id	sid	tid	token	lemma	pos
brj-23756	1	1	peer	peer	NOUN
brj-23756	1	2	-	-	PUNCT
brj-23756	1	3	review	review	NOUN
brj-23756	1	4	article	article	NOUN
brj-23756	1	5	peer	peer	NOUN
brj-23756	1	6	-	-	PUNCT
brj-23756	1	7	reviewed	review	VERB
brj-23756	1	8	article	article	NOUN
brj-23756	1	9	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	1	10	masoumi	masoumi	NOUN
brj-23756	1	11	&	&	CCONJ
brj-23756	1	12	bond	bond	PROPN
brj-23756	1	13	(	(	PUNCT
brj-23756	1	14	2024	2024	NUM
brj-23756	1	15	)	)	PUNCT
brj-23756	1	16	.	.	PUNCT
brj-23756	2	1	“	"	PUNCT
brj-23756	2	2	ann	ann	PROPN
brj-23756	2	3	prediction	prediction	NOUN
brj-23756	2	4	of	of	ADP
brj-23756	2	5	tmw	tmw	NOUN
brj-23756	2	6	,	,	PUNCT
brj-23756	2	7	”	"	PUNCT
brj-23756	2	8	bioresources	bioresource	NOUN
brj-23756	2	9	19(4	19(4	NUM
brj-23756	2	10	)	)	PUNCT
brj-23756	2	11	,	,	PUNCT
brj-23756	2	12	6983	6983	NUM
brj-23756	2	13	-	-	SYM
brj-23756	2	14	6993	6993	NUM
brj-23756	2	15	.	.	PUNCT
brj-23756	3	1	6983	6983	NUM
brj-23756	3	2	prediction	prediction	NOUN
brj-23756	3	3	of	of	ADP
brj-23756	3	4	equilibrium	equilibrium	NOUN
brj-23756	3	5	moisture	moisture	NOUN
brj-23756	3	6	content	content	NOUN
brj-23756	3	7	and	and	CCONJ
brj-23756	3	8	swelling	swell	VERB
brj-23756	3	9	of	of	ADP
brj-23756	3	10	thermally	thermally	ADV
brj-23756	3	11	modified	modify	VERB
brj-23756	3	12	hardwoods	hardwood	NOUN
brj-23756	3	13	by	by	ADP
brj-23756	3	14	artificial	artificial	ADJ
brj-23756	3	15	neural	neural	ADJ
brj-23756	3	16	networks	network	NOUN
brj-23756	3	17	abasali	abasali	VERB
brj-23756	3	18	masoumi	masoumi	PROPN
brj-23756	3	19	,	,	PUNCT
brj-23756	3	20	a	a	PRON
brj-23756	3	21	,	,	PUNCT
brj-23756	3	22	*	*	ADJ
brj-23756	3	23	and	and	CCONJ
brj-23756	3	24	brian	brian	PROPN
brj-23756	3	25	h.	h.	PROPN
brj-23756	3	26	bond	bond	PROPN
brj-23756	3	27	b	b	PROPN
brj-23756	3	28	in	in	ADP
brj-23756	3	29	this	this	DET
brj-23756	3	30	study	study	NOUN
brj-23756	3	31	artificial	artificial	ADJ
brj-23756	3	32	neural	neural	ADJ
brj-23756	3	33	network	network	NOUN
brj-23756	3	34	(	(	PUNCT
brj-23756	3	35	ann	ann	PROPN
brj-23756	3	36	)	)	PUNCT
brj-23756	3	37	models	model	NOUN
brj-23756	3	38	were	be	AUX
brj-23756	3	39	developed	develop	VERB
brj-23756	3	40	for	for	ADP
brj-23756	3	41	predicting	predict	VERB
brj-23756	3	42	the	the	DET
brj-23756	3	43	effects	effect	NOUN
brj-23756	3	44	of	of	ADP
brj-23756	3	45	wood	wood	NOUN
brj-23756	3	46	species	specie	NOUN
brj-23756	3	47	,	,	PUNCT
brj-23756	3	48	density	density	NOUN
brj-23756	3	49	,	,	PUNCT
brj-23756	3	50	modifying	modify	VERB
brj-23756	3	51	time	time	NOUN
brj-23756	3	52	,	,	PUNCT
brj-23756	3	53	and	and	CCONJ
brj-23756	3	54	temperature	temperature	NOUN
brj-23756	3	55	on	on	ADP
brj-23756	3	56	the	the	DET
brj-23756	3	57	equilibrium	equilibrium	NOUN
brj-23756	3	58	moisture	moisture	NOUN
brj-23756	3	59	content	content	NOUN
brj-23756	3	60	(	(	PUNCT
brj-23756	3	61	emc	emc	PROPN
brj-23756	3	62	)	)	PUNCT
brj-23756	3	63	and	and	CCONJ
brj-23756	3	64	swelling	swell	VERB
brj-23756	3	65	of	of	ADP
brj-23756	3	66	six	six	NUM
brj-23756	3	67	different	different	ADJ
brj-23756	3	68	thermally	thermally	ADV
brj-23756	3	69	modified	modify	VERB
brj-23756	3	70	hardwood	hardwood	NOUN
brj-23756	3	71	species	specie	NOUN
brj-23756	3	72	,	,	PUNCT
brj-23756	3	73	as	as	SCONJ
brj-23756	3	74	previously	previously	ADV
brj-23756	3	75	published	publish	VERB
brj-23756	3	76	by	by	ADP
brj-23756	3	77	the	the	DET
brj-23756	3	78	authors	author	NOUN
brj-23756	3	79	.	.	PUNCT
brj-23756	4	1	lumber	lumber	NOUN
brj-23756	4	2	of	of	ADP
brj-23756	4	3	yellow	yellow	ADJ
brj-23756	4	4	-	-	PUNCT
brj-23756	4	5	poplar	poplar	NOUN
brj-23756	4	6	(	(	PUNCT
brj-23756	4	7	liriodendron	liriodendron	NOUN
brj-23756	4	8	tulipifera	tulipifera	NOUN
brj-23756	4	9	)	)	PUNCT
brj-23756	4	10	,	,	PUNCT
brj-23756	4	11	red	red	ADJ
brj-23756	4	12	oak	oak	NOUN
brj-23756	4	13	(	(	PUNCT
brj-23756	4	14	quercus	quercus	ADJ
brj-23756	4	15	borealis	boreali	NOUN
brj-23756	4	16	)	)	PUNCT
brj-23756	4	17	,	,	PUNCT
brj-23756	4	18	white	white	ADJ
brj-23756	4	19	ash	ash	NOUN
brj-23756	4	20	(	(	PUNCT
brj-23756	4	21	fraxinus	fraxinus	NOUN
brj-23756	4	22	americana	americana	PROPN
brj-23756	4	23	)	)	PUNCT
brj-23756	4	24	,	,	PUNCT
brj-23756	4	25	red	red	ADJ
brj-23756	4	26	maple	maple	PROPN
brj-23756	4	27	(	(	PUNCT
brj-23756	4	28	acer	acer	NOUN
brj-23756	4	29	rubrum	rubrum	NOUN
brj-23756	4	30	)	)	PUNCT
brj-23756	4	31	,	,	PUNCT
brj-23756	4	32	hickory	hickory	NOUN
brj-23756	4	33	(	(	PUNCT
brj-23756	4	34	carya	carya	NOUN
brj-23756	4	35	glabra	glabra	NOUN
brj-23756	4	36	)	)	PUNCT
brj-23756	4	37	,	,	PUNCT
brj-23756	4	38	and	and	CCONJ
brj-23756	4	39	black	black	ADJ
brj-23756	4	40	cherry	cherry	NOUN
brj-23756	4	41	(	(	PUNCT
brj-23756	4	42	prunus	prunus	NOUN
brj-23756	4	43	serotina	serotina	PROPN
brj-23756	4	44	)	)	PUNCT
brj-23756	4	45	were	be	AUX
brj-23756	4	46	selected	select	VERB
brj-23756	4	47	.	.	PUNCT
brj-23756	5	1	treatment	treatment	NOUN
brj-23756	5	2	type	type	NOUN
brj-23756	5	3	,	,	PUNCT
brj-23756	5	4	species	specie	NOUN
brj-23756	5	5	,	,	PUNCT
brj-23756	5	6	temperature	temperature	NOUN
brj-23756	5	7	,	,	PUNCT
brj-23756	5	8	time	time	NOUN
brj-23756	5	9	,	,	PUNCT
brj-23756	5	10	and	and	CCONJ
brj-23756	5	11	density	density	NOUN
brj-23756	5	12	were	be	AUX
brj-23756	5	13	used	use	VERB
brj-23756	5	14	as	as	ADP
brj-23756	5	15	inputs	input	NOUN
brj-23756	5	16	for	for	ADP
brj-23756	5	17	the	the	DET
brj-23756	5	18	models	model	NOUN
brj-23756	5	19	.	.	PUNCT
brj-23756	6	1	using	use	VERB
brj-23756	6	2	keras	keras	PROPN
brj-23756	6	3	and	and	CCONJ
brj-23756	6	4	pytorch	pytorch	NOUN
brj-23756	6	5	libraries	library	NOUN
brj-23756	6	6	in	in	ADP
brj-23756	6	7	python	python	PROPN
brj-23756	6	8	,	,	PUNCT
brj-23756	6	9	different	different	ADJ
brj-23756	6	10	feed	feed	NOUN
brj-23756	6	11	forward	forward	ADV
brj-23756	6	12	and	and	CCONJ
brj-23756	6	13	back	back	ADJ
brj-23756	6	14	propagation	propagation	NOUN
brj-23756	6	15	multilayer	multilayer	PROPN
brj-23756	6	16	ann	ann	PROPN
brj-23756	6	17	models	model	NOUN
brj-23756	6	18	were	be	AUX
brj-23756	6	19	created	create	VERB
brj-23756	6	20	and	and	CCONJ
brj-23756	6	21	tested	test	VERB
brj-23756	6	22	.	.	PUNCT
brj-23756	7	1	the	the	DET
brj-23756	7	2	best	good	ADJ
brj-23756	7	3	prediction	prediction	NOUN
brj-23756	7	4	models	model	NOUN
brj-23756	7	5	,	,	PUNCT
brj-23756	7	6	determined	determine	VERB
brj-23756	7	7	based	base	VERB
brj-23756	7	8	on	on	ADP
brj-23756	7	9	the	the	DET
brj-23756	7	10	errors	error	NOUN
brj-23756	7	11	in	in	ADP
brj-23756	7	12	training	training	NOUN
brj-23756	7	13	iterations	iteration	NOUN
brj-23756	7	14	,	,	PUNCT
brj-23756	7	15	were	be	AUX
brj-23756	7	16	selected	select	VERB
brj-23756	7	17	and	and	CCONJ
brj-23756	7	18	used	use	VERB
brj-23756	7	19	for	for	ADP
brj-23756	7	20	testing	testing	NOUN
brj-23756	7	21	.	.	PUNCT
brj-23756	8	1	based	base	VERB
brj-23756	8	2	on	on	ADP
brj-23756	8	3	the	the	DET
brj-23756	8	4	performance	performance	NOUN
brj-23756	8	5	analysis	analysis	NOUN
brj-23756	8	6	,	,	PUNCT
brj-23756	8	7	the	the	DET
brj-23756	8	8	prediction	prediction	NOUN
brj-23756	8	9	ann	ann	PROPN
brj-23756	8	10	models	model	NOUN
brj-23756	8	11	were	be	AUX
brj-23756	8	12	accurate	accurate	ADJ
brj-23756	8	13	,	,	PUNCT
brj-23756	8	14	reliable	reliable	ADJ
brj-23756	8	15	,	,	PUNCT
brj-23756	8	16	and	and	CCONJ
brj-23756	8	17	effective	effective	ADJ
brj-23756	8	18	tools	tool	NOUN
brj-23756	8	19	in	in	ADP
brj-23756	8	20	terms	term	NOUN
brj-23756	8	21	of	of	ADP
brj-23756	8	22	time	time	NOUN
brj-23756	8	23	and	and	CCONJ
brj-23756	8	24	cost	cost	NOUN
brj-23756	8	25	-	-	PUNCT
brj-23756	8	26	effectiveness	effectiveness	NOUN
brj-23756	8	27	,	,	PUNCT
brj-23756	8	28	for	for	ADP
brj-23756	8	29	predicting	predict	VERB
brj-23756	8	30	the	the	DET
brj-23756	8	31	emc	emc	PROPN
brj-23756	8	32	and	and	CCONJ
brj-23756	8	33	swelling	swell	VERB
brj-23756	8	34	characteristics	characteristic	NOUN
brj-23756	8	35	of	of	ADP
brj-23756	8	36	thermally	thermally	ADV
brj-23756	8	37	modified	modify	VERB
brj-23756	8	38	wood	wood	NOUN
brj-23756	8	39	.	.	PUNCT
brj-23756	9	1	the	the	DET
brj-23756	9	2	multiple	multiple	ADJ
brj-23756	9	3	-	-	PUNCT
brj-23756	9	4	input	input	NOUN
brj-23756	9	5	model	model	NOUN
brj-23756	9	6	was	be	AUX
brj-23756	9	7	more	more	ADV
brj-23756	9	8	accurate	accurate	ADJ
brj-23756	9	9	than	than	ADP
brj-23756	9	10	the	the	DET
brj-23756	9	11	single	single	ADJ
brj-23756	9	12	-	-	PUNCT
brj-23756	9	13	input	input	NOUN
brj-23756	9	14	model	model	NOUN
brj-23756	9	15	and	and	CCONJ
brj-23756	9	16	it	it	PRON
brj-23756	9	17	provided	provide	VERB
brj-23756	9	18	a	a	DET
brj-23756	9	19	prediction	prediction	NOUN
brj-23756	9	20	with	with	ADP
brj-23756	9	21	r2	r2	NOUN
brj-23756	9	22	of	of	ADP
brj-23756	9	23	0.9975	0.9975	NUM
brj-23756	9	24	,	,	PUNCT
brj-23756	9	25	0.92	0.92	NUM
brj-23756	9	26	,	,	PUNCT
brj-23756	9	27	and	and	CCONJ
brj-23756	9	28	mape	mape	NOUN
brj-23756	9	29	of	of	ADP
brj-23756	9	30	1.36	1.36	NUM
brj-23756	9	31	,	,	PUNCT
brj-23756	9	32	7.77	7.77	NUM
brj-23756	9	33	for	for	ADP
brj-23756	9	34	emc	emc	NOUN
brj-23756	9	35	and	and	CCONJ
brj-23756	9	36	swelling	swelling	NOUN
brj-23756	9	37	.	.	PUNCT
brj-23756	10	1	doi	doi	NOUN
brj-23756	10	2	:	:	PUNCT
brj-23756	10	3	10.15376	10.15376	NUM
brj-23756	10	4	/	/	SYM
brj-23756	10	5	biores.19.4.6983	biores.19.4.6983	NOUN
brj-23756	10	6	-	-	PUNCT
brj-23756	10	7	6993	6993	NUM
brj-23756	10	8	keywords	keyword	NOUN
brj-23756	10	9	:	:	PUNCT
brj-23756	10	10	thermally	thermally	ADV
brj-23756	10	11	modified	modify	VERB
brj-23756	10	12	wood	wood	NOUN
brj-23756	10	13	;	;	PUNCT
brj-23756	10	14	emc	emc	PROPN
brj-23756	10	15	,	,	PUNCT
brj-23756	10	16	swelling	swelling	NOUN
brj-23756	10	17	;	;	PUNCT
brj-23756	10	18	ann	ann	PROPN
brj-23756	10	19	contact	contact	NOUN
brj-23756	10	20	information	information	NOUN
brj-23756	10	21	:	:	PUNCT
brj-23756	11	1	a	a	DET
brj-23756	11	2	:	:	PUNCT
brj-23756	11	3	ph.d	ph.d	PROPN
brj-23756	11	4	.	.	PUNCT
brj-23756	11	5	candidate	candidate	PROPN
brj-23756	11	6	,	,	PUNCT
brj-23756	11	7	department	department	NOUN
brj-23756	11	8	of	of	ADP
brj-23756	11	9	sustainable	sustainable	ADJ
brj-23756	11	10	biomaterials	biomaterial	NOUN
brj-23756	11	11	,	,	PUNCT
brj-23756	11	12	virginia	virginia	PROPN
brj-23756	11	13	polytechnic	polytechnic	PROPN
brj-23756	11	14	institute	institute	PROPN
brj-23756	11	15	and	and	CCONJ
brj-23756	11	16	state	state	PROPN
brj-23756	11	17	university	university	PROPN
brj-23756	11	18	,	,	PUNCT
brj-23756	11	19	blacksburg	blacksburg	PROPN
brj-23756	11	20	,	,	PUNCT
brj-23756	11	21	va	va	PROPN
brj-23756	11	22	,	,	PUNCT
brj-23756	11	23	usa	usa	PROPN
brj-23756	11	24	;	;	PUNCT
brj-23756	11	25	b	b	NUM
brj-23756	11	26	:	:	PUNCT
brj-23756	11	27	professor	professor	NOUN
brj-23756	11	28	and	and	CCONJ
brj-23756	11	29	associate	associate	ADJ
brj-23756	11	30	dean	dean	NOUN
brj-23756	11	31	of	of	ADP
brj-23756	11	32	extension	extension	NOUN
brj-23756	11	33	,	,	PUNCT
brj-23756	11	34	outreach	outreach	NOUN
brj-23756	11	35	and	and	CCONJ
brj-23756	11	36	engagement	engagement	NOUN
brj-23756	11	37	,	,	PUNCT
brj-23756	11	38	department	department	NOUN
brj-23756	11	39	of	of	ADP
brj-23756	11	40	sustainable	sustainable	ADJ
brj-23756	11	41	biomaterials	biomaterial	NOUN
brj-23756	11	42	,	,	PUNCT
brj-23756	11	43	brooks	brooks	PROPN
brj-23756	11	44	forest	forest	PROPN
brj-23756	11	45	products	product	NOUN
brj-23756	11	46	center	center	PROPN
brj-23756	11	47	,	,	PUNCT
brj-23756	11	48	virginia	virginia	PROPN
brj-23756	11	49	polytechnic	polytechnic	PROPN
brj-23756	11	50	institute	institute	PROPN
brj-23756	11	51	and	and	CCONJ
brj-23756	11	52	state	state	PROPN
brj-23756	11	53	university	university	PROPN
brj-23756	11	54	,	,	PUNCT
brj-23756	11	55	blacksburg	blacksburg	PROPN
brj-23756	11	56	,	,	PUNCT
brj-23756	11	57	va	va	PROPN
brj-23756	11	58	24061	24061	NUM
brj-23756	11	59	,	,	PUNCT
brj-23756	11	60	usa	usa	PROPN
brj-23756	11	61	;	;	PUNCT
brj-23756	11	62	*	*	PUNCT
brj-23756	11	63	corresponding	correspond	VERB
brj-23756	11	64	author	author	NOUN
brj-23756	11	65	:	:	PUNCT
brj-23756	11	66	masoumi@vt.edu	masoumi@vt.edu	NOUN
brj-23756	11	67	introduction	introduction	NOUN
brj-23756	11	68	the	the	DET
brj-23756	11	69	utilization	utilization	NOUN
brj-23756	11	70	of	of	ADP
brj-23756	11	71	thermally	thermally	ADV
brj-23756	11	72	modified	modify	VERB
brj-23756	11	73	woods	wood	NOUN
brj-23756	11	74	(	(	PUNCT
brj-23756	11	75	tmw	tmw	NOUN
brj-23756	11	76	)	)	PUNCT
brj-23756	11	77	has	have	AUX
brj-23756	11	78	gained	gain	VERB
brj-23756	11	79	significant	significant	ADJ
brj-23756	11	80	traction	traction	NOUN
brj-23756	11	81	as	as	ADP
brj-23756	11	82	a	a	DET
brj-23756	11	83	sustainable	sustainable	ADJ
brj-23756	11	84	material	material	NOUN
brj-23756	11	85	across	across	ADP
brj-23756	11	86	diverse	diverse	ADJ
brj-23756	11	87	applications	application	NOUN
brj-23756	11	88	(	(	PUNCT
brj-23756	11	89	espinoza	espinoza	PROPN
brj-23756	11	90	et	et	PROPN
brj-23756	11	91	al	al	PROPN
brj-23756	11	92	.	.	PROPN
brj-23756	11	93	2015	2015	NUM
brj-23756	11	94	;	;	PUNCT
brj-23756	11	95	bond	bond	NOUN
brj-23756	11	96	et	et	PROPN
brj-23756	11	97	al	al	PROPN
brj-23756	11	98	.	.	PROPN
brj-23756	11	99	2023	2023	NUM
brj-23756	11	100	)	)	PUNCT
brj-23756	11	101	.	.	PUNCT
brj-23756	12	1	thermal	thermal	ADJ
brj-23756	12	2	modification	modification	NOUN
brj-23756	12	3	(	(	PUNCT
brj-23756	12	4	tm	tm	NOUN
brj-23756	12	5	)	)	PUNCT
brj-23756	12	6	,	,	PUNCT
brj-23756	12	7	a	a	DET
brj-23756	12	8	process	process	NOUN
brj-23756	12	9	involving	involve	VERB
brj-23756	12	10	the	the	DET
brj-23756	12	11	controlled	control	VERB
brj-23756	12	12	heating	heating	NOUN
brj-23756	12	13	of	of	ADP
brj-23756	12	14	wood	wood	NOUN
brj-23756	12	15	within	within	ADP
brj-23756	12	16	the	the	DET
brj-23756	12	17	temperature	temperature	NOUN
brj-23756	12	18	range	range	NOUN
brj-23756	12	19	of	of	ADP
brj-23756	12	20	180	180	NUM
brj-23756	12	21	to	to	PART
brj-23756	12	22	240	240	NUM
brj-23756	12	23	°	°	NOUN
brj-23756	12	24	c	c	NOUN
brj-23756	12	25	,	,	PUNCT
brj-23756	12	26	changes	change	VERB
brj-23756	12	27	its	its	PRON
brj-23756	12	28	chemical	chemical	NOUN
brj-23756	12	29	,	,	PUNCT
brj-23756	12	30	physical	physical	ADJ
brj-23756	12	31	,	,	PUNCT
brj-23756	12	32	and	and	CCONJ
brj-23756	12	33	mechanical	mechanical	ADJ
brj-23756	12	34	properties	property	NOUN
brj-23756	12	35	(	(	PUNCT
brj-23756	12	36	tjeerdsma	tjeerdsma	NOUN
brj-23756	12	37	and	and	CCONJ
brj-23756	12	38	militz	militz	PROPN
brj-23756	12	39	2005	2005	NUM
brj-23756	12	40	;	;	PUNCT
brj-23756	12	41	esteves	esteves	PROPN
brj-23756	12	42	and	and	CCONJ
brj-23756	12	43	pereira	pereira	PROPN
brj-23756	12	44	2009	2009	NUM
brj-23756	12	45	;	;	PUNCT
brj-23756	12	46	militz	militz	PROPN
brj-23756	12	47	and	and	CCONJ
brj-23756	12	48	altgen	altgen	PROPN
brj-23756	12	49	2014	2014	NUM
brj-23756	12	50	;	;	PUNCT
brj-23756	12	51	hill	hill	PROPN
brj-23756	12	52	et	et	PROPN
brj-23756	12	53	al	al	PROPN
brj-23756	12	54	.	.	PROPN
brj-23756	12	55	2021	2021	NUM
brj-23756	12	56	)	)	PUNCT
brj-23756	12	57	.	.	PUNCT
brj-23756	13	1	the	the	DET
brj-23756	13	2	primary	primary	ADJ
brj-23756	13	3	objective	objective	NOUN
brj-23756	13	4	of	of	ADP
brj-23756	13	5	tm	tm	PROPN
brj-23756	13	6	is	be	AUX
brj-23756	13	7	to	to	PART
brj-23756	13	8	enhance	enhance	VERB
brj-23756	13	9	the	the	DET
brj-23756	13	10	dimensional	dimensional	ADJ
brj-23756	13	11	stability	stability	NOUN
brj-23756	13	12	of	of	ADP
brj-23756	13	13	wood	wood	NOUN
brj-23756	13	14	,	,	PUNCT
brj-23756	13	15	rendering	render	VERB
brj-23756	13	16	it	it	PRON
brj-23756	13	17	well	well	ADV
brj-23756	13	18	-	-	PUNCT
brj-23756	13	19	suited	suit	VERB
brj-23756	13	20	for	for	ADP
brj-23756	13	21	applications	application	NOUN
brj-23756	13	22	in	in	ADP
brj-23756	13	23	varying	vary	VERB
brj-23756	13	24	moisture	moisture	NOUN
brj-23756	13	25	conditions	condition	NOUN
brj-23756	13	26	,	,	PUNCT
brj-23756	13	27	particularly	particularly	ADV
brj-23756	13	28	in	in	ADP
brj-23756	13	29	outdoor	outdoor	ADJ
brj-23756	13	30	applications	application	NOUN
brj-23756	13	31	.	.	PUNCT
brj-23756	14	1	the	the	DET
brj-23756	14	2	tmw	tmw	PROPN
brj-23756	14	3	exhibits	exhibit	VERB
brj-23756	14	4	altered	alter	VERB
brj-23756	14	5	equilibrium	equilibrium	NOUN
brj-23756	14	6	moisture	moisture	NOUN
brj-23756	14	7	content	content	NOUN
brj-23756	14	8	(	(	PUNCT
brj-23756	14	9	emc	emc	PROPN
brj-23756	14	10	)	)	PUNCT
brj-23756	14	11	and	and	CCONJ
brj-23756	14	12	swelling	swell	VERB
brj-23756	14	13	compared	compare	VERB
brj-23756	14	14	to	to	ADP
brj-23756	14	15	unmodified	unmodified	ADJ
brj-23756	14	16	wood	wood	NOUN
brj-23756	14	17	(	(	PUNCT
brj-23756	14	18	masoumi	masoumi	NOUN
brj-23756	14	19	and	and	CCONJ
brj-23756	14	20	bond	bond	NOUN
brj-23756	14	21	2024a	2024a	NOUN
brj-23756	14	22	)	)	PUNCT
brj-23756	14	23	.	.	PUNCT
brj-23756	15	1	peer	peer	NOUN
brj-23756	15	2	-	-	PUNCT
brj-23756	15	3	reviewed	review	VERB
brj-23756	15	4	article	article	NOUN
brj-23756	15	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	15	6	masoumi	masoumi	NOUN
brj-23756	15	7	&	&	CCONJ
brj-23756	15	8	bond	bond	PROPN
brj-23756	15	9	(	(	PUNCT
brj-23756	15	10	2024	2024	NUM
brj-23756	15	11	)	)	PUNCT
brj-23756	15	12	.	.	PUNCT
brj-23756	16	1	“	"	PUNCT
brj-23756	16	2	ann	ann	PROPN
brj-23756	16	3	prediction	prediction	NOUN
brj-23756	16	4	of	of	ADP
brj-23756	16	5	tmw	tmw	NOUN
brj-23756	16	6	,	,	PUNCT
brj-23756	16	7	”	"	PUNCT
brj-23756	16	8	bioresources	bioresource	NOUN
brj-23756	16	9	19(4	19(4	NUM
brj-23756	16	10	)	)	PUNCT
brj-23756	16	11	,	,	PUNCT
brj-23756	16	12	6983	6983	NUM
brj-23756	16	13	-	-	SYM
brj-23756	16	14	6993	6993	NUM
brj-23756	16	15	.	.	PUNCT
brj-23756	17	1	6984	6984	NUM
brj-23756	17	2	notably	notably	ADV
brj-23756	17	3	,	,	PUNCT
brj-23756	17	4	hardwoods	hardwood	NOUN
brj-23756	17	5	exhibit	exhibit	VERB
brj-23756	17	6	distinct	distinct	ADJ
brj-23756	17	7	chemical	chemical	NOUN
brj-23756	17	8	and	and	CCONJ
brj-23756	17	9	anatomical	anatomical	ADJ
brj-23756	17	10	properties	property	NOUN
brj-23756	17	11	that	that	PRON
brj-23756	17	12	differ	differ	VERB
brj-23756	17	13	from	from	ADP
brj-23756	17	14	softwoods	softwood	NOUN
brj-23756	17	15	and	and	CCONJ
brj-23756	17	16	across	across	ADP
brj-23756	17	17	different	different	ADJ
brj-23756	17	18	plantations	plantation	NOUN
brj-23756	17	19	(	(	PUNCT
brj-23756	17	20	oladi	oladi	NOUN
brj-23756	17	21	et	et	PROPN
brj-23756	17	22	al	al	PROPN
brj-23756	17	23	.	.	PROPN
brj-23756	17	24	2013	2013	NUM
brj-23756	17	25	)	)	PUNCT
brj-23756	17	26	.	.	PUNCT
brj-23756	18	1	the	the	DET
brj-23756	18	2	appalachian	appalachian	ADJ
brj-23756	18	3	region	region	NOUN
brj-23756	18	4	in	in	ADP
brj-23756	18	5	north	north	PROPN
brj-23756	18	6	america	america	PROPN
brj-23756	18	7	stands	stand	VERB
brj-23756	18	8	out	out	ADP
brj-23756	18	9	as	as	ADP
brj-23756	18	10	a	a	DET
brj-23756	18	11	hub	hub	NOUN
brj-23756	18	12	for	for	ADP
brj-23756	18	13	several	several	ADJ
brj-23756	18	14	hardwood	hardwood	ADJ
brj-23756	18	15	species	specie	NOUN
brj-23756	18	16	,	,	PUNCT
brj-23756	18	17	with	with	ADP
brj-23756	18	18	yellow	yellow	ADJ
brj-23756	18	19	poplar	poplar	NOUN
brj-23756	18	20	(	(	PUNCT
brj-23756	18	21	yp	yp	NOUN
brj-23756	18	22	)	)	PUNCT
brj-23756	18	23	emerging	emerge	VERB
brj-23756	18	24	as	as	ADP
brj-23756	18	25	a	a	DET
brj-23756	18	26	prominent	prominent	ADJ
brj-23756	18	27	species	specie	NOUN
brj-23756	18	28	,	,	PUNCT
brj-23756	18	29	representing	represent	VERB
brj-23756	18	30	35	35	NUM
brj-23756	18	31	%	%	NOUN
brj-23756	18	32	of	of	ADP
brj-23756	18	33	the	the	DET
brj-23756	18	34	region	region	NOUN
brj-23756	18	35	's	's	PART
brj-23756	18	36	growth	growth	NOUN
brj-23756	18	37	and	and	CCONJ
brj-23756	18	38	production	production	NOUN
brj-23756	18	39	(	(	PUNCT
brj-23756	18	40	appalachian	appalachian	ADJ
brj-23756	18	41	hardwood	hardwood	NOUN
brj-23756	18	42	species	specie	NOUN
brj-23756	18	43	guide	guide	VERB
brj-23756	18	44	2023	2023	NUM
brj-23756	18	45	)	)	PUNCT
brj-23756	18	46	.	.	PUNCT
brj-23756	19	1	artificial	artificial	ADJ
brj-23756	19	2	neural	neural	ADJ
brj-23756	19	3	network	network	NOUN
brj-23756	19	4	(	(	PUNCT
brj-23756	19	5	ann	ann	PROPN
brj-23756	19	6	)	)	PUNCT
brj-23756	19	7	models	model	NOUN
brj-23756	19	8	are	be	AUX
brj-23756	19	9	pivotal	pivotal	ADJ
brj-23756	19	10	for	for	ADP
brj-23756	19	11	deciphering	decipher	VERB
brj-23756	19	12	complex	complex	ADJ
brj-23756	19	13	scenarios	scenario	NOUN
brj-23756	19	14	and	and	CCONJ
brj-23756	19	15	revealing	reveal	VERB
brj-23756	19	16	hidden	hidden	ADJ
brj-23756	19	17	relationships	relationship	NOUN
brj-23756	19	18	between	between	ADP
brj-23756	19	19	input	input	NOUN
brj-23756	19	20	and	and	CCONJ
brj-23756	19	21	output	output	NOUN
brj-23756	19	22	variables	variable	NOUN
brj-23756	19	23	.	.	PUNCT
brj-23756	20	1	this	this	DET
brj-23756	20	2	transformative	transformative	ADJ
brj-23756	20	3	technology	technology	NOUN
brj-23756	20	4	,	,	PUNCT
brj-23756	20	5	mirroring	mirror	VERB
brj-23756	20	6	the	the	DET
brj-23756	20	7	human	human	ADJ
brj-23756	20	8	brain	brain	NOUN
brj-23756	20	9	’s	’s	PART
brj-23756	20	10	learning	learning	NOUN
brj-23756	20	11	process	process	NOUN
brj-23756	20	12	,	,	PUNCT
brj-23756	20	13	excels	excel	VERB
brj-23756	20	14	in	in	ADP
brj-23756	20	15	pattern	pattern	NOUN
brj-23756	20	16	recognition	recognition	NOUN
brj-23756	20	17	,	,	PUNCT
