aiti-8538	18	17	channels	channel	NOUN
aiti-8538	18	18	are	be	AUX
aiti-8538	18	19	considered	consider	VERB
aiti-8538	18	20	.	.	PUNCT
aiti-8538	19	1	the	the	DET
aiti-8538	19	2	edge	edge	NOUN
aiti-8538	19	3	features	feature	VERB
aiti-8538	19	4	extraction	extraction	NOUN
aiti-8538	19	5	method	method	NOUN
aiti-8538	19	6	extracts	extract	NOUN
aiti-8538	19	7	edges	edge	NOUN
aiti-8538	19	8	as	as	ADP
aiti-8538	19	9	features	feature	NOUN
aiti-8538	19	10	and	and	CCONJ
aiti-8538	19	11	uses	use	VERB
aiti-8538	19	12	that	that	PRON
aiti-8538	19	13	as	as	ADP
aiti-8538	19	14	the	the	DET
aiti-8538	19	15	input	input	NOUN
aiti-8538	19	16	for	for	ADP
aiti-8538	19	17	the	the	DET
aiti-8538	19	18	model	model	NOUN
aiti-8538	19	19	.	.	PUNCT
aiti-8538	20	1	edges	edge	NOUN
aiti-8538	20	2	can	can	AUX
aiti-8538	20	3	be	be	AUX
aiti-8538	20	4	extracted	extract	VERB
aiti-8538	20	5	by	by	ADP
aiti-8538	20	6	using	use	VERB
aiti-8538	20	7	different	different	ADJ
aiti-8538	20	8	algorithms	algorithm	NOUN
aiti-8538	20	9	,	,	PUNCT
aiti-8538	20	10	e.g.	e.g.	ADV
aiti-8538	20	11	,	,	PUNCT
aiti-8538	20	12	prewitt	prewitt	PROPN
aiti-8538	20	13	edge	edge	PROPN
aiti-8538	20	14	detection	detection	PROPN
aiti-8538	20	15	,	,	PUNCT
aiti-8538	20	16	sobel	sobel	PROPN
aiti-8538	20	17	edge	edge	PROPN
aiti-8538	20	18	detection	detection	PROPN
aiti-8538	20	19	,	,	PUNCT
aiti-8538	20	20	*	*	PUNCT
aiti-8538	20	21	corresponding	correspond	VERB
aiti-8538	20	22	author	author	NOUN
aiti-8538	20	23	.	.	PUNCT
aiti-8538	21	1	e	e	X
aiti-8538	21	2	-	-	NOUN
aiti-8538	21	3	mail	mail	NOUN
aiti-8538	21	4	address	address	NOUN
aiti-8538	21	5	:	:	PUNCT
aiti-8538	21	6	prof.sharma.rahul@gmail.com	prof.sharma.rahul@gmail.com	X
aiti-8538	22	1	tel	tel	PROPN
aiti-8538	22	2	.	.	PUNCT
aiti-8538	22	3	:	:	PUNCT
aiti-8538	23	1	+91	+91	NOUN
aiti-8538	23	2	-	-	PUNCT
aiti-8538	23	3	7006957906	7006957906	NUM
aiti-8538	23	4	;	;	PUNCT
aiti-8538	23	5	fax	fax	NOUN
aiti-8538	23	6	:	:	PUNCT
aiti-8538	23	7	+91	+91	NOUN
aiti-8538	23	8	-	-	PUNCT
aiti-8538	23	9	1922	1922	NUM
aiti-8538	23	10	-	-	SYM
aiti-8538	23	11	234315	234315	NUM
aiti-8538	23	12	advances	advance	NOUN
aiti-8538	23	13	in	in	ADP
aiti-8538	23	14	technology	technology	NOUN
aiti-8538	23	15	innovation	innovation	NOUN
aiti-8538	23	16	,	,	PUNCT
aiti-8538	23	17	vol	vol	NOUN
aiti-8538	23	18	.	.	PROPN
aiti-8538	23	19	7	7	NUM
aiti-8538	23	20	,	,	PUNCT
aiti-8538	23	21	no	no	INTJ
aiti-8538	23	22	.	.	NOUN
aiti-8538	23	23	2	2	NUM
aiti-8538	23	24	,	,	PUNCT
aiti-8538	23	25	2022	2022	NUM
aiti-8538	23	26	,	,	PUNCT
aiti-8538	23	27	pp	pp	ADJ
aiti-8538	23	28	.	.	PUNCT
aiti-8538	24	1	105	105	NUM
aiti-8538	24	2	-	-	SYM
aiti-8538	24	3	117	117	NUM
aiti-8538	24	4	laplacian	laplacian	ADJ
aiti-8538	24	5	edge	edge	NOUN
aiti-8538	24	6	detection	detection	NOUN
aiti-8538	24	7	,	,	PUNCT
aiti-8538	24	8	canny	canny	ADJ
aiti-8538	24	9	edge	edge	NOUN
aiti-8538	24	10	detection	detection	NOUN
aiti-8538	24	11	,	,	PUNCT
aiti-8538	24	12	etc	etc	X
aiti-8538	24	13	.	.	X
aiti-8538	24	14	low	low	ADJ
aiti-8538	24	15	-	-	PUNCT
aiti-8538	24	16	level	level	NOUN
aiti-8538	24	17	features	feature	NOUN
aiti-8538	24	18	of	of	ADP
aiti-8538	24	19	an	an	DET
aiti-8538	24	20	image	image	NOUN
aiti-8538	24	21	can	can	AUX
aiti-8538	24	22	also	also	ADV
aiti-8538	24	23	be	be	AUX
aiti-8538	24	24	extracted	extract	VERB
aiti-8538	24	25	by	by	ADP
aiti-8538	24	26	using	use	VERB
aiti-8538	24	27	different	different	ADJ
aiti-8538	24	28	techniques	technique	NOUN
aiti-8538	24	29	,	,	PUNCT
aiti-8538	24	30	e.g.	e.g.	ADV
aiti-8538	24	31	,	,	PUNCT
aiti-8538	24	32	histogram	histogram	NOUN
aiti-8538	24	33	of	of	ADP
aiti-8538	24	34	oriented	orient	VERB
aiti-8538	24	35	gradients	gradient	NOUN
aiti-8538	24	36	(	(	PUNCT
aiti-8538	24	37	hog	hog	PROPN
aiti-8538	24	38	)	)	PUNCT
aiti-8538	24	39	,	,	PUNCT
aiti-8538	24	40	generalized	generalize	VERB
aiti-8538	24	41	search	search	NOUN
aiti-8538	24	42	tree	tree	NOUN
aiti-8538	24	43	(	(	PUNCT
aiti-8538	24	44	gist	gist	NOUN
aiti-8538	24	45	)	)	PUNCT
aiti-8538	24	46	,	,	PUNCT
aiti-8538	24	47	scale	scale	NOUN
aiti-8538	24	48	-	-	PUNCT
aiti-8538	24	49	invariant	invariant	ADJ
aiti-8538	24	50	feature	feature	NOUN
aiti-8538	24	51	transform	transform	NOUN
aiti-8538	24	52	(	(	PUNCT
aiti-8538	24	53	sift	sift	NOUN
aiti-8538	24	54	)	)	PUNCT
aiti-8538	24	55	,	,	PUNCT
aiti-8538	24	56	speeded	speed	VERB
aiti-8538	24	57	up	up	ADP
aiti-8538	24	58	robust	robust	ADJ
aiti-8538	24	59	feature	feature	NOUN
aiti-8538	24	60	(	(	PUNCT
aiti-8538	24	61	surf	surf	NOUN
aiti-8538	24	62	)	)	PUNCT
aiti-8538	24	63	,	,	PUNCT
aiti-8538	24	64	etc	etc	X
aiti-8538	25	1	[	[	X
aiti-8538	25	2	1	1	NUM
aiti-8538	25	3	]	]	PUNCT
aiti-8538	25	4	.	.	PUNCT
aiti-8538	26	1	training	train	VERB
aiti-8538	26	2	image	image	NOUN
aiti-8538	26	3	classification	classification	NOUN
aiti-8538	26	4	algorithms	algorithm	NOUN
aiti-8538	26	5	by	by	ADP
aiti-8538	26	6	using	use	VERB
aiti-8538	26	7	these	these	DET
aiti-8538	26	8	hand	hand	NOUN
aiti-8538	26	9	-	-	PUNCT
aiti-8538	26	10	crafted	craft	VERB
aiti-8538	26	11	features	feature	NOUN
aiti-8538	26	12	is	be	AUX
aiti-8538	26	13	time	time	NOUN
aiti-8538	26	14	-	-	PUNCT
aiti-8538	26	15	consuming	consume	VERB
aiti-8538	26	16	and	and	CCONJ
aiti-8538	26	17	requires	require	VERB
aiti-8538	26	18	technical	technical	ADJ
aiti-8538	26	19	expertise	expertise	NOUN
aiti-8538	26	20	.	.	PUNCT
aiti-8538	27	1	the	the	DET
aiti-8538	27	2	pretrained	pretraine	VERB
aiti-8538	27	3	convolution	convolution	NOUN
aiti-8538	27	4	neural	neural	ADJ
aiti-8538	27	5	network	network	NOUN
aiti-8538	27	6	(	(	PUNCT
aiti-8538	27	7	cnn	cnn	PROPN
aiti-8538	27	8	)	)	PUNCT
aiti-8538	27	9	models	model	NOUN
aiti-8538	27	10	are	be	AUX
aiti-8538	27	11	trained	train	VERB
aiti-8538	27	12	using	use	VERB
aiti-8538	27	13	large	large	ADJ
aiti-8538	27	14	,	,	PUNCT
aiti-8538	27	15	distributed	distribute	VERB
aiti-8538	27	16	,	,	PUNCT
aiti-8538	27	17	and	and	CCONJ
aiti-8538	27	18	standard	standard	ADJ
aiti-8538	27	19	datasets	dataset	NOUN
aiti-8538	27	20	.	.	PUNCT
aiti-8538	28	1	the	the	DET
aiti-8538	28	2	weights	weight	NOUN
aiti-8538	28	3	learned	learn	VERB
aiti-8538	28	4	by	by	ADP
aiti-8538	28	5	the	the	DET
aiti-8538	28	6	pretrained	pretraine	VERB
aiti-8538	28	7	model	model	NOUN
aiti-8538	28	8	can	can	AUX
aiti-8538	28	9	be	be	AUX
aiti-8538	28	10	reused	reuse	VERB
aiti-8538	28	11	to	to	PART
aiti-8538	28	12	solve	solve	VERB
aiti-8538	28	13	a	a	DET
aiti-8538	28	14	similar	similar	ADJ
aiti-8538	28	15	problem	problem	NOUN
aiti-8538	28	16	.	.	PUNCT
aiti-8538	29	1	transfer	transfer	NOUN
aiti-8538	29	2	learning	learning	NOUN
aiti-8538	29	3	employing	employ	VERB
aiti-8538	29	4	the	the	DET
aiti-8538	29	5	pretrained	pretraine	VERB
aiti-8538	29	6	cnn	cnn	PROPN
aiti-8538	29	7	enables	enable	VERB
aiti-8538	29	8	filters	filter	NOUN
aiti-8538	29	9	to	to	PART
aiti-8538	29	10	extract	extract	VERB
aiti-8538	29	11	valuable	valuable	ADJ
aiti-8538	29	12	characteristics	characteristic	NOUN
aiti-8538	29	13	from	from	ADP
aiti-8538	29	14	images	image	NOUN
aiti-8538	29	15	[	[	X
aiti-8538	29	16	2	2	NUM
aiti-8538	29	17	]	]	PUNCT
aiti-8538	29	18	.	.	PUNCT
aiti-8538	30	1	the	the	DET
aiti-8538	30	2	cnn	cnn	PROPN
aiti-8538	30	3	model	model	NOUN
aiti-8538	30	4	has	have	VERB
aiti-8538	30	5	different	different	ADJ
aiti-8538	30	6	layers	layer	NOUN
aiti-8538	30	7	such	such	ADJ
aiti-8538	30	8	as	as	ADP
aiti-8538	30	9	the	the	DET
aiti-8538	30	10	convolutional	convolutional	ADJ
aiti-8538	30	11	layer	layer	NOUN
aiti-8538	30	12	,	,	PUNCT
aiti-8538	30	13	pooling	pool	VERB
aiti-8538	30	14	layer	layer	NOUN
aiti-8538	30	15	,	,	PUNCT
aiti-8538	30	16	dropout	dropout	NOUN
aiti-8538	30	17	layers	layer	NOUN
aiti-8538	30	18	,	,	PUNCT
aiti-8538	30	19	non	non	ADJ
aiti-8538	30	20	-	-	ADJ
aiti-8538	30	21	linear	linear	ADJ
aiti-8538	30	22	layers	layer	NOUN
aiti-8538	30	23	,	,	PUNCT
aiti-8538	30	24	and	and	CCONJ
aiti-8538	30	25	fully	fully	ADV
aiti-8538	30	26	connected	connected	ADJ
aiti-8538	30	27	layers	layer	NOUN
aiti-8538	30	28	.	.	PUNCT
aiti-8538	31	1	the	the	DET
aiti-8538	31	2	feature	feature	NOUN
aiti-8538	31	3	maps	map	NOUN
aiti-8538	31	4	of	of	ADP
aiti-8538	31	5	these	these	DET
aiti-8538	31	6	layers	layer	NOUN
aiti-8538	31	7	can	can	AUX
aiti-8538	31	8	be	be	AUX
aiti-8538	31	9	viewed	view	VERB
aiti-8538	31	10	and	and	CCONJ
aiti-8538	31	11	give	give	VERB
aiti-8538	31	12	a	a	DET
aiti-8538	31	13	distinct	distinct	ADJ
aiti-8538	31	14	representation	representation	NOUN
aiti-8538	31	15	of	of	ADP
aiti-8538	31	16	the	the	DET
aiti-8538	31	17	input	input	NOUN
aiti-8538	31	18	image	image	NOUN
aiti-8538	31	19	.	.	PUNCT
aiti-8538	32	1	the	the	DET
aiti-8538	32	2	vgg16	vgg16	NOUN
aiti-8538	32	3	architecture	architecture	NOUN
aiti-8538	32	4	is	be	AUX
aiti-8538	32	5	employed	employ	VERB
aiti-8538	32	6	to	to	PART
aiti-8538	32	7	extract	extract	VERB
aiti-8538	32	8	important	important	ADJ
aiti-8538	32	9	features	feature	NOUN
aiti-8538	32	10	of	of	ADP
aiti-8538	32	11	images	image	NOUN
aiti-8538	32	12	[	[	X
aiti-8538	32	13	3	3	NUM
aiti-8538	32	14	]	]	PUNCT
aiti-8538	32	15	.	.	PUNCT
aiti-8538	33	1	to	to	PART
aiti-8538	33	2	improve	improve	VERB
aiti-8538	33	3	the	the	DET
aiti-8538	33	4	space	space	NOUN
aiti-8538	33	5	and	and	CCONJ
aiti-8538	33	6	time	time	NOUN
aiti-8538	33	7	complexity	complexity	NOUN
aiti-8538	33	8	,	,	PUNCT
aiti-8538	33	9	principle	principle	ADJ
aiti-8538	33	10	component	component	NOUN
aiti-8538	33	11	analysis	analysis	NOUN
aiti-8538	33	12	(	(	PUNCT
aiti-8538	33	13	pca	pca	NOUN
aiti-8538	33	14	)	)	PUNCT
aiti-8538	33	15	is	be	AUX
aiti-8538	33	16	applied	apply	VERB
aiti-8538	33	17	as	as	ADP
aiti-8538	33	18	feature	feature	NOUN
aiti-8538	33	19	reduction	reduction	NOUN
aiti-8538	33	20	technique	technique	NOUN
aiti-8538	33	21	.	.	PUNCT
aiti-8538	34	1	deep	deep	ADJ
aiti-8538	34	2	learning	learning	NOUN
aiti-8538	34	3	techniques	technique	NOUN
aiti-8538	34	4	are	be	AUX
aiti-8538	34	5	successfully	successfully	ADV
aiti-8538	34	6	applied	apply	VERB
aiti-8538	34	7	for	for	ADP
aiti-8538	34	8	solving	solve	VERB
aiti-8538	34	9	the	the	DET
aiti-8538	34	10	problems	problem	NOUN
aiti-8538	34	11	related	relate	VERB
aiti-8538	34	12	to	to	ADP
aiti-8538	34	13	healthcare	healthcare	PROPN
aiti-8538	34	14	,	,	PUNCT
aiti-8538	34	15	agriculture	agriculture	NOUN
aiti-8538	34	16	,	,	PUNCT
aiti-8538	34	17	engineering	engineering	NOUN
aiti-8538	34	18	,	,	PUNCT
aiti-8538	34	19	entertainment	entertainment	NOUN
aiti-8538	34	20	,	,	PUNCT
aiti-8538	34	21	music	music	NOUN
aiti-8538	34	22	composition	composition	NOUN
aiti-8538	34	23	,	,	PUNCT
aiti-8538	34	24	advertising	advertising	NOUN
aiti-8538	34	25	,	,	PUNCT
aiti-8538	34	26	signal	signal	NOUN
aiti-8538	34	27	processing	processing	NOUN
aiti-8538	34	28	,	,	PUNCT
aiti-8538	34	29	image	image	NOUN
aiti-8538	34	30	recognition	recognition	NOUN
aiti-8538	34	31	,	,	PUNCT
aiti-8538	34	32	robotics	robotic	NOUN
aiti-8538	34	33	,	,	PUNCT
aiti-8538	34	34	image	image	NOUN
aiti-8538	34	35	coloring	coloring	NOUN
aiti-8538	34	36	,	,	PUNCT
aiti-8538	34	37	image	image	NOUN
aiti-8538	34	38	captioning	captioning	NOUN
aiti-8538	34	39	,	,	PUNCT
aiti-8538	34	40	etc	etc	X
aiti-8538	34	41	.	.	X
aiti-8538	35	1	the	the	DET
aiti-8538	35	2	application	application	NOUN
aiti-8538	35	3	of	of	ADP
aiti-8538	35	4	machine	machine	NOUN
aiti-8538	35	5	learning	learn	VERB
aiti-8538	35	6	techniques	technique	NOUN
aiti-8538	35	7	for	for	ADP
aiti-8538	35	8	solving	solve	VERB
aiti-8538	35	9	real	real	ADJ
aiti-8538	35	10	-	-	PUNCT
aiti-8538	35	11	time	time	NOUN
aiti-8538	35	12	applications	application	NOUN
aiti-8538	35	13	efficiently	efficiently	ADV
aiti-8538	35	14	and	and	CCONJ
aiti-8538	35	15	accurately	accurately	ADV
aiti-8538	35	16	is	be	AUX
aiti-8538	35	17	gaining	gain	VERB
aiti-8538	35	18	importance	importance	NOUN
aiti-8538	35	19	.	.	PUNCT
aiti-8538	36	1	real	real	ADJ
aiti-8538	36	2	-	-	PUNCT
aiti-8538	36	3	time	time	NOUN
aiti-8538	36	4	applications	application	NOUN
aiti-8538	36	5	often	often	ADV
aiti-8538	36	6	require	require	VERB
aiti-8538	36	7	handling	handle	VERB
aiti-8538	36	8	huge	huge	ADJ
aiti-8538	36	9	data	datum	NOUN
aiti-8538	36	10	.	.	PUNCT
aiti-8538	37	1	extracting	extract	VERB
aiti-8538	37	2	significant	significant	ADJ
aiti-8538	37	3	features	feature	NOUN
aiti-8538	37	4	and	and	CCONJ
aiti-8538	37	5	reducing	reduce	VERB
aiti-8538	37	6	noise	noise	NOUN
aiti-8538	37	7	are	be	AUX
aiti-8538	37	8	required	require	VERB
aiti-8538	37	9	for	for	ADP
aiti-8538	37	10	improving	improve	VERB
aiti-8538	37	11	computational	computational	ADJ
aiti-8538	37	12	speed	speed	NOUN
aiti-8538	37	13	and	and	CCONJ
aiti-8538	37	14	accuracy	accuracy	NOUN
aiti-8538	37	15	.	.	PUNCT
aiti-8538	38	1	some	some	PRON
aiti-8538	38	2	of	of	ADP
aiti-8538	38	3	the	the	DET
aiti-8538	38	4	current	current	ADJ
aiti-8538	38	5	work	work	NOUN
aiti-8538	38	6	in	in	ADP
aiti-8538	38	7	this	this	DET
aiti-8538	38	8	field	field	NOUN
aiti-8538	38	9	includes	include	VERB
aiti-8538	38	10	handling	handle	VERB
aiti-8538	38	11	multi	multi	ADJ
aiti-8538	38	12	-	-	ADJ
aiti-8538	38	13	sensor	sensor	ADJ
aiti-8538	38	14	data	datum	NOUN
aiti-8538	38	15	for	for	ADP
aiti-8538	38	16	chatter	chatter	NOUN
aiti-8538	38	17	detection	detection	NOUN
aiti-8538	38	18	using	use	VERB
aiti-8538	38	19	metaheuristic	metaheuristic	ADJ
aiti-8538	38	20	algorithms	algorithm	NOUN
aiti-8538	38	21	and	and	CCONJ
aiti-8538	38	22	recursive	recursive	ADJ
aiti-8538	38	23	feature	feature	NOUN
aiti-8538	38	24	elimination	elimination	NOUN
aiti-8538	38	25	[	[	X
aiti-8538	38	26	4	4	NUM
aiti-8538	38	27	]	]	PUNCT
aiti-8538	38	28	,	,	PUNCT
aiti-8538	38	29	feature	feature	NOUN
aiti-8538	38	30	selection	selection	NOUN
aiti-8538	38	31	using	use	VERB
aiti-8538	38	32	fuzzy	fuzzy	ADJ
aiti-8538	38	33	entropy	entropy	NOUN
aiti-8538	38	34	measures	measure	NOUN
aiti-8538	38	35	with	with	ADP
aiti-8538	38	36	a	a	DET
aiti-8538	38	37	similarity	similarity	NOUN
aiti-8538	38	38	classifier	classifier	NOUN
aiti-8538	38	39	[	[	X
aiti-8538	38	40	5	5	NUM
aiti-8538	38	41	]	]	PUNCT
aiti-8538	38	42	,	,	PUNCT
aiti-8538	38	43	effective	effective	ADJ
aiti-8538	38	44	feature	feature	NOUN
aiti-8538	38	45	selection	selection	NOUN
aiti-8538	38	46	using	use	VERB
aiti-8538	38	47	enhanced	enhanced	ADJ
aiti-8538	38	48	pca	pca	NOUN
aiti-8538	39	1	[	[	X
aiti-8538	39	2	6	6	NUM
aiti-8538	39	3	]	]	PUNCT
aiti-8538	39	4	,	,	PUNCT
aiti-8538	39	5	artificial	artificial	ADJ
aiti-8538	39	6	bee	bee	NOUN
aiti-8538	39	7	colony	colony	NOUN
aiti-8538	39	8	for	for	ADP
aiti-8538	39	9	feature	feature	NOUN
aiti-8538	39	10	selection	selection	NOUN
aiti-8538	39	11	of	of	ADP
aiti-8538	39	12	breast	breast	NOUN
aiti-8538	39	13	cancer	cancer	NOUN
aiti-8538	39	14	data	datum	NOUN
aiti-8538	40	1	[	[	X
aiti-8538	40	2	7	7	NUM
aiti-8538	40	3	]	]	PUNCT
aiti-8538	40	4	,	,	PUNCT
aiti-8538	40	5	etc	etc	X
aiti-8538	40	6	.	.	X
aiti-8538	41	1	recent	recent	ADJ
aiti-8538	41	2	studies	study	NOUN
aiti-8538	41	3	have	have	AUX
aiti-8538	41	4	integrated	integrate	VERB
aiti-8538	41	5	deep	deep	ADJ
aiti-8538	41	6	learning	learning	NOUN
aiti-8538	41	7	with	with	ADP
aiti-8538	41	8	the	the	DET
aiti-8538	41	9	internet	internet	NOUN
aiti-8538	41	10	of	of	ADP
aiti-8538	41	11	things	thing	NOUN
aiti-8538	41	12	(	(	PUNCT
aiti-8538	41	13	iot	iot	NOUN
aiti-8538	41	14	)	)	PUNCT
aiti-8538	41	15	for	for	ADP
aiti-8538	41	16	performing	perform	VERB
aiti-8538	41	17	real	real	ADJ
aiti-8538	41	18	-	-	PUNCT
aiti-8538	41	19	time	time	NOUN
aiti-8538	41	20	complex	complex	ADJ
aiti-8538	41	21	sensing	sensing	NOUN
aiti-8538	41	22	and	and	CCONJ
aiti-8538	41	23	recognition	recognition	NOUN
aiti-8538	41	24	tasks	task	NOUN
aiti-8538	41	25	.	.	PUNCT
aiti-8538	42	1	iot	iot	PROPN
aiti-8538	42	2	applications	application	NOUN
aiti-8538	42	3	generate	generate	VERB
aiti-8538	42	4	a	a	DET
aiti-8538	42	5	huge	huge	ADJ
aiti-8538	42	6	volume	volume	NOUN
aiti-8538	42	7	of	of	ADP
aiti-8538	42	8	data	datum	NOUN
aiti-8538	42	9	that	that	PRON
aiti-8538	42	10	requires	require	VERB
aiti-8538	42	11	preprocessing	preprocesse	VERB
aiti-8538	42	12	and	and	CCONJ
aiti-8538	42	13	dimensionality	dimensionality	NOUN
aiti-8538	42	14	reduction	reduction	NOUN
aiti-8538	42	15	.	.	PUNCT
aiti-8538	43	1	some	some	PRON
aiti-8538	43	2	of	of	ADP
aiti-8538	43	3	the	the	DET
aiti-8538	43	4	tasks	task	NOUN
aiti-8538	43	5	involving	involve	VERB
aiti-8538	43	6	iot	iot	NOUN
aiti-8538	43	7	and	and	CCONJ
aiti-8538	43	8	deep	deep	ADJ
aiti-8538	43	9	learning	learning	NOUN
aiti-8538	43	10	include	include	VERB
aiti-8538	43	11	reducing	reduce	VERB
aiti-8538	43	12	the	the	DET
aiti-8538	43	13	energy	energy	NOUN
aiti-8538	43	14	consumption	consumption	NOUN
aiti-8538	43	15	of	of	ADP
aiti-8538	43	16	iot	iot	PROPN
aiti-8538	43	17	enabled	enable	VERB
aiti-8538	43	18	devices	device	NOUN
aiti-8538	43	19	[	[	X
aiti-8538	43	20	8	8	NUM
aiti-8538	43	21	]	]	PUNCT
aiti-8538	43	22	,	,	PUNCT
aiti-8538	43	23	online	online	ADJ
aiti-8538	43	24	automated	automate	VERB
aiti-8538	43	25	monitoring	monitoring	NOUN
aiti-8538	43	26	of	of	ADP
aiti-8538	43	27	cyber	cyber	NOUN
aiti-8538	43	28	-	-	PUNCT
aiti-8538	43	29	attacks	attack	NOUN
aiti-8538	43	30	[	[	X
aiti-8538	43	31	9	9	NUM
aiti-8538	43	32	]	]	PUNCT
aiti-8538	43	33	,	,	PUNCT
aiti-8538	43	34	automatic	automatic	ADJ
aiti-8538	43	35	online	online	ADJ
aiti-8538	43	36	detection	detection	NOUN
aiti-8538	43	37	of	of	ADP
aiti-8538	43	38	defects	defect	NOUN
aiti-8538	43	39	in	in	ADP
aiti-8538	43	40	gas	gas	NOUN
aiti-8538	43	41	-	-	PUNCT
aiti-8538	43	42	insulated	insulate	VERB
aiti-8538	43	43	switchgear	switchgear	NOUN
aiti-8538	44	1	[	[	X
aiti-8538	44	2	10	10	NUM
aiti-8538	44	3	]	]	PUNCT
aiti-8538	44	4	,	,	PUNCT
aiti-8538	44	5	anomaly	anomaly	NOUN
aiti-8538	44	6	prediction	prediction	NOUN
aiti-8538	44	7	in	in	ADP
aiti-8538	44	8	iot	iot	ADJ
aiti-8538	44	9	networks	network	NOUN
aiti-8538	44	10	[	[	X
aiti-8538	44	11	11	11	NUM
aiti-8538	44	12	]	]	PUNCT
aiti-8538	44	13	,	,	PUNCT
aiti-8538	44	14	real	real	ADJ
aiti-8538	44	15	-	-	PUNCT
aiti-8538	44	16	time	time	NOUN
aiti-8538	44	17	monitoring	monitoring	NOUN
aiti-8538	44	18	of	of	ADP
aiti-8538	44	19	agriculture	agriculture	NOUN
aiti-8538	44	20	fields	field	NOUN
aiti-8538	44	21	[	[	X
aiti-8538	44	22	12	12	NUM
aiti-8538	44	23	]	]	PUNCT
aiti-8538	44	24	,	,	PUNCT
aiti-8538	44	25	etc	etc	X
aiti-8538	44	26	.	.	X
aiti-8538	45	1	the	the	DET
aiti-8538	45	2	main	main	ADJ
aiti-8538	45	3	objective	objective	NOUN
aiti-8538	45	4	of	of	ADP
aiti-8538	45	5	this	this	DET
aiti-8538	45	6	study	study	NOUN
aiti-8538	45	7	is	be	AUX
aiti-8538	45	8	to	to	PART
aiti-8538	45	9	propose	propose	VERB
aiti-8538	45	10	an	an	DET
aiti-8538	45	11	efficient	efficient	ADJ
aiti-8538	45	12	and	and	CCONJ
aiti-8538	45	13	accurate	accurate	ADJ
aiti-8538	45	14	approach	approach	NOUN
aiti-8538	45	15	for	for	ADP
aiti-8538	45	16	image	image	NOUN
aiti-8538	45	17	recognition	recognition	NOUN
aiti-8538	45	18	based	base	VERB
aiti-8538	45	19	on	on	ADP
aiti-8538	45	20	the	the	DET
aiti-8538	45	21	integrated	integrate	VERB
aiti-8538	45	22	approach	approach	NOUN
aiti-8538	45	23	.	.	PUNCT
aiti-8538	46	1	some	some	PRON
aiti-8538	46	2	of	of	ADP
aiti-8538	46	3	the	the	DET
aiti-8538	46	4	challenges	challenge	NOUN
aiti-8538	46	5	faced	face	VERB
aiti-8538	46	6	by	by	ADP
aiti-8538	46	7	deep	deep	ADJ
aiti-8538	46	8	learning	learning	NOUN
aiti-8538	46	9	image	image	NOUN
aiti-8538	46	10	classifiers	classifier	NOUN
aiti-8538	46	11	are	be	AUX
aiti-8538	46	12	computationally	computationally	ADV
aiti-8538	46	13	expensive	expensive	ADJ
aiti-8538	46	14	,	,	PUNCT
aiti-8538	46	15	energy	energy	NOUN
aiti-8538	46	16	-	-	PUNCT
aiti-8538	46	17	intensive	intensive	ADJ
aiti-8538	46	18	,	,	PUNCT
aiti-8538	46	19	and	and	CCONJ
aiti-8538	46	20	time	time	NOUN
aiti-8538	46	21	-	-	PUNCT
aiti-8538	46	22	consuming	consume	VERB
aiti-8538	46	23	,	,	PUNCT
aiti-8538	46	24	and	and	CCONJ
aiti-8538	46	25	have	have	VERB
aiti-8538	46	26	high	high	ADJ
aiti-8538	46	27	memory	memory	NOUN
aiti-8538	46	28	requirements	requirement	NOUN
aiti-8538	46	29	.	.	PUNCT
aiti-8538	47	1	thus	thus	ADV
aiti-8538	47	2	,	,	PUNCT
aiti-8538	47	3	there	there	PRON
aiti-8538	47	4	is	be	VERB
aiti-8538	47	5	a	a	DET
aiti-8538	47	6	need	need	NOUN
aiti-8538	47	7	for	for	ADP
aiti-8538	47	8	scaling	scale	VERB
aiti-8538	47	9	up	up	ADP
aiti-8538	47	10	the	the	DET
aiti-8538	47	11	performance	performance	NOUN
aiti-8538	47	12	of	of	ADP
aiti-8538	47	13	image	image	NOUN
aiti-8538	47	14	classifiers	classifier	NOUN
aiti-8538	47	15	and	and	CCONJ
aiti-8538	47	16	overcoming	overcome	VERB
aiti-8538	47	17	the	the	DET
aiti-8538	47	18	bottlenecks	bottleneck	NOUN
aiti-8538	47	19	faced	face	VERB
aiti-8538	47	20	during	during	ADP
aiti-8538	47	21	model	model	NOUN
aiti-8538	47	22	training	training	NOUN
aiti-8538	47	23	and	and	CCONJ
aiti-8538	47	24	image	image	NOUN
aiti-8538	47	25	classification	classification	NOUN
aiti-8538	47	26	.	.	PUNCT
aiti-8538	48	1	the	the	DET
aiti-8538	48	2	proposed	propose	VERB
aiti-8538	48	3	approach	approach	NOUN
aiti-8538	48	4	reduces	reduce	VERB
aiti-8538	48	5	the	the	DET
aiti-8538	48	6	computational	computational	ADJ
aiti-8538	48	7	complexity	complexity	NOUN
aiti-8538	48	8	and	and	CCONJ
aiti-8538	48	9	maintains	maintain	VERB
aiti-8538	48	10	high	high	ADJ
aiti-8538	48	11	classification	classification	NOUN
aiti-8538	48	12	accuracy	accuracy	NOUN
aiti-8538	48	13	.	.	PUNCT
aiti-8538	49	1	the	the	DET
aiti-8538	49	2	contribution	contribution	NOUN
aiti-8538	49	3	of	of	ADP
aiti-8538	49	4	the	the	DET
aiti-8538	49	5	work	work	NOUN
aiti-8538	49	6	is	be	AUX
aiti-8538	49	7	summarized	summarize	VERB
aiti-8538	49	8	in	in	ADP
aiti-8538	49	9	the	the	DET
aiti-8538	49	10	following	follow	VERB
aiti-8538	49	11	points	point	NOUN
aiti-8538	49	12	.	.	PUNCT
aiti-8538	50	1	(	(	PUNCT
aiti-8538	50	2	1	1	X
aiti-8538	50	3	)	)	PUNCT
aiti-8538	50	4	an	an	DET
aiti-8538	50	5	integrated	integrate	VERB
aiti-8538	50	6	approach	approach	NOUN
aiti-8538	50	7	is	be	AUX
aiti-8538	50	8	proposed	propose	VERB
aiti-8538	50	9	employing	employ	VERB
aiti-8538	50	10	deep	deep	ADJ
aiti-8538	50	11	learning	learning	NOUN
aiti-8538	50	12	for	for	ADP
aiti-8538	50	13	feature	feature	NOUN
aiti-8538	50	14	extraction	extraction	NOUN
aiti-8538	50	15	,	,	PUNCT
aiti-8538	50	16	pca	pca	NOUN
aiti-8538	50	17	for	for	ADP
aiti-8538	50	18	feature	feature	NOUN
aiti-8538	50	19	reduction	reduction	NOUN
aiti-8538	50	20	,	,	PUNCT
aiti-8538	50	21	and	and	CCONJ
aiti-8538	50	22	image	image	NOUN
aiti-8538	50	23	classification	classification	NOUN
aiti-8538	50	24	module	module	NOUN
aiti-8538	50	25	with	with	ADP
aiti-8538	50	26	multilayer	multilayer	PROPN
aiti-8538	50	27	perceptron	perceptron	PROPN
aiti-8538	50	28	,	,	PUNCT
aiti-8538	50	29	support	support	VERB
aiti-8538	50	30	vector	vector	NOUN
aiti-8538	50	31	machine	machine	NOUN
aiti-8538	50	32	(	(	PUNCT
aiti-8538	50	33	svm	svm	PROPN
aiti-8538	50	34	)	)	PUNCT
aiti-8538	50	35	,	,	PUNCT
aiti-8538	50	36	and	and	CCONJ
aiti-8538	50	37	random	random	ADJ
aiti-8538	50	38	forest	forest	NOUN
aiti-8538	50	39	(	(	PUNCT
aiti-8538	50	40	rf	rf	NOUN
aiti-8538	50	41	)	)	PUNCT
aiti-8538	50	42	.	.	PUNCT
aiti-8538	51	1	(	(	PUNCT
aiti-8538	51	2	2	2	X
aiti-8538	51	3	)	)	PUNCT
aiti-8538	51	4	the	the	DET
aiti-8538	51	5	accuracy	accuracy	NOUN
aiti-8538	51	6	of	of	ADP
aiti-8538	51	7	multilayer	multilayer	PROPN
aiti-8538	51	8	perceptron	perceptron	PROPN
aiti-8538	51	9	,	,	PUNCT
aiti-8538	51	10	svm	svm	PROPN
aiti-8538	51	11	,	,	PUNCT
aiti-8538	51	12	and	and	CCONJ
aiti-8538	51	13	rf	rf	NOUN
aiti-8538	51	14	classifiers	classifier	NOUN
aiti-8538	51	15	is	be	AUX
aiti-8538	51	16	enhanced	enhance	VERB
aiti-8538	51	17	by	by	ADP
aiti-8538	51	18	tuning	tune	VERB
aiti-8538	51	19	their	their	PRON
aiti-8538	51	20	hyperparameters	hyperparameter	NOUN
aiti-8538	51	21	using	use	VERB
aiti-8538	51	22	a	a	DET
aiti-8538	51	23	grid	grid	NOUN
aiti-8538	51	24	search	search	NOUN
aiti-8538	51	25	algorithm	algorithm	NOUN
aiti-8538	51	26	.	.	PUNCT
aiti-8538	52	1	(	(	PUNCT
aiti-8538	52	2	3	3	X
aiti-8538	52	3	)	)	PUNCT
aiti-8538	52	4	with	with	ADP
aiti-8538	52	5	the	the	DET
aiti-8538	52	6	pca	pca	PROPN
aiti-8538	52	7	reduced	reduce	VERB
aiti-8538	52	8	features	feature	NOUN
aiti-8538	52	9	,	,	PUNCT
aiti-8538	52	10	efficient	efficient	ADJ
aiti-8538	52	11	utilization	utilization	NOUN
aiti-8538	52	12	of	of	ADP
aiti-8538	52	13	computation	computation	NOUN
aiti-8538	52	14	resources	resource	NOUN
aiti-8538	52	15	is	be	AUX
aiti-8538	52	16	conducted	conduct	VERB
aiti-8538	52	17	with	with	ADP
aiti-8538	52	18	the	the	DET
aiti-8538	52	19	improved	improved	ADJ
aiti-8538	52	20	computational	computational	ADJ
aiti-8538	52	21	efficiency	efficiency	NOUN
aiti-8538	52	22	of	of	ADP
aiti-8538	52	23	model	model	NOUN
aiti-8538	52	24	training	training	NOUN
aiti-8538	52	25	and	and	CCONJ
aiti-8538	52	26	testing	testing	NOUN
aiti-8538	52	27	.	.	PUNCT
aiti-8538	53	1	this	this	DET
aiti-8538	53	2	study	study	NOUN
aiti-8538	53	3	is	be	AUX
aiti-8538	53	4	organized	organize	VERB
aiti-8538	53	5	as	as	ADP
aiti-8538	53	6	follow	follow	NOUN
aiti-8538	53	7	.	.	PUNCT
aiti-8538	54	1	section	section	NOUN
aiti-8538	54	2	2	2	NUM
aiti-8538	54	3	presents	present	NOUN
aiti-8538	54	4	literature	literature	NOUN
aiti-8538	54	5	review	review	PROPN
aiti-8538	54	6	.	.	PUNCT
aiti-8538	55	1	section	section	NOUN
aiti-8538	55	2	3	3	NUM
aiti-8538	55	3	introduces	introduce	NOUN
aiti-8538	55	4	transfer	transfer	NOUN
aiti-8538	55	5	learning	learning	NOUN
aiti-8538	55	6	,	,	PUNCT
aiti-8538	55	7	the	the	DET
aiti-8538	55	8	pca	pca	NOUN
aiti-8538	55	9	algorithm	algorithm	NOUN
aiti-8538	55	10	,	,	PUNCT
aiti-8538	55	11	and	and	CCONJ
aiti-8538	55	12	the	the	DET
aiti-8538	55	13	proposed	propose	VERB
aiti-8538	55	14	image	image	NOUN
aiti-8538	55	15	classification	classification	NOUN
aiti-8538	55	16	approach	approach	NOUN
aiti-8538	55	17	.	.	PUNCT
aiti-8538	56	1	section	section	NOUN
aiti-8538	56	2	4	4	NUM
aiti-8538	56	3	presents	present	VERB
aiti-8538	56	4	the	the	DET
aiti-8538	56	5	results	result	NOUN
aiti-8538	56	6	and	and	CCONJ
aiti-8538	56	7	discussion	discussion	NOUN
aiti-8538	56	8	.	.	PUNCT
aiti-8538	57	1	finally	finally	ADV
aiti-8538	57	2	,	,	PUNCT
aiti-8538	57	3	section	section	NOUN
aiti-8538	57	4	5	5	NUM
aiti-8538	57	5	presents	present	VERB
aiti-8538	57	6	the	the	DET
aiti-8538	57	7	conclusion	conclusion	NOUN
aiti-8538	57	8	.	.	PUNCT
aiti-8538	58	1	106	106	NUM
aiti-8538	58	2	advances	advance	NOUN
aiti-8538	58	3	in	in	ADP
aiti-8538	58	4	technology	technology	NOUN
aiti-8538	58	5	innovation	innovation	NOUN
aiti-8538	58	6	,	,	PUNCT
aiti-8538	58	7	vol	vol	NOUN
aiti-8538	58	8	.	.	PROPN
aiti-8538	58	9	7	7	NUM
aiti-8538	58	10	,	,	PUNCT
aiti-8538	58	11	no	no	INTJ
aiti-8538	58	12	.	.	NOUN
aiti-8538	58	13	2	2	NUM
aiti-8538	58	14	,	,	PUNCT
aiti-8538	58	15	2022	2022	NUM
aiti-8538	58	16	,	,	PUNCT
aiti-8538	58	17	pp	pp	ADJ
aiti-8538	58	18	.	.	PUNCT
aiti-8538	59	1	105	105	NUM
aiti-8538	59	2	-	-	SYM
aiti-8538	59	3	117	117	NUM
aiti-8538	59	4	2	2	NUM
aiti-8538	59	5	.	.	PUNCT
aiti-8538	59	6	related	relate	VERB
aiti-8538	59	7	work	work	NOUN
aiti-8538	59	8	several	several	ADJ
aiti-8538	59	9	techniques	technique	NOUN
aiti-8538	59	10	were	be	AUX
aiti-8538	59	11	proposed	propose	VERB
aiti-8538	59	12	for	for	ADP
aiti-8538	59	13	image	image	NOUN
aiti-8538	59	14	classification	classification	NOUN
aiti-8538	59	15	.	.	PUNCT
aiti-8538	60	1	kaur	kaur	PROPN
aiti-8538	60	2	et	et	PROPN
aiti-8538	60	3	al	al	PROPN
aiti-8538	60	4	.	.	PUNCT
aiti-8538	61	1	[	[	X
aiti-8538	61	2	13	13	NUM
aiti-8538	61	3	]	]	PUNCT
aiti-8538	61	4	explored	explore	VERB
aiti-8538	61	5	various	various	ADJ
aiti-8538	61	6	pretrained	pretraine	VERB
aiti-8538	61	7	cnn	cnn	PROPN
aiti-8538	61	8	models	model	NOUN
aiti-8538	61	9	for	for	ADP
aiti-8538	61	10	the	the	DET
aiti-8538	61	11	classification	classification	NOUN
aiti-8538	61	12	of	of	ADP
aiti-8538	61	13	magnetic	magnetic	ADJ
aiti-8538	61	14	resonance	resonance	NOUN
aiti-8538	61	15	brain	brain	NOUN
aiti-8538	61	16	images	image	NOUN
aiti-8538	61	17	.	.	PUNCT
aiti-8538	62	1	their	their	PRON
aiti-8538	62	2	proposed	propose	VERB
aiti-8538	62	3	pretrained	pretraine	VERB
aiti-8538	62	4	deep	deep	ADJ
aiti-8538	62	5	cnn	cnn	PROPN
aiti-8538	62	6	models	model	NOUN
aiti-8538	62	7	were	be	AUX
aiti-8538	62	8	demonstrated	demonstrate	VERB
aiti-8538	62	9	.	.	PUNCT
aiti-8538	63	1	the	the	DET
aiti-8538	63	2	authors	author	NOUN
aiti-8538	63	3	explored	explore	VERB
aiti-8538	63	4	8	8	NUM
aiti-8538	63	5	different	different	ADJ
aiti-8538	63	6	pretrained	pretraine	VERB
aiti-8538	63	7	cnn	cnn	PROPN
aiti-8538	63	8	models	model	NOUN
aiti-8538	63	9	out	out	ADP
aiti-8538	63	10	of	of	ADP
aiti-8538	63	11	which	which	PRON
aiti-8538	63	12	the	the	DET
aiti-8538	63	13	alexnet	alexnet	ADJ
aiti-8538	63	14	model	model	NOUN
aiti-8538	63	15	illustrated	illustrate	VERB
aiti-8538	63	16	best	good	ADJ
aiti-8538	63	17	classification	classification	NOUN
aiti-8538	63	18	accuracy	accuracy	NOUN
aiti-8538	63	19	.	.	PUNCT
aiti-8538	64	1	pires	pire	NOUN
aiti-8538	64	2	de	de	PROPN
aiti-8538	64	3	lima	lima	PROPN
aiti-8538	64	4	et	et	PROPN
aiti-8538	64	5	al	al	PROPN
aiti-8538	64	6	.	.	PUNCT
aiti-8538	65	1	[	[	X
aiti-8538	65	2	14	14	NUM
aiti-8538	65	3	]	]	PUNCT
aiti-8538	65	4	presented	present	VERB
aiti-8538	65	5	remote	remote	ADV
aiti-8538	65	6	-	-	PUNCT
aiti-8538	65	7	sensing	sense	VERB
aiti-8538	65	8	image	image	NOUN
aiti-8538	65	9	classification	classification	NOUN
aiti-8538	65	10	using	use	VERB
aiti-8538	65	11	transfer	transfer	NOUN
aiti-8538	65	12	learning	learning	NOUN
aiti-8538	65	13	.	.	PUNCT
aiti-8538	66	1	their	their	PRON
aiti-8538	66	2	cnn	cnn	PROPN
aiti-8538	66	3	models	model	NOUN
aiti-8538	66	4	trained	train	VERB
aiti-8538	66	5	on	on	ADP
aiti-8538	66	6	diverse	diverse	ADJ
aiti-8538	66	7	natural	natural	ADJ
aiti-8538	66	8	image	image	NOUN
aiti-8538	66	9	datasets	dataset	NOUN
aiti-8538	66	10	gave	give	VERB
aiti-8538	66	11	better	well	ADJ
aiti-8538	66	12	results	result	NOUN
aiti-8538	66	13	for	for	ADP
aiti-8538	66	14	remote	remote	ADV
aiti-8538	66	15	-	-	PUNCT
aiti-8538	66	16	sensing	sense	VERB
aiti-8538	66	17	image	image	NOUN
aiti-8538	66	18	classification	classification	NOUN
aiti-8538	66	19	tasks	task	NOUN
aiti-8538	66	20	.	.	PUNCT
aiti-8538	67	1	the	the	DET
aiti-8538	67	2	study	study	NOUN
aiti-8538	67	3	demonstrates	demonstrate	VERB
aiti-8538	67	4	that	that	SCONJ
aiti-8538	67	5	the	the	DET
aiti-8538	67	6	pretrained	pretraine	VERB
aiti-8538	67	7	model	model	NOUN
aiti-8538	67	8	trained	train	VERB
aiti-8538	67	9	on	on	ADP
aiti-8538	67	10	a	a	DET
aiti-8538	67	11	generalized	generalize	VERB
aiti-8538	67	12	dataset	dataset	NOUN
aiti-8538	67	13	can	can	AUX
aiti-8538	67	14	be	be	AUX
aiti-8538	67	15	accurately	accurately	ADV
aiti-8538	67	16	applied	apply	VERB
aiti-8538	67	17	to	to	ADP
aiti-8538	67	18	remote	remote	ADJ
aiti-8538	67	19	-	-	PUNCT
aiti-8538	67	20	sensing	sense	VERB
aiti-8538	67	21	image	image	NOUN
aiti-8538	67	22	classification	classification	NOUN
aiti-8538	67	23	.	.	PUNCT
aiti-8538	68	1	the	the	DET
aiti-8538	68	2	results	result	NOUN
aiti-8538	68	3	show	show	VERB
aiti-8538	68	4	that	that	SCONJ
aiti-8538	68	5	the	the	DET
aiti-8538	68	6	pretrained	pretraine	VERB
aiti-8538	68	7	models	model	NOUN
aiti-8538	68	8	can	can	AUX
aiti-8538	68	9	easily	easily	ADV
aiti-8538	68	10	be	be	AUX
aiti-8538	68	11	used	use	VERB
aiti-8538	68	12	for	for	ADP
aiti-8538	68	13	feature	feature	NOUN
aiti-8538	68	14	extraction	extraction	NOUN
aiti-8538	68	15	of	of	ADP
