id	sid	tid	token	lemma	pos
brj-23416	1	1	peer	peer	NOUN
brj-23416	1	2	-	-	PUNCT
brj-23416	1	3	review	review	NOUN
brj-23416	1	4	article	article	NOUN
brj-23416	1	5	peer	peer	NOUN
brj-23416	1	6	-	-	PUNCT
brj-23416	1	7	reviewed	review	VERB
brj-23416	1	8	article	article	NOUN
brj-23416	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	1	10	akyüz	akyüz	PROPN
brj-23416	1	11	et	et	PROPN
brj-23416	1	12	al	al	PROPN
brj-23416	1	13	.	.	PROPN
brj-23416	2	1	(	(	PUNCT
brj-23416	2	2	2024	2024	NUM
brj-23416	2	3	)	)	PUNCT
brj-23416	2	4	.	.	PUNCT
brj-23416	3	1	“	"	PUNCT
brj-23416	3	2	stock	stock	NOUN
brj-23416	3	3	exchange	exchange	NOUN
brj-23416	3	4	values	value	NOUN
brj-23416	3	5	,	,	PUNCT
brj-23416	3	6	”	"	PUNCT
brj-23416	3	7	bioresources	bioresource	NOUN
brj-23416	3	8	19(3	19(3	NUM
brj-23416	3	9	)	)	PUNCT
brj-23416	3	10	,	,	PUNCT
brj-23416	3	11	5141	5141	NUM
brj-23416	3	12	-	-	SYM
brj-23416	3	13	5157	5157	NUM
brj-23416	3	14	.	.	PUNCT
brj-23416	4	1	5140	5140	NUM
brj-23416	4	2	prediction	prediction	NOUN
brj-23416	4	3	of	of	ADP
brj-23416	4	4	values	value	NOUN
brj-23416	4	5	of	of	ADP
brj-23416	4	6	borsa	borsa	PROPN
brj-23416	4	7	istanbul	istanbul	PROPN
brj-23416	4	8	forest	forest	PROPN
brj-23416	4	9	,	,	PUNCT
brj-23416	4	10	paper	paper	NOUN
brj-23416	4	11	,	,	PUNCT
brj-23416	4	12	and	and	CCONJ
brj-23416	4	13	printing	printing	NOUN
brj-23416	4	14	index	index	NOUN
brj-23416	4	15	using	use	VERB
brj-23416	4	16	machine	machine	NOUN
brj-23416	4	17	learning	learn	VERB
brj-23416	4	18	methods	method	NOUN
brj-23416	4	19	i̇lker	i̇lker	NOUN
brj-23416	4	20	akyüz	akyüz	NOUN
brj-23416	4	21	,	,	PUNCT
brj-23416	4	22	a	a	DET
brj-23416	4	23	kinyas	kinyas	NOUN
brj-23416	4	24	polat	polat	NOUN
brj-23416	4	25	,	,	PUNCT
brj-23416	4	26	b	b	PROPN
brj-23416	4	27	selahattin	selahattin	ADP
brj-23416	4	28	bardak	bardak	PROPN
brj-23416	4	29	,	,	PUNCT
brj-23416	4	30	c	c	X
brj-23416	4	31	,	,	PUNCT
brj-23416	4	32	*	*	PUNCT
brj-23416	4	33	and	and	CCONJ
brj-23416	4	34	nadir	nadir	PROPN
brj-23416	4	35	ersen	ersen	NOUN
brj-23416	4	36	d	d	PROPN
brj-23416	4	37	*	*	PUNCT
brj-23416	4	38	corresponding	correspond	VERB
brj-23416	4	39	author	author	NOUN
brj-23416	4	40	:	:	PUNCT
brj-23416	4	41	sbardak@sinop.edu.tr	sbardak@sinop.edu.tr	ADP
brj-23416	4	42	doi:10.15376	doi:10.15376	NOUN
brj-23416	4	43	/	/	SYM
brj-23416	4	44	biores.19.3.5141	biores.19.3.5141	NOUN
brj-23416	4	45	-	-	PUNCT
brj-23416	4	46	5157	5157	NUM
brj-23416	4	47	graphical	graphical	ADJ
brj-23416	4	48	abstract	abstract	NOUN
brj-23416	4	49	mailto:sbardak@sinop.edu	mailto:sbardak@sinop.edu	PROPN
brj-23416	4	50	peer	peer	NOUN
brj-23416	4	51	-	-	PUNCT
brj-23416	4	52	reviewed	review	VERB
brj-23416	4	53	article	article	NOUN
brj-23416	4	54	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	4	55	akyüz	akyüz	PROPN
brj-23416	4	56	et	et	PROPN
brj-23416	4	57	al	al	PROPN
brj-23416	4	58	.	.	PROPN
brj-23416	5	1	(	(	PUNCT
brj-23416	5	2	2024	2024	NUM
brj-23416	5	3	)	)	PUNCT
brj-23416	5	4	.	.	PUNCT
brj-23416	6	1	“	"	PUNCT
brj-23416	6	2	stock	stock	NOUN
brj-23416	6	3	exchange	exchange	NOUN
brj-23416	6	4	values	value	NOUN
brj-23416	6	5	,	,	PUNCT
brj-23416	6	6	”	"	PUNCT
brj-23416	6	7	bioresources	bioresource	NOUN
brj-23416	6	8	19(3	19(3	NUM
brj-23416	6	9	)	)	PUNCT
brj-23416	6	10	,	,	PUNCT
brj-23416	6	11	5141	5141	NUM
brj-23416	6	12	-	-	SYM
brj-23416	6	13	5157	5157	NUM
brj-23416	6	14	.	.	PUNCT
brj-23416	7	1	5141	5141	NUM
brj-23416	7	2	prediction	prediction	NOUN
brj-23416	7	3	of	of	ADP
brj-23416	7	4	values	value	NOUN
brj-23416	7	5	of	of	ADP
brj-23416	7	6	borsa	borsa	PROPN
brj-23416	7	7	istanbul	istanbul	PROPN
brj-23416	7	8	forest	forest	PROPN
brj-23416	7	9	,	,	PUNCT
brj-23416	7	10	paper	paper	NOUN
brj-23416	7	11	,	,	PUNCT
brj-23416	7	12	and	and	CCONJ
brj-23416	7	13	printing	printing	NOUN
brj-23416	7	14	index	index	NOUN
brj-23416	7	15	using	use	VERB
brj-23416	7	16	machine	machine	NOUN
brj-23416	7	17	learning	learn	VERB
brj-23416	7	18	methods	method	NOUN
brj-23416	7	19	i̇lker	i̇lker	NOUN
brj-23416	7	20	akyüz	akyüz	NOUN
brj-23416	7	21	,	,	PUNCT
brj-23416	7	22	a	a	DET
brj-23416	7	23	kinyas	kinyas	NOUN
brj-23416	7	24	polat	polat	NOUN
brj-23416	7	25	,	,	PUNCT
brj-23416	7	26	b	b	PROPN
brj-23416	7	27	selahattin	selahattin	ADP
brj-23416	7	28	bardak	bardak	PROPN
brj-23416	7	29	,	,	PUNCT
brj-23416	7	30	c	c	X
brj-23416	7	31	,	,	PUNCT
brj-23416	7	32	*	*	PUNCT
brj-23416	7	33	and	and	CCONJ
brj-23416	7	34	nadir	nadir	PROPN
brj-23416	7	35	ersen	ersen	PROPN
brj-23416	8	1	d	d	INTJ
brj-23416	8	2	it	it	PRON
brj-23416	8	3	is	be	AUX
brj-23416	8	4	difficult	difficult	ADJ
brj-23416	8	5	to	to	PART
brj-23416	8	6	predict	predict	VERB
brj-23416	8	7	index	index	NOUN
brj-23416	8	8	values	value	NOUN
brj-23416	8	9	or	or	CCONJ
brj-23416	8	10	stock	stock	NOUN
brj-23416	8	11	prices	price	NOUN
brj-23416	8	12	with	with	ADP
brj-23416	8	13	a	a	DET
brj-23416	8	14	single	single	ADJ
brj-23416	8	15	financial	financial	ADJ
brj-23416	8	16	formula	formula	NOUN
brj-23416	8	17	.	.	PUNCT
brj-23416	9	1	they	they	PRON
brj-23416	9	2	are	be	AUX
brj-23416	9	3	affected	affect	VERB
brj-23416	9	4	by	by	ADP
brj-23416	9	5	many	many	ADJ
brj-23416	9	6	factors	factor	NOUN
brj-23416	9	7	,	,	PUNCT
brj-23416	9	8	such	such	ADJ
brj-23416	9	9	as	as	ADP
brj-23416	9	10	political	political	ADJ
brj-23416	9	11	conditions	condition	NOUN
brj-23416	9	12	,	,	PUNCT
brj-23416	9	13	global	global	ADJ
brj-23416	9	14	economy	economy	NOUN
brj-23416	9	15	,	,	PUNCT
brj-23416	9	16	unexpected	unexpected	ADJ
brj-23416	9	17	events	event	NOUN
brj-23416	9	18	,	,	PUNCT
brj-23416	9	19	market	market	NOUN
brj-23416	9	20	anomalies	anomaly	NOUN
brj-23416	9	21	,	,	PUNCT
brj-23416	9	22	and	and	CCONJ
brj-23416	9	23	the	the	DET
brj-23416	9	24	characteristics	characteristic	NOUN
brj-23416	9	25	of	of	ADP
brj-23416	9	26	the	the	DET
brj-23416	9	27	relevant	relevant	ADJ
brj-23416	9	28	companies	company	NOUN
brj-23416	9	29	,	,	PUNCT
brj-23416	9	30	and	and	CCONJ
brj-23416	9	31	many	many	ADJ
brj-23416	9	32	computer	computer	NOUN
brj-23416	9	33	science	science	NOUN
brj-23416	9	34	techniques	technique	NOUN
brj-23416	9	35	are	be	AUX
brj-23416	9	36	being	be	AUX
brj-23416	9	37	used	use	VERB
brj-23416	9	38	to	to	PART
brj-23416	9	39	make	make	VERB
brj-23416	9	40	more	more	ADV
brj-23416	9	41	accurate	accurate	ADJ
brj-23416	9	42	predictions	prediction	NOUN
brj-23416	9	43	about	about	ADP
brj-23416	9	44	them	they	PRON
brj-23416	9	45	.	.	PUNCT
brj-23416	10	1	this	this	DET
brj-23416	10	2	study	study	NOUN
brj-23416	10	3	aimed	aim	VERB
brj-23416	10	4	to	to	PART
brj-23416	10	5	predict	predict	VERB
brj-23416	10	6	the	the	DET
brj-23416	10	7	values	value	NOUN
brj-23416	10	8	of	of	ADP
brj-23416	10	9	the	the	DET
brj-23416	10	10	xkagt	xkagt	PROPN
brj-23416	10	11	index	index	NOUN
brj-23416	10	12	by	by	ADP
brj-23416	10	13	using	use	VERB
brj-23416	10	14	the	the	DET
brj-23416	10	15	monthly	monthly	ADJ
brj-23416	10	16	closing	closing	NOUN
brj-23416	10	17	values	value	NOUN
brj-23416	10	18	of	of	ADP
brj-23416	10	19	the	the	DET
brj-23416	10	20	borsa	borsa	PROPN
brj-23416	10	21	istanbul	istanbul	PROPN
brj-23416	10	22	(	(	PUNCT
brj-23416	10	23	bist	bist	NOUN
brj-23416	10	24	)	)	PUNCT
brj-23416	10	25	forestry	forestry	NOUN
brj-23416	10	26	,	,	PUNCT
brj-23416	10	27	paper	paper	NOUN
brj-23416	10	28	and	and	CCONJ
brj-23416	10	29	printing	printing	NOUN
brj-23416	10	30	(	(	PUNCT
brj-23416	10	31	xkagt	xkagt	PROPN
brj-23416	10	32	)	)	PUNCT
brj-23416	10	33	index	index	NOUN
brj-23416	10	34	between	between	ADP
brj-23416	10	35	2002	2002	NUM
brj-23416	10	36	and	and	CCONJ
brj-23416	10	37	2023	2023	NUM
brj-23416	10	38	,	,	PUNCT
brj-23416	10	39	and	and	CCONJ
brj-23416	10	40	the	the	DET
brj-23416	10	41	machine	machine	NOUN
brj-23416	10	42	learning	learn	VERB
brj-23416	10	43	techniques	technique	VERB
brj-23416	10	44	artificial	artificial	ADJ
brj-23416	10	45	neural	neural	ADJ
brj-23416	10	46	networks	network	NOUN
brj-23416	10	47	(	(	PUNCT
brj-23416	10	48	ann	ann	PROPN
brj-23416	10	49	)	)	PUNCT
brj-23416	10	50	,	,	PUNCT
brj-23416	10	51	random	random	ADJ
brj-23416	10	52	forest	forest	NOUN
brj-23416	10	53	(	(	PUNCT
brj-23416	10	54	rf	rf	NOUN
brj-23416	10	55	)	)	PUNCT
brj-23416	10	56	,	,	PUNCT
brj-23416	10	57	k	k	X
brj-23416	10	58	-	-	PUNCT
brj-23416	10	59	nearest	near	ADJ
brj-23416	10	60	neighbor	neighbor	NOUN
brj-23416	10	61	(	(	PUNCT
brj-23416	10	62	knn	knn	PROPN
brj-23416	10	63	)	)	PUNCT
brj-23416	10	64	,	,	PUNCT
brj-23416	10	65	and	and	CCONJ
brj-23416	10	66	gradient	gradient	ADJ
brj-23416	10	67	boosting	boost	VERB
brj-23416	10	68	machine	machine	NOUN
brj-23416	10	69	(	(	PUNCT
brj-23416	10	70	gbm	gbm	NOUN
brj-23416	10	71	)	)	PUNCT
brj-23416	10	72	.	.	PUNCT
brj-23416	11	1	furthermore	furthermore	ADV
brj-23416	11	2	,	,	PUNCT
brj-23416	11	3	the	the	DET
brj-23416	11	4	performances	performance	NOUN
brj-23416	11	5	of	of	ADP
brj-23416	11	6	four	four	NUM
brj-23416	11	7	machine	machine	NOUN
brj-23416	11	8	learning	learn	VERB
brj-23416	11	9	techniques	technique	NOUN
brj-23416	11	10	were	be	AUX
brj-23416	11	11	compared	compare	VERB
brj-23416	11	12	.	.	PUNCT
brj-23416	12	1	factors	factor	NOUN
brj-23416	12	2	affecting	affect	VERB
brj-23416	12	3	stock	stock	NOUN
brj-23416	12	4	prices	price	NOUN
brj-23416	12	5	are	be	AUX
brj-23416	12	6	generally	generally	ADV
brj-23416	12	7	classified	classify	VERB
brj-23416	12	8	as	as	ADP
brj-23416	12	9	macroeconomic	macroeconomic	ADJ
brj-23416	12	10	and	and	CCONJ
brj-23416	12	11	microeconomic	microeconomic	ADJ
brj-23416	12	12	factors	factor	NOUN
brj-23416	12	13	.	.	PUNCT
brj-23416	13	1	as	as	ADP
brj-23416	13	2	a	a	DET
brj-23416	13	3	result	result	NOUN
brj-23416	13	4	of	of	ADP
brj-23416	13	5	examining	examine	VERB
brj-23416	13	6	the	the	DET
brj-23416	13	7	studies	study	NOUN
brj-23416	13	8	on	on	ADP
brj-23416	13	9	determining	determine	VERB
brj-23416	13	10	the	the	DET
brj-23416	13	11	macroeconomic	macroeconomic	ADJ
brj-23416	13	12	factors	factor	NOUN
brj-23416	13	13	affecting	affect	VERB
brj-23416	13	14	the	the	DET
brj-23416	13	15	stock	stock	NOUN
brj-23416	13	16	markets	market	NOUN
brj-23416	13	17	,	,	PUNCT
brj-23416	13	18	10	10	NUM
brj-23416	13	19	macroeconomic	macroeconomic	ADJ
brj-23416	13	20	factors	factor	NOUN
brj-23416	13	21	were	be	AUX
brj-23416	13	22	determined	determine	VERB
brj-23416	13	23	as	as	ADP
brj-23416	13	24	input	input	NOUN
brj-23416	13	25	.	.	PUNCT
brj-23416	14	1	the	the	DET
brj-23416	14	2	macroeconomic	macroeconomic	ADJ
brj-23416	14	3	variables	variable	NOUN
brj-23416	14	4	used	use	VERB
brj-23416	14	5	were	be	AUX
brj-23416	14	6	crude	crude	ADJ
brj-23416	14	7	oil	oil	NOUN
brj-23416	14	8	price	price	NOUN
brj-23416	14	9	,	,	PUNCT
brj-23416	14	10	exchange	exchange	NOUN
brj-23416	14	11	rate	rate	NOUN
brj-23416	14	12	of	of	ADP
brj-23416	14	13	usd	usd	NOUN
brj-23416	14	14	/	/	SYM
brj-23416	14	15	try	try	NOUN
brj-23416	14	16	,	,	PUNCT
brj-23416	14	17	dollar	dollar	NOUN
brj-23416	14	18	index	index	NOUN
brj-23416	14	19	,	,	PUNCT
brj-23416	14	20	bist100	bist100	PROPN
brj-23416	14	21	index	index	NOUN
brj-23416	14	22	,	,	PUNCT
brj-23416	14	23	gold	gold	NOUN
brj-23416	14	24	price	price	NOUN
brj-23416	14	25	,	,	PUNCT
brj-23416	14	26	money	money	NOUN
brj-23416	14	27	supply	supply	NOUN
brj-23416	14	28	(	(	PUNCT
brj-23416	14	29	m2	m2	PROPN
brj-23416	14	30	)	)	PUNCT
brj-23416	14	31	,	,	PUNCT
brj-23416	14	32	s&p	s&p	PROPN
brj-23416	14	33	500	500	NUM
brj-23416	14	34	index	index	NOUN
brj-23416	14	35	,	,	PUNCT
brj-23416	14	36	us	we	PRON
brj-23416	14	37	10	10	NUM
brj-23416	14	38	-	-	PUNCT
brj-23416	14	39	year	year	NOUN
brj-23416	14	40	bond	bond	NOUN
brj-23416	14	41	interest	interest	NOUN
brj-23416	14	42	,	,	PUNCT
brj-23416	14	43	export	export	NOUN
brj-23416	14	44	-	-	PUNCT
brj-23416	14	45	import	import	NOUN
brj-23416	14	46	coverage	coverage	NOUN
brj-23416	14	47	rate	rate	NOUN
brj-23416	14	48	in	in	ADP
brj-23416	14	49	the	the	DET
brj-23416	14	50	forest	forest	NOUN
brj-23416	14	51	products	product	NOUN
brj-23416	14	52	sector	sector	NOUN
brj-23416	14	53	,	,	PUNCT
brj-23416	14	54	and	and	CCONJ
brj-23416	14	55	deposits	deposit	VERB
brj-23416	14	56	interest	interest	NOUN
brj-23416	14	57	rate	rate	NOUN
brj-23416	14	58	.	.	PUNCT
brj-23416	15	1	it	it	PRON
brj-23416	15	2	was	be	AUX
brj-23416	15	3	determined	determine	VERB
brj-23416	15	4	that	that	SCONJ
brj-23416	15	5	all	all	DET
brj-23416	15	6	machine	machine	NOUN
brj-23416	15	7	learning	learn	VERB
brj-23416	15	8	techniques	technique	NOUN
brj-23416	15	9	used	use	VERB
brj-23416	15	10	in	in	ADP
brj-23416	15	11	the	the	DET
brj-23416	15	12	study	study	NOUN
brj-23416	15	13	performed	perform	VERB
brj-23416	15	14	successfully	successfully	ADV
brj-23416	15	15	in	in	ADP
brj-23416	15	16	predicting	predict	VERB
brj-23416	15	17	the	the	DET
brj-23416	15	18	index	index	NOUN
brj-23416	15	19	value	value	NOUN
brj-23416	15	20	,	,	PUNCT
brj-23416	15	21	but	but	CCONJ
brj-23416	15	22	the	the	DET
brj-23416	15	23	k	k	NOUN
brj-23416	15	24	-	-	PUNCT
brj-23416	15	25	nearest	near	ADJ
brj-23416	15	26	neighbor	neighbor	NOUN
brj-23416	15	27	algorithm	algorithm	NOUN
brj-23416	15	28	showed	show	VERB
brj-23416	15	29	the	the	DET
brj-23416	15	30	best	good	ADJ
brj-23416	15	31	performance	performance	NOUN
brj-23416	15	32	with	with	ADP
brj-23416	15	33	r2=0.996	r2=0.996	NOUN
brj-23416	15	34	,	,	PUNCT
brj-23416	15	35	rmse=71.36	rmse=71.36	NOUN
brj-23416	15	36	,	,	PUNCT
brj-23416	15	37	and	and	CCONJ
brj-23416	15	38	a	a	DET
brj-23416	15	39	mae	mae	PROPN
brj-23416	15	40	of	of	ADP
brj-23416	15	41	40.8	40.8	NUM
brj-23416	15	42	.	.	PUNCT
brj-23416	16	1	therefore	therefore	ADV
brj-23416	16	2	,	,	PUNCT
brj-23416	16	3	in	in	ADP
brj-23416	16	4	line	line	NOUN
brj-23416	16	5	with	with	ADP
brj-23416	16	6	the	the	DET
brj-23416	16	7	current	current	ADJ
brj-23416	16	8	variables	variable	NOUN
brj-23416	16	9	,	,	PUNCT
brj-23416	16	10	investors	investor	NOUN
brj-23416	16	11	can	can	AUX
brj-23416	16	12	make	make	VERB
brj-23416	16	13	analyzes	analyze	NOUN
brj-23416	16	14	using	use	VERB
brj-23416	16	15	any	any	PRON
brj-23416	16	16	of	of	ADP
brj-23416	16	17	the	the	DET
brj-23416	16	18	ann	ann	PROPN
brj-23416	16	19	,	,	PUNCT
brj-23416	16	20	rf	rf	PROPN
brj-23416	16	21	,	,	PUNCT
brj-23416	16	22	knn	knn	PROPN
brj-23416	16	23	,	,	PUNCT
brj-23416	16	24	and	and	CCONJ
brj-23416	16	25	gbm	gbm	PROPN
brj-23416	16	26	techniques	technique	NOUN
brj-23416	16	27	to	to	PART
brj-23416	16	28	predict	predict	VERB
brj-23416	16	29	the	the	DET
brj-23416	16	30	future	future	ADJ
brj-23416	16	31	index	index	NOUN
brj-23416	16	32	value	value	NOUN
brj-23416	16	33	,	,	PUNCT
brj-23416	16	34	which	which	PRON
brj-23416	16	35	will	will	AUX
brj-23416	16	36	lead	lead	VERB
brj-23416	16	37	them	they	PRON
brj-23416	16	38	to	to	ADP
brj-23416	16	39	accurate	accurate	ADJ
brj-23416	16	40	results	result	NOUN
brj-23416	16	41	.	.	PUNCT
brj-23416	17	1	doi:10.15376	doi:10.15376	NOUN
brj-23416	17	2	/	/	SYM
brj-23416	17	3	biores.19.3.5141	biores.19.3.5141	NOUN
brj-23416	17	4	-	-	PUNCT
brj-23416	17	5	5157	5157	NUM
brj-23416	17	6	keywords	keyword	NOUN
brj-23416	17	7	:	:	PUNCT
brj-23416	17	8	machine	machine	NOUN
brj-23416	17	9	learning	learning	NOUN
brj-23416	17	10	;	;	PUNCT
brj-23416	17	11	forest	forest	NOUN
brj-23416	17	12	industry	industry	NOUN
brj-23416	17	13	;	;	PUNCT
brj-23416	17	14	index	index	NOUN
brj-23416	17	15	prediction	prediction	NOUN
brj-23416	17	16	;	;	PUNCT
brj-23416	17	17	xkagt	xkagt	PROPN
brj-23416	17	18	contact	contact	NOUN
brj-23416	17	19	information	information	NOUN
brj-23416	17	20	:	:	PUNCT
brj-23416	17	21	a	a	DET
brj-23416	17	22	:	:	PUNCT
brj-23416	17	23	department	department	NOUN
brj-23416	17	24	of	of	ADP
brj-23416	17	25	forest	forest	NOUN
brj-23416	17	26	industry	industry	NOUN
brj-23416	17	27	engineering	engineering	NOUN
brj-23416	17	28	,	,	PUNCT
brj-23416	17	29	karadeniz	karadeniz	PROPN
brj-23416	17	30	technical	technical	PROPN
brj-23416	17	31	university	university	PROPN
brj-23416	17	32	,	,	PUNCT
brj-23416	17	33	61100	61100	NUM
brj-23416	17	34	,	,	PUNCT
brj-23416	17	35	trabzon	trabzon	PROPN
brj-23416	17	36	,	,	PUNCT
brj-23416	17	37	türkiye	türkiye	PROPN
brj-23416	17	38	;	;	PUNCT
brj-23416	17	39	b	b	X
brj-23416	17	40	:	:	PUNCT
brj-23416	17	41	department	department	NOUN
brj-23416	17	42	of	of	ADP
brj-23416	17	43	metallurgical	metallurgical	ADJ
brj-23416	17	44	and	and	CCONJ
brj-23416	17	45	materials	material	NOUN
brj-23416	17	46	engineering	engineering	NOUN
brj-23416	17	47	,	,	PUNCT
brj-23416	17	48	sinop	sinop	NOUN
brj-23416	17	49	university	university	NOUN
brj-23416	17	50	,	,	PUNCT
brj-23416	17	51	57000	57000	NUM
brj-23416	17	52	,	,	PUNCT
brj-23416	17	53	sinop	sinop	NOUN
brj-23416	17	54	,	,	PUNCT
brj-23416	17	55	türkiye	türkiye	PROPN
brj-23416	17	56	;	;	PUNCT
brj-23416	17	57	c	c	X
brj-23416	17	58	:	:	PUNCT
brj-23416	17	59	department	department	NOUN
brj-23416	17	60	of	of	ADP
brj-23416	17	61	computer	computer	NOUN
brj-23416	17	62	engineering	engineering	NOUN
brj-23416	17	63	,	,	PUNCT
brj-23416	17	64	sinop	sinop	NOUN
brj-23416	17	65	university	university	NOUN
brj-23416	17	66	,	,	PUNCT
brj-23416	17	67	57000	57000	NUM
brj-23416	17	68	,	,	PUNCT
brj-23416	17	69	sinop	sinop	NOUN
brj-23416	17	70	,	,	PUNCT
brj-23416	17	71	türkiye	türkiye	PROPN
brj-23416	17	72	;	;	PUNCT
brj-23416	17	73	d	d	X
brj-23416	17	74	:	:	PUNCT
brj-23416	17	75	department	department	NOUN
brj-23416	17	76	of	of	ADP
brj-23416	17	77	forestry	forestry	PROPN
brj-23416	17	78	,	,	PUNCT
brj-23416	17	79	artvin	artvin	PROPN
brj-23416	17	80	çoruh	çoruh	PROPN
brj-23416	17	81	university	university	PROPN
brj-23416	17	82	,	,	PUNCT
brj-23416	17	83	08000	08000	NUM
brj-23416	17	84	,	,	PUNCT
brj-23416	17	85	artvin	artvin	PROPN
brj-23416	17	86	,	,	PUNCT
brj-23416	17	87	türkiye	türkiye	PROPN
brj-23416	17	88	;	;	PUNCT
brj-23416	17	89	*	*	PUNCT
brj-23416	17	90	corresponding	correspond	VERB
brj-23416	17	91	author	author	NOUN
brj-23416	17	92	:	:	PUNCT
brj-23416	17	93	sbardak@sinop.edu.tr	sbardak@sinop.edu.tr	PRON
brj-23416	17	94	introduction	introduction	NOUN
brj-23416	17	95	the	the	DET
brj-23416	17	96	forest	forest	NOUN
brj-23416	17	97	-	-	PUNCT
brj-23416	17	98	based	base	VERB
brj-23416	17	99	sector	sector	NOUN
brj-23416	17	100	has	have	VERB
brj-23416	17	101	significant	significant	ADJ
brj-23416	17	102	economic	economic	ADJ
brj-23416	17	103	and	and	CCONJ
brj-23416	17	104	social	social	ADJ
brj-23416	17	105	value	value	NOUN
brj-23416	17	106	on	on	ADP
brj-23416	17	107	both	both	CCONJ
brj-23416	17	108	a	a	DET
brj-23416	17	109	national	national	ADJ
brj-23416	17	110	and	and	CCONJ
brj-23416	17	111	global	global	ADJ
brj-23416	17	112	scale	scale	NOUN
brj-23416	17	113	.	.	PUNCT
brj-23416	18	1	this	this	DET
brj-23416	18	2	sector	sector	NOUN
brj-23416	18	3	,	,	PUNCT
brj-23416	18	4	which	which	PRON
brj-23416	18	5	constitutes	constitute	VERB
brj-23416	18	6	25	25	NUM
brj-23416	18	7	%	%	NOUN
brj-23416	18	8	of	of	ADP
brj-23416	18	9	türkiye	türkiye	PROPN
brj-23416	18	10	’s	’s	PART
brj-23416	18	11	total	total	ADJ
brj-23416	18	12	manufacturing	manufacturing	NOUN
brj-23416	18	13	industry	industry	NOUN
brj-23416	18	14	,	,	PUNCT
brj-23416	18	15	affects	affect	VERB
brj-23416	18	16	many	many	ADJ
brj-23416	18	17	sectors	sector	NOUN
brj-23416	18	18	directly	directly	ADV
brj-23416	18	19	and	and	CCONJ
brj-23416	18	20	indirectly	indirectly	ADV
brj-23416	18	21	(	(	PUNCT
brj-23416	18	22	kahraman	kahraman	NOUN
brj-23416	18	23	2023).in	2023).in	NUM
brj-23416	18	24	addition	addition	NOUN
brj-23416	18	25	,	,	PUNCT
brj-23416	18	26	this	this	DET
brj-23416	18	27	sector	sector	NOUN
brj-23416	18	28	is	be	AUX
brj-23416	18	29	constantly	constantly	ADV
brj-23416	18	30	developing	develop	VERB
brj-23416	18	31	its	its	PRON
brj-23416	18	32	product	product	NOUN
brj-23416	18	33	diversity	diversity	NOUN
brj-23416	18	34	with	with	ADP
brj-23416	18	35	new	new	ADJ
brj-23416	18	36	technologies	technology	NOUN
brj-23416	18	37	,	,	PUNCT
brj-23416	18	38	and	and	CCONJ
brj-23416	18	39	it	it	PRON
brj-23416	18	40	has	have	VERB
brj-23416	18	41	a	a	DET
brj-23416	18	42	3.3	3.3	NUM
brj-23416	18	43	%	%	NOUN
brj-23416	18	44	share	share	NOUN
brj-23416	18	45	of	of	ADP
brj-23416	18	46	türkiye	türkiye	PROPN
brj-23416	18	47	’s	’s	PART
brj-23416	18	48	total	total	ADJ
brj-23416	18	49	exports	export	NOUN
brj-23416	18	50	(	(	PUNCT
brj-23416	18	51	yıldız	yıldız	NOUN
brj-23416	18	52	and	and	CCONJ
brj-23416	18	53	erdoğan	erdoğan	NOUN
brj-23416	18	54	2020	2020	NUM
brj-23416	18	55	;	;	PUNCT
brj-23416	18	56	aegean	aegean	PROPN
brj-23416	18	57	exporters	exporter	NOUN
brj-23416	18	58	’	'	PUNCT
brj-23416	18	59	associations	association	NOUN
brj-23416	18	60	2024	2024	NUM
brj-23416	18	61	)	)	PUNCT
brj-23416	18	62	.	.	PUNCT
brj-23416	19	1	the	the	DET
brj-23416	19	2	performance	performance	NOUN
brj-23416	19	3	of	of	ADP
brj-23416	19	4	companies	company	NOUN
brj-23416	19	5	in	in	ADP
brj-23416	19	6	the	the	DET
brj-23416	19	7	forest	forest	NOUN
brj-23416	19	8	-	-	PUNCT
brj-23416	19	9	based	base	VERB
brj-23416	19	10	sector	sector	NOUN
brj-23416	19	11	can	can	AUX
brj-23416	19	12	be	be	AUX
brj-23416	19	13	a	a	DET
brj-23416	19	14	valuable	valuable	ADJ
brj-23416	19	15	indicator	indicator	NOUN
brj-23416	19	16	for	for	ADP
brj-23416	19	17	understanding	understand	VERB
brj-23416	19	18	the	the	DET
brj-23416	19	19	general	general	ADJ
brj-23416	19	20	economic	economic	ADJ
brj-23416	19	21	situation	situation	NOUN
brj-23416	19	22	and	and	CCONJ
brj-23416	19	23	industrial	industrial	ADJ
brj-23416	19	24	developments	development	NOUN
brj-23416	19	25	.	.	PUNCT
brj-23416	20	1	at	at	ADP
brj-23416	20	2	this	this	DET
brj-23416	20	3	mailto:sbardak@sinop.edu.tr	mailto:sbardak@sinop.edu.tr	ADJ
brj-23416	20	4	peer	peer	NOUN
brj-23416	20	5	-	-	PUNCT
brj-23416	20	6	reviewed	review	VERB
brj-23416	20	7	article	article	NOUN
brj-23416	20	8	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	20	9	akyüz	akyüz	PROPN
brj-23416	20	10	et	et	PROPN
brj-23416	20	11	al	al	PROPN
brj-23416	20	12	.	.	PROPN
brj-23416	21	1	(	(	PUNCT
brj-23416	21	2	2024	2024	NUM
brj-23416	21	3	)	)	PUNCT
brj-23416	21	4	.	.	PUNCT
brj-23416	22	1	“	"	PUNCT
brj-23416	22	2	stock	stock	NOUN
brj-23416	22	3	exchange	exchange	NOUN
brj-23416	22	4	values	value	NOUN
brj-23416	22	5	,	,	PUNCT
brj-23416	22	6	”	"	PUNCT
brj-23416	22	7	bioresources	bioresource	NOUN
brj-23416	22	8	19(3	19(3	NUM
brj-23416	22	9	)	)	PUNCT
brj-23416	22	10	,	,	PUNCT
brj-23416	22	11	5141	5141	NUM
brj-23416	22	12	-	-	SYM
brj-23416	22	13	5157	5157	NUM
brj-23416	22	14	.	.	PUNCT
brj-23416	23	1	5142	5142	NUM
brj-23416	23	2	point	point	NOUN
brj-23416	23	3	,	,	PUNCT
brj-23416	23	4	the	the	DET
brj-23416	23	5	forestry	forestry	NOUN
brj-23416	23	6	,	,	PUNCT
brj-23416	23	7	paper	paper	NOUN
brj-23416	23	8	,	,	PUNCT
brj-23416	23	9	printing	printing	NOUN
brj-23416	23	10	index	index	NOUN
brj-23416	23	11	(	(	PUNCT
brj-23416	23	12	xkagt	xkagt	PROPN
brj-23416	23	13	)	)	PUNCT
brj-23416	23	14	emerges	emerge	VERB
brj-23416	23	15	as	as	ADP
brj-23416	23	16	an	an	DET
brj-23416	23	17	important	important	ADJ
brj-23416	23	18	tool	tool	NOUN
brj-23416	23	19	to	to	PART
brj-23416	23	20	monitor	monitor	VERB
brj-23416	23	21	the	the	DET
brj-23416	23	22	performance	performance	NOUN
brj-23416	23	23	of	of	ADP
brj-23416	23	24	companies	company	NOUN
brj-23416	23	25	operating	operate	VERB
brj-23416	23	26	in	in	ADP
brj-23416	23	27	these	these	DET
brj-23416	23	28	sectors	sector	NOUN
brj-23416	23	29	.	.	PUNCT
brj-23416	24	1	as	as	ADP
brj-23416	24	2	of	of	ADP
brj-23416	24	3	april	april	PROPN
brj-23416	24	4	30	30	NUM
brj-23416	24	5	,	,	PUNCT
brj-23416	24	6	2024	2024	NUM
brj-23416	24	7	,	,	PUNCT
brj-23416	24	8	17	17	NUM
brj-23416	24	9	companies	company	NOUN
brj-23416	24	10	are	be	AUX
brj-23416	24	11	traded	trade	VERB
brj-23416	24	12	in	in	ADP
brj-23416	24	13	the	the	DET
brj-23416	24	14	bist	bist	NOUN
brj-23416	24	15	forest	forest	NOUN
brj-23416	24	16	,	,	PUNCT
brj-23416	24	17	paper	paper	NOUN
brj-23416	24	18	,	,	PUNCT
brj-23416	24	19	printing	printing	NOUN
brj-23416	24	20	(	(	PUNCT
brj-23416	24	21	xkagt	xkagt	PROPN
brj-23416	24	22	)	)	PUNCT
brj-23416	24	23	index	index	NOUN
brj-23416	24	24	,	,	PUNCT
brj-23416	24	25	which	which	PRON
brj-23416	24	26	started	start	VERB
brj-23416	24	27	to	to	PART
brj-23416	24	28	be	be	AUX
brj-23416	24	29	calculated	calculate	VERB
brj-23416	24	30	on	on	ADP
brj-23416	24	31	dec	dec	PROPN
brj-23416	24	32	27	27	NUM
brj-23416	24	33	,	,	PUNCT
brj-23416	24	34	1996	1996	NUM
brj-23416	24	35	.	.	PUNCT
brj-23416	25	1	the	the	DET
brj-23416	25	2	number	number	NOUN
brj-23416	25	3	of	of	ADP
brj-23416	25	4	investors	investor	NOUN
brj-23416	25	5	of	of	ADP
brj-23416	25	6	the	the	DET
brj-23416	25	7	bist	bist	ADJ
brj-23416	25	8	xkagt	xkagt	PROPN
brj-23416	25	9	index	index	NOUN
brj-23416	25	10	was	be	AUX
brj-23416	25	11	625,342	625,342	NUM
brj-23416	25	12	as	as	ADP
brj-23416	25	13	of	of	ADP
brj-23416	25	14	april	april	PROPN
brj-23416	25	15	30	30	NUM
brj-23416	25	16	,	,	PUNCT
brj-23416	25	17	2024	2024	NUM
brj-23416	25	18	,	,	PUNCT
brj-23416	25	19	and	and	CCONJ
brj-23416	25	20	the	the	DET
brj-23416	25	21	number	number	NOUN
brj-23416	25	22	of	of	ADP
brj-23416	25	23	investors	investor	NOUN
brj-23416	25	24	of	of	ADP
brj-23416	25	25	bist	bist	ADJ
brj-23416	25	26	xkagt	xkagt	PROPN
brj-23416	25	27	index	index	NOUN
brj-23416	25	28	constitutes	constitute	VERB
brj-23416	25	29	7.65	7.65	NUM
brj-23416	25	30	%	%	NOUN
brj-23416	25	31	of	of	ADP
brj-23416	25	32	bist	bist	NOUN
brj-23416	25	33	all	all	DET
brj-23416	25	34	index	index	NOUN
brj-23416	25	35	(	(	PUNCT
brj-23416	25	36	borsa	borsa	PROPN
brj-23416	25	37	istanbul	istanbul	PROPN
brj-23416	25	38	2023	2023	NUM
brj-23416	25	39	;	;	PUNCT
brj-23416	25	40	kahraman	kahraman	NOUN
brj-23416	25	41	2023	2023	NUM
brj-23416	25	42	)	)	PUNCT
brj-23416	25	43	.	.	PUNCT
brj-23416	26	1	xkagt	xkagt	PROPN
brj-23416	26	2	directly	directly	ADV
brj-23416	26	3	measures	measure	VERB
brj-23416	26	4	the	the	DET
brj-23416	26	5	positive	positive	ADJ
brj-23416	26	6	or	or	CCONJ
brj-23416	26	7	negative	negative	ADJ
brj-23416	26	8	changes	change	NOUN
brj-23416	26	9	that	that	PRON
brj-23416	26	10	may	may	AUX
brj-23416	26	11	occur	occur	VERB
brj-23416	26	12	in	in	ADP
brj-23416	26	13	the	the	DET
brj-23416	26	14	wood	wood	NOUN
brj-23416	26	15	-	-	PUNCT
brj-23416	26	16	based	base	VERB
brj-23416	26	17	sector	sector	NOUN
brj-23416	26	18	and	and	CCONJ
brj-23416	26	19	plays	play	VERB
brj-23416	26	20	important	important	ADJ
brj-23416	26	21	roles	role	NOUN
brj-23416	26	22	in	in	ADP
brj-23416	26	23	managing	manage	VERB
brj-23416	26	24	strategic	strategic	ADJ
brj-23416	26	25	investment	investment	NOUN
brj-23416	26	26	decisions	decision	NOUN
brj-23416	26	27	in	in	ADP
brj-23416	26	28	this	this	DET
brj-23416	26	29	sector	sector	NOUN
brj-23416	26	30	(	(	PUNCT
brj-23416	26	31	kaderli	kaderli	NOUN
brj-23416	26	32	et	et	PROPN
brj-23416	26	33	al	al	PROPN
brj-23416	26	34	.	.	PROPN
brj-23416	26	35	2013	2013	NUM
brj-23416	26	36	)	)	PUNCT
brj-23416	26	37	.	.	PUNCT
brj-23416	27	1	increasing	increase	VERB
brj-23416	27	2	instability	instability	NOUN
brj-23416	27	3	in	in	ADP
brj-23416	27	4	stock	stock	NOUN
brj-23416	27	5	markets	market	NOUN
brj-23416	27	6	means	mean	VERB
brj-23416	27	7	that	that	SCONJ
brj-23416	27	8	investors	investor	NOUN
brj-23416	27	9	assume	assume	VERB
brj-23416	27	10	risks	risk	NOUN
brj-23416	27	11	.	.	PUNCT
brj-23416	28	1	increased	increase	VERB
brj-23416	28	2	instability	instability	NOUN
brj-23416	28	3	can	can	AUX
brj-23416	28	4	also	also	ADV
brj-23416	28	5	be	be	AUX
brj-23416	28	6	a	a	DET
brj-23416	28	7	situation	situation	NOUN
brj-23416	28	8	that	that	PRON
brj-23416	28	9	can	can	AUX
brj-23416	28	10	lead	lead	VERB
brj-23416	28	11	investors	investor	NOUN
brj-23416	28	12	to	to	ADP
brj-23416	28	13	losses	loss	NOUN
brj-23416	28	14	(	(	PUNCT
brj-23416	28	15	kurt	kurt	NOUN
brj-23416	28	16	and	and	CCONJ
brj-23416	28	17	senal	senal	PROPN
brj-23416	28	18	2018).therefore	2018).therefore	NUM
brj-23416	28	19	,	,	PUNCT
brj-23416	28	20	the	the	DET
brj-23416	28	21	prediction	prediction	NOUN
brj-23416	28	22	of	of	ADP
brj-23416	28	23	xkagt	xkagt	PROPN
brj-23416	28	24	values	value	NOUN
brj-23416	28	25	is	be	AUX
brj-23416	28	26	very	very	ADV
brj-23416	28	27	important	important	ADJ
brj-23416	28	28	for	for	ADP
brj-23416	28	29	investors	investor	NOUN
brj-23416	28	30	,	,	PUNCT
brj-23416	28	31	sector	sector	NOUN
brj-23416	28	32	actors	actor	NOUN
brj-23416	28	33	,	,	PUNCT
brj-23416	28	34	and	and	CCONJ
brj-23416	28	35	people	people	NOUN
brj-23416	28	36	and	and	CCONJ
brj-23416	28	37	organizations	organization	NOUN
brj-23416	28	38	connected	connect	VERB
brj-23416	28	39	to	to	ADP
brj-23416	28	40	the	the	DET
brj-23416	28	41	sector	sector	NOUN
brj-23416	28	42	.	.	PUNCT
brj-23416	29	1	since	since	SCONJ
brj-23416	29	2	stock	stock	NOUN
brj-23416	29	3	indices	index	NOUN
brj-23416	29	4	reflect	reflect	VERB
brj-23416	29	5	the	the	DET
brj-23416	29	6	values	value	NOUN
brj-23416	29	7	and	and	CCONJ
brj-23416	29	8	performances	performance	NOUN
brj-23416	29	9	of	of	ADP
brj-23416	29	10	the	the	DET
brj-23416	29	11	stocks	stock	NOUN
brj-23416	29	12	in	in	ADP
brj-23416	29	13	the	the	DET
brj-23416	29	14	portfolio	portfolio	NOUN
brj-23416	29	15	,	,	PUNCT
brj-23416	29	16	they	they	PRON
brj-23416	29	17	are	be	AUX
brj-23416	29	18	an	an	DET
brj-23416	29	19	effective	effective	ADJ
brj-23416	29	20	indicator	indicator	NOUN
brj-23416	29	21	in	in	ADP
brj-23416	29	22	investors	investor	NOUN
brj-23416	29	23	’	’	PART
brj-23416	29	24	decisions	decision	NOUN
brj-23416	29	25	to	to	PART
brj-23416	29	26	invest	invest	VERB
brj-23416	29	27	or	or	CCONJ
brj-23416	29	28	abandon	abandon	VERB
brj-23416	29	29	investment	investment	NOUN
brj-23416	29	30	in	in	ADP
brj-23416	29	31	the	the	DET
brj-23416	29	32	capital	capital	NOUN
brj-23416	29	33	markets	market	NOUN
brj-23416	29	34	.	.	PUNCT
brj-23416	30	1	additionally	additionally	ADV
brj-23416	30	2	,	,	PUNCT
brj-23416	30	3	indices	index	NOUN
brj-23416	30	4	are	be	AUX
brj-23416	30	5	one	one	NUM
brj-23416	30	6	of	of	ADP
brj-23416	30	7	the	the	DET
brj-23416	30	8	indicators	indicator	NOUN
brj-23416	30	9	that	that	SCONJ
brj-23416	30	10	foreign	foreign	ADJ
brj-23416	30	11	investors	investor	NOUN
brj-23416	30	12	use	use	VERB
brj-23416	30	13	to	to	PART
brj-23416	30	14	analyze	analyze	VERB
brj-23416	30	15	the	the	DET
brj-23416	30	16	general	general	ADJ
brj-23416	30	17	economic	economic	ADJ
brj-23416	30	18	and	and	CCONJ
brj-23416	30	19	political	political	ADJ
brj-23416	30	20	performance	performance	NOUN
brj-23416	30	21	of	of	ADP
brj-23416	30	22	the	the	DET
brj-23416	30	23	country	country	NOUN
brj-23416	30	24	in	in	ADP
brj-23416	30	25	which	which	PRON
brj-23416	30	26	they	they	PRON
brj-23416	30	27	intend	intend	VERB
brj-23416	30	28	to	to	PART
brj-23416	30	29	invest	invest	VERB
brj-23416	30	30	(	(	PUNCT
brj-23416	30	31	bayramoğlu	bayramoğlu	NOUN
brj-23416	30	32	2007	2007	NUM
brj-23416	30	33	)	)	PUNCT
brj-23416	30	34	.	.	PUNCT
brj-23416	31	1	predicting	predict	VERB
brj-23416	31	2	how	how	SCONJ
brj-23416	31	3	the	the	DET
brj-23416	31	4	stock	stock	NOUN
brj-23416	31	5	market	market	NOUN
brj-23416	31	6	will	will	AUX
brj-23416	31	7	perform	perform	VERB
brj-23416	31	8	is	be	AUX
brj-23416	31	9	one	one	NUM
brj-23416	31	10	of	of	ADP
brj-23416	31	11	the	the	DET
brj-23416	31	12	most	most	ADV
brj-23416	31	13	difficult	difficult	ADJ
brj-23416	31	14	things	thing	NOUN
brj-23416	31	15	to	to	PART
brj-23416	31	16	do	do	VERB
brj-23416	31	17	(	(	PUNCT
brj-23416	31	18	cavalcante	cavalcante	VERB
brj-23416	31	19	et	et	PROPN
brj-23416	31	20	al	al	PROPN
brj-23416	31	21	.	.	PROPN
brj-23416	31	22	2016	2016	NUM
brj-23416	31	23	)	)	PUNCT
brj-23416	31	24	.	.	PUNCT
brj-23416	32	1	nowadays	nowadays	ADV
brj-23416	32	2	,	,	PUNCT
brj-23416	32	3	artificial	artificial	ADJ
brj-23416	32	4	intelligence	intelligence	NOUN
brj-23416	32	5	is	be	AUX
brj-23416	32	6	used	use	VERB
brj-23416	32	7	to	to	PART
brj-23416	32	8	solve	solve	VERB
brj-23416	32	9	uncertainties	uncertainty	NOUN
brj-23416	32	10	in	in	ADP
brj-23416	32	11	matters	matter	NOUN
brj-23416	32	12	such	such	ADJ
brj-23416	32	13	as	as	ADP
brj-23416	32	14	measuring	measure	VERB
brj-23416	32	15	index	index	NOUN
brj-23416	32	16	performances	performance	NOUN
brj-23416	32	17	and	and	CCONJ
brj-23416	32	18	predicting	predict	VERB
brj-23416	32	19	stock	stock	NOUN
brj-23416	32	20	prices	price	NOUN
brj-23416	32	21	(	(	PUNCT
brj-23416	32	22	özcan	özcan	NOUN
brj-23416	32	23	akdağ	akdağ	NOUN
brj-23416	32	24	et	et	PROPN
brj-23416	32	25	al	al	PROPN
brj-23416	32	26	.	.	PROPN
brj-23416	32	27	2022	2022	NUM
brj-23416	32	28	)	)	PUNCT
brj-23416	32	29	.	.	PUNCT
brj-23416	33	1	machine	machine	NOUN
brj-23416	33	2	learning	learning	NOUN
brj-23416	33	3	is	be	AUX
brj-23416	33	4	a	a	DET
brj-23416	33	5	field	field	NOUN
brj-23416	33	6	of	of	ADP
brj-23416	33	7	artificial	artificial	ADJ
brj-23416	33	8	intelligence	intelligence	NOUN
brj-23416	33	9	that	that	PRON
brj-23416	33	10	is	be	AUX
brj-23416	33	11	being	be	AUX
brj-23416	33	12	used	use	VERB
brj-23416	33	13	as	as	ADP
brj-23416	33	14	a	a	DET
brj-23416	33	15	game	game	NOUN
brj-23416	33	16	changer	changer	NOUN
brj-23416	33	17	in	in	ADP
brj-23416	33	18	predicting	predict	VERB
brj-23416	33	19	stock	stock	NOUN
brj-23416	33	20	market	market	NOUN
brj-23416	33	21	index	index	NOUN
brj-23416	33	22	prices	price	NOUN
brj-23416	33	23	(	(	PUNCT
brj-23416	33	24	ravikumar	ravikumar	NOUN
brj-23416	33	25	and	and	CCONJ
brj-23416	33	26	saraf	saraf	PROPN
brj-23416	33	27	2020	2020	NUM
brj-23416	33	28	)	)	PUNCT
brj-23416	33	29	.	.	PUNCT
brj-23416	34	1	research	research	NOUN
brj-23416	34	2	on	on	ADP
brj-23416	34	3	stock	stock	NOUN
brj-23416	34	4	market	market	NOUN
brj-23416	34	5	index	index	NOUN
brj-23416	34	6	predictions	prediction	NOUN
brj-23416	34	7	based	base	VERB
brj-23416	34	8	on	on	ADP
brj-23416	34	9	machine	machine	NOUN
brj-23416	34	10	learning	learn	VERB
brj-23416	34	11	algorithms	algorithm	NOUN
brj-23416	34	12	has	have	AUX
brj-23416	34	13	attracted	attract	VERB
brj-23416	34	14	more	more	ADJ
brj-23416	34	15	attention	attention	NOUN
brj-23416	34	16	recently	recently	ADV
brj-23416	34	17	(	(	PUNCT
brj-23416	34	18	hu	hu	PROPN
brj-23416	34	19	et	et	PROPN
brj-23416	34	20	al	al	PROPN
brj-23416	34	21	.	.	PROPN
brj-23416	34	22	2022	2022	NUM
brj-23416	34	23	)	)	PUNCT
brj-23416	34	24	.	.	PUNCT
brj-23416	35	1	various	various	ADJ
brj-23416	35	2	machine	machine	NOUN
brj-23416	35	3	learning	learning	NOUN
brj-23416	35	4	methods	method	NOUN
brj-23416	35	5	,	,	PUNCT
brj-23416	35	6	such	such	ADJ
brj-23416	35	7	as	as	ADP
brj-23416	35	8	artificial	artificial	ADJ
brj-23416	35	9	neural	neural	ADJ
brj-23416	35	10	networks	network	NOUN
brj-23416	35	11	,	,	PUNCT
brj-23416	35	12	decision	decision	NOUN
brj-23416	35	13	trees	tree	NOUN
brj-23416	35	14	,	,	PUNCT
brj-23416	35	15	k	k	NOUN
brj-23416	35	16	-	-	PUNCT
brj-23416	35	17	nearest	near	ADJ
brj-23416	35	18	neighbors	neighbor	NOUN
brj-23416	35	19	,	,	PUNCT
brj-23416	35	20	support	support	NOUN
brj-23416	35	21	vector	vector	NOUN
brj-23416	35	22	machines	machine	NOUN
brj-23416	35	23	,	,	PUNCT
brj-23416	35	24	support	support	VERB
brj-23416	35	25	vector	vector	NOUN
brj-23416	35	26	regression	regression	NOUN
brj-23416	35	27	,	,	PUNCT
brj-23416	35	28	bagging	bagging	NOUN
brj-23416	35	29	,	,	PUNCT
brj-23416	35	30	gradient	gradient	NOUN
brj-23416	35	31	boosting	boosting	NOUN
brj-23416	35	32	,	,	PUNCT
brj-23416	35	33	random	random	ADJ
brj-23416	35	34	forest	forest	NOUN
brj-23416	35	35	,	,	PUNCT
brj-23416	35	36	etc	etc	X
brj-23416	35	37	.	.	X
brj-23416	35	38	,	,	PUNCT
brj-23416	35	39	are	be	AUX
brj-23416	35	40	being	be	AUX
brj-23416	35	41	used	use	VERB
brj-23416	35	42	to	to	PART
brj-23416	35	43	predict	predict	VERB
brj-23416	35	44	stock	stock	NOUN
brj-23416	35	45	market	market	NOUN
brj-23416	35	46	movement	movement	NOUN
brj-23416	35	47	(	(	PUNCT
brj-23416	35	48	ceylan	ceylan	PROPN
brj-23416	35	49	2018	2018	NUM
brj-23416	35	50	)	)	PUNCT
brj-23416	35	51	.	.	PUNCT
brj-23416	36	1	there	there	PRON
brj-23416	36	2	is	be	VERB
brj-23416	36	3	no	no	DET
brj-23416	36	4	best	good	ADJ
brj-23416	36	5	algorithm	algorithm	NOUN
brj-23416	36	6	that	that	PRON
brj-23416	36	7	can	can	AUX
brj-23416	36	8	predict	predict	VERB
brj-23416	36	9	the	the	DET
brj-23416	36	10	stock	stock	NOUN
brj-23416	36	11	market	market	NOUN
brj-23416	36	12	movement	movement	NOUN
brj-23416	36	13	with	with	ADP
brj-23416	36	14	high	high	ADJ
brj-23416	36	15	accuracy	accuracy	NOUN
brj-23416	36	16	.	.	PUNCT
brj-23416	37	1	therefore	therefore	ADV
brj-23416	37	2	,	,	PUNCT
brj-23416	37	3	performance	performance	NOUN
brj-23416	37	4	analysis	analysis	NOUN
brj-23416	37	5	of	of	ADP
brj-23416	37	6	different	different	ADJ
brj-23416	37	7	machine	machine	NOUN
brj-23416	37	8	learning	learn	VERB
brj-23416	37	9	algorithms	algorithm	NOUN
brj-23416	37	10	is	be	AUX
brj-23416	37	11	needed	need	VERB
brj-23416	37	12	to	to	PART
brj-23416	37	13	reach	reach	VERB
brj-23416	37	14	the	the	DET
brj-23416	37	15	best	good	ADJ
brj-23416	37	16	machine	machine	NOUN
brj-23416	37	17	learning	learn	VERB
brj-23416	37	18	algorithm	algorithm	NOUN
brj-23416	37	19	that	that	PRON
brj-23416	37	20	provides	provide	VERB
brj-23416	37	21	the	the	DET
brj-23416	37	22	most	most	ADV
brj-23416	37	23	optimal	optimal	ADJ
brj-23416	37	24	and	and	CCONJ
brj-23416	37	25	precise	precise	ADJ
brj-23416	37	26	prediction	prediction	NOUN
brj-23416	37	27	of	of	ADP
brj-23416	37	28	stock	stock	NOUN
brj-23416	37	29	market	market	NOUN
brj-23416	37	30	movement	movement	NOUN
brj-23416	37	31	.	.	PUNCT
brj-23416	38	1	the	the	DET
brj-23416	38	2	use	use	NOUN
brj-23416	38	3	of	of	ADP
brj-23416	38	4	different	different	ADJ
brj-23416	38	5	machine	machine	NOUN
brj-23416	38	6	learning	learning	NOUN
brj-23416	38	7	methods	method	NOUN
brj-23416	38	8	has	have	VERB
brj-23416	38	9	important	important	ADJ
brj-23416	38	10	practical	practical	ADJ
brj-23416	38	11	implications	implication	NOUN
brj-23416	38	12	in	in	ADP
brj-23416	38	13	guiding	guide	VERB
brj-23416	38	14	investors	investor	NOUN
brj-23416	38	15	in	in	ADP
brj-23416	38	16	choosing	choose	VERB
brj-23416	38	17	algorithms	algorithm	NOUN
brj-23416	38	18	,	,	PUNCT