brj-23756	20	18	classification	classification	NOUN
brj-23756	20	19	,	,	PUNCT
brj-23756	20	20	and	and	CCONJ
brj-23756	20	21	prediction	prediction	NOUN
brj-23756	20	22	tasks	task	NOUN
brj-23756	20	23	across	across	ADP
brj-23756	20	24	various	various	ADJ
brj-23756	20	25	domains	domain	NOUN
brj-23756	20	26	such	such	ADJ
brj-23756	20	27	as	as	ADP
brj-23756	20	28	finance	finance	NOUN
brj-23756	20	29	,	,	PUNCT
brj-23756	20	30	healthcare	healthcare	PROPN
brj-23756	20	31	,	,	PUNCT
brj-23756	20	32	and	and	CCONJ
brj-23756	20	33	image	image	NOUN
brj-23756	20	34	recognition	recognition	NOUN
brj-23756	20	35	(	(	PUNCT
brj-23756	20	36	sen	sen	PROPN
brj-23756	20	37	et	et	PROPN
brj-23756	20	38	al	al	PROPN
brj-23756	20	39	.	.	PROPN
brj-23756	20	40	2023	2023	NUM
brj-23756	20	41	)	)	PUNCT
brj-23756	20	42	.	.	PUNCT
brj-23756	21	1	the	the	DET
brj-23756	21	2	analysis	analysis	NOUN
brj-23756	21	3	of	of	ADP
brj-23756	21	4	anns	anns	NOUN
brj-23756	21	5	encompasses	encompass	VERB
brj-23756	21	6	two	two	NUM
brj-23756	21	7	key	key	ADJ
brj-23756	21	8	facets	facet	NOUN
brj-23756	21	9	:	:	PUNCT
brj-23756	21	10	architecture	architecture	NOUN
brj-23756	21	11	and	and	CCONJ
brj-23756	21	12	mathematical	mathematical	ADJ
brj-23756	21	13	functions	function	NOUN
brj-23756	21	14	.	.	PUNCT
brj-23756	22	1	the	the	DET
brj-23756	22	2	architecture	architecture	NOUN
brj-23756	22	3	involves	involve	VERB
brj-23756	22	4	the	the	DET
brj-23756	22	5	arrangement	arrangement	NOUN
brj-23756	22	6	and	and	CCONJ
brj-23756	22	7	interconnections	interconnection	NOUN
brj-23756	22	8	of	of	ADP
brj-23756	22	9	layers	layer	NOUN
brj-23756	22	10	and	and	CCONJ
brj-23756	22	11	nodes	node	NOUN
brj-23756	22	12	,	,	PUNCT
brj-23756	22	13	highlighting	highlight	VERB
brj-23756	22	14	the	the	DET
brj-23756	22	15	network	network	NOUN
brj-23756	22	16	’s	’s	PART
brj-23756	22	17	information	information	NOUN
brj-23756	22	18	processing	processing	NOUN
brj-23756	22	19	capacity	capacity	NOUN
brj-23756	22	20	.	.	PUNCT
brj-23756	23	1	simultaneously	simultaneously	ADV
brj-23756	23	2	,	,	PUNCT
brj-23756	23	3	mathematical	mathematical	ADJ
brj-23756	23	4	functions	function	NOUN
brj-23756	23	5	,	,	PUNCT
brj-23756	23	6	embedded	embed	VERB
brj-23756	23	7	in	in	ADP
brj-23756	23	8	activation	activation	NOUN
brj-23756	23	9	functions	function	NOUN
brj-23756	23	10	and	and	CCONJ
brj-23756	23	11	weight	weight	NOUN
brj-23756	23	12	adjustments	adjustment	NOUN
brj-23756	23	13	,	,	PUNCT
brj-23756	23	14	contribute	contribute	VERB
brj-23756	23	15	to	to	ADP
brj-23756	23	16	the	the	DET
brj-23756	23	17	network	network	NOUN
brj-23756	23	18	’s	’s	PART
brj-23756	23	19	adaptability	adaptability	NOUN
brj-23756	23	20	and	and	CCONJ
brj-23756	23	21	learning	learning	NOUN
brj-23756	23	22	ability	ability	NOUN
brj-23756	23	23	,	,	PUNCT
brj-23756	23	24	which	which	PRON
brj-23756	23	25	is	be	AUX
brj-23756	23	26	crucial	crucial	ADJ
brj-23756	23	27	for	for	ADP
brj-23756	23	28	its	its	PRON
brj-23756	23	29	predictive	predictive	ADJ
brj-23756	23	30	prowess	prowess	NOUN
brj-23756	23	31	.	.	PUNCT
brj-23756	24	1	multilayer	multilayer	PROPN
brj-23756	24	2	perceptron	perceptron	PROPN
brj-23756	24	3	(	(	PUNCT
brj-23756	24	4	mlp	mlp	PROPN
brj-23756	24	5	)	)	PUNCT
brj-23756	24	6	ann	ann	PROPN
brj-23756	24	7	models	model	NOUN
brj-23756	24	8	,	,	PUNCT
brj-23756	24	9	particularly	particularly	ADV
brj-23756	24	10	notable	notable	ADJ
brj-23756	24	11	for	for	ADP
brj-23756	24	12	their	their	PRON
brj-23756	24	13	predictive	predictive	ADJ
brj-23756	24	14	capabilities	capability	NOUN
brj-23756	24	15	,	,	PUNCT
brj-23756	24	16	have	have	AUX
brj-23756	24	17	been	be	AUX
brj-23756	24	18	extensively	extensively	ADV
brj-23756	24	19	researched	research	VERB
brj-23756	24	20	and	and	CCONJ
brj-23756	24	21	applied	apply	VERB
brj-23756	24	22	in	in	ADP
brj-23756	24	23	diverse	diverse	ADJ
brj-23756	24	24	fields	field	NOUN
brj-23756	24	25	.	.	PUNCT
brj-23756	25	1	their	their	PRON
brj-23756	25	2	proficiency	proficiency	NOUN
brj-23756	25	3	in	in	ADP
brj-23756	25	4	discerning	discern	VERB
brj-23756	25	5	complex	complex	ADJ
brj-23756	25	6	patterns	pattern	NOUN
brj-23756	25	7	within	within	ADP
brj-23756	25	8	data	datum	NOUN
brj-23756	25	9	sets	set	NOUN
brj-23756	25	10	makes	make	VERB
brj-23756	25	11	them	they	PRON
brj-23756	25	12	adept	adept	ADJ
brj-23756	25	13	at	at	ADP
brj-23756	25	14	handling	handle	VERB
brj-23756	25	15	intricate	intricate	ADJ
brj-23756	25	16	relationships	relationship	NOUN
brj-23756	25	17	and	and	CCONJ
brj-23756	25	18	non	non	ADJ
brj-23756	25	19	-	-	ADJ
brj-23756	25	20	linear	linear	ADJ
brj-23756	25	21	dependencies	dependency	NOUN
brj-23756	25	22	,	,	PUNCT
brj-23756	25	23	surpassing	surpass	VERB
brj-23756	25	24	traditional	traditional	ADJ
brj-23756	25	25	analytical	analytical	ADJ
brj-23756	25	26	approaches	approach	NOUN
brj-23756	25	27	(	(	PUNCT
brj-23756	25	28	sen	sen	PROPN
brj-23756	25	29	et	et	PROPN
brj-23756	25	30	al	al	PROPN
brj-23756	25	31	.	.	PROPN
brj-23756	25	32	2023	2023	NUM
brj-23756	25	33	)	)	PUNCT
brj-23756	25	34	.	.	PUNCT
brj-23756	26	1	the	the	DET
brj-23756	26	2	novel	novel	ADJ
brj-23756	26	3	deep	deep	ADJ
brj-23756	26	4	learning	learning	NOUN
brj-23756	26	5	principles	principle	NOUN
brj-23756	26	6	have	have	AUX
brj-23756	26	7	given	give	VERB
brj-23756	26	8	rise	rise	NOUN
brj-23756	26	9	to	to	ADP
brj-23756	26	10	deep	deep	ADJ
brj-23756	26	11	neural	neural	ADJ
brj-23756	26	12	networks	network	NOUN
brj-23756	26	13	,	,	PUNCT
brj-23756	26	14	capable	capable	ADJ
brj-23756	26	15	of	of	ADP
brj-23756	26	16	handling	handle	VERB
brj-23756	26	17	vast	vast	ADJ
brj-23756	26	18	amounts	amount	NOUN
brj-23756	26	19	of	of	ADP
brj-23756	26	20	data	datum	NOUN
brj-23756	26	21	and	and	CCONJ
brj-23756	26	22	extracting	extract	VERB
brj-23756	26	23	hierarchical	hierarchical	ADJ
brj-23756	26	24	features	feature	NOUN
brj-23756	26	25	.	.	PUNCT
brj-23756	27	1	this	this	DET
brj-23756	27	2	dynamic	dynamic	ADJ
brj-23756	27	3	landscape	landscape	NOUN
brj-23756	27	4	ensures	ensure	VERB
brj-23756	27	5	that	that	SCONJ
brj-23756	27	6	ann	ann	PROPN
brj-23756	27	7	models	model	NOUN
brj-23756	27	8	remain	remain	VERB
brj-23756	27	9	at	at	ADP
brj-23756	27	10	the	the	DET
brj-23756	27	11	forefront	forefront	NOUN
brj-23756	27	12	of	of	ADP
brj-23756	27	13	cutting	cut	VERB
brj-23756	27	14	-	-	PUNCT
brj-23756	27	15	edge	edge	NOUN
brj-23756	27	16	technological	technological	ADJ
brj-23756	27	17	solutions	solution	NOUN
brj-23756	27	18	,	,	PUNCT
brj-23756	27	19	empowering	empower	VERB
brj-23756	27	20	to	to	PART
brj-23756	27	21	explore	explore	VERB
brj-23756	27	22	new	new	ADJ
brj-23756	27	23	frontiers	frontier	NOUN
brj-23756	27	24	in	in	ADP
brj-23756	27	25	data	datum	NOUN
brj-23756	27	26	analysis	analysis	NOUN
brj-23756	27	27	and	and	CCONJ
brj-23756	27	28	decision	decision	NOUN
brj-23756	27	29	-	-	PUNCT
brj-23756	27	30	making	making	NOUN
brj-23756	27	31	.	.	PUNCT
brj-23756	28	1	recent	recent	ADJ
brj-23756	28	2	studies	study	NOUN
brj-23756	28	3	have	have	AUX
brj-23756	28	4	reported	report	VERB
brj-23756	28	5	the	the	DET
brj-23756	28	6	possibility	possibility	NOUN
brj-23756	28	7	of	of	ADP
brj-23756	28	8	predicting	predict	VERB
brj-23756	28	9	emc	emc	PROPN
brj-23756	28	10	,	,	PUNCT
brj-23756	28	11	swelling	swelling	NOUN
brj-23756	28	12	,	,	PUNCT
brj-23756	28	13	and	and	CCONJ
brj-23756	28	14	shrinkage	shrinkage	NOUN
brj-23756	28	15	of	of	ADP
brj-23756	28	16	wood	wood	NOUN
brj-23756	28	17	based	base	VERB
brj-23756	28	18	on	on	ADP
brj-23756	28	19	factors	factor	NOUN
brj-23756	28	20	such	such	ADJ
brj-23756	28	21	as	as	ADP
brj-23756	28	22	wood	wood	NOUN
brj-23756	28	23	species	specie	NOUN
brj-23756	28	24	,	,	PUNCT
brj-23756	28	25	treatment	treatment	NOUN
brj-23756	28	26	time	time	NOUN
brj-23756	28	27	,	,	PUNCT
brj-23756	28	28	and	and	CCONJ
brj-23756	28	29	treatment	treatment	NOUN
brj-23756	28	30	temperature	temperature	NOUN
brj-23756	28	31	(	(	PUNCT
brj-23756	28	32	tiryaki	tiryaki	NOUN
brj-23756	28	33	et	et	PROPN
brj-23756	28	34	al	al	PROPN
brj-23756	28	35	.	.	PROPN
brj-23756	28	36	2016	2016	NUM
brj-23756	28	37	;	;	PUNCT
brj-23756	28	38	chen	chen	PROPN
brj-23756	28	39	et	et	PROPN
brj-23756	28	40	al	al	PROPN
brj-23756	28	41	.	.	PROPN
brj-23756	28	42	2022	2022	NUM
brj-23756	28	43	)	)	PUNCT
brj-23756	28	44	.	.	PUNCT
brj-23756	29	1	chen	chen	PROPN
brj-23756	29	2	et	et	PROPN
brj-23756	29	3	al	al	PROPN
brj-23756	29	4	.	.	PROPN
brj-23756	29	5	(	(	PUNCT
brj-23756	29	6	2022	2022	NUM
brj-23756	29	7	)	)	PUNCT
brj-23756	29	8	reported	report	VERB
brj-23756	29	9	predicting	predict	VERB
brj-23756	29	10	the	the	DET
brj-23756	29	11	emc	emc	PROPN
brj-23756	29	12	and	and	CCONJ
brj-23756	29	13	specific	specific	ADJ
brj-23756	29	14	gravity	gravity	NOUN
brj-23756	29	15	using	use	VERB
brj-23756	29	16	a	a	DET
brj-23756	29	17	back	back	ADJ
brj-23756	29	18	-	-	PUNCT
brj-23756	29	19	propagation	propagation	NOUN
brj-23756	29	20	neural	neural	ADJ
brj-23756	29	21	network	network	NOUN
brj-23756	29	22	.	.	PUNCT
brj-23756	30	1	additionally	additionally	ADV
brj-23756	30	2	,	,	PUNCT
brj-23756	30	3	nasir	nasir	PROPN
brj-23756	30	4	et	et	PROPN
brj-23756	30	5	al	al	PROPN
brj-23756	30	6	.	.	PROPN
brj-23756	31	1	(	(	PUNCT
brj-23756	31	2	2019	2019	NUM
brj-23756	31	3	)	)	PUNCT
brj-23756	31	4	reported	report	VERB
brj-23756	31	5	the	the	DET
brj-23756	31	6	possibility	possibility	NOUN
brj-23756	31	7	of	of	ADP
brj-23756	31	8	predicting	predict	VERB
brj-23756	31	9	the	the	DET
brj-23756	31	10	swelling	swell	VERB
brj-23756	31	11	coefficient	coefficient	NOUN
brj-23756	31	12	and	and	CCONJ
brj-23756	31	13	water	water	NOUN
brj-23756	31	14	absorption	absorption	NOUN
brj-23756	31	15	with	with	ADP
brj-23756	31	16	the	the	DET
brj-23756	31	17	group	group	NOUN
brj-23756	31	18	method	method	NOUN
brj-23756	31	19	of	of	ADP
brj-23756	31	20	data	data	NOUN
brj-23756	31	21	handling	handling	NOUN
brj-23756	31	22	(	(	PUNCT
brj-23756	31	23	gmdh	gmdh	ADJ
brj-23756	31	24	)	)	PUNCT
brj-23756	31	25	neural	neural	ADJ
brj-23756	31	26	network	network	NOUN
brj-23756	31	27	.	.	PUNCT
brj-23756	32	1	these	these	DET
brj-23756	32	2	studies	study	NOUN
brj-23756	32	3	have	have	AUX
brj-23756	32	4	used	use	VERB
brj-23756	32	5	single	single	ADJ
brj-23756	32	6	or	or	CCONJ
brj-23756	32	7	few	few	ADJ
brj-23756	32	8	species	specie	NOUN
brj-23756	32	9	in	in	ADP
brj-23756	32	10	making	make	VERB
brj-23756	32	11	models	model	NOUN
brj-23756	32	12	.	.	PUNCT
brj-23756	33	1	however	however	ADV
brj-23756	33	2	,	,	PUNCT
brj-23756	33	3	a	a	DET
brj-23756	33	4	model	model	NOUN
brj-23756	33	5	containing	contain	VERB
brj-23756	33	6	a	a	DET
brj-23756	33	7	variety	variety	NOUN
brj-23756	33	8	of	of	ADP
brj-23756	33	9	species	specie	NOUN
brj-23756	33	10	,	,	PUNCT
brj-23756	33	11	particularly	particularly	ADV
brj-23756	33	12	hardwoods	hardwood	NOUN
brj-23756	33	13	that	that	PRON
brj-23756	33	14	have	have	VERB
brj-23756	33	15	very	very	ADV
brj-23756	33	16	diverse	diverse	ADJ
brj-23756	33	17	properties	property	NOUN
brj-23756	33	18	,	,	PUNCT
brj-23756	33	19	is	be	AUX
brj-23756	33	20	lacking	lack	VERB
brj-23756	33	21	in	in	ADP
brj-23756	33	22	the	the	DET
brj-23756	33	23	literature	literature	NOUN
brj-23756	33	24	.	.	PUNCT
brj-23756	34	1	this	this	DET
brj-23756	34	2	study	study	NOUN
brj-23756	34	3	aimed	aim	VERB
brj-23756	34	4	to	to	PART
brj-23756	34	5	develop	develop	VERB
brj-23756	34	6	a	a	DET
brj-23756	34	7	single	single	ADJ
brj-23756	34	8	-	-	PUNCT
brj-23756	34	9	input	input	NOUN
brj-23756	34	10	(	(	PUNCT
brj-23756	34	11	as	as	ADP
brj-23756	34	12	a	a	DET
brj-23756	34	13	more	more	ADJ
brj-23756	34	14	time	time	NOUN
brj-23756	34	15	and	and	CCONJ
brj-23756	34	16	cost	cost	NOUN
brj-23756	34	17	-	-	PUNCT
brj-23756	34	18	effective	effective	ADJ
brj-23756	34	19	model	model	NOUN
brj-23756	34	20	)	)	PUNCT
brj-23756	34	21	and	and	CCONJ
brj-23756	34	22	multiple	multiple	ADJ
brj-23756	34	23	-	-	PUNCT
brj-23756	34	24	input	input	NOUN
brj-23756	34	25	model	model	NOUN
brj-23756	34	26	specifically	specifically	ADV
brj-23756	34	27	tailored	tailor	VERB
brj-23756	34	28	to	to	PART
brj-23756	34	29	predict	predict	VERB
brj-23756	34	30	emc	emc	PROPN
brj-23756	34	31	and	and	CCONJ
brj-23756	34	32	swelling	swell	VERB
brj-23756	34	33	in	in	ADP
brj-23756	34	34	thermally	thermally	ADV
brj-23756	34	35	modified	modify	VERB
brj-23756	34	36	hardwood	hardwood	NOUN
brj-23756	34	37	timber	timber	NOUN
brj-23756	34	38	of	of	ADP
brj-23756	34	39	six	six	NUM
brj-23756	34	40	different	different	ADJ
brj-23756	34	41	types	type	NOUN
brj-23756	34	42	with	with	ADP
brj-23756	34	43	different	different	ADJ
brj-23756	34	44	densities	density	NOUN
brj-23756	34	45	and	and	CCONJ
brj-23756	34	46	anatomy	anatomy	NOUN
brj-23756	34	47	.	.	PUNCT
brj-23756	35	1	these	these	DET
brj-23756	35	2	species	specie	NOUN
brj-23756	35	3	,	,	PUNCT
brj-23756	35	4	native	native	ADJ
brj-23756	35	5	to	to	ADP
brj-23756	35	6	the	the	DET
brj-23756	35	7	appalachian	appalachian	ADJ
brj-23756	35	8	region	region	NOUN
brj-23756	35	9	in	in	ADP
brj-23756	35	10	north	north	PROPN
brj-23756	35	11	america	america	PROPN
brj-23756	35	12	,	,	PUNCT
brj-23756	35	13	have	have	AUX
brj-23756	35	14	recently	recently	ADV
brj-23756	35	15	gained	gain	VERB
brj-23756	35	16	attention	attention	NOUN
brj-23756	35	17	for	for	ADP
brj-23756	35	18	their	their	PRON
brj-23756	35	19	potential	potential	ADJ
brj-23756	35	20	use	use	NOUN
brj-23756	35	21	in	in	ADP
brj-23756	35	22	structural	structural	ADJ
brj-23756	35	23	applications	application	NOUN
brj-23756	35	24	.	.	PUNCT
brj-23756	36	1	in	in	ADP
brj-23756	36	2	the	the	DET
brj-23756	36	3	authors	author	NOUN
brj-23756	36	4	’	’	PART
brj-23756	36	5	previous	previous	ADJ
brj-23756	36	6	studies	study	NOUN
brj-23756	36	7	,	,	PUNCT
brj-23756	36	8	their	their	PRON
brj-23756	36	9	physical	physical	ADJ
brj-23756	36	10	properties	property	NOUN
brj-23756	36	11	were	be	AUX
brj-23756	36	12	published	publish	VERB
brj-23756	36	13	.	.	PUNCT
brj-23756	37	1	using	use	VERB
brj-23756	37	2	key	key	ADJ
brj-23756	37	3	features	feature	NOUN
brj-23756	37	4	,	,	PUNCT
brj-23756	37	5	such	such	ADJ
brj-23756	37	6	as	as	ADP
brj-23756	37	7	wood	wood	NOUN
brj-23756	37	8	species	specie	NOUN
brj-23756	37	9	,	,	PUNCT
brj-23756	37	10	density	density	NOUN
brj-23756	37	11	,	,	PUNCT
brj-23756	37	12	treatment	treatment	NOUN
brj-23756	37	13	time	time	NOUN
brj-23756	37	14	,	,	PUNCT
brj-23756	37	15	and	and	CCONJ
brj-23756	37	16	treatment	treatment	NOUN
brj-23756	37	17	temperature	temperature	NOUN
brj-23756	37	18	,	,	PUNCT
brj-23756	37	19	the	the	DET
brj-23756	37	20	authors	author	NOUN
brj-23756	37	21	sought	seek	VERB
brj-23756	37	22	to	to	PART
brj-23756	37	23	contribute	contribute	VERB
brj-23756	37	24	to	to	ADP
brj-23756	37	25	the	the	DET
brj-23756	37	26	ongoing	ongoing	ADJ
brj-23756	37	27	exploration	exploration	NOUN
brj-23756	37	28	of	of	ADP
brj-23756	37	29	ann	ann	PROPN
brj-23756	37	30	applications	application	NOUN
brj-23756	37	31	in	in	ADP
brj-23756	37	32	predicting	predict	VERB
brj-23756	37	33	the	the	DET
brj-23756	37	34	intricate	intricate	ADJ
brj-23756	37	35	properties	property	NOUN
brj-23756	37	36	of	of	ADP
brj-23756	37	37	tmw	tmw	NOUN
brj-23756	37	38	,	,	PUNCT
brj-23756	37	39	thereby	thereby	ADV
brj-23756	37	40	enhancing	enhance	VERB
brj-23756	37	41	the	the	DET
brj-23756	37	42	understanding	understanding	NOUN
brj-23756	37	43	and	and	CCONJ
brj-23756	37	44	utilization	utilization	NOUN
brj-23756	37	45	of	of	ADP
brj-23756	37	46	this	this	DET
brj-23756	37	47	sustainable	sustainable	ADJ
brj-23756	37	48	material	material	NOUN
brj-23756	37	49	in	in	ADP
brj-23756	37	50	diverse	diverse	ADJ
brj-23756	37	51	applications	application	NOUN
brj-23756	37	52	.	.	PUNCT
brj-23756	38	1	the	the	DET
brj-23756	38	2	model	model	NOUN
brj-23756	38	3	was	be	AUX
brj-23756	38	4	trained	train	VERB
brj-23756	38	5	and	and	CCONJ
brj-23756	38	6	tested	test	VERB
brj-23756	38	7	using	use	VERB
brj-23756	38	8	key	key	ADJ
brj-23756	38	9	features	feature	NOUN
brj-23756	38	10	of	of	ADP
brj-23756	38	11	the	the	DET
brj-23756	38	12	given	give	VERB
brj-23756	38	13	appalachian	appalachian	ADJ
brj-23756	38	14	species	specie	NOUN
brj-23756	38	15	.	.	PUNCT
brj-23756	39	1	it	it	PRON
brj-23756	39	2	is	be	AUX
brj-23756	39	3	designed	design	VERB
brj-23756	39	4	to	to	PART
brj-23756	39	5	take	take	VERB
brj-23756	39	6	the	the	DET
brj-23756	39	7	features	feature	NOUN
brj-23756	39	8	of	of	ADP
brj-23756	39	9	new	new	ADJ
brj-23756	39	10	species	specie	NOUN
brj-23756	39	11	and	and	CCONJ
brj-23756	39	12	predict	predict	VERB
brj-23756	39	13	their	their	PRON
brj-23756	39	14	emc	emc	NOUN
brj-23756	39	15	and	and	CCONJ
brj-23756	39	16	swelling	swell	VERB
brj-23756	39	17	properties	property	NOUN
brj-23756	39	18	.	.	PUNCT
brj-23756	40	1	the	the	DET
brj-23756	40	2	application	application	NOUN
brj-23756	40	3	of	of	ADP
brj-23756	40	4	these	these	DET
brj-23756	40	5	models	model	NOUN
brj-23756	40	6	would	would	AUX
brj-23756	40	7	be	be	AUX
brj-23756	40	8	to	to	PART
brj-23756	40	9	optimize	optimize	VERB
brj-23756	40	10	the	the	DET
brj-23756	40	11	process	process	NOUN
brj-23756	40	12	to	to	PART
brj-23756	40	13	reach	reach	VERB
brj-23756	40	14	the	the	DET
brj-23756	40	15	optimal	optimal	ADJ
brj-23756	40	16	properties	property	NOUN
brj-23756	40	17	of	of	ADP
brj-23756	40	18	the	the	DET
brj-23756	40	19	process	process	NOUN
brj-23756	40	20	and	and	CCONJ
brj-23756	40	21	products	product	NOUN
brj-23756	40	22	(	(	PUNCT
brj-23756	40	23	masoumi	masoumi	NOUN
brj-23756	40	24	and	and	CCONJ
brj-23756	40	25	bond	bond	NOUN
brj-23756	40	26	2024b	2024b	NUM
brj-23756	40	27	)	)	PUNCT
brj-23756	40	28	.	.	PUNCT
brj-23756	41	1	peer	peer	NOUN
brj-23756	41	2	-	-	PUNCT
brj-23756	41	3	reviewed	review	VERB
brj-23756	41	4	article	article	NOUN
brj-23756	41	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	41	6	masoumi	masoumi	NOUN
brj-23756	41	7	&	&	CCONJ
brj-23756	41	8	bond	bond	PROPN
brj-23756	41	9	(	(	PUNCT
brj-23756	41	10	2024	2024	NUM
brj-23756	41	11	)	)	PUNCT
brj-23756	41	12	.	.	PUNCT
brj-23756	42	1	“	"	PUNCT
brj-23756	42	2	ann	ann	PROPN
brj-23756	42	3	prediction	prediction	NOUN
brj-23756	42	4	of	of	ADP
brj-23756	42	5	tmw	tmw	NOUN
brj-23756	42	6	,	,	PUNCT
brj-23756	42	7	”	"	PUNCT
brj-23756	42	8	bioresources	bioresource	NOUN
brj-23756	42	9	19(4	19(4	NUM
brj-23756	42	10	)	)	PUNCT
brj-23756	42	11	,	,	PUNCT
brj-23756	42	12	6983	6983	NUM
brj-23756	42	13	-	-	SYM
brj-23756	42	14	6993	6993	NUM
brj-23756	42	15	.	.	PUNCT
brj-23756	43	1	6985	6985	NUM
brj-23756	43	2	experimental	experimental	ADJ
brj-23756	43	3	data	datum	NOUN
brj-23756	43	4	collection	collection	NOUN
brj-23756	43	5	the	the	DET
brj-23756	43	6	data	datum	NOUN
brj-23756	43	7	used	use	VERB
brj-23756	43	8	in	in	ADP
brj-23756	43	9	this	this	DET
brj-23756	43	10	study	study	NOUN
brj-23756	43	11	was	be	AUX
brj-23756	43	12	previously	previously	ADV
brj-23756	43	13	published	publish	VERB
brj-23756	43	14	by	by	ADP
brj-23756	43	15	masoumi	masoumi	NOUN
brj-23756	43	16	and	and	CCONJ
brj-23756	43	17	bond	bond	NOUN
brj-23756	43	18	(	(	PUNCT
brj-23756	43	19	2024a	2024a	NUM
brj-23756	43	20	)	)	PUNCT
brj-23756	43	21	and	and	CCONJ
brj-23756	43	22	masoumi	masoumi	NOUN
brj-23756	43	23	et	et	PROPN
brj-23756	43	24	al	al	PROPN
brj-23756	43	25	.	.	PROPN
brj-23756	44	1	(	(	PUNCT
brj-23756	44	2	2024	2024	NUM
brj-23756	44	3	)	)	PUNCT
brj-23756	44	4	.	.	PUNCT
brj-23756	45	1	test	test	NOUN
brj-23756	45	2	specimens	specimen	NOUN
brj-23756	45	3	were	be	AUX
brj-23756	45	4	prepared	prepare	VERB
brj-23756	45	5	from	from	ADP
brj-23756	45	6	randomly	randomly	ADV
brj-23756	45	7	selected	select	VERB
brj-23756	45	8	lumber	lumber	NOUN
brj-23756	45	9	.	.	PUNCT
brj-23756	46	1	the	the	DET
brj-23756	46	2	lumber	lumber	NOUN
brj-23756	46	3	was	be	AUX
brj-23756	46	4	kiln	kiln	NOUN
brj-23756	46	5	-	-	PUNCT
brj-23756	46	6	dried	dry	VERB
brj-23756	46	7	to	to	ADP
brj-23756	46	8	6	6	NUM
brj-23756	46	9	to	to	PART
brj-23756	46	10	8	8	NUM
brj-23756	46	11	%	%	NOUN
brj-23756	46	12	mc	mc	NOUN
brj-23756	46	13	prior	prior	ADV
brj-23756	46	14	to	to	ADP
brj-23756	46	15	modification	modification	NOUN
brj-23756	46	16	.	.	PUNCT
brj-23756	47	1	thermal	thermal	ADJ
brj-23756	47	2	modification	modification	NOUN
brj-23756	47	3	of	of	ADP
brj-23756	47	4	the	the	DET
brj-23756	47	5	lumber	lumber	NOUN
brj-23756	47	6	was	be	AUX
brj-23756	47	7	conducted	conduct	VERB
brj-23756	47	8	in	in	ADP
brj-23756	47	9	an	an	DET
brj-23756	47	10	industrial	industrial	ADJ
brj-23756	47	11	dry	dry	ADJ
brj-23756	47	12	-	-	PUNCT
brj-23756	47	13	open	open	ADJ
brj-23756	47	14	vessel	vessel	NOUN
brj-23756	47	15	thermo	thermo	NOUN
brj-23756	47	16	-	-	PUNCT
brj-23756	47	17	vacuum	vacuum	NOUN
brj-23756	47	18	and	and	CCONJ
brj-23756	47	19	the	the	DET
brj-23756	47	20	maximum	maximum	ADJ
brj-23756	47	21	modification	modification	NOUN
brj-23756	47	22	temperature	temperature	NOUN
brj-23756	47	23	,	,	PUNCT
brj-23756	47	24	duration	duration	NOUN
brj-23756	47	25	,	,	PUNCT
brj-23756	47	26	and	and	CCONJ
brj-23756	47	27	density	density	NOUN
brj-23756	47	28	of	of	ADP
brj-23756	47	29	the	the	DET
brj-23756	47	30	unmodified	unmodified	ADJ
brj-23756	47	31	and	and	CCONJ
brj-23756	47	32	modified	modify	VERB
brj-23756	47	33	lumber	lumber	NOUN
brj-23756	47	34	is	be	AUX
brj-23756	47	35	presented	present	VERB
brj-23756	47	36	in	in	ADP
brj-23756	47	37	table	table	NOUN
brj-23756	47	38	1	1	NUM
brj-23756	47	39	.	.	PUNCT
