id	sid	tid	token	lemma	pos
aiti-9227	1	1	3___aiti#9227___258	3___aiti#9227___258	NUM
aiti-9227	1	2	-	-	SYM
aiti-9227	1	3	269	269	NUM
aiti-9227	1	4	advances	advance	NOUN
aiti-9227	1	5	in	in	ADP
aiti-9227	1	6	technology	technology	NOUN
aiti-9227	1	7	innovation	innovation	NOUN
aiti-9227	1	8	,	,	PUNCT
aiti-9227	1	9	vol	vol	NOUN
aiti-9227	1	10	.	.	PROPN
aiti-9227	1	11	7	7	NUM
aiti-9227	1	12	,	,	PUNCT
aiti-9227	1	13	no	no	INTJ
aiti-9227	1	14	.	.	NOUN
aiti-9227	1	15	4	4	NUM
aiti-9227	1	16	,	,	PUNCT
aiti-9227	1	17	2022	2022	NUM
aiti-9227	1	18	,	,	PUNCT
aiti-9227	1	19	pp	pp	ADJ
aiti-9227	1	20	.	.	PUNCT
aiti-9227	2	1	258	258	NUM
aiti-9227	2	2	-	-	SYM
aiti-9227	2	3	269	269	NUM
aiti-9227	2	4	effects	effect	NOUN
aiti-9227	2	5	of	of	ADP
aiti-9227	2	6	data	datum	NOUN
aiti-9227	2	7	standardization	standardization	NOUN
aiti-9227	2	8	on	on	ADP
aiti-9227	2	9	hyperparameter	hyperparameter	NOUN
aiti-9227	2	10	optimization	optimization	NOUN
aiti-9227	2	11	with	with	ADP
aiti-9227	2	12	the	the	DET
aiti-9227	2	13	grid	grid	NOUN
aiti-9227	2	14	search	search	NOUN
aiti-9227	2	15	algorithm	algorithm	NOUN
aiti-9227	2	16	based	base	VERB
aiti-9227	2	17	on	on	ADP
aiti-9227	2	18	deep	deep	ADJ
aiti-9227	2	19	learning	learning	NOUN
aiti-9227	2	20	:	:	PUNCT
aiti-9227	2	21	a	a	DET
aiti-9227	2	22	case	case	NOUN
aiti-9227	2	23	study	study	NOUN
aiti-9227	2	24	of	of	ADP
aiti-9227	2	25	electric	electric	ADJ
aiti-9227	2	26	load	load	NOUN
aiti-9227	2	27	forecasting	forecasting	NOUN
aiti-9227	2	28	tran	tran	PROPN
aiti-9227	2	29	thanh	thanh	PROPN
aiti-9227	2	30	ngoc	ngoc	PROPN
aiti-9227	2	31	,	,	PUNCT
aiti-9227	2	32	le	le	PROPN
aiti-9227	2	33	van	van	PROPN
aiti-9227	2	34	dai	dai	PROPN
aiti-9227	2	35	*	*	PUNCT
aiti-9227	2	36	,	,	PUNCT
aiti-9227	2	37	lam	lam	PROPN
aiti-9227	2	38	binh	binh	PROPN
aiti-9227	2	39	minh	minh	PROPN
aiti-9227	2	40	faculty	faculty	NOUN
aiti-9227	2	41	of	of	ADP
aiti-9227	2	42	electrical	electrical	ADJ
aiti-9227	2	43	engineering	engineering	NOUN
aiti-9227	2	44	technology	technology	NOUN
aiti-9227	2	45	,	,	PUNCT
aiti-9227	2	46	industrial	industrial	ADJ
aiti-9227	2	47	university	university	PROPN
aiti-9227	2	48	of	of	ADP
aiti-9227	2	49	ho	ho	PROPN
aiti-9227	2	50	chi	chi	PROPN
aiti-9227	2	51	minh	minh	PROPN
aiti-9227	2	52	city	city	PROPN
aiti-9227	2	53	,	,	PUNCT
aiti-9227	2	54	ho	ho	PROPN
aiti-9227	2	55	chi	chi	PROPN
aiti-9227	2	56	minh	minh	PROPN
aiti-9227	2	57	city	city	PROPN
aiti-9227	2	58	,	,	PUNCT
aiti-9227	2	59	vietnam	vietnam	PROPN
aiti-9227	2	60	received	receive	VERB
aiti-9227	2	61	04	04	NUM
aiti-9227	2	62	january	january	PROPN
aiti-9227	2	63	2022	2022	NUM
aiti-9227	2	64	;	;	PUNCT
aiti-9227	2	65	received	receive	VERB
aiti-9227	2	66	in	in	ADP
aiti-9227	2	67	revised	revise	VERB
aiti-9227	2	68	form	form	NOUN
aiti-9227	2	69	24	24	NUM
aiti-9227	2	70	march	march	NOUN
aiti-9227	2	71	2022	2022	NUM
aiti-9227	2	72	;	;	PUNCT
aiti-9227	2	73	accepted	accept	VERB
aiti-9227	2	74	25	25	NUM
aiti-9227	2	75	march	march	NOUN
aiti-9227	2	76	2022	2022	NUM
aiti-9227	2	77	doi	doi	NOUN
aiti-9227	2	78	:	:	PUNCT
aiti-9227	2	79	https://doi.org/10.46604/aiti.2022.9227	https://doi.org/10.46604/aiti.2022.9227	PROPN
aiti-9227	2	80	abstract	abstract	NOUN
aiti-9227	2	81	this	this	DET
aiti-9227	2	82	study	study	NOUN
aiti-9227	2	83	investigates	investigate	VERB
aiti-9227	2	84	data	data	NOUN
aiti-9227	2	85	standardization	standardization	NOUN
aiti-9227	2	86	methods	method	NOUN
aiti-9227	2	87	based	base	VERB
aiti-9227	2	88	on	on	ADP
aiti-9227	2	89	the	the	DET
aiti-9227	2	90	grid	grid	NOUN
aiti-9227	2	91	search	search	NOUN
aiti-9227	2	92	(	(	PUNCT
aiti-9227	2	93	gs	gs	NOUN
aiti-9227	2	94	)	)	PUNCT
aiti-9227	2	95	algorithm	algorithm	NOUN
aiti-9227	2	96	for	for	ADP
aiti-9227	2	97	energy	energy	NOUN
aiti-9227	2	98	load	load	NOUN
aiti-9227	2	99	forecasting	forecasting	NOUN
aiti-9227	2	100	,	,	PUNCT
aiti-9227	2	101	including	include	VERB
aiti-9227	2	102	zero	zero	NUM
aiti-9227	2	103	-	-	PUNCT
aiti-9227	2	104	mean	mean	ADJ
aiti-9227	2	105	,	,	PUNCT
aiti-9227	2	106	min	min	PROPN
aiti-9227	2	107	-	-	PUNCT
aiti-9227	2	108	max	max	PROPN
aiti-9227	2	109	,	,	PUNCT
aiti-9227	2	110	max	max	PROPN
aiti-9227	2	111	,	,	PUNCT
aiti-9227	2	112	decimal	decimal	ADJ
aiti-9227	2	113	,	,	PUNCT
aiti-9227	2	114	sigmoid	sigmoid	NOUN
aiti-9227	2	115	,	,	PUNCT
aiti-9227	2	116	softmax	softmax	NOUN
aiti-9227	2	117	,	,	PUNCT
aiti-9227	2	118	median	median	NOUN
aiti-9227	2	119	,	,	PUNCT
aiti-9227	2	120	and	and	CCONJ
aiti-9227	2	121	robust	robust	ADJ
aiti-9227	2	122	,	,	PUNCT
aiti-9227	2	123	to	to	PART
aiti-9227	2	124	determine	determine	VERB
aiti-9227	2	125	the	the	DET
aiti-9227	2	126	hyperparameters	hyperparameter	NOUN
aiti-9227	2	127	of	of	ADP
aiti-9227	2	128	deep	deep	ADJ
aiti-9227	2	129	learning	learning	NOUN
aiti-9227	2	130	(	(	PUNCT
aiti-9227	2	131	dl	dl	NOUN
aiti-9227	2	132	)	)	PUNCT
aiti-9227	2	133	models	model	NOUN
aiti-9227	2	134	.	.	PUNCT
aiti-9227	3	1	the	the	DET
aiti-9227	3	2	considered	consider	VERB
aiti-9227	3	3	dl	dl	PROPN
aiti-9227	3	4	models	model	NOUN
aiti-9227	3	5	are	be	AUX
aiti-9227	3	6	the	the	DET
aiti-9227	3	7	convolutional	convolutional	ADJ
aiti-9227	3	8	neural	neural	ADJ
aiti-9227	3	9	network	network	NOUN
aiti-9227	3	10	(	(	PUNCT
aiti-9227	3	11	cnn	cnn	PROPN
aiti-9227	3	12	)	)	PUNCT
aiti-9227	3	13	and	and	CCONJ
aiti-9227	3	14	long	long	ADJ
aiti-9227	3	15	short	short	ADJ
aiti-9227	3	16	-	-	PUNCT
aiti-9227	3	17	term	term	NOUN
aiti-9227	3	18	memory	memory	NOUN
aiti-9227	3	19	network	network	NOUN
aiti-9227	3	20	(	(	PUNCT
aiti-9227	3	21	lstmn	lstmn	NOUN
aiti-9227	3	22	)	)	PUNCT
aiti-9227	3	23	.	.	PUNCT
aiti-9227	4	1	the	the	DET
aiti-9227	4	2	procedure	procedure	NOUN
aiti-9227	4	3	is	be	AUX
aiti-9227	4	4	made	make	VERB
aiti-9227	4	5	over	over	ADP
aiti-9227	4	6	(	(	PUNCT
aiti-9227	4	7	i	i	NOUN
aiti-9227	4	8	)	)	PUNCT
aiti-9227	4	9	setting	set	VERB
aiti-9227	4	10	the	the	DET
aiti-9227	4	11	configuration	configuration	NOUN
aiti-9227	4	12	for	for	ADP
aiti-9227	4	13	cnn	cnn	PROPN
aiti-9227	4	14	and	and	CCONJ
aiti-9227	4	15	lstmn	lstmn	PROPN
aiti-9227	4	16	,	,	PUNCT
aiti-9227	4	17	(	(	PUNCT
aiti-9227	4	18	ii	ii	NOUN
aiti-9227	4	19	)	)	PUNCT
aiti-9227	4	20	establishing	establish	VERB
aiti-9227	4	21	the	the	DET
aiti-9227	4	22	hyperparameter	hyperparameter	NOUN
aiti-9227	4	23	values	value	NOUN
aiti-9227	4	24	of	of	ADP
aiti-9227	4	25	cnn	cnn	PROPN
aiti-9227	4	26	and	and	CCONJ
aiti-9227	4	27	lstmn	lstmn	NOUN
aiti-9227	4	28	models	model	NOUN
aiti-9227	4	29	based	base	VERB
aiti-9227	4	30	on	on	ADP
aiti-9227	4	31	epoch	epoch	NOUN
aiti-9227	4	32	,	,	PUNCT
aiti-9227	4	33	batch	batch	NOUN
aiti-9227	4	34	,	,	PUNCT
aiti-9227	4	35	optimizer	optimizer	NOUN
aiti-9227	4	36	,	,	PUNCT
aiti-9227	4	37	dropout	dropout	NOUN
aiti-9227	4	38	,	,	PUNCT
aiti-9227	4	39	filters	filter	NOUN
aiti-9227	4	40	,	,	PUNCT
aiti-9227	4	41	and	and	CCONJ
aiti-9227	4	42	kernel	kernel	PROPN
aiti-9227	4	43	,	,	PUNCT
aiti-9227	4	44	(	(	PUNCT
aiti-9227	4	45	iii	iii	X
aiti-9227	4	46	)	)	PUNCT
aiti-9227	4	47	using	use	VERB
aiti-9227	4	48	eight	eight	NUM
aiti-9227	4	49	data	datum	NOUN
aiti-9227	4	50	standardization	standardization	NOUN
aiti-9227	4	51	methods	method	NOUN
aiti-9227	4	52	to	to	PART
aiti-9227	4	53	standardize	standardize	VERB
aiti-9227	4	54	the	the	DET
aiti-9227	4	55	input	input	NOUN
aiti-9227	4	56	data	datum	NOUN
aiti-9227	4	57	,	,	PUNCT
aiti-9227	4	58	and	and	CCONJ
aiti-9227	4	59	(	(	PUNCT
aiti-9227	4	60	iv	iv	X
aiti-9227	4	61	)	)	PUNCT
aiti-9227	4	62	using	use	VERB
aiti-9227	4	63	the	the	DET
aiti-9227	4	64	gs	gs	PROPN
aiti-9227	4	65	algorithm	algorithm	NOUN
aiti-9227	4	66	to	to	PART
aiti-9227	4	67	search	search	VERB
aiti-9227	4	68	the	the	DET
aiti-9227	4	69	optimal	optimal	ADJ
aiti-9227	4	70	hyperparameters	hyperparameter	NOUN
aiti-9227	4	71	based	base	VERB
aiti-9227	4	72	on	on	ADP
aiti-9227	4	73	the	the	DET
aiti-9227	4	74	mean	mean	ADJ
aiti-9227	4	75	absolute	absolute	ADJ
aiti-9227	4	76	error	error	NOUN
aiti-9227	4	77	(	(	PUNCT
aiti-9227	4	78	mae	mae	PROPN
aiti-9227	4	79	)	)	PUNCT
aiti-9227	4	80	and	and	CCONJ
aiti-9227	4	81	mean	mean	VERB
aiti-9227	4	82	absolute	absolute	ADJ
aiti-9227	4	83	percent	percent	NOUN
aiti-9227	4	84	error	error	NOUN
aiti-9227	4	85	(	(	PUNCT
aiti-9227	4	86	mape	mape	NOUN
aiti-9227	4	87	)	)	PUNCT
aiti-9227	4	88	indexes	index	NOUN
aiti-9227	4	89	.	.	PUNCT
aiti-9227	5	1	the	the	DET
aiti-9227	5	2	effectiveness	effectiveness	NOUN
aiti-9227	5	3	of	of	ADP
aiti-9227	5	4	the	the	DET
aiti-9227	5	5	proposed	propose	VERB
aiti-9227	5	6	method	method	NOUN
aiti-9227	5	7	is	be	AUX
aiti-9227	5	8	verified	verify	VERB
aiti-9227	5	9	on	on	ADP
aiti-9227	5	10	the	the	DET
aiti-9227	5	11	power	power	NOUN
aiti-9227	5	12	load	load	NOUN
aiti-9227	5	13	data	datum	NOUN
aiti-9227	5	14	of	of	ADP
aiti-9227	5	15	the	the	DET
aiti-9227	5	16	australian	australian	ADJ
aiti-9227	5	17	state	state	NOUN
aiti-9227	5	18	of	of	ADP
aiti-9227	5	19	queensland	queensland	PROPN
aiti-9227	5	20	and	and	CCONJ
aiti-9227	5	21	vietnamese	vietnamese	ADJ
aiti-9227	5	22	ho	ho	PROPN
aiti-9227	5	23	chi	chi	PROPN
aiti-9227	5	24	minh	minh	PROPN
aiti-9227	5	25	city	city	PROPN
aiti-9227	5	26	.	.	PUNCT
aiti-9227	6	1	the	the	DET
aiti-9227	6	2	simulation	simulation	NOUN
aiti-9227	6	3	results	result	NOUN
aiti-9227	6	4	show	show	VERB
aiti-9227	6	5	that	that	SCONJ
aiti-9227	6	6	the	the	DET
aiti-9227	6	7	proposed	propose	VERB
aiti-9227	6	8	data	data	NOUN
aiti-9227	6	9	standardization	standardization	NOUN
aiti-9227	6	10	methods	method	NOUN
aiti-9227	6	11	are	be	AUX
aiti-9227	6	12	appropriate	appropriate	ADJ
aiti-9227	6	13	,	,	PUNCT
aiti-9227	6	14	except	except	SCONJ
aiti-9227	6	15	for	for	ADP
aiti-9227	6	16	the	the	DET
aiti-9227	6	17	zero	zero	NUM
aiti-9227	6	18	-	-	PUNCT
aiti-9227	6	19	mean	mean	NOUN
aiti-9227	6	20	and	and	CCONJ
aiti-9227	6	21	min	min	ADJ
aiti-9227	6	22	-	-	ADJ
aiti-9227	6	23	max	max	PROPN
aiti-9227	6	24	methods	method	NOUN
aiti-9227	6	25	.	.	PUNCT
aiti-9227	7	1	keywords	keyword	NOUN
aiti-9227	7	2	:	:	PUNCT
aiti-9227	7	3	deep	deep	ADJ
aiti-9227	7	4	learning	learning	NOUN
aiti-9227	7	5	,	,	PUNCT
aiti-9227	7	6	grid	grid	NOUN
aiti-9227	7	7	search	search	NOUN
aiti-9227	7	8	,	,	PUNCT
aiti-9227	7	9	data	datum	NOUN
aiti-9227	7	10	standardization	standardization	NOUN
aiti-9227	7	11	method	method	NOUN
aiti-9227	7	12	,	,	PUNCT
aiti-9227	7	13	hyperparameter	hyperparameter	NOUN
aiti-9227	7	14	,	,	PUNCT
aiti-9227	7	15	electric	electric	ADJ
aiti-9227	7	16	load	load	NOUN
aiti-9227	7	17	forecasting	forecast	VERB
aiti-9227	7	18	1	1	NUM
aiti-9227	7	19	.	.	PUNCT
aiti-9227	7	20	introduction	introduction	NOUN
aiti-9227	7	21	according	accord	VERB
aiti-9227	7	22	to	to	ADP
aiti-9227	7	23	the	the	DET
aiti-9227	7	24	united	united	PROPN
aiti-9227	7	25	states	states	PROPN
aiti-9227	7	26	energy	energy	PROPN
aiti-9227	7	27	information	information	PROPN
aiti-9227	7	28	administration	administration	NOUN
aiti-9227	7	29	(	(	PUNCT
aiti-9227	7	30	us	us	PROPN
aiti-9227	7	31	-	-	PUNCT
aiti-9227	7	32	eia	eia	PROPN
aiti-9227	7	33	)	)	PUNCT
aiti-9227	7	34	,	,	PUNCT
aiti-9227	7	35	worldwide	worldwide	ADJ
aiti-9227	7	36	energy	energy	NOUN
aiti-9227	7	37	demand	demand	NOUN
aiti-9227	7	38	is	be	AUX
aiti-9227	7	39	expected	expect	VERB
aiti-9227	7	40	to	to	PART
aiti-9227	7	41	rise	rise	VERB
aiti-9227	7	42	by	by	ADP
aiti-9227	7	43	50	50	NUM
aiti-9227	7	44	%	%	NOUN
aiti-9227	7	45	,	,	PUNCT
aiti-9227	7	46	with	with	ADP
aiti-9227	7	47	rising	rise	VERB
aiti-9227	7	48	countries	country	NOUN
aiti-9227	7	49	in	in	ADP
aiti-9227	7	50	asia	asia	PROPN
aiti-9227	7	51	leading	lead	VERB
aiti-9227	7	52	the	the	DET
aiti-9227	7	53	way	way	NOUN
aiti-9227	7	54	.	.	PUNCT
aiti-9227	8	1	this	this	DET
aiti-9227	8	2	rising	rise	VERB
aiti-9227	8	3	demand	demand	NOUN
aiti-9227	8	4	would	would	AUX
aiti-9227	8	5	place	place	VERB
aiti-9227	8	6	considerable	considerable	ADJ
aiti-9227	8	7	strain	strain	NOUN
aiti-9227	8	8	on	on	ADP
aiti-9227	8	9	the	the	DET
aiti-9227	8	10	current	current	ADJ
aiti-9227	8	11	energy	energy	NOUN
aiti-9227	8	12	infrastructure	infrastructure	NOUN
aiti-9227	8	13	and	and	CCONJ
aiti-9227	8	14	jeopardize	jeopardize	VERB
aiti-9227	8	15	global	global	ADJ
aiti-9227	8	16	environmental	environmental	ADJ
aiti-9227	8	17	health	health	NOUN
aiti-9227	8	18	by	by	ADP
aiti-9227	8	19	increasing	increase	VERB
aiti-9227	8	20	greenhouse	greenhouse	NOUN
aiti-9227	8	21	gas	gas	NOUN
aiti-9227	8	22	emissions	emission	NOUN
aiti-9227	8	23	from	from	ADP
aiti-9227	8	24	conventional	conventional	ADJ
aiti-9227	8	25	power	power	NOUN
aiti-9227	8	26	sources	source	NOUN
aiti-9227	8	27	[	[	X
aiti-9227	8	28	1	1	NUM
aiti-9227	8	29	]	]	PUNCT
aiti-9227	8	30	.	.	PUNCT
aiti-9227	9	1	in	in	ADP
aiti-9227	9	2	the	the	DET
aiti-9227	9	3	united	united	PROPN
aiti-9227	9	4	states	states	PROPN
aiti-9227	9	5	and	and	CCONJ
aiti-9227	9	6	europe	europe	PROPN
aiti-9227	9	7	,	,	PUNCT
aiti-9227	9	8	an	an	DET
aiti-9227	9	9	estimated	estimate	VERB
aiti-9227	9	10	40	40	NUM
aiti-9227	9	11	%	%	NOUN
aiti-9227	9	12	of	of	ADP
aiti-9227	9	13	electricity	electricity	NOUN
aiti-9227	9	14	consumption	consumption	NOUN
aiti-9227	9	15	and	and	CCONJ
aiti-9227	9	16	38	38	NUM
aiti-9227	9	17	%	%	NOUN
aiti-9227	9	18	of	of	ADP
aiti-9227	9	19	co2	co2	NOUN
aiti-9227	9	20	emissions	emission	NOUN
aiti-9227	9	21	come	come	VERB
aiti-9227	9	22	from	from	ADP
aiti-9227	9	23	the	the	DET
aiti-9227	9	24	construction	construction	NOUN
aiti-9227	9	25	industry	industry	NOUN
aiti-9227	10	1	[	[	X
aiti-9227	10	2	2	2	NUM
aiti-9227	10	3	]	]	PUNCT
aiti-9227	10	4	.	.	PUNCT
aiti-9227	11	1	currently	currently	ADV
aiti-9227	11	2	,	,	PUNCT
aiti-9227	11	3	the	the	DET
aiti-9227	11	4	construction	construction	NOUN
aiti-9227	11	5	industry	industry	NOUN
aiti-9227	11	6	tends	tend	VERB
aiti-9227	11	7	to	to	PART
aiti-9227	11	8	use	use	VERB
aiti-9227	11	9	sustainable	sustainable	ADJ
aiti-9227	11	10	energy	energy	NOUN
aiti-9227	11	11	sources	source	NOUN
aiti-9227	11	12	to	to	PART
aiti-9227	11	13	replace	replace	VERB
aiti-9227	11	14	limited	limited	ADJ
aiti-9227	11	15	energy	energy	NOUN
aiti-9227	11	16	sources	source	NOUN
aiti-9227	11	17	.	.	PUNCT
aiti-9227	12	1	as	as	ADP
aiti-9227	12	2	a	a	DET
aiti-9227	12	3	result	result	NOUN
aiti-9227	12	4	,	,	PUNCT
aiti-9227	12	5	the	the	DET
aiti-9227	12	6	use	use	NOUN
aiti-9227	12	7	of	of	ADP
aiti-9227	12	8	renewable	renewable	ADJ
aiti-9227	12	9	energy	energy	NOUN
aiti-9227	12	10	sources	source	NOUN
aiti-9227	12	11	has	have	AUX
aiti-9227	12	12	been	be	AUX
aiti-9227	12	13	increasing	increase	VERB
aiti-9227	12	14	,	,	PUNCT
aiti-9227	12	15	the	the	DET
aiti-9227	12	16	design	design	NOUN
aiti-9227	12	17	of	of	ADP
aiti-9227	12	18	buildings	building	NOUN
aiti-9227	12	19	must	must	AUX
aiti-9227	12	20	be	be	AUX
aiti-9227	12	21	improved	improve	VERB
aiti-9227	12	22	,	,	PUNCT
aiti-9227	12	23	and	and	CCONJ
aiti-9227	12	24	the	the	DET
aiti-9227	12	25	building	building	NOUN
aiti-9227	12	26	energy	energy	NOUN
aiti-9227	12	27	demand	demand	NOUN
aiti-9227	12	28	needs	need	VERB
aiti-9227	12	29	to	to	PART
aiti-9227	12	30	be	be	AUX
aiti-9227	12	31	forecasted	forecast	VERB
aiti-9227	12	32	.	.	PUNCT
aiti-9227	13	1	therefore	therefore	ADV
aiti-9227	13	2	,	,	PUNCT
aiti-9227	13	3	it	it	PRON
aiti-9227	13	4	is	be	AUX
aiti-9227	13	5	necessary	necessary	ADJ
aiti-9227	13	6	to	to	PART
aiti-9227	13	7	apply	apply	VERB
aiti-9227	13	8	the	the	DET
aiti-9227	13	9	energy	energy	NOUN
aiti-9227	13	10	load	load	NOUN
aiti-9227	13	11	forecasting	forecasting	NOUN
aiti-9227	13	12	method	method	NOUN
aiti-9227	13	13	because	because	SCONJ
aiti-9227	13	14	it	it	PRON
aiti-9227	13	15	has	have	VERB
aiti-9227	13	16	both	both	CCONJ
aiti-9227	13	17	economic	economic	ADJ
aiti-9227	13	18	and	and	CCONJ
aiti-9227	13	19	infrastructure	infrastructure	NOUN
aiti-9227	13	20	advantages	advantage	NOUN
aiti-9227	13	21	.	.	PUNCT
aiti-9227	14	1	this	this	DET
aiti-9227	14	2	method	method	NOUN
aiti-9227	14	3	can	can	AUX
aiti-9227	14	4	predict	predict	VERB
aiti-9227	14	5	future	future	ADJ
aiti-9227	14	6	electricity	electricity	NOUN
aiti-9227	14	7	consumption	consumption	NOUN
aiti-9227	14	8	and	and	CCONJ
aiti-9227	14	9	help	help	VERB
aiti-9227	14	10	power	power	NOUN
aiti-9227	14	11	companies	company	NOUN
aiti-9227	14	12	make	make	VERB
aiti-9227	14	13	economically	economically	ADV
aiti-9227	14	14	viable	viable	ADJ
aiti-9227	14	15	plans	plan	NOUN
aiti-9227	14	16	and	and	CCONJ
aiti-9227	14	17	decisions	decision	NOUN
aiti-9227	14	18	[	[	X
aiti-9227	14	19	3	3	NUM
aiti-9227	14	20	]	]	PUNCT
aiti-9227	14	21	.	.	PUNCT
aiti-9227	15	1	short	short	ADJ
aiti-9227	15	2	-	-	PUNCT
aiti-9227	15	3	term	term	NOUN
aiti-9227	15	4	load	load	NOUN
aiti-9227	15	5	forecasting	forecasting	NOUN
aiti-9227	15	6	plays	play	VERB
aiti-9227	15	7	an	an	DET
aiti-9227	15	8	important	important	ADJ
aiti-9227	15	9	role	role	NOUN
aiti-9227	15	10	in	in	ADP
aiti-9227	15	11	the	the	DET
aiti-9227	15	12	power	power	NOUN
aiti-9227	15	13	industry	industry	NOUN
aiti-9227	15	14	,	,	PUNCT
aiti-9227	15	15	including	include	VERB
aiti-9227	15	16	power	power	NOUN
aiti-9227	15	17	system	system	NOUN
aiti-9227	15	18	planning	planning	NOUN
aiti-9227	15	19	,	,	PUNCT
aiti-9227	15	20	power	power	NOUN
aiti-9227	15	21	generation	generation	NOUN
aiti-9227	15	22	planning	planning	NOUN
aiti-9227	15	23	,	,	PUNCT
aiti-9227	15	24	and	and	CCONJ
aiti-9227	15	25	the	the	DET
aiti-9227	15	26	power	power	NOUN
aiti-9227	15	27	supply	supply	NOUN
aiti-9227	15	28	-	-	PUNCT
aiti-9227	15	29	demand	demand	NOUN
aiti-9227	15	30	balance	balance	NOUN
aiti-9227	15	31	[	[	PUNCT
aiti-9227	15	32	4	4	NUM
aiti-9227	15	33	-	-	SYM
aiti-9227	15	34	5	5	NUM
aiti-9227	15	35	]	]	PUNCT
aiti-9227	15	36	.	.	PUNCT
aiti-9227	16	1	if	if	SCONJ
aiti-9227	16	2	load	load	NOUN
aiti-9227	16	3	forecasting	forecasting	NOUN
aiti-9227	16	4	is	be	AUX
aiti-9227	16	5	accurate	accurate	ADJ
aiti-9227	16	6	,	,	PUNCT
aiti-9227	16	7	significant	significant	ADJ
aiti-9227	16	8	cost	cost	NOUN
aiti-9227	16	9	reductions	reduction	NOUN
aiti-9227	16	10	in	in	ADP
aiti-9227	16	11	control	control	NOUN
aiti-9227	16	12	operations	operation	NOUN
aiti-9227	16	13	and	and	CCONJ
aiti-9227	16	14	decision	decision	NOUN
aiti-9227	16	15	-	-	PUNCT
aiti-9227	16	16	making	making	NOUN
aiti-9227	16	17	,	,	PUNCT
aiti-9227	16	18	such	such	ADJ
aiti-9227	16	19	as	as	ADP
aiti-9227	16	20	dispatch	dispatch	NOUN
aiti-9227	16	21	,	,	PUNCT
aiti-9227	16	22	unit	unit	NOUN
aiti-9227	16	23	commitment	commitment	NOUN
aiti-9227	16	24	,	,	PUNCT
aiti-9227	16	25	fuel	fuel	NOUN
aiti-9227	16	26	allocation	allocation	NOUN
aiti-9227	16	27	,	,	PUNCT
aiti-9227	16	28	power	power	NOUN
aiti-9227	16	29	system	system	NOUN
aiti-9227	16	30	security	security	NOUN
aiti-9227	16	31	assessment	assessment	NOUN
aiti-9227	16	32	,	,	PUNCT
aiti-9227	16	33	and	and	CCONJ
aiti-9227	16	34	off	off	ADP
aiti-9227	16	35	-	-	PUNCT
aiti-9227	16	36	line	line	NOUN
aiti-9227	16	37	analysis	analysis	NOUN
aiti-9227	16	38	,	,	PUNCT
aiti-9227	16	39	would	would	AUX
aiti-9227	16	40	be	be	AUX
aiti-9227	16	41	realized	realize	VERB
aiti-9227	16	42	.	.	PUNCT
aiti-9227	17	1	on	on	ADP
aiti-9227	17	2	the	the	DET
aiti-9227	17	3	contrary	contrary	NOUN
aiti-9227	17	4	,	,	PUNCT
aiti-9227	17	5	if	if	SCONJ
aiti-9227	17	6	there	there	PRON
aiti-9227	17	7	is	be	VERB
aiti-9227	17	8	an	an	DET
aiti-9227	17	9	error	error	NOUN
aiti-9227	17	10	in	in	ADP
aiti-9227	17	11	the	the	DET
aiti-9227	17	12	forecast	forecast	NOUN
aiti-9227	17	13	of	of	ADP
aiti-9227	17	14	electricity	electricity	NOUN
aiti-9227	17	15	demand	demand	NOUN
aiti-9227	17	16	,	,	PUNCT
aiti-9227	17	17	there	there	PRON
aiti-9227	17	18	will	will	AUX
aiti-9227	17	19	be	be	AUX
aiti-9227	17	20	an	an	DET
aiti-9227	17	21	increase	increase	NOUN
aiti-9227	17	22	in	in	ADP
aiti-9227	17	23	operating	operating	NOUN
aiti-9227	17	24	costs	cost	NOUN
aiti-9227	17	25	.	.	PUNCT
aiti-9227	18	1	*	*	PUNCT
aiti-9227	18	2	corresponding	correspond	VERB
aiti-9227	18	3	author	author	NOUN
aiti-9227	18	4	.	.	PUNCT
aiti-9227	19	1	e	e	X
aiti-9227	19	2	-	-	NOUN
aiti-9227	19	3	mail	mail	NOUN
aiti-9227	19	4	address	address	NOUN
aiti-9227	19	5	:	:	PUNCT
aiti-9227	19	6	levandai@iuh.edu.vn	levandai@iuh.edu.vn	ADJ
aiti-9227	19	7	advances	advance	NOUN
aiti-9227	19	8	in	in	ADP
aiti-9227	19	9	technology	technology	NOUN
aiti-9227	19	10	innovation	innovation	NOUN
aiti-9227	19	11	,	,	PUNCT
aiti-9227	19	12	vol	vol	NOUN
aiti-9227	19	13	.	.	PROPN
aiti-9227	19	14	7	7	NUM
aiti-9227	19	15	,	,	PUNCT
aiti-9227	19	16	no	no	INTJ
aiti-9227	19	17	.	.	NOUN
aiti-9227	19	18	4	4	NUM
aiti-9227	19	19	,	,	PUNCT
aiti-9227	19	20	2022	2022	NUM
aiti-9227	19	21	,	,	PUNCT
aiti-9227	19	22	pp	pp	ADJ
aiti-9227	19	23	.	.	PUNCT
aiti-9227	20	1	258	258	NUM
aiti-9227	20	2	-	-	SYM
aiti-9227	20	3	269	269	NUM
aiti-9227	20	4	in	in	ADP
aiti-9227	20	5	the	the	DET
aiti-9227	20	6	past	past	ADJ
aiti-9227	20	7	decade	decade	NOUN
aiti-9227	20	8	,	,	PUNCT
aiti-9227	20	9	many	many	ADJ
aiti-9227	20	10	methodologies	methodology	NOUN
aiti-9227	20	11	and	and	CCONJ
aiti-9227	20	12	techniques	technique	NOUN
aiti-9227	20	13	have	have	AUX
aiti-9227	20	14	been	be	AUX
aiti-9227	20	15	proposed	propose	VERB
aiti-9227	20	16	to	to	PART
aiti-9227	20	17	solve	solve	VERB
aiti-9227	20	18	the	the	DET
aiti-9227	20	19	problem	problem	NOUN
aiti-9227	20	20	of	of	ADP
aiti-9227	20	21	short	short	ADJ
aiti-9227	20	22	-	-	PUNCT
aiti-9227	20	23	term	term	NOUN
aiti-9227	20	24	power	power	NOUN
aiti-9227	20	25	load	load	NOUN
aiti-9227	20	26	forecasting	forecasting	NOUN
aiti-9227	20	27	.	.	PUNCT
aiti-9227	21	1	they	they	PRON
aiti-9227	21	2	can	can	AUX
aiti-9227	21	3	be	be	AUX
aiti-9227	21	4	classified	classify	VERB
aiti-9227	21	5	into	into	ADP
aiti-9227	21	6	two	two	NUM
aiti-9227	21	7	groups	group	NOUN
aiti-9227	21	8	of	of	ADP
aiti-9227	21	9	methods	method	NOUN
aiti-9227	21	10	.	.	PUNCT
aiti-9227	22	1	the	the	DET
aiti-9227	22	2	first	first	ADJ
aiti-9227	22	3	group	group	NOUN
aiti-9227	22	4	relates	relate	VERB
aiti-9227	22	5	to	to	ADP
aiti-9227	22	6	using	use	VERB
aiti-9227	22	7	statistical	statistical	ADJ
aiti-9227	22	8	methods	method	NOUN
aiti-9227	22	9	,	,	PUNCT
aiti-9227	22	10	such	such	ADJ
aiti-9227	22	11	as	as	ADP
aiti-9227	22	12	multiple	multiple	ADJ
aiti-9227	22	13	regression	regression	NOUN
aiti-9227	22	14	,	,	PUNCT
aiti-9227	22	15	exponential	exponential	ADJ
aiti-9227	22	16	smoothing	smoothing	NOUN
aiti-9227	22	17	,	,	PUNCT
aiti-9227	22	18	and	and	CCONJ
aiti-9227	22	19	autoregressive	autoregressive	ADJ
aiti-9227	22	20	integrated	integrated	ADJ
aiti-9227	22	21	moving	move	VERB
aiti-9227	22	22	average	average	ADJ
aiti-9227	22	23	(	(	PUNCT
aiti-9227	22	24	arima	arima	NOUN
aiti-9227	22	25	)	)	PUNCT
aiti-9227	23	1	[	[	X
aiti-9227	23	2	6	6	NUM
aiti-9227	23	3	-	-	SYM
aiti-9227	23	4	7	7	NUM
aiti-9227	23	5	]	]	PUNCT
aiti-9227	23	6	.	.	PUNCT
aiti-9227	24	1	the	the	DET
aiti-9227	24	2	second	second	ADJ
aiti-9227	24	3	group	group	NOUN
aiti-9227	24	4	employs	employ	VERB
aiti-9227	24	5	artificial	artificial	ADJ
aiti-9227	24	6	intelligence	intelligence	NOUN
aiti-9227	24	7	techniques	technique	NOUN
aiti-9227	24	8	,	,	PUNCT
aiti-9227	24	9	such	such	ADJ
aiti-9227	24	10	as	as	ADP
aiti-9227	24	11	support	support	NOUN
aiti-9227	24	12	vector	vector	NOUN
aiti-9227	24	13	machines	machine	NOUN
aiti-9227	24	14	(	(	PUNCT
aiti-9227	24	15	svm	svm	PROPN
aiti-9227	24	16	)	)	PUNCT
aiti-9227	24	17	and	and	CCONJ
aiti-9227	24	18	artificial	artificial	ADJ
aiti-9227	24	19	neural	neural	ADJ
aiti-9227	24	20	networks	network	NOUN
aiti-9227	24	21	(	(	PUNCT
aiti-9227	24	22	anns	anns	PROPN
aiti-9227	24	23	)	)	PUNCT
aiti-9227	25	1	[	[	X
aiti-9227	25	2	8	8	NUM
aiti-9227	25	3	-	-	SYM
aiti-9227	25	4	9	9	NUM
aiti-9227	25	5	]	]	PUNCT
aiti-9227	25	6	.	.	PUNCT
aiti-9227	26	1	recent	recent	ADJ
aiti-9227	26	2	development	development	NOUN
aiti-9227	26	3	based	base	VERB
aiti-9227	26	4	on	on	ADP
aiti-9227	26	5	anns	ann	NOUN
aiti-9227	26	6	and	and	CCONJ
aiti-9227	26	7	deep	deep	ADJ
aiti-9227	26	8	learning	learning	NOUN
aiti-9227	26	9	(	(	PUNCT
aiti-9227	26	10	dl	dl	NOUN
aiti-9227	26	11	)	)	PUNCT
aiti-9227	26	12	networks	network	NOUN
aiti-9227	26	13	is	be	AUX
aiti-9227	26	14	one	one	NUM
aiti-9227	26	15	of	of	ADP
aiti-9227	26	16	the	the	DET
aiti-9227	26	17	methods	method	NOUN
aiti-9227	26	18	applied	apply	VERB
aiti-9227	26	19	to	to	PART
aiti-9227	26	20	solve	solve	VERB
aiti-9227	26	21	the	the	DET
aiti-9227	26	22	problem	problem	NOUN
aiti-9227	26	23	of	of	ADP
aiti-9227	26	24	load	load	NOUN
aiti-9227	26	25	forecasting	forecasting	NOUN
aiti-9227	26	26	.	.	PUNCT
aiti-9227	27	1	the	the	DET
aiti-9227	27	2	dl	dl	PROPN
aiti-9227	27	3	architecture	architecture	NOUN
aiti-9227	27	4	includes	include	VERB
aiti-9227	27	5	different	different	ADJ
aiti-9227	27	6	models	model	NOUN
aiti-9227	27	7	,	,	PUNCT
aiti-9227	27	8	such	such	ADJ
aiti-9227	27	9	as	as	ADP
aiti-9227	27	10	long	long	ADJ
aiti-9227	27	11	short	short	ADJ
aiti-9227	27	12	-	-	PUNCT
aiti-9227	27	13	term	term	NOUN
aiti-9227	27	14	memory	memory	NOUN
aiti-9227	27	15	networks	network	NOUN
aiti-9227	27	16	(	(	PUNCT
aiti-9227	27	17	lstmn	lstmn	NOUN
aiti-9227	27	18	)	)	PUNCT
aiti-9227	27	19	,	,	PUNCT
aiti-9227	27	20	convolutional	convolutional	ADJ
aiti-9227	27	21	neural	neural	ADJ
aiti-9227	27	22	networks	network	NOUN
aiti-9227	27	23	(	(	PUNCT
aiti-9227	27	24	cnn	cnn	PROPN
aiti-9227	27	25	)	)	PUNCT
aiti-9227	27	26	,	,	PUNCT
aiti-9227	27	27	deep	deep	ADJ
aiti-9227	27	28	belief	belief	NOUN
aiti-9227	27	29	networks	network	NOUN
aiti-9227	27	30	,	,	PUNCT
aiti-9227	27	31	and	and	CCONJ
aiti-9227	27	32	deep	deep	ADJ
aiti-9227	27	33	boltzmann	boltzmann	PROPN
aiti-9227	27	34	machine	machine	NOUN
aiti-9227	27	35	networks	network	NOUN
aiti-9227	27	36	.	.	PUNCT
aiti-9227	28	1	among	among	ADP
aiti-9227	28	2	them	they	PRON
aiti-9227	28	3	,	,	PUNCT
aiti-9227	28	4	lstmn	lstmn	PROPN
aiti-9227	28	5	and	and	CCONJ
aiti-9227	28	6	cnn	cnn	PROPN
aiti-9227	28	7	are	be	AUX
aiti-9227	28	8	popular	popular	ADJ
aiti-9227	28	9	in	in	ADP
aiti-9227	28	10	the	the	DET
aiti-9227	28	11	problem	problem	NOUN
aiti-9227	28	12	of	of	ADP
aiti-9227	28	13	power	power	NOUN
aiti-9227	28	14	load	load	NOUN
aiti-9227	28	15	forecasting	forecasting	NOUN
aiti-9227	28	16	[	[	X
aiti-9227	28	17	10	10	NUM
aiti-9227	28	18	-	-	SYM
aiti-9227	28	19	11	11	NUM
aiti-9227	28	20	]	]	PUNCT
aiti-9227	28	21	.	.	PUNCT
aiti-9227	29	1	the	the	DET
aiti-9227	29	2	main	main	ADJ
aiti-9227	29	3	feature	feature	NOUN
aiti-9227	29	4	of	of	ADP
aiti-9227	29	5	the	the	DET
aiti-9227	29	6	dl	dl	PROPN
aiti-9227	29	7	model	model	NOUN
aiti-9227	29	8	is	be	AUX
aiti-9227	29	9	that	that	SCONJ
aiti-9227	29	10	the	the	DET
aiti-9227	29	11	accuracy	accuracy	NOUN
aiti-9227	29	12	of	of	ADP
aiti-9227	29	13	the	the	DET
aiti-9227	29	14	load	load	NOUN
aiti-9227	29	15	-	-	PUNCT
aiti-9227	29	16	forecasted	forecast	VERB
aiti-9227	29	17	results	result	NOUN
aiti-9227	29	18	highly	highly	ADV
aiti-9227	29	19	depends	depend	VERB
aiti-9227	29	20	on	on	ADP
aiti-9227	29	21	its	its	PRON
aiti-9227	29	22	hyperparameters	hyperparameter	NOUN
aiti-9227	29	23	.	.	PUNCT
aiti-9227	30	1	therefore	therefore	ADV
aiti-9227	30	2	,	,	PUNCT
aiti-9227	30	3	determining	determine	VERB
aiti-9227	30	4	these	these	DET
aiti-9227	30	5	hyperparameters	hyperparameter	NOUN
aiti-9227	30	6	for	for	ADP
aiti-9227	30	7	dl	dl	PROPN
aiti-9227	30	8	models	model	NOUN
aiti-9227	30	9	is	be	AUX
aiti-9227	30	10	important	important	ADJ
aiti-9227	30	11	[	[	X
aiti-9227	30	12	12	12	NUM
aiti-9227	30	13	-	-	SYM
aiti-9227	30	14	13	13	NUM
aiti-9227	30	15	]	]	PUNCT
aiti-9227	30	16	.	.	PUNCT
aiti-9227	31	1	recently	recently	ADV
aiti-9227	31	2	,	,	PUNCT
aiti-9227	31	3	some	some	DET
aiti-9227	31	4	algorithms	algorithm	NOUN
aiti-9227	31	5	,	,	PUNCT
aiti-9227	31	6	such	such	ADJ
aiti-9227	31	7	as	as	ADP
aiti-9227	31	8	grid	grid	NOUN
aiti-9227	31	9	search	search	NOUN
aiti-9227	31	10	(	(	PUNCT
aiti-9227	31	11	gs	gs	NOUN
aiti-9227	31	12	)	)	PUNCT
aiti-9227	31	13	,	,	PUNCT
aiti-9227	31	14	random	random	ADJ
aiti-9227	31	15	search	search	NOUN
aiti-9227	31	16	(	(	PUNCT
aiti-9227	31	17	rs	rs	NOUN
aiti-9227	31	18	)	)	PUNCT
aiti-9227	31	19	,	,	PUNCT
aiti-9227	31	20	and	and	CCONJ
aiti-9227	31	21	genetic	genetic	ADJ
aiti-9227	31	22	algorithm	algorithm	NOUN
aiti-9227	31	23	(	(	PUNCT
aiti-9227	31	24	ga	ga	PROPN
aiti-9227	31	25	)	)	PUNCT
aiti-9227	31	26	,	,	PUNCT
aiti-9227	31	27	have	have	AUX
aiti-9227	31	28	been	be	AUX
aiti-9227	31	29	applied	apply	VERB
aiti-9227	31	30	to	to	PART
aiti-9227	31	31	determine	determine	VERB
aiti-9227	31	32	the	the	DET
aiti-9227	31	33	hyperparameters	hyperparameter	NOUN
aiti-9227	31	34	of	of	ADP
