id	sid	tid	token	lemma	pos
fcis-28908	1	1	frontiers	frontier	NOUN
fcis-28908	1	2	in	in	ADP
fcis-28908	1	3	computing	computing	NOUN
fcis-28908	1	4	and	and	CCONJ
fcis-28908	1	5	intelligent	intelligent	ADJ
fcis-28908	1	6	systems	system	NOUN
fcis-28908	1	7	issn	issn	VERB
fcis-28908	1	8	:	:	PUNCT
fcis-28908	1	9	2832	2832	NUM
fcis-28908	1	10	-	-	SYM
fcis-28908	1	11	6024	6024	NUM
fcis-28908	1	12	|	|	NOUN
fcis-28908	1	13	vol	vol	NOUN
fcis-28908	1	14	.	.	PROPN
fcis-28908	2	1	10	10	NUM
fcis-28908	2	2	,	,	PUNCT
fcis-28908	2	3	no	no	INTJ
fcis-28908	2	4	.	.	NOUN
fcis-28908	2	5	3	3	NUM
fcis-28908	2	6	,	,	PUNCT
fcis-28908	2	7	2024	2024	NUM
fcis-28908	3	1	128	128	NUM
fcis-28908	3	2	implementing	implement	VERB
fcis-28908	3	3	insect	insect	NOUN
fcis-28908	3	4	classification	classification	NOUN
fcis-28908	3	5	based	base	VERB
fcis-28908	3	6	on	on	ADP
fcis-28908	3	7	convolutional	convolutional	ADJ
fcis-28908	3	8	neural	neural	ADJ
fcis-28908	3	9	networks	network	NOUN
fcis-28908	3	10	and	and	CCONJ
fcis-28908	3	11	tensorflow	tensorflow	PROPN
fcis-28908	3	12	zhiqiang	zhiqiang	PROPN
fcis-28908	3	13	zhang	zhang	PROPN
fcis-28908	4	1	*	*	PUNCT
fcis-28908	4	2	zhejiang	zhejiang	PROPN
fcis-28908	4	3	dongfang	dongfang	PROPN
fcis-28908	4	4	polytechnic	polytechnic	PROPN
fcis-28908	4	5	,	,	PUNCT
fcis-28908	4	6	wenzhou	wenzhou	PROPN
fcis-28908	4	7	zhejiang	zhejiang	PROPN
fcis-28908	4	8	,	,	PUNCT
fcis-28908	4	9	325000	325000	NUM
fcis-28908	4	10	,	,	PUNCT
fcis-28908	4	11	china	china	PROPN
fcis-28908	4	12	*	*	PUNCT
fcis-28908	4	13	corresponding	correspond	VERB
fcis-28908	4	14	author	author	NOUN
fcis-28908	4	15	:	:	PUNCT
fcis-28908	4	16	zhiqiang	zhiqiang	PROPN
fcis-28908	4	17	zhang	zhang	PROPN
fcis-28908	4	18	abstract	abstract	PROPN
fcis-28908	4	19	:	:	PUNCT
fcis-28908	4	20	insects	insect	NOUN
fcis-28908	4	21	are	be	AUX
fcis-28908	4	22	one	one	NUM
fcis-28908	4	23	of	of	ADP
fcis-28908	4	24	the	the	DET
fcis-28908	4	25	most	most	ADV
fcis-28908	4	26	diverse	diverse	ADJ
fcis-28908	4	27	biological	biological	ADJ
fcis-28908	4	28	groups	group	NOUN
fcis-28908	4	29	on	on	ADP
fcis-28908	4	30	earth	earth	NOUN
fcis-28908	4	31	,	,	PUNCT
fcis-28908	4	32	playing	play	VERB
fcis-28908	4	33	a	a	DET
fcis-28908	4	34	crucial	crucial	ADJ
fcis-28908	4	35	role	role	NOUN
fcis-28908	4	36	in	in	ADP
fcis-28908	4	37	human	human	ADJ
fcis-28908	4	38	agricultural	agricultural	ADJ
fcis-28908	4	39	production	production	NOUN
fcis-28908	4	40	.	.	PUNCT
fcis-28908	5	1	however	however	ADV
fcis-28908	5	2	,	,	PUNCT
fcis-28908	5	3	there	there	PRON
fcis-28908	5	4	is	be	VERB
fcis-28908	5	5	a	a	DET
fcis-28908	5	6	relative	relative	ADJ
fcis-28908	5	7	shortage	shortage	NOUN
fcis-28908	5	8	of	of	ADP
fcis-28908	5	9	professionals	professional	NOUN
fcis-28908	5	10	capable	capable	ADJ
fcis-28908	5	11	of	of	ADP
fcis-28908	5	12	classifying	classify	VERB
fcis-28908	5	13	insects	insect	NOUN
fcis-28908	5	14	.	.	PUNCT
fcis-28908	6	1	with	with	ADP
fcis-28908	6	2	the	the	DET
fcis-28908	6	3	rapid	rapid	ADJ
fcis-28908	6	4	advancement	advancement	NOUN
fcis-28908	6	5	of	of	ADP
fcis-28908	6	6	computer	computer	NOUN
fcis-28908	6	7	vision	vision	NOUN
fcis-28908	6	8	technology	technology	NOUN
fcis-28908	6	9	,	,	PUNCT
fcis-28908	6	10	image	image	NOUN
fcis-28908	6	11	classification	classification	NOUN
fcis-28908	6	12	techniques	technique	NOUN
fcis-28908	6	13	have	have	AUX
fcis-28908	6	14	been	be	AUX
fcis-28908	6	15	employed	employ	VERB
fcis-28908	6	16	to	to	PART
fcis-28908	6	17	classify	classify	VERB
fcis-28908	6	18	insects	insect	NOUN
fcis-28908	6	19	.	.	PUNCT
fcis-28908	7	1	traditional	traditional	ADJ
fcis-28908	7	2	insect	insect	NOUN
fcis-28908	7	3	image	image	NOUN
fcis-28908	7	4	classification	classification	NOUN
fcis-28908	7	5	requires	require	VERB
fcis-28908	7	6	manual	manual	ADJ
fcis-28908	7	7	extraction	extraction	NOUN
fcis-28908	7	8	of	of	ADP
fcis-28908	7	9	image	image	NOUN
fcis-28908	7	10	features	feature	NOUN
fcis-28908	7	11	,	,	PUNCT
fcis-28908	7	12	a	a	DET
fcis-28908	7	13	process	process	NOUN
fcis-28908	7	14	that	that	PRON
fcis-28908	7	15	is	be	AUX
fcis-28908	7	16	both	both	PRON
fcis-28908	7	17	time	time	NOUN
fcis-28908	7	18	-	-	PUNCT
fcis-28908	7	19	consuming	consume	VERB
fcis-28908	7	20	and	and	CCONJ
fcis-28908	7	21	labor	labor	NOUN
fcis-28908	7	22	-	-	PUNCT
fcis-28908	7	23	intensive	intensive	ADJ
fcis-28908	7	24	,	,	PUNCT
fcis-28908	7	25	and	and	CCONJ
fcis-28908	7	26	the	the	DET
fcis-28908	7	27	accuracy	accuracy	NOUN
fcis-28908	7	28	of	of	ADP
fcis-28908	7	29	the	the	DET
fcis-28908	7	30	identification	identification	NOUN
fcis-28908	7	31	results	result	NOUN
fcis-28908	7	32	is	be	AUX
fcis-28908	7	33	relatively	relatively	ADV
fcis-28908	7	34	low	low	ADJ
fcis-28908	7	35	.	.	PUNCT
fcis-28908	8	1	to	to	PART
fcis-28908	8	2	address	address	VERB
fcis-28908	8	3	these	these	DET
fcis-28908	8	4	issues	issue	NOUN
fcis-28908	8	5	,	,	PUNCT
fcis-28908	8	6	this	this	DET
fcis-28908	8	7	study	study	NOUN
fcis-28908	8	8	adopts	adopt	VERB
fcis-28908	8	9	deep	deep	ADJ
fcis-28908	8	10	learning	learning	NOUN
fcis-28908	8	11	technology	technology	NOUN
fcis-28908	8	12	and	and	CCONJ
fcis-28908	8	13	uses	use	VERB
fcis-28908	8	14	google	google	PROPN
fcis-28908	8	15	's	's	PART
fcis-28908	8	16	tensorflow	tensorflow	NOUN
fcis-28908	8	17	framework	framework	NOUN
fcis-28908	8	18	to	to	PART
fcis-28908	8	19	build	build	VERB
fcis-28908	8	20	a	a	DET
fcis-28908	8	21	convolutional	convolutional	ADJ
fcis-28908	8	22	neural	neural	ADJ
fcis-28908	8	23	network	network	NOUN
fcis-28908	8	24	(	(	PUNCT
fcis-28908	8	25	cnn	cnn	PROPN
fcis-28908	8	26	)	)	PUNCT
fcis-28908	8	27	model	model	NOUN
fcis-28908	8	28	for	for	ADP
fcis-28908	8	29	insect	insect	NOUN
fcis-28908	8	30	classification	classification	NOUN
fcis-28908	8	31	.	.	PUNCT
fcis-28908	9	1	the	the	DET
fcis-28908	9	2	article	article	NOUN
fcis-28908	9	3	further	far	ADV
fcis-28908	9	4	analyzes	analyze	VERB
fcis-28908	9	5	the	the	DET
fcis-28908	9	6	impact	impact	NOUN
fcis-28908	9	7	of	of	ADP
fcis-28908	9	8	different	different	ADJ
fcis-28908	9	9	optimizers	optimizer	NOUN
fcis-28908	9	10	and	and	CCONJ
fcis-28908	9	11	learning	learn	VERB
fcis-28908	9	12	rates	rate	NOUN
fcis-28908	9	13	on	on	ADP
fcis-28908	9	14	the	the	DET
fcis-28908	9	15	model	model	NOUN
fcis-28908	9	16	's	's	PART
fcis-28908	9	17	classification	classification	NOUN
fcis-28908	9	18	performance	performance	NOUN
fcis-28908	9	19	.	.	PUNCT
fcis-28908	10	1	experimental	experimental	ADJ
fcis-28908	10	2	results	result	NOUN
fcis-28908	10	3	show	show	VERB
fcis-28908	10	4	that	that	SCONJ
fcis-28908	10	5	using	use	VERB
fcis-28908	10	6	the	the	DET
fcis-28908	10	7	adam	adam	PROPN
fcis-28908	10	8	optimizer	optimizer	NOUN
fcis-28908	10	9	with	with	ADP
fcis-28908	10	10	a	a	DET
fcis-28908	10	11	learning	learn	VERB
fcis-28908	10	12	rate	rate	NOUN
fcis-28908	10	13	of	of	ADP
fcis-28908	10	14	0.009	0.009	NUM
fcis-28908	10	15	yields	yield	NOUN
fcis-28908	10	16	the	the	DET
fcis-28908	10	17	highest	high	ADJ
fcis-28908	10	18	recognition	recognition	NOUN
fcis-28908	10	19	accuracy	accuracy	NOUN
fcis-28908	10	20	for	for	ADP
fcis-28908	10	21	the	the	DET
fcis-28908	10	22	cnn	cnn	PROPN
fcis-28908	10	23	model	model	NOUN
fcis-28908	10	24	,	,	PUNCT
fcis-28908	10	25	reaching	reach	VERB
fcis-28908	10	26	up	up	ADP
fcis-28908	10	27	to	to	PART
fcis-28908	10	28	92	92	NUM
fcis-28908	10	29	%	%	NOUN
fcis-28908	10	30	.	.	PUNCT
fcis-28908	11	1	keywords	keyword	NOUN
fcis-28908	11	2	:	:	PUNCT
fcis-28908	11	3	deep	deep	ADJ
fcis-28908	11	4	learning	learning	NOUN
fcis-28908	11	5	techniques	technique	NOUN
fcis-28908	11	6	;	;	PUNCT
fcis-28908	11	7	insect	insect	VERB
fcis-28908	11	8	image	image	NOUN
fcis-28908	11	9	classification	classification	NOUN
fcis-28908	11	10	;	;	PUNCT
fcis-28908	11	11	convolutional	convolutional	ADJ
fcis-28908	11	12	neural	neural	ADJ
fcis-28908	11	13	network	network	NOUN
fcis-28908	11	14	architecture	architecture	NOUN
fcis-28908	11	15	;	;	PUNCT
fcis-28908	11	16	tensorflow	tensorflow	NOUN
fcis-28908	11	17	framework	framework	NOUN
fcis-28908	11	18	.	.	PUNCT
fcis-28908	12	1	1	1	X
fcis-28908	12	2	.	.	X
fcis-28908	12	3	introduction	introduction	NOUN
fcis-28908	12	4	as	as	ADP
fcis-28908	12	5	a	a	DET
fcis-28908	12	6	major	major	ADJ
fcis-28908	12	7	agricultural	agricultural	ADJ
fcis-28908	12	8	country	country	NOUN
fcis-28908	12	9	,	,	PUNCT
fcis-28908	12	10	china	china	PROPN
fcis-28908	12	11	frequently	frequently	ADV
fcis-28908	12	12	faces	face	VERB
fcis-28908	12	13	the	the	DET
fcis-28908	12	14	challenge	challenge	NOUN
fcis-28908	12	15	of	of	ADP
fcis-28908	12	16	pests	pest	NOUN
fcis-28908	12	17	and	and	CCONJ
fcis-28908	12	18	diseases	disease	NOUN
fcis-28908	12	19	in	in	ADP
fcis-28908	12	20	traditional	traditional	ADJ
fcis-28908	12	21	agricultural	agricultural	ADJ
fcis-28908	12	22	production	production	NOUN
fcis-28908	12	23	activities	activity	NOUN
fcis-28908	12	24	.	.	PUNCT
fcis-28908	13	1	the	the	DET
fcis-28908	13	2	key	key	NOUN
fcis-28908	13	3	to	to	ADP
fcis-28908	13	4	solving	solve	VERB
fcis-28908	13	5	this	this	DET
fcis-28908	13	6	problem	problem	NOUN
fcis-28908	13	7	lies	lie	VERB
fcis-28908	13	8	in	in	ADP
fcis-28908	13	9	the	the	DET
fcis-28908	13	10	accurate	accurate	ADJ
fcis-28908	13	11	identification	identification	NOUN
fcis-28908	13	12	of	of	ADP
fcis-28908	13	13	insects	insect	NOUN
fcis-28908	13	14	.	.	PUNCT
fcis-28908	14	1	traditionally	traditionally	ADV
fcis-28908	14	2	,	,	PUNCT
fcis-28908	14	3	entomologists	entomologist	NOUN
fcis-28908	14	4	rely	rely	VERB
fcis-28908	14	5	on	on	ADP
fcis-28908	14	6	their	their	PRON
fcis-28908	14	7	unique	unique	ADJ
fcis-28908	14	8	expertise	expertise	NOUN
fcis-28908	14	9	and	and	CCONJ
fcis-28908	14	10	observation	observation	NOUN
fcis-28908	14	11	of	of	ADP
fcis-28908	14	12	insects	insect	NOUN
fcis-28908	14	13	'	'	PART
fcis-28908	14	14	external	external	ADJ
fcis-28908	14	15	features	feature	NOUN
fcis-28908	14	16	,	,	PUNCT
fcis-28908	14	17	comparing	compare	VERB
fcis-28908	14	18	them	they	PRON
fcis-28908	14	19	with	with	ADP
fcis-28908	14	20	insect	insect	NOUN
fcis-28908	14	21	atlases	atlas	NOUN
fcis-28908	14	22	to	to	PART
fcis-28908	14	23	classify	classify	VERB
fcis-28908	14	24	insects	insect	NOUN
fcis-28908	14	25	,	,	PUNCT
fcis-28908	14	26	a	a	DET
fcis-28908	14	27	process	process	NOUN
fcis-28908	14	28	that	that	PRON
fcis-28908	14	29	is	be	AUX
fcis-28908	14	30	both	both	CCONJ
fcis-28908	14	31	timeconsuming	timeconsuming	ADJ
fcis-28908	14	32	and	and	CCONJ
fcis-28908	14	33	laborious	laborious	ADJ
fcis-28908	14	34	.	.	PUNCT
fcis-28908	15	1	with	with	ADP
fcis-28908	15	2	technological	technological	ADJ
fcis-28908	15	3	advancements	advancement	NOUN
fcis-28908	15	4	,	,	PUNCT
fcis-28908	15	5	this	this	DET
fcis-28908	15	6	traditional	traditional	ADJ
fcis-28908	15	7	classification	classification	NOUN
fcis-28908	15	8	method	method	NOUN
fcis-28908	15	9	is	be	AUX
fcis-28908	15	10	gradually	gradually	ADV
fcis-28908	15	11	being	be	AUX
fcis-28908	15	12	replaced	replace	VERB
fcis-28908	15	13	by	by	ADP
fcis-28908	15	14	image	image	NOUN
fcis-28908	15	15	-	-	PUNCT
fcis-28908	15	16	based	base	VERB
fcis-28908	15	17	insect	insect	NOUN
fcis-28908	15	18	classification	classification	NOUN
fcis-28908	15	19	techniques	technique	NOUN
fcis-28908	15	20	.	.	PUNCT
fcis-28908	16	1	currently	currently	ADV
fcis-28908	16	2	,	,	PUNCT
fcis-28908	16	3	commonly	commonly	ADV
fcis-28908	16	4	used	use	VERB
fcis-28908	16	5	insect	insect	NOUN
fcis-28908	16	6	classification	classification	NOUN
fcis-28908	16	7	techniques	technique	NOUN
fcis-28908	16	8	include	include	VERB
fcis-28908	16	9	image	image	NOUN
fcis-28908	16	10	recognition	recognition	NOUN
fcis-28908	16	11	,	,	PUNCT
fcis-28908	16	12	microwave	microwave	NOUN
fcis-28908	16	13	radar	radar	NOUN
fcis-28908	16	14	detection	detection	NOUN
fcis-28908	16	15	,	,	PUNCT
fcis-28908	16	16	biophotonic	biophotonic	NOUN
fcis-28908	16	17	detection	detection	NOUN
fcis-28908	16	18	,	,	PUNCT
fcis-28908	16	19	sample	sample	NOUN
fcis-28908	16	20	detection	detection	NOUN
fcis-28908	16	21	,	,	PUNCT
fcis-28908	16	22	near	near	ADV
fcis-28908	16	23	-	-	PUNCT
fcis-28908	16	24	infrared	infrared	ADJ
fcis-28908	16	25	and	and	CCONJ
fcis-28908	16	26	hyperspectral	hyperspectral	ADJ
fcis-28908	16	27	techniques	technique	NOUN
fcis-28908	16	28	,	,	PUNCT
fcis-28908	16	29	as	as	ADV
fcis-28908	16	30	well	well	ADV
fcis-28908	16	31	as	as	ADP
fcis-28908	16	32	acoustic	acoustic	ADJ
fcis-28908	16	33	detection	detection	NOUN
fcis-28908	16	34	methods	method	NOUN
fcis-28908	16	35	.	.	PUNCT
fcis-28908	17	1	in	in	ADP
fcis-28908	17	2	recent	recent	ADJ
fcis-28908	17	3	years	year	NOUN
fcis-28908	17	4	,	,	PUNCT
fcis-28908	17	5	with	with	ADP
fcis-28908	17	6	the	the	DET
fcis-28908	17	7	rapid	rapid	ADJ
fcis-28908	17	8	development	development	NOUN
fcis-28908	17	9	of	of	ADP
fcis-28908	17	10	the	the	DET
fcis-28908	17	11	field	field	NOUN
fcis-28908	17	12	of	of	ADP
fcis-28908	17	13	artificial	artificial	ADJ
fcis-28908	17	14	intelligence	intelligence	NOUN
fcis-28908	17	15	,	,	PUNCT
fcis-28908	17	16	deep	deep	ADJ
fcis-28908	17	17	learning	learning	NOUN
fcis-28908	17	18	technology	technology	NOUN
fcis-28908	17	19	has	have	AUX
fcis-28908	17	20	made	make	VERB
fcis-28908	17	21	significant	significant	ADJ
fcis-28908	17	22	progress	progress	NOUN
fcis-28908	17	23	in	in	ADP
fcis-28908	17	24	various	various	ADJ
fcis-28908	17	25	domains	domain	NOUN
fcis-28908	17	26	such	such	ADJ
fcis-28908	17	27	as	as	ADP
fcis-28908	17	28	natural	natural	ADJ
fcis-28908	17	29	language	language	NOUN
fcis-28908	17	30	processing	processing	NOUN
fcis-28908	17	31	and	and	CCONJ
fcis-28908	17	32	machine	machine	NOUN
fcis-28908	17	33	vision	vision	NOUN
fcis-28908	17	34	.	.	PUNCT
fcis-28908	18	1	therefore	therefore	ADV
fcis-28908	18	2	,	,	PUNCT
fcis-28908	18	3	researchers	researcher	NOUN
fcis-28908	18	4	have	have	AUX
fcis-28908	18	5	begun	begin	VERB
fcis-28908	18	6	to	to	PART
fcis-28908	18	7	explore	explore	VERB
fcis-28908	18	8	the	the	DET
fcis-28908	18	9	possibility	possibility	NOUN
fcis-28908	18	10	of	of	ADP
fcis-28908	18	11	applying	apply	VERB
fcis-28908	18	12	deep	deep	ADJ
fcis-28908	18	13	learning	learning	NOUN
fcis-28908	18	14	technology	technology	NOUN
fcis-28908	18	15	to	to	PART
fcis-28908	18	16	insect	insect	VERB
fcis-28908	18	17	image	image	NOUN
fcis-28908	18	18	classification	classification	NOUN
fcis-28908	18	19	.	.	PUNCT
fcis-28908	19	1	this	this	DET
fcis-28908	19	2	study	study	NOUN
fcis-28908	19	3	aims	aim	VERB
fcis-28908	19	4	to	to	PART
fcis-28908	19	5	utilize	utilize	VERB
fcis-28908	19	6	an	an	DET
fcis-28908	19	7	image	image	NOUN
fcis-28908	19	8	classification	classification	NOUN
fcis-28908	19	9	method	method	NOUN
fcis-28908	19	10	based	base	VERB
fcis-28908	19	11	on	on	ADP
fcis-28908	19	12	deep	deep	ADJ
fcis-28908	19	13	learning	learning	NOUN
fcis-28908	19	14	to	to	PART
fcis-28908	19	15	address	address	VERB
fcis-28908	19	16	the	the	DET
fcis-28908	19	17	problem	problem	NOUN
fcis-28908	19	18	of	of	ADP
fcis-28908	19	19	insect	insect	NOUN
fcis-28908	19	20	classification	classification	NOUN
fcis-28908	19	21	,	,	PUNCT
fcis-28908	19	22	with	with	ADP
fcis-28908	19	23	the	the	DET
fcis-28908	19	24	goal	goal	NOUN
fcis-28908	19	25	of	of	ADP
fcis-28908	19	26	providing	provide	VERB
fcis-28908	19	27	new	new	ADJ
