id	sid	tid	token	lemma	pos
cana-1287	1	1	communications	communication	NOUN
cana-1287	1	2	on	on	ADP
cana-1287	1	3	applied	apply	VERB
cana-1287	1	4	nonlinear	nonlinear	ADJ
cana-1287	1	5	analysis	analysis	NOUN
cana-1287	1	6	issn	issn	NOUN
cana-1287	1	7	:	:	PUNCT
cana-1287	1	8	1074	1074	NUM
cana-1287	1	9	-	-	PUNCT
cana-1287	1	10	133x	133x	NUM
cana-1287	1	11	vol	vol	NOUN
cana-1287	1	12	31	31	NUM
cana-1287	1	13	no	no	NOUN
cana-1287	1	14	.	.	PUNCT
cana-1287	2	1	7s	7	NOUN
cana-1287	2	2	(	(	PUNCT
cana-1287	2	3	2024	2024	NUM
cana-1287	2	4	)	)	PUNCT
cana-1287	2	5	92	92	NUM
cana-1287	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	2	7	nonlinear	nonlinear	ADJ
cana-1287	2	8	regression	regression	NOUN
cana-1287	2	9	based	base	VERB
cana-1287	2	10	deep	deep	ADJ
cana-1287	2	11	radial	radial	ADJ
cana-1287	2	12	basis	basis	NOUN
cana-1287	2	13	function	function	NOUN
cana-1287	2	14	and	and	CCONJ
cana-1287	2	15	advanced	advanced	ADJ
cana-1287	2	16	image	image	NOUN
cana-1287	2	17	processing	processing	NOUN
cana-1287	2	18	in	in	ADP
cana-1287	2	19	soil	soil	NOUN
cana-1287	2	20	estimation	estimation	NOUN
cana-1287	2	21	1atish	1atish	NUM
cana-1287	2	22	mane	mane	NOUN
cana-1287	2	23	,	,	PUNCT
cana-1287	2	24	assistant	assistant	NOUN
cana-1287	2	25	professor	professor	NOUN
cana-1287	2	26	,	,	PUNCT
cana-1287	2	27	mechanical	mechanical	ADJ
cana-1287	2	28	engineering	engineering	NOUN
cana-1287	2	29	department	department	PROPN
cana-1287	2	30	,	,	PUNCT
cana-1287	2	31	bharati	bharati	PROPN
cana-1287	2	32	vidyapeeth	vidyapeeth	PROPN
cana-1287	2	33	's	's	PART
cana-1287	2	34	college	college	NOUN
cana-1287	2	35	of	of	ADP
cana-1287	2	36	engineering	engineering	NOUN
cana-1287	2	37	,	,	PUNCT
cana-1287	2	38	lavale	lavale	NOUN
cana-1287	2	39	,	,	PUNCT
cana-1287	2	40	pune	pune	NOUN
cana-1287	2	41	,	,	PUNCT
cana-1287	2	42	maharashtra	maharashtra	PROPN
cana-1287	2	43	,	,	PUNCT
cana-1287	2	44	india	india	PROPN
cana-1287	2	45	.	.	PUNCT
cana-1287	3	1	mane.atish@bharatividyapeeth.edu	mane.atish@bharatividyapeeth.edu	X
cana-1287	3	2	2k	2k	NUM
cana-1287	3	3	.	.	PUNCT
cana-1287	4	1	shailaja	shailaja	PROPN
cana-1287	4	2	,	,	PUNCT
cana-1287	4	3	associate	associate	NOUN
cana-1287	4	4	professor	professor	NOUN
cana-1287	4	5	,	,	PUNCT
cana-1287	4	6	department	department	PROPN
cana-1287	4	7	of	of	ADP
cana-1287	4	8	cse	cse	PROPN
cana-1287	4	9	,	,	PUNCT
cana-1287	4	10	anurag	anurag	PROPN
cana-1287	4	11	university	university	PROPN
cana-1287	4	12	,	,	PUNCT
cana-1287	4	13	hyderabad	hyderabad	PROPN
cana-1287	4	14	,	,	PUNCT
cana-1287	4	15	india	india	PROPN
cana-1287	4	16	.	.	PUNCT
cana-1287	5	1	kshailajacse@anurag.edu.in	kshailajacse@anurag.edu.in	PROPN
cana-1287	5	2	3girish	3girish	NUM
cana-1287	5	3	madhaorao	madhaorao	NOUN
cana-1287	5	4	lonare	lonare	NOUN
cana-1287	5	5	,	,	PUNCT
cana-1287	5	6	associate	associate	NOUN
cana-1287	5	7	professor	professor	NOUN
cana-1287	5	8	,	,	PUNCT
cana-1287	5	9	department	department	NOUN
cana-1287	5	10	of	of	ADP
cana-1287	5	11	mechanical	mechanical	ADJ
cana-1287	5	12	engineering	engineering	NOUN
cana-1287	5	13	,	,	PUNCT
cana-1287	5	14	bharati	bharati	PROPN
cana-1287	5	15	vidyapeeth	vidyapeeth	PROPN
cana-1287	5	16	college	college	PROPN
cana-1287	5	17	of	of	ADP
cana-1287	5	18	engineering	engineering	PROPN
cana-1287	5	19	,	,	PUNCT
cana-1287	5	20	navi	navi	PROPN
cana-1287	5	21	mumbai	mumbai	PROPN
cana-1287	5	22	,	,	PUNCT
cana-1287	5	23	india	india	PROPN
cana-1287	5	24	.	.	PUNCT
cana-1287	6	1	girish.lonare@bvcoenm.edu.in	girish.lonare@bvcoenm.edu.in	PROPN
cana-1287	6	2	4satyajee	4satyajee	NUM
cana-1287	6	3	srivastava	srivastava	PROPN
cana-1287	6	4	,	,	PUNCT
cana-1287	6	5	professor	professor	NOUN
cana-1287	6	6	,	,	PUNCT
cana-1287	6	7	department	department	PROPN
cana-1287	6	8	of	of	ADP
cana-1287	6	9	cse	cse	PROPN
cana-1287	6	10	,	,	PUNCT
cana-1287	6	11	m.m	m.m	PROPN
cana-1287	6	12	.	.	PROPN
cana-1287	6	13	engineering	engineering	PROPN
cana-1287	6	14	college	college	PROPN
cana-1287	6	15	,	,	PUNCT
cana-1287	6	16	maharishi	maharishi	PROPN
cana-1287	6	17	markandeshwar	markandeshwar	PROPN
cana-1287	6	18	(	(	PUNCT
cana-1287	6	19	deemed	deem	VERB
cana-1287	6	20	to	to	PART
cana-1287	6	21	be	be	AUX
cana-1287	6	22	university	university	NOUN
cana-1287	6	23	)	)	PUNCT
cana-1287	6	24	,	,	PUNCT
cana-1287	6	25	mullana	mullana	PROPN
cana-1287	6	26	,	,	PUNCT
cana-1287	6	27	ambala	ambala	PROPN
cana-1287	6	28	,	,	PUNCT
cana-1287	6	29	haryana	haryana	PROPN
cana-1287	6	30	,	,	PUNCT
cana-1287	6	31	india	india	PROPN
cana-1287	6	32	.	.	PUNCT
cana-1287	7	1	drsatyajee@gmail.com	drsatyajee@gmail.com	X
cana-1287	8	1	5komalavalli	5komalavalli	NUM
cana-1287	8	2	d	d	NOUN
cana-1287	8	3	,	,	PUNCT
cana-1287	8	4	assistant	assistant	NOUN
cana-1287	8	5	professor	professor	NOUN
cana-1287	8	6	,	,	PUNCT
cana-1287	8	7	department	department	NOUN
cana-1287	8	8	of	of	ADP
cana-1287	8	9	information	information	NOUN
cana-1287	8	10	technology	technology	PROPN
cana-1287	8	11	,	,	PUNCT
cana-1287	8	12	sona	sona	PROPN
cana-1287	8	13	college	college	PROPN
cana-1287	8	14	of	of	ADP
cana-1287	8	15	technology	technology	NOUN
cana-1287	8	16	,	,	PUNCT
cana-1287	8	17	salem	salem	NOUN
cana-1287	8	18	,	,	PUNCT
cana-1287	8	19	tamilnadu	tamilnadu	NOUN
cana-1287	8	20	,	,	PUNCT
cana-1287	8	21	india	india	PROPN
cana-1287	8	22	.	.	PUNCT
cana-1287	9	1	komsdayalan@gmail.com	komsdayalan@gmail.com	X
cana-1287	10	1	6ghanasham	6ghanasham	NUM
cana-1287	10	2	c.	c.	PROPN
cana-1287	10	3	sarode	sarode	PROPN
cana-1287	10	4	,	,	PUNCT
cana-1287	10	5	associate	associate	NOUN
cana-1287	10	6	professor	professor	NOUN
cana-1287	10	7	,	,	PUNCT
cana-1287	10	8	department	department	NOUN
cana-1287	10	9	of	of	ADP
cana-1287	10	10	civil	civil	ADJ
cana-1287	10	11	engineering	engineering	NOUN
cana-1287	10	12	,	,	PUNCT
cana-1287	10	13	dr	dr	PROPN
cana-1287	10	14	.	.	PROPN
cana-1287	11	1	d.y	d.y	PROPN
cana-1287	11	2	.	.	PROPN
cana-1287	11	3	patil	patil	PROPN
cana-1287	11	4	institute	institute	PROPN
cana-1287	11	5	of	of	ADP
cana-1287	11	6	technology	technology	NOUN
cana-1287	11	7	pimpri	pimpri	NOUN
cana-1287	11	8	,	,	PUNCT
cana-1287	11	9	pune	pune	NOUN
cana-1287	11	10	,	,	PUNCT
cana-1287	11	11	maharashtra	maharashtra	PROPN
cana-1287	11	12	,	,	PUNCT
cana-1287	11	13	india	india	PROPN
cana-1287	11	14	.	.	PUNCT
cana-1287	12	1	ghanshyam.sarode@dypvp.edu.in	ghanshyam.sarode@dypvp.edu.in	PROPN
cana-1287	12	2	article	article	PROPN
cana-1287	12	3	history	history	NOUN
cana-1287	12	4	:	:	PUNCT
cana-1287	12	5	received	receive	VERB
cana-1287	12	6	:	:	PUNCT
cana-1287	12	7	01	01	NUM
cana-1287	12	8	-	-	PUNCT
cana-1287	12	9	06	06	NUM
cana-1287	12	10	-	-	PUNCT
cana-1287	12	11	2024	2024	NUM
cana-1287	12	12	revised	revise	VERB
cana-1287	12	13	:	:	PUNCT
cana-1287	12	14	03	03	NUM
cana-1287	12	15	-	-	PUNCT
cana-1287	12	16	07	07	NUM
cana-1287	12	17	-	-	PUNCT
cana-1287	12	18	2024	2024	NUM
cana-1287	12	19	accepted	accept	VERB
cana-1287	12	20	:	:	PUNCT
cana-1287	12	21	29	29	NUM
cana-1287	12	22	-	-	SYM
cana-1287	12	23	07	07	NUM
cana-1287	12	24	-	-	PUNCT
cana-1287	12	25	2024	2024	NUM
cana-1287	12	26	abstract	abstract	NOUN
cana-1287	12	27	:	:	PUNCT
cana-1287	12	28	agricultural	agricultural	ADJ
cana-1287	12	29	planning	planning	NOUN
cana-1287	12	30	,	,	PUNCT
cana-1287	12	31	environmental	environmental	ADJ
cana-1287	12	32	management	management	NOUN
cana-1287	12	33	,	,	PUNCT
cana-1287	12	34	and	and	CCONJ
cana-1287	12	35	soil	soil	NOUN
cana-1287	12	36	estimation	estimation	NOUN
cana-1287	12	37	all	all	PRON
cana-1287	12	38	depend	depend	VERB
cana-1287	12	39	on	on	ADP
cana-1287	12	40	exact	exact	ADJ
cana-1287	12	41	prediction	prediction	NOUN
cana-1287	12	42	of	of	ADP
cana-1287	12	43	soil	soil	NOUN
cana-1287	12	44	properties	property	NOUN
cana-1287	12	45	.	.	PUNCT
cana-1287	13	1	conventional	conventional	ADJ
cana-1287	13	2	methods	method	NOUN
cana-1287	13	3	find	find	VERB
cana-1287	13	4	it	it	PRON
cana-1287	13	5	challenging	challenge	VERB
cana-1287	13	6	to	to	PART
cana-1287	13	7	manage	manage	VERB
cana-1287	13	8	the	the	DET
cana-1287	13	9	complex	complex	ADJ
cana-1287	13	10	,	,	PUNCT
cana-1287	13	11	nonlinear	nonlinear	ADJ
cana-1287	13	12	connections	connection	NOUN
cana-1287	13	13	between	between	ADP
cana-1287	13	14	soil	soil	NOUN
cana-1287	13	15	parameters	parameter	NOUN
cana-1287	13	16	and	and	CCONJ
cana-1287	13	17	visual	visual	ADJ
cana-1287	13	18	characteristics	characteristic	NOUN
cana-1287	13	19	.	.	PUNCT
cana-1287	14	1	this	this	DET
cana-1287	14	2	work	work	NOUN
cana-1287	14	3	handles	handle	VERB
cana-1287	14	4	this	this	DET
cana-1287	14	5	challenge	challenge	NOUN
cana-1287	14	6	using	use	VERB
cana-1287	14	7	advanced	advanced	ADJ
cana-1287	14	8	image	image	NOUN
cana-1287	14	9	processing	processing	NOUN
cana-1287	14	10	techniques	technique	NOUN
cana-1287	14	11	along	along	ADP
cana-1287	14	12	with	with	ADP
cana-1287	14	13	a	a	DET
cana-1287	14	14	deep	deep	ADJ
cana-1287	14	15	radial	radial	ADJ
cana-1287	14	16	basis	basis	NOUN
cana-1287	14	17	function	function	NOUN
cana-1287	14	18	(	(	PUNCT
cana-1287	14	19	rbf	rbf	PROPN
cana-1287	14	20	)	)	PUNCT
cana-1287	14	21	network	network	NOUN
cana-1287	14	22	for	for	ADP
cana-1287	14	23	nonlinear	nonlinear	ADJ
cana-1287	14	24	regression	regression	NOUN
cana-1287	14	25	.	.	PUNCT
cana-1287	15	1	the	the	DET
cana-1287	15	2	deep	deep	PROPN
cana-1287	15	3	rbf	rbf	PROPN
cana-1287	15	4	network	network	NOUN
cana-1287	15	5	detects	detect	NOUN
cana-1287	15	6	complicated	complicate	VERB
cana-1287	15	7	nonlinear	nonlinear	ADJ
cana-1287	15	8	correlations	correlation	NOUN
cana-1287	15	9	in	in	ADP
cana-1287	15	10	soil	soil	NOUN
cana-1287	15	11	data	datum	NOUN
cana-1287	15	12	by	by	ADP
cana-1287	15	13	using	use	VERB
cana-1287	15	14	numerous	numerous	ADJ
cana-1287	15	15	hidden	hidden	ADJ
cana-1287	15	16	layers	layer	NOUN
cana-1287	15	17	with	with	ADP
cana-1287	15	18	radial	radial	ADJ
cana-1287	15	19	basis	basis	NOUN
cana-1287	15	20	functions	function	NOUN
cana-1287	15	21	.	.	PUNCT
cana-1287	16	1	from	from	ADP
cana-1287	16	2	remote	remote	ADJ
cana-1287	16	3	sensing	sensing	NOUN
cana-1287	16	4	images	image	NOUN
cana-1287	16	5	,	,	PUNCT
cana-1287	16	6	image	image	NOUN
cana-1287	16	7	processing	processing	NOUN
cana-1287	16	8	methods	method	NOUN
cana-1287	16	9	enhance	enhance	VERB
cana-1287	16	10	the	the	DET
cana-1287	16	11	feature	feature	NOUN
cana-1287	16	12	extraction	extraction	NOUN
cana-1287	16	13	process	process	NOUN
cana-1287	16	14	thereby	thereby	ADV
cana-1287	16	15	enhancing	enhance	VERB
cana-1287	16	16	the	the	DET
cana-1287	16	17	accuracy	accuracy	NOUN
cana-1287	16	18	of	of	ADP
cana-1287	16	19	soil	soil	NOUN
cana-1287	16	20	property	property	NOUN
cana-1287	16	21	forecasts	forecast	NOUN
cana-1287	16	22	.	.	PUNCT
cana-1287	17	1	experimental	experimental	ADJ
cana-1287	17	2	data	datum	NOUN
cana-1287	17	3	shows	show	VERB
cana-1287	17	4	that	that	SCONJ
cana-1287	17	5	the	the	DET
cana-1287	17	6	proposed	propose	VERB
cana-1287	17	7	method	method	NOUN
cana-1287	17	8	beats	beat	VERB
cana-1287	17	9	more	more	ADJ
cana-1287	17	10	conventional	conventional	ADJ
cana-1287	17	11	linear	linear	NOUN
cana-1287	17	12	regression	regression	NOUN
cana-1287	17	13	models	model	NOUN
cana-1287	17	14	rather	rather	ADV
cana-1287	17	15	significantly	significantly	ADV
cana-1287	17	16	.	.	PUNCT
cana-1287	18	1	against	against	ADP
cana-1287	18	2	an	an	DET
cana-1287	18	3	mse	mse	NOUN
cana-1287	18	4	of	of	ADP
cana-1287	18	5	0.056	0.056	NUM
cana-1287	18	6	and	and	CCONJ
cana-1287	18	7	r²	r²	NOUN
cana-1287	18	8	of	of	ADP
cana-1287	18	9	0.76	0.76	NUM
cana-1287	18	10	for	for	ADP
cana-1287	18	11	linear	linear	ADJ
cana-1287	18	12	models	model	NOUN
cana-1287	18	13	,	,	PUNCT
cana-1287	18	14	the	the	DET
cana-1287	18	15	deep	deep	ADJ
cana-1287	18	16	rbf	rbf	PROPN
cana-1287	18	17	model	model	NOUN
cana-1287	18	18	especially	especially	ADV
cana-1287	18	19	obtained	obtain	VERB
cana-1287	18	20	a	a	DET
cana-1287	18	21	mean	mean	ADJ
cana-1287	18	22	squared	square	VERB
cana-1287	18	23	error	error	NOUN
cana-1287	18	24	(	(	PUNCT
cana-1287	18	25	mse	mse	NOUN
cana-1287	18	26	)	)	PUNCT
cana-1287	18	27	of	of	ADP
cana-1287	18	28	0.032	0.032	NUM
cana-1287	18	29	and	and	CCONJ
cana-1287	18	30	a	a	DET
cana-1287	18	31	coefficient	coefficient	NOUN
cana-1287	18	32	of	of	ADP
cana-1287	18	33	determination	determination	NOUN
cana-1287	18	34	(	(	PUNCT
cana-1287	18	35	r²	r²	NOUN
cana-1287	18	36	)	)	PUNCT
cana-1287	18	37	of	of	ADP
cana-1287	18	38	0.87	0.87	NUM
cana-1287	18	39	.	.	PUNCT
cana-1287	19	1	these	these	DET
cana-1287	19	2	results	result	NOUN
cana-1287	19	3	show	show	VERB
cana-1287	19	4	how	how	SCONJ
cana-1287	19	5	effectively	effectively	ADV
cana-1287	19	6	nonlinear	nonlinear	ADJ
cana-1287	19	7	regression	regression	NOUN
cana-1287	19	8	estimates	estimate	VERB
cana-1287	19	9	dirt	dirt	NOUN
cana-1287	19	10	combined	combine	VERB
cana-1287	19	11	with	with	ADP
cana-1287	19	12	contemporary	contemporary	ADJ
cana-1287	19	13	image	image	NOUN
cana-1287	19	14	processing	processing	NOUN
cana-1287	19	15	.	.	PUNCT
cana-1287	20	1	keywords	keyword	NOUN
cana-1287	20	2	:	:	PUNCT
cana-1287	20	3	nonlinear	nonlinear	ADJ
cana-1287	20	4	regression	regression	NOUN
cana-1287	20	5	,	,	PUNCT
cana-1287	20	6	deep	deep	ADJ
cana-1287	20	7	radial	radial	ADJ
cana-1287	20	8	basis	basis	NOUN
cana-1287	20	9	function	function	NOUN
cana-1287	20	10	,	,	PUNCT
cana-1287	20	11	soil	soil	NOUN
cana-1287	20	12	estimation	estimation	NOUN
cana-1287	20	13	,	,	PUNCT
cana-1287	20	14	image	image	NOUN
cana-1287	20	15	processing	processing	NOUN
cana-1287	20	16	,	,	PUNCT
cana-1287	20	17	remote	remote	ADJ
cana-1287	20	18	sensing	sensing	NOUN
cana-1287	20	19	.	.	PUNCT
cana-1287	21	1	1	1	X
cana-1287	21	2	.	.	X
cana-1287	21	3	introduction	introduction	NOUN
cana-1287	21	4	recent	recent	ADJ
cana-1287	21	5	years	year	NOUN
cana-1287	21	6	'	'	PART
cana-1287	21	7	developments	development	NOUN
cana-1287	21	8	in	in	ADP
cana-1287	21	9	remote	remote	ADJ
cana-1287	21	10	sensing	sensing	NOUN
cana-1287	21	11	technologies	technology	NOUN
cana-1287	21	12	and	and	CCONJ
cana-1287	21	13	machine	machine	NOUN
cana-1287	21	14	learning	learning	NOUN
cana-1287	21	15	algorithms	algorithm	NOUN
cana-1287	21	16	have	have	AUX
cana-1287	21	17	transformed	transform	VERB
cana-1287	21	18	numerous	numerous	ADJ
cana-1287	21	19	fields	field	NOUN
cana-1287	21	20	including	include	VERB
cana-1287	21	21	urban	urban	ADJ
cana-1287	21	22	planning	planning	NOUN
cana-1287	21	23	,	,	PUNCT
cana-1287	21	24	environmental	environmental	ADJ
cana-1287	21	25	monitoring	monitoring	NOUN
cana-1287	21	26	,	,	PUNCT
cana-1287	21	27	and	and	CCONJ
cana-1287	22	1	agricultural	agricultural	ADJ
cana-1287	22	2	[	[	X
cana-1287	22	3	1	1	NUM
cana-1287	22	4	]	]	PUNCT
cana-1287	22	5	.	.	PUNCT
cana-1287	23	1	precision	precision	NOUN
cana-1287	23	2	agriculture	agriculture	NOUN
cana-1287	23	3	depends	depend	VERB
cana-1287	23	4	mostly	mostly	ADV
cana-1287	23	5	on	on	ADP
cana-1287	23	6	soil	soil	NOUN
cana-1287	23	7	nutrient	nutrient	NOUN
cana-1287	23	8	evaluation	evaluation	NOUN
cana-1287	23	9	,	,	PUNCT
cana-1287	23	10	hence	hence	ADV
cana-1287	23	11	proper	proper	ADJ
cana-1287	23	12	soil	soil	NOUN
cana-1287	23	13	information	information	NOUN
cana-1287	23	14	is	be	AUX
cana-1287	23	15	essential	essential	ADJ
cana-1287	23	16	to	to	PART
cana-1287	23	17	maximize	maximize	VERB
cana-1287	23	18	crop	crop	NOUN
cana-1287	23	19	yields	yield	NOUN
cana-1287	23	20	and	and	CCONJ
cana-1287	23	21	control	control	VERB
cana-1287	23	22	soil	soil	NOUN
cana-1287	23	23	condition	condition	NOUN
cana-1287	24	1	[	[	X
cana-1287	24	2	2	2	NUM
cana-1287	24	3	]	]	PUNCT
cana-1287	24	4	.	.	PUNCT
cana-1287	25	1	labor	labor	NOUN
cana-1287	25	2	-	-	PUNCT
cana-1287	25	3	intensive	intensive	ADJ
cana-1287	25	4	and	and	CCONJ
cana-1287	25	5	sometimes	sometimes	ADV
cana-1287	25	6	limited	limit	VERB
cana-1287	25	7	in	in	ADP
cana-1287	25	8	spatial	spatial	ADJ
cana-1287	25	9	resolution	resolution	NOUN
cana-1287	25	10	conventional	conventional	ADJ
cana-1287	25	11	soil	soil	NOUN
cana-1287	25	12	sample	sample	NOUN
cana-1287	25	13	techniques	technique	NOUN
cana-1287	25	14	demand	demand	VERB
cana-1287	25	15	innovative	innovative	ADJ
cana-1287	25	16	solutions	solution	NOUN
cana-1287	25	17	using	use	VERB
cana-1287	25	18	remote	remote	ADJ
cana-1287	25	19	sensing	sense	VERB
cana-1287	25	20	data	datum	NOUN
cana-1287	25	21	and	and	CCONJ
cana-1287	25	22	advanced	advanced	ADJ
cana-1287	25	23	computer	computer	NOUN
cana-1287	25	24	technologies	technology	NOUN
cana-1287	26	1	[	[	X
cana-1287	26	2	3	3	NUM
cana-1287	26	3	]	]	PUNCT
cana-1287	26	4	.	.	PUNCT
cana-1287	27	1	communications	communication	NOUN
cana-1287	27	2	on	on	ADP
cana-1287	27	3	applied	apply	VERB
cana-1287	27	4	nonlinear	nonlinear	ADJ
cana-1287	27	5	analysis	analysis	NOUN
cana-1287	27	6	issn	issn	NOUN
cana-1287	27	7	:	:	PUNCT
cana-1287	27	8	1074	1074	NUM
cana-1287	27	9	-	-	PUNCT
cana-1287	27	10	133x	133x	NUM
cana-1287	27	11	vol	vol	NOUN
cana-1287	27	12	31	31	NUM
cana-1287	27	13	no	no	NOUN
cana-1287	27	14	.	.	PUNCT
cana-1287	28	1	7s	7	NOUN
cana-1287	28	2	(	(	PUNCT
cana-1287	28	3	2024	2024	NUM
cana-1287	28	4	)	)	PUNCT
cana-1287	28	5	93	93	NUM
cana-1287	29	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	29	2	remote	remote	ADJ
cana-1287	29	3	sensing	sense	VERB
cana-1287	29	4	technologies	technology	NOUN
cana-1287	29	5	'	'	PART
cana-1287	29	6	high	high	ADJ
cana-1287	29	7	-	-	PUNCT
cana-1287	29	8	resolution	resolution	NOUN
cana-1287	29	9	spatial	spatial	ADJ
cana-1287	29	10	data	datum	NOUN
cana-1287	29	11	—	—	PUNCT
cana-1287	29	12	that	that	PRON
cana-1287	29	13	of	of	ADP
cana-1287	29	14	airborne	airborne	ADJ
cana-1287	29	15	surveys	survey	NOUN
cana-1287	29	16	and	and	CCONJ
cana-1287	29	17	satellite	satellite	NOUN
cana-1287	29	18	images	image	NOUN
cana-1287	29	19	—	—	PUNCT
cana-1287	29	20	allows	allow	VERB
cana-1287	29	21	one	one	PRON
cana-1287	29	22	to	to	PART
cana-1287	29	23	assess	assess	VERB
cana-1287	29	24	soil	soil	NOUN
cana-1287	29	25	parameters	parameter	NOUN
cana-1287	29	26	over	over	ADP
cana-1287	29	27	large	large	ADJ
cana-1287	29	28	distances	distance	NOUN
cana-1287	29	29	[	[	PUNCT
cana-1287	29	30	4	4	NUM
cana-1287	29	31	]	]	PUNCT
cana-1287	29	32	.	.	PUNCT
cana-1287	30	1	but	but	CCONJ
cana-1287	30	2	occasionally	occasionally	ADV
cana-1287	30	3	raw	raw	ADJ
cana-1287	30	4	remote	remote	ADJ
cana-1287	30	5	sensing	sense	VERB
cana-1287	30	6	data	datum	NOUN
cana-1287	30	7	is	be	AUX
cana-1287	30	8	noisy	noisy	ADJ
cana-1287	30	9	and	and	CCONJ
cana-1287	30	10	complex	complex	ADJ
cana-1287	30	11	,	,	PUNCT
cana-1287	30	12	which	which	PRON
cana-1287	30	13	makes	make	VERB
cana-1287	30	14	it	it	PRON
