id	sid	tid	token	lemma	pos
ajst-15420	1	1	academic	academic	ADJ
ajst-15420	1	2	journal	journal	NOUN
ajst-15420	1	3	of	of	ADP
ajst-15420	1	4	science	science	NOUN
ajst-15420	1	5	and	and	CCONJ
ajst-15420	1	6	technology	technology	NOUN
ajst-15420	1	7	issn	issn	NOUN
ajst-15420	1	8	:	:	PUNCT
ajst-15420	1	9	2771	2771	NUM
ajst-15420	1	10	-	-	SYM
ajst-15420	1	11	3032	3032	NUM
ajst-15420	1	12	|	|	NOUN
ajst-15420	1	13	vol	vol	NOUN
ajst-15420	1	14	.	.	PROPN
ajst-15420	1	15	8	8	NUM
ajst-15420	1	16	,	,	PUNCT
ajst-15420	1	17	no	no	INTJ
ajst-15420	1	18	.	.	NOUN
ajst-15420	1	19	3	3	NUM
ajst-15420	1	20	,	,	PUNCT
ajst-15420	1	21	2023	2023	NUM
ajst-15420	1	22	1	1	NUM
ajst-15420	1	23	mechanical	mechanical	ADJ
ajst-15420	1	24	performance	performance	NOUN
ajst-15420	1	25	forecast	forecast	NOUN
ajst-15420	1	26	for	for	ADP
ajst-15420	1	27	bp	bp	PROPN
ajst-15420	1	28	neural	neural	ADJ
ajst-15420	1	29	network	network	NOUN
ajst-15420	1	30	materials	material	NOUN
ajst-15420	1	31	optimized	optimize	VERB
ajst-15420	1	32	by	by	ADP
ajst-15420	1	33	genetic	genetic	ADJ
ajst-15420	1	34	algorithm	algorithm	NOUN
ajst-15420	1	35	xiangxiang	xiangxiang	PROPN
ajst-15420	2	1	wu1	wu1	PROPN
ajst-15420	2	2	,	,	PUNCT
ajst-15420	2	3	*	*	PUNCT
ajst-15420	2	4	,	,	PUNCT
ajst-15420	2	5	shihao	shihao	PROPN
ajst-15420	2	6	wang1	wang1	PROPN
ajst-15420	3	1	1school	1school	NUM
ajst-15420	3	2	of	of	ADP
ajst-15420	3	3	mechanical	mechanical	ADJ
ajst-15420	3	4	engineering	engineering	NOUN
ajst-15420	3	5	,	,	PUNCT
ajst-15420	3	6	tianjin	tianjin	PROPN
ajst-15420	3	7	university	university	PROPN
ajst-15420	3	8	of	of	ADP
ajst-15420	3	9	technology	technology	NOUN
ajst-15420	3	10	and	and	CCONJ
ajst-15420	3	11	education	education	NOUN
ajst-15420	3	12	,	,	PUNCT
ajst-15420	3	13	tianjin	tianjin	PROPN
ajst-15420	3	14	300222	300222	NUM
ajst-15420	3	15	,	,	PUNCT
ajst-15420	3	16	china	china	PROPN
ajst-15420	3	17	*	*	PUNCT
ajst-15420	3	18	corresponding	correspond	VERB
ajst-15420	3	19	author	author	NOUN
ajst-15420	3	20	:	:	PUNCT
ajst-15420	4	1	wu	wu	PROPN
ajst-15420	4	2	xiangxiang	xiangxiang	PROPN
ajst-15420	5	1	(	(	PUNCT
ajst-15420	5	2	email	email	NOUN
ajst-15420	5	3	:	:	PUNCT
ajst-15420	5	4	wuxiangxiang0739@163.com	wuxiangxiang0739@163.com	NUM
ajst-15420	5	5	)	)	PUNCT
ajst-15420	5	6	abstract	abstract	NOUN
ajst-15420	5	7	:	:	PUNCT
ajst-15420	5	8	the	the	DET
ajst-15420	5	9	mechanical	mechanical	ADJ
ajst-15420	5	10	properties	property	NOUN
ajst-15420	5	11	of	of	ADP
ajst-15420	5	12	steel	steel	NOUN
ajst-15420	5	13	materials	material	NOUN
ajst-15420	5	14	play	play	VERB
ajst-15420	5	15	a	a	DET
ajst-15420	5	16	crucial	crucial	ADJ
ajst-15420	5	17	role	role	NOUN
ajst-15420	5	18	in	in	ADP
ajst-15420	5	19	their	their	PRON
ajst-15420	5	20	design	design	NOUN
ajst-15420	5	21	,	,	PUNCT
ajst-15420	5	22	selection	selection	NOUN
ajst-15420	5	23	,	,	PUNCT
ajst-15420	5	24	and	and	CCONJ
ajst-15420	5	25	application	application	NOUN
ajst-15420	5	26	.	.	PUNCT
ajst-15420	6	1	in	in	ADP
ajst-15420	6	2	order	order	NOUN
ajst-15420	6	3	to	to	PART
ajst-15420	6	4	better	well	ADV
ajst-15420	6	5	predict	predict	VERB
ajst-15420	6	6	the	the	DET
ajst-15420	6	7	mechanical	mechanical	ADJ
ajst-15420	6	8	properties	property	NOUN
ajst-15420	6	9	through	through	ADP
ajst-15420	6	10	chemical	chemical	NOUN
ajst-15420	6	11	composition	composition	NOUN
ajst-15420	6	12	and	and	CCONJ
ajst-15420	6	13	process	process	NOUN
ajst-15420	6	14	parameters	parameter	NOUN
ajst-15420	6	15	,	,	PUNCT
ajst-15420	6	16	this	this	DET
ajst-15420	6	17	paper	paper	NOUN
ajst-15420	6	18	uses	use	VERB
ajst-15420	6	19	genetic	genetic	ADJ
ajst-15420	6	20	algorithm	algorithm	NOUN
ajst-15420	6	21	to	to	PART
ajst-15420	6	22	optimize	optimize	VERB
ajst-15420	6	23	the	the	DET
ajst-15420	6	24	bp	bp	PROPN
ajst-15420	6	25	neural	neural	ADJ
ajst-15420	6	26	network	network	NOUN
ajst-15420	6	27	to	to	PART
ajst-15420	6	28	establish	establish	VERB
ajst-15420	6	29	a	a	DET
ajst-15420	6	30	mechanical	mechanical	ADJ
ajst-15420	6	31	property	property	NOUN
ajst-15420	6	32	prediction	prediction	NOUN
ajst-15420	6	33	model	model	NOUN
ajst-15420	6	34	for	for	ADP
ajst-15420	6	35	steel	steel	NOUN
ajst-15420	6	36	materials	material	NOUN
ajst-15420	6	37	.	.	PUNCT
ajst-15420	7	1	the	the	DET
ajst-15420	7	2	model	model	NOUN
ajst-15420	7	3	can	can	AUX
ajst-15420	7	4	predict	predict	VERB
ajst-15420	7	5	three	three	NUM
ajst-15420	7	6	mechanical	mechanical	ADJ
ajst-15420	7	7	properties	property	NOUN
ajst-15420	7	8	,	,	PUNCT
ajst-15420	7	9	including	include	VERB
ajst-15420	7	10	yield	yield	NOUN
ajst-15420	7	11	strength	strength	NOUN
ajst-15420	7	12	,	,	PUNCT
ajst-15420	7	13	tensile	tensile	NOUN
ajst-15420	7	14	strength	strength	NOUN
ajst-15420	7	15	,	,	PUNCT
ajst-15420	7	16	and	and	CCONJ
ajst-15420	7	17	elongation	elongation	NOUN
ajst-15420	7	18	,	,	PUNCT
ajst-15420	7	19	through	through	ADP
ajst-15420	7	20	chemical	chemical	ADJ
ajst-15420	7	21	composition	composition	NOUN
ajst-15420	7	22	and	and	CCONJ
ajst-15420	7	23	process	process	NOUN
ajst-15420	7	24	parameters	parameter	NOUN
ajst-15420	7	25	.	.	PUNCT
ajst-15420	8	1	after	after	ADP
ajst-15420	8	2	optimization	optimization	NOUN
ajst-15420	8	3	by	by	ADP
ajst-15420	8	4	genetic	genetic	ADJ
ajst-15420	8	5	algorithm	algorithm	NOUN
ajst-15420	8	6	,	,	PUNCT
ajst-15420	8	7	the	the	DET
ajst-15420	8	8	problems	problem	NOUN
ajst-15420	8	9	of	of	ADP
ajst-15420	8	10	insufficient	insufficient	ADJ
ajst-15420	8	11	convergence	convergence	NOUN
ajst-15420	8	12	effect	effect	NOUN
ajst-15420	8	13	,	,	PUNCT
ajst-15420	8	14	random	random	ADJ
ajst-15420	8	15	initialization	initialization	NOUN
ajst-15420	8	16	of	of	ADP
ajst-15420	8	17	weights	weight	NOUN
ajst-15420	8	18	and	and	CCONJ
ajst-15420	8	19	thresholds	threshold	NOUN
ajst-15420	8	20	in	in	ADP
ajst-15420	8	21	the	the	DET
ajst-15420	8	22	bp	bp	PROPN
ajst-15420	8	23	neural	neural	PROPN
ajst-15420	8	24	network	network	NOUN
ajst-15420	8	25	model	model	NOUN
ajst-15420	8	26	are	be	AUX
ajst-15420	8	27	improved	improve	VERB
ajst-15420	8	28	,	,	PUNCT
ajst-15420	8	29	and	and	CCONJ
ajst-15420	8	30	the	the	DET
ajst-15420	8	31	prediction	prediction	NOUN
ajst-15420	8	32	error	error	NOUN
ajst-15420	8	33	is	be	AUX
ajst-15420	8	34	significantly	significantly	ADV
ajst-15420	8	35	reduced	reduce	VERB
ajst-15420	8	36	.	.	PUNCT
ajst-15420	9	1	the	the	DET
ajst-15420	9	2	experimental	experimental	ADJ
ajst-15420	9	3	results	result	NOUN
ajst-15420	9	4	show	show	VERB
ajst-15420	9	5	that	that	SCONJ
ajst-15420	9	6	the	the	DET
ajst-15420	9	7	ga	ga	PROPN
ajst-15420	9	8	-	-	PUNCT
ajst-15420	9	9	bp	bp	PROPN
ajst-15420	9	10	algorithm	algorithm	NOUN
ajst-15420	9	11	model	model	NOUN
ajst-15420	9	12	has	have	VERB
ajst-15420	9	13	excellent	excellent	ADJ
ajst-15420	9	14	performance	performance	NOUN
ajst-15420	9	15	in	in	ADP
ajst-15420	9	16	predicting	predict	VERB
ajst-15420	9	17	the	the	DET
ajst-15420	9	18	mechanical	mechanical	ADJ
ajst-15420	9	19	properties	property	NOUN
ajst-15420	9	20	of	of	ADP
ajst-15420	9	21	steel	steel	NOUN
ajst-15420	9	22	materials	material	NOUN
ajst-15420	9	23	.	.	PUNCT
ajst-15420	10	1	keywords	keyword	NOUN
ajst-15420	10	2	:	:	PUNCT
ajst-15420	10	3	bp	bp	PROPN
ajst-15420	10	4	neural	neural	ADJ
ajst-15420	10	5	network	network	NOUN
ajst-15420	10	6	,	,	PUNCT
ajst-15420	10	7	genetic	genetic	ADJ
ajst-15420	10	8	algorithm	algorithm	NOUN
ajst-15420	10	9	,	,	PUNCT
ajst-15420	10	10	mechanical	mechanical	ADJ
ajst-15420	10	11	performance	performance	NOUN
ajst-15420	10	12	.	.	PUNCT
ajst-15420	11	1	1	1	X
ajst-15420	11	2	.	.	X
ajst-15420	11	3	introduction	introduction	NOUN
ajst-15420	11	4	since	since	SCONJ
ajst-15420	11	5	ancient	ancient	ADJ
ajst-15420	11	6	times	time	NOUN
ajst-15420	11	7	,	,	PUNCT
ajst-15420	11	8	steel	steel	NOUN
ajst-15420	11	9	,	,	PUNCT
ajst-15420	11	10	a	a	DET
ajst-15420	11	11	material	material	NOUN
ajst-15420	11	12	with	with	ADP
ajst-15420	11	13	excellent	excellent	ADJ
ajst-15420	11	14	properties	property	NOUN
ajst-15420	11	15	,	,	PUNCT
ajst-15420	11	16	has	have	AUX
ajst-15420	11	17	been	be	AUX
ajst-15420	11	18	widely	widely	ADV
ajst-15420	11	19	used	use	VERB
ajst-15420	11	20	in	in	ADP
ajst-15420	11	21	various	various	ADJ
ajst-15420	11	22	fields	field	NOUN
ajst-15420	11	23	.	.	PUNCT
ajst-15420	12	1	in	in	ADP
ajst-15420	12	2	order	order	NOUN
ajst-15420	12	3	to	to	PART
ajst-15420	12	4	better	well	ADV
ajst-15420	12	5	utilize	utilize	VERB
ajst-15420	12	6	the	the	DET
ajst-15420	12	7	properties	property	NOUN
ajst-15420	12	8	of	of	ADP
ajst-15420	12	9	steel	steel	NOUN
ajst-15420	12	10	,	,	PUNCT
ajst-15420	12	11	people	people	NOUN
ajst-15420	12	12	continue	continue	VERB
ajst-15420	12	13	to	to	PART
ajst-15420	12	14	explore	explore	VERB
ajst-15420	12	15	and	and	CCONJ
ajst-15420	12	16	innovate	innovate	VERB
ajst-15420	12	17	to	to	PART
ajst-15420	12	18	understand	understand	VERB
ajst-15420	12	19	and	and	CCONJ
ajst-15420	12	20	predict	predict	VERB
ajst-15420	12	21	its	its	PRON
ajst-15420	12	22	performance	performance	NOUN
ajst-15420	12	23	.	.	PUNCT
ajst-15420	13	1	with	with	ADP
ajst-15420	13	2	the	the	DET
ajst-15420	13	3	development	development	NOUN
ajst-15420	13	4	of	of	ADP
ajst-15420	13	5	technology	technology	NOUN
ajst-15420	13	6	,	,	PUNCT
ajst-15420	13	7	especially	especially	ADV
ajst-15420	13	8	the	the	DET
ajst-15420	13	9	advancement	advancement	NOUN
ajst-15420	13	10	of	of	ADP
ajst-15420	13	11	materials	material	NOUN
ajst-15420	13	12	science	science	NOUN
ajst-15420	13	13	and	and	CCONJ
ajst-15420	13	14	computer	computer	NOUN
ajst-15420	13	15	technology	technology	NOUN
ajst-15420	13	16	,	,	PUNCT
ajst-15420	13	17	the	the	DET
ajst-15420	13	18	methods	method	NOUN
ajst-15420	13	19	for	for	ADP
ajst-15420	13	20	predicting	predict	VERB
ajst-15420	13	21	steel	steel	NOUN
ajst-15420	13	22	performance	performance	NOUN
ajst-15420	13	23	have	have	AUX
ajst-15420	13	24	gradually	gradually	ADV
ajst-15420	13	25	become	become	VERB
ajst-15420	13	26	more	more	ADV
ajst-15420	13	27	scientific	scientific	ADJ
ajst-15420	13	28	and	and	CCONJ
ajst-15420	13	29	accurate	accurate	ADJ
ajst-15420	13	30	.	.	PUNCT
ajst-15420	14	1	in	in	ADP
ajst-15420	14	2	ancient	ancient	ADJ
ajst-15420	14	3	times	time	NOUN
ajst-15420	14	4	,	,	PUNCT
ajst-15420	14	5	the	the	DET
ajst-15420	14	6	prediction	prediction	NOUN
ajst-15420	14	7	of	of	ADP
ajst-15420	14	8	steel	steel	NOUN
ajst-15420	14	9	performance	performance	NOUN
ajst-15420	14	10	mainly	mainly	ADV
ajst-15420	14	11	relied	rely	VERB
ajst-15420	14	12	on	on	ADP
ajst-15420	14	13	experience	experience	NOUN
ajst-15420	14	14	and	and	CCONJ
ajst-15420	14	15	practice	practice	NOUN
ajst-15420	14	16	.	.	PUNCT
ajst-15420	15	1	craftsmen	craftsman	NOUN
ajst-15420	15	2	gradually	gradually	ADV
ajst-15420	15	3	mastered	master	VERB
ajst-15420	15	4	the	the	DET
ajst-15420	15	5	smelting	smelting	NOUN
ajst-15420	15	6	of	of	ADP
ajst-15420	15	7	iron	iron	NOUN
ajst-15420	15	8	ore	ore	NOUN
ajst-15420	15	9	and	and	CCONJ
ajst-15420	15	10	the	the	DET
ajst-15420	15	11	forging	forge	VERB
ajst-15420	15	12	technology	technology	NOUN
ajst-15420	15	13	of	of	ADP
ajst-15420	15	14	steel	steel	NOUN
ajst-15420	15	15	through	through	ADP
ajst-15420	15	16	continuous	continuous	ADJ
ajst-15420	15	17	trial	trial	NOUN
ajst-15420	15	18	and	and	CCONJ
ajst-15420	15	19	error	error	NOUN
ajst-15420	15	20	.	.	PUNCT
ajst-15420	16	1	they	they	PRON
ajst-15420	16	2	accumulated	accumulate	VERB
ajst-15420	16	3	rich	rich	ADJ
ajst-15420	16	4	practical	practical	ADJ
ajst-15420	16	5	experience	experience	NOUN
ajst-15420	16	6	by	by	ADP
ajst-15420	16	7	observing	observe	VERB
ajst-15420	16	8	and	and	CCONJ
ajst-15420	16	9	feeling	feel	VERB
ajst-15420	16	10	the	the	DET
ajst-15420	16	11	performance	performance	NOUN
ajst-15420	16	12	of	of	ADP
ajst-15420	16	13	steel	steel	NOUN
ajst-15420	16	14	in	in	ADP
ajst-15420	16	15	various	various	ADJ
ajst-15420	16	16	environments	environment	NOUN
ajst-15420	16	17	,	,	PUNCT
ajst-15420	16	18	thereby	thereby	ADV
ajst-15420	16	19	gaining	gain	VERB
ajst-15420	16	20	a	a	DET
ajst-15420	16	21	preliminary	preliminary	ADJ
ajst-15420	16	22	understanding	understanding	NOUN
ajst-15420	16	23	of	of	ADP
ajst-15420	16	24	steel	steel	NOUN
ajst-15420	16	25	's	's	PART
ajst-15420	16	26	performance	performance	NOUN
ajst-15420	16	27	.	.	PUNCT
ajst-15420	17	1	these	these	DET
ajst-15420	17	2	experiences	experience	NOUN
ajst-15420	17	3	were	be	AUX
ajst-15420	17	4	often	often	ADV
ajst-15420	17	5	handed	hand	VERB
ajst-15420	17	6	down	down	ADP
ajst-15420	17	7	by	by	ADP
ajst-15420	17	8	word	word	NOUN
ajst-15420	17	9	of	of	ADP
ajst-15420	17	10	mouth	mouth	NOUN
ajst-15420	17	11	and	and	CCONJ
ajst-15420	17	12	passed	pass	VERB
ajst-15420	17	13	on	on	ADP
ajst-15420	17	14	to	to	ADP
ajst-15420	17	15	later	later	ADJ
ajst-15420	17	16	generations	generation	NOUN
ajst-15420	17	17	,	,	PUNCT
ajst-15420	17	18	becoming	become	VERB
ajst-15420	17	19	a	a	DET
ajst-15420	17	20	valuable	valuable	ADJ
ajst-15420	17	21	asset	asset	NOUN
ajst-15420	17	22	for	for	SCONJ
ajst-15420	17	23	them	they	PRON
ajst-15420	17	24	to	to	PART
ajst-15420	17	25	learn	learn	VERB
ajst-15420	17	26	from	from	ADP
ajst-15420	17	27	.	.	PUNCT
ajst-15420	18	1	with	with	ADP
ajst-15420	18	2	the	the	DET
ajst-15420	18	3	arrival	arrival	NOUN
ajst-15420	18	4	of	of	ADP
ajst-15420	18	5	the	the	DET
ajst-15420	18	6	industrial	industrial	ADJ
ajst-15420	18	7	revolution	revolution	NOUN
ajst-15420	18	8	,	,	PUNCT
ajst-15420	18	9	the	the	DET
ajst-15420	18	10	demand	demand	NOUN
ajst-15420	18	11	for	for	ADP
ajst-15420	18	12	steel	steel	NOUN
ajst-15420	18	13	increased	increase	VERB
ajst-15420	18	14	significantly	significantly	ADV
ajst-15420	18	15	,	,	PUNCT
ajst-15420	18	16	and	and	CCONJ
ajst-15420	18	17	higher	high	ADJ
ajst-15420	18	18	accuracy	accuracy	NOUN
ajst-15420	18	19	was	be	AUX
ajst-15420	18	20	required	require	VERB
ajst-15420	18	21	for	for	ADP
ajst-15420	18	22	predicting	predict	VERB
ajst-15420	18	23	steel	steel	NOUN
ajst-15420	18	24	performance	performance	NOUN
ajst-15420	18	25	.	.	PUNCT
ajst-15420	19	1	at	at	ADP
ajst-15420	19	2	this	this	DET
ajst-15420	19	3	time	time	NOUN
ajst-15420	19	4	,	,	PUNCT
ajst-15420	19	5	materials	material	NOUN
ajst-15420	19	6	science	science	NOUN
ajst-15420	19	7	began	begin	VERB
ajst-15420	19	8	to	to	PART
ajst-15420	19	9	develop	develop	VERB
ajst-15420	19	10	,	,	PUNCT
ajst-15420	19	11	and	and	CCONJ
ajst-15420	19	12	people	people	NOUN
ajst-15420	19	13	gained	gain	VERB
ajst-15420	19	14	a	a	DET
ajst-15420	19	15	deeper	deep	ADJ
ajst-15420	19	16	understanding	understanding	NOUN
ajst-15420	19	17	of	of	ADP
ajst-15420	19	18	the	the	DET
ajst-15420	19	19	influence	influence	NOUN
ajst-15420	19	20	of	of	ADP
ajst-15420	19	21	steel	steel	NOUN
ajst-15420	19	22	's	's	PART
ajst-15420	19	23	microstructure	microstructure	ADJ
ajst-15420	19	24	and	and	CCONJ
ajst-15420	19	25	chemical	chemical	NOUN
ajst-15420	19	26	composition	composition	NOUN
ajst-15420	19	27	on	on	ADP
ajst-15420	19	28	its	its	PRON
ajst-15420	19	29	performance	performance	NOUN
ajst-15420	19	30	.	.	PUNCT
ajst-15420	20	1	simultaneously	simultaneously	ADV
ajst-15420	20	2	,	,	PUNCT
ajst-15420	20	3	engineers	engineer	NOUN
ajst-15420	20	4	began	begin	VERB
ajst-15420	20	5	to	to	PART
ajst-15420	20	6	use	use	VERB
ajst-15420	20	7	mathematical	mathematical	ADJ
ajst-15420	20	8	and	and	CCONJ
ajst-15420	20	9	physical	physical	ADJ
ajst-15420	20	10	models	model	NOUN
ajst-15420	20	11	to	to	PART
ajst-15420	20	12	describe	describe	VERB
ajst-15420	20	13	and	and	CCONJ
ajst-15420	20	14	predict	predict	VERB
ajst-15420	20	15	steel	steel	NOUN
ajst-15420	20	16	performance	performance	NOUN
ajst-15420	20	17	.	.	PUNCT
ajst-15420	21	1	these	these	DET