fcis-28908	19	28	support	support	NOUN
fcis-28908	19	29	and	and	CCONJ
fcis-28908	19	30	assistance	assistance	NOUN
fcis-28908	19	31	for	for	ADP
fcis-28908	19	32	pest	pest	NOUN
fcis-28908	19	33	and	and	CCONJ
fcis-28908	19	34	disease	disease	NOUN
fcis-28908	19	35	identification	identification	NOUN
fcis-28908	19	36	in	in	ADP
fcis-28908	19	37	the	the	DET
fcis-28908	19	38	real	real	ADJ
fcis-28908	19	39	world	world	NOUN
fcis-28908	19	40	.	.	PUNCT
fcis-28908	20	1	2	2	X
fcis-28908	20	2	.	.	X
fcis-28908	20	3	deep	deep	ADJ
fcis-28908	20	4	learning	learning	NOUN
fcis-28908	20	5	approach	approach	VERB
fcis-28908	20	6	deep	deep	ADJ
fcis-28908	20	7	learning	learning	NOUN
fcis-28908	20	8	(	(	PUNCT
fcis-28908	20	9	dl	dl	PROPN
fcis-28908	20	10	)	)	PUNCT
fcis-28908	20	11	,	,	PUNCT
fcis-28908	20	12	especially	especially	ADV
fcis-28908	20	13	convolutional	convolutional	ADJ
fcis-28908	20	14	neural	neural	ADJ
fcis-28908	20	15	networks	network	NOUN
fcis-28908	20	16	(	(	PUNCT
fcis-28908	20	17	cnn	cnn	PROPN
fcis-28908	20	18	)	)	PUNCT
fcis-28908	20	19	,	,	PUNCT
fcis-28908	20	20	plays	play	VERB
fcis-28908	20	21	a	a	DET
fcis-28908	20	22	crucial	crucial	ADJ
fcis-28908	20	23	role	role	NOUN
fcis-28908	20	24	in	in	ADP
fcis-28908	20	25	the	the	DET
fcis-28908	20	26	field	field	NOUN
fcis-28908	20	27	of	of	ADP
fcis-28908	20	28	machine	machine	NOUN
fcis-28908	20	29	learning	learning	NOUN
fcis-28908	20	30	(	(	PUNCT
fcis-28908	20	31	ml	ml	NOUN
fcis-28908	20	32	)	)	PUNCT
fcis-28908	20	33	,	,	PUNCT
fcis-28908	20	34	particularly	particularly	ADV
fcis-28908	20	35	for	for	ADP
fcis-28908	20	36	tasks	task	NOUN
fcis-28908	20	37	such	such	ADJ
fcis-28908	20	38	as	as	ADP
fcis-28908	20	39	image	image	NOUN
fcis-28908	20	40	classification	classification	NOUN
fcis-28908	20	41	.	.	PUNCT
fcis-28908	21	1	through	through	ADP
fcis-28908	21	2	a	a	DET
fcis-28908	21	3	carefully	carefully	ADV
fcis-28908	21	4	designed	design	VERB
fcis-28908	21	5	multi	multi	ADJ
fcis-28908	21	6	-	-	ADJ
fcis-28908	21	7	layer	layer	ADJ
fcis-28908	21	8	structure	structure	NOUN
fcis-28908	21	9	,	,	PUNCT
fcis-28908	21	10	cnns	cnns	PROPN
fcis-28908	21	11	possess	possess	VERB
fcis-28908	21	12	the	the	DET
fcis-28908	21	13	ability	ability	NOUN
fcis-28908	21	14	to	to	PART
fcis-28908	21	15	automatically	automatically	ADV
fcis-28908	21	16	identify	identify	VERB
fcis-28908	21	17	and	and	CCONJ
fcis-28908	21	18	extract	extract	VERB
fcis-28908	21	19	data	data	NOUN
fcis-28908	21	20	features	feature	NOUN
fcis-28908	21	21	.	.	PUNCT
fcis-28908	22	1	by	by	ADP
fcis-28908	22	2	utilizing	utilize	VERB
fcis-28908	22	3	local	local	ADJ
fcis-28908	22	4	connections	connection	NOUN
fcis-28908	22	5	,	,	PUNCT
fcis-28908	22	6	weight	weight	NOUN
fcis-28908	22	7	sharing	sharing	NOUN
fcis-28908	22	8	,	,	PUNCT
fcis-28908	22	9	and	and	CCONJ
fcis-28908	22	10	pooling	pool	VERB
fcis-28908	22	11	techniques	technique	NOUN
fcis-28908	22	12	,	,	PUNCT
fcis-28908	22	13	cnns	cnn	NOUN
fcis-28908	22	14	can	can	AUX
fcis-28908	22	15	effectively	effectively	ADV
fcis-28908	22	16	capture	capture	VERB
fcis-28908	22	17	key	key	ADJ
fcis-28908	22	18	features	feature	NOUN
fcis-28908	22	19	in	in	ADP
fcis-28908	22	20	images	image	NOUN
fcis-28908	22	21	,	,	PUNCT
fcis-28908	22	22	thereby	thereby	ADV
fcis-28908	22	23	significantly	significantly	ADV
fcis-28908	22	24	simplifying	simplify	VERB
fcis-28908	22	25	the	the	DET
fcis-28908	22	26	complexity	complexity	NOUN
fcis-28908	22	27	of	of	ADP
fcis-28908	22	28	feature	feature	NOUN
fcis-28908	22	29	extraction	extraction	NOUN
fcis-28908	22	30	.	.	PUNCT
fcis-28908	23	1	meanwhile	meanwhile	ADV
fcis-28908	23	2	,	,	PUNCT
fcis-28908	23	3	recursive	recursive	ADJ
fcis-28908	23	4	neural	neural	ADJ
fcis-28908	23	5	networks	network	NOUN
fcis-28908	23	6	(	(	PUNCT
fcis-28908	23	7	rnn	rnn	PROPN
fcis-28908	23	8	)	)	PUNCT
fcis-28908	23	9	are	be	AUX
fcis-28908	23	10	more	more	ADV
fcis-28908	23	11	suitable	suitable	ADJ
fcis-28908	23	12	for	for	ADP
fcis-28908	23	13	processing	process	VERB
fcis-28908	23	14	data	datum	NOUN
fcis-28908	23	15	with	with	ADP
fcis-28908	23	16	sequential	sequential	ADJ
fcis-28908	23	17	features	feature	NOUN
fcis-28908	23	18	,	,	PUNCT
fcis-28908	23	19	such	such	ADJ
fcis-28908	23	20	as	as	ADP
fcis-28908	23	21	natural	natural	ADJ
fcis-28908	23	22	language	language	NOUN
fcis-28908	23	23	,	,	PUNCT
fcis-28908	23	24	by	by	ADP
fcis-28908	23	25	using	use	VERB
fcis-28908	23	26	recurrent	recurrent	ADJ
fcis-28908	23	27	connections	connection	NOUN
fcis-28908	23	28	to	to	PART
fcis-28908	23	29	remember	remember	VERB
fcis-28908	23	30	previous	previous	ADJ
fcis-28908	23	31	information	information	NOUN
fcis-28908	23	32	.	.	PUNCT
fcis-28908	24	1	these	these	DET
fcis-28908	24	2	deep	deep	ADJ
fcis-28908	24	3	learning	learning	NOUN
fcis-28908	24	4	techniques	technique	NOUN
fcis-28908	24	5	have	have	AUX
fcis-28908	24	6	achieved	achieve	VERB
fcis-28908	24	7	remarkable	remarkable	ADJ
fcis-28908	24	8	results	result	NOUN
fcis-28908	24	9	in	in	ADP
fcis-28908	24	10	various	various	ADJ
fcis-28908	24	11	fields	field	NOUN
fcis-28908	24	12	such	such	ADJ
fcis-28908	24	13	as	as	ADP
fcis-28908	24	14	computer	computer	NOUN
fcis-28908	24	15	vision	vision	NOUN
fcis-28908	24	16	(	(	PUNCT
fcis-28908	24	17	cv	cv	PROPN
fcis-28908	24	18	)	)	PUNCT
fcis-28908	24	19	and	and	CCONJ
fcis-28908	24	20	natural	natural	ADJ
fcis-28908	24	21	language	language	NOUN
fcis-28908	24	22	processing	processing	NOUN
fcis-28908	24	23	(	(	PUNCT
fcis-28908	24	24	nlp	nlp	NOUN
fcis-28908	24	25	)	)	PUNCT
fcis-28908	24	26	,	,	PUNCT
fcis-28908	24	27	becoming	become	VERB
fcis-28908	24	28	important	important	ADJ
fcis-28908	24	29	drivers	driver	NOUN
fcis-28908	24	30	for	for	ADP
fcis-28908	24	31	the	the	DET
fcis-28908	24	32	development	development	NOUN
fcis-28908	24	33	of	of	ADP
fcis-28908	24	34	artificial	artificial	ADJ
fcis-28908	24	35	intelligence	intelligence	NOUN
fcis-28908	24	36	.	.	PUNCT
fcis-28908	25	1	their	their	PRON
fcis-28908	25	2	ability	ability	NOUN
fcis-28908	25	3	to	to	PART
fcis-28908	25	4	automatically	automatically	ADV
fcis-28908	25	5	learn	learn	VERB
fcis-28908	25	6	and	and	CCONJ
fcis-28908	25	7	analyze	analyze	VERB
fcis-28908	25	8	complex	complex	ADJ
fcis-28908	25	9	data	datum	NOUN
fcis-28908	25	10	lays	lay	VERB
fcis-28908	25	11	a	a	DET
fcis-28908	25	12	solid	solid	ADJ
fcis-28908	25	13	foundation	foundation	NOUN
fcis-28908	25	14	for	for	ADP
fcis-28908	25	15	higher	high	ADJ
fcis-28908	25	16	-	-	PUNCT
fcis-28908	25	17	level	level	NOUN
fcis-28908	25	18	ai	ai	NOUN
fcis-28908	25	19	applications	application	NOUN
fcis-28908	25	20	.	.	PUNCT
fcis-28908	26	1	3	3	X
fcis-28908	26	2	.	.	X
fcis-28908	26	3	tensorflow	tensorflow	NOUN
fcis-28908	26	4	and	and	CCONJ
fcis-28908	26	5	convolutional	convolutional	ADJ
fcis-28908	26	6	neural	neural	ADJ
fcis-28908	26	7	networks	network	NOUN
fcis-28908	26	8	(	(	PUNCT
fcis-28908	26	9	1	1	X
fcis-28908	26	10	)	)	PUNCT
fcis-28908	26	11	tensorflow	tensorflow	NOUN
fcis-28908	26	12	tensorflow	tensorflow	NOUN
fcis-28908	26	13	is	be	AUX
fcis-28908	26	14	a	a	DET
fcis-28908	26	15	powerful	powerful	ADJ
fcis-28908	26	16	open	open	ADJ
fcis-28908	26	17	-	-	PUNCT
fcis-28908	26	18	source	source	NOUN
fcis-28908	26	19	machine	machine	NOUN
fcis-28908	26	20	learning	learn	VERB
fcis-28908	26	21	framework	framework	NOUN
fcis-28908	26	22	developed	develop	VERB
fcis-28908	26	23	and	and	CCONJ
fcis-28908	26	24	maintained	maintain	VERB
fcis-28908	26	25	by	by	ADP
fcis-28908	26	26	google	google	PROPN
fcis-28908	26	27	,	,	PUNCT
fcis-28908	26	28	boasting	boast	VERB
fcis-28908	26	29	a	a	DET
fcis-28908	26	30	wide	wide	ADJ
fcis-28908	26	31	range	range	NOUN
fcis-28908	26	32	of	of	ADP
fcis-28908	26	33	functionalities	functionality	NOUN
fcis-28908	26	34	.	.	PUNCT
fcis-28908	27	1	developers	developer	NOUN
fcis-28908	27	2	can	can	AUX
fcis-28908	27	3	construct	construct	VERB
fcis-28908	27	4	complex	complex	ADJ
fcis-28908	27	5	numerical	numerical	ADJ
fcis-28908	27	6	computation	computation	NOUN
fcis-28908	27	7	models	model	NOUN
fcis-28908	27	8	through	through	ADP
fcis-28908	27	9	data	datum	NOUN
fcis-28908	27	10	flow	flow	NOUN
fcis-28908	27	11	graphs	graph	NOUN
fcis-28908	27	12	,	,	PUNCT
fcis-28908	27	13	particularly	particularly	ADV
fcis-28908	27	14	excelling	excel	VERB
fcis-28908	27	15	in	in	ADP
fcis-28908	27	16	deep	deep	ADJ
fcis-28908	27	17	learning	learning	NOUN
fcis-28908	27	18	and	and	CCONJ
fcis-28908	27	19	various	various	ADJ
fcis-28908	27	20	machine	machine	NOUN
fcis-28908	27	21	learning	learn	VERB
fcis-28908	27	22	applications	application	NOUN
fcis-28908	27	23	.	.	PUNCT
fcis-28908	28	1	this	this	DET
fcis-28908	28	2	library	library	NOUN
fcis-28908	28	3	is	be	AUX
fcis-28908	28	4	not	not	PART
fcis-28908	28	5	only	only	ADV
fcis-28908	28	6	compatible	compatible	ADJ
fcis-28908	28	7	with	with	ADP
fcis-28908	28	8	multiple	multiple	ADJ
fcis-28908	28	9	hardware	hardware	NOUN
fcis-28908	28	10	platforms	platform	NOUN
fcis-28908	28	11	but	but	CCONJ
fcis-28908	28	12	also	also	ADV
fcis-28908	28	13	provides	provide	VERB
fcis-28908	28	14	rich	rich	ADJ
fcis-28908	28	15	application	application	NOUN
fcis-28908	28	16	programming	programming	NOUN
fcis-28908	28	17	interfaces	interface	NOUN
fcis-28908	28	18	(	(	PUNCT
fcis-28908	28	19	apis	apis	ADJ
fcis-28908	28	20	)	)	PUNCT
fcis-28908	28	21	and	and	CCONJ
fcis-28908	28	22	numerous	numerous	ADJ
fcis-28908	28	23	visualization	visualization	NOUN
fcis-28908	28	24	development	development	NOUN
fcis-28908	28	25	tools	tool	NOUN
fcis-28908	28	26	,	,	PUNCT
fcis-28908	28	27	greatly	greatly	ADV
fcis-28908	28	28	simplifying	simplify	VERB
fcis-28908	28	29	the	the	DET
fcis-28908	28	30	process	process	NOUN
fcis-28908	28	31	of	of	ADP
fcis-28908	28	32	building	building	NOUN
fcis-28908	28	33	and	and	CCONJ
fcis-28908	28	34	optimizing	optimize	VERB
fcis-28908	28	35	algorithm	algorithm	NOUN
fcis-28908	28	36	models	model	NOUN
fcis-28908	28	37	.	.	PUNCT
fcis-28908	29	1	therefore	therefore	ADV
fcis-28908	29	2	,	,	PUNCT
fcis-28908	29	3	it	it	PRON
fcis-28908	29	4	has	have	AUX
fcis-28908	29	5	become	become	VERB
fcis-28908	29	6	an	an	DET
fcis-28908	29	7	ideal	ideal	ADJ
fcis-28908	29	8	choice	choice	NOUN
fcis-28908	29	9	for	for	ADP
fcis-28908	29	10	developing	develop	VERB
fcis-28908	29	11	artificial	artificial	ADJ
fcis-28908	29	12	intelligence	intelligence	NOUN
fcis-28908	29	13	applications	application	NOUN
fcis-28908	29	14	.	.	PUNCT
fcis-28908	30	1	(	(	PUNCT
fcis-28908	30	2	2	2	X
fcis-28908	30	3	)	)	PUNCT
fcis-28908	30	4	convolutional	convolutional	ADJ
fcis-28908	30	5	neural	neural	ADJ
fcis-28908	30	6	networks	network	NOUN
fcis-28908	30	7	convolution	convolution	NOUN
fcis-28908	30	8	is	be	AUX
fcis-28908	30	9	a	a	DET
fcis-28908	30	10	unique	unique	ADJ
fcis-28908	30	11	mathematical	mathematical	ADJ
fcis-28908	30	12	operation	operation	NOUN
fcis-28908	30	13	that	that	PRON
fcis-28908	30	14	produces	produce	VERB
fcis-28908	30	15	a	a	DET
fcis-28908	30	16	third	third	ADJ
fcis-28908	30	17	function	function	NOUN
fcis-28908	30	18	through	through	ADP
fcis-28908	30	19	the	the	DET
fcis-28908	30	20	combination	combination	NOUN
fcis-28908	30	21	of	of	ADP
fcis-28908	30	22	two	two	NUM
fcis-28908	30	23	functions	function	NOUN
fcis-28908	30	24	,	,	PUNCT
fcis-28908	30	25	f	f	PROPN
fcis-28908	30	26	and	and	CCONJ
fcis-28908	30	27	g.	g.	VERB
fcis-28908	30	28	its	its	PRON
fcis-28908	30	29	core	core	NOUN
fcis-28908	30	30	essence	essence	NOUN
fcis-28908	30	31	is	be	AUX
fcis-28908	30	32	a	a	DET
fcis-28908	30	33	type	type	NOUN
fcis-28908	30	34	of	of	ADP
fcis-28908	30	35	integral	integral	ADJ
fcis-28908	30	36	transform	transform	NOUN
fcis-28908	30	37	.	.	PUNCT
fcis-28908	31	1	the	the	DET
fcis-28908	31	2	process	process	NOUN
fcis-28908	31	3	of	of	ADP
fcis-28908	31	4	this	this	DET
fcis-28908	31	5	operation	operation	NOUN
fcis-28908	31	6	involves	involve	VERB
fcis-28908	31	7	flipping	flip	VERB
fcis-28908	31	8	and	and	CCONJ
fcis-28908	31	9	shifting	shift	VERB
fcis-28908	31	10	one	one	NUM
fcis-28908	31	11	function	function	NOUN
fcis-28908	31	12	,	,	PUNCT
fcis-28908	31	13	multiplying	multiply	VERB
fcis-28908	31	14	it	it	PRON
fcis-28908	31	15	by	by	ADP
fcis-28908	31	16	another	another	DET
fcis-28908	31	17	function	function	NOUN
fcis-28908	31	18	,	,	PUNCT
fcis-28908	31	19	and	and	CCONJ
fcis-28908	31	20	then	then	ADV
fcis-28908	31	21	integrating	integrate	VERB
fcis-28908	31	22	the	the	DET
fcis-28908	31	23	product	product	NOUN
fcis-28908	31	24	over	over	ADP
fcis-28908	31	25	the	the	DET
fcis-28908	31	26	shift	shift	NOUN
fcis-28908	31	27	,	,	PUNCT
fcis-28908	31	28	thereby	thereby	ADV
fcis-28908	31	29	obtaining	obtain	VERB
fcis-28908	31	30	the	the	DET
fcis-28908	31	31	result	result	NOUN
fcis-28908	31	32	of	of	ADP
fcis-28908	31	33	the	the	DET
fcis-28908	31	34	convolution	convolution	NOUN
fcis-28908	31	35	.	.	PUNCT
fcis-28908	32	1	convolution	convolution	NOUN
fcis-28908	32	2	operations	operation	NOUN
fcis-28908	32	3	are	be	AUX
fcis-28908	32	4	widely	widely	ADV
fcis-28908	32	5	used	use	VERB
fcis-28908	32	6	in	in	ADP
fcis-28908	32	7	various	various	ADJ
fcis-28908	32	8	fields	field	NOUN
fcis-28908	32	9	such	such	ADJ
fcis-28908	32	10	as	as	ADP
fcis-28908	32	11	signal	signal	ADJ
fcis-28908	32	12	processing	processing	NOUN
fcis-28908	32	13	,	,	PUNCT
fcis-28908	32	14	image	image	NOUN
fcis-28908	32	15	processing	processing	NOUN
fcis-28908	32	16	,	,	PUNCT
fcis-28908	32	17	and	and	CCONJ
fcis-28908	32	18	machine	machine	NOUN
fcis-28908	32	19	learning	learning	NOUN
fcis-28908	32	20	.	.	PUNCT
fcis-28908	33	1	image	image	NOUN
fcis-28908	33	2	convolution	convolution	NOUN
fcis-28908	33	3	is	be	AUX
fcis-28908	33	4	a	a	DET
fcis-28908	33	5	mathematical	mathematical	ADJ
fcis-28908	33	6	process	process	NOUN
fcis-28908	33	7	involving	involve	VERB
fcis-28908	33	8	two	two	NUM
fcis-28908	33	9	129	129	NUM
fcis-28908	33	10	matrices	matrix	NOUN
fcis-28908	33	11	:	:	PUNCT
fcis-28908	33	12	the	the	DET
fcis-28908	33	13	feature	feature	NOUN
fcis-28908	33	14	image	image	NOUN
fcis-28908	33	15	matrix	matrix	NOUN
fcis-28908	33	16	and	and	CCONJ
fcis-28908	33	17	the	the	DET
fcis-28908	33	18	convolution	convolution	NOUN
fcis-28908	33	19	kernel	kernel	NOUN
fcis-28908	33	20	matrix	matrix	NOUN
fcis-28908	33	21	.	.	PUNCT
fcis-28908	34	1	in	in	ADP
fcis-28908	34	2	this	this	DET
fcis-28908	34	3	process	process	NOUN
fcis-28908	34	4	,	,	PUNCT
fcis-28908	34	5	the	the	DET
fcis-28908	34	6	convolution	convolution	NOUN
fcis-28908	34	7	kernel	kernel	NOUN
fcis-28908	34	8	(	(	PUNCT
fcis-28908	34	9	also	also	ADV
fcis-28908	34	10	known	know	VERB
fcis-28908	34	11	as	as	ADP
fcis-28908	34	12	a	a	DET
fcis-28908	34	13	filter	filter	NOUN
fcis-28908	34	14	)	)	PUNCT
fcis-28908	34	15	slides	slide	VERB
fcis-28908	34	16	over	over	ADP
fcis-28908	34	17	the	the	DET
fcis-28908	34	18	input	input	NOUN
fcis-28908	34	19	feature	feature	NOUN
fcis-28908	34	20	image	image	NOUN
fcis-28908	34	21	with	with	ADP
fcis-28908	34	22	a	a	DET
fcis-28908	34	23	specific	specific	ADJ
fcis-28908	34	24	stride	stride	NOUN
fcis-28908	34	25	.	.	PUNCT
fcis-28908	35	1	at	at	ADP
fcis-28908	35	2	each	each	DET
fcis-28908	35	3	position	position	NOUN
fcis-28908	35	4	,	,	PUNCT
fcis-28908	35	5	each	each	DET
fcis-28908	35	6	element	element	NOUN
fcis-28908	35	7	of	of	ADP
fcis-28908	35	8	the	the	DET