cana-1287	30	15	challenging	challenging	ADJ
cana-1287	30	16	to	to	PART
cana-1287	30	17	get	get	VERB
cana-1287	30	18	meaningful	meaningful	ADJ
cana-1287	30	19	soil	soil	NOUN
cana-1287	30	20	nutrient	nutrient	NOUN
cana-1287	30	21	information	information	NOUN
cana-1287	31	1	[	[	X
cana-1287	31	2	5	5	NUM
cana-1287	31	3	]	]	PUNCT
cana-1287	31	4	.	.	PUNCT
cana-1287	32	1	non	non	ADJ
cana-1287	32	2	-	-	ADJ
cana-1287	32	3	linear	linear	ADJ
cana-1287	32	4	interactions	interaction	NOUN
cana-1287	32	5	between	between	ADP
cana-1287	32	6	soil	soil	NOUN
cana-1287	32	7	properties	property	NOUN
cana-1287	32	8	and	and	CCONJ
cana-1287	32	9	image	image	NOUN
cana-1287	32	10	attributes	attribute	NOUN
cana-1287	32	11	need	need	VERB
cana-1287	32	12	for	for	ADP
cana-1287	32	13	sophisticated	sophisticated	ADJ
cana-1287	32	14	data	datum	NOUN
cana-1287	32	15	processing	processing	NOUN
cana-1287	32	16	methods	method	NOUN
cana-1287	32	17	[	[	X
cana-1287	32	18	6	6	NUM
cana-1287	32	19	]	]	PUNCT
cana-1287	32	20	.	.	PUNCT
cana-1287	33	1	learning	learn	VERB
cana-1287	33	2	complex	complex	ADJ
cana-1287	33	3	patterns	pattern	NOUN
cana-1287	33	4	from	from	ADP
cana-1287	33	5	data	datum	NOUN
cana-1287	33	6	has	have	AUX
cana-1287	33	7	enabled	enable	VERB
cana-1287	33	8	machine	machine	NOUN
cana-1287	33	9	learning	learning	NOUN
cana-1287	33	10	techniques	technique	NOUN
cana-1287	33	11	—	—	PUNCT
cana-1287	33	12	especially	especially	ADV
cana-1287	33	13	deep	deep	ADJ
cana-1287	33	14	learning	learning	NOUN
cana-1287	33	15	models	model	NOUN
cana-1287	33	16	—	—	PUNCT
cana-1287	33	17	show	show	VERB
cana-1287	33	18	promise	promise	NOUN
cana-1287	33	19	in	in	ADP
cana-1287	33	20	addressing	address	VERB
cana-1287	33	21	these	these	DET
cana-1287	33	22	challenges	challenge	NOUN
cana-1287	33	23	[	[	X
cana-1287	33	24	7	7	NUM
cana-1287	33	25	]	]	PUNCT
cana-1287	33	26	.	.	PUNCT
cana-1287	34	1	among	among	ADP
cana-1287	34	2	these	these	DET
cana-1287	34	3	techniques	technique	NOUN
cana-1287	34	4	for	for	ADP
cana-1287	34	5	modeling	model	VERB
cana-1287	34	6	non	non	ADJ
cana-1287	34	7	-	-	ADJ
cana-1287	34	8	linear	linear	ADJ
cana-1287	34	9	connections	connection	NOUN
cana-1287	34	10	,	,	PUNCT
cana-1287	34	11	radial	radial	ADJ
cana-1287	34	12	basis	basis	NOUN
cana-1287	34	13	function	function	NOUN
cana-1287	34	14	(	(	PUNCT
cana-1287	34	15	rbf	rbf	PROPN
cana-1287	34	16	)	)	PUNCT
cana-1287	34	17	networks	network	NOUN
cana-1287	34	18	have	have	AUX
cana-1287	34	19	become	become	VERB
cana-1287	34	20	a	a	DET
cana-1287	34	21	powerful	powerful	ADJ
cana-1287	34	22	tool	tool	NOUN
cana-1287	34	23	because	because	SCONJ
cana-1287	34	24	of	of	ADP
cana-1287	34	25	their	their	PRON
cana-1287	34	26	flexibility	flexibility	NOUN
cana-1287	34	27	and	and	CCONJ
cana-1287	34	28	ability	ability	NOUN
cana-1287	34	29	to	to	PART
cana-1287	34	30	preserve	preserve	VERB
cana-1287	34	31	intricate	intricate	ADJ
cana-1287	34	32	data	datum	NOUN
cana-1287	34	33	patterns	pattern	NOUN
cana-1287	34	34	[	[	X
cana-1287	34	35	8	8	NUM
cana-1287	34	36	]	]	PUNCT
cana-1287	34	37	.	.	PUNCT
cana-1287	35	1	deep	deep	PROPN
cana-1287	35	2	rbf	rbf	PROPN
cana-1287	35	3	networks	network	NOUN
cana-1287	35	4	applied	apply	VERB
cana-1287	35	5	to	to	ADP
cana-1287	35	6	soil	soil	NOUN
cana-1287	35	7	nutrient	nutrient	NOUN
cana-1287	35	8	assessment	assessment	NOUN
cana-1287	35	9	still	still	ADV
cana-1287	35	10	provide	provide	VERB
cana-1287	35	11	several	several	ADJ
cana-1287	35	12	challenges	challenge	NOUN
cana-1287	35	13	despite	despite	SCONJ
cana-1287	35	14	their	their	PRON
cana-1287	35	15	potential	potential	ADJ
cana-1287	36	1	[	[	X
cana-1287	36	2	9	9	NUM
cana-1287	36	3	]	]	PUNCT
cana-1287	36	4	.	.	PUNCT
cana-1287	37	1	one	one	NUM
cana-1287	37	2	of	of	ADP
cana-1287	37	3	key	key	ADJ
cana-1287	37	4	challenges	challenge	NOUN
cana-1287	37	5	is	be	AUX
cana-1287	37	6	efficient	efficient	ADJ
cana-1287	37	7	retrieval	retrieval	NOUN
cana-1287	37	8	of	of	ADP
cana-1287	37	9	relevant	relevant	ADJ
cana-1287	37	10	information	information	NOUN
cana-1287	37	11	from	from	ADP
cana-1287	37	12	noisy	noisy	ADJ
cana-1287	37	13	and	and	CCONJ
cana-1287	37	14	highdimensional	highdimensional	ADJ
cana-1287	37	15	remote	remote	ADJ
cana-1287	37	16	sensing	sense	VERB
cana-1287	37	17	data	datum	NOUN
cana-1287	37	18	.	.	PUNCT
cana-1287	38	1	conventional	conventional	ADJ
cana-1287	38	2	feature	feature	NOUN
cana-1287	38	3	extraction	extraction	NOUN
cana-1287	38	4	methods	method	NOUN
cana-1287	38	5	may	may	AUX
cana-1287	38	6	miss	miss	VERB
cana-1287	38	7	the	the	DET
cana-1287	38	8	complex	complex	ADJ
cana-1287	38	9	,	,	PUNCT
cana-1287	38	10	non	non	ADJ
cana-1287	38	11	-	-	ADJ
cana-1287	38	12	linear	linear	ADJ
cana-1287	38	13	interaction	interaction	NOUN
cana-1287	38	14	between	between	ADP
cana-1287	38	15	picture	picture	NOUN
cana-1287	38	16	elements	element	NOUN
cana-1287	38	17	and	and	CCONJ
cana-1287	38	18	soil	soil	NOUN
cana-1287	38	19	conditions	condition	NOUN
cana-1287	38	20	.	.	PUNCT
cana-1287	39	1	moreover	moreover	ADV
cana-1287	39	2	,	,	PUNCT
cana-1287	39	3	the	the	DET
cana-1287	39	4	training	training	NOUN
cana-1287	39	5	of	of	ADP
cana-1287	39	6	deep	deep	ADJ
cana-1287	39	7	rbf	rbf	PROPN
cana-1287	39	8	networks	network	NOUN
cana-1287	39	9	requires	require	VERB
cana-1287	39	10	careful	careful	ADJ
cana-1287	39	11	selection	selection	NOUN
cana-1287	39	12	of	of	ADP
cana-1287	39	13	parameters	parameter	NOUN
cana-1287	39	14	that	that	PRON
cana-1287	39	15	could	could	AUX
cana-1287	39	16	significantly	significantly	ADV
cana-1287	39	17	influence	influence	VERB
cana-1287	39	18	the	the	DET
cana-1287	39	19	performance	performance	NOUN
cana-1287	39	20	of	of	ADP
cana-1287	39	21	the	the	DET
cana-1287	39	22	model	model	NOUN
cana-1287	39	23	:	:	PUNCT
cana-1287	39	24	the	the	DET
cana-1287	39	25	number	number	NOUN
cana-1287	39	26	of	of	ADP
cana-1287	39	27	rbf	rbf	PROPN
cana-1287	39	28	units	unit	NOUN
cana-1287	39	29	,	,	PUNCT
cana-1287	39	30	centers	center	NOUN
cana-1287	39	31	,	,	PUNCT
cana-1287	39	32	and	and	CCONJ
cana-1287	39	33	widths	width	NOUN
cana-1287	39	34	.	.	PUNCT
cana-1287	40	1	moreover	moreover	ADV
cana-1287	40	2	,	,	PUNCT
cana-1287	40	3	the	the	DET
cana-1287	40	4	power	power	NOUN
cana-1287	40	5	of	of	ADP
cana-1287	40	6	the	the	DET
cana-1287	40	7	model	model	NOUN
cana-1287	40	8	to	to	PART
cana-1287	40	9	generalize	generalize	VERB
cana-1287	40	10	properly	properly	ADV
cana-1287	40	11	to	to	ADP
cana-1287	40	12	unprocessed	unprocessed	ADJ
cana-1287	40	13	data	datum	NOUN
cana-1287	40	14	[	[	X
cana-1287	40	15	10	10	NUM
cana-1287	40	16	]	]	PUNCT
cana-1287	40	17	determines	determine	VERB
cana-1287	40	18	its	its	PRON
cana-1287	40	19	practical	practical	ADJ
cana-1287	40	20	usefulness	usefulness	NOUN
cana-1287	40	21	.	.	PUNCT
cana-1287	41	1	the	the	DET
cana-1287	41	2	fundamental	fundamental	ADJ
cana-1287	41	3	problem	problem	NOUN
cana-1287	41	4	this	this	DET
cana-1287	41	5	work	work	NOUN
cana-1287	41	6	tackles	tackle	NOUN
cana-1287	41	7	is	be	AUX
cana-1287	41	8	the	the	DET
cana-1287	41	9	precise	precise	ADJ
cana-1287	41	10	assessment	assessment	NOUN
cana-1287	41	11	of	of	ADP
cana-1287	41	12	soil	soil	NOUN
cana-1287	41	13	nutrients	nutrient	NOUN
cana-1287	41	14	obtained	obtain	VERB
cana-1287	41	15	from	from	ADP
cana-1287	41	16	processed	process	VERB
cana-1287	41	17	advanced	advanced	ADJ
cana-1287	41	18	machine	machine	NOUN
cana-1287	41	19	learning	learn	VERB
cana-1287	41	20	approaches	approach	NOUN
cana-1287	41	21	based	base	VERB
cana-1287	41	22	on	on	ADP
cana-1287	41	23	remote	remote	ADJ
cana-1287	41	24	sensing	sense	VERB
cana-1287	41	25	data	datum	NOUN
cana-1287	41	26	.	.	PUNCT
cana-1287	42	1	more	more	ADV
cana-1287	42	2	especially	especially	ADV
cana-1287	42	3	,	,	PUNCT
cana-1287	42	4	the	the	DET
cana-1287	42	5	focus	focus	NOUN
cana-1287	42	6	is	be	AUX
cana-1287	42	7	on	on	ADP
cana-1287	42	8	creating	create	VERB
cana-1287	42	9	a	a	DET
cana-1287	42	10	deep	deep	ADJ
cana-1287	42	11	rbf	rbf	PROPN
cana-1287	42	12	network	network	NOUN
cana-1287	42	13	able	able	ADJ
cana-1287	42	14	to	to	PART
cana-1287	42	15	control	control	VERB
cana-1287	42	16	the	the	DET
cana-1287	42	17	non	non	ADJ
cana-1287	42	18	-	-	ADJ
cana-1287	42	19	linear	linear	ADJ
cana-1287	42	20	correlations	correlation	NOUN
cana-1287	42	21	between	between	ADP
cana-1287	42	22	distant	distant	ADJ
cana-1287	42	23	sensing	sense	VERB
cana-1287	42	24	properties	property	NOUN
cana-1287	42	25	and	and	CCONJ
cana-1287	42	26	soil	soil	NOUN
cana-1287	42	27	nutrient	nutrient	NOUN
cana-1287	42	28	levels	level	NOUN
cana-1287	42	29	.	.	PUNCT
cana-1287	43	1	by	by	ADP
cana-1287	43	2	means	mean	NOUN
cana-1287	43	3	of	of	ADP
cana-1287	43	4	non	non	ADJ
cana-1287	43	5	-	-	ADJ
cana-1287	43	6	linear	linear	ADJ
cana-1287	43	7	regression	regression	NOUN
cana-1287	43	8	for	for	ADP
cana-1287	43	9	feature	feature	NOUN
cana-1287	43	10	extraction	extraction	NOUN
cana-1287	43	11	and	and	CCONJ
cana-1287	43	12	deep	deep	ADJ
cana-1287	43	13	rbf	rbf	PROPN
cana-1287	43	14	networks	network	NOUN
cana-1287	43	15	for	for	ADP
cana-1287	43	16	classification	classification	NOUN
cana-1287	43	17	,	,	PUNCT
cana-1287	43	18	one	one	PRON
cana-1287	43	19	can	can	AUX
cana-1287	43	20	enhance	enhance	VERB
cana-1287	43	21	the	the	DET
cana-1287	43	22	scalability	scalability	NOUN
cana-1287	43	23	and	and	CCONJ
cana-1287	43	24	precision	precision	NOUN
cana-1287	43	25	of	of	ADP
cana-1287	43	26	soil	soil	NOUN
cana-1287	43	27	nutrient	nutrient	NOUN
cana-1287	43	28	estimate	estimate	NOUN
cana-1287	43	29	.	.	PUNCT
cana-1287	44	1	the	the	DET
cana-1287	44	2	objectives	objective	NOUN
cana-1287	44	3	are	be	AUX
cana-1287	44	4	given	give	VERB
cana-1287	44	5	below	below	ADP
cana-1287	44	6	:	:	PUNCT
cana-1287	44	7	1	1	X
cana-1287	44	8	.	.	PUNCT
cana-1287	44	9	by	by	ADP
cana-1287	44	10	use	use	NOUN
cana-1287	44	11	of	of	ADP
cana-1287	44	12	complex	complex	ADJ
cana-1287	44	13	relationships	relationship	NOUN
cana-1287	44	14	between	between	ADP
cana-1287	44	15	picture	picture	NOUN
cana-1287	44	16	features	feature	NOUN
cana-1287	44	17	and	and	CCONJ
cana-1287	44	18	soil	soil	NOUN
cana-1287	44	19	properties	property	NOUN
cana-1287	44	20	,	,	PUNCT
cana-1287	44	21	one	one	PRON
cana-1287	44	22	can	can	AUX
cana-1287	44	23	develop	develop	VERB
cana-1287	44	24	and	and	CCONJ
cana-1287	44	25	implement	implement	VERB
cana-1287	44	26	a	a	DET
cana-1287	44	27	non	non	ADJ
cana-1287	44	28	-	-	ADJ
cana-1287	44	29	linear	linear	ADJ
cana-1287	44	30	regression	regression	NOUN
cana-1287	44	31	method	method	NOUN
cana-1287	44	32	to	to	PART
cana-1287	44	33	extract	extract	VERB
cana-1287	44	34	relevant	relevant	ADJ
cana-1287	44	35	data	datum	NOUN
cana-1287	44	36	from	from	ADP
cana-1287	44	37	remote	remote	ADJ
cana-1287	44	38	sensing	sensing	NOUN
cana-1287	44	39	images	image	NOUN
cana-1287	44	40	.	.	PUNCT
cana-1287	45	1	2	2	X
cana-1287	45	2	.	.	X
cana-1287	45	3	the	the	DET
cana-1287	45	4	second	second	NOUN
cana-1287	45	5	is	be	AUX
cana-1287	45	6	to	to	PART
cana-1287	45	7	develop	develop	VERB
cana-1287	45	8	and	and	CCONJ
cana-1287	45	9	instruct	instruct	VERB
cana-1287	45	10	a	a	DET
cana-1287	45	11	deep	deep	ADJ
cana-1287	45	12	rbf	rbf	PROPN
cana-1287	45	13	network	network	NOUN
cana-1287	45	14	capable	capable	ADJ
cana-1287	45	15	of	of	ADP
cana-1287	45	16	very	very	ADV
cana-1287	45	17	accurate	accurate	ADJ
cana-1287	45	18	soil	soil	NOUN
cana-1287	45	19	nutrient	nutrient	NOUN
cana-1287	45	20	level	level	NOUN
cana-1287	45	21	forecasting	forecasting	NOUN
cana-1287	45	22	and	and	CCONJ
cana-1287	45	23	control	control	NOUN
cana-1287	45	24	of	of	ADP
cana-1287	45	25	acquired	acquire	VERB
cana-1287	45	26	properties	property	NOUN
cana-1287	45	27	.	.	PUNCT
cana-1287	46	1	3	3	X
cana-1287	46	2	.	.	X
cana-1287	46	3	evaluate	evaluate	VERB
cana-1287	46	4	the	the	DET
cana-1287	46	5	proposed	propose	VERB
cana-1287	46	6	deep	deep	ADJ
cana-1287	46	7	rbf	rbf	PROPN
cana-1287	46	8	network	network	NOUN
cana-1287	46	9	against	against	ADP
cana-1287	46	10	current	current	ADJ
cana-1287	46	11	methods	method	NOUN
cana-1287	46	12	including	include	VERB
cana-1287	46	13	artificial	artificial	ADJ
cana-1287	46	14	neural	neural	ADJ
cana-1287	46	15	networks	network	NOUN
cana-1287	46	16	(	(	PUNCT
cana-1287	46	17	ann	ann	PROPN
cana-1287	46	18	)	)	PUNCT
cana-1287	46	19	,	,	PUNCT
cana-1287	46	20	bi	bi	ADJ
cana-1287	46	21	-	-	ADJ
cana-1287	46	22	directional	directional	ADJ
cana-1287	46	23	gated	gate	VERB
cana-1287	46	24	recurrent	recurrent	ADJ
cana-1287	46	25	units	unit	NOUN
cana-1287	46	26	with	with	ADP
cana-1287	46	27	dynamic	dynamic	ADJ
cana-1287	46	28	memory	memory	NOUN
cana-1287	46	29	network	network	NOUN
cana-1287	46	30	(	(	PUNCT
cana-1287	46	31	bi	bi	NOUN
cana-1287	46	32	-	-	ADJ
cana-1287	46	33	grudmn	grudmn	ADJ
cana-1287	46	34	)	)	PUNCT
cana-1287	46	35	,	,	PUNCT
cana-1287	46	36	random	random	ADJ
cana-1287	46	37	forest	forest	NOUN
cana-1287	46	38	regression	regression	NOUN
cana-1287	46	39	(	(	PUNCT
cana-1287	46	40	rfr	rfr	PROPN
cana-1287	46	41	)	)	PUNCT
cana-1287	46	42	,	,	PUNCT
cana-1287	46	43	and	and	CCONJ
cana-1287	46	44	recurrent	recurrent	ADJ
cana-1287	46	45	neural	neural	ADJ
cana-1287	46	46	network	network	NOUN
cana-1287	46	47	(	(	PUNCT
cana-1287	46	48	rnn	rnn	PROPN
cana-1287	46	49	)	)	PUNCT
cana-1287	46	50	by	by	ADP
cana-1287	46	51	using	use	VERB
cana-1287	46	52	several	several	ADJ
cana-1287	46	53	performance	performance	NOUN
cana-1287	46	54	criteria	criterion	NOUN
cana-1287	46	55	.	.	PUNCT
cana-1287	47	1	novelty	novelty	NOUN
cana-1287	47	2	in	in	ADP
cana-1287	47	3	this	this	DET
cana-1287	47	4	work	work	NOUN
cana-1287	47	5	,	,	PUNCT
cana-1287	47	6	the	the	DET
cana-1287	47	7	new	new	ADJ
cana-1287	47	8	combining	combining	NOUN
cana-1287	47	9	of	of	ADP
cana-1287	47	10	deep	deep	ADJ
cana-1287	47	11	rbf	rbf	PROPN
cana-1287	47	12	networks	network	NOUN
cana-1287	47	13	for	for	ADP
cana-1287	47	14	classification	classification	NOUN
cana-1287	47	15	with	with	ADP
cana-1287	47	16	non	non	ADJ
cana-1287	47	17	-	-	ADJ
cana-1287	47	18	linear	linear	ADJ
cana-1287	47	19	regression	regression	NOUN
cana-1287	47	20	for	for	ADP
cana-1287	47	21	feature	feature	NOUN
cana-1287	47	22	extraction	extraction	NOUN
cana-1287	47	23	is	be	AUX
cana-1287	47	24	presented	present	VERB
cana-1287	47	25	.	.	PUNCT
cana-1287	48	1	although	although	SCONJ
cana-1287	48	2	non	non	ADJ
cana-1287	48	3	-	-	ADJ
cana-1287	48	4	linear	linear	ADJ
cana-1287	48	5	regression	regression	NOUN
cana-1287	48	6	approaches	approach	NOUN
cana-1287	48	7	have	have	AUX
cana-1287	48	8	been	be	AUX
cana-1287	48	9	applied	apply	VERB
cana-1287	48	10	independently	independently	ADV
cana-1287	48	11	to	to	PART
cana-1287	48	12	feature	feature	VERB
cana-1287	48	13	extraction	extraction	NOUN
cana-1287	48	14	,	,	PUNCT
cana-1287	48	15	their	their	PRON
cana-1287	48	16	combined	combine	VERB
cana-1287	48	17	use	use	NOUN
cana-1287	48	18	with	with	ADP
cana-1287	48	19	deep	deep	ADJ
cana-1287	48	20	rbf	rbf	PROPN
cana-1287	48	21	networks	network	NOUN
cana-1287	48	22	has	have	AUX
cana-1287	48	23	not	not	PART
cana-1287	48	24	been	be	AUX
cana-1287	48	25	examined	examine	VERB
cana-1287	48	26	in	in	ADP
cana-1287	48	27	the	the	DET
cana-1287	48	28	framework	framework	NOUN
cana-1287	48	29	of	of	ADP
cana-1287	48	30	soil	soil	NOUN
cana-1287	48	31	nutrient	nutrient	NOUN
cana-1287	48	32	assessment	assessment	NOUN
cana-1287	48	33	.	.	PUNCT
cana-1287	49	1	this	this	DET
cana-1287	49	2	approach	approach	NOUN
cana-1287	49	3	allows	allow	VERB
cana-1287	49	4	one	one	NUM
cana-1287	49	5	more	more	ADV
cana-1287	49	6	precisely	precisely	ADV
cana-1287	49	7	and	and	CCONJ
cana-1287	49	8	dependably	dependably	ADV
cana-1287	49	9	record	record	ADJ
cana-1287	49	10	complex	complex	ADJ
cana-1287	49	11	data	datum	NOUN
cana-1287	49	12	links	link	NOUN
cana-1287	49	13	,	,	PUNCT
cana-1287	49	14	so	so	ADV
cana-1287	49	15	guiding	guide	VERB
cana-1287	49	16	soil	soil	NOUN
cana-1287	49	17	nutrient	nutrient	NOUN
cana-1287	49	18	estimations	estimation	NOUN
cana-1287	49	19	.	.	PUNCT
cana-1287	50	1	the	the	DET
cana-1287	50	2	main	main	ADJ
cana-1287	50	3	contribution	contribution	NOUN
cana-1287	50	4	of	of	ADP
cana-1287	50	5	the	the	DET
cana-1287	50	6	proposed	propose	VERB
cana-1287	50	7	work	work	NOUN
cana-1287	50	8	involves	involve	VERB
cana-1287	50	9	the	the	DET
cana-1287	50	10	following	follow	VERB
cana-1287	50	11	:	:	PUNCT
cana-1287	50	12	communications	communication	NOUN
cana-1287	50	13	on	on	ADP
cana-1287	50	14	applied	apply	VERB
cana-1287	50	15	nonlinear	nonlinear	ADJ
cana-1287	50	16	analysis	analysis	NOUN
cana-1287	50	17	issn	issn	NOUN
cana-1287	50	18	:	:	PUNCT
cana-1287	50	19	1074	1074	NUM
cana-1287	50	20	-	-	PUNCT
cana-1287	50	21	133x	133x	NUM
cana-1287	50	22	vol	vol	NOUN
cana-1287	50	23	31	31	NUM
cana-1287	50	24	no	no	NOUN
cana-1287	50	25	.	.	PUNCT
cana-1287	51	1	7s	7	NOUN
cana-1287	51	2	(	(	PUNCT
cana-1287	51	3	2024	2024	NUM
cana-1287	51	4	)	)	PUNCT
cana-1287	51	5	94	94	NUM
cana-1287	52	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	52	2	1	1	NUM
cana-1287	52	3	.	.	PUNCT
cana-1287	52	4	first	first	ADV
cana-1287	52	5	by	by	ADP
cana-1287	52	6	means	mean	NOUN
cana-1287	52	7	of	of	ADP
cana-1287	52	8	non	non	ADJ
cana-1287	52	9	-	-	ADJ
cana-1287	52	10	linear	linear	ADJ
cana-1287	52	11	regression	regression	NOUN
cana-1287	52	12	,	,	PUNCT
cana-1287	52	13	the	the	DET
cana-1287	52	14	proposed	propose	VERB
cana-1287	52	15	method	method	NOUN
cana-1287	52	16	presents	present	VERB
cana-1287	52	17	a	a	DET
cana-1287	52	18	sophisticated	sophisticated	ADJ
cana-1287	52	19	method	method	NOUN
cana-1287	52	20	of	of	ADP
cana-1287	52	21	feature	feature	NOUN
cana-1287	52	22	extraction	extraction	NOUN
cana-1287	52	23	,	,	PUNCT
cana-1287	52	24	therefore	therefore	ADV
cana-1287	52	25	providing	provide	VERB
cana-1287	52	26	a	a	DET
cana-1287	52	27	more	more	ADV
cana-1287	52	28	accurate	accurate	ADJ
cana-1287	52	29	representation	representation	NOUN
cana-1287	52	30	of	of	ADP
cana-1287	52	31	the	the	DET
cana-1287	52	32	fundamental	fundamental	ADJ
cana-1287	52	33	soil	soil	NOUN
cana-1287	52	34	properties	property	NOUN
cana-1287	52	35	from	from	ADP
cana-1287	52	36	remote	remote	ADJ
cana-1287	52	37	sensing	sense	VERB
cana-1287	52	38	data	datum	NOUN
cana-1287	52	39	.	.	PUNCT
cana-1287	53	1	2	2	X
cana-1287	53	2	.	.	X
cana-1287	53	3	the	the	DET
cana-1287	53	4	second	second	NOUN
cana-1287	53	5	is	be	AUX
cana-1287	53	6	a	a	DET
cana-1287	53	7	robust	robust	ADJ
cana-1287	53	8	classification	classification	NOUN
cana-1287	53	9	system	system	NOUN
cana-1287	53	10	leveraging	leverage	VERB
cana-1287	53	11	deep	deep	ADJ
cana-1287	53	12	rbf	rbf	PROPN
cana-1287	53	13	networks	network	NOUN