ajst-15420	21	2	models	model	NOUN
ajst-15420	21	3	were	be	AUX
ajst-15420	21	4	based	base	VERB
ajst-15420	21	5	on	on	ADP
ajst-15420	21	6	simple	simple	ADJ
ajst-15420	21	7	physical	physical	ADJ
ajst-15420	21	8	and	and	CCONJ
ajst-15420	21	9	chemical	chemical	NOUN
ajst-15420	21	10	principles	principle	NOUN
ajst-15420	21	11	,	,	PUNCT
ajst-15420	21	12	such	such	ADJ
ajst-15420	21	13	as	as	ADP
ajst-15420	21	14	elastic	elastic	ADJ
ajst-15420	21	15	mechanics	mechanic	NOUN
ajst-15420	21	16	and	and	CCONJ
ajst-15420	21	17	plasticity	plasticity	NOUN
ajst-15420	21	18	mechanics	mechanic	NOUN
ajst-15420	21	19	,	,	PUNCT
ajst-15420	21	20	providing	provide	VERB
ajst-15420	21	21	a	a	DET
ajst-15420	21	22	more	more	ADV
ajst-15420	21	23	scientific	scientific	ADJ
ajst-15420	21	24	basis	basis	NOUN
ajst-15420	21	25	for	for	ADP
ajst-15420	21	26	steel	steel	NOUN
ajst-15420	21	27	performance	performance	NOUN
ajst-15420	21	28	prediction	prediction	NOUN
ajst-15420	21	29	.	.	PUNCT
ajst-15420	22	1	in	in	ADP
ajst-15420	22	2	the	the	DET
ajst-15420	22	3	20th	20th	ADJ
ajst-15420	22	4	century	century	NOUN
ajst-15420	22	5	,	,	PUNCT
ajst-15420	22	6	the	the	DET
ajst-15420	22	7	rapid	rapid	ADJ
ajst-15420	22	8	development	development	NOUN
ajst-15420	22	9	of	of	ADP
ajst-15420	22	10	computer	computer	NOUN
ajst-15420	22	11	technology	technology	NOUN
ajst-15420	22	12	brought	bring	VERB
ajst-15420	22	13	revolutionary	revolutionary	ADJ
ajst-15420	22	14	changes	change	NOUN
ajst-15420	22	15	to	to	ADP
ajst-15420	22	16	steel	steel	NOUN
ajst-15420	22	17	performance	performance	NOUN
ajst-15420	22	18	prediction	prediction	NOUN
ajst-15420	22	19	.	.	PUNCT
ajst-15420	23	1	computers	computer	NOUN
ajst-15420	23	2	'	'	PART
ajst-15420	23	3	powerful	powerful	ADJ
ajst-15420	23	4	computational	computational	ADJ
ajst-15420	23	5	capabilities	capability	NOUN
ajst-15420	23	6	enabled	enable	VERB
ajst-15420	23	7	complex	complex	ADJ
ajst-15420	23	8	physical	physical	ADJ
ajst-15420	23	9	and	and	CCONJ
ajst-15420	23	10	chemical	chemical	NOUN
ajst-15420	23	11	models	model	NOUN
ajst-15420	23	12	to	to	PART
ajst-15420	23	13	be	be	AUX
ajst-15420	23	14	implemented	implement	VERB
ajst-15420	23	15	and	and	CCONJ
ajst-15420	23	16	processed	process	VERB
ajst-15420	23	17	large	large	ADJ
ajst-15420	23	18	amounts	amount	NOUN
ajst-15420	23	19	of	of	ADP
ajst-15420	23	20	data	datum	NOUN
ajst-15420	23	21	.	.	PUNCT
ajst-15420	24	1	during	during	ADP
ajst-15420	24	2	this	this	DET
ajst-15420	24	3	period	period	NOUN
ajst-15420	24	4	,	,	PUNCT
ajst-15420	24	5	methods	method	NOUN
ajst-15420	24	6	such	such	ADJ
ajst-15420	24	7	as	as	ADP
ajst-15420	24	8	finite	finite	ADJ
ajst-15420	24	9	element	element	NOUN
ajst-15420	24	10	analysis	analysis	NOUN
ajst-15420	24	11	(	(	PUNCT
ajst-15420	24	12	fea	fea	NOUN
ajst-15420	24	13	)	)	PUNCT
ajst-15420	24	14	and	and	CCONJ
ajst-15420	24	15	finite	finite	ADJ
ajst-15420	24	16	difference	difference	NOUN
ajst-15420	24	17	analysis	analysis	NOUN
ajst-15420	24	18	(	(	PUNCT
ajst-15420	24	19	fda	fda	PROPN
ajst-15420	24	20	)	)	PUNCT
ajst-15420	24	21	began	begin	VERB
ajst-15420	24	22	to	to	PART
ajst-15420	24	23	be	be	AUX
ajst-15420	24	24	widely	widely	ADV
ajst-15420	24	25	used	use	VERB
ajst-15420	24	26	for	for	ADP
ajst-15420	24	27	simulating	simulate	VERB
ajst-15420	24	28	and	and	CCONJ
ajst-15420	24	29	predicting	predict	VERB
ajst-15420	24	30	steel	steel	NOUN
ajst-15420	24	31	performance	performance	NOUN
ajst-15420	24	32	.	.	PUNCT
ajst-15420	25	1	these	these	DET
ajst-15420	25	2	methods	method	NOUN
ajst-15420	25	3	can	can	AUX
ajst-15420	25	4	consider	consider	VERB
ajst-15420	25	5	the	the	DET
ajst-15420	25	6	influence	influence	NOUN
ajst-15420	25	7	of	of	ADP
ajst-15420	25	8	steel	steel	NOUN
ajst-15420	25	9	's	's	PART
ajst-15420	25	10	microstructure	microstructure	ADJ
ajst-15420	25	11	and	and	CCONJ
ajst-15420	25	12	chemical	chemical	NOUN
ajst-15420	25	13	composition	composition	NOUN
ajst-15420	25	14	on	on	ADP
ajst-15420	25	15	its	its	PRON
ajst-15420	25	16	performance	performance	NOUN
ajst-15420	25	17	,	,	PUNCT
ajst-15420	25	18	providing	provide	VERB
ajst-15420	25	19	more	more	ADV
ajst-15420	25	20	accurate	accurate	ADJ
ajst-15420	25	21	predictions	prediction	NOUN
ajst-15420	25	22	.	.	PUNCT
ajst-15420	26	1	at	at	ADP
ajst-15420	26	2	the	the	DET
ajst-15420	26	3	same	same	ADJ
ajst-15420	26	4	time	time	NOUN
ajst-15420	26	5	,	,	PUNCT
ajst-15420	26	6	the	the	DET
ajst-15420	26	7	development	development	NOUN
ajst-15420	26	8	of	of	ADP
ajst-15420	26	9	computer	computer	NOUN
ajst-15420	26	10	software	software	NOUN
ajst-15420	26	11	also	also	ADV
ajst-15420	26	12	enabled	enable	VERB
ajst-15420	26	13	researchers	researcher	NOUN
ajst-15420	26	14	to	to	PART
ajst-15420	26	15	easily	easily	ADV
ajst-15420	26	16	design	design	VERB
ajst-15420	26	17	and	and	CCONJ
ajst-15420	26	18	optimize	optimize	VERB
ajst-15420	26	19	steel	steel	NOUN
ajst-15420	26	20	materials	material	NOUN
ajst-15420	26	21	.	.	PUNCT
ajst-15420	27	1	for	for	ADP
ajst-15420	27	2	example	example	NOUN
ajst-15420	27	3	,	,	PUNCT
ajst-15420	27	4	through	through	ADP
ajst-15420	27	5	finite	finite	ADJ
ajst-15420	27	6	element	element	NOUN
ajst-15420	27	7	analysis	analysis	NOUN
ajst-15420	27	8	,	,	PUNCT
ajst-15420	27	9	researchers	researcher	NOUN
ajst-15420	27	10	can	can	AUX
ajst-15420	27	11	simulate	simulate	VERB
ajst-15420	27	12	the	the	DET
ajst-15420	27	13	mechanical	mechanical	ADJ
ajst-15420	27	14	behavior	behavior	NOUN
ajst-15420	27	15	of	of	ADP
ajst-15420	27	16	steel	steel	NOUN
ajst-15420	27	17	under	under	ADP
ajst-15420	27	18	various	various	ADJ
ajst-15420	27	19	conditions	condition	NOUN
ajst-15420	27	20	to	to	PART
ajst-15420	27	21	predict	predict	VERB
ajst-15420	27	22	its	its	PRON
ajst-15420	27	23	strength	strength	NOUN
ajst-15420	27	24	,	,	PUNCT
ajst-15420	27	25	plasticity	plasticity	NOUN
ajst-15420	27	26	,	,	PUNCT
ajst-15420	27	27	and	and	CCONJ
ajst-15420	27	28	toughness	toughness	NOUN
ajst-15420	27	29	.	.	PUNCT
ajst-15420	28	1	in	in	ADP
ajst-15420	28	2	addition	addition	NOUN
ajst-15420	28	3	,	,	PUNCT
ajst-15420	28	4	some	some	DET
ajst-15420	28	5	computer	computer	NOUN
ajst-15420	28	6	software	software	NOUN
ajst-15420	28	7	based	base	VERB
ajst-15420	28	8	on	on	ADP
ajst-15420	28	9	physical	physical	ADJ
ajst-15420	28	10	models	model	NOUN
ajst-15420	28	11	has	have	AUX
ajst-15420	28	12	been	be	AUX
ajst-15420	28	13	developed	develop	VERB
ajst-15420	28	14	to	to	PART
ajst-15420	28	15	predict	predict	VERB
ajst-15420	28	16	steel	steel	NOUN
ajst-15420	28	17	's	's	PART
ajst-15420	28	18	electromagnetic	electromagnetic	ADJ
ajst-15420	28	19	performance	performance	NOUN
ajst-15420	28	20	,	,	PUNCT
ajst-15420	28	21	thermal	thermal	ADJ
ajst-15420	28	22	performance	performance	NOUN
ajst-15420	28	23	,	,	PUNCT
ajst-15420	28	24	and	and	CCONJ
ajst-15420	28	25	so	so	ADV
ajst-15420	28	26	on	on	ADV
ajst-15420	28	27	.	.	PUNCT
ajst-15420	29	1	however	however	ADV
ajst-15420	29	2	,	,	PUNCT
ajst-15420	29	3	even	even	ADV
ajst-15420	29	4	with	with	ADP
ajst-15420	29	5	the	the	DET
ajst-15420	29	6	support	support	NOUN
ajst-15420	29	7	of	of	ADP
ajst-15420	29	8	computer	computer	NOUN
ajst-15420	29	9	technology	technology	NOUN
ajst-15420	29	10	,	,	PUNCT
ajst-15420	29	11	traditional	traditional	ADJ
ajst-15420	29	12	physical	physical	ADJ
ajst-15420	29	13	and	and	CCONJ
ajst-15420	29	14	chemical	chemical	NOUN
ajst-15420	29	15	models	model	NOUN
ajst-15420	29	16	still	still	ADV
ajst-15420	29	17	have	have	VERB
ajst-15420	29	18	certain	certain	ADJ
ajst-15420	29	19	limitations	limitation	NOUN
ajst-15420	29	20	and	and	CCONJ
ajst-15420	29	21	can	can	AUX
ajst-15420	29	22	not	not	PART
ajst-15420	29	23	fully	fully	ADV
ajst-15420	29	24	consider	consider	VERB
ajst-15420	29	25	the	the	DET
ajst-15420	29	26	complex	complex	ADJ
ajst-15420	29	27	physical	physical	ADJ
ajst-15420	29	28	and	and	CCONJ
ajst-15420	29	29	chemical	chemical	NOUN
ajst-15420	29	30	processes	process	NOUN
ajst-15420	29	31	in	in	ADP
ajst-15420	29	32	steel	steel	NOUN
ajst-15420	29	33	.	.	PUNCT
ajst-15420	30	1	at	at	ADP
ajst-15420	30	2	this	this	DET
ajst-15420	30	3	point	point	NOUN
ajst-15420	30	4	,	,	PUNCT
ajst-15420	30	5	the	the	DET
ajst-15420	30	6	rapid	rapid	ADJ
ajst-15420	30	7	development	development	NOUN
ajst-15420	30	8	of	of	ADP
ajst-15420	30	9	artificial	artificial	ADJ
ajst-15420	30	10	intelligence	intelligence	NOUN
ajst-15420	30	11	(	(	PUNCT
ajst-15420	30	12	ai	ai	AUX
ajst-15420	30	13	)	)	PUNCT
ajst-15420	30	14	brought	bring	VERB
ajst-15420	30	15	a	a	DET
ajst-15420	30	16	new	new	ADJ
ajst-15420	30	17	breakthrough	breakthrough	NOUN
ajst-15420	30	18	for	for	ADP
ajst-15420	30	19	steel	steel	NOUN
ajst-15420	30	20	performance	performance	NOUN
ajst-15420	30	21	prediction	prediction	NOUN
ajst-15420	30	22	.	.	PUNCT
ajst-15420	31	1	compared	compare	VERB
ajst-15420	31	2	with	with	ADP
ajst-15420	31	3	traditional	traditional	ADJ
ajst-15420	31	4	physical	physical	ADJ
ajst-15420	31	5	and	and	CCONJ
ajst-15420	31	6	chemical	chemical	NOUN
ajst-15420	31	7	models	model	NOUN
ajst-15420	31	8	,	,	PUNCT
ajst-15420	31	9	machine	machine	NOUN
ajst-15420	31	10	learning	learn	VERB
ajst-15420	31	11	algorithms	algorithm	NOUN
ajst-15420	31	12	can	can	AUX
ajst-15420	31	13	process	process	VERB
ajst-15420	31	14	more	more	ADJ
ajst-15420	31	15	influencing	influence	VERB
ajst-15420	31	16	factors	factor	NOUN
ajst-15420	31	17	and	and	CCONJ
ajst-15420	31	18	automatically	automatically	ADV
ajst-15420	31	19	adjust	adjust	VERB
ajst-15420	31	20	model	model	NOUN
ajst-15420	31	21	parameters	parameter	NOUN
ajst-15420	31	22	to	to	PART
ajst-15420	31	23	optimize	optimize	VERB
ajst-15420	31	24	prediction	prediction	NOUN
ajst-15420	31	25	results[1,2	results[1,2	PROPN
ajst-15420	31	26	]	]	PUNCT
ajst-15420	31	27	.	.	PUNCT
ajst-15420	32	1	the	the	DET
ajst-15420	32	2	application	application	NOUN
ajst-15420	32	3	of	of	ADP
ajst-15420	32	4	machine	machine	NOUN
ajst-15420	32	5	learning	learn	VERB
ajst-15420	32	6	algorithms	algorithm	NOUN
ajst-15420	32	7	in	in	ADP
ajst-15420	32	8	steel	steel	NOUN
ajst-15420	32	9	performance	performance	NOUN
ajst-15420	32	10	prediction	prediction	NOUN
ajst-15420	32	11	can	can	AUX
ajst-15420	32	12	be	be	AUX
ajst-15420	32	13	divided	divide	VERB
ajst-15420	32	14	into	into	ADP
ajst-15420	32	15	two	two	NUM
ajst-15420	32	16	stages	stage	NOUN
ajst-15420	32	17	.	.	PUNCT
ajst-15420	33	1	in	in	ADP
ajst-15420	33	2	the	the	DET
ajst-15420	33	3	first	first	ADJ
ajst-15420	33	4	stage	stage	NOUN
ajst-15420	33	5	,	,	PUNCT
ajst-15420	33	6	machine	machine	NOUN
ajst-15420	33	7	learning	learning	NOUN
ajst-15420	33	8	is	be	AUX
ajst-15420	33	9	mainly	mainly	ADV
ajst-15420	33	10	used	use	VERB
ajst-15420	33	11	for	for	ADP
ajst-15420	33	12	feature	feature	NOUN
ajst-15420	33	13	extraction	extraction	NOUN
ajst-15420	33	14	and	and	CCONJ
ajst-15420	33	15	classification	classification	NOUN
ajst-15420	33	16	.	.	PUNCT
ajst-15420	34	1	by	by	ADP
ajst-15420	34	2	analyzing	analyze	VERB
ajst-15420	34	3	the	the	DET
ajst-15420	34	4	feature	feature	NOUN
ajst-15420	34	5	data	datum	NOUN
ajst-15420	34	6	of	of	ADP
ajst-15420	34	7	a	a	DET
ajst-15420	34	8	large	large	ADJ
ajst-15420	34	9	number	number	NOUN
ajst-15420	34	10	of	of	ADP
ajst-15420	34	11	known	know	VERB
ajst-15420	34	12	performance	performance	NOUN
ajst-15420	34	13	steel	steel	NOUN
ajst-15420	34	14	samples	sample	NOUN
ajst-15420	34	15	,	,	PUNCT
ajst-15420	34	16	machine	machine	NOUN
ajst-15420	34	17	learning	learning	NOUN
ajst-15420	34	18	algorithms	algorithm	NOUN
ajst-15420	34	19	can	can	AUX
ajst-15420	34	20	learn	learn	VERB
ajst-15420	34	21	the	the	DET
ajst-15420	34	22	mapping	mapping	NOUN
ajst-15420	34	23	relationship	relationship	NOUN
ajst-15420	34	24	between	between	ADP
ajst-15420	34	25	features	feature	NOUN
ajst-15420	34	26	and	and	CCONJ
ajst-15420	34	27	performance	performance	NOUN
ajst-15420	34	28	and	and	CCONJ
ajst-15420	34	29	classify	classify	VERB
ajst-15420	34	30	or	or	CCONJ
ajst-15420	34	31	predict	predict	VERB
ajst-15420	34	32	new	new	ADJ
ajst-15420	34	33	steel	steel	NOUN
ajst-15420	34	34	samples	sample	NOUN
ajst-15420	34	35	.	.	PUNCT
ajst-15420	35	1	for	for	ADP
ajst-15420	35	2	example	example	NOUN
ajst-15420	35	3	,	,	PUNCT
ajst-15420	35	4	machine	machine	NOUN
ajst-15420	35	5	learning	learn	VERB
ajst-15420	35	6	algorithms	algorithm	NOUN
ajst-15420	35	7	such	such	ADJ
ajst-15420	35	8	as	as	ADP
ajst-15420	35	9	bp	bp	PROPN
ajst-15420	35	10	neural	neural	ADJ
ajst-15420	35	11	network	network	NOUN
ajst-15420	35	12	and	and	CCONJ
ajst-15420	35	13	random	random	ADJ
ajst-15420	35	14	forest	forest	NOUN
ajst-15420	35	15	are	be	AUX
ajst-15420	35	16	widely	widely	ADV
ajst-15420	35	17	used	use	VERB
ajst-15420	35	18	for	for	ADP
ajst-15420	35	19	steel	steel	NOUN
ajst-15420	35	20	material	material	NOUN
ajst-15420	35	21	classification	classification	NOUN
ajst-15420	35	22	and	and	CCONJ
ajst-15420	35	23	identification[3,4	identification[3,4	NOUN
ajst-15420	35	24	]	]	PUNCT
ajst-15420	35	25	.	.	PUNCT
ajst-15420	36	1	by	by	ADP
ajst-15420	36	2	analyzing	analyze	VERB
ajst-15420	36	3	material	material	NOUN
ajst-15420	36	4	features	feature	NOUN
ajst-15420	36	5	such	such	ADJ
ajst-15420	36	6	as	as	ADP
ajst-15420	36	7	composition	composition	NOUN
ajst-15420	36	8	,	,	PUNCT
ajst-15420	36	9	microstructure	microstructure	NOUN
ajst-15420	36	10	,	,	PUNCT
ajst-15420	36	11	and	and	CCONJ
ajst-15420	36	12	heat	heat	NOUN
ajst-15420	36	13	treatment	treatment	NOUN
ajst-15420	36	14	process	process	NOUN
ajst-15420	36	15	data	datum	NOUN
ajst-15420	36	16	,	,	PUNCT
ajst-15420	36	17	machine	machine	NOUN
ajst-15420	36	18	learning	learning	NOUN
ajst-15420	36	19	algorithms	algorithm	NOUN
ajst-15420	36	20	can	can	AUX
ajst-15420	36	21	determine	determine	VERB
ajst-15420	36	22	the	the	DET
ajst-15420	36	23	type	type	NOUN
ajst-15420	36	24	,	,	PUNCT
ajst-15420	36	25	grade	grade	NOUN
ajst-15420	36	26	,	,	PUNCT
ajst-15420	36	27	or	or	CCONJ
ajst-15420	36	28	performance	performance	NOUN
ajst-15420	36	29	indicators	indicator	NOUN
ajst-15420	36	30	of	of	ADP
ajst-15420	36	31	the	the	DET
ajst-15420	36	32	material	material	NOUN
ajst-15420	36	33	to	to	PART
ajst-15420	36	34	provide	provide	VERB
ajst-15420	36	35	guidance	guidance	NOUN
ajst-15420	36	36	for	for	ADP
ajst-15420	36	37	material	material	NOUN
ajst-15420	36	38	design	design	NOUN
ajst-15420	36	39	and	and	CCONJ
ajst-15420	36	40	application	application	NOUN
ajst-15420	36	41	.	.	PUNCT
ajst-15420	37	1	with	with	ADP
ajst-15420	37	2	the	the	DET
ajst-15420	37	3	development	development	NOUN
ajst-15420	37	4	of	of	ADP
ajst-15420	37	5	deep	deep	ADJ
ajst-15420	37	6	learning	learning	NOUN
ajst-15420	37	7	technology	technology	NOUN
ajst-15420	37	8	,	,	PUNCT
ajst-15420	37	9	the	the	DET
ajst-15420	37	10	application	application	NOUN
ajst-15420	37	11	of	of	ADP
ajst-15420	37	12	machine	machine	NOUN
ajst-15420	37	13	learning	learning	NOUN
ajst-15420	37	14	in	in	ADP
ajst-15420	37	15	steel	steel	NOUN
ajst-15420	37	16	performance	performance	NOUN
ajst-15420	37	17	prediction	prediction	NOUN
ajst-15420	37	18	has	have	AUX
ajst-15420	37	19	entered	enter	VERB
ajst-15420	37	20	the	the	DET
ajst-15420	37	21	second	second	ADJ
ajst-15420	37	22	stage	stage	NOUN
ajst-15420	37	23	.	.	PUNCT
ajst-15420	38	1	deep	deep	ADJ
ajst-15420	38	2	learning	learning	NOUN
ajst-15420	38	3	algorithms	algorithm	NOUN
ajst-15420	38	4	can	can	AUX
ajst-15420	38	5	handle	handle	VERB
ajst-15420	38	6	more	more	ADJ
ajst-15420	38	7	complex	complex	ADJ
ajst-15420	38	8	features	feature	NOUN
ajst-15420	38	9	and	and	CCONJ
ajst-15420	38	10	models	model	NOUN
ajst-15420	38	11	and	and	CCONJ
ajst-15420	38	12	can	can	AUX
ajst-15420	38	13	automatically	automatically	ADV
ajst-15420	38	14	learn	learn	VERB
