id	sid	tid	token	lemma	pos
fcis-26723	1	1	frontiers	frontier	NOUN
fcis-26723	1	2	in	in	ADP
fcis-26723	1	3	computing	computing	NOUN
fcis-26723	1	4	and	and	CCONJ
fcis-26723	1	5	intelligent	intelligent	ADJ
fcis-26723	1	6	systems	system	NOUN
fcis-26723	1	7	issn	issn	VERB
fcis-26723	1	8	:	:	PUNCT
fcis-26723	1	9	2832	2832	NUM
fcis-26723	1	10	-	-	SYM
fcis-26723	1	11	6024	6024	NUM
fcis-26723	1	12	|	|	NOUN
fcis-26723	1	13	vol	vol	NOUN
fcis-26723	1	14	.	.	PROPN
fcis-26723	2	1	10	10	NUM
fcis-26723	2	2	,	,	PUNCT
fcis-26723	2	3	no	no	INTJ
fcis-26723	2	4	.	.	NOUN
fcis-26723	2	5	1	1	NUM
fcis-26723	2	6	,	,	PUNCT
fcis-26723	2	7	2024	2024	NUM
fcis-26723	2	8	103	103	NUM
fcis-26723	2	9	prediction	prediction	NOUN
fcis-26723	2	10	of	of	ADP
fcis-26723	2	11	final	final	ADJ
fcis-26723	2	12	grade	grade	NOUN
fcis-26723	2	13	of	of	ADP
fcis-26723	2	14	advanced	advanced	ADJ
fcis-26723	2	15	mathematics	mathematic	NOUN
fcis-26723	2	16	based	base	VERB
fcis-26723	2	17	on	on	ADP
fcis-26723	2	18	bp	bp	PROPN
fcis-26723	2	19	neural	neural	ADJ
fcis-26723	2	20	network	network	PROPN
fcis-26723	2	21	daohai	daohai	PROPN
fcis-26723	2	22	zhang	zhang	PROPN
fcis-26723	2	23	1	1	NUM
fcis-26723	2	24	,	,	PUNCT
fcis-26723	2	25	shang	shang	PROPN
fcis-26723	2	26	zhang	zhang	PROPN
fcis-26723	2	27	2	2	NUM
fcis-26723	2	28	1	1	NUM
fcis-26723	2	29	school	school	NOUN
fcis-26723	2	30	of	of	ADP
fcis-26723	2	31	information	information	NOUN
fcis-26723	2	32	network	network	NOUN
fcis-26723	2	33	security	security	NOUN
fcis-26723	2	34	,	,	PUNCT
fcis-26723	2	35	xinjiang	xinjiang	PROPN
fcis-26723	2	36	university	university	PROPN
fcis-26723	2	37	of	of	ADP
fcis-26723	2	38	political	political	ADJ
fcis-26723	2	39	science	science	NOUN
fcis-26723	2	40	and	and	CCONJ
fcis-26723	2	41	law	law	NOUN
fcis-26723	2	42	,	,	PUNCT
fcis-26723	2	43	tumushuke	tumushuke	ADJ
fcis-26723	2	44	xinjiang	xinjiang	PROPN
fcis-26723	2	45	,	,	PUNCT
fcis-26723	2	46	843900	843900	NUM
fcis-26723	2	47	,	,	PUNCT
fcis-26723	2	48	china	china	PROPN
fcis-26723	2	49	2	2	NUM
fcis-26723	2	50	office	office	NOUN
fcis-26723	2	51	of	of	ADP
fcis-26723	2	52	academic	academic	ADJ
fcis-26723	2	53	affairs	affair	NOUN
fcis-26723	2	54	,	,	PUNCT
fcis-26723	2	55	xinjiang	xinjiang	PROPN
fcis-26723	2	56	university	university	PROPN
fcis-26723	2	57	of	of	ADP
fcis-26723	2	58	political	political	ADJ
fcis-26723	2	59	science	science	NOUN
fcis-26723	2	60	and	and	CCONJ
fcis-26723	2	61	law	law	NOUN
fcis-26723	2	62	,	,	PUNCT
fcis-26723	2	63	tumushuke	tumushuke	ADJ
fcis-26723	2	64	xinjiang	xinjiang	PROPN
fcis-26723	2	65	,	,	PUNCT
fcis-26723	2	66	843900	843900	NUM
fcis-26723	2	67	,	,	PUNCT
fcis-26723	2	68	china	china	PROPN
fcis-26723	2	69	abstract	abstract	NOUN
fcis-26723	2	70	:	:	PUNCT
fcis-26723	2	71	in	in	ADP
fcis-26723	2	72	recent	recent	ADJ
fcis-26723	2	73	years	year	NOUN
fcis-26723	2	74	,	,	PUNCT
fcis-26723	2	75	with	with	ADP
fcis-26723	2	76	the	the	DET
fcis-26723	2	77	continuous	continuous	ADJ
fcis-26723	2	78	expansion	expansion	NOUN
fcis-26723	2	79	of	of	ADP
fcis-26723	2	80	college	college	NOUN
fcis-26723	2	81	enrollment	enrollment	NOUN
fcis-26723	2	82	,	,	PUNCT
fcis-26723	2	83	the	the	DET
fcis-26723	2	84	number	number	NOUN
fcis-26723	2	85	of	of	ADP
fcis-26723	2	86	students	student	NOUN
fcis-26723	2	87	is	be	AUX
fcis-26723	2	88	increasing	increase	VERB
fcis-26723	2	89	year	year	NOUN
fcis-26723	2	90	by	by	ADP
fcis-26723	2	91	year	year	NOUN
fcis-26723	2	92	,	,	PUNCT
fcis-26723	2	93	so	so	CCONJ
fcis-26723	2	94	the	the	DET
fcis-26723	2	95	scale	scale	NOUN
fcis-26723	2	96	of	of	ADP
fcis-26723	2	97	classroom	classroom	NOUN
fcis-26723	2	98	teaching	teaching	NOUN
fcis-26723	2	99	is	be	AUX
fcis-26723	2	100	also	also	ADV
fcis-26723	2	101	getting	get	VERB
fcis-26723	2	102	larger	large	ADJ
fcis-26723	2	103	and	and	CCONJ
fcis-26723	2	104	larger	large	ADJ
fcis-26723	2	105	,	,	PUNCT
fcis-26723	2	106	so	so	SCONJ
fcis-26723	2	107	that	that	SCONJ
fcis-26723	2	108	it	it	PRON
fcis-26723	2	109	is	be	AUX
fcis-26723	2	110	difficult	difficult	ADJ
fcis-26723	2	111	for	for	SCONJ
fcis-26723	2	112	teachers	teacher	NOUN
fcis-26723	2	113	to	to	PART
fcis-26723	2	114	track	track	VERB
fcis-26723	2	115	and	and	CCONJ
fcis-26723	2	116	understand	understand	VERB
fcis-26723	2	117	the	the	DET
fcis-26723	2	118	learning	learn	VERB
fcis-26723	2	119	situation	situation	NOUN
fcis-26723	2	120	of	of	ADP
fcis-26723	2	121	each	each	DET
fcis-26723	2	122	student	student	NOUN
fcis-26723	2	123	,	,	PUNCT
fcis-26723	2	124	which	which	PRON
fcis-26723	2	125	affects	affect	VERB
fcis-26723	2	126	the	the	DET
fcis-26723	2	127	quality	quality	NOUN
fcis-26723	2	128	of	of	ADP
fcis-26723	2	129	teaching	teaching	NOUN
fcis-26723	2	130	to	to	ADP
fcis-26723	2	131	a	a	DET
fcis-26723	2	132	certain	certain	ADJ
fcis-26723	2	133	extent	extent	NOUN
fcis-26723	2	134	.	.	PUNCT
fcis-26723	3	1	at	at	ADP
fcis-26723	3	2	the	the	DET
fcis-26723	3	3	same	same	ADJ
fcis-26723	3	4	time	time	NOUN
fcis-26723	3	5	,	,	PUNCT
fcis-26723	3	6	every	every	DET
fcis-26723	3	7	year	year	NOUN
fcis-26723	3	8	,	,	PUNCT
fcis-26723	3	9	a	a	DET
fcis-26723	3	10	certain	certain	ADJ
fcis-26723	3	11	number	number	NOUN
fcis-26723	3	12	of	of	ADP
fcis-26723	3	13	students	student	NOUN
fcis-26723	3	14	in	in	ADP
fcis-26723	3	15	colleges	college	NOUN
fcis-26723	3	16	and	and	CCONJ
fcis-26723	3	17	universities	university	NOUN
fcis-26723	3	18	fail	fail	VERB
fcis-26723	3	19	the	the	DET
fcis-26723	3	20	exam	exam	NOUN
fcis-26723	3	21	,	,	PUNCT
fcis-26723	3	22	repeat	repeat	VERB
fcis-26723	3	23	the	the	DET
fcis-26723	3	24	grade	grade	NOUN
fcis-26723	3	25	,	,	PUNCT
fcis-26723	3	26	or	or	CCONJ
fcis-26723	3	27	even	even	ADV
fcis-26723	3	28	drop	drop	VERB
fcis-26723	3	29	out	out	ADP
fcis-26723	3	30	.	.	PUNCT
fcis-26723	4	1	if	if	SCONJ
fcis-26723	4	2	these	these	DET
fcis-26723	4	3	problems	problem	NOUN
fcis-26723	4	4	can	can	AUX
fcis-26723	4	5	not	not	PART
fcis-26723	4	6	be	be	AUX
fcis-26723	4	7	solved	solve	VERB
fcis-26723	4	8	in	in	ADP
fcis-26723	4	9	time	time	NOUN
fcis-26723	4	10	,	,	PUNCT
fcis-26723	4	11	they	they	PRON
fcis-26723	4	12	will	will	AUX
fcis-26723	4	13	seriously	seriously	ADV
fcis-26723	4	14	affect	affect	VERB
fcis-26723	4	15	the	the	DET
fcis-26723	4	16	psychological	psychological	ADJ
fcis-26723	4	17	health	health	NOUN
fcis-26723	4	18	and	and	CCONJ
fcis-26723	4	19	future	future	ADJ
fcis-26723	4	20	development	development	NOUN
fcis-26723	4	21	of	of	ADP
fcis-26723	4	22	students	student	NOUN
fcis-26723	4	23	,	,	PUNCT
fcis-26723	4	24	and	and	CCONJ
fcis-26723	4	25	also	also	ADV
fcis-26723	4	26	affect	affect	VERB
fcis-26723	4	27	the	the	DET
fcis-26723	4	28	employment	employment	NOUN
fcis-26723	4	29	rate	rate	NOUN
fcis-26723	4	30	of	of	ADP
fcis-26723	4	31	graduates	graduate	NOUN
fcis-26723	4	32	,	,	PUNCT
fcis-26723	4	33	and	and	CCONJ
fcis-26723	4	34	thus	thus	ADV
fcis-26723	4	35	affect	affect	VERB
fcis-26723	4	36	the	the	DET
fcis-26723	4	37	development	development	NOUN
fcis-26723	4	38	of	of	ADP
fcis-26723	4	39	schools	school	NOUN
fcis-26723	4	40	.	.	PUNCT
fcis-26723	5	1	how	how	SCONJ
fcis-26723	5	2	to	to	PART
fcis-26723	5	3	predict	predict	VERB
fcis-26723	5	4	the	the	DET
fcis-26723	5	5	situation	situation	NOUN
fcis-26723	5	6	of	of	ADP
fcis-26723	5	7	students	student	NOUN
fcis-26723	5	8	,	,	PUNCT
fcis-26723	5	9	so	so	SCONJ
fcis-26723	5	10	that	that	SCONJ
fcis-26723	5	11	students	student	NOUN
fcis-26723	5	12	in	in	ADP
fcis-26723	5	13	the	the	DET
fcis-26723	5	14	study	study	NOUN
fcis-26723	5	15	of	of	ADP
fcis-26723	5	16	trouble	trouble	NOUN
fcis-26723	5	17	,	,	PUNCT
fcis-26723	5	18	hesitation	hesitation	NOUN
fcis-26723	5	19	,	,	PUNCT
fcis-26723	5	20	timely	timely	ADJ
fcis-26723	5	21	interference	interference	NOUN
fcis-26723	5	22	,	,	PUNCT
fcis-26723	5	23	so	so	SCONJ
fcis-26723	5	24	that	that	SCONJ
fcis-26723	5	25	students	student	NOUN
fcis-26723	5	26	successfully	successfully	ADV
fcis-26723	5	27	complete	complete	VERB
fcis-26723	5	28	the	the	DET
fcis-26723	5	29	university	university	NOUN
fcis-26723	5	30	study	study	NOUN
fcis-26723	5	31	life	life	NOUN
fcis-26723	5	32	.	.	PUNCT
fcis-26723	6	1	through	through	ADP
fcis-26723	6	2	the	the	DET
fcis-26723	6	3	analysis	analysis	NOUN
fcis-26723	6	4	of	of	ADP
fcis-26723	6	5	the	the	DET
fcis-26723	6	6	failed	fail	VERB
fcis-26723	6	7	students	student	NOUN
fcis-26723	6	8	,	,	PUNCT
fcis-26723	6	9	it	it	PRON
fcis-26723	6	10	is	be	AUX
fcis-26723	6	11	found	find	VERB
fcis-26723	6	12	that	that	SCONJ
fcis-26723	6	13	about	about	ADV
fcis-26723	6	14	80	80	NUM
fcis-26723	6	15	%	%	NOUN
fcis-26723	6	16	of	of	ADP
fcis-26723	6	17	the	the	DET
fcis-26723	6	18	failed	fail	VERB
fcis-26723	6	19	students	student	NOUN
fcis-26723	6	20	are	be	AUX
fcis-26723	6	21	advanced	advanced	ADJ
fcis-26723	6	22	mathematics	mathematic	NOUN
fcis-26723	6	23	.	.	PUNCT
fcis-26723	7	1	therefore	therefore	ADV
fcis-26723	7	2	,	,	PUNCT
fcis-26723	7	3	this	this	DET
fcis-26723	7	4	paper	paper	NOUN
fcis-26723	7	5	will	will	AUX
fcis-26723	7	6	use	use	VERB
fcis-26723	7	7	the	the	DET
fcis-26723	7	8	machine	machine	NOUN
fcis-26723	7	9	learning	learn	VERB
fcis-26723	7	10	technology	technology	NOUN
fcis-26723	7	11	based	base	VERB
fcis-26723	7	12	on	on	ADP
fcis-26723	7	13	bp	bp	PROPN
fcis-26723	7	14	neural	neural	ADJ
fcis-26723	7	15	network	network	NOUN
fcis-26723	7	16	to	to	PART
fcis-26723	7	17	analyze	analyze	VERB
fcis-26723	7	18	the	the	DET
fcis-26723	7	19	scores	score	NOUN
fcis-26723	7	20	of	of	ADP
fcis-26723	7	21	senior	senior	ADJ
fcis-26723	7	22	students	student	NOUN
fcis-26723	7	23	in	in	ADP
fcis-26723	7	24	the	the	DET
fcis-26723	7	25	final	final	ADJ
fcis-26723	7	26	exam	exam	NOUN
fcis-26723	7	27	of	of	ADP
fcis-26723	7	28	advanced	advanced	ADJ
fcis-26723	7	29	mathematics	mathematic	NOUN
fcis-26723	7	30	.	.	PUNCT
fcis-26723	8	1	according	accord	VERB
fcis-26723	8	2	to	to	ADP
fcis-26723	8	3	the	the	DET
fcis-26723	8	4	different	different	ADJ
fcis-26723	8	5	scores	score	NOUN
fcis-26723	8	6	,	,	PUNCT
fcis-26723	8	7	the	the	DET
fcis-26723	8	8	scores	score	NOUN
fcis-26723	8	9	will	will	AUX
fcis-26723	8	10	be	be	AUX
fcis-26723	8	11	divided	divide	VERB
fcis-26723	8	12	into	into	ADP
fcis-26723	8	13	four	four	NUM
fcis-26723	8	14	categories	category	NOUN
fcis-26723	8	15	:	:	PUNCT
fcis-26723	8	16	excellent	excellent	ADJ
fcis-26723	8	17	,	,	PUNCT
fcis-26723	8	18	good	good	ADJ
fcis-26723	8	19	,	,	PUNCT
fcis-26723	8	20	medium	medium	ADJ
fcis-26723	8	21	and	and	CCONJ
fcis-26723	8	22	failed	fail	VERB
fcis-26723	8	23	.	.	PUNCT
fcis-26723	9	1	through	through	ADP
fcis-26723	9	2	the	the	DET
fcis-26723	9	3	establishment	establishment	NOUN
fcis-26723	9	4	of	of	ADP
fcis-26723	9	5	the	the	DET
fcis-26723	9	6	grade	grade	NOUN
fcis-26723	9	7	prediction	prediction	NOUN
fcis-26723	9	8	model	model	NOUN
fcis-26723	9	9	,	,	PUNCT
fcis-26723	9	10	the	the	DET
fcis-26723	9	11	paper	paper	NOUN
fcis-26723	9	12	predicts	predict	VERB
fcis-26723	9	13	the	the	DET
fcis-26723	9	14	grade	grade	NOUN
fcis-26723	9	15	of	of	ADP
fcis-26723	9	16	the	the	DET
fcis-26723	9	17	college	college	NOUN
fcis-26723	9	18	students	student	NOUN
fcis-26723	9	19	in	in	ADP
fcis-26723	9	20	the	the	DET
fcis-26723	9	21	final	final	ADJ
fcis-26723	9	22	exam	exam	NOUN
fcis-26723	9	23	of	of	ADP
fcis-26723	9	24	higher	high	ADJ
fcis-26723	9	25	mathematics	mathematic	NOUN
fcis-26723	9	26	,	,	PUNCT
fcis-26723	9	27	and	and	CCONJ
fcis-26723	9	28	then	then	ADV
fcis-26723	9	29	gives	give	VERB
fcis-26723	9	30	the	the	DET
fcis-26723	9	31	students	student	NOUN
fcis-26723	9	32	learning	learn	VERB
fcis-26723	9	33	guidance	guidance	NOUN
fcis-26723	9	34	.	.	PUNCT
fcis-26723	10	1	keywords	keyword	NOUN
fcis-26723	10	2	:	:	PUNCT
fcis-26723	10	3	college	college	NOUN
fcis-26723	10	4	students	student	NOUN
fcis-26723	10	5	;	;	PUNCT
fcis-26723	10	6	bp	bp	PROPN
fcis-26723	10	7	neural	neural	ADJ
fcis-26723	10	8	network	network	NOUN
fcis-26723	10	9	;	;	PUNCT
fcis-26723	10	10	advanced	advanced	ADJ
fcis-26723	10	11	mathematics	mathematic	NOUN
fcis-26723	10	12	;	;	PUNCT
fcis-26723	10	13	grade	grade	NOUN
fcis-26723	10	14	prediction	prediction	NOUN
fcis-26723	10	15	.	.	PUNCT
fcis-26723	11	1	1	1	X
fcis-26723	11	2	.	.	X
fcis-26723	11	3	background	background	NOUN
fcis-26723	11	4	and	and	CCONJ
fcis-26723	11	5	significance	significance	NOUN
fcis-26723	11	6	of	of	ADP
fcis-26723	11	7	the	the	DET
fcis-26723	11	8	research	research	NOUN
fcis-26723	11	9	under	under	ADP
fcis-26723	11	10	the	the	DET
fcis-26723	11	11	background	background	NOUN
fcis-26723	11	12	of	of	ADP
fcis-26723	11	13	the	the	DET
fcis-26723	11	14	information	information	NOUN
fcis-26723	11	15	age	age	NOUN
fcis-26723	11	16	,	,	PUNCT
fcis-26723	11	17	all	all	DET
fcis-26723	11	18	walks	walk	NOUN
fcis-26723	11	19	of	of	ADP
fcis-26723	11	20	life	life	NOUN
fcis-26723	11	21	have	have	AUX
fcis-26723	11	22	accumulated	accumulate	VERB
fcis-26723	11	23	a	a	DET
fcis-26723	11	24	huge	huge	ADJ
fcis-26723	11	25	amount	amount	NOUN
fcis-26723	11	26	of	of	ADP
fcis-26723	11	27	data	datum	NOUN
fcis-26723	11	28	,	,	PUNCT
fcis-26723	11	29	and	and	CCONJ
fcis-26723	11	30	the	the	DET
fcis-26723	11	31	amount	amount	NOUN
fcis-26723	11	32	of	of	ADP
fcis-26723	11	33	data	datum	NOUN
fcis-26723	11	34	is	be	AUX
fcis-26723	11	35	still	still	ADV
fcis-26723	11	36	growing	grow	VERB
fcis-26723	11	37	at	at	ADP
fcis-26723	11	38	an	an	DET
fcis-26723	11	39	exponential	exponential	ADJ
fcis-26723	11	40	rate	rate	NOUN
fcis-26723	11	41	.	.	PUNCT
fcis-26723	12	1	massive	massive	ADJ
fcis-26723	12	2	data	datum	NOUN
fcis-26723	12	3	often	often	ADV
fcis-26723	12	4	implies	imply	VERB
fcis-26723	12	5	some	some	DET
fcis-26723	12	6	valuable	valuable	ADJ
fcis-26723	12	7	knowledge	knowledge	NOUN
fcis-26723	12	8	and	and	CCONJ
fcis-26723	12	9	valuable	valuable	ADJ
fcis-26723	12	10	information	information	NOUN
fcis-26723	12	11	,	,	PUNCT
fcis-26723	12	12	which	which	PRON
fcis-26723	12	13	is	be	AUX
fcis-26723	12	14	a	a	DET
fcis-26723	12	15	very	very	ADV
fcis-26723	12	16	valuable	valuable	ADJ
fcis-26723	12	17	"	"	PUNCT
fcis-26723	12	18	soft	soft	ADJ
fcis-26723	12	19	resource	resource	NOUN
fcis-26723	12	20	"	"	PUNCT
fcis-26723	12	21	in	in	ADP
fcis-26723	12	22	various	various	ADJ
fcis-26723	12	23	fields	field	NOUN
fcis-26723	12	24	and	and	CCONJ
fcis-26723	12	25	industries	industry	NOUN
fcis-26723	12	26	.	.	PUNCT
fcis-26723	13	1	machine	machine	NOUN
fcis-26723	13	2	learning	learn	VERB
fcis-26723	13	3	technology	technology	NOUN
fcis-26723	13	4	based	base	VERB
fcis-26723	13	5	on	on	ADP
fcis-26723	13	6	bp	bp	PROPN
fcis-26723	13	7	neural	neural	ADJ
fcis-26723	13	8	network	network	NOUN
fcis-26723	13	9	can	can	AUX
fcis-26723	13	10	find	find	VERB
fcis-26723	13	11	some	some	DET
fcis-26723	13	12	laws	law	NOUN
fcis-26723	13	13	inherent	inherent	ADJ
fcis-26723	13	14	in	in	ADP
fcis-26723	13	15	data	datum	NOUN
fcis-26723	13	16	,	,	PUNCT
fcis-26723	13	17	extract	extract	VERB
fcis-26723	13	18	valuable	valuable	ADJ
fcis-26723	13	19	information	information	NOUN
fcis-26723	13	20	and	and	CCONJ
fcis-26723	13	21	knowledge[1	knowledge[1	NOUN
fcis-26723	13	22	]	]	X
fcis-26723	13	23	,	,	PUNCT
fcis-26723	13	24	so	so	SCONJ
fcis-26723	13	25	as	as	SCONJ
fcis-26723	13	26	to	to	PART
fcis-26723	13	27	serve	serve	VERB
fcis-26723	13	28	to	to	PART
fcis-26723	13	29	solve	solve	VERB
fcis-26723	13	30	problems	problem	NOUN
fcis-26723	13	31	in	in	ADP
fcis-26723	13	32	various	various	ADJ
fcis-26723	13	33	fields	field	NOUN
fcis-26723	13	34	,	,	PUNCT
fcis-26723	13	35	and	and	CCONJ
fcis-26723	13	36	help	help	VERB
fcis-26723	13	37	managers	manager	NOUN
fcis-26723	13	38	to	to	PART
fcis-26723	13	39	make	make	VERB
fcis-26723	13	40	more	more	ADJ
fcis-26723	13	41	"	"	PUNCT
fcis-26723	13	42	scientific	scientific	ADJ
fcis-26723	13	43	"	"	PUNCT
fcis-26723	13	44	decisions	decision	NOUN
fcis-26723	13	45	.	.	PUNCT
fcis-26723	14	1	based	base	VERB
fcis-26723	14	2	on	on	ADP
fcis-26723	14	3	bp	bp	PROPN
fcis-26723	14	4	neural	neural	ADJ
fcis-26723	14	5	network	network	NOUN
fcis-26723	14	6	machine	machine	NOUN
fcis-26723	14	7	learning	learn	VERB
fcis-26723	14	8	technology	technology	NOUN
fcis-26723	14	9	,	,	PUNCT
fcis-26723	14	10	intelligent	intelligent	ADJ
fcis-26723	14	11	analysis	analysis	NOUN
fcis-26723	14	12	and	and	CCONJ
fcis-26723	14	13	processing	processing	NOUN
fcis-26723	14	14	of	of	ADP
fcis-26723	14	15	data	datum	NOUN
fcis-26723	14	16	,	,	PUNCT
fcis-26723	14	17	so	so	SCONJ
fcis-26723	14	18	as	as	SCONJ
fcis-26723	14	19	to	to	PART
fcis-26723	14	20	use	use	VERB
fcis-26723	14	21	the	the	DET
fcis-26723	14	22	hidden	hide	VERB
fcis-26723	14	23	rules	rule	NOUN
fcis-26723	14	24	in	in	ADP
fcis-26723	14	25	the	the	DET
fcis-26723	14	26	data	datum	NOUN
fcis-26723	14	27	to	to	PART
fcis-26723	14	28	predict	predict	VERB
fcis-26723	14	29	the	the	DET
fcis-26723	14	30	development	development	NOUN
fcis-26723	14	31	trend	trend	NOUN
fcis-26723	14	32	of	of	ADP
fcis-26723	14	33	things	thing	NOUN
fcis-26723	14	34	,	,	PUNCT
fcis-26723	14	35	has	have	AUX
fcis-26723	14	36	become	become	VERB
fcis-26723	14	37	the	the	DET
fcis-26723	14	38	consensus	consensus	NOUN
fcis-26723	14	39	of	of	ADP
fcis-26723	14	40	today	today	NOUN
fcis-26723	14	41	's	's	PART
fcis-26723	14	42	industry	industry	NOUN
fcis-26723	14	43	and	and	CCONJ
fcis-26723	14	44	academia	academia	NOUN
fcis-26723	14	45	.	.	PUNCT
fcis-26723	15	1	at	at	ADP
fcis-26723	15	2	present	present	ADJ
fcis-26723	15	3	,	,	PUNCT
fcis-26723	15	4	machine	machine	NOUN
fcis-26723	15	5	learning	learning	NOUN
fcis-26723	15	6	technology	technology	NOUN
fcis-26723	15	7	based	base	VERB
fcis-26723	15	8	on	on	ADP
fcis-26723	15	9	bp	bp	PROPN
fcis-26723	15	10	neural	neural	ADJ
fcis-26723	15	11	network	network	NOUN
fcis-26723	15	12	has	have	AUX
fcis-26723	15	13	been	be	AUX
fcis-26723	15	14	widely	widely	ADV
fcis-26723	15	15	used	use	VERB
fcis-26723	15	16	in	in	ADP
fcis-26723	15	17	many	many	ADJ
fcis-26723	15	18	fields	field	NOUN
fcis-26723	15	19	such	such	ADJ
fcis-26723	15	20	as	as	ADP
fcis-26723	15	21	finance	finance	NOUN
fcis-26723	15	22	,	,	PUNCT
fcis-26723	15	23	medical	medical	ADJ
fcis-26723	15	24	treatment	treatment	NOUN
fcis-26723	15	25	,	,	PUNCT
fcis-26723	15	26	e	e	NOUN
fcis-26723	15	27	-	-	NOUN
fcis-26723	15	28	commerce	commerce	NOUN
fcis-26723	15	29	,	,	PUNCT
fcis-26723	15	30	energy	energy	NOUN
fcis-26723	15	31	and	and	CCONJ
fcis-26723	15	32	manufacturing	manufacturing	NOUN
fcis-26723	15	33	,	,	PUNCT
fcis-26723	15	34	transportation	transportation	NOUN
fcis-26723	15	35	and	and	CCONJ
fcis-26723	15	36	transportation	transportation	NOUN
fcis-26723	15	37	.	.	PUNCT
fcis-26723	16	1	big	big	ADJ
fcis-26723	16	2	data	data	PROPN
fcis-26723	16	3	has	have	VERB
fcis-26723	16	4	an	an	DET
fcis-26723	16	5	important	important	ADJ
fcis-26723	16	6	impact	impact	NOUN
fcis-26723	16	7	on	on	ADP
fcis-26723	16	8	all	all	DET
fcis-26723	16	9	walks	walk	NOUN
fcis-26723	16	10	of	of	ADP
fcis-26723	16	11	life	life	NOUN
fcis-26723	16	12	,	,	PUNCT
fcis-26723	16	13	and	and	CCONJ
fcis-26723	16	14	education	education	NOUN
fcis-26723	16	15	is	be	AUX
fcis-26723	16	16	no	no	DET
fcis-26723	16	17	exception	exception	NOUN
fcis-26723	16	18	.	.	PUNCT