aiti-9227	31	35	the	the	DET
aiti-9227	31	36	dl	dl	PROPN
aiti-9227	31	37	model	model	NOUN
aiti-9227	31	38	,	,	PUNCT
aiti-9227	31	39	among	among	ADP
aiti-9227	31	40	which	which	PRON
aiti-9227	31	41	the	the	DET
aiti-9227	31	42	gs	gs	PROPN
aiti-9227	31	43	algorithm	algorithm	NOUN
aiti-9227	31	44	was	be	AUX
aiti-9227	31	45	widely	widely	ADV
aiti-9227	31	46	applied	apply	VERB
aiti-9227	31	47	[	[	PUNCT
aiti-9227	31	48	14	14	NUM
aiti-9227	31	49	-	-	SYM
aiti-9227	31	50	15	15	NUM
aiti-9227	31	51	]	]	PUNCT
aiti-9227	31	52	.	.	PUNCT
aiti-9227	32	1	in	in	ADP
aiti-9227	32	2	addition	addition	NOUN
aiti-9227	32	3	,	,	PUNCT
aiti-9227	32	4	the	the	DET
aiti-9227	32	5	characteristics	characteristic	NOUN
aiti-9227	32	6	of	of	ADP
aiti-9227	32	7	the	the	DET
aiti-9227	32	8	input	input	NOUN
aiti-9227	32	9	data	datum	NOUN
aiti-9227	32	10	are	be	AUX
aiti-9227	32	11	also	also	ADV
aiti-9227	32	12	important	important	ADJ
aiti-9227	32	13	factors	factor	NOUN
aiti-9227	32	14	affecting	affect	VERB
aiti-9227	32	15	the	the	DET
aiti-9227	32	16	accuracy	accuracy	NOUN
aiti-9227	32	17	of	of	ADP
aiti-9227	32	18	the	the	DET
aiti-9227	32	19	dl	dl	PROPN
aiti-9227	32	20	model	model	NOUN
aiti-9227	32	21	.	.	PUNCT
aiti-9227	33	1	moolayil	moolayil	NOUN
aiti-9227	34	1	[	[	X
aiti-9227	34	2	16	16	NUM
aiti-9227	34	3	]	]	PUNCT
aiti-9227	34	4	introduced	introduce	VERB
aiti-9227	34	5	some	some	DET
aiti-9227	34	6	data	data	NOUN
aiti-9227	34	7	standardization	standardization	NOUN
aiti-9227	34	8	methods	method	NOUN
aiti-9227	34	9	to	to	PART
aiti-9227	34	10	solve	solve	VERB
aiti-9227	34	11	this	this	DET
aiti-9227	34	12	problem	problem	NOUN
aiti-9227	34	13	.	.	PUNCT
aiti-9227	35	1	however	however	ADV
aiti-9227	35	2	,	,	PUNCT
aiti-9227	35	3	raschka	raschka	PROPN
aiti-9227	35	4	et	et	PROPN
aiti-9227	35	5	al	al	PROPN
aiti-9227	35	6	.	.	PUNCT
aiti-9227	36	1	[	[	X
aiti-9227	36	2	17	17	NUM
aiti-9227	36	3	]	]	PUNCT
aiti-9227	36	4	and	and	CCONJ
aiti-9227	36	5	yang	yang	PROPN
aiti-9227	36	6	et	et	PROPN
aiti-9227	36	7	al	al	PROPN
aiti-9227	36	8	.	.	PUNCT
aiti-9227	37	1	[	[	X
aiti-9227	37	2	18	18	NUM
aiti-9227	37	3	]	]	PUNCT
aiti-9227	37	4	did	do	AUX
aiti-9227	37	5	not	not	PART
aiti-9227	37	6	show	show	VERB
aiti-9227	37	7	any	any	DET
aiti-9227	37	8	interest	interest	NOUN
aiti-9227	37	9	in	in	ADP
aiti-9227	37	10	employing	employ	VERB
aiti-9227	37	11	data	datum	NOUN
aiti-9227	37	12	normalization	normalization	NOUN
aiti-9227	37	13	on	on	ADP
aiti-9227	37	14	the	the	DET
aiti-9227	37	15	gs	gs	PROPN
aiti-9227	37	16	algorithm	algorithm	NOUN
aiti-9227	37	17	.	.	PUNCT
aiti-9227	38	1	as	as	ADP
aiti-9227	38	2	a	a	DET
aiti-9227	38	3	result	result	NOUN
aiti-9227	38	4	,	,	PUNCT
aiti-9227	38	5	this	this	PRON
aiti-9227	38	6	may	may	AUX
aiti-9227	38	7	be	be	AUX
aiti-9227	38	8	a	a	DET
aiti-9227	38	9	leading	lead	VERB
aiti-9227	38	10	disadvantage	disadvantage	NOUN
aiti-9227	38	11	for	for	ADP
aiti-9227	38	12	these	these	DET
aiti-9227	38	13	studies	study	NOUN
aiti-9227	38	14	.	.	PUNCT
aiti-9227	39	1	to	to	PART
aiti-9227	39	2	overcome	overcome	VERB
aiti-9227	39	3	this	this	DET
aiti-9227	39	4	disadvantage	disadvantage	NOUN
aiti-9227	39	5	,	,	PUNCT
aiti-9227	39	6	this	this	DET
aiti-9227	39	7	study	study	NOUN
aiti-9227	39	8	proposes	propose	VERB
aiti-9227	39	9	an	an	DET
aiti-9227	39	10	input	input	NOUN
aiti-9227	39	11	data	data	NOUN
aiti-9227	39	12	standardization	standardization	NOUN
aiti-9227	39	13	method	method	NOUN
aiti-9227	39	14	on	on	ADP
aiti-9227	39	15	the	the	DET
aiti-9227	39	16	gs	gs	PROPN
aiti-9227	39	17	algorithm	algorithm	NOUN
aiti-9227	39	18	to	to	PART
aiti-9227	39	19	determine	determine	VERB
aiti-9227	39	20	the	the	DET
aiti-9227	39	21	hyperparameters	hyperparameter	NOUN
aiti-9227	39	22	of	of	ADP
aiti-9227	39	23	the	the	DET
aiti-9227	39	24	dl	dl	PROPN
aiti-9227	39	25	model	model	NOUN
aiti-9227	39	26	,	,	PUNCT
aiti-9227	39	27	including	include	VERB
aiti-9227	39	28	cnn	cnn	PROPN
aiti-9227	39	29	and	and	CCONJ
aiti-9227	39	30	lstmn	lstmn	NOUN
aiti-9227	39	31	,	,	PUNCT
aiti-9227	39	32	for	for	ADP
aiti-9227	39	33	energy	energy	NOUN
aiti-9227	39	34	load	load	NOUN
aiti-9227	39	35	forecasting	forecasting	NOUN
aiti-9227	39	36	.	.	PUNCT
aiti-9227	40	1	for	for	ADP
aiti-9227	40	2	this	this	DET
aiti-9227	40	3	proposed	propose	VERB
aiti-9227	40	4	method	method	NOUN
aiti-9227	40	5	,	,	PUNCT
aiti-9227	40	6	the	the	DET
aiti-9227	40	7	model	model	NOUN
aiti-9227	40	8	data	datum	NOUN
aiti-9227	40	9	are	be	AUX
aiti-9227	40	10	split	split	VERB
aiti-9227	40	11	into	into	ADP
aiti-9227	40	12	training	training	NOUN
aiti-9227	40	13	and	and	CCONJ
aiti-9227	40	14	testing	testing	NOUN
aiti-9227	40	15	sets	set	NOUN
aiti-9227	40	16	.	.	PUNCT
aiti-9227	41	1	for	for	ADP
aiti-9227	41	2	the	the	DET
aiti-9227	41	3	training	training	NOUN
aiti-9227	41	4	step	step	NOUN
aiti-9227	41	5	,	,	PUNCT
aiti-9227	41	6	the	the	DET
aiti-9227	41	7	gs	gs	PROPN
aiti-9227	41	8	algorithm	algorithm	NOUN
aiti-9227	41	9	is	be	AUX
aiti-9227	41	10	performed	perform	VERB
aiti-9227	41	11	to	to	PART
aiti-9227	41	12	determine	determine	VERB
aiti-9227	41	13	the	the	DET
aiti-9227	41	14	hyperparameters	hyperparameter	NOUN
aiti-9227	41	15	of	of	ADP
aiti-9227	41	16	the	the	DET
aiti-9227	41	17	dl	dl	PROPN
aiti-9227	41	18	model	model	NOUN
aiti-9227	41	19	corresponding	correspond	VERB
aiti-9227	41	20	to	to	ADP
aiti-9227	41	21	each	each	DET
aiti-9227	41	22	data	datum	NOUN
aiti-9227	41	23	normalization	normalization	NOUN
aiti-9227	41	24	method	method	NOUN
aiti-9227	41	25	.	.	PUNCT
aiti-9227	42	1	for	for	ADP
aiti-9227	42	2	the	the	DET
aiti-9227	42	3	testing	testing	NOUN
aiti-9227	42	4	step	step	NOUN
aiti-9227	42	5	,	,	PUNCT
aiti-9227	42	6	the	the	DET
aiti-9227	42	7	predicted	predict	VERB
aiti-9227	42	8	errors	error	NOUN
aiti-9227	42	9	of	of	ADP
aiti-9227	42	10	these	these	DET
aiti-9227	42	11	optimal	optimal	ADJ
aiti-9227	42	12	models	model	NOUN
aiti-9227	42	13	are	be	AUX
aiti-9227	42	14	compared	compare	VERB
aiti-9227	42	15	,	,	PUNCT
aiti-9227	42	16	and	and	CCONJ
aiti-9227	42	17	thereby	thereby	ADV
aiti-9227	42	18	the	the	DET
aiti-9227	42	19	proposed	propose	VERB
aiti-9227	42	20	methodology	methodology	NOUN
aiti-9227	42	21	can	can	AUX
aiti-9227	42	22	evaluate	evaluate	VERB
aiti-9227	42	23	the	the	DET
aiti-9227	42	24	impact	impact	NOUN
aiti-9227	42	25	of	of	ADP
aiti-9227	42	26	the	the	DET
aiti-9227	42	27	data	datum	NOUN
aiti-9227	42	28	normalization	normalization	NOUN
aiti-9227	42	29	methods	method	NOUN
aiti-9227	42	30	on	on	ADP
aiti-9227	42	31	the	the	DET
aiti-9227	42	32	gs	gs	PROPN
aiti-9227	42	33	algorithm	algorithm	NOUN
aiti-9227	42	34	to	to	ADP
aiti-9227	42	35	the	the	DET
aiti-9227	42	36	dl	dl	PROPN
aiti-9227	42	37	model	model	NOUN
aiti-9227	42	38	.	.	PUNCT
aiti-9227	43	1	the	the	DET
aiti-9227	43	2	error	error	NOUN
aiti-9227	43	3	value	value	NOUN
aiti-9227	43	4	in	in	ADP
aiti-9227	43	5	the	the	DET
aiti-9227	43	6	dl	dl	PROPN
aiti-9227	43	7	model	model	NOUN
aiti-9227	43	8	is	be	AUX
aiti-9227	43	9	usually	usually	ADV
aiti-9227	43	10	determined	determine	VERB
aiti-9227	43	11	based	base	VERB
aiti-9227	43	12	on	on	ADP
aiti-9227	43	13	the	the	DET
aiti-9227	43	14	error	error	NOUN
aiti-9227	43	15	evaluation	evaluation	NOUN
aiti-9227	43	16	indexes	index	NOUN
aiti-9227	43	17	of	of	ADP
aiti-9227	43	18	the	the	DET
aiti-9227	43	19	actual	actual	ADJ
aiti-9227	43	20	value	value	NOUN
aiti-9227	43	21	and	and	CCONJ
aiti-9227	43	22	predicted	predict	VERB
aiti-9227	43	23	value	value	NOUN
aiti-9227	43	24	of	of	ADP
aiti-9227	43	25	the	the	DET
aiti-9227	43	26	model	model	NOUN
aiti-9227	43	27	,	,	PUNCT
aiti-9227	43	28	such	such	ADJ
aiti-9227	43	29	as	as	ADP
aiti-9227	43	30	the	the	DET
aiti-9227	43	31	mean	mean	ADJ
aiti-9227	43	32	absolute	absolute	ADJ
aiti-9227	43	33	error	error	NOUN
aiti-9227	43	34	(	(	PUNCT
aiti-9227	43	35	mae	mae	PROPN
aiti-9227	43	36	)	)	PUNCT
aiti-9227	43	37	and	and	CCONJ
aiti-9227	43	38	the	the	DET
aiti-9227	43	39	mean	mean	ADJ
aiti-9227	43	40	absolute	absolute	ADJ
aiti-9227	43	41	percent	percent	NOUN
aiti-9227	43	42	error	error	NOUN
aiti-9227	43	43	(	(	PUNCT
aiti-9227	43	44	mape	mape	NOUN
aiti-9227	43	45	)	)	PUNCT
aiti-9227	43	46	.	.	PUNCT
aiti-9227	44	1	the	the	DET
aiti-9227	44	2	forecasting	forecasting	NOUN
aiti-9227	44	3	results	result	NOUN
aiti-9227	44	4	of	of	ADP
aiti-9227	44	5	the	the	DET
aiti-9227	44	6	dl	dl	PROPN
aiti-9227	44	7	model	model	NOUN
aiti-9227	44	8	are	be	AUX
aiti-9227	44	9	significantly	significantly	ADV
aiti-9227	44	10	affected	affect	VERB
aiti-9227	44	11	by	by	ADP
aiti-9227	44	12	the	the	DET
aiti-9227	44	13	scale	scale	NOUN
aiti-9227	44	14	and	and	CCONJ
aiti-9227	44	15	size	size	NOUN
aiti-9227	44	16	of	of	ADP
aiti-9227	44	17	the	the	DET
aiti-9227	44	18	data	datum	NOUN
aiti-9227	44	19	.	.	PUNCT
aiti-9227	45	1	therefore	therefore	ADV
aiti-9227	45	2	,	,	PUNCT
aiti-9227	45	3	it	it	PRON
aiti-9227	45	4	is	be	AUX
aiti-9227	45	5	necessary	necessary	ADJ
aiti-9227	45	6	to	to	PART
aiti-9227	45	7	standardize	standardize	VERB
aiti-9227	45	8	the	the	DET
aiti-9227	45	9	data	datum	NOUN
aiti-9227	45	10	during	during	ADP
aiti-9227	45	11	training	training	NOUN
aiti-9227	45	12	and	and	CCONJ
aiti-9227	45	13	forecasting	forecasting	NOUN
aiti-9227	45	14	for	for	ADP
aiti-9227	45	15	the	the	DET
aiti-9227	45	16	dl	dl	PROPN
aiti-9227	45	17	model	model	NOUN
aiti-9227	45	18	.	.	PUNCT
aiti-9227	46	1	in	in	ADP
aiti-9227	46	2	this	this	DET
aiti-9227	46	3	study	study	NOUN
aiti-9227	46	4	,	,	PUNCT
aiti-9227	46	5	the	the	DET
aiti-9227	46	6	methods	method	NOUN
aiti-9227	46	7	,	,	PUNCT
aiti-9227	46	8	such	such	ADJ
aiti-9227	46	9	as	as	ADP
aiti-9227	46	10	zero	zero	NUM
aiti-9227	46	11	-	-	PUNCT
aiti-9227	46	12	mean	mean	ADJ
aiti-9227	46	13	,	,	PUNCT
aiti-9227	46	14	min	min	PROPN
aiti-9227	46	15	-	-	PUNCT
aiti-9227	46	16	max	max	PROPN
aiti-9227	46	17	,	,	PUNCT
aiti-9227	46	18	max	max	PROPN
aiti-9227	46	19	,	,	PUNCT
aiti-9227	46	20	decimal	decimal	ADJ
aiti-9227	46	21	,	,	PUNCT
aiti-9227	46	22	sigmoid	sigmoid	NOUN
aiti-9227	46	23	,	,	PUNCT
aiti-9227	46	24	softmax	softmax	NOUN
aiti-9227	46	25	,	,	PUNCT
aiti-9227	46	26	median	median	NOUN
aiti-9227	46	27	,	,	PUNCT
aiti-9227	46	28	and	and	CCONJ
aiti-9227	46	29	robust	robust	ADJ
aiti-9227	46	30	,	,	PUNCT
aiti-9227	46	31	are	be	AUX
aiti-9227	46	32	proposed	propose	VERB
aiti-9227	46	33	to	to	PART
aiti-9227	46	34	standardize	standardize	VERB
aiti-9227	46	35	the	the	DET
aiti-9227	46	36	input	input	NOUN
aiti-9227	46	37	data	datum	NOUN
aiti-9227	46	38	of	of	ADP
aiti-9227	46	39	the	the	DET
aiti-9227	46	40	dl	dl	PROPN
aiti-9227	46	41	model	model	NOUN
aiti-9227	46	42	,	,	PUNCT
aiti-9227	46	43	and	and	CCONJ
aiti-9227	46	44	the	the	DET
aiti-9227	46	45	hyperparameter	hyperparameter	NOUN
aiti-9227	46	46	values	value	NOUN
aiti-9227	46	47	of	of	ADP
aiti-9227	46	48	cnn	cnn	PROPN
aiti-9227	46	49	and	and	CCONJ
aiti-9227	46	50	lstmn	lstmn	NOUN
aiti-9227	46	51	models	model	NOUN
aiti-9227	46	52	are	be	AUX
aiti-9227	46	53	established	establish	VERB
aiti-9227	46	54	based	base	VERB
aiti-9227	46	55	on	on	ADP
aiti-9227	46	56	epoch	epoch	NOUN
aiti-9227	46	57	,	,	PUNCT
aiti-9227	46	58	batch	batch	NOUN
aiti-9227	46	59	,	,	PUNCT
aiti-9227	46	60	optimizer	optimizer	NOUN
aiti-9227	46	61	,	,	PUNCT
aiti-9227	46	62	dropout	dropout	NOUN
aiti-9227	46	63	,	,	PUNCT
aiti-9227	46	64	filters	filter	NOUN
aiti-9227	46	65	,	,	PUNCT
aiti-9227	46	66	and	and	CCONJ
aiti-9227	46	67	kernel	kernel	PROPN
aiti-9227	46	68	.	.	PUNCT
aiti-9227	47	1	the	the	DET
aiti-9227	47	2	novelty	novelty	NOUN
aiti-9227	47	3	and	and	CCONJ
aiti-9227	47	4	contributions	contribution	NOUN
aiti-9227	47	5	of	of	ADP
aiti-9227	47	6	this	this	DET
aiti-9227	47	7	study	study	NOUN
aiti-9227	47	8	include	include	VERB
aiti-9227	47	9	the	the	DET
aiti-9227	47	10	following	follow	VERB
aiti-9227	47	11	aspects	aspect	NOUN
aiti-9227	47	12	:	:	PUNCT
aiti-9227	47	13	(	(	PUNCT
aiti-9227	47	14	i	i	NOUN
aiti-9227	47	15	)	)	PUNCT
aiti-9227	47	16	introduce	introduce	VERB
aiti-9227	47	17	a	a	DET
aiti-9227	47	18	data	data	NOUN
aiti-9227	47	19	standardization	standardization	NOUN
aiti-9227	47	20	method	method	NOUN
aiti-9227	47	21	on	on	ADP
aiti-9227	47	22	the	the	DET
aiti-9227	47	23	gs	gs	PROPN
aiti-9227	47	24	algorithm	algorithm	NOUN
aiti-9227	47	25	to	to	PART
aiti-9227	47	26	determine	determine	VERB
aiti-9227	47	27	the	the	DET
aiti-9227	47	28	optimal	optimal	ADJ
aiti-9227	47	29	hyperparameters	hyperparameter	NOUN
aiti-9227	47	30	of	of	ADP
aiti-9227	47	31	the	the	DET
aiti-9227	47	32	dl	dl	PROPN
aiti-9227	47	33	model	model	NOUN
aiti-9227	47	34	,	,	PUNCT
aiti-9227	47	35	including	include	VERB
aiti-9227	47	36	cnn	cnn	PROPN
aiti-9227	47	37	and	and	CCONJ
aiti-9227	47	38	lstmn	lstmn	VERB
aiti-9227	47	39	for	for	ADP
aiti-9227	47	40	the	the	DET
aiti-9227	47	41	energy	energy	NOUN
aiti-9227	47	42	load	load	NOUN
aiti-9227	47	43	forecasting	forecasting	NOUN
aiti-9227	47	44	;	;	PUNCT
aiti-9227	47	45	(	(	PUNCT
aiti-9227	47	46	ii	ii	NOUN
aiti-9227	47	47	)	)	PUNCT
aiti-9227	47	48	consider	consider	VERB
aiti-9227	47	49	the	the	DET
aiti-9227	47	50	error	error	NOUN
aiti-9227	47	51	evaluation	evaluation	NOUN
aiti-9227	47	52	indexes	index	NOUN
aiti-9227	47	53	of	of	ADP
aiti-9227	47	54	mae	mae	PROPN
aiti-9227	47	55	and	and	CCONJ
aiti-9227	47	56	mape	mape	NOUN
aiti-9227	47	57	of	of	ADP
aiti-9227	47	58	actual	actual	ADJ
aiti-9227	47	59	and	and	CCONJ
aiti-9227	47	60	predicted	predict	VERB
aiti-9227	47	61	values	value	NOUN
aiti-9227	47	62	for	for	ADP
aiti-9227	47	63	determining	determine	VERB
aiti-9227	47	64	the	the	DET
aiti-9227	47	65	optimal	optimal	ADJ
aiti-9227	47	66	hyperparameters	hyperparameter	NOUN
aiti-9227	47	67	of	of	ADP
aiti-9227	47	68	the	the	DET
aiti-9227	47	69	dl	dl	PROPN
aiti-9227	47	70	model	model	NOUN
aiti-9227	47	71	through	through	ADP
aiti-9227	47	72	the	the	DET
aiti-9227	47	73	epoch	epoch	NOUN
aiti-9227	47	74	,	,	PUNCT
aiti-9227	47	75	batch	batch	NOUN
aiti-9227	47	76	,	,	PUNCT
aiti-9227	47	77	optimizer	optimizer	NOUN
aiti-9227	47	78	,	,	PUNCT
aiti-9227	47	79	dropout	dropout	NOUN
aiti-9227	47	80	,	,	PUNCT
aiti-9227	47	81	filters	filter	NOUN
aiti-9227	47	82	,	,	PUNCT
aiti-9227	47	83	and	and	CCONJ
aiti-9227	47	84	kernel	kernel	PROPN
aiti-9227	47	85	;	;	PUNCT
aiti-9227	47	86	(	(	PUNCT
aiti-9227	47	87	iii	iii	X
aiti-9227	47	88	)	)	PUNCT
aiti-9227	47	89	conclude	conclude	VERB
aiti-9227	47	90	that	that	SCONJ
aiti-9227	47	91	the	the	DET
aiti-9227	47	92	zero	zero	NUM
aiti-9227	47	93	-	-	PUNCT
aiti-9227	47	94	mean	mean	NOUN
aiti-9227	47	95	and	and	CCONJ
aiti-9227	47	96	min	min	NOUN
aiti-9227	47	97	-	-	ADJ
aiti-9227	47	98	max	max	PROPN
aiti-9227	47	99	are	be	AUX
aiti-9227	47	100	two	two	NUM
aiti-9227	47	101	of	of	ADP
aiti-9227	47	102	the	the	DET
aiti-9227	47	103	data	data	NOUN
aiti-9227	47	104	standardization	standardization	NOUN
aiti-9227	47	105	methods	method	NOUN
aiti-9227	47	106	and	and	CCONJ
aiti-9227	47	107	not	not	PART
aiti-9227	47	108	the	the	DET
aiti-9227	47	109	best	good	ADJ
aiti-9227	47	110	methods	method	NOUN
aiti-9227	47	111	on	on	ADP
aiti-9227	47	112	the	the	DET
aiti-9227	47	113	gs	gs	PROPN
aiti-9227	47	114	algorithm	algorithm	NOUN
aiti-9227	47	115	for	for	ADP
aiti-9227	47	116	determining	determine	VERB
aiti-9227	47	117	the	the	DET
aiti-9227	47	118	optimal	optimal	ADJ
aiti-9227	47	119	hyperparameters	hyperparameter	NOUN
aiti-9227	47	120	of	of	ADP
aiti-9227	47	121	the	the	DET
aiti-9227	47	122	dl	dl	PROPN
aiti-9227	47	123	model	model	NOUN
aiti-9227	47	124	.	.	PUNCT
aiti-9227	48	1	this	this	DET
aiti-9227	48	2	study	study	NOUN
aiti-9227	48	3	consists	consist	VERB
aiti-9227	48	4	of	of	ADP
aiti-9227	48	5	five	five	NUM
aiti-9227	48	6	sections	section	NOUN
aiti-9227	48	7	.	.	PUNCT
aiti-9227	49	1	section	section	NOUN
aiti-9227	49	2	1	1	NUM
aiti-9227	49	3	presents	present	VERB
aiti-9227	49	4	the	the	DET
aiti-9227	49	5	urgency	urgency	NOUN
aiti-9227	49	6	,	,	PUNCT
aiti-9227	49	7	settlement	settlement	NOUN
aiti-9227	49	8	,	,	PUNCT
aiti-9227	49	9	and	and	CCONJ
aiti-9227	49	10	unresolved	unresolved	ADJ
aiti-9227	49	11	issues	issue	NOUN
aiti-9227	49	12	of	of	ADP
aiti-9227	49	13	the	the	DET
aiti-9227	49	14	load	load	NOUN
aiti-9227	49	15	forecasting	forecasting	NOUN
aiti-9227	49	16	problem	problem	NOUN
aiti-9227	49	17	.	.	PUNCT
aiti-9227	50	1	section	section	NOUN
aiti-9227	50	2	2	2	NUM
aiti-9227	50	3	describes	describe	VERB
aiti-9227	50	4	the	the	DET
aiti-9227	50	5	principle	principle	NOUN
aiti-9227	50	6	,	,	PUNCT
aiti-9227	50	7	hyperparameters	hyperparameter	NOUN
aiti-9227	50	8	,	,	PUNCT
aiti-9227	50	9	gs	gs	NOUN
aiti-9227	50	10	algorithm	algorithm	NOUN
aiti-9227	50	11	,	,	PUNCT
aiti-9227	50	12	and	and	CCONJ
aiti-9227	50	13	data	datum	NOUN
aiti-9227	50	14	normalization	normalization	NOUN
aiti-9227	50	15	method	method	NOUN
aiti-9227	50	16	for	for	ADP
aiti-9227	50	17	the	the	DET
aiti-9227	50	18	dl	dl	PROPN
aiti-9227	50	19	model	model	NOUN
aiti-9227	50	20	.	.	PUNCT
aiti-9227	51	1	the	the	DET
aiti-9227	51	2	experimental	experimental	ADJ
aiti-9227	51	3	procedures	procedure	NOUN
aiti-9227	51	4	and	and	CCONJ
aiti-9227	51	5	settings	setting	NOUN
aiti-9227	51	6	are	be	AUX
aiti-9227	51	7	presented	present	VERB
aiti-9227	51	8	in	in	ADP
aiti-9227	51	9	section	section	NOUN
aiti-9227	51	10	3	3	NUM
aiti-9227	51	11	.	.	PUNCT
aiti-9227	51	12	section	section	NOUN
aiti-9227	51	13	4	4	NUM
aiti-9227	51	14	presents	present	VERB
aiti-9227	51	15	the	the	DET
aiti-9227	51	16	results	result	NOUN
aiti-9227	51	17	and	and	CCONJ
aiti-9227	51	18	discussion	discussion	NOUN
aiti-9227	51	19	.	.	PUNCT
aiti-9227	52	1	finally	finally	ADV
aiti-9227	52	2	,	,	PUNCT
aiti-9227	52	3	the	the	DET
aiti-9227	52	4	conclusions	conclusion	NOUN
aiti-9227	52	5	and	and	CCONJ
aiti-9227	52	6	future	future	ADJ
aiti-9227	52	7	research	research	NOUN
aiti-9227	52	8	aspects	aspect	NOUN
aiti-9227	52	9	are	be	AUX
aiti-9227	52	10	presented	present	VERB
aiti-9227	52	11	in	in	ADP
aiti-9227	52	12	section	section	NOUN
aiti-9227	52	13	5	5	NUM
aiti-9227	52	14	.	.	SYM
aiti-9227	52	15	2	2	NUM
aiti-9227	52	16	.	.	X
aiti-9227	52	17	methodology	methodology	NOUN
aiti-9227	52	18	2.1	2.1	NUM
aiti-9227	52	19	.	.	PUNCT
aiti-9227	53	1	deep	deep	ADJ
aiti-9227	53	2	learning	learning	NOUN
aiti-9227	53	3	structures	structure	VERB
aiti-9227	53	4	artificial	artificial	ADJ
aiti-9227	53	5	intelligence	intelligence	NOUN
aiti-9227	53	6	is	be	AUX
aiti-9227	53	7	the	the	DET
aiti-9227	53	8	ability	ability	NOUN
aiti-9227	53	9	of	of	ADP
aiti-9227	53	10	a	a	DET
aiti-9227	53	11	machine	machine	NOUN
aiti-9227	53	12	to	to	PART
aiti-9227	53	13	imitate	imitate	VERB
aiti-9227	53	14	intelligent	intelligent	ADJ
aiti-9227	53	15	human	human	ADJ
aiti-9227	53	16	behavior	behavior	NOUN
aiti-9227	53	17	.	.	PUNCT
aiti-9227	54	1	machine	machine	NOUN
aiti-9227	54	2	learning	learning	NOUN
aiti-9227	54	3	(	(	PUNCT
aiti-9227	54	4	ml	ml	NOUN
aiti-9227	54	5	)	)	PUNCT
aiti-9227	54	6	is	be	AUX
aiti-9227	54	7	part	part	NOUN
aiti-9227	54	8	of	of	ADP
aiti-9227	54	9	artificial	artificial	ADJ
aiti-9227	54	10	intelligence	intelligence	NOUN
aiti-9227	54	11	that	that	PRON
aiti-9227	54	12	allows	allow	VERB
aiti-9227	54	13	a	a	DET
aiti-9227	54	14	system	system	NOUN
aiti-9227	54	15	to	to	PART
aiti-9227	54	16	learn	learn	VERB
aiti-9227	54	17	and	and	CCONJ
aiti-9227	54	18	automatically	automatically	ADV
aiti-9227	54	19	improve	improve	VERB
aiti-9227	54	20	from	from	ADP
aiti-9227	54	21	experience	experience	NOUN
aiti-9227	54	22	.	.	PUNCT
aiti-9227	55	1	dl	dl	PROPN
aiti-9227	55	2	is	be	AUX
aiti-9227	55	3	an	an	DET
aiti-9227	55	4	application	application	NOUN
aiti-9227	55	5	of	of	ADP
aiti-9227	55	6	ml	ml	ADP
aiti-9227	55	7	that	that	SCONJ
aiti-9227	55	8	259	259	NUM
aiti-9227	55	9	advances	advance	NOUN
aiti-9227	55	10	in	in	ADP
aiti-9227	55	11	technology	technology	NOUN
aiti-9227	55	12	innovation	innovation	NOUN
aiti-9227	55	13	,	,	PUNCT
aiti-9227	55	14	vol	vol	NOUN
aiti-9227	55	15	.	.	PROPN
aiti-9227	56	1	7	7	NUM
aiti-9227	56	2	,	,	PUNCT
aiti-9227	56	3	no	no	INTJ
aiti-9227	56	4	.	.	NOUN
aiti-9227	56	5	4	4	NUM
aiti-9227	56	6	,	,	PUNCT
aiti-9227	56	7	2022	2022	NUM
aiti-9227	56	8	,	,	PUNCT
aiti-9227	56	9	pp	pp	ADJ
aiti-9227	56	10	.	.	PUNCT
aiti-9227	57	1	258	258	NUM
aiti-9227	57	2	-	-	SYM
aiti-9227	57	3	269	269	NUM
aiti-9227	57	4	uses	use	VERB
aiti-9227	57	5	complex	complex	ADJ
aiti-9227	57	6	algorithms	algorithm	NOUN
aiti-9227	57	7	and	and	CCONJ
aiti-9227	57	8	deep	deep	ADJ
aiti-9227	57	9	neural	neural	ADJ
aiti-9227	57	10	nets	net	NOUN
aiti-9227	57	11	to	to	PART
aiti-9227	57	12	train	train	VERB
aiti-9227	57	13	a	a	DET
aiti-9227	57	14	model	model	NOUN
aiti-9227	57	15	.	.	PUNCT
aiti-9227	58	1	dl	dl	PROPN
aiti-9227	58	2	models	model	NOUN
aiti-9227	58	3	are	be	AUX
aiti-9227	58	4	built	build	VERB
aiti-9227	58	5	using	use	VERB
aiti-9227	58	6	several	several	ADJ
aiti-9227	58	7	algorithms	algorithm	NOUN
aiti-9227	58	8	,	,	PUNCT
aiti-9227	58	9	such	such	ADJ
aiti-9227	58	10	as	as	ADP
aiti-9227	58	11	cnns	cnn	NOUN
aiti-9227	58	12	,	,	PUNCT
aiti-9227	58	13	lstmns	lstmn	NOUN
aiti-9227	58	14	,	,	PUNCT
aiti-9227	58	15	recurrent	recurrent	ADJ
aiti-9227	58	16	neural	neural	ADJ
aiti-9227	58	17	networks	network	NOUN
aiti-9227	58	18	(	(	PUNCT
aiti-9227	58	19	rnns	rnns	PROPN
aiti-9227	58	20	)	)	PUNCT
aiti-9227	58	21	,	,	PUNCT
aiti-9227	58	22	generative	generative	ADJ
aiti-9227	58	23	adversarial	adversarial	ADJ
aiti-9227	58	24	networks	network	NOUN
aiti-9227	58	25	(	(	PUNCT
aiti-9227	58	26	gans	gan	NOUN
aiti-9227	58	27	)	)	PUNCT
aiti-9227	58	28	,	,	PUNCT
aiti-9227	58	29	radial	radial	ADJ
aiti-9227	58	30	basis	basis	NOUN
aiti-9227	58	31	function	function	NOUN
aiti-9227	58	32	networks	network	NOUN
aiti-9227	58	33	(	(	PUNCT
aiti-9227	58	34	rbfns	rbfns	NOUN
aiti-9227	58	35	)	)	PUNCT
aiti-9227	58	36	,	,	PUNCT
aiti-9227	58	37	and	and	CCONJ
aiti-9227	58	38	so	so	ADV
aiti-9227	58	39	on	on	ADP
aiti-9227	58	40	[	[	X
aiti-9227	58	41	19	19	NUM
aiti-9227	58	42	]	]	PUNCT
aiti-9227	58	43	.	.	PUNCT
aiti-9227	59	1	in	in	ADP
aiti-9227	59	2	this	this	DET
aiti-9227	59	3	study	study	NOUN
aiti-9227	59	4	,	,	PUNCT
aiti-9227	59	5	two	two	NUM
aiti-9227	59	6	widespread	widespread	ADJ
aiti-9227	59	7	dl	dl	PROPN
aiti-9227	59	8	networks	network	NOUN
aiti-9227	59	9	,	,	PUNCT
aiti-9227	59	10	lstmn	lstmn	NOUN
aiti-9227	59	11	and	and	CCONJ
aiti-9227	59	12	cnn	cnn	PROPN
aiti-9227	59	13	,	,	PUNCT
aiti-9227	59	14	are	be	AUX
aiti-9227	59	15	used	use	VERB
aiti-9227	59	16	to	to	PART
aiti-9227	59	17	resolve	resolve	VERB
aiti-9227	59	18	the	the	DET
aiti-9227	59	19	problem	problem	NOUN
aiti-9227	59	20	.	.	PUNCT
aiti-9227	60	1	the	the	DET
aiti-9227	60	2	procedure	procedure	NOUN
aiti-9227	60	3	is	be	AUX
aiti-9227	60	4	performed	perform	VERB
aiti-9227	60	5	as	as	SCONJ
aiti-9227	60	6	follows	follow	VERB
aiti-9227	60	7	.	.	PUNCT
aiti-9227	61	1	2.1.1	2.1.1	X
aiti-9227	61	2	.	.	PUNCT
aiti-9227	61	3	lstmn	lstmn	NOUN
aiti-9227	61	4	network	network	NOUN
aiti-9227	61	5	the	the	DET
aiti-9227	61	6	difference	difference	NOUN
aiti-9227	61	7	between	between	ADP
aiti-9227	61	8	the	the	DET
aiti-9227	61	9	rnn	rnn	NOUN
aiti-9227	61	10	and	and	CCONJ
aiti-9227	61	11	feed	feed	NOUN
aiti-9227	61	12	-	-	PUNCT
aiti-9227	61	13	forward	forward	ADV
aiti-9227	61	14	neural	neural	ADJ
aiti-9227	61	15	network	network	NOUN
aiti-9227	61	16	(	(	PUNCT
aiti-9227	61	17	ffnn	ffnn	NOUN
aiti-9227	61	18	)	)	PUNCT
aiti-9227	61	19	is	be	AUX
aiti-9227	61	20	that	that	SCONJ
aiti-9227	61	21	the	the	DET
aiti-9227	61	22	rnn	rnn	NOUN
aiti-9227	61	23	is	be	AUX
aiti-9227	61	24	a	a	DET
aiti-9227	61	25	model	model	NOUN
aiti-9227	61	26	that	that	PRON
aiti-9227	61	27	can	can	AUX
aiti-9227	61	28	create	create	VERB
aiti-9227	61	29	a	a	DET
aiti-9227	61	30	correlation	correlation	NOUN
aiti-9227	61	31	between	between	ADP
aiti-9227	61	32	the	the	DET
aiti-9227	61	33	previous	previous	ADJ
aiti-9227	61	34	information	information	NOUN
aiti-9227	61	35	and	and	CCONJ
aiti-9227	61	36	the	the	DET
aiti-9227	61	37	current	current	ADJ
aiti-9227	61	38	state	state	NOUN
aiti-9227	61	39	.	.	PUNCT
aiti-9227	62	1	a	a	DET
aiti-9227	62	2	simple	simple	ADJ
aiti-9227	62	3	rnn	rnn	NOUN
aiti-9227	62	4	structure	structure	NOUN
aiti-9227	62	5	is	be	AUX
aiti-9227	62	6	shown	show	VERB
aiti-9227	62	7	in	in	ADP
aiti-9227	62	8	fig	fig	NOUN
aiti-9227	62	9	.	.	PUNCT
aiti-9227	63	1	1	1	NUM
aiti-9227	63	2	,	,	PUNCT
aiti-9227	63	3	in	in	ADP
aiti-9227	63	4	which	which	PRON
aiti-9227	63	5	the	the	DET
aiti-9227	63	6	output	output	NOUN
aiti-9227	63	7	signal	signal	NOUN
aiti-9227	63	8	is	be	AUX
aiti-9227	63	9	determined	determine	VERB
aiti-9227	63	10	based	base	VERB
aiti-9227	63	11	on	on	ADP
aiti-9227	63	12	a	a	DET
aiti-9227	63	13	linear	linear	ADJ
aiti-9227	63	14	transformation	transformation	NOUN
aiti-9227	63	15	and	and	CCONJ
aiti-9227	63	16	nonlinear	nonlinear	ADJ
aiti-9227	63	17	activation	activation	NOUN
aiti-9227	63	18	.	.	PUNCT
aiti-9227	64	1	the	the	DET
aiti-9227	64	2	output	output	NOUN
aiti-9227	64	3	signal	signal	NOUN
aiti-9227	64	4	can	can	AUX
aiti-9227	64	5	be	be	AUX
aiti-9227	64	6	calculated	calculate	VERB
aiti-9227	64	7	under	under	ADP
aiti-9227	64	8	the	the	DET
aiti-9227	64	9	tangent	tangent	NOUN
aiti-9227	64	10	function	function	NOUN
aiti-9227	64	11	as	as	SCONJ
aiti-9227	64	12	follows	follow	VERB
aiti-9227	64	13	[	[	X
aiti-9227	64	14	20	20	NUM
aiti-9227	64	15	]	]	X
aiti-9227	64	16	:	:	PUNCT
aiti-9227	64	17	1tanh	1tanh	NUM
aiti-9227	64	18	(	(	PUNCT
aiti-9227	64	19	(	(	PUNCT
aiti-9227	64	20	,	,	PUNCT
aiti-9227	64	21	)	)	PUNCT
aiti-9227	64	22	)	)	PUNCT
aiti-9227	65	1	t	t	PROPN
aiti-9227	65	2	t	t	PROPN
aiti-9227	65	3	t	t	PROPN
aiti-9227	65	4	h	h	PROPN
aiti-9227	66	1	w	w	PROPN
aiti-9227	66	2	h	h	NOUN
aiti-9227	66	3	x	x	X
aiti-9227	66	4	b−=	b−=	X
aiti-9227	67	1	+	+	CCONJ
aiti-9227	67	2	(	(	PUNCT
aiti-9227	67	3	1	1	X
aiti-9227	67	4	)	)	PUNCT
aiti-9227	67	5	where	where	SCONJ
aiti-9227	67	6	ht-1	ht-1	PRON
aiti-9227	67	7	denotes	denote	VERB
aiti-9227	67	8	the	the	DET
aiti-9227	67	9	(	(	PUNCT
aiti-9227	67	10	t-1)th	t-1)th	PROPN
aiti-9227	67	11	output	output	NOUN
aiti-9227	67	12	signal	signal	NOUN
aiti-9227	67	13	,	,	PUNCT
aiti-9227	67	14	xt	xt	PROPN
aiti-9227	67	15	denotes	denote	VERB
aiti-9227	67	16	the	the	DET
aiti-9227	67	17	t	t	PROPN
aiti-9227	67	18	th	th	X
aiti-9227	67	19	input	input	NOUN
aiti-9227	67	20	signal	signal	NOUN
aiti-9227	67	21	,	,	PUNCT
aiti-9227	67	22	and	and	CCONJ
aiti-9227	67	23	b	b	X
aiti-9227	67	24	denotes	denote	VERB
aiti-9227	67	25	the	the	DET
aiti-9227	67	26	bias	bias	NOUN
aiti-9227	67	27	.	.	PUNCT
aiti-9227	68	1	the	the	DET
aiti-9227	68	2	lstmn	lstmn	NOUN
aiti-9227	68	3	is	be	AUX
aiti-9227	68	4	a	a	DET
aiti-9227	68	5	modified	modify	VERB
aiti-9227	68	6	rnn	rnn	NOUN
aiti-9227	68	7	model	model	NOUN
aiti-9227	68	8	developed	develop	VERB
aiti-9227	68	9	by	by	ADP
aiti-9227	68	10	song	song	NOUN
aiti-9227	68	11	et	et	PROPN
aiti-9227	68	12	al	al	PROPN
aiti-9227	68	13	.	.	PUNCT
aiti-9227	69	1	[	[	X
aiti-9227	69	2	21	21	NUM
aiti-9227	69	3	]	]	PUNCT
aiti-9227	69	4	.	.	PUNCT
aiti-9227	70	1	the	the	DET
aiti-9227	70	2	difference	difference	NOUN
aiti-9227	70	3	between	between	ADP
aiti-9227	70	4	the	the	DET
aiti-9227	70	5	lstmn	lstmn	NOUN
aiti-9227	70	6	and	and	CCONJ
aiti-9227	70	7	the	the	DET
aiti-9227	70	8	rnn	rnn	NOUN
aiti-9227	70	9	is	be	AUX
aiti-9227	70	10	that	that	SCONJ
aiti-9227	70	11	the	the	DET
aiti-9227	70	12	lstmn	lstmn	NOUN
aiti-9227	70	13	can	can	AUX
aiti-9227	70	14	process	process	VERB
aiti-9227	70	15	long	long	ADJ
aiti-9227	70	16	-	-	PUNCT
aiti-9227	70	17	term	term	NOUN
aiti-9227	70	18	dependencies	dependency	NOUN
aiti-9227	70	19	.	.	PUNCT
aiti-9227	71	1	fig	fig	NOUN
aiti-9227	71	2	.	.	PUNCT
aiti-9227	72	1	2	2	NUM
aiti-9227	72	2	describes	describe	VERB
aiti-9227	72	3	the	the	DET
aiti-9227	72	4	lstmn	lstmn	NOUN
aiti-9227	72	5	structure	structure	NOUN
aiti-9227	72	6	.	.	PUNCT
aiti-9227	73	1	each	each	DET
aiti-9227	73	2	block	block	NOUN
aiti-9227	73	3	has	have	VERB
aiti-9227	73	4	two	two	NUM
aiti-9227	73	5	parallel	parallel	ADJ
aiti-9227	73	6	lines	line	NOUN
aiti-9227	73	7	going	go	VERB
aiti-9227	73	8	in	in	ADV
aiti-9227	73	9	and	and	CCONJ
aiti-9227	73	10	out	out	ADV
aiti-9227	73	11	,	,	PUNCT
aiti-9227	73	12	representing	represent	VERB
aiti-9227	73	13	the	the	DET
aiti-9227	73	14	cell	cell	NOUN
aiti-9227	73	15	state	state	NOUN
aiti-9227	73	16	and	and	CCONJ
aiti-9227	73	17	hidden	hide	VERB
aiti-9227	73	18	state	state	NOUN
aiti-9227	73	19	information	information	NOUN
aiti-9227	73	20	.	.	PUNCT
aiti-9227	74	1	the	the	DET
aiti-9227	74	2	general	general	ADJ
aiti-9227	74	3	structure	structure	NOUN
aiti-9227	74	4	of	of	ADP