fcis-28908	35	9	convolution	convolution	NOUN
fcis-28908	35	10	kernel	kernel	NOUN
fcis-28908	35	11	is	be	AUX
fcis-28908	35	12	multiplied	multiply	VERB
fcis-28908	35	13	by	by	ADP
fcis-28908	35	14	the	the	DET
fcis-28908	35	15	corresponding	corresponding	ADJ
fcis-28908	35	16	element	element	NOUN
fcis-28908	35	17	in	in	ADP
fcis-28908	35	18	the	the	DET
fcis-28908	35	19	feature	feature	NOUN
fcis-28908	35	20	image	image	NOUN
fcis-28908	35	21	,	,	PUNCT
fcis-28908	35	22	and	and	CCONJ
fcis-28908	35	23	then	then	ADV
fcis-28908	35	24	these	these	DET
fcis-28908	35	25	products	product	NOUN
fcis-28908	35	26	are	be	AUX
fcis-28908	35	27	summed	sum	VERB
fcis-28908	35	28	to	to	PART
fcis-28908	35	29	obtain	obtain	VERB
fcis-28908	35	30	a	a	DET
fcis-28908	35	31	single	single	ADJ
fcis-28908	35	32	value	value	NOUN
fcis-28908	35	33	.	.	PUNCT
fcis-28908	36	1	this	this	DET
fcis-28908	36	2	process	process	NOUN
fcis-28908	36	3	is	be	AUX
fcis-28908	36	4	repeated	repeat	VERB
fcis-28908	36	5	across	across	ADP
fcis-28908	36	6	the	the	DET
fcis-28908	36	7	entire	entire	ADJ
fcis-28908	36	8	feature	feature	NOUN
fcis-28908	36	9	image	image	NOUN
fcis-28908	36	10	,	,	PUNCT
fcis-28908	36	11	typically	typically	ADV
fcis-28908	36	12	with	with	ADP
fcis-28908	36	13	a	a	DET
fcis-28908	36	14	fixed	fix	VERB
fcis-28908	36	15	stride	stride	NOUN
fcis-28908	36	16	(	(	PUNCT
fcis-28908	36	17	as	as	SCONJ
fcis-28908	36	18	shown	show	VERB
fcis-28908	36	19	in	in	ADP
fcis-28908	36	20	figure	figure	NOUN
fcis-28908	36	21	1	1	NUM
fcis-28908	36	22	,	,	PUNCT
fcis-28908	36	23	where	where	SCONJ
fcis-28908	36	24	the	the	DET
fcis-28908	36	25	stride	stride	NOUN
fcis-28908	36	26	is	be	AUX
fcis-28908	36	27	1	1	NUM
fcis-28908	36	28	)	)	PUNCT
fcis-28908	36	29	for	for	ADP
fcis-28908	36	30	sliding	slide	VERB
fcis-28908	36	31	the	the	DET
fcis-28908	36	32	convolution	convolution	NOUN
fcis-28908	36	33	kernel	kernel	NOUN
fcis-28908	36	34	,	,	PUNCT
fcis-28908	36	35	until	until	SCONJ
fcis-28908	36	36	the	the	DET
fcis-28908	36	37	entire	entire	ADJ
fcis-28908	36	38	feature	feature	NOUN
fcis-28908	36	39	image	image	NOUN
fcis-28908	36	40	is	be	AUX
fcis-28908	36	41	traversed	traverse	VERB
fcis-28908	36	42	,	,	PUNCT
fcis-28908	36	43	resulting	result	VERB
fcis-28908	36	44	in	in	ADP
fcis-28908	36	45	the	the	DET
fcis-28908	36	46	final	final	ADJ
fcis-28908	36	47	output	output	NOUN
fcis-28908	36	48	feature	feature	NOUN
fcis-28908	36	49	map	map	NOUN
fcis-28908	36	50	.	.	PUNCT
fcis-28908	37	1	figure	figure	VERB
fcis-28908	37	2	1	1	NUM
fcis-28908	37	3	.	.	PUNCT
fcis-28908	38	1	convolution	convolution	NOUN
fcis-28908	38	2	operation	operation	NOUN
fcis-28908	38	3	process	process	NOUN
fcis-28908	38	4	pooling	pooling	NOUN
fcis-28908	38	5	,	,	PUNCT
fcis-28908	38	6	also	also	ADV
fcis-28908	38	7	known	know	VERB
fcis-28908	38	8	as	as	ADP
fcis-28908	38	9	subsampling	subsample	VERB
fcis-28908	38	10	or	or	CCONJ
fcis-28908	38	11	downsampling	downsampling	NOUN
fcis-28908	38	12	,	,	PUNCT
fcis-28908	38	13	is	be	AUX
fcis-28908	38	14	based	base	VERB
fcis-28908	38	15	on	on	ADP
fcis-28908	38	16	the	the	DET
fcis-28908	38	17	principle	principle	NOUN
fcis-28908	38	18	that	that	SCONJ
fcis-28908	38	19	neighboring	neighboring	ADJ
fcis-28908	38	20	regions	region	NOUN
fcis-28908	38	21	in	in	ADP
fcis-28908	38	22	an	an	DET
fcis-28908	38	23	image	image	NOUN
fcis-28908	38	24	share	share	VERB
fcis-28908	38	25	highly	highly	ADV
fcis-28908	38	26	similar	similar	ADJ
fcis-28908	38	27	feature	feature	NOUN
fcis-28908	38	28	.	.	PUNCT
fcis-28908	39	1	in	in	ADP
fcis-28908	39	2	the	the	DET
fcis-28908	39	3	output	output	NOUN
fcis-28908	39	4	of	of	ADP
fcis-28908	39	5	a	a	DET
fcis-28908	39	6	convolutional	convolutional	ADJ
fcis-28908	39	7	layer	layer	NOUN
fcis-28908	39	8	,	,	PUNCT
fcis-28908	39	9	due	due	ADP
fcis-28908	39	10	to	to	ADP
fcis-28908	39	11	the	the	DET
fcis-28908	39	12	high	high	ADJ
fcis-28908	39	13	degree	degree	NOUN
fcis-28908	39	14	of	of	ADP
fcis-28908	39	15	overlap	overlap	NOUN
fcis-28908	39	16	in	in	ADP
fcis-28908	39	17	features	feature	NOUN
fcis-28908	39	18	among	among	ADP
fcis-28908	39	19	adjacent	adjacent	ADJ
fcis-28908	39	20	image	image	NOUN
fcis-28908	39	21	regions	region	NOUN
fcis-28908	39	22	,	,	PUNCT
fcis-28908	39	23	there	there	PRON
fcis-28908	39	24	is	be	VERB
fcis-28908	39	25	a	a	DET
fcis-28908	39	26	significant	significant	ADJ
fcis-28908	39	27	amount	amount	NOUN
fcis-28908	39	28	of	of	ADP
fcis-28908	39	29	redundant	redundant	ADJ
fcis-28908	39	30	information	information	NOUN
fcis-28908	39	31	.	.	PUNCT
fcis-28908	40	1	pooling	pool	VERB
fcis-28908	40	2	techniques	technique	NOUN
fcis-28908	40	3	aggregate	aggregate	VERB
fcis-28908	40	4	global	global	ADJ
fcis-28908	40	5	features	feature	NOUN
fcis-28908	40	6	of	of	ADP
fcis-28908	40	7	the	the	DET
fcis-28908	40	8	image	image	NOUN
fcis-28908	40	9	to	to	PART
fcis-28908	40	10	reduce	reduce	VERB
fcis-28908	40	11	this	this	DET
fcis-28908	40	12	redundancy	redundancy	NOUN
fcis-28908	40	13	,	,	PUNCT
fcis-28908	40	14	lower	low	ADJ
fcis-28908	40	15	the	the	DET
fcis-28908	40	16	computational	computational	ADJ
fcis-28908	40	17	complexity	complexity	NOUN
fcis-28908	40	18	of	of	ADP
fcis-28908	40	19	convolutional	convolutional	ADJ
fcis-28908	40	20	neural	neural	ADJ
fcis-28908	40	21	networks	network	NOUN
fcis-28908	40	22	,	,	PUNCT
fcis-28908	40	23	and	and	CCONJ
fcis-28908	40	24	prevent	prevent	VERB
fcis-28908	40	25	the	the	DET
fcis-28908	40	26	neural	neural	ADJ
fcis-28908	40	27	network	network	NOUN
fcis-28908	40	28	model	model	NOUN
fcis-28908	40	29	from	from	ADP
fcis-28908	40	30	overfitting	overfitte	VERB
fcis-28908	40	31	.	.	PUNCT
fcis-28908	41	1	in	in	ADP
fcis-28908	41	2	image	image	NOUN
fcis-28908	41	3	processing	processing	NOUN
fcis-28908	41	4	,	,	PUNCT
fcis-28908	41	5	two	two	NUM
fcis-28908	41	6	widely	widely	ADV
fcis-28908	41	7	used	use	VERB
fcis-28908	41	8	pooling	pooling	NOUN
fcis-28908	41	9	methods	method	NOUN
fcis-28908	41	10	are	be	AUX
fcis-28908	41	11	average	average	ADJ
fcis-28908	41	12	pooling	pooling	NOUN
fcis-28908	41	13	and	and	CCONJ
fcis-28908	41	14	max	max	PROPN
fcis-28908	41	15	pooling	pooling	NOUN
fcis-28908	41	16	.	.	PUNCT
fcis-28908	42	1	average	average	ADJ
fcis-28908	42	2	pooling	pooling	NOUN
fcis-28908	42	3	calculates	calculate	VERB
fcis-28908	42	4	the	the	DET
fcis-28908	42	5	mean	mean	ADJ
fcis-28908	42	6	value	value	NOUN
fcis-28908	42	7	of	of	ADP
fcis-28908	42	8	all	all	DET
fcis-28908	42	9	pixel	pixel	ADJ
fcis-28908	42	10	values	value	NOUN
fcis-28908	42	11	within	within	ADP
fcis-28908	42	12	a	a	DET
fcis-28908	42	13	specific	specific	ADJ
fcis-28908	42	14	region	region	NOUN
fcis-28908	42	15	to	to	PART
fcis-28908	42	16	represent	represent	VERB
fcis-28908	42	17	the	the	DET
fcis-28908	42	18	features	feature	NOUN
fcis-28908	42	19	of	of	ADP
fcis-28908	42	20	that	that	DET
fcis-28908	42	21	region	region	NOUN
fcis-28908	42	22	,	,	PUNCT
fcis-28908	42	23	while	while	SCONJ
fcis-28908	42	24	max	max	PROPN
fcis-28908	42	25	pooling	pooling	NOUN
fcis-28908	42	26	selects	select	VERB
fcis-28908	42	27	the	the	DET
fcis-28908	42	28	maximum	maximum	ADJ
fcis-28908	42	29	pixel	pixel	PROPN
fcis-28908	42	30	value	value	NOUN
fcis-28908	42	31	from	from	ADP
fcis-28908	42	32	the	the	DET
fcis-28908	42	33	region	region	NOUN
fcis-28908	42	34	as	as	ADP
fcis-28908	42	35	its	its	PRON
fcis-28908	42	36	representative	representative	NOUN
fcis-28908	42	37	.	.	PUNCT
fcis-28908	43	1	both	both	DET
fcis-28908	43	2	methods	method	NOUN
fcis-28908	43	3	can	can	AUX
fcis-28908	43	4	effectively	effectively	ADV
fcis-28908	43	5	compress	compress	VERB
fcis-28908	43	6	data	datum	NOUN
fcis-28908	43	7	while	while	SCONJ
fcis-28908	43	8	preserving	preserve	VERB
fcis-28908	43	9	key	key	ADJ
fcis-28908	43	10	features	feature	NOUN
fcis-28908	43	11	in	in	ADP
fcis-28908	43	12	the	the	DET
fcis-28908	43	13	image	image	NOUN
fcis-28908	43	14	.	.	PUNCT
fcis-28908	44	1	taking	take	VERB
fcis-28908	44	2	figure	figure	NOUN
fcis-28908	44	3	2	2	NUM
fcis-28908	44	4	as	as	ADP
fcis-28908	44	5	an	an	DET
fcis-28908	44	6	example	example	NOUN
fcis-28908	44	7	,	,	PUNCT
fcis-28908	44	8	it	it	PRON
fcis-28908	44	9	illustrates	illustrate	VERB
fcis-28908	44	10	the	the	DET
fcis-28908	44	11	computation	computation	NOUN
fcis-28908	44	12	process	process	NOUN
fcis-28908	44	13	of	of	ADP
fcis-28908	44	14	image	image	NOUN
fcis-28908	44	15	pooling	pooling	NOUN
fcis-28908	44	16	,	,	PUNCT
fcis-28908	44	17	where	where	SCONJ
fcis-28908	44	18	the	the	DET
fcis-28908	44	19	sliding	slide	VERB
fcis-28908	44	20	stride	stride	NOUN
fcis-28908	44	21	is	be	AUX
fcis-28908	44	22	set	set	VERB
fcis-28908	44	23	to	to	ADP
fcis-28908	44	24	2	2	NUM
fcis-28908	44	25	.	.	PUNCT
fcis-28908	45	1	this	this	PRON
fcis-28908	45	2	means	mean	VERB
fcis-28908	45	3	that	that	SCONJ
fcis-28908	45	4	in	in	ADP
fcis-28908	45	5	the	the	DET
fcis-28908	45	6	pooling	pooling	NOUN
fcis-28908	45	7	operation	operation	NOUN
fcis-28908	45	8	,	,	PUNCT
fcis-28908	45	9	after	after	ADP
fcis-28908	45	10	processing	process	VERB
fcis-28908	45	11	one	one	NUM
fcis-28908	45	12	region	region	NOUN
fcis-28908	45	13	,	,	PUNCT
fcis-28908	45	14	the	the	DET
fcis-28908	45	15	system	system	NOUN
fcis-28908	45	16	skips	skip	VERB
fcis-28908	45	17	the	the	DET
fcis-28908	45	18	adjacent	adjacent	ADJ
fcis-28908	45	19	region	region	NOUN
fcis-28908	45	20	and	and	CCONJ
fcis-28908	45	21	proceeds	proceed	NOUN
fcis-28908	45	22	to	to	PART
fcis-28908	45	23	process	process	VERB
fcis-28908	45	24	the	the	DET
fcis-28908	45	25	next	next	ADJ
fcis-28908	45	26	one	one	NUM
fcis-28908	45	27	.	.	PUNCT
fcis-28908	46	1	this	this	DET
fcis-28908	46	2	processing	processing	NOUN
fcis-28908	46	3	strategy	strategy	NOUN
fcis-28908	46	4	not	not	PART
fcis-28908	46	5	only	only	ADV
fcis-28908	46	6	improves	improve	VERB
fcis-28908	46	7	computational	computational	ADJ
fcis-28908	46	8	efficiency	efficiency	NOUN
fcis-28908	46	9	but	but	CCONJ
fcis-28908	46	10	also	also	ADV
fcis-28908	46	11	reduces	reduce	VERB
fcis-28908	46	12	information	information	NOUN
fcis-28908	46	13	redundancy	redundancy	NOUN
fcis-28908	46	14	to	to	ADP
fcis-28908	46	15	some	some	DET
fcis-28908	46	16	extent	extent	NOUN
fcis-28908	46	17	,	,	PUNCT
fcis-28908	46	18	thereby	thereby	ADV
fcis-28908	46	19	helping	help	VERB
fcis-28908	46	20	convolutional	convolutional	ADJ
fcis-28908	46	21	neural	neural	ADJ
fcis-28908	46	22	networks	network	NOUN
fcis-28908	46	23	more	more	ADV
fcis-28908	46	24	effectively	effectively	ADV
fcis-28908	46	25	extract	extract	VERB
fcis-28908	46	26	and	and	CCONJ
fcis-28908	46	27	utilize	utilize	VERB
fcis-28908	46	28	image	image	NOUN
fcis-28908	46	29	features	feature	NOUN
fcis-28908	46	30	.	.	PUNCT
fcis-28908	47	1	the	the	DET
fcis-28908	47	2	pooling	pool	VERB
fcis-28908	47	3	computation	computation	NOUN
fcis-28908	47	4	process	process	NOUN
fcis-28908	47	5	can	can	AUX
fcis-28908	47	6	be	be	AUX
fcis-28908	47	7	explained	explain	VERB
fcis-28908	47	8	through	through	ADP
fcis-28908	47	9	a	a	DET
fcis-28908	47	10	concise	concise	ADJ
fcis-28908	47	11	diagram	diagram	NOUN
fcis-28908	47	12	.	.	PUNCT
fcis-28908	48	1	in	in	ADP
fcis-28908	48	2	figure	figure	NOUN
fcis-28908	48	3	2	2	NUM
fcis-28908	48	4	,	,	PUNCT
fcis-28908	48	5	there	there	PRON
fcis-28908	48	6	is	be	VERB
fcis-28908	48	7	a	a	DET
fcis-28908	48	8	4x4	4x4	NUM
fcis-28908	48	9	feature	feature	NOUN
fcis-28908	48	10	map	map	NOUN
fcis-28908	48	11	undergoing	undergo	VERB
fcis-28908	48	12	2x2	2x2	NUM
fcis-28908	48	13	mean	mean	ADJ
fcis-28908	48	14	pooling	pooling	NOUN
fcis-28908	48	15	and	and	CCONJ
fcis-28908	48	16	max	max	PROPN
fcis-28908	48	17	pooling	pooling	NOUN
fcis-28908	48	18	.	.	PUNCT
fcis-28908	49	1	the	the	DET
fcis-28908	49	2	pooling	pool	VERB
fcis-28908	49	3	window	window	NOUN
fcis-28908	49	4	slides	slide	NOUN
fcis-28908	49	5	over	over	ADP
fcis-28908	49	6	the	the	DET
fcis-28908	49	7	feature	feature	NOUN
fcis-28908	49	8	map	map	NOUN
fcis-28908	49	9	,	,	PUNCT
fcis-28908	49	10	selecting	select	VERB
fcis-28908	49	11	either	either	CCONJ
fcis-28908	49	12	the	the	DET
fcis-28908	49	13	mean	mean	NOUN
fcis-28908	49	14	or	or	CCONJ
fcis-28908	49	15	the	the	DET
fcis-28908	49	16	maximum	maximum	ADJ
fcis-28908	49	17	value	value	NOUN
fcis-28908	49	18	from	from	ADP
fcis-28908	49	19	within	within	ADP
fcis-28908	49	20	the	the	DET
fcis-28908	49	21	window	window	NOUN
fcis-28908	49	22	for	for	ADP
fcis-28908	49	23	the	the	DET
fcis-28908	49	24	output	output	NOUN
fcis-28908	49	25	.	.	PUNCT
fcis-28908	50	1	ultimately	ultimately	ADV
fcis-28908	50	2	,	,	PUNCT
fcis-28908	50	3	a	a	DET
fcis-28908	50	4	2x2	2x2	NUM
fcis-28908	50	5	output	output	NOUN
fcis-28908	50	6	feature	feature	NOUN
fcis-28908	50	7	map	map	NOUN
fcis-28908	50	8	is	be	AUX
fcis-28908	50	9	obtained	obtain	VERB
fcis-28908	50	10	,	,	PUNCT
fcis-28908	50	11	achieving	achieve	VERB
fcis-28908	50	12	dimensionality	dimensionality	NOUN
fcis-28908	50	13	reduction	reduction	NOUN
fcis-28908	50	14	of	of	ADP
fcis-28908	50	15	the	the	DET
fcis-28908	50	16	feature	feature	NOUN
fcis-28908	50	17	map	map	NOUN
fcis-28908	50	18	.	.	PUNCT
fcis-28908	51	1	this	this	DET
fcis-28908	51	2	process	process	NOUN
fcis-28908	51	3	helps	help	VERB
fcis-28908	51	4	extract	extract	VERB
fcis-28908	51	5	more	more	ADV
fcis-28908	51	6	prominent	prominent	ADJ
fcis-28908	51	7	features	feature	NOUN
fcis-28908	51	8	while	while	SCONJ
fcis-28908	51	9	effectively	effectively	ADV
fcis-28908	51	10	reducing	reduce	VERB
fcis-28908	51	11	the	the	DET
fcis-28908	51	12	number	number	NOUN
fcis-28908	51	13	of	of	ADP
fcis-28908	51	14	model	model	NOUN
fcis-28908	51	15	parameters	parameter	NOUN
fcis-28908	51	16	and	and	CCONJ
fcis-28908	51	17	computational	computational	ADJ
fcis-28908	51	18	complexity	complexity	NOUN
fcis-28908	51	19	.	.	PUNCT
fcis-28908	52	1	activation	activation	NOUN
fcis-28908	52	2	functions	function	NOUN
fcis-28908	52	3	enable	enable	VERB
fcis-28908	52	4	neural	neural	ADJ
fcis-28908	52	5	networks	network	NOUN
fcis-28908	52	6	to	to	PART
fcis-28908	52	7	possess	possess	VERB
fcis-28908	52	8	powerful	powerful	ADJ
fcis-28908	52	9	learning	learn	VERB
fcis-28908	52	10	capabilities	capability	NOUN
fcis-28908	52	11	and	and	CCONJ
fcis-28908	52	12	effectively	effectively	ADV
fcis-28908	52	13	simulate	simulate	VERB
fcis-28908	52	14	complex	complex	ADJ
fcis-28908	52	15	nonlinear	nonlinear	ADJ
fcis-28908	52	16	relationships	relationship	NOUN
fcis-28908	52	17	by	by	ADP
fcis-28908	52	18	applying	apply	VERB
fcis-28908	52	19	nonlinear	nonlinear	ADJ
fcis-28908	52	20	transformations	transformation	NOUN
fcis-28908	52	21	to	to	ADP
fcis-28908	52	22	each	each	DET
fcis-28908	52	23	node	node	NOUN
fcis-28908	52	24	in	in	ADP
fcis-28908	52	25	the	the	DET
fcis-28908	52	26	network	network	NOUN
fcis-28908	52	27	.	.	PUNCT
fcis-28908	53	1	without	without	ADP
fcis-28908	53	2	activation	activation	NOUN
fcis-28908	53	3	functions	function	NOUN
fcis-28908	53	4	,	,	PUNCT
fcis-28908	53	5	the	the	DET
fcis-28908	53	6	functionality	functionality	NOUN
fcis-28908	53	7	of	of	ADP
fcis-28908	53	8	neural	neural	ADJ
fcis-28908	53	9	networks	network	NOUN
fcis-28908	53	10	would	would	AUX
fcis-28908	53	11	be	be	AUX
fcis-28908	53	12	severely	severely	ADV
fcis-28908	53	13	diminished	diminish	VERB
fcis-28908	53	14	,	,	PUNCT
fcis-28908	53	15	limiting	limit	VERB
fcis-28908	53	16	them	they	PRON
