id	sid	tid	token	lemma	pos
ajst-17687	1	1	academic	academic	ADJ
ajst-17687	1	2	journal	journal	NOUN
ajst-17687	1	3	of	of	ADP
ajst-17687	1	4	science	science	NOUN
ajst-17687	1	5	and	and	CCONJ
ajst-17687	1	6	technology	technology	NOUN
ajst-17687	1	7	issn	issn	NOUN
ajst-17687	1	8	:	:	PUNCT
ajst-17687	1	9	2771	2771	NUM
ajst-17687	1	10	-	-	SYM
ajst-17687	1	11	3032	3032	NUM
ajst-17687	1	12	|	|	NOUN
ajst-17687	1	13	vol	vol	NOUN
ajst-17687	1	14	.	.	PROPN
ajst-17687	2	1	9	9	NUM
ajst-17687	2	2	,	,	PUNCT
ajst-17687	2	3	no	no	INTJ
ajst-17687	2	4	.	.	NOUN
ajst-17687	2	5	2	2	NUM
ajst-17687	2	6	,	,	PUNCT
ajst-17687	2	7	2024	2024	NUM
ajst-17687	2	8	133	133	NUM
ajst-17687	2	9	optimizing	optimize	VERB
ajst-17687	2	10	energy	energy	NOUN
ajst-17687	2	11	efficiency	efficiency	NOUN
ajst-17687	2	12	in	in	ADP
ajst-17687	2	13	eaf	eaf	PROPN
ajst-17687	2	14	steel	steel	NOUN
ajst-17687	2	15	production	production	NOUN
ajst-17687	2	16	:	:	PUNCT
ajst-17687	2	17	a	a	DET
ajst-17687	2	18	data‐driven	data‐driven	ADJ
ajst-17687	2	19	approach	approach	NOUN
ajst-17687	2	20	yubin	yubin	PROPN
ajst-17687	2	21	zhao	zhao	PROPN
ajst-17687	2	22	,	,	PUNCT
ajst-17687	2	23	yize	yize	VERB
ajst-17687	2	24	qi	qi	PROPN
ajst-17687	2	25	*	*	PROPN
ajst-17687	2	26	,	,	PUNCT
ajst-17687	2	27	jose	jose	PROPN
ajst-17687	2	28	diogo	diogo	PROPN
ajst-17687	2	29	franco	franco	PROPN
ajst-17687	2	30	viveiros	viveiros	PROPN
ajst-17687	2	31	school	school	NOUN
ajst-17687	2	32	of	of	ADP
ajst-17687	2	33	engineering	engineering	NOUN
ajst-17687	2	34	,	,	PUNCT
ajst-17687	2	35	cardiff	cardiff	PROPN
ajst-17687	2	36	university	university	NOUN
ajst-17687	2	37	,	,	PUNCT
ajst-17687	2	38	the	the	DET
ajst-17687	2	39	parade	parade	NOUN
ajst-17687	2	40	,	,	PUNCT
ajst-17687	2	41	cardiff	cardiff	NOUN
ajst-17687	2	42	,	,	PUNCT
ajst-17687	2	43	wales	wale	NOUN
ajst-17687	2	44	,	,	PUNCT
ajst-17687	2	45	cf24	cf24	PROPN
ajst-17687	2	46	3aa	3aa	PROPN
ajst-17687	2	47	,	,	PUNCT
ajst-17687	2	48	uk	uk	PROPN
ajst-17687	2	49	*	*	PUNCT
ajst-17687	2	50	corresponding	correspond	VERB
ajst-17687	2	51	author	author	NOUN
ajst-17687	2	52	abstract	abstract	NOUN
ajst-17687	2	53	:	:	PUNCT
ajst-17687	2	54	the	the	DET
ajst-17687	2	55	article	article	NOUN
ajst-17687	2	56	’s	’s	PART
ajst-17687	2	57	main	main	ADJ
ajst-17687	2	58	research	research	NOUN
ajst-17687	2	59	mission	mission	NOUN
ajst-17687	2	60	is	be	AUX
ajst-17687	2	61	making	make	VERB
ajst-17687	2	62	predictions	prediction	NOUN
ajst-17687	2	63	about	about	ADP
ajst-17687	2	64	energy	energy	NOUN
ajst-17687	2	65	usage	usage	NOUN
ajst-17687	2	66	within	within	ADP
ajst-17687	2	67	an	an	DET
ajst-17687	2	68	eaf	eaf	NOUN
ajst-17687	2	69	steel	steel	NOUN
ajst-17687	2	70	production	production	NOUN
ajst-17687	2	71	operation	operation	NOUN
ajst-17687	2	72	and	and	CCONJ
ajst-17687	2	73	being	be	AUX
ajst-17687	2	74	able	able	ADJ
ajst-17687	2	75	to	to	PART
ajst-17687	2	76	reliably	reliably	ADV
ajst-17687	2	77	predict	predict	VERB
ajst-17687	2	78	energy	energy	NOUN
ajst-17687	2	79	consumption	consumption	NOUN
ajst-17687	2	80	.	.	PUNCT
ajst-17687	3	1	besides	besides	SCONJ
ajst-17687	3	2	,	,	PUNCT
ajst-17687	3	3	we	we	PRON
ajst-17687	3	4	analysed	analyse	VERB
ajst-17687	3	5	the	the	DET
ajst-17687	3	6	relationship	relationship	NOUN
ajst-17687	3	7	between	between	ADP
ajst-17687	3	8	energy	energy	NOUN
ajst-17687	3	9	consumption	consumption	NOUN
ajst-17687	3	10	in	in	ADP
ajst-17687	3	11	electric	electric	ADJ
ajst-17687	3	12	arc	arc	NOUN
ajst-17687	3	13	furnace	furnace	NOUN
ajst-17687	3	14	(	(	PUNCT
ajst-17687	3	15	eaf	eaf	NOUN
ajst-17687	3	16	)	)	PUNCT
ajst-17687	3	17	production	production	NOUN
ajst-17687	3	18	processes	process	NOUN
ajst-17687	3	19	by	by	ADP
ajst-17687	3	20	the	the	DET
ajst-17687	3	21	method	method	NOUN
ajst-17687	3	22	of	of	ADP
ajst-17687	3	23	data	datum	NOUN
ajst-17687	3	24	mining	mining	NOUN
ajst-17687	3	25	,	,	PUNCT
ajst-17687	3	26	so	so	SCONJ
ajst-17687	3	27	we	we	PRON
ajst-17687	3	28	could	could	AUX
ajst-17687	3	29	obtain	obtain	VERB
ajst-17687	3	30	accurate	accurate	ADJ
ajst-17687	3	31	forecasts	forecast	NOUN
ajst-17687	3	32	to	to	PART
ajst-17687	3	33	optimize	optimize	VERB
ajst-17687	3	34	production	production	NOUN
ajst-17687	3	35	costs	cost	NOUN
ajst-17687	3	36	and	and	CCONJ
ajst-17687	3	37	maximize	maximize	VERB
ajst-17687	3	38	energy	energy	NOUN
ajst-17687	3	39	efficiency	efficiency	NOUN
ajst-17687	3	40	.	.	PUNCT
ajst-17687	4	1	last	last	ADJ
ajst-17687	4	2	but	but	CCONJ
ajst-17687	4	3	not	not	PART
ajst-17687	4	4	least	least	ADJ
ajst-17687	4	5	,	,	PUNCT
ajst-17687	4	6	by	by	ADP
ajst-17687	4	7	using	use	VERB
ajst-17687	4	8	decision	decision	NOUN
ajst-17687	4	9	tree	tree	NOUN
ajst-17687	4	10	,	,	PUNCT
ajst-17687	4	11	random	random	ADJ
ajst-17687	4	12	tree	tree	NOUN
ajst-17687	4	13	algorithms	algorithm	NOUN
ajst-17687	4	14	and	and	CCONJ
ajst-17687	4	15	multilayer	multilayer	ADJ
ajst-17687	4	16	perceptron	perceptron	PROPN
ajst-17687	4	17	models	model	NOUN
ajst-17687	4	18	,	,	PUNCT
ajst-17687	4	19	we	we	PRON
ajst-17687	4	20	can	can	AUX
ajst-17687	4	21	easily	easily	ADV
ajst-17687	4	22	achieve	achieve	VERB
ajst-17687	4	23	the	the	DET
ajst-17687	4	24	intended	intend	VERB
ajst-17687	4	25	goals	goal	NOUN
ajst-17687	4	26	of	of	ADP
ajst-17687	4	27	efficient	efficient	ADJ
ajst-17687	4	28	use	use	NOUN
ajst-17687	4	29	of	of	ADP
ajst-17687	4	30	energy	energy	NOUN
ajst-17687	4	31	and	and	CCONJ
ajst-17687	4	32	provide	provide	VERB
ajst-17687	4	33	a	a	DET
ajst-17687	4	34	better	well	ADJ
ajst-17687	4	35	plan	plan	NOUN
ajst-17687	4	36	for	for	ADP
ajst-17687	4	37	electric	electric	ADJ
ajst-17687	4	38	arc	arc	NOUN
ajst-17687	4	39	furnace	furnace	NOUN
ajst-17687	4	40	(	(	PUNCT
ajst-17687	4	41	eaf	eaf	NOUN
ajst-17687	4	42	)	)	PUNCT
ajst-17687	4	43	.	.	PUNCT
ajst-17687	5	1	keywords	keyword	NOUN
ajst-17687	5	2	:	:	PUNCT
ajst-17687	5	3	eaf	eaf	NOUN
ajst-17687	5	4	,	,	PUNCT
ajst-17687	5	5	weka	weka	PROPN
ajst-17687	5	6	,	,	PUNCT
ajst-17687	5	7	data	datum	NOUN
ajst-17687	5	8	analysis	analysis	NOUN
ajst-17687	5	9	,	,	PUNCT
ajst-17687	5	10	data	data	NOUN
ajst-17687	5	11	modelling	modelling	NOUN
ajst-17687	5	12	.	.	PUNCT
ajst-17687	6	1	1	1	X
ajst-17687	6	2	.	.	X
ajst-17687	6	3	introduction	introduction	NOUN
ajst-17687	6	4	nowadays	nowadays	ADV
ajst-17687	6	5	,	,	PUNCT
ajst-17687	6	6	with	with	ADP
ajst-17687	6	7	the	the	DET
ajst-17687	6	8	development	development	NOUN
ajst-17687	6	9	of	of	ADP
ajst-17687	6	10	modern	modern	ADJ
ajst-17687	6	11	industry	industry	NOUN
ajst-17687	6	12	,	,	PUNCT
ajst-17687	6	13	we	we	PRON
ajst-17687	6	14	ca	can	AUX
ajst-17687	6	15	n’t	not	PART
ajst-17687	6	16	ignore	ignore	VERB
ajst-17687	6	17	that	that	SCONJ
ajst-17687	6	18	the	the	DET
ajst-17687	6	19	steel	steel	NOUN
ajst-17687	6	20	industry	industry	NOUN
ajst-17687	6	21	is	be	AUX
ajst-17687	6	22	still	still	ADV
ajst-17687	6	23	playing	play	VERB
ajst-17687	6	24	a	a	DET
ajst-17687	6	25	very	very	ADV
ajst-17687	6	26	important	important	ADJ
ajst-17687	6	27	role	role	NOUN
ajst-17687	6	28	in	in	ADP
ajst-17687	6	29	the	the	DET
ajst-17687	6	30	industry	industry	NOUN
ajst-17687	6	31	of	of	ADP
ajst-17687	6	32	any	any	DET
ajst-17687	6	33	country	country	NOUN
ajst-17687	6	34	around	around	ADP
ajst-17687	6	35	whole	whole	ADJ
ajst-17687	6	36	world	world	NOUN
ajst-17687	6	37	.	.	PUNCT
ajst-17687	7	1	britain	britain	PROPN
ajst-17687	7	2	and	and	CCONJ
ajst-17687	7	3	other	other	ADJ
ajst-17687	7	4	european	european	ADJ
ajst-17687	7	5	countries	country	NOUN
ajst-17687	7	6	are	be	AUX
ajst-17687	7	7	the	the	DET
ajst-17687	7	8	earliest	early	ADJ
ajst-17687	7	9	batch	batch	NOUN
ajst-17687	7	10	of	of	ADP
ajst-17687	7	11	countries	country	NOUN
ajst-17687	7	12	to	to	PART
ajst-17687	7	13	develop	develop	VERB
ajst-17687	7	14	the	the	DET
ajst-17687	7	15	steel	steel	NOUN
ajst-17687	7	16	during	during	ADP
ajst-17687	7	17	the	the	DET
ajst-17687	7	18	first	first	ADJ
ajst-17687	7	19	industrial	industrial	ADJ
ajst-17687	7	20	revolution	revolution	NOUN
ajst-17687	7	21	,	,	PUNCT
ajst-17687	7	22	so	so	ADV
ajst-17687	7	23	over	over	ADP
ajst-17687	7	24	the	the	DET
ajst-17687	7	25	past	past	ADJ
ajst-17687	7	26	century	century	NOUN
ajst-17687	7	27	,	,	PUNCT
ajst-17687	7	28	scientist	scientist	NOUN
ajst-17687	7	29	developed	develop	VERB
ajst-17687	7	30	many	many	ADJ
ajst-17687	7	31	ways	way	NOUN
ajst-17687	7	32	in	in	ADP
ajst-17687	7	33	order	order	NOUN
ajst-17687	7	34	to	to	PART
ajst-17687	7	35	do	do	AUX
ajst-17687	7	36	the	the	DET
ajst-17687	7	37	steel	steel	NOUN
ajst-17687	7	38	production	production	NOUN
ajst-17687	7	39	,	,	PUNCT
ajst-17687	7	40	such	such	ADJ
ajst-17687	7	41	as	as	ADP
ajst-17687	7	42	smelting	smelting	NOUN
ajst-17687	7	43	reduction	reduction	NOUN
ajst-17687	7	44	,	,	PUNCT
ajst-17687	7	45	gas	gas	NOUN
ajst-17687	7	46	-	-	PUNCT
ajst-17687	7	47	based	base	VERB
ajst-17687	7	48	direct	direct	ADJ
ajst-17687	7	49	reduction	reduction	NOUN
ajst-17687	7	50	and	and	CCONJ
ajst-17687	7	51	direct	direct	ADJ
ajst-17687	7	52	reduction	reduction	NOUN
ajst-17687	7	53	ironmaking	ironmaking	NOUN
ajst-17687	7	54	,	,	PUNCT
ajst-17687	7	55	or	or	CCONJ
ajst-17687	7	56	dri	dri	PROPN
ajst-17687	7	57	.	.	PUNCT
ajst-17687	8	1	besides	besides	SCONJ
ajst-17687	8	2	,	,	PUNCT
ajst-17687	8	3	one	one	NUM
ajst-17687	8	4	of	of	ADP
ajst-17687	8	5	the	the	DET
ajst-17687	8	6	most	most	ADV
ajst-17687	8	7	widely	widely	ADV
ajst-17687	8	8	used	use	VERB
ajst-17687	8	9	is	be	AUX
ajst-17687	8	10	the	the	DET
ajst-17687	8	11	electric	electric	ADJ
ajst-17687	8	12	arc	arc	NOUN
ajst-17687	8	13	furnace	furnace	NOUN
ajst-17687	8	14	.	.	PUNCT
ajst-17687	9	1	although	although	SCONJ
ajst-17687	9	2	the	the	DET
ajst-17687	9	3	electric	electric	ADJ
ajst-17687	9	4	arc	arc	NOUN
ajst-17687	9	5	furnace	furnace	NOUN
ajst-17687	9	6	is	be	AUX
ajst-17687	9	7	the	the	DET
ajst-17687	9	8	most	most	ADV
ajst-17687	9	9	widely	widely	ADV
ajst-17687	9	10	used	use	VERB
ajst-17687	9	11	way	way	NOUN
ajst-17687	9	12	of	of	ADP
ajst-17687	9	13	steel	steel	NOUN
ajst-17687	9	14	production	production	NOUN
ajst-17687	9	15	,	,	PUNCT
ajst-17687	9	16	there	there	PRON
ajst-17687	9	17	are	be	VERB
ajst-17687	9	18	still	still	ADV
ajst-17687	9	19	many	many	ADJ
ajst-17687	9	20	problems	problem	NOUN
ajst-17687	9	21	occurred	occur	VERB
ajst-17687	9	22	during	during	ADP
ajst-17687	9	23	the	the	DET
ajst-17687	9	24	process	process	NOUN
ajst-17687	9	25	,	,	PUNCT
ajst-17687	9	26	which	which	PRON
ajst-17687	9	27	can	can	AUX
ajst-17687	9	28	cause	cause	VERB
ajst-17687	9	29	a	a	DET
ajst-17687	9	30	series	series	NOUN
ajst-17687	9	31	of	of	ADP
ajst-17687	9	32	problems	problem	NOUN
ajst-17687	9	33	.	.	PUNCT
ajst-17687	10	1	like	like	ADP
ajst-17687	10	2	furnace	furnace	NOUN
ajst-17687	10	3	process	process	NOUN
ajst-17687	10	4	control	control	NOUN
ajst-17687	10	5	,	,	PUNCT
ajst-17687	10	6	slag	slag	NOUN
ajst-17687	10	7	control	control	NOUN
ajst-17687	10	8	and	and	CCONJ
ajst-17687	10	9	improvement	improvement	NOUN
ajst-17687	10	10	of	of	ADP
ajst-17687	10	11	energy	energy	NOUN
ajst-17687	10	12	efficiency	efficiency	NOUN
ajst-17687	10	13	.	.	PUNCT
ajst-17687	11	1	in	in	ADP
ajst-17687	11	2	order	order	NOUN
ajst-17687	11	3	to	to	PART
ajst-17687	11	4	solve	solve	VERB
ajst-17687	11	5	the	the	DET
ajst-17687	11	6	problems	problem	NOUN
ajst-17687	11	7	,	,	PUNCT
ajst-17687	11	8	we	we	PRON
ajst-17687	11	9	must	must	AUX
ajst-17687	11	10	take	take	VERB
ajst-17687	11	11	the	the	DET
ajst-17687	11	12	full	full	ADJ
ajst-17687	11	13	advantages	advantage	NOUN
ajst-17687	11	14	of	of	ADP
ajst-17687	11	15	date	date	NOUN
ajst-17687	11	16	,	,	PUNCT
ajst-17687	11	17	which	which	PRON
ajst-17687	11	18	,	,	PUNCT
ajst-17687	11	19	in	in	ADP
ajst-17687	11	20	that	that	DET
ajst-17687	11	21	case	case	NOUN
ajst-17687	11	22	,	,	PUNCT
ajst-17687	11	23	a	a	DET
ajst-17687	11	24	suitable	suitable	ADJ
ajst-17687	11	25	type	type	NOUN
ajst-17687	11	26	of	of	ADP
ajst-17687	11	27	data	data	NOUN
ajst-17687	11	28	mining	mining	NOUN
ajst-17687	11	29	formulation	formulation	NOUN
ajst-17687	11	30	has	have	VERB
ajst-17687	11	31	to	to	PART
ajst-17687	11	32	be	be	AUX
ajst-17687	11	33	considered	consider	VERB
ajst-17687	11	34	,	,	PUNCT
ajst-17687	11	35	and	and	CCONJ
ajst-17687	11	36	it	it	PRON
ajst-17687	11	37	not	not	PART
ajst-17687	11	38	only	only	ADV
ajst-17687	11	39	can	can	AUX
ajst-17687	11	40	deal	deal	VERB
ajst-17687	11	41	with	with	ADP
ajst-17687	11	42	the	the	DET
ajst-17687	11	43	problems	problem	NOUN
ajst-17687	11	44	we	we	PRON
ajst-17687	11	45	meet	meet	VERB
