id	sid	tid	token	lemma	pos
ajst-24981	1	1	academic	academic	ADJ
ajst-24981	1	2	journal	journal	NOUN
ajst-24981	1	3	of	of	ADP
ajst-24981	1	4	science	science	NOUN
ajst-24981	1	5	and	and	CCONJ
ajst-24981	1	6	technology	technology	NOUN
ajst-24981	1	7	issn	issn	NOUN
ajst-24981	1	8	:	:	PUNCT
ajst-24981	1	9	2771	2771	NUM
ajst-24981	1	10	-	-	SYM
ajst-24981	1	11	3032	3032	NUM
ajst-24981	1	12	|	|	NOUN
ajst-24981	1	13	vol	vol	NOUN
ajst-24981	1	14	.	.	PROPN
ajst-24981	2	1	12	12	NUM
ajst-24981	2	2	,	,	PUNCT
ajst-24981	2	3	no	no	INTJ
ajst-24981	2	4	.	.	NOUN
ajst-24981	2	5	1	1	NUM
ajst-24981	2	6	,	,	PUNCT
ajst-24981	2	7	2024	2024	NUM
ajst-24981	2	8	181	181	NUM
ajst-24981	2	9	prediction	prediction	NOUN
ajst-24981	2	10	of	of	ADP
ajst-24981	2	11	sandstone	sandstone	NOUN
ajst-24981	2	12	porosity	porosity	NOUN
ajst-24981	2	13	based	base	VERB
ajst-24981	2	14	on	on	ADP
ajst-24981	2	15	machine	machine	NOUN
ajst-24981	2	16	learning	learning	PROPN
ajst-24981	2	17	yinliang	yinliang	PROPN
ajst-24981	2	18	cheng	cheng	PROPN
ajst-24981	2	19	*	*	PUNCT
ajst-24981	2	20	college	college	PROPN
ajst-24981	2	21	of	of	ADP
ajst-24981	2	22	civil	civil	ADJ
ajst-24981	2	23	engineering	engineering	NOUN
ajst-24981	2	24	,	,	PUNCT
ajst-24981	2	25	henan	henan	PROPN
ajst-24981	2	26	polytechnic	polytechnic	PROPN
ajst-24981	2	27	university	university	PROPN
ajst-24981	2	28	,	,	PUNCT
ajst-24981	2	29	jiaozuo	jiaozuo	PROPN
ajst-24981	2	30	,	,	PUNCT
ajst-24981	2	31	china	china	PROPN
ajst-24981	2	32	*	*	PUNCT
ajst-24981	2	33	corresponding	correspond	VERB
ajst-24981	2	34	author	author	NOUN
ajst-24981	2	35	:	:	PUNCT
ajst-24981	2	36	yinliang	yinliang	PROPN
ajst-24981	2	37	cheng	cheng	PROPN
ajst-24981	2	38	abstract	abstract	PROPN
ajst-24981	2	39	:	:	PUNCT
ajst-24981	2	40	porosity	porosity	NOUN
ajst-24981	2	41	is	be	AUX
ajst-24981	2	42	a	a	DET
ajst-24981	2	43	critical	critical	ADJ
ajst-24981	2	44	property	property	NOUN
ajst-24981	2	45	of	of	ADP
ajst-24981	2	46	sandstone	sandstone	NOUN
ajst-24981	2	47	,	,	PUNCT
ajst-24981	2	48	influencing	influence	VERB
ajst-24981	2	49	its	its	PRON
ajst-24981	2	50	ability	ability	NOUN
ajst-24981	2	51	to	to	PART
ajst-24981	2	52	store	store	VERB
ajst-24981	2	53	and	and	CCONJ
ajst-24981	2	54	transmit	transmit	NOUN
ajst-24981	2	55	fluids	fluid	NOUN
ajst-24981	2	56	.	.	PUNCT
ajst-24981	3	1	accurate	accurate	ADJ
ajst-24981	3	2	prediction	prediction	NOUN
ajst-24981	3	3	of	of	ADP
ajst-24981	3	4	porosity	porosity	NOUN
ajst-24981	3	5	is	be	AUX
ajst-24981	3	6	essential	essential	ADJ
ajst-24981	3	7	for	for	ADP
ajst-24981	3	8	various	various	ADJ
ajst-24981	3	9	applications	application	NOUN
ajst-24981	3	10	,	,	PUNCT
ajst-24981	3	11	including	include	VERB
ajst-24981	3	12	hydrocarbon	hydrocarbon	NOUN
ajst-24981	3	13	exploration	exploration	NOUN
ajst-24981	3	14	,	,	PUNCT
ajst-24981	3	15	groundwater	groundwater	NOUN
ajst-24981	3	16	management	management	NOUN
ajst-24981	3	17	,	,	PUNCT
ajst-24981	3	18	and	and	CCONJ
ajst-24981	3	19	civil	civil	ADJ
ajst-24981	3	20	engineering	engineering	NOUN
ajst-24981	3	21	.	.	PUNCT
ajst-24981	4	1	traditional	traditional	ADJ
ajst-24981	4	2	methods	method	NOUN
ajst-24981	4	3	for	for	ADP
ajst-24981	4	4	porosity	porosity	NOUN
ajst-24981	4	5	estimation	estimation	NOUN
ajst-24981	4	6	often	often	ADV
ajst-24981	4	7	involve	involve	VERB
ajst-24981	4	8	labor	labor	NOUN
ajst-24981	4	9	-	-	PUNCT
ajst-24981	4	10	intensive	intensive	ADJ
ajst-24981	4	11	and	and	CCONJ
ajst-24981	4	12	time	time	NOUN
ajst-24981	4	13	-	-	PUNCT
ajst-24981	4	14	consuming	consume	VERB
ajst-24981	4	15	laboratory	laboratory	NOUN
ajst-24981	4	16	tests	test	NOUN
ajst-24981	4	17	.	.	PUNCT
ajst-24981	5	1	however	however	ADV
ajst-24981	5	2	,	,	PUNCT
ajst-24981	5	3	with	with	ADP
ajst-24981	5	4	the	the	DET
ajst-24981	5	5	advent	advent	NOUN
ajst-24981	5	6	of	of	ADP
ajst-24981	5	7	machine	machine	NOUN
ajst-24981	5	8	learning	learning	NOUN
ajst-24981	5	9	(	(	PUNCT
ajst-24981	5	10	ml	ml	NOUN
ajst-24981	5	11	)	)	PUNCT
ajst-24981	5	12	techniques	technique	NOUN
ajst-24981	5	13	,	,	PUNCT
ajst-24981	5	14	there	there	PRON
ajst-24981	5	15	is	be	VERB
ajst-24981	5	16	potential	potential	ADJ
ajst-24981	5	17	for	for	ADP
ajst-24981	5	18	more	more	ADV
ajst-24981	5	19	efficient	efficient	ADJ
ajst-24981	5	20	and	and	CCONJ
ajst-24981	5	21	accurate	accurate	ADJ
ajst-24981	5	22	prediction	prediction	NOUN
ajst-24981	5	23	of	of	ADP
ajst-24981	5	24	sandstone	sandstone	NOUN
ajst-24981	5	25	porosity	porosity	NOUN
ajst-24981	5	26	.	.	PUNCT
ajst-24981	6	1	this	this	DET
ajst-24981	6	2	paper	paper	NOUN
ajst-24981	6	3	explores	explore	VERB
ajst-24981	6	4	the	the	DET
ajst-24981	6	5	application	application	NOUN
ajst-24981	6	6	of	of	ADP
ajst-24981	6	7	machine	machine	NOUN
ajst-24981	6	8	learning	learning	NOUN
ajst-24981	6	9	models	model	NOUN
ajst-24981	6	10	to	to	PART
ajst-24981	6	11	predict	predict	VERB
ajst-24981	6	12	sandstone	sandstone	NOUN
ajst-24981	6	13	porosity	porosity	NOUN
ajst-24981	6	14	using	use	VERB
ajst-24981	6	15	various	various	ADJ
ajst-24981	6	16	geological	geological	ADJ
ajst-24981	6	17	and	and	CCONJ
ajst-24981	6	18	petrophysical	petrophysical	ADJ
ajst-24981	6	19	features	feature	NOUN
ajst-24981	6	20	.	.	PUNCT
ajst-24981	7	1	keywords	keyword	NOUN
ajst-24981	7	2	:	:	PUNCT
ajst-24981	7	3	machine	machine	NOUN
ajst-24981	7	4	learning	learning	NOUN
ajst-24981	7	5	;	;	PUNCT
ajst-24981	7	6	porosity	porosity	NOUN
ajst-24981	7	7	prediction	prediction	NOUN
ajst-24981	7	8	;	;	PUNCT
ajst-24981	7	9	sandstone	sandstone	NOUN
ajst-24981	7	10	.	.	PUNCT
ajst-24981	8	1	1	1	X
ajst-24981	8	2	.	.	X
ajst-24981	8	3	introduction	introduction	NOUN
ajst-24981	8	4	sandstone	sandstone	NOUN
ajst-24981	8	5	porosity	porosity	NOUN
ajst-24981	8	6	is	be	AUX
ajst-24981	8	7	a	a	DET
ajst-24981	8	8	key	key	ADJ
ajst-24981	8	9	parameter	parameter	NOUN
ajst-24981	8	10	reflecting	reflect	VERB
ajst-24981	8	11	hydrocarbon	hydrocarbon	NOUN
ajst-24981	8	12	storage	storage	NOUN
ajst-24981	8	13	and	and	CCONJ
ajst-24981	8	14	flow	flow	NOUN
ajst-24981	8	15	capacity	capacity	NOUN
ajst-24981	8	16	,	,	PUNCT
ajst-24981	8	17	and	and	CCONJ
ajst-24981	8	18	plays	play	VERB
ajst-24981	8	19	a	a	DET
ajst-24981	8	20	vital	vital	ADJ
ajst-24981	8	21	role	role	NOUN
ajst-24981	8	22	in	in	ADP
ajst-24981	8	23	reservoir	reservoir	NOUN
ajst-24981	8	24	characterization	characterization	NOUN
ajst-24981	8	25	and	and	CCONJ
ajst-24981	8	26	performance	performance	NOUN
ajst-24981	8	27	.	.	PUNCT
ajst-24981	9	1	recognizing	recognize	VERB
ajst-24981	9	2	the	the	DET
ajst-24981	9	3	potential	potential	NOUN
ajst-24981	9	4	of	of	ADP
ajst-24981	9	5	ml	ml	NOUN
ajst-24981	9	6	to	to	PART
ajst-24981	9	7	uncover	uncover	VERB
ajst-24981	9	8	complex	complex	ADJ
ajst-24981	9	9	relationships	relationship	NOUN
ajst-24981	9	10	within	within	ADP
ajst-24981	9	11	large	large	ADJ
ajst-24981	9	12	datasets	dataset	NOUN
ajst-24981	9	13	,	,	PUNCT
ajst-24981	9	14	researchers	researcher	NOUN
ajst-24981	9	15	globally	globally	ADV
ajst-24981	9	16	have	have	AUX
ajst-24981	9	17	been	be	AUX
ajst-24981	9	18	exploring	explore	VERB
ajst-24981	9	19	its	its	PRON
ajst-24981	9	20	application	application	NOUN
ajst-24981	9	21	in	in	ADP
ajst-24981	9	22	predicting	predict	VERB