cana-1287	53	14	that	that	PRON
cana-1287	53	15	makes	make	VERB
cana-1287	53	16	advantage	advantage	NOUN
cana-1287	53	17	of	of	ADP
cana-1287	53	18	the	the	DET
cana-1287	53	19	obtained	obtain	VERB
cana-1287	53	20	knowledge	knowledge	NOUN
cana-1287	53	21	to	to	PART
cana-1287	53	22	raise	raise	VERB
cana-1287	53	23	prediction	prediction	NOUN
cana-1287	53	24	accuracy	accuracy	NOUN
cana-1287	53	25	.	.	PUNCT
cana-1287	54	1	3	3	X
cana-1287	54	2	.	.	X
cana-1287	54	3	the	the	DET
cana-1287	54	4	paper	paper	NOUN
cana-1287	54	5	offers	offer	VERB
cana-1287	54	6	a	a	DET
cana-1287	54	7	comprehensive	comprehensive	ADJ
cana-1287	54	8	performance	performance	NOUN
cana-1287	54	9	comparison	comparison	NOUN
cana-1287	54	10	of	of	ADP
cana-1287	54	11	the	the	DET
cana-1287	54	12	deep	deep	ADJ
cana-1287	54	13	rbf	rbf	PROPN
cana-1287	54	14	network	network	NOUN
cana-1287	54	15	with	with	ADP
cana-1287	54	16	current	current	ADJ
cana-1287	54	17	methods	method	NOUN
cana-1287	54	18	,	,	PUNCT
cana-1287	54	19	therefore	therefore	ADV
cana-1287	54	20	offering	offer	VERB
cana-1287	54	21	interesting	interesting	ADJ
cana-1287	54	22	study	study	NOUN
cana-1287	54	23	of	of	ADP
cana-1287	54	24	the	the	DET
cana-1287	54	25	advantages	advantage	NOUN
cana-1287	54	26	and	and	CCONJ
cana-1287	54	27	efficiency	efficiency	NOUN
cana-1287	54	28	of	of	ADP
cana-1287	54	29	the	the	DET
cana-1287	54	30	recommended	recommend	VERB
cana-1287	54	31	methodology	methodology	NOUN
cana-1287	54	32	.	.	PUNCT
cana-1287	55	1	2	2	X
cana-1287	55	2	.	.	NUM
cana-1287	55	3	related	relate	VERB
cana-1287	55	4	works	work	NOUN
cana-1287	55	5	estimating	estimate	VERB
cana-1287	55	6	surface	surface	NOUN
cana-1287	55	7	soil	soil	NOUN
cana-1287	55	8	moisture	moisture	NOUN
cana-1287	55	9	using	use	VERB
cana-1287	55	10	satellite	satellite	NOUN
cana-1287	55	11	photos	photo	NOUN
cana-1287	55	12	on	on	ADP
cana-1287	55	13	a	a	DET
cana-1287	55	14	large	large	ADJ
cana-1287	55	15	alluvial	alluvial	ADJ
cana-1287	55	16	fan	fan	NOUN
cana-1287	55	17	of	of	ADP
cana-1287	55	18	the	the	DET
cana-1287	55	19	kosi	kosi	PROPN
cana-1287	55	20	river	river	NOUN
cana-1287	55	21	in	in	ADP
cana-1287	55	22	the	the	DET
cana-1287	55	23	himalayan	himalayan	PROPN
cana-1287	55	24	foreland	foreland	PROPN
cana-1287	55	25	,	,	PUNCT
cana-1287	55	26	the	the	DET
cana-1287	55	27	research	research	NOUN
cana-1287	55	28	[	[	X
cana-1287	55	29	11	11	NUM
cana-1287	55	30	]	]	PUNCT
cana-1287	55	31	proposed	propose	VERB
cana-1287	55	32	a	a	DET
cana-1287	55	33	unique	unique	ADJ
cana-1287	55	34	design	design	NOUN
cana-1287	55	35	.	.	PUNCT
cana-1287	56	1	one	one	NUM
cana-1287	56	2	finds	find	VERB
cana-1287	56	3	this	this	DET
cana-1287	56	4	fan	fan	NOUN
cana-1287	56	5	in	in	ADP
cana-1287	56	6	the	the	DET
cana-1287	56	7	himalayan	himalayan	ADJ
cana-1287	56	8	forest	forest	NOUN
cana-1287	56	9	.	.	PUNCT
cana-1287	57	1	an	an	DET
cana-1287	57	2	artificial	artificial	ADJ
cana-1287	57	3	neural	neural	ADJ
cana-1287	57	4	network	network	NOUN
cana-1287	57	5	(	(	PUNCT
cana-1287	57	6	ann	ann	PROPN
cana-1287	57	7	)	)	PUNCT
cana-1287	57	8	,	,	PUNCT
cana-1287	57	9	completely	completely	ADV
cana-1287	57	10	coupled	couple	VERB
cana-1287	57	11	and	and	CCONJ
cana-1287	57	12	feed	feed	NOUN
cana-1287	57	13	-	-	PUNCT
cana-1287	57	14	forward	forward	NOUN
cana-1287	57	15	,	,	PUNCT
cana-1287	57	16	is	be	AUX
cana-1287	57	17	essential	essential	ADJ
cana-1287	57	18	component	component	NOUN
cana-1287	57	19	of	of	ADP
cana-1287	57	20	the	the	DET
cana-1287	57	21	model	model	NOUN
cana-1287	57	22	.	.	PUNCT
cana-1287	58	1	by	by	ADP
cana-1287	58	2	means	mean	NOUN
cana-1287	58	3	of	of	ADP
cana-1287	58	4	linear	linear	PROPN
cana-1287	58	5	data	datum	NOUN
cana-1287	58	6	fusion	fusion	NOUN
cana-1287	58	7	and	and	CCONJ
cana-1287	58	8	graphical	graphical	ADJ
cana-1287	58	9	indicators	indicator	NOUN
cana-1287	58	10	,	,	PUNCT
cana-1287	58	11	we	we	PRON
cana-1287	58	12	have	have	AUX
cana-1287	58	13	efficiently	efficiently	ADV
cana-1287	58	14	derived	derive	VERB
cana-1287	58	15	nine	nine	NUM
cana-1287	58	16	distinct	distinct	ADJ
cana-1287	58	17	features	feature	NOUN
cana-1287	58	18	from	from	ADP
cana-1287	58	19	the	the	DET
cana-1287	58	20	satellite	satellite	NOUN
cana-1287	58	21	products	product	NOUN
cana-1287	58	22	of	of	ADP
cana-1287	58	23	sentinel-1	sentinel-1	NUM
cana-1287	58	24	,	,	PUNCT
cana-1287	58	25	sentinel-2	sentinel-2	NUM
cana-1287	58	26	,	,	PUNCT
cana-1287	58	27	and	and	CCONJ
cana-1287	58	28	shuttle	shuttle	PROPN
cana-1287	58	29	radar	radar	PROPN
cana-1287	58	30	topographic	topographic	PROPN
cana-1287	58	31	mission	mission	NOUN
cana-1287	58	32	.	.	PUNCT
cana-1287	59	1	these	these	PRON
cana-1287	59	2	in	in	ADP
cana-1287	59	3	that	that	DET
cana-1287	59	4	order	order	NOUN
cana-1287	59	5	consist	consist	NOUN
cana-1287	59	6	of	of	ADP
cana-1287	59	7	digital	digital	ADJ
cana-1287	59	8	elevation	elevation	NOUN
cana-1287	59	9	model	model	NOUN
cana-1287	59	10	,	,	PUNCT
cana-1287	59	11	red	red	ADJ
cana-1287	59	12	and	and	CCONJ
cana-1287	59	13	near	near	ADV
cana-1287	59	14	-	-	PUNCT
cana-1287	59	15	infrared	infrare	VERB
cana-1287	59	16	bands	band	NOUN
cana-1287	59	17	,	,	PUNCT
cana-1287	59	18	and	and	CCONJ
cana-1287	59	19	dual	dual	ADV
cana-1287	59	20	-	-	PUNCT
cana-1287	59	21	polarized	polarize	VERB
cana-1287	59	22	radar	radar	NOUN
cana-1287	59	23	backscatter	backscatter	NOUN
cana-1287	59	24	.	.	PUNCT
cana-1287	60	1	using	use	VERB
cana-1287	60	2	a	a	DET
cana-1287	60	3	calibrated	calibrate	VERB
cana-1287	60	4	tdr	tdr	PROPN
cana-1287	60	5	sensor	sensor	NOUN
cana-1287	60	6	,	,	PUNCT
cana-1287	60	7	soil	soil	NOUN
cana-1287	60	8	moisture	moisture	NOUN
cana-1287	60	9	was	be	AUX
cana-1287	60	10	monitored	monitor	VERB
cana-1287	60	11	at	at	ADP
cana-1287	60	12	224	224	NUM
cana-1287	60	13	independent	independent	ADJ
cana-1287	60	14	points	point	NOUN
cana-1287	60	15	dispersed	disperse	VERB
cana-1287	60	16	around	around	ADP
cana-1287	60	17	the	the	DET
cana-1287	60	18	fan	fan	NOUN
cana-1287	60	19	.	.	PUNCT
cana-1287	61	1	with	with	ADP
cana-1287	61	2	a	a	DET
cana-1287	61	3	correlation	correlation	NOUN
cana-1287	61	4	coefficient	coefficient	NOUN
cana-1287	61	5	(	(	PUNCT
cana-1287	61	6	r	r	NOUN
cana-1287	61	7	)	)	PUNCT
cana-1287	61	8	of	of	ADP
cana-1287	61	9	0.80	0.80	NUM
cana-1287	61	10	,	,	PUNCT
cana-1287	61	11	root	root	NOUN
cana-1287	61	12	mean	mean	VERB
cana-1287	61	13	square	square	ADJ
cana-1287	61	14	error	error	NOUN
cana-1287	61	15	(	(	PUNCT
cana-1287	61	16	rmse	rmse	NOUN
cana-1287	61	17	)	)	PUNCT
cana-1287	61	18	of	of	ADP
cana-1287	61	19	0.040	0.040	NUM
cana-1287	61	20	m3	m3	PROPN
cana-1287	61	21	/	/	SYM
cana-1287	61	22	m3	m3	PROPN
cana-1287	61	23	,	,	PUNCT
cana-1287	61	24	and	and	CCONJ
cana-1287	61	25	a	a	DET
cana-1287	61	26	bias	bias	NOUN
cana-1287	61	27	of	of	ADP
cana-1287	61	28	0.004	0.004	NUM
cana-1287	61	29	m3	m3	PROPN
cana-1287	61	30	/	/	SYM
cana-1287	61	31	m3	m3	PROPN
cana-1287	61	32	,	,	PUNCT
cana-1287	61	33	we	we	PRON
cana-1287	61	34	predicted	predict	VERB
cana-1287	61	35	soil	soil	NOUN
cana-1287	61	36	moisture	moisture	NOUN
cana-1287	61	37	and	and	CCONJ
cana-1287	61	38	found	find	VERB
cana-1287	61	39	that	that	SCONJ
cana-1287	61	40	the	the	DET
cana-1287	61	41	ann	ann	PROPN
cana-1287	61	42	model	model	NOUN
cana-1287	61	43	exceeded	exceed	VERB
cana-1287	61	44	all	all	PRON
cana-1287	61	45	of	of	ADP
cana-1287	61	46	the	the	DET
cana-1287	61	47	benchmark	benchmark	NOUN
cana-1287	61	48	methodologies	methodology	NOUN
cana-1287	61	49	.	.	PUNCT
cana-1287	62	1	benchmark	benchmark	NOUN
cana-1287	62	2	technique	technique	NOUN
cana-1287	62	3	comparison	comparison	NOUN
cana-1287	62	4	of	of	ADP
cana-1287	62	5	the	the	DET
cana-1287	62	6	ann	ann	PROPN
cana-1287	62	7	model	model	NOUN
cana-1287	62	8	helped	help	VERB
cana-1287	62	9	to	to	PART
cana-1287	62	10	achieve	achieve	VERB
cana-1287	62	11	this	this	PRON
cana-1287	62	12	.	.	PUNCT
cana-1287	63	1	in	in	ADP
cana-1287	63	2	[	[	X
cana-1287	63	3	12	12	NUM
cana-1287	63	4	]	]	PUNCT
cana-1287	63	5	a	a	DET
cana-1287	63	6	novel	novel	ADJ
cana-1287	63	7	approach	approach	NOUN
cana-1287	63	8	for	for	ADP
cana-1287	63	9	retrieving	retrieve	VERB
cana-1287	63	10	soil	soil	NOUN
cana-1287	63	11	moisture	moisture	NOUN
cana-1287	63	12	was	be	AUX
cana-1287	63	13	applied	apply	VERB
cana-1287	63	14	.	.	PUNCT
cana-1287	64	1	the	the	DET
cana-1287	64	2	first	first	ADJ
cana-1287	64	3	phase	phase	NOUN
cana-1287	64	4	is	be	AUX
cana-1287	64	5	the	the	DET
cana-1287	64	6	process	process	NOUN
cana-1287	64	7	of	of	ADP
cana-1287	64	8	acquiring	acquire	VERB
cana-1287	64	9	images	image	NOUN
cana-1287	64	10	.	.	PUNCT
cana-1287	65	1	deriving	derive	VERB
cana-1287	65	2	vi	vi	PROPN
cana-1287	65	3	indices	index	NOUN
cana-1287	65	4	with	with	ADP
cana-1287	65	5	ndvi	ndvi	PROPN
cana-1287	65	6	,	,	PUNCT
cana-1287	65	7	glai	glai	PROPN
cana-1287	65	8	,	,	PUNCT
cana-1287	65	9	gndvi	gndvi	PROPN
cana-1287	65	10	,	,	PUNCT
cana-1287	65	11	and	and	CCONJ
cana-1287	65	12	wdrvi	wdrvi	ADJ
cana-1287	65	13	properties	property	NOUN
cana-1287	65	14	is	be	AUX
cana-1287	65	15	one	one	NUM
cana-1287	65	16	of	of	ADP
cana-1287	65	17	the	the	DET
cana-1287	65	18	processes	process	NOUN
cana-1287	65	19	that	that	PRON
cana-1287	65	20	follows	follow	VERB
cana-1287	65	21	last	last	ADJ
cana-1287	65	22	one	one	NUM
cana-1287	65	23	.	.	PUNCT
cana-1287	66	1	the	the	DET
cana-1287	66	2	use	use	NOUN
cana-1287	66	3	of	of	ADP
cana-1287	66	4	a	a	DET
cana-1287	66	5	better	well	ADJ
cana-1287	66	6	water	water	NOUN
cana-1287	66	7	cloud	cloud	NOUN
cana-1287	66	8	model	model	NOUN
cana-1287	66	9	(	(	PUNCT
cana-1287	66	10	wcm	wcm	NOUN
cana-1287	66	11	)	)	PUNCT
cana-1287	66	12	is	be	AUX
cana-1287	66	13	another	another	DET
cana-1287	66	14	element	element	NOUN
cana-1287	66	15	of	of	ADP
cana-1287	66	16	the	the	DET
cana-1287	66	17	attempt	attempt	NOUN
cana-1287	66	18	to	to	PART
cana-1287	66	19	fix	fix	VERB
cana-1287	66	20	the	the	DET
cana-1287	66	21	effects	effect	NOUN
cana-1287	66	22	arising	arise	VERB
cana-1287	66	23	on	on	ADP
cana-1287	66	24	the	the	DET
cana-1287	66	25	plants	plant	NOUN
cana-1287	66	26	.	.	PUNCT
cana-1287	67	1	not	not	PART
cana-1287	67	2	least	least	ADJ
cana-1287	67	3	of	of	ADP
cana-1287	67	4	all	all	PRON
cana-1287	67	5	,	,	PUNCT
cana-1287	67	6	a	a	DET
cana-1287	67	7	superior	superior	ADJ
cana-1287	67	8	score	score	NOUN
cana-1287	67	9	level	level	NOUN
cana-1287	67	10	fusion	fusion	NOUN
cana-1287	67	11	model	model	NOUN
cana-1287	67	12	under	under	ADP
cana-1287	67	13	responsibility	responsibility	NOUN
cana-1287	67	14	of	of	ADP
cana-1287	67	15	soil	soil	NOUN
cana-1287	67	16	moisture	moisture	NOUN
cana-1287	67	17	drainage	drainage	NOUN
cana-1287	67	18	provides	provide	VERB
cana-1287	67	19	the	the	DET
cana-1287	67	20	data	datum	NOUN
cana-1287	67	21	.	.	PUNCT
cana-1287	68	1	deep	deep	ADJ
cana-1287	68	2	max	max	PROPN
cana-1287	68	3	out	out	ADP
cana-1287	68	4	network	network	NOUN
cana-1287	68	5	(	(	PUNCT
cana-1287	68	6	dmn	dmn	PROPN
cana-1287	68	7	)	)	PUNCT
cana-1287	68	8	and	and	CCONJ
cana-1287	68	9	bidirectional	bidirectional	ADJ
cana-1287	68	10	gated	gate	VERB
cana-1287	68	11	recurrent	recurrent	ADJ
cana-1287	68	12	unit	unit	NOUN
cana-1287	68	13	(	(	PUNCT
cana-1287	68	14	bi	bi	NOUN
cana-1287	68	15	-	-	NOUN
cana-1287	68	16	gru	gru	NOUN
cana-1287	68	17	)	)	PUNCT
cana-1287	68	18	are	be	AUX
cana-1287	68	19	included	include	VERB
cana-1287	68	20	into	into	ADP
cana-1287	68	21	this	this	DET
cana-1287	68	22	model	model	NOUN
cana-1287	68	23	.	.	PUNCT
cana-1287	69	1	arriving	arrive	VERB
cana-1287	69	2	at	at	ADP
cana-1287	69	3	0.9565	0.9565	NUM
cana-1287	69	4	,	,	PUNCT
cana-1287	69	5	the	the	DET
cana-1287	69	6	rmse	rmse	NOUN
cana-1287	69	7	of	of	ADP
cana-1287	69	8	the	the	DET
cana-1287	69	9	method	method	NOUN
cana-1287	69	10	combining	combine	VERB
cana-1287	69	11	bi	bi	NOUN
cana-1287	69	12	-	-	NOUN
cana-1287	69	13	gru	gru	PROPN
cana-1287	69	14	and	and	CCONJ
cana-1287	69	15	dmn	dmn	PROPN
cana-1287	69	16	was	be	AUX
cana-1287	69	17	found	find	VERB
cana-1287	69	18	to	to	PART
cana-1287	69	19	be	be	AUX
cana-1287	69	20	smaller	small	ADJ
cana-1287	69	21	than	than	ADP
cana-1287	69	22	those	those	PRON
cana-1287	69	23	of	of	ADP
cana-1287	69	24	the	the	DET
cana-1287	69	25	hybrid	hybrid	ADJ
cana-1287	69	26	classifier	classifier	NOUN
cana-1287	69	27	approaches	approach	NOUN
cana-1287	69	28	.	.	PUNCT
cana-1287	70	1	these	these	PRON
cana-1287	70	2	confirmed	confirm	VERB
cana-1287	70	3	this	this	PRON
cana-1287	70	4	.	.	PUNCT
cana-1287	71	1	in	in	ADP
cana-1287	71	2	[	[	X
cana-1287	71	3	13	13	NUM
cana-1287	71	4	]	]	PUNCT
cana-1287	71	5	utilizing	utilize	VERB
cana-1287	71	6	uav	uav	PROPN
cana-1287	71	7	-	-	PUNCT
cana-1287	71	8	based	base	VERB
cana-1287	71	9	multimodal	multimodal	NOUN
cana-1287	71	10	data	datum	NOUN
cana-1287	71	11	,	,	PUNCT
cana-1287	71	12	tracked	track	VERB
cana-1287	71	13	the	the	DET
cana-1287	71	14	soil	soil	NOUN
cana-1287	71	15	moisture	moisture	NOUN
cana-1287	71	16	content	content	NOUN
cana-1287	71	17	(	(	PUNCT
cana-1287	71	18	smc	smc	PROPN
cana-1287	71	19	)	)	PUNCT
cana-1287	71	20	of	of	ADP
cana-1287	71	21	a	a	DET
cana-1287	71	22	maize	maize	NOUN
cana-1287	71	23	crop	crop	NOUN
cana-1287	71	24	subjecting	subject	VERB
cana-1287	71	25	different	different	ADJ
cana-1287	71	26	degrees	degree	NOUN
cana-1287	71	27	of	of	ADP
cana-1287	71	28	irrigation	irrigation	NOUN
cana-1287	71	29	over	over	ADP
cana-1287	71	30	two	two	NUM
cana-1287	71	31	years	year	NOUN
cana-1287	71	32	.	.	PUNCT
cana-1287	72	1	the	the	DET
cana-1287	72	2	results	result	NOUN
cana-1287	72	3	revealed	reveal	VERB
cana-1287	72	4	incorporating	incorporate	VERB
cana-1287	72	5	data	datum	NOUN
cana-1287	72	6	from	from	ADP
cana-1287	72	7	various	various	ADJ
cana-1287	72	8	modalities	modality	NOUN
cana-1287	72	9	—	—	PUNCT
cana-1287	72	10	including	include	VERB
cana-1287	72	11	thermal	thermal	ADJ
cana-1287	72	12	and	and	CCONJ
cana-1287	72	13	multispectral	multispectral	ADJ
cana-1287	72	14	data	datum	NOUN
cana-1287	72	15	—	—	PUNCT
cana-1287	72	16	among	among	ADP
cana-1287	72	17	other	other	ADJ
cana-1287	72	18	modalities	modality	NOUN
cana-1287	72	19	increases	increase	VERB
cana-1287	72	20	the	the	DET
cana-1287	72	21	accuracy	accuracy	NOUN
cana-1287	72	22	of	of	ADP
cana-1287	72	23	smc	smc	PROPN
cana-1287	72	24	predictions	prediction	NOUN
cana-1287	72	25	.	.	PUNCT
cana-1287	73	1	from	from	ADP
cana-1287	73	2	the	the	DET
cana-1287	73	3	three	three	NUM
cana-1287	73	4	smc	smc	PROPN
cana-1287	73	5	regression	regression	NOUN
cana-1287	73	6	models	model	NOUN
cana-1287	73	7	produced	produce	VERB
cana-1287	73	8	,	,	PUNCT
cana-1287	73	9	the	the	DET
cana-1287	73	10	rfr	rfr	PROPN
cana-1287	73	11	model	model	NOUN
cana-1287	73	12	produced	produce	VERB
cana-1287	73	13	the	the	DET
cana-1287	73	14	best	well	ADV
cana-1287	73	15	accurate	accurate	ADJ
cana-1287	73	16	smc	smc	PROPN
cana-1287	73	17	estimate	estimate	NOUN
cana-1287	73	18	for	for	ADP
cana-1287	73	19	both	both	DET
cana-1287	73	20	growing	grow	VERB
cana-1287	73	21	seasons	season	NOUN
cana-1287	73	22	.	.	PUNCT
cana-1287	74	1	this	this	PRON
cana-1287	74	2	was	be	AUX
cana-1287	74	3	true	true	ADJ
cana-1287	74	4	independent	independent	ADJ
cana-1287	74	5	of	of	ADP
cana-1287	74	6	the	the	DET
cana-1287	74	7	combinations	combination	NOUN
cana-1287	74	8	of	of	ADP
cana-1287	74	9	sensors	sensor	NOUN
cana-1287	74	10	used	use	VERB
cana-1287	74	11	.	.	PUNCT
cana-1287	75	1	the	the	DET
cana-1287	75	2	rfr	rfr	PROPN
cana-1287	75	3	model	model	NOUN
cana-1287	75	4	using	use	VERB
cana-1287	75	5	all	all	DET
cana-1287	75	6	three	three	NUM
cana-1287	75	7	data	datum	NOUN
cana-1287	75	8	sources	source	NOUN
cana-1287	75	9	generated	generate	VERB
cana-1287	75	10	the	the	DET
cana-1287	75	11	most	most	ADV
cana-1287	75	12	accurate	accurate	ADJ
cana-1287	75	13	and	and	CCONJ
cana-1287	75	14	consistent	consistent	ADJ
cana-1287	75	15	smc	smc	NOUN
cana-1287	75	16	estimate	estimate	NOUN
cana-1287	75	17	in	in	ADP
cana-1287	75	18	the	the	DET
cana-1287	75	19	vegetative	vegetative	ADJ
cana-1287	75	20	stage	stage	NOUN
cana-1287	75	21	.	.	PUNCT
cana-1287	76	1	its	its	PRON
cana-1287	76	2	r2	r2	PROPN
cana-1287	76	3	was	be	AUX
cana-1287	76	4	0.68	0.68	NUM
cana-1287	76	5	for	for	ADP
cana-1287	76	6	10and	10and	NOUN
cana-1287	76	7	20	20	NUM
cana-1287	76	8	cm	cm	NOUN
cana-1287	76	9	soil	soil	NOUN
cana-1287	76	10	depths	depth	NOUN
cana-1287	76	11	respectively	respectively	ADV
cana-1287	76	12	;	;	PUNCT
cana-1287	76	13	its	its	PRON
cana-1287	76	14	rrmse	rrmse	NOUN
cana-1287	76	15	was	be	AUX
cana-1287	76	16	20.82	20.82	NUM
cana-1287	76	17	%	%	NOUN
cana-1287	76	18	and	and	CCONJ
cana-1287	76	19	19.36	19.36	NUM
cana-1287	76	20	%	%	NOUN
cana-1287	76	21	.	.	PUNCT
cana-1287	77	1	the	the	DET
cana-1287	77	2	rfr	rfr	PROPN
cana-1287	77	3	model	model	NOUN
cana-1287	77	4	performed	perform	VERB
cana-1287	77	5	really	really	ADV
cana-1287	77	6	wonderfully	wonderfully	ADV
cana-1287	77	7	applying	apply	VERB
cana-1287	77	8	these	these	DET
cana-1287	77	9	ideas	idea	NOUN
cana-1287	77	10	.	.	PUNCT
cana-1287	78	1	using	use	VERB
cana-1287	78	2	well	well	ADV
cana-1287	78	3	-	-	PUNCT
cana-1287	78	4	watered	water	VERB
cana-1287	78	5	,	,	PUNCT
cana-1287	78	6	mild	mild	ADJ
cana-1287	78	7	to	to	ADP
cana-1287	78	8	modest	modest	ADJ
cana-1287	78	9	deficit	deficit	NOUN
cana-1287	78	10	irrigation	irrigation	NOUN
cana-1287	78	11	treatments	treatment	NOUN
cana-1287	78	12	,	,	PUNCT
cana-1287	78	13	it	it	PRON
cana-1287	78	14	also	also	ADV
cana-1287	78	15	produced	produce	VERB
cana-1287	78	16	the	the	DET
cana-1287	78	17	best	good	ADJ
cana-1287	78	18	smc	smc	PROPN
cana-1287	78	19	estimation	estimation	NOUN
cana-1287	78	20	accuracy	accuracy	NOUN
cana-1287	78	21	for	for	ADP
cana-1287	78	22	both	both	DET
cana-1287	78	23	soil	soil	NOUN
cana-1287	78	24	depths	depth	NOUN
cana-1287	78	25	.	.	PUNCT
cana-1287	79	1	this	this	PRON
cana-1287	79	2	pertained	pertain	VERB
cana-1287	79	3	to	to	ADP
cana-1287	79	4	both	both	DET
cana-1287	79	5	treatments	treatment	NOUN
cana-1287	79	6	.	.	PUNCT
cana-1287	80	1	the	the	DET
cana-1287	80	2	high	high	ADJ
cana-1287	80	3	spatial	spatial	ADJ
cana-1287	80	4	-	-	PUNCT
cana-1287	80	5	temporal	temporal	ADJ
cana-1287	80	6	maps	map	NOUN
cana-1287	80	7	produced	produce	VERB
cana-1287	80	8	by	by	ADP
cana-1287	80	9	smc	smc	PROPN
cana-1287	80	10	—	—	PUNCT
cana-1287	80	11	derived	derive	VERB
cana-1287	80	12	from	from	ADP
cana-1287	80	13	multimodal	multimodal	NOUN
cana-1287	80	14	data	datum	NOUN
cana-1287	80	15	acquired	acquire	VERB
cana-1287	80	16	by	by	ADP
cana-1287	80	17	uavs	uavs	NOUN
cana-1287	80	18	—	—	PUNCT
cana-1287	80	19	have	have	VERB
cana-1287	80	20	communications	communication	NOUN
cana-1287	80	21	on	on	ADP