ajst-15420	38	15	and	and	CCONJ
ajst-15420	38	16	optimize	optimize	VERB
ajst-15420	38	17	model	model	NOUN
ajst-15420	38	18	parameters	parameter	NOUN
ajst-15420	38	19	.	.	PUNCT
ajst-15420	39	1	in	in	ADP
ajst-15420	39	2	steel	steel	NOUN
ajst-15420	39	3	performance	performance	NOUN
ajst-15420	39	4	prediction	prediction	NOUN
ajst-15420	39	5	,	,	PUNCT
ajst-15420	39	6	deep	deep	ADJ
ajst-15420	39	7	learning	learn	VERB
ajst-15420	39	8	2	2	NUM
ajst-15420	39	9	algorithms	algorithm	NOUN
ajst-15420	39	10	can	can	AUX
ajst-15420	39	11	better	well	ADV
ajst-15420	39	12	consider	consider	VERB
ajst-15420	39	13	the	the	DET
ajst-15420	39	14	influence	influence	NOUN
ajst-15420	39	15	of	of	ADP
ajst-15420	39	16	steel	steel	NOUN
ajst-15420	39	17	's	's	PART
ajst-15420	39	18	microstructure	microstructure	ADJ
ajst-15420	39	19	and	and	CCONJ
ajst-15420	39	20	chemical	chemical	NOUN
ajst-15420	39	21	composition	composition	NOUN
ajst-15420	39	22	on	on	ADP
ajst-15420	39	23	performance	performance	NOUN
ajst-15420	39	24	.	.	PUNCT
ajst-15420	40	1	at	at	ADP
ajst-15420	40	2	the	the	DET
ajst-15420	40	3	same	same	ADJ
ajst-15420	40	4	time	time	NOUN
ajst-15420	40	5	,	,	PUNCT
ajst-15420	40	6	deep	deep	ADJ
ajst-15420	40	7	learning	learning	NOUN
ajst-15420	40	8	can	can	AUX
ajst-15420	40	9	also	also	ADV
ajst-15420	40	10	combine	combine	VERB
ajst-15420	40	11	a	a	DET
ajst-15420	40	12	large	large	ADJ
ajst-15420	40	13	amount	amount	NOUN
ajst-15420	40	14	of	of	ADP
ajst-15420	40	15	historical	historical	ADJ
ajst-15420	40	16	data	datum	NOUN
ajst-15420	40	17	and	and	CCONJ
ajst-15420	40	18	real	real	ADJ
ajst-15420	40	19	-	-	PUNCT
ajst-15420	40	20	time	time	NOUN
ajst-15420	40	21	data	datum	NOUN
ajst-15420	40	22	for	for	ADP
ajst-15420	40	23	prediction	prediction	NOUN
ajst-15420	40	24	and	and	CCONJ
ajst-15420	40	25	analysis	analysis	NOUN
ajst-15420	40	26	,	,	PUNCT
ajst-15420	40	27	providing	provide	VERB
ajst-15420	40	28	more	more	ADV
ajst-15420	40	29	accurate	accurate	ADJ
ajst-15420	40	30	and	and	CCONJ
ajst-15420	40	31	real	real	ADJ
ajst-15420	40	32	-	-	PUNCT
ajst-15420	40	33	time	time	NOUN
ajst-15420	40	34	decision	decision	NOUN
ajst-15420	40	35	support	support	NOUN
ajst-15420	40	36	for	for	ADP
ajst-15420	40	37	material	material	NOUN
ajst-15420	40	38	design	design	NOUN
ajst-15420	40	39	and	and	CCONJ
ajst-15420	40	40	optimization	optimization	NOUN
ajst-15420	40	41	.	.	PUNCT
ajst-15420	41	1	for	for	ADP
ajst-15420	41	2	example	example	NOUN
ajst-15420	41	3	,	,	PUNCT
ajst-15420	41	4	deep	deep	ADJ
ajst-15420	41	5	learning	learning	NOUN
ajst-15420	41	6	algorithms	algorithm	NOUN
ajst-15420	41	7	such	such	ADJ
ajst-15420	41	8	as	as	ADP
ajst-15420	41	9	convolutional	convolutional	ADJ
ajst-15420	41	10	neural	neural	ADJ
ajst-15420	41	11	network	network	NOUN
ajst-15420	41	12	(	(	PUNCT
ajst-15420	41	13	cnn	cnn	PROPN
ajst-15420	41	14	)	)	PUNCT
ajst-15420	41	15	and	and	CCONJ
ajst-15420	41	16	recurrent	recurrent	ADJ
ajst-15420	41	17	neural	neural	ADJ
ajst-15420	41	18	network	network	NOUN
ajst-15420	41	19	(	(	PUNCT
ajst-15420	41	20	rnn	rnn	PROPN
ajst-15420	41	21	)	)	PUNCT
ajst-15420	41	22	have	have	AUX
ajst-15420	41	23	been	be	AUX
ajst-15420	41	24	widely	widely	ADV
ajst-15420	41	25	used	use	VERB
ajst-15420	41	26	in	in	ADP
ajst-15420	41	27	steel	steel	NOUN
ajst-15420	41	28	material	material	NOUN
ajst-15420	41	29	performance	performance	NOUN
ajst-15420	41	30	prediction	prediction	NOUN
ajst-15420	41	31	and	and	CCONJ
ajst-15420	41	32	process	process	NOUN
ajst-15420	41	33	optimization[5	optimization[5	NOUN
ajst-15420	41	34	]	]	PUNCT
ajst-15420	41	35	.	.	PUNCT
ajst-15420	42	1	overall	overall	ADV
ajst-15420	42	2	,	,	PUNCT
ajst-15420	42	3	the	the	DET
ajst-15420	42	4	prediction	prediction	NOUN
ajst-15420	42	5	of	of	ADP
ajst-15420	42	6	steel	steel	NOUN
ajst-15420	42	7	performance	performance	NOUN
ajst-15420	42	8	is	be	AUX
ajst-15420	42	9	a	a	DET
ajst-15420	42	10	continuous	continuous	ADJ
ajst-15420	42	11	process	process	NOUN
ajst-15420	42	12	of	of	ADP
ajst-15420	42	13	development	development	NOUN
ajst-15420	42	14	and	and	CCONJ
ajst-15420	42	15	progress	progress	NOUN
ajst-15420	42	16	.	.	PUNCT
ajst-15420	43	1	from	from	ADP
ajst-15420	43	2	ancient	ancient	ADJ
ajst-15420	43	3	empirical	empirical	ADJ
ajst-15420	43	4	practices	practice	NOUN
ajst-15420	43	5	to	to	ADP
ajst-15420	43	6	modern	modern	ADJ
ajst-15420	43	7	applications	application	NOUN
ajst-15420	43	8	of	of	ADP
ajst-15420	43	9	computer	computer	NOUN
ajst-15420	43	10	models	model	NOUN
ajst-15420	43	11	and	and	CCONJ
ajst-15420	43	12	artificial	artificial	ADJ
ajst-15420	43	13	intelligence	intelligence	NOUN
ajst-15420	43	14	algorithms	algorithm	NOUN
ajst-15420	43	15	,	,	PUNCT
ajst-15420	43	16	this	this	DET
ajst-15420	43	17	field	field	NOUN
ajst-15420	43	18	has	have	AUX
ajst-15420	43	19	been	be	AUX
ajst-15420	43	20	constantly	constantly	ADV
ajst-15420	43	21	pushed	push	VERB
ajst-15420	43	22	forward	forward	ADV
ajst-15420	43	23	by	by	ADP
ajst-15420	43	24	technological	technological	ADJ
ajst-15420	43	25	advancements	advancement	NOUN
ajst-15420	43	26	.	.	PUNCT
ajst-15420	44	1	with	with	ADP
ajst-15420	44	2	the	the	DET
ajst-15420	44	3	continuous	continuous	ADJ
ajst-15420	44	4	development	development	NOUN
ajst-15420	44	5	and	and	CCONJ
ajst-15420	44	6	improvement	improvement	NOUN
ajst-15420	44	7	of	of	ADP
ajst-15420	44	8	technology	technology	NOUN
ajst-15420	44	9	,	,	PUNCT
ajst-15420	44	10	the	the	DET
ajst-15420	44	11	prediction	prediction	NOUN
ajst-15420	44	12	of	of	ADP
ajst-15420	44	13	steel	steel	NOUN
ajst-15420	44	14	performance	performance	NOUN
ajst-15420	44	15	in	in	ADP
ajst-15420	44	16	the	the	DET
ajst-15420	44	17	future	future	NOUN
ajst-15420	44	18	will	will	AUX
ajst-15420	44	19	become	become	VERB
ajst-15420	44	20	more	more	ADV
ajst-15420	44	21	accurate	accurate	ADJ
ajst-15420	44	22	,	,	PUNCT
ajst-15420	44	23	efficient	efficient	ADJ
ajst-15420	44	24	,	,	PUNCT
ajst-15420	44	25	and	and	CCONJ
ajst-15420	44	26	innovative	innovative	ADJ
ajst-15420	44	27	,	,	PUNCT
ajst-15420	44	28	with	with	ADP
ajst-15420	44	29	broader	broad	ADJ
ajst-15420	44	30	application	application	NOUN
ajst-15420	44	31	scenarios	scenario	NOUN
ajst-15420	44	32	.	.	PUNCT
ajst-15420	45	1	2	2	X
ajst-15420	45	2	.	.	X
ajst-15420	45	3	model	model	NOUN
ajst-15420	45	4	establishment	establishment	NOUN
ajst-15420	45	5	2.1	2.1	NUM
ajst-15420	45	6	.	.	PUNCT
ajst-15420	46	1	bp	bp	PROPN
ajst-15420	46	2	neural	neural	PROPN
ajst-15420	46	3	network	network	PROPN
ajst-15420	46	4	back	back	ADV
ajst-15420	46	5	propagation	propagation	NOUN
ajst-15420	46	6	neural	neural	ADJ
ajst-15420	46	7	network	network	NOUN
ajst-15420	46	8	(	(	PUNCT
ajst-15420	46	9	bpnn	bpnn	NOUN
ajst-15420	46	10	)	)	PUNCT
ajst-15420	46	11	is	be	AUX
ajst-15420	46	12	a	a	DET
ajst-15420	46	13	multilayer	multilayer	ADJ
ajst-15420	46	14	feedforward	feedforward	NOUN
ajst-15420	46	15	neural	neural	ADJ
ajst-15420	46	16	network	network	NOUN
ajst-15420	46	17	that	that	PRON
ajst-15420	46	18	trains	train	VERB
ajst-15420	46	19	the	the	DET
ajst-15420	46	20	network	network	NOUN
ajst-15420	46	21	through	through	ADP
ajst-15420	46	22	the	the	DET
ajst-15420	46	23	method	method	NOUN
ajst-15420	46	24	of	of	ADP
ajst-15420	46	25	error	error	NOUN
ajst-15420	46	26	backpropagation[6,7	backpropagation[6,7	NOUN
ajst-15420	46	27	]	]	PUNCT
ajst-15420	46	28	.	.	PUNCT
ajst-15420	47	1	it	it	PRON
ajst-15420	47	2	consists	consist	VERB
ajst-15420	47	3	of	of	ADP
ajst-15420	47	4	an	an	DET
ajst-15420	47	5	input	input	NOUN
ajst-15420	47	6	layer	layer	NOUN
ajst-15420	47	7	,	,	PUNCT
ajst-15420	47	8	a	a	DET
ajst-15420	47	9	hidden	hidden	ADJ
ajst-15420	47	10	layer	layer	NOUN
ajst-15420	47	11	,	,	PUNCT
ajst-15420	47	12	and	and	CCONJ
ajst-15420	47	13	an	an	DET
ajst-15420	47	14	output	output	NOUN
ajst-15420	47	15	layer	layer	NOUN
ajst-15420	47	16	,	,	PUNCT
ajst-15420	47	17	and	and	CCONJ
ajst-15420	47	18	approximates	approximate	VERB
ajst-15420	47	19	the	the	DET
ajst-15420	47	20	target	target	NOUN
ajst-15420	47	21	function	function	NOUN
ajst-15420	47	22	by	by	ADP
ajst-15420	47	23	adjusting	adjust	VERB
ajst-15420	47	24	the	the	DET
ajst-15420	47	25	network	network	NOUN
ajst-15420	47	26	weights	weight	NOUN
ajst-15420	47	27	and	and	CCONJ
ajst-15420	47	28	thresholds	threshold	NOUN
ajst-15420	47	29	.	.	PUNCT
ajst-15420	48	1	the	the	DET
ajst-15420	48	2	core	core	ADJ
ajst-15420	48	3	idea	idea	NOUN
ajst-15420	48	4	of	of	ADP
ajst-15420	48	5	bpnn	bpnn	NOUN
ajst-15420	48	6	is	be	AUX
ajst-15420	48	7	the	the	DET
ajst-15420	48	8	learning	learning	NOUN
ajst-15420	48	9	rule	rule	NOUN
ajst-15420	48	10	,	,	PUNCT
ajst-15420	48	11	which	which	PRON
ajst-15420	48	12	approximates	approximate	VERB
ajst-15420	48	13	the	the	DET
ajst-15420	48	14	target	target	NOUN
ajst-15420	48	15	function	function	NOUN
ajst-15420	48	16	by	by	ADP
ajst-15420	48	17	learning	learn	VERB
ajst-15420	48	18	the	the	DET
ajst-15420	48	19	characteristics	characteristic	NOUN
ajst-15420	48	20	of	of	ADP
ajst-15420	48	21	sample	sample	NOUN
ajst-15420	48	22	data	datum	NOUN
ajst-15420	48	23	.	.	PUNCT
ajst-15420	49	1	it	it	PRON
ajst-15420	49	2	uses	use	VERB
ajst-15420	49	3	the	the	DET
ajst-15420	49	4	gradient	gradient	ADJ
ajst-15420	49	5	descent	descent	NOUN
ajst-15420	49	6	method	method	NOUN
ajst-15420	49	7	to	to	PART
ajst-15420	49	8	adjust	adjust	VERB
ajst-15420	49	9	the	the	DET
ajst-15420	49	10	weights	weight	NOUN
ajst-15420	49	11	and	and	CCONJ
ajst-15420	49	12	thresholds	threshold	NOUN
ajst-15420	49	13	,	,	PUNCT
ajst-15420	49	14	and	and	CCONJ
ajst-15420	49	15	adjusts	adjust	VERB
ajst-15420	49	16	the	the	DET
ajst-15420	49	17	weights	weight	NOUN
ajst-15420	49	18	and	and	CCONJ
ajst-15420	49	19	thresholds	threshold	NOUN
ajst-15420	49	20	of	of	ADP
ajst-15420	49	21	the	the	DET
ajst-15420	49	22	hidden	hide	VERB
ajst-15420	49	23	layer	layer	NOUN
ajst-15420	49	24	by	by	ADP
ajst-15420	49	25	calculating	calculate	VERB
ajst-15420	49	26	the	the	DET
ajst-15420	49	27	error	error	NOUN
ajst-15420	49	28	of	of	ADP
ajst-15420	49	29	the	the	DET
ajst-15420	49	30	output	output	NOUN
ajst-15420	49	31	layer	layer	NOUN
ajst-15420	49	32	,	,	PUNCT
ajst-15420	49	33	so	so	SCONJ
ajst-15420	49	34	that	that	SCONJ
ajst-15420	49	35	the	the	DET
ajst-15420	49	36	error	error	NOUN
ajst-15420	49	37	between	between	ADP
ajst-15420	49	38	the	the	DET
ajst-15420	49	39	output	output	NOUN
ajst-15420	49	40	value	value	NOUN
ajst-15420	49	41	of	of	ADP
ajst-15420	49	42	the	the	DET
ajst-15420	49	43	network	network	NOUN
ajst-15420	49	44	and	and	CCONJ
ajst-15420	49	45	the	the	DET
ajst-15420	49	46	target	target	NOUN
ajst-15420	49	47	value	value	NOUN
ajst-15420	49	48	is	be	AUX
ajst-15420	49	49	minimized[8	minimized[8	NOUN
ajst-15420	49	50	]	]	X
ajst-15420	49	51	.	.	PUNCT
ajst-15420	50	1	bpnn	bpnn	PROPN
ajst-15420	50	2	has	have	VERB
ajst-15420	50	3	the	the	DET
ajst-15420	50	4	characteristics	characteristic	NOUN
ajst-15420	50	5	of	of	ADP
ajst-15420	50	6	self	self	NOUN
ajst-15420	50	7	-	-	PUNCT
ajst-15420	50	8	learning	learning	NOUN
ajst-15420	50	9	,	,	PUNCT
ajst-15420	50	10	selforganization	selforganization	NOUN
ajst-15420	50	11	,	,	PUNCT
ajst-15420	50	12	and	and	CCONJ
ajst-15420	50	13	strong	strong	ADJ
ajst-15420	50	14	adaptability	adaptability	NOUN
ajst-15420	50	15	,	,	PUNCT
ajst-15420	50	16	and	and	CCONJ
ajst-15420	50	17	can	can	AUX
ajst-15420	50	18	handle	handle	VERB
ajst-15420	50	19	complex	complex	ADJ
ajst-15420	50	20	nonlinear	nonlinear	ADJ
ajst-15420	50	21	problems	problem	NOUN
ajst-15420	50	22	.	.	PUNCT
ajst-15420	51	1	it	it	PRON
ajst-15420	51	2	is	be	AUX
ajst-15420	51	3	widely	widely	ADV
ajst-15420	51	4	used	use	VERB
ajst-15420	51	5	in	in	ADP
ajst-15420	51	6	classification	classification	NOUN
ajst-15420	51	7	,	,	PUNCT
ajst-15420	51	8	function	function	NOUN
ajst-15420	51	9	approximation	approximation	NOUN
ajst-15420	51	10	,	,	PUNCT
ajst-15420	51	11	optimization	optimization	NOUN
ajst-15420	51	12	,	,	PUNCT
ajst-15420	51	13	and	and	CCONJ
ajst-15420	51	14	other	other	ADJ
ajst-15420	51	15	fields	field	NOUN
ajst-15420	51	16	,	,	PUNCT
ajst-15420	51	17	and	and	CCONJ
ajst-15420	51	18	has	have	AUX
ajst-15420	51	19	also	also	ADV
ajst-15420	51	20	been	be	AUX
ajst-15420	51	21	widely	widely	ADV
ajst-15420	51	22	applied	apply	VERB
ajst-15420	51	23	in	in	ADP
ajst-15420	51	24	steel	steel	NOUN
ajst-15420	51	25	property	property	NOUN
ajst-15420	51	26	prediction[9	prediction[9	ADV
ajst-15420	51	27	]	]	PUNCT
ajst-15420	51	28	.	.	PUNCT
ajst-15420	52	1	bpnn	bpnn	PROPN
ajst-15420	52	2	includes	include	VERB
ajst-15420	52	3	input	input	NOUN
ajst-15420	52	4	layer	layer	NOUN
ajst-15420	52	5	,	,	PUNCT
ajst-15420	52	6	hidden	hide	VERB
ajst-15420	52	7	layer	layer	NOUN
ajst-15420	52	8	,	,	PUNCT
ajst-15420	52	9	and	and	CCONJ
ajst-15420	52	10	output	output	NOUN
ajst-15420	52	11	layer	layer	NOUN
ajst-15420	52	12	.	.	PUNCT
ajst-15420	53	1	the	the	DET
ajst-15420	53	2	number	number	NOUN
ajst-15420	53	3	of	of	ADP
ajst-15420	53	4	nodes	node	NOUN
ajst-15420	53	5	in	in	ADP
ajst-15420	53	6	the	the	DET
ajst-15420	53	7	input	input	NOUN
ajst-15420	53	8	layer	layer	NOUN
ajst-15420	53	9	is	be	AUX
ajst-15420	53	10	equal	equal	ADJ
ajst-15420	53	11	to	to	ADP
ajst-15420	53	12	the	the	DET
ajst-15420	53	13	number	number	NOUN
ajst-15420	53	14	of	of	ADP
ajst-15420	53	15	input	input	NOUN
ajst-15420	53	16	data	data	NOUN
ajst-15420	53	17	features	feature	NOUN
ajst-15420	53	18	,	,	PUNCT
ajst-15420	53	19	and	and	CCONJ
ajst-15420	53	20	the	the	DET
ajst-15420	53	21	number	number	NOUN
ajst-15420	53	22	of	of	ADP
ajst-15420	53	23	nodes	node	NOUN
ajst-15420	53	24	in	in	ADP
ajst-15420	53	25	the	the	DET
ajst-15420	53	26	hidden	hide	VERB
ajst-15420	53	27	layer	layer	NOUN
ajst-15420	53	28	can	can	AUX
ajst-15420	53	29	be	be	AUX
ajst-15420	53	30	adjusted	adjust	VERB
ajst-15420	53	31	according	accord	VERB
ajst-15420	53	32	to	to	ADP
ajst-15420	53	33	the	the	DET
ajst-15420	53	34	complexity	complexity	NOUN
ajst-15420	53	35	of	of	ADP
ajst-15420	53	36	the	the	DET
ajst-15420	53	37	problem	problem	NOUN
ajst-15420	53	38	.	.	PUNCT
ajst-15420	54	1	the	the	DET
ajst-15420	54	2	number	number	NOUN
ajst-15420	54	3	of	of	ADP
ajst-15420	54	4	nodes	node	NOUN
ajst-15420	54	5	in	in	ADP
ajst-15420	54	6	the	the	DET
ajst-15420	54	7	output	output	NOUN
ajst-15420	54	8	layer	layer	NOUN
ajst-15420	54	9	is	be	AUX
ajst-15420	54	10	equal	equal	ADJ
ajst-15420	54	11	to	to	ADP
ajst-15420	54	12	the	the	DET
ajst-15420	54	13	number	number	NOUN
ajst-15420	54	14	of	of	ADP
ajst-15420	54	15	predicted	predict	VERB
ajst-15420	54	16	target	target	NOUN
ajst-15420	54	17	variables	variable	NOUN
ajst-15420	54	18	.	.	PUNCT
ajst-15420	55	1	there	there	PRON
ajst-15420	55	2	are	be	VERB
ajst-15420	55	3	one	one	NUM
ajst-15420	55	4	or	or	CCONJ
ajst-15420	55	5	more	more	ADJ
ajst-15420	55	6	layers	layer	NOUN
ajst-15420	55	7	of	of	ADP
ajst-15420	55	8	neurons	neuron	NOUN
ajst-15420	55	9	between	between	ADP
ajst-15420	55	10	the	the	DET
ajst-15420	55	11	hidden	hide	VERB
ajst-15420	55	12	layer	layer	NOUN
ajst-15420	55	13	and	and	CCONJ
ajst-15420	55	14	the	the	DET
ajst-15420	55	15	output	output	NOUN
ajst-15420	55	16	layer	layer	NOUN
ajst-15420	55	17	,	,	PUNCT
ajst-15420	55	18	which	which	PRON
ajst-15420	55	19	are	be	AUX
ajst-15420	55	20	connected	connect	VERB
ajst-15420	55	21	together	together	ADV
ajst-15420	55	22	through	through	ADP
ajst-15420	55	23	full	full	ADJ
ajst-15420	55	24	connection	connection	NOUN
ajst-15420	55	25	.	.	PUNCT
ajst-15420	56	1	in	in	ADP
ajst-15420	56	2	the	the	DET
ajst-15420	56	3	training	training	NOUN
ajst-15420	56	4	process	process	NOUN
ajst-15420	56	5	,	,	PUNCT
ajst-15420	56	6	the	the	DET
ajst-15420	56	7	input	input	NOUN
ajst-15420	56	8	data	data	NOUN