brj-23756	48	1	cubes	cube	NOUN
brj-23756	48	2	of	of	ADP
brj-23756	48	3	each	each	DET
brj-23756	48	4	treatment	treatment	NOUN
brj-23756	48	5	type	type	NOUN
brj-23756	48	6	of	of	ADP
brj-23756	48	7	every	every	DET
brj-23756	48	8	species	specie	NOUN
brj-23756	48	9	with	with	ADP
brj-23756	48	10	dimensions	dimension	NOUN
brj-23756	48	11	of	of	ADP
brj-23756	48	12	1	1	NUM
brj-23756	48	13	in	in	ADP
brj-23756	48	14	×	×	NOUN
brj-23756	48	15	1	1	NUM
brj-23756	48	16	in	in	ADP
brj-23756	48	17	×	×	NOUN
brj-23756	48	18	1	1	NUM
brj-23756	48	19	in	in	ADP
brj-23756	48	20	(	(	PUNCT
brj-23756	48	21	l	l	NOUN
brj-23756	48	22	×	×	NOUN
brj-23756	48	23	r	r	NOUN
brj-23756	48	24	×	×	PROPN
brj-23756	48	25	t	t	PROPN
brj-23756	48	26	)	)	PUNCT
brj-23756	48	27	were	be	AUX
brj-23756	48	28	cut	cut	VERB
brj-23756	48	29	from	from	ADP
brj-23756	48	30	the	the	DET
brj-23756	48	31	lumber	lumber	NOUN
brj-23756	48	32	.	.	PUNCT
brj-23756	49	1	physical	physical	ADJ
brj-23756	49	2	experiments	experiment	NOUN
brj-23756	49	3	were	be	AUX
brj-23756	49	4	conducted	conduct	VERB
brj-23756	49	5	based	base	VERB
brj-23756	49	6	on	on	ADP
brj-23756	49	7	the	the	DET
brj-23756	49	8	astm	astm	PROPN
brj-23756	49	9	d143	d143	PROPN
brj-23756	49	10	-	-	PUNCT
brj-23756	49	11	22	22	NUM
brj-23756	49	12	(	(	PUNCT
brj-23756	49	13	2022	2022	NUM
brj-23756	49	14	)	)	PUNCT
brj-23756	49	15	standard	standard	NOUN
brj-23756	49	16	.	.	PUNCT
brj-23756	50	1	thirty	thirty	NUM
brj-23756	50	2	cubes	cube	NOUN
brj-23756	50	3	of	of	ADP
brj-23756	50	4	each	each	DET
brj-23756	50	5	species	specie	NOUN
brj-23756	50	6	were	be	AUX
brj-23756	50	7	taken	take	VERB
brj-23756	50	8	and	and	CCONJ
brj-23756	50	9	conditioned	condition	VERB
brj-23756	50	10	by	by	ADP
brj-23756	50	11	placing	place	VERB
brj-23756	50	12	them	they	PRON
brj-23756	50	13	in	in	ADP
brj-23756	50	14	a	a	DET
brj-23756	50	15	climatic	climatic	ADJ
brj-23756	50	16	chamber	chamber	NOUN
brj-23756	50	17	in	in	ADP
brj-23756	50	18	21	21	NUM
brj-23756	50	19	°	°	NOUN
brj-23756	50	20	c	c	NOUN
brj-23756	50	21	and	and	CCONJ
brj-23756	50	22	65	65	NUM
brj-23756	50	23	%	%	NOUN
brj-23756	50	24	relative	relative	ADJ
brj-23756	50	25	humidity	humidity	NOUN
brj-23756	50	26	(	(	PUNCT
brj-23756	50	27	rh	rh	PROPN
brj-23756	50	28	)	)	PUNCT
brj-23756	50	29	,	,	PUNCT
brj-23756	50	30	which	which	PRON
brj-23756	50	31	is	be	AUX
brj-23756	50	32	equal	equal	ADJ
brj-23756	50	33	to	to	ADP
brj-23756	50	34	12	12	NUM
brj-23756	50	35	%	%	NOUN
brj-23756	50	36	rh	rh	NOUN
brj-23756	50	37	for	for	ADP
brj-23756	50	38	20	20	NUM
brj-23756	50	39	days	day	NOUN
brj-23756	50	40	until	until	SCONJ
brj-23756	50	41	unmodified	unmodified	ADJ
brj-23756	50	42	specimens	specimen	NOUN
brj-23756	50	43	reached	reach	VERB
brj-23756	50	44	the	the	DET
brj-23756	50	45	equilibrium	equilibrium	NOUN
brj-23756	50	46	moisture	moisture	NOUN
brj-23756	50	47	content	content	NOUN
brj-23756	50	48	,	,	PUNCT
brj-23756	50	49	as	as	SCONJ
brj-23756	50	50	measurements	measurement	NOUN
brj-23756	50	51	five	five	NUM
brj-23756	50	52	days	day	NOUN
brj-23756	50	53	after	after	ADP
brj-23756	50	54	this	this	DET
brj-23756	50	55	time	time	NOUN
brj-23756	50	56	showed	show	VERB
brj-23756	50	57	no	no	DET
brj-23756	50	58	moisture	moisture	NOUN
brj-23756	50	59	absorption	absorption	NOUN
brj-23756	50	60	.	.	PUNCT
brj-23756	51	1	after	after	ADP
brj-23756	51	2	conditioning	conditioning	NOUN
brj-23756	51	3	,	,	PUNCT
brj-23756	51	4	the	the	DET
brj-23756	51	5	samples	sample	NOUN
brj-23756	51	6	were	be	AUX
brj-23756	51	7	weighed	weigh	VERB
brj-23756	51	8	,	,	PUNCT
brj-23756	51	9	and	and	CCONJ
brj-23756	51	10	their	their	PRON
brj-23756	51	11	dimensions	dimension	NOUN
brj-23756	51	12	were	be	AUX
brj-23756	51	13	measured	measure	VERB
brj-23756	51	14	using	use	VERB
brj-23756	51	15	a	a	DET
brj-23756	51	16	0.01	0.01	NUM
brj-23756	51	17	-	-	PUNCT
brj-23756	51	18	g	g	NOUN
brj-23756	51	19	accuracy	accuracy	NOUN
brj-23756	51	20	balance	balance	NOUN
brj-23756	51	21	and	and	CCONJ
brj-23756	51	22	a	a	DET
brj-23756	51	23	0.01	0.01	NUM
brj-23756	51	24	-	-	PUNCT
brj-23756	51	25	mm	mm	NOUN
brj-23756	51	26	accuracy	accuracy	NOUN
brj-23756	51	27	digital	digital	ADJ
brj-23756	51	28	caliper	caliper	NOUN
brj-23756	51	29	.	.	PUNCT
brj-23756	52	1	samples	sample	NOUN
brj-23756	52	2	were	be	AUX
brj-23756	52	3	then	then	ADV
brj-23756	52	4	submerged	submerge	VERB
brj-23756	52	5	in	in	ADP
brj-23756	52	6	distilled	distil	VERB
brj-23756	52	7	water	water	NOUN
brj-23756	52	8	for	for	ADP
brj-23756	52	9	20	20	NUM
brj-23756	52	10	days	day	NOUN
brj-23756	52	11	.	.	PUNCT
brj-23756	53	1	subsequently	subsequently	ADV
brj-23756	53	2	,	,	PUNCT
brj-23756	53	3	samples	sample	NOUN
brj-23756	53	4	were	be	AUX
brj-23756	53	5	left	leave	VERB
brj-23756	53	6	to	to	PART
brj-23756	53	7	dry	dry	VERB
brj-23756	53	8	at	at	ADP
brj-23756	53	9	room	room	NOUN
brj-23756	53	10	temperature	temperature	NOUN
brj-23756	53	11	for	for	ADP
brj-23756	53	12	3	3	NUM
brj-23756	53	13	days	day	NOUN
brj-23756	53	14	to	to	PART
brj-23756	53	15	avoid	avoid	VERB
brj-23756	53	16	cracking	cracking	NOUN
brj-23756	53	17	and	and	CCONJ
brj-23756	53	18	then	then	ADV
brj-23756	53	19	placed	place	VERB
brj-23756	53	20	in	in	ADP
brj-23756	53	21	the	the	DET
brj-23756	53	22	oven	oven	NOUN
brj-23756	53	23	at	at	ADP
brj-23756	53	24	a	a	DET
brj-23756	53	25	temperature	temperature	NOUN
brj-23756	53	26	of	of	ADP
brj-23756	53	27	103	103	NUM
brj-23756	53	28	±	±	NUM
brj-23756	53	29	2	2	NUM
brj-23756	53	30	°	°	ADP
brj-23756	53	31	c	c	NOUN
brj-23756	53	32	for	for	ADP
brj-23756	53	33	24	24	NUM
brj-23756	53	34	h.	h.	PROPN
brj-23756	53	35	after	after	ADP
brj-23756	53	36	each	each	DET
brj-23756	53	37	phase	phase	NOUN
brj-23756	53	38	,	,	PUNCT
brj-23756	53	39	the	the	DET
brj-23756	53	40	weight	weight	NOUN
brj-23756	53	41	and	and	CCONJ
brj-23756	53	42	dimensions	dimension	NOUN
brj-23756	53	43	of	of	ADP
brj-23756	53	44	the	the	DET
brj-23756	53	45	samples	sample	NOUN
brj-23756	53	46	were	be	AUX
brj-23756	53	47	measured	measure	VERB
brj-23756	53	48	.	.	PUNCT
brj-23756	54	1	table	table	NOUN
brj-23756	54	2	1	1	NUM
brj-23756	54	3	.	.	PUNCT
brj-23756	54	4	modification	modification	NOUN
brj-23756	54	5	temperature	temperature	NOUN
brj-23756	54	6	,	,	PUNCT
brj-23756	54	7	time	time	NOUN
brj-23756	54	8	,	,	PUNCT
brj-23756	54	9	and	and	CCONJ
brj-23756	54	10	density	density	NOUN
brj-23756	54	11	for	for	ADP
brj-23756	54	12	different	different	ADJ
brj-23756	54	13	wood	wood	NOUN
brj-23756	54	14	species	specie	NOUN
brj-23756	54	15	artificial	artificial	ADJ
brj-23756	54	16	neural	neural	ADJ
brj-23756	54	17	network	network	NOUN
brj-23756	54	18	models	model	NOUN
brj-23756	54	19	the	the	DET
brj-23756	54	20	authors	author	NOUN
brj-23756	54	21	utilized	utilize	VERB
brj-23756	54	22	a	a	DET
brj-23756	54	23	dataset	dataset	NOUN
brj-23756	54	24	comprising	comprise	VERB
brj-23756	54	25	360	360	NUM
brj-23756	54	26	data	datum	NOUN
brj-23756	54	27	points	point	NOUN
brj-23756	54	28	representing	represent	VERB
brj-23756	54	29	emc	emc	NOUN
brj-23756	54	30	and	and	CCONJ
brj-23756	54	31	volumetric	volumetric	NOUN
brj-23756	54	32	swelling	swelling	NOUN
brj-23756	54	33	of	of	ADP
brj-23756	54	34	six	six	NUM
brj-23756	54	35	hardwood	hardwood	NOUN
brj-23756	54	36	species	specie	NOUN
brj-23756	54	37	to	to	PART
brj-23756	54	38	predict	predict	VERB
brj-23756	54	39	emc	emc	PROPN
brj-23756	54	40	and	and	CCONJ
brj-23756	54	41	swelling	swell	VERB
brj-23756	54	42	using	use	VERB
brj-23756	54	43	a	a	DET
brj-23756	54	44	single	single	ADJ
brj-23756	54	45	input	input	NOUN
brj-23756	54	46	and	and	CCONJ
brj-23756	54	47	multiple	multiple	ADJ
brj-23756	54	48	input	input	NOUN
brj-23756	54	49	multilayer	multilayer	ADJ
brj-23756	54	50	perceptron	perceptron	PROPN
brj-23756	54	51	(	(	PUNCT
brj-23756	54	52	mlp	mlp	NOUN
brj-23756	54	53	)	)	PUNCT
brj-23756	54	54	fully	fully	ADV
brj-23756	54	55	connected	connect	VERB
brj-23756	54	56	artificial	artificial	ADJ
brj-23756	54	57	neural	neural	ADJ
brj-23756	54	58	network	network	NOUN
brj-23756	54	59	implemented	implement	VERB
brj-23756	54	60	in	in	ADP
brj-23756	54	61	python	python	PROPN
brj-23756	54	62	3.11	3.11	NUM
brj-23756	54	63	,	,	PUNCT
brj-23756	54	64	leveraging	leverage	VERB
brj-23756	54	65	keras	keras	PROPN
brj-23756	54	66	and	and	CCONJ
brj-23756	54	67	pytorch	pytorch	NOUN
brj-23756	54	68	.	.	PUNCT
brj-23756	55	1	the	the	DET
brj-23756	55	2	single	single	ADJ
brj-23756	55	3	input	input	NOUN
brj-23756	55	4	model	model	NOUN
brj-23756	55	5	used	use	VERB
brj-23756	55	6	one	one	NUM
brj-23756	55	7	of	of	ADP
brj-23756	55	8	the	the	DET
brj-23756	55	9	parameters	parameter	NOUN
brj-23756	55	10	as	as	ADP
brj-23756	55	11	input	input	NOUN
brj-23756	55	12	in	in	ADP
brj-23756	55	13	every	every	DET
brj-23756	55	14	processing	processing	NOUN
brj-23756	55	15	set	set	NOUN
brj-23756	55	16	,	,	PUNCT
brj-23756	55	17	and	and	CCONJ
brj-23756	55	18	multiple	multiple	ADJ
brj-23756	55	19	input	input	NOUN
brj-23756	55	20	used	use	VERB
brj-23756	55	21	all	all	PRON
brj-23756	55	22	of	of	ADP
brj-23756	55	23	the	the	DET
brj-23756	55	24	parameters	parameter	NOUN
brj-23756	55	25	in	in	ADP
brj-23756	55	26	a	a	DET
brj-23756	55	27	model	model	NOUN
brj-23756	55	28	.	.	PUNCT
brj-23756	56	1	in	in	ADP
brj-23756	56	2	either	either	DET
brj-23756	56	3	case	case	NOUN
brj-23756	56	4	,	,	PUNCT
brj-23756	56	5	either	either	CCONJ
brj-23756	56	6	emc	emc	PROPN
brj-23756	56	7	or	or	CCONJ
brj-23756	56	8	swelling	swelling	NOUN
brj-23756	56	9	was	be	AUX
brj-23756	56	10	considered	consider	VERB
brj-23756	56	11	as	as	ADP
brj-23756	56	12	the	the	DET
brj-23756	56	13	output	output	NOUN
brj-23756	56	14	.	.	PUNCT
brj-23756	57	1	keras	keras	PROPN
brj-23756	57	2	and	and	CCONJ
brj-23756	57	3	pytorch	pytorch	NOUN
brj-23756	57	4	are	be	AUX
brj-23756	57	5	two	two	NUM
brj-23756	57	6	powerful	powerful	ADJ
brj-23756	57	7	open	open	ADJ
brj-23756	57	8	-	-	PUNCT
brj-23756	57	9	source	source	NOUN
brj-23756	57	10	machine	machine	NOUN
brj-23756	57	11	-	-	PUNCT
brj-23756	57	12	learning	learn	VERB
brj-23756	57	13	libraries	library	NOUN
brj-23756	57	14	.	.	PUNCT
brj-23756	58	1	keras	keras	PROPN
brj-23756	58	2	is	be	AUX
brj-23756	58	3	python	python	NOUN
brj-23756	58	4	-	-	PUNCT
brj-23756	58	5	based	base	VERB
brj-23756	58	6	and	and	CCONJ
brj-23756	58	7	is	be	AUX
brj-23756	58	8	used	use	VERB
brj-23756	58	9	in	in	ADP
brj-23756	58	10	deep	deep	ADJ
brj-23756	58	11	learning	learning	NOUN
brj-23756	58	12	for	for	ADP
brj-23756	58	13	neural	neural	ADJ
brj-23756	58	14	networks	network	NOUN
brj-23756	58	15	.	.	PUNCT
brj-23756	59	1	pytorch	pytorch	NOUN
brj-23756	59	2	is	be	AUX
brj-23756	59	3	an	an	DET
brj-23756	59	4	open	open	ADJ
brj-23756	59	5	-	-	PUNCT
brj-23756	59	6	source	source	NOUN
brj-23756	59	7	machine	machine	NOUN
brj-23756	59	8	learning	learn	VERB
brj-23756	59	9	library	library	NOUN
brj-23756	59	10	that	that	PRON
brj-23756	59	11	can	can	AUX
brj-23756	59	12	be	be	AUX
brj-23756	59	13	integrated	integrate	VERB
brj-23756	59	14	with	with	ADP
brj-23756	59	15	python	python	NOUN
brj-23756	59	16	and	and	CCONJ
brj-23756	59	17	can	can	AUX
brj-23756	59	18	debug	debug	VERB
brj-23756	59	19	neural	neural	ADJ
brj-23756	59	20	networks	network	NOUN
brj-23756	59	21	easily	easily	ADV
brj-23756	59	22	.	.	PUNCT
brj-23756	60	1	the	the	DET
brj-23756	60	2	mathematical	mathematical	ADJ
brj-23756	60	3	formula	formula	NOUN
brj-23756	60	4	for	for	ADP
brj-23756	60	5	mlp	mlp	PROPN
brj-23756	60	6	is	be	AUX
brj-23756	60	7	given	give	VERB
brj-23756	60	8	in	in	ADP
brj-23756	60	9	eq	eq	ADJ
brj-23756	60	10	.	.	PROPN
brj-23756	60	11	4	4	NUM
brj-23756	60	12	.	.	NOUN
brj-23756	60	13	model	model	NOUN
brj-23756	60	14	architecture	architecture	NOUN
brj-23756	60	15	the	the	DET
brj-23756	60	16	ann	ann	PROPN
brj-23756	60	17	model	model	NOUN
brj-23756	60	18	consisted	consist	VERB
brj-23756	60	19	of	of	ADP
brj-23756	60	20	an	an	DET
brj-23756	60	21	input	input	NOUN
brj-23756	60	22	layer	layer	NOUN
brj-23756	60	23	representing	represent	VERB
brj-23756	60	24	modified	modified	ADJ
brj-23756	60	25	and	and	CCONJ
brj-23756	60	26	unmodified	unmodified	ADJ
brj-23756	60	27	,	,	PUNCT
brj-23756	60	28	wood	wood	NOUN
brj-23756	60	29	species	specie	NOUN
brj-23756	60	30	,	,	PUNCT
brj-23756	60	31	temperature	temperature	NOUN
brj-23756	60	32	,	,	PUNCT
brj-23756	60	33	time	time	NOUN
brj-23756	60	34	,	,	PUNCT
brj-23756	60	35	and	and	CCONJ
brj-23756	60	36	density	density	NOUN
brj-23756	60	37	,	,	PUNCT
brj-23756	60	38	and	and	CCONJ
brj-23756	60	39	an	an	DET
brj-23756	60	40	output	output	NOUN
brj-23756	60	41	layer	layer	NOUN
brj-23756	60	42	of	of	ADP
brj-23756	60	43	emc	emc	NOUN
brj-23756	60	44	or	or	CCONJ
brj-23756	60	45	swelling	swelling	NOUN
brj-23756	60	46	.	.	PUNCT
brj-23756	61	1	the	the	DET
brj-23756	61	2	wood	wood	NOUN
brj-23756	61	3	species	specie	NOUN
brj-23756	61	4	yp	yp	PROPN
brj-23756	61	5	red	red	PROPN
brj-23756	61	6	oak	oak	PROPN
brj-23756	61	7	ash	ash	NOUN
brj-23756	61	8	red	red	PROPN
brj-23756	61	9	maple	maple	PROPN
brj-23756	61	10	hickory	hickory	PROPN
brj-23756	61	11	black	black	ADJ
brj-23756	61	12	cherry	cherry	NOUN
brj-23756	61	13	temperature	temperature	NOUN
brj-23756	61	14	(	(	PUNCT
brj-23756	61	15	°	°	ADP
brj-23756	61	16	c	c	NOUN
brj-23756	61	17	)	)	PUNCT
brj-23756	61	18	a	a	DET
brj-23756	61	19	b	b	NOUN
brj-23756	61	20	c	c	NOUN
brj-23756	61	21	c	c	NOUN
brj-23756	61	22	c	c	NOUN
brj-23756	61	23	d	d	X
brj-23756	61	24	time	time	NOUN
brj-23756	61	25	(	(	PUNCT
brj-23756	61	26	min	min	NOUN
brj-23756	61	27	)	)	PUNCT
brj-23756	61	28	a	a	DET
brj-23756	61	29	c	c	NOUN
brj-23756	61	30	c	c	NOUN
brj-23756	61	31	c	c	NOUN
brj-23756	61	32	c	c	PROPN
brj-23756	61	33	b	b	PROPN
brj-23756	61	34	unmodified	unmodified	ADJ
brj-23756	61	35	density	density	NOUN
brj-23756	61	36	(	(	PUNCT
brj-23756	61	37	g	g	NOUN
brj-23756	61	38	/	/	SYM
brj-23756	61	39	cm2	cm2	NOUN
brj-23756	61	40	)	)	PUNCT
brj-23756	61	41	0.44	0.44	NUM
brj-23756	61	42	0.74	0.74	NUM
brj-23756	61	43	0.74	0.74	NUM
brj-23756	61	44	0.61	0.61	NUM
brj-23756	61	45	0.77	0.77	NUM
brj-23756	61	46	0.56	0.56	NUM
brj-23756	61	47	modified	modify	VERB
brj-23756	61	48	density	density	NOUN
brj-23756	61	49	(	(	PUNCT
brj-23756	61	50	g	g	NOUN
brj-23756	61	51	/	/	SYM
brj-23756	61	52	cm2	cm2	NOUN
brj-23756	61	53	)	)	PUNCT
brj-23756	61	54	0.37	0.37	NUM
brj-23756	61	55	0.46	0.46	NUM
brj-23756	61	56	0.32	0.32	NUM
brj-23756	61	57	0.45	0.45	NUM
brj-23756	61	58	0.59	0.59	NUM
brj-23756	61	59	0.46	0.46	NUM
brj-23756	61	60	as	as	SCONJ
brj-23756	61	61	the	the	DET
brj-23756	61	62	modification	modification	NOUN
brj-23756	61	63	schedule	schedule	NOUN
brj-23756	61	64	is	be	AUX
brj-23756	61	65	proprietary	proprietary	ADJ
brj-23756	61	66	,	,	PUNCT
brj-23756	61	67	a	a	DET
brj-23756	61	68	,	,	PUNCT
brj-23756	61	69	b	b	NOUN
brj-23756	61	70	,	,	PUNCT
brj-23756	61	71	c	c	X
brj-23756	61	72	,	,	PUNCT
brj-23756	61	73	d	d	PROPN
brj-23756	61	74	represent	represent	VERB
brj-23756	61	75	the	the	DET
brj-23756	61	76	class	class	NOUN
brj-23756	61	77	of	of	ADP
brj-23756	61	78	time	time	NOUN
brj-23756	61	79	and	and	CCONJ
brj-23756	61	80	temperature	temperature	NOUN
brj-23756	61	81	peer	peer	NOUN
brj-23756	61	82	-	-	PUNCT
brj-23756	61	83	reviewed	review	VERB
brj-23756	61	84	article	article	NOUN
brj-23756	61	85	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	61	86	masoumi	masoumi	NOUN
brj-23756	61	87	&	&	CCONJ
brj-23756	61	88	bond	bond	PROPN
brj-23756	61	89	(	(	PUNCT
brj-23756	61	90	2024	2024	NUM
brj-23756	61	91	)	)	PUNCT
brj-23756	61	92	.	.	PUNCT
brj-23756	62	1	“	"	PUNCT
brj-23756	62	2	ann	ann	PROPN
brj-23756	62	3	prediction	prediction	NOUN
brj-23756	62	4	of	of	ADP
brj-23756	62	5	tmw	tmw	NOUN
brj-23756	62	6	,	,	PUNCT
brj-23756	62	7	”	"	PUNCT
brj-23756	62	8	bioresources	bioresource	NOUN
brj-23756	62	9	19(4	19(4	NUM
brj-23756	62	10	)	)	PUNCT
brj-23756	62	11	,	,	PUNCT
brj-23756	62	12	6983	6983	NUM
brj-23756	62	13	-	-	SYM
brj-23756	62	14	6993	6993	NUM
brj-23756	62	15	.	.	PUNCT
brj-23756	63	1	6986	6986	NUM
brj-23756	63	2	architecture	architecture	NOUN
brj-23756	63	3	involved	involve	VERB
brj-23756	63	4	an	an	DET
brj-23756	63	5	input	input	NOUN
brj-23756	63	6	layer	layer	NOUN
brj-23756	63	7	,	,	PUNCT
brj-23756	63	8	a	a	DET
brj-23756	63	9	hidden	hidden	ADJ
brj-23756	63	10	layer	layer	NOUN
brj-23756	63	11	with	with	ADP
brj-23756	63	12	relu	relu	NOUN
brj-23756	63	13	activation	activation	NOUN
brj-23756	63	14	(	(	PUNCT
brj-23756	63	15	rectified	rectified	ADJ
brj-23756	63	16	linear	linear	ADJ
brj-23756	63	17	activation	activation	NOUN
brj-23756	63	18	function	function	NOUN
brj-23756	63	19	that	that	PRON
brj-23756	63	20	is	be	AUX
brj-23756	63	21	the	the	DET
brj-23756	63	22	default	default	NOUN
brj-23756	63	23	function	function	NOUN
brj-23756	63	24	that	that	PRON
brj-23756	63	25	will	will	AUX
brj-23756	63	26	output	output	VERB
brj-23756	63	27	the	the	DET
brj-23756	63	28	input	input	NOUN
brj-23756	63	29	directly	directly	ADV
brj-23756	63	30	,	,	PUNCT
brj-23756	63	31	if	if	SCONJ
brj-23756	63	32	it	it	PRON
brj-23756	63	33	is	be	AUX
brj-23756	63	34	positive	positive	ADJ
brj-23756	63	35	,	,	PUNCT
brj-23756	63	36	otherwise	otherwise	ADV
brj-23756	63	37	prints	print	NOUN
brj-23756	63	38	zero	zero	NUM
brj-23756	63	39	in	in	ADP
brj-23756	63	40	output	output	NOUN
brj-23756	63	41	)	)	PUNCT
brj-23756	63	42	,	,	PUNCT
brj-23756	63	43	and	and	CCONJ
brj-23756	63	44	an	an	DET
brj-23756	63	45	output	output	NOUN
brj-23756	63	46	layer	layer	NOUN
brj-23756	63	47	.	.	PUNCT
brj-23756	64	1	the	the	DET
brj-23756	64	2	hidden	hide	VERB
brj-23756	64	3	layer	layer	NOUN
brj-23756	64	4	is	be	AUX
brj-23756	64	5	the	the	DET
brj-23756	64	6	layer	layer	NOUN
brj-23756	64	7	of	of	ADP
brj-23756	64	8	neurons	neuron	NOUN
brj-23756	64	9	that	that	PRON
brj-23756	64	10	is	be	AUX
brj-23756	64	11	neither	neither	CCONJ
brj-23756	64	12	the	the	DET
brj-23756	64	13	input	input	NOUN
brj-23756	64	14	nor	nor	CCONJ
brj-23756	64	15	the	the	DET
brj-23756	64	16	output	output	NOUN
brj-23756	64	17	layer	layer	NOUN
brj-23756	64	18	and	and	CCONJ
brj-23756	64	19	is	be	AUX
brj-23756	64	20	what	what	PRON
brj-23756	64	21	makes	make	VERB
brj-23756	64	22	neural	neural	ADJ
brj-23756	64	23	networks	network	NOUN
brj-23756	64	24	deep	deep	ADJ
brj-23756	64	25	and	and	CCONJ
brj-23756	64	26	enables	enable	VERB
brj-23756	64	27	them	they	PRON
brj-23756	64	28	to	to	PART
brj-23756	64	29	learn	learn	VERB
brj-23756	64	30	complex	complex	ADJ
brj-23756	64	31	data	datum	NOUN
brj-23756	64	32	.	.	PUNCT
brj-23756	65	1	the	the	DET
brj-23756	65	2	activation	activation	NOUN
brj-23756	65	3	function	function	NOUN
brj-23756	65	4	is	be	AUX
brj-23756	65	5	used	use	VERB
brj-23756	65	6	to	to	PART
brj-23756	65	7	determine	determine	VERB
brj-23756	65	8	the	the	DET
brj-23756	65	9	output	output	NOUN
brj-23756	65	10	of	of	ADP
brj-23756	65	11	a	a	DET
brj-23756	65	12	neuron	neuron	NOUN
brj-23756	65	13	by	by	ADP
brj-23756	65	14	calculating	calculate	VERB
brj-23756	65	15	the	the	DET
brj-23756	65	16	weighted	weighted	ADJ
brj-23756	65	17	sum	sum	NOUN
brj-23756	65	18	of	of	ADP
brj-23756	65	19	inputs	input	NOUN
brj-23756	65	20	and	and	CCONJ
brj-23756	65	21	adds	add	VERB
brj-23756	65	22	a	a	DET
brj-23756	65	23	bias	bias	NOUN
brj-23756	65	24	to	to	ADP
brj-23756	65	25	it	it	PRON
brj-23756	65	26	.	.	PUNCT
brj-23756	66	1	the	the	DET
brj-23756	66	2	model	model	NOUN
brj-23756	66	3	,	,	PUNCT
brj-23756	66	4	implemented	implement	VERB
brj-23756	66	5	using	use	VERB
brj-23756	66	6	the	the	DET
brj-23756	66	7	keras	keras	PROPN
brj-23756	66	8	and	and	CCONJ
brj-23756	66	9	pytorch	pytorch	NOUN
brj-23756	66	10	deep	deep	ADJ
brj-23756	66	11	learning	learning	NOUN
brj-23756	66	12	libraries	library	NOUN
brj-23756	66	13	,	,	PUNCT
brj-23756	66	14	adapted	adapt	VERB
brj-23756	66	15	its	its	PRON
brj-23756	66	16	input	input	NOUN
brj-23756	66	17	size	size	NOUN
brj-23756	66	18	based	base	VERB
brj-23756	66	19	on	on	ADP
brj-23756	66	20	the	the	DET
brj-23756	66	21	number	number	NOUN
brj-23756	66	22	of	of	ADP
brj-23756	66	23	features	feature	NOUN
brj-23756	66	24	extracted	extract	VERB
brj-23756	66	25	from	from	ADP
brj-23756	66	26	the	the	DET
brj-23756	66	27	dataset	dataset	NOUN
brj-23756	66	28	.	.	PUNCT
brj-23756	67	1	various	various	ADJ
brj-23756	67	2	configurations	configuration	NOUN
brj-23756	67	3	were	be	AUX
brj-23756	67	4	experimented	experiment	VERB
brj-23756	67	5	with	with	ADP
brj-23756	67	6	,	,	PUNCT
brj-23756	67	7	including	include	VERB
brj-23756	67	8	different	different	ADJ
brj-23756	67	9	architectures	architecture	NOUN
brj-23756	67	10	(	(	PUNCT
brj-23756	67	11	single	single	ADJ
brj-23756	67	12	or	or	CCONJ
brj-23756	67	13	multiple	multiple	ADJ
brj-23756	67	14	inputs	input	NOUN
brj-23756	67	15	,	,	PUNCT
brj-23756	67	16	varying	vary	VERB
brj-23756	67	17	hidden	hidden	ADJ
brj-23756	67	18	layer	layer	NOUN
brj-23756	67	19	neurons	neuron	NOUN
brj-23756	67	20	)	)	PUNCT
brj-23756	67	21	,	,	PUNCT
brj-23756	67	22	optimizers	optimizer	NOUN
brj-23756	67	23	(	(	PUNCT
brj-23756	67	24	adam	adam	PROPN
brj-23756	67	25	and	and	CCONJ
brj-23756	67	26	sgd	sgd	NOUN
brj-23756	67	27	)	)	PUNCT
brj-23756	67	28	,	,	PUNCT
brj-23756	67	29	learning	learn	VERB
brj-23756	67	30	rates	rate	NOUN
brj-23756	67	31	,	,	PUNCT
brj-23756	67	32	epochs	epoch	NOUN
brj-23756	67	33	,	,	PUNCT
brj-23756	67	34	and	and	CCONJ
brj-23756	67	35	regularizations	regularization	NOUN
brj-23756	67	36	(	(	PUNCT
brj-23756	67	37	l1	l1	PROPN
brj-23756	67	38	,	,	PUNCT