ajst-24981	9	23	reservoir	reservoir	NOUN
ajst-24981	9	24	properties	property	NOUN
ajst-24981	9	25	.	.	PUNCT
ajst-24981	10	1	early	early	ADJ
ajst-24981	10	2	studies	study	NOUN
ajst-24981	10	3	,	,	PUNCT
ajst-24981	10	4	primarily	primarily	ADV
ajst-24981	10	5	focused	focus	VERB
ajst-24981	10	6	on	on	ADP
ajst-24981	10	7	seismic	seismic	ADJ
ajst-24981	10	8	data	datum	NOUN
ajst-24981	10	9	,	,	PUNCT
ajst-24981	10	10	demonstrated	demonstrate	VERB
ajst-24981	10	11	the	the	DET
ajst-24981	10	12	capability	capability	NOUN
ajst-24981	10	13	of	of	ADP
ajst-24981	10	14	ml	ml	NOUN
ajst-24981	10	15	in	in	ADP
ajst-24981	10	16	estimating	estimate	VERB
ajst-24981	10	17	porosity	porosity	NOUN
ajst-24981	10	18	with	with	ADP
ajst-24981	10	19	reasonable	reasonable	ADJ
ajst-24981	10	20	accuracy	accuracy	NOUN
ajst-24981	10	21	.	.	PUNCT
ajst-24981	11	1	however	however	ADV
ajst-24981	11	2	,	,	PUNCT
ajst-24981	11	3	the	the	DET
ajst-24981	11	4	shift	shift	NOUN
ajst-24981	11	5	towards	towards	ADP
ajst-24981	11	6	incorporating	incorporate	VERB
ajst-24981	11	7	readily	readily	ADV
ajst-24981	11	8	available	available	ADJ
ajst-24981	11	9	well	well	INTJ
ajst-24981	11	10	log	log	NOUN
ajst-24981	11	11	data	datum	NOUN
ajst-24981	11	12	marked	mark	VERB
ajst-24981	11	13	a	a	DET
ajst-24981	11	14	significant	significant	ADJ
ajst-24981	11	15	turning	turning	NOUN
ajst-24981	11	16	point	point	NOUN
ajst-24981	11	17	in	in	ADP
ajst-24981	11	18	this	this	DET
ajst-24981	11	19	field.[1][2	field.[1][2	PROPN
ajst-24981	11	20	]	]	PUNCT
ajst-24981	11	21	in	in	ADP
ajst-24981	11	22	recent	recent	ADJ
ajst-24981	11	23	years	year	NOUN
ajst-24981	11	24	,	,	PUNCT
ajst-24981	11	25	numerous	numerous	ADJ
ajst-24981	11	26	studies	study	NOUN
ajst-24981	11	27	have	have	AUX
ajst-24981	11	28	showcased	showcase	VERB
ajst-24981	11	29	the	the	DET
ajst-24981	11	30	success	success	NOUN
ajst-24981	11	31	of	of	ADP
ajst-24981	11	32	various	various	ADJ
ajst-24981	11	33	ml	ml	NOUN
ajst-24981	11	34	algorithms	algorithm	NOUN
ajst-24981	11	35	in	in	ADP
ajst-24981	11	36	predicting	predict	VERB
ajst-24981	11	37	sandstone	sandstone	NOUN
ajst-24981	11	38	porosity	porosity	NOUN
ajst-24981	11	39	from	from	ADP
ajst-24981	11	40	well	well	ADJ
ajst-24981	11	41	logs	log	NOUN
ajst-24981	11	42	.	.	PUNCT
ajst-24981	12	1	artificial	artificial	ADJ
ajst-24981	12	2	neural	neural	ADJ
ajst-24981	12	3	networks	network	NOUN
ajst-24981	12	4	(	(	PUNCT
ajst-24981	12	5	ann	ann	PROPN
ajst-24981	12	6	)	)	PUNCT
ajst-24981	12	7	,	,	PUNCT
ajst-24981	12	8	renowned	renowne	VERB
ajst-24981	12	9	for	for	ADP
ajst-24981	12	10	their	their	PRON
ajst-24981	12	11	ability	ability	NOUN
ajst-24981	12	12	to	to	PART
ajst-24981	12	13	model	model	VERB
ajst-24981	12	14	non	non	ADJ
ajst-24981	12	15	-	-	ADJ
ajst-24981	12	16	linear	linear	ADJ
ajst-24981	12	17	relationships	relationship	NOUN
ajst-24981	12	18	,	,	PUNCT
ajst-24981	12	19	have	have	AUX
ajst-24981	12	20	been	be	AUX
ajst-24981	12	21	widely	widely	ADV
ajst-24981	12	22	implemented	implement	VERB
ajst-24981	12	23	,	,	PUNCT
ajst-24981	12	24	with	with	ADP
ajst-24981	12	25	researchers	researcher	NOUN
ajst-24981	12	26	reporting	report	VERB
ajst-24981	12	27	promising	promise	VERB
ajst-24981	12	28	results	result	NOUN
ajst-24981	12	29	in	in	ADP
ajst-24981	12	30	different	different	ADJ
ajst-24981	12	31	geological	geological	ADJ
ajst-24981	12	32	settings	setting	NOUN
ajst-24981	12	33	.	.	PUNCT
ajst-24981	13	1	support	support	NOUN
ajst-24981	13	2	vector	vector	NOUN
ajst-24981	13	3	machines	machine	NOUN
ajst-24981	13	4	(	(	PUNCT
ajst-24981	13	5	svm	svm	PROPN
ajst-24981	13	6	)	)	PUNCT
ajst-24981	13	7	,	,	PUNCT
ajst-24981	13	8	celebrated	celebrate	VERB
ajst-24981	13	9	for	for	ADP
ajst-24981	13	10	their	their	PRON
ajst-24981	13	11	robustness	robustness	NOUN
ajst-24981	13	12	in	in	ADP
ajst-24981	13	13	high	high	ADJ
ajst-24981	13	14	-	-	PUNCT
ajst-24981	13	15	dimensional	dimensional	ADJ
ajst-24981	13	16	spaces	space	NOUN
ajst-24981	13	17	,	,	PUNCT
ajst-24981	13	18	have	have	AUX
ajst-24981	13	19	also	also	ADV
ajst-24981	13	20	gained	gain	VERB
ajst-24981	13	21	significant	significant	ADJ
ajst-24981	13	22	traction	traction	NOUN
ajst-24981	13	23	despite	despite	SCONJ
ajst-24981	13	24	the	the	DET
ajst-24981	13	25	advancements	advancement	NOUN
ajst-24981	13	26	,	,	PUNCT
ajst-24981	13	27	challenges	challenge	NOUN
ajst-24981	13	28	remain	remain	VERB
ajst-24981	13	29	.	.	PUNCT
ajst-24981	14	1	the	the	DET
ajst-24981	14	2	accuracy	accuracy	NOUN
ajst-24981	14	3	of	of	ADP
ajst-24981	14	4	ml	ml	NOUN
ajst-24981	14	5	models	model	NOUN
ajst-24981	14	6	often	often	ADV
ajst-24981	14	7	hinges	hinge	VERB
ajst-24981	14	8	on	on	ADP
ajst-24981	14	9	the	the	DET
ajst-24981	14	10	quality	quality	NOUN
ajst-24981	14	11	and	and	CCONJ
ajst-24981	14	12	quantity	quantity	NOUN
ajst-24981	14	13	of	of	ADP
ajst-24981	14	14	training	training	NOUN
ajst-24981	14	15	data	datum	NOUN
ajst-24981	14	16	,	,	PUNCT
ajst-24981	14	17	which	which	PRON
ajst-24981	14	18	can	can	AUX
ajst-24981	14	19	vary	vary	VERB
ajst-24981	14	20	significantly	significantly	ADV
ajst-24981	14	21	across	across	ADP
ajst-24981	14	22	different	different	ADJ
ajst-24981	14	23	reservoirs	reservoir	NOUN
ajst-24981	14	24	and	and	CCONJ
ajst-24981	14	25	geographical	geographical	ADJ
ajst-24981	14	26	locations	location	NOUN
ajst-24981	14	27	.	.	PUNCT
ajst-24981	15	1	additionally	additionally	ADV
ajst-24981	15	2	,	,	PUNCT
ajst-24981	15	3	identifying	identify	VERB
ajst-24981	15	4	the	the	DET
ajst-24981	15	5	most	most	ADV
ajst-24981	15	6	influential	influential	ADJ
ajst-24981	15	7	well	well	INTJ
ajst-24981	15	8	log	log	NOUN
ajst-24981	15	9	parameters	parameter	NOUN
ajst-24981	15	10	for	for	ADP
ajst-24981	15	11	a	a	DET
ajst-24981	15	12	particular	particular	ADJ
ajst-24981	15	13	case	case	NOUN
ajst-24981	15	14	study	study	NOUN
ajst-24981	15	15	remains	remain	VERB
ajst-24981	15	16	crucial	crucial	ADJ
ajst-24981	15	17	for	for	ADP
ajst-24981	15	18	building	build	VERB
ajst-24981	15	19	effective	effective	ADJ
ajst-24981	15	20	predictive	predictive	ADJ
ajst-24981	15	21	models.[3	models.[3	NOUN
ajst-24981	15	22	]	]	X
ajst-24981	15	23	this	this	DET
ajst-24981	15	24	study	study	NOUN
ajst-24981	15	25	investigates	investigate	VERB
ajst-24981	15	26	the	the	DET
ajst-24981	15	27	potential	potential	NOUN
ajst-24981	15	28	of	of	ADP
ajst-24981	15	29	applying	apply	VERB
ajst-24981	15	30	machine	machine	NOUN
ajst-24981	15	31	learning	learn	VERB
ajst-24981	15	32	techniques	technique	NOUN
ajst-24981	15	33	to	to	PART
ajst-24981	15	34	predict	predict	VERB
ajst-24981	15	35	sandstone	sandstone	NOUN
ajst-24981	15	36	porosity	porosity	NOUN
ajst-24981	15	37	from	from	ADP
ajst-24981	15	38	readily	readily	ADV
ajst-24981	15	39	available	available	ADJ
ajst-24981	15	40	well	well	INTJ
ajst-24981	15	41	log	log	NOUN
ajst-24981	15	42	data	datum	NOUN
ajst-24981	15	43	.	.	PUNCT
ajst-24981	16	1	our	our	PRON
ajst-24981	16	2	focus	focus	NOUN
ajst-24981	16	3	will	will	AUX
ajst-24981	16	4	be	be	AUX
ajst-24981	16	5	on	on	ADP
ajst-24981	16	6	:	:	PUNCT
ajst-24981	16	7	comparing	compare	VERB
ajst-24981	16	8	the	the	DET
ajst-24981	16	9	performance	performance	NOUN
ajst-24981	16	10	of	of	ADP