brj-23416	38	19	weighing	weigh	VERB
brj-23416	38	20	risk	risk	NOUN
brj-23416	38	21	-	-	PUNCT
brj-23416	38	22	return	return	NOUN
brj-23416	38	23	,	,	PUNCT
brj-23416	38	24	and	and	CCONJ
brj-23416	38	25	making	make	VERB
brj-23416	38	26	more	more	ADV
brj-23416	38	27	rational	rational	ADJ
brj-23416	38	28	investments	investment	NOUN
brj-23416	38	29	(	(	PUNCT
brj-23416	38	30	sakhare	sakhare	NOUN
brj-23416	38	31	and	and	CCONJ
brj-23416	38	32	imambi	imambi	NOUN
brj-23416	38	33	2019	2019	NUM
brj-23416	38	34	;	;	PUNCT
brj-23416	38	35	vijh	vijh	NOUN
brj-23416	38	36	et	et	PROPN
brj-23416	38	37	al	al	PROPN
brj-23416	38	38	.	.	PROPN
brj-23416	38	39	2020	2020	NUM
brj-23416	38	40	;	;	PUNCT
brj-23416	38	41	hu	hu	PROPN
brj-23416	38	42	et	et	PROPN
brj-23416	38	43	al	al	PROPN
brj-23416	38	44	.	.	PROPN
brj-23416	38	45	2022	2022	NUM
brj-23416	38	46	)	)	PUNCT
brj-23416	38	47	.	.	PUNCT
brj-23416	39	1	when	when	SCONJ
brj-23416	39	2	the	the	DET
brj-23416	39	3	literature	literature	NOUN
brj-23416	39	4	on	on	ADP
brj-23416	39	5	machine	machine	NOUN
brj-23416	39	6	learning	learn	VERB
brj-23416	39	7	applications	application	NOUN
brj-23416	39	8	in	in	ADP
brj-23416	39	9	the	the	DET
brj-23416	39	10	forest	forest	NOUN
brj-23416	39	11	-	-	PUNCT
brj-23416	39	12	based	base	VERB
brj-23416	39	13	industry	industry	NOUN
brj-23416	39	14	was	be	AUX
brj-23416	39	15	examined	examine	VERB
brj-23416	39	16	,	,	PUNCT
brj-23416	39	17	it	it	PRON
brj-23416	39	18	was	be	AUX
brj-23416	39	19	found	find	VERB
brj-23416	39	20	that	that	SCONJ
brj-23416	39	21	the	the	DET
brj-23416	39	22	number	number	NOUN
brj-23416	39	23	of	of	ADP
brj-23416	39	24	studies	study	NOUN
brj-23416	39	25	on	on	ADP
brj-23416	39	26	stock	stock	NOUN
brj-23416	39	27	market	market	NOUN
brj-23416	39	28	index	index	NOUN
brj-23416	39	29	value	value	NOUN
brj-23416	39	30	and	and	CCONJ
brj-23416	39	31	stock	stock	NOUN
brj-23416	39	32	price	price	NOUN
brj-23416	39	33	predictions	prediction	NOUN
brj-23416	39	34	in	in	ADP
brj-23416	39	35	the	the	DET
brj-23416	39	36	forest	forest	NOUN
brj-23416	39	37	-	-	PUNCT
brj-23416	39	38	based	base	VERB
brj-23416	39	39	industry	industry	NOUN
brj-23416	39	40	is	be	AUX
brj-23416	39	41	limited	limit	VERB
brj-23416	39	42	.	.	PUNCT
brj-23416	40	1	in	in	ADP
brj-23416	40	2	particular	particular	ADJ
brj-23416	40	3	,	,	PUNCT
brj-23416	40	4	there	there	PRON
brj-23416	40	5	are	be	VERB
brj-23416	40	6	studies	study	NOUN
brj-23416	40	7	on	on	ADP
brj-23416	40	8	price	price	NOUN
brj-23416	40	9	and	and	CCONJ
brj-23416	40	10	demand	demand	NOUN
brj-23416	40	11	forecasting	forecasting	NOUN
brj-23416	40	12	of	of	ADP
brj-23416	40	13	forest	forest	NOUN
brj-23416	40	14	products	product	NOUN
brj-23416	40	15	.	.	PUNCT
brj-23416	41	1	yıldırım	yıldırım	PROPN
brj-23416	41	2	et	et	PROPN
brj-23416	41	3	al	al	PROPN
brj-23416	41	4	.	.	PROPN
brj-23416	42	1	(	(	PUNCT
brj-23416	42	2	2011	2011	NUM
brj-23416	42	3	)	)	PUNCT
brj-23416	42	4	predicted	predict	VERB
brj-23416	42	5	financial	financial	ADJ
brj-23416	42	6	return	return	NOUN
brj-23416	42	7	of	of	ADP
brj-23416	42	8	paper	paper	NOUN
brj-23416	42	9	sector	sector	NOUN
brj-23416	42	10	in	in	ADP
brj-23416	42	11	turkey	turkey	NOUN
brj-23416	42	12	using	use	VERB
brj-23416	42	13	artificial	artificial	ADJ
brj-23416	42	14	neural	neural	ADJ
brj-23416	42	15	networks	network	NOUN
brj-23416	42	16	(	(	PUNCT
brj-23416	42	17	ann	ann	PROPN
brj-23416	42	18	)	)	PUNCT
brj-23416	42	19	and	and	CCONJ
brj-23416	42	20	multiple	multiple	ADJ
brj-23416	42	21	linear	linear	ADJ
brj-23416	42	22	regression	regression	NOUN
brj-23416	42	23	(	(	PUNCT
brj-23416	42	24	mlr	mlr	NOUN
brj-23416	42	25	)	)	PUNCT
brj-23416	42	26	.	.	PUNCT
brj-23416	43	1	as	as	ADP
brj-23416	43	2	a	a	DET
brj-23416	43	3	result	result	NOUN
brj-23416	43	4	of	of	ADP
brj-23416	43	5	the	the	DET
brj-23416	43	6	study	study	NOUN
brj-23416	43	7	,	,	PUNCT
brj-23416	43	8	it	it	PRON
brj-23416	43	9	was	be	AUX
brj-23416	43	10	observed	observe	VERB
brj-23416	43	11	that	that	SCONJ
brj-23416	43	12	although	although	SCONJ
brj-23416	43	13	both	both	DET
brj-23416	43	14	methods	method	NOUN
brj-23416	43	15	provide	provide	VERB
brj-23416	43	16	successful	successful	ADJ
brj-23416	43	17	results	result	NOUN
brj-23416	43	18	,	,	PUNCT
brj-23416	43	19	ann	ann	PROPN
brj-23416	43	20	performed	perform	VERB
brj-23416	43	21	significantly	significantly	ADV
brj-23416	43	22	better	well	ADV
brj-23416	43	23	than	than	ADP
brj-23416	43	24	mlr	mlr	PROPN
brj-23416	43	25	.	.	PUNCT
brj-23416	44	1	yücesan	yücesan	PROPN
brj-23416	44	2	et	et	PROPN
brj-23416	44	3	al	al	PROPN
brj-23416	44	4	.	.	PROPN
brj-23416	45	1	(	(	PUNCT
brj-23416	45	2	2017	2017	NUM
brj-23416	45	3	)	)	PUNCT
brj-23416	45	4	predicted	predict	VERB
brj-23416	45	5	the	the	DET
brj-23416	45	6	monthly	monthly	ADJ
brj-23416	45	7	sales	sale	NOUN
brj-23416	45	8	of	of	ADP
brj-23416	45	9	a	a	DET
brj-23416	45	10	furniture	furniture	NOUN
brj-23416	45	11	manufacturer	manufacturer	NOUN
brj-23416	45	12	located	locate	VERB
brj-23416	45	13	in	in	ADP
brj-23416	45	14	the	the	DET
brj-23416	45	15	black	black	ADJ
brj-23416	45	16	sea	sea	NOUN
brj-23416	45	17	region	region	NOUN
brj-23416	45	18	of	of	ADP
brj-23416	45	19	turkey	turkey	NOUN
brj-23416	45	20	with	with	ADP
brj-23416	45	21	ann	ann	PROPN
brj-23416	45	22	model	model	NOUN
brj-23416	45	23	based	base	VERB
brj-23416	45	24	on	on	ADP
brj-23416	45	25	bayesian	bayesian	NOUN
brj-23416	45	26	rules	rule	NOUN
brj-23416	45	27	training	training	NOUN
brj-23416	45	28	in	in	ADP
brj-23416	45	29	the	the	DET
brj-23416	45	30	study	study	NOUN
brj-23416	45	31	.	.	PUNCT
brj-23416	46	1	it	it	PRON
brj-23416	46	2	has	have	AUX
brj-23416	46	3	been	be	AUX
brj-23416	46	4	concluded	conclude	VERB
brj-23416	46	5	that	that	SCONJ
brj-23416	46	6	the	the	DET
brj-23416	46	7	ann	ann	PROPN
brj-23416	46	8	model	model	NOUN
brj-23416	46	9	is	be	AUX
brj-23416	46	10	an	an	DET
brj-23416	46	11	applicable	applicable	ADJ
brj-23416	46	12	model	model	NOUN
brj-23416	46	13	in	in	ADP
brj-23416	46	14	predicting	predict	VERB
brj-23416	46	15	the	the	DET
brj-23416	46	16	sales	sale	NOUN
brj-23416	46	17	of	of	ADP
brj-23416	46	18	the	the	DET
brj-23416	46	19	furniture	furniture	NOUN
brj-23416	46	20	factory	factory	NOUN
brj-23416	46	21	.	.	PUNCT
brj-23416	47	1	verly	verly	ADV
brj-23416	47	2	lopes	lope	VERB
brj-23416	47	3	et	et	PROPN
brj-23416	47	4	al	al	PROPN
brj-23416	47	5	.	.	PROPN
brj-23416	48	1	(	(	PUNCT
brj-23416	48	2	2021	2021	NUM
brj-23416	48	3	)	)	PUNCT
brj-23416	48	4	predicted	predict	VERB
brj-23416	48	5	random	random	ADJ
brj-23416	48	6	length	length	NOUN
brj-23416	48	7	lumber	lumber	NOUN
brj-23416	48	8	stock	stock	NOUN
brj-23416	48	9	price	price	NOUN
brj-23416	48	10	using	use	VERB
brj-23416	48	11	lstm	lstm	ADJ
brj-23416	48	12	artificial	artificial	ADJ
brj-23416	48	13	recurrent	recurrent	ADJ
brj-23416	48	14	neural	neural	ADJ
brj-23416	48	15	networks	network	NOUN
brj-23416	48	16	.	.	PUNCT
brj-23416	49	1	as	as	ADP
brj-23416	49	2	a	a	DET
brj-23416	49	3	result	result	NOUN
brj-23416	49	4	of	of	ADP
brj-23416	49	5	the	the	DET
brj-23416	49	6	study	study	NOUN
brj-23416	49	7	,	,	PUNCT
brj-23416	49	8	it	it	PRON
brj-23416	49	9	was	be	AUX
brj-23416	49	10	observed	observe	VERB
brj-23416	49	11	that	that	SCONJ
brj-23416	49	12	the	the	DET
brj-23416	49	13	lstm	lstm	PROPN
brj-23416	49	14	network	network	NOUN
brj-23416	49	15	can	can	AUX
brj-23416	49	16	efficiently	efficiently	ADV
brj-23416	49	17	capture	capture	VERB
brj-23416	49	18	non	non	ADJ
brj-23416	49	19	-	-	ADJ
brj-23416	49	20	linear	linear	ADJ
brj-23416	49	21	temporal	temporal	ADJ
brj-23416	49	22	relationships	relationship	NOUN
brj-23416	49	23	.	.	PUNCT
brj-23416	50	1	kurniawan	kurniawan	PROPN
brj-23416	50	2	et	et	PROPN
brj-23416	50	3	al	al	PROPN
brj-23416	50	4	.	.	PROPN
brj-23416	51	1	(	(	PUNCT
brj-23416	51	2	2021	2021	NUM
brj-23416	51	3	)	)	PUNCT
brj-23416	51	4	aimed	aim	VERB
brj-23416	51	5	to	to	PART
brj-23416	51	6	predict	predict	VERB
brj-23416	51	7	the	the	DET
brj-23416	51	8	raw	raw	ADJ
brj-23416	51	9	paper	paper	NOUN
brj-23416	51	10	material	material	NOUN
brj-23416	51	11	of	of	ADP
brj-23416	51	12	the	the	DET
brj-23416	51	13	printing	print	VERB
brj-23416	51	14	company	company	NOUN
brj-23416	51	15	peer	peer	NOUN
brj-23416	51	16	-	-	PUNCT
brj-23416	51	17	reviewed	review	VERB
brj-23416	51	18	article	article	NOUN
brj-23416	51	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	51	20	akyüz	akyüz	PROPN
brj-23416	51	21	et	et	PROPN
brj-23416	51	22	al	al	PROPN
brj-23416	51	23	.	.	PROPN
brj-23416	52	1	(	(	PUNCT
brj-23416	52	2	2024	2024	NUM
brj-23416	52	3	)	)	PUNCT
brj-23416	52	4	.	.	PUNCT
brj-23416	53	1	“	"	PUNCT
brj-23416	53	2	stock	stock	NOUN
brj-23416	53	3	exchange	exchange	NOUN
brj-23416	53	4	values	value	NOUN
brj-23416	53	5	,	,	PUNCT
brj-23416	53	6	”	"	PUNCT
brj-23416	53	7	bioresources	bioresource	NOUN
brj-23416	53	8	19(3	19(3	NUM
brj-23416	53	9	)	)	PUNCT
brj-23416	53	10	,	,	PUNCT
brj-23416	53	11	5141	5141	NUM
brj-23416	53	12	-	-	SYM
brj-23416	53	13	5157	5157	NUM
brj-23416	53	14	.	.	PUNCT
brj-23416	54	1	5143	5143	NUM
brj-23416	54	2	with	with	ADP
brj-23416	54	3	the	the	DET
brj-23416	54	4	short	short	ADJ
brj-23416	54	5	-	-	PUNCT
brj-23416	54	6	term	term	NOUN
brj-23416	54	7	memory	memory	NOUN
brj-23416	54	8	method	method	NOUN
brj-23416	54	9	,	,	PUNCT
brj-23416	54	10	and	and	CCONJ
brj-23416	54	11	it	it	PRON
brj-23416	54	12	was	be	AUX
brj-23416	54	13	determined	determine	VERB
brj-23416	54	14	that	that	SCONJ
brj-23416	54	15	the	the	DET
brj-23416	54	16	method	method	NOUN
brj-23416	54	17	could	could	AUX
brj-23416	54	18	be	be	AUX
brj-23416	54	19	used	use	VERB
brj-23416	54	20	in	in	ADP
brj-23416	54	21	raw	raw	ADJ
brj-23416	54	22	paper	paper	NOUN
brj-23416	54	23	material	material	NOUN
brj-23416	54	24	prediction	prediction	NOUN
brj-23416	54	25	.	.	PUNCT
brj-23416	55	1	in	in	ADP
brj-23416	55	2	the	the	DET
brj-23416	55	3	master	master	NOUN
brj-23416	55	4	's	's	PART
brj-23416	55	5	thesis	thesis	NOUN
brj-23416	55	6	by	by	ADP
brj-23416	55	7	ghorbanali	ghorbanali	PROPN
brj-23416	55	8	(	(	PUNCT
brj-23416	55	9	2022	2022	NUM
brj-23416	55	10	)	)	PUNCT
brj-23416	55	11	,	,	PUNCT
brj-23416	55	12	the	the	DET
brj-23416	55	13	prices	price	NOUN
brj-23416	55	14	of	of	ADP
brj-23416	55	15	products	product	NOUN
brj-23416	55	16	sold	sell	VERB
brj-23416	55	17	from	from	ADP
brj-23416	55	18	a	a	DET
brj-23416	55	19	large	large	ADJ
brj-23416	55	20	furniture	furniture	NOUN
brj-23416	55	21	company	company	NOUN
brj-23416	55	22	were	be	AUX
brj-23416	55	23	predicted	predict	VERB
brj-23416	55	24	using	use	VERB
brj-23416	55	25	different	different	ADJ
brj-23416	55	26	machine	machine	NOUN
brj-23416	55	27	learning	learning	NOUN
brj-23416	55	28	methods	method	NOUN
brj-23416	55	29	like	like	ADP
brj-23416	55	30	linear	linear	PROPN
brj-23416	55	31	regression	regression	NOUN
brj-23416	55	32	,	,	PUNCT
brj-23416	55	33	bayesian	bayesian	NOUN
brj-23416	55	34	ridge	ridge	NOUN
brj-23416	55	35	regression	regression	NOUN
brj-23416	55	36	,	,	PUNCT
brj-23416	55	37	light	light	ADJ
brj-23416	55	38	gbm	gbm	NOUN
brj-23416	55	39	,	,	PUNCT
brj-23416	55	40	and	and	CCONJ
brj-23416	55	41	xgboost	xgboost	X
brj-23416	55	42	.	.	PUNCT
brj-23416	56	1	results	result	NOUN
brj-23416	56	2	showed	show	VERB
brj-23416	56	3	that	that	SCONJ
brj-23416	56	4	xgboost	xgboost	PROPN
brj-23416	56	5	machine	machine	NOUN
brj-23416	56	6	learning	learning	NOUN
brj-23416	56	7	model	model	NOUN
brj-23416	56	8	achieved	achieve	VERB
brj-23416	56	9	better	well	ADJ
brj-23416	56	10	prediction	prediction	NOUN
brj-23416	56	11	than	than	ADP
brj-23416	56	12	other	other	ADJ
brj-23416	56	13	models	model	NOUN
brj-23416	56	14	.	.	PUNCT
brj-23416	57	1	yaneva	yaneva	PROPN
brj-23416	57	2	and	and	CCONJ
brj-23416	57	3	kulina	kulina	PROPN
brj-23416	57	4	(	(	PUNCT
brj-23416	57	5	2023	2023	NUM
brj-23416	57	6	)	)	PUNCT
brj-23416	57	7	aimed	aim	VERB
brj-23416	57	8	to	to	PART
brj-23416	57	9	predict	predict	VERB
brj-23416	57	10	furniture	furniture	NOUN
brj-23416	57	11	demand	demand	NOUN
brj-23416	57	12	with	with	ADP
brj-23416	57	13	machine	machine	NOUN
brj-23416	57	14	learning	learn	VERB
brj-23416	57	15	techniques	technique	NOUN
brj-23416	57	16	.	.	PUNCT
brj-23416	58	1	for	for	ADP
brj-23416	58	2	this	this	DET
brj-23416	58	3	purpose	purpose	NOUN
brj-23416	58	4	,	,	PUNCT
brj-23416	58	5	daily	daily	ADJ
brj-23416	58	6	data	datum	NOUN
brj-23416	58	7	of	of	ADP
brj-23416	58	8	a	a	DET
brj-23416	58	9	large	large	ADJ
brj-23416	58	10	furniture	furniture	NOUN
brj-23416	58	11	manufacturer	manufacturer	NOUN
brj-23416	58	12	in	in	ADP
brj-23416	58	13	bulgaria	bulgaria	PROPN
brj-23416	58	14	was	be	AUX
brj-23416	58	15	used	use	VERB
brj-23416	58	16	.	.	PUNCT
brj-23416	59	1	in	in	ADP
brj-23416	59	2	the	the	DET
brj-23416	59	3	study	study	NOUN
brj-23416	59	4	predicting	predict	VERB
brj-23416	59	5	timber	timber	NOUN
brj-23416	59	6	prices	price	NOUN
brj-23416	59	7	in	in	ADP
brj-23416	59	8	poland	poland	PROPN
brj-23416	59	9	with	with	ADP
brj-23416	59	10	artificial	artificial	ADJ
brj-23416	59	11	neural	neural	ADJ
brj-23416	59	12	networks	network	NOUN
brj-23416	59	13	(	(	PUNCT
brj-23416	59	14	rbf	rbf	PROPN
brj-23416	59	15	and	and	CCONJ
brj-23416	59	16	mlp	mlp	PROPN
brj-23416	59	17	)	)	PUNCT
brj-23416	59	18	and	and	CCONJ
brj-23416	59	19	classical	classical	ADJ
brj-23416	59	20	models	model	NOUN
brj-23416	59	21	(	(	PUNCT
brj-23416	59	22	arima	arima	NOUN
brj-23416	59	23	,	,	PUNCT
brj-23416	59	24	ets	et	NOUN
brj-23416	59	25	,	,	PUNCT
brj-23416	59	26	bats	bat	NOUN
brj-23416	59	27	,	,	PUNCT
brj-23416	59	28	and	and	CCONJ
brj-23416	59	29	tbats	tbat	NOUN
brj-23416	59	30	)	)	PUNCT
brj-23416	59	31	by	by	ADP
brj-23416	59	32	kozuch	kozuch	PROPN
brj-23416	59	33	et	et	PROPN
brj-23416	59	34	al	al	PROPN
brj-23416	59	35	.	.	PROPN
brj-23416	59	36	(	(	PUNCT
brj-23416	59	37	2023	2023	NUM
brj-23416	59	38	)	)	PUNCT
brj-23416	59	39	,	,	PUNCT
brj-23416	59	40	they	they	PRON
brj-23416	59	41	stated	state	VERB
brj-23416	59	42	that	that	SCONJ
brj-23416	59	43	neural	neural	ADJ
brj-23416	59	44	networks	network	NOUN
brj-23416	59	45	provide	provide	VERB
brj-23416	59	46	more	more	ADV
brj-23416	59	47	accurate	accurate	ADJ
brj-23416	59	48	results	result	NOUN
brj-23416	59	49	compared	compare	VERB
brj-23416	59	50	to	to	ADP
brj-23416	59	51	classical	classical	ADJ
brj-23416	59	52	methods	method	NOUN
brj-23416	59	53	that	that	PRON
brj-23416	59	54	are	be	AUX
brj-23416	59	55	widely	widely	ADV
brj-23416	59	56	used	use	VERB
brj-23416	59	57	in	in	ADP
brj-23416	59	58	predicting	predict	VERB
brj-23416	59	59	timber	timber	NOUN
brj-23416	59	60	prices	price	NOUN
brj-23416	59	61	.	.	PUNCT
brj-23416	60	1	bardak	bardak	PROPN
brj-23416	60	2	(	(	PUNCT
brj-23416	60	3	2023	2023	NUM
brj-23416	60	4	)	)	PUNCT
brj-23416	60	5	tried	try	VERB
brj-23416	60	6	to	to	PART
brj-23416	60	7	predict	predict	VERB
brj-23416	60	8	the	the	DET
brj-23416	60	9	prices	price	NOUN
brj-23416	60	10	of	of	ADP
brj-23416	60	11	bookcase	bookcase	NOUN
brj-23416	60	12	and	and	CCONJ
brj-23416	60	13	dresser	dresser	NOUN
brj-23416	60	14	type	type	NOUN
brj-23416	60	15	furniture	furniture	NOUN
brj-23416	60	16	using	use	VERB
brj-23416	60	17	data	datum	NOUN
brj-23416	60	18	that	that	PRON
brj-23416	60	19	obtained	obtain	VERB
brj-23416	60	20	different	different	ADJ
brj-23416	60	21	public	public	ADJ
brj-23416	60	22	e	e	NOUN
brj-23416	60	23	-	-	NOUN
brj-23416	60	24	commerce	commerce	NOUN
brj-23416	60	25	sites	site	NOUN
brj-23416	60	26	in	in	ADP
brj-23416	60	27	the	the	DET
brj-23416	60	28	united	united	PROPN
brj-23416	60	29	states	states	PROPN
brj-23416	60	30	and	and	CCONJ
brj-23416	60	31	deep	deep	ADJ
brj-23416	60	32	learning	learning	NOUN
brj-23416	60	33	and	and	CCONJ
brj-23416	60	34	random	random	ADJ
brj-23416	60	35	forest	forest	NOUN
brj-23416	60	36	algorithms	algorithm	NOUN
brj-23416	60	37	.	.	PUNCT
brj-23416	61	1	it	it	PRON
brj-23416	61	2	was	be	AUX
brj-23416	61	3	concluded	conclude	VERB
brj-23416	61	4	that	that	SCONJ
brj-23416	61	5	deep	deep	ADJ
brj-23416	61	6	learning	learning	NOUN
brj-23416	61	7	and	and	CCONJ
brj-23416	61	8	random	random	ADJ
brj-23416	61	9	forest	forest	NOUN
brj-23416	61	10	algorithms	algorithm	NOUN
brj-23416	61	11	are	be	AUX
brj-23416	61	12	suitable	suitable	ADJ
brj-23416	61	13	for	for	ADP
brj-23416	61	14	predicting	predict	VERB
brj-23416	61	15	furniture	furniture	NOUN
brj-23416	61	16	prices	price	NOUN
brj-23416	61	17	.	.	PUNCT
brj-23416	62	1	studies	study	NOUN
brj-23416	62	2	on	on	ADP
brj-23416	62	3	different	different	ADJ
brj-23416	62	4	stock	stock	NOUN
brj-23416	62	5	market	market	NOUN
brj-23416	62	6	index	index	NOUN
brj-23416	62	7	prediction	prediction	NOUN
brj-23416	62	8	with	with	ADP
brj-23416	62	9	machine	machine	NOUN
brj-23416	62	10	learning	learning	NOUN
brj-23416	62	11	methods	method	NOUN
brj-23416	62	12	can	can	AUX
brj-23416	62	13	be	be	AUX
brj-23416	62	14	listed	list	VERB
brj-23416	62	15	as	as	ADP
brj-23416	62	16	:	:	PUNCT
brj-23416	62	17	prediction	prediction	NOUN
brj-23416	62	18	of	of	ADP
brj-23416	62	19	korea	korea	PROPN
brj-23416	62	20	composite	composite	ADJ
brj-23416	62	21	stock	stock	NOUN
brj-23416	62	22	price	price	NOUN
brj-23416	62	23	index	index	NOUN
brj-23416	62	24	200	200	NUM
brj-23416	62	25	(	(	PUNCT
brj-23416	62	26	pyo	pyo	PROPN
brj-23416	62	27	et	et	PROPN
brj-23416	62	28	al	al	PROPN
brj-23416	62	29	.	.	PROPN
brj-23416	62	30	2017	2017	NUM
brj-23416	62	31	)	)	PUNCT
brj-23416	62	32	,	,	PUNCT
brj-23416	62	33	prediction	prediction	NOUN
brj-23416	62	34	of	of	ADP
brj-23416	62	35	swedish	swedish	ADJ
brj-23416	62	36	omx	omx	PROPN
brj-23416	62	37	30	30	NUM
brj-23416	62	38	,	,	PUNCT
brj-23416	62	39	british	british	PROPN
brj-23416	62	40	ftse	ftse	PROPN
brj-23416	62	41	100	100	NUM
brj-23416	62	42	,	,	PUNCT
brj-23416	62	43	and	and	CCONJ
brj-23416	62	44	australian	australian	ADJ
brj-23416	62	45	s&p	s&p	PROPN
brj-23416	62	46	/	/	SYM
brj-23416	62	47	asx	asx	NOUN
brj-23416	62	48	200	200	NUM
brj-23416	62	49	indexes	index	NOUN
brj-23416	62	50	(	(	PUNCT
brj-23416	62	51	johnsson	johnsson	PROPN
brj-23416	62	52	2018	2018	NUM
brj-23416	62	53	)	)	PUNCT
brj-23416	62	54	,	,	PUNCT
brj-23416	62	55	prediction	prediction	NOUN
brj-23416	62	56	of	of	ADP
brj-23416	62	57	japanese	japanese	ADJ
brj-23416	62	58	nikkei	nikkei	PROPN
brj-23416	62	59	225	225	NUM
brj-23416	62	60	and	and	CCONJ
brj-23416	62	61	japanese	japanese	ADJ
brj-23416	62	62	nikkei	nikkei	NOUN
brj-23416	62	63	400	400	NUM
brj-23416	62	64	indexes	index	NOUN
brj-23416	62	65	(	(	PUNCT
brj-23416	62	66	harahap	harahap	PROPN
brj-23416	62	67	et	et	PROPN
brj-23416	62	68	al	al	PROPN
brj-23416	62	69	.	.	PROPN
brj-23416	62	70	2020	2020	NUM
brj-23416	62	71	)	)	PUNCT
brj-23416	62	72	,	,	PUNCT
brj-23416	62	73	prediction	prediction	NOUN
brj-23416	62	74	of	of	ADP
brj-23416	62	75	the	the	DET
brj-23416	62	76	bist	bist	NOUN
brj-23416	62	77	30	30	NUM
brj-23416	62	78	,	,	PUNCT
brj-23416	62	79	bist	bist	NOUN
brj-23416	62	80	50	50	NUM
brj-23416	62	81	and	and	CCONJ
brj-23416	62	82	bist	bist	NOUN
brj-23416	62	83	100	100	NUM
brj-23416	62	84	price	price	NOUN
brj-23416	62	85	indices	index	NOUN
brj-23416	62	86	(	(	PUNCT
brj-23416	62	87	yiğit	yiğit	PROPN
brj-23416	62	88	et	et	PROPN
brj-23416	62	89	al	al	PROPN
brj-23416	62	90	.	.	PROPN
brj-23416	62	91	2020	2020	NUM
brj-23416	62	92	)	)	PUNCT
brj-23416	62	93	,	,	PUNCT
brj-23416	62	94	prediction	prediction	NOUN
brj-23416	62	95	of	of	ADP
brj-23416	62	96	s&p	s&p	PROPN
brj-23416	62	97	500	500	NUM
brj-23416	62	98	index	index	NOUN
brj-23416	62	99	(	(	PUNCT
brj-23416	62	100	abraham	abraham	PROPN
brj-23416	62	101	2021	2021	NUM
brj-23416	62	102	)	)	PUNCT
brj-23416	62	103	,	,	PUNCT
brj-23416	62	104	prediction	prediction	NOUN
brj-23416	62	105	of	of	ADP
brj-23416	62	106	national	national	PROPN
brj-23416	62	107	association	association	PROPN
brj-23416	62	108	of	of	ADP
brj-23416	62	109	securities	security	NOUN
brj-23416	62	110	dealers	dealer	NOUN
brj-23416	62	111	automate	automate	VERB
brj-23416	62	112	(	(	PUNCT
brj-23416	62	113	nasdaq	nasdaq	PROPN
brj-23416	62	114	)	)	PUNCT
brj-23416	62	115	,	,	PUNCT
brj-23416	62	116	new	new	PROPN
brj-23416	62	117	york	york	PROPN
brj-23416	62	118	stock	stock	PROPN
brj-23416	62	119	exchange	exchange	PROPN
brj-23416	62	120	(	(	PUNCT
brj-23416	62	121	nyse	nyse	PROPN
brj-23416	62	122	)	)	PUNCT
brj-23416	62	123	,	,	PUNCT
brj-23416	62	124	nikkei	nikkei	NOUN
brj-23416	62	125	,	,	PUNCT
brj-23416	62	126	and	and	CCONJ
brj-23416	62	127	financial	financial	ADJ
brj-23416	62	128	time	time	NOUN
brj-23416	62	129	stock	stock	PROPN
brj-23416	62	130	exchange	exchange	PROPN
brj-23416	62	131	(	(	PUNCT
brj-23416	62	132	ftse	ftse	NOUN
brj-23416	62	133	)	)	PUNCT
brj-23416	62	134	index	index	NOUN
brj-23416	62	135	(	(	PUNCT
brj-23416	62	136	subasi	subasi	NOUN
brj-23416	62	137	et	et	PROPN
brj-23416	62	138	al	al	PROPN
brj-23416	62	139	.	.	PROPN
brj-23416	62	140	2021	2021	NUM
brj-23416	62	141	)	)	PUNCT
brj-23416	62	142	,	,	PUNCT
brj-23416	62	143	prediction	prediction	NOUN
brj-23416	62	144	of	of	ADP
brj-23416	62	145	bist	bist	ADJ
brj-23416	62	146	transportation	transportation	NOUN
brj-23416	62	147	index	index	NOUN
brj-23416	62	148	(	(	PUNCT
brj-23416	62	149	özcan	özcan	NOUN
brj-23416	62	150	akdağ	akdağ	NOUN
brj-23416	62	151	et	et	PROPN
brj-23416	62	152	al	al	PROPN
brj-23416	62	153	.	.	PROPN
brj-23416	62	154	2022	2022	NUM
brj-23416	62	155	)	)	PUNCT
brj-23416	62	156	,	,	PUNCT
brj-23416	62	157	prediction	prediction	NOUN
brj-23416	62	158	of	of	ADP
brj-23416	62	159	indian	indian	ADJ
brj-23416	62	160	stock	stock	NOUN
brj-23416	62	161	market	market	NOUN
brj-23416	62	162	nifty	nifty	ADJ
brj-23416	62	163	50	50	NUM
brj-23416	62	164	index	index	NOUN
brj-23416	62	165	(	(	PUNCT
brj-23416	62	166	singh	singh	NOUN
brj-23416	62	167	2022	2022	NUM
brj-23416	62	168	)	)	PUNCT
brj-23416	62	169	,	,	PUNCT
brj-23416	62	170	prediction	prediction	NOUN
brj-23416	62	171	of	of	ADP
brj-23416	62	172	the	the	DET
brj-23416	62	173	dow	dow	PROPN
brj-23416	62	174	jones	jones	PROPN
brj-23416	62	175	stock	stock	PROPN
brj-23416	62	176	index	index	PROPN
brj-23416	62	177	movement	movement	NOUN
brj-23416	62	178	(	(	PUNCT
brj-23416	62	179	alihodzic	alihodzic	ADV
brj-23416	62	180	et	et	NOUN
brj-23416	62	181	al	al	PROPN
brj-23416	62	182	.	.	PROPN
brj-23416	62	183	2022	2022	NUM
brj-23416	62	184	)	)	PUNCT
brj-23416	62	185	,	,	PUNCT
brj-23416	62	186	prediction	prediction	NOUN
brj-23416	62	187	of	of	ADP
brj-23416	62	188	bist	bist	ADJ
brj-23416	62	189	100	100	NUM
brj-23416	62	190	index	index	NOUN
brj-23416	62	191	(	(	PUNCT
brj-23416	62	192	ünvan	ünvan	NOUN
brj-23416	62	193	and	and	CCONJ
brj-23416	62	194	ergenç	ergenç	NOUN
brj-23416	62	195	2023	2023	NUM
brj-23416	62	196	)	)	PUNCT
brj-23416	62	197	,	,	PUNCT
brj-23416	62	198	prediction	prediction	NOUN
brj-23416	62	199	of	of	ADP
brj-23416	62	200	borsa	borsa	PROPN
brj-23416	62	201	istanbul	istanbul	PROPN
brj-23416	62	202	banks	banks	PROPN
brj-23416	62	203	index	index	NOUN
brj-23416	62	204	(	(	PUNCT
brj-23416	62	205	armağan	armağan	PROPN
brj-23416	62	206	2023	2023	NUM
brj-23416	62	207	)	)	PUNCT
brj-23416	62	208	,	,	PUNCT
brj-23416	62	209	prediction	prediction	NOUN
brj-23416	62	210	of	of	ADP
brj-23416	62	211	taiwan	taiwan	PROPN
brj-23416	62	212	50	50	NUM
brj-23416	62	213	exchange	exchange	NOUN
brj-23416	62	214	traded	trade	VERB
brj-23416	62	215	funds	fund	NOUN
brj-23416	62	216	(	(	PUNCT
brj-23416	62	217	etf	etf	NOUN
brj-23416	62	218	)	)	PUNCT
brj-23416	62	219	index	index	NOUN
brj-23416	62	220	(	(	PUNCT
brj-23416	62	221	fan	fan	NOUN
brj-23416	62	222	et	et	PROPN
brj-23416	62	223	al	al	PROPN
brj-23416	62	224	.	.	PROPN
brj-23416	62	225	2024	2024	NUM
brj-23416	62	226	)	)	PUNCT
brj-23416	62	227	,	,	PUNCT
brj-23416	62	228	prediction	prediction	NOUN
brj-23416	62	229	of	of	ADP
brj-23416	62	230	masi	masi	PROPN
brj-23416	62	231	,	,	PUNCT
brj-23416	62	232	cac	cac	PROPN
brj-23416	62	233	40	40	NUM
brj-23416	62	234	,	,	PUNCT
brj-23416	62	235	dax	dax	PROPN
brj-23416	62	236	,	,	PUNCT
brj-23416	62	237	ftse	ftse	PROPN
brj-23416	62	238	250	250	NUM
brj-23416	62	239	,	,	PUNCT
brj-23416	62	240	nasdaq	nasdaq	NOUN
brj-23416	62	241	,	,	PUNCT
brj-23416	62	242	and	and	CCONJ
brj-23416	62	243	hkex	hkex	ADJ
brj-23416	62	244	indexes	index	NOUN
brj-23416	62	245	,	,	PUNCT
brj-23416	62	246	representing	represent	VERB
brj-23416	62	247	the	the	DET
brj-23416	62	248	moroccan	moroccan	ADJ
brj-23416	62	249	,	,	PUNCT
brj-23416	62	250	french	french	ADJ
brj-23416	62	251	,	,	PUNCT
brj-23416	62	252	german	german	ADJ
brj-23416	62	253	,	,	PUNCT
brj-23416	62	254	british	british	PROPN
brj-23416	62	255	,	,	PUNCT
brj-23416	62	256	us	we	PRON
brj-23416	62	257	,	,	PUNCT
brj-23416	62	258	and	and	CCONJ
brj-23416	62	259	hong	hong	PROPN
brj-23416	62	260	kong	kong	PROPN
brj-23416	62	261	(	(	PUNCT
brj-23416	62	262	oukhouya	oukhouya	PROPN
brj-23416	62	263	et	et	PROPN
brj-23416	62	264	al	al	PROPN
brj-23416	62	265	.	.	PROPN
brj-23416	62	266	2024	2024	NUM
brj-23416	62	267	)	)	PUNCT
brj-23416	62	268	.	.	PUNCT
brj-23416	63	1	in	in	ADP
brj-23416	63	2	order	order	NOUN
brj-23416	63	3	to	to	PART
brj-23416	63	4	be	be	AUX
brj-23416	63	5	useful	useful	ADJ
brj-23416	63	6	to	to	ADP
brj-23416	63	7	sector	sector	NOUN
brj-23416	63	8	representatives	representative	NOUN
brj-23416	63	9	,	,	PUNCT
brj-23416	63	10	current	current	ADJ
brj-23416	63	11	and	and	CCONJ
brj-23416	63	12	potential	potential	ADJ
brj-23416	63	13	investors	investor	NOUN
brj-23416	63	14	,	,	PUNCT
brj-23416	63	15	and	and	CCONJ
brj-23416	63	16	other	other	ADJ
brj-23416	63	17	individuals	individual	NOUN
brj-23416	63	18	and	and	CCONJ
brj-23416	63	19	organizations	organization	NOUN
brj-23416	63	20	affected	affect	VERB
brj-23416	63	21	by	by	ADP
brj-23416	63	22	changes	change	NOUN
brj-23416	63	23	taking	take	VERB
brj-23416	63	24	place	place	NOUN
brj-23416	63	25	in	in	ADP
brj-23416	63	26	the	the	DET
brj-23416	63	27	industry	industry	NOUN
brj-23416	63	28	in	in	ADP
brj-23416	63	29	the	the	DET
brj-23416	63	30	forest	forest	NOUN
brj-23416	63	31	products	product	NOUN
brj-23416	63	32	,	,	PUNCT
brj-23416	63	33	furniture	furniture	NOUN
brj-23416	63	34	,	,	PUNCT
brj-23416	63	35	paper	paper	NOUN
brj-23416	63	36	and	and	CCONJ
brj-23416	63	37	publishing	publishing	NOUN
brj-23416	63	38	sectors	sector	NOUN
brj-23416	63	39	,	,	PUNCT
brj-23416	63	40	a	a	DET
brj-23416	63	41	prediction	prediction	NOUN
brj-23416	63	42	study	study	NOUN
brj-23416	63	43	was	be	AUX
brj-23416	63	44	made	make	VERB
brj-23416	63	45	on	on	ADP
brj-23416	63	46	the	the	DET
brj-23416	63	47	xkagt	xkagt	PROPN
brj-23416	63	48	index	index	NOUN
brj-23416	63	49	values	value	NOUN
brj-23416	63	50	by	by	ADP
brj-23416	63	51	creating	create	VERB
brj-23416	63	52	artificial	artificial	ADJ
brj-23416	63	53	neural	neural	ADJ
brj-23416	63	54	network	network	NOUN
brj-23416	63	55	,	,	PUNCT
brj-23416	63	56	gradient	gradient	ADJ
brj-23416	63	57	boosting	boost	VERB
brj-23416	63	58	machine	machine	NOUN
brj-23416	63	59	,	,	PUNCT
brj-23416	63	60	random	random	ADJ
brj-23416	63	61	forest	forest	NOUN
brj-23416	63	62	,	,	PUNCT
brj-23416	63	63	and	and	CCONJ
brj-23416	63	64	k	k	X
brj-23416	63	65	-	-	PUNCT
brj-23416	63	66	nearest	near	ADJ
brj-23416	63	67	neighbor	neighbor	NOUN
brj-23416	63	68	models	model	NOUN
brj-23416	63	69	with	with	ADP
brj-23416	63	70	monthly	monthly	ADJ
brj-23416	63	71	data	datum	NOUN
brj-23416	63	72	based	base	VERB
brj-23416	63	73	ten	ten	NUM
brj-23416	63	74	macroeconomic	macroeconomic	ADJ
brj-23416	63	75	variables	variable	NOUN
brj-23416	63	76	,	,	PUNCT
brj-23416	63	77	and	and	CCONJ
brj-23416	63	78	these	these	DET
brj-23416	63	79	techniques	technique	NOUN
brj-23416	63	80	were	be	AUX
brj-23416	63	81	compared	compare	VERB
brj-23416	63	82	.	.	PUNCT
brj-23416	64	1	moreover	moreover	ADV
brj-23416	64	2	,	,	PUNCT
brj-23416	64	3	there	there	PRON
brj-23416	64	4	are	be	VERB
brj-23416	64	5	limited	limited	ADJ
brj-23416	64	6	prediction	prediction	NOUN
brj-23416	64	7	studies	study	NOUN
brj-23416	64	8	on	on	ADP
brj-23416	64	9	stock	stock	NOUN
brj-23416	64	10	market	market	NOUN
brj-23416	64	11	price	price	NOUN
brj-23416	64	12	of	of	ADP
brj-23416	64	13	the	the	DET
brj-23416	64	14	forest	forest	NOUN
brj-23416	64	15	-	-	PUNCT
brj-23416	64	16	based	base	VERB
brj-23416	64	17	sector	sector	NOUN
brj-23416	64	18	using	use	VERB
brj-23416	64	19	machine	machine	NOUN
brj-23416	64	20	learning	learning	NOUN
brj-23416	64	21	methods	method	NOUN
brj-23416	64	22	.	.	PUNCT
brj-23416	65	1	in	in	ADP
brj-23416	65	2	this	this	DET
brj-23416	65	3	respect	respect	NOUN
brj-23416	65	4	,	,	PUNCT
brj-23416	65	5	this	this	DET
brj-23416	65	6	study	study	NOUN
brj-23416	65	7	contributes	contribute	VERB
brj-23416	65	8	to	to	ADP
brj-23416	65	9	the	the	DET
brj-23416	65	10	literature	literature	NOUN
brj-23416	65	11	.	.	PUNCT
brj-23416	66	1	experimental	experimental	ADJ
brj-23416	66	2	data	datum	NOUN
brj-23416	66	3	collection	collection	NOUN
brj-23416	66	4	and	and	CCONJ
brj-23416	66	5	processing	processing	NOUN
brj-23416	66	6	in	in	ADP
brj-23416	66	7	this	this	DET
brj-23416	66	8	study	study	NOUN
brj-23416	66	9	,	,	PUNCT
brj-23416	66	10	a	a	DET
brj-23416	66	11	dependent	dependent	ADJ
brj-23416	66	12	variable	variable	NOUN
brj-23416	66	13	and	and	CCONJ
brj-23416	66	14	10	10	NUM
brj-23416	66	15	macroeconomic	macroeconomic	ADJ
brj-23416	66	16	(	(	PUNCT
brj-23416	66	17	independent	independent	ADJ
brj-23416	66	18	)	)	PUNCT
brj-23416	66	19	variables	variable	NOUN
brj-23416	66	20	that	that	PRON
brj-23416	66	21	were	be	AUX
brj-23416	66	22	determined	determine	VERB
brj-23416	66	23	to	to	PART
brj-23416	66	24	have	have	VERB
brj-23416	66	25	a	a	DET
brj-23416	66	26	direct	direct	ADJ
brj-23416	66	27	or	or	CCONJ
brj-23416	66	28	indirect	indirect	ADJ
brj-23416	66	29	effect	effect	NOUN
brj-23416	66	30	on	on	ADP
brj-23416	66	31	the	the	DET
brj-23416	66	32	stock	stock	NOUN
brj-23416	66	33	were	be	AUX
brj-23416	66	34	used	use	VERB
brj-23416	66	35	.	.	PUNCT
brj-23416	67	1	in	in	ADP
brj-23416	67	2	determining	determine	VERB
brj-23416	67	3	the	the	DET
brj-23416	67	4	independent	independent	ADJ
brj-23416	67	5	variables	variable	NOUN
brj-23416	67	6	,	,	PUNCT
brj-23416	67	7	studies	study	NOUN
brj-23416	67	8	in	in	ADP
brj-23416	67	9	the	the	DET
brj-23416	67	10	literature	literature	NOUN
brj-23416	67	11	(	(	PUNCT
brj-23416	67	12	budak	budak	PROPN
brj-23416	67	13	et	et	PROPN
brj-23416	67	14	al	al	PROPN
brj-23416	67	15	.	.	PROPN
brj-23416	67	16	2017	2017	NUM
brj-23416	67	17	;	;	PUNCT
brj-23416	67	18	gürsoy	gürsoy	NOUN
brj-23416	67	19	2019	2019	NUM
brj-23416	67	20	;	;	PUNCT
brj-23416	67	21	durmuş	durmuş	PROPN
brj-23416	67	22	et	et	PROPN
brj-23416	67	23	al	al	PROPN
brj-23416	67	24	.	.	PROPN
brj-23416	67	25	2019	2019	NUM
brj-23416	67	26	;	;	PUNCT
brj-23416	67	27	fattah	fattah	PROPN
brj-23416	67	28	and	and	CCONJ
brj-23416	67	29	kocabıyık	kocabıyık	PROPN
brj-23416	67	30	2020	2020	NUM
brj-23416	67	31	;	;	PUNCT
brj-23416	67	32	özcan	özcan	NOUN
brj-23416	67	33	akdağ	akdağ	NOUN
brj-23416	67	34	et	et	PROPN
brj-23416	67	35	al	al	PROPN
brj-23416	67	36	.	.	PROPN
brj-23416	67	37	2022	2022	NUM
brj-23416	67	38	)	)	PUNCT
brj-23416	67	39	and	and	CCONJ
brj-23416	67	40	expert	expert	NOUN
brj-23416	67	41	opinions	opinion	NOUN
brj-23416	67	42	were	be	AUX
brj-23416	67	43	used	use	VERB
brj-23416	67	44	.	.	PUNCT
brj-23416	68	1	the	the	DET
brj-23416	68	2	independent	independent	ADJ
brj-23416	68	3	variables	variable	NOUN
brj-23416	68	4	in	in	ADP
brj-23416	68	5	the	the	DET
brj-23416	68	6	machine	machine	NOUN
brj-23416	68	7	learning	learning	NOUN
brj-23416	68	8	models	model	NOUN
brj-23416	68	9	were	be	AUX
brj-23416	68	10	peer	peer	NOUN
brj-23416	68	11	-	-	PUNCT
brj-23416	68	12	reviewed	review	VERB
brj-23416	68	13	article	article	NOUN
brj-23416	68	14	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	68	15	akyüz	akyüz	PROPN
brj-23416	68	16	et	et	PROPN
brj-23416	68	17	al	al	PROPN
brj-23416	68	18	.	.	PROPN
brj-23416	69	1	(	(	PUNCT
brj-23416	69	2	2024	2024	NUM
brj-23416	69	3	)	)	PUNCT
brj-23416	69	4	.	.	PUNCT
brj-23416	70	1	“	"	PUNCT
brj-23416	70	2	stock	stock	NOUN
brj-23416	70	3	exchange	exchange	NOUN
brj-23416	70	4	values	value	NOUN
brj-23416	70	5	,	,	PUNCT
brj-23416	70	6	”	"	PUNCT
brj-23416	70	7	bioresources	bioresource	NOUN
brj-23416	70	8	19(3	19(3	NUM
brj-23416	70	9	)	)	PUNCT
brj-23416	70	10	,	,	PUNCT
brj-23416	70	11	5141	5141	NUM
brj-23416	70	12	-	-	SYM
brj-23416	70	13	5157	5157	NUM
brj-23416	70	14	.	.	PUNCT
brj-23416	71	1	5144	5144	NUM
brj-23416	71	2	crude	crude	ADJ
brj-23416	71	3	oil	oil	NOUN
brj-23416	71	4	price	price	NOUN
brj-23416	71	5	,	,	PUNCT
brj-23416	71	6	exchange	exchange	NOUN
brj-23416	71	7	rate	rate	NOUN
brj-23416	71	8	of	of	ADP
brj-23416	71	9	usd	usd	NOUN
brj-23416	71	10	/	/	SYM
brj-23416	71	11	try	try	NOUN
brj-23416	71	12	,	,	PUNCT
brj-23416	71	13	dollar	dollar	NOUN
brj-23416	71	14	index	index	NOUN
brj-23416	71	15	,	,	PUNCT
brj-23416	71	16	bist	bist	NOUN
brj-23416	71	17	100	100	NUM
brj-23416	71	18	index	index	NOUN
brj-23416	71	19	,	,	PUNCT
brj-23416	71	20	gold	gold	NOUN
brj-23416	71	21	price	price	NOUN
brj-23416	71	22	,	,	PUNCT
brj-23416	71	23	money	money	NOUN
brj-23416	71	24	supply	supply	NOUN
brj-23416	71	25	(	(	PUNCT
brj-23416	71	26	m2	m2	PROPN
brj-23416	71	27	)	)	PUNCT
brj-23416	71	28	,	,	PUNCT
brj-23416	71	29	s&p	s&p	PROPN
brj-23416	71	30	500	500	NUM
brj-23416	71	31	index	index	NOUN
brj-23416	71	32	,	,	PUNCT
brj-23416	71	33	us	we	PRON
brj-23416	71	34	10	10	NUM
brj-23416	71	35	-	-	PUNCT
brj-23416	71	36	year	year	NOUN
brj-23416	71	37	bond	bond	NOUN
brj-23416	71	38	interest	interest	NOUN
brj-23416	71	39	,	,	PUNCT
brj-23416	71	40	export	export	NOUN
brj-23416	71	41	-	-	PUNCT
brj-23416	71	42	import	import	NOUN
brj-23416	71	43	coverage	coverage	NOUN
brj-23416	71	44	rate	rate	NOUN
brj-23416	71	45	in	in	ADP
brj-23416	71	46	the	the	DET
brj-23416	71	47	forest	forest	NOUN
brj-23416	71	48	products	product	NOUN
brj-23416	71	49	sector	sector	NOUN
brj-23416	71	50	,	,	PUNCT
brj-23416	71	51	and	and	CCONJ
brj-23416	71	52	deposit	deposit	NOUN
brj-23416	71	53	interest	interest	NOUN
brj-23416	71	54	rate	rate	NOUN
brj-23416	71	55	.	.	PUNCT
brj-23416	72	1	information	information	NOUN
brj-23416	72	2	regarding	regard	VERB
brj-23416	72	3	the	the	DET
brj-23416	72	4	variables	variable	NOUN
brj-23416	72	5	used	use	VERB
brj-23416	72	6	in	in	ADP
brj-23416	72	7	the	the	DET
brj-23416	72	8	analysis	analysis	NOUN
brj-23416	72	9	is	be	AUX
brj-23416	72	10	given	give	VERB
brj-23416	72	11	in	in	ADP
brj-23416	72	12	table	table	NOUN
brj-23416	72	13	1	1	NUM
brj-23416	72	14	.	.	PUNCT
brj-23416	72	15	table	table	NOUN
brj-23416	72	16	1	1	NUM
brj-23416	72	17	.	.	PUNCT
brj-23416	73	1	variables	variable	NOUN
brj-23416	73	2	used	use	VERB
brj-23416	73	3	in	in	ADP
brj-23416	73	4	analysis	analysis	NOUN
brj-23416	73	5	data	datum	NOUN
brj-23416	73	6	set	set	VERB
brj-23416	73	7	variables	variable	NOUN
brj-23416	73	8	access	access	NOUN
brj-23416	73	9	source	source	NOUN
brj-23416	73	10	variable	variable	ADJ
brj-23416	73	11	type	type	NOUN
brj-23416	73	12	date	date	NOUN
brj-23416	73	13	range	range	VERB
brj-23416	73	14	monthly	monthly	ADJ
brj-23416	73	15	xkagt	xkagt	NOUN
brj-23416	73	16	index	index	NOUN
brj-23416	73	17	investing.com	investing.com	X
brj-23416	73	18	numerical	numerical	VERB
brj-23416	73	19	2002:012023:11	2002:012023:11	NUM
brj-23416	73	20	bist	bist	ADJ
brj-23416	73	21	100	100	NUM
brj-23416	73	22	index	index	NOUN
brj-23416	73	23	investing.com	investing.com	X
brj-23416	73	24	numerical	numerical	ADJ
brj-23416	73	25	gold	gold	NOUN
brj-23416	73	26	price	price	NOUN
brj-23416	73	27	investing.com	investing.com	X
brj-23416	73	28	numerical	numerical	ADJ
brj-23416	73	29	s&p	s&p	PROPN
brj-23416	73	30	500	500	NUM
brj-23416	73	31	index	index	NOUN
brj-23416	73	32	investing.com	investing.com	X
brj-23416	73	33	numerical	numerical	VERB
brj-23416	73	34	us	we	PRON
brj-23416	73	35	10	10	NUM
brj-23416	73	36	-	-	PUNCT
brj-23416	73	37	year	year	NOUN
brj-23416	73	38	bond	bond	NOUN
brj-23416	73	39	interest	interest	NOUN
brj-23416	73	40	investing.com	investing.com	X
brj-23416	73	41	numerical	numerical	ADJ
brj-23416	73	42	crude	crude	ADJ
brj-23416	73	43	oil	oil	NOUN
brj-23416	73	44	price	price	NOUN
brj-23416	73	45	investing.com	investing.com	X
brj-23416	73	46	numerical	numerical	ADJ
brj-23416	73	47	exchange	exchange	NOUN
brj-23416	73	48	rate	rate	NOUN
brj-23416	73	49	of	of	ADP
brj-23416	73	50	usd	usd	NOUN
brj-23416	73	51	/	/	SYM
brj-23416	73	52	try	try	VERB
brj-23416	73	53	investing.com	investing.com	X
brj-23416	73	54	numerical	numerical	ADJ
brj-23416	73	55	dollar	dollar	NOUN
brj-23416	73	56	index	index	NOUN
brj-23416	73	57	investing.com	investing.com	X
brj-23416	73	58	numerical	numerical	ADJ
brj-23416	73	59	deposit	deposit	NOUN
brj-23416	73	60	interest	interest	NOUN
brj-23416	73	61	rate	rate	PROPN
brj-23416	73	62	central	central	PROPN
brj-23416	73	63	bank	bank	PROPN
brj-23416	73	64	of	of	ADP
brj-23416	73	65	the	the	DET
brj-23416	73	66	republic	republic	NOUN
brj-23416	73	67	of	of	ADP
brj-23416	73	68	türkiye	türkiye	PROPN
brj-23416	73	69	(	(	PUNCT
brj-23416	73	70	cbrt	cbrt	PROPN
brj-23416	73	71	)	)	PUNCT
brj-23416	73	72	numerical	numerical	ADJ
brj-23416	73	73	money	money	NOUN
brj-23416	73	74	supply	supply	NOUN
brj-23416	73	75	(	(	PUNCT
brj-23416	73	76	m2	m2	PROPN
brj-23416	73	77	)	)	PUNCT
brj-23416	73	78	central	central	ADJ
brj-23416	73	79	bank	bank	NOUN
brj-23416	73	80	of	of	ADP
brj-23416	73	81	the	the	DET
brj-23416	73	82	republic	republic	NOUN
brj-23416	73	83	of	of	ADP
brj-23416	73	84	türkiye	türkiye	PROPN
brj-23416	73	85	(	(	PUNCT
brj-23416	73	86	cbrt	cbrt	PROPN
brj-23416	73	87	)	)	PUNCT
brj-23416	73	88	numerical	numerical	PROPN
brj-23416	73	89	export	export	PROPN
brj-23416	73	90	trademap.org	trademap.org	X
brj-23416	73	91	numerical	numerical	ADJ
brj-23416	73	92	import	import	PROPN
brj-23416	73	93	trademap.org	trademap.org	X
brj-23416	73	94	numerical	numerical	ADJ
brj-23416	73	95	before	before	SCONJ
brj-23416	73	96	machine	machine	NOUN
brj-23416	73	97	learning	learning	NOUN
brj-23416	73	98	models	model	NOUN
brj-23416	73	99	are	be	AUX
brj-23416	73	100	established	establish	VERB
brj-23416	73	101	,	,	PUNCT
brj-23416	73	102	the	the	DET
brj-23416	73	103	data	datum	NOUN