aiti-9227	74	5	lstmn	lstmn	NOUN
aiti-9227	74	6	has	have	VERB
aiti-9227	74	7	four	four	NUM
aiti-9227	74	8	layers	layer	NOUN
aiti-9227	74	9	of	of	ADP
aiti-9227	74	10	neural	neural	ADJ
aiti-9227	74	11	networks	network	NOUN
aiti-9227	74	12	composed	compose	VERB
aiti-9227	74	13	of	of	ADP
aiti-9227	74	14	three	three	NUM
aiti-9227	74	15	inputs	input	NOUN
aiti-9227	74	16	(	(	PUNCT
aiti-9227	74	17	i.e.	i.e.	X
aiti-9227	74	18	,	,	PUNCT
aiti-9227	74	19	ct-1	ct-1	ADJ
aiti-9227	74	20	,	,	PUNCT
aiti-9227	74	21	ht-1	ht-1	X
aiti-9227	74	22	,	,	PUNCT
aiti-9227	74	23	and	and	CCONJ
aiti-9227	74	24	xt	xt	NUM
aiti-9227	74	25	)	)	PUNCT
aiti-9227	74	26	and	and	CCONJ
aiti-9227	74	27	two	two	NUM
aiti-9227	74	28	outputs	output	NOUN
aiti-9227	74	29	(	(	PUNCT
aiti-9227	74	30	i.e.	i.e.	X
aiti-9227	74	31	,	,	PUNCT
aiti-9227	74	32	ct	ct	PROPN
aiti-9227	74	33	and	and	CCONJ
aiti-9227	74	34	ht	ht	PROPN
aiti-9227	74	35	)	)	PUNCT
aiti-9227	74	36	.	.	PUNCT
aiti-9227	75	1	therefore	therefore	ADV
aiti-9227	75	2	,	,	PUNCT
aiti-9227	75	3	this	this	DET
aiti-9227	75	4	lstmn	lstmn	NOUN
aiti-9227	75	5	structure	structure	NOUN
aiti-9227	75	6	can	can	AUX
aiti-9227	75	7	be	be	AUX
aiti-9227	75	8	described	describe	VERB
aiti-9227	75	9	by	by	ADP
aiti-9227	75	10	the	the	DET
aiti-9227	75	11	following	follow	VERB
aiti-9227	75	12	equations	equation	NOUN
aiti-9227	75	13	[	[	X
aiti-9227	75	14	21	21	NUM
aiti-9227	75	15	]	]	X
aiti-9227	75	16	:	:	PUNCT
aiti-9227	75	17	the	the	DET
aiti-9227	75	18	authors	author	NOUN
aiti-9227	75	19	identify	identify	VERB
aiti-9227	75	20	the	the	DET
aiti-9227	75	21	information	information	NOUN
aiti-9227	75	22	from	from	ADP
aiti-9227	75	23	the	the	DET
aiti-9227	75	24	previous	previous	ADJ
aiti-9227	75	25	cell	cell	NOUN
aiti-9227	75	26	state	state	NOUN
aiti-9227	75	27	ct-1	ct-1	PUNCT
aiti-9227	75	28	that	that	PRON
aiti-9227	75	29	should	should	AUX
aiti-9227	75	30	be	be	AUX
aiti-9227	75	31	removed	remove	VERB
aiti-9227	75	32	by	by	ADP
aiti-9227	75	33	the	the	DET
aiti-9227	75	34	following	follow	VERB
aiti-9227	75	35	forget	forget	VERB
aiti-9227	75	36	gate	gate	PROPN
aiti-9227	75	37	ft	ft	PROPN
aiti-9227	75	38	.	.	PROPN
aiti-9227	75	39	1	1	NUM
aiti-9227	75	40	(	(	PUNCT
aiti-9227	75	41	)	)	PUNCT
aiti-9227	75	42	)	)	PUNCT
aiti-9227	75	43	(	(	PUNCT
aiti-9227	75	44	,	,	PUNCT
aiti-9227	75	45	t	t	PROPN
aiti-9227	75	46	f	f	PROPN
aiti-9227	76	1	t	t	PROPN
aiti-9227	76	2	t	t	PROPN
aiti-9227	76	3	f	f	PROPN
aiti-9227	76	4	f	f	PROPN
aiti-9227	76	5	w	w	PROPN
aiti-9227	76	6	h	h	NOUN
aiti-9227	76	7	x	x	INTJ
aiti-9227	76	8	b−=	b−=	NOUN
aiti-9227	77	1	×	×	PROPN
aiti-9227	78	1	+	+	NOUN
aiti-9227	78	2	σ	σ	X
aiti-9227	78	3	(	(	PUNCT
aiti-9227	78	4	2	2	NUM
aiti-9227	78	5	)	)	PUNCT
aiti-9227	78	6	the	the	DET
aiti-9227	78	7	authors	author	NOUN
aiti-9227	78	8	identify	identify	VERB
aiti-9227	78	9	the	the	DET
aiti-9227	78	10	input	input	NOUN
aiti-9227	78	11	signal	signal	NOUN
aiti-9227	78	12	xt	xt	PROPN
aiti-9227	78	13	that	that	PRON
aiti-9227	78	14	should	should	AUX
aiti-9227	78	15	be	be	AUX
aiti-9227	78	16	stored	store	VERB
aiti-9227	78	17	in	in	ADP
aiti-9227	78	18	the	the	DET
aiti-9227	78	19	cell	cell	NOUN
aiti-9227	78	20	state	state	NOUN
aiti-9227	78	21	ct	ct	NUM
aiti-9227	78	22	in	in	ADP
aiti-9227	78	23	the	the	DET
aiti-9227	78	24	input	input	NOUN
aiti-9227	78	25	gate	gate	NOUN
aiti-9227	78	26	,	,	PUNCT
aiti-9227	78	27	in	in	ADP
aiti-9227	78	28	which	which	PRON
aiti-9227	78	29	the	the	DET
aiti-9227	78	30	input	input	NOUN
aiti-9227	78	31	information	information	NOUN
aiti-9227	78	32	and	and	CCONJ
aiti-9227	78	33	the	the	DET
aiti-9227	78	34	candidacy	candidacy	NOUN
aiti-9227	78	35	cell	cell	NOUN
aiti-9227	78	36	state	state	NOUN
aiti-9227	78	37	�	�	PROPN
aiti-9227	78	38	�	�	PROPN
aiti-9227	78	39	�	�	PROPN
aiti-9227	78	40	should	should	AUX
aiti-9227	78	41	be	be	AUX
aiti-9227	78	42	updated	update	VERB
aiti-9227	78	43	by	by	ADP
aiti-9227	78	44	:	:	PUNCT
aiti-9227	78	45	1	1	NUM
aiti-9227	78	46	(	(	PUNCT
aiti-9227	78	47	(	(	PUNCT
aiti-9227	78	48	,	,	PUNCT
aiti-9227	78	49	)	)	PUNCT
aiti-9227	78	50	)	)	PUNCT
aiti-9227	79	1	t	t	NOUN
aiti-9227	80	1	i	i	PRON
aiti-9227	80	2	t	t	NOUN
aiti-9227	81	1	t	t	X
aiti-9227	82	1	i	i	PRON
aiti-9227	83	1	i	i	INTJ
aiti-9227	84	1	w	w	VERB
aiti-9227	84	2	h	h	NOUN
aiti-9227	85	1	x	x	INTJ
aiti-9227	85	2	b−=	b−=	NOUN
aiti-9227	86	1	×	×	PROPN
aiti-9227	87	1	+	+	NOUN
aiti-9227	87	2	σ	σ	X
aiti-9227	87	3	(	(	PUNCT
aiti-9227	87	4	3	3	NUM
aiti-9227	87	5	)	)	PUNCT
aiti-9227	87	6	1tanh	1tanh	NUM
aiti-9227	87	7	(	(	PUNCT
aiti-9227	87	8	(	(	PUNCT
aiti-9227	87	9	,	,	PUNCT
aiti-9227	87	10	)	)	PUNCT
aiti-9227	87	11	)	)	PUNCT
aiti-9227	88	1	t	t	PROPN
aiti-9227	89	1	c	c	PROPN
aiti-9227	89	2	t	t	PROPN
aiti-9227	89	3	t	t	PROPN
aiti-9227	89	4	c	c	PROPN
aiti-9227	89	5	c	c	PROPN
aiti-9227	89	6	w	w	PROPN
aiti-9227	89	7	h	h	NOUN
aiti-9227	89	8	x	x	INTJ
aiti-9227	89	9	b−=	b−=	PUNCT
aiti-9227	90	1	×	×	VERB
aiti-9227	91	1	+	+	SYM
aiti-9227	91	2	ɶ	ɶ	X
aiti-9227	91	3	(	(	PUNCT
aiti-9227	91	4	4	4	NUM
aiti-9227	91	5	)	)	PUNCT
aiti-9227	91	6	the	the	DET
aiti-9227	91	7	previous	previous	ADJ
aiti-9227	91	8	cell	cell	NOUN
aiti-9227	91	9	state	state	NOUN
aiti-9227	91	10	ct	ct	PROPN
aiti-9227	91	11	is	be	AUX
aiti-9227	91	12	updated	update	VERB
aiti-9227	91	13	by	by	ADP
aiti-9227	91	14	combining	combine	VERB
aiti-9227	91	15	ct-1	ct-1	PUNCT
aiti-9227	91	16	and	and	CCONJ
aiti-9227	91	17	�	�	PROPN
aiti-9227	91	18	�	�	PROPN
aiti-9227	91	19	�	�	PROPN
aiti-9227	91	20	:	:	PUNCT
aiti-9227	91	21	1	1	NUM
aiti-9227	91	22	t	t	NOUN
aiti-9227	91	23	t	t	NOUN
aiti-9227	91	24	t	t	PROPN
aiti-9227	91	25	t	t	PROPN
aiti-9227	91	26	t	t	PROPN
aiti-9227	91	27	c	c	NOUN
aiti-9227	92	1	f	f	PROPN
aiti-9227	92	2	c	c	NOUN
aiti-9227	93	1	i	i	PRON
aiti-9227	93	2	c−=	c−=	VERB
aiti-9227	93	3	×	×	NOUN
aiti-9227	94	1	+	+	CCONJ
aiti-9227	94	2	×	×	PROPN
aiti-9227	94	3	ɶ	ɶ	PROPN
aiti-9227	94	4	(	(	PUNCT
aiti-9227	94	5	5	5	NUM
aiti-9227	94	6	)	)	PUNCT
aiti-9227	94	7	the	the	DET
aiti-9227	94	8	outcome	outcome	NOUN
aiti-9227	94	9	ht	ht	INTJ
aiti-9227	94	10	in	in	ADP
aiti-9227	94	11	the	the	DET
aiti-9227	94	12	output	output	NOUN
aiti-9227	94	13	gate	gate	NOUN
aiti-9227	94	14	is	be	AUX
aiti-9227	94	15	confirmed	confirm	VERB
aiti-9227	94	16	based	base	VERB
aiti-9227	94	17	on	on	ADP
aiti-9227	94	18	the	the	DET
aiti-9227	94	19	output	output	NOUN
aiti-9227	94	20	information	information	NOUN
aiti-9227	94	21	ot	ot	INTJ
aiti-9227	94	22	and	and	CCONJ
aiti-9227	94	23	ct	ct	PRON
aiti-9227	94	24	:	:	PUNCT
aiti-9227	94	25	1	1	NUM
aiti-9227	94	26	(	(	PUNCT
aiti-9227	94	27	)	)	PUNCT
aiti-9227	94	28	)	)	PUNCT
aiti-9227	94	29	(	(	PUNCT
aiti-9227	94	30	,	,	PUNCT
aiti-9227	94	31	t	t	X
aiti-9227	95	1	o	o	X
aiti-9227	95	2	t	t	NOUN
aiti-9227	95	3	t	t	X
aiti-9227	95	4	o	o	X
aiti-9227	95	5	o	o	X
aiti-9227	95	6	w	w	NOUN
aiti-9227	95	7	h	h	NOUN
aiti-9227	95	8	x	x	INTJ
aiti-9227	95	9	b−=	b−=	NOUN
aiti-9227	96	1	×	×	PROPN
aiti-9227	97	1	+	+	NOUN
aiti-9227	97	2	σ	σ	X
aiti-9227	97	3	(	(	PUNCT
aiti-9227	97	4	6	6	NUM
aiti-9227	97	5	)	)	PUNCT
aiti-9227	97	6	tanh	tanh	NOUN
aiti-9227	97	7	(	(	PUNCT
aiti-9227	97	8	)	)	PUNCT
aiti-9227	97	9	t	t	NOUN
aiti-9227	97	10	t	t	PROPN
aiti-9227	97	11	t	t	PROPN
aiti-9227	97	12	h	h	NOUN
aiti-9227	97	13	o	o	NOUN
aiti-9227	97	14	c=	c=	NOUN
aiti-9227	97	15	×	×	NOUN
aiti-9227	97	16	(	(	PUNCT
aiti-9227	97	17	7	7	NUM
aiti-9227	97	18	)	)	PUNCT
aiti-9227	97	19	in	in	ADP
aiti-9227	97	20	which	which	PRON
aiti-9227	97	21	w	w	NOUN
aiti-9227	97	22	is	be	AUX
aiti-9227	97	23	the	the	DET
aiti-9227	97	24	input	input	NOUN
aiti-9227	97	25	weight	weight	NOUN
aiti-9227	97	26	and	and	CCONJ
aiti-9227	97	27	f	f	X
aiti-9227	97	28	,	,	PUNCT
aiti-9227	97	29	i	i	PRON
aiti-9227	97	30	,	,	PUNCT
aiti-9227	97	31	and	and	CCONJ
aiti-9227	97	32	o	o	PROPN
aiti-9227	97	33	represent	represent	VERB
aiti-9227	97	34	the	the	DET
aiti-9227	97	35	forget	forget	NOUN
aiti-9227	97	36	,	,	PUNCT
aiti-9227	97	37	input	input	NOUN
aiti-9227	97	38	,	,	PUNCT
aiti-9227	97	39	and	and	CCONJ
aiti-9227	97	40	output	output	NOUN
aiti-9227	97	41	gates	gate	NOUN
aiti-9227	97	42	,	,	PUNCT
aiti-9227	97	43	respectively	respectively	ADV
aiti-9227	97	44	.	.	PUNCT
aiti-9227	98	1	a	a	DET
aiti-9227	98	2	a	a	DET
aiti-9227	98	3	ht-1	ht-1	PUNCT
aiti-9227	98	4	ht	ht	PROPN
aiti-9227	98	5	ht+1	ht+1	PROPN
aiti-9227	98	6	xt-1	xt-1	NUM
aiti-9227	98	7	xt	xt	PROPN
aiti-9227	98	8	xt+1	xt+1	PROPN
aiti-9227	98	9	tanh	tanh	PROPN
aiti-9227	98	10	fig	fig	PROPN
aiti-9227	98	11	.	.	PUNCT
aiti-9227	99	1	1	1	NUM
aiti-9227	99	2	simple	simple	ADJ
aiti-9227	99	3	rnn	rnn	NOUN
aiti-9227	99	4	architecture	architecture	NOUN
aiti-9227	99	5	260	260	NUM
aiti-9227	99	6	advances	advance	NOUN
aiti-9227	99	7	in	in	ADP
aiti-9227	99	8	technology	technology	NOUN
aiti-9227	99	9	innovation	innovation	NOUN
aiti-9227	99	10	,	,	PUNCT
aiti-9227	99	11	vol	vol	NOUN
aiti-9227	99	12	.	.	PROPN
aiti-9227	99	13	7	7	NUM
aiti-9227	99	14	,	,	PUNCT
aiti-9227	99	15	no	no	INTJ
aiti-9227	99	16	.	.	NOUN
aiti-9227	99	17	4	4	NUM
aiti-9227	99	18	,	,	PUNCT
aiti-9227	99	19	2022	2022	NUM
aiti-9227	99	20	,	,	PUNCT
aiti-9227	99	21	pp	pp	ADJ
aiti-9227	99	22	.	.	PUNCT
aiti-9227	100	1	258	258	NUM
aiti-9227	100	2	-	-	SYM
aiti-9227	100	3	269	269	NUM
aiti-9227	100	4	ht	ht	PROPN
aiti-9227	100	5	ct-1	ct-1	PUNCT
aiti-9227	100	6	ht-1	ht-1	PROPN
aiti-9227	100	7	xt	xt	PROPN
aiti-9227	100	8	σ	σ	PROPN
aiti-9227	100	9	tanh	tanh	PROPN
aiti-9227	100	10	ct	ct	PROPN
aiti-9227	100	11	ht	ht	PROPN
aiti-9227	101	1	ft	ft	INTJ
aiti-9227	101	2	it	it	PRON
aiti-9227	101	3	ot	ot	NOUN
aiti-9227	101	4	tcɶ	tcɶ	PROPN
aiti-9227	101	5	σ	σ	PROPN
aiti-9227	101	6	σ	σ	PROPN
aiti-9227	101	7	tanh	tanh	PROPN
aiti-9227	101	8	fig	fig	NOUN
aiti-9227	101	9	.	.	PUNCT
aiti-9227	101	10	2	2	NUM
aiti-9227	101	11	lstmn	lstmn	NOUN
aiti-9227	101	12	architecture	architecture	NOUN
aiti-9227	101	13	2.1.2	2.1.2	NUM
aiti-9227	101	14	.	.	PUNCT
aiti-9227	101	15	cnn	cnn	PROPN
aiti-9227	101	16	network	network	PROPN
aiti-9227	101	17	cnn	cnn	PROPN
aiti-9227	101	18	is	be	AUX
aiti-9227	101	19	a	a	DET
aiti-9227	101	20	feedforward	feedforward	ADJ
aiti-9227	101	21	neural	neural	ADJ
aiti-9227	101	22	network	network	NOUN
aiti-9227	101	23	with	with	ADP
aiti-9227	101	24	a	a	DET
aiti-9227	101	25	structure	structure	NOUN
aiti-9227	101	26	similar	similar	ADJ
aiti-9227	101	27	to	to	ADP
aiti-9227	101	28	that	that	PRON
aiti-9227	101	29	of	of	ADP
aiti-9227	101	30	human	human	ADJ
aiti-9227	101	31	neurons	neuron	NOUN
aiti-9227	101	32	.	.	PUNCT
aiti-9227	102	1	fig	fig	NOUN
aiti-9227	102	2	.	.	PUNCT
aiti-9227	103	1	3	3	NUM
aiti-9227	103	2	depicts	depict	VERB
aiti-9227	103	3	the	the	DET
aiti-9227	103	4	cnn	cnn	PROPN
aiti-9227	103	5	structure	structure	NOUN
aiti-9227	103	6	developed	develop	VERB
aiti-9227	103	7	based	base	VERB
aiti-9227	103	8	on	on	ADP
aiti-9227	103	9	the	the	DET
aiti-9227	103	10	convolutional	convolutional	ADJ
aiti-9227	103	11	cnn	cnn	NOUN
aiti-9227	103	12	structure	structure	NOUN
aiti-9227	103	13	introduced	introduce	VERB
aiti-9227	103	14	in	in	ADP
aiti-9227	103	15	the	the	DET
aiti-9227	103	16	work	work	NOUN
aiti-9227	103	17	of	of	ADP
aiti-9227	103	18	bon	bon	X
aiti-9227	103	19	et	et	PROPN
aiti-9227	103	20	al	al	PROPN
aiti-9227	103	21	.	.	PUNCT
aiti-9227	104	1	[	[	X
aiti-9227	104	2	22	22	NUM
aiti-9227	104	3	]	]	PUNCT
aiti-9227	104	4	,	,	PUNCT
aiti-9227	104	5	which	which	PRON
aiti-9227	104	6	includes	include	VERB
aiti-9227	104	7	convolution	convolution	NOUN
aiti-9227	104	8	,	,	PUNCT
aiti-9227	104	9	pooling	pooling	NOUN
aiti-9227	104	10	,	,	PUNCT
aiti-9227	104	11	and	and	CCONJ
aiti-9227	104	12	fully	fully	ADV
aiti-9227	104	13	linked	link	VERB
aiti-9227	104	14	layers	layer	NOUN
aiti-9227	104	15	.	.	PUNCT
aiti-9227	105	1	the	the	DET
aiti-9227	105	2	input	input	NOUN
aiti-9227	105	3	data	datum	NOUN
aiti-9227	105	4	are	be	AUX
aiti-9227	105	5	convolved	convolve	VERB
aiti-9227	105	6	using	use	VERB
aiti-9227	105	7	many	many	ADJ
aiti-9227	105	8	filters	filter	NOUN
aiti-9227	105	9	for	for	ADP
aiti-9227	105	10	the	the	DET
aiti-9227	105	11	convolution	convolution	NOUN
aiti-9227	105	12	layer	layer	NOUN
aiti-9227	105	13	,	,	PUNCT
aiti-9227	105	14	and	and	CCONJ
aiti-9227	105	15	a	a	DET
aiti-9227	105	16	feature	feature	NOUN
aiti-9227	105	17	map	map	NOUN
aiti-9227	105	18	is	be	AUX
aiti-9227	105	19	formed	form	VERB
aiti-9227	105	20	when	when	SCONJ
aiti-9227	105	21	a	a	DET
aiti-9227	105	22	bias	bias	NOUN
aiti-9227	105	23	term	term	NOUN
aiti-9227	105	24	is	be	AUX
aiti-9227	105	25	added	add	VERB
aiti-9227	105	26	.	.	PUNCT
aiti-9227	106	1	then	then	ADV
aiti-9227	106	2	,	,	PUNCT
aiti-9227	106	3	a	a	DET
aiti-9227	106	4	nonlinear	nonlinear	ADJ
aiti-9227	106	5	function	function	NOUN
aiti-9227	106	6	is	be	AUX
aiti-9227	106	7	applied	apply	VERB
aiti-9227	106	8	.	.	PUNCT
aiti-9227	107	1	the	the	DET
aiti-9227	107	2	pooling	pool	VERB
aiti-9227	107	3	layer	layer	NOUN
aiti-9227	107	4	’s	’s	PART
aiti-9227	107	5	primary	primary	ADJ
aiti-9227	107	6	goal	goal	NOUN
aiti-9227	107	7	is	be	AUX
aiti-9227	107	8	to	to	PART
aiti-9227	107	9	lower	lower	VERB
aiti-9227	107	10	the	the	DET
aiti-9227	107	11	resolution	resolution	NOUN
aiti-9227	107	12	of	of	ADP
aiti-9227	107	13	the	the	DET
aiti-9227	107	14	feature	feature	NOUN
aiti-9227	107	15	maps	map	NOUN
aiti-9227	107	16	to	to	PART
aiti-9227	107	17	aggregate	aggregate	VERB
aiti-9227	107	18	the	the	DET
aiti-9227	107	19	input	input	NOUN
aiti-9227	107	20	data	datum	NOUN
aiti-9227	107	21	.	.	PUNCT
aiti-9227	108	1	there	there	PRON
aiti-9227	108	2	are	be	VERB
aiti-9227	108	3	several	several	ADJ
aiti-9227	108	4	sorts	sort	NOUN
aiti-9227	108	5	of	of	ADP
aiti-9227	108	6	pooling	pool	VERB
aiti-9227	108	7	procedures	procedure	NOUN
aiti-9227	108	8	,	,	PUNCT
aiti-9227	108	9	the	the	DET
aiti-9227	108	10	most	most	ADV
aiti-9227	108	11	prevalent	prevalent	NOUN
aiti-9227	108	12	of	of	ADP
aiti-9227	108	13	which	which	PRON
aiti-9227	108	14	is	be	AUX
aiti-9227	108	15	the	the	DET
aiti-9227	108	16	max	max	PROPN
aiti-9227	108	17	-	-	PUNCT
aiti-9227	108	18	pooling	pool	VERB
aiti-9227	108	19	strategy	strategy	NOUN
aiti-9227	108	20	.	.	PUNCT
aiti-9227	109	1	finally	finally	ADV
aiti-9227	109	2	,	,	PUNCT
aiti-9227	109	3	fully	fully	ADV
aiti-9227	109	4	connected	connected	ADJ
aiti-9227	109	5	layers	layer	NOUN
aiti-9227	109	6	process	process	VERB
aiti-9227	109	7	the	the	DET
aiti-9227	109	8	convolutional	convolutional	ADJ
aiti-9227	109	9	layers	layer	NOUN
aiti-9227	109	10	’	'	PUNCT
aiti-9227	109	11	outputs	output	NOUN
aiti-9227	109	12	[	[	X
aiti-9227	109	13	21	21	NUM
aiti-9227	109	14	]	]	PUNCT
aiti-9227	109	15	.	.	PUNCT
aiti-9227	109	16	...	...	PUNCT
aiti-9227	110	1	...	...	PUNCT
aiti-9227	111	1	input	input	NOUN
aiti-9227	111	2	convolution	convolution	NOUN
aiti-9227	111	3	layer	layer	NOUN
aiti-9227	111	4	pooling	pool	VERB
aiti-9227	111	5	layer	layer	NOUN
aiti-9227	111	6	fully	fully	ADV
aiti-9227	111	7	connected	connected	ADJ
aiti-9227	111	8	layer	layer	NOUN
aiti-9227	111	9	output	output	NOUN
aiti-9227	111	10	fig	fig	NOUN
aiti-9227	111	11	.	.	PUNCT
aiti-9227	112	1	3	3	NUM
aiti-9227	112	2	the	the	DET
aiti-9227	112	3	architecture	architecture	NOUN
aiti-9227	112	4	of	of	ADP
aiti-9227	112	5	the	the	DET
aiti-9227	112	6	cnn	cnn	PROPN
aiti-9227	112	7	model	model	NOUN
aiti-9227	112	8	2.2	2.2	NUM
aiti-9227	112	9	.	.	PUNCT
aiti-9227	113	1	hyperparameters	hyperparameter	NOUN
aiti-9227	113	2	in	in	ADP
aiti-9227	113	3	general	general	ADJ
aiti-9227	113	4	,	,	PUNCT
aiti-9227	113	5	the	the	DET
aiti-9227	113	6	accuracy	accuracy	NOUN
aiti-9227	113	7	of	of	ADP
aiti-9227	113	8	a	a	DET
aiti-9227	113	9	dl	dl	PROPN
aiti-9227	113	10	model	model	NOUN
aiti-9227	113	11	depends	depend	VERB
aiti-9227	113	12	on	on	ADP
aiti-9227	113	13	its	its	PRON
aiti-9227	113	14	hyperparameters	hyperparameter	NOUN
aiti-9227	113	15	.	.	PUNCT
aiti-9227	114	1	therefore	therefore	ADV
aiti-9227	114	2	,	,	PUNCT
aiti-9227	114	3	determining	determine	VERB
aiti-9227	114	4	the	the	DET
aiti-9227	114	5	hyperparameters	hyperparameter	NOUN
aiti-9227	114	6	for	for	SCONJ
aiti-9227	114	7	dl	dl	PROPN
aiti-9227	114	8	plays	play	VERB
aiti-9227	114	9	an	an	DET
aiti-9227	114	10	extremely	extremely	ADV
aiti-9227	114	11	important	important	ADJ
aiti-9227	114	12	role	role	NOUN
aiti-9227	114	13	.	.	PUNCT
aiti-9227	115	1	a	a	DET
aiti-9227	115	2	hyperparameter	hyperparameter	NOUN
aiti-9227	115	3	is	be	AUX
aiti-9227	115	4	a	a	DET
aiti-9227	115	5	configuration	configuration	NOUN
aiti-9227	115	6	that	that	PRON
aiti-9227	115	7	is	be	AUX
aiti-9227	115	8	external	external	ADJ
aiti-9227	115	9	to	to	ADP
aiti-9227	115	10	the	the	DET
aiti-9227	115	11	model	model	NOUN
aiti-9227	115	12	,	,	PUNCT
aiti-9227	115	13	whose	whose	DET
aiti-9227	115	14	value	value	NOUN
aiti-9227	115	15	can	can	AUX
aiti-9227	115	16	not	not	PART
aiti-9227	115	17	be	be	AUX
aiti-9227	115	18	estimated	estimate	VERB
aiti-9227	115	19	from	from	ADP
aiti-9227	115	20	data	datum	NOUN
aiti-9227	115	21	and	and	CCONJ
aiti-9227	115	22	all	all	DET
aiti-9227	115	23	values	value	NOUN
aiti-9227	115	24	are	be	AUX
aiti-9227	115	25	set	set	VERB
aiti-9227	115	26	before	before	ADP
aiti-9227	115	27	the	the	DET
aiti-9227	115	28	started	start	VERB
aiti-9227	115	29	network	network	NOUN
aiti-9227	115	30	training	training	NOUN
aiti-9227	115	31	.	.	PUNCT
aiti-9227	116	1	in	in	ADP
aiti-9227	116	2	this	this	DET
aiti-9227	116	3	study	study	NOUN
aiti-9227	116	4	,	,	PUNCT
aiti-9227	116	5	the	the	DET
aiti-9227	116	6	hyperparameter	hyperparameter	NOUN
aiti-9227	116	7	values	value	NOUN
aiti-9227	116	8	of	of	ADP
aiti-9227	116	9	lstm	lstm	PROPN
aiti-9227	116	10	and	and	CCONJ
aiti-9227	116	11	cnn	cnn	PROPN
aiti-9227	116	12	are	be	AUX
aiti-9227	116	13	established	establish	VERB
aiti-9227	116	14	,	,	PUNCT
aiti-9227	116	15	as	as	SCONJ
aiti-9227	116	16	listed	list	VERB
aiti-9227	116	17	in	in	ADP
aiti-9227	116	18	table	table	NOUN
aiti-9227	116	19	1	1	NUM
aiti-9227	116	20	.	.	PUNCT
aiti-9227	117	1	epoch	epoch	PROPN
aiti-9227	117	2	refers	refer	VERB
aiti-9227	117	3	to	to	ADP
aiti-9227	117	4	the	the	DET
aiti-9227	117	5	number	number	NOUN
aiti-9227	117	6	of	of	ADP
aiti-9227	117	7	times	time	NOUN
aiti-9227	117	8	to	to	PART
aiti-9227	117	9	expose	expose	VERB
aiti-9227	117	10	the	the	DET
aiti-9227	117	11	model	model	NOUN
aiti-9227	117	12	to	to	ADP
aiti-9227	117	13	the	the	DET
aiti-9227	117	14	whole	whole	ADJ
aiti-9227	117	15	training	training	NOUN
aiti-9227	117	16	dataset	dataset	NOUN
aiti-9227	117	17	;	;	PUNCT
aiti-9227	117	18	batch	batch	NOUN
aiti-9227	117	19	refers	refer	VERB
aiti-9227	117	20	to	to	ADP
aiti-9227	117	21	the	the	DET
aiti-9227	117	22	number	number	NOUN
aiti-9227	117	23	of	of	ADP
aiti-9227	117	24	samples	sample	NOUN
aiti-9227	117	25	within	within	ADP
aiti-9227	117	26	an	an	DET
aiti-9227	117	27	epoch	epoch	NOUN
aiti-9227	117	28	after	after	ADP
aiti-9227	117	29	which	which	PRON
aiti-9227	117	30	the	the	DET
aiti-9227	117	31	weights	weight	NOUN
aiti-9227	117	32	are	be	AUX
aiti-9227	117	33	updated	update	VERB
aiti-9227	117	34	;	;	PUNCT
aiti-9227	117	35	dropout	dropout	NOUN
aiti-9227	117	36	refers	refer	VERB
aiti-9227	117	37	to	to	ADP
aiti-9227	117	38	the	the	DET
aiti-9227	117	39	process	process	NOUN
aiti-9227	117	40	of	of	ADP
aiti-9227	117	41	randomly	randomly	ADV
aiti-9227	117	42	omitting	omit	VERB
aiti-9227	117	43	a	a	DET
aiti-9227	117	44	fraction	fraction	NOUN
aiti-9227	117	45	of	of	ADP
aiti-9227	117	46	the	the	DET
aiti-9227	117	47	hidden	hidden	ADJ
aiti-9227	117	48	neurons	neuron	NOUN
aiti-9227	117	49	.	.	PUNCT
aiti-9227	118	1	for	for	ADP
aiti-9227	118	2	each	each	DET
aiti-9227	118	3	training	training	NOUN
aiti-9227	118	4	case	case	NOUN
aiti-9227	118	5	,	,	PUNCT
aiti-9227	118	6	each	each	DET
aiti-9227	118	7	hidden	hide	VERB
aiti-9227	118	8	neuron	neuron	NOUN
aiti-9227	118	9	is	be	AUX
aiti-9227	118	10	randomly	randomly	ADV
aiti-9227	118	11	omitted	omit	VERB
aiti-9227	118	12	from	from	ADP
aiti-9227	118	13	the	the	DET
aiti-9227	118	14	network	network	NOUN
aiti-9227	118	15	with	with	ADP
aiti-9227	118	16	a	a	DET
aiti-9227	118	17	fixed	fix	VERB
aiti-9227	118	18	probability	probability	NOUN
aiti-9227	118	19	p	p	X
aiti-9227	118	20	,	,	PUNCT
aiti-9227	118	21	where	where	SCONJ
aiti-9227	118	22	p	p	NOUN
aiti-9227	118	23	can	can	AUX
aiti-9227	118	24	be	be	AUX
aiti-9227	118	25	chosen	choose	VERB
aiti-9227	118	26	in	in	ADP
aiti-9227	118	27	the	the	DET
aiti-9227	118	28	range	range	NOUN
aiti-9227	118	29	[	[	X
aiti-9227	118	30	0,1	0,1	NUM
aiti-9227	118	31	]	]	PUNCT
aiti-9227	118	32	.	.	PUNCT
aiti-9227	119	1	optimizer	optimizer	NOUN
aiti-9227	119	2	refers	refer	VERB
aiti-9227	119	3	to	to	ADP
aiti-9227	119	4	the	the	DET
aiti-9227	119	5	optimization	optimization	NOUN
aiti-9227	119	6	algorithm	algorithm	NOUN
aiti-9227	119	7	which	which	PRON
aiti-9227	119	8	plays	play	VERB
aiti-9227	119	9	an	an	DET
aiti-9227	119	10	important	important	ADJ
aiti-9227	119	11	role	role	NOUN
aiti-9227	119	12	in	in	ADP
aiti-9227	119	13	improving	improve	VERB
aiti-9227	119	14	the	the	DET
aiti-9227	119	15	accuracy	accuracy	NOUN
aiti-9227	119	16	of	of	ADP
aiti-9227	119	17	the	the	DET
aiti-9227	119	18	dl	dl	PROPN
aiti-9227	119	19	network	network	NOUN
aiti-9227	119	20	.	.	PUNCT
aiti-9227	120	1	the	the	DET
aiti-9227	120	2	optimizer	optimizer	NOUN
aiti-9227	120	3	is	be	AUX
aiti-9227	120	4	a	a	DET
aiti-9227	120	5	mathematical	mathematical	ADJ
aiti-9227	120	6	algorithm	algorithm	NOUN
aiti-9227	120	7	that	that	PRON
aiti-9227	120	8	uses	use	VERB
aiti-9227	120	9	derivatives	derivative	NOUN
aiti-9227	120	10	,	,	PUNCT
aiti-9227	120	11	partial	partial	ADJ
aiti-9227	120	12	derivatives	derivative	NOUN
aiti-9227	120	13	,	,	PUNCT
aiti-9227	120	14	and	and	CCONJ
aiti-9227	120	15	the	the	DET
aiti-9227	120	16	chain	chain	NOUN
aiti-9227	120	17	rule	rule	NOUN
aiti-9227	120	18	in	in	ADP
aiti-9227	120	19	calculus	calculus	NOUN
aiti-9227	120	20	to	to	PART
aiti-9227	120	21	understand	understand	VERB
aiti-9227	120	22	how	how	SCONJ
aiti-9227	120	23	much	much	ADJ
aiti-9227	120	24	change	change	NOUN
aiti-9227	120	25	the	the	DET
aiti-9227	120	26	network	network	NOUN
aiti-9227	120	27	will	will	AUX
aiti-9227	120	28	see	see	VERB
aiti-9227	120	29	in	in	ADP
aiti-9227	120	30	the	the	DET
aiti-9227	120	31	loss	loss	NOUN
aiti-9227	120	32	function	function	NOUN
aiti-9227	120	33	by	by	ADP
aiti-9227	120	34	making	make	VERB
aiti-9227	120	35	a	a	DET
aiti-9227	120	36	small	small	ADJ
aiti-9227	120	37	change	change	NOUN
aiti-9227	120	38	in	in	ADP
aiti-9227	120	39	the	the	DET
aiti-9227	120	40	weight	weight	NOUN
aiti-9227	120	41	of	of	ADP
aiti-9227	120	42	the	the	DET
aiti-9227	120	43	neurons	neuron	NOUN
aiti-9227	120	44	.	.	PUNCT
aiti-9227	121	1	filter	filter	NOUN
aiti-9227	121	2	is	be	AUX
aiti-9227	121	3	one	one	NUM
aiti-9227	121	4	of	of	ADP
aiti-9227	121	5	the	the	DET
aiti-9227	121	6	most	most	ADV
aiti-9227	121	7	important	important	ADJ
aiti-9227	121	8	cnn	cnn	PROPN
aiti-9227	121	9	hyperparameters	hyperparameter	NOUN
aiti-9227	121	10	,	,	PUNCT
aiti-9227	121	11	which	which	PRON
aiti-9227	121	12	is	be	AUX
aiti-9227	121	13	the	the	DET
aiti-9227	121	14	number	number	NOUN
aiti-9227	121	15	of	of	ADP
aiti-9227	121	16	filters	filter	NOUN
aiti-9227	121	17	that	that	PRON
aiti-9227	121	18	will	will	AUX
aiti-9227	121	19	be	be	AUX
aiti-9227	121	20	learned	learn	VERB
aiti-9227	121	21	by	by	ADP
aiti-9227	121	22	the	the	DET
aiti-9227	121	23	convolutional	convolutional	ADJ
aiti-9227	121	24	layer	layer	NOUN
aiti-9227	121	25	.	.	PUNCT
aiti-9227	122	1	in	in	ADP
aiti-9227	122	2	a	a	DET
aiti-9227	122	3	cnn	cnn	NOUN
aiti-9227	122	4	,	,	PUNCT
aiti-9227	122	5	a	a	DET
aiti-9227	122	6	convolution	convolution	NOUN
aiti-9227	122	7	filter	filter	NOUN
aiti-9227	122	8	iterates	iterate	VERB
aiti-9227	122	9	over	over	ADP
aiti-9227	122	10	all	all	PRON
aiti-9227	122	11	of	of	ADP
aiti-9227	122	12	the	the	DET
aiti-9227	122	13	input	input	NOUN
aiti-9227	122	14	components	component	NOUN
aiti-9227	122	15	,	,	PUNCT
aiti-9227	122	16	executing	execute	VERB
aiti-9227	122	17	convolution	convolution	NOUN
aiti-9227	122	18	operations	operation	NOUN
aiti-9227	122	19	to	to	PART
aiti-9227	122	20	extract	extract	VERB
aiti-9227	122	21	input	input	NOUN
aiti-9227	122	22	characteristics	characteristic	NOUN
aiti-9227	122	23	;	;	PUNCT
aiti-9227	122	24	32	32	NUM
aiti-9227	122	25	,	,	PUNCT
aiti-9227	122	26	64	64	NUM
aiti-9227	122	27	,	,	PUNCT
aiti-9227	122	28	128	128	NUM
aiti-9227	122	29	,	,	PUNCT
aiti-9227	122	30	and	and	CCONJ
aiti-9227	122	31	so	so	ADV
aiti-9227	122	32	on	on	ADV
aiti-9227	122	33	are	be	AUX
aiti-9227	122	34	the	the	DET
aiti-9227	122	35	most	most	ADV
aiti-9227	122	36	frequent	frequent	ADJ
aiti-9227	122	37	number	number	NOUN
aiti-9227	122	38	of	of	ADP
aiti-9227	122	39	filters	filter	NOUN
aiti-9227	122	40	.	.	PUNCT
aiti-9227	123	1	the	the	DET
aiti-9227	123	2	convolution	convolution	NOUN
aiti-9227	123	3	window	window	NOUN
aiti-9227	123	4	width	width	NOUN
aiti-9227	123	5	and	and	CCONJ
aiti-9227	123	6	height	height	NOUN
aiti-9227	123	7	are	be	AUX
aiti-9227	123	8	determined	determine	VERB
aiti-9227	123	9	by	by	ADP
aiti-9227	123	10	kernel	kernel	PROPN
aiti-9227	123	11	size	size	NOUN
aiti-9227	123	12	.	.	PUNCT
aiti-9227	124	1	the	the	DET
aiti-9227	124	2	kernel	kernel	PROPN
aiti-9227	124	3	size	size	NOUN
aiti-9227	124	4	might	might	AUX
aiti-9227	124	5	be	be	AUX
aiti-9227	124	6	an	an	DET
aiti-9227	124	7	odd	odd	ADJ
aiti-9227	124	8	integer	integer	NOUN
aiti-9227	124	9	,	,	PUNCT
aiti-9227	124	10	such	such	ADJ
aiti-9227	124	11	as	as	ADP
aiti-9227	124	12	(	(	PUNCT
aiti-9227	124	13	3	3	NUM
aiti-9227	124	14	,	,	PUNCT
aiti-9227	124	15	3	3	NUM
aiti-9227	124	16	)	)	PUNCT
aiti-9227	124	17	,	,	PUNCT
aiti-9227	124	18	(	(	PUNCT
aiti-9227	124	19	5	5	NUM
aiti-9227	124	20	,	,	PUNCT
aiti-9227	124	21	5	5	NUM
aiti-9227	124	22	)	)	PUNCT
aiti-9227	124	23	,	,	PUNCT
aiti-9227	124	24	(	(	PUNCT
aiti-9227	124	25	7	7	NUM
aiti-9227	124	26	,	,	PUNCT
aiti-9227	124	27	7	7	NUM
aiti-9227	124	28	)	)	PUNCT
aiti-9227	124	29	,	,	PUNCT
aiti-9227	124	30	and	and	CCONJ
aiti-9227	124	31	so	so	ADV
aiti-9227	124	32	on	on	ADV
aiti-9227	124	33	.	.	PUNCT
aiti-9227	125	1	there	there	PRON
aiti-9227	125	2	have	have	AUX
aiti-9227	125	3	been	be	AUX
aiti-9227	125	4	many	many	ADJ
aiti-9227	125	5	proposed	propose	VERB
aiti-9227	125	6	methods	method	NOUN
aiti-9227	125	7	to	to	PART
aiti-9227	125	8	determine	determine	VERB
aiti-9227	125	9	the	the	DET
aiti-9227	125	10	dl	dl	PROPN
aiti-9227	125	11	hyperparameters	hyperparameter	NOUN
aiti-9227	125	12	in	in	ADP
aiti-9227	125	13	recent	recent	ADJ
aiti-9227	125	14	years	year	NOUN
aiti-9227	125	15	,	,	PUNCT
aiti-9227	125	16	such	such	ADJ
aiti-9227	125	17	as	as	ADP
aiti-9227	125	18	the	the	DET
aiti-9227	125	19	gs	gs	NOUN
aiti-9227	125	20	,	,	PUNCT
aiti-9227	125	21	rs	rs	ADJ
aiti-9227	125	22	,	,	PUNCT
aiti-9227	125	23	gradient	gradient	NOUN
aiti-9227	125	24	-	-	PUNCT
aiti-9227	125	25	based	base	VERB
aiti-9227	125	26	optimization	optimization	NOUN
aiti-9227	125	27	,	,	PUNCT
aiti-9227	125	28	bayesian	bayesian	NOUN
aiti-9227	125	29	optimization	optimization	NOUN
aiti-9227	125	30	(	(	PUNCT
aiti-9227	125	31	bo	bo	PROPN
aiti-9227	125	32	)	)	PUNCT
aiti-9227	125	33	,	,	PUNCT
aiti-9227	125	34	ga	ga	PROPN
aiti-9227	125	35	,	,	PUNCT
aiti-9227	125	36	and	and	CCONJ
aiti-9227	125	37	particle	particle	NOUN
aiti-9227	125	38	swarm	swarm	NOUN
aiti-9227	125	39	optimization	optimization	NOUN
aiti-9227	125	40	(	(	PUNCT
aiti-9227	125	41	pso	pso	NOUN
aiti-9227	125	42	)	)	PUNCT
aiti-9227	125	43	.	.	PUNCT
aiti-9227	126	1	of	of	ADP
aiti-9227	126	2	these	these	DET
aiti-9227	126	3	methods	method	NOUN
aiti-9227	126	4	,	,	PUNCT
aiti-9227	126	5	gs	gs	PROPN
aiti-9227	126	6	is	be	AUX
aiti-9227	126	7	widely	widely	ADV
aiti-9227	126	8	employed	employ	VERB
aiti-9227	126	9	due	due	ADP
aiti-9227	126	10	to	to	ADP
aiti-9227	126	11	its	its	PRON
aiti-9227	126	12	simplicity	simplicity	NOUN
aiti-9227	126	13	and	and	CCONJ
aiti-9227	126	14	efficiency	efficiency	NOUN
aiti-9227	126	15	.	.	PUNCT
aiti-9227	127	1	therefore	therefore	ADV
aiti-9227	127	2	,	,	PUNCT
aiti-9227	127	3	gs	gs	PROPN
aiti-9227	127	4	is	be	AUX
aiti-9227	127	5	the	the	DET
aiti-9227	127	6	choice	choice	NOUN
aiti-9227	127	7	to	to	PART
aiti-9227	127	8	determine	determine	VERB
aiti-9227	127	9	the	the	DET
aiti-9227	127	10	dl	dl	PROPN
aiti-9227	127	11	hyperparameters	hyperparameter	NOUN
aiti-9227	127	12	in	in	ADP
aiti-9227	127	13	this	this	DET
aiti-9227	127	14	study	study	NOUN
aiti-9227	127	15	.	.	PUNCT
aiti-9227	128	1	261	261	NUM
aiti-9227	128	2	advances	advance	NOUN
aiti-9227	128	3	in	in	ADP
aiti-9227	128	4	technology	technology	NOUN
aiti-9227	128	5	innovation	innovation	NOUN