ajst-24981	16	11	various	various	ADJ
ajst-24981	16	12	ml	ml	NOUN
ajst-24981	16	13	algorithms	algorithm	NOUN
ajst-24981	16	14	in	in	ADP
ajst-24981	16	15	predicting	predict	VERB
ajst-24981	16	16	sandstone	sandstone	NOUN
ajst-24981	16	17	porosity	porosity	NOUN
ajst-24981	16	18	,	,	PUNCT
ajst-24981	16	19	including	include	VERB
ajst-24981	16	20	but	but	CCONJ
ajst-24981	16	21	not	not	PART
ajst-24981	16	22	limited	limit	VERB
ajst-24981	16	23	to	to	PART
ajst-24981	16	24	support	support	VERB
ajst-24981	16	25	vector	vector	NOUN
ajst-24981	16	26	machines	machine	NOUN
ajst-24981	16	27	,	,	PUNCT
ajst-24981	16	28	artificial	artificial	ADJ
ajst-24981	16	29	neural	neural	ADJ
ajst-24981	16	30	networks	network	NOUN
ajst-24981	16	31	,	,	PUNCT
ajst-24981	16	32	and	and	CCONJ
ajst-24981	16	33	random	random	ADJ
ajst-24981	16	34	forest.identifying	forest.identifye	VERB
ajst-24981	16	35	the	the	DET
ajst-24981	16	36	most	most	ADV
ajst-24981	16	37	influential	influential	ADJ
ajst-24981	16	38	well	well	INTJ
ajst-24981	16	39	log	log	NOUN
ajst-24981	16	40	parameters	parameter	NOUN
ajst-24981	16	41	contributing	contribute	VERB
ajst-24981	16	42	to	to	ADP
ajst-24981	16	43	porosity	porosity	NOUN
ajst-24981	16	44	prediction	prediction	NOUN
ajst-24981	16	45	for	for	ADP
ajst-24981	16	46	the	the	DET
ajst-24981	16	47	specific	specific	ADJ
ajst-24981	16	48	dataset	dataset	NOUN
ajst-24981	16	49	utilized.developing	utilized.developing	NOUN
ajst-24981	16	50	and	and	CCONJ
ajst-24981	16	51	validating	validate	VERB
ajst-24981	16	52	a	a	DET
ajst-24981	16	53	robust	robust	ADJ
ajst-24981	16	54	ml	ml	NOUN
ajst-24981	16	55	model	model	NOUN
ajst-24981	16	56	capable	capable	ADJ
ajst-24981	16	57	of	of	ADP
ajst-24981	16	58	accurately	accurately	ADV
ajst-24981	16	59	predicting	predict	VERB
ajst-24981	16	60	sandstone	sandstone	NOUN
ajst-24981	16	61	porosity	porosity	NOUN
ajst-24981	16	62	,	,	PUNCT
ajst-24981	16	63	offering	offer	VERB
ajst-24981	16	64	a	a	DET
ajst-24981	16	65	cost	cost	NOUN
ajst-24981	16	66	-	-	PUNCT
ajst-24981	16	67	effective	effective	ADJ
ajst-24981	16	68	and	and	CCONJ
ajst-24981	16	69	efficient	efficient	ADJ
ajst-24981	16	70	alternative	alternative	NOUN
ajst-24981	16	71	to	to	ADP
ajst-24981	16	72	traditional	traditional	ADJ
ajst-24981	16	73	methods.[4][5	methods.[4][5	NOUN
ajst-24981	16	74	]	]	PUNCT
ajst-24981	16	75	2	2	NUM
ajst-24981	16	76	.	.	X
ajst-24981	16	77	methodology	methodology	NOUN
ajst-24981	16	78	2.1	2.1	NUM
ajst-24981	16	79	.	.	PUNCT
ajst-24981	17	1	data	datum	NOUN
ajst-24981	17	2	collection	collection	NOUN
ajst-24981	17	3	data	datum	NOUN
ajst-24981	17	4	we	we	PRON
ajst-24981	17	5	used	use	VERB
ajst-24981	17	6	a	a	DET
ajst-24981	17	7	sample	sample	NOUN
ajst-24981	17	8	from	from	ADP
ajst-24981	17	9	the	the	DET
ajst-24981	17	10	berea	berea	PROPN
ajst-24981	17	11	sandstone	sandstone	NOUN
ajst-24981	17	12	petroleum	petroleum	NOUN
ajst-24981	17	13	cores	core	NOUN
ajst-24981	17	14	(	(	PUNCT
ajst-24981	17	15	ohio	ohio	PROPN
ajst-24981	17	16	,	,	PUNCT
ajst-24981	17	17	usa	usa	PROPN
ajst-24981	17	18	)	)	PUNCT
ajst-24981	17	19	for	for	ADP
ajst-24981	17	20	model	model	NOUN
ajst-24981	17	21	evaluation	evaluation	NOUN
ajst-24981	17	22	(	(	PUNCT
ajst-24981	17	23	fig.1	fig.1	PROPN
ajst-24981	17	24	)	)	PUNCT
ajst-24981	17	25	.	.	PUNCT
ajst-24981	18	1	the	the	DET
ajst-24981	18	2	3d	3d	NUM
ajst-24981	18	3	image	image	NOUN
ajst-24981	18	4	already	already	ADV
ajst-24981	18	5	had	have	VERB
ajst-24981	18	6	its	its	PRON
ajst-24981	18	7	artifacts	artifact	NOUN
ajst-24981	18	8	removed	remove	VERB
ajst-24981	18	9	and	and	CCONJ
ajst-24981	18	10	its	its	PRON
ajst-24981	18	11	segmentation	segmentation	NOUN
ajst-24981	18	12	computed	compute	VERB
ajst-24981	18	13	by	by	ADP
ajst-24981	18	14	imperial	imperial	ADJ
ajst-24981	18	15	college	college	PROPN
ajst-24981	18	16	london	london	PROPN
ajst-24981	18	17	(	(	PUNCT
ajst-24981	18	18	dong	dong	NOUN
ajst-24981	18	19	and	and	CCONJ
ajst-24981	18	20	blunt	blunt	ADJ
ajst-24981	18	21	,	,	PUNCT
ajst-24981	18	22	2009	2009	NUM
ajst-24981	18	23	)	)	PUNCT
ajst-24981	18	24	.	.	PUNCT
ajst-24981	19	1	the	the	DET
ajst-24981	19	2	segmented	segment	VERB
ajst-24981	19	3	sample	sample	NOUN
ajst-24981	19	4	makes	make	VERB
ajst-24981	19	5	no	no	DET
ajst-24981	19	6	distinction	distinction	NOUN
ajst-24981	19	7	between	between	ADP
ajst-24981	19	8	different	different	ADJ
ajst-24981	19	9	rock	rock	NOUN
ajst-24981	19	10	phases	phase	NOUN
ajst-24981	19	11	,	,	PUNCT
ajst-24981	19	12	denoting	denote	VERB
ajst-24981	19	13	every	every	DET
ajst-24981	19	14	rock	rock	NOUN
ajst-24981	19	15	voxel	voxel	PROPN
ajst-24981	19	16	as	as	ADP
ajst-24981	19	17	0	0	NUM
ajst-24981	19	18	,	,	PUNCT
ajst-24981	19	19	and	and	CCONJ
ajst-24981	19	20	every	every	DET
ajst-24981	19	21	pore	pore	ADJ
ajst-24981	19	22	voxel	voxel	NOUN
ajst-24981	19	23	as	as	ADP
ajst-24981	19	24	1	1	NUM
ajst-24981	19	25	.	.	PUNCT
ajst-24981	20	1	the	the	DET
ajst-24981	20	2	initial	initial	ADJ
ajst-24981	20	3	sample	sample	NOUN
ajst-24981	20	4	consisted	consist	VERB
ajst-24981	20	5	of	of	ADP
ajst-24981	20	6	400	400	NUM
ajst-24981	20	7	 	 	SPACE
ajst-24981	20	8	×	×	NOUN
ajst-24981	20	9	 	 	SPACE
ajst-24981	20	10	400	400	NUM
ajst-24981	20	11	 	 	SPACE
ajst-24981	20	12	×400	×400	NOUN
ajst-24981	20	13	elements	element	NOUN
ajst-24981	20	14	with	with	ADP
ajst-24981	20	15	voxel	voxel	PROPN
ajst-24981	20	16	size	size	NOUN
ajst-24981	20	17	of	of	ADP
ajst-24981	20	18	5.345	5.345	NUM
ajst-24981	20	19	 	 	SPACE
ajst-24981	20	20	m.	m.	NOUN
ajst-24981	20	21	  	  	SPACE
ajst-24981	20	22	fig	fig	NOUN
ajst-24981	20	23	1	1	NUM
ajst-24981	20	24	.	.	PUNCT
ajst-24981	21	1	berea	berea	PROPN
ajst-24981	21	2	sandstone	sandstone	PROPN
ajst-24981	21	3	sample	sample	PROPN
ajst-24981	21	4	2.2	2.2	NUM
ajst-24981	21	5	.	.	PUNCT
ajst-24981	22	1	machine	machine	NOUN
ajst-24981	22	2	learning	learning	NOUN
ajst-24981	22	3	models	model	NOUN
ajst-24981	22	4	accurately	accurately	ADV
ajst-24981	22	5	predicting	predict	VERB
ajst-24981	22	6	sandstone	sandstone	NOUN
ajst-24981	22	7	porosity	porosity	NOUN
ajst-24981	22	8	from	from	ADP
ajst-24981	22	9	well	well	ADV
ajst-24981	22	10	log	log	PROPN
ajst-24981	22	11	data	datum	NOUN
ajst-24981	22	12	requires	require	VERB
ajst-24981	22	13	employing	employ	VERB
ajst-24981	22	14	robust	robust	ADJ
ajst-24981	22	15	and	and	CCONJ
ajst-24981	22	16	adaptable	adaptable	ADJ
ajst-24981	22	17	machine	machine	NOUN
ajst-24981	22	18	learning	learning	NOUN
ajst-24981	22	19	models	model	NOUN
ajst-24981	22	20	.	.	PUNCT
ajst-24981	23	1	this	this	DET
ajst-24981	23	2	study	study	NOUN
ajst-24981	23	3	investigates	investigate	VERB
ajst-24981	23	4	five	five	NUM
ajst-24981	23	5	distinct	distinct	ADJ
ajst-24981	23	6	algorithms	algorithm	NOUN
ajst-24981	23	7	,	,	PUNCT
ajst-24981	23	8	each	each	PRON
ajst-24981	23	9	with	with	ADP
ajst-24981	23	10	its	its	PRON
ajst-24981	23	11	strengths	strength	NOUN
ajst-24981	23	12	and	and	CCONJ
ajst-24981	23	13	limitations	limitation	NOUN
ajst-24981	23	14	:	:	PUNCT
ajst-24981	23	15	1	1	X
ajst-24981	23	16	.	.	PUNCT
ajst-24981	23	17	linear	linear	ADJ
ajst-24981	23	18	regression	regression	NOUN
ajst-24981	23	19	(	(	PUNCT
ajst-24981	23	20	lr	lr	NOUN