cana-1287	80	22	applied	apply	VERB
cana-1287	80	23	nonlinear	nonlinear	ADJ
cana-1287	80	24	analysis	analysis	NOUN
cana-1287	80	25	issn	issn	NOUN
cana-1287	80	26	:	:	PUNCT
cana-1287	80	27	1074	1074	NUM
cana-1287	80	28	-	-	PUNCT
cana-1287	80	29	133x	133x	NUM
cana-1287	80	30	vol	vol	NOUN
cana-1287	80	31	31	31	NUM
cana-1287	80	32	no	no	NOUN
cana-1287	80	33	.	.	PUNCT
cana-1287	81	1	7s	7	NOUN
cana-1287	81	2	(	(	PUNCT
cana-1287	81	3	2024	2024	NUM
cana-1287	81	4	)	)	PUNCT
cana-1287	81	5	95	95	NUM
cana-1287	82	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	82	2	great	great	ADJ
cana-1287	82	3	potential	potential	NOUN
cana-1287	82	4	to	to	PART
cana-1287	82	5	enhance	enhance	VERB
cana-1287	82	6	decision	decision	NOUN
cana-1287	82	7	-	-	PUNCT
cana-1287	82	8	making	making	NOUN
cana-1287	82	9	in	in	ADP
cana-1287	82	10	the	the	DET
cana-1287	82	11	scope	scope	NOUN
cana-1287	82	12	of	of	ADP
cana-1287	82	13	irrigation	irrigation	NOUN
cana-1287	82	14	scheduling	scheduling	NOUN
cana-1287	82	15	at	at	ADP
cana-1287	82	16	the	the	DET
cana-1287	82	17	farmer	farmer	NOUN
cana-1287	82	18	-	-	PUNCT
cana-1287	82	19	scale	scale	NOUN
cana-1287	82	20	based	base	VERB
cana-1287	82	21	on	on	ADP
cana-1287	82	22	the	the	DET
cana-1287	82	23	outcomes	outcome	NOUN
cana-1287	82	24	of	of	ADP
cana-1287	82	25	the	the	DET
cana-1287	82	26	study	study	NOUN
cana-1287	82	27	.	.	PUNCT
cana-1287	83	1	one	one	PRON
cana-1287	83	2	can	can	AUX
cana-1287	83	3	acquire	acquire	VERB
cana-1287	83	4	an	an	DET
cana-1287	83	5	initial	initial	ADJ
cana-1287	83	6	approximation	approximation	NOUN
cana-1287	83	7	of	of	ADP
cana-1287	83	8	its	its	PRON
cana-1287	83	9	possible	possible	ADJ
cana-1287	83	10	value	value	NOUN
cana-1287	83	11	in	in	ADP
cana-1287	83	12	[	[	X
cana-1287	83	13	14	14	NUM
cana-1287	83	14	]	]	PUNCT
cana-1287	83	15	.	.	PUNCT
cana-1287	84	1	geographic	geographic	ADJ
cana-1287	84	2	vulnerability	vulnerability	NOUN
cana-1287	84	3	in	in	ADP
cana-1287	84	4	an	an	DET
cana-1287	84	5	iranian	iranian	ADJ
cana-1287	84	6	watershed	watershe	VERB
cana-1287	84	7	with	with	ADP
cana-1287	84	8	a	a	DET
cana-1287	84	9	history	history	NOUN
cana-1287	84	10	of	of	ADP
cana-1287	84	11	considerable	considerable	ADJ
cana-1287	84	12	water	water	NOUN
cana-1287	84	13	erosion	erosion	NOUN
cana-1287	84	14	was	be	AUX
cana-1287	84	15	produced	produce	VERB
cana-1287	84	16	by	by	ADP
cana-1287	84	17	means	mean	NOUN
cana-1287	84	18	of	of	ADP
cana-1287	84	19	three	three	NUM
cana-1287	84	20	deep	deep	ADJ
cana-1287	84	21	learning	learning	NOUN
cana-1287	84	22	algorithms	algorithm	NOUN
cana-1287	84	23	.	.	PUNCT
cana-1287	85	1	reference	reference	NOUN
cana-1287	85	2	method	method	NOUN
cana-1287	85	3	algorithm	algorithm	NOUN
cana-1287	85	4	methodology	methodology	NOUN
cana-1287	85	5	outcomes	outcome	NOUN
cana-1287	86	1	[	[	X
cana-1287	86	2	11	11	NUM
cana-1287	86	3	]	]	X
cana-1287	86	4	ann	ann	PROPN
cana-1287	86	5	for	for	ADP
cana-1287	86	6	soil	soil	NOUN
cana-1287	86	7	moisture	moisture	NOUN
cana-1287	86	8	fully	fully	ADV
cana-1287	86	9	connected	connect	VERB
cana-1287	86	10	feedforward	feedforward	NOUN
cana-1287	86	11	ann	ann	PROPN
cana-1287	86	12	extracted	extract	VERB
cana-1287	86	13	nine	nine	NUM
cana-1287	86	14	features	feature	NOUN
cana-1287	86	15	from	from	ADP
cana-1287	86	16	sentinel-1	sentinel-1	NUM
cana-1287	86	17	,	,	PUNCT
cana-1287	86	18	sentinel-2	sentinel-2	NUM
cana-1287	86	19	,	,	PUNCT
cana-1287	86	20	and	and	CCONJ
cana-1287	86	21	srtm	srtm	NOUN
cana-1287	86	22	products	product	NOUN
cana-1287	86	23	;	;	PUNCT
cana-1287	86	24	measured	measure	VERB
cana-1287	86	25	soil	soil	NOUN
cana-1287	86	26	moisture	moisture	NOUN
cana-1287	86	27	with	with	ADP
cana-1287	86	28	tdr	tdr	PROPN
cana-1287	86	29	probes	probe	NOUN
cana-1287	86	30	.	.	PUNCT
cana-1287	87	1	correlation	correlation	NOUN
cana-1287	87	2	coefficient	coefficient	NOUN
cana-1287	87	3	(	(	PUNCT
cana-1287	87	4	r	r	NOUN
cana-1287	87	5	):	):	PUNCT
cana-1287	87	6	0.80	0.80	NUM
cana-1287	87	7	rmse	rmse	NOUN
cana-1287	87	8	:	:	PUNCT
cana-1287	87	9	0.040	0.040	NUM
cana-1287	87	10	m³/m³	m³/m³	PROPN
cana-1287	87	11	bias	bias	NOUN
cana-1287	87	12	:	:	PUNCT
cana-1287	87	13	0.004	0.004	NUM
cana-1287	87	14	m³/m³	m³/m³	NOUN
cana-1287	88	1	[	[	X
cana-1287	88	2	12	12	NUM
cana-1287	88	3	]	]	PUNCT
cana-1287	88	4	hybrid	hybrid	ADJ
cana-1287	88	5	soil	soil	NOUN
cana-1287	88	6	moisture	moisture	NOUN
cana-1287	88	7	retrieval	retrieval	NOUN
cana-1287	88	8	deep	deep	ADJ
cana-1287	88	9	max	max	PROPN
cana-1287	88	10	out	out	ADP
cana-1287	88	11	network	network	NOUN
cana-1287	88	12	(	(	PUNCT
cana-1287	88	13	dmn	dmn	PROPN
cana-1287	88	14	)	)	PUNCT
cana-1287	88	15	,	,	PUNCT
cana-1287	88	16	bigru	bigru	AUX
cana-1287	88	17	acquired	acquire	VERB
cana-1287	88	18	images	image	NOUN
cana-1287	88	19	and	and	CCONJ
cana-1287	88	20	derived	derive	VERB
cana-1287	88	21	vi	vi	NOUN
cana-1287	88	22	indices	index	NOUN
cana-1287	88	23	(	(	PUNCT
cana-1287	88	24	ndvi	ndvi	PROPN
cana-1287	88	25	,	,	PUNCT
cana-1287	88	26	glai	glai	PROPN
cana-1287	88	27	,	,	PUNCT
cana-1287	88	28	gndvi	gndvi	PROPN
cana-1287	88	29	,	,	PUNCT
cana-1287	88	30	wdrvi	wdrvi	PROPN
cana-1287	88	31	)	)	PUNCT
cana-1287	88	32	;	;	PUNCT
cana-1287	88	33	used	use	VERB
cana-1287	88	34	water	water	NOUN
cana-1287	88	35	cloud	cloud	NOUN
cana-1287	88	36	model	model	NOUN
cana-1287	88	37	(	(	PUNCT
cana-1287	88	38	wcm	wcm	NOUN
cana-1287	88	39	)	)	PUNCT
cana-1287	88	40	for	for	ADP
cana-1287	88	41	vegetation	vegetation	NOUN
cana-1287	88	42	impact	impact	NOUN
cana-1287	88	43	;	;	PUNCT
cana-1287	88	44	combined	combined	ADJ
cana-1287	88	45	dmn	dmn	NOUN
cana-1287	88	46	and	and	CCONJ
cana-1287	88	47	bi	bi	PROPN
cana-1287	88	48	-	-	PROPN
cana-1287	88	49	gru	gru	NOUN
cana-1287	88	50	with	with	ADP
cana-1287	88	51	score	score	NOUN
cana-1287	88	52	-	-	PUNCT
cana-1287	88	53	level	level	NOUN
cana-1287	88	54	fusion	fusion	NOUN
cana-1287	88	55	.	.	PUNCT
cana-1287	89	1	rmse	rmse	NOUN
cana-1287	89	2	:	:	PUNCT
cana-1287	89	3	0.9565	0.9565	NUM
cana-1287	89	4	me	i	PRON
cana-1287	89	5	:	:	PUNCT
cana-1287	89	6	0.7287	0.7287	NUM
cana-1287	89	7	lower	low	ADJ
cana-1287	89	8	errors	error	NOUN
cana-1287	89	9	compared	compare	VERB
cana-1287	89	10	to	to	ADP
cana-1287	89	11	methods	method	NOUN
cana-1287	89	12	without	without	ADP
cana-1287	89	13	vegetation	vegetation	NOUN
cana-1287	89	14	index	index	NOUN
cana-1287	89	15	and	and	CCONJ
cana-1287	89	16	standard	standard	ADJ
cana-1287	89	17	wcm	wcm	NOUN
cana-1287	89	18	.	.	PUNCT
cana-1287	90	1	[	[	X
cana-1287	90	2	13	13	NUM
cana-1287	90	3	]	]	X
cana-1287	90	4	uav	uav	PROPN
cana-1287	90	5	-	-	PUNCT
cana-1287	90	6	based	base	VERB
cana-1287	90	7	multimodal	multimodal	NOUN
cana-1287	90	8	data	datum	NOUN
cana-1287	90	9	fusion	fusion	NOUN
cana-1287	90	10	plsr	plsr	NOUN
cana-1287	90	11	,	,	PUNCT
cana-1287	90	12	knn	knn	PROPN
cana-1287	90	13	,	,	PUNCT
cana-1287	90	14	rfr	rfr	PROPN
cana-1287	90	15	used	use	VERB
cana-1287	90	16	thermal	thermal	ADJ
cana-1287	90	17	and	and	CCONJ
cana-1287	90	18	multispectral	multispectral	ADJ
cana-1287	90	19	data	datum	NOUN
cana-1287	90	20	for	for	ADP
cana-1287	90	21	soil	soil	NOUN
cana-1287	90	22	moisture	moisture	NOUN
cana-1287	90	23	content	content	NOUN
cana-1287	90	24	(	(	PUNCT
cana-1287	90	25	smc	smc	PROPN
cana-1287	90	26	)	)	PUNCT
cana-1287	90	27	estimation	estimation	NOUN
cana-1287	90	28	in	in	ADP
cana-1287	90	29	maize	maize	NOUN
cana-1287	90	30	fields	field	NOUN
cana-1287	90	31	;	;	PUNCT
cana-1287	90	32	compared	compare	VERB
cana-1287	90	33	three	three	NUM
cana-1287	90	34	ml	ml	NOUN
cana-1287	90	35	algorithms	algorithm	NOUN
cana-1287	90	36	.	.	PUNCT
cana-1287	91	1	r²	r²	VERB
cana-1287	91	2	for	for	ADP
cana-1287	91	3	rfr	rfr	PROPN
cana-1287	91	4	:	:	PUNCT
cana-1287	91	5	0.68	0.68	NUM
cana-1287	91	6	(	(	PUNCT
cana-1287	91	7	10	10	NUM
cana-1287	91	8	cm	cm	NOUN
cana-1287	91	9	)	)	PUNCT
cana-1287	91	10	,	,	PUNCT
cana-1287	91	11	0.78	0.78	NUM
cana-1287	91	12	(	(	PUNCT
cana-1287	91	13	20	20	NUM
cana-1287	91	14	cm	cm	NOUN
cana-1287	91	15	)	)	PUNCT
cana-1287	91	16	rrmse	rrmse	NOUN
cana-1287	91	17	for	for	ADP
cana-1287	91	18	rfr	rfr	PROPN
cana-1287	91	19	:	:	PUNCT
cana-1287	91	20	20.82	20.82	NUM
cana-1287	91	21	%	%	NOUN
cana-1287	91	22	(	(	PUNCT
cana-1287	91	23	10	10	NUM
cana-1287	91	24	cm	cm	NOUN
cana-1287	91	25	)	)	PUNCT
cana-1287	91	26	,	,	PUNCT
cana-1287	91	27	19.36	19.36	NUM
cana-1287	91	28	%	%	NOUN
cana-1287	91	29	(	(	PUNCT
cana-1287	91	30	20	20	NUM
cana-1287	91	31	cm	cm	NOUN
cana-1287	91	32	)	)	PUNCT
cana-1287	92	1	[	[	X
cana-1287	92	2	14	14	NUM
cana-1287	92	3	]	]	X
cana-1287	92	4	deep	deep	ADJ
cana-1287	92	5	learning	learning	NOUN
cana-1287	92	6	for	for	ADP
cana-1287	92	7	swe	swe	NOUN
cana-1287	92	8	susceptibility	susceptibility	PROPN
cana-1287	92	9	cnn	cnn	PROPN
cana-1287	92	10	,	,	PUNCT
cana-1287	92	11	rnn	rnn	PROPN
cana-1287	92	12	,	,	PUNCT
cana-1287	92	13	lstm	lstm	NOUN
cana-1287	92	14	used	use	VERB
cana-1287	92	15	elevation	elevation	NOUN
cana-1287	92	16	and	and	CCONJ
cana-1287	92	17	other	other	ADJ
cana-1287	92	18	geoenvironmental	geoenvironmental	ADJ
cana-1287	92	19	factors	factor	NOUN
cana-1287	92	20	;	;	PUNCT
cana-1287	92	21	compared	compare	VERB
cana-1287	92	22	cnn	cnn	PROPN
cana-1287	92	23	,	,	PUNCT
cana-1287	92	24	rnn	rnn	PROPN
cana-1287	92	25	,	,	PUNCT
cana-1287	92	26	and	and	CCONJ
cana-1287	92	27	lstm	lstm	NOUN
cana-1287	92	28	for	for	ADP
cana-1287	92	29	swe	swe	NOUN
cana-1287	92	30	susceptibility	susceptibility	PROPN
cana-1287	92	31	prediction	prediction	PROPN
cana-1287	92	32	.	.	PUNCT
cana-1287	93	1	rnn	rnn	NOUN
cana-1287	93	2	performance	performance	NOUN
cana-1287	93	3	:	:	PUNCT
cana-1287	93	4	marginally	marginally	ADV
cana-1287	93	5	superior	superior	ADJ
cana-1287	93	6	40	40	NUM
cana-1287	93	7	%	%	NOUN
cana-1287	93	8	of	of	ADP
cana-1287	93	9	catchment	catchment	NOUN
cana-1287	93	10	although	although	SCONJ
cana-1287	93	11	erosion	erosion	NOUN
cana-1287	93	12	prediction	prediction	NOUN
cana-1287	93	13	and	and	CCONJ
cana-1287	93	14	soil	soil	NOUN
cana-1287	93	15	moisture	moisture	NOUN
cana-1287	93	16	have	have	AUX
cana-1287	93	17	gotten	get	VERB
cana-1287	93	18	better	well	ADJ
cana-1287	93	19	,	,	PUNCT
cana-1287	93	20	combining	combine	VERB
cana-1287	93	21	multi	multi	ADJ
cana-1287	93	22	-	-	ADJ
cana-1287	93	23	source	source	NOUN
cana-1287	93	24	remote	remote	ADJ
cana-1287	93	25	sensing	sense	VERB
cana-1287	93	26	data	datum	NOUN
cana-1287	93	27	with	with	ADP
cana-1287	93	28	deep	deep	ADJ
cana-1287	93	29	learning	learning	NOUN
cana-1287	93	30	models	model	NOUN
cana-1287	93	31	for	for	ADP
cana-1287	93	32	best	good	ADJ
cana-1287	93	33	accuracy	accuracy	NOUN
cana-1287	93	34	still	still	ADV
cana-1287	93	35	has	have	VERB
cana-1287	93	36	challenges	challenge	NOUN
cana-1287	93	37	.	.	PUNCT
cana-1287	94	1	many	many	ADJ
cana-1287	94	2	times	time	NOUN
cana-1287	94	3	depending	depend	VERB
cana-1287	94	4	on	on	ADP
cana-1287	94	5	single	single	ADJ
cana-1287	94	6	data	datum	NOUN
cana-1287	94	7	sources	source	NOUN
cana-1287	94	8	or	or	CCONJ
cana-1287	94	9	simpler	simple	ADJ
cana-1287	94	10	algorithms	algorithm	NOUN
cana-1287	94	11	,	,	PUNCT
cana-1287	94	12	present	present	ADJ
cana-1287	94	13	methods	method	NOUN
cana-1287	94	14	restrict	restrict	VERB
cana-1287	94	15	their	their	PRON
cana-1287	94	16	capacity	capacity	NOUN
cana-1287	94	17	to	to	PART
cana-1287	94	18	reflect	reflect	VERB
cana-1287	94	19	complex	complex	ADJ
cana-1287	94	20	soil	soil	NOUN
cana-1287	94	21	qualities	quality	NOUN
cana-1287	94	22	and	and	CCONJ
cana-1287	94	23	environmental	environmental	ADJ
cana-1287	94	24	interactions	interaction	NOUN
cana-1287	94	25	.	.	PUNCT
cana-1287	95	1	moreover	moreover	ADV
cana-1287	95	2	,	,	PUNCT
cana-1287	95	3	current	current	ADJ
cana-1287	95	4	models	model	NOUN
cana-1287	95	5	might	might	AUX
cana-1287	95	6	not	not	PART
cana-1287	95	7	be	be	AUX
cana-1287	95	8	able	able	ADJ
cana-1287	95	9	to	to	PART
cana-1287	95	10	extend	extend	VERB
cana-1287	95	11	over	over	ADP
cana-1287	95	12	many	many	ADJ
cana-1287	95	13	various	various	ADJ
cana-1287	95	14	geographical	geographical	ADJ
cana-1287	95	15	regions	region	NOUN
cana-1287	95	16	or	or	CCONJ
cana-1287	95	17	soil	soil	NOUN
cana-1287	95	18	types	type	NOUN
cana-1287	95	19	.	.	PUNCT
cana-1287	96	1	furthermore	furthermore	ADV
cana-1287	96	2	much	much	ADV
cana-1287	96	3	needed	need	VERB
cana-1287	96	4	are	be	AUX
cana-1287	96	5	improved	improve	VERB
cana-1287	96	6	feature	feature	NOUN
cana-1287	96	7	extraction	extraction	NOUN
cana-1287	96	8	techniques	technique	NOUN
cana-1287	96	9	able	able	ADJ
cana-1287	96	10	to	to	PART
cana-1287	96	11	control	control	VERB
cana-1287	96	12	the	the	DET
cana-1287	96	13	non	non	ADJ
cana-1287	96	14	-	-	ADJ
cana-1287	96	15	linearity	linearity	ADJ
cana-1287	96	16	and	and	CCONJ
cana-1287	96	17	large	large	ADJ
cana-1287	96	18	dimensionality	dimensionality	NOUN
cana-1287	96	19	of	of	ADP
cana-1287	96	20	remote	remote	ADJ
cana-1287	96	21	sensing	sense	VERB
cana-1287	96	22	data	datum	NOUN
cana-1287	96	23	and	and	CCONJ
cana-1287	96	24	for	for	ADP
cana-1287	96	25	new	new	ADJ
cana-1287	96	26	ways	way	NOUN
cana-1287	96	27	combining	combine	VERB
cana-1287	96	28	advanced	advanced	ADJ
cana-1287	96	29	feature	feature	NOUN
cana-1287	96	30	extraction	extraction	NOUN
cana-1287	96	31	with	with	ADP
cana-1287	96	32	deep	deep	ADJ
cana-1287	96	33	learning	learning	NOUN
cana-1287	96	34	models	model	NOUN
cana-1287	96	35	to	to	PART
cana-1287	96	36	address	address	VERB
cana-1287	96	37	these	these	DET
cana-1287	96	38	limitations	limitation	NOUN
cana-1287	96	39	.	.	PUNCT
cana-1287	97	1	communications	communication	NOUN
cana-1287	97	2	on	on	ADP
cana-1287	97	3	applied	apply	VERB
cana-1287	97	4	nonlinear	nonlinear	ADJ
cana-1287	97	5	analysis	analysis	NOUN
cana-1287	97	6	issn	issn	NOUN
cana-1287	97	7	:	:	PUNCT
cana-1287	97	8	1074	1074	NUM
cana-1287	97	9	-	-	PUNCT
cana-1287	97	10	133x	133x	NUM
cana-1287	97	11	vol	vol	NOUN
cana-1287	97	12	31	31	NUM
cana-1287	97	13	no	no	NOUN
cana-1287	97	14	.	.	PUNCT
cana-1287	98	1	7s	7	NOUN
cana-1287	98	2	(	(	PUNCT
cana-1287	98	3	2024	2024	NUM
cana-1287	98	4	)	)	PUNCT
cana-1287	98	5	96	96	NUM
cana-1287	98	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	98	7	3	3	X
cana-1287	98	8	.	.	NUM
cana-1287	98	9	proposed	propose	VERB
cana-1287	98	10	method	method	NOUN
cana-1287	98	11	combining	combine	VERB
cana-1287	98	12	modern	modern	ADJ
cana-1287	98	13	image	image	NOUN
cana-1287	98	14	processing	processing	NOUN
cana-1287	98	15	techniques	technique	NOUN
cana-1287	98	16	with	with	ADP
cana-1287	98	17	a	a	DET
cana-1287	98	18	deep	deep	ADJ
cana-1287	98	19	radial	radial	ADJ
cana-1287	98	20	basis	basis	NOUN
cana-1287	98	21	function	function	NOUN
cana-1287	98	22	(	(	PUNCT
cana-1287	98	23	drbf	drbf	NOUN
cana-1287	98	24	)	)	PUNCT
cana-1287	98	25	network	network	NOUN
cana-1287	98	26	helps	help	VERB
cana-1287	98	27	to	to	PART
cana-1287	98	28	increase	increase	VERB
cana-1287	98	29	soil	soil	NOUN
cana-1287	98	30	estimate	estimate	NOUN
cana-1287	98	31	accuracy	accuracy	NOUN
cana-1287	98	32	.	.	PUNCT
cana-1287	99	1	as	as	SCONJ
cana-1287	99	2	figure	figure	NOUN
cana-1287	99	3	1	1	NUM
cana-1287	99	4	shows	show	NOUN
cana-1287	99	5	,	,	PUNCT
cana-1287	99	6	the	the	DET
cana-1287	99	7	approach	approach	NOUN
cana-1287	99	8	comprises	comprise	VERB
cana-1287	99	9	in	in	ADP
cana-1287	99	10	several	several	ADJ
cana-1287	99	11	basic	basic	ADJ
cana-1287	99	12	phases	phase	NOUN
cana-1287	99	13	:	:	PUNCT
cana-1287	99	14	figure	figure	NOUN
cana-1287	99	15	1	1	NUM
cana-1287	99	16	:	:	PUNCT
cana-1287	99	17	proposed	propose	VERB
cana-1287	99	18	framework	framework	NOUN
cana-1287	99	19	1	1	NUM
cana-1287	99	20	.	.	PUNCT
cana-1287	99	21	image	image	NOUN
cana-1287	99	22	acquisition	acquisition	NOUN
cana-1287	99	23	and	and	CCONJ
cana-1287	99	24	preprocessing	preprocessing	NOUN
cana-1287	99	25	:	:	PUNCT
cana-1287	99	26	preprocessing	preprocesse	VERB
cana-1287	99	27	high	high	ADJ
cana-1287	99	28	-	-	PUNCT
cana-1287	99	29	resolution	resolution	NOUN
cana-1287	99	30	remote	remote	ADJ
cana-1287	99	31	sensing	sensing	NOUN
cana-1287	99	32	images	image	NOUN
cana-1287	99	33	helps	help	VERB
cana-1287	99	34	to	to	PART
cana-1287	99	35	remove	remove	VERB
cana-1287	99	36	noise	noise	NOUN
cana-1287	99	37	and	and	CCONJ
cana-1287	99	38	enhance	enhance	VERB
cana-1287	99	39	features	feature	VERB
cana-1287	99	40	relevant	relevant	ADJ
cana-1287	99	41	to	to	PART
cana-1287	99	42	soil	soil	NOUN
cana-1287	99	43	conditions	condition	NOUN
cana-1287	99	44	.	.	PUNCT
cana-1287	100	1	filtering	filtering	NOUN
cana-1287	100	2	and	and	CCONJ
cana-1287	100	3	histogram	histogram	NOUN
cana-1287	100	4	equalization	equalization	NOUN
cana-1287	100	5	among	among	ADP
cana-1287	100	6	other	other	ADJ
cana-1287	100	7	methods	method	NOUN
cana-1287	100	8	improve	improve	VERB
cana-1287	100	9	image	image	NOUN
cana-1287	100	10	quality	quality	NOUN
cana-1287	100	11	.	.	PUNCT
cana-1287	101	1	2	2	X
cana-1287	101	2	.	.	X
cana-1287	101	3	feature	feature	NOUN
cana-1287	101	4	extraction	extraction	NOUN
cana-1287	101	5	:	:	PUNCT
cana-1287	101	6	edge	edge	NOUN
cana-1287	101	7	detection	detection	NOUN
cana-1287	101	8	and	and	CCONJ
cana-1287	101	9	texture	texture	ADJ
cana-1287	101	10	analysis	analysis	NOUN
cana-1287	101	11	among	among	ADP
cana-1287	101	12	other	other	ADJ
cana-1287	101	13	advanced	advanced	ADJ
cana-1287	101	14	image	image	NOUN
cana-1287	101	15	processing	processing	NOUN
cana-1287	101	16	methods	method	NOUN
cana-1287	101	17	extract	extract	VERB
cana-1287	101	18	relevant	relevant	ADJ
cana-1287	101	19	information	information	NOUN
cana-1287	101	20	from	from	ADP
cana-1287	101	21	the	the	DET
cana-1287	101	22	preprocessed	preprocesse	VERB
cana-1287	101	23	images	image	NOUN
cana-1287	101	24	.	.	PUNCT
cana-1287	102	1	as	as	ADP
cana-1287	102	2	inputs	input	NOUN
cana-1287	102	3	,	,	PUNCT
cana-1287	102	4	the	the	DET
cana-1287	102	5	rbf	rbf	PROPN
cana-1287	102	6	network	network	NOUN
cana-1287	102	7	makes	make	VERB
cana-1287	102	8	advantage	advantage	NOUN
cana-1287	102	9	of	of	ADP
cana-1287	102	10	these	these	DET
cana-1287	102	11	characteristics	characteristic	NOUN
cana-1287	102	12	—	—	PUNCT
cana-1287	102	13	which	which	PRON
cana-1287	102	14	could	could	AUX
cana-1287	102	15	include	include	VERB
cana-1287	102	16	color	color	NOUN
cana-1287	102	17	histograms	histogram	NOUN
cana-1287	102	18	and	and	CCONJ
cana-1287	102	19	texture	texture	ADJ
cana-1287	102	20	patterns	pattern	NOUN
cana-1287	102	21	.	.	PUNCT
cana-1287	103	1	3	3	X
cana-1287	103	2	.	.	X
cana-1287	103	3	deep	deep	ADJ
cana-1287	103	4	radial	radial	ADJ
cana-1287	103	5	basis	basis	NOUN
cana-1287	103	6	function	function	NOUN