aiti-9227	128	6	,	,	PUNCT
aiti-9227	128	7	vol	vol	NOUN
aiti-9227	128	8	.	.	PROPN
aiti-9227	128	9	7	7	NUM
aiti-9227	128	10	,	,	PUNCT
aiti-9227	128	11	no	no	INTJ
aiti-9227	128	12	.	.	NOUN
aiti-9227	128	13	4	4	NUM
aiti-9227	128	14	,	,	PUNCT
aiti-9227	128	15	2022	2022	NUM
aiti-9227	128	16	,	,	PUNCT
aiti-9227	128	17	pp	pp	ADJ
aiti-9227	128	18	.	.	PUNCT
aiti-9227	129	1	258	258	NUM
aiti-9227	129	2	-	-	SYM
aiti-9227	129	3	269	269	NUM
aiti-9227	129	4	table	table	NOUN
aiti-9227	129	5	1	1	NUM
aiti-9227	129	6	graph	graph	NOUN
aiti-9227	129	7	representations	representation	NOUN
aiti-9227	129	8	lstmn	lstmn	NOUN
aiti-9227	129	9	model	model	NOUN
aiti-9227	129	10	cnn	cnn	PROPN
aiti-9227	129	11	model	model	PROPN
aiti-9227	129	12	epoch	epoch	PROPN
aiti-9227	129	13	epoch	epoch	PROPN
aiti-9227	129	14	batch	batch	NOUN
aiti-9227	129	15	batch	batch	NOUN
aiti-9227	129	16	optimizer	optimizer	NOUN
aiti-9227	129	17	optimizer	optimizer	NOUN
aiti-9227	129	18	dropout	dropout	NOUN
aiti-9227	129	19	filter	filter	NOUN
aiti-9227	129	20	kernel	kernel	NOUN
aiti-9227	129	21	2.3	2.3	NUM
aiti-9227	129	22	.	.	PUNCT
aiti-9227	130	1	grid	grid	NOUN
aiti-9227	130	2	search	search	NOUN
aiti-9227	130	3	method	method	NOUN
aiti-9227	130	4	the	the	DET
aiti-9227	130	5	gs	gs	PROPN
aiti-9227	130	6	is	be	AUX
aiti-9227	130	7	a	a	DET
aiti-9227	130	8	comprehensive	comprehensive	ADJ
aiti-9227	130	9	search	search	NOUN
aiti-9227	130	10	process	process	NOUN
aiti-9227	130	11	through	through	ADP
aiti-9227	130	12	the	the	DET
aiti-9227	130	13	predefined	predefine	VERB
aiti-9227	130	14	subclass	subclass	NOUN
aiti-9227	130	15	of	of	ADP
aiti-9227	130	16	the	the	DET
aiti-9227	130	17	value	value	NOUN
aiti-9227	130	18	’s	’s	PART
aiti-9227	130	19	combinatory	combinatory	NOUN
aiti-9227	130	20	of	of	ADP
aiti-9227	130	21	the	the	DET
aiti-9227	130	22	model	model	NOUN
aiti-9227	130	23	’s	’s	PART
aiti-9227	130	24	hyperparameters	hyperparameter	NOUN
aiti-9227	130	25	.	.	PUNCT
aiti-9227	131	1	the	the	DET
aiti-9227	131	2	operation	operation	NOUN
aiti-9227	131	3	principle	principle	NOUN
aiti-9227	131	4	of	of	ADP
aiti-9227	131	5	gs	gs	PROPN
aiti-9227	131	6	is	be	AUX
aiti-9227	131	7	illustrated	illustrate	VERB
aiti-9227	131	8	in	in	ADP
aiti-9227	131	9	fig	fig	NOUN
aiti-9227	131	10	.	.	PUNCT
aiti-9227	132	1	4	4	X
aiti-9227	132	2	.	.	X
aiti-9227	132	3	it	it	PRON
aiti-9227	132	4	is	be	AUX
aiti-9227	132	5	composed	compose	VERB
aiti-9227	132	6	of	of	ADP
aiti-9227	132	7	two	two	NUM
aiti-9227	132	8	hyperparameters	hyperparameter	NOUN
aiti-9227	132	9	,	,	PUNCT
aiti-9227	132	10	x	x	PUNCT
aiti-9227	132	11	and	and	CCONJ
aiti-9227	132	12	y	y	PROPN
aiti-9227	133	1	[	[	X
aiti-9227	133	2	23	23	NUM
aiti-9227	133	3	-	-	SYM
aiti-9227	133	4	24	24	NUM
aiti-9227	133	5	]	]	PUNCT
aiti-9227	133	6	.	.	PUNCT
aiti-9227	134	1	the	the	DET
aiti-9227	134	2	x	x	PUNCT
aiti-9227	134	3	is	be	AUX
aiti-9227	134	4	established	establish	VERB
aiti-9227	134	5	by	by	ADP
aiti-9227	134	6	three	three	NUM
aiti-9227	134	7	values	value	NOUN
aiti-9227	134	8	{	{	PUNCT
aiti-9227	134	9	x1	x1	PROPN
aiti-9227	134	10	,	,	PUNCT
aiti-9227	134	11	x2	x2	PROPN
aiti-9227	134	12	,	,	PUNCT
aiti-9227	134	13	x3	x3	ADJ
aiti-9227	134	14	}	}	PUNCT
aiti-9227	134	15	,	,	PUNCT
aiti-9227	134	16	and	and	CCONJ
aiti-9227	134	17	the	the	DET
aiti-9227	134	18	y	y	PROPN
aiti-9227	134	19	is	be	AUX
aiti-9227	134	20	established	establish	VERB
aiti-9227	134	21	by	by	ADP
aiti-9227	134	22	three	three	NUM
aiti-9227	134	23	values	value	NOUN
aiti-9227	134	24	{	{	PUNCT
aiti-9227	134	25	y1	y1	NOUN
aiti-9227	134	26	,	,	PUNCT
aiti-9227	134	27	y2	y2	PROPN
aiti-9227	134	28	,	,	PUNCT
aiti-9227	134	29	y3	y3	PROPN
aiti-9227	134	30	}	}	PUNCT
aiti-9227	134	31	.	.	PUNCT
aiti-9227	135	1	as	as	ADP
aiti-9227	135	2	a	a	DET
aiti-9227	135	3	result	result	NOUN
aiti-9227	135	4	,	,	PUNCT
aiti-9227	135	5	their	their	PRON
aiti-9227	135	6	combination	combination	NOUN
aiti-9227	135	7	is	be	AUX
aiti-9227	135	8	nine	nine	NUM
aiti-9227	135	9	value	value	NOUN
aiti-9227	135	10	pairs	pair	NOUN
aiti-9227	135	11	.	.	PUNCT
aiti-9227	136	1	the	the	DET
aiti-9227	136	2	gs	gs	PROPN
aiti-9227	136	3	will	will	AUX
aiti-9227	136	4	perform	perform	VERB
aiti-9227	136	5	a	a	DET
aiti-9227	136	6	search	search	NOUN
aiti-9227	136	7	for	for	ADP
aiti-9227	136	8	the	the	DET
aiti-9227	136	9	optimal	optimal	ADJ
aiti-9227	136	10	model	model	NOUN
aiti-9227	136	11	based	base	VERB
aiti-9227	136	12	on	on	ADP
aiti-9227	136	13	these	these	DET
aiti-9227	136	14	values	value	NOUN
aiti-9227	136	15	,	,	PUNCT
aiti-9227	136	16	and	and	CCONJ
aiti-9227	136	17	the	the	DET
aiti-9227	136	18	optimal	optimal	ADJ
aiti-9227	136	19	hyperparameter	hyperparameter	NOUN
aiti-9227	136	20	corresponds	correspond	VERB
aiti-9227	136	21	to	to	ADP
aiti-9227	136	22	the	the	DET
aiti-9227	136	23	dl	dl	PROPN
aiti-9227	136	24	model	model	NOUN
aiti-9227	136	25	with	with	ADP
aiti-9227	136	26	the	the	DET
aiti-9227	136	27	smallest	small	ADJ
aiti-9227	136	28	error	error	NOUN
aiti-9227	136	29	.	.	PUNCT
aiti-9227	137	1	x	x	X
aiti-9227	138	1	y	y	PROPN
aiti-9227	138	2	3	3	NUM
aiti-9227	138	3	y	y	PROPN
aiti-9227	138	4	2	2	NUM
aiti-9227	138	5	y	y	PROPN
aiti-9227	138	6	1	1	NUM
aiti-9227	138	7	y	y	PROPN
aiti-9227	138	8	2	2	NUM
aiti-9227	138	9	x	x	SYM
aiti-9227	138	10	1	1	NUM
aiti-9227	138	11	x	x	SYM
aiti-9227	138	12	3	3	NUM
aiti-9227	138	13	x	x	SYM
aiti-9227	138	14	fig	fig	NOUN
aiti-9227	138	15	.	.	PUNCT
aiti-9227	138	16	4	4	NUM
aiti-9227	138	17	the	the	DET
aiti-9227	138	18	operation	operation	NOUN
aiti-9227	138	19	principle	principle	NOUN
aiti-9227	138	20	of	of	ADP
aiti-9227	138	21	the	the	DET
aiti-9227	138	22	gs	gs	PROPN
aiti-9227	138	23	method	method	NOUN
aiti-9227	138	24	the	the	DET
aiti-9227	138	25	error	error	NOUN
aiti-9227	138	26	value	value	NOUN
aiti-9227	138	27	in	in	ADP
aiti-9227	138	28	the	the	DET
aiti-9227	138	29	dl	dl	PROPN
aiti-9227	138	30	model	model	NOUN
aiti-9227	138	31	is	be	AUX
aiti-9227	138	32	usually	usually	ADV
aiti-9227	138	33	determined	determine	VERB
aiti-9227	138	34	based	base	VERB
aiti-9227	138	35	on	on	ADP
aiti-9227	138	36	the	the	DET
aiti-9227	138	37	error	error	NOUN
aiti-9227	138	38	evaluation	evaluation	NOUN
aiti-9227	138	39	indexes	index	NOUN
aiti-9227	138	40	of	of	ADP
aiti-9227	138	41	the	the	DET
aiti-9227	138	42	actual	actual	ADJ
aiti-9227	138	43	and	and	CCONJ
aiti-9227	138	44	predicted	predict	VERB
aiti-9227	138	45	values	value	NOUN
aiti-9227	138	46	of	of	ADP
aiti-9227	138	47	the	the	DET
aiti-9227	138	48	model	model	NOUN
aiti-9227	138	49	,	,	PUNCT
aiti-9227	138	50	such	such	ADJ
aiti-9227	138	51	as	as	ADP
aiti-9227	138	52	mean	mean	ADJ
aiti-9227	138	53	square	square	ADJ
aiti-9227	138	54	error	error	NOUN
aiti-9227	138	55	(	(	PUNCT
aiti-9227	138	56	mse	mse	NOUN
aiti-9227	138	57	)	)	PUNCT
aiti-9227	138	58	,	,	PUNCT
aiti-9227	138	59	mae	mae	PROPN
aiti-9227	138	60	,	,	PUNCT
aiti-9227	138	61	and	and	CCONJ
aiti-9227	138	62	mape	mape	NOUN
aiti-9227	138	63	.	.	PUNCT
aiti-9227	139	1	these	these	DET
aiti-9227	139	2	evaluation	evaluation	NOUN
aiti-9227	139	3	indexes	index	NOUN
aiti-9227	139	4	can	can	AUX
aiti-9227	139	5	be	be	AUX
aiti-9227	139	6	described	describe	VERB
aiti-9227	139	7	as	as	SCONJ
aiti-9227	139	8	follows	follow	VERB
aiti-9227	139	9	[	[	X
aiti-9227	139	10	25	25	NUM
aiti-9227	139	11	]	]	SYM
aiti-9227	139	12	:	:	PUNCT
aiti-9227	139	13	2	2	NUM
aiti-9227	139	14	1	1	NUM
aiti-9227	139	15	1	1	NUM
aiti-9227	139	16	ˆmse	ˆmse	NOUN
aiti-9227	140	1	n	n	CCONJ
aiti-9227	141	1	i	i	PRON
aiti-9227	142	1	i	i	PRON
aiti-9227	143	1	i	i	VERB
aiti-9227	143	2	y	y	VERB
aiti-9227	143	3	y	y	PROPN
aiti-9227	143	4	n	n	NOUN
aiti-9227	143	5	=	=	SYM
aiti-9227	143	6	=	=	SYM
aiti-9227	143	7	−∑	−∑	PROPN
aiti-9227	143	8	(	(	PUNCT
aiti-9227	143	9	8)	8)	NUM
aiti-9227	143	10	1	1	NUM
aiti-9227	143	11	1	1	NUM
aiti-9227	143	12	ˆmae	ˆmae	NOUN
aiti-9227	144	1	n	n	INTJ
aiti-9227	144	2	i	i	PRON
aiti-9227	145	1	i	i	PRON
aiti-9227	146	1	i	i	VERB
aiti-9227	146	2	y	y	VERB
aiti-9227	146	3	y	y	PROPN
aiti-9227	146	4	n	n	NOUN
aiti-9227	146	5	=	=	SYM
aiti-9227	146	6	=	=	SYM
aiti-9227	146	7	−∑	−∑	PROPN
aiti-9227	146	8	(	(	PUNCT
aiti-9227	146	9	9	9	NUM
aiti-9227	146	10	)	)	SYM
aiti-9227	146	11	1	1	NUM
aiti-9227	146	12	ˆ1	ˆ1	NOUN
aiti-9227	146	13	mape	mape	NOUN
aiti-9227	146	14	100	100	NUM
aiti-9227	147	1	n	n	NOUN
aiti-9227	148	1	i	i	PRON
aiti-9227	149	1	i	i	PRON
aiti-9227	150	1	i	i	PRON
aiti-9227	151	1	i	i	VERB
aiti-9227	151	2	y	y	VERB
aiti-9227	151	3	y	y	PROPN
aiti-9227	151	4	n	n	ADV
aiti-9227	151	5	y=	y=	PRON
aiti-9227	151	6	−	−	PROPN
aiti-9227	152	1	=	=	SYM
aiti-9227	152	2	×∑	×∑	NOUN
aiti-9227	152	3	(	(	PUNCT
aiti-9227	152	4	10	10	NUM
aiti-9227	152	5	)	)	PUNCT
aiti-9227	152	6	where	where	SCONJ
aiti-9227	152	7	�	�	PROPN
aiti-9227	152	8	�	�	PROPN
aiti-9227	152	9	is	be	AUX
aiti-9227	152	10	the	the	DET
aiti-9227	152	11	actual	actual	ADJ
aiti-9227	152	12	value	value	NOUN
aiti-9227	152	13	i	i	PRON
aiti-9227	152	14	th	th	X
aiti-9227	152	15	,	,	PUNCT
aiti-9227	152	16	and	and	CCONJ
aiti-9227	152	17	ˆ	ˆ	X
aiti-9227	152	18	i	i	PRON
aiti-9227	152	19	y	y	PROPN
aiti-9227	152	20	is	be	AUX
aiti-9227	152	21	the	the	DET
aiti-9227	152	22	predicted	predict	VERB
aiti-9227	152	23	value	value	NOUN
aiti-9227	152	24	i	i	PRON
aiti-9227	152	25	th	th	X
aiti-9227	152	26	.	.	PUNCT
aiti-9227	153	1	2.4	2.4	NUM
aiti-9227	153	2	.	.	PUNCT
aiti-9227	154	1	data	datum	NOUN
aiti-9227	154	2	normalization	normalization	NOUN
aiti-9227	154	3	many	many	ADJ
aiti-9227	154	4	studies	study	NOUN
aiti-9227	154	5	have	have	AUX
aiti-9227	154	6	shown	show	VERB
aiti-9227	154	7	that	that	SCONJ
aiti-9227	154	8	the	the	DET
aiti-9227	154	9	forecasting	forecasting	NOUN
aiti-9227	154	10	results	result	NOUN
aiti-9227	154	11	of	of	ADP
aiti-9227	154	12	the	the	DET
aiti-9227	154	13	dl	dl	PROPN
aiti-9227	154	14	model	model	NOUN
aiti-9227	154	15	are	be	AUX
aiti-9227	154	16	significantly	significantly	ADV
aiti-9227	154	17	affected	affect	VERB
aiti-9227	154	18	by	by	ADP
aiti-9227	154	19	the	the	DET
aiti-9227	154	20	scale	scale	NOUN
aiti-9227	154	21	and	and	CCONJ
aiti-9227	154	22	size	size	NOUN
aiti-9227	154	23	of	of	ADP
aiti-9227	154	24	the	the	DET
aiti-9227	154	25	data	datum	NOUN
aiti-9227	154	26	[	[	X
aiti-9227	154	27	26	26	NUM
aiti-9227	154	28	]	]	PUNCT
aiti-9227	154	29	.	.	PUNCT
aiti-9227	155	1	thus	thus	ADV
aiti-9227	155	2	,	,	PUNCT
aiti-9227	155	3	it	it	PRON
aiti-9227	155	4	is	be	AUX
aiti-9227	155	5	necessary	necessary	ADJ
aiti-9227	155	6	to	to	PART
aiti-9227	155	7	standardize	standardize	VERB
aiti-9227	155	8	the	the	DET
aiti-9227	155	9	data	datum	NOUN
aiti-9227	155	10	during	during	ADP
aiti-9227	155	11	training	training	NOUN
aiti-9227	155	12	and	and	CCONJ
aiti-9227	155	13	forecasting	forecasting	NOUN
aiti-9227	155	14	for	for	ADP
aiti-9227	155	15	the	the	DET
aiti-9227	155	16	dl	dl	PROPN
aiti-9227	155	17	model	model	NOUN
aiti-9227	155	18	.	.	PUNCT
aiti-9227	156	1	in	in	ADP
aiti-9227	156	2	this	this	DET
aiti-9227	156	3	study	study	NOUN
aiti-9227	156	4	,	,	PUNCT
aiti-9227	156	5	the	the	DET
aiti-9227	156	6	methods	method	NOUN
aiti-9227	156	7	zero	zero	NUM
aiti-9227	156	8	-	-	PUNCT
aiti-9227	156	9	mean	mean	ADJ
aiti-9227	156	10	,	,	PUNCT
aiti-9227	156	11	min	min	PROPN
aiti-9227	156	12	-	-	PUNCT
aiti-9227	156	13	max	max	PROPN
aiti-9227	156	14	,	,	PUNCT
aiti-9227	156	15	max	max	PROPN
aiti-9227	156	16	,	,	PUNCT
aiti-9227	156	17	decimal	decimal	ADJ
aiti-9227	156	18	,	,	PUNCT
aiti-9227	156	19	sigmoid	sigmoid	NOUN
aiti-9227	156	20	,	,	PUNCT
aiti-9227	156	21	softmax	softmax	NOUN
aiti-9227	156	22	,	,	PUNCT
aiti-9227	156	23	median	median	NOUN
aiti-9227	156	24	,	,	PUNCT
aiti-9227	156	25	and	and	CCONJ
aiti-9227	156	26	robust	robust	ADJ
aiti-9227	156	27	are	be	AUX
aiti-9227	156	28	proposed	propose	VERB
aiti-9227	156	29	to	to	PART
aiti-9227	156	30	standardize	standardize	VERB
aiti-9227	156	31	the	the	DET
aiti-9227	156	32	input	input	NOUN
aiti-9227	156	33	data	datum	NOUN
aiti-9227	156	34	of	of	ADP
aiti-9227	156	35	the	the	DET
aiti-9227	156	36	dl	dl	PROPN
aiti-9227	156	37	model	model	NOUN
aiti-9227	156	38	.	.	PUNCT
aiti-9227	157	1	the	the	DET
aiti-9227	157	2	mathematical	mathematical	ADJ
aiti-9227	157	3	models	model	NOUN
aiti-9227	157	4	of	of	ADP
aiti-9227	157	5	these	these	DET
aiti-9227	157	6	methods	method	NOUN
aiti-9227	157	7	can	can	AUX
aiti-9227	157	8	be	be	AUX
aiti-9227	157	9	described	describe	VERB
aiti-9227	157	10	as	as	SCONJ
aiti-9227	157	11	follows	follow	VERB
aiti-9227	157	12	[	[	X
aiti-9227	157	13	16	16	NUM
aiti-9227	157	14	]	]	X
aiti-9227	157	15	:	:	PUNCT
aiti-9227	157	16	mean	mean	VERB
aiti-9227	157	17	std	std	NOUN
aiti-9227	157	18	zero	zero	NUM
aiti-9227	157	19	-	-	PUNCT
aiti-9227	157	20	mean	mean	NOUN
aiti-9227	157	21	normalization	normalization	NOUN
aiti-9227	157	22	:	:	PUNCT
aiti-9227	157	23	x	x	PUNCT
aiti-9227	157	24	x	x	PUNCT
aiti-9227	157	25	x	x	PUNCT
aiti-9227	157	26	x	x	NOUN
aiti-9227	157	27	′	′	NOUN
aiti-9227	158	1	−	−	PROPN
aiti-9227	159	1	=	=	SYM
aiti-9227	160	1	(	(	PUNCT
aiti-9227	160	2	11	11	NUM
aiti-9227	160	3	)	)	PUNCT
aiti-9227	160	4	min	min	PROPN
aiti-9227	160	5	max	max	PROPN
aiti-9227	160	6	min	min	PROPN
aiti-9227	160	7	min	min	PROPN
aiti-9227	160	8	-	-	PROPN
aiti-9227	160	9	max	max	PROPN
aiti-9227	160	10	normalization	normalization	NOUN
aiti-9227	160	11	:	:	PUNCT
aiti-9227	160	12	x	x	SYM
aiti-9227	160	13	x	x	PUNCT
aiti-9227	160	14	x	x	PUNCT
aiti-9227	160	15	x	x	PUNCT
aiti-9227	160	16	x	x	X
aiti-9227	160	17	−	−	NOUN
aiti-9227	160	18	=	=	PUNCT
aiti-9227	160	19	−	−	PROPN
aiti-9227	161	1	′	′	NUM
aiti-9227	161	2	(	(	PUNCT
aiti-9227	161	3	12	12	NUM
aiti-9227	161	4	)	)	PUNCT
aiti-9227	161	5	262	262	NUM
aiti-9227	161	6	advances	advance	NOUN
aiti-9227	161	7	in	in	ADP
aiti-9227	161	8	technology	technology	NOUN
aiti-9227	161	9	innovation	innovation	NOUN
aiti-9227	161	10	,	,	PUNCT
aiti-9227	161	11	vol	vol	NOUN
aiti-9227	161	12	.	.	PROPN
aiti-9227	161	13	7	7	NUM
aiti-9227	161	14	,	,	PUNCT
aiti-9227	161	15	no	no	INTJ
aiti-9227	161	16	.	.	NOUN
aiti-9227	161	17	4	4	NUM
aiti-9227	161	18	,	,	PUNCT
aiti-9227	161	19	2022	2022	NUM
aiti-9227	161	20	,	,	PUNCT
aiti-9227	161	21	pp	pp	ADJ
aiti-9227	161	22	.	.	PUNCT
aiti-9227	162	1	258	258	NUM
aiti-9227	162	2	-	-	SYM
aiti-9227	162	3	269	269	NUM
aiti-9227	162	4	max	max	PROPN
aiti-9227	162	5	max	max	PROPN
aiti-9227	162	6	normalization	normalization	PROPN
aiti-9227	162	7	:	:	PUNCT
aiti-9227	162	8	x	x	SYM
aiti-9227	162	9	x	x	PUNCT
aiti-9227	162	10	x	x	PUNCT
aiti-9227	162	11	=	=	NOUN
aiti-9227	162	12	′	′	NUM
aiti-9227	162	13	(	(	PUNCT
aiti-9227	162	14	13	13	NUM
aiti-9227	162	15	)	)	PUNCT
aiti-9227	162	16	decimal	decimal	ADJ
aiti-9227	162	17	normalization	normalization	NOUN
aiti-9227	162	18	:	:	PUNCT
aiti-9227	162	19	10	10	NUM
aiti-9227	162	20	j	j	NOUN
aiti-9227	162	21	x	x	X
aiti-9227	162	22	x′	x′	PROPN
aiti-9227	162	23	=	=	SYM
aiti-9227	162	24	(	(	PUNCT
aiti-9227	162	25	14	14	NUM
aiti-9227	162	26	)	)	PUNCT
aiti-9227	162	27	min1	min1	NOUN
aiti-9227	162	28	sigmoid	sigmoid	NOUN
aiti-9227	162	29	normalization	normalization	NOUN
aiti-9227	162	30	:	:	PUNCT
aiti-9227	162	31	,	,	PUNCT
aiti-9227	162	32	1	1	NUM
aiti-9227	162	33	a	a	DET
aiti-9227	162	34	std	std	NOUN
aiti-9227	162	35	x	x	NOUN
aiti-9227	162	36	x	x	SYM
aiti-9227	162	37	x	x	X
aiti-9227	162	38	a	a	DET
aiti-9227	162	39	xe	xe	PROPN
aiti-9227	162	40	−	−	PROPN
aiti-9227	162	41	′	′	NUM
aiti-9227	162	42	−	−	PROPN
aiti-9227	163	1	=	=	PUNCT
aiti-9227	163	2	=	=	PUNCT
aiti-9227	164	1	+	+	NUM
aiti-9227	164	2	∀	∀	X
aiti-9227	164	3	(	(	PUNCT
aiti-9227	164	4	15	15	NUM
aiti-9227	164	5	)	)	PUNCT
aiti-9227	164	6	min1	min1	PROPN
aiti-9227	164	7	softmax	softmax	PROPN
aiti-9227	164	8	normalization	normalization	PROPN
aiti-9227	164	9	:	:	PUNCT
aiti-9227	164	10	,	,	PUNCT
aiti-9227	165	1	1	1	X
aiti-9227	165	2	a	a	PRON
aiti-9227	165	3	a	a	DET
aiti-9227	165	4	std	std	NOUN
aiti-9227	165	5	x	x	SYM
aiti-9227	165	6	xe	xe	PROPN
aiti-9227	165	7	x	x	PROPN
aiti-9227	165	8	a	a	DET
aiti-9227	165	9	xe	xe	PROPN
aiti-9227	165	10	−	−	PROPN
aiti-9227	165	11	−	−	PROPN
aiti-9227	165	12	−−=	−−=	NOUN
aiti-9227	166	1	=	=	NOUN
aiti-9227	167	1	′	′	NOUN
aiti-9227	167	2	+	+	NUM
aiti-9227	167	3	∀	∀	X
aiti-9227	167	4	(	(	PUNCT
aiti-9227	167	5	16	16	NUM
aiti-9227	167	6	)	)	PUNCT
aiti-9227	167	7	med	med	ADJ
aiti-9227	167	8	median	median	ADJ
aiti-9227	167	9	normalization	normalization	NOUN
aiti-9227	167	10	:	:	PUNCT
aiti-9227	168	1	x	x	SYM
aiti-9227	168	2	x	x	PUNCT
aiti-9227	168	3	x	x	PUNCT
aiti-9227	168	4	=	=	NOUN
aiti-9227	168	5	′	′	NUM
aiti-9227	168	6	(	(	PUNCT
aiti-9227	168	7	17	17	NUM
aiti-9227	168	8	)	)	PUNCT
aiti-9227	168	9	med	me	VERB
aiti-9227	168	10	75	75	NUM
aiti-9227	168	11	25robust	25robust	PROPN
aiti-9227	168	12	normalization	normalization	NOUN
aiti-9227	168	13	:	:	PUNCT
aiti-9227	168	14	,	,	PUNCT
aiti-9227	168	15	x	x	PUNCT
aiti-9227	168	16	x	x	PUNCT
aiti-9227	168	17	x	x	X
aiti-9227	168	18	iqr	iqr	NOUN
aiti-9227	168	19	x	x	SYM
aiti-9227	168	20	x	x	X
aiti-9227	168	21	iqr	iqr	NOUN
aiti-9227	168	22	−	−	PROPN
aiti-9227	169	1	=	=	SYM
aiti-9227	169	2	=	=	SYM
aiti-9227	169	3	−′	−′	NUM
aiti-9227	169	4	∀	∀	X
aiti-9227	169	5	(	(	PUNCT
aiti-9227	169	6	18	18	NUM
aiti-9227	169	7	)	)	PUNCT
aiti-9227	169	8	where	where	SCONJ
aiti-9227	169	9	�	�	PROPN
aiti-9227	169	10	and	and	CCONJ
aiti-9227	169	11	�	�	PROPN
aiti-9227	169	12	�	�	PROPN
aiti-9227	169	13	are	be	AUX
aiti-9227	169	14	the	the	DET
aiti-9227	169	15	original	original	ADJ
aiti-9227	169	16	and	and	CCONJ
aiti-9227	169	17	standardized	standardized	ADJ
aiti-9227	169	18	date	date	NOUN
aiti-9227	169	19	value	value	NOUN
aiti-9227	169	20	,	,	PUNCT
aiti-9227	169	21	respectively	respectively	ADV
aiti-9227	169	22	;	;	PUNCT
aiti-9227	169	23	xmean	xmean	PROPN
aiti-9227	169	24	,	,	PUNCT
aiti-9227	169	25	xstd	xstd	PROPN
aiti-9227	169	26	,	,	PUNCT
aiti-9227	169	27	xmin	xmin	PROPN
aiti-9227	169	28	,	,	PUNCT
aiti-9227	169	29	xmax	xmax	PROPN
aiti-9227	169	30	,	,	PUNCT
aiti-9227	169	31	and	and	CCONJ
aiti-9227	169	32	xmed	xmed	PROPN
aiti-9227	169	33	are	be	AUX
aiti-9227	169	34	the	the	DET
aiti-9227	169	35	mean	mean	ADJ
aiti-9227	169	36	,	,	PUNCT
aiti-9227	169	37	standard	standard	ADJ
aiti-9227	169	38	deviation	deviation	NOUN
aiti-9227	169	39	,	,	PUNCT
aiti-9227	169	40	min	min	PROPN
aiti-9227	169	41	,	,	PUNCT
aiti-9227	169	42	max	max	PROPN
aiti-9227	169	43	,	,	PUNCT
aiti-9227	169	44	and	and	CCONJ
aiti-9227	169	45	median	median	ADJ
aiti-9227	169	46	values	value	NOUN
aiti-9227	169	47	of	of	ADP
aiti-9227	169	48	x	x	PRON
aiti-9227	169	49	,	,	PUNCT
aiti-9227	169	50	respectively	respectively	ADV
aiti-9227	169	51	;	;	PUNCT
aiti-9227	169	52	x25	x25	NUM
aiti-9227	169	53	and	and	CCONJ
aiti-9227	169	54	x75	x75	NUM
aiti-9227	169	55	are	be	AUX
aiti-9227	169	56	the	the	DET
aiti-9227	169	57	25	25	NUM
aiti-9227	169	58	th	th	X
aiti-9227	169	59	quantile	quantile	NOUN
aiti-9227	169	60	and	and	CCONJ
aiti-9227	169	61	the	the	DET
aiti-9227	169	62	75	75	NUM
aiti-9227	169	63	th	th	X
aiti-9227	169	64	quantile	quantile	ADJ
aiti-9227	169	65	values	value	NOUN
aiti-9227	169	66	of	of	ADP
aiti-9227	169	67	x	x	PRON
aiti-9227	169	68	,	,	PUNCT
aiti-9227	169	69	respectively	respectively	ADV
aiti-9227	169	70	;	;	PUNCT
aiti-9227	169	71	j	j	PROPN
aiti-9227	169	72	is	be	AUX
aiti-9227	169	73	the	the	DET
aiti-9227	169	74	smallest	small	ADJ
aiti-9227	169	75	integer	integer	NOUN
aiti-9227	169	76	that	that	PRON
aiti-9227	169	77	satisfies	satisfy	VERB
aiti-9227	169	78	the	the	DET
aiti-9227	169	79	condition	condition	NOUN
aiti-9227	169	80	of	of	ADP
aiti-9227	169	81	max|	max|	PROPN
aiti-9227	169	82	�	�	PROPN
aiti-9227	169	83	�	�	PROPN
aiti-9227	169	84	|	|	ADV
aiti-9227	169	85	≤	≤	NOUN
aiti-9227	169	86	1	1	NUM
aiti-9227	169	87	.	.	X
aiti-9227	169	88	2.5	2.5	NUM
aiti-9227	169	89	.	.	PUNCT
aiti-9227	170	1	grid	grid	NOUN
aiti-9227	170	2	search	search	NOUN
aiti-9227	170	3	method	method	NOUN
aiti-9227	170	4	based	base	VERB
aiti-9227	170	5	on	on	ADP
aiti-9227	170	6	data	data	NOUN
aiti-9227	170	7	normalization	normalization	NOUN
aiti-9227	170	8	based	base	VERB
aiti-9227	170	9	on	on	ADP
aiti-9227	170	10	the	the	DET
aiti-9227	170	11	theoretical	theoretical	ADJ
aiti-9227	170	12	base	base	NOUN
aiti-9227	170	13	of	of	ADP
aiti-9227	170	14	the	the	DET
aiti-9227	170	15	dl	dl	PROPN
aiti-9227	170	16	structure	structure	NOUN
aiti-9227	170	17	,	,	PUNCT
aiti-9227	170	18	hyperparameter	hyperparameter	NOUN
aiti-9227	170	19	,	,	PUNCT
aiti-9227	170	20	and	and	CCONJ
aiti-9227	170	21	the	the	DET
aiti-9227	170	22	date	date	NOUN
aiti-9227	170	23	standardization	standardization	NOUN
aiti-9227	170	24	method	method	NOUN
aiti-9227	170	25	,	,	PUNCT
aiti-9227	170	26	as	as	SCONJ
aiti-9227	170	27	presented	present	VERB
aiti-9227	170	28	in	in	ADP
aiti-9227	170	29	sections	section	NOUN
aiti-9227	170	30	2.1	2.1	NUM
aiti-9227	170	31	,	,	PUNCT
aiti-9227	170	32	2.2	2.2	NUM
aiti-9227	170	33	,	,	PUNCT
aiti-9227	170	34	and	and	CCONJ
aiti-9227	170	35	2.4	2.4	NUM
aiti-9227	170	36	,	,	PUNCT
aiti-9227	170	37	the	the	DET
aiti-9227	170	38	proposed	propose	VERB
aiti-9227	170	39	gs	gs	PROPN
aiti-9227	170	40	algorithm	algorithm	PROPN
aiti-9227	170	41	applied	apply	VERB
aiti-9227	170	42	to	to	PART
aiti-9227	170	43	standardize	standardize	VERB
aiti-9227	170	44	the	the	DET
aiti-9227	170	45	data	datum	NOUN
aiti-9227	170	46	for	for	ADP
aiti-9227	170	47	the	the	DET
aiti-9227	170	48	dl	dl	PROPN
aiti-9227	170	49	model	model	NOUN
aiti-9227	170	50	is	be	AUX
aiti-9227	170	51	shown	show	VERB
aiti-9227	170	52	in	in	ADP
aiti-9227	170	53	fig	fig	NOUN
aiti-9227	170	54	.	.	PUNCT
aiti-9227	171	1	5	5	X
aiti-9227	171	2	.	.	PUNCT
aiti-9227	171	3	the	the	DET
aiti-9227	171	4	procedure	procedure	NOUN
aiti-9227	171	5	is	be	AUX
aiti-9227	171	6	done	do	VERB
aiti-9227	171	7	in	in	ADP
aiti-9227	171	8	the	the	DET
aiti-9227	171	9	six	six	NUM
aiti-9227	171	10	steps	step	NOUN
aiti-9227	171	11	below	below	ADV
aiti-9227	171	12	.	.	PUNCT
aiti-9227	172	1	the	the	DET
aiti-9227	172	2	procedure	procedure	NOUN
aiti-9227	172	3	is	be	AUX
aiti-9227	172	4	applied	apply	VERB
aiti-9227	172	5	to	to	ADP
aiti-9227	172	6	each	each	DET
aiti-9227	172	7	data	datum	NOUN
aiti-9227	172	8	standardization	standardization	NOUN
aiti-9227	172	9	method	method	NOUN
aiti-9227	172	10	,	,	PUNCT
aiti-9227	172	11	as	as	SCONJ
aiti-9227	172	12	introduced	introduce	VERB
aiti-9227	172	13	in	in	ADP
aiti-9227	172	14	section	section	NOUN
aiti-9227	172	15	2.4	2.4	NUM
aiti-9227	172	16	,	,	PUNCT
aiti-9227	172	17	to	to	PART
aiti-9227	172	18	determine	determine	VERB
aiti-9227	172	19	the	the	DET
aiti-9227	172	20	error	error	NOUN
aiti-9227	172	21	value	value	NOUN
aiti-9227	172	22	.	.	PUNCT
aiti-9227	173	1	this	this	DET
aiti-9227	173	2	error	error	NOUN
aiti-9227	173	3	value	value	NOUN
aiti-9227	173	4	is	be	AUX
aiti-9227	173	5	then	then	ADV
aiti-9227	173	6	compared	compare	VERB
aiti-9227	173	7	to	to	PART
aiti-9227	173	8	evaluate	evaluate	VERB
aiti-9227	173	9	the	the	DET
aiti-9227	173	10	effect	effect	NOUN
aiti-9227	173	11	of	of	ADP
aiti-9227	173	12	these	these	DET
aiti-9227	173	13	methods	method	NOUN
aiti-9227	173	14	on	on	ADP
aiti-9227	173	15	the	the	DET
aiti-9227	173	16	gs	gs	PROPN
aiti-9227	173	17	algorithm	algorithm	NOUN
aiti-9227	173	18	for	for	ADP
aiti-9227	173	19	the	the	DET
aiti-9227	173	20	dl	dl	PROPN
aiti-9227	173	21	network	network	NOUN
aiti-9227	173	22	.	.	PUNCT
aiti-9227	174	1	step	step	NOUN
aiti-9227	174	2	1	1	NUM
aiti-9227	174	3	:	:	PUNCT
aiti-9227	174	4	the	the	DET
aiti-9227	174	5	original	original	ADJ
aiti-9227	174	6	data	data	NOUN
aiti-9227	174	7	is	be	AUX
aiti-9227	174	8	processed	process	VERB
aiti-9227	174	9	and	and	CCONJ
aiti-9227	174	10	the	the	DET
aiti-9227	174	11	input	input	NOUN
aiti-9227	174	12	-	-	PUNCT
aiti-9227	174	13	target	target	NOUN
aiti-9227	174	14	pairs	pair	NOUN
aiti-9227	174	15	of	of	ADP
aiti-9227	174	16	(	(	PUNCT
aiti-9227	174	17	�	�	PROPN
aiti-9227	174	18	�	�	PROPN
aiti-9227	174	19	�	�	PROPN
aiti-9227	174	20	�	�	PROPN
aiti-9227	174	21	�	�	PROPN
aiti-9227	174	22	,	,	PUNCT
aiti-9227	174	23	�	�	PROPN
aiti-9227	174	24	�	�	PROPN
aiti-9227	174	25	�	�	PROPN
aiti-9227	174	26	�	�	PROPN
aiti-9227	174	27	�	�	PROPN
aiti-9227	174	28	)	)	PUNCT
aiti-9227	174	29	and	and	CCONJ
aiti-9227	174	30	(	(	PUNCT
aiti-9227	174	31	�	�	PROPN
aiti-9227	174	32	�	�	PROPN
aiti-9227	174	33	�	�	PROPN
aiti-9227	174	34	�	�	PROPN
aiti-9227	174	35	�	�	PROPN
aiti-9227	174	36	,	,	PUNCT
aiti-9227	174	37	�	�	PROPN
aiti-9227	174	38	�	�	PROPN
aiti-9227	174	39	�	�	PROPN
aiti-9227	174	40	�	�	PROPN
aiti-9227	174	41	�	�	PROPN
aiti-9227	174	42	)	)	PUNCT
aiti-9227	174	43	are	be	AUX
aiti-9227	174	44	determined	determine	VERB
aiti-9227	174	45	corresponding	correspond	VERB
aiti-9227	174	46	to	to	ADP
aiti-9227	174	47	the	the	DET
aiti-9227	174	48	training	training	NOUN
aiti-9227	174	49	and	and	CCONJ
aiti-9227	174	50	test	test	NOUN
aiti-9227	174	51	processes	process	NOUN
aiti-9227	174	52	,	,	PUNCT
aiti-9227	174	53	respectively	respectively	ADV
aiti-9227	174	54	.	.	PUNCT
aiti-9227	175	1	step	step	NOUN
aiti-9227	175	2	2	2	NUM
aiti-9227	175	3	:	:	PUNCT
aiti-9227	175	4	the	the	DET
aiti-9227	175	5	training	training	NOUN
aiti-9227	175	6	and	and	CCONJ
aiti-9227	175	7	test	test	NOUN
aiti-9227	175	8	data	datum	NOUN
aiti-9227	175	9	are	be	AUX
aiti-9227	175	10	standardized	standardize	VERB
aiti-9227	175	11	by	by	ADP
aiti-9227	175	12	using	use	VERB
aiti-9227	175	13	the	the	DET
aiti-9227	175	14	methods	method	NOUN
aiti-9227	175	15	described	describe	VERB
aiti-9227	175	16	in	in	ADP
aiti-9227	175	17	section	section	NOUN
aiti-9227	175	18	2.4	2.4	NUM
aiti-9227	175	19	to	to	PART
aiti-9227	175	20	determine	determine	VERB
aiti-9227	175	21	(	(	PUNCT
aiti-9227	175	22	�	�	PROPN
aiti-9227	175	23	�	�	PROPN
aiti-9227	175	24	�	�	PROPN
aiti-9227	175	25	�	�	PROPN
aiti-9227	175	26	�	�	PROPN
aiti-9227	175	27	�	�	PROPN
aiti-9227	175	28	,	,	PUNCT
aiti-9227	175	29	�	�	PROPN
aiti-9227	175	30	�	�	PROPN
aiti-9227	175	31	�	�	PROPN
aiti-9227	175	32	�	�	PROPN
aiti-9227	175	33	�	�	PROPN
aiti-9227	175	34	�	�	PROPN
aiti-9227	175	35	)	)	PUNCT
aiti-9227	175	36	and	and	CCONJ
aiti-9227	175	37	(	(	PUNCT
aiti-9227	175	38	�	�	PROPN
aiti-9227	175	39	�	�	PROPN
aiti-9227	175	40	�	�	PROPN
aiti-9227	175	41	�	�	PROPN
aiti-9227	175	42	�	�	PROPN
aiti-9227	175	43	�	�	PROPN
aiti-9227	175	44	,	,	PUNCT
aiti-9227	175	45	�	�	PROPN
aiti-9227	175	46	�	�	PROPN
aiti-9227	175	47	�	�	PROPN
aiti-9227	175	48	�	�	PROPN
aiti-9227	175	49	�	�	PROPN
aiti-9227	175	50	�	�	PROPN
aiti-9227	175	51	)	)	PUNCT
aiti-9227	175	52	.	.	PUNCT
aiti-9227	176	1	the	the	DET
aiti-9227	176	2	next	next	ADJ
aiti-9227	176	3	step	step	NOUN
aiti-9227	176	4	is	be	AUX
aiti-9227	176	5	the	the	DET
aiti-9227	176	6	training	training	NOUN
aiti-9227	176	7	process	process	NOUN
aiti-9227	176	8	.	.	PUNCT
aiti-9227	177	1	step	step	NOUN
aiti-9227	177	2	3	3	NUM
aiti-9227	177	3	:	:	PUNCT
aiti-9227	177	4	the	the	DET
aiti-9227	177	5	gs	gs	PROPN
aiti-9227	177	6	algorithm	algorithm	NOUN
aiti-9227	177	7	is	be	AUX
aiti-9227	177	8	applied	apply	VERB
aiti-9227	177	9	to	to	PART
aiti-9227	177	10	determine	determine	VERB
aiti-9227	177	11	the	the	DET
aiti-9227	177	12	optimal	optimal	ADJ
aiti-9227	177	13	hyperparameters	hyperparameter	NOUN
aiti-9227	177	14	of	of	ADP
aiti-9227	177	15	the	the	DET
aiti-9227	177	16	dl	dl	PROPN
aiti-9227	177	17	model	model	NOUN
aiti-9227	177	18	from	from	ADP
aiti-9227	177	19	the	the	DET
aiti-9227	177	20	variable	variable	ADJ
aiti-9227	177	21	value	value	NOUN
aiti-9227	177	22	combination	combination	NOUN
aiti-9227	177	23	of	of	ADP
aiti-9227	177	24	each	each	DET
aiti-9227	177	25	hyperparameter	hyperparameter	NOUN
aiti-9227	177	26	cfg	cfg	PROPN
aiti-9227	177	27	=	=	PRON
aiti-9227	177	28	{	{	PUNCT
aiti-9227	177	29	cfgi	cfgi	NOUN
aiti-9227	177	30	}	}	PUNCT
aiti-9227	177	31	,	,	PUNCT
aiti-9227	177	32	i=	i=	PROPN
aiti-9227	177	33	1	1	NUM
aiti-9227	177	34	:	:	SYM
aiti-9227	177	35	n	n	CCONJ
aiti-9227	177	36	,	,	PUNCT
aiti-9227	177	37	in	in	ADP
aiti-9227	177	38	which	which	PRON
aiti-9227	177	39	n	n	ADV
aiti-9227	177	40	is	be	AUX
aiti-9227	177	41	the	the	DET
aiti-9227	177	42	total	total	ADJ
aiti-9227	177	43	number	number	NOUN
aiti-9227	177	44	of	of	ADP
aiti-9227	177	45	combinations	combination	NOUN
aiti-9227	177	46	.	.	PUNCT
aiti-9227	178	1	the	the	DET
aiti-9227	178	2	vector	vector	NOUN
aiti-9227	178	3	cfgi	cfgi	NOUN
aiti-9227	178	4	depends	depend	VERB
aiti-9227	178	5	on	on	ADP
aiti-9227	178	6	the	the	DET
aiti-9227	178	7	dl	dl	PROPN
aiti-9227	178	8	model	model	NOUN
aiti-9227	178	9	.	.	PUNCT
aiti-9227	179	1	for	for	ADP
aiti-9227	179	2	the	the	DET
aiti-9227	179	3	lstmn	lstmn	NOUN