brj-23416	73	104	set	set	VERB
brj-23416	73	105	must	must	AUX
brj-23416	73	106	go	go	VERB
brj-23416	73	107	through	through	ADP
brj-23416	73	108	some	some	DET
brj-23416	73	109	data	datum	NOUN
brj-23416	73	110	preprocessing	preprocessing	NOUN
brj-23416	73	111	.	.	PUNCT
brj-23416	74	1	missing	miss	VERB
brj-23416	74	2	data	data	NOUN
brj-23416	74	3	analysis	analysis	NOUN
brj-23416	74	4	and	and	CCONJ
brj-23416	74	5	outlier	outlier	NOUN
brj-23416	74	6	data	datum	NOUN
brj-23416	74	7	analysis	analysis	NOUN
brj-23416	74	8	were	be	AUX
brj-23416	74	9	performed	perform	VERB
brj-23416	74	10	from	from	ADP
brj-23416	74	11	the	the	DET
brj-23416	74	12	data	datum	NOUN
brj-23416	74	13	preprocessing	preprocessing	NOUN
brj-23416	74	14	.	.	PUNCT
brj-23416	75	1	it	it	PRON
brj-23416	75	2	was	be	AUX
brj-23416	75	3	checked	check	VERB
brj-23416	75	4	whether	whether	SCONJ
brj-23416	75	5	there	there	PRON
brj-23416	75	6	was	be	VERB
brj-23416	75	7	any	any	DET
brj-23416	75	8	missing	missing	ADJ
brj-23416	75	9	data	datum	NOUN
brj-23416	75	10	in	in	ADP
brj-23416	75	11	the	the	DET
brj-23416	75	12	data	datum	NOUN
brj-23416	75	13	set	set	VERB
brj-23416	75	14	with	with	ADP
brj-23416	75	15	the	the	DET
brj-23416	75	16	isnull	isnull	NOUN
brj-23416	75	17	parameter	parameter	NOUN
brj-23416	75	18	of	of	ADP
brj-23416	75	19	the	the	DET
brj-23416	75	20	python	python	NOUN
brj-23416	75	21	programming	programming	NOUN
brj-23416	75	22	language	language	NOUN
brj-23416	75	23	and	and	CCONJ
brj-23416	75	24	no	no	DET
brj-23416	75	25	missing	miss	VERB
brj-23416	75	26	data	datum	NOUN
brj-23416	75	27	was	be	AUX
brj-23416	75	28	found	find	VERB
brj-23416	75	29	.	.	PUNCT
brj-23416	76	1	outlier	outlier	NOUN
brj-23416	76	2	analysis	analysis	NOUN
brj-23416	76	3	was	be	AUX
brj-23416	76	4	performed	perform	VERB
brj-23416	76	5	using	use	VERB
brj-23416	76	6	the	the	DET
brj-23416	76	7	boxplot	boxplot	NOUN
brj-23416	76	8	method	method	NOUN
brj-23416	76	9	,	,	PUNCT
brj-23416	76	10	and	and	CCONJ
brj-23416	76	11	some	some	DET
brj-23416	76	12	results	result	NOUN
brj-23416	76	13	are	be	AUX
brj-23416	76	14	given	give	VERB
brj-23416	76	15	in	in	ADP
brj-23416	76	16	fig	fig	NOUN
brj-23416	76	17	.	.	PUNCT
brj-23416	77	1	1	1	X
brj-23416	77	2	.	.	X
brj-23416	77	3	as	as	SCONJ
brj-23416	77	4	seen	see	VERB
brj-23416	77	5	in	in	ADP
brj-23416	77	6	the	the	DET
brj-23416	77	7	figure	figure	NOUN
brj-23416	77	8	,	,	PUNCT
brj-23416	77	9	it	it	PRON
brj-23416	77	10	was	be	AUX
brj-23416	77	11	determined	determine	VERB
brj-23416	77	12	that	that	SCONJ
brj-23416	77	13	there	there	PRON
brj-23416	77	14	were	be	VERB
brj-23416	77	15	no	no	DET
brj-23416	77	16	outlier	outlier	ADJ
brj-23416	77	17	values	value	NOUN
brj-23416	77	18	.	.	PUNCT
brj-23416	78	1	after	after	ADP
brj-23416	78	2	the	the	DET
brj-23416	78	3	preprocessing	preprocessing	NOUN
brj-23416	78	4	,	,	PUNCT
brj-23416	78	5	the	the	DET
brj-23416	78	6	data	datum	NOUN
brj-23416	78	7	was	be	AUX
brj-23416	78	8	split	split	VERB
brj-23416	78	9	into	into	ADP
brj-23416	78	10	two	two	NUM
brj-23416	78	11	:	:	SYM
brj-23416	78	12	80	80	NUM
brj-23416	78	13	%	%	NOUN
brj-23416	78	14	(	(	PUNCT
brj-23416	78	15	211	211	NUM
brj-23416	78	16	)	)	PUNCT
brj-23416	78	17	training	training	NOUN
brj-23416	78	18	data	datum	NOUN
brj-23416	78	19	and	and	CCONJ
brj-23416	78	20	20	20	NUM
brj-23416	78	21	%	%	NOUN
brj-23416	78	22	(	(	PUNCT
brj-23416	78	23	52	52	NUM
brj-23416	78	24	)	)	PUNCT
brj-23416	78	25	test	test	NOUN
brj-23416	78	26	data	datum	NOUN
brj-23416	78	27	.	.	PUNCT
brj-23416	79	1	modeling	model	VERB
brj-23416	79	2	experiments	experiment	NOUN
brj-23416	79	3	were	be	AUX
brj-23416	79	4	conducted	conduct	VERB
brj-23416	79	5	using	use	VERB
brj-23416	79	6	python	python	NOUN
brj-23416	79	7	coding	coding	NOUN
brj-23416	79	8	on	on	ADP
brj-23416	79	9	a	a	DET
brj-23416	79	10	data	data	NOUN
brj-23416	79	11	set	set	VERB
brj-23416	79	12	consisting	consist	VERB
brj-23416	79	13	of	of	ADP
brj-23416	79	14	10	10	NUM
brj-23416	79	15	independent	independent	ADJ
brj-23416	79	16	and	and	CCONJ
brj-23416	79	17	1	1	NUM
brj-23416	79	18	dependent	dependent	ADJ
brj-23416	79	19	variable	variable	NOUN
brj-23416	79	20	.	.	PUNCT
brj-23416	80	1	artificial	artificial	ADJ
brj-23416	80	2	neural	neural	ADJ
brj-23416	80	3	networks	network	NOUN
brj-23416	80	4	,	,	PUNCT
brj-23416	80	5	random	random	ADJ
brj-23416	80	6	forest	forest	NOUN
brj-23416	80	7	,	,	PUNCT
brj-23416	81	1	k	k	PROPN
brj-23416	81	2	nearest	near	ADJ
brj-23416	81	3	neighbors	neighbor	NOUN
brj-23416	81	4	,	,	PUNCT
brj-23416	81	5	and	and	CCONJ
brj-23416	81	6	gradient	gradient	ADJ
brj-23416	81	7	boosting	boost	VERB
brj-23416	81	8	machine	machine	NOUN
brj-23416	81	9	algorithms	algorithm	NOUN
brj-23416	81	10	were	be	AUX
brj-23416	81	11	used	use	VERB
brj-23416	81	12	,	,	PUNCT
brj-23416	81	13	and	and	CCONJ
brj-23416	81	14	four	four	NUM
brj-23416	81	15	different	different	ADJ
brj-23416	81	16	models	model	NOUN
brj-23416	81	17	were	be	AUX
brj-23416	81	18	established	establish	VERB
brj-23416	81	19	.	.	PUNCT
brj-23416	82	1	python	python	NOUN
brj-23416	82	2	programming	programming	NOUN
brj-23416	82	3	language	language	NOUN
brj-23416	82	4	was	be	AUX
brj-23416	82	5	used	use	VERB
brj-23416	82	6	to	to	PART
brj-23416	82	7	apply	apply	VERB
brj-23416	82	8	machine	machine	NOUN
brj-23416	82	9	learning	learning	NOUN
brj-23416	82	10	techniques	technique	NOUN
brj-23416	82	11	.	.	PUNCT
brj-23416	83	1	the	the	DET
brj-23416	83	2	most	most	ADV
brj-23416	83	3	common	common	ADJ
brj-23416	83	4	programming	programming	NOUN
brj-23416	83	5	language	language	NOUN
brj-23416	83	6	used	use	VERB
brj-23416	83	7	in	in	ADP
brj-23416	83	8	57	57	NUM
brj-23416	83	9	%	%	NOUN
brj-23416	83	10	of	of	ADP
brj-23416	83	11	artificial	artificial	ADJ
brj-23416	83	12	intelligence	intelligence	NOUN
brj-23416	83	13	studies	study	NOUN
brj-23416	83	14	is	be	AUX
brj-23416	83	15	python	python	NOUN
brj-23416	83	16	,	,	PUNCT
brj-23416	83	17	which	which	PRON
brj-23416	83	18	has	have	VERB
brj-23416	83	19	various	various	ADJ
brj-23416	83	20	libraries	library	NOUN
brj-23416	83	21	that	that	PRON
brj-23416	83	22	can	can	AUX
brj-23416	83	23	be	be	AUX
brj-23416	83	24	used	use	VERB
brj-23416	83	25	in	in	ADP
brj-23416	83	26	different	different	ADJ
brj-23416	83	27	areas	area	NOUN
brj-23416	83	28	(	(	PUNCT
brj-23416	83	29	kızrak	kızrak	NOUN
brj-23416	83	30	2018	2018	NUM
brj-23416	83	31	)	)	PUNCT
brj-23416	83	32	.	.	PUNCT
brj-23416	84	1	peer	peer	NOUN
brj-23416	84	2	-	-	PUNCT
brj-23416	84	3	reviewed	review	VERB
brj-23416	84	4	article	article	NOUN
brj-23416	84	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	84	6	akyüz	akyüz	PROPN
brj-23416	84	7	et	et	PROPN
brj-23416	84	8	al	al	PROPN
brj-23416	84	9	.	.	PROPN
brj-23416	85	1	(	(	PUNCT
brj-23416	85	2	2024	2024	NUM
brj-23416	85	3	)	)	PUNCT
brj-23416	85	4	.	.	PUNCT
brj-23416	86	1	“	"	PUNCT
brj-23416	86	2	stock	stock	NOUN
brj-23416	86	3	exchange	exchange	NOUN
brj-23416	86	4	values	value	NOUN
brj-23416	86	5	,	,	PUNCT
brj-23416	86	6	”	"	PUNCT
brj-23416	86	7	bioresources	bioresource	NOUN
brj-23416	86	8	19(3	19(3	NUM
brj-23416	86	9	)	)	PUNCT
brj-23416	86	10	,	,	PUNCT
brj-23416	86	11	5141	5141	NUM
brj-23416	86	12	-	-	SYM
brj-23416	86	13	5157	5157	NUM
brj-23416	86	14	.	.	PUNCT
brj-23416	87	1	5145	5145	NUM
brj-23416	87	2	fig	fig	NOUN
brj-23416	87	3	.	.	PUNCT
brj-23416	88	1	1	1	X
brj-23416	88	2	.	.	X
brj-23416	88	3	outlier	outlier	NOUN
brj-23416	88	4	analysis	analysis	NOUN
brj-23416	88	5	of	of	ADP
brj-23416	88	6	variables	variables	ADJ
brj-23416	88	7	artificial	artificial	ADJ
brj-23416	88	8	neural	neural	ADJ
brj-23416	88	9	networks	network	NOUN
brj-23416	88	10	(	(	PUNCT
brj-23416	88	11	ann	ann	PROPN
brj-23416	88	12	)	)	PUNCT
brj-23416	88	13	the	the	DET
brj-23416	88	14	prediction	prediction	NOUN
brj-23416	88	15	method	method	NOUN
brj-23416	88	16	with	with	ADP
brj-23416	88	17	artificial	artificial	ADJ
brj-23416	88	18	neural	neural	ADJ
brj-23416	88	19	networks	network	NOUN
brj-23416	88	20	was	be	AUX
brj-23416	88	21	implemented	implement	VERB
brj-23416	88	22	using	use	VERB
brj-23416	88	23	pandas	panda	NOUN
brj-23416	88	24	,	,	PUNCT
brj-23416	88	25	numpy	numpy	NOUN
brj-23416	88	26	,	,	PUNCT
brj-23416	88	27	pytorch	pytorch	NOUN
brj-23416	88	28	,	,	PUNCT
brj-23416	88	29	and	and	CCONJ
brj-23416	88	30	optuna	optuna	ADJ
brj-23416	88	31	libraries	library	NOUN
brj-23416	88	32	.	.	PUNCT
brj-23416	89	1	the	the	DET
brj-23416	89	2	pandas	panda	NOUN
brj-23416	89	3	and	and	CCONJ
brj-23416	89	4	numpy	numpy	NOUN
brj-23416	89	5	libraries	library	NOUN
brj-23416	89	6	were	be	AUX
brj-23416	89	7	utilized	utilize	VERB
brj-23416	89	8	to	to	PART
brj-23416	89	9	manipulate	manipulate	VERB
brj-23416	89	10	and	and	CCONJ
brj-23416	89	11	process	process	NOUN
brj-23416	89	12	datasets	dataset	NOUN
brj-23416	89	13	.	.	PUNCT
brj-23416	90	1	the	the	DET
brj-23416	90	2	pytorch	pytorch	NOUN
brj-23416	90	3	library	library	NOUN
brj-23416	90	4	(	(	PUNCT
brj-23416	90	5	hassan	hassan	PROPN
brj-23416	90	6	et	et	PROPN
brj-23416	90	7	al	al	PROPN
brj-23416	90	8	.	.	PROPN
brj-23416	90	9	2022	2022	NUM
brj-23416	90	10	)	)	PUNCT
brj-23416	90	11	was	be	AUX
brj-23416	90	12	employed	employ	VERB
brj-23416	90	13	in	in	ADP
brj-23416	90	14	both	both	CCONJ
brj-23416	90	15	the	the	DET
brj-23416	90	16	training	training	NOUN
brj-23416	90	17	and	and	CCONJ
brj-23416	90	18	testing	testing	NOUN
brj-23416	90	19	phases	phase	NOUN
brj-23416	90	20	of	of	ADP
brj-23416	90	21	the	the	DET
brj-23416	90	22	model	model	NOUN
brj-23416	90	23	.	.	PUNCT
brj-23416	91	1	the	the	DET
brj-23416	91	2	use	use	NOUN
brj-23416	91	3	of	of	ADP
brj-23416	91	4	the	the	DET
brj-23416	91	5	optuna	optuna	PROPN
brj-23416	91	6	library	library	NOUN
brj-23416	91	7	(	(	PUNCT
brj-23416	91	8	akl	akl	PROPN
brj-23416	91	9	et	et	PROPN
brj-23416	91	10	al	al	PROPN
brj-23416	91	11	.	.	PROPN
brj-23416	91	12	2019	2019	NUM
brj-23416	91	13	;	;	PUNCT
brj-23416	91	14	yu	yu	PROPN
brj-23416	91	15	and	and	CCONJ
brj-23416	91	16	zhu	zhu	PROPN
brj-23416	91	17	2020	2020	NUM
brj-23416	91	18	;	;	PUNCT
brj-23416	91	19	abdolrasol	abdolrasol	VERB
brj-23416	91	20	et	et	PROPN
brj-23416	91	21	al	al	PROPN
brj-23416	91	22	.	.	PROPN
brj-23416	91	23	2021	2021	NUM
brj-23416	91	24	;	;	PUNCT
brj-23416	91	25	rimal	rimal	NOUN
brj-23416	91	26	et	et	PROPN
brj-23416	91	27	al	al	PROPN
brj-23416	91	28	.	.	PROPN
brj-23416	91	29	2024	2024	NUM
brj-23416	91	30	)	)	PUNCT
brj-23416	91	31	was	be	AUX
brj-23416	91	32	employed	employ	VERB
brj-23416	91	33	to	to	PART
brj-23416	91	34	optimize	optimize	VERB
brj-23416	91	35	hyperparameters	hyperparameter	NOUN
brj-23416	91	36	,	,	PUNCT
brj-23416	91	37	including	include	VERB
brj-23416	91	38	the	the	DET
brj-23416	91	39	learning	learning	NOUN
brj-23416	91	40	rate	rate	NOUN
brj-23416	91	41	and	and	CCONJ
brj-23416	91	42	the	the	DET
brj-23416	91	43	quantity	quantity	NOUN
brj-23416	91	44	of	of	ADP
brj-23416	91	45	neurons	neuron	NOUN
brj-23416	91	46	in	in	ADP
brj-23416	91	47	the	the	DET
brj-23416	91	48	hidden	hide	VERB
brj-23416	91	49	layer	layer	NOUN
brj-23416	91	50	of	of	ADP
brj-23416	91	51	the	the	DET
brj-23416	91	52	model	model	NOUN
brj-23416	91	53	.	.	PUNCT
brj-23416	92	1	in	in	ADP
brj-23416	92	2	the	the	DET
brj-23416	92	3	beginning	beginning	NOUN
brj-23416	92	4	,	,	PUNCT
brj-23416	92	5	the	the	DET
brj-23416	92	6	data	datum	NOUN
brj-23416	92	7	set	set	VERB
brj-23416	92	8	was	be	AUX
brj-23416	92	9	partitioned	partition	VERB
brj-23416	92	10	into	into	ADP
brj-23416	92	11	targets	target	NOUN
brj-23416	92	12	(	(	PUNCT
brj-23416	92	13	y	y	NOUN
brj-23416	92	14	)	)	PUNCT
brj-23416	92	15	and	and	CCONJ
brj-23416	92	16	features	feature	NOUN
brj-23416	92	17	(	(	PUNCT
brj-23416	92	18	x	x	X
brj-23416	92	19	)	)	PUNCT
brj-23416	92	20	for	for	ADP
brj-23416	92	21	the	the	DET
brj-23416	92	22	purpose	purpose	NOUN
brj-23416	92	23	of	of	ADP
brj-23416	92	24	training	train	VERB
brj-23416	92	25	the	the	DET
brj-23416	92	26	model	model	NOUN
brj-23416	92	27	.	.	PUNCT
brj-23416	93	1	the	the	DET
brj-23416	93	2	data	datum	NOUN
brj-23416	93	3	set	set	VERB
brj-23416	93	4	was	be	AUX
brj-23416	93	5	subsequently	subsequently	ADV
brj-23416	93	6	partitioned	partition	VERB
brj-23416	93	7	into	into	ADP
brj-23416	93	8	test	test	NOUN
brj-23416	93	9	and	and	CCONJ
brj-23416	93	10	training	training	NOUN
brj-23416	93	11	sets	set	NOUN
brj-23416	93	12	before	before	ADP
brj-23416	93	13	being	be	AUX
brj-23416	93	14	transformed	transform	VERB
brj-23416	93	15	into	into	ADP
brj-23416	93	16	pytorch	pytorch	NOUN
brj-23416	93	17	tensors	tensor	NOUN
brj-23416	93	18	.	.	PUNCT
brj-23416	94	1	a	a	DET
brj-23416	94	2	batch	batch	NOUN
brj-23416	94	3	processing	processing	NOUN
brj-23416	94	4	strategy	strategy	NOUN
brj-23416	94	5	was	be	AUX
brj-23416	94	6	utilized	utilize	VERB
brj-23416	94	7	throughout	throughout	ADP
brj-23416	94	8	the	the	DET
brj-23416	94	9	model	model	NOUN
brj-23416	94	10	training	training	NOUN
brj-23416	94	11	phase	phase	NOUN
brj-23416	94	12	to	to	PART
brj-23416	94	13	run	run	VERB
brj-23416	94	14	over	over	ADP
brj-23416	94	15	the	the	DET
brj-23416	94	16	dataset	dataset	NOUN
brj-23416	94	17	and	and	CCONJ
brj-23416	94	18	modify	modify	VERB
brj-23416	94	19	the	the	DET
brj-23416	94	20	model	model	NOUN
brj-23416	94	21	weights	weight	NOUN
brj-23416	94	22	.	.	PUNCT
brj-23416	95	1	upon	upon	SCONJ
brj-23416	95	2	completion	completion	NOUN
brj-23416	95	3	of	of	ADP
brj-23416	95	4	the	the	DET
brj-23416	95	5	training	training	NOUN
brj-23416	95	6	process	process	NOUN
brj-23416	95	7	,	,	PUNCT
brj-23416	95	8	the	the	DET
brj-23416	95	9	model	model	NOUN
brj-23416	95	10	was	be	AUX
brj-23416	95	11	assessed	assess	VERB
brj-23416	95	12	on	on	ADP
brj-23416	95	13	the	the	DET
brj-23416	95	14	test	test	NOUN
brj-23416	95	15	set	set	VERB
brj-23416	95	16	by	by	ADP
brj-23416	95	17	calculating	calculate	VERB
brj-23416	95	18	several	several	ADJ
brj-23416	95	19	performance	performance	NOUN
brj-23416	95	20	measures	measure	NOUN
brj-23416	95	21	(	(	PUNCT
brj-23416	95	22	mse	mse	PROPN
brj-23416	95	23	,	,	PUNCT
brj-23416	95	24	mae	mae	PROPN
brj-23416	95	25	,	,	PUNCT
brj-23416	95	26	and	and	CCONJ
brj-23416	95	27	r²	r²	NOUN
brj-23416	95	28	)	)	PUNCT
brj-23416	95	29	.	.	PUNCT
brj-23416	96	1	in	in	ADP
brj-23416	96	2	this	this	DET
brj-23416	96	3	model	model	NOUN
brj-23416	96	4	,	,	PUNCT
brj-23416	96	5	both	both	CCONJ
brj-23416	96	6	the	the	DET
brj-23416	96	7	adam	adam	PROPN
brj-23416	96	8	optimization	optimization	NOUN
brj-23416	96	9	technique	technique	NOUN
brj-23416	96	10	and	and	CCONJ
brj-23416	96	11	the	the	DET
brj-23416	96	12	relu	relu	NOUN
brj-23416	96	13	activation	activation	NOUN
brj-23416	96	14	function	function	NOUN
brj-23416	96	15	were	be	AUX
brj-23416	96	16	implemented	implement	VERB
brj-23416	96	17	(	(	PUNCT
brj-23416	96	18	ahmad	ahmad	PROPN
brj-23416	96	19	et	et	PROPN
brj-23416	96	20	al	al	PROPN
brj-23416	96	21	.	.	PROPN
brj-23416	96	22	2022	2022	NUM
brj-23416	96	23	)	)	PUNCT
brj-23416	96	24	.	.	PUNCT
brj-23416	97	1	the	the	DET
brj-23416	97	2	relu	relu	NOUN
brj-23416	97	3	function	function	NOUN
brj-23416	97	4	was	be	AUX
brj-23416	97	5	selected	select	VERB
brj-23416	97	6	for	for	ADP
brj-23416	97	7	the	the	DET
brj-23416	97	8	activation	activation	NOUN
brj-23416	97	9	of	of	ADP
brj-23416	97	10	neurons	neuron	NOUN
brj-23416	97	11	in	in	ADP
brj-23416	97	12	the	the	DET
brj-23416	97	13	hidden	hide	VERB
brj-23416	97	14	layer	layer	NOUN
brj-23416	97	15	as	as	ADP
brj-23416	97	16	part	part	NOUN
brj-23416	97	17	of	of	ADP
brj-23416	97	18	the	the	DET
brj-23416	97	19	model	model	NOUN
brj-23416	97	20	's	's	PART
brj-23416	97	21	design	design	NOUN
brj-23416	97	22	.	.	PUNCT
brj-23416	98	1	weight	weight	NOUN
brj-23416	98	2	adjustments	adjustment	NOUN
brj-23416	98	3	during	during	ADP
brj-23416	98	4	the	the	DET
brj-23416	98	5	model	model	NOUN
brj-23416	98	6	's	's	PART
brj-23416	98	7	training	training	NOUN
brj-23416	98	8	were	be	AUX
brj-23416	98	9	performed	perform	VERB
brj-23416	98	10	using	use	VERB
brj-23416	98	11	the	the	DET
brj-23416	98	12	adam	adam	PROPN
brj-23416	98	13	optimization	optimization	NOUN
brj-23416	98	14	technique	technique	NOUN
brj-23416	98	15	.	.	PUNCT
brj-23416	99	1	the	the	DET
brj-23416	99	2	performance	performance	NOUN
brj-23416	99	3	of	of	ADP
brj-23416	99	4	any	any	DET
brj-23416	99	5	machine	machine	NOUN
brj-23416	99	6	learning	learn	VERB
brj-23416	99	7	model	model	NOUN
brj-23416	99	8	directly	directly	ADV
brj-23416	99	9	depends	depend	VERB
brj-23416	99	10	on	on	ADP
brj-23416	99	11	the	the	DET
brj-23416	99	12	selected	select	VERB
brj-23416	99	13	hyperparameter	hyperparameter	NOUN
brj-23416	99	14	.	.	PUNCT
brj-23416	100	1	there	there	PRON
brj-23416	100	2	are	be	VERB
brj-23416	100	3	different	different	ADJ
brj-23416	100	4	hyperparameter	hyperparameter	NOUN
brj-23416	100	5	values	value	NOUN
brj-23416	100	6	for	for	ADP
brj-23416	100	7	the	the	DET
brj-23416	100	8	algorithms	algorithm	NOUN
brj-23416	100	9	used	use	VERB
brj-23416	100	10	in	in	ADP
brj-23416	100	11	modeling	modeling	NOUN
brj-23416	100	12	and	and	CCONJ
brj-23416	100	13	separate	separate	ADJ
brj-23416	100	14	calculations	calculation	NOUN
brj-23416	100	15	have	have	AUX
brj-23416	100	16	been	be	AUX
brj-23416	100	17	made	make	VERB
brj-23416	100	18	for	for	ADP
brj-23416	100	19	each	each	DET
brj-23416	100	20	algorithm	algorithm	NOUN
brj-23416	100	21	.	.	PUNCT
brj-23416	101	1	table	table	NOUN
brj-23416	101	2	2	2	NUM
brj-23416	101	3	presents	present	VERB
brj-23416	101	4	peer	peer	NOUN
brj-23416	101	5	-	-	PUNCT
brj-23416	101	6	reviewed	review	VERB
brj-23416	101	7	article	article	NOUN
brj-23416	101	8	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	101	9	akyüz	akyüz	PROPN
brj-23416	101	10	et	et	PROPN
brj-23416	101	11	al	al	PROPN
brj-23416	101	12	.	.	PROPN
brj-23416	102	1	(	(	PUNCT
brj-23416	102	2	2024	2024	NUM
brj-23416	102	3	)	)	PUNCT
brj-23416	102	4	.	.	PUNCT
brj-23416	103	1	“	"	PUNCT
brj-23416	103	2	stock	stock	NOUN
brj-23416	103	3	exchange	exchange	NOUN
brj-23416	103	4	values	value	NOUN
brj-23416	103	5	,	,	PUNCT
brj-23416	103	6	”	"	PUNCT
brj-23416	103	7	bioresources	bioresource	NOUN
brj-23416	103	8	19(3	19(3	NUM
brj-23416	103	9	)	)	PUNCT
brj-23416	103	10	,	,	PUNCT
brj-23416	103	11	5141	5141	NUM
brj-23416	103	12	-	-	SYM
brj-23416	103	13	5157	5157	NUM
brj-23416	103	14	.	.	PUNCT
brj-23416	104	1	5146	5146	NUM
brj-23416	104	2	the	the	DET
brj-23416	104	3	usage	usage	NOUN
brj-23416	104	4	values	value	NOUN
brj-23416	104	5	of	of	ADP
brj-23416	104	6	hyperparameters	hyperparameter	NOUN
brj-23416	104	7	used	use	VERB
brj-23416	104	8	for	for	ADP
brj-23416	104	9	ann	ann	PROPN
brj-23416	104	10	.	.	PUNCT
brj-23416	105	1	as	as	SCONJ
brj-23416	105	2	seen	see	VERB
brj-23416	105	3	in	in	ADP
brj-23416	105	4	table	table	NOUN
brj-23416	105	5	2	2	NUM
brj-23416	105	6	,	,	PUNCT
brj-23416	105	7	hidden	hidden	ADJ
brj-23416	105	8	size	size	NOUN
brj-23416	105	9	,	,	PUNCT
brj-23416	105	10	number	number	NOUN
brj-23416	105	11	epoch	epoch	NOUN
brj-23416	105	12	and	and	CCONJ
brj-23416	105	13	weight	weight	NOUN
brj-23416	105	14	hyperparameters	hyperparameter	NOUN
brj-23416	105	15	were	be	AUX
brj-23416	105	16	analyzed	analyze	VERB
brj-23416	105	17	for	for	ADP
brj-23416	105	18	the	the	DET
brj-23416	105	19	ann	ann	PROPN
brj-23416	105	20	model	model	NOUN
brj-23416	105	21	.	.	PUNCT
brj-23416	106	1	hidden	hide	VERB
brj-23416	106	2	size	size	NOUN
brj-23416	106	3	represents	represent	VERB
brj-23416	106	4	the	the	DET
brj-23416	106	5	number	number	NOUN
brj-23416	106	6	of	of	ADP
brj-23416	106	7	neurons	neuron	NOUN
brj-23416	106	8	in	in	ADP
brj-23416	106	9	the	the	DET
brj-23416	106	10	hidden	hide	VERB
brj-23416	106	11	layer	layer	NOUN
brj-23416	106	12	.	.	PUNCT
brj-23416	107	1	this	this	DET
brj-23416	107	2	parameter	parameter	NOUN
brj-23416	107	3	was	be	AUX
brj-23416	107	4	chosen	choose	VERB
brj-23416	107	5	within	within	ADP
brj-23416	107	6	the	the	DET
brj-23416	107	7	range	range	NOUN
brj-23416	107	8	of	of	ADP
brj-23416	107	9	10	10	NUM
brj-23416	107	10	to	to	PART
brj-23416	107	11	100	100	NUM
brj-23416	107	12	.	.	PUNCT
brj-23416	107	13	number	number	NOUN
brj-23416	107	14	epoch	epoch	PROPN
brj-23416	107	15	specifies	specify	VERB
brj-23416	107	16	the	the	DET
brj-23416	107	17	number	number	NOUN
brj-23416	107	18	of	of	ADP
brj-23416	107	19	forward	forward	ADV
brj-23416	107	20	and	and	CCONJ
brj-23416	107	21	backward	backward	ADJ
brj-23416	107	22	passes	pass	VERB
brj-23416	107	23	the	the	DET
brj-23416	107	24	complete	complete	ADJ
brj-23416	107	25	training	training	NOUN
brj-23416	107	26	dataset	dataset	NOUN
brj-23416	107	27	undergoes	undergoe	NOUN
brj-23416	107	28	through	through	ADP
brj-23416	107	29	the	the	DET
brj-23416	107	30	ann	ann	PROPN
brj-23416	107	31	.	.	PUNCT
brj-23416	108	1	the	the	DET
brj-23416	108	2	number	number	NOUN
brj-23416	108	3	epoch	epoch	NOUN
brj-23416	108	4	value	value	NOUN
brj-23416	108	5	was	be	AUX
brj-23416	108	6	determined	determine	VERB
brj-23416	108	7	within	within	ADP
brj-23416	108	8	the	the	DET
brj-23416	108	9	range	range	NOUN
brj-23416	108	10	of	of	ADP
brj-23416	108	11	50	50	NUM
brj-23416	108	12	to	to	PART
brj-23416	108	13	200.weight	200.weight	NUM
brj-23416	108	14	value	value	NOUN
brj-23416	108	15	was	be	AUX
brj-23416	108	16	chosen	choose	VERB
brj-23416	108	17	within	within	ADP
brj-23416	108	18	the	the	DET
brj-23416	108	19	range	range	NOUN
brj-23416	108	20	of	of	ADP
brj-23416	108	21	0.0001	0.0001	NUM
brj-23416	108	22	to	to	PART
brj-23416	108	23	0.2	0.2	NUM
brj-23416	108	24	.	.	PUNCT
brj-23416	109	1	table	table	NOUN
brj-23416	109	2	2	2	NUM
brj-23416	109	3	.	.	PUNCT
brj-23416	110	1	ann	ann	PROPN
brj-23416	110	2	hyperparameter	hyperparameter	PROPN
brj-23416	110	3	candidates	candidate	NOUN
brj-23416	110	4	hyperparameters	hyperparameter	VERB
brj-23416	110	5	search	search	NOUN
brj-23416	110	6	space	space	NOUN
brj-23416	110	7	hidden	hide	VERB
brj-23416	110	8	size	size	NOUN
brj-23416	110	9	10	10	NUM
brj-23416	110	10	-	-	SYM
brj-23416	110	11	100	100	NUM
brj-23416	110	12	number	number	NOUN
brj-23416	110	13	epochs	epoch	VERB
brj-23416	110	14	50	50	NUM
brj-23416	110	15	-	-	SYM
brj-23416	110	16	200	200	NUM
brj-23416	110	17	weights	weight	NOUN
brj-23416	110	18	0.0001	0.0001	NUM
brj-23416	110	19	-	-	SYM
brj-23416	110	20	0.2	0.2	NUM
brj-23416	110	21	k	k	PROPN
brj-23416	110	22	nearest	near	ADJ
brj-23416	110	23	neighbor	neighbor	NOUN
brj-23416	110	24	(	(	PUNCT
brj-23416	110	25	knn	knn	PROPN
brj-23416	110	26	)	)	PUNCT
brj-23416	110	27	the	the	DET
brj-23416	110	28	k	k	NOUN
brj-23416	110	29	-	-	PUNCT
brj-23416	110	30	nearest	near	ADJ
brj-23416	110	31	neighbor	neighbor	NOUN
brj-23416	110	32	algorithm	algorithm	NOUN
brj-23416	110	33	,	,	PUNCT
brj-23416	110	34	defined	define	VERB
brj-23416	110	35	by	by	ADP
brj-23416	110	36	fix	fix	NOUN
brj-23416	110	37	and	and	CCONJ
brj-23416	110	38	hodges	hodge	NOUN
brj-23416	110	39	in	in	ADP
brj-23416	110	40	1951	1951	NUM
brj-23416	110	41	,	,	PUNCT
brj-23416	110	42	is	be	AUX
brj-23416	110	43	a	a	DET
brj-23416	110	44	nonparametric	nonparametric	ADJ
brj-23416	110	45	machine	machine	NOUN
brj-23416	110	46	learning	learning	NOUN
brj-23416	110	47	technique	technique	NOUN
brj-23416	110	48	used	use	VERB
brj-23416	110	49	for	for	ADP
brj-23416	110	50	classification	classification	NOUN
brj-23416	110	51	and	and	CCONJ
brj-23416	110	52	regression	regression	NOUN
brj-23416	110	53	based	base	VERB
brj-23416	110	54	on	on	ADP
brj-23416	110	55	linear	linear	ADJ
brj-23416	110	56	supervised	supervise	VERB
brj-23416	110	57	pattern	pattern	NOUN
brj-23416	110	58	recognition	recognition	NOUN
brj-23416	110	59	(	(	PUNCT
brj-23416	110	60	lu	lu	PROPN
brj-23416	110	61	and	and	CCONJ
brj-23416	110	62	zhu	zhu	PROPN
brj-23416	110	63	2014	2014	NUM
brj-23416	110	64	;	;	PUNCT
brj-23416	110	65	bhuvaneswari	bhuvaneswari	NOUN
brj-23416	110	66	and	and	CCONJ
brj-23416	110	67	therese	therese	PROPN
brj-23416	110	68	2015	2015	NUM
brj-23416	110	69	)	)	PUNCT
brj-23416	110	70	.	.	PUNCT
brj-23416	111	1	the	the	DET
brj-23416	111	2	application	application	NOUN
brj-23416	111	3	process	process	NOUN
brj-23416	111	4	of	of	ADP
brj-23416	111	5	the	the	DET
brj-23416	111	6	nearest	near	ADJ
brj-23416	111	7	neighbor	neighbor	NOUN
brj-23416	111	8	technique	technique	NOUN
brj-23416	111	9	in	in	ADP
brj-23416	111	10	this	this	DET
brj-23416	111	11	study	study	NOUN
brj-23416	111	12	is	be	AUX
brj-23416	111	13	as	as	SCONJ
brj-23416	111	14	follows	follow	VERB
brj-23416	111	15	:	:	PUNCT
brj-23416	111	16	first	first	ADV
brj-23416	111	17	,	,	PUNCT
brj-23416	111	18	the	the	DET
brj-23416	111	19	pandas	panda	NOUN
brj-23416	111	20	library	library	NOUN
brj-23416	111	21	was	be	AUX
brj-23416	111	22	included	include	VERB
brj-23416	111	23	into	into	ADP
brj-23416	111	24	the	the	DET
brj-23416	111	25	model	model	NOUN
brj-23416	111	26	to	to	PART
brj-23416	111	27	facilitate	facilitate	VERB
brj-23416	111	28	data	datum	NOUN
brj-23416	111	29	manipulation	manipulation	NOUN
brj-23416	111	30	and	and	CCONJ
brj-23416	111	31	analysis	analysis	NOUN
brj-23416	111	32	.	.	PUNCT
brj-23416	112	1	then	then	ADV
brj-23416	112	2	,	,	PUNCT
brj-23416	112	3	utilizing	utilize	VERB
brj-23416	112	4	the	the	DET
brj-23416	112	5	k	k	NOUN
brj-23416	112	6	-	-	PUNCT
brj-23416	112	7	neighbors	neighbor	NOUN
brj-23416	112	8	regressor	regressor	NOUN
brj-23416	112	9	(	(	PUNCT
brj-23416	112	10	yao	yao	NOUN
brj-23416	112	11	2023	2023	NUM
brj-23416	112	12	)	)	PUNCT
brj-23416	112	13	from	from	ADP
brj-23416	112	14	the	the	DET
brj-23416	112	15	sklearn.neighbors	sklearn.neighbor	NOUN
brj-23416	112	16	module	module	NOUN
brj-23416	112	17	,	,	PUNCT
brj-23416	112	18	value	value	NOUN
brj-23416	112	19	predictions	prediction	NOUN
brj-23416	112	20	have	have	AUX
brj-23416	112	21	been	be	AUX
brj-23416	112	22	generated	generate	VERB
brj-23416	112	23	according	accord	VERB
brj-23416	112	24	to	to	ADP
brj-23416	112	25	the	the	DET
brj-23416	112	26	closeness	closeness	NOUN
brj-23416	112	27	of	of	ADP
brj-23416	112	28	data	datum	NOUN
brj-23416	112	29	points	point	NOUN
brj-23416	112	30	.	.	PUNCT
brj-23416	113	1	the	the	DET
brj-23416	113	2	gridsearchcv	gridsearchcv	NOUN
brj-23416	113	3	framework	framework	NOUN
brj-23416	113	4	(	(	PUNCT
brj-23416	113	5	kudari	kudari	PROPN
brj-23416	113	6	et	et	PROPN
brj-23416	113	7	al	al	PROPN
brj-23416	113	8	.	.	PROPN
brj-23416	113	9	2021	2021	NUM
brj-23416	113	10	)	)	PUNCT
brj-23416	113	11	was	be	AUX
brj-23416	113	12	employed	employ	VERB
brj-23416	113	13	to	to	PART
brj-23416	113	14	ascertain	ascertain	VERB
brj-23416	113	15	and	and	CCONJ
brj-23416	113	16	optimize	optimize	VERB
brj-23416	113	17	the	the	DET
brj-23416	113	18	model	model	NOUN
brj-23416	113	19	's	's	PART
brj-23416	113	20	hyperparameters	hyperparameter	NOUN
brj-23416	113	21	.	.	PUNCT
brj-23416	114	1	in	in	ADP
brj-23416	114	2	the	the	DET
brj-23416	114	3	k	k	PROPN
brj-23416	114	4	nearest	near	ADJ
brj-23416	114	5	neighbor	neighbor	PROPN
brj-23416	114	6	algorithm	algorithm	PROPN
brj-23416	114	7	model	model	PROPN
brj-23416	114	8	,	,	PUNCT
brj-23416	114	9	learning	learn	VERB
brj-23416	114	10	rate	rate	NOUN
brj-23416	114	11	,	,	PUNCT
brj-23416	114	12	algorithm	algorithm	NOUN
brj-23416	114	13	,	,	PUNCT
brj-23416	114	14	and	and	CCONJ
brj-23416	114	15	n	n	PRON
brj-23416	114	16	neighbors	neighbor	NOUN
brj-23416	114	17	hyperparameters	hyperparameter	NOUN
brj-23416	114	18	were	be	AUX
brj-23416	114	19	analyzed	analyze	VERB
brj-23416	114	20	and	and	CCONJ
brj-23416	114	21	the	the	DET
brj-23416	114	22	usage	usage	NOUN
brj-23416	114	23	values	value	NOUN
brj-23416	114	24	of	of	ADP
brj-23416	114	25	the	the	DET
brj-23416	114	26	hyperparameters	hyperparameter	NOUN
brj-23416	114	27	were	be	AUX
brj-23416	114	28	given	give	VERB
brj-23416	114	29	in	in	ADP
brj-23416	114	30	table	table	NOUN
brj-23416	114	31	3	3	NUM
brj-23416	114	32	.	.	PUNCT
brj-23416	115	1	as	as	SCONJ
brj-23416	115	2	it	it	PRON
brj-23416	115	3	has	have	VERB
brj-23416	115	4	a	a	DET
brj-23416	115	5	direct	direct	ADJ
brj-23416	115	6	impact	impact	NOUN
brj-23416	115	7	on	on	ADP
brj-23416	115	8	the	the	DET
brj-23416	115	9	performance	performance	NOUN
brj-23416	115	10	of	of	ADP
brj-23416	115	11	the	the	DET
brj-23416	115	12	model	model	NOUN
brj-23416	115	13	,	,	PUNCT
brj-23416	115	14	the	the	DET
brj-23416	115	15	selection	selection	NOUN
brj-23416	115	16	of	of	ADP
brj-23416	115	17	the	the	DET
brj-23416	115	18	number	number	NOUN
brj-23416	115	19	of	of	ADP
brj-23416	115	20	nearest	near	ADJ
brj-23416	115	21	neighbors	neighbor	NOUN
brj-23416	115	22	(	(	PUNCT
brj-23416	115	23	k	k	X
brj-23416	115	24	)	)	PUNCT
brj-23416	115	25	is	be	AUX
brj-23416	115	26	critical	critical	ADJ
brj-23416	115	27	.	.	PUNCT
brj-23416	116	1	"	"	PUNCT
brj-23416	116	2	k	k	X
brj-23416	116	3	"	"	PUNCT
brj-23416	116	4	denotes	denote	NOUN
brj-23416	116	5	as	as	ADP
brj-23416	116	6	"	"	PUNCT
brj-23416	116	7	n	n	DET
brj-23416	116	8	neighbors	neighbor	NOUN
brj-23416	116	9	"	"	PUNCT
brj-23416	116	10	(	(	PUNCT
brj-23416	116	11	for	for	ADP
brj-23416	116	12	regression	regression	NOUN
brj-23416	116	13	)	)	PUNCT
brj-23416	116	14	in	in	ADP
brj-23416	116	15	python	python	NOUN
brj-23416	116	16	coding	coding	NOUN
brj-23416	116	17	.	.	PUNCT
brj-23416	117	1	demonstrating	demonstrate	VERB
brj-23416	117	2	sensitivity	sensitivity	NOUN
brj-23416	117	3	to	to	PART
brj-23416	117	4	noise	noise	VERB
brj-23416	117	5	in	in	ADP
brj-23416	117	6	the	the	DET
brj-23416	117	7	data	datum	NOUN
brj-23416	117	8	,	,	PUNCT
brj-23416	117	9	a	a	DET
brj-23416	117	10	reduced	reduced	ADJ
brj-23416	117	11	value	value	NOUN
brj-23416	117	12	of	of	ADP
brj-23416	117	13	'	'	PUNCT
brj-23416	117	14	k	k	X
brj-23416	117	15	'	'	PUNCT
brj-23416	117	16	may	may	AUX
brj-23416	117	17	result	result	VERB
brj-23416	117	18	in	in	ADP
brj-23416	117	19	overfitting	overfitte	VERB
brj-23416	117	20	.	.	PUNCT
brj-23416	118	1	a	a	DET
brj-23416	118	2	greater	great	ADJ
brj-23416	118	3	value	value	NOUN
brj-23416	118	4	of	of	ADP
brj-23416	118	5	'	'	PUNCT
brj-23416	118	6	k	k	X
brj-23416	118	7	'	'	PUNCT
brj-23416	118	8	,	,	PUNCT
brj-23416	118	9	on	on	ADP
brj-23416	118	10	the	the	DET
brj-23416	118	11	other	other	ADJ
brj-23416	118	12	hand	hand	NOUN
brj-23416	118	13	,	,	PUNCT
brj-23416	118	14	might	might	AUX
brj-23416	118	15	result	result	VERB
brj-23416	118	16	in	in	ADP
brj-23416	118	17	underfitting	underfitte	VERB
brj-23416	118	18	due	due	ADP
brj-23416	118	19	to	to	ADP
brj-23416	118	20	the	the	DET
brj-23416	118	21	smoothing	smoothing	NOUN
brj-23416	118	22	of	of	ADP
brj-23416	118	23	the	the	DET
brj-23416	118	24	decision	decision	NOUN
brj-23416	118	25	boundary	boundary	NOUN
brj-23416	118	26	.	.	PUNCT
brj-23416	119	1	this	this	DET
brj-23416	119	2	parameter	parameter	NOUN
brj-23416	119	3	was	be	AUX
brj-23416	119	4	chosen	choose	VERB
brj-23416	119	5	within	within	ADP
brj-23416	119	6	the	the	DET
brj-23416	119	7	range	range	NOUN
brj-23416	119	8	of	of	ADP
brj-23416	119	9	2	2	NUM
brj-23416	119	10	to	to	ADP
brj-23416	119	11	9	9	NUM
brj-23416	119	12	.	.	PUNCT
brj-23416	120	1	the	the	DET
brj-23416	120	2	manner	manner	NOUN
brj-23416	120	3	in	in	ADP
brj-23416	120	4	which	which	PRON
brj-23416	120	5	the	the	DET
brj-23416	120	6	distance	distance	NOUN
brj-23416	120	7	or	or	CCONJ
brj-23416	120	8	uniform	uniform	ADJ
brj-23416	120	9	weight	weight	NOUN
brj-23416	120	10	parameter	parameter	NOUN
brj-23416	120	11	is	be	AUX
brj-23416	120	12	utilized	utilize	VERB
brj-23416	120	13	to	to	PART
brj-23416	120	14	determine	determine	VERB
brj-23416	120	15	the	the	DET
brj-23416	120	16	contribution	contribution	NOUN
brj-23416	120	17	of	of	ADP
brj-23416	120	18	the	the	DET
brj-23416	120	19	neighbors	neighbor	NOUN
brj-23416	120	20	to	to	ADP
brj-23416	120	21	the	the	DET
brj-23416	120	22	output	output	NOUN
brj-23416	120	23	prediction	prediction	NOUN
brj-23416	120	24	process	process	NOUN
brj-23416	120	25	is	be	AUX
brj-23416	120	26	how	how	SCONJ
brj-23416	120	27	the	the	DET
brj-23416	120	28	neighbor	neighbor	NOUN
brj-23416	120	29	contributions	contribution	NOUN
brj-23416	120	30	are	be	AUX
brj-23416	120	31	weighted	weight	VERB
brj-23416	120	32	.	.	PUNCT
brj-23416	121	1	the	the	DET
brj-23416	121	2	process	process	NOUN
brj-23416	121	3	by	by	ADP
brj-23416	121	4	which	which	PRON
brj-23416	121	5	each	each	DET
brj-23416	121	6	neighbor	neighbor	NOUN
brj-23416	121	7	contributes	contribute	VERB
brj-23416	121	8	equally	equally	ADV
brj-23416	121	9	to	to	ADP
brj-23416	121	10	the	the	DET
brj-23416	121	11	forecast	forecast	NOUN
brj-23416	121	12	is	be	AUX
brj-23416	121	13	referred	refer	VERB
brj-23416	121	14	to	to	ADP
brj-23416	121	15	as	as	ADP
brj-23416	121	16	"	"	PUNCT
brj-23416	121	17	uniform	uniform	ADJ
brj-23416	121	18	weighting	weighting	NOUN
brj-23416	121	19	.	.	PUNCT
brj-23416	121	20	"	"	PUNCT
brj-23416	122	1	each	each	DET
brj-23416	122	2	neighbor	neighbor	NOUN
brj-23416	122	3	is	be	AUX
brj-23416	122	4	accorded	accord	VERB
brj-23416	122	5	equal	equal	ADJ
brj-23416	122	6	importance	importance	NOUN
brj-23416	122	7	in	in	ADP
brj-23416	122	8	this	this	DET
brj-23416	122	9	setting	setting	NOUN
brj-23416	122	10	.	.	PUNCT
brj-23416	123	1	in	in	ADP
brj-23416	123	2	contrast	contrast	NOUN
brj-23416	123	3	,	,	PUNCT
brj-23416	123	4	neighbors	neighbor	NOUN
brj-23416	123	5	are	be	AUX
brj-23416	123	6	weighed	weigh	VERB
brj-23416	123	7	according	accord	VERB
brj-23416	123	8	to	to	ADP
brj-23416	123	9	their	their	PRON
brj-23416	123	10	distance	distance	NOUN
brj-23416	123	11	from	from	ADP
brj-23416	123	12	the	the	DET
brj-23416	123	13	query	query	NOUN
brj-23416	123	14	point	point	NOUN
brj-23416	123	15	;	;	PUNCT
brj-23416	123	16	this	this	PRON
brj-23416	123	17	is	be	AUX
brj-23416	123	18	referred	refer	VERB
brj-23416	123	19	to	to	ADP
brj-23416	123	20	as	as	ADP
brj-23416	123	21	"	"	PUNCT
brj-23416	123	22	distance	distance	NOUN
brj-23416	123	23	"	"	PUNCT
brj-23416	123	24	weighting	weighting	NOUN
brj-23416	123	25	.	.	PUNCT
brj-23416	124	1	algorithm	algorithm	NOUN
brj-23416	124	2	is	be	AUX
brj-23416	124	3	another	another	DET
brj-23416	124	4	crucial	crucial	ADJ
brj-23416	124	5	element	element	NOUN
brj-23416	124	6	.	.	PUNCT
brj-23416	125	1	the	the	DET
brj-23416	125	2	algorithm	algorithm	NOUN
brj-23416	125	3	employed	employ	VERB
brj-23416	125	4	for	for	ADP
brj-23416	125	5	calculating	calculate	VERB
brj-23416	125	6	the	the	DET
brj-23416	125	7	nearest	near	ADJ
brj-23416	125	8	neighbors	neighbor	NOUN
brj-23416	125	9	is	be	AUX
brj-23416	125	10	determined	determine	VERB
brj-23416	125	11	by	by	ADP
brj-23416	125	12	this	this	DET
brj-23416	125	13	parameter	parameter	NOUN
brj-23416	125	14	.	.	PUNCT
brj-23416	126	1	"	"	PUNCT
brj-23416	126	2	ball	ball	NOUN
brj-23416	126	3	tree	tree	NOUN
brj-23416	126	4	,	,	PUNCT
brj-23416	126	5	"	"	PUNCT
brj-23416	126	6	"	"	PUNCT
brj-23416	126	7	kd	kd	PROPN
brj-23416	126	8	tree	tree	NOUN
brj-23416	126	9	,	,	PUNCT
brj-23416	126	10	"	"	PUNCT
brj-23416	126	11	and	and	CCONJ
brj-23416	126	12	"	"	PUNCT
brj-23416	126	13	brute	brute	ADJ
brj-23416	126	14	"	"	PUNCT
brj-23416	126	15	are	be	AUX
brj-23416	126	16	among	among	ADP
brj-23416	126	17	viable	viable	ADJ
brj-23416	126	18	choices	choice	NOUN
brj-23416	126	19	.	.	PUNCT
brj-23416	127	1	through	through	ADP
brj-23416	127	2	testing	test	VERB
brj-23416	127	3	each	each	PRON
brj-23416	127	4	of	of	ADP
brj-23416	127	5	them	they	PRON
brj-23416	127	6	,	,	PUNCT
brj-23416	127	7	the	the	DET
brj-23416	127	8	code	code	NOUN
brj-23416	127	9	's	's	PART
brj-23416	127	10	auto	auto	NOUN
brj-23416	127	11	-	-	PUNCT
brj-23416	127	12	function	function	NOUN
brj-23416	127	13	ascertains	ascertain	NOUN
brj-23416	127	14	which	which	DET
brj-23416	127	15	one	one	NOUN
brj-23416	127	16	is	be	AUX
brj-23416	127	17	most	most	ADV
brj-23416	127	18	appropriate	appropriate	ADJ
brj-23416	127	19	.	.	PUNCT
brj-23416	128	1	peer	peer	NOUN
brj-23416	128	2	-	-	PUNCT
brj-23416	128	3	reviewed	review	VERB
brj-23416	128	4	article	article	NOUN
brj-23416	128	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	128	6	akyüz	akyüz	PROPN
brj-23416	128	7	et	et	PROPN
brj-23416	128	8	al	al	PROPN
brj-23416	128	9	.	.	PROPN
brj-23416	129	1	(	(	PUNCT
brj-23416	129	2	2024	2024	NUM
brj-23416	129	3	)	)	PUNCT
brj-23416	129	4	.	.	PUNCT
brj-23416	130	1	“	"	PUNCT
brj-23416	130	2	stock	stock	NOUN
brj-23416	130	3	exchange	exchange	NOUN
brj-23416	130	4	values	value	NOUN
brj-23416	130	5	,	,	PUNCT
brj-23416	130	6	”	"	PUNCT
brj-23416	130	7	bioresources	bioresource	NOUN
brj-23416	130	8	19(3	19(3	NUM
brj-23416	130	9	)	)	PUNCT
brj-23416	130	10	,	,	PUNCT
brj-23416	130	11	5141	5141	NUM
brj-23416	130	12	-	-	SYM
brj-23416	130	13	5157	5157	NUM
brj-23416	130	14	.	.	PUNCT
brj-23416	131	1	5147	5147	NUM
brj-23416	131	2	table	table	NOUN
brj-23416	131	3	3	3	NUM
brj-23416	131	4	.	.	PUNCT
brj-23416	132	1	knn	knn	PROPN
brj-23416	132	2	hyperparameter	hyperparameter	PROPN
brj-23416	132	3	candidates	candidate	NOUN
brj-23416	132	4	hyperparameters	hyperparameter	VERB
brj-23416	132	5	search	search	NOUN
brj-23416	132	6	space	space	NOUN
brj-23416	132	7	n	n	NUM
brj-23416	132	8	neighbors	neighbor	NOUN
brj-23416	132	9	2	2	NUM
brj-23416	132	10	,	,	PUNCT
brj-23416	132	11	3	3	NUM
brj-23416	132	12	,	,	PUNCT
brj-23416	132	13	4	4	NUM
brj-23416	132	14	,	,	PUNCT
brj-23416	132	15	5	5	NUM
brj-23416	132	16	,	,	PUNCT
brj-23416	132	17	7	7	NUM
brj-23416	132	18	,	,	PUNCT
brj-23416	132	19	9	9	NUM
brj-23416	132	20	algorithm	algorithm	NOUN
brj-23416	132	21	auto	auto	NOUN
brj-23416	132	22	,	,	PUNCT
brj-23416	132	23	ball	ball	NOUN
brj-23416	132	24	tree	tree	NOUN
brj-23416	132	25	,	,	PUNCT
brj-23416	132	26	kd	kd	PROPN
brj-23416	132	27	tree	tree	NOUN
brj-23416	132	28	,	,	PUNCT
brj-23416	132	29	brute	brute	ADJ
brj-23416	132	30	learning	learning	NOUN
brj-23416	132	31	rate	rate	NOUN
brj-23416	132	32	uniform	uniform	NOUN
brj-23416	132	33	,	,	PUNCT
brj-23416	132	34	distance	distance	NOUN
brj-23416	132	35	gradient	gradient	NOUN
brj-23416	132	36	boosting	boost	VERB
brj-23416	132	37	machine	machine	NOUN
brj-23416	132	38	(	(	PUNCT
brj-23416	132	39	gbm	gbm	PROPN
brj-23416	132	40	)	)	PUNCT
brj-23416	132	41	for	for	ADP
brj-23416	132	42	the	the	DET
brj-23416	132	43	gbm	gbm	PROPN
brj-23416	132	44	model	model	NOUN
brj-23416	132	45	,	,	PUNCT
brj-23416	132	46	inaddition	inaddition	NOUN