brj-23756	67	39	l2	l2	NOUN
brj-23756	67	40	,	,	PUNCT
brj-23756	67	41	and	and	CCONJ
brj-23756	67	42	dropout	dropout	NOUN
brj-23756	67	43	)	)	PUNCT
brj-23756	67	44	.	.	PUNCT
brj-23756	68	1	the	the	DET
brj-23756	68	2	models	model	NOUN
brj-23756	68	3	were	be	AUX
brj-23756	68	4	assessed	assess	VERB
brj-23756	68	5	based	base	VERB
brj-23756	68	6	on	on	ADP
brj-23756	68	7	mean	mean	ADJ
brj-23756	68	8	squared	square	VERB
brj-23756	68	9	error	error	NOUN
brj-23756	68	10	(	(	PUNCT
brj-23756	68	11	mse	mse	NOUN
brj-23756	68	12	)	)	PUNCT
brj-23756	68	13	,	,	PUNCT
brj-23756	68	14	mape	mape	NOUN
brj-23756	68	15	,	,	PUNCT
brj-23756	68	16	and	and	CCONJ
brj-23756	68	17	r2	r2	PROPN
brj-23756	68	18	values	value	NOUN
brj-23756	68	19	,	,	PUNCT
brj-23756	68	20	which	which	PRON
brj-23756	68	21	are	be	AUX
brj-23756	68	22	the	the	DET
brj-23756	68	23	best	good	ADJ
brj-23756	68	24	criteria	criterion	NOUN
brj-23756	68	25	for	for	ADP
brj-23756	68	26	measuring	measure	VERB
brj-23756	68	27	the	the	DET
brj-23756	68	28	performance	performance	NOUN
brj-23756	68	29	of	of	ADP
brj-23756	68	30	anns	anns	NOUN
brj-23756	68	31	(	(	PUNCT
brj-23756	68	32	sen	sen	PROPN
brj-23756	68	33	et	et	PROPN
brj-23756	68	34	al	al	PROPN
brj-23756	68	35	.	.	PROPN
brj-23756	68	36	2023	2023	NUM
brj-23756	68	37	)	)	PUNCT
brj-23756	68	38	.	.	PUNCT
brj-23756	69	1	the	the	DET
brj-23756	69	2	ideal	ideal	ADJ
brj-23756	69	3	model	model	NOUN
brj-23756	69	4	has	have	VERB
brj-23756	69	5	data	datum	NOUN
brj-23756	69	6	with	with	ADP
brj-23756	69	7	the	the	DET
brj-23756	69	8	lowest	low	ADJ
brj-23756	69	9	mse	mse	NOUN
brj-23756	69	10	,	,	PUNCT
brj-23756	69	11	mape	mape	NOUN
brj-23756	69	12	<	<	NOUN
brj-23756	69	13	10	10	NUM
brj-23756	69	14	and	and	CCONJ
brj-23756	69	15	r2	r2	NOUN
brj-23756	69	16	above	above	ADP
brj-23756	69	17	0.90	0.90	NUM
brj-23756	69	18	and	and	CCONJ
brj-23756	69	19	as	as	ADV
brj-23756	69	20	close	close	ADJ
brj-23756	69	21	as	as	ADP
brj-23756	69	22	to	to	ADP
brj-23756	69	23	1	1	NUM
brj-23756	69	24	.	.	PUNCT
brj-23756	70	1	ultimately	ultimately	ADV
brj-23756	70	2	,	,	PUNCT
brj-23756	70	3	the	the	DET
brj-23756	70	4	best	good	ADJ
brj-23756	70	5	model	model	NOUN
brj-23756	70	6	was	be	AUX
brj-23756	70	7	chosen	choose	VERB
brj-23756	70	8	for	for	ADP
brj-23756	70	9	prediction	prediction	NOUN
brj-23756	70	10	.	.	PUNCT
brj-23756	71	1	fig	fig	NOUN
brj-23756	71	2	.	.	PUNCT
brj-23756	72	1	1	1	NUM
brj-23756	72	2	.	.	X
brj-23756	72	3	schematic	schematic	ADJ
brj-23756	72	4	architecture	architecture	NOUN
brj-23756	72	5	of	of	ADP
brj-23756	72	6	a	a	DET
brj-23756	72	7	:	:	PUNCT
brj-23756	72	8	basic	basic	ADJ
brj-23756	72	9	single	single	ADJ
brj-23756	72	10	input	input	NOUN
brj-23756	72	11	;	;	PUNCT
brj-23756	72	12	b	b	X
brj-23756	72	13	:	:	PUNCT
brj-23756	72	14	basic	basic	ADJ
brj-23756	72	15	multiple	multiple	ADJ
brj-23756	72	16	input	input	NOUN
brj-23756	72	17	;	;	PUNCT
brj-23756	72	18	and	and	CCONJ
brj-23756	72	19	c	c	X
brj-23756	72	20	:	:	PUNCT
brj-23756	72	21	multiple	multiple	ADJ
brj-23756	72	22	input	input	NOUN
brj-23756	72	23	ann	ann	PROPN
brj-23756	72	24	model	model	NOUN
brj-23756	72	25	used	use	VERB
brj-23756	72	26	in	in	ADP
brj-23756	72	27	this	this	DET
brj-23756	72	28	study	study	NOUN
brj-23756	72	29	peer	peer	NOUN
brj-23756	72	30	-	-	PUNCT
brj-23756	72	31	reviewed	review	VERB
brj-23756	72	32	article	article	NOUN
brj-23756	72	33	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	72	34	masoumi	masoumi	NOUN
brj-23756	72	35	&	&	CCONJ
brj-23756	72	36	bond	bond	PROPN
brj-23756	72	37	(	(	PUNCT
brj-23756	72	38	2024	2024	NUM
brj-23756	72	39	)	)	PUNCT
brj-23756	72	40	.	.	PUNCT
brj-23756	73	1	“	"	PUNCT
brj-23756	73	2	ann	ann	PROPN
brj-23756	73	3	prediction	prediction	NOUN
brj-23756	73	4	of	of	ADP
brj-23756	73	5	tmw	tmw	NOUN
brj-23756	73	6	,	,	PUNCT
brj-23756	73	7	”	"	PUNCT
brj-23756	73	8	bioresources	bioresource	NOUN
brj-23756	73	9	19(4	19(4	NUM
brj-23756	73	10	)	)	PUNCT
brj-23756	73	11	,	,	PUNCT
brj-23756	73	12	6983	6983	NUM
brj-23756	73	13	-	-	SYM
brj-23756	73	14	6993	6993	NUM
brj-23756	73	15	.	.	PUNCT
brj-23756	74	1	6987	6987	NUM
brj-23756	74	2	equations	equation	NOUN
brj-23756	74	3	mse	mse	NOUN
brj-23756	74	4	=	=	SYM
brj-23756	74	5	1	1	NUM
brj-23756	74	6	𝑛	𝑛	NOUN
brj-23756	74	7	∑	∑	PROPN
brj-23756	74	8	(	(	PUNCT
brj-23756	74	9	yi	yi	NOUN
brj-23756	74	10	−	−	PROPN
brj-23756	74	11	yp	yp	PROPN
brj-23756	74	12	)	)	PUNCT
brj-23756	74	13	2𝑛	2𝑛	PROPN
brj-23756	74	14	𝑖=1	𝑖=1	PUNCT
brj-23756	74	15	(	(	PUNCT
brj-23756	74	16	1	1	X
brj-23756	74	17	)	)	PUNCT
brj-23756	74	18	r2	r2	NOUN
brj-23756	74	19	=	=	SYM
brj-23756	74	20	1	1	NUM
brj-23756	74	21	−	−	NOUN
brj-23756	74	22	∑	∑	PROPN
brj-23756	74	23	(	(	PUNCT
brj-23756	74	24	yi−yp	yi−yp	NOUN
brj-23756	74	25	)	)	PUNCT
brj-23756	74	26	2𝑛	2𝑛	PROPN
brj-23756	75	1	𝑖=1	𝑖=1	PUNCT
brj-23756	75	2	∑	∑	PROPN
brj-23756	75	3	(	(	PUNCT
brj-23756	75	4	yi−ȳ)2𝑛	yi−ȳ)2𝑛	NOUN
brj-23756	75	5	𝑖=1	𝑖=1	PROPN
brj-23756	75	6	(	(	PUNCT
brj-23756	75	7	2	2	X
brj-23756	75	8	)	)	PUNCT
brj-23756	75	9	mape	mape	NOUN
brj-23756	75	10	=	=	SYM
brj-23756	75	11	1	1	NUM
brj-23756	75	12	𝑛	𝑛	NOUN
brj-23756	75	13	∑	∑	PUNCT
brj-23756	75	14	(	(	PUNCT
brj-23756	75	15	|yi−yp|	|yi−yp|	X
brj-23756	75	16	yi	yi	NOUN
brj-23756	75	17	)	)	PUNCT
brj-23756	75	18	100𝑛	100𝑛	NOUN
brj-23756	75	19	𝑖=1	𝑖=1	PUNCT
brj-23756	75	20	(	(	PUNCT
brj-23756	75	21	3	3	X
brj-23756	75	22	)	)	PUNCT
brj-23756	75	23	y	y	NOUN
brj-23756	75	24	=	=	SYM
brj-23756	75	25	g(𝜃	g(𝜃	PROPN
brj-23756	75	26	+	+	CCONJ
brj-23756	75	27	∑	∑	PROPN
brj-23756	75	28	𝑣j	𝑣j	ADP
brj-23756	75	29	𝑚	𝑚	X
brj-23756	75	30	𝑗=1	𝑗=1	X
brj-23756	76	1	[	[	X
brj-23756	76	2	∑	∑	PUNCT
brj-23756	76	3	𝑓(𝑊𝑖𝑗xi	𝑓(𝑊𝑖𝑗xi	PRON
brj-23756	76	4	+	+	NOUN
brj-23756	76	5	𝛽𝑗	𝛽𝑗	X
brj-23756	76	6	)	)	PUNCT
brj-23756	76	7	𝑛	𝑛	PRON
brj-23756	76	8	𝑖=1	𝑖=1	PROPN
brj-23756	76	9	]	]	PUNCT
brj-23756	76	10	)	)	PUNCT
brj-23756	76	11	(	(	PUNCT
brj-23756	76	12	4	4	X
brj-23756	76	13	)	)	PUNCT
brj-23756	76	14	in	in	ADP
brj-23756	76	15	these	these	DET
brj-23756	76	16	equations	equation	NOUN
brj-23756	76	17	,	,	PUNCT
brj-23756	76	18	y	y	PROPN
brj-23756	76	19	is	be	AUX
brj-23756	76	20	the	the	DET
brj-23756	76	21	prediction	prediction	NOUN
brj-23756	76	22	of	of	ADP
brj-23756	76	23	the	the	DET
brj-23756	76	24	dependent	dependent	ADJ
brj-23756	76	25	variable	variable	NOUN
brj-23756	76	26	;	;	PUNCT
brj-23756	76	27	g	g	PROPN
brj-23756	76	28	(	(	PUNCT
brj-23756	76	29	)	)	PUNCT
brj-23756	76	30	represents	represent	VERB
brj-23756	76	31	the	the	DET
brj-23756	76	32	activation	activation	NOUN
brj-23756	76	33	functions	function	NOUN
brj-23756	76	34	of	of	ADP
brj-23756	76	35	output	output	NOUN
brj-23756	76	36	neurons	neuron	NOUN
brj-23756	76	37	;	;	PUNCT
brj-23756	76	38	θ	θ	PROPN
brj-23756	76	39	is	be	AUX
brj-23756	76	40	the	the	DET
brj-23756	76	41	bias	bias	NOUN
brj-23756	76	42	value	value	NOUN
brj-23756	76	43	of	of	ADP
brj-23756	76	44	output	output	NOUN
brj-23756	76	45	neuron	neuron	NOUN
brj-23756	76	46	;	;	PUNCT
brj-23756	76	47	vj	vj	PROPN
brj-23756	76	48	is	be	AUX
brj-23756	76	49	the	the	DET
brj-23756	76	50	weight	weight	NOUN
brj-23756	76	51	of	of	ADP
brj-23756	76	52	the	the	DET
brj-23756	76	53	connection	connection	NOUN
brj-23756	76	54	between	between	ADP
brj-23756	76	55	the	the	DET
brj-23756	76	56	jth	jth	PROPN
brj-23756	76	57	hidden	hide	VERB
brj-23756	76	58	and	and	CCONJ
brj-23756	76	59	output	output	NOUN
brj-23756	76	60	neuron	neuron	NOUN
brj-23756	76	61	;	;	PUNCT
brj-23756	76	62	f	f	PROPN
brj-23756	76	63	is	be	AUX
brj-23756	76	64	the	the	DET
brj-23756	76	65	activation	activation	NOUN
brj-23756	76	66	functions	function	NOUN
brj-23756	76	67	of	of	ADP
brj-23756	76	68	hidden	hidden	ADJ
brj-23756	76	69	neurons	neuron	NOUN
brj-23756	76	70	;	;	PUNCT
brj-23756	76	71	wij	wij	PROPN
brj-23756	76	72	is	be	AUX
brj-23756	76	73	the	the	DET
brj-23756	76	74	weight	weight	NOUN
brj-23756	76	75	of	of	ADP
brj-23756	76	76	connection	connection	NOUN
brj-23756	76	77	between	between	ADP
brj-23756	76	78	the	the	DET
brj-23756	76	79	ith	ith	PROPN
brj-23756	76	80	input	input	PROPN
brj-23756	76	81	neuron	neuron	PROPN
brj-23756	76	82	and	and	CCONJ
brj-23756	76	83	jth	jth	PROPN
brj-23756	76	84	hidden	hide	VERB
brj-23756	76	85	neuron	neuron	PROPN
brj-23756	76	86	;	;	PUNCT
brj-23756	76	87	xi	xi	X
brj-23756	76	88	is	be	AUX
brj-23756	76	89	the	the	DET
brj-23756	76	90	input	input	NOUN
brj-23756	76	91	value	value	NOUN
brj-23756	76	92	of	of	ADP
brj-23756	76	93	ith	ith	PROPN
brj-23756	76	94	independent	independent	ADJ
brj-23756	76	95	variable	variable	NOUN
brj-23756	76	96	;	;	PUNCT
brj-23756	76	97	and	and	CCONJ
brj-23756	76	98	βj	βj	PRON
brj-23756	76	99	is	be	AUX
brj-23756	76	100	the	the	DET
brj-23756	76	101	bias	bias	NOUN
brj-23756	76	102	value	value	NOUN
brj-23756	76	103	of	of	ADP
brj-23756	76	104	the	the	DET
brj-23756	76	105	jth	jth	PROPN
brj-23756	76	106	hidden	hide	VERB
brj-23756	76	107	neuron	neuron	PROPN
brj-23756	76	108	.	.	PUNCT
brj-23756	77	1	training	training	NOUN
brj-23756	77	2	and	and	CCONJ
brj-23756	77	3	testing	test	VERB
brj-23756	77	4	the	the	DET
brj-23756	77	5	dataset	dataset	NOUN
brj-23756	77	6	was	be	AUX
brj-23756	77	7	split	split	VERB
brj-23756	77	8	into	into	ADP
brj-23756	77	9	training	training	NOUN
brj-23756	77	10	and	and	CCONJ
brj-23756	77	11	testing	testing	NOUN
brj-23756	77	12	sets	set	NOUN
brj-23756	77	13	(	(	PUNCT
brj-23756	77	14	80	80	NUM
brj-23756	77	15	%	%	NOUN
brj-23756	77	16	for	for	ADP
brj-23756	77	17	training	training	NOUN
brj-23756	77	18	,	,	PUNCT
brj-23756	77	19	20	20	NUM
brj-23756	77	20	%	%	NOUN
brj-23756	77	21	for	for	ADP
brj-23756	77	22	testing	testing	NOUN
brj-23756	77	23	)	)	PUNCT
brj-23756	77	24	using	use	VERB
brj-23756	77	25	the	the	DET
brj-23756	77	26	train_test_split	train_test_split	ADJ
brj-23756	77	27	function	function	NOUN
brj-23756	77	28	.	.	PUNCT
brj-23756	78	1	the	the	DET
brj-23756	78	2	neural	neural	ADJ
brj-23756	78	3	network	network	NOUN
brj-23756	78	4	model	model	NOUN
brj-23756	78	5	was	be	AUX
brj-23756	78	6	trained	train	VERB
brj-23756	78	7	using	use	VERB
brj-23756	78	8	mean	mean	NOUN
brj-23756	78	9	squared	square	VERB
brj-23756	78	10	error	error	NOUN
brj-23756	78	11	loss	loss	NOUN
brj-23756	78	12	as	as	ADP
brj-23756	78	13	the	the	DET
brj-23756	78	14	loss	loss	NOUN
brj-23756	78	15	function	function	NOUN
brj-23756	78	16	and	and	CCONJ
brj-23756	78	17	the	the	DET
brj-23756	78	18	adam	adam	PROPN
brj-23756	78	19	and	and	CCONJ
brj-23756	78	20	sgd	sgd	PROPN
brj-23756	78	21	,	,	PUNCT
brj-23756	78	22	which	which	PRON
brj-23756	78	23	are	be	AUX
brj-23756	78	24	the	the	DET
brj-23756	78	25	most	most	ADV
brj-23756	78	26	common	common	ADJ
brj-23756	78	27	optimizers	optimizer	NOUN
brj-23756	78	28	.	.	PUNCT
brj-23756	79	1	the	the	DET
brj-23756	79	2	training	training	NOUN
brj-23756	79	3	process	process	NOUN
brj-23756	79	4	involved	involve	VERB
brj-23756	79	5	iterating	iterate	VERB
brj-23756	79	6	through	through	ADP
brj-23756	79	7	1000	1000	NUM
brj-23756	79	8	epochs	epoch	NOUN
brj-23756	79	9	(	(	PUNCT
brj-23756	79	10	iteration	iteration	NOUN
brj-23756	79	11	)	)	PUNCT
brj-23756	79	12	and	and	CCONJ
brj-23756	79	13	updating	update	VERB
brj-23756	79	14	the	the	DET
brj-23756	79	15	model	model	NOUN
brj-23756	79	16	’s	’s	PART
brj-23756	79	17	parameters	parameter	NOUN
brj-23756	79	18	to	to	PART
brj-23756	79	19	minimize	minimize	VERB
brj-23756	79	20	the	the	DET
brj-23756	79	21	training	training	NOUN
brj-23756	79	22	loss	loss	NOUN
brj-23756	79	23	,	,	PUNCT
brj-23756	79	24	which	which	PRON
brj-23756	79	25	shows	show	VERB
brj-23756	79	26	how	how	SCONJ
brj-23756	79	27	well	well	ADV
brj-23756	79	28	the	the	DET
brj-23756	79	29	model	model	NOUN
brj-23756	79	30	is	be	AUX
brj-23756	79	31	fitting	fit	VERB
brj-23756	79	32	the	the	DET
brj-23756	79	33	training	training	NOUN
brj-23756	79	34	data	datum	NOUN
brj-23756	79	35	.	.	PUNCT
brj-23756	80	1	subsequently	subsequently	ADV
brj-23756	80	2	,	,	PUNCT
brj-23756	80	3	the	the	DET
brj-23756	80	4	model	model	NOUN
brj-23756	80	5	was	be	AUX
brj-23756	80	6	validated	validate	VERB
brj-23756	80	7	using	use	VERB
brj-23756	80	8	the	the	DET
brj-23756	80	9	test	test	NOUN
brj-23756	80	10	set	set	VERB
brj-23756	80	11	to	to	PART
brj-23756	80	12	evaluate	evaluate	VERB
brj-23756	80	13	its	its	PRON
brj-23756	80	14	ability	ability	NOUN
brj-23756	80	15	to	to	PART
brj-23756	80	16	predict	predict	VERB
brj-23756	80	17	.	.	PUNCT
brj-23756	81	1	test	test	NOUN
brj-23756	81	2	loss	loss	NOUN
brj-23756	81	3	or	or	CCONJ
brj-23756	81	4	mean	mean	VERB
brj-23756	81	5	squared	square	VERB
brj-23756	81	6	error	error	NOUN
brj-23756	81	7	(	(	PUNCT
brj-23756	81	8	mse	mse	NOUN
brj-23756	81	9	)	)	PUNCT
brj-23756	81	10	,	,	PUNCT
brj-23756	81	11	r2	r2	NOUN
brj-23756	81	12	,	,	PUNCT
brj-23756	81	13	and	and	CCONJ
brj-23756	81	14	the	the	DET
brj-23756	81	15	mean	mean	ADJ
brj-23756	81	16	absolute	absolute	ADJ
brj-23756	81	17	percentage	percentage	NOUN
brj-23756	81	18	error	error	NOUN
brj-23756	81	19	(	(	PUNCT
brj-23756	81	20	mape	mape	NOUN
brj-23756	81	21	)	)	PUNCT
brj-23756	81	22	were	be	AUX
brj-23756	81	23	used	use	VERB
brj-23756	81	24	for	for	ADP
brj-23756	81	25	evaluation	evaluation	NOUN
brj-23756	81	26	and	and	CCONJ
brj-23756	81	27	their	their	PRON
brj-23756	81	28	mathematical	mathematical	ADJ
brj-23756	81	29	expression	expression	NOUN
brj-23756	81	30	are	be	AUX
brj-23756	81	31	presented	present	VERB
brj-23756	81	32	in	in	ADP
brj-23756	81	33	eqs	eqs	PROPN
brj-23756	81	34	.	.	PROPN
brj-23756	81	35	1	1	NUM
brj-23756	81	36	to	to	PART
brj-23756	81	37	3	3	NUM
brj-23756	81	38	.	.	PUNCT
brj-23756	81	39	test	test	NOUN
brj-23756	81	40	loss	loss	NOUN
brj-23756	81	41	or	or	CCONJ
brj-23756	81	42	mean	mean	VERB
brj-23756	81	43	square	square	ADJ
brj-23756	81	44	error	error	NOUN
brj-23756	81	45	(	(	PUNCT
brj-23756	81	46	mse	mse	NOUN
brj-23756	81	47	)	)	PUNCT
brj-23756	81	48	indicates	indicate	VERB
brj-23756	81	49	how	how	SCONJ
brj-23756	81	50	well	well	ADV
brj-23756	81	51	the	the	DET
brj-23756	81	52	model	model	NOUN
brj-23756	81	53	applies	apply	VERB
brj-23756	81	54	new	new	ADJ
brj-23756	81	55	data	datum	NOUN
brj-23756	81	56	.	.	PUNCT
brj-23756	82	1	a	a	DET
brj-23756	82	2	lower	low	ADJ
brj-23756	82	3	mse	mse	NOUN
brj-23756	82	4	suggests	suggest	VERB
brj-23756	82	5	better	well	ADJ
brj-23756	82	6	accuracy	accuracy	NOUN
brj-23756	82	7	.	.	PUNCT
brj-23756	83	1	the	the	DET
brj-23756	83	2	r2	r2	PROPN
brj-23756	83	3	score	score	NOUN
brj-23756	83	4	represents	represent	VERB
brj-23756	83	5	the	the	DET
brj-23756	83	6	proportion	proportion	NOUN
brj-23756	83	7	of	of	ADP
brj-23756	83	8	variance	variance	NOUN
brj-23756	83	9	in	in	ADP
brj-23756	83	10	the	the	DET
brj-23756	83	11	dependent	dependent	ADJ
brj-23756	83	12	variable	variable	NOUN
brj-23756	83	13	explained	explain	VERB
brj-23756	83	14	by	by	ADP
brj-23756	83	15	the	the	DET
brj-23756	83	16	independent	independent	ADJ
brj-23756	83	17	variables	variable	NOUN
brj-23756	83	18	.	.	PUNCT
brj-23756	84	1	a	a	DET
brj-23756	84	2	higher	high	ADJ
brj-23756	84	3	r2	r2	NOUN
brj-23756	84	4	score	score	NOUN
brj-23756	84	5	(	(	PUNCT
brj-23756	84	6	closer	close	ADJ
brj-23756	84	7	to	to	PART
brj-23756	84	8	1	1	NUM
brj-23756	84	9	)	)	PUNCT
brj-23756	84	10	indicates	indicate	VERB
brj-23756	84	11	better	well	ADJ
brj-23756	84	12	predictive	predictive	ADJ
brj-23756	84	13	performance	performance	NOUN
brj-23756	84	14	.	.	PUNCT
brj-23756	85	1	mape	mape	NOUN
brj-23756	85	2	provides	provide	VERB
brj-23756	85	3	a	a	DET
brj-23756	85	4	measure	measure	NOUN
brj-23756	85	5	of	of	ADP
brj-23756	85	6	how	how	SCONJ
brj-23756	85	7	far	far	ADV
brj-23756	85	8	the	the	DET
brj-23756	85	9	predicted	predict	VERB
brj-23756	85	10	values	value	NOUN
brj-23756	85	11	are	be	AUX
brj-23756	85	12	from	from	ADP
brj-23756	85	13	the	the	DET
brj-23756	85	14	actual	actual	ADJ
brj-23756	85	15	values	value	NOUN
brj-23756	85	16	as	as	ADP
brj-23756	85	17	a	a	DET
brj-23756	85	18	percentage	percentage	NOUN
brj-23756	85	19	and	and	CCONJ
brj-23756	85	20	should	should	AUX
brj-23756	85	21	be	be	AUX
brj-23756	85	22	defined	define	VERB
brj-23756	85	23	and	and	CCONJ
brj-23756	85	24	calculated	calculate	VERB
brj-23756	85	25	before	before	ADP
brj-23756	85	26	printing	print	VERB
brj-23756	85	27	the	the	DET
brj-23756	85	28	data	datum	NOUN
brj-23756	85	29	in	in	ADP
brj-23756	85	30	the	the	DET
brj-23756	85	31	coding	coding	NOUN
brj-23756	85	32	.	.	PUNCT
brj-23756	86	1	in	in	ADP
brj-23756	86	2	the	the	DET
brj-23756	86	3	training	training	NOUN
brj-23756	86	4	process	process	NOUN
brj-23756	86	5	the	the	DET
brj-23756	86	6	learning	learning	NOUN
brj-23756	86	7	rate	rate	NOUN
brj-23756	86	8	was	be	AUX
brj-23756	86	9	set	set	VERB
brj-23756	86	10	to	to	ADP
brj-23756	86	11	lr	lr	X
brj-23756	86	12	=	=	NOUN
brj-23756	86	13	0.0001	0.0001	NUM
brj-23756	86	14	to	to	ADP
brj-23756	86	15	0.001	0.001	NUM
brj-23756	86	16	and	and	CCONJ
brj-23756	86	17	0.005	0.005	NUM
brj-23756	86	18	,	,	PUNCT
brj-23756	86	19	for	for	ADP
brj-23756	86	20	fine	fine	ADV
brj-23756	86	21	-	-	PUNCT
brj-23756	86	22	tuning	tune	VERB
brj-23756	86	23	model	model	NOUN
brj-23756	86	24	parameters	parameter	NOUN
brj-23756	86	25	for	for	ADP
brj-23756	86	26	optimal	optimal	ADJ
brj-23756	86	27	performance	performance	NOUN
brj-23756	86	28	.	.	PUNCT
brj-23756	87	1	results	result	NOUN
brj-23756	87	2	and	and	CCONJ
brj-23756	87	3	discussion	discussion	NOUN
brj-23756	87	4	model	model	NOUN
brj-23756	87	5	performance	performance	NOUN
brj-23756	87	6	analysis	analysis	NOUN
brj-23756	87	7	the	the	DET
brj-23756	87	8	data	datum	NOUN
brj-23756	87	9	for	for	ADP
brj-23756	87	10	performance	performance	NOUN
brj-23756	87	11	analysis	analysis	NOUN
brj-23756	87	12	,	,	PUNCT
brj-23756	87	13	such	such	ADJ
brj-23756	87	14	as	as	ADP
brj-23756	87	15	r2	r2	PROPN
brj-23756	87	16	,	,	PUNCT
brj-23756	87	17	mse	mse	NOUN
brj-23756	87	18	,	,	PUNCT
brj-23756	87	19	and	and	CCONJ
brj-23756	87	20	mape	mape	NOUN
brj-23756	87	21	,	,	PUNCT
brj-23756	87	22	values	value	NOUN
brj-23756	87	23	of	of	ADP
brj-23756	87	24	the	the	DET
brj-23756	87	25	emc	emc	PROPN
brj-23756	87	26	and	and	CCONJ
brj-23756	87	27	swelling	swell	VERB
brj-23756	87	28	parameters	parameter	NOUN
brj-23756	87	29	,	,	PUNCT
brj-23756	87	30	shown	show	VERB
brj-23756	87	31	in	in	ADP
brj-23756	87	32	table	table	NOUN
brj-23756	87	33	2	2	NUM
brj-23756	87	34	,	,	PUNCT
brj-23756	87	35	indicate	indicate	VERB
brj-23756	87	36	that	that	SCONJ
brj-23756	87	37	the	the	DET
brj-23756	87	38	ann	ann	PROPN
brj-23756	87	39	models	model	NOUN
brj-23756	87	40	were	be	AUX
brj-23756	87	41	successful	successful	ADJ
brj-23756	87	42	.	.	PUNCT
brj-23756	88	1	masoumi	masoumi	NOUN
brj-23756	88	2	and	and	CCONJ
brj-23756	88	3	bond	bond	NOUN
brj-23756	88	4	2024b	2024b	NOUN
brj-23756	88	5	compared	compare	VERB
brj-23756	88	6	ann	ann	PROPN
brj-23756	88	7	with	with	ADP
brj-23756	88	8	random	random	ADJ
brj-23756	88	9	forest	forest	NOUN
brj-23756	88	10	and	and	CCONJ
brj-23756	88	11	gradient	gradient	ADJ
brj-23756	88	12	boosting	boost	VERB
brj-23756	88	13	regression	regression	NOUN
brj-23756	88	14	models	model	NOUN
brj-23756	88	15	and	and	CCONJ
brj-23756	88	16	demonstrated	demonstrate	VERB
brj-23756	88	17	that	that	SCONJ
brj-23756	88	18	ann	ann	PROPN
brj-23756	88	19	models	model	NOUN
brj-23756	88	20	are	be	AUX
brj-23756	88	21	more	more	ADV
brj-23756	88	22	accurate	accurate	ADJ
brj-23756	88	23	than	than	ADP
brj-23756	88	24	peer	peer	NOUN
brj-23756	88	25	-	-	PUNCT
brj-23756	88	26	reviewed	review	VERB
brj-23756	88	27	article	article	NOUN
brj-23756	88	28	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	88	29	masoumi	masoumi	NOUN
brj-23756	88	30	&	&	CCONJ
brj-23756	88	31	bond	bond	PROPN
brj-23756	88	32	(	(	PUNCT
brj-23756	88	33	2024	2024	NUM
brj-23756	88	34	)	)	PUNCT
brj-23756	88	35	.	.	PUNCT
brj-23756	89	1	“	"	PUNCT
brj-23756	89	2	ann	ann	PROPN
brj-23756	89	3	prediction	prediction	NOUN
brj-23756	89	4	of	of	ADP
brj-23756	89	5	tmw	tmw	NOUN
brj-23756	89	6	,	,	PUNCT
brj-23756	89	7	”	"	PUNCT
brj-23756	89	8	bioresources	bioresource	NOUN
brj-23756	89	9	19(4	19(4	NUM
brj-23756	89	10	)	)	PUNCT
brj-23756	89	11	,	,	PUNCT
brj-23756	89	12	6983	6983	NUM
brj-23756	89	13	-	-	SYM
brj-23756	89	14	6993	6993	NUM
brj-23756	89	15	.	.	PUNCT
brj-23756	90	1	6988	6988	NUM
brj-23756	90	2	traditional	traditional	ADJ
brj-23756	90	3	regression	regression	NOUN
brj-23756	90	4	models	model	NOUN
brj-23756	90	5	.	.	PUNCT
brj-23756	91	1	both	both	DET
brj-23756	91	2	pytorch	pytorch	NOUN
brj-23756	91	3	and	and	CCONJ
brj-23756	91	4	keras	keras	PROPN
brj-23756	91	5	performed	perform	VERB
brj-23756	91	6	similar	similar	ADJ
brj-23756	91	7	accuracy	accuracy	NOUN
brj-23756	91	8	in	in	ADP
brj-23756	91	9	terms	term	NOUN
brj-23756	91	10	of	of	ADP
brj-23756	91	11	r2	r2	PROPN
brj-23756	91	12	,	,	PUNCT
brj-23756	91	13	mse	mse	NOUN
brj-23756	91	14	,	,	PUNCT
brj-23756	91	15	and	and	CCONJ
brj-23756	91	16	mape	mape	NOUN
brj-23756	91	17	in	in	ADP
brj-23756	91	18	both	both	CCONJ
brj-23756	91	19	single	single	ADJ
brj-23756	91	20	and	and	CCONJ
brj-23756	91	21	multiple	multiple	ADJ
brj-23756	91	22	-	-	PUNCT
brj-23756	91	23	input	input	NOUN
brj-23756	91	24	ann	ann	PROPN
brj-23756	91	25	models	model	NOUN
brj-23756	91	26	.	.	PUNCT
brj-23756	92	1	the	the	DET
brj-23756	92	2	pytorch	pytorch	NOUN
brj-23756	92	3	has	have	VERB
brj-23756	92	4	better	well	ADJ
brj-23756	92	5	flexibility	flexibility	NOUN
brj-23756	92	6	and	and	CCONJ
brj-23756	92	7	debugging	debug	VERB