fcis-28908	53	17	to	to	ADP
fcis-28908	53	18	simple	simple	ADJ
fcis-28908	53	19	linear	linear	ADJ
fcis-28908	53	20	transformations	transformation	NOUN
fcis-28908	53	21	.	.	PUNCT
fcis-28908	54	1	undoubtedly	undoubtedly	ADV
fcis-28908	54	2	,	,	PUNCT
fcis-28908	54	3	this	this	PRON
fcis-28908	54	4	would	would	AUX
fcis-28908	54	5	greatly	greatly	ADV
fcis-28908	54	6	restrict	restrict	VERB
fcis-28908	54	7	their	their	PRON
fcis-28908	54	8	expressive	expressive	ADJ
fcis-28908	54	9	power	power	NOUN
fcis-28908	54	10	and	and	CCONJ
fcis-28908	54	11	learning	learning	NOUN
fcis-28908	54	12	ability	ability	NOUN
fcis-28908	54	13	,	,	PUNCT
fcis-28908	54	14	rendering	render	VERB
fcis-28908	54	15	them	they	PRON
fcis-28908	54	16	unable	unable	ADJ
fcis-28908	54	17	to	to	PART
fcis-28908	54	18	effectively	effectively	ADV
fcis-28908	54	19	capture	capture	VERB
fcis-28908	54	20	the	the	DET
fcis-28908	54	21	complex	complex	ADJ
fcis-28908	54	22	and	and	CCONJ
fcis-28908	54	23	diverse	diverse	ADJ
fcis-28908	54	24	data	datum	NOUN
fcis-28908	54	25	relationships	relationship	NOUN
fcis-28908	54	26	in	in	ADP
fcis-28908	54	27	the	the	DET
fcis-28908	54	28	real	real	ADJ
fcis-28908	54	29	world	world	NOUN
fcis-28908	54	30	.	.	PUNCT
fcis-28908	55	1	figure	figure	NOUN
fcis-28908	55	2	2	2	NUM
fcis-28908	55	3	.	.	X
fcis-28908	55	4	pooling	pool	VERB
fcis-28908	55	5	operation	operation	NOUN
fcis-28908	55	6	process	process	NOUN
fcis-28908	55	7	to	to	PART
fcis-28908	55	8	meet	meet	VERB
fcis-28908	55	9	the	the	DET
fcis-28908	55	10	needs	need	NOUN
fcis-28908	55	11	of	of	ADP
fcis-28908	55	12	different	different	ADJ
fcis-28908	55	13	tasks	task	NOUN
fcis-28908	55	14	,	,	PUNCT
fcis-28908	55	15	researchers	researcher	NOUN
fcis-28908	55	16	have	have	AUX
fcis-28908	55	17	designed	design	VERB
fcis-28908	55	18	various	various	ADJ
fcis-28908	55	19	activation	activation	NOUN
fcis-28908	55	20	functions	function	NOUN
fcis-28908	55	21	,	,	PUNCT
fcis-28908	55	22	such	such	ADJ
fcis-28908	55	23	as	as	ADP
fcis-28908	55	24	sigmoid	sigmoid	NOUN
fcis-28908	55	25	,	,	PUNCT
fcis-28908	55	26	tanh	tanh	NOUN
fcis-28908	55	27	,	,	PUNCT
fcis-28908	55	28	relu	relu	NOUN
fcis-28908	55	29	,	,	PUNCT
fcis-28908	55	30	leaky	leaky	ADJ
fcis-28908	55	31	relu	relu	NOUN
fcis-28908	55	32	,	,	PUNCT
fcis-28908	55	33	elu	elu	PROPN
fcis-28908	55	34	,	,	PUNCT
fcis-28908	55	35	and	and	CCONJ
fcis-28908	55	36	softmax	softmax	NOUN
fcis-28908	55	37	.	.	PUNCT
fcis-28908	56	1	these	these	DET
fcis-28908	56	2	activation	activation	NOUN
fcis-28908	56	3	functions	function	NOUN
fcis-28908	56	4	each	each	PRON
fcis-28908	56	5	have	have	VERB
fcis-28908	56	6	unique	unique	ADJ
fcis-28908	56	7	characteristics	characteristic	NOUN
fcis-28908	56	8	,	,	PUNCT
fcis-28908	56	9	mathematical	mathematical	ADJ
fcis-28908	56	10	properties	property	NOUN
fcis-28908	56	11	,	,	PUNCT
fcis-28908	56	12	and	and	CCONJ
fcis-28908	56	13	application	application	NOUN
fcis-28908	56	14	scenarios	scenario	NOUN
fcis-28908	56	15	,	,	PUNCT
fcis-28908	56	16	providing	provide	VERB
fcis-28908	56	17	diverse	diverse	ADJ
fcis-28908	56	18	options	option	NOUN
fcis-28908	56	19	for	for	ADP
fcis-28908	56	20	neural	neural	ADJ
fcis-28908	56	21	network	network	NOUN
fcis-28908	56	22	learning	learning	NOUN
fcis-28908	56	23	.	.	PUNCT
fcis-28908	57	1	for	for	ADP
fcis-28908	57	2	instance	instance	NOUN
fcis-28908	57	3	,	,	PUNCT
fcis-28908	57	4	the	the	DET
fcis-28908	57	5	sigmoid	sigmoid	NOUN
fcis-28908	57	6	function	function	NOUN
fcis-28908	57	7	compresses	compress	VERB
fcis-28908	57	8	output	output	NOUN
fcis-28908	57	9	values	value	NOUN
fcis-28908	57	10	between	between	ADP
fcis-28908	57	11	0	0	NUM
fcis-28908	57	12	and	and	CCONJ
fcis-28908	57	13	1	1	NUM
fcis-28908	57	14	,	,	PUNCT
fcis-28908	57	15	making	make	VERB
fcis-28908	57	16	it	it	PRON
fcis-28908	57	17	particularly	particularly	ADV
fcis-28908	57	18	suitable	suitable	ADJ
fcis-28908	57	19	for	for	ADP
fcis-28908	57	20	the	the	DET
fcis-28908	57	21	output	output	NOUN
fcis-28908	57	22	layer	layer	NOUN
fcis-28908	57	23	of	of	ADP
fcis-28908	57	24	binary	binary	ADJ
fcis-28908	57	25	classification	classification	NOUN
fcis-28908	57	26	problems	problem	NOUN
fcis-28908	57	27	.	.	PUNCT
fcis-28908	58	1	the	the	DET
fcis-28908	58	2	relu	relu	NOUN
fcis-28908	58	3	function	function	NOUN
fcis-28908	58	4	is	be	AUX
fcis-28908	58	5	favored	favor	VERB
fcis-28908	58	6	by	by	ADP
fcis-28908	58	7	many	many	ADJ
fcis-28908	58	8	scholars	scholar	NOUN
fcis-28908	58	9	due	due	ADP
fcis-28908	58	10	to	to	ADP
fcis-28908	58	11	its	its	PRON
fcis-28908	58	12	simple	simple	ADJ
fcis-28908	58	13	computation	computation	NOUN
fcis-28908	58	14	method	method	NOUN
fcis-28908	58	15	and	and	CCONJ
fcis-28908	58	16	effectiveness	effectiveness	NOUN
fcis-28908	58	17	in	in	ADP
fcis-28908	58	18	mitigating	mitigate	VERB
fcis-28908	58	19	the	the	DET
fcis-28908	58	20	vanishing	vanish	VERB
fcis-28908	58	21	gradient	gradient	NOUN
fcis-28908	58	22	problem	problem	NOUN
fcis-28908	58	23	.	.	PUNCT
fcis-28908	59	1	however	however	ADV
fcis-28908	59	2	,	,	PUNCT
fcis-28908	59	3	it	it	PRON
fcis-28908	59	4	should	should	AUX
fcis-28908	59	5	be	be	AUX
fcis-28908	59	6	noted	note	VERB
fcis-28908	59	7	that	that	SCONJ
fcis-28908	59	8	it	it	PRON
fcis-28908	59	9	may	may	AUX
fcis-28908	59	10	suffer	suffer	VERB
fcis-28908	59	11	from	from	ADP
fcis-28908	59	12	the	the	DET
fcis-28908	59	13	"	"	PUNCT
fcis-28908	59	14	dead	dead	ADJ
fcis-28908	59	15	zone	zone	NOUN
fcis-28908	59	16	"	"	PUNCT
fcis-28908	59	17	phenomenon	phenomenon	NOUN
fcis-28908	59	18	when	when	SCONJ
fcis-28908	59	19	processing	process	VERB
fcis-28908	59	20	negative	negative	ADJ
fcis-28908	59	21	inputs	input	NOUN
fcis-28908	59	22	.	.	PUNCT
fcis-28908	60	1	in	in	ADP
fcis-28908	60	2	contrast	contrast	NOUN
fcis-28908	60	3	,	,	PUNCT
fcis-28908	60	4	the	the	DET
fcis-28908	60	5	softmax	softmax	NOUN
fcis-28908	60	6	function	function	NOUN
fcis-28908	60	7	is	be	AUX
fcis-28908	60	8	widely	widely	ADV
fcis-28908	60	9	used	use	VERB
fcis-28908	60	10	in	in	ADP
fcis-28908	60	11	multi	multi	ADJ
fcis-28908	60	12	-	-	ADJ
fcis-28908	60	13	class	class	ADJ
fcis-28908	60	14	classification	classification	NOUN
fcis-28908	60	15	problems	problem	NOUN
fcis-28908	60	16	,	,	PUNCT
fcis-28908	60	17	effectively	effectively	ADV
fcis-28908	60	18	converting	convert	VERB
fcis-28908	60	19	network	network	NOUN
fcis-28908	60	20	outputs	output	NOUN
fcis-28908	60	21	into	into	ADP
fcis-28908	60	22	probability	probability	NOUN
fcis-28908	60	23	distributions	distribution	NOUN
fcis-28908	60	24	.	.	PUNCT
fcis-28908	61	1	in	in	ADP
fcis-28908	61	2	summary	summary	NOUN
fcis-28908	61	3	,	,	PUNCT
fcis-28908	61	4	activation	activation	NOUN
fcis-28908	61	5	functions	function	NOUN
fcis-28908	61	6	play	play	VERB
fcis-28908	61	7	a	a	DET
fcis-28908	61	8	crucial	crucial	ADJ
fcis-28908	61	9	role	role	NOUN
fcis-28908	61	10	in	in	ADP
fcis-28908	61	11	deep	deep	ADJ
fcis-28908	61	12	learning	learning	NOUN
fcis-28908	61	13	.	.	PUNCT
fcis-28908	62	1	they	they	PRON
fcis-28908	62	2	endow	endow	VERB
fcis-28908	62	3	neural	neural	ADJ
fcis-28908	62	4	networks	network	NOUN
fcis-28908	62	5	with	with	ADP
fcis-28908	62	6	the	the	DET
fcis-28908	62	7	ability	ability	NOUN
fcis-28908	62	8	to	to	PART
fcis-28908	62	9	learn	learn	VERB
fcis-28908	62	10	and	and	CCONJ
fcis-28908	62	11	simulate	simulate	VERB
fcis-28908	62	12	complex	complex	ADJ
fcis-28908	62	13	nonlinear	nonlinear	ADJ
fcis-28908	62	14	relationships	relationship	NOUN
fcis-28908	62	15	,	,	PUNCT
fcis-28908	62	16	serving	serve	VERB
fcis-28908	62	17	as	as	ADP
fcis-28908	62	18	a	a	DET
fcis-28908	62	19	core	core	NOUN
fcis-28908	62	20	technology	technology	NOUN
fcis-28908	62	21	driving	drive	VERB
fcis-28908	62	22	the	the	DET
fcis-28908	62	23	continuous	continuous	ADJ
fcis-28908	62	24	evolution	evolution	NOUN
fcis-28908	62	25	of	of	ADP
fcis-28908	62	26	deep	deep	ADJ
fcis-28908	62	27	learning	learning	NOUN
fcis-28908	62	28	techniques	technique	NOUN
fcis-28908	62	29	.	.	PUNCT
fcis-28908	63	1	when	when	SCONJ
fcis-28908	63	2	constructing	construct	VERB
fcis-28908	63	3	a	a	DET
fcis-28908	63	4	neural	neural	ADJ
fcis-28908	63	5	network	network	NOUN
fcis-28908	63	6	for	for	ADP
fcis-28908	63	7	multi	multi	ADJ
fcis-28908	63	8	-	-	ADJ
fcis-28908	63	9	class	class	ADJ
fcis-28908	63	10	classification	classification	NOUN
fcis-28908	63	11	problems	problem	NOUN
fcis-28908	63	12	,	,	PUNCT
fcis-28908	63	13	the	the	DET
fcis-28908	63	14	softmax	softmax	NOUN
fcis-28908	63	15	classifier	classifier	NOUN
fcis-28908	63	16	is	be	AUX
fcis-28908	63	17	a	a	DET
fcis-28908	63	18	common	common	ADJ
fcis-28908	63	19	choice	choice	NOUN
fcis-28908	63	20	.	.	PUNCT
fcis-28908	64	1	it	it	PRON
fcis-28908	64	2	maps	map	VERB
fcis-28908	64	3	the	the	DET
fcis-28908	64	4	input	input	NOUN
fcis-28908	64	5	x	x	X
fcis-28908	64	6	to	to	ADP
fcis-28908	64	7	a	a	DET
fcis-28908	64	8	k	k	ADV
fcis-28908	64	9	-	-	ADJ
fcis-28908	64	10	dimensional	dimensional	ADJ
fcis-28908	64	11	probability	probability	NOUN
fcis-28908	64	12	distribution	distribution	NOUN
fcis-28908	64	13	,	,	PUNCT
fcis-28908	64	14	where	where	SCONJ
fcis-28908	64	15	k	k	PROPN
fcis-28908	64	16	is	be	AUX
fcis-28908	64	17	the	the	DET
fcis-28908	64	18	total	total	ADJ
fcis-28908	64	19	number	number	NOUN
fcis-28908	64	20	of	of	ADP
fcis-28908	64	21	classes	class	NOUN
fcis-28908	64	22	.	.	PUNCT
fcis-28908	65	1	for	for	ADP
fcis-28908	65	2	a	a	DET
fcis-28908	65	3	given	give	VERB
fcis-28908	65	4	input	input	NOUN
fcis-28908	65	5	x	x	X
fcis-28908	65	6	,	,	PUNCT
fcis-28908	65	7	the	the	DET
fcis-28908	65	8	softmax	softmax	NOUN
fcis-28908	65	9	function	function	NOUN
fcis-28908	65	10	calculates	calculate	VERB
fcis-28908	65	11	the	the	DET
fcis-28908	65	12	probability	probability	NOUN
fcis-28908	65	13	of	of	ADP
fcis-28908	65	14	each	each	DET
fcis-28908	65	15	class	class	NOUN
fcis-28908	65	16	j	j	PROPN
fcis-28908	65	17	,	,	PUNCT
fcis-28908	65	18	using	use	VERB
fcis-28908	65	19	the	the	DET
fcis-28908	65	20	following	follow	VERB
fcis-28908	65	21	formula	formula	NOUN
fcis-28908	65	22	.	.	PUNCT
fcis-28908	66	1	|	|	ADV
fcis-28908	66	2	∑	∑	ADV
fcis-28908	66	3	here	here	ADV
fcis-28908	66	4	,	,	PUNCT
fcis-28908	66	5	denotes	denote	VERB
fcis-28908	66	6	the	the	DET
fcis-28908	66	7	probability	probability	NOUN
fcis-28908	66	8	that	that	SCONJ
fcis-28908	66	9	the	the	DET
fcis-28908	66	10	label	label	NOUN
fcis-28908	66	11	y	y	PROPN
fcis-28908	66	12	belongs	belong	VERB
fcis-28908	66	13	to	to	ADP
fcis-28908	66	14	class	class	PROPN
fcis-28908	66	15	j	j	PROPN
fcis-28908	66	16	given	give	VERB
fcis-28908	66	17	the	the	DET
fcis-28908	66	18	input	input	NOUN
fcis-28908	66	19	x	x	NOUN
fcis-28908	66	20	,	,	PUNCT
fcis-28908	66	21	typically	typically	ADV
fcis-28908	66	22	computed	compute	VERB
fcis-28908	66	23	as	as	ADP
fcis-28908	66	24	the	the	DET
fcis-28908	66	25	dot	dot	NOUN
fcis-28908	66	26	product	product	NOUN
fcis-28908	66	27	of	of	ADP
fcis-28908	66	28	the	the	DET
fcis-28908	66	29	input	input	NOUN
fcis-28908	66	30	x	x	X
fcis-28908	66	31	and	and	CCONJ
fcis-28908	66	32	the	the	DET
fcis-28908	66	33	weights	weight	NOUN
fcis-28908	66	34	.	.	PUNCT
fcis-28908	67	1	in	in	ADP
fcis-28908	67	2	the	the	DET
fcis-28908	67	3	formula	formula	NOUN
fcis-28908	67	4	above	above	ADV
fcis-28908	67	5	,	,	PUNCT
fcis-28908	67	6	the	the	DET
fcis-28908	67	7	denominator	denominator	NOUN
fcis-28908	67	8	represents	represent	VERB
fcis-28908	67	9	normalization	normalization	NOUN
fcis-28908	67	10	,	,	PUNCT
fcis-28908	67	11	ensuring	ensure	VERB
fcis-28908	67	12	that	that	SCONJ
fcis-28908	67	13	the	the	DET
fcis-28908	67	14	sum	sum	NOUN
fcis-28908	67	15	of	of	ADP
fcis-28908	67	16	probabilities	probability	NOUN
fcis-28908	67	17	across	across	ADP
fcis-28908	67	18	all	all	DET
fcis-28908	67	19	classes	class	NOUN
fcis-28908	67	20	equals	equal	VERB
fcis-28908	67	21	1	1	NUM
fcis-28908	67	22	.	.	PUNCT
fcis-28908	67	23	to	to	PART
fcis-28908	67	24	train	train	VERB
fcis-28908	67	25	this	this	DET
fcis-28908	67	26	model	model	NOUN
fcis-28908	67	27	,	,	PUNCT
fcis-28908	67	28	we	we	PRON
fcis-28908	67	29	define	define	VERB
fcis-28908	67	30	a	a	DET
fcis-28908	67	31	loss	loss	NOUN
fcis-28908	67	32	function	function	NOUN
fcis-28908	67	33	l(w	l(w	NOUN
fcis-28908	67	34	)	)	PUNCT
fcis-28908	67	35	to	to	PART
fcis-28908	67	36	measure	measure	VERB
fcis-28908	67	37	the	the	DET
fcis-28908	67	38	discrepancy	discrepancy	NOUN
fcis-28908	67	39	between	between	ADP
fcis-28908	67	40	the	the	DET
fcis-28908	67	41	predicted	predict	VERB
fcis-28908	67	42	probability	probability	NOUN
fcis-28908	67	43	distribution	distribution	NOUN
fcis-28908	67	44	and	and	CCONJ
fcis-28908	67	45	the	the	DET
fcis-28908	67	46	true	true	ADJ
fcis-28908	67	47	values	value	NOUN
fcis-28908	67	48	.	.	PUNCT
fcis-28908	68	1	a	a	DET
fcis-28908	68	2	commonly	commonly	ADV
fcis-28908	68	3	used	use	VERB
fcis-28908	68	4	loss	loss	NOUN
fcis-28908	68	5	function	function	NOUN
fcis-28908	68	6	is	be	AUX
fcis-28908	68	7	the	the	DET
fcis-28908	68	8	cross	cross	ADJ
fcis-28908	68	9	-	-	ADJ
fcis-28908	68	10	entropy	entropy	ADJ
fcis-28908	68	11	loss	loss	NOUN
fcis-28908	68	12	,	,	PUNCT
fcis-28908	68	13	which	which	PRON
fcis-28908	68	14	for	for	ADP
fcis-28908	68	15	the	the	DET
fcis-28908	68	16	softmax	softmax	NOUN
fcis-28908	68	17	classifier	classifier	NOUN
fcis-28908	68	18	takes	take	VERB
fcis-28908	68	19	the	the	DET
fcis-28908	68	20	following	follow	VERB
fcis-28908	68	21	form	form	NOUN
fcis-28908	68	22	.	.	PUNCT
fcis-28908	69	1	130	130	NUM
fcis-28908	69	2	1	1	NUM
fcis-28908	69	3	log	log	NOUN
fcis-28908	69	4	e	e	X
fcis-28908	69	5	∑	∑	PUNCT
fcis-28908	69	6	e	e	PROPN
fcis-28908	69	7	2	2	NUM
fcis-28908	69	8	here	here	ADV
fcis-28908	69	9	,	,	PUNCT
fcis-28908	69	10	the	the	DET
fcis-28908	69	11	indicator	indicator	NOUN
fcis-28908	69	12	function	function	NOUN
fcis-28908	69	13	takes	take	VERB
fcis-28908	69	14	the	the	DET
fcis-28908	69	15	value	value	NOUN
fcis-28908	69	16	of	of	ADP
fcis-28908	69	17	1	1	NUM
fcis-28908	69	18	when	when	SCONJ
fcis-28908	69	19	the	the	DET
fcis-28908	69	20	variable	variable	NOUN
fcis-28908	69	21	equals	equal	VERB
fcis-28908	69	22	j	j	PROPN
fcis-28908	69	23	and	and	CCONJ
fcis-28908	69	24	0	0	NUM
fcis-28908	69	25	otherwise	otherwise	ADV
fcis-28908	69	26	.	.	PUNCT
fcis-28908	70	1	the	the	DET
fcis-28908	70	2	first	first	ADJ
fcis-28908	70	3	component	component	NOUN
fcis-28908	70	4	is	be	AUX
fcis-28908	70	5	the	the	DET
fcis-28908	70	6	cross	cross	ADJ
fcis-28908	70	7	-	-	ADJ
fcis-28908	70	8	entropy	entropy	ADJ
fcis-28908	70	9	loss	loss	NOUN
fcis-28908	70	10	,	,	PUNCT
fcis-28908	70	11	which	which	PRON
fcis-28908	70	12	evaluates	evaluate	VERB
fcis-28908	70	13	the	the	DET
fcis-28908	70	14	deviation	deviation	NOUN
fcis-28908	70	15	between	between	ADP
fcis-28908	70	16	the	the	DET
fcis-28908	70	17	predicted	predict	VERB
fcis-28908	70	18	probability	probability	NOUN
fcis-28908	70	19	distribution	distribution	NOUN
fcis-28908	70	20	and	and	CCONJ
fcis-28908	70	21	the	the	DET
fcis-28908	70	22	actual	actual	ADJ
fcis-28908	70	23	target	target	NOUN
fcis-28908	70	24	values	value	NOUN
fcis-28908	70	25	.	.	PUNCT
fcis-28908	71	1	secondly	secondly	ADV
fcis-28908	71	2	,	,	PUNCT
fcis-28908	71	3	there	there	PRON
fcis-28908	71	4	is	be	VERB
fcis-28908	71	5	a	a	DET
fcis-28908	71	6	regularization	regularization	NOUN