aiti-8538	68	16	the	the	DET
aiti-8538	68	17	unseen	unseen	ADJ
aiti-8538	68	18	images	image	NOUN
aiti-8538	68	19	related	relate	VERB
aiti-8538	68	20	to	to	ADP
aiti-8538	68	21	different	different	ADJ
aiti-8538	68	22	domains	domain	NOUN
aiti-8538	68	23	.	.	PUNCT
aiti-8538	69	1	xue	xue	PROPN
aiti-8538	69	2	et	et	PROPN
aiti-8538	69	3	al	al	PROPN
aiti-8538	69	4	.	.	PUNCT
aiti-8538	70	1	[	[	X
aiti-8538	70	2	15	15	NUM
aiti-8538	70	3	]	]	PUNCT
aiti-8538	70	4	presented	present	VERB
aiti-8538	70	5	an	an	DET
aiti-8538	70	6	ensemble	ensemble	ADJ
aiti-8538	70	7	learning	learning	NOUN
aiti-8538	70	8	strategy	strategy	NOUN
aiti-8538	70	9	based	base	VERB
aiti-8538	70	10	on	on	ADP
aiti-8538	70	11	inception	inception	NOUN
aiti-8538	70	12	-	-	PUNCT
aiti-8538	70	13	v3	v3	NOUN
aiti-8538	70	14	,	,	PUNCT
aiti-8538	70	15	xception	xception	PROPN
aiti-8538	70	16	,	,	PUNCT
aiti-8538	70	17	vgg16	vgg16	NOUN
aiti-8538	70	18	,	,	PUNCT
aiti-8538	70	19	and	and	CCONJ
aiti-8538	70	20	resnet-50	resnet-50	PROPN
aiti-8538	70	21	cnn	cnn	PROPN
aiti-8538	70	22	models	model	NOUN
aiti-8538	70	23	.	.	PUNCT
aiti-8538	71	1	the	the	DET
aiti-8538	71	2	ensemble	ensemble	ADJ
aiti-8538	71	3	learning	learning	NOUN
aiti-8538	71	4	technique	technique	NOUN
aiti-8538	71	5	employed	employ	VERB
aiti-8538	71	6	a	a	DET
aiti-8538	71	7	weighted	weighted	ADJ
aiti-8538	71	8	voting	voting	NOUN
aiti-8538	71	9	approach	approach	NOUN
aiti-8538	71	10	with	with	ADP
aiti-8538	71	11	an	an	DET
aiti-8538	71	12	accuracy	accuracy	NOUN
aiti-8538	71	13	of	of	ADP
aiti-8538	71	14	98.61	98.61	NUM
aiti-8538	71	15	%	%	NOUN
aiti-8538	71	16	.	.	PUNCT
aiti-8538	72	1	the	the	DET
aiti-8538	72	2	high	high	ADJ
aiti-8538	72	3	computational	computational	ADJ
aiti-8538	72	4	requirements	requirement	NOUN
aiti-8538	72	5	of	of	ADP
aiti-8538	72	6	the	the	DET
aiti-8538	72	7	classifier	classifier	NOUN
aiti-8538	72	8	which	which	PRON
aiti-8538	72	9	used	use	VERB
aiti-8538	72	10	four	four	NUM
aiti-8538	72	11	pretrained	pretraine	VERB
aiti-8538	72	12	models	model	NOUN
aiti-8538	72	13	as	as	ADP
aiti-8538	72	14	base	base	NOUN
aiti-8538	72	15	learners	learner	NOUN
aiti-8538	72	16	are	be	AUX
aiti-8538	72	17	one	one	NUM
aiti-8538	72	18	of	of	ADP
aiti-8538	72	19	the	the	DET
aiti-8538	72	20	limitations	limitation	NOUN
aiti-8538	72	21	of	of	ADP
aiti-8538	72	22	their	their	PRON
aiti-8538	72	23	proposed	propose	VERB
aiti-8538	72	24	approach	approach	NOUN
aiti-8538	72	25	.	.	PUNCT
aiti-8538	73	1	garcia	garcia	PROPN
aiti-8538	73	2	-	-	PUNCT
aiti-8538	73	3	dominguez	dominguez	PROPN
aiti-8538	73	4	et	et	PROPN
aiti-8538	73	5	al	al	PROPN
aiti-8538	73	6	.	.	PUNCT
aiti-8538	74	1	[	[	X
aiti-8538	74	2	16	16	NUM
aiti-8538	74	3	]	]	PUNCT
aiti-8538	74	4	proposed	propose	VERB
aiti-8538	74	5	an	an	DET
aiti-8538	74	6	automatic	automatic	ADJ
aiti-8538	74	7	optimum	optimum	ADJ
aiti-8538	74	8	image	image	NOUN
aiti-8538	74	9	feature	feature	NOUN
aiti-8538	74	10	selector	selector	NOUN
aiti-8538	74	11	and	and	CCONJ
aiti-8538	74	12	classification	classification	NOUN
aiti-8538	74	13	open	open	ADJ
aiti-8538	74	14	-	-	PUNCT
aiti-8538	74	15	source	source	NOUN
aiti-8538	74	16	tool	tool	NOUN
aiti-8538	74	17	“	"	PUNCT
aiti-8538	74	18	frimcla	frimcla	NOUN
aiti-8538	74	19	”	"	PUNCT
aiti-8538	74	20	[	[	X
aiti-8538	74	21	16	16	NUM
aiti-8538	74	22	]	]	PUNCT
aiti-8538	74	23	.	.	PUNCT
aiti-8538	75	1	the	the	DET
aiti-8538	75	2	tool	tool	NOUN
aiti-8538	75	3	based	base	VERB
aiti-8538	75	4	upon	upon	SCONJ
aiti-8538	75	5	the	the	DET
aiti-8538	75	6	statistical	statistical	ADJ
aiti-8538	75	7	study	study	NOUN
aiti-8538	75	8	automatically	automatically	ADV
aiti-8538	75	9	selects	select	VERB
aiti-8538	75	10	the	the	DET
aiti-8538	75	11	feature	feature	NOUN
aiti-8538	75	12	extraction	extraction	NOUN
aiti-8538	75	13	and	and	CCONJ
aiti-8538	75	14	classification	classification	NOUN
aiti-8538	75	15	techniques	technique	NOUN
aiti-8538	75	16	.	.	PUNCT
aiti-8538	76	1	for	for	ADP
aiti-8538	76	2	feature	feature	NOUN
aiti-8538	76	3	selection	selection	NOUN
aiti-8538	76	4	,	,	PUNCT
aiti-8538	76	5	the	the	DET
aiti-8538	76	6	pretrained	pretraine	VERB
aiti-8538	76	7	models	model	NOUN
aiti-8538	76	8	as	as	ADV
aiti-8538	76	9	well	well	ADV
aiti-8538	76	10	as	as	ADP
aiti-8538	76	11	traditional	traditional	ADJ
aiti-8538	76	12	methods	method	NOUN
aiti-8538	76	13	are	be	AUX
aiti-8538	76	14	explored	explore	VERB
aiti-8538	76	15	.	.	PUNCT
aiti-8538	77	1	transfer	transfer	NOUN
aiti-8538	77	2	learning	learn	VERB
aiti-8538	77	3	techniques	technique	NOUN
aiti-8538	77	4	are	be	AUX
aiti-8538	77	5	popularly	popularly	ADV
aiti-8538	77	6	used	use	VERB
aiti-8538	77	7	for	for	ADP
aiti-8538	77	8	feature	feature	NOUN
aiti-8538	77	9	selection	selection	NOUN
aiti-8538	77	10	in	in	ADP
aiti-8538	77	11	computer	computer	NOUN
aiti-8538	77	12	vision	vision	NOUN
aiti-8538	77	13	.	.	PUNCT
aiti-8538	78	1	sert	sert	NOUN
aiti-8538	78	2	and	and	CCONJ
aiti-8538	78	3	boyacı	boyacı	NOUN
aiti-8538	78	4	[	[	X
aiti-8538	78	5	17	17	NUM
aiti-8538	78	6	]	]	PUNCT
aiti-8538	78	7	presented	present	VERB
aiti-8538	78	8	a	a	DET
aiti-8538	78	9	feature	feature	NOUN
aiti-8538	78	10	fusion	fusion	NOUN
aiti-8538	78	11	transfer	transfer	NOUN
aiti-8538	78	12	learning	learning	NOUN
aiti-8538	78	13	-	-	PUNCT
aiti-8538	78	14	based	base	VERB
aiti-8538	78	15	approach	approach	NOUN
aiti-8538	78	16	for	for	ADP
aiti-8538	78	17	the	the	DET
aiti-8538	78	18	recognition	recognition	NOUN
aiti-8538	78	19	of	of	ADP
aiti-8538	78	20	sketch	sketch	ADJ
aiti-8538	78	21	drawings	drawing	NOUN
aiti-8538	78	22	.	.	PUNCT
aiti-8538	79	1	for	for	ADP
aiti-8538	79	2	improving	improve	VERB
aiti-8538	79	3	the	the	DET
aiti-8538	79	4	efficiency	efficiency	NOUN
aiti-8538	79	5	of	of	ADP
aiti-8538	79	6	feature	feature	NOUN
aiti-8538	79	7	reduction	reduction	NOUN
aiti-8538	79	8	,	,	PUNCT
aiti-8538	79	9	pca	pca	PROPN
aiti-8538	79	10	was	be	AUX
aiti-8538	79	11	proposed	propose	VERB
aiti-8538	79	12	.	.	PUNCT
aiti-8538	80	1	the	the	DET
aiti-8538	80	2	selected	select	VERB
aiti-8538	80	3	features	feature	NOUN
aiti-8538	80	4	were	be	AUX
aiti-8538	80	5	input	input	NOUN
aiti-8538	80	6	to	to	PART
aiti-8538	80	7	svm	svm	VERB
aiti-8538	80	8	for	for	ADP
aiti-8538	80	9	classification	classification	NOUN
aiti-8538	80	10	.	.	PUNCT
aiti-8538	81	1	the	the	DET
aiti-8538	81	2	proposed	propose	VERB
aiti-8538	81	3	approach	approach	NOUN
aiti-8538	81	4	was	be	AUX
aiti-8538	81	5	able	able	ADJ
aiti-8538	81	6	to	to	PART
aiti-8538	81	7	classify	classify	VERB
aiti-8538	81	8	freehand	freehand	NOUN
aiti-8538	81	9	sketches	sketch	NOUN
aiti-8538	81	10	of	of	ADP
aiti-8538	81	11	the	the	DET
aiti-8538	81	12	sketchy	sketchy	ADJ
aiti-8538	81	13	dataset	dataset	NOUN
aiti-8538	81	14	with	with	ADP
aiti-8538	81	15	an	an	DET
aiti-8538	81	16	accuracy	accuracy	NOUN
aiti-8538	81	17	of	of	ADP
aiti-8538	81	18	97.91	97.91	NUM
aiti-8538	81	19	%	%	NOUN
aiti-8538	81	20	.	.	PUNCT
aiti-8538	82	1	chen	chen	PROPN
aiti-8538	82	2	et	et	PROPN
aiti-8538	82	3	al	al	PROPN
aiti-8538	82	4	.	.	PUNCT
aiti-8538	83	1	[	[	X
aiti-8538	83	2	18	18	NUM
aiti-8538	83	3	]	]	PUNCT
aiti-8538	83	4	proposed	propose	VERB
aiti-8538	83	5	an	an	DET
aiti-8538	83	6	automated	automate	VERB
aiti-8538	83	7	robot	robot	NOUN
aiti-8538	83	8	arm	arm	PROPN
aiti-8538	83	9	control	control	PROPN
aiti-8538	83	10	program	program	NOUN
aiti-8538	83	11	enabling	enable	VERB
aiti-8538	83	12	human	human	ADJ
aiti-8538	83	13	-	-	PUNCT
aiti-8538	83	14	robot	robot	NOUN
aiti-8538	83	15	interaction	interaction	NOUN
aiti-8538	83	16	using	use	VERB
aiti-8538	83	17	the	the	DET
aiti-8538	83	18	yolov4	yolov4	PROPN
aiti-8538	83	19	algorithm	algorithm	NOUN
aiti-8538	83	20	.	.	PUNCT
aiti-8538	84	1	eight	eight	NUM
aiti-8538	84	2	different	different	ADJ
aiti-8538	84	3	hand	hand	NOUN
aiti-8538	84	4	gestures	gesture	NOUN
aiti-8538	84	5	were	be	AUX
aiti-8538	84	6	recorded	record	VERB
aiti-8538	84	7	in	in	ADP
aiti-8538	84	8	a	a	DET
aiti-8538	84	9	controlled	control	VERB
aiti-8538	84	10	laboratory	laboratory	NOUN
aiti-8538	84	11	environment	environment	NOUN
aiti-8538	84	12	and	and	CCONJ
aiti-8538	84	13	image	image	NOUN
aiti-8538	84	14	features	feature	NOUN
aiti-8538	84	15	were	be	AUX
aiti-8538	84	16	classified	classify	VERB
aiti-8538	84	17	by	by	ADP
aiti-8538	84	18	employing	employ	VERB
aiti-8538	84	19	a	a	DET
aiti-8538	84	20	deep	deep	ADJ
aiti-8538	84	21	cnn	cnn	PROPN
aiti-8538	84	22	.	.	PUNCT
aiti-8538	85	1	the	the	DET
aiti-8538	85	2	proposed	propose	VERB
aiti-8538	85	3	approach	approach	NOUN
aiti-8538	85	4	classified	classify	VERB
aiti-8538	85	5	the	the	DET
aiti-8538	85	6	images	image	NOUN
aiti-8538	85	7	accurately	accurately	ADV
aiti-8538	85	8	and	and	CCONJ
aiti-8538	85	9	the	the	DET
aiti-8538	85	10	recognized	recognize	VERB
aiti-8538	85	11	hand	hand	NOUN
aiti-8538	85	12	gestures	gesture	NOUN
aiti-8538	85	13	were	be	AUX
aiti-8538	85	14	used	use	VERB
aiti-8538	85	15	to	to	PART
aiti-8538	85	16	control	control	VERB
aiti-8538	85	17	robot	robot	NOUN
aiti-8538	85	18	arm	arm	NOUN
aiti-8538	85	19	movement	movement	NOUN
aiti-8538	85	20	.	.	PUNCT
aiti-8538	86	1	khan	khan	PROPN
aiti-8538	86	2	et	et	PROPN
aiti-8538	86	3	al	al	PROPN
aiti-8538	86	4	.	.	PUNCT
aiti-8538	87	1	[	[	X
aiti-8538	87	2	19	19	NUM
aiti-8538	87	3	]	]	PUNCT
aiti-8538	87	4	presented	present	VERB
aiti-8538	87	5	an	an	DET
aiti-8538	87	6	improved	improved	ADJ
aiti-8538	87	7	saliency	saliency	NOUN
aiti-8538	87	8	-	-	PUNCT
aiti-8538	87	9	based	base	VERB
aiti-8538	87	10	segmentation	segmentation	NOUN
aiti-8538	87	11	technique	technique	NOUN
aiti-8538	87	12	for	for	ADP
aiti-8538	87	13	the	the	DET
aiti-8538	87	14	extraction	extraction	NOUN
aiti-8538	87	15	of	of	ADP
aiti-8538	87	16	the	the	DET
aiti-8538	87	17	infected	infected	ADJ
aiti-8538	87	18	area	area	NOUN
aiti-8538	87	19	of	of	ADP
aiti-8538	87	20	cucumber	cucumber	NOUN
aiti-8538	87	21	leaves	leave	NOUN
aiti-8538	87	22	.	.	PUNCT
aiti-8538	88	1	the	the	DET
aiti-8538	88	2	deep	deep	ADJ
aiti-8538	88	3	neural	neural	ADJ
aiti-8538	88	4	networks	network	NOUN
aiti-8538	88	5	vgg	vgg	NOUN
aiti-8538	88	6	-	-	PUNCT
aiti-8538	88	7	vd-19	vd-19	NUM
aiti-8538	88	8	and	and	CCONJ
aiti-8538	88	9	vgg	vgg	PROPN
aiti-8538	88	10	-	-	PUNCT
aiti-8538	88	11	s	s	PROPN
aiti-8538	88	12	,	,	PUNCT
aiti-8538	88	13	m	m	PROPN
aiti-8538	88	14	,	,	PUNCT
aiti-8538	88	15	f	f	PROPN
aiti-8538	88	16	were	be	AUX
aiti-8538	88	17	employed	employ	VERB
aiti-8538	88	18	for	for	ADP
aiti-8538	88	19	the	the	DET
aiti-8538	88	20	extraction	extraction	NOUN
aiti-8538	88	21	of	of	ADP
aiti-8538	88	22	useful	useful	ADJ
aiti-8538	88	23	features	feature	NOUN
aiti-8538	88	24	.	.	PUNCT
aiti-8538	89	1	subsequently	subsequently	ADV
aiti-8538	89	2	,	,	PUNCT
aiti-8538	89	3	the	the	DET
aiti-8538	89	4	features	feature	NOUN
aiti-8538	89	5	obtained	obtain	VERB
aiti-8538	89	6	were	be	AUX
aiti-8538	89	7	fused	fuse	VERB
aiti-8538	89	8	and	and	CCONJ
aiti-8538	89	9	were	be	AUX
aiti-8538	89	10	used	use	VERB
aiti-8538	89	11	for	for	ADP
aiti-8538	89	12	image	image	NOUN
aiti-8538	89	13	classification	classification	NOUN
aiti-8538	89	14	by	by	ADP
aiti-8538	89	15	svm	svm	PROPN
aiti-8538	89	16	with	with	ADP
aiti-8538	89	17	an	an	DET
aiti-8538	89	18	accuracy	accuracy	NOUN
aiti-8538	89	19	of	of	ADP
aiti-8538	89	20	98.08	98.08	NUM
aiti-8538	89	21	%	%	NOUN
aiti-8538	89	22	.	.	PUNCT
aiti-8538	90	1	kaur	kaur	PROPN
aiti-8538	90	2	et	et	PROPN
aiti-8538	90	3	al	al	PROPN
aiti-8538	90	4	.	.	PUNCT
aiti-8538	91	1	[	[	X
aiti-8538	91	2	20	20	NUM
aiti-8538	91	3	]	]	PUNCT
aiti-8538	91	4	employed	employ	VERB
aiti-8538	91	5	the	the	DET
aiti-8538	91	6	pretrained	pretraine	VERB
aiti-8538	91	7	model	model	NOUN
aiti-8538	91	8	alexnet	alexnet	PROPN
aiti-8538	91	9	to	to	PART
aiti-8538	91	10	identify	identify	VERB
aiti-8538	91	11	the	the	DET
aiti-8538	91	12	patients	patient	NOUN
aiti-8538	91	13	with	with	ADP
aiti-8538	91	14	parkinson	parkinson	NOUN
aiti-8538	91	15	disease	disease	NOUN
aiti-8538	91	16	.	.	PUNCT
aiti-8538	92	1	the	the	DET
aiti-8538	92	2	final	final	ADJ
aiti-8538	92	3	layers	layer	NOUN
aiti-8538	92	4	of	of	ADP
aiti-8538	92	5	the	the	DET
aiti-8538	92	6	model	model	NOUN
aiti-8538	92	7	were	be	AUX
aiti-8538	92	8	modified	modify	VERB
aiti-8538	92	9	and	and	CCONJ
aiti-8538	92	10	the	the	DET
aiti-8538	92	11	transfer	transfer	NOUN
aiti-8538	92	12	learning	learning	NOUN
aiti-8538	92	13	-	-	PUNCT
aiti-8538	92	14	based	base	VERB
aiti-8538	92	15	model	model	NOUN
aiti-8538	92	16	was	be	AUX
aiti-8538	92	17	able	able	ADJ
aiti-8538	92	18	to	to	PART
aiti-8538	92	19	diagnose	diagnose	VERB
aiti-8538	92	20	the	the	DET
aiti-8538	92	21	disease	disease	NOUN
aiti-8538	92	22	with	with	ADP
aiti-8538	92	23	an	an	DET
aiti-8538	92	24	accuracy	accuracy	NOUN
aiti-8538	92	25	of	of	ADP
aiti-8538	92	26	89.23	89.23	NUM
aiti-8538	92	27	%	%	NOUN
aiti-8538	92	28	.	.	PUNCT
aiti-8538	93	1	li	li	PROPN
aiti-8538	93	2	et	et	PROPN
aiti-8538	93	3	al	al	PROPN
aiti-8538	93	4	.	.	PUNCT
aiti-8538	94	1	[	[	X
aiti-8538	94	2	21	21	NUM
aiti-8538	94	3	]	]	PUNCT
aiti-8538	94	4	presented	present	VERB
aiti-8538	94	5	a	a	DET
aiti-8538	94	6	shallow	shallow	ADJ
aiti-8538	94	7	vgg16	vgg16	NOUN
aiti-8538	94	8	neural	neural	ADJ
aiti-8538	94	9	network	network	NOUN
aiti-8538	94	10	-	-	PUNCT
aiti-8538	94	11	based	base	VERB
aiti-8538	94	12	approach	approach	NOUN
aiti-8538	94	13	for	for	ADP
aiti-8538	94	14	plant	plant	NOUN
aiti-8538	94	15	disease	disease	NOUN
aiti-8538	94	16	detection	detection	NOUN
aiti-8538	94	17	.	.	PUNCT
aiti-8538	95	1	the	the	DET
aiti-8538	95	2	block	block	NOUN
aiti-8538	95	3	of	of	ADP
aiti-8538	95	4	the	the	DET
aiti-8538	95	5	vgg16	vgg16	NOUN
aiti-8538	95	6	pretrained	pretraine	VERB
aiti-8538	95	7	model	model	NOUN
aiti-8538	95	8	was	be	AUX
aiti-8538	95	9	used	use	VERB
aiti-8538	95	10	for	for	ADP
aiti-8538	95	11	the	the	DET
aiti-8538	95	12	extraction	extraction	NOUN
aiti-8538	95	13	of	of	ADP
aiti-8538	95	14	prominent	prominent	ADJ
aiti-8538	95	15	image	image	NOUN
aiti-8538	95	16	features	feature	NOUN
aiti-8538	95	17	.	.	PUNCT
aiti-8538	96	1	the	the	DET
aiti-8538	96	2	pca	pca	PROPN
aiti-8538	96	3	reduced	reduce	VERB
aiti-8538	96	4	features	feature	NOUN
aiti-8538	96	5	were	be	AUX
aiti-8538	96	6	input	input	NOUN
aiti-8538	96	7	to	to	PART
aiti-8538	96	8	svm	svm	VERB
aiti-8538	96	9	or	or	CCONJ
aiti-8538	96	10	rf	rf	ADJ
aiti-8538	96	11	algorithms	algorithm	NOUN
aiti-8538	96	12	.	.	PUNCT
aiti-8538	97	1	experimental	experimental	ADJ
aiti-8538	97	2	results	result	NOUN
aiti-8538	97	3	illustrate	illustrate	VERB
aiti-8538	97	4	that	that	SCONJ
aiti-8538	97	5	the	the	DET
aiti-8538	97	6	simple	simple	ADJ
aiti-8538	97	7	shallow	shallow	ADJ
aiti-8538	97	8	neural	neural	ADJ
aiti-8538	97	9	network	network	NOUN
aiti-8538	97	10	and	and	CCONJ
aiti-8538	97	11	statistical	statistical	ADJ
aiti-8538	97	12	machine	machine	NOUN
aiti-8538	97	13	learning	learn	VERB
aiti-8538	97	14	algorithms	algorithm	NOUN
aiti-8538	97	15	are	be	AUX
aiti-8538	97	16	very	very	ADV
aiti-8538	97	17	useful	useful	ADJ
aiti-8538	97	18	for	for	ADP
aiti-8538	97	19	plant	plant	NOUN
aiti-8538	97	20	disease	disease	NOUN
aiti-8538	97	21	detection	detection	NOUN
aiti-8538	97	22	.	.	PUNCT
aiti-8538	98	1	sujatha	sujatha	PROPN
aiti-8538	98	2	et	et	PROPN
aiti-8538	98	3	al	al	PROPN
aiti-8538	98	4	.	.	PUNCT
aiti-8538	99	1	[	[	X
aiti-8538	99	2	22	22	NUM
aiti-8538	99	3	]	]	PUNCT
aiti-8538	99	4	presented	present	VERB
aiti-8538	99	5	the	the	DET
aiti-8538	99	6	analysis	analysis	NOUN
aiti-8538	99	7	of	of	ADP
aiti-8538	99	8	different	different	ADJ
aiti-8538	99	9	machine	machine	NOUN
aiti-8538	99	10	learning	learning	NOUN
aiti-8538	99	11	and	and	CCONJ
aiti-8538	99	12	pretrained	pretraine	VERB
aiti-8538	99	13	deep	deep	ADJ
aiti-8538	99	14	learning	learning	NOUN
aiti-8538	99	15	algorithms	algorithm	NOUN
aiti-8538	99	16	for	for	ADP
aiti-8538	99	17	the	the	DET
aiti-8538	99	18	classification	classification	NOUN
aiti-8538	99	19	of	of	ADP
aiti-8538	99	20	diagnosis	diagnosis	NOUN
aiti-8538	99	21	of	of	ADP
aiti-8538	99	22	citrus	citrus	NOUN
aiti-8538	99	23	plant	plant	NOUN
aiti-8538	99	24	diseases	disease	NOUN
aiti-8538	99	25	.	.	PUNCT
aiti-8538	100	1	the	the	DET
aiti-8538	100	2	experimental	experimental	ADJ
aiti-8538	100	3	work	work	NOUN
aiti-8538	100	4	shows	show	VERB
aiti-8538	100	5	that	that	SCONJ
aiti-8538	100	6	deep	deep	ADJ
aiti-8538	100	7	learning	learning	NOUN
aiti-8538	100	8	algorithms	algorithm	NOUN
aiti-8538	100	9	performed	perform	VERB
aiti-8538	100	10	better	well	ADV
aiti-8538	100	11	than	than	ADP
aiti-8538	100	12	machine	machine	NOUN
aiti-8538	100	13	learning	learn	VERB
aiti-8538	100	14	algorithms	algorithm	NOUN
aiti-8538	100	15	for	for	ADP
aiti-8538	100	16	disease	disease	NOUN
aiti-8538	100	17	classification	classification	NOUN
aiti-8538	100	18	.	.	PUNCT
aiti-8538	101	1	the	the	DET
aiti-8538	101	2	vgg16	vgg16	PROPN
aiti-8538	101	3	model	model	NOUN
aiti-8538	101	4	outperformed	outperform	VERB
aiti-8538	101	5	other	other	ADJ
aiti-8538	101	6	classifiers	classifier	NOUN
aiti-8538	101	7	(	(	PUNCT
aiti-8538	101	8	vgg19	vgg19	NOUN
aiti-8538	101	9	,	,	PUNCT
aiti-8538	101	10	inception	inception	NOUN
aiti-8538	101	11	-	-	PUNCT
aiti-8538	101	12	v3	v3	NOUN
aiti-8538	101	13	,	,	PUNCT
aiti-8538	101	14	rf	rf	NOUN
aiti-8538	101	15	,	,	PUNCT
aiti-8538	101	16	stochastic	stochastic	ADJ
aiti-8538	101	17	gradient	gradient	ADJ
aiti-8538	101	18	descent	descent	NOUN
aiti-8538	101	19	,	,	PUNCT
aiti-8538	101	20	and	and	CCONJ
aiti-8538	101	21	svm	svm	ADJ
aiti-8538	101	22	)	)	PUNCT
aiti-8538	101	23	.	.	PUNCT
aiti-8538	102	1	behera	behera	PROPN
aiti-8538	102	2	et	et	PROPN
aiti-8538	102	3	al	al	PROPN
aiti-8538	102	4	.	.	PUNCT
aiti-8538	103	1	[	[	X
aiti-8538	103	2	23	23	NUM
aiti-8538	103	3	]	]	PUNCT
aiti-8538	103	4	presented	present	VERB
aiti-8538	103	5	papaya	papaya	NOUN
aiti-8538	103	6	maturity	maturity	NOUN
aiti-8538	103	7	stage	stage	NOUN
aiti-8538	103	8	prediction	prediction	NOUN
aiti-8538	103	9	models	model	NOUN
aiti-8538	103	10	using	use	VERB
aiti-8538	103	11	k	k	NOUN
aiti-8538	103	12	-	-	PUNCT
aiti-8538	103	13	nearest	near	ADJ
aiti-8538	103	14	neighbor	neighbor	NOUN
aiti-8538	103	15	(	(	PUNCT
aiti-8538	103	16	knn	knn	PROPN
aiti-8538	103	17	)	)	PUNCT
aiti-8538	103	18	,	,	PUNCT
aiti-8538	103	19	svm	svm	PROPN
aiti-8538	103	20	,	,	PUNCT
aiti-8538	103	21	naïve	naïve	ADJ
aiti-8538	103	22	bayes	bayes	NOUN
aiti-8538	103	23	,	,	PUNCT
aiti-8538	103	24	and	and	CCONJ
aiti-8538	103	25	deep	deep	ADJ
aiti-8538	103	26	learning	learn	VERB
aiti-8538	103	27	neural	neural	ADJ
aiti-8538	103	28	networks	network	NOUN
aiti-8538	103	29	(	(	PUNCT
aiti-8538	103	30	alexnet	alexnet	ADJ
aiti-8538	103	31	,	,	PUNCT
aiti-8538	103	32	vgg16	vgg16	PROPN
aiti-8538	103	33	,	,	PUNCT
aiti-8538	103	34	vgg19	vgg19	PROPN
aiti-8538	103	35	,	,	PUNCT
aiti-8538	103	36	googlenet	googlenet	NOUN
aiti-8538	103	37	,	,	PUNCT
aiti-8538	103	38	resnet50	resnet50	NOUN
aiti-8538	103	39	,	,	PUNCT
aiti-8538	103	40	etc	etc	X
aiti-8538	103	41	)	)	PUNCT
aiti-8538	103	42	.	.	PUNCT
aiti-8538	104	1	both	both	CCONJ
aiti-8538	104	2	the	the	DET
aiti-8538	104	3	vgg16	vgg16	PROPN
aiti-8538	104	4	and	and	CCONJ
aiti-8538	104	5	vgg19	vgg19	PROPN
aiti-8538	104	6	models	model	NOUN
aiti-8538	104	7	were	be	AUX
aiti-8538	104	8	able	able	ADJ
aiti-8538	104	9	to	to	PART
aiti-8538	104	10	classify	classify	VERB
aiti-8538	104	11	papaya	papaya	NOUN
aiti-8538	104	12	images	image	NOUN
aiti-8538	104	13	with	with	ADP
aiti-8538	104	14	an	an	DET
aiti-8538	104	15	accuracy	accuracy	NOUN
aiti-8538	104	16	of	of	ADP
aiti-8538	104	17	100	100	NUM
aiti-8538	104	18	%	%	NOUN
aiti-8538	104	19	.	.	PUNCT
aiti-8538	105	1	the	the	DET
aiti-8538	105	2	vgg19	vgg19	PROPN
aiti-8538	105	3	model	model	NOUN
aiti-8538	105	4	achieved	achieve	VERB
aiti-8538	105	5	100	100	NUM
aiti-8538	105	6	%	%	NOUN
aiti-8538	105	7	training	training	NOUN
aiti-8538	105	8	accuracy	accuracy	NOUN
aiti-8538	105	9	in	in	ADP
aiti-8538	105	10	10	10	NUM
aiti-8538	105	11	epochs	epoch	NOUN
aiti-8538	105	12	.	.	PUNCT
aiti-8538	106	1	chiu	chiu	PROPN
aiti-8538	106	2	et	et	PROPN
aiti-8538	106	3	al	al	PROPN
aiti-8538	106	4	.	.	PUNCT
aiti-8538	107	1	[	[	X
aiti-8538	107	2	24	24	NUM
aiti-8538	107	3	]	]	PUNCT
aiti-8538	107	4	proposed	propose	VERB
aiti-8538	107	5	an	an	DET
aiti-8538	107	6	efficient	efficient	ADJ
aiti-8538	107	7	method	method	NOUN
aiti-8538	107	8	for	for	ADP
aiti-8538	107	9	predicting	predict	VERB
aiti-8538	107	10	breast	breast	NOUN
aiti-8538	107	11	cancer	cancer	NOUN
aiti-8538	107	12	on	on	ADP
aiti-8538	107	13	the	the	DET
aiti-8538	107	14	dataset	dataset	NOUN
aiti-8538	107	15	obtained	obtain	VERB
aiti-8538	107	16	from	from	ADP
aiti-8538	107	17	university	university	NOUN
aiti-8538	107	18	hospital	hospital	NOUN
aiti-8538	107	19	care	care	NOUN
aiti-8538	107	20	of	of	ADP
aiti-8538	107	21	coimbra	coimbra	PROPN
aiti-8538	107	22	.	.	PUNCT
aiti-8538	108	1	for	for	ADP
aiti-8538	108	2	reducing	reduce	VERB
aiti-8538	108	3	the	the	DET
aiti-8538	108	4	dimensionality	dimensionality	NOUN
aiti-8538	108	5	of	of	ADP
aiti-8538	108	6	the	the	DET
aiti-8538	108	7	dataset	dataset	NOUN
aiti-8538	108	8	,	,	PUNCT
aiti-8538	108	9	the	the	DET
aiti-8538	108	10	pca	pca	PROPN
aiti-8538	108	11	algorithm	algorithm	NOUN
aiti-8538	108	12	was	be	AUX
aiti-8538	108	13	used	use	VERB
aiti-8538	108	14	.	.	PUNCT
aiti-8538	109	1	the	the	DET
aiti-8538	109	2	features	feature	NOUN
aiti-8538	109	3	were	be	AUX
aiti-8538	109	4	extracted	extract	VERB
aiti-8538	109	5	by	by	ADP
aiti-8538	109	6	using	use	VERB
aiti-8538	109	7	a	a	DET
aiti-8538	109	8	transfer	transfer	NOUN
aiti-8538	109	9	learning	learn	VERB
aiti-8538	109	10	neural	neural	ADJ
aiti-8538	109	11	network	network	NOUN
aiti-8538	109	12	and	and	CCONJ
aiti-8538	109	13	107	107	NUM
aiti-8538	109	14	advances	advance	NOUN
aiti-8538	109	15	in	in	ADP
aiti-8538	109	16	technology	technology	NOUN
aiti-8538	109	17	innovation	innovation	NOUN
aiti-8538	109	18	,	,	PUNCT
aiti-8538	109	19	vol	vol	NOUN
aiti-8538	109	20	.	.	PROPN
aiti-8538	109	21	7	7	NUM
aiti-8538	109	22	,	,	PUNCT
aiti-8538	109	23	no	no	INTJ
aiti-8538	109	24	.	.	NOUN
aiti-8538	109	25	2	2	NUM
aiti-8538	109	26	,	,	PUNCT
aiti-8538	109	27	2022	2022	NUM
aiti-8538	109	28	,	,	PUNCT
aiti-8538	109	29	pp	pp	ADJ
aiti-8538	109	30	.	.	PUNCT
aiti-8538	110	1	105	105	NUM
aiti-8538	110	2	-	-	SYM
aiti-8538	110	3	117	117	NUM
aiti-8538	110	4	classification	classification	NOUN
aiti-8538	110	5	by	by	ADP
aiti-8538	110	6	svm	svm	PROPN
aiti-8538	110	7	.	.	PROPN
aiti-8538	111	1	loey	loey	PROPN
aiti-8538	111	2	et	et	PROPN
aiti-8538	111	3	al	al	PROPN
aiti-8538	111	4	.	.	PUNCT
aiti-8538	112	1	[	[	X
aiti-8538	112	2	25	25	NUM
aiti-8538	112	3	]	]	PUNCT
aiti-8538	112	4	presented	present	VERB
aiti-8538	112	5	a	a	DET
aiti-8538	112	6	hybrid	hybrid	ADJ
aiti-8538	112	7	approach	approach	NOUN
aiti-8538	112	8	employing	employ	VERB
aiti-8538	112	9	the	the	DET
aiti-8538	112	10	pretrained	pretraine	VERB
aiti-8538	112	11	cnn	cnn	PROPN
aiti-8538	112	12	model	model	PROPN
aiti-8538	112	13	resnet50	resnet50	PROPN
aiti-8538	112	14	for	for	ADP
aiti-8538	112	15	feature	feature	NOUN
aiti-8538	112	16	extraction	extraction	NOUN
aiti-8538	112	17	of	of	ADP
aiti-8538	112	18	the	the	DET
aiti-8538	112	19	images	image	NOUN
aiti-8538	112	20	of	of	ADP
aiti-8538	112	21	real	real	ADJ
aiti-8538	112	22	-	-	PUNCT
aiti-8538	112	23	world	world	NOUN
aiti-8538	112	24	masked	mask	VERB
aiti-8538	112	25	face	face	NOUN
aiti-8538	112	26	dataset	dataset	NOUN
aiti-8538	112	27	(	(	PUNCT
aiti-8538	112	28	rmfd	rmfd	NOUN
aiti-8538	112	29	)	)	PUNCT
aiti-8538	112	30	,	,	PUNCT
aiti-8538	112	31	simulated	simulate	VERB
aiti-8538	112	32	masked	mask	VERB
aiti-8538	112	33	face	face	NOUN
aiti-8538	112	34	dataset	dataset	NOUN
aiti-8538	112	35	(	(	PUNCT
aiti-8538	112	36	smfd	smfd	NOUN
aiti-8538	112	37	)	)	PUNCT
aiti-8538	112	38	,	,	PUNCT
aiti-8538	112	39	and	and	CCONJ
aiti-8538	112	40	labelled	label	VERB
aiti-8538	112	41	faces	face	NOUN
aiti-8538	112	42	in	in	ADP
aiti-8538	112	43	the	the	DET
aiti-8538	112	44	wild	wild	ADJ
aiti-8538	112	45	(	(	PUNCT
aiti-8538	112	46	lfw	lfw	NOUN
aiti-8538	112	47	)	)	PUNCT
aiti-8538	112	48	dataset	dataset	NOUN
aiti-8538	112	49	.	.	PUNCT
aiti-8538	113	1	the	the	DET
aiti-8538	113	2	extracted	extract	VERB
aiti-8538	113	3	features	feature	NOUN
aiti-8538	113	4	were	be	AUX
aiti-8538	113	5	classified	classify	VERB
aiti-8538	113	6	by	by	ADP
aiti-8538	113	7	svm	svm	ADJ
aiti-8538	113	8	decision	decision	NOUN
aiti-8538	113	9	tree	tree	NOUN
aiti-8538	113	10	and	and	CCONJ
aiti-8538	113	11	ensemble	ensemble	ADJ
aiti-8538	113	12	learning	learning	NOUN
aiti-8538	113	13	techniques	technique	NOUN
aiti-8538	113	14	.	.	PUNCT
aiti-8538	114	1	using	use	VERB
aiti-8538	114	2	the	the	DET
aiti-8538	114	3	proposed	propose	VERB
aiti-8538	114	4	approach	approach	NOUN
aiti-8538	114	5	,	,	PUNCT
aiti-8538	114	6	the	the	DET
aiti-8538	114	7	svm	svm	ADJ
aiti-8538	114	8	model	model	NOUN
aiti-8538	114	9	classified	classify	VERB
aiti-8538	114	10	rmfd	rmfd	NOUN
aiti-8538	114	11	with	with	ADP
aiti-8538	114	12	99.64	99.64	NUM
aiti-8538	114	13	%	%	NOUN
aiti-8538	114	14	,	,	PUNCT
aiti-8538	114	15	smfd	smfd	ADJ
aiti-8538	114	16	with	with	ADP
aiti-8538	114	17	99.49	99.49	NUM
aiti-8538	114	18	%	%	NOUN
aiti-8538	114	19	,	,	PUNCT
aiti-8538	114	20	and	and	CCONJ
aiti-8538	114	21	lwf	lwf	VERB
aiti-8538	114	22	with	with	ADP
aiti-8538	114	23	100	100	NUM
aiti-8538	114	24	%	%	NOUN
aiti-8538	114	25	accuracy	accuracy	NOUN
aiti-8538	114	26	.	.	PUNCT
aiti-8538	115	1	bhagwat	bhagwat	NOUN
aiti-8538	115	2	et	et	PROPN
aiti-8538	115	3	al	al	PROPN
aiti-8538	115	4	.	.	PUNCT
aiti-8538	116	1	[	[	X
aiti-8538	116	2	26	26	NUM
aiti-8538	116	3	]	]	PUNCT
aiti-8538	116	4	presented	present	VERB
aiti-8538	116	5	a	a	DET
aiti-8538	116	6	review	review	NOUN
aiti-8538	116	7	of	of	ADP
aiti-8538	116	8	traditional	traditional	ADJ
aiti-8538	116	9	machine	machine	NOUN
aiti-8538	116	10	learning	learning	NOUN
aiti-8538	116	11	and	and	CCONJ
aiti-8538	116	12	deep	deep	ADJ
aiti-8538	116	13	learning	learning	NOUN
aiti-8538	116	14	techniques	technique	NOUN
aiti-8538	116	15	for	for	ADP
aiti-8538	116	16	early	early	ADJ
aiti-8538	116	17	and	and	CCONJ
aiti-8538	116	18	accurate	accurate	ADJ
aiti-8538	116	19	detection	detection	NOUN
aiti-8538	116	20	of	of	ADP
aiti-8538	116	21	plant	plant	NOUN
aiti-8538	116	22	diseases	disease	NOUN
aiti-8538	116	23	.	.	PUNCT
aiti-8538	117	1	early	early	ADJ
aiti-8538	117	2	detection	detection	NOUN
aiti-8538	117	3	of	of	ADP
aiti-8538	117	4	plant	plant	NOUN
aiti-8538	117	5	diseases	disease	NOUN
aiti-8538	117	6	is	be	AUX
aiti-8538	117	7	a	a	DET
aiti-8538	117	8	challenging	challenging	ADJ
aiti-8538	117	9	task	task	NOUN
aiti-8538	117	10	that	that	PRON
aiti-8538	117	11	needs	need	VERB
aiti-8538	117	12	to	to	PART
aiti-8538	117	13	be	be	AUX
aiti-8538	117	14	addressed	address	VERB
aiti-8538	117	15	for	for	ADP
aiti-8538	117	16	precision	precision	NOUN
aiti-8538	117	17	agriculture	agriculture	NOUN
aiti-8538	117	18	.	.	PUNCT
aiti-8538	118	1	deep	deep	ADJ
aiti-8538	118	2	learning	learning	NOUN
aiti-8538	118	3	architectures	architecture	NOUN
aiti-8538	118	4	illustrate	illustrate	VERB
aiti-8538	118	5	their	their	PRON
aiti-8538	118	6	significance	significance	NOUN
aiti-8538	118	7	for	for	ADP
aiti-8538	118	8	important	important	ADJ
aiti-8538	118	9	feature	feature	NOUN
aiti-8538	118	10	extraction	extraction	NOUN
aiti-8538	118	11	and	and	CCONJ
aiti-8538	118	12	classification	classification	NOUN
aiti-8538	118	13	.	.	PUNCT
aiti-8538	119	1	deep	deep	ADJ
aiti-8538	119	2	neural	neural	ADJ
aiti-8538	119	3	networks	network	NOUN
aiti-8538	119	4	require	require	VERB
aiti-8538	119	5	large	large	ADJ
aiti-8538	119	6	datasets	dataset	NOUN
aiti-8538	119	7	for	for	ADP
aiti-8538	119	8	training	training	NOUN
aiti-8538	119	9	.	.	PUNCT
aiti-8538	120	1	collecting	collect	VERB
aiti-8538	120	2	a	a	DET
aiti-8538	120	3	large	large	ADJ
aiti-8538	120	4	dataset	dataset	NOUN
aiti-8538	120	5	is	be	AUX
aiti-8538	120	6	very	very	ADV
aiti-8538	120	7	time	time	NOUN
aiti-8538	120	8	-	-	PUNCT
aiti-8538	120	9	consuming	consume	VERB
aiti-8538	120	10	and	and	CCONJ
aiti-8538	120	11	expensive	expensive	ADJ
aiti-8538	120	12	.	.	PUNCT
aiti-8538	121	1	transfer	transfer	NOUN
aiti-8538	121	2	learning	learning	NOUN
aiti-8538	121	3	using	use	VERB
aiti-8538	121	4	pretrained	pretraine	VERB
aiti-8538	121	5	models	model	NOUN
aiti-8538	121	6	(	(	PUNCT
aiti-8538	121	7	e.g.	e.g.	ADV
aiti-8538	121	8	,	,	PUNCT
aiti-8538	121	9	single	single	ADJ
aiti-8538	121	10	-	-	PUNCT
aiti-8538	121	11	shot	shot	NOUN
aiti-8538	121	12	multibox	multibox	NOUN
aiti-8538	121	13	detector	detector	NOUN
aiti-8538	121	14	,	,	PUNCT
aiti-8538	121	15	vggnet	vggnet	NOUN
aiti-8538	121	16	,	,	PUNCT
aiti-8538	121	17	resnet50	resnet50	NOUN
aiti-8538	121	18	,	,	PUNCT
aiti-8538	121	19	inceptionv2	inceptionv2	NOUN
aiti-8538	121	20	,	,	PUNCT
aiti-8538	121	21	googlenet	googlenet	NOUN
aiti-8538	121	22	,	,	PUNCT
aiti-8538	121	23	mobilenet	mobilenet	NOUN
aiti-8538	121	24	,	,	PUNCT
aiti-8538	121	25	etc	etc	X
aiti-8538	121	26	.	.	X
aiti-8538	121	27	)	)	PUNCT
aiti-8538	121	28	is	be	AUX
aiti-8538	121	29	very	very	ADV
aiti-8538	121	30	useful	useful	ADJ
aiti-8538	121	31	to	to	PART
aiti-8538	121	32	overcome	overcome	VERB
aiti-8538	121	33	these	these	DET
aiti-8538	121	34	limitations	limitation	NOUN
aiti-8538	121	35	and	and	CCONJ
aiti-8538	121	36	recognize	recognize	VERB
aiti-8538	121	37	the	the	DET
aiti-8538	121	38	plant	plant	NOUN
aiti-8538	121	39	diseases	disease	NOUN
aiti-8538	121	40	with	with	ADP
aiti-8538	121	41	high	high	ADJ
aiti-8538	121	42	accuracy	accuracy	NOUN
aiti-8538	121	43	.	.	PUNCT
aiti-8538	122	1	training	train	VERB
aiti-8538	122	2	a	a	DET
aiti-8538	122	3	deep	deep	ADJ
aiti-8538	122	4	learning	learning	NOUN
aiti-8538	122	5	model	model	NOUN
aiti-8538	122	6	with	with	ADP
aiti-8538	122	7	large	large	ADJ
aiti-8538	122	8	data	datum	NOUN
aiti-8538	122	9	of	of	ADP
aiti-8538	122	10	diverse	diverse	ADJ
aiti-8538	122	11	examples	example	NOUN
aiti-8538	122	12	is	be	AUX
aiti-8538	122	13	highly	highly	ADV
aiti-8538	122	14	desirable	desirable	ADJ
aiti-8538	122	15	to	to	PART
aiti-8538	122	16	avoid	avoid	VERB
aiti-8538	122	17	over	over	ADV
aiti-8538	122	18	-	-	PUNCT
aiti-8538	122	19	fitting	fitting	ADJ
aiti-8538	122	20	.	.	PUNCT
aiti-8538	123	1	generally	generally	ADV
aiti-8538	123	2	,	,	PUNCT
aiti-8538	123	3	the	the	DET
aiti-8538	123	4	aim	aim	NOUN
aiti-8538	123	5	is	be	AUX
aiti-8538	123	6	to	to	PART
aiti-8538	123	7	develop	develop	VERB
aiti-8538	123	8	a	a	DET
aiti-8538	123	9	model	model	NOUN
aiti-8538	123	10	which	which	PRON
aiti-8538	123	11	generalizes	generalize	VERB
aiti-8538	123	12	well	well	ADV
aiti-8538	123	13	on	on	ADP
aiti-8538	123	14	the	the	DET
aiti-8538	123	15	unseen	unseen	ADJ
aiti-8538	123	16	test	test	NOUN
aiti-8538	123	17	data	datum	NOUN
aiti-8538	123	18	by	by	ADP
aiti-8538	123	19	minimizing	minimize	VERB
aiti-8538	123	20	both	both	DET
aiti-8538	123	21	the	the	DET
aiti-8538	123	22	bias	bias	NOUN
aiti-8538	123	23	and	and	CCONJ
aiti-8538	123	24	variance	variance	NOUN
aiti-8538	123	25	.	.	PUNCT
aiti-8538	124	1	to	to	PART
aiti-8538	124	2	solve	solve	VERB
aiti-8538	124	3	a	a	DET
aiti-8538	124	4	complex	complex	ADJ
aiti-8538	124	5	problem	problem	NOUN
aiti-8538	124	6	,	,	PUNCT
aiti-8538	124	7	the	the	DET
aiti-8538	124	8	model	model	NOUN
aiti-8538	124	9	needs	need	VERB
aiti-8538	124	10	to	to	PART
aiti-8538	124	11	learn	learn	VERB
aiti-8538	124	12	more	more	ADJ
aiti-8538	124	13	parameters	parameter	NOUN
aiti-8538	124	14	so	so	SCONJ