aiti-9227	179	4	model	model	NOUN
aiti-9227	179	5	,	,	PUNCT
aiti-9227	179	6	cfgi	cfgi	NOUN
aiti-9227	179	7	=	=	SYM
aiti-9227	179	8	{	{	PUNCT
aiti-9227	179	9	ei	ei	PROPN
aiti-9227	179	10	,	,	PUNCT
aiti-9227	179	11	bi	bi	NOUN
aiti-9227	179	12	,	,	PUNCT
aiti-9227	179	13	oi	oi	INTJ
aiti-9227	179	14	,	,	PUNCT
aiti-9227	179	15	di	di	NOUN
aiti-9227	179	16	}	}	PUNCT
aiti-9227	179	17	is	be	AUX
aiti-9227	179	18	used	use	VERB
aiti-9227	179	19	,	,	PUNCT
aiti-9227	179	20	whereas	whereas	SCONJ
aiti-9227	179	21	cfgi	cfgi	NOUN
aiti-9227	179	22	=	=	PRON
aiti-9227	179	23	{	{	PUNCT
aiti-9227	179	24	ei	ei	NOUN
aiti-9227	179	25	,	,	PUNCT
aiti-9227	179	26	bi	bi	NOUN
aiti-9227	179	27	,	,	PUNCT
aiti-9227	179	28	oi	oi	INTJ
aiti-9227	179	29	,	,	PUNCT
aiti-9227	179	30	fi	fi	NOUN
aiti-9227	179	31	,	,	PUNCT
aiti-9227	179	32	ki	ki	INTJ
aiti-9227	179	33	}	}	PUNCT
aiti-9227	179	34	is	be	AUX
aiti-9227	179	35	used	use	VERB
aiti-9227	179	36	for	for	ADP
aiti-9227	179	37	the	the	DET
aiti-9227	179	38	cnn	cnn	PROPN
aiti-9227	179	39	model	model	NOUN
aiti-9227	179	40	,	,	PUNCT
aiti-9227	179	41	in	in	ADP
aiti-9227	179	42	which	which	PRON
aiti-9227	179	43	e	e	NOUN
aiti-9227	179	44	,	,	PUNCT
aiti-9227	179	45	b	b	PROPN
aiti-9227	179	46	,	,	PUNCT
aiti-9227	179	47	o	o	NOUN
aiti-9227	179	48	,	,	PUNCT
aiti-9227	179	49	d	d	X
aiti-9227	179	50	,	,	PUNCT
aiti-9227	179	51	f	f	X
aiti-9227	179	52	,	,	PUNCT
aiti-9227	179	53	and	and	CCONJ
aiti-9227	179	54	k	k	PROPN
aiti-9227	179	55	notate	notate	VERB
aiti-9227	179	56	the	the	DET
aiti-9227	179	57	hyperparameters	hyperparameter	NOUN
aiti-9227	179	58	of	of	ADP
aiti-9227	179	59	epoch	epoch	NOUN
aiti-9227	179	60	,	,	PUNCT
aiti-9227	179	61	batch	batch	NOUN
aiti-9227	179	62	,	,	PUNCT
aiti-9227	179	63	optimizer	optimizer	NOUN
aiti-9227	179	64	,	,	PUNCT
aiti-9227	179	65	dropout	dropout	NOUN
aiti-9227	179	66	,	,	PUNCT
aiti-9227	179	67	filters	filter	NOUN
aiti-9227	179	68	,	,	PUNCT
aiti-9227	179	69	and	and	CCONJ
aiti-9227	179	70	kernel	kernel	PROPN
aiti-9227	179	71	,	,	PUNCT
aiti-9227	179	72	respectively	respectively	ADV
aiti-9227	179	73	.	.	PUNCT
aiti-9227	180	1	for	for	ADP
aiti-9227	180	2	this	this	DET
aiti-9227	180	3	step	step	NOUN
aiti-9227	180	4	,	,	PUNCT
aiti-9227	180	5	to	to	PART
aiti-9227	180	6	overcome	overcome	VERB
aiti-9227	180	7	overfitting	overfitte	VERB
aiti-9227	180	8	during	during	ADP
aiti-9227	180	9	training	training	NOUN
aiti-9227	180	10	,	,	PUNCT
aiti-9227	180	11	the	the	DET
aiti-9227	180	12	cross	cross	NOUN
aiti-9227	180	13	-	-	NOUN
aiti-9227	180	14	validation	validation	ADJ
aiti-9227	180	15	(	(	PUNCT
aiti-9227	180	16	cv	cv	NOUN
aiti-9227	180	17	)	)	PUNCT
aiti-9227	180	18	technique	technique	NOUN
aiti-9227	180	19	is	be	AUX
aiti-9227	180	20	applied	apply	VERB
aiti-9227	180	21	,	,	PUNCT
aiti-9227	180	22	and	and	CCONJ
aiti-9227	180	23	the	the	DET
aiti-9227	180	24	dl	dl	PROPN
aiti-9227	180	25	model	model	NOUN
aiti-9227	180	26	is	be	AUX
aiti-9227	180	27	run	run	VERB
aiti-9227	180	28	repeatedly	repeatedly	ADV
aiti-9227	180	29	at	at	ADP
aiti-9227	180	30	the	the	DET
aiti-9227	180	31	same	same	ADJ
aiti-9227	180	32	time	time	NOUN
aiti-9227	180	33	.	.	PUNCT
aiti-9227	181	1	then	then	ADV
aiti-9227	181	2	,	,	PUNCT
aiti-9227	181	3	the	the	DET
aiti-9227	181	4	average	average	ADJ
aiti-9227	181	5	value	value	NOUN
aiti-9227	181	6	of	of	ADP
aiti-9227	181	7	the	the	DET
aiti-9227	181	8	model	model	NOUN
aiti-9227	181	9	is	be	AUX
aiti-9227	181	10	used	use	VERB
aiti-9227	181	11	to	to	PART
aiti-9227	181	12	increase	increase	VERB
aiti-9227	181	13	its	its	PRON
aiti-9227	181	14	reliability	reliability	NOUN
aiti-9227	181	15	.	.	PUNCT
aiti-9227	182	1	the	the	DET
aiti-9227	182	2	next	next	ADJ
aiti-9227	182	3	steps	step	NOUN
aiti-9227	182	4	are	be	AUX
aiti-9227	182	5	the	the	DET
aiti-9227	182	6	test	test	NOUN
aiti-9227	182	7	processes	process	NOUN
aiti-9227	182	8	:	:	PUNCT
aiti-9227	182	9	step	step	NOUN
aiti-9227	182	10	4	4	NUM
aiti-9227	182	11	:	:	PUNCT
aiti-9227	182	12	the	the	DET
aiti-9227	182	13	dl	dl	PROPN
aiti-9227	182	14	is	be	AUX
aiti-9227	182	15	used	use	VERB
aiti-9227	182	16	with	with	ADP
aiti-9227	182	17	the	the	DET
aiti-9227	182	18	obtained	obtain	VERB
aiti-9227	182	19	hyperparameters	hyperparameter	NOUN
aiti-9227	182	20	in	in	ADP
aiti-9227	182	21	step	step	NOUN
aiti-9227	182	22	3	3	NUM
aiti-9227	182	23	to	to	PART
aiti-9227	182	24	predict	predict	VERB
aiti-9227	182	25	the	the	DET
aiti-9227	182	26	�	�	PROPN
aiti-9227	182	27	�	�	PROPN
aiti-9227	182	28	�	�	PROPN
aiti-9227	182	29	�	�	PROPN
aiti-9227	182	30	�	�	PROPN
aiti-9227	182	31	�	�	PROPN
aiti-9227	182	32	�	�	PROPN
aiti-9227	182	33	�	�	PROPN
aiti-9227	182	34	�	�	PROPN
aiti-9227	182	35	.	.	PUNCT
aiti-9227	183	1	step	step	VERB
aiti-9227	183	2	5	5	NUM
aiti-9227	183	3	:	:	PUNCT
aiti-9227	183	4	�	�	PROPN
aiti-9227	183	5	�	�	PROPN
aiti-9227	183	6	�	�	PROPN
aiti-9227	183	7	�	�	PROPN
aiti-9227	183	8	�	�	PROPN
aiti-9227	183	9	�	�	PROPN
aiti-9227	183	10	�	�	PROPN
aiti-9227	183	11	�	�	PROPN
aiti-9227	183	12	is	be	AUX
aiti-9227	183	13	calculated	calculate	VERB
aiti-9227	183	14	by	by	ADP
aiti-9227	183	15	using	use	VERB
aiti-9227	183	16	the	the	DET
aiti-9227	183	17	value	value	NOUN
aiti-9227	183	18	�	�	PROPN
aiti-9227	183	19	�	�	PROPN
aiti-9227	183	20	�	�	PROPN
aiti-9227	183	21	�	�	PROPN
aiti-9227	183	22	�	�	PROPN
aiti-9227	183	23	�	�	PROPN
aiti-9227	183	24	�	�	PROPN
aiti-9227	183	25	�	�	PROPN
aiti-9227	183	26	�	�	PROPN
aiti-9227	183	27	.	.	PUNCT
aiti-9227	184	1	step	step	VERB
aiti-9227	184	2	6	6	NUM
aiti-9227	184	3	:	:	PUNCT
aiti-9227	184	4	the	the	DET
aiti-9227	184	5	error	error	NOUN
aiti-9227	184	6	value	value	NOUN
aiti-9227	184	7	of	of	ADP
aiti-9227	184	8	the	the	DET
aiti-9227	184	9	dl	dl	PROPN
aiti-9227	184	10	is	be	AUX
aiti-9227	184	11	determined	determine	VERB
aiti-9227	184	12	based	base	VERB
aiti-9227	184	13	on	on	ADP
aiti-9227	184	14	the	the	DET
aiti-9227	184	15	difference	difference	NOUN
aiti-9227	184	16	between	between	ADP
aiti-9227	184	17	�	�	PROPN
aiti-9227	184	18	�	�	PROPN
aiti-9227	184	19	�	�	PROPN
aiti-9227	184	20	�	�	PROPN
aiti-9227	184	21	�	�	PROPN
aiti-9227	184	22	�	�	PROPN
aiti-9227	184	23	�	�	PROPN
aiti-9227	184	24	�	�	PROPN
aiti-9227	184	25	and	and	CCONJ
aiti-9227	184	26	�	�	PROPN
aiti-9227	184	27	�	�	PROPN
aiti-9227	184	28	�	�	PROPN
aiti-9227	184	29	�	�	PROPN
aiti-9227	184	30	�	�	PROPN
aiti-9227	184	31	by	by	ADP
aiti-9227	184	32	using	use	VERB
aiti-9227	184	33	eqs	eqs	PROPN
aiti-9227	184	34	.	.	PUNCT
aiti-9227	184	35	(	(	PUNCT
aiti-9227	184	36	8)-(10	8)-(10	NUM
aiti-9227	184	37	)	)	PUNCT
aiti-9227	184	38	.	.	PUNCT
aiti-9227	185	1	263	263	NUM
aiti-9227	185	2	advances	advance	NOUN
aiti-9227	185	3	in	in	ADP
aiti-9227	185	4	technology	technology	NOUN
aiti-9227	185	5	innovation	innovation	NOUN
aiti-9227	185	6	,	,	PUNCT
aiti-9227	185	7	vol	vol	NOUN
aiti-9227	185	8	.	.	PROPN
aiti-9227	185	9	7	7	NUM
aiti-9227	185	10	,	,	PUNCT
aiti-9227	185	11	no	no	INTJ
aiti-9227	185	12	.	.	NOUN
aiti-9227	185	13	4	4	NUM
aiti-9227	185	14	,	,	PUNCT
aiti-9227	185	15	2022	2022	NUM
aiti-9227	185	16	,	,	PUNCT
aiti-9227	185	17	pp	pp	ADJ
aiti-9227	185	18	.	.	PUNCT
aiti-9227	186	1	258	258	NUM
aiti-9227	186	2	-	-	SYM
aiti-9227	186	3	269	269	NUM
aiti-9227	186	4	process	process	NOUN
aiti-9227	186	5	data	datum	NOUN
aiti-9227	186	6	scaling	scale	VERB
aiti-9227	186	7	data	datum	NOUN
aiti-9227	186	8	data	data	PROPN
aiti-9227	186	9	trainx	trainx	PROPN
aiti-9227	186	10	trainy	trainy	ADJ
aiti-9227	186	11	testx	testx	NOUN
aiti-9227	186	12	testy	testy	PROPN
aiti-9227	186	13	mse	mse	PROPN
aiti-9227	186	14	'	'	PUNCT
aiti-9227	186	15	trainx	trainx	NOUN
aiti-9227	186	16	'	'	PUNCT
aiti-9227	186	17	train	train	NOUN
aiti-9227	186	18	y	y	NOUN
aiti-9227	186	19	using	use	VERB
aiti-9227	186	20	cnn	cnn	PROPN
aiti-9227	186	21	and	and	CCONJ
aiti-9227	186	22	lstmn	lstmn	NOUN
aiti-9227	186	23	models	model	NOUN
aiti-9227	186	24	(	(	PUNCT
aiti-9227	186	25	testing	testing	NOUN
aiti-9227	186	26	process	process	NOUN
aiti-9227	186	27	)	)	PUNCT
aiti-9227	186	28	un	un	ADJ
aiti-9227	186	29	-	-	ADJ
aiti-9227	186	30	scaling	scaling	ADJ
aiti-9227	186	31	data	datum	NOUN
aiti-9227	186	32	'	'	PART
aiti-9227	186	33	testx	testx	NOUN
aiti-9227	186	34	'	'	PUNCT
aiti-9227	186	35	testy	testy	ADJ
aiti-9227	186	36	'	'	PUNCT
aiti-9227	186	37	predict	predict	VERB
aiti-9227	186	38	y	y	PRON
aiti-9227	186	39	evaluate	evaluate	VERB
aiti-9227	186	40	by	by	ADP
aiti-9227	186	41	using	use	VERB
aiti-9227	186	42	eqs	eqs	PROPN
aiti-9227	186	43	.	.	PUNCT
aiti-9227	187	1	(	(	PUNCT
aiti-9227	187	2	8)-(10	8)-(10	NUM
aiti-9227	187	3	)	)	PUNCT
aiti-9227	187	4	predicty	predicty	NOUN
aiti-9227	187	5	testy	testy	PROPN
aiti-9227	187	6	mae	mae	PROPN
aiti-9227	187	7	mape	mape	NOUN
aiti-9227	187	8	using	use	VERB
aiti-9227	187	9	cnn	cnn	PROPN
aiti-9227	187	10	and	and	CCONJ
aiti-9227	187	11	lstmn	lstmn	NOUN
aiti-9227	187	12	models	model	NOUN
aiti-9227	187	13	(	(	PUNCT
aiti-9227	187	14	training	training	NOUN
aiti-9227	187	15	process	process	NOUN
aiti-9227	187	16	)	)	PUNCT
aiti-9227	187	17	cfgopt	cfgopt	VERB
aiti-9227	187	18	cfg	cfg	PROPN
aiti-9227	187	19	fig	fig	NOUN
aiti-9227	187	20	.	.	PUNCT
aiti-9227	188	1	5	5	NUM
aiti-9227	188	2	the	the	DET
aiti-9227	188	3	gs	gs	PROPN
aiti-9227	188	4	methodology	methodology	NOUN
aiti-9227	188	5	based	base	VERB
aiti-9227	188	6	on	on	ADP
aiti-9227	188	7	data	datum	NOUN
aiti-9227	188	8	normalization	normalization	NOUN
aiti-9227	188	9	3	3	NUM
aiti-9227	188	10	.	.	PUNCT
aiti-9227	188	11	simulation	simulation	NOUN
aiti-9227	188	12	data	data	PROPN
aiti-9227	188	13	setup	setup	NOUN
aiti-9227	188	14	3.1	3.1	NUM
aiti-9227	188	15	.	.	PUNCT
aiti-9227	189	1	data	datum	NOUN
aiti-9227	189	2	the	the	DET
aiti-9227	189	3	half	half	ADJ
aiti-9227	189	4	-	-	PUNCT
aiti-9227	189	5	hourly	hourly	ADJ
aiti-9227	189	6	load	load	NOUN
aiti-9227	189	7	demand	demand	NOUN
aiti-9227	189	8	data	datum	NOUN
aiti-9227	189	9	of	of	ADP
aiti-9227	189	10	queensland	queensland	PROPN
aiti-9227	189	11	state	state	PROPN
aiti-9227	189	12	,	,	PUNCT
aiti-9227	189	13	australia	australia	PROPN
aiti-9227	189	14	,	,	PUNCT
aiti-9227	189	15	and	and	CCONJ
aiti-9227	189	16	the	the	DET
aiti-9227	189	17	hourly	hourly	ADJ
aiti-9227	189	18	load	load	NOUN
aiti-9227	189	19	demand	demand	NOUN
aiti-9227	189	20	data	datum	NOUN
aiti-9227	189	21	of	of	ADP
aiti-9227	189	22	ho	ho	PROPN
aiti-9227	189	23	chi	chi	PROPN
aiti-9227	189	24	minh	minh	PROPN
aiti-9227	189	25	city	city	PROPN
aiti-9227	189	26	,	,	PUNCT
aiti-9227	189	27	vietnam	vietnam	PROPN
aiti-9227	189	28	,	,	PUNCT
aiti-9227	189	29	are	be	AUX
aiti-9227	189	30	used	use	VERB
aiti-9227	189	31	to	to	PART
aiti-9227	189	32	verify	verify	VERB
aiti-9227	189	33	the	the	DET
aiti-9227	189	34	effectiveness	effectiveness	NOUN
aiti-9227	189	35	of	of	ADP
aiti-9227	189	36	the	the	DET
aiti-9227	189	37	proposed	propose	VERB
aiti-9227	189	38	method	method	NOUN
aiti-9227	189	39	.	.	PUNCT
aiti-9227	190	1	the	the	DET
aiti-9227	190	2	selected	select	VERB
aiti-9227	190	3	data	datum	NOUN
aiti-9227	190	4	are	be	AUX
aiti-9227	190	5	divided	divide	VERB
aiti-9227	190	6	into	into	ADP
aiti-9227	190	7	two	two	NUM
aiti-9227	190	8	different	different	ADJ
aiti-9227	190	9	cases	case	NOUN
aiti-9227	190	10	,	,	PUNCT
aiti-9227	190	11	corresponding	correspond	VERB
aiti-9227	190	12	to	to	ADP
aiti-9227	190	13	the	the	DET
aiti-9227	190	14	lstmn	lstmn	NOUN
aiti-9227	190	15	and	and	CCONJ
aiti-9227	190	16	the	the	DET
aiti-9227	190	17	cnn	cnn	PROPN
aiti-9227	190	18	.	.	PUNCT
aiti-9227	191	1	these	these	DET
aiti-9227	191	2	datasets	dataset	NOUN
aiti-9227	191	3	have	have	VERB
aiti-9227	191	4	different	different	ADJ
aiti-9227	191	5	periods	period	NOUN
aiti-9227	191	6	,	,	PUNCT
aiti-9227	191	7	and	and	CCONJ
aiti-9227	191	8	their	their	PRON
aiti-9227	191	9	statistical	statistical	ADJ
aiti-9227	191	10	properties	property	NOUN
aiti-9227	191	11	are	be	AUX
aiti-9227	191	12	shown	show	VERB
aiti-9227	191	13	in	in	ADP
aiti-9227	191	14	table	table	NOUN
aiti-9227	192	1	2	2	NUM
aiti-9227	192	2	.	.	PUNCT
aiti-9227	192	3	fig	fig	NOUN
aiti-9227	192	4	.	.	PUNCT
aiti-9227	193	1	6	6	NUM
aiti-9227	193	2	shows	show	VERB
aiti-9227	193	3	the	the	DET
aiti-9227	193	4	ytrain	ytrain	NOUN
aiti-9227	193	5	value	value	NOUN
aiti-9227	193	6	waveform	waveform	NOUN
aiti-9227	193	7	in	in	ADP
aiti-9227	193	8	case	case	NOUN
aiti-9227	193	9	1	1	NUM
aiti-9227	193	10	according	accord	VERB
aiti-9227	193	11	to	to	ADP
aiti-9227	193	12	the	the	DET
aiti-9227	193	13	data	datum	NOUN
aiti-9227	193	14	normalization	normalization	NOUN
aiti-9227	193	15	methods	method	NOUN
aiti-9227	193	16	.	.	PUNCT
aiti-9227	194	1	6000	6000	NUM
aiti-9227	194	2	5000	5000	NUM
aiti-9227	194	3	(	(	PUNCT
aiti-9227	194	4	1)-none	1)-none	NUM
aiti-9227	194	5	1.0	1.0	NUM
aiti-9227	194	6	-1.0	-1.0	PROPN
aiti-9227	194	7	0.0	0.0	NUM
aiti-9227	194	8	(	(	PUNCT
aiti-9227	194	9	2)-zero	2)-zero	NUM
aiti-9227	194	10	-	-	PUNCT
aiti-9227	194	11	mean	mean	NOUN
aiti-9227	194	12	0.75	0.75	NUM
aiti-9227	194	13	0.25	0.25	NUM
aiti-9227	194	14	0.50	0.50	NUM
aiti-9227	194	15	(	(	PUNCT
aiti-9227	194	16	3)-min	3)-min	NUM
aiti-9227	194	17	-	-	PROPN
aiti-9227	194	18	max	max	PROPN
aiti-9227	194	19	0.9	0.9	NUM
aiti-9227	194	20	0.7	0.7	NUM
aiti-9227	194	21	0.8	0.8	NUM
aiti-9227	194	22	(	(	PUNCT
aiti-9227	194	23	4)-max	4)-max	NUM
aiti-9227	194	24	0.6	0.6	NUM
aiti-9227	194	25	0.5	0.5	NUM
aiti-9227	194	26	(	(	PUNCT
aiti-9227	194	27	5)-decimal	5)-decimal	NOUN
aiti-9227	194	28	0.75	0.75	NUM
aiti-9227	194	29	0.25	0.25	NUM
aiti-9227	194	30	0.50	0.50	NUM
aiti-9227	194	31	(	(	PUNCT
aiti-9227	194	32	6)-sigmoid	6)-sigmoid	NUM
aiti-9227	194	33	0.5	0.5	NUM
aiti-9227	194	34	-0.5	-0.5	NUM
aiti-9227	194	35	0.0	0.0	NUM
aiti-9227	194	36	(	(	PUNCT
aiti-9227	194	37	7)-softmax	7)-softmax	NUM
aiti-9227	194	38	1.0	1.0	NUM
aiti-9227	194	39	0.8	0.8	NUM
aiti-9227	194	40	(	(	PUNCT
aiti-9227	194	41	8)-median	8)-median	NUM
aiti-9227	194	42	0.0	0.0	NUM
aiti-9227	194	43	-1.0	-1.0	PROPN
aiti-9227	194	44	(	(	PUNCT
aiti-9227	194	45	9)-robust	9)-robust	NUM
aiti-9227	194	46	m	m	PROPN
aiti-9227	194	47	w	w	NOUN
aiti-9227	194	48	p	p	X
aiti-9227	194	49	u	u	NOUN
aiti-9227	194	50	p	p	NOUN
aiti-9227	194	51	u	u	NOUN
aiti-9227	194	52	p	p	NOUN
aiti-9227	194	53	u	u	NOUN
aiti-9227	194	54	p	p	NOUN
aiti-9227	194	55	u	u	NOUN
aiti-9227	194	56	p	p	NOUN
aiti-9227	194	57	u	u	NOUN
aiti-9227	194	58	p	p	NOUN
aiti-9227	194	59	u	u	NOUN
aiti-9227	194	60	p	p	NOUN
aiti-9227	194	61	u	u	NOUN
aiti-9227	194	62	p	p	NOUN
aiti-9227	194	63	u	u	NOUN
aiti-9227	194	64	0	0	NUM
aiti-9227	194	65	100	100	NUM
aiti-9227	194	66	200	200	NUM
aiti-9227	194	67	300	300	NUM
aiti-9227	194	68	time	time	NOUN
aiti-9227	194	69	(	(	PUNCT
aiti-9227	194	70	half	half	NOUN
aiti-9227	194	71	hour	hour	NOUN
aiti-9227	194	72	)	)	PUNCT
aiti-9227	194	73	(	(	PUNCT
aiti-9227	194	74	2)-zero	2)-zero	NUM
aiti-9227	194	75	-	-	PUNCT
aiti-9227	194	76	mean	mean	NOUN
aiti-9227	194	77	(	(	PUNCT
aiti-9227	194	78	7)-softmax	7)-softmax	NUM
aiti-9227	194	79	time	time	NOUN
aiti-9227	194	80	(	(	PUNCT
aiti-9227	194	81	half	half	NOUN
aiti-9227	194	82	hour	hour	NOUN
aiti-9227	194	83	)	)	PUNCT
aiti-9227	194	84	3000	3000	NUM
aiti-9227	194	85	2000	2000	NUM
aiti-9227	194	86	(	(	PUNCT
aiti-9227	194	87	1)-none	1)-none	NUM
aiti-9227	194	88	m	m	PROPN
aiti-9227	194	89	w	w	NOUN
aiti-9227	194	90	-1.0	-1.0	PROPN
aiti-9227	194	91	0.25	0.25	NUM
aiti-9227	194	92	0.50	0.50	NUM
aiti-9227	194	93	(	(	PUNCT
aiti-9227	194	94	3)-min	3)-min	NUM
aiti-9227	194	95	-	-	PROPN
aiti-9227	194	96	max	max	PROPN
aiti-9227	194	97	p	p	PROPN
aiti-9227	194	98	u	u	PROPN
aiti-9227	194	99	0.6	0.6	NUM
aiti-9227	194	100	0.8	0.8	NUM
aiti-9227	194	101	(	(	PUNCT
aiti-9227	194	102	4)-max	4)-max	NUM
aiti-9227	194	103	p	p	NOUN
aiti-9227	194	104	u	u	PROPN
aiti-9227	194	105	0.3	0.3	NUM
aiti-9227	194	106	0.2	0.2	NUM
aiti-9227	194	107	(	(	PUNCT
aiti-9227	194	108	5)-decimal	5)-decimal	NUM
aiti-9227	194	109	p	p	NOUN
aiti-9227	194	110	u	u	NOUN
aiti-9227	194	111	0.75	0.75	NUM
aiti-9227	194	112	0.25	0.25	NUM
aiti-9227	194	113	0.50	0.50	NUM
aiti-9227	194	114	(	(	PUNCT
aiti-9227	194	115	6)-sigmoid	6)-sigmoid	NUM
aiti-9227	194	116	p	p	NOUN
aiti-9227	194	117	u	u	NOUN
aiti-9227	194	118	1.00	1.00	NUM
aiti-9227	194	119	0.75	0.75	NUM
aiti-9227	194	120	(	(	PUNCT
aiti-9227	194	121	8)-median	8)-median	NUM
aiti-9227	194	122	p	p	NOUN
aiti-9227	194	123	u	u	PROPN
aiti-9227	194	124	0.0	0.0	NUM
aiti-9227	194	125	-0.5	-0.5	NOUN
aiti-9227	194	126	(	(	PUNCT
aiti-9227	194	127	9)-robust	9)-robust	NUM
aiti-9227	194	128	p	p	NOUN
aiti-9227	194	129	u	u	NOUN
aiti-9227	194	130	0	0	NUM
aiti-9227	194	131	50	50	NUM
aiti-9227	194	132	100	100	NUM
aiti-9227	194	133	150	150	NUM
aiti-9227	194	134	0.5	0.5	NUM
aiti-9227	194	135	1.0	1.0	NUM
aiti-9227	194	136	0.0p	0.0p	NOUN
aiti-9227	194	137	u	u	NOUN
aiti-9227	194	138	0.5	0.5	NUM
aiti-9227	194	139	-0.5	-0.5	NUM
aiti-9227	194	140	0.0p	0.0p	NOUN
aiti-9227	194	141	u	u	NOUN
aiti-9227	194	142	(	(	PUNCT
aiti-9227	194	143	a	a	NOUN
aiti-9227	194	144	)	)	PUNCT
aiti-9227	194	145	queensland	queensland	NOUN
aiti-9227	194	146	state	state	NOUN
aiti-9227	194	147	(	(	PUNCT
aiti-9227	194	148	b	b	NOUN
aiti-9227	194	149	)	)	PUNCT
aiti-9227	194	150	ho	ho	PROPN
aiti-9227	194	151	chi	chi	PROPN
aiti-9227	194	152	minh	minh	PROPN
aiti-9227	194	153	city	city	PROPN
aiti-9227	194	154	fig	fig	NOUN
aiti-9227	194	155	.	.	PUNCT
aiti-9227	195	1	6	6	NUM
aiti-9227	195	2	the	the	DET
aiti-9227	195	3	ytrain	ytrain	NOUN
aiti-9227	195	4	test	test	NOUN
aiti-9227	195	5	value	value	NOUN
aiti-9227	195	6	waveform	waveform	NOUN
aiti-9227	195	7	in	in	ADP
aiti-9227	195	8	case	case	NOUN
aiti-9227	195	9	1	1	NUM
aiti-9227	195	10	according	accord	VERB
aiti-9227	195	11	to	to	ADP
aiti-9227	195	12	the	the	DET
aiti-9227	195	13	data	datum	NOUN
aiti-9227	195	14	normalization	normalization	NOUN
aiti-9227	195	15	methods	method	VERB
aiti-9227	195	16	264	264	NUM
aiti-9227	195	17	advances	advance	NOUN
aiti-9227	195	18	in	in	ADP
aiti-9227	195	19	technology	technology	NOUN
aiti-9227	195	20	innovation	innovation	NOUN
aiti-9227	195	21	,	,	PUNCT
aiti-9227	195	22	vol	vol	NOUN
aiti-9227	195	23	.	.	PROPN
aiti-9227	196	1	7	7	NUM
aiti-9227	196	2	,	,	PUNCT
aiti-9227	196	3	no	no	INTJ
aiti-9227	196	4	.	.	NOUN
aiti-9227	196	5	4	4	NUM
aiti-9227	196	6	,	,	PUNCT
aiti-9227	196	7	2022	2022	NUM
aiti-9227	196	8	,	,	PUNCT
aiti-9227	196	9	pp	pp	ADJ
aiti-9227	196	10	.	.	PUNCT
aiti-9227	197	1	258	258	NUM
aiti-9227	197	2	-	-	SYM
aiti-9227	197	3	269	269	NUM
aiti-9227	197	4	table	table	NOUN
aiti-9227	197	5	2	2	NUM
aiti-9227	197	6	data	datum	NOUN
aiti-9227	197	7	characteristics	characteristic	NOUN
aiti-9227	197	8	description	description	NOUN
aiti-9227	197	9	case	case	NOUN
aiti-9227	197	10	1	1	NUM
aiti-9227	197	11	:	:	PUNCT
aiti-9227	197	12	lstm	lstm	NOUN
aiti-9227	197	13	model	model	NOUN
aiti-9227	197	14	case	case	NOUN
aiti-9227	197	15	2	2	NUM
aiti-9227	197	16	:	:	PUNCT
aiti-9227	197	17	cnn	cnn	PROPN
aiti-9227	197	18	model	model	PROPN
aiti-9227	197	19	queensland	queensland	PROPN
aiti-9227	197	20	state	state	PROPN
aiti-9227	197	21	ho	ho	PROPN
aiti-9227	197	22	chi	chi	PROPN
aiti-9227	197	23	minh	minh	PROPN
aiti-9227	197	24	city	city	PROPN
aiti-9227	197	25	queensland	queensland	PROPN
aiti-9227	197	26	state	state	PROPN
aiti-9227	197	27	ho	ho	PROPN
aiti-9227	197	28	chi	chi	PROPN
aiti-9227	197	29	minh	minh	PROPN
aiti-9227	197	30	city	city	PROPN
aiti-9227	197	31	xtrain	xtrain	PROPN
aiti-9227	197	32	xtest	xtest	PROPN
aiti-9227	197	33	xtrain	xtrain	PROPN
aiti-9227	197	34	xtest	xtest	PROPN
aiti-9227	197	35	xtrain	xtrain	PROPN
aiti-9227	198	1	xtest	xtest	PROPN
aiti-9227	198	2	xtrain	xtrain	PROPN
aiti-9227	198	3	xtest	xtest	PROPN
aiti-9227	198	4	time	time	NOUN
aiti-9227	198	5	(	(	PUNCT
aiti-9227	198	6	day	day	NOUN
aiti-9227	198	7	)	)	PUNCT
aiti-9227	199	1	05/10/14	05/10/14	PROPN
aiti-9227	200	1	05/23/14	05/23/14	PROPN
aiti-9227	200	2	05/24/14	05/24/14	PROPN
aiti-9227	201	1	05/30/14	05/30/14	PROPN
aiti-9227	202	1	25/11/18	25/11/18	PROPN
aiti-9227	203	1	22/12/18	22/12/18	X
aiti-9227	204	1	12/23/18	12/23/18	NUM
aiti-9227	204	2	12/29/18	12/29/18	X
aiti-9227	204	3	03/29/14	03/29/14	X
aiti-9227	205	1	05/23/14	05/23/14	PROPN
aiti-9227	205	2	05/24/14	05/24/14	PROPN
aiti-9227	206	1	05/30/14	05/30/14	PROPN
aiti-9227	206	2	10/28/14	10/28/14	PROPN
aiti-9227	206	3	12/22/14	12/22/14	NUM
aiti-9227	206	4	12/23/18	12/23/18	NUM
aiti-9227	206	5	12/29/18	12/29/18	PROPN
aiti-9227	206	6	size	size	NOUN
aiti-9227	206	7	(	(	PUNCT
aiti-9227	206	8	672,48	672,48	NUM
aiti-9227	206	9	)	)	PUNCT
aiti-9227	206	10	(	(	PUNCT
aiti-9227	206	11	336	336	NUM
aiti-9227	206	12	,	,	PUNCT
aiti-9227	206	13	48	48	NUM
aiti-9227	206	14	)	)	PUNCT
aiti-9227	206	15	(	(	PUNCT
aiti-9227	206	16	672	672	NUM
aiti-9227	206	17	,	,	PUNCT
aiti-9227	206	18	24	24	NUM
aiti-9227	206	19	)	)	PUNCT
aiti-9227	206	20	(	(	PUNCT
aiti-9227	206	21	168	168	NUM
aiti-9227	206	22	,	,	PUNCT
aiti-9227	206	23	24	24	NUM
aiti-9227	206	24	)	)	PUNCT
aiti-9227	206	25	(	(	PUNCT
aiti-9227	206	26	2688	2688	NUM
aiti-9227	206	27	,	,	PUNCT
aiti-9227	206	28	48	48	NUM
aiti-9227	206	29	)	)	PUNCT
aiti-9227	206	30	(	(	PUNCT
aiti-9227	206	31	336	336	NUM
aiti-9227	206	32	,	,	PUNCT
aiti-9227	206	33	48	48	NUM
aiti-9227	206	34	)	)	PUNCT
aiti-9227	206	35	(	(	PUNCT
aiti-9227	206	36	1344	1344	NUM
aiti-9227	206	37	,	,	PUNCT
aiti-9227	206	38	24	24	NUM
aiti-9227	206	39	)	)	PUNCT
aiti-9227	206	40	(	(	PUNCT
aiti-9227	206	41	168	168	NUM
aiti-9227	206	42	,	,	PUNCT
aiti-9227	206	43	24	24	NUM
aiti-9227	206	44	)	)	PUNCT
aiti-9227	206	45	min	min	NOUN
aiti-9227	206	46	(	(	PUNCT
aiti-9227	206	47	mw	mw	PROPN
aiti-9227	206	48	)	)	PUNCT
aiti-9227	206	49	4,304.46	4,304.46	NUM
aiti-9227	207	1	4,404.48	4,404.48	NUM
aiti-9227	208	1	1,347.70	1,347.70	NUM
aiti-9227	208	2	1,873.90	1,873.90	NUM
aiti-9227	208	3	4,279.21	4,279.21	NUM
aiti-9227	209	1	4,404.48	4,404.48	NUM
aiti-9227	209	2	1,347.70	1,347.70	NUM
aiti-9227	209	3	1,873.90	1,873.90	NUM
aiti-9227	209	4	mean	mean	NOUN
aiti-9227	209	5	(	(	PUNCT
aiti-9227	209	6	mw	mw	PROPN
aiti-9227	209	7	)	)	PUNCT
aiti-9227	209	8	5,535.20	5,535.20	NUM
aiti-9227	209	9	5,591.45	5,591.45	NUM
aiti-9227	209	10	2,917.94	2,917.94	NUM
aiti-9227	209	11	2,844.65	2,844.65	NUM
aiti-9227	209	12	5,589.60	5,589.60	NUM
aiti-9227	209	13	5,591.45	5,591.45	NUM
aiti-9227	209	14	2,951.42	2,951.42	NUM
aiti-9227	209	15	2,844.65	2,844.65	NUM
aiti-9227	209	16	max	max	PROPN
aiti-9227	209	17	(	(	PUNCT
aiti-9227	209	18	mw	mw	PROPN
aiti-9227	209	19	)	)	PUNCT
aiti-9227	209	20	6,917.66	6,917.66	NUM
aiti-9227	209	21	6,824.76	6,824.76	NUM
aiti-9227	209	22	3,945.90	3,945.90	NUM
aiti-9227	209	23	3,695.20	3,695.20	NUM
aiti-9227	209	24	6,984.78	6,984.78	NUM
aiti-9227	209	25	6,824.76	6,824.76	NUM
aiti-9227	209	26	3,945.9	3,945.9	NUM
aiti-9227	209	27	3,695.20	3,695.20	NUM
aiti-9227	209	28	std	std	NOUN
aiti-9227	209	29	(	(	PUNCT
aiti-9227	209	30	mw	mw	PROPN
aiti-9227	209	31	)	)	PUNCT
aiti-9227	209	32	6,38.78	6,38.78	NOUN
aiti-9227	209	33	6,54.69	6,54.69	NUM
aiti-9227	209	34	6,02.94	6,02.94	NUM
aiti-9227	209	35	553.73	553.73	NUM
aiti-9227	209	36	679.70	679.70	NUM
aiti-9227	209	37	654.69	654.69	NUM
aiti-9227	209	38	589.33	589.33	NUM
aiti-9227	209	39	553.73	553.73	NUM
aiti-9227	209	40	3.2	3.2	NUM
aiti-9227	209	41	.	.	PUNCT
aiti-9227	210	1	simulation	simulation	NOUN
aiti-9227	210	2	value	value	NOUN
aiti-9227	210	3	setup	setup	VERB
aiti-9227	210	4	the	the	DET
aiti-9227	210	5	values	value	NOUN
aiti-9227	210	6	of	of	ADP
aiti-9227	210	7	the	the	DET
aiti-9227	210	8	optimal	optimal	ADJ
aiti-9227	210	9	hyperparameters	hyperparameter	NOUN
aiti-9227	210	10	for	for	ADP
aiti-9227	210	11	the	the	DET
aiti-9227	210	12	dl	dl	PROPN
aiti-9227	210	13	model	model	NOUN
aiti-9227	210	14	are	be	AUX
aiti-9227	210	15	listed	list	VERB
aiti-9227	210	16	in	in	ADP
aiti-9227	210	17	table	table	NOUN
aiti-9227	210	18	3	3	NUM
aiti-9227	210	19	.	.	PUNCT
aiti-9227	211	1	for	for	ADP
aiti-9227	211	2	the	the	DET
aiti-9227	211	3	lstm	lstm	PROPN
aiti-9227	211	4	model	model	NOUN
aiti-9227	211	5	,	,	PUNCT
aiti-9227	211	6	the	the	DET
aiti-9227	211	7	total	total	ADJ
aiti-9227	211	8	number	number	NOUN
aiti-9227	211	9	of	of	ADP
aiti-9227	211	10	the	the	DET
aiti-9227	211	11	hyperparameter	hyperparameter	NOUN
aiti-9227	211	12	combinations	combination	NOUN
aiti-9227	211	13	,	,	PUNCT
aiti-9227	211	14	represented	represent	VERB
aiti-9227	211	15	by	by	ADP
aiti-9227	211	16	cfgi	cfgi	NOUN
aiti-9227	211	17	=	=	SYM
aiti-9227	211	18	{	{	PUNCT
aiti-9227	211	19	ei	ei	PROPN
aiti-9227	211	20	,	,	PUNCT
aiti-9227	211	21	bi	bi	NOUN
aiti-9227	211	22	,	,	PUNCT
aiti-9227	211	23	oi	oi	INTJ
aiti-9227	211	24	,	,	PUNCT
aiti-9227	211	25	di	di	NOUN
aiti-9227	211	26	}	}	PUNCT
aiti-9227	211	27	,	,	PUNCT
aiti-9227	211	28	is	be	AUX
aiti-9227	211	29	81	81	NUM
aiti-9227	211	30	.	.	PUNCT
aiti-9227	212	1	the	the	DET
aiti-9227	212	2	set	set	VERB
aiti-9227	212	3	value	value	NOUN
aiti-9227	212	4	for	for	ADP
aiti-9227	212	5	the	the	DET
aiti-9227	212	6	cv	cv	PROPN
aiti-9227	212	7	cycle	cycle	NOUN
aiti-9227	212	8	is	be	AUX
aiti-9227	212	9	2	2	NUM
aiti-9227	212	10	(	(	PUNCT
aiti-9227	212	11	i.e.	i.e.	X
aiti-9227	212	12	,	,	PUNCT
aiti-9227	212	13	the	the	DET
aiti-9227	212	14	training	training	NOUN
aiti-9227	212	15	dataset	dataset	NOUN
aiti-9227	212	16	is	be	AUX
aiti-9227	212	17	divided	divide	VERB
aiti-9227	212	18	into	into	ADP
aiti-9227	212	19	two	two	NUM
aiti-9227	212	20	subsets	subset	NOUN
aiti-9227	212	21	corresponding	correspond	VERB
aiti-9227	212	22	to	to	ADP
aiti-9227	212	23	two	two	NUM
aiti-9227	212	24	times	time	NOUN
aiti-9227	212	25	of	of	ADP
aiti-9227	212	26	training	training	NOUN
aiti-9227	212	27	and	and	CCONJ
aiti-9227	212	28	testing	testing	NOUN
aiti-9227	212	29	)	)	PUNCT
aiti-9227	212	30	.	.	PUNCT
aiti-9227	213	1	for	for	ADP
aiti-9227	213	2	the	the	DET
aiti-9227	213	3	cnn	cnn	PROPN
aiti-9227	213	4	model	model	NOUN
aiti-9227	213	5	,	,	PUNCT
aiti-9227	213	6	the	the	DET
aiti-9227	213	7	total	total	ADJ
aiti-9227	213	8	number	number	NOUN
aiti-9227	213	9	of	of	ADP
aiti-9227	213	10	the	the	DET
aiti-9227	213	11	hyperparameter	hyperparameter	NOUN
aiti-9227	213	12	combinations	combination	NOUN
aiti-9227	213	13	,	,	PUNCT
aiti-9227	213	14	written	write	VERB
aiti-9227	213	15	as	as	ADP
aiti-9227	213	16	cfgi	cfgi	NOUN
aiti-9227	213	17	=	=	SYM
aiti-9227	213	18	{	{	PUNCT
aiti-9227	213	19	ei	ei	PROPN
aiti-9227	213	20	,	,	PUNCT
aiti-9227	213	21	bi	bi	NOUN
aiti-9227	213	22	,	,	PUNCT
aiti-9227	213	23	oi	oi	INTJ
aiti-9227	213	24	,	,	PUNCT
aiti-9227	213	25	fi	fi	NOUN
aiti-9227	213	26	,	,	PUNCT
aiti-9227	213	27	ki	ki	PROPN
aiti-9227	213	28	}	}	PUNCT
aiti-9227	213	29	,	,	PUNCT
aiti-9227	213	30	is	be	AUX
aiti-9227	213	31	243	243	NUM
aiti-9227	213	32	.	.	PUNCT
aiti-9227	214	1	the	the	DET
aiti-9227	214	2	set	set	VERB
aiti-9227	214	3	value	value	NOUN
aiti-9227	214	4	for	for	ADP
aiti-9227	214	5	the	the	DET
aiti-9227	214	6	cv	cv	PROPN
aiti-9227	214	7	cycle	cycle	NOUN
aiti-9227	214	8	is	be	AUX
aiti-9227	214	9	equal	equal	ADJ
aiti-9227	214	10	to	to	ADP
aiti-9227	214	11	3	3	NUM
aiti-9227	214	12	(	(	PUNCT
aiti-9227	214	13	i.e.	i.e.	X
aiti-9227	214	14	,	,	PUNCT
aiti-9227	214	15	the	the	DET
aiti-9227	214	16	training	training	NOUN
aiti-9227	214	17	dataset	dataset	NOUN
aiti-9227	214	18	is	be	AUX
aiti-9227	214	19	divided	divide	VERB
aiti-9227	214	20	into	into	ADP
aiti-9227	214	21	three	three	NUM
aiti-9227	214	22	subsets	subset	NOUN
aiti-9227	214	23	corresponding	correspond	VERB
aiti-9227	214	24	to	to	ADP
aiti-9227	214	25	three	three	NUM
aiti-9227	214	26	times	time	NOUN
aiti-9227	214	27	of	of	ADP
aiti-9227	214	28	training	training	NOUN
aiti-9227	214	29	and	and	CCONJ
aiti-9227	214	30	testing	testing	NOUN
aiti-9227	214	31	)	)	PUNCT
aiti-9227	214	32	.	.	PUNCT
aiti-9227	215	1	the	the	DET
aiti-9227	215	2	set	set	VERB
aiti-9227	215	3	value	value	NOUN
aiti-9227	215	4	for	for	ADP
aiti-9227	215	5	the	the	DET
aiti-9227	215	6	number	number	NOUN
aiti-9227	215	7	of	of	ADP
aiti-9227	215	8	repetitions	repetition	NOUN
aiti-9227	215	9	is	be	AUX
aiti-9227	215	10	two	two	NUM
aiti-9227	215	11	times	time	NOUN
aiti-9227	215	12	for	for	ADP
aiti-9227	215	13	both	both	CCONJ
aiti-9227	215	14	lstm	lstm	NOUN
aiti-9227	215	15	and	and	CCONJ
aiti-9227	215	16	cnn	cnn	PROPN
aiti-9227	215	17	(	(	PUNCT
aiti-9227	215	18	i.e.	i.e.	X
aiti-9227	215	19	,	,	PUNCT
aiti-9227	215	20	each	each	DET
aiti-9227	215	21	model	model	NOUN
aiti-9227	215	22	is	be	AUX
aiti-9227	215	23	trained	train	VERB
aiti-9227	215	24	twice	twice	ADV
aiti-9227	215	25	)	)	PUNCT
aiti-9227	215	26	.	.	PUNCT
aiti-9227	216	1	the	the	DET
aiti-9227	216	2	error	error	NOUN
aiti-9227	216	3	measurement	measurement	NOUN