ajst-17687	11	46	during	during	ADP
ajst-17687	11	47	the	the	DET
ajst-17687	11	48	steel	steel	NOUN
ajst-17687	11	49	production	production	NOUN
ajst-17687	11	50	,	,	PUNCT
ajst-17687	11	51	but	but	CCONJ
ajst-17687	11	52	also	also	ADV
ajst-17687	11	53	have	have	VERB
ajst-17687	11	54	potential	potential	ADJ
ajst-17687	11	55	opportunity	opportunity	NOUN
ajst-17687	11	56	,	,	PUNCT
ajst-17687	11	57	which	which	PRON
ajst-17687	11	58	can	can	AUX
ajst-17687	11	59	lead	lead	VERB
ajst-17687	11	60	the	the	DET
ajst-17687	11	61	steel	steel	NOUN
ajst-17687	11	62	industry	industry	NOUN
ajst-17687	11	63	into	into	ADP
ajst-17687	11	64	a	a	DET
ajst-17687	11	65	brighter	bright	ADJ
ajst-17687	11	66	future	future	NOUN
ajst-17687	11	67	.	.	PUNCT
ajst-17687	12	1	2	2	X
ajst-17687	12	2	.	.	X
ajst-17687	12	3	data	datum	NOUN
ajst-17687	12	4	understanding	understand	VERB
ajst-17687	12	5	2.1	2.1	NUM
ajst-17687	12	6	.	.	PUNCT
ajst-17687	13	1	data	datum	NOUN
ajst-17687	13	2	exploration	exploration	NOUN
ajst-17687	13	3	and	and	CCONJ
ajst-17687	13	4	description	description	NOUN
ajst-17687	13	5	we	we	PRON
ajst-17687	13	6	can	can	AUX
ajst-17687	13	7	analyse	analyse	VERB
ajst-17687	13	8	the	the	DET
ajst-17687	13	9	data	datum	NOUN
ajst-17687	13	10	given	give	VERB
ajst-17687	13	11	in	in	ADP
ajst-17687	13	12	appendix	appendix	NOUN
ajst-17687	13	13	(	(	PUNCT
ajst-17687	13	14	fig	fig	NOUN
ajst-17687	13	15	1	1	NUM
ajst-17687	13	16	,	,	PUNCT
ajst-17687	13	17	fig2	fig2	PROPN
ajst-17687	13	18	)	)	PUNCT
ajst-17687	13	19	,	,	PUNCT
ajst-17687	13	20	this	this	DET
ajst-17687	13	21	way	way	NOUN
ajst-17687	13	22	making	make	VERB
ajst-17687	13	23	any	any	DET
ajst-17687	13	24	change	change	NOUN
ajst-17687	13	25	to	to	ADP
ajst-17687	13	26	the	the	DET
ajst-17687	13	27	data	datum	NOUN
ajst-17687	13	28	,	,	PUNCT
ajst-17687	13	29	if	if	SCONJ
ajst-17687	13	30	needed	need	VERB
ajst-17687	13	31	,	,	PUNCT
ajst-17687	13	32	and	and	CCONJ
ajst-17687	13	33	get	get	VERB
ajst-17687	13	34	a	a	DET
ajst-17687	13	35	brief	brief	ADJ
ajst-17687	13	36	introduction	introduction	NOUN
ajst-17687	13	37	about	about	ADP
ajst-17687	13	38	dataset	dataset	NOUN
ajst-17687	13	39	.	.	PUNCT
ajst-17687	14	1	the	the	DET
ajst-17687	14	2	dataset	dataset	NOUN
ajst-17687	14	3	includes	include	VERB
ajst-17687	14	4	21	21	NUM
ajst-17687	14	5	variables	variable	NOUN
ajst-17687	14	6	and	and	CCONJ
ajst-17687	14	7	3499	3499	NUM
ajst-17687	14	8	data	datum	NOUN
ajst-17687	14	9	points	point	NOUN
ajst-17687	14	10	,	,	PUNCT
ajst-17687	14	11	including	include	VERB
ajst-17687	14	12	precise	precise	ADJ
ajst-17687	14	13	measures	measure	NOUN
ajst-17687	14	14	of	of	ADP
ajst-17687	14	15	energy	energy	NOUN
ajst-17687	14	16	usage	usage	NOUN
ajst-17687	14	17	,	,	PUNCT
ajst-17687	14	18	the	the	DET
ajst-17687	14	19	manufacture	manufacture	NOUN
ajst-17687	14	20	of	of	ADP
ajst-17687	14	21	recycled	recycled	ADJ
ajst-17687	14	22	steel	steel	NOUN
ajst-17687	14	23	,	,	PUNCT
ajst-17687	14	24	and	and	CCONJ
ajst-17687	14	25	the	the	DET
ajst-17687	14	26	utilization	utilization	NOUN
ajst-17687	14	27	of	of	ADP
ajst-17687	14	28	metal	metal	NOUN
ajst-17687	14	29	materials	material	NOUN
ajst-17687	14	30	.	.	PUNCT
ajst-17687	15	1	when	when	SCONJ
ajst-17687	15	2	having	have	VERB
ajst-17687	15	3	a	a	DET
ajst-17687	15	4	preliminary	preliminary	ADJ
ajst-17687	15	5	investigation	investigation	NOUN
ajst-17687	15	6	of	of	ADP
ajst-17687	15	7	the	the	DET
ajst-17687	15	8	values	value	NOUN
ajst-17687	15	9	imported	import	VERB
ajst-17687	15	10	into	into	ADP
ajst-17687	15	11	weka	weka	PROPN
ajst-17687	15	12	,	,	PUNCT
ajst-17687	15	13	it	it	PRON
ajst-17687	15	14	is	be	AUX
ajst-17687	15	15	notable	notable	ADJ
ajst-17687	15	16	cases	case	NOUN
ajst-17687	15	17	of	of	ADP
ajst-17687	15	18	unclear	unclear	ADJ
ajst-17687	15	19	and	and	CCONJ
ajst-17687	15	20	missing	missing	ADJ
ajst-17687	15	21	data	datum	NOUN
ajst-17687	15	22	.	.	PUNCT
ajst-17687	16	1	instead	instead	ADV
ajst-17687	16	2	of	of	ADP
ajst-17687	16	3	having	have	VERB
ajst-17687	16	4	a	a	DET
ajst-17687	16	5	focus	focus	NOUN
ajst-17687	16	6	on	on	ADP
ajst-17687	16	7	production	production	NOUN
ajst-17687	16	8	,	,	PUNCT
ajst-17687	16	9	the	the	DET
ajst-17687	16	10	main	main	ADJ
ajst-17687	16	11	objective	objective	NOUN
ajst-17687	16	12	of	of	ADP
ajst-17687	16	13	this	this	DET
ajst-17687	16	14	data	datum	NOUN
ajst-17687	16	15	mining	mining	NOUN
ajst-17687	16	16	project	project	NOUN
ajst-17687	16	17	for	for	ADP
ajst-17687	16	18	steel	steel	NOUN
ajst-17687	16	19	production	production	NOUN
ajst-17687	16	20	in	in	ADP
ajst-17687	16	21	eaf	eaf	PROPN
ajst-17687	16	22	’s	’s	PART
ajst-17687	16	23	is	be	AUX
ajst-17687	16	24	to	to	PART
ajst-17687	16	25	optimize	optimize	VERB
ajst-17687	16	26	the	the	DET
ajst-17687	16	27	energy	energy	NOUN
ajst-17687	16	28	use	use	NOUN
ajst-17687	16	29	.	.	PUNCT
ajst-17687	17	1	in	in	ADP
ajst-17687	17	2	terms	term	NOUN
ajst-17687	17	3	of	of	ADP
ajst-17687	17	4	optimizing	optimize	VERB
ajst-17687	17	5	energy	energy	NOUN
ajst-17687	17	6	use	use	NOUN
ajst-17687	17	7	,	,	PUNCT
ajst-17687	17	8	if	if	SCONJ
ajst-17687	17	9	“	"	PUNCT
ajst-17687	17	10	billet	billet	NOUN
ajst-17687	17	11	tons	ton	NOUN
ajst-17687	17	12	”	"	PUNCT
ajst-17687	17	13	represent	represent	VERB
ajst-17687	17	14	the	the	DET
ajst-17687	17	15	amount	amount	NOUN
ajst-17687	17	16	of	of	ADP
ajst-17687	17	17	steel	steel	NOUN
ajst-17687	17	18	produced	produce	VERB
ajst-17687	17	19	per	per	ADP
ajst-17687	17	20	batch	batch	NOUN
ajst-17687	17	21	,	,	PUNCT
ajst-17687	17	22	then	then	ADV
ajst-17687	17	23	this	this	DET
ajst-17687	17	24	parameter	parameter	NOUN
ajst-17687	17	25	may	may	AUX
ajst-17687	17	26	wo	will	AUX
ajst-17687	17	27	n’t	not	PART
ajst-17687	17	28	have	have	VERB
ajst-17687	17	29	a	a	DET
ajst-17687	17	30	significant	significant	ADJ
ajst-17687	17	31	impact	impact	NOUN
ajst-17687	17	32	on	on	ADP
ajst-17687	17	33	optimizing	optimize	VERB
ajst-17687	17	34	the	the	DET
ajst-17687	17	35	energy	energy	NOUN
ajst-17687	17	36	use	use	NOUN
ajst-17687	17	37	.	.	PUNCT
ajst-17687	18	1	this	this	DET
ajst-17687	18	2	attribute	attribute	NOUN
ajst-17687	18	3	(	(	PUNCT
ajst-17687	18	4	“	"	PUNCT
ajst-17687	18	5	billet	billet	NOUN
ajst-17687	18	6	tons	ton	NOUN
ajst-17687	18	7	”	"	PUNCT
ajst-17687	18	8	)	)	PUNCT
ajst-17687	18	9	did	do	AUX
ajst-17687	18	10	n’t	not	PART
ajst-17687	18	11	have	have	VERB
ajst-17687	18	12	a	a	DET
ajst-17687	18	13	significant	significant	ADJ
ajst-17687	18	14	contribution	contribution	NOUN
ajst-17687	18	15	on	on	ADP
ajst-17687	18	16	predicting	predict	VERB
ajst-17687	18	17	the	the	DET
ajst-17687	18	18	energy	energy	NOUN
ajst-17687	18	19	consumption	consumption	NOUN
ajst-17687	18	20	and	and	CCONJ
ajst-17687	18	21	was	be	AUX
ajst-17687	18	22	considered	consider	VERB
ajst-17687	18	23	for	for	ADP
ajst-17687	18	24	us	we	PRON
ajst-17687	18	25	as	as	ADP
ajst-17687	18	26	unimportant	unimportant	ADJ
ajst-17687	18	27	for	for	ADP
ajst-17687	18	28	the	the	DET
ajst-17687	18	29	feature	feature	NOUN
ajst-17687	18	30	selection	selection	NOUN
ajst-17687	18	31	in	in	ADP
ajst-17687	18	32	the	the	DET
ajst-17687	18	33	pre	pre	ADJ
ajst-17687	18	34	-	-	ADJ
ajst-17687	18	35	processing	processing	ADJ
ajst-17687	18	36	stage	stage	NOUN
ajst-17687	18	37	,	,	PUNCT
ajst-17687	18	38	so	so	CCONJ
ajst-17687	18	39	the	the	DET
ajst-17687	18	40	decision	decision	NOUN
ajst-17687	18	41	was	be	AUX
ajst-17687	18	42	to	to	PART
ajst-17687	18	43	remove	remove	VERB
ajst-17687	18	44	it	it	PRON
ajst-17687	18	45	.	.	PUNCT
ajst-17687	19	1	furthermore	furthermore	ADV
ajst-17687	19	2	,	,	PUNCT
ajst-17687	19	3	each	each	DET
ajst-17687	19	4	sample	sample	NOUN
ajst-17687	19	5	was	be	AUX
ajst-17687	19	6	assigned	assign	VERB
ajst-17687	19	7	a	a	DET
ajst-17687	19	8	serial	serial	ADJ
ajst-17687	19	9	number	number	NOUN
ajst-17687	19	10	called	call	VERB
ajst-17687	19	11	“	"	PUNCT
ajst-17687	19	12	heat	heat	NOUN
ajst-17687	19	13	number	number	NOUN
ajst-17687	19	14	”	"	PUNCT
ajst-17687	19	15	,	,	PUNCT
ajst-17687	19	16	which	which	PRON
ajst-17687	19	17	was	be	AUX
ajst-17687	19	18	optional	optional	ADJ
ajst-17687	19	19	and	and	CCONJ
ajst-17687	19	20	did	do	AUX
ajst-17687	19	21	n’t	not	PART
ajst-17687	19	22	have	have	VERB
ajst-17687	19	23	any	any	DET
ajst-17687	19	24	significance	significance	NOUN
ajst-17687	19	25	importance	importance	NOUN
ajst-17687	19	26	in	in	ADP
ajst-17687	19	27	aiding	aid	VERB
ajst-17687	19	28	the	the	DET
ajst-17687	19	29	optimization	optimization	NOUN
ajst-17687	19	30	.	.	PUNCT
ajst-17687	20	1	2.2	2.2	NUM
ajst-17687	20	2	.	.	PUNCT
ajst-17687	21	1	data	datum	NOUN
ajst-17687	21	2	processing	processing	NOUN
ajst-17687	21	3	with	with	ADP
ajst-17687	21	4	the	the	DET
ajst-17687	21	5	intention	intention	NOUN
ajst-17687	21	6	of	of	ADP
ajst-17687	21	7	further	far	ADV
ajst-17687	21	8	reducing	reduce	VERB
ajst-17687	21	9	the	the	DET
ajst-17687	21	10	attribute	attribute	NOUN
ajst-17687	21	11	and	and	CCONJ
ajst-17687	21	12	simplifying	simplify	VERB
ajst-17687	21	13	the	the	DET
ajst-17687	21	14	model	model	NOUN
ajst-17687	21	15	even	even	ADV
ajst-17687	21	16	more	more	ADV
ajst-17687	21	17	,	,	PUNCT
ajst-17687	21	18	the	the	DET
ajst-17687	21	19	option	option	NOUN
ajst-17687	21	20	“	"	PUNCT
ajst-17687	21	21	correlationattributeeval	correlationattributeeval	NOUN
ajst-17687	21	22	”	"	PUNCT
ajst-17687	21	23	was	be	AUX
ajst-17687	21	24	selected	select	VERB
ajst-17687	21	25	in	in	ADP
ajst-17687	21	26	the	the	DET
ajst-17687	21	27	attribute	attribute	NOUN
ajst-17687	21	28	evaluator	evaluator	NOUN
ajst-17687	21	29	panel	panel	NOUN
ajst-17687	21	30	and	and	CCONJ
ajst-17687	21	31	the	the	DET
ajst-17687	21	32	cut	cut	VERB
ajst-17687	21	33	-	-	PUNCT
ajst-17687	21	34	off	off	ADP
ajst-17687	21	35	value	value	NOUN
ajst-17687	21	36	was	be	AUX
ajst-17687	21	37	set	set	VERB
ajst-17687	21	38	to	to	ADP
ajst-17687	21	39	0.02	0.02	NUM
ajst-17687	21	40	.	.	PUNCT
ajst-17687	22	1	figure	figure	NOUN
ajst-17687	22	2	1	1	NUM
ajst-17687	22	3	.	.	PUNCT
ajst-17687	22	4	select	select	VERB
ajst-17687	22	5	attributes	attribute	NOUN
ajst-17687	22	6	tab	tab	NOUN
ajst-17687	22	7	on	on	ADP
ajst-17687	22	8	weka	weka	PROPN
ajst-17687	22	9	.	.	PUNCT
ajst-17687	23	1	the	the	DET
ajst-17687	23	2	properties	property	NOUN
ajst-17687	23	3	with	with	ADP
ajst-17687	23	4	values	value	NOUN
ajst-17687	23	5	between	between	ADP
ajst-17687	23	6	-0.02	-0.02	NUM
ajst-17687	23	7	and	and	CCONJ
ajst-17687	23	8	0.02	0.02	NUM
ajst-17687	23	9	showed	show	VERB
ajst-17687	23	10	a	a	DET
ajst-17687	23	11	low	low	ADJ
ajst-17687	23	12	relevance	relevance	NOUN
ajst-17687	23	13	to	to	ADP
ajst-17687	23	14	the	the	DET
ajst-17687	23	15	experiment	experiment	NOUN
ajst-17687	23	16	,	,	PUNCT
ajst-17687	23	17	so	so	SCONJ
ajst-17687	23	18	they	they	PRON
ajst-17687	23	19	were	be	AUX
ajst-17687	23	20	deleted	delete	VERB
ajst-17687	23	21	together	together	ADV
ajst-17687	23	22	with	with	ADP
ajst-17687	23	23	the	the	DET
ajst-17687	23	24	attribute	attribute	NOUN
ajst-17687	23	25	“	"	PUNCT
ajst-17687	23	26	argon	argon	NOUN
ajst-17687	23	27	(	(	PUNCT
ajst-17687	23	28	kg	kg	NOUN
ajst-17687	23	29	)	)	PUNCT
ajst-17687	23	30	”	"	PUNCT
ajst-17687	23	31	.	.	PUNCT
ajst-17687	24	1	we	we	PRON
ajst-17687	24	2	also	also	ADV
ajst-17687	24	3	discovered	discover	VERB
ajst-17687	24	4	during	during	ADP
ajst-17687	24	5	the	the	DET
ajst-17687	24	6	data	datum	NOUN
ajst-17687	24	7	filtering	filtering	NOUN
ajst-17687	24	8	process	process	NOUN
ajst-17687	24	9	that	that	PRON
ajst-17687	24	10	the	the	DET
ajst-17687	24	11	steel	steel	NOUN
ajst-17687	24	12	grade	grade	NOUN
ajst-17687	24	13	attribute	attribute	NOUN
ajst-17687	24	14	has	have	VERB
ajst-17687	24	15	two	two	NUM
ajst-17687	24	16	outliers	outlier	NOUN
ajst-17687	24	17	of	of	ADP
ajst-17687	24	18	00/01/1900	00/01/1900	NUM
ajst-17687	24	19	and	and	CCONJ
ajst-17687	24	20	four	four	NUM
ajst-17687	24	21	consecutive	consecutive	ADJ
ajst-17687	24	22	outliers	outlier	NOUN
ajst-17687	24	23	of	of	ADP
ajst-17687	24	24	0	0	NUM
ajst-17687	24	25	.	.	PUNCT
ajst-17687	25	1	we	we	PRON
ajst-17687	25	2	made	make	VERB
ajst-17687	25	3	the	the	DET
ajst-17687	25	4	decision	decision	NOUN
ajst-17687	25	5	to	to	PART
ajst-17687	25	6	include	include	VERB
ajst-17687	25	7	a	a	DET
ajst-17687	25	8	134	134	NUM
ajst-17687	25	9	filter	filter	NOUN
ajst-17687	25	10	so	so	SCONJ
ajst-17687	25	11	the	the	DET
ajst-17687	25	12	outliers	outlier	NOUN
ajst-17687	25	13	can	can	AUX
ajst-17687	25	14	be	be	AUX
ajst-17687	25	15	managed	manage	VERB