aiti-8538	124	15	that	that	SCONJ
aiti-8538	124	16	the	the	DET
aiti-8538	124	17	data	datum	NOUN
aiti-8538	124	18	size	size	NOUN
aiti-8538	124	19	for	for	ADP
aiti-8538	124	20	training	training	NOUN
aiti-8538	124	21	increases	increase	NOUN
aiti-8538	124	22	.	.	PUNCT
aiti-8538	125	1	transfer	transfer	NOUN
aiti-8538	125	2	learning	learning	NOUN
aiti-8538	125	3	is	be	AUX
aiti-8538	125	4	commonly	commonly	ADV
aiti-8538	125	5	employed	employ	VERB
aiti-8538	125	6	to	to	PART
aiti-8538	125	7	solve	solve	VERB
aiti-8538	125	8	computer	computer	NOUN
aiti-8538	125	9	vision	vision	NOUN
aiti-8538	125	10	problems	problem	NOUN
aiti-8538	125	11	with	with	ADP
aiti-8538	125	12	a	a	DET
aiti-8538	125	13	small	small	ADJ
aiti-8538	125	14	dataset	dataset	NOUN
aiti-8538	125	15	,	,	PUNCT
aiti-8538	125	16	and	and	CCONJ
aiti-8538	125	17	the	the	DET
aiti-8538	125	18	acquisition	acquisition	NOUN
aiti-8538	125	19	of	of	ADP
aiti-8538	125	20	more	more	ADJ
aiti-8538	125	21	data	datum	NOUN
aiti-8538	125	22	is	be	AUX
aiti-8538	125	23	time	time	NOUN
aiti-8538	125	24	-	-	PUNCT
aiti-8538	125	25	consuming	consume	VERB
aiti-8538	125	26	or	or	CCONJ
aiti-8538	125	27	expensive	expensive	ADJ
aiti-8538	125	28	.	.	PUNCT
aiti-8538	126	1	ren	ren	NOUN
aiti-8538	126	2	et	et	PROPN
aiti-8538	126	3	al	al	PROPN
aiti-8538	126	4	.	.	PUNCT
aiti-8538	127	1	[	[	X
aiti-8538	127	2	27	27	NUM
aiti-8538	127	3	]	]	PUNCT
aiti-8538	127	4	demonstrated	demonstrate	VERB
aiti-8538	127	5	accurate	accurate	ADJ
aiti-8538	127	6	image	image	NOUN
aiti-8538	127	7	classification	classification	NOUN
aiti-8538	127	8	employing	employ	VERB
aiti-8538	127	9	transfer	transfer	NOUN
aiti-8538	127	10	learning	learn	VERB
aiti-8538	127	11	algorithms	algorithm	NOUN
aiti-8538	127	12	on	on	ADP
aiti-8538	127	13	small	small	ADJ
aiti-8538	127	14	or	or	CCONJ
aiti-8538	127	15	poor	poor	ADJ
aiti-8538	127	16	image	image	NOUN
aiti-8538	127	17	datasets	dataset	NOUN
aiti-8538	127	18	.	.	PUNCT
aiti-8538	128	1	the	the	DET
aiti-8538	128	2	minimum	minimum	ADJ
aiti-8538	128	3	data	datum	NOUN
aiti-8538	128	4	size	size	NOUN
aiti-8538	128	5	requirements	requirement	NOUN
aiti-8538	128	6	for	for	ADP
aiti-8538	128	7	the	the	DET
aiti-8538	128	8	training	training	NOUN
aiti-8538	128	9	transfer	transfer	NOUN
aiti-8538	128	10	learning	learning	NOUN
aiti-8538	128	11	model	model	NOUN
aiti-8538	128	12	were	be	AUX
aiti-8538	128	13	explored	explore	VERB
aiti-8538	128	14	.	.	PUNCT
aiti-8538	129	1	the	the	DET
aiti-8538	129	2	proposed	propose	VERB
aiti-8538	129	3	model	model	NOUN
aiti-8538	129	4	was	be	AUX
aiti-8538	129	5	able	able	ADJ
aiti-8538	129	6	to	to	PART
aiti-8538	129	7	be	be	AUX
aiti-8538	129	8	trained	train	VERB
aiti-8538	129	9	using	use	VERB
aiti-8538	129	10	small	small	ADJ
aiti-8538	129	11	,	,	PUNCT
aiti-8538	129	12	poor	poor	ADJ
aiti-8538	129	13	-	-	PUNCT
aiti-8538	129	14	resolution	resolution	NOUN
aiti-8538	129	15	,	,	PUNCT
aiti-8538	129	16	unfavorably	unfavorably	ADV
aiti-8538	129	17	cropped	crop	VERB
aiti-8538	129	18	,	,	PUNCT
aiti-8538	129	19	or	or	CCONJ
aiti-8538	129	20	rotated	rotate	VERB
aiti-8538	129	21	images	image	NOUN
aiti-8538	129	22	.	.	PUNCT
aiti-8538	130	1	transfer	transfer	NOUN
aiti-8538	130	2	learning	learn	VERB
aiti-8538	130	3	algorithm	algorithm	NOUN
aiti-8538	130	4	was	be	AUX
aiti-8538	130	5	able	able	ADJ
aiti-8538	130	6	to	to	PART
aiti-8538	130	7	achieve	achieve	VERB
aiti-8538	130	8	above	above	ADP
aiti-8538	130	9	75	75	NUM
aiti-8538	130	10	%	%	NOUN
aiti-8538	130	11	accuracy	accuracy	NOUN
aiti-8538	130	12	with	with	ADP
aiti-8538	130	13	different	different	ADJ
aiti-8538	130	14	data	datum	NOUN
aiti-8538	130	15	sizes	size	NOUN
aiti-8538	130	16	.	.	PUNCT
aiti-8538	131	1	weimann	weimann	PROPN
aiti-8538	131	2	et	et	PROPN
aiti-8538	131	3	al	al	PROPN
aiti-8538	131	4	.	.	PUNCT
aiti-8538	132	1	[	[	X
aiti-8538	132	2	28	28	NUM
aiti-8538	132	3	]	]	PUNCT
aiti-8538	132	4	presented	present	VERB
aiti-8538	132	5	a	a	DET
aiti-8538	132	6	transfer	transfer	NOUN
aiti-8538	132	7	learning	learning	NOUN
aiti-8538	132	8	-	-	PUNCT
aiti-8538	132	9	based	base	VERB
aiti-8538	132	10	approach	approach	NOUN
aiti-8538	132	11	for	for	ADP
aiti-8538	132	12	the	the	DET
aiti-8538	132	13	classification	classification	NOUN
aiti-8538	132	14	of	of	ADP
aiti-8538	132	15	atrial	atrial	ADJ
aiti-8538	132	16	fibrillation	fibrillation	NOUN
aiti-8538	132	17	.	.	PUNCT
aiti-8538	133	1	the	the	DET
aiti-8538	133	2	cnn	cnn	PROPN
aiti-8538	133	3	model	model	NOUN
aiti-8538	133	4	was	be	AUX
aiti-8538	133	5	first	first	ADV
aiti-8538	133	6	trained	train	VERB
aiti-8538	133	7	on	on	ADP
aiti-8538	133	8	a	a	DET
aiti-8538	133	9	large	large	ADJ
aiti-8538	133	10	public	public	ADJ
aiti-8538	133	11	dataset	dataset	NOUN
aiti-8538	133	12	and	and	CCONJ
aiti-8538	133	13	then	then	ADV
aiti-8538	133	14	the	the	DET
aiti-8538	133	15	weights	weight	NOUN
aiti-8538	133	16	learned	learn	VERB
aiti-8538	133	17	were	be	AUX
aiti-8538	133	18	fine	fine	ADV
aiti-8538	133	19	-	-	PUNCT
aiti-8538	133	20	tuned	tune	VERB
aiti-8538	133	21	on	on	ADP
aiti-8538	133	22	a	a	DET
aiti-8538	133	23	small	small	ADJ
aiti-8538	133	24	dataset	dataset	NOUN
aiti-8538	133	25	of	of	ADP
aiti-8538	133	26	atrial	atrial	ADJ
aiti-8538	133	27	fibrillation	fibrillation	NOUN
aiti-8538	133	28	.	.	PUNCT
aiti-8538	134	1	the	the	DET
aiti-8538	134	2	proposed	propose	VERB
aiti-8538	134	3	approach	approach	NOUN
aiti-8538	134	4	employing	employ	VERB
aiti-8538	134	5	transfer	transfer	NOUN
aiti-8538	134	6	learning	learning	NOUN
aiti-8538	134	7	improved	improve	VERB
aiti-8538	134	8	the	the	DET
aiti-8538	134	9	performance	performance	NOUN
aiti-8538	134	10	of	of	ADP
aiti-8538	134	11	the	the	DET
aiti-8538	134	12	cnn	cnn	PROPN
aiti-8538	134	13	model	model	NOUN
aiti-8538	134	14	by	by	ADP
aiti-8538	134	15	6.57	6.57	NUM
aiti-8538	134	16	%	%	NOUN
aiti-8538	134	17	.	.	PUNCT
aiti-8538	135	1	liu	liu	PROPN
aiti-8538	135	2	et	et	PROPN
aiti-8538	135	3	al	al	PROPN
aiti-8538	135	4	.	.	PUNCT
aiti-8538	136	1	[	[	X
aiti-8538	136	2	29	29	NUM
aiti-8538	136	3	]	]	PUNCT
aiti-8538	136	4	presented	present	VERB
aiti-8538	136	5	an	an	DET
aiti-8538	136	6	automatic	automatic	ADJ
aiti-8538	136	7	manufacturing	manufacturing	NOUN
aiti-8538	136	8	defect	defect	NOUN
aiti-8538	136	9	detection	detection	NOUN
aiti-8538	136	10	system	system	NOUN
aiti-8538	136	11	with	with	ADP
aiti-8538	136	12	limited	limited	ADJ
aiti-8538	136	13	training	training	NOUN
aiti-8538	136	14	samples	sample	NOUN
aiti-8538	136	15	employing	employ	VERB
aiti-8538	136	16	transfer	transfer	NOUN
aiti-8538	136	17	learning	learning	NOUN
aiti-8538	136	18	.	.	PUNCT
aiti-8538	137	1	the	the	DET
aiti-8538	137	2	proposed	propose	VERB
aiti-8538	137	3	model	model	NOUN
aiti-8538	137	4	was	be	AUX
aiti-8538	137	5	able	able	ADJ
aiti-8538	137	6	to	to	PART
aiti-8538	137	7	extract	extract	VERB
aiti-8538	137	8	the	the	DET
aiti-8538	137	9	discriminative	discriminative	NOUN
aiti-8538	137	10	features	feature	NOUN
aiti-8538	137	11	and	and	CCONJ
aiti-8538	137	12	was	be	AUX
aiti-8538	137	13	able	able	ADJ
aiti-8538	137	14	to	to	PART
aiti-8538	137	15	detect	detect	VERB
aiti-8538	137	16	defects	defect	NOUN
aiti-8538	137	17	with	with	ADP
aiti-8538	137	18	an	an	DET
aiti-8538	137	19	accuracy	accuracy	NOUN
aiti-8538	137	20	of	of	ADP
aiti-8538	137	21	99	99	NUM
aiti-8538	137	22	%	%	NOUN
aiti-8538	137	23	improving	improve	VERB
aiti-8538	137	24	the	the	DET
aiti-8538	137	25	model	model	NOUN
aiti-8538	137	26	accuracy	accuracy	NOUN
aiti-8538	137	27	by	by	ADP
aiti-8538	137	28	11	11	NUM
aiti-8538	137	29	%	%	NOUN
aiti-8538	137	30	.	.	PUNCT
aiti-8538	138	1	li	li	PROPN
aiti-8538	138	2	et	et	PROPN
aiti-8538	138	3	al	al	PROPN
aiti-8538	138	4	.	.	PUNCT
aiti-8538	139	1	[	[	X
aiti-8538	139	2	30	30	NUM
aiti-8538	139	3	]	]	PUNCT
aiti-8538	139	4	proposed	propose	VERB
aiti-8538	139	5	an	an	DET
aiti-8538	139	6	approach	approach	NOUN
aiti-8538	139	7	demonstrating	demonstrate	VERB
aiti-8538	139	8	the	the	DET
aiti-8538	139	9	usefulness	usefulness	NOUN
aiti-8538	139	10	of	of	ADP
aiti-8538	139	11	transfer	transfer	NOUN
aiti-8538	139	12	learning	learn	VERB
aiti-8538	139	13	to	to	PART
aiti-8538	139	14	train	train	VERB
aiti-8538	139	15	deep	deep	ADJ
aiti-8538	139	16	learning	learning	NOUN
aiti-8538	139	17	models	model	NOUN
aiti-8538	139	18	on	on	ADP
aiti-8538	139	19	small	small	ADJ
aiti-8538	139	20	training	training	NOUN
aiti-8538	139	21	data	datum	NOUN
aiti-8538	139	22	for	for	ADP
aiti-8538	139	23	the	the	DET
aiti-8538	139	24	diagnosis	diagnosis	NOUN
aiti-8538	139	25	of	of	ADP
aiti-8538	139	26	covid-19	covid-19	PROPN
aiti-8538	139	27	.	.	PUNCT
aiti-8538	140	1	the	the	DET
aiti-8538	140	2	transfer	transfer	NOUN
aiti-8538	140	3	learning	learn	VERB
aiti-8538	140	4	using	use	VERB
aiti-8538	140	5	the	the	DET
aiti-8538	140	6	chexnet	chexnet	NOUN
aiti-8538	140	7	model	model	NOUN
aiti-8538	140	8	already	already	ADV
aiti-8538	140	9	trained	train	VERB
aiti-8538	140	10	for	for	ADP
aiti-8538	140	11	the	the	DET
aiti-8538	140	12	classification	classification	NOUN
aiti-8538	140	13	of	of	ADP
aiti-8538	140	14	chest	chest	NOUN
aiti-8538	140	15	x	x	NOUN
aiti-8538	140	16	-	-	NOUN
aiti-8538	140	17	ray	ray	NOUN
aiti-8538	140	18	images	image	NOUN
aiti-8538	140	19	was	be	AUX
aiti-8538	140	20	employed	employ	VERB
aiti-8538	140	21	.	.	PUNCT
aiti-8538	141	1	the	the	DET
aiti-8538	141	2	already	already	ADV
aiti-8538	141	3	learned	learn	VERB
aiti-8538	141	4	knowledge	knowledge	NOUN
aiti-8538	141	5	was	be	AUX
aiti-8538	141	6	fine	fine	ADV
aiti-8538	141	7	-	-	PUNCT
aiti-8538	141	8	tuned	tune	VERB
aiti-8538	141	9	on	on	ADP
aiti-8538	141	10	the	the	DET
aiti-8538	141	11	limited	limited	ADJ
aiti-8538	141	12	covid-19	covid-19	PROPN
aiti-8538	141	13	dataset	dataset	NOUN
aiti-8538	141	14	.	.	PUNCT
aiti-8538	142	1	the	the	DET
aiti-8538	142	2	proposed	propose	VERB
aiti-8538	142	3	approach	approach	NOUN
aiti-8538	142	4	outperformed	outperform	VERB
aiti-8538	142	5	several	several	ADJ
aiti-8538	142	6	other	other	ADJ
aiti-8538	142	7	methods	method	NOUN
aiti-8538	142	8	.	.	PUNCT
aiti-8538	143	1	the	the	DET
aiti-8538	143	2	first	first	ADJ
aiti-8538	143	3	few	few	ADJ
aiti-8538	143	4	layers	layer	NOUN
aiti-8538	143	5	of	of	ADP
aiti-8538	143	6	a	a	DET
aiti-8538	143	7	deep	deep	ADJ
aiti-8538	143	8	cnn	cnn	PROPN
aiti-8538	143	9	model	model	NOUN
aiti-8538	143	10	extract	extract	VERB
aiti-8538	143	11	generic	generic	ADJ
aiti-8538	143	12	features	feature	NOUN
aiti-8538	143	13	,	,	PUNCT
aiti-8538	143	14	and	and	CCONJ
aiti-8538	143	15	the	the	DET
aiti-8538	143	16	final	final	ADJ
aiti-8538	143	17	layers	layer	NOUN
aiti-8538	143	18	extract	extract	VERB
aiti-8538	143	19	the	the	DET
aiti-8538	143	20	specific	specific	ADJ
aiti-8538	143	21	features	feature	NOUN
aiti-8538	143	22	of	of	ADP
aiti-8538	143	23	an	an	DET
aiti-8538	143	24	image	image	NOUN
aiti-8538	143	25	.	.	PUNCT
aiti-8538	144	1	yosinski	yosinski	ADJ
aiti-8538	144	2	et	et	PROPN
aiti-8538	144	3	al	al	PROPN
aiti-8538	144	4	.	.	PUNCT
aiti-8538	145	1	[	[	X
aiti-8538	145	2	31	31	NUM
aiti-8538	145	3	]	]	PUNCT
aiti-8538	145	4	discussed	discuss	VERB
aiti-8538	145	5	the	the	DET
aiti-8538	145	6	applicability	applicability	NOUN
aiti-8538	145	7	of	of	ADP
aiti-8538	145	8	transferring	transfer	VERB
aiti-8538	145	9	features	feature	NOUN
aiti-8538	145	10	from	from	ADP
aiti-8538	145	11	the	the	DET
aiti-8538	145	12	base	base	NOUN
aiti-8538	145	13	task	task	NOUN
aiti-8538	145	14	to	to	ADP
aiti-8538	145	15	the	the	DET
aiti-8538	145	16	target	target	NOUN
aiti-8538	145	17	task	task	NOUN
aiti-8538	145	18	.	.	PUNCT
aiti-8538	146	1	the	the	DET
aiti-8538	146	2	generalization	generalization	NOUN
aiti-8538	146	3	performance	performance	NOUN
aiti-8538	146	4	of	of	ADP
aiti-8538	146	5	the	the	DET
aiti-8538	146	6	model	model	NOUN
aiti-8538	146	7	is	be	AUX
aiti-8538	146	8	enhanced	enhance	VERB
aiti-8538	146	9	by	by	ADP
aiti-8538	146	10	transferring	transfer	VERB
aiti-8538	146	11	features	feature	NOUN
aiti-8538	146	12	from	from	ADP
aiti-8538	146	13	the	the	DET
aiti-8538	146	14	base	base	NOUN
aiti-8538	146	15	layers	layer	NOUN
aiti-8538	146	16	.	.	PUNCT
aiti-8538	147	1	the	the	DET
aiti-8538	147	2	performance	performance	NOUN
aiti-8538	147	3	is	be	AUX
aiti-8538	147	4	enhanced	enhance	VERB
aiti-8538	147	5	when	when	SCONJ
aiti-8538	147	6	the	the	DET
aiti-8538	147	7	base	base	NOUN
aiti-8538	147	8	task	task	NOUN
aiti-8538	147	9	and	and	CCONJ
aiti-8538	147	10	target	target	NOUN
aiti-8538	147	11	task	task	NOUN
aiti-8538	147	12	are	be	AUX
aiti-8538	147	13	similar	similar	ADJ
aiti-8538	147	14	.	.	PUNCT
aiti-8538	148	1	using	use	VERB
aiti-8538	148	2	the	the	DET
aiti-8538	148	3	pre	pre	ADJ
aiti-8538	148	4	-	-	ADJ
aiti-8538	148	5	learned	learned	ADJ
aiti-8538	148	6	weights	weight	NOUN
aiti-8538	148	7	for	for	ADP
aiti-8538	148	8	the	the	DET
aiti-8538	148	9	neural	neural	ADJ
aiti-8538	148	10	network	network	NOUN
aiti-8538	148	11	is	be	AUX
aiti-8538	148	12	better	well	ADJ
aiti-8538	148	13	than	than	ADP
aiti-8538	148	14	initializing	initialize	VERB
aiti-8538	148	15	the	the	DET
aiti-8538	148	16	weights	weight	NOUN
aiti-8538	148	17	randomly	randomly	ADV
aiti-8538	148	18	.	.	PUNCT
aiti-8538	149	1	mahajan	mahajan	PROPN
aiti-8538	149	2	et	et	PROPN
aiti-8538	149	3	al	al	PROPN
aiti-8538	149	4	.	.	PUNCT
aiti-8538	150	1	[	[	X
aiti-8538	150	2	32	32	NUM
aiti-8538	150	3	]	]	PUNCT
aiti-8538	150	4	discussed	discuss	VERB
aiti-8538	150	5	an	an	DET
aiti-8538	150	6	efficient	efficient	ADJ
aiti-8538	150	7	plant	plant	NOUN
aiti-8538	150	8	species	specie	NOUN
aiti-8538	150	9	recognition	recognition	NOUN
aiti-8538	150	10	model	model	NOUN
aiti-8538	150	11	which	which	PRON
aiti-8538	150	12	used	use	VERB
aiti-8538	150	13	transfer	transfer	NOUN
aiti-8538	150	14	learning	learning	NOUN
aiti-8538	150	15	for	for	ADP
aiti-8538	150	16	feature	feature	NOUN
aiti-8538	150	17	extraction	extraction	NOUN
aiti-8538	150	18	and	and	CCONJ
aiti-8538	150	19	enhanced	enhance	VERB
aiti-8538	150	20	multiclass	multiclass	ADJ
aiti-8538	150	21	adaptive	adaptive	ADJ
aiti-8538	150	22	boosting	boost	VERB
aiti-8538	150	23	for	for	ADP
aiti-8538	150	24	image	image	NOUN
aiti-8538	150	25	classification	classification	NOUN
aiti-8538	150	26	.	.	PUNCT
aiti-8538	151	1	the	the	DET
aiti-8538	151	2	open	open	ADJ
aiti-8538	151	3	-	-	PUNCT
aiti-8538	151	4	source	source	NOUN
aiti-8538	151	5	plant	plant	NOUN
aiti-8538	151	6	species	specie	NOUN
aiti-8538	151	7	dataset	dataset	VERB
aiti-8538	151	8	flavia	flavia	PROPN
aiti-8538	151	9	was	be	AUX
aiti-8538	151	10	used	use	VERB
aiti-8538	151	11	in	in	ADP
aiti-8538	151	12	the	the	DET
aiti-8538	151	13	study	study	NOUN
aiti-8538	151	14	.	.	PUNCT
aiti-8538	152	1	using	use	VERB
aiti-8538	152	2	10	10	NUM
aiti-8538	152	3	-	-	ADJ
aiti-8538	152	4	fold	fold	ADJ
aiti-8538	152	5	cross	cross	NOUN
aiti-8538	152	6	-	-	NOUN
aiti-8538	152	7	validation	validation	NOUN
aiti-8538	152	8	,	,	PUNCT
aiti-8538	152	9	an	an	DET
aiti-8538	152	10	accuracy	accuracy	NOUN
aiti-8538	152	11	of	of	ADP
aiti-8538	152	12	95.85	95.85	NUM
aiti-8538	152	13	%	%	NOUN
aiti-8538	152	14	was	be	AUX
aiti-8538	152	15	achieved	achieve	VERB
aiti-8538	152	16	.	.	PUNCT
aiti-8538	153	1	singh	singh	PROPN
aiti-8538	153	2	et	et	PROPN
aiti-8538	153	3	al	al	PROPN
aiti-8538	153	4	.	.	PUNCT
aiti-8538	154	1	[	[	X
aiti-8538	154	2	33	33	NUM
aiti-8538	154	3	]	]	PUNCT
aiti-8538	154	4	applied	apply	VERB
aiti-8538	154	5	a	a	DET
aiti-8538	154	6	novel	novel	ADJ
aiti-8538	154	7	nature	nature	NOUN
aiti-8538	154	8	-	-	PUNCT
aiti-8538	154	9	inspired	inspire	VERB
aiti-8538	154	10	multi	multi	ADJ
aiti-8538	154	11	-	-	ADJ
aiti-8538	154	12	population	population	NOUN
aiti-8538	154	13	parallel	parallel	ADJ
aiti-8538	154	14	three	three	NUM
aiti-8538	154	15	-	-	PUNCT
aiti-8538	154	16	parent	parent	NOUN
aiti-8538	154	17	genetic	genetic	ADJ
aiti-8538	154	18	algorithm	algorithm	NOUN
aiti-8538	154	19	(	(	PUNCT
aiti-8538	154	20	p3pga	p3pga	NOUN
aiti-8538	154	21	)	)	PUNCT
aiti-8538	154	22	for	for	ADP
aiti-8538	154	23	the	the	DET
aiti-8538	154	24	optimization	optimization	NOUN
aiti-8538	154	25	of	of	ADP
aiti-8538	154	26	routing	route	VERB
aiti-8538	154	27	problems	problem	NOUN
aiti-8538	154	28	in	in	ADP
aiti-8538	154	29	wireless	wireless	ADJ
aiti-8538	154	30	mesh	mesh	NOUN
aiti-8538	154	31	networks	network	NOUN
aiti-8538	154	32	.	.	PUNCT
aiti-8538	155	1	the	the	DET
aiti-8538	155	2	p3pga	p3pga	NOUN
aiti-8538	155	3	benchmarked	benchmarke	VERB
aiti-8538	155	4	tests	test	NOUN
aiti-8538	155	5	on	on	ADP
aiti-8538	155	6	cec-2014	cec-2014	NOUN
aiti-8538	155	7	tests	test	NOUN
aiti-8538	155	8	and	and	CCONJ
aiti-8538	155	9	their	their	PRON
aiti-8538	155	10	application	application	NOUN
aiti-8538	155	11	for	for	ADP
aiti-8538	155	12	finding	find	VERB
aiti-8538	155	13	minimum	minimum	ADJ
aiti-8538	155	14	cost	cost	NOUN
aiti-8538	155	15	routes	route	NOUN
aiti-8538	155	16	illustrate	illustrate	VERB
aiti-8538	155	17	faster	fast	ADJ
aiti-8538	155	18	convergence	convergence	NOUN
aiti-8538	155	19	time	time	NOUN
aiti-8538	155	20	compared	compare	VERB
aiti-8538	155	21	to	to	ADP
aiti-8538	155	22	other	other	ADJ
aiti-8538	155	23	popular	popular	ADJ
aiti-8538	155	24	nature	nature	NOUN
aiti-8538	155	25	-	-	PUNCT
aiti-8538	155	26	inspired	inspire	VERB
aiti-8538	155	27	algorithms	algorithm	NOUN
aiti-8538	155	28	.	.	PUNCT
aiti-8538	156	1	the	the	DET
aiti-8538	156	2	optimization	optimization	NOUN
aiti-8538	156	3	techniques	technique	NOUN
aiti-8538	156	4	enhance	enhance	VERB
aiti-8538	156	5	the	the	DET
aiti-8538	156	6	performance	performance	NOUN
aiti-8538	156	7	of	of	ADP
aiti-8538	156	8	image	image	NOUN
aiti-8538	156	9	classification	classification	NOUN
aiti-8538	156	10	algorithms	algorithm	NOUN
aiti-8538	156	11	.	.	PUNCT
aiti-8538	157	1	table	table	NOUN
aiti-8538	157	2	1	1	NUM
aiti-8538	157	3	presents	present	VERB
aiti-8538	157	4	some	some	PRON
aiti-8538	157	5	of	of	ADP
aiti-8538	157	6	the	the	DET
aiti-8538	157	7	recent	recent	ADJ
aiti-8538	157	8	studies	study	NOUN
aiti-8538	157	9	on	on	ADP
aiti-8538	157	10	feature	feature	NOUN
aiti-8538	157	11	extraction	extraction	NOUN
aiti-8538	157	12	,	,	PUNCT
aiti-8538	157	13	feature	feature	NOUN
aiti-8538	157	14	reduction	reduction	NOUN
aiti-8538	157	15	,	,	PUNCT
aiti-8538	157	16	and	and	CCONJ
aiti-8538	157	17	classification	classification	NOUN
aiti-8538	157	18	.	.	PUNCT
aiti-8538	158	1	108	108	NUM
aiti-8538	158	2	advances	advance	NOUN
aiti-8538	158	3	in	in	ADP
aiti-8538	158	4	technology	technology	NOUN
aiti-8538	158	5	innovation	innovation	NOUN
aiti-8538	158	6	,	,	PUNCT
aiti-8538	158	7	vol	vol	NOUN
aiti-8538	158	8	.	.	PROPN
aiti-8538	158	9	7	7	NUM
aiti-8538	158	10	,	,	PUNCT
aiti-8538	158	11	no	no	INTJ
aiti-8538	158	12	.	.	NOUN
aiti-8538	158	13	2	2	NUM
aiti-8538	158	14	,	,	PUNCT
aiti-8538	158	15	2022	2022	NUM
aiti-8538	158	16	,	,	PUNCT
aiti-8538	158	17	pp	pp	ADJ
aiti-8538	158	18	.	.	PUNCT
aiti-8538	159	1	105	105	NUM
aiti-8538	159	2	-	-	SYM
aiti-8538	159	3	117	117	NUM
aiti-8538	159	4	table	table	NOUN
aiti-8538	159	5	1	1	NUM
aiti-8538	159	6	description	description	NOUN
aiti-8538	159	7	,	,	PUNCT
aiti-8538	159	8	technique	technique	NOUN
aiti-8538	159	9	,	,	PUNCT
aiti-8538	159	10	and	and	CCONJ
aiti-8538	159	11	limitation	limitation	NOUN
aiti-8538	159	12	of	of	ADP
aiti-8538	159	13	recent	recent	ADJ
aiti-8538	159	14	studies	study	NOUN
aiti-8538	159	15	ref	ref	VERB
aiti-8538	159	16	.	.	PUNCT
aiti-8538	160	1	description	description	NOUN
aiti-8538	160	2	technique	technique	NOUN
aiti-8538	160	3	limitation	limitation	NOUN
aiti-8538	160	4	[	[	X
aiti-8538	160	5	34	34	NUM
aiti-8538	160	6	]	]	X
aiti-8538	160	7	real	real	ADJ
aiti-8538	160	8	-	-	PUNCT
aiti-8538	160	9	time	time	NOUN
aiti-8538	160	10	accurate	accurate	ADJ
aiti-8538	160	11	and	and	CCONJ
aiti-8538	160	12	efficient	efficient	ADJ
aiti-8538	160	13	monitoring	monitoring	NOUN
aiti-8538	160	14	of	of	ADP
aiti-8538	160	15	water	water	NOUN
aiti-8538	160	16	quality	quality	NOUN
aiti-8538	160	17	of	of	ADP
aiti-8538	160	18	water	water	NOUN
aiti-8538	160	19	resources	resource	NOUN
aiti-8538	160	20	for	for	ADP
aiti-8538	160	21	decision	decision	NOUN
aiti-8538	160	22	making	make	VERB
aiti-8538	160	23	long	long	ADJ
aiti-8538	160	24	short	short	ADJ
aiti-8538	160	25	-	-	PUNCT
aiti-8538	160	26	term	term	NOUN
aiti-8538	160	27	memory	memory	NOUN
aiti-8538	160	28	recurrent	recurrent	NOUN
aiti-8538	160	29	neural	neural	ADJ
aiti-8538	160	30	networks	network	NOUN
aiti-8538	160	31	(	(	PUNCT
aiti-8538	160	32	lstm	lstm	NOUN
aiti-8538	160	33	rnns	rnns	PROPN
aiti-8538	160	34	)	)	PUNCT
aiti-8538	160	35	,	,	PUNCT
aiti-8538	160	36	pca	pca	PROPN
aiti-8538	160	37	,	,	PUNCT
aiti-8538	160	38	linear	linear	ADJ
aiti-8538	160	39	discriminate	discriminate	VERB
aiti-8538	160	40	analysis	analysis	NOUN
aiti-8538	160	41	(	(	PUNCT
aiti-8538	160	42	lda	lda	PROPN
aiti-8538	160	43	)	)	PUNCT
aiti-8538	160	44	,	,	PUNCT
aiti-8538	160	45	and	and	CCONJ
aiti-8538	160	46	independent	independent	ADJ
aiti-8538	160	47	component	component	NOUN
aiti-8538	160	48	analysis	analysis	NOUN
aiti-8538	160	49	(	(	PUNCT
aiti-8538	160	50	ica	ica	PROPN
aiti-8538	160	51	)	)	PUNCT
aiti-8538	160	52	for	for	SCONJ
aiti-8538	160	53	feature	feature	NOUN
aiti-8538	160	54	reduction	reduction	NOUN
aiti-8538	160	55	classification	classification	NOUN
aiti-8538	160	56	performance	performance	NOUN
aiti-8538	160	57	is	be	AUX
aiti-8538	160	58	not	not	PART
aiti-8538	160	59	tested	test	VERB
aiti-8538	160	60	using	use	VERB
aiti-8538	160	61	deep	deep	ADJ
aiti-8538	160	62	learning	learning	NOUN
aiti-8538	160	63	classifiers	classifier	NOUN
aiti-8538	160	64	.	.	PUNCT
aiti-8538	161	1	[	[	X
aiti-8538	161	2	35	35	NUM
aiti-8538	161	3	]	]	X
aiti-8538	161	4	non	non	ADJ
aiti-8538	161	5	-	-	ADJ
aiti-8538	161	6	linear	linear	ADJ
aiti-8538	161	7	ensemble	ensemble	ADJ
aiti-8538	161	8	learning	learning	NOUN
aiti-8538	161	9	for	for	ADP
aiti-8538	161	10	improving	improve	VERB
aiti-8538	161	11	feature	feature	NOUN
aiti-8538	161	12	extraction	extraction	NOUN
aiti-8538	161	13	and	and	CCONJ
aiti-8538	161	14	forecasting	forecast	VERB
aiti-8538	161	15	the	the	DET
aiti-8538	161	16	carbon	carbon	NOUN
aiti-8538	161	17	price	price	NOUN
aiti-8538	161	18	deep	deep	ADJ
aiti-8538	161	19	learning	learning	NOUN
aiti-8538	161	20	,	,	PUNCT
aiti-8538	161	21	lstm	lstm	PROPN
aiti-8538	161	22	,	,	PUNCT
aiti-8538	161	23	rf	rf	PROPN
aiti-8538	161	24	,	,	PUNCT
aiti-8538	161	25	rnn	rnn	NOUN
aiti-8538	161	26	,	,	PUNCT
aiti-8538	161	27	and	and	CCONJ
aiti-8538	161	28	bagging	bag	VERB
aiti-8538	161	29	algorithm	algorithm	NOUN
aiti-8538	161	30	the	the	DET
aiti-8538	161	31	proposed	propose	VERB
aiti-8538	161	32	ensemble	ensemble	ADJ
aiti-8538	161	33	learning	learning	NOUN
aiti-8538	161	34	approach	approach	NOUN
aiti-8538	161	35	needs	need	VERB
aiti-8538	161	36	to	to	PART
aiti-8538	161	37	select	select	VERB
aiti-8538	161	38	optimal	optimal	ADJ
aiti-8538	161	39	models	model	NOUN
aiti-8538	161	40	rather	rather	ADV
aiti-8538	161	41	than	than	ADP
aiti-8538	161	42	random	random	ADJ
aiti-8538	161	43	selection	selection	NOUN
aiti-8538	161	44	.	.	PUNCT
aiti-8538	162	1	[	[	X
aiti-8538	162	2	36	36	NUM
aiti-8538	162	3	]	]	X
aiti-8538	162	4	covid-19	covid-19	PROPN
aiti-8538	162	5	prediction	prediction	NOUN
aiti-8538	162	6	by	by	ADP
aiti-8538	162	7	inspecting	inspect	VERB
aiti-8538	162	8	x	x	NOUN
aiti-8538	162	9	-	-	NOUN
aiti-8538	162	10	ray	ray	NOUN
aiti-8538	162	11	images	image	NOUN
aiti-8538	162	12	transfer	transfer	NOUN
aiti-8538	162	13	learning	learn	VERB
aiti-8538	162	14	feature	feature	NOUN
aiti-8538	162	15	extraction	extraction	NOUN
aiti-8538	162	16	resnet18	resnet18	NOUN
aiti-8538	162	17	,	,	PUNCT
aiti-8538	162	18	resnet50	resnet50	NOUN
aiti-8538	162	19	,	,	PUNCT
aiti-8538	162	20	resnet101	resnet101	PROPN
aiti-8538	162	21	,	,	PUNCT
aiti-8538	162	22	vgg16	vgg16	PROPN
aiti-8538	162	23	,	,	PUNCT
aiti-8538	162	24	vgg19	vgg19	PROPN
aiti-8538	162	25	,	,	PUNCT
aiti-8538	162	26	and	and	CCONJ
aiti-8538	162	27	svm	svm	VERB
aiti-8538	162	28	for	for	ADP
aiti-8538	162	29	classification	classification	NOUN
aiti-8538	162	30	with	with	ADP
aiti-8538	162	31	an	an	DET
aiti-8538	162	32	accuracy	accuracy	NOUN
aiti-8538	162	33	of	of	ADP
aiti-8538	162	34	94.7	94.7	NUM
aiti-8538	162	35	%	%	NOUN
aiti-8538	162	36	the	the	DET
aiti-8538	162	37	severity	severity	NOUN
aiti-8538	162	38	of	of	ADP
aiti-8538	162	39	the	the	DET
aiti-8538	162	40	disease	disease	NOUN
aiti-8538	162	41	is	be	AUX
aiti-8538	162	42	not	not	PART
aiti-8538	162	43	detected	detect	VERB
aiti-8538	162	44	.	.	PUNCT
aiti-8538	163	1	[	[	X
aiti-8538	163	2	37	37	NUM
aiti-8538	163	3	]	]	X
aiti-8538	163	4	detection	detection	NOUN
aiti-8538	163	5	of	of	ADP
aiti-8538	163	6	alzheimer	alzheimer	PROPN
aiti-8538	163	7	disease	disease	NOUN
aiti-8538	163	8	by	by	ADP
aiti-8538	163	9	classification	classification	NOUN
aiti-8538	163	10	of	of	ADP
aiti-8538	163	11	functional	functional	ADJ
aiti-8538	163	12	mri	mri	NOUN
aiti-8538	163	13	images	image	NOUN
aiti-8538	163	14	knn	knn	PROPN
aiti-8538	163	15	,	,	PUNCT
aiti-8538	163	16	svm	svm	ADJ
aiti-8538	163	17	,	,	PUNCT
aiti-8538	163	18	decision	decision	NOUN
aiti-8538	163	19	tree	tree	NOUN
aiti-8538	163	20	,	,	PUNCT
aiti-8538	163	21	lda	lda	PROPN
aiti-8538	163	22	,	,	PUNCT
aiti-8538	163	23	rf	rf	ADJ
aiti-8538	163	24	,	,	PUNCT
aiti-8538	163	25	and	and	CCONJ
aiti-8538	163	26	cnn	cnn	PROPN
aiti-8538	163	27	limited	limit	VERB
aiti-8538	163	28	dataset	dataset	NOUN
aiti-8538	163	29	is	be	AUX
aiti-8538	163	30	used	use	VERB
aiti-8538	163	31	.	.	PUNCT
aiti-8538	164	1	[	[	X
aiti-8538	164	2	38	38	NUM
aiti-8538	164	3	]	]	PUNCT
aiti-8538	164	4	detection	detection	NOUN
aiti-8538	164	5	of	of	ADP
aiti-8538	164	6	external	external	ADJ
aiti-8538	164	7	defects	defect	NOUN
aiti-8538	164	8	in	in	ADP
aiti-8538	164	9	the	the	DET
aiti-8538	164	10	tomatoes	tomato	NOUN
aiti-8538	164	11	resnet18	resnet18	NOUN
aiti-8538	164	12	,	,	PUNCT
aiti-8538	164	13	resnet34	resnet34	NOUN
aiti-8538	164	14	,	,	PUNCT
aiti-8538	164	15	resnet50	resnet50	NOUN
aiti-8538	164	16	,	,	PUNCT
aiti-8538	164	17	resnet101	resnet101	PROPN
aiti-8538	164	18	,	,	PUNCT
aiti-8538	164	19	resnet152	resnet152	PROPN
aiti-8538	164	20	,	,	PUNCT
aiti-8538	164	21	grid	grid	NOUN
aiti-8538	164	22	search	search	NOUN
aiti-8538	164	23	with	with	ADP
aiti-8538	164	24	a	a	DET
aiti-8538	164	25	high	high	ADJ
aiti-8538	164	26	accuracy	accuracy	NOUN
aiti-8538	164	27	of	of	ADP
aiti-8538	164	28	94.6	94.6	NUM
aiti-8538	164	29	%	%	NOUN
aiti-8538	164	30	deep	deep	ADJ
aiti-8538	164	31	autoencoders	autoencoder	NOUN
aiti-8538	164	32	and	and	CCONJ
aiti-8538	164	33	one	one	NUM
aiti-8538	164	34	-	-	PUNCT
aiti-8538	164	35	class	class	NOUN
aiti-8538	164	36	classifiers	classifier	NOUN
aiti-8538	164	37	used	use	VERB
aiti-8538	164	38	for	for	ADP
aiti-8538	164	39	improving	improve	VERB
aiti-8538	164	40	accuracy	accuracy	NOUN
aiti-8538	164	41	need	need	NOUN
aiti-8538	164	42	to	to	PART
aiti-8538	164	43	be	be	AUX
aiti-8538	164	44	explored	explore	VERB
aiti-8538	164	45	.	.	PUNCT
aiti-8538	165	1	the	the	DET
aiti-8538	165	2	classifiers	classifier	NOUN
aiti-8538	165	3	need	need	VERB
aiti-8538	165	4	to	to	PART
aiti-8538	165	5	be	be	AUX
aiti-8538	165	6	generalized	generalize	VERB
aiti-8538	165	7	.	.	PUNCT
aiti-8538	166	1	[	[	X
aiti-8538	166	2	39	39	NUM
aiti-8538	166	3	]	]	PUNCT
aiti-8538	166	4	selection	selection	NOUN
aiti-8538	166	5	of	of	ADP
aiti-8538	166	6	optimum	optimum	ADJ
aiti-8538	166	7	features	feature	NOUN
aiti-8538	166	8	for	for	ADP
aiti-8538	166	9	medical	medical	ADJ
aiti-8538	166	10	image	image	NOUN
aiti-8538	166	11	classification	classification	NOUN
aiti-8538	166	12	gray	gray	ADJ
aiti-8538	166	13	level	level	NOUN
aiti-8538	166	14	co	co	NOUN
aiti-8538	166	15	-	-	NOUN
aiti-8538	166	16	occurrence	occurrence	ADJ
aiti-8538	166	17	matrices	matrix	NOUN
aiti-8538	166	18	(	(	PUNCT
aiti-8538	166	19	glcm	glcm	PROPN
aiti-8538	166	20	)	)	PUNCT
aiti-8538	166	21	,	,	PUNCT
aiti-8538	166	22	grey	grey	ADJ
aiti-8538	166	23	-	-	PUNCT
aiti-8538	166	24	level	level	NOUN
aiti-8538	166	25	run	run	NOUN
aiti-8538	166	26	length	length	NOUN
aiti-8538	166	27	matrix	matrix	NOUN
aiti-8538	166	28	(	(	PUNCT
aiti-8538	166	29	glrlm	glrlm	PROPN
aiti-8538	166	30	)	)	PUNCT
aiti-8538	166	31	,	,	PUNCT
aiti-8538	166	32	crow	crow	NOUN
aiti-8538	166	33	search	search	NOUN
aiti-8538	166	34	optimization	optimization	NOUN
aiti-8538	166	35	algorithm	algorithm	NOUN
aiti-8538	166	36	for	for	ADP
aiti-8538	166	37	feature	feature	NOUN
aiti-8538	166	38	selection	selection	NOUN
aiti-8538	166	39	,	,	PUNCT
aiti-8538	166	40	and	and	CCONJ
aiti-8538	166	41	deep	deep	ADJ
aiti-8538	166	42	learning	learn	VERB
aiti-8538	166	43	further	further	ADJ
aiti-8538	166	44	segmentation	segmentation	NOUN
aiti-8538	166	45	and	and	CCONJ
aiti-8538	166	46	feature	feature	NOUN
aiti-8538	166	47	reduction	reduction	NOUN
aiti-8538	166	48	are	be	AUX
aiti-8538	166	49	needed	need	VERB
aiti-8538	166	50	.	.	PUNCT
aiti-8538	167	1	[	[	X
aiti-8538	167	2	40	40	NUM
aiti-8538	167	3	]	]	X
aiti-8538	167	4	detection	detection	NOUN
aiti-8538	167	5	of	of	ADP
aiti-8538	167	6	pain	pain	NOUN
aiti-8538	167	7	intensity	intensity	NOUN
aiti-8538	167	8	by	by	ADP
aiti-8538	167	9	analyzing	analyze	VERB
aiti-8538	167	10	facial	facial	ADJ
aiti-8538	167	11	expressions	expression	NOUN
aiti-8538	167	12	vgg	vgg	NOUN
aiti-8538	167	13	-	-	PUNCT
aiti-8538	167	14	face	face	NOUN
aiti-8538	167	15	for	for	ADP
aiti-8538	167	16	feature	feature	NOUN
aiti-8538	167	17	extraction	extraction	NOUN
aiti-8538	167	18	,	,	PUNCT
aiti-8538	167	19	pca	pca	NOUN
aiti-8538	167	20	for	for	ADP
aiti-8538	167	21	improving	improve	VERB
aiti-8538	167	22	computational	computational	ADJ
aiti-8538	167	23	efficiency	efficiency	NOUN
aiti-8538	167	24	,	,	PUNCT
aiti-8538	167	25	and	and	CCONJ
aiti-8538	167	26	lstm	lstm	NOUN
aiti-8538	167	27	for	for	ADP
aiti-8538	167	28	classification	classification	NOUN
aiti-8538	167	29	a	a	DET
aiti-8538	167	30	limited	limited	ADJ
aiti-8538	167	31	number	number	NOUN
aiti-8538	167	32	of	of	ADP
aiti-8538	167	33	images	image	NOUN
aiti-8538	167	34	and	and	CCONJ
aiti-8538	167	35	non	non	NOUN
aiti-8538	167	36	-	-	NOUN
aiti-8538	167	37	availability	availability	NOUN
aiti-8538	167	38	of	of	ADP
aiti-8538	167	39	a	a	DET
aiti-8538	167	40	standard	standard	ADJ
aiti-8538	167	41	dataset	dataset	NOUN
aiti-8538	167	42	[	[	X
aiti-8538	167	43	41	41	NUM
aiti-8538	167	44	]	]	PUNCT
aiti-8538	167	45	transfer	transfer	NOUN
aiti-8538	167	46	learning	learning	NOUN
aiti-8538	167	47	for	for	ADP
aiti-8538	167	48	image	image	NOUN
aiti-8538	167	49	classification	classification	NOUN
aiti-8538	167	50	of	of	ADP
aiti-8538	167	51	caltech	caltech	PROPN
aiti-8538	167	52	101	101	NUM
aiti-8538	167	53	dataset	dataset	PROPN
aiti-8538	167	54	vgg19	vgg19	PROPN
aiti-8538	167	55	,	,	PUNCT
aiti-8538	167	56	sift	sift	ADJ
aiti-8538	167	57	,	,	PUNCT
aiti-8538	167	58	surf	surf	NOUN
aiti-8538	167	59	,	,	PUNCT
aiti-8538	167	60	oriented	orient	VERB
aiti-8538	167	61	fast	fast	ADV
aiti-8538	167	62	and	and	CCONJ
aiti-8538	167	63	rotated	rotate	VERB
aiti-8538	167	64	brief	brief	ADJ
aiti-8538	167	65	(	(	PUNCT
aiti-8538	167	66	orb	orb	PROPN
aiti-8538	167	67	)	)	PUNCT
aiti-8538	167	68	,	,	PUNCT
aiti-8538	167	69	shi	shi	PROPN
aiti-8538	167	70	-	-	PUNCT
aiti-8538	167	71	tomasi	tomasi	PROPN
aiti-8538	167	72	corner	corner	NOUN
aiti-8538	167	73	detector	detector	NOUN
aiti-8538	167	74	algorithm	algorithm	NOUN
aiti-8538	167	75	for	for	ADP
aiti-8538	167	76	feature	feature	NOUN
aiti-8538	167	77	extraction	extraction	NOUN
aiti-8538	167	78	,	,	PUNCT