cana-1287	103	7	network	network	NOUN
cana-1287	103	8	training	training	NOUN
cana-1287	103	9	:	:	PUNCT
cana-1287	103	10	comprising	comprise	VERB
cana-1287	103	11	several	several	ADJ
cana-1287	103	12	hidden	hidden	ADJ
cana-1287	103	13	layers	layer	NOUN
cana-1287	103	14	,	,	PUNCT
cana-1287	103	15	each	each	PRON
cana-1287	103	16	using	use	VERB
cana-1287	103	17	radial	radial	ADJ
cana-1287	103	18	basis	basis	NOUN
cana-1287	103	19	functions	function	NOUN
cana-1287	103	20	to	to	PART
cana-1287	103	21	replicate	replicate	VERB
cana-1287	103	22	the	the	DET
cana-1287	103	23	complex	complex	ADJ
cana-1287	103	24	,	,	PUNCT
cana-1287	103	25	nonlinear	nonlinear	ADJ
cana-1287	103	26	interactions	interaction	NOUN
cana-1287	103	27	between	between	ADP
cana-1287	103	28	soil	soil	NOUN
cana-1287	103	29	parameters	parameter	NOUN
cana-1287	103	30	and	and	CCONJ
cana-1287	103	31	input	input	NOUN
cana-1287	103	32	variables	variable	NOUN
cana-1287	103	33	,	,	PUNCT
cana-1287	103	34	the	the	DET
cana-1287	103	35	training	training	NOUN
cana-1287	103	36	process	process	NOUN
cana-1287	103	37	lowers	lower	VERB
cana-1287	103	38	a	a	DET
cana-1287	103	39	loss	loss	NOUN
cana-1287	103	40	function	function	NOUN
cana-1287	103	41	—	—	PUNCT
cana-1287	103	42	typically	typically	ADV
cana-1287	103	43	mean	mean	VERB
cana-1287	103	44	squared	square	VERB
cana-1287	103	45	error	error	NOUN
cana-1287	103	46	(	(	PUNCT
cana-1287	103	47	mse	mse	NOUN
cana-1287	103	48	)	)	PUNCT
cana-1287	103	49	using	use	VERB
cana-1287	103	50	optimization	optimization	NOUN
cana-1287	103	51	techniques	technique	NOUN
cana-1287	103	52	like	like	ADP
cana-1287	103	53	gradient	gradient	ADJ
cana-1287	103	54	descent	descent	NOUN
cana-1287	103	55	.	.	PUNCT
cana-1287	104	1	pseudocode	pseudocode	NOUN
cana-1287	104	2	:	:	PUNCT
cana-1287	104	3	#	#	NOUN
cana-1287	104	4	step	step	NOUN
cana-1287	104	5	1	1	NUM
cana-1287	104	6	:	:	PUNCT
cana-1287	104	7	image	image	NOUN
cana-1287	104	8	acquisition	acquisition	NOUN
cana-1287	104	9	and	and	CCONJ
cana-1287	104	10	preprocessing	preprocessing	NOUN
cana-1287	104	11	images	image	NOUN
cana-1287	104	12	=	=	SYM
cana-1287	104	13	acquire_images	acquire_image	NOUN
cana-1287	104	14	(	(	PUNCT
cana-1287	104	15	)	)	PUNCT
cana-1287	104	16	#	#	NOUN
cana-1287	104	17	function	function	NOUN
cana-1287	104	18	to	to	PART
cana-1287	104	19	acquire	acquire	VERB
cana-1287	104	20	remote	remote	ADJ
cana-1287	104	21	sensing	sensing	NOUN
cana-1287	104	22	images	image	NOUN
cana-1287	104	23	preprocessed_images	preprocessed_image	NOUN
cana-1287	104	24	=	=	SYM
cana-1287	104	25	preprocess_images(images	preprocess_images(image	NOUN
cana-1287	104	26	)	)	PUNCT
cana-1287	104	27	#	#	NOUN
cana-1287	104	28	apply	apply	VERB
cana-1287	104	29	preprocessing	preprocesse	VERB
cana-1287	104	30	techniques	technique	NOUN
cana-1287	104	31	#	#	NOUN
cana-1287	104	32	step	step	NOUN
cana-1287	104	33	2	2	NUM
cana-1287	104	34	:	:	PUNCT
cana-1287	104	35	feature	feature	NOUN
cana-1287	104	36	extraction	extraction	NOUN
cana-1287	104	37	features	feature	VERB
cana-1287	104	38	=	=	PUNCT
cana-1287	105	1	[	[	X
cana-1287	105	2	]	]	X
cana-1287	105	3	for	for	ADP
cana-1287	105	4	image	image	NOUN
cana-1287	105	5	in	in	ADP
cana-1287	105	6	preprocessed_images	preprocessed_image	NOUN
cana-1287	105	7	:	:	PUNCT
cana-1287	105	8	feature_vector	feature_vector	NOUN
cana-1287	105	9	=	=	NOUN
cana-1287	105	10	extract_features(image	extract_features(image	NOUN
cana-1287	105	11	)	)	PUNCT
cana-1287	105	12	#	#	NOUN
cana-1287	105	13	extract	extract	NOUN
cana-1287	105	14	features	feature	NOUN
cana-1287	105	15	from	from	ADP
cana-1287	105	16	the	the	DET
cana-1287	105	17	image	image	NOUN
cana-1287	105	18	features.append(feature_vector	features.append(feature_vector	NOUN
cana-1287	105	19	)	)	PUNCT
cana-1287	105	20	#	#	NOUN
cana-1287	105	21	step	step	NOUN
cana-1287	105	22	3	3	NUM
cana-1287	105	23	:	:	PUNCT
cana-1287	105	24	deep	deep	ADJ
cana-1287	105	25	radial	radial	ADJ
cana-1287	105	26	basis	basis	NOUN
cana-1287	105	27	function	function	NOUN
cana-1287	105	28	network	network	NOUN
cana-1287	105	29	training	training	NOUN
cana-1287	105	30	rbf_network	rbf_network	NOUN
cana-1287	105	31	=	=	SYM
cana-1287	105	32	initialize_deep_rbf_network	initialize_deep_rbf_network	NOUN
cana-1287	105	33	(	(	PUNCT
cana-1287	105	34	)	)	PUNCT
cana-1287	105	35	#	#	NOUN
cana-1287	105	36	initialize	initialize	NOUN
cana-1287	105	37	the	the	DET
cana-1287	105	38	deep	deep	ADJ
cana-1287	105	39	rbf	rbf	PROPN
cana-1287	105	40	network	network	PROPN
cana-1287	105	41	load	load	VERB
cana-1287	105	42	dataset	dataset	ADJ
cana-1287	105	43	preprocessing	preprocesse	VERB
cana-1287	105	44	feature	feature	NOUN
cana-1287	105	45	extraction	extraction	NOUN
cana-1287	105	46	deep	deep	ADJ
cana-1287	105	47	radial	radial	ADJ
cana-1287	105	48	basis	basis	NOUN
cana-1287	105	49	function	function	NOUN
cana-1287	105	50	network	network	NOUN
cana-1287	105	51	training	train	VERB
cana-1287	105	52	classified	classify	VERB
cana-1287	105	53	output	output	NOUN
cana-1287	105	54	communications	communication	NOUN
cana-1287	105	55	on	on	ADP
cana-1287	105	56	applied	apply	VERB
cana-1287	105	57	nonlinear	nonlinear	ADJ
cana-1287	105	58	analysis	analysis	NOUN
cana-1287	105	59	issn	issn	NOUN
cana-1287	105	60	:	:	PUNCT
cana-1287	105	61	1074	1074	NUM
cana-1287	105	62	-	-	PUNCT
cana-1287	105	63	133x	133x	NUM
cana-1287	105	64	vol	vol	NOUN
cana-1287	105	65	31	31	NUM
cana-1287	105	66	no	no	NOUN
cana-1287	105	67	.	.	PUNCT
cana-1287	106	1	7s	7	NOUN
cana-1287	106	2	(	(	PUNCT
cana-1287	106	3	2024	2024	NUM
cana-1287	106	4	)	)	PUNCT
cana-1287	106	5	97	97	NUM
cana-1287	107	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	107	2	features_train	features_train	NOUN
cana-1287	107	3	,	,	PUNCT
cana-1287	107	4	soil_properties_train	soil_properties_train	PROPN
cana-1287	107	5	=	=	SYM
cana-1287	107	6	split_data(features	split_data(feature	NOUN
cana-1287	107	7	,	,	PUNCT
cana-1287	107	8	soil_properties	soil_propertie	NOUN
cana-1287	107	9	)	)	PUNCT
cana-1287	107	10	rbf_network.train(features_train	rbf_network.train(features_train	PROPN
cana-1287	107	11	,	,	PUNCT
cana-1287	107	12	soil_properties_train	soil_properties_train	NOUN
cana-1287	107	13	)	)	PUNCT
cana-1287	107	14	#	#	NOUN
cana-1287	107	15	train	train	NOUN
cana-1287	107	16	the	the	DET
cana-1287	107	17	rbf	rbf	PROPN
cana-1287	107	18	network	network	NOUN
cana-1287	107	19	#	#	NOUN
cana-1287	107	20	step	step	NOUN
cana-1287	107	21	4	4	NUM
cana-1287	107	22	:	:	PUNCT
cana-1287	107	23	prediction	prediction	NOUN
cana-1287	107	24	and	and	CCONJ
cana-1287	107	25	evaluation	evaluation	NOUN
cana-1287	107	26	predictions	prediction	NOUN
cana-1287	107	27	=	=	SYM
cana-1287	107	28	rbf_network.predict(features	rbf_network.predict(feature	NOUN
cana-1287	107	29	)	)	PUNCT
cana-1287	107	30	#	#	NOUN
cana-1287	107	31	predict	predict	VERB
cana-1287	107	32	soil	soil	NOUN
cana-1287	107	33	properties	property	NOUN
cana-1287	107	34	using	use	VERB
cana-1287	107	35	the	the	DET
cana-1287	107	36	trained	train	VERB
cana-1287	107	37	model	model	NOUN
cana-1287	107	38	evaluation_metrics	evaluation_metric	NOUN
cana-1287	107	39	=	=	SYM
cana-1287	107	40	evaluate(predictions	evaluate(prediction	NOUN
cana-1287	107	41	,	,	PUNCT
cana-1287	107	42	soil_properties	soil_properties	PROPN
cana-1287	107	43	)	)	PUNCT
cana-1287	107	44	#	#	NOUN
cana-1287	107	45	evaluate	evaluate	VERB
cana-1287	107	46	the	the	DET
cana-1287	107	47	model	model	NOUN
cana-1287	107	48	’s	’s	PART
cana-1287	107	49	performance	performance	NOUN
cana-1287	107	50	3.1	3.1	NUM
cana-1287	107	51	.	.	PUNCT
cana-1287	108	1	preprocessing	preprocesse	VERB
cana-1287	108	2	preprocessing	preprocessing	NOUN
cana-1287	108	3	is	be	AUX
cana-1287	108	4	crucial	crucial	ADJ
cana-1287	108	5	for	for	ADP
cana-1287	108	6	preparing	prepare	VERB
cana-1287	108	7	remote	remote	ADJ
cana-1287	108	8	sensing	sensing	NOUN
cana-1287	108	9	images	image	NOUN
cana-1287	108	10	for	for	ADP
cana-1287	108	11	accurate	accurate	ADJ
cana-1287	108	12	soil	soil	NOUN
cana-1287	108	13	nutrient	nutrient	NOUN
cana-1287	108	14	classification	classification	NOUN
cana-1287	108	15	with	with	ADP
cana-1287	108	16	deep	deep	ADJ
cana-1287	108	17	radial	radial	ADJ
cana-1287	108	18	basis	basis	NOUN
cana-1287	108	19	function	function	NOUN
cana-1287	108	20	(	(	PUNCT
cana-1287	108	21	rbf	rbf	PROPN
cana-1287	108	22	)	)	PUNCT
cana-1287	108	23	networks	network	NOUN
cana-1287	108	24	.	.	PUNCT
cana-1287	109	1	preprocessing	preprocesse	VERB
cana-1287	109	2	generally	generally	ADV
cana-1287	109	3	seeks	seek	VERB
cana-1287	109	4	to	to	PART
cana-1287	109	5	enhance	enhance	VERB
cana-1287	109	6	the	the	DET
cana-1287	109	7	image	image	NOUN
cana-1287	109	8	quality	quality	NOUN
cana-1287	109	9	and	and	CCONJ
cana-1287	109	10	extract	extract	VERB
cana-1287	109	11	relevant	relevant	ADJ
cana-1287	109	12	features	feature	NOUN
cana-1287	109	13	that	that	PRON
cana-1287	109	14	could	could	AUX
cana-1287	109	15	considerably	considerably	ADV
cana-1287	109	16	boost	boost	VERB
cana-1287	109	17	the	the	DET
cana-1287	109	18	performance	performance	NOUN
cana-1287	109	19	of	of	ADP
cana-1287	109	20	next	next	ADJ
cana-1287	109	21	modeling	modeling	NOUN
cana-1287	109	22	.	.	PUNCT
cana-1287	110	1	•	•	NUM
cana-1287	110	2	image	image	NOUN
cana-1287	110	3	capture	capture	NOUN
cana-1287	110	4	starts	start	VERB
cana-1287	110	5	with	with	ADP
cana-1287	110	6	compiling	compile	VERB
cana-1287	110	7	high	high	ADJ
cana-1287	110	8	-	-	PUNCT
cana-1287	110	9	resolution	resolution	NOUN
cana-1287	110	10	remote	remote	ADJ
cana-1287	110	11	sensing	sense	VERB
cana-1287	110	12	ground	ground	NOUN
cana-1287	110	13	images	image	NOUN
cana-1287	110	14	.	.	PUNCT
cana-1287	111	1	many	many	ADJ
cana-1287	111	2	times	time	NOUN
cana-1287	111	3	,	,	PUNCT
cana-1287	111	4	these	these	DET
cana-1287	111	5	images	image	NOUN
cana-1287	111	6	are	be	AUX
cana-1287	111	7	affected	affect	VERB
cana-1287	111	8	by	by	ADP
cana-1287	111	9	several	several	ADJ
cana-1287	111	10	aberrations	aberration	NOUN
cana-1287	111	11	including	include	VERB
cana-1287	111	12	noise	noise	NOUN
cana-1287	111	13	,	,	PUNCT
cana-1287	111	14	illumination	illumination	NOUN
cana-1287	111	15	variations	variation	NOUN
cana-1287	111	16	,	,	PUNCT
cana-1287	111	17	and	and	CCONJ
cana-1287	111	18	atmospheric	atmospheric	ADJ
cana-1287	111	19	conditions	condition	NOUN
cana-1287	111	20	.	.	PUNCT
cana-1287	112	1	starting	start	VERB
cana-1287	112	2	the	the	DET
cana-1287	112	3	preparation	preparation	NOUN
cana-1287	112	4	process	process	NOUN
cana-1287	112	5	are	be	AUX
cana-1287	112	6	noise	noise	NOUN
cana-1287	112	7	reduction	reduction	NOUN
cana-1287	112	8	techniques	technique	NOUN
cana-1287	112	9	whereby	whereby	SCONJ
cana-1287	112	10	random	random	ADJ
cana-1287	112	11	changes	change	NOUN
cana-1287	112	12	are	be	AUX
cana-1287	112	13	smooth	smooth	ADJ
cana-1287	112	14	out	out	ADP
cana-1287	112	15	and	and	CCONJ
cana-1287	112	16	significant	significant	ADJ
cana-1287	112	17	visual	visual	ADJ
cana-1287	112	18	properties	property	NOUN
cana-1287	112	19	are	be	AUX
cana-1287	112	20	maintained	maintain	VERB
cana-1287	112	21	using	use	VERB
cana-1287	112	22	filters	filter	NOUN
cana-1287	112	23	such	such	ADJ
cana-1287	112	24	as	as	ADP
cana-1287	112	25	gaussian	gaussian	ADJ
cana-1287	112	26	blur	blur	NOUN
cana-1287	112	27	or	or	CCONJ
cana-1287	112	28	median	median	ADJ
cana-1287	112	29	filtering	filtering	NOUN
cana-1287	112	30	.	.	PUNCT
cana-1287	113	1	this	this	DET
cana-1287	113	2	stage	stage	NOUN
cana-1287	113	3	helps	help	VERB
cana-1287	113	4	to	to	PART
cana-1287	113	5	reduce	reduce	VERB
cana-1287	113	6	the	the	DET
cana-1287	113	7	impact	impact	NOUN
cana-1287	113	8	of	of	ADP
cana-1287	113	9	extraneous	extraneous	ADJ
cana-1287	113	10	noise	noise	NOUN
cana-1287	113	11	on	on	ADP
cana-1287	113	12	the	the	DET
cana-1287	113	13	feature	feature	NOUN
cana-1287	113	14	extraction	extraction	NOUN
cana-1287	113	15	process	process	NOUN
cana-1287	113	16	.	.	PUNCT
cana-1287	114	1	•	•	NUM
cana-1287	114	2	after	after	ADP
cana-1287	114	3	noise	noise	NOUN
cana-1287	114	4	reduction	reduction	NOUN
cana-1287	114	5	,	,	PUNCT
cana-1287	114	6	significant	significant	ADJ
cana-1287	114	7	feature	feature	NOUN
cana-1287	114	8	visibility	visibility	NOUN
cana-1287	114	9	inside	inside	ADP
cana-1287	114	10	the	the	DET
cana-1287	114	11	images	image	NOUN
cana-1287	114	12	is	be	AUX
cana-1287	114	13	enhanced	enhance	VERB
cana-1287	114	14	by	by	ADP
cana-1287	114	15	histogram	histogram	NOUN
cana-1287	114	16	equalization	equalization	NOUN
cana-1287	114	17	.	.	PUNCT
cana-1287	115	1	this	this	DET
cana-1287	115	2	technique	technique	NOUN
cana-1287	115	3	controls	control	VERB
cana-1287	115	4	the	the	DET
cana-1287	115	5	contrast	contrast	NOUN
cana-1287	115	6	of	of	ADP
cana-1287	115	7	the	the	DET
cana-1287	115	8	image	image	NOUN
cana-1287	115	9	,	,	PUNCT
cana-1287	115	10	therefore	therefore	ADV
cana-1287	115	11	improving	improve	VERB
cana-1287	115	12	the	the	DET
cana-1287	115	13	distinguishability	distinguishability	NOUN
cana-1287	115	14	of	of	ADP
cana-1287	115	15	the	the	DET
cana-1287	115	16	patterns	pattern	NOUN
cana-1287	115	17	and	and	CCONJ
cana-1287	115	18	textures	texture	NOUN
cana-1287	115	19	of	of	ADP
cana-1287	115	20	the	the	DET
cana-1287	115	21	ground	ground	NOUN
cana-1287	115	22	.	.	PUNCT
cana-1287	116	1	more	more	ADV
cana-1287	116	2	effective	effective	ADJ
cana-1287	116	3	recording	recording	NOUN
cana-1287	116	4	of	of	ADP
cana-1287	116	5	important	important	ADJ
cana-1287	116	6	soil	soil	NOUN
cana-1287	116	7	parameters	parameter	NOUN
cana-1287	116	8	is	be	AUX
cana-1287	116	9	guaranteed	guarantee	VERB
cana-1287	116	10	by	by	ADP
cana-1287	116	11	improved	improve	VERB
cana-1287	116	12	feature	feature	NOUN
cana-1287	116	13	extraction	extraction	NOUN
cana-1287	116	14	made	make	VERB
cana-1287	116	15	possible	possible	ADJ
cana-1287	116	16	by	by	ADP
cana-1287	116	17	better	well	ADJ
cana-1287	116	18	contrast	contrast	NOUN
cana-1287	116	19	.	.	PUNCT
cana-1287	117	1	•	•	NOUN
cana-1287	117	2	feature	feature	NOUN
cana-1287	117	3	extraction	extraction	NOUN
cana-1287	117	4	then	then	ADV
cana-1287	117	5	asks	ask	VERB
cana-1287	117	6	for	for	ADP
cana-1287	117	7	among	among	ADP
cana-1287	117	8	other	other	ADJ
cana-1287	117	9	methods	method	NOUN
cana-1287	117	10	texture	texture	VERB
cana-1287	117	11	analysis	analysis	NOUN
cana-1287	117	12	and	and	CCONJ
cana-1287	117	13	edge	edge	NOUN
cana-1287	117	14	identification	identification	NOUN
cana-1287	117	15	.	.	PUNCT
cana-1287	118	1	crucially	crucially	ADV
cana-1287	118	2	for	for	ADP
cana-1287	118	3	understanding	understanding	NOUN
cana-1287	118	4	of	of	ADP
cana-1287	118	5	soil	soil	NOUN
cana-1287	118	6	features	feature	NOUN
cana-1287	118	7	,	,	PUNCT
cana-1287	118	8	canny	canny	ADJ
cana-1287	118	9	edge	edge	NOUN
cana-1287	118	10	detector	detector	NOUN
cana-1287	118	11	finds	find	VERB
cana-1287	118	12	transitions	transition	NOUN
cana-1287	118	13	in	in	ADP
cana-1287	118	14	soil	soil	NOUN
cana-1287	118	15	textures	texture	NOUN
cana-1287	118	16	and	and	CCONJ
cana-1287	118	17	borders	border	NOUN
cana-1287	118	18	.	.	PUNCT
cana-1287	119	1	texture	texture	ADJ
cana-1287	119	2	analysis	analysis	NOUN
cana-1287	119	3	allows	allow	VERB
cana-1287	119	4	one	one	NUM
cana-1287	119	5	to	to	PART
cana-1287	119	6	measure	measure	VERB
cana-1287	119	7	trends	trend	NOUN
cana-1287	119	8	and	and	CCONJ
cana-1287	119	9	variations	variation	NOUN
cana-1287	119	10	in	in	ADP
cana-1287	119	11	the	the	DET
cana-1287	119	12	surface	surface	NOUN
cana-1287	119	13	of	of	ADP
cana-1287	119	14	the	the	DET
cana-1287	119	15	soil	soil	NOUN
cana-1287	119	16	,	,	PUNCT
cana-1287	119	17	so	so	ADV
cana-1287	119	18	providing	provide	VERB
cana-1287	119	19	more	more	ADJ
cana-1287	119	20	knowledge	knowledge	NOUN
cana-1287	119	21	of	of	ADP
cana-1287	119	22	soil	soil	NOUN
cana-1287	119	23	characteristics	characteristic	NOUN
cana-1287	119	24	.	.	PUNCT
cana-1287	120	1	•	•	NUM
cana-1287	120	2	the	the	DET
cana-1287	120	3	last	last	ADJ
cana-1287	120	4	stage	stage	NOUN
cana-1287	120	5	in	in	ADP
cana-1287	120	6	preprocessing	preprocessing	NOUN
cana-1287	120	7	is	be	AUX
cana-1287	120	8	normalizing	normalize	VERB
cana-1287	120	9	the	the	DET
cana-1287	120	10	acquired	acquire	VERB
cana-1287	120	11	properties	property	NOUN
cana-1287	120	12	into	into	ADP
cana-1287	120	13	a	a	DET
cana-1287	120	14	consistent	consistent	ADJ
cana-1287	120	15	range	range	NOUN
cana-1287	120	16	.	.	PUNCT
cana-1287	121	1	this	this	PRON
cana-1287	121	2	ensures	ensure	VERB
cana-1287	121	3	that	that	SCONJ
cana-1287	121	4	every	every	DET
cana-1287	121	5	feature	feature	NOUN
cana-1287	121	6	equally	equally	ADV
cana-1287	121	7	supports	support	VERB
cana-1287	121	8	the	the	DET
cana-1287	121	9	model	model	NOUN
cana-1287	121	10	and	and	CCONJ
cana-1287	121	11	maintains	maintain	VERB
cana-1287	121	12	any	any	DET
cana-1287	121	13	one	one	NUM
cana-1287	121	14	characteristic	characteristic	NOUN
cana-1287	121	15	from	from	ADP
cana-1287	121	16	free	free	ADJ
cana-1287	121	17	from	from	ADP
cana-1287	121	18	influence	influence	NOUN
cana-1287	121	19	not	not	PART
cana-1287	121	20	dominating	dominate	VERB
cana-1287	121	21	the	the	DET
cana-1287	121	22	prediction	prediction	NOUN
cana-1287	121	23	process	process	NOUN
cana-1287	121	24	.	.	PUNCT
cana-1287	122	1	by	by	ADP
cana-1287	122	2	means	mean	NOUN
cana-1287	122	3	of	of	ADP
cana-1287	122	4	normalizing	normalize	VERB
cana-1287	122	5	the	the	DET
cana-1287	122	6	data	datum	NOUN
cana-1287	122	7	,	,	PUNCT
cana-1287	122	8	the	the	DET
cana-1287	122	9	deep	deep	ADJ
cana-1287	122	10	rbf	rbf	PROPN
cana-1287	122	11	network	network	NOUN
cana-1287	122	12	learns	learn	VERB
cana-1287	122	13	the	the	DET
cana-1287	122	14	correlations	correlation	NOUN
cana-1287	122	15	between	between	ADP
cana-1287	122	16	image	image	NOUN
cana-1287	122	17	attributes	attribute	NOUN
cana-1287	122	18	and	and	CCONJ
cana-1287	122	19	soil	soil	NOUN
cana-1287	122	20	characteristics	characteristic	NOUN
cana-1287	122	21	.	.	PUNCT
cana-1287	123	1	3.2	3.2	NUM
cana-1287	123	2	.	.	PUNCT
cana-1287	124	1	non	non	ADJ
cana-1287	124	2	-	-	ADJ
cana-1287	124	3	linear	linear	ADJ
cana-1287	124	4	regression	regression	NOUN
cana-1287	124	5	for	for	ADP
cana-1287	124	6	feature	feature	NOUN
cana-1287	124	7	extraction	extraction	NOUN
cana-1287	124	8	it	it	PRON
cana-1287	124	9	is	be	AUX
cana-1287	124	10	designed	design	VERB
cana-1287	124	11	to	to	PART
cana-1287	124	12	capture	capture	VERB
cana-1287	124	13	complex	complex	ADJ
cana-1287	124	14	relationships	relationship	NOUN
cana-1287	124	15	between	between	ADP
cana-1287	124	16	soil	soil	NOUN
cana-1287	124	17	characteristics	characteristic	NOUN