ajst-17687	25	16	so	so	SCONJ
ajst-17687	25	17	we	we	PRON
ajst-17687	25	18	can	can	AUX
ajst-17687	25	19	keep	keep	VERB
ajst-17687	25	20	them	they	PRON
ajst-17687	25	21	from	from	ADP
ajst-17687	25	22	throwing	throw	VERB
ajst-17687	25	23	off	off	ADP
ajst-17687	25	24	the	the	DET
ajst-17687	25	25	algorithm	algorithm	NOUN
ajst-17687	25	26	.	.	PUNCT
ajst-17687	26	1	figure	figure	NOUN
ajst-17687	26	2	2	2	NUM
ajst-17687	26	3	.	.	PUNCT
ajst-17687	26	4	genericobjecteditor	genericobjecteditor	PROPN
ajst-17687	26	5	tab	tab	PROPN
ajst-17687	26	6	on	on	ADP
ajst-17687	26	7	weka	weka	PROPN
ajst-17687	26	8	.	.	PUNCT
ajst-17687	27	1	thus	thus	ADV
ajst-17687	27	2	,	,	PUNCT
ajst-17687	27	3	the	the	DET
ajst-17687	27	4	missing	miss	VERB
ajst-17687	27	5	data	datum	NOUN
ajst-17687	27	6	can	can	AUX
ajst-17687	27	7	now	now	ADV
ajst-17687	27	8	be	be	AUX
ajst-17687	27	9	checked	check	VERB
ajst-17687	27	10	and	and	CCONJ
ajst-17687	27	11	handled	handle	VERB
ajst-17687	27	12	,	,	PUNCT
ajst-17687	27	13	which	which	PRON
ajst-17687	27	14	can	can	AUX
ajst-17687	27	15	be	be	AUX
ajst-17687	27	16	judged	judge	VERB
ajst-17687	27	17	by	by	ADP
ajst-17687	27	18	the	the	DET
ajst-17687	27	19	values	value	NOUN
ajst-17687	27	20	displayed	display	VERB
ajst-17687	27	21	by	by	ADP
ajst-17687	27	22	“	"	PUNCT
ajst-17687	27	23	missing	miss	VERB
ajst-17687	27	24	”	"	PUNCT
ajst-17687	27	25	in	in	ADP
ajst-17687	27	26	the	the	DET
ajst-17687	27	27	selected	select	VERB
ajst-17687	27	28	attribute	attribute	NOUN
ajst-17687	27	29	.	.	PUNCT
ajst-17687	28	1	the	the	DET
ajst-17687	28	2	“	"	PUNCT
ajst-17687	28	3	replacemissingvalues	replacemissingvalue	NOUN
ajst-17687	28	4	”	"	PUNCT
ajst-17687	28	5	function	function	NOUN
ajst-17687	28	6	in	in	ADP
ajst-17687	28	7	weka	weka	PROPN
ajst-17687	28	8	can	can	AUX
ajst-17687	28	9	be	be	AUX
ajst-17687	28	10	responsible	responsible	ADJ
ajst-17687	28	11	for	for	ADP
ajst-17687	28	12	handling	handle	VERB
ajst-17687	28	13	the	the	DET
ajst-17687	28	14	missing	miss	VERB
ajst-17687	28	15	data	datum	NOUN
ajst-17687	28	16	.	.	PUNCT
ajst-17687	29	1	in	in	ADP
ajst-17687	29	2	conclusion	conclusion	NOUN
ajst-17687	29	3	,	,	PUNCT
ajst-17687	29	4	those	those	DET
ajst-17687	29	5	things	thing	NOUN
ajst-17687	29	6	which	which	PRON
ajst-17687	29	7	can	can	AUX
ajst-17687	29	8	get	get	VERB
ajst-17687	29	9	from	from	ADP
ajst-17687	29	10	the	the	DET
ajst-17687	29	11	dataset	dataset	NOUN
ajst-17687	29	12	are	be	AUX
ajst-17687	29	13	as	as	ADP
ajst-17687	29	14	following	follow	VERB
ajst-17687	29	15	:	:	PUNCT
ajst-17687	29	16	1	1	X
ajst-17687	29	17	)	)	PUNCT
ajst-17687	29	18	there	there	PRON
ajst-17687	29	19	is	be	VERB
ajst-17687	29	20	a	a	DET
ajst-17687	29	21	moderate	moderate	ADJ
ajst-17687	29	22	positive	positive	ADJ
ajst-17687	29	23	correlation	correlation	NOUN
ajst-17687	29	24	between	between	ADP
ajst-17687	29	25	heat	heat	NOUN
ajst-17687	29	26	number	number	NOUN
ajst-17687	29	27	and	and	CCONJ
ajst-17687	29	28	eaf	eaf	PROPN
ajst-17687	29	29	(	(	PUNCT
ajst-17687	29	30	mwh	mwh	PROPN
ajst-17687	29	31	)	)	PUNCT
ajst-17687	29	32	about	about	ADV
ajst-17687	29	33	0.56	0.56	NUM
ajst-17687	29	34	,	,	PUNCT
ajst-17687	29	35	which	which	PRON
ajst-17687	29	36	may	may	AUX
ajst-17687	29	37	show	show	VERB
ajst-17687	29	38	that	that	SCONJ
ajst-17687	29	39	an	an	DET
ajst-17687	29	40	increase	increase	NOUN
ajst-17687	29	41	in	in	ADP
ajst-17687	29	42	energy	energy	NOUN
ajst-17687	29	43	consumption	consumption	NOUN
ajst-17687	29	44	as	as	SCONJ
ajst-17687	29	45	production	production	NOUN
ajst-17687	29	46	continues	continue	VERB
ajst-17687	29	47	.	.	PUNCT
ajst-17687	30	1	2	2	X
ajst-17687	30	2	)	)	PUNCT
ajst-17687	30	3	the	the	DET
ajst-17687	30	4	carbon	carbon	NOUN
ajst-17687	30	5	injected	inject	VERB
ajst-17687	30	6	(	(	PUNCT
ajst-17687	30	7	kg	kg	NOUN
ajst-17687	30	8	)	)	PUNCT
ajst-17687	30	9	and	and	CCONJ
ajst-17687	30	10	eaf	eaf	PROPN
ajst-17687	30	11	(	(	PUNCT
ajst-17687	30	12	mwh	mwh	PROPN
ajst-17687	30	13	)	)	PUNCT
ajst-17687	30	14	have	have	VERB
ajst-17687	30	15	moderate	moderate	ADJ
ajst-17687	30	16	positive	positive	ADJ
ajst-17687	30	17	correlation	correlation	NOUN
ajst-17687	30	18	about	about	ADP
ajst-17687	30	19	0.39	0.39	NUM
ajst-17687	30	20	,	,	PUNCT
ajst-17687	30	21	which	which	PRON
ajst-17687	30	22	may	may	AUX
ajst-17687	30	23	suggested	suggest	VERB
ajst-17687	30	24	that	that	SCONJ
ajst-17687	30	25	the	the	DET
ajst-17687	30	26	amount	amount	NOUN
ajst-17687	30	27	of	of	ADP
ajst-17687	30	28	carbon	carbon	NOUN
ajst-17687	30	29	used	use	VERB
ajst-17687	30	30	may	may	AUX
ajst-17687	30	31	have	have	VERB
ajst-17687	30	32	an	an	DET
ajst-17687	30	33	impact	impact	NOUN
ajst-17687	30	34	on	on	ADP
ajst-17687	30	35	energy	energy	NOUN
ajst-17687	30	36	consumption	consumption	NOUN
ajst-17687	30	37	.	.	PUNCT
ajst-17687	31	1	3	3	X
ajst-17687	31	2	)	)	PUNCT
ajst-17687	31	3	as	as	ADP
ajst-17687	31	4	for	for	ADP
ajst-17687	31	5	total	total	ADJ
ajst-17687	31	6	scrap	scrap	NOUN
ajst-17687	31	7	mix	mix	NOUN
ajst-17687	31	8	and	and	CCONJ
ajst-17687	31	9	eaf	eaf	PROPN
ajst-17687	31	10	(	(	PUNCT
ajst-17687	31	11	mwh	mwh	PROPN
ajst-17687	31	12	)	)	PUNCT
ajst-17687	31	13	,	,	PUNCT
ajst-17687	31	14	their	their	PRON
ajst-17687	31	15	moderate	moderate	ADJ
ajst-17687	31	16	positive	positive	ADJ
ajst-17687	31	17	correlation	correlation	NOUN
ajst-17687	31	18	is	be	AUX
ajst-17687	31	19	only	only	ADV
ajst-17687	31	20	0.34	0.34	NUM
ajst-17687	31	21	,	,	PUNCT
ajst-17687	31	22	so	so	SCONJ
ajst-17687	31	23	it	it	PRON
ajst-17687	31	24	’s	’	VERB
ajst-17687	31	25	goes	go	VERB
ajst-17687	31	26	without	without	ADP
ajst-17687	31	27	saying	say	VERB
ajst-17687	31	28	that	that	SCONJ
ajst-17687	31	29	total	total	ADJ
ajst-17687	31	30	waste	waste	NOUN
ajst-17687	31	31	may	may	AUX
ajst-17687	31	32	be	be	AUX
ajst-17687	31	33	a	a	DET
ajst-17687	31	34	key	key	ADJ
ajst-17687	31	35	factor	factor	NOUN
ajst-17687	31	36	affecting	affect	VERB
ajst-17687	31	37	energy	energy	NOUN
ajst-17687	31	38	consumption	consumption	NOUN
ajst-17687	31	39	.	.	PUNCT
ajst-17687	32	1	so	so	ADV
ajst-17687	32	2	in	in	ADP
ajst-17687	32	3	order	order	NOUN
ajst-17687	32	4	to	to	PART
ajst-17687	32	5	improve	improve	VERB
ajst-17687	32	6	energy	energy	NOUN
ajst-17687	32	7	efficiency	efficiency	NOUN
ajst-17687	32	8	.	.	PUNCT
ajst-17687	33	1	the	the	DET
ajst-17687	33	2	following	follow	VERB
ajst-17687	33	3	elements	element	NOUN
ajst-17687	33	4	need	need	VERB
ajst-17687	33	5	to	to	PART
ajst-17687	33	6	be	be	AUX
ajst-17687	33	7	optimized	optimize	VERB
ajst-17687	33	8	,	,	PUNCT
ajst-17687	33	9	such	such	ADJ
ajst-17687	33	10	as	as	ADP
ajst-17687	33	11	the	the	DET
ajst-17687	33	12	time	time	NOUN
ajst-17687	33	13	of	of	ADP
ajst-17687	33	14	the	the	DET
ajst-17687	33	15	heat	heat	NOUN
ajst-17687	33	16	,	,	PUNCT
ajst-17687	33	17	amount	amount	NOUN
ajst-17687	33	18	of	of	ADP
ajst-17687	33	19	carbon	carbon	NOUN
ajst-17687	33	20	injected	inject	VERB
ajst-17687	33	21	and	and	CCONJ
ajst-17687	33	22	scrap	scrap	NOUN
ajst-17687	33	23	mix	mix	NOUN
ajst-17687	33	24	.	.	PUNCT
ajst-17687	34	1	besides	besides	SCONJ
ajst-17687	34	2	,	,	PUNCT
ajst-17687	34	3	focusing	focus	VERB
ajst-17687	34	4	on	on	ADP
ajst-17687	34	5	carbon	carbon	NOUN
ajst-17687	34	6	usage	usage	NOUN
ajst-17687	34	7	and	and	CCONJ
ajst-17687	34	8	waste	waste	NOUN
ajst-17687	34	9	mix	mix	NOUN
ajst-17687	34	10	optimization	optimization	NOUN
ajst-17687	34	11	may	may	AUX
ajst-17687	34	12	help	help	VERB
ajst-17687	34	13	reduce	reduce	VERB
ajst-17687	34	14	energy	energy	NOUN
ajst-17687	34	15	consumption	consumption	NOUN
ajst-17687	34	16	,	,	PUNCT
ajst-17687	34	17	thereby	thereby	ADV
ajst-17687	34	18	lowering	lower	VERB
ajst-17687	34	19	costs	cost	NOUN
ajst-17687	34	20	and	and	CCONJ
ajst-17687	34	21	increasing	increase	VERB
ajst-17687	34	22	production	production	NOUN
ajst-17687	34	23	efficiency	efficiency	NOUN
ajst-17687	34	24	.	.	PUNCT
ajst-17687	35	1	figure	figure	VERB
ajst-17687	35	2	3	3	NUM
ajst-17687	35	3	.	.	PUNCT
ajst-17687	35	4	process	process	NOUN
ajst-17687	35	5	tab	tab	NOUN
ajst-17687	35	6	in	in	ADP
ajst-17687	35	7	weka	weka	PROPN
ajst-17687	35	8	.	.	PUNCT
ajst-17687	36	1	3	3	X
ajst-17687	36	2	.	.	X
ajst-17687	36	3	methodology	methodology	NOUN
ajst-17687	36	4	the	the	DET
ajst-17687	36	5	algorithms	algorithm	NOUN
ajst-17687	36	6	that	that	PRON
ajst-17687	36	7	we	we	PRON
ajst-17687	36	8	decided	decide	VERB
ajst-17687	36	9	to	to	PART
ajst-17687	36	10	choose	choose	VERB
ajst-17687	36	11	to	to	PART
ajst-17687	36	12	analyse	analyse	VERB
ajst-17687	36	13	the	the	DET
ajst-17687	36	14	data	datum	NOUN
ajst-17687	36	15	were	be	AUX
ajst-17687	36	16	the	the	DET
ajst-17687	36	17	j48	j48	NOUN
ajst-17687	36	18	for	for	ADP
ajst-17687	36	19	decision	decision	NOUN
ajst-17687	36	20	trees	tree	NOUN
ajst-17687	36	21	and	and	CCONJ
ajst-17687	36	22	randomtree	randomtree	NOUN
ajst-17687	36	23	.	.	PUNCT
ajst-17687	37	1	we	we	PRON
ajst-17687	37	2	choose	choose	VERB
ajst-17687	37	3	these	these	DET
ajst-17687	37	4	two	two	NUM
ajst-17687	37	5	models	model	NOUN
ajst-17687	37	6	because	because	SCONJ
ajst-17687	37	7	they	they	PRON
ajst-17687	37	8	both	both	PRON
ajst-17687	37	9	have	have	VERB
ajst-17687	37	10	great	great	ADJ
ajst-17687	37	11	advantages	advantage	NOUN
ajst-17687	37	12	,	,	PUNCT
ajst-17687	37	13	such	such	ADJ
ajst-17687	37	14	as	as	ADP
ajst-17687	37	15	high	high	ADJ
ajst-17687	37	16	accuracy	accuracy	NOUN
ajst-17687	37	17	when	when	SCONJ
ajst-17687	37	18	dealing	deal	VERB
ajst-17687	37	19	with	with	ADP
ajst-17687	37	20	small	small	ADJ
ajst-17687	37	21	and	and	CCONJ
ajst-17687	37	22	medium	medium	ADJ
ajst-17687	37	23	sized	sized	ADJ
ajst-17687	37	24	datasets	dataset	NOUN
ajst-17687	37	25	.	.	PUNCT
ajst-17687	38	1	also	also	ADV
ajst-17687	38	2	,	,	PUNCT
ajst-17687	38	3	the	the	DET
ajst-17687	38	4	model	model	NOUN
ajst-17687	38	5	multiplayer	multiplayer	PROPN
ajst-17687	38	6	percepton	percepton	PROPN
ajst-17687	38	7	was	be	AUX
ajst-17687	38	8	used	use	VERB
ajst-17687	38	9	.	.	PUNCT
ajst-17687	39	1	this	this	DET
ajst-17687	39	2	model	model	NOUN
ajst-17687	39	3	has	have	VERB
ajst-17687	39	4	its	its	PRON
ajst-17687	39	5	unique	unique	ADJ
ajst-17687	39	6	advantages	advantage	NOUN
ajst-17687	39	7	when	when	SCONJ
ajst-17687	39	8	dealing	deal	VERB
ajst-17687	39	9	with	with	ADP
ajst-17687	39	10	complex	complex	ADJ
ajst-17687	39	11	energy	energy	NOUN
ajst-17687	39	12	systems	system	NOUN
ajst-17687	39	13	,	,	PUNCT
ajst-17687	39	14	where	where	SCONJ
ajst-17687	39	15	the	the	DET
ajst-17687	39	16	model	model	NOUN
ajst-17687	39	17	can	can	AUX
ajst-17687	39	18	adjust	adjust	VERB
ajst-17687	39	19	to	to	ADP
ajst-17687	39	20	the	the	DET
ajst-17687	39	21	number	number	NOUN
ajst-17687	39	22	of	of	ADP
ajst-17687	39	23	layers	layer	NOUN
ajst-17687	39	24	and	and	CCONJ
ajst-17687	39	25	neurons	neuron	NOUN
ajst-17687	39	26	to	to	PART
ajst-17687	39	27	better	well	ADV
ajst-17687	39	28	adapt	adapt	VERB
ajst-17687	39	29	to	to	ADP
ajst-17687	39	30	the	the	DET
ajst-17687	39	31	more	more	ADV
ajst-17687	39	32	complex	complex	ADJ
ajst-17687	39	33	non	non	ADJ
ajst-17687	39	34	-	-	ADJ
ajst-17687	39	35	linear	linear	ADJ
ajst-17687	39	36	relationships	relationship	NOUN
ajst-17687	39	37	.	.	PUNCT
ajst-17687	40	1	these	these	DET
ajst-17687	40	2	three	three	NUM
ajst-17687	40	3	models	model	NOUN
ajst-17687	40	4	were	be	AUX
ajst-17687	40	5	chosen	choose	VERB
ajst-17687	40	6	because	because	SCONJ
ajst-17687	40	7	they	they	PRON
ajst-17687	40	8	are	be	AUX
ajst-17687	40	9	very	very	ADV
ajst-17687	40	10	distinct	distinct	ADJ
ajst-17687	40	11	learning	learning	NOUN
ajst-17687	40	12	and	and	CCONJ
ajst-17687	40	13	pattern	pattern	NOUN
ajst-17687	40	14	recognition	recognition	NOUN
ajst-17687	40	15	approaches	approach	NOUN
ajst-17687	40	16	.	.	PUNCT
ajst-17687	41	1	furthermore	furthermore	ADV
ajst-17687	41	2	,	,	PUNCT
ajst-17687	41	3	this	this	PRON
ajst-17687	41	4	will	will	AUX
ajst-17687	41	5	allow	allow	VERB
ajst-17687	41	6	us	we	PRON
ajst-17687	41	7	to	to	PART
ajst-17687	41	8	have	have	VERB
ajst-17687	41	9	a	a	DET
ajst-17687	41	10	more	more	ADV
ajst-17687	41	11	comprehensive	comprehensive	ADJ
ajst-17687	41	12	understanding	understanding	NOUN
ajst-17687	41	13	of	of	ADP
ajst-17687	41	14	the	the	DET
ajst-17687	41	15	relationship	relationship	NOUN
ajst-17687	41	16	between	between	ADP
ajst-17687	41	17	energy	energy	NOUN
ajst-17687	41	18	consumption	consumption	NOUN
ajst-17687	41	19	and	and	CCONJ
ajst-17687	41	20	attributes	attribute	NOUN
ajst-17687	41	21	.	.	PUNCT