fcis-26723	17	1	based	base	VERB
fcis-26723	17	2	on	on	ADP
fcis-26723	17	3	the	the	DET
fcis-26723	17	4	era	era	NOUN
fcis-26723	17	5	of	of	ADP
fcis-26723	17	6	big	big	ADJ
fcis-26723	17	7	data	datum	NOUN
fcis-26723	17	8	,	,	PUNCT
fcis-26723	17	9	education	education	NOUN
fcis-26723	17	10	big	big	ADJ
fcis-26723	17	11	data	datum	NOUN
fcis-26723	17	12	has	have	AUX
fcis-26723	17	13	emerged	emerge	VERB
fcis-26723	17	14	as	as	ADP
fcis-26723	17	15	an	an	DET
fcis-26723	17	16	emerging	emerge	VERB
fcis-26723	17	17	discipline	discipline	NOUN
fcis-26723	17	18	.	.	PUNCT
fcis-26723	18	1	introducing	introduce	VERB
fcis-26723	18	2	the	the	DET
fcis-26723	18	3	concept	concept	NOUN
fcis-26723	18	4	of	of	ADP
fcis-26723	18	5	big	big	ADJ
fcis-26723	18	6	data	datum	NOUN
fcis-26723	18	7	into	into	ADP
fcis-26723	18	8	education	education	NOUN
fcis-26723	18	9	can	can	AUX
fcis-26723	18	10	make	make	VERB
fcis-26723	18	11	the	the	DET
fcis-26723	18	12	process	process	NOUN
fcis-26723	18	13	of	of	ADP
fcis-26723	18	14	education	education	NOUN
fcis-26723	18	15	and	and	CCONJ
fcis-26723	18	16	being	be	AUX
fcis-26723	18	17	educated	educate	VERB
fcis-26723	18	18	more	more	ADV
fcis-26723	18	19	clear	clear	ADJ
fcis-26723	18	20	,	,	PUNCT
fcis-26723	18	21	accurate	accurate	ADJ
fcis-26723	18	22	and	and	CCONJ
fcis-26723	18	23	rapid	rapid	ADJ
fcis-26723	18	24	,	,	PUNCT
fcis-26723	18	25	and	and	CCONJ
fcis-26723	18	26	even	even	ADV
fcis-26723	18	27	"	"	PUNCT
fcis-26723	18	28	predict	predict	VERB
fcis-26723	18	29	"	"	PUNCT
fcis-26723	18	30	success	success	NOUN
fcis-26723	18	31	or	or	CCONJ
fcis-26723	18	32	failure	failure	NOUN
fcis-26723	18	33	in	in	ADP
fcis-26723	18	34	advance	advance	NOUN
fcis-26723	18	35	.	.	PUNCT
fcis-26723	19	1	the	the	DET
fcis-26723	19	2	"	"	PUNCT
fcis-26723	19	3	big	big	ADJ
fcis-26723	19	4	"	"	PUNCT
fcis-26723	19	5	of	of	ADP
fcis-26723	19	6	educational	educational	ADJ
fcis-26723	19	7	big	big	ADJ
fcis-26723	19	8	data	datum	NOUN
fcis-26723	19	9	is	be	AUX
fcis-26723	19	10	not	not	PART
fcis-26723	19	11	only	only	ADV
fcis-26723	19	12	the	the	DET
fcis-26723	19	13	large	large	ADJ
fcis-26723	19	14	quantity	quantity	NOUN
fcis-26723	19	15	,	,	PUNCT
fcis-26723	19	16	but	but	CCONJ
fcis-26723	19	17	also	also	ADV
fcis-26723	19	18	emphasizes	emphasize	VERB
fcis-26723	19	19	the	the	DET
fcis-26723	19	20	great	great	ADJ
fcis-26723	19	21	value	value	NOUN
fcis-26723	19	22	.	.	PUNCT
fcis-26723	20	1	educational	educational	ADJ
fcis-26723	20	2	big	big	ADJ
fcis-26723	20	3	data	datum	NOUN
fcis-26723	20	4	is	be	AUX
fcis-26723	20	5	a	a	DET
fcis-26723	20	6	potentially	potentially	ADV
fcis-26723	20	7	huge	huge	ADJ
fcis-26723	20	8	wealth	wealth	NOUN
fcis-26723	20	9	that	that	PRON
fcis-26723	20	10	is	be	AUX
fcis-26723	20	11	difficult	difficult	ADJ
fcis-26723	20	12	to	to	PART
fcis-26723	20	13	estimate	estimate	VERB
fcis-26723	20	14	,	,	PUNCT
fcis-26723	20	15	which	which	PRON
fcis-26723	20	16	greatly	greatly	ADV
fcis-26723	20	17	promotes	promote	VERB
fcis-26723	20	18	the	the	DET
fcis-26723	20	19	research	research	NOUN
fcis-26723	20	20	on	on	ADP
fcis-26723	20	21	educational	educational	ADJ
fcis-26723	20	22	issues	issue	NOUN
fcis-26723	20	23	[	[	X
fcis-26723	20	24	2	2	NUM
fcis-26723	20	25	]	]	PUNCT
fcis-26723	20	26	.	.	PUNCT
fcis-26723	21	1	the	the	DET
fcis-26723	21	2	development	development	NOUN
fcis-26723	21	3	of	of	ADP
fcis-26723	21	4	machine	machine	NOUN
fcis-26723	21	5	learning	learn	VERB
fcis-26723	21	6	technology	technology	NOUN
fcis-26723	21	7	based	base	VERB
fcis-26723	21	8	on	on	ADP
fcis-26723	21	9	bp	bp	PROPN
fcis-26723	21	10	neural	neural	ADJ
fcis-26723	21	11	network	network	NOUN
fcis-26723	21	12	is	be	AUX
fcis-26723	21	13	realizing	realize	VERB
fcis-26723	21	14	the	the	DET
fcis-26723	21	15	infinite	infinite	ADJ
fcis-26723	21	16	appreciation	appreciation	NOUN
fcis-26723	21	17	of	of	ADP
fcis-26723	21	18	this	this	DET
fcis-26723	21	19	wealth	wealth	NOUN
fcis-26723	21	20	and	and	CCONJ
fcis-26723	21	21	promoting	promote	VERB
fcis-26723	21	22	the	the	DET
fcis-26723	21	23	development	development	NOUN
fcis-26723	21	24	of	of	ADP
fcis-26723	21	25	students	student	NOUN
fcis-26723	21	26	in	in	ADP
fcis-26723	21	27	the	the	DET
fcis-26723	21	28	right	right	ADJ
fcis-26723	21	29	direction	direction	NOUN
fcis-26723	21	30	.	.	PUNCT
fcis-26723	22	1	student	student	NOUN
fcis-26723	22	2	achievement	achievement	NOUN
fcis-26723	22	3	prediction	prediction	NOUN
fcis-26723	22	4	,	,	PUNCT
fcis-26723	22	5	also	also	ADV
fcis-26723	22	6	known	know	VERB
fcis-26723	22	7	as	as	ADP
fcis-26723	22	8	student	student	NOUN
fcis-26723	22	9	academic	academic	ADJ
fcis-26723	22	10	performance	performance	NOUN
fcis-26723	22	11	prediction	prediction	NOUN
fcis-26723	22	12	,	,	PUNCT
fcis-26723	22	13	is	be	AUX
fcis-26723	22	14	one	one	NUM
fcis-26723	22	15	of	of	ADP
fcis-26723	22	16	the	the	DET
fcis-26723	22	17	important	important	ADJ
fcis-26723	22	18	research	research	NOUN
fcis-26723	22	19	issues	issue	NOUN
fcis-26723	22	20	in	in	ADP
fcis-26723	22	21	education	education	NOUN
fcis-26723	22	22	data[3	data[3	CCONJ
fcis-26723	22	23	]	]	PUNCT
fcis-26723	22	24	.	.	PUNCT
fcis-26723	23	1	the	the	DET
fcis-26723	23	2	purpose	purpose	NOUN
fcis-26723	23	3	is	be	AUX
fcis-26723	23	4	to	to	PART
fcis-26723	23	5	predict	predict	VERB
fcis-26723	23	6	students	student	NOUN
fcis-26723	23	7	'	'	PART
fcis-26723	23	8	future	future	ADJ
fcis-26723	23	9	academic	academic	ADJ
fcis-26723	23	10	performance	performance	NOUN
fcis-26723	23	11	by	by	ADP
fcis-26723	23	12	using	use	VERB
fcis-26723	23	13	studentrelated	studentrelate	VERB
fcis-26723	23	14	information[4	information[4	NOUN
fcis-26723	23	15	]	]	PUNCT
fcis-26723	23	16	.	.	PUNCT
fcis-26723	24	1	this	this	DET
fcis-26723	24	2	paper	paper	NOUN
fcis-26723	24	3	obtains	obtain	VERB
fcis-26723	24	4	the	the	DET
fcis-26723	24	5	behavior	behavior	NOUN
fcis-26723	24	6	data	datum	NOUN
fcis-26723	24	7	of	of	ADP
fcis-26723	24	8	previous	previous	ADJ
fcis-26723	24	9	students	student	NOUN
fcis-26723	24	10	through	through	ADP
fcis-26723	24	11	questionnaires	questionnaire	NOUN
fcis-26723	24	12	,	,	PUNCT
fcis-26723	24	13	derives	derive	VERB
fcis-26723	24	14	the	the	DET
fcis-26723	24	15	grade	grade	NOUN
fcis-26723	24	16	information	information	NOUN
fcis-26723	24	17	and	and	CCONJ
fcis-26723	24	18	average	average	ADJ
fcis-26723	24	19	grade	grade	NOUN
fcis-26723	24	20	point	point	NOUN
fcis-26723	24	21	of	of	ADP
fcis-26723	24	22	previous	previous	ADJ
fcis-26723	24	23	students	student	NOUN
fcis-26723	24	24	through	through	ADP
fcis-26723	24	25	the	the	DET
fcis-26723	24	26	educational	educational	ADJ
fcis-26723	24	27	administration	administration	NOUN
fcis-26723	24	28	system	system	NOUN
fcis-26723	24	29	,	,	PUNCT
fcis-26723	24	30	and	and	CCONJ
fcis-26723	24	31	uses	use	VERB
fcis-26723	24	32	the	the	DET
fcis-26723	24	33	machine	machine	NOUN
fcis-26723	24	34	learning	learn	VERB
fcis-26723	24	35	technology	technology	NOUN
fcis-26723	24	36	based	base	VERB
fcis-26723	24	37	on	on	ADP
fcis-26723	24	38	bp	bp	PROPN
fcis-26723	24	39	neural	neural	ADJ
fcis-26723	24	40	network	network	NOUN
fcis-26723	24	41	to	to	PART
fcis-26723	24	42	establish	establish	VERB
fcis-26723	24	43	a	a	DET
fcis-26723	24	44	grade	grade	NOUN
fcis-26723	24	45	prediction	prediction	NOUN
fcis-26723	24	46	model	model	NOUN
fcis-26723	24	47	to	to	PART
fcis-26723	24	48	predict	predict	VERB
fcis-26723	24	49	the	the	DET
fcis-26723	24	50	final	final	ADJ
fcis-26723	24	51	exam	exam	NOUN
fcis-26723	24	52	scores	score	NOUN
fcis-26723	24	53	of	of	ADP
fcis-26723	24	54	lower	low	ADJ
fcis-26723	24	55	grade	grade	NOUN
fcis-26723	24	56	students	student	NOUN
fcis-26723	24	57	.	.	PUNCT
fcis-26723	25	1	make	make	VERB
fcis-26723	25	2	academic	academic	ADJ
fcis-26723	25	3	prediction	prediction	NOUN
fcis-26723	25	4	for	for	ADP
fcis-26723	25	5	students	student	NOUN
fcis-26723	25	6	,	,	PUNCT
fcis-26723	25	7	and	and	CCONJ
fcis-26723	25	8	then	then	ADV
fcis-26723	25	9	remind	remind	VERB
fcis-26723	25	10	students	student	NOUN
fcis-26723	25	11	to	to	PART
fcis-26723	25	12	reflect	reflect	VERB
fcis-26723	25	13	on	on	ADP
fcis-26723	25	14	time	time	NOUN
fcis-26723	25	15	,	,	PUNCT
fcis-26723	25	16	check	check	VERB
fcis-26723	25	17	the	the	DET
fcis-26723	25	18	gaps	gap	NOUN
fcis-26723	25	19	and	and	CCONJ
fcis-26723	25	20	make	make	VERB
fcis-26723	25	21	progress	progress	NOUN
fcis-26723	25	22	.	.	PUNCT
fcis-26723	26	1	2	2	X
fcis-26723	26	2	.	.	X
fcis-26723	26	3	research	research	NOUN
fcis-26723	26	4	status	status	NOUN
fcis-26723	26	5	in	in	ADP
fcis-26723	26	6	recent	recent	ADJ
fcis-26723	26	7	years	year	NOUN
fcis-26723	26	8	,	,	PUNCT
fcis-26723	26	9	the	the	DET
fcis-26723	26	10	study	study	NOUN
fcis-26723	26	11	of	of	ADP
fcis-26723	26	12	learning	learn	VERB
fcis-26723	26	13	prediction	prediction	NOUN
fcis-26723	26	14	based	base	VERB
fcis-26723	26	15	on	on	ADP
fcis-26723	26	16	data	datum	NOUN
fcis-26723	26	17	mining	mining	NOUN
fcis-26723	26	18	in	in	ADP
fcis-26723	26	19	foreign	foreign	ADJ
fcis-26723	26	20	countries	country	NOUN
fcis-26723	26	21	focuses	focus	VERB
fcis-26723	26	22	on	on	ADP
fcis-26723	26	23	three	three	NUM
fcis-26723	26	24	aspects	aspect	NOUN
fcis-26723	26	25	:	:	PUNCT
fcis-26723	26	26	the	the	DET
fcis-26723	26	27	selection	selection	NOUN
fcis-26723	26	28	of	of	ADP
fcis-26723	26	29	prediction	prediction	NOUN
fcis-26723	26	30	indicators	indicator	NOUN
fcis-26723	26	31	,	,	PUNCT
fcis-26723	26	32	the	the	DET
fcis-26723	26	33	detection	detection	NOUN
fcis-26723	26	34	of	of	ADP
fcis-26723	26	35	prediction	prediction	NOUN
fcis-26723	26	36	start	start	VERB
fcis-26723	26	37	time	time	NOUN
fcis-26723	26	38	,	,	PUNCT
fcis-26723	26	39	and	and	CCONJ
fcis-26723	26	40	the	the	DET
fcis-26723	26	41	evaluation	evaluation	NOUN
fcis-26723	26	42	of	of	ADP
fcis-26723	26	43	prediction	prediction	NOUN
fcis-26723	26	44	model	model	NOUN
fcis-26723	26	45	effect	effect	NOUN
fcis-26723	26	46	;	;	PUNCT
fcis-26723	26	47	at	at	ADP
fcis-26723	26	48	the	the	DET
fcis-26723	26	49	level	level	NOUN
fcis-26723	26	50	of	of	ADP
fcis-26723	26	51	research	research	NOUN
fcis-26723	26	52	data	datum	NOUN
fcis-26723	26	53	,	,	PUNCT
fcis-26723	26	54	the	the	DET
fcis-26723	26	55	existing	exist	VERB
fcis-26723	26	56	research	research	NOUN
fcis-26723	26	57	data	datum	NOUN
fcis-26723	26	58	are	be	AUX
fcis-26723	26	59	mostly	mostly	ADV
fcis-26723	26	60	obtained	obtain	VERB
fcis-26723	26	61	from	from	ADP
fcis-26723	26	62	the	the	DET
fcis-26723	26	63	learning	learning	NOUN
fcis-26723	26	64	management	management	NOUN
fcis-26723	26	65	system	system	NOUN
fcis-26723	26	66	,	,	PUNCT
fcis-26723	26	67	and	and	CCONJ
fcis-26723	26	68	the	the	DET
fcis-26723	26	69	daily	daily	ADJ
fcis-26723	26	70	behavior	behavior	NOUN
fcis-26723	26	71	data	datum	NOUN
fcis-26723	26	72	of	of	ADP
fcis-26723	26	73	students	student	NOUN
fcis-26723	26	74	has	have	AUX
fcis-26723	26	75	not	not	PART
fcis-26723	26	76	been	be	AUX
fcis-26723	26	77	fully	fully	ADV
fcis-26723	26	78	utilized	utilize	VERB
fcis-26723	26	79	.	.	PUNCT
fcis-26723	27	1	in	in	ADP
fcis-26723	27	2	terms	term	NOUN
fcis-26723	27	3	of	of	ADP
fcis-26723	27	4	empirical	empirical	ADJ
fcis-26723	27	5	research	research	NOUN
fcis-26723	27	6	,	,	PUNCT
fcis-26723	27	7	there	there	PRON
fcis-26723	27	8	are	be	VERB
fcis-26723	27	9	few	few	ADJ
fcis-26723	27	10	empirical	empirical	ADJ
fcis-26723	27	11	studies	study	NOUN
fcis-26723	27	12	and	and	CCONJ
fcis-26723	27	13	insufficient	insufficient	ADJ
fcis-26723	27	14	attention	attention	NOUN
fcis-26723	27	15	to	to	ADP
fcis-26723	27	16	dynamic	dynamic	ADJ
fcis-26723	27	17	data	datum	NOUN
fcis-26723	27	18	in	in	ADP
fcis-26723	27	19	the	the	DET
fcis-26723	27	20	selection	selection	NOUN
fcis-26723	27	21	of	of	ADP
fcis-26723	27	22	learning	learn	VERB
fcis-26723	27	23	prediction	prediction	NOUN
fcis-26723	27	24	indicators[5	indicators[5	PROPN
fcis-26723	27	25	]	]	PUNCT
fcis-26723	27	26	.	.	PUNCT
fcis-26723	28	1	compared	compare	VERB
fcis-26723	28	2	with	with	ADP
fcis-26723	28	3	foreign	foreign	ADJ
fcis-26723	28	4	countries	country	NOUN
fcis-26723	28	5	,	,	PUNCT
fcis-26723	28	6	the	the	DET
fcis-26723	28	7	research	research	NOUN
fcis-26723	28	8	on	on	ADP
fcis-26723	28	9	learning	learn	VERB
fcis-26723	28	10	prediction	prediction	NOUN
fcis-26723	28	11	in	in	ADP
fcis-26723	28	12	china	china	PROPN
fcis-26723	28	13	mainly	mainly	ADV
fcis-26723	28	14	stays	stay	VERB
fcis-26723	28	15	at	at	ADP
fcis-26723	28	16	the	the	DET
fcis-26723	28	17	theoretical	theoretical	ADJ
fcis-26723	28	18	level	level	NOUN
fcis-26723	28	19	,	,	PUNCT
fcis-26723	28	20	mostly	mostly	ADV
fcis-26723	28	21	focusing	focus	VERB
fcis-26723	28	22	on	on	ADP
fcis-26723	28	23	the	the	DET
fcis-26723	28	24	study	study	NOUN
fcis-26723	28	25	of	of	ADP
fcis-26723	28	26	learning	learn	VERB
fcis-26723	28	27	prediction	prediction	NOUN
fcis-26723	28	28	mechanism	mechanism	NOUN
fcis-26723	28	29	and	and	CCONJ
fcis-26723	28	30	prediction	prediction	NOUN
fcis-26723	28	31	model	model	NOUN
fcis-26723	28	32	design	design	NOUN
fcis-26723	28	33	,	,	PUNCT
fcis-26723	28	34	and	and	CCONJ
fcis-26723	28	35	the	the	DET
fcis-26723	28	36	research	research	NOUN
fcis-26723	28	37	on	on	ADP
fcis-26723	28	38	application	application	NOUN
fcis-26723	28	39	level	level	NOUN
fcis-26723	28	40	is	be	AUX
fcis-26723	28	41	lacking	lack	VERB
fcis-26723	28	42	.	.	PUNCT
fcis-26723	29	1	in	in	ADP
fcis-26723	29	2	terms	term	NOUN
fcis-26723	29	3	of	of	ADP
fcis-26723	29	4	the	the	DET
fcis-26723	29	5	prediction	prediction	NOUN
fcis-26723	29	6	mechanism	mechanism	NOUN
fcis-26723	29	7	,	,	PUNCT
fcis-26723	29	8	chen	chen	PROPN
fcis-26723	29	9	qinhua	qinhua	PROPN
fcis-26723	29	10	studied	study	VERB
fcis-26723	29	11	the	the	DET
fcis-26723	29	12	academic	academic	ADJ
fcis-26723	29	13	prediction	prediction	NOUN
fcis-26723	29	14	mechanism	mechanism	NOUN
fcis-26723	29	15	based	base	VERB
fcis-26723	29	16	on	on	ADP
fcis-26723	29	17	the	the	DET
fcis-26723	29	18	teaching	teaching	NOUN
fcis-26723	29	19	management	management	NOUN
fcis-26723	29	20	of	of	ADP
fcis-26723	29	21	the	the	DET
fcis-26723	29	22	credit	credit	NOUN
fcis-26723	29	23	system	system	NOUN
fcis-26723	29	24	,	,	PUNCT
fcis-26723	29	25	divided	divide	VERB
fcis-26723	29	26	the	the	DET
fcis-26723	29	27	prediction	prediction	NOUN
fcis-26723	29	28	level	level	NOUN
fcis-26723	29	29	and	and	CCONJ
fcis-26723	29	30	initially	initially	ADV
fcis-26723	29	31	designed	design	VERB
fcis-26723	29	32	the	the	DET
fcis-26723	29	33	working	work	VERB
fcis-26723	29	34	system	system	NOUN
fcis-26723	29	35	of	of	ADP
fcis-26723	29	36	the	the	DET
fcis-26723	29	37	academic	academic	ADJ
fcis-26723	29	38	prediction	prediction	NOUN
fcis-26723	29	39	mechanism	mechanism	NOUN
fcis-26723	29	40	for	for	ADP
fcis-26723	29	41	college	college	NOUN
fcis-26723	29	42	students[6	students[6	NOUN
fcis-26723	29	43	]	]	PUNCT
fcis-26723	29	44	.	.	PUNCT
fcis-26723	30	1	wang	wang	PROPN
fcis-26723	30	2	zihua	zihua	PROPN
fcis-26723	30	3	et	et	PROPN
fcis-26723	30	4	al	al	PROPN
fcis-26723	30	5	.	.	PROPN
fcis-26723	30	6	designed	design	VERB
fcis-26723	30	7	a	a	DET
fcis-26723	30	8	prediction	prediction	NOUN
fcis-26723	30	9	system	system	NOUN
fcis-26723	30	10	for	for	ADP
fcis-26723	30	11	academic	academic	ADJ
fcis-26723	30	12	difficulties	difficulty	NOUN
fcis-26723	30	13	in	in	ADP
fcis-26723	30	14	view	view	NOUN
fcis-26723	30	15	of	of	ADP
fcis-26723	30	16	the	the	DET
fcis-26723	30	17	increasing	increase	VERB
fcis-26723	30	18	number	number	NOUN
fcis-26723	30	19	of	of	ADP
fcis-26723	30	20	students	student	NOUN
fcis-26723	30	21	with	with	ADP
fcis-26723	30	22	academic	academic	ADJ
fcis-26723	30	23	difficulties	difficulty	NOUN
fcis-26723	30	24	.	.	PUNCT
fcis-26723	31	1	through	through	ADP
fcis-26723	31	2	104	104	NUM
fcis-26723	31	3	comprehensive	comprehensive	ADJ
fcis-26723	31	4	analysis	analysis	NOUN
fcis-26723	31	5	of	of	ADP
fcis-26723	31	6	students	student	NOUN
fcis-26723	31	7	'	'	PART
fcis-26723	31	8	academic	academic	ADJ
fcis-26723	31	9	conditions	condition	NOUN
fcis-26723	31	10	,	,	PUNCT
fcis-26723	31	11	students	student	NOUN
fcis-26723	31	12	were	be	AUX
fcis-26723	31	13	evaluated	evaluate	VERB
fcis-26723	31	14	in	in	ADP
fcis-26723	31	15	combination	combination	NOUN
fcis-26723	31	16	with	with	ADP
fcis-26723	31	17	professional	professional	ADJ
fcis-26723	31	18	training	training	NOUN
fcis-26723	31	19	programs	program	NOUN
fcis-26723	31	20	and	and	CCONJ
fcis-26723	31	21	teaching	teaching	NOUN
fcis-26723	31	22	requirements	requirement	NOUN
fcis-26723	31	23	,	,	PUNCT
fcis-26723	31	24	and	and	CCONJ
fcis-26723	31	25	warnings	warning	NOUN
fcis-26723	31	26	were	be	AUX
fcis-26723	31	27	given	give	VERB
fcis-26723	31	28	to	to	ADP
fcis-26723	31	29	different	different	ADJ
fcis-26723	31	30	degrees	degree	NOUN
fcis-26723	31	31	according	accord	VERB
fcis-26723	31	32	to	to	ADP
fcis-26723	31	33	the	the	DET
fcis-26723	31	34	evaluation	evaluation	NOUN
fcis-26723	31	35	results[7	results[7	NOUN
fcis-26723	31	36	]	]	PUNCT
fcis-26723	31	37	.	.	PUNCT
fcis-26723	32	1	shang	shang	PROPN
fcis-26723	32	2	weiwei	weiwei	PROPN
fcis-26723	32	3	et	et	PROPN
fcis-26723	32	4	al	al	PROPN
fcis-26723	32	5	.	.	PROPN
fcis-26723	32	6	studied	study	VERB
fcis-26723	32	7	the	the	DET
fcis-26723	32	8	academic	academic	ADJ
fcis-26723	32	9	prediction	prediction	NOUN
fcis-26723	32	10	mechanism	mechanism	NOUN
fcis-26723	32	11	of	of	ADP
fcis-26723	32	12	local	local	ADJ
fcis-26723	32	13	college	college	NOUN
fcis-26723	32	14	students	student	NOUN
fcis-26723	32	15	in	in	ADP
fcis-26723	32	16	view	view	NOUN
fcis-26723	32	17	of	of	ADP
fcis-26723	32	18	the	the	DET
fcis-26723	32	19	poor	poor	ADJ
fcis-26723	32	20	student	student	NOUN
fcis-26723	32	21	source	source	NOUN
fcis-26723	32	22	quality	quality	NOUN
fcis-26723	32	23	in	in	ADP
fcis-26723	32	24	local	local	ADJ
fcis-26723	32	25	colleges	college	NOUN
fcis-26723	32	26	and	and	CCONJ
fcis-26723	32	27	universities[8	universities[8	PROPN
fcis-26723	32	28	]	]	PUNCT
fcis-26723	32	29	.	.	PUNCT
fcis-26723	33	1	he	he	PRON
fcis-26723	33	2	kui	kui	PROPN
fcis-26723	33	3	improved	improve	VERB
fcis-26723	33	4	the	the	DET
fcis-26723	33	5	student	student	NOUN
fcis-26723	33	6	academic	academic	ADJ
fcis-26723	33	7	prediction	prediction	NOUN
fcis-26723	33	8	mechanism	mechanism	NOUN
fcis-26723	33	9	by	by	ADP
fcis-26723	33	10	establishing	establish	VERB
fcis-26723	33	11	effective	effective	ADJ
fcis-26723	33	12	linkage	linkage	NOUN
fcis-26723	33	13	between	between	ADP
fcis-26723	33	14	the	the	DET
fcis-26723	33	15	school	school	NOUN
fcis-26723	33	16	educational	educational	PROPN
fcis-26723	33	17	administration	administration	PROPN
fcis-26723	33	18	department	department	PROPN
fcis-26723	33	19	,	,	PUNCT
fcis-26723	33	20	the	the	DET
fcis-26723	33	21	student	student	NOUN
fcis-26723	33	22	administration	administration	PROPN
fcis-26723	33	23	department	department	PROPN
fcis-26723	33	24	and	and	CCONJ
fcis-26723	33	25	the	the	DET
fcis-26723	33	26	parents	parent	NOUN
fcis-26723	33	27	of	of	ADP
fcis-26723	33	28	students	student	NOUN
fcis-26723	33	29	with	with	ADP
fcis-26723	33	30	learning	learn	VERB