aiti-8538	167	79	gaussian	gaussian	ADJ
aiti-8538	167	80	naïve	naïve	ADJ
aiti-8538	167	81	bayes	bayes	NOUN
aiti-8538	167	82	,	,	PUNCT
aiti-8538	167	83	decision	decision	NOUN
aiti-8538	167	84	tree	tree	NOUN
aiti-8538	167	85	,	,	PUNCT
aiti-8538	167	86	rf	rf	NOUN
aiti-8538	167	87	,	,	PUNCT
aiti-8538	167	88	and	and	CCONJ
aiti-8538	167	89	xgbclassifer	xgbclassifer	VERB
aiti-8538	167	90	for	for	SCONJ
aiti-8538	167	91	classification	classification	NOUN
aiti-8538	167	92	multiple	multiple	ADJ
aiti-8538	167	93	feature	feature	NOUN
aiti-8538	167	94	extraction	extraction	NOUN
aiti-8538	167	95	techniques	technique	NOUN
aiti-8538	167	96	are	be	AUX
aiti-8538	167	97	required	require	VERB
aiti-8538	167	98	for	for	ADP
aiti-8538	167	99	improving	improve	VERB
aiti-8538	167	100	accuracy	accuracy	NOUN
aiti-8538	167	101	and	and	CCONJ
aiti-8538	167	102	required	require	VERB
aiti-8538	167	103	more	more	ADV
aiti-8538	167	104	computational	computational	ADJ
aiti-8538	167	105	resources	resource	NOUN
aiti-8538	167	106	.	.	PUNCT
aiti-8538	168	1	the	the	DET
aiti-8538	168	2	vgg16	vgg16	NOUN
aiti-8538	168	3	architecture	architecture	NOUN
aiti-8538	168	4	proposed	propose	VERB
aiti-8538	168	5	by	by	ADP
aiti-8538	168	6	simonyan	simonyan	PROPN
aiti-8538	168	7	et	et	PROPN
aiti-8538	168	8	al	al	PROPN
aiti-8538	168	9	.	.	PUNCT
aiti-8538	169	1	[	[	X
aiti-8538	169	2	42	42	NUM
aiti-8538	169	3	]	]	PUNCT
aiti-8538	169	4	secured	secure	VERB
aiti-8538	169	5	the	the	DET
aiti-8538	169	6	first	first	ADJ
aiti-8538	169	7	place	place	NOUN
aiti-8538	169	8	in	in	ADP
aiti-8538	169	9	the	the	DET
aiti-8538	169	10	object	object	NOUN
aiti-8538	169	11	localization	localization	NOUN
aiti-8538	169	12	track	track	NOUN
aiti-8538	169	13	of	of	ADP
aiti-8538	169	14	the	the	DET
aiti-8538	169	15	imagenet	imagenet	NOUN
aiti-8538	169	16	challenge	challenge	NOUN
aiti-8538	169	17	and	and	CCONJ
aiti-8538	169	18	was	be	AUX
aiti-8538	169	19	able	able	ADJ
aiti-8538	169	20	to	to	PART
aiti-8538	169	21	detect	detect	VERB
aiti-8538	169	22	an	an	DET
aiti-8538	169	23	object	object	NOUN
aiti-8538	169	24	within	within	ADP
aiti-8538	169	25	an	an	DET
aiti-8538	169	26	input	input	NOUN
aiti-8538	169	27	image	image	NOUN
aiti-8538	169	28	with	with	ADP
aiti-8538	169	29	high	high	ADJ
aiti-8538	169	30	accuracy	accuracy	NOUN
aiti-8538	169	31	.	.	PUNCT
aiti-8538	170	1	in	in	ADP
aiti-8538	170	2	the	the	DET
aiti-8538	170	3	image	image	NOUN
aiti-8538	170	4	classification	classification	NOUN
aiti-8538	170	5	track	track	NOUN
aiti-8538	170	6	of	of	ADP
aiti-8538	170	7	the	the	DET
aiti-8538	170	8	imagenet	imagenet	NOUN
aiti-8538	170	9	challenge	challenge	NOUN
aiti-8538	170	10	,	,	PUNCT
aiti-8538	170	11	the	the	DET
aiti-8538	170	12	vgg16	vgg16	PROPN
aiti-8538	170	13	neural	neural	ADJ
aiti-8538	170	14	network	network	NOUN
aiti-8538	170	15	achieved	achieve	VERB
aiti-8538	170	16	92.7	92.7	NUM
aiti-8538	170	17	%	%	NOUN
aiti-8538	170	18	top-5	top-5	NOUN
aiti-8538	170	19	test	test	NOUN
aiti-8538	170	20	accuracy	accuracy	NOUN
aiti-8538	170	21	for	for	ADP
aiti-8538	170	22	classifying	classify	VERB
aiti-8538	170	23	over	over	ADP
aiti-8538	170	24	14	14	NUM
aiti-8538	170	25	million	million	NUM
aiti-8538	170	26	images	image	NOUN
aiti-8538	170	27	belonging	belong	VERB
aiti-8538	170	28	to	to	ADP
aiti-8538	170	29	1000	1000	NUM
aiti-8538	170	30	different	different	ADJ
aiti-8538	170	31	classes	class	NOUN
aiti-8538	170	32	.	.	PUNCT
aiti-8538	171	1	the	the	DET
aiti-8538	171	2	vgg16	vgg16	NOUN
aiti-8538	171	3	model	model	NOUN
aiti-8538	171	4	was	be	AUX
aiti-8538	171	5	tested	test	VERB
aiti-8538	171	6	with	with	ADP
aiti-8538	171	7	voc-2007	voc-2007	NOUN
aiti-8538	171	8	,	,	PUNCT
aiti-8538	171	9	voc-2012	voc-2012	NOUN
aiti-8538	171	10	,	,	PUNCT
aiti-8538	171	11	caltech-101	caltech-101	NOUN
aiti-8538	171	12	,	,	PUNCT
aiti-8538	171	13	and	and	CCONJ
aiti-8538	171	14	caltech-256	caltech-256	PROPN
aiti-8538	171	15	benchmarked	benchmarke	VERB
aiti-8538	171	16	datasets	dataset	NOUN
aiti-8538	171	17	.	.	PUNCT
aiti-8538	172	1	the	the	DET
aiti-8538	172	2	experimental	experimental	ADJ
aiti-8538	172	3	results	result	NOUN
aiti-8538	172	4	illustrate	illustrate	VERB
aiti-8538	172	5	that	that	SCONJ
aiti-8538	172	6	the	the	DET
aiti-8538	172	7	vgg16	vgg16	NOUN
aiti-8538	172	8	network	network	NOUN
aiti-8538	172	9	can	can	AUX
aiti-8538	172	10	generalize	generalize	VERB
aiti-8538	172	11	well	well	ADV
aiti-8538	172	12	to	to	ADP
aiti-8538	172	13	the	the	DET
aiti-8538	172	14	unseen	unseen	ADJ
aiti-8538	172	15	data	datum	NOUN
aiti-8538	172	16	.	.	PUNCT
aiti-8538	173	1	3	3	X
aiti-8538	173	2	.	.	X
aiti-8538	173	3	materials	material	NOUN
aiti-8538	173	4	and	and	CCONJ
aiti-8538	173	5	methods	method	NOUN
aiti-8538	173	6	3.1	3.1	NUM
aiti-8538	173	7	.	.	PUNCT
aiti-8538	174	1	transfer	transfer	NOUN
aiti-8538	174	2	learning	learning	NOUN
aiti-8538	174	3	using	use	VERB
aiti-8538	174	4	deep	deep	ADJ
aiti-8538	174	5	convolutional	convolutional	ADJ
aiti-8538	174	6	neural	neural	ADJ
aiti-8538	174	7	networks	network	NOUN
aiti-8538	174	8	the	the	DET
aiti-8538	174	9	already	already	ADV
aiti-8538	174	10	trained	train	VERB
aiti-8538	174	11	models	model	NOUN
aiti-8538	174	12	are	be	AUX
aiti-8538	174	13	very	very	ADV
aiti-8538	174	14	useful	useful	ADJ
aiti-8538	174	15	to	to	PART
aiti-8538	174	16	overcome	overcome	VERB
aiti-8538	174	17	the	the	DET
aiti-8538	174	18	limited	limited	ADJ
aiti-8538	174	19	training	training	NOUN
aiti-8538	174	20	dataset	dataset	NOUN
aiti-8538	174	21	challenge	challenge	NOUN
aiti-8538	174	22	by	by	ADP
aiti-8538	174	23	reusing	reuse	VERB
aiti-8538	174	24	the	the	DET
aiti-8538	174	25	knowledge	knowledge	NOUN
aiti-8538	174	26	already	already	ADV
aiti-8538	174	27	learned	learn	VERB
aiti-8538	174	28	from	from	ADP
aiti-8538	174	29	large	large	ADJ
aiti-8538	174	30	training	training	NOUN
aiti-8538	174	31	samples	sample	NOUN
aiti-8538	174	32	.	.	PUNCT
aiti-8538	175	1	the	the	DET
aiti-8538	175	2	transfer	transfer	NOUN
aiti-8538	175	3	learning	learn	VERB
aiti-8538	175	4	using	use	VERB
aiti-8538	175	5	cnn	cnn	PROPN
aiti-8538	175	6	models	model	NOUN
aiti-8538	175	7	reuses	reuse	VERB
aiti-8538	175	8	the	the	DET
aiti-8538	175	9	knowledge	knowledge	NOUN
aiti-8538	175	10	learned	learn	VERB
aiti-8538	175	11	by	by	ADP
aiti-8538	175	12	the	the	DET
aiti-8538	175	13	neural	neural	ADJ
aiti-8538	175	14	network	network	NOUN
aiti-8538	175	15	trained	train	VERB
aiti-8538	175	16	on	on	ADP
aiti-8538	175	17	a	a	DET
aiti-8538	175	18	large	large	ADJ
aiti-8538	175	19	dataset	dataset	NOUN
aiti-8538	175	20	and	and	CCONJ
aiti-8538	175	21	applies	apply	VERB
aiti-8538	175	22	the	the	DET
aiti-8538	175	23	knowledge	knowledge	NOUN
aiti-8538	175	24	for	for	ADP
aiti-8538	175	25	solving	solve	VERB
aiti-8538	175	26	another	another	DET
aiti-8538	175	27	problem	problem	NOUN
aiti-8538	175	28	.	.	PUNCT
aiti-8538	176	1	vgg16	vgg16	PROPN
aiti-8538	176	2	,	,	PUNCT
aiti-8538	176	3	vgg19	vgg19	PROPN
aiti-8538	176	4	,	,	PUNCT
aiti-8538	176	5	resnet50	resnet50	NOUN
aiti-8538	176	6	,	,	PUNCT
aiti-8538	176	7	resnet101	resnet101	PROPN
aiti-8538	176	8	,	,	PUNCT
aiti-8538	176	9	inceptionv3	inceptionv3	NOUN
aiti-8538	176	10	,	,	PUNCT
aiti-8538	176	11	densenet121	densenet121	PROPN
aiti-8538	176	12	,	,	PUNCT
aiti-8538	176	13	xception	xception	NOUN
aiti-8538	176	14	,	,	PUNCT
aiti-8538	176	15	etc	etc	X
aiti-8538	176	16	are	be	AUX
aiti-8538	176	17	some	some	PRON
aiti-8538	176	18	of	of	ADP
aiti-8538	176	19	the	the	DET
aiti-8538	176	20	popular	popular	ADJ
aiti-8538	176	21	neural	neural	ADJ
aiti-8538	176	22	network	network	NOUN
aiti-8538	176	23	architectures	architecture	NOUN
aiti-8538	176	24	which	which	PRON
aiti-8538	176	25	have	have	AUX
aiti-8538	176	26	demonstrated	demonstrate	VERB
aiti-8538	176	27	excellent	excellent	ADJ
aiti-8538	176	28	image	image	NOUN
aiti-8538	176	29	classification	classification	NOUN
aiti-8538	176	30	performance	performance	NOUN
aiti-8538	176	31	.	.	PUNCT
aiti-8538	177	1	in	in	ADP
aiti-8538	177	2	this	this	DET
aiti-8538	177	3	study	study	NOUN
aiti-8538	177	4	,	,	PUNCT
aiti-8538	177	5	the	the	DET
aiti-8538	177	6	vgg16	vgg16	NOUN
aiti-8538	177	7	model	model	NOUN
aiti-8538	177	8	as	as	SCONJ
aiti-8538	177	9	shown	show	VERB
aiti-8538	177	10	in	in	ADP
aiti-8538	177	11	fig	fig	NOUN
aiti-8538	177	12	.	.	PUNCT
aiti-8538	177	13	1	1	NUM
aiti-8538	177	14	is	be	AUX
aiti-8538	177	15	used	use	VERB
aiti-8538	177	16	for	for	ADP
aiti-8538	177	17	the	the	DET
aiti-8538	177	18	extraction	extraction	NOUN
aiti-8538	177	19	of	of	ADP
aiti-8538	177	20	useful	useful	ADJ
aiti-8538	177	21	image	image	NOUN
aiti-8538	177	22	features	feature	NOUN
aiti-8538	177	23	.	.	PUNCT
aiti-8538	178	1	fig	fig	NOUN
aiti-8538	178	2	.	.	PUNCT
aiti-8538	179	1	1	1	NUM
aiti-8538	179	2	vgg16	vgg16	NOUN
aiti-8538	179	3	network	network	NOUN
aiti-8538	179	4	architecture	architecture	NOUN
aiti-8538	179	5	as	as	ADP
aiti-8538	179	6	a	a	DET
aiti-8538	179	7	feature	feature	NOUN
aiti-8538	179	8	extractor	extractor	NOUN
aiti-8538	179	9	109	109	NUM
aiti-8538	179	10	advances	advance	NOUN
aiti-8538	179	11	in	in	ADP
aiti-8538	179	12	technology	technology	NOUN
aiti-8538	179	13	innovation	innovation	NOUN
aiti-8538	179	14	,	,	PUNCT
aiti-8538	179	15	vol	vol	NOUN
aiti-8538	179	16	.	.	PROPN
aiti-8538	179	17	7	7	NUM
aiti-8538	179	18	,	,	PUNCT
aiti-8538	179	19	no	no	INTJ
aiti-8538	179	20	.	.	NOUN
aiti-8538	179	21	2	2	NUM
aiti-8538	179	22	,	,	PUNCT
aiti-8538	179	23	2022	2022	NUM
aiti-8538	179	24	,	,	PUNCT
aiti-8538	179	25	pp	pp	ADJ
aiti-8538	179	26	.	.	PUNCT
aiti-8538	180	1	105	105	NUM
aiti-8538	180	2	-	-	SYM
aiti-8538	180	3	117	117	NUM
aiti-8538	180	4	the	the	DET
aiti-8538	180	5	vgg16	vgg16	NOUN
aiti-8538	180	6	model	model	NOUN
aiti-8538	180	7	architecture	architecture	NOUN
aiti-8538	180	8	has	have	VERB
aiti-8538	180	9	13	13	NUM
aiti-8538	180	10	convolution	convolution	NOUN
aiti-8538	180	11	layers	layer	NOUN
aiti-8538	180	12	of	of	ADP
aiti-8538	180	13	3	3	NUM
aiti-8538	180	14	×	×	NOUN
aiti-8538	180	15	3	3	NUM
aiti-8538	180	16	filters	filter	NOUN
aiti-8538	180	17	stacked	stack	VERB
aiti-8538	180	18	together	together	ADV
aiti-8538	180	19	along	along	ADV
aiti-8538	180	20	with	with	ADP
aiti-8538	180	21	5	5	NUM
aiti-8538	180	22	max	max	ADV
aiti-8538	180	23	-	-	PUNCT
aiti-8538	180	24	pooling	pool	VERB
aiti-8538	180	25	layers	layer	NOUN
aiti-8538	180	26	of	of	ADP
aiti-8538	180	27	the	the	DET
aiti-8538	180	28	size	size	NOUN
aiti-8538	180	29	2	2	NUM
aiti-8538	180	30	×	×	NOUN
aiti-8538	180	31	2	2	NUM
aiti-8538	180	32	.	.	PUNCT
aiti-8538	181	1	after	after	ADP
aiti-8538	181	2	the	the	DET
aiti-8538	181	3	last	last	ADJ
aiti-8538	181	4	max	max	NOUN
aiti-8538	181	5	-	-	PUNCT
aiti-8538	181	6	pooling	pool	VERB
aiti-8538	181	7	layer	layer	NOUN
aiti-8538	181	8	,	,	PUNCT
aiti-8538	181	9	there	there	PRON
aiti-8538	181	10	are	be	VERB
aiti-8538	181	11	three	three	NUM
aiti-8538	181	12	dense	dense	ADJ
aiti-8538	181	13	layers	layer	NOUN
aiti-8538	181	14	.	.	PUNCT
aiti-8538	182	1	vgg16	vgg16	PROPN
aiti-8538	182	2	uses	use	VERB
aiti-8538	182	3	the	the	DET
aiti-8538	182	4	relu	relu	NOUN
aiti-8538	182	5	activation	activation	NOUN
aiti-8538	182	6	function	function	NOUN
aiti-8538	182	7	and	and	CCONJ
aiti-8538	182	8	softmax	softmax	NOUN
aiti-8538	182	9	layer	layer	NOUN
aiti-8538	182	10	in	in	ADP
aiti-8538	182	11	the	the	DET
aiti-8538	182	12	final	final	ADJ
aiti-8538	182	13	dense	dense	ADJ
aiti-8538	182	14	layer	layer	NOUN
aiti-8538	182	15	.	.	PUNCT
aiti-8538	183	1	the	the	DET
aiti-8538	183	2	imagenet	imagenet	NOUN
aiti-8538	183	3	pretrained	pretraine	VERB
aiti-8538	183	4	model	model	NOUN
aiti-8538	183	5	vgg16	vgg16	PROPN
aiti-8538	183	6	has	have	VERB
aiti-8538	183	7	fewer	few	ADJ
aiti-8538	183	8	tunable	tunable	ADJ
aiti-8538	183	9	hyperparameters	hyperparameter	NOUN
aiti-8538	183	10	and	and	CCONJ
aiti-8538	183	11	achieves	achieve	VERB
aiti-8538	183	12	high	high	ADJ
aiti-8538	183	13	classification	classification	NOUN
aiti-8538	183	14	accuracy	accuracy	NOUN
aiti-8538	183	15	.	.	PUNCT
aiti-8538	184	1	the	the	DET
aiti-8538	184	2	low	low	ADJ
aiti-8538	184	3	-	-	PUNCT
aiti-8538	184	4	level	level	NOUN
aiti-8538	184	5	features	feature	NOUN
aiti-8538	184	6	already	already	ADV
aiti-8538	184	7	learned	learn	VERB
aiti-8538	184	8	by	by	ADP
aiti-8538	184	9	a	a	DET
aiti-8538	184	10	pretrained	pretraine	VERB
aiti-8538	184	11	model	model	NOUN
aiti-8538	184	12	are	be	AUX
aiti-8538	184	13	used	use	VERB
aiti-8538	184	14	for	for	ADP
aiti-8538	184	15	learning	learn	VERB
aiti-8538	184	16	another	another	DET
aiti-8538	184	17	problem	problem	NOUN
aiti-8538	184	18	.	.	PUNCT
aiti-8538	185	1	the	the	DET
aiti-8538	185	2	extracted	extract	VERB
aiti-8538	185	3	features	feature	NOUN
aiti-8538	185	4	are	be	AUX
aiti-8538	185	5	input	input	NOUN
aiti-8538	185	6	to	to	ADP
aiti-8538	185	7	pca	pca	PROPN
aiti-8538	185	8	to	to	PART
aiti-8538	185	9	select	select	VERB
aiti-8538	185	10	the	the	DET
aiti-8538	185	11	most	most	ADV
aiti-8538	185	12	relevant	relevant	ADJ
aiti-8538	185	13	features	feature	NOUN
aiti-8538	185	14	,	,	PUNCT
aiti-8538	185	15	enhance	enhance	VERB
aiti-8538	185	16	the	the	DET
aiti-8538	185	17	performance	performance	NOUN
aiti-8538	185	18	,	,	PUNCT
aiti-8538	185	19	and	and	CCONJ
aiti-8538	185	20	reduce	reduce	VERB
aiti-8538	185	21	the	the	DET
aiti-8538	185	22	training	training	NOUN
aiti-8538	185	23	time	time	NOUN
aiti-8538	185	24	.	.	PUNCT
aiti-8538	186	1	3.2	3.2	NUM
aiti-8538	186	2	.	.	PUNCT
aiti-8538	187	1	the	the	DET
aiti-8538	187	2	pca	pca	PROPN
aiti-8538	187	3	algorithm	algorithm	PROPN
aiti-8538	187	4	pca	pca	PROPN
aiti-8538	187	5	was	be	AUX
aiti-8538	187	6	introduced	introduce	VERB
aiti-8538	187	7	by	by	ADP
aiti-8538	187	8	karl	karl	PROPN
aiti-8538	187	9	pearson	pearson	PROPN
aiti-8538	188	1	[	[	X
aiti-8538	188	2	43	43	NUM
aiti-8538	188	3	]	]	PUNCT
aiti-8538	188	4	.	.	PUNCT
aiti-8538	189	1	pca	pca	PROPN
aiti-8538	189	2	is	be	AUX
aiti-8538	189	3	also	also	ADV
aiti-8538	189	4	known	know	VERB
aiti-8538	189	5	as	as	ADP
aiti-8538	189	6	hotelling	hotelle	VERB
aiti-8538	189	7	transform	transform	NOUN
aiti-8538	189	8	(	(	PUNCT
aiti-8538	189	9	ht	ht	NOUN
aiti-8538	189	10	)	)	PUNCT
aiti-8538	189	11	and	and	CCONJ
aiti-8538	189	12	is	be	AUX
aiti-8538	189	13	a	a	DET
aiti-8538	189	14	very	very	ADV
aiti-8538	189	15	popular	popular	ADJ
aiti-8538	189	16	dimensionality	dimensionality	NOUN
aiti-8538	189	17	reduction	reduction	NOUN
aiti-8538	189	18	technique	technique	NOUN
aiti-8538	189	19	used	use	VERB
aiti-8538	189	20	effectively	effectively	ADV
aiti-8538	189	21	in	in	ADP
aiti-8538	189	22	the	the	DET
aiti-8538	189	23	areas	area	NOUN
aiti-8538	189	24	of	of	ADP
aiti-8538	189	25	image	image	NOUN
aiti-8538	189	26	and	and	CCONJ
aiti-8538	189	27	signal	signal	NOUN
aiti-8538	189	28	processing	processing	NOUN
aiti-8538	189	29	.	.	PUNCT
aiti-8538	190	1	pca	pca	PROPN
aiti-8538	190	2	is	be	AUX
aiti-8538	190	3	used	use	VERB
aiti-8538	190	4	for	for	ADP
aiti-8538	190	5	reducing	reduce	VERB
aiti-8538	190	6	the	the	DET
aiti-8538	190	7	size	size	NOUN
aiti-8538	190	8	of	of	ADP
aiti-8538	190	9	the	the	DET
aiti-8538	190	10	feature	feature	NOUN
aiti-8538	190	11	vectors	vector	NOUN
aiti-8538	190	12	that	that	PRON
aiti-8538	190	13	are	be	AUX
aiti-8538	190	14	used	use	VERB
aiti-8538	190	15	for	for	ADP
aiti-8538	190	16	object	object	NOUN
aiti-8538	190	17	recognition	recognition	NOUN
aiti-8538	190	18	and	and	CCONJ
aiti-8538	190	19	object	object	VERB
aiti-8538	190	20	classification	classification	NOUN
aiti-8538	190	21	.	.	PUNCT
aiti-8538	191	1	pca	pca	PROPN
aiti-8538	191	2	can	can	AUX
aiti-8538	191	3	be	be	AUX
aiti-8538	191	4	implemented	implement	VERB
aiti-8538	191	5	using	use	VERB
aiti-8538	191	6	eigenvalues	eigenvalue	NOUN
aiti-8538	191	7	decomposition	decomposition	NOUN
aiti-8538	191	8	(	(	PUNCT
aiti-8538	191	9	evd	evd	NOUN
aiti-8538	191	10	)	)	PUNCT
aiti-8538	191	11	or	or	CCONJ
aiti-8538	191	12	singular	singular	ADJ
aiti-8538	191	13	value	value	NOUN
aiti-8538	191	14	decomposition	decomposition	NOUN
aiti-8538	191	15	(	(	PUNCT
aiti-8538	191	16	svd	svd	PROPN
aiti-8538	191	17	)	)	PUNCT
aiti-8538	191	18	.	.	PUNCT
aiti-8538	192	1	pca	pca	PROPN
aiti-8538	192	2	transforms	transform	VERB
aiti-8538	192	3	the	the	DET
aiti-8538	192	4	feature	feature	NOUN
aiti-8538	192	5	vectors	vector	NOUN
aiti-8538	192	6	with	with	ADP
aiti-8538	192	7	a	a	DET
aiti-8538	192	8	large	large	ADJ
aiti-8538	192	9	number	number	NOUN
aiti-8538	192	10	of	of	ADP
aiti-8538	192	11	correlated	correlate	VERB
aiti-8538	192	12	variables	variable	NOUN
aiti-8538	192	13	into	into	ADP
aiti-8538	192	14	a	a	DET
aiti-8538	192	15	smaller	small	ADJ
aiti-8538	192	16	number	number	NOUN
aiti-8538	192	17	of	of	ADP
aiti-8538	192	18	uncorrelated	uncorrelated	ADJ
aiti-8538	192	19	variables	variable	NOUN
aiti-8538	192	20	also	also	ADV
aiti-8538	192	21	known	know	VERB
aiti-8538	192	22	as	as	ADP
aiti-8538	192	23	principal	principal	ADJ
aiti-8538	192	24	components	component	NOUN
aiti-8538	192	25	(	(	PUNCT
aiti-8538	192	26	pcs	pc	NOUN
aiti-8538	192	27	)	)	PUNCT
aiti-8538	192	28	.	.	PUNCT
aiti-8538	193	1	consider	consider	VERB
aiti-8538	193	2	a	a	DET
aiti-8538	193	3	dataset	dataset	NOUN
aiti-8538	193	4	having	have	VERB
aiti-8538	193	5	k	k	PROPN
aiti-8538	193	6	images	image	NOUN
aiti-8538	193	7	with	with	ADP
aiti-8538	193	8	n	n	NOUN
aiti-8538	193	9	=	=	SYM
aiti-8538	193	10	n	n	CCONJ
aiti-8538	193	11	×	×	NOUN
aiti-8538	193	12	n	n	PRON
aiti-8538	193	13	pixels	pixel	NOUN
aiti-8538	193	14	,	,	PUNCT
aiti-8538	193	15	the	the	DET
aiti-8538	193	16	dataset	dataset	NOUN
aiti-8538	193	17	is	be	AUX
aiti-8538	193	18	represented	represent	VERB
aiti-8538	193	19	by	by	ADP
aiti-8538	193	20	d	d	PROPN
aiti-8538	193	21	=	=	SYM
aiti-8538	193	22	n	n	PROPN
aiti-8538	193	23	×	×	PROPN
aiti-8538	193	24	k	k	NOUN
aiti-8538	193	25	matrix	matrix	NOUN
aiti-8538	193	26	where	where	SCONJ
aiti-8538	193	27	di	di	NOUN
aiti-8538	193	28	represents	represent	VERB
aiti-8538	193	29	the	the	DET
aiti-8538	193	30	ith	ith	PROPN
aiti-8538	193	31	row	row	NOUN
aiti-8538	193	32	of	of	ADP
aiti-8538	193	33	the	the	DET
aiti-8538	193	34	matrix	matrix	NOUN
aiti-8538	193	35	or	or	CCONJ
aiti-8538	193	36	ith	ith	NOUN
aiti-8538	193	37	image	image	NOUN
aiti-8538	193	38	of	of	ADP
aiti-8538	193	39	the	the	DET
aiti-8538	193	40	dataset	dataset	NOUN
aiti-8538	193	41	.	.	PUNCT
aiti-8538	194	1	algorithm	algorithm	PROPN
aiti-8538	194	2	1	1	NUM
aiti-8538	194	3	gives	give	VERB
aiti-8538	194	4	the	the	DET
aiti-8538	194	5	pca	pca	PROPN
aiti-8538	194	6	algorithm	algorithm	NOUN
aiti-8538	194	7	.	.	PUNCT
aiti-8538	195	1	algorithm	algorithm	NOUN
aiti-8538	195	2	1	1	NUM
aiti-8538	195	3	:	:	PUNCT
aiti-8538	195	4	pca	pca	NOUN
aiti-8538	195	5	step	step	NOUN
aiti-8538	195	6	1	1	NUM
aiti-8538	195	7	:	:	PUNCT
aiti-8538	195	8	for	for	ADP
aiti-8538	195	9	i	i	PRON
aiti-8538	195	10	=	=	NOUN
aiti-8538	195	11	1	1	NUM
aiti-8538	195	12	to	to	ADP
aiti-8538	195	13	n	n	CCONJ
aiti-8538	195	14	,	,	PUNCT
aiti-8538	195	15	calculate	calculate	VERB
aiti-8538	195	16	the	the	DET
aiti-8538	195	17	mean	mean	NOUN
aiti-8538	195	18	of	of	ADP
aiti-8538	195	19	di	di	NOUN
aiti-8538	195	20	using	use	VERB
aiti-8538	195	21	eq	eq	X
aiti-8538	195	22	.	.	PUNCT
aiti-8538	196	1	(	(	PUNCT
aiti-8538	196	2	1	1	NUM
aiti-8538	196	3	):	):	PUNCT
aiti-8538	196	4	1==	1==	NUM
aiti-8538	196	5	∑	∑	PROPN
aiti-8538	196	6	i	i	PRON
aiti-8538	196	7	k	k	PROPN
aiti-8538	197	1	ij	ij	INTJ
aiti-8538	197	2	j	j	PROPN
aiti-8538	197	3	d	d	X
aiti-8538	197	4	k	k	PROPN
aiti-8538	197	5	µ	µ	X
aiti-8538	197	6	(	(	PUNCT
aiti-8538	197	7	1	1	NUM
aiti-8538	197	8	)	)	PUNCT
aiti-8538	197	9	step	step	NOUN
aiti-8538	197	10	2	2	NUM
aiti-8538	197	11	:	:	PUNCT
aiti-8538	197	12	shift	shift	VERB
aiti-8538	197	13	the	the	DET
aiti-8538	197	14	origin	origin	NOUN
aiti-8538	197	15	to	to	ADP
aiti-8538	197	16	the	the	DET
aiti-8538	197	17	mean	mean	NOUN
aiti-8538	197	18	of	of	ADP
aiti-8538	197	19	the	the	DET
aiti-8538	197	20	data	datum	NOUN
aiti-8538	197	21	by	by	ADP
aiti-8538	197	22	subtracting	subtract	VERB
aiti-8538	197	23	the	the	DET
aiti-8538	197	24	mean	mean	ADJ
aiti-8538	197	25	�	�	PROPN
aiti-8538	197	26	�	�	PROPN
aiti-8538	197	27	from	from	ADP
aiti-8538	197	28	each	each	DET
aiti-8538	197	29	column	column	NOUN
aiti-8538	197	30	vector	vector	PROPN
aiti-8538	197	31	�	�	PROPN
aiti-8538	197	32	�	�	PROPN
aiti-8538	197	33	�	�	PROPN
aiti-8538	197	34	as	as	SCONJ
aiti-8538	197	35	shown	show	VERB
aiti-8538	197	36	in	in	ADP
aiti-8538	197	37	eq	eq	ADP
aiti-8538	197	38	.	.	PUNCT
aiti-8538	198	1	(	(	PUNCT
aiti-8538	198	2	2	2	NUM
aiti-8538	198	3	):	):	PUNCT
aiti-8538	198	4	φ	φ	PROPN
aiti-8538	198	5	=	=	SYM
aiti-8538	198	6	−ij	−ij	PROPN
aiti-8538	198	7	ij	ij	INTJ
aiti-8538	198	8	i	i	PROPN
aiti-8538	198	9	d	d	PROPN
aiti-8538	198	10	µ	µ	X
aiti-8538	198	11	(	(	PUNCT
aiti-8538	198	12	2	2	NUM
aiti-8538	198	13	)	)	PUNCT
aiti-8538	198	14	step	step	NOUN
aiti-8538	198	15	3	3	NUM
aiti-8538	198	16	:	:	PUNCT
aiti-8538	198	17	compute	compute	VERB
aiti-8538	198	18	the	the	DET
aiti-8538	198	19	covariance	covariance	NOUN
aiti-8538	198	20	matrix	matrix	NOUN
aiti-8538	198	21	of	of	ADP
aiti-8538	198	22	mean	mean	ADJ
aiti-8538	198	23	-	-	PUNCT
aiti-8538	198	24	centered	center	VERB
aiti-8538	198	25	data	datum	NOUN
aiti-8538	198	26	using	use	VERB
aiti-8538	198	27	eq	eq	ADP
aiti-8538	198	28	.	.	PUNCT
aiti-8538	199	1	(	(	PUNCT
aiti-8538	199	2	3	3	NUM
aiti-8538	199	3	):	):	PUNCT
aiti-8538	199	4	=	=	VERB
aiti-8538	199	5	φφt	φφt	VERB
aiti-8538	199	6	c	c	X
aiti-8538	199	7	(	(	PUNCT
aiti-8538	199	8	3	3	NUM
aiti-8538	199	9	)	)	PUNCT
aiti-8538	199	10	where	where	SCONJ
aiti-8538	199	11	t	t	PROPN
aiti-8538	199	12	represents	represent	VERB
aiti-8538	199	13	the	the	DET
aiti-8538	199	14	transposition	transposition	NOUN
aiti-8538	199	15	matrix	matrix	NOUN
aiti-8538	199	16	.	.	PUNCT
aiti-8538	200	1	step	step	NOUN
aiti-8538	200	2	4	4	NUM
aiti-8538	200	3	:	:	PUNCT
aiti-8538	200	4	find	find	VERB
aiti-8538	200	5	eigenvalues	eigenvalue	NOUN
aiti-8538	200	6	�	�	PROPN
aiti-8538	200	7	�	�	PROPN
aiti-8538	200	8	,	,	PUNCT
aiti-8538	200	9	�	�	PROPN
aiti-8538	200	10	�	�	PROPN
aiti-8538	200	11	,	,	PUNCT
aiti-8538	200	12	�	�	PROPN
aiti-8538	200	13	�	�	PROPN
aiti-8538	200	14	,	,	PUNCT
aiti-8538	200	15	…	…	PUNCT
aiti-8538	200	16	,	,	PUNCT
aiti-8538	200	17	�	�	PROPN
aiti-8538	200	18	and	and	CCONJ
aiti-8538	200	19	eigenvectors	eigenvector	NOUN
aiti-8538	200	20	�	�	PROPN
aiti-8538	200	21	�	�	PROPN
aiti-8538	200	22	,	,	PUNCT
aiti-8538	200	23	�	�	PROPN
aiti-8538	200	24	�	�	PROPN
aiti-8538	200	25	,	,	PUNCT
aiti-8538	200	26	�	�	PROPN
aiti-8538	200	27	�	�	PROPN
aiti-8538	200	28	,	,	PUNCT
aiti-8538	200	29	…	…	PUNCT
aiti-8538	200	30	,	,	PUNCT
aiti-8538	200	31	�	�	PROPN
aiti-8538	200	32	of	of	ADP
aiti-8538	200	33	c	c	PROPN
aiti-8538	200	34	where	where	SCONJ
aiti-8538	200	35	�	�	PROPN
aiti-8538	200	36	�	�	PROPN
aiti-8538	200	37	>	>	SYM
aiti-8538	200	38	�	�	PROPN
aiti-8538	200	39	�	�	PROPN
aiti-8538	200	40	>	>	SYM
aiti-8538	200	41	�	�	PROPN
aiti-8538	200	42	�	�	PROPN
aiti-8538	200	43	>	>	SYM
aiti-8538	200	44	…	…	PUNCT
aiti-8538	200	45	�	�	PROPN
aiti-8538	200	46	.	.	PUNCT
aiti-8538	201	1	step	step	NOUN
aiti-8538	201	2	5	5	NUM
aiti-8538	201	3	:	:	PUNCT
aiti-8538	201	4	arrange	arrange	VERB
aiti-8538	201	5	the	the	DET
aiti-8538	201	6	eigenvectors	eigenvector	NOUN
aiti-8538	201	7	in	in	ADP
aiti-8538	201	8	descending	descend	VERB
aiti-8538	201	9	order	order	NOUN
aiti-8538	201	10	and	and	CCONJ
aiti-8538	201	11	return	return	VERB
aiti-8538	201	12	top	top	NOUN
aiti-8538	201	13	k	k	PROPN
aiti-8538	201	14	eigenvalues	eigenvalue	VERB
aiti-8538	201	15	corresponding	correspond	VERB
aiti-8538	201	16	to	to	ADP
aiti-8538	201	17	k	k	X
aiti-8538	201	18	number	number	NOUN
aiti-8538	201	19	of	of	ADP
aiti-8538	201	20	the	the	DET
aiti-8538	201	21	largest	large	ADJ
aiti-8538	201	22	eigenvalues	eigenvalue	NOUN
aiti-8538	201	23	also	also	ADV
aiti-8538	201	24	known	know	VERB
aiti-8538	201	25	as	as	ADP
aiti-8538	201	26	pcs	pc	NOUN
aiti-8538	201	27	.	.	PUNCT
aiti-8538	202	1	the	the	DET
aiti-8538	202	2	pca	pca	PROPN
aiti-8538	202	3	algorithm	algorithm	NOUN
aiti-8538	202	4	is	be	AUX
aiti-8538	202	5	a	a	DET
aiti-8538	202	6	popular	popular	ADJ
aiti-8538	202	7	dimensionality	dimensionality	NOUN
aiti-8538	202	8	reduction	reduction	NOUN
aiti-8538	202	9	technique	technique	NOUN
aiti-8538	202	10	which	which	PRON
aiti-8538	202	11	transforms	transform	VERB
aiti-8538	202	12	a	a	DET
aiti-8538	202	13	large	large	ADJ
aiti-8538	202	14	set	set	NOUN
aiti-8538	202	15	of	of	ADP
aiti-8538	202	16	correlated	correlate	VERB
aiti-8538	202	17	variables	variable	NOUN
aiti-8538	202	18	into	into	ADP
aiti-8538	202	19	fewer	few	ADJ
aiti-8538	202	20	uncorrelated	uncorrelated	ADJ
aiti-8538	202	21	variables	variable	NOUN
aiti-8538	202	22	.	.	PUNCT
aiti-8538	203	1	pca	pca	NOUN
aiti-8538	203	2	computes	compute	VERB
aiti-8538	203	3	uncorrelated	uncorrelated	ADJ
aiti-8538	203	4	variables	variable	NOUN
aiti-8538	203	5	by	by	ADP
aiti-8538	203	6	transforming	transform	VERB
aiti-8538	203	7	the	the	DET
aiti-8538	203	8	data	datum	NOUN
aiti-8538	203	9	to	to	ADP
aiti-8538	203	10	a	a	DET
aiti-8538	203	11	new	new	ADJ
aiti-8538	203	12	coordinate	coordinate	NOUN
aiti-8538	203	13	system	system	NOUN
aiti-8538	203	14	maintaining	maintain	VERB
aiti-8538	203	15	as	as	ADV
aiti-8538	203	16	much	much	ADJ
aiti-8538	203	17	variance	variance	NOUN
aiti-8538	203	18	as	as	ADP
aiti-8538	203	19	possible	possible	ADJ
aiti-8538	203	20	.	.	PUNCT
aiti-8538	204	1	pca	pca	PROPN
aiti-8538	204	2	is	be	AUX
aiti-8538	204	3	employed	employ	VERB
aiti-8538	204	4	for	for	ADP
aiti-8538	204	5	reducing	reduce	VERB
aiti-8538	204	6	a	a	DET
aiti-8538	204	7	large	large	ADJ
aiti-8538	204	8	number	number	NOUN
aiti-8538	204	9	of	of	ADP
aiti-8538	204	10	image	image	NOUN
aiti-8538	204	11	features	feature	NOUN
aiti-8538	204	12	into	into	ADP
aiti-8538	204	13	the	the	DET
aiti-8538	204	14	reduced	reduce	VERB
aiti-8538	204	15	one	one	NUM
aiti-8538	204	16	-	-	PUNCT
aiti-8538	204	17	dimensional	dimensional	ADJ
aiti-8538	204	18	feature	feature	NOUN
aiti-8538	204	19	set	set	NOUN
aiti-8538	204	20	representation	representation	NOUN
aiti-8538	204	21	(	(	PUNCT
aiti-8538	204	22	i.e.	i.e.	X
aiti-8538	204	23	,	,	PUNCT
aiti-8538	204	24	pcs	pc	NOUN
aiti-8538	204	25	)	)	PUNCT
aiti-8538	204	26	as	as	SCONJ
aiti-8538	204	27	shown	show	VERB
aiti-8538	204	28	in	in	ADP
aiti-8538	204	29	fig	fig	NOUN
aiti-8538	204	30	.	.	PUNCT
aiti-8538	205	1	2	2	X
aiti-8538	205	2	.	.	X
aiti-8538	205	3	pca	pca	PROPN
aiti-8538	205	4	flat	flat	ADJ
aiti-8538	205	5	image	image	NOUN
aiti-8538	205	6	1d	1d	NUM
aiti-8538	205	7	vector	vector	NOUN
aiti-8538	205	8	of	of	ADP
aiti-8538	205	9	length	length	NOUN
aiti-8538	205	10	1	1	NUM
aiti-8538	205	11	×	×	NOUN
aiti-8538	205	12	n	n	CCONJ
aiti-8538	205	13	(	(	PUNCT
aiti-8538	205	14	spatial	spatial	ADJ
aiti-8538	205	15	domain	domain	NOUN
aiti-8538	205	16	)	)	PUNCT
aiti-8538	205	17	1d	1d	NUM
aiti-8538	205	18	vector	vector	NOUN
aiti-8538	205	19	of	of	ADP
aiti-8538	205	20	length	length	NOUN
aiti-8538	205	21	1	1	NUM
aiti-8538	205	22	×	×	NOUN
aiti-8538	205	23	k	k	NOUN
aiti-8538	205	24	(	(	PUNCT
aiti-8538	205	25	where	where	SCONJ
aiti-8538	205	26	k	k	X
aiti-8538	205	27	<	<	X
aiti-8538	205	28	n	n	CCONJ
aiti-8538	205	29	)	)	PUNCT
aiti-8538	205	30	(	(	PUNCT
aiti-8538	205	31	pca	pca	NOUN
aiti-8538	205	32	space	space	NOUN
aiti-8538	205	33	)	)	PUNCT
aiti-8538	205	34	fig	fig	NOUN
aiti-8538	205	35	.	.	PUNCT
aiti-8538	206	1	2	2	NUM
aiti-8538	206	2	pca	pca	NOUN
aiti-8538	206	3	feature	feature	NOUN
aiti-8538	206	4	reduction	reduction	NOUN
aiti-8538	206	5	110	110	NUM
aiti-8538	206	6	advances	advance	NOUN
aiti-8538	206	7	in	in	ADP
aiti-8538	206	8	technology	technology	NOUN
aiti-8538	206	9	innovation	innovation	NOUN
aiti-8538	206	10	,	,	PUNCT
aiti-8538	206	11	vol	vol	NOUN
aiti-8538	206	12	.	.	PROPN
aiti-8538	206	13	7	7	NUM
aiti-8538	206	14	,	,	PUNCT
aiti-8538	206	15	no	no	INTJ
aiti-8538	206	16	.	.	NOUN
aiti-8538	206	17	2	2	NUM
aiti-8538	206	18	,	,	PUNCT
aiti-8538	206	19	2022	2022	NUM
aiti-8538	206	20	,	,	PUNCT
aiti-8538	206	21	pp	pp	ADJ
aiti-8538	206	22	.	.	PUNCT
aiti-8538	207	1	105	105	NUM
aiti-8538	207	2	-	-	SYM
aiti-8538	207	3	117	117	NUM
aiti-8538	207	4	for	for	ADP
aiti-8538	207	5	a	a	DET
aiti-8538	207	6	given	give	VERB
aiti-8538	207	7	set	set	NOUN
aiti-8538	207	8	of	of	ADP
aiti-8538	207	9	data	datum	NOUN
aiti-8538	207	10	,	,	PUNCT
aiti-8538	207	11	pca	pca	PROPN
aiti-8538	207	12	finds	find	VERB
aiti-8538	207	13	a	a	DET
aiti-8538	207	14	new	new	ADJ
aiti-8538	207	15	axis	axis	NOUN
aiti-8538	207	16	system	system	NOUN
aiti-8538	207	17	defined	define	VERB
aiti-8538	207	18	by	by	ADP
aiti-8538	207	19	the	the	DET
aiti-8538	207	20	principal	principal	ADJ
aiti-8538	207	21	directions	direction	NOUN
aiti-8538	207	22	of	of	ADP
aiti-8538	207	23	variance	variance	NOUN
aiti-8538	207	24	.	.	PUNCT
aiti-8538	208	1	suppose	suppose	VERB
aiti-8538	208	2	the	the	DET
aiti-8538	208	3	dataset	dataset	NOUN
aiti-8538	208	4	has	have	VERB
aiti-8538	208	5	400	400	NUM
aiti-8538	208	6	images	image	NOUN
aiti-8538	208	7	of	of	ADP
aiti-8538	208	8	the	the	DET
aiti-8538	208	9	size	size	NOUN
aiti-8538	208	10	256	256	NUM
aiti-8538	208	11	×	×	NOUN
aiti-8538	208	12	256	256	NUM
aiti-8538	208	13	,	,	PUNCT
aiti-8538	208	14	then	then	ADV
aiti-8538	208	15	the	the	DET
aiti-8538	208	16	size	size	NOUN
aiti-8538	208	17	of	of	ADP
aiti-8538	208	18	the	the	DET
aiti-8538	208	19	data	data	NOUN
aiti-8538	208	20	matrix	matrix	NOUN
aiti-8538	208	21	will	will	AUX
aiti-8538	208	22	be	be	AUX
aiti-8538	208	23	400	400	NUM
aiti-8538	208	24	×	×	NOUN
aiti-8538	208	25	(	(	PUNCT
aiti-8538	208	26	256	256	NUM
aiti-8538	208	27	×	×	NOUN
aiti-8538	208	28	256	256	NUM
aiti-8538	208	29	)	)	PUNCT
aiti-8538	208	30	=	=	SYM
aiti-8538	208	31	400	400	NUM
aiti-8538	208	32	×	×	NOUN
aiti-8538	208	33	65536	65536	NUM
aiti-8538	208	34	.	.	PUNCT
aiti-8538	209	1	therefore	therefore	ADV
aiti-8538	209	2	,	,	PUNCT
aiti-8538	209	3	for	for	ADP
aiti-8538	209	4	each	each	PRON
aiti-8538	209	5	of	of	ADP
aiti-8538	209	6	the	the	DET
aiti-8538	209	7	400	400	NUM
aiti-8538	209	8	images	image	NOUN
aiti-8538	209	9	,	,	PUNCT
aiti-8538	209	10	the	the	DET
aiti-8538	209	11	data	data	NOUN
aiti-8538	209	12	matrix	matrix	NOUN
aiti-8538	209	13	will	will	AUX
aiti-8538	209	14	have	have	VERB
aiti-8538	209	15	65536	65536	NUM
aiti-8538	209	16	columns	column	NOUN
aiti-8538	209	17	.	.	PUNCT
aiti-8538	210	1	the	the	DET
aiti-8538	210	2	size	size	NOUN
aiti-8538	210	3	of	of	ADP
aiti-8538	210	4	the	the	DET
aiti-8538	210	5	covariant	covariant	ADJ
aiti-8538	210	6	matrix	matrix	NOUN
aiti-8538	210	7	and	and	CCONJ
aiti-8538	210	8	transformation	transformation	NOUN
aiti-8538	210	9	matrix	matrix	NOUN
aiti-8538	210	10	becomes	become	VERB
aiti-8538	210	11	65536	65536	NUM
aiti-8538	210	12	×	×	NOUN
aiti-8538	210	13	65536	65536	NUM
aiti-8538	210	14	.	.	PUNCT
aiti-8538	211	1	now	now	ADV
aiti-8538	211	2	,	,	PUNCT
aiti-8538	211	3	using	use	VERB
aiti-8538	211	4	pca	pca	NOUN
aiti-8538	211	5	,	,	PUNCT
aiti-8538	211	6	50	50	NUM
aiti-8538	211	7	columns	column	NOUN
aiti-8538	211	8	of	of	ADP
aiti-8538	211	9	the	the	DET
aiti-8538	211	10	transformation	transformation	NOUN
aiti-8538	211	11	matrix	matrix	NOUN
aiti-8538	211	12	corresponding	correspond	VERB
aiti-8538	211	13	to	to	ADP
aiti-8538	211	14	the	the	DET
aiti-8538	211	15	50	50	NUM
aiti-8538	211	16	largest	large	ADJ
aiti-8538	211	17	eigenvalues	eigenvalue	NOUN
aiti-8538	211	18	are	be	AUX
aiti-8538	211	19	selected	select	VERB