ajst-15420	56	9	enters	enter	VERB
ajst-15420	56	10	the	the	DET
ajst-15420	56	11	network	network	NOUN
ajst-15420	56	12	through	through	ADP
ajst-15420	56	13	the	the	DET
ajst-15420	56	14	input	input	NOUN
ajst-15420	56	15	layer	layer	NOUN
ajst-15420	56	16	,	,	PUNCT
ajst-15420	56	17	and	and	CCONJ
ajst-15420	56	18	is	be	AUX
ajst-15420	56	19	calculated	calculate	VERB
ajst-15420	56	20	and	and	CCONJ
ajst-15420	56	21	processed	process	VERB
ajst-15420	56	22	by	by	ADP
ajst-15420	56	23	the	the	DET
ajst-15420	56	24	hidden	hide	VERB
ajst-15420	56	25	layer	layer	NOUN
ajst-15420	56	26	and	and	CCONJ
ajst-15420	56	27	output	output	NOUN
ajst-15420	56	28	layer	layer	NOUN
ajst-15420	56	29	to	to	PART
ajst-15420	56	30	obtain	obtain	VERB
ajst-15420	56	31	the	the	DET
ajst-15420	56	32	predicted	predict	VERB
ajst-15420	56	33	results	result	NOUN
ajst-15420	56	34	.	.	PUNCT
ajst-15420	57	1	then	then	ADV
ajst-15420	57	2	,	,	PUNCT
ajst-15420	57	3	the	the	DET
ajst-15420	57	4	predicted	predict	VERB
ajst-15420	57	5	results	result	NOUN
ajst-15420	57	6	are	be	AUX
ajst-15420	57	7	compared	compare	VERB
ajst-15420	57	8	with	with	ADP
ajst-15420	57	9	the	the	DET
ajst-15420	57	10	actual	actual	ADJ
ajst-15420	57	11	results	result	NOUN
ajst-15420	57	12	,	,	PUNCT
ajst-15420	57	13	the	the	DET
ajst-15420	57	14	error	error	NOUN
ajst-15420	57	15	is	be	AUX
ajst-15420	57	16	calculated	calculate	VERB
ajst-15420	57	17	,	,	PUNCT
ajst-15420	57	18	and	and	CCONJ
ajst-15420	57	19	it	it	PRON
ajst-15420	57	20	is	be	AUX
ajst-15420	57	21	backpropagated	backpropagate	VERB
ajst-15420	57	22	to	to	ADP
ajst-15420	57	23	the	the	DET
ajst-15420	57	24	hidden	hide	VERB
ajst-15420	57	25	layer	layer	NOUN
ajst-15420	57	26	and	and	CCONJ
ajst-15420	57	27	input	input	NOUN
ajst-15420	57	28	layer	layer	NOUN
ajst-15420	57	29	to	to	PART
ajst-15420	57	30	adjust	adjust	VERB
ajst-15420	57	31	the	the	DET
ajst-15420	57	32	weights	weight	NOUN
ajst-15420	57	33	and	and	CCONJ
ajst-15420	57	34	thresholds	threshold	NOUN
ajst-15420	57	35	of	of	ADP
ajst-15420	57	36	neurons	neuron	NOUN
ajst-15420	57	37	,	,	PUNCT
ajst-15420	57	38	so	so	SCONJ
ajst-15420	57	39	that	that	SCONJ
ajst-15420	57	40	the	the	DET
ajst-15420	57	41	error	error	NOUN
ajst-15420	57	42	between	between	ADP
ajst-15420	57	43	the	the	DET
ajst-15420	57	44	network	network	NOUN
ajst-15420	57	45	output	output	NOUN
ajst-15420	57	46	value	value	NOUN
ajst-15420	57	47	and	and	CCONJ
ajst-15420	57	48	the	the	DET
ajst-15420	57	49	target	target	NOUN
ajst-15420	57	50	value	value	NOUN
ajst-15420	57	51	is	be	AUX
ajst-15420	57	52	minimized	minimize	VERB
ajst-15420	57	53	.	.	PUNCT
ajst-15420	58	1	the	the	DET
ajst-15420	58	2	schematic	schematic	ADJ
ajst-15420	58	3	diagram	diagram	NOUN
ajst-15420	58	4	of	of	ADP
ajst-15420	58	5	bpnn	bpnn	NOUN
ajst-15420	58	6	is	be	AUX
ajst-15420	58	7	shown	show	VERB
ajst-15420	58	8	in	in	ADP
ajst-15420	58	9	figure	figure	NOUN
ajst-15420	58	10	1	1	NUM
ajst-15420	58	11	.	.	PUNCT
ajst-15420	58	12	figure	figure	NOUN
ajst-15420	58	13	1	1	NUM
ajst-15420	58	14	.	.	PUNCT
ajst-15420	58	15	bp	bp	PROPN
ajst-15420	58	16	neural	neural	PROPN
ajst-15420	58	17	network	network	PROPN
ajst-15420	58	18	schematic	schematic	ADJ
ajst-15420	58	19	diagram[10	diagram[10	X
ajst-15420	58	20	]	]	PUNCT
ajst-15420	58	21	overall	overall	ADV
ajst-15420	58	22	,	,	PUNCT
ajst-15420	58	23	bpnn	bpnn	PROPN
ajst-15420	58	24	is	be	AUX
ajst-15420	58	25	a	a	DET
ajst-15420	58	26	widely	widely	ADV
ajst-15420	58	27	applicable	applicable	ADJ
ajst-15420	58	28	neural	neural	ADJ
ajst-15420	58	29	network	network	NOUN
ajst-15420	58	30	model	model	NOUN
ajst-15420	58	31	that	that	PRON
ajst-15420	58	32	can	can	AUX
ajst-15420	58	33	handle	handle	VERB
ajst-15420	58	34	complex	complex	ADJ
ajst-15420	58	35	nonlinear	nonlinear	ADJ
ajst-15420	58	36	problems	problem	NOUN
ajst-15420	58	37	and	and	CCONJ
ajst-15420	58	38	has	have	VERB
ajst-15420	58	39	the	the	DET
ajst-15420	58	40	characteristics	characteristic	NOUN
ajst-15420	58	41	of	of	ADP
ajst-15420	58	42	self	self	NOUN
ajst-15420	58	43	-	-	PUNCT
ajst-15420	58	44	learning	learning	NOUN
ajst-15420	58	45	,	,	PUNCT
ajst-15420	58	46	self	self	NOUN
ajst-15420	58	47	-	-	PUNCT
ajst-15420	58	48	organization	organization	NOUN
ajst-15420	58	49	,	,	PUNCT
ajst-15420	58	50	and	and	CCONJ
ajst-15420	58	51	strong	strong	ADJ
ajst-15420	58	52	adaptability	adaptability	NOUN
ajst-15420	58	53	.	.	PUNCT
ajst-15420	59	1	in	in	ADP
ajst-15420	59	2	steel	steel	NOUN
ajst-15420	59	3	property	property	NOUN
ajst-15420	59	4	prediction	prediction	NOUN
ajst-15420	59	5	,	,	PUNCT
ajst-15420	59	6	bpnn	bpnn	PROPN
ajst-15420	59	7	can	can	AUX
ajst-15420	59	8	help	help	VERB
ajst-15420	59	9	researchers	researcher	NOUN
ajst-15420	59	10	better	well	ADV
ajst-15420	59	11	understand	understand	VERB
ajst-15420	59	12	and	and	CCONJ
ajst-15420	59	13	predict	predict	VERB
ajst-15420	59	14	the	the	DET
ajst-15420	59	15	performance	performance	NOUN
ajst-15420	59	16	of	of	ADP
ajst-15420	59	17	steel	steel	NOUN
ajst-15420	59	18	,	,	PUNCT
ajst-15420	59	19	providing	provide	VERB
ajst-15420	59	20	important	important	ADJ
ajst-15420	59	21	support	support	NOUN
ajst-15420	59	22	for	for	ADP
ajst-15420	59	23	material	material	NOUN
ajst-15420	59	24	design	design	NOUN
ajst-15420	59	25	and	and	CCONJ
ajst-15420	59	26	optimization	optimization	NOUN
ajst-15420	59	27	.	.	PUNCT
ajst-15420	60	1	2.2	2.2	NUM
ajst-15420	60	2	.	.	PUNCT
ajst-15420	60	3	genetic	genetic	ADJ
ajst-15420	60	4	algorithm	algorithm	NOUN
ajst-15420	60	5	although	although	SCONJ
ajst-15420	60	6	bp	bp	PROPN
ajst-15420	60	7	neural	neural	ADJ
ajst-15420	60	8	network	network	NOUN
ajst-15420	60	9	has	have	VERB
ajst-15420	60	10	extensive	extensive	ADJ
ajst-15420	60	11	applications	application	NOUN
ajst-15420	60	12	,	,	PUNCT
ajst-15420	60	13	it	it	PRON
ajst-15420	60	14	also	also	ADV
ajst-15420	60	15	has	have	VERB
ajst-15420	60	16	some	some	DET
ajst-15420	60	17	drawbacks	drawback	NOUN
ajst-15420	60	18	.	.	PUNCT
ajst-15420	61	1	firstly	firstly	ADV
ajst-15420	61	2	,	,	PUNCT
ajst-15420	61	3	the	the	DET
ajst-15420	61	4	learning	learn	VERB
ajst-15420	61	5	convergence	convergence	NOUN
ajst-15420	61	6	speed	speed	NOUN
ajst-15420	61	7	of	of	ADP
ajst-15420	61	8	bp	bp	PROPN
ajst-15420	61	9	neural	neural	ADJ
ajst-15420	61	10	network	network	NOUN
ajst-15420	61	11	is	be	AUX
ajst-15420	61	12	slow	slow	ADJ
ajst-15420	61	13	,	,	PUNCT
ajst-15420	61	14	and	and	CCONJ
ajst-15420	61	15	even	even	ADV
ajst-15420	61	16	for	for	ADP
ajst-15420	61	17	a	a	DET
ajst-15420	61	18	simple	simple	ADJ
ajst-15420	61	19	problem	problem	NOUN
ajst-15420	61	20	,	,	PUNCT
ajst-15420	61	21	it	it	PRON
ajst-15420	61	22	may	may	AUX
ajst-15420	61	23	require	require	VERB
ajst-15420	61	24	hundreds	hundred	NOUN
ajst-15420	61	25	or	or	CCONJ
ajst-15420	61	26	even	even	ADV
ajst-15420	61	27	thousands	thousand	NOUN
ajst-15420	61	28	of	of	ADP
ajst-15420	61	29	iterations	iteration	NOUN
ajst-15420	61	30	to	to	PART
ajst-15420	61	31	converge	converge	VERB
ajst-15420	61	32	.	.	PUNCT
ajst-15420	62	1	secondly	secondly	ADV
ajst-15420	62	2	,	,	PUNCT
ajst-15420	62	3	bp	bp	PROPN
ajst-15420	62	4	neural	neural	ADJ
ajst-15420	62	5	network	network	NOUN
ajst-15420	62	6	can	can	AUX
ajst-15420	62	7	not	not	PART
ajst-15420	62	8	guarantee	guarantee	VERB
ajst-15420	62	9	convergence	convergence	NOUN
ajst-15420	62	10	to	to	ADP
ajst-15420	62	11	the	the	DET
ajst-15420	62	12	global	global	ADJ
ajst-15420	62	13	minimum	minimum	NOUN
ajst-15420	62	14	point	point	NOUN
ajst-15420	62	15	,	,	PUNCT
ajst-15420	62	16	which	which	PRON
ajst-15420	62	17	may	may	AUX
ajst-15420	62	18	lead	lead	VERB
ajst-15420	62	19	to	to	ADP
ajst-15420	62	20	unsatisfactory	unsatisfactory	ADJ
ajst-15420	62	21	training	training	NOUN
ajst-15420	62	22	results	result	NOUN
ajst-15420	62	23	.	.	PUNCT
ajst-15420	63	1	in	in	ADP
ajst-15420	63	2	addition	addition	NOUN
ajst-15420	63	3	,	,	PUNCT
ajst-15420	63	4	the	the	DET
ajst-15420	63	5	selection	selection	NOUN
ajst-15420	63	6	of	of	ADP
ajst-15420	63	7	network	network	NOUN
ajst-15420	63	8	structure	structure	NOUN
ajst-15420	63	9	,	,	PUNCT
ajst-15420	63	10	initial	initial	ADJ
ajst-15420	63	11	connection	connection	NOUN
ajst-15420	63	12	weights	weight	NOUN
ajst-15420	63	13	and	and	CCONJ
ajst-15420	63	14	thresholds	threshold	NOUN
ajst-15420	63	15	has	have	VERB
ajst-15420	63	16	a	a	DET
ajst-15420	63	17	great	great	ADJ
ajst-15420	63	18	influence	influence	NOUN
ajst-15420	63	19	on	on	ADP
ajst-15420	63	20	network	network	NOUN
ajst-15420	63	21	training	training	NOUN
ajst-15420	63	22	,	,	PUNCT
ajst-15420	63	23	but	but	CCONJ
ajst-15420	63	24	the	the	DET
ajst-15420	63	25	determination	determination	NOUN
ajst-15420	63	26	of	of	ADP
ajst-15420	63	27	these	these	DET
ajst-15420	63	28	parameters	parameter	NOUN
ajst-15420	63	29	often	often	ADV
ajst-15420	63	30	relies	rely	VERB
ajst-15420	63	31	on	on	ADP
ajst-15420	63	32	experience	experience	NOUN
ajst-15420	63	33	and	and	CCONJ
ajst-15420	63	34	trial	trial	NOUN
ajst-15420	63	35	and	and	CCONJ
ajst-15420	63	36	error	error	NOUN
ajst-15420	63	37	.	.	PUNCT
ajst-15420	64	1	furthermore	furthermore	ADV
ajst-15420	64	2	,	,	PUNCT
ajst-15420	64	3	bp	bp	PROPN
ajst-15420	64	4	neural	neural	ADJ
ajst-15420	64	5	network	network	NOUN
ajst-15420	64	6	also	also	ADV
ajst-15420	64	7	has	have	VERB
ajst-15420	64	8	the	the	DET
ajst-15420	64	9	problem	problem	NOUN
ajst-15420	64	10	of	of	ADP
ajst-15420	64	11	overfitting	overfitte	VERB
ajst-15420	64	12	.	.	PUNCT
ajst-15420	65	1	when	when	SCONJ
ajst-15420	65	2	the	the	DET
ajst-15420	65	3	training	training	NOUN
ajst-15420	65	4	sample	sample	NOUN
ajst-15420	65	5	is	be	AUX
ajst-15420	65	6	insufficient	insufficient	ADJ
ajst-15420	65	7	or	or	CCONJ
ajst-15420	65	8	the	the	DET
ajst-15420	65	9	network	network	NOUN
ajst-15420	65	10	structure	structure	NOUN
ajst-15420	65	11	is	be	AUX
ajst-15420	65	12	too	too	ADV
ajst-15420	65	13	complex	complex	ADJ
ajst-15420	65	14	,	,	PUNCT
ajst-15420	65	15	the	the	DET
ajst-15420	65	16	network	network	NOUN
ajst-15420	65	17	may	may	AUX
ajst-15420	65	18	overfit	overfit	VERB
ajst-15420	65	19	the	the	DET
ajst-15420	65	20	training	training	NOUN
ajst-15420	65	21	sample	sample	NOUN
ajst-15420	65	22	,	,	PUNCT
ajst-15420	65	23	leading	lead	VERB
ajst-15420	65	24	to	to	ADP
ajst-15420	65	25	a	a	DET
ajst-15420	65	26	decrease	decrease	NOUN
ajst-15420	65	27	in	in	ADP
ajst-15420	65	28	generalization	generalization	NOUN
ajst-15420	65	29	ability	ability	NOUN
ajst-15420	65	30	.	.	PUNCT
ajst-15420	66	1	to	to	PART
ajst-15420	66	2	avoid	avoid	VERB
ajst-15420	66	3	overfitting	overfitte	VERB
ajst-15420	66	4	,	,	PUNCT
ajst-15420	66	5	it	it	PRON
ajst-15420	66	6	is	be	AUX
ajst-15420	66	7	necessary	necessary	ADJ
ajst-15420	66	8	to	to	PART
ajst-15420	66	9	select	select	VERB
ajst-15420	66	10	the	the	DET
ajst-15420	66	11	network	network	NOUN
ajst-15420	66	12	structure	structure	NOUN
ajst-15420	66	13	and	and	CCONJ
ajst-15420	66	14	parameters	parameter	NOUN
ajst-15420	66	15	reasonably	reasonably	ADV
ajst-15420	66	16	and	and	CCONJ
ajst-15420	66	17	use	use	VERB
ajst-15420	66	18	regularization	regularization	NOUN
ajst-15420	66	19	methods	method	NOUN
ajst-15420	66	20	to	to	PART
ajst-15420	66	21	enhance	enhance	VERB
ajst-15420	66	22	the	the	DET
ajst-15420	66	23	generalization	generalization	NOUN
ajst-15420	66	24	ability	ability	NOUN
ajst-15420	66	25	of	of	ADP
ajst-15420	66	26	the	the	DET
ajst-15420	66	27	network	network	NOUN
ajst-15420	66	28	.	.	PUNCT
ajst-15420	67	1	in	in	ADP
ajst-15420	67	2	addition	addition	NOUN
ajst-15420	67	3	,	,	PUNCT
ajst-15420	67	4	the	the	DET
ajst-15420	67	5	training	training	NOUN
ajst-15420	67	6	process	process	NOUN
ajst-15420	67	7	of	of	ADP
ajst-15420	67	8	bp	bp	PROPN
ajst-15420	67	9	neural	neural	ADJ
ajst-15420	67	10	network	network	NOUN
ajst-15420	67	11	is	be	AUX
ajst-15420	67	12	easy	easy	ADJ
ajst-15420	67	13	to	to	PART
ajst-15420	67	14	be	be	AUX
ajst-15420	67	15	affected	affect	VERB
ajst-15420	67	16	by	by	ADP
ajst-15420	67	17	local	local	ADJ
ajst-15420	67	18	minimum	minimum	ADJ
ajst-15420	67	19	values	value	NOUN
ajst-15420	67	20	,	,	PUNCT
ajst-15420	67	21	which	which	PRON
ajst-15420	67	22	may	may	AUX
ajst-15420	67	23	lead	lead	VERB
ajst-15420	67	24	to	to	ADP
ajst-15420	67	25	the	the	DET
ajst-15420	67	26	training	training	NOUN
ajst-15420	67	27	results	result	NOUN
ajst-15420	67	28	being	be	AUX
ajst-15420	67	29	trapped	trap	VERB
ajst-15420	67	30	in	in	ADP
ajst-15420	67	31	local	local	ADJ
ajst-15420	67	32	optimal	optimal	ADJ
ajst-15420	67	33	solutions	solution	NOUN
ajst-15420	67	34	,	,	PUNCT
ajst-15420	67	35	rather	rather	ADV
ajst-15420	67	36	than	than	ADP
ajst-15420	67	37	obtaining	obtain	VERB
ajst-15420	67	38	global	global	ADJ
ajst-15420	67	39	optimal	optimal	ADJ
ajst-15420	67	40	solutions	solution	NOUN
ajst-15420	67	41	.	.	PUNCT
ajst-15420	68	1	to	to	PART
ajst-15420	68	2	solve	solve	VERB
ajst-15420	68	3	this	this	DET
ajst-15420	68	4	problem	problem	NOUN
ajst-15420	68	5	,	,	PUNCT
ajst-15420	68	6	this	this	DET
ajst-15420	68	7	article	article	NOUN
ajst-15420	68	8	proposes	propose	VERB
ajst-15420	68	9	to	to	PART
ajst-15420	68	10	use	use	VERB
ajst-15420	68	11	genetic	genetic	ADJ
ajst-15420	68	12	algorithm	algorithm	NOUN
ajst-15420	68	13	optimization	optimization	NOUN
ajst-15420	68	14	to	to	PART
ajst-15420	68	15	optimize	optimize	VERB
ajst-15420	68	16	the	the	DET
ajst-15420	68	17	drawbacks	drawback	NOUN
ajst-15420	68	18	of	of	ADP
ajst-15420	68	19	traditional	traditional	ADJ
ajst-15420	68	20	bp	bp	PROPN
ajst-15420	68	21	neural	neural	PROPN
ajst-15420	68	22	network[11	network[11	PROPN
ajst-15420	68	23	]	]	PUNCT
ajst-15420	68	24	.	.	PUNCT
ajst-15420	69	1	genetic	genetic	ADJ
ajst-15420	69	2	algorithm	algorithm	NOUN
ajst-15420	69	3	is	be	AUX
ajst-15420	69	4	an	an	DET
ajst-15420	69	5	optimization	optimization	NOUN
ajst-15420	69	6	algorithm	algorithm	NOUN
ajst-15420	69	7	based	base	VERB
ajst-15420	69	8	on	on	ADP
ajst-15420	69	9	principles	principle	NOUN
ajst-15420	69	10	of	of	ADP
ajst-15420	69	11	biological	biological	ADJ
ajst-15420	69	12	genetics	genetic	NOUN
ajst-15420	69	13	,	,	PUNCT
ajst-15420	69	14	which	which	PRON
ajst-15420	69	15	simulates	simulate	VERB
ajst-15420	69	16	natural	natural	ADJ
ajst-15420	69	17	selection	selection	NOUN
ajst-15420	69	18	and	and	CCONJ
ajst-15420	69	19	genetic	genetic	ADJ
ajst-15420	69	20	mechanisms	mechanism	NOUN
ajst-15420	69	21	to	to	PART
ajst-15420	69	22	find	find	VERB
ajst-15420	69	23	optimal	optimal	ADJ
ajst-15420	69	24	solutions	solution	NOUN
ajst-15420	69	25	.	.	PUNCT
ajst-15420	70	1	it	it	PRON
ajst-15420	70	2	was	be	AUX
ajst-15420	70	3	proposed	propose	VERB
ajst-15420	70	4	by	by	ADP
ajst-15420	70	5	american	american	ADJ
ajst-15420	70	6	scientist	scientist	PROPN
ajst-15420	70	7	john	john	PROPN
ajst-15420	70	8	holland	holland	PROPN
ajst-15420	70	9	in	in	ADP
ajst-15420	70	10	the	the	DET
ajst-15420	70	11	1970s	1970	NOUN
ajst-15420	70	12	and	and	CCONJ
ajst-15420	70	13	is	be	AUX
ajst-15420	70	14	widely	widely	ADV
ajst-15420	70	15	used	use	VERB
ajst-15420	70	16	in	in	ADP
ajst-15420	70	17	various	various	ADJ
ajst-15420	70	18	optimization	optimization	NOUN
ajst-15420	70	19	problems	problem	NOUN
ajst-15420	70	20	,	,	PUNCT
ajst-15420	70	21	such	such	ADJ
ajst-15420	70	22	as	as	ADP
ajst-15420	70	23	function	function	NOUN
ajst-15420	70	24	optimization	optimization	NOUN
ajst-15420	70	25	,	,	PUNCT
ajst-15420	70	26	machine	machine	NOUN
ajst-15420	70	27	learning	learning	NOUN
ajst-15420	70	28	,	,	PUNCT
ajst-15420	70	29	image	image	NOUN
ajst-15420	70	30	processing	processing	NOUN
ajst-15420	70	31	,	,	PUNCT
ajst-15420	70	32	etc	etc	X
ajst-15420	70	33	.	.	X
ajst-15420	71	1	the	the	DET
ajst-15420	71	2	core	core	ADJ