fcis-26723	33	31	difficulties[9	difficulties[9	NUM
fcis-26723	33	32	]	]	PUNCT
fcis-26723	33	33	.	.	PUNCT
fcis-26723	34	1	in	in	ADP
fcis-26723	34	2	terms	term	NOUN
fcis-26723	34	3	of	of	ADP
fcis-26723	34	4	prediction	prediction	NOUN
fcis-26723	34	5	model	model	NOUN
fcis-26723	34	6	design	design	NOUN
fcis-26723	34	7	,	,	PUNCT
fcis-26723	34	8	wu	wu	PROPN
fcis-26723	34	9	haifeng	haifeng	PROPN
fcis-26723	34	10	et	et	PROPN
fcis-26723	34	11	al	al	PROPN
fcis-26723	34	12	.	.	PROPN
fcis-26723	34	13	built	build	VERB
fcis-26723	34	14	a	a	DET
fcis-26723	34	15	prediction	prediction	NOUN
fcis-26723	34	16	model	model	NOUN
fcis-26723	34	17	based	base	VERB
fcis-26723	34	18	on	on	ADP
fcis-26723	34	19	cluster	cluster	NOUN
fcis-26723	34	20	analysis	analysis	NOUN
fcis-26723	34	21	and	and	CCONJ
fcis-26723	34	22	association	association	NOUN
fcis-26723	34	23	analysis	analysis	NOUN
fcis-26723	34	24	,	,	PUNCT
fcis-26723	34	25	so	so	SCONJ
fcis-26723	34	26	as	as	SCONJ
fcis-26723	34	27	to	to	PART
fcis-26723	34	28	timely	timely	ADV
fcis-26723	34	29	discover	discover	VERB
fcis-26723	34	30	and	and	CCONJ
fcis-26723	34	31	predict	predict	VERB
fcis-26723	34	32	students	student	NOUN
fcis-26723	34	33	of	of	ADP
fcis-26723	34	34	three	three	NUM
fcis-26723	34	35	types	type	NOUN
fcis-26723	34	36	:	:	PUNCT
fcis-26723	34	37	low	low	ADJ
fcis-26723	34	38	,	,	PUNCT
fcis-26723	34	39	landslide	landslide	NOUN
fcis-26723	34	40	and	and	CCONJ
fcis-26723	34	41	potential	potential	ADJ
fcis-26723	34	42	type	type	NOUN
fcis-26723	35	1	[	[	X
fcis-26723	35	2	10	10	NUM
fcis-26723	35	3	]	]	PUNCT
fcis-26723	35	4	.	.	PUNCT
fcis-26723	36	1	from	from	ADP
fcis-26723	36	2	the	the	DET
fcis-26723	36	3	perspective	perspective	NOUN
fcis-26723	36	4	of	of	ADP
fcis-26723	36	5	counselors	counselor	NOUN
fcis-26723	36	6	,	,	PUNCT
fcis-26723	36	7	wan	wan	PROPN
fcis-26723	36	8	xinghuo	xinghuo	PROPN
fcis-26723	36	9	et	et	PROPN
fcis-26723	36	10	al	al	PROPN
fcis-26723	36	11	.	.	PROPN
fcis-26723	36	12	built	build	VERB
fcis-26723	36	13	an	an	DET
fcis-26723	36	14	innovative	innovative	ADJ
fcis-26723	36	15	management	management	NOUN
fcis-26723	36	16	model	model	NOUN
fcis-26723	36	17	of	of	ADP
fcis-26723	36	18	college	college	NOUN
fcis-26723	36	19	students	student	NOUN
fcis-26723	36	20	'	'	PART
fcis-26723	36	21	academic	academic	ADJ
fcis-26723	36	22	forecasting	forecasting	NOUN
fcis-26723	36	23	based	base	VERB
fcis-26723	36	24	on	on	ADP
fcis-26723	36	25	the	the	DET
fcis-26723	36	26	kernel	kernel	PROPN
fcis-26723	36	27	principal	principal	PROPN
fcis-26723	36	28	component	component	NOUN
fcis-26723	36	29	analysis	analysis	NOUN
fcis-26723	36	30	method	method	NOUN
fcis-26723	36	31	,	,	PUNCT
fcis-26723	36	32	which	which	PRON
fcis-26723	36	33	combined	combine	VERB
fcis-26723	36	34	dynamic	dynamic	ADJ
fcis-26723	36	35	qualitative	qualitative	NOUN
fcis-26723	36	36	forecasting	forecasting	NOUN
fcis-26723	36	37	and	and	CCONJ
fcis-26723	36	38	quantitative	quantitative	ADJ
fcis-26723	36	39	forecasting[11	forecasting[11	NOUN
fcis-26723	36	40	]	]	PUNCT
fcis-26723	36	41	.	.	PUNCT
fcis-26723	37	1	zhang	zhang	PROPN
fcis-26723	37	2	fusheng	fusheng	PROPN
fcis-26723	37	3	et	et	PROPN
fcis-26723	37	4	al	al	PROPN
fcis-26723	37	5	.	.	PROPN
fcis-26723	37	6	proposed	propose	VERB
fcis-26723	37	7	a	a	DET
fcis-26723	37	8	dynamic	dynamic	ADJ
fcis-26723	37	9	academic	academic	ADJ
fcis-26723	37	10	forecasting	forecasting	NOUN
fcis-26723	37	11	system	system	NOUN
fcis-26723	37	12	reference	reference	NOUN
fcis-26723	37	13	model	model	NOUN
fcis-26723	37	14	with	with	ADP
fcis-26723	37	15	two	two	NUM
fcis-26723	37	16	-	-	PUNCT
fcis-26723	37	17	way	way	NOUN
fcis-26723	37	18	forecasting	forecasting	NOUN
fcis-26723	37	19	threshold	threshold	NOUN
fcis-26723	37	20	and	and	CCONJ
fcis-26723	37	21	implemented	implement	VERB
fcis-26723	37	22	a	a	DET
fcis-26723	37	23	specific	specific	ADJ
fcis-26723	37	24	help	help	NOUN
fcis-26723	37	25	plan	plan	NOUN
fcis-26723	37	26	for	for	ADP
fcis-26723	37	27	individual	individual	ADJ
fcis-26723	37	28	forecasting	forecasting	NOUN
fcis-26723	37	29	characteristics[12	characteristics[12	NOUN
fcis-26723	37	30	]	]	PUNCT
fcis-26723	37	31	.	.	PUNCT
fcis-26723	38	1	through	through	ADP
fcis-26723	38	2	in	in	ADP
fcis-26723	38	3	-	-	PUNCT
fcis-26723	38	4	depth	depth	NOUN
fcis-26723	38	5	mining	mining	NOUN
fcis-26723	38	6	and	and	CCONJ
fcis-26723	38	7	research	research	NOUN
fcis-26723	38	8	on	on	ADP
fcis-26723	38	9	outlier	outlier	NOUN
fcis-26723	38	10	data	datum	NOUN
fcis-26723	38	11	,	,	PUNCT
fcis-26723	38	12	jin	jin	PROPN
fcis-26723	38	13	yifu	yifu	PROPN
fcis-26723	38	14	et	et	PROPN
fcis-26723	38	15	al	al	PROPN
fcis-26723	38	16	.	.	PROPN
fcis-26723	38	17	proposed	propose	VERB
fcis-26723	38	18	a	a	DET
fcis-26723	38	19	prediction	prediction	NOUN
fcis-26723	38	20	information	information	NOUN
fcis-26723	38	21	discovery	discovery	NOUN
fcis-26723	38	22	and	and	CCONJ
fcis-26723	38	23	generation	generation	NOUN
fcis-26723	38	24	model	model	NOUN
fcis-26723	38	25	called	call	VERB
fcis-26723	38	26	laoma	laoma	PROPN
fcis-26723	38	27	,	,	PUNCT
fcis-26723	38	28	which	which	PRON
fcis-26723	38	29	integrates	integrate	VERB
fcis-26723	38	30	data	datum	NOUN
fcis-26723	38	31	from	from	ADP
fcis-26723	38	32	three	three	NUM
fcis-26723	38	33	aspects	aspect	NOUN
fcis-26723	38	34	:	:	PUNCT
fcis-26723	38	35	curriculum	curriculum	NOUN
fcis-26723	38	36	,	,	PUNCT
fcis-26723	38	37	classroom	classroom	NOUN
fcis-26723	38	38	and	and	CCONJ
fcis-26723	38	39	extracurricular	extracurricular	NOUN
fcis-26723	38	40	.	.	PUNCT
fcis-26723	39	1	based	base	VERB
fcis-26723	39	2	on	on	ADP
fcis-26723	39	3	this	this	DET
fcis-26723	39	4	model	model	NOUN
fcis-26723	39	5	,	,	PUNCT
fcis-26723	39	6	two	two	NUM
fcis-26723	39	7	kinds	kind	NOUN
fcis-26723	39	8	of	of	ADP
fcis-26723	39	9	six	six	NUM
fcis-26723	39	10	-	-	PUNCT
fcis-26723	39	11	level	level	NOUN
fcis-26723	39	12	signal	signal	NOUN
fcis-26723	39	13	systems	system	NOUN
fcis-26723	39	14	and	and	CCONJ
fcis-26723	39	15	feedback	feedback	NOUN
fcis-26723	39	16	mechanisms	mechanism	NOUN
fcis-26723	39	17	for	for	ADP
fcis-26723	39	18	academic	academic	ADJ
fcis-26723	39	19	prediction	prediction	NOUN
fcis-26723	39	20	were	be	AUX
fcis-26723	39	21	established[13	established[13	PROPN
fcis-26723	39	22	]	]	PUNCT
fcis-26723	39	23	.	.	PUNCT
fcis-26723	40	1	based	base	VERB
fcis-26723	40	2	on	on	ADP
fcis-26723	40	3	the	the	DET
fcis-26723	40	4	improved	improve	VERB
fcis-26723	40	5	apriori	apriori	ADJ
fcis-26723	40	6	algorithm	algorithm	NOUN
fcis-26723	40	7	,	,	PUNCT
fcis-26723	40	8	du	du	PROPN
fcis-26723	40	9	juan	juan	PROPN
fcis-26723	40	10	et	et	PROPN
fcis-26723	40	11	al	al	PROPN
fcis-26723	40	12	.	.	PROPN
fcis-26723	40	13	studied	study	VERB
fcis-26723	40	14	the	the	DET
fcis-26723	40	15	prediction	prediction	NOUN
fcis-26723	40	16	system	system	NOUN
fcis-26723	40	17	of	of	ADP
fcis-26723	40	18	college	college	NOUN
fcis-26723	40	19	students	student	NOUN
fcis-26723	40	20	'	'	PART
fcis-26723	40	21	grades	grade	NOUN
fcis-26723	40	22	and	and	CCONJ
fcis-26723	40	23	found	find	VERB
fcis-26723	40	24	the	the	DET
fcis-26723	40	25	correlation	correlation	NOUN
fcis-26723	40	26	between	between	ADP
fcis-26723	40	27	different	different	ADJ
fcis-26723	40	28	disciplines	discipline	NOUN
fcis-26723	40	29	through	through	ADP
fcis-26723	40	30	the	the	DET
fcis-26723	40	31	mining	mining	NOUN
fcis-26723	40	32	and	and	CCONJ
fcis-26723	40	33	analysis	analysis	NOUN
fcis-26723	40	34	of	of	ADP
fcis-26723	40	35	students	student	NOUN
fcis-26723	40	36	'	'	PART
fcis-26723	40	37	grades[14	grades[14	PROPN
fcis-26723	40	38	]	]	PUNCT
fcis-26723	40	39	.	.	PUNCT
fcis-26723	41	1	cui	cui	PROPN
fcis-26723	41	2	qiang	qiang	PROPN
fcis-26723	41	3	et	et	PROPN
fcis-26723	41	4	al	al	PROPN
fcis-26723	41	5	.	.	PROPN
fcis-26723	41	6	used	use	VERB
fcis-26723	41	7	bp	bp	PROPN
fcis-26723	41	8	neural	neural	PROPN
fcis-26723	41	9	network	network	NOUN
fcis-26723	41	10	model	model	NOUN
fcis-26723	41	11	to	to	PART
fcis-26723	41	12	predict	predict	VERB
fcis-26723	41	13	students	student	NOUN
fcis-26723	41	14	'	'	PART
fcis-26723	41	15	academic	academic	ADJ
fcis-26723	41	16	achievement[15	achievement[15	NOUN
fcis-26723	41	17	]	]	PUNCT
fcis-26723	41	18	.	.	PUNCT
fcis-26723	42	1	the	the	DET
fcis-26723	42	2	study	study	NOUN
fcis-26723	42	3	of	of	ADP
fcis-26723	42	4	student	student	NOUN
fcis-26723	42	5	learning	learning	NOUN
fcis-26723	42	6	prediction	prediction	NOUN
fcis-26723	42	7	is	be	AUX
fcis-26723	42	8	mainly	mainly	ADV
fcis-26723	42	9	divided	divide	VERB
fcis-26723	42	10	into	into	ADP
fcis-26723	42	11	curriculum	curriculum	NOUN
fcis-26723	42	12	prediction	prediction	NOUN
fcis-26723	42	13	and	and	CCONJ
fcis-26723	42	14	academic	academic	ADJ
fcis-26723	42	15	prediction	prediction	NOUN
fcis-26723	42	16	.	.	PUNCT
fcis-26723	43	1	curriculum	curriculum	NOUN
fcis-26723	43	2	prediction	prediction	NOUN
fcis-26723	43	3	is	be	AUX
fcis-26723	43	4	aimed	aim	VERB
fcis-26723	43	5	at	at	ADP
fcis-26723	43	6	a	a	DET
fcis-26723	43	7	specific	specific	ADJ
fcis-26723	43	8	course	course	NOUN
fcis-26723	43	9	,	,	PUNCT
fcis-26723	43	10	and	and	CCONJ
fcis-26723	43	11	the	the	DET
fcis-26723	43	12	data	datum	NOUN
fcis-26723	43	13	involved	involve	VERB
fcis-26723	43	14	are	be	AUX
fcis-26723	43	15	mainly	mainly	ADV
fcis-26723	43	16	the	the	DET
fcis-26723	43	17	learning	learning	NOUN
fcis-26723	43	18	process	process	NOUN
fcis-26723	43	19	data	datum	NOUN
fcis-26723	43	20	of	of	ADP
fcis-26723	43	21	the	the	DET
fcis-26723	43	22	course	course	NOUN
fcis-26723	43	23	.	.	PUNCT
fcis-26723	44	1	academic	academic	ADJ
fcis-26723	44	2	prediction	prediction	NOUN
fcis-26723	44	3	can	can	AUX
fcis-26723	44	4	be	be	AUX
fcis-26723	44	5	divided	divide	VERB
fcis-26723	44	6	into	into	ADP
fcis-26723	44	7	two	two	NUM
fcis-26723	44	8	categories	category	NOUN
fcis-26723	44	9	:	:	PUNCT
fcis-26723	44	10	the	the	DET
fcis-26723	44	11	academic	academic	ADJ
fcis-26723	44	12	prediction	prediction	NOUN
fcis-26723	44	13	studied	study	VERB
fcis-26723	44	14	in	in	ADP
fcis-26723	44	15	the	the	DET
fcis-26723	44	16	field	field	NOUN
fcis-26723	44	17	of	of	ADP
fcis-26723	44	18	education	education	NOUN
fcis-26723	44	19	directly	directly	ADV
fcis-26723	44	20	divides	divide	VERB
fcis-26723	44	21	students	student	NOUN
fcis-26723	44	22	into	into	ADP
fcis-26723	44	23	grades	grade	NOUN
fcis-26723	44	24	at	at	ADP
fcis-26723	44	25	the	the	DET
fcis-26723	44	26	end	end	NOUN
fcis-26723	44	27	of	of	ADP
fcis-26723	44	28	each	each	DET
fcis-26723	44	29	semester	semester	NOUN
fcis-26723	44	30	and	and	CCONJ
fcis-26723	44	31	issues	issue	VERB
fcis-26723	44	32	predictions	prediction	NOUN
fcis-26723	44	33	to	to	ADP
fcis-26723	44	34	students	student	NOUN
fcis-26723	44	35	at	at	ADP
fcis-26723	44	36	the	the	DET
fcis-26723	44	37	beginning	beginning	NOUN
fcis-26723	44	38	of	of	ADP
fcis-26723	44	39	the	the	DET
fcis-26723	44	40	next	next	ADJ
fcis-26723	44	41	semester	semester	NOUN
fcis-26723	44	42	;	;	PUNCT
fcis-26723	44	43	academic	academic	ADJ
fcis-26723	44	44	prediction	prediction	NOUN
fcis-26723	44	45	using	use	VERB
fcis-26723	44	46	statistical	statistical	ADJ
fcis-26723	44	47	theory	theory	NOUN
fcis-26723	44	48	is	be	AUX
fcis-26723	44	49	to	to	PART
fcis-26723	44	50	predict	predict	VERB
fcis-26723	44	51	whether	whether	SCONJ
fcis-26723	44	52	students	student	NOUN
fcis-26723	44	53	can	can	AUX
fcis-26723	44	54	successfully	successfully	ADV
fcis-26723	44	55	graduate	graduate	VERB
fcis-26723	44	56	,	,	PUNCT
fcis-26723	44	57	and	and	CCONJ
fcis-26723	44	58	to	to	PART
fcis-26723	44	59	mine	mine	VERB
fcis-26723	44	60	the	the	DET
fcis-26723	44	61	correlation	correlation	NOUN
fcis-26723	44	62	relationship	relationship	NOUN
fcis-26723	44	63	between	between	ADP
fcis-26723	44	64	courses	course	NOUN
fcis-26723	44	65	by	by	ADP
fcis-26723	44	66	using	use	VERB
fcis-26723	44	67	correlation	correlation	NOUN
fcis-26723	44	68	analysis	analysis	NOUN
fcis-26723	44	69	,	,	PUNCT
fcis-26723	44	70	and	and	CCONJ
fcis-26723	44	71	to	to	PART
fcis-26723	44	72	predict	predict	VERB
fcis-26723	44	73	students	student	NOUN
fcis-26723	44	74	'	'	PART
fcis-26723	44	75	academic	academic	ADJ
fcis-26723	44	76	completion	completion	NOUN
fcis-26723	44	77	through	through	ADP
fcis-26723	44	78	early	early	ADJ
fcis-26723	44	79	course	course	NOUN
fcis-26723	44	80	results	result	NOUN
fcis-26723	44	81	.	.	PUNCT
fcis-26723	45	1	3	3	X
fcis-26723	45	2	.	.	X
fcis-26723	45	3	research	research	NOUN
fcis-26723	45	4	content	content	NOUN
fcis-26723	45	5	and	and	CCONJ
fcis-26723	45	6	research	research	NOUN
fcis-26723	45	7	methods	method	NOUN
fcis-26723	45	8	3.1	3.1	NUM
fcis-26723	45	9	.	.	PUNCT
fcis-26723	46	1	research	research	NOUN
fcis-26723	46	2	content	content	NOUN
fcis-26723	46	3	this	this	DET
fcis-26723	46	4	paper	paper	NOUN
fcis-26723	46	5	proposes	propose	VERB
fcis-26723	46	6	a	a	DET
fcis-26723	46	7	grade	grade	NOUN
fcis-26723	46	8	prediction	prediction	NOUN
fcis-26723	46	9	model	model	NOUN
fcis-26723	46	10	based	base	VERB
fcis-26723	46	11	on	on	ADP
fcis-26723	46	12	bp	bp	PROPN
fcis-26723	46	13	neural	neural	ADJ
fcis-26723	46	14	network	network	NOUN
fcis-26723	46	15	,	,	PUNCT
fcis-26723	46	16	which	which	PRON
fcis-26723	46	17	can	can	AUX
fcis-26723	46	18	predict	predict	VERB
fcis-26723	46	19	the	the	DET
fcis-26723	46	20	grade	grade	NOUN
fcis-26723	46	21	of	of	ADP
fcis-26723	46	22	students	student	NOUN
fcis-26723	46	23	at	at	ADP
fcis-26723	46	24	the	the	DET
fcis-26723	46	25	end	end	NOUN
fcis-26723	46	26	of	of	ADP
fcis-26723	46	27	the	the	DET
fcis-26723	46	28	semester	semester	NOUN
fcis-26723	46	29	according	accord	VERB
fcis-26723	46	30	to	to	ADP
fcis-26723	46	31	their	their	PRON
fcis-26723	46	32	daily	daily	ADJ
fcis-26723	46	33	learning	learn	VERB
fcis-26723	46	34	behavior	behavior	NOUN
fcis-26723	46	35	characteristics	characteristic	NOUN
fcis-26723	46	36	.	.	PUNCT
fcis-26723	47	1	by	by	ADP
fcis-26723	47	2	using	use	VERB
fcis-26723	47	3	online	online	ADJ
fcis-26723	47	4	questionnaire	questionnaire	NOUN
fcis-26723	47	5	to	to	PART
fcis-26723	47	6	investigate	investigate	VERB
fcis-26723	47	7	students	student	NOUN
fcis-26723	47	8	'	'	PART
fcis-26723	47	9	behavior	behavior	NOUN
fcis-26723	47	10	information	information	NOUN
fcis-26723	47	11	data	datum	NOUN
fcis-26723	47	12	and	and	CCONJ
fcis-26723	47	13	students	student	NOUN
fcis-26723	47	14	'	'	PART
fcis-26723	47	15	final	final	ADJ
fcis-26723	47	16	grades	grade	NOUN
fcis-26723	47	17	derived	derive	VERB
fcis-26723	47	18	from	from	ADP
fcis-26723	47	19	the	the	DET
fcis-26723	47	20	educational	educational	ADJ
fcis-26723	47	21	administration	administration	NOUN
fcis-26723	47	22	system	system	NOUN
fcis-26723	47	23	,	,	PUNCT
fcis-26723	47	24	the	the	DET
fcis-26723	47	25	students	student	NOUN
fcis-26723	47	26	'	'	PART
fcis-26723	47	27	final	final	ADJ
fcis-26723	47	28	exam	exam	NOUN
fcis-26723	47	29	course	course	NOUN
fcis-26723	47	30	score	score	NOUN
fcis-26723	47	31	information	information	NOUN
fcis-26723	47	32	and	and	CCONJ
fcis-26723	47	33	students	student	NOUN
fcis-26723	47	34	'	'	PART
fcis-26723	47	35	daily	daily	ADJ
fcis-26723	47	36	behavior	behavior	NOUN
fcis-26723	47	37	characteristic	characteristic	ADJ
fcis-26723	47	38	data	datum	NOUN
fcis-26723	47	39	are	be	AUX
fcis-26723	47	40	combined	combine	VERB
fcis-26723	47	41	to	to	PART
fcis-26723	47	42	form	form	VERB
fcis-26723	47	43	a	a	DET
fcis-26723	47	44	data	data	NOUN
fcis-26723	47	45	set	set	VERB
fcis-26723	47	46	of	of	ADP
fcis-26723	47	47	students	student	NOUN
fcis-26723	47	48	'	'	PART
fcis-26723	47	49	daily	daily	ADJ
fcis-26723	47	50	behavior	behavior	NOUN
fcis-26723	47	51	course	course	NOUN
fcis-26723	47	52	score	score	NOUN
fcis-26723	47	53	,	,	PUNCT
fcis-26723	47	54	and	and	CCONJ
fcis-26723	47	55	the	the	DET
fcis-26723	47	56	machine	machine	NOUN
fcis-26723	47	57	learning	learn	VERB
fcis-26723	47	58	technology	technology	NOUN
fcis-26723	47	59	based	base	VERB
fcis-26723	47	60	on	on	ADP
fcis-26723	47	61	bp	bp	PROPN
fcis-26723	47	62	neural	neural	ADJ
fcis-26723	47	63	network	network	NOUN
fcis-26723	47	64	is	be	AUX
fcis-26723	47	65	used	use	VERB
fcis-26723	47	66	to	to	PART
fcis-26723	47	67	analyze	analyze	VERB
fcis-26723	47	68	the	the	DET
fcis-26723	47	69	data	datum	NOUN
fcis-26723	47	70	set	set	VERB
fcis-26723	47	71	of	of	ADP
fcis-26723	47	72	students	student	NOUN
fcis-26723	47	73	'	'	PART
fcis-26723	47	74	daily	daily	ADJ
fcis-26723	47	75	behavior	behavior	NOUN
fcis-26723	47	76	course	course	NOUN
fcis-26723	47	77	score	score	NOUN
fcis-26723	47	78	.	.	PUNCT
fcis-26723	48	1	determine	determine	VERB
fcis-26723	48	2	the	the	DET
fcis-26723	48	3	relationship	relationship	NOUN
fcis-26723	48	4	between	between	ADP
fcis-26723	48	5	students	student	NOUN
fcis-26723	48	6	'	'	PART
fcis-26723	48	7	daily	daily	ADJ
fcis-26723	48	8	behavior	behavior	NOUN
fcis-26723	48	9	and	and	CCONJ
fcis-26723	48	10	final	final	ADJ
fcis-26723	48	11	exam	exam	NOUN
fcis-26723	48	12	scores	score	NOUN
fcis-26723	48	13	.	.	PUNCT
fcis-26723	49	1	3.2	3.2	NUM
fcis-26723	49	2	.	.	PUNCT
fcis-26723	50	1	research	research	NOUN
fcis-26723	50	2	methods	method	NOUN
fcis-26723	50	3	3.2.1	3.2.1	NUM
fcis-26723	50	4	.	.	PUNCT
fcis-26723	51	1	obtaining	obtain	VERB
fcis-26723	51	2	the	the	DET
fcis-26723	51	3	data	datum	NOUN
fcis-26723	51	4	set	set	VERB
fcis-26723	51	5	through	through	ADP
fcis-26723	51	6	the	the	DET
fcis-26723	51	7	combination	combination	NOUN
fcis-26723	51	8	of	of	ADP
fcis-26723	51	9	the	the	DET
fcis-26723	51	10	questionnaire	questionnaire	NOUN
fcis-26723	51	11	survey	survey	NOUN
fcis-26723	51	12	and	and	CCONJ
fcis-26723	51	13	the	the	DET
fcis-26723	51	14	teaching	teaching	NOUN
fcis-26723	51	15	administration	administration	NOUN
fcis-26723	51	16	system	system	NOUN
fcis-26723	51	17	to	to	PART
fcis-26723	51	18	export	export	VERB
fcis-26723	51	19	the	the	DET
fcis-26723	51	20	final	final	ADJ
fcis-26723	51	21	exam	exam	NOUN
fcis-26723	51	22	results	result	NOUN
fcis-26723	51	23	of	of	ADP
fcis-26723	51	24	students	student	NOUN
fcis-26723	51	25	,	,	PUNCT
fcis-26723	51	26	table	table	NOUN
fcis-26723	51	27	1	1	NUM
fcis-26723	51	28	is	be	AUX
fcis-26723	51	29	part	part	NOUN
fcis-26723	51	30	of	of	ADP
fcis-26723	51	31	the	the	DET
fcis-26723	51	32	data	datum	NOUN
fcis-26723	51	33	set	set	VERB
fcis-26723	51	34	,	,	PUNCT
fcis-26723	51	35	and	and	CCONJ
fcis-26723	51	36	the	the	DET
fcis-26723	51	37	data	data	NOUN
fcis-26723	51	38	statistics	statistic	NOUN
fcis-26723	51	39	are	be	AUX
fcis-26723	51	40	based	base	VERB
fcis-26723	51	41	on	on	ADP
fcis-26723	51	42	weekly	weekly	ADJ
fcis-26723	51	43	units	unit	NOUN
fcis-26723	51	44	.	.	PUNCT
fcis-26723	52	1	grades	grade	NOUN
fcis-26723	52	2	with	with	ADP
fcis-26723	52	3	a	a	DET
fcis-26723	52	4	,	,	PUNCT
fcis-26723	52	5	b	b	NOUN
fcis-26723	52	6	,	,	PUNCT
fcis-26723	52	7	c	c	NOUN
fcis-26723	52	8	,	,	PUNCT
fcis-26723	52	9	d	d	X
fcis-26723	52	10	to	to	PART
fcis-26723	52	11	indicate	indicate	VERB
fcis-26723	52	12	excellent	excellent	ADJ
fcis-26723	52	13	,	,	PUNCT
fcis-26723	52	14	good	good	ADJ
fcis-26723	52	15	,	,	PUNCT
fcis-26723	52	16	medium	medium	ADJ