fcis-28908	71	7	term	term	NOUN
fcis-28908	71	8	aimed	aim	VERB
fcis-28908	71	9	at	at	ADP
fcis-28908	71	10	mitigating	mitigate	VERB
fcis-28908	71	11	overfitting	overfitting	NOUN
fcis-28908	71	12	of	of	ADP
fcis-28908	71	13	the	the	DET
fcis-28908	71	14	model	model	NOUN
fcis-28908	71	15	,	,	PUNCT
fcis-28908	71	16	which	which	PRON
fcis-28908	71	17	includes	include	VERB
fcis-28908	71	18	a	a	DET
fcis-28908	71	19	regularization	regularization	NOUN
fcis-28908	71	20	strength	strength	NOUN
fcis-28908	71	21	parameter	parameter	NOUN
fcis-28908	71	22	indicating	indicate	VERB
fcis-28908	71	23	the	the	DET
fcis-28908	71	24	degree	degree	NOUN
fcis-28908	71	25	of	of	ADP
fcis-28908	71	26	constraint	constraint	NOUN
fcis-28908	71	27	on	on	ADP
fcis-28908	71	28	each	each	DET
fcis-28908	71	29	element	element	NOUN
fcis-28908	71	30	in	in	ADP
fcis-28908	71	31	the	the	DET
fcis-28908	71	32	weight	weight	NOUN
fcis-28908	71	33	matrix	matrix	NOUN
fcis-28908	71	34	w.	w.	NOUN
fcis-28908	71	35	during	during	ADP
fcis-28908	71	36	the	the	DET
fcis-28908	71	37	training	training	NOUN
fcis-28908	71	38	process	process	NOUN
fcis-28908	71	39	of	of	ADP
fcis-28908	71	40	the	the	DET
fcis-28908	71	41	model	model	NOUN
fcis-28908	71	42	,	,	PUNCT
fcis-28908	71	43	an	an	DET
fcis-28908	71	44	optimization	optimization	NOUN
fcis-28908	71	45	algorithm	algorithm	NOUN
fcis-28908	71	46	(	(	PUNCT
fcis-28908	71	47	such	such	ADJ
fcis-28908	71	48	as	as	ADP
fcis-28908	71	49	gradient	gradient	ADJ
fcis-28908	71	50	descent	descent	NOUN
fcis-28908	71	51	)	)	PUNCT
fcis-28908	71	52	is	be	AUX
fcis-28908	71	53	employed	employ	VERB
fcis-28908	71	54	to	to	PART
fcis-28908	71	55	gradually	gradually	ADV
fcis-28908	71	56	adjust	adjust	VERB
fcis-28908	71	57	the	the	DET
fcis-28908	71	58	weights	weight	NOUN
fcis-28908	71	59	w	w	NOUN
fcis-28908	71	60	in	in	ADP
fcis-28908	71	61	order	order	NOUN
fcis-28908	71	62	to	to	PART
fcis-28908	71	63	minimize	minimize	VERB
fcis-28908	71	64	the	the	DET
fcis-28908	71	65	value	value	NOUN
fcis-28908	71	66	of	of	ADP
fcis-28908	71	67	the	the	DET
fcis-28908	71	68	loss	loss	NOUN
fcis-28908	71	69	function	function	NOUN
fcis-28908	71	70	.	.	PUNCT
fcis-28908	72	1	in	in	ADP
fcis-28908	72	2	this	this	DET
fcis-28908	72	3	way	way	NOUN
fcis-28908	72	4	,	,	PUNCT
fcis-28908	72	5	the	the	DET
fcis-28908	72	6	model	model	NOUN
fcis-28908	72	7	can	can	AUX
fcis-28908	72	8	more	more	ADV
fcis-28908	72	9	effectively	effectively	ADV
fcis-28908	72	10	fit	fit	VERB
fcis-28908	72	11	the	the	DET
fcis-28908	72	12	training	training	NOUN
fcis-28908	72	13	data	datum	NOUN
fcis-28908	72	14	and	and	CCONJ
fcis-28908	72	15	make	make	VERB
fcis-28908	72	16	accurate	accurate	ADJ
fcis-28908	72	17	classification	classification	NOUN
fcis-28908	72	18	predictions	prediction	NOUN
fcis-28908	72	19	for	for	ADP
fcis-28908	72	20	new	new	ADJ
fcis-28908	72	21	inputs	input	NOUN
fcis-28908	72	22	.	.	PUNCT
fcis-28908	73	1	4	4	X
fcis-28908	73	2	.	.	X
fcis-28908	73	3	establishment	establishment	NOUN
fcis-28908	73	4	of	of	ADP
fcis-28908	73	5	insect	insect	NOUN
fcis-28908	73	6	image	image	NOUN
fcis-28908	73	7	model	model	NOUN
fcis-28908	73	8	(	(	PUNCT
fcis-28908	73	9	1	1	X
fcis-28908	73	10	)	)	PUNCT
fcis-28908	73	11	insect	insect	NOUN
fcis-28908	73	12	image	image	NOUN
fcis-28908	73	13	data	datum	NOUN
fcis-28908	73	14	collection	collection	NOUN
fcis-28908	73	15	prior	prior	ADV
fcis-28908	73	16	to	to	ADP
fcis-28908	73	17	model	model	NOUN
fcis-28908	73	18	training	training	NOUN
fcis-28908	73	19	,	,	PUNCT
fcis-28908	73	20	we	we	PRON
fcis-28908	73	21	selected	select	VERB
fcis-28908	73	22	12	12	NUM
fcis-28908	73	23	different	different	ADJ
fcis-28908	73	24	types	type	NOUN
fcis-28908	73	25	of	of	ADP
fcis-28908	73	26	insects	insect	NOUN
fcis-28908	73	27	as	as	ADP
fcis-28908	73	28	our	our	PRON
fcis-28908	73	29	training	training	NOUN
fcis-28908	73	30	dataset	dataset	NOUN
fcis-28908	73	31	.	.	PUNCT
fcis-28908	74	1	subsequently	subsequently	ADV
fcis-28908	74	2	,	,	PUNCT
fcis-28908	74	3	utilizing	utilize	VERB
fcis-28908	74	4	advanced	advanced	ADJ
fcis-28908	74	5	web	web	NOUN
fcis-28908	74	6	scraping	scrape	VERB
fcis-28908	74	7	techniques	technique	NOUN
fcis-28908	74	8	,	,	PUNCT
fcis-28908	74	9	we	we	PRON
fcis-28908	74	10	collected	collect	VERB
fcis-28908	74	11	300	300	NUM
fcis-28908	74	12	pieces	piece	NOUN
fcis-28908	74	13	of	of	ADP
fcis-28908	74	14	relevant	relevant	ADJ
fcis-28908	74	15	data	datum	NOUN
fcis-28908	74	16	for	for	ADP
fcis-28908	74	17	each	each	DET
fcis-28908	74	18	type	type	NOUN
fcis-28908	74	19	of	of	ADP
fcis-28908	74	20	insect	insect	NOUN
fcis-28908	74	21	.	.	PUNCT
fcis-28908	75	1	after	after	SCONJ
fcis-28908	75	2	a	a	DET
fcis-28908	75	3	rigorous	rigorous	ADJ
fcis-28908	75	4	manual	manual	ADJ
fcis-28908	75	5	review	review	NOUN
fcis-28908	75	6	and	and	CCONJ
fcis-28908	75	7	screening	screen	VERB
fcis-28908	75	8	process	process	NOUN
fcis-28908	75	9	,	,	PUNCT
fcis-28908	75	10	inaccurate	inaccurate	ADJ
fcis-28908	75	11	and	and	CCONJ
fcis-28908	75	12	unsuitable	unsuitable	ADJ
fcis-28908	75	13	images	image	NOUN
fcis-28908	75	14	were	be	AUX
fcis-28908	75	15	removed	remove	VERB
fcis-28908	75	16	,	,	PUNCT
fcis-28908	75	17	leaving	leave	VERB
fcis-28908	75	18	us	we	PRON
fcis-28908	75	19	with	with	ADP
fcis-28908	75	20	215	215	NUM
fcis-28908	75	21	highquality	highquality	NOUN
fcis-28908	75	22	training	training	NOUN
fcis-28908	75	23	data	datum	NOUN
fcis-28908	75	24	entries	entry	NOUN
fcis-28908	75	25	.	.	PUNCT
fcis-28908	76	1	to	to	PART
fcis-28908	76	2	construct	construct	VERB
fcis-28908	76	3	a	a	DET
fcis-28908	76	4	balanced	balanced	ADJ
fcis-28908	76	5	and	and	CCONJ
fcis-28908	76	6	effective	effective	ADJ
fcis-28908	76	7	dataset	dataset	NOUN
fcis-28908	76	8	,	,	PUNCT
fcis-28908	76	9	we	we	PRON
fcis-28908	76	10	split	split	VERB
fcis-28908	76	11	the	the	DET
fcis-28908	76	12	data	datum	NOUN
fcis-28908	76	13	at	at	ADP
fcis-28908	76	14	a	a	DET
fcis-28908	76	15	ratio	ratio	NOUN
fcis-28908	76	16	of	of	ADP
fcis-28908	76	17	approximately	approximately	ADV
fcis-28908	76	18	7:3	7:3	NUM
fcis-28908	76	19	,	,	PUNCT
fcis-28908	76	20	ensuring	ensure	VERB
fcis-28908	76	21	that	that	SCONJ
fcis-28908	76	22	each	each	DET
fcis-28908	76	23	type	type	NOUN
fcis-28908	76	24	of	of	ADP
fcis-28908	76	25	insect	insect	NOUN
fcis-28908	76	26	has	have	VERB
fcis-28908	76	27	150	150	NUM
fcis-28908	76	28	images	image	NOUN
fcis-28908	76	29	in	in	ADP
fcis-28908	76	30	the	the	DET
fcis-28908	76	31	training	training	NOUN
fcis-28908	76	32	set	set	NOUN
fcis-28908	76	33	and	and	CCONJ
fcis-28908	76	34	65	65	NUM
fcis-28908	76	35	images	image	NOUN
fcis-28908	76	36	in	in	ADP
fcis-28908	76	37	the	the	DET
fcis-28908	76	38	test	test	NOUN
fcis-28908	76	39	set	set	NOUN
fcis-28908	76	40	.	.	PUNCT
fcis-28908	77	1	(	(	PUNCT
fcis-28908	77	2	2	2	X
fcis-28908	77	3	)	)	PUNCT
fcis-28908	77	4	insect	insect	NOUN
fcis-28908	77	5	image	image	NOUN
fcis-28908	77	6	data	datum	NOUN
fcis-28908	77	7	preprocessing	preprocessing	NOUN
fcis-28908	77	8	:	:	PUNCT
fcis-28908	77	9	one	one	NUM
fcis-28908	77	10	-	-	PUNCT
fcis-28908	77	11	hot	hot	ADJ
fcis-28908	77	12	encoding	encoding	NOUN
fcis-28908	77	13	one	one	NUM
fcis-28908	77	14	-	-	PUNCT
fcis-28908	77	15	hot	hot	ADJ
fcis-28908	77	16	encoding	encoding	NOUN
fcis-28908	77	17	is	be	AUX
fcis-28908	77	18	a	a	DET
fcis-28908	77	19	method	method	NOUN
fcis-28908	77	20	of	of	ADP
fcis-28908	77	21	encoding	encode	VERB
fcis-28908	77	22	where	where	SCONJ
fcis-28908	77	23	each	each	DET
fcis-28908	77	24	state	state	NOUN
fcis-28908	77	25	corresponds	correspond	VERB
fcis-28908	77	26	to	to	ADP
fcis-28908	77	27	an	an	DET
fcis-28908	77	28	independent	independent	ADJ
fcis-28908	77	29	register	register	NOUN
fcis-28908	77	30	bit	bit	NOUN
fcis-28908	77	31	,	,	PUNCT
fcis-28908	77	32	and	and	CCONJ
fcis-28908	77	33	at	at	ADP
fcis-28908	77	34	any	any	DET
fcis-28908	77	35	given	give	VERB
fcis-28908	77	36	time	time	NOUN
fcis-28908	77	37	,	,	PUNCT
fcis-28908	77	38	only	only	ADV
fcis-28908	77	39	one	one	NUM
fcis-28908	77	40	bit	bit	NOUN
fcis-28908	77	41	is	be	AUX
fcis-28908	77	42	active	active	ADJ
fcis-28908	77	43	(	(	PUNCT
fcis-28908	77	44	i.e.	i.e.	X
fcis-28908	77	45	,	,	PUNCT
fcis-28908	77	46	has	have	AUX
fcis-28908	77	47	a	a	DET
fcis-28908	77	48	value	value	NOUN
fcis-28908	77	49	of	of	ADP
fcis-28908	77	50	1	1	NUM
fcis-28908	77	51	)	)	PUNCT
fcis-28908	77	52	while	while	SCONJ
fcis-28908	77	53	the	the	DET
fcis-28908	77	54	rest	rest	NOUN
fcis-28908	77	55	are	be	AUX
fcis-28908	77	56	0	0	NUM
fcis-28908	77	57	.	.	PUNCT
fcis-28908	78	1	in	in	ADP
fcis-28908	78	2	the	the	DET
fcis-28908	78	3	experiment	experiment	NOUN
fcis-28908	78	4	,	,	PUNCT
fcis-28908	78	5	the	the	DET
fcis-28908	78	6	image	image	NOUN
fcis-28908	78	7	dataset	dataset	VERB
fcis-28908	78	8	is	be	AUX
fcis-28908	78	9	first	first	ADV
fcis-28908	78	10	converted	convert	VERB
fcis-28908	78	11	into	into	ADP
fcis-28908	78	12	an	an	DET
fcis-28908	78	13	array	array	NOUN
fcis-28908	78	14	format	format	NOUN
fcis-28908	78	15	and	and	CCONJ
fcis-28908	78	16	shuffled	shuffle	VERB
fcis-28908	78	17	to	to	PART
fcis-28908	78	18	enhance	enhance	VERB
fcis-28908	78	19	the	the	DET
fcis-28908	78	20	model	model	NOUN
fcis-28908	78	21	's	's	PART
fcis-28908	78	22	generalization	generalization	NOUN
fcis-28908	78	23	ability	ability	NOUN
fcis-28908	78	24	.	.	PUNCT
fcis-28908	79	1	then	then	ADV
fcis-28908	79	2	,	,	PUNCT
fcis-28908	79	3	one	one	NUM
fcis-28908	79	4	-	-	PUNCT
fcis-28908	79	5	hot	hot	ADJ
fcis-28908	79	6	encoding	encoding	NOUN
fcis-28908	79	7	is	be	AUX
fcis-28908	79	8	applied	apply	VERB
fcis-28908	79	9	to	to	ADP
fcis-28908	79	10	the	the	DET
fcis-28908	79	11	processed	process	VERB
fcis-28908	79	12	dataset	dataset	NOUN
fcis-28908	79	13	to	to	PART
fcis-28908	79	14	convert	convert	VERB
fcis-28908	79	15	it	it	PRON
fcis-28908	79	16	into	into	ADP
fcis-28908	79	17	a	a	DET
fcis-28908	79	18	form	form	NOUN
fcis-28908	79	19	suitable	suitable	ADJ
fcis-28908	79	20	for	for	ADP
fcis-28908	79	21	subsequent	subsequent	ADJ
fcis-28908	79	22	model	model	NOUN
fcis-28908	79	23	training	training	NOUN
fcis-28908	79	24	.	.	PUNCT
fcis-28908	80	1	(	(	PUNCT
fcis-28908	80	2	3	3	X
fcis-28908	80	3	)	)	PUNCT
fcis-28908	80	4	construction	construction	NOUN
fcis-28908	80	5	of	of	ADP
fcis-28908	80	6	tensorflow	tensorflow	ADJ
fcis-28908	80	7	convolutional	convolutional	ADJ
fcis-28908	80	8	neural	neural	ADJ
fcis-28908	80	9	network	network	NOUN
fcis-28908	80	10	when	when	SCONJ
fcis-28908	80	11	constructing	construct	VERB
fcis-28908	80	12	the	the	DET
fcis-28908	80	13	convolutional	convolutional	ADJ
fcis-28908	80	14	neural	neural	ADJ
fcis-28908	80	15	network	network	NOUN
fcis-28908	80	16	,	,	PUNCT
fcis-28908	80	17	the	the	DET
fcis-28908	80	18	classic	classic	NOUN
fcis-28908	80	19	lenet-5	lenet-5	NUM
fcis-28908	80	20	model	model	NOUN
fcis-28908	80	21	is	be	AUX
fcis-28908	80	22	chosen	choose	VERB
fcis-28908	80	23	as	as	ADP
fcis-28908	80	24	the	the	DET
fcis-28908	80	25	basic	basic	ADJ
fcis-28908	80	26	architecture	architecture	NOUN
fcis-28908	80	27	,	,	PUNCT
fcis-28908	80	28	which	which	PRON
fcis-28908	80	29	includes	include	VERB
fcis-28908	80	30	two	two	NUM
fcis-28908	80	31	convolutional	convolutional	ADJ
fcis-28908	80	32	pooling	pool	VERB
fcis-28908	80	33	layers	layer	NOUN
fcis-28908	80	34	and	and	CCONJ
fcis-28908	80	35	three	three	NUM
fcis-28908	80	36	fully	fully	ADV
fcis-28908	80	37	connected	connected	ADJ
fcis-28908	80	38	layers	layer	NOUN
fcis-28908	80	39	.	.	PUNCT
fcis-28908	81	1	to	to	PART
fcis-28908	81	2	mitigate	mitigate	VERB
fcis-28908	81	3	the	the	DET
fcis-28908	81	4	issue	issue	NOUN
fcis-28908	81	5	of	of	ADP
fcis-28908	81	6	model	model	NOUN
fcis-28908	81	7	parameter	parameter	PROPN
fcis-28908	81	8	overfitting	overfitting	NOUN
fcis-28908	81	9	,	,	PUNCT
fcis-28908	81	10	the	the	DET
fcis-28908	81	11	dropout	dropout	NOUN
fcis-28908	81	12	mechanism	mechanism	NOUN
fcis-28908	81	13	with	with	ADP
fcis-28908	81	14	a	a	DET
fcis-28908	81	15	dropout	dropout	NOUN
fcis-28908	81	16	rate	rate	NOUN
fcis-28908	81	17	of	of	ADP
fcis-28908	81	18	0.5	0.5	NUM
fcis-28908	81	19	is	be	AUX
fcis-28908	81	20	introduced	introduce	VERB
fcis-28908	81	21	into	into	ADP
fcis-28908	81	22	the	the	DET
fcis-28908	81	23	network	network	NOUN
fcis-28908	81	24	.	.	PUNCT
fcis-28908	82	1	the	the	DET
fcis-28908	82	2	focus	focus	NOUN
fcis-28908	82	3	of	of	ADP
fcis-28908	82	4	this	this	DET
fcis-28908	82	5	study	study	NOUN
fcis-28908	82	6	is	be	AUX
fcis-28908	82	7	to	to	PART
fcis-28908	82	8	explore	explore	VERB
fcis-28908	82	9	how	how	SCONJ
fcis-28908	82	10	the	the	DET
fcis-28908	82	11	application	application	NOUN
fcis-28908	82	12	of	of	ADP
fcis-28908	82	13	different	different	ADJ
fcis-28908	82	14	optimizers	optimizer	NOUN
fcis-28908	82	15	and	and	CCONJ
fcis-28908	82	16	the	the	DET
fcis-28908	82	17	configuration	configuration	NOUN
fcis-28908	82	18	of	of	ADP
fcis-28908	82	19	learning	learn	VERB
fcis-28908	82	20	rates	rate	NOUN
fcis-28908	82	21	affect	affect	VERB
fcis-28908	82	22	the	the	DET
fcis-28908	82	23	final	final	ADJ
fcis-28908	82	24	test	test	NOUN
fcis-28908	82	25	performance	performance	NOUN
fcis-28908	82	26	of	of	ADP
fcis-28908	82	27	the	the	DET
fcis-28908	82	28	model	model	NOUN
fcis-28908	82	29	.	.	PUNCT
fcis-28908	83	1	during	during	ADP
fcis-28908	83	2	the	the	DET
fcis-28908	83	3	comparative	comparative	ADJ
fcis-28908	83	4	testing	testing	NOUN
fcis-28908	83	5	of	of	ADP
fcis-28908	83	6	optimizers	optimizer	NOUN
fcis-28908	83	7	,	,	PUNCT
fcis-28908	83	8	the	the	DET
fcis-28908	83	9	experiment	experiment	NOUN
fcis-28908	83	10	will	will	AUX
fcis-28908	83	11	refer	refer	VERB
fcis-28908	83	12	to	to	ADP
fcis-28908	83	13	widely	widely	ADV
fcis-28908	83	14	adopted	adopt	VERB
fcis-28908	83	15	parameter	parameter	NOUN
fcis-28908	83	16	values	value	NOUN
fcis-28908	83	17	and	and	CCONJ
fcis-28908	83	18	make	make	VERB
fcis-28908	83	19	adjustments	adjustment	NOUN
fcis-28908	83	20	and	and	CCONJ
fcis-28908	83	21	experiments	experiment	NOUN
fcis-28908	83	22	within	within	ADP
fcis-28908	83	23	a	a	DET
fcis-28908	83	24	small	small	ADJ
fcis-28908	83	25	range	range	NOUN
fcis-28908	83	26	of	of	ADP
fcis-28908	83	27	these	these	DET
fcis-28908	83	28	reference	reference	NOUN
fcis-28908	83	29	values	value	NOUN
fcis-28908	83	30	,	,	PUNCT
fcis-28908	83	31	with	with	ADP
fcis-28908	83	32	the	the	DET
fcis-28908	83	33	aim	aim	NOUN
fcis-28908	83	34	of	of	ADP
fcis-28908	83	35	observing	observe	VERB
fcis-28908	83	36	and	and	CCONJ
fcis-28908	83	37	analyzing	analyze	VERB
fcis-28908	83	38	changes	change	NOUN
fcis-28908	83	39	in	in	ADP
fcis-28908	83	40	the	the	DET
fcis-28908	83	41	model	model	NOUN
fcis-28908	83	42	's	's	PART
fcis-28908	83	43	prediction	prediction	NOUN
fcis-28908	83	44	accuracy	accuracy	NOUN
fcis-28908	83	45	.	.	PUNCT
fcis-28908	84	1	adam	adam	PROPN
fcis-28908	84	2	,	,	PUNCT
fcis-28908	84	3	which	which	PRON
fcis-28908	84	4	stands	stand	VERB
fcis-28908	84	5	for	for	ADP
fcis-28908	84	6	adaptive	adaptive	ADJ
fcis-28908	84	7	moment	moment	NOUN
fcis-28908	84	8	estimation	estimation	NOUN
fcis-28908	84	9	,	,	PUNCT
fcis-28908	84	10	is	be	AUX
fcis-28908	84	11	a	a	DET
fcis-28908	84	12	variant	variant	NOUN
fcis-28908	84	13	of	of	ADP
fcis-28908	84	14	the	the	DET
fcis-28908	84	15	gradient	gradient	ADJ
fcis-28908	84	16	descent	descent	NOUN