ajst-15420	71	3	idea	idea	NOUN
ajst-15420	71	4	of	of	ADP
ajst-15420	71	5	genetic	genetic	ADJ
ajst-15420	71	6	algorithm	algorithm	NOUN
ajst-15420	71	7	is	be	AUX
ajst-15420	71	8	to	to	PART
ajst-15420	71	9	regard	regard	VERB
ajst-15420	71	10	the	the	DET
ajst-15420	71	11	solution	solution	NOUN
ajst-15420	71	12	of	of	ADP
ajst-15420	71	13	the	the	DET
ajst-15420	71	14	problem	problem	NOUN
ajst-15420	71	15	as	as	ADP
ajst-15420	71	16	a	a	DET
ajst-15420	71	17	group	group	NOUN
ajst-15420	71	18	of	of	ADP
ajst-15420	71	19	biological	biological	ADJ
ajst-15420	71	20	individuals	individual	NOUN
ajst-15420	71	21	and	and	CCONJ
ajst-15420	71	22	simulate	simulate	VERB
ajst-15420	71	23	the	the	DET
ajst-15420	71	24	selection	selection	NOUN
ajst-15420	71	25	,	,	PUNCT
ajst-15420	71	26	crossover	crossover	NOUN
ajst-15420	71	27	and	and	CCONJ
ajst-15420	71	28	mutation	mutation	NOUN
ajst-15420	71	29	operations	operation	NOUN
ajst-15420	71	30	in	in	ADP
ajst-15420	71	31	the	the	DET
ajst-15420	71	32	process	process	NOUN
ajst-15420	71	33	of	of	ADP
ajst-15420	71	34	biological	biological	ADJ
ajst-15420	71	35	evolution	evolution	NOUN
ajst-15420	71	36	to	to	PART
ajst-15420	71	37	continuously	continuously	ADV
ajst-15420	71	38	optimize	optimize	VERB
ajst-15420	71	39	the	the	DET
ajst-15420	71	40	quality	quality	NOUN
ajst-15420	71	41	of	of	ADP
ajst-15420	71	42	the	the	DET
ajst-15420	71	43	solution	solution	NOUN
ajst-15420	71	44	.	.	PUNCT
ajst-15420	72	1	in	in	ADP
ajst-15420	72	2	each	each	DET
ajst-15420	72	3	generation	generation	NOUN
ajst-15420	72	4	,	,	PUNCT
ajst-15420	72	5	the	the	DET
ajst-15420	72	6	fitness	fitness	NOUN
ajst-15420	72	7	function	function	NOUN
ajst-15420	72	8	is	be	AUX
ajst-15420	72	9	used	use	VERB
ajst-15420	72	10	to	to	PART
ajst-15420	72	11	evaluate	evaluate	VERB
ajst-15420	72	12	the	the	DET
ajst-15420	72	13	quality	quality	NOUN
ajst-15420	72	14	of	of	ADP
ajst-15420	72	15	each	each	DET
ajst-15420	72	16	individual	individual	NOUN
ajst-15420	72	17	,	,	PUNCT
ajst-15420	72	18	and	and	CCONJ
ajst-15420	72	19	individuals	individual	NOUN
ajst-15420	72	20	with	with	ADP
ajst-15420	72	21	high	high	ADJ
ajst-15420	72	22	fitness	fitness	NOUN
ajst-15420	72	23	are	be	AUX
ajst-15420	72	24	selected	select	VERB
ajst-15420	72	25	for	for	ADP
ajst-15420	72	26	crossover	crossover	NOUN
ajst-15420	72	27	and	and	CCONJ
ajst-15420	72	28	mutation	mutation	NOUN
ajst-15420	72	29	operations	operation	NOUN
ajst-15420	72	30	to	to	PART
ajst-15420	72	31	generate	generate	VERB
ajst-15420	72	32	new	new	ADJ
ajst-15420	72	33	individuals	individual	NOUN
ajst-15420	72	34	and	and	CCONJ
ajst-15420	72	35	gradually	gradually	ADV
ajst-15420	72	36	evolve	evolve	VERB
ajst-15420	72	37	towards	towards	ADP
ajst-15420	72	38	better	well	ADJ
ajst-15420	72	39	solutions	solution	NOUN
ajst-15420	72	40	.	.	PUNCT
ajst-15420	73	1	genetic	genetic	ADJ
ajst-15420	73	2	algorithm	algorithm	NOUN
ajst-15420	73	3	has	have	VERB
ajst-15420	73	4	the	the	DET
ajst-15420	73	5	advantages	advantage	NOUN
ajst-15420	73	6	of	of	ADP
ajst-15420	73	7	adaptability	adaptability	NOUN
ajst-15420	73	8	,	,	PUNCT
ajst-15420	73	9	parallelism	parallelism	NOUN
ajst-15420	73	10	and	and	CCONJ
ajst-15420	73	11	robustness	robustness	NOUN
ajst-15420	73	12	,	,	PUNCT
ajst-15420	73	13	and	and	CCONJ
ajst-15420	73	14	can	can	AUX
ajst-15420	73	15	find	find	VERB
ajst-15420	73	16	optimal	optimal	ADJ
ajst-15420	73	17	solutions	solution	NOUN
ajst-15420	73	18	in	in	ADP
ajst-15420	73	19	complex	complex	ADJ
ajst-15420	73	20	search	search	NOUN
ajst-15420	73	21	spaces	space	NOUN
ajst-15420	73	22	.	.	PUNCT
ajst-15420	74	1	it	it	PRON
ajst-15420	74	2	does	do	AUX
ajst-15420	74	3	not	not	PART
ajst-15420	74	4	require	require	VERB
ajst-15420	74	5	explicit	explicit	ADJ
ajst-15420	74	6	constraints	constraint	NOUN
ajst-15420	74	7	3	3	NUM
ajst-15420	74	8	or	or	CCONJ
ajst-15420	74	9	restrictions	restriction	NOUN
ajst-15420	74	10	on	on	ADP
ajst-15420	74	11	the	the	DET
ajst-15420	74	12	problem	problem	NOUN
ajst-15420	74	13	,	,	PUNCT
ajst-15420	74	14	can	can	AUX
ajst-15420	74	15	automatically	automatically	ADV
ajst-15420	74	16	adjust	adjust	VERB
ajst-15420	74	17	the	the	DET
ajst-15420	74	18	search	search	NOUN
ajst-15420	74	19	direction	direction	NOUN
ajst-15420	74	20	and	and	CCONJ
ajst-15420	74	21	range	range	NOUN
ajst-15420	74	22	,	,	PUNCT
ajst-15420	74	23	and	and	CCONJ
ajst-15420	74	24	has	have	VERB
ajst-15420	74	25	a	a	DET
ajst-15420	74	26	strong	strong	ADJ
ajst-15420	74	27	global	global	ADJ
ajst-15420	74	28	search	search	NOUN
ajst-15420	74	29	ability	ability	NOUN
ajst-15420	74	30	.	.	PUNCT
ajst-15420	75	1	at	at	ADP
ajst-15420	75	2	the	the	DET
ajst-15420	75	3	same	same	ADJ
ajst-15420	75	4	time	time	NOUN
ajst-15420	75	5	,	,	PUNCT
ajst-15420	75	6	genetic	genetic	ADJ
ajst-15420	75	7	algorithm	algorithm	NOUN
ajst-15420	75	8	is	be	AUX
ajst-15420	75	9	also	also	ADV
ajst-15420	75	10	suitable	suitable	ADJ
ajst-15420	75	11	for	for	ADP
ajst-15420	75	12	problems	problem	NOUN
ajst-15420	75	13	with	with	ADP
ajst-15420	75	14	multiple	multiple	ADJ
ajst-15420	75	15	peak	peak	NOUN
ajst-15420	75	16	distributions	distribution	NOUN
ajst-15420	75	17	and	and	CCONJ
ajst-15420	75	18	can	can	AUX
ajst-15420	75	19	find	find	VERB
ajst-15420	75	20	multiple	multiple	ADJ
ajst-15420	75	21	optimal	optimal	ADJ
ajst-15420	75	22	solutions	solution	NOUN
ajst-15420	75	23	.	.	PUNCT
ajst-15420	76	1	2.3	2.3	NUM
ajst-15420	76	2	.	.	PUNCT
ajst-15420	76	3	model	model	NOUN
ajst-15420	76	4	calculation	calculation	NOUN
ajst-15420	76	5	in	in	ADP
ajst-15420	76	6	this	this	DET
ajst-15420	76	7	experiment	experiment	NOUN
ajst-15420	76	8	,	,	PUNCT
ajst-15420	76	9	the	the	DET
ajst-15420	76	10	chemical	chemical	NOUN
ajst-15420	76	11	composition	composition	NOUN
ajst-15420	76	12	and	and	CCONJ
ajst-15420	76	13	processing	processing	NOUN
ajst-15420	76	14	technology	technology	NOUN
ajst-15420	76	15	of	of	ADP
ajst-15420	76	16	steel	steel	NOUN
ajst-15420	76	17	materials	material	NOUN
ajst-15420	76	18	are	be	AUX
ajst-15420	76	19	used	use	VERB
ajst-15420	76	20	as	as	ADP
ajst-15420	76	21	input	input	NOUN
ajst-15420	76	22	values	value	NOUN
ajst-15420	76	23	,	,	PUNCT
ajst-15420	76	24	and	and	CCONJ
ajst-15420	76	25	the	the	DET
ajst-15420	76	26	output	output	NOUN
ajst-15420	76	27	values	value	NOUN
ajst-15420	76	28	are	be	AUX
ajst-15420	76	29	yield	yield	NOUN
ajst-15420	76	30	strength	strength	NOUN
ajst-15420	76	31	,	,	PUNCT
ajst-15420	76	32	tensile	tensile	NOUN
ajst-15420	76	33	strength	strength	NOUN
ajst-15420	76	34	,	,	PUNCT
ajst-15420	76	35	and	and	CCONJ
ajst-15420	76	36	elongation	elongation	NOUN
ajst-15420	76	37	.	.	PUNCT
ajst-15420	77	1	based	base	VERB
ajst-15420	77	2	on	on	ADP
ajst-15420	77	3	the	the	DET
ajst-15420	77	4	collected	collect	VERB
ajst-15420	77	5	data	datum	NOUN
ajst-15420	77	6	,	,	PUNCT
ajst-15420	77	7	we	we	PRON
ajst-15420	77	8	conducted	conduct	VERB
ajst-15420	77	9	standardization	standardization	NOUN
ajst-15420	77	10	processing	processing	NOUN
ajst-15420	77	11	and	and	CCONJ
ajst-15420	77	12	data	datum	NOUN
ajst-15420	77	13	division	division	NOUN
ajst-15420	77	14	.	.	PUNCT
ajst-15420	78	1	we	we	PRON
ajst-15420	78	2	selected	select	VERB
ajst-15420	78	3	80	80	NUM
ajst-15420	78	4	%	%	NOUN
ajst-15420	78	5	of	of	ADP
ajst-15420	78	6	the	the	DET
ajst-15420	78	7	data	datum	NOUN
ajst-15420	78	8	set	set	VERB
ajst-15420	78	9	as	as	ADP
ajst-15420	78	10	the	the	DET
ajst-15420	78	11	training	training	NOUN
ajst-15420	78	12	group	group	NOUN
ajst-15420	78	13	,	,	PUNCT
ajst-15420	78	14	and	and	CCONJ
ajst-15420	78	15	the	the	DET
ajst-15420	78	16	remaining	remain	VERB
ajst-15420	78	17	20	20	NUM
ajst-15420	78	18	%	%	NOUN
ajst-15420	78	19	as	as	ADP
ajst-15420	78	20	the	the	DET
ajst-15420	78	21	test	test	NOUN
ajst-15420	78	22	group	group	NOUN
ajst-15420	78	23	.	.	PUNCT
ajst-15420	79	1	root	root	NOUN
ajst-15420	79	2	mean	mean	VERB
ajst-15420	79	3	square	square	ADJ
ajst-15420	79	4	error	error	NOUN
ajst-15420	79	5	(	(	PUNCT
ajst-15420	79	6	rmse	rmse	NOUN
ajst-15420	79	7	)	)	PUNCT
ajst-15420	79	8	and	and	CCONJ
ajst-15420	79	9	coefficient	coefficient	NOUN
ajst-15420	79	10	of	of	ADP
ajst-15420	79	11	determination	determination	NOUN
ajst-15420	79	12	r	r	NOUN
ajst-15420	79	13	-	-	PUNCT
ajst-15420	79	14	square	square	ADJ
ajst-15420	79	15	(	(	PUNCT
ajst-15420	79	16	r2	r2	PROPN
ajst-15420	79	17	)	)	PUNCT
ajst-15420	79	18	are	be	AUX
ajst-15420	79	19	commonly	commonly	ADV
ajst-15420	79	20	used	use	VERB
ajst-15420	79	21	methods	method	NOUN
ajst-15420	79	22	to	to	PART
ajst-15420	79	23	evaluate	evaluate	VERB
ajst-15420	79	24	the	the	DET
ajst-15420	79	25	quality	quality	NOUN
ajst-15420	79	26	of	of	ADP
ajst-15420	79	27	machine	machine	NOUN
ajst-15420	79	28	learning	learning	NOUN
ajst-15420	79	29	models	model	NOUN
ajst-15420	79	30	,	,	PUNCT
ajst-15420	79	31	with	with	ADP
ajst-15420	79	32	expressions	expression	NOUN
ajst-15420	79	33	listed	list	VERB
ajst-15420	79	34	in	in	ADP
ajst-15420	79	35	equations	equation	NOUN
ajst-15420	79	36	1	1	NUM
ajst-15420	79	37	and	and	CCONJ
ajst-15420	79	38	2	2	NUM
ajst-15420	79	39	,	,	PUNCT
ajst-15420	79	40	respectively	respectively	ADV
ajst-15420	79	41	.	.	PUNCT
ajst-15420	80	1	where	where	SCONJ
ajst-15420	80	2	n	n	PRON
ajst-15420	80	3	is	be	AUX
ajst-15420	80	4	the	the	DET
ajst-15420	80	5	total	total	ADJ
ajst-15420	80	6	number	number	NOUN
ajst-15420	80	7	of	of	ADP
ajst-15420	80	8	samples	sample	NOUN
ajst-15420	80	9	,	,	PUNCT
ajst-15420	80	10	𝑦	𝑦	NOUN
ajst-15420	80	11	is	be	AUX
ajst-15420	80	12	the	the	DET
ajst-15420	80	13	true	true	ADJ
ajst-15420	80	14	value	value	NOUN
ajst-15420	80	15	,	,	PUNCT
ajst-15420	80	16	and	and	CCONJ
ajst-15420	80	17	y	y	PROPN
ajst-15420	80	18	is	be	AUX
ajst-15420	80	19	the	the	DET
ajst-15420	80	20	machine	machine	NOUN
ajst-15420	80	21	learning	learn	VERB
ajst-15420	80	22	prediction	prediction	NOUN
ajst-15420	80	23	value	value	NOUN
ajst-15420	80	24	.	.	PUNCT
ajst-15420	81	1	𝑅𝑀𝑆𝐸	𝑅𝑀𝑆𝐸	PROPN
ajst-15420	81	2	𝑦	𝑦	PRON
ajst-15420	81	3	𝑦	𝑦	NOUN
ajst-15420	81	4	/𝑛	/𝑛	X
ajst-15420	81	5	(	(	PUNCT
ajst-15420	81	6	1	1	NUM
ajst-15420	81	7	)	)	PUNCT
ajst-15420	81	8	𝑅2	𝑅2	NOUN
ajst-15420	81	9	1	1	NUM
ajst-15420	81	10	∑	∑	PROPN
ajst-15420	81	11	∑	∑	PROPN
ajst-15420	81	12	(	(	PUNCT
ajst-15420	81	13	2	2	NUM
ajst-15420	81	14	)	)	PUNCT
ajst-15420	81	15	the	the	DET
ajst-15420	81	16	rmse	rmse	ADJ
ajst-15420	81	17	value	value	NOUN
ajst-15420	81	18	indicates	indicate	VERB
ajst-15420	81	19	the	the	DET
ajst-15420	81	20	error	error	NOUN
ajst-15420	81	21	between	between	ADP
ajst-15420	81	22	the	the	DET
ajst-15420	81	23	predicted	predict	VERB
ajst-15420	81	24	value	value	NOUN
ajst-15420	81	25	and	and	CCONJ
ajst-15420	81	26	the	the	DET
ajst-15420	81	27	true	true	ADJ
ajst-15420	81	28	value	value	NOUN
ajst-15420	81	29	,	,	PUNCT
ajst-15420	81	30	and	and	CCONJ
ajst-15420	81	31	the	the	PRON
ajst-15420	81	32	smaller	small	ADJ
ajst-15420	81	33	the	the	DET
ajst-15420	81	34	error	error	NOUN
ajst-15420	81	35	,	,	PUNCT
ajst-15420	81	36	the	the	PRON
ajst-15420	81	37	closer	close	ADV
ajst-15420	81	38	the	the	DET
ajst-15420	81	39	predicted	predict	VERB
ajst-15420	81	40	value	value	NOUN
ajst-15420	81	41	is	be	AUX
ajst-15420	81	42	to	to	ADP
ajst-15420	81	43	the	the	DET
ajst-15420	81	44	true	true	ADJ
ajst-15420	81	45	value	value	NOUN
ajst-15420	81	46	.	.	PUNCT
ajst-15420	82	1	the	the	DET
ajst-15420	82	2	r2	r2	PROPN
ajst-15420	82	3	value	value	NOUN
ajst-15420	82	4	indicates	indicate	VERB
ajst-15420	82	5	the	the	DET
ajst-15420	82	6	closeness	closeness	NOUN
ajst-15420	82	7	of	of	ADP
ajst-15420	82	8	the	the	DET
ajst-15420	82	9	model	model	NOUN
ajst-15420	82	10	's	's	PART
ajst-15420	82	11	predicted	predict	VERB
ajst-15420	82	12	value	value	NOUN
ajst-15420	82	13	to	to	ADP
ajst-15420	82	14	the	the	DET
ajst-15420	82	15	true	true	ADJ
ajst-15420	82	16	value	value	NOUN
ajst-15420	82	17	,	,	PUNCT
ajst-15420	82	18	so	so	CCONJ
ajst-15420	82	19	the	the	PRON
ajst-15420	82	20	larger	large	ADJ
ajst-15420	82	21	the	the	DET
ajst-15420	82	22	r2	r2	PROPN
ajst-15420	82	23	value	value	NOUN
ajst-15420	82	24	,	,	PUNCT
ajst-15420	82	25	the	the	PRON
ajst-15420	82	26	more	more	ADV
ajst-15420	82	27	accurate	accurate	ADJ
ajst-15420	82	28	the	the	DET
ajst-15420	82	29	prediction	prediction	NOUN
ajst-15420	82	30	result	result	NOUN
ajst-15420	82	31	.	.	PUNCT
ajst-15420	83	1	in	in	ADP
ajst-15420	83	2	order	order	NOUN
ajst-15420	83	3	to	to	PART
ajst-15420	83	4	compare	compare	VERB
ajst-15420	83	5	the	the	DET
ajst-15420	83	6	optimization	optimization	NOUN
ajst-15420	83	7	effect	effect	NOUN
ajst-15420	83	8	of	of	ADP
ajst-15420	83	9	genetic	genetic	ADJ
ajst-15420	83	10	algorithms	algorithm	NOUN
ajst-15420	83	11	,	,	PUNCT
ajst-15420	83	12	this	this	DET
ajst-15420	83	13	article	article	NOUN
ajst-15420	83	14	predicts	predict	VERB
ajst-15420	83	15	the	the	DET
ajst-15420	83	16	optimized	optimize	VERB
ajst-15420	83	17	bp	bp	PROPN
ajst-15420	83	18	neural	neural	ADJ
ajst-15420	83	19	network	network	NOUN
ajst-15420	83	20	model	model	NOUN
ajst-15420	83	21	and	and	CCONJ
ajst-15420	83	22	the	the	DET
ajst-15420	83	23	unoptimized	unoptimized	ADJ
ajst-15420	83	24	bp	bp	PROPN
ajst-15420	83	25	neural	neural	PROPN
ajst-15420	83	26	network	network	NOUN
ajst-15420	83	27	model	model	NOUN
ajst-15420	83	28	separately	separately	ADV
ajst-15420	83	29	.	.	PUNCT
ajst-15420	84	1	the	the	DET
ajst-15420	84	2	number	number	NOUN
ajst-15420	84	3	of	of	ADP
ajst-15420	84	4	iterations	iteration	NOUN
ajst-15420	84	5	in	in	ADP
ajst-15420	84	6	the	the	DET
ajst-15420	84	7	neural	neural	ADJ
ajst-15420	84	8	network	network	NOUN
ajst-15420	84	9	training	training	NOUN
ajst-15420	84	10	stage	stage	NOUN
ajst-15420	84	11	is	be	AUX
ajst-15420	84	12	1000	1000	NUM
ajst-15420	84	13	,	,	PUNCT
ajst-15420	84	14	the	the	DET
ajst-15420	84	15	learning	learning	NOUN
ajst-15420	84	16	rate	rate	NOUN
ajst-15420	84	17	is	be	AUX
ajst-15420	84	18	set	set	VERB
ajst-15420	84	19	to	to	ADP
ajst-15420	84	20	0.01	0.01	NUM
ajst-15420	84	21	,	,	PUNCT
ajst-15420	84	22	and	and	CCONJ
ajst-15420	84	23	the	the	DET
ajst-15420	84	24	error	error	NOUN
ajst-15420	84	25	threshold	threshold	NOUN
ajst-15420	84	26	is	be	AUX
ajst-15420	84	27	set	set	VERB
ajst-15420	84	28	to	to	ADP
ajst-15420	84	29	0.00001	0.00001	NUM
ajst-15420	84	30	.	.	PUNCT
ajst-15420	85	1	the	the	DET
ajst-15420	85	2	transfer	transfer	NOUN
ajst-15420	85	3	function	function	NOUN
ajst-15420	85	4	of	of	ADP
ajst-15420	85	5	the	the	DET
ajst-15420	85	6	hidden	hide	VERB
ajst-15420	85	7	layer	layer	NOUN
ajst-15420	85	8	is	be	AUX
ajst-15420	85	9	the	the	DET
ajst-15420	85	10	tan	tan	NOUN
ajst-15420	85	11	-	-	PUNCT
ajst-15420	85	12	sigmoid	sigmoid	NOUN
ajst-15420	85	13	function	function	NOUN
ajst-15420	85	14	,	,	PUNCT
ajst-15420	85	15	and	and	CCONJ
ajst-15420	85	16	the	the	DET
ajst-15420	85	17	transfer	transfer	NOUN
ajst-15420	85	18	function	function	NOUN
ajst-15420	85	19	of	of	ADP
ajst-15420	85	20	the	the	DET
ajst-15420	85	21	output	output	NOUN
ajst-15420	85	22	layer	layer	NOUN
ajst-15420	85	23	is	be	AUX
ajst-15420	85	24	the	the	DET
ajst-15420	85	25	linear	linear	ADJ
ajst-15420	85	26	function	function	NOUN
ajst-15420	85	27	.	.	PUNCT
ajst-15420	86	1	the	the	DET
ajst-15420	86	2	fitness	fitness	NOUN
ajst-15420	86	3	obtained	obtain	VERB
ajst-15420	86	4	by	by	ADP
ajst-15420	86	5	the	the	DET
ajst-15420	86	6	optimized	optimize	VERB
ajst-15420	86	7	bp	bp	PROPN
ajst-15420	86	8	neural	neural	PROPN
ajst-15420	86	9	network	network	NOUN
ajst-15420	86	10	model	model	NOUN
ajst-15420	86	11	using	use	VERB
ajst-15420	86	12	genetic	genetic	ADJ
ajst-15420	86	13	algorithms	algorithm	NOUN
ajst-15420	86	14	after	after	SCONJ
ajst-15420	86	15	50	50	NUM