fcis-26723	52	17	,	,	PUNCT
fcis-26723	52	18	poor	poor	ADJ
fcis-26723	52	19	four	four	NUM
fcis-26723	52	20	grades	grade	NOUN
fcis-26723	52	21	.	.	PUNCT
fcis-26723	53	1	table	table	NOUN
fcis-26723	53	2	1	1	NUM
fcis-26723	53	3	.	.	PUNCT
fcis-26723	53	4	partial	partial	ADJ
fcis-26723	53	5	data	datum	NOUN
fcis-26723	53	6	of	of	ADP
fcis-26723	53	7	the	the	DET
fcis-26723	53	8	dataset	dataset	NOUN
fcis-26723	53	9	student	student	NOUN
fcis-26723	53	10	selfstudy	selfstudy	NOUN
fcis-26723	53	11	attend	attend	VERB
fcis-26723	53	12	lecture	lecture	NOUN
fcis-26723	53	13	online	online	ADV
fcis-26723	53	14	learning	learn	VERB
fcis-26723	53	15	after	after	ADP
fcis-26723	53	16	-	-	PUNCT
fcis-26723	53	17	class	class	NOUN
fcis-26723	53	18	tutoring	tutoring	NOUN
fcis-26723	53	19	complete	complete	VERB
fcis-26723	53	20	the	the	DET
fcis-26723	53	21	exercise	exercise	NOUN
fcis-26723	53	22	admission	admission	NOUN
fcis-26723	53	23	score	score	NOUN
fcis-26723	53	24	other	other	ADJ
fcis-26723	53	25	learning	learn	VERB
fcis-26723	53	26	grade	grade	NOUN
fcis-26723	53	27	level	level	NOUN
fcis-26723	53	28	s1	s1	PROPN
fcis-26723	53	29	6h	6h	NUM
fcis-26723	53	30	4h	4h	NUM
fcis-26723	53	31	3h	3h	NUM
fcis-26723	53	32	2h	2h	NUM
fcis-26723	53	33	y	y	NOUN
fcis-26723	53	34	a	a	DET
fcis-26723	53	35	2	2	NUM
fcis-26723	53	36	a	a	DET
fcis-26723	53	37	3h	3h	NUM
fcis-26723	53	38	3h	3h	NUM
fcis-26723	53	39	1h	1h	NUM
fcis-26723	53	40	0h	0h	NOUN
fcis-26723	54	1	n	n	NOUN
fcis-26723	54	2	d	d	PROPN
fcis-26723	54	3	1	1	NUM
fcis-26723	54	4	c	c	NOUN
fcis-26723	54	5	3h	3h	NUM
fcis-26723	54	6	3h	3h	NUM
fcis-26723	54	7	0h	0h	PROPN
fcis-26723	55	1	0h	0h	PROPN
fcis-26723	56	1	y	y	PROPN
fcis-26723	56	2	b	b	PROPN
fcis-26723	56	3	0	0	NUM
fcis-26723	56	4	d	d	NOUN
fcis-26723	56	5	…	…	PUNCT
fcis-26723	56	6	…	…	PUNCT
fcis-26723	56	7	…	…	PUNCT
fcis-26723	56	8	…	…	PUNCT
fcis-26723	56	9	…	…	PUNCT
fcis-26723	56	10	…	…	PUNCT
fcis-26723	56	11	…	…	PUNCT
fcis-26723	56	12	…	…	PUNCT
fcis-26723	56	13	…	…	PUNCT
fcis-26723	56	14	4h	4h	NUM
fcis-26723	56	15	3h	3h	NUM
fcis-26723	56	16	3h	3h	NUM
fcis-26723	56	17	1h	1h	NUM
fcis-26723	56	18	n	n	PROPN
fcis-26723	56	19	c	c	NOUN
fcis-26723	56	20	0	0	NUM
fcis-26723	56	21	b	b	X
fcis-26723	56	22	3.2.2	3.2.2	NUM
fcis-26723	56	23	.	.	PUNCT
fcis-26723	57	1	data	datum	NOUN
fcis-26723	57	2	preprocessing	preprocessing	NOUN
fcis-26723	57	3	in	in	ADP
fcis-26723	57	4	the	the	DET
fcis-26723	57	5	process	process	NOUN
fcis-26723	57	6	of	of	ADP
fcis-26723	57	7	data	datum	NOUN
fcis-26723	57	8	learning	learn	VERB
fcis-26723	57	9	by	by	ADP
fcis-26723	57	10	bp	bp	PROPN
fcis-26723	57	11	neural	neural	PROPN
fcis-26723	57	12	network	network	PROPN
fcis-26723	57	13	prediction	prediction	NOUN
fcis-26723	57	14	model	model	NOUN
fcis-26723	57	15	,	,	PUNCT
fcis-26723	57	16	the	the	DET
fcis-26723	57	17	data	datum	NOUN
fcis-26723	57	18	of	of	ADP
fcis-26723	57	19	students	student	NOUN
fcis-26723	57	20	surveyed	survey	VERB
fcis-26723	57	21	by	by	ADP
fcis-26723	57	22	questionnaire	questionnaire	NOUN
fcis-26723	57	23	is	be	AUX
fcis-26723	57	24	not	not	PART
fcis-26723	57	25	completely	completely	ADV
fcis-26723	57	26	accurate	accurate	ADJ
fcis-26723	57	27	,	,	PUNCT
fcis-26723	57	28	and	and	CCONJ
fcis-26723	57	29	students	student	NOUN
fcis-26723	57	30	may	may	AUX
fcis-26723	57	31	be	be	AUX
fcis-26723	57	32	subjective	subjective	ADJ
fcis-26723	57	33	when	when	SCONJ
fcis-26723	57	34	filling	fill	VERB
fcis-26723	57	35	in	in	ADP
fcis-26723	57	36	the	the	DET
fcis-26723	57	37	data	datum	NOUN
fcis-26723	57	38	.	.	PUNCT
fcis-26723	58	1	therefore	therefore	ADV
fcis-26723	58	2	,	,	PUNCT
fcis-26723	58	3	the	the	DET
fcis-26723	58	4	original	original	ADJ
fcis-26723	58	5	data	data	NOUN
fcis-26723	58	6	is	be	AUX
fcis-26723	58	7	generally	generally	ADV
fcis-26723	58	8	incomplete	incomplete	ADJ
fcis-26723	58	9	and	and	CCONJ
fcis-26723	58	10	inconsistent	inconsistent	ADJ
fcis-26723	58	11	dirty	dirty	ADJ
fcis-26723	58	12	data	datum	NOUN
fcis-26723	58	13	,	,	PUNCT
fcis-26723	58	14	which	which	PRON
fcis-26723	58	15	can	can	AUX
fcis-26723	58	16	not	not	PART
fcis-26723	58	17	be	be	AUX
fcis-26723	58	18	directly	directly	ADV
fcis-26723	58	19	used	use	VERB
fcis-26723	58	20	as	as	ADP
fcis-26723	58	21	the	the	DET
fcis-26723	58	22	training	training	NOUN
fcis-26723	58	23	data	datum	NOUN
fcis-26723	58	24	set	set	VERB
fcis-26723	58	25	of	of	ADP
fcis-26723	58	26	neural	neural	ADJ
fcis-26723	58	27	networks	network	NOUN
fcis-26723	58	28	.	.	PUNCT
fcis-26723	59	1	it	it	PRON
fcis-26723	59	2	may	may	AUX
fcis-26723	59	3	cause	cause	VERB
fcis-26723	59	4	the	the	DET
fcis-26723	59	5	training	training	NOUN
fcis-26723	59	6	effect	effect	NOUN
fcis-26723	59	7	of	of	ADP
fcis-26723	59	8	neural	neural	ADJ
fcis-26723	59	9	network	network	NOUN
fcis-26723	59	10	is	be	AUX
fcis-26723	59	11	not	not	PART
fcis-26723	59	12	very	very	ADV
fcis-26723	59	13	ideal	ideal	ADJ
fcis-26723	59	14	.	.	PUNCT
fcis-26723	60	1	in	in	ADP
fcis-26723	60	2	order	order	NOUN
fcis-26723	60	3	to	to	PART
fcis-26723	60	4	improve	improve	VERB
fcis-26723	60	5	the	the	DET
fcis-26723	60	6	training	training	NOUN
fcis-26723	60	7	effect	effect	NOUN
fcis-26723	60	8	of	of	ADP
fcis-26723	60	9	the	the	DET
fcis-26723	60	10	neural	neural	ADJ
fcis-26723	60	11	network	network	NOUN
fcis-26723	60	12	,	,	PUNCT
fcis-26723	60	13	this	this	DET
fcis-26723	60	14	paper	paper	NOUN
fcis-26723	60	15	preprocesses	preprocesse	VERB
fcis-26723	60	16	the	the	DET
fcis-26723	60	17	original	original	ADJ
fcis-26723	60	18	data	data	NOUN
fcis-26723	60	19	samples	sample	NOUN
fcis-26723	60	20	before	before	ADP
fcis-26723	60	21	training	train	VERB
fcis-26723	60	22	the	the	DET
fcis-26723	60	23	neural	neural	ADJ
fcis-26723	60	24	network	network	NOUN
fcis-26723	60	25	.	.	PUNCT
fcis-26723	61	1	data	datum	NOUN
fcis-26723	61	2	preprocessing	preprocessing	NOUN
fcis-26723	61	3	is	be	AUX
fcis-26723	61	4	very	very	ADV
fcis-26723	61	5	important	important	ADJ
fcis-26723	61	6	when	when	SCONJ
fcis-26723	61	7	using	use	VERB
fcis-26723	61	8	neural	neural	ADJ
fcis-26723	61	9	network	network	NOUN
fcis-26723	61	10	to	to	PART
fcis-26723	61	11	predict	predict	VERB
fcis-26723	61	12	performance	performance	NOUN
fcis-26723	61	13	.	.	PUNCT
fcis-26723	62	1	the	the	DET
fcis-26723	62	2	following	follow	VERB
fcis-26723	62	3	is	be	AUX
fcis-26723	62	4	a	a	DET
fcis-26723	62	5	detailed	detailed	ADJ
fcis-26723	62	6	introduction	introduction	NOUN
fcis-26723	62	7	to	to	ADP
fcis-26723	62	8	the	the	DET
fcis-26723	62	9	preprocessing	preprocessing	NOUN
fcis-26723	62	10	of	of	ADP
fcis-26723	62	11	original	original	ADJ
fcis-26723	62	12	data	datum	NOUN
fcis-26723	62	13	.	.	PUNCT
fcis-26723	63	1	(	(	PUNCT
fcis-26723	63	2	1	1	X
fcis-26723	63	3	)	)	PUNCT
fcis-26723	63	4	reasons	reason	NOUN
fcis-26723	63	5	for	for	ADP
fcis-26723	63	6	data	datum	NOUN
fcis-26723	63	7	normalization	normalization	NOUN
fcis-26723	63	8	since	since	SCONJ
fcis-26723	63	9	each	each	DET
fcis-26723	63	10	sample	sample	NOUN
fcis-26723	63	11	has	have	VERB
fcis-26723	63	12	multiple	multiple	ADJ
fcis-26723	63	13	eigenvalues	eigenvalue	NOUN
fcis-26723	63	14	,	,	PUNCT
fcis-26723	63	15	that	that	ADV
fcis-26723	63	16	is	is	ADV
fcis-26723	63	17	,	,	PUNCT
fcis-26723	63	18	each	each	DET
fcis-26723	63	19	grade	grade	NOUN
fcis-26723	63	20	level	level	NOUN
fcis-26723	63	21	has	have	VERB
fcis-26723	63	22	multiple	multiple	ADJ
fcis-26723	63	23	eigenvectors	eigenvector	NOUN
fcis-26723	63	24	.	.	PUNCT
fcis-26723	64	1	the	the	DET
fcis-26723	64	2	number	number	NOUN
fcis-26723	64	3	of	of	ADP
fcis-26723	64	4	feature	feature	NOUN
fcis-26723	64	5	vectors	vector	NOUN
fcis-26723	64	6	in	in	ADP
fcis-26723	64	7	each	each	DET
fcis-26723	64	8	grade	grade	NOUN
fcis-26723	64	9	means	mean	VERB
fcis-26723	64	10	that	that	SCONJ
fcis-26723	64	11	the	the	DET
fcis-26723	64	12	bp	bp	PROPN
fcis-26723	64	13	neural	neural	PROPN
fcis-26723	64	14	network	network	NOUN
fcis-26723	64	15	prediction	prediction	NOUN
fcis-26723	64	16	model	model	NOUN
fcis-26723	64	17	has	have	VERB
fcis-26723	64	18	the	the	DET
fcis-26723	64	19	same	same	ADJ
fcis-26723	64	20	number	number	NOUN
fcis-26723	64	21	of	of	ADP
fcis-26723	64	22	input	input	NOUN
fcis-26723	64	23	nodes	node	NOUN
fcis-26723	64	24	.	.	PUNCT
fcis-26723	65	1	since	since	SCONJ
fcis-26723	65	2	these	these	DET
fcis-26723	65	3	feature	feature	NOUN
fcis-26723	65	4	vectors	vector	NOUN
fcis-26723	65	5	are	be	AUX
fcis-26723	65	6	quantities	quantity	NOUN
fcis-26723	65	7	with	with	ADP
fcis-26723	65	8	dimensions	dimension	NOUN
fcis-26723	65	9	,	,	PUNCT
fcis-26723	65	10	and	and	CCONJ
fcis-26723	65	11	the	the	DET
fcis-26723	65	12	magnitude	magnitude	NOUN
fcis-26723	65	13	of	of	ADP
fcis-26723	65	14	each	each	DET
fcis-26723	65	15	feature	feature	NOUN
fcis-26723	65	16	vector	vector	NOUN
fcis-26723	65	17	is	be	AUX
fcis-26723	65	18	different	different	ADJ
fcis-26723	65	19	,	,	PUNCT
fcis-26723	65	20	which	which	PRON
fcis-26723	65	21	is	be	AUX
fcis-26723	65	22	not	not	PART
fcis-26723	65	23	conducive	conducive	ADJ
fcis-26723	65	24	to	to	ADP
fcis-26723	65	25	the	the	DET
fcis-26723	65	26	comparison	comparison	NOUN
fcis-26723	65	27	between	between	ADP
fcis-26723	65	28	different	different	ADJ
fcis-26723	65	29	samples	sample	NOUN
fcis-26723	65	30	of	of	ADP
fcis-26723	65	31	the	the	DET
fcis-26723	65	32	same	same	ADJ
fcis-26723	65	33	feature	feature	NOUN
fcis-26723	65	34	vector	vector	NOUN
fcis-26723	65	35	.	.	PUNCT
fcis-26723	66	1	in	in	ADP
fcis-26723	66	2	order	order	NOUN
fcis-26723	66	3	to	to	PART
fcis-26723	66	4	make	make	VERB
fcis-26723	66	5	the	the	DET
fcis-26723	66	6	original	original	ADJ
fcis-26723	66	7	data	datum	NOUN
fcis-26723	66	8	sample	sample	NOUN
fcis-26723	66	9	easy	easy	ADJ
fcis-26723	66	10	to	to	PART
fcis-26723	66	11	be	be	AUX
fcis-26723	66	12	processed	process	VERB
fcis-26723	66	13	by	by	ADP
fcis-26723	66	14	the	the	DET
fcis-26723	66	15	prediction	prediction	NOUN
fcis-26723	66	16	system	system	NOUN
fcis-26723	66	17	,	,	PUNCT
fcis-26723	66	18	it	it	PRON
fcis-26723	66	19	is	be	AUX
fcis-26723	66	20	usually	usually	ADV
fcis-26723	66	21	105	105	NUM
fcis-26723	66	22	necessary	necessary	ADJ
fcis-26723	66	23	to	to	PART
fcis-26723	66	24	eliminate	eliminate	VERB
fcis-26723	66	25	the	the	DET
fcis-26723	66	26	dimensional	dimensional	ADJ
fcis-26723	66	27	interference	interference	NOUN
fcis-26723	66	28	and	and	CCONJ
fcis-26723	66	29	analyze	analyze	VERB
fcis-26723	66	30	the	the	DET
fcis-26723	66	31	data	data	NOUN
fcis-26723	66	32	sample	sample	NOUN
fcis-26723	66	33	only	only	ADV
fcis-26723	66	34	from	from	ADP
fcis-26723	66	35	the	the	DET
fcis-26723	66	36	quantitative	quantitative	ADJ
fcis-26723	66	37	level	level	NOUN
fcis-26723	66	38	of	of	ADP
fcis-26723	66	39	its	its	PRON
fcis-26723	66	40	features	feature	NOUN
fcis-26723	66	41	,	,	PUNCT
fcis-26723	66	42	which	which	PRON
fcis-26723	66	43	has	have	VERB
fcis-26723	66	44	the	the	DET
fcis-26723	66	45	advantage	advantage	NOUN
fcis-26723	66	46	of	of	ADP
fcis-26723	66	47	avoiding	avoid	VERB
fcis-26723	66	48	the	the	DET
fcis-26723	66	49	phenomenon	phenomenon	NOUN
fcis-26723	66	50	of	of	ADP
fcis-26723	66	51	neuron	neuron	PROPN
fcis-26723	66	52	output	output	NOUN
fcis-26723	66	53	saturation	saturation	NOUN
fcis-26723	66	54	caused	cause	VERB
fcis-26723	66	55	by	by	ADP
fcis-26723	66	56	too	too	ADV
fcis-26723	66	57	large	large	ADJ
fcis-26723	66	58	absolute	absolute	ADJ
fcis-26723	66	59	value	value	NOUN
fcis-26723	66	60	of	of	ADP
fcis-26723	66	61	the	the	DET
fcis-26723	66	62	input	input	NOUN
fcis-26723	66	63	data	datum	NOUN
fcis-26723	66	64	sample	sample	NOUN
fcis-26723	66	65	.	.	PUNCT
fcis-26723	67	1	after	after	ADP
fcis-26723	67	2	the	the	DET
fcis-26723	67	3	normalization	normalization	NOUN
fcis-26723	67	4	of	of	ADP
fcis-26723	67	5	the	the	DET
fcis-26723	67	6	original	original	ADJ
fcis-26723	67	7	data	datum	NOUN
fcis-26723	67	8	sample	sample	NOUN
fcis-26723	67	9	,	,	PUNCT
fcis-26723	67	10	each	each	DET
fcis-26723	67	11	data	datum	NOUN
fcis-26723	67	12	index	index	NOUN
fcis-26723	67	13	reaches	reach	VERB
fcis-26723	67	14	the	the	DET
fcis-26723	67	15	same	same	ADJ
fcis-26723	67	16	order	order	NOUN
fcis-26723	67	17	of	of	ADP
fcis-26723	67	18	magnitude	magnitude	NOUN
fcis-26723	67	19	.	.	PUNCT
fcis-26723	68	1	comparative	comparative	ADJ
fcis-26723	68	2	evaluation	evaluation	NOUN
fcis-26723	68	3	can	can	AUX
fcis-26723	68	4	be	be	AUX
fcis-26723	68	5	performed	perform	VERB
fcis-26723	68	6	.	.	PUNCT
fcis-26723	69	1	(	(	PUNCT
fcis-26723	69	2	2	2	X
fcis-26723	69	3	)	)	PUNCT
fcis-26723	69	4	data	datum	NOUN
fcis-26723	69	5	normalization	normalization	NOUN
fcis-26723	69	6	method	method	VERB
fcis-26723	69	7	the	the	DET
fcis-26723	69	8	data	data	NOUN
fcis-26723	69	9	normalization	normalization	NOUN
fcis-26723	69	10	method	method	NOUN
fcis-26723	69	11	used	use	VERB
fcis-26723	69	12	in	in	ADP
fcis-26723	69	13	this	this	DET
fcis-26723	69	14	paper	paper	NOUN
fcis-26723	69	15	is	be	AUX
fcis-26723	69	16	minmax	minmax	ADJ
fcis-26723	69	17	normalization	normalization	NOUN
fcis-26723	69	18	method	method	NOUN
fcis-26723	69	19	.	.	PUNCT
fcis-26723	70	1	min	min	ADJ
fcis-26723	70	2	-	-	PUNCT
fcis-26723	70	3	max	max	PROPN
fcis-26723	70	4	normalization	normalization	NOUN
fcis-26723	70	5	,	,	PUNCT
fcis-26723	70	6	also	also	ADV
fcis-26723	70	7	known	know	VERB
fcis-26723	70	8	as	as	ADP
fcis-26723	70	9	deviation	deviation	NOUN
fcis-26723	70	10	normalization	normalization	NOUN
fcis-26723	70	11	,	,	PUNCT
fcis-26723	70	12	is	be	AUX
fcis-26723	70	13	based	base	VERB
fcis-26723	70	14	on	on	ADP
fcis-26723	70	15	a	a	DET
fcis-26723	70	16	linear	linear	ADJ
fcis-26723	70	17	transformation	transformation	NOUN
fcis-26723	70	18	of	of	ADP
fcis-26723	70	19	raw	raw	ADJ
fcis-26723	70	20	data	datum	NOUN
fcis-26723	70	21	with	with	ADP
fcis-26723	70	22	a	a	DET
fcis-26723	70	23	very	very	ADV
fcis-26723	70	24	large	large	ADJ
fcis-26723	70	25	range	range	NOUN
fcis-26723	70	26	of	of	ADP
fcis-26723	70	27	values	value	NOUN
fcis-26723	70	28	,	,	PUNCT
fcis-26723	70	29	so	so	SCONJ
fcis-26723	70	30	that	that	SCONJ
fcis-26723	70	31	the	the	DET
fcis-26723	70	32	resulting	result	VERB
fcis-26723	70	33	values	value	NOUN
fcis-26723	70	34	are	be	AUX
fcis-26723	70	35	mapped	map	VERB
fcis-26723	70	36	between	between	ADP
fcis-26723	70	37	[	[	X
fcis-26723	70	38	0,1	0,1	NUM
fcis-26723	70	39	]	]	PUNCT
fcis-26723	70	40	.	.	PUNCT
fcis-26723	71	1	for	for	ADP
fcis-26723	71	2	the	the	DET
fcis-26723	71	3	conversion	conversion	NOUN
fcis-26723	71	4	function	function	NOUN
fcis-26723	71	5	,	,	PUNCT
fcis-26723	71	6	see	see	VERB
fcis-26723	71	7	formula	formula	NOUN
fcis-26723	71	8	(	(	PUNCT
fcis-26723	71	9	1	1	NUM
fcis-26723	71	10	)	)	PUNCT
fcis-26723	71	11	.	.	PUNCT
fcis-26723	72	1	(	(	PUNCT
fcis-26723	72	2	1	1	X
fcis-26723	72	3	)	)	PUNCT
fcis-26723	72	4	formula	formula	NOUN
fcis-26723	72	5	:	:	PUNCT
fcis-26723	72	6	maxvalue	maxvalue	NOUN
fcis-26723	72	7	-	-	PUNCT
fcis-26723	72	8	represents	represent	VERB
fcis-26723	72	9	the	the	DET
fcis-26723	72	10	maximum	maximum	ADJ
fcis-26723	72	11	value	value	NOUN
fcis-26723	72	12	of	of	ADP
fcis-26723	72	13	the	the	DET
fcis-26723	72	14	sample	sample	NOUN
fcis-26723	72	15	data	datum	NOUN
fcis-26723	72	16	;	;	PUNCT
fcis-26723	72	17	minvalue	minvalue	NOUN
fcis-26723	72	18	-	-	PUNCT
fcis-26723	72	19	represents	represent	VERB
fcis-26723	72	20	the	the	DET
fcis-26723	72	21	minimum	minimum	ADJ
fcis-26723	72	22	value	value	NOUN
fcis-26723	72	23	of	of	ADP
fcis-26723	72	24	the	the	DET
fcis-26723	72	25	sample	sample	NOUN
fcis-26723	72	26	data	datum	NOUN
fcis-26723	72	27	;	;	PUNCT
fcis-26723	72	28	x	x	X
fcis-26723	72	29	-	-	PUNCT
fcis-26723	72	30	represents	represent	VERB
fcis-26723	72	31	the	the	DET
fcis-26723	72	32	original	original	ADJ
fcis-26723	72	33	data	datum	NOUN
fcis-26723	72	34	of	of	ADP
fcis-26723	72	35	the	the	DET
fcis-26723	72	36	sample	sample	NOUN
fcis-26723	72	37	;	;	PUNCT
fcis-26723	72	38	y	y	X
fcis-26723	72	39	-	-	PUNCT
fcis-26723	72	40	indicates	indicate	VERB
fcis-26723	72	41	normalized	normalize	VERB
fcis-26723	72	42	data	datum	NOUN
fcis-26723	72	43	.	.	PUNCT
fcis-26723	73	1	min	min	PROPN
fcis-26723	73	2	-	-	ADJ
fcis-26723	73	3	max	max	ADJ
fcis-26723	73	4	one	one	NUM
fcis-26723	73	5	disadvantage	disadvantage	NOUN
fcis-26723	73	6	of	of	ADP
fcis-26723	73	7	the	the	DET
fcis-26723	73	8	normalization	normalization	NOUN
fcis-26723	73	9	method	method	NOUN
fcis-26723	73	10	is	be	AUX
fcis-26723	73	11	that	that	SCONJ
fcis-26723	73	12	whenever	whenever	SCONJ
fcis-26723	73	13	new	new	ADJ
fcis-26723	73	14	data	data	NOUN
fcis-26723	73	15	samples	sample	NOUN
fcis-26723	73	16	are	be	AUX
fcis-26723	73	17	added	add	VERB
fcis-26723	73	18	,	,	PUNCT
fcis-26723	73	19	the	the	DET
fcis-26723	73	20	sum	sum	NOUN
fcis-26723	73	21	may	may	AUX
fcis-26723	73	22	change	change	VERB
fcis-26723	73	23	,	,	PUNCT
fcis-26723	73	24	and	and	CCONJ
fcis-26723	73	25	then	then	ADV
fcis-26723	73	26	the	the	DET
fcis-26723	73	27	sum	sum	NOUN
fcis-26723	73	28	must	must	AUX
fcis-26723	73	29	be	be	AUX
fcis-26723	73	30	redefined	redefine	VERB
fcis-26723	73	31	.	.	PUNCT
fcis-26723	74	1	however	however	ADV
fcis-26723	74	2	,	,	PUNCT
fcis-26723	74	3	this	this	DET
fcis-26723	74	4	shortcoming	shortcoming	NOUN
fcis-26723	74	5	has	have	VERB
fcis-26723	74	6	no	no	DET
fcis-26723	74	7	impact	impact	NOUN
fcis-26723	74	8	on	on	ADP
fcis-26723	74	9	this	this	DET
fcis-26723	74	10	paper	paper	NOUN
fcis-26723	74	11	,	,	PUNCT
fcis-26723	74	12	because	because	SCONJ
fcis-26723	74	13	the	the	DET
fcis-26723	74	14	data	data	NOUN
fcis-26723	74	15	sets	set	NOUN
fcis-26723	74	16	used	use	VERB
fcis-26723	74	17	for	for	ADP
fcis-26723	74	18	bp	bp	PROPN
fcis-26723	74	19	neural	neural	ADJ
fcis-26723	74	20	network	network	NOUN
fcis-26723	74	21	training	training	NOUN
fcis-26723	74	22	in	in	ADP
fcis-26723	74	23	this	this	DET
fcis-26723	74	24	paper	paper	NOUN
fcis-26723	74	25	have	have	AUX
fcis-26723	74	26	been	be	AUX
fcis-26723	74	27	determined	determine	VERB
fcis-26723	74	28	in	in	ADP
fcis-26723	74	29	advance	advance	NOUN
fcis-26723	74	30	,	,	PUNCT
fcis-26723	74	31	and	and	CCONJ
fcis-26723	74	32	no	no	DET
fcis-26723	74	33	new	new	ADJ
fcis-26723	74	34	data	datum	NOUN
fcis-26723	74	35	has	have	AUX
fcis-26723	74	36	been	be	AUX
fcis-26723	74	37	added	add	VERB
fcis-26723	74	38	,	,	PUNCT
fcis-26723	74	39	so	so	CCONJ
fcis-26723	74	40	it	it	PRON
fcis-26723	74	41	is	be	AUX
fcis-26723	74	42	completely	completely	ADV
fcis-26723	74	43	feasible	feasible	ADJ
fcis-26723	74	44	in	in	ADP
fcis-26723	74	45	theory	theory	NOUN
fcis-26723	74	46	to	to	PART
fcis-26723	74	47	adopt	adopt	VERB
fcis-26723	74	48	normalization	normalization	NOUN
fcis-26723	74	49	method	method	NOUN
fcis-26723	74	50	to	to	PART
fcis-26723	74	51	normalize	normalize	VERB
fcis-26723	74	52	the	the	DET
fcis-26723	74	53	collected	collect	VERB
fcis-26723	74	54	data	data	NOUN
fcis-26723	74	55	sets	set	NOUN
fcis-26723	74	56	.	.	PUNCT
fcis-26723	75	1	(	(	PUNCT
fcis-26723	75	2	3	3	X
fcis-26723	75	3	)	)	PUNCT
fcis-26723	75	4	data	datum	NOUN
fcis-26723	75	5	normalization	normalization	NOUN
fcis-26723	75	6	results	result	VERB
fcis-26723	75	7	the	the	DET