cana-1287	124	18	and	and	CCONJ
cana-1287	124	19	picture	picture	NOUN
cana-1287	124	20	data	datum	NOUN
cana-1287	124	21	,	,	PUNCT
cana-1287	124	22	non	non	ADJ
cana-1287	124	23	-	-	ADJ
cana-1287	124	24	linear	linear	ADJ
cana-1287	124	25	regression	regression	NOUN
cana-1287	124	26	for	for	ADP
cana-1287	124	27	feature	feature	NOUN
cana-1287	124	28	extraction	extraction	NOUN
cana-1287	124	29	is	be	AUX
cana-1287	124	30	a	a	DET
cana-1287	124	31	sophisticated	sophisticated	ADJ
cana-1287	124	32	technique	technique	NOUN
cana-1287	124	33	permitting	permit	VERB
cana-1287	124	34	more	more	ADV
cana-1287	124	35	exact	exact	ADJ
cana-1287	124	36	soil	soil	NOUN
cana-1287	124	37	nutrient	nutrient	NOUN
cana-1287	124	38	classification	classification	NOUN
cana-1287	124	39	.	.	PUNCT
cana-1287	125	1	the	the	DET
cana-1287	125	2	process	process	NOUN
cana-1287	125	3	comprises	comprise	VERB
cana-1287	125	4	in	in	ADP
cana-1287	125	5	several	several	ADJ
cana-1287	125	6	crucial	crucial	ADJ
cana-1287	125	7	phases	phase	NOUN
cana-1287	125	8	:	:	PUNCT
cana-1287	125	9	model	model	NOUN
cana-1287	125	10	development	development	PROPN
cana-1287	125	11	:	:	PUNCT
cana-1287	125	12	the	the	DET
cana-1287	125	13	first	first	ADJ
cana-1287	125	14	stage	stage	NOUN
cana-1287	125	15	in	in	ADP
cana-1287	125	16	non	non	ADJ
cana-1287	125	17	-	-	ADJ
cana-1287	125	18	linear	linear	ADJ
cana-1287	125	19	regression	regression	NOUN
cana-1287	125	20	for	for	ADP
cana-1287	125	21	feature	feature	NOUN
cana-1287	125	22	extraction	extraction	NOUN
cana-1287	125	23	is	be	AUX
cana-1287	125	24	developing	develop	VERB
cana-1287	125	25	a	a	DET
cana-1287	125	26	model	model	NOUN
cana-1287	125	27	able	able	ADJ
cana-1287	125	28	to	to	PART
cana-1287	125	29	capture	capture	VERB
cana-1287	125	30	the	the	DET
cana-1287	125	31	intricate	intricate	ADJ
cana-1287	125	32	,	,	PUNCT
cana-1287	125	33	non	non	ADJ
cana-1287	125	34	-	-	ADJ
cana-1287	125	35	linear	linear	ADJ
cana-1287	125	36	interactions	interaction	NOUN
cana-1287	125	37	between	between	ADP
cana-1287	125	38	the	the	DET
cana-1287	125	39	soil	soil	NOUN
cana-1287	125	40	attributes	attribute	NOUN
cana-1287	125	41	depicted	depict	VERB
cana-1287	125	42	in	in	ADP
cana-1287	125	43	the	the	DET
cana-1287	125	44	images	image	NOUN
cana-1287	125	45	and	and	CCONJ
cana-1287	125	46	the	the	DET
cana-1287	125	47	actual	actual	ADJ
cana-1287	125	48	soil	soil	NOUN
cana-1287	125	49	nutrient	nutrient	NOUN
cana-1287	125	50	levels	level	NOUN
cana-1287	125	51	.	.	PUNCT
cana-1287	126	1	whereas	whereas	SCONJ
cana-1287	126	2	linear	linear	ADJ
cana-1287	126	3	regression	regression	NOUN
cana-1287	126	4	models	model	NOUN
cana-1287	126	5	imply	imply	VERB
cana-1287	126	6	a	a	DET
cana-1287	126	7	straight	straight	ADJ
cana-1287	126	8	-	-	PUNCT
cana-1287	126	9	line	line	NOUN
cana-1287	126	10	relationship	relationship	NOUN
cana-1287	126	11	,	,	PUNCT
cana-1287	126	12	non	non	ADJ
cana-1287	126	13	-	-	ADJ
cana-1287	126	14	linear	linear	ADJ
cana-1287	126	15	regression	regression	NOUN
cana-1287	126	16	models	model	NOUN
cana-1287	126	17	can	can	AUX
cana-1287	126	18	fit	fit	VERB
cana-1287	126	19	curves	curve	NOUN
cana-1287	126	20	and	and	CCONJ
cana-1287	126	21	more	more	ADV
cana-1287	126	22	complex	complex	ADJ
cana-1287	126	23	interactions	interaction	NOUN
cana-1287	126	24	.	.	PUNCT
cana-1287	127	1	in	in	ADP
cana-1287	127	2	this	this	DET
cana-1287	127	3	work	work	NOUN
cana-1287	127	4	a	a	DET
cana-1287	127	5	non	non	ADJ
cana-1287	127	6	-	-	ADJ
cana-1287	127	7	linear	linear	ADJ
cana-1287	127	8	regression	regression	NOUN
cana-1287	127	9	model	model	NOUN
cana-1287	127	10	—	—	PUNCT
cana-1287	127	11	such	such	ADJ
cana-1287	127	12	as	as	ADP
cana-1287	127	13	gaussian	gaussian	ADJ
cana-1287	127	14	processes	process	NOUN
cana-1287	127	15	or	or	CCONJ
cana-1287	127	16	polynomial	polynomial	ADJ
cana-1287	127	17	regression	regression	NOUN
cana-1287	127	18	—	—	PUNCT
cana-1287	127	19	fits	fit	VERB
cana-1287	127	20	the	the	DET
cana-1287	127	21	data	datum	NOUN
cana-1287	127	22	.	.	PUNCT
cana-1287	128	1	using	use	VERB
cana-1287	128	2	a	a	DET
cana-1287	128	3	dataset	dataset	NOUN
cana-1287	128	4	with	with	ADP
cana-1287	128	5	known	know	VERB
cana-1287	128	6	soil	soil	NOUN
cana-1287	128	7	nutrient	nutrient	NOUN
cana-1287	128	8	levels	level	NOUN
cana-1287	128	9	,	,	PUNCT
cana-1287	128	10	the	the	DET
cana-1287	128	11	method	method	NOUN
cana-1287	128	12	learns	learn	VERB
cana-1287	128	13	how	how	SCONJ
cana-1287	128	14	various	various	ADJ
cana-1287	128	15	image	image	NOUN
cana-1287	128	16	features	feature	NOUN
cana-1287	128	17	connect	connect	VERB
cana-1287	128	18	to	to	ADP
cana-1287	128	19	soil	soil	NOUN
cana-1287	128	20	parameters	parameter	NOUN
cana-1287	128	21	.	.	PUNCT
cana-1287	129	1	communications	communication	NOUN
cana-1287	129	2	on	on	ADP
cana-1287	129	3	applied	apply	VERB
cana-1287	129	4	nonlinear	nonlinear	ADJ
cana-1287	129	5	analysis	analysis	NOUN
cana-1287	129	6	issn	issn	NOUN
cana-1287	129	7	:	:	PUNCT
cana-1287	129	8	1074	1074	NUM
cana-1287	129	9	-	-	PUNCT
cana-1287	129	10	133x	133x	NUM
cana-1287	129	11	vol	vol	NOUN
cana-1287	129	12	31	31	NUM
cana-1287	129	13	no	no	NOUN
cana-1287	129	14	.	.	PUNCT
cana-1287	130	1	7s	7	NOUN
cana-1287	130	2	(	(	PUNCT
cana-1287	130	3	2024	2024	NUM
cana-1287	130	4	)	)	PUNCT
cana-1287	130	5	98	98	NUM
cana-1287	130	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	130	7	feature	feature	NOUN
cana-1287	130	8	mapping	mapping	NOUN
cana-1287	130	9	:	:	PUNCT
cana-1287	130	10	once	once	SCONJ
cana-1287	130	11	the	the	DET
cana-1287	130	12	non	non	ADJ
cana-1287	130	13	-	-	ADJ
cana-1287	130	14	linear	linear	ADJ
cana-1287	130	15	regression	regression	NOUN
cana-1287	130	16	model	model	NOUN
cana-1287	130	17	is	be	AUX
cana-1287	130	18	developed	develop	VERB
cana-1287	130	19	,	,	PUNCT
cana-1287	130	20	it	it	PRON
cana-1287	130	21	is	be	AUX
cana-1287	130	22	employed	employ	VERB
cana-1287	130	23	to	to	PART
cana-1287	130	24	move	move	VERB
cana-1287	130	25	raw	raw	ADJ
cana-1287	130	26	image	image	NOUN
cana-1287	130	27	data	datum	NOUN
cana-1287	130	28	to	to	ADP
cana-1287	130	29	a	a	DET
cana-1287	130	30	feature	feature	NOUN
cana-1287	130	31	space	space	NOUN
cana-1287	130	32	wherein	wherein	SCONJ
cana-1287	130	33	more	more	ADV
cana-1287	130	34	effectively	effectively	ADV
cana-1287	130	35	links	link	NOUN
cana-1287	130	36	between	between	ADP
cana-1287	130	37	picture	picture	NOUN
cana-1287	130	38	features	feature	NOUN
cana-1287	130	39	and	and	CCONJ
cana-1287	130	40	soil	soil	NOUN
cana-1287	130	41	attributes	attribute	NOUN
cana-1287	130	42	are	be	AUX
cana-1287	130	43	communicated	communicate	VERB
cana-1287	130	44	.	.	PUNCT
cana-1287	131	1	this	this	PRON
cana-1287	131	2	creates	create	VERB
cana-1287	131	3	a	a	DET
cana-1287	131	4	set	set	NOUN
cana-1287	131	5	of	of	ADP
cana-1287	131	6	derived	derive	VERB
cana-1287	131	7	features	feature	NOUN
cana-1287	131	8	from	from	ADP
cana-1287	131	9	the	the	DET
cana-1287	131	10	original	original	ADJ
cana-1287	131	11	image	image	NOUN
cana-1287	131	12	data	datum	NOUN
cana-1287	131	13	by	by	ADP
cana-1287	131	14	means	mean	NOUN
cana-1287	131	15	of	of	ADP
cana-1287	131	16	non	non	ADJ
cana-1287	131	17	-	-	ADJ
cana-1287	131	18	linear	linear	ADJ
cana-1287	131	19	functions	function	NOUN
cana-1287	131	20	.	.	PUNCT
cana-1287	132	1	poisson	poisson	PROPN
cana-1287	132	2	regression	regression	PROPN
cana-1287	132	3	,	,	PUNCT
cana-1287	132	4	for	for	ADP
cana-1287	132	5	instance	instance	NOUN
cana-1287	132	6	,	,	PUNCT
cana-1287	132	7	can	can	AUX
cana-1287	132	8	generate	generate	VERB
cana-1287	132	9	new	new	ADJ
cana-1287	132	10	features	feature	NOUN
cana-1287	132	11	dependent	dependent	ADJ
cana-1287	132	12	on	on	ADP
cana-1287	132	13	poisson	poisson	NOUN
cana-1287	132	14	combinations	combination	NOUN
cana-1287	132	15	of	of	ADP
cana-1287	132	16	the	the	DET
cana-1287	132	17	original	original	ADJ
cana-1287	132	18	features	feature	NOUN
cana-1287	132	19	,	,	PUNCT
cana-1287	132	20	therefore	therefore	ADV
cana-1287	132	21	capturing	capture	VERB
cana-1287	132	22	interactions	interaction	NOUN
cana-1287	132	23	and	and	CCONJ
cana-1287	132	24	higher	high	ADJ
cana-1287	132	25	-	-	PUNCT
cana-1287	132	26	order	order	NOUN
cana-1287	132	27	effects	effect	NOUN
cana-1287	132	28	lacking	lack	VERB
cana-1287	132	29	from	from	ADP
cana-1287	132	30	the	the	DET
cana-1287	132	31	raw	raw	ADJ
cana-1287	132	32	data	datum	NOUN
cana-1287	132	33	.	.	PUNCT
cana-1287	133	1	feature	feature	NOUN
cana-1287	133	2	extraction	extraction	NOUN
cana-1287	133	3	:	:	PUNCT
cana-1287	133	4	new	new	ADJ
cana-1287	133	5	images	image	NOUN
cana-1287	133	6	are	be	AUX
cana-1287	133	7	fed	feed	VERB
cana-1287	133	8	the	the	DET
cana-1287	133	9	trained	train	VERB
cana-1287	133	10	non	non	ADJ
cana-1287	133	11	-	-	ADJ
cana-1287	133	12	linear	linear	ADJ
cana-1287	133	13	regression	regression	NOUN
cana-1287	133	14	model	model	NOUN
cana-1287	133	15	in	in	ADP
cana-1287	133	16	the	the	DET
cana-1287	133	17	feature	feature	NOUN
cana-1287	133	18	extraction	extraction	NOUN
cana-1287	133	19	stage	stage	NOUN
cana-1287	133	20	in	in	ADP
cana-1287	133	21	order	order	NOUN
cana-1287	133	22	to	to	PART
cana-1287	133	23	identify	identify	VERB
cana-1287	133	24	relevant	relevant	ADJ
cana-1287	133	25	features	feature	NOUN
cana-1287	133	26	.	.	PUNCT
cana-1287	134	1	processing	process	VERB
cana-1287	134	2	the	the	DET
cana-1287	134	3	raw	raw	ADJ
cana-1287	134	4	visual	visual	ADJ
cana-1287	134	5	data	datum	NOUN
cana-1287	134	6	and	and	CCONJ
cana-1287	134	7	using	use	VERB
cana-1287	134	8	learnt	learn	VERB
cana-1287	134	9	non	non	ADJ
cana-1287	134	10	-	-	ADJ
cana-1287	134	11	linear	linear	ADJ
cana-1287	134	12	mappings	mapping	NOUN
cana-1287	134	13	,	,	PUNCT
cana-1287	134	14	the	the	DET
cana-1287	134	15	model	model	NOUN
cana-1287	134	16	produces	produce	VERB
cana-1287	134	17	a	a	DET
cana-1287	134	18	set	set	NOUN
cana-1287	134	19	of	of	ADP
cana-1287	134	20	characteristics	characteristic	NOUN
cana-1287	134	21	underlining	underline	VERB
cana-1287	134	22	important	important	ADJ
cana-1287	134	23	patterns	pattern	NOUN
cana-1287	134	24	and	and	CCONJ
cana-1287	134	25	connections	connection	NOUN
cana-1287	134	26	.	.	PUNCT
cana-1287	135	1	these	these	DET
cana-1287	135	2	properties	property	NOUN
cana-1287	135	3	,	,	PUNCT
cana-1287	135	4	which	which	PRON
cana-1287	135	5	mirror	mirror	VERB
cana-1287	135	6	soil	soil	NOUN
cana-1287	135	7	nutrient	nutrient	NOUN
cana-1287	135	8	levels	level	NOUN
cana-1287	135	9	,	,	PUNCT
cana-1287	135	10	could	could	AUX
cana-1287	135	11	combine	combine	VERB
cana-1287	135	12	complex	complex	ADJ
cana-1287	135	13	combinations	combination	NOUN
cana-1287	135	14	of	of	ADP
cana-1287	135	15	basic	basic	ADJ
cana-1287	135	16	image	image	NOUN
cana-1287	135	17	components	component	NOUN
cana-1287	135	18	including	include	VERB
cana-1287	135	19	color	color	NOUN
cana-1287	135	20	variations	variation	NOUN
cana-1287	135	21	and	and	CCONJ
cana-1287	135	22	texture	texture	NOUN
cana-1287	135	23	patterns	pattern	NOUN
cana-1287	135	24	.	.	PUNCT
cana-1287	136	1	1	1	X
cana-1287	136	2	.	.	X
cana-1287	136	3	polynomial	polynomial	ADJ
cana-1287	136	4	regression	regression	NOUN
cana-1287	136	5	(	(	PUNCT
cana-1287	136	6	quadratic	quadratic	ADJ
cana-1287	136	7	):	):	PUNCT
cana-1287	136	8	2	2	NUM
cana-1287	136	9	0	0	NUM
cana-1287	136	10	1	1	NUM
cana-1287	136	11	2y	2y	NUM
cana-1287	136	12	x	x	X
cana-1287	136	13	x	x	PROPN
cana-1287	136	14			PROPN
cana-1287	136	15	=	=	PROPN
cana-1287	136	16	+	+	PUNCT
cana-1287	136	17	+	+	PUNCT
cana-1287	136	18	+	+	ADJ
cana-1287	136	19			NOUN
cana-1287	136	20	this	this	DET
cana-1287	136	21	equation	equation	NOUN
cana-1287	136	22	models	model	VERB
cana-1287	136	23	the	the	DET
cana-1287	136	24	relationship	relationship	NOUN
cana-1287	136	25	between	between	ADP
cana-1287	136	26	the	the	DET
cana-1287	136	27	predictor	predictor	NOUN
cana-1287	136	28	x	x	PUNCT
cana-1287	136	29	and	and	CCONJ
cana-1287	136	30	the	the	DET
cana-1287	136	31	response	response	NOUN
cana-1287	136	32	variable	variable	ADJ
cana-1287	136	33	y	y	PROPN
cana-1287	136	34	as	as	ADP
cana-1287	136	35	a	a	DET
cana-1287	136	36	quadratic	quadratic	ADJ
cana-1287	136	37	function	function	NOUN
cana-1287	136	38	.	.	PUNCT
cana-1287	137	1	2	2	X
cana-1287	137	2	.	.	X
cana-1287	137	3	polynomial	polynomial	ADJ
cana-1287	137	4	regression	regression	NOUN
cana-1287	137	5	(	(	PUNCT
cana-1287	137	6	cubic	cubic	ADJ
cana-1287	137	7	):	):	PUNCT
cana-1287	137	8	2	2	NUM
cana-1287	137	9	3	3	NUM
cana-1287	137	10	0	0	NUM
cana-1287	137	11	1	1	NUM
cana-1287	137	12	2	2	NUM
cana-1287	137	13	3	3	NUM
cana-1287	137	14	y	y	NOUN
cana-1287	137	15	x	x	PUNCT
cana-1287	137	16	x	x	PUNCT
cana-1287	137	17	x	x	PROPN
cana-1287	137	18			PROPN
cana-1287	137	19			PROPN
cana-1287	137	20			PROPN
cana-1287	137	21	=	=	NOUN
cana-1287	137	22	+	+	PUNCT
cana-1287	138	1	+	+	PUNCT
cana-1287	138	2	+	+	CCONJ
cana-1287	138	3	+	+	CCONJ
cana-1287	138	4	extends	extend	VERB
cana-1287	138	5	the	the	DET
cana-1287	138	6	quadratic	quadratic	ADJ
cana-1287	138	7	model	model	NOUN
cana-1287	138	8	by	by	ADP
cana-1287	138	9	adding	add	VERB
cana-1287	138	10	a	a	DET
cana-1287	138	11	cubic	cubic	ADJ
cana-1287	138	12	term	term	NOUN
cana-1287	138	13	to	to	PART
cana-1287	138	14	capture	capture	VERB
cana-1287	138	15	more	more	ADJ
cana-1287	138	16	complex	complex	ADJ
cana-1287	138	17	relationships	relationship	NOUN
cana-1287	138	18	.	.	PUNCT
cana-1287	139	1	3	3	X
cana-1287	139	2	.	.	X
cana-1287	139	3	exponential	exponential	ADJ
cana-1287	139	4	regression	regression	NOUN
cana-1287	139	5	:	:	PUNCT
cana-1287	139	6	1	1	NUM
cana-1287	139	7	0	0	NUM
cana-1287	139	8	x	x	SYM
cana-1287	139	9	y	y	NOUN
cana-1287	139	10	e	e	ADP
cana-1287	139	11	=	=	PROPN
cana-1287	140	1	+	+	NOUN
cana-1287	140	2			NOUN
cana-1287	140	3	models	model	VERB
cana-1287	140	4	exponential	exponential	ADJ
cana-1287	140	5	growth	growth	NOUN
cana-1287	140	6	or	or	CCONJ
cana-1287	140	7	decay	decay	NOUN
cana-1287	140	8	,	,	PUNCT
cana-1287	140	9	where	where	SCONJ
cana-1287	140	10	β1\beta_1β1	β1\beta_1β1	PROPN
cana-1287	140	11	represents	represent	VERB
cana-1287	140	12	the	the	DET
cana-1287	140	13	growth	growth	NOUN
cana-1287	140	14	rate	rate	NOUN
cana-1287	140	15	.	.	PUNCT
cana-1287	141	1	4	4	X
cana-1287	141	2	.	.	X
cana-1287	141	3	logarithmic	logarithmic	ADJ
cana-1287	141	4	regression	regression	NOUN
cana-1287	141	5	:	:	PUNCT
cana-1287	141	6	0	0	NUM
cana-1287	141	7	1	1	NUM
cana-1287	141	8	ln	ln	ADJ
cana-1287	141	9	(	(	PUNCT
cana-1287	141	10	)	)	PUNCT
cana-1287	141	11	y	y	PROPN
cana-1287	141	12	x	x	PROPN
cana-1287	142	1	=	=	PROPN
cana-1287	143	1	+	+	CCONJ
cana-1287	144	1	+	+	ADJ
cana-1287	144	2			NOUN
cana-1287	144	3	useful	useful	ADJ
cana-1287	144	4	for	for	ADP
cana-1287	144	5	modeling	model	VERB
cana-1287	144	6	relationships	relationship	NOUN
cana-1287	144	7	where	where	SCONJ
cana-1287	144	8	the	the	DET
cana-1287	144	9	effect	effect	NOUN
cana-1287	144	10	of	of	ADP
cana-1287	144	11	x	x	PUNCT
cana-1287	144	12	diminishes	diminish	VERB
cana-1287	144	13	as	as	ADP
cana-1287	144	14	x	x	NOUN
cana-1287	144	15	increases	increase	NOUN
cana-1287	144	16	.	.	PUNCT
cana-1287	145	1	5	5	X
cana-1287	145	2	.	.	X
cana-1287	145	3	power	power	NOUN
cana-1287	145	4	law	law	NOUN
cana-1287	145	5	regression	regression	NOUN
cana-1287	145	6	:	:	PUNCT
cana-1287	145	7	1	1	NUM
cana-1287	145	8	0y	0y	NOUN
cana-1287	145	9	x	x	X
cana-1287	145	10	=	=	PROPN
cana-1287	145	11	+	+	NOUN
cana-1287	145	12	ò	ò	PROPN
cana-1287	145	13	models	model	VERB
cana-1287	145	14	relationships	relationship	NOUN
cana-1287	145	15	where	where	SCONJ
cana-1287	145	16	y	y	PROPN
cana-1287	145	17	is	be	AUX
cana-1287	145	18	proportional	proportional	ADJ
cana-1287	145	19	to	to	ADP
cana-1287	145	20	a	a	DET
cana-1287	145	21	power	power	NOUN
cana-1287	145	22	of	of	ADP
cana-1287	145	23	x.	x.	NOUN
cana-1287	145	24	6	6	NUM
cana-1287	145	25	.	.	PUNCT
cana-1287	145	26	rational	rational	ADJ
cana-1287	145	27	function	function	NOUN
cana-1287	145	28	regression	regression	NOUN
cana-1287	145	29	:	:	PUNCT
cana-1287	145	30	0	0	NUM
cana-1287	145	31	1	1	NUM
cana-1287	145	32	21	21	NUM
cana-1287	145	33	x	x	SYM
cana-1287	145	34	y	y	NOUN
cana-1287	145	35	x	x	PROPN
cana-1287	145	36			PROPN
cana-1287	145	37			PROPN
cana-1287	145	38			PROPN
cana-1287	145	39	+	+	CCONJ
cana-1287	145	40	=	=	PUNCT
cana-1287	146	1	+	+	CCONJ
cana-1287	147	1	+	+	CCONJ
cana-1287	147	2	ò	ò	NOUN
cana-1287	147	3	useful	useful	ADJ
cana-1287	147	4	for	for	ADP
cana-1287	147	5	modeling	model	VERB
cana-1287	147	6	relationships	relationship	NOUN
cana-1287	147	7	that	that	PRON
cana-1287	147	8	asymptotically	asymptotically	ADV
cana-1287	147	9	approach	approach	VERB
cana-1287	147	10	a	a	DET
cana-1287	147	11	limit	limit	NOUN
cana-1287	147	12	.	.	PUNCT
cana-1287	148	1	communications	communication	NOUN
cana-1287	148	2	on	on	ADP
cana-1287	148	3	applied	apply	VERB
cana-1287	148	4	nonlinear	nonlinear	ADJ
cana-1287	148	5	analysis	analysis	NOUN
cana-1287	148	6	issn	issn	NOUN
cana-1287	148	7	:	:	PUNCT
cana-1287	148	8	1074	1074	NUM
cana-1287	148	9	-	-	PUNCT
cana-1287	148	10	133x	133x	NUM
cana-1287	148	11	vol	vol	NOUN
cana-1287	148	12	31	31	NUM
cana-1287	148	13	no	no	NOUN
cana-1287	148	14	.	.	PUNCT
cana-1287	149	1	7s	7	NOUN
cana-1287	149	2	(	(	PUNCT
cana-1287	149	3	2024	2024	NUM
cana-1287	149	4	)	)	PUNCT
cana-1287	149	5	99	99	NUM
cana-1287	150	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	150	2	7	7	X
cana-1287	150	3	.	.	PUNCT
cana-1287	150	4	gaussian	gaussian	ADJ
cana-1287	150	5	process	process	NOUN
cana-1287	150	6	regression	regression	NOUN
cana-1287	150	7	:	:	PUNCT
cana-1287	150	8	(	(	PUNCT
cana-1287	150	9	)	)	PUNCT
cana-1287	150	10	y	y	PROPN
cana-1287	150	11	x=	x=	PUNCT
cana-1287	151	1	+	+	ADJ
cana-1287	151	2	ò	ò	X
cana-1287	151	3	where	where	SCONJ
cana-1287	151	4	(	(	PUNCT
cana-1287	151	5	)	)	PUNCT
cana-1287	151	6	(	(	PUNCT
cana-1287	151	7	,	,	PUNCT
cana-1287	151	8	)	)	PUNCT
cana-1287	151	9	d	d	NOUN
cana-1287	151	10	x	x	PUNCT
cana-1287	151	11	x	x	PUNCT
cana-1287	151	12	x	x	SYM
cana-1287	151	13	x	x	X
cana-1287	151	14	x	x	X
cana-1287	151	15			VERB
cana-1287	151	16	−	−	NOUN
cana-1287	151	17			CCONJ
cana-1287	151	18	=	=	PROPN
cana-1287	151	19			PUNCT
cana-1287	151	20	uses	use	VERB
cana-1287	151	21	a	a	DET
cana-1287	151	22	kernel	kernel	NOUN
cana-1287	151	23	function	function	NOUN
cana-1287	151	24	κ\kappaκ	κ\kappaκ	NOUN
cana-1287	151	25	to	to	PART
cana-1287	151	26	model	model	VERB
cana-1287	151	27	the	the	DET
cana-1287	151	28	mean	mean	ADJ
cana-1287	151	29	function	function	NOUN
cana-1287	151	30	μ(x	μ(x	NOUN
cana-1287	151	31	)	)	PUNCT
cana-1287	151	32	and	and	CCONJ
cana-1287	151	33	capture	capture	VERB