ajst-15420	86	16	iterations	iteration	NOUN
ajst-15420	86	17	is	be	AUX
ajst-15420	86	18	shown	show	VERB
ajst-15420	86	19	in	in	ADP
ajst-15420	86	20	figure	figure	NOUN
ajst-15420	86	21	4	4	NUM
ajst-15420	86	22	.	.	PUNCT
ajst-15420	87	1	in	in	ADP
ajst-15420	87	2	genetic	genetic	ADJ
ajst-15420	87	3	algorithms	algorithm	NOUN
ajst-15420	87	4	,	,	PUNCT
ajst-15420	87	5	the	the	DET
ajst-15420	87	6	parameter	parameter	NOUN
ajst-15420	87	7	selection	selection	NOUN
ajst-15420	87	8	population	population	NOUN
ajst-15420	87	9	size	size	NOUN
ajst-15420	87	10	is	be	AUX
ajst-15420	87	11	40	40	NUM
ajst-15420	87	12	,	,	PUNCT
ajst-15420	87	13	the	the	DET
ajst-15420	87	14	genetic	genetic	ADJ
ajst-15420	87	15	generation	generation	NOUN
ajst-15420	87	16	is	be	AUX
ajst-15420	87	17	50	50	NUM
ajst-15420	87	18	,	,	PUNCT
ajst-15420	87	19	the	the	DET
ajst-15420	87	20	crossover	crossover	NOUN
ajst-15420	87	21	probability	probability	NOUN
ajst-15420	87	22	is	be	AUX
ajst-15420	87	23	0.6	0.6	NUM
ajst-15420	87	24	,	,	PUNCT
ajst-15420	87	25	and	and	CCONJ
ajst-15420	87	26	the	the	DET
ajst-15420	87	27	mutation	mutation	NOUN
ajst-15420	87	28	probability	probability	NOUN
ajst-15420	87	29	is	be	AUX
ajst-15420	87	30	0.05	0.05	NUM
ajst-15420	87	31	for	for	ADP
ajst-15420	87	32	optimization	optimization	NOUN
ajst-15420	87	33	.	.	PUNCT
ajst-15420	88	1	table	table	NOUN
ajst-15420	88	2	1	1	NUM
ajst-15420	88	3	presents	present	VERB
ajst-15420	88	4	the	the	DET
ajst-15420	88	5	evaluation	evaluation	NOUN
ajst-15420	88	6	results	result	NOUN
ajst-15420	88	7	of	of	ADP
ajst-15420	88	8	the	the	DET
ajst-15420	88	9	prediction	prediction	NOUN
ajst-15420	88	10	accuracy	accuracy	NOUN
ajst-15420	88	11	using	use	VERB
ajst-15420	88	12	rmse	rmse	NOUN
ajst-15420	88	13	and	and	CCONJ
ajst-15420	88	14	r2	r2	NOUN
ajst-15420	88	15	.	.	PUNCT
ajst-15420	89	1	it	it	PRON
ajst-15420	89	2	can	can	AUX
ajst-15420	89	3	be	be	AUX
ajst-15420	89	4	observed	observe	VERB
ajst-15420	89	5	that	that	SCONJ
ajst-15420	89	6	the	the	DET
ajst-15420	89	7	r2	r2	NOUN
ajst-15420	89	8	of	of	ADP
ajst-15420	89	9	the	the	DET
ajst-15420	89	10	bp	bp	PROPN
ajst-15420	89	11	neural	neural	PROPN
ajst-15420	89	12	network	network	NOUN
ajst-15420	89	13	model	model	NOUN
ajst-15420	89	14	for	for	ADP
ajst-15420	89	15	yield	yield	NOUN
ajst-15420	89	16	strength	strength	NOUN
ajst-15420	89	17	can	can	AUX
ajst-15420	89	18	be	be	AUX
ajst-15420	89	19	improved	improve	VERB
ajst-15420	89	20	from	from	ADP
ajst-15420	89	21	0.90	0.90	NUM
ajst-15420	89	22	to	to	ADP
ajst-15420	89	23	0.97	0.97	NUM
ajst-15420	89	24	after	after	ADP
ajst-15420	89	25	genetic	genetic	ADJ
ajst-15420	89	26	algorithm	algorithm	NOUN
ajst-15420	89	27	optimization	optimization	NOUN
ajst-15420	89	28	,	,	PUNCT
ajst-15420	89	29	and	and	CCONJ
ajst-15420	89	30	the	the	DET
ajst-15420	89	31	rmse	rmse	NOUN
ajst-15420	89	32	is	be	AUX
ajst-15420	89	33	reduced	reduce	VERB
ajst-15420	89	34	from	from	ADP
ajst-15420	89	35	120.14	120.14	NUM
ajst-15420	89	36	to	to	ADP
ajst-15420	89	37	57.67	57.67	NUM
ajst-15420	89	38	.	.	PUNCT
ajst-15420	90	1	for	for	ADP
ajst-15420	90	2	tensile	tensile	NOUN
ajst-15420	90	3	strength	strength	NOUN
ajst-15420	90	4	,	,	PUNCT
ajst-15420	90	5	the	the	DET
ajst-15420	90	6	r2	r2	PROPN
ajst-15420	90	7	is	be	AUX
ajst-15420	90	8	improved	improve	VERB
ajst-15420	90	9	from	from	ADP
ajst-15420	90	10	0.95	0.95	NUM
ajst-15420	90	11	to	to	ADP
ajst-15420	90	12	0.98	0.98	NUM
ajst-15420	90	13	,	,	PUNCT
ajst-15420	90	14	and	and	CCONJ
ajst-15420	90	15	the	the	DET
ajst-15420	90	16	rmse	rmse	NOUN
ajst-15420	90	17	is	be	AUX
ajst-15420	90	18	reduced	reduce	VERB
ajst-15420	90	19	from	from	ADP
ajst-15420	90	20	108.16	108.16	NUM
ajst-15420	90	21	to	to	ADP
ajst-15420	90	22	54.60	54.60	NUM
ajst-15420	90	23	.	.	PUNCT
ajst-15420	91	1	for	for	ADP
ajst-15420	91	2	elongation	elongation	NOUN
ajst-15420	91	3	,	,	PUNCT
ajst-15420	91	4	the	the	DET
ajst-15420	91	5	r2	r2	NOUN
ajst-15420	91	6	is	be	AUX
ajst-15420	91	7	improved	improve	VERB
ajst-15420	91	8	from	from	ADP
ajst-15420	91	9	0.96	0.96	NUM
ajst-15420	91	10	to	to	ADP
ajst-15420	91	11	0.98	0.98	NUM
ajst-15420	91	12	,	,	PUNCT
ajst-15420	91	13	and	and	CCONJ
ajst-15420	91	14	the	the	DET
ajst-15420	91	15	rmse	rmse	NOUN
ajst-15420	91	16	is	be	AUX
ajst-15420	91	17	reduced	reduce	VERB
ajst-15420	91	18	from	from	ADP
ajst-15420	91	19	1.70	1.70	NUM
ajst-15420	91	20	to	to	ADP
ajst-15420	91	21	1.01	1.01	NUM
ajst-15420	91	22	.	.	PUNCT
ajst-15420	92	1	these	these	DET
ajst-15420	92	2	results	result	NOUN
ajst-15420	92	3	indicate	indicate	VERB
ajst-15420	92	4	that	that	SCONJ
ajst-15420	92	5	genetic	genetic	ADJ
ajst-15420	92	6	algorithm	algorithm	NOUN
ajst-15420	92	7	optimization	optimization	NOUN
ajst-15420	92	8	of	of	ADP
ajst-15420	92	9	bp	bp	PROPN
ajst-15420	92	10	neural	neural	PROPN
ajst-15420	92	11	network	network	NOUN
ajst-15420	92	12	plays	play	VERB
ajst-15420	92	13	a	a	DET
ajst-15420	92	14	good	good	ADJ
ajst-15420	92	15	role	role	NOUN
ajst-15420	92	16	in	in	ADP
ajst-15420	92	17	improving	improve	VERB
ajst-15420	92	18	prediction	prediction	NOUN
ajst-15420	92	19	accuracy	accuracy	NOUN
ajst-15420	92	20	.	.	PUNCT
ajst-15420	93	1	genetic	genetic	ADJ
ajst-15420	93	2	algorithm	algorithm	NOUN
ajst-15420	93	3	can	can	AUX
ajst-15420	93	4	be	be	AUX
ajst-15420	93	5	used	use	VERB
ajst-15420	93	6	to	to	PART
ajst-15420	93	7	optimize	optimize	VERB
ajst-15420	93	8	the	the	DET
ajst-15420	93	9	weights	weight	NOUN
ajst-15420	93	10	and	and	CCONJ
ajst-15420	93	11	thresholds	threshold	NOUN
ajst-15420	93	12	of	of	ADP
ajst-15420	93	13	neural	neural	ADJ
ajst-15420	93	14	connections	connection	NOUN
ajst-15420	93	15	in	in	ADP
ajst-15420	93	16	bp	bp	PROPN
ajst-15420	93	17	neural	neural	ADJ
ajst-15420	93	18	network	network	NOUN
ajst-15420	93	19	.	.	PUNCT
ajst-15420	94	1	the	the	DET
ajst-15420	94	2	weights	weight	NOUN
ajst-15420	94	3	and	and	CCONJ
ajst-15420	94	4	thresholds	threshold	NOUN
ajst-15420	94	5	of	of	ADP
ajst-15420	94	6	neural	neural	ADJ
ajst-15420	94	7	connections	connection	NOUN
ajst-15420	94	8	in	in	ADP
ajst-15420	94	9	bp	bp	PROPN
ajst-15420	94	10	neural	neural	ADJ
ajst-15420	94	11	network	network	NOUN
ajst-15420	94	12	determine	determine	VERB
ajst-15420	94	13	the	the	DET
ajst-15420	94	14	performance	performance	NOUN
ajst-15420	94	15	and	and	CCONJ
ajst-15420	94	16	prediction	prediction	NOUN
ajst-15420	94	17	ability	ability	NOUN
ajst-15420	94	18	of	of	ADP
ajst-15420	94	19	the	the	DET
ajst-15420	94	20	neural	neural	ADJ
ajst-15420	94	21	network	network	NOUN
ajst-15420	94	22	,	,	PUNCT
ajst-15420	94	23	and	and	CCONJ
ajst-15420	94	24	optimizing	optimize	VERB
ajst-15420	94	25	them	they	PRON
ajst-15420	94	26	can	can	AUX
ajst-15420	94	27	make	make	VERB
ajst-15420	94	28	the	the	DET
ajst-15420	94	29	neural	neural	ADJ
ajst-15420	94	30	network	network	NOUN
ajst-15420	94	31	more	more	ADV
ajst-15420	94	32	accurate	accurate	ADJ
ajst-15420	94	33	in	in	ADP
ajst-15420	94	34	classification	classification	NOUN
ajst-15420	94	35	or	or	CCONJ
ajst-15420	94	36	prediction[12	prediction[12	NOUN
ajst-15420	94	37	]	]	X
ajst-15420	94	38	.	.	PUNCT
ajst-15420	95	1	genetic	genetic	ADJ
ajst-15420	95	2	algorithm	algorithm	NOUN
ajst-15420	95	3	can	can	AUX
ajst-15420	95	4	encode	encode	VERB
ajst-15420	95	5	the	the	DET
ajst-15420	95	6	weights	weight	NOUN
ajst-15420	95	7	and	and	CCONJ
ajst-15420	95	8	thresholds	threshold	NOUN
ajst-15420	95	9	of	of	ADP
ajst-15420	95	10	neural	neural	ADJ
ajst-15420	95	11	connections	connection	NOUN
ajst-15420	95	12	in	in	ADP
ajst-15420	95	13	bp	bp	PROPN
ajst-15420	95	14	neural	neural	ADJ
ajst-15420	95	15	network	network	NOUN
ajst-15420	95	16	into	into	ADP
ajst-15420	95	17	gene	gene	NOUN
ajst-15420	95	18	sequences	sequence	NOUN
ajst-15420	95	19	,	,	PUNCT
ajst-15420	95	20	and	and	CCONJ
ajst-15420	95	21	then	then	ADV
ajst-15420	95	22	use	use	VERB
ajst-15420	95	23	the	the	DET
ajst-15420	95	24	evolutionary	evolutionary	ADJ
ajst-15420	95	25	process	process	NOUN
ajst-15420	95	26	of	of	ADP
ajst-15420	95	27	genetic	genetic	ADJ
ajst-15420	95	28	algorithm	algorithm	NOUN
ajst-15420	95	29	,	,	PUNCT
ajst-15420	95	30	including	include	VERB
ajst-15420	95	31	selection	selection	NOUN
ajst-15420	95	32	,	,	PUNCT
ajst-15420	95	33	crossover	crossover	NOUN
ajst-15420	95	34	and	and	CCONJ
ajst-15420	95	35	mutation	mutation	NOUN
ajst-15420	95	36	operations	operation	NOUN
ajst-15420	95	37	,	,	PUNCT
ajst-15420	95	38	to	to	PART
ajst-15420	95	39	continuously	continuously	ADV
ajst-15420	95	40	optimize	optimize	VERB
ajst-15420	95	41	gene	gene	NOUN
ajst-15420	95	42	sequences	sequence	NOUN
ajst-15420	95	43	to	to	PART
ajst-15420	95	44	find	find	VERB
ajst-15420	95	45	better	well	ADJ
ajst-15420	95	46	weight	weight	NOUN
ajst-15420	95	47	and	and	CCONJ
ajst-15420	95	48	threshold	threshold	NOUN
ajst-15420	95	49	combinations	combination	NOUN
ajst-15420	95	50	.	.	PUNCT
ajst-15420	96	1	in	in	ADP
ajst-15420	96	2	each	each	DET
ajst-15420	96	3	generation	generation	NOUN
ajst-15420	96	4	of	of	ADP
ajst-15420	96	5	evolution	evolution	NOUN
ajst-15420	96	6	,	,	PUNCT
ajst-15420	96	7	the	the	DET
ajst-15420	96	8	fitness	fitness	NOUN
ajst-15420	96	9	function	function	NOUN
ajst-15420	96	10	is	be	AUX
ajst-15420	96	11	used	use	VERB
ajst-15420	96	12	to	to	PART
ajst-15420	96	13	evaluate	evaluate	VERB
ajst-15420	96	14	the	the	DET
ajst-15420	96	15	adaptability	adaptability	NOUN
ajst-15420	96	16	of	of	ADP
ajst-15420	96	17	each	each	DET
ajst-15420	96	18	individual	individual	NOUN
ajst-15420	96	19	,	,	PUNCT
ajst-15420	96	20	which	which	PRON
ajst-15420	96	21	is	be	AUX
ajst-15420	96	22	then	then	ADV
ajst-15420	96	23	selected	select	VERB
ajst-15420	96	24	,	,	PUNCT
ajst-15420	96	25	crossover	crossover	NOUN
ajst-15420	96	26	,	,	PUNCT
ajst-15420	96	27	and	and	CCONJ
ajst-15420	96	28	mutated	mutate	VERB
ajst-15420	96	29	,	,	PUNCT
ajst-15420	96	30	ultimately	ultimately	ADV
ajst-15420	96	31	obtaining	obtain	VERB
ajst-15420	96	32	more	more	ADV
ajst-15420	96	33	adaptogenic	adaptogenic	ADJ
ajst-15420	96	34	new	new	ADJ
ajst-15420	96	35	individuals	individual	NOUN
ajst-15420	96	36	and	and	CCONJ
ajst-15420	96	37	optimizing	optimize	VERB
ajst-15420	96	38	the	the	DET
ajst-15420	96	39	combinations	combination	NOUN
ajst-15420	96	40	of	of	ADP
ajst-15420	96	41	weights	weight	NOUN
ajst-15420	96	42	and	and	CCONJ
ajst-15420	96	43	thresholds	threshold	NOUN
ajst-15420	96	44	.	.	PUNCT
ajst-15420	97	1	table	table	NOUN
ajst-15420	97	2	1	1	NUM
ajst-15420	97	3	.	.	PUNCT
ajst-15420	97	4	evaluation	evaluation	NOUN
ajst-15420	97	5	criteria	criterion	NOUN
ajst-15420	97	6	r2	r2	PROPN
ajst-15420	97	7	rmse	rmse	PROPN
ajst-15420	97	8	bp	bp	PROPN
ajst-15420	97	9	ga	ga	PROPN
ajst-15420	97	10	-	-	PUNCT
ajst-15420	97	11	bp	bp	PROPN
ajst-15420	97	12	bp	bp	PROPN
ajst-15420	97	13	ga	ga	PROPN
ajst-15420	97	14	-	-	PUNCT
ajst-15420	97	15	bp	bp	PROPN
ajst-15420	97	16	yield	yield	NOUN
ajst-15420	97	17	strength	strength	NOUN
ajst-15420	97	18	/	/	SYM
ajst-15420	97	19	mpa	mpa	PROPN
ajst-15420	97	20	0.90	0.90	NUM
ajst-15420	97	21	0.97	0.97	NUM
ajst-15420	97	22	120.14	120.14	NUM
ajst-15420	97	23	57.67	57.67	NUM
ajst-15420	97	24	tensile	tensile	NOUN
ajst-15420	97	25	strength	strength	NOUN
ajst-15420	97	26	/	/	SYM
ajst-15420	97	27	mpa	mpa	PROPN
ajst-15420	97	28	0.95	0.95	NUM
ajst-15420	97	29	0.98	0.98	NUM
ajst-15420	97	30	108.16	108.16	NUM
ajst-15420	97	31	1.70	1.70	NUM
ajst-15420	97	32	elongation/%	elongation/%	NUM
ajst-15420	97	33	0.96	0.96	NUM
ajst-15420	97	34	0.98	0.98	NUM
ajst-15420	97	35	54.60	54.60	NUM
ajst-15420	97	36	1.01	1.01	NUM
ajst-15420	97	37	figures	figure	NOUN
ajst-15420	97	38	2	2	NUM
ajst-15420	97	39	,	,	PUNCT
ajst-15420	97	40	3	3	NUM
ajst-15420	97	41	,	,	PUNCT
ajst-15420	97	42	and	and	CCONJ
ajst-15420	97	43	4	4	NUM
ajst-15420	97	44	show	show	VERB
ajst-15420	97	45	the	the	DET
ajst-15420	97	46	prediction	prediction	NOUN
ajst-15420	97	47	accuracy	accuracy	NOUN
ajst-15420	97	48	of	of	ADP
ajst-15420	97	49	the	the	DET
ajst-15420	97	50	bp	bp	PROPN
ajst-15420	97	51	neural	neural	ADJ
ajst-15420	97	52	network	network	NOUN
ajst-15420	97	53	for	for	ADP
ajst-15420	97	54	yield	yield	NOUN
ajst-15420	97	55	strength	strength	NOUN
ajst-15420	97	56	,	,	PUNCT
ajst-15420	97	57	tensile	tensile	NOUN
ajst-15420	97	58	strength	strength	NOUN
ajst-15420	97	59	,	,	PUNCT
ajst-15420	97	60	and	and	CCONJ
ajst-15420	97	61	elongation	elongation	NOUN
ajst-15420	97	62	before	before	ADV
ajst-15420	97	63	and	and	CCONJ
ajst-15420	97	64	after	after	ADP
ajst-15420	97	65	genetic	genetic	ADJ
ajst-15420	97	66	algorithm	algorithm	NOUN
ajst-15420	97	67	optimization	optimization	NOUN
ajst-15420	97	68	.	.	PUNCT
ajst-15420	98	1	it	it	PRON
ajst-15420	98	2	can	can	AUX
ajst-15420	98	3	be	be	AUX
ajst-15420	98	4	observed	observe	VERB
ajst-15420	98	5	from	from	ADP
ajst-15420	98	6	the	the	DET
ajst-15420	98	7	figures	figure	NOUN
ajst-15420	98	8	that	that	SCONJ
ajst-15420	98	9	the	the	DET
ajst-15420	98	10	prediction	prediction	NOUN
ajst-15420	98	11	results	result	NOUN
ajst-15420	98	12	of	of	ADP
ajst-15420	98	13	the	the	DET
ajst-15420	98	14	three	three	NUM
ajst-15420	98	15	properties	property	NOUN
ajst-15420	98	16	have	have	AUX
ajst-15420	98	17	been	be	AUX
ajst-15420	98	18	optimized	optimize	VERB
ajst-15420	98	19	to	to	ADP
ajst-15420	98	20	a	a	DET
ajst-15420	98	21	certain	certain	ADJ
ajst-15420	98	22	extent	extent	NOUN
ajst-15420	98	23	after	after	ADP
ajst-15420	98	24	genetic	genetic	ADJ
ajst-15420	98	25	algorithm	algorithm	NOUN
ajst-15420	98	26	optimization	optimization	NOUN
ajst-15420	98	27	.	.	PUNCT
ajst-15420	99	1	this	this	PRON
ajst-15420	99	2	indicates	indicate	VERB
ajst-15420	99	3	that	that	SCONJ
ajst-15420	99	4	genetic	genetic	ADJ
ajst-15420	99	5	algorithm	algorithm	NOUN
ajst-15420	99	6	has	have	VERB
ajst-15420	99	7	a	a	DET
ajst-15420	99	8	good	good	ADJ
ajst-15420	99	9	effect	effect	NOUN
ajst-15420	99	10	on	on	ADP
ajst-15420	99	11	optimizing	optimize	VERB
ajst-15420	99	12	the	the	DET
ajst-15420	99	13	bp	bp	PROPN
ajst-15420	99	14	neural	neural	ADJ
ajst-15420	99	15	network	network	NOUN
ajst-15420	99	16	and	and	CCONJ
ajst-15420	99	17	improving	improve	VERB
ajst-15420	99	18	prediction	prediction	NOUN
ajst-15420	99	19	accuracy	accuracy	NOUN
ajst-15420	99	20	.	.	PUNCT
ajst-15420	100	1	however	however	ADV
ajst-15420	100	2	,	,	PUNCT
ajst-15420	100	3	several	several	ADJ
ajst-15420	100	4	groups	group	NOUN
ajst-15420	100	5	of	of	ADP
ajst-15420	100	6	prediction	prediction	NOUN
ajst-15420	100	7	points	point	NOUN
ajst-15420	100	8	have	have	VERB
ajst-15420	100	9	relatively	relatively	ADV
ajst-15420	100	10	large	large	ADJ
ajst-15420	100	11	errors	error	NOUN
ajst-15420	100	12	.	.	PUNCT
ajst-15420	101	1	one	one	NUM
ajst-15420	101	2	possible	possible	ADJ
ajst-15420	101	3	reason	reason	NOUN
ajst-15420	101	4	for	for	ADP
ajst-15420	101	5	this	this	PRON
ajst-15420	101	6	is	be	AUX
ajst-15420	101	7	that	that	SCONJ
ajst-15420	101	8	the	the	DET
ajst-15420	101	9	amount	amount	NOUN
ajst-15420	101	10	of	of	ADP
ajst-15420	101	11	data	datum	NOUN
ajst-15420	101	12	collected	collect	VERB
ajst-15420	101	13	in	in	ADP
ajst-15420	101	14	this	this	DET
ajst-15420	101	15	experiment	experiment	NOUN
ajst-15420	101	16	is	be	AUX
ajst-15420	101	17	relatively	relatively	ADV
ajst-15420	101	18	small	small	ADJ
ajst-15420	101	19	,	,	PUNCT
ajst-15420	101	20	making	make	VERB
ajst-15420	101	21	it	it	PRON
ajst-15420	101	22	difficult	difficult	ADJ
ajst-15420	101	23	to	to	PART
ajst-15420	101	24	achieve	achieve	VERB
ajst-15420	101	25	better	well	ADJ
ajst-15420	101	26	predictions	prediction	NOUN
ajst-15420	101	27	.	.	PUNCT
ajst-15420	102	1	in	in	ADP
ajst-15420	102	2	subsequent	subsequent	ADJ
ajst-15420	102	3	optimization	optimization	NOUN
ajst-15420	102	4	,	,	PUNCT