fcis-26723	75	8	original	original	ADJ
fcis-26723	75	9	dataset	dataset	NOUN
fcis-26723	75	10	used	use	VERB
fcis-26723	75	11	for	for	ADP
fcis-26723	75	12	grade	grade	NOUN
fcis-26723	75	13	prediction	prediction	NOUN
fcis-26723	75	14	in	in	ADP
fcis-26723	75	15	this	this	DET
fcis-26723	75	16	paper	paper	NOUN
fcis-26723	75	17	has	have	VERB
fcis-26723	75	18	4	4	NUM
fcis-26723	75	19	grades	grade	NOUN
fcis-26723	75	20	,	,	PUNCT
fcis-26723	75	21	each	each	PRON
fcis-26723	75	22	with	with	ADP
fcis-26723	75	23	7	7	NUM
fcis-26723	75	24	feature	feature	NOUN
fcis-26723	75	25	vectors	vector	NOUN
fcis-26723	75	26	.	.	PUNCT
fcis-26723	76	1	in	in	ADP
fcis-26723	76	2	the	the	DET
fcis-26723	76	3	original	original	ADJ
fcis-26723	76	4	data	datum	NOUN
fcis-26723	76	5	set	set	VERB
fcis-26723	76	6	,	,	PUNCT
fcis-26723	76	7	the	the	DET
fcis-26723	76	8	data	datum	NOUN
fcis-26723	76	9	are	be	AUX
fcis-26723	76	10	all	all	ADV
fcis-26723	76	11	dimensional	dimensional	ADJ
fcis-26723	76	12	,	,	PUNCT
fcis-26723	76	13	and	and	CCONJ
fcis-26723	76	14	the	the	DET
fcis-26723	76	15	value	value	NOUN
fcis-26723	76	16	difference	difference	NOUN
fcis-26723	76	17	between	between	ADP
fcis-26723	76	18	different	different	ADJ
fcis-26723	76	19	feature	feature	NOUN
fcis-26723	76	20	vectors	vector	NOUN
fcis-26723	76	21	of	of	ADP
fcis-26723	76	22	the	the	DET
fcis-26723	76	23	same	same	ADJ
fcis-26723	76	24	score	score	NOUN
fcis-26723	76	25	level	level	NOUN
fcis-26723	76	26	and	and	CCONJ
fcis-26723	76	27	the	the	DET
fcis-26723	76	28	same	same	ADJ
fcis-26723	76	29	feature	feature	NOUN
fcis-26723	76	30	vectors	vector	NOUN
fcis-26723	76	31	of	of	ADP
fcis-26723	76	32	different	different	ADJ
fcis-26723	76	33	score	score	NOUN
fcis-26723	76	34	level	level	NOUN
fcis-26723	76	35	is	be	AUX
fcis-26723	76	36	very	very	ADV
fcis-26723	76	37	large	large	ADJ
fcis-26723	76	38	.	.	PUNCT
fcis-26723	77	1	in	in	ADP
fcis-26723	77	2	order	order	NOUN
fcis-26723	77	3	to	to	PART
fcis-26723	77	4	see	see	VERB
fcis-26723	77	5	the	the	DET
fcis-26723	77	6	value	value	NOUN
fcis-26723	77	7	range	range	NOUN
fcis-26723	77	8	of	of	ADP
fcis-26723	77	9	the	the	DET
fcis-26723	77	10	original	original	ADJ
fcis-26723	77	11	data	datum	NOUN
fcis-26723	77	12	set	set	VERB
fcis-26723	77	13	more	more	ADV
fcis-26723	77	14	directly	directly	ADV
fcis-26723	77	15	,	,	PUNCT
fcis-26723	77	16	the	the	DET
fcis-26723	77	17	value	value	NOUN
fcis-26723	77	18	distribution	distribution	NOUN
fcis-26723	77	19	of	of	ADP
fcis-26723	77	20	the	the	DET
fcis-26723	77	21	data	datum	NOUN
fcis-26723	77	22	after	after	SCONJ
fcis-26723	77	23	normalization	normalization	NOUN
fcis-26723	77	24	is	be	AUX
fcis-26723	77	25	shown	show	VERB
fcis-26723	77	26	in	in	ADP
fcis-26723	77	27	figure	figure	NOUN
fcis-26723	77	28	1	1	NUM
fcis-26723	77	29	.	.	PUNCT
fcis-26723	78	1	before	before	SCONJ
fcis-26723	78	2	the	the	DET
fcis-26723	78	3	original	original	ADJ
fcis-26723	78	4	data	datum	NOUN
fcis-26723	78	5	was	be	AUX
fcis-26723	78	6	normalized	normalize	VERB
fcis-26723	78	7	,	,	PUNCT
fcis-26723	78	8	the	the	DET
fcis-26723	78	9	data	data	NOUN
fcis-26723	78	10	size	size	NOUN
fcis-26723	78	11	distribution	distribution	NOUN
fcis-26723	78	12	was	be	AUX
fcis-26723	78	13	very	very	ADV
fcis-26723	78	14	wide	wide	ADJ
fcis-26723	78	15	,	,	PUNCT
fcis-26723	78	16	ranging	range	VERB
fcis-26723	78	17	from	from	ADP
fcis-26723	78	18	0	0	NUM
fcis-26723	78	19	to	to	ADP
fcis-26723	78	20	100	100	NUM
fcis-26723	78	21	,	,	PUNCT
fcis-26723	78	22	that	that	ADV
fcis-26723	78	23	is	is	ADV
fcis-26723	78	24	,	,	PUNCT
fcis-26723	78	25	the	the	DET
fcis-26723	78	26	data	datum	NOUN
fcis-26723	78	27	was	be	AUX
fcis-26723	78	28	distributed	distribute	VERB
fcis-26723	78	29	in	in	ADP
fcis-26723	78	30	the	the	DET
fcis-26723	78	31	interval	interval	NOUN
fcis-26723	78	32	[	[	X
fcis-26723	78	33	0,100	0,100	NUM
fcis-26723	78	34	]	]	PUNCT
fcis-26723	78	35	.	.	PUNCT
fcis-26723	79	1	such	such	ADJ
fcis-26723	79	2	data	data	NOUN
fcis-26723	79	3	value	value	NOUN
fcis-26723	79	4	range	range	NOUN
fcis-26723	79	5	seriously	seriously	ADV
fcis-26723	79	6	affected	affect	VERB
fcis-26723	79	7	the	the	DET
fcis-26723	79	8	training	training	NOUN
fcis-26723	79	9	effect	effect	NOUN
fcis-26723	79	10	of	of	ADP
fcis-26723	79	11	bp	bp	PROPN
fcis-26723	79	12	neural	neural	ADJ
fcis-26723	79	13	network	network	NOUN
fcis-26723	79	14	.	.	PUNCT
fcis-26723	80	1	the	the	DET
fcis-26723	80	2	score	score	NOUN
fcis-26723	80	3	prediction	prediction	NOUN
fcis-26723	80	4	system	system	NOUN
fcis-26723	80	5	in	in	ADP
fcis-26723	80	6	this	this	DET
fcis-26723	80	7	paper	paper	NOUN
fcis-26723	80	8	is	be	AUX
fcis-26723	80	9	based	base	VERB
fcis-26723	80	10	on	on	ADP
fcis-26723	80	11	bp	bp	PROPN
fcis-26723	80	12	neural	neural	ADJ
fcis-26723	80	13	network	network	NOUN
fcis-26723	80	14	,	,	PUNCT
fcis-26723	80	15	if	if	SCONJ
fcis-26723	80	16	the	the	DET
fcis-26723	80	17	training	training	NOUN
fcis-26723	80	18	effect	effect	NOUN
fcis-26723	80	19	of	of	ADP
fcis-26723	80	20	neural	neural	ADJ
fcis-26723	80	21	network	network	NOUN
fcis-26723	80	22	is	be	AUX
fcis-26723	80	23	not	not	PART
fcis-26723	80	24	good	good	ADJ
fcis-26723	80	25	,	,	PUNCT
fcis-26723	80	26	as	as	ADP
fcis-26723	80	27	a	a	DET
fcis-26723	80	28	result	result	NOUN
fcis-26723	80	29	,	,	PUNCT
fcis-26723	80	30	the	the	DET
fcis-26723	80	31	accuracy	accuracy	NOUN
fcis-26723	80	32	of	of	ADP
fcis-26723	80	33	the	the	DET
fcis-26723	80	34	prediction	prediction	NOUN
fcis-26723	80	35	results	result	NOUN
fcis-26723	80	36	of	of	ADP
fcis-26723	80	37	the	the	DET
fcis-26723	80	38	performance	performance	NOUN
fcis-26723	80	39	prediction	prediction	NOUN
fcis-26723	80	40	system	system	NOUN
fcis-26723	80	41	is	be	AUX
fcis-26723	80	42	not	not	PART
fcis-26723	80	43	very	very	ADV
fcis-26723	80	44	high	high	ADJ
fcis-26723	80	45	,	,	PUNCT
fcis-26723	80	46	so	so	CCONJ
fcis-26723	80	47	the	the	DET
fcis-26723	80	48	original	original	ADJ
fcis-26723	80	49	data	datum	NOUN
fcis-26723	80	50	set	set	VERB
fcis-26723	80	51	must	must	AUX
fcis-26723	80	52	be	be	AUX
fcis-26723	80	53	normalized	normalize	VERB
fcis-26723	80	54	before	before	SCONJ
fcis-26723	80	55	it	it	PRON
fcis-26723	80	56	can	can	AUX
fcis-26723	80	57	be	be	AUX
fcis-26723	80	58	used	use	VERB
fcis-26723	80	59	to	to	PART
fcis-26723	80	60	train	train	VERB
fcis-26723	80	61	the	the	DET
fcis-26723	80	62	neural	neural	ADJ
fcis-26723	80	63	network	network	NOUN
fcis-26723	80	64	.	.	PUNCT
fcis-26723	81	1	fig	fig	NOUN
fcis-26723	81	2	1	1	NUM
fcis-26723	81	3	.	.	PUNCT
fcis-26723	81	4	data	datum	NOUN
fcis-26723	81	5	value	value	NOUN
fcis-26723	81	6	distribution	distribution	NOUN
fcis-26723	81	7	after	after	ADP
fcis-26723	81	8	normalization	normalization	NOUN
fcis-26723	81	9	3.2.3	3.2.3	NUM
fcis-26723	81	10	.	.	PUNCT
fcis-26723	82	1	bp	bp	PROPN
fcis-26723	82	2	neural	neural	ADJ
fcis-26723	82	3	network	network	NOUN
fcis-26723	82	4	structure	structure	NOUN
fcis-26723	82	5	the	the	DET
fcis-26723	82	6	classic	classic	ADJ
fcis-26723	82	7	bp	bp	PROPN
fcis-26723	82	8	network	network	NOUN
fcis-26723	82	9	is	be	AUX
fcis-26723	82	10	a	a	DET
fcis-26723	82	11	three	three	NUM
fcis-26723	82	12	-	-	PUNCT
fcis-26723	82	13	layer	layer	NOUN
fcis-26723	82	14	network	network	NOUN
fcis-26723	82	15	structure	structure	NOUN
fcis-26723	82	16	,	,	PUNCT
fcis-26723	82	17	namely	namely	ADV
fcis-26723	82	18	the	the	DET
fcis-26723	82	19	input	input	NOUN
fcis-26723	82	20	layer	layer	NOUN
fcis-26723	82	21	,	,	PUNCT
fcis-26723	82	22	the	the	DET
fcis-26723	82	23	hidden	hidden	ADJ
fcis-26723	82	24	layer	layer	NOUN
fcis-26723	82	25	and	and	CCONJ
fcis-26723	82	26	the	the	DET
fcis-26723	82	27	output	output	NOUN
fcis-26723	82	28	layer	layer	NOUN
fcis-26723	82	29	.	.	PUNCT
fcis-26723	83	1	the	the	DET
fcis-26723	83	2	input	input	NOUN
fcis-26723	83	3	layer	layer	NOUN
fcis-26723	83	4	is	be	AUX
fcis-26723	83	5	responsible	responsible	ADJ
fcis-26723	83	6	for	for	ADP
fcis-26723	83	7	data	datum	NOUN
fcis-26723	83	8	input	input	NOUN
fcis-26723	83	9	and	and	CCONJ
fcis-26723	83	10	does	do	AUX
fcis-26723	83	11	not	not	PART
fcis-26723	83	12	process	process	VERB
fcis-26723	83	13	the	the	DET
fcis-26723	83	14	data	datum	NOUN
fcis-26723	83	15	,	,	PUNCT
fcis-26723	83	16	while	while	SCONJ
fcis-26723	83	17	the	the	DET
fcis-26723	83	18	hidden	hidden	ADJ
fcis-26723	83	19	layer	layer	NOUN
fcis-26723	83	20	may	may	AUX
fcis-26723	83	21	be	be	AUX
fcis-26723	83	22	one	one	NUM
fcis-26723	83	23	or	or	CCONJ
fcis-26723	83	24	more	more	ADJ
fcis-26723	83	25	layers	layer	NOUN
fcis-26723	83	26	,	,	PUNCT
fcis-26723	83	27	which	which	PRON
fcis-26723	83	28	is	be	AUX
fcis-26723	83	29	responsible	responsible	ADJ
fcis-26723	83	30	for	for	ADP
fcis-26723	83	31	mapping	mapping	NOUN
fcis-26723	83	32	and	and	CCONJ
fcis-26723	83	33	transforming	transform	VERB
fcis-26723	83	34	the	the	DET
fcis-26723	83	35	data	datum	NOUN
fcis-26723	83	36	,	,	PUNCT
fcis-26723	83	37	and	and	CCONJ
fcis-26723	83	38	the	the	DET
fcis-26723	83	39	output	output	NOUN
fcis-26723	83	40	is	be	AUX
fcis-26723	83	41	responsible	responsible	ADJ
fcis-26723	83	42	for	for	ADP
fcis-26723	83	43	classifying	classify	VERB
fcis-26723	83	44	and	and	CCONJ
fcis-26723	83	45	output	output	NOUN
fcis-26723	83	46	.	.	PUNCT
fcis-26723	84	1	in	in	ADP
fcis-26723	84	2	terms	term	NOUN
fcis-26723	84	3	of	of	ADP
fcis-26723	84	4	network	network	NOUN
fcis-26723	84	5	structure	structure	NOUN
fcis-26723	84	6	,	,	PUNCT
fcis-26723	84	7	bp	bp	PROPN
fcis-26723	84	8	network	network	PROPN
fcis-26723	84	9	is	be	AUX
fcis-26723	84	10	no	no	DET
fcis-26723	84	11	connection	connection	NOUN
fcis-26723	84	12	within	within	ADP
fcis-26723	84	13	the	the	DET
fcis-26723	84	14	layer	layer	NOUN
fcis-26723	84	15	,	,	PUNCT
fcis-26723	84	16	and	and	CCONJ
fcis-26723	84	17	the	the	DET
fcis-26723	84	18	layer	layer	NOUN
fcis-26723	84	19	is	be	AUX
fcis-26723	84	20	fully	fully	ADV
fcis-26723	84	21	connected	connect	VERB
fcis-26723	84	22	.	.	PUNCT
fcis-26723	85	1	although	although	SCONJ
fcis-26723	85	2	multiple	multiple	ADJ
fcis-26723	85	3	hidden	hidden	ADJ
fcis-26723	85	4	layers	layer	NOUN
fcis-26723	85	5	can	can	AUX
fcis-26723	85	6	reduce	reduce	VERB
fcis-26723	85	7	the	the	DET
fcis-26723	85	8	error	error	NOUN
fcis-26723	85	9	between	between	ADP
fcis-26723	85	10	the	the	DET
fcis-26723	85	11	output	output	NOUN
fcis-26723	85	12	result	result	NOUN
fcis-26723	85	13	and	and	CCONJ
fcis-26723	85	14	the	the	DET
fcis-26723	85	15	expected	expect	VERB
fcis-26723	85	16	result	result	NOUN
fcis-26723	85	17	,	,	PUNCT
fcis-26723	85	18	the	the	DET
fcis-26723	85	19	structure	structure	NOUN
fcis-26723	85	20	of	of	ADP
fcis-26723	85	21	the	the	DET
fcis-26723	85	22	network	network	NOUN
fcis-26723	85	23	is	be	AUX
fcis-26723	85	24	very	very	ADV
fcis-26723	85	25	complex	complex	ADJ
fcis-26723	85	26	,	,	PUNCT
fcis-26723	85	27	which	which	PRON
fcis-26723	85	28	increases	increase	VERB
fcis-26723	85	29	the	the	DET
fcis-26723	85	30	running	running	NOUN
fcis-26723	85	31	time	time	NOUN
fcis-26723	85	32	of	of	ADP
fcis-26723	85	33	the	the	DET
fcis-26723	85	34	network	network	NOUN
fcis-26723	85	35	and	and	CCONJ
fcis-26723	85	36	is	be	AUX
fcis-26723	85	37	prone	prone	ADJ
fcis-26723	85	38	to	to	ADP
fcis-26723	85	39	overfitting	overfitte	VERB
fcis-26723	85	40	problems	problem	NOUN
fcis-26723	85	41	.	.	PUNCT
fcis-26723	86	1	however	however	ADV
fcis-26723	86	2	,	,	PUNCT
fcis-26723	86	3	due	due	ADP
fcis-26723	86	4	to	to	ADP
fcis-26723	86	5	the	the	DET
fcis-26723	86	6	large	large	ADJ
fcis-26723	86	7	reduction	reduction	NOUN
fcis-26723	86	8	of	of	ADP
fcis-26723	86	9	parameters	parameter	NOUN
fcis-26723	86	10	,	,	PUNCT
fcis-26723	86	11	the	the	DET
fcis-26723	86	12	single	single	ADJ
fcis-26723	86	13	hidden	hidden	ADJ
fcis-26723	86	14	layer	layer	NOUN
fcis-26723	86	15	may	may	AUX
fcis-26723	86	16	lead	lead	VERB
fcis-26723	86	17	to	to	ADP
fcis-26723	86	18	low	low	ADJ
fcis-26723	86	19	training	training	NOUN
fcis-26723	86	20	accuracy	accuracy	NOUN
fcis-26723	86	21	,	,	PUNCT
fcis-26723	86	22	weak	weak	ADJ
fcis-26723	86	23	network	network	NOUN
fcis-26723	86	24	generalization	generalization	NOUN
fcis-26723	86	25	ability	ability	NOUN
fcis-26723	86	26	and	and	CCONJ
fcis-26723	86	27	underfitting	underfitting	NOUN
fcis-26723	86	28	problems	problem	NOUN
fcis-26723	86	29	,	,	PUNCT
fcis-26723	86	30	so	so	ADV
fcis-26723	86	31	specific	specific	ADJ
fcis-26723	86	32	problems	problem	NOUN
fcis-26723	86	33	need	need	VERB
fcis-26723	86	34	to	to	PART
fcis-26723	86	35	be	be	AUX
fcis-26723	86	36	selected	select	VERB
fcis-26723	86	37	for	for	ADP
fcis-26723	86	38	specific	specific	ADJ
fcis-26723	86	39	network	network	NOUN
fcis-26723	86	40	structures	structure	NOUN
fcis-26723	86	41	.	.	PUNCT
fcis-26723	87	1	fig	fig	NOUN
fcis-26723	87	2	2	2	NUM
fcis-26723	87	3	.	.	PUNCT
fcis-26723	87	4	schematic	schematic	ADJ
fcis-26723	87	5	diagram	diagram	NOUN
fcis-26723	87	6	of	of	ADP
fcis-26723	87	7	three	three	NUM
fcis-26723	87	8	-	-	PUNCT
fcis-26723	87	9	layer	layer	NOUN
fcis-26723	87	10	topology	topology	NOUN
fcis-26723	87	11	of	of	ADP
fcis-26723	87	12	bp	bp	PROPN
fcis-26723	87	13	neural	neural	ADJ
fcis-26723	87	14	network	network	NOUN
fcis-26723	87	15	when	when	SCONJ
fcis-26723	87	16	solving	solve	VERB
fcis-26723	87	17	real	real	ADJ
fcis-26723	87	18	problems	problem	NOUN
fcis-26723	87	19	,	,	PUNCT
fcis-26723	87	20	the	the	DET
fcis-26723	87	21	number	number	NOUN
fcis-26723	87	22	of	of	ADP
fcis-26723	87	23	hidden	hide	VERB
fcis-26723	87	24	layer	layer	NOUN
fcis-26723	87	25	nodes	node	NOUN
fcis-26723	87	26	of	of	ADP
fcis-26723	87	27	bp	bp	PROPN
fcis-26723	87	28	neural	neural	ADJ
fcis-26723	87	29	network	network	NOUN
fcis-26723	87	30	is	be	AUX
fcis-26723	87	31	generally	generally	ADV
fcis-26723	87	32	set	set	VERB
fcis-26723	87	33	according	accord	VERB
fcis-26723	87	34	to	to	ADP
fcis-26723	87	35	106	106	NUM
fcis-26723	87	36	experience	experience	NOUN
fcis-26723	87	37	.	.	PUNCT
fcis-26723	88	1	a	a	DET
fcis-26723	88	2	three	three	NUM
fcis-26723	88	3	-	-	PUNCT
fcis-26723	88	4	layer	layer	NOUN
fcis-26723	88	5	neural	neural	ADJ
fcis-26723	88	6	network	network	NOUN
fcis-26723	88	7	can	can	AUX
fcis-26723	88	8	approximate	approximate	VERB
fcis-26723	88	9	the	the	DET
fcis-26723	88	10	function	function	NOUN
fcis-26723	88	11	of	of	ADP
fcis-26723	88	12	any	any	DET
fcis-26723	88	13	function	function	NOUN
fcis-26723	88	14	with	with	ADP
fcis-26723	88	15	any	any	DET
fcis-26723	88	16	accuracy	accuracy	NOUN
fcis-26723	88	17	,	,	PUNCT
fcis-26723	88	18	just	just	ADV
fcis-26723	88	19	like	like	ADP
fcis-26723	88	20	a	a	DET
fcis-26723	88	21	complicated	complicated	ADJ
fcis-26723	88	22	function	function	NOUN
fcis-26723	88	23	model	model	NOUN
fcis-26723	88	24	,	,	PUNCT
fcis-26723	88	25	then	then	ADV
fcis-26723	88	26	any	any	DET
fcis-26723	88	27	sub	sub	ADJ
fcis-26723	88	28	-	-	ADJ
fcis-26723	88	29	function	function	NOUN
fcis-26723	88	30	will	will	AUX
fcis-26723	88	31	be	be	AUX
fcis-26723	88	32	included	include	VERB
fcis-26723	88	33	.	.	PUNCT
fcis-26723	89	1	therefore	therefore	ADV
fcis-26723	89	2	,	,	PUNCT
fcis-26723	89	3	the	the	DET
fcis-26723	89	4	three	three	NUM
fcis-26723	89	5	-	-	PUNCT
fcis-26723	89	6	layer	layer	NOUN
fcis-26723	89	7	bp	bp	PROPN
fcis-26723	89	8	neural	neural	ADJ
fcis-26723	89	9	network	network	NOUN
fcis-26723	89	10	can	can	AUX
fcis-26723	89	11	effectively	effectively	ADV
fcis-26723	89	12	solve	solve	VERB
fcis-26723	89	13	most	most	ADJ
fcis-26723	89	14	problems	problem	NOUN
fcis-26723	89	15	in	in	ADP
fcis-26723	89	16	practical	practical	ADJ
fcis-26723	89	17	applications	application	NOUN
fcis-26723	89	18	.	.	PUNCT
fcis-26723	90	1	based	base	VERB
fcis-26723	90	2	on	on	ADP
fcis-26723	90	3	the	the	DET
fcis-26723	90	4	above	above	ADJ
fcis-26723	90	5	content	content	NOUN
fcis-26723	90	6	,	,	PUNCT
fcis-26723	90	7	the	the	DET
fcis-26723	90	8	fault	fault	NOUN
fcis-26723	90	9	diagnosis	diagnosis	NOUN
fcis-26723	90	10	model	model	NOUN
fcis-26723	90	11	in	in	ADP
fcis-26723	90	12	this	this	DET
fcis-26723	90	13	paper	paper	NOUN
fcis-26723	90	14	is	be	AUX
fcis-26723	90	15	also	also	ADV
fcis-26723	90	16	based	base	VERB
fcis-26723	90	17	on	on	ADP
fcis-26723	90	18	three	three	NUM
fcis-26723	90	19	-	-	PUNCT
fcis-26723	90	20	layer	layer	NOUN
fcis-26723	90	21	bp	bp	PROPN
fcis-26723	90	22	neural	neural	ADJ
fcis-26723	90	23	network	network	NOUN
fcis-26723	90	24	,	,	PUNCT
fcis-26723	90	25	which	which	PRON
fcis-26723	90	26	is	be	AUX
fcis-26723	90	27	composed	compose	VERB
fcis-26723	90	28	of	of	ADP
fcis-26723	90	29	an	an	DET
fcis-26723	90	30	input	input	NOUN
fcis-26723	90	31	layer	layer	NOUN
fcis-26723	90	32	,	,	PUNCT
fcis-26723	90	33	a	a	DET
fcis-26723	90	34	hidden	hidden	ADJ
fcis-26723	90	35	layer	layer	NOUN
fcis-26723	90	36	and	and	CCONJ
fcis-26723	90	37	an	an	DET
fcis-26723	90	38	output	output	NOUN
fcis-26723	90	39	layer	layer	NOUN
fcis-26723	90	40	.	.	PUNCT
fcis-26723	91	1	the	the	DET
fcis-26723	91	2	bp	bp	PROPN
fcis-26723	91	3	neural	neural	PROPN
fcis-26723	91	4	network	network	NOUN
fcis-26723	91	5	with	with	ADP
fcis-26723	91	6	three	three	NUM
fcis-26723	91	7	-	-	PUNCT
fcis-26723	91	8	layer	layer	NOUN
fcis-26723	91	9	topology	topology	NOUN
fcis-26723	91	10	is	be	AUX
fcis-26723	91	11	shown	show	VERB
fcis-26723	91	12	in	in	ADP
fcis-26723	91	13	figure	figure	NOUN
fcis-26723	91	14	2	2	NUM
fcis-26723	91	15	,	,	PUNCT
fcis-26723	91	16	where	where	SCONJ
fcis-26723	91	17	,	,	PUNCT
fcis-26723	91	18	and	and	CCONJ
fcis-26723	91	19	are	be	AUX
fcis-26723	91	20	input	input	NOUN
fcis-26723	91	21	vectors	vector	NOUN
fcis-26723	91	22	and	and	CCONJ
fcis-26723	91	23	,	,	PUNCT
fcis-26723	91	24	and	and	CCONJ
fcis-26723	91	25	are	be	AUX
fcis-26723	91	26	output	output	NOUN
fcis-26723	91	27	vectors	vector	NOUN
fcis-26723	91	28	.	.	PUNCT
fcis-26723	92	1	the	the	DET
fcis-26723	92	2	basis	basis	NOUN
fcis-26723	92	3	function	function	NOUN
fcis-26723	92	4	between	between	ADP
fcis-26723	92	5	neurons	neuron	NOUN
fcis-26723	92	6	at	at	ADP
fcis-26723	92	7	each	each	DET
fcis-26723	92	8	layer	layer	NOUN
fcis-26723	92	9	of	of	ADP
fcis-26723	92	10	the	the	DET
fcis-26723	92	11	network	network	NOUN
fcis-26723	92	12	adopts	adopt	VERB
fcis-26723	92	13	linear	linear	ADJ
fcis-26723	92	14	basis	basis	NOUN
fcis-26723	92	15	function	function	NOUN
fcis-26723	92	16	,	,	PUNCT
fcis-26723	92	17	whose	whose	DET
fcis-26723	92	18	expression	expression	NOUN
fcis-26723	92	19	is	be	AUX
fcis-26723	92	20	shown	show	VERB
fcis-26723	92	21	in	in	ADP
fcis-26723	92	22	equation	equation	NOUN
fcis-26723	92	23	(	(	PUNCT
fcis-26723	92	24	2	2	NUM
fcis-26723	92	25	)	)	PUNCT
fcis-26723	92	26	,	,	PUNCT
fcis-26723	92	27	the	the	DET
fcis-26723	92	28	activation	activation	NOUN
fcis-26723	92	29	function	function	VERB
fcis-26723	92	30	f	f	X
fcis-26723	92	31	(	(	PUNCT
fcis-26723	92	32	)	)	PUNCT
fcis-26723	92	33	adopts	adopt	VERB
fcis-26723	92	34	sigmoid	sigmoid	NOUN
fcis-26723	92	35	function	function	NOUN
fcis-26723	92	36	,	,	PUNCT
fcis-26723	92	37	whose	whose	DET
fcis-26723	92	38	expression	expression	NOUN
fcis-26723	92	39	is	be	AUX
fcis-26723	92	40	shown	show	VERB
fcis-26723	92	41	in	in	ADP