fcis-28908	84	17	algorithm	algorithm	NOUN
fcis-28908	84	18	.	.	PUNCT
fcis-28908	85	1	its	its	PRON
fcis-28908	85	2	characteristic	characteristic	NOUN
fcis-28908	85	3	lies	lie	VERB
fcis-28908	85	4	in	in	ADP
fcis-28908	85	5	controlling	control	VERB
fcis-28908	85	6	the	the	DET
fcis-28908	85	7	learning	learning	NOUN
fcis-28908	85	8	rate	rate	NOUN
fcis-28908	85	9	of	of	ADP
fcis-28908	85	10	the	the	DET
fcis-28908	85	11	parameters	parameter	NOUN
fcis-28908	85	12	within	within	ADP
fcis-28908	85	13	a	a	DET
fcis-28908	85	14	certain	certain	ADJ
fcis-28908	85	15	range	range	NOUN
fcis-28908	85	16	during	during	ADP
fcis-28908	85	17	each	each	DET
fcis-28908	85	18	iteration	iteration	NOUN
fcis-28908	85	19	,	,	PUNCT
fcis-28908	85	20	ensuring	ensure	VERB
fcis-28908	85	21	relative	relative	ADJ
fcis-28908	85	22	stability	stability	NOUN
fcis-28908	85	23	of	of	ADP
fcis-28908	85	24	the	the	DET
fcis-28908	85	25	parameters	parameter	NOUN
fcis-28908	85	26	even	even	ADV
fcis-28908	85	27	when	when	SCONJ
fcis-28908	85	28	faced	face	VERB
fcis-28908	85	29	with	with	ADP
fcis-28908	85	30	large	large	ADJ
fcis-28908	85	31	gradients	gradient	NOUN
fcis-28908	85	32	.	.	PUNCT
fcis-28908	86	1	as	as	SCONJ
fcis-28908	86	2	shown	show	VERB
fcis-28908	86	3	in	in	ADP
fcis-28908	86	4	figure	figure	NOUN
fcis-28908	86	5	3	3	NUM
fcis-28908	86	6	,	,	PUNCT
fcis-28908	86	7	the	the	DET
fcis-28908	86	8	adam	adam	PROPN
fcis-28908	86	9	optimizer	optimizer	NOUN
fcis-28908	86	10	demonstrates	demonstrate	VERB
fcis-28908	86	11	excellent	excellent	ADJ
fcis-28908	86	12	prediction	prediction	NOUN
fcis-28908	86	13	accuracy	accuracy	NOUN
fcis-28908	86	14	.	.	PUNCT
fcis-28908	87	1	in	in	ADP
fcis-28908	87	2	the	the	DET
fcis-28908	87	3	insect	insect	NOUN
fcis-28908	87	4	recognition	recognition	NOUN
fcis-28908	87	5	task	task	NOUN
fcis-28908	87	6	(	(	PUNCT
fcis-28908	87	7	as	as	SCONJ
fcis-28908	87	8	illustrated	illustrate	VERB
fcis-28908	87	9	in	in	ADP
fcis-28908	87	10	figure	figure	NOUN
fcis-28908	87	11	4	4	NUM
fcis-28908	87	12	)	)	PUNCT
fcis-28908	87	13	,	,	PUNCT
fcis-28908	87	14	despite	despite	SCONJ
fcis-28908	87	15	the	the	DET
fcis-28908	87	16	limited	limited	ADJ
fcis-28908	87	17	size	size	NOUN
fcis-28908	87	18	of	of	ADP
fcis-28908	87	19	the	the	DET
fcis-28908	87	20	training	training	NOUN
fcis-28908	87	21	dataset	dataset	NOUN
fcis-28908	87	22	,	,	PUNCT
fcis-28908	87	23	the	the	DET
fcis-28908	87	24	adam	adam	PROPN
fcis-28908	87	25	optimizer	optimizer	NOUN
fcis-28908	87	26	achieves	achieve	VERB
fcis-28908	87	27	a	a	DET
fcis-28908	87	28	prediction	prediction	NOUN
fcis-28908	87	29	accuracy	accuracy	NOUN
fcis-28908	87	30	of	of	ADP
fcis-28908	87	31	approximately	approximately	ADV
fcis-28908	87	32	90	90	NUM
fcis-28908	87	33	%	%	NOUN
fcis-28908	87	34	when	when	SCONJ
fcis-28908	87	35	the	the	DET
fcis-28908	87	36	learning	learning	NOUN
fcis-28908	87	37	rate	rate	NOUN
fcis-28908	87	38	is	be	AUX
fcis-28908	87	39	set	set	VERB
fcis-28908	87	40	between	between	ADP
fcis-28908	87	41	0.01	0.01	NUM
fcis-28908	87	42	and	and	CCONJ
fcis-28908	87	43	0.001	0.001	NUM
fcis-28908	87	44	,	,	PUNCT
fcis-28908	87	45	indicating	indicate	VERB
fcis-28908	87	46	good	good	ADJ
fcis-28908	87	47	performance	performance	NOUN
fcis-28908	87	48	.	.	PUNCT
fcis-28908	88	1	however	however	ADV
fcis-28908	88	2	,	,	PUNCT
fcis-28908	88	3	when	when	SCONJ
fcis-28908	88	4	the	the	DET
fcis-28908	88	5	learning	learning	NOUN
fcis-28908	88	6	rate	rate	NOUN
fcis-28908	88	7	exceeds	exceed	VERB
fcis-28908	88	8	0.01	0.01	NUM
fcis-28908	88	9	,	,	PUNCT
fcis-28908	88	10	the	the	DET
fcis-28908	88	11	prediction	prediction	NOUN
fcis-28908	88	12	accuracy	accuracy	NOUN
fcis-28908	88	13	begins	begin	VERB
fcis-28908	88	14	to	to	PART
fcis-28908	88	15	gradually	gradually	ADV
fcis-28908	88	16	decline	decline	VERB
fcis-28908	88	17	.	.	PUNCT
fcis-28908	89	1	figure	figure	VERB
fcis-28908	89	2	3	3	NUM
fcis-28908	89	3	.	.	PUNCT
fcis-28908	89	4	prediction	prediction	NOUN
fcis-28908	89	5	accuracy	accuracy	NOUN
fcis-28908	89	6	of	of	ADP
fcis-28908	89	7	the	the	DET
fcis-28908	89	8	adam	adam	PROPN
fcis-28908	89	9	optimizer	optimizer	NOUN
fcis-28908	89	10	(	(	PUNCT
fcis-28908	89	11	a	a	NOUN
fcis-28908	89	12	)	)	PUNCT
fcis-28908	89	13	category	category	NOUN
fcis-28908	89	14	1	1	NUM
fcis-28908	89	15	(	(	PUNCT
fcis-28908	89	16	b	b	NOUN
fcis-28908	89	17	)	)	PUNCT
fcis-28908	89	18	category	category	NOUN
fcis-28908	89	19	2	2	NUM
fcis-28908	89	20	figure	figure	NOUN
fcis-28908	89	21	4	4	NUM
fcis-28908	89	22	.	.	PUNCT
fcis-28908	90	1	illustration	illustration	NOUN
fcis-28908	90	2	of	of	ADP
fcis-28908	90	3	insect	insect	NOUN
fcis-28908	90	4	recognition	recognition	NOUN
fcis-28908	90	5	results	result	NOUN
fcis-28908	90	6	figure	figure	VERB
fcis-28908	90	7	5	5	NUM
fcis-28908	90	8	.	.	PUNCT
fcis-28908	90	9	training	training	NOUN
fcis-28908	90	10	results	result	NOUN
fcis-28908	90	11	using	use	VERB
fcis-28908	90	12	gd	gd	ADP
fcis-28908	90	13	optimizer	optimizer	NOUN
fcis-28908	90	14	the	the	DET
fcis-28908	90	15	standard	standard	ADJ
fcis-28908	90	16	gradient	gradient	ADJ
fcis-28908	90	17	descent	descent	NOUN
fcis-28908	90	18	(	(	PUNCT
fcis-28908	90	19	gd	gd	NOUN
fcis-28908	90	20	)	)	PUNCT
fcis-28908	90	21	method	method	NOUN
fcis-28908	90	22	is	be	AUX
fcis-28908	90	23	a	a	DET
fcis-28908	90	24	commonly	commonly	ADV
fcis-28908	90	25	used	use	VERB
fcis-28908	90	26	approach	approach	NOUN
fcis-28908	90	27	in	in	ADP
fcis-28908	90	28	machine	machine	NOUN
fcis-28908	90	29	learning	learn	VERB
fcis-28908	90	30	for	for	ADP
fcis-28908	90	31	solving	solve	VERB
fcis-28908	90	32	model	model	NOUN
fcis-28908	90	33	parameters	parameter	NOUN
fcis-28908	90	34	.	.	PUNCT
fcis-28908	91	1	this	this	DET
fcis-28908	91	2	method	method	NOUN
fcis-28908	91	3	iteratively	iteratively	ADV
fcis-28908	91	4	updates	update	VERB
fcis-28908	91	5	the	the	DET
fcis-28908	91	6	parameters	parameter	NOUN
fcis-28908	91	7	in	in	ADP
fcis-28908	91	8	the	the	DET
fcis-28908	91	9	direction	direction	NOUN
fcis-28908	91	10	of	of	ADP
fcis-28908	91	11	gradient	gradient	ADJ
fcis-28908	91	12	descent	descent	NOUN
fcis-28908	91	13	to	to	PART
fcis-28908	91	14	gradually	gradually	ADV
fcis-28908	91	15	approach	approach	VERB
fcis-28908	91	16	the	the	DET
fcis-28908	91	17	minimum	minimum	ADJ
fcis-28908	91	18	value	value	NOUN
fcis-28908	91	19	of	of	ADP
fcis-28908	91	20	the	the	DET
fcis-28908	91	21	loss	loss	NOUN
fcis-28908	91	22	function	function	NOUN
fcis-28908	91	23	.	.	PUNCT
fcis-28908	92	1	131	131	NUM
fcis-28908	92	2	(	(	PUNCT
fcis-28908	92	3	a	a	NOUN
fcis-28908	92	4	)	)	PUNCT
fcis-28908	92	5	category	category	NOUN
fcis-28908	92	6	3	3	NUM
fcis-28908	92	7	(	(	PUNCT
fcis-28908	92	8	b	b	NOUN
fcis-28908	92	9	)	)	PUNCT
fcis-28908	92	10	category	category	NOUN
fcis-28908	92	11	4	4	NUM
fcis-28908	92	12	figure	figure	NOUN
fcis-28908	92	13	6	6	NUM
fcis-28908	92	14	.	.	PUNCT
fcis-28908	92	15	schematic	schematic	ADJ
fcis-28908	92	16	diagram	diagram	NOUN
fcis-28908	92	17	of	of	ADP
fcis-28908	92	18	insect	insect	NOUN
fcis-28908	92	19	recognition	recognition	NOUN
fcis-28908	92	20	effect	effect	NOUN
fcis-28908	92	21	(	(	PUNCT
fcis-28908	92	22	a	a	X
fcis-28908	92	23	)	)	PUNCT
fcis-28908	92	24	category	category	NOUN
fcis-28908	92	25	5	5	NUM
fcis-28908	92	26	(	(	PUNCT
fcis-28908	92	27	b	b	NOUN
fcis-28908	92	28	)	)	PUNCT
fcis-28908	92	29	category	category	NOUN
fcis-28908	92	30	6	6	NUM
fcis-28908	92	31	figure	figure	NOUN
fcis-28908	92	32	7	7	NUM
fcis-28908	92	33	.	.	PUNCT
fcis-28908	92	34	illustration	illustration	NOUN
fcis-28908	92	35	of	of	ADP
fcis-28908	92	36	insect	insect	NOUN
fcis-28908	92	37	recognition	recognition	NOUN
fcis-28908	92	38	results	result	NOUN
fcis-28908	92	39	figure	figure	VERB
fcis-28908	92	40	5	5	NUM
fcis-28908	92	41	displays	display	VERB
fcis-28908	92	42	the	the	DET
fcis-28908	92	43	training	training	NOUN
fcis-28908	92	44	results	result	NOUN
fcis-28908	92	45	of	of	ADP
fcis-28908	92	46	the	the	DET
fcis-28908	92	47	gd	gd	PROPN
fcis-28908	92	48	optimizer	optimizer	NOUN
fcis-28908	92	49	,	,	PUNCT
fcis-28908	92	50	while	while	SCONJ
fcis-28908	92	51	figure	figure	VERB
fcis-28908	92	52	6	6	NUM
fcis-28908	92	53	showcases	showcase	VERB
fcis-28908	92	54	its	its	PRON
fcis-28908	92	55	performance	performance	NOUN
fcis-28908	92	56	in	in	ADP
fcis-28908	92	57	the	the	DET
fcis-28908	92	58	insect	insect	NOUN
fcis-28908	92	59	recognition	recognition	NOUN
fcis-28908	92	60	task	task	NOUN
fcis-28908	92	61	.	.	PUNCT
fcis-28908	93	1	however	however	ADV
fcis-28908	93	2	,	,	PUNCT
fcis-28908	93	3	in	in	ADP
fcis-28908	93	4	this	this	DET
fcis-28908	93	5	experiment	experiment	NOUN
fcis-28908	93	6	,	,	PUNCT
fcis-28908	93	7	the	the	DET
fcis-28908	93	8	gd	gd	NOUN
fcis-28908	93	9	optimizer	optimizer	NOUN
fcis-28908	93	10	did	do	AUX
fcis-28908	93	11	not	not	PART
fcis-28908	93	12	perform	perform	VERB
fcis-28908	93	13	well	well	ADV
fcis-28908	93	14	.	.	PUNCT
fcis-28908	94	1	when	when	SCONJ
fcis-28908	94	2	the	the	DET
fcis-28908	94	3	learning	learning	NOUN
fcis-28908	94	4	rate	rate	NOUN
fcis-28908	94	5	was	be	AUX
fcis-28908	94	6	set	set	VERB
fcis-28908	94	7	to	to	ADP
fcis-28908	94	8	0.05	0.05	NUM
fcis-28908	94	9	,	,	PUNCT
fcis-28908	94	10	the	the	DET
fcis-28908	94	11	prediction	prediction	NOUN
fcis-28908	94	12	accuracy	accuracy	NOUN
fcis-28908	94	13	was	be	AUX
fcis-28908	94	14	very	very	ADV
fcis-28908	94	15	low	low	ADJ
fcis-28908	94	16	,	,	PUNCT
fcis-28908	94	17	indicating	indicate	VERB
fcis-28908	94	18	that	that	SCONJ
fcis-28908	94	19	the	the	DET
fcis-28908	94	20	model	model	NOUN
fcis-28908	94	21	hardly	hardly	ADV
fcis-28908	94	22	learned	learn	VERB
fcis-28908	94	23	effectively	effectively	ADV
fcis-28908	94	24	.	.	PUNCT
fcis-28908	95	1	although	although	SCONJ
fcis-28908	95	2	attempts	attempt	NOUN
fcis-28908	95	3	were	be	AUX
fcis-28908	95	4	made	make	VERB
fcis-28908	95	5	to	to	PART
fcis-28908	95	6	significantly	significantly	ADV
fcis-28908	95	7	adjust	adjust	VERB
fcis-28908	95	8	the	the	DET
fcis-28908	95	9	learning	learning	NOUN
fcis-28908	95	10	rate	rate	NOUN
fcis-28908	95	11	,	,	PUNCT
fcis-28908	95	12	the	the	DET
fcis-28908	95	13	situation	situation	NOUN
fcis-28908	95	14	improved	improve	VERB
fcis-28908	95	15	only	only	ADV
fcis-28908	95	16	slightly	slightly	ADV
fcis-28908	95	17	,	,	PUNCT
fcis-28908	95	18	and	and	CCONJ
fcis-28908	95	19	the	the	DET
fcis-28908	95	20	prediction	prediction	NOUN
fcis-28908	95	21	accuracy	accuracy	NOUN
fcis-28908	95	22	still	still	ADV
fcis-28908	95	23	failed	fail	VERB
fcis-28908	95	24	to	to	PART
fcis-28908	95	25	reach	reach	VERB
fcis-28908	95	26	a	a	DET
fcis-28908	95	27	satisfactory	satisfactory	ADJ
fcis-28908	95	28	level	level	NOUN
fcis-28908	95	29	.	.	PUNCT
fcis-28908	96	1	when	when	SCONJ
fcis-28908	96	2	the	the	DET
fcis-28908	96	3	learning	learning	NOUN
fcis-28908	96	4	rate	rate	NOUN
fcis-28908	96	5	was	be	AUX
fcis-28908	96	6	set	set	VERB
fcis-28908	96	7	too	too	ADV
fcis-28908	96	8	low	low	ADJ
fcis-28908	96	9	,	,	PUNCT
fcis-28908	96	10	there	there	PRON
fcis-28908	96	11	was	be	VERB
fcis-28908	96	12	no	no	DET
fcis-28908	96	13	significant	significant	ADJ
fcis-28908	96	14	improvement	improvement	NOUN
fcis-28908	96	15	in	in	ADP
fcis-28908	96	16	model	model	NOUN
fcis-28908	96	17	performance	performance	NOUN
fcis-28908	96	18	.	.	PUNCT
fcis-28908	97	1	in	in	ADP
fcis-28908	97	2	summary	summary	NOUN
fcis-28908	97	3	,	,	PUNCT
fcis-28908	97	4	under	under	ADP
fcis-28908	97	5	the	the	DET
fcis-28908	97	6	conditions	condition	NOUN
fcis-28908	97	7	of	of	ADP
fcis-28908	97	8	this	this	DET
fcis-28908	97	9	experiment	experiment	NOUN
fcis-28908	97	10	,	,	PUNCT
fcis-28908	97	11	the	the	DET
fcis-28908	97	12	gd	gd	NOUN
fcis-28908	97	13	optimizer	optimizer	NOUN
fcis-28908	97	14	is	be	AUX
fcis-28908	97	15	not	not	PART
fcis-28908	97	16	an	an	DET
fcis-28908	97	17	ideal	ideal	ADJ
fcis-28908	97	18	choice	choice	NOUN
fcis-28908	97	19	.	.	PUNCT
fcis-28908	98	1	adagrad	adagrad	PROPN
fcis-28908	98	2	is	be	AUX
fcis-28908	98	3	an	an	DET
fcis-28908	98	4	optimization	optimization	NOUN
fcis-28908	98	5	algorithm	algorithm	NOUN
fcis-28908	98	6	built	build	VERB
fcis-28908	98	7	upon	upon	SCONJ
fcis-28908	98	8	stochastic	stochastic	ADJ
fcis-28908	98	9	gradient	gradient	ADJ
fcis-28908	98	10	descent	descent	NOUN
fcis-28908	98	11	(	(	PUNCT
fcis-28908	98	12	sgd	sgd	PROPN
fcis-28908	98	13	)	)	PUNCT
fcis-28908	98	14	,	,	PUNCT
fcis-28908	98	15	with	with	ADP
fcis-28908	98	16	its	its	PRON
fcis-28908	98	17	core	core	NOUN
fcis-28908	98	18	idea	idea	NOUN
fcis-28908	98	19	being	be	AUX
fcis-28908	98	20	to	to	PART
fcis-28908	98	21	adjust	adjust	VERB
fcis-28908	98	22	model	model	NOUN
fcis-28908	98	23	parameters	parameter	NOUN
fcis-28908	98	24	using	use	VERB
fcis-28908	98	25	different	different	ADJ
fcis-28908	98	26	learning	learning	NOUN
fcis-28908	98	27	rates	rate	NOUN
fcis-28908	98	28	based	base	VERB
fcis-28908	98	29	on	on	ADP
fcis-28908	98	30	the	the	DET
fcis-28908	98	31	frequency	frequency	NOUN
fcis-28908	98	32	of	of	ADP
fcis-28908	98	33	data	datum	NOUN
fcis-28908	98	34	occurrence	occurrence	NOUN
fcis-28908	98	35	.	.	PUNCT
fcis-28908	99	1	specifically	specifically	ADV
fcis-28908	99	2	,	,	PUNCT
fcis-28908	99	3	for	for	ADP
fcis-28908	99	4	frequently	frequently	ADV
fcis-28908	99	5	occurring	occur	VERB
fcis-28908	99	6	datasets	dataset	NOUN
fcis-28908	99	7	,	,	PUNCT
fcis-28908	99	8	a	a	DET
fcis-28908	99	9	smaller	small	ADJ
fcis-28908	99	10	learning	learning	NOUN
fcis-28908	99	11	rate	rate	NOUN
fcis-28908	99	12	is	be	AUX
fcis-28908	99	13	used	use	VERB
fcis-28908	99	14	for	for	ADP
fcis-28908	99	15	finetuning	finetuning	NOUN
fcis-28908	99	16	;	;	PUNCT
fcis-28908	99	17	whereas	whereas	SCONJ
fcis-28908	99	18	for	for	ADP
fcis-28908	99	19	rare	rare	ADJ
fcis-28908	99	20	datasets	dataset	NOUN
fcis-28908	99	21	,	,	PUNCT
fcis-28908	99	22	a	a	DET
fcis-28908	99	23	larger	large	ADJ
fcis-28908	99	24	learning	learning	NOUN
fcis-28908	99	25	rate	rate	NOUN
fcis-28908	99	26	is	be	AUX
fcis-28908	99	27	employed	employ	VERB
fcis-28908	99	28	to	to	PART
fcis-28908	99	29	facilitate	facilitate	VERB
fcis-28908	99	30	faster	fast	ADJ
fcis-28908	99	31	learning	learning	NOUN
fcis-28908	99	32	.	.	PUNCT
fcis-28908	100	1	this	this	DET
fcis-28908	100	2	characteristic	characteristic	NOUN
fcis-28908	100	3	makes	make	VERB
fcis-28908	100	4	the	the	DET
fcis-28908	100	5	adagrad	adagrad	ADJ
fcis-28908	100	6	algorithm	algorithm	NOUN
fcis-28908	100	7	particularly	particularly	ADV
fcis-28908	100	8	excellent	excellent	ADJ
fcis-28908	100	9	in	in	ADP
fcis-28908	100	10	handling	handle	VERB
fcis-28908	100	11	sparse	sparse	ADJ
fcis-28908	100	12	data	datum	NOUN
fcis-28908	100	13	scenarios	scenario	NOUN
fcis-28908	100	14	.	.	PUNCT
fcis-28908	101	1	in	in	ADP
fcis-28908	101	2	this	this	DET
fcis-28908	101	3	experiment	experiment	NOUN
fcis-28908	101	4	,	,	PUNCT
fcis-28908	101	5	the	the	DET
fcis-28908	101	6	performance	performance	NOUN
fcis-28908	101	7	results	result	NOUN
fcis-28908	101	8	of	of	ADP
fcis-28908	101	9	the	the	DET
fcis-28908	101	10	adagrad	adagrad	ADJ
fcis-28908	101	11	optimizer	optimizer	NOUN
fcis-28908	101	12	are	be	AUX
fcis-28908	101	13	listed	list	VERB
fcis-28908	101	14	in	in	ADP
fcis-28908	101	15	table	table	NOUN
fcis-28908	101	16	1	1	NUM
fcis-28908	101	17	.	.	PUNCT
fcis-28908	102	1	additionally	additionally	ADV