ajst-17687	42	1	j48	j48	ADJ
ajst-17687	42	2	decision	decision	NOUN
ajst-17687	42	3	tree	tree	NOUN
ajst-17687	42	4	:	:	PUNCT
ajst-17687	42	5	this	this	PRON
ajst-17687	42	6	is	be	AUX
ajst-17687	42	7	a	a	DET
ajst-17687	42	8	decision	decision	NOUN
ajst-17687	42	9	tree	tree	NOUN
ajst-17687	42	10	algorithm	algorithm	NOUN
ajst-17687	42	11	,	,	PUNCT
ajst-17687	42	12	which	which	PRON
ajst-17687	42	13	it	it	PRON
ajst-17687	42	14	is	be	AUX
ajst-17687	42	15	often	often	ADV
ajst-17687	42	16	used	use	VERB
ajst-17687	42	17	the	the	DET
ajst-17687	42	18	divide	divide	NOUN
ajst-17687	42	19	-	-	PUNCT
ajst-17687	42	20	and	and	CCONJ
ajst-17687	42	21	-	-	PUNCT
ajst-17687	42	22	conquer	conquer	NOUN
ajst-17687	42	23	method	method	NOUN
ajst-17687	42	24	in	in	ADP
ajst-17687	42	25	order	order	NOUN
ajst-17687	42	26	to	to	PART
ajst-17687	42	27	divide	divide	VERB
ajst-17687	42	28	the	the	DET
ajst-17687	42	29	instances	instance	NOUN
ajst-17687	42	30	,	,	PUNCT
ajst-17687	42	31	this	this	DET
ajst-17687	42	32	way	way	NOUN
ajst-17687	42	33	according	accord	VERB
ajst-17687	42	34	to	to	ADP
ajst-17687	42	35	the	the	DET
ajst-17687	42	36	similarity	similarity	NOUN
ajst-17687	42	37	.	.	PUNCT
ajst-17687	43	1	the	the	DET
ajst-17687	43	2	j48	j48	PROPN
ajst-17687	43	3	algorithm	algorithm	NOUN
ajst-17687	43	4	is	be	AUX
ajst-17687	43	5	an	an	DET
ajst-17687	43	6	application	application	NOUN
ajst-17687	43	7	of	of	ADP
ajst-17687	43	8	the	the	DET
ajst-17687	43	9	c4.5	c4.5	PROPN
ajst-17687	43	10	algorithm	algorithm	NOUN
ajst-17687	43	11	,	,	PUNCT
ajst-17687	43	12	which	which	PRON
ajst-17687	43	13	is	be	AUX
ajst-17687	43	14	a	a	DET
ajst-17687	43	15	very	very	ADV
ajst-17687	43	16	stable	stable	ADJ
ajst-17687	43	17	and	and	CCONJ
ajst-17687	43	18	reliable	reliable	ADJ
ajst-17687	43	19	algorithm	algorithm	NOUN
ajst-17687	43	20	,	,	PUNCT
ajst-17687	43	21	as	as	SCONJ
ajst-17687	43	22	we	we	PRON
ajst-17687	43	23	can	can	AUX
ajst-17687	43	24	determine	determine	VERB
ajst-17687	43	25	through	through	ADP
ajst-17687	43	26	long	long	ADJ
ajst-17687	43	27	-	-	PUNCT
ajst-17687	43	28	term	term	NOUN
ajst-17687	43	29	verification	verification	NOUN
ajst-17687	43	30	.	.	PUNCT
ajst-17687	44	1	-multiplayer	-multiplayer	PROPN
ajst-17687	44	2	perceptron	perceptron	PROPN
ajst-17687	44	3	:	:	PUNCT
ajst-17687	44	4	this	this	PRON
ajst-17687	44	5	is	be	AUX
ajst-17687	44	6	a	a	DET
ajst-17687	44	7	very	very	ADV
ajst-17687	44	8	common	common	ADJ
ajst-17687	44	9	multilayer	multilayer	ADJ
ajst-17687	44	10	feed	feed	NOUN
ajst-17687	44	11	-	-	PUNCT
ajst-17687	44	12	forward	forward	ADV
ajst-17687	44	13	artificial	artificial	ADJ
ajst-17687	44	14	neural	neural	ADJ
ajst-17687	44	15	network	network	NOUN
ajst-17687	44	16	model	model	NOUN
ajst-17687	44	17	.	.	PUNCT
ajst-17687	45	1	it	it	PRON
ajst-17687	45	2	’s	’	VERB
ajst-17687	45	3	an	an	DET
ajst-17687	45	4	algorithm	algorithm	NOUN
ajst-17687	45	5	often	often	ADV
ajst-17687	45	6	used	use	VERB
ajst-17687	45	7	for	for	ADP
ajst-17687	45	8	classification	classification	NOUN
ajst-17687	45	9	and	and	CCONJ
ajst-17687	45	10	prediction	prediction	NOUN
ajst-17687	45	11	of	of	ADP
ajst-17687	45	12	data	datum	NOUN
ajst-17687	45	13	,	,	PUNCT
ajst-17687	45	14	and	and	CCONJ
ajst-17687	45	15	it	it	PRON
ajst-17687	45	16	includes	include	VERB
ajst-17687	45	17	one	one	NUM
ajst-17687	45	18	or	or	CCONJ
ajst-17687	45	19	more	more	ADV
ajst-17687	45	20	hidden	hidden	ADJ
ajst-17687	45	21	layers	layer	NOUN
ajst-17687	45	22	that	that	PRON
ajst-17687	45	23	allows	allow	VERB
ajst-17687	45	24	it	it	PRON
ajst-17687	45	25	to	to	PART
ajst-17687	45	26	learn	learn	VERB
ajst-17687	45	27	non	non	ADJ
ajst-17687	45	28	-	-	ADJ
ajst-17687	45	29	linear	linear	ADJ
ajst-17687	45	30	patterns	pattern	NOUN
ajst-17687	45	31	in	in	ADP
ajst-17687	45	32	the	the	DET
ajst-17687	45	33	data	datum	NOUN
ajst-17687	45	34	,	,	PUNCT
ajst-17687	45	35	being	be	AUX
ajst-17687	45	36	a	a	DET
ajst-17687	45	37	acceptable	acceptable	ADJ
ajst-17687	45	38	tool	tool	NOUN
ajst-17687	45	39	for	for	ADP
ajst-17687	45	40	deep	deep	ADJ
ajst-17687	45	41	learning	learning	NOUN
ajst-17687	45	42	.	.	PUNCT
ajst-17687	46	1	-randomtree	-randomtree	NOUN
ajst-17687	46	2	:	:	PUNCT
ajst-17687	46	3	this	this	DET
ajst-17687	46	4	algorithm	algorithm	NOUN
ajst-17687	46	5	can	can	AUX
ajst-17687	46	6	be	be	AUX
ajst-17687	46	7	used	use	VERB
ajst-17687	46	8	for	for	ADP
ajst-17687	46	9	both	both	CCONJ
ajst-17687	46	10	classification	classification	NOUN
ajst-17687	46	11	and	and	CCONJ
ajst-17687	46	12	regression	regression	NOUN
ajst-17687	46	13	problems	problem	NOUN
ajst-17687	46	14	.	.	PUNCT
ajst-17687	47	1	it	it	PRON
ajst-17687	47	2	is	be	AUX
ajst-17687	47	3	an	an	DET
ajst-17687	47	4	important	important	ADJ
ajst-17687	47	5	algorithm	algorithm	NOUN
ajst-17687	47	6	in	in	ADP
ajst-17687	47	7	trees	tree	NOUN
ajst-17687	47	8	.	.	PUNCT
ajst-17687	48	1	each	each	PRON
ajst-17687	48	2	of	of	ADP
ajst-17687	48	3	its	its	PRON
ajst-17687	48	4	internal	internal	ADJ
ajst-17687	48	5	nodes	node	NOUN
ajst-17687	48	6	represents	represent	VERB
ajst-17687	48	7	a	a	DET
ajst-17687	48	8	test	test	NOUN
ajst-17687	48	9	of	of	ADP
ajst-17687	48	10	a	a	DET
ajst-17687	48	11	characteristics	characteristic	NOUN
ajst-17687	48	12	,	,	PUNCT
ajst-17687	48	13	each	each	DET
ajst-17687	48	14	branch	branch	NOUN
ajst-17687	48	15	represents	represent	VERB
ajst-17687	48	16	the	the	DET
ajst-17687	48	17	result	result	NOUN
ajst-17687	48	18	of	of	ADP
ajst-17687	48	19	the	the	DET
ajst-17687	48	20	test	test	NOUN
ajst-17687	48	21	,	,	PUNCT
ajst-17687	48	22	and	and	CCONJ
ajst-17687	48	23	each	each	DET
ajst-17687	48	24	leaf	leaf	NOUN
ajst-17687	48	25	node	node	NOUN
ajst-17687	48	26	represents	represent	VERB
ajst-17687	48	27	a	a	DET
ajst-17687	48	28	prediction	prediction	NOUN
ajst-17687	48	29	.	.	PUNCT
ajst-17687	49	1	when	when	SCONJ
ajst-17687	49	2	writing	write	VERB
ajst-17687	49	3	up	up	ADP
ajst-17687	49	4	the	the	DET
ajst-17687	49	5	methodology	methodology	NOUN
ajst-17687	49	6	,	,	PUNCT
ajst-17687	49	7	we	we	PRON
ajst-17687	49	8	always	always	ADV
ajst-17687	49	9	tried	try	VERB
ajst-17687	49	10	to	to	PART
ajst-17687	49	11	maintain	maintain	VERB
ajst-17687	49	12	the	the	DET
ajst-17687	49	13	importance	importance	NOUN
ajst-17687	49	14	of	of	ADP
ajst-17687	49	15	validating	validate	VERB
ajst-17687	49	16	the	the	DET
ajst-17687	49	17	results	result	NOUN
ajst-17687	49	18	of	of	ADP
ajst-17687	49	19	this	this	DET
ajst-17687	49	20	experiment	experiment	NOUN
ajst-17687	49	21	to	to	PART
ajst-17687	49	22	ensure	ensure	VERB
ajst-17687	49	23	the	the	DET
ajst-17687	49	24	accuracy	accuracy	NOUN
ajst-17687	49	25	and	and	CCONJ
ajst-17687	49	26	stability	stability	NOUN
ajst-17687	49	27	of	of	ADP
ajst-17687	49	28	the	the	DET
ajst-17687	49	29	results	result	NOUN
ajst-17687	49	30	.	.	PUNCT
ajst-17687	50	1	in	in	ADP
ajst-17687	50	2	addition	addition	NOUN
ajst-17687	50	3	,	,	PUNCT
ajst-17687	50	4	we	we	PRON
ajst-17687	50	5	also	also	ADV
ajst-17687	50	6	paid	pay	VERB
ajst-17687	50	7	attention	attention	NOUN
ajst-17687	50	8	to	to	ADP
ajst-17687	50	9	the	the	DET
ajst-17687	50	10	iterative	iterative	NOUN
ajst-17687	50	11	improvement	improvement	NOUN
ajst-17687	50	12	of	of	ADP
ajst-17687	50	13	this	this	DET
ajst-17687	50	14	experiment	experiment	NOUN
ajst-17687	50	15	.	.	PUNCT
ajst-17687	51	1	the	the	DET
ajst-17687	51	2	algorithm	algorithm	NOUN
ajst-17687	51	3	was	be	AUX
ajst-17687	51	4	replaced	replace	VERB
ajst-17687	51	5	several	several	ADJ
ajst-17687	51	6	times	time	NOUN
ajst-17687	51	7	to	to	PART
ajst-17687	51	8	find	find	VERB
ajst-17687	51	9	the	the	DET
ajst-17687	51	10	most	most	ADV
ajst-17687	51	11	suitable	suitable	ADJ
ajst-17687	51	12	algorithm	algorithm	NOUN
ajst-17687	51	13	for	for	ADP
ajst-17687	51	14	this	this	DET
ajst-17687	51	15	experiment	experiment	NOUN
ajst-17687	51	16	.	.	PUNCT
ajst-17687	52	1	4	4	X
ajst-17687	52	2	.	.	X
ajst-17687	52	3	model	model	NOUN
ajst-17687	52	4	building	build	VERB
ajst-17687	52	5	4.1	4.1	NUM
ajst-17687	52	6	.	.	PUNCT
ajst-17687	52	7	multilayerperceptron	multilayerperceptron	PROPN
ajst-17687	52	8	the	the	DET
ajst-17687	52	9	multiplayerpercepton	multiplayerpercepton	PROPN
ajst-17687	52	10	is	be	AUX
ajst-17687	52	11	a	a	DET
ajst-17687	52	12	neutral	neutral	ADJ
ajst-17687	52	13	model	model	NOUN
ajst-17687	52	14	in	in	ADP
ajst-17687	52	15	weka	weka	PROPN
ajst-17687	52	16	,	,	PUNCT
ajst-17687	52	17	commonly	commonly	ADV
ajst-17687	52	18	used	use	VERB
ajst-17687	52	19	in	in	ADP
ajst-17687	52	20	data	data	NOUN
ajst-17687	52	21	mining	mining	NOUN
ajst-17687	52	22	and	and	CCONJ
ajst-17687	52	23	machine	machine	NOUN
ajst-17687	52	24	learning	learning	NOUN
ajst-17687	52	25	.	.	PUNCT
ajst-17687	53	1	this	this	DET
ajst-17687	53	2	algorithm	algorithm	NOUN
ajst-17687	53	3	has	have	VERB
ajst-17687	53	4	its	its	PRON
ajst-17687	53	5	own	own	ADJ
ajst-17687	53	6	benefits	benefit	NOUN
ajst-17687	53	7	when	when	SCONJ
ajst-17687	53	8	working	work	VERB
ajst-17687	53	9	with	with	ADP
ajst-17687	53	10	small	small	ADJ
ajst-17687	53	11	to	to	PART
ajst-17687	53	12	135	135	NUM
ajst-17687	53	13	medium	medium	ADJ
ajst-17687	53	14	sized	sized	ADJ
ajst-17687	53	15	data	datum	NOUN
ajst-17687	53	16	,	,	PUNCT
ajst-17687	53	17	even	even	ADV
ajst-17687	53	18	though	though	SCONJ
ajst-17687	53	19	it	it	PRON
ajst-17687	53	20	is	be	AUX
ajst-17687	53	21	not	not	PART
ajst-17687	53	22	as	as	ADV
ajst-17687	53	23	accurate	accurate	ADJ
ajst-17687	53	24	as	as	ADP
ajst-17687	53	25	deep	deep	ADJ
ajst-17687	53	26	learning	learning	NOUN
ajst-17687	53	27	algorithms	algorithm	NOUN
ajst-17687	53	28	when	when	SCONJ
ajst-17687	53	29	working	work	VERB
ajst-17687	53	30	with	with	ADP
ajst-17687	53	31	larger	large	ADJ
ajst-17687	53	32	datasets	dataset	NOUN
ajst-17687	53	33	.	.	PUNCT
ajst-17687	54	1	we	we	PRON
ajst-17687	54	2	determined	determine	VERB
ajst-17687	54	3	that	that	SCONJ
ajst-17687	54	4	it	it	PRON
ajst-17687	54	5	was	be	AUX
ajst-17687	54	6	best	good	ADJ
ajst-17687	54	7	to	to	PART
ajst-17687	54	8	split	split	VERB
ajst-17687	54	9	the	the	DET
ajst-17687	54	10	data	datum	NOUN
ajst-17687	54	11	into	into	ADP
ajst-17687	54	12	ten	ten	NUM
ajst-17687	54	13	sections	section	NOUN
ajst-17687	54	14	,	,	PUNCT
ajst-17687	54	15	nine	nine	NUM
ajst-17687	54	16	for	for	ADP
ajst-17687	54	17	training	training	NOUN
ajst-17687	54	18	and	and	CCONJ
ajst-17687	54	19	one	one	NUM
ajst-17687	54	20	for	for	ADP
ajst-17687	54	21	testing	testing	NOUN
ajst-17687	54	22	,	,	PUNCT
ajst-17687	54	23	based	base	VERB
ajst-17687	54	24	on	on	ADP
ajst-17687	54	25	our	our	PRON
ajst-17687	54	26	experience	experience	NOUN
ajst-17687	54	27	.	.	PUNCT
ajst-17687	55	1	with	with	ADP
ajst-17687	55	2	this	this	PRON
ajst-17687	55	3	said	say	VERB
ajst-17687	55	4	,	,	PUNCT
ajst-17687	55	5	all	all	DET
ajst-17687	55	6	the	the	DET
ajst-17687	55	7	data	datum	NOUN
ajst-17687	55	8	shown	show	VERB
ajst-17687	55	9	below	below	ADP
ajst-17687	55	10	was	be	AUX
ajst-17687	55	11	acquired	acquire	VERB
ajst-17687	55	12	using	use	VERB
ajst-17687	55	13	a	a	DET
ajst-17687	55	14	cross	cross	NOUN
ajst-17687	55	15	-	-	NOUN
ajst-17687	55	16	validation	validation	NOUN
ajst-17687	55	17	of	of	ADP
ajst-17687	55	18	10	10	NUM
ajst-17687	55	19	.	.	PUNCT
ajst-17687	56	1	note	note	VERB
ajst-17687	56	2	that	that	SCONJ
ajst-17687	56	3	the	the	DET
ajst-17687	56	4	target	target	NOUN
ajst-17687	56	5	attribute	attribute	NOUN
ajst-17687	56	6	must	must	AUX
ajst-17687	56	7	be	be	AUX
ajst-17687	56	8	changed	change	VERB
ajst-17687	56	9	to	to	ADP
ajst-17687	56	10	(	(	PUNCT
ajst-17687	56	11	num)eaf	num)eaf	PROPN
ajst-17687	56	12	(	(	PUNCT
ajst-17687	56	13	mwh	mwh	PROPN
ajst-17687	56	14	)	)	PUNCT
ajst-17687	56	15	table	table	NOUN
ajst-17687	56	16	1	1	NUM
ajst-17687	56	17	.	.	PUNCT
ajst-17687	56	18	model	model	NOUN
ajst-17687	56	19	training	training	NOUN
ajst-17687	56	20	results	result	NOUN
ajst-17687	56	21	after	after	ADP
ajst-17687	56	22	data	data	NOUN
ajst-17687	56	23	segmentation	segmentation	NOUN
ajst-17687	56	24	.	.	PUNCT
ajst-17687	57	1	learning	learn	VERB
ajst-17687	57	2	rate	rate	NOUN
ajst-17687	57	3	0.3	0.3	NUM
ajst-17687	57	4	0.3	0.3	NUM
ajst-17687	57	5	0.3	0.3	NUM
ajst-17687	57	6	0.3	0.3	NUM
ajst-17687	57	7	0.4	0.4	NUM
ajst-17687	57	8	0.5	0.5	NUM
ajst-17687	57	9	0.3	0.3	NUM
ajst-17687	57	10	0.3	0.3	NUM
ajst-17687	57	11	0.3	0.3	NUM
ajst-17687	57	12	momentum	momentum	NOUN
ajst-17687	57	13	0.2	0.2	NUM
ajst-17687	57	14	0.2	0.2	NUM
ajst-17687	57	15	0.2	0.2	NUM
ajst-17687	57	16	0.2	0.2	NUM