fcis-26723	92	42	equation	equation	NOUN
fcis-26723	92	43	(	(	PUNCT
fcis-26723	92	44	3	3	NUM
fcis-26723	92	45	)	)	PUNCT
fcis-26723	92	46	.	.	PUNCT
fcis-26723	93	1	(	(	PUNCT
fcis-26723	93	2	2	2	X
fcis-26723	93	3	)	)	PUNCT
fcis-26723	93	4	(	(	PUNCT
fcis-26723	93	5	3	3	X
fcis-26723	93	6	)	)	PUNCT
fcis-26723	93	7	formula	formula	NOUN
fcis-26723	93	8	:	:	PUNCT
fcis-26723	93	9	-is	-is	ADP
fcis-26723	93	10	the	the	DET
fcis-26723	93	11	output	output	NOUN
fcis-26723	93	12	of	of	ADP
fcis-26723	93	13	the	the	DET
fcis-26723	93	14	i	i	PROPN
fcis-26723	93	15	-	-	PUNCT
fcis-26723	93	16	th	th	X
fcis-26723	93	17	neuron	neuron	NOUN
fcis-26723	93	18	in	in	ADP
fcis-26723	93	19	the	the	DET
fcis-26723	93	20	hidden	hide	VERB
fcis-26723	93	21	layer	layer	NOUN
fcis-26723	93	22	or	or	CCONJ
fcis-26723	93	23	output	output	NOUN
fcis-26723	93	24	layer	layer	NOUN
fcis-26723	93	25	;	;	PUNCT
fcis-26723	93	26	i	i	PRON
fcis-26723	93	27	-	-	PUNCT
fcis-26723	93	28	is	be	AUX
fcis-26723	93	29	the	the	DET
fcis-26723	93	30	number	number	NOUN
fcis-26723	93	31	of	of	ADP
fcis-26723	93	32	nodes	node	NOUN
fcis-26723	93	33	in	in	ADP
fcis-26723	93	34	the	the	DET
fcis-26723	93	35	hidden	hide	VERB
fcis-26723	93	36	layer	layer	NOUN
fcis-26723	93	37	or	or	CCONJ
fcis-26723	93	38	output	output	NOUN
fcis-26723	93	39	layer	layer	NOUN
fcis-26723	93	40	;	;	PUNCT
fcis-26723	93	41	m	m	NOUN
fcis-26723	93	42	-	-	PUNCT
fcis-26723	93	43	is	be	AUX
fcis-26723	93	44	the	the	DET
fcis-26723	93	45	number	number	NOUN
fcis-26723	93	46	of	of	ADP
fcis-26723	93	47	nodes	node	NOUN
fcis-26723	93	48	in	in	ADP
fcis-26723	93	49	the	the	DET
fcis-26723	93	50	input	input	NOUN
fcis-26723	93	51	layer	layer	NOUN
fcis-26723	93	52	or	or	CCONJ
fcis-26723	93	53	hidden	hide	VERB
fcis-26723	93	54	layer	layer	NOUN
fcis-26723	93	55	;	;	PUNCT
fcis-26723	93	56	-is	-is	ADP
fcis-26723	93	57	the	the	DET
fcis-26723	93	58	connection	connection	NOUN
fcis-26723	93	59	weight	weight	NOUN
fcis-26723	93	60	between	between	ADP
fcis-26723	93	61	the	the	DET
fcis-26723	93	62	input	input	NOUN
fcis-26723	93	63	layer	layer	NOUN
fcis-26723	93	64	and	and	CCONJ
fcis-26723	93	65	the	the	DET
fcis-26723	93	66	hidden	hide	VERB
fcis-26723	93	67	layer	layer	NOUN
fcis-26723	93	68	or	or	CCONJ
fcis-26723	93	69	the	the	DET
fcis-26723	93	70	hidden	hide	VERB
fcis-26723	93	71	layer	layer	NOUN
fcis-26723	93	72	and	and	CCONJ
fcis-26723	93	73	the	the	DET
fcis-26723	93	74	output	output	NOUN
fcis-26723	93	75	layer	layer	NOUN
fcis-26723	93	76	;	;	PUNCT
fcis-26723	93	77	-is	-is	ADP
fcis-26723	93	78	the	the	DET
fcis-26723	93	79	threshold	threshold	NOUN
fcis-26723	93	80	of	of	ADP
fcis-26723	93	81	the	the	DET
fcis-26723	93	82	hidden	hide	VERB
fcis-26723	93	83	layer	layer	NOUN
fcis-26723	93	84	or	or	CCONJ
fcis-26723	93	85	the	the	DET
fcis-26723	93	86	output	output	NOUN
fcis-26723	93	87	layer	layer	NOUN
fcis-26723	93	88	;	;	PUNCT
fcis-26723	93	89	factivate	factivate	VERB
fcis-26723	93	90	a	a	DET
fcis-26723	93	91	function	function	NOUN
fcis-26723	93	92	for	for	ADP
fcis-26723	93	93	the	the	DET
fcis-26723	93	94	hidden	hidden	ADJ
fcis-26723	93	95	or	or	CCONJ
fcis-26723	93	96	output	output	NOUN
fcis-26723	93	97	layer	layer	NOUN
fcis-26723	93	98	.	.	PUNCT
fcis-26723	94	1	set	set	VERB
fcis-26723	94	2	the	the	DET
fcis-26723	94	3	output	output	NOUN
fcis-26723	94	4	of	of	ADP
fcis-26723	94	5	neurons	neuron	NOUN
fcis-26723	94	6	at	at	ADP
fcis-26723	94	7	the	the	DET
fcis-26723	94	8	jth	jth	PROPN
fcis-26723	94	9	node	node	PROPN
fcis-26723	94	10	of	of	ADP
fcis-26723	94	11	layer	layer	NOUN
fcis-26723	94	12	l	l	NOUN
fcis-26723	94	13	as	as	ADP
fcis-26723	94	14	,	,	PUNCT
fcis-26723	94	15	and	and	CCONJ
fcis-26723	94	16	the	the	DET
fcis-26723	94	17	threshold	threshold	NOUN
fcis-26723	94	18	of	of	ADP
fcis-26723	94	19	neurons	neuron	NOUN
fcis-26723	94	20	at	at	ADP
fcis-26723	94	21	the	the	DET
fcis-26723	94	22	jth	jth	PROPN
fcis-26723	94	23	node	node	PROPN
fcis-26723	94	24	of	of	ADP
fcis-26723	94	25	layer	layer	NOUN
fcis-26723	94	26	l	l	NOUN
fcis-26723	94	27	as	as	ADV
fcis-26723	94	28	.	.	PUNCT
fcis-26723	95	1	the	the	DET
fcis-26723	95	2	expression	expression	NOUN
fcis-26723	95	3	of	of	ADP
fcis-26723	95	4	is	be	AUX
fcis-26723	95	5	shown	show	VERB
fcis-26723	95	6	in	in	ADP
fcis-26723	95	7	equation	equation	NOUN
fcis-26723	95	8	(	(	PUNCT
fcis-26723	95	9	4	4	NUM
fcis-26723	95	10	)	)	PUNCT
fcis-26723	95	11	.	.	PUNCT
fcis-26723	96	1	p	p	X
fcis-26723	96	2	g	g	PROPN
fcis-26723	96	3	∑	∑	PROPN
fcis-26723	96	4	+	+	CCONJ
fcis-26723	96	5	)	)	PUNCT
fcis-26723	96	6	(	(	PUNCT
fcis-26723	96	7	4	4	X
fcis-26723	96	8	)	)	PUNCT
fcis-26723	96	9	formula	formula	NOUN
fcis-26723	96	10	:	:	PUNCT
fcis-26723	97	1	k	k	X
fcis-26723	97	2	,	,	PUNCT
fcis-26723	97	3	j	j	NOUN
fcis-26723	97	4	-	-	PUNCT
fcis-26723	97	5	represents	represent	VERB
fcis-26723	97	6	the	the	DET
fcis-26723	97	7	k	k	PROPN
fcis-26723	97	8	or	or	CCONJ
fcis-26723	97	9	jth	jth	PROPN
fcis-26723	97	10	node	node	PROPN
fcis-26723	97	11	;	;	PUNCT
fcis-26723	97	12	g	g	PROPN
fcis-26723	97	13	(	(	PUNCT
fcis-26723	97	14	)	)	PUNCT
fcis-26723	97	15	-denotes	-denote	NOUN
fcis-26723	97	16	the	the	DET
fcis-26723	97	17	sigmoid	sigmoid	NOUN
fcis-26723	97	18	function	function	NOUN
fcis-26723	97	19	;	;	PUNCT
fcis-26723	98	1	l	l	X
fcis-26723	98	2	-	-	PUNCT
fcis-26723	98	3	represents	represent	VERB
fcis-26723	98	4	the	the	DET
fcis-26723	98	5	l	l	ADJ
fcis-26723	98	6	-	-	ADJ
fcis-26723	98	7	layer	layer	NOUN
fcis-26723	98	8	neural	neural	ADJ
fcis-26723	98	9	network	network	NOUN
fcis-26723	98	10	,	,	PUNCT
fcis-26723	98	11	and	and	CCONJ
fcis-26723	98	12	l	l	NOUN
fcis-26723	98	13	is	be	AUX
fcis-26723	98	14	equal	equal	ADJ
fcis-26723	98	15	to	to	ADP
fcis-26723	98	16	2	2	NUM
fcis-26723	98	17	or	or	CCONJ
fcis-26723	98	18	3	3	NUM
fcis-26723	98	19	;	;	PUNCT
fcis-26723	98	20	-indicates	-indicate	NOUN
fcis-26723	98	21	that	that	SCONJ
fcis-26723	98	22	the	the	DET
fcis-26723	98	23	output	output	NOUN
fcis-26723	98	24	of	of	ADP
fcis-26723	98	25	neurons	neuron	NOUN
fcis-26723	98	26	at	at	ADP
fcis-26723	98	27	the	the	DET
fcis-26723	98	28	jth	jth	PROPN
fcis-26723	98	29	node	node	NOUN
fcis-26723	98	30	of	of	ADP
fcis-26723	98	31	layer	layer	NOUN
fcis-26723	98	32	l	l	NOUN
fcis-26723	98	33	is	be	AUX
fcis-26723	98	34	;	;	PUNCT
fcis-26723	98	35	-represents	-represent	NOUN
fcis-26723	98	36	the	the	DET
fcis-26723	98	37	output	output	NOUN
fcis-26723	98	38	of	of	ADP
fcis-26723	98	39	neurons	neuron	NOUN
fcis-26723	98	40	at	at	ADP
fcis-26723	98	41	the	the	DET
fcis-26723	98	42	k	k	PROPN
fcis-26723	98	43	node	node	NOUN
fcis-26723	98	44	of	of	ADP
fcis-26723	98	45	layer	layer	NOUN
fcis-26723	98	46	l-1	l-1	NOUN
fcis-26723	98	47	;	;	PUNCT
fcis-26723	98	48	-represents	-represent	NOUN
fcis-26723	98	49	the	the	DET
fcis-26723	98	50	connection	connection	NOUN
fcis-26723	98	51	weight	weight	NOUN
fcis-26723	98	52	of	of	ADP
fcis-26723	98	53	the	the	DET
fcis-26723	98	54	kth	kth	PROPN
fcis-26723	98	55	input	input	NOUN
fcis-26723	98	56	node	node	NOUN
fcis-26723	98	57	of	of	ADP
fcis-26723	98	58	layer	layer	NOUN
fcis-26723	98	59	l-1	l-1	NOUN
fcis-26723	98	60	to	to	ADP
fcis-26723	98	61	the	the	DET
fcis-26723	98	62	jth	jth	PROPN
fcis-26723	98	63	output	output	PROPN
fcis-26723	98	64	node	node	NOUN
fcis-26723	98	65	of	of	ADP
fcis-26723	98	66	layer	layer	NOUN
fcis-26723	98	67	l	l	NOUN
fcis-26723	98	68	;	;	PUNCT
fcis-26723	98	69	-represents	-represent	NOUN
fcis-26723	98	70	the	the	DET
fcis-26723	98	71	negative	negative	ADJ
fcis-26723	98	72	value	value	NOUN
fcis-26723	98	73	of	of	ADP
fcis-26723	98	74	the	the	DET
fcis-26723	98	75	threshold	threshold	NOUN
fcis-26723	98	76	corresponding	correspond	VERB
fcis-26723	98	77	to	to	ADP
fcis-26723	98	78	the	the	DET
fcis-26723	98	79	jth	jth	PROPN
fcis-26723	98	80	output	output	PROPN
fcis-26723	98	81	node	node	NOUN
fcis-26723	98	82	of	of	ADP
fcis-26723	98	83	layer	layer	NOUN
fcis-26723	98	84	l.	l.	PROPN
fcis-26723	98	85	4	4	NUM
fcis-26723	98	86	.	.	PUNCT
fcis-26723	98	87	bp	bp	PROPN
fcis-26723	98	88	neural	neural	PROPN
fcis-26723	98	89	network	network	PROPN
fcis-26723	98	90	learning	learn	VERB
fcis-26723	98	91	algorithm	algorithm	PROPN
fcis-26723	98	92	4.1	4.1	NUM
fcis-26723	98	93	.	.	PUNCT
fcis-26723	98	94	training	training	NOUN
fcis-26723	98	95	process	process	NOUN
fcis-26723	98	96	of	of	ADP
fcis-26723	98	97	bp	bp	PROPN
fcis-26723	98	98	algorithm	algorithm	NOUN
fcis-26723	98	99	(	(	PUNCT
fcis-26723	98	100	1	1	NUM
fcis-26723	98	101	)	)	PUNCT
fcis-26723	98	102	propagation	propagation	NOUN
fcis-26723	98	103	from	from	ADP
fcis-26723	98	104	the	the	DET
fcis-26723	98	105	input	input	NOUN
fcis-26723	98	106	layer	layer	NOUN
fcis-26723	98	107	through	through	ADP
fcis-26723	98	108	the	the	DET
fcis-26723	98	109	hidden	hidden	ADJ
fcis-26723	98	110	layer	layer	NOUN
fcis-26723	98	111	to	to	ADP
fcis-26723	98	112	the	the	DET
fcis-26723	98	113	output	output	NOUN
fcis-26723	98	114	layer	layer	NOUN
fcis-26723	98	115	,	,	PUNCT
fcis-26723	98	116	this	this	DET
fcis-26723	98	117	process	process	NOUN
fcis-26723	98	118	is	be	AUX
fcis-26723	98	119	called	call	VERB
fcis-26723	98	120	forward	forward	ADV
fcis-26723	98	121	propagation	propagation	NOUN
fcis-26723	98	122	;	;	PUNCT
fcis-26723	98	123	(	(	PUNCT
fcis-26723	98	124	2	2	X
fcis-26723	98	125	)	)	PUNCT
fcis-26723	98	126	the	the	DET
fcis-26723	98	127	error	error	NOUN
fcis-26723	98	128	between	between	ADP
fcis-26723	98	129	the	the	DET
fcis-26723	98	130	real	real	ADJ
fcis-26723	98	131	output	output	NOUN
fcis-26723	98	132	and	and	CCONJ
fcis-26723	98	133	the	the	DET
fcis-26723	98	134	expected	expect	VERB
fcis-26723	98	135	output	output	NOUN
fcis-26723	98	136	of	of	ADP
fcis-26723	98	137	the	the	DET
fcis-26723	98	138	neural	neural	ADJ
fcis-26723	98	139	network	network	NOUN
fcis-26723	98	140	is	be	AUX
fcis-26723	98	141	regarded	regard	VERB
fcis-26723	98	142	as	as	ADP
fcis-26723	98	143	an	an	DET
fcis-26723	98	144	error	error	NOUN
fcis-26723	98	145	signal	signal	NOUN
fcis-26723	98	146	.	.	PUNCT
fcis-26723	99	1	if	if	SCONJ
fcis-26723	99	2	the	the	DET
fcis-26723	99	3	error	error	NOUN
fcis-26723	99	4	exceeds	exceed	VERB
fcis-26723	99	5	the	the	DET
fcis-26723	99	6	set	set	NOUN
fcis-26723	99	7	threshold	threshold	NOUN
fcis-26723	99	8	,	,	PUNCT
fcis-26723	99	9	the	the	DET
fcis-26723	99	10	connection	connection	NOUN
fcis-26723	99	11	weight	weight	NOUN
fcis-26723	99	12	and	and	CCONJ
fcis-26723	99	13	threshold	threshold	NOUN
fcis-26723	99	14	of	of	ADP
fcis-26723	99	15	each	each	DET
fcis-26723	99	16	layer	layer	NOUN
fcis-26723	99	17	are	be	AUX
fcis-26723	99	18	adjusted	adjust	VERB
fcis-26723	99	19	by	by	ADP
fcis-26723	99	20	gradient	gradient	ADJ
fcis-26723	99	21	descent	descent	NOUN
fcis-26723	99	22	algorithm	algorithm	NOUN
fcis-26723	99	23	from	from	ADP
fcis-26723	99	24	the	the	DET
fcis-26723	99	25	output	output	NOUN
fcis-26723	99	26	layer	layer	NOUN
fcis-26723	99	27	to	to	ADP
fcis-26723	99	28	the	the	DET
fcis-26723	99	29	input	input	NOUN
fcis-26723	99	30	layer	layer	NOUN
fcis-26723	99	31	.	.	PUNCT
fcis-26723	100	1	this	this	DET
fcis-26723	100	2	process	process	NOUN
fcis-26723	100	3	is	be	AUX
fcis-26723	100	4	called	call	VERB
fcis-26723	100	5	error	error	NOUN
fcis-26723	100	6	back	back	NOUN
fcis-26723	100	7	propagation	propagation	NOUN
fcis-26723	100	8	;	;	PUNCT
fcis-26723	100	9	(	(	PUNCT
fcis-26723	100	10	3	3	X
fcis-26723	100	11	)	)	PUNCT
fcis-26723	100	12	forward	forward	ADJ
fcis-26723	100	13	propagation	propagation	NOUN
fcis-26723	100	14	and	and	CCONJ
fcis-26723	100	15	error	error	NOUN
fcis-26723	100	16	backpropagation	backpropagation	NOUN
fcis-26723	100	17	are	be	AUX
fcis-26723	100	18	repeated	repeat	VERB
fcis-26723	100	19	for	for	ADP
fcis-26723	100	20	many	many	ADJ
fcis-26723	100	21	times	time	NOUN
fcis-26723	100	22	,	,	PUNCT
fcis-26723	100	23	during	during	ADP
fcis-26723	100	24	which	which	PRON
fcis-26723	100	25	the	the	DET
fcis-26723	100	26	weights	weight	NOUN
fcis-26723	100	27	and	and	CCONJ
fcis-26723	100	28	thresholds	threshold	NOUN
fcis-26723	100	29	of	of	ADP
fcis-26723	100	30	the	the	DET
fcis-26723	100	31	neural	neural	ADJ
fcis-26723	100	32	network	network	NOUN
fcis-26723	100	33	are	be	AUX
fcis-26723	100	34	constantly	constantly	ADV
fcis-26723	100	35	adjusted	adjust	VERB
fcis-26723	100	36	.	.	PUNCT
fcis-26723	101	1	this	this	DET
fcis-26723	101	2	process	process	NOUN
fcis-26723	101	3	is	be	AUX
fcis-26723	101	4	called	call	VERB
fcis-26723	101	5	memory	memory	NOUN
fcis-26723	101	6	training	training	NOUN
fcis-26723	101	7	process	process	NOUN
fcis-26723	101	8	;	;	PUNCT
fcis-26723	101	9	(	(	PUNCT
fcis-26723	101	10	4	4	X
fcis-26723	101	11	)	)	PUNCT
fcis-26723	101	12	with	with	ADP
fcis-26723	101	13	the	the	DET
fcis-26723	101	14	continuous	continuous	ADJ
fcis-26723	101	15	progress	progress	NOUN
fcis-26723	101	16	of	of	ADP
fcis-26723	101	17	the	the	DET
fcis-26723	101	18	above	above	ADJ
fcis-26723	101	19	process	process	NOUN
fcis-26723	101	20	,	,	PUNCT
fcis-26723	101	21	the	the	DET
fcis-26723	101	22	global	global	ADJ
fcis-26723	101	23	error	error	NOUN
fcis-26723	101	24	of	of	ADP
fcis-26723	101	25	the	the	DET
fcis-26723	101	26	network	network	NOUN
fcis-26723	101	27	tends	tend	VERB
fcis-26723	101	28	to	to	ADP
fcis-26723	101	29	the	the	DET
fcis-26723	101	30	minimum	minimum	ADJ
fcis-26723	101	31	value	value	NOUN
fcis-26723	101	32	or	or	CCONJ
fcis-26723	101	33	reaches	reach	VERB
fcis-26723	101	34	the	the	DET
fcis-26723	101	35	termination	termination	NOUN
fcis-26723	101	36	condition	condition	NOUN
fcis-26723	101	37	set	set	VERB
fcis-26723	101	38	in	in	ADP
fcis-26723	101	39	advance	advance	NOUN
fcis-26723	101	40	,	,	PUNCT
fcis-26723	101	41	which	which	PRON
fcis-26723	101	42	is	be	AUX
fcis-26723	101	43	called	call	VERB
fcis-26723	101	44	the	the	DET
fcis-26723	101	45	learning	learn	VERB
fcis-26723	101	46	convergence	convergence	NOUN
fcis-26723	101	47	process	process	NOUN
fcis-26723	101	48	.	.	PUNCT
fcis-26723	102	1	the	the	DET
fcis-26723	102	2	main	main	ADJ
fcis-26723	102	3	objective	objective	NOUN
fcis-26723	102	4	of	of	ADP
fcis-26723	102	5	the	the	DET
fcis-26723	102	6	bp	bp	PROPN
fcis-26723	102	7	algorithm	algorithm	PROPN
fcis-26723	102	8	is	be	AUX
fcis-26723	102	9	to	to	PART
fcis-26723	102	10	compute	compute	VERB
fcis-26723	102	11	the	the	DET
fcis-26723	102	12	partial	partial	ADJ
fcis-26723	102	13	derivatives	derivative	NOUN
fcis-26723	102	14	∂e/∂ω	∂e/∂ω	PROPN
fcis-26723	102	15	and	and	CCONJ
fcis-26723	102	16	∂e/∂θ	∂e/∂θ	PROPN
fcis-26723	102	17	of	of	ADP
fcis-26723	102	18	the	the	DET
fcis-26723	102	19	cost	cost	NOUN
fcis-26723	102	20	function	function	NOUN
fcis-26723	102	21	e	e	NOUN
fcis-26723	102	22	with	with	ADP
fcis-26723	102	23	respect	respect	NOUN
fcis-26723	102	24	to	to	ADP
fcis-26723	102	25	all	all	DET
fcis-26723	102	26	network	network	NOUN
fcis-26723	102	27	connection	connection	NOUN
fcis-26723	102	28	weights	weight	VERB
fcis-26723	102	29	ω	ω	PROPN
fcis-26723	102	30	and	and	CCONJ
fcis-26723	102	31	thresholds	threshold	VERB
fcis-26723	102	32	θ	θ	PROPN
fcis-26723	102	33	.	.	PUNCT
fcis-26723	103	1	the	the	DET
fcis-26723	103	2	expression	expression	NOUN
fcis-26723	103	3	of	of	ADP
fcis-26723	103	4	the	the	DET
fcis-26723	103	5	cost	cost	NOUN
fcis-26723	103	6	function	function	NOUN
fcis-26723	103	7	e	e	NOUN
fcis-26723	103	8	is	be	AUX
fcis-26723	103	9	shown	show	VERB
fcis-26723	103	10	in	in	ADP
fcis-26723	103	11	equation	equation	NOUN
fcis-26723	103	12	(	(	PUNCT
fcis-26723	103	13	5	5	NUM
fcis-26723	103	14	)	)	PUNCT
fcis-26723	103	15	.	.	PUNCT
fcis-26723	104	1	e	e	PROPN
fcis-26723	104	2	ω	ω	PROPN
fcis-26723	104	3	,	,	PUNCT
fcis-26723	104	4	θ	θ	PROPN
fcis-26723	104	5	∑	∑	PUNCT
fcis-26723	104	6	||	||	PROPN
fcis-26723	104	7	||	||	PROPN
fcis-26723	105	1	(	(	PUNCT
fcis-26723	105	2	5	5	NUM
fcis-26723	105	3	)	)	PUNCT
fcis-26723	105	4	formula	formula	NOUN
fcis-26723	105	5	:	:	PUNCT
fcis-26723	105	6	n	n	CCONJ
fcis-26723	105	7	-	-	PUNCT
fcis-26723	105	8	is	be	AUX
fcis-26723	105	9	the	the	DET
fcis-26723	105	10	total	total	ADJ
fcis-26723	105	11	number	number	NOUN
fcis-26723	105	12	of	of	ADP
fcis-26723	105	13	training	training	NOUN
fcis-26723	105	14	samples	sample	NOUN
fcis-26723	105	15	x	x	PRON
fcis-26723	105	16	;	;	PUNCT
fcis-26723	105	17	ω	ω	NUM
fcis-26723	105	18	,	,	PUNCT
fcis-26723	105	19	θ	θ	PROPN
fcis-26723	105	20	-	-	PUNCT
fcis-26723	105	21	is	be	AUX
fcis-26723	105	22	the	the	DET
fcis-26723	105	23	connection	connection	NOUN
fcis-26723	105	24	weight	weight	NOUN
fcis-26723	105	25	and	and	CCONJ
fcis-26723	105	26	threshold	threshold	NOUN
fcis-26723	105	27	value	value	NOUN
fcis-26723	105	28	;	;	PUNCT
fcis-26723	105	29	y(x)-the	y(x)-the	PRON
fcis-26723	105	30	expected	expect	VERB
fcis-26723	105	31	output	output	NOUN
fcis-26723	105	32	of	of	ADP
fcis-26723	105	33	training	training	NOUN
fcis-26723	105	34	sample	sample	NOUN
fcis-26723	105	35	x	x	X
fcis-26723	105	36	;	;	PUNCT
fcis-26723	105	37	n	n	CCONJ
fcis-26723	105	38	-	-	PUNCT
fcis-26723	105	39	is	be	AUX
fcis-26723	105	40	the	the	DET
fcis-26723	105	41	total	total	ADJ
fcis-26723	105	42	number	number	NOUN
fcis-26723	105	43	of	of	ADP
fcis-26723	105	44	layers	layer	NOUN
fcis-26723	105	45	of	of	ADP
fcis-26723	105	46	the	the	DET
fcis-26723	105	47	network	network	NOUN
fcis-26723	105	48	.	.	PUNCT
fcis-26723	106	1	in	in	ADP
fcis-26723	106	2	this	this	DET
fcis-26723	106	3	paper	paper	NOUN
fcis-26723	106	4	,	,	PUNCT
fcis-26723	106	5	a	a	DET
fcis-26723	106	6	3	3	NUM
fcis-26723	106	7	-	-	PUNCT
fcis-26723	106	8	layer	layer	NOUN
fcis-26723	106	9	neural	neural	ADJ
fcis-26723	106	10	network	network	NOUN
fcis-26723	106	11	is	be	AUX
fcis-26723	106	12	used	use	VERB
fcis-26723	106	13	,	,	PUNCT
fcis-26723	106	14	that	that	ADV
fcis-26723	106	15	is	is	ADV
fcis-26723	106	16	,	,	PUNCT
fcis-26723	106	17	n=3	n=3	PRON
fcis-26723	106	18	;	;	PUNCT
fcis-26723	106	19	pn(x)-the	pn(x)-the	DET
fcis-26723	106	20	real	real	ADJ
fcis-26723	106	21	output	output	NOUN
fcis-26723	106	22	of	of	ADP
fcis-26723	106	23	the	the	DET
fcis-26723	106	24	training	training	NOUN
fcis-26723	106	25	sample	sample	NOUN
fcis-26723	106	26	in	in	ADP
fcis-26723	106	27	the	the	DET
fcis-26723	106	28	output	output	NOUN
fcis-26723	106	29	layer	layer	NOUN
fcis-26723	106	30	.	.	PUNCT
fcis-26723	107	1	4.2	4.2	NUM
fcis-26723	107	2	.	.	PUNCT
fcis-26723	107	3	performance	performance	NOUN
fcis-26723	107	4	prediction	prediction	NOUN
fcis-26723	107	5	experiment	experiment	NOUN
fcis-26723	107	6	based	base	VERB
fcis-26723	107	7	on	on	ADP
fcis-26723	107	8	bp	bp	PROPN
fcis-26723	107	9	neural	neural	PROPN
fcis-26723	107	10	network	network	PROPN
fcis-26723	107	11	4.2.1	4.2.1	NUM
fcis-26723	107	12	.	.	PUNCT
fcis-26723	108	1	parameter	parameter	NOUN
fcis-26723	108	2	selection	selection	NOUN
fcis-26723	108	3	(	(	PUNCT
fcis-26723	108	4	1	1	X
fcis-26723	108	5	)	)	PUNCT
fcis-26723	108	6	set	set	VERB
fcis-26723	108	7	the	the	DET
fcis-26723	108	8	number	number	NOUN
fcis-26723	108	9	of	of	ADP
fcis-26723	108	10	nodes	node	NOUN
fcis-26723	108	11	in	in	ADP
fcis-26723	108	12	the	the	DET
fcis-26723	108	13	input	input	NOUN
fcis-26723	108	14	layer	layer	NOUN
fcis-26723	108	15	input	input	NOUN
fcis-26723	108	16	layer	layer	NOUN