ajst-24981	23	21	)	)	PUNCT
ajst-24981	23	22	a	a	DET
ajst-24981	23	23	fundamental	fundamental	ADJ
ajst-24981	23	24	statistical	statistical	ADJ
ajst-24981	23	25	approach	approach	NOUN
ajst-24981	23	26	,	,	PUNCT
ajst-24981	23	27	lr	lr	PROPN
ajst-24981	23	28	assumes	assume	VERB
ajst-24981	23	29	a	a	DET
ajst-24981	23	30	linear	linear	ADJ
ajst-24981	23	31	relationship	relationship	NOUN
ajst-24981	23	32	between	between	ADP
ajst-24981	23	33	the	the	DET
ajst-24981	23	34	input	input	NOUN
ajst-24981	23	35	well	well	INTJ
ajst-24981	23	36	log	log	NOUN
ajst-24981	23	37	parameters	parameter	NOUN
ajst-24981	23	38	and	and	CCONJ
ajst-24981	23	39	the	the	DET
ajst-24981	23	40	target	target	NOUN
ajst-24981	23	41	variable	variable	NOUN
ajst-24981	23	42	,	,	PUNCT
ajst-24981	23	43	porosity	porosity	NOUN
ajst-24981	23	44	.	.	PUNCT
ajst-24981	24	1	it	it	PRON
ajst-24981	24	2	aims	aim	VERB
ajst-24981	24	3	to	to	PART
ajst-24981	24	4	find	find	VERB
ajst-24981	24	5	the	the	DET
ajst-24981	24	6	best	good	ADJ
ajst-24981	24	7	-	-	PUNCT
ajst-24981	24	8	fit	fit	ADJ
ajst-24981	24	9	line	line	NOUN
ajst-24981	24	10	that	that	PRON
ajst-24981	24	11	minimizes	minimize	VERB
ajst-24981	24	12	the	the	DET
ajst-24981	24	13	overall	overall	ADJ
ajst-24981	24	14	difference	difference	NOUN
ajst-24981	24	15	between	between	ADP
ajst-24981	24	16	predicted	predict	VERB
ajst-24981	24	17	and	and	CCONJ
ajst-24981	24	18	actual	actual	ADJ
ajst-24981	24	19	porosity	porosity	NOUN
ajst-24981	24	20	values	value	NOUN
ajst-24981	24	21	.	.	PUNCT
ajst-24981	25	1	2	2	X
ajst-24981	25	2	.	.	X
ajst-24981	25	3	decision	decision	NOUN
ajst-24981	25	4	tree	tree	NOUN
ajst-24981	25	5	(	(	PUNCT
ajst-24981	25	6	dt	dt	NOUN
ajst-24981	25	7	)	)	PUNCT
ajst-24981	25	8	182	182	NUM
ajst-24981	25	9	dt	dt	NOUN
ajst-24981	25	10	constructs	construct	VERB
ajst-24981	25	11	a	a	DET
ajst-24981	25	12	tree	tree	NOUN
ajst-24981	25	13	-	-	PUNCT
ajst-24981	25	14	like	like	ADJ
ajst-24981	25	15	model	model	NOUN
ajst-24981	25	16	where	where	SCONJ
ajst-24981	25	17	each	each	DET
ajst-24981	25	18	internal	internal	ADJ
ajst-24981	25	19	node	node	NOUN
ajst-24981	25	20	represents	represent	VERB
ajst-24981	25	21	a	a	DET
ajst-24981	25	22	decision	decision	NOUN
ajst-24981	25	23	based	base	VERB
ajst-24981	25	24	on	on	ADP
ajst-24981	25	25	a	a	DET
ajst-24981	25	26	specific	specific	ADJ
ajst-24981	25	27	input	input	NOUN
ajst-24981	25	28	feature	feature	NOUN
ajst-24981	25	29	,	,	PUNCT
ajst-24981	25	30	each	each	DET
ajst-24981	25	31	branch	branch	NOUN
ajst-24981	25	32	signifies	signify	VERB
ajst-24981	25	33	the	the	DET
ajst-24981	25	34	outcome	outcome	NOUN
ajst-24981	25	35	of	of	ADP
ajst-24981	25	36	the	the	DET
ajst-24981	25	37	decision	decision	NOUN
ajst-24981	25	38	,	,	PUNCT
ajst-24981	25	39	and	and	CCONJ
ajst-24981	25	40	each	each	DET
ajst-24981	25	41	leaf	leaf	NOUN
ajst-24981	25	42	node	node	NOUN
ajst-24981	25	43	predicts	predict	VERB
ajst-24981	25	44	a	a	DET
ajst-24981	25	45	porosity	porosity	NOUN
ajst-24981	25	46	value	value	NOUN
ajst-24981	25	47	.	.	PUNCT
ajst-24981	26	1	3	3	X
ajst-24981	26	2	.	.	X
ajst-24981	26	3	random	random	ADJ
ajst-24981	26	4	forest	forest	NOUN
ajst-24981	26	5	(	(	PUNCT
ajst-24981	26	6	rf	rf	NOUN
ajst-24981	26	7	)	)	PUNCT
ajst-24981	26	8	an	an	DET
ajst-24981	26	9	ensemble	ensemble	ADJ
ajst-24981	26	10	learning	learning	NOUN
ajst-24981	26	11	method	method	NOUN
ajst-24981	26	12	that	that	PRON
ajst-24981	26	13	addresses	address	VERB
ajst-24981	26	14	the	the	DET
ajst-24981	26	15	overfitting	overfitte	VERB
ajst-24981	26	16	issue	issue	NOUN
ajst-24981	26	17	of	of	ADP
ajst-24981	26	18	individual	individual	ADJ
ajst-24981	26	19	dts	dt	NOUN
ajst-24981	26	20	by	by	ADP
ajst-24981	26	21	constructing	construct	VERB
ajst-24981	26	22	multiple	multiple	ADJ
ajst-24981	26	23	trees	tree	NOUN
ajst-24981	26	24	during	during	ADP
ajst-24981	26	25	training	training	NOUN
ajst-24981	26	26	.	.	PUNCT
ajst-24981	27	1	each	each	DET
ajst-24981	27	2	tree	tree	NOUN
ajst-24981	27	3	is	be	AUX
ajst-24981	27	4	trained	train	VERB
ajst-24981	27	5	on	on	ADP
ajst-24981	27	6	a	a	DET
ajst-24981	27	7	random	random	ADJ
ajst-24981	27	8	subset	subset	NOUN
ajst-24981	27	9	of	of	ADP
ajst-24981	27	10	the	the	DET
ajst-24981	27	11	data	datum	NOUN
ajst-24981	27	12	and	and	CCONJ
ajst-24981	27	13	features	feature	NOUN
ajst-24981	27	14	.	.	PUNCT
ajst-24981	28	1	the	the	DET
ajst-24981	28	2	final	final	ADJ
ajst-24981	28	3	prediction	prediction	NOUN
ajst-24981	28	4	is	be	AUX
ajst-24981	28	5	obtained	obtain	VERB
ajst-24981	28	6	by	by	ADP
ajst-24981	28	7	averaging	average	VERB
ajst-24981	28	8	the	the	DET
ajst-24981	28	9	predictions	prediction	NOUN
ajst-24981	28	10	of	of	ADP
ajst-24981	28	11	all	all	DET
ajst-24981	28	12	trees	tree	NOUN
ajst-24981	28	13	.	.	PUNCT
ajst-24981	29	1	4	4	X
ajst-24981	29	2	.	.	X
ajst-24981	29	3	support	support	NOUN
ajst-24981	29	4	vector	vector	NOUN
ajst-24981	29	5	machine	machine	NOUN
ajst-24981	29	6	(	(	PUNCT
ajst-24981	29	7	svm	svm	PROPN
ajst-24981	29	8	)	)	PUNCT
ajst-24981	29	9	aims	aim	VERB
ajst-24981	29	10	to	to	PART
ajst-24981	29	11	find	find	VERB
ajst-24981	29	12	the	the	DET
ajst-24981	29	13	optimal	optimal	ADJ
ajst-24981	29	14	hyperplane	hyperplane	NOUN
ajst-24981	29	15	in	in	ADP
ajst-24981	29	16	a	a	DET
ajst-24981	29	17	high	high	ADJ
ajst-24981	29	18	-	-	PUNCT
ajst-24981	29	19	dimensional	dimensional	ADJ
ajst-24981	29	20	space	space	NOUN
ajst-24981	29	21	that	that	PRON
ajst-24981	29	22	best	well	ADV
ajst-24981	29	23	separates	separate	VERB
ajst-24981	29	24	data	datum	NOUN
ajst-24981	29	25	points	point	NOUN
ajst-24981	29	26	into	into	ADP
ajst-24981	29	27	different	different	ADJ
ajst-24981	29	28	classes	class	NOUN
ajst-24981	29	29	(	(	PUNCT
ajst-24981	29	30	in	in	ADP
ajst-24981	29	31	classification	classification	NOUN
ajst-24981	29	32	)	)	PUNCT
ajst-24981	29	33	or	or	CCONJ
ajst-24981	29	34	predicts	predict	VERB
ajst-24981	29	35	a	a	DET
ajst-24981	29	36	continuous	continuous	ADJ
ajst-24981	29	37	target	target	NOUN
ajst-24981	29	38	variable	variable	NOUN
ajst-24981	29	39	(	(	PUNCT
ajst-24981	29	40	in	in	ADP
ajst-24981	29	41	regression	regression	NOUN
ajst-24981	29	42	)	)	PUNCT
ajst-24981	29	43	.	.	PUNCT
ajst-24981	30	1	svm	svm	PROPN
ajst-24981	30	2	utilizes	utilizes	ADJ
ajst-24981	30	3	kernel	kernel	PROPN
ajst-24981	30	4	functions	function	NOUN
ajst-24981	30	5	to	to	PART
ajst-24981	30	6	transform	transform	VERB
ajst-24981	30	7	data	datum	NOUN
ajst-24981	30	8	into	into	ADP
ajst-24981	30	9	higher	high	ADJ
ajst-24981	30	10	dimensions	dimension	NOUN
ajst-24981	30	11	,	,	PUNCT
ajst-24981	30	12	enabling	enable	VERB
ajst-24981	30	13	it	it	PRON
ajst-24981	30	14	to	to	PART
ajst-24981	30	15	capture	capture	VERB
ajst-24981	30	16	non	non	ADJ
ajst-24981	30	17	-	-	ADJ
ajst-24981	30	18	linear	linear	ADJ
ajst-24981	30	19	relationships	relationship	NOUN
ajst-24981	30	20	.	.	PUNCT
ajst-24981	31	1	5	5	X
ajst-24981	31	2	.	.	X
ajst-24981	31	3	neural	neural	ADJ
ajst-24981	31	4	network	network	NOUN
ajst-24981	31	5	(	(	PUNCT
ajst-24981	31	6	nn	nn	NOUN
ajst-24981	31	7	)	)	PUNCT
ajst-24981	31	8	inspired	inspire	VERB
ajst-24981	31	9	by	by	ADP
ajst-24981	31	10	the	the	DET
ajst-24981	31	11	biological	biological	ADJ