cana-1287	151	34	non	non	ADJ
cana-1287	151	35	-	-	ADJ
cana-1287	151	36	linear	linear	ADJ
cana-1287	151	37	relationships	relationship	NOUN
cana-1287	151	38	.	.	PUNCT
cana-1287	152	1	8	8	X
cana-1287	152	2	.	.	X
cana-1287	152	3	sigmoid	sigmoid	NOUN
cana-1287	152	4	function	function	NOUN
cana-1287	152	5	regression	regression	NOUN
cana-1287	152	6	:	:	PUNCT
cana-1287	152	7	0	0	NUM
cana-1287	152	8	1	1	NUM
cana-1287	152	9	(	(	PUNCT
cana-1287	152	10	)	)	PUNCT
cana-1287	152	11	1	1	NUM
cana-1287	152	12	1	1	NUM
cana-1287	152	13	x	x	SYM
cana-1287	152	14	y	y	PROPN
cana-1287	152	15	e	e	PROPN
cana-1287	152	16			PROPN
cana-1287	152	17	−	−	PROPN
cana-1287	152	18	+	+	X
cana-1287	152	19	=	=	PUNCT
cana-1287	153	1	+	+	CCONJ
cana-1287	153	2	+	+	NUM
cana-1287	153	3	ò	ò	PROPN
cana-1287	153	4	models	model	VERB
cana-1287	153	5	relationships	relationship	NOUN
cana-1287	153	6	with	with	ADP
cana-1287	153	7	an	an	DET
cana-1287	153	8	s	s	ADV
cana-1287	153	9	-	-	PUNCT
cana-1287	153	10	shaped	shape	VERB
cana-1287	153	11	curve	curve	NOUN
cana-1287	153	12	,	,	PUNCT
cana-1287	153	13	useful	useful	ADJ
cana-1287	153	14	for	for	ADP
cana-1287	153	15	binary	binary	ADJ
cana-1287	153	16	classification	classification	NOUN
cana-1287	153	17	and	and	CCONJ
cana-1287	153	18	probabilities	probability	NOUN
cana-1287	153	19	.	.	PUNCT
cana-1287	154	1	9	9	X
cana-1287	154	2	.	.	X
cana-1287	154	3	radial	radial	ADJ
cana-1287	154	4	basis	basis	NOUN
cana-1287	154	5	function	function	NOUN
cana-1287	154	6	(	(	PUNCT
cana-1287	154	7	rbf	rbf	PROPN
cana-1287	154	8	)	)	PUNCT
cana-1287	154	9	regression	regression	NOUN
cana-1287	154	10	:	:	PUNCT
cana-1287	154	11	0	0	NUM
cana-1287	154	12	1	1	NUM
cana-1287	154	13	(	(	PUNCT
cana-1287	154	14	)	)	PUNCT
cana-1287	154	15	n	n	CCONJ
cana-1287	155	1	i	i	PRON
cana-1287	156	1	i	i	PRON
cana-1287	157	1	i	i	VERB
cana-1287	157	2	y	y	VERB
cana-1287	157	3	x	x	SYM
cana-1287	157	4	x	x	PROPN
cana-1287	157	5			PROPN
cana-1287	157	6			NOUN
cana-1287	157	7	=	=	PUNCT
cana-1287	157	8	=	=	PUNCT
cana-1287	158	1	−	−	PROPN
cana-1287	159	1	+	+	CCONJ
cana-1287	159	2	+	+	ADJ
cana-1287	159	3			X
cana-1287	159	4	ò‖	ò‖	NOUN
cana-1287	159	5	‖	‖	PROPN
cana-1287	159	6	where	where	SCONJ
cana-1287	159	7	2	2	NUM
cana-1287	159	8	(	(	PUNCT
cana-1287	159	9	)	)	PUNCT
cana-1287	159	10	ix	ix	ADP
cana-1287	159	11	x	x	X
cana-1287	159	12	ix	ix	PROPN
cana-1287	159	13	x	x	X
cana-1287	159	14	e	e	NOUN
cana-1287	159	15			NOUN
cana-1287	159	16	−	−	PROPN
cana-1287	159	17	−	−	NOUN
cana-1287	159	18	−	−	PROPN
cana-1287	159	19	=	=	SYM
cana-1287	159	20	‖	‖	PROPN
cana-1287	159	21	‖	‖	PROPN
cana-1287	159	22	‖	‖	PROPN
cana-1287	159	23	‖	‖	PROPN
cana-1287	159	24	is	be	AUX
cana-1287	159	25	the	the	DET
cana-1287	159	26	gaussian	gaussian	ADJ
cana-1287	159	27	radial	radial	ADJ
cana-1287	159	28	basis	basis	NOUN
cana-1287	159	29	function	function	NOUN
cana-1287	159	30	.	.	PUNCT
cana-1287	160	1	10	10	NUM
cana-1287	160	2	.	.	X
cana-1287	161	1	piecewise	piecewise	NOUN
cana-1287	161	2	regression	regression	NOUN
cana-1287	161	3	:	:	PUNCT
cana-1287	161	4	0,1	0,1	NUM
cana-1287	161	5	1,1	1,1	NUM
cana-1287	161	6	0,2	0,2	NUM
cana-1287	161	7	1,2	1,2	NUM
cana-1287	161	8	for	for	ADP
cana-1287	161	9	for	for	ADP
cana-1287	161	10	x	x	SYM
cana-1287	161	11	x	x	X
cana-1287	161	12	c	c	NOUN
cana-1287	161	13	y	y	NOUN
cana-1287	161	14	x	x	PUNCT
cana-1287	161	15	x	x	SYM
cana-1287	161	16	c	c	PROPN
cana-1287	161	17			PROPN
cana-1287	161	18			PROPN
cana-1287	161	19			PROPN
cana-1287	161	20			PROPN
cana-1287	161	21	+	+	CCONJ
cana-1287	161	22			NOUN
cana-1287	161	23	=	=	SYM
cana-1287	162	1	+	+	NOUN
cana-1287	162	2			NUM
cana-1287	162	3	+	+	CCONJ
cana-1287	162	4			PROPN
cana-1287	162	5	ò	ò	ADP
cana-1287	162	6	dimensionality	dimensionality	NOUN
cana-1287	162	7	reduction	reduction	NOUN
cana-1287	162	8	and	and	CCONJ
cana-1287	162	9	normalization	normalization	NOUN
cana-1287	162	10	:	:	PUNCT
cana-1287	162	11	moreover	moreover	ADV
cana-1287	162	12	,	,	PUNCT
cana-1287	162	13	normalizing	normalize	VERB
cana-1287	162	14	guarantees	guarantee	NOUN
cana-1287	162	15	that	that	SCONJ
cana-1287	162	16	the	the	DET
cana-1287	162	17	acquired	acquire	VERB
cana-1287	162	18	features	feature	NOUN
cana-1287	162	19	have	have	VERB
cana-1287	162	20	a	a	DET
cana-1287	162	21	constant	constant	ADJ
cana-1287	162	22	scale	scale	NOUN
cana-1287	162	23	,	,	PUNCT
cana-1287	162	24	which	which	PRON
cana-1287	162	25	facilitates	facilitate	VERB
cana-1287	162	26	better	well	ADJ
cana-1287	162	27	integration	integration	NOUN
cana-1287	162	28	with	with	ADP
cana-1287	162	29	the	the	DET
cana-1287	162	30	deep	deep	ADJ
cana-1287	162	31	radial	radial	ADJ
cana-1287	162	32	basis	basis	NOUN
cana-1287	162	33	function	function	NOUN
cana-1287	162	34	(	(	PUNCT
cana-1287	162	35	rbf	rbf	PROPN
cana-1287	162	36	)	)	PUNCT
cana-1287	162	37	network	network	NOUN
cana-1287	162	38	for	for	ADP
cana-1287	162	39	classification	classification	NOUN
cana-1287	162	40	.	.	PUNCT
cana-1287	163	1	non	non	ADJ
cana-1287	163	2	-	-	ADJ
cana-1287	163	3	linear	linear	ADJ
cana-1287	163	4	regression	regression	NOUN
cana-1287	163	5	for	for	ADP
cana-1287	163	6	feature	feature	NOUN
cana-1287	163	7	extraction	extraction	NOUN
cana-1287	163	8	translates	translate	VERB
cana-1287	163	9	raw	raw	ADJ
cana-1287	163	10	image	image	NOUN
cana-1287	163	11	data	datum	NOUN
cana-1287	163	12	into	into	ADP
cana-1287	163	13	a	a	DET
cana-1287	163	14	meaningful	meaningful	ADJ
cana-1287	163	15	set	set	NOUN
cana-1287	163	16	of	of	ADP
cana-1287	163	17	features	feature	NOUN
cana-1287	163	18	by	by	ADP
cana-1287	163	19	way	way	NOUN
cana-1287	163	20	of	of	ADP
cana-1287	163	21	complex	complex	ADJ
cana-1287	163	22	,	,	PUNCT
cana-1287	163	23	non	non	ADJ
cana-1287	163	24	-	-	ADJ
cana-1287	163	25	linear	linear	ADJ
cana-1287	163	26	connections	connection	NOUN
cana-1287	163	27	between	between	ADP
cana-1287	163	28	soil	soil	NOUN
cana-1287	163	29	conditions	condition	NOUN
cana-1287	163	30	and	and	CCONJ
cana-1287	163	31	image	image	NOUN
cana-1287	163	32	attributes	attribute	NOUN
cana-1287	163	33	.	.	PUNCT
cana-1287	164	1	more	more	ADJ
cana-1287	164	2	exact	exact	ADJ
cana-1287	164	3	estimates	estimate	NOUN
cana-1287	164	4	of	of	ADP
cana-1287	164	5	soil	soil	NOUN
cana-1287	164	6	nitrogen	nitrogen	NOUN
cana-1287	164	7	levels	level	NOUN
cana-1287	164	8	follow	follow	VERB
cana-1287	164	9	from	from	ADP
cana-1287	164	10	this	this	DET
cana-1287	164	11	enhanced	enhance	VERB
cana-1287	164	12	performance	performance	NOUN
cana-1287	164	13	of	of	ADP
cana-1287	164	14	the	the	DET
cana-1287	164	15	deep	deep	ADJ
cana-1287	164	16	rbf	rbf	PROPN
cana-1287	164	17	network	network	PROPN
cana-1287	164	18	.	.	PUNCT
cana-1287	165	1	table	table	NOUN
cana-1287	165	2	2	2	NUM
cana-1287	165	3	:	:	PUNCT
cana-1287	165	4	performance	performance	NOUN
cana-1287	165	5	metrics	metric	NOUN
cana-1287	165	6	for	for	ADP
cana-1287	165	7	non	non	ADJ
cana-1287	165	8	-	-	ADJ
cana-1287	165	9	linear	linear	ADJ
cana-1287	165	10	regression	regression	NOUN
cana-1287	165	11	models	model	NOUN
cana-1287	165	12	model	model	VERB
cana-1287	165	13	dataset	dataset	PROPN
cana-1287	165	14	mse	mse	PROPN
cana-1287	165	15	accuracy	accuracy	NOUN
cana-1287	165	16	(	(	PUNCT
cana-1287	165	17	%	%	INTJ
cana-1287	165	18	)	)	PUNCT
cana-1287	165	19	polynomial	polynomial	ADJ
cana-1287	165	20	regression	regression	NOUN
cana-1287	165	21	(	(	PUNCT
cana-1287	165	22	quadratic	quadratic	ADJ
cana-1287	165	23	)	)	PUNCT
cana-1287	165	24	training	training	NOUN
cana-1287	165	25	0.045	0.045	NUM
cana-1287	165	26	82.5	82.5	NUM
cana-1287	165	27	testing	testing	NOUN
cana-1287	165	28	0.048	0.048	NUM
cana-1287	165	29	80.0	80.0	NUM
cana-1287	165	30	validation	validation	NOUN
cana-1287	165	31	0.050	0.050	NUM
cana-1287	165	32	78.5	78.5	NUM
cana-1287	165	33	polynomial	polynomial	ADJ
cana-1287	165	34	regression	regression	NOUN
cana-1287	165	35	(	(	PUNCT
cana-1287	165	36	cubic	cubic	ADJ
cana-1287	165	37	)	)	PUNCT
cana-1287	165	38	training	training	NOUN
cana-1287	165	39	0.040	0.040	NUM
cana-1287	165	40	85.0	85.0	NUM
cana-1287	165	41	testing	testing	NOUN
cana-1287	165	42	0.042	0.042	NUM
cana-1287	165	43	83.0	83.0	NUM
cana-1287	165	44	validation	validation	NOUN
cana-1287	165	45	0.045	0.045	NUM
cana-1287	165	46	81.0	81.0	NUM
cana-1287	165	47	exponential	exponential	ADJ
cana-1287	165	48	regression	regression	NOUN
cana-1287	165	49	training	train	VERB
cana-1287	165	50	0.048	0.048	NUM
cana-1287	165	51	80.0	80.0	NUM
cana-1287	165	52	testing	testing	NOUN
cana-1287	165	53	0.050	0.050	NUM
cana-1287	165	54	78.0	78.0	NUM
cana-1287	165	55	validation	validation	NOUN
cana-1287	165	56	0.052	0.052	NUM
cana-1287	165	57	76.5	76.5	NUM
cana-1287	165	58	logarithmic	logarithmic	ADJ
cana-1287	165	59	regression	regression	NOUN
cana-1287	165	60	training	training	NOUN
cana-1287	165	61	0.042	0.042	NUM
cana-1287	165	62	83.0	83.0	NUM
cana-1287	165	63	testing	testing	NOUN
cana-1287	165	64	0.045	0.045	NUM
cana-1287	165	65	81.0	81.0	NUM
cana-1287	165	66	communications	communication	NOUN
cana-1287	165	67	on	on	ADP
cana-1287	165	68	applied	apply	VERB
cana-1287	165	69	nonlinear	nonlinear	ADJ
cana-1287	165	70	analysis	analysis	NOUN
cana-1287	165	71	issn	issn	NOUN
cana-1287	165	72	:	:	PUNCT
cana-1287	165	73	1074	1074	NUM
cana-1287	165	74	-	-	PUNCT
cana-1287	165	75	133x	133x	NUM
cana-1287	165	76	vol	vol	NOUN
cana-1287	165	77	31	31	NUM
cana-1287	165	78	no	no	NOUN
cana-1287	165	79	.	.	PUNCT
cana-1287	166	1	7s	7	NOUN
cana-1287	166	2	(	(	PUNCT
cana-1287	166	3	2024	2024	NUM
cana-1287	166	4	)	)	PUNCT
cana-1287	166	5	100	100	NUM
cana-1287	167	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	167	2	validation	validation	NOUN
cana-1287	167	3	0.047	0.047	NUM
cana-1287	167	4	79.5	79.5	NUM
cana-1287	167	5	power	power	NOUN
cana-1287	167	6	law	law	NOUN
cana-1287	167	7	regression	regression	NOUN
cana-1287	167	8	training	train	VERB
cana-1287	167	9	0.046	0.046	NUM
cana-1287	167	10	81.5	81.5	NUM
cana-1287	167	11	testing	test	VERB
cana-1287	167	12	0.048	0.048	NUM
cana-1287	167	13	80.0	80.0	NUM
cana-1287	167	14	validation	validation	NOUN
cana-1287	167	15	0.050	0.050	NUM
cana-1287	167	16	78.0	78.0	NUM
cana-1287	167	17	rational	rational	ADJ
cana-1287	167	18	function	function	NOUN
cana-1287	167	19	regression	regression	NOUN
cana-1287	167	20	training	train	VERB
cana-1287	167	21	0.043	0.043	NUM
cana-1287	167	22	82.0	82.0	NUM
cana-1287	167	23	testing	testing	NOUN
cana-1287	167	24	0.045	0.045	NUM
cana-1287	167	25	80.5	80.5	NUM
cana-1287	167	26	validation	validation	NOUN
cana-1287	167	27	0.047	0.047	NUM
cana-1287	167	28	78.5	78.5	NUM
cana-1287	167	29	gaussian	gaussian	ADJ
cana-1287	167	30	process	process	NOUN
cana-1287	167	31	regression	regression	NOUN
cana-1287	167	32	training	training	NOUN
cana-1287	167	33	0.039	0.039	NUM
cana-1287	167	34	84.5	84.5	NUM
cana-1287	167	35	testing	test	VERB
cana-1287	167	36	0.041	0.041	NUM
cana-1287	167	37	82.0	82.0	NUM
cana-1287	167	38	validation	validation	NOUN
cana-1287	167	39	0.043	0.043	NUM
cana-1287	167	40	80.0	80.0	NUM
cana-1287	167	41	sigmoid	sigmoid	NOUN
cana-1287	167	42	function	function	NOUN
cana-1287	167	43	regression	regression	NOUN
cana-1287	167	44	training	train	VERB
cana-1287	167	45	0.047	0.047	NUM
cana-1287	167	46	79.5	79.5	NUM
cana-1287	167	47	testing	testing	NOUN
cana-1287	167	48	0.049	0.049	NUM
cana-1287	167	49	77.5	77.5	NUM
cana-1287	167	50	validation	validation	NOUN
cana-1287	167	51	0.051	0.051	NUM
cana-1287	167	52	75.0	75.0	NUM
cana-1287	167	53	rbf	rbf	PROPN
cana-1287	167	54	regression	regression	NOUN
cana-1287	167	55	training	train	VERB
cana-1287	167	56	0.038	0.038	NUM
cana-1287	167	57	85.5	85.5	NUM
cana-1287	167	58	testing	testing	NOUN
cana-1287	167	59	0.040	0.040	NUM
cana-1287	167	60	83.5	83.5	NUM
cana-1287	167	61	validation	validation	NOUN
cana-1287	167	62	0.042	0.042	NUM
cana-1287	167	63	81.0	81.0	NUM
cana-1287	167	64	piecewise	piecewise	NOUN
cana-1287	167	65	regression	regression	NOUN
cana-1287	167	66	training	train	VERB
cana-1287	167	67	0.044	0.044	NUM
cana-1287	167	68	82.5	82.5	NUM
cana-1287	167	69	testing	testing	NOUN
cana-1287	167	70	0.046	0.046	NUM
cana-1287	167	71	80.0	80.0	NUM
cana-1287	167	72	validation	validation	NOUN
cana-1287	167	73	0.048	0.048	NUM
cana-1287	167	74	78.5	78.5	NUM
cana-1287	167	75	among	among	ADP
cana-1287	167	76	the	the	DET
cana-1287	167	77	non	non	ADJ
cana-1287	167	78	-	-	ADJ
cana-1287	167	79	linear	linear	ADJ
cana-1287	167	80	regression	regression	NOUN
cana-1287	167	81	models	model	NOUN
cana-1287	167	82	evaluated	evaluate	VERB
cana-1287	167	83	in	in	ADP
cana-1287	167	84	table	table	NOUN
cana-1287	167	85	2	2	NUM
cana-1287	167	86	,	,	PUNCT
cana-1287	167	87	radial	radial	ADJ
cana-1287	167	88	basis	basis	NOUN
cana-1287	167	89	function	function	NOUN
cana-1287	167	90	(	(	PUNCT
cana-1287	167	91	rbf	rbf	PROPN
cana-1287	167	92	)	)	PUNCT
cana-1287	167	93	regression	regression	NOUN
cana-1287	167	94	consistently	consistently	ADV
cana-1287	167	95	demonstrates	demonstrate	VERB
cana-1287	167	96	the	the	DET
cana-1287	167	97	lowest	low	ADJ
cana-1287	167	98	mean	mean	NOUN
cana-1287	167	99	squared	square	VERB
cana-1287	167	100	error	error	NOUN
cana-1287	167	101	(	(	PUNCT
cana-1287	167	102	mse	mse	NOUN
cana-1287	167	103	)	)	PUNCT
cana-1287	167	104	and	and	CCONJ
cana-1287	167	105	highest	high	ADJ
cana-1287	167	106	accuracy	accuracy	NOUN
cana-1287	167	107	throughout	throughout	ADP
cana-1287	167	108	all	all	DET
cana-1287	167	109	datasets	dataset	NOUN
cana-1287	167	110	.	.	PUNCT
cana-1287	168	1	with	with	ADP
cana-1287	168	2	an	an	DET
cana-1287	168	3	mse	mse	NOUN
cana-1287	168	4	of	of	ADP
cana-1287	168	5	0.038	0.038	NUM
cana-1287	168	6	in	in	ADP
cana-1287	168	7	training	training	NOUN
cana-1287	168	8	and	and	CCONJ
cana-1287	168	9	0.040	0.040	NUM
cana-1287	168	10	in	in	ADP
cana-1287	168	11	testing	testing	NOUN
cana-1287	168	12	and	and	CCONJ
cana-1287	168	13	accuracy	accuracy	NOUN
cana-1287	168	14	of	of	ADP
cana-1287	168	15	85.5	85.5	NUM
cana-1287	168	16	%	%	NOUN
cana-1287	168	17	and	and	CCONJ
cana-1287	168	18	83.5	83.5	NUM
cana-1287	168	19	%	%	NOUN
cana-1287	168	20	respective	respective	ADJ
cana-1287	168	21	,	,	PUNCT
cana-1287	168	22	the	the	DET
cana-1287	168	23	rbf	rbf	PROPN
cana-1287	168	24	model	model	PROPN
cana-1287	168	25	fares	fare	NOUN
cana-1287	168	26	remarkably	remarkably	ADV
cana-1287	168	27	well	well	ADV
cana-1287	168	28	in	in	ADP
cana-1287	168	29	identifying	identify	VERB
cana-1287	168	30	complex	complex	ADJ
cana-1287	168	31	relationships	relationship	NOUN
cana-1287	168	32	in	in	ADP
cana-1287	168	33	the	the	DET
cana-1287	168	34	data	datum	NOUN
cana-1287	168	35	.	.	PUNCT
cana-1287	169	1	this	this	PRON
cana-1287	169	2	implies	imply	VERB
cana-1287	169	3	rather	rather	ADV
cana-1287	169	4	good	good	ADJ
cana-1287	169	5	feature	feature	NOUN
cana-1287	169	6	extraction	extraction	NOUN
cana-1287	169	7	quality	quality	NOUN
cana-1287	169	8	.	.	PUNCT
cana-1287	170	1	gaussian	gaussian	ADJ
cana-1287	170	2	process	process	NOUN
cana-1287	170	3	regression	regression	NOUN
cana-1287	170	4	likewise	likewise	ADV
cana-1287	170	5	performs	perform	VERB
cana-1287	170	6	rather	rather	ADV
cana-1287	170	7	well	well	ADV
cana-1287	170	8	with	with	ADP
cana-1287	170	9	second	second	ADV
cana-1287	170	10	lowest	low	ADJ
cana-1287	170	11	mse	mse	NOUN
cana-1287	170	12	of	of	ADP
cana-1287	170	13	0.039	0.039	NUM
cana-1287	170	14	in	in	ADP
cana-1287	170	15	training	training	NOUN
cana-1287	170	16	and	and	CCONJ
cana-1287	170	17	0.041	0.041	NUM
cana-1287	170	18	in	in	ADP
cana-1287	170	19	testing	testing	NOUN
cana-1287	170	20	and	and	CCONJ
cana-1287	170	21	accuracy	accuracy	NOUN
cana-1287	170	22	of	of	ADP
cana-1287	170	23	84.5	84.5	NUM
cana-1287	170	24	%	%	NOUN
cana-1287	170	25	and	and	CCONJ
cana-1287	170	26	82.0	82.0	NUM
cana-1287	170	27	%	%	NOUN
cana-1287	170	28	,	,	PUNCT
cana-1287	170	29	respectively	respectively	ADV
cana-1287	170	30	.	.	PUNCT
cana-1287	171	1	since	since	SCONJ
cana-1287	171	2	this	this	DET
cana-1287	171	3	model	model	NOUN
cana-1287	171	4	provides	provide	VERB
cana-1287	171	5	flexibility	flexibility	NOUN
cana-1287	171	6	in	in	ADP
cana-1287	171	7	non	non	ADJ
cana-1287	171	8	-	-	ADJ
cana-1287	171	9	linear	linear	ADJ
cana-1287	171	10	relationship	relationship	NOUN
cana-1287	171	11	modeling	modeling	NOUN
cana-1287	171	12	,	,	PUNCT
cana-1287	171	13	its	its	PRON
cana-1287	171	14	probabilistic	probabilistic	ADJ
cana-1287	171	15	approach	approach	NOUN
cana-1287	171	16	is	be	AUX
cana-1287	171	17	what	what	PRON
cana-1287	171	18	gives	give	VERB
cana-1287	171	19	it	it	PRON
cana-1287	171	20	strength	strength	NOUN
cana-1287	171	21	.	.	PUNCT
cana-1287	172	1	polynom	polynom	PROPN
cana-1287	172	2	regression	regression	PROPN
cana-1287	172	3	(	(	PUNCT
cana-1287	172	4	cubic	cubic	NOUN
cana-1287	172	5	)	)	PUNCT
cana-1287	172	6	shows	show	VERB
cana-1287	172	7	good	good	ADJ
cana-1287	172	8	performance	performance	NOUN
cana-1287	172	9	demonstrating	demonstrate	VERB
cana-1287	172	10	its	its	PRON
cana-1287	172	11	efficacy	efficacy	NOUN
cana-1287	172	12	in	in	ADP
cana-1287	172	13	caputreing	caputree	VERB
cana-1287	172	14	more	more	ADJ
cana-1287	172	15	complex	complex	ADJ
cana-1287	172	16	patterns	pattern	NOUN
cana-1287	172	17	than	than	ADP
cana-1287	172	18	quadratic	quadratic	ADJ
cana-1287	172	19	regression	regression	NOUN
cana-1287	172	20	with	with	ADP
cana-1287	172	21	accuracy	accuracy	NOUN
cana-1287	172	22	of	of	ADP
cana-1287	172	23	85.0	85.0	NUM
cana-1287	172	24	%	%	NOUN
cana-1287	172	25	and	and	CCONJ
cana-1287	172	26	83.0	83.0	NUM
cana-1287	172	27	%	%	NOUN
cana-1287	172	28	with	with	ADP
cana-1287	172	29	mse	mse	NOUN
cana-1287	172	30	values	value	NOUN
cana-1287	172	31	of	of	ADP
cana-1287	172	32	0.040	0.040	NUM
cana-1287	172	33	in	in	ADP
cana-1287	172	34	training	training	NOUN
cana-1287	172	35	and	and	CCONJ
cana-1287	172	36	0.042	0.042	NUM
cana-1287	172	37	in	in	ADP
cana-1287	172	38	testing	testing	NOUN
cana-1287	172	39	.	.	PUNCT
cana-1287	173	1	particularly	particularly	ADV
cana-1287	173	2	in	in	ADP
cana-1287	173	3	validation	validation	NOUN
cana-1287	173	4	datasets	dataset	NOUN
cana-1287	173	5	,	,	PUNCT
cana-1287	173	6	models	model	NOUN
cana-1287	173	7	such	such	ADJ
cana-1287	173	8	sigmoid	sigmoid	NOUN
cana-1287	173	9	function	function	NOUN
cana-1287	173	10	regression	regression	NOUN
cana-1287	173	11	and	and	CCONJ
cana-1287	173	12	exponential	exponential	ADJ
cana-1287	173	13	regression	regression	NOUN
cana-1287	173	14	show	show	VERB
cana-1287	173	15	lower	low	ADJ
cana-1287	173	16	accuracy	accuracy	NOUN
cana-1287	173	17	and	and	CCONJ
cana-1287	173	18	increased	increase	VERB
cana-1287	173	19	mse	mse	NOUN
cana-1287	173	20	,	,	PUNCT