fcis-26723	108	17	nodes	node	NOUN
fcis-26723	108	18	are	be	AUX
fcis-26723	108	19	input	input	ADJ
fcis-26723	108	20	interfaces	interface	NOUN
fcis-26723	108	21	of	of	ADP
fcis-26723	108	22	data	datum	NOUN
fcis-26723	108	23	samples	sample	NOUN
fcis-26723	108	24	,	,	PUNCT
fcis-26723	108	25	and	and	CCONJ
fcis-26723	108	26	their	their	PRON
fcis-26723	108	27	number	number	NOUN
fcis-26723	108	28	depends	depend	VERB
fcis-26723	108	29	on	on	ADP
fcis-26723	108	30	the	the	DET
fcis-26723	108	31	number	number	NOUN
fcis-26723	108	32	of	of	ADP
fcis-26723	108	33	features	feature	NOUN
fcis-26723	108	34	of	of	ADP
fcis-26723	108	35	data	datum	NOUN
fcis-26723	108	36	samples	sample	NOUN
fcis-26723	108	37	.	.	PUNCT
fcis-26723	109	1	the	the	DET
fcis-26723	109	2	dimensionality	dimensionality	NOUN
fcis-26723	109	3	of	of	ADP
fcis-26723	109	4	the	the	DET
fcis-26723	109	5	data	datum	NOUN
fcis-26723	109	6	set	set	VERB
fcis-26723	109	7	used	use	VERB
fcis-26723	109	8	for	for	ADP
fcis-26723	109	9	neural	neural	ADJ
fcis-26723	109	10	network	network	NOUN
fcis-26723	109	11	training	training	NOUN
fcis-26723	109	12	in	in	ADP
fcis-26723	109	13	this	this	DET
fcis-26723	109	14	paper	paper	NOUN
fcis-26723	109	15	is	be	AUX
fcis-26723	109	16	reduced	reduce	VERB
fcis-26723	109	17	by	by	ADP
fcis-26723	109	18	principal	principal	ADJ
fcis-26723	109	19	component	component	NOUN
fcis-26723	109	20	analysis	analysis	NOUN
fcis-26723	109	21	.	.	PUNCT
fcis-26723	110	1	the	the	DET
fcis-26723	110	2	data	datum	NOUN
fcis-26723	110	3	after	after	ADP
fcis-26723	110	4	dimensionality	dimensionality	NOUN
fcis-26723	110	5	reduction	reduction	NOUN
fcis-26723	110	6	has	have	VERB
fcis-26723	110	7	5	5	NUM
fcis-26723	110	8	features	feature	NOUN
fcis-26723	110	9	,	,	PUNCT
fcis-26723	110	10	so	so	CCONJ
fcis-26723	110	11	the	the	DET
fcis-26723	110	12	number	number	NOUN
fcis-26723	110	13	of	of	ADP
fcis-26723	110	14	nodes	node	NOUN
fcis-26723	110	15	in	in	ADP
fcis-26723	110	16	the	the	DET
fcis-26723	110	17	input	input	NOUN
fcis-26723	110	18	layer	layer	NOUN
fcis-26723	110	19	is	be	AUX
fcis-26723	110	20	5	5	NUM
fcis-26723	110	21	.	.	PUNCT
fcis-26723	111	1	(	(	PUNCT
fcis-26723	111	2	2	2	X
fcis-26723	111	3	)	)	PUNCT
fcis-26723	111	4	setting	set	VERB
fcis-26723	111	5	the	the	DET
fcis-26723	111	6	number	number	NOUN
fcis-26723	111	7	of	of	ADP
fcis-26723	111	8	nodes	node	NOUN
fcis-26723	111	9	in	in	ADP
fcis-26723	111	10	the	the	DET
fcis-26723	111	11	output	output	NOUN
fcis-26723	111	12	layer	layer	NOUN
fcis-26723	111	13	when	when	SCONJ
fcis-26723	111	14	bp	bp	PROPN
fcis-26723	111	15	neural	neural	ADJ
fcis-26723	111	16	network	network	NOUN
fcis-26723	111	17	is	be	AUX
fcis-26723	111	18	used	use	VERB
fcis-26723	111	19	for	for	ADP
fcis-26723	111	20	gear	gear	NOUN
fcis-26723	111	21	performance	performance	NOUN
fcis-26723	111	22	recognition	recognition	NOUN
fcis-26723	111	23	,	,	PUNCT
fcis-26723	111	24	the	the	DET
fcis-26723	111	25	output	output	NOUN
fcis-26723	111	26	performance	performance	NOUN
fcis-26723	111	27	type	type	NOUN
fcis-26723	111	28	of	of	ADP
fcis-26723	111	29	the	the	DET
fcis-26723	111	30	neural	neural	ADJ
fcis-26723	111	31	network	network	NOUN
fcis-26723	111	32	is	be	AUX
fcis-26723	111	33	expressed	express	VERB
fcis-26723	111	34	as	as	ADP
fcis-26723	111	35	a	a	DET
fcis-26723	111	36	combination	combination	NOUN
fcis-26723	111	37	of	of	ADP
fcis-26723	111	38	0	0	NUM
fcis-26723	111	39	or	or	CCONJ
fcis-26723	111	40	1	1	NUM
fcis-26723	111	41	,	,	PUNCT
fcis-26723	111	42	and	and	CCONJ
fcis-26723	111	43	the	the	DET
fcis-26723	111	44	number	number	NOUN
fcis-26723	111	45	of	of	ADP
fcis-26723	111	46	nodes	node	NOUN
fcis-26723	111	47	in	in	ADP
fcis-26723	111	48	the	the	DET
fcis-26723	111	49	output	output	NOUN
fcis-26723	111	50	layer	layer	NOUN
fcis-26723	111	51	can	can	AUX
fcis-26723	111	52	be	be	AUX
fcis-26723	111	53	determined	determine	VERB
fcis-26723	111	54	according	accord	VERB
fcis-26723	111	55	to	to	ADP
fcis-26723	111	56	the	the	DET
fcis-26723	111	57	number	number	NOUN
fcis-26723	111	58	of	of	ADP
fcis-26723	111	59	performance	performance	NOUN
fcis-26723	111	60	types	type	NOUN
fcis-26723	111	61	.	.	PUNCT
fcis-26723	112	1	since	since	SCONJ
fcis-26723	112	2	the	the	DET
fcis-26723	112	3	number	number	NOUN
fcis-26723	112	4	of	of	ADP
fcis-26723	112	5	achievement	achievement	NOUN
fcis-26723	112	6	categories	category	NOUN
fcis-26723	112	7	in	in	ADP
fcis-26723	112	8	this	this	DET
fcis-26723	112	9	paper	paper	NOUN
fcis-26723	112	10	is	be	AUX
fcis-26723	112	11	4	4	NUM
fcis-26723	112	12	,	,	PUNCT
fcis-26723	112	13	the	the	DET
fcis-26723	112	14	output	output	NOUN
fcis-26723	112	15	layer	layer	NOUN
fcis-26723	112	16	nodes	node	NOUN
fcis-26723	112	17	of	of	ADP
fcis-26723	112	18	the	the	DET
fcis-26723	112	19	neural	neural	ADJ
fcis-26723	112	20	network	network	NOUN
fcis-26723	112	21	in	in	ADP
fcis-26723	112	22	this	this	DET
fcis-26723	112	23	paper	paper	NOUN
fcis-26723	112	24	are	be	AUX
fcis-26723	112	25	4	4	NUM
fcis-26723	112	26	.	.	PUNCT
fcis-26723	113	1	(	(	PUNCT
fcis-26723	113	2	3	3	X
fcis-26723	113	3	)	)	PUNCT
fcis-26723	113	4	setting	set	VERB
fcis-26723	113	5	the	the	DET
fcis-26723	113	6	number	number	NOUN
fcis-26723	113	7	of	of	ADP
fcis-26723	113	8	nodes	node	NOUN
fcis-26723	113	9	in	in	ADP
fcis-26723	113	10	the	the	DET
fcis-26723	113	11	hidden	hide	VERB
fcis-26723	113	12	layer	layer	NOUN
fcis-26723	113	13	the	the	DET
fcis-26723	113	14	selection	selection	NOUN
fcis-26723	113	15	of	of	ADP
fcis-26723	113	16	the	the	DET
fcis-26723	113	17	number	number	NOUN
fcis-26723	113	18	of	of	ADP
fcis-26723	113	19	hidden	hide	VERB
fcis-26723	113	20	layer	layer	NOUN
fcis-26723	113	21	nodes	node	NOUN
fcis-26723	113	22	in	in	ADP
fcis-26723	113	23	bp	bp	PROPN
fcis-26723	113	24	neural	neural	ADJ
fcis-26723	113	25	network	network	NOUN
fcis-26723	113	26	has	have	VERB
fcis-26723	113	27	a	a	DET
fcis-26723	113	28	great	great	ADJ
fcis-26723	113	29	influence	influence	NOUN
fcis-26723	113	30	on	on	ADP
fcis-26723	113	31	gear	gear	NOUN
fcis-26723	113	32	grade	grade	NOUN
fcis-26723	113	33	classification	classification	NOUN
fcis-26723	113	34	.	.	PUNCT
fcis-26723	114	1	with	with	ADP
fcis-26723	114	2	fewer	few	ADJ
fcis-26723	114	3	hidden	hidden	ADJ
fcis-26723	114	4	layer	layer	NOUN
fcis-26723	114	5	nodes	node	NOUN
fcis-26723	114	6	,	,	PUNCT
fcis-26723	114	7	the	the	DET
fcis-26723	114	8	ability	ability	NOUN
fcis-26723	114	9	of	of	ADP
fcis-26723	114	10	neural	neural	ADJ
fcis-26723	114	11	network	network	NOUN
fcis-26723	114	12	to	to	PART
fcis-26723	114	13	obtain	obtain	VERB
fcis-26723	114	14	information	information	NOUN
fcis-26723	114	15	is	be	AUX
fcis-26723	114	16	poor	poor	ADJ
fcis-26723	114	17	,	,	PUNCT
fcis-26723	114	18	and	and	CCONJ
fcis-26723	114	19	the	the	DET
fcis-26723	114	20	error	error	NOUN
fcis-26723	114	21	rate	rate	NOUN
fcis-26723	114	22	of	of	ADP
fcis-26723	114	23	gear	gear	NOUN
fcis-26723	114	24	performance	performance	NOUN
fcis-26723	114	25	prediction	prediction	NOUN
fcis-26723	114	26	is	be	AUX
fcis-26723	114	27	high	high	ADJ
fcis-26723	114	28	.	.	PUNCT
fcis-26723	115	1	when	when	SCONJ
fcis-26723	115	2	the	the	DET
fcis-26723	115	3	number	number	NOUN
fcis-26723	115	4	of	of	ADP
fcis-26723	115	5	hidden	hide	VERB
fcis-26723	115	6	layer	layer	NOUN
fcis-26723	115	7	nodes	node	NOUN
fcis-26723	115	8	is	be	AUX
fcis-26723	115	9	large	large	ADJ
fcis-26723	115	10	,	,	PUNCT
fcis-26723	115	11	the	the	DET
fcis-26723	115	12	neural	neural	ADJ
fcis-26723	115	13	network	network	NOUN
fcis-26723	115	14	is	be	AUX
fcis-26723	115	15	prone	prone	ADJ
fcis-26723	115	16	to	to	ADP
fcis-26723	115	17	overfitting	overfitte	VERB
fcis-26723	115	18	,	,	PUNCT
fcis-26723	115	19	and	and	CCONJ
fcis-26723	115	20	the	the	DET
fcis-26723	115	21	training	training	NOUN
fcis-26723	115	22	time	time	NOUN
fcis-26723	115	23	of	of	ADP
fcis-26723	115	24	the	the	DET
fcis-26723	115	25	network	network	NOUN
fcis-26723	115	26	will	will	AUX
fcis-26723	115	27	be	be	AUX
fcis-26723	115	28	increased	increase	VERB
fcis-26723	115	29	.	.	PUNCT
fcis-26723	116	1	however	however	ADV
fcis-26723	116	2	,	,	PUNCT
fcis-26723	116	3	as	as	ADV
fcis-26723	116	4	far	far	ADV
fcis-26723	116	5	as	as	SCONJ
fcis-26723	116	6	the	the	DET
fcis-26723	116	7	current	current	ADJ
fcis-26723	116	8	theory	theory	NOUN
fcis-26723	116	9	is	be	AUX
fcis-26723	116	10	concerned	concern	VERB
fcis-26723	116	11	,	,	PUNCT
fcis-26723	116	12	there	there	PRON
fcis-26723	116	13	is	be	VERB
fcis-26723	116	14	no	no	DET
fcis-26723	116	15	best	good	ADJ
fcis-26723	116	16	way	way	NOUN
fcis-26723	116	17	to	to	PART
fcis-26723	116	18	determine	determine	VERB
fcis-26723	116	19	the	the	DET
fcis-26723	116	20	hidden	hide	VERB
fcis-26723	116	21	layer	layer	NOUN
fcis-26723	116	22	nodes	node	NOUN
fcis-26723	116	23	of	of	ADP
fcis-26723	116	24	bp	bp	PROPN
fcis-26723	116	25	network	network	NOUN
fcis-26723	116	26	.	.	PUNCT
fcis-26723	117	1	in	in	ADP
fcis-26723	117	2	this	this	DET
fcis-26723	117	3	paper	paper	NOUN
fcis-26723	117	4	,	,	PUNCT
fcis-26723	117	5	empirical	empirical	ADJ
fcis-26723	117	6	method	method	NOUN
fcis-26723	117	7	and	and	CCONJ
fcis-26723	117	8	trial	trial	NOUN
fcis-26723	117	9	and	and	CCONJ
fcis-26723	117	10	error	error	NOUN
fcis-26723	117	11	method	method	NOUN
fcis-26723	117	12	are	be	AUX
fcis-26723	117	13	used	use	VERB
fcis-26723	117	14	to	to	PART
fcis-26723	117	15	select	select	VERB
fcis-26723	117	16	the	the	DET
fcis-26723	117	17	appropriate	appropriate	ADJ
fcis-26723	117	18	number	number	NOUN
fcis-26723	117	19	of	of	ADP
fcis-26723	117	20	hidden	hide	VERB
fcis-26723	117	21	layer	layer	NOUN
fcis-26723	117	22	nodes	node	NOUN
fcis-26723	117	23	,	,	PUNCT
fcis-26723	117	24	and	and	CCONJ
fcis-26723	117	25	the	the	DET
fcis-26723	117	26	number	number	NOUN
fcis-26723	117	27	of	of	ADP
fcis-26723	117	28	hidden	hide	VERB
fcis-26723	117	29	layer	layer	NOUN
fcis-26723	117	30	nodes	node	NOUN
fcis-26723	117	31	in	in	ADP
fcis-26723	117	32	this	this	DET
fcis-26723	117	33	paper	paper	NOUN
fcis-26723	117	34	is	be	AUX
fcis-26723	117	35	12	12	NUM
fcis-26723	117	36	.	.	PUNCT
fcis-26723	118	1	(	(	PUNCT
fcis-26723	118	2	4	4	X
fcis-26723	118	3	)	)	PUNCT
fcis-26723	118	4	other	other	ADJ
fcis-26723	118	5	parameter	parameter	NOUN
fcis-26723	118	6	settings	setting	NOUN
fcis-26723	118	7	in	in	ADP
fcis-26723	118	8	bp	bp	PROPN
fcis-26723	118	9	neural	neural	ADJ
fcis-26723	118	10	network	network	NOUN
fcis-26723	118	11	,	,	PUNCT
fcis-26723	118	12	the	the	DET
fcis-26723	118	13	learning	learn	VERB
fcis-26723	118	14	factor	factor	NOUN
fcis-26723	118	15	η	η	PROPN
fcis-26723	118	16	=	=	PROPN
fcis-26723	118	17	0.1	0.1	NUM
fcis-26723	118	18	and	and	CCONJ
fcis-26723	118	19	the	the	DET
fcis-26723	118	20	critical	critical	ADJ
fcis-26723	118	21	value	value	NOUN
fcis-26723	118	22	of	of	ADP
fcis-26723	118	23	training	training	NOUN
fcis-26723	118	24	error	error	NOUN
fcis-26723	118	25	mse	mse	NOUN
fcis-26723	118	26	is	be	AUX
fcis-26723	118	27	set	set	VERB
fcis-26723	118	28	to	to	ADP
fcis-26723	118	29	0.001	0.001	NUM
fcis-26723	118	30	.	.	PUNCT
fcis-26723	119	1	107	107	NUM
fcis-26723	119	2	4.2.2	4.2.2	NUM
fcis-26723	119	3	.	.	PUNCT
fcis-26723	120	1	experimental	experimental	ADJ
fcis-26723	120	2	results	result	NOUN
fcis-26723	120	3	and	and	CCONJ
fcis-26723	120	4	analysis	analysis	NOUN
fcis-26723	120	5	the	the	DET
fcis-26723	120	6	accuracy	accuracy	NOUN
fcis-26723	120	7	of	of	ADP
fcis-26723	120	8	the	the	DET
fcis-26723	120	9	bp	bp	PROPN
fcis-26723	120	10	god	god	PROPN
fcis-26723	120	11	network	network	NOUN
fcis-26723	120	12	performance	performance	NOUN
fcis-26723	120	13	prediction	prediction	NOUN
fcis-26723	120	14	model	model	NOUN
fcis-26723	120	15	for	for	ADP
fcis-26723	120	16	the	the	DET
fcis-26723	120	17	gearbox	gearbox	ADJ
fcis-26723	120	18	gear	gear	NOUN
fcis-26723	120	19	performance	performance	NOUN
fcis-26723	120	20	prediction	prediction	NOUN
fcis-26723	120	21	is	be	AUX
fcis-26723	120	22	shown	show	VERB
fcis-26723	120	23	in	in	ADP
fcis-26723	120	24	figure	figure	NOUN
fcis-26723	120	25	3	3	NUM
fcis-26723	120	26	.	.	PUNCT
fcis-26723	120	27	figure	figure	NOUN
fcis-26723	120	28	3	3	NUM
fcis-26723	120	29	is	be	AUX
fcis-26723	120	30	the	the	DET
fcis-26723	120	31	confusion	confusion	NOUN
fcis-26723	120	32	matrix	matrix	NOUN
fcis-26723	120	33	.	.	PUNCT
fcis-26723	121	1	the	the	DET
fcis-26723	121	2	horizontal	horizontal	ADJ
fcis-26723	121	3	axis	axis	NOUN
fcis-26723	121	4	of	of	ADP
fcis-26723	121	5	the	the	DET
fcis-26723	121	6	confusion	confusion	NOUN
fcis-26723	121	7	matrix	matrix	NOUN
fcis-26723	121	8	represents	represent	VERB
fcis-26723	121	9	the	the	DET
fcis-26723	121	10	target	target	NOUN
fcis-26723	121	11	category	category	NOUN
fcis-26723	121	12	,	,	PUNCT
fcis-26723	121	13	the	the	DET
fcis-26723	121	14	vertical	vertical	ADJ
fcis-26723	121	15	axis	axis	NOUN
fcis-26723	121	16	represents	represent	VERB
fcis-26723	121	17	the	the	DET
fcis-26723	121	18	output	output	NOUN
fcis-26723	121	19	category	category	NOUN
fcis-26723	121	20	of	of	ADP
fcis-26723	121	21	the	the	DET
fcis-26723	121	22	prediction	prediction	NOUN
fcis-26723	121	23	model	model	NOUN
fcis-26723	121	24	,	,	PUNCT
fcis-26723	121	25	the	the	DET
fcis-26723	121	26	first	first	ADJ
fcis-26723	121	27	four	four	NUM
fcis-26723	121	28	diagonal	diagonal	ADJ
fcis-26723	121	29	lines	line	NOUN
fcis-26723	121	30	represent	represent	VERB
fcis-26723	121	31	the	the	DET
fcis-26723	121	32	accuracy	accuracy	NOUN
fcis-26723	121	33	rate	rate	NOUN
fcis-26723	121	34	of	of	ADP
fcis-26723	121	35	the	the	DET
fcis-26723	121	36	prediction	prediction	NOUN
fcis-26723	121	37	results	result	NOUN
fcis-26723	121	38	of	of	ADP
fcis-26723	121	39	each	each	DET
fcis-26723	121	40	grade	grade	NOUN
fcis-26723	121	41	level	level	NOUN
fcis-26723	121	42	,	,	PUNCT
fcis-26723	121	43	and	and	CCONJ
fcis-26723	121	44	the	the	DET
fcis-26723	121	45	bottom	bottom	ADJ
fcis-26723	121	46	diagonal	diagonal	ADJ
fcis-26723	121	47	lines	line	NOUN
fcis-26723	121	48	represent	represent	VERB
fcis-26723	121	49	the	the	DET
fcis-26723	121	50	total	total	ADJ
fcis-26723	121	51	prediction	prediction	NOUN
fcis-26723	121	52	accuracy	accuracy	NOUN
fcis-26723	121	53	rate	rate	NOUN
fcis-26723	121	54	,	,	PUNCT
fcis-26723	121	55	which	which	PRON
fcis-26723	121	56	is	be	AUX
fcis-26723	121	57	generally	generally	ADV
fcis-26723	121	58	used	use	VERB
fcis-26723	121	59	as	as	ADP
fcis-26723	121	60	the	the	DET
fcis-26723	121	61	measurement	measurement	NOUN
fcis-26723	121	62	standard	standard	NOUN
fcis-26723	121	63	for	for	ADP
fcis-26723	121	64	the	the	DET
fcis-26723	121	65	performance	performance	NOUN
fcis-26723	121	66	of	of	ADP
fcis-26723	121	67	the	the	DET
fcis-26723	121	68	prediction	prediction	NOUN
fcis-26723	121	69	model	model	NOUN
fcis-26723	121	70	.	.	PUNCT
fcis-26723	122	1	it	it	PRON
fcis-26723	122	2	can	can	AUX
fcis-26723	122	3	be	be	AUX
fcis-26723	122	4	seen	see	VERB
fcis-26723	122	5	from	from	ADP
fcis-26723	122	6	the	the	DET
fcis-26723	122	7	figure	figure	NOUN
fcis-26723	122	8	that	that	SCONJ
fcis-26723	122	9	the	the	DET
fcis-26723	122	10	accuracy	accuracy	NOUN
fcis-26723	122	11	of	of	ADP
fcis-26723	122	12	prediction	prediction	NOUN
fcis-26723	122	13	results	result	NOUN
fcis-26723	122	14	of	of	ADP
fcis-26723	122	15	bp	bp	PROPN
fcis-26723	122	16	god	god	PROPN
fcis-26723	122	17	network	network	NOUN
fcis-26723	122	18	performance	performance	NOUN
fcis-26723	122	19	prediction	prediction	NOUN
fcis-26723	122	20	model	model	NOUN
fcis-26723	122	21	is	be	AUX
fcis-26723	122	22	69.3	69.3	NUM
fcis-26723	122	23	%	%	NOUN
fcis-26723	122	24	.	.	PUNCT
fcis-26723	123	1	fig	fig	NOUN
fcis-26723	123	2	3	3	NUM
fcis-26723	123	3	.	.	PUNCT
fcis-26723	123	4	prediction	prediction	NOUN
fcis-26723	123	5	results	result	NOUN
fcis-26723	123	6	of	of	ADP
fcis-26723	123	7	bp	bp	PROPN
fcis-26723	123	8	god	god	PROPN
fcis-26723	123	9	network	network	NOUN
fcis-26723	123	10	performance	performance	NOUN
fcis-26723	123	11	prediction	prediction	NOUN
fcis-26723	123	12	model	model	NOUN
fcis-26723	123	13	5	5	NUM
fcis-26723	123	14	.	.	PUNCT
fcis-26723	124	1	summary	summary	NOUN
fcis-26723	124	2	this	this	DET
fcis-26723	124	3	paper	paper	NOUN
fcis-26723	124	4	takes	take	VERB
fcis-26723	124	5	student	student	NOUN
fcis-26723	124	6	achievement	achievement	NOUN
fcis-26723	124	7	as	as	ADP
fcis-26723	124	8	the	the	DET
fcis-26723	124	9	research	research	NOUN
fcis-26723	124	10	object	object	NOUN
fcis-26723	124	11	,	,	PUNCT
fcis-26723	124	12	mainly	mainly	ADV
fcis-26723	124	13	aiming	aim	VERB
fcis-26723	124	14	at	at	ADP
fcis-26723	124	15	the	the	DET
fcis-26723	124	16	historical	historical	ADJ
fcis-26723	124	17	achievement	achievement	NOUN
fcis-26723	124	18	of	of	ADP
fcis-26723	124	19	senior	senior	ADJ
fcis-26723	124	20	students	student	NOUN
fcis-26723	124	21	to	to	PART
fcis-26723	124	22	train	train	VERB
fcis-26723	124	23	the	the	DET
fcis-26723	124	24	prediction	prediction	NOUN
fcis-26723	124	25	model	model	NOUN
fcis-26723	124	26	,	,	PUNCT
fcis-26723	124	27	this	this	DET
fcis-26723	124	28	paper	paper	NOUN
fcis-26723	124	29	puts	put	VERB
fcis-26723	124	30	forward	forward	ADV
fcis-26723	124	31	the	the	DET
fcis-26723	124	32	achievement	achievement	NOUN
fcis-26723	124	33	prediction	prediction	NOUN
fcis-26723	124	34	based	base	VERB
fcis-26723	124	35	on	on	ADP
fcis-26723	124	36	bp	bp	PROPN
fcis-26723	124	37	neural	neural	ADJ
fcis-26723	124	38	network	network	NOUN
fcis-26723	124	39	.	.	PUNCT
fcis-26723	125	1	considering	consider	VERB
fcis-26723	125	2	that	that	SCONJ
fcis-26723	125	3	the	the	DET
fcis-26723	125	4	original	original	ADJ
fcis-26723	125	5	data	datum	NOUN
fcis-26723	125	6	set	set	VERB
fcis-26723	125	7	of	of	ADP
fcis-26723	125	8	students	student	NOUN
fcis-26723	125	9	is	be	AUX
fcis-26723	125	10	too	too	ADV
fcis-26723	125	11	large	large	ADJ
fcis-26723	125	12	and	and	CCONJ
fcis-26723	125	13	the	the	DET
fcis-26723	125	14	range	range	NOUN
fcis-26723	125	15	of	of	ADP
fcis-26723	125	16	values	value	NOUN
fcis-26723	125	17	is	be	AUX
fcis-26723	125	18	too	too	ADV
fcis-26723	125	19	large	large	ADJ
fcis-26723	125	20	,	,	PUNCT
fcis-26723	125	21	the	the	DET
fcis-26723	125	22	original	original	ADJ
fcis-26723	125	23	data	datum	NOUN
fcis-26723	125	24	set	set	VERB
fcis-26723	125	25	needs	need	VERB
fcis-26723	125	26	to	to	PART
fcis-26723	125	27	be	be	AUX
fcis-26723	125	28	pre	pre	VERB
fcis-26723	125	29	-	-	VERB
fcis-26723	125	30	processed	processed	ADJ
fcis-26723	125	31	.	.	PUNCT
fcis-26723	126	1	this	this	DET
fcis-26723	126	2	paper	paper	NOUN
fcis-26723	126	3	proposes	propose	VERB
fcis-26723	126	4	to	to	PART
fcis-26723	126	5	use	use	VERB
fcis-26723	126	6	the	the	DET
fcis-26723	126	7	min	min	ADJ
fcis-26723	126	8	-	-	ADJ
fcis-26723	126	9	max	max	PROPN
fcis-26723	126	10	normalization	normalization	NOUN
fcis-26723	126	11	method	method	NOUN
fcis-26723	126	12	to	to	PART
fcis-26723	126	13	pre	pre	VERB
fcis-26723	126	14	-	-	VERB
fcis-26723	126	15	process	process	VERB
fcis-26723	126	16	the	the	DET
fcis-26723	126	17	data	datum	NOUN
fcis-26723	126	18	,	,	PUNCT
fcis-26723	126	19	so	so	SCONJ
fcis-26723	126	20	that	that	SCONJ
fcis-26723	126	21	the	the	DET