ajst-17687	57	17	0.2	0.2	NUM
ajst-17687	57	18	0.2	0.2	NUM
ajst-17687	57	19	0.3	0.3	NUM
ajst-17687	57	20	0.4	0.4	NUM
ajst-17687	57	21	0.5	0.5	NUM
ajst-17687	57	22	percent	percent	NOUN
ajst-17687	57	23	split	split	VERB
ajst-17687	57	24	60	60	NUM
ajst-17687	57	25	66	66	NUM
ajst-17687	57	26	76	76	NUM
ajst-17687	57	27	86	86	NUM
ajst-17687	57	28	66	66	NUM
ajst-17687	57	29	66	66	NUM
ajst-17687	57	30	66	66	NUM
ajst-17687	57	31	66	66	NUM
ajst-17687	57	32	66	66	NUM
ajst-17687	57	33	correlation	correlation	NOUN
ajst-17687	57	34	coefficient	coefficient	VERB
ajst-17687	58	1	0.8081	0.8081	PRON
ajst-17687	58	2	0.7924	0.7924	NUM
ajst-17687	58	3	0.8166	0.8166	NUM
ajst-17687	58	4	0.8142	0.8142	NUM
ajst-17687	58	5	0.7857	0.7857	NUM
ajst-17687	58	6	0.7644	0.7644	NUM
ajst-17687	58	7	0.7738	0.7738	NUM
ajst-17687	58	8	0.7921	0.7921	NUM
ajst-17687	58	9	0.7911	0.7911	NUM
ajst-17687	58	10	mean	mean	NOUN
ajst-17687	58	11	absolute	absolute	ADJ
ajst-17687	58	12	error	error	NOUN
ajst-17687	58	13	2.0465	2.0465	NUM
ajst-17687	58	14	1.7199	1.7199	NUM
ajst-17687	58	15	1.9788	1.9788	NUM
ajst-17687	58	16	1.8565	1.8565	NUM
ajst-17687	58	17	1.7555	1.7555	NUM
ajst-17687	58	18	1.7918	1.7918	NUM
ajst-17687	58	19	1.7573	1.7573	NUM
ajst-17687	58	20	1.681	1.681	NUM
ajst-17687	58	21	1.6878	1.6878	NUM
ajst-17687	58	22	root	root	NOUN
ajst-17687	58	23	mean	mean	NOUN
ajst-17687	58	24	squared	square	VERB
ajst-17687	58	25	error	error	NOUN
ajst-17687	58	26	2.6707	2.6707	NUM
ajst-17687	58	27	2.4518	2.4518	NUM
ajst-17687	58	28	2.54	2.54	NUM
ajst-17687	58	29	2.4501	2.4501	NUM
ajst-17687	58	30	2.5537	2.5537	NUM
ajst-17687	58	31	2.5789	2.5789	NUM
ajst-17687	58	32	2.5564	2.5564	NUM
ajst-17687	58	33	2.4446	2.4446	NUM
ajst-17687	58	34	2.4931	2.4931	NUM
ajst-17687	58	35	relative	relative	ADJ
ajst-17687	58	36	absolute	absolute	ADJ
ajst-17687	58	37	error	error	NOUN
ajst-17687	58	38	70.321	70.321	NUM
ajst-17687	58	39	58.078	58.078	NUM
ajst-17687	58	40	66.7905	66.7905	NUM
ajst-17687	58	41	62.2219	62.2219	NUM
ajst-17687	58	42	59.2815	59.2815	NUM
ajst-17687	58	43	60.5079	60.5079	NUM
ajst-17687	58	44	59.34	59.34	NUM
ajst-17687	58	45	56.766	56.766	NUM
ajst-17687	58	46	56.9932	56.9932	NUM
ajst-17687	58	47	root	root	NOUN
ajst-17687	58	48	relative	relative	ADJ
ajst-17687	58	49	squared	square	VERB
ajst-17687	58	50	error	error	NOUN
ajst-17687	58	51	70.8842	70.8842	NUM
ajst-17687	58	52	63.3657	63.3657	NUM
ajst-17687	58	53	67.0566	67.0566	NUM
ajst-17687	58	54	64.2228	64.2228	NUM
ajst-17687	58	55	65.9995	65.9995	NUM
ajst-17687	58	56	66.6507	66.6507	NUM
ajst-17687	58	57	66.07	66.07	NUM
ajst-17687	58	58	63.1805	63.1805	NUM
ajst-17687	58	59	64.4343	64.4343	NUM
ajst-17687	58	60	figure	figure	NOUN
ajst-17687	58	61	4	4	NUM
ajst-17687	58	62	.	.	PUNCT
ajst-17687	58	63	classify	classify	VERB
ajst-17687	58	64	tab	tab	NOUN
ajst-17687	58	65	on	on	ADP
ajst-17687	58	66	weka	weka	PROPN
ajst-17687	58	67	.	.	PUNCT
ajst-17687	59	1	looking	look	VERB
ajst-17687	59	2	at	at	ADP
ajst-17687	59	3	the	the	DET
ajst-17687	59	4	data	datum	NOUN
ajst-17687	59	5	above	above	ADV
ajst-17687	59	6	in	in	ADP
ajst-17687	59	7	fig.6	fig.6	PROPN
ajst-17687	59	8	,	,	PUNCT
ajst-17687	59	9	we	we	PRON
ajst-17687	59	10	can	can	AUX
ajst-17687	59	11	see	see	VERB
ajst-17687	59	12	that	that	SCONJ
ajst-17687	59	13	when	when	SCONJ
ajst-17687	59	14	the	the	DET
ajst-17687	59	15	values	value	NOUN
ajst-17687	59	16	of	of	ADP
ajst-17687	59	17	the	the	DET
ajst-17687	59	18	learning	learning	NOUN
ajst-17687	59	19	rate	rate	NOUN
ajst-17687	59	20	is	be	AUX
ajst-17687	59	21	0.3	0.3	NUM
ajst-17687	59	22	,	,	PUNCT
ajst-17687	59	23	the	the	DET
ajst-17687	59	24	momentum	momentum	NOUN
ajst-17687	59	25	is	be	AUX
ajst-17687	59	26	0.4	0.4	NUM
ajst-17687	59	27	and	and	CCONJ
ajst-17687	59	28	the	the	DET
ajst-17687	59	29	percentage	percentage	NOUN
ajst-17687	59	30	split	split	NOUN
ajst-17687	59	31	is	be	AUX
ajst-17687	59	32	66	66	NUM
ajst-17687	59	33	%	%	NOUN
ajst-17687	59	34	.	.	PUNCT
ajst-17687	60	1	the	the	DET
ajst-17687	60	2	values	value	NOUN
ajst-17687	60	3	of	of	ADP
ajst-17687	60	4	mean	mean	ADJ
ajst-17687	60	5	absolute	absolute	ADJ
ajst-17687	60	6	error	error	NOUN
ajst-17687	60	7	,	,	PUNCT
ajst-17687	60	8	root	root	NOUN
ajst-17687	60	9	mean	mean	VERB
ajst-17687	60	10	squared	square	VERB
ajst-17687	60	11	error	error	NOUN
ajst-17687	60	12	,	,	PUNCT
ajst-17687	60	13	relative	relative	ADJ
ajst-17687	60	14	absolute	absolute	ADJ
ajst-17687	60	15	error	error	NOUN
ajst-17687	60	16	and	and	CCONJ
ajst-17687	60	17	root	root	NOUN
ajst-17687	60	18	relative	relative	ADJ
ajst-17687	60	19	squared	square	VERB
ajst-17687	60	20	error	error	NOUN
ajst-17687	60	21	in	in	ADP
ajst-17687	60	22	the	the	DET
ajst-17687	60	23	data	datum	NOUN
ajst-17687	60	24	are	be	AUX
ajst-17687	60	25	minimum	minimum	ADJ
ajst-17687	60	26	,	,	PUNCT
ajst-17687	60	27	so	so	SCONJ
ajst-17687	60	28	it	it	PRON
ajst-17687	60	29	can	can	AUX
ajst-17687	60	30	easily	easily	ADV
ajst-17687	60	31	get	get	VERB
ajst-17687	60	32	that	that	SCONJ
ajst-17687	60	33	the	the	DET
ajst-17687	60	34	error	error	NOUN
ajst-17687	60	35	between	between	ADP
ajst-17687	60	36	the	the	DET
ajst-17687	60	37	actual	actual	ADJ
ajst-17687	60	38	value	value	NOUN
ajst-17687	60	39	and	and	CCONJ
ajst-17687	60	40	the	the	DET
ajst-17687	60	41	predicted	predict	VERB
ajst-17687	60	42	value	value	NOUN
ajst-17687	60	43	is	be	AUX
ajst-17687	60	44	minimum	minimum	ADJ
ajst-17687	60	45	in	in	ADP
ajst-17687	60	46	this	this	DET
ajst-17687	60	47	data	data	NOUN
ajst-17687	60	48	.	.	PUNCT
ajst-17687	61	1	figure	figure	NOUN
ajst-17687	61	2	5	5	NUM
ajst-17687	61	3	.	.	PUNCT
ajst-17687	61	4	program	program	NOUN
ajst-17687	61	5	execution	execution	NOUN
ajst-17687	61	6	process	process	NOUN
ajst-17687	61	7	and	and	CCONJ
ajst-17687	61	8	regression	regression	NOUN
ajst-17687	61	9	diagram	diagram	NOUN
ajst-17687	61	10	.	.	PUNCT
ajst-17687	62	1	4.2	4.2	NUM
ajst-17687	62	2	.	.	PUNCT
ajst-17687	63	1	j48	j48	PROPN
ajst-17687	63	2	the	the	DET
ajst-17687	63	3	j48	j48	PROPN
ajst-17687	63	4	algorithm	algorithm	NOUN
ajst-17687	63	5	offers	offer	VERB
ajst-17687	63	6	a	a	DET
ajst-17687	63	7	high	high	ADJ
ajst-17687	63	8	degree	degree	NOUN
ajst-17687	63	9	of	of	ADP
ajst-17687	63	10	accuracy	accuracy	NOUN
ajst-17687	63	11	when	when	SCONJ
ajst-17687	63	12	analysing	analyse	VERB
ajst-17687	63	13	small	small	ADJ
ajst-17687	63	14	to	to	ADP
ajst-17687	63	15	medium	medium	ADJ
ajst-17687	63	16	sized	sized	ADJ
ajst-17687	63	17	datasets	dataset	NOUN
ajst-17687	63	18	,	,	PUNCT
ajst-17687	63	19	a	a	DET
ajst-17687	63	20	clear	clear	ADJ
ajst-17687	63	21	structure	structure	NOUN
ajst-17687	63	22	and	and	CCONJ
ajst-17687	63	23	,	,	PUNCT
ajst-17687	63	24	results	result	NOUN
ajst-17687	63	25	that	that	PRON
ajst-17687	63	26	are	be	AUX
ajst-17687	63	27	simple	simple	ADJ
ajst-17687	63	28	to	to	PART
ajst-17687	63	29	read	read	VERB
ajst-17687	63	30	and	and	CCONJ
ajst-17687	63	31	comprehend	comprehend	VERB
ajst-17687	63	32	.	.	PUNCT
ajst-17687	64	1	the	the	DET
ajst-17687	64	2	algorithm	algorithm	NOUN
ajst-17687	64	3	can	can	AUX
ajst-17687	64	4	also	also	ADV
ajst-17687	64	5	handle	handle	VERB
ajst-17687	64	6	attributes	attribute	NOUN
ajst-17687	64	7	with	with	ADP
ajst-17687	64	8	values	value	NOUN
ajst-17687	64	9	missing	miss	VERB
ajst-17687	64	10	and	and	CCONJ
ajst-17687	64	11	has	have	VERB
ajst-17687	64	12	a	a	DET
ajst-17687	64	13	high	high	ADJ
ajst-17687	64	14	fault	fault	NOUN
ajst-17687	64	15	tolerance	tolerance	NOUN
ajst-17687	64	16	.	.	PUNCT
ajst-17687	65	1	it	it	PRON
ajst-17687	65	2	usually	usually	ADV
ajst-17687	65	3	struggles	struggle	VERB
ajst-17687	65	4	to	to	PART
ajst-17687	65	5	manage	manage	VERB
ajst-17687	65	6	136	136	NUM
ajst-17687	65	7	characteristics	characteristic	NOUN
ajst-17687	65	8	that	that	PRON
ajst-17687	65	9	often	often	ADV
ajst-17687	65	10	require	require	VERB
ajst-17687	65	11	constant	constant	ADJ
ajst-17687	65	12	performance	performance	NOUN
ajst-17687	65	13	,	,	PUNCT
ajst-17687	65	14	tough	tough	ADJ
ajst-17687	65	15	.	.	PUNCT
ajst-17687	66	1	when	when	SCONJ
ajst-17687	66	2	using	use	VERB
ajst-17687	66	3	the	the	DET
ajst-17687	66	4	default	default	NOUN
ajst-17687	66	5	parameter	parameter	NOUN
ajst-17687	66	6	,	,	PUNCT
ajst-17687	66	7	we	we	PRON
ajst-17687	66	8	must	must	AUX
ajst-17687	66	9	first	first	ADV
ajst-17687	66	10	use	use	VERB
ajst-17687	66	11	the	the	DET
ajst-17687	66	12	option	option	NOUN
ajst-17687	66	13	discretize	discretize	NOUN
ajst-17687	66	14	in	in	ADP
ajst-17687	66	15	order	order	NOUN
ajst-17687	66	16	to	to	PART
ajst-17687	66	17	change	change	VERB
ajst-17687	66	18	the	the	DET
ajst-17687	66	19	attribute	attribute	NOUN
ajst-17687	66	20	from	from	ADP
ajst-17687	66	21	numeric	numeric	ADJ
ajst-17687	66	22	to	to	ADP
ajst-17687	66	23	nominal	nominal	ADJ
ajst-17687	66	24	.	.	PUNCT
ajst-17687	67	1	alternatively	alternatively	ADV
ajst-17687	67	2	,	,	PUNCT
ajst-17687	67	3	we	we	PRON
ajst-17687	67	4	can	can	AUX
ajst-17687	67	5	also	also	ADV
ajst-17687	67	6	use	use	VERB
ajst-17687	67	7	the	the	DET
ajst-17687	67	8	numericaltonominal	numericaltonominal	ADJ
ajst-17687	67	9	attribute	attribute	NOUN
ajst-17687	67	10	to	to	PART
ajst-17687	67	11	change	change	VERB
ajst-17687	67	12	the	the	DET
ajst-17687	67	13	attribute	attribute	NOUN
ajst-17687	67	14	from	from	ADP
ajst-17687	67	15	numeric	numeric	ADJ
ajst-17687	67	16	to	to	ADP
ajst-17687	67	17	nominal	nominal	ADJ
ajst-17687	67	18	.	.	PUNCT
ajst-17687	68	1	figure	figure	NOUN
ajst-17687	68	2	6	6	NUM
ajst-17687	68	3	.	.	PUNCT
ajst-17687	69	1	eaf	eaf	PROPN
ajst-17687	69	2	data	datum	NOUN
ajst-17687	69	3	type	type	NOUN
ajst-17687	69	4	conversion	conversion	NOUN
ajst-17687	69	5	process	process	NOUN
ajst-17687	69	6	.	.	PUNCT
ajst-17687	70	1	we	we	PRON
ajst-17687	70	2	then	then	ADV
ajst-17687	70	3	turned	turn	VERB
ajst-17687	70	4	cross	cross	NOUN
ajst-17687	70	5	-	-	ADJ
ajst-17687	70	6	validation	validation	ADJ
ajst-17687	70	7	to	to	ADP
ajst-17687	70	8	10	10	NUM
ajst-17687	70	9	and	and	CCONJ
ajst-17687	70	10	used	use	VERB
ajst-17687	70	11	the	the	DET
ajst-17687	70	12	algorithm	algorithm	NOUN
ajst-17687	70	13	j48	j48	NOUN
ajst-17687	70	14	to	to	PART
ajst-17687	70	15	run	run	VERB
ajst-17687	70	16	different	different	ADJ
ajst-17687	70	17	percentage	percentage	NOUN
ajst-17687	70	18	split	split	NOUN
ajst-17687	70	19	values	value	NOUN
ajst-17687	70	20	.	.	PUNCT
ajst-17687	71	1	table	table	NOUN
ajst-17687	71	2	2	2	NUM
ajst-17687	71	3	.	.	PUNCT
ajst-17687	71	4	model	model	NOUN
ajst-17687	71	5	training	training	NOUN
ajst-17687	71	6	results	result	NOUN
ajst-17687	71	7	after	after	ADP
ajst-17687	71	8	data	data	NOUN
ajst-17687	71	9	segmentation	segmentation	NOUN
ajst-17687	71	10	.	.	PUNCT
ajst-17687	72	1	percent	percent	NOUN
ajst-17687	72	2	split	split	VERB
ajst-17687	72	3	56	56	NUM
ajst-17687	72	4	66	66	NUM
ajst-17687	72	5	76	76	NUM
ajst-17687	72	6	86	86	NUM
ajst-17687	72	7	96	96	NUM
ajst-17687	72	8	correctly	correctly	ADV
ajst-17687	72	9	classified	classified	ADJ
ajst-17687	72	10	instances	instance	NOUN
ajst-17687	72	11	68.2911	68.2911	NUM
ajst-17687	72	12	69.6883	69.6883	NUM
ajst-17687	72	13	68.2956	68.2956	NUM
ajst-17687	72	14	70.2041	70.2041	NUM
ajst-17687	72	15	69.2857	69.2857	NUM
ajst-17687	72	16	incorrectly	incorrectly	ADV
ajst-17687	72	17	classified	classified	ADJ
ajst-17687	72	18	instances	instance	NOUN
ajst-17687	72	19	31.7089	31.7089	NUM
ajst-17687	72	20	30.3117	30.3117	NUM
ajst-17687	72	21	31.7044	31.7044	NUM
ajst-17687	72	22	29.7959	29.7959	NUM
ajst-17687	72	23	30.7143	30.7143	NUM
ajst-17687	72	24	mean	mean	NOUN
ajst-17687	72	25	absolute	absolute	ADJ
ajst-17687	72	26	error	error	NOUN
ajst-17687	72	27	0.0808	0.0808	NUM
ajst-17687	72	28	0.0806	0.0806	NUM
ajst-17687	72	29	0.0808	0.0808	NUM
ajst-17687	72	30	0.0795	0.0795	NUM
ajst-17687	72	31	0.0816	0.0816	NUM
ajst-17687	72	32	root	root	NOUN
ajst-17687	72	33	mean	mean	VERB
ajst-17687	72	34	squared	square	VERB
ajst-17687	72	35	error	error	NOUN
ajst-17687	72	36	0.2239	0.2239	NUM
ajst-17687	72	37	0.2157	0.2157	NUM
ajst-17687	72	38	0.2225	0.2225	NUM
ajst-17687	72	39	0.2169	0.2169	NUM
ajst-17687	72	40	0.2164	0.2164	NUM
ajst-17687	72	41	relative	relative	ADJ
ajst-17687	72	42	absolute	absolute	ADJ
ajst-17687	72	43	error	error	NOUN
ajst-17687	72	44	69.8711	69.8711	NUM
ajst-17687	72	45	69.8533	69.8533	NUM
ajst-17687	72	46	69.1381	69.1381	NUM
ajst-17687	72	47	67.8949	67.8949	NUM
ajst-17687	72	48	68.4355	68.4355	NUM
ajst-17687	72	49	root	root	NOUN
ajst-17687	72	50	relative	relative	ADJ