aiti-9227	216	4	of	of	ADP
aiti-9227	216	5	the	the	DET
aiti-9227	216	6	gs	gs	PROPN
aiti-9227	216	7	algorithm	algorithm	NOUN
aiti-9227	216	8	used	use	VERB
aiti-9227	216	9	in	in	ADP
aiti-9227	216	10	the	the	DET
aiti-9227	216	11	training	training	NOUN
aiti-9227	216	12	process	process	NOUN
aiti-9227	216	13	is	be	AUX
aiti-9227	216	14	mae	mae	PROPN
aiti-9227	216	15	.	.	PROPN
aiti-9227	216	16	table	table	NOUN
aiti-9227	216	17	3	3	NUM
aiti-9227	216	18	the	the	DET
aiti-9227	216	19	values	value	NOUN
aiti-9227	216	20	of	of	ADP
aiti-9227	216	21	the	the	DET
aiti-9227	216	22	optimal	optimal	ADJ
aiti-9227	216	23	hyperparameters	hyperparameter	NOUN
aiti-9227	216	24	for	for	ADP
aiti-9227	216	25	the	the	DET
aiti-9227	216	26	dl	dl	PROPN
aiti-9227	216	27	model	model	PROPN
aiti-9227	216	28	hyperparameter	hyperparameter	PROPN
aiti-9227	216	29	lstm	lstm	PROPN
aiti-9227	216	30	model	model	PROPN
aiti-9227	216	31	cnn	cnn	PROPN
aiti-9227	216	32	model	model	PROPN
aiti-9227	216	33	epoch	epoch	PROPN
aiti-9227	216	34	(	(	PUNCT
aiti-9227	216	35	e	e	NOUN
aiti-9227	216	36	)	)	PUNCT
aiti-9227	216	37	100	100	NUM
aiti-9227	216	38	,	,	PUNCT
aiti-9227	216	39	300	300	NUM
aiti-9227	216	40	,	,	PUNCT
aiti-9227	216	41	500	500	NUM
aiti-9227	216	42	300	300	NUM
aiti-9227	216	43	,	,	PUNCT
aiti-9227	216	44	500	500	NUM
aiti-9227	216	45	,	,	PUNCT
aiti-9227	216	46	700	700	NUM
aiti-9227	216	47	batch	batch	NOUN
aiti-9227	216	48	(	(	PUNCT
aiti-9227	216	49	b	b	NOUN
aiti-9227	216	50	)	)	PUNCT
aiti-9227	216	51	10	10	NUM
aiti-9227	216	52	,	,	PUNCT
aiti-9227	216	53	30	30	NUM
aiti-9227	216	54	,	,	PUNCT
aiti-9227	216	55	50	50	NUM
aiti-9227	216	56	30	30	NUM
aiti-9227	216	57	,	,	PUNCT
aiti-9227	216	58	50	50	NUM
aiti-9227	216	59	,	,	PUNCT
aiti-9227	216	60	70	70	NUM
aiti-9227	216	61	optimizer	optimizer	NOUN
aiti-9227	216	62	(	(	PUNCT
aiti-9227	216	63	o	o	NOUN
aiti-9227	216	64	)	)	PUNCT
aiti-9227	216	65	adadelta	adadelta	ADJ
aiti-9227	216	66	,	,	PUNCT
aiti-9227	216	67	adam	adam	PROPN
aiti-9227	216	68	,	,	PUNCT
aiti-9227	216	69	adamax	adamax	PROPN
aiti-9227	216	70	adagrad	adagrad	NOUN
aiti-9227	216	71	,	,	PUNCT
aiti-9227	216	72	adam	adam	PROPN
aiti-9227	216	73	,	,	PUNCT
aiti-9227	216	74	sgd	sgd	PROPN
aiti-9227	216	75	dropout	dropout	NOUN
aiti-9227	216	76	rate	rate	NOUN
aiti-9227	216	77	(	(	PUNCT
aiti-9227	216	78	d	d	NOUN
aiti-9227	216	79	)	)	PUNCT
aiti-9227	216	80	0.1	0.1	NUM
aiti-9227	216	81	,	,	PUNCT
aiti-9227	216	82	0.3	0.3	NUM
aiti-9227	216	83	,	,	PUNCT
aiti-9227	216	84	0.5	0.5	NUM
aiti-9227	216	85	filter	filter	NOUN
aiti-9227	216	86	(	(	PUNCT
aiti-9227	216	87	f	f	NOUN
aiti-9227	216	88	)	)	PUNCT
aiti-9227	216	89	48	48	NUM
aiti-9227	216	90	,	,	PUNCT
aiti-9227	216	91	80	80	NUM
aiti-9227	216	92	,	,	PUNCT
aiti-9227	216	93	112	112	NUM
aiti-9227	216	94	kernel	kernel	NOUN
aiti-9227	216	95	(	(	PUNCT
aiti-9227	216	96	k	k	NOUN
aiti-9227	216	97	)	)	PUNCT
aiti-9227	216	98	3	3	NUM
aiti-9227	216	99	,	,	PUNCT
aiti-9227	216	100	5	5	NUM
aiti-9227	216	101	,	,	PUNCT
aiti-9227	216	102	7	7	NUM
aiti-9227	216	103	number	number	NOUN
aiti-9227	216	104	of	of	ADP
aiti-9227	216	105	combinations	combination	NOUN
aiti-9227	216	106	(	(	PUNCT
aiti-9227	216	107	cfg	cfg	PROPN
aiti-9227	216	108	)	)	PUNCT
aiti-9227	216	109	81	81	NUM
aiti-9227	216	110	243	243	NUM
aiti-9227	216	111	4	4	NUM
aiti-9227	216	112	.	.	PUNCT
aiti-9227	216	113	experimental	experimental	ADJ
aiti-9227	216	114	results	result	NOUN
aiti-9227	216	115	and	and	CCONJ
aiti-9227	216	116	analyses	analyse	VERB
aiti-9227	216	117	tables	table	NOUN
aiti-9227	216	118	4	4	NUM
aiti-9227	216	119	and	and	CCONJ
aiti-9227	216	120	5	5	NUM
aiti-9227	216	121	show	show	VERB
aiti-9227	216	122	the	the	DET
aiti-9227	216	123	experimental	experimental	ADJ
aiti-9227	216	124	results	result	NOUN
aiti-9227	216	125	produced	produce	VERB
aiti-9227	216	126	while	while	SCONJ
aiti-9227	216	127	using	use	VERB
aiti-9227	216	128	lstm	lstm	NOUN
aiti-9227	216	129	and	and	CCONJ
aiti-9227	216	130	cnn	cnn	PROPN
aiti-9227	216	131	based	base	VERB
aiti-9227	216	132	on	on	ADP
aiti-9227	216	133	the	the	DET
aiti-9227	216	134	gs	gs	PROPN
aiti-9227	216	135	algorithm	algorithm	NOUN
aiti-9227	216	136	during	during	ADP
aiti-9227	216	137	training	training	NOUN
aiti-9227	216	138	for	for	ADP
aiti-9227	216	139	the	the	DET
aiti-9227	216	140	data	datum	NOUN
aiti-9227	216	141	normalization	normalization	NOUN
aiti-9227	216	142	scenarios	scenario	NOUN
aiti-9227	216	143	,	,	PUNCT
aiti-9227	216	144	respectively	respectively	ADV
aiti-9227	216	145	.	.	PUNCT
aiti-9227	217	1	these	these	DET
aiti-9227	217	2	tables	table	NOUN
aiti-9227	217	3	illustrate	illustrate	VERB
aiti-9227	217	4	that	that	SCONJ
aiti-9227	217	5	the	the	DET
aiti-9227	217	6	dl	dl	PROPN
aiti-9227	217	7	model	model	PROPN
aiti-9227	217	8	’s	’s	PART
aiti-9227	217	9	ideal	ideal	ADJ
aiti-9227	217	10	hyperparameters	hyperparameter	NOUN
aiti-9227	217	11	have	have	VERB
aiti-9227	217	12	distinct	distinct	ADJ
aiti-9227	217	13	values	value	NOUN
aiti-9227	217	14	for	for	ADP
aiti-9227	217	15	each	each	DET
aiti-9227	217	16	data	datum	NOUN
aiti-9227	217	17	normalization	normalization	NOUN
aiti-9227	217	18	approach	approach	NOUN
aiti-9227	217	19	and	and	CCONJ
aiti-9227	217	20	for	for	ADP
aiti-9227	217	21	various	various	ADJ
aiti-9227	217	22	queensland	queensland	NOUN
aiti-9227	217	23	and	and	CCONJ
aiti-9227	217	24	ho	ho	PROPN
aiti-9227	217	25	chi	chi	PROPN
aiti-9227	217	26	minh	minh	PROPN
aiti-9227	217	27	city	city	NOUN
aiti-9227	217	28	datasets	dataset	NOUN
aiti-9227	217	29	.	.	PUNCT
aiti-9227	218	1	in	in	ADP
aiti-9227	218	2	addition	addition	NOUN
aiti-9227	218	3	,	,	PUNCT
aiti-9227	218	4	it	it	PRON
aiti-9227	218	5	shows	show	VERB
aiti-9227	218	6	the	the	DET
aiti-9227	218	7	same	same	ADJ
aiti-9227	218	8	values	value	NOUN
aiti-9227	218	9	of	of	ADP
aiti-9227	218	10	the	the	DET
aiti-9227	218	11	optimal	optimal	ADJ
aiti-9227	218	12	hyperparameter	hyperparameter	NOUN
aiti-9227	218	13	set	set	VERB
aiti-9227	218	14	in	in	ADP
aiti-9227	218	15	some	some	DET
aiti-9227	218	16	cases	case	NOUN
aiti-9227	218	17	.	.	PUNCT
aiti-9227	219	1	for	for	ADP
aiti-9227	219	2	example	example	NOUN
aiti-9227	219	3	,	,	PUNCT
aiti-9227	219	4	in	in	ADP
aiti-9227	219	5	the	the	DET
aiti-9227	219	6	instance	instance	NOUN
aiti-9227	219	7	of	of	ADP
aiti-9227	219	8	queensland	queensland	PROPN
aiti-9227	219	9	state	state	NOUN
aiti-9227	219	10	data	datum	NOUN
aiti-9227	219	11	where	where	SCONJ
aiti-9227	219	12	the	the	DET
aiti-9227	219	13	lstmn	lstmn	NOUN
aiti-9227	219	14	is	be	AUX
aiti-9227	219	15	applied	apply	VERB
aiti-9227	219	16	,	,	PUNCT
aiti-9227	219	17	the	the	DET
aiti-9227	219	18	max	max	PROPN
aiti-9227	219	19	and	and	CCONJ
aiti-9227	219	20	median	median	ADJ
aiti-9227	219	21	approaches	approach	NOUN
aiti-9227	219	22	yield	yield	VERB
aiti-9227	219	23	the	the	DET
aiti-9227	219	24	same	same	ADJ
aiti-9227	219	25	values	value	NOUN
aiti-9227	219	26	of	of	ADP
aiti-9227	219	27	the	the	DET
aiti-9227	219	28	ideal	ideal	ADJ
aiti-9227	219	29	hyperparameters	hyperparameter	NOUN
aiti-9227	219	30	as	as	ADP
aiti-9227	219	31	the	the	DET
aiti-9227	219	32	normal	normal	ADJ
aiti-9227	219	33	method	method	NOUN
aiti-9227	219	34	(	(	PUNCT
aiti-9227	219	35	original	original	ADJ
aiti-9227	219	36	data	datum	NOUN
aiti-9227	219	37	)	)	PUNCT
aiti-9227	219	38	.	.	PUNCT
aiti-9227	220	1	table	table	NOUN
aiti-9227	220	2	4	4	NUM
aiti-9227	220	3	the	the	DET
aiti-9227	220	4	obtained	obtain	VERB
aiti-9227	220	5	results	result	NOUN
aiti-9227	220	6	of	of	ADP
aiti-9227	220	7	optimal	optimal	ADJ
aiti-9227	220	8	hyperparameters	hyperparameter	NOUN
aiti-9227	220	9	when	when	SCONJ
aiti-9227	220	10	using	use	VERB
aiti-9227	220	11	lstmn	lstmn	NOUN
aiti-9227	220	12	method	method	PROPN
aiti-9227	220	13	queensland	queensland	PROPN
aiti-9227	220	14	state	state	PROPN
aiti-9227	220	15	ho	ho	PROPN
aiti-9227	220	16	chi	chi	PROPN
aiti-9227	220	17	minh	minh	PROPN
aiti-9227	220	18	city	city	PROPN
aiti-9227	220	19	epoch	epoch	NOUN
aiti-9227	220	20	batch	batch	NOUN
aiti-9227	220	21	dropout	dropout	NOUN
aiti-9227	220	22	optimizer	optimizer	NOUN
aiti-9227	220	23	epoch	epoch	NOUN
aiti-9227	220	24	batch	batch	NOUN
aiti-9227	220	25	dropout	dropout	NOUN
aiti-9227	220	26	optimizer	optimizer	NOUN
aiti-9227	220	27	normal	normal	ADJ
aiti-9227	220	28	500	500	NUM
aiti-9227	220	29	10	10	NUM
aiti-9227	220	30	0.1	0.1	NUM
aiti-9227	220	31	adam	adam	NOUN
aiti-9227	220	32	500	500	NUM
aiti-9227	220	33	10	10	NUM
aiti-9227	220	34	0.3	0.3	NUM
aiti-9227	220	35	adam	adam	PROPN
aiti-9227	220	36	zero	zero	NUM
aiti-9227	220	37	-	-	PUNCT
aiti-9227	220	38	mean	mean	NOUN
aiti-9227	220	39	500	500	NUM
aiti-9227	220	40	10	10	NUM
aiti-9227	220	41	0.1	0.1	NUM
aiti-9227	220	42	adamax	adamax	NOUN
aiti-9227	220	43	500	500	NUM
aiti-9227	220	44	30	30	NUM
aiti-9227	220	45	0.1	0.1	NUM
aiti-9227	220	46	adam	adam	PROPN
aiti-9227	220	47	min	min	PROPN
aiti-9227	220	48	-	-	PROPN
aiti-9227	220	49	max	max	PROPN
aiti-9227	220	50	500	500	NUM
aiti-9227	220	51	10	10	NUM
aiti-9227	220	52	0.1	0.1	NUM
aiti-9227	220	53	adamax	adamax	NOUN
aiti-9227	220	54	500	500	NUM
aiti-9227	220	55	10	10	NUM
aiti-9227	220	56	0.1	0.1	NUM
aiti-9227	220	57	adam	adam	PROPN
aiti-9227	220	58	max	max	PROPN
aiti-9227	220	59	500	500	NUM
aiti-9227	220	60	10	10	NUM
aiti-9227	220	61	0.1	0.1	NUM
aiti-9227	220	62	adam	adam	NOUN
aiti-9227	220	63	300	300	NUM
aiti-9227	220	64	10	10	NUM
aiti-9227	220	65	0.1	0.1	NUM
aiti-9227	220	66	adam	adam	PROPN
aiti-9227	220	67	decimal	decimal	ADJ
aiti-9227	220	68	500	500	NUM
aiti-9227	220	69	10	10	NUM
aiti-9227	220	70	0.3	0.3	NUM
aiti-9227	220	71	adam	adam	PROPN
aiti-9227	220	72	500	500	NUM
aiti-9227	220	73	10	10	NUM
aiti-9227	220	74	0.3	0.3	NUM
aiti-9227	220	75	adam	adam	PROPN
aiti-9227	220	76	sigmoid	sigmoid	NOUN
aiti-9227	220	77	500	500	NUM
aiti-9227	220	78	10	10	NUM
aiti-9227	220	79	0.1	0.1	NUM
aiti-9227	220	80	adamax	adamax	NOUN
aiti-9227	220	81	500	500	NUM
aiti-9227	220	82	10	10	NUM
aiti-9227	220	83	0.1	0.1	NUM
aiti-9227	220	84	adam	adam	PROPN
aiti-9227	220	85	softmax	softmax	PROPN
aiti-9227	220	86	500	500	NUM
aiti-9227	220	87	10	10	NUM
aiti-9227	220	88	0.1	0.1	NUM
aiti-9227	220	89	adamax	adamax	NOUN
aiti-9227	220	90	500	500	NUM
aiti-9227	220	91	10	10	NUM
aiti-9227	220	92	0.1	0.1	NUM
aiti-9227	220	93	adam	adam	PROPN
aiti-9227	220	94	median	median	PROPN
aiti-9227	220	95	500	500	NUM
aiti-9227	220	96	10	10	NUM
aiti-9227	220	97	0.1	0.1	NUM
aiti-9227	220	98	adam	adam	NOUN
aiti-9227	220	99	300	300	NUM
aiti-9227	220	100	10	10	NUM
aiti-9227	220	101	0.1	0.1	NUM
aiti-9227	220	102	adam	adam	PROPN
aiti-9227	220	103	robust	robust	ADJ
aiti-9227	220	104	500	500	NUM
aiti-9227	220	105	50	50	NUM
aiti-9227	220	106	0.1	0.1	NUM
aiti-9227	220	107	adam	adam	NOUN
aiti-9227	220	108	500	500	NUM
aiti-9227	220	109	10	10	NUM
aiti-9227	220	110	0.1	0.1	NUM
aiti-9227	220	111	adam	adam	PROPN
aiti-9227	220	112	265	265	NUM
aiti-9227	220	113	advances	advance	NOUN
aiti-9227	220	114	in	in	ADP
aiti-9227	220	115	technology	technology	NOUN
aiti-9227	220	116	innovation	innovation	NOUN
aiti-9227	220	117	,	,	PUNCT
aiti-9227	220	118	vol	vol	NOUN
aiti-9227	220	119	.	.	PROPN
aiti-9227	221	1	7	7	NUM
aiti-9227	221	2	,	,	PUNCT
aiti-9227	221	3	no	no	INTJ
aiti-9227	221	4	.	.	NOUN
aiti-9227	221	5	4	4	NUM
aiti-9227	221	6	,	,	PUNCT
aiti-9227	221	7	2022	2022	NUM
aiti-9227	221	8	,	,	PUNCT
aiti-9227	221	9	pp	pp	ADJ
aiti-9227	221	10	.	.	PUNCT
aiti-9227	222	1	258	258	NUM
aiti-9227	222	2	-	-	SYM
aiti-9227	222	3	269	269	NUM
aiti-9227	222	4	table	table	NOUN
aiti-9227	222	5	5	5	NUM
aiti-9227	222	6	the	the	DET
aiti-9227	222	7	obtained	obtain	VERB
aiti-9227	222	8	results	result	NOUN
aiti-9227	222	9	of	of	ADP
aiti-9227	222	10	optimal	optimal	ADJ
aiti-9227	222	11	hyperparameters	hyperparameter	NOUN
aiti-9227	222	12	when	when	SCONJ
aiti-9227	222	13	using	use	VERB
aiti-9227	222	14	cnn	cnn	PROPN
aiti-9227	222	15	method	method	PROPN
aiti-9227	222	16	queensland	queensland	PROPN
aiti-9227	222	17	state	state	PROPN
aiti-9227	222	18	ho	ho	PROPN
aiti-9227	222	19	chi	chi	PROPN
aiti-9227	222	20	minh	minh	PROPN
aiti-9227	222	21	city	city	PROPN
aiti-9227	222	22	epoch	epoch	NOUN
aiti-9227	222	23	batch	batch	NOUN
aiti-9227	222	24	optimizer	optimizer	NOUN
aiti-9227	222	25	filter	filter	NOUN
aiti-9227	222	26	kernel	kernel	PROPN
aiti-9227	222	27	epoch	epoch	PROPN
aiti-9227	222	28	batch	batch	NOUN
aiti-9227	222	29	optimizer	optimizer	NOUN
aiti-9227	222	30	filter	filter	NOUN
aiti-9227	222	31	kernel	kernel	NOUN
aiti-9227	222	32	normal	normal	ADJ
aiti-9227	222	33	700	700	NUM
aiti-9227	222	34	50	50	NUM
aiti-9227	222	35	adam	adam	NOUN
aiti-9227	222	36	112	112	NUM
aiti-9227	222	37	7	7	NUM
aiti-9227	222	38	700	700	NUM
aiti-9227	222	39	30	30	NUM
aiti-9227	222	40	adam	adam	NOUN
aiti-9227	222	41	80	80	NUM
aiti-9227	222	42	5	5	NUM
aiti-9227	222	43	zero	zero	NUM
aiti-9227	222	44	-	-	PUNCT
aiti-9227	222	45	mean	mean	NOUN
aiti-9227	222	46	700	700	NUM
aiti-9227	222	47	70	70	NUM
aiti-9227	222	48	adam	adam	NOUN
aiti-9227	222	49	112	112	NUM
aiti-9227	222	50	3	3	NUM
aiti-9227	222	51	500	500	NUM
aiti-9227	222	52	50	50	NUM
aiti-9227	222	53	adam	adam	NOUN
aiti-9227	222	54	112	112	NUM
aiti-9227	222	55	7	7	NUM
aiti-9227	222	56	min	min	NOUN
aiti-9227	222	57	-	-	PUNCT
aiti-9227	222	58	max	max	PROPN
aiti-9227	222	59	500	500	NUM
aiti-9227	222	60	50	50	NUM
aiti-9227	222	61	adam	adam	NOUN
aiti-9227	222	62	112	112	NUM
aiti-9227	222	63	3	3	NUM
aiti-9227	222	64	700	700	NUM
aiti-9227	222	65	50	50	NUM
aiti-9227	222	66	adam	adam	NOUN
aiti-9227	222	67	112	112	NUM
aiti-9227	222	68	7	7	NUM
aiti-9227	222	69	max	max	PROPN
aiti-9227	222	70	700	700	NUM
aiti-9227	222	71	30	30	NUM
aiti-9227	222	72	adam	adam	NOUN
aiti-9227	222	73	112	112	NUM
aiti-9227	222	74	3	3	NUM
aiti-9227	222	75	700	700	NUM
aiti-9227	222	76	30	30	NUM
aiti-9227	222	77	adam	adam	NOUN
aiti-9227	222	78	112	112	NUM
aiti-9227	222	79	5	5	NUM
aiti-9227	222	80	decimal	decimal	ADJ
aiti-9227	222	81	700	700	NUM
aiti-9227	222	82	50	50	NUM
aiti-9227	222	83	adam	adam	NOUN
aiti-9227	222	84	112	112	NUM
aiti-9227	222	85	7	7	NUM
aiti-9227	222	86	700	700	NUM
aiti-9227	222	87	70	70	NUM
aiti-9227	222	88	adam	adam	PROPN
aiti-9227	222	89	112	112	NUM
aiti-9227	222	90	7	7	NUM
aiti-9227	222	91	sigmoid	sigmoid	NOUN
aiti-9227	222	92	700	700	NUM
aiti-9227	222	93	50	50	NUM
aiti-9227	222	94	adam	adam	NOUN
aiti-9227	222	95	80	80	NUM
aiti-9227	222	96	7	7	NUM
aiti-9227	222	97	700	700	NUM
aiti-9227	222	98	30	30	NUM
aiti-9227	222	99	adam	adam	NOUN
aiti-9227	222	100	80	80	NUM
aiti-9227	222	101	7	7	NUM
aiti-9227	222	102	softmax	softmax	NOUN
aiti-9227	222	103	300	300	NUM
aiti-9227	222	104	70	70	NUM
aiti-9227	222	105	adam	adam	NOUN
aiti-9227	222	106	80	80	NUM
aiti-9227	222	107	7	7	NUM
aiti-9227	222	108	700	700	NUM
aiti-9227	222	109	50	50	NUM
aiti-9227	222	110	adam	adam	NOUN
aiti-9227	222	111	80	80	NUM
aiti-9227	222	112	7	7	NUM
aiti-9227	222	113	median	median	NOUN
aiti-9227	222	114	700	700	NUM
aiti-9227	222	115	50	50	NUM
aiti-9227	222	116	adam	adam	NOUN
aiti-9227	222	117	80	80	NUM
aiti-9227	222	118	5	5	NUM
aiti-9227	222	119	700	700	NUM
aiti-9227	222	120	30	30	NUM
aiti-9227	222	121	adam	adam	NOUN
aiti-9227	222	122	80	80	NUM
aiti-9227	222	123	7	7	NUM
aiti-9227	222	124	robust	robust	ADJ
aiti-9227	222	125	500	500	NUM
aiti-9227	222	126	70	70	NUM
aiti-9227	222	127	adam	adam	NOUN
aiti-9227	222	128	112	112	NUM
aiti-9227	222	129	7	7	NUM
aiti-9227	222	130	700	700	NUM
aiti-9227	222	131	50	50	NUM
aiti-9227	222	132	adam	adam	NOUN
aiti-9227	222	133	80	80	NUM
aiti-9227	222	134	5	5	NUM
aiti-9227	222	135	table	table	NOUN
aiti-9227	222	136	6	6	NUM
aiti-9227	222	137	the	the	DET
aiti-9227	222	138	mae	mae	PROPN
aiti-9227	222	139	when	when	SCONJ
aiti-9227	222	140	using	use	VERB
aiti-9227	222	141	the	the	DET
aiti-9227	222	142	lstmn	lstmn	NOUN
aiti-9227	222	143	method	method	NOUN
aiti-9227	222	144	mae	mae	PROPN
aiti-9227	222	145	(	(	PUNCT
aiti-9227	222	146	mw	mw	PROPN
aiti-9227	222	147	)	)	PUNCT
aiti-9227	222	148	mape	mape	NOUN
aiti-9227	222	149	(	(	PUNCT
aiti-9227	222	150	%	%	NOUN
aiti-9227	222	151	)	)	PUNCT
aiti-9227	222	152	training	training	NOUN
aiti-9227	222	153	test	test	NOUN
aiti-9227	222	154	training	training	NOUN
aiti-9227	222	155	queensland	queensland	PROPN
aiti-9227	222	156	ho	ho	PROPN
aiti-9227	222	157	chi	chi	PROPN
aiti-9227	222	158	minh	minh	PROPN
aiti-9227	222	159	city	city	PROPN
aiti-9227	222	160	queensland	queensland	PROPN
aiti-9227	222	161	ho	ho	PROPN
aiti-9227	222	162	chi	chi	PROPN
aiti-9227	222	163	minh	minh	PROPN
aiti-9227	222	164	city	city	PROPN
aiti-9227	222	165	queensland	queensland	PROPN
aiti-9227	222	166	ho	ho	PROPN
aiti-9227	222	167	chi	chi	PROPN
aiti-9227	222	168	minh	minh	PROPN
aiti-9227	222	169	city	city	NOUN
aiti-9227	222	170	normal	normal	ADJ
aiti-9227	222	171	546.17	546.17	NUM
aiti-9227	222	172	534.95	534.95	NUM
aiti-9227	222	173	567.59	567.59	NUM
aiti-9227	222	174	504.53	504.53	NUM
aiti-9227	222	175	10.68	10.68	NUM
aiti-9227	222	176	20.07	20.07	NUM
aiti-9227	222	177	standard	standard	NOUN
aiti-9227	222	178	33.31	33.31	NUM
aiti-9227	222	179	26.68	26.68	NUM
aiti-9227	222	180	39.94	39.94	NUM
aiti-9227	222	181	48.58	48.58	NUM
aiti-9227	222	182	0.73	0.73	NUM
aiti-9227	222	183	1.73	1.73	NUM
aiti-9227	222	184	min	min	NOUN
aiti-9227	222	185	-	-	PROPN
aiti-9227	222	186	max	max	PROPN
aiti-9227	222	187	40.21	40.21	NUM
aiti-9227	222	188	35.37	35.37	NUM
aiti-9227	222	189	39.14	39.14	NUM
aiti-9227	222	190	46.81	46.81	NUM
aiti-9227	222	191	0.70	0.70	NUM
aiti-9227	222	192	1.76	1.76	NUM
aiti-9227	222	193	max	max	PROPN
aiti-9227	222	194	44.04	44.04	NUM
aiti-9227	222	195	50.82	50.82	NUM
aiti-9227	222	196	44.18	44.18	NUM
aiti-9227	222	197	50.09	50.09	NUM
aiti-9227	222	198	0.81	0.81	NUM
aiti-9227	222	199	1.85	1.85	NUM
aiti-9227	222	200	decimal	decimal	NOUN
aiti-9227	222	201	45.63	45.63	NUM
aiti-9227	222	202	47.32	47.32	NUM
aiti-9227	222	203	43.74	43.74	NUM
aiti-9227	222	204	53.42	53.42	NUM
aiti-9227	222	205	0.80	0.80	NUM
aiti-9227	222	206	2.00	2.00	NUM
aiti-9227	222	207	sigmoid	sigmoid	NOUN
aiti-9227	222	208	40.19	40.19	NUM
aiti-9227	222	209	56.87	56.87	NUM
aiti-9227	222	210	40.96	40.96	NUM
aiti-9227	222	211	68.12	68.12	NUM
aiti-9227	222	212	0.73	0.73	NUM
aiti-9227	222	213	2.43	2.43	NUM
aiti-9227	222	214	softmax	softmax	NOUN
aiti-9227	222	215	34.43	34.43	NUM
aiti-9227	222	216	30.88	30.88	NUM
aiti-9227	222	217	36.64	36.64	NUM
aiti-9227	222	218	43.95	43.95	NUM
aiti-9227	222	219	0.66	0.66	NUM
aiti-9227	222	220	1.61	1.61	NUM
aiti-9227	222	221	median	median	NOUN
aiti-9227	222	222	41.98	41.98	NUM
aiti-9227	222	223	65.02	65.02	NUM
aiti-9227	222	224	42.44	42.44	NUM
aiti-9227	222	225	60.23	60.23	NUM
aiti-9227	222	226	0.77	0.77	NUM
aiti-9227	222	227	2.24	2.24	NUM
aiti-9227	222	228	robust	robust	ADJ
aiti-9227	222	229	34.16	34.16	NUM
aiti-9227	222	230	28.62	28.62	NUM
aiti-9227	222	231	37.24	37.24	NUM
aiti-9227	222	232	39.31	39.31	NUM
aiti-9227	222	233	0.67	0.67	NUM
aiti-9227	222	234	1.42	1.42	NUM
aiti-9227	222	235	table	table	NOUN
aiti-9227	222	236	7	7	NUM
aiti-9227	222	237	the	the	DET
aiti-9227	222	238	mae	mae	PROPN
aiti-9227	222	239	when	when	SCONJ
aiti-9227	222	240	using	use	VERB
aiti-9227	222	241	cnn	cnn	PROPN
aiti-9227	222	242	method	method	PROPN
aiti-9227	222	243	mae	mae	PROPN
aiti-9227	222	244	(	(	PUNCT
aiti-9227	222	245	mw	mw	PROPN
aiti-9227	222	246	)	)	PUNCT
aiti-9227	222	247	mape	mape	NOUN
aiti-9227	222	248	(	(	PUNCT
aiti-9227	222	249	%	%	NOUN
aiti-9227	222	250	)	)	PUNCT
aiti-9227	222	251	training	training	NOUN
aiti-9227	222	252	test	test	NOUN
aiti-9227	222	253	training	training	NOUN
aiti-9227	222	254	queensland	queensland	PROPN
aiti-9227	222	255	ho	ho	PROPN
aiti-9227	222	256	chi	chi	PROPN
aiti-9227	222	257	minh	minh	PROPN
aiti-9227	222	258	city	city	PROPN
aiti-9227	222	259	queensland	queensland	PROPN
aiti-9227	222	260	ho	ho	PROPN
aiti-9227	222	261	chi	chi	PROPN
aiti-9227	222	262	minh	minh	PROPN
aiti-9227	222	263	city	city	PROPN
aiti-9227	222	264	queensland	queensland	PROPN
aiti-9227	222	265	ho	ho	PROPN
aiti-9227	222	266	chi	chi	PROPN
aiti-9227	222	267	minh	minh	PROPN
aiti-9227	222	268	city	city	NOUN
aiti-9227	222	269	normal	normal	ADJ
aiti-9227	222	270	52.66	52.66	NUM
aiti-9227	222	271	38.95	38.95	NUM
aiti-9227	222	272	52.94	52.94	NUM
aiti-9227	222	273	46.97	46.97	NUM
aiti-9227	222	274	0.94	0.94	NUM
aiti-9227	222	275	1.71	1.71	NUM
aiti-9227	222	276	standard	standard	NOUN
aiti-9227	222	277	32.98	32.98	NUM
aiti-9227	222	278	25.60	25.60	NUM
aiti-9227	222	279	37.03	37.03	NUM
aiti-9227	222	280	38.72	38.72	NUM
aiti-9227	222	281	0.67	0.67	NUM
aiti-9227	222	282	1.41	1.41	NUM
aiti-9227	222	283	min	min	NOUN
aiti-9227	222	284	-	-	PUNCT
aiti-9227	222	285	max	max	PROPN
aiti-9227	222	286	35.98	35.98	NUM
aiti-9227	222	287	26.02	26.02	NUM
aiti-9227	222	288	37.18	37.18	NUM
aiti-9227	222	289	38.65	38.65	NUM
aiti-9227	222	290	0.66	0.66	NUM
aiti-9227	222	291	1.42	1.42	NUM
aiti-9227	222	292	max	max	PROPN
aiti-9227	222	293	44.57	44.57	NUM
aiti-9227	222	294	34.58	34.58	NUM
aiti-9227	222	295	41.64	41.64	NUM
aiti-9227	222	296	38.62	38.62	NUM
aiti-9227	222	297	0.74	0.74	NUM
aiti-9227	222	298	1.42	1.42	NUM
aiti-9227	222	299	decimal	decimal	ADJ
aiti-9227	222	300	40.76	40.76	NUM
aiti-9227	222	301	37.25	37.25	NUM
aiti-9227	222	302	39.38	39.38	NUM
aiti-9227	222	303	43.80	43.80	NUM
aiti-9227	222	304	0.71	0.71	NUM
aiti-9227	222	305	1.61	1.61	NUM
aiti-9227	222	306	sigmoid	sigmoid	NOUN
aiti-9227	222	307	35.79	35.79	NUM
aiti-9227	222	308	30.40	30.40	NUM
aiti-9227	222	309	38.01	38.01	NUM
aiti-9227	222	310	42.89	42.89	NUM
aiti-9227	222	311	0.68	0.68	NUM
aiti-9227	222	312	1.56	1.56	NUM
aiti-9227	222	313	softmax	softmax	NOUN
aiti-9227	222	314	33.17	33.17	NUM
aiti-9227	222	315	24.48	24.48	NUM
aiti-9227	222	316	35.51	35.51	NUM
aiti-9227	222	317	36.19	36.19	NUM
aiti-9227	222	318	0.64	0.64	NUM
aiti-9227	222	319	1.37	1.37	NUM
aiti-9227	222	320	median	median	NOUN
aiti-9227	222	321	40.78	40.78	NUM
aiti-9227	222	322	34.01	34.01	NUM
aiti-9227	222	323	39.48	39.48	NUM
aiti-9227	222	324	39.25	39.25	NUM
aiti-9227	222	325	0.70	0.70	NUM
aiti-9227	222	326	1.45	1.45	NUM
aiti-9227	222	327	robust	robust	ADJ
aiti-9227	222	328	26.21	26.21	NUM
aiti-9227	222	329	23.63	23.63	NUM
aiti-9227	222	330	33.56	33.56	NUM
aiti-9227	222	331	36.03	36.03	NUM
aiti-9227	222	332	0.60	0.60	NUM
aiti-9227	222	333	1.32	1.32	NUM
aiti-9227	222	334	table	table	NOUN
aiti-9227	222	335	6	6	NUM
aiti-9227	222	336	presents	present	NOUN
aiti-9227	222	337	the	the	DET
aiti-9227	222	338	mae	mae	PROPN
aiti-9227	222	339	and	and	CCONJ
aiti-9227	222	340	the	the	DET
aiti-9227	222	341	mape	mape	NOUN
aiti-9227	222	342	error	error	NOUN
aiti-9227	222	343	of	of	ADP
aiti-9227	222	344	the	the	DET
aiti-9227	222	345	training	training	NOUN
aiti-9227	222	346	and	and	CCONJ
aiti-9227	222	347	test	test	NOUN
aiti-9227	222	348	stages	stage	NOUN
aiti-9227	222	349	of	of	ADP
aiti-9227	222	350	the	the	DET
aiti-9227	222	351	lstmn	lstmn	NOUN
aiti-9227	222	352	model	model	NOUN
aiti-9227	222	353	.	.	PUNCT
aiti-9227	223	1	fig	fig	NOUN
aiti-9227	223	2	.	.	PUNCT
aiti-9227	224	1	7	7	NUM
aiti-9227	224	2	shows	show	VERB
aiti-9227	224	3	the	the	DET
aiti-9227	224	4	boxplot	boxplot	NOUN
aiti-9227	224	5	chart	chart	NOUN
aiti-9227	224	6	of	of	ADP
aiti-9227	224	7	these	these	DET
aiti-9227	224	8	maes	maes	PROPN
aiti-9227	224	9	and	and	CCONJ
aiti-9227	224	10	mapes	mapes	PROPN
aiti-9227	224	11	corresponding	correspond	VERB
aiti-9227	224	12	to	to	ADP
aiti-9227	224	13	table	table	NOUN
aiti-9227	224	14	6	6	NUM
aiti-9227	224	15	.	.	PUNCT
aiti-9227	225	1	the	the	DET
aiti-9227	225	2	obtained	obtain	VERB
aiti-9227	225	3	results	result	NOUN
aiti-9227	225	4	show	show	VERB
aiti-9227	225	5	the	the	DET
aiti-9227	225	6	effectiveness	effectiveness	NOUN
aiti-9227	225	7	of	of	ADP
aiti-9227	225	8	the	the	DET
aiti-9227	225	9	data	datum	NOUN
aiti-9227	225	10	normalization	normalization	NOUN
aiti-9227	225	11	method	method	NOUN
aiti-9227	225	12	for	for	ADP
aiti-9227	225	13	the	the	DET
aiti-9227	225	14	gs	gs	PROPN
aiti-9227	225	15	algorithm	algorithm	NOUN
aiti-9227	225	16	in	in	ADP
aiti-9227	225	17	the	the	DET
aiti-9227	225	18	lstm	lstm	PROPN
aiti-9227	225	19	model	model	NOUN
aiti-9227	225	20	.	.	PUNCT
aiti-9227	226	1	specifically	specifically	ADV
aiti-9227	226	2	,	,	PUNCT
aiti-9227	226	3	the	the	DET
aiti-9227	226	4	mae	mae	PROPN
aiti-9227	226	5	is	be	AUX
aiti-9227	226	6	significantly	significantly	ADV
aiti-9227	226	7	reduced	reduce	VERB
aiti-9227	226	8	when	when	SCONJ
aiti-9227	226	9	a	a	DET
aiti-9227	226	10	data	data	NOUN
aiti-9227	226	11	normalization	normalization	NOUN
aiti-9227	226	12	method	method	NOUN
aiti-9227	226	13	is	be	AUX
aiti-9227	226	14	applied	apply	VERB
aiti-9227	226	15	.	.	PUNCT
aiti-9227	227	1	for	for	ADP
aiti-9227	227	2	queensland	queensland	PROPN
aiti-9227	227	3	data	data	PROPN
aiti-9227	227	4	,	,	PUNCT
aiti-9227	227	5	the	the	DET
aiti-9227	227	6	mae	mae	PROPN
aiti-9227	227	7	of	of	ADP
aiti-9227	227	8	the	the	DET
aiti-9227	227	9	test	test	NOUN
aiti-9227	227	10	process	process	NOUN
aiti-9227	227	11	is	be	AUX
aiti-9227	227	12	567.59	567.59	NUM
aiti-9227	227	13	mw	mw	NOUN
aiti-9227	227	14	and	and	CCONJ
aiti-9227	227	15	the	the	DET
aiti-9227	227	16	mape	mape	NOUN
aiti-9227	227	17	is	be	AUX
aiti-9227	227	18	10.68	10.68	NUM
aiti-9227	227	19	%	%	NOUN
aiti-9227	227	20	without	without	ADP
aiti-9227	227	21	using	use	VERB
aiti-9227	227	22	any	any	DET
aiti-9227	227	23	data	data	NOUN
aiti-9227	227	24	normalization	normalization	NOUN
aiti-9227	227	25	technique	technique	NOUN
aiti-9227	227	26	,	,	PUNCT
aiti-9227	227	27	while	while	SCONJ
aiti-9227	227	28	they	they	PRON
aiti-9227	227	29	decrease	decrease	VERB
aiti-9227	227	30	to	to	ADP
aiti-9227	227	31	44.18	44.18	NUM
aiti-9227	227	32	mw	mw	NOUN
aiti-9227	227	33	and	and	CCONJ
aiti-9227	227	34	0.81	0.81	NUM
aiti-9227	227	35	%	%	NOUN
aiti-9227	227	36	at	at	ADP
aiti-9227	227	37	max	max	PROPN
aiti-9227	227	38	,	,	PUNCT
aiti-9227	227	39	respectively	respectively	ADV
aiti-9227	227	40	,	,	PUNCT
aiti-9227	227	41	when	when	SCONJ
aiti-9227	227	42	the	the	DET
aiti-9227	227	43	proposed	propose	VERB
aiti-9227	227	44	data	data	NOUN
aiti-9227	227	45	normalization	normalization	NOUN
aiti-9227	227	46	methods	method	NOUN
aiti-9227	227	47	are	be	AUX
aiti-9227	227	48	applied	apply	VERB
aiti-9227	227	49	.	.	PUNCT
aiti-9227	228	1	similarly	similarly	ADV
aiti-9227	228	2	,	,	PUNCT
aiti-9227	228	3	table	table	NOUN
aiti-9227	228	4	7	7	NUM
aiti-9227	228	5	and	and	CCONJ
aiti-9227	228	6	fig	fig	NOUN
aiti-9227	228	7	.	.	PUNCT
aiti-9227	228	8	8	8	NUM
aiti-9227	228	9	show	show	VERB
aiti-9227	228	10	the	the	DET
aiti-9227	228	11	mae	mae	PROPN
aiti-9227	228	12	,	,	PUNCT
aiti-9227	228	13	the	the	DET
aiti-9227	228	14	mape	mape	NOUN
aiti-9227	228	15	,	,	PUNCT
aiti-9227	228	16	and	and	CCONJ
aiti-9227	228	17	their	their	PRON
aiti-9227	228	18	boxplots	boxplot	NOUN
aiti-9227	228	19	of	of	ADP
aiti-9227	228	20	the	the	DET
aiti-9227	228	21	training	training	NOUN
aiti-9227	228	22	and	and	CCONJ
aiti-9227	228	23	test	test	NOUN
aiti-9227	228	24	stages	stage	NOUN
aiti-9227	228	25	for	for	ADP
aiti-9227	228	26	the	the	DET
aiti-9227	228	27	cnn	cnn	PROPN
aiti-9227	228	28	model	model	NOUN
aiti-9227	228	29	.	.	PUNCT
aiti-9227	229	1	again	again	ADV
aiti-9227	229	2	,	,	PUNCT
aiti-9227	229	3	the	the	DET
aiti-9227	229	4	observed	observe	VERB
aiti-9227	229	5	results	result	NOUN
aiti-9227	229	6	show	show	VERB
aiti-9227	229	7	that	that	SCONJ
aiti-9227	229	8	applying	apply	VERB
aiti-9227	229	9	data	datum	NOUN
aiti-9227	229	10	normalization	normalization	NOUN
aiti-9227	229	11	methods	method	NOUN
aiti-9227	229	12	significantly	significantly	ADV
aiti-9227	229	13	reduces	reduce	VERB
aiti-9227	229	14	both	both	CCONJ
aiti-9227	229	15	the	the	DET
aiti-9227	229	16	mae	mae	PROPN
aiti-9227	229	17	and	and	CCONJ
aiti-9227	229	18	mpae	mpae	NOUN
aiti-9227	229	19	.	.	PUNCT
aiti-9227	230	1	in	in	ADP
aiti-9227	230	2	other	other	ADJ
aiti-9227	230	3	words	word	NOUN
aiti-9227	230	4	,	,	PUNCT
aiti-9227	230	5	the	the	DET
aiti-9227	230	6	performance	performance	NOUN
aiti-9227	230	7	of	of	ADP
aiti-9227	230	8	the	the	DET
aiti-9227	230	9	gs	gs	PROPN
aiti-9227	230	10	algorithm	algorithm	NOUN
aiti-9227	230	11	is	be	AUX
aiti-9227	230	12	greatly	greatly	ADV
aiti-9227	230	13	improved	improve	VERB
aiti-9227	230	14	with	with	ADP
aiti-9227	230	15	data	datum	NOUN
aiti-9227	230	16	normalization	normalization	NOUN
aiti-9227	230	17	.	.	PUNCT
aiti-9227	231	1	moreover	moreover	ADV
aiti-9227	231	2	,	,	PUNCT
aiti-9227	231	3	the	the	DET
aiti-9227	231	4	effectiveness	effectiveness	NOUN
aiti-9227	231	5	of	of	ADP