ajst-24981	31	12	nervous	nervous	ADJ
ajst-24981	31	13	system	system	NOUN
ajst-24981	31	14	,	,	PUNCT
ajst-24981	31	15	nns	nn	NOUN
ajst-24981	31	16	consist	consist	VERB
ajst-24981	31	17	of	of	ADP
ajst-24981	31	18	interconnected	interconnected	ADJ
ajst-24981	31	19	nodes	node	NOUN
ajst-24981	31	20	organized	organize	VERB
ajst-24981	31	21	in	in	ADP
ajst-24981	31	22	layers	layer	NOUN
ajst-24981	31	23	.	.	PUNCT
ajst-24981	32	1	each	each	DET
ajst-24981	32	2	connection	connection	NOUN
ajst-24981	32	3	between	between	ADP
ajst-24981	32	4	nodes	node	NOUN
ajst-24981	32	5	has	have	VERB
ajst-24981	32	6	an	an	DET
ajst-24981	32	7	associated	associated	ADJ
ajst-24981	32	8	weight	weight	NOUN
ajst-24981	32	9	,	,	PUNCT
ajst-24981	32	10	representing	represent	VERB
ajst-24981	32	11	the	the	DET
ajst-24981	32	12	strength	strength	NOUN
ajst-24981	32	13	of	of	ADP
ajst-24981	32	14	the	the	DET
ajst-24981	32	15	connection	connection	NOUN
ajst-24981	32	16	.	.	PUNCT
ajst-24981	33	1	nns	nns	PROPN
ajst-24981	33	2	learn	learn	VERB
ajst-24981	33	3	by	by	ADP
ajst-24981	33	4	adjusting	adjust	VERB
ajst-24981	33	5	these	these	DET
ajst-24981	33	6	weights	weight	NOUN
ajst-24981	33	7	to	to	PART
ajst-24981	33	8	minimize	minimize	VERB
ajst-24981	33	9	the	the	DET
ajst-24981	33	10	difference	difference	NOUN
ajst-24981	33	11	between	between	ADP
ajst-24981	33	12	predicted	predict	VERB
ajst-24981	33	13	and	and	CCONJ
ajst-24981	33	14	actual	actual	ADJ
ajst-24981	33	15	values	value	NOUN
ajst-24981	33	16	.	.	PUNCT
ajst-24981	34	1	2.3	2.3	NUM
ajst-24981	34	2	.	.	PUNCT
ajst-24981	34	3	model	model	NOUN
ajst-24981	34	4	training	training	NOUN
ajst-24981	34	5	and	and	CCONJ
ajst-24981	34	6	evaluation	evaluation	NOUN
ajst-24981	34	7	strategy	strategy	NOUN
ajst-24981	34	8	to	to	PART
ajst-24981	34	9	evaluate	evaluate	VERB
ajst-24981	34	10	the	the	DET
ajst-24981	34	11	performance	performance	NOUN
ajst-24981	34	12	of	of	ADP
ajst-24981	34	13	these	these	DET
ajst-24981	34	14	models	model	NOUN
ajst-24981	34	15	rigorously	rigorously	ADV
ajst-24981	34	16	and	and	CCONJ
ajst-24981	34	17	ensure	ensure	VERB
ajst-24981	34	18	their	their	PRON
ajst-24981	34	19	generalization	generalization	NOUN
ajst-24981	34	20	capabilities	capability	NOUN
ajst-24981	34	21	,	,	PUNCT
ajst-24981	34	22	we	we	PRON
ajst-24981	34	23	implemented	implement	VERB
ajst-24981	34	24	a	a	DET
ajst-24981	34	25	systematic	systematic	ADJ
ajst-24981	34	26	training	training	NOUN
ajst-24981	34	27	and	and	CCONJ
ajst-24981	34	28	evaluation	evaluation	NOUN
ajst-24981	34	29	strategy	strategy	NOUN
ajst-24981	34	30	:	:	PUNCT
ajst-24981	34	31	1	1	X
ajst-24981	34	32	.	.	PUNCT
ajst-24981	34	33	dataset	dataset	ADJ
ajst-24981	34	34	splitting	splitting	NOUN
ajst-24981	34	35	:	:	PUNCT
ajst-24981	34	36	the	the	DET
ajst-24981	34	37	collected	collect	VERB
ajst-24981	34	38	dataset	dataset	NOUN
ajst-24981	34	39	was	be	AUX
ajst-24981	34	40	randomly	randomly	ADV
ajst-24981	34	41	divided	divide	VERB
ajst-24981	34	42	into	into	ADP
ajst-24981	34	43	two	two	NUM
ajst-24981	34	44	subsets	subset	NOUN
ajst-24981	34	45	:	:	PUNCT
ajst-24981	34	46	training	training	NOUN
ajst-24981	34	47	set	set	NOUN
ajst-24981	34	48	(	(	PUNCT
ajst-24981	34	49	80	80	NUM
ajst-24981	34	50	%	%	NOUN
ajst-24981	34	51	):	):	PUNCT
ajst-24981	34	52	used	use	VERB
ajst-24981	34	53	to	to	PART
ajst-24981	34	54	train	train	VERB
ajst-24981	34	55	each	each	DET
ajst-24981	34	56	ml	ml	NOUN
ajst-24981	34	57	model	model	NOUN
ajst-24981	34	58	,	,	PUNCT
ajst-24981	34	59	enabling	enable	VERB
ajst-24981	34	60	them	they	PRON
ajst-24981	34	61	to	to	PART
ajst-24981	34	62	learn	learn	VERB
ajst-24981	34	63	the	the	DET
ajst-24981	34	64	relationship	relationship	NOUN
ajst-24981	34	65	between	between	ADP
ajst-24981	34	66	input	input	NOUN
ajst-24981	34	67	well	well	INTJ
ajst-24981	34	68	log	log	NOUN
ajst-24981	34	69	parameters	parameter	NOUN
ajst-24981	34	70	and	and	CCONJ
ajst-24981	34	71	sandstone	sandstone	NOUN
ajst-24981	34	72	porosity	porosity	NOUN
ajst-24981	34	73	.	.	PUNCT
ajst-24981	35	1	test	test	NOUN
ajst-24981	35	2	set	set	NOUN
ajst-24981	35	3	(	(	PUNCT
ajst-24981	35	4	20	20	NUM
ajst-24981	35	5	%	%	NOUN
ajst-24981	35	6	):	):	PUNCT
ajst-24981	35	7	held	hold	VERB
ajst-24981	35	8	back	back	ADV
ajst-24981	35	9	from	from	ADP
ajst-24981	35	10	training	training	NOUN
ajst-24981	35	11	and	and	CCONJ
ajst-24981	35	12	used	use	VERB
ajst-24981	35	13	exclusively	exclusively	ADV
ajst-24981	35	14	to	to	PART
ajst-24981	35	15	evaluate	evaluate	VERB
ajst-24981	35	16	the	the	DET
ajst-24981	35	17	performance	performance	NOUN
ajst-24981	35	18	of	of	ADP
ajst-24981	35	19	the	the	DET
ajst-24981	35	20	trained	train	VERB
ajst-24981	35	21	models	model	NOUN
ajst-24981	35	22	on	on	ADP
ajst-24981	35	23	unseen	unseen	ADJ
ajst-24981	35	24	data	datum	NOUN
ajst-24981	35	25	,	,	PUNCT
ajst-24981	35	26	providing	provide	VERB
ajst-24981	35	27	an	an	DET
ajst-24981	35	28	unbiased	unbiased	ADJ
ajst-24981	35	29	assessment	assessment	NOUN
ajst-24981	35	30	of	of	ADP
ajst-24981	35	31	their	their	PRON
ajst-24981	35	32	generalization	generalization	NOUN
ajst-24981	35	33	ability	ability	NOUN
ajst-24981	35	34	.	.	PUNCT
ajst-24981	36	1	2	2	X
ajst-24981	36	2	.	.	X
ajst-24981	36	3	performance	performance	NOUN
ajst-24981	36	4	metrics	metric	NOUN
ajst-24981	36	5	:	:	PUNCT
ajst-24981	36	6	three	three	NUM
ajst-24981	36	7	commonly	commonly	ADV
ajst-24981	36	8	used	use	VERB
ajst-24981	36	9	metrics	metric	NOUN
ajst-24981	36	10	were	be	AUX
ajst-24981	36	11	employed	employ	VERB
ajst-24981	36	12	to	to	PART
ajst-24981	36	13	quantify	quantify	VERB
ajst-24981	36	14	and	and	CCONJ
ajst-24981	36	15	compare	compare	VERB
ajst-24981	36	16	the	the	DET
ajst-24981	36	17	predictive	predictive	ADJ
ajst-24981	36	18	accuracy	accuracy	NOUN
ajst-24981	36	19	of	of	ADP
ajst-24981	36	20	each	each	DET
ajst-24981	36	21	trained	train	VERB
ajst-24981	36	22	ml	ml	NOUN
ajst-24981	36	23	model	model	NOUN
ajst-24981	36	24	:	:	PUNCT
ajst-24981	36	25	mean	mean	VERB
ajst-24981	36	26	absolute	absolute	ADJ
ajst-24981	36	27	error	error	NOUN
ajst-24981	36	28	(	(	PUNCT
ajst-24981	36	29	mae	mae	PROPN
ajst-24981	36	30	):	):	PUNCT
ajst-24981	36	31	quantifies	quantifie	NOUN
ajst-24981	36	32	the	the	DET
ajst-24981	36	33	average	average	ADJ
ajst-24981	36	34	absolute	absolute	ADJ
ajst-24981	36	35	difference	difference	NOUN
ajst-24981	36	36	between	between	ADP
ajst-24981	36	37	the	the	DET
ajst-24981	36	38	predicted	predict	VERB
ajst-24981	36	39	porosity	porosity	NOUN
ajst-24981	36	40	values	value	NOUN
ajst-24981	36	41	and	and	CCONJ
ajst-24981	36	42	the	the	DET
ajst-24981	36	43	actual	actual	ADJ
ajst-24981	36	44	porosity	porosity	NOUN
ajst-24981	36	45	values	value	NOUN
ajst-24981	36	46	in	in	ADP
ajst-24981	36	47	the	the	DET
ajst-24981	36	48	test	test	NOUN
ajst-24981	36	49	set	set	NOUN
ajst-24981	36	50	.	.	PUNCT
ajst-24981	37	1	lower	low	ADJ
ajst-24981	37	2	mae	mae	PROPN
ajst-24981	37	3	values	value	NOUN
ajst-24981	37	4	indicate	indicate	VERB
ajst-24981	37	5	better	well	ADJ
ajst-24981	37	6	predictive	predictive	ADJ
ajst-24981	37	7	accuracy	accuracy	NOUN
ajst-24981	37	8	.	.	PUNCT
ajst-24981	38	1	root	root	NOUN
ajst-24981	38	2	mean	mean	VERB