fcis-26723	126	22	preprocessed	preprocesse	VERB
fcis-26723	126	23	data	datum	NOUN
fcis-26723	126	24	can	can	AUX
fcis-26723	126	25	be	be	AUX
fcis-26723	126	26	used	use	VERB
fcis-26723	126	27	as	as	ADP
fcis-26723	126	28	the	the	DET
fcis-26723	126	29	input	input	NOUN
fcis-26723	126	30	data	datum	NOUN
fcis-26723	126	31	of	of	ADP
fcis-26723	126	32	the	the	DET
fcis-26723	126	33	prediction	prediction	NOUN
fcis-26723	126	34	model	model	NOUN
fcis-26723	126	35	.	.	PUNCT
fcis-26723	127	1	the	the	DET
fcis-26723	127	2	grade	grade	NOUN
fcis-26723	127	3	prediction	prediction	NOUN
fcis-26723	127	4	model	model	NOUN
fcis-26723	127	5	in	in	ADP
fcis-26723	127	6	this	this	DET
fcis-26723	127	7	paper	paper	NOUN
fcis-26723	127	8	is	be	AUX
fcis-26723	127	9	based	base	VERB
fcis-26723	127	10	on	on	ADP
fcis-26723	127	11	bp	bp	PROPN
fcis-26723	127	12	neural	neural	ADJ
fcis-26723	127	13	network	network	NOUN
fcis-26723	127	14	.	.	PUNCT
fcis-26723	128	1	acknowledgments	acknowledgment	NOUN
fcis-26723	128	2	xinjiang	xinjiang	PROPN
fcis-26723	128	3	university	university	PROPN
fcis-26723	128	4	of	of	ADP
fcis-26723	128	5	political	political	ADJ
fcis-26723	128	6	science	science	NOUN
fcis-26723	128	7	and	and	CCONJ
fcis-26723	128	8	law	law	NOUN
fcis-26723	128	9	president	president	NOUN
fcis-26723	128	10	's	's	PART
fcis-26723	128	11	fund+xzzk2021001	fund+xzzk2021001	NOUN
fcis-26723	128	12	.	.	PUNCT
fcis-26723	129	1	references	reference	NOUN
fcis-26723	129	2	[	[	X
fcis-26723	129	3	1	1	NUM
fcis-26723	129	4	]	]	PUNCT
fcis-26723	129	5	ertel	ertel	NOUN
fcis-26723	129	6	w.	w.	NOUN
fcis-26723	129	7	machine	machine	NOUN
fcis-26723	129	8	learning	learning	PROPN
fcis-26723	129	9	and	and	CCONJ
fcis-26723	129	10	data	datum	NOUN
fcis-26723	129	11	mining	mining	NOUN
fcis-26723	129	12	.	.	PUNCT
fcis-26723	130	1	springer	springer	PROPN
fcis-26723	130	2	london	london	PROPN
fcis-26723	130	3	,	,	PUNCT
fcis-26723	130	4	2011	2011	NUM
fcis-26723	130	5	.	.	PUNCT
fcis-26723	131	1	[	[	X
fcis-26723	131	2	2	2	NUM
fcis-26723	131	3	]	]	X
fcis-26723	131	4	mostow	mostow	PROPN
fcis-26723	131	5	j	j	PROPN
fcis-26723	131	6	,	,	PUNCT
fcis-26723	131	7	beck	beck	PROPN
fcis-26723	131	8	j	j	PROPN
fcis-26723	131	9	.	.	PUNCT
fcis-26723	132	1	some	some	DET
fcis-26723	132	2	useful	useful	ADJ
fcis-26723	132	3	tactics	tactic	NOUN
fcis-26723	132	4	to	to	PART
fcis-26723	132	5	modify	modify	VERB
fcis-26723	132	6	,	,	PUNCT
fcis-26723	132	7	map	map	VERB
fcis-26723	132	8	and	and	CCONJ
fcis-26723	132	9	mine	mine	NOUN
fcis-26723	132	10	data	datum	NOUN
fcis-26723	132	11	from	from	ADP
fcis-26723	132	12	intelligent	intelligent	ADJ
fcis-26723	132	13	tutors[j	tutors[j	PROPN
fcis-26723	132	14	]	]	PUNCT
fcis-26723	132	15	.	.	PUNCT
fcis-26723	133	1	natural	natural	ADJ
fcis-26723	133	2	language	language	NOUN
fcis-26723	133	3	engineering	engineering	NOUN
fcis-26723	133	4	,	,	PUNCT
fcis-26723	133	5	2006	2006	NUM
fcis-26723	133	6	,	,	PUNCT
fcis-26723	133	7	12(pt2):195	12(pt2):195	NUM
fcis-26723	133	8	-	-	SYM
fcis-26723	133	9	208	208	NUM
fcis-26723	133	10	.	.	PUNCT
fcis-26723	134	1	[	[	X
fcis-26723	134	2	3	3	X
fcis-26723	134	3	]	]	PUNCT
fcis-26723	134	4	hippe	hippe	PROPN
fcis-26723	134	5	j	j	PROPN
fcis-26723	134	6	m.	m.	NOUN
fcis-26723	134	7	a	a	DET
fcis-26723	134	8	review	review	NOUN
fcis-26723	134	9	of	of	ADP
fcis-26723	134	10	the	the	DET
fcis-26723	134	11	state	state	NOUN
fcis-26723	134	12	of	of	ADP
fcis-26723	134	13	the	the	DET
fcis-26723	134	14	art	art	NOUN
fcis-26723	134	15	.	.	PUNCT
fcis-26723	135	1	[	[	X
fcis-26723	135	2	4	4	NUM
fcis-26723	135	3	]	]	X
fcis-26723	135	4	zhou	zhou	PROPN
fcis-26723	135	5	qing	qing	PROPN
fcis-26723	135	6	,	,	PUNCT
fcis-26723	135	7	mou	mou	PROPN
fcis-26723	135	8	chao	chao	PROPN
fcis-26723	135	9	,	,	PUNCT
fcis-26723	135	10	yang	yang	PROPN
fcis-26723	135	11	dan	dan	PROPN
fcis-26723	135	12	.	.	PROPN
fcis-26723	135	13	review	review	PROPN
fcis-26723	135	14	on	on	ADP
fcis-26723	135	15	research	research	NOUN
fcis-26723	135	16	progress	progress	NOUN
fcis-26723	135	17	of	of	ADP
fcis-26723	135	18	educational	educational	ADJ
fcis-26723	135	19	data	datum	NOUN
fcis-26723	135	20	mining	mining	NOUN
fcis-26723	136	1	[	[	X
fcis-26723	136	2	j	j	X
fcis-26723	136	3	]	]	X
fcis-26723	136	4	.	.	PUNCT
fcis-26723	137	1	journal	journal	PROPN
fcis-26723	137	2	of	of	ADP
fcis-26723	137	3	software	software	NOUN
fcis-26723	137	4	,	,	PUNCT
fcis-26723	137	5	2015	2015	NUM
fcis-26723	137	6	,	,	PUNCT
fcis-26723	137	7	26(11):17	26(11):17	NUM
fcis-26723	137	8	.	.	PUNCT
fcis-26723	138	1	(	(	PUNCT
fcis-26723	138	2	in	in	ADP
fcis-26723	138	3	chinese	chinese	PROPN
fcis-26723	138	4	)	)	PUNCT
fcis-26723	139	1	[	[	X
fcis-26723	139	2	5	5	NUM
fcis-26723	139	3	]	]	PUNCT
fcis-26723	139	4	xiao	xiao	PROPN
fcis-26723	139	5	wei	wei	PROPN
fcis-26723	139	6	,	,	PUNCT
fcis-26723	139	7	ni	ni	PROPN
fcis-26723	139	8	chuanbin	chuanbin	NOUN
fcis-26723	139	9	,	,	PUNCT
fcis-26723	139	10	li	li	PROPN
fcis-26723	139	11	rui	rui	PROPN
fcis-26723	139	12	.	.	PUNCT
fcis-26723	140	1	foreign	foreign	ADJ
fcis-26723	140	2	research	research	NOUN
fcis-26723	140	3	on	on	ADP
fcis-26723	140	4	learning	learn	VERB
fcis-26723	140	5	prediction	prediction	NOUN
fcis-26723	140	6	based	base	VERB
fcis-26723	140	7	on	on	ADP
fcis-26723	140	8	data	data	NOUN
fcis-26723	140	9	mining	mining	NOUN
fcis-26723	140	10	:	:	PUNCT
fcis-26723	140	11	review	review	NOUN
fcis-26723	140	12	and	and	CCONJ
fcis-26723	140	13	prospect	prospect	VERB
fcis-26723	141	1	[	[	X
fcis-26723	141	2	j	j	X
fcis-26723	141	3	]	]	X
fcis-26723	141	4	.	.	PUNCT
fcis-26723	142	1	china	china	PROPN
fcis-26723	142	2	distance	distance	PROPN
fcis-26723	142	3	education	education	NOUN
fcis-26723	142	4	,	,	PUNCT
fcis-26723	142	5	2018(2):9	2018(2):9	PROPN
fcis-26723	142	6	.	.	PUNCT
fcis-26723	143	1	(	(	PUNCT
fcis-26723	143	2	in	in	ADP
fcis-26723	143	3	chinese	chinese	PROPN
fcis-26723	143	4	)	)	PUNCT
fcis-26723	144	1	[	[	X
fcis-26723	144	2	6	6	NUM
fcis-26723	144	3	]	]	X
fcis-26723	144	4	chen	chen	PROPN
fcis-26723	144	5	qinhua	qinhua	PROPN
fcis-26723	144	6	.	.	PUNCT
fcis-26723	144	7	research	research	NOUN
fcis-26723	144	8	on	on	ADP
fcis-26723	144	9	the	the	DET
fcis-26723	144	10	academic	academic	ADJ
fcis-26723	144	11	prediction	prediction	NOUN
fcis-26723	144	12	mechanism	mechanism	NOUN
fcis-26723	144	13	of	of	ADP
fcis-26723	144	14	college	college	NOUN
fcis-26723	144	15	students	student	NOUN
fcis-26723	144	16	under	under	ADP
fcis-26723	144	17	the	the	DET
fcis-26723	144	18	credit	credit	NOUN
fcis-26723	144	19	system	system	NOUN
fcis-26723	145	1	[	[	X
fcis-26723	145	2	j	j	X
fcis-26723	145	3	]	]	X
fcis-26723	145	4	.	.	PUNCT
fcis-26723	146	1	journal	journal	PROPN
fcis-26723	146	2	of	of	ADP
fcis-26723	146	3	guangxi	guangxi	PROPN
fcis-26723	146	4	normal	normal	ADJ
fcis-26723	146	5	university	university	NOUN
fcis-26723	146	6	:	:	PUNCT
fcis-26723	146	7	philosophy	philosophy	NOUN
fcis-26723	146	8	and	and	CCONJ
fcis-26723	146	9	social	social	ADJ
fcis-26723	146	10	sciences	science	NOUN
fcis-26723	146	11	edition	edition	NOUN
fcis-26723	146	12	,	,	PUNCT
fcis-26723	146	13	2007(s2):6.(in	2007(s2):6.(in	NUM
fcis-26723	146	14	chinese	chinese	NOUN
fcis-26723	146	15	)	)	PUNCT
fcis-26723	147	1	[	[	X
fcis-26723	147	2	7	7	X
fcis-26723	147	3	]	]	X
fcis-26723	147	4	wang	wang	PROPN
fcis-26723	147	5	zihua	zihua	PROPN
fcis-26723	147	6	,	,	PUNCT
fcis-26723	147	7	zhang	zhang	PROPN
fcis-26723	147	8	baojing	baojing	PROPN
fcis-26723	147	9	,	,	PUNCT
fcis-26723	147	10	shi	shi	PROPN
fcis-26723	147	11	yuan	yuan	PROPN
fcis-26723	147	12	,	,	PUNCT
fcis-26723	147	13	et	et	PROPN
fcis-26723	147	14	al	al	PROPN
fcis-26723	147	15	.	.	PROPN
fcis-26723	147	16	study	study	NOUN
fcis-26723	147	17	on	on	ADP
fcis-26723	147	18	the	the	DET
fcis-26723	147	19	prediction	prediction	NOUN
fcis-26723	147	20	mechanism	mechanism	NOUN
fcis-26723	147	21	of	of	ADP
fcis-26723	147	22	college	college	NOUN
fcis-26723	147	23	students	student	NOUN
fcis-26723	147	24	'	'	PART
fcis-26723	147	25	academic	academic	ADJ
fcis-26723	147	26	difficulties	difficulty	NOUN
fcis-26723	147	27	[	[	X
fcis-26723	147	28	j	j	X
fcis-26723	147	29	]	]	X
fcis-26723	147	30	.	.	PUNCT
fcis-26723	148	1	journal	journal	PROPN
fcis-26723	148	2	of	of	ADP
fcis-26723	148	3	college	college	PROPN
fcis-26723	148	4	of	of	ADP
fcis-26723	148	5	adult	adult	NOUN
fcis-26723	148	6	education	education	NOUN
fcis-26723	148	7	,	,	PUNCT
fcis-26723	148	8	hebei	hebei	PROPN
fcis-26723	148	9	university	university	PROPN
fcis-26723	148	10	of	of	ADP
fcis-26723	148	11	technology	technology	NOUN
fcis-26723	148	12	,	,	PUNCT
fcis-26723	148	13	2009(3):4	2009(3):4	PROPN
fcis-26723	148	14	.	.	PUNCT
fcis-26723	149	1	(	(	PUNCT
fcis-26723	149	2	in	in	ADP
fcis-26723	149	3	chinese	chinese	PROPN
fcis-26723	149	4	)	)	PUNCT
fcis-26723	150	1	[	[	X
fcis-26723	150	2	8	8	NUM
fcis-26723	150	3	]	]	X
fcis-26723	150	4	shang	shang	PROPN
fcis-26723	150	5	wei	wei	PROPN
fcis-26723	150	6	,	,	PUNCT
fcis-26723	150	7	champion	champion	NOUN
fcis-26723	150	8	luo	luo	PROPN
fcis-26723	150	9	.	.	PUNCT
fcis-26723	150	10	study	study	NOUN
fcis-26723	150	11	on	on	ADP
fcis-26723	150	12	academic	academic	ADJ
fcis-26723	150	13	prediction	prediction	NOUN
fcis-26723	150	14	mechanism	mechanism	NOUN
fcis-26723	150	15	of	of	ADP
fcis-26723	150	16	local	local	ADJ
fcis-26723	150	17	college	college	NOUN
fcis-26723	150	18	students	student	NOUN
fcis-26723	151	1	[	[	X
fcis-26723	151	2	j	j	X
fcis-26723	151	3	]	]	X
fcis-26723	151	4	.	.	PUNCT
fcis-26723	152	1	contemporary	contemporary	ADJ
fcis-26723	152	2	educational	educational	ADJ
fcis-26723	152	3	practice	practice	NOUN
fcis-26723	152	4	and	and	CCONJ
fcis-26723	152	5	teaching	teach	VERB
fcis-26723	152	6	research	research	NOUN
fcis-26723	152	7	:	:	PUNCT
fcis-26723	152	8	electronic	electronic	ADJ
fcis-26723	152	9	edition	edition	NOUN
fcis-26723	152	10	,	,	PUNCT
fcis-26723	152	11	2016(9):2	2016(9):2	NUM
fcis-26723	152	12	.	.	PUNCT
fcis-26723	153	1	(	(	PUNCT
fcis-26723	153	2	in	in	ADP
fcis-26723	153	3	chinese	chinese	PROPN
fcis-26723	153	4	)	)	PUNCT
fcis-26723	154	1	[	[	X
fcis-26723	154	2	9	9	NUM
fcis-26723	154	3	]	]	PUNCT
fcis-26723	154	4	he	he	PRON
fcis-26723	154	5	kui	kui	PROPN
fcis-26723	154	6	.	.	PUNCT
fcis-26723	155	1	a	a	DET
fcis-26723	155	2	study	study	NOUN
fcis-26723	155	3	on	on	ADP
fcis-26723	155	4	the	the	DET
fcis-26723	155	5	construction	construction	NOUN
fcis-26723	155	6	of	of	ADP
fcis-26723	155	7	academic	academic	ADJ
fcis-26723	155	8	prediction	prediction	NOUN
fcis-26723	155	9	mechanism	mechanism	NOUN
fcis-26723	155	10	for	for	ADP
fcis-26723	155	11	college	college	NOUN
fcis-26723	155	12	students	student	NOUN
fcis-26723	155	13	under	under	ADP
fcis-26723	155	14	the	the	DET
fcis-26723	155	15	credit	credit	NOUN
fcis-26723	155	16	system	system	NOUN
fcis-26723	156	1	[	[	X
fcis-26723	156	2	j	j	X
fcis-26723	156	3	]	]	X
fcis-26723	156	4	.	.	PUNCT
fcis-26723	157	1	journal	journal	PROPN
fcis-26723	157	2	of	of	ADP
fcis-26723	157	3	hubei	hubei	PROPN
fcis-26723	157	4	adult	adult	PROPN
fcis-26723	157	5	education	education	NOUN
fcis-26723	157	6	university	university	NOUN
fcis-26723	157	7	,	,	PUNCT
fcis-26723	157	8	2017	2017	NUM
fcis-26723	157	9	,	,	PUNCT
fcis-26723	157	10	23(3):4.(in	23(3):4.(in	NUM
fcis-26723	157	11	chinese	chinese	ADJ
fcis-26723	157	12	)	)	PUNCT
fcis-26723	158	1	[	[	X
fcis-26723	158	2	10	10	NUM
fcis-26723	158	3	]	]	X
fcis-26723	158	4	wu	wu	PROPN
fcis-26723	158	5	haifeng	haifeng	PROPN
fcis-26723	158	6	,	,	PUNCT
fcis-26723	158	7	cheng	cheng	PROPN
fcis-26723	158	8	yusheng	yusheng	PROPN
fcis-26723	158	9	,	,	PUNCT
fcis-26723	158	10	hu	hu	PROPN
fcis-26723	158	11	xuegang	xuegang	PROPN
fcis-26723	158	12	.	.	PUNCT
fcis-26723	159	1	design	design	NOUN
fcis-26723	159	2	of	of	ADP
fcis-26723	159	3	university	university	NOUN
fcis-26723	159	4	achievement	achievement	NOUN
fcis-26723	159	5	prediction	prediction	NOUN
fcis-26723	159	6	model	model	NOUN
fcis-26723	159	7	based	base	VERB
fcis-26723	159	8	on	on	ADP
fcis-26723	159	9	data	data	NOUN
fcis-26723	159	10	mining	mining	NOUN
fcis-26723	159	11	technology	technology	NOUN
fcis-26723	159	12	[	[	X
fcis-26723	159	13	c]//	c]//	PROPN
fcis-26723	159	14	2010.(in	2010.(in	NUM
fcis-26723	159	15	chinese	chinese	NOUN
fcis-26723	159	16	)	)	PUNCT
fcis-26723	160	1	[	[	X
fcis-26723	160	2	11	11	NUM
fcis-26723	160	3	]	]	X
fcis-26723	160	4	wan	wan	PROPN
fcis-26723	160	5	xinghuo	xinghuo	PROPN
fcis-26723	160	6	,	,	PUNCT
fcis-26723	160	7	zheng	zheng	PROPN
fcis-26723	160	8	junling	junling	PROPN
fcis-26723	160	9	,	,	PUNCT
fcis-26723	160	10	jin	jin	PROPN
fcis-26723	160	11	yongchao	yongchao	PROPN
fcis-26723	160	12	.	.	PUNCT
fcis-26723	161	1	academic	academic	ADJ
fcis-26723	161	2	prediction	prediction	NOUN
fcis-26723	161	3	model	model	NOUN
fcis-26723	161	4	based	base	VERB
fcis-26723	161	5	on	on	ADP
fcis-26723	161	6	kpca	kpca	NOUN
fcis-26723	161	7	and	and	CCONJ
fcis-26723	161	8	its	its	PRON
fcis-26723	161	9	application	application	NOUN
fcis-26723	161	10	[	[	X
fcis-26723	161	11	j	j	X
fcis-26723	161	12	]	]	X
fcis-26723	161	13	.	.	PUNCT
fcis-26723	162	1	mathematical	mathematical	ADJ
fcis-26723	162	2	theory	theory	NOUN
fcis-26723	162	3	and	and	CCONJ
fcis-26723	162	4	applications	application	NOUN
fcis-26723	162	5	,	,	PUNCT
fcis-26723	162	6	2013	2013	NUM
fcis-26723	162	7	,	,	PUNCT
fcis-26723	162	8	33(4):6.(in	33(4):6.(in	NUM
fcis-26723	162	9	chinese	chinese	ADJ
fcis-26723	162	10	)	)	PUNCT
fcis-26723	163	1	[	[	X
fcis-26723	163	2	12	12	NUM
fcis-26723	163	3	]	]	X
fcis-26723	163	4	zhang	zhang	PROPN
fcis-26723	163	5	fusheng	fusheng	PROPN
fcis-26723	163	6	,	,	PUNCT
fcis-26723	163	7	lu	lu	PROPN
fcis-26723	163	8	kaidong	kaidong	PROPN
fcis-26723	163	9	,	,	PUNCT
fcis-26723	163	10	han	han	PROPN
fcis-26723	163	11	yijia	yijia	PROPN
fcis-26723	163	12	.	.	PUNCT
fcis-26723	164	1	research	research	NOUN
fcis-26723	164	2	on	on	ADP
fcis-26723	164	3	the	the	DET
fcis-26723	164	4	academic	academic	ADJ
fcis-26723	164	5	monitoring	monitoring	NOUN
fcis-26723	164	6	and	and	CCONJ
fcis-26723	164	7	prediction	prediction	NOUN
fcis-26723	164	8	system	system	NOUN
fcis-26723	164	9	of	of	ADP
fcis-26723	164	10	college	college	NOUN
fcis-26723	164	11	students	student	NOUN
fcis-26723	164	12	based	base	VERB
fcis-26723	164	13	on	on	ADP
fcis-26723	164	14	campus	campus	NOUN
fcis-26723	164	15	cloud	cloud	NOUN
fcis-26723	165	1	[	[	X
fcis-26723	165	2	j	j	X
fcis-26723	165	3	]	]	X
fcis-26723	165	4	.	.	PUNCT
fcis-26723	166	1	china	china	PROPN
fcis-26723	166	2	education	education	PROPN
fcis-26723	166	3	informatization	informatization	PROPN
fcis-26723	166	4	:	:	PUNCT
fcis-26723	166	5	higher	high	ADJ
fcis-26723	166	6	vocational	vocational	ADJ
fcis-26723	166	7	education	education	NOUN
fcis-26723	166	8	,	,	PUNCT
fcis-26723	166	9	2015(5):3.(in	2015(5):3.(in	NUM
fcis-26723	166	10	chinese	chinese	NOUN
fcis-26723	166	11	)	)	PUNCT
fcis-26723	167	1	[	[	X
fcis-26723	167	2	13	13	NUM
fcis-26723	167	3	]	]	PUNCT
fcis-26723	167	4	jin	jin	NOUN
fcis-26723	167	5	yifu	yifu	PROPN
fcis-26723	167	6	,	,	PUNCT
fcis-26723	167	7	wu	wu	PROPN
fcis-26723	167	8	tao	tao	PROPN
fcis-26723	167	9	,	,	PUNCT
fcis-26723	167	10	zhang	zhang	PROPN
fcis-26723	167	11	zishi	zishi	PROPN
fcis-26723	167	12	,	,	PUNCT
fcis-26723	167	13	et	et	PROPN
fcis-26723	167	14	al	al	PROPN
fcis-26723	167	15	.	.	PROPN
fcis-26723	167	16	design	design	NOUN
fcis-26723	167	17	and	and	CCONJ
fcis-26723	167	18	analysis	analysis	NOUN
fcis-26723	167	19	of	of	ADP
fcis-26723	167	20	academic	academic	ADJ
fcis-26723	167	21	prediction	prediction	NOUN
fcis-26723	167	22	system	system	NOUN
fcis-26723	167	23	in	in	ADP
fcis-26723	167	24	big	big	ADJ
fcis-26723	167	25	data	datum	NOUN
fcis-26723	167	26	environment	environment	NOUN
fcis-26723	168	1	[	[	X
fcis-26723	168	2	j	j	X
fcis-26723	168	3	]	]	X
fcis-26723	168	4	.	.	PUNCT
fcis-26723	169	1	china	china	PROPN
fcis-26723	169	2	audio	audio	ADJ
fcis-26723	169	3	-	-	ADJ
fcis-26723	169	4	visual	visual	ADJ
fcis-26723	169	5	education	education	NOUN
fcis-26723	169	6	,	,	PUNCT
fcis-26723	169	7	2016(2):5	2016(2):5	PROPN
fcis-26723	169	8	.	.	PUNCT
fcis-26723	170	1	(	(	PUNCT
fcis-26723	170	2	in	in	ADP
fcis-26723	170	3	chinese	chinese	PROPN
fcis-26723	170	4	)	)	PUNCT
fcis-26723	171	1	[	[	X
fcis-26723	171	2	14	14	NUM
fcis-26723	171	3	]	]	X
fcis-26723	171	4	du	du	PROPN
fcis-26723	171	5	juan	juan	PROPN
fcis-26723	171	6	and	and	CCONJ
fcis-26723	171	7	zhai	zhai	PROPN
fcis-26723	171	8	sheping	sheping	PROPN
fcis-26723	171	9	.	.	PUNCT
fcis-26723	172	1	research	research	NOUN
fcis-26723	172	2	on	on	ADP
fcis-26723	172	3	college	college	NOUN
fcis-26723	172	4	student	student	NOUN
fcis-26723	172	5	achievement	achievement	NOUN
fcis-26723	172	6	prediction	prediction	NOUN
fcis-26723	172	7	system	system	NOUN
fcis-26723	172	8	based	base	VERB
fcis-26723	172	9	on	on	ADP
fcis-26723	172	10	improved	improve	VERB
fcis-26723	172	11	apriori	apriori	ADJ
fcis-26723	172	12	algorithm	algorithm	NOUN
fcis-26723	173	1	[	[	X
fcis-26723	173	2	j	j	X
fcis-26723	173	3	]	]	X
fcis-26723	173	4	.	.	PUNCT
fcis-26723	174	1	modern	modern	ADJ
fcis-26723	174	2	information	information	NOUN
fcis-26723	174	3	technology	technology	NOUN
fcis-26723	174	4	,	,	PUNCT
fcis-26723	174	5	2018	2018	NUM
fcis-26723	174	6	,	,	PUNCT
fcis-26723	174	7	2(2):3	2(2):3	X
fcis-26723	174	8	.	.	PUNCT
fcis-26723	175	1	(	(	PUNCT
fcis-26723	175	2	in	in	ADP
fcis-26723	175	3	chinese	chinese	PROPN
fcis-26723	175	4	)	)	PUNCT
fcis-26723	176	1	[	[	X
fcis-26723	176	2	15	15	NUM
fcis-26723	176	3	]	]	X
fcis-26723	176	4	cui	cui	PROPN
fcis-26723	176	5	qiang	qiang	PROPN
fcis-26723	176	6	,	,	PUNCT
fcis-26723	176	7	sun	sun	PROPN
fcis-26723	176	8	jiyan	jiyan	PROPN
fcis-26723	176	9	.	.	PUNCT
fcis-26723	177	1	the	the	DET
fcis-26723	177	2	application	application	NOUN
fcis-26723	177	3	of	of	ADP
fcis-26723	177	4	big	big	ADJ
fcis-26723	177	5	data	datum	NOUN
fcis-26723	177	6	in	in	ADP
fcis-26723	177	7	promoting	promote	VERB
fcis-26723	177	8	the	the	DET
fcis-26723	177	9	quality	quality	NOUN
fcis-26723	177	10	of	of	ADP
fcis-26723	177	11	ideological	ideological	ADJ
fcis-26723	177	12	and	and	CCONJ
fcis-26723	177	13	political	political	ADJ
fcis-26723	177	14	education	education	NOUN
fcis-26723	177	15	in	in	ADP
fcis-26723	177	16	colleges	college	NOUN
fcis-26723	177	17	and	and	CCONJ
fcis-26723	177	18	universities	university	NOUN
fcis-26723	177	19	-taking	-take	VERB
fcis-26723	177	20	dalian	dalian	PROPN
fcis-26723	177	21	university	university	PROPN
fcis-26723	177	22	of	of	ADP
fcis-26723	177	23	technology	technology	NOUN
fcis-26723	177	24	academic	academic	ADJ
fcis-26723	177	25	prediction	prediction	NOUN
fcis-26723	177	26	model	model	NOUN
fcis-26723	177	27	as	as	ADP
fcis-26723	177	28	an	an	DET
fcis-26723	177	29	example	example	NOUN
fcis-26723	178	1	[	[	X
fcis-26723	178	2	j	j	X
fcis-26723	178	3	]	]	X
fcis-26723	178	4	.	.	PUNCT
fcis-26723	179	1	journal	journal	PROPN
fcis-26723	179	2	of	of	ADP
fcis-26723	179	3	college	college	NOUN
fcis-26723	179	4	counselors	counselor	NOUN
fcis-26723	179	5	,	,	PUNCT
fcis-26723	179	6	2019	2019	NUM
fcis-26723	179	7	,	,	PUNCT
fcis-26723	179	8	11(5):5.(in	11(5):5.(in	NUM
fcis-26723	179	9	chinese	chinese	ADJ
fcis-26723	179	10	)	)	PUNCT