aiti-9227	231	6	applying	apply	VERB
aiti-9227	231	7	data	datum	NOUN
aiti-9227	231	8	normalization	normalization	NOUN
aiti-9227	231	9	techniques	technique	NOUN
aiti-9227	231	10	can	can	AUX
aiti-9227	231	11	be	be	AUX
aiti-9227	231	12	divided	divide	VERB
aiti-9227	231	13	into	into	ADP
aiti-9227	231	14	three	three	NUM
aiti-9227	231	15	groups	group	NOUN
aiti-9227	231	16	.	.	PUNCT
aiti-9227	232	1	the	the	DET
aiti-9227	232	2	first	first	ADJ
aiti-9227	232	3	group	group	NOUN
aiti-9227	232	4	of	of	ADP
aiti-9227	232	5	applying	apply	VERB
aiti-9227	232	6	the	the	DET
aiti-9227	232	7	softmax	softmax	NOUN
aiti-9227	232	8	and	and	CCONJ
aiti-9227	232	9	robust	robust	ADJ
aiti-9227	232	10	methods	method	NOUN
aiti-9227	232	11	yields	yield	VERB
aiti-9227	232	12	small	small	ADJ
aiti-9227	232	13	maes	maes	PROPN
aiti-9227	232	14	.	.	PUNCT
aiti-9227	233	1	the	the	DET
aiti-9227	233	2	second	second	ADJ
aiti-9227	233	3	group	group	NOUN
aiti-9227	233	4	,	,	PUNCT
aiti-9227	233	5	which	which	PRON
aiti-9227	233	6	presents	present	VERB
aiti-9227	233	7	medium	medium	ADJ
aiti-9227	233	8	maes	maes	PROPN
aiti-9227	233	9	,	,	PUNCT
aiti-9227	233	10	includes	include	VERB
aiti-9227	233	11	the	the	DET
aiti-9227	233	12	zero	zero	NUM
aiti-9227	233	13	-	-	PUNCT
aiti-9227	233	14	mean	mean	NOUN
aiti-9227	233	15	and	and	CCONJ
aiti-9227	233	16	the	the	DET
aiti-9227	233	17	min	min	PROPN
aiti-9227	233	18	-	-	PUNCT
aiti-9227	233	19	max	max	NOUN
aiti-9227	233	20	.	.	PUNCT
aiti-9227	234	1	the	the	DET
aiti-9227	234	2	third	third	ADJ
aiti-9227	234	3	group	group	NOUN
aiti-9227	234	4	that	that	PRON
aiti-9227	234	5	provides	provide	VERB
aiti-9227	234	6	medium	medium	ADJ
aiti-9227	234	7	maes	maes	PROPN
aiti-9227	234	8	consists	consist	VERB
aiti-9227	234	9	of	of	ADP
aiti-9227	234	10	the	the	DET
aiti-9227	234	11	max	max	PROPN
aiti-9227	234	12	,	,	PUNCT
aiti-9227	234	13	decimal	decimal	ADJ
aiti-9227	234	14	,	,	PUNCT
aiti-9227	234	15	sigmoid	sigmoid	NOUN
aiti-9227	234	16	,	,	PUNCT
aiti-9227	234	17	and	and	CCONJ
aiti-9227	234	18	median	median	ADJ
aiti-9227	234	19	methods	method	NOUN
aiti-9227	234	20	.	.	PUNCT
aiti-9227	235	1	266	266	NUM
aiti-9227	235	2	advances	advance	NOUN
aiti-9227	235	3	in	in	ADP
aiti-9227	235	4	technology	technology	NOUN
aiti-9227	235	5	innovation	innovation	NOUN
aiti-9227	235	6	,	,	PUNCT
aiti-9227	235	7	vol	vol	NOUN
aiti-9227	235	8	.	.	PROPN
aiti-9227	235	9	7	7	NUM
aiti-9227	235	10	,	,	PUNCT
aiti-9227	235	11	no	no	INTJ
aiti-9227	235	12	.	.	NOUN
aiti-9227	235	13	4	4	NUM
aiti-9227	235	14	,	,	PUNCT
aiti-9227	235	15	2022	2022	NUM
aiti-9227	235	16	,	,	PUNCT
aiti-9227	235	17	pp	pp	ADJ
aiti-9227	235	18	.	.	PUNCT
aiti-9227	236	1	258	258	NUM
aiti-9227	236	2	-	-	SYM
aiti-9227	236	3	269	269	NUM
aiti-9227	236	4	500	500	NUM
aiti-9227	236	5	400	400	NUM
aiti-9227	236	6	300	300	NUM
aiti-9227	236	7	200	200	NUM
aiti-9227	236	8	100	100	NUM
aiti-9227	236	9	0	0	NUM
aiti-9227	236	10	1	1	NUM
aiti-9227	236	11	2	2	NUM
aiti-9227	236	12	3	3	NUM
aiti-9227	236	13	4	4	NUM
aiti-9227	236	14	5	5	NUM
aiti-9227	236	15	6	6	NUM
aiti-9227	236	16	7	7	NUM
aiti-9227	236	17	8	8	NUM
aiti-9227	236	18	9	9	NUM
aiti-9227	236	19	queensland	queensland	NOUN
aiti-9227	236	20	ho	ho	PROPN
aiti-9227	236	21	chi	chi	PROPN
aiti-9227	236	22	minh	minh	PROPN
aiti-9227	236	23	normalization	normalization	NOUN
aiti-9227	236	24	m	m	VERB
aiti-9227	236	25	a	a	DET
aiti-9227	236	26	e	e	X
aiti-9227	236	27	(	(	PUNCT
aiti-9227	236	28	m	m	PROPN
aiti-9227	236	29	w	w	NOUN
aiti-9227	236	30	)	)	PUNCT
aiti-9227	236	31	(	(	PUNCT
aiti-9227	236	32	a	a	X
aiti-9227	236	33	)	)	PUNCT
aiti-9227	236	34	mae	mae	PROPN
aiti-9227	236	35	of	of	ADP
aiti-9227	236	36	the	the	DET
aiti-9227	236	37	training	training	NOUN
aiti-9227	236	38	process	process	NOUN
aiti-9227	237	1	500	500	NUM
aiti-9227	237	2	400	400	NUM
aiti-9227	237	3	300	300	NUM
aiti-9227	237	4	200	200	NUM
aiti-9227	237	5	100	100	NUM
aiti-9227	237	6	0	0	NUM
aiti-9227	237	7	1	1	NUM
aiti-9227	237	8	2	2	NUM
aiti-9227	237	9	3	3	NUM
aiti-9227	237	10	4	4	NUM
aiti-9227	237	11	5	5	NUM
aiti-9227	237	12	6	6	NUM
aiti-9227	237	13	7	7	NUM
aiti-9227	237	14	8	8	NUM
aiti-9227	237	15	9	9	NUM
aiti-9227	237	16	queensland	queensland	NOUN
aiti-9227	237	17	ho	ho	PROPN
aiti-9227	237	18	chi	chi	PROPN
aiti-9227	237	19	minh	minh	PROPN
aiti-9227	237	20	normalization	normalization	NOUN
aiti-9227	237	21	m	m	VERB
aiti-9227	237	22	a	a	DET
aiti-9227	237	23	e	e	X
aiti-9227	237	24	(	(	PUNCT
aiti-9227	237	25	m	m	PROPN
aiti-9227	237	26	w	w	PROPN
aiti-9227	237	27	)	)	PUNCT
aiti-9227	237	28	17.5	17.5	NUM
aiti-9227	237	29	12.5	12.5	NUM
aiti-9227	237	30	10.0	10.0	NUM
aiti-9227	237	31	7.5	7.5	NUM
aiti-9227	237	32	2.5	2.5	NUM
aiti-9227	237	33	0	0	NUM
aiti-9227	237	34	1	1	NUM
aiti-9227	237	35	2	2	NUM
aiti-9227	237	36	3	3	NUM
aiti-9227	237	37	4	4	NUM
aiti-9227	237	38	5	5	NUM
aiti-9227	237	39	6	6	NUM
aiti-9227	237	40	7	7	NUM
aiti-9227	237	41	8	8	NUM
aiti-9227	237	42	9	9	NUM
aiti-9227	237	43	queensland	queensland	NOUN
aiti-9227	237	44	ho	ho	PROPN
aiti-9227	237	45	chi	chi	PROPN
aiti-9227	237	46	minh	minh	PROPN
aiti-9227	237	47	normalization	normalization	NOUN
aiti-9227	237	48	m	m	VERB
aiti-9227	237	49	a	a	DET
aiti-9227	237	50	p	p	X
aiti-9227	237	51	e	e	X
aiti-9227	237	52	(	(	PUNCT
aiti-9227	237	53	%	%	NOUN
aiti-9227	237	54	)	)	PUNCT
aiti-9227	237	55	5.0	5.0	NUM
aiti-9227	237	56	15.0	15.0	NUM
aiti-9227	237	57	20.0	20.0	NUM
aiti-9227	237	58	(	(	PUNCT
aiti-9227	237	59	b	b	NOUN
aiti-9227	237	60	)	)	PUNCT
aiti-9227	237	61	mae	mae	PROPN
aiti-9227	237	62	of	of	ADP
aiti-9227	237	63	the	the	DET
aiti-9227	237	64	test	test	NOUN
aiti-9227	237	65	process	process	NOUN
aiti-9227	237	66	(	(	PUNCT
aiti-9227	237	67	c	c	NOUN
aiti-9227	237	68	)	)	PUNCT
aiti-9227	237	69	mape	mape	NOUN
aiti-9227	237	70	of	of	ADP
aiti-9227	237	71	the	the	DET
aiti-9227	237	72	test	test	NOUN
aiti-9227	237	73	process	process	NOUN
aiti-9227	237	74	fig	fig	NOUN
aiti-9227	237	75	.	.	PUNCT
aiti-9227	238	1	7	7	NUM
aiti-9227	238	2	the	the	DET
aiti-9227	238	3	boxplot	boxplot	NOUN
aiti-9227	238	4	of	of	ADP
aiti-9227	238	5	the	the	DET
aiti-9227	238	6	maes	maes	PROPN
aiti-9227	238	7	and	and	CCONJ
aiti-9227	238	8	mapes	mapes	PROPN
aiti-9227	238	9	when	when	SCONJ
aiti-9227	238	10	using	use	VERB
aiti-9227	238	11	lstmn	lstmn	NOUN
aiti-9227	238	12	50	50	NUM
aiti-9227	238	13	40	40	NUM
aiti-9227	238	14	30	30	NUM
aiti-9227	238	15	20	20	NUM
aiti-9227	238	16	10	10	NUM
aiti-9227	238	17	0	0	NUM
aiti-9227	238	18	1	1	NUM
aiti-9227	238	19	2	2	NUM
aiti-9227	238	20	3	3	NUM
aiti-9227	238	21	4	4	NUM
aiti-9227	238	22	5	5	NUM
aiti-9227	238	23	6	6	NUM
aiti-9227	238	24	7	7	NUM
aiti-9227	238	25	8	8	NUM
aiti-9227	238	26	9	9	NUM
aiti-9227	238	27	queensland	queensland	NOUN
aiti-9227	238	28	ho	ho	PROPN
aiti-9227	238	29	chi	chi	PROPN
aiti-9227	238	30	minh	minh	PROPN
aiti-9227	238	31	normalization	normalization	NOUN
aiti-9227	238	32	m	m	VERB
aiti-9227	238	33	a	a	DET
aiti-9227	238	34	e	e	X
aiti-9227	238	35	(	(	PUNCT
aiti-9227	238	36	m	m	PROPN
aiti-9227	238	37	w	w	NOUN
aiti-9227	238	38	)	)	PUNCT
aiti-9227	238	39	(	(	PUNCT
aiti-9227	238	40	a	a	X
aiti-9227	238	41	)	)	PUNCT
aiti-9227	238	42	mae	mae	PROPN
aiti-9227	238	43	of	of	ADP
aiti-9227	238	44	the	the	DET
aiti-9227	238	45	training	training	NOUN
aiti-9227	238	46	process	process	NOUN
aiti-9227	238	47	50	50	NUM
aiti-9227	238	48	40	40	NUM
aiti-9227	238	49	30	30	NUM
aiti-9227	238	50	20	20	NUM
aiti-9227	238	51	10	10	NUM
aiti-9227	238	52	0	0	NUM
aiti-9227	238	53	1	1	NUM
aiti-9227	238	54	2	2	NUM
aiti-9227	238	55	3	3	NUM
aiti-9227	238	56	4	4	NUM
aiti-9227	238	57	5	5	NUM
aiti-9227	238	58	6	6	NUM
aiti-9227	238	59	7	7	NUM
aiti-9227	238	60	8	8	NUM
aiti-9227	238	61	9	9	NUM
aiti-9227	238	62	queensland	queensland	NOUN
aiti-9227	238	63	ho	ho	PROPN
aiti-9227	238	64	chi	chi	PROPN
aiti-9227	238	65	minh	minh	PROPN
aiti-9227	238	66	normalization	normalization	NOUN
aiti-9227	238	67	m	m	VERB
aiti-9227	238	68	a	a	DET
aiti-9227	238	69	e	e	X
aiti-9227	238	70	(	(	PUNCT
aiti-9227	238	71	m	m	PROPN
aiti-9227	238	72	w	w	PROPN
aiti-9227	238	73	)	)	PUNCT
aiti-9227	238	74	1.6	1.6	NUM
aiti-9227	238	75	1.2	1.2	NUM
aiti-9227	238	76	1.0	1.0	NUM
aiti-9227	238	77	0.6	0.6	NUM
aiti-9227	238	78	0.2	0.2	NUM
aiti-9227	238	79	0	0	NUM
aiti-9227	238	80	1	1	NUM
aiti-9227	238	81	2	2	NUM
aiti-9227	238	82	3	3	NUM
aiti-9227	238	83	4	4	NUM
aiti-9227	238	84	5	5	NUM
aiti-9227	238	85	6	6	NUM
aiti-9227	238	86	7	7	NUM
aiti-9227	238	87	8	8	NUM
aiti-9227	238	88	9	9	NUM
aiti-9227	238	89	queensland	queensland	NOUN
aiti-9227	238	90	ho	ho	PROPN
aiti-9227	238	91	chi	chi	PROPN
aiti-9227	238	92	minh	minh	PROPN
aiti-9227	238	93	normalization	normalization	NOUN
aiti-9227	238	94	m	m	VERB
aiti-9227	238	95	a	a	DET
aiti-9227	238	96	p	p	X
aiti-9227	238	97	e	e	X
aiti-9227	238	98	(	(	PUNCT
aiti-9227	238	99	%	%	NOUN
aiti-9227	238	100	)	)	PUNCT
aiti-9227	238	101	0.4	0.4	NUM
aiti-9227	238	102	0.8	0.8	NUM
aiti-9227	238	103	1.4	1.4	NUM
aiti-9227	238	104	(	(	PUNCT
aiti-9227	238	105	b	b	NOUN
aiti-9227	238	106	)	)	PUNCT
aiti-9227	238	107	mae	mae	PROPN
aiti-9227	238	108	of	of	ADP
aiti-9227	238	109	the	the	DET
aiti-9227	238	110	testing	testing	NOUN
aiti-9227	238	111	process	process	NOUN
aiti-9227	238	112	(	(	PUNCT
aiti-9227	238	113	c	c	NOUN
aiti-9227	238	114	)	)	PUNCT
aiti-9227	238	115	mape	mape	NOUN
aiti-9227	238	116	of	of	ADP
aiti-9227	238	117	the	the	DET
aiti-9227	238	118	test	test	NOUN
aiti-9227	238	119	process	process	NOUN
aiti-9227	238	120	fig	fig	NOUN
aiti-9227	238	121	.	.	PUNCT
aiti-9227	239	1	8	8	NUM
aiti-9227	239	2	the	the	DET
aiti-9227	239	3	boxplot	boxplot	NOUN
aiti-9227	239	4	of	of	ADP
aiti-9227	239	5	the	the	DET
aiti-9227	239	6	maes	maes	PROPN
aiti-9227	239	7	and	and	CCONJ
aiti-9227	239	8	mapes	mapes	PROPN
aiti-9227	239	9	when	when	SCONJ
aiti-9227	239	10	using	use	VERB
aiti-9227	239	11	cnn	cnn	PROPN
aiti-9227	239	12	267	267	NUM
aiti-9227	239	13	advances	advance	NOUN
aiti-9227	239	14	in	in	ADP
aiti-9227	239	15	technology	technology	NOUN
aiti-9227	239	16	innovation	innovation	NOUN
aiti-9227	239	17	,	,	PUNCT
aiti-9227	239	18	vol	vol	NOUN
aiti-9227	239	19	.	.	PROPN
aiti-9227	239	20	7	7	NUM
aiti-9227	239	21	,	,	PUNCT
aiti-9227	239	22	no	no	INTJ
aiti-9227	239	23	.	.	NOUN
aiti-9227	239	24	4	4	NUM
aiti-9227	239	25	,	,	PUNCT
aiti-9227	239	26	2022	2022	NUM
aiti-9227	239	27	,	,	PUNCT
aiti-9227	239	28	pp	pp	ADJ
aiti-9227	239	29	.	.	PUNCT
aiti-9227	240	1	258	258	NUM
aiti-9227	240	2	-	-	SYM
aiti-9227	240	3	269	269	NUM
aiti-9227	240	4	5	5	NUM
aiti-9227	240	5	.	.	PUNCT
aiti-9227	240	6	conclusions	conclusion	NOUN
aiti-9227	240	7	this	this	DET
aiti-9227	240	8	study	study	NOUN
aiti-9227	240	9	presents	present	VERB
aiti-9227	240	10	an	an	DET
aiti-9227	240	11	approach	approach	NOUN
aiti-9227	240	12	to	to	PART
aiti-9227	240	13	examine	examine	VERB
aiti-9227	240	14	the	the	DET
aiti-9227	240	15	effect	effect	NOUN
aiti-9227	240	16	of	of	ADP
aiti-9227	240	17	data	datum	NOUN
aiti-9227	240	18	normalization	normalization	NOUN
aiti-9227	240	19	methods	method	NOUN
aiti-9227	240	20	on	on	ADP
aiti-9227	240	21	the	the	DET
aiti-9227	240	22	gs	gs	PROPN
aiti-9227	240	23	algorithm	algorithm	NOUN
aiti-9227	240	24	for	for	ADP
aiti-9227	240	25	determining	determine	VERB
aiti-9227	240	26	the	the	DET
aiti-9227	240	27	optimal	optimal	ADJ
aiti-9227	240	28	hyperparameters	hyperparameter	NOUN
aiti-9227	240	29	of	of	ADP
aiti-9227	240	30	the	the	DET
aiti-9227	240	31	dl	dl	PROPN
aiti-9227	240	32	model	model	NOUN
aiti-9227	240	33	,	,	PUNCT
aiti-9227	240	34	including	include	VERB
aiti-9227	240	35	the	the	DET
aiti-9227	240	36	lstmn	lstmn	NOUN
aiti-9227	240	37	and	and	CCONJ
aiti-9227	240	38	cnn	cnn	PROPN
aiti-9227	240	39	,	,	PUNCT
aiti-9227	240	40	for	for	ADP
aiti-9227	240	41	energy	energy	NOUN
aiti-9227	240	42	load	load	NOUN
aiti-9227	240	43	forecasting	forecasting	NOUN
aiti-9227	240	44	.	.	PUNCT
aiti-9227	241	1	the	the	DET
aiti-9227	241	2	power	power	NOUN
aiti-9227	241	3	load	load	NOUN
aiti-9227	241	4	data	datum	NOUN
aiti-9227	241	5	of	of	ADP
aiti-9227	241	6	the	the	DET
aiti-9227	241	7	australian	australian	ADJ
aiti-9227	241	8	state	state	NOUN
aiti-9227	241	9	of	of	ADP
aiti-9227	241	10	queensland	queensland	PROPN
aiti-9227	241	11	and	and	CCONJ
aiti-9227	241	12	the	the	DET
aiti-9227	241	13	vietnamese	vietnamese	ADJ
aiti-9227	241	14	city	city	NOUN
aiti-9227	241	15	of	of	ADP
aiti-9227	241	16	ho	ho	PROPN
aiti-9227	241	17	chi	chi	PROPN
aiti-9227	241	18	minh	minh	PROPN
aiti-9227	241	19	were	be	AUX
aiti-9227	241	20	used	use	VERB
aiti-9227	241	21	to	to	PART
aiti-9227	241	22	verify	verify	VERB
aiti-9227	241	23	the	the	DET
aiti-9227	241	24	reliability	reliability	NOUN
aiti-9227	241	25	of	of	ADP
aiti-9227	241	26	the	the	DET
aiti-9227	241	27	proposed	propose	VERB
aiti-9227	241	28	method	method	NOUN
aiti-9227	241	29	.	.	PUNCT
aiti-9227	242	1	the	the	DET
aiti-9227	242	2	error	error	NOUN
aiti-9227	242	3	evaluation	evaluation	NOUN
aiti-9227	242	4	indexes	index	NOUN
aiti-9227	242	5	of	of	ADP
aiti-9227	242	6	mae	mae	PROPN
aiti-9227	242	7	and	and	CCONJ
aiti-9227	242	8	mape	mape	NOUN
aiti-9227	242	9	of	of	ADP
aiti-9227	242	10	the	the	DET
aiti-9227	242	11	actual	actual	ADJ
aiti-9227	242	12	and	and	CCONJ
aiti-9227	242	13	predicted	predict	VERB
aiti-9227	242	14	values	value	NOUN
aiti-9227	242	15	are	be	AUX
aiti-9227	242	16	established	establish	VERB
aiti-9227	242	17	based	base	VERB
aiti-9227	242	18	on	on	ADP
aiti-9227	242	19	the	the	DET
aiti-9227	242	20	epoch	epoch	NOUN
aiti-9227	242	21	,	,	PUNCT
aiti-9227	242	22	batch	batch	NOUN
aiti-9227	242	23	,	,	PUNCT
aiti-9227	242	24	optimizer	optimizer	NOUN
aiti-9227	242	25	,	,	PUNCT
aiti-9227	242	26	dropout	dropout	NOUN
aiti-9227	242	27	,	,	PUNCT
aiti-9227	242	28	filters	filter	NOUN
aiti-9227	242	29	,	,	PUNCT
aiti-9227	242	30	and	and	CCONJ
aiti-9227	242	31	kernel	kernel	PROPN
aiti-9227	242	32	to	to	PART
aiti-9227	242	33	determine	determine	VERB
aiti-9227	242	34	the	the	DET
aiti-9227	242	35	optimal	optimal	ADJ
aiti-9227	242	36	hyperparameters	hyperparameter	NOUN
aiti-9227	242	37	of	of	ADP
aiti-9227	242	38	the	the	DET
aiti-9227	242	39	dl	dl	PROPN
aiti-9227	242	40	model	model	NOUN
aiti-9227	242	41	.	.	PUNCT
aiti-9227	243	1	the	the	DET
aiti-9227	243	2	effectiveness	effectiveness	NOUN
aiti-9227	243	3	of	of	ADP
aiti-9227	243	4	applying	apply	VERB
aiti-9227	243	5	data	datum	NOUN
aiti-9227	243	6	normalization	normalization	NOUN
aiti-9227	243	7	techniques	technique	NOUN
aiti-9227	243	8	can	can	AUX
aiti-9227	243	9	be	be	AUX
aiti-9227	243	10	divided	divide	VERB
aiti-9227	243	11	into	into	ADP
aiti-9227	243	12	three	three	NUM
aiti-9227	243	13	groups	group	NOUN
aiti-9227	243	14	.	.	PUNCT
aiti-9227	244	1	the	the	DET
aiti-9227	244	2	first	first	ADJ
aiti-9227	244	3	group	group	NOUN
aiti-9227	244	4	of	of	ADP
aiti-9227	244	5	the	the	DET
aiti-9227	244	6	applications	application	NOUN
aiti-9227	244	7	of	of	ADP
aiti-9227	244	8	the	the	DET
aiti-9227	244	9	softmax	softmax	NOUN
aiti-9227	244	10	and	and	CCONJ
aiti-9227	244	11	robust	robust	ADJ
aiti-9227	244	12	methods	method	NOUN
aiti-9227	244	13	yielded	yield	VERB
aiti-9227	244	14	small	small	ADJ
aiti-9227	244	15	maes	maes	PROPN
aiti-9227	244	16	.	.	PUNCT
aiti-9227	245	1	the	the	DET
aiti-9227	245	2	second	second	ADJ
aiti-9227	245	3	group	group	NOUN
aiti-9227	245	4	,	,	PUNCT
aiti-9227	245	5	which	which	PRON
aiti-9227	245	6	presented	present	VERB
aiti-9227	245	7	medium	medium	ADJ
aiti-9227	245	8	maes	maes	PROPN
aiti-9227	245	9	,	,	PUNCT
aiti-9227	245	10	included	include	VERB
aiti-9227	245	11	the	the	DET
aiti-9227	245	12	zero	zero	NUM
aiti-9227	245	13	-	-	PUNCT
aiti-9227	245	14	mean	mean	NOUN
aiti-9227	245	15	and	and	CCONJ
aiti-9227	245	16	the	the	DET
aiti-9227	245	17	min	min	PROPN
aiti-9227	245	18	-	-	PUNCT
aiti-9227	245	19	max	max	NOUN
aiti-9227	245	20	.	.	PUNCT
aiti-9227	246	1	the	the	DET
aiti-9227	246	2	third	third	ADJ
aiti-9227	246	3	group	group	NOUN
aiti-9227	246	4	that	that	PRON
aiti-9227	246	5	provided	provide	VERB
aiti-9227	246	6	medium	medium	ADJ
aiti-9227	246	7	maes	maes	PROPN
aiti-9227	246	8	consisted	consist	VERB
aiti-9227	246	9	of	of	ADP
aiti-9227	246	10	the	the	DET
aiti-9227	246	11	max	max	PROPN
aiti-9227	246	12	,	,	PUNCT
aiti-9227	246	13	decimal	decimal	ADJ
aiti-9227	246	14	,	,	PUNCT
aiti-9227	246	15	sigmoid	sigmoid	NOUN
aiti-9227	246	16	,	,	PUNCT
aiti-9227	246	17	and	and	CCONJ
aiti-9227	246	18	median	median	ADJ
aiti-9227	246	19	methods	method	NOUN
aiti-9227	246	20	.	.	PUNCT
aiti-9227	247	1	the	the	DET
aiti-9227	247	2	results	result	NOUN
aiti-9227	247	3	showed	show	VERB
aiti-9227	247	4	that	that	SCONJ
aiti-9227	247	5	both	both	PRON
aiti-9227	247	6	mae	mae	PROPN
aiti-9227	247	7	and	and	CCONJ
aiti-9227	247	8	mape	mape	NOUN
aiti-9227	247	9	were	be	AUX
aiti-9227	247	10	much	much	ADV
aiti-9227	247	11	smaller	small	ADJ
aiti-9227	247	12	when	when	SCONJ
aiti-9227	247	13	applying	apply	VERB
aiti-9227	247	14	data	datum	NOUN
aiti-9227	247	15	normalization	normalization	NOUN
aiti-9227	247	16	.	.	PUNCT
aiti-9227	248	1	in	in	ADP
aiti-9227	248	2	addition	addition	NOUN
aiti-9227	248	3	,	,	PUNCT
aiti-9227	248	4	out	out	ADP
aiti-9227	248	5	of	of	ADP
aiti-9227	248	6	the	the	DET
aiti-9227	248	7	eight	eight	NUM
aiti-9227	248	8	proposed	propose	VERB
aiti-9227	248	9	data	datum	NOUN
aiti-9227	248	10	normalization	normalization	NOUN
aiti-9227	248	11	methods	method	NOUN
aiti-9227	248	12	,	,	PUNCT
aiti-9227	248	13	zero	zero	NUM
aiti-9227	248	14	-	-	PUNCT
aiti-9227	248	15	mean	mean	NOUN
aiti-9227	248	16	or	or	CCONJ
aiti-9227	248	17	min	min	NOUN
aiti-9227	248	18	-	-	ADJ
aiti-9227	248	19	max	max	PROPN
aiti-9227	248	20	was	be	AUX
aiti-9227	248	21	not	not	PART
aiti-9227	248	22	the	the	DET
aiti-9227	248	23	best	good	ADJ
aiti-9227	248	24	method	method	NOUN
aiti-9227	248	25	for	for	ADP
aiti-9227	248	26	the	the	DET
aiti-9227	248	27	gs	gs	PROPN
aiti-9227	248	28	algorithm	algorithm	NOUN
aiti-9227	248	29	for	for	ADP
aiti-9227	248	30	determining	determine	VERB
aiti-9227	248	31	the	the	DET
aiti-9227	248	32	optimal	optimal	ADJ
aiti-9227	248	33	hyperparameters	hyperparameter	NOUN
aiti-9227	248	34	of	of	ADP
aiti-9227	248	35	the	the	DET
aiti-9227	248	36	dl	dl	PROPN
aiti-9227	248	37	model	model	NOUN
aiti-9227	248	38	.	.	PUNCT
aiti-9227	249	1	conflicts	conflict	NOUN
aiti-9227	249	2	of	of	ADP
aiti-9227	249	3	interest	interest	NOUN
aiti-9227	249	4	the	the	DET
aiti-9227	249	5	authors	author	NOUN
aiti-9227	249	6	declare	declare	VERB
aiti-9227	249	7	no	no	DET
aiti-9227	249	8	conflicts	conflict	NOUN
aiti-9227	249	9	of	of	ADP
aiti-9227	249	10	interest	interest	NOUN
aiti-9227	249	11	.	.	PUNCT
aiti-9227	250	1	references	reference	NOUN
aiti-9227	250	2	[	[	X
aiti-9227	250	3	1	1	NUM
aiti-9227	250	4	]	]	PUNCT
aiti-9227	250	5	a.	a.	NOUN
aiti-9227	250	6	m.	m.	NOUN
aiti-9227	250	7	omer	omer	PROPN
aiti-9227	250	8	,	,	PUNCT
aiti-9227	250	9	“	"	PUNCT
aiti-9227	250	10	energy	energy	NOUN
aiti-9227	250	11	use	use	NOUN
aiti-9227	250	12	and	and	CCONJ
aiti-9227	250	13	environmental	environmental	ADJ
aiti-9227	250	14	impacts	impact	NOUN
aiti-9227	250	15	:	:	PUNCT
aiti-9227	250	16	a	a	DET
aiti-9227	250	17	general	general	ADJ
aiti-9227	250	18	review	review	NOUN
aiti-9227	250	19	,	,	PUNCT
aiti-9227	250	20	”	"	PUNCT
aiti-9227	250	21	journal	journal	NOUN
aiti-9227	250	22	of	of	ADP
aiti-9227	250	23	renewable	renewable	ADJ
aiti-9227	250	24	and	and	CCONJ
aiti-9227	250	25	sustainable	sustainable	ADJ
aiti-9227	250	26	energy	energy	NOUN
aiti-9227	250	27	,	,	PUNCT
aiti-9227	250	28	vol	vol	NOUN
aiti-9227	250	29	.	.	PROPN
aiti-9227	250	30	1	1	NUM
aiti-9227	250	31	,	,	PUNCT
aiti-9227	250	32	no	no	INTJ
aiti-9227	250	33	.	.	NOUN
aiti-9227	250	34	5	5	NUM
aiti-9227	250	35	,	,	PUNCT
aiti-9227	250	36	article	article	NOUN
aiti-9227	250	37	no	no	INTJ
aiti-9227	250	38	.	.	PROPN
aiti-9227	250	39	053100	053100	NUM
aiti-9227	250	40	,	,	PUNCT
aiti-9227	250	41	2009	2009	NUM
aiti-9227	250	42	.	.	PUNCT
aiti-9227	251	1	[	[	X
aiti-9227	251	2	2	2	NUM
aiti-9227	251	3	]	]	PUNCT
aiti-9227	251	4	k.	k.	PROPN
aiti-9227	251	5	amasyali	amasyali	PROPN
aiti-9227	251	6	,	,	PUNCT
aiti-9227	251	7	et	et	PROPN
aiti-9227	251	8	al	al	PROPN
aiti-9227	251	9	.	.	PROPN
aiti-9227	251	10	,	,	PUNCT
aiti-9227	251	11	“	"	PUNCT
aiti-9227	251	12	a	a	DET
aiti-9227	251	13	review	review	NOUN
aiti-9227	251	14	of	of	ADP
aiti-9227	251	15	data	data	NOUN
aiti-9227	251	16	-	-	PUNCT
aiti-9227	251	17	driven	drive	VERB
aiti-9227	251	18	building	building	NOUN
aiti-9227	251	19	energy	energy	NOUN
aiti-9227	251	20	consumption	consumption	NOUN
aiti-9227	251	21	prediction	prediction	NOUN
aiti-9227	251	22	studies	study	NOUN
aiti-9227	251	23	,	,	PUNCT
aiti-9227	251	24	”	"	PUNCT
aiti-9227	251	25	renewable	renewable	ADJ
aiti-9227	251	26	and	and	CCONJ
aiti-9227	251	27	sustainable	sustainable	ADJ
aiti-9227	251	28	energy	energy	NOUN
aiti-9227	251	29	reviews	review	NOUN
aiti-9227	251	30	,	,	PUNCT
aiti-9227	251	31	vol	vol	NOUN
aiti-9227	251	32	.	.	PROPN
aiti-9227	251	33	81	81	NUM
aiti-9227	251	34	,	,	PUNCT
aiti-9227	251	35	pp	pp	ADJ
aiti-9227	251	36	.	.	PUNCT
aiti-9227	252	1	1192	1192	NUM
aiti-9227	252	2	-	-	SYM
aiti-9227	252	3	1205	1205	NUM
aiti-9227	252	4	,	,	PUNCT
aiti-9227	252	5	january	january	PROPN
aiti-9227	252	6	2018	2018	NUM
aiti-9227	252	7	.	.	PUNCT
aiti-9227	253	1	[	[	X
aiti-9227	253	2	3	3	X
aiti-9227	253	3	]	]	X
aiti-9227	253	4	e.	e.	PROPN
aiti-9227	253	5	naml	naml	PROPN
aiti-9227	253	6	,	,	PUNCT
aiti-9227	253	7	et	et	PROPN
aiti-9227	253	8	al	al	PROPN
aiti-9227	253	9	.	.	PROPN
aiti-9227	253	10	,	,	PUNCT
aiti-9227	253	11	“	"	PUNCT
aiti-9227	253	12	artificial	artificial	ADJ
aiti-9227	253	13	intelligence	intelligence	NOUN
aiti-9227	253	14	-	-	PUNCT
aiti-9227	253	15	based	base	VERB
aiti-9227	253	16	prediction	prediction	NOUN
aiti-9227	253	17	models	model	NOUN
aiti-9227	253	18	for	for	ADP
aiti-9227	253	19	energy	energy	NOUN
aiti-9227	253	20	performance	performance	NOUN
aiti-9227	253	21	of	of	ADP
aiti-9227	253	22	residential	residential	ADJ
aiti-9227	253	23	buildings	building	NOUN
aiti-9227	253	24	,	,	PUNCT
aiti-9227	253	25	”	"	PUNCT
aiti-9227	253	26	recycling	recycling	NOUN
aiti-9227	253	27	and	and	CCONJ
aiti-9227	253	28	reuse	reuse	NOUN
aiti-9227	253	29	approach	approach	NOUN
aiti-9227	253	30	for	for	ADP
aiti-9227	253	31	better	well	ADJ
aiti-9227	253	32	sustainability	sustainability	NOUN
aiti-9227	253	33	,	,	PUNCT
aiti-9227	253	34	pp	pp	ADJ
aiti-9227	253	35	.	.	PUNCT
aiti-9227	253	36	141	141	NUM
aiti-9227	253	37	-	-	SYM
aiti-9227	253	38	149	149	NUM
aiti-9227	253	39	,	,	PUNCT
aiti-9227	253	40	2019	2019	NUM
aiti-9227	253	41	.	.	PUNCT
aiti-9227	254	1	[	[	X
aiti-9227	254	2	4	4	X
aiti-9227	254	3	]	]	X
aiti-9227	254	4	y.	y.	PROPN
aiti-9227	254	5	jiang	jiang	PROPN
aiti-9227	254	6	,	,	PUNCT
aiti-9227	254	7	et	et	PROPN
aiti-9227	254	8	al	al	PROPN
aiti-9227	254	9	.	.	PROPN
aiti-9227	254	10	,	,	PUNCT
aiti-9227	254	11	“	"	PUNCT
aiti-9227	254	12	stochastic	stochastic	ADJ
aiti-9227	254	13	receding	recede	VERB
aiti-9227	254	14	horizon	horizon	NOUN
aiti-9227	254	15	control	control	NOUN
aiti-9227	254	16	of	of	ADP
aiti-9227	254	17	active	active	ADJ
aiti-9227	254	18	distribution	distribution	NOUN
aiti-9227	254	19	networks	network	NOUN
aiti-9227	254	20	with	with	ADP
aiti-9227	254	21	distributed	distribute	VERB
aiti-9227	254	22	renewables	renewable	NOUN
aiti-9227	254	23	,	,	PUNCT
aiti-9227	254	24	”	"	PUNCT
aiti-9227	254	25	ieee	ieee	NOUN
aiti-9227	254	26	transactions	transaction	NOUN
aiti-9227	254	27	on	on	ADP
aiti-9227	254	28	power	power	NOUN
aiti-9227	254	29	systems	system	NOUN
aiti-9227	254	30	,	,	PUNCT
aiti-9227	254	31	vol	vol	NOUN
aiti-9227	254	32	.	.	PROPN
aiti-9227	255	1	34	34	NUM
aiti-9227	255	2	,	,	PUNCT
aiti-9227	255	3	no	no	INTJ
aiti-9227	255	4	.	.	NOUN
aiti-9227	255	5	2	2	NUM
aiti-9227	255	6	,	,	PUNCT
aiti-9227	255	7	pp	pp	ADJ
aiti-9227	255	8	.	.	PUNCT
aiti-9227	256	1	1325	1325	NUM
aiti-9227	256	2	-	-	SYM
aiti-9227	256	3	1341	1341	NUM
aiti-9227	256	4	,	,	PUNCT
aiti-9227	256	5	march	march	PROPN
aiti-9227	256	6	2019	2019	NUM
aiti-9227	256	7	.	.	PUNCT
aiti-9227	257	1	[	[	X
aiti-9227	257	2	5	5	X
aiti-9227	257	3	]	]	PUNCT
aiti-9227	257	4	j.	j.	PROPN
aiti-9227	257	5	c.	c.	PROPN
aiti-9227	257	6	lópez	lópez	PROPN
aiti-9227	257	7	,	,	PUNCT
aiti-9227	257	8	et	et	PROPN
aiti-9227	257	9	al	al	PROPN
aiti-9227	257	10	.	.	PROPN
aiti-9227	257	11	,	,	PUNCT
aiti-9227	257	12	“	"	PUNCT
aiti-9227	257	13	parsimonious	parsimonious	ADJ
aiti-9227	257	14	short	short	ADJ
aiti-9227	257	15	-	-	PUNCT
aiti-9227	257	16	term	term	NOUN
aiti-9227	257	17	load	load	NOUN
aiti-9227	257	18	forecasting	forecasting	NOUN
aiti-9227	257	19	for	for	ADP
aiti-9227	257	20	optimal	optimal	ADJ
aiti-9227	257	21	operation	operation	NOUN
aiti-9227	257	22	planning	planning	NOUN
aiti-9227	257	23	of	of	ADP
aiti-9227	257	24	electrical	electrical	ADJ
aiti-9227	257	25	distribution	distribution	NOUN
aiti-9227	257	26	systems	system	NOUN
aiti-9227	257	27	,	,	PUNCT
aiti-9227	257	28	”	"	PUNCT
aiti-9227	257	29	ieee	ieee	NOUN
aiti-9227	257	30	transactions	transaction	NOUN
aiti-9227	257	31	on	on	ADP
aiti-9227	257	32	power	power	NOUN
aiti-9227	257	33	systems	system	NOUN
aiti-9227	257	34	,	,	PUNCT
aiti-9227	257	35	vol	vol	NOUN
aiti-9227	257	36	.	.	PROPN
aiti-9227	258	1	34	34	NUM
aiti-9227	258	2	,	,	PUNCT
aiti-9227	258	3	no	no	INTJ
aiti-9227	258	4	.	.	NOUN
aiti-9227	258	5	2	2	NUM
aiti-9227	258	6	,	,	PUNCT
aiti-9227	258	7	pp	pp	ADJ
aiti-9227	258	8	.	.	PUNCT
aiti-9227	259	1	1427	1427	NUM
aiti-9227	259	2	-	-	SYM
aiti-9227	259	3	1437	1437	NUM
aiti-9227	259	4	,	,	PUNCT
aiti-9227	259	5	march	march	PROPN
aiti-9227	259	6	2019	2019	NUM
aiti-9227	259	7	.	.	PUNCT
aiti-9227	260	1	[	[	X
aiti-9227	260	2	6	6	NUM
aiti-9227	260	3	]	]	PUNCT
aiti-9227	260	4	s.	s.	PROPN
aiti-9227	260	5	khan	khan	PROPN
aiti-9227	260	6	,	,	PUNCT
aiti-9227	260	7	et	et	PROPN
aiti-9227	260	8	al	al	PROPN
aiti-9227	260	9	.	.	PROPN
aiti-9227	260	10	,	,	PUNCT
aiti-9227	260	11	“	"	PUNCT
aiti-9227	260	12	forecasting	forecasting	NOUN
aiti-9227	260	13	day	day	NOUN
aiti-9227	260	14	,	,	PUNCT
aiti-9227	260	15	week	week	NOUN
aiti-9227	260	16	and	and	CCONJ
aiti-9227	260	17	month	month	NOUN
aiti-9227	260	18	ahead	ahead	ADV
aiti-9227	260	19	electricity	electricity	NOUN
aiti-9227	260	20	load	load	NOUN
aiti-9227	260	21	consumption	consumption	NOUN
aiti-9227	260	22	of	of	ADP
aiti-9227	260	23	a	a	DET
aiti-9227	260	24	building	building	NOUN
aiti-9227	260	25	using	use	VERB
aiti-9227	260	26	empirical	empirical	ADJ
aiti-9227	260	27	mode	mode	NOUN
aiti-9227	260	28	decomposition	decomposition	NOUN
aiti-9227	260	29	and	and	CCONJ
aiti-9227	260	30	extreme	extreme	ADJ
aiti-9227	260	31	learning	learning	NOUN
aiti-9227	260	32	machine	machine	NOUN
aiti-9227	260	33	,	,	PUNCT
aiti-9227	260	34	”	"	PUNCT
aiti-9227	260	35	15th	15th	ADJ
aiti-9227	260	36	international	international	ADJ
aiti-9227	260	37	wireless	wireless	ADJ
aiti-9227	260	38	communications	communication	NOUN
aiti-9227	260	39	and	and	CCONJ
aiti-9227	260	40	mobile	mobile	NOUN
aiti-9227	260	41	computing	computing	NOUN
aiti-9227	260	42	conference	conference	NOUN
aiti-9227	260	43	,	,	PUNCT
aiti-9227	260	44	pp	pp	ADP
aiti-9227	260	45	.	.	PUNCT
aiti-9227	260	46	1600	1600	NUM
aiti-9227	260	47	-	-	SYM
aiti-9227	260	48	1605	1605	NUM
aiti-9227	260	49	,	,	PUNCT
aiti-9227	260	50	june	june	PROPN
aiti-9227	260	51	2019	2019	NUM
aiti-9227	260	52	.	.	PUNCT
aiti-9227	261	1	[	[	X
aiti-9227	261	2	7	7	X
aiti-9227	261	3	]	]	PUNCT
aiti-9227	261	4	t.	t.	PROPN
aiti-9227	261	5	t.	t.	PROPN
aiti-9227	261	6	ngoc	ngoc	PROPN
aiti-9227	261	7	,	,	PUNCT
aiti-9227	261	8	et	et	PROPN
aiti-9227	261	9	al	al	PROPN
aiti-9227	261	10	.	.	PROPN
aiti-9227	261	11	,	,	PUNCT
aiti-9227	261	12	“	"	PUNCT
aiti-9227	261	13	grid	grid	NOUN
aiti-9227	261	14	search	search	NOUN
aiti-9227	261	15	of	of	ADP
aiti-9227	261	16	exponential	exponential	ADJ
aiti-9227	261	17	smoothing	smoothing	NOUN
aiti-9227	261	18	method	method	NOUN
aiti-9227	261	19	:	:	PUNCT
aiti-9227	261	20	a	a	DET
aiti-9227	261	21	case	case	NOUN
aiti-9227	261	22	study	study	NOUN
aiti-9227	261	23	of	of	ADP
aiti-9227	261	24	ho	ho	PROPN
aiti-9227	261	25	chi	chi	PROPN
aiti-9227	261	26	minh	minh	PROPN
aiti-9227	261	27	city	city	PROPN
aiti-9227	261	28	load	load	NOUN
aiti-9227	261	29	demand	demand	NOUN
aiti-9227	261	30	,	,	PUNCT
aiti-9227	261	31	”	"	PUNCT
aiti-9227	261	32	indonesian	indonesian	ADJ
aiti-9227	261	33	journal	journal	NOUN
aiti-9227	261	34	of	of	ADP
aiti-9227	261	35	electrical	electrical	ADJ
aiti-9227	261	36	engineering	engineering	NOUN
aiti-9227	261	37	and	and	CCONJ
aiti-9227	261	38	computer	computer	NOUN
aiti-9227	261	39	science	science	NOUN