ajst-15420	102	5	we	we	PRON
ajst-15420	102	6	should	should	AUX
ajst-15420	102	7	continue	continue	VERB
ajst-15420	102	8	to	to	PART
ajst-15420	102	9	increase	increase	VERB
ajst-15420	102	10	the	the	DET
ajst-15420	102	11	amount	amount	NOUN
ajst-15420	102	12	of	of	ADP
ajst-15420	102	13	experimental	experimental	ADJ
ajst-15420	102	14	data	datum	NOUN
ajst-15420	102	15	to	to	PART
ajst-15420	102	16	improve	improve	VERB
ajst-15420	102	17	the	the	DET
ajst-15420	102	18	prediction	prediction	NOUN
ajst-15420	102	19	accuracy[13	accuracy[13	PROPN
ajst-15420	102	20	]	]	PUNCT
ajst-15420	102	21	.	.	PUNCT
ajst-15420	103	1	figure	figure	NOUN
ajst-15420	103	2	2	2	NUM
ajst-15420	103	3	.	.	PUNCT
ajst-15420	103	4	breeding	breeding	NOUN
ajst-15420	103	5	intensity	intensity	NOUN
ajst-15420	103	6	prediction	prediction	NOUN
ajst-15420	103	7	comparison	comparison	NOUN
ajst-15420	103	8	of	of	ADP
ajst-15420	103	9	aenetic	aenetic	ADJ
ajst-15420	103	10	algorithms	algorithm	NOUN
ajst-15420	103	11	4	4	NUM
ajst-15420	103	12	figure	figure	NOUN
ajst-15420	103	13	3	3	NUM
ajst-15420	103	14	.	.	PUNCT
ajst-15420	104	1	the	the	DET
ajst-15420	104	2	comparison	comparison	NOUN
ajst-15420	104	3	of	of	ADP
ajst-15420	104	4	the	the	DET
ajst-15420	104	5	hereditary	hereditary	ADJ
ajst-15420	104	6	algorithm	algorithm	NOUN
ajst-15420	104	7	before	before	ADP
ajst-15420	104	8	and	and	CCONJ
ajst-15420	104	9	after	after	ADP
ajst-15420	104	10	the	the	DET
ajst-15420	104	11	tensile	tensile	NOUN
ajst-15420	104	12	strength	strength	NOUN
ajst-15420	104	13	prediction	prediction	NOUN
ajst-15420	104	14	figure	figure	NOUN
ajst-15420	104	15	4	4	NUM
ajst-15420	104	16	.	.	NOUN
ajst-15420	104	17	comparison	comparison	NOUN
ajst-15420	104	18	of	of	ADP
ajst-15420	104	19	elongation	elongation	NOUN
ajst-15420	104	20	prediction	prediction	NOUN
ajst-15420	104	21	before	before	ADP
ajst-15420	104	22	and	and	CCONJ
ajst-15420	104	23	after	after	ADP
ajst-15420	104	24	genetic	genetic	ADJ
ajst-15420	104	25	algorithm	algorithm	NOUN
ajst-15420	104	26	2.4	2.4	NUM
ajst-15420	104	27	.	.	PUNCT
ajst-15420	105	1	model	model	NOUN
ajst-15420	105	2	detection	detection	NOUN
ajst-15420	105	3	table	table	NOUN
ajst-15420	105	4	2	2	NUM
ajst-15420	105	5	presents	present	VERB
ajst-15420	105	6	the	the	DET
ajst-15420	105	7	prediction	prediction	NOUN
ajst-15420	105	8	results	result	NOUN
ajst-15420	105	9	of	of	ADP
ajst-15420	105	10	two	two	NUM
ajst-15420	105	11	sets	set	NOUN
ajst-15420	105	12	of	of	ADP
ajst-15420	105	13	experimental	experimental	ADJ
ajst-15420	105	14	data	datum	NOUN
ajst-15420	105	15	using	use	VERB
ajst-15420	105	16	the	the	DET
ajst-15420	105	17	genetic	genetic	ADJ
ajst-15420	105	18	algorithm	algorithm	NOUN
ajst-15420	105	19	-	-	PUNCT
ajst-15420	105	20	optimized	optimize	VERB
ajst-15420	105	21	bp	bp	PROPN
ajst-15420	105	22	neural	neural	ADJ
ajst-15420	105	23	network	network	NOUN
ajst-15420	105	24	model	model	NOUN
ajst-15420	105	25	and	and	CCONJ
ajst-15420	105	26	the	the	DET
ajst-15420	105	27	unoptimized	unoptimized	ADJ
ajst-15420	105	28	bp	bp	PROPN
ajst-15420	105	29	neural	neural	ADJ
ajst-15420	105	30	network	network	NOUN
ajst-15420	105	31	model	model	NOUN
ajst-15420	105	32	for	for	ADP
ajst-15420	105	33	mechanical	mechanical	ADJ
ajst-15420	105	34	property	property	NOUN
ajst-15420	105	35	prediction	prediction	NOUN
ajst-15420	105	36	.	.	PUNCT
ajst-15420	106	1	it	it	PRON
ajst-15420	106	2	can	can	AUX
ajst-15420	106	3	be	be	AUX
ajst-15420	106	4	observed	observe	VERB
ajst-15420	106	5	from	from	ADP
ajst-15420	106	6	the	the	DET
ajst-15420	106	7	table	table	NOUN
ajst-15420	106	8	that	that	SCONJ
ajst-15420	106	9	the	the	DET
ajst-15420	106	10	genetic	genetic	ADJ
ajst-15420	106	11	algorithm	algorithm	NOUN
ajst-15420	106	12	-	-	PUNCT
ajst-15420	106	13	optimized	optimize	VERB
ajst-15420	106	14	bp	bp	PROPN
ajst-15420	106	15	neural	neural	PROPN
ajst-15420	106	16	network	network	NOUN
ajst-15420	106	17	model	model	NOUN
ajst-15420	106	18	provides	provide	VERB
ajst-15420	106	19	more	more	ADV
ajst-15420	106	20	accurate	accurate	ADJ
ajst-15420	106	21	predictions	prediction	NOUN
ajst-15420	106	22	than	than	ADP
ajst-15420	106	23	the	the	DET
ajst-15420	106	24	unoptimized	unoptimized	ADJ
ajst-15420	106	25	bp	bp	PROPN
ajst-15420	106	26	neural	neural	ADJ
ajst-15420	106	27	network	network	NOUN
ajst-15420	106	28	model	model	NOUN
ajst-15420	106	29	for	for	ADP
ajst-15420	106	30	the	the	DET
ajst-15420	106	31	mechanical	mechanical	ADJ
ajst-15420	106	32	properties	property	NOUN
ajst-15420	106	33	.	.	PUNCT
ajst-15420	107	1	this	this	PRON
ajst-15420	107	2	further	far	ADV
ajst-15420	107	3	confirms	confirm	VERB
ajst-15420	107	4	that	that	SCONJ
ajst-15420	107	5	genetic	genetic	ADJ
ajst-15420	107	6	algorithm	algorithm	NOUN
ajst-15420	107	7	optimization	optimization	NOUN
ajst-15420	107	8	can	can	AUX
ajst-15420	107	9	improve	improve	VERB
ajst-15420	107	10	the	the	DET
ajst-15420	107	11	prediction	prediction	NOUN
ajst-15420	107	12	accuracy	accuracy	NOUN
ajst-15420	107	13	of	of	ADP
ajst-15420	107	14	bp	bp	PROPN
ajst-15420	107	15	neural	neural	ADJ
ajst-15420	107	16	network	network	NOUN
ajst-15420	107	17	models	model	NOUN
ajst-15420	107	18	.	.	PUNCT
ajst-15420	108	1	table	table	NOUN
ajst-15420	108	2	2	2	NUM
ajst-15420	108	3	.	.	X
ajst-15420	108	4	optimize	optimize	VERB
ajst-15420	108	5	the	the	DET
ajst-15420	108	6	results	result	NOUN
ajst-15420	108	7	of	of	ADP
ajst-15420	108	8	the	the	DET
ajst-15420	108	9	results	result	NOUN
ajst-15420	108	10	before	before	ADP
ajst-15420	108	11	and	and	CCONJ
ajst-15420	108	12	after	after	ADP
ajst-15420	108	13	ture	ture	PROPN
ajst-15420	108	14	bp	bp	PROPN
ajst-15420	108	15	ga	ga	PROPN
ajst-15420	108	16	-	-	PUNCT
ajst-15420	108	17	bp	bp	PROPN
ajst-15420	108	18	yield	yield	NOUN
ajst-15420	108	19	strength	strength	NOUN
ajst-15420	108	20	/	/	SYM
ajst-15420	108	21	mpa	mpa	PROPN
ajst-15420	108	22	990	990	NUM
ajst-15420	108	23	1024.32	1024.32	NUM
ajst-15420	108	24	995.48	995.48	NUM
ajst-15420	108	25	tensile	tensile	NOUN
ajst-15420	108	26	strength	strength	NOUN
ajst-15420	108	27	/	/	SYM
ajst-15420	108	28	mpa	mpa	PROPN
ajst-15420	108	29	1039	1039	NUM
ajst-15420	108	30	1018.48	1018.48	NUM
ajst-15420	108	31	1022.83	1022.83	NUM
ajst-15420	108	32	elongation/%	elongation/%	NUM
ajst-15420	108	33	16.3	16.3	NUM
ajst-15420	108	34	17.35	17.35	NUM
ajst-15420	108	35	16.89	16.89	NUM
ajst-15420	108	36	3	3	NUM
ajst-15420	108	37	.	.	PUNCT
ajst-15420	108	38	conclusion	conclusion	NOUN
ajst-15420	108	39	optimizing	optimize	VERB
ajst-15420	108	40	the	the	DET
ajst-15420	108	41	bp	bp	PROPN
ajst-15420	108	42	neural	neural	PROPN
ajst-15420	108	43	network	network	NOUN
ajst-15420	108	44	prediction	prediction	NOUN
ajst-15420	108	45	of	of	ADP
ajst-15420	108	46	mechanical	mechanical	ADJ
ajst-15420	108	47	properties	property	NOUN
ajst-15420	108	48	based	base	VERB
ajst-15420	108	49	on	on	ADP
ajst-15420	108	50	genetic	genetic	ADJ
ajst-15420	108	51	algorithms	algorithm	NOUN
ajst-15420	108	52	,	,	PUNCT
ajst-15420	108	53	using	use	VERB
ajst-15420	108	54	the	the	DET
ajst-15420	108	55	genetic	genetic	ADJ
ajst-15420	108	56	algorithm	algorithm	NOUN
ajst-15420	108	57	's	's	PART
ajst-15420	108	58	ability	ability	NOUN
ajst-15420	108	59	to	to	PART
ajst-15420	108	60	select	select	VERB
ajst-15420	108	61	the	the	DET
ajst-15420	108	62	fittest	fit	ADJ
ajst-15420	108	63	parameters	parameter	NOUN
ajst-15420	108	64	,	,	PUNCT
ajst-15420	108	65	selecting	select	VERB
ajst-15420	108	66	chemical	chemical	NOUN
ajst-15420	108	67	composition	composition	NOUN
ajst-15420	108	68	and	and	CCONJ
ajst-15420	108	69	processing	processing	NOUN
ajst-15420	108	70	technology	technology	NOUN
ajst-15420	108	71	to	to	PART
ajst-15420	108	72	predict	predict	VERB
ajst-15420	108	73	the	the	DET
ajst-15420	108	74	yield	yield	NOUN
ajst-15420	108	75	strength	strength	NOUN
ajst-15420	108	76	,	,	PUNCT
ajst-15420	108	77	tensile	tensile	NOUN
ajst-15420	108	78	strength	strength	NOUN
ajst-15420	108	79	,	,	PUNCT
ajst-15420	108	80	and	and	CCONJ
ajst-15420	108	81	elongation	elongation	NOUN
ajst-15420	108	82	of	of	ADP
ajst-15420	108	83	steel	steel	NOUN
ajst-15420	108	84	materials	material	NOUN
ajst-15420	108	85	.	.	PUNCT
ajst-15420	109	1	demonstrating	demonstrate	VERB
ajst-15420	109	2	the	the	DET
ajst-15420	109	3	prediction	prediction	NOUN
ajst-15420	109	4	accuracy	accuracy	NOUN
ajst-15420	109	5	of	of	ADP
ajst-15420	109	6	the	the	DET
ajst-15420	109	7	bp	bp	PROPN
ajst-15420	109	8	neural	neural	PROPN
ajst-15420	109	9	network	network	PROPN
ajst-15420	109	10	and	and	CCONJ
ajst-15420	109	11	ga	ga	PROPN
ajst-15420	109	12	-	-	PUNCT
ajst-15420	109	13	bp	bp	PROPN
ajst-15420	109	14	neural	neural	ADJ
ajst-15420	109	15	network	network	NOUN
ajst-15420	109	16	for	for	ADP
ajst-15420	109	17	the	the	DET
ajst-15420	109	18	three	three	NUM
ajst-15420	109	19	mechanical	mechanical	ADJ
ajst-15420	109	20	properties	property	NOUN
ajst-15420	109	21	through	through	ADP
ajst-15420	109	22	r2	r2	PROPN
ajst-15420	109	23	and	and	CCONJ
ajst-15420	109	24	rmse	rmse	NOUN
ajst-15420	109	25	.	.	PUNCT
ajst-15420	110	1	selecting	select	VERB
ajst-15420	110	2	data	datum	NOUN
ajst-15420	110	3	to	to	PART
ajst-15420	110	4	verify	verify	VERB
ajst-15420	110	5	the	the	DET
ajst-15420	110	6	reliability	reliability	NOUN
ajst-15420	110	7	of	of	ADP
ajst-15420	110	8	the	the	DET
ajst-15420	110	9	rf	rf	ADJ
ajst-15420	110	10	algorithm	algorithm	NOUN
ajst-15420	110	11	model	model	NOUN
ajst-15420	110	12	,	,	PUNCT
ajst-15420	110	13	the	the	DET
ajst-15420	110	14	results	result	NOUN
ajst-15420	110	15	show	show	VERB
ajst-15420	110	16	that	that	SCONJ
ajst-15420	110	17	the	the	DET
ajst-15420	110	18	ga	ga	PROPN
ajst-15420	110	19	-	-	PUNCT
ajst-15420	110	20	bp	bp	PROPN
ajst-15420	110	21	prediction	prediction	NOUN
ajst-15420	110	22	effect	effect	NOUN
ajst-15420	110	23	is	be	AUX
ajst-15420	110	24	better	well	ADJ
ajst-15420	110	25	than	than	ADP
ajst-15420	110	26	the	the	DET
ajst-15420	110	27	bp	bp	PROPN
ajst-15420	110	28	neural	neural	ADJ
ajst-15420	110	29	network	network	NOUN
ajst-15420	110	30	for	for	ADP
ajst-15420	110	31	the	the	DET
ajst-15420	110	32	yield	yield	NOUN
ajst-15420	110	33	strength	strength	NOUN
ajst-15420	110	34	,	,	PUNCT
ajst-15420	110	35	tensile	tensile	NOUN
ajst-15420	110	36	strength	strength	NOUN
ajst-15420	110	37	,	,	PUNCT
ajst-15420	110	38	and	and	CCONJ
ajst-15420	110	39	elongation	elongation	NOUN
ajst-15420	110	40	.	.	PUNCT
ajst-15420	111	1	ga	ga	PROPN
ajst-15420	111	2	-	-	PUNCT
ajst-15420	111	3	bp	bp	PROPN
ajst-15420	111	4	is	be	AUX
ajst-15420	111	5	feasible	feasible	ADJ
ajst-15420	111	6	for	for	ADP
ajst-15420	111	7	the	the	DET
ajst-15420	111	8	prediction	prediction	NOUN
ajst-15420	111	9	of	of	ADP
ajst-15420	111	10	mechanical	mechanical	ADJ
ajst-15420	111	11	properties	property	NOUN
ajst-15420	111	12	of	of	ADP
ajst-15420	111	13	steel	steel	NOUN
ajst-15420	111	14	materials	material	NOUN
ajst-15420	111	15	.	.	PUNCT
ajst-15420	112	1	references	reference	NOUN
ajst-15420	112	2	[	[	X
ajst-15420	112	3	1	1	NUM
ajst-15420	112	4	]	]	PUNCT
ajst-15420	112	5	a.	a.	PROPN
ajst-15420	112	6	k.	k.	PROPN
ajst-15420	112	7	tyagi	tyagi	PROPN
ajst-15420	112	8	,	,	PUNCT
ajst-15420	112	9	p.chahal	p.chahal	ADV
ajst-15420	112	10	,	,	PUNCT
ajst-15420	112	11	“	"	PUNCT
ajst-15420	112	12	artificial	artificial	ADJ
ajst-15420	112	13	intelligence	intelligence	NOUN
ajst-15420	112	14	and	and	CCONJ
ajst-15420	112	15	machine	machine	NOUN
ajst-15420	112	16	learning	learn	VERB
ajst-15420	112	17	algorithms	algorithm	NOUN
ajst-15420	112	18	.	.	PUNCT
ajst-15420	113	1	in	in	ADP
ajst-15420	113	2	research	research	NOUN
ajst-15420	113	3	anthology	anthology	NOUN
ajst-15420	113	4	on	on	ADP
ajst-15420	113	5	machine	machine	NOUN
ajst-15420	113	6	learning	learn	VERB
ajst-15420	113	7	techniques	technique	NOUN
ajst-15420	113	8	,	,	PUNCT
ajst-15420	113	9	”	"	PUNCT
ajst-15420	113	10	methods	method	NOUN
ajst-15420	113	11	,	,	PUNCT
ajst-15420	113	12	and	and	CCONJ
ajst-15420	113	13	applications	application	NOUN
ajst-15420	113	14	igi	igi	PROPN
ajst-15420	113	15	global	global	NOUN
ajst-15420	113	16	,	,	PUNCT
ajst-15420	113	17	2022	2022	NUM
ajst-15420	113	18	,	,	PUNCT
ajst-15420	113	19	pp	pp	ADJ
ajst-15420	113	20	.	.	PUNCT
ajst-15420	114	1	421	421	NUM
ajst-15420	114	2	-	-	SYM
ajst-15420	114	3	446	446	NUM
ajst-15420	114	4	.	.	PUNCT
ajst-15420	115	1	[	[	X
ajst-15420	115	2	2	2	X
ajst-15420	115	3	]	]	X
ajst-15420	115	4	y.	y.	PROPN
ajst-15420	115	5	juan	juan	PROPN
ajst-15420	115	6	,	,	PUNCT
ajst-15420	115	7	y.	y.	PROPN
ajst-15420	115	8	dai	dai	PROPN
ajst-15420	115	9	,	,	PUNCT
ajst-15420	115	10	and	and	CCONJ
ajst-15420	115	11	y.	y.	PROPN
ajst-15420	115	12	yang	yang	PROPN
ajst-15420	115	13	,	,	PUNCT
ajst-15420	115	14	“	"	PUNCT
ajst-15420	115	15	accelerating	accelerate	VERB
ajst-15420	115	16	materials	material	NOUN
ajst-15420	115	17	discovery	discovery	NOUN
ajst-15420	115	18	using	use	VERB
ajst-15420	115	19	machine	machine	NOUN
ajst-15420	115	20	learning	learn	VERB
ajst-15420	116	1	[	[	X
ajst-15420	116	2	j	j	X
ajst-15420	116	3	]	]	X
ajst-15420	116	4	”	"	PUNCT
ajst-15420	116	5	.	.	PUNCT
ajst-15420	117	1	journal	journal	PROPN
ajst-15420	117	2	of	of	ADP
ajst-15420	117	3	materials	material	NOUN
ajst-15420	117	4	science	science	NOUN
ajst-15420	117	5	&	&	CCONJ
ajst-15420	117	6	technology	technology	PROPN
ajst-15420	117	7	,	,	PUNCT
ajst-15420	117	8	vol	vol	NOUN
ajst-15420	117	9	.	.	PROPN
ajst-15420	117	10	79	79	NUM
ajst-15420	117	11	,	,	PUNCT
ajst-15420	117	12	2021	2021	NUM
ajst-15420	117	13	,	,	PUNCT
ajst-15420	117	14	pp	pp	ADJ
ajst-15420	117	15	.	.	PUNCT
ajst-15420	118	1	178	178	NUM
ajst-15420	118	2	-	-	SYM
ajst-15420	118	3	190	190	NUM
ajst-15420	118	4	.	.	PUNCT
ajst-15420	119	1	[	[	X
ajst-15420	119	2	3	3	X
ajst-15420	119	3	]	]	PUNCT
ajst-15420	119	4	z.	z.	PROPN
ajst-15420	119	5	xu	xu	PROPN
ajst-15420	119	6	,	,	PUNCT
ajst-15420	119	7	x.	x.	PROPN
ajst-15420	119	8	huang	huang	PROPN
ajst-15420	119	9	,	,	PUNCT
ajst-15420	119	10	and	and	CCONJ
ajst-15420	119	11	l.	l.	PROPN
ajst-15420	119	12	lin	lin	PROPN
ajst-15420	119	13	,	,	PUNCT
ajst-15420	119	14	“	"	PUNCT
ajst-15420	119	15	bp	bp	PROPN
ajst-15420	119	16	neural	neural	ADJ
ajst-15420	119	17	networks	network	NOUN
ajst-15420	119	18	and	and	CCONJ
ajst-15420	119	19	random	random	ADJ
ajst-15420	119	20	forest	forest	NOUN
ajst-15420	119	21	models	model	NOUN
ajst-15420	119	22	to	to	PART
ajst-15420	119	23	detect	detect	VERB
ajst-15420	119	24	damage	damage	NOUN
ajst-15420	119	25	by	by	ADP
ajst-15420	119	26	dendrolimus	dendrolimus	NOUN
ajst-15420	119	27	punctatus	punctatus	PROPN
ajst-15420	119	28	walker[j	walker[j	NOUN
ajst-15420	119	29	]	]	PUNCT
ajst-15420	119	30	”	"	PUNCT
ajst-15420	119	31	.	.	PUNCT
ajst-15420	120	1	journal	journal	PROPN
ajst-15420	120	2	of	of	ADP
ajst-15420	120	3	forestry	forestry	PROPN
ajst-15420	120	4	research	research	NOUN
ajst-15420	120	5	,	,	PUNCT
ajst-15420	120	6	vol	vol	NOUN
ajst-15420	120	7	.	.	PROPN
ajst-15420	120	8	31	31	NUM
ajst-15420	120	9	,	,	PUNCT
ajst-15420	120	10	2020	2020	NUM
ajst-15420	120	11	,	,	PUNCT
ajst-15420	120	12	pp	pp	ADV
ajst-15420	120	13	.	.	PUNCT
ajst-15420	121	1	107	107	NUM
ajst-15420	121	2	-	-	SYM
ajst-15420	121	3	121	121	NUM
ajst-15420	121	4	.	.	PUNCT
ajst-15420	122	1	[	[	X
ajst-15420	122	2	4	4	X
ajst-15420	122	3	]	]	PUNCT
ajst-15420	122	4	w.	w.	PROPN
ajst-15420	122	5	jin	jin	PROPN
ajst-15420	122	6	,	,	PUNCT
ajst-15420	122	7	z.	z.	PROPN
ajst-15420	122	8	j.	j.	PROPN
ajst-15420	122	9	li	li	PROPN
ajst-15420	122	10	,	,	PUNCT
ajst-15420	122	11	and	and	CCONJ
ajst-15420	122	12	l.	l.	PROPN
ajst-15420	122	13	s.	s.	PROPN
ajst-15420	122	14	wei	wei	PROPN
ajst-15420	122	15	,	,	PUNCT
ajst-15420	122	16	“	"	PUNCT