brj-23756	92	8	capabilities	capability	NOUN
brj-23756	92	9	to	to	PART
brj-23756	92	10	check	check	VERB
brj-23756	92	11	and	and	CCONJ
brj-23756	92	12	ensure	ensure	VERB
brj-23756	92	13	the	the	DET
brj-23756	92	14	validity	validity	NOUN
brj-23756	92	15	of	of	ADP
brj-23756	92	16	keras	keras	PROPN
brj-23756	92	17	.	.	PUNCT
brj-23756	93	1	the	the	DET
brj-23756	93	2	models	model	NOUN
brj-23756	93	3	implemented	implement	VERB
brj-23756	93	4	by	by	ADP
brj-23756	93	5	both	both	DET
brj-23756	93	6	keras	keras	PROPN
brj-23756	93	7	and	and	CCONJ
brj-23756	93	8	pytorch	pytorch	NOUN
brj-23756	93	9	provided	provide	VERB
brj-23756	93	10	similar	similar	ADJ
brj-23756	93	11	results	result	NOUN
brj-23756	93	12	.	.	PUNCT
brj-23756	94	1	the	the	DET
brj-23756	94	2	single	single	ADJ
brj-23756	94	3	input	input	NOUN
brj-23756	94	4	model	model	NOUN
brj-23756	94	5	was	be	AUX
brj-23756	94	6	acceptable	acceptable	ADJ
brj-23756	94	7	just	just	ADV
brj-23756	94	8	in	in	ADP
brj-23756	94	9	features	feature	NOUN
brj-23756	94	10	of	of	ADP
brj-23756	94	11	treatment	treatment	NOUN
brj-23756	94	12	and	and	CCONJ
brj-23756	94	13	time	time	NOUN
brj-23756	94	14	(	(	PUNCT
brj-23756	94	15	r2	r2	PROPN
brj-23756	94	16	>	>	PUNCT
brj-23756	94	17	0.90	0.90	NUM
brj-23756	94	18	or	or	CCONJ
brj-23756	94	19	mape	mape	NOUN
brj-23756	94	20	<	<	X
brj-23756	94	21	10	10	NUM
brj-23756	94	22	)	)	PUNCT
brj-23756	94	23	for	for	ADP
brj-23756	94	24	emc	emc	PROPN
brj-23756	94	25	.	.	PUNCT
brj-23756	95	1	however	however	ADV
brj-23756	95	2	,	,	PUNCT
brj-23756	95	3	for	for	ADP
brj-23756	95	4	swelling	swelling	NOUN
brj-23756	95	5	,	,	PUNCT
brj-23756	95	6	the	the	DET
brj-23756	95	7	single	single	ADJ
brj-23756	95	8	input	input	NOUN
brj-23756	95	9	model	model	NOUN
brj-23756	95	10	did	do	AUX
brj-23756	95	11	not	not	PART
brj-23756	95	12	provide	provide	VERB
brj-23756	95	13	as	as	ADP
brj-23756	95	14	good	good	ADJ
brj-23756	95	15	performance	performance	NOUN
brj-23756	95	16	as	as	SCONJ
brj-23756	95	17	all	all	DET
brj-23756	95	18	r2	r2	NOUN
brj-23756	95	19	values	value	NOUN
brj-23756	95	20	were	be	AUX
brj-23756	95	21	smaller	small	ADJ
brj-23756	95	22	than	than	ADP
brj-23756	95	23	0.90	0.90	NUM
brj-23756	95	24	,	,	PUNCT
brj-23756	95	25	and	and	CCONJ
brj-23756	95	26	mape	mape	NOUN
brj-23756	95	27	values	value	NOUN
brj-23756	95	28	were	be	AUX
brj-23756	95	29	higher	high	ADJ
brj-23756	95	30	than	than	ADP
brj-23756	95	31	10	10	NUM
brj-23756	95	32	.	.	PUNCT
brj-23756	95	33	swelling	swell	VERB
brj-23756	95	34	in	in	ADP
brj-23756	95	35	wood	wood	NOUN
brj-23756	95	36	,	,	PUNCT
brj-23756	95	37	especially	especially	ADV
brj-23756	95	38	in	in	ADP
brj-23756	95	39	tmw	tmw	PROPN
brj-23756	95	40	is	be	AUX
brj-23756	95	41	affected	affect	VERB
brj-23756	95	42	by	by	ADP
brj-23756	95	43	multiple	multiple	ADJ
brj-23756	95	44	features	feature	NOUN
brj-23756	95	45	,	,	PUNCT
brj-23756	95	46	such	such	ADJ
brj-23756	95	47	as	as	ADP
brj-23756	95	48	wood	wood	NOUN
brj-23756	95	49	species	specie	NOUN
brj-23756	95	50	,	,	PUNCT
brj-23756	95	51	anatomy	anatomy	NOUN
brj-23756	95	52	,	,	PUNCT
brj-23756	95	53	and	and	CCONJ
brj-23756	95	54	density	density	NOUN
brj-23756	95	55	,	,	PUNCT
brj-23756	95	56	that	that	PRON
brj-23756	95	57	are	be	AUX
brj-23756	95	58	unique	unique	ADJ
brj-23756	95	59	to	to	ADP
brj-23756	95	60	each	each	DET
brj-23756	95	61	species	specie	NOUN
brj-23756	95	62	,	,	PUNCT
brj-23756	95	63	modifying	modify	VERB
brj-23756	95	64	time	time	NOUN
brj-23756	95	65	and	and	CCONJ
brj-23756	95	66	temperature	temperature	NOUN
brj-23756	95	67	that	that	PRON
brj-23756	95	68	changes	change	VERB
brj-23756	95	69	the	the	DET
brj-23756	95	70	physical	physical	ADJ
brj-23756	95	71	properties	property	NOUN
brj-23756	95	72	to	to	ADP
brj-23756	95	73	different	different	ADJ
brj-23756	95	74	extents	extent	NOUN
brj-23756	95	75	by	by	ADP
brj-23756	95	76	changing	change	VERB
brj-23756	95	77	the	the	DET
brj-23756	95	78	chemical	chemical	ADJ
brj-23756	95	79	and	and	CCONJ
brj-23756	95	80	structural	structural	ADJ
brj-23756	95	81	properties	property	NOUN
brj-23756	95	82	differently	differently	ADV
brj-23756	95	83	in	in	ADP
brj-23756	95	84	different	different	ADJ
brj-23756	95	85	species	specie	NOUN
brj-23756	95	86	.	.	PUNCT
brj-23756	96	1	in	in	ADP
brj-23756	96	2	the	the	DET
brj-23756	96	3	multiple	multiple	ADJ
brj-23756	96	4	-	-	PUNCT
brj-23756	96	5	input	input	NOUN
brj-23756	96	6	model	model	NOUN
brj-23756	96	7	,	,	PUNCT
brj-23756	96	8	r2	r2	PROPN
brj-23756	96	9	values	value	NOUN
brj-23756	96	10	were	be	AUX
brj-23756	96	11	higher	high	ADJ
brj-23756	96	12	than	than	ADP
brj-23756	96	13	0.90	0.90	NUM
brj-23756	96	14	and	and	CCONJ
brj-23756	96	15	mape	mape	NOUN
brj-23756	96	16	values	value	NOUN
brj-23756	96	17	were	be	AUX
brj-23756	96	18	smaller	small	ADJ
brj-23756	96	19	than	than	ADP
brj-23756	96	20	10	10	NUM
brj-23756	96	21	%	%	NOUN
brj-23756	96	22	in	in	ADP
brj-23756	96	23	predicted	predict	VERB
brj-23756	96	24	data	datum	NOUN
brj-23756	96	25	for	for	ADP
brj-23756	96	26	both	both	DET
brj-23756	96	27	emc	emc	PROPN
brj-23756	96	28	and	and	CCONJ
brj-23756	96	29	swelling	swell	VERB
brj-23756	96	30	in	in	ADP
brj-23756	96	31	tm	tm	DET
brj-23756	96	32	hardwoods	hardwood	NOUN
brj-23756	96	33	by	by	ADP
brj-23756	96	34	having	have	VERB
brj-23756	96	35	the	the	DET
brj-23756	96	36	r2	r2	PROPN
brj-23756	96	37	and	and	CCONJ
brj-23756	96	38	mape	mape	NOUN
brj-23756	96	39	values	value	NOUN
brj-23756	96	40	of	of	ADP
brj-23756	96	41	0.9975	0.9975	NUM
brj-23756	96	42	,	,	PUNCT
brj-23756	96	43	0.92	0.92	NUM
brj-23756	96	44	,	,	PUNCT
brj-23756	96	45	and	and	CCONJ
brj-23756	96	46	mape	mape	NOUN
brj-23756	96	47	values	value	NOUN
brj-23756	96	48	of	of	ADP
brj-23756	96	49	1.36	1.36	NUM
brj-23756	96	50	,	,	PUNCT
brj-23756	96	51	7.77	7.77	NUM
brj-23756	96	52	for	for	ADP
brj-23756	96	53	emc	emc	NOUN
brj-23756	96	54	and	and	CCONJ
brj-23756	96	55	swelling	swelling	NOUN
brj-23756	96	56	,	,	PUNCT
brj-23756	96	57	respectively	respectively	ADV
brj-23756	96	58	.	.	PUNCT
brj-23756	97	1	particularly	particularly	ADV
brj-23756	97	2	,	,	PUNCT
brj-23756	97	3	having	have	VERB
brj-23756	97	4	r2	r2	NOUN
brj-23756	97	5	value	value	NOUN
brj-23756	97	6	of	of	ADP
brj-23756	97	7	0.997	0.997	NUM
brj-23756	97	8	in	in	ADP
brj-23756	97	9	emc	emc	PROPN
brj-23756	97	10	and	and	CCONJ
brj-23756	97	11	mape	mape	NOUN
brj-23756	97	12	of	of	ADP
brj-23756	97	13	1.36	1.36	NUM
brj-23756	97	14	shows	show	NOUN
brj-23756	97	15	very	very	ADV
brj-23756	97	16	high	high	ADJ
brj-23756	97	17	accuracy	accuracy	NOUN
brj-23756	97	18	of	of	ADP
brj-23756	97	19	the	the	DET
brj-23756	97	20	performance	performance	NOUN
brj-23756	97	21	in	in	ADP
brj-23756	97	22	multiple	multiple	ADJ
brj-23756	97	23	input	input	NOUN
brj-23756	97	24	models	model	NOUN
brj-23756	97	25	in	in	ADP
brj-23756	97	26	predicting	predict	VERB
brj-23756	97	27	emc	emc	PROPN
brj-23756	97	28	.	.	PUNCT
brj-23756	98	1	an	an	DET
brj-23756	98	2	r2	r2	PROPN
brj-23756	98	3	value	value	NOUN
brj-23756	98	4	over	over	ADP
brj-23756	98	5	0.90	0.90	NUM
brj-23756	98	6	indicates	indicate	VERB
brj-23756	98	7	an	an	DET
brj-23756	98	8	excellent	excellent	ADJ
brj-23756	98	9	correlation	correlation	NOUN
brj-23756	98	10	between	between	ADP
brj-23756	98	11	the	the	DET
brj-23756	98	12	calculated	calculate	VERB
brj-23756	98	13	and	and	CCONJ
brj-23756	98	14	predicted	predict	VERB
brj-23756	98	15	data	datum	NOUN
brj-23756	98	16	.	.	PUNCT
brj-23756	99	1	moreover	moreover	ADV
brj-23756	99	2	,	,	PUNCT
brj-23756	99	3	mape	mape	NOUN
brj-23756	99	4	is	be	AUX
brj-23756	99	5	a	a	DET
brj-23756	99	6	decisive	decisive	ADJ
brj-23756	99	7	factor	factor	NOUN
brj-23756	99	8	for	for	ADP
brj-23756	99	9	evaluating	evaluate	VERB
brj-23756	99	10	for	for	ADP
brj-23756	99	11	prediction	prediction	NOUN
brj-23756	99	12	performance	performance	NOUN
brj-23756	99	13	(	(	PUNCT
brj-23756	99	14	aydin	aydin	NOUN
brj-23756	99	15	et	et	PROPN
brj-23756	99	16	al	al	PROPN
brj-23756	99	17	.	.	PROPN
brj-23756	99	18	2015	2015	NUM
brj-23756	99	19	)	)	PUNCT
brj-23756	99	20	and	and	CCONJ
brj-23756	99	21	10	10	NUM
brj-23756	99	22	%	%	NOUN
brj-23756	99	23	is	be	AUX
brj-23756	99	24	considered	consider	VERB
brj-23756	99	25	a	a	DET
brj-23756	99	26	highly	highly	ADV
brj-23756	99	27	accurate	accurate	ADJ
brj-23756	99	28	prediction	prediction	NOUN
brj-23756	99	29	.	.	PUNCT
brj-23756	100	1	table	table	NOUN
brj-23756	100	2	2	2	NUM
brj-23756	100	3	.	.	X
brj-23756	100	4	data	datum	NOUN
brj-23756	100	5	for	for	ADP
brj-23756	100	6	performance	performance	NOUN
brj-23756	100	7	analysis	analysis	NOUN
brj-23756	100	8	of	of	ADP
brj-23756	100	9	single	single	ADJ
brj-23756	100	10	and	and	CCONJ
brj-23756	100	11	multiple	multiple	ADJ
brj-23756	100	12	input	input	NOUN
brj-23756	100	13	ann	ann	PROPN
brj-23756	100	14	single	single	ADJ
brj-23756	100	15	criteria	criterion	NOUN
brj-23756	100	16	input	input	VERB
brj-23756	100	17	output	output	NOUN
brj-23756	100	18	treatment	treatment	NOUN
brj-23756	100	19	species	specie	NOUN
brj-23756	100	20	temperature	temperature	NOUN
brj-23756	100	21	time	time	NOUN
brj-23756	100	22	density	density	NOUN
brj-23756	100	23	input	input	NOUN
brj-23756	100	24	r2	r2	PROPN
brj-23756	100	25	0.95	0.95	NUM
brj-23756	100	26	(	(	PUNCT
brj-23756	100	27	0.86	0.86	NUM
brj-23756	100	28	)	)	PUNCT
brj-23756	100	29	0.03	0.03	NUM
brj-23756	100	30	(	(	PUNCT
brj-23756	100	31	0.3	0.3	NUM
brj-23756	100	32	)	)	PUNCT
brj-23756	100	33	0.86	0.86	NUM
brj-23756	100	34	(	(	PUNCT
brj-23756	100	35	0.86	0.86	NUM
brj-23756	100	36	)	)	PUNCT
brj-23756	100	37	0.91	0.91	NUM
brj-23756	100	38	(	(	PUNCT
brj-23756	100	39	0.91	0.91	NUM
brj-23756	100	40	)	)	PUNCT
brj-23756	100	41	0.66	0.66	NUM
brj-23756	100	42	(	(	PUNCT
brj-23756	100	43	0.75	0.75	NUM
brj-23756	100	44	)	)	PUNCT
brj-23756	100	45	emc	emc	PROPN
brj-23756	100	46	mse	mse	PROPN
brj-23756	100	47	0.95	0.95	NUM
brj-23756	100	48	(	(	PUNCT
brj-23756	100	49	0.95	0.95	NUM
brj-23756	100	50	)	)	PUNCT
brj-23756	100	51	6.95	6.95	NUM
brj-23756	100	52	(	(	PUNCT
brj-23756	100	53	6.94	6.94	NUM
brj-23756	100	54	)	)	PUNCT
brj-23756	100	55	0.93	0.93	NUM
brj-23756	100	56	(	(	PUNCT
brj-23756	100	57	0.96	0.96	NUM
brj-23756	100	58	)	)	PUNCT
brj-23756	100	59	0.64	0.64	NUM
brj-23756	100	60	(	(	PUNCT
brj-23756	100	61	0.64	0.64	NUM
brj-23756	100	62	)	)	PUNCT
brj-23756	100	63	2.37	2.37	NUM
brj-23756	100	64	(	(	PUNCT
brj-23756	100	65	1.76	1.76	NUM
brj-23756	100	66	)	)	PUNCT
brj-23756	100	67	emc	emc	PROPN
brj-23756	100	68	mape	mape	NOUN
brj-23756	100	69	12.45	12.45	NUM
brj-23756	100	70	(	(	PUNCT
brj-23756	100	71	11.79	11.79	NUM
brj-23756	100	72	)	)	PUNCT
brj-23756	100	73	37.62	37.62	NUM
brj-23756	100	74	(	(	PUNCT
brj-23756	100	75	37.88	37.88	NUM
brj-23756	100	76	)	)	PUNCT
brj-23756	100	77	12.34	12.34	NUM
brj-23756	100	78	(	(	PUNCT
brj-23756	100	79	12.41	12.41	NUM
brj-23756	100	80	)	)	PUNCT
brj-23756	100	81	7.75	7.75	NUM
brj-23756	100	82	(	(	PUNCT
brj-23756	100	83	7.74	7.74	NUM
brj-23756	100	84	)	)	PUNCT
brj-23756	100	85	15.32	15.32	NUM
brj-23756	100	86	(	(	PUNCT
brj-23756	100	87	12.60	12.60	NUM
brj-23756	100	88	)	)	PUNCT
brj-23756	100	89	emc	emc	PROPN
brj-23756	100	90	r2	r2	PROPN
brj-23756	100	91	0.2	0.2	NUM
brj-23756	100	92	(	(	PUNCT
brj-23756	100	93	0.20	0.20	NUM
brj-23756	100	94	)	)	PUNCT
brj-23756	100	95	0.54	0.54	NUM
brj-23756	100	96	(	(	PUNCT
brj-23756	100	97	0.54	0.54	NUM
brj-23756	100	98	)	)	PUNCT
brj-23756	100	99	0.20	0.20	NUM
brj-23756	100	100	(	(	PUNCT
brj-23756	100	101	0.20	0.20	NUM
brj-23756	100	102	)	)	PUNCT
brj-23756	100	103	0.2	0.2	NUM
brj-23756	100	104	(	(	PUNCT
brj-23756	100	105	0.2	0.2	NUM
brj-23756	100	106	)	)	PUNCT
brj-23756	100	107	0.70	0.70	NUM
brj-23756	100	108	(	(	PUNCT
brj-23756	100	109	0.71	0.71	NUM
brj-23756	100	110	)	)	PUNCT
brj-23756	100	111	swelling	swell	VERB
brj-23756	100	112	mse	mse	NOUN
brj-23756	100	113	21.38	21.38	NUM
brj-23756	100	114	(	(	PUNCT
brj-23756	100	115	21.4	21.4	NUM
brj-23756	100	116	)	)	PUNCT
brj-23756	100	117	12.16	12.16	NUM
brj-23756	100	118	(	(	PUNCT
brj-23756	100	119	12.22	12.22	NUM
brj-23756	100	120	)	)	PUNCT
brj-23756	100	121	21.38	21.38	NUM
brj-23756	100	122	(	(	PUNCT
brj-23756	100	123	21.47	21.47	NUM
brj-23756	100	124	)	)	PUNCT
brj-23756	100	125	21.045	21.045	NUM
brj-23756	100	126	(	(	PUNCT
brj-23756	100	127	21.49	21.49	NUM
brj-23756	100	128	)	)	PUNCT
brj-23756	100	129	7.84	7.84	NUM
brj-23756	100	130	(	(	PUNCT
brj-23756	100	131	7.67	7.67	NUM
brj-23756	100	132	)	)	PUNCT
brj-23756	100	133	swelling	swell	VERB
brj-23756	100	134	mape	mape	NOUN
brj-23756	100	135	36.79	36.79	NUM
brj-23756	100	136	(	(	PUNCT
brj-23756	100	137	37.5	37.5	NUM
brj-23756	100	138	)	)	PUNCT
brj-23756	100	139	34.24	34.24	NUM
brj-23756	100	140	(	(	PUNCT
brj-23756	100	141	34.5	34.5	NUM
brj-23756	100	142	)	)	PUNCT
brj-23756	100	143	37.32	37.32	NUM
brj-23756	100	144	(	(	PUNCT
brj-23756	100	145	37.98	37.98	NUM
brj-23756	100	146	)	)	PUNCT
brj-23756	100	147	38.08	38.08	NUM
brj-23756	100	148	(	(	PUNCT
brj-23756	100	149	38.47	38.47	NUM
brj-23756	100	150	)	)	PUNCT
brj-23756	100	151	17.35	17.35	NUM
brj-23756	100	152	(	(	PUNCT
brj-23756	100	153	17.18	17.18	NUM
brj-23756	100	154	)	)	PUNCT
brj-23756	100	155	swelling	swell	VERB
brj-23756	100	156	multiple	multiple	ADJ
brj-23756	100	157	input	input	NOUN
brj-23756	100	158	r2	r2	PROPN
brj-23756	100	159	0.9976	0.9976	NUM
brj-23756	100	160	(	(	PUNCT
brj-23756	100	161	0.9970	0.9970	NUM
brj-23756	100	162	)	)	PUNCT
brj-23756	100	163	all	all	DET
brj-23756	100	164	the	the	DET
brj-23756	100	165	features	feature	NOUN
brj-23756	100	166	emc	emc	PROPN
brj-23756	100	167	mse	mse	PROPN
brj-23756	100	168	0.017	0.017	NUM
brj-23756	100	169	(	(	PUNCT
brj-23756	100	170	0.02	0.02	NUM
brj-23756	100	171	)	)	PUNCT
brj-23756	100	172	emc	emc	PROPN
brj-23756	100	173	mape	mape	NOUN
brj-23756	100	174	1.36	1.36	NUM
brj-23756	100	175	(	(	PUNCT
brj-23756	100	176	1.54	1.54	NUM
brj-23756	100	177	)	)	PUNCT
brj-23756	100	178	emc	emc	PROPN
brj-23756	100	179	r2	r2	PROPN
brj-23756	100	180	0.92	0.92	NUM
brj-23756	100	181	(	(	PUNCT
brj-23756	100	182	0.92	0.92	NUM
brj-23756	100	183	)	)	PUNCT
brj-23756	100	184	all	all	DET
brj-23756	100	185	the	the	DET
brj-23756	100	186	features	feature	NOUN
brj-23756	100	187	swelling	swell	VERB
brj-23756	100	188	mse	mse	NOUN
brj-23756	100	189	2.0	2.0	NUM
brj-23756	100	190	(	(	PUNCT
brj-23756	100	191	2.1	2.1	NUM
brj-23756	100	192	)	)	PUNCT
brj-23756	100	193	swelling	swell	VERB
brj-23756	100	194	mape	mape	NOUN
brj-23756	100	195	7.77	7.77	NUM
brj-23756	100	196	(	(	PUNCT
brj-23756	100	197	8.25	8.25	NUM
brj-23756	100	198	)	)	PUNCT
brj-23756	100	199	swelling	swell	VERB
brj-23756	100	200	*	*	PUNCT
brj-23756	100	201	keras	keras	PROPN
brj-23756	100	202	data	data	PROPN
brj-23756	100	203	is	be	AUX
brj-23756	100	204	presented	present	VERB
brj-23756	100	205	in	in	ADP
brj-23756	100	206	parentheses	parenthesis	NOUN
brj-23756	100	207	the	the	DET
brj-23756	100	208	multiple	multiple	ADJ
brj-23756	100	209	input	input	NOUN
brj-23756	100	210	model	model	NOUN
brj-23756	100	211	performed	perform	VERB
brj-23756	100	212	an	an	DET
brj-23756	100	213	acceptable	acceptable	ADJ
brj-23756	100	214	performance	performance	NOUN
brj-23756	100	215	in	in	ADP
brj-23756	100	216	predicting	predict	VERB
brj-23756	100	217	emc	emc	PROPN
brj-23756	100	218	and	and	CCONJ
brj-23756	100	219	swelling	swelling	NOUN
brj-23756	100	220	of	of	ADP
brj-23756	100	221	wood	wood	NOUN
brj-23756	100	222	and	and	CCONJ
brj-23756	100	223	the	the	DET
brj-23756	100	224	single	single	ADJ
brj-23756	100	225	input	input	NOUN
brj-23756	100	226	model	model	NOUN
brj-23756	100	227	was	be	AUX
brj-23756	100	228	unable	unable	ADJ
brj-23756	100	229	to	to	PART
brj-23756	100	230	predict	predict	VERB
brj-23756	100	231	accurately	accurately	ADV
brj-23756	100	232	that	that	SCONJ
brj-23756	100	233	these	these	DET
brj-23756	100	234	results	result	NOUN
brj-23756	100	235	were	be	AUX
brj-23756	100	236	in	in	ADP
brj-23756	100	237	close	close	ADJ
brj-23756	100	238	accordance	accordance	NOUN
brj-23756	100	239	with	with	ADP
brj-23756	100	240	the	the	DET
brj-23756	100	241	findings	finding	NOUN
brj-23756	100	242	of	of	ADP
brj-23756	100	243	haftkhani	haftkhani	PROPN
brj-23756	100	244	et	et	PROPN
brj-23756	100	245	al	al	PROPN
brj-23756	100	246	.	.	PROPN
brj-23756	101	1	(	(	PUNCT
brj-23756	101	2	2022	2022	NUM
brj-23756	101	3	)	)	PUNCT
brj-23756	101	4	,	,	PUNCT
brj-23756	101	5	where	where	SCONJ
brj-23756	101	6	they	they	PRON
brj-23756	101	7	peer	peer	NOUN
brj-23756	101	8	-	-	PUNCT
brj-23756	101	9	reviewed	review	VERB
brj-23756	101	10	article	article	NOUN
brj-23756	101	11	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	101	12	masoumi	masoumi	NOUN
brj-23756	101	13	&	&	CCONJ
brj-23756	101	14	bond	bond	PROPN
brj-23756	101	15	(	(	PUNCT
brj-23756	101	16	2024	2024	NUM
brj-23756	101	17	)	)	PUNCT
brj-23756	101	18	.	.	PUNCT
brj-23756	102	1	“	"	PUNCT
brj-23756	102	2	ann	ann	PROPN
brj-23756	102	3	prediction	prediction	NOUN
brj-23756	102	4	of	of	ADP
brj-23756	102	5	tmw	tmw	NOUN
brj-23756	102	6	,	,	PUNCT
brj-23756	102	7	”	"	PUNCT
brj-23756	102	8	bioresources	bioresource	NOUN
brj-23756	102	9	19(4	19(4	NUM
brj-23756	102	10	)	)	PUNCT
brj-23756	102	11	,	,	PUNCT
brj-23756	102	12	6983	6983	NUM
brj-23756	102	13	-	-	SYM
brj-23756	102	14	6993	6993	NUM
brj-23756	102	15	.	.	PUNCT
brj-23756	103	1	6989	6989	NUM
brj-23756	103	2	modeled	model	VERB
brj-23756	103	3	the	the	DET
brj-23756	103	4	water	water	NOUN
brj-23756	103	5	absorption	absorption	NOUN
brj-23756	103	6	and	and	CCONJ
brj-23756	103	7	swelling	swelling	NOUN
brj-23756	103	8	of	of	ADP
brj-23756	103	9	tm	tm	DET
brj-23756	103	10	fir	fir	PROPN
brj-23756	103	11	(	(	PUNCT
brj-23756	103	12	abies	abie	NOUN
brj-23756	103	13	sp	sp	PROPN
brj-23756	103	14	.	.	PUNCT
brj-23756	103	15	)	)	PUNCT
brj-23756	104	1	wood	wood	NOUN
brj-23756	105	1	and	and	CCONJ
brj-23756	105	2	reported	report	VERB
brj-23756	105	3	the	the	DET
brj-23756	105	4	superiority	superiority	NOUN
brj-23756	105	5	of	of	ADP
brj-23756	105	6	the	the	DET
brj-23756	105	7	multiple	multiple	ADJ
brj-23756	105	8	input	input	NOUN
brj-23756	105	9	ann	ann	PROPN
brj-23756	105	10	model	model	NOUN
brj-23756	105	11	with	with	ADP
brj-23756	105	12	r2	r2	PROPN
brj-23756	105	13	and	and	CCONJ
brj-23756	105	14	mape	mape	NOUN
brj-23756	105	15	of	of	ADP
brj-23756	105	16	0.996	0.996	NUM
brj-23756	105	17	and	and	CCONJ
brj-23756	105	18	2.8	2.8	NUM
brj-23756	105	19	.	.	PUNCT
brj-23756	105	20	table	table	NOUN
brj-23756	105	21	3	3	NUM
brj-23756	105	22	.	.	PUNCT
brj-23756	105	23	experimental	experimental	ADJ
brj-23756	105	24	data	datum	NOUN
brj-23756	105	25	and	and	CCONJ
brj-23756	105	26	predicted	predict	VERB
brj-23756	105	27	results	result	NOUN
brj-23756	105	28	equilibrium	equilibrium	NOUN
brj-23756	105	29	moisture	moisture	NOUN
brj-23756	105	30	content	content	NOUN
brj-23756	105	31	swelling	swell	VERB
brj-23756	105	32	predicted	predict	VERB
brj-23756	105	33	actual	actual	ADJ
brj-23756	105	34	error	error	NOUN
brj-23756	105	35	predicted	predict	VERB
brj-23756	105	36	actual	actual	ADJ
brj-23756	105	37	error	error	NOUN
brj-23756	105	38	7.41	7.41	NUM
brj-23756	105	39	7.4	7.4	NUM
brj-23756	105	40	-0.01	-0.01	NUM
brj-23756	105	41	21.25	21.25	NUM
brj-23756	105	42	21.57	21.57	NUM
brj-23756	105	43	0.32	0.32	NUM
brj-23756	105	44	5.01	5.01	NUM
brj-23756	105	45	5	5	NUM
brj-23756	105	46	-0.01	-0.01	NUM
brj-23756	105	47	10.49	10.49	NUM
brj-23756	105	48	10	10	NUM
brj-23756	105	49	-0.49	-0.49	ADP
brj-23756	105	50	4.19	4.19	NUM
brj-23756	105	51	4.1	4.1	NUM
brj-23756	105	52	-0.09	-0.09	NUM
brj-23756	105	53	10.80	10.80	NUM
brj-23756	105	54	9.06	9.06	NUM
brj-23756	105	55	-1.74	-1.74	NUM
brj-23756	105	56	10.74	10.74	NUM
brj-23756	105	57	10.75	10.75	NUM
brj-23756	105	58	0.01	0.01	NUM
brj-23756	105	59	16.01	16.01	NUM
brj-23756	105	60	15.96	15.96	NUM
brj-23756	105	61	-0.05	-0.05	NUM
brj-23756	105	62	4.98	4.98	NUM
brj-23756	105	63	5	5	NUM
brj-23756	105	64	0.02	0.02	NUM
brj-23756	105	65	10.61	10.61	NUM
brj-23756	105	66	8.92	8.92	NUM
brj-23756	105	67	-1.69	-1.69	NOUN
brj-23756	105	68	4.08	4.08	NUM
brj-23756	105	69	3.8	3.8	NUM
brj-23756	105	70	-0.28	-0.28	NUM
brj-23756	105	71	10.29	10.29	NUM
brj-23756	105	72	13.79	13.79	NUM
brj-23756	105	73	3.50	3.50	NUM
brj-23756	105	74	10.28	10.28	NUM
brj-23756	105	75	10.25	10.25	NUM
brj-23756	105	76	-0.03	-0.03	NUM
brj-23756	105	77	17.55	17.55	NUM
brj-23756	105	78	18.84	18.84	NUM
brj-23756	105	79	1.29	1.29	NUM
brj-23756	105	80	4.96	4.96	NUM
brj-23756	105	81	4.96	4.96	NUM
brj-23756	105	82	0.00	0.00	NUM
brj-23756	105	83	10.73	10.73	NUM
brj-23756	105	84	11.63	11.63	NUM
brj-23756	105	85	0.90	0.90	NUM
brj-23756	105	86	9.85	9.85	NUM
brj-23756	105	87	9.5	9.5	NUM
brj-23756	105	88	-0.35	-0.35	NUM
brj-23756	105	89	13.84	13.84	NUM
brj-23756	105	90	13.55	13.55	NUM
brj-23756	105	91	-0.29	-0.29	NUM
brj-23756	105	92	10.27	10.27	NUM
brj-23756	105	93	10.1	10.1	NUM
brj-23756	105	94	-0.17	-0.17	NUM
brj-23756	105	95	12.88	12.88	NUM
brj-23756	105	96	13.57	13.57	NUM
brj-23756	105	97	0.69	0.69	NUM
brj-23756	105	98	11.65	11.65	NUM
brj-23756	105	99	11.56	11.56	NUM
brj-23756	105	100	-0.09	-0.09	NUM
brj-23756	105	101	11.85	11.85	NUM
brj-23756	105	102	12.7	12.7	NUM
brj-23756	105	103	0.85	0.85	NUM
brj-23756	105	104	6.08	6.08	NUM
brj-23756	105	105	5.9	5.9	NUM
brj-23756	105	106	-0.18	-0.18	NUM
brj-23756	105	107	4.43	4.43	NUM
brj-23756	105	108	4.82	4.82	NUM
brj-23756	105	109	0.39	0.39	NUM