brj-23416	132	47	to	to	ADP
brj-23416	132	48	the	the	DET
brj-23416	132	49	sklearn.ensemble	sklearn.ensemble	ADJ
brj-23416	132	50	module	module	NOUN
brj-23416	132	51	's	's	PART
brj-23416	132	52	gradient	gradient	NOUN
brj-23416	132	53	boosting	boost	VERB
brj-23416	132	54	regressor	regressor	NOUN
brj-23416	132	55	method	method	NOUN
brj-23416	132	56	(	(	PUNCT
brj-23416	132	57	rao	rao	NOUN
brj-23416	132	58	et	et	PROPN
brj-23416	132	59	al	al	PROPN
brj-23416	132	60	.	.	PROPN
brj-23416	132	61	2023	2023	NUM
brj-23416	132	62	)	)	PUNCT
brj-23416	132	63	,	,	PUNCT
brj-23416	132	64	the	the	DET
brj-23416	132	65	pandas	panda	NOUN
brj-23416	132	66	and	and	CCONJ
brj-23416	132	67	numpy	numpy	NOUN
brj-23416	132	68	libraries	library	NOUN
brj-23416	132	69	have	have	AUX
brj-23416	132	70	been	be	AUX
brj-23416	132	71	utilized	utilize	VERB
brj-23416	132	72	.	.	PUNCT
brj-23416	133	1	achieving	achieve	VERB
brj-23416	133	2	optimal	optimal	ADJ
brj-23416	133	3	tuning	tuning	NOUN
brj-23416	133	4	of	of	ADP
brj-23416	133	5	hyperparameters	hyperparameter	NOUN
brj-23416	133	6	is	be	AUX
brj-23416	133	7	critical	critical	ADJ
brj-23416	133	8	to	to	ADP
brj-23416	133	9	improving	improve	VERB
brj-23416	133	10	the	the	DET
brj-23416	133	11	accuracy	accuracy	NOUN
brj-23416	133	12	of	of	ADP
brj-23416	133	13	predictions	prediction	NOUN
brj-23416	133	14	and	and	CCONJ
brj-23416	133	15	the	the	DET
brj-23416	133	16	ability	ability	NOUN
brj-23416	133	17	of	of	ADP
brj-23416	133	18	the	the	DET
brj-23416	133	19	model	model	NOUN
brj-23416	133	20	to	to	PART
brj-23416	133	21	generalize	generalize	VERB
brj-23416	133	22	.	.	PUNCT
brj-23416	134	1	improving	improve	VERB
brj-23416	134	2	hyperparameters	hyperparameter	NOUN
brj-23416	134	3	delineates	delineate	VERB
brj-23416	134	4	a	a	DET
brj-23416	134	5	domain	domain	NOUN
brj-23416	134	6	containing	contain	VERB
brj-23416	134	7	probable	probable	ADJ
brj-23416	134	8	hyperparameter	hyperparameter	NOUN
brj-23416	134	9	values	value	NOUN
brj-23416	134	10	for	for	ADP
brj-23416	134	11	the	the	DET
brj-23416	134	12	gradient	gradient	NOUN
brj-23416	134	13	boosting	boost	VERB
brj-23416	134	14	model	model	NOUN
brj-23416	134	15	(	(	PUNCT
brj-23416	134	16	gbm	gbm	NOUN
brj-23416	134	17	)	)	PUNCT
brj-23416	134	18	.	.	PUNCT
brj-23416	135	1	this	this	DET
brj-23416	135	2	procedure	procedure	NOUN
brj-23416	135	3	involves	involve	VERB
brj-23416	135	4	the	the	DET
brj-23416	135	5	utilization	utilization	NOUN
brj-23416	135	6	of	of	ADP
brj-23416	135	7	several	several	ADJ
brj-23416	135	8	hyperparameters	hyperparameter	NOUN
brj-23416	135	9	,	,	PUNCT
brj-23416	135	10	including	include	VERB
brj-23416	135	11	the	the	DET
brj-23416	135	12	minimum	minimum	ADJ
brj-23416	135	13	sample	sample	NOUN
brj-23416	135	14	leaf	leaf	NOUN
brj-23416	135	15	,	,	PUNCT
brj-23416	135	16	learning	learning	NOUN
brj-23416	135	17	rate	rate	NOUN
brj-23416	135	18	,	,	PUNCT
brj-23416	135	19	maximum	maximum	ADJ
brj-23416	135	20	depth	depth	NOUN
brj-23416	135	21	,	,	PUNCT
brj-23416	135	22	and	and	CCONJ
brj-23416	135	23	n	n	PRON
brj-23416	135	24	estimators	estimator	NOUN
brj-23416	135	25	(	(	PUNCT
brj-23416	135	26	table	table	NOUN
brj-23416	135	27	4	4	NUM
brj-23416	135	28	)	)	PUNCT
brj-23416	135	29	.	.	PUNCT
brj-23416	136	1	the	the	DET
brj-23416	136	2	number	number	NOUN
brj-23416	136	3	of	of	ADP
brj-23416	136	4	trees	tree	NOUN
brj-23416	136	5	in	in	ADP
brj-23416	136	6	the	the	DET
brj-23416	136	7	forest	forest	NOUN
brj-23416	136	8	is	be	AUX
brj-23416	136	9	expressed	express	VERB
brj-23416	136	10	with	with	ADP
brj-23416	136	11	n	n	PRON
brj-23416	136	12	estimators	estimator	NOUN
brj-23416	136	13	.	.	PUNCT
brj-23416	137	1	in	in	ADP
brj-23416	137	2	general	general	ADJ
brj-23416	137	3	,	,	PUNCT
brj-23416	137	4	a	a	DET
brj-23416	137	5	greater	great	ADJ
brj-23416	137	6	number	number	NOUN
brj-23416	137	7	of	of	ADP
brj-23416	137	8	"	"	PUNCT
brj-23416	137	9	n	n	DET
brj-23416	137	10	estimators	estimator	NOUN
brj-23416	137	11	"	"	PUNCT
brj-23416	137	12	indicates	indicate	VERB
brj-23416	137	13	a	a	DET
brj-23416	137	14	model	model	NOUN
brj-23416	137	15	with	with	ADP
brj-23416	137	16	greater	great	ADJ
brj-23416	137	17	complexity	complexity	NOUN
brj-23416	137	18	.	.	PUNCT
brj-23416	138	1	this	this	DET
brj-23416	138	2	parameter	parameter	NOUN
brj-23416	138	3	was	be	AUX
brj-23416	138	4	chosen	choose	VERB
brj-23416	138	5	as	as	ADP
brj-23416	138	6	100	100	NUM
brj-23416	138	7	,	,	PUNCT
brj-23416	138	8	200	200	NUM
brj-23416	138	9	,	,	PUNCT
brj-23416	138	10	and	and	CCONJ
brj-23416	138	11	300	300	NUM
brj-23416	138	12	.	.	PUNCT
brj-23416	139	1	learning	learn	VERB
brj-23416	139	2	rate	rate	NOUN
brj-23416	139	3	is	be	AUX
brj-23416	139	4	the	the	DET
brj-23416	139	5	term	term	NOUN
brj-23416	139	6	used	use	VERB
brj-23416	139	7	to	to	PART
brj-23416	139	8	describe	describe	VERB
brj-23416	139	9	the	the	DET
brj-23416	139	10	speed	speed	NOUN
brj-23416	139	11	at	at	ADP
brj-23416	139	12	which	which	PRON
brj-23416	139	13	modifications	modification	NOUN
brj-23416	139	14	are	be	AUX
brj-23416	139	15	implemented	implement	VERB
brj-23416	139	16	at	at	ADP
brj-23416	139	17	each	each	DET
brj-23416	139	18	stage	stage	NOUN
brj-23416	139	19	.	.	PUNCT
brj-23416	140	1	in	in	ADP
brj-23416	140	2	this	this	DET
brj-23416	140	3	study	study	NOUN
brj-23416	140	4	,	,	PUNCT
brj-23416	140	5	learning	learn	VERB
brj-23416	140	6	rates	rate	NOUN
brj-23416	140	7	such	such	ADJ
brj-23416	140	8	as	as	ADP
brj-23416	140	9	0.01	0.01	NUM
brj-23416	140	10	,	,	PUNCT
brj-23416	140	11	0.1	0.1	NUM
brj-23416	140	12	,	,	PUNCT
brj-23416	140	13	and	and	CCONJ
brj-23416	140	14	0.2	0.2	NUM
brj-23416	140	15	have	have	AUX
brj-23416	140	16	been	be	AUX
brj-23416	140	17	tried	try	VERB
brj-23416	140	18	.	.	PUNCT
brj-23416	141	1	the	the	DET
brj-23416	141	2	parameter	parameter	NOUN
brj-23416	141	3	named	name	VERB
brj-23416	141	4	max	max	NOUN
brj-23416	141	5	depth	depth	NOUN
brj-23416	141	6	delineates	delineate	VERB
brj-23416	141	7	the	the	DET
brj-23416	141	8	maximum	maximum	ADJ
brj-23416	141	9	depth	depth	NOUN
brj-23416	141	10	that	that	SCONJ
brj-23416	141	11	a	a	DET
brj-23416	141	12	tree	tree	NOUN
brj-23416	141	13	inside	inside	ADP
brj-23416	141	14	the	the	DET
brj-23416	141	15	model	model	NOUN
brj-23416	141	16	can	can	AUX
brj-23416	141	17	attain	attain	VERB
brj-23416	141	18	.	.	PUNCT
brj-23416	142	1	this	this	DET
brj-23416	142	2	parameter	parameter	NOUN
brj-23416	142	3	was	be	AUX
brj-23416	142	4	chosen	choose	VERB
brj-23416	142	5	as	as	ADP
brj-23416	142	6	3	3	NUM
brj-23416	142	7	,	,	PUNCT
brj-23416	142	8	4	4	NUM
brj-23416	142	9	,	,	PUNCT
brj-23416	142	10	and	and	CCONJ
brj-23416	142	11	5	5	NUM
brj-23416	142	12	.	.	X
brj-23416	143	1	another	another	DET
brj-23416	143	2	hyperparameter	hyperparameter	NOUN
brj-23416	143	3	utilized	utilize	VERB
brj-23416	143	4	by	by	ADP
brj-23416	143	5	decision	decision	NOUN
brj-23416	143	6	tree	tree	NOUN
brj-23416	143	7	algorithms	algorithm	NOUN
brj-23416	143	8	is	be	AUX
brj-23416	143	9	"	"	PUNCT
brj-23416	143	10	min	min	NOUN
brj-23416	143	11	samples	sample	NOUN
brj-23416	143	12	split	split	VERB
brj-23416	143	13	.	.	PUNCT
brj-23416	143	14	"	"	PUNCT
brj-23416	144	1	it	it	PRON
brj-23416	144	2	specifies	specify	VERB
brj-23416	144	3	the	the	DET
brj-23416	144	4	minimum	minimum	ADJ
brj-23416	144	5	number	number	NOUN
brj-23416	144	6	of	of	ADP
brj-23416	144	7	samples	sample	NOUN
brj-23416	144	8	that	that	PRON
brj-23416	144	9	are	be	AUX
brj-23416	144	10	necessary	necessary	ADJ
brj-23416	144	11	to	to	PART
brj-23416	144	12	further	far	ADV
brj-23416	144	13	divide	divide	VERB
brj-23416	144	14	an	an	DET
brj-23416	144	15	internal	internal	ADJ
brj-23416	144	16	node	node	NOUN
brj-23416	144	17	into	into	ADP
brj-23416	144	18	child	child	NOUN
brj-23416	144	19	nodes	node	NOUN
brj-23416	144	20	.	.	PUNCT
brj-23416	145	1	in	in	ADP
brj-23416	145	2	this	this	DET
brj-23416	145	3	study	study	NOUN
brj-23416	145	4	,	,	PUNCT
brj-23416	145	5	it	it	PRON
brj-23416	145	6	was	be	AUX
brj-23416	145	7	chosen	choose	VERB
brj-23416	145	8	as	as	ADP
brj-23416	145	9	2	2	NUM
brj-23416	145	10	,	,	PUNCT
brj-23416	145	11	3	3	NUM
brj-23416	145	12	,	,	PUNCT
brj-23416	145	13	and	and	CCONJ
brj-23416	145	14	4	4	X
brj-23416	145	15	.	.	X
brj-23416	145	16	a	a	DET
brj-23416	145	17	hyperparameter	hyperparameter	NOUN
brj-23416	145	18	utilized	utilize	VERB
brj-23416	145	19	in	in	ADP
brj-23416	145	20	ensemble	ensemble	ADJ
brj-23416	145	21	methods	method	NOUN
brj-23416	145	22	such	such	ADJ
brj-23416	145	23	as	as	ADP
brj-23416	145	24	random	random	ADJ
brj-23416	145	25	forests	forest	NOUN
brj-23416	145	26	and	and	CCONJ
brj-23416	145	27	gradient	gradient	ADJ
brj-23416	145	28	boosting	boost	VERB
brj-23416	145	29	machines	machine	NOUN
brj-23416	145	30	,	,	PUNCT
brj-23416	145	31	"	"	PUNCT
brj-23416	145	32	min	min	NOUN
brj-23416	145	33	samples	sample	NOUN
brj-23416	145	34	leaf	leaf	NOUN
brj-23416	145	35	"	"	PUNCT
brj-23416	145	36	is	be	AUX
brj-23416	145	37	a	a	DET
brj-23416	145	38	component	component	NOUN
brj-23416	145	39	of	of	ADP
brj-23416	145	40	decision	decision	NOUN
brj-23416	145	41	tree	tree	NOUN
brj-23416	145	42	algorithms	algorithm	NOUN
brj-23416	145	43	(	(	PUNCT
brj-23416	145	44	gbms	gbms	NOUN
brj-23416	145	45	)	)	PUNCT
brj-23416	145	46	.	.	PUNCT
brj-23416	146	1	the	the	DET
brj-23416	146	2	numbers	number	NOUN
brj-23416	146	3	1	1	NUM
brj-23416	146	4	,	,	PUNCT
brj-23416	146	5	2	2	NUM
brj-23416	146	6	,	,	PUNCT
brj-23416	146	7	and	and	CCONJ
brj-23416	146	8	3	3	NUM
brj-23416	146	9	were	be	AUX
brj-23416	146	10	utilized	utilize	VERB
brj-23416	146	11	as	as	ADP
brj-23416	146	12	min	min	NOUN
brj-23416	146	13	samples	sample	NOUN
brj-23416	146	14	leaf	leaf	NOUN
brj-23416	146	15	.	.	PUNCT
brj-23416	147	1	gbm	gbm	PROPN
brj-23416	147	2	is	be	AUX
brj-23416	147	3	the	the	DET
brj-23416	147	4	moniker	moniker	NOUN
brj-23416	147	5	given	give	VERB
brj-23416	147	6	to	to	ADP
brj-23416	147	7	the	the	DET
brj-23416	147	8	gradientboostingregressor	gradientboostingregressor	NOUN
brj-23416	147	9	model	model	NOUN
brj-23416	147	10	that	that	PRON
brj-23416	147	11	is	be	AUX
brj-23416	147	12	constructed	construct	VERB
brj-23416	147	13	with	with	ADP
brj-23416	147	14	random	random	ADJ
brj-23416	147	15	state	state	NOUN
brj-23416	147	16	set	set	VERB
brj-23416	147	17	to	to	ADP
brj-23416	147	18	42	42	NUM
brj-23416	147	19	.	.	PUNCT
brj-23416	148	1	table	table	NOUN
brj-23416	148	2	4	4	NUM
brj-23416	148	3	.	.	PUNCT
brj-23416	149	1	gbm	gbm	PROPN
brj-23416	149	2	hyperparameter	hyperparameter	NOUN
brj-23416	149	3	candidates	candidate	NOUN
brj-23416	149	4	hyperparameters	hyperparameter	VERB
brj-23416	149	5	search	search	NOUN
brj-23416	149	6	space	space	NOUN
brj-23416	149	7	n	n	NOUN
brj-23416	149	8	estimators	estimator	NOUN
brj-23416	149	9	100	100	NUM
brj-23416	149	10	,	,	PUNCT
brj-23416	149	11	200	200	NUM
brj-23416	149	12	,	,	PUNCT
brj-23416	149	13	300	300	NUM
brj-23416	149	14	learning	learning	NOUN
brj-23416	149	15	rate	rate	NOUN
brj-23416	149	16	0.01	0.01	NUM
brj-23416	149	17	,	,	PUNCT
brj-23416	149	18	0.1	0.1	NUM
brj-23416	149	19	,	,	PUNCT
brj-23416	149	20	0.2	0.2	NUM
brj-23416	149	21	max	max	NOUN
brj-23416	149	22	depth	depth	NOUN
brj-23416	149	23	3	3	NUM
brj-23416	149	24	,	,	PUNCT
brj-23416	149	25	4	4	NUM
brj-23416	149	26	,	,	PUNCT
brj-23416	149	27	5	5	NUM
brj-23416	149	28	min	min	NOUN
brj-23416	149	29	samples	sample	NOUN
brj-23416	149	30	leaf	leaf	NOUN
brj-23416	149	31	1	1	NUM
brj-23416	149	32	,	,	PUNCT
brj-23416	149	33	2	2	NUM
brj-23416	149	34	,	,	PUNCT
brj-23416	149	35	3	3	NUM
brj-23416	149	36	min	min	NOUN
brj-23416	149	37	samples	sample	NOUN
brj-23416	149	38	split	split	VERB
brj-23416	149	39	2	2	NUM
brj-23416	149	40	,	,	PUNCT
brj-23416	149	41	3	3	NUM
brj-23416	149	42	,	,	PUNCT
brj-23416	149	43	4	4	NUM
brj-23416	149	44	random	random	ADJ
brj-23416	149	45	forest	forest	NOUN
brj-23416	149	46	(	(	PUNCT
brj-23416	149	47	rf	rf	NOUN
brj-23416	149	48	)	)	PUNCT
brj-23416	149	49	in	in	ADP
brj-23416	149	50	this	this	DET
brj-23416	149	51	study	study	NOUN
brj-23416	149	52	,	,	PUNCT
brj-23416	149	53	firstly	firstly	ADV
brj-23416	149	54	,	,	PUNCT
brj-23416	149	55	the	the	DET
brj-23416	149	56	data	data	NOUN
brj-23416	149	57	collection	collection	NOUN
brj-23416	149	58	was	be	AUX
brj-23416	149	59	loaded	load	VERB
brj-23416	149	60	and	and	CCONJ
brj-23416	149	61	processed	process	VERB
brj-23416	149	62	with	with	ADP
brj-23416	149	63	the	the	DET
brj-23416	149	64	pandas	panda	NOUN
brj-23416	149	65	library	library	NOUN
brj-23416	149	66	.	.	PUNCT
brj-23416	150	1	in	in	ADP
brj-23416	150	2	the	the	DET
brj-23416	150	3	realm	realm	NOUN
brj-23416	150	4	of	of	ADP
brj-23416	150	5	broad	broad	ADJ
brj-23416	150	6	mathematical	mathematical	ADJ
brj-23416	150	7	and	and	CCONJ
brj-23416	150	8	numerical	numerical	ADJ
brj-23416	150	9	operations	operation	NOUN
brj-23416	150	10	,	,	PUNCT
brj-23416	150	11	numpy	numpy	NOUN
brj-23416	150	12	has	have	AUX
brj-23416	150	13	been	be	AUX
brj-23416	150	14	favored	favor	VERB
brj-23416	150	15	.	.	PUNCT
brj-23416	151	1	utilization	utilization	NOUN
brj-23416	151	2	has	have	AUX
brj-23416	151	3	been	be	AUX
brj-23416	151	4	made	make	VERB
brj-23416	151	5	of	of	ADP
brj-23416	151	6	the	the	DET
brj-23416	151	7	random	random	ADJ
brj-23416	151	8	forest	forest	NOUN
brj-23416	151	9	regressor	regressor	NOUN
brj-23416	151	10	function	function	NOUN
brj-23416	151	11	(	(	PUNCT
brj-23416	151	12	el	el	PROPN
brj-23416	151	13	mrabet	mrabet	PROPN
brj-23416	151	14	et	et	PROPN
brj-23416	151	15	al	al	PROPN
brj-23416	151	16	.	.	PROPN
brj-23416	151	17	2022	2022	NUM
brj-23416	151	18	)	)	PUNCT
brj-23416	151	19	from	from	ADP
brj-23416	151	20	the	the	DET
brj-23416	151	21	sklearn.ensemble	sklearn.ensemble	ADJ
brj-23416	151	22	module	module	NOUN
brj-23416	151	23	.	.	PUNCT
brj-23416	152	1	when	when	SCONJ
brj-23416	152	2	optimizing	optimize	VERB
brj-23416	152	3	hyperparameters	hyperparameter	NOUN
brj-23416	152	4	for	for	ADP
brj-23416	152	5	the	the	DET
brj-23416	152	6	random	random	ADJ
brj-23416	152	7	forest	forest	NOUN
brj-23416	152	8	model	model	NOUN
brj-23416	152	9	,	,	PUNCT
brj-23416	152	10	several	several	ADJ
brj-23416	152	11	factors	factor	NOUN
brj-23416	152	12	must	must	AUX
brj-23416	152	13	be	be	AUX
brj-23416	152	14	considered	consider	VERB
brj-23416	152	15	(	(	PUNCT
brj-23416	152	16	contreras	contreras	PROPN
brj-23416	152	17	et	et	PROPN
brj-23416	152	18	al	al	PROPN
brj-23416	152	19	.	.	PROPN
brj-23416	152	20	2021	2021	NUM
brj-23416	152	21	;	;	PUNCT
brj-23416	152	22	virro	virro	PROPN
brj-23416	152	23	et	et	PROPN
brj-23416	152	24	al	al	PROPN
brj-23416	152	25	.	.	PROPN
brj-23416	152	26	2022	2022	NUM
brj-23416	152	27	;	;	PUNCT
brj-23416	152	28	lee	lee	PROPN
brj-23416	152	29	et	et	PROPN
brj-23416	152	30	al	al	PROPN
brj-23416	152	31	.	.	PROPN
brj-23416	152	32	2023	2023	NUM
brj-23416	152	33	;	;	PUNCT
brj-23416	152	34	sandunil	sandunil	NOUN
brj-23416	152	35	et	et	PROPN
brj-23416	152	36	al	al	PROPN
brj-23416	152	37	.	.	PROPN
brj-23416	152	38	2023	2023	NUM
brj-23416	152	39	)	)	PUNCT
brj-23416	152	40	.	.	PUNCT
brj-23416	153	1	as	as	SCONJ
brj-23416	153	2	can	can	AUX
brj-23416	153	3	be	be	AUX
brj-23416	153	4	seen	see	VERB
brj-23416	153	5	in	in	ADP
brj-23416	153	6	table	table	NOUN
brj-23416	153	7	5	5	NUM
brj-23416	153	8	,	,	PUNCT
brj-23416	153	9	six	six	NUM
brj-23416	153	10	hyperparameter	hyperparameter	NOUN
brj-23416	153	11	analyzes	analyze	NOUN
brj-23416	153	12	were	be	AUX
brj-23416	153	13	performed	perform	VERB
brj-23416	153	14	to	to	PART
brj-23416	153	15	tune	tune	VERB
brj-23416	153	16	the	the	DET
brj-23416	153	17	random	random	ADJ
brj-23416	153	18	forests	forest	NOUN
brj-23416	153	19	model	model	NOUN
brj-23416	153	20	.	.	PUNCT
brj-23416	154	1	these	these	DET
brj-23416	154	2	peer	peer	NOUN
brj-23416	154	3	-	-	PUNCT
brj-23416	154	4	reviewed	review	VERB
brj-23416	154	5	article	article	NOUN
brj-23416	154	6	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	154	7	akyüz	akyüz	PROPN
brj-23416	154	8	et	et	PROPN
brj-23416	154	9	al	al	PROPN
brj-23416	154	10	.	.	PROPN
brj-23416	155	1	(	(	PUNCT
brj-23416	155	2	2024	2024	NUM
brj-23416	155	3	)	)	PUNCT
brj-23416	155	4	.	.	PUNCT
brj-23416	156	1	“	"	PUNCT
brj-23416	156	2	stock	stock	NOUN
brj-23416	156	3	exchange	exchange	NOUN
brj-23416	156	4	values	value	NOUN
brj-23416	156	5	,	,	PUNCT
brj-23416	156	6	”	"	PUNCT
brj-23416	156	7	bioresources	bioresource	NOUN
brj-23416	156	8	19(3	19(3	NUM
brj-23416	156	9	)	)	PUNCT
brj-23416	156	10	,	,	PUNCT
brj-23416	156	11	5141	5141	NUM
brj-23416	156	12	-	-	SYM
brj-23416	156	13	5157	5157	NUM
brj-23416	156	14	.	.	PUNCT
brj-23416	157	1	5148	5148	NUM
brj-23416	157	2	parameters	parameter	NOUN
brj-23416	157	3	are	be	AUX
brj-23416	157	4	:	:	PUNCT
brj-23416	157	5	n	n	PRON
brj-23416	157	6	estimators	estimator	NOUN
brj-23416	157	7	,	,	PUNCT
brj-23416	157	8	max	max	PROPN
brj-23416	157	9	features	feature	NOUN
brj-23416	157	10	,	,	PUNCT
brj-23416	157	11	min	min	NOUN
brj-23416	157	12	samples	sample	NOUN
brj-23416	157	13	split	split	VERB
brj-23416	157	14	,	,	PUNCT
brj-23416	157	15	learning	learn	VERB
brj-23416	157	16	rate	rate	NOUN
brj-23416	157	17	,	,	PUNCT
brj-23416	157	18	max	max	PROPN
brj-23416	157	19	depth	depth	NOUN
brj-23416	157	20	,	,	PUNCT
brj-23416	157	21	and	and	CCONJ
brj-23416	157	22	min	min	PROPN
brj-23416	157	23	sample	sample	NOUN
brj-23416	157	24	leaf	leaf	NOUN
brj-23416	157	25	.	.	PUNCT
brj-23416	158	1	with	with	ADP
brj-23416	158	2	the	the	DET
brj-23416	158	3	exception	exception	NOUN
brj-23416	158	4	of	of	ADP
brj-23416	158	5	the	the	DET
brj-23416	158	6	max	max	PROPN
brj-23416	158	7	feature	feature	NOUN
brj-23416	158	8	parameter	parameter	NOUN
brj-23416	158	9	,	,	PUNCT
brj-23416	158	10	all	all	DET
brj-23416	158	11	other	other	ADJ
brj-23416	158	12	parameters	parameter	NOUN
brj-23416	158	13	are	be	AUX
brj-23416	158	14	identical	identical	ADJ
brj-23416	158	15	to	to	ADP
brj-23416	158	16	those	those	PRON
brj-23416	158	17	of	of	ADP
brj-23416	158	18	the	the	DET
brj-23416	158	19	bgm	bgm	PROPN
brj-23416	158	20	model	model	NOUN
brj-23416	158	21	.	.	PUNCT
brj-23416	159	1	the	the	DET
brj-23416	159	2	"	"	PUNCT
brj-23416	159	3	max	max	PROPN
brj-23416	159	4	features	feature	NOUN
brj-23416	159	5	"	"	PUNCT
brj-23416	159	6	is	be	AUX
brj-23416	159	7	a	a	DET
brj-23416	159	8	hyperparameter	hyperparameter	NOUN
brj-23416	159	9	used	use	VERB
brj-23416	159	10	in	in	ADP
brj-23416	159	11	the	the	DET
brj-23416	159	12	random	random	ADJ
brj-23416	159	13	forest	forest	NOUN
brj-23416	159	14	technique	technique	NOUN
brj-23416	159	15	,	,	PUNCT
brj-23416	159	16	which	which	PRON
brj-23416	159	17	governs	govern	VERB
brj-23416	159	18	the	the	DET
brj-23416	159	19	quantity	quantity	NOUN
brj-23416	159	20	of	of	ADP
brj-23416	159	21	features	feature	NOUN
brj-23416	159	22	to	to	PART
brj-23416	159	23	be	be	AUX
brj-23416	159	24	taken	take	VERB
brj-23416	159	25	into	into	ADP
brj-23416	159	26	account	account	NOUN
brj-23416	159	27	while	while	SCONJ
brj-23416	159	28	constructing	construct	VERB
brj-23416	159	29	trees	tree	NOUN
brj-23416	159	30	and	and	CCONJ
brj-23416	159	31	identifying	identify	VERB
brj-23416	159	32	the	the	DET
brj-23416	159	33	optimal	optimal	ADJ
brj-23416	159	34	split	split	NOUN
brj-23416	159	35	.	.	PUNCT
brj-23416	160	1	the	the	DET
brj-23416	160	2	choices	choice	NOUN
brj-23416	160	3	are	be	AUX
brj-23416	160	4	sqrt	sqrt	NOUN
brj-23416	160	5	,	,	PUNCT
brj-23416	160	6	log2	log2	PROPN
brj-23416	160	7	,	,	PUNCT
brj-23416	160	8	and	and	CCONJ
brj-23416	160	9	none	none	NOUN
brj-23416	160	10	.	.	PUNCT
brj-23416	161	1	the	the	DET
brj-23416	161	2	values	value	NOUN
brj-23416	161	3	of	of	ADP
brj-23416	161	4	other	other	ADJ
brj-23416	161	5	hyperparameters	hyperparameter	NOUN
brj-23416	161	6	were	be	AUX
brj-23416	161	7	determined	determine	VERB
brj-23416	161	8	as	as	ADP
brj-23416	161	9	n	n	DET
brj-23416	161	10	estimators	estimator	NOUN
brj-23416	161	11	:	:	PUNCT
brj-23416	161	12	100	100	NUM
brj-23416	161	13	,	,	PUNCT
brj-23416	161	14	200	200	NUM
brj-23416	161	15	,	,	PUNCT
brj-23416	161	16	300	300	NUM
brj-23416	161	17	,	,	PUNCT
brj-23416	161	18	learning	learn	VERB
brj-23416	161	19	rate	rate	NOUN
brj-23416	161	20	:	:	PUNCT
brj-23416	161	21	0.01	0.01	NUM
brj-23416	161	22	,	,	PUNCT
brj-23416	161	23	0.1	0.1	NUM
brj-23416	161	24	,	,	PUNCT
brj-23416	161	25	0.2	0.2	NUM
brj-23416	161	26	,	,	PUNCT
brj-23416	161	27	max	max	PROPN
brj-23416	161	28	depth	depth	NOUN
brj-23416	161	29	:	:	PUNCT
brj-23416	161	30	4	4	NUM
brj-23416	161	31	,	,	PUNCT
brj-23416	161	32	6	6	NUM
brj-23416	161	33	,	,	PUNCT
brj-23416	161	34	8	8	NUM
brj-23416	161	35	,	,	PUNCT
brj-23416	161	36	10	10	NUM
brj-23416	161	37	,	,	PUNCT
brj-23416	161	38	min	min	NOUN
brj-23416	161	39	sample	sample	NOUN
brj-23416	161	40	leaf	leaf	NOUN
brj-23416	161	41	:	:	PUNCT
brj-23416	161	42	1	1	NUM
brj-23416	161	43	,	,	PUNCT
brj-23416	161	44	2	2	NUM
brj-23416	161	45	,	,	PUNCT
brj-23416	161	46	3	3	NUM
brj-23416	161	47	,	,	PUNCT
brj-23416	161	48	min	min	PROPN
brj-23416	161	49	sample	sample	NOUN
brj-23416	161	50	split	split	NOUN
brj-23416	161	51	:	:	PUNCT
brj-23416	162	1	2	2	NUM
brj-23416	162	2	,	,	PUNCT
brj-23416	162	3	5	5	NUM
brj-23416	162	4	,	,	PUNCT
brj-23416	162	5	10	10	NUM
brj-23416	162	6	.	.	PUNCT
brj-23416	162	7	table	table	NOUN
brj-23416	162	8	5	5	NUM
brj-23416	162	9	.	.	PUNCT
brj-23416	163	1	rf	rf	VERB
brj-23416	163	2	hyperparameter	hyperparameter	NOUN
brj-23416	163	3	candidates	candidate	NOUN
brj-23416	163	4	hyperparameters	hyperparameter	VERB
brj-23416	163	5	search	search	NOUN
brj-23416	163	6	space	space	NOUN
brj-23416	163	7	n	n	NOUN
brj-23416	163	8	estimators	estimator	NOUN
brj-23416	163	9	100,200,300	100,200,300	NUM
brj-23416	163	10	learning	learning	NOUN
brj-23416	163	11	rate	rate	NOUN
brj-23416	163	12	0.01,0.1,0.2	0.01,0.1,0.2	NOUN
brj-23416	164	1	max	max	PROPN
brj-23416	164	2	depth	depth	NOUN
brj-23416	164	3	4,6,8,10	4,6,8,10	PRON
brj-23416	164	4	max	max	PROPN
brj-23416	164	5	features	feature	NOUN
brj-23416	164	6	sqrt	sqrt	NOUN
brj-23416	164	7	,	,	PUNCT
brj-23416	164	8	log2	log2	PROPN
brj-23416	164	9	,	,	PUNCT
brj-23416	164	10	none	none	NOUN
brj-23416	164	11	min	min	NOUN
brj-23416	164	12	samples	sample	NOUN
brj-23416	164	13	leaf	leaf	NOUN
brj-23416	164	14	1,2,4	1,2,4	NUM
brj-23416	164	15	min	min	NOUN
brj-23416	164	16	samples	sample	NOUN
brj-23416	164	17	split	split	VERB
brj-23416	164	18	2,5,10	2,5,10	NUM
brj-23416	164	19	evaluation	evaluation	NOUN
brj-23416	164	20	of	of	ADP
brj-23416	164	21	models	model	NOUN
brj-23416	164	22	coefficient	coefficient	NOUN
brj-23416	164	23	of	of	ADP
brj-23416	164	24	determination	determination	NOUN
brj-23416	164	25	(	(	PUNCT
brj-23416	164	26	r2	r2	PROPN
brj-23416	164	27	)	)	PUNCT
brj-23416	164	28	,	,	PUNCT
brj-23416	164	29	mean	mean	VERB
brj-23416	164	30	absolute	absolute	ADJ
brj-23416	164	31	error	error	NOUN
brj-23416	164	32	(	(	PUNCT
brj-23416	164	33	mae	mae	PROPN
brj-23416	164	34	)	)	PUNCT
brj-23416	164	35	,	,	PUNCT
brj-23416	164	36	and	and	CCONJ
brj-23416	164	37	root	root	NOUN
brj-23416	164	38	mean	mean	VERB
brj-23416	164	39	square	square	ADJ
brj-23416	164	40	error	error	NOUN
brj-23416	164	41	(	(	PUNCT
brj-23416	164	42	rmse	rmse	NOUN
brj-23416	164	43	)	)	PUNCT
brj-23416	164	44	were	be	AUX
brj-23416	164	45	used	use	VERB
brj-23416	164	46	to	to	PART
brj-23416	164	47	measure	measure	VERB
brj-23416	164	48	the	the	DET
brj-23416	164	49	performance	performance	NOUN
brj-23416	164	50	of	of	ADP
brj-23416	164	51	all	all	DET
brj-23416	164	52	machine	machine	NOUN
brj-23416	164	53	learning	learning	NOUN
brj-23416	164	54	models	model	NOUN
brj-23416	164	55	.	.	PUNCT
brj-23416	165	1	table	table	NOUN
brj-23416	165	2	6	6	NUM
brj-23416	165	3	includes	include	VERB
brj-23416	165	4	the	the	DET
brj-23416	165	5	mathematical	mathematical	ADJ
brj-23416	165	6	formulas	formula	NOUN
brj-23416	165	7	and	and	CCONJ
brj-23416	165	8	definitions	definition	NOUN
brj-23416	165	9	of	of	ADP
brj-23416	165	10	the	the	DET
brj-23416	165	11	performance	performance	NOUN
brj-23416	165	12	metrics	metric	NOUN
brj-23416	165	13	used	use	VERB
brj-23416	165	14	in	in	ADP
brj-23416	165	15	the	the	DET
brj-23416	165	16	research	research	NOUN
brj-23416	165	17	.	.	PUNCT
brj-23416	166	1	k	k	ADJ
brj-23416	166	2	-	-	PUNCT
brj-23416	166	3	cross	cross	ADJ
brj-23416	166	4	validation	validation	NOUN
brj-23416	166	5	technique	technique	NOUN
brj-23416	166	6	was	be	AUX
brj-23416	166	7	utilized	utilize	VERB
brj-23416	166	8	to	to	PART
brj-23416	166	9	evaluate	evaluate	VERB
brj-23416	166	10	model	model	NOUN
brj-23416	166	11	performances	performance	NOUN
brj-23416	166	12	more	more	ADV
brj-23416	166	13	accurately	accurately	ADV
brj-23416	166	14	and	and	CCONJ
brj-23416	166	15	objectively	objectively	ADV
brj-23416	166	16	.	.	PUNCT
brj-23416	167	1	in	in	ADP
brj-23416	167	2	this	this	DET
brj-23416	167	3	study	study	NOUN
brj-23416	167	4	,	,	PUNCT
brj-23416	167	5	3	3	NUM
brj-23416	167	6	-	-	ADJ
brj-23416	167	7	fold	fold	ADJ
brj-23416	167	8	cross	cross	NOUN
brj-23416	167	9	validation	validation	NOUN
brj-23416	167	10	was	be	AUX
brj-23416	167	11	used	use	VERB
brj-23416	167	12	on	on	ADP
brj-23416	167	13	the	the	DET
brj-23416	167	14	training	training	NOUN
brj-23416	167	15	set	set	NOUN
brj-23416	167	16	.	.	PUNCT
brj-23416	168	1	table	table	NOUN
brj-23416	168	2	6	6	NUM
brj-23416	168	3	.	.	PUNCT
brj-23416	169	1	performance	performance	NOUN
brj-23416	169	2	metrics	metric	NOUN
brj-23416	169	3	used	use	VERB
brj-23416	169	4	in	in	ADP
brj-23416	169	5	the	the	DET
brj-23416	169	6	study	study	NOUN
brj-23416	169	7	(	(	PUNCT
brj-23416	169	8	botchkarev	botchkarev	NOUN
brj-23416	169	9	2018	2018	NUM
brj-23416	169	10	;	;	PUNCT
brj-23416	169	11	correajullian	correajullian	PROPN
brj-23416	169	12	et	et	PROPN
brj-23416	169	13	al	al	PROPN
brj-23416	169	14	.	.	PROPN
brj-23416	169	15	2020	2020	NUM
brj-23416	169	16	;	;	PUNCT
brj-23416	169	17	gao	gao	PROPN
brj-23416	169	18	2023	2023	NUM
brj-23416	169	19	)	)	PUNCT
brj-23416	169	20	performance	performance	NOUN
brj-23416	169	21	metrics	metric	NOUN
brj-23416	169	22	mathematical	mathematical	ADJ
brj-23416	169	23	formulas	formula	NOUN
brj-23416	169	24	definitions	definition	NOUN
brj-23416	169	25	coefficient	coefficient	NOUN
brj-23416	169	26	of	of	ADP
brj-23416	169	27	determination	determination	NOUN
brj-23416	169	28	𝑅2	𝑅2	NOUN
brj-23416	169	29	=	=	NOUN
brj-23416	169	30	1	1	NUM
brj-23416	169	31	−	−	NOUN
brj-23416	169	32	∑	∑	PUNCT
brj-23416	169	33	(	(	PUNCT
brj-23416	169	34	𝑦𝑖	𝑦𝑖	PROPN
brj-23416	169	35	−	−	PROPN
brj-23416	169	36	𝑦	𝑦	SYM
brj-23416	169	37	�	�	PROPN
brj-23416	169	38	̂	̂	SYM
brj-23416	169	39	�	�	NOUN
brj-23416	169	40	)	)	PUNCT
brj-23416	169	41	2𝑁	2𝑁	NOUN
brj-23416	169	42	𝑖=1	𝑖=1	PUNCT
brj-23416	169	43	∑	∑	PROPN
brj-23416	169	44	(	(	PUNCT
brj-23416	169	45	𝑦𝑖	𝑦𝑖	PROPN
brj-23416	169	46	−	−	PROPN
brj-23416	169	47	�	�	NOUN
brj-23416	169	48	̅	̅	NOUN
brj-23416	169	49	�	�	NOUN
brj-23416	169	50	)2𝑁	)2𝑁	PROPN
brj-23416	169	51	𝑖=1	𝑖=1	PUNCT
brj-23416	170	1	it	it	PRON
brj-23416	170	2	shows	show	VERB
brj-23416	170	3	the	the	DET
brj-23416	170	4	extent	extent	NOUN
brj-23416	170	5	to	to	PART
brj-23416	170	6	which	which	PRON
brj-23416	170	7	the	the	DET
brj-23416	170	8	model	model	NOUN
brj-23416	170	9	can	can	AUX
brj-23416	170	10	explain	explain	VERB
brj-23416	170	11	the	the	DET
brj-23416	170	12	deviation	deviation	NOUN
brj-23416	170	13	between	between	ADP
brj-23416	170	14	the	the	DET
brj-23416	170	15	data	datum	NOUN
brj-23416	170	16	.	.	PUNCT
brj-23416	171	1	mean	mean	VERB
brj-23416	171	2	absolute	absolute	ADJ
brj-23416	171	3	error	error	NOUN
brj-23416	171	4	𝑀𝐴𝐸	𝑀𝐴𝐸	NOUN
brj-23416	171	5	=	=	SYM
brj-23416	171	6	1	1	NUM
brj-23416	171	7	𝑁	𝑁	PROPN
brj-23416	171	8	∑|𝑦𝑖	∑|𝑦𝑖	PROPN
brj-23416	171	9	−	−	PROPN
brj-23416	171	10	𝑦	𝑦	SYM
brj-23416	171	11	�	�	NOUN
brj-23416	171	12	̂	̂	NOUN
brj-23416	171	13	�	�	NOUN
brj-23416	171	14	|	|	ADV
brj-23416	171	15	𝑁	𝑁	PROPN
brj-23416	171	16	𝑖=1	𝑖=1	PROPN
brj-23416	171	17	it	it	PRON
brj-23416	171	18	represents	represent	VERB
brj-23416	171	19	the	the	DET
brj-23416	171	20	average	average	NOUN
brj-23416	171	21	of	of	ADP
brj-23416	171	22	the	the	DET
brj-23416	171	23	absolute	absolute	ADJ
brj-23416	171	24	values	value	NOUN
brj-23416	171	25	of	of	ADP
brj-23416	171	26	the	the	DET
brj-23416	171	27	errors	error	NOUN
brj-23416	171	28	.	.	PUNCT
brj-23416	172	1	root	root	NOUN
brj-23416	172	2	mean	mean	VERB
brj-23416	172	3	square	square	ADJ
brj-23416	172	4	error	error	NOUN
brj-23416	172	5	𝑅𝑀𝑆𝐸	𝑅𝑀𝑆𝐸	NOUN
brj-23416	172	6	=	=	NOUN
brj-23416	172	7	√	√	ADP
brj-23416	172	8	1	1	NUM
brj-23416	173	1	𝑁	𝑁	PROPN
brj-23416	173	2	∑(𝑦𝑖	∑(𝑦𝑖	PROPN
brj-23416	173	3	−	−	PROPN
brj-23416	173	4	𝑦	𝑦	SYM
brj-23416	173	5	�	�	PROPN
brj-23416	173	6	̂	̂	SYM
brj-23416	173	7	�	�	NOUN
brj-23416	173	8	)	)	PUNCT
brj-23416	173	9	2	2	NUM
brj-23416	174	1	𝑁	𝑁	PROPN
brj-23416	174	2	𝑖=1	𝑖=1	PROPN
brj-23416	175	1	it	it	PRON
brj-23416	175	2	is	be	AUX
brj-23416	175	3	used	use	VERB
brj-23416	175	4	to	to	PART
brj-23416	175	5	measure	measure	VERB
brj-23416	175	6	the	the	DET
brj-23416	175	7	magnitude	magnitude	NOUN
brj-23416	175	8	of	of	ADP
brj-23416	175	9	the	the	DET
brj-23416	175	10	variance	variance	NOUN
brj-23416	175	11	between	between	ADP
brj-23416	175	12	predicted	predict	VERB
brj-23416	175	13	and	and	CCONJ
brj-23416	175	14	actual	actual	ADJ
brj-23416	175	15	values	value	NOUN
brj-23416	175	16	.	.	PUNCT
brj-23416	176	1	results	result	NOUN
brj-23416	176	2	and	and	CCONJ
brj-23416	176	3	discussion	discussion	NOUN
brj-23416	176	4	after	after	ADP
brj-23416	176	5	preprocessing	preprocesse	VERB
brj-23416	176	6	the	the	DET
brj-23416	176	7	data	datum	NOUN
brj-23416	176	8	and	and	CCONJ
brj-23416	176	9	splitting	split	VERB
brj-23416	176	10	the	the	DET
brj-23416	176	11	dataset	dataset	NOUN
brj-23416	176	12	into	into	ADP
brj-23416	176	13	training	training	NOUN
brj-23416	176	14	set	set	NOUN
brj-23416	176	15	and	and	CCONJ
brj-23416	176	16	test	test	NOUN
brj-23416	176	17	set	set	VERB
brj-23416	176	18	,	,	PUNCT
brj-23416	176	19	the	the	DET
brj-23416	176	20	aim	aim	NOUN
brj-23416	176	21	was	be	AUX
brj-23416	176	22	to	to	PART
brj-23416	176	23	make	make	VERB
brj-23416	176	24	parameter	parameter	NOUN
brj-23416	176	25	entries	entry	NOUN
brj-23416	176	26	for	for	ADP
brj-23416	176	27	the	the	DET
brj-23416	176	28	models	model	NOUN
brj-23416	176	29	and	and	CCONJ
brj-23416	176	30	to	to	PART
brj-23416	176	31	obtain	obtain	VERB
brj-23416	176	32	the	the	DET
brj-23416	176	33	best	good	ADJ
brj-23416	176	34	performance	performance	NOUN
brj-23416	176	35	with	with	ADP
brj-23416	176	36	these	these	DET
brj-23416	176	37	parameter	parameter	NOUN
brj-23416	176	38	entries	entry	NOUN
brj-23416	176	39	.	.	PUNCT
brj-23416	177	1	while	while	SCONJ
brj-23416	177	2	selecting	select	VERB
brj-23416	177	3	the	the	DET
brj-23416	177	4	best	good	ADJ
brj-23416	177	5	parameter	parameter	NOUN
brj-23416	177	6	values	value	NOUN
brj-23416	177	7	,	,	PUNCT
brj-23416	177	8	it	it	PRON
brj-23416	177	9	was	be	AUX
brj-23416	177	10	taken	take	VERB
brj-23416	177	11	as	as	ADP
brj-23416	177	12	a	a	DET
brj-23416	177	13	basis	basis	NOUN
brj-23416	177	14	that	that	SCONJ
brj-23416	177	15	the	the	DET
brj-23416	177	16	total	total	ADJ
brj-23416	177	17	error	error	NOUN
brj-23416	177	18	value	value	NOUN
brj-23416	177	19	was	be	AUX
brj-23416	177	20	minimum	minimum	ADJ
brj-23416	177	21	.	.	PUNCT
brj-23416	178	1	the	the	DET
brj-23416	178	2	hyperparameters	hyperparameter	NOUN
brj-23416	178	3	of	of	ADP
brj-23416	178	4	the	the	DET
brj-23416	178	5	gbm	gbm	NOUN
brj-23416	178	6	,	,	PUNCT
brj-23416	178	7	knn	knn	PROPN
brj-23416	178	8	,	,	PUNCT
brj-23416	178	9	peer	peer	NOUN
brj-23416	178	10	-	-	PUNCT
brj-23416	178	11	reviewed	review	VERB
brj-23416	178	12	article	article	NOUN
brj-23416	178	13	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	178	14	akyüz	akyüz	PROPN
brj-23416	178	15	et	et	PROPN
brj-23416	178	16	al	al	PROPN
brj-23416	178	17	.	.	PROPN
brj-23416	179	1	(	(	PUNCT
brj-23416	179	2	2024	2024	NUM
brj-23416	179	3	)	)	PUNCT
brj-23416	179	4	.	.	PUNCT
brj-23416	180	1	“	"	PUNCT
brj-23416	180	2	stock	stock	NOUN
brj-23416	180	3	exchange	exchange	NOUN
brj-23416	180	4	values	value	NOUN
brj-23416	180	5	,	,	PUNCT
brj-23416	180	6	”	"	PUNCT
brj-23416	180	7	bioresources	bioresource	NOUN
brj-23416	180	8	19(3	19(3	NUM
brj-23416	180	9	)	)	PUNCT
brj-23416	180	10	,	,	PUNCT
brj-23416	180	11	5141	5141	NUM
brj-23416	180	12	-	-	SYM
brj-23416	180	13	5157	5157	NUM
brj-23416	180	14	.	.	PUNCT
brj-23416	181	1	5149	5149	NUM
brj-23416	182	1	and	and	CCONJ
brj-23416	182	2	rf	rf	PRON
brj-23416	182	3	were	be	AUX
brj-23416	182	4	optimized	optimize	VERB
brj-23416	182	5	using	use	VERB
brj-23416	182	6	gridsearchcv	gridsearchcv	NOUN
brj-23416	182	7	,	,	PUNCT
brj-23416	182	8	whereas	whereas	SCONJ
brj-23416	182	9	the	the	DET
brj-23416	182	10	hyperparameters	hyperparameter	NOUN
brj-23416	182	11	of	of	ADP
brj-23416	182	12	the	the	DET
brj-23416	182	13	ann	ann	PROPN
brj-23416	182	14	were	be	AUX
brj-23416	182	15	optimized	optimize	VERB
brj-23416	182	16	using	use	VERB
brj-23416	182	17	adam	adam	PROPN
brj-23416	182	18	and	and	CCONJ
brj-23416	182	19	optuna	optuna	ADJ
brj-23416	182	20	optimization	optimization	NOUN
brj-23416	182	21	technique	technique	NOUN
brj-23416	182	22	.	.	PUNCT
brj-23416	183	1	the	the	DET
brj-23416	183	2	best	good	ADJ
brj-23416	183	3	values	value	NOUN
brj-23416	183	4	of	of	ADP
brj-23416	183	5	hyperparameter	hyperparameter	NOUN
brj-23416	183	6	of	of	ADP
brj-23416	183	7	all	all	DET
brj-23416	183	8	machine	machine	NOUN
brj-23416	183	9	learning	learning	NOUN
brj-23416	183	10	models	model	NOUN
brj-23416	183	11	used	use	VERB
brj-23416	183	12	in	in	ADP
brj-23416	183	13	the	the	DET
brj-23416	183	14	study	study	NOUN
brj-23416	183	15	are	be	AUX
brj-23416	183	16	given	give	VERB
brj-23416	183	17	in	in	ADP
brj-23416	183	18	table	table	NOUN
brj-23416	183	19	7	7	NUM
brj-23416	183	20	.	.	PUNCT
brj-23416	184	1	when	when	SCONJ
brj-23416	184	2	the	the	DET
brj-23416	184	3	best	good	ADJ
brj-23416	184	4	values	value	NOUN
brj-23416	184	5	of	of	ADP
brj-23416	184	6	the	the	DET
brj-23416	184	7	parameters	parameter	NOUN
brj-23416	184	8	of	of	ADP
brj-23416	184	9	all	all	DET
brj-23416	184	10	machine	machine	NOUN
brj-23416	184	11	learning	learning	NOUN
brj-23416	184	12	models	model	NOUN
brj-23416	184	13	are	be	AUX
brj-23416	184	14	examined	examine	VERB
brj-23416	184	15	,	,	PUNCT
brj-23416	184	16	it	it	PRON
brj-23416	184	17	was	be	AUX
brj-23416	184	18	determined	determine	VERB
brj-23416	184	19	that	that	SCONJ
brj-23416	184	20	max	max	PROPN
brj-23416	184	21	depth=4	depth=4	PROPN
brj-23416	184	22	,	,	PUNCT
brj-23416	184	23	min	min	NOUN
brj-23416	184	24	samples	sample	NOUN
brj-23416	184	25	leaf=1	leaf=1	PROPN
brj-23416	184	26	,	,	PUNCT
brj-23416	184	27	min	min	PROPN
brj-23416	184	28	samples	sample	NOUN
brj-23416	184	29	split=4	split=4	PROPN
brj-23416	184	30	,	,	PUNCT
brj-23416	184	31	n	n	CCONJ
brj-23416	184	32	estimators=300	estimators=300	ADJ
brj-23416	184	33	,	,	PUNCT
brj-23416	184	34	and	and	CCONJ
brj-23416	184	35	learning	learn	VERB
brj-23416	184	36	rate	rate	NOUN
brj-23416	184	37	:	:	PUNCT
brj-23416	184	38	0.1	0.1	NUM
brj-23416	184	39	for	for	ADP
brj-23416	184	40	gbm	gbm	NOUN
brj-23416	184	41	,	,	PUNCT
brj-23416	184	42	algorithm	algorithm	NOUN
brj-23416	184	43	=	=	SYM
brj-23416	184	44	auto	auto	NOUN
brj-23416	184	45	,	,	PUNCT
brj-23416	184	46	weights	weight	NOUN
brj-23416	184	47	=	=	SYM
brj-23416	184	48	uniform	uniform	NOUN
brj-23416	184	49	,	,	PUNCT
brj-23416	184	50	and	and	CCONJ
brj-23416	184	51	n	n	DET
brj-23416	184	52	neighbors=2	neighbors=2	PROPN
brj-23416	184	53	for	for	ADP
brj-23416	184	54	knn	knn	PROPN
brj-23416	184	55	,	,	PUNCT
brj-23416	184	56	learning	learn	VERB
brj-23416	184	57	rate=	rate=	NUM
brj-23416	184	58	0.074	0.074	NUM
brj-23416	184	59	,	,	PUNCT
brj-23416	184	60	hidden	hide	VERB
brj-23416	184	61	size=58	size=58	NOUN
brj-23416	184	62	,	,	PUNCT
brj-23416	184	63	and	and	CCONJ
brj-23416	184	64	number	number	NOUN
brj-23416	184	65	epochs=194	epochs=194	PROPN
brj-23416	184	66	for	for	ADP
brj-23416	184	67	ann	ann	PROPN
brj-23416	184	68	,	,	PUNCT
brj-23416	184	69	and	and	CCONJ
brj-23416	184	70	max	max	PROPN
brj-23416	184	71	depth=10	depth=10	PROPN
brj-23416	184	72	,	,	PUNCT
brj-23416	184	73	max	max	PROPN
brj-23416	184	74	features=	features=	NUM
brj-23416	184	75	sqrt	sqrt	NOUN
brj-23416	184	76	,	,	PUNCT
brj-23416	184	77	min	min	NOUN
brj-23416	184	78	samples	sample	NOUN
brj-23416	184	79	leaf=1	leaf=1	PROPN
brj-23416	184	80	,	,	PUNCT
brj-23416	184	81	min	min	NOUN
brj-23416	184	82	samples	sample	NOUN
brj-23416	184	83	split=2	split=2	ADJ
brj-23416	184	84	,	,	PUNCT
brj-23416	184	85	n	n	CCONJ
brj-23416	184	86	estimators=100	estimators=100	NOUN
brj-23416	184	87	,	,	PUNCT
brj-23416	184	88	and	and	CCONJ
brj-23416	184	89	learning	learn	VERB
brj-23416	184	90	rate	rate	NOUN
brj-23416	184	91	:	:	PUNCT
brj-23416	184	92	0.1	0.1	NUM
brj-23416	184	93	for	for	ADP
brj-23416	184	94	rf	rf	NOUN
brj-23416	184	95	.	.	NOUN
brj-23416	184	96	predictions	prediction	NOUN
brj-23416	184	97	were	be	AUX
brj-23416	184	98	produced	produce	VERB
brj-23416	184	99	for	for	ADP
brj-23416	184	100	both	both	CCONJ
brj-23416	184	101	the	the	DET
brj-23416	184	102	training	training	NOUN
brj-23416	184	103	set	set	NOUN
brj-23416	184	104	and	and	CCONJ
brj-23416	184	105	the	the	DET
brj-23416	184	106	test	test	NOUN
brj-23416	184	107	set	set	VERB
brj-23416	184	108	using	use	VERB
brj-23416	184	109	the	the	DET
brj-23416	184	110	best	good	ADJ
brj-23416	184	111	parameters	parameter	NOUN
brj-23416	184	112	.	.	PUNCT
brj-23416	185	1	table	table	NOUN
brj-23416	185	2	7	7	NUM
brj-23416	185	3	.	.	X
brj-23416	185	4	optimal	optimal	ADJ
brj-23416	185	5	model	model	NOUN
brj-23416	185	6	parameters	parameter	NOUN
brj-23416	185	7	for	for	ADP
brj-23416	185	8	all	all	DET
brj-23416	185	9	machine	machine	NOUN
brj-23416	185	10	learning	learn	VERB
brj-23416	185	11	algorithms	algorithms	NOUN
brj-23416	185	12	models	model	NOUN
brj-23416	185	13	hyperparameter	hyperparameter	NOUN
brj-23416	185	14	values	value	NOUN
brj-23416	185	15	gbm	gbm	NOUN
brj-23416	185	16	n	n	PRON
brj-23416	185	17	estimators	estimator	NOUN
brj-23416	185	18	300	300	NUM