fcis-28908	102	2	,	,	PUNCT
fcis-28908	102	3	the	the	DET
fcis-28908	102	4	effectiveness	effectiveness	NOUN
fcis-28908	102	5	of	of	ADP
fcis-28908	102	6	insect	insect	NOUN
fcis-28908	102	7	classification	classification	NOUN
fcis-28908	102	8	is	be	AUX
fcis-28908	102	9	illustrated	illustrate	VERB
fcis-28908	102	10	in	in	ADP
fcis-28908	102	11	figure	figure	NOUN
fcis-28908	102	12	7	7	NUM
fcis-28908	102	13	.	.	PUNCT
fcis-28908	102	14	when	when	SCONJ
fcis-28908	102	15	the	the	DET
fcis-28908	102	16	initial	initial	ADJ
fcis-28908	102	17	learning	learning	NOUN
fcis-28908	102	18	rate	rate	NOUN
fcis-28908	102	19	of	of	ADP
fcis-28908	102	20	the	the	DET
fcis-28908	102	21	adagrad	adagrad	ADJ
fcis-28908	102	22	optimizer	optimizer	NOUN
fcis-28908	102	23	is	be	AUX
fcis-28908	102	24	set	set	VERB
fcis-28908	102	25	to	to	ADP
fcis-28908	102	26	0.005	0.005	NUM
fcis-28908	102	27	,	,	PUNCT
fcis-28908	102	28	further	far	ADV
fcis-28908	102	29	reducing	reduce	VERB
fcis-28908	102	30	the	the	DET
fcis-28908	102	31	learning	learning	NOUN
fcis-28908	102	32	rate	rate	NOUN
fcis-28908	102	33	leads	lead	VERB
fcis-28908	102	34	to	to	ADP
fcis-28908	102	35	a	a	DET
fcis-28908	102	36	significant	significant	ADJ
fcis-28908	102	37	decrease	decrease	NOUN
fcis-28908	102	38	in	in	ADP
fcis-28908	102	39	prediction	prediction	NOUN
fcis-28908	102	40	accuracy	accuracy	NOUN
fcis-28908	102	41	.	.	PUNCT
fcis-28908	103	1	conversely	conversely	ADV
fcis-28908	103	2	,	,	PUNCT
fcis-28908	103	3	as	as	SCONJ
fcis-28908	103	4	the	the	DET
fcis-28908	103	5	learning	learning	NOUN
fcis-28908	103	6	rate	rate	NOUN
fcis-28908	103	7	is	be	AUX
fcis-28908	103	8	gradually	gradually	ADV
fcis-28908	103	9	increased	increase	VERB
fcis-28908	103	10	,	,	PUNCT
fcis-28908	103	11	the	the	DET
fcis-28908	103	12	prediction	prediction	NOUN
fcis-28908	103	13	accuracy	accuracy	NOUN
fcis-28908	103	14	shows	show	VERB
fcis-28908	103	15	an	an	DET
fcis-28908	103	16	improving	improve	VERB
fcis-28908	103	17	trend	trend	NOUN
fcis-28908	103	18	.	.	PUNCT
fcis-28908	104	1	particularly	particularly	ADV
fcis-28908	104	2	when	when	SCONJ
fcis-28908	104	3	the	the	DET
fcis-28908	104	4	learning	learning	NOUN
fcis-28908	104	5	rate	rate	NOUN
fcis-28908	104	6	is	be	AUX
fcis-28908	104	7	adjusted	adjust	VERB
fcis-28908	104	8	to	to	ADP
fcis-28908	104	9	0.01	0.01	NUM
fcis-28908	104	10	,	,	PUNCT
fcis-28908	104	11	the	the	DET
fcis-28908	104	12	test	test	NOUN
fcis-28908	104	13	accuracy	accuracy	NOUN
fcis-28908	104	14	approaches	approach	VERB
fcis-28908	104	15	90	90	NUM
fcis-28908	104	16	%	%	NOUN
fcis-28908	104	17	.	.	PUNCT
fcis-28908	105	1	nevertheless	nevertheless	ADV
fcis-28908	105	2	,	,	PUNCT
fcis-28908	105	3	the	the	DET
fcis-28908	105	4	overall	overall	ADJ
fcis-28908	105	5	accuracy	accuracy	NOUN
fcis-28908	105	6	remains	remain	VERB
fcis-28908	105	7	stable	stable	ADJ
fcis-28908	105	8	at	at	ADP
fcis-28908	105	9	around	around	ADP
fcis-28908	105	10	80	80	NUM
fcis-28908	105	11	%	%	NOUN
fcis-28908	105	12	.	.	PUNCT
fcis-28908	106	1	table	table	NOUN
fcis-28908	106	2	1	1	NUM
fcis-28908	106	3	.	.	PUNCT
fcis-28908	106	4	presents	present	VERB
fcis-28908	106	5	the	the	DET
fcis-28908	106	6	training	training	NOUN
fcis-28908	106	7	results	result	NOUN
fcis-28908	106	8	of	of	ADP
fcis-28908	106	9	the	the	DET
fcis-28908	106	10	adagrad	adagrad	ADJ
fcis-28908	106	11	optimizer	optimizer	NOUN
fcis-28908	106	12	.	.	PUNCT
fcis-28908	107	1	learning	learn	VERB
fcis-28908	107	2	rate	rate	NOUN
fcis-28908	107	3	training	training	NOUN
fcis-28908	107	4	accuracy	accuracy	NOUN
fcis-28908	107	5	test	test	NOUN
fcis-28908	107	6	accuracy	accuracy	NOUN
fcis-28908	107	7	cost	cost	VERB
fcis-28908	107	8	0.05	0.05	NUM
fcis-28908	107	9	1	1	NUM
fcis-28908	107	10	0.8125	0.8125	NUM
fcis-28908	107	11	0	0	NUM
fcis-28908	108	1	0.01	0.01	NUM
fcis-28908	108	2	1	1	NUM
fcis-28908	108	3	0.8950	0.8950	NUM
fcis-28908	108	4	0	0	NUM
fcis-28908	108	5	0.009	0.009	NUM
fcis-28908	108	6	1	1	NUM
fcis-28908	108	7	0.8425	0.8425	NUM
fcis-28908	108	8	0	0	NUM
fcis-28908	108	9	0.005	0.005	NUM
fcis-28908	108	10	0.92308	0.92308	NUM
fcis-28908	108	11	0.7850	0.7850	NUM
fcis-28908	108	12	19063848	19063848	NUM
fcis-28908	108	13	0.001	0.001	NUM
fcis-28908	108	14	0.92308	0.92308	NUM
fcis-28908	108	15	0.6675	0.6675	NUM
fcis-28908	108	16	59120256	59120256	NUM
fcis-28908	108	17	5	5	NUM
fcis-28908	108	18	.	.	PUNCT
fcis-28908	108	19	conclusion	conclusion	NOUN
fcis-28908	108	20	in	in	ADP
fcis-28908	108	21	this	this	DET
fcis-28908	108	22	paper	paper	NOUN
fcis-28908	108	23	,	,	PUNCT
fcis-28908	108	24	a	a	DET
fcis-28908	108	25	convolutional	convolutional	ADJ
fcis-28908	108	26	neural	neural	ADJ
fcis-28908	108	27	network	network	NOUN
fcis-28908	108	28	model	model	NOUN
fcis-28908	108	29	was	be	AUX
fcis-28908	108	30	constructed	construct	VERB
fcis-28908	108	31	based	base	VERB
fcis-28908	108	32	on	on	ADP
fcis-28908	108	33	the	the	DET
fcis-28908	108	34	tensorflow	tensorflow	NOUN
fcis-28908	108	35	framework	framework	NOUN
fcis-28908	108	36	for	for	ADP
fcis-28908	108	37	insect	insect	NOUN
fcis-28908	108	38	classification	classification	NOUN
fcis-28908	108	39	tasks	task	NOUN
fcis-28908	108	40	.	.	PUNCT
fcis-28908	109	1	additionally	additionally	ADV
fcis-28908	109	2	,	,	PUNCT
fcis-28908	109	3	the	the	DET
fcis-28908	109	4	impact	impact	NOUN
fcis-28908	109	5	of	of	ADP
fcis-28908	109	6	three	three	NUM
fcis-28908	109	7	different	different	ADJ
fcis-28908	109	8	optimizers	optimizer	NOUN
fcis-28908	109	9	and	and	CCONJ
fcis-28908	109	10	their	their	PRON
fcis-28908	109	11	learning	learning	NOUN
fcis-28908	109	12	rates	rate	NOUN
fcis-28908	109	13	on	on	ADP
fcis-28908	109	14	the	the	DET
fcis-28908	109	15	model	model	NOUN
fcis-28908	109	16	's	's	PART
fcis-28908	109	17	final	final	ADJ
fcis-28908	109	18	test	test	NOUN
fcis-28908	109	19	performance	performance	NOUN
fcis-28908	109	20	was	be	AUX
fcis-28908	109	21	explored	explore	VERB
fcis-28908	109	22	.	.	PUNCT
fcis-28908	110	1	three	three	NUM
fcis-28908	110	2	mainstream	mainstream	ADJ
fcis-28908	110	3	optimizers	optimizer	NOUN
fcis-28908	110	4	—	—	PUNCT
fcis-28908	110	5	adam	adam	PROPN
fcis-28908	110	6	optimizer	optimizer	NOUN
fcis-28908	110	7	,	,	PUNCT
fcis-28908	110	8	gradient	gradient	ADJ
fcis-28908	110	9	descent	descent	NOUN
fcis-28908	110	10	(	(	PUNCT
fcis-28908	110	11	gd	gd	NOUN
fcis-28908	110	12	)	)	PUNCT
fcis-28908	110	13	optimizer	optimizer	NOUN
fcis-28908	110	14	,	,	PUNCT
fcis-28908	110	15	and	and	CCONJ
fcis-28908	110	16	adagrad	adagrad	ADJ
fcis-28908	110	17	optimizer	optimizer	NOUN
fcis-28908	110	18	—	—	PUNCT
fcis-28908	110	19	were	be	AUX
fcis-28908	110	20	selected	select	VERB
fcis-28908	110	21	for	for	ADP
fcis-28908	110	22	comparative	comparative	ADJ
fcis-28908	110	23	analysis	analysis	NOUN
fcis-28908	110	24	,	,	PUNCT
fcis-28908	110	25	with	with	ADP
fcis-28908	110	26	different	different	ADJ
fcis-28908	110	27	learning	learning	NOUN
fcis-28908	110	28	rates	rate	NOUN
fcis-28908	110	29	set	set	VERB
fcis-28908	110	30	for	for	ADP
fcis-28908	110	31	each	each	DET
fcis-28908	110	32	optimizer	optimizer	NOUN
fcis-28908	110	33	.	.	PUNCT
fcis-28908	111	1	experimental	experimental	ADJ
fcis-28908	111	2	validation	validation	NOUN
fcis-28908	111	3	revealed	reveal	VERB
fcis-28908	111	4	that	that	SCONJ
fcis-28908	111	5	the	the	DET
fcis-28908	111	6	adam	adam	PROPN
fcis-28908	111	7	optimizer	optimizer	NOUN
fcis-28908	111	8	exhibited	exhibit	VERB
fcis-28908	111	9	the	the	DET
fcis-28908	111	10	best	good	ADJ
fcis-28908	111	11	performance	performance	NOUN
fcis-28908	111	12	when	when	SCONJ
fcis-28908	111	13	the	the	DET
fcis-28908	111	14	learning	learning	NOUN
fcis-28908	111	15	rate	rate	NOUN
fcis-28908	111	16	was	be	AUX
fcis-28908	111	17	set	set	VERB
fcis-28908	111	18	to	to	ADP
fcis-28908	111	19	0.009	0.009	NUM
fcis-28908	111	20	,	,	PUNCT
fcis-28908	111	21	achieving	achieve	VERB
fcis-28908	111	22	a	a	DET
fcis-28908	111	23	prediction	prediction	NOUN
fcis-28908	111	24	accuracy	accuracy	NOUN
fcis-28908	111	25	of	of	ADP
fcis-28908	111	26	up	up	ADP
fcis-28908	111	27	to	to	PART
fcis-28908	111	28	92	92	NUM
fcis-28908	111	29	%	%	NOUN
fcis-28908	111	30	,	,	PUNCT
fcis-28908	111	31	which	which	PRON
fcis-28908	111	32	was	be	AUX
fcis-28908	111	33	significantly	significantly	ADV
fcis-28908	111	34	better	well	ADJ
fcis-28908	111	35	than	than	ADP
fcis-28908	111	36	other	other	ADJ
fcis-28908	111	37	configurations	configuration	NOUN
fcis-28908	111	38	.	.	PUNCT
fcis-28908	112	1	however	however	ADV
fcis-28908	112	2	,	,	PUNCT
fcis-28908	112	3	due	due	ADP
fcis-28908	112	4	to	to	ADP
fcis-28908	112	5	the	the	DET
fcis-28908	112	6	relatively	relatively	ADV
fcis-28908	112	7	small	small	ADJ
fcis-28908	112	8	size	size	NOUN
fcis-28908	112	9	of	of	ADP
fcis-28908	112	10	the	the	DET
fcis-28908	112	11	dataset	dataset	NOUN
fcis-28908	112	12	used	use	VERB
fcis-28908	112	13	in	in	ADP
fcis-28908	112	14	this	this	DET
fcis-28908	112	15	experiment	experiment	NOUN
fcis-28908	112	16	and	and	CCONJ
fcis-28908	112	17	the	the	DET
fcis-28908	112	18	shallow	shallow	ADJ
fcis-28908	112	19	architecture	architecture	NOUN
fcis-28908	112	20	of	of	ADP
fcis-28908	112	21	the	the	DET
fcis-28908	112	22	chosen	choose	VERB
fcis-28908	112	23	convolutional	convolutional	ADJ
fcis-28908	112	24	neural	neural	ADJ
fcis-28908	112	25	network	network	NOUN
fcis-28908	112	26	,	,	PUNCT
fcis-28908	112	27	there	there	PRON
fcis-28908	112	28	is	be	VERB
fcis-28908	112	29	still	still	ADV
fcis-28908	112	30	considerable	considerable	ADJ
fcis-28908	112	31	room	room	NOUN
fcis-28908	112	32	for	for	ADP
fcis-28908	112	33	improvement	improvement	NOUN
fcis-28908	112	34	in	in	ADP
fcis-28908	112	35	prediction	prediction	NOUN
fcis-28908	112	36	accuracy	accuracy	NOUN
fcis-28908	112	37	.	.	PUNCT
fcis-28908	113	1	this	this	PRON
fcis-28908	113	2	suggests	suggest	VERB
fcis-28908	113	3	that	that	SCONJ
fcis-28908	113	4	in	in	ADP
fcis-28908	113	5	future	future	ADJ
fcis-28908	113	6	research	research	NOUN
fcis-28908	113	7	,	,	PUNCT
fcis-28908	113	8	the	the	DET
fcis-28908	113	9	accuracy	accuracy	NOUN
fcis-28908	113	10	of	of	ADP
fcis-28908	113	11	insect	insect	NOUN
fcis-28908	113	12	classification	classification	NOUN
fcis-28908	113	13	can	can	AUX
fcis-28908	113	14	be	be	AUX
fcis-28908	113	15	further	far	ADV
fcis-28908	113	16	enhanced	enhance	VERB
fcis-28908	113	17	by	by	ADP
fcis-28908	113	18	expanding	expand	VERB
fcis-28908	113	19	the	the	DET
fcis-28908	113	20	dataset	dataset	NOUN
fcis-28908	113	21	size	size	NOUN
fcis-28908	113	22	,	,	PUNCT
fcis-28908	113	23	deepening	deepen	VERB
fcis-28908	113	24	the	the	DET
fcis-28908	113	25	network	network	NOUN
fcis-28908	113	26	structure	structure	NOUN
fcis-28908	113	27	,	,	PUNCT
fcis-28908	113	28	or	or	CCONJ
fcis-28908	113	29	further	far	ADV
fcis-28908	113	30	optimizing	optimize	VERB
fcis-28908	113	31	model	model	NOUN
fcis-28908	113	32	parameters	parameter	NOUN
fcis-28908	113	33	.	.	PUNCT
fcis-28908	114	1	acknowledgments	acknowledgment	NOUN
fcis-28908	114	2	funded	fund	VERB
fcis-28908	114	3	project	project	NOUN
fcis-28908	114	4	:	:	PUNCT
fcis-28908	114	5	2024	2024	NUM
fcis-28908	114	6	annual	annual	ADJ
fcis-28908	114	7	school	school	NOUN
fcis-28908	114	8	-	-	PUNCT
fcis-28908	114	9	level	level	NOUN
fcis-28908	114	10	project	project	NOUN
fcis-28908	114	11	of	of	ADP
fcis-28908	114	12	zhejiang	zhejiang	PROPN
fcis-28908	114	13	dongfang	dongfang	PROPN
fcis-28908	114	14	polytechnic	polytechnic	PROPN
fcis-28908	114	15	(	(	PUNCT
fcis-28908	114	16	project	project	NOUN
fcis-28908	114	17	number	number	NOUN
fcis-28908	114	18	:	:	PUNCT
fcis-28908	114	19	df2024	df2024	PROPN
fcis-28908	114	20	szz01	szz01	PROPN
fcis-28908	114	21	)	)	PUNCT
fcis-28908	114	22	.	.	PUNCT
fcis-28908	115	1	references	reference	NOUN
fcis-28908	115	2	[	[	X
fcis-28908	115	3	1	1	NUM
fcis-28908	115	4	]	]	X
fcis-28908	115	5	deng	deng	PROPN
fcis-28908	115	6	c	c	PROPN
fcis-28908	115	7	,	,	PUNCT
fcis-28908	115	8	han	han	PROPN
fcis-28908	115	9	y	y	PROPN
fcis-28908	115	10	,	,	PUNCT
fcis-28908	115	11	zhao	zhao	PROPN
fcis-28908	115	12	b.	b.	PROPN
fcis-28908	116	1	high	high	ADJ
fcis-28908	116	2	-	-	PUNCT
fcis-28908	116	3	performance	performance	NOUN
fcis-28908	116	4	visual	visual	ADJ
fcis-28908	116	5	tracking	tracking	NOUN
fcis-28908	116	6	with	with	ADP
fcis-28908	116	7	extreme	extreme	ADJ
fcis-28908	116	8	learning	learning	NOUN
fcis-28908	116	9	machine	machine	NOUN
fcis-28908	116	10	framework[j	framework[j	PROPN
fcis-28908	116	11	]	]	PUNCT
fcis-28908	116	12	.	.	PUNCT
fcis-28908	117	1	ieee	ieee	NOUN
fcis-28908	117	2	transactions	transaction	NOUN
fcis-28908	117	3	on	on	ADP
fcis-28908	117	4	cybernetics	cybernetic	NOUN
fcis-28908	117	5	,	,	PUNCT
fcis-28908	117	6	2019	2019	NUM
fcis-28908	117	7	,	,	PUNCT
fcis-28908	117	8	26(3	26(3	NUM
fcis-28908	117	9	):	):	PUNCT
fcis-28908	117	10	1	1	NUM
fcis-28908	117	11	-	-	SYM
fcis-28908	117	12	12	12	NUM
fcis-28908	117	13	.	.	PUNCT
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fcis-28908	118	3	]	]	X
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fcis-28908	118	5	h.g	h.g	PROPN
fcis-28908	118	6	.	.	PROPN
fcis-28908	118	7	,	,	PUNCT
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fcis-28908	118	10	.	.	PROPN
fcis-28908	118	11	,	,	PUNCT
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fcis-28908	118	14	.	.	PROPN
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fcis-28908	118	16	on	on	ADP
fcis-28908	118	17	rbf	rbf	PROPN
fcis-28908	118	18	neural	neural	ADJ
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fcis-28908	118	20	structure	structure	NOUN
fcis-28908	118	21	design	design	NOUN
fcis-28908	118	22	based	base	VERB
fcis-28908	118	23	on	on	ADP
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fcis-28908	118	26	]	]	PUNCT
fcis-28908	118	27	.	.	PUNCT
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fcis-28908	119	2	automatica	automatica	PROPN
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fcis-28908	119	4	,	,	PUNCT
fcis-28908	119	5	2012	2012	NUM
fcis-28908	119	6	,	,	PUNCT
fcis-28908	119	7	38(7	38(7	NUM
fcis-28908	119	8	):	):	PUNCT
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fcis-28908	119	10	-	-	SYM
fcis-28908	119	11	1090	1090	NUM
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fcis-28908	120	1	[	[	X
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fcis-28908	120	23	on	on	ADP
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fcis-28908	120	27	.	.	PUNCT
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fcis-28908	121	6	,	,	PUNCT
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fcis-28908	121	8	):	):	PUNCT
fcis-28908	121	9	1083	1083	NUM
fcis-28908	121	10	-	-	SYM
fcis-28908	121	11	1090	1090	NUM
fcis-28908	121	12	.	.	PUNCT
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fcis-28908	122	3	]	]	X
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fcis-28908	122	6	,	,	PUNCT
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fcis-28908	122	9	,	,	PUNCT
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fcis-28908	122	19	bee	bee	NOUN
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fcis-28908	122	22	to	to	PART
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fcis-28908	122	32	force	force	NOUN
fcis-28908	122	33	in	in	ADP
fcis-28908	122	34	hot	hot	ADJ
fcis-28908	122	35	strip	strip	NOUN