brj-23756	105	110	7.40	7.40	NUM
brj-23756	105	111	7.6	7.6	NUM
brj-23756	105	112	0.20	0.20	NUM
brj-23756	105	113	20.32	20.32	NUM
brj-23756	105	114	23.67	23.67	NUM
brj-23756	105	115	3.35	3.35	NUM
brj-23756	105	116	6.08	6.08	NUM
brj-23756	105	117	6	6	NUM
brj-23756	105	118	-0.08	-0.08	NUM
brj-23756	105	119	4.43	4.43	NUM
brj-23756	105	120	4.49	4.49	NUM
brj-23756	105	121	0.06	0.06	NUM
brj-23756	105	122	4.79	4.79	NUM
brj-23756	105	123	4.7	4.7	NUM
brj-23756	105	124	-0.09	-0.09	NUM
brj-23756	105	125	8.22	8.22	NUM
brj-23756	105	126	7.35	7.35	NUM
brj-23756	105	127	-0.87	-0.87	NUM
brj-23756	105	128	10.26	10.26	NUM
brj-23756	105	129	10.2	10.2	NUM
brj-23756	105	130	-0.06	-0.06	NUM
brj-23756	105	131	12.53	12.53	NUM
brj-23756	105	132	14.29	14.29	NUM
brj-23756	105	133	1.76	1.76	NUM
brj-23756	105	134	9.55	9.55	NUM
brj-23756	105	135	9.6	9.6	NUM
brj-23756	105	136	0.05	0.05	NUM
brj-23756	105	137	20.12	20.12	NUM
brj-23756	105	138	20.37	20.37	NUM
brj-23756	105	139	0.25	0.25	NUM
brj-23756	105	140	10.74	10.74	NUM
brj-23756	105	141	10.7	10.7	NUM
brj-23756	105	142	-0.04	-0.04	NUM
brj-23756	105	143	16.01	16.01	NUM
brj-23756	105	144	16.3	16.3	NUM
brj-23756	105	145	0.29	0.29	NUM
brj-23756	105	146	10.25	10.25	NUM
brj-23756	105	147	10.2	10.2	NUM
brj-23756	105	148	-0.05	-0.05	NUM
brj-23756	105	149	13.13	13.13	NUM
brj-23756	105	150	12.88	12.88	NUM
brj-23756	105	151	-0.25	-0.25	NUM
brj-23756	105	152	6.06	6.06	NUM
brj-23756	105	153	6.1	6.1	NUM
brj-23756	105	154	0.04	0.04	NUM
brj-23756	105	155	4.04	4.04	NUM
brj-23756	105	156	3.88	3.88	NUM
brj-23756	105	157	-0.16	-0.16	NUM
brj-23756	105	158	…	…	PUNCT
brj-23756	105	159	…	…	PUNCT
brj-23756	105	160	…	…	PUNCT
brj-23756	105	161	…	…	PUNCT
brj-23756	105	162	…	…	PUNCT
brj-23756	105	163	…	…	PUNCT
brj-23756	105	164	…	…	PUNCT
brj-23756	105	165	…	…	PUNCT
brj-23756	105	166	…	…	PUNCT
brj-23756	105	167	…	…	PUNCT
brj-23756	105	168	…	…	PUNCT
brj-23756	105	169	…	…	PUNCT
brj-23756	105	170	…	…	PUNCT
brj-23756	105	171	……	……	NOUN
brj-23756	105	172	……	……	NOUN
brj-23756	105	173	……	……	NOUN
brj-23756	105	174	……	……	NOUN
brj-23756	105	175	……	……	NOUN
brj-23756	105	176	……	……	NOUN
brj-23756	105	177	……	……	NOUN
brj-23756	105	178	……	……	NOUN
brj-23756	105	179	……	……	NOUN
brj-23756	105	180	……	……	NOUN
brj-23756	105	181	……	……	NOUN
brj-23756	105	182	……	……	NOUN
brj-23756	105	183	……	……	NOUN
brj-23756	105	184	4.98	4.98	NUM
brj-23756	105	185	5	5	NUM
brj-23756	105	186	0.02	0.02	NUM
brj-23756	105	187	10.61	10.61	NUM
brj-23756	105	188	10.91	10.91	NUM
brj-23756	105	189	0.30	0.30	NUM
brj-23756	105	190	4.19	4.19	NUM
brj-23756	105	191	4.2	4.2	NUM
brj-23756	105	192	0.01	0.01	NUM
brj-23756	105	193	10.80	10.80	NUM
brj-23756	105	194	7	7	NUM
brj-23756	105	195	-3.80	-3.80	NOUN
brj-23756	105	196	7.41	7.41	NUM
brj-23756	105	197	7.4	7.4	NUM
brj-23756	105	198	-0.01	-0.01	NUM
brj-23756	105	199	21.25	21.25	NUM
brj-23756	105	200	16.54	16.54	NUM
brj-23756	105	201	-4.71	-4.71	NUM
brj-23756	105	202	10.25	10.25	NUM
brj-23756	105	203	10.1	10.1	NUM
brj-23756	105	204	-0.15	-0.15	NUM
brj-23756	105	205	13.49	13.49	NUM
brj-23756	105	206	13.58	13.58	NUM
brj-23756	105	207	0.09	0.09	NUM
brj-23756	105	208	10.73	10.73	NUM
brj-23756	105	209	10.75	10.75	NUM
brj-23756	105	210	0.02	0.02	NUM
brj-23756	105	211	16.12	16.12	NUM
brj-23756	105	212	15.67	15.67	NUM
brj-23756	105	213	-0.45	-0.45	NUM
brj-23756	105	214	10.28	10.28	NUM
brj-23756	105	215	10.4	10.4	NUM
brj-23756	105	216	0.12	0.12	NUM
brj-23756	105	217	17.50	17.50	NUM
brj-23756	105	218	17.68	17.68	NUM
brj-23756	105	219	0.18	0.18	NUM
brj-23756	105	220	11.65	11.65	NUM
brj-23756	105	221	11.75	11.75	NUM
brj-23756	105	222	0.10	0.10	NUM
brj-23756	105	223	11.85	11.85	NUM
brj-23756	105	224	11.62	11.62	NUM
brj-23756	105	225	-0.23	-0.23	NUM
brj-23756	105	226	9.79	9.79	NUM
brj-23756	105	227	10.3	10.3	NUM
brj-23756	105	228	0.51	0.51	NUM
brj-23756	105	229	12.98	12.98	NUM
brj-23756	105	230	13.45	13.45	NUM
brj-23756	105	231	0.47	0.47	NUM
brj-23756	105	232	10.28	10.28	NUM
brj-23756	105	233	10.3	10.3	NUM
brj-23756	105	234	0.02	0.02	NUM
brj-23756	105	235	17.50	17.50	NUM
brj-23756	105	236	18.43	18.43	NUM
brj-23756	105	237	0.93	0.93	NUM
brj-23756	105	238	6.08	6.08	NUM
brj-23756	105	239	6.1	6.1	NUM
brj-23756	105	240	0.02	0.02	NUM
brj-23756	105	241	4.43	4.43	NUM
brj-23756	105	242	4.65	4.65	NUM
brj-23756	105	243	0.22	0.22	NUM
brj-23756	105	244	6.10	6.10	NUM
brj-23756	105	245	6	6	NUM
brj-23756	105	246	-0.10	-0.10	NUM
brj-23756	105	247	6.99	6.99	NUM
brj-23756	105	248	5.86	5.86	NUM
brj-23756	105	249	-1.13	-1.13	NOUN
brj-23756	105	250	11.72	11.72	NUM
brj-23756	105	251	11.76	11.76	NUM
brj-23756	105	252	0.04	0.04	NUM
brj-23756	105	253	11.57	11.57	NUM
brj-23756	105	254	11.51	11.51	NUM
brj-23756	105	255	-0.06	-0.06	NUM
brj-23756	105	256	6.06	6.06	NUM
brj-23756	105	257	6.2	6.2	NUM
brj-23756	105	258	0.14	0.14	NUM
brj-23756	105	259	4.04	4.04	NUM
brj-23756	105	260	4.49	4.49	NUM
brj-23756	105	261	0.45	0.45	NUM
brj-23756	105	262	9.55	9.55	NUM
brj-23756	105	263	9.6	9.6	NUM
brj-23756	105	264	0.05	0.05	NUM
brj-23756	105	265	20.12	20.12	NUM
brj-23756	105	266	21.47	21.47	NUM
brj-23756	105	267	1.35	1.35	NUM
brj-23756	105	268	6.08	6.08	NUM
brj-23756	105	269	6	6	NUM
brj-23756	105	270	-0.08	-0.08	NUM
brj-23756	105	271	4.43	4.43	NUM
brj-23756	105	272	3.74	3.74	NUM
brj-23756	105	273	-0.69	-0.69	NUM
brj-23756	105	274	4.79	4.79	NUM
brj-23756	105	275	4.8	4.8	NUM
brj-23756	105	276	0.01	0.01	NUM
brj-23756	105	277	7.87	7.87	NUM
brj-23756	105	278	7.95	7.95	NUM
brj-23756	105	279	0.08	0.08	NUM
brj-23756	105	280	9.55	9.55	NUM
brj-23756	105	281	9.5	9.5	NUM
brj-23756	105	282	-0.05	-0.05	NUM
brj-23756	105	283	20.08	20.08	NUM
brj-23756	105	284	21.2	21.2	NUM
brj-23756	105	285	1.12	1.12	NUM
brj-23756	105	286	9.55	9.55	NUM
brj-23756	105	287	9.6	9.6	NUM
brj-23756	105	288	0.05	0.05	NUM
brj-23756	105	289	20.08	20.08	NUM
brj-23756	105	290	19.6	19.6	NUM
brj-23756	105	291	-0.48	-0.48	NUM
brj-23756	105	292	peer	peer	NOUN
brj-23756	105	293	-	-	PUNCT
brj-23756	105	294	reviewed	review	VERB
brj-23756	105	295	article	article	NOUN
brj-23756	105	296	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	105	297	masoumi	masoumi	NOUN
brj-23756	105	298	&	&	CCONJ
brj-23756	105	299	bond	bond	PROPN
brj-23756	105	300	(	(	PUNCT
brj-23756	105	301	2024	2024	NUM
brj-23756	105	302	)	)	PUNCT
brj-23756	105	303	.	.	PUNCT
brj-23756	106	1	“	"	PUNCT
brj-23756	106	2	ann	ann	PROPN
brj-23756	106	3	prediction	prediction	NOUN
brj-23756	106	4	of	of	ADP
brj-23756	106	5	tmw	tmw	NOUN
brj-23756	106	6	,	,	PUNCT
brj-23756	106	7	”	"	PUNCT
brj-23756	106	8	bioresources	bioresource	NOUN
brj-23756	106	9	19(4	19(4	NUM
brj-23756	106	10	)	)	PUNCT
brj-23756	106	11	,	,	PUNCT
brj-23756	106	12	6983	6983	NUM
brj-23756	106	13	-	-	SYM
brj-23756	106	14	6993	6993	NUM
brj-23756	106	15	.	.	PUNCT
brj-23756	107	1	6990	6990	NUM
brj-23756	107	2	ozsahin	ozsahin	NOUN
brj-23756	107	3	and	and	CCONJ
brj-23756	107	4	murat	murat	PROPN
brj-23756	107	5	(	(	PUNCT
brj-23756	107	6	2018	2018	NUM
brj-23756	107	7	)	)	PUNCT
brj-23756	107	8	developed	develop	VERB
brj-23756	107	9	an	an	DET
brj-23756	107	10	ann	ann	PROPN
brj-23756	107	11	model	model	NOUN
brj-23756	107	12	for	for	ADP
brj-23756	107	13	predicting	predict	VERB
brj-23756	107	14	emc	emc	PROPN
brj-23756	107	15	in	in	ADP
brj-23756	107	16	tm	tm	DET
brj-23756	107	17	fir	fir	NOUN
brj-23756	107	18	(	(	PUNCT
brj-23756	107	19	abies	abie	NOUN
brj-23756	107	20	bornmuelleriana	bornmuelleriana	PROPN
brj-23756	107	21	maff	maff	PROPN
brj-23756	107	22	)	)	PUNCT
brj-23756	107	23	and	and	CCONJ
brj-23756	107	24	hornbeam	hornbeam	PROPN
brj-23756	107	25	(	(	PUNCT
brj-23756	107	26	carpinus	carpinus	PROPN
brj-23756	107	27	betulus	betulus	PROPN
brj-23756	107	28	l.	l.	PROPN
brj-23756	107	29	)	)	PUNCT
brj-23756	107	30	and	and	CCONJ
brj-23756	107	31	reported	report	VERB
brj-23756	107	32	a	a	DET
brj-23756	107	33	mape	mape	NOUN
brj-23756	107	34	value	value	NOUN
brj-23756	107	35	of	of	ADP
brj-23756	107	36	3.21	3.21	NUM
brj-23756	107	37	.	.	PUNCT
brj-23756	108	1	additionally	additionally	ADV
brj-23756	108	2	,	,	PUNCT
brj-23756	108	3	in	in	ADP
brj-23756	108	4	another	another	DET
brj-23756	108	5	study	study	NOUN
brj-23756	108	6	for	for	ADP
brj-23756	108	7	the	the	DET
brj-23756	108	8	same	same	ADJ
brj-23756	108	9	species	specie	NOUN
brj-23756	108	10	,	,	PUNCT
brj-23756	108	11	chen	chen	PROPN
brj-23756	108	12	et	et	PROPN
brj-23756	108	13	al	al	PROPN
brj-23756	108	14	.	.	PROPN
brj-23756	108	15	(	(	PUNCT
brj-23756	108	16	2022	2022	NUM
brj-23756	108	17	)	)	PUNCT
brj-23756	108	18	reported	report	VERB
brj-23756	108	19	r2	r2	PROPN
brj-23756	108	20	values	value	NOUN
brj-23756	108	21	of	of	ADP
brj-23756	108	22	0.99	0.99	NUM
brj-23756	108	23	and	and	CCONJ
brj-23756	108	24	0.98	0.98	NUM
brj-23756	108	25	.	.	PUNCT
brj-23756	109	1	the	the	DET
brj-23756	109	2	multiple	multiple	ADJ
brj-23756	109	3	-	-	PUNCT
brj-23756	109	4	input	input	NOUN
brj-23756	109	5	ann	ann	PROPN
brj-23756	109	6	model	model	NOUN
brj-23756	109	7	was	be	AUX
brj-23756	109	8	shown	show	VERB
brj-23756	109	9	to	to	PART
brj-23756	109	10	be	be	AUX
brj-23756	109	11	effective	effective	ADJ
brj-23756	109	12	and	and	CCONJ
brj-23756	109	13	reliable	reliable	ADJ
brj-23756	109	14	in	in	ADP
brj-23756	109	15	predicting	predict	VERB
brj-23756	109	16	emc	emc	PROPN
brj-23756	109	17	and	and	CCONJ
brj-23756	109	18	swelling	swelling	NOUN
brj-23756	109	19	.	.	PUNCT
brj-23756	110	1	tiryaki	tiryaki	PROPN
brj-23756	110	2	et	et	PROPN
brj-23756	110	3	al	al	PROPN
brj-23756	110	4	.	.	PUNCT
brj-23756	111	1	(	(	PUNCT
brj-23756	111	2	2016	2016	NUM
brj-23756	111	3	)	)	PUNCT
brj-23756	111	4	confirmed	confirm	VERB
brj-23756	111	5	predicting	predict	VERB
brj-23756	111	6	volumetric	volumetric	NOUN
brj-23756	111	7	swelling	swell	VERB
brj-23756	111	8	by	by	ADP
brj-23756	111	9	ann	ann	PROPN
brj-23756	111	10	models	model	NOUN
brj-23756	111	11	using	use	VERB
brj-23756	111	12	wood	wood	NOUN
brj-23756	111	13	species	specie	NOUN
brj-23756	111	14	,	,	PUNCT
brj-23756	111	15	treatment	treatment	NOUN
brj-23756	111	16	time	time	NOUN
brj-23756	111	17	,	,	PUNCT
brj-23756	111	18	and	and	CCONJ
brj-23756	111	19	temperature	temperature	NOUN
brj-23756	111	20	.	.	PUNCT
brj-23756	112	1	other	other	ADJ
brj-23756	112	2	studies	study	NOUN
brj-23756	112	3	have	have	AUX
brj-23756	112	4	reported	report	VERB
brj-23756	112	5	the	the	DET
brj-23756	112	6	effectiveness	effectiveness	NOUN
brj-23756	112	7	of	of	ADP
brj-23756	112	8	ann	ann	PROPN
brj-23756	112	9	could	could	AUX
brj-23756	112	10	predict	predict	VERB
brj-23756	112	11	tmw	tmw	NOUN
brj-23756	112	12	properties	property	NOUN
brj-23756	112	13	(	(	PUNCT
brj-23756	112	14	nasir	nasir	PROPN
brj-23756	112	15	et	et	PROPN
brj-23756	112	16	al	al	PROPN
brj-23756	112	17	.	.	PROPN
brj-23756	112	18	2019	2019	NUM
brj-23756	112	19	)	)	PUNCT
brj-23756	112	20	.	.	PUNCT
brj-23756	113	1	fitting	fitting	ADJ
brj-23756	113	2	effect	effect	NOUN
brj-23756	113	3	figure	figure	NOUN
brj-23756	113	4	2	2	NUM
brj-23756	113	5	shows	show	VERB
brj-23756	113	6	the	the	DET
brj-23756	113	7	correlation	correlation	NOUN
brj-23756	113	8	between	between	ADP
brj-23756	113	9	the	the	DET
brj-23756	113	10	experimental	experimental	ADJ
brj-23756	113	11	data	datum	NOUN
brj-23756	113	12	values	value	NOUN
brj-23756	113	13	and	and	CCONJ
brj-23756	113	14	the	the	DET
brj-23756	113	15	values	value	NOUN
brj-23756	113	16	predicted	predict	VERB
brj-23756	113	17	by	by	ADP
brj-23756	113	18	the	the	DET
brj-23756	113	19	developed	develop	VERB
brj-23756	113	20	ann	ann	PROPN
brj-23756	113	21	models	model	NOUN
brj-23756	113	22	.	.	PUNCT
brj-23756	114	1	there	there	PRON
brj-23756	114	2	was	be	VERB
brj-23756	114	3	a	a	DET
brj-23756	114	4	significant	significant	ADJ
brj-23756	114	5	correlation	correlation	NOUN
brj-23756	114	6	between	between	ADP
brj-23756	114	7	actual	actual	ADJ
brj-23756	114	8	and	and	CCONJ
brj-23756	114	9	predicted	predict	VERB
brj-23756	114	10	values	value	NOUN
brj-23756	114	11	in	in	ADP
brj-23756	114	12	testing	test	VERB
brj-23756	114	13	both	both	CCONJ
brj-23756	114	14	in	in	ADP
brj-23756	114	15	emc	emc	PROPN
brj-23756	114	16	and	and	CCONJ
brj-23756	114	17	swelling	swell	VERB
brj-23756	114	18	with	with	ADP
brj-23756	114	19	r2	r2	PROPN
brj-23756	114	20	0.9975	0.9975	NUM
brj-23756	114	21	and	and	CCONJ
brj-23756	114	22	0.92	0.92	NUM
brj-23756	114	23	.	.	PUNCT
brj-23756	115	1	the	the	DET
brj-23756	115	2	same	same	ADJ
brj-23756	115	3	accuracies	accuracy	NOUN
brj-23756	115	4	have	have	AUX
brj-23756	115	5	been	be	AUX
brj-23756	115	6	reported	report	VERB
brj-23756	115	7	by	by	ADP
brj-23756	115	8	other	other	ADJ
brj-23756	115	9	researchers	researcher	NOUN
brj-23756	115	10	such	such	ADJ
brj-23756	115	11	as	as	ADP
brj-23756	115	12	chen	chen	PROPN
brj-23756	115	13	et	et	PROPN
brj-23756	115	14	al	al	PROPN
brj-23756	115	15	.	.	PROPN
brj-23756	116	1	(	(	PUNCT
brj-23756	116	2	2022	2022	NUM
brj-23756	116	3	)	)	PUNCT
brj-23756	116	4	,	,	PUNCT
brj-23756	116	5	0.99	0.99	NUM
brj-23756	116	6	,	,	PUNCT
brj-23756	116	7	for	for	ADP
brj-23756	116	8	emc	emc	NOUN
brj-23756	116	9	and	and	CCONJ
brj-23756	116	10	chai	chai	NOUN
brj-23756	116	11	et	et	PROPN
brj-23756	116	12	al	al	PROPN
brj-23756	116	13	.	.	PROPN
brj-23756	117	1	(	(	PUNCT
brj-23756	117	2	2018	2018	NUM
brj-23756	117	3	)	)	PUNCT
brj-23756	117	4	0.974	0.974	NUM
brj-23756	117	5	.	.	PUNCT
brj-23756	118	1	figure	figure	NOUN
brj-23756	118	2	3	3	NUM
brj-23756	118	3	shows	show	VERB
brj-23756	118	4	the	the	DET
brj-23756	118	5	training	training	NOUN
brj-23756	118	6	test	test	NOUN
brj-23756	118	7	for	for	ADP
brj-23756	118	8	emc	emc	PROPN
brj-23756	118	9	and	and	CCONJ
brj-23756	118	10	swelling	swelling	NOUN
brj-23756	118	11	.	.	PUNCT
brj-23756	119	1	fig	fig	NOUN
brj-23756	119	2	.	.	PUNCT
brj-23756	120	1	2	2	X
brj-23756	120	2	.	.	X
brj-23756	120	3	fitting	fitting	ADJ
brj-23756	120	4	effect	effect	NOUN
brj-23756	120	5	of	of	ADP
brj-23756	120	6	actual	actual	ADJ
brj-23756	120	7	vs	vs	ADP
brj-23756	120	8	predicted	predict	VERB
brj-23756	120	9	values	value	NOUN
brj-23756	120	10	in	in	ADP
brj-23756	120	11	emc	emc	PROPN
brj-23756	120	12	and	and	CCONJ
brj-23756	120	13	swelling	swell	VERB
brj-23756	120	14	fig	fig	NOUN
brj-23756	120	15	.	.	PUNCT
brj-23756	121	1	3	3	X
brj-23756	121	2	.	.	X
brj-23756	121	3	loss	loss	NOUN
brj-23756	121	4	trend	trend	NOUN
brj-23756	121	5	in	in	ADP
brj-23756	121	6	training	training	NOUN
brj-23756	121	7	test	test	NOUN
brj-23756	121	8	for	for	ADP
brj-23756	121	9	emc	emc	PROPN
brj-23756	121	10	and	and	CCONJ
brj-23756	121	11	swelling	swell	VERB
brj-23756	121	12	peer	peer	NOUN
brj-23756	121	13	-	-	PUNCT
brj-23756	121	14	reviewed	review	VERB
brj-23756	121	15	article	article	NOUN
brj-23756	121	16	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	121	17	masoumi	masoumi	NOUN
brj-23756	121	18	&	&	CCONJ
brj-23756	121	19	bond	bond	PROPN
brj-23756	121	20	(	(	PUNCT
brj-23756	121	21	2024	2024	NUM
brj-23756	121	22	)	)	PUNCT
brj-23756	121	23	.	.	PUNCT
brj-23756	122	1	“	"	PUNCT
brj-23756	122	2	ann	ann	PROPN
brj-23756	122	3	prediction	prediction	NOUN
brj-23756	122	4	of	of	ADP
brj-23756	122	5	tmw	tmw	NOUN
brj-23756	122	6	,	,	PUNCT
brj-23756	122	7	”	"	PUNCT
brj-23756	122	8	bioresources	bioresource	NOUN
brj-23756	122	9	19(4	19(4	NUM
brj-23756	122	10	)	)	PUNCT
brj-23756	122	11	,	,	PUNCT
brj-23756	122	12	6983	6983	NUM
brj-23756	122	13	-	-	SYM
brj-23756	122	14	6993	6993	NUM
brj-23756	122	15	.	.	PUNCT
brj-23756	123	1	6991	6991	NUM
brj-23756	123	2	during	during	ADP
brj-23756	123	3	training	training	NOUN
brj-23756	123	4	,	,	PUNCT
brj-23756	123	5	the	the	DET
brj-23756	123	6	loss	loss	NOUN
brj-23756	123	7	was	be	AUX
brj-23756	123	8	recorded	record	VERB
brj-23756	123	9	every	every	DET
brj-23756	123	10	100	100	NUM
brj-23756	123	11	epochs	epoch	NOUN
brj-23756	123	12	,	,	PUNCT
brj-23756	123	13	it	it	PRON
brj-23756	123	14	is	be	AUX
brj-23756	123	15	essential	essential	ADJ
brj-23756	123	16	to	to	PART
brj-23756	123	17	observe	observe	VERB
brj-23756	123	18	the	the	DET
brj-23756	123	19	loss	loss	NOUN
brj-23756	123	20	trend	trend	NOUN
brj-23756	123	21	to	to	PART
brj-23756	123	22	ensure	ensure	VERB
brj-23756	123	23	that	that	SCONJ
brj-23756	123	24	the	the	DET
brj-23756	123	25	model	model	NOUN
brj-23756	123	26	is	be	AUX
brj-23756	123	27	converging	converge	VERB
brj-23756	123	28	as	as	SCONJ
brj-23756	123	29	the	the	DET
brj-23756	123	30	decreasing	decrease	VERB
brj-23756	123	31	trend	trend	NOUN
brj-23756	123	32	indicates	indicate	VERB
brj-23756	123	33	that	that	SCONJ
brj-23756	123	34	the	the	DET
brj-23756	123	35	model	model	NOUN
brj-23756	123	36	is	be	AUX
brj-23756	123	37	learning	learn	VERB
brj-23756	123	38	from	from	ADP
brj-23756	123	39	the	the	DET
brj-23756	123	40	data	datum	NOUN
brj-23756	123	41	.	.	PUNCT
brj-23756	124	1	for	for	ADP
brj-23756	124	2	emc	emc	PROPN
brj-23756	124	3	,	,	PUNCT
brj-23756	124	4	the	the	DET
brj-23756	124	5	loss	loss	NOUN
brj-23756	124	6	both	both	PRON
brj-23756	124	7	in	in	ADP
brj-23756	124	8	training	training	NOUN
brj-23756	124	9	and	and	CCONJ
brj-23756	124	10	testing	testing	NOUN
brj-23756	124	11	immediately	immediately	ADV
brj-23756	124	12	dropped	drop	VERB
brj-23756	124	13	to	to	ADP
brj-23756	124	14	its	its	PRON
brj-23756	124	15	lowest	low	ADJ
brj-23756	124	16	value	value	NOUN
brj-23756	124	17	after	after	ADP
brj-23756	124	18	20	20	NUM
brj-23756	124	19	epochs	epoch	NOUN
brj-23756	124	20	and	and	CCONJ
brj-23756	124	21	kept	keep	VERB
brj-23756	124	22	a	a	DET
brj-23756	124	23	constant	constant	ADJ
brj-23756	124	24	value	value	NOUN
brj-23756	124	25	toward	toward	ADP
brj-23756	124	26	the	the	DET
brj-23756	124	27	end	end	NOUN
brj-23756	124	28	of	of	ADP
brj-23756	124	29	iterations	iteration	NOUN
brj-23756	124	30	.	.	PUNCT
brj-23756	125	1	however	however	ADV
brj-23756	125	2	,	,	PUNCT
brj-23756	125	3	for	for	ADP
brj-23756	125	4	swelling	swell	VERB
brj-23756	125	5	it	it	PRON
brj-23756	125	6	dropped	drop	VERB
brj-23756	125	7	after	after	ADP
brj-23756	125	8	200	200	NUM
brj-23756	125	9	epochs	epoch	NOUN
brj-23756	125	10	as	as	SCONJ
brj-23756	125	11	the	the	DET
brj-23756	125	12	loss	loss	NOUN
brj-23756	125	13	was	be	AUX
brj-23756	125	14	constant	constant	ADJ
brj-23756	125	15	in	in	ADP
brj-23756	125	16	training	training	NOUN
brj-23756	125	17	and	and	CCONJ
brj-23756	125	18	testing	testing	NOUN
brj-23756	125	19	,	,	PUNCT
brj-23756	125	20	and	and	CCONJ
brj-23756	125	21	in	in	ADP
brj-23756	125	22	training	train	VERB
brj-23756	125	23	the	the	DET
brj-23756	125	24	loss	loss	NOUN
brj-23756	125	25	was	be	AUX
brj-23756	125	26	higher	high	ADJ
brj-23756	125	27	than	than	ADP
brj-23756	125	28	testing	testing	NOUN
brj-23756	125	29	because	because	SCONJ
brj-23756	125	30	emc	emc	PROPN
brj-23756	125	31	data	datum	NOUN
brj-23756	125	32	were	be	AUX
brj-23756	125	33	more	more	ADV
brj-23756	125	34	uniform	uniform	ADJ
brj-23756	125	35	and	and	CCONJ
brj-23756	125	36	swelling	swell	VERB
brj-23756	125	37	data	datum	NOUN
brj-23756	125	38	were	be	AUX
brj-23756	125	39	very	very	ADV
brj-23756	125	40	diverse	diverse	ADJ
brj-23756	125	41	in	in	ADP
brj-23756	125	42	all	all	DET
brj-23756	125	43	the	the	DET
brj-23756	125	44	tested	test	VERB
brj-23756	125	45	hardwood	hardwood	NOUN
brj-23756	125	46	species	specie	NOUN
brj-23756	125	47	.	.	PUNCT
brj-23756	126	1	the	the	DET
brj-23756	126	2	anns	ann	NOUN
brj-23756	126	3	have	have	VERB
brj-23756	126	4	several	several	ADJ
brj-23756	126	5	advantages	advantage	NOUN
brj-23756	126	6	over	over	ADP
brj-23756	126	7	traditional	traditional	ADJ
brj-23756	126	8	statistical	statistical	ADJ
brj-23756	126	9	tools	tool	NOUN
brj-23756	126	10	,	,	PUNCT
brj-23756	126	11	including	include	VERB
brj-23756	126	12	the	the	DET
brj-23756	126	13	ability	ability	NOUN
brj-23756	126	14	to	to	PART
brj-23756	126	15	model	model	VERB
brj-23756	126	16	complex	complex	ADJ
brj-23756	126	17	,	,	PUNCT
brj-23756	126	18	non	non	ADJ
brj-23756	126	19	-	-	ADJ
brj-23756	126	20	linear	linear	ADJ
brj-23756	126	21	relationships	relationship	NOUN
brj-23756	126	22	and	and	CCONJ
brj-23756	126	23	handle	handle	VERB
brj-23756	126	24	high	high	ADJ
brj-23756	126	25	-	-	PUNCT
brj-23756	126	26	dimensional	dimensional	ADJ
brj-23756	126	27	data	datum	NOUN
brj-23756	126	28	.	.	PUNCT
brj-23756	127	1	they	they	PRON
brj-23756	127	2	excel	excel	VERB
brj-23756	127	3	at	at	ADP
brj-23756	127	4	learning	learn	VERB
brj-23756	127	5	from	from	ADP
brj-23756	127	6	data	datum	NOUN
brj-23756	127	7	,	,	PUNCT
brj-23756	127	8	recognizing	recognize	VERB
brj-23756	127	9	patterns	pattern	NOUN
brj-23756	127	10	,	,	PUNCT
brj-23756	127	11	and	and	CCONJ
brj-23756	127	12	automatically	automatically	ADV
brj-23756	127	13	extracting	extract	VERB
brj-23756	127	14	features	feature	NOUN
brj-23756	127	15	,	,	PUNCT
brj-23756	127	16	reducing	reduce	VERB
brj-23756	127	17	the	the	DET
brj-23756	127	18	need	need	NOUN
brj-23756	127	19	for	for	ADP
brj-23756	127	20	manual	manual	ADJ
brj-23756	127	21	intervention	intervention	NOUN
brj-23756	127	22	.	.	PUNCT
brj-23756	128	1	anns	anns	PROPN
brj-23756	128	2	are	be	AUX
brj-23756	128	3	robust	robust	ADJ
brj-23756	128	4	against	against	ADP
brj-23756	128	5	noisy	noisy	ADJ
brj-23756	128	6	and	and	CCONJ
brj-23756	128	7	incomplete	incomplete	ADJ
brj-23756	128	8	data	datum	NOUN
brj-23756	128	9	,	,	PUNCT
brj-23756	128	10	can	can	AUX
brj-23756	128	11	generalize	generalize	VERB
brj-23756	128	12	well	well	ADV
brj-23756	128	13	to	to	ADP