brj-23416	185	19	learning	learning	NOUN
brj-23416	185	20	rate	rate	NOUN
brj-23416	185	21	0,1	0,1	NUM
brj-23416	185	22	max	max	NOUN
brj-23416	185	23	depth	depth	NOUN
brj-23416	185	24	4	4	NUM
brj-23416	185	25	min	min	NOUN
brj-23416	185	26	samples	sample	NOUN
brj-23416	185	27	leaf	leaf	NOUN
brj-23416	185	28	1	1	NUM
brj-23416	185	29	min	min	NOUN
brj-23416	185	30	samples	sample	NOUN
brj-23416	185	31	split	split	VERB
brj-23416	185	32	4	4	NUM
brj-23416	185	33	knn	knn	NOUN
brj-23416	185	34	n	n	PROPN
brj-23416	185	35	neighbors	neighbor	NOUN
brj-23416	185	36	2	2	NUM
brj-23416	185	37	algorithm	algorithm	NOUN
brj-23416	185	38	auto	auto	NOUN
brj-23416	185	39	learning	learning	NOUN
brj-23416	185	40	rate	rate	NOUN
brj-23416	185	41	uniform	uniform	PROPN
brj-23416	185	42	ann	ann	PROPN
brj-23416	185	43	hidden	hide	VERB
brj-23416	185	44	size	size	NOUN
brj-23416	185	45	58	58	NUM
brj-23416	185	46	number	number	NOUN
brj-23416	185	47	epochs	epoch	VERB
brj-23416	185	48	194	194	NUM
brj-23416	185	49	weights	weight	NOUN
brj-23416	186	1	0,074	0,074	NUM
brj-23416	186	2	rf	rf	NUM
brj-23416	186	3	n	n	PRON
brj-23416	186	4	estimators	estimator	NOUN
brj-23416	186	5	100	100	NUM
brj-23416	186	6	learning	learn	VERB
brj-23416	186	7	rate	rate	NOUN
brj-23416	186	8	0,1	0,1	NUM
brj-23416	186	9	max	max	NOUN
brj-23416	186	10	depth	depth	NOUN
brj-23416	186	11	10	10	NUM
brj-23416	186	12	max	max	PROPN
brj-23416	186	13	features	feature	NOUN
brj-23416	186	14	sqrt	sqrt	VERB
brj-23416	186	15	min	min	NOUN
brj-23416	186	16	samples	sample	NOUN
brj-23416	186	17	leaf	leaf	NOUN
brj-23416	186	18	1	1	NUM
brj-23416	186	19	min	min	NOUN
brj-23416	186	20	samples	sample	NOUN
brj-23416	186	21	split	split	VERB
brj-23416	186	22	2	2	NUM
brj-23416	186	23	the	the	DET
brj-23416	186	24	performances	performance	NOUN
brj-23416	186	25	of	of	ADP
brj-23416	186	26	the	the	DET
brj-23416	186	27	prediction	prediction	NOUN
brj-23416	186	28	results	result	NOUN
brj-23416	186	29	were	be	AUX
brj-23416	186	30	evaluated	evaluate	VERB
brj-23416	186	31	with	with	ADP
brj-23416	186	32	rmse	rmse	PROPN
brj-23416	186	33	,	,	PUNCT
brj-23416	186	34	mae	mae	PROPN
brj-23416	186	35	and	and	CCONJ
brj-23416	186	36	r2	r2	PROPN
brj-23416	186	37	evaluation	evaluation	NOUN
brj-23416	186	38	criteria	criterion	NOUN
brj-23416	186	39	.	.	PUNCT
brj-23416	187	1	when	when	SCONJ
brj-23416	187	2	the	the	DET
brj-23416	187	3	performances	performance	NOUN
brj-23416	187	4	of	of	ADP
brj-23416	187	5	the	the	DET
brj-23416	187	6	models	model	NOUN
brj-23416	187	7	in	in	ADP
brj-23416	187	8	table	table	NOUN
brj-23416	187	9	8were	8were	NUM
brj-23416	187	10	examined	examine	VERB
brj-23416	187	11	,	,	PUNCT
brj-23416	187	12	it	it	PRON
brj-23416	187	13	was	be	AUX
brj-23416	187	14	found	find	VERB
brj-23416	187	15	that	that	SCONJ
brj-23416	187	16	the	the	DET
brj-23416	187	17	mae	mae	PROPN
brj-23416	187	18	values	value	NOUN
brj-23416	187	19	of	of	ADP
brj-23416	187	20	gbm	gbm	PROPN
brj-23416	187	21	is	be	AUX
brj-23416	187	22	0.620	0.620	NUM
brj-23416	187	23	for	for	ADP
brj-23416	187	24	the	the	DET
brj-23416	187	25	training	training	NOUN
brj-23416	187	26	phase	phase	NOUN
brj-23416	187	27	and	and	CCONJ
brj-23416	187	28	61.6	61.6	NUM
brj-23416	187	29	for	for	ADP
brj-23416	187	30	the	the	DET
brj-23416	187	31	testing	testing	NOUN
brj-23416	187	32	phase	phase	NOUN
brj-23416	187	33	,	,	PUNCT
brj-23416	187	34	the	the	DET
brj-23416	187	35	mae	mae	PROPN
brj-23416	187	36	values	value	NOUN
brj-23416	187	37	of	of	ADP
brj-23416	187	38	rf	rf	NUM
brj-23416	187	39	was	be	AUX
brj-23416	187	40	25.25	25.25	NUM
brj-23416	187	41	for	for	ADP
brj-23416	187	42	the	the	DET
brj-23416	187	43	training	training	NOUN
brj-23416	187	44	and	and	CCONJ
brj-23416	187	45	57.72	57.72	NUM
brj-23416	187	46	for	for	ADP
brj-23416	187	47	the	the	DET
brj-23416	187	48	testing	testing	NOUN
brj-23416	187	49	,	,	PUNCT
brj-23416	187	50	the	the	DET
brj-23416	187	51	mae	mae	PROPN
brj-23416	187	52	values	value	NOUN
brj-23416	187	53	of	of	ADP
brj-23416	187	54	knn	knn	PROPN
brj-23416	187	55	were	be	AUX
brj-23416	187	56	36.20	36.20	NUM
brj-23416	187	57	for	for	ADP
brj-23416	187	58	training	training	NOUN
brj-23416	187	59	and	and	CCONJ
brj-23416	187	60	40.8	40.8	NUM
brj-23416	187	61	for	for	ADP
brj-23416	187	62	testing	testing	NOUN
brj-23416	187	63	,	,	PUNCT
brj-23416	187	64	and	and	CCONJ
brj-23416	187	65	the	the	DET
brj-23416	187	66	mae	mae	PROPN
brj-23416	187	67	values	value	NOUN
brj-23416	187	68	of	of	ADP
brj-23416	187	69	ann	ann	PROPN
brj-23416	187	70	were	be	AUX
brj-23416	187	71	33.48	33.48	NUM
brj-23416	187	72	for	for	ADP
brj-23416	187	73	the	the	DET
brj-23416	187	74	training	training	NOUN
brj-23416	187	75	and	and	CCONJ
brj-23416	187	76	63.31	63.31	NUM
brj-23416	187	77	for	for	ADP
brj-23416	187	78	the	the	DET
brj-23416	187	79	testing	testing	NOUN
brj-23416	187	80	.	.	PUNCT
brj-23416	188	1	an	an	DET
brj-23416	188	2	important	important	ADJ
brj-23416	188	3	criterion	criterion	NOUN
brj-23416	188	4	used	use	VERB
brj-23416	188	5	to	to	PART
brj-23416	188	6	evaluate	evaluate	VERB
brj-23416	188	7	the	the	DET
brj-23416	188	8	validity	validity	NOUN
brj-23416	188	9	of	of	ADP
brj-23416	188	10	the	the	DET
brj-23416	188	11	model	model	NOUN
brj-23416	188	12	was	be	AUX
brj-23416	188	13	the	the	DET
brj-23416	188	14	correlation	correlation	NOUN
brj-23416	188	15	coefficient	coefficient	NOUN
brj-23416	188	16	(	(	PUNCT
brj-23416	188	17	r2	r2	PROPN
brj-23416	188	18	)	)	PUNCT
brj-23416	188	19	between	between	ADP
brj-23416	188	20	the	the	DET
brj-23416	188	21	experimental	experimental	ADJ
brj-23416	188	22	and	and	CCONJ
brj-23416	188	23	prediction	prediction	NOUN
brj-23416	188	24	results	result	NOUN
brj-23416	188	25	.	.	PUNCT
brj-23416	189	1	r2	r2	NOUN
brj-23416	189	2	value	value	NOUN
brj-23416	189	3	takes	take	VERB
brj-23416	189	4	a	a	DET
brj-23416	189	5	value	value	NOUN
brj-23416	189	6	between	between	ADP
brj-23416	189	7	0	0	NUM
brj-23416	189	8	and	and	CCONJ
brj-23416	189	9	1	1	NUM
brj-23416	189	10	.	.	PUNCT
brj-23416	190	1	if	if	SCONJ
brj-23416	190	2	this	this	DET
brj-23416	190	3	value	value	NOUN
brj-23416	190	4	approaches	approach	VERB
brj-23416	190	5	1	1	NUM
brj-23416	190	6	,	,	PUNCT
brj-23416	190	7	the	the	DET
brj-23416	190	8	model	model	NOUN
brj-23416	190	9	is	be	AUX
brj-23416	190	10	quite	quite	ADV
brj-23416	190	11	compatible	compatible	ADJ
brj-23416	190	12	with	with	ADP
brj-23416	190	13	the	the	DET
brj-23416	190	14	data	datum	NOUN
brj-23416	190	15	(	(	PUNCT
brj-23416	190	16	özşahin	özşahin	NOUN
brj-23416	190	17	2012	2012	NUM
brj-23416	190	18	)	)	PUNCT
brj-23416	190	19	.	.	PUNCT
brj-23416	191	1	when	when	SCONJ
brj-23416	191	2	the	the	DET
brj-23416	191	3	r2	r2	PROPN
brj-23416	191	4	values	value	NOUN
brj-23416	191	5	of	of	ADP
brj-23416	191	6	the	the	DET
brj-23416	191	7	models	model	NOUN
brj-23416	191	8	were	be	AUX
brj-23416	191	9	analyzed	analyze	VERB
brj-23416	191	10	,	,	PUNCT
brj-23416	191	11	the	the	DET
brj-23416	191	12	r2	r2	PROPN
brj-23416	191	13	values	value	NOUN
brj-23416	191	14	in	in	ADP
brj-23416	191	15	the	the	DET
brj-23416	191	16	training	training	NOUN
brj-23416	191	17	and	and	CCONJ
brj-23416	191	18	test	test	NOUN
brj-23416	191	19	data	datum	NOUN
brj-23416	191	20	sets	set	NOUN
brj-23416	191	21	of	of	ADP
brj-23416	191	22	gbm	gbm	PROPN
brj-23416	191	23	were	be	AUX
brj-23416	191	24	0.999	0.999	NUM
brj-23416	191	25	and	and	CCONJ
brj-23416	191	26	0.978	0.978	NUM
brj-23416	191	27	,	,	PUNCT
brj-23416	191	28	respectively	respectively	ADV
brj-23416	191	29	,	,	PUNCT
brj-23416	191	30	the	the	DET
brj-23416	191	31	r2	r2	PROPN
brj-23416	191	32	values	value	NOUN
brj-23416	191	33	in	in	ADP
brj-23416	191	34	the	the	DET
brj-23416	191	35	training	training	NOUN
brj-23416	191	36	and	and	CCONJ
brj-23416	191	37	test	test	NOUN
brj-23416	191	38	data	datum	NOUN
brj-23416	191	39	sets	set	NOUN
brj-23416	191	40	of	of	ADP
brj-23416	191	41	rf	rf	NUM
brj-23416	191	42	were	be	AUX
brj-23416	191	43	0.996	0.996	NUM
brj-23416	191	44	and	and	CCONJ
brj-23416	191	45	0.989	0.989	NUM
brj-23416	191	46	,	,	PUNCT
brj-23416	191	47	respectively	respectively	ADV
brj-23416	191	48	,	,	PUNCT
brj-23416	191	49	the	the	DET
brj-23416	191	50	r2	r2	PROPN
brj-23416	191	51	values	value	NOUN
brj-23416	191	52	in	in	ADP
brj-23416	191	53	the	the	DET
brj-23416	191	54	training	training	NOUN
brj-23416	191	55	and	and	CCONJ
brj-23416	191	56	test	test	NOUN
brj-23416	191	57	data	datum	NOUN
brj-23416	191	58	of	of	ADP
brj-23416	191	59	knn	knn	PROPN
brj-23416	191	60	were	be	AUX
brj-23416	191	61	0.993	0.993	NUM
brj-23416	191	62	and	and	CCONJ
brj-23416	191	63	0.996	0.996	NUM
brj-23416	191	64	,	,	PUNCT
brj-23416	191	65	respectively	respectively	ADV
brj-23416	191	66	.	.	PUNCT
brj-23416	192	1	ther2	ther2	PROPN
brj-23416	193	1	values	value	NOUN
brj-23416	193	2	in	in	ADP
brj-23416	193	3	the	the	DET
brj-23416	193	4	training	training	NOUN
brj-23416	193	5	and	and	CCONJ
brj-23416	193	6	test	test	NOUN
brj-23416	193	7	data	datum	NOUN
brj-23416	193	8	sets	set	NOUN
brj-23416	193	9	of	of	ADP
brj-23416	193	10	ann	ann	PROPN
brj-23416	193	11	were	be	AUX
brj-23416	193	12	0.997	0.997	NUM
brj-23416	193	13	and	and	CCONJ
brj-23416	193	14	0.991	0.991	NUM
brj-23416	193	15	,	,	PUNCT
brj-23416	193	16	respectively	respectively	ADV
brj-23416	193	17	.	.	PUNCT
brj-23416	194	1	peer	peer	NOUN
brj-23416	194	2	-	-	PUNCT
brj-23416	194	3	reviewed	review	VERB
brj-23416	194	4	article	article	NOUN
brj-23416	194	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	194	6	akyüz	akyüz	PROPN
brj-23416	194	7	et	et	PROPN
brj-23416	194	8	al	al	PROPN
brj-23416	194	9	.	.	PROPN
brj-23416	195	1	(	(	PUNCT
brj-23416	195	2	2024	2024	NUM
brj-23416	195	3	)	)	PUNCT
brj-23416	195	4	.	.	PUNCT
brj-23416	196	1	“	"	PUNCT
brj-23416	196	2	stock	stock	NOUN
brj-23416	196	3	exchange	exchange	NOUN
brj-23416	196	4	values	value	NOUN
brj-23416	196	5	,	,	PUNCT
brj-23416	196	6	”	"	PUNCT
brj-23416	196	7	bioresources	bioresource	NOUN
brj-23416	196	8	19(3	19(3	NUM
brj-23416	196	9	)	)	PUNCT
brj-23416	196	10	,	,	PUNCT
brj-23416	196	11	5141	5141	NUM
brj-23416	196	12	-	-	SYM
brj-23416	196	13	5157	5157	NUM
brj-23416	196	14	.	.	PUNCT
brj-23416	197	1	5150	5150	NUM
brj-23416	197	2	table	table	NOUN
brj-23416	197	3	8	8	NUM
brj-23416	197	4	.	.	PUNCT
brj-23416	197	5	comparison	comparison	NOUN
brj-23416	197	6	of	of	ADP
brj-23416	197	7	the	the	DET
brj-23416	197	8	performance	performance	NOUN
brj-23416	197	9	of	of	ADP
brj-23416	197	10	the	the	DET
brj-23416	197	11	models	model	NOUN
brj-23416	197	12	models	model	VERB
brj-23416	197	13	training	training	NOUN
brj-23416	197	14	test	test	NOUN
brj-23416	197	15	rmse	rmse	PROPN
brj-23416	197	16	mae	mae	PROPN
brj-23416	197	17	r2	r2	PROPN
brj-23416	197	18	rmse	rmse	PROPN
brj-23416	197	19	mae	mae	PROPN
brj-23416	197	20	r2	r2	PROPN
brj-23416	197	21	gbm	gbm	PROPN
brj-23416	197	22	0.781	0.781	NUM
brj-23416	197	23	0.620	0.620	NUM
brj-23416	197	24	0.999	0.999	NUM
brj-23416	197	25	165.6	165.6	NUM
brj-23416	197	26	61.6	61.6	NUM
brj-23416	197	27	0.978	0.978	NUM
brj-23416	197	28	rf	rf	NUM
brj-23416	197	29	68.00	68.00	NUM
brj-23416	197	30	25.25	25.25	NUM
brj-23416	197	31	0.996	0.996	NUM
brj-23416	197	32	114.4	114.4	NUM
brj-23416	197	33	57.72	57.72	NUM
brj-23416	197	34	0.989	0.989	NUM
brj-23416	197	35	knn	knn	PROPN
brj-23416	197	36	88.46	88.46	NUM
brj-23416	198	1	36.20	36.20	NUM
brj-23416	198	2	0.993	0.993	NUM
brj-23416	198	3	71.36	71.36	NUM
brj-23416	198	4	40.8	40.8	NUM
brj-23416	198	5	0.996	0.996	NUM
brj-23416	198	6	ann	ann	PROPN
brj-23416	198	7	56.24	56.24	NUM
brj-23416	198	8	33.48	33.48	NUM
brj-23416	198	9	0.997	0.997	NUM
brj-23416	198	10	104.9	104.9	NUM
brj-23416	198	11	63.31	63.31	NUM
brj-23416	198	12	0.991	0.991	NUM
brj-23416	198	13	fig	fig	NOUN
brj-23416	198	14	.	.	PUNCT
brj-23416	199	1	2	2	X
brj-23416	199	2	.	.	X
brj-23416	199	3	comparison	comparison	NOUN
brj-23416	199	4	of	of	ADP
brj-23416	199	5	actual	actual	ADJ
brj-23416	199	6	values	value	NOUN
brj-23416	199	7	and	and	CCONJ
brj-23416	199	8	predicted	predict	VERB
brj-23416	199	9	values	value	NOUN
brj-23416	199	10	obtained	obtain	VERB
brj-23416	199	11	with	with	ADP
brj-23416	199	12	the	the	DET
brj-23416	199	13	all	all	DET
brj-23416	199	14	machine	machine	NOUN
brj-23416	199	15	learning	learning	NOUN
brj-23416	199	16	methods	method	NOUN
brj-23416	199	17	when	when	SCONJ
brj-23416	199	18	looking	look	VERB
brj-23416	199	19	at	at	ADP
brj-23416	199	20	fig	fig	NOUN
brj-23416	199	21	.	.	PUNCT
brj-23416	200	1	2	2	NUM
brj-23416	200	2	,	,	PUNCT
brj-23416	200	3	it	it	PRON
brj-23416	200	4	can	can	AUX
brj-23416	200	5	be	be	AUX
brj-23416	200	6	seen	see	VERB
brj-23416	200	7	that	that	SCONJ
brj-23416	200	8	the	the	DET
brj-23416	200	9	predicted	predict	VERB
brj-23416	200	10	values	value	NOUN
brj-23416	200	11	obtained	obtain	VERB
brj-23416	200	12	by	by	ADP
brj-23416	200	13	gbm	gbm	PROPN
brj-23416	200	14	,	,	PUNCT
brj-23416	200	15	rf	rf	PROPN
brj-23416	200	16	,	,	PUNCT
brj-23416	200	17	knn	knn	PROPN
brj-23416	200	18	,	,	PUNCT
brj-23416	200	19	and	and	CCONJ
brj-23416	200	20	ann	ann	PROPN
brj-23416	200	21	models	model	NOUN
brj-23416	200	22	were	be	AUX
brj-23416	200	23	close	close	ADJ
brj-23416	200	24	to	to	ADP
brj-23416	200	25	the	the	DET
brj-23416	200	26	actual	actual	ADJ
brj-23416	200	27	values	value	NOUN
brj-23416	200	28	.	.	PUNCT
brj-23416	201	1	although	although	SCONJ
brj-23416	201	2	all	all	DET
brj-23416	201	3	models	model	NOUN
brj-23416	201	4	used	use	VERB
brj-23416	201	5	in	in	ADP
brj-23416	201	6	index	index	NOUN
brj-23416	201	7	prediction	prediction	NOUN
brj-23416	201	8	gave	give	VERB
brj-23416	201	9	successful	successful	ADJ
brj-23416	201	10	results	result	NOUN
brj-23416	201	11	,	,	PUNCT
brj-23416	201	12	it	it	PRON
brj-23416	201	13	was	be	AUX
brj-23416	201	14	observed	observe	VERB
brj-23416	201	15	that	that	SCONJ
brj-23416	201	16	gbm	gbm	PROPN
brj-23416	201	17	’s	’s	PART
brj-23416	201	18	rmse	rmse	PROPN
brj-23416	201	19	and	and	CCONJ
brj-23416	201	20	mae	mae	PROPN
brj-23416	201	21	values	value	NOUN
brj-23416	201	22	were	be	AUX
brj-23416	201	23	lower	low	ADJ
brj-23416	201	24	and	and	CCONJ
brj-23416	201	25	its	its	PRON
brj-23416	201	26	r2	r2	NOUN
brj-23416	201	27	value	value	NOUN
brj-23416	201	28	was	be	AUX
brj-23416	201	29	higher	high	ADJ
brj-23416	201	30	than	than	ADP
brj-23416	201	31	other	other	ADJ
brj-23416	201	32	models	model	NOUN
brj-23416	201	33	in	in	ADP
brj-23416	201	34	the	the	DET
brj-23416	201	35	training	training	NOUN
brj-23416	201	36	phase	phase	NOUN
brj-23416	201	37	,	,	PUNCT
brj-23416	201	38	and	and	CCONJ
brj-23416	201	39	that	that	SCONJ
brj-23416	201	40	knn	knn	PROPN
brj-23416	201	41	was	be	AUX
brj-23416	201	42	the	the	DET
brj-23416	201	43	model	model	NOUN
brj-23416	201	44	with	with	ADP
brj-23416	201	45	the	the	DET
brj-23416	201	46	best	good	ADJ
brj-23416	201	47	evaluation	evaluation	NOUN
brj-23416	201	48	criteria	criterion	NOUN
brj-23416	201	49	in	in	ADP
brj-23416	201	50	the	the	DET
brj-23416	201	51	testing	testing	NOUN
brj-23416	201	52	phase	phase	NOUN
brj-23416	201	53	.	.	PUNCT
brj-23416	202	1	in	in	ADP
brj-23416	202	2	the	the	DET
brj-23416	202	3	literature	literature	NOUN
brj-23416	202	4	,	,	PUNCT
brj-23416	202	5	there	there	PRON
brj-23416	202	6	are	be	VERB
brj-23416	202	7	studies	study	NOUN
brj-23416	202	8	on	on	ADP
brj-23416	202	9	the	the	DET
brj-23416	202	10	successful	successful	ADJ
brj-23416	202	11	performance	performance	NOUN
brj-23416	202	12	of	of	ADP
brj-23416	202	13	machine	machine	NOUN
brj-23416	202	14	learning	learning	NOUN
brj-23416	202	15	methods	method	NOUN
brj-23416	202	16	in	in	ADP
brj-23416	202	17	stock	stock	NOUN
brj-23416	202	18	price	price	NOUN
brj-23416	202	19	prediction	prediction	NOUN
brj-23416	202	20	.	.	PUNCT
brj-23416	203	1	in	in	ADP
brj-23416	203	2	other	other	ADJ
brj-23416	203	3	words	word	NOUN
brj-23416	203	4	,	,	PUNCT
brj-23416	203	5	previous	previous	ADJ
brj-23416	203	6	study	study	NOUN
brj-23416	203	7	results	result	NOUN
brj-23416	203	8	overlap	overlap	VERB
brj-23416	203	9	with	with	ADP
brj-23416	203	10	the	the	DET
brj-23416	203	11	results	result	NOUN
brj-23416	203	12	of	of	ADP
brj-23416	203	13	this	this	DET
brj-23416	203	14	study	study	NOUN
brj-23416	203	15	.	.	PUNCT
brj-23416	204	1	yıldırım	yıldırım	PROPN
brj-23416	204	2	et	et	PROPN
brj-23416	204	3	al	al	PROPN
brj-23416	204	4	.	.	PROPN
brj-23416	205	1	(	(	PUNCT
brj-23416	205	2	2011	2011	NUM
brj-23416	205	3	)	)	PUNCT
brj-23416	205	4	found	find	VERB
brj-23416	205	5	that	that	SCONJ
brj-23416	205	6	the	the	DET
brj-23416	205	7	ann	ann	PROPN
brj-23416	205	8	and	and	CCONJ
brj-23416	205	9	the	the	DET
brj-23416	205	10	multiple	multiple	ADJ
brj-23416	205	11	linear	linear	PROPN
brj-23416	205	12	regression	regression	NOUN
brj-23416	205	13	methods	method	NOUN
brj-23416	205	14	provide	provide	VERB
brj-23416	205	15	good	good	ADJ
brj-23416	205	16	performance	performance	NOUN
brj-23416	205	17	for	for	ADP
brj-23416	205	18	paper	paper	NOUN
brj-23416	205	19	sector	sector	NOUN
brj-23416	205	20	financial	financial	ADJ
brj-23416	205	21	return	return	NOUN
brj-23416	205	22	prediction	prediction	NOUN
brj-23416	205	23	and	and	CCONJ
brj-23416	205	24	ann	ann	PROPN
brj-23416	205	25	was	be	AUX
brj-23416	205	26	significantly	significantly	ADV
brj-23416	205	27	better	well	ADJ
brj-23416	205	28	than	than	ADP
brj-23416	205	29	the	the	DET
brj-23416	205	30	multiple	multiple	ADJ
brj-23416	205	31	linear	linear	PROPN
brj-23416	205	32	regression	regression	NOUN
brj-23416	205	33	model	model	NOUN
brj-23416	205	34	.	.	PUNCT
brj-23416	206	1	roy	roy	PROPN
brj-23416	206	2	et	et	PROPN
brj-23416	206	3	al	al	PROPN
brj-23416	206	4	.	.	PROPN
brj-23416	206	5	(	(	PUNCT
brj-23416	206	6	2020	2020	NUM
brj-23416	206	7	)	)	PUNCT
brj-23416	206	8	found	find	VERB
brj-23416	206	9	that	that	SCONJ
brj-23416	206	10	random	random	ADJ
brj-23416	206	11	forest	forest	NOUN
brj-23416	206	12	and	and	CCONJ
brj-23416	206	13	gradient	gradient	NOUN
brj-23416	206	14	boosting	boost	VERB
brj-23416	206	15	machine	machine	NOUN
brj-23416	206	16	methods	method	NOUN
brj-23416	206	17	provide	provide	VERB
brj-23416	206	18	satisfactory	satisfactory	ADJ
brj-23416	206	19	performance	performance	NOUN
brj-23416	206	20	in	in	ADP
brj-23416	206	21	predicting	predict	VERB
brj-23416	206	22	stock	stock	NOUN
brj-23416	206	23	prices	price	NOUN
brj-23416	206	24	.	.	PUNCT
brj-23416	207	1	verly	verly	ADV
brj-23416	207	2	lopes	lope	VERB
brj-23416	207	3	et	et	PROPN
brj-23416	207	4	al	al	PROPN
brj-23416	207	5	.	.	PROPN
brj-23416	208	1	(	(	PUNCT
brj-23416	208	2	2021	2021	NUM
brj-23416	208	3	)	)	PUNCT
brj-23416	208	4	revealed	reveal	VERB
brj-23416	208	5	that	that	SCONJ
brj-23416	208	6	the	the	DET
brj-23416	208	7	lstm	lstm	ADJ
brj-23416	208	8	artificial	artificial	ADJ
brj-23416	208	9	recurrent	recurrent	ADJ
brj-23416	208	10	neural	neural	ADJ
brj-23416	208	11	networks	network	NOUN
brj-23416	208	12	predicted	predict	VERB
brj-23416	208	13	the	the	DET
brj-23416	208	14	random	random	ADJ
brj-23416	208	15	length	length	NOUN
brj-23416	208	16	lumber	lumber	NOUN
brj-23416	208	17	stock	stock	NOUN
brj-23416	208	18	price	price	NOUN
brj-23416	208	19	with	with	ADP
brj-23416	208	20	low	low	ADJ
brj-23416	208	21	error	error	NOUN
brj-23416	208	22	terms	term	NOUN
brj-23416	208	23	for	for	ADP
brj-23416	208	24	mse	mse	NOUN
brj-23416	208	25	,	,	PUNCT
brj-23416	208	26	rmse	rmse	NOUN
brj-23416	208	27	,	,	PUNCT
brj-23416	208	28	and	and	CCONJ
brj-23416	208	29	mae	mae	PROPN
brj-23416	208	30	.	.	PROPN
brj-23416	208	31	singh	singh	PROPN
brj-23416	208	32	(	(	PUNCT
brj-23416	208	33	2021	2021	NUM
brj-23416	208	34	)	)	PUNCT
brj-23416	208	35	found	find	VERB
brj-23416	208	36	that	that	SCONJ
brj-23416	208	37	machine	machine	NOUN
brj-23416	208	38	learning	learn	VERB
brj-23416	208	39	techniques	technique	NOUN
brj-23416	208	40	including	include	VERB
brj-23416	208	41	ann	ann	PROPN
brj-23416	208	42	,	,	PUNCT
brj-23416	208	43	lr	lr	PROPN
brj-23416	208	44	,	,	PUNCT
brj-23416	208	45	sgd	sgd	PROPN
brj-23416	208	46	,	,	PUNCT
brj-23416	208	47	svm	svm	ADJ
brj-23416	208	48	,	,	PUNCT
brj-23416	208	49	adaboost	adaboost	ADV
brj-23416	208	50	,	,	PUNCT
brj-23416	208	51	rf	rf	ADJ
brj-23416	208	52	,	,	PUNCT
brj-23416	208	53	knn	knn	PROPN
brj-23416	208	54	and	and	CCONJ
brj-23416	208	55	dt	dt	PROPN
brj-23416	208	56	give	give	VERB
brj-23416	208	57	significant	significant	ADJ
brj-23416	208	58	results	result	NOUN
brj-23416	208	59	in	in	ADP
brj-23416	208	60	nifty	nifty	ADJ
brj-23416	208	61	50	50	NUM
brj-23416	208	62	index	index	NOUN
brj-23416	208	63	prediction	prediction	NOUN
brj-23416	208	64	,	,	PUNCT
brj-23416	208	65	but	but	CCONJ
brj-23416	208	66	adaboost	adaboost	ADV
brj-23416	208	67	,	,	PUNCT
brj-23416	208	68	knn	knn	PROPN
brj-23416	208	69	,	,	PUNCT
brj-23416	208	70	rf	rf	ADJ
brj-23416	208	71	and	and	CCONJ
brj-23416	208	72	dt	dt	AUX
brj-23416	208	73	under	under	AUX
brj-23416	208	74	performed	perform	VERB
brj-23416	208	75	with	with	ADP
brj-23416	208	76	increase	increase	NOUN
brj-23416	208	77	in	in	ADP
brj-23416	208	78	the	the	DET
brj-23416	208	79	size	size	NOUN
brj-23416	208	80	of	of	ADP
brj-23416	208	81	data	datum	NOUN
brj-23416	208	82	set	set	VERB
brj-23416	208	83	.	.	PUNCT
brj-23416	209	1	armağan	armağan	PROPN
brj-23416	209	2	(	(	PUNCT
brj-23416	209	3	2023	2023	NUM
brj-23416	209	4	)	)	PUNCT
brj-23416	209	5	determined	determine	VERB
brj-23416	209	6	that	that	SCONJ
brj-23416	209	7	artificial	artificial	ADJ
brj-23416	209	8	intelligence	intelligence	NOUN
brj-23416	209	9	-	-	PUNCT
brj-23416	209	10	based	base	VERB
brj-23416	209	11	peer	peer	NOUN
brj-23416	209	12	-	-	PUNCT
brj-23416	209	13	reviewed	review	VERB
brj-23416	209	14	article	article	NOUN
brj-23416	209	15	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	209	16	akyüz	akyüz	PROPN
brj-23416	209	17	et	et	PROPN
brj-23416	209	18	al	al	PROPN
brj-23416	209	19	.	.	PROPN
brj-23416	210	1	(	(	PUNCT
brj-23416	210	2	2024	2024	NUM
brj-23416	210	3	)	)	PUNCT
brj-23416	210	4	.	.	PUNCT
brj-23416	211	1	“	"	PUNCT
brj-23416	211	2	stock	stock	NOUN
brj-23416	211	3	exchange	exchange	NOUN
brj-23416	211	4	values	value	NOUN
brj-23416	211	5	,	,	PUNCT
brj-23416	211	6	”	"	PUNCT
brj-23416	211	7	bioresources	bioresource	NOUN
brj-23416	211	8	19(3	19(3	NUM
brj-23416	211	9	)	)	PUNCT
brj-23416	211	10	,	,	PUNCT
brj-23416	211	11	5141	5141	NUM
brj-23416	211	12	-	-	SYM
brj-23416	211	13	5157	5157	NUM
brj-23416	211	14	.	.	PUNCT
brj-23416	212	1	5151	5151	NUM
brj-23416	212	2	deep	deep	ADJ
brj-23416	212	3	learning	learning	NOUN
brj-23416	212	4	models	model	NOUN
brj-23416	212	5	perform	perform	VERB
brj-23416	212	6	better	well	ADV
brj-23416	212	7	compared	compare	VERB
brj-23416	212	8	to	to	ADP
brj-23416	212	9	traditional	traditional	ADJ
brj-23416	212	10	model	model	NOUN
brj-23416	212	11	in	in	ADP
brj-23416	212	12	bist	bist	ADJ
brj-23416	212	13	banks	bank	NOUN
brj-23416	212	14	index	index	NOUN
brj-23416	212	15	prediction	prediction	NOUN
brj-23416	212	16	and	and	CCONJ
brj-23416	212	17	convolutional	convolutional	ADJ
brj-23416	212	18	neural	neural	ADJ
brj-23416	212	19	networks	network	NOUN
brj-23416	212	20	model	model	NOUN
brj-23416	212	21	(	(	PUNCT
brj-23416	212	22	cnnm	cnnm	ADJ
brj-23416	212	23	)	)	PUNCT
brj-23416	212	24	gives	give	VERB
brj-23416	212	25	the	the	DET
brj-23416	212	26	best	good	ADJ
brj-23416	212	27	results	result	NOUN
brj-23416	212	28	.	.	PUNCT
brj-23416	213	1	oukhouya	oukhouya	PROPN
brj-23416	213	2	et	et	PROPN
brj-23416	213	3	al	al	PROPN
brj-23416	213	4	.	.	PROPN
brj-23416	214	1	(	(	PUNCT
brj-23416	214	2	2024	2024	NUM
brj-23416	214	3	)	)	PUNCT
brj-23416	214	4	reported	report	VERB
brj-23416	214	5	that	that	SCONJ
brj-23416	214	6	machine	machine	NOUN
brj-23416	214	7	learning	learning	NOUN
brj-23416	214	8	models	model	NOUN
brj-23416	214	9	show	show	VERB
brj-23416	214	10	very	very	ADV
brj-23416	214	11	good	good	ADJ
brj-23416	214	12	results	result	NOUN
brj-23416	214	13	in	in	ADP
brj-23416	214	14	predicting	predict	VERB
brj-23416	214	15	daily	daily	ADJ
brj-23416	214	16	prices	price	NOUN
brj-23416	214	17	of	of	ADP
brj-23416	214	18	stock	stock	NOUN
brj-23416	214	19	indices	index	NOUN
brj-23416	214	20	.	.	PUNCT
brj-23416	215	1	conclusions	conclusion	NOUN
brj-23416	215	2	1	1	X
brj-23416	215	3	.	.	PUNCT
brj-23416	216	1	the	the	DET
brj-23416	216	2	purpose	purpose	NOUN
brj-23416	216	3	of	of	ADP
brj-23416	216	4	this	this	DET
brj-23416	216	5	study	study	NOUN
brj-23416	216	6	was	be	AUX
brj-23416	216	7	to	to	PART
brj-23416	216	8	guide	guide	VERB
brj-23416	216	9	investors	investor	NOUN
brj-23416	216	10	or	or	CCONJ
brj-23416	216	11	those	those	PRON
brj-23416	216	12	considering	consider	VERB
brj-23416	216	13	investing	invest	VERB
brj-23416	216	14	in	in	ADP
brj-23416	216	15	analyzing	analyze	VERB
brj-23416	216	16	and	and	CCONJ
brj-23416	216	17	speculating	speculate	VERB
brj-23416	216	18	on	on	ADP
brj-23416	216	19	the	the	DET
brj-23416	216	20	stock	stock	NOUN
brj-23416	216	21	price	price	NOUN
brj-23416	216	22	trend	trend	NOUN
brj-23416	216	23	,	,	PUNCT
brj-23416	216	24	which	which	PRON
brj-23416	216	25	will	will	AUX
brj-23416	216	26	help	help	VERB
brj-23416	216	27	them	they	PRON
brj-23416	216	28	in	in	ADP
brj-23416	216	29	purchasing	purchase	VERB
brj-23416	216	30	stocks	stock	NOUN
brj-23416	216	31	to	to	PART
brj-23416	216	32	make	make	VERB
brj-23416	216	33	maximum	maximum	ADJ
brj-23416	216	34	profits	profit	NOUN
brj-23416	216	35	.	.	PUNCT
brj-23416	217	1	2	2	X
brj-23416	217	2	.	.	X
brj-23416	217	3	it	it	PRON
brj-23416	217	4	is	be	AUX
brj-23416	217	5	seen	see	VERB
brj-23416	217	6	that	that	SCONJ
brj-23416	217	7	models	model	NOUN
brj-23416	217	8	created	create	VERB
brj-23416	217	9	with	with	ADP
brj-23416	217	10	gradient	gradient	ADJ
brj-23416	217	11	boosting	boosting	NOUN
brj-23416	217	12	,	,	PUNCT
brj-23416	217	13	random	random	ADJ
brj-23416	217	14	forest	forest	NOUN
brj-23416	217	15	,	,	PUNCT
brj-23416	217	16	k	k	X
brj-23416	217	17	-	-	PUNCT
brj-23416	217	18	nearest	near	ADJ
brj-23416	217	19	neighbors	neighbor	NOUN
brj-23416	217	20	and	and	CCONJ
brj-23416	217	21	artificial	artificial	ADJ
brj-23416	217	22	neural	neural	ADJ
brj-23416	217	23	networks	network	NOUN
brj-23416	217	24	methods	method	NOUN
brj-23416	217	25	,	,	PUNCT
brj-23416	217	26	which	which	PRON
brj-23416	217	27	are	be	AUX
brj-23416	217	28	machine	machine	NOUN
brj-23416	217	29	learning	learning	NOUN
brj-23416	217	30	methods	method	NOUN
brj-23416	217	31	,	,	PUNCT
brj-23416	217	32	are	be	AUX
brj-23416	217	33	successful	successful	ADJ
brj-23416	217	34	in	in	ADP
brj-23416	217	35	predicting	predict	VERB
brj-23416	217	36	the	the	DET
brj-23416	217	37	next	next	ADJ
brj-23416	217	38	day	day	NOUN
brj-23416	217	39	's	's	PART
brj-23416	217	40	closing	closing	NOUN
brj-23416	217	41	values	value	NOUN
brj-23416	217	42	of	of	ADP
brj-23416	217	43	the	the	DET
brj-23416	217	44	bist	bist	NOUN
brj-23416	217	45	forest	forest	NOUN
brj-23416	217	46	,	,	PUNCT
brj-23416	217	47	paper	paper	NOUN
brj-23416	217	48	and	and	CCONJ
brj-23416	217	49	printing	printing	NOUN
brj-23416	217	50	index	index	NOUN
brj-23416	217	51	.	.	PUNCT
brj-23416	218	1	r2	r2	NOUN
brj-23416	218	2	values	value	NOUN
brj-23416	218	3	of	of	ADP
brj-23416	218	4	all	all	DET
brj-23416	218	5	models	model	NOUN
brj-23416	218	6	were	be	AUX
brj-23416	218	7	above	above	ADP
brj-23416	218	8	0.97	0.97	NUM
brj-23416	218	9	in	in	ADP
brj-23416	218	10	both	both	DET
brj-23416	218	11	training	training	NOUN
brj-23416	218	12	and	and	CCONJ
brj-23416	218	13	testing	testing	NOUN
brj-23416	218	14	phases	phase	NOUN
brj-23416	218	15	.	.	PUNCT
brj-23416	219	1	3	3	X
brj-23416	219	2	.	.	X
brj-23416	219	3	although	although	SCONJ
brj-23416	219	4	all	all	DET
brj-23416	219	5	models	model	NOUN
brj-23416	219	6	performed	perform	VERB
brj-23416	219	7	well	well	ADV
brj-23416	219	8	in	in	ADP
brj-23416	219	9	index	index	NOUN
brj-23416	219	10	prediction	prediction	NOUN
brj-23416	219	11	,	,	PUNCT
brj-23416	219	12	it	it	PRON
brj-23416	219	13	was	be	AUX
brj-23416	219	14	observed	observe	VERB
brj-23416	219	15	that	that	SCONJ
brj-23416	219	16	gbm	gbm	NOUN
brj-23416	219	17	in	in	ADP
brj-23416	219	18	the	the	DET
brj-23416	219	19	training	training	NOUN
brj-23416	219	20	phase	phase	NOUN
brj-23416	219	21	and	and	CCONJ
brj-23416	219	22	knn	knn	VERB
brj-23416	219	23	in	in	ADP
brj-23416	219	24	the	the	DET
brj-23416	219	25	testing	testing	NOUN
brj-23416	219	26	phase	phase	NOUN
brj-23416	219	27	were	be	AUX
brj-23416	219	28	more	more	ADV
brj-23416	219	29	successful	successful	ADJ
brj-23416	219	30	than	than	ADP
brj-23416	219	31	the	the	DET
brj-23416	219	32	others	other	NOUN
brj-23416	219	33	.	.	PUNCT
brj-23416	220	1	4	4	X
brj-23416	220	2	.	.	X
brj-23416	220	3	industry	industry	NOUN
brj-23416	220	4	actors	actor	NOUN
brj-23416	220	5	,	,	PUNCT
brj-23416	220	6	current	current	ADJ
brj-23416	220	7	and	and	CCONJ
brj-23416	220	8	potential	potential	ADJ
brj-23416	220	9	investors	investor	NOUN
brj-23416	220	10	can	can	AUX
brj-23416	220	11	have	have	VERB
brj-23416	220	12	an	an	DET
brj-23416	220	13	idea	idea	NOUN
brj-23416	220	14	about	about	ADP
brj-23416	220	15	future	future	ADJ
brj-23416	220	16	index	index	NOUN
brj-23416	220	17	values	value	NOUN
brj-23416	220	18	by	by	ADP
brj-23416	220	19	analyzing	analyze	VERB
brj-23416	220	20	any	any	PRON
brj-23416	220	21	of	of	ADP
brj-23416	220	22	the	the	DET
brj-23416	220	23	machine	machine	NOUN
brj-23416	220	24	learning	learn	VERB
brj-23416	220	25	techniques	technique	NOUN
brj-23416	220	26	used	use	VERB
brj-23416	220	27	in	in	ADP
brj-23416	220	28	the	the	DET
brj-23416	220	29	study	study	NOUN
brj-23416	220	30	in	in	ADP
brj-23416	220	31	line	line	NOUN
brj-23416	220	32	with	with	ADP
brj-23416	220	33	the	the	DET
brj-23416	220	34	variables	variable	NOUN
brj-23416	220	35	used	use	VERB
brj-23416	220	36	in	in	ADP
brj-23416	220	37	the	the	DET
brj-23416	220	38	study	study	NOUN
brj-23416	220	39	.	.	PUNCT
brj-23416	221	1	5	5	X
brj-23416	221	2	.	.	X
brj-23416	221	3	the	the	DET
brj-23416	221	4	use	use	NOUN
brj-23416	221	5	of	of	ADP
brj-23416	221	6	only	only	ADV
brj-23416	221	7	10	10	NUM
brj-23416	221	8	macroeconomic	macroeconomic	ADJ
brj-23416	221	9	variables	variable	NOUN
brj-23416	221	10	and	and	CCONJ
brj-23416	221	11	four	four	NUM
brj-23416	221	12	machine	machine	NOUN
brj-23416	221	13	learning	learning	NOUN
brj-23416	221	14	methods	method	NOUN
brj-23416	221	15	as	as	ADP
brj-23416	221	16	input	input	NOUN
brj-23416	221	17	in	in	ADP
brj-23416	221	18	the	the	DET
brj-23416	221	19	study	study	NOUN
brj-23416	221	20	is	be	AUX
brj-23416	221	21	a	a	DET
brj-23416	221	22	limitation	limitation	NOUN
brj-23416	221	23	of	of	ADP
brj-23416	221	24	the	the	DET
brj-23416	221	25	study	study	NOUN
brj-23416	221	26	.	.	PUNCT
brj-23416	222	1	since	since	SCONJ
brj-23416	222	2	stock	stock	NOUN
brj-23416	222	3	market	market	NOUN
brj-23416	222	4	index	index	NOUN
brj-23416	222	5	values	value	NOUN
brj-23416	222	6	are	be	AUX
brj-23416	222	7	affected	affect	VERB
brj-23416	222	8	by	by	ADP
brj-23416	222	9	many	many	ADJ
brj-23416	222	10	factors	factor	NOUN
brj-23416	222	11	in	in	ADP
brj-23416	222	12	both	both	CCONJ
brj-23416	222	13	short	short	ADJ
brj-23416	222	14	and	and	CCONJ
brj-23416	222	15	long	long	ADJ
brj-23416	222	16	time	time	NOUN
brj-23416	222	17	periods	period	NOUN
brj-23416	222	18	,	,	PUNCT
brj-23416	222	19	it	it	PRON
brj-23416	222	20	would	would	AUX
brj-23416	222	21	be	be	AUX
brj-23416	222	22	meaningful	meaningful	ADJ
brj-23416	222	23	for	for	SCONJ
brj-23416	222	24	further	further	ADJ
brj-23416	222	25	research	research	NOUN
brj-23416	222	26	to	to	PART
brj-23416	222	27	include	include	VERB
brj-23416	222	28	and	and	CCONJ
brj-23416	222	29	analyze	analyze	VERB
brj-23416	222	30	market	market	NOUN
brj-23416	222	31	psychology	psychology	NOUN
brj-23416	222	32	,	,	PUNCT
brj-23416	222	33	and	and	CCONJ
brj-23416	222	34	companies	company	NOUN
brj-23416	222	35	’	'	PUNCT
brj-23416	222	36	financial	financial	ADJ
brj-23416	222	37	ratios	ratio	NOUN
brj-23416	222	38	such	such	ADJ
brj-23416	222	39	as	as	ADP
brj-23416	222	40	current	current	ADJ
brj-23416	222	41	ratio	ratio	NOUN
brj-23416	222	42	,	,	PUNCT
brj-23416	222	43	cash	cash	NOUN
brj-23416	222	44	ratio	ratio	NOUN
brj-23416	222	45	,	,	PUNCT
brj-23416	222	46	leverage	leverage	NOUN
brj-23416	222	47	ratio	ratio	NOUN
brj-23416	222	48	as	as	ADV
brj-23416	222	49	well	well	ADV
brj-23416	222	50	as	as	ADP
brj-23416	222	51	macroeconomic	macroeconomic	ADJ
brj-23416	222	52	economic	economic	ADJ
brj-23416	222	53	variables	variable	NOUN
brj-23416	222	54	.	.	PUNCT
brj-23416	223	1	6	6	X
brj-23416	223	2	.	.	X
brj-23416	223	3	research	research	NOUN
brj-23416	223	4	can	can	AUX
brj-23416	223	5	be	be	AUX
brj-23416	223	6	carried	carry	VERB
brj-23416	223	7	out	out	ADP
brj-23416	223	8	to	to	PART
brj-23416	223	9	predict	predict	VERB
brj-23416	223	10	the	the	DET
brj-23416	223	11	stock	stock	NOUN
brj-23416	223	12	price	price	NOUN
brj-23416	223	13	movements	movement	NOUN
brj-23416	223	14	of	of	ADP
brj-23416	223	15	individual	individual	ADJ
brj-23416	223	16	companies	company	NOUN
brj-23416	223	17	included	include	VERB
brj-23416	223	18	in	in	ADP
brj-23416	223	19	the	the	DET
brj-23416	223	20	xkagt	xkagt	PROPN
brj-23416	223	21	index	index	NOUN
brj-23416	223	22	.	.	PUNCT
brj-23416	224	1	7	7	X
brj-23416	224	2	.	.	X
brj-23416	224	3	different	different	ADJ
brj-23416	224	4	studies	study	NOUN
brj-23416	224	5	can	can	AUX
brj-23416	224	6	be	be	AUX
brj-23416	224	7	carried	carry	VERB
brj-23416	224	8	out	out	ADP
brj-23416	224	9	by	by	ADP
brj-23416	224	10	using	use	VERB
brj-23416	224	11	different	different	ADJ
brj-23416	224	12	machine	machine	NOUN
brj-23416	224	13	learning	learning	NOUN
brj-23416	224	14	(	(	PUNCT
brj-23416	224	15	e.g.	e.g.	ADV
brj-23416	224	16	,	,	PUNCT
brj-23416	224	17	decision	decision	NOUN
brj-23416	224	18	tree	tree	NOUN
brj-23416	224	19	,	,	PUNCT
brj-23416	224	20	support	support	VERB
brj-23416	224	21	vector	vector	NOUN
brj-23416	224	22	machine	machine	NOUN
brj-23416	224	23	-	-	PUNCT
brj-23416	224	24	svm	svm	NOUN
brj-23416	224	25	)	)	PUNCT
brj-23416	224	26	and	and	CCONJ
brj-23416	224	27	deep	deep	ADJ
brj-23416	224	28	learning	learning	NOUN
brj-23416	224	29	methods	method	NOUN
brj-23416	224	30	(	(	PUNCT
brj-23416	224	31	e.g.	e.g.	ADV
brj-23416	224	32	,	,	PUNCT
brj-23416	224	33	convolution	convolution	NOUN
brj-23416	224	34	neural	neural	ADJ
brj-23416	224	35	network	network	NOUN
brj-23416	224	36	-	-	PUNCT
brj-23416	224	37	cnn	cnn	PROPN
brj-23416	224	38	,	,	PUNCT
brj-23416	224	39	simple	simple	ADJ
brj-23416	224	40	recurrent	recurrent	ADJ
brj-23416	224	41	network	network	NOUN
brj-23416	224	42	-	-	PUNCT
brj-23416	224	43	srn	srn	NOUN
brj-23416	224	44	)	)	PUNCT
brj-23416	224	45	and	and	CCONJ
brj-23416	224	46	hybrid	hybrid	VERB
brj-23416	224	47	the	the	DET
brj-23416	224	48	methods	method	NOUN
brj-23416	224	49	(	(	PUNCT
brj-23416	224	50	e.g.	e.g.	ADV
brj-23416	224	51	,	,	PUNCT
brj-23416	224	52	lstm	lstm	NOUN
brj-23416	224	53	-	-	PUNCT
brj-23416	224	54	xgboost	xgboost	PROPN
brj-23416	224	55	)	)	PUNCT
brj-23416	224	56	.	.	PUNCT
brj-23416	225	1	references	reference	NOUN
brj-23416	225	2	cited	cite	VERB
brj-23416	225	3	abdolrasol	abdolrasol	PROPN
brj-23416	225	4	,	,	PUNCT
brj-23416	225	5	m.	m.	NOUN
brj-23416	225	6	g.	g.	PROPN
brj-23416	225	7	,	,	PUNCT
brj-23416	225	8	hussain	hussain	PROPN
brj-23416	225	9	,	,	PUNCT
brj-23416	225	10	s.	s.	PROPN
brj-23416	225	11	s.	s.	PROPN
brj-23416	225	12	,	,	PUNCT
brj-23416	225	13	ustun	ustun	NOUN
brj-23416	225	14	,	,	PUNCT
brj-23416	225	15	t.	t.	PROPN
brj-23416	225	16	s.	s.	PROPN
brj-23416	225	17	,	,	PUNCT
brj-23416	225	18	sarker	sarker	NOUN
brj-23416	225	19	,	,	PUNCT
brj-23416	225	20	m.	m.	PROPN
brj-23416	225	21	r.	r.	PROPN
brj-23416	225	22	,	,	PUNCT
brj-23416	225	23	hannan	hannan	PROPN
brj-23416	225	24	,	,	PUNCT
brj-23416	225	25	m.	m.	NOUN
brj-23416	225	26	a.	a.	PROPN
brj-23416	225	27	,	,	PUNCT
brj-23416	225	28	mohamed	mohamed	PROPN
brj-23416	225	29	,	,	PUNCT
brj-23416	225	30	r.	r.	PROPN
brj-23416	225	31	,	,	PUNCT
brj-23416	225	32	abd	abd	PROPN
brj-23416	225	33	ali	ali	PROPN
brj-23416	225	34	,	,	PUNCT
brj-23416	225	35	j.	j.	PROPN
brj-23416	225	36	,	,	PUNCT
brj-23416	225	37	mekhilef	mekhilef	PROPN
brj-23416	225	38	,	,	PUNCT
brj-23416	225	39	s.	s.	PROPN
brj-23416	225	40	,	,	PUNCT
brj-23416	225	41	and	and	CCONJ
brj-23416	225	42	milad	milad	NOUN
brj-23416	225	43	,	,	PUNCT
brj-23416	225	44	a.	a.	NOUN
brj-23416	225	45	(	(	PUNCT
brj-23416	225	46	2021	2021	NUM
brj-23416	225	47	)	)	PUNCT
brj-23416	225	48	.	.	PUNCT
brj-23416	226	1	“	"	PUNCT
brj-23416	226	2	artificial	artificial	ADJ
brj-23416	226	3	neural	neural	ADJ
brj-23416	226	4	networks	network	NOUN
brj-23416	226	5	based	base	VERB
brj-23416	226	6	optimization	optimization	NOUN
brj-23416	226	7	techniques	technique	NOUN
brj-23416	226	8	:	:	PUNCT
brj-23416	226	9	a	a	DET
brj-23416	226	10	review	review	NOUN
brj-23416	226	11	,	,	PUNCT
brj-23416	226	12	”	"	PUNCT
brj-23416	226	13	electronics	electronics	NOUN
brj-23416	226	14	10(21	10(21	NUM
brj-23416	226	15	)	)	PUNCT
brj-23416	226	16	,	,	PUNCT
brj-23416	226	17	1	1	NUM
brj-23416	226	18	-	-	SYM
brj-23416	226	19	43	43	NUM
brj-23416	226	20	.	.	PUNCT
brj-23416	227	1	doi	doi	NOUN
brj-23416	227	2	:	:	PUNCT
brj-23416	227	3	10.3390	10.3390	NUM
brj-23416	227	4	/	/	SYM
brj-23416	227	5	electronics10212689	electronics10212689	PROPN
brj-23416	227	6	.	.	PUNCT
brj-23416	228	1	abraham	abraham	PROPN
brj-23416	228	2	,	,	PUNCT
brj-23416	228	3	a.	a.	NOUN
brj-23416	228	4	(	(	PUNCT
brj-23416	228	5	2021	2021	NUM
brj-23416	228	6	)	)	PUNCT
brj-23416	228	7	.	.	PUNCT
brj-23416	229	1	“	"	PUNCT
brj-23416	229	2	forecasting	forecast	VERB
brj-23416	229	3	stock	stock	NOUN
brj-23416	229	4	market	market	NOUN
brj-23416	229	5	prices	price	NOUN
brj-23416	229	6	:	:	PUNCT
brj-23416	229	7	a	a	DET
brj-23416	229	8	machine	machine	NOUN
brj-23416	229	9	learning	learn	VERB
brj-23416	229	10	approach	approach	NOUN
brj-23416	229	11	,	,	PUNCT
brj-23416	229	12	”	"	PUNCT
brj-23416	229	13	all	all	DET
brj-23416	229	14	graduate	graduate	NOUN
brj-23416	229	15	plan	plan	NOUN
brj-23416	229	16	b	b	NOUN
brj-23416	229	17	and	and	CCONJ
brj-23416	229	18	other	other	ADJ
brj-23416	229	19	reports	report	NOUN
brj-23416	229	20	,	,	PUNCT