aiti-9227	261	40	,	,	PUNCT
aiti-9227	261	41	vol	vol	NOUN
aiti-9227	261	42	.	.	PROPN
aiti-9227	261	43	19	19	NUM
aiti-9227	261	44	,	,	PUNCT
aiti-9227	261	45	no	no	INTJ
aiti-9227	261	46	.	.	NOUN
aiti-9227	261	47	3	3	NUM
aiti-9227	261	48	,	,	PUNCT
aiti-9227	261	49	pp	pp	ADJ
aiti-9227	261	50	.	.	PUNCT
aiti-9227	261	51	1121	1121	NUM
aiti-9227	261	52	-	-	SYM
aiti-9227	261	53	1130	1130	NUM
aiti-9227	261	54	,	,	PUNCT
aiti-9227	261	55	september	september	PROPN
aiti-9227	261	56	2020	2020	NUM
aiti-9227	261	57	.	.	PUNCT
aiti-9227	262	1	[	[	X
aiti-9227	262	2	8	8	NUM
aiti-9227	262	3	]	]	PUNCT
aiti-9227	262	4	t.	t.	PROPN
aiti-9227	262	5	t.	t.	PROPN
aiti-9227	262	6	ngoc	ngoc	PROPN
aiti-9227	262	7	,	,	PUNCT
aiti-9227	262	8	et	et	PROPN
aiti-9227	262	9	al	al	PROPN
aiti-9227	262	10	.	.	PROPN
aiti-9227	262	11	,	,	PUNCT
aiti-9227	262	12	“	"	PUNCT
aiti-9227	262	13	grid	grid	NOUN
aiti-9227	262	14	search	search	NOUN
aiti-9227	262	15	of	of	ADP
aiti-9227	262	16	multilayer	multilayer	ADJ
aiti-9227	262	17	perceptron	perceptron	PROPN
aiti-9227	262	18	based	base	VERB
aiti-9227	262	19	on	on	ADP
aiti-9227	262	20	the	the	DET
aiti-9227	262	21	walk	walk	VERB
aiti-9227	262	22	-	-	PUNCT
aiti-9227	262	23	forward	forward	NOUN
aiti-9227	262	24	validation	validation	NOUN
aiti-9227	262	25	methodology	methodology	NOUN
aiti-9227	262	26	,	,	PUNCT
aiti-9227	262	27	”	"	PUNCT
aiti-9227	262	28	international	international	ADJ
aiti-9227	262	29	journal	journal	NOUN
aiti-9227	262	30	of	of	ADP
aiti-9227	262	31	electrical	electrical	ADJ
aiti-9227	262	32	and	and	CCONJ
aiti-9227	262	33	computer	computer	NOUN
aiti-9227	262	34	engineering	engineering	NOUN
aiti-9227	262	35	,	,	PUNCT
aiti-9227	262	36	vol	vol	NOUN
aiti-9227	262	37	.	.	PROPN
aiti-9227	262	38	11	11	NUM
aiti-9227	262	39	,	,	PUNCT
aiti-9227	262	40	no	no	INTJ
aiti-9227	262	41	.	.	NOUN
aiti-9227	262	42	2	2	NUM
aiti-9227	262	43	,	,	PUNCT
aiti-9227	262	44	pp	pp	ADJ
aiti-9227	262	45	.	.	PUNCT
aiti-9227	262	46	1742	1742	NUM
aiti-9227	262	47	-	-	SYM
aiti-9227	262	48	1751	1751	NUM
aiti-9227	262	49	,	,	PUNCT
aiti-9227	262	50	april	april	PROPN
aiti-9227	262	51	2021	2021	NUM
aiti-9227	262	52	.	.	PUNCT
aiti-9227	263	1	[	[	X
aiti-9227	263	2	9	9	NUM
aiti-9227	263	3	]	]	PUNCT
aiti-9227	263	4	k.	k.	PROPN
aiti-9227	263	5	krishnakumari	krishnakumari	PROPN
aiti-9227	263	6	,	,	PUNCT
aiti-9227	263	7	et	et	PROPN
aiti-9227	263	8	al	al	PROPN
aiti-9227	263	9	.	.	PROPN
aiti-9227	263	10	,	,	PUNCT
aiti-9227	263	11	“	"	PUNCT
aiti-9227	263	12	hyperparameter	hyperparameter	NOUN
aiti-9227	263	13	tuning	tune	VERB
aiti-9227	263	14	in	in	ADP
aiti-9227	263	15	convolutional	convolutional	ADJ
aiti-9227	263	16	neural	neural	ADJ
aiti-9227	263	17	networks	network	NOUN
aiti-9227	263	18	for	for	ADP
aiti-9227	263	19	domain	domain	NOUN
aiti-9227	263	20	adaptation	adaptation	NOUN
aiti-9227	263	21	in	in	ADP
aiti-9227	263	22	sentiment	sentiment	NOUN
aiti-9227	263	23	classification	classification	NOUN
aiti-9227	263	24	(	(	PUNCT
aiti-9227	263	25	htcnn	htcnn	NOUN
aiti-9227	263	26	-	-	PUNCT
aiti-9227	263	27	dasc	dasc	NOUN
aiti-9227	263	28	)	)	PUNCT
aiti-9227	263	29	,	,	PUNCT
aiti-9227	263	30	”	"	PUNCT
aiti-9227	263	31	soft	soft	ADJ
aiti-9227	263	32	computing	computing	NOUN
aiti-9227	263	33	,	,	PUNCT
aiti-9227	263	34	vol	vol	NOUN
aiti-9227	263	35	.	.	PROPN
aiti-9227	263	36	24	24	NUM
aiti-9227	263	37	,	,	PUNCT
aiti-9227	263	38	no	no	INTJ
aiti-9227	263	39	.	.	NOUN
aiti-9227	263	40	5	5	NUM
aiti-9227	263	41	,	,	PUNCT
aiti-9227	263	42	pp	pp	ADJ
aiti-9227	263	43	.	.	PUNCT
aiti-9227	264	1	3511	3511	NUM
aiti-9227	264	2	-	-	SYM
aiti-9227	264	3	3527	3527	NUM
aiti-9227	264	4	,	,	PUNCT
aiti-9227	264	5	march	march	PROPN
aiti-9227	264	6	2020	2020	NUM
aiti-9227	264	7	.	.	PUNCT
aiti-9227	265	1	[	[	X
aiti-9227	265	2	10	10	NUM
aiti-9227	265	3	]	]	X
aiti-9227	265	4	y.	y.	PROPN
aiti-9227	265	5	yu	yu	PROPN
aiti-9227	265	6	,	,	PUNCT
aiti-9227	265	7	et	et	PROPN
aiti-9227	265	8	al	al	PROPN
aiti-9227	265	9	.	.	PROPN
aiti-9227	265	10	,	,	PUNCT
aiti-9227	265	11	“	"	PUNCT
aiti-9227	265	12	short	short	ADJ
aiti-9227	265	13	-	-	PUNCT
aiti-9227	265	14	term	term	NOUN
aiti-9227	265	15	load	load	NOUN
aiti-9227	265	16	forecasting	forecasting	NOUN
aiti-9227	265	17	using	use	VERB
aiti-9227	265	18	deep	deep	ADJ
aiti-9227	265	19	belief	belief	NOUN
aiti-9227	265	20	network	network	NOUN
aiti-9227	265	21	with	with	ADP
aiti-9227	265	22	empirical	empirical	ADJ
aiti-9227	265	23	mode	mode	NOUN
aiti-9227	265	24	decomposition	decomposition	NOUN
aiti-9227	265	25	and	and	CCONJ
aiti-9227	265	26	local	local	ADJ
aiti-9227	265	27	predictor	predictor	NOUN
aiti-9227	265	28	,	,	PUNCT
aiti-9227	265	29	”	"	PUNCT
aiti-9227	265	30	ieee	ieee	NOUN
aiti-9227	265	31	power	power	NOUN
aiti-9227	265	32	and	and	CCONJ
aiti-9227	265	33	energy	energy	NOUN
aiti-9227	265	34	society	society	NOUN
aiti-9227	265	35	general	general	ADJ
aiti-9227	265	36	meeting	meeting	NOUN
aiti-9227	265	37	,	,	PUNCT
aiti-9227	265	38	pp	pp	CCONJ
aiti-9227	265	39	.	.	PUNCT
aiti-9227	266	1	1	1	NUM
aiti-9227	266	2	-	-	SYM
aiti-9227	266	3	5	5	NUM
aiti-9227	266	4	,	,	PUNCT
aiti-9227	266	5	august	august	PROPN
aiti-9227	266	6	2018	2018	NUM
aiti-9227	266	7	.	.	PUNCT
aiti-9227	267	1	[	[	X
aiti-9227	267	2	11	11	NUM
aiti-9227	267	3	]	]	PUNCT
aiti-9227	267	4	x.	x.	NOUN
aiti-9227	267	5	wang	wang	PROPN
aiti-9227	267	6	,	,	PUNCT
aiti-9227	267	7	et	et	PROPN
aiti-9227	267	8	al	al	PROPN
aiti-9227	267	9	.	.	PROPN
aiti-9227	267	10	,	,	PUNCT
aiti-9227	267	11	“	"	PUNCT
aiti-9227	267	12	lstm	lstm	NOUN
aiti-9227	267	13	-	-	PUNCT
aiti-9227	267	14	based	base	VERB
aiti-9227	267	15	short	short	ADJ
aiti-9227	267	16	-	-	PUNCT
aiti-9227	267	17	term	term	NOUN
aiti-9227	267	18	load	load	NOUN
aiti-9227	267	19	forecasting	forecasting	NOUN
aiti-9227	267	20	for	for	ADP
aiti-9227	267	21	building	build	VERB
aiti-9227	267	22	electricity	electricity	NOUN
aiti-9227	267	23	consumption	consumption	NOUN
aiti-9227	267	24	,	,	PUNCT
aiti-9227	267	25	”	"	PUNCT
aiti-9227	267	26	ieee	ieee	NOUN
aiti-9227	267	27	28th	28th	ADJ
aiti-9227	267	28	international	international	ADJ
aiti-9227	267	29	symposium	symposium	NOUN
aiti-9227	267	30	on	on	ADP
aiti-9227	267	31	industrial	industrial	ADJ
aiti-9227	267	32	electronics	electronic	NOUN
aiti-9227	267	33	,	,	PUNCT
aiti-9227	267	34	pp	pp	ADJ
aiti-9227	267	35	.	.	PUNCT
aiti-9227	267	36	1418	1418	NUM
aiti-9227	267	37	-	-	SYM
aiti-9227	267	38	1423	1423	NUM
aiti-9227	267	39	,	,	PUNCT
aiti-9227	267	40	june	june	PROPN
aiti-9227	267	41	2019	2019	NUM
aiti-9227	267	42	.	.	PUNCT
aiti-9227	268	1	[	[	X
aiti-9227	268	2	12	12	NUM
aiti-9227	268	3	]	]	PUNCT
aiti-9227	268	4	m.	m.	NOUN
aiti-9227	268	5	zahid	zahid	PROPN
aiti-9227	268	6	,	,	PUNCT
aiti-9227	268	7	et	et	PROPN
aiti-9227	268	8	al	al	PROPN
aiti-9227	268	9	.	.	PROPN
aiti-9227	268	10	,	,	PUNCT
aiti-9227	268	11	“	"	PUNCT
aiti-9227	268	12	electricity	electricity	NOUN
aiti-9227	268	13	price	price	NOUN
aiti-9227	268	14	and	and	CCONJ
aiti-9227	268	15	load	load	NOUN
aiti-9227	268	16	forecasting	forecasting	NOUN
aiti-9227	268	17	using	use	VERB
aiti-9227	268	18	enhanced	enhance	VERB
aiti-9227	268	19	convolutional	convolutional	ADJ
aiti-9227	268	20	neural	neural	ADJ
aiti-9227	268	21	network	network	NOUN
aiti-9227	268	22	and	and	CCONJ
aiti-9227	268	23	enhanced	enhance	VERB
aiti-9227	268	24	support	support	NOUN
aiti-9227	268	25	vector	vector	NOUN
aiti-9227	268	26	regression	regression	NOUN
aiti-9227	268	27	in	in	ADP
aiti-9227	268	28	smart	smart	ADJ
aiti-9227	268	29	grids	grid	NOUN
aiti-9227	268	30	,	,	PUNCT
aiti-9227	268	31	”	"	PUNCT
aiti-9227	268	32	electronics	electronic	NOUN
aiti-9227	268	33	,	,	PUNCT
aiti-9227	268	34	vol	vol	NOUN
aiti-9227	268	35	.	.	PROPN
aiti-9227	268	36	8	8	NUM
aiti-9227	268	37	,	,	PUNCT
aiti-9227	268	38	no	no	INTJ
aiti-9227	268	39	.	.	NOUN
aiti-9227	268	40	2	2	NUM
aiti-9227	268	41	,	,	PUNCT
aiti-9227	268	42	article	article	NOUN
aiti-9227	268	43	no	no	NOUN
aiti-9227	268	44	.	.	PROPN
aiti-9227	268	45	122	122	NUM
aiti-9227	268	46	,	,	PUNCT
aiti-9227	268	47	2019	2019	NUM
aiti-9227	268	48	.	.	PUNCT
aiti-9227	269	1	[	[	X
aiti-9227	269	2	13	13	NUM
aiti-9227	269	3	]	]	X
aiti-9227	269	4	n.	n.	PROPN
aiti-9227	269	5	m.	m.	PROPN
aiti-9227	269	6	aszemi	aszemi	PROPN
aiti-9227	269	7	,	,	PUNCT
aiti-9227	269	8	et	et	PROPN
aiti-9227	269	9	al	al	PROPN
aiti-9227	269	10	.	.	PROPN
aiti-9227	269	11	,	,	PUNCT
aiti-9227	269	12	“	"	PUNCT
aiti-9227	269	13	hyperparameter	hyperparameter	NOUN
aiti-9227	269	14	optimization	optimization	NOUN
aiti-9227	269	15	in	in	ADP
aiti-9227	269	16	convolutional	convolutional	ADJ
aiti-9227	269	17	neural	neural	ADJ
aiti-9227	269	18	network	network	NOUN
aiti-9227	269	19	using	use	VERB
aiti-9227	269	20	genetic	genetic	ADJ
aiti-9227	269	21	algorithms	algorithm	NOUN
aiti-9227	269	22	,	,	PUNCT
aiti-9227	269	23	”	"	PUNCT
aiti-9227	269	24	international	international	ADJ
aiti-9227	269	25	journal	journal	NOUN
aiti-9227	269	26	of	of	ADP
aiti-9227	269	27	advanced	advanced	ADJ
aiti-9227	269	28	computer	computer	NOUN
aiti-9227	269	29	science	science	NOUN
aiti-9227	269	30	and	and	CCONJ
aiti-9227	269	31	applications	application	NOUN
aiti-9227	269	32	,	,	PUNCT
aiti-9227	269	33	vol	vol	NOUN
aiti-9227	269	34	.	.	PROPN
aiti-9227	269	35	10	10	NUM
aiti-9227	269	36	,	,	PUNCT
aiti-9227	269	37	no	no	INTJ
aiti-9227	269	38	.	.	NOUN
aiti-9227	269	39	6	6	NUM
aiti-9227	269	40	,	,	PUNCT
aiti-9227	269	41	pp	pp	ADJ
aiti-9227	269	42	.	.	PUNCT
aiti-9227	270	1	269	269	NUM
aiti-9227	270	2	-	-	SYM
aiti-9227	270	3	278	278	NUM
aiti-9227	270	4	,	,	PUNCT
aiti-9227	270	5	march	march	PROPN
aiti-9227	270	6	2019	2019	NUM
aiti-9227	270	7	.	.	PUNCT
aiti-9227	271	1	[	[	X
aiti-9227	271	2	14	14	NUM
aiti-9227	271	3	]	]	PUNCT
aiti-9227	271	4	j.	j.	PROPN
aiti-9227	271	5	brownlee	brownlee	PROPN
aiti-9227	271	6	,	,	PUNCT
aiti-9227	271	7	deep	deep	ADJ
aiti-9227	271	8	learning	learning	NOUN
aiti-9227	271	9	for	for	ADP
aiti-9227	271	10	time	time	NOUN
aiti-9227	271	11	series	series	PROPN
aiti-9227	271	12	forecasting	forecasting	PROPN
aiti-9227	271	13	:	:	PUNCT
aiti-9227	271	14	predict	predict	VERB
aiti-9227	271	15	the	the	DET
aiti-9227	271	16	future	future	NOUN
aiti-9227	271	17	with	with	ADP
aiti-9227	271	18	mlps	mlp	NOUN
aiti-9227	271	19	,	,	PUNCT
aiti-9227	271	20	cnns	cnn	NOUN
aiti-9227	271	21	,	,	PUNCT
aiti-9227	271	22	and	and	CCONJ
aiti-9227	271	23	lstms	lstms	NOUN
aiti-9227	271	24	in	in	ADP
aiti-9227	271	25	python	python	PROPN
aiti-9227	271	26	,	,	PUNCT
aiti-9227	271	27	new	new	PROPN
aiti-9227	271	28	york	york	PROPN
aiti-9227	271	29	:	:	PUNCT
aiti-9227	271	30	machine	machine	NOUN
aiti-9227	271	31	learning	learn	VERB
aiti-9227	271	32	mastery	mastery	NOUN
aiti-9227	271	33	,	,	PUNCT
aiti-9227	271	34	2018	2018	NUM
aiti-9227	271	35	.	.	PUNCT
aiti-9227	272	1	[	[	X
aiti-9227	272	2	15	15	NUM
aiti-9227	272	3	]	]	X
aiti-9227	272	4	s.	s.	PROPN
aiti-9227	272	5	mukhopadhyay	mukhopadhyay	PROPN
aiti-9227	272	6	,	,	PUNCT
aiti-9227	272	7	deep	deep	ADJ
aiti-9227	272	8	learning	learning	NOUN
aiti-9227	272	9	and	and	CCONJ
aiti-9227	272	10	neural	neural	ADJ
aiti-9227	272	11	networks	network	NOUN
aiti-9227	272	12	,	,	PUNCT
aiti-9227	272	13	advanced	advanced	ADJ
aiti-9227	272	14	data	datum	NOUN
aiti-9227	272	15	analytics	analytic	NOUN
aiti-9227	272	16	using	use	VERB
aiti-9227	272	17	python	python	PROPN
aiti-9227	272	18	,	,	PUNCT
aiti-9227	272	19	berkeley	berkeley	PROPN
aiti-9227	272	20	:	:	PUNCT
aiti-9227	272	21	apress	apress	NOUN
aiti-9227	272	22	,	,	PUNCT
aiti-9227	272	23	2018	2018	NUM
aiti-9227	272	24	.	.	PUNCT
aiti-9227	273	1	268	268	NUM
aiti-9227	273	2	advances	advance	NOUN
aiti-9227	273	3	in	in	ADP
aiti-9227	273	4	technology	technology	NOUN
aiti-9227	273	5	innovation	innovation	NOUN
aiti-9227	273	6	,	,	PUNCT
aiti-9227	273	7	vol	vol	NOUN
aiti-9227	273	8	.	.	PROPN
aiti-9227	273	9	7	7	NUM
aiti-9227	273	10	,	,	PUNCT
aiti-9227	273	11	no	no	INTJ
aiti-9227	273	12	.	.	NOUN
aiti-9227	273	13	4	4	NUM
aiti-9227	273	14	,	,	PUNCT
aiti-9227	273	15	2022	2022	NUM
aiti-9227	273	16	,	,	PUNCT
aiti-9227	273	17	pp	pp	ADJ
aiti-9227	273	18	.	.	PUNCT
aiti-9227	274	1	258	258	NUM
aiti-9227	274	2	-	-	SYM
aiti-9227	274	3	269	269	NUM
aiti-9227	274	4	[	[	SYM
aiti-9227	274	5	16	16	NUM
aiti-9227	274	6	]	]	X
aiti-9227	274	7	j.	j.	PROPN
aiti-9227	274	8	moolayil	moolayil	PROPN
aiti-9227	274	9	,	,	PUNCT
aiti-9227	274	10	learn	learn	VERB
aiti-9227	274	11	keras	keras	PROPN
aiti-9227	274	12	for	for	ADP
aiti-9227	274	13	deep	deep	ADJ
aiti-9227	274	14	neural	neural	ADJ
aiti-9227	274	15	networks	network	NOUN
aiti-9227	274	16	:	:	PUNCT
aiti-9227	274	17	a	a	DET
aiti-9227	274	18	fast	fast	ADJ
aiti-9227	274	19	-	-	PUNCT
aiti-9227	274	20	track	track	NOUN
aiti-9227	274	21	approach	approach	NOUN
aiti-9227	274	22	to	to	ADP
aiti-9227	274	23	modern	modern	ADJ
aiti-9227	274	24	deep	deep	ADJ
aiti-9227	274	25	learning	learning	NOUN
aiti-9227	274	26	with	with	ADP
aiti-9227	274	27	python	python	PROPN
aiti-9227	274	28	,	,	PUNCT
aiti-9227	274	29	new	new	PROPN
aiti-9227	274	30	york	york	PROPN
aiti-9227	274	31	:	:	PUNCT
aiti-9227	274	32	springer	springer	NOUN
aiti-9227	274	33	,	,	PUNCT
aiti-9227	274	34	2019	2019	NUM
aiti-9227	274	35	.	.	PUNCT
aiti-9227	275	1	[	[	X
aiti-9227	275	2	17	17	NUM
aiti-9227	275	3	]	]	X
aiti-9227	275	4	s.	s.	PROPN
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aiti-9227	275	6	,	,	PUNCT
aiti-9227	275	7	et	et	PROPN
aiti-9227	275	8	al	al	PROPN
aiti-9227	275	9	.	.	PROPN
aiti-9227	275	10	,	,	PUNCT
aiti-9227	275	11	python	python	PROPN
aiti-9227	275	12	machine	machine	NOUN
aiti-9227	275	13	learning	learning	PROPN
aiti-9227	275	14	:	:	PUNCT
aiti-9227	275	15	machine	machine	NOUN
aiti-9227	275	16	learning	learning	NOUN
aiti-9227	275	17	and	and	CCONJ
aiti-9227	275	18	deep	deep	ADJ
aiti-9227	275	19	learning	learning	NOUN
aiti-9227	275	20	with	with	ADP
aiti-9227	275	21	python	python	PROPN
aiti-9227	275	22	,	,	PUNCT
aiti-9227	275	23	scikit	scikit	NOUN
aiti-9227	275	24	-	-	PUNCT
aiti-9227	275	25	learn	learn	VERB
aiti-9227	275	26	,	,	PUNCT
aiti-9227	275	27	and	and	CCONJ
aiti-9227	275	28	tensorflow	tensorflow	NOUN
aiti-9227	275	29	2	2	NUM
aiti-9227	275	30	,	,	PUNCT
aiti-9227	275	31	hoboken	hoboken	PROPN
aiti-9227	275	32	:	:	PUNCT
aiti-9227	275	33	wiley	wiley	PROPN
aiti-9227	275	34	,	,	PUNCT
aiti-9227	275	35	2019	2019	NUM
aiti-9227	275	36	.	.	PUNCT
aiti-9227	276	1	[	[	X
aiti-9227	276	2	18	18	NUM
aiti-9227	276	3	]	]	PUNCT
aiti-9227	276	4	l.	l.	PROPN
aiti-9227	276	5	yang	yang	PROPN
aiti-9227	276	6	,	,	PUNCT
aiti-9227	276	7	et	et	PROPN
aiti-9227	276	8	al	al	PROPN
aiti-9227	276	9	.	.	PROPN
aiti-9227	276	10	,	,	PUNCT
aiti-9227	276	11	“	"	PUNCT
aiti-9227	276	12	on	on	ADP
aiti-9227	276	13	hyperparameter	hyperparameter	NOUN
aiti-9227	276	14	optimization	optimization	NOUN
aiti-9227	276	15	of	of	ADP
aiti-9227	276	16	machine	machine	NOUN
aiti-9227	276	17	learning	learn	VERB
aiti-9227	276	18	algorithms	algorithm	NOUN
aiti-9227	276	19	:	:	PUNCT
aiti-9227	276	20	theory	theory	NOUN
aiti-9227	276	21	and	and	CCONJ
aiti-9227	276	22	practice	practice	NOUN
aiti-9227	276	23	,	,	PUNCT
aiti-9227	276	24	”	"	PUNCT
aiti-9227	276	25	neurocomputing	neurocomputing	NOUN
aiti-9227	276	26	,	,	PUNCT
aiti-9227	276	27	vol	vol	NOUN
aiti-9227	276	28	.	.	PROPN
aiti-9227	277	1	415	415	NUM
aiti-9227	277	2	,	,	PUNCT
aiti-9227	278	1	pp	pp	ADJ
aiti-9227	278	2	.	.	PUNCT
aiti-9227	279	1	295	295	NUM
aiti-9227	279	2	-	-	SYM
aiti-9227	279	3	316	316	NUM
aiti-9227	279	4	,	,	PUNCT
aiti-9227	279	5	july	july	PROPN
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aiti-9227	279	7	.	.	PUNCT
aiti-9227	280	1	[	[	X
aiti-9227	280	2	19	19	NUM
aiti-9227	280	3	]	]	X
aiti-9227	280	4	s.	s.	PROPN
aiti-9227	280	5	motepe	motepe	PROPN
aiti-9227	280	6	,	,	PUNCT
aiti-9227	280	7	et	et	PROPN
aiti-9227	280	8	al	al	PROPN
aiti-9227	280	9	.	.	PROPN
aiti-9227	280	10	,	,	PUNCT
aiti-9227	280	11	“	"	PUNCT
aiti-9227	280	12	power	power	NOUN
aiti-9227	280	13	distribution	distribution	NOUN
aiti-9227	280	14	networks	network	NOUN
aiti-9227	280	15	load	load	NOUN
aiti-9227	280	16	forecasting	forecasting	NOUN
aiti-9227	280	17	using	use	VERB
aiti-9227	280	18	deep	deep	ADJ
aiti-9227	280	19	belief	belief	NOUN
aiti-9227	280	20	networks	network	NOUN
aiti-9227	280	21	:	:	PUNCT
aiti-9227	280	22	the	the	DET
aiti-9227	280	23	south	south	ADJ
aiti-9227	280	24	african	african	ADJ
aiti-9227	280	25	case	case	NOUN
aiti-9227	280	26	,	,	PUNCT
aiti-9227	280	27	”	"	PUNCT
aiti-9227	280	28	ieee	ieee	PROPN
aiti-9227	280	29	jordan	jordan	PROPN
aiti-9227	280	30	international	international	PROPN
aiti-9227	280	31	joint	joint	ADJ
aiti-9227	280	32	conference	conference	NOUN
aiti-9227	280	33	on	on	ADP
aiti-9227	280	34	electrical	electrical	ADJ
aiti-9227	280	35	engineering	engineering	NOUN
aiti-9227	280	36	and	and	CCONJ
aiti-9227	280	37	information	information	NOUN
aiti-9227	280	38	technology	technology	NOUN
aiti-9227	280	39	,	,	PUNCT
aiti-9227	280	40	pp	pp	ADJ
aiti-9227	280	41	.	.	PUNCT
aiti-9227	281	1	507	507	NUM
aiti-9227	281	2	-	-	SYM
aiti-9227	281	3	512	512	NUM
aiti-9227	281	4	,	,	PUNCT
aiti-9227	281	5	april	april	PROPN
aiti-9227	281	6	2019	2019	NUM
aiti-9227	281	7	.	.	PUNCT
aiti-9227	282	1	[	[	X
aiti-9227	282	2	20	20	NUM
aiti-9227	282	3	]	]	PUNCT
aiti-9227	282	4	x.	x.	PROPN
aiti-9227	282	5	zhang	zhang	PROPN
aiti-9227	282	6	,	,	PUNCT
aiti-9227	282	7	et	et	PROPN
aiti-9227	282	8	al	al	PROPN
aiti-9227	282	9	.	.	PROPN
aiti-9227	282	10	,	,	PUNCT
aiti-9227	282	11	“	"	PUNCT
aiti-9227	282	12	deep	deep	ADJ
aiti-9227	282	13	neural	neural	ADJ
aiti-9227	282	14	network	network	NOUN
aiti-9227	282	15	hyperparameter	hyperparameter	NOUN
aiti-9227	282	16	optimization	optimization	NOUN
aiti-9227	282	17	with	with	ADP
aiti-9227	282	18	orthogonal	orthogonal	ADJ
aiti-9227	282	19	array	array	NOUN
aiti-9227	282	20	tuning	tune	VERB
aiti-9227	282	21	,	,	PUNCT
aiti-9227	282	22	”	"	PUNCT
aiti-9227	282	23	international	international	ADJ
aiti-9227	282	24	conference	conference	NOUN
aiti-9227	282	25	on	on	ADP
aiti-9227	282	26	neural	neural	ADJ
aiti-9227	282	27	information	information	NOUN
aiti-9227	282	28	processing	processing	NOUN
aiti-9227	282	29	,	,	PUNCT
aiti-9227	282	30	pp	pp	ADJ
aiti-9227	282	31	.	.	PUNCT
aiti-9227	283	1	287	287	NUM
aiti-9227	283	2	-	-	SYM
aiti-9227	283	3	295	295	NUM
aiti-9227	283	4	,	,	PUNCT
aiti-9227	283	5	december	december	PROPN
aiti-9227	283	6	2019	2019	NUM
aiti-9227	283	7	.	.	PUNCT
aiti-9227	284	1	[	[	X
aiti-9227	284	2	21	21	NUM
aiti-9227	284	3	]	]	PUNCT
aiti-9227	284	4	x.	x.	NOUN
aiti-9227	284	5	song	song	PROPN
aiti-9227	284	6	,	,	PUNCT
aiti-9227	284	7	et	et	PROPN
aiti-9227	284	8	al	al	PROPN
aiti-9227	284	9	.	.	PROPN
aiti-9227	284	10	,	,	PUNCT
aiti-9227	284	11	“	"	PUNCT
aiti-9227	284	12	time	time	NOUN
aiti-9227	284	13	-	-	PUNCT
aiti-9227	284	14	series	series	NOUN
aiti-9227	284	15	well	well	ADJ
aiti-9227	284	16	performance	performance	NOUN
aiti-9227	284	17	prediction	prediction	NOUN
aiti-9227	284	18	based	base	VERB
aiti-9227	284	19	on	on	ADP
aiti-9227	284	20	long	long	ADJ
aiti-9227	284	21	short	short	ADJ
aiti-9227	284	22	-	-	PUNCT
aiti-9227	284	23	term	term	NOUN
aiti-9227	284	24	memory	memory	NOUN
aiti-9227	284	25	(	(	PUNCT
aiti-9227	284	26	lstm	lstm	ADJ
aiti-9227	284	27	)	)	PUNCT
aiti-9227	284	28	neural	neural	ADJ
aiti-9227	284	29	network	network	NOUN
aiti-9227	284	30	model	model	NOUN
aiti-9227	284	31	,	,	PUNCT
aiti-9227	284	32	”	"	PUNCT
aiti-9227	284	33	journal	journal	NOUN
aiti-9227	284	34	of	of	ADP
aiti-9227	284	35	petroleum	petroleum	NOUN
aiti-9227	284	36	science	science	NOUN
aiti-9227	284	37	and	and	CCONJ
aiti-9227	284	38	engineering	engineering	NOUN
aiti-9227	284	39	,	,	PUNCT
aiti-9227	284	40	vol	vol	NOUN
aiti-9227	284	41	.	.	PROPN
aiti-9227	284	42	186	186	NUM
aiti-9227	284	43	,	,	PUNCT
aiti-9227	284	44	article	article	NOUN
aiti-9227	284	45	no	no	NOUN
aiti-9227	284	46	.	.	PROPN
aiti-9227	284	47	106682	106682	NUM
aiti-9227	284	48	,	,	PUNCT
aiti-9227	284	49	march	march	PROPN
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aiti-9227	284	51	.	.	PUNCT
aiti-9227	285	1	[	[	X
aiti-9227	285	2	22	22	NUM
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aiti-9227	285	4	n.	n.	PROPN
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aiti-9227	285	6	bon	bon	PROPN
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aiti-9227	285	8	et	et	PROPN
aiti-9227	285	9	al	al	PROPN
aiti-9227	285	10	.	.	PROPN
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aiti-9227	285	12	“	"	PUNCT
aiti-9227	285	13	fault	fault	VERB
aiti-9227	285	14	identification	identification	NOUN
aiti-9227	285	15	,	,	PUNCT
aiti-9227	285	16	classification	classification	NOUN
aiti-9227	285	17	,	,	PUNCT
aiti-9227	285	18	and	and	CCONJ
aiti-9227	285	19	location	location	NOUN
aiti-9227	285	20	on	on	ADP
aiti-9227	285	21	transmission	transmission	NOUN
aiti-9227	285	22	lines	line	NOUN
aiti-9227	285	23	using	use	VERB
aiti-9227	285	24	combined	combined	ADJ
aiti-9227	285	25	machine	machine	NOUN
aiti-9227	285	26	learning	learning	NOUN
aiti-9227	285	27	methods	method	NOUN
aiti-9227	285	28	,	,	PUNCT
aiti-9227	285	29	”	"	PUNCT
aiti-9227	285	30	international	international	ADJ
aiti-9227	285	31	journal	journal	NOUN
aiti-9227	285	32	of	of	ADP
aiti-9227	285	33	engineering	engineering	NOUN
aiti-9227	285	34	and	and	CCONJ
aiti-9227	285	35	technology	technology	NOUN
aiti-9227	285	36	innovation	innovation	NOUN
aiti-9227	285	37	,	,	PUNCT
aiti-9227	285	38	vol	vol	NOUN
aiti-9227	285	39	.	.	PROPN
aiti-9227	285	40	12	12	NUM
aiti-9227	285	41	,	,	PUNCT
aiti-9227	285	42	no	no	INTJ
aiti-9227	285	43	.	.	NOUN
aiti-9227	285	44	2	2	NUM
aiti-9227	285	45	,	,	PUNCT
aiti-9227	285	46	pp	pp	ADJ
aiti-9227	285	47	.	.	PUNCT
aiti-9227	286	1	91	91	NUM
aiti-9227	286	2	-	-	SYM
aiti-9227	286	3	109	109	NUM
aiti-9227	286	4	,	,	PUNCT
aiti-9227	286	5	february	february	NOUN
aiti-9227	286	6	2022	2022	NUM
aiti-9227	286	7	.	.	PUNCT
aiti-9227	287	1	[	[	X
aiti-9227	287	2	23	23	NUM
aiti-9227	287	3	]	]	X
aiti-9227	287	4	u.	u.	NOUN
aiti-9227	287	5	michelucci	michelucci	PROPN
aiti-9227	287	6	,	,	PUNCT
aiti-9227	287	7	applied	apply	VERB
aiti-9227	287	8	deep	deep	ADJ
aiti-9227	287	9	learning	learning	NOUN
aiti-9227	287	10	:	:	PUNCT
aiti-9227	287	11	a	a	DET
aiti-9227	287	12	case	case	NOUN
aiti-9227	287	13	-	-	PUNCT
aiti-9227	287	14	based	base	VERB
aiti-9227	287	15	approach	approach	NOUN
aiti-9227	287	16	to	to	ADP
aiti-9227	287	17	understanding	understand	VERB
aiti-9227	287	18	deep	deep	ADJ
aiti-9227	287	19	neural	neural	ADJ
aiti-9227	287	20	networks	network	NOUN
aiti-9227	287	21	,	,	PUNCT
aiti-9227	287	22	new	new	PROPN
aiti-9227	287	23	york	york	PROPN
aiti-9227	287	24	:	:	PUNCT
aiti-9227	287	25	apress	apress	PROPN
aiti-9227	287	26	,	,	PUNCT
aiti-9227	287	27	2018	2018	NUM
aiti-9227	287	28	.	.	PUNCT
aiti-9227	288	1	[	[	X
aiti-9227	288	2	24	24	NUM
aiti-9227	288	3	]	]	X
aiti-9227	288	4	s.	s.	PROPN
aiti-9227	288	5	v.	v.	PROPN
aiti-9227	288	6	subramanian	subramanian	PROPN
aiti-9227	288	7	,	,	PUNCT
aiti-9227	288	8	et	et	PROPN
aiti-9227	288	9	al	al	PROPN
aiti-9227	288	10	.	.	PROPN
aiti-9227	288	11	,	,	PUNCT
aiti-9227	288	12	“	"	PUNCT
aiti-9227	288	13	deep	deep	ADJ
aiti-9227	288	14	-	-	PUNCT
aiti-9227	288	15	learning	learn	VERB
aiti-9227	288	16	based	base	VERB
aiti-9227	288	17	time	time	NOUN
aiti-9227	288	18	series	series	PROPN
aiti-9227	288	19	forecasting	forecasting	NOUN
aiti-9227	288	20	of	of	ADP
aiti-9227	288	21	go	go	VERB
aiti-9227	288	22	-	-	PUNCT
aiti-9227	288	23	around	around	ADP
aiti-9227	288	24	incidents	incident	NOUN
aiti-9227	288	25	in	in	ADP
aiti-9227	288	26	the	the	DET
aiti-9227	288	27	national	national	ADJ
aiti-9227	288	28	airspace	airspace	NOUN
aiti-9227	288	29	system	system	NOUN
aiti-9227	288	30	,	,	PUNCT
aiti-9227	288	31	”	"	PUNCT
aiti-9227	288	32	aiaa	aiaa	ADJ
aiti-9227	288	33	modeling	modeling	NOUN
aiti-9227	288	34	and	and	CCONJ
aiti-9227	288	35	simulation	simulation	NOUN
aiti-9227	288	36	technologies	technology	NOUN
aiti-9227	288	37	conference	conference	NOUN
aiti-9227	288	38	,	,	PUNCT
aiti-9227	288	39	article	article	NOUN
aiti-9227	288	40	no	no	NOUN
aiti-9227	288	41	.	.	PROPN
aiti-9227	288	42	0424	0424	NUM
aiti-9227	288	43	,	,	PUNCT
aiti-9227	288	44	january	january	PROPN
aiti-9227	288	45	2018	2018	NUM
aiti-9227	288	46	.	.	PUNCT
aiti-9227	289	1	[	[	X
aiti-9227	289	2	25	25	NUM
aiti-9227	289	3	]	]	PUNCT
aiti-9227	289	4	t.	t.	PROPN
aiti-9227	289	5	t.	t.	PROPN
aiti-9227	289	6	ngoc	ngoc	PROPN
aiti-9227	289	7	,	,	PUNCT
aiti-9227	289	8	et	et	PROPN
aiti-9227	289	9	al	al	PROPN
aiti-9227	289	10	.	.	PROPN
aiti-9227	289	11	,	,	PUNCT
aiti-9227	289	12	“	"	PUNCT
aiti-9227	289	13	support	support	NOUN
aiti-9227	289	14	vector	vector	NOUN
aiti-9227	289	15	regression	regression	NOUN
aiti-9227	289	16	based	base	VERB
aiti-9227	289	17	on	on	ADP
aiti-9227	289	18	grid	grid	NOUN
aiti-9227	289	19	search	search	NOUN
aiti-9227	289	20	method	method	NOUN
aiti-9227	289	21	of	of	ADP
aiti-9227	289	22	hyperparameters	hyperparameter	NOUN
aiti-9227	289	23	for	for	ADP
aiti-9227	289	24	load	load	NOUN
aiti-9227	289	25	forecasting	forecasting	NOUN
aiti-9227	289	26	,	,	PUNCT
aiti-9227	289	27	”	"	PUNCT
aiti-9227	289	28	acta	acta	PROPN
aiti-9227	289	29	polytechnica	polytechnica	PROPN
aiti-9227	289	30	hungarica	hungarica	PROPN
aiti-9227	289	31	,	,	PUNCT
aiti-9227	289	32	vol	vol	NOUN
aiti-9227	289	33	.	.	PROPN
aiti-9227	289	34	18	18	NUM
aiti-9227	289	35	,	,	PUNCT
aiti-9227	289	36	no	no	INTJ
aiti-9227	289	37	.	.	NOUN
aiti-9227	289	38	2	2	NUM
aiti-9227	289	39	,	,	PUNCT
aiti-9227	289	40	pp	pp	ADJ
aiti-9227	289	41	.	.	PUNCT
aiti-9227	290	1	143	143	NUM
aiti-9227	290	2	-	-	SYM
aiti-9227	290	3	158	158	NUM
aiti-9227	290	4	,	,	PUNCT
aiti-9227	290	5	january	january	PROPN
aiti-9227	290	6	2021	2021	NUM
aiti-9227	290	7	.	.	PUNCT
aiti-9227	291	1	[	[	X
aiti-9227	291	2	26	26	NUM
aiti-9227	291	3	]	]	PUNCT
aiti-9227	291	4	a.	a.	PROPN
aiti-9227	291	5	s.	s.	PROPN
aiti-9227	291	6	girsang	girsang	PROPN
aiti-9227	291	7	,	,	PUNCT
aiti-9227	291	8	et	et	PROPN
aiti-9227	291	9	al	al	PROPN
aiti-9227	291	10	.	.	PROPN
aiti-9227	291	11	,	,	PUNCT
aiti-9227	291	12	“	"	PUNCT
aiti-9227	291	13	stock	stock	NOUN
aiti-9227	291	14	price	price	NOUN
aiti-9227	291	15	prediction	prediction	NOUN
aiti-9227	291	16	using	use	VERB
aiti-9227	291	17	lstm	lstm	NOUN
aiti-9227	291	18	and	and	CCONJ
aiti-9227	291	19	search	search	NOUN
aiti-9227	291	20	economics	economic	NOUN
aiti-9227	291	21	optimization	optimization	NOUN
aiti-9227	291	22	,	,	PUNCT
aiti-9227	291	23	”	"	PUNCT
aiti-9227	291	24	iaeng	iaeng	PROPN
aiti-9227	291	25	international	international	PROPN
aiti-9227	291	26	journal	journal	PROPN
aiti-9227	291	27	of	of	ADP
aiti-9227	291	28	computer	computer	NOUN
aiti-9227	291	29	science	science	NOUN
aiti-9227	291	30	,	,	PUNCT
aiti-9227	291	31	vol	vol	NOUN
aiti-9227	291	32	.	.	PROPN
aiti-9227	292	1	47	47	NUM
aiti-9227	292	2	,	,	PUNCT
aiti-9227	292	3	no	no	INTJ
aiti-9227	292	4	.	.	NOUN
aiti-9227	292	5	4	4	NUM
aiti-9227	292	6	,	,	PUNCT
aiti-9227	292	7	pp	pp	ADJ
aiti-9227	292	8	.	.	PUNCT
aiti-9227	293	1	758	758	NUM
aiti-9227	293	2	-	-	SYM
aiti-9227	293	3	764	764	NUM
aiti-9227	293	4	,	,	PUNCT
aiti-9227	293	5	november	november	PROPN
aiti-9227	293	6	2020	2020	NUM
aiti-9227	293	7	.	.	PUNCT
aiti-9227	294	1	copyright	copyright	NOUN
aiti-9227	294	2	©	©	PROPN
aiti-9227	294	3	by	by	ADP
aiti-9227	294	4	the	the	DET
aiti-9227	294	5	authors	author	NOUN
aiti-9227	294	6	.	.	PUNCT
aiti-9227	295	1	licensee	licensee	PROPN
aiti-9227	295	2	taeti	taeti	PROPN
aiti-9227	295	3	,	,	PUNCT
aiti-9227	295	4	taiwan	taiwan	PROPN
aiti-9227	295	5	.	.	PUNCT
aiti-9227	296	1	this	this	DET
aiti-9227	296	2	article	article	NOUN
aiti-9227	296	3	is	be	AUX
aiti-9227	296	4	an	an	DET
aiti-9227	296	5	open	open	ADJ
aiti-9227	296	6	access	access	NOUN
aiti-9227	296	7	article	article	NOUN
aiti-9227	296	8	distributed	distribute	VERB
aiti-9227	296	9	under	under	ADP
aiti-9227	296	10	the	the	DET
aiti-9227	296	11	terms	term	NOUN
aiti-9227	296	12	and	and	CCONJ
aiti-9227	296	13	conditions	condition	NOUN
aiti-9227	296	14	of	of	ADP
aiti-9227	296	15	the	the	DET
aiti-9227	296	16	creative	creative	ADJ
aiti-9227	296	17	commons	common	NOUN
aiti-9227	296	18	attribution	attribution	NOUN
aiti-9227	296	19	(	(	PUNCT
aiti-9227	296	20	cc	cc	NOUN
aiti-9227	296	21	by	by	ADP
aiti-9227	296	22	-	-	PUNCT
aiti-9227	296	23	nc	nc	NOUN
aiti-9227	296	24	)	)	PUNCT
aiti-9227	296	25	license	license	NOUN
aiti-9227	296	26	(	(	PUNCT
aiti-9227	296	27	https://creativecommons.org/licenses/by-nc/4.0/	https://creativecommons.org/licenses/by-nc/4.0/	NOUN
aiti-9227	296	28	)	)	PUNCT
aiti-9227	296	29	.	.	PUNCT
aiti-9227	297	1	269	269	NUM