ajst-17687	72	51	squared	square	VERB
ajst-17687	72	52	error	error	NOUN
ajst-17687	72	53	92.7772	92.7772	NUM
ajst-17687	72	54	89.8878	89.8878	NUM
ajst-17687	72	55	91.0512	91.0512	NUM
ajst-17687	72	56	88.7971	88.7971	NUM
ajst-17687	72	57	87.1663	87.1663	NUM
ajst-17687	72	58	figure	figure	NOUN
ajst-17687	72	59	6	6	NUM
ajst-17687	72	60	.	.	PUNCT
ajst-17687	72	61	classify	classify	VERB
ajst-17687	72	62	tab	tab	NOUN
ajst-17687	72	63	on	on	ADP
ajst-17687	72	64	weka	weka	PROPN
ajst-17687	72	65	.	.	PUNCT
ajst-17687	73	1	we	we	PRON
ajst-17687	73	2	observed	observe	VERB
ajst-17687	73	3	the	the	DET
ajst-17687	73	4	following	follow	VERB
ajst-17687	73	5	values	value	NOUN
ajst-17687	73	6	through	through	ADP
ajst-17687	73	7	the	the	DET
ajst-17687	73	8	window	window	NOUN
ajst-17687	73	9	classifier	classifier	NOUN
ajst-17687	73	10	on	on	ADP
ajst-17687	73	11	output	output	NOUN
ajst-17687	73	12	.	.	PUNCT
ajst-17687	74	1	we	we	PRON
ajst-17687	74	2	can	can	AUX
ajst-17687	74	3	deduct	deduct	VERB
ajst-17687	74	4	from	from	ADP
ajst-17687	74	5	the	the	DET
ajst-17687	74	6	size	size	NOUN
ajst-17687	74	7	of	of	ADP
ajst-17687	74	8	the	the	DET
ajst-17687	74	9	values	value	NOUN
ajst-17687	74	10	in	in	ADP
ajst-17687	74	11	the	the	DET
ajst-17687	74	12	above	above	ADJ
ajst-17687	74	13	table	table	NOUN
ajst-17687	74	14	that	that	SCONJ
ajst-17687	74	15	the	the	DET
ajst-17687	74	16	accuracy	accuracy	NOUN
ajst-17687	74	17	is	be	AUX
ajst-17687	74	18	the	the	DET
ajst-17687	74	19	highest	high	ADJ
ajst-17687	74	20	when	when	SCONJ
ajst-17687	74	21	the	the	DET
ajst-17687	74	22	percentage	percentage	NOUN
ajst-17687	74	23	split	split	NOUN
ajst-17687	74	24	value	value	NOUN
ajst-17687	74	25	is	be	AUX
ajst-17687	74	26	56%.in	56%.in	PROPN
ajst-17687	74	27	weka	weka	NOUN
ajst-17687	74	28	,	,	PUNCT
ajst-17687	74	29	selecting	select	VERB
ajst-17687	74	30	visualise	visualise	NOUN
ajst-17687	74	31	offers	offer	VERB
ajst-17687	74	32	a	a	DET
ajst-17687	74	33	more	more	ADV
ajst-17687	74	34	userfriendly	userfriendly	ADJ
ajst-17687	74	35	way	way	NOUN
ajst-17687	74	36	to	to	PART
ajst-17687	74	37	present	present	VERB
ajst-17687	74	38	the	the	DET
ajst-17687	74	39	data	datum	NOUN
ajst-17687	74	40	so	so	SCONJ
ajst-17687	74	41	that	that	SCONJ
ajst-17687	74	42	we	we	PRON
ajst-17687	74	43	can	can	AUX
ajst-17687	74	44	see	see	VERB
ajst-17687	74	45	how	how	SCONJ
ajst-17687	74	46	various	various	ADJ
ajst-17687	74	47	attributes	attribute	NOUN
ajst-17687	74	48	relate	relate	VERB
ajst-17687	74	49	to	to	ADP
ajst-17687	74	50	one	one	NUM
ajst-17687	74	51	another	another	DET
ajst-17687	74	52	.	.	PUNCT
ajst-17687	75	1	moreover	moreover	ADV
ajst-17687	75	2	,	,	PUNCT
ajst-17687	75	3	users	user	NOUN
ajst-17687	75	4	can	can	AUX
ajst-17687	75	5	also	also	ADV
ajst-17687	75	6	spot	spot	VERB
ajst-17687	75	7	outliers	outlier	NOUN
ajst-17687	75	8	by	by	ADP
ajst-17687	75	9	examining	examine	VERB
ajst-17687	75	10	the	the	DET
ajst-17687	75	11	image	image	NOUN
ajst-17687	75	12	.	.	PUNCT
ajst-17687	76	1	137	137	NUM
ajst-17687	76	2	figure	figure	NOUN
ajst-17687	76	3	7	7	NUM
ajst-17687	76	4	.	.	PUNCT
ajst-17687	76	5	program	program	NOUN
ajst-17687	76	6	execution	execution	NOUN
ajst-17687	76	7	process	process	NOUN
ajst-17687	76	8	and	and	CCONJ
ajst-17687	76	9	regression	regression	NOUN
ajst-17687	76	10	diagram	diagram	PROPN
ajst-17687	76	11	4.3	4.3	NUM
ajst-17687	76	12	.	.	PUNCT
ajst-17687	76	13	randomtree	randomtree	NOUN
ajst-17687	76	14	in	in	ADP
ajst-17687	76	15	weka	weka	PROPN
ajst-17687	76	16	,	,	PUNCT
ajst-17687	76	17	the	the	DET
ajst-17687	76	18	randomtree	randomtree	NOUN
ajst-17687	76	19	algorithm	algorithm	NOUN
ajst-17687	76	20	is	be	AUX
ajst-17687	76	21	often	often	ADV
ajst-17687	76	22	used	use	VERB
ajst-17687	76	23	for	for	ADP
ajst-17687	76	24	machine	machine	NOUN
ajst-17687	76	25	learning	learning	NOUN
ajst-17687	76	26	and	and	CCONJ
ajst-17687	76	27	also	also	ADV
ajst-17687	76	28	data	data	VERB
ajst-17687	76	29	mining	mining	NOUN
ajst-17687	76	30	.	.	PUNCT
ajst-17687	77	1	it	it	PRON
ajst-17687	77	2	produces	produce	VERB
ajst-17687	77	3	a	a	DET
ajst-17687	77	4	decision	decision	NOUN
ajst-17687	77	5	tree	tree	NOUN
ajst-17687	77	6	model	model	NOUN
ajst-17687	77	7	with	with	ADP
ajst-17687	77	8	an	an	DET
ajst-17687	77	9	easy	easy	ADJ
ajst-17687	77	10	-	-	PUNCT
ajst-17687	77	11	to	to	PART
ajst-17687	77	12	-	-	PUNCT
ajst-17687	77	13	understand	understand	VERB
ajst-17687	77	14	structure	structure	NOUN
ajst-17687	77	15	.	.	PUNCT
ajst-17687	78	1	it	it	PRON
ajst-17687	78	2	can	can	AUX
ajst-17687	78	3	handle	handle	VERB
ajst-17687	78	4	a	a	DET
ajst-17687	78	5	variety	variety	NOUN
ajst-17687	78	6	of	of	ADP
ajst-17687	78	7	data	datum	NOUN
ajst-17687	78	8	types	type	NOUN
ajst-17687	78	9	,	,	PUNCT
ajst-17687	78	10	these	these	PRON
ajst-17687	78	11	including	include	VERB
ajst-17687	78	12	nominal	nominal	ADJ
ajst-17687	78	13	and	and	CCONJ
ajst-17687	78	14	numerical	numerical	ADJ
ajst-17687	78	15	data	datum	NOUN
ajst-17687	78	16	,	,	PUNCT
ajst-17687	78	17	which	which	PRON
ajst-17687	78	18	makes	make	VERB
ajst-17687	78	19	the	the	DET
ajst-17687	78	20	operation	operation	NOUN
ajst-17687	78	21	simpler	simple	ADJ
ajst-17687	78	22	,	,	PUNCT
ajst-17687	78	23	although	although	SCONJ
ajst-17687	78	24	there	there	PRON
ajst-17687	78	25	is	be	VERB
ajst-17687	78	26	some	some	DET
ajst-17687	78	27	randomness	randomness	NOUN
ajst-17687	78	28	present	present	ADJ
ajst-17687	78	29	and	and	CCONJ
ajst-17687	78	30	the	the	DET
ajst-17687	78	31	accuracy	accuracy	NOUN
ajst-17687	78	32	in	in	ADP
ajst-17687	78	33	not	not	PART
ajst-17687	78	34	the	the	DET
ajst-17687	78	35	highest	high	ADJ
ajst-17687	78	36	.	.	PUNCT
ajst-17687	79	1	the	the	DET
ajst-17687	79	2	cross	cross	NOUN
ajst-17687	79	3	-	-	ADJ
ajst-17687	79	4	validation	validation	NOUN
ajst-17687	79	5	is	be	AUX
ajst-17687	79	6	set	set	VERB
ajst-17687	79	7	,	,	PUNCT
ajst-17687	79	8	folds	fold	VERB
ajst-17687	79	9	10	10	NUM
ajst-17687	80	1	and	and	CCONJ
ajst-17687	80	2	then	then	ADV
ajst-17687	80	3	the	the	DET
ajst-17687	80	4	randomtree	randomtree	NOUN
ajst-17687	80	5	algorithm	algorithm	NOUN
ajst-17687	80	6	is	be	AUX
ajst-17687	80	7	used	use	VERB
ajst-17687	80	8	to	to	PART
ajst-17687	80	9	classify	classify	VERB
ajst-17687	80	10	the	the	DET
ajst-17687	80	11	data	datum	NOUN
ajst-17687	80	12	.	.	PUNCT
ajst-17687	81	1	the	the	DET
ajst-17687	81	2	arithmetic	arithmetic	NOUN
ajst-17687	81	3	resulting	result	VERB
ajst-17687	81	4	from	from	ADP
ajst-17687	81	5	varying	vary	VERB
ajst-17687	81	6	the	the	DET
ajst-17687	81	7	percentage	percentage	NOUN
ajst-17687	81	8	split	split	NOUN
ajst-17687	81	9	values	value	NOUN
ajst-17687	81	10	produces	produce	VERB
ajst-17687	81	11	the	the	DET
ajst-17687	81	12	following	follow	VERB
ajst-17687	81	13	data	datum	NOUN
ajst-17687	81	14	.	.	PUNCT
ajst-17687	82	1	table	table	NOUN
ajst-17687	83	1	3	3	NUM
ajst-17687	83	2	.	.	PUNCT
ajst-17687	83	3	model	model	NOUN
ajst-17687	83	4	learning	learn	VERB
ajst-17687	83	5	training	training	NOUN
ajst-17687	83	6	results	result	NOUN
ajst-17687	83	7	after	after	SCONJ
ajst-17687	83	8	data	data	NOUN
ajst-17687	83	9	segmentation	segmentation	NOUN
ajst-17687	83	10	percent	percent	NOUN
ajst-17687	83	11	split	split	VERB
ajst-17687	83	12	56	56	NUM
ajst-17687	83	13	%	%	NOUN
ajst-17687	83	14	66	66	NUM
ajst-17687	83	15	%	%	NOUN
ajst-17687	83	16	76	76	NUM
ajst-17687	83	17	%	%	NOUN
ajst-17687	83	18	86	86	NUM
ajst-17687	83	19	%	%	NOUN
ajst-17687	83	20	96	96	NUM
ajst-17687	83	21	%	%	NOUN
ajst-17687	83	22	correctly	correctly	ADV
ajst-17687	83	23	classified	classify	VERB
ajst-17687	83	24	instances	instance	NOUN
ajst-17687	83	25	64.9773	64.9773	NUM
ajst-17687	83	26	63.3305	63.3305	NUM
ajst-17687	83	27	62.5745	62.5745	NUM
ajst-17687	83	28	66.3265	66.3265	NUM
ajst-17687	83	29	68.5714	68.5714	NUM
ajst-17687	83	30	incorrectly	incorrectly	ADV
ajst-17687	83	31	classified	classified	ADJ
ajst-17687	83	32	instances	instance	NOUN
ajst-17687	83	33	35.0227	35.0227	NUM
ajst-17687	83	34	36.6695	36.6695	NUM
ajst-17687	83	35	37.4255	37.4255	NUM
ajst-17687	83	36	33.6735	33.6735	NUM
ajst-17687	83	37	31.4286	31.4286	NUM
ajst-17687	83	38	mean	mean	NOUN
ajst-17687	83	39	absolute	absolute	ADJ
ajst-17687	83	40	error	error	NOUN
ajst-17687	83	41	0.0728	0.0728	NOUN
ajst-17687	83	42	0.0755	0.0755	NUM
ajst-17687	84	1	0.076	0.076	NUM
ajst-17687	84	2	0.068	0.068	NUM
ajst-17687	84	3	0.0657	0.0657	NUM
ajst-17687	84	4	root	root	NOUN
ajst-17687	84	5	mean	mean	VERB
ajst-17687	84	6	squared	square	VERB
ajst-17687	84	7	error	error	NOUN
ajst-17687	84	8	0.2559	0.2559	NUM
ajst-17687	84	9	0.2635	0.2635	NUM
ajst-17687	84	10	0.2633	0.2633	NUM
ajst-17687	84	11	0.2488	0.2488	NUM
ajst-17687	84	12	0.2376	0.2376	NUM
ajst-17687	84	13	relative	relative	ADJ
ajst-17687	84	14	absolute	absolute	ADJ
ajst-17687	84	15	error	error	NOUN
ajst-17687	84	16	62.9523	62.9523	NOUN
ajst-17687	84	17	64.9629	64.9629	NUM
ajst-17687	84	18	65.0131	65.0131	NUM
ajst-17687	84	19	58.0774	58.0774	NUM
ajst-17687	84	20	55.0952	55.0952	NUM
ajst-17687	84	21	root	root	NOUN
ajst-17687	84	22	relative	relative	ADJ
ajst-17687	84	23	squared	square	VERB
ajst-17687	84	24	error	error	NOUN
ajst-17687	84	25	106.0335	106.0335	NUM
ajst-17687	84	26	108.4655	108.4655	NUM
ajst-17687	84	27	107.7565	107.7565	NUM
ajst-17687	84	28	101.8892	101.8892	NUM
ajst-17687	84	29	95.6949	95.6949	NUM
ajst-17687	84	30	from	from	ADP
ajst-17687	84	31	the	the	DET
ajst-17687	84	32	data	datum	NOUN
ajst-17687	84	33	shown	show	VERB
ajst-17687	84	34	in	in	ADP
ajst-17687	84	35	the	the	DET
ajst-17687	84	36	above	above	ADJ
ajst-17687	84	37	table	table	NOUN
ajst-17687	84	38	we	we	PRON
ajst-17687	84	39	can	can	AUX
ajst-17687	84	40	conclude	conclude	VERB
ajst-17687	84	41	that	that	SCONJ
ajst-17687	84	42	when	when	SCONJ
ajst-17687	84	43	the	the	DET
ajst-17687	84	44	value	value	NOUN
ajst-17687	84	45	of	of	ADP
ajst-17687	84	46	the	the	DET
ajst-17687	84	47	percentage	percentage	NOUN
ajst-17687	84	48	split	split	NOUN
ajst-17687	84	49	is	be	AUX
ajst-17687	84	50	96	96	NUM
ajst-17687	84	51	%	%	NOUN
ajst-17687	84	52	,	,	PUNCT
ajst-17687	84	53	the	the	DET
ajst-17687	84	54	accuracy	accuracy	NOUN
ajst-17687	84	55	of	of	ADP
ajst-17687	84	56	this	this	DET
ajst-17687	84	57	data	data	NOUN
ajst-17687	84	58	is	be	AUX
ajst-17687	84	59	the	the	DET
ajst-17687	84	60	highest	high	ADJ
ajst-17687	84	61	and	and	CCONJ
ajst-17687	84	62	the	the	DET
ajst-17687	84	63	error	error	NOUN
ajst-17687	84	64	is	be	AUX
ajst-17687	84	65	the	the	DET
ajst-17687	84	66	least	least	ADJ
ajst-17687	84	67	.	.	PUNCT
ajst-17687	85	1	figure	figure	NOUN
ajst-17687	85	2	8	8	NUM
ajst-17687	85	3	.	.	PUNCT
ajst-17687	86	1	classify	classify	VERB
ajst-17687	86	2	tab	tab	NOUN
ajst-17687	86	3	with	with	ADP
ajst-17687	86	4	the	the	DET
ajst-17687	86	5	percentage	percentage	NOUN
ajst-17687	86	6	split	split	NOUN
ajst-17687	86	7	set	set	VERB
ajst-17687	86	8	to	to	ADP
ajst-17687	86	9	96	96	NUM
ajst-17687	86	10	on	on	ADP
ajst-17687	86	11	weka	weka	PROPN
ajst-17687	86	12	.	.	PUNCT
ajst-17687	87	1	thus	thus	ADV
ajst-17687	87	2	,	,	PUNCT
ajst-17687	87	3	in	in	ADP
ajst-17687	87	4	order	order	NOUN
ajst-17687	87	5	to	to	PART
ajst-17687	87	6	be	be	AUX
ajst-17687	87	7	able	able	ADJ
ajst-17687	87	8	to	to	PART
ajst-17687	87	9	observe	observe	VERB
ajst-17687	87	10	the	the	DET
ajst-17687	87	11	data	datum	NOUN
ajst-17687	87	12	in	in	ADP
ajst-17687	87	13	a	a	DET
ajst-17687	87	14	better	well	ADJ
ajst-17687	87	15	visionally	visionally	ADV
ajst-17687	87	16	way	way	NOUN
ajst-17687	87	17	,	,	PUNCT
ajst-17687	87	18	the	the	DET
ajst-17687	87	19	graphical	graphical	ADJ
ajst-17687	87	20	decision	decision	NOUN
ajst-17687	87	21	tree	tree	NOUN
ajst-17687	87	22	can	can	AUX
ajst-17687	87	23	also	also	ADV
ajst-17687	87	24	be	be	AUX
ajst-17687	87	25	opened	open	VERB
ajst-17687	87	26	.	.	PUNCT
ajst-17687	88	1	138	138	NUM
ajst-17687	88	2	figure	figure	NOUN
ajst-17687	88	3	9	9	NUM
ajst-17687	88	4	.	.	PUNCT
ajst-17687	88	5	visualize	visualize	VERB
ajst-17687	88	6	tree	tree	NOUN
ajst-17687	88	7	.	.	PUNCT
ajst-17687	89	1	5	5	X
ajst-17687	89	2	.	.	X
ajst-17687	89	3	results	result	NOUN
ajst-17687	89	4	,	,	PUNCT
ajst-17687	89	5	performance	performance	NOUN
ajst-17687	89	6	and	and	CCONJ
ajst-17687	89	7	evaluation	evaluation	NOUN
ajst-17687	89	8	in	in	ADP
ajst-17687	89	9	this	this	DET
ajst-17687	89	10	part	part	NOUN
ajst-17687	89	11	,	,	PUNCT
ajst-17687	89	12	the	the	DET
ajst-17687	89	13	performance	performance	NOUN
ajst-17687	89	14	results	result	NOUN