brj-23416	229	21	spring	spring	NOUN
brj-23416	229	22	1920	1920	NUM
brj-23416	229	23	to	to	ADP
brj-23416	229	24	spring	spring	NOUN
brj-23416	229	25	2023	2023	NUM
brj-23416	229	26	.	.	PUNCT
brj-23416	230	1	1610	1610	NUM
brj-23416	230	2	.	.	PUNCT
brj-23416	231	1	(	(	PUNCT
brj-23416	231	2	https://digitalcommons.usu.edu/gradreports/1610	https://digitalcommons.usu.edu/gradreports/1610	PROPN
brj-23416	231	3	)	)	PUNCT
brj-23416	231	4	,	,	PUNCT
brj-23416	231	5	accessed	access	VERB
brj-23416	231	6	20	20	NUM
brj-23416	231	7	may	may	PROPN
brj-23416	231	8	2023	2023	NUM
brj-23416	231	9	.	.	PUNCT
brj-23416	232	1	https://digitalcommons.usu.edu/gradreports/1610	https://digitalcommons.usu.edu/gradreports/1610	PROPN
brj-23416	232	2	peer	peer	NOUN
brj-23416	232	3	-	-	PUNCT
brj-23416	232	4	reviewed	review	VERB
brj-23416	232	5	article	article	NOUN
brj-23416	232	6	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	232	7	akyüz	akyüz	PROPN
brj-23416	232	8	et	et	PROPN
brj-23416	232	9	al	al	PROPN
brj-23416	232	10	.	.	PROPN
brj-23416	233	1	(	(	PUNCT
brj-23416	233	2	2024	2024	NUM
brj-23416	233	3	)	)	PUNCT
brj-23416	233	4	.	.	PUNCT
brj-23416	234	1	“	"	PUNCT
brj-23416	234	2	stock	stock	NOUN
brj-23416	234	3	exchange	exchange	NOUN
brj-23416	234	4	values	value	NOUN
brj-23416	234	5	,	,	PUNCT
brj-23416	234	6	”	"	PUNCT
brj-23416	234	7	bioresources	bioresource	NOUN
brj-23416	234	8	19(3	19(3	NUM
brj-23416	234	9	)	)	PUNCT
brj-23416	234	10	,	,	PUNCT
brj-23416	234	11	5141	5141	NUM
brj-23416	234	12	-	-	SYM
brj-23416	234	13	5157	5157	NUM
brj-23416	234	14	.	.	PUNCT
brj-23416	235	1	5152	5152	NUM
brj-23416	235	2	aegean	aegean	PROPN
brj-23416	235	3	exporters	exporter	NOUN
brj-23416	235	4	’	'	PUNCT
brj-23416	235	5	associations	association	NOUN
brj-23416	235	6	.	.	PUNCT
brj-23416	236	1	(	(	PUNCT
brj-23416	236	2	2024	2024	NUM
brj-23416	236	3	)	)	PUNCT
brj-23416	236	4	.	.	PUNCT
brj-23416	237	1	“	"	PUNCT
brj-23416	237	2	turkey	turkey	PROPN
brj-23416	237	3	's	's	PART
brj-23416	237	4	furniture	furniture	NOUN
brj-23416	237	5	,	,	PUNCT
brj-23416	237	6	paper	paper	NOUN
brj-23416	237	7	and	and	CCONJ
brj-23416	237	8	forest	forest	NOUN
brj-23416	237	9	products	product	NOUN
brj-23416	237	10	export	export	NOUN
brj-23416	237	11	data	datum	NOUN
brj-23416	237	12	for	for	ADP
brj-23416	237	13	2022	2022	NUM
brj-23416	237	14	,	,	PUNCT
brj-23416	237	15	”	"	PUNCT
brj-23416	237	16	(	(	PUNCT
brj-23416	237	17	https://upload.eib.org.tr	https://upload.eib.org.tr	PROPN
brj-23416	237	18	)	)	PUNCT
brj-23416	237	19	,	,	PUNCT
brj-23416	237	20	accessed	access	VERB
brj-23416	237	21	16	16	NUM
brj-23416	237	22	february	february	NOUN
brj-23416	237	23	2024	2024	NUM
brj-23416	237	24	.	.	PUNCT
brj-23416	238	1	ahmad	ahmad	PROPN
brj-23416	238	2	,	,	PUNCT
brj-23416	238	3	g.	g.	PROPN
brj-23416	238	4	n.	n.	PROPN
brj-23416	238	5	,	,	PUNCT
brj-23416	238	6	fatima	fatima	PROPN
brj-23416	238	7	,	,	PUNCT
brj-23416	238	8	h.	h.	PROPN
brj-23416	238	9	,	,	PUNCT
brj-23416	238	10	ullah	ullah	PROPN
brj-23416	238	11	,	,	PUNCT
brj-23416	238	12	s.	s.	PROPN
brj-23416	238	13	,	,	PUNCT
brj-23416	238	14	and	and	CCONJ
brj-23416	238	15	saidi	saidi	PROPN
brj-23416	238	16	,	,	PUNCT
brj-23416	238	17	a.	a.	PROPN
brj-23416	238	18	s.	s.	PROPN
brj-23416	238	19	(	(	PUNCT
brj-23416	238	20	2022	2022	NUM
brj-23416	238	21	)	)	PUNCT
brj-23416	238	22	.	.	PUNCT
brj-23416	239	1	“	"	PUNCT
brj-23416	239	2	efficient	efficient	ADJ
brj-23416	239	3	medical	medical	ADJ
brj-23416	239	4	diagnosis	diagnosis	NOUN
brj-23416	239	5	of	of	ADP
brj-23416	239	6	human	human	ADJ
brj-23416	239	7	heart	heart	NOUN
brj-23416	239	8	diseases	disease	NOUN
brj-23416	239	9	using	use	VERB
brj-23416	239	10	machine	machine	NOUN
brj-23416	239	11	learning	learn	VERB
brj-23416	239	12	techniques	technique	NOUN
brj-23416	239	13	with	with	ADP
brj-23416	239	14	and	and	CCONJ
brj-23416	239	15	without	without	ADP
brj-23416	239	16	gridsearchcv	gridsearchcv	NOUN
brj-23416	239	17	,	,	PUNCT
brj-23416	239	18	”	"	PUNCT
brj-23416	239	19	ieee	ieee	NOUN
brj-23416	239	20	access	access	NOUN
brj-23416	239	21	10	10	NUM
brj-23416	239	22	,	,	PUNCT
brj-23416	239	23	80151	80151	NUM
brj-23416	239	24	-	-	SYM
brj-23416	239	25	80173	80173	NUM
brj-23416	239	26	.	.	PUNCT
brj-23416	240	1	doi	doi	NOUN
brj-23416	240	2	:	:	PUNCT
brj-23416	240	3	10.1109	10.1109	NUM
brj-23416	240	4	/	/	SYM
brj-23416	240	5	access.2022.3165792	access.2022.3165792	NUM
brj-23416	240	6	akl	akl	PROPN
brj-23416	240	7	,	,	PUNCT
brj-23416	240	8	a.	a.	PROPN
brj-23416	240	9	,	,	PUNCT
brj-23416	240	10	el	el	PROPN
brj-23416	240	11	-	-	PUNCT
brj-23416	240	12	henawy	henawy	PROPN
brj-23416	240	13	,	,	PUNCT
brj-23416	240	14	i.	i.	PROPN
brj-23416	240	15	,	,	PUNCT
brj-23416	240	16	salah	salah	PROPN
brj-23416	240	17	,	,	PUNCT
brj-23416	240	18	a.	a.	NOUN
brj-23416	240	19	,	,	PUNCT
brj-23416	240	20	and	and	CCONJ
brj-23416	240	21	li	li	PROPN
brj-23416	240	22	,	,	PUNCT
brj-23416	240	23	k.	k.	PROPN
brj-23416	240	24	(	(	PUNCT
brj-23416	240	25	2019	2019	NUM
brj-23416	240	26	)	)	PUNCT
brj-23416	240	27	.	.	PUNCT
brj-23416	241	1	“	"	PUNCT
brj-23416	241	2	optimizing	optimize	VERB
brj-23416	241	3	deep	deep	ADJ
brj-23416	241	4	neural	neural	ADJ
brj-23416	241	5	networks	network	NOUN
brj-23416	241	6	hyperparameter	hyperparameter	NOUN
brj-23416	241	7	positions	position	NOUN
brj-23416	241	8	and	and	CCONJ
brj-23416	241	9	values	value	NOUN
brj-23416	241	10	,	,	PUNCT
brj-23416	241	11	”	"	PUNCT
brj-23416	241	12	journal	journal	NOUN
brj-23416	241	13	of	of	ADP
brj-23416	241	14	intelligent	intelligent	ADJ
brj-23416	241	15	&	&	CCONJ
brj-23416	241	16	fuzzy	fuzzy	ADJ
brj-23416	241	17	systems	system	NOUN
brj-23416	241	18	37(5	37(5	NUM
brj-23416	241	19	)	)	PUNCT
brj-23416	241	20	,	,	PUNCT
brj-23416	241	21	6665	6665	NUM
brj-23416	241	22	-	-	SYM
brj-23416	241	23	6681	6681	NUM
brj-23416	241	24	.	.	PUNCT
brj-23416	242	1	doi	doi	NOUN
brj-23416	242	2	:	:	PUNCT
brj-23416	242	3	10.3233	10.3233	NUM
brj-23416	242	4	/	/	SYM
brj-23416	242	5	jifs-190033	jifs-190033	ADJ
brj-23416	242	6	alihodžić	alihodžić	NOUN
brj-23416	242	7	,	,	PUNCT
brj-23416	242	8	a.	a.	NOUN
brj-23416	242	9	,	,	PUNCT
brj-23416	242	10	zvorničanin	zvorničanin	PROPN
brj-23416	242	11	,	,	PUNCT
brj-23416	242	12	e.	e.	PROPN
brj-23416	242	13	,	,	PUNCT
brj-23416	242	14	and	and	CCONJ
brj-23416	242	15	čunjalo	čunjalo	NOUN
brj-23416	242	16	,	,	PUNCT
brj-23416	242	17	f.	f.	PROPN
brj-23416	242	18	(	(	PUNCT
brj-23416	242	19	2022	2022	NUM
brj-23416	242	20	)	)	PUNCT
brj-23416	242	21	.	.	PUNCT
brj-23416	243	1	“	"	PUNCT
brj-23416	243	2	a	a	DET
brj-23416	243	3	comparison	comparison	NOUN
brj-23416	243	4	of	of	ADP
brj-23416	243	5	machine	machine	NOUN
brj-23416	243	6	learning	learning	NOUN
brj-23416	243	7	methods	method	NOUN
brj-23416	243	8	for	for	ADP
brj-23416	243	9	forecasting	forecast	VERB
brj-23416	243	10	dow	dow	PROPN
brj-23416	243	11	jones	jones	PROPN
brj-23416	243	12	stock	stock	PROPN
brj-23416	243	13	index	index	PROPN
brj-23416	243	14	,	,	PUNCT
brj-23416	243	15	”	"	PUNCT
brj-23416	243	16	in	in	ADP
brj-23416	243	17	:	:	PUNCT
brj-23416	243	18	large	large	ADJ
brj-23416	243	19	-	-	PUNCT
brj-23416	243	20	scale	scale	NOUN
brj-23416	243	21	scientific	scientific	ADJ
brj-23416	243	22	computing	computing	NOUN
brj-23416	243	23	,	,	PUNCT
brj-23416	243	24	i.	i.	PROPN
brj-23416	243	25	lirkov	lirkov	PROPN
brj-23416	243	26	,	,	PUNCT
brj-23416	243	27	s.	s.	PROPN
brj-23416	243	28	margenov	margenov	PROPN
brj-23416	243	29	(	(	PUNCT
brj-23416	243	30	eds	ed	NOUN
brj-23416	243	31	.	.	PUNCT
brj-23416	243	32	)	)	PUNCT
brj-23416	243	33	,	,	PUNCT
brj-23416	243	34	springer	springer	NOUN
brj-23416	243	35	,	,	PUNCT
brj-23416	243	36	cham	cham	PROPN
brj-23416	243	37	.	.	PUNCT
brj-23416	244	1	doi	doi	NOUN
brj-23416	244	2	:	:	PUNCT
brj-23416	244	3	10.1007/978	10.1007/978	NUM
brj-23416	244	4	-	-	SYM
brj-23416	244	5	3	3	NUM
brj-23416	244	6	-	-	PUNCT
brj-23416	244	7	03097549	03097549	NUM
brj-23416	244	8	-	-	PUNCT
brj-23416	244	9	4_24	4_24	NUM
brj-23416	244	10	armağan	armağan	PROPN
brj-23416	244	11	,	,	PUNCT
brj-23416	244	12	i̇.	i̇.	PROPN
brj-23416	244	13	ü.	ü.	NOUN
brj-23416	244	14	(	(	PUNCT
brj-23416	244	15	2023	2023	NUM
brj-23416	244	16	)	)	PUNCT
brj-23416	244	17	.	.	PUNCT
brj-23416	245	1	“	"	PUNCT
brj-23416	245	2	price	price	NOUN
brj-23416	245	3	prediction	prediction	NOUN
brj-23416	245	4	of	of	ADP
brj-23416	245	5	the	the	DET
brj-23416	245	6	borsa	borsa	PROPN
brj-23416	245	7	istanbul	istanbul	PROPN
brj-23416	245	8	banks	banks	PROPN
brj-23416	245	9	index	index	NOUN
brj-23416	245	10	with	with	ADP
brj-23416	245	11	traditional	traditional	ADJ
brj-23416	245	12	methods	method	NOUN
brj-23416	245	13	and	and	CCONJ
brj-23416	245	14	artificial	artificial	ADJ
brj-23416	245	15	neural	neural	ADJ
brj-23416	245	16	networks	network	NOUN
brj-23416	245	17	,	,	PUNCT
brj-23416	245	18	”	"	PUNCT
brj-23416	245	19	borsa	borsa	PROPN
brj-23416	245	20	istanbul	istanbul	PROPN
brj-23416	245	21	review	review	PROPN
brj-23416	245	22	23(s1	23(s1	PROPN
brj-23416	245	23	)	)	PUNCT
brj-23416	245	24	,	,	PUNCT
brj-23416	245	25	s30	s30	PROPN
brj-23416	245	26	-	-	PUNCT
brj-23416	245	27	s39	s39	PROPN
brj-23416	245	28	.	.	PUNCT
brj-23416	246	1	doi	doi	NOUN
brj-23416	246	2	:	:	PUNCT
brj-23416	246	3	10.1016	10.1016	NUM
brj-23416	246	4	/	/	SYM
brj-23416	246	5	j.bir.2023.10.005	j.bir.2023.10.005	PROPN
brj-23416	246	6	bardak	bardak	PROPN
brj-23416	246	7	,	,	PUNCT
brj-23416	246	8	t.	t.	PROPN
brj-23416	246	9	(	(	PUNCT
brj-23416	246	10	2023	2023	NUM
brj-23416	246	11	)	)	PUNCT
brj-23416	246	12	.	.	PUNCT
brj-23416	247	1	“	"	PUNCT
brj-23416	247	2	predicting	predict	VERB
brj-23416	247	3	prices	price	NOUN
brj-23416	247	4	of	of	ADP
brj-23416	247	5	case	case	NOUN
brj-23416	247	6	furniture	furniture	NOUN
brj-23416	247	7	products	product	NOUN
brj-23416	247	8	using	use	VERB
brj-23416	247	9	web	web	NOUN
brj-23416	247	10	mining	mining	NOUN
brj-23416	247	11	techniques	technique	NOUN
brj-23416	247	12	,	,	PUNCT
brj-23416	247	13	”	"	PUNCT
brj-23416	247	14	bioresources	bioresource	NOUN
brj-23416	247	15	18(4	18(4	NUM
brj-23416	247	16	)	)	PUNCT
brj-23416	247	17	,	,	PUNCT
brj-23416	247	18	7412	7412	NUM
brj-23416	247	19	-	-	SYM
brj-23416	247	20	7427	7427	NUM
brj-23416	247	21	.	.	PUNCT
brj-23416	248	1	doi	doi	NOUN
brj-23416	248	2	:	:	PUNCT
brj-23416	248	3	10.15376	10.15376	NUM
brj-23416	248	4	/	/	SYM
brj-23416	248	5	biores.18.4.7412	biores.18.4.7412	PROPN
brj-23416	248	6	-	-	PUNCT
brj-23416	248	7	7427	7427	NUM
brj-23416	248	8	bayramoğlu	bayramoğlu	NOUN
brj-23416	248	9	,	,	PUNCT
brj-23416	248	10	m.	m.	PROPN
brj-23416	248	11	f.	f.	PROPN
brj-23416	248	12	(	(	PUNCT
brj-23416	248	13	2007	2007	NUM
brj-23416	248	14	)	)	PUNCT
brj-23416	248	15	.	.	PUNCT
brj-23416	249	1	using	use	VERB
brj-23416	249	2	artificial	artificial	ADJ
brj-23416	249	3	neural	neural	ADJ
brj-23416	249	4	networks	network	NOUN
brj-23416	249	5	models	model	NOUN
brj-23416	249	6	for	for	ADP
brj-23416	249	7	predicting	predict	VERB
brj-23416	249	8	financial	financial	ADJ
brj-23416	249	9	indexes	index	NOUN
brj-23416	249	10	:	:	PUNCT
brj-23416	249	11	an	an	DET
brj-23416	249	12	application	application	NOUN
brj-23416	249	13	on	on	ADP
brj-23416	249	14	predicting	predict	VERB
brj-23416	249	15	of	of	ADP
brj-23416	249	16	daily	daily	ADV
brj-23416	249	17	lowest	low	ADJ
brj-23416	249	18	and	and	CCONJ
brj-23416	249	19	highest	high	ADJ
brj-23416	249	20	values	value	NOUN
brj-23416	249	21	of	of	ADP
brj-23416	249	22	ise	ise	PROPN
brj-23416	249	23	national	national	ADJ
brj-23416	249	24	100	100	NUM
brj-23416	249	25	index	index	NOUN
brj-23416	249	26	,	,	PUNCT
brj-23416	249	27	master	master	NOUN
brj-23416	249	28	’s	’s	PART
brj-23416	249	29	thesis	thesis	NOUN
brj-23416	249	30	,	,	PUNCT
brj-23416	249	31	zonguldak	zonguldak	PROPN
brj-23416	249	32	karaelmas	karaelmas	PROPN
brj-23416	249	33	university	university	PROPN
brj-23416	249	34	,	,	PUNCT
brj-23416	249	35	zonguldak	zonguldak	PROPN
brj-23416	249	36	,	,	PUNCT
brj-23416	249	37	türkiye	türkiye	PROPN
brj-23416	249	38	.	.	PUNCT
brj-23416	249	39	bhuvaneswari	bhuvaneswari	PROPN
brj-23416	249	40	,	,	PUNCT
brj-23416	249	41	p.	p.	NOUN
brj-23416	249	42	,	,	PUNCT
brj-23416	249	43	and	and	CCONJ
brj-23416	249	44	therese	therese	PROPN
brj-23416	249	45	,	,	PUNCT
brj-23416	249	46	a.	a.	PROPN
brj-23416	249	47	b.	b.	PROPN
brj-23416	249	48	(	(	PUNCT
brj-23416	249	49	2015	2015	NUM
brj-23416	249	50	)	)	PUNCT
brj-23416	249	51	.	.	PUNCT
brj-23416	250	1	“	"	PUNCT
brj-23416	250	2	detection	detection	NOUN
brj-23416	250	3	of	of	ADP
brj-23416	250	4	cancer	cancer	NOUN
brj-23416	250	5	in	in	ADP
brj-23416	250	6	lung	lung	NOUN
brj-23416	250	7	with	with	ADP
brj-23416	250	8	k	k	PROPN
brj-23416	250	9	-	-	PUNCT
brj-23416	250	10	nn	nn	ADJ
brj-23416	250	11	classification	classification	NOUN
brj-23416	250	12	using	use	VERB
brj-23416	250	13	genetic	genetic	ADJ
brj-23416	250	14	algorithm	algorithm	NOUN
brj-23416	250	15	,	,	PUNCT
brj-23416	250	16	”	"	PUNCT
brj-23416	250	17	procedia	procedia	NOUN
brj-23416	250	18	materials	material	NOUN
brj-23416	250	19	science	science	NOUN
brj-23416	250	20	10	10	NUM
brj-23416	250	21	,	,	PUNCT
brj-23416	250	22	433	433	NUM
brj-23416	250	23	-	-	SYM
brj-23416	250	24	440	440	NUM
brj-23416	250	25	.	.	PUNCT
brj-23416	251	1	doi	doi	NOUN
brj-23416	251	2	:	:	PUNCT
brj-23416	251	3	10.1016	10.1016	NUM
brj-23416	251	4	/	/	SYM
brj-23416	251	5	j.	j.	PROPN
brj-23416	251	6	mspro.2015.06.077	mspro.2015.06.077	PROPN
brj-23416	251	7	borsa	borsa	PROPN
brj-23416	251	8	istanbul	istanbul	PROPN
brj-23416	251	9	.	.	PUNCT
brj-23416	252	1	(	(	PUNCT
brj-23416	252	2	2023	2023	NUM
brj-23416	252	3	)	)	PUNCT
brj-23416	252	4	.	.	PUNCT
brj-23416	253	1	“	"	PUNCT
brj-23416	253	2	indices	index	NOUN
brj-23416	253	3	,	,	PUNCT
brj-23416	253	4	bist	bist	ADJ
brj-23416	253	5	stock	stock	NOUN
brj-23416	253	6	indices	index	NOUN
brj-23416	253	7	,	,	PUNCT
brj-23416	253	8	”	"	PUNCT
brj-23416	253	9	(	(	PUNCT
brj-23416	253	10	https://www.borsaistanbul	https://www.borsaistanbul	PROPN
brj-23416	253	11	.	.	PUNCT
brj-23416	253	12	com	com	NOUN
brj-23416	253	13	/	/	SYM
brj-23416	253	14	en	en	NOUN
brj-23416	253	15	/	/	SYM
brj-23416	253	16	index/1	index/1	ADJ
brj-23416	253	17	/	/	SYM
brj-23416	253	18	stock	stock	NOUN
brj-23416	253	19	-	-	PUNCT
brj-23416	253	20	indices	index	NOUN
brj-23416	253	21	)	)	PUNCT
brj-23416	253	22	,	,	PUNCT
brj-23416	253	23	accessed	access	VERB
brj-23416	253	24	30	30	NUM
brj-23416	253	25	may	may	PROPN
brj-23416	253	26	2024	2024	NUM
brj-23416	253	27	botchkarev	botchkarev	NOUN
brj-23416	253	28	,	,	PUNCT
brj-23416	253	29	a.	a.	NOUN
brj-23416	253	30	(	(	PUNCT
brj-23416	253	31	2018	2018	NUM
brj-23416	253	32	)	)	PUNCT
brj-23416	253	33	.	.	PUNCT
brj-23416	254	1	“	"	PUNCT
brj-23416	254	2	performance	performance	NOUN
brj-23416	254	3	metrics	metric	NOUN
brj-23416	254	4	(	(	PUNCT
brj-23416	254	5	error	error	NOUN
brj-23416	254	6	measures	measure	NOUN
brj-23416	254	7	)	)	PUNCT
brj-23416	254	8	in	in	ADP
brj-23416	254	9	machine	machine	NOUN
brj-23416	254	10	learning	learn	VERB
brj-23416	254	11	regression	regression	NOUN
brj-23416	254	12	,	,	PUNCT
brj-23416	254	13	forecasting	forecasting	NOUN
brj-23416	254	14	and	and	CCONJ
brj-23416	254	15	prognostics	prognostic	NOUN
brj-23416	254	16	:	:	PUNCT
brj-23416	254	17	properties	property	NOUN
brj-23416	254	18	and	and	CCONJ
brj-23416	254	19	typology	typology	NOUN
brj-23416	254	20	,	,	PUNCT
brj-23416	254	21	”	"	PUNCT
brj-23416	254	22	(	(	PUNCT
brj-23416	254	23	https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf	https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf	PROPN
brj-23416	254	24	)	)	PUNCT
brj-23416	254	25	,	,	PUNCT
brj-23416	254	26	accessed	access	VERB
brj-23416	254	27	28	28	NUM
brj-23416	254	28	september	september	PROPN
brj-23416	254	29	2023	2023	NUM
brj-23416	254	30	.	.	PUNCT
brj-23416	255	1	budak	budak	PROPN
brj-23416	255	2	,	,	PUNCT
brj-23416	255	3	s.	s.	PROPN
brj-23416	255	4	,	,	PUNCT
brj-23416	255	5	ölmez	ölmez	NOUN
brj-23416	255	6	cangi	cangi	NOUN
brj-23416	255	7	,	,	PUNCT
brj-23416	255	8	s.	s.	PROPN
brj-23416	255	9	,	,	PUNCT
brj-23416	255	10	and	and	CCONJ
brj-23416	255	11	tuna	tuna	NOUN
brj-23416	255	12	,	,	PUNCT
brj-23416	255	13	i̇.	i̇.	PROPN
brj-23416	255	14	(	(	PUNCT
brj-23416	255	15	2017	2017	NUM
brj-23416	255	16	)	)	PUNCT
brj-23416	255	17	.	.	PUNCT
brj-23416	256	1	“	"	PUNCT
brj-23416	256	2	the	the	DET
brj-23416	256	3	effect	effect	NOUN
brj-23416	256	4	of	of	ADP
brj-23416	256	5	basic	basic	ADJ
brj-23416	256	6	macroeconomic	macroeconomic	ADJ
brj-23416	256	7	variables	variable	NOUN
brj-23416	256	8	on	on	ADP
brj-23416	256	9	bist	bist	ADJ
brj-23416	256	10	indexes	index	NOUN
brj-23416	256	11	,	,	PUNCT
brj-23416	256	12	”	"	PUNCT
brj-23416	256	13	the	the	DET
brj-23416	256	14	journal	journal	NOUN
brj-23416	256	15	of	of	ADP
brj-23416	256	16	academic	academic	ADJ
brj-23416	256	17	social	social	ADJ
brj-23416	256	18	science	science	NOUN
brj-23416	256	19	55	55	NUM
brj-23416	256	20	,	,	PUNCT
brj-23416	256	21	199	199	NUM
brj-23416	256	22	-	-	SYM
brj-23416	256	23	214	214	NUM
brj-23416	256	24	.	.	PUNCT
brj-23416	257	1	doi	doi	NOUN
brj-23416	257	2	:	:	PUNCT
brj-23416	257	3	10.16992	10.16992	NUM
brj-23416	257	4	/	/	SYM
brj-23416	257	5	asos.12826	asos.12826	PROPN
brj-23416	257	6	cavalcante	cavalcante	NOUN
brj-23416	257	7	,	,	PUNCT
brj-23416	257	8	r.	r.	PROPN
brj-23416	257	9	c.	c.	PROPN
brj-23416	257	10	,	,	PUNCT
brj-23416	257	11	brasileiro	brasileiro	PROPN
brj-23416	257	12	,	,	PUNCT
brj-23416	257	13	r.	r.	PROPN
brj-23416	257	14	c.	c.	PROPN
brj-23416	257	15	,	,	PUNCT
brj-23416	257	16	souza	souza	PROPN
brj-23416	257	17	,	,	PUNCT
brj-23416	257	18	v.	v.	PROPN
brj-23416	257	19	l.	l.	PROPN
brj-23416	257	20	f.	f.	PROPN
brj-23416	257	21	,	,	PUNCT
brj-23416	257	22	nobrega	nobrega	PROPN
brj-23416	257	23	,	,	PUNCT
brj-23416	257	24	j.	j.	PROPN
brj-23416	257	25	p.	p.	PROPN
brj-23416	257	26	,	,	PUNCT
brj-23416	257	27	and	and	CCONJ
brj-23416	257	28	oliveira	oliveira	PROPN
brj-23416	257	29	,	,	PUNCT
brj-23416	257	30	a.	a.	PROPN
brj-23416	257	31	l.	l.	PROPN
brj-23416	257	32	i.	i.	PROPN
brj-23416	257	33	(	(	PUNCT
brj-23416	257	34	2016	2016	NUM
brj-23416	257	35	)	)	PUNCT
brj-23416	257	36	.	.	PUNCT
brj-23416	258	1	“	"	PUNCT
brj-23416	258	2	computational	computational	ADJ
brj-23416	258	3	intelligence	intelligence	NOUN
brj-23416	258	4	and	and	CCONJ
brj-23416	258	5	financial	financial	ADJ
brj-23416	258	6	markets	market	NOUN
brj-23416	258	7	:	:	PUNCT
brj-23416	258	8	a	a	DET
brj-23416	258	9	survey	survey	NOUN
brj-23416	258	10	and	and	CCONJ
brj-23416	258	11	future	future	ADJ
brj-23416	258	12	directions	direction	NOUN
brj-23416	258	13	,	,	PUNCT
brj-23416	258	14	”	"	PUNCT
brj-23416	258	15	expert	expert	NOUN
brj-23416	258	16	systems	system	NOUN
brj-23416	258	17	with	with	ADP
brj-23416	258	18	applications	application	NOUN
brj-23416	258	19	,	,	PUNCT
brj-23416	258	20	55	55	NUM
brj-23416	258	21	,	,	PUNCT
brj-23416	258	22	194	194	NUM
brj-23416	258	23	-	-	SYM
brj-23416	258	24	211	211	NUM
brj-23416	258	25	.	.	PUNCT
brj-23416	259	1	doi	doi	NOUN
brj-23416	259	2	:	:	PUNCT
brj-23416	259	3	10.1016	10.1016	NUM
brj-23416	259	4	/	/	SYM
brj-23416	259	5	j.eswa.2016.02.006	j.eswa.2016.02.006	NOUN
brj-23416	259	6	ceylan	ceylan	NOUN
brj-23416	259	7	,	,	PUNCT
brj-23416	259	8	t.	t.	PROPN
brj-23416	259	9	(	(	PUNCT
brj-23416	259	10	2018	2018	NUM
brj-23416	259	11	)	)	PUNCT
brj-23416	259	12	.	.	PUNCT
brj-23416	260	1	approaches	approach	NOUN
brj-23416	260	2	to	to	ADP
brj-23416	260	3	the	the	DET
brj-23416	260	4	application	application	NOUN
brj-23416	260	5	of	of	ADP
brj-23416	260	6	machine	machine	NOUN
brj-23416	260	7	learning	learning	NOUN
brj-23416	260	8	in	in	ADP
brj-23416	260	9	the	the	DET
brj-23416	260	10	retail	retail	ADJ
brj-23416	260	11	sector	sector	NOUN
brj-23416	260	12	,	,	PUNCT
brj-23416	260	13	phd	phd	NOUN
brj-23416	260	14	thesis	thesis	NOUN
brj-23416	260	15	.	.	PUNCT
brj-23416	261	1	yıldız	yıldız	PROPN
brj-23416	261	2	technical	technical	PROPN
brj-23416	261	3	university	university	PROPN
brj-23416	261	4	,	,	PUNCT
brj-23416	261	5	i̇stanbul	i̇stanbul	ADV
brj-23416	261	6	,	,	PUNCT
brj-23416	262	1	türkiye	türkiye	PROPN
brj-23416	262	2	.	.	PUNCT
brj-23416	262	3	contreras	contreras	PROPN
brj-23416	262	4	,	,	PUNCT
brj-23416	262	5	p.	p.	PROPN
brj-23416	262	6	,	,	PUNCT
brj-23416	262	7	orellana	orellana	PROPN
brj-23416	262	8	-	-	PUNCT
brj-23416	262	9	alvear	alvear	PROPN
brj-23416	262	10	,	,	PUNCT
brj-23416	262	11	j.	j.	PROPN
brj-23416	262	12	,	,	PUNCT
brj-23416	262	13	muñoz	muñoz	PROPN
brj-23416	262	14	,	,	PUNCT
brj-23416	262	15	p.	p.	PROPN
brj-23416	262	16	,	,	PUNCT
brj-23416	262	17	bendix	bendix	PROPN
brj-23416	262	18	,	,	PUNCT
brj-23416	262	19	j.	j.	PROPN
brj-23416	262	20	,	,	PUNCT
brj-23416	262	21	and	and	CCONJ
brj-23416	262	22	célleri	célleri	ADJ
brj-23416	262	23	,	,	PUNCT
brj-23416	262	24	r.	r.	PROPN
brj-23416	262	25	(	(	PUNCT
brj-23416	262	26	2021	2021	NUM
brj-23416	262	27	)	)	PUNCT
brj-23416	262	28	.	.	PUNCT
brj-23416	263	1	“	"	PUNCT
brj-23416	263	2	influence	influence	NOUN
brj-23416	263	3	of	of	ADP
brj-23416	263	4	random	random	ADJ
brj-23416	263	5	forest	forest	NOUN
brj-23416	263	6	hyperparameterization	hyperparameterization	NOUN
brj-23416	263	7	on	on	ADP
brj-23416	263	8	short	short	ADJ
brj-23416	263	9	-	-	PUNCT
brj-23416	263	10	term	term	NOUN
brj-23416	263	11	runoff	runoff	NOUN
brj-23416	263	12	forecasting	forecasting	NOUN
brj-23416	263	13	in	in	ADP
brj-23416	263	14	an	an	DET
brj-23416	263	15	andean	andean	ADJ
brj-23416	263	16	mountain	mountain	NOUN
brj-23416	263	17	catchment	catchment	NOUN
brj-23416	263	18	,	,	PUNCT
brj-23416	263	19	”	"	PUNCT
brj-23416	263	20	atmosphere	atmosphere	NOUN
brj-23416	263	21	12(2	12(2	NUM
brj-23416	263	22	)	)	PUNCT
brj-23416	263	23	,	,	PUNCT
brj-23416	263	24	article	article	NOUN
brj-23416	263	25	238	238	NUM
brj-23416	263	26	.	.	PUNCT
brj-23416	264	1	doi	doi	NOUN
brj-23416	264	2	:	:	PUNCT
brj-23416	264	3	10.3390	10.3390	NUM
brj-23416	264	4	/	/	SYM
brj-23416	264	5	atmos12020238	atmos12020238	ADP
brj-23416	264	6	correa	correa	PROPN
brj-23416	264	7	-	-	PUNCT
brj-23416	264	8	jullian	jullian	PROPN
brj-23416	264	9	,	,	PUNCT
brj-23416	264	10	c.	c.	PROPN
brj-23416	264	11	,	,	PUNCT
brj-23416	264	12	cardemil	cardemil	PROPN
brj-23416	264	13	,	,	PUNCT
brj-23416	264	14	j.	j.	PROPN
brj-23416	264	15	m.	m.	PROPN
brj-23416	264	16	,	,	PUNCT
brj-23416	264	17	droguett	droguett	PROPN
brj-23416	264	18	,	,	PUNCT
brj-23416	264	19	e.	e.	PROPN
brj-23416	264	20	l.	l.	PROPN
brj-23416	264	21	,	,	PUNCT
brj-23416	264	22	and	and	CCONJ
brj-23416	264	23	behzad	behzad	PROPN
brj-23416	264	24	,	,	PUNCT
brj-23416	264	25	m.	m.	NOUN
brj-23416	264	26	(	(	PUNCT
brj-23416	264	27	2020	2020	NUM
brj-23416	264	28	)	)	PUNCT
brj-23416	264	29	.	.	PUNCT
brj-23416	265	1	“	"	PUNCT
brj-23416	265	2	assessment	assessment	NOUN
brj-23416	265	3	of	of	ADP
brj-23416	265	4	deep	deep	ADJ
brj-23416	265	5	learning	learning	NOUN
brj-23416	265	6	techniques	technique	NOUN
brj-23416	265	7	for	for	ADP
brj-23416	265	8	prognosis	prognosis	NOUN
brj-23416	265	9	of	of	ADP
brj-23416	265	10	solar	solar	ADJ
brj-23416	265	11	thermal	thermal	ADJ
brj-23416	265	12	systems	system	NOUN
brj-23416	265	13	,	,	PUNCT
brj-23416	265	14	”	"	PUNCT
brj-23416	265	15	renewable	renewable	ADJ
brj-23416	265	16	energy	energy	NOUN
brj-23416	265	17	145	145	NUM
brj-23416	265	18	,	,	PUNCT
brj-23416	265	19	2178	2178	NUM
brj-23416	265	20	-	-	SYM
brj-23416	265	21	2191	2191	NUM
brj-23416	265	22	.	.	PUNCT
brj-23416	266	1	doi	doi	NOUN
brj-23416	266	2	:	:	PUNCT
brj-23416	266	3	10.1016	10.1016	NUM
brj-23416	266	4	/	/	SYM
brj-23416	266	5	j.renene.2019.07.100	j.renene.2019.07.100	PROPN
brj-23416	266	6	https://upload.eib.org.tr/	https://upload.eib.org.tr/	ADP
brj-23416	266	7	peer	peer	NOUN
brj-23416	266	8	-	-	PUNCT
brj-23416	266	9	reviewed	review	VERB
brj-23416	266	10	article	article	NOUN
brj-23416	266	11	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	266	12	akyüz	akyüz	PROPN
brj-23416	266	13	et	et	PROPN
brj-23416	266	14	al	al	PROPN
brj-23416	266	15	.	.	PROPN
brj-23416	266	16	(	(	PUNCT
brj-23416	266	17	2024	2024	NUM
brj-23416	266	18	)	)	PUNCT
brj-23416	266	19	.	.	PUNCT
brj-23416	267	1	“	"	PUNCT
brj-23416	267	2	stock	stock	NOUN
brj-23416	267	3	exchange	exchange	NOUN
brj-23416	267	4	values	value	NOUN
brj-23416	267	5	,	,	PUNCT
brj-23416	267	6	”	"	PUNCT
brj-23416	267	7	bioresources	bioresource	NOUN
brj-23416	267	8	19(3	19(3	NUM
brj-23416	267	9	)	)	PUNCT
brj-23416	267	10	,	,	PUNCT
brj-23416	267	11	5141	5141	NUM
brj-23416	267	12	-	-	SYM
brj-23416	267	13	5157	5157	NUM
brj-23416	267	14	.	.	PUNCT
brj-23416	268	1	5153	5153	NUM
brj-23416	268	2	durmuş	durmuş	NOUN
brj-23416	268	3	,	,	PUNCT
brj-23416	268	4	s.	s.	PROPN
brj-23416	268	5	,	,	PUNCT
brj-23416	268	6	yılmaz	yılmaz	PROPN
brj-23416	268	7	,	,	PUNCT
brj-23416	268	8	t.	t.	PROPN
brj-23416	268	9	,	,	PUNCT
brj-23416	268	10	and	and	CCONJ
brj-23416	268	11	şahin	şahin	PROPN
brj-23416	268	12	,	,	PUNCT
brj-23416	268	13	d.	d.	PROPN
brj-23416	268	14	(	(	PUNCT
brj-23416	268	15	2019	2019	NUM
brj-23416	268	16	)	)	PUNCT
brj-23416	268	17	.	.	PUNCT
brj-23416	269	1	“	"	PUNCT
brj-23416	269	2	the	the	DET
brj-23416	269	3	effect	effect	NOUN
brj-23416	269	4	of	of	ADP
brj-23416	269	5	macroeconomic	macroeconomic	ADJ
brj-23416	269	6	indicators	indicator	NOUN
brj-23416	269	7	on	on	ADP
brj-23416	269	8	index	index	NOUN
brj-23416	269	9	revenues	revenue	NOUN
brj-23416	269	10	:	:	PUNCT
brj-23416	269	11	bist	bist	ADJ
brj-23416	269	12	example	example	NOUN
brj-23416	269	13	,	,	PUNCT
brj-23416	269	14	”	"	PUNCT
brj-23416	269	15	eurasia	eurasia	PROPN
brj-23416	269	16	international	international	PROPN
brj-23416	269	17	research	research	PROPN
brj-23416	269	18	journal	journal	PROPN
brj-23416	269	19	7(16	7(16	NUM
brj-23416	269	20	)	)	PUNCT
brj-23416	269	21	,	,	PUNCT
brj-23416	269	22	870	870	NUM
brj-23416	269	23	-	-	SYM
brj-23416	269	24	886	886	NUM
brj-23416	269	25	.	.	PUNCT
brj-23416	270	1	el	el	PROPN
brj-23416	270	2	mrabet	mrabet	PROPN
brj-23416	270	3	,	,	PUNCT
brj-23416	270	4	z.	z.	PROPN
brj-23416	270	5	,	,	PUNCT
brj-23416	270	6	sugunaraj	sugunaraj	ADJ
brj-23416	270	7	,	,	PUNCT
brj-23416	270	8	n.	n.	NOUN
brj-23416	270	9	,	,	PUNCT
brj-23416	270	10	ranganathan	ranganathan	PROPN
brj-23416	270	11	,	,	PUNCT
brj-23416	270	12	p.	p.	NOUN
brj-23416	270	13	,	,	PUNCT
brj-23416	270	14	and	and	CCONJ
brj-23416	270	15	abhyankar	abhyankar	PROPN
brj-23416	270	16	,	,	PUNCT
brj-23416	270	17	s.	s.	PROPN
brj-23416	270	18	(	(	PUNCT
brj-23416	270	19	2022	2022	NUM
brj-23416	270	20	)	)	PUNCT
brj-23416	270	21	.	.	PUNCT
brj-23416	271	1	“	"	PUNCT
brj-23416	271	2	random	random	ADJ
brj-23416	271	3	forest	forest	NOUN
brj-23416	271	4	regressor	regressor	NOUN
brj-23416	271	5	-	-	PUNCT
brj-23416	271	6	based	base	VERB
brj-23416	271	7	approach	approach	NOUN
brj-23416	271	8	for	for	ADP
brj-23416	271	9	detecting	detect	VERB
brj-23416	271	10	fault	fault	NOUN
brj-23416	271	11	location	location	NOUN
brj-23416	271	12	and	and	CCONJ
brj-23416	271	13	duration	duration	NOUN
brj-23416	271	14	in	in	ADP
brj-23416	271	15	power	power	NOUN
brj-23416	271	16	systems	system	NOUN
brj-23416	271	17	,	,	PUNCT
brj-23416	271	18	”	"	PUNCT
brj-23416	271	19	sensors	sensor	NOUN
brj-23416	271	20	22(2	22(2	NUM
brj-23416	271	21	)	)	PUNCT
brj-23416	271	22	,	,	PUNCT
brj-23416	271	23	458	458	NUM
brj-23416	271	24	.	.	PUNCT
brj-23416	272	1	doi	doi	NOUN
brj-23416	272	2	:	:	PUNCT
brj-23416	272	3	10.3390	10.3390	NUM
brj-23416	272	4	/	/	SYM
brj-23416	272	5	s22020458	s22020458	ADJ
brj-23416	272	6	fan	fan	NOUN
brj-23416	272	7	,	,	PUNCT
brj-23416	272	8	m.	m.	PROPN
brj-23416	272	9	h.	h.	PROPN
brj-23416	272	10	,	,	PUNCT
brj-23416	272	11	huang	huang	PROPN
brj-23416	272	12	,	,	PUNCT
brj-23416	272	13	j.	j.	PROPN
brj-23416	272	14	l.	l.	PROPN
brj-23416	272	15	,	,	PUNCT
brj-23416	272	16	and	and	CCONJ
brj-23416	272	17	chen	chen	PROPN
brj-23416	272	18	,	,	PUNCT
brj-23416	272	19	m.	m.	NOUN
brj-23416	272	20	y.	y.	PROPN
brj-23416	272	21	(	(	PUNCT
brj-23416	272	22	2024	2024	NUM
brj-23416	272	23	)	)	PUNCT
brj-23416	272	24	.	.	PUNCT
brj-23416	273	1	“	"	PUNCT
brj-23416	273	2	stock	stock	NOUN
brj-23416	273	3	and	and	CCONJ
brj-23416	273	4	futures	future	NOUN
brj-23416	273	5	market	market	NOUN
brj-23416	273	6	prediction	prediction	NOUN
brj-23416	273	7	using	use	VERB
brj-23416	273	8	deep	deep	ADJ
brj-23416	273	9	learning	learning	NOUN
brj-23416	273	10	approach	approach	NOUN
brj-23416	273	11	,	,	PUNCT
brj-23416	273	12	”	"	PUNCT
brj-23416	273	13	in	in	ADP
brj-23416	273	14	:	:	PUNCT
brj-23416	273	15	investment	investment	NOUN
brj-23416	273	16	strategies	strategy	NOUN
brj-23416	273	17	new	new	ADJ
brj-23416	273	18	advances	advance	NOUN
brj-23416	273	19	and	and	CCONJ
brj-23416	273	20	challenges	challenge	NOUN
brj-23416	273	21	,	,	PUNCT
brj-23416	273	22	g.	g.	PROPN
brj-23416	273	23	prelipcean	prelipcean	PROPN
brj-23416	273	24	,	,	PUNCT
brj-23416	273	25	m.	m.	NOUN
brj-23416	273	26	boscoinau	boscoinau	PROPN
brj-23416	273	27	(	(	PUNCT
brj-23416	273	28	eds	ed	NOUN
brj-23416	273	29	.	.	PUNCT
brj-23416	273	30	)	)	PUNCT
brj-23416	273	31	,	,	PUNCT
brj-23416	273	32	intechopen	intechopen	ADJ
brj-23416	273	33	,	,	PUNCT
brj-23416	273	34	london	london	PROPN
brj-23416	273	35	,	,	PUNCT
brj-23416	273	36	united	united	ADJ
brj-23416	273	37	kingdom	kingdom	PROPN
brj-23416	273	38	.	.	PUNCT
brj-23416	274	1	doi	doi	NOUN
brj-23416	274	2	:	:	PUNCT
brj-23416	274	3	10.5772	10.5772	NUM
brj-23416	274	4	/	/	SYM
brj-23416	274	5	intechopen.114116	intechopen.114116	ADV
brj-23416	274	6	fattah	fattah	ADJ
brj-23416	274	7	,	,	PUNCT
brj-23416	274	8	a.	a.	NOUN
brj-23416	274	9	,	,	PUNCT
brj-23416	274	10	and	and	CCONJ
brj-23416	274	11	kocabıyık	kocabıyık	PROPN
brj-23416	274	12	,	,	PUNCT
brj-23416	274	13	t.	t.	PROPN
brj-23416	274	14	(	(	PUNCT
brj-23416	274	15	2020	2020	NUM
brj-23416	274	16	)	)	PUNCT
brj-23416	274	17	.	.	PUNCT
brj-23416	275	1	“	"	PUNCT
brj-23416	275	2	the	the	DET
brj-23416	275	3	effect	effect	NOUN
brj-23416	275	4	of	of	ADP
brj-23416	275	5	macroeconomic	macroeconomic	ADJ
brj-23416	275	6	variables	variable	NOUN
brj-23416	275	7	on	on	ADP
brj-23416	275	8	stock	stock	NOUN
brj-23416	275	9	market	market	NOUN
brj-23416	275	10	indices	index	NOUN
brj-23416	275	11	:	:	PUNCT
brj-23416	275	12	a	a	DET
brj-23416	275	13	comparison	comparison	NOUN
brj-23416	275	14	of	of	ADP
brj-23416	275	15	turkey	turkey	NOUN
brj-23416	275	16	and	and	CCONJ
brj-23416	275	17	the	the	DET
brj-23416	275	18	usa	usa	PROPN
brj-23416	275	19	,	,	PUNCT
brj-23416	275	20	”	"	PUNCT
brj-23416	275	21	journal	journal	NOUN
brj-23416	275	22	of	of	ADP
brj-23416	275	23	financial	financial	ADJ
brj-23416	275	24	research	research	NOUN
brj-23416	275	25	and	and	CCONJ
brj-23416	275	26	studies	study	NOUN
brj-23416	275	27	12(2	12(2	NUM
brj-23416	275	28	)	)	PUNCT
brj-23416	275	29	,	,	PUNCT
brj-23416	275	30	116	116	NUM
brj-23416	275	31	-	-	SYM
brj-23416	275	32	151	151	NUM
brj-23416	275	33	.	.	PUNCT
brj-23416	276	1	doi	doi	NOUN
brj-23416	276	2	:	:	PUNCT
brj-23416	276	3	10.14784	10.14784	NUM
brj-23416	276	4	/	/	SYM
brj-23416	276	5	marufacd.691108	marufacd.691108	PROPN
brj-23416	276	6	gao	gao	PROPN
brj-23416	276	7	,	,	PUNCT
brj-23416	276	8	s.	s.	PROPN
brj-23416	276	9	(	(	PUNCT
brj-23416	276	10	2023	2023	NUM
brj-23416	276	11	)	)	PUNCT
brj-23416	276	12	.	.	PUNCT
brj-23416	277	1	“	"	PUNCT
brj-23416	277	2	trend	trend	NOUN
brj-23416	277	3	-	-	PUNCT
brj-23416	277	4	based	base	VERB
brj-23416	277	5	k	k	NOUN
brj-23416	277	6	-	-	PUNCT
brj-23416	277	7	nearest	near	ADJ
brj-23416	277	8	neighbor	neighbor	NOUN
brj-23416	277	9	algorithm	algorithm	NOUN
brj-23416	277	10	in	in	ADP
brj-23416	277	11	stock	stock	NOUN
brj-23416	277	12	price	price	NOUN
brj-23416	277	13	prediction	prediction	NOUN
brj-23416	277	14	,	,	PUNCT
brj-23416	277	15	”	"	PUNCT
brj-23416	277	16	in	in	ADP
brj-23416	277	17	:	:	PUNCT
brj-23416	277	18	proceedings	proceeding	NOUN
brj-23416	277	19	of	of	ADP
brj-23416	277	20	the	the	DET
brj-23416	277	21	3rd	3rd	ADJ
brj-23416	277	22	international	international	ADJ
brj-23416	277	23	conference	conference	NOUN
brj-23416	277	24	on	on	ADP
brj-23416	277	25	digital	digital	ADJ
brj-23416	277	26	economy	economy	NOUN
brj-23416	277	27	and	and	CCONJ
brj-23416	277	28	computer	computer	NOUN
brj-23416	277	29	application	application	NOUN
brj-23416	277	30	(	(	PUNCT
brj-23416	277	31	deca	deca	NOUN
brj-23416	277	32	2023	2023	NUM
brj-23416	277	33	)	)	PUNCT
brj-23416	277	34	,	,	PUNCT
brj-23416	277	35	shanghai	shanghai	PROPN
brj-23416	277	36	,	,	PUNCT
brj-23416	277	37	china	china	PROPN
brj-23416	277	38	,	,	PUNCT
brj-23416	277	39	pp	pp	ADJ
brj-23416	277	40	.	.	PUNCT
brj-23416	278	1	746	746	NUM
brj-23416	278	2	-	-	SYM
brj-23416	278	3	756	756	NUM
brj-23416	278	4	.	.	PUNCT
brj-23416	279	1	ghorbanali	ghorbanali	VERB
brj-23416	279	2	,	,	PUNCT
brj-23416	279	3	m.	m.	NOUN
brj-23416	279	4	(	(	PUNCT
brj-23416	279	5	2022	2022	NUM
brj-23416	279	6	)	)	PUNCT
brj-23416	279	7	.	.	PUNCT
brj-23416	280	1	automation	automation	NOUN
brj-23416	280	2	of	of	ADP
brj-23416	280	3	price	price	NOUN
brj-23416	280	4	prediction	prediction	NOUN
brj-23416	280	5	using	use	VERB
brj-23416	280	6	machine	machine	NOUN
brj-23416	280	7	learning	learning	NOUN
brj-23416	280	8	in	in	ADP
brj-23416	280	9	a	a	DET
brj-23416	280	10	large	large	ADJ
brj-23416	280	11	furniture	furniture	NOUN
brj-23416	280	12	company	company	NOUN
brj-23416	280	13	,	,	PUNCT
brj-23416	280	14	master	master	NOUN
brj-23416	280	15	’s	’s	PART
brj-23416	280	16	thesis	thesis	NOUN
brj-23416	280	17	.	.	PUNCT
brj-23416	281	1	malmö	malmö	PROPN
brj-23416	281	2	university	university	PROPN
brj-23416	281	3	,	,	PUNCT
brj-23416	281	4	malmö	malmö	PROPN
brj-23416	281	5	,	,	PUNCT
brj-23416	281	6	sweden	sweden	PROPN
brj-23416	281	7	.	.	PUNCT
brj-23416	282	1	gürsoy	gürsoy	PROPN
brj-23416	282	2	,	,	PUNCT
brj-23416	282	3	a.	a.	NOUN
brj-23416	282	4	(	(	PUNCT
brj-23416	282	5	2019	2019	NUM
brj-23416	282	6	)	)	PUNCT
brj-23416	282	7	.	.	PUNCT
brj-23416	283	1	“	"	PUNCT
brj-23416	283	2	the	the	DET
brj-23416	283	3	effect	effect	NOUN
brj-23416	283	4	of	of	ADP
brj-23416	283	5	macroeconomic	macroeconomic	ADJ
brj-23416	283	6	variables	variable	NOUN
brj-23416	283	7	on	on	ADP
brj-23416	283	8	stock	stock	NOUN
brj-23416	283	9	returns	return	NOUN
brj-23416	283	10	:	:	PUNCT
brj-23416	283	11	case	case	NOUN
brj-23416	283	12	of	of	ADP
brj-23416	283	13	banking	banking	NOUN
brj-23416	283	14	sector	sector	NOUN
brj-23416	283	15	,	,	PUNCT
brj-23416	283	16	”	"	PUNCT
brj-23416	283	17	journal	journal	NOUN
brj-23416	283	18	of	of	ADP
brj-23416	283	19	economics	economic	NOUN
brj-23416	283	20	and	and	CCONJ
brj-23416	283	21	financial	financial	ADJ
brj-23416	283	22	research	research	NOUN
brj-23416	283	23	1(1	1(1	NUM
brj-23416	283	24	-	-	SYM
brj-23416	283	25	2	2	NUM
brj-23416	283	26	)	)	PUNCT
brj-23416	283	27	,	,	PUNCT
brj-23416	283	28	1	1	NUM
brj-23416	283	29	-	-	SYM
brj-23416	283	30	25	25	NUM
brj-23416	283	31	.	.	PUNCT
brj-23416	283	32	harahap	harahap	PROPN
brj-23416	283	33	,	,	PUNCT
brj-23416	283	34	l.	l.	PROPN
brj-23416	283	35	a.	a.	PROPN
brj-23416	283	36	,	,	PUNCT
brj-23416	283	37	lipikorn	lipikorn	PROPN
brj-23416	283	38	,	,	PUNCT
brj-23416	283	39	r.	r.	PROPN
brj-23416	283	40	,	,	PUNCT
brj-23416	283	41	and	and	CCONJ
brj-23416	283	42	kitamoto	kitamoto	NOUN
brj-23416	283	43	,	,	PUNCT
brj-23416	283	44	a.	a.	NOUN
brj-23416	283	45	(	(	PUNCT
brj-23416	283	46	2020	2020	NUM
brj-23416	283	47	)	)	PUNCT
brj-23416	283	48	.	.	PUNCT
brj-23416	284	1	“	"	PUNCT
brj-23416	284	2	nikkei	nikkei	PROPN
brj-23416	284	3	stock	stock	NOUN
brj-23416	284	4	market	market	NOUN
brj-23416	284	5	price	price	NOUN
brj-23416	284	6	index	index	NOUN
brj-23416	284	7	prediction	prediction	NOUN
brj-23416	284	8	using	use	VERB
brj-23416	284	9	machine	machine	NOUN
brj-23416	284	10	learning	learning	NOUN
brj-23416	284	11	,	,	PUNCT
brj-23416	284	12	”	"	PUNCT
brj-23416	284	13	journal	journal	NOUN
brj-23416	284	14	of	of	ADP
brj-23416	284	15	physics	physics	PROPN
brj-23416	284	16	:	:	PUNCT
brj-23416	284	17	conference	conference	NOUN
brj-23416	284	18	series	series	NOUN
brj-23416	284	19	1566	1566	NUM
brj-23416	284	20	,	,	PUNCT
brj-23416	284	21	1	1	NUM
brj-23416	284	22	-	-	SYM
brj-23416	284	23	6	6	NUM
brj-23416	284	24	.	.	PUNCT
brj-23416	285	1	doi	doi	NOUN
brj-23416	285	2	:	:	PUNCT
brj-23416	285	3	10.1088/1742	10.1088/1742	NUM
brj-23416	285	4	-	-	SYM
brj-23416	285	5	6596/1566/1/012043	6596/1566/1/012043	NUM
brj-23416	285	6	hassan	hassan	PROPN
brj-23416	285	7	,	,	PUNCT
brj-23416	285	8	w.	w.	PROPN
brj-23416	285	9	h.	h.	PROPN
brj-23416	285	10	,	,	PUNCT
brj-23416	285	11	hussein	hussein	PROPN
brj-23416	285	12	,	,	PUNCT
brj-23416	285	13	h.	h.	PROPN
brj-23416	285	14	h.	h.	PROPN
brj-23416	285	15	,	,	PUNCT
brj-23416	285	16	alshammari	alshammari	PROPN
brj-23416	285	17	,	,	PUNCT
brj-23416	285	18	m.	m.	PROPN
brj-23416	285	19	h.	h.	PROPN
brj-23416	285	20	,	,	PUNCT
brj-23416	285	21	jalal	jalal	PROPN
brj-23416	285	22	,	,	PUNCT
brj-23416	285	23	h.	h.	PROPN
brj-23416	285	24	k.	k.	PROPN
brj-23416	285	25	,	,	PUNCT
brj-23416	285	26	and	and	CCONJ
brj-23416	285	27	rasheed	rasheed	VERB
brj-23416	285	28	,	,	PUNCT
brj-23416	285	29	s.	s.	PROPN
brj-23416	285	30	e.	e.	PROPN
brj-23416	285	31	(	(	PUNCT
brj-23416	285	32	2022	2022	NUM
brj-23416	285	33	)	)	PUNCT
brj-23416	285	34	.	.	PUNCT
brj-23416	286	1	“	"	PUNCT
brj-23416	286	2	evaluation	evaluation	NOUN
brj-23416	286	3	of	of	ADP
brj-23416	286	4	gene	gene	NOUN
brj-23416	286	5	expression	expression	NOUN
brj-23416	286	6	programming	programming	NOUN
brj-23416	286	7	and	and	CCONJ
brj-23416	286	8	artificial	artificial	ADJ
brj-23416	286	9	neural	neural	ADJ
brj-23416	286	10	networks	network	NOUN
brj-23416	286	11	in	in	ADP
brj-23416	286	12	pytorch	pytorch	NOUN
brj-23416	286	13	for	for	ADP
brj-23416	286	14	the	the	DET
brj-23416	286	15	prediction	prediction	NOUN
brj-23416	286	16	of	of	ADP
brj-23416	286	17	local	local	ADJ
brj-23416	286	18	scour	scour	ADJ
brj-23416	286	19	depth	depth	NOUN
brj-23416	286	20	around	around	ADP
brj-23416	286	21	a	a	DET
brj-23416	286	22	bridge	bridge	NOUN
brj-23416	286	23	pier	pier	NOUN
brj-23416	286	24	,	,	PUNCT
brj-23416	286	25	”	"	PUNCT
brj-23416	286	26	results	result	NOUN
brj-23416	286	27	in	in	ADP