aiti-8538	211	20	.	.	PUNCT
aiti-8538	212	1	then	then	ADV
aiti-8538	212	2	,	,	PUNCT
aiti-8538	212	3	the	the	DET
aiti-8538	212	4	size	size	NOUN
aiti-8538	212	5	of	of	ADP
aiti-8538	212	6	the	the	DET
aiti-8538	212	7	reduced	reduce	VERB
aiti-8538	212	8	transformation	transformation	NOUN
aiti-8538	212	9	matrix	matrix	NOUN
aiti-8538	212	10	p	p	NOUN
aiti-8538	212	11	is	be	AUX
aiti-8538	212	12	computed	compute	VERB
aiti-8538	212	13	as	as	ADP
aiti-8538	212	14	65536	65536	NUM
aiti-8538	212	15	×	×	NOUN
aiti-8538	212	16	50	50	NUM
aiti-8538	212	17	.	.	PUNCT
aiti-8538	213	1	the	the	DET
aiti-8538	213	2	next	next	ADJ
aiti-8538	213	3	step	step	NOUN
aiti-8538	213	4	is	be	AUX
aiti-8538	213	5	to	to	PART
aiti-8538	213	6	obtain	obtain	VERB
aiti-8538	213	7	the	the	DET
aiti-8538	213	8	transformed	transform	VERB
aiti-8538	213	9	dataset	dataset	VERB
aiti-8538	213	10	t	t	PROPN
aiti-8538	213	11	using	use	VERB
aiti-8538	213	12	eq	eq	ADP
aiti-8538	213	13	.	.	PUNCT
aiti-8538	214	1	(	(	PUNCT
aiti-8538	214	2	4	4	NUM
aiti-8538	214	3	)	)	PUNCT
aiti-8538	214	4	.	.	PUNCT
aiti-8538	215	1	×	×	PROPN
aiti-8538	215	2	×=	×=	PROPN
aiti-8538	215	3	φ	φ	PROPN
aiti-8538	215	4	s	s	PROPN
aiti-8538	215	5	n	n	CCONJ
aiti-8538	215	6	n	n	PRON
aiti-8538	215	7	k	k	PROPN
aiti-8538	215	8	t	t	PROPN
aiti-8538	215	9	p	p	X
aiti-8538	215	10	(	(	PUNCT
aiti-8538	215	11	4	4	NUM
aiti-8538	215	12	)	)	PUNCT
aiti-8538	215	13	the	the	DET
aiti-8538	215	14	size	size	NOUN
aiti-8538	215	15	of	of	ADP
aiti-8538	215	16	the	the	DET
aiti-8538	215	17	transformed	transform	VERB
aiti-8538	215	18	dataset	dataset	NOUN
aiti-8538	215	19	is	be	AUX
aiti-8538	215	20	400	400	NUM
aiti-8538	215	21	×	×	NOUN
aiti-8538	215	22	50	50	NUM
aiti-8538	215	23	.	.	PUNCT
aiti-8538	216	1	thus	thus	ADV
aiti-8538	216	2	,	,	PUNCT
aiti-8538	216	3	the	the	DET
aiti-8538	216	4	initial	initial	ADJ
aiti-8538	216	5	data	data	NOUN
aiti-8538	216	6	matrix	matrix	NOUN
aiti-8538	216	7	of	of	ADP
aiti-8538	216	8	the	the	DET
aiti-8538	216	9	size	size	NOUN
aiti-8538	216	10	400	400	NUM
aiti-8538	216	11	×	×	NOUN
aiti-8538	216	12	65536	65536	NUM
aiti-8538	216	13	is	be	AUX
aiti-8538	216	14	reduced	reduce	VERB
aiti-8538	216	15	to	to	ADP
aiti-8538	216	16	the	the	DET
aiti-8538	216	17	new	new	ADJ
aiti-8538	216	18	representation	representation	NOUN
aiti-8538	216	19	,	,	PUNCT
aiti-8538	216	20	i.e.	i.e.	X
aiti-8538	216	21	,	,	PUNCT
aiti-8538	216	22	the	the	DET
aiti-8538	216	23	transformed	transform	VERB
aiti-8538	216	24	matrix	matrix	NOUN
aiti-8538	216	25	t	t	NOUN
aiti-8538	216	26	of	of	ADP
aiti-8538	216	27	the	the	DET
aiti-8538	216	28	size	size	NOUN
aiti-8538	216	29	400	400	NUM
aiti-8538	216	30	×	×	NOUN
aiti-8538	216	31	50	50	NUM
aiti-8538	216	32	.	.	PUNCT
aiti-8538	216	33	3.3	3.3	NUM
aiti-8538	216	34	.	.	PUNCT
aiti-8538	217	1	the	the	DET
aiti-8538	217	2	proposed	propose	VERB
aiti-8538	217	3	approach	approach	NOUN
aiti-8538	217	4	in	in	ADP
aiti-8538	217	5	this	this	DET
aiti-8538	217	6	section	section	NOUN
aiti-8538	217	7	,	,	PUNCT
aiti-8538	217	8	the	the	DET
aiti-8538	217	9	proposed	propose	VERB
aiti-8538	217	10	approach	approach	NOUN
aiti-8538	217	11	for	for	ADP
aiti-8538	217	12	image	image	NOUN
aiti-8538	217	13	classification	classification	NOUN
aiti-8538	217	14	is	be	AUX
aiti-8538	217	15	described	describe	VERB
aiti-8538	217	16	in	in	ADP
aiti-8538	217	17	details	detail	NOUN
aiti-8538	217	18	.	.	PUNCT
aiti-8538	218	1	deep	deep	ADJ
aiti-8538	218	2	neural	neural	ADJ
aiti-8538	218	3	networks	network	NOUN
aiti-8538	218	4	extract	extract	VERB
aiti-8538	218	5	useful	useful	ADJ
aiti-8538	218	6	feature	feature	NOUN
aiti-8538	218	7	maps	map	NOUN
aiti-8538	218	8	for	for	ADP
aiti-8538	218	9	the	the	DET
aiti-8538	218	10	recognition	recognition	NOUN
aiti-8538	218	11	of	of	ADP
aiti-8538	218	12	images	image	NOUN
aiti-8538	218	13	.	.	PUNCT
aiti-8538	219	1	recent	recent	ADJ
aiti-8538	219	2	studies	study	NOUN
aiti-8538	219	3	in	in	ADP
aiti-8538	219	4	computer	computer	NOUN
aiti-8538	219	5	vision	vision	NOUN
aiti-8538	219	6	have	have	AUX
aiti-8538	219	7	successfully	successfully	ADV
aiti-8538	219	8	used	use	VERB
aiti-8538	219	9	cnn	cnn	PROPN
aiti-8538	219	10	for	for	ADP
aiti-8538	219	11	feature	feature	NOUN
aiti-8538	219	12	representation	representation	NOUN
aiti-8538	219	13	.	.	PUNCT
aiti-8538	220	1	building	building	NOUN
aiti-8538	220	2	and	and	CCONJ
aiti-8538	220	3	training	train	VERB
aiti-8538	220	4	an	an	DET
aiti-8538	220	5	efficient	efficient	ADJ
aiti-8538	220	6	cnn	cnn	NOUN
aiti-8538	220	7	from	from	ADP
aiti-8538	220	8	scratch	scratch	NOUN
aiti-8538	220	9	requires	require	VERB
aiti-8538	220	10	technical	technical	ADJ
aiti-8538	220	11	and	and	CCONJ
aiti-8538	220	12	domain	domain	NOUN
aiti-8538	220	13	expertise	expertise	NOUN
aiti-8538	220	14	.	.	PUNCT
aiti-8538	221	1	designing	design	VERB
aiti-8538	221	2	an	an	DET
aiti-8538	221	3	optimized	optimize	VERB
aiti-8538	221	4	cnn	cnn	NOUN
aiti-8538	221	5	architecture	architecture	NOUN
aiti-8538	221	6	for	for	ADP
aiti-8538	221	7	a	a	DET
aiti-8538	221	8	real	real	ADJ
aiti-8538	221	9	-	-	PUNCT
aiti-8538	221	10	world	world	NOUN
aiti-8538	221	11	computer	computer	NOUN
aiti-8538	221	12	vision	vision	PROPN
aiti-8538	221	13	task	task	PROPN
aiti-8538	221	14	is	be	AUX
aiti-8538	221	15	a	a	DET
aiti-8538	221	16	complicated	complicated	ADJ
aiti-8538	221	17	and	and	CCONJ
aiti-8538	221	18	time	time	NOUN
aiti-8538	221	19	-	-	PUNCT
aiti-8538	221	20	consuming	consume	VERB
aiti-8538	221	21	task	task	NOUN
aiti-8538	221	22	.	.	PUNCT
aiti-8538	222	1	in	in	ADP
aiti-8538	222	2	this	this	DET
aiti-8538	222	3	study	study	NOUN
aiti-8538	222	4	,	,	PUNCT
aiti-8538	222	5	the	the	DET
aiti-8538	222	6	vgg16	vgg16	NOUN
aiti-8538	222	7	architecture	architecture	NOUN
aiti-8538	222	8	is	be	AUX
aiti-8538	222	9	employed	employ	VERB
aiti-8538	222	10	for	for	ADP
aiti-8538	222	11	feature	feature	NOUN
aiti-8538	222	12	extraction	extraction	NOUN
aiti-8538	222	13	.	.	PUNCT
aiti-8538	223	1	the	the	DET
aiti-8538	223	2	weights	weight	NOUN
aiti-8538	223	3	of	of	ADP
aiti-8538	223	4	the	the	DET
aiti-8538	223	5	convolution	convolution	NOUN
aiti-8538	223	6	base	base	NOUN
aiti-8538	223	7	layers	layer	NOUN
aiti-8538	223	8	of	of	ADP
aiti-8538	223	9	the	the	DET
aiti-8538	223	10	vgg16	vgg16	NOUN
aiti-8538	223	11	model	model	NOUN
aiti-8538	223	12	are	be	AUX
aiti-8538	223	13	reused	reuse	VERB
aiti-8538	223	14	and	and	CCONJ
aiti-8538	223	15	are	be	AUX
aiti-8538	223	16	not	not	PART
aiti-8538	223	17	updated	update	VERB
aiti-8538	223	18	.	.	PUNCT
aiti-8538	224	1	the	the	DET
aiti-8538	224	2	vgg16	vgg16	PROPN
aiti-8538	224	3	neural	neural	ADJ
aiti-8538	224	4	network	network	NOUN
aiti-8538	224	5	processes	process	VERB
aiti-8538	224	6	the	the	DET
aiti-8538	224	7	input	input	NOUN
aiti-8538	224	8	image	image	NOUN
aiti-8538	224	9	to	to	PART
aiti-8538	224	10	extract	extract	VERB
aiti-8538	224	11	activation	activation	NOUN
aiti-8538	224	12	maps	map	NOUN
aiti-8538	224	13	that	that	PRON
aiti-8538	224	14	describe	describe	VERB
aiti-8538	224	15	features	feature	NOUN
aiti-8538	224	16	in	in	ADP
aiti-8538	224	17	an	an	DET
aiti-8538	224	18	image	image	NOUN
aiti-8538	224	19	[	[	X
aiti-8538	224	20	44	44	NUM
aiti-8538	224	21	]	]	PUNCT
aiti-8538	224	22	.	.	PUNCT
aiti-8538	225	1	the	the	DET
aiti-8538	225	2	features	feature	NOUN
aiti-8538	225	3	are	be	AUX
aiti-8538	225	4	extracted	extract	VERB
aiti-8538	225	5	from	from	ADP
aiti-8538	225	6	the	the	DET
aiti-8538	225	7	block5_pool	block5_pool	PROPN
aiti-8538	225	8	layer	layer	NOUN
aiti-8538	225	9	of	of	ADP
aiti-8538	225	10	the	the	DET
aiti-8538	225	11	vgg16	vgg16	NOUN
aiti-8538	225	12	architecture	architecture	NOUN
aiti-8538	225	13	.	.	PUNCT
aiti-8538	226	1	the	the	DET
aiti-8538	226	2	features	feature	NOUN
aiti-8538	226	3	of	of	ADP
aiti-8538	226	4	the	the	DET
aiti-8538	226	5	images	image	NOUN
aiti-8538	226	6	extracted	extract	VERB
aiti-8538	226	7	from	from	ADP
aiti-8538	226	8	the	the	DET
aiti-8538	226	9	vgg16	vgg16	NOUN
aiti-8538	226	10	model	model	NOUN
aiti-8538	226	11	are	be	AUX
aiti-8538	226	12	converted	convert	VERB
aiti-8538	226	13	into	into	ADP
aiti-8538	226	14	a	a	DET
aiti-8538	226	15	vector	vector	NOUN
aiti-8538	226	16	of	of	ADP
aiti-8538	226	17	32768	32768	NUM
aiti-8538	226	18	numbers	number	NOUN
aiti-8538	226	19	.	.	PUNCT
aiti-8538	227	1	the	the	DET
aiti-8538	227	2	pca	pca	PROPN
aiti-8538	227	3	feature	feature	NOUN
aiti-8538	227	4	reduction	reduction	NOUN
aiti-8538	227	5	technique	technique	NOUN
aiti-8538	227	6	is	be	AUX
aiti-8538	227	7	employed	employ	VERB
aiti-8538	227	8	to	to	PART
aiti-8538	227	9	reduce	reduce	VERB
aiti-8538	227	10	the	the	DET
aiti-8538	227	11	dimensionality	dimensionality	NOUN
aiti-8538	227	12	of	of	ADP
aiti-8538	227	13	vgg16	vgg16	NOUN
aiti-8538	227	14	features	feature	NOUN
aiti-8538	227	15	and	and	CCONJ
aiti-8538	227	16	at	at	ADP
aiti-8538	227	17	the	the	DET
aiti-8538	227	18	same	same	ADJ
aiti-8538	227	19	time	time	NOUN
aiti-8538	227	20	maintain	maintain	VERB
aiti-8538	227	21	the	the	DET
aiti-8538	227	22	distinctive	distinctive	ADJ
aiti-8538	227	23	properties	property	NOUN
aiti-8538	227	24	of	of	ADP
aiti-8538	227	25	the	the	DET
aiti-8538	227	26	features	feature	NOUN
aiti-8538	227	27	thereby	thereby	ADV
aiti-8538	227	28	improving	improve	VERB
aiti-8538	227	29	the	the	DET
aiti-8538	227	30	training	training	NOUN
aiti-8538	227	31	/	/	SYM
aiti-8538	227	32	prediction	prediction	NOUN
aiti-8538	227	33	time	time	NOUN
aiti-8538	227	34	.	.	PUNCT
aiti-8538	228	1	the	the	DET
aiti-8538	228	2	reduced	reduce	VERB
aiti-8538	228	3	features	feature	NOUN
aiti-8538	228	4	are	be	AUX
aiti-8538	228	5	fed	feed	VERB
aiti-8538	228	6	to	to	ADP
aiti-8538	228	7	the	the	DET
aiti-8538	228	8	multilayer	multilayer	PROPN
aiti-8538	228	9	perceptron	perceptron	PROPN
aiti-8538	228	10	model	model	NOUN
aiti-8538	228	11	or	or	CCONJ
aiti-8538	228	12	svm	svm	ADJ
aiti-8538	228	13	model	model	NOUN
aiti-8538	228	14	.	.	PUNCT
aiti-8538	229	1	the	the	DET
aiti-8538	229	2	hyperparameters	hyperparameter	NOUN
aiti-8538	229	3	of	of	ADP
aiti-8538	229	4	these	these	DET
aiti-8538	229	5	image	image	NOUN
aiti-8538	229	6	classifiers	classifier	NOUN
aiti-8538	229	7	are	be	AUX
aiti-8538	229	8	optimized	optimize	VERB
aiti-8538	229	9	using	use	VERB
aiti-8538	229	10	a	a	DET
aiti-8538	229	11	grid	grid	NOUN
aiti-8538	229	12	-	-	PUNCT
aiti-8538	229	13	search	search	NOUN
aiti-8538	229	14	algorithm	algorithm	NOUN
aiti-8538	229	15	[	[	X
aiti-8538	229	16	45	45	NUM
aiti-8538	229	17	]	]	PUNCT
aiti-8538	229	18	.	.	PUNCT
aiti-8538	230	1	fig	fig	NOUN
aiti-8538	230	2	.	.	PUNCT
aiti-8538	231	1	3	3	NUM
aiti-8538	231	2	shows	show	VERB
aiti-8538	231	3	the	the	DET
aiti-8538	231	4	proposed	propose	VERB
aiti-8538	231	5	image	image	NOUN
aiti-8538	231	6	recognition	recognition	NOUN
aiti-8538	231	7	approach	approach	NOUN
aiti-8538	231	8	.	.	PUNCT
aiti-8538	232	1	fig	fig	NOUN
aiti-8538	232	2	.	.	PUNCT
aiti-8538	233	1	3	3	NUM
aiti-8538	233	2	the	the	DET
aiti-8538	233	3	proposed	propose	VERB
aiti-8538	233	4	image	image	NOUN
aiti-8538	233	5	recognition	recognition	NOUN
aiti-8538	233	6	approach	approach	NOUN
aiti-8538	233	7	(	(	PUNCT
aiti-8538	233	8	fashion	fashion	NOUN
aiti-8538	233	9	-	-	PUNCT
aiti-8538	233	10	mnist	mnist	NOUN
aiti-8538	233	11	dataset	dataset	NOUN
aiti-8538	233	12	)	)	PUNCT
aiti-8538	233	13	3.4	3.4	NUM
aiti-8538	233	14	.	.	PUNCT
aiti-8538	234	1	datasets	dataset	VERB
aiti-8538	234	2	the	the	DET
aiti-8538	234	3	proposed	propose	VERB
aiti-8538	234	4	image	image	NOUN
aiti-8538	234	5	classification	classification	NOUN
aiti-8538	234	6	approach	approach	NOUN
aiti-8538	234	7	is	be	AUX
aiti-8538	234	8	validated	validate	VERB
aiti-8538	234	9	on	on	ADP
aiti-8538	234	10	four	four	NUM
aiti-8538	234	11	different	different	ADJ
aiti-8538	234	12	image	image	NOUN
aiti-8538	234	13	datasets	dataset	NOUN
aiti-8538	234	14	,	,	PUNCT
aiti-8538	234	15	namely	namely	ADV
aiti-8538	234	16	,	,	PUNCT
aiti-8538	234	17	orl	orl	PROPN
aiti-8538	234	18	dataset	dataset	NOUN
aiti-8538	234	19	of	of	ADP
aiti-8538	234	20	faces	face	NOUN
aiti-8538	234	21	,	,	PUNCT
aiti-8538	234	22	fashion	fashion	NOUN
aiti-8538	234	23	-	-	PUNCT
aiti-8538	234	24	mnist	mnist	NOUN
aiti-8538	234	25	dataset	dataset	NOUN
aiti-8538	234	26	,	,	PUNCT
aiti-8538	234	27	corn	corn	NOUN
aiti-8538	234	28	leaf	leaf	NOUN
aiti-8538	234	29	disease	disease	NOUN
aiti-8538	234	30	dataset	dataset	NOUN
aiti-8538	234	31	,	,	PUNCT
aiti-8538	234	32	and	and	CCONJ
aiti-8538	234	33	rice	rice	NOUN
aiti-8538	234	34	leaf	leaf	NOUN
aiti-8538	234	35	disease	disease	NOUN
aiti-8538	234	36	dataset	dataset	VERB
aiti-8538	234	37	.	.	PUNCT
aiti-8538	235	1	the	the	DET
aiti-8538	235	2	training	training	NOUN
aiti-8538	235	3	and	and	CCONJ
aiti-8538	235	4	testing	testing	NOUN
aiti-8538	235	5	datasets	dataset	NOUN
aiti-8538	235	6	are	be	AUX
aiti-8538	235	7	randomly	randomly	ADV
aiti-8538	235	8	divided	divide	VERB
aiti-8538	235	9	in	in	ADP
aiti-8538	235	10	the	the	DET
aiti-8538	235	11	ratio	ratio	NOUN
aiti-8538	235	12	of	of	ADP
aiti-8538	235	13	80:20	80:20	NUM
aiti-8538	235	14	.	.	PUNCT
aiti-8538	236	1	some	some	PRON
aiti-8538	236	2	of	of	ADP
aiti-8538	236	3	the	the	DET
aiti-8538	236	4	sample	sample	NOUN
aiti-8538	236	5	images	image	NOUN
aiti-8538	236	6	of	of	ADP
aiti-8538	236	7	the	the	DET
aiti-8538	236	8	dataset	dataset	NOUN
aiti-8538	236	9	are	be	AUX
aiti-8538	236	10	shown	show	VERB
aiti-8538	236	11	in	in	ADP
aiti-8538	236	12	fig	fig	NOUN
aiti-8538	236	13	.	.	PUNCT
aiti-8538	237	1	4	4	NUM
aiti-8538	237	2	.	.	NOUN
aiti-8538	237	3	111	111	NUM
aiti-8538	237	4	advances	advance	NOUN
aiti-8538	237	5	in	in	ADP
aiti-8538	237	6	technology	technology	NOUN
aiti-8538	237	7	innovation	innovation	NOUN
aiti-8538	237	8	,	,	PUNCT
aiti-8538	237	9	vol	vol	NOUN
aiti-8538	237	10	.	.	PROPN
aiti-8538	237	11	7	7	NUM
aiti-8538	237	12	,	,	PUNCT
aiti-8538	237	13	no	no	INTJ
aiti-8538	237	14	.	.	NOUN
aiti-8538	237	15	2	2	NUM
aiti-8538	237	16	,	,	PUNCT
aiti-8538	237	17	2022	2022	NUM
aiti-8538	237	18	,	,	PUNCT
aiti-8538	237	19	pp	pp	ADJ
aiti-8538	237	20	.	.	PUNCT
aiti-8538	238	1	105	105	NUM
aiti-8538	238	2	-	-	SYM
aiti-8538	238	3	117	117	NUM
aiti-8538	238	4	s1/2	s1/2	VERB
aiti-8538	238	5	s2/3	s2/3	NUM
aiti-8538	238	6	s3/8	s3/8	ADV
aiti-8538	238	7	s7/10	s7/10	ADV
aiti-8538	238	8	s6/5	s6/5	NOUN
aiti-8538	238	9	s8/7	s8/7	NOUN
aiti-8538	238	10	s10/4	s10/4	PROPN
aiti-8538	238	11	s15/7	s15/7	INTJ
aiti-8538	238	12	(	(	PUNCT
aiti-8538	238	13	a	a	X
aiti-8538	238	14	)	)	PUNCT
aiti-8538	238	15	orl	orl	PROPN
aiti-8538	238	16	dataset	dataset	NOUN
aiti-8538	238	17	of	of	ADP
aiti-8538	238	18	faces	face	NOUN
aiti-8538	238	19	dress	dress	NOUN
aiti-8538	238	20	coat	coat	NOUN
aiti-8538	238	21	pullover	pullover	NOUN
aiti-8538	238	22	bag	bag	NOUN
aiti-8538	238	23	pullover	pullover	NOUN
aiti-8538	238	24	coat	coat	NOUN
aiti-8538	238	25	ankle	ankle	NOUN
aiti-8538	238	26	boot	boot	NOUN
aiti-8538	238	27	shirt	shirt	NOUN
aiti-8538	238	28	(	(	PUNCT
aiti-8538	238	29	b	b	NOUN
aiti-8538	238	30	)	)	PUNCT
aiti-8538	238	31	fashion	fashion	NOUN
aiti-8538	238	32	-	-	PUNCT
aiti-8538	238	33	mnist	mnist	NOUN
aiti-8538	238	34	dataset	dataset	NOUN
aiti-8538	238	35	blight	blight	NOUN
aiti-8538	238	36	common	common	ADJ
aiti-8538	238	37	rust	rust	NOUN
aiti-8538	238	38	gray	gray	ADJ
aiti-8538	238	39	spot	spot	NOUN
aiti-8538	238	40	health	health	NOUN
aiti-8538	238	41	(	(	PUNCT
aiti-8538	238	42	c	c	NOUN
aiti-8538	238	43	)	)	PUNCT
aiti-8538	238	44	corn	corn	NOUN
aiti-8538	238	45	leaf	leaf	NOUN
aiti-8538	238	46	diseases	disease	NOUN
aiti-8538	238	47	dataset	dataset	VERB
aiti-8538	238	48	bacterail	bacterail	NOUN
aiti-8538	238	49	blight	blight	NOUN
aiti-8538	238	50	blast	blast	NOUN
aiti-8538	238	51	brown	brown	ADJ
aiti-8538	238	52	spot	spot	NOUN
aiti-8538	238	53	tungro	tungro	NOUN
aiti-8538	238	54	(	(	PUNCT
aiti-8538	238	55	d	d	NOUN
aiti-8538	238	56	)	)	PUNCT
aiti-8538	238	57	rice	rice	NOUN
aiti-8538	238	58	leaf	leaf	NOUN
aiti-8538	238	59	disease	disease	NOUN
aiti-8538	238	60	dataset	dataset	VERB
aiti-8538	238	61	fig	fig	NOUN
aiti-8538	238	62	.	.	PUNCT
aiti-8538	239	1	4	4	NUM
aiti-8538	239	2	sample	sample	NOUN
aiti-8538	239	3	images	image	NOUN
aiti-8538	239	4	of	of	ADP
aiti-8538	239	5	datasets	dataset	NOUN
aiti-8538	239	6	table	table	NOUN
aiti-8538	239	7	2	2	NUM
aiti-8538	239	8	dataset	dataset	NOUN
aiti-8538	239	9	images	image	NOUN
aiti-8538	239	10	used	use	VERB
aiti-8538	239	11	for	for	ADP
aiti-8538	239	12	the	the	DET
aiti-8538	239	13	evaluation	evaluation	NOUN
aiti-8538	239	14	of	of	ADP
aiti-8538	239	15	the	the	DET
aiti-8538	239	16	proposed	propose	VERB
aiti-8538	239	17	approach	approach	NOUN
aiti-8538	239	18	image	image	NOUN
aiti-8538	239	19	dataset	dataset	NOUN
aiti-8538	239	20	training	training	NOUN
aiti-8538	239	21	images	image	NOUN
aiti-8538	239	22	testing	testing	NOUN
aiti-8538	239	23	images	image	NOUN
aiti-8538	239	24	total	total	ADJ
aiti-8538	239	25	images	image	NOUN
aiti-8538	239	26	orl	orl	VERB
aiti-8538	239	27	160	160	NUM
aiti-8538	239	28	40	40	NUM
aiti-8538	239	29	200	200	NUM
aiti-8538	239	30	corn	corn	NOUN
aiti-8538	239	31	leaf	leaf	NOUN
aiti-8538	239	32	disease	disease	NOUN
aiti-8538	239	33	3350	3350	NUM
aiti-8538	239	34	838	838	NUM
aiti-8538	239	35	4188	4188	NUM
aiti-8538	239	36	rice	rice	NOUN
aiti-8538	239	37	leaf	leaf	NOUN
aiti-8538	239	38	disease	disease	NOUN
aiti-8538	239	39	4105	4105	NUM
aiti-8538	239	40	1027	1027	NUM
aiti-8538	239	41	5132	5132	NUM
aiti-8538	239	42	fashion	fashion	NOUN
aiti-8538	239	43	-	-	PUNCT
aiti-8538	239	44	mnist	mnist	NOUN
aiti-8538	239	45	60000	60000	NUM
aiti-8538	239	46	10000	10000	NUM
aiti-8538	239	47	70000	70000	NUM
aiti-8538	239	48	table	table	NOUN
aiti-8538	239	49	2	2	NUM
aiti-8538	239	50	shows	show	VERB
aiti-8538	239	51	the	the	DET
aiti-8538	239	52	number	number	NOUN
aiti-8538	239	53	of	of	ADP
aiti-8538	239	54	training	training	NOUN
aiti-8538	239	55	and	and	CCONJ
aiti-8538	239	56	testing	testing	NOUN
aiti-8538	239	57	images	image	NOUN
aiti-8538	239	58	of	of	ADP
aiti-8538	239	59	different	different	ADJ
aiti-8538	239	60	datasets	dataset	NOUN
aiti-8538	239	61	used	use	VERB
aiti-8538	239	62	for	for	ADP
aiti-8538	239	63	the	the	DET
aiti-8538	239	64	evaluation	evaluation	NOUN
aiti-8538	239	65	of	of	ADP
aiti-8538	239	66	the	the	DET
aiti-8538	239	67	proposed	propose	VERB
aiti-8538	239	68	approach	approach	NOUN
aiti-8538	239	69	.	.	PUNCT
aiti-8538	240	1	in	in	ADP
aiti-8538	240	2	this	this	DET
aiti-8538	240	3	study	study	NOUN
aiti-8538	240	4	,	,	PUNCT
aiti-8538	240	5	10	10	NUM
aiti-8538	240	6	different	different	ADJ
aiti-8538	240	7	images	image	NOUN
aiti-8538	240	8	of	of	ADP
aiti-8538	240	9	each	each	PRON
aiti-8538	240	10	of	of	ADP
aiti-8538	240	11	20	20	NUM
aiti-8538	240	12	distinct	distinct	ADJ
aiti-8538	240	13	subjects	subject	NOUN
aiti-8538	240	14	from	from	ADP
aiti-8538	240	15	the	the	DET
aiti-8538	240	16	orl	orl	PROPN
aiti-8538	240	17	dataset	dataset	NOUN
aiti-8538	240	18	are	be	AUX
aiti-8538	240	19	used	use	VERB
aiti-8538	240	20	.	.	PUNCT
aiti-8538	241	1	the	the	DET
aiti-8538	241	2	orl	orl	PROPN
aiti-8538	241	3	database	database	NOUN
aiti-8538	241	4	includes	include	VERB
aiti-8538	241	5	grayscale	grayscale	NOUN
aiti-8538	241	6	face	face	NOUN
aiti-8538	241	7	images	image	NOUN
aiti-8538	241	8	of	of	ADP
aiti-8538	241	9	92	92	NUM
aiti-8538	241	10	×	×	NOUN
aiti-8538	241	11	112	112	NUM
aiti-8538	241	12	pixels	pixel	NOUN
aiti-8538	241	13	collected	collect	VERB
aiti-8538	241	14	in	in	ADP
aiti-8538	241	15	varying	vary	VERB
aiti-8538	241	16	lighting	lighting	NOUN
aiti-8538	241	17	conditions	condition	NOUN
aiti-8538	241	18	with	with	ADP
aiti-8538	241	19	similar	similar	ADJ
aiti-8538	241	20	backgrounds	background	NOUN
aiti-8538	241	21	.	.	PUNCT
aiti-8538	242	1	the	the	DET
aiti-8538	242	2	dataset	dataset	NOUN
aiti-8538	242	3	captures	capture	VERB
aiti-8538	242	4	different	different	ADJ
aiti-8538	242	5	facial	facial	ADJ
aiti-8538	242	6	expressions	expression	NOUN
aiti-8538	242	7	of	of	ADP
aiti-8538	242	8	persons	person	NOUN
aiti-8538	242	9	organized	organize	VERB
aiti-8538	242	10	in	in	ADP
aiti-8538	242	11	a	a	DET
aiti-8538	242	12	directory	directory	NOUN
aiti-8538	242	13	for	for	ADP
aiti-8538	242	14	each	each	DET
aiti-8538	242	15	subject	subject	NOUN
aiti-8538	242	16	with	with	ADP
aiti-8538	242	17	names	name	NOUN
aiti-8538	242	18	in	in	ADP
aiti-8538	242	19	the	the	DET
aiti-8538	242	20	format	format	NOUN
aiti-8538	242	21	s1	s1	NOUN
aiti-8538	242	22	,	,	PUNCT
aiti-8538	242	23	s2	s2	PROPN
aiti-8538	242	24	,	,	PUNCT
aiti-8538	242	25	s3	s3	PROPN
aiti-8538	242	26	,	,	PUNCT
aiti-8538	242	27	etc	etc	X
aiti-8538	242	28	.	.	X
aiti-8538	243	1	fashion	fashion	NOUN
aiti-8538	243	2	-	-	PUNCT
aiti-8538	243	3	mnist	mnist	NOUN
aiti-8538	243	4	is	be	AUX
aiti-8538	243	5	a	a	DET
aiti-8538	243	6	standard	standard	ADJ
aiti-8538	243	7	dataset	dataset	NOUN
aiti-8538	243	8	having	have	VERB
aiti-8538	243	9	60000	60000	NUM
aiti-8538	243	10	grayscale	grayscale	NOUN
aiti-8538	243	11	images	image	NOUN
aiti-8538	243	12	of	of	ADP
aiti-8538	243	13	28	28	NUM
aiti-8538	243	14	×	×	NOUN
aiti-8538	243	15	28	28	NUM
aiti-8538	243	16	pixels	pixel	NOUN
aiti-8538	243	17	corresponding	correspond	VERB
aiti-8538	243	18	to	to	ADP
aiti-8538	243	19	10	10	NUM
aiti-8538	243	20	different	different	ADJ
aiti-8538	243	21	types	type	NOUN
aiti-8538	243	22	of	of	ADP
aiti-8538	243	23	clothing	clothing	NOUN
aiti-8538	243	24	.	.	PUNCT
aiti-8538	244	1	fashion	fashion	NOUN
aiti-8538	244	2	-	-	PUNCT
aiti-8538	244	3	mnist	mnist	NOUN
aiti-8538	244	4	dataset	dataset	NOUN
aiti-8538	244	5	is	be	AUX
aiti-8538	244	6	popularly	popularly	ADV
aiti-8538	244	7	used	use	VERB
aiti-8538	244	8	for	for	ADP
aiti-8538	244	9	benchmarking	benchmarke	VERB
aiti-8538	244	10	computer	computer	NOUN
aiti-8538	244	11	vision	vision	NOUN
aiti-8538	244	12	and	and	CCONJ
aiti-8538	244	13	deep	deep	ADJ
aiti-8538	244	14	learning	learning	NOUN
aiti-8538	244	15	algorithms	algorithm	NOUN
aiti-8538	244	16	.	.	PUNCT
aiti-8538	245	1	4	4	X
aiti-8538	245	2	.	.	NOUN
aiti-8538	245	3	results	result	NOUN
aiti-8538	245	4	and	and	CCONJ
aiti-8538	245	5	discussion	discussion	NOUN
aiti-8538	245	6	as	as	SCONJ
aiti-8538	245	7	discussed	discuss	VERB
aiti-8538	245	8	in	in	ADP
aiti-8538	245	9	section	section	NOUN
aiti-8538	245	10	4	4	NUM
aiti-8538	245	11	,	,	PUNCT
aiti-8538	245	12	the	the	DET
aiti-8538	245	13	images	image	NOUN
aiti-8538	245	14	are	be	AUX
aiti-8538	245	15	fed	feed	VERB
aiti-8538	245	16	to	to	ADP
aiti-8538	245	17	the	the	DET
aiti-8538	245	18	vgg16	vgg16	NOUN
aiti-8538	245	19	models	model	NOUN
aiti-8538	245	20	for	for	ADP
aiti-8538	245	21	feature	feature	NOUN
aiti-8538	245	22	extraction	extraction	NOUN
aiti-8538	245	23	.	.	PUNCT
aiti-8538	246	1	the	the	DET
aiti-8538	246	2	extracted	extract	VERB
aiti-8538	246	3	features	feature	NOUN
aiti-8538	246	4	represent	represent	VERB
aiti-8538	246	5	useful	useful	ADJ
aiti-8538	246	6	information	information	NOUN
aiti-8538	246	7	required	require	VERB
aiti-8538	246	8	for	for	ADP
aiti-8538	246	9	image	image	NOUN
aiti-8538	246	10	classification	classification	NOUN
aiti-8538	246	11	.	.	PUNCT
aiti-8538	247	1	the	the	DET
aiti-8538	247	2	features	feature	NOUN
aiti-8538	247	3	are	be	AUX
aiti-8538	247	4	classified	classify	VERB
aiti-8538	247	5	using	use	VERB
aiti-8538	247	6	multilayer	multilayer	ADJ
aiti-8538	247	7	perceptron	perceptron	PROPN
aiti-8538	247	8	,	,	PUNCT
aiti-8538	247	9	svm	svm	PROPN
aiti-8538	247	10	,	,	PUNCT
aiti-8538	247	11	and	and	CCONJ
aiti-8538	247	12	rf	rf	NOUN
aiti-8538	247	13	image	image	NOUN
aiti-8538	247	14	classifiers	classifier	NOUN
aiti-8538	247	15	.	.	PUNCT
aiti-8538	248	1	the	the	DET
aiti-8538	248	2	hyperparameters	hyperparameter	NOUN
aiti-8538	248	3	of	of	ADP
aiti-8538	248	4	these	these	DET
aiti-8538	248	5	classifiers	classifier	NOUN
aiti-8538	248	6	are	be	AUX
aiti-8538	248	7	optimized	optimize	VERB
aiti-8538	248	8	by	by	ADP
aiti-8538	248	9	using	use	VERB
aiti-8538	248	10	a	a	DET
aiti-8538	248	11	grid	grid	NOUN
aiti-8538	248	12	search	search	NOUN
aiti-8538	248	13	algorithm	algorithm	NOUN
aiti-8538	248	14	.	.	PUNCT
aiti-8538	249	1	the	the	DET
aiti-8538	249	2	multilayer	multilayer	PROPN
aiti-8538	249	3	perceptron	perceptron	PROPN
aiti-8538	249	4	model	model	NOUN
aiti-8538	249	5	is	be	AUX
aiti-8538	249	6	trained	train	VERB
aiti-8538	249	7	for	for	ADP
aiti-8538	249	8	50	50	NUM
aiti-8538	249	9	epochs	epoch	NOUN
aiti-8538	249	10	.	.	PUNCT
aiti-8538	250	1	the	the	DET
aiti-8538	250	2	svm	svm	ADJ
aiti-8538	250	3	algorithm	algorithm	NOUN
aiti-8538	250	4	used	use	VERB
aiti-8538	250	5	in	in	ADP
aiti-8538	250	6	the	the	DET
aiti-8538	250	7	study	study	NOUN
aiti-8538	250	8	is	be	AUX
aiti-8538	250	9	evaluated	evaluate	VERB
aiti-8538	250	10	by	by	ADP
aiti-8538	250	11	fitting	fit	VERB
aiti-8538	250	12	5	5	NUM
aiti-8538	250	13	folds	fold	NOUN
aiti-8538	250	14	for	for	ADP
aiti-8538	250	15	each	each	PRON
aiti-8538	250	16	of	of	ADP
aiti-8538	250	17	the	the	DET
aiti-8538	250	18	50	50	NUM
aiti-8538	250	19	candidates	candidate	NOUN
aiti-8538	250	20	totaling	total	VERB
aiti-8538	250	21	250	250	NUM
aiti-8538	250	22	fits	fit	NOUN
aiti-8538	250	23	.	.	PUNCT
aiti-8538	251	1	the	the	DET
aiti-8538	251	2	hyperparameters	hyperparameter	NOUN
aiti-8538	251	3	optimized	optimize	VERB
aiti-8538	251	4	by	by	ADP
aiti-8538	251	5	the	the	DET
aiti-8538	251	6	grid	grid	NOUN
aiti-8538	251	7	search	search	NOUN
aiti-8538	251	8	algorithm	algorithm	NOUN
aiti-8538	251	9	for	for	ADP
aiti-8538	251	10	svm	svm	NOUN
aiti-8538	251	11	are	be	AUX
aiti-8538	251	12	c	c	NOUN
aiti-8538	251	13	=	=	SYM
aiti-8538	251	14	1	1	NUM
aiti-8538	251	15	,	,	PUNCT
aiti-8538	251	16	gamma	gamma	NOUN
aiti-8538	251	17	=	=	PROPN
aiti-8538	251	18	0.01	0.01	NUM
aiti-8538	251	19	and	and	CCONJ
aiti-8538	251	20	kernel	kernel	NOUN
aiti-8538	251	21	=	=	SYM
aiti-8538	251	22	'	'	PUNCT
aiti-8538	251	23	rbf	rbf	PROPN
aiti-8538	251	24	'	'	PUNCT
aiti-8538	251	25	when	when	SCONJ
aiti-8538	251	26	the	the	DET
aiti-8538	251	27	orl	orl	PROPN
aiti-8538	251	28	images	image	NOUN
aiti-8538	251	29	are	be	AUX
aiti-8538	251	30	classified	classify	VERB
aiti-8538	251	31	using	use	VERB
aiti-8538	251	32	vgg16	vgg16	NOUN
aiti-8538	251	33	features	feature	NOUN
aiti-8538	251	34	.	.	PUNCT
aiti-8538	252	1	the	the	DET
aiti-8538	252	2	accuracy	accuracy	NOUN
aiti-8538	252	3	and	and	CCONJ
aiti-8538	252	4	time	time	NOUN
aiti-8538	252	5	of	of	ADP
aiti-8538	252	6	image	image	NOUN
aiti-8538	252	7	classification	classification	NOUN
aiti-8538	252	8	are	be	AUX
aiti-8538	252	9	recorded	record	VERB
aiti-8538	252	10	and	and	CCONJ
aiti-8538	252	11	are	be	AUX
aiti-8538	252	12	shown	show	VERB
aiti-8538	252	13	in	in	ADP
aiti-8538	252	14	table	table	NOUN
aiti-8538	252	15	3	3	NUM
aiti-8538	252	16	.	.	SYM
aiti-8538	252	17	112	112	NUM
aiti-8538	252	18	advances	advance	NOUN
aiti-8538	252	19	in	in	ADP
aiti-8538	252	20	technology	technology	NOUN
aiti-8538	252	21	innovation	innovation	NOUN
aiti-8538	252	22	,	,	PUNCT
aiti-8538	252	23	vol	vol	NOUN
aiti-8538	252	24	.	.	PROPN
aiti-8538	253	1	7	7	NUM
aiti-8538	253	2	,	,	PUNCT
aiti-8538	253	3	no	no	INTJ
aiti-8538	253	4	.	.	NOUN
aiti-8538	253	5	2	2	NUM
aiti-8538	253	6	,	,	PUNCT
aiti-8538	253	7	2022	2022	NUM
aiti-8538	253	8	,	,	PUNCT
aiti-8538	253	9	pp	pp	ADJ
aiti-8538	253	10	.	.	PUNCT
aiti-8538	254	1	105	105	NUM
aiti-8538	254	2	-	-	SYM
aiti-8538	254	3	117	117	NUM
aiti-8538	254	4	dimensionality	dimensionality	NOUN
aiti-8538	254	5	reduction	reduction	NOUN
aiti-8538	254	6	is	be	AUX
aiti-8538	254	7	required	require	VERB
aiti-8538	254	8	for	for	ADP
aiti-8538	254	9	improving	improve	VERB
aiti-8538	254	10	the	the	DET
aiti-8538	254	11	performance	performance	NOUN
aiti-8538	254	12	of	of	ADP
aiti-8538	254	13	the	the	DET
aiti-8538	254	14	model	model	NOUN
aiti-8538	254	15	.	.	PUNCT
aiti-8538	255	1	linear	linear	PROPN
aiti-8538	255	2	discriminate	discriminate	VERB
aiti-8538	255	3	analysis	analysis	NOUN
aiti-8538	255	4	(	(	PUNCT
aiti-8538	255	5	lda	lda	PROPN
aiti-8538	255	6	)	)	PUNCT
aiti-8538	255	7	,	,	PUNCT
aiti-8538	255	8	as	as	ADV
aiti-8538	255	9	well	well	ADV
aiti-8538	255	10	as	as	ADP
aiti-8538	255	11	pca	pca	PROPN
aiti-8538	255	12	,	,	PUNCT
aiti-8538	255	13	can	can	AUX
aiti-8538	255	14	be	be	AUX
aiti-8538	255	15	used	use	VERB
aiti-8538	255	16	for	for	ADP
aiti-8538	255	17	feature	feature	NOUN
aiti-8538	255	18	reduction	reduction	NOUN
aiti-8538	255	19	.	.	PUNCT
aiti-8538	256	1	lda	lda	PROPN
aiti-8538	256	2	uses	use	VERB
aiti-8538	256	3	class	class	NOUN
aiti-8538	256	4	information	information	NOUN
aiti-8538	256	5	to	to	PART
aiti-8538	256	6	obtain	obtain	VERB
aiti-8538	256	7	new	new	ADJ
aiti-8538	256	8	features	feature	NOUN
aiti-8538	256	9	whereas	whereas	SCONJ
aiti-8538	256	10	pca	pca	NOUN
aiti-8538	256	11	evaluates	evaluate	VERB
aiti-8538	256	12	pcs	pc	NOUN
aiti-8538	256	13	by	by	ADP
aiti-8538	256	14	making	make	VERB
aiti-8538	256	15	use	use	NOUN
aiti-8538	256	16	of	of	ADP
aiti-8538	256	17	variance	variance	NOUN
aiti-8538	256	18	matrix	matrix	NOUN
aiti-8538	256	19	,	,	PUNCT
aiti-8538	256	20	covariance	covariance	NOUN
aiti-8538	256	21	matrix	matrix	NOUN
aiti-8538	256	22	,	,	PUNCT
aiti-8538	256	23	eigenvector	eigenvector	NOUN
aiti-8538	256	24	,	,	PUNCT
aiti-8538	256	25	and	and	CCONJ
aiti-8538	256	26	eigenvalues	eigenvalue	NOUN
aiti-8538	256	27	of	of	ADP
aiti-8538	256	28	each	each	DET
aiti-8538	256	29	feature	feature	NOUN
aiti-8538	256	30	.	.	PUNCT
aiti-8538	257	1	pca	pca	PROPN
aiti-8538	257	2	is	be	AUX
aiti-8538	257	3	used	use	VERB
aiti-8538	257	4	to	to	PART
aiti-8538	257	5	speed	speed	VERB
aiti-8538	257	6	up	up	ADP
aiti-8538	257	7	the	the	DET
aiti-8538	257	8	computation	computation	NOUN
aiti-8538	257	9	and	and	CCONJ
aiti-8538	257	10	improve	improve	VERB
aiti-8538	257	11	the	the	DET
aiti-8538	257	12	performance	performance	NOUN
aiti-8538	257	13	of	of	ADP
aiti-8538	257	14	the	the	DET
aiti-8538	257	15	image	image	NOUN
aiti-8538	257	16	classifier	classifier	NOUN
aiti-8538	257	17	.	.	PUNCT
aiti-8538	258	1	figs	fig	NOUN
aiti-8538	258	2	.	.	PUNCT
aiti-8538	259	1	5	5	NUM
aiti-8538	259	2	-	-	SYM
aiti-8538	259	3	6	6	NUM
aiti-8538	259	4	give	give	VERB
aiti-8538	259	5	the	the	DET
aiti-8538	259	6	pca	pca	NOUN
aiti-8538	259	7	reduced	reduce	VERB
aiti-8538	259	8	features	feature	VERB
aiti-8538	259	9	representation	representation	NOUN
aiti-8538	259	10	of	of	ADP
aiti-8538	259	11	corn	corn	NOUN
aiti-8538	259	12	leaf	leaf	NOUN
aiti-8538	259	13	disease	disease	NOUN
aiti-8538	259	14	dataset	dataset	NOUN
aiti-8538	259	15	and	and	CCONJ
aiti-8538	259	16	fashion	fashion	NOUN
aiti-8538	259	17	-	-	PUNCT
aiti-8538	259	18	mnist	mnist	NOUN
aiti-8538	259	19	dataset	dataset	NOUN
aiti-8538	259	20	respectively	respectively	ADV
aiti-8538	259	21	.	.	PUNCT
aiti-8538	260	1	the	the	DET
aiti-8538	260	2	evaluated	evaluate	VERB
aiti-8538	260	3	pcs	pc	NOUN
aiti-8538	260	4	are	be	AUX
aiti-8538	260	5	input	input	NOUN
aiti-8538	260	6	to	to	ADP
aiti-8538	260	7	multilayer	multilayer	PROPN
aiti-8538	260	8	perceptron	perceptron	PROPN
aiti-8538	260	9	,	,	PUNCT
aiti-8538	260	10	svm	svm	PROPN
aiti-8538	260	11	,	,	PUNCT
aiti-8538	260	12	and	and	CCONJ
aiti-8538	260	13	rf	rf	NOUN
aiti-8538	260	14	classifiers	classifier	NOUN
aiti-8538	260	15	.	.	PUNCT
aiti-8538	261	1	tables	table	NOUN
aiti-8538	261	2	4	4	NUM
aiti-8538	261	3	-	-	SYM
aiti-8538	261	4	6	6	NUM
aiti-8538	261	5	show	show	VERB
aiti-8538	261	6	the	the	DET
aiti-8538	261	7	classification	classification	NOUN
aiti-8538	261	8	time	time	NOUN
aiti-8538	261	9	and	and	CCONJ
aiti-8538	261	10	the	the	DET