ajst-17687	89	15	and	and	CCONJ
ajst-17687	89	16	evaluation	evaluation	NOUN
ajst-17687	89	17	outcomes	outcome	NOUN
ajst-17687	89	18	of	of	ADP
ajst-17687	89	19	the	the	DET
ajst-17687	89	20	models	model	NOUN
ajst-17687	89	21	will	will	AUX
ajst-17687	89	22	be	be	AUX
ajst-17687	89	23	discussed	discuss	VERB
ajst-17687	89	24	,	,	PUNCT
ajst-17687	89	25	the	the	DET
ajst-17687	89	26	ones	one	NOUN
ajst-17687	89	27	we	we	PRON
ajst-17687	89	28	used	use	VERB
ajst-17687	89	29	on	on	ADP
ajst-17687	89	30	our	our	PRON
ajst-17687	89	31	group	group	NOUN
ajst-17687	89	32	’s	’s	PART
ajst-17687	89	33	case	case	NOUN
ajst-17687	89	34	study	study	NOUN
ajst-17687	89	35	.	.	PUNCT
ajst-17687	90	1	in	in	ADP
ajst-17687	90	2	order	order	NOUN
ajst-17687	90	3	to	to	PART
ajst-17687	90	4	accomplish	accomplish	VERB
ajst-17687	90	5	the	the	DET
ajst-17687	90	6	target	target	NOUN
ajst-17687	90	7	of	of	ADP
ajst-17687	90	8	energy	energy	NOUN
ajst-17687	90	9	consumption	consumption	NOUN
ajst-17687	90	10	prediction	prediction	NOUN
ajst-17687	90	11	in	in	ADP
ajst-17687	90	12	the	the	DET
ajst-17687	90	13	eaf	eaf	NOUN
ajst-17687	90	14	production	production	NOUN
ajst-17687	90	15	process	process	NOUN
ajst-17687	90	16	,	,	PUNCT
ajst-17687	90	17	we	we	PRON
ajst-17687	90	18	took	take	VERB
ajst-17687	90	19	into	into	ADP
ajst-17687	90	20	account	account	NOUN
ajst-17687	90	21	the	the	DET
ajst-17687	90	22	next	next	ADJ
ajst-17687	90	23	ways	way	NOUN
ajst-17687	90	24	:	:	PUNCT
ajst-17687	90	25	performance	performance	NOUN
ajst-17687	90	26	metrics	metric	NOUN
ajst-17687	90	27	rationale	rationale	NOUN
ajst-17687	90	28	:	:	PUNCT
ajst-17687	90	29	accuracy	accuracy	NOUN
ajst-17687	90	30	and	and	CCONJ
ajst-17687	90	31	error	error	NOUN
ajst-17687	90	32	metrics	metric	NOUN
ajst-17687	90	33	:	:	PUNCT
ajst-17687	90	34	for	for	ADP
ajst-17687	90	35	the	the	DET
ajst-17687	90	36	target	target	NOUN
ajst-17687	90	37	of	of	ADP
ajst-17687	90	38	providing	provide	VERB
ajst-17687	90	39	a	a	DET
ajst-17687	90	40	comprehensive	comprehensive	ADJ
ajst-17687	90	41	picture	picture	NOUN
ajst-17687	90	42	of	of	ADP
ajst-17687	90	43	the	the	DET
ajst-17687	90	44	model	model	NOUN
ajst-17687	90	45	’s	’s	PART
ajst-17687	90	46	prediction	prediction	NOUN
ajst-17687	90	47	accuracy	accuracy	NOUN
ajst-17687	90	48	and	and	CCONJ
ajst-17687	90	49	error	error	NOUN
ajst-17687	90	50	size	size	NOUN
ajst-17687	90	51	.	.	PUNCT
ajst-17687	91	1	we	we	PRON
ajst-17687	91	2	took	take	VERB
ajst-17687	91	3	the	the	DET
ajst-17687	91	4	metrics	metric	NOUN
ajst-17687	91	5	such	such	ADJ
ajst-17687	91	6	as	as	ADP
ajst-17687	91	7	mean	mean	ADJ
ajst-17687	91	8	absolute	absolute	ADJ
ajst-17687	91	9	error	error	NOUN
ajst-17687	91	10	,	,	PUNCT
ajst-17687	91	11	root	root	NOUN
ajst-17687	91	12	mean	mean	VERB
ajst-17687	91	13	squared	square	VERB
ajst-17687	91	14	error	error	NOUN
ajst-17687	91	15	,	,	PUNCT
ajst-17687	91	16	and	and	CCONJ
ajst-17687	91	17	correctly	correctly	ADV
ajst-17687	91	18	classified	classified	ADJ
ajst-17687	91	19	instances	instance	NOUN
ajst-17687	91	20	.	.	PUNCT
ajst-17687	92	1	relative	relative	ADJ
ajst-17687	92	2	errors	error	NOUN
ajst-17687	92	3	:	:	PUNCT
ajst-17687	92	4	for	for	ADP
ajst-17687	92	5	the	the	DET
ajst-17687	92	6	relative	relative	ADJ
ajst-17687	92	7	errors	error	NOUN
ajst-17687	92	8	,	,	PUNCT
ajst-17687	92	9	we	we	PRON
ajst-17687	92	10	choose	choose	VERB
ajst-17687	92	11	relative	relative	ADJ
ajst-17687	92	12	absolute	absolute	ADJ
ajst-17687	92	13	error	error	NOUN
ajst-17687	92	14	and	and	CCONJ
ajst-17687	92	15	root	root	NOUN
ajst-17687	92	16	relative	relative	ADJ
ajst-17687	92	17	squared	square	VERB
ajst-17687	92	18	error	error	NOUN
ajst-17687	92	19	,	,	PUNCT
ajst-17687	92	20	so	so	SCONJ
ajst-17687	92	21	we	we	PRON
ajst-17687	92	22	could	could	AUX
ajst-17687	92	23	know	know	VERB
ajst-17687	92	24	how	how	SCONJ
ajst-17687	92	25	our	our	PRON
ajst-17687	92	26	model	model	NOUN
ajst-17687	92	27	would	would	AUX
ajst-17687	92	28	perform	perform	VERB
ajst-17687	92	29	against	against	ADP
ajst-17687	92	30	baseline	baseline	NOUN
ajst-17687	92	31	predictors	predictor	NOUN
ajst-17687	92	32	,	,	PUNCT
ajst-17687	92	33	so	so	SCONJ
ajst-17687	92	34	we	we	PRON
ajst-17687	92	35	could	could	AUX
ajst-17687	92	36	get	get	VERB
ajst-17687	92	37	a	a	DET
ajst-17687	92	38	better	well	ADJ
ajst-17687	92	39	result	result	NOUN
ajst-17687	92	40	.	.	PUNCT
ajst-17687	93	1	performance	performance	NOUN
ajst-17687	93	2	results	result	NOUN
ajst-17687	93	3	:	:	PUNCT
ajst-17687	93	4	multiplayer	multiplayer	NOUN
ajst-17687	93	5	perceptron	perceptron	PROPN
ajst-17687	93	6	:	:	PUNCT
ajst-17687	94	1	i	i	PRON
ajst-17687	94	2	believe	believe	VERB
ajst-17687	94	3	our	our	PRON
ajst-17687	94	4	model	model	NOUN
ajst-17687	94	5	performs	perform	VERB
ajst-17687	94	6	well	well	ADV
ajst-17687	94	7	in	in	ADP
ajst-17687	94	8	terms	term	NOUN
ajst-17687	94	9	of	of	ADP
ajst-17687	94	10	accuracy	accuracy	NOUN
ajst-17687	94	11	,	,	PUNCT
ajst-17687	94	12	but	but	CCONJ
ajst-17687	94	13	we	we	PRON
ajst-17687	94	14	can	can	AUX
ajst-17687	94	15	still	still	ADV
ajst-17687	94	16	see	see	VERB
ajst-17687	94	17	from	from	ADP
ajst-17687	94	18	the	the	DET
ajst-17687	94	19	error	error	NOUN
ajst-17687	94	20	rate	rate	NOUN
ajst-17687	94	21	that	that	SCONJ
ajst-17687	94	22	there	there	PRON
ajst-17687	94	23	is	be	VERB
ajst-17687	94	24	some	some	DET
ajst-17687	94	25	variability	variability	NOUN
ajst-17687	94	26	in	in	ADP
ajst-17687	94	27	the	the	DET
ajst-17687	94	28	predictions	prediction	NOUN
ajst-17687	94	29	.	.	PUNCT
ajst-17687	95	1	besides	besides	SCONJ
ajst-17687	95	2	,	,	PUNCT
ajst-17687	95	3	we	we	PRON
ajst-17687	95	4	should	should	AUX
ajst-17687	95	5	also	also	ADV
ajst-17687	95	6	notice	notice	VERB
ajst-17687	95	7	that	that	SCONJ
ajst-17687	95	8	the	the	DET
ajst-17687	95	9	correlation	correlation	NOUN
ajst-17687	95	10	coefficient	coefficient	NOUN
ajst-17687	95	11	is	be	AUX
ajst-17687	95	12	significantly	significantly	ADV
ajst-17687	95	13	high	high	ADJ
ajst-17687	95	14	,	,	PUNCT
ajst-17687	95	15	which	which	PRON
ajst-17687	95	16	means	mean	VERB
ajst-17687	95	17	there	there	PRON
ajst-17687	95	18	is	be	VERB
ajst-17687	95	19	a	a	DET
ajst-17687	95	20	relationship	relationship	NOUN
ajst-17687	95	21	between	between	ADP
ajst-17687	95	22	the	the	DET
ajst-17687	95	23	predicted	predict	VERB
ajst-17687	95	24	and	and	CCONJ
ajst-17687	95	25	actual	actual	ADJ
ajst-17687	95	26	values	value	NOUN
ajst-17687	95	27	.	.	PUNCT
ajst-17687	96	1	j48	j48	ADJ
ajst-17687	96	2	decision	decision	NOUN
ajst-17687	96	3	tree	tree	NOUN
ajst-17687	96	4	:	:	PUNCT
ajst-17687	96	5	based	base	VERB
ajst-17687	96	6	on	on	ADP
ajst-17687	96	7	the	the	DET
ajst-17687	96	8	database	database	NOUN
ajst-17687	96	9	we	we	PRON
ajst-17687	96	10	have	have	VERB
ajst-17687	96	11	,	,	PUNCT
ajst-17687	96	12	there	there	PRON
ajst-17687	96	13	is	be	VERB
ajst-17687	96	14	even	even	ADV
ajst-17687	96	15	a	a	DET
ajst-17687	96	16	high	high	ADJ
ajst-17687	96	17	tolerance	tolerance	NOUN
ajst-17687	96	18	for	for	ADP
ajst-17687	96	19	missing	miss	VERB
ajst-17687	96	20	attribute	attribute	NOUN
ajst-17687	96	21	values	value	NOUN
ajst-17687	96	22	,	,	PUNCT
ajst-17687	96	23	we	we	PRON
ajst-17687	96	24	think	think	VERB
ajst-17687	96	25	it	it	PRON
ajst-17687	96	26	’s	’	VERB
ajst-17687	96	27	still	still	ADV
ajst-17687	96	28	beneficial	beneficial	ADJ
ajst-17687	96	29	to	to	ADP
ajst-17687	96	30	the	the	DET
ajst-17687	96	31	nature	nature	NOUN
ajst-17687	96	32	of	of	ADP
ajst-17687	96	33	our	our	PRON
ajst-17687	96	34	dataset	dataset	NOUN
ajst-17687	96	35	.	.	PUNCT
ajst-17687	97	1	randomtree	randomtree	NOUN
ajst-17687	97	2	:	:	PUNCT
ajst-17687	97	3	with	with	ADP
ajst-17687	97	4	the	the	DET
ajst-17687	97	5	help	help	NOUN
ajst-17687	97	6	of	of	ADP
ajst-17687	97	7	the	the	DET
ajst-17687	97	8	randomtree	randomtree	NOUN
ajst-17687	97	9	,	,	PUNCT
ajst-17687	97	10	it	it	PRON
ajst-17687	97	11	’s	’	VERB
ajst-17687	97	12	easier	easy	ADJ
ajst-17687	97	13	to	to	PART
ajst-17687	97	14	deal	deal	VERB
ajst-17687	97	15	with	with	ADP
ajst-17687	97	16	various	various	ADJ
ajst-17687	97	17	data	datum	NOUN
ajst-17687	97	18	types	type	NOUN
ajst-17687	97	19	,	,	PUNCT
ajst-17687	97	20	including	include	VERB
ajst-17687	97	21	nominal	nominal	ADJ
ajst-17687	97	22	and	and	CCONJ
ajst-17687	97	23	numerical	numerical	ADJ
ajst-17687	97	24	.	.	PUNCT
ajst-17687	98	1	after	after	SCONJ
ajst-17687	98	2	we	we	PRON
ajst-17687	98	3	conducted	conduct	VERB
ajst-17687	98	4	a	a	DET
ajst-17687	98	5	comprehensive	comprehensive	ADJ
ajst-17687	98	6	data	data	NOUN
ajst-17687	98	7	analysis	analysis	NOUN
ajst-17687	98	8	by	by	ADP
ajst-17687	98	9	weka	weka	PROPN
ajst-17687	98	10	,	,	PUNCT
ajst-17687	98	11	we	we	PRON
ajst-17687	98	12	can	can	AUX
ajst-17687	98	13	get	get	VERB
ajst-17687	98	14	the	the	DET
ajst-17687	98	15	performance	performance	NOUN
ajst-17687	98	16	metrics	metric	NOUN
ajst-17687	98	17	.	.	PUNCT
ajst-17687	99	1	in	in	ADP
ajst-17687	99	2	addition	addition	NOUN
ajst-17687	99	3	,	,	PUNCT
ajst-17687	99	4	the	the	DET
ajst-17687	99	5	error	error	NOUN
ajst-17687	99	6	rates	rate	NOUN
ajst-17687	99	7	,	,	PUNCT
ajst-17687	99	8	accuracy	accuracy	NOUN
ajst-17687	99	9	percentages	percentage	NOUN
ajst-17687	99	10	,	,	PUNCT
ajst-17687	99	11	and	and	CCONJ
ajst-17687	99	12	the	the	DET
ajst-17687	99	13	correlation	correlation	NOUN
ajst-17687	99	14	coefficients	coefficient	NOUN
ajst-17687	99	15	were	be	AUX
ajst-17687	99	16	derived	derive	VERB
ajst-17687	99	17	from	from	ADP
ajst-17687	99	18	tests	test	NOUN
ajst-17687	99	19	and	and	CCONJ
ajst-17687	99	20	validations	validation	NOUN
ajst-17687	99	21	.	.	PUNCT
ajst-17687	100	1	as	as	ADP
ajst-17687	100	2	for	for	ADP
ajst-17687	100	3	the	the	DET
ajst-17687	100	4	pictures	picture	NOUN
ajst-17687	100	5	and	and	CCONJ
ajst-17687	100	6	graphs	graph	NOUN
ajst-17687	100	7	which	which	PRON
ajst-17687	100	8	are	be	AUX
ajst-17687	100	9	referenced	reference	VERB
ajst-17687	100	10	in	in	ADP
ajst-17687	100	11	the	the	DET
ajst-17687	100	12	methodology	methodology	NOUN
ajst-17687	100	13	section	section	NOUN
ajst-17687	100	14	,	,	PUNCT
ajst-17687	100	15	they	they	PRON
ajst-17687	100	16	provide	provide	VERB
ajst-17687	100	17	a	a	DET
ajst-17687	100	18	representative	representative	ADJ
ajst-17687	100	19	and	and	CCONJ
ajst-17687	100	20	quantitative	quantitative	ADJ
ajst-17687	100	21	evidence	evidence	NOUN
ajst-17687	100	22	of	of	ADP
ajst-17687	100	23	each	each	DET
ajst-17687	100	24	model	model	NOUN
ajst-17687	100	25	’s	’s	PART
ajst-17687	100	26	performance	performance	NOUN
ajst-17687	100	27	.	.	PUNCT
ajst-17687	101	1	last	last	ADJ
ajst-17687	101	2	but	but	CCONJ
ajst-17687	101	3	not	not	PART
ajst-17687	101	4	least	least	ADJ
ajst-17687	101	5	,	,	PUNCT
ajst-17687	101	6	from	from	ADP
ajst-17687	101	7	the	the	DET
ajst-17687	101	8	presentation	presentation	NOUN
ajst-17687	101	9	analysis	analysis	NOUN
ajst-17687	101	10	of	of	ADP
ajst-17687	101	11	the	the	DET
ajst-17687	101	12	three	three	NUM
ajst-17687	101	13	different	different	ADJ
ajst-17687	101	14	models	model	NOUN
ajst-17687	101	15	,	,	PUNCT
ajst-17687	101	16	it	it	PRON
ajst-17687	101	17	goes	go	VERB
ajst-17687	101	18	without	without	ADP
ajst-17687	101	19	saying	say	VERB
ajst-17687	101	20	that	that	SCONJ
ajst-17687	101	21	every	every	DET
ajst-17687	101	22	one	one	NUM
ajst-17687	101	23	of	of	ADP
ajst-17687	101	24	them	they	PRON
ajst-17687	101	25	have	have	VERB
ajst-17687	101	26	their	their	PRON
ajst-17687	101	27	advantages	advantage	NOUN
ajst-17687	101	28	and	and	CCONJ
ajst-17687	101	29	disadvantages	disadvantage	NOUN
ajst-17687	101	30	.	.	PUNCT
ajst-17687	102	1	we	we	PRON
ajst-17687	102	2	believe	believe	VERB
ajst-17687	102	3	that	that	SCONJ
ajst-17687	102	4	in	in	ADP
ajst-17687	102	5	order	order	NOUN
ajst-17687	102	6	to	to	PART
ajst-17687	102	7	do	do	AUX
ajst-17687	102	8	a	a	DET
ajst-17687	102	9	good	good	ADJ
ajst-17687	102	10	prediction	prediction	NOUN
ajst-17687	102	11	of	of	ADP
ajst-17687	102	12	the	the	DET
ajst-17687	102	13	energy	energy	NOUN
ajst-17687	102	14	consumption	consumption	NOUN
ajst-17687	102	15	,	,	PUNCT
ajst-17687	102	16	we	we	PRON
ajst-17687	102	17	should	should	AUX
ajst-17687	102	18	take	take	VERB
ajst-17687	102	19	all	all	PRON
ajst-17687	102	20	of	of	ADP
ajst-17687	102	21	them	they	PRON
ajst-17687	102	22	into	into	ADP
ajst-17687	102	23	account	account	NOUN
ajst-17687	102	24	,	,	PUNCT
ajst-17687	102	25	which	which	PRON
ajst-17687	102	26	would	would	AUX
ajst-17687	102	27	be	be	AUX