brj-23416	286	28	engineering	engineer	VERB
brj-23416	286	29	13	13	NUM
brj-23416	286	30	,	,	PUNCT
brj-23416	286	31	article	article	NOUN
brj-23416	286	32	100353	100353	NUM
brj-23416	286	33	.	.	PUNCT
brj-23416	287	1	hu	hu	PROPN
brj-23416	287	2	,	,	PUNCT
brj-23416	287	3	q.	q.	PROPN
brj-23416	287	4	,	,	PUNCT
brj-23416	287	5	qin	qin	PROPN
brj-23416	287	6	,	,	PUNCT
brj-23416	287	7	s.	s.	PROPN
brj-23416	287	8	,	,	PUNCT
brj-23416	287	9	and	and	CCONJ
brj-23416	287	10	zhang	zhang	PROPN
brj-23416	287	11	,	,	PUNCT
brj-23416	287	12	s.	s.	PROPN
brj-23416	287	13	(	(	PUNCT
brj-23416	287	14	2022	2022	NUM
brj-23416	287	15	)	)	PUNCT
brj-23416	287	16	.	.	PUNCT
brj-23416	288	1	“	"	PUNCT
brj-23416	288	2	comparison	comparison	NOUN
brj-23416	288	3	of	of	ADP
brj-23416	288	4	stock	stock	NOUN
brj-23416	288	5	price	price	NOUN
brj-23416	288	6	prediction	prediction	NOUN
brj-23416	288	7	based	base	VERB
brj-23416	288	8	on	on	ADP
brj-23416	288	9	different	different	ADJ
brj-23416	288	10	machine	machine	NOUN
brj-23416	288	11	learning	learning	NOUN
brj-23416	288	12	approaches	approach	NOUN
brj-23416	288	13	,	,	PUNCT
brj-23416	288	14	”	"	PUNCT
brj-23416	288	15	in	in	ADP
brj-23416	288	16	:	:	PUNCT
brj-23416	288	17	proceedings	proceeding	NOUN
brj-23416	288	18	of	of	ADP
brj-23416	288	19	the	the	DET
brj-23416	288	20	2022	2022	NUM
brj-23416	288	21	international	international	ADJ
brj-23416	288	22	conference	conference	NOUN
brj-23416	288	23	on	on	ADP
brj-23416	288	24	bigdata	bigdata	PROPN
brj-23416	288	25	blockchain	blockchain	PROPN
brj-23416	288	26	and	and	CCONJ
brj-23416	288	27	economy	economy	NOUN
brj-23416	288	28	management	management	NOUN
brj-23416	288	29	(	(	PUNCT
brj-23416	288	30	icbbem	icbbem	NOUN
brj-23416	288	31	2022	2022	NUM
brj-23416	288	32	)	)	PUNCT
brj-23416	288	33	,	,	PUNCT
brj-23416	288	34	wuhan	wuhan	PROPN
brj-23416	288	35	,	,	PUNCT
brj-23416	288	36	hubei	hubei	PROPN
brj-23416	288	37	province	province	PROPN
brj-23416	288	38	,	,	PUNCT
brj-23416	288	39	china	china	PROPN
brj-23416	288	40	,	,	PUNCT
brj-23416	288	41	pp	pp	ADJ
brj-23416	288	42	.	.	PUNCT
brj-23416	289	1	215	215	NUM
brj-23416	289	2	-	-	SYM
brj-23416	289	3	231	231	NUM
brj-23416	289	4	.	.	PUNCT
brj-23416	290	1	johnsson	johnsson	PROPN
brj-23416	290	2	,	,	PUNCT
brj-23416	290	3	o.	o.	PROPN
brj-23416	290	4	(	(	PUNCT
brj-23416	290	5	2018	2018	NUM
brj-23416	290	6	)	)	PUNCT
brj-23416	290	7	.	.	PUNCT
brj-23416	291	1	predicting	predict	VERB
brj-23416	291	2	stock	stock	NOUN
brj-23416	291	3	index	index	NOUN
brj-23416	291	4	volatility	volatility	NOUN
brj-23416	291	5	using	use	VERB
brj-23416	291	6	artificial	artificial	ADJ
brj-23416	291	7	neural	neural	ADJ
brj-23416	291	8	networks	network	NOUN
brj-23416	291	9	:	:	PUNCT
brj-23416	291	10	an	an	DET
brj-23416	291	11	empirical	empirical	ADJ
brj-23416	291	12	study	study	NOUN
brj-23416	291	13	of	of	ADP
brj-23416	291	14	the	the	DET
brj-23416	291	15	omxs30	omxs30	NOUN
brj-23416	291	16	,	,	PUNCT
brj-23416	291	17	ftse100	ftse100	PROPN
brj-23416	291	18	&	&	CCONJ
brj-23416	291	19	s&p	s&p	PROPN
brj-23416	291	20	/	/	SYM
brj-23416	291	21	asx200	asx200	PROPN
brj-23416	291	22	,	,	PUNCT
brj-23416	291	23	master	master	NOUN
brj-23416	291	24	’s	’s	PART
brj-23416	291	25	thesis	thesis	NOUN
brj-23416	291	26	.	.	PUNCT
brj-23416	292	1	lund	lund	PROPN
brj-23416	292	2	university	university	PROPN
brj-23416	292	3	,	,	PUNCT
brj-23416	292	4	lund	lund	PROPN
brj-23416	292	5	,	,	PUNCT
brj-23416	292	6	sweden	sweden	PROPN
brj-23416	292	7	.	.	PUNCT
brj-23416	293	1	kaderli	kaderli	PROPN
brj-23416	293	2	,	,	PUNCT
brj-23416	293	3	y.	y.	PROPN
brj-23416	293	4	,	,	PUNCT
brj-23416	293	5	petek	petek	AUX
brj-23416	293	6	,	,	PUNCT
brj-23416	293	7	a.	a.	NOUN
brj-23416	293	8	,	,	PUNCT
brj-23416	293	9	doğaner	doğaner	NOUN
brj-23416	293	10	,	,	PUNCT
brj-23416	293	11	m.	m.	NOUN
brj-23416	293	12	,	,	PUNCT
brj-23416	293	13	and	and	CCONJ
brj-23416	293	14	babayiğit	babayiğit	PROPN
brj-23416	293	15	,	,	PUNCT
brj-23416	293	16	g.	g.	PROPN
brj-23416	293	17	(	(	PUNCT
brj-23416	293	18	2013	2013	NUM
brj-23416	293	19	)	)	PUNCT
brj-23416	293	20	.	.	PUNCT
brj-23416	294	1	“	"	PUNCT
brj-23416	294	2	the	the	DET
brj-23416	294	3	sensitivity	sensitivity	NOUN
brj-23416	294	4	to	to	AUX
brj-23416	294	5	market	market	NOUN
brj-23416	294	6	index	index	NOUN
brj-23416	294	7	and	and	CCONJ
brj-23416	294	8	non	non	ADJ
brj-23416	294	9	-	-	ADJ
brj-23416	294	10	systematic	systematic	ADJ
brj-23416	294	11	risk	risk	NOUN
brj-23416	294	12	measurement	measurement	NOUN
brj-23416	294	13	of	of	ADP
brj-23416	294	14	sector	sector	NOUN
brj-23416	294	15	indices	index	NOUN
brj-23416	294	16	in	in	ADP
brj-23416	294	17	borsa	borsa	PROPN
brj-23416	294	18	i̇stanbul	i̇stanbul	PROPN
brj-23416	294	19	,	,	PUNCT
brj-23416	294	20	”	"	PUNCT
brj-23416	294	21	anadolu	anadolu	PROPN
brj-23416	294	22	university	university	PROPN
brj-23416	294	23	journal	journal	NOUN
brj-23416	294	24	of	of	ADP
brj-23416	294	25	social	social	ADJ
brj-23416	294	26	sciences	science	NOUN
brj-23416	294	27	13(3	13(3	NUM
brj-23416	294	28	)	)	PUNCT
brj-23416	294	29	,	,	PUNCT
brj-23416	294	30	55	55	NUM
brj-23416	294	31	-	-	SYM
brj-23416	294	32	64	64	NUM
brj-23416	294	33	.	.	PUNCT
brj-23416	295	1	kahraman	kahraman	NOUN
brj-23416	295	2	,	,	PUNCT
brj-23416	295	3	y.	y.	PROPN
brj-23416	295	4	e.	e.	PROPN
brj-23416	295	5	(	(	PUNCT
brj-23416	295	6	2023	2023	NUM
brj-23416	295	7	)	)	PUNCT
brj-23416	295	8	.	.	PUNCT
brj-23416	296	1	“	"	PUNCT
brj-23416	296	2	the	the	DET
brj-23416	296	3	effect	effect	NOUN
brj-23416	296	4	of	of	ADP
brj-23416	296	5	profitability	profitability	NOUN
brj-23416	296	6	on	on	ADP
brj-23416	296	7	business	business	NOUN
brj-23416	296	8	success	success	NOUN
brj-23416	296	9	in	in	ADP
brj-23416	296	10	bist	bist	ADJ
brj-23416	296	11	forest	forest	NOUN
brj-23416	296	12	,	,	PUNCT
brj-23416	296	13	paper	paper	NOUN
brj-23416	296	14	,	,	PUNCT
brj-23416	296	15	printing	printing	NOUN
brj-23416	296	16	index	index	NOUN
brj-23416	296	17	,	,	PUNCT
brj-23416	296	18	”	"	PUNCT
brj-23416	296	19	third	third	ADJ
brj-23416	296	20	sector	sector	NOUN
brj-23416	296	21	social	social	ADJ
brj-23416	296	22	economic	economic	ADJ
brj-23416	296	23	review	review	NOUN
brj-23416	296	24	58(3	58(3	NUM
brj-23416	296	25	)	)	PUNCT
brj-23416	296	26	,	,	PUNCT
brj-23416	296	27	2125	2125	NUM
brj-23416	296	28	-	-	SYM
brj-23416	296	29	2143	2143	NUM
brj-23416	296	30	.	.	PUNCT
brj-23416	297	1	doi	doi	NOUN
brj-23416	297	2	:	:	PUNCT
brj-23416	297	3	10.15659/3.sektor	10.15659/3.sektor	NUM
brj-23416	297	4	-	-	PUNCT
brj-23416	297	5	sosyal	sosyal	NOUN
brj-23416	297	6	-	-	PUNCT
brj-23416	297	7	ekonomi.23.08.2161	ekonomi.23.08.2161	ADJ
brj-23416	297	8	kızrak	kızrak	NOUN
brj-23416	297	9	,	,	PUNCT
brj-23416	297	10	a.	a.	NOUN
brj-23416	297	11	(	(	PUNCT
brj-23416	297	12	2018	2018	NUM
brj-23416	297	13	)	)	PUNCT
brj-23416	297	14	.	.	PUNCT
brj-23416	298	1	“	"	PUNCT
brj-23416	298	2	a	a	DET
brj-23416	298	3	getting	get	VERB
brj-23416	298	4	started	start	VERB
brj-23416	298	5	guide	guide	NOUN
brj-23416	298	6	to	to	ADP
brj-23416	298	7	artificial	artificial	ADJ
brj-23416	298	8	intelligence	intelligence	NOUN
brj-23416	298	9	and	and	CCONJ
brj-23416	298	10	deep	deep	ADJ
brj-23416	298	11	learning	learning	NOUN
brj-23416	298	12	,	,	PUNCT
brj-23416	298	13	”	"	PUNCT
brj-23416	298	14	(	(	PUNCT
brj-23416	298	15	https://ayyucekizrak.medium.com/yapay-zekaya-ba%c5%9flama-rehberi91e79d3de8e1	https://ayyucekizrak.medium.com/yapay-zekaya-ba%c5%9flama-rehberi91e79d3de8e1	NOUN
brj-23416	298	16	)	)	PUNCT
brj-23416	298	17	,	,	PUNCT
brj-23416	298	18	accessed	access	VERB
brj-23416	298	19	09	09	NUM
brj-23416	298	20	february	february	NOUN
brj-23416	298	21	2024	2024	NUM
brj-23416	298	22	.	.	PUNCT
brj-23416	299	1	kozuch	kozuch	NOUN
brj-23416	299	2	,	,	PUNCT
brj-23416	299	3	a.	a.	PROPN
brj-23416	299	4	,	,	PUNCT
brj-23416	299	5	cywicka	cywicka	PROPN
brj-23416	299	6	,	,	PUNCT
brj-23416	299	7	d.	d.	PROPN
brj-23416	299	8	,	,	PUNCT
brj-23416	299	9	and	and	CCONJ
brj-23416	299	10	adamowicz	adamowicz	PROPN
brj-23416	299	11	,	,	PUNCT
brj-23416	299	12	k.	k.	PROPN
brj-23416	299	13	(	(	PUNCT
brj-23416	299	14	2023	2023	NUM
brj-23416	299	15	)	)	PUNCT
brj-23416	299	16	.	.	PUNCT
brj-23416	300	1	“	"	PUNCT
brj-23416	300	2	a	a	DET
brj-23416	300	3	comparison	comparison	NOUN
brj-23416	300	4	of	of	ADP
brj-23416	300	5	artificial	artificial	ADJ
brj-23416	300	6	neural	neural	ADJ
brj-23416	300	7	network	network	NOUN
brj-23416	300	8	and	and	CCONJ
brj-23416	300	9	time	time	NOUN
brj-23416	300	10	series	series	NOUN
brj-23416	300	11	models	model	NOUN
brj-23416	300	12	for	for	ADP
brj-23416	300	13	timber	timber	NOUN
brj-23416	300	14	price	price	NOUN
brj-23416	300	15	forecasting	forecasting	NOUN
brj-23416	300	16	,	,	PUNCT
brj-23416	300	17	”	"	PUNCT
brj-23416	300	18	forests	forest	NOUN
brj-23416	300	19	14(2	14(2	NUM
brj-23416	300	20	)	)	PUNCT
brj-23416	300	21	,	,	PUNCT
brj-23416	300	22	article	article	NOUN
brj-23416	300	23	177	177	NUM
brj-23416	300	24	.	.	PUNCT
brj-23416	301	1	doi	doi	NOUN
brj-23416	301	2	:	:	PUNCT
brj-23416	301	3	10.3390	10.3390	NUM
brj-23416	301	4	/	/	SYM
brj-23416	301	5	f14020177	f14020177	ADJ
brj-23416	301	6	kudari	kudari	NOUN
brj-23416	301	7	,	,	PUNCT
brj-23416	301	8	j.	j.	PROPN
brj-23416	301	9	m.	m.	PROPN
brj-23416	301	10	,	,	PUNCT
brj-23416	301	11	jebakumari	jebakumari	PROPN
brj-23416	301	12	,	,	PUNCT
brj-23416	301	13	a.	a.	PROPN
brj-23416	301	14	,	,	PUNCT
brj-23416	301	15	kumar	kumar	PROPN
brj-23416	301	16	,	,	PUNCT
brj-23416	301	17	s.	s.	PROPN
brj-23416	301	18	,	,	PUNCT
brj-23416	301	19	jebakumari	jebakumari	PROPN
brj-23416	301	20	,	,	PUNCT
brj-23416	301	21	s.	s.	PROPN
brj-23416	301	22	a.	a.	PROPN
brj-23416	301	23	,	,	PUNCT
brj-23416	301	24	and	and	CCONJ
brj-23416	301	25	sushma	sushma	PROPN
brj-23416	301	26	,	,	PUNCT
brj-23416	301	27	b.	b.	PROPN
brj-23416	301	28	s.	s.	PROPN
brj-23416	301	29	(	(	PUNCT
brj-23416	301	30	2021	2021	NUM
brj-23416	301	31	)	)	PUNCT
brj-23416	301	32	.	.	PUNCT
brj-23416	302	1	“	"	PUNCT
brj-23416	302	2	image	image	NOUN
brj-23416	302	3	classifier	classifier	NOUN
brj-23416	302	4	using	use	VERB
brj-23416	302	5	the	the	DET
brj-23416	302	6	adam	adam	PROPN
brj-23416	302	7	optimizer	optimizer	NOUN
brj-23416	302	8	and	and	CCONJ
brj-23416	302	9	the	the	DET
brj-23416	302	10	relu	relu	NOUN
brj-23416	302	11	activation	activation	NOUN
brj-23416	302	12	function	function	NOUN
brj-23416	302	13	,	,	PUNCT
brj-23416	302	14	”	"	PUNCT
brj-23416	302	15	peer	peer	NOUN
brj-23416	302	16	-	-	PUNCT
brj-23416	302	17	reviewed	review	VERB
brj-23416	302	18	article	article	NOUN
brj-23416	302	19	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	302	20	akyüz	akyüz	PROPN
brj-23416	302	21	et	et	PROPN
brj-23416	302	22	al	al	PROPN
brj-23416	302	23	.	.	PROPN
brj-23416	303	1	(	(	PUNCT
brj-23416	303	2	2024	2024	NUM
brj-23416	303	3	)	)	PUNCT
brj-23416	303	4	.	.	PUNCT
brj-23416	304	1	“	"	PUNCT
brj-23416	304	2	stock	stock	NOUN
brj-23416	304	3	exchange	exchange	NOUN
brj-23416	304	4	values	value	NOUN
brj-23416	304	5	,	,	PUNCT
brj-23416	304	6	”	"	PUNCT
brj-23416	304	7	bioresources	bioresource	NOUN
brj-23416	304	8	19(3	19(3	NUM
brj-23416	304	9	)	)	PUNCT
brj-23416	304	10	,	,	PUNCT
brj-23416	304	11	5141	5141	NUM
brj-23416	304	12	-	-	SYM
brj-23416	304	13	5157	5157	NUM
brj-23416	304	14	.	.	PUNCT
brj-23416	305	1	5154	5154	NUM
brj-23416	305	2	international	international	ADJ
brj-23416	305	3	journal	journal	NOUN
brj-23416	305	4	of	of	ADP
brj-23416	305	5	advanced	advanced	ADJ
brj-23416	305	6	research	research	NOUN
brj-23416	305	7	in	in	ADP
brj-23416	305	8	engineering	engineering	NOUN
brj-23416	305	9	and	and	CCONJ
brj-23416	305	10	technology	technology	NOUN
brj-23416	305	11	(	(	PUNCT
brj-23416	305	12	ijaret	ijaret	NOUN
brj-23416	305	13	)	)	PUNCT
brj-23416	305	14	12(3	12(3	NUM
brj-23416	305	15	)	)	PUNCT
brj-23416	305	16	,	,	PUNCT
brj-23416	305	17	56	56	NUM
brj-23416	305	18	-	-	SYM
brj-23416	305	19	60	60	NUM
brj-23416	305	20	.	.	PUNCT
brj-23416	306	1	doi	doi	NOUN
brj-23416	306	2	:	:	PUNCT
brj-23416	306	3	10.34218	10.34218	NUM
brj-23416	306	4	/	/	SYM
brj-23416	306	5	ijaret.12.3.2021.006	ijaret.12.3.2021.006	ADJ
brj-23416	306	6	kurniawan	kurniawan	PROPN
brj-23416	306	7	,	,	PUNCT
brj-23416	306	8	f.	f.	PROPN
brj-23416	306	9	,	,	PUNCT
brj-23416	306	10	herwindiati	herwindiati	PROPN
brj-23416	306	11	,	,	PUNCT
brj-23416	306	12	d.	d.	PROPN
brj-23416	306	13	e.	e.	PROPN
brj-23416	306	14	,	,	PUNCT
brj-23416	306	15	and	and	CCONJ
brj-23416	306	16	lauro	lauro	PROPN
brj-23416	306	17	,	,	PUNCT
brj-23416	306	18	m.	m.	NOUN
brj-23416	306	19	d.	d.	PROPN
brj-23416	306	20	(	(	PUNCT
brj-23416	306	21	2021	2021	NUM
brj-23416	306	22	)	)	PUNCT
brj-23416	306	23	.	.	PUNCT
brj-23416	307	1	“	"	PUNCT
brj-23416	307	2	raw	raw	ADJ
brj-23416	307	3	paper	paper	NOUN
brj-23416	307	4	material	material	NOUN
brj-23416	307	5	stock	stock	NOUN
brj-23416	307	6	forecasting	forecasting	NOUN
brj-23416	307	7	with	with	ADP
brj-23416	307	8	long	long	ADJ
brj-23416	307	9	short	short	ADJ
brj-23416	307	10	-	-	PUNCT
brj-23416	307	11	term	term	NOUN
brj-23416	307	12	memory	memory	NOUN
brj-23416	307	13	,	,	PUNCT
brj-23416	307	14	”	"	PUNCT
brj-23416	307	15	in	in	ADP
brj-23416	307	16	:	:	PUNCT
brj-23416	307	17	9th	9th	ADJ
brj-23416	307	18	international	international	ADJ
brj-23416	307	19	conference	conference	NOUN
brj-23416	307	20	on	on	ADP
brj-23416	307	21	information	information	NOUN
brj-23416	307	22	and	and	CCONJ
brj-23416	307	23	communication	communication	NOUN
brj-23416	307	24	technology	technology	NOUN
brj-23416	307	25	(	(	PUNCT
brj-23416	307	26	icoict	icoict	PROPN
brj-23416	307	27	)	)	PUNCT
brj-23416	307	28	,	,	PUNCT
brj-23416	307	29	yogyakarta	yogyakarta	PROPN
brj-23416	307	30	,	,	PUNCT
brj-23416	307	31	indonesia	indonesia	PROPN
brj-23416	307	32	,	,	PUNCT
brj-23416	307	33	pp	pp	PROPN
brj-23416	307	34	.	.	PUNCT
brj-23416	308	1	342	342	NUM
brj-23416	308	2	-	-	SYM
brj-23416	308	3	347	347	NUM
brj-23416	308	4	.	.	PUNCT
brj-23416	309	1	doi	doi	NOUN
brj-23416	309	2	:	:	PUNCT
brj-23416	309	3	10.1109	10.1109	NUM
brj-23416	309	4	/	/	SYM
brj-23416	309	5	icoict52021.2021.9527528	icoict52021.2021.9527528	PROPN
brj-23416	309	6	kurt	kurt	NOUN
brj-23416	309	7	,	,	PUNCT
brj-23416	309	8	f.	f.	PROPN
brj-23416	309	9	e.	e.	PROPN
brj-23416	309	10	,	,	PUNCT
brj-23416	309	11	and	and	CCONJ
brj-23416	309	12	senal	senal	ADJ
brj-23416	309	13	,	,	PUNCT
brj-23416	309	14	s.	s.	PROPN
brj-23416	309	15	(	(	PUNCT
brj-23416	309	16	2018	2018	NUM
brj-23416	309	17	)	)	PUNCT
brj-23416	309	18	.	.	PUNCT
brj-23416	310	1	“	"	PUNCT
brj-23416	310	2	modeling	model	VERB
brj-23416	310	3	volatility	volatility	NOUN
brj-23416	310	4	of	of	ADP
brj-23416	310	5	insurance	insurance	NOUN
brj-23416	310	6	shares	share	NOUN
brj-23416	310	7	in	in	ADP
brj-23416	310	8	istanbul	istanbul	PROPN
brj-23416	310	9	stock	stock	NOUN
brj-23416	310	10	market	market	NOUN
brj-23416	310	11	:	:	PUNCT
brj-23416	310	12	an	an	DET
brj-23416	310	13	application	application	NOUN
brj-23416	310	14	with	with	ADP
brj-23416	310	15	arch	arch	ADJ
brj-23416	310	16	-	-	PUNCT
brj-23416	310	17	m	m	NOUN
brj-23416	310	18	models	model	NOUN
brj-23416	310	19	,	,	PUNCT
brj-23416	310	20	”	"	PUNCT
brj-23416	310	21	journal	journal	NOUN
brj-23416	310	22	of	of	ADP
brj-23416	310	23	süleyman	süleyman	PROPN
brj-23416	310	24	demirel	demirel	PROPN
brj-23416	310	25	university	university	PROPN
brj-23416	310	26	institute	institute	PROPN
brj-23416	310	27	of	of	ADP
brj-23416	310	28	social	social	ADJ
brj-23416	310	29	sciences	science	NOUN
brj-23416	310	30	32	32	NUM
brj-23416	310	31	,	,	PUNCT
brj-23416	310	32	314	314	NUM
brj-23416	310	33	-	-	SYM
brj-23416	310	34	332	332	NUM
brj-23416	310	35	.	.	PUNCT
brj-23416	311	1	le	le	PROPN
brj-23416	311	2	,	,	PUNCT
brj-23416	311	3	h.	h.	PROPN
brj-23416	311	4	l.	l.	PROPN
brj-23416	311	5	,	,	PUNCT
brj-23416	311	6	tran	tran	PROPN
brj-23416	311	7	,	,	PUNCT
brj-23416	311	8	d.	d.	PROPN
brj-23416	311	9	h.	h.	PROPN
brj-23416	311	10	,	,	PUNCT
brj-23416	311	11	and	and	CCONJ
brj-23416	311	12	van	van	PROPN
brj-23416	311	13	chau	chau	PROPN
brj-23416	311	14	,	,	PUNCT
brj-23416	311	15	d.	d.	PROPN
brj-23416	311	16	(	(	PUNCT
brj-23416	311	17	2023	2023	NUM
brj-23416	311	18	)	)	PUNCT
brj-23416	311	19	.	.	PUNCT
brj-23416	312	1	“	"	PUNCT
brj-23416	312	2	a	a	DET
brj-23416	312	3	survey	survey	NOUN
brj-23416	312	4	on	on	ADP
brj-23416	312	5	the	the	DET
brj-23416	312	6	impact	impact	NOUN
brj-23416	312	7	of	of	ADP
brj-23416	312	8	hyperparameters	hyperparameter	NOUN
brj-23416	312	9	on	on	ADP
brj-23416	312	10	random	random	ADJ
brj-23416	312	11	forest	forest	NOUN
brj-23416	312	12	performance	performance	NOUN
brj-23416	312	13	using	use	VERB
brj-23416	312	14	multiple	multiple	ADJ
brj-23416	312	15	accelerometer	accelerometer	ADJ
brj-23416	312	16	datasets,”international	datasets,”international	ADJ
brj-23416	312	17	journal	journal	NOUN
brj-23416	312	18	for	for	ADP
brj-23416	312	19	computers	computer	NOUN
brj-23416	312	20	&	&	CCONJ
brj-23416	312	21	their	their	PRON
brj-23416	312	22	applications	application	NOUN
brj-23416	312	23	30(4	30(4	NUM
brj-23416	312	24	)	)	PUNCT
brj-23416	312	25	,	,	PUNCT
brj-23416	312	26	351	351	NUM
brj-23416	312	27	-	-	SYM
brj-23416	312	28	361	361	NUM
brj-23416	312	29	.	.	PUNCT
brj-23416	313	1	lu	lu	PROPN
brj-23416	313	2	,	,	PUNCT
brj-23416	313	3	l.	l.	PROPN
brj-23416	313	4	,	,	PUNCT
brj-23416	313	5	and	and	CCONJ
brj-23416	313	6	zhu	zhu	PROPN
brj-23416	313	7	,	,	PUNCT
brj-23416	313	8	z.	z.	PROPN
brj-23416	313	9	(	(	PUNCT
brj-23416	313	10	2014	2014	NUM
brj-23416	313	11	)	)	PUNCT
brj-23416	313	12	.	.	PUNCT
brj-23416	314	1	“	"	PUNCT
brj-23416	314	2	prediction	prediction	NOUN
brj-23416	314	3	model	model	NOUN
brj-23416	314	4	for	for	ADP
brj-23416	314	5	eating	eat	VERB
brj-23416	314	6	property	property	NOUN
brj-23416	314	7	of	of	ADP
brj-23416	314	8	indica	indica	ADJ
brj-23416	314	9	rice	rice	NOUN
brj-23416	314	10	,	,	PUNCT
brj-23416	314	11	”	"	PUNCT
brj-23416	314	12	journal	journal	NOUN
brj-23416	314	13	of	of	ADP
brj-23416	314	14	food	food	NOUN
brj-23416	314	15	quality	quality	NOUN
brj-23416	314	16	37	37	NUM
brj-23416	314	17	,	,	PUNCT
brj-23416	314	18	274	274	NUM
brj-23416	314	19	-	-	SYM
brj-23416	314	20	280	280	NUM
brj-23416	314	21	.	.	PUNCT
brj-23416	315	1	doi	doi	NOUN
brj-23416	315	2	:	:	PUNCT
brj-23416	315	3	10.1111	10.1111	NUM
brj-23416	315	4	/	/	SYM
brj-23416	315	5	jfq.12095	jfq.12095	NOUN
brj-23416	315	6	oukhouya	oukhouya	PROPN
brj-23416	315	7	,	,	PUNCT
brj-23416	315	8	h.	h.	PROPN
brj-23416	315	9	,	,	PUNCT
brj-23416	315	10	kadiri	kadiri	PROPN
brj-23416	315	11	,	,	PUNCT
brj-23416	315	12	h.	h.	PROPN
brj-23416	315	13	,	,	PUNCT
brj-23416	315	14	el	el	PROPN
brj-23416	315	15	himdi	himdi	PROPN
brj-23416	315	16	,	,	PUNCT
brj-23416	315	17	k.	k.	PROPN
brj-23416	315	18	,	,	PUNCT
brj-23416	315	19	and	and	CCONJ
brj-23416	315	20	guerbaz	guerbaz	PROPN
brj-23416	315	21	,	,	PUNCT
brj-23416	315	22	r.	r.	PROPN
brj-23416	315	23	(	(	PUNCT
brj-23416	315	24	2024	2024	NUM
brj-23416	315	25	)	)	PUNCT
brj-23416	315	26	.	.	PUNCT
brj-23416	316	1	“	"	PUNCT
brj-23416	316	2	forecasting	forecast	VERB
brj-23416	316	3	international	international	ADJ
brj-23416	316	4	stock	stock	NOUN
brj-23416	316	5	market	market	NOUN
brj-23416	316	6	trends	trend	NOUN
brj-23416	316	7	:	:	PUNCT
brj-23416	316	8	xgboost	xgboost	ADJ
brj-23416	316	9	,	,	PUNCT
brj-23416	316	10	lstm	lstm	ADJ
brj-23416	316	11	,	,	PUNCT
brj-23416	316	12	lstm	lstm	PROPN
brj-23416	316	13	-	-	PUNCT
brj-23416	316	14	xgboost	xgboost	PROPN
brj-23416	316	15	,	,	PUNCT
brj-23416	316	16	and	and	CCONJ
brj-23416	316	17	back	back	ADV
brj-23416	316	18	testing	test	VERB
brj-23416	316	19	xgboost	xgboost	PROPN
brj-23416	316	20	models	model	NOUN
brj-23416	316	21	,	,	PUNCT
brj-23416	316	22	”	"	PUNCT
brj-23416	316	23	statistics	statistic	NOUN
brj-23416	316	24	,	,	PUNCT
brj-23416	316	25	optimization	optimization	NOUN
brj-23416	316	26	&	&	CCONJ
brj-23416	316	27	information	information	NOUN
brj-23416	316	28	computing	computing	PROPN
brj-23416	316	29	12(1	12(1	PROPN
brj-23416	316	30	)	)	PUNCT
brj-23416	316	31	,	,	PUNCT
brj-23416	316	32	200	200	NUM
brj-23416	316	33	-	-	SYM
brj-23416	316	34	209	209	NUM
brj-23416	316	35	.	.	PUNCT
brj-23416	317	1	doi	doi	NOUN
brj-23416	317	2	:	:	PUNCT
brj-23416	317	3	10.19139	10.19139	NUM
brj-23416	317	4	/	/	SYM
brj-23416	317	5	soic-2310	soic-2310	ADJ
brj-23416	317	6	-	-	PUNCT
brj-23416	317	7	5070	5070	NUM
brj-23416	317	8	-	-	PUNCT
brj-23416	317	9	1822	1822	NUM
brj-23416	317	10	özcan	özcan	NOUN
brj-23416	317	11	akdağ	akdağ	NOUN
brj-23416	317	12	,	,	PUNCT
brj-23416	317	13	n.	n.	NOUN
brj-23416	317	14	,	,	PUNCT
brj-23416	317	15	karaatlı	karaatlı	NOUN
brj-23416	317	16	,	,	PUNCT
brj-23416	317	17	m.	m.	NOUN
brj-23416	317	18	,	,	PUNCT
brj-23416	317	19	and	and	CCONJ
brj-23416	317	20	kocabıyık	kocabıyık	PROPN
brj-23416	317	21	,	,	PUNCT
brj-23416	317	22	t.	t.	PROPN
brj-23416	317	23	(	(	PUNCT
brj-23416	317	24	2022	2022	NUM
brj-23416	317	25	)	)	PUNCT
brj-23416	317	26	.	.	PUNCT
brj-23416	318	1	“	"	PUNCT
brj-23416	318	2	forecasting	forecasting	NOUN
brj-23416	318	3	of	of	ADP
brj-23416	318	4	bist	bist	ADJ
brj-23416	318	5	transportation	transportation	NOUN
brj-23416	318	6	index	index	NOUN
brj-23416	318	7	with	with	ADP
brj-23416	318	8	ann	ann	PROPN
brj-23416	318	9	narx	narx	PROPN
brj-23416	318	10	model	model	PROPN
brj-23416	318	11	,	,	PUNCT
brj-23416	318	12	”	"	PUNCT
brj-23416	318	13	alanya	alanya	PROPN
brj-23416	318	14	academic	academic	PROPN
brj-23416	318	15	review	review	PROPN
brj-23416	318	16	journal	journal	PROPN
brj-23416	318	17	6(3	6(3	NOUN
brj-23416	318	18	)	)	PUNCT
brj-23416	318	19	,	,	PUNCT
brj-23416	318	20	2721	2721	NUM
brj-23416	318	21	-	-	SYM
brj-23416	318	22	2746	2746	NUM
brj-23416	318	23	.	.	PUNCT
brj-23416	319	1	doi	doi	NOUN
brj-23416	319	2	:	:	PUNCT
brj-23416	319	3	10.29023	10.29023	NUM
brj-23416	319	4	/	/	SYM
brj-23416	319	5	alanyaakademik.1088404	alanyaakademik.1088404	PROPN
brj-23416	319	6	özşahin	özşahin	NOUN
brj-23416	319	7	,	,	PUNCT
brj-23416	319	8	ş	ş	X
brj-23416	319	9	.	.	PUNCT
brj-23416	319	10	(	(	PUNCT
brj-23416	319	11	2012	2012	NUM
brj-23416	319	12	)	)	PUNCT
brj-23416	319	13	.	.	PUNCT
brj-23416	320	1	“	"	PUNCT
brj-23416	320	2	the	the	DET
brj-23416	320	3	use	use	NOUN
brj-23416	320	4	of	of	ADP
brj-23416	320	5	an	an	DET
brj-23416	320	6	artificial	artificial	ADJ
brj-23416	320	7	neural	neural	ADJ
brj-23416	320	8	network	network	NOUN
brj-23416	320	9	for	for	ADP
brj-23416	320	10	modelling	model	VERB
brj-23416	320	11	the	the	DET
brj-23416	320	12	moisture	moisture	NOUN
brj-23416	320	13	absorption	absorption	NOUN
brj-23416	320	14	and	and	CCONJ
brj-23416	320	15	thickness	thickness	NOUN
brj-23416	320	16	swelling	swell	VERB
brj-23416	320	17	of	of	ADP
brj-23416	320	18	oriented	orient	VERB
brj-23416	320	19	strand	strand	NOUN
brj-23416	320	20	board	board	NOUN
brj-23416	320	21	,	,	PUNCT
brj-23416	320	22	”	"	PUNCT
brj-23416	320	23	bioresources	bioresource	NOUN
brj-23416	320	24	7(1	7(1	NUM
brj-23416	320	25	)	)	PUNCT
brj-23416	320	26	,	,	PUNCT
brj-23416	320	27	1053	1053	NUM
brj-23416	320	28	-	-	SYM
brj-23416	320	29	1067	1067	NUM
brj-23416	320	30	.	.	PUNCT
brj-23416	321	1	pyo	pyo	PROPN
brj-23416	321	2	,	,	PUNCT
brj-23416	321	3	s.	s.	PROPN
brj-23416	321	4	,	,	PUNCT
brj-23416	321	5	lee	lee	PROPN
brj-23416	321	6	,	,	PUNCT
brj-23416	321	7	j.	j.	PROPN
brj-23416	321	8	,	,	PUNCT
brj-23416	321	9	cha	cha	PROPN
brj-23416	321	10	,	,	PUNCT
brj-23416	321	11	m.	m.	NOUN
brj-23416	321	12	,	,	PUNCT
brj-23416	321	13	and	and	CCONJ
brj-23416	321	14	jang	jang	PROPN
brj-23416	321	15	,	,	PUNCT
brj-23416	321	16	h.	h.	PROPN
brj-23416	321	17	(	(	PUNCT
brj-23416	321	18	2017	2017	NUM
brj-23416	321	19	)	)	PUNCT
brj-23416	321	20	.	.	PUNCT
brj-23416	322	1	“	"	PUNCT
brj-23416	322	2	predictability	predictability	NOUN
brj-23416	322	3	of	of	ADP
brj-23416	322	4	machine	machine	NOUN
brj-23416	322	5	learning	learn	VERB
brj-23416	322	6	techniques	technique	NOUN
brj-23416	322	7	to	to	PART
brj-23416	322	8	forecast	forecast	VERB
brj-23416	322	9	the	the	DET
brj-23416	322	10	trends	trend	NOUN
brj-23416	322	11	of	of	ADP
brj-23416	322	12	market	market	NOUN
brj-23416	322	13	index	index	NOUN
brj-23416	322	14	prices	price	NOUN
brj-23416	322	15	:	:	PUNCT
brj-23416	322	16	hypothesis	hypothesis	NOUN
brj-23416	322	17	testing	testing	NOUN
brj-23416	322	18	for	for	ADP
brj-23416	322	19	the	the	DET
brj-23416	322	20	korean	korean	ADJ
brj-23416	322	21	stock	stock	NOUN
brj-23416	322	22	markets	market	NOUN
brj-23416	322	23	,	,	PUNCT
brj-23416	322	24	”	"	PUNCT
brj-23416	322	25	plos	plos	PROPN
brj-23416	322	26	one	one	NUM
brj-23416	322	27	,	,	PUNCT
brj-23416	322	28	public	public	ADJ
brj-23416	322	29	library	library	NOUN
brj-23416	322	30	of	of	ADP
brj-23416	322	31	science	science	NOUN
brj-23416	322	32	12(11	12(11	NUM
brj-23416	322	33	)	)	PUNCT
brj-23416	322	34	,	,	PUNCT
brj-23416	322	35	1	1	NUM
brj-23416	322	36	-	-	SYM
brj-23416	322	37	17	17	NUM
brj-23416	322	38	.	.	PUNCT
brj-23416	323	1	doi	doi	NOUN
brj-23416	323	2	:	:	PUNCT
brj-23416	323	3	10.1371	10.1371	NUM
brj-23416	323	4	/	/	SYM
brj-23416	323	5	journal.pone.0188107	journal.pone.0188107	PROPN
brj-23416	323	6	rao	rao	PROPN
brj-23416	323	7	,	,	PUNCT
brj-23416	323	8	n.	n.	PROPN
brj-23416	323	9	s.	s.	PROPN
brj-23416	323	10	s.	s.	PROPN
brj-23416	324	1	v.	v.	PROPN
brj-23416	324	2	s.	s.	PROPN
brj-23416	324	3	,	,	PUNCT
brj-23416	324	4	thangaraj	thangaraj	PROPN
brj-23416	324	5	,	,	PUNCT
brj-23416	324	6	s.	s.	PROPN
brj-23416	324	7	j.	j.	PROPN
brj-23416	324	8	j.	j.	PROPN
brj-23416	324	9	,	,	PUNCT
brj-23416	324	10	and	and	CCONJ
brj-23416	324	11	kumari	kumari	PROPN
brj-23416	324	12	,	,	PUNCT
brj-23416	324	13	v.	v.	ADP
brj-23416	324	14	s.	s.	PROPN
brj-23416	324	15	(	(	PUNCT
brj-23416	324	16	2023	2023	NUM
brj-23416	324	17	)	)	PUNCT
brj-23416	324	18	.	.	PUNCT
brj-23416	325	1	“	"	PUNCT
brj-23416	325	2	flight	flight	NOUN
brj-23416	325	3	ticket	ticket	NOUN
brj-23416	325	4	prediction	prediction	NOUN
brj-23416	325	5	using	use	VERB
brj-23416	325	6	gradient	gradient	NOUN
brj-23416	325	7	boosting	boost	VERB
brj-23416	325	8	regressor	regressor	NOUN
brj-23416	325	9	compared	compare	VERB
brj-23416	325	10	with	with	ADP
brj-23416	325	11	linear	linear	PROPN
brj-23416	325	12	regression	regression	NOUN
brj-23416	325	13	,	,	PUNCT
brj-23416	325	14	”	"	PUNCT
brj-23416	325	15	in	in	ADP
brj-23416	325	16	:	:	PUNCT
brj-23416	325	17	2023	2023	NUM
brj-23416	325	18	eighth	eighth	ADJ
brj-23416	325	19	international	international	ADJ
brj-23416	325	20	conference	conference	NOUN
brj-23416	325	21	on	on	ADP
brj-23416	325	22	science	science	NOUN
brj-23416	325	23	technology	technology	NOUN
brj-23416	325	24	engineering	engineering	NOUN
brj-23416	325	25	and	and	CCONJ
brj-23416	325	26	mathematics	mathematics	PROPN
brj-23416	325	27	(	(	PUNCT
brj-23416	325	28	iconstem	iconstem	NOUN
brj-23416	325	29	)	)	PUNCT
brj-23416	325	30	,	,	PUNCT
brj-23416	325	31	chennai	chennai	PROPN
brj-23416	325	32	,	,	PUNCT
brj-23416	325	33	india	india	PROPN
brj-23416	325	34	,	,	PUNCT
brj-23416	325	35	pp	pp	ADJ
brj-23416	325	36	.	.	PUNCT
brj-23416	326	1	1	1	NUM
brj-23416	326	2	-	-	SYM
brj-23416	326	3	6	6	NUM
brj-23416	326	4	.	.	PUNCT
brj-23416	326	5	ravikumar	ravikumar	PROPN
brj-23416	326	6	,	,	PUNCT
brj-23416	326	7	s.	s.	PROPN
brj-23416	326	8	,	,	PUNCT
brj-23416	326	9	and	and	CCONJ
brj-23416	326	10	saraf	saraf	PROPN
brj-23416	326	11	,	,	PUNCT
brj-23416	326	12	p.	p.	NOUN
brj-23416	326	13	(	(	PUNCT
brj-23416	326	14	2020	2020	NUM
brj-23416	326	15	)	)	PUNCT
brj-23416	326	16	.	.	PUNCT
brj-23416	327	1	“	"	PUNCT
brj-23416	327	2	prediction	prediction	NOUN
brj-23416	327	3	of	of	ADP
brj-23416	327	4	stock	stock	NOUN
brj-23416	327	5	prices	price	NOUN
brj-23416	327	6	using	use	VERB
brj-23416	327	7	machine	machine	NOUN
brj-23416	327	8	learning	learning	NOUN
brj-23416	327	9	(	(	PUNCT
brj-23416	327	10	regression	regression	NOUN
brj-23416	327	11	,	,	PUNCT
brj-23416	327	12	classification	classification	NOUN
brj-23416	327	13	)	)	PUNCT
brj-23416	327	14	algorithms	algorithm	NOUN
brj-23416	327	15	,	,	PUNCT
brj-23416	327	16	”	"	PUNCT
brj-23416	327	17	in	in	ADP
brj-23416	327	18	:	:	PUNCT
brj-23416	327	19	proceedings	proceeding	NOUN
brj-23416	327	20	of	of	ADP
brj-23416	327	21	2020	2020	NUM
brj-23416	327	22	international	international	ADJ
brj-23416	327	23	conference	conference	NOUN
brj-23416	327	24	for	for	ADP
brj-23416	327	25	emerging	emerge	VERB
brj-23416	327	26	technology	technology	NOUN
brj-23416	327	27	(	(	PUNCT
brj-23416	327	28	incet	incet	PROPN
brj-23416	327	29	)	)	PUNCT
brj-23416	327	30	,	,	PUNCT
brj-23416	327	31	belgaum	belgaum	PROPN
brj-23416	327	32	,	,	PUNCT
brj-23416	327	33	india	india	PROPN
brj-23416	327	34	,	,	PUNCT
brj-23416	327	35	pp	pp	ADJ
brj-23416	327	36	.	.	PUNCT
brj-23416	328	1	1	1	NUM
brj-23416	328	2	-	-	SYM
brj-23416	328	3	5	5	NUM
brj-23416	328	4	.	.	PUNCT
brj-23416	328	5	rimal	rimal	NOUN
brj-23416	328	6	,	,	PUNCT
brj-23416	328	7	y.	y.	PROPN
brj-23416	328	8	,	,	PUNCT
brj-23416	328	9	sharma	sharma	PROPN
brj-23416	328	10	,	,	PUNCT
brj-23416	328	11	n.	n.	NOUN
brj-23416	328	12	,	,	PUNCT
brj-23416	328	13	and	and	CCONJ
brj-23416	328	14	alsadoon	alsadoon	NOUN
brj-23416	328	15	,	,	PUNCT
brj-23416	328	16	a.	a.	NOUN
brj-23416	328	17	(	(	PUNCT
brj-23416	328	18	2024	2024	NUM
brj-23416	328	19	)	)	PUNCT
brj-23416	328	20	.	.	PUNCT
brj-23416	329	1	“	"	PUNCT
brj-23416	329	2	the	the	DET
brj-23416	329	3	accuracy	accuracy	NOUN
brj-23416	329	4	of	of	ADP
brj-23416	329	5	machine	machine	NOUN
brj-23416	329	6	learning	learning	NOUN
brj-23416	329	7	models	model	NOUN
brj-23416	329	8	relies	rely	VERB
brj-23416	329	9	on	on	ADP
brj-23416	329	10	hyperparameter	hyperparameter	NOUN
brj-23416	329	11	tuning	tuning	NOUN
brj-23416	329	12	:	:	PUNCT
brj-23416	329	13	student	student	NOUN
brj-23416	329	14	result	result	NOUN
brj-23416	329	15	classification	classification	NOUN
brj-23416	329	16	using	use	VERB
brj-23416	329	17	random	random	ADJ
brj-23416	329	18	forest	forest	NOUN
brj-23416	329	19	,	,	PUNCT
brj-23416	329	20	randomized	randomize	VERB
brj-23416	329	21	search	search	NOUN
brj-23416	329	22	,	,	PUNCT
brj-23416	329	23	grid	grid	NOUN
brj-23416	329	24	search	search	NOUN
brj-23416	329	25	,	,	PUNCT
brj-23416	329	26	bayesian	bayesian	NOUN
brj-23416	329	27	,	,	PUNCT
brj-23416	329	28	genetic	genetic	ADJ
brj-23416	329	29	,	,	PUNCT
brj-23416	329	30	and	and	CCONJ
brj-23416	329	31	optunaalgorithms	optunaalgorithm	NOUN
brj-23416	329	32	,	,	PUNCT
brj-23416	329	33	”	"	PUNCT
brj-23416	329	34	multimedia	multimedia	NOUN
brj-23416	329	35	tools	tool	NOUN
brj-23416	329	36	and	and	CCONJ
brj-23416	329	37	applications	application	NOUN
brj-23416	329	38	,	,	PUNCT
brj-23416	329	39	1	1	NUM
brj-23416	329	40	-	-	SYM
brj-23416	329	41	16	16	NUM
brj-23416	329	42	.	.	PUNCT
brj-23416	330	1	doi	doi	NOUN
brj-23416	330	2	:	:	PUNCT
brj-23416	330	3	10.1007	10.1007	NUM
brj-23416	330	4	/	/	SYM
brj-23416	330	5	s11042	s11042	NOUN
brj-23416	330	6	-	-	PUNCT
brj-23416	330	7	024	024	NUM
brj-23416	330	8	-	-	PUNCT
brj-23416	330	9	18426	18426	NUM
brj-23416	330	10	-	-	SYM
brj-23416	330	11	2	2	NUM
brj-23416	330	12	roy	roy	PROPN
brj-23416	330	13	,	,	PUNCT
brj-23416	330	14	s.	s.	PROPN
brj-23416	330	15	s.	s.	PROPN
brj-23416	330	16	,	,	PUNCT
brj-23416	330	17	chopra	chopra	PROPN
brj-23416	330	18	,	,	PUNCT
brj-23416	330	19	r.	r.	PROPN
brj-23416	330	20	,	,	PUNCT
brj-23416	330	21	lee	lee	PROPN
brj-23416	330	22	,	,	PUNCT
brj-23416	330	23	k.	k.	PROPN
brj-23416	330	24	c.	c.	PROPN
brj-23416	330	25	,	,	PUNCT
brj-23416	330	26	spampinato	spampinato	PROPN
brj-23416	330	27	,	,	PUNCT
brj-23416	330	28	c.	c.	PROPN
brj-23416	330	29	,	,	PUNCT
brj-23416	330	30	and	and	CCONJ
brj-23416	330	31	mohammadi	mohammadi	NOUN
brj-23416	330	32	-	-	PUNCT
brj-23416	330	33	ivatlood	ivatlood	NOUN
brj-23416	330	34	,	,	PUNCT
brj-23416	330	35	b.	b.	PROPN
brj-23416	330	36	(	(	PUNCT
brj-23416	330	37	2020	2020	NUM
brj-23416	330	38	)	)	PUNCT
brj-23416	330	39	.	.	PUNCT
brj-23416	331	1	“	"	PUNCT
brj-23416	331	2	random	random	ADJ
brj-23416	331	3	forest	forest	NOUN
brj-23416	331	4	,	,	PUNCT
brj-23416	331	5	gradient	gradient	ADJ
brj-23416	331	6	boosted	boost	VERB
brj-23416	331	7	machines	machine	NOUN
brj-23416	331	8	and	and	CCONJ
brj-23416	331	9	deep	deep	ADJ
brj-23416	331	10	neural	neural	ADJ
brj-23416	331	11	network	network	NOUN
brj-23416	331	12	for	for	ADP
brj-23416	331	13	stock	stock	NOUN
brj-23416	331	14	price	price	NOUN
brj-23416	331	15	forecasting	forecasting	NOUN
brj-23416	331	16	:	:	PUNCT
brj-23416	331	17	a	a	DET
brj-23416	331	18	comparative	comparative	ADJ
brj-23416	331	19	analysis	analysis	NOUN
brj-23416	331	20	on	on	ADP
brj-23416	331	21	south	south	ADJ
brj-23416	331	22	korean	korean	ADJ
brj-23416	331	23	companies	company	NOUN
brj-23416	331	24	,	,	PUNCT
brj-23416	331	25	”	"	PUNCT
brj-23416	331	26	international	international	ADJ
brj-23416	331	27	journal	journal	NOUN
brj-23416	331	28	of	of	ADP
brj-23416	331	29	ad	ad	X
brj-23416	331	30	hoc	hoc	X
brj-23416	331	31	and	and	CCONJ
brj-23416	331	32	ubiquitous	ubiquitous	ADJ
brj-23416	331	33	computing	compute	VERB
brj-23416	331	34	33(1	33(1	NOUN
brj-23416	331	35	)	)	PUNCT
brj-23416	331	36	,	,	PUNCT
brj-23416	331	37	62	62	NUM
brj-23416	331	38	-	-	SYM
brj-23416	331	39	71	71	NUM
brj-23416	331	40	.	.	PUNCT
brj-23416	331	41	doi	doi	NOUN
brj-23416	331	42	:	:	PUNCT
brj-23416	331	43	10.1504	10.1504	NUM
brj-23416	331	44	/	/	SYM
brj-23416	331	45	ijahuc.2020.104715	ijahuc.2020.104715	NOUN
brj-23416	331	46	sakhare	sakhare	NOUN
brj-23416	331	47	,	,	PUNCT
brj-23416	331	48	n.	n.	PROPN
brj-23416	331	49	n.	n.	PROPN
brj-23416	331	50	,	,	PUNCT
brj-23416	331	51	and	and	CCONJ
brj-23416	331	52	imambi	imambi	NOUN
brj-23416	331	53	,	,	PUNCT
brj-23416	331	54	s.	s.	PROPN
brj-23416	331	55	s.	s.	PROPN
brj-23416	331	56	(	(	PUNCT
brj-23416	331	57	2019	2019	NUM
brj-23416	331	58	)	)	PUNCT
brj-23416	331	59	.	.	PUNCT
brj-23416	332	1	“	"	PUNCT
brj-23416	332	2	performance	performance	NOUN
brj-23416	332	3	analysis	analysis	NOUN
brj-23416	332	4	of	of	ADP
brj-23416	332	5	regression	regression	NOUN
brj-23416	332	6	based	base	VERB
brj-23416	332	7	machine	machine	NOUN
brj-23416	332	8	learning	learn	VERB
brj-23416	332	9	techniques	technique	NOUN
brj-23416	332	10	for	for	ADP
brj-23416	332	11	prediction	prediction	NOUN
brj-23416	332	12	of	of	ADP
brj-23416	332	13	stock	stock	NOUN
brj-23416	332	14	market	market	NOUN
brj-23416	332	15	movement	movement	NOUN
brj-23416	332	16	,	,	PUNCT
brj-23416	332	17	”	"	PUNCT
brj-23416	332	18	international	international	ADJ
brj-23416	332	19	journal	journal	NOUN
brj-23416	332	20	of	of	ADP
brj-23416	332	21	recent	recent	ADJ
brj-23416	332	22	technology	technology	NOUN
brj-23416	332	23	and	and	CCONJ
brj-23416	332	24	engineering	engineering	NOUN
brj-23416	332	25	7(6s	7(6s	NUM
brj-23416	332	26	)	)	PUNCT
brj-23416	332	27	,	,	PUNCT
brj-23416	332	28	655	655	NUM
brj-23416	332	29	-	-	SYM
brj-23416	332	30	662	662	NUM
brj-23416	332	31	.	.	PUNCT
brj-23416	332	32	peer	peer	NOUN
brj-23416	332	33	-	-	PUNCT
brj-23416	332	34	reviewed	review	VERB
brj-23416	332	35	article	article	NOUN
brj-23416	332	36	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	332	37	akyüz	akyüz	PROPN
brj-23416	332	38	et	et	PROPN
brj-23416	332	39	al	al	PROPN
brj-23416	332	40	.	.	PROPN
brj-23416	333	1	(	(	PUNCT
brj-23416	333	2	2024	2024	NUM
brj-23416	333	3	)	)	PUNCT
brj-23416	333	4	.	.	PUNCT
brj-23416	334	1	“	"	PUNCT
brj-23416	334	2	stock	stock	NOUN
brj-23416	334	3	exchange	exchange	NOUN
brj-23416	334	4	values	value	NOUN
brj-23416	334	5	,	,	PUNCT
brj-23416	334	6	”	"	PUNCT
brj-23416	334	7	bioresources	bioresource	NOUN
brj-23416	334	8	19(3	19(3	NUM
brj-23416	334	9	)	)	PUNCT
brj-23416	334	10	,	,	PUNCT
brj-23416	334	11	5141	5141	NUM
brj-23416	334	12	-	-	SYM
brj-23416	334	13	5157	5157	NUM
brj-23416	334	14	.	.	PUNCT
brj-23416	335	1	5155	5155	NUM
brj-23416	335	2	sandunil	sandunil	NOUN
brj-23416	335	3	,	,	PUNCT
brj-23416	335	4	k.	k.	PROPN
brj-23416	335	5	,	,	PUNCT
brj-23416	335	6	bennour	bennour	NOUN
brj-23416	335	7	,	,	PUNCT
brj-23416	335	8	z.	z.	PROPN
brj-23416	335	9	,	,	PUNCT
brj-23416	335	10	ben	ben	PROPN
brj-23416	335	11	mahmud	mahmud	PROPN
brj-23416	335	12	,	,	PUNCT
brj-23416	335	13	h.	h.	PROPN
brj-23416	335	14	,	,	PUNCT
brj-23416	335	15	and	and	CCONJ
brj-23416	335	16	giwelli	giwelli	PROPN
brj-23416	335	17	,	,	PUNCT
brj-23416	335	18	a.	a.	NOUN
brj-23416	335	19	(	(	PUNCT
brj-23416	335	20	2023	2023	NUM
brj-23416	335	21	)	)	PUNCT
brj-23416	335	22	.	.	PUNCT
brj-23416	336	1	“	"	PUNCT
brj-23416	336	2	effects	effect	NOUN
brj-23416	336	3	of	of	ADP
brj-23416	336	4	tuning	tune	VERB
brj-23416	336	5	hyperparameters	hyperparameter	NOUN
brj-23416	336	6	in	in	ADP
brj-23416	336	7	random	random	ADJ
brj-23416	336	8	forest	forest	NOUN
brj-23416	336	9	regression	regression	NOUN
brj-23416	336	10	on	on	ADP
brj-23416	336	11	reservoir	reservoir	PROPN
brj-23416	336	12	's	's	PART
brj-23416	336	13	porosity	porosity	NOUN
brj-23416	336	14	prediction	prediction	NOUN
brj-23416	336	15	.	.	PUNCT
brj-23416	337	1	case	case	NOUN
brj-23416	337	2	study	study	NOUN
brj-23416	337	3	:	:	PUNCT
brj-23416	337	4	volve	volve	PROPN
brj-23416	337	5	oil	oil	NOUN
brj-23416	337	6	field	field	NOUN
brj-23416	337	7	,	,	PUNCT
brj-23416	337	8	north	north	NOUN
brj-23416	337	9	sea	sea	NOUN
brj-23416	337	10	,	,	PUNCT
brj-23416	337	11	”	"	PUNCT
brj-23416	337	12	in	in	ADP
brj-23416	337	13	:	:	PUNCT
brj-23416	337	14	57th	57th	ADJ
brj-23416	337	15	us	us	PROPN
brj-23416	337	16	rock	rock	NOUN
brj-23416	337	17	mechanics	mechanic	NOUN
brj-23416	337	18	/	/	SYM
brj-23416	337	19	geomechanics	geomechanic	NOUN
brj-23416	337	20	symposium	symposium	NOUN
brj-23416	337	21	,	,	PUNCT
brj-23416	337	22	atlanta	atlanta	PROPN
brj-23416	337	23	,	,	PUNCT
brj-23416	337	24	usa	usa	PROPN
brj-23416	337	25	.	.	PROPN
brj-23416	337	26	doi	doi	PROPN
brj-23416	337	27	:	:	PUNCT
brj-23416	337	28	10.56952	10.56952	NUM
brj-23416	337	29	/	/	SYM
brj-23416	337	30	arma-2023	arma-2023	NOUN
brj-23416	337	31	-	-	PUNCT
brj-23416	337	32	0012	0012	NUM
brj-23416	337	33	.	.	PUNCT
brj-23416	338	1	singh	singh	PROPN
brj-23416	338	2	,	,	PUNCT
brj-23416	338	3	g.	g.	PROPN
brj-23416	338	4	(	(	PUNCT
brj-23416	338	5	2022	2022	NUM
brj-23416	338	6	)	)	PUNCT
brj-23416	338	7	.	.	PUNCT