aiti-8538	261	11	accuracy	accuracy	NOUN
aiti-8538	261	12	of	of	ADP
aiti-8538	261	13	the	the	DET
aiti-8538	261	14	multilayer	multilayer	PROPN
aiti-8538	261	15	perceptron	perceptron	PROPN
aiti-8538	261	16	,	,	PUNCT
aiti-8538	261	17	svm	svm	PROPN
aiti-8538	261	18	,	,	PUNCT
aiti-8538	261	19	and	and	CCONJ
aiti-8538	262	1	rf	rf	NOUN
aiti-8538	262	2	classifiers	classifier	NOUN
aiti-8538	262	3	respectively	respectively	ADV
aiti-8538	262	4	.	.	PUNCT
aiti-8538	263	1	with	with	ADP
aiti-8538	263	2	100	100	NUM
aiti-8538	263	3	pcs	pc	NOUN
aiti-8538	263	4	,	,	PUNCT
aiti-8538	263	5	the	the	DET
aiti-8538	263	6	multilayer	multilayer	ADJ
aiti-8538	263	7	perceptron	perceptron	PROPN
aiti-8538	263	8	gives	give	VERB
aiti-8538	263	9	the	the	DET
aiti-8538	263	10	best	good	ADJ
aiti-8538	263	11	accuracy	accuracy	NOUN
aiti-8538	263	12	and	and	CCONJ
aiti-8538	263	13	outperforms	outperform	VERB
aiti-8538	263	14	the	the	DET
aiti-8538	263	15	svm	svm	PROPN
aiti-8538	263	16	and	and	CCONJ
aiti-8538	263	17	rf	rf	NOUN
aiti-8538	263	18	classifiers	classifier	NOUN
aiti-8538	263	19	.	.	PUNCT
aiti-8538	264	1	the	the	DET
aiti-8538	264	2	image	image	NOUN
aiti-8538	264	3	classification	classification	NOUN
aiti-8538	264	4	performance	performance	NOUN
aiti-8538	264	5	recorded	record	VERB
aiti-8538	264	6	in	in	ADP
aiti-8538	264	7	table	table	NOUN
aiti-8538	264	8	3	3	NUM
aiti-8538	264	9	and	and	CCONJ
aiti-8538	264	10	table	table	NOUN
aiti-8538	264	11	4	4	NUM
aiti-8538	264	12	demonstrate	demonstrate	VERB
aiti-8538	264	13	that	that	SCONJ
aiti-8538	264	14	the	the	DET
aiti-8538	264	15	proposed	propose	VERB
aiti-8538	264	16	approach	approach	NOUN
aiti-8538	264	17	reduces	reduce	VERB
aiti-8538	264	18	the	the	DET
aiti-8538	264	19	computational	computational	ADJ
aiti-8538	264	20	complexity	complexity	NOUN
aiti-8538	264	21	of	of	ADP
aiti-8538	264	22	the	the	DET
aiti-8538	264	23	image	image	NOUN
aiti-8538	264	24	classifiers	classifier	NOUN
aiti-8538	264	25	.	.	PUNCT
aiti-8538	265	1	for	for	ADP
aiti-8538	265	2	example	example	NOUN
aiti-8538	265	3	,	,	PUNCT
aiti-8538	265	4	for	for	ADP
aiti-8538	265	5	the	the	DET
aiti-8538	265	6	classification	classification	NOUN
aiti-8538	265	7	of	of	ADP
aiti-8538	265	8	rice	rice	NOUN
aiti-8538	265	9	leaf	leaf	NOUN
aiti-8538	265	10	disease	disease	NOUN
aiti-8538	265	11	test	test	NOUN
aiti-8538	265	12	images	image	NOUN
aiti-8538	265	13	,	,	PUNCT
aiti-8538	265	14	vgg16	vgg16	NOUN
aiti-8538	265	15	and	and	CCONJ
aiti-8538	265	16	multilayer	multilayer	PROPN
aiti-8538	265	17	perceptron	perceptron	PROPN
aiti-8538	265	18	give	give	VERB
aiti-8538	265	19	an	an	DET
aiti-8538	265	20	accuracy	accuracy	NOUN
aiti-8538	265	21	of	of	ADP
aiti-8538	265	22	97.27	97.27	NUM
aiti-8538	265	23	%	%	NOUN
aiti-8538	265	24	whereas	whereas	SCONJ
aiti-8538	265	25	using	use	VERB
aiti-8538	265	26	the	the	DET
aiti-8538	265	27	proposed	propose	VERB
aiti-8538	265	28	approach	approach	NOUN
aiti-8538	265	29	with	with	ADP
aiti-8538	265	30	100	100	NUM
aiti-8538	265	31	pcs	pc	NOUN
aiti-8538	265	32	the	the	DET
aiti-8538	265	33	classification	classification	NOUN
aiti-8538	265	34	accuracy	accuracy	NOUN
aiti-8538	265	35	of	of	ADP
aiti-8538	265	36	98.93	98.93	NUM
aiti-8538	265	37	%	%	NOUN
aiti-8538	265	38	is	be	AUX
aiti-8538	265	39	achieved	achieve	VERB
aiti-8538	265	40	.	.	PUNCT
aiti-8538	266	1	in	in	ADP
aiti-8538	266	2	addition	addition	NOUN
aiti-8538	266	3	,	,	PUNCT
aiti-8538	266	4	the	the	DET
aiti-8538	266	5	improvement	improvement	NOUN
aiti-8538	266	6	in	in	ADP
aiti-8538	266	7	classification	classification	NOUN
aiti-8538	266	8	time	time	NOUN
aiti-8538	266	9	by	by	ADP
aiti-8538	266	10	107.17	107.17	NUM
aiti-8538	266	11	seconds	second	NOUN
aiti-8538	266	12	is	be	AUX
aiti-8538	266	13	also	also	ADV
aiti-8538	266	14	observed	observe	VERB
aiti-8538	266	15	.	.	PUNCT
aiti-8538	267	1	table	table	NOUN
aiti-8538	267	2	3	3	NUM
aiti-8538	267	3	image	image	NOUN
aiti-8538	267	4	classification	classification	NOUN
aiti-8538	267	5	without	without	ADP
aiti-8538	267	6	pca	pca	PROPN
aiti-8538	267	7	dataset	dataset	VERB
aiti-8538	267	8	artificial	artificial	ADJ
aiti-8538	267	9	neural	neural	ADJ
aiti-8538	267	10	network	network	NOUN
aiti-8538	267	11	(	(	PUNCT
aiti-8538	267	12	ann	ann	PROPN
aiti-8538	267	13	)	)	PUNCT
aiti-8538	267	14	support	support	NOUN
aiti-8538	267	15	vector	vector	NOUN
aiti-8538	267	16	machine	machine	NOUN
aiti-8538	267	17	(	(	PUNCT
aiti-8538	267	18	svm	svm	ADJ
aiti-8538	267	19	)	)	PUNCT
aiti-8538	267	20	random	random	ADJ
aiti-8538	267	21	forest	forest	NOUN
aiti-8538	267	22	(	(	PUNCT
aiti-8538	267	23	rf	rf	NOUN
aiti-8538	267	24	)	)	PUNCT
aiti-8538	267	25	accuracy	accuracy	NOUN
aiti-8538	267	26	(	(	PUNCT
aiti-8538	267	27	%	%	NOUN
aiti-8538	267	28	)	)	PUNCT
aiti-8538	267	29	time	time	NOUN
aiti-8538	267	30	(	(	PUNCT
aiti-8538	267	31	sec	sec	PROPN
aiti-8538	267	32	)	)	PUNCT
aiti-8538	267	33	accuracy	accuracy	NOUN
aiti-8538	267	34	(	(	PUNCT
aiti-8538	267	35	%	%	NOUN
aiti-8538	267	36	)	)	PUNCT
aiti-8538	267	37	time	time	NOUN
aiti-8538	267	38	(	(	PUNCT
aiti-8538	267	39	sec	sec	PROPN
aiti-8538	267	40	)	)	PUNCT
aiti-8538	267	41	accuracy	accuracy	NOUN
aiti-8538	267	42	(	(	PUNCT
aiti-8538	267	43	%	%	NOUN
aiti-8538	267	44	)	)	PUNCT
aiti-8538	267	45	time	time	NOUN
aiti-8538	267	46	(	(	PUNCT
aiti-8538	267	47	sec	sec	PROPN
aiti-8538	267	48	)	)	PUNCT
aiti-8538	267	49	orl	orl	VERB
aiti-8538	267	50	95.0	95.0	NUM
aiti-8538	267	51	6.45	6.45	NUM
aiti-8538	267	52	92.5	92.5	NUM
aiti-8538	267	53	0.82	0.82	NUM
aiti-8538	267	54	92.5	92.5	NUM
aiti-8538	267	55	120.45	120.45	NUM
aiti-8538	267	56	corn	corn	NOUN
aiti-8538	267	57	leaf	leaf	NOUN
aiti-8538	267	58	disease	disease	NOUN
aiti-8538	267	59	89.14	89.14	NUM
aiti-8538	267	60	142.8	142.8	NUM
aiti-8538	267	61	88.78	88.78	NUM
aiti-8538	267	62	55	55	NUM
aiti-8538	267	63	86.51	86.51	NUM
aiti-8538	267	64	123.56	123.56	NUM
aiti-8538	267	65	rice	rice	NOUN
aiti-8538	267	66	leaf	leaf	NOUN
aiti-8538	267	67	disease	disease	NOUN
aiti-8538	267	68	93.81	93.81	NUM
aiti-8538	267	69	140.4	140.4	NUM
aiti-8538	267	70	98.54	98.54	NUM
aiti-8538	267	71	61.2	61.2	NUM
aiti-8538	267	72	97.27	97.27	NUM
aiti-8538	267	73	121.4	121.4	NUM
aiti-8538	267	74	fashion	fashion	NOUN
aiti-8538	267	75	-	-	PUNCT
aiti-8538	267	76	mnist	mnist	NOUN
aiti-8538	267	77	91.56	91.56	NUM
aiti-8538	267	78	174.26	174.26	NUM
aiti-8538	267	79	90.1	90.1	NUM
aiti-8538	267	80	110.3	110.3	NUM
aiti-8538	267	81	87.78	87.78	NUM
aiti-8538	267	82	153.41	153.41	NUM
aiti-8538	267	83	(	(	PUNCT
aiti-8538	267	84	a	a	PRON
aiti-8538	267	85	)	)	PUNCT
aiti-8538	267	86	pc	pc	NOUN
aiti-8538	267	87	=	=	SYM
aiti-8538	267	88	2	2	NUM
aiti-8538	267	89	(	(	PUNCT
aiti-8538	267	90	b	b	NOUN
aiti-8538	267	91	)	)	PUNCT
aiti-8538	267	92	pc	pc	NOUN
aiti-8538	267	93	=	=	SYM
aiti-8538	267	94	3	3	NUM
aiti-8538	267	95	fig	fig	NOUN
aiti-8538	267	96	.	.	PUNCT
aiti-8538	268	1	5	5	NUM
aiti-8538	268	2	corn	corn	NOUN
aiti-8538	268	3	leaf	leaf	NOUN
aiti-8538	268	4	disease	disease	NOUN
aiti-8538	268	5	dataset	dataset	NOUN
aiti-8538	268	6	(	(	PUNCT
aiti-8538	268	7	a	a	PRON
aiti-8538	268	8	)	)	PUNCT
aiti-8538	268	9	pc	pc	NOUN
aiti-8538	268	10	=	=	SYM
aiti-8538	268	11	2	2	NUM
aiti-8538	268	12	(	(	PUNCT
aiti-8538	268	13	b	b	NOUN
aiti-8538	268	14	)	)	PUNCT
aiti-8538	268	15	pc	pc	NOUN
aiti-8538	268	16	=	=	SYM
aiti-8538	268	17	3	3	NUM
aiti-8538	268	18	fig	fig	NOUN
aiti-8538	268	19	.	.	PUNCT
aiti-8538	269	1	6	6	NUM
aiti-8538	269	2	fashion	fashion	NOUN
aiti-8538	269	3	-	-	PUNCT
aiti-8538	269	4	mnist	mnist	NOUN
aiti-8538	269	5	dataset	dataset	VERB
aiti-8538	269	6	113	113	NUM
aiti-8538	269	7	advances	advance	NOUN
aiti-8538	269	8	in	in	ADP
aiti-8538	269	9	technology	technology	NOUN
aiti-8538	269	10	innovation	innovation	NOUN
aiti-8538	269	11	,	,	PUNCT
aiti-8538	269	12	vol	vol	NOUN
aiti-8538	269	13	.	.	PROPN
aiti-8538	270	1	7	7	NUM
aiti-8538	270	2	,	,	PUNCT
aiti-8538	270	3	no	no	INTJ
aiti-8538	270	4	.	.	NOUN
aiti-8538	270	5	2	2	NUM
aiti-8538	270	6	,	,	PUNCT
aiti-8538	270	7	2022	2022	NUM
aiti-8538	270	8	,	,	PUNCT
aiti-8538	270	9	pp	pp	ADJ
aiti-8538	270	10	.	.	PUNCT
aiti-8538	271	1	105	105	NUM
aiti-8538	271	2	-	-	SYM
aiti-8538	271	3	117	117	NUM
aiti-8538	271	4	table	table	NOUN
aiti-8538	271	5	4	4	NUM
aiti-8538	271	6	accuracy	accuracy	NOUN
aiti-8538	271	7	/	/	SYM
aiti-8538	271	8	time	time	NOUN
aiti-8538	271	9	of	of	ADP
aiti-8538	271	10	multilayer	multilayer	ADJ
aiti-8538	271	11	perceptron	perceptron	PROPN
aiti-8538	271	12	classifier	classifier	NOUN
aiti-8538	271	13	(	(	PUNCT
aiti-8538	271	14	20	20	NUM
aiti-8538	271	15	epochs	epoch	NOUN
aiti-8538	271	16	)	)	PUNCT
aiti-8538	271	17	using	use	VERB
aiti-8538	271	18	pca	pca	PROPN
aiti-8538	271	19	features	feature	NOUN
aiti-8538	271	20	dataset	dataset	VERB
aiti-8538	271	21	pc	pc	NOUN
aiti-8538	271	22	=	=	SYM
aiti-8538	271	23	10	10	NUM
aiti-8538	271	24	pc	pc	NOUN
aiti-8538	271	25	=	=	SYM
aiti-8538	271	26	20	20	NUM
aiti-8538	271	27	pc	pc	NOUN
aiti-8538	271	28	=	=	NOUN
aiti-8538	271	29	50	50	NUM
aiti-8538	271	30	pc	pc	NOUN
aiti-8538	271	31	=	=	NOUN
aiti-8538	271	32	100	100	NUM
aiti-8538	271	33	accuracy	accuracy	NOUN
aiti-8538	271	34	(	(	PUNCT
aiti-8538	271	35	%	%	NOUN
aiti-8538	271	36	)	)	PUNCT
aiti-8538	271	37	time	time	NOUN
aiti-8538	271	38	(	(	PUNCT
aiti-8538	271	39	sec	sec	PROPN
aiti-8538	271	40	)	)	PUNCT
aiti-8538	271	41	accuracy	accuracy	NOUN
aiti-8538	271	42	(	(	PUNCT
aiti-8538	271	43	%	%	NOUN
aiti-8538	271	44	)	)	PUNCT
aiti-8538	271	45	time	time	NOUN
aiti-8538	271	46	(	(	PUNCT
aiti-8538	271	47	sec	sec	PROPN
aiti-8538	271	48	)	)	PUNCT
aiti-8538	271	49	accuracy	accuracy	NOUN
aiti-8538	271	50	(	(	PUNCT
aiti-8538	271	51	%	%	NOUN
aiti-8538	271	52	)	)	PUNCT
aiti-8538	271	53	time	time	NOUN
aiti-8538	271	54	(	(	PUNCT
aiti-8538	271	55	sec	sec	PROPN
aiti-8538	271	56	)	)	PUNCT
aiti-8538	271	57	accuracy	accuracy	NOUN
aiti-8538	271	58	(	(	PUNCT
aiti-8538	271	59	%	%	NOUN
aiti-8538	271	60	)	)	PUNCT
aiti-8538	271	61	time	time	NOUN
aiti-8538	271	62	(	(	PUNCT
aiti-8538	271	63	sec	sec	PROPN
aiti-8538	271	64	)	)	PUNCT
aiti-8538	271	65	orl	orl	VERB
aiti-8538	271	66	87.5	87.5	NUM
aiti-8538	271	67	1.74	1.74	NUM
aiti-8538	271	68	90.0	90.0	NUM
aiti-8538	271	69	1.81	1.81	NUM
aiti-8538	271	70	92.2	92.2	NUM
aiti-8538	271	71	1.83	1.83	NUM
aiti-8538	271	72	95.24	95.24	NUM
aiti-8538	271	73	1.898	1.898	NUM
aiti-8538	271	74	corn	corn	NOUN
aiti-8538	271	75	leaf	leaf	NOUN
aiti-8538	271	76	disease	disease	NOUN
aiti-8538	271	77	83.05	83.05	NUM
aiti-8538	271	78	10.05	10.05	NUM
aiti-8538	271	79	85.08	85.08	NUM
aiti-8538	271	80	10.84	10.84	NUM
aiti-8538	271	81	86.99	86.99	NUM
aiti-8538	271	82	10.91	10.91	NUM
aiti-8538	271	83	92.36	92.36	NUM
aiti-8538	271	84	10.95	10.95	NUM
aiti-8538	271	85	rice	rice	NOUN
aiti-8538	271	86	leaf	leaf	NOUN
aiti-8538	271	87	disease	disease	NOUN
aiti-8538	271	88	93.86	93.86	NUM
aiti-8538	271	89	10	10	NUM
aiti-8538	271	90	95.33	95.33	NUM
aiti-8538	271	91	10.82	10.82	NUM
aiti-8538	271	92	97.27	97.27	NUM
aiti-8538	271	93	11.018	11.018	NUM
aiti-8538	271	94	98.93	98.93	NUM
aiti-8538	271	95	14.23	14.23	NUM
aiti-8538	271	96	fashion	fashion	NOUN
aiti-8538	271	97	-	-	PUNCT
aiti-8538	271	98	mnist	mnist	NOUN
aiti-8538	271	99	75.11	75.11	NUM
aiti-8538	271	100	15.35	15.35	NUM
aiti-8538	271	101	81.45	81.45	NUM
aiti-8538	271	102	17.75	17.75	NUM
aiti-8538	271	103	84.15	84.15	NUM
aiti-8538	271	104	18.45	18.45	NUM
aiti-8538	271	105	92.93	92.93	NUM
aiti-8538	271	106	19.68	19.68	NUM
aiti-8538	271	107	table	table	NOUN
aiti-8538	271	108	5	5	NUM
aiti-8538	271	109	accuracy	accuracy	NOUN
aiti-8538	271	110	/	/	SYM
aiti-8538	271	111	time	time	NOUN
aiti-8538	271	112	of	of	ADP
aiti-8538	271	113	svm	svm	ADJ
aiti-8538	271	114	classifier	classifier	NOUN
aiti-8538	271	115	using	use	VERB
aiti-8538	271	116	pca	pca	PROPN
aiti-8538	271	117	algorithm	algorithm	NOUN
aiti-8538	271	118	dataset	dataset	VERB
aiti-8538	271	119	pc	pc	NOUN
aiti-8538	271	120	=	=	SYM
aiti-8538	271	121	10	10	NUM
aiti-8538	271	122	pc	pc	NOUN
aiti-8538	271	123	=	=	SYM
aiti-8538	271	124	20	20	NUM
aiti-8538	271	125	pc	pc	NOUN
aiti-8538	271	126	=	=	NOUN
aiti-8538	271	127	50	50	NUM
aiti-8538	271	128	pc	pc	NOUN
aiti-8538	271	129	=	=	SYM
aiti-8538	271	130	100	100	NUM
aiti-8538	271	131	accuracy	accuracy	NOUN
aiti-8538	271	132	(	(	PUNCT
aiti-8538	271	133	%	%	NOUN
aiti-8538	271	134	)	)	PUNCT
aiti-8538	271	135	time	time	NOUN
aiti-8538	271	136	(	(	PUNCT
aiti-8538	271	137	sec	sec	PROPN
aiti-8538	271	138	)	)	PUNCT
aiti-8538	271	139	accuracy	accuracy	NOUN
aiti-8538	271	140	(	(	PUNCT
aiti-8538	271	141	%	%	NOUN
aiti-8538	271	142	)	)	PUNCT
aiti-8538	271	143	time	time	NOUN
aiti-8538	271	144	(	(	PUNCT
aiti-8538	271	145	sec	sec	PROPN
aiti-8538	271	146	)	)	PUNCT
aiti-8538	271	147	accuracy	accuracy	NOUN
aiti-8538	271	148	(	(	PUNCT
aiti-8538	271	149	%	%	NOUN
aiti-8538	271	150	)	)	PUNCT
aiti-8538	271	151	time	time	NOUN
aiti-8538	271	152	(	(	PUNCT
aiti-8538	271	153	sec	sec	PROPN
aiti-8538	271	154	)	)	PUNCT
aiti-8538	271	155	accuracy	accuracy	NOUN
aiti-8538	271	156	(	(	PUNCT
aiti-8538	271	157	%	%	NOUN
aiti-8538	271	158	)	)	PUNCT
aiti-8538	271	159	time	time	NOUN
aiti-8538	271	160	(	(	PUNCT
aiti-8538	271	161	sec	sec	PROPN
aiti-8538	271	162	)	)	PUNCT
aiti-8538	271	163	orl	orl	VERB
aiti-8538	271	164	82.50	82.50	NUM
aiti-8538	271	165	1.83	1.83	NUM
aiti-8538	271	166	85.5	85.5	NUM
aiti-8538	271	167	2.02	2.02	NUM
aiti-8538	271	168	90.01	90.01	NUM
aiti-8538	271	169	3.30	3.30	NUM
aiti-8538	271	170	92.2	92.2	NUM
aiti-8538	271	171	4.5	4.5	NUM
aiti-8538	271	172	corn	corn	NOUN
aiti-8538	271	173	leaf	leaf	NOUN
aiti-8538	271	174	disease	disease	NOUN
aiti-8538	271	175	81.503	81.503	NUM
aiti-8538	271	176	1.89	1.89	NUM
aiti-8538	271	177	83.89	83.89	NUM
aiti-8538	271	178	2.47	2.47	NUM
aiti-8538	271	179	87.11	87.11	NUM
aiti-8538	271	180	3.8	3.8	NUM
aiti-8538	271	181	89.14	89.14	NUM
aiti-8538	271	182	5.15	5.15	NUM
aiti-8538	271	183	rice	rice	NOUN
aiti-8538	271	184	leaf	leaf	NOUN
aiti-8538	271	185	disease	disease	NOUN
aiti-8538	271	186	72.15	72.15	NUM
aiti-8538	271	187	5.56	5.56	NUM
aiti-8538	271	188	80.62	80.62	NUM
aiti-8538	271	189	6.73	6.73	NUM
aiti-8538	272	1	85.78	85.78	NUM
aiti-8538	272	2	8.84	8.84	NUM
aiti-8538	272	3	92.11	92.11	NUM
aiti-8538	272	4	11.88	11.88	NUM
aiti-8538	272	5	fashion	fashion	NOUN
aiti-8538	272	6	-	-	PUNCT
aiti-8538	272	7	mnist	mnist	NOUN
aiti-8538	272	8	71.54	71.54	NUM
aiti-8538	272	9	11.56	11.56	NUM
aiti-8538	272	10	76.15	76.15	NUM
aiti-8538	272	11	12.67	12.67	NUM
aiti-8538	272	12	83.57	83.57	NUM
aiti-8538	272	13	15.86	15.86	NUM
aiti-8538	272	14	89.15	89.15	NUM
aiti-8538	272	15	18.65	18.65	NUM
aiti-8538	272	16	table	table	NOUN
aiti-8538	272	17	6	6	NUM
aiti-8538	272	18	accuracy	accuracy	NOUN
aiti-8538	272	19	/	/	SYM
aiti-8538	272	20	time	time	NOUN
aiti-8538	272	21	of	of	ADP
aiti-8538	272	22	rf	rf	NOUN
aiti-8538	272	23	classifier	classifier	NOUN
aiti-8538	272	24	using	use	VERB
aiti-8538	272	25	pca	pca	PROPN
aiti-8538	272	26	algorithm	algorithm	NOUN
aiti-8538	272	27	dataset	dataset	VERB
aiti-8538	272	28	pc	pc	NOUN
aiti-8538	273	1	=	=	SYM
aiti-8538	273	2	10	10	NUM
aiti-8538	273	3	pc	pc	NOUN
aiti-8538	273	4	=	=	SYM
aiti-8538	273	5	20	20	NUM
aiti-8538	273	6	pc	pc	NOUN
aiti-8538	273	7	=	=	NOUN
aiti-8538	273	8	50	50	NUM
aiti-8538	273	9	pc	pc	NOUN
aiti-8538	273	10	=	=	SYM
aiti-8538	273	11	100	100	NUM
aiti-8538	273	12	accuracy	accuracy	NOUN
aiti-8538	273	13	(	(	PUNCT
aiti-8538	273	14	%	%	NOUN
aiti-8538	273	15	)	)	PUNCT
aiti-8538	273	16	time	time	NOUN
aiti-8538	273	17	(	(	PUNCT
aiti-8538	273	18	sec	sec	PROPN
aiti-8538	273	19	)	)	PUNCT
aiti-8538	273	20	accuracy	accuracy	NOUN
aiti-8538	273	21	(	(	PUNCT
aiti-8538	273	22	%	%	NOUN
aiti-8538	273	23	)	)	PUNCT
aiti-8538	273	24	time	time	NOUN
aiti-8538	273	25	(	(	PUNCT
aiti-8538	273	26	sec	sec	PROPN
aiti-8538	273	27	)	)	PUNCT
aiti-8538	273	28	accuracy	accuracy	NOUN
aiti-8538	273	29	(	(	PUNCT
aiti-8538	273	30	%	%	NOUN
aiti-8538	273	31	)	)	PUNCT
aiti-8538	273	32	time	time	NOUN
aiti-8538	273	33	(	(	PUNCT
aiti-8538	273	34	sec	sec	PROPN
aiti-8538	273	35	)	)	PUNCT
aiti-8538	273	36	accuracy	accuracy	NOUN
aiti-8538	273	37	(	(	PUNCT
aiti-8538	273	38	%	%	NOUN
aiti-8538	273	39	)	)	PUNCT
aiti-8538	273	40	time	time	NOUN
aiti-8538	273	41	(	(	PUNCT
aiti-8538	273	42	sec	sec	PROPN
aiti-8538	273	43	)	)	PUNCT
aiti-8538	273	44	orl	orl	VERB
aiti-8538	273	45	77.50	77.50	NUM
aiti-8538	273	46	1.14	1.14	NUM
aiti-8538	273	47	92.5	92.5	NUM
aiti-8538	273	48	1.9	1.9	NUM
aiti-8538	273	49	87.5	87.5	NUM
aiti-8538	273	50	2.7	2.7	NUM
aiti-8538	273	51	90.05	90.05	NUM
aiti-8538	273	52	3.45	3.45	NUM
aiti-8538	273	53	corn	corn	NOUN
aiti-8538	273	54	leaf	leaf	NOUN
aiti-8538	273	55	disease	disease	NOUN
aiti-8538	273	56	78.64	78.64	NUM
aiti-8538	273	57	1.47	1.47	NUM
aiti-8538	273	58	85.91	85.91	NUM
aiti-8538	273	59	1.93	1.93	NUM
aiti-8538	273	60	86.04	86.04	NUM
aiti-8538	273	61	2.41	2.41	NUM
aiti-8538	273	62	88.07	88.07	NUM
aiti-8538	273	63	4.52	4.52	NUM
aiti-8538	273	64	rice	rice	NOUN
aiti-8538	273	65	leaf	leaf	NOUN
aiti-8538	273	66	disease	disease	NOUN
aiti-8538	273	67	75.17	75.17	NUM
aiti-8538	273	68	3.25	3.25	NUM
aiti-8538	273	69	78.57	78.57	NUM
aiti-8538	273	70	4.84	4.84	NUM
aiti-8538	273	71	83.74	83.74	NUM
aiti-8538	273	72	5.42	5.42	NUM
aiti-8538	273	73	88.12	88.12	NUM
aiti-8538	273	74	9.19	9.19	NUM
aiti-8538	273	75	fashion	fashion	NOUN
aiti-8538	273	76	-	-	PUNCT
aiti-8538	273	77	mnist	mnist	NOUN
aiti-8538	273	78	73.21	73.21	NUM
aiti-8538	273	79	10.67	10.67	NUM
aiti-8538	273	80	75.95	75.95	NUM
aiti-8538	273	81	9.41	9.41	NUM
aiti-8538	273	82	81.21	81.21	NUM
aiti-8538	273	83	13.47	13.47	NUM
aiti-8538	273	84	84.37	84.37	NUM
aiti-8538	273	85	15.62	15.62	NUM
aiti-8538	273	86	(	(	PUNCT
aiti-8538	273	87	a	a	X
aiti-8538	273	88	)	)	PUNCT
aiti-8538	273	89	orl	orl	PROPN
aiti-8538	273	90	dataset	dataset	NOUN
aiti-8538	273	91	of	of	ADP
aiti-8538	273	92	faces	face	NOUN
aiti-8538	273	93	(	(	PUNCT
aiti-8538	273	94	b	b	NOUN
aiti-8538	273	95	)	)	PUNCT
aiti-8538	273	96	corn	corn	NOUN
aiti-8538	273	97	leaf	leaf	NOUN
aiti-8538	273	98	disease	disease	NOUN
aiti-8538	273	99	dataset	dataset	NOUN
aiti-8538	273	100	(	(	PUNCT
aiti-8538	273	101	c	c	NOUN
aiti-8538	273	102	)	)	PUNCT
aiti-8538	273	103	rice	rice	NOUN
aiti-8538	273	104	leaf	leaf	NOUN
aiti-8538	273	105	disease	disease	NOUN
aiti-8538	273	106	dataset	dataset	NOUN
aiti-8538	273	107	(	(	PUNCT
aiti-8538	273	108	d	d	NOUN
aiti-8538	273	109	)	)	PUNCT
aiti-8538	273	110	fashion	fashion	NOUN
aiti-8538	273	111	-	-	PUNCT
aiti-8538	273	112	mnist	mnist	NOUN
aiti-8538	273	113	dataset	dataset	NOUN
aiti-8538	273	114	fig	fig	NOUN
aiti-8538	273	115	.	.	PUNCT
aiti-8538	274	1	7	7	NUM
aiti-8538	274	2	confusion	confusion	NOUN
aiti-8538	274	3	matrix	matrix	NOUN
aiti-8538	274	4	of	of	ADP
aiti-8538	274	5	different	different	ADJ
aiti-8538	274	6	datasets	dataset	NOUN
aiti-8538	274	7	114	114	NUM
aiti-8538	274	8	advances	advance	NOUN
aiti-8538	274	9	in	in	ADP
aiti-8538	274	10	technology	technology	NOUN
aiti-8538	274	11	innovation	innovation	NOUN
aiti-8538	274	12	,	,	PUNCT
aiti-8538	274	13	vol	vol	NOUN
aiti-8538	274	14	.	.	PROPN
aiti-8538	274	15	7	7	NUM
aiti-8538	274	16	,	,	PUNCT
aiti-8538	274	17	no	no	INTJ
aiti-8538	274	18	.	.	NOUN
aiti-8538	274	19	2	2	NUM
aiti-8538	274	20	,	,	PUNCT
aiti-8538	274	21	2022	2022	NUM
aiti-8538	274	22	,	,	PUNCT
aiti-8538	274	23	pp	pp	ADJ
aiti-8538	274	24	.	.	PUNCT
aiti-8538	275	1	105	105	NUM
aiti-8538	275	2	-	-	SYM
aiti-8538	275	3	117	117	NUM
aiti-8538	275	4	fig	fig	NOUN
aiti-8538	275	5	.	.	PUNCT
aiti-8538	276	1	7	7	NUM
aiti-8538	276	2	shows	show	VERB
aiti-8538	276	3	the	the	DET
aiti-8538	276	4	confusion	confusion	NOUN
aiti-8538	276	5	matrix	matrix	NOUN
aiti-8538	276	6	of	of	ADP
aiti-8538	276	7	the	the	DET
aiti-8538	276	8	datasets	dataset	NOUN
aiti-8538	276	9	which	which	PRON
aiti-8538	276	10	can	can	AUX
aiti-8538	276	11	be	be	AUX
aiti-8538	276	12	used	use	VERB
aiti-8538	276	13	for	for	ADP
aiti-8538	276	14	evaluating	evaluate	VERB
aiti-8538	276	15	the	the	DET
aiti-8538	276	16	performance	performance	NOUN
aiti-8538	276	17	of	of	ADP
aiti-8538	276	18	the	the	DET
aiti-8538	276	19	proposed	propose	VERB
aiti-8538	276	20	approach	approach	NOUN
aiti-8538	276	21	.	.	PUNCT
aiti-8538	277	1	a	a	DET
aiti-8538	277	2	confusion	confusion	NOUN
aiti-8538	277	3	matrix	matrix	NOUN
aiti-8538	277	4	is	be	AUX
aiti-8538	277	5	very	very	ADV
aiti-8538	277	6	useful	useful	ADJ
aiti-8538	277	7	for	for	ADP
aiti-8538	277	8	evaluating	evaluate	VERB
aiti-8538	277	9	performance	performance	NOUN
aiti-8538	277	10	measures	measure	NOUN
aiti-8538	277	11	like	like	ADP
aiti-8538	277	12	accuracy	accuracy	NOUN
aiti-8538	277	13	,	,	PUNCT
aiti-8538	277	14	precision	precision	NOUN
aiti-8538	277	15	,	,	PUNCT
aiti-8538	277	16	sensitivity	sensitivity	NOUN
aiti-8538	277	17	,	,	PUNCT
aiti-8538	277	18	specificity	specificity	NOUN
aiti-8538	277	19	,	,	PUNCT
aiti-8538	277	20	negative	negative	ADJ
aiti-8538	277	21	predictive	predictive	ADJ
aiti-8538	277	22	value	value	NOUN
aiti-8538	277	23	,	,	PUNCT
aiti-8538	277	24	etc	etc	X
aiti-8538	277	25	.	.	X
aiti-8538	278	1	learning	learn	VERB
aiti-8538	278	2	redundant	redundant	ADJ
aiti-8538	278	3	and	and	CCONJ
aiti-8538	278	4	less	less	ADV
aiti-8538	278	5	relevant	relevant	ADJ
aiti-8538	278	6	information	information	NOUN
aiti-8538	278	7	does	do	AUX
aiti-8538	278	8	not	not	PART
aiti-8538	278	9	give	give	VERB
aiti-8538	278	10	generalized	generalized	ADJ
aiti-8538	278	11	results	result	NOUN
aiti-8538	278	12	.	.	PUNCT
aiti-8538	279	1	training	training	NOUN
aiti-8538	279	2	models	model	NOUN
aiti-8538	279	3	with	with	ADP
aiti-8538	279	4	higher	high	ADJ
aiti-8538	279	5	dimension	dimension	NOUN
aiti-8538	279	6	data	datum	NOUN
aiti-8538	279	7	can	can	AUX
aiti-8538	279	8	suffer	suffer	VERB
aiti-8538	279	9	from	from	ADP
aiti-8538	279	10	overfitting	overfitte	VERB
aiti-8538	279	11	.	.	PUNCT
aiti-8538	280	1	the	the	DET
aiti-8538	280	2	experimental	experimental	ADJ
aiti-8538	280	3	result	result	NOUN
aiti-8538	280	4	illustrates	illustrate	VERB
aiti-8538	280	5	that	that	SCONJ
aiti-8538	280	6	the	the	DET
aiti-8538	280	7	proposed	propose	VERB
aiti-8538	280	8	approach	approach	NOUN
aiti-8538	280	9	improves	improve	VERB
aiti-8538	280	10	the	the	DET
aiti-8538	280	11	performance	performance	NOUN
aiti-8538	280	12	of	of	ADP
aiti-8538	280	13	classifiers	classifier	NOUN
aiti-8538	280	14	.	.	PUNCT
aiti-8538	281	1	5	5	X
aiti-8538	281	2	.	.	X
aiti-8538	281	3	conclusions	conclusion	NOUN
aiti-8538	281	4	efficiently	efficiently	ADV
aiti-8538	281	5	classifying	classify	VERB
aiti-8538	281	6	images	image	NOUN
aiti-8538	281	7	and	and	CCONJ
aiti-8538	281	8	avoiding	avoid	VERB
aiti-8538	281	9	overfitting	overfitting	NOUN
aiti-8538	281	10	is	be	AUX
aiti-8538	281	11	a	a	DET
aiti-8538	281	12	challenging	challenging	ADJ
aiti-8538	281	13	task	task	NOUN
aiti-8538	281	14	.	.	PUNCT
aiti-8538	282	1	in	in	ADP
aiti-8538	282	2	this	this	DET
aiti-8538	282	3	study	study	NOUN
aiti-8538	282	4	,	,	PUNCT
aiti-8538	282	5	an	an	DET
aiti-8538	282	6	efficient	efficient	ADJ
aiti-8538	282	7	approach	approach	NOUN
aiti-8538	282	8	employing	employ	VERB
aiti-8538	282	9	the	the	DET
aiti-8538	282	10	vgg16	vgg16	NOUN
aiti-8538	282	11	model	model	NOUN
aiti-8538	282	12	for	for	ADP
aiti-8538	282	13	feature	feature	NOUN
aiti-8538	282	14	extraction	extraction	NOUN
aiti-8538	282	15	and	and	CCONJ
aiti-8538	282	16	pca	pca	NOUN
aiti-8538	282	17	for	for	ADP
aiti-8538	282	18	feature	feature	NOUN
aiti-8538	282	19	reduction	reduction	NOUN
aiti-8538	282	20	was	be	AUX
aiti-8538	282	21	presented	present	VERB
aiti-8538	282	22	.	.	PUNCT
aiti-8538	283	1	the	the	DET
aiti-8538	283	2	extracted	extract	VERB
aiti-8538	283	3	features	feature	NOUN
aiti-8538	283	4	were	be	AUX
aiti-8538	283	5	classified	classify	VERB
aiti-8538	283	6	without	without	ADP
aiti-8538	283	7	applying	apply	VERB
aiti-8538	283	8	feature	feature	NOUN
aiti-8538	283	9	reduction	reduction	NOUN
aiti-8538	283	10	techniques	technique	NOUN
aiti-8538	283	11	and	and	CCONJ
aiti-8538	283	12	the	the	DET
aiti-8538	283	13	results	result	NOUN
aiti-8538	283	14	were	be	AUX
aiti-8538	283	15	recorded	record	VERB
aiti-8538	283	16	.	.	PUNCT
aiti-8538	284	1	the	the	DET
aiti-8538	284	2	performance	performance	NOUN
aiti-8538	284	3	of	of	ADP
aiti-8538	284	4	the	the	DET
aiti-8538	284	5	model	model	NOUN
aiti-8538	284	6	was	be	AUX
aiti-8538	284	7	improved	improve	VERB
aiti-8538	284	8	by	by	ADP
aiti-8538	284	9	integrating	integrate	VERB
aiti-8538	284	10	the	the	DET
aiti-8538	284	11	pca	pca	NOUN
aiti-8538	284	12	feature	feature	NOUN
aiti-8538	284	13	reduction	reduction	NOUN
aiti-8538	284	14	algorithm	algorithm	NOUN
aiti-8538	284	15	.	.	PUNCT
aiti-8538	285	1	the	the	DET
aiti-8538	285	2	features	feature	NOUN
aiti-8538	285	3	extracted	extract	VERB
aiti-8538	285	4	by	by	ADP
aiti-8538	285	5	the	the	DET
aiti-8538	285	6	vgg16	vgg16	NOUN
aiti-8538	285	7	model	model	NOUN
aiti-8538	285	8	were	be	AUX
aiti-8538	285	9	fed	feed	VERB
aiti-8538	285	10	to	to	ADP
aiti-8538	285	11	pca	pca	PROPN
aiti-8538	285	12	,	,	PUNCT
aiti-8538	285	13	and	and	CCONJ
aiti-8538	285	14	the	the	DET
aiti-8538	285	15	optimum	optimum	ADJ
aiti-8538	285	16	number	number	NOUN
aiti-8538	285	17	of	of	ADP
aiti-8538	285	18	pcs	pc	NOUN
aiti-8538	285	19	for	for	ADP
aiti-8538	285	20	achieving	achieve	VERB
aiti-8538	285	21	the	the	DET
aiti-8538	285	22	best	good	ADJ
aiti-8538	285	23	accuracy	accuracy	NOUN
aiti-8538	285	24	was	be	AUX
aiti-8538	285	25	evaluated	evaluate	VERB
aiti-8538	285	26	.	.	PUNCT
aiti-8538	286	1	for	for	ADP
aiti-8538	286	2	the	the	DET
aiti-8538	286	3	classification	classification	NOUN
aiti-8538	286	4	of	of	ADP
aiti-8538	286	5	images	image	NOUN
aiti-8538	286	6	,	,	PUNCT
aiti-8538	286	7	the	the	DET
aiti-8538	286	8	features	feature	NOUN
aiti-8538	286	9	extracted	extract	VERB
aiti-8538	286	10	by	by	ADP
aiti-8538	286	11	the	the	DET
aiti-8538	286	12	vgg16	vgg16	NOUN
aiti-8538	286	13	model	model	NOUN
aiti-8538	286	14	were	be	AUX
aiti-8538	286	15	classified	classify	VERB
aiti-8538	286	16	using	use	VERB
aiti-8538	286	17	multilayer	multilayer	ADJ
aiti-8538	286	18	perceptron	perceptron	PROPN
aiti-8538	286	19	,	,	PUNCT
aiti-8538	286	20	svm	svm	PROPN
aiti-8538	286	21	,	,	PUNCT
aiti-8538	286	22	and	and	CCONJ
aiti-8538	286	23	rf	rf	NOUN
aiti-8538	286	24	algorithms	algorithm	NOUN
aiti-8538	286	25	.	.	PUNCT
aiti-8538	287	1	the	the	DET
aiti-8538	287	2	proposed	propose	VERB
aiti-8538	287	3	approach	approach	NOUN
aiti-8538	287	4	employing	employ	VERB
aiti-8538	287	5	transfer	transfer	NOUN
aiti-8538	287	6	learning	learning	NOUN
aiti-8538	287	7	was	be	AUX
aiti-8538	287	8	validated	validate	VERB
aiti-8538	287	9	using	use	VERB
aiti-8538	287	10	four	four	NUM
aiti-8538	287	11	benchmarked	benchmarked	ADJ
aiti-8538	287	12	image	image	NOUN
aiti-8538	287	13	datasets	dataset	NOUN
aiti-8538	287	14	,	,	PUNCT
aiti-8538	287	15	considering	consider	VERB
aiti-8538	287	16	performance	performance	NOUN
aiti-8538	287	17	metrics	metric	NOUN
aiti-8538	287	18	.	.	PUNCT
aiti-8538	288	1	the	the	DET
aiti-8538	288	2	proposed	propose	VERB
aiti-8538	288	3	approach	approach	NOUN
aiti-8538	288	4	accurately	accurately	ADV
aiti-8538	288	5	classified	classify	VERB
aiti-8538	288	6	the	the	DET
aiti-8538	288	7	images	image	NOUN
aiti-8538	288	8	with	with	ADP
aiti-8538	288	9	improved	improved	ADJ
aiti-8538	288	10	computational	computational	ADJ
aiti-8538	288	11	efficiency	efficiency	NOUN
aiti-8538	288	12	compared	compare	VERB
aiti-8538	288	13	to	to	ADP
aiti-8538	288	14	other	other	ADJ
aiti-8538	288	15	methods	method	NOUN
aiti-8538	288	16	with	with	ADP
aiti-8538	288	17	the	the	DET
aiti-8538	288	18	same	same	ADJ
aiti-8538	288	19	hardware	hardware	NOUN
aiti-8538	288	20	resources	resource	NOUN
aiti-8538	288	21	.	.	PUNCT
aiti-8538	289	1	the	the	DET
aiti-8538	289	2	models	model	NOUN
aiti-8538	289	3	trained	train	VERB
aiti-8538	289	4	using	use	VERB
aiti-8538	289	5	pca	pca	NOUN
aiti-8538	289	6	-	-	PUNCT
aiti-8538	289	7	reduced	reduce	VERB
aiti-8538	289	8	features	feature	NOUN
aiti-8538	289	9	demonstrated	demonstrate	VERB
aiti-8538	289	10	significant	significant	ADJ
aiti-8538	289	11	improvement	improvement	NOUN
aiti-8538	289	12	in	in	ADP
aiti-8538	289	13	running	run	VERB
aiti-8538	289	14	speed	speed	NOUN
aiti-8538	289	15	.	.	PUNCT
aiti-8538	290	1	the	the	DET
aiti-8538	290	2	scope	scope	NOUN
aiti-8538	290	3	of	of	ADP
aiti-8538	290	4	the	the	DET
aiti-8538	290	5	proposed	propose	VERB
aiti-8538	290	6	framework	framework	NOUN
aiti-8538	290	7	can	can	AUX
aiti-8538	290	8	further	far	ADV
aiti-8538	290	9	be	be	AUX
aiti-8538	290	10	expanded	expand	VERB
aiti-8538	290	11	by	by	ADP
aiti-8538	290	12	optimizing	optimize	VERB
aiti-8538	290	13	the	the	DET
aiti-8538	290	14	pcs	pc	NOUN
aiti-8538	290	15	by	by	ADP
aiti-8538	290	16	using	use	VERB
aiti-8538	290	17	nature	nature	NOUN
aiti-8538	290	18	-	-	PUNCT
aiti-8538	290	19	inspired	inspire	VERB
aiti-8538	290	20	search	search	NOUN
aiti-8538	290	21	and	and	CCONJ
aiti-8538	290	22	optimization	optimization	NOUN
aiti-8538	290	23	algorithms	algorithm	NOUN
aiti-8538	290	24	.	.	PUNCT
aiti-8538	291	1	furthermore	furthermore	ADV
aiti-8538	291	2	,	,	PUNCT
aiti-8538	291	3	the	the	DET
aiti-8538	291	4	feature	feature	NOUN
aiti-8538	291	5	extracted	extract	VERB
aiti-8538	291	6	by	by	ADP
aiti-8538	291	7	transfer	transfer	NOUN
aiti-8538	291	8	learning	learning	NOUN
aiti-8538	291	9	-	-	PUNCT
aiti-8538	291	10	based	base	VERB
aiti-8538	291	11	neural	neural	ADJ
aiti-8538	291	12	networks	network	NOUN
aiti-8538	291	13	can	can	AUX
aiti-8538	291	14	be	be	AUX
aiti-8538	291	15	fused	fuse	VERB
aiti-8538	291	16	or	or	CCONJ
aiti-8538	291	17	an	an	DET
aiti-8538	291	18	ensemble	ensemble	ADJ
aiti-8538	291	19	model	model	NOUN
aiti-8538	291	20	can	can	AUX
aiti-8538	291	21	be	be	AUX
aiti-8538	291	22	explored	explore	VERB
aiti-8538	291	23	to	to	PART
aiti-8538	291	24	enhance	enhance	VERB
aiti-8538	291	25	the	the	DET
aiti-8538	291	26	accuracy	accuracy	NOUN
aiti-8538	291	27	of	of	ADP
aiti-8538	291	28	the	the	DET
aiti-8538	291	29	classifier	classifier	NOUN
aiti-8538	291	30	.	.	PUNCT
aiti-8538	292	1	moreover	moreover	ADV
aiti-8538	292	2	,	,	PUNCT
aiti-8538	292	3	fuzzy	fuzzy	ADJ
aiti-8538	292	4	rough	rough	ADJ
aiti-8538	292	5	sets	set	NOUN
aiti-8538	292	6	,	,	PUNCT
aiti-8538	292	7	ranking	ranking	NOUN
aiti-8538	292	8	-	-	PUNCT
aiti-8538	292	9	based	base	VERB
aiti-8538	292	10	models	model	NOUN
aiti-8538	292	11	,	,	PUNCT
aiti-8538	292	12	and	and	CCONJ
aiti-8538	292	13	regularized	regularize	VERB
aiti-8538	292	14	regression	regression	NOUN
aiti-8538	292	15	models	model	NOUN
aiti-8538	292	16	will	will	AUX
aiti-8538	292	17	be	be	AUX
aiti-8538	292	18	researched	research	VERB
aiti-8538	292	19	for	for	ADP
aiti-8538	292	20	feature	feature	NOUN
aiti-8538	292	21	selection	selection	NOUN
aiti-8538	292	22	enhancing	enhance	VERB
aiti-8538	292	23	the	the	DET
aiti-8538	292	24	performance	performance	NOUN
aiti-8538	292	25	of	of	ADP
aiti-8538	292	26	image	image	NOUN
aiti-8538	292	27	classifiers	classifier	NOUN
aiti-8538	292	28	.	.	PUNCT
aiti-8538	293	1	conflicts	conflict	NOUN
aiti-8538	293	2	of	of	ADP
aiti-8538	293	3	interest	interest	NOUN
aiti-8538	293	4	the	the	DET
aiti-8538	293	5	authors	author	NOUN
aiti-8538	293	6	declare	declare	VERB
aiti-8538	293	7	no	no	DET
aiti-8538	293	8	conflict	conflict	NOUN
aiti-8538	293	9	of	of	ADP
aiti-8538	293	10	interest	interest	NOUN
aiti-8538	293	11	.	.	PUNCT
aiti-8538	294	1	references	reference	NOUN
aiti-8538	294	2	[	[	X
aiti-8538	294	3	1	1	NUM
aiti-8538	294	4	]	]	X