ajst-24981	38	3	squared	square	VERB
ajst-24981	38	4	error	error	NOUN
ajst-24981	38	5	(	(	PUNCT
ajst-24981	38	6	rmse	rmse	NOUN
ajst-24981	38	7	):	):	PUNCT
ajst-24981	38	8	measures	measure	NOUN
ajst-24981	38	9	the	the	DET
ajst-24981	38	10	square	square	ADJ
ajst-24981	38	11	root	root	NOUN
ajst-24981	38	12	of	of	ADP
ajst-24981	38	13	the	the	DET
ajst-24981	38	14	average	average	ADJ
ajst-24981	38	15	squared	square	VERB
ajst-24981	38	16	difference	difference	NOUN
ajst-24981	38	17	between	between	ADP
ajst-24981	38	18	predicted	predict	VERB
ajst-24981	38	19	and	and	CCONJ
ajst-24981	38	20	actual	actual	ADJ
ajst-24981	38	21	values	value	NOUN
ajst-24981	38	22	.	.	PUNCT
ajst-24981	39	1	rmse	rmse	PROPN
ajst-24981	39	2	gives	give	VERB
ajst-24981	39	3	a	a	DET
ajst-24981	39	4	relatively	relatively	ADV
ajst-24981	39	5	high	high	ADJ
ajst-24981	39	6	weight	weight	NOUN
ajst-24981	39	7	to	to	ADP
ajst-24981	39	8	large	large	ADJ
ajst-24981	39	9	errors	error	NOUN
ajst-24981	39	10	,	,	PUNCT
ajst-24981	39	11	making	make	VERB
ajst-24981	39	12	it	it	PRON
ajst-24981	39	13	more	more	ADV
ajst-24981	39	14	sensitive	sensitive	ADJ
ajst-24981	39	15	to	to	ADP
ajst-24981	39	16	outliers	outlier	NOUN
ajst-24981	39	17	compared	compare	VERB
ajst-24981	39	18	to	to	ADP
ajst-24981	39	19	mae	mae	PROPN
ajst-24981	39	20	.	.	PUNCT
ajst-24981	40	1	r	r	X
ajst-24981	40	2	-	-	PUNCT
ajst-24981	40	3	squared	square	VERB
ajst-24981	40	4	(	(	PUNCT
ajst-24981	40	5	r²	r²	NOUN
ajst-24981	40	6	):	):	PUNCT
ajst-24981	40	7	represents	represent	VERB
ajst-24981	40	8	the	the	DET
ajst-24981	40	9	proportion	proportion	NOUN
ajst-24981	40	10	of	of	ADP
ajst-24981	40	11	the	the	DET
ajst-24981	40	12	variance	variance	NOUN
ajst-24981	40	13	in	in	ADP
ajst-24981	40	14	the	the	DET
ajst-24981	40	15	target	target	NOUN
ajst-24981	40	16	variable	variable	NOUN
ajst-24981	40	17	(	(	PUNCT
ajst-24981	40	18	porosity	porosity	NOUN
ajst-24981	40	19	)	)	PUNCT
ajst-24981	40	20	that	that	PRON
ajst-24981	40	21	is	be	AUX
ajst-24981	40	22	explained	explain	VERB
ajst-24981	40	23	by	by	ADP
ajst-24981	40	24	the	the	DET
ajst-24981	40	25	input	input	NOUN
ajst-24981	40	26	features	feature	NOUN
ajst-24981	40	27	.	.	PUNCT
ajst-24981	41	1	r²	r²	VERB
ajst-24981	41	2	ranges	range	VERB
ajst-24981	41	3	from	from	ADP
ajst-24981	41	4	0	0	NUM
ajst-24981	41	5	to	to	ADP
ajst-24981	41	6	1	1	NUM
ajst-24981	41	7	,	,	PUNCT
ajst-24981	41	8	where	where	SCONJ
ajst-24981	41	9	higher	high	ADJ
ajst-24981	41	10	values	value	NOUN
ajst-24981	41	11	indicate	indicate	VERB
ajst-24981	41	12	a	a	DET
ajst-24981	41	13	better	well	ADJ
ajst-24981	41	14	fit	fit	NOUN
ajst-24981	41	15	of	of	ADP
ajst-24981	41	16	the	the	DET
ajst-24981	41	17	model	model	NOUN
ajst-24981	41	18	to	to	ADP
ajst-24981	41	19	the	the	DET
ajst-24981	41	20	data	datum	NOUN
ajst-24981	41	21	.	.	PUNCT
ajst-24981	42	1	by	by	ADP
ajst-24981	42	2	comparing	compare	VERB
ajst-24981	42	3	the	the	DET
ajst-24981	42	4	performance	performance	NOUN
ajst-24981	42	5	of	of	ADP
ajst-24981	42	6	the	the	DET
ajst-24981	42	7	five	five	NUM
ajst-24981	42	8	ml	ml	NOUN
ajst-24981	42	9	models	model	NOUN
ajst-24981	42	10	across	across	ADP
ajst-24981	42	11	these	these	DET
ajst-24981	42	12	evaluation	evaluation	NOUN
ajst-24981	42	13	metrics	metric	NOUN
ajst-24981	42	14	,	,	PUNCT
ajst-24981	42	15	we	we	PRON
ajst-24981	42	16	aim	aim	VERB
ajst-24981	42	17	to	to	PART
ajst-24981	42	18	identify	identify	VERB
ajst-24981	42	19	the	the	DET
ajst-24981	42	20	most	most	ADV
ajst-24981	42	21	suitable	suitable	ADJ
ajst-24981	42	22	approach	approach	NOUN
ajst-24981	42	23	for	for	ADP
ajst-24981	42	24	predicting	predict	VERB
ajst-24981	42	25	sandstone	sandstone	NOUN
ajst-24981	42	26	porosity	porosity	NOUN
ajst-24981	42	27	from	from	ADP
ajst-24981	42	28	well	well	ADV
ajst-24981	42	29	log	log	VERB
ajst-24981	42	30	data	datum	NOUN
ajst-24981	42	31	within	within	ADP
ajst-24981	42	32	our	our	PRON
ajst-24981	42	33	specific	specific	ADJ
ajst-24981	42	34	dataset	dataset	NOUN
ajst-24981	42	35	and	and	CCONJ
ajst-24981	42	36	geological	geological	ADJ
ajst-24981	42	37	context	context	NOUN
ajst-24981	42	38	.	.	PUNCT
ajst-24981	43	1	the	the	DET
ajst-24981	43	2	findings	finding	NOUN
ajst-24981	43	3	will	will	AUX
ajst-24981	43	4	provide	provide	VERB
ajst-24981	43	5	valuable	valuable	ADJ
ajst-24981	43	6	insights	insight	NOUN
ajst-24981	43	7	for	for	ADP
ajst-24981	43	8	selecting	select	VERB
ajst-24981	43	9	the	the	DET
ajst-24981	43	10	most	most	ADV
ajst-24981	43	11	accurate	accurate	ADJ
ajst-24981	43	12	and	and	CCONJ
ajst-24981	43	13	effective	effective	ADJ
ajst-24981	43	14	model	model	NOUN
ajst-24981	43	15	for	for	ADP
ajst-24981	43	16	porosity	porosity	NOUN
ajst-24981	43	17	prediction	prediction	NOUN
ajst-24981	43	18	in	in	ADP
ajst-24981	43	19	future	future	ADJ
ajst-24981	43	20	studies.[6][7	studies.[6][7	PROPN
ajst-24981	43	21	]	]	X
ajst-24981	43	22	3	3	X
ajst-24981	43	23	.	.	NOUN
ajst-24981	43	24	results	result	NOUN
ajst-24981	43	25	and	and	CCONJ
ajst-24981	43	26	discussion	discussion	NOUN
ajst-24981	43	27	3.1	3.1	NUM
ajst-24981	43	28	.	.	PUNCT
ajst-24981	43	29	model	model	NOUN
ajst-24981	43	30	performance	performance	NOUN
ajst-24981	43	31	table	table	NOUN
ajst-24981	43	32	1	1	NUM
ajst-24981	43	33	.	.	PUNCT
ajst-24981	43	34	summarizes	summarize	VERB
ajst-24981	43	35	the	the	DET
ajst-24981	43	36	performance	performance	NOUN
ajst-24981	43	37	metrics	metric	NOUN
ajst-24981	43	38	of	of	ADP
ajst-24981	43	39	each	each	DET
ajst-24981	43	40	ml	ml	PROPN
ajst-24981	43	41	model	model	PROPN
ajst-24981	43	42	model	model	PROPN
ajst-24981	43	43	mae	mae	PROPN
ajst-24981	43	44	rmse	rmse	PROPN
ajst-24981	43	45	r²	r²	NOUN
ajst-24981	43	46	linear	linear	PROPN
ajst-24981	43	47	regression	regression	NOUN
ajst-24981	43	48	0.45	0.45	NUM
ajst-24981	43	49	0.58	0.58	NUM
ajst-24981	43	50	0.82	0.82	NUM
ajst-24981	43	51	decision	decision	NOUN
ajst-24981	43	52	tree	tree	NOUN
ajst-24981	43	53	0.35	0.35	NUM
ajst-24981	43	54	0.47	0.47	NUM
ajst-24981	43	55	0.87	0.87	NUM
ajst-24981	43	56	random	random	ADJ
ajst-24981	43	57	forest	forest	NOUN
ajst-24981	43	58	0.28	0.28	NUM
ajst-24981	43	59	0.36	0.36	NUM
ajst-24981	43	60	0.92	0.92	NUM
ajst-24981	43	61	supportvectormachine	supportvectormachine	NOUN
ajst-24981	43	62	0.32	0.32	NUM
ajst-24981	43	63	0.42	0.42	NUM
ajst-24981	43	64	0.89	0.89	NUM
ajst-24981	43	65	neural	neural	ADJ
ajst-24981	43	66	network	network	NOUN
ajst-24981	43	67	0.30	0.30	NUM
ajst-24981	43	68	0.40	0.40	NUM
ajst-24981	43	69	0.91	0.91	NUM
ajst-24981	43	70	3.2	3.2	NUM
ajst-24981	43	71	.	.	PUNCT
ajst-24981	44	1	analysis	analysis	NOUN
ajst-24981	44	2	of	of	ADP
ajst-24981	44	3	results	result	NOUN
ajst-24981	44	4	the	the	DET
ajst-24981	44	5	random	random	ADJ
ajst-24981	44	6	forest	forest	NOUN
ajst-24981	44	7	model	model	NOUN
ajst-24981	44	8	exhibited	exhibit	VERB
ajst-24981	44	9	the	the	DET
ajst-24981	44	10	best	good	ADJ
ajst-24981	44	11	performance	performance	NOUN
ajst-24981	44	12	,	,	PUNCT
ajst-24981	44	13	with	with	ADP
ajst-24981	44	14	the	the	DET
ajst-24981	44	15	lowest	low	ADJ
ajst-24981	44	16	mae	mae	PROPN
ajst-24981	44	17	and	and	CCONJ
ajst-24981	44	18	rmse	rmse	NOUN
ajst-24981	44	19	,	,	PUNCT
ajst-24981	44	20	and	and	CCONJ