brj-23416	339	1	“	"	PUNCT
brj-23416	339	2	machine	machine	NOUN
brj-23416	339	3	learning	learning	NOUN
brj-23416	339	4	models	model	NOUN
brj-23416	339	5	in	in	ADP
brj-23416	339	6	stock	stock	NOUN
brj-23416	339	7	market	market	NOUN
brj-23416	339	8	prediction	prediction	NOUN
brj-23416	339	9	,	,	PUNCT
brj-23416	339	10	”	"	PUNCT
brj-23416	339	11	international	international	ADJ
brj-23416	339	12	journal	journal	NOUN
brj-23416	339	13	of	of	ADP
brj-23416	339	14	innovative	innovative	ADJ
brj-23416	339	15	technology	technology	NOUN
brj-23416	339	16	and	and	CCONJ
brj-23416	339	17	exploring	explore	VERB
brj-23416	339	18	engineering	engineering	NOUN
brj-23416	339	19	(	(	PUNCT
brj-23416	339	20	ijitee	ijitee	NOUN
brj-23416	339	21	)	)	PUNCT
brj-23416	339	22	11(3	11(3	NUM
brj-23416	339	23	)	)	PUNCT
brj-23416	339	24	,	,	PUNCT
brj-23416	339	25	18	18	NUM
brj-23416	339	26	-	-	SYM
brj-23416	339	27	28	28	NUM
brj-23416	339	28	.	.	PUNCT
brj-23416	340	1	doi	doi	NOUN
brj-23416	340	2	:	:	PUNCT
brj-23416	340	3	10.35940	10.35940	NUM
brj-23416	340	4	/	/	SYM
brj-23416	340	5	ijitee.c9733.0111322	ijitee.c9733.0111322	PROPN
brj-23416	340	6	subasi	subasi	PROPN
brj-23416	340	7	,	,	PUNCT
brj-23416	340	8	a.	a.	NOUN
brj-23416	340	9	,	,	PUNCT
brj-23416	340	10	amir	amir	PROPN
brj-23416	340	11	,	,	PUNCT
brj-23416	340	12	f.	f.	PROPN
brj-23416	340	13	,	,	PUNCT
brj-23416	340	14	bagedo	bagedo	PROPN
brj-23416	340	15	,	,	PUNCT
brj-23416	340	16	k.	k.	PROPN
brj-23416	340	17	,	,	PUNCT
brj-23416	340	18	shams	sham	NOUN
brj-23416	340	19	,	,	PUNCT
brj-23416	340	20	a.	a.	NOUN
brj-23416	340	21	,	,	PUNCT
brj-23416	340	22	and	and	CCONJ
brj-23416	340	23	sarirete	sarirete	NOUN
brj-23416	340	24	,	,	PUNCT
brj-23416	340	25	a.	a.	NOUN
brj-23416	340	26	(	(	PUNCT
brj-23416	340	27	2021	2021	NUM
brj-23416	340	28	)	)	PUNCT
brj-23416	340	29	.	.	PUNCT
brj-23416	341	1	“	"	PUNCT
brj-23416	341	2	stock	stock	NOUN
brj-23416	341	3	market	market	NOUN
brj-23416	341	4	prediction	prediction	NOUN
brj-23416	341	5	using	use	VERB
brj-23416	341	6	machine	machine	NOUN
brj-23416	341	7	learning	learning	NOUN
brj-23416	341	8	,	,	PUNCT
brj-23416	341	9	”	"	PUNCT
brj-23416	341	10	procedia	procedia	NOUN
brj-23416	341	11	computer	computer	NOUN
brj-23416	341	12	science	science	NOUN
brj-23416	341	13	194	194	NUM
brj-23416	341	14	,	,	PUNCT
brj-23416	341	15	173	173	NUM
brj-23416	341	16	-	-	SYM
brj-23416	341	17	179	179	NUM
brj-23416	341	18	.	.	PUNCT
brj-23416	342	1	doi	doi	NOUN
brj-23416	342	2	:	:	PUNCT
brj-23416	342	3	10.1016	10.1016	NUM
brj-23416	342	4	/	/	SYM
brj-23416	342	5	j.procs.2021.10.071	j.procs.2021.10.071	PROPN
brj-23416	342	6	ünvan	ünvan	PROPN
brj-23416	342	7	,	,	PUNCT
brj-23416	342	8	y.	y.	PROPN
brj-23416	342	9	a.	a.	PROPN
brj-23416	342	10	,	,	PUNCT
brj-23416	342	11	and	and	CCONJ
brj-23416	342	12	ergenç	ergenç	PROPN
brj-23416	342	13	,	,	PUNCT
brj-23416	342	14	c.	c.	PROPN
brj-23416	342	15	(	(	PUNCT
brj-23416	342	16	2023	2023	NUM
brj-23416	342	17	)	)	PUNCT
brj-23416	342	18	.	.	PUNCT
brj-23416	343	1	“	"	PUNCT
brj-23416	343	2	stock	stock	NOUN
brj-23416	343	3	market	market	NOUN
brj-23416	343	4	forecasting	forecasting	NOUN
brj-23416	343	5	with	with	ADP
brj-23416	343	6	machine	machine	NOUN
brj-23416	343	7	learning	learning	NOUN
brj-23416	343	8	:	:	PUNCT
brj-23416	343	9	the	the	DET
brj-23416	343	10	case	case	NOUN
brj-23416	343	11	of	of	ADP
brj-23416	343	12	bist-100	bist-100	NOUN
brj-23416	343	13	index	index	NOUN
brj-23416	343	14	,	,	PUNCT
brj-23416	343	15	”	"	PUNCT
brj-23416	343	16	international	international	ADJ
brj-23416	343	17	research	research	NOUN
brj-23416	343	18	journal	journal	NOUN
brj-23416	343	19	of	of	ADP
brj-23416	343	20	modernization	modernization	NOUN
brj-23416	343	21	in	in	ADP
brj-23416	343	22	engineering	engineering	NOUN
brj-23416	343	23	technology	technology	NOUN
brj-23416	343	24	and	and	CCONJ
brj-23416	343	25	science	science	NOUN
brj-23416	343	26	5(6	5(6	NUM
brj-23416	343	27	)	)	PUNCT
brj-23416	343	28	,	,	PUNCT
brj-23416	343	29	1304-1312.doi	1304-1312.doi	NUM
brj-23416	343	30	:	:	PUNCT
brj-23416	343	31	10.56726	10.56726	NUM
brj-23416	343	32	/	/	SYM
brj-23416	343	33	irjmets41781	irjmets41781	PROPN
brj-23416	343	34	verly	verly	ADV
brj-23416	343	35	lopes	lope	NOUN
brj-23416	343	36	,	,	PUNCT
brj-23416	343	37	d.j	d.j	PROPN
brj-23416	343	38	.	.	PROPN
brj-23416	343	39	,	,	PUNCT
brj-23416	343	40	bobadilha	bobadilha	NOUN
brj-23416	343	41	,	,	PUNCT
brj-23416	343	42	g.d.s	g.d.s	NOUN
brj-23416	343	43	.	.	PUNCT
brj-23416	343	44	,	,	PUNCT
brj-23416	343	45	and	and	CCONJ
brj-23416	343	46	peres	peres	PROPN
brj-23416	343	47	vieira	vieira	PROPN
brj-23416	343	48	bedette	bedette	PROPN
brj-23416	343	49	,	,	PUNCT
brj-23416	343	50	a.	a.	NOUN
brj-23416	343	51	(	(	PUNCT
brj-23416	343	52	2021	2021	NUM
brj-23416	343	53	)	)	PUNCT
brj-23416	343	54	.	.	PUNCT
brj-23416	344	1	“	"	PUNCT
brj-23416	344	2	analysis	analysis	NOUN
brj-23416	344	3	of	of	ADP
brj-23416	344	4	lumber	lumber	NOUN
brj-23416	344	5	prices	price	NOUN
brj-23416	344	6	time	time	NOUN
brj-23416	344	7	series	series	PROPN
brj-23416	344	8	using	use	VERB
brj-23416	344	9	long	long	ADJ
brj-23416	344	10	short	short	ADJ
brj-23416	344	11	-	-	PUNCT
brj-23416	344	12	term	term	NOUN
brj-23416	344	13	memory	memory	NOUN
brj-23416	344	14	artificial	artificial	ADJ
brj-23416	344	15	neural	neural	ADJ
brj-23416	344	16	networks	network	NOUN
brj-23416	344	17	,	,	PUNCT
brj-23416	344	18	”	"	PUNCT
brj-23416	344	19	forests	forest	VERB
brj-23416	344	20	12	12	NUM
brj-23416	344	21	,	,	PUNCT
brj-23416	344	22	article	article	NOUN
brj-23416	344	23	428	428	NUM
brj-23416	344	24	.	.	PUNCT
brj-23416	345	1	doi	doi	NOUN
brj-23416	345	2	:	:	PUNCT
brj-23416	345	3	10.3390	10.3390	NUM
brj-23416	345	4	/	/	SYM
brj-23416	345	5	f12040428	f12040428	VERB
brj-23416	345	6	vijh	vijh	NOUN
brj-23416	345	7	,	,	PUNCT
brj-23416	345	8	m.	m.	NOUN
brj-23416	345	9	,	,	PUNCT
brj-23416	345	10	chandola	chandola	PROPN
brj-23416	345	11	,	,	PUNCT
brj-23416	345	12	d.	d.	PROPN
brj-23416	345	13	,	,	PUNCT
brj-23416	345	14	tikkiwal	tikkiwal	NOUN
brj-23416	345	15	,	,	PUNCT
brj-23416	345	16	v.	v.	CCONJ
brj-23416	345	17	a.	a.	NOUN
brj-23416	345	18	,	,	PUNCT
brj-23416	345	19	and	and	CCONJ
brj-23416	345	20	kumar	kumar	PROPN
brj-23416	345	21	,	,	PUNCT
brj-23416	345	22	a.	a.	NOUN
brj-23416	345	23	(	(	PUNCT
brj-23416	345	24	2020	2020	NUM
brj-23416	345	25	)	)	PUNCT
brj-23416	345	26	.	.	PUNCT
brj-23416	346	1	“	"	PUNCT
brj-23416	346	2	stock	stock	NOUN
brj-23416	346	3	closing	closing	NOUN
brj-23416	346	4	price	price	NOUN
brj-23416	346	5	prediction	prediction	NOUN
brj-23416	346	6	using	use	VERB
brj-23416	346	7	machine	machine	NOUN
brj-23416	346	8	learning	learning	NOUN
brj-23416	346	9	techniques	technique	NOUN
brj-23416	346	10	,	,	PUNCT
brj-23416	346	11	”	"	PUNCT
brj-23416	346	12	procedia	procedia	NOUN
brj-23416	346	13	computer	computer	NOUN
brj-23416	346	14	science	science	NOUN
brj-23416	346	15	,	,	PUNCT
brj-23416	346	16	167	167	NUM
brj-23416	346	17	,	,	PUNCT
brj-23416	346	18	599	599	NUM
brj-23416	346	19	-	-	SYM
brj-23416	346	20	606	606	NUM
brj-23416	346	21	.	.	PUNCT
brj-23416	347	1	doi	doi	NOUN
brj-23416	347	2	:	:	PUNCT
brj-23416	347	3	10.1016	10.1016	NUM
brj-23416	347	4	/	/	SYM
brj-23416	347	5	j.procs.2020.03.326	j.procs.2020.03.326	PRON
brj-23416	347	6	virro	virro	PROPN
brj-23416	347	7	,	,	PUNCT
brj-23416	347	8	h.	h.	PROPN
brj-23416	347	9	,	,	PUNCT
brj-23416	347	10	kmoch	kmoch	PROPN
brj-23416	347	11	,	,	PUNCT
brj-23416	347	12	a.	a.	NOUN
brj-23416	347	13	,	,	PUNCT
brj-23416	347	14	vainu	vainu	NOUN
brj-23416	347	15	,	,	PUNCT
brj-23416	347	16	m.	m.	NOUN
brj-23416	347	17	,	,	PUNCT
brj-23416	347	18	and	and	CCONJ
brj-23416	347	19	uuemaa	uuemaa	VERB
brj-23416	347	20	,	,	PUNCT
brj-23416	347	21	e.	e.	PROPN
brj-23416	347	22	(	(	PUNCT
brj-23416	347	23	2022	2022	NUM
brj-23416	347	24	)	)	PUNCT
brj-23416	347	25	.	.	PUNCT
brj-23416	348	1	“	"	PUNCT
brj-23416	348	2	random	random	ADJ
brj-23416	348	3	forest	forest	NOUN
brj-23416	348	4	-	-	PUNCT
brj-23416	348	5	based	base	VERB
brj-23416	348	6	modeling	modeling	NOUN
brj-23416	348	7	of	of	ADP
brj-23416	348	8	stream	stream	NOUN
brj-23416	348	9	nutrients	nutrient	NOUN
brj-23416	348	10	at	at	ADP
brj-23416	348	11	national	national	ADJ
brj-23416	348	12	level	level	NOUN
brj-23416	348	13	in	in	ADP
brj-23416	348	14	a	a	DET
brj-23416	348	15	data	data	NOUN
brj-23416	348	16	-	-	PUNCT
brj-23416	348	17	scarce	scarce	NOUN
brj-23416	348	18	region	region	NOUN
brj-23416	348	19	,	,	PUNCT
brj-23416	348	20	”	"	PUNCT
brj-23416	348	21	science	science	NOUN
brj-23416	348	22	of	of	ADP
brj-23416	348	23	the	the	DET
brj-23416	348	24	total	total	ADJ
brj-23416	348	25	environment	environment	NOUN
brj-23416	348	26	840	840	NUM
brj-23416	348	27	,	,	PUNCT
brj-23416	348	28	article	article	NOUN
brj-23416	348	29	156613	156613	NUM
brj-23416	348	30	.	.	PUNCT
brj-23416	349	1	doi	doi	NOUN
brj-23416	349	2	:	:	PUNCT
brj-23416	349	3	10.1016	10.1016	NUM
brj-23416	349	4	/	/	SYM
brj-23416	349	5	j.scitotenv.2022.156613	j.scitotenv.2022.156613	PROPN
brj-23416	349	6	yao	yao	PROPN
brj-23416	349	7	,	,	PUNCT
brj-23416	349	8	b.	b.	PROPN
brj-23416	349	9	(	(	PUNCT
brj-23416	349	10	2023	2023	NUM
brj-23416	349	11	)	)	PUNCT
brj-23416	349	12	.	.	PUNCT
brj-23416	350	1	“	"	PUNCT
brj-23416	350	2	walmart	walmart	NOUN
brj-23416	350	3	sales	sale	NOUN
brj-23416	350	4	prediction	prediction	NOUN
brj-23416	350	5	based	base	VERB
brj-23416	350	6	on	on	ADP
brj-23416	350	7	decision	decision	NOUN
brj-23416	350	8	tree	tree	NOUN
brj-23416	350	9	,	,	PUNCT
brj-23416	350	10	random	random	ADJ
brj-23416	350	11	forest	forest	NOUN
brj-23416	350	12	,	,	PUNCT
brj-23416	350	13	and	and	CCONJ
brj-23416	350	14	k	k	PROPN
brj-23416	350	15	neighbors	neighbor	NOUN
brj-23416	350	16	regressor	regressor	NOUN
brj-23416	350	17	,	,	PUNCT
brj-23416	350	18	”	"	PUNCT
brj-23416	350	19	highlights	highlight	NOUN
brj-23416	350	20	in	in	ADP
brj-23416	350	21	business	business	NOUN
brj-23416	350	22	,	,	PUNCT
brj-23416	350	23	economics	economic	NOUN
brj-23416	350	24	and	and	CCONJ
brj-23416	350	25	management	management	NOUN
brj-23416	350	26	5	5	NUM
brj-23416	350	27	,	,	PUNCT
brj-23416	350	28	330335.doi	330335.doi	NUM
brj-23416	350	29	:	:	PUNCT
brj-23416	350	30	10.54097	10.54097	NUM
brj-23416	350	31	/	/	SYM
brj-23416	350	32	hbem.v5i.5100	hbem.v5i.5100	NUM
brj-23416	350	33	yavena	yavena	NOUN
brj-23416	350	34	,	,	PUNCT
brj-23416	350	35	p.	p.	PROPN
brj-23416	350	36	e.	e.	PROPN
brj-23416	350	37	,	,	PUNCT
brj-23416	350	38	and	and	CCONJ
brj-23416	350	39	kulina	kulina	PROPN
brj-23416	350	40	,	,	PUNCT
brj-23416	350	41	h.	h.	PROPN
brj-23416	350	42	n.	n.	PROPN
brj-23416	350	43	(	(	PUNCT
brj-23416	350	44	2023	2023	NUM
brj-23416	350	45	)	)	PUNCT
brj-23416	350	46	.	.	PUNCT
brj-23416	351	1	“	"	PUNCT
brj-23416	351	2	furniture	furniture	NOUN
brj-23416	351	3	market	market	NOUN
brj-23416	351	4	demand	demand	NOUN
brj-23416	351	5	forecasting	forecasting	NOUN
brj-23416	351	6	using	use	VERB
brj-23416	351	7	machine	machine	NOUN
brj-23416	351	8	learning	learning	NOUN
brj-23416	351	9	approaches	approach	NOUN
brj-23416	351	10	,	,	PUNCT
brj-23416	351	11	”	"	PUNCT
brj-23416	351	12	journal	journal	NOUN
brj-23416	351	13	of	of	ADP
brj-23416	351	14	physics	physics	PROPN
brj-23416	351	15	:	:	PUNCT
brj-23416	351	16	conference	conference	NOUN
brj-23416	351	17	series	series	NOUN
brj-23416	351	18	2675	2675	NUM
brj-23416	351	19	,	,	PUNCT
brj-23416	351	20	1	1	NUM
brj-23416	351	21	-	-	SYM
brj-23416	351	22	12	12	NUM
brj-23416	351	23	.	.	PUNCT
brj-23416	352	1	doi	doi	NOUN
brj-23416	352	2	:	:	PUNCT
brj-23416	352	3	10.1088/1742	10.1088/1742	NUM
brj-23416	352	4	-	-	SYM
brj-23416	352	5	6596/2675/1/012008	6596/2675/1/012008	NUM
brj-23416	352	6	yıldırım	yıldırım	NOUN
brj-23416	352	7	,	,	PUNCT
brj-23416	352	8	i̇.	i̇.	NOUN
brj-23416	352	9	,	,	PUNCT
brj-23416	352	10	özşahin	özşahin	NOUN
brj-23416	352	11	,	,	PUNCT
brj-23416	352	12	ş	ş	X
brj-23416	352	13	.	.	NUM
brj-23416	352	14	,	,	PUNCT
brj-23416	352	15	and	and	CCONJ
brj-23416	352	16	akyüz	akyüz	NOUN
brj-23416	352	17	,	,	PUNCT
brj-23416	352	18	k.	k.	PROPN
brj-23416	352	19	c.	c.	PROPN
brj-23416	352	20	(	(	PUNCT
brj-23416	352	21	2011	2011	NUM
brj-23416	352	22	)	)	PUNCT
brj-23416	352	23	.	.	PUNCT
brj-23416	353	1	“	"	PUNCT
brj-23416	353	2	prediction	prediction	NOUN
brj-23416	353	3	of	of	ADP
brj-23416	353	4	the	the	DET
brj-23416	353	5	financial	financial	ADJ
brj-23416	353	6	return	return	NOUN
brj-23416	353	7	of	of	ADP
brj-23416	353	8	the	the	DET
brj-23416	353	9	paper	paper	NOUN
brj-23416	353	10	sector	sector	NOUN
brj-23416	353	11	with	with	ADP
brj-23416	353	12	artificial	artificial	ADJ
brj-23416	353	13	neural	neural	ADJ
brj-23416	353	14	networks	network	NOUN
brj-23416	353	15	,	,	PUNCT
brj-23416	353	16	”	"	PUNCT
brj-23416	353	17	bioresources	bioresource	NOUN
brj-23416	353	18	6(4	6(4	NUM
brj-23416	353	19	)	)	PUNCT
brj-23416	353	20	,	,	PUNCT
brj-23416	353	21	4076	4076	NUM
brj-23416	353	22	-	-	SYM
brj-23416	353	23	4091	4091	NUM
brj-23416	353	24	.	.	PUNCT
brj-23416	354	1	doi	doi	NOUN
brj-23416	354	2	:	:	PUNCT
brj-23416	354	3	10.15376	10.15376	NUM
brj-23416	354	4	/	/	SYM
brj-23416	354	5	biores.6.4.4076	biores.6.4.4076	NUM
brj-23416	354	6	-	-	PUNCT
brj-23416	354	7	4091	4091	NUM
brj-23416	354	8	yıldız	yıldız	PROPN
brj-23416	354	9	,	,	PUNCT
brj-23416	354	10	b.	b.	PROPN
brj-23416	354	11	,	,	PUNCT
brj-23416	354	12	and	and	CCONJ
brj-23416	354	13	erdoğan	erdoğan	NOUN
brj-23416	354	14	,	,	PUNCT
brj-23416	354	15	m.	m.	NOUN
brj-23416	354	16	(	(	PUNCT
brj-23416	354	17	2020	2020	NUM
brj-23416	354	18	)	)	PUNCT
brj-23416	354	19	.	.	PUNCT
brj-23416	355	1	“	"	PUNCT
brj-23416	355	2	measuring	measure	VERB
brj-23416	355	3	financial	financial	ADJ
brj-23416	355	4	performance	performance	NOUN
brj-23416	355	5	of	of	ADP
brj-23416	355	6	enterprises	enterprise	NOUN
brj-23416	355	7	operating	operate	VERB
brj-23416	355	8	in	in	ADP
brj-23416	355	9	forest	forest	NOUN
brj-23416	355	10	,	,	PUNCT
brj-23416	355	11	paper	paper	NOUN
brj-23416	355	12	and	and	CCONJ
brj-23416	355	13	printing	printing	NOUN
brj-23416	355	14	sector	sector	NOUN
brj-23416	355	15	:	:	PUNCT
brj-23416	355	16	a	a	DET
brj-23416	355	17	topsis	topsis	NOUN
brj-23416	355	18	application	application	NOUN
brj-23416	355	19	in	in	ADP
brj-23416	355	20	borsa	borsa	PROPN
brj-23416	355	21	istanbul	istanbul	PROPN
brj-23416	355	22	,	,	PUNCT
brj-23416	355	23	”	"	PUNCT
brj-23416	355	24	journal	journal	NOUN
brj-23416	355	25	of	of	ADP
brj-23416	355	26	business	business	NOUN
brj-23416	355	27	research	research	NOUN
brj-23416	355	28	-	-	PUNCT
brj-23416	355	29	turk	turk	NOUN
brj-23416	355	30	12(1	12(1	PROPN
brj-23416	355	31	)	)	PUNCT
brj-23416	355	32	,	,	PUNCT
brj-23416	355	33	938	938	NUM
brj-23416	355	34	-	-	SYM
brj-23416	355	35	954	954	NUM
brj-23416	355	36	.	.	PUNCT
brj-23416	356	1	doi	doi	NOUN
brj-23416	356	2	:	:	PUNCT
brj-23416	356	3	10.20491	10.20491	NUM
brj-23416	356	4	/	/	SYM
brj-23416	356	5	isarder.2020.886	isarder.2020.886	NOUN
brj-23416	356	6	yiğit	yiğit	PROPN
brj-23416	356	7	,	,	PUNCT
brj-23416	356	8	ö.	ö.	PROPN
brj-23416	356	9	e.	e.	PROPN
brj-23416	356	10	,	,	PUNCT
brj-23416	356	11	alp	alp	PROPN
brj-23416	356	12	,	,	PUNCT
brj-23416	356	13	s.	s.	PROPN
brj-23416	356	14	,	,	PUNCT
brj-23416	356	15	and	and	CCONJ
brj-23416	356	16	öz	öz	PROPN
brj-23416	356	17	,	,	PUNCT
brj-23416	356	18	e.	e.	PROPN
brj-23416	356	19	(	(	PUNCT
brj-23416	356	20	2020	2020	NUM
brj-23416	356	21	)	)	PUNCT
brj-23416	356	22	.	.	PUNCT
brj-23416	357	1	“	"	PUNCT
brj-23416	357	2	prediction	prediction	NOUN
brj-23416	357	3	of	of	ADP
brj-23416	357	4	bist	bist	ADJ
brj-23416	357	5	price	price	NOUN
brj-23416	357	6	indices	index	NOUN
brj-23416	357	7	:	:	PUNCT
brj-23416	357	8	a	a	DET
brj-23416	357	9	comparative	comparative	ADJ
brj-23416	357	10	study	study	NOUN
brj-23416	357	11	between	between	ADP
brj-23416	357	12	traditional	traditional	ADJ
brj-23416	357	13	and	and	CCONJ
brj-23416	357	14	deep	deep	ADJ
brj-23416	357	15	learning	learning	NOUN
brj-23416	357	16	methods	method	NOUN
brj-23416	357	17	,	,	PUNCT
brj-23416	357	18	”	"	PUNCT
brj-23416	357	19	sigma	sigma	VERB
brj-23416	357	20	j	j	PROPN
brj-23416	357	21	eng	eng	PROPN
brj-23416	357	22	nat	nat	PROPN
brj-23416	357	23	sci	sci	PROPN
brj-23416	357	24	38(4	38(4	NUM
brj-23416	357	25	)	)	PUNCT
brj-23416	357	26	,	,	PUNCT
brj-23416	357	27	1693	1693	NUM
brj-23416	357	28	-	-	SYM
brj-23416	357	29	1704	1704	NUM
brj-23416	357	30	.	.	PUNCT
brj-23416	358	1	yu	yu	PROPN
brj-23416	358	2	,	,	PUNCT
brj-23416	358	3	t.	t.	PROPN
brj-23416	358	4	,	,	PUNCT
brj-23416	358	5	and	and	CCONJ
brj-23416	358	6	zhu	zhu	PROPN
brj-23416	358	7	,	,	PUNCT
brj-23416	358	8	h.	h.	PROPN
brj-23416	358	9	(	(	PUNCT
brj-23416	358	10	2020	2020	NUM
brj-23416	358	11	)	)	PUNCT
brj-23416	358	12	.	.	PUNCT
brj-23416	359	1	“	"	PUNCT
brj-23416	359	2	hyper	hyper	ADJ
brj-23416	359	3	-	-	ADJ
brj-23416	359	4	parameter	parameter	ADJ
brj-23416	359	5	optimization	optimization	NOUN
brj-23416	359	6	:	:	PUNCT
brj-23416	359	7	a	a	DET
brj-23416	359	8	review	review	NOUN
brj-23416	359	9	of	of	ADP
brj-23416	359	10	algorithms	algorithm	NOUN
brj-23416	359	11	and	and	CCONJ
brj-23416	359	12	applications	application	NOUN
brj-23416	359	13	,	,	PUNCT
brj-23416	359	14	”	"	PUNCT
brj-23416	359	15	(	(	PUNCT
brj-23416	359	16	https://arxiv.org/pdf/2003.05689.pdf	https://arxiv.org/pdf/2003.05689.pdf	PROPN
brj-23416	359	17	)	)	PUNCT
brj-23416	359	18	,	,	PUNCT
brj-23416	359	19	accessed	access	VERB
brj-23416	359	20	26	26	NUM
brj-23416	359	21	february	february	NOUN
brj-23416	359	22	2024	2024	NUM
brj-23416	359	23	.	.	PUNCT
brj-23416	360	1	yücesan	yücesan	PROPN
brj-23416	360	2	,	,	PUNCT
brj-23416	360	3	m.	m.	NOUN
brj-23416	360	4	,	,	PUNCT
brj-23416	360	5	gül	gül	PROPN
brj-23416	360	6	,	,	PUNCT
brj-23416	360	7	m.	m.	NOUN
brj-23416	360	8	,	,	PUNCT
brj-23416	360	9	and	and	CCONJ
brj-23416	360	10	çelik	çelik	PROPN
brj-23416	360	11	,	,	PUNCT
brj-23416	360	12	e.	e.	PROPN
brj-23416	360	13	(	(	PUNCT
brj-23416	360	14	2017	2017	NUM
brj-23416	360	15	)	)	PUNCT
brj-23416	360	16	.	.	PUNCT
brj-23416	361	1	“	"	PUNCT
brj-23416	361	2	application	application	NOUN
brj-23416	361	3	of	of	ADP
brj-23416	361	4	artificial	artificial	ADJ
brj-23416	361	5	neural	neural	ADJ
brj-23416	361	6	networks	network	NOUN
brj-23416	361	7	using	use	VERB
brj-23416	361	8	bayesian	bayesian	NOUN
brj-23416	361	9	training	training	NOUN
brj-23416	361	10	rule	rule	NOUN
brj-23416	361	11	in	in	ADP
brj-23416	361	12	sales	sale	NOUN
brj-23416	361	13	forecasting	forecasting	NOUN
brj-23416	361	14	for	for	ADP
brj-23416	361	15	furniture	furniture	NOUN
brj-23416	361	16	industry	industry	NOUN
brj-23416	361	17	,	,	PUNCT
brj-23416	361	18	”	"	PUNCT
brj-23416	361	19	drvna	drvna	VERB
brj-23416	361	20	industrija	industrija	ADJ
brj-23416	361	21	68(3	68(3	NUM
brj-23416	361	22	)	)	PUNCT
brj-23416	361	23	,	,	PUNCT
brj-23416	361	24	219-228.doi	219-228.doi	NUM
brj-23416	361	25	:	:	PUNCT
brj-23416	361	26	10.5552	10.5552	NUM
brj-23416	361	27	/	/	SYM
brj-23416	361	28	drind.2017.1706	drind.2017.1706	PROPN
brj-23416	361	29	article	article	NOUN
brj-23416	361	30	submitted	submit	VERB
brj-23416	361	31	:	:	PUNCT
brj-23416	361	32	march	march	PROPN
brj-23416	361	33	8	8	NUM
brj-23416	361	34	,	,	PUNCT
brj-23416	361	35	2024	2024	NUM
brj-23416	361	36	;	;	PUNCT
brj-23416	361	37	peer	peer	NOUN
brj-23416	361	38	review	review	NOUN
brj-23416	361	39	completed	complete	VERB
brj-23416	361	40	:	:	PUNCT
brj-23416	361	41	may	may	AUX
brj-23416	361	42	19	19	NUM
brj-23416	361	43	,	,	PUNCT
brj-23416	361	44	2024	2024	NUM
brj-23416	361	45	;	;	PUNCT
brj-23416	361	46	revised	revise	VERB
brj-23416	361	47	version	version	NOUN
brj-23416	361	48	received	receive	VERB
brj-23416	361	49	and	and	CCONJ
brj-23416	361	50	accepted	accept	VERB
brj-23416	361	51	:	:	PUNCT
brj-23416	361	52	june	june	PROPN
brj-23416	361	53	4	4	NUM
brj-23416	361	54	,	,	PUNCT
brj-23416	361	55	2024	2024	NUM
brj-23416	361	56	;	;	PUNCT
brj-23416	361	57	published	publish	VERB
brj-23416	361	58	:	:	PUNCT
brj-23416	361	59	june	june	PROPN
brj-23416	361	60	13	13	NUM
brj-23416	361	61	,	,	PUNCT
brj-23416	361	62	2024	2024	NUM
brj-23416	361	63	.	.	PUNCT
brj-23416	362	1	doi	doi	NOUN
brj-23416	362	2	:	:	PUNCT
brj-23416	362	3	10.15376	10.15376	NUM
brj-23416	362	4	/	/	SYM
brj-23416	362	5	biores.19.3.5141	biores.19.3.5141	NOUN
brj-23416	362	6	-	-	PUNCT
brj-23416	362	7	5157	5157	NUM
brj-23416	362	8	peer	peer	NOUN
brj-23416	362	9	-	-	PUNCT
brj-23416	362	10	reviewed	review	VERB
brj-23416	362	11	article	article	NOUN
brj-23416	362	12	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	362	13	akyüz	akyüz	PROPN
brj-23416	362	14	et	et	PROPN
brj-23416	362	15	al	al	PROPN
brj-23416	362	16	.	.	PROPN
brj-23416	363	1	(	(	PUNCT
brj-23416	363	2	2024	2024	NUM
brj-23416	363	3	)	)	PUNCT
brj-23416	363	4	.	.	PUNCT
brj-23416	364	1	“	"	PUNCT
brj-23416	364	2	stock	stock	NOUN
brj-23416	364	3	exchange	exchange	NOUN
brj-23416	364	4	values	value	NOUN
brj-23416	364	5	,	,	PUNCT
brj-23416	364	6	”	"	PUNCT
brj-23416	364	7	bioresources	bioresource	NOUN
brj-23416	364	8	19(3	19(3	NUM
brj-23416	364	9	)	)	PUNCT
brj-23416	364	10	,	,	PUNCT
brj-23416	364	11	5141	5141	NUM
brj-23416	364	12	-	-	SYM
brj-23416	364	13	5157	5157	NUM
brj-23416	364	14	.	.	PUNCT
brj-23416	365	1	5156	5156	NUM
brj-23416	365	2	appendix	appendix	NOUN
brj-23416	365	3	appendix	appendix	VERB
brj-23416	365	4	1.the	1.the	DET
brj-23416	365	5	running	run	VERB
brj-23416	365	6	code	code	NOUN
brj-23416	365	7	for	for	ADP
brj-23416	365	8	gbm	gbm	NOUN
brj-23416	365	9	process	process	NOUN
brj-23416	365	10	#	#	NOUN
brj-23416	365	11	hyperparameter	hyperparameter	NOUN
brj-23416	365	12	adjustment	adjustment	NOUN
brj-23416	365	13	param_grid	param_grid	NOUN
brj-23416	365	14	=	=	PUNCT
brj-23416	365	15	{	{	PUNCT
brj-23416	365	16	'	'	PUNCT
brj-23416	365	17	n_estimators	n_estimator	NOUN
brj-23416	365	18	'	'	PUNCT
brj-23416	365	19	:	:	PUNCT
brj-23416	366	1	[	[	X
brj-23416	366	2	100	100	NUM
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brj-23416	366	4	200	200	NUM
brj-23416	366	5	,	,	PUNCT
brj-23416	366	6	300	300	NUM
brj-23416	366	7	]	]	PUNCT
brj-23416	366	8	,	,	PUNCT
brj-23416	366	9	'	'	PUNCT
brj-23416	366	10	learning_rate	learning_rate	PROPN
brj-23416	366	11	'	'	PUNCT
brj-23416	366	12	:	:	PUNCT
brj-23416	367	1	[	[	X
brj-23416	367	2	0.01	0.01	NUM
brj-23416	367	3	,	,	PUNCT
brj-23416	367	4	0.1	0.1	NUM
brj-23416	367	5	,	,	PUNCT
brj-23416	367	6	0.2	0.2	NUM
brj-23416	367	7	]	]	PUNCT
brj-23416	367	8	,	,	PUNCT
brj-23416	367	9	'	'	PUNCT
brj-23416	367	10	max_depth	max_depth	NOUN
brj-23416	367	11	'	'	PUNCT
brj-23416	367	12	:	:	PUNCT
brj-23416	368	1	[	[	X
brj-23416	368	2	3	3	NUM
brj-23416	368	3	,	,	PUNCT
brj-23416	368	4	4	4	NUM
brj-23416	368	5	,	,	PUNCT
brj-23416	368	6	5	5	NUM
brj-23416	368	7	]	]	PUNCT
brj-23416	368	8	,	,	PUNCT
brj-23416	368	9	'	'	PUNCT
brj-23416	368	10	min_samples_split	min_samples_split	ADJ
brj-23416	368	11	'	'	PUNCT
brj-23416	368	12	:	:	PUNCT
brj-23416	369	1	[	[	X
brj-23416	369	2	2	2	NUM
brj-23416	369	3	,	,	PUNCT
brj-23416	369	4	3	3	NUM
brj-23416	369	5	,	,	PUNCT
brj-23416	369	6	4	4	NUM
brj-23416	369	7	]	]	PUNCT
brj-23416	369	8	,	,	PUNCT
brj-23416	369	9	'	'	PUNCT
brj-23416	369	10	min_samples_leaf	min_samples_leaf	ADJ
brj-23416	369	11	'	'	PUNCT
brj-23416	369	12	:	:	PUNCT
brj-23416	370	1	[	[	X
brj-23416	370	2	1	1	NUM
brj-23416	370	3	,	,	PUNCT
brj-23416	370	4	2	2	NUM
brj-23416	370	5	,	,	PUNCT
brj-23416	370	6	3	3	NUM
brj-23416	370	7	]	]	PUNCT
brj-23416	370	8	}	}	PUNCT
brj-23416	370	9	#	#	NOUN
brj-23416	370	10	model	model	NOUN
brj-23416	370	11	building	building	NOUN
brj-23416	370	12	by	by	ADP
brj-23416	370	13	gridsearchcv	gridsearchcv	NOUN
brj-23416	370	14	gbm	gbm	NOUN
brj-23416	370	15	=	=	SYM
brj-23416	370	16	gradientboostingregressor(random_state=42	gradientboostingregressor(random_state=42	NOUN
brj-23416	370	17	)	)	PUNCT
brj-23416	370	18	grid_search	grid_search	NOUN
brj-23416	370	19	=	=	NOUN
brj-23416	370	20	gridsearchcv(estimator	gridsearchcv(estimator	NOUN
brj-23416	370	21	=	=	SYM
brj-23416	370	22	gbm	gbm	NOUN
brj-23416	370	23	,	,	PUNCT
brj-23416	370	24	param_grid	param_grid	NOUN
brj-23416	370	25	=	=	SYM
brj-23416	370	26	param_grid	param_grid	ADJ
brj-23416	370	27	,	,	PUNCT
brj-23416	370	28	cv=3	cv=3	PROPN
brj-23416	370	29	,	,	PUNCT
brj-23416	370	30	n_jobs=-1	n_jobs=-1	PROPN
brj-23416	370	31	,	,	PUNCT
brj-23416	370	32	verbose=2	verbose=2	NOUN
brj-23416	370	33	)	)	PUNCT
brj-23416	370	34	grid_search.fit(x_train	grid_search.fit(x_train	PROPN
brj-23416	370	35	,	,	PUNCT
brj-23416	370	36	y_train	y_train	ADV
brj-23416	370	37	)	)	PUNCT
brj-23416	370	38	appendix	appendix	VERB
brj-23416	370	39	2.the	2.the	PRON
brj-23416	370	40	running	running	NOUN
brj-23416	370	41	code	code	NOUN
brj-23416	370	42	for	for	ADP
brj-23416	370	43	knn	knn	PROPN
brj-23416	370	44	process	process	NOUN
brj-23416	370	45	#	#	NOUN
brj-23416	370	46	determining	determine	VERB
brj-23416	370	47	the	the	DET
brj-23416	370	48	hyperparameter	hyperparameter	NOUN
brj-23416	370	49	range	range	NOUN
brj-23416	370	50	for	for	ADP
brj-23416	370	51	knn	knn	PROPN
brj-23416	370	52	param_grid_knn	param_grid_knn	PROPN
brj-23416	370	53	=	=	SYM
brj-23416	370	54	{	{	PUNCT
brj-23416	370	55	'	'	PUNCT
brj-23416	370	56	n_neighbors	n_neighbor	NOUN
brj-23416	370	57	'	'	PUNCT
brj-23416	370	58	:	:	PUNCT
brj-23416	371	1	[	[	X
brj-23416	371	2	2	2	NUM
brj-23416	371	3	,	,	PUNCT
brj-23416	371	4	3	3	NUM
brj-23416	371	5	,	,	PUNCT
brj-23416	371	6	4	4	NUM
brj-23416	371	7	,	,	PUNCT
brj-23416	371	8	5	5	NUM
brj-23416	371	9	,	,	PUNCT
brj-23416	371	10	7	7	NUM
brj-23416	371	11	,	,	PUNCT
brj-23416	371	12	9	9	NUM
brj-23416	371	13	]	]	PUNCT
brj-23416	371	14	,	,	PUNCT
brj-23416	371	15	'	'	PUNCT
brj-23416	371	16	weights	weight	NOUN
brj-23416	371	17	'	'	PUNCT
brj-23416	371	18	:	:	PUNCT
brj-23416	372	1	[	[	X
brj-23416	372	2	'	'	PUNCT
brj-23416	372	3	uniform	uniform	ADJ
brj-23416	372	4	'	'	PUNCT
brj-23416	372	5	,	,	PUNCT
brj-23416	372	6	'	'	PUNCT
brj-23416	372	7	distance	distance	NOUN
brj-23416	372	8	'	'	PUNCT
brj-23416	372	9	]	]	PUNCT
brj-23416	372	10	,	,	PUNCT
brj-23416	372	11	'	'	PUNCT
brj-23416	372	12	algorithm	algorithm	NOUN
brj-23416	372	13	'	'	PUNCT
brj-23416	372	14	:	:	PUNCT
brj-23416	373	1	[	[	X
brj-23416	373	2	'	'	PUNCT
brj-23416	373	3	auto	auto	NOUN
brj-23416	373	4	'	'	PUNCT
brj-23416	373	5	,	,	PUNCT
brj-23416	373	6	'	'	PUNCT
brj-23416	373	7	ball_tree	ball_tree	NOUN
brj-23416	373	8	'	'	PUNCT
brj-23416	373	9	,	,	PUNCT
brj-23416	373	10	'	'	PUNCT
brj-23416	373	11	kd_tree	kd_tree	X
brj-23416	373	12	'	'	PUNCT
brj-23416	373	13	,	,	PUNCT
brj-23416	373	14	'	'	PUNCT
brj-23416	373	15	brute	brute	ADJ
brj-23416	373	16	'	'	PUNCT
brj-23416	373	17	]	]	PUNCT
brj-23416	373	18	}	}	PUNCT
brj-23416	373	19	#	#	NOUN
brj-23416	373	20	hyperparametre	hyperparametre	NOUN
brj-23416	373	21	optimization	optimization	NOUN
brj-23416	373	22	with	with	ADP
brj-23416	373	23	gridsearchcv	gridsearchcv	NOUN
brj-23416	373	24	knn	knn	NOUN
brj-23416	373	25	=	=	SYM
brj-23416	373	26	kneighborsregressor	kneighborsregressor	NOUN
brj-23416	373	27	(	(	PUNCT
brj-23416	373	28	)	)	PUNCT
brj-23416	373	29	grid_search_knn	grid_search_knn	NOUN
brj-23416	373	30	=	=	SYM
brj-23416	373	31	gridsearchcv(estimator	gridsearchcv(estimator	PROPN
brj-23416	373	32	=	=	SYM
brj-23416	373	33	knn	knn	PROPN
brj-23416	373	34	,	,	PUNCT
brj-23416	373	35	param_grid	param_grid	ADJ
brj-23416	373	36	=	=	SYM
brj-23416	373	37	param_grid_knn	param_grid_knn	NOUN
brj-23416	373	38	,	,	PUNCT
brj-23416	373	39	cv=3	cv=3	PROPN
brj-23416	373	40	,	,	PUNCT
brj-23416	373	41	n_jobs=-1	n_jobs=-1	PROPN
brj-23416	373	42	,	,	PUNCT
brj-23416	373	43	verbose=2	verbose=2	NOUN
brj-23416	373	44	)	)	PUNCT
brj-23416	373	45	grid_search_knn.fit(x_train	grid_search_knn.fit(x_train	ADJ
brj-23416	373	46	,	,	PUNCT
brj-23416	373	47	y_train	y_train	X
brj-23416	373	48	)	)	PUNCT
brj-23416	373	49	appendix	appendix	VERB
brj-23416	373	50	3.the	3.the	PRON
brj-23416	373	51	running	run	VERB
brj-23416	373	52	code	code	NOUN
brj-23416	373	53	for	for	ADP
brj-23416	373	54	ann	ann	PROPN
brj-23416	373	55	process	process	NOUN
brj-23416	373	56	#	#	NOUN
brj-23416	373	57	objective	objective	ADJ
brj-23416	373	58	function	function	NOUN
brj-23416	373	59	for	for	ADP
brj-23416	373	60	hyperparameter	hyperparameter	NOUN
brj-23416	373	61	optimization	optimization	NOUN
brj-23416	373	62	;	;	PUNCT
brj-23416	373	63	def	def	PROPN
brj-23416	373	64	objective(trial	objective(trial	PROPN
brj-23416	373	65	):	):	PUNCT
brj-23416	373	66	hidden_size	hidden_size	NOUN
brj-23416	373	67	=	=	SYM
brj-23416	373	68	trial.suggest_int('hidden_size	trial.suggest_int('hidden_size	PROPN
brj-23416	373	69	'	'	PUNCT
brj-23416	373	70	,	,	PUNCT
brj-23416	373	71	10	10	NUM
brj-23416	373	72	,	,	PUNCT
brj-23416	373	73	100	100	NUM
brj-23416	373	74	)	)	PUNCT
brj-23416	373	75	lr	lr	NOUN
brj-23416	373	76	=	=	PUNCT
brj-23416	373	77	trial.suggest_float('lr	trial.suggest_float('lr	NUM
brj-23416	373	78	'	'	PUNCT
brj-23416	373	79	,	,	PUNCT
brj-23416	373	80	1e-4	1e-4	PROPN
brj-23416	373	81	,	,	PUNCT
brj-23416	373	82	1e-1	1e-1	PROPN
brj-23416	373	83	,	,	PUNCT
brj-23416	373	84	log	log	NOUN
brj-23416	373	85	=	=	SYM
brj-23416	373	86	true	true	ADJ
brj-23416	373	87	)	)	PUNCT
brj-23416	373	88	num_epochs	num_epochs	NOUN
brj-23416	373	89	=	=	SYM
brj-23416	373	90	trial.suggest_int('num_epochs	trial.suggest_int('num_epoch	NOUN
brj-23416	373	91	'	'	PUNCT
brj-23416	373	92	,	,	PUNCT
brj-23416	373	93	50	50	NUM
brj-23416	373	94	,	,	PUNCT
brj-23416	373	95	200	200	NUM
brj-23416	373	96	)	)	PUNCT
brj-23416	373	97	model	model	NOUN
brj-23416	373	98	=	=	PUNCT
brj-23416	373	99	annmodel(x_train.shape[1	annmodel(x_train.shape[1	PROPN
brj-23416	373	100	]	]	X
brj-23416	373	101	,	,	PUNCT
brj-23416	373	102	hidden_size	hidden_size	PROPN
brj-23416	373	103	,	,	PUNCT
brj-23416	373	104	1	1	NUM
brj-23416	373	105	)	)	PUNCT
brj-23416	373	106	optimizer	optimizer	NOUN
brj-23416	373	107	=	=	SYM
brj-23416	373	108	optim.adam(model.parameters	optim.adam(model.parameter	NOUN
brj-23416	373	109	(	(	PUNCT
brj-23416	373	110	)	)	PUNCT
brj-23416	373	111	,	,	PUNCT
brj-23416	373	112	lr	lr	PROPN
brj-23416	373	113	=	=	NOUN
brj-23416	373	114	lr	lr	NOUN
brj-23416	373	115	)	)	PUNCT
brj-23416	373	116	criterion	criterion	NOUN
brj-23416	373	117	=	=	SYM
brj-23416	373	118	nn.mseloss	nn.mseloss	X
brj-23416	373	119	(	(	PUNCT
brj-23416	373	120	)	)	PUNCT
brj-23416	373	121	for	for	ADP
brj-23416	373	122	epoch	epoch	NOUN
brj-23416	373	123	in	in	ADP
brj-23416	373	124	range(num_epochs	range(num_epochs	PROPN
brj-23416	373	125	):	):	PUNCT
brj-23416	373	126	model.train	model.train	NOUN
brj-23416	373	127	(	(	PUNCT
brj-23416	373	128	)	)	PUNCT
brj-23416	373	129	for	for	ADP
brj-23416	373	130	batch_x	batch_x	NUM
brj-23416	373	131	,	,	PUNCT
brj-23416	373	132	batch_y	batch_y	VERB
brj-23416	373	133	in	in	ADP
brj-23416	373	134	train_loader	train_loader	NOUN
brj-23416	373	135	:	:	PUNCT
brj-23416	373	136	optimizer.zero_grad	optimizer.zero_grad	ADJ
brj-23416	373	137	(	(	PUNCT
brj-23416	373	138	)	)	PUNCT
brj-23416	373	139	peer	peer	NOUN
brj-23416	373	140	-	-	PUNCT
brj-23416	373	141	reviewed	review	VERB
brj-23416	373	142	article	article	NOUN
brj-23416	373	143	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23416	373	144	akyüz	akyüz	PROPN
brj-23416	373	145	et	et	PROPN
brj-23416	373	146	al	al	PROPN
brj-23416	373	147	.	.	PROPN
brj-23416	374	1	(	(	PUNCT
brj-23416	374	2	2024	2024	NUM
brj-23416	374	3	)	)	PUNCT
brj-23416	374	4	.	.	PUNCT
brj-23416	375	1	“	"	PUNCT
brj-23416	375	2	stock	stock	NOUN
brj-23416	375	3	exchange	exchange	NOUN
brj-23416	375	4	values	value	NOUN
brj-23416	375	5	,	,	PUNCT
brj-23416	375	6	”	"	PUNCT
brj-23416	375	7	bioresources	bioresource	NOUN
brj-23416	375	8	19(3	19(3	NUM
brj-23416	375	9	)	)	PUNCT
brj-23416	375	10	,	,	PUNCT
brj-23416	375	11	5141	5141	NUM
brj-23416	375	12	-	-	SYM
brj-23416	375	13	5157	5157	NUM
brj-23416	375	14	.	.	PUNCT
brj-23416	376	1	5157	5157	NUM
brj-23416	376	2	outputs	output	NOUN
brj-23416	376	3	=	=	SYM
brj-23416	376	4	model(batch_x	model(batch_x	X
brj-23416	376	5	)	)	PUNCT
brj-23416	376	6	loss	loss	NOUN
brj-23416	376	7	=	=	SYM
brj-23416	376	8	criterion(outputs	criterion(output	NOUN
brj-23416	376	9	,	,	PUNCT
brj-23416	376	10	batch_y	batch_y	NOUN
brj-23416	376	11	)	)	PUNCT
brj-23416	376	12	loss.backward	loss.backward	NUM
brj-23416	376	13	(	(	PUNCT
brj-23416	376	14	)	)	PUNCT
brj-23416	376	15	optimizer.step	optimizer.step	NOUN
brj-23416	376	16	(	(	PUNCT
brj-23416	376	17	)	)	PUNCT
brj-23416	376	18	model.eval	model.eval	NOUN
brj-23416	376	19	(	(	PUNCT
brj-23416	376	20	)	)	PUNCT
brj-23416	376	21	with	with	ADP
brj-23416	376	22	torch.no_grad	torch.no_grad	PROPN
brj-23416	376	23	(	(	PUNCT
brj-23416	376	24	):	):	PUNCT
brj-23416	376	25	y_pred	y_pre	VERB
brj-23416	376	26	=	=	SYM
brj-23416	376	27	model(x_test_t	model(x_test_t	NOUN
brj-23416	376	28	)	)	PUNCT
brj-23416	376	29	mse	mse	NOUN
brj-23416	376	30	=	=	PUNCT
brj-23416	376	31	mean_squared_error(y_test	mean_squared_error(y_t	ADJ
brj-23416	376	32	,	,	PUNCT
brj-23416	376	33	y_pred.numpy	y_pred.numpy	NUM
brj-23416	376	34	(	(	PUNCT
brj-23416	376	35	)	)	PUNCT
brj-23416	376	36	)	)	PUNCT
brj-23416	376	37	return	return	VERB
brj-23416	376	38	mse	mse	NOUN
brj-23416	376	39	#	#	NOUN
brj-23416	376	40	optuna	optuna	PROPN
brj-23416	376	41	optimization	optimization	NOUN
brj-23416	376	42	study	study	NOUN
brj-23416	376	43	=	=	SYM
brj-23416	376	44	optuna.create_study(direction='minimize	optuna.create_study(direction='minimize	PROPN
brj-23416	376	45	'	'	PUNCT
brj-23416	376	46	)	)	PUNCT
brj-23416	376	47	study.optimize(objective	study.optimize(objective	PROPN
brj-23416	376	48	,	,	PUNCT
brj-23416	376	49	n_trials=50	n_trials=50	ADJ
brj-23416	376	50	)	)	PUNCT
brj-23416	376	51	appendix	appendix	VERB
brj-23416	376	52	4.the	4.the	PRON
brj-23416	376	53	running	run	VERB
brj-23416	376	54	code	code	NOUN
brj-23416	376	55	for	for	ADP
brj-23416	376	56	rf	rf	NOUN
brj-23416	376	57	process	process	NOUN
brj-23416	376	58	#	#	NOUN
brj-23416	376	59	determining	determine	VERB
brj-23416	376	60	the	the	DET
brj-23416	376	61	hyperparameter	hyperparameter	NOUN
brj-23416	376	62	range	range	NOUN
brj-23416	376	63	param_grid	param_grid	VERB
brj-23416	377	1	=	=	PUNCT
brj-23416	377	2	{	{	PUNCT
brj-23416	377	3	'	'	PUNCT
brj-23416	377	4	n_estimators	n_estimator	NOUN
brj-23416	377	5	'	'	PUNCT
brj-23416	377	6	:	:	PUNCT
brj-23416	378	1	[	[	X
brj-23416	378	2	100	100	NUM
brj-23416	378	3	,	,	PUNCT
brj-23416	378	4	200	200	NUM
brj-23416	378	5	,	,	PUNCT
brj-23416	378	6	300	300	NUM
brj-23416	378	7	]	]	PUNCT
brj-23416	378	8	,	,	PUNCT
brj-23416	378	9	'	'	PUNCT
brj-23416	378	10	max_features	max_feature	NOUN
brj-23416	378	11	'	'	PUNCT
brj-23416	378	12	:	:	PUNCT
brj-23416	379	1	[	[	X
brj-23416	379	2	'	'	PUNCT
brj-23416	379	3	sqrt	sqrt	NOUN
brj-23416	379	4	'	'	PUNCT
brj-23416	379	5	,	,	PUNCT
brj-23416	379	6	'	'	PUNCT
brj-23416	379	7	log2	log2	PROPN
brj-23416	379	8	'	'	PUNCT
brj-23416	379	9	,	,	PUNCT
brj-23416	379	10	none	none	NOUN
brj-23416	379	11	]	]	PUNCT
brj-23416	379	12	,	,	PUNCT
brj-23416	379	13	#	#	NOUN
brj-23416	379	14	'	'	PUNCT
brj-23416	379	15	auto	auto	NOUN
brj-23416	379	16	'	'	PUNCT
brj-23416	379	17	yerine	yerine	ADJ
brj-23416	379	18	'	'	PUNCT
brj-23416	379	19	sqrt	sqrt	NOUN
brj-23416	379	20	'	'	PUNCT
brj-23416	379	21	,	,	PUNCT
brj-23416	379	22	'	'	PUNCT
brj-23416	379	23	log2	log2	PROPN
brj-23416	379	24	'	'	PART
brj-23416	379	25	veya	veya	NOUN
brj-23416	379	26	none	none	NOUN
brj-23416	379	27	'	'	PUNCT
brj-23416	379	28	max_depth	max_depth	NOUN
brj-23416	379	29	'	'	PUNCT
brj-23416	379	30	:	:	PUNCT
brj-23416	380	1	[	[	X
brj-23416	380	2	4	4	NUM
brj-23416	380	3	,	,	PUNCT
brj-23416	380	4	6	6	NUM
brj-23416	380	5	,	,	PUNCT
brj-23416	380	6	8	8	NUM
brj-23416	380	7	,	,	PUNCT
brj-23416	380	8	10	10	NUM
brj-23416	380	9	]	]	PUNCT
brj-23416	380	10	,	,	PUNCT
brj-23416	380	11	'	'	PUNCT
brj-23416	380	12	min_samples_split	min_samples_split	ADJ
brj-23416	380	13	'	'	PUNCT
brj-23416	380	14	:	:	PUNCT
brj-23416	381	1	[	[	X
brj-23416	381	2	2	2	NUM
brj-23416	381	3	,	,	PUNCT
brj-23416	381	4	5	5	NUM
brj-23416	381	5	,	,	PUNCT
brj-23416	381	6	10	10	NUM
brj-23416	381	7	]	]	PUNCT
brj-23416	381	8	,	,	PUNCT
brj-23416	381	9	'	'	PUNCT
brj-23416	381	10	min_samples_leaf	min_samples_leaf	ADJ
brj-23416	381	11	'	'	PUNCT
brj-23416	381	12	:	:	PUNCT
brj-23416	382	1	[	[	X
brj-23416	382	2	1	1	NUM
brj-23416	382	3	,	,	PUNCT
brj-23416	382	4	2	2	NUM
brj-23416	382	5	,	,	PUNCT
brj-23416	382	6	4	4	NUM
brj-23416	382	7	]	]	PUNCT
brj-23416	382	8	}	}	PUNCT
brj-23416	382	9	#	#	NOUN
brj-23416	382	10	hyperparameter	hyperparameter	NOUN
brj-23416	382	11	optimization	optimization	NOUN
brj-23416	382	12	with	with	ADP
brj-23416	382	13	gridsearchcv	gridsearchcv	NOUN
brj-23416	382	14	rf	rf	NOUN
brj-23416	382	15	=	=	PUNCT
brj-23416	382	16	randomforestregressor(random_state=42	randomforestregressor(random_state=42	NOUN
brj-23416	382	17	)	)	PUNCT
brj-23416	382	18	grid_search	grid_search	NOUN
brj-23416	383	1	=	=	NOUN
brj-23416	383	2	gridsearchcv(estimator	gridsearchcv(estimator	NOUN
brj-23416	383	3	=	=	SYM
brj-23416	383	4	rf	rf	NOUN
brj-23416	383	5	,	,	PUNCT
brj-23416	383	6	param_grid	param_grid	NOUN
brj-23416	383	7	=	=	SYM
brj-23416	383	8	param_grid	param_grid	ADJ
brj-23416	383	9	,	,	PUNCT
brj-23416	383	10	cv=3	cv=3	PROPN
brj-23416	383	11	,	,	PUNCT
brj-23416	383	12	n_jobs=-1	n_jobs=-1	PROPN
brj-23416	383	13	,	,	PUNCT
brj-23416	383	14	verbose=2	verbose=2	NOUN
brj-23416	383	15	)	)	PUNCT
brj-23416	384	1	grid_search.fit(x_train	grid_search.fit(x_train	PROPN
brj-23416	384	2	,	,	PUNCT
brj-23416	384	3	y_train	y_train	X
brj-23416	384	4	)	)	PUNCT
brj-23416	384	5	#	#	NOUN
brj-23416	384	6	retrain	retrain	VERB
brj-23416	384	7	the	the	DET
brj-23416	384	8	model	model	NOUN
brj-23416	384	9	using	use	VERB
brj-23416	384	10	the	the	DET
brj-23416	384	11	best	good	ADJ
brj-23416	384	12	parameters	parameter	NOUN
brj-23416	384	13	best_rf	best_rf	NOUN
brj-23416	384	14	=	=	SYM
brj-23416	384	15	grid_search.best_estimator	grid_search.best_estimator	NOUN
brj-23416	384	16	_	_	NOUN
brj-23416	384	17	best_rf.fit(x_train	best_rf.fit(x_train	NOUN
brj-23416	384	18	,	,	PUNCT
brj-23416	384	19	y_train	y_train	X
brj-23416	384	20	)	)	PUNCT