aiti-8538	294	5	r.	r.	PROPN
aiti-8538	294	6	guha	guha	PROPN
aiti-8538	294	7	,	,	PUNCT
aiti-8538	294	8	a.	a.	PROPN
aiti-8538	294	9	h.	h.	PROPN
aiti-8538	294	10	khan	khan	PROPN
aiti-8538	294	11	,	,	PUNCT
aiti-8538	294	12	p.	p.	PROPN
aiti-8538	294	13	k.	k.	PROPN
aiti-8538	295	1	singh	singh	PROPN
aiti-8538	295	2	,	,	PUNCT
aiti-8538	295	3	r.	r.	PROPN
aiti-8538	295	4	sarkar	sarkar	PROPN
aiti-8538	295	5	,	,	PUNCT
aiti-8538	295	6	and	and	CCONJ
aiti-8538	295	7	d.	d.	PROPN
aiti-8538	295	8	bhattacharjee	bhattacharjee	PROPN
aiti-8538	295	9	,	,	PUNCT
aiti-8538	295	10	“	"	PUNCT
aiti-8538	295	11	cga	cga	PROPN
aiti-8538	295	12	:	:	PUNCT
aiti-8538	295	13	a	a	DET
aiti-8538	295	14	new	new	ADJ
aiti-8538	295	15	feature	feature	NOUN
aiti-8538	295	16	selection	selection	NOUN
aiti-8538	295	17	model	model	NOUN
aiti-8538	295	18	for	for	ADP
aiti-8538	295	19	visual	visual	ADJ
aiti-8538	295	20	human	human	ADJ
aiti-8538	295	21	action	action	NOUN
aiti-8538	295	22	recognition	recognition	NOUN
aiti-8538	295	23	,	,	PUNCT
aiti-8538	295	24	”	"	PUNCT
aiti-8538	295	25	neural	neural	ADJ
aiti-8538	295	26	computing	computing	NOUN
aiti-8538	295	27	and	and	CCONJ
aiti-8538	295	28	applications	application	NOUN
aiti-8538	295	29	,	,	PUNCT
aiti-8538	295	30	vol	vol	NOUN
aiti-8538	295	31	.	.	PROPN
aiti-8538	296	1	33	33	NUM
aiti-8538	296	2	,	,	PUNCT
aiti-8538	296	3	no	no	INTJ
aiti-8538	296	4	.	.	NOUN
aiti-8538	296	5	10	10	NUM
aiti-8538	296	6	,	,	PUNCT
aiti-8538	296	7	pp	pp	ADJ
aiti-8538	296	8	.	.	PUNCT
aiti-8538	297	1	5267	5267	NUM
aiti-8538	297	2	-	-	SYM
aiti-8538	297	3	5286	5286	NUM
aiti-8538	297	4	,	,	PUNCT
aiti-8538	297	5	may	may	AUX
aiti-8538	297	6	2021	2021	NUM
aiti-8538	297	7	.	.	PUNCT
aiti-8538	298	1	[	[	X
aiti-8538	298	2	2	2	X
aiti-8538	298	3	]	]	PUNCT
aiti-8538	298	4	s.	s.	PROPN
aiti-8538	298	5	ahuja	ahuja	PROPN
aiti-8538	298	6	,	,	PUNCT
aiti-8538	298	7	b.	b.	PROPN
aiti-8538	298	8	k.	k.	PROPN
aiti-8538	298	9	panigrahi	panigrahi	PROPN
aiti-8538	298	10	,	,	PUNCT
aiti-8538	298	11	n.	n.	PROPN
aiti-8538	298	12	dey	dey	PROPN
aiti-8538	298	13	,	,	PUNCT
aiti-8538	298	14	v.	v.	ADP
aiti-8538	298	15	rajinikanth	rajinikanth	NOUN
aiti-8538	298	16	,	,	PUNCT
aiti-8538	298	17	and	and	CCONJ
aiti-8538	298	18	t.	t.	PROPN
aiti-8538	298	19	k.	k.	PROPN
aiti-8538	298	20	gandhi	gandhi	PROPN
aiti-8538	298	21	,	,	PUNCT
aiti-8538	298	22	“	"	PUNCT
aiti-8538	298	23	deep	deep	ADJ
aiti-8538	298	24	transfer	transfer	NOUN
aiti-8538	298	25	learning	learning	NOUN
aiti-8538	298	26	-	-	PUNCT
aiti-8538	298	27	based	base	VERB
aiti-8538	298	28	automated	automate	VERB
aiti-8538	298	29	detection	detection	NOUN
aiti-8538	298	30	of	of	ADP
aiti-8538	298	31	covid-19	covid-19	PROPN
aiti-8538	298	32	from	from	ADP
aiti-8538	298	33	lung	lung	PROPN
aiti-8538	298	34	ct	ct	PROPN
aiti-8538	298	35	scan	scan	PROPN
aiti-8538	298	36	slices	slice	NOUN
aiti-8538	298	37	,	,	PUNCT
aiti-8538	298	38	”	"	PUNCT
aiti-8538	298	39	applied	apply	VERB
aiti-8538	298	40	intelligence	intelligence	NOUN
aiti-8538	298	41	,	,	PUNCT
aiti-8538	298	42	vol	vol	NOUN
aiti-8538	298	43	.	.	PROPN
aiti-8538	299	1	51	51	NUM
aiti-8538	299	2	,	,	PUNCT
aiti-8538	299	3	no	no	INTJ
aiti-8538	299	4	.	.	NOUN
aiti-8538	299	5	1	1	NUM
aiti-8538	299	6	,	,	PUNCT
aiti-8538	299	7	pp	pp	ADJ
aiti-8538	299	8	.	.	PUNCT
aiti-8538	300	1	571	571	NUM
aiti-8538	300	2	-	-	SYM
aiti-8538	300	3	585	585	NUM
aiti-8538	300	4	,	,	PUNCT
aiti-8538	300	5	january	january	NOUN
aiti-8538	300	6	2021	2021	NUM
aiti-8538	300	7	.	.	PUNCT
aiti-8538	301	1	[	[	X
aiti-8538	301	2	3	3	X
aiti-8538	301	3	]	]	X
aiti-8538	301	4	j.	j.	PROPN
aiti-8538	301	5	pardede	pardede	PROPN
aiti-8538	301	6	,	,	PUNCT
aiti-8538	301	7	b.	b.	PROPN
aiti-8538	301	8	sitohang	sitohang	PROPN
aiti-8538	301	9	,	,	PUNCT
aiti-8538	301	10	s.	s.	PROPN
aiti-8538	301	11	akbar	akbar	PROPN
aiti-8538	301	12	,	,	PUNCT
aiti-8538	301	13	and	and	CCONJ
aiti-8538	301	14	m.	m.	PROPN
aiti-8538	301	15	l.	l.	PROPN
aiti-8538	301	16	khodra	khodra	PROPN
aiti-8538	301	17	,	,	PUNCT
aiti-8538	301	18	“	"	PUNCT
aiti-8538	301	19	implementation	implementation	NOUN
aiti-8538	301	20	of	of	ADP
aiti-8538	301	21	transfer	transfer	NOUN
aiti-8538	301	22	learning	learning	NOUN
aiti-8538	301	23	using	use	VERB
aiti-8538	301	24	vgg16	vgg16	NOUN
aiti-8538	301	25	on	on	ADP
aiti-8538	301	26	fruit	fruit	NOUN
aiti-8538	301	27	ripeness	ripeness	NOUN
aiti-8538	301	28	detection	detection	NOUN
aiti-8538	301	29	,	,	PUNCT
aiti-8538	301	30	”	"	PUNCT
aiti-8538	301	31	international	international	ADJ
aiti-8538	301	32	journal	journal	NOUN
aiti-8538	301	33	of	of	ADP
aiti-8538	301	34	intelligent	intelligent	ADJ
aiti-8538	301	35	systems	system	NOUN
aiti-8538	301	36	and	and	CCONJ
aiti-8538	301	37	applications	application	NOUN
aiti-8538	301	38	,	,	PUNCT
aiti-8538	301	39	vol	vol	NOUN
aiti-8538	301	40	.	.	PROPN
aiti-8538	301	41	13	13	NUM
aiti-8538	301	42	,	,	PUNCT
aiti-8538	301	43	no	no	INTJ
aiti-8538	301	44	.	.	NOUN
aiti-8538	301	45	2	2	NUM
aiti-8538	301	46	,	,	PUNCT
aiti-8538	301	47	pp	pp	ADJ
aiti-8538	301	48	.	.	PUNCT
aiti-8538	302	1	52	52	NUM
aiti-8538	302	2	-	-	SYM
aiti-8538	302	3	61	61	NUM
aiti-8538	302	4	,	,	PUNCT
aiti-8538	302	5	2021	2021	NUM
aiti-8538	302	6	.	.	PUNCT
aiti-8538	303	1	[	[	X
aiti-8538	303	2	4	4	X
aiti-8538	303	3	]	]	PUNCT
aiti-8538	303	4	m.	m.	PROPN
aiti-8538	303	5	q.	q.	PROPN
aiti-8538	303	6	tran	tran	PROPN
aiti-8538	303	7	,	,	PUNCT
aiti-8538	303	8	m.	m.	PROPN
aiti-8538	303	9	k.	k.	PROPN
aiti-8538	303	10	liu	liu	PROPN
aiti-8538	303	11	,	,	PUNCT
aiti-8538	303	12	and	and	CCONJ
aiti-8538	303	13	m.	m.	NOUN
aiti-8538	303	14	elsisi	elsisi	VERB
aiti-8538	303	15	,	,	PUNCT
aiti-8538	303	16	“	"	PUNCT
aiti-8538	303	17	effective	effective	ADJ
aiti-8538	303	18	multi	multi	ADJ
aiti-8538	303	19	-	-	ADJ
aiti-8538	303	20	sensor	sensor	ADJ
aiti-8538	303	21	data	datum	NOUN
aiti-8538	303	22	fusion	fusion	NOUN
aiti-8538	303	23	for	for	ADP
aiti-8538	303	24	chatter	chatter	NOUN
aiti-8538	303	25	detection	detection	NOUN
aiti-8538	303	26	in	in	ADP
aiti-8538	303	27	milling	milling	NOUN
aiti-8538	303	28	process	process	NOUN
aiti-8538	303	29	,	,	PUNCT
aiti-8538	303	30	”	"	PUNCT
aiti-8538	303	31	isa	isa	NOUN
aiti-8538	303	32	transactions	transaction	NOUN
aiti-8538	303	33	,	,	PUNCT
aiti-8538	303	34	in	in	ADP
aiti-8538	303	35	press	press	NOUN
aiti-8538	303	36	.	.	PUNCT
aiti-8538	304	1	[	[	X
aiti-8538	304	2	5	5	X
aiti-8538	304	3	]	]	PUNCT
aiti-8538	304	4	m.	m.	PROPN
aiti-8538	304	5	q.	q.	PROPN
aiti-8538	304	6	tran	tran	PROPN
aiti-8538	304	7	,	,	PUNCT
aiti-8538	304	8	m.	m.	NOUN
aiti-8538	304	9	elsisi	elsisi	VERB
aiti-8538	304	10	,	,	PUNCT
aiti-8538	304	11	and	and	CCONJ
aiti-8538	304	12	m.	m.	PROPN
aiti-8538	304	13	k.	k.	PROPN
aiti-8538	304	14	liu	liu	PROPN
aiti-8538	304	15	,	,	PUNCT
aiti-8538	304	16	“	"	PUNCT
aiti-8538	304	17	effective	effective	ADJ
aiti-8538	304	18	feature	feature	NOUN
aiti-8538	304	19	selection	selection	NOUN
aiti-8538	304	20	with	with	ADP
aiti-8538	304	21	fuzzy	fuzzy	ADJ
aiti-8538	304	22	entropy	entropy	NOUN
aiti-8538	304	23	and	and	CCONJ
aiti-8538	304	24	similarity	similarity	NOUN
aiti-8538	304	25	classifier	classifier	NOUN
aiti-8538	304	26	for	for	ADP
aiti-8538	304	27	chatter	chatter	NOUN
aiti-8538	304	28	vibration	vibration	NOUN
aiti-8538	304	29	diagnosis	diagnosis	NOUN
aiti-8538	304	30	,	,	PUNCT
aiti-8538	304	31	”	"	PUNCT
aiti-8538	304	32	measurement	measurement	NOUN
aiti-8538	304	33	,	,	PUNCT
aiti-8538	304	34	vol	vol	NOUN
aiti-8538	304	35	.	.	PROPN
aiti-8538	304	36	184	184	NUM
aiti-8538	304	37	,	,	PUNCT
aiti-8538	304	38	109962	109962	NUM
aiti-8538	304	39	,	,	PUNCT
aiti-8538	304	40	november	november	PROPN
aiti-8538	304	41	2021	2021	NUM
aiti-8538	304	42	.	.	PUNCT
aiti-8538	305	1	[	[	X
aiti-8538	305	2	6	6	NUM
aiti-8538	305	3	]	]	X
aiti-8538	305	4	d.	d.	PROPN
aiti-8538	305	5	hemavathi	hemavathi	PROPN
aiti-8538	305	6	and	and	CCONJ
aiti-8538	305	7	h.	h.	PROPN
aiti-8538	305	8	srimathi	srimathi	PROPN
aiti-8538	305	9	,	,	PUNCT
aiti-8538	305	10	“	"	PUNCT
aiti-8538	305	11	effective	effective	ADJ
aiti-8538	305	12	feature	feature	NOUN
aiti-8538	305	13	selection	selection	NOUN
aiti-8538	305	14	technique	technique	NOUN
aiti-8538	305	15	in	in	ADP
aiti-8538	305	16	an	an	DET
aiti-8538	305	17	integrated	integrated	ADJ
aiti-8538	305	18	environment	environment	NOUN
aiti-8538	305	19	using	use	VERB
aiti-8538	305	20	enhanced	enhance	VERB
aiti-8538	305	21	principal	principal	ADJ
aiti-8538	305	22	component	component	NOUN
aiti-8538	305	23	analysis	analysis	NOUN
aiti-8538	305	24	,	,	PUNCT
aiti-8538	305	25	”	"	PUNCT
aiti-8538	305	26	journal	journal	NOUN
aiti-8538	305	27	of	of	ADP
aiti-8538	305	28	ambient	ambient	ADJ
aiti-8538	305	29	intelligence	intelligence	NOUN
aiti-8538	305	30	and	and	CCONJ
aiti-8538	305	31	humanized	humanize	VERB
aiti-8538	305	32	computing	computing	NOUN
aiti-8538	305	33	,	,	PUNCT
aiti-8538	305	34	vol	vol	NOUN
aiti-8538	305	35	.	.	PROPN
aiti-8538	305	36	12	12	NUM
aiti-8538	305	37	,	,	PUNCT
aiti-8538	305	38	no	no	INTJ
aiti-8538	305	39	.	.	NOUN
aiti-8538	305	40	3	3	NUM
aiti-8538	305	41	,	,	PUNCT
aiti-8538	305	42	pp	pp	ADJ
aiti-8538	305	43	.	.	PUNCT
aiti-8538	305	44	3679	3679	NUM
aiti-8538	305	45	-	-	SYM
aiti-8538	305	46	3688	3688	NUM
aiti-8538	305	47	,	,	PUNCT
aiti-8538	305	48	2021	2021	NUM
aiti-8538	305	49	.	.	PUNCT
aiti-8538	306	1	[	[	X
aiti-8538	306	2	7	7	X
aiti-8538	306	3	]	]	PUNCT
aiti-8538	306	4	s.	s.	PROPN
aiti-8538	306	5	punitha	punitha	PROPN
aiti-8538	306	6	,	,	PUNCT
aiti-8538	306	7	f.	f.	PROPN
aiti-8538	306	8	al	al	PROPN
aiti-8538	306	9	-	-	PUNCT
aiti-8538	306	10	turjman	turjman	NOUN
aiti-8538	306	11	,	,	PUNCT
aiti-8538	306	12	and	and	CCONJ
aiti-8538	306	13	t.	t.	PROPN
aiti-8538	306	14	stephan	stephan	PROPN
aiti-8538	306	15	,	,	PUNCT
aiti-8538	306	16	“	"	PUNCT
aiti-8538	306	17	an	an	DET
aiti-8538	306	18	automated	automate	VERB
aiti-8538	306	19	breast	breast	NOUN
aiti-8538	306	20	cancer	cancer	NOUN
aiti-8538	306	21	diagnosis	diagnosis	NOUN
aiti-8538	306	22	using	use	VERB
aiti-8538	306	23	feature	feature	NOUN
aiti-8538	306	24	selection	selection	NOUN
aiti-8538	306	25	and	and	CCONJ
aiti-8538	306	26	parameter	parameter	NOUN
aiti-8538	306	27	optimization	optimization	NOUN
aiti-8538	306	28	in	in	ADP
aiti-8538	306	29	ann	ann	PROPN
aiti-8538	306	30	,	,	PUNCT
aiti-8538	306	31	”	"	PUNCT
aiti-8538	306	32	computers	computer	NOUN
aiti-8538	306	33	and	and	CCONJ
aiti-8538	306	34	electrical	electrical	ADJ
aiti-8538	306	35	engineering	engineering	NOUN
aiti-8538	306	36	,	,	PUNCT
aiti-8538	306	37	vol	vol	NOUN
aiti-8538	306	38	.	.	PROPN
aiti-8538	306	39	90	90	NUM
aiti-8538	306	40	,	,	PUNCT
aiti-8538	306	41	106958	106958	NUM
aiti-8538	306	42	,	,	PUNCT
aiti-8538	306	43	march	march	PROPN
aiti-8538	306	44	2021	2021	NUM
aiti-8538	306	45	.	.	PUNCT
aiti-8538	307	1	[	[	X
aiti-8538	307	2	8	8	NUM
aiti-8538	307	3	]	]	X
aiti-8538	307	4	m.	m.	NOUN
aiti-8538	307	5	elsisi	elsisi	VERB
aiti-8538	307	6	,	,	PUNCT
aiti-8538	307	7	m.	m.	NOUN
aiti-8538	307	8	q.	q.	PROPN
aiti-8538	307	9	tran	tran	PROPN
aiti-8538	307	10	,	,	PUNCT
aiti-8538	307	11	k.	k.	PROPN
aiti-8538	307	12	mahmoud	mahmoud	PROPN
aiti-8538	307	13	,	,	PUNCT
aiti-8538	307	14	m.	m.	NOUN
aiti-8538	307	15	lehtonen	lehtonen	PROPN
aiti-8538	307	16	,	,	PUNCT
aiti-8538	307	17	and	and	CCONJ
aiti-8538	307	18	m.	m.	NOUN
aiti-8538	307	19	m.	m.	PROPN
aiti-8538	307	20	darwish	darwish	PROPN
aiti-8538	307	21	,	,	PUNCT
aiti-8538	307	22	“	"	PUNCT
aiti-8538	307	23	deep	deep	ADJ
aiti-8538	307	24	learning	learning	NOUN
aiti-8538	307	25	-	-	PUNCT
aiti-8538	307	26	based	base	VERB
aiti-8538	307	27	industry	industry	NOUN
aiti-8538	307	28	4.0	4.0	NUM
aiti-8538	307	29	and	and	CCONJ
aiti-8538	307	30	internet	internet	NOUN
aiti-8538	307	31	of	of	ADP
aiti-8538	307	32	things	thing	NOUN
aiti-8538	307	33	towards	towards	ADP
aiti-8538	307	34	effective	effective	ADJ
aiti-8538	307	35	energy	energy	NOUN
aiti-8538	307	36	management	management	NOUN
aiti-8538	307	37	for	for	ADP
aiti-8538	307	38	smart	smart	ADJ
aiti-8538	307	39	buildings	building	NOUN
aiti-8538	307	40	,	,	PUNCT
aiti-8538	307	41	”	"	PUNCT
aiti-8538	307	42	sensors	sensor	NOUN
aiti-8538	307	43	,	,	PUNCT
aiti-8538	307	44	vol	vol	NOUN
aiti-8538	307	45	.	.	PROPN
aiti-8538	307	46	21	21	NUM
aiti-8538	307	47	,	,	PUNCT
aiti-8538	307	48	no	no	INTJ
aiti-8538	307	49	.	.	NOUN
aiti-8538	307	50	4	4	NUM
aiti-8538	307	51	,	,	PUNCT
aiti-8538	307	52	1038	1038	NUM
aiti-8538	307	53	,	,	PUNCT
aiti-8538	307	54	february	february	NOUN
aiti-8538	307	55	2021	2021	NUM
aiti-8538	307	56	.	.	PUNCT
aiti-8538	308	1	[	[	X
aiti-8538	308	2	9	9	NUM
aiti-8538	308	3	]	]	PUNCT
aiti-8538	308	4	m.	m.	NOUN
aiti-8538	308	5	q.	q.	PROPN
aiti-8538	308	6	tran	tran	PROPN
aiti-8538	308	7	,	,	PUNCT
aiti-8538	308	8	m.	m.	PROPN
aiti-8538	308	9	elsisi	elsisi	VERB
aiti-8538	308	10	,	,	PUNCT
aiti-8538	308	11	k.	k.	PROPN
aiti-8538	308	12	mahmoud	mahmoud	PROPN
aiti-8538	308	13	,	,	PUNCT
aiti-8538	308	14	m.	m.	PROPN
aiti-8538	308	15	k.	k.	PROPN
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aiti-8538	308	17	,	,	PUNCT
aiti-8538	308	18	m.	m.	NOUN
aiti-8538	308	19	lehtonen	lehtonen	PROPN
aiti-8538	308	20	,	,	PUNCT
aiti-8538	308	21	and	and	CCONJ
aiti-8538	308	22	m.	m.	NOUN
aiti-8538	308	23	m.	m.	PROPN
aiti-8538	308	24	darwish	darwish	PROPN
aiti-8538	308	25	,	,	PUNCT
aiti-8538	308	26	“	"	PUNCT
aiti-8538	308	27	experimental	experimental	ADJ
aiti-8538	308	28	setup	setup	NOUN
aiti-8538	308	29	for	for	ADP
aiti-8538	308	30	online	online	ADJ
aiti-8538	308	31	fault	fault	NOUN
aiti-8538	308	32	diagnosis	diagnosis	NOUN
aiti-8538	308	33	of	of	ADP
aiti-8538	308	34	induction	induction	NOUN
aiti-8538	308	35	machines	machine	NOUN
aiti-8538	308	36	via	via	ADP
aiti-8538	308	37	promising	promise	VERB
aiti-8538	308	38	iot	iot	NOUN
aiti-8538	308	39	and	and	CCONJ
aiti-8538	308	40	machine	machine	NOUN
aiti-8538	308	41	learning	learning	NOUN
aiti-8538	308	42	:	:	PUNCT
aiti-8538	308	43	towards	towards	ADP
aiti-8538	308	44	industry	industry	NOUN
aiti-8538	308	45	4.0	4.0	NUM
aiti-8538	308	46	empowerment	empowerment	NOUN
aiti-8538	308	47	,	,	PUNCT
aiti-8538	308	48	”	"	PUNCT
aiti-8538	308	49	ieee	ieee	NOUN
aiti-8538	308	50	access	access	NOUN
aiti-8538	308	51	,	,	PUNCT
aiti-8538	308	52	vol	vol	NOUN
aiti-8538	308	53	.	.	NOUN
aiti-8538	308	54	9	9	NUM
aiti-8538	308	55	,	,	PUNCT
aiti-8538	308	56	pp	pp	ADJ
aiti-8538	308	57	.	.	PUNCT
aiti-8538	309	1	115429	115429	NUM
aiti-8538	309	2	-	-	SYM
aiti-8538	309	3	115441	115441	NUM
aiti-8538	309	4	,	,	PUNCT
aiti-8538	309	5	2021	2021	NUM
aiti-8538	309	6	.	.	PUNCT
aiti-8538	310	1	115	115	NUM
aiti-8538	310	2	advances	advance	NOUN
aiti-8538	310	3	in	in	ADP
aiti-8538	310	4	technology	technology	NOUN
aiti-8538	310	5	innovation	innovation	NOUN
aiti-8538	310	6	,	,	PUNCT
aiti-8538	310	7	vol	vol	NOUN
aiti-8538	310	8	.	.	PROPN
aiti-8538	310	9	7	7	NUM
aiti-8538	310	10	,	,	PUNCT
aiti-8538	310	11	no	no	INTJ
aiti-8538	310	12	.	.	NOUN
aiti-8538	310	13	2	2	NUM
aiti-8538	310	14	,	,	PUNCT
aiti-8538	310	15	2022	2022	NUM
aiti-8538	310	16	,	,	PUNCT
aiti-8538	310	17	pp	pp	ADJ
aiti-8538	310	18	.	.	PUNCT
aiti-8538	311	1	105	105	NUM
aiti-8538	311	2	-	-	SYM
aiti-8538	311	3	117	117	NUM
aiti-8538	311	4	[	[	SYM
aiti-8538	311	5	10	10	NUM
aiti-8538	311	6	]	]	X
aiti-8538	311	7	m.	m.	NOUN
aiti-8538	311	8	elsisi	elsisi	VERB
aiti-8538	311	9	,	,	PUNCT
aiti-8538	311	10	m.	m.	NOUN
aiti-8538	311	11	q.	q.	PROPN
aiti-8538	311	12	tran	tran	PROPN
aiti-8538	311	13	,	,	PUNCT
aiti-8538	311	14	k.	k.	PROPN
aiti-8538	311	15	mahmoud	mahmoud	PROPN
aiti-8538	311	16	,	,	PUNCT
aiti-8538	311	17	d.	d.	PROPN
aiti-8538	311	18	e.	e.	PROPN
aiti-8538	311	19	a.	a.	PROPN
aiti-8538	311	20	mansour	mansour	PROPN
aiti-8538	311	21	,	,	PUNCT
aiti-8538	311	22	m.	m.	PROPN
aiti-8538	311	23	lehtonen	lehtonen	PROPN
aiti-8538	311	24	,	,	PUNCT
aiti-8538	311	25	and	and	CCONJ
aiti-8538	311	26	m.	m.	NOUN
aiti-8538	311	27	m.	m.	PROPN
aiti-8538	311	28	darwish	darwish	PROPN
aiti-8538	311	29	,	,	PUNCT
aiti-8538	311	30	“	"	PUNCT
aiti-8538	311	31	towards	towards	ADP
aiti-8538	311	32	secured	secure	VERB
aiti-8538	311	33	online	online	ADJ
aiti-8538	311	34	monitoring	monitoring	NOUN
aiti-8538	311	35	for	for	ADP
aiti-8538	311	36	digitalized	digitalized	ADJ
aiti-8538	311	37	gis	gis	NOUN
aiti-8538	311	38	against	against	ADP
aiti-8538	311	39	cyber	cyber	NOUN
aiti-8538	311	40	-	-	PUNCT
aiti-8538	311	41	attacks	attack	NOUN
aiti-8538	311	42	based	base	VERB
aiti-8538	311	43	on	on	ADP
aiti-8538	311	44	iot	iot	NOUN
aiti-8538	311	45	and	and	CCONJ
aiti-8538	311	46	machine	machine	NOUN
aiti-8538	311	47	learning	learning	NOUN
aiti-8538	311	48	,	,	PUNCT
aiti-8538	311	49	”	"	PUNCT
aiti-8538	311	50	ieee	ieee	NOUN
aiti-8538	311	51	access	access	NOUN
aiti-8538	311	52	,	,	PUNCT
aiti-8538	311	53	vol	vol	NOUN
aiti-8538	311	54	.	.	NOUN
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aiti-8538	311	56	,	,	PUNCT
aiti-8538	311	57	pp	pp	ADJ
aiti-8538	311	58	.	.	PUNCT
aiti-8538	312	1	78415	78415	NUM
aiti-8538	312	2	-	-	SYM
aiti-8538	312	3	78427	78427	NUM
aiti-8538	312	4	,	,	PUNCT
aiti-8538	312	5	2021	2021	NUM
aiti-8538	312	6	.	.	PUNCT
aiti-8538	313	1	[	[	X
aiti-8538	313	2	11	11	NUM
aiti-8538	313	3	]	]	X
aiti-8538	313	4	i.	i.	PROPN
aiti-8538	313	5	ullah	ullah	PROPN
aiti-8538	313	6	and	and	CCONJ
aiti-8538	313	7	q.	q.	PROPN
aiti-8538	313	8	h.	h.	PROPN
aiti-8538	313	9	mahmoud	mahmoud	PROPN
aiti-8538	313	10	,	,	PUNCT
aiti-8538	313	11	“	"	PUNCT
aiti-8538	313	12	design	design	NOUN
aiti-8538	313	13	and	and	CCONJ
aiti-8538	313	14	development	development	NOUN
aiti-8538	313	15	of	of	ADP
aiti-8538	313	16	a	a	DET
aiti-8538	313	17	deep	deep	ADJ
aiti-8538	313	18	learning	learning	NOUN
aiti-8538	313	19	-	-	PUNCT
aiti-8538	313	20	based	base	VERB
aiti-8538	313	21	model	model	NOUN
aiti-8538	313	22	for	for	ADP
aiti-8538	313	23	anomaly	anomaly	NOUN
aiti-8538	313	24	detection	detection	NOUN
aiti-8538	313	25	in	in	ADP
aiti-8538	313	26	iot	iot	PROPN
aiti-8538	313	27	networks	network	NOUN
aiti-8538	313	28	,	,	PUNCT
aiti-8538	313	29	”	"	PUNCT
aiti-8538	313	30	ieee	ieee	NOUN
aiti-8538	313	31	access	access	NOUN
aiti-8538	313	32	,	,	PUNCT
aiti-8538	313	33	vol	vol	NOUN
aiti-8538	313	34	.	.	NOUN
aiti-8538	313	35	9	9	NUM
aiti-8538	313	36	,	,	PUNCT
aiti-8538	313	37	pp	pp	ADJ
aiti-8538	313	38	.	.	PUNCT
aiti-8538	313	39	103906	103906	NUM
aiti-8538	313	40	-	-	SYM
aiti-8538	313	41	103926	103926	NUM
aiti-8538	313	42	,	,	PUNCT
aiti-8538	313	43	2021	2021	NUM
aiti-8538	313	44	.	.	PUNCT
aiti-8538	314	1	[	[	X
aiti-8538	314	2	12	12	NUM
aiti-8538	314	3	]	]	X
aiti-8538	314	4	g.	g.	PROPN
aiti-8538	314	5	delnevo	delnevo	PROPN
aiti-8538	314	6	,	,	PUNCT
aiti-8538	314	7	r.	r.	PROPN
aiti-8538	314	8	girau	girau	PROPN
aiti-8538	314	9	,	,	PUNCT
aiti-8538	314	10	c.	c.	PROPN
aiti-8538	314	11	ceccarini	ceccarini	PROPN
aiti-8538	314	12	,	,	PUNCT
aiti-8538	314	13	and	and	CCONJ
aiti-8538	314	14	c.	c.	PROPN
aiti-8538	314	15	prandi	prandi	PROPN
aiti-8538	314	16	,	,	PUNCT
aiti-8538	314	17	“	"	PUNCT
aiti-8538	314	18	a	a	DET
aiti-8538	314	19	deep	deep	ADJ
aiti-8538	314	20	learning	learning	NOUN
aiti-8538	314	21	and	and	CCONJ
aiti-8538	314	22	social	social	ADJ
aiti-8538	314	23	iot	iot	NOUN
aiti-8538	314	24	approach	approach	NOUN
aiti-8538	314	25	for	for	ADP
aiti-8538	314	26	plants	plant	NOUN
aiti-8538	314	27	disease	disease	NOUN
aiti-8538	314	28	prediction	prediction	NOUN
aiti-8538	314	29	toward	toward	ADP
aiti-8538	314	30	a	a	DET
aiti-8538	314	31	sustainable	sustainable	ADJ
aiti-8538	314	32	agriculture	agriculture	NOUN
aiti-8538	314	33	,	,	PUNCT
aiti-8538	314	34	”	"	PUNCT
aiti-8538	314	35	ieee	ieee	NOUN
aiti-8538	314	36	internet	internet	NOUN
aiti-8538	314	37	of	of	ADP
aiti-8538	314	38	things	thing	NOUN
aiti-8538	314	39	journal	journal	NOUN
aiti-8538	314	40	,	,	PUNCT
aiti-8538	314	41	in	in	ADP
aiti-8538	314	42	press	press	NOUN
aiti-8538	314	43	.	.	PUNCT
aiti-8538	315	1	[	[	X
aiti-8538	315	2	13	13	NUM
aiti-8538	315	3	]	]	PUNCT
aiti-8538	315	4	t.	t.	PROPN
aiti-8538	315	5	kaur	kaur	PROPN
aiti-8538	315	6	and	and	CCONJ
aiti-8538	315	7	t.	t.	PROPN
aiti-8538	315	8	k.	k.	PROPN
aiti-8538	315	9	gandhi	gandhi	PROPN
aiti-8538	315	10	,	,	PUNCT
aiti-8538	315	11	“	"	PUNCT
aiti-8538	315	12	deep	deep	ADJ
aiti-8538	315	13	convolutional	convolutional	ADJ
aiti-8538	315	14	neural	neural	ADJ
aiti-8538	315	15	networks	network	NOUN
aiti-8538	315	16	with	with	ADP
aiti-8538	315	17	transfer	transfer	NOUN
aiti-8538	315	18	learning	learning	NOUN
aiti-8538	315	19	for	for	ADP
aiti-8538	315	20	automated	automate	VERB
aiti-8538	315	21	brain	brain	NOUN
aiti-8538	315	22	image	image	NOUN
aiti-8538	315	23	classification	classification	NOUN
aiti-8538	315	24	,	,	PUNCT
aiti-8538	315	25	”	"	PUNCT
aiti-8538	315	26	machine	machine	NOUN
aiti-8538	315	27	vision	vision	NOUN
aiti-8538	315	28	and	and	CCONJ
aiti-8538	315	29	applications	application	NOUN
aiti-8538	315	30	,	,	PUNCT
aiti-8538	315	31	vol	vol	NOUN
aiti-8538	315	32	.	.	PROPN
aiti-8538	315	33	31	31	NUM
aiti-8538	315	34	,	,	PUNCT
aiti-8538	315	35	no	no	INTJ
aiti-8538	315	36	.	.	NOUN
aiti-8538	315	37	3	3	NUM
aiti-8538	315	38	,	,	PUNCT
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aiti-8538	315	40	,	,	PUNCT
aiti-8538	315	41	march	march	PROPN
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aiti-8538	315	43	.	.	PUNCT
aiti-8538	316	1	[	[	X
aiti-8538	316	2	14	14	NUM
aiti-8538	316	3	]	]	X
aiti-8538	316	4	r.	r.	PROPN
aiti-8538	316	5	pires	pires	PROPN
aiti-8538	316	6	de	de	PROPN
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aiti-8538	316	8	and	and	CCONJ
aiti-8538	316	9	k.	k.	PROPN
aiti-8538	316	10	marfurt	marfurt	PROPN
aiti-8538	316	11	,	,	PUNCT
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aiti-8538	316	13	convolutional	convolutional	ADJ
aiti-8538	316	14	neural	neural	ADJ
aiti-8538	316	15	network	network	NOUN
aiti-8538	316	16	for	for	ADP
aiti-8538	316	17	remote	remote	ADV
aiti-8538	316	18	-	-	PUNCT
aiti-8538	316	19	sensing	sense	VERB
aiti-8538	316	20	scene	scene	NOUN
aiti-8538	316	21	classification	classification	NOUN
aiti-8538	316	22	:	:	PUNCT
aiti-8538	316	23	transfer	transfer	VERB
aiti-8538	316	24	learning	learn	VERB
aiti-8538	316	25	analysis	analysis	NOUN
aiti-8538	316	26	,	,	PUNCT
aiti-8538	316	27	”	"	PUNCT
aiti-8538	316	28	remote	remote	ADJ
aiti-8538	316	29	sensing	sensing	NOUN
aiti-8538	316	30	,	,	PUNCT
aiti-8538	316	31	vol	vol	NOUN
aiti-8538	316	32	.	.	PROPN
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aiti-8538	317	2	,	,	PUNCT
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aiti-8538	317	4	.	.	NOUN
aiti-8538	317	5	1	1	NUM
aiti-8538	317	6	,	,	PUNCT
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aiti-8538	317	8	,	,	PUNCT
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aiti-8538	317	11	.	.	PUNCT
aiti-8538	318	1	[	[	X
aiti-8538	318	2	15	15	NUM
aiti-8538	318	3	]	]	X
aiti-8538	318	4	d.	d.	PROPN
aiti-8538	318	5	xue	xue	PROPN
aiti-8538	318	6	,	,	PUNCT
aiti-8538	318	7	x.	x.	NOUN
aiti-8538	318	8	zhou	zhou	PROPN
aiti-8538	318	9	,	,	PUNCT
aiti-8538	318	10	c.	c.	PROPN
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aiti-8538	318	12	,	,	PUNCT
aiti-8538	318	13	y.	y.	PROPN
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aiti-8538	318	15	,	,	PUNCT
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aiti-8538	318	17	m.	m.	PROPN
aiti-8538	318	18	rahaman	rahaman	PROPN
aiti-8538	318	19	,	,	PUNCT
aiti-8538	318	20	j.	j.	PROPN
aiti-8538	318	21	zhang	zhang	PROPN
aiti-8538	318	22	,	,	PUNCT
aiti-8538	318	23	et	et	PROPN
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aiti-8538	318	25	.	.	PROPN
aiti-8538	318	26	,	,	PUNCT
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aiti-8538	318	28	an	an	DET
aiti-8538	318	29	application	application	NOUN
aiti-8538	318	30	of	of	ADP
aiti-8538	318	31	transfer	transfer	NOUN
aiti-8538	318	32	learning	learning	NOUN
aiti-8538	318	33	and	and	CCONJ
aiti-8538	318	34	ensemble	ensemble	ADJ
aiti-8538	318	35	learning	learning	NOUN
aiti-8538	318	36	techniques	technique	NOUN
aiti-8538	318	37	for	for	ADP
aiti-8538	318	38	cervical	cervical	ADJ
aiti-8538	318	39	histopathology	histopathology	NOUN
aiti-8538	318	40	image	image	NOUN
aiti-8538	318	41	classification	classification	NOUN
aiti-8538	318	42	,	,	PUNCT
aiti-8538	318	43	”	"	PUNCT
aiti-8538	318	44	ieee	ieee	NOUN
aiti-8538	318	45	access	access	NOUN
aiti-8538	318	46	,	,	PUNCT
aiti-8538	318	47	vol	vol	NOUN
aiti-8538	318	48	.	.	PROPN
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aiti-8538	318	50	,	,	PUNCT
aiti-8538	318	51	pp	pp	ADJ
aiti-8538	318	52	.	.	PUNCT
aiti-8538	319	1	104603	104603	NUM
aiti-8538	319	2	-	-	SYM
aiti-8538	319	3	104618	104618	NUM
aiti-8538	319	4	,	,	PUNCT
aiti-8538	319	5	2020	2020	NUM
aiti-8538	319	6	.	.	PUNCT
aiti-8538	320	1	[	[	X
aiti-8538	320	2	16	16	NUM
aiti-8538	320	3	]	]	PUNCT
aiti-8538	320	4	m.	m.	NOUN
aiti-8538	320	5	garcia	garcia	PROPN
aiti-8538	320	6	-	-	PUNCT
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aiti-8538	320	8	,	,	PUNCT
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aiti-8538	320	11	,	,	PUNCT
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aiti-8538	320	19	v.	v.	ADP
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aiti-8538	320	24	:	:	PUNCT
aiti-8538	320	25	a	a	DET
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aiti-8538	320	27	for	for	ADP
aiti-8538	320	28	image	image	NOUN
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aiti-8538	320	30	using	use	VERB
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aiti-8538	320	33	transfer	transfer	VERB
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aiti-8538	320	36	,	,	PUNCT
aiti-8538	320	37	”	"	PUNCT
aiti-8538	320	38	ieee	ieee	NOUN
aiti-8538	320	39	access	access	NOUN
aiti-8538	320	40	,	,	PUNCT
aiti-8538	320	41	vol	vol	NOUN
aiti-8538	320	42	.	.	PROPN
aiti-8538	320	43	8	8	NUM
aiti-8538	320	44	,	,	PUNCT
aiti-8538	320	45	pp	pp	ADJ
aiti-8538	320	46	.	.	PUNCT
aiti-8538	321	1	53443	53443	NUM
aiti-8538	321	2	-	-	SYM
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aiti-8538	321	4	,	,	PUNCT
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aiti-8538	322	3	]	]	PUNCT
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aiti-8538	322	7	e.	e.	PROPN
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aiti-8538	322	15	learning	learning	NOUN
aiti-8538	322	16	,	,	PUNCT
aiti-8538	322	17	”	"	PUNCT
aiti-8538	322	18	multimedia	multimedia	NOUN
aiti-8538	322	19	tools	tool	NOUN
aiti-8538	322	20	and	and	CCONJ
aiti-8538	322	21	applications	application	NOUN
aiti-8538	322	22	,	,	PUNCT
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aiti-8538	322	24	.	.	PROPN
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aiti-8538	322	26	,	,	PUNCT
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aiti-8538	322	28	.	.	NOUN
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aiti-8538	322	30	,	,	PUNCT
aiti-8538	322	31	pp	pp	ADJ
aiti-8538	322	32	.	.	PUNCT
aiti-8538	322	33	17095	17095	NUM
aiti-8538	322	34	-	-	SYM
aiti-8538	322	35	17112	17112	NUM
aiti-8538	322	36	,	,	PUNCT
aiti-8538	322	37	june	june	PROPN
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aiti-8538	322	39	.	.	PUNCT
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aiti-8538	323	2	18	18	NUM
aiti-8538	323	3	]	]	PUNCT
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aiti-8538	323	7	and	and	CCONJ
aiti-8538	323	8	l.	l.	PROPN
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aiti-8538	323	11	,	,	PUNCT
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aiti-8538	323	31	,	,	PUNCT
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aiti-8538	323	34	journal	journal	NOUN
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aiti-8538	323	37	and	and	CCONJ
aiti-8538	323	38	technology	technology	NOUN
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aiti-8538	323	40	,	,	PUNCT
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aiti-8538	323	42	.	.	PROPN
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aiti-8538	323	44	,	,	PUNCT
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aiti-8538	323	48	,	,	PUNCT
aiti-8538	323	49	pp	pp	ADJ
aiti-8538	323	50	.	.	PUNCT
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aiti-8538	324	2	-	-	SYM
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aiti-8538	324	7	.	.	PUNCT
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aiti-8538	325	21	t.	t.	PROPN
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aiti-8538	325	49	applications	application	NOUN
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aiti-8538	325	64	,	,	PUNCT
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aiti-8538	325	67	.	.	PUNCT
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aiti-8538	326	27	data	datum	NOUN
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aiti-8538	326	29	,	,	PUNCT
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aiti-8538	326	31	multimedia	multimedia	NOUN
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aiti-8538	326	33	and	and	CCONJ
aiti-8538	326	34	applications	application	NOUN
aiti-8538	326	35	,	,	PUNCT
aiti-8538	326	36	vol	vol	NOUN
aiti-8538	326	37	.	.	PROPN
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aiti-8538	326	39	,	,	PUNCT
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aiti-8538	326	43	,	,	PUNCT
aiti-8538	326	44	pp	pp	ADJ
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aiti-8538	328	4	y.	y.	PROPN
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aiti-8538	328	11	x.	x.	PROPN
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aiti-8538	328	29	electronics	electronic	NOUN
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aiti-8538	328	36	,	,	PUNCT
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aiti-8538	328	38	,	,	PUNCT
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aiti-8538	328	41	.	.	PUNCT
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aiti-8538	329	4	r.	r.	PROPN
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aiti-8538	329	10	,	,	PUNCT
aiti-8538	329	11	n.	n.	PROPN
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aiti-8538	329	13	jhanjhi	jhanjhi	PROPN
aiti-8538	329	14	,	,	PUNCT
aiti-8538	329	15	and	and	CCONJ
aiti-8538	329	16	s.	s.	PROPN
aiti-8538	329	17	n.	n.	PROPN
aiti-8538	329	18	brohi	brohi	PROPN
aiti-8538	329	19	,	,	PUNCT
aiti-8538	329	20	“	"	PUNCT
aiti-8538	329	21	performance	performance	NOUN
aiti-8538	329	22	of	of	ADP
aiti-8538	329	23	deep	deep	ADJ
aiti-8538	329	24	learning	learning	NOUN
aiti-8538	329	25	vs	vs	ADP
aiti-8538	329	26	machine	machine	NOUN
aiti-8538	329	27	learning	learn	VERB
aiti-8538	329	28	in	in	ADP
aiti-8538	329	29	plant	plant	NOUN
aiti-8538	329	30	leaf	leaf	NOUN
aiti-8538	329	31	disease	disease	NOUN
aiti-8538	329	32	detection	detection	NOUN
aiti-8538	329	33	,	,	PUNCT
aiti-8538	329	34	”	"	PUNCT
aiti-8538	329	35	microprocessors	microprocessor	NOUN
aiti-8538	329	36	and	and	CCONJ
aiti-8538	329	37	microsystems	microsystem	NOUN
aiti-8538	329	38	,	,	PUNCT
aiti-8538	329	39	vol	vol	NOUN
aiti-8538	329	40	.	.	PROPN
aiti-8538	329	41	80	80	NUM
aiti-8538	329	42	,	,	PUNCT
aiti-8538	329	43	103615	103615	NUM
aiti-8538	329	44	,	,	PUNCT
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aiti-8538	329	46	.	.	PUNCT
aiti-8538	330	1	[	[	X
aiti-8538	330	2	23	23	NUM
aiti-8538	330	3	]	]	PUNCT
aiti-8538	330	4	s.	s.	PROPN
aiti-8538	330	5	k.	k.	PROPN
aiti-8538	330	6	behera	behera	PROPN
aiti-8538	330	7	,	,	PUNCT
aiti-8538	330	8	a.	a.	PROPN
aiti-8538	330	9	k.	k.	PROPN
aiti-8538	330	10	rath	rath	PROPN
aiti-8538	330	11	,	,	PUNCT