ajst-24981	44	21	the	the	DET
ajst-24981	44	22	highest	high	ADJ
ajst-24981	44	23	r²	r²	NOUN
ajst-24981	44	24	value	value	NOUN
ajst-24981	44	25	.	.	PUNCT
ajst-24981	45	1	this	this	PRON
ajst-24981	45	2	indicates	indicate	VERB
ajst-24981	45	3	that	that	SCONJ
ajst-24981	45	4	random	random	ADJ
ajst-24981	45	5	forest	forest	NOUN
ajst-24981	45	6	is	be	AUX
ajst-24981	45	7	highly	highly	ADV
ajst-24981	45	8	effective	effective	ADJ
ajst-24981	45	9	in	in	ADP
ajst-24981	45	10	capturing	capture	VERB
ajst-24981	45	11	the	the	DET
ajst-24981	45	12	complex	complex	ADJ
ajst-24981	45	13	relationships	relationship	NOUN
ajst-24981	45	14	between	between	ADP
ajst-24981	45	15	the	the	DET
ajst-24981	45	16	input	input	NOUN
ajst-24981	45	17	parameters	parameter	NOUN
ajst-24981	45	18	and	and	CCONJ
ajst-24981	45	19	the	the	DET
ajst-24981	45	20	permeability	permeability	NOUN
ajst-24981	45	21	.	.	PUNCT
ajst-24981	46	1	the	the	DET
ajst-24981	46	2	svm	svm	PROPN
ajst-24981	46	3	and	and	CCONJ
ajst-24981	46	4	neural	neural	ADJ
ajst-24981	46	5	network	network	NOUN
ajst-24981	46	6	models	model	NOUN
ajst-24981	46	7	also	also	ADV
ajst-24981	46	8	showed	show	VERB
ajst-24981	46	9	strong	strong	ADJ
ajst-24981	46	10	performance	performance	NOUN
ajst-24981	46	11	,	,	PUNCT
ajst-24981	46	12	suggesting	suggest	VERB
ajst-24981	46	13	their	their	PRON
ajst-24981	46	14	potential	potential	NOUN
ajst-24981	46	15	for	for	ADP
ajst-24981	46	16	predictive	predictive	ADJ
ajst-24981	46	17	tasks	task	NOUN
ajst-24981	46	18	in	in	ADP
ajst-24981	46	19	grouting	grout	VERB
ajst-24981	46	20	reinforcement	reinforcement	NOUN
ajst-24981	46	21	engineering	engineering	NOUN
ajst-24981	46	22	.	.	PUNCT
ajst-24981	47	1	3.3	3.3	NUM
ajst-24981	47	2	.	.	PUNCT
ajst-24981	48	1	feature	feature	NOUN
ajst-24981	48	2	importance	importance	NOUN
ajst-24981	48	3	figure	figure	NOUN
ajst-24981	48	4	1	1	NUM
ajst-24981	48	5	illustrates	illustrate	VERB
ajst-24981	48	6	the	the	DET
ajst-24981	48	7	feature	feature	NOUN
ajst-24981	48	8	importance	importance	NOUN
ajst-24981	48	9	as	as	SCONJ
ajst-24981	48	10	determined	determine	VERB
ajst-24981	48	11	by	by	ADP
ajst-24981	48	12	the	the	DET
ajst-24981	48	13	random	random	ADJ
ajst-24981	48	14	forest	forest	NOUN
ajst-24981	48	15	model	model	NOUN
ajst-24981	48	16	.	.	PUNCT
ajst-24981	49	1	curing	cure	VERB
ajst-24981	49	2	time	time	NOUN
ajst-24981	49	3	emerged	emerge	VERB
ajst-24981	49	4	as	as	ADP
ajst-24981	49	5	the	the	DET
ajst-24981	49	6	most	most	ADV
ajst-24981	49	7	significant	significant	ADJ
ajst-24981	49	8	factor	factor	NOUN
ajst-24981	49	9	,	,	PUNCT
ajst-24981	49	10	followed	follow	VERB
ajst-24981	49	11	by	by	ADP
ajst-24981	49	12	sand	sand	NOUN
ajst-24981	49	13	water	water	NOUN
ajst-24981	49	14	content	content	NOUN
ajst-24981	49	15	and	and	CCONJ
ajst-24981	49	16	cement	cement	NOUN
ajst-24981	49	17	-	-	PUNCT
ajst-24981	49	18	water	water	NOUN
ajst-24981	49	19	ratio	ratio	NOUN
ajst-24981	49	20	.	.	PUNCT
ajst-24981	50	1	this	this	PRON
ajst-24981	50	2	aligns	align	VERB
ajst-24981	50	3	with	with	ADP
ajst-24981	50	4	previous	previous	ADJ
ajst-24981	50	5	studies	study	NOUN
ajst-24981	50	6	[	[	X
ajst-24981	50	7	6][7	6][7	NUM
ajst-24981	50	8	]	]	PUNCT
ajst-24981	50	9	,	,	PUNCT
ajst-24981	50	10	confirming	confirm	VERB
ajst-24981	50	11	the	the	DET
ajst-24981	50	12	critical	critical	ADJ
ajst-24981	50	13	role	role	NOUN
ajst-24981	50	14	of	of	ADP
ajst-24981	50	15	curing	cure	VERB
ajst-24981	50	16	time	time	NOUN
ajst-24981	50	17	in	in	ADP
ajst-24981	50	18	determining	determine	VERB
ajst-24981	50	19	the	the	DET
ajst-24981	50	20	permeability	permeability	NOUN
ajst-24981	50	21	of	of	ADP
ajst-24981	50	22	reinforced	reinforce	VERB
ajst-24981	50	23	sand	sand	NOUN
ajst-24981	50	24	.	.	PUNCT
ajst-24981	51	1	4	4	X
ajst-24981	51	2	.	.	X
ajst-24981	51	3	summary	summary	NOUN
ajst-24981	51	4	this	this	DET
ajst-24981	51	5	study	study	NOUN
ajst-24981	51	6	demonstrates	demonstrate	VERB
ajst-24981	51	7	the	the	DET
ajst-24981	51	8	potential	potential	NOUN
ajst-24981	51	9	of	of	ADP
ajst-24981	51	10	machine	machine	NOUN
ajst-24981	51	11	learning	learning	NOUN
ajst-24981	51	12	models	model	NOUN
ajst-24981	51	13	in	in	ADP
ajst-24981	51	14	predicting	predict	VERB
ajst-24981	51	15	the	the	DET
ajst-24981	51	16	permeability	permeability	NOUN
ajst-24981	51	17	of	of	ADP
ajst-24981	51	18	grouted	grouted	ADJ
ajst-24981	51	19	sand	sand	NOUN
ajst-24981	51	20	in	in	ADP
ajst-24981	51	21	medium	medium	ADJ
ajst-24981	51	22	sand	sand	NOUN
ajst-24981	51	23	strata	strata	NOUN
ajst-24981	51	24	.	.	PUNCT
ajst-24981	52	1	the	the	DET
ajst-24981	52	2	results	result	NOUN
ajst-24981	52	3	indicate	indicate	VERB
ajst-24981	52	4	that	that	SCONJ
ajst-24981	52	5	random	random	ADJ
ajst-24981	52	6	forest	forest	NOUN
ajst-24981	52	7	and	and	CCONJ
ajst-24981	52	8	svm	svm	ADJ
ajst-24981	52	9	models	model	NOUN
ajst-24981	52	10	can	can	AUX
ajst-24981	52	11	provide	provide	VERB
ajst-24981	52	12	accurate	accurate	ADJ
ajst-24981	52	13	predictions	prediction	NOUN
ajst-24981	52	14	,	,	PUNCT
ajst-24981	52	15	thereby	thereby	ADV
ajst-24981	52	16	aiding	aid	VERB
ajst-24981	52	17	in	in	ADP
ajst-24981	52	18	the	the	DET
ajst-24981	52	19	optimization	optimization	NOUN
ajst-24981	52	20	of	of	ADP
ajst-24981	52	21	grouting	grout	VERB
ajst-24981	52	22	processes	process	NOUN
ajst-24981	52	23	.	.	PUNCT
ajst-24981	53	1	future	future	ADJ
ajst-24981	53	2	work	work	NOUN
ajst-24981	53	3	will	will	AUX
ajst-24981	53	4	focus	focus	VERB
ajst-24981	53	5	on	on	ADP
ajst-24981	53	6	expanding	expand	VERB
ajst-24981	53	7	the	the	DET
ajst-24981	53	8	dataset	dataset	NOUN
ajst-24981	53	9	and	and	CCONJ
ajst-24981	53	10	exploring	explore	VERB
ajst-24981	53	11	additional	additional	ADJ
ajst-24981	53	12	ml	ml	NOUN
ajst-24981	53	13	techniques	technique	NOUN
ajst-24981	53	14	to	to	PART
ajst-24981	53	15	further	far	ADV
ajst-24981	53	16	enhance	enhance	VERB
ajst-24981	53	17	predictive	predictive	ADJ
ajst-24981	53	18	accuracy	accuracy	NOUN
ajst-24981	53	19	.	.	PUNCT
ajst-24981	54	1	conflicts	conflict	NOUN
ajst-24981	54	2	of	of	ADP
ajst-24981	54	3	interest	interest	NOUN
ajst-24981	54	4	the	the	DET
ajst-24981	54	5	authors	author	NOUN
ajst-24981	54	6	declare	declare	VERB
ajst-24981	54	7	that	that	SCONJ
ajst-24981	54	8	they	they	PRON
ajst-24981	54	9	have	have	VERB
ajst-24981	54	10	no	no	DET
ajst-24981	54	11	conflict	conflict	NOUN
ajst-24981	54	12	of	of	ADP
ajst-24981	54	13	interest	interest	NOUN
ajst-24981	54	14	.	.	PUNCT
ajst-24981	55	1	references	reference	NOUN
ajst-24981	55	2	[	[	X
ajst-24981	55	3	1	1	X
ajst-24981	55	4	]	]	X
ajst-24981	55	5	barboza	barboza	NOUN
ajst-24981	55	6	f	f	NOUN
ajst-24981	55	7	,	,	PUNCT
ajst-24981	55	8	kimura	kimura	NOUN
ajst-24981	55	9	h	h	NOUN
ajst-24981	55	10	,	,	PUNCT
ajst-24981	55	11	altman	altman	PROPN
ajst-24981	55	12	e.	e.	PROPN
ajst-24981	55	13	machine	machine	PROPN
ajst-24981	55	14	learning	learning	NOUN
ajst-24981	55	15	models	model	NOUN
ajst-24981	55	16	and	and	CCONJ
ajst-24981	55	17	bankruptcy	bankruptcy	NOUN
ajst-24981	55	18	prediction[j	prediction[j	PROPN
ajst-24981	55	19	]	]	PUNCT
ajst-24981	55	20	.	.	PUNCT
ajst-24981	56	1	expert	expert	NOUN
ajst-24981	56	2	systems	system	NOUN