brj-23756	128	14	unseen	unseen	ADJ
brj-23756	128	15	data	datum	NOUN
brj-23756	128	16	,	,	PUNCT
brj-23756	128	17	and	and	CCONJ
brj-23756	128	18	benefit	benefit	VERB
brj-23756	128	19	from	from	ADP
brj-23756	128	20	parallel	parallel	ADJ
brj-23756	128	21	processing	processing	NOUN
brj-23756	128	22	capabilities	capability	NOUN
brj-23756	128	23	for	for	ADP
brj-23756	128	24	efficient	efficient	ADJ
brj-23756	128	25	computation	computation	NOUN
brj-23756	128	26	.	.	PUNCT
brj-23756	129	1	their	their	PRON
brj-23756	129	2	flexibility	flexibility	NOUN
brj-23756	129	3	and	and	CCONJ
brj-23756	129	4	versatility	versatility	NOUN
brj-23756	129	5	make	make	VERB
brj-23756	129	6	them	they	PRON
brj-23756	129	7	suitable	suitable	ADJ
brj-23756	129	8	for	for	ADP
brj-23756	129	9	a	a	DET
brj-23756	129	10	wide	wide	ADJ
brj-23756	129	11	range	range	NOUN
brj-23756	129	12	of	of	ADP
brj-23756	129	13	applications	application	NOUN
brj-23756	129	14	,	,	PUNCT
brj-23756	129	15	although	although	SCONJ
brj-23756	129	16	they	they	PRON
brj-23756	129	17	require	require	VERB
brj-23756	129	18	large	large	ADJ
brj-23756	129	19	datasets	dataset	NOUN
brj-23756	129	20	,	,	PUNCT
brj-23756	129	21	computational	computational	ADJ
brj-23756	129	22	resources	resource	NOUN
brj-23756	129	23	,	,	PUNCT
brj-23756	129	24	and	and	CCONJ
brj-23756	129	25	expertise	expertise	NOUN
brj-23756	129	26	in	in	ADP
brj-23756	129	27	design	design	NOUN
brj-23756	129	28	and	and	CCONJ
brj-23756	129	29	training	training	NOUN
brj-23756	129	30	.	.	PUNCT
brj-23756	130	1	this	this	DET
brj-23756	130	2	model	model	NOUN
brj-23756	130	3	is	be	AUX
brj-23756	130	4	implemented	implement	VERB
brj-23756	130	5	and	and	CCONJ
brj-23756	130	6	is	be	AUX
brj-23756	130	7	designed	design	VERB
brj-23756	130	8	to	to	PART
brj-23756	130	9	take	take	VERB
brj-23756	130	10	the	the	DET
brj-23756	130	11	known	know	VERB
brj-23756	130	12	features	feature	NOUN
brj-23756	130	13	of	of	ADP
brj-23756	130	14	new	new	ADJ
brj-23756	130	15	species	specie	NOUN
brj-23756	130	16	and	and	CCONJ
brj-23756	130	17	predict	predict	VERB
brj-23756	130	18	their	their	PRON
brj-23756	130	19	emc	emc	NOUN
brj-23756	130	20	and	and	CCONJ
brj-23756	130	21	swelling	swell	VERB
brj-23756	130	22	properties	property	NOUN
brj-23756	130	23	.	.	PUNCT
brj-23756	131	1	conclusions	conclusion	NOUN
brj-23756	131	2	1	1	X
brj-23756	131	3	.	.	PUNCT
brj-23756	132	1	the	the	DET
brj-23756	132	2	pytorch	pytorch	NOUN
brj-23756	132	3	system	system	NOUN
brj-23756	132	4	was	be	AUX
brj-23756	132	5	used	use	VERB
brj-23756	132	6	in	in	ADP
brj-23756	132	7	this	this	DET
brj-23756	132	8	study	study	NOUN
brj-23756	132	9	,	,	PUNCT
brj-23756	132	10	as	as	SCONJ
brj-23756	132	11	it	it	PRON
brj-23756	132	12	has	have	VERB
brj-23756	132	13	better	well	ADJ
brj-23756	132	14	flexibility	flexibility	NOUN
brj-23756	132	15	and	and	CCONJ
brj-23756	132	16	debugging	debug	VERB
brj-23756	132	17	capabilities	capability	NOUN
brj-23756	132	18	to	to	PART
brj-23756	132	19	check	check	VERB
brj-23756	132	20	and	and	CCONJ
brj-23756	132	21	ensure	ensure	VERB
brj-23756	132	22	the	the	DET
brj-23756	132	23	validity	validity	NOUN
brj-23756	132	24	of	of	ADP
brj-23756	132	25	keras	keras	PROPN
brj-23756	132	26	.	.	PUNCT
brj-23756	133	1	the	the	DET
brj-23756	133	2	models	model	NOUN
brj-23756	133	3	implemented	implement	VERB
brj-23756	133	4	by	by	ADP
brj-23756	133	5	both	both	DET
brj-23756	133	6	keras	keras	PROPN
brj-23756	133	7	and	and	CCONJ
brj-23756	133	8	pytorch	pytorch	NOUN
brj-23756	133	9	provided	provide	VERB
brj-23756	133	10	similar	similar	ADJ
brj-23756	133	11	results	result	NOUN
brj-23756	133	12	.	.	PUNCT
brj-23756	134	1	2	2	X
brj-23756	134	2	.	.	X
brj-23756	134	3	the	the	DET
brj-23756	134	4	multiple	multiple	ADJ
brj-23756	134	5	-	-	PUNCT
brj-23756	134	6	input	input	NOUN
brj-23756	134	7	artificial	artificial	ADJ
brj-23756	134	8	neural	neural	ADJ
brj-23756	134	9	network	network	NOUN
brj-23756	134	10	(	(	PUNCT
brj-23756	134	11	ann	ann	PROPN
brj-23756	134	12	)	)	PUNCT
brj-23756	134	13	model	model	NOUN
brj-23756	134	14	was	be	AUX
brj-23756	134	15	successful	successful	ADJ
brj-23756	134	16	in	in	ADP
brj-23756	134	17	accurately	accurately	ADV
brj-23756	134	18	predicting	predict	VERB
brj-23756	134	19	the	the	DET
brj-23756	134	20	equilibrium	equilibrium	NOUN
brj-23756	134	21	moisture	moisture	NOUN
brj-23756	134	22	content	content	NOUN
brj-23756	134	23	(	(	PUNCT
brj-23756	134	24	emc	emc	PROPN
brj-23756	134	25	)	)	PUNCT
brj-23756	134	26	and	and	CCONJ
brj-23756	134	27	swelling	swell	VERB
brj-23756	134	28	using	use	VERB
brj-23756	134	29	various	various	ADJ
brj-23756	134	30	input	input	NOUN
brj-23756	134	31	features	feature	NOUN
brj-23756	134	32	.	.	PUNCT
brj-23756	135	1	the	the	DET
brj-23756	135	2	ann	ann	PROPN
brj-23756	135	3	learned	learn	VERB
brj-23756	135	4	emc	emc	PROPN
brj-23756	135	5	data	datum	NOUN
brj-23756	135	6	faster	fast	ADV
brj-23756	135	7	than	than	ADP
brj-23756	135	8	swelling	swell	VERB
brj-23756	135	9	data	datum	NOUN
brj-23756	135	10	.	.	PUNCT
brj-23756	136	1	hardwoods	hardwood	NOUN
brj-23756	136	2	have	have	VERB
brj-23756	136	3	different	different	ADJ
brj-23756	136	4	properties	property	NOUN
brj-23756	136	5	that	that	PRON
brj-23756	136	6	lead	lead	VERB
brj-23756	136	7	to	to	ADP
brj-23756	136	8	variation	variation	NOUN
brj-23756	136	9	in	in	ADP
brj-23756	136	10	their	their	PRON
brj-23756	136	11	properties	property	NOUN
brj-23756	136	12	in	in	ADP
brj-23756	136	13	a	a	DET
brj-23756	136	14	modification	modification	NOUN
brj-23756	136	15	,	,	PUNCT
brj-23756	136	16	making	make	VERB
brj-23756	136	17	it	it	PRON
brj-23756	136	18	difficult	difficult	ADJ
brj-23756	136	19	to	to	PART
brj-23756	136	20	predict	predict	VERB
brj-23756	136	21	their	their	PRON
brj-23756	136	22	properties	property	NOUN
brj-23756	136	23	.	.	PUNCT
brj-23756	137	1	3	3	X
brj-23756	137	2	.	.	X
brj-23756	137	3	the	the	DET
brj-23756	137	4	single	single	ADJ
brj-23756	137	5	-	-	PUNCT
brj-23756	137	6	input	input	NOUN
brj-23756	137	7	model	model	NOUN
brj-23756	137	8	showed	show	VERB
brj-23756	137	9	less	less	ADJ
brj-23756	137	10	accuracy	accuracy	NOUN
brj-23756	137	11	in	in	ADP
brj-23756	137	12	predicting	predict	VERB
brj-23756	137	13	the	the	DET
brj-23756	137	14	emc	emc	NOUN
brj-23756	137	15	and	and	CCONJ
brj-23756	137	16	swelling	swell	VERB
brj-23756	137	17	using	use	VERB
brj-23756	137	18	just	just	ADV
brj-23756	137	19	one	one	NUM
brj-23756	137	20	feature	feature	NOUN
brj-23756	137	21	as	as	ADP
brj-23756	137	22	an	an	DET
brj-23756	137	23	input	input	NOUN
brj-23756	137	24	.	.	PUNCT
brj-23756	138	1	this	this	PRON
brj-23756	138	2	implies	imply	VERB
brj-23756	138	3	that	that	SCONJ
brj-23756	138	4	multiple	multiple	ADJ
brj-23756	138	5	features	feature	NOUN
brj-23756	138	6	are	be	AUX
brj-23756	138	7	contributing	contribute	VERB
brj-23756	138	8	to	to	ADP
brj-23756	138	9	the	the	DET
brj-23756	138	10	emc	emc	NOUN
brj-23756	138	11	and	and	CCONJ
brj-23756	138	12	particularly	particularly	ADV
brj-23756	138	13	swelling	swell	VERB
brj-23756	138	14	changes	change	NOUN
brj-23756	138	15	.	.	PUNCT
brj-23756	139	1	4	4	X
brj-23756	139	2	.	.	X
brj-23756	139	3	the	the	DET
brj-23756	139	4	accuracy	accuracy	NOUN
brj-23756	139	5	of	of	ADP
brj-23756	139	6	the	the	DET
brj-23756	139	7	prediction	prediction	NOUN
brj-23756	139	8	of	of	ADP
brj-23756	139	9	the	the	DET
brj-23756	139	10	developed	develop	VERB
brj-23756	139	11	ann	ann	PROPN
brj-23756	139	12	models	model	NOUN
brj-23756	139	13	was	be	AUX
brj-23756	139	14	shown	show	VERB
brj-23756	139	15	by	by	ADP
brj-23756	139	16	the	the	DET
brj-23756	139	17	r2	r2	PROPN
brj-23756	139	18	values	value	NOUN
brj-23756	139	19	of	of	ADP
brj-23756	139	20	0.9975	0.9975	NUM
brj-23756	139	21	and	and	CCONJ
brj-23756	139	22	0.92	0.92	NUM
brj-23756	139	23	for	for	ADP
brj-23756	139	24	emc	emc	NOUN
brj-23756	139	25	and	and	CCONJ
brj-23756	139	26	swelling	swelling	NOUN
brj-23756	139	27	,	,	PUNCT
brj-23756	139	28	respectively	respectively	ADV
brj-23756	139	29	,	,	PUNCT
brj-23756	139	30	which	which	PRON
brj-23756	139	31	especially	especially	ADV
brj-23756	139	32	for	for	ADP
brj-23756	139	33	emc	emc	PROPN
brj-23756	139	34	was	be	AUX
brj-23756	139	35	higher	high	ADJ
brj-23756	139	36	than	than	ADP
brj-23756	139	37	previously	previously	ADV
brj-23756	139	38	introduced	introduce	VERB
brj-23756	139	39	models	model	NOUN
brj-23756	139	40	and	and	CCONJ
brj-23756	139	41	for	for	ADP
brj-23756	139	42	the	the	DET
brj-23756	139	43	swelling	swelling	NOUN
brj-23756	139	44	agrees	agree	VERB
brj-23756	139	45	with	with	ADP
brj-23756	139	46	models	model	NOUN
brj-23756	139	47	in	in	ADP
brj-23756	139	48	other	other	ADJ
brj-23756	139	49	studies	study	NOUN
brj-23756	139	50	.	.	PUNCT
brj-23756	140	1	acknowledgments	acknowledgment	NOUN
brj-23756	140	2	the	the	DET
brj-23756	140	3	authors	author	NOUN
brj-23756	140	4	are	be	AUX
brj-23756	140	5	grateful	grateful	ADJ
brj-23756	140	6	to	to	ADP
brj-23756	140	7	the	the	DET
brj-23756	140	8	bingaman	bingaman	NOUN
brj-23756	140	9	lumber	lumber	NOUN
brj-23756	140	10	company	company	NOUN
brj-23756	140	11	for	for	ADP
brj-23756	140	12	providing	provide	VERB
brj-23756	140	13	thermally	thermally	ADV
brj-23756	140	14	modified	modify	VERB
brj-23756	140	15	lumber	lumber	NOUN
brj-23756	140	16	and	and	CCONJ
brj-23756	140	17	information	information	NOUN
brj-23756	140	18	regarding	regard	VERB
brj-23756	140	19	the	the	DET
brj-23756	140	20	thermal	thermal	ADJ
brj-23756	140	21	modifying	modifying	NOUN
brj-23756	140	22	process	process	NOUN
brj-23756	140	23	.	.	PUNCT
brj-23756	141	1	peer	peer	NOUN
brj-23756	141	2	-	-	PUNCT
brj-23756	141	3	reviewed	review	VERB
brj-23756	141	4	article	article	NOUN
brj-23756	141	5	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	141	6	masoumi	masoumi	NOUN
brj-23756	141	7	&	&	CCONJ
brj-23756	141	8	bond	bond	PROPN
brj-23756	141	9	(	(	PUNCT
brj-23756	141	10	2024	2024	NUM
brj-23756	141	11	)	)	PUNCT
brj-23756	141	12	.	.	PUNCT
brj-23756	142	1	“	"	PUNCT
brj-23756	142	2	ann	ann	PROPN
brj-23756	142	3	prediction	prediction	NOUN
brj-23756	142	4	of	of	ADP
brj-23756	142	5	tmw	tmw	NOUN
brj-23756	142	6	,	,	PUNCT
brj-23756	142	7	”	"	PUNCT
brj-23756	142	8	bioresources	bioresource	NOUN
brj-23756	142	9	19(4	19(4	NUM
brj-23756	142	10	)	)	PUNCT
brj-23756	142	11	,	,	PUNCT
brj-23756	142	12	6983	6983	NUM
brj-23756	142	13	-	-	SYM
brj-23756	142	14	6993	6993	NUM
brj-23756	142	15	.	.	PUNCT
brj-23756	143	1	6992	6992	NUM
brj-23756	143	2	references	reference	NOUN
brj-23756	143	3	cited	cite	VERB
brj-23756	143	4	appalachian	appalachian	ADJ
brj-23756	143	5	hardwood	hardwood	NOUN
brj-23756	143	6	manufacturers	manufacturer	NOUN
brj-23756	143	7	,	,	PUNCT
brj-23756	143	8	inc	inc	PROPN
brj-23756	143	9	.	.	PROPN
brj-23756	143	10	(	(	PUNCT
brj-23756	143	11	2023	2023	NUM
brj-23756	143	12	)	)	PUNCT
brj-23756	143	13	.	.	PUNCT
brj-23756	144	1	“	"	PUNCT
brj-23756	144	2	appalachian	appalachian	ADJ
brj-23756	144	3	hardwood	hardwood	NOUN
brj-23756	144	4	species	specie	NOUN
brj-23756	144	5	guide	guide	NOUN
brj-23756	144	6	,	,	PUNCT
brj-23756	144	7	”	"	PUNCT
brj-23756	144	8	(	(	PUNCT
brj-23756	144	9	https://www.appalachianwood.org/species/species.htm	https://www.appalachianwood.org/species/species.htm	NOUN
brj-23756	144	10	)	)	PUNCT
brj-23756	144	11	,	,	PUNCT
brj-23756	144	12	accessed	access	VERB
brj-23756	144	13	25	25	NUM
brj-23756	144	14	nov	nov	PROPN
brj-23756	144	15	2023	2023	NUM
brj-23756	144	16	.	.	PUNCT
brj-23756	145	1	astm	astm	PROPN
brj-23756	145	2	d143	d143	PROPN
brj-23756	145	3	-	-	PUNCT
brj-23756	145	4	22	22	NUM
brj-23756	145	5	(	(	PUNCT
brj-23756	145	6	2022	2022	NUM
brj-23756	145	7	)	)	PUNCT
brj-23756	145	8	.	.	PUNCT
brj-23756	146	1	“	"	PUNCT
brj-23756	146	2	standard	standard	ADJ
brj-23756	146	3	test	test	NOUN
brj-23756	146	4	methods	method	NOUN
brj-23756	146	5	for	for	ADP
brj-23756	146	6	small	small	ADJ
brj-23756	146	7	clear	clear	ADJ
brj-23756	146	8	specimens	specimen	NOUN
brj-23756	146	9	of	of	ADP
brj-23756	146	10	timber	timber	NOUN
brj-23756	146	11	,	,	PUNCT
brj-23756	146	12	”	"	PUNCT
brj-23756	146	13	astm	astm	PROPN
brj-23756	146	14	international	international	PROPN
brj-23756	146	15	,	,	PUNCT
brj-23756	146	16	west	west	PROPN
brj-23756	146	17	conshohocken	conshohocken	PROPN
brj-23756	146	18	,	,	PUNCT
brj-23756	146	19	pa	pa	PROPN
brj-23756	146	20	,	,	PUNCT
brj-23756	146	21	usa	usa	PROPN
brj-23756	146	22	.	.	PROPN
brj-23756	146	23	doi	doi	PROPN
brj-23756	146	24	:	:	PUNCT
brj-23756	146	25	10.1520	10.1520	NUM
brj-23756	146	26	/	/	SYM
brj-23756	146	27	d0143	d0143	NOUN
brj-23756	146	28	-	-	PUNCT
brj-23756	146	29	22	22	NUM
brj-23756	146	30	.	.	PUNCT
brj-23756	147	1	aydin	aydin	NOUN
brj-23756	147	2	,	,	PUNCT
brj-23756	147	3	g.	g.	PROPN
brj-23756	147	4	,	,	PUNCT
brj-23756	147	5	karakurt	karakurt	PROPN
brj-23756	147	6	,	,	PUNCT
brj-23756	147	7	i.	i.	NOUN
brj-23756	147	8	,	,	PUNCT
brj-23756	147	9	and	and	CCONJ
brj-23756	147	10	hamzacebi	hamzacebi	NOUN
brj-23756	147	11	,	,	PUNCT
brj-23756	147	12	c.	c.	NOUN
brj-23756	147	13	(	(	PUNCT
brj-23756	147	14	2015	2015	NUM
brj-23756	147	15	)	)	PUNCT
brj-23756	147	16	.	.	PUNCT
brj-23756	148	1	“	"	PUNCT
brj-23756	148	2	performance	performance	NOUN
brj-23756	148	3	prediction	prediction	NOUN
brj-23756	148	4	of	of	ADP
brj-23756	148	5	diamond	diamond	NOUN
brj-23756	148	6	sawblades	sawblade	NOUN
brj-23756	148	7	using	use	VERB
brj-23756	148	8	artificial	artificial	ADJ
brj-23756	148	9	neural	neural	ADJ
brj-23756	148	10	network	network	NOUN
brj-23756	148	11	and	and	CCONJ
brj-23756	148	12	regression	regression	NOUN
brj-23756	148	13	analysis	analysis	NOUN
brj-23756	148	14	,	,	PUNCT
brj-23756	148	15	”	"	PUNCT
brj-23756	148	16	arabian	arabian	ADJ
brj-23756	148	17	journal	journal	NOUN
brj-23756	148	18	of	of	ADP
brj-23756	148	19	science	science	NOUN
brj-23756	148	20	and	and	CCONJ
brj-23756	148	21	engineering	engineering	NOUN
brj-23756	148	22	40(7	40(7	NOUN
brj-23756	148	23	)	)	PUNCT
brj-23756	148	24	,	,	PUNCT
brj-23756	148	25	2003	2003	NUM
brj-23756	148	26	-	-	SYM
brj-23756	148	27	2012	2012	NUM
brj-23756	148	28	.	.	PUNCT
brj-23756	149	1	doi	doi	NOUN
brj-23756	149	2	:	:	PUNCT
brj-23756	149	3	10.1007	10.1007	NUM
brj-23756	149	4	/	/	SYM
brj-23756	149	5	s13369	s13369	PROPN
brj-23756	149	6	-	-	PUNCT
brj-23756	149	7	015	015	NUM
brj-23756	149	8	-	-	PUNCT
brj-23756	149	9	1589	1589	NUM
brj-23756	149	10	-	-	PUNCT
brj-23756	149	11	x	x	NOUN
brj-23756	149	12	bond	bond	NOUN
brj-23756	149	13	,	,	PUNCT
brj-23756	149	14	b.	b.	PROPN
brj-23756	149	15	,	,	PUNCT
brj-23756	149	16	gonzalez	gonzalez	PROPN
brj-23756	149	17	,	,	PUNCT
brj-23756	149	18	j.	j.	PROPN
brj-23756	149	19	,	,	PUNCT
brj-23756	149	20	masoumi	masoumi	PROPN
brj-23756	149	21	,	,	PUNCT
brj-23756	149	22	a.	a.	PROPN
brj-23756	149	23	,	,	PUNCT
brj-23756	149	24	xavier	xavier	PROPN
brj-23756	149	25	zambrano	zambrano	PROPN
brj-23756	149	26	balma	balma	PROPN
brj-23756	149	27	,	,	PUNCT
brj-23756	149	28	f.	f.	PROPN
brj-23756	149	29	,	,	PUNCT
brj-23756	149	30	and	and	CCONJ
brj-23756	149	31	tylor	tylor	PROPN
brj-23756	149	32	,	,	PUNCT
brj-23756	149	33	a.	a.	PROPN
brj-23756	149	34	(	(	PUNCT
brj-23756	149	35	2023	2023	NUM
brj-23756	149	36	)	)	PUNCT
brj-23756	149	37	.	.	PUNCT
brj-23756	150	1	“	"	PUNCT
brj-23756	150	2	thermally	thermally	ADV
brj-23756	150	3	modified	modify	VERB
brj-23756	150	4	wood	wood	NOUN
brj-23756	150	5	as	as	ADP
brj-23756	150	6	a	a	DET
brj-23756	150	7	sustainable	sustainable	ADJ
brj-23756	150	8	alternative	alternative	NOUN
brj-23756	150	9	,	,	PUNCT
brj-23756	150	10	”	"	PUNCT
brj-23756	150	11	in	in	ADP
brj-23756	150	12	:	:	PUNCT
brj-23756	150	13	ptf	ptf	PROPN
brj-23756	150	14	bpi	bpi	PROPN
brj-23756	150	15	2023	2023	NUM
brj-23756	150	16	,	,	PUNCT
brj-23756	150	17	processing	processing	NOUN
brj-23756	150	18	technologies	technology	NOUN
brj-23756	150	19	for	for	ADP
brj-23756	150	20	the	the	DET
brj-23756	150	21	forest	forest	NOUN
brj-23756	150	22	&	&	CCONJ
brj-23756	150	23	biobased	biobase	VERB
brj-23756	150	24	products	product	NOUN
brj-23756	150	25	industries	industry	NOUN
brj-23756	150	26	,	,	PUNCT
brj-23756	150	27	simons	simons	PROPN
brj-23756	150	28	islands	islands	PROPN
brj-23756	150	29	,	,	PUNCT
brj-23756	150	30	ga	ga	PROPN
brj-23756	150	31	,	,	PUNCT
brj-23756	150	32	usa	usa	PROPN
brj-23756	150	33	,	,	PUNCT
brj-23756	150	34	pp	pp	ADJ
brj-23756	150	35	.	.	PUNCT
brj-23756	151	1	1	1	NUM
brj-23756	151	2	-	-	SYM
brj-23756	151	3	15	15	NUM
brj-23756	151	4	.	.	PUNCT
brj-23756	152	1	chai	chai	NOUN
brj-23756	152	2	,	,	PUNCT
brj-23756	152	3	h.	h.	PROPN
brj-23756	152	4	,	,	PUNCT
brj-23756	152	5	chen	chen	PROPN
brj-23756	152	6	,	,	PUNCT
brj-23756	152	7	x.	x.	PROPN
brj-23756	152	8	,	,	PUNCT
brj-23756	152	9	cai	cai	PROPN
brj-23756	152	10	,	,	PUNCT
brj-23756	152	11	y.	y.	PROPN
brj-23756	152	12	,	,	PUNCT
brj-23756	152	13	and	and	CCONJ
brj-23756	152	14	zhao	zhao	PROPN
brj-23756	152	15	,	,	PUNCT
brj-23756	152	16	j.	j.	PROPN
brj-23756	152	17	(	(	PUNCT
brj-23756	152	18	2018	2018	NUM
brj-23756	152	19	)	)	PUNCT
brj-23756	152	20	.	.	PUNCT
brj-23756	153	1	“	"	PUNCT
brj-23756	153	2	artificial	artificial	ADJ
brj-23756	153	3	neural	neural	ADJ
brj-23756	153	4	network	network	NOUN
brj-23756	153	5	modeling	modeling	NOUN
brj-23756	153	6	for	for	ADP
brj-23756	153	7	predicting	predict	VERB
brj-23756	153	8	wood	wood	NOUN
brj-23756	153	9	moisture	moisture	NOUN
brj-23756	153	10	content	content	NOUN
brj-23756	153	11	in	in	ADP
brj-23756	153	12	high	high	ADJ
brj-23756	153	13	frequency	frequency	NOUN
brj-23756	153	14	vacuum	vacuum	NOUN
brj-23756	153	15	drying	dry	VERB
brj-23756	153	16	process	process	NOUN
brj-23756	153	17	,	,	PUNCT
brj-23756	153	18	”	"	PUNCT
brj-23756	153	19	forests	forest	NOUN
brj-23756	153	20	10(1	10(1	NUM
brj-23756	153	21	)	)	PUNCT
brj-23756	153	22	,	,	PUNCT
brj-23756	153	23	article	article	NOUN
brj-23756	153	24	16	16	NUM
brj-23756	153	25	.	.	PUNCT
brj-23756	154	1	doi	doi	NOUN
brj-23756	154	2	:	:	PUNCT
brj-23756	154	3	10.3390	10.3390	NUM
brj-23756	154	4	/	/	SYM
brj-23756	154	5	f10010016	f10010016	PROPN
brj-23756	154	6	chen	chen	PROPN
brj-23756	154	7	,	,	PUNCT
brj-23756	154	8	y.	y.	PROPN
brj-23756	154	9	,	,	PUNCT
brj-23756	154	10	wang	wang	PROPN
brj-23756	154	11	,	,	PUNCT
brj-23756	154	12	w.	w.	PROPN
brj-23756	154	13	,	,	PUNCT
brj-23756	154	14	and	and	CCONJ
brj-23756	154	15	li	li	PROPN
brj-23756	154	16	,	,	PUNCT
brj-23756	154	17	n.	n.	PROPN
brj-23756	154	18	(	(	PUNCT
brj-23756	154	19	2022	2022	NUM
brj-23756	154	20	)	)	PUNCT
brj-23756	154	21	.	.	PUNCT
brj-23756	155	1	“	"	PUNCT
brj-23756	155	2	prediction	prediction	NOUN
brj-23756	155	3	of	of	ADP
brj-23756	155	4	the	the	DET
brj-23756	155	5	equilibrium	equilibrium	NOUN
brj-23756	155	6	moisture	moisture	NOUN
brj-23756	155	7	content	content	NOUN
brj-23756	155	8	and	and	CCONJ
brj-23756	155	9	specific	specific	ADJ
brj-23756	155	10	gravity	gravity	NOUN
brj-23756	155	11	of	of	ADP
brj-23756	155	12	thermally	thermally	ADV
brj-23756	155	13	modified	modify	VERB
brj-23756	155	14	wood	wood	NOUN
brj-23756	155	15	via	via	ADP
brj-23756	155	16	an	an	DET
brj-23756	155	17	aquila	aquila	ADJ
brj-23756	155	18	optimization	optimization	NOUN
brj-23756	155	19	algorithm	algorithm	NOUN
brj-23756	155	20	back	back	ADJ
brj-23756	155	21	-	-	PUNCT
brj-23756	155	22	propagation	propagation	NOUN
brj-23756	155	23	neural	neural	ADJ
brj-23756	155	24	network	network	NOUN
brj-23756	155	25	model	model	NOUN
brj-23756	155	26	,	,	PUNCT
brj-23756	155	27	”	"	PUNCT
brj-23756	155	28	bioresources	bioresource	NOUN
brj-23756	155	29	17(3	17(3	NUM
brj-23756	155	30	)	)	PUNCT
brj-23756	155	31	,	,	PUNCT
brj-23756	155	32	4816	4816	NUM
brj-23756	155	33	-	-	SYM
brj-23756	155	34	4836	4836	NUM
brj-23756	155	35	.	.	PUNCT
brj-23756	156	1	doi	doi	NOUN
brj-23756	156	2	:	:	PUNCT
brj-23756	156	3	10.15376	10.15376	NUM
brj-23756	156	4	/	/	SYM
brj-23756	156	5	biores.17.3.4816	biores.17.3.4816	PROPN
brj-23756	156	6	-	-	PUNCT
brj-23756	156	7	4836	4836	NUM
brj-23756	156	8	espinoza	espinoza	PROPN
brj-23756	156	9	,	,	PUNCT
brj-23756	156	10	o.	o.	PROPN
brj-23756	156	11	,	,	PUNCT
brj-23756	156	12	buehlmann	buehlmann	PROPN
brj-23756	156	13	,	,	PUNCT
brj-23756	156	14	u.	u.	PROPN
brj-23756	156	15	,	,	PUNCT
brj-23756	156	16	and	and	CCONJ
brj-23756	156	17	laguarda	laguarda	PROPN
brj-23756	156	18	-	-	PUNCT
brj-23756	156	19	mallo	mallo	PROPN
brj-23756	156	20	,	,	PUNCT
brj-23756	156	21	m.	m.	NOUN
brj-23756	156	22	f.	f.	PROPN
brj-23756	156	23	(	(	PUNCT
brj-23756	156	24	2015	2015	NUM
brj-23756	156	25	)	)	PUNCT
brj-23756	156	26	.	.	PUNCT
brj-23756	157	1	“	"	PUNCT
brj-23756	157	2	thermally	thermally	ADV
brj-23756	157	3	modified	modify	VERB
brj-23756	157	4	wood	wood	NOUN
brj-23756	157	5	:	:	PUNCT
brj-23756	157	6	marketing	marketing	NOUN
brj-23756	157	7	strategies	strategy	NOUN
brj-23756	157	8	of	of	ADP
brj-23756	157	9	u.s	u.s	PROPN
brj-23756	157	10	.	.	PROPN
brj-23756	157	11	producers	producer	NOUN
brj-23756	157	12	,	,	PUNCT
brj-23756	157	13	”	"	PUNCT
brj-23756	157	14	bioresources	bioresource	NOUN
brj-23756	157	15	10(4	10(4	NUM
brj-23756	157	16	)	)	PUNCT
brj-23756	157	17	,	,	PUNCT
brj-23756	157	18	6942	6942	NUM
brj-23756	157	19	-	-	SYM
brj-23756	157	20	6952	6952	NUM
brj-23756	157	21	.	.	PUNCT
brj-23756	158	1	doi	doi	NOUN
brj-23756	158	2	:	:	PUNCT
brj-23756	158	3	10.13576	10.13576	NUM
brj-23756	158	4	/	/	SYM
brj-23756	158	5	biores.10.4.6942	biores.10.4.6942	PROPN
brj-23756	158	6	-	-	PUNCT