aiti-8538	330	12	and	and	CCONJ
aiti-8538	330	13	p.	p.	PROPN
aiti-8538	330	14	k.	k.	PROPN
aiti-8538	331	1	sethy	sethy	ADJ
aiti-8538	331	2	,	,	PUNCT
aiti-8538	331	3	“	"	PUNCT
aiti-8538	331	4	maturity	maturity	NOUN
aiti-8538	331	5	status	status	NOUN
aiti-8538	331	6	classification	classification	NOUN
aiti-8538	331	7	of	of	ADP
aiti-8538	331	8	papaya	papaya	NOUN
aiti-8538	331	9	fruits	fruit	NOUN
aiti-8538	331	10	based	base	VERB
aiti-8538	331	11	on	on	ADP
aiti-8538	331	12	machine	machine	NOUN
aiti-8538	331	13	learning	learning	NOUN
aiti-8538	331	14	and	and	CCONJ
aiti-8538	331	15	transfer	transfer	VERB
aiti-8538	331	16	learning	learning	NOUN
aiti-8538	331	17	approach	approach	NOUN
aiti-8538	331	18	,	,	PUNCT
aiti-8538	331	19	”	"	PUNCT
aiti-8538	331	20	information	information	NOUN
aiti-8538	331	21	processing	processing	NOUN
aiti-8538	331	22	in	in	ADP
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aiti-8538	331	24	,	,	PUNCT
aiti-8538	331	25	vol	vol	NOUN
aiti-8538	331	26	.	.	PROPN
aiti-8538	331	27	8	8	NUM
aiti-8538	331	28	,	,	PUNCT
aiti-8538	331	29	no	no	INTJ
aiti-8538	331	30	.	.	NOUN
aiti-8538	331	31	2	2	NUM
aiti-8538	331	32	,	,	PUNCT
aiti-8538	331	33	pp	pp	ADJ
aiti-8538	331	34	.	.	PUNCT
aiti-8538	332	1	244	244	NUM
aiti-8538	332	2	-	-	SYM
aiti-8538	332	3	250	250	NUM
aiti-8538	332	4	,	,	PUNCT
aiti-8538	332	5	june	june	PROPN
aiti-8538	332	6	2021	2021	NUM
aiti-8538	332	7	.	.	PUNCT
aiti-8538	333	1	[	[	X
aiti-8538	333	2	24	24	NUM
aiti-8538	333	3	]	]	PUNCT
aiti-8538	333	4	h.	h.	PROPN
aiti-8538	333	5	j.	j.	PROPN
aiti-8538	333	6	chiu	chiu	PROPN
aiti-8538	333	7	,	,	PUNCT
aiti-8538	333	8	t.	t.	PROPN
aiti-8538	333	9	h.	h.	PROPN
aiti-8538	333	10	s.	s.	PROPN
aiti-8538	333	11	li	li	PROPN
aiti-8538	333	12	,	,	PUNCT
aiti-8538	333	13	and	and	CCONJ
aiti-8538	333	14	p.	p.	PROPN
aiti-8538	333	15	h.	h.	PROPN
aiti-8538	333	16	kuo	kuo	PROPN
aiti-8538	333	17	,	,	PUNCT
aiti-8538	333	18	“	"	PUNCT
aiti-8538	333	19	breast	breast	NOUN
aiti-8538	333	20	cancer	cancer	NOUN
aiti-8538	333	21	-	-	PUNCT
aiti-8538	333	22	detection	detection	NOUN
aiti-8538	333	23	system	system	NOUN
aiti-8538	333	24	using	use	VERB
aiti-8538	333	25	pca	pca	PROPN
aiti-8538	333	26	,	,	PUNCT
aiti-8538	333	27	multilayer	multilayer	PROPN
aiti-8538	333	28	perceptron	perceptron	PROPN
aiti-8538	333	29	,	,	PUNCT
aiti-8538	333	30	transfer	transfer	NOUN
aiti-8538	333	31	learning	learning	NOUN
aiti-8538	333	32	,	,	PUNCT
aiti-8538	333	33	and	and	CCONJ
aiti-8538	333	34	support	support	VERB
aiti-8538	333	35	vector	vector	NOUN
aiti-8538	333	36	machine	machine	NOUN
aiti-8538	333	37	,	,	PUNCT
aiti-8538	333	38	”	"	PUNCT
aiti-8538	333	39	ieee	ieee	NOUN
aiti-8538	333	40	access	access	NOUN
aiti-8538	333	41	,	,	PUNCT
aiti-8538	333	42	vol	vol	NOUN
aiti-8538	333	43	.	.	PROPN
aiti-8538	333	44	8	8	NUM
aiti-8538	333	45	,	,	PUNCT
aiti-8538	333	46	pp	pp	ADJ
aiti-8538	333	47	.	.	PUNCT
aiti-8538	334	1	204309	204309	NUM
aiti-8538	334	2	-	-	SYM
aiti-8538	334	3	204324	204324	NUM
aiti-8538	334	4	,	,	PUNCT
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aiti-8538	334	6	.	.	PUNCT
aiti-8538	335	1	[	[	X
aiti-8538	335	2	25	25	NUM
aiti-8538	335	3	]	]	PUNCT
aiti-8538	335	4	m.	m.	NOUN
aiti-8538	335	5	loey	loey	PROPN
aiti-8538	335	6	,	,	PUNCT
aiti-8538	335	7	g.	g.	PROPN
aiti-8538	335	8	manogaran	manogaran	PROPN
aiti-8538	335	9	,	,	PUNCT
aiti-8538	335	10	m.	m.	NOUN
aiti-8538	335	11	h.	h.	PROPN
aiti-8538	335	12	n.	n.	PROPN
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aiti-8538	335	14	,	,	PUNCT
aiti-8538	335	15	and	and	CCONJ
aiti-8538	335	16	n.	n.	PROPN
aiti-8538	335	17	e.	e.	PROPN
aiti-8538	335	18	m.	m.	PROPN
aiti-8538	335	19	khalifa	khalifa	PROPN
aiti-8538	335	20	,	,	PUNCT
aiti-8538	335	21	“	"	PUNCT
aiti-8538	335	22	a	a	DET
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aiti-8538	335	25	transfer	transfer	NOUN
aiti-8538	335	26	learning	learning	NOUN
aiti-8538	335	27	model	model	NOUN
aiti-8538	335	28	with	with	ADP
aiti-8538	335	29	machine	machine	NOUN
aiti-8538	335	30	learning	learning	NOUN
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aiti-8538	335	32	for	for	ADP
aiti-8538	335	33	face	face	NOUN
aiti-8538	335	34	mask	mask	NOUN
aiti-8538	335	35	detection	detection	NOUN
aiti-8538	335	36	in	in	ADP
aiti-8538	335	37	the	the	DET
aiti-8538	335	38	era	era	NOUN
aiti-8538	335	39	of	of	ADP
aiti-8538	335	40	the	the	DET
aiti-8538	335	41	covid-19	covid-19	PROPN
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aiti-8538	335	43	,	,	PUNCT
aiti-8538	335	44	”	"	PUNCT
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aiti-8538	335	46	,	,	PUNCT
aiti-8538	335	47	vol	vol	NOUN
aiti-8538	335	48	.	.	PROPN
aiti-8538	335	49	167	167	NUM
aiti-8538	335	50	,	,	PUNCT
aiti-8538	335	51	108288	108288	NUM
aiti-8538	335	52	,	,	PUNCT
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aiti-8538	335	55	.	.	PUNCT
aiti-8538	336	1	[	[	X
aiti-8538	336	2	26	26	NUM
aiti-8538	336	3	]	]	X
aiti-8538	336	4	r.	r.	PROPN
aiti-8538	336	5	bhagwat	bhagwat	PROPN
aiti-8538	336	6	and	and	CCONJ
aiti-8538	336	7	y.	y.	PROPN
aiti-8538	336	8	dandawat	dandawat	PROPN
aiti-8538	336	9	,	,	PUNCT
aiti-8538	336	10	“	"	PUNCT
aiti-8538	336	11	a	a	DET
aiti-8538	336	12	review	review	NOUN
aiti-8538	336	13	on	on	ADP
aiti-8538	336	14	advances	advance	NOUN
aiti-8538	336	15	in	in	ADP
aiti-8538	336	16	automated	automate	VERB
aiti-8538	336	17	plant	plant	NOUN
aiti-8538	336	18	disease	disease	NOUN
aiti-8538	336	19	detection	detection	NOUN
aiti-8538	336	20	,	,	PUNCT
aiti-8538	336	21	”	"	PUNCT
aiti-8538	336	22	international	international	ADJ
aiti-8538	336	23	journal	journal	NOUN
aiti-8538	336	24	of	of	ADP
aiti-8538	336	25	engineering	engineering	NOUN
aiti-8538	336	26	and	and	CCONJ
aiti-8538	336	27	technology	technology	NOUN
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aiti-8538	336	29	,	,	PUNCT
aiti-8538	336	30	vol	vol	NOUN
aiti-8538	336	31	.	.	PROPN
aiti-8538	336	32	11	11	NUM
aiti-8538	336	33	,	,	PUNCT
aiti-8538	336	34	no	no	INTJ
aiti-8538	336	35	.	.	NOUN
aiti-8538	336	36	4	4	NUM
aiti-8538	336	37	,	,	PUNCT
aiti-8538	336	38	pp	pp	ADJ
aiti-8538	336	39	.	.	PUNCT
aiti-8538	337	1	251	251	NUM
aiti-8538	337	2	-	-	SYM
aiti-8538	337	3	264	264	NUM
aiti-8538	337	4	,	,	PUNCT
aiti-8538	337	5	september	september	PROPN
aiti-8538	337	6	2021	2021	NUM
aiti-8538	337	7	.	.	PUNCT
aiti-8538	338	1	[	[	X
aiti-8538	338	2	27	27	NUM
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aiti-8538	338	4	s.	s.	PROPN
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aiti-8538	338	6	and	and	CCONJ
aiti-8538	338	7	c.	c.	PROPN
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aiti-8538	338	9	li	li	PROPN
aiti-8538	338	10	,	,	PUNCT
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aiti-8538	338	17	image	image	VERB
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aiti-8538	338	19	,	,	PUNCT
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aiti-8538	338	25	,	,	PUNCT
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aiti-8538	339	1	[	[	X
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aiti-8538	339	4	k.	k.	PROPN
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aiti-8538	339	17	,	,	PUNCT
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aiti-8538	339	21	,	,	PUNCT
aiti-8538	339	22	vol	vol	NOUN
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aiti-8538	339	29	,	,	PUNCT
aiti-8538	339	30	pp	pp	ADJ
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aiti-8538	340	1	1	1	NUM
aiti-8538	340	2	-	-	SYM
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aiti-8538	340	4	,	,	PUNCT
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aiti-8538	340	6	,	,	PUNCT
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aiti-8538	340	8	.	.	PUNCT
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aiti-8538	341	9	,	,	PUNCT
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aiti-8538	341	46	,	,	PUNCT
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aiti-8538	342	2	-	-	SYM
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aiti-8538	342	7	.	.	PUNCT
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aiti-8538	343	10	h.	h.	PROPN
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aiti-8538	343	13	and	and	CCONJ
aiti-8538	343	14	b.	b.	PROPN
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aiti-8538	343	39	based	base	VERB
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aiti-8538	343	42	vol	vol	NOUN
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aiti-8538	344	17	“	"	PUNCT
aiti-8538	344	18	how	how	SCONJ
aiti-8538	344	19	transferable	transferable	ADJ
aiti-8538	344	20	are	be	AUX
aiti-8538	344	21	features	feature	NOUN
aiti-8538	344	22	in	in	ADP
aiti-8538	344	23	deep	deep	ADJ
aiti-8538	344	24	neural	neural	ADJ
aiti-8538	344	25	networks	network	NOUN
aiti-8538	344	26	?	?	PUNCT
aiti-8538	344	27	”	"	PUNCT
aiti-8538	345	1	https://arxiv.org/pdf/1411.1792.pdf	https://arxiv.org/pdf/1411.1792.pdf	PROPN
aiti-8538	345	2	,	,	PUNCT
aiti-8538	345	3	november	november	PROPN
aiti-8538	345	4	06	06	NUM
aiti-8538	345	5	,	,	PUNCT
aiti-8538	345	6	2014	2014	NUM
aiti-8538	345	7	.	.	PUNCT
aiti-8538	346	1	[	[	X
aiti-8538	346	2	32	32	NUM
aiti-8538	346	3	]	]	PUNCT
aiti-8538	346	4	s.	s.	PROPN
aiti-8538	346	5	mahajan	mahajan	PROPN
aiti-8538	346	6	,	,	PUNCT
aiti-8538	346	7	a.	a.	PROPN
aiti-8538	346	8	raina	raina	PROPN
aiti-8538	346	9	,	,	PUNCT
aiti-8538	346	10	x.	x.	PROPN
aiti-8538	346	11	z.	z.	PROPN
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aiti-8538	346	13	,	,	PUNCT
aiti-8538	346	14	and	and	CCONJ
aiti-8538	346	15	a.	a.	PROPN
aiti-8538	346	16	k.	k.	PROPN
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aiti-8538	346	18	,	,	PUNCT
aiti-8538	346	19	“	"	PUNCT
aiti-8538	346	20	plant	plant	NOUN
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aiti-8538	346	22	using	use	VERB
aiti-8538	346	23	morphological	morphological	ADJ
aiti-8538	346	24	feature	feature	NOUN
aiti-8538	346	25	extraction	extraction	NOUN
aiti-8538	346	26	and	and	CCONJ
aiti-8538	346	27	transfer	transfer	NOUN
aiti-8538	346	28	learning	learning	NOUN
aiti-8538	346	29	over	over	ADP
aiti-8538	346	30	svm	svm	PROPN
aiti-8538	346	31	and	and	CCONJ
aiti-8538	346	32	adaboost	adaboost	ADV
aiti-8538	346	33	,	,	PUNCT
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aiti-8538	346	36	,	,	PUNCT
aiti-8538	346	37	vol	vol	NOUN
aiti-8538	346	38	.	.	PROPN
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aiti-8538	346	40	,	,	PUNCT
aiti-8538	346	41	no	no	INTJ
aiti-8538	346	42	.	.	NOUN
aiti-8538	346	43	2	2	NUM
aiti-8538	346	44	,	,	PUNCT
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aiti-8538	346	46	,	,	PUNCT
aiti-8538	346	47	februay	februay	NOUN
aiti-8538	346	48	2021	2021	NUM
aiti-8538	346	49	.	.	PUNCT
aiti-8538	347	1	[	[	X
aiti-8538	347	2	33	33	NUM
aiti-8538	347	3	]	]	PUNCT
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aiti-8538	347	5	singh	singh	PROPN
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aiti-8538	347	7	s.	s.	PROPN
aiti-8538	347	8	kumar	kumar	PROPN
aiti-8538	347	9	,	,	PUNCT
aiti-8538	347	10	a.	a.	PROPN
aiti-8538	347	11	singh	singh	PROPN
aiti-8538	347	12	,	,	PUNCT
aiti-8538	347	13	and	and	CCONJ
aiti-8538	347	14	s.	s.	PROPN
aiti-8538	347	15	s.	s.	PROPN
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aiti-8538	347	17	,	,	PUNCT
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aiti-8538	347	19	parallel	parallel	ADJ
aiti-8538	347	20	3	3	NUM
aiti-8538	347	21	-	-	PUNCT
aiti-8538	347	22	parent	parent	NOUN
aiti-8538	347	23	genetic	genetic	ADJ
aiti-8538	347	24	algorithm	algorithm	NOUN
aiti-8538	347	25	with	with	ADP
aiti-8538	347	26	application	application	NOUN
aiti-8538	347	27	to	to	ADP
aiti-8538	347	28	routing	route	VERB
aiti-8538	347	29	in	in	ADP
aiti-8538	347	30	wireless	wireless	ADJ
aiti-8538	347	31	mesh	mesh	NOUN
aiti-8538	347	32	networks	network	NOUN
aiti-8538	347	33	,	,	PUNCT
aiti-8538	347	34	”	"	PUNCT
aiti-8538	347	35	implementations	implementation	NOUN
aiti-8538	347	36	and	and	CCONJ
aiti-8538	347	37	applications	application	NOUN
aiti-8538	347	38	of	of	ADP
aiti-8538	347	39	machine	machine	NOUN
aiti-8538	347	40	learning	learning	NOUN
aiti-8538	347	41	,	,	PUNCT
aiti-8538	347	42	vol	vol	NOUN
aiti-8538	347	43	.	.	PROPN
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aiti-8538	347	45	,	,	PUNCT
aiti-8538	347	46	pp	pp	ADJ
aiti-8538	347	47	.	.	PUNCT
aiti-8538	348	1	1	1	NUM
aiti-8538	348	2	-	-	SYM
aiti-8538	348	3	27	27	NUM
aiti-8538	348	4	,	,	PUNCT
aiti-8538	348	5	2020	2020	NUM
aiti-8538	348	6	.	.	PUNCT
aiti-8538	349	1	116	116	NUM
aiti-8538	349	2	advances	advance	NOUN
aiti-8538	349	3	in	in	ADP
aiti-8538	349	4	technology	technology	NOUN
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aiti-8538	349	6	,	,	PUNCT
aiti-8538	349	7	vol	vol	NOUN
aiti-8538	349	8	.	.	PROPN
aiti-8538	349	9	7	7	NUM
aiti-8538	349	10	,	,	PUNCT
aiti-8538	349	11	no	no	INTJ
aiti-8538	349	12	.	.	NOUN
aiti-8538	349	13	2	2	NUM
aiti-8538	349	14	,	,	PUNCT
aiti-8538	349	15	2022	2022	NUM
aiti-8538	349	16	,	,	PUNCT
aiti-8538	349	17	pp	pp	ADJ
aiti-8538	349	18	.	.	PUNCT
aiti-8538	350	1	105	105	NUM
aiti-8538	350	2	-	-	SYM
aiti-8538	350	3	117	117	NUM
aiti-8538	350	4	[	[	SYM
aiti-8538	350	5	34	34	NUM
aiti-8538	350	6	]	]	X
aiti-8538	350	7	s.	s.	PROPN
aiti-8538	350	8	dilmi	dilmi	PROPN
aiti-8538	350	9	and	and	CCONJ
aiti-8538	350	10	m.	m.	NOUN
aiti-8538	350	11	ladjal	ladjal	PROPN
aiti-8538	350	12	,	,	PUNCT
aiti-8538	350	13	“	"	PUNCT
aiti-8538	350	14	a	a	DET
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aiti-8538	350	16	approach	approach	NOUN
aiti-8538	350	17	for	for	ADP
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aiti-8538	350	21	based	base	VERB
aiti-8538	350	22	on	on	ADP
aiti-8538	350	23	the	the	DET
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aiti-8538	350	25	of	of	ADP
aiti-8538	350	26	deep	deep	ADJ
aiti-8538	350	27	learning	learning	NOUN
aiti-8538	350	28	and	and	CCONJ
aiti-8538	350	29	feature	feature	NOUN
aiti-8538	350	30	extraction	extraction	NOUN
aiti-8538	350	31	techniques	technique	NOUN
aiti-8538	350	32	,	,	PUNCT
aiti-8538	350	33	”	"	PUNCT
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aiti-8538	350	35	and	and	CCONJ
aiti-8538	350	36	intelligent	intelligent	ADJ
aiti-8538	350	37	laboratory	laboratory	NOUN
aiti-8538	350	38	systems	system	NOUN
aiti-8538	350	39	,	,	PUNCT
aiti-8538	350	40	vol	vol	NOUN
aiti-8538	350	41	.	.	PROPN
aiti-8538	351	1	214	214	NUM
aiti-8538	351	2	,	,	PUNCT
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aiti-8538	351	4	,	,	PUNCT
aiti-8538	351	5	july	july	PROPN
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aiti-8538	351	7	.	.	PUNCT
aiti-8538	352	1	[	[	X
aiti-8538	352	2	35	35	NUM
aiti-8538	352	3	]	]	PUNCT
aiti-8538	352	4	j.	j.	PROPN
aiti-8538	352	5	wang	wang	PROPN
aiti-8538	352	6	,	,	PUNCT
aiti-8538	352	7	x.	x.	PROPN
aiti-8538	352	8	sun	sun	PROPN
aiti-8538	352	9	,	,	PUNCT
aiti-8538	352	10	q.	q.	PROPN
aiti-8538	352	11	cheng	cheng	PROPN
aiti-8538	352	12	,	,	PUNCT
aiti-8538	352	13	and	and	CCONJ
aiti-8538	352	14	q.	q.	PROPN
aiti-8538	352	15	cui	cui	PROPN
aiti-8538	352	16	,	,	PUNCT
aiti-8538	352	17	“	"	PUNCT
aiti-8538	352	18	an	an	DET
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aiti-8538	352	22	-	-	PUNCT
aiti-8538	352	23	based	base	VERB
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aiti-8538	352	28	improved	improved	ADJ
aiti-8538	352	29	feature	feature	NOUN
aiti-8538	352	30	extraction	extraction	NOUN
aiti-8538	352	31	and	and	CCONJ
aiti-8538	352	32	deep	deep	ADJ
aiti-8538	352	33	learning	learning	NOUN
aiti-8538	352	34	for	for	ADP
aiti-8538	352	35	carbon	carbon	NOUN
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aiti-8538	352	37	forecasting	forecasting	NOUN
aiti-8538	352	38	,	,	PUNCT
aiti-8538	352	39	”	"	PUNCT
aiti-8538	352	40	science	science	NOUN
aiti-8538	352	41	of	of	ADP
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aiti-8538	352	43	total	total	ADJ
aiti-8538	352	44	environment	environment	NOUN
aiti-8538	352	45	,	,	PUNCT
aiti-8538	352	46	vol	vol	NOUN
aiti-8538	352	47	.	.	PROPN
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aiti-8538	352	49	,	,	PUNCT
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aiti-8538	352	51	,	,	PUNCT
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aiti-8538	352	54	.	.	PUNCT
aiti-8538	353	1	[	[	X
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aiti-8538	353	3	]	]	PUNCT
aiti-8538	353	4	a.	a.	NOUN
aiti-8538	353	5	m.	m.	PROPN
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aiti-8538	353	8	a.	a.	NOUN
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aiti-8538	353	10	,	,	PUNCT
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aiti-8538	353	12	deep	deep	ADJ
aiti-8538	353	13	learning	learning	NOUN
aiti-8538	353	14	approaches	approach	NOUN
aiti-8538	353	15	for	for	ADP
aiti-8538	353	16	covid-19	covid-19	PROPN
aiti-8538	353	17	detection	detection	NOUN
aiti-8538	353	18	based	base	VERB
aiti-8538	353	19	on	on	ADP
aiti-8538	353	20	chest	chest	NOUN
aiti-8538	353	21	x	x	NOUN
aiti-8538	353	22	-	-	NOUN
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aiti-8538	353	29	with	with	ADP
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aiti-8538	353	31	,	,	PUNCT
aiti-8538	353	32	vol	vol	NOUN
aiti-8538	353	33	.	.	NOUN
aiti-8538	353	34	164	164	NUM
aiti-8538	353	35	,	,	PUNCT
aiti-8538	353	36	114054	114054	NUM
aiti-8538	353	37	,	,	PUNCT
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aiti-8538	353	39	2021	2021	NUM
aiti-8538	353	40	.	.	PUNCT
aiti-8538	354	1	[	[	X
aiti-8538	354	2	37	37	NUM
aiti-8538	354	3	]	]	PUNCT
aiti-8538	354	4	m.	m.	NOUN
aiti-8538	354	5	amini	amini	PROPN
aiti-8538	354	6	,	,	PUNCT
aiti-8538	354	7	m.	m.	NOUN
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aiti-8538	354	9	,	,	PUNCT
aiti-8538	354	10	a.	a.	NOUN
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aiti-8538	354	13	and	and	CCONJ
aiti-8538	354	14	m.	m.	PROPN
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aiti-8538	354	17	“	"	PUNCT
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aiti-8538	354	30	feature	feature	NOUN
aiti-8538	354	31	extraction	extraction	NOUN
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aiti-8538	354	33	and	and	CCONJ
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aiti-8538	354	36	network	network	NOUN
aiti-8538	354	37	(	(	PUNCT
aiti-8538	354	38	cnn	cnn	PROPN
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aiti-8538	354	46	in	in	ADP
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aiti-8538	354	52	,	,	PUNCT
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aiti-8538	354	54	,	,	PUNCT
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aiti-8538	355	1	[	[	X
aiti-8538	355	2	38	38	NUM
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aiti-8538	355	4	a.	a.	NOUN
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aiti-8538	355	13	and	and	CCONJ
aiti-8538	355	14	j.	j.	PROPN
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aiti-8538	355	18	“	"	PUNCT
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aiti-8538	355	26	on	on	ADP
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aiti-8538	355	28	using	use	VERB
aiti-8538	355	29	deep	deep	ADJ
aiti-8538	355	30	learning	learning	NOUN
aiti-8538	355	31	,	,	PUNCT
aiti-8538	355	32	”	"	PUNCT
aiti-8538	355	33	biosystems	biosystems	PROPN
aiti-8538	355	34	engineering	engineering	NOUN
aiti-8538	355	35	,	,	PUNCT
aiti-8538	355	36	vol	vol	NOUN
aiti-8538	355	37	.	.	PROPN
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aiti-8538	355	39	,	,	PUNCT
aiti-8538	355	40	pp	pp	ADJ
aiti-8538	355	41	.	.	PUNCT
aiti-8538	356	1	131	131	NUM
aiti-8538	356	2	-	-	SYM
aiti-8538	356	3	144	144	NUM
aiti-8538	356	4	,	,	PUNCT
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aiti-8538	356	7	.	.	PUNCT
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aiti-8538	357	2	39	39	NUM
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aiti-8538	357	4	r.	r.	PROPN
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aiti-8538	357	9	s.	s.	PROPN
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aiti-8538	357	12	,	,	PUNCT
aiti-8538	357	13	i.	i.	PROPN
aiti-8538	357	14	v.	v.	PROPN
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aiti-8538	357	17	d.	d.	PROPN
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aiti-8538	357	20	,	,	PUNCT
aiti-8538	357	21	d.	d.	PROPN
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aiti-8538	357	24	and	and	CCONJ
aiti-8538	357	25	k.	k.	PROPN
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aiti-8538	357	39	learning	learning	NOUN
aiti-8538	357	40	model	model	NOUN
aiti-8538	357	41	in	in	ADP
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aiti-8538	357	43	of	of	ADP
aiti-8538	357	44	medical	medical	ADJ
aiti-8538	357	45	things	thing	NOUN
aiti-8538	357	46	,	,	PUNCT
aiti-8538	357	47	”	"	PUNCT
aiti-8538	357	48	ieee	ieee	NOUN
aiti-8538	357	49	access	access	NOUN
aiti-8538	357	50	,	,	PUNCT
aiti-8538	357	51	vol	vol	NOUN
aiti-8538	357	52	.	.	PROPN
aiti-8538	357	53	8	8	NUM
aiti-8538	357	54	,	,	PUNCT
aiti-8538	357	55	pp	pp	ADJ
aiti-8538	357	56	.	.	PUNCT
aiti-8538	358	1	58006	58006	NUM
aiti-8538	358	2	-	-	SYM
aiti-8538	358	3	58017	58017	NUM
aiti-8538	358	4	,	,	PUNCT
aiti-8538	358	5	2020	2020	NUM
aiti-8538	358	6	.	.	PUNCT
aiti-8538	359	1	[	[	X
aiti-8538	359	2	40	40	NUM
aiti-8538	359	3	]	]	PUNCT
aiti-8538	359	4	g.	g.	PROPN
aiti-8538	359	5	bargshady	bargshady	PROPN
aiti-8538	359	6	,	,	PUNCT
aiti-8538	359	7	x.	x.	PROPN
aiti-8538	359	8	zhou	zhou	PROPN
aiti-8538	359	9	,	,	PUNCT
aiti-8538	359	10	r.	r.	PROPN
aiti-8538	359	11	c.	c.	PROPN
aiti-8538	359	12	deo	deo	PROPN
aiti-8538	359	13	,	,	PUNCT
aiti-8538	359	14	j.	j.	PROPN
aiti-8538	359	15	soar	soar	PROPN
aiti-8538	359	16	,	,	PUNCT
aiti-8538	359	17	f.	f.	PROPN
aiti-8538	359	18	whittaker	whittaker	PROPN
aiti-8538	359	19	,	,	PUNCT
aiti-8538	359	20	and	and	CCONJ
aiti-8538	359	21	h.	h.	PROPN
aiti-8538	359	22	wang	wang	PROPN
aiti-8538	359	23	,	,	PUNCT
aiti-8538	359	24	“	"	PUNCT
aiti-8538	359	25	enhanced	enhance	VERB
aiti-8538	359	26	deep	deep	ADJ
aiti-8538	359	27	learning	learning	NOUN
aiti-8538	359	28	algorithm	algorithm	NOUN
aiti-8538	359	29	development	development	NOUN
aiti-8538	359	30	to	to	PART
aiti-8538	359	31	detect	detect	VERB
aiti-8538	359	32	pain	pain	NOUN
aiti-8538	359	33	intensity	intensity	NOUN
aiti-8538	359	34	from	from	ADP
aiti-8538	359	35	facial	facial	ADJ
aiti-8538	359	36	expression	expression	NOUN
aiti-8538	359	37	images	image	NOUN
aiti-8538	359	38	,	,	PUNCT
aiti-8538	359	39	”	"	PUNCT
aiti-8538	359	40	expert	expert	NOUN
aiti-8538	359	41	systems	system	NOUN
aiti-8538	359	42	with	with	ADP
aiti-8538	359	43	applications	application	NOUN
aiti-8538	359	44	,	,	PUNCT
aiti-8538	359	45	vol	vol	NOUN
aiti-8538	359	46	.	.	PROPN
aiti-8538	359	47	149	149	NUM
aiti-8538	359	48	,	,	PUNCT
aiti-8538	359	49	113305	113305	NUM
aiti-8538	359	50	,	,	PUNCT
aiti-8538	359	51	july	july	PROPN
aiti-8538	359	52	2020	2020	NUM
aiti-8538	359	53	.	.	PUNCT
aiti-8538	360	1	[	[	X
aiti-8538	360	2	41	41	NUM
aiti-8538	360	3	]	]	PUNCT
aiti-8538	360	4	m.	m.	NOUN
aiti-8538	360	5	bansal	bansal	PROPN
aiti-8538	360	6	,	,	PUNCT
aiti-8538	360	7	m.	m.	NOUN
aiti-8538	360	8	kumar	kumar	PROPN
aiti-8538	360	9	,	,	PUNCT
aiti-8538	360	10	m.	m.	NOUN
aiti-8538	360	11	sachdeva	sachdeva	PROPN
aiti-8538	360	12	,	,	PUNCT
aiti-8538	360	13	and	and	CCONJ
aiti-8538	360	14	a.	a.	NOUN
aiti-8538	360	15	mittal	mittal	PROPN
aiti-8538	360	16	,	,	PUNCT
aiti-8538	360	17	“	"	PUNCT
aiti-8538	360	18	transfer	transfer	NOUN
aiti-8538	360	19	learning	learning	NOUN
aiti-8538	360	20	for	for	ADP
aiti-8538	360	21	image	image	NOUN
aiti-8538	360	22	classification	classification	NOUN
aiti-8538	360	23	using	use	VERB
aiti-8538	360	24	vgg19	vgg19	NOUN
aiti-8538	360	25	:	:	PUNCT
aiti-8538	360	26	caltech-101	caltech-101	NOUN
aiti-8538	360	27	image	image	NOUN
aiti-8538	360	28	data	datum	NOUN
aiti-8538	360	29	set	set	VERB
aiti-8538	360	30	,	,	PUNCT
aiti-8538	360	31	”	"	PUNCT
aiti-8538	360	32	journal	journal	NOUN
aiti-8538	360	33	of	of	ADP
aiti-8538	360	34	ambient	ambient	ADJ
aiti-8538	360	35	intelligence	intelligence	NOUN
aiti-8538	360	36	and	and	CCONJ
aiti-8538	360	37	humanized	humanize	VERB
aiti-8538	360	38	computing	computing	NOUN
aiti-8538	360	39	,	,	PUNCT
aiti-8538	360	40	in	in	ADP
aiti-8538	360	41	press	press	NOUN
aiti-8538	360	42	.	.	PUNCT
aiti-8538	361	1	[	[	X
aiti-8538	361	2	42	42	NUM
aiti-8538	361	3	]	]	PUNCT
aiti-8538	361	4	k.	k.	PROPN
aiti-8538	361	5	simonyan	simonyan	PROPN
aiti-8538	361	6	and	and	CCONJ
aiti-8538	361	7	a.	a.	NOUN
aiti-8538	361	8	zisserman	zisserman	PROPN
aiti-8538	361	9	,	,	PUNCT
aiti-8538	361	10	“	"	PUNCT
aiti-8538	361	11	very	very	ADV
aiti-8538	361	12	deep	deep	ADJ
aiti-8538	361	13	convolutional	convolutional	ADJ
aiti-8538	361	14	networks	network	NOUN
aiti-8538	361	15	for	for	ADP
aiti-8538	361	16	large	large	ADJ
aiti-8538	361	17	-	-	PUNCT
aiti-8538	361	18	scale	scale	NOUN
aiti-8538	361	19	image	image	NOUN
aiti-8538	361	20	recognition	recognition	NOUN
aiti-8538	361	21	,	,	PUNCT
aiti-8538	361	22	”	"	PUNCT
aiti-8538	361	23	https://arxiv.org/pdf/1409.1556.pdf	https://arxiv.org/pdf/1409.1556.pdf	PROPN
aiti-8538	361	24	,	,	PUNCT
aiti-8538	361	25	april	april	PROPN
aiti-8538	361	26	10	10	NUM
aiti-8538	361	27	,	,	PUNCT
aiti-8538	361	28	2015	2015	NUM
aiti-8538	361	29	.	.	PUNCT
aiti-8538	362	1	[	[	X
aiti-8538	362	2	43	43	NUM
aiti-8538	362	3	]	]	PUNCT
aiti-8538	362	4	k.	k.	PROPN
aiti-8538	362	5	pearson	pearson	PROPN
aiti-8538	362	6	,	,	PUNCT
aiti-8538	362	7	“	"	PUNCT
aiti-8538	362	8	liii	liii	NOUN
aiti-8538	362	9	.	.	PUNCT
aiti-8538	363	1	on	on	ADP
aiti-8538	363	2	lines	line	NOUN
aiti-8538	363	3	and	and	CCONJ
aiti-8538	363	4	planes	plane	NOUN
aiti-8538	363	5	of	of	ADP
aiti-8538	363	6	closest	close	ADJ
aiti-8538	363	7	fit	fit	ADJ
aiti-8538	363	8	to	to	ADP
aiti-8538	363	9	systems	system	NOUN
aiti-8538	363	10	of	of	ADP
aiti-8538	363	11	points	point	NOUN
aiti-8538	363	12	in	in	ADP
aiti-8538	363	13	space	space	NOUN
aiti-8538	363	14	,	,	PUNCT
aiti-8538	363	15	”	"	PUNCT
aiti-8538	363	16	the	the	DET
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aiti-8538	363	18	,	,	PUNCT
aiti-8538	363	19	edinburgh	edinburgh	PROPN
aiti-8538	363	20	,	,	PUNCT
aiti-8538	363	21	and	and	CCONJ
aiti-8538	363	22	dublin	dublin	PROPN
aiti-8538	363	23	philosophical	philosophical	PROPN
aiti-8538	363	24	magazine	magazine	NOUN
aiti-8538	363	25	and	and	CCONJ
aiti-8538	363	26	journal	journal	NOUN
aiti-8538	363	27	of	of	ADP
aiti-8538	363	28	science	science	NOUN
aiti-8538	363	29	,	,	PUNCT
aiti-8538	363	30	vol	vol	NOUN
aiti-8538	363	31	.	.	PROPN
aiti-8538	363	32	2	2	NUM
aiti-8538	363	33	,	,	PUNCT
aiti-8538	363	34	no	no	INTJ
aiti-8538	363	35	.	.	NOUN
aiti-8538	363	36	11	11	NUM
aiti-8538	363	37	,	,	PUNCT
aiti-8538	363	38	pp	pp	ADJ
aiti-8538	363	39	.	.	PUNCT
aiti-8538	364	1	559	559	NUM
aiti-8538	364	2	-	-	SYM
aiti-8538	364	3	572	572	NUM
aiti-8538	364	4	,	,	PUNCT
aiti-8538	364	5	1901	1901	NUM
aiti-8538	364	6	.	.	PUNCT
aiti-8538	365	1	[	[	X
aiti-8538	365	2	44	44	NUM
aiti-8538	365	3	]	]	PUNCT
aiti-8538	365	4	m.	m.	NOUN
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aiti-8538	365	6	,	,	PUNCT
aiti-8538	365	7	z.	z.	PROPN
aiti-8538	365	8	cömert	cömert	PROPN
aiti-8538	365	9	,	,	PUNCT
aiti-8538	365	10	and	and	CCONJ
aiti-8538	365	11	b.	b.	PROPN
aiti-8538	365	12	ergen	ergen	PROPN
aiti-8538	365	13	,	,	PUNCT
aiti-8538	365	14	“	"	PUNCT
aiti-8538	365	15	classification	classification	NOUN
aiti-8538	365	16	of	of	ADP
aiti-8538	365	17	brain	brain	NOUN
aiti-8538	365	18	mri	mri	NOUN
aiti-8538	365	19	using	use	VERB
aiti-8538	365	20	hyper	hyper	ADJ
aiti-8538	365	21	column	column	NOUN
aiti-8538	365	22	technique	technique	NOUN
aiti-8538	365	23	with	with	ADP
aiti-8538	365	24	convolutional	convolutional	ADJ
aiti-8538	365	25	neural	neural	ADJ
aiti-8538	365	26	network	network	NOUN
aiti-8538	365	27	and	and	CCONJ
aiti-8538	365	28	feature	feature	NOUN
aiti-8538	365	29	selection	selection	NOUN
aiti-8538	365	30	method	method	NOUN
aiti-8538	365	31	,	,	PUNCT
aiti-8538	365	32	”	"	PUNCT
aiti-8538	365	33	expert	expert	NOUN
aiti-8538	365	34	systems	system	NOUN
aiti-8538	365	35	with	with	ADP
aiti-8538	365	36	applications	application	NOUN
aiti-8538	365	37	,	,	PUNCT
aiti-8538	365	38	vol	vol	NOUN
aiti-8538	365	39	.	.	PROPN
aiti-8538	365	40	149	149	NUM
aiti-8538	365	41	,	,	PUNCT
aiti-8538	365	42	113274	113274	NUM
aiti-8538	365	43	,	,	PUNCT
aiti-8538	365	44	july	july	PROPN
aiti-8538	365	45	2020	2020	NUM
aiti-8538	365	46	.	.	PUNCT
aiti-8538	366	1	[	[	X
aiti-8538	366	2	45	45	NUM
aiti-8538	366	3	]	]	PUNCT
aiti-8538	366	4	l.	l.	PROPN
aiti-8538	366	5	yao	yao	PROPN
aiti-8538	366	6	,	,	PUNCT
aiti-8538	366	7	z.	z.	PROPN
aiti-8538	366	8	fang	fang	PROPN
aiti-8538	366	9	,	,	PUNCT
aiti-8538	366	10	y.	y.	PROPN
aiti-8538	366	11	xiao	xiao	PROPN
aiti-8538	366	12	,	,	PUNCT
aiti-8538	366	13	j.	j.	PROPN
aiti-8538	366	14	hou	hou	PROPN
aiti-8538	366	15	,	,	PUNCT
aiti-8538	366	16	and	and	CCONJ
aiti-8538	366	17	z.	z.	PROPN
aiti-8538	366	18	fu	fu	PROPN
aiti-8538	366	19	,	,	PUNCT
aiti-8538	366	20	“	"	PUNCT
aiti-8538	366	21	an	an	DET
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aiti-8538	366	23	fault	fault	NOUN
aiti-8538	366	24	diagnosis	diagnosis	NOUN
aiti-8538	366	25	method	method	NOUN
aiti-8538	366	26	for	for	ADP
aiti-8538	366	27	lithium	lithium	NOUN
aiti-8538	366	28	battery	battery	NOUN
aiti-8538	366	29	systems	system	NOUN
aiti-8538	366	30	based	base	VERB
aiti-8538	366	31	on	on	ADP
aiti-8538	366	32	grid	grid	NOUN
aiti-8538	366	33	search	search	NOUN
aiti-8538	366	34	support	support	NOUN
aiti-8538	366	35	vector	vector	NOUN
aiti-8538	366	36	machine	machine	NOUN
aiti-8538	366	37	,	,	PUNCT
aiti-8538	366	38	”	"	PUNCT
aiti-8538	366	39	energy	energy	NOUN
aiti-8538	366	40	,	,	PUNCT
aiti-8538	366	41	vol	vol	NOUN
aiti-8538	366	42	.	.	PROPN
aiti-8538	366	43	214	214	NUM
aiti-8538	366	44	,	,	PUNCT
aiti-8538	366	45	118866	118866	NUM
aiti-8538	366	46	,	,	PUNCT
aiti-8538	366	47	january	january	PROPN
aiti-8538	366	48	2021	2021	NUM
aiti-8538	366	49	.	.	PUNCT
aiti-8538	367	1	copyright	copyright	NOUN
aiti-8538	367	2	©	©	PROPN
aiti-8538	367	3	by	by	ADP
aiti-8538	367	4	the	the	DET
aiti-8538	367	5	authors	author	NOUN
aiti-8538	367	6	.	.	PUNCT
aiti-8538	368	1	licensee	licensee	PROPN
aiti-8538	368	2	taeti	taeti	PROPN
aiti-8538	368	3	,	,	PUNCT
aiti-8538	368	4	taiwan	taiwan	PROPN
aiti-8538	368	5	.	.	PUNCT
aiti-8538	369	1	this	this	DET
aiti-8538	369	2	article	article	NOUN
aiti-8538	369	3	is	be	AUX
aiti-8538	369	4	an	an	DET
aiti-8538	369	5	open	open	ADJ
aiti-8538	369	6	access	access	NOUN
aiti-8538	369	7	article	article	NOUN
aiti-8538	369	8	distributed	distribute	VERB
aiti-8538	369	9	under	under	ADP
aiti-8538	369	10	the	the	DET
aiti-8538	369	11	terms	term	NOUN
aiti-8538	369	12	and	and	CCONJ
aiti-8538	369	13	conditions	condition	NOUN
aiti-8538	369	14	of	of	ADP
aiti-8538	369	15	the	the	DET
aiti-8538	369	16	creative	creative	ADJ
aiti-8538	369	17	commons	common	NOUN
aiti-8538	369	18	attribution	attribution	NOUN
aiti-8538	369	19	(	(	PUNCT
aiti-8538	369	20	cc	cc	NOUN
aiti-8538	369	21	by	by	ADP
aiti-8538	369	22	-	-	PUNCT
aiti-8538	369	23	nc	nc	NOUN
aiti-8538	369	24	)	)	PUNCT
aiti-8538	369	25	license	license	NOUN
aiti-8538	369	26	(	(	PUNCT
aiti-8538	369	27	https://creativecommons.org/licenses/by-nc/4.0/	https://creativecommons.org/licenses/by-nc/4.0/	NOUN
aiti-8538	369	28	)	)	PUNCT
aiti-8538	369	29	.	.	PUNCT
aiti-8538	370	1	117	117	NUM