ajst-24981	56	3	with	with	ADP
ajst-24981	56	4	applications	application	NOUN
ajst-24981	56	5	,	,	PUNCT
ajst-24981	56	6	2017	2017	NUM
ajst-24981	56	7	,	,	PUNCT
ajst-24981	56	8	83	83	NUM
ajst-24981	56	9	:	:	SYM
ajst-24981	56	10	405	405	NUM
ajst-24981	56	11	-	-	SYM
ajst-24981	56	12	417	417	NUM
ajst-24981	56	13	.	.	PUNCT
ajst-24981	57	1	[	[	X
ajst-24981	57	2	2	2	NUM
ajst-24981	57	3	]	]	PUNCT
ajst-24981	57	4	bischl	bischl	PROPN
ajst-24981	57	5	b	b	PROPN
ajst-24981	57	6	,	,	PUNCT
ajst-24981	57	7	lang	lang	PROPN
ajst-24981	57	8	m	m	PROPN
ajst-24981	57	9	,	,	PUNCT
ajst-24981	57	10	kotthoff	kotthoff	PROPN
ajst-24981	57	11	l	l	PROPN
ajst-24981	57	12	,	,	PUNCT
ajst-24981	57	13	et	et	PROPN
ajst-24981	57	14	al	al	PROPN
ajst-24981	57	15	.	.	PUNCT
ajst-24981	57	16	mlr	mlr	PROPN
ajst-24981	57	17	:	:	PUNCT
ajst-24981	57	18	machine	machine	NOUN
ajst-24981	57	19	learning	learning	NOUN
ajst-24981	57	20	in	in	ADP
ajst-24981	57	21	r[j	r[j	NOUN
ajst-24981	57	22	]	]	PUNCT
ajst-24981	57	23	.	.	PUNCT
ajst-24981	58	1	the	the	DET
ajst-24981	58	2	journal	journal	NOUN
ajst-24981	58	3	of	of	ADP
ajst-24981	58	4	machine	machine	NOUN
ajst-24981	58	5	learning	learn	VERB
ajst-24981	58	6	research	research	NOUN
ajst-24981	58	7	,	,	PUNCT
ajst-24981	58	8	2016	2016	NUM
ajst-24981	58	9	,	,	PUNCT
ajst-24981	58	10	17(1	17(1	NUM
ajst-24981	58	11	):	):	PUNCT
ajst-24981	58	12	5938	5938	NUM
ajst-24981	58	13	-	-	SYM
ajst-24981	58	14	5942	5942	NUM
ajst-24981	58	15	.	.	PUNCT
ajst-24981	59	1	[	[	X
ajst-24981	59	2	3	3	NUM
ajst-24981	59	3	]	]	X
ajst-24981	59	4	awad	awad	PROPN
ajst-24981	59	5	m	m	PROPN
ajst-24981	59	6	,	,	PUNCT
ajst-24981	59	7	khanna	khanna	PROPN
ajst-24981	59	8	r	r	PROPN
ajst-24981	59	9	,	,	PUNCT
ajst-24981	59	10	awad	awad	PROPN
ajst-24981	59	11	m	m	PROPN
ajst-24981	59	12	,	,	PUNCT
ajst-24981	59	13	et	et	PROPN
ajst-24981	59	14	al	al	PROPN
ajst-24981	59	15	.	.	PUNCT
ajst-24981	59	16	support	support	PROPN
ajst-24981	59	17	vector	vector	NOUN
ajst-24981	59	18	regression[j	regression[j	PROPN
ajst-24981	59	19	]	]	PUNCT
ajst-24981	59	20	.	.	PUNCT
ajst-24981	60	1	efficient	efficient	ADJ
ajst-24981	60	2	learning	learning	PROPN
ajst-24981	60	3	machines	machine	NOUN
ajst-24981	60	4	:	:	PUNCT
ajst-24981	60	5	theories	theory	NOUN
ajst-24981	60	6	,	,	PUNCT
ajst-24981	60	7	concepts	concept	NOUN
ajst-24981	60	8	,	,	PUNCT
ajst-24981	60	9	183	183	NUM
ajst-24981	60	10	and	and	CCONJ
ajst-24981	60	11	applications	application	NOUN
ajst-24981	60	12	for	for	ADP
ajst-24981	60	13	engineers	engineer	NOUN
ajst-24981	60	14	and	and	CCONJ
ajst-24981	60	15	system	system	NOUN
ajst-24981	60	16	designers	designer	NOUN
ajst-24981	60	17	,	,	PUNCT
ajst-24981	60	18	2015	2015	NUM
ajst-24981	60	19	:	:	PUNCT
ajst-24981	60	20	6780	6780	NUM
ajst-24981	60	21	.	.	PUNCT
ajst-24981	61	1	[	[	X
ajst-24981	61	2	4	4	X
ajst-24981	61	3	]	]	X
ajst-24981	61	4	hao	hao	PROPN
ajst-24981	61	5	x	x	X
ajst-24981	61	6	,	,	PUNCT
ajst-24981	61	7	zhang	zhang	PROPN
ajst-24981	61	8	g	g	PROPN
ajst-24981	61	9	,	,	PUNCT
ajst-24981	61	10	ma	ma	PROPN
ajst-24981	61	11	s	s	PROPN
ajst-24981	61	12	.	.	PUNCT
ajst-24981	62	1	deep	deep	ADJ
ajst-24981	62	2	learning[j	learning[j	PROPN
ajst-24981	62	3	]	]	PUNCT
ajst-24981	62	4	.	.	PUNCT
ajst-24981	63	1	international	international	ADJ
ajst-24981	63	2	journal	journal	PROPN
ajst-24981	63	3	of	of	ADP
ajst-24981	63	4	semantic	semantic	ADJ
ajst-24981	63	5	computing	computing	NOUN
ajst-24981	63	6	,	,	PUNCT
ajst-24981	63	7	2016	2016	NUM
ajst-24981	63	8	,	,	PUNCT
ajst-24981	63	9	10(03):417	10(03):417	NUM
ajst-24981	63	10	-	-	SYM
ajst-24981	63	11	439	439	NUM
ajst-24981	63	12	.	.	PUNCT
ajst-24981	64	1	[	[	X
ajst-24981	64	2	5	5	NUM
ajst-24981	64	3	]	]	X
ajst-24981	64	4	lecun	lecun	PROPN
ajst-24981	64	5	y	y	PROPN
ajst-24981	64	6	,	,	PUNCT
ajst-24981	64	7	bengio	bengio	PROPN
ajst-24981	64	8	y.	y.	PROPN
ajst-24981	64	9	convolutional	convolutional	ADJ
ajst-24981	64	10	networks	network	NOUN
ajst-24981	64	11	for	for	ADP
ajst-24981	64	12	images	image	NOUN
ajst-24981	64	13	,	,	PUNCT
ajst-24981	64	14	speech	speech	NOUN
ajst-24981	64	15	,	,	PUNCT
ajst-24981	64	16	and	and	CCONJ
ajst-24981	64	17	time	time	NOUN
ajst-24981	64	18	series[j	series[j	PROPN
ajst-24981	64	19	]	]	PUNCT
ajst-24981	64	20	.	.	PUNCT
ajst-24981	65	1	the	the	DET
ajst-24981	65	2	handbook	handbook	NOUN
ajst-24981	65	3	of	of	ADP
ajst-24981	65	4	brain	brain	NOUN
ajst-24981	65	5	theory	theory	NOUN
ajst-24981	65	6	and	and	CCONJ
ajst-24981	65	7	neural	neural	ADJ
ajst-24981	65	8	networks	network	NOUN
ajst-24981	65	9	,	,	PUNCT
ajst-24981	65	10	1995	1995	NUM
ajst-24981	65	11	,	,	PUNCT
ajst-24981	65	12	3361(10	3361(10	NUM
ajst-24981	65	13	):	):	PUNCT
ajst-24981	65	14	1995	1995	NUM
ajst-24981	65	15	.	.	PUNCT
ajst-24981	66	1	[	[	X
ajst-24981	66	2	6	6	NUM
ajst-24981	66	3	]	]	X
ajst-24981	66	4	cang	cang	PROPN
ajst-24981	66	5	r	r	PROPN
ajst-24981	66	6	,	,	PUNCT
ajst-24981	66	7	xu	xu	PROPN
ajst-24981	66	8	y	y	PROPN
ajst-24981	66	9	,	,	PUNCT
ajst-24981	66	10	chen	chen	PROPN
ajst-24981	66	11	s	s	PROPN
ajst-24981	66	12	,	,	PUNCT
ajst-24981	66	13	et	et	PROPN
ajst-24981	66	14	al	al	PROPN
ajst-24981	66	15	.	.	PROPN
ajst-24981	66	16	microstructure	microstructure	ADJ
ajst-24981	66	17	representation	representation	NOUN
ajst-24981	66	18	and	and	CCONJ
ajst-24981	66	19	reconstruction	reconstruction	NOUN
ajst-24981	66	20	of	of	ADP
ajst-24981	66	21	heterogeneous	heterogeneous	ADJ
ajst-24981	66	22	materials	material	NOUN
ajst-24981	66	23	via	via	ADP
ajst-24981	66	24	deep	deep	ADJ
ajst-24981	66	25	belief	belief	NOUN
ajst-24981	66	26	network	network	NOUN
ajst-24981	66	27	for	for	ADP
ajst-24981	66	28	computational	computational	ADJ
ajst-24981	66	29	material	material	NOUN
ajst-24981	66	30	design[j	design[j	PROPN
ajst-24981	66	31	]	]	PUNCT
ajst-24981	66	32	.	.	PUNCT
ajst-24981	67	1	journal	journal	PROPN
ajst-24981	67	2	of	of	ADP
ajst-24981	67	3	mechanical	mechanical	ADJ
ajst-24981	67	4	design	design	NOUN
ajst-24981	67	5	,	,	PUNCT
ajst-24981	67	6	2017	2017	NUM
ajst-24981	67	7	,	,	PUNCT
ajst-24981	67	8	139(7	139(7	NUM
ajst-24981	67	9	):	):	PUNCT
ajst-24981	67	10	071404	071404	NUM
ajst-24981	67	11	.	.	PUNCT
ajst-24981	68	1	[	[	X
ajst-24981	68	2	7	7	X
ajst-24981	68	3	]	]	X
ajst-24981	68	4	mosser	mosser	NOUN
ajst-24981	68	5	l	l	NOUN
ajst-24981	68	6	,	,	PUNCT
ajst-24981	68	7	dubrule	dubrule	VERB
ajst-24981	68	8	o	o	NOUN
ajst-24981	68	9	,	,	PUNCT
ajst-24981	68	10	blunt	blunt	ADJ
ajst-24981	68	11	m	m	PROPN
ajst-24981	68	12	j	j	NOUN
ajst-24981	68	13	.	.	PUNCT
ajst-24981	69	1	reconstruction	reconstruction	NOUN
ajst-24981	69	2	of	of	ADP
ajst-24981	69	3	threedimensional	threedimensional	ADJ
ajst-24981	69	4	porous	porous	ADJ
ajst-24981	69	5	media	medium	NOUN
ajst-24981	69	6	using	use	VERB
ajst-24981	69	7	generative	generative	ADJ
ajst-24981	69	8	adversarial	adversarial	ADJ
ajst-24981	69	9	neural	neural	ADJ
ajst-24981	69	10	networks[j	networks[j	PROPN
ajst-24981	69	11	]	]	PUNCT
ajst-24981	69	12	.	.	PUNCT
ajst-24981	70	1	physical	physical	ADJ
ajst-24981	70	2	review	review	PROPN
ajst-24981	70	3	e	e	NOUN
ajst-24981	70	4	,	,	PUNCT
ajst-24981	70	5	2017	2017	NUM
ajst-24981	70	6	,	,	PUNCT
ajst-24981	70	7	96(4):043309	96(4):043309	NOUN
ajst-24981	70	8	.	.	PUNCT