brj-23756	158	7	6952	6952	NUM
brj-23756	158	8	esteves	esteves	PROPN
brj-23756	158	9	,	,	PUNCT
brj-23756	158	10	b.	b.	PROPN
brj-23756	158	11	m.	m.	PROPN
brj-23756	158	12	,	,	PUNCT
brj-23756	158	13	and	and	CCONJ
brj-23756	158	14	pereira	pereira	PROPN
brj-23756	158	15	,	,	PUNCT
brj-23756	158	16	h.	h.	PROPN
brj-23756	158	17	m.	m.	PROPN
brj-23756	158	18	(	(	PUNCT
brj-23756	158	19	2009	2009	NUM
brj-23756	158	20	)	)	PUNCT
brj-23756	158	21	.	.	PUNCT
brj-23756	159	1	“	"	PUNCT
brj-23756	159	2	wood	wood	NOUN
brj-23756	159	3	modification	modification	NOUN
brj-23756	159	4	by	by	ADP
brj-23756	159	5	heat	heat	NOUN
brj-23756	159	6	treatment	treatment	NOUN
brj-23756	159	7	:	:	PUNCT
brj-23756	159	8	a	a	DET
brj-23756	159	9	review	review	NOUN
brj-23756	159	10	,	,	PUNCT
brj-23756	159	11	”	"	PUNCT
brj-23756	159	12	bioresources	bioresource	NOUN
brj-23756	159	13	4(1	4(1	NOUN
brj-23756	159	14	)	)	PUNCT
brj-23756	159	15	,	,	PUNCT
brj-23756	159	16	370	370	NUM
brj-23756	159	17	-	-	SYM
brj-23756	159	18	404	404	NUM
brj-23756	159	19	.	.	PUNCT
brj-23756	160	1	doi	doi	NOUN
brj-23756	160	2	:	:	PUNCT
brj-23756	160	3	10.13576	10.13576	NUM
brj-23756	160	4	/	/	SYM
brj-23756	160	5	biores.4.1.370	biores.4.1.370	NOUN
brj-23756	160	6	-	-	PUNCT
brj-23756	160	7	404	404	NUM
brj-23756	160	8	haftkhani	haftkhani	NOUN
brj-23756	160	9	,	,	PUNCT
brj-23756	160	10	a.	a.	NOUN
brj-23756	160	11	r	r	NOUN
brj-23756	160	12	,	,	PUNCT
brj-23756	160	13	abdoli	abdoli	NOUN
brj-23756	160	14	,	,	PUNCT
brj-23756	160	15	f.	f.	PROPN
brj-23756	160	16	,	,	PUNCT
brj-23756	160	17	rashidijouybari	rashidijouybari	PROPN
brj-23756	160	18	,	,	PUNCT
brj-23756	160	19	i.	i.	NOUN
brj-23756	160	20	,	,	PUNCT
brj-23756	160	21	and	and	CCONJ
brj-23756	160	22	garcia	garcia	PROPN
brj-23756	160	23	,	,	PUNCT
brj-23756	160	24	r.	r.	PROPN
brj-23756	160	25	a.	a.	PROPN
brj-23756	160	26	(	(	PUNCT
brj-23756	160	27	2022	2022	NUM
brj-23756	160	28	)	)	PUNCT
brj-23756	160	29	.	.	PUNCT
brj-23756	161	1	“	"	PUNCT
brj-23756	161	2	prediction	prediction	NOUN
brj-23756	161	3	of	of	ADP
brj-23756	161	4	water	water	NOUN
brj-23756	161	5	absorption	absorption	NOUN
brj-23756	161	6	and	and	CCONJ
brj-23756	161	7	swelling	swelling	NOUN
brj-23756	161	8	of	of	ADP
brj-23756	161	9	thermally	thermally	ADV
brj-23756	161	10	modified	modify	VERB
brj-23756	161	11	fir	fir	NOUN
brj-23756	161	12	wood	wood	NOUN
brj-23756	161	13	by	by	ADP
brj-23756	161	14	artificial	artificial	ADJ
brj-23756	161	15	neural	neural	ADJ
brj-23756	161	16	network	network	NOUN
brj-23756	161	17	models	model	NOUN
brj-23756	161	18	,	,	PUNCT
brj-23756	161	19	”	"	PUNCT
brj-23756	161	20	european	european	ADJ
brj-23756	161	21	journal	journal	PROPN
brj-23756	161	22	of	of	ADP
brj-23756	161	23	wood	wood	NOUN
brj-23756	161	24	and	and	CCONJ
brj-23756	161	25	wood	wood	NOUN
brj-23756	161	26	products	product	NOUN
brj-23756	161	27	80	80	NUM
brj-23756	161	28	,	,	PUNCT
brj-23756	161	29	1135–1150	1135–1150	NUM
brj-23756	161	30	.	.	PUNCT
brj-23756	162	1	doi	doi	NOUN
brj-23756	162	2	:	:	PUNCT
brj-23756	162	3	10.1007	10.1007	NUM
brj-23756	162	4	/	/	SYM
brj-23756	162	5	s00107	s00107	PROPN
brj-23756	162	6	-	-	PUNCT
brj-23756	162	7	022	022	NUM
brj-23756	162	8	-	-	PUNCT
brj-23756	162	9	01839	01839	NUM
brj-23756	162	10	-	-	PUNCT
brj-23756	162	11	x	x	SYM
brj-23756	162	12	hill	hill	PROPN
brj-23756	162	13	,	,	PUNCT
brj-23756	162	14	c.	c.	PROPN
brj-23756	162	15	,	,	PUNCT
brj-23756	162	16	altgen	altgen	PROPN
brj-23756	162	17	,	,	PUNCT
brj-23756	162	18	m.	m.	NOUN
brj-23756	162	19	,	,	PUNCT
brj-23756	162	20	and	and	CCONJ
brj-23756	162	21	rautkari	rautkari	ADJ
brj-23756	162	22	,	,	PUNCT
brj-23756	162	23	l.	l.	PROPN
brj-23756	162	24	(	(	PUNCT
brj-23756	162	25	2021	2021	NUM
brj-23756	162	26	)	)	PUNCT
brj-23756	162	27	.	.	PUNCT
brj-23756	163	1	“	"	PUNCT
brj-23756	163	2	thermal	thermal	ADJ
brj-23756	163	3	modification	modification	NOUN
brj-23756	163	4	of	of	ADP
brj-23756	163	5	wood	wood	NOUN
brj-23756	163	6	—	—	PUNCT
brj-23756	163	7	a	a	DET
brj-23756	163	8	review	review	NOUN
brj-23756	163	9	:	:	PUNCT
brj-23756	163	10	chemical	chemical	NOUN
brj-23756	163	11	changes	change	NOUN
brj-23756	163	12	and	and	CCONJ
brj-23756	163	13	hygroscopicity	hygroscopicity	NOUN
brj-23756	163	14	,	,	PUNCT
brj-23756	163	15	”	"	PUNCT
brj-23756	163	16	journal	journal	NOUN
brj-23756	163	17	of	of	ADP
brj-23756	163	18	materials	material	NOUN
brj-23756	163	19	science	science	NOUN
brj-23756	163	20	56(11	56(11	NUM
brj-23756	163	21	)	)	PUNCT
brj-23756	163	22	,	,	PUNCT
brj-23756	163	23	65816614	65816614	NUM
brj-23756	163	24	.	.	PUNCT
brj-23756	164	1	doi	doi	NOUN
brj-23756	164	2	:	:	PUNCT
brj-23756	164	3	10.1007	10.1007	NUM
brj-23756	164	4	/	/	SYM
brj-23756	164	5	s10853	s10853	NOUN
brj-23756	164	6	-	-	PUNCT
brj-23756	164	7	020	020	NUM
brj-23756	164	8	-	-	PUNCT
brj-23756	164	9	05722	05722	NUM
brj-23756	164	10	-	-	PUNCT
brj-23756	164	11	z	z	NOUN
brj-23756	164	12	masoumi	masoumi	NOUN
brj-23756	164	13	,	,	PUNCT
brj-23756	164	14	a.	a.	NOUN
brj-23756	164	15	,	,	PUNCT
brj-23756	164	16	and	and	CCONJ
brj-23756	164	17	bond	bond	NOUN
brj-23756	164	18	,	,	PUNCT
brj-23756	164	19	b.	b.	PROPN
brj-23756	164	20	(	(	PUNCT
brj-23756	164	21	2024a	2024a	NUM
brj-23756	164	22	)	)	PUNCT
brj-23756	164	23	.	.	PUNCT
brj-23756	165	1	“	"	PUNCT
brj-23756	165	2	dimensional	dimensional	ADJ
brj-23756	165	3	stability	stability	NOUN
brj-23756	165	4	and	and	CCONJ
brj-23756	165	5	equilibrium	equilibrium	NOUN
brj-23756	165	6	moisture	moisture	NOUN
brj-23756	165	7	content	content	NOUN
brj-23756	165	8	of	of	ADP
brj-23756	165	9	thermally	thermally	ADV
brj-23756	165	10	modified	modify	VERB
brj-23756	165	11	hardwoods	hardwood	NOUN
brj-23756	165	12	,	,	PUNCT
brj-23756	165	13	”	"	PUNCT
brj-23756	165	14	bioresources	bioresource	NOUN
brj-23756	165	15	19(1	19(1	NUM
brj-23756	165	16	)	)	PUNCT
brj-23756	165	17	,	,	PUNCT
brj-23756	165	18	1218	1218	NUM
brj-23756	165	19	-	-	SYM
brj-23756	165	20	1228	1228	NUM
brj-23756	165	21	.	.	PUNCT
brj-23756	166	1	doi	doi	NOUN
brj-23756	166	2	:	:	PUNCT
brj-23756	166	3	10.15376	10.15376	NUM
brj-23756	166	4	/	/	SYM
brj-23756	166	5	biores.19.1.1218	biores.19.1.1218	NOUN
brj-23756	166	6	-	-	PUNCT
brj-23756	166	7	1228	1228	NUM
brj-23756	166	8	masoumi	masoumi	NOUN
brj-23756	166	9	,	,	PUNCT
brj-23756	166	10	a.	a.	NOUN
brj-23756	166	11	,	,	PUNCT
brj-23756	166	12	and	and	CCONJ
brj-23756	166	13	bond	bond	NOUN
brj-23756	166	14	,	,	PUNCT
brj-23756	166	15	b.	b.	PROPN
brj-23756	166	16	(	(	PUNCT
brj-23756	166	17	2024b	2024b	NUM
brj-23756	166	18	)	)	PUNCT
brj-23756	166	19	.	.	PUNCT
brj-23756	167	1	“	"	PUNCT
brj-23756	167	2	machine	machine	NOUN
brj-23756	167	3	learning	learning	NOUN
brj-23756	167	4	-	-	PUNCT
brj-23756	167	5	based	base	VERB
brj-23756	167	6	prediction	prediction	NOUN
brj-23756	167	7	of	of	ADP
brj-23756	167	8	processing	processing	NOUN
brj-23756	167	9	time	time	NOUN
brj-23756	167	10	in	in	ADP
brj-23756	167	11	furniture	furniture	NOUN
brj-23756	167	12	manufacturing	manufacturing	NOUN
brj-23756	167	13	to	to	PART
brj-23756	167	14	estimate	estimate	VERB
brj-23756	167	15	lead	lead	NOUN
brj-23756	167	16	time	time	NOUN
brj-23756	167	17	and	and	CCONJ
brj-23756	167	18	pricing	pricing	NOUN
brj-23756	167	19	,	,	PUNCT
brj-23756	167	20	”	"	PUNCT
brj-23756	167	21	european	european	ADJ
brj-23756	167	22	journal	journal	PROPN
brj-23756	167	23	of	of	ADP
brj-23756	167	24	wood	wood	NOUN
brj-23756	167	25	and	and	CCONJ
brj-23756	167	26	wood	wood	NOUN
brj-23756	167	27	products	product	NOUN
brj-23756	167	28	(	(	PUNCT
brj-23756	167	29	submitted	submit	VERB
brj-23756	167	30	)	)	PUNCT
brj-23756	167	31	masoumi	masoumi	NOUN
brj-23756	167	32	,	,	PUNCT
brj-23756	167	33	a.	a.	NOUN
brj-23756	167	34	,	,	PUNCT
brj-23756	167	35	bond	bond	NOUN
brj-23756	167	36	,	,	PUNCT
brj-23756	167	37	b.	b.	PROPN
brj-23756	167	38	,	,	PUNCT
brj-23756	167	39	and	and	CCONJ
brj-23756	167	40	zink	zink	NOUN
brj-23756	167	41	sharp	sharp	ADJ
brj-23756	167	42	,	,	PUNCT
brj-23756	167	43	a.	a.	NOUN
brj-23756	167	44	(	(	PUNCT
brj-23756	167	45	2024	2024	NUM
brj-23756	167	46	)	)	PUNCT
brj-23756	167	47	.	.	PUNCT
brj-23756	168	1	“	"	PUNCT
brj-23756	168	2	kinetics	kinetic	NOUN
brj-23756	168	3	of	of	ADP
brj-23756	168	4	moisture	moisture	NOUN
brj-23756	168	5	absorption	absorption	NOUN
brj-23756	168	6	,	,	PUNCT
brj-23756	168	7	swelling	swelling	NOUN
brj-23756	168	8	and	and	CCONJ
brj-23756	168	9	shrinkage	shrinkage	NOUN
brj-23756	168	10	of	of	ADP
brj-23756	168	11	thermally	thermally	ADV
brj-23756	168	12	modified	modify	VERB
brj-23756	168	13	hardwoods	hardwood	NOUN
brj-23756	168	14	,	,	PUNCT
brj-23756	168	15	”	"	PUNCT
brj-23756	168	16	annual	annual	ADJ
brj-23756	168	17	international	international	ADJ
brj-23756	168	18	conference	conference	NOUN
brj-23756	168	19	of	of	ADP
brj-23756	168	20	forest	forest	NOUN
brj-23756	168	21	products	product	NOUN
brj-23756	168	22	society	society	NOUN
brj-23756	168	23	,	,	PUNCT
brj-23756	168	24	june	june	PROPN
brj-23756	168	25	4	4	NUM
brj-23756	168	26	-	-	SYM
brj-23756	168	27	6	6	NUM
brj-23756	168	28	,	,	PUNCT
brj-23756	168	29	2024	2024	NUM
brj-23756	168	30	,	,	PUNCT
brj-23756	168	31	knoxville	knoxville	PROPN
brj-23756	168	32	,	,	PUNCT
brj-23756	168	33	tn	tn	PROPN
brj-23756	168	34	.	.	PUNCT
brj-23756	168	35	doi	doi	PROPN
brj-23756	168	36	:	:	PUNCT
brj-23756	168	37	10.13140	10.13140	NUM
brj-23756	168	38	/	/	SYM
brj-23756	168	39	rg.2.2.28417.67687	rg.2.2.28417.67687	PROPN
brj-23756	168	40	militz	militz	PROPN
brj-23756	168	41	,	,	PUNCT
brj-23756	168	42	h.	h.	PROPN
brj-23756	168	43	,	,	PUNCT
brj-23756	168	44	and	and	CCONJ
brj-23756	168	45	altgen	altgen	PROPN
brj-23756	168	46	,	,	PUNCT
brj-23756	168	47	m.	m.	NOUN
brj-23756	168	48	(	(	PUNCT
brj-23756	168	49	2014	2014	NUM
brj-23756	168	50	)	)	PUNCT
brj-23756	168	51	.	.	PUNCT
brj-23756	169	1	“	"	PUNCT
brj-23756	169	2	processes	process	NOUN
brj-23756	169	3	and	and	CCONJ
brj-23756	169	4	properties	property	NOUN
brj-23756	169	5	of	of	ADP
brj-23756	169	6	thermally	thermally	ADV
brj-23756	169	7	modified	modify	VERB
brj-23756	169	8	wood	wood	NOUN
brj-23756	169	9	manufactured	manufacture	VERB
brj-23756	169	10	in	in	ADP
brj-23756	169	11	europe	europe	PROPN
brj-23756	169	12	,	,	PUNCT
brj-23756	169	13	”	"	PUNCT
brj-23756	169	14	acs	acs	PROPN
brj-23756	169	15	symposium	symposium	NOUN
brj-23756	169	16	series	series	PROPN
brj-23756	169	17	1158	1158	NUM
brj-23756	169	18	,	,	PUNCT
brj-23756	169	19	269	269	NUM
brj-23756	169	20	-	-	SYM
brj-23756	169	21	285	285	NUM
brj-23756	169	22	.	.	PUNCT
brj-23756	170	1	doi	doi	NOUN
brj-23756	170	2	:	:	PUNCT
brj-23756	170	3	10.1021	10.1021	NUM
brj-23756	170	4	/	/	SYM
brj-23756	170	5	bk2014	bk2014	NOUN
brj-23756	170	6	-	-	PUNCT
brj-23756	170	7	1158.ch016	1158.ch016	NUM
brj-23756	170	8	nasir	nasir	PROPN
brj-23756	170	9	,	,	PUNCT
brj-23756	170	10	v.	v.	PROPN
brj-23756	170	11	,	,	PUNCT
brj-23756	170	12	nourian	nourian	PROPN
brj-23756	170	13	,	,	PUNCT
brj-23756	170	14	s.	s.	PROPN
brj-23756	170	15	,	,	PUNCT
brj-23756	170	16	avramidis	avramidis	PROPN
brj-23756	170	17	,	,	PUNCT
brj-23756	170	18	s.	s.	PROPN
brj-23756	170	19	,	,	PUNCT
brj-23756	170	20	and	and	CCONJ
brj-23756	170	21	cool	cool	ADJ
brj-23756	170	22	,	,	PUNCT
brj-23756	170	23	j.	j.	PROPN
brj-23756	170	24	(	(	PUNCT
brj-23756	170	25	2019	2019	NUM
brj-23756	170	26	)	)	PUNCT
brj-23756	170	27	.	.	PUNCT
brj-23756	171	1	“	"	PUNCT
brj-23756	171	2	prediction	prediction	NOUN
brj-23756	171	3	of	of	ADP
brj-23756	171	4	physical	physical	ADJ
brj-23756	171	5	and	and	CCONJ
brj-23756	171	6	mechanical	mechanical	ADJ
brj-23756	171	7	properties	property	NOUN
brj-23756	171	8	of	of	ADP
brj-23756	171	9	thermally	thermally	ADV
brj-23756	171	10	modified	modify	VERB
brj-23756	171	11	wood	wood	NOUN
brj-23756	171	12	based	base	VERB
brj-23756	171	13	on	on	ADP
brj-23756	171	14	color	color	NOUN
brj-23756	171	15	change	change	NOUN
brj-23756	171	16	evaluated	evaluate	VERB
brj-23756	171	17	https://doi.org/10.1007/s00107-022-01839-x	https://doi.org/10.1007/s00107-022-01839-x	PROPN
brj-23756	171	18	http://dx.doi.org/10.13140/rg.2.2.28417.67687	http://dx.doi.org/10.13140/rg.2.2.28417.67687	PROPN
brj-23756	171	19	peer	peer	NOUN
brj-23756	171	20	-	-	PUNCT
brj-23756	171	21	reviewed	review	VERB
brj-23756	171	22	article	article	NOUN
brj-23756	171	23	bioresources.cnr.ncsu.edu	bioresources.cnr.ncsu.edu	X
brj-23756	171	24	masoumi	masoumi	NOUN
brj-23756	171	25	&	&	CCONJ
brj-23756	171	26	bond	bond	PROPN
brj-23756	171	27	(	(	PUNCT
brj-23756	171	28	2024	2024	NUM
brj-23756	171	29	)	)	PUNCT
brj-23756	171	30	.	.	PUNCT
brj-23756	172	1	“	"	PUNCT
brj-23756	172	2	ann	ann	PROPN
brj-23756	172	3	prediction	prediction	NOUN
brj-23756	172	4	of	of	ADP
brj-23756	172	5	tmw	tmw	NOUN
brj-23756	172	6	,	,	PUNCT
brj-23756	172	7	”	"	PUNCT
brj-23756	172	8	bioresources	bioresource	NOUN
brj-23756	172	9	19(4	19(4	NUM
brj-23756	172	10	)	)	PUNCT
brj-23756	172	11	,	,	PUNCT
brj-23756	172	12	6983	6983	NUM
brj-23756	172	13	-	-	SYM
brj-23756	172	14	6993	6993	NUM
brj-23756	172	15	.	.	PUNCT
brj-23756	173	1	6993	6993	NUM
brj-23756	173	2	by	by	ADP
brj-23756	173	3	means	mean	NOUN
brj-23756	173	4	of	of	ADP
brj-23756	173	5	“	"	PUNCT
brj-23756	173	6	group	group	NOUN
brj-23756	173	7	method	method	NOUN
brj-23756	173	8	of	of	ADP
brj-23756	173	9	data	data	PROPN
brj-23756	173	10	handling”(gmdh	handling”(gmdh	PROPN
brj-23756	173	11	)	)	PUNCT
brj-23756	173	12	neural	neural	ADJ
brj-23756	173	13	network	network	NOUN
brj-23756	173	14	,	,	PUNCT
brj-23756	173	15	”	"	PUNCT
brj-23756	173	16	holzforschung	holzforschung	PROPN
brj-23756	173	17	73(4	73(4	NOUN
brj-23756	173	18	)	)	PUNCT
brj-23756	174	1	,	,	PUNCT
brj-23756	174	2	381	381	NUM
brj-23756	174	3	-	-	SYM
brj-23756	174	4	392	392	NUM
brj-23756	174	5	.	.	PUNCT
brj-23756	174	6	doi	doi	NOUN
brj-23756	174	7	:	:	PUNCT
brj-23756	174	8	10.1515	10.1515	NUM
brj-23756	174	9	/	/	SYM
brj-23756	174	10	hf-2018	hf-2018	NOUN
brj-23756	174	11	-	-	PUNCT
brj-23756	174	12	0146	0146	NUM
brj-23756	174	13	oladi	oladi	NOUN
brj-23756	174	14	,	,	PUNCT
brj-23756	174	15	r.	r.	PROPN
brj-23756	174	16	,	,	PUNCT
brj-23756	174	17	matini	matini	PROPN
brj-23756	174	18	behzad	behzad	PROPN
brj-23756	174	19	,	,	PUNCT
brj-23756	174	20	h.	h.	PROPN
brj-23756	174	21	,	,	PUNCT
brj-23756	174	22	sharifi	sharifi	PROPN
brj-23756	174	23	,	,	PUNCT
brj-23756	174	24	z.	z.	PROPN
brj-23756	174	25	,	,	PUNCT
brj-23756	174	26	and	and	CCONJ
brj-23756	174	27	masoumi	masoumi	NOUN
brj-23756	174	28	,	,	PUNCT
brj-23756	174	29	a.	a.	NOUN
brj-23756	174	30	(	(	PUNCT
brj-23756	174	31	2013	2013	NUM
brj-23756	174	32	)	)	PUNCT
brj-23756	174	33	.	.	PUNCT
brj-23756	175	1	“	"	PUNCT
brj-23756	175	2	comparing	compare	VERB
brj-23756	175	3	the	the	DET
brj-23756	175	4	wood	wood	NOUN
brj-23756	175	5	anatomy	anatomy	NOUN
brj-23756	175	6	of	of	ADP
brj-23756	175	7	the	the	DET
brj-23756	175	8	field	field	NOUN
brj-23756	175	9	elms	elm	NOUN
brj-23756	175	10	(	(	PUNCT
brj-23756	175	11	ulmus	ulmus	PROPN
brj-23756	175	12	carpinifolia	carpinifolia	PROPN
brj-23756	175	13	borkh	borkh	PROPN
brj-23756	175	14	.	.	PUNCT
brj-23756	175	15	)	)	PUNCT
brj-23756	176	1	native	native	ADJ
brj-23756	176	2	to	to	PART
brj-23756	176	3	gorgan	gorgan	VERB
brj-23756	176	4	and	and	CCONJ
brj-23756	176	5	komijan	komijan	NOUN
brj-23756	176	6	,	,	PUNCT
brj-23756	176	7	”	"	PUNCT
brj-23756	176	8	journal	journal	NOUN
brj-23756	176	9	of	of	ADP
brj-23756	176	10	forest	forest	NOUN
brj-23756	176	11	and	and	CCONJ
brj-23756	176	12	wood	wood	NOUN
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brj-23756	177	10	and	and	CCONJ
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brj-23756	177	12	,	,	PUNCT
brj-23756	177	13	m.	m.	NOUN
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brj-23756	177	16	)	)	PUNCT
brj-23756	177	17	.	.	PUNCT
brj-23756	178	1	“	"	PUNCT
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brj-23756	178	4	equilibrium	equilibrium	NOUN
brj-23756	178	5	moisture	moisture	NOUN
brj-23756	178	6	content	content	NOUN
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brj-23756	178	14	wood	wood	NOUN
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brj-23756	178	31	-	-	SYM
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brj-23756	179	1	doi	doi	NOUN
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brj-23756	179	8	-	-	PUNCT
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brj-23756	179	10	-	-	SYM
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brj-23756	179	17	)	)	PUNCT
brj-23756	179	18	.	.	PUNCT
brj-23756	180	1	“	"	PUNCT
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brj-23756	180	5	,	,	PUNCT
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brj-23756	180	8	:	:	PUNCT
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brj-23756	180	11	deep	deep	ADJ
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brj-23756	181	2	-	-	SYM
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brj-23756	181	4	.	.	PUNCT
brj-23756	182	1	doi	doi	NOUN
brj-23756	182	2	:	:	PUNCT
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brj-23756	182	4	-	-	SYM
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brj-23756	182	6	-	-	PUNCT
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brj-23756	182	8	-	-	PUNCT
brj-23756	182	9	29555	29555	NUM
brj-23756	182	10	-	-	PUNCT
brj-23756	182	11	3_7	3_7	NUM
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brj-23756	182	13	,	,	PUNCT
brj-23756	182	14	b.	b.	PROPN
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brj-23756	182	17	and	and	CCONJ
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brj-23756	182	23	)	)	PUNCT
brj-23756	182	24	.	.	PUNCT
brj-23756	183	1	“	"	PUNCT
brj-23756	183	2	chemical	chemical	ADJ
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brj-23756	183	5	hydrothermal	hydrothermal	NOUN
brj-23756	183	6	treated	treat	VERB
brj-23756	183	7	wood	wood	NOUN
brj-23756	183	8	:	:	PUNCT
brj-23756	183	9	ftir	ftir	VERB
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brj-23756	183	28	-	-	SYM
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brj-23756	183	30	.	.	PUNCT
brj-23756	184	1	doi	doi	NOUN
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brj-23756	184	4	/	/	SYM
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brj-23756	184	8	-	-	PUNCT
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brj-23756	184	14	s.	s.	PROPN
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brj-23756	184	18	s.	s.	PROPN
brj-23756	184	19	,	,	PUNCT
brj-23756	184	20	aydin	aydin	NOUN
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brj-23756	184	22	a.	a.	NOUN
brj-23756	184	23	,	,	PUNCT
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brj-23756	184	26	,	,	PUNCT
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brj-23756	184	28	(	(	PUNCT
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brj-23756	184	30	)	)	PUNCT
brj-23756	184	31	.	.	PUNCT
brj-23756	185	1	“	"	PUNCT
brj-23756	185	2	analysis	analysis	NOUN
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brj-23756	185	4	volumetric	volumetric	NOUN
brj-23756	185	5	swelling	swelling	NOUN
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brj-23756	185	7	shrinkage	shrinkage	NOUN
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brj-23756	185	10	-	-	PUNCT
brj-23756	185	11	treated	treat	VERB
brj-23756	185	12	woods	wood	NOUN
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brj-23756	185	14	experimental	experimental	ADJ
brj-23756	185	15	and	and	CCONJ
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brj-23756	185	17	neural	neural	ADJ
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brj-23756	185	27	18(3	18(3	PROPN
brj-23756	185	28	)	)	PUNCT
brj-23756	185	29	,	,	PUNCT
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brj-23756	185	31	-	-	SYM
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brj-23756	185	33	.	.	PUNCT
brj-23756	186	1	doi	doi	NOUN
brj-23756	186	2	:	:	PUNCT
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brj-23756	186	6	-	-	PUNCT
brj-23756	186	7	221x2016005000043	221x2016005000043	NUM
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brj-23756	186	10	:	:	PUNCT
brj-23756	186	11	july	july	PROPN
brj-23756	186	12	1	1	NUM
brj-23756	186	13	,	,	PUNCT
brj-23756	186	14	2024	2024	NUM
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brj-23756	186	16	peer	peer	NOUN
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brj-23756	186	19	:	:	PUNCT
brj-23756	186	20	july	july	PROPN
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brj-23756	186	22	,	,	PUNCT
brj-23756	186	23	2024	2024	NUM
brj-23756	186	24	;	;	PUNCT
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brj-23756	186	28	and	and	CCONJ
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brj-23756	186	30	:	:	PUNCT
brj-23756	186	31	july	july	PROPN
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brj-23756	186	33	,	,	PUNCT
brj-23756	186	34	2024	2024	NUM
brj-23756	186	35	;	;	PUNCT
brj-23756	186	36	published	publish	VERB
brj-23756	186	37	:	:	PUNCT
brj-23756	186	38	august	august	PROPN
brj-23756	186	39	1	1	NUM
brj-23756	186	40	,	,	PUNCT
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brj-23756	186	42	.	.	PUNCT
brj-23756	187	1	doi	doi	NOUN
brj-23756	187	2	:	:	PUNCT
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brj-23756	187	4	/	/	SYM
brj-23756	187	5	biores.19.4.6983	biores.19.4.6983	NOUN
brj-23756	187	6	-	-	PUNCT
brj-23756	187	7	6993	6993	NUM
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