ajst-15420	122	17	the	the	DET
ajst-15420	122	18	improvements	improvement	NOUN
ajst-15420	122	19	of	of	ADP
ajst-15420	122	20	bp	bp	PROPN
ajst-15420	122	21	neural	neural	ADJ
ajst-15420	122	22	network	network	NOUN
ajst-15420	122	23	learning	learn	VERB
ajst-15420	122	24	algorithm[c]//wcc	algorithm[c]//wcc	PROPN
ajst-15420	122	25	2000	2000	NUM
ajst-15420	122	26	-	-	PUNCT
ajst-15420	122	27	icsp	icsp	ADJ
ajst-15420	122	28	2000	2000	NUM
ajst-15420	122	29	”	"	PUNCT
ajst-15420	122	30	.	.	PUNCT
ajst-15420	123	1	2000	2000	NUM
ajst-15420	123	2	5th	5th	ADJ
ajst-15420	123	3	international	international	ADJ
ajst-15420	123	4	conference	conference	NOUN
ajst-15420	123	5	on	on	ADP
ajst-15420	123	6	signal	signal	ADJ
ajst-15420	123	7	processing	processing	NOUN
ajst-15420	123	8	proceedings	proceeding	NOUN
ajst-15420	123	9	.	.	PUNCT
ajst-15420	124	1	16th	16th	ADJ
ajst-15420	124	2	world	world	PROPN
ajst-15420	124	3	computer	computer	NOUN
ajst-15420	124	4	congress	congress	PROPN
ajst-15420	124	5	2000	2000	NUM
ajst-15420	124	6	.	.	PUNCT
ajst-15420	125	1	ieee	ieee	NOUN
ajst-15420	125	2	,	,	PUNCT
ajst-15420	125	3	vol	vol	NOUN
ajst-15420	125	4	.	.	PROPN
ajst-15420	125	5	3	3	NUM
ajst-15420	125	6	,	,	PUNCT
ajst-15420	125	7	2000	2000	NUM
ajst-15420	125	8	,	,	PUNCT
ajst-15420	125	9	pp.1647	pp.1647	PROPN
ajst-15420	125	10	-	-	SYM
ajst-15420	125	11	1649	1649	NUM
ajst-15420	125	12	.	.	PUNCT
ajst-15420	126	1	[	[	X
ajst-15420	126	2	5	5	NUM
ajst-15420	126	3	]	]	PUNCT
ajst-15420	126	4	i.	i.	NOUN
ajst-15420	126	5	banerjee	banerjee	PROPN
ajst-15420	126	6	,	,	PUNCT
ajst-15420	126	7	y.	y.	PROPN
ajst-15420	126	8	ling	ling	PROPN
ajst-15420	126	9	,	,	PUNCT
ajst-15420	126	10	and	and	CCONJ
ajst-15420	126	11	m.	m.	PROPN
ajst-15420	126	12	c.	c.	PROPN
ajst-15420	126	13	chen	chen	PROPN
ajst-15420	126	14	,	,	PUNCT
ajst-15420	126	15	“	"	PUNCT
ajst-15420	126	16	comparative	comparative	ADJ
ajst-15420	126	17	effectiveness	effectiveness	NOUN
ajst-15420	126	18	of	of	ADP
ajst-15420	126	19	convolutional	convolutional	ADJ
ajst-15420	126	20	neural	neural	ADJ
ajst-15420	126	21	network	network	NOUN
ajst-15420	126	22	(	(	PUNCT
ajst-15420	126	23	cnn	cnn	PROPN
ajst-15420	126	24	)	)	PUNCT
ajst-15420	126	25	and	and	CCONJ
ajst-15420	126	26	recurrent	recurrent	ADJ
ajst-15420	126	27	neural	neural	ADJ
ajst-15420	126	28	network	network	NOUN
ajst-15420	126	29	(	(	PUNCT
ajst-15420	126	30	rnn	rnn	PROPN
ajst-15420	126	31	)	)	PUNCT
ajst-15420	126	32	architectures	architecture	NOUN
ajst-15420	126	33	for	for	ADP
ajst-15420	126	34	radiology	radiology	NOUN
ajst-15420	126	35	text	text	NOUN
ajst-15420	126	36	report	report	NOUN
ajst-15420	126	37	classification[j	classification[j	X
ajst-15420	126	38	]	]	X
ajst-15420	126	39	”	"	PUNCT
ajst-15420	126	40	.	.	PUNCT
ajst-15420	127	1	artificial	artificial	ADJ
ajst-15420	127	2	intelligence	intelligence	NOUN
ajst-15420	127	3	in	in	ADP
ajst-15420	127	4	medicine	medicine	NOUN
ajst-15420	127	5	,	,	PUNCT
ajst-15420	127	6	vol	vol	NOUN
ajst-15420	127	7	.	.	PROPN
ajst-15420	127	8	97	97	NUM
ajst-15420	127	9	,	,	PUNCT
ajst-15420	127	10	2019	2019	NUM
ajst-15420	127	11	,	,	PUNCT
ajst-15420	127	12	pp	pp	ADJ
ajst-15420	127	13	.	.	PUNCT
ajst-15420	128	1	79	79	NUM
ajst-15420	128	2	-	-	SYM
ajst-15420	128	3	88	88	NUM
ajst-15420	128	4	.	.	PUNCT
ajst-15420	129	1	[	[	X
ajst-15420	129	2	6	6	NUM
ajst-15420	129	3	]	]	PUNCT
ajst-15420	129	4	a.	a.	NOUN
ajst-15420	129	5	yang	yang	PROPN
ajst-15420	129	6	,	,	PUNCT
ajst-15420	129	7	y.	y.	PROPN
ajst-15420	129	8	zhuansun	zhuansun	PROPN
ajst-15420	129	9	,	,	PUNCT
ajst-15420	129	10	and	and	CCONJ
ajst-15420	129	11	c.	c.	PROPN
ajst-15420	129	12	liu,”design	liu,”design	NOUN
ajst-15420	129	13	of	of	ADP
ajst-15420	129	14	intrusion	intrusion	NOUN
ajst-15420	129	15	detection	detection	NOUN
ajst-15420	129	16	system	system	NOUN
ajst-15420	129	17	for	for	ADP
ajst-15420	129	18	internet	internet	NOUN
ajst-15420	129	19	of	of	ADP
ajst-15420	129	20	things	thing	NOUN
ajst-15420	129	21	based	base	VERB
ajst-15420	129	22	on	on	ADP
ajst-15420	129	23	improved	improved	ADJ
ajst-15420	129	24	bp	bp	PROPN
ajst-15420	129	25	neural	neural	ADJ
ajst-15420	129	26	network[j	network[j	PROPN
ajst-15420	129	27	]	]	PUNCT
ajst-15420	129	28	”	"	PUNCT
ajst-15420	129	29	.	.	PUNCT
ajst-15420	130	1	ieee	ieee	NOUN
ajst-15420	130	2	access	access	NOUN
ajst-15420	130	3	,	,	PUNCT
ajst-15420	130	4	vol	vol	NOUN
ajst-15420	130	5	.	.	PROPN
ajst-15420	130	6	7	7	NUM
ajst-15420	130	7	,	,	PUNCT
ajst-15420	130	8	2019	2019	NUM
ajst-15420	130	9	,	,	PUNCT
ajst-15420	130	10	pp	pp	ADJ
ajst-15420	130	11	.	.	PUNCT
ajst-15420	130	12	106043106052	106043106052	NUM
ajst-15420	130	13	.	.	PUNCT
ajst-15420	131	1	[	[	X
ajst-15420	131	2	7	7	X
ajst-15420	131	3	]	]	X
ajst-15420	131	4	d.	d.	PROPN
ajst-15420	131	5	e.	e.	PROPN
ajst-15420	131	6	rumelhart	rumelhart	PROPN
ajst-15420	131	7	,	,	PUNCT
ajst-15420	131	8	g.	g.	PROPN
ajst-15420	131	9	e.	e.	PROPN
ajst-15420	131	10	hinton	hinton	PROPN
ajst-15420	131	11	,	,	PUNCT
ajst-15420	131	12	and	and	CCONJ
ajst-15420	131	13	r.	r.	PROPN
ajst-15420	131	14	j.	j.	PROPN
ajst-15420	131	15	williams	williams	PROPN
ajst-15420	131	16	,	,	PUNCT
ajst-15420	131	17	“	"	PUNCT
ajst-15420	131	18	learning	learn	VERB
ajst-15420	131	19	representations	representation	NOUN
ajst-15420	131	20	by	by	ADP
ajst-15420	131	21	back	back	ADV
ajst-15420	131	22	-	-	PUNCT
ajst-15420	131	23	propagating	propagate	VERB
ajst-15420	131	24	errors[j	errors[j	NOUN
ajst-15420	131	25	]	]	PUNCT
ajst-15420	131	26	”	"	PUNCT
ajst-15420	131	27	.	.	PUNCT
ajst-15420	132	1	nature	nature	NOUN
ajst-15420	132	2	,	,	PUNCT
ajst-15420	132	3	vol	vol	NOUN
ajst-15420	132	4	.	.	PROPN
ajst-15420	132	5	323	323	NUM
ajst-15420	132	6	,	,	PUNCT
ajst-15420	132	7	1986	1986	NUM
ajst-15420	132	8	,	,	PUNCT
ajst-15420	132	9	pp	pp	ADJ
ajst-15420	132	10	.	.	PUNCT
ajst-15420	133	1	533	533	NUM
ajst-15420	133	2	-	-	SYM
ajst-15420	133	3	536	536	NUM
ajst-15420	133	4	.	.	PUNCT
ajst-15420	134	1	[	[	X
ajst-15420	134	2	8	8	NUM
ajst-15420	134	3	]	]	PUNCT
ajst-15420	134	4	s.	s.	PROPN
ajst-15420	134	5	l.	l.	PROPN
ajst-15420	134	6	yu	yu	PROPN
ajst-15420	134	7	,	,	PUNCT
ajst-15420	134	8	and	and	CCONJ
ajst-15420	134	9	j.	j.	PROPN
ajst-15420	134	10	liu	liu	PROPN
ajst-15420	134	11	,	,	PUNCT
ajst-15420	134	12	“	"	PUNCT
ajst-15420	134	13	convolutional	convolutional	ADJ
ajst-15420	134	14	recurrent	recurrent	ADJ
ajst-15420	134	15	neural	neural	ADJ
ajst-15420	134	16	networks	network	NOUN
ajst-15420	134	17	for	for	ADP
ajst-15420	134	18	text	text	NOUN
ajst-15420	134	19	classification[j	classification[j	NOUN
ajst-15420	134	20	]	]	X
ajst-15420	134	21	”	"	PUNCT
ajst-15420	134	22	.	.	PUNCT
ajst-15420	135	1	journal	journal	PROPN
ajst-15420	135	2	of	of	ADP
ajst-15420	135	3	database	database	PROPN
ajst-15420	135	4	management	management	PROPN
ajst-15420	135	5	(	(	PUNCT
ajst-15420	135	6	jdm	jdm	NOUN
ajst-15420	135	7	)	)	PUNCT
ajst-15420	135	8	,	,	PUNCT
ajst-15420	135	9	vol	vol	NOUN
ajst-15420	135	10	.	.	PUNCT
ajst-15420	135	11	32(4	32(4	NUM
ajst-15420	135	12	)	)	PUNCT
ajst-15420	135	13	,	,	PUNCT
ajst-15420	135	14	2021	2021	NUM
ajst-15420	135	15	,	,	PUNCT
ajst-15420	135	16	pp	pp	ADJ
ajst-15420	135	17	.	.	PUNCT
ajst-15420	136	1	65	65	NUM
ajst-15420	136	2	-	-	SYM
ajst-15420	136	3	82	82	NUM
ajst-15420	136	4	.	.	NOUN
ajst-15420	136	5	5	5	NUM
ajst-15420	137	1	[	[	SYM
ajst-15420	137	2	9	9	NUM
ajst-15420	137	3	]	]	PUNCT
ajst-15420	137	4	s.	s.	PROPN
ajst-15420	137	5	wu	wu	PROPN
ajst-15420	137	6	,	,	PUNCT
ajst-15420	137	7	j.	j.	PROPN
ajst-15420	137	8	yang	yang	PROPN
ajst-15420	137	9	,	,	PUNCT
ajst-15420	137	10	and	and	CCONJ
ajst-15420	137	11	g.	g.	PROPN
ajst-15420	137	12	cao	cao	PROPN
ajst-15420	137	13	,	,	PUNCT
ajst-15420	137	14	“	"	PUNCT
ajst-15420	137	15	prediction	prediction	NOUN
ajst-15420	137	16	of	of	ADP
ajst-15420	137	17	the	the	DET
ajst-15420	137	18	charpy	charpy	ADJ
ajst-15420	137	19	v	v	NOUN
ajst-15420	137	20	-	-	PUNCT
ajst-15420	137	21	notch	notch	NOUN
ajst-15420	137	22	impact	impact	NOUN
ajst-15420	137	23	energy	energy	NOUN
ajst-15420	137	24	of	of	ADP
ajst-15420	137	25	low	low	ADJ
ajst-15420	137	26	carbon	carbon	NOUN
ajst-15420	137	27	steel	steel	NOUN
ajst-15420	137	28	using	use	VERB
ajst-15420	137	29	a	a	DET
ajst-15420	137	30	shallow	shallow	ADJ
ajst-15420	137	31	neural	neural	ADJ
ajst-15420	137	32	network	network	NOUN
ajst-15420	137	33	and	and	CCONJ
ajst-15420	137	34	deep	deep	ADJ
ajst-15420	137	35	learning[j	learning[j	NOUN
ajst-15420	137	36	]	]	PUNCT
ajst-15420	137	37	”	"	PUNCT
ajst-15420	137	38	.	.	PUNCT
ajst-15420	138	1	international	international	ADJ
ajst-15420	138	2	journal	journal	PROPN
ajst-15420	138	3	of	of	ADP
ajst-15420	138	4	minerals	mineral	NOUN
ajst-15420	138	5	,	,	PUNCT
ajst-15420	138	6	metallurgy	metallurgy	NOUN
ajst-15420	138	7	and	and	CCONJ
ajst-15420	138	8	materials	material	NOUN
ajst-15420	138	9	,	,	PUNCT
ajst-15420	138	10	vol	vol	NOUN
ajst-15420	138	11	.	.	PUNCT
ajst-15420	139	1	28(8	28(8	NUM
ajst-15420	139	2	)	)	PUNCT
ajst-15420	139	3	,	,	PUNCT
ajst-15420	139	4	2021	2021	NUM
ajst-15420	139	5	,	,	PUNCT
ajst-15420	139	6	pp	pp	ADJ
ajst-15420	139	7	.	.	PUNCT
ajst-15420	139	8	13091320	13091320	NUM
ajst-15420	139	9	.	.	PUNCT
ajst-15420	140	1	[	[	X
ajst-15420	140	2	10	10	NUM
ajst-15420	140	3	]	]	X
ajst-15420	140	4	f.	f.	NOUN
ajst-15420	140	5	he	he	PRON
ajst-15420	140	6	,	,	PUNCT
ajst-15420	140	7	and	and	CCONJ
ajst-15420	140	8	l.	l.	PROPN
ajst-15420	140	9	zhang	zhang	PROPN
ajst-15420	140	10	,	,	PUNCT
ajst-15420	140	11	“	"	PUNCT
ajst-15420	140	12	mold	mold	NOUN
ajst-15420	140	13	breakout	breakout	NOUN
ajst-15420	140	14	prediction	prediction	NOUN
ajst-15420	140	15	in	in	ADP
ajst-15420	140	16	slab	slab	NOUN
ajst-15420	140	17	continuous	continuous	ADJ
ajst-15420	140	18	casting	casting	NOUN
ajst-15420	140	19	based	base	VERB
ajst-15420	140	20	on	on	ADP
ajst-15420	140	21	combined	combined	ADJ
ajst-15420	140	22	method	method	NOUN
ajst-15420	140	23	of	of	ADP
ajst-15420	140	24	ga	ga	PROPN
ajst-15420	140	25	-	-	PUNCT
ajst-15420	140	26	bp	bp	PROPN
ajst-15420	140	27	neural	neural	ADJ
ajst-15420	140	28	network	network	NOUN
ajst-15420	140	29	and	and	CCONJ
ajst-15420	140	30	logic	logic	PROPN
ajst-15420	140	31	rules[j	rules[j	PROPN
ajst-15420	140	32	]	]	PUNCT
ajst-15420	140	33	”	"	PUNCT
ajst-15420	140	34	.	.	PUNCT
ajst-15420	141	1	the	the	DET
ajst-15420	141	2	international	international	ADJ
ajst-15420	141	3	journal	journal	NOUN
ajst-15420	141	4	of	of	ADP
ajst-15420	141	5	advanced	advanced	ADJ
ajst-15420	141	6	manufacturing	manufacturing	NOUN
ajst-15420	141	7	technology	technology	NOUN
ajst-15420	141	8	,	,	PUNCT
ajst-15420	141	9	vol	vol	NOUN
ajst-15420	141	10	.	.	PROPN
ajst-15420	141	11	95	95	NUM
ajst-15420	141	12	,	,	PUNCT
ajst-15420	141	13	2018	2018	NUM
ajst-15420	141	14	,	,	PUNCT
ajst-15420	141	15	pp	pp	ADJ
ajst-15420	141	16	.	.	PUNCT
ajst-15420	142	1	4081	4081	NUM
ajst-15420	142	2	-	-	SYM
ajst-15420	142	3	4089	4089	NUM
ajst-15420	142	4	.	.	PUNCT
ajst-15420	143	1	[	[	X
ajst-15420	143	2	11	11	NUM
ajst-15420	143	3	]	]	PUNCT
ajst-15420	143	4	a.	a.	NOUN
ajst-15420	143	5	lambora	lambora	PROPN
ajst-15420	143	6	,	,	PUNCT
ajst-15420	143	7	k.	k.	PROPN
ajst-15420	143	8	gupta	gupta	PROPN
ajst-15420	143	9	,	,	PUNCT
ajst-15420	143	10	and	and	CCONJ
ajst-15420	143	11	k.	k.	PROPN
ajst-15420	143	12	chopra	chopra	PROPN
ajst-15420	143	13	,	,	PUNCT
ajst-15420	143	14	“	"	PUNCT
ajst-15420	143	15	genetic	genetic	ADJ
ajst-15420	143	16	algorithm	algorithm	NOUN
ajst-15420	143	17	-	-	PUNCT
ajst-15420	143	18	a	a	DET
ajst-15420	143	19	literature	literature	NOUN
ajst-15420	143	20	review[c]//2019	review[c]//2019	PUNCT
ajst-15420	143	21	international	international	ADJ
ajst-15420	143	22	conference	conference	NOUN
ajst-15420	143	23	on	on	ADP
ajst-15420	143	24	machine	machine	NOUN
ajst-15420	143	25	learning	learning	NOUN
ajst-15420	143	26	,	,	PUNCT
ajst-15420	143	27	big	big	ADJ
ajst-15420	143	28	data	datum	NOUN
ajst-15420	143	29	,	,	PUNCT
ajst-15420	143	30	cloud	cloud	NOUN
ajst-15420	143	31	and	and	CCONJ
ajst-15420	143	32	parallel	parallel	ADJ
ajst-15420	143	33	computing	computing	NOUN
ajst-15420	143	34	(	(	PUNCT
ajst-15420	143	35	comitcon	comitcon	NOUN
ajst-15420	143	36	)	)	PUNCT
ajst-15420	143	37	”	"	PUNCT
ajst-15420	143	38	.	.	PUNCT
ajst-15420	144	1	ieee	ieee	PROPN
ajst-15420	144	2	,	,	PUNCT
ajst-15420	144	3	2019	2019	NUM
ajst-15420	144	4	,	,	PUNCT
ajst-15420	144	5	pp	pp	ADJ
ajst-15420	144	6	.	.	PUNCT
ajst-15420	145	1	380	380	NUM
ajst-15420	145	2	-	-	SYM
ajst-15420	145	3	384	384	NUM
ajst-15420	145	4	.	.	PUNCT
ajst-15420	146	1	[	[	X
ajst-15420	146	2	12	12	NUM
ajst-15420	146	3	]	]	X
ajst-15420	146	4	s.	s.	PROPN
ajst-15420	146	5	ding	ding	PROPN
ajst-15420	146	6	,	,	PUNCT
ajst-15420	146	7	c.	c.	PROPN
ajst-15420	146	8	su	su	PROPN
ajst-15420	146	9	,	,	PUNCT
ajst-15420	146	10	j.	j.	PROPN
ajst-15420	146	11	yu	yu	PROPN
ajst-15420	146	12	,	,	PUNCT
ajst-15420	146	13	“	"	PUNCT
ajst-15420	146	14	an	an	DET
ajst-15420	146	15	optimizing	optimize	VERB
ajst-15420	146	16	bp	bp	PROPN
ajst-15420	146	17	neural	neural	ADJ
ajst-15420	146	18	network	network	NOUN
ajst-15420	146	19	algorithm	algorithm	NOUN
ajst-15420	146	20	based	base	VERB
ajst-15420	146	21	on	on	ADP
ajst-15420	146	22	genetic	genetic	ADJ
ajst-15420	146	23	algorithm[j	algorithm[j	PROPN
ajst-15420	146	24	]	]	PUNCT
ajst-15420	146	25	”	"	PUNCT
ajst-15420	146	26	.	.	PUNCT
ajst-15420	147	1	artificial	artificial	ADJ
ajst-15420	147	2	intelligence	intelligence	NOUN
ajst-15420	147	3	review	review	NOUN
ajst-15420	147	4	,	,	PUNCT
ajst-15420	147	5	vol	vol	NOUN
ajst-15420	147	6	.	.	PROPN
ajst-15420	147	7	36	36	NUM
ajst-15420	147	8	,	,	PUNCT
ajst-15420	147	9	2011	2011	NUM
ajst-15420	147	10	,	,	PUNCT
ajst-15420	147	11	pp	pp	ADJ
ajst-15420	147	12	.	.	PUNCT
ajst-15420	148	1	153	153	NUM
ajst-15420	148	2	-	-	SYM
ajst-15420	148	3	162	162	NUM
ajst-15420	148	4	.	.	PUNCT
ajst-15420	149	1	[	[	X
ajst-15420	149	2	13	13	NUM
ajst-15420	149	3	]	]	PUNCT
ajst-15420	149	4	h.	h.	PROPN
ajst-15420	149	5	wang	wang	PROPN
ajst-15420	149	6	,	,	PUNCT
ajst-15420	149	7	z.	z.	PROPN
ajst-15420	149	8	zhang	zhang	PROPN
ajst-15420	149	9	,	,	PUNCT
ajst-15420	149	10	and	and	CCONJ
ajst-15420	149	11	l.	l.	PROPN
ajst-15420	149	12	liu	liu	PROPN
ajst-15420	149	13	,	,	PUNCT
ajst-15420	149	14	“	"	PUNCT
ajst-15420	149	15	prediction	prediction	NOUN
ajst-15420	149	16	and	and	CCONJ
ajst-15420	149	17	fitting	fitting	NOUN
ajst-15420	149	18	of	of	ADP
ajst-15420	149	19	weld	weld	NOUN
ajst-15420	149	20	morphology	morphology	NOUN
ajst-15420	149	21	of	of	ADP
ajst-15420	149	22	al	al	PROPN
ajst-15420	149	23	alloy	alloy	NOUN
ajst-15420	149	24	-	-	PUNCT
ajst-15420	149	25	cfrp	cfrp	NOUN
ajst-15420	149	26	welding	welding	NOUN
ajst-15420	149	27	-	-	PUNCT
ajst-15420	149	28	rivet	rivet	NOUN
ajst-15420	149	29	hybrid	hybrid	NOUN
ajst-15420	149	30	bonding	bonding	NOUN
ajst-15420	149	31	joint	joint	NOUN
ajst-15420	149	32	based	base	VERB
ajst-15420	149	33	on	on	ADP
ajst-15420	149	34	ga	ga	PROPN
ajst-15420	149	35	-	-	PUNCT
ajst-15420	149	36	bp	bp	PROPN
ajst-15420	149	37	neural	neural	ADJ
ajst-15420	149	38	network[j	network[j	PROPN
ajst-15420	149	39	]	]	PUNCT
ajst-15420	149	40	”	"	PUNCT
ajst-15420	149	41	.	.	PUNCT
ajst-15420	150	1	journal	journal	NOUN
ajst-15420	150	2	of	of	ADP
ajst-15420	150	3	manufacturing	manufacturing	NOUN
ajst-15420	150	4	processes	process	NOUN
ajst-15420	150	5	,	,	PUNCT
ajst-15420	150	6	vol	vol	NOUN
ajst-15420	150	7	.	.	PROPN
ajst-15420	150	8	63	63	NUM
ajst-15420	150	9	,	,	PUNCT
ajst-15420	150	10	2021	2021	NUM
ajst-15420	150	11	,	,	PUNCT
ajst-15420	150	12	pp	pp	ADV
ajst-15420	150	13	.	.	PUNCT
ajst-15420	151	1	109	109	NUM
ajst-15420	151	2	-	-	SYM
ajst-15420	151	3	120	120	NUM
ajst-15420	151	4	.	.	PUNCT