fcis-28908	122	36	rolling	roll	VERB
fcis-28908	122	37	132	132	NUM
fcis-28908	122	38	process[j	process[j	NUM
fcis-28908	122	39	]	]	PUNCT
fcis-28908	122	40	.	.	PUNCT
fcis-28908	123	1	journal	journal	PROPN
fcis-28908	123	2	of	of	ADP
fcis-28908	123	3	chemical	chemical	PROPN
fcis-28908	123	4	and	and	CCONJ
fcis-28908	123	5	pharmaceutical	pharmaceutical	NOUN
fcis-28908	123	6	research	research	NOUN
fcis-28908	123	7	,	,	PUNCT
fcis-28908	123	8	2013	2013	NUM
fcis-28908	123	9	,	,	PUNCT
fcis-28908	123	10	5(9	5(9	NUM
fcis-28908	123	11	):	):	PUNCT
fcis-28908	123	12	563	563	NUM
fcis-28908	123	13	-	-	SYM
fcis-28908	123	14	570	570	NUM
fcis-28908	123	15	.	.	PUNCT
fcis-28908	124	1	[	[	X
fcis-28908	124	2	5	5	NUM
fcis-28908	124	3	]	]	X
fcis-28908	124	4	yan	yan	PROPN
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fcis-28908	124	6	,	,	PUNCT
fcis-28908	124	7	tang	tang	PROPN
fcis-28908	124	8	d	d	NOUN
fcis-28908	124	9	,	,	PUNCT
fcis-28908	124	10	lin	lin	PROPN
fcis-28908	124	11	y.	y.	PROPN
fcis-28908	124	12	a	a	DET
fcis-28908	124	13	data	data	NOUN
fcis-28908	124	14	-	-	PUNCT
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fcis-28908	124	16	soft	soft	ADJ
fcis-28908	124	17	sensor	sensor	NOUN
fcis-28908	124	18	modeling	modeling	NOUN
fcis-28908	124	19	method	method	NOUN
fcis-28908	124	20	based	base	VERB
fcis-28908	124	21	on	on	ADP
fcis-28908	124	22	deep	deep	ADJ
fcis-28908	124	23	learning	learning	NOUN
fcis-28908	124	24	and	and	CCONJ
fcis-28908	124	25	its	its	PRON
fcis-28908	124	26	application[j	application[j	PROPN
fcis-28908	124	27	]	]	PUNCT
fcis-28908	124	28	.	.	PUNCT
fcis-28908	125	1	ieee	ieee	NOUN
fcis-28908	125	2	transactions	transaction	NOUN
fcis-28908	125	3	on	on	ADP
fcis-28908	125	4	industrial	industrial	ADJ
fcis-28908	125	5	electronics	electronic	NOUN
fcis-28908	125	6	,	,	PUNCT
fcis-28908	125	7	2017	2017	NUM
fcis-28908	125	8	,	,	PUNCT
fcis-28908	125	9	64(5	64(5	PROPN
fcis-28908	125	10	):	):	PUNCT
fcis-28908	125	11	4237	4237	NUM
fcis-28908	125	12	-	-	SYM
fcis-28908	125	13	4245	4245	NUM
fcis-28908	125	14	.	.	PUNCT
fcis-28908	126	1	[	[	X
fcis-28908	126	2	6	6	NUM
fcis-28908	126	3	]	]	PUNCT
fcis-28908	126	4	yao	yao	PROPN
fcis-28908	126	5	l	l	NOUN
fcis-28908	126	6	,	,	PUNCT
fcis-28908	126	7	ge	ge	PROPN
fcis-28908	126	8	z.	z.	PROPN
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fcis-28908	126	10	learning	learning	NOUN
fcis-28908	126	11	of	of	ADP
fcis-28908	126	12	semi	semi	ADJ
fcis-28908	126	13	-	-	ADJ
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fcis-28908	126	15	process	process	NOUN
fcis-28908	126	16	data	datum	NOUN
fcis-28908	126	17	with	with	ADP
fcis-28908	126	18	hierarchical	hierarchical	ADJ
fcis-28908	126	19	extreme	extreme	ADJ
fcis-28908	126	20	learning	learning	NOUN
fcis-28908	126	21	machine	machine	NOUN
fcis-28908	126	22	and	and	CCONJ
fcis-28908	126	23	soft	soft	ADJ
fcis-28908	126	24	sensor	sensor	NOUN
fcis-28908	126	25	application[j	application[j	PROPN
fcis-28908	126	26	]	]	PUNCT
fcis-28908	126	27	.	.	PUNCT
fcis-28908	127	1	ieee	ieee	NOUN
fcis-28908	127	2	transactions	transaction	NOUN
fcis-28908	127	3	on	on	ADP
fcis-28908	127	4	industrial	industrial	ADJ
fcis-28908	127	5	electronics	electronic	NOUN
fcis-28908	127	6	,	,	PUNCT
fcis-28908	127	7	2017	2017	NUM
fcis-28908	127	8	,	,	PUNCT
fcis-28908	127	9	65(2	65(2	NUM
fcis-28908	127	10	):	):	PUNCT
fcis-28908	127	11	1490	1490	NUM
fcis-28908	127	12	-	-	SYM
fcis-28908	127	13	1498	1498	NUM
fcis-28908	127	14	.	.	PUNCT
fcis-28908	128	1	[	[	X
fcis-28908	128	2	7	7	X
fcis-28908	128	3	]	]	X
fcis-28908	128	4	vincent	vincent	NOUN
fcis-28908	128	5	p	p	NOUN
fcis-28908	128	6	,	,	PUNCT
fcis-28908	128	7	larochelle	larochelle	PROPN
fcis-28908	128	8	h	h	NOUN
fcis-28908	128	9	,	,	PUNCT
fcis-28908	128	10	lajoie	lajoie	PROPN
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fcis-28908	128	12	,	,	PUNCT
fcis-28908	128	13	et	et	PROPN
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fcis-28908	128	19	:	:	PUNCT
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fcis-28908	128	21	useful	useful	ADJ
fcis-28908	128	22	representations	representation	NOUN
fcis-28908	128	23	in	in	ADP
fcis-28908	128	24	a	a	DET
fcis-28908	128	25	deep	deep	ADJ
fcis-28908	128	26	network	network	NOUN
fcis-28908	128	27	with	with	ADP
fcis-28908	128	28	a	a	DET
fcis-28908	128	29	local	local	ADJ
fcis-28908	128	30	denoising	denoise	VERB
fcis-28908	128	31	criterion[j	criterion[j	NOUN
fcis-28908	128	32	]	]	PUNCT
fcis-28908	128	33	.	.	PUNCT
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fcis-28908	129	7	2010	2010	NUM
fcis-28908	129	8	,	,	PUNCT
fcis-28908	129	9	11(dec	11(dec	NUM
fcis-28908	129	10	):	):	PUNCT
fcis-28908	129	11	3371	3371	NUM
fcis-28908	129	12	-	-	SYM
fcis-28908	129	13	3408	3408	NUM
fcis-28908	129	14	.	.	PUNCT
fcis-28908	130	1	[	[	X
fcis-28908	130	2	8	8	NUM
fcis-28908	130	3	]	]	X
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fcis-28908	130	5	h	h	PROPN
fcis-28908	130	6	,	,	PUNCT
fcis-28908	130	7	zhang	zhang	PROPN
fcis-28908	130	8	s	s	PROPN
fcis-28908	130	9	,	,	PUNCT
fcis-28908	130	10	yin	yin	PROPN
fcis-28908	130	11	y.	y.	PROPN
fcis-28908	130	12	online	online	PROPN
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fcis-28908	130	14	elm	elm	PROPN
fcis-28908	130	15	algorithm	algorithm	NOUN
fcis-28908	130	16	with	with	ADP
fcis-28908	130	17	forgetting	forget	VERB
fcis-28908	130	18	factor	factor	NOUN
fcis-28908	130	19	for	for	ADP
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fcis-28908	130	21	applications[j	applications[j	NOUN
fcis-28908	130	22	]	]	PUNCT
fcis-28908	130	23	.	.	PUNCT
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fcis-28908	131	2	,	,	PUNCT
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fcis-28908	131	4	,	,	PUNCT
fcis-28908	131	5	261	261	NUM
fcis-28908	131	6	:	:	SYM
fcis-28908	131	7	144	144	NUM
fcis-28908	131	8	-	-	SYM
fcis-28908	131	9	152	152	NUM
fcis-28908	131	10	.	.	PUNCT
fcis-28908	132	1	[	[	X
fcis-28908	132	2	9	9	NUM
fcis-28908	132	3	]	]	X
fcis-28908	132	4	lekamalage	lekamalage	X
fcis-28908	132	5	c.k.l	c.k.l	PROPN
fcis-28908	132	6	.	.	PROPN
fcis-28908	132	7	,	,	PUNCT
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fcis-28908	132	9	k.	k.	PROPN
fcis-28908	132	10	,	,	PUNCT
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fcis-28908	132	12	g.	g.	PROPN
fcis-28908	132	13	,	,	PUNCT
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fcis-28908	132	26	]	]	PUNCT
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fcis-28908	133	7	.	.	PUNCT
fcis-28908	133	8	)	)	PUNCT
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fcis-28908	133	12	conference	conference	NOUN
fcis-28908	133	13	on	on	ADP
fcis-28908	133	14	image	image	NOUN
fcis-28908	133	15	processing	processing	NOUN
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fcis-28908	133	19	.	.	PUNCT
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fcis-28908	134	2	:	:	PUNCT
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fcis-28908	134	4	,	,	PUNCT
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fcis-28908	135	1	[	[	X
fcis-28908	135	2	10	10	NUM
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fcis-28908	135	5	g.b	g.b	PROPN
fcis-28908	135	6	.	.	PROPN
fcis-28908	135	7	,	,	PUNCT
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fcis-28908	135	9	q.y	q.y	PROPN
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fcis-28908	136	3	machine	machine	NOUN
fcis-28908	136	4	:	:	PUNCT
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fcis-28908	136	6	and	and	CCONJ
fcis-28908	136	7	applications[j	applications[j	PROPN
fcis-28908	136	8	]	]	PUNCT
fcis-28908	136	9	.	.	PUNCT
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fcis-28908	137	2	,	,	PUNCT
fcis-28908	137	3	2006	2006	NUM
fcis-28908	137	4	,	,	PUNCT
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fcis-28908	137	6	-	-	SYM
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fcis-28908	137	10	-	-	SYM
fcis-28908	137	11	501	501	NUM
fcis-28908	137	12	.	.	PUNCT
fcis-28908	138	1	[	[	X
fcis-28908	138	2	11	11	NUM
fcis-28908	138	3	]	]	PUNCT
fcis-28908	138	4	he	he	PRON
fcis-28908	138	5	q	q	PROPN
fcis-28908	138	6	,	,	PUNCT
fcis-28908	138	7	wang	wang	PROPN
fcis-28908	138	8	h	h	PROPN
fcis-28908	138	9	,	,	PUNCT
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fcis-28908	138	11	g.q	g.q	PROPN
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fcis-28908	138	13	,	,	PUNCT
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fcis-28908	138	23	condition	condition	NOUN
fcis-28908	138	24	monitoring	monitoring	NOUN
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fcis-28908	138	27	correlation	correlation	NOUN
fcis-28908	138	28	pca	pca	PROPN
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fcis-28908	138	30	elm[j	elm[j	VERB
fcis-28908	138	31	]	]	PUNCT
fcis-28908	138	32	.	.	PUNCT
fcis-28908	139	1	acta	acta	PROPN
fcis-28908	139	2	metrologica	metrologica	PROPN
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fcis-28908	139	4	,	,	PUNCT
fcis-28908	139	5	2018	2018	NUM
fcis-28908	139	6	,	,	PUNCT
fcis-28908	139	7	39(1	39(1	NUM
fcis-28908	139	8	):	):	PUNCT
fcis-28908	139	9	8993	8993	NUM
fcis-28908	139	10	.	.	PUNCT
fcis-28908	140	1	[	[	X
fcis-28908	140	2	12	12	NUM
fcis-28908	140	3	]	]	PUNCT
fcis-28908	140	4	he	he	PRON
fcis-28908	140	5	q	q	PROPN
fcis-28908	140	6	,	,	PUNCT
fcis-28908	140	7	wang	wang	PROPN
fcis-28908	140	8	h	h	PROPN
fcis-28908	140	9	,	,	PUNCT
fcis-28908	140	10	jiang	jiang	PROPN
fcis-28908	140	11	g.q	g.q	PROPN
fcis-28908	140	12	.	.	PROPN
fcis-28908	140	13	,	,	PUNCT
fcis-28908	140	14	et	et	PROPN
fcis-28908	140	15	al	al	PROPN
fcis-28908	140	16	.	.	PUNCT
fcis-28908	141	1	wind	wind	NOUN
fcis-28908	141	2	turbine	turbine	NOUN
fcis-28908	141	3	main	main	ADJ
fcis-28908	141	4	bearing	bearing	NOUN
fcis-28908	141	5	condition	condition	NOUN
fcis-28908	141	6	monitoring	monitoring	NOUN
fcis-28908	141	7	based	base	VERB
fcis-28908	141	8	on	on	ADP
fcis-28908	141	9	correlation	correlation	NOUN
fcis-28908	141	10	pca	pca	PROPN
fcis-28908	141	11	and	and	CCONJ
fcis-28908	141	12	elm[j	elm[j	VERB
fcis-28908	141	13	]	]	PUNCT
fcis-28908	141	14	.	.	PUNCT
fcis-28908	142	1	acta	acta	PROPN
fcis-28908	142	2	metrologica	metrologica	PROPN
fcis-28908	142	3	sinica	sinica	PROPN
fcis-28908	142	4	,	,	PUNCT
fcis-28908	142	5	2018	2018	NUM
fcis-28908	142	6	,	,	PUNCT
fcis-28908	142	7	39(1	39(1	NUM
fcis-28908	142	8	):	):	PUNCT
fcis-28908	142	9	89	89	NUM
fcis-28908	142	10	-	-	SYM
fcis-28908	142	11	93	93	NUM
fcis-28908	142	12	.	.	PUNCT
fcis-28908	143	1	[	[	X
fcis-28908	143	2	13	13	NUM
fcis-28908	143	3	]	]	SYM
fcis-28908	143	4	toh	toh	PROPN
fcis-28908	143	5	k.	k.	PROPN
fcis-28908	143	6	deterministic	deterministic	PROPN
fcis-28908	143	7	neural	neural	ADJ
fcis-28908	143	8	classification[j	classification[j	PROPN
fcis-28908	143	9	]	]	X
fcis-28908	143	10	.	.	PUNCT
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fcis-28908	144	2	computation	computation	NOUN
fcis-28908	144	3	,	,	PUNCT
fcis-28908	144	4	2008	2008	NUM
fcis-28908	144	5	,	,	PUNCT
fcis-28908	144	6	20(6	20(6	NUM
fcis-28908	144	7	):	):	PUNCT
fcis-28908	144	8	1565	1565	NUM
fcis-28908	144	9	-	-	SYM
fcis-28908	144	10	1595	1595	NUM
fcis-28908	144	11	.	.	PUNCT
fcis-28908	145	1	[	[	X
fcis-28908	145	2	14	14	NUM
fcis-28908	145	3	]	]	X
fcis-28908	145	4	lu	lu	PROPN
fcis-28908	145	5	c.b	c.b	PROPN
fcis-28908	145	6	.	.	PROPN
fcis-28908	145	7	,	,	PUNCT
fcis-28908	145	8	mei	mei	PROPN
fcis-28908	145	9	y.	y.	PROPN
fcis-28908	145	10	an	an	DET
fcis-28908	145	11	imputation	imputation	NOUN
fcis-28908	145	12	method	method	NOUN
fcis-28908	145	13	for	for	ADP
fcis-28908	145	14	missing	miss	VERB
fcis-28908	145	15	data	datum	NOUN
fcis-28908	145	16	based	base	VERB
fcis-28908	145	17	on	on	ADP
fcis-28908	145	18	an	an	DET
fcis-28908	145	19	extreme	extreme	ADJ
fcis-28908	145	20	learning	learning	NOUN
fcis-28908	145	21	machine	machine	NOUN
fcis-28908	145	22	auto	auto	NOUN
fcis-28908	145	23	-	-	PUNCT
fcis-28908	145	24	encoder[j	encoder[j	NOUN
fcis-28908	145	25	]	]	PUNCT
fcis-28908	145	26	.	.	PUNCT
fcis-28908	146	1	ieee	ieee	NOUN
fcis-28908	146	2	access	access	NOUN
fcis-28908	146	3	,	,	PUNCT
fcis-28908	146	4	2018	2018	NUM
fcis-28908	146	5	,	,	PUNCT
fcis-28908	146	6	6	6	NUM
fcis-28908	146	7	:	:	SYM
fcis-28908	146	8	52930	52930	NUM
fcis-28908	146	9	-	-	SYM
fcis-28908	146	10	52935	52935	NUM
fcis-28908	146	11	.	.	PUNCT
fcis-28908	147	1	[	[	X
fcis-28908	147	2	15	15	NUM
fcis-28908	147	3	]	]	X
fcis-28908	147	4	gopakumar	gopakumar	PROPN
fcis-28908	147	5	v	v	PROPN
fcis-28908	147	6	,	,	PUNCT
fcis-28908	147	7	tiwari	tiwari	PROPN
fcis-28908	147	8	s	s	PART
fcis-28908	147	9	,	,	PUNCT
fcis-28908	147	10	rahman	rahman	PROPN
fcis-28908	147	11	i.	i.	PROPN
fcis-28908	148	1	a	a	DET
fcis-28908	148	2	deep	deep	ADJ
fcis-28908	148	3	learning	learning	NOUN
fcis-28908	148	4	based	base	VERB
fcis-28908	148	5	data	data	NOUN
fcis-28908	148	6	-	-	PUNCT
fcis-28908	148	7	driven	drive	VERB
fcis-28908	148	8	soft	soft	ADJ
fcis-28908	148	9	sensor	sensor	NOUN
fcis-28908	148	10	for	for	ADP
fcis-28908	148	11	bioprocesses[j	bioprocesses[j	PROPN
fcis-28908	148	12	]	]	PUNCT
fcis-28908	148	13	.	.	PUNCT
fcis-28908	149	1	biochemical	biochemical	ADJ
fcis-28908	149	2	engineering	engineering	NOUN
fcis-28908	149	3	journal	journal	NOUN
fcis-28908	149	4	,	,	PUNCT
fcis-28908	149	5	2018	2018	NUM
fcis-28908	149	6	,	,	PUNCT
fcis-28908	149	7	136	136	NUM
fcis-28908	149	8	:	:	PUNCT
fcis-28908	149	9	28	28	NUM
fcis-28908	149	10	-	-	SYM
fcis-28908	149	11	39	39	NUM
fcis-28908	149	12	.	.	PUNCT
fcis-28908	150	1	[	[	X
fcis-28908	150	2	16	16	NUM
fcis-28908	150	3	]	]	X
fcis-28908	150	4	su	su	PROPN
fcis-28908	150	5	x	x	PROPN
fcis-28908	150	6	,	,	PUNCT
fcis-28908	150	7	zhang	zhang	PROPN
fcis-28908	150	8	s	s	PROPN
fcis-28908	150	9	,	,	PUNCT
fcis-28908	150	10	yin	yin	PROPN
fcis-28908	150	11	y	y	PROPN
fcis-28908	150	12	,	,	PUNCT
fcis-28908	150	13	et	et	PROPN
fcis-28908	150	14	al	al	PROPN
fcis-28908	150	15	.	.	PROPN
fcis-28908	150	16	prediction	prediction	NOUN
fcis-28908	150	17	of	of	ADP
fcis-28908	150	18	hot	hot	ADJ
fcis-28908	150	19	metal	metal	NOUN
fcis-28908	150	20	silicon	silicon	NOUN
fcis-28908	150	21	content	content	NOUN
fcis-28908	150	22	for	for	ADP
fcis-28908	150	23	blast	blast	NOUN
fcis-28908	150	24	furnace	furnace	NOUN
fcis-28908	150	25	based	base	VERB
fcis-28908	150	26	on	on	ADP
fcis-28908	150	27	multi	multi	ADJ
fcis-28908	150	28	-	-	ADJ
fcis-28908	150	29	layer	layer	ADJ
fcis-28908	150	30	online	online	ADJ
fcis-28908	150	31	sequential	sequential	ADJ
fcis-28908	150	32	extreme	extreme	ADJ
fcis-28908	150	33	learning	learning	NOUN
fcis-28908	150	34	machine[a	machine[a	NOUN
fcis-28908	150	35	]	]	PUNCT
fcis-28908	150	36	.	.	PUNCT
fcis-28908	151	1	in	in	ADP
fcis-28908	151	2	:	:	PUNCT
fcis-28908	151	3	chen	chen	PROPN
fcis-28908	151	4	x.	x.	PROPN
fcis-28908	151	5	(	(	PUNCT
fcis-28908	151	6	ed	ed	NOUN
fcis-28908	151	7	.	.	PUNCT
fcis-28908	151	8	)	)	PUNCT
fcis-28908	152	1	37th	37th	ADJ
fcis-28908	152	2	chinese	chinese	ADJ
fcis-28908	152	3	control	control	NOUN
fcis-28908	152	4	conference[c	conference[c	VERB
fcis-28908	152	5	]	]	PUNCT
fcis-28908	152	6	.	.	PUNCT
fcis-28908	153	1	wuhan	wuhan	PROPN
fcis-28908	153	2	:	:	PUNCT
fcis-28908	153	3	ieee	ieee	NOUN
fcis-28908	153	4	,	,	PUNCT
fcis-28908	153	5	2018	2018	NUM
fcis-28908	153	6	.	.	PUNCT