ajst-17687	102	28	the	the	DET
ajst-17687	102	29	most	most	ADV
ajst-17687	102	30	effective	effective	ADJ
ajst-17687	102	31	way	way	NOUN
ajst-17687	102	32	.	.	PUNCT
ajst-17687	103	1	6	6	X
ajst-17687	103	2	.	.	X
ajst-17687	103	3	conclusion	conclusion	NOUN
ajst-17687	103	4	and	and	CCONJ
ajst-17687	103	5	reflection	reflection	NOUN
ajst-17687	103	6	this	this	DET
ajst-17687	103	7	study	study	NOUN
ajst-17687	103	8	has	have	VERB
ajst-17687	103	9	as	as	ADP
ajst-17687	103	10	its	its	PRON
ajst-17687	103	11	main	main	ADJ
ajst-17687	103	12	aims	aim	NOUN
ajst-17687	103	13	to	to	PART
ajst-17687	103	14	reveal	reveal	VERB
ajst-17687	103	15	the	the	DET
ajst-17687	103	16	complex	complex	ADJ
ajst-17687	103	17	relationships	relationship	NOUN
ajst-17687	103	18	of	of	ADP
ajst-17687	103	19	energy	energy	NOUN
ajst-17687	103	20	consumption	consumption	NOUN
ajst-17687	103	21	in	in	ADP
ajst-17687	103	22	the	the	DET
ajst-17687	103	23	eaf	eaf	NOUN
ajst-17687	103	24	production	production	NOUN
ajst-17687	103	25	process	process	NOUN
ajst-17687	103	26	and	and	CCONJ
ajst-17687	103	27	provide	provide	VERB
ajst-17687	103	28	more	more	ADV
ajst-17687	103	29	accurate	accurate	ADJ
ajst-17687	103	30	predictions	prediction	NOUN
ajst-17687	103	31	to	to	PART
ajst-17687	103	32	optimize	optimize	VERB
ajst-17687	103	33	production	production	NOUN
ajst-17687	103	34	costs	cost	NOUN
ajst-17687	103	35	and	and	CCONJ
ajst-17687	103	36	maximize	maximize	VERB
ajst-17687	103	37	energy	energy	NOUN
ajst-17687	103	38	efficiency	efficiency	NOUN
ajst-17687	103	39	.	.	PUNCT
ajst-17687	104	1	besides	besides	SCONJ
ajst-17687	104	2	,	,	PUNCT
ajst-17687	104	3	this	this	DET
ajst-17687	104	4	article	article	NOUN
ajst-17687	104	5	emphasized	emphasize	VERB
ajst-17687	104	6	the	the	DET
ajst-17687	104	7	importance	importance	NOUN
ajst-17687	104	8	of	of	ADP
ajst-17687	104	9	using	use	VERB
ajst-17687	104	10	different	different	ADJ
ajst-17687	104	11	models	model	NOUN
ajst-17687	104	12	in	in	ADP
ajst-17687	104	13	weka	weka	PROPN
ajst-17687	104	14	.	.	PUNCT
ajst-17687	105	1	this	this	PRON
ajst-17687	105	2	not	not	PART
ajst-17687	105	3	only	only	ADV
ajst-17687	105	4	upgrades	upgrade	VERB
ajst-17687	105	5	the	the	DET
ajst-17687	105	6	existing	exist	VERB
ajst-17687	105	7	production	production	NOUN
ajst-17687	105	8	model	model	NOUN
ajst-17687	105	9	,	,	PUNCT
ajst-17687	105	10	but	but	CCONJ
ajst-17687	105	11	also	also	ADV
ajst-17687	105	12	helps	help	VERB
ajst-17687	105	13	the	the	DET
ajst-17687	105	14	new	new	ADJ
ajst-17687	105	15	insights	insight	NOUN
ajst-17687	105	16	for	for	ADP
ajst-17687	105	17	the	the	DET
ajst-17687	105	18	development	development	NOUN
ajst-17687	105	19	of	of	ADP
ajst-17687	105	20	smart	smart	ADJ
ajst-17687	105	21	manufacturing	manufacturing	NOUN
ajst-17687	105	22	in	in	ADP
ajst-17687	105	23	the	the	DET
ajst-17687	105	24	era	era	NOUN
ajst-17687	105	25	of	of	ADP
ajst-17687	105	26	industry	industry	NOUN
ajst-17687	105	27	4.0	4.0	NUM
ajst-17687	105	28	.	.	PUNCT
ajst-17687	106	1	throughout	throughout	ADP
ajst-17687	106	2	the	the	DET
ajst-17687	106	3	hole	hole	NOUN
ajst-17687	106	4	development	development	NOUN
ajst-17687	106	5	of	of	ADP
ajst-17687	106	6	this	this	DET
ajst-17687	106	7	case	case	NOUN
ajst-17687	106	8	study	study	NOUN
ajst-17687	106	9	in	in	ADP
ajst-17687	106	10	weka	weka	PROPN
ajst-17687	106	11	,	,	PUNCT
ajst-17687	106	12	different	different	ADJ
ajst-17687	106	13	models	model	NOUN
ajst-17687	106	14	were	be	AUX
ajst-17687	106	15	used	use	VERB
ajst-17687	106	16	,	,	PUNCT
ajst-17687	106	17	each	each	DET
ajst-17687	106	18	one	one	NOUN
ajst-17687	106	19	being	be	AUX
ajst-17687	106	20	a	a	DET
ajst-17687	106	21	different	different	ADJ
ajst-17687	106	22	method	method	NOUN
ajst-17687	106	23	of	of	ADP
ajst-17687	106	24	solving	solve	VERB
ajst-17687	106	25	from	from	ADP
ajst-17687	106	26	each	each	DET
ajst-17687	106	27	other	other	ADJ
ajst-17687	106	28	,	,	PUNCT
ajst-17687	106	29	to	to	PART
ajst-17687	106	30	make	make	VERB
ajst-17687	106	31	sure	sure	ADJ
ajst-17687	106	32	the	the	DET
ajst-17687	106	33	prediction	prediction	NOUN
ajst-17687	106	34	of	of	ADP
ajst-17687	106	35	the	the	DET
ajst-17687	106	36	energy	energy	NOUN
ajst-17687	106	37	consumption	consumption	NOUN
ajst-17687	106	38	would	would	AUX
ajst-17687	106	39	be	be	AUX
ajst-17687	106	40	the	the	DET
ajst-17687	106	41	best	good	ADJ
ajst-17687	106	42	.	.	PUNCT
ajst-17687	107	1	but	but	CCONJ
ajst-17687	107	2	there	there	PRON
ajst-17687	107	3	are	be	VERB
ajst-17687	107	4	still	still	ADV
ajst-17687	107	5	several	several	ADJ
ajst-17687	107	6	things	thing	NOUN
ajst-17687	107	7	which	which	PRON
ajst-17687	107	8	need	need	VERB
ajst-17687	107	9	to	to	PART
ajst-17687	107	10	be	be	AUX
ajst-17687	107	11	consideration	consideration	NOUN
ajst-17687	107	12	.	.	PUNCT
ajst-17687	108	1	1	1	NUM
ajst-17687	108	2	)	)	PUNCT
ajst-17687	108	3	in	in	ADP
ajst-17687	108	4	order	order	NOUN
ajst-17687	108	5	to	to	PART
ajst-17687	108	6	get	get	VERB
ajst-17687	108	7	the	the	DET
ajst-17687	108	8	best	good	ADJ
ajst-17687	108	9	prediction	prediction	NOUN
ajst-17687	108	10	model	model	NOUN
ajst-17687	108	11	,	,	PUNCT
ajst-17687	108	12	different	different	ADJ
ajst-17687	108	13	software	software	NOUN
ajst-17687	108	14	should	should	AUX
ajst-17687	108	15	be	be	AUX
ajst-17687	108	16	taken	take	VERB
ajst-17687	108	17	into	into	ADP
ajst-17687	108	18	use	use	NOUN
ajst-17687	108	19	.	.	PUNCT
ajst-17687	109	1	2	2	X
ajst-17687	109	2	)	)	PUNCT
ajst-17687	109	3	in	in	ADP
ajst-17687	109	4	the	the	DET
ajst-17687	109	5	section	section	NOUN
ajst-17687	109	6	of	of	ADP
ajst-17687	109	7	data	datum	NOUN
ajst-17687	109	8	understanding	understanding	NOUN
ajst-17687	109	9	,	,	PUNCT
ajst-17687	109	10	mathematical	mathematical	ADJ
ajst-17687	109	11	knowledge	knowledge	NOUN
ajst-17687	109	12	should	should	AUX
ajst-17687	109	13	be	be	AUX
ajst-17687	109	14	used	use	VERB
ajst-17687	109	15	as	as	ADP
ajst-17687	109	16	a	a	DET
ajst-17687	109	17	tool	tool	NOUN
ajst-17687	109	18	to	to	PART
ajst-17687	109	19	assist	assist	VERB
ajst-17687	109	20	analysis	analysis	NOUN
ajst-17687	109	21	finally	finally	ADV
ajst-17687	109	22	,	,	PUNCT
ajst-17687	109	23	this	this	DET
ajst-17687	109	24	case	case	NOUN
ajst-17687	109	25	study	study	NOUN
ajst-17687	109	26	showed	show	VERB
ajst-17687	109	27	that	that	SCONJ
ajst-17687	109	28	with	with	ADP
ajst-17687	109	29	the	the	DET
ajst-17687	109	30	right	right	ADJ
ajst-17687	109	31	data	datum	NOUN
ajst-17687	109	32	preprocessing	preprocessing	NOUN
ajst-17687	109	33	,	,	PUNCT
ajst-17687	109	34	processing	processing	NOUN
ajst-17687	109	35	and	and	CCONJ
ajst-17687	109	36	with	with	ADP
ajst-17687	109	37	the	the	DET
ajst-17687	109	38	right	right	ADJ
ajst-17687	109	39	models	model	NOUN
ajst-17687	109	40	,	,	PUNCT
ajst-17687	109	41	it	it	PRON
ajst-17687	109	42	is	be	AUX
ajst-17687	109	43	possible	possible	ADJ
ajst-17687	109	44	to	to	PART
ajst-17687	109	45	get	get	VERB
ajst-17687	109	46	a	a	DET
ajst-17687	109	47	high	high	ADJ
ajst-17687	109	48	accuracy	accuracy	NOUN
ajst-17687	109	49	.	.	PUNCT
ajst-17687	110	1	references	reference	NOUN
ajst-17687	110	2	[	[	X
ajst-17687	110	3	1	1	NUM
ajst-17687	110	4	]	]	X
ajst-17687	110	5	huang	huang	PROPN
ajst-17687	110	6	,	,	PUNCT
ajst-17687	110	7	f.	f.	PROPN
ajst-17687	110	8	2021	2021	NUM
ajst-17687	110	9	.	.	PUNCT
ajst-17687	111	1	network	network	NOUN
ajst-17687	111	2	activities	activity	NOUN
ajst-17687	111	3	recognition	recognition	NOUN
ajst-17687	111	4	and	and	CCONJ
ajst-17687	111	5	analysis	analysis	NOUN
ajst-17687	111	6	based	base	VERB
ajst-17687	111	7	on	on	ADP
ajst-17687	111	8	supervised	supervised	ADJ
ajst-17687	111	9	machine	machine	NOUN
ajst-17687	111	10	learning	learn	VERB
ajst-17687	111	11	classification	classification	NOUN
ajst-17687	111	12	methods	method	NOUN
ajst-17687	111	13	using	use	VERB
ajst-17687	111	14	j48	j48	PROPN
ajst-17687	111	15	and	and	CCONJ
ajst-17687	111	16	na\"ive	na\"ive	ADJ
ajst-17687	111	17	bayes	bayes	PROPN
ajst-17687	111	18	algorithm	algorithm	PROPN
ajst-17687	111	19	.	.	PUNCT
ajst-17687	112	1	available	available	ADJ
ajst-17687	112	2	at	at	ADP
ajst-17687	112	3	:	:	PUNCT
ajst-17687	112	4	https://arxiv.org/abs/2105.13698	https://arxiv.org/abs/2105.13698	PROPN
ajst-17687	113	1	[	[	X
ajst-17687	113	2	accessed	access	VERB
ajst-17687	113	3	:	:	PUNCT
ajst-17687	113	4	20	20	NUM
ajst-17687	113	5	november	november	PROPN
ajst-17687	113	6	2023	2023	NUM
ajst-17687	113	7	]	]	PUNCT
ajst-17687	113	8	.	.	PUNCT
ajst-17687	114	1	[	[	X
ajst-17687	114	2	2	2	NUM
ajst-17687	114	3	]	]	PUNCT
ajst-17687	114	4	iopscience	iopscience	NOUN
ajst-17687	114	5	.	.	PUNCT
ajst-17687	115	1	2017	2017	NUM
ajst-17687	115	2	.	.	PUNCT
ajst-17687	116	1	iop	iop	PROPN
ajst-17687	116	2	conference	conference	PROPN
ajst-17687	116	3	series	series	PROPN
ajst-17687	116	4	:	:	PUNCT
ajst-17687	116	5	materials	material	NOUN
ajst-17687	116	6	science	science	NOUN
ajst-17687	116	7	and	and	CCONJ
ajst-17687	116	8	engineering	engineering	NOUN
ajst-17687	116	9	iopscience	iopscience	NOUN
ajst-17687	116	10	.	.	PUNCT
ajst-17687	117	1	available	available	ADJ
ajst-17687	117	2	at	at	ADP
ajst-17687	117	3	:	:	PUNCT
ajst-17687	117	4	https://iopscience.iop.org/journal/1757-899x	https://iopscience.iop.org/journal/1757-899x	NOUN
ajst-17687	117	5	[	[	X
ajst-17687	117	6	3	3	NUM
ajst-17687	117	7	]	]	PUNCT
ajst-17687	117	8	raheli	raheli	NOUN
ajst-17687	117	9	,	,	PUNCT
ajst-17687	117	10	b.	b.	PROPN
ajst-17687	117	11	,	,	PUNCT
ajst-17687	117	12	aalami	aalami	PROPN
ajst-17687	117	13	,	,	PUNCT
ajst-17687	117	14	m.t	m.t	PROPN
ajst-17687	117	15	.	.	PROPN
ajst-17687	117	16	,	,	PUNCT
ajst-17687	117	17	el	el	NOUN
ajst-17687	117	18	-	-	PUNCT
ajst-17687	117	19	shafie	shafie	NOUN
ajst-17687	117	20	,	,	PUNCT
ajst-17687	117	21	a.	a.	NOUN
ajst-17687	117	22	,	,	PUNCT
ajst-17687	117	23	ghorbani	ghorbani	NOUN
ajst-17687	117	24	,	,	PUNCT
ajst-17687	117	25	m.a	m.a	PROPN
ajst-17687	117	26	.	.	PROPN
ajst-17687	117	27	and	and	CCONJ
ajst-17687	117	28	deo	deo	PROPN
ajst-17687	117	29	,	,	PUNCT
ajst-17687	117	30	r.c	r.c	PROPN
ajst-17687	117	31	.	.	PROPN
ajst-17687	117	32	2017	2017	NUM
ajst-17687	117	33	.	.	PUNCT
ajst-17687	118	1	uncertainty	uncertainty	NOUN
ajst-17687	118	2	assessment	assessment	NOUN
ajst-17687	118	3	of	of	ADP
ajst-17687	118	4	the	the	DET
ajst-17687	118	5	multilayer	multilayer	ADJ
ajst-17687	118	6	perceptron	perceptron	PROPN
ajst-17687	118	7	(	(	PUNCT
ajst-17687	118	8	mlp	mlp	NOUN
ajst-17687	118	9	)	)	PUNCT
ajst-17687	118	10	neural	neural	ADJ
ajst-17687	118	11	network	network	NOUN
ajst-17687	118	12	model	model	NOUN
ajst-17687	118	13	with	with	ADP
ajst-17687	118	14	implementation	implementation	NOUN
ajst-17687	118	15	of	of	ADP
ajst-17687	118	16	the	the	DET
ajst-17687	118	17	novel	novel	ADJ
ajst-17687	118	18	hybrid	hybrid	ADJ
ajst-17687	118	19	mlp	mlp	NOUN
ajst-17687	118	20	-	-	PUNCT
ajst-17687	118	21	ffa	ffa	PROPN
ajst-17687	118	22	method	method	NOUN
ajst-17687	118	23	for	for	ADP
ajst-17687	118	24	prediction	prediction	NOUN
ajst-17687	118	25	of	of	ADP
ajst-17687	118	26	biochemical	biochemical	ADJ
ajst-17687	118	27	oxygen	oxygen	NOUN
ajst-17687	118	28	demand	demand	NOUN
ajst-17687	118	29	and	and	CCONJ
ajst-17687	118	30	dissolved	dissolve	VERB
ajst-17687	118	31	oxygen	oxygen	NOUN
ajst-17687	118	32	:	:	PUNCT
ajst-17687	118	33	a	a	DET
ajst-17687	118	34	case	case	NOUN
ajst-17687	118	35	study	study	NOUN
ajst-17687	118	36	of	of	ADP
ajst-17687	118	37	langat	langat	ADJ
ajst-17687	118	38	river	river	NOUN
ajst-17687	118	39	.	.	PUNCT
ajst-17687	119	1	environmental	environmental	ADJ
ajst-17687	119	2	earth	earth	NOUN
ajst-17687	119	3	sciences	sciences	PROPN
ajst-17687	119	4	76(14	76(14	PROPN
ajst-17687	119	5	)	)	PUNCT
ajst-17687	119	6	.	.	PUNCT
ajst-17687	120	1	doi	doi	NOUN
ajst-17687	120	2	:	:	PUNCT
ajst-17687	120	3	https://doi.org/10.1007/s12665-017-6842-z	https://doi.org/10.1007/s12665-017-6842-z	NOUN
ajst-17687	120	4	139	139	NUM
ajst-17687	120	5	appendix	appendix	PROPN
ajst-17687	120	6	fig	fig	NOUN
ajst-17687	120	7	.	.	PUNCT
ajst-17687	121	1	1	1	NUM
ajst-17687	121	2	dataset	dataset	NOUN
ajst-17687	121	3	in	in	ADP
ajst-17687	121	4	an	an	DET
ajst-17687	121	5	excel	excel	NOUN
ajst-17687	121	6	file	file	NOUN
ajst-17687	121	7	.	.	PUNCT
ajst-17687	122	1	fig	fig	NOUN
ajst-17687	122	2	.	.	PUNCT
ajst-17687	123	1	2	2	NUM
ajst-17687	123	2	dataset	dataset	NOUN
ajst-17687	123	3	opened	open	VERB
ajst-17687	123	4	in	in	ADP
ajst-17687	123	5	weka	weka	PROPN
ajst-17687	123	6	.	.	PUNCT