cana-1287	173	21	which	which	PRON
cana-1287	173	22	reflects	reflect	VERB
cana-1287	173	23	their	their	PRON
cana-1287	173	24	constraints	constraint	NOUN
cana-1287	173	25	in	in	ADP
cana-1287	173	26	handling	handle	VERB
cana-1287	173	27	distinct	distinct	ADJ
cana-1287	173	28	non	non	ADJ
cana-1287	173	29	-	-	ADJ
cana-1287	173	30	linear	linear	ADJ
cana-1287	173	31	patterns	pattern	NOUN
cana-1287	173	32	compared	compare	VERB
cana-1287	173	33	to	to	ADP
cana-1287	173	34	more	more	ADV
cana-1287	173	35	flexible	flexible	ADJ
cana-1287	173	36	models	model	NOUN
cana-1287	173	37	like	like	ADP
cana-1287	173	38	rbf	rbf	PROPN
cana-1287	173	39	and	and	CCONJ
cana-1287	173	40	gaussian	gaussian	ADJ
cana-1287	173	41	processes	process	NOUN
cana-1287	173	42	.	.	PUNCT
cana-1287	174	1	3.3	3.3	NUM
cana-1287	174	2	.	.	PUNCT
cana-1287	175	1	deep	deep	ADJ
cana-1287	175	2	radial	radial	ADJ
cana-1287	175	3	basis	basis	NOUN
cana-1287	175	4	function	function	NOUN
cana-1287	175	5	(	(	PUNCT
cana-1287	175	6	rbf	rbf	PROPN
cana-1287	175	7	)	)	PUNCT
cana-1287	175	8	classification	classification	NOUN
cana-1287	175	9	deep	deep	ADJ
cana-1287	175	10	radial	radial	ADJ
cana-1287	175	11	basis	basis	NOUN
cana-1287	175	12	function	function	NOUN
cana-1287	175	13	(	(	PUNCT
cana-1287	175	14	rbf	rbf	PROPN
cana-1287	175	15	)	)	PUNCT
cana-1287	175	16	networks	network	NOUN
cana-1287	175	17	are	be	AUX
cana-1287	175	18	a	a	DET
cana-1287	175	19	specialized	specialized	ADJ
cana-1287	175	20	type	type	NOUN
cana-1287	175	21	of	of	ADP
cana-1287	175	22	neural	neural	ADJ
cana-1287	175	23	network	network	NOUN
cana-1287	175	24	meant	mean	VERB
cana-1287	175	25	to	to	PART
cana-1287	175	26	control	control	VERB
cana-1287	175	27	complex	complex	ADJ
cana-1287	175	28	,	,	PUNCT
cana-1287	175	29	non	non	ADJ
cana-1287	175	30	-	-	ADJ
cana-1287	175	31	linear	linear	ADJ
cana-1287	175	32	interactions	interaction	NOUN
cana-1287	175	33	throughout	throughout	ADP
cana-1287	175	34	a	a	DET
cana-1287	175	35	sequence	sequence	NOUN
cana-1287	175	36	of	of	ADP
cana-1287	175	37	radial	radial	ADJ
cana-1287	175	38	basis	basis	NOUN
cana-1287	175	39	functions	function	NOUN
cana-1287	175	40	.	.	PUNCT
cana-1287	176	1	rbf	rbf	PROPN
cana-1287	176	2	units	unit	NOUN
cana-1287	176	3	layered	layer	VERB
cana-1287	176	4	several	several	ADJ
cana-1287	176	5	times	time	NOUN
cana-1287	176	6	allow	allow	VERB
cana-1287	176	7	the	the	DET
cana-1287	176	8	deep	deep	ADJ
cana-1287	176	9	rbf	rbf	PROPN
cana-1287	176	10	network	network	NOUN
cana-1287	176	11	to	to	PART
cana-1287	176	12	effectively	effectively	ADV
cana-1287	176	13	mimic	mimic	VERB
cana-1287	176	14	intricate	intricate	ADJ
cana-1287	176	15	data	datum	NOUN
cana-1287	176	16	patterns	pattern	NOUN
cana-1287	176	17	.	.	PUNCT
cana-1287	177	1	it	it	PRON
cana-1287	177	2	goes	go	VERB
cana-1287	177	3	like	like	ADP
cana-1287	177	4	this	this	PRON
cana-1287	177	5	:	:	PUNCT
cana-1287	177	6	a	a	DET
cana-1287	177	7	deep	deep	ADJ
cana-1287	177	8	rbf	rbf	PROPN
cana-1287	177	9	network	network	NOUN
cana-1287	177	10	consists	consist	VERB
cana-1287	177	11	in	in	ADP
cana-1287	177	12	an	an	DET
cana-1287	177	13	input	input	NOUN
cana-1287	177	14	layer	layer	NOUN
cana-1287	177	15	,	,	PUNCT
cana-1287	177	16	numerous	numerous	ADJ
cana-1287	177	17	hidden	hidden	ADJ
cana-1287	177	18	layers	layer	NOUN
cana-1287	177	19	applying	apply	VERB
cana-1287	177	20	radial	radial	ADJ
cana-1287	177	21	basis	basis	NOUN
cana-1287	177	22	functions	function	NOUN
cana-1287	177	23	,	,	PUNCT
cana-1287	177	24	and	and	CCONJ
cana-1287	177	25	an	an	DET
cana-1287	177	26	output	output	NOUN
cana-1287	177	27	layer	layer	NOUN
cana-1287	177	28	.	.	PUNCT
cana-1287	178	1	every	every	DET
cana-1287	178	2	layer	layer	NOUN
cana-1287	178	3	gradually	gradually	ADV
cana-1287	178	4	alters	alter	VERB
cana-1287	178	5	the	the	DET
cana-1287	178	6	data	datum	NOUN
cana-1287	178	7	;	;	PUNCT
cana-1287	178	8	the	the	DET
cana-1287	178	9	rbf	rbf	PROPN
cana-1287	178	10	units	unit	NOUN
cana-1287	178	11	in	in	ADP
cana-1287	178	12	the	the	DET
cana-1287	178	13	buried	bury	VERB
cana-1287	178	14	layers	layer	NOUN
cana-1287	178	15	document	document	NOUN
cana-1287	178	16	communications	communication	NOUN
cana-1287	178	17	on	on	ADP
cana-1287	178	18	applied	apply	VERB
cana-1287	178	19	nonlinear	nonlinear	ADJ
cana-1287	178	20	analysis	analysis	NOUN
cana-1287	178	21	issn	issn	NOUN
cana-1287	178	22	:	:	PUNCT
cana-1287	178	23	1074	1074	NUM
cana-1287	178	24	-	-	PUNCT
cana-1287	178	25	133x	133x	NUM
cana-1287	178	26	vol	vol	NOUN
cana-1287	178	27	31	31	NUM
cana-1287	178	28	no	no	NOUN
cana-1287	178	29	.	.	PUNCT
cana-1287	179	1	7s	7	NOUN
cana-1287	179	2	(	(	PUNCT
cana-1287	179	3	2024	2024	NUM
cana-1287	179	4	)	)	PUNCT
cana-1287	179	5	101	101	NUM
cana-1287	180	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	180	2	certain	certain	ADJ
cana-1287	180	3	aspects	aspect	NOUN
cana-1287	180	4	of	of	ADP
cana-1287	180	5	the	the	DET
cana-1287	180	6	input	input	NOUN
cana-1287	180	7	properties	property	NOUN
cana-1287	180	8	.	.	PUNCT
cana-1287	181	1	the	the	DET
cana-1287	181	2	following	follow	VERB
cana-1287	181	3	formulas	formula	NOUN
cana-1287	181	4	enable	enable	VERB
cana-1287	181	5	one	one	NUM
cana-1287	181	6	to	to	PART
cana-1287	181	7	define	define	VERB
cana-1287	181	8	the	the	DET
cana-1287	181	9	overall	overall	ADJ
cana-1287	181	10	structure	structure	NOUN
cana-1287	181	11	:	:	PUNCT
cana-1287	181	12	1	1	X
cana-1287	181	13	.	.	PUNCT
cana-1287	181	14	input	input	NOUN
cana-1287	181	15	layer	layer	NOUN
cana-1287	181	16	:	:	PUNCT
cana-1287	181	17	receiving	receive	VERB
cana-1287	181	18	the	the	DET
cana-1287	181	19	raw	raw	ADJ
cana-1287	181	20	features	feature	NOUN
cana-1287	181	21	x	x	X
cana-1287	181	22	,	,	PUNCT
cana-1287	181	23	the	the	DET
cana-1287	181	24	input	input	NOUN
cana-1287	181	25	layer	layer	NOUN
cana-1287	181	26	transmits	transmit	VERB
cana-1287	181	27	them	they	PRON
cana-1287	181	28	to	to	ADP
cana-1287	181	29	the	the	DET
cana-1287	181	30	first	first	ADJ
cana-1287	181	31	hidden	hide	VERB
cana-1287	181	32	layer	layer	NOUN
cana-1287	181	33	.	.	PUNCT
cana-1287	182	1	let	let	VERB
cana-1287	182	2	x	x	PART
cana-1287	182	3	to	to	PART
cana-1287	182	4	be	be	AUX
cana-1287	182	5	a	a	DET
cana-1287	182	6	n	n	CCONJ
cana-1287	182	7	dimensionally	dimensionally	ADV
cana-1287	182	8	input	input	NOUN
cana-1287	182	9	vector	vector	NOUN
cana-1287	182	10	.	.	PUNCT
cana-1287	183	1	2	2	NUM
cana-1287	183	2	.	.	X
cana-1287	183	3	hidden	hide	VERB
cana-1287	183	4	layer	layer	NOUN
cana-1287	183	5	transformation	transformation	NOUN
cana-1287	183	6	:	:	PUNCT
cana-1287	183	7	every	every	DET
cana-1287	183	8	hidden	hide	VERB
cana-1287	183	9	layer	layer	NOUN
cana-1287	183	10	is	be	AUX
cana-1287	183	11	made	make	VERB
cana-1287	183	12	of	of	ADP
cana-1287	183	13	rbf	rbf	PROPN
cana-1287	183	14	units	unit	NOUN
cana-1287	183	15	,	,	PUNCT
cana-1287	183	16	which	which	PRON
cana-1287	183	17	radially	radially	ADV
cana-1287	183	18	base	base	VERB
cana-1287	183	19	the	the	DET
cana-1287	183	20	input	input	NOUN
cana-1287	183	21	characteristics	characteristic	NOUN
cana-1287	183	22	.	.	PUNCT
cana-1287	184	1	the	the	DET
cana-1287	184	2	i	i	PROPN
cana-1287	184	3	-	-	PUNCT
cana-1287	184	4	th	th	PROPN
cana-1287	184	5	rbf	rbf	PROPN
cana-1287	184	6	unit	unit	NOUN
cana-1287	184	7	's	's	PART
cana-1287	184	8	output	output	NOUN
cana-1287	184	9	is	be	AUX
cana-1287	184	10	computed	compute	VERB
cana-1287	184	11	as	as	ADP
cana-1287	184	12	for	for	ADP
cana-1287	184	13	a	a	DET
cana-1287	184	14	given	give	VERB
cana-1287	184	15	hidden	hide	VERB
cana-1287	184	16	layer	layer	NOUN
cana-1287	184	17	l	l	NOUN
cana-1287	184	18	with	with	ADP
cana-1287	184	19	mmm	mmm	PROPN
cana-1287	184	20	rbf	rbf	PROPN
cana-1287	184	21	units	unit	NOUN
cana-1287	184	22	:	:	PUNCT
cana-1287	184	23	(	(	PUNCT
cana-1287	184	24	)	)	PUNCT
cana-1287	184	25	(	(	PUNCT
cana-1287	185	1	)	)	PUNCT
cana-1287	185	2	(	(	PUNCT
cana-1287	185	3	)	)	PUNCT
cana-1287	185	4	2l	2l	NOUN
cana-1287	185	5	l	l	NOUN
cana-1287	186	1	i	i	PRON
cana-1287	186	2	ih	ih	VERB
cana-1287	186	3	x	x	PROPN
cana-1287	186	4	=	=	PROPN
cana-1287	186	5	−‖	−‖	PROPN
cana-1287	186	6	‖	‖	ADJ
cana-1287	186	7	where	where	SCONJ
cana-1287	186	8	,	,	PUNCT
cana-1287	186	9			NOUN
cana-1287	186	10	radial	radial	ADJ
cana-1287	186	11	basis	basis	NOUN
cana-1287	186	12	function	function	NOUN
cana-1287	186	13	,	,	PUNCT
cana-1287	186	14	(	(	PUNCT
cana-1287	186	15	)	)	PUNCT
cana-1287	186	16	l	l	NOUN
cana-1287	186	17	i	i	NUM
cana-1287	186	18	center	center	NOUN
cana-1287	186	19	of	of	ADP
cana-1287	186	20	the	the	DET
cana-1287	186	21	i	i	PROPN
cana-1287	186	22	-	-	PUNCT
cana-1287	186	23	th	th	PROPN
cana-1287	186	24	rbf	rbf	PROPN
cana-1287	186	25	unit	unit	NOUN
cana-1287	186	26	in	in	ADP
cana-1287	186	27	layer	layer	NOUN
cana-1287	186	28	l	l	NOUN
cana-1287	186	29	,	,	PUNCT
cana-1287	186	30	and	and	CCONJ
cana-1287	186	31	(	(	PUNCT
cana-1287	186	32	)	)	PUNCT
cana-1287	186	33	2l	2l	NOUN
cana-1287	186	34	ix	ix	ADP
cana-1287	186	35	−‖	−‖	PROPN
cana-1287	187	1	‖	‖	PROPN
cana-1287	187	2	squared	square	VERB
cana-1287	187	3	euclidean	euclidean	ADJ
cana-1287	187	4	distance	distance	NOUN
cana-1287	187	5	between	between	ADP
cana-1287	187	6	the	the	DET
cana-1287	187	7	input	input	NOUN
cana-1287	187	8	x	x	X
cana-1287	187	9	and	and	CCONJ
cana-1287	187	10	the	the	DET
cana-1287	187	11	center	center	NOUN
cana-1287	187	12	(	(	PUNCT
cana-1287	187	13	)	)	PUNCT
cana-1287	187	14	l	l	NOUN
cana-1287	187	15	i	i	NUM
cana-1287	187	16	.	.	PUNCT
cana-1287	188	1	for	for	ADP
cana-1287	188	2	a	a	DET
cana-1287	188	3	gaussian	gaussian	ADJ
cana-1287	188	4	function	function	NOUN
cana-1287	188	5	,	,	PUNCT
cana-1287	188	6	ϕ	ϕ	PROPN
cana-1287	188	7	is	be	AUX
cana-1287	188	8	defined	define	VERB
cana-1287	188	9	as	as	ADP
cana-1287	188	10	:	:	PUNCT
cana-1287	188	11	(	(	PUNCT
cana-1287	188	12	)	)	PUNCT
cana-1287	188	13	rr	rr	PROPN
cana-1287	188	14	e	e	NOUN
cana-1287	188	15			NOUN
cana-1287	188	16	−=	−=	PUNCT
cana-1287	188	17	where	where	SCONJ
cana-1287	188	18	γ	γ	PROPN
cana-1287	188	19	parameter	parameter	NOUN
cana-1287	188	20	controlling	control	VERB
cana-1287	188	21	the	the	DET
cana-1287	188	22	width	width	NOUN
cana-1287	188	23	of	of	ADP
cana-1287	188	24	the	the	DET
cana-1287	188	25	gaussian	gaussian	ADJ
cana-1287	188	26	function	function	NOUN
cana-1287	188	27	.	.	PUNCT
cana-1287	189	1	3	3	X
cana-1287	189	2	.	.	X
cana-1287	189	3	output	output	NOUN
cana-1287	189	4	layer	layer	NOUN
cana-1287	189	5	computation	computation	NOUN
cana-1287	189	6	:	:	PUNCT
cana-1287	189	7	the	the	DET
cana-1287	189	8	outputs	output	NOUN
cana-1287	189	9	of	of	ADP
cana-1287	189	10	the	the	DET
cana-1287	189	11	last	last	ADJ
cana-1287	189	12	hidden	hide	VERB
cana-1287	189	13	layer	layer	NOUN
cana-1287	189	14	are	be	AUX
cana-1287	189	15	linearly	linearly	ADV
cana-1287	189	16	combined	combine	VERB
cana-1287	189	17	in	in	ADP
cana-1287	189	18	an	an	DET
cana-1287	189	19	output	output	NOUN
cana-1287	189	20	layer	layer	NOUN
cana-1287	189	21	computation	computation	NOUN
cana-1287	189	22	to	to	PART
cana-1287	189	23	provide	provide	VERB
cana-1287	189	24	the	the	DET
cana-1287	189	25	final	final	ADJ
cana-1287	189	26	prediction	prediction	NOUN
cana-1287	189	27	.	.	PUNCT
cana-1287	190	1	let	let	AUX
cana-1287	190	2	indicate	indicate	VERB
cana-1287	190	3	the	the	DET
cana-1287	190	4	weight	weight	NOUN
cana-1287	190	5	connecting	connect	VERB
cana-1287	190	6	the	the	DET
cana-1287	190	7	j	j	PROPN
cana-1287	190	8	-	-	PUNCT
cana-1287	190	9	th	th	PROPN
cana-1287	190	10	rbf	rbf	PROPN
cana-1287	190	11	unit	unit	NOUN
cana-1287	190	12	to	to	ADP
cana-1287	190	13	the	the	DET
cana-1287	190	14	output	output	NOUN
cana-1287	190	15	is	be	AUX
cana-1287	190	16	(	(	PUNCT
cana-1287	190	17	)	)	PUNCT
cana-1287	190	18	l	l	NOUN
cana-1287	190	19	jw	jw	PROPN
cana-1287	190	20	.	.	PUNCT
cana-1287	191	1	the	the	DET
cana-1287	191	2	output	output	NOUN
cana-1287	191	3	of	of	ADP
cana-1287	191	4	the	the	DET
cana-1287	191	5	network	network	NOUN
cana-1287	191	6	y	y	PROPN
cana-1287	191	7	comes	come	VERB
cana-1287	191	8	from	from	ADP
cana-1287	191	9	:	:	PUNCT
cana-1287	191	10	(	(	PUNCT
cana-1287	191	11	)	)	PUNCT
cana-1287	191	12	(	(	PUNCT
cana-1287	191	13	)	)	PUNCT
cana-1287	191	14	1	1	NUM
cana-1287	191	15	m	m	NOUN
cana-1287	191	16	l	l	NOUN
cana-1287	191	17	l	l	NOUN
cana-1287	192	1	j	j	PROPN
cana-1287	193	1	j	j	PROPN
cana-1287	193	2	j	j	PROPN
cana-1287	193	3	y	y	PROPN
cana-1287	193	4	w	w	PROPN
cana-1287	193	5	h	h	PROPN
cana-1287	193	6	b	b	PROPN
cana-1287	194	1	=	=	PUNCT
cana-1287	194	2	=	=	SYM
cana-1287	194	3	+	+	NOUN
cana-1287	194	4			X
cana-1287	194	5	where	where	SCONJ
cana-1287	194	6	b	b	X
cana-1287	194	7	bias	bias	NOUN
cana-1287	194	8	term	term	NOUN
cana-1287	194	9	,	,	PUNCT
cana-1287	194	10	and	and	CCONJ
cana-1287	194	11	(	(	PUNCT
cana-1287	194	12	)	)	PUNCT
cana-1287	194	13	l	l	NOUN
cana-1287	194	14	jh	jh	PROPN
cana-1287	194	15	output	output	NOUN
cana-1287	194	16	of	of	ADP
cana-1287	194	17	the	the	DET
cana-1287	194	18	j	j	PROPN
cana-1287	194	19	-	-	PUNCT
cana-1287	194	20	th	th	PROPN
cana-1287	194	21	rbf	rbf	PROPN
cana-1287	194	22	unit	unit	NOUN
cana-1287	194	23	in	in	ADP
cana-1287	194	24	the	the	DET
cana-1287	194	25	last	last	ADJ
cana-1287	194	26	hidden	hide	VERB
cana-1287	194	27	layer	layer	NOUN
cana-1287	194	28	.	.	PUNCT
cana-1287	195	1	training	training	NOUN
cana-1287	195	2	process	process	NOUN
cana-1287	195	3	learning	learn	VERB
cana-1287	195	4	the	the	DET
cana-1287	195	5	weights	weight	NOUN
cana-1287	195	6	of	of	ADP
cana-1287	195	7	the	the	DET
cana-1287	195	8	output	output	NOUN
cana-1287	195	9	layer	layer	NOUN
cana-1287	195	10	and	and	CCONJ
cana-1287	195	11	determining	determine	VERB
cana-1287	195	12	the	the	DET
cana-1287	195	13	characteristics	characteristic	NOUN
cana-1287	195	14	of	of	ADP
cana-1287	195	15	the	the	DET
cana-1287	195	16	rbf	rbf	PROPN
cana-1287	195	17	units	unit	NOUN
cana-1287	195	18	—	—	PUNCT
cana-1287	195	19	centers	center	NOUN
cana-1287	195	20	and	and	CCONJ
cana-1287	195	21	widths	width	NOUN
cana-1287	195	22	—	—	PUNCT
cana-1287	195	23	two	two	NUM
cana-1287	195	24	main	main	ADJ
cana-1287	195	25	goals	goal	NOUN
cana-1287	195	26	define	define	VERB
cana-1287	195	27	training	train	VERB
cana-1287	195	28	a	a	DET
cana-1287	195	29	deep	deep	ADJ
cana-1287	195	30	rbf	rbf	PROPN
cana-1287	195	31	network	network	NOUN
cana-1287	195	32	.	.	PUNCT
cana-1287	196	1	the	the	DET
cana-1287	196	2	typical	typical	ADJ
cana-1287	196	3	process	process	NOUN
cana-1287	196	4	involves	involve	VERB
cana-1287	196	5	:	:	PUNCT
cana-1287	196	6	1	1	X
cana-1287	196	7	.	.	X
cana-1287	196	8	initialization	initialization	NOUN
cana-1287	196	9	:	:	PUNCT
cana-1287	196	10	usually	usually	ADV
cana-1287	196	11	depending	depend	VERB
cana-1287	196	12	on	on	ADP
cana-1287	196	13	data	datum	NOUN
cana-1287	196	14	distribution	distribution	NOUN
cana-1287	196	15	,	,	PUNCT
cana-1287	196	16	the	the	DET
cana-1287	196	17	widths	width	NOUN
cana-1287	196	18	γ	γ	NOUN
cana-1287	196	19	are	be	AUX
cana-1287	196	20	specified	specify	VERB
cana-1287	196	21	;	;	PUNCT
cana-1287	196	22	clustering	cluster	VERB
cana-1287	196	23	methods	method	NOUN
cana-1287	196	24	like	like	ADP
cana-1287	196	25	k	k	X
cana-1287	196	26	-	-	PUNCT
cana-1287	196	27	means	means	NOUN
cana-1287	196	28	can	can	AUX
cana-1287	196	29	be	be	AUX
cana-1287	196	30	used	use	VERB
cana-1287	196	31	to	to	PART
cana-1287	196	32	generate	generate	VERB
cana-1287	196	33	the	the	DET
cana-1287	196	34	centers	center	NOUN
cana-1287	196	35	(	(	PUNCT
cana-1287	196	36	)	)	PUNCT
cana-1287	196	37	l	l	NOUN
cana-1287	196	38	i	i	NUM
cana-1287	196	39	of	of	ADP
cana-1287	196	40	the	the	DET
cana-1287	196	41	rbf	rbf	PROPN
cana-1287	196	42	units	unit	NOUN
cana-1287	196	43	.	.	PUNCT
cana-1287	197	1	2	2	X
cana-1287	197	2	.	.	X
cana-1287	197	3	forward	forward	ADJ
cana-1287	197	4	propagation	propagation	NOUN
cana-1287	197	5	:	:	PUNCT
cana-1287	197	6	training	training	NOUN
cana-1287	197	7	makes	make	VERB
cana-1287	197	8	advantage	advantage	NOUN
cana-1287	197	9	of	of	ADP
cana-1287	197	10	forward	forward	ADJ
cana-1287	197	11	propagation	propagation	NOUN
cana-1287	197	12	,	,	PUNCT
cana-1287	197	13	that	that	ADV
cana-1287	197	14	is	is	ADV
cana-1287	197	15	,	,	PUNCT
cana-1287	197	16	sends	send	VERB
cana-1287	197	17	the	the	DET
cana-1287	197	18	input	input	NOUN
cana-1287	197	19	data	datum	NOUN
cana-1287	197	20	across	across	ADP
cana-1287	197	21	the	the	DET
cana-1287	197	22	network	network	NOUN
cana-1287	197	23	using	use	VERB
cana-1287	197	24	the	the	DET
cana-1287	197	25	equations	equation	NOUN
cana-1287	197	26	above	above	ADV
cana-1287	197	27	to	to	PART
cana-1287	197	28	produce	produce	VERB
cana-1287	197	29	the	the	DET
cana-1287	197	30	predicted	predict	VERB
cana-1287	197	31	output	output	NOUN
cana-1287	197	32	.	.	PUNCT
cana-1287	198	1	3	3	X
cana-1287	198	2	.	.	X
cana-1287	198	3	error	error	NOUN
cana-1287	198	4	calculation	calculation	NOUN
cana-1287	198	5	:	:	PUNCT
cana-1287	198	6	using	use	VERB
cana-1287	198	7	a	a	DET
cana-1287	198	8	loss	loss	NOUN
cana-1287	198	9	function	function	NOUN
cana-1287	198	10	—	—	PUNCT
cana-1287	198	11	such	such	ADJ
cana-1287	198	12	mean	mean	NOUN
cana-1287	198	13	squared	square	VERB
cana-1287	198	14	error	error	NOUN
cana-1287	198	15	(	(	PUNCT
cana-1287	198	16	mse	mse	NOUN
cana-1287	198	17	—	—	PUNCT
cana-1287	198	18	the	the	DET
cana-1287	198	19	prediction	prediction	NOUN
cana-1287	198	20	error	error	NOUN
cana-1287	198	21	is	be	AUX
cana-1287	198	22	calculated	calculate	VERB
cana-1287	198	23	between	between	ADP
cana-1287	198	24	the	the	DET
cana-1287	198	25	target	target	NOUN
cana-1287	198	26	values	value	NOUN
cana-1287	198	27	as	as	ADP
cana-1287	198	28	real	real	ADJ
cana-1287	198	29	and	and	CCONJ
cana-1287	198	30	the	the	DET
cana-1287	198	31	expected	expect	VERB
cana-1287	198	32	output	output	NOUN
cana-1287	198	33	:	:	PUNCT
cana-1287	198	34	communications	communication	NOUN
cana-1287	198	35	on	on	ADP
cana-1287	198	36	applied	apply	VERB
cana-1287	198	37	nonlinear	nonlinear	ADJ
cana-1287	198	38	analysis	analysis	NOUN
cana-1287	198	39	issn	issn	NOUN
cana-1287	198	40	:	:	PUNCT
cana-1287	198	41	1074	1074	NUM
cana-1287	198	42	-	-	PUNCT
cana-1287	198	43	133x	133x	NUM
cana-1287	198	44	vol	vol	NOUN
cana-1287	198	45	31	31	NUM
cana-1287	198	46	no	no	NOUN
cana-1287	198	47	.	.	PUNCT
cana-1287	199	1	7s	7	NOUN
cana-1287	199	2	(	(	PUNCT
cana-1287	199	3	2024	2024	NUM
cana-1287	199	4	)	)	PUNCT
cana-1287	199	5	102	102	NUM
cana-1287	199	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	199	7	2	2	NUM
cana-1287	199	8	1	1	NUM
cana-1287	199	9	1	1	NUM
cana-1287	199	10	ˆloss	ˆloss	ADV
cana-1287	199	11	(	(	PUNCT
cana-1287	199	12	)	)	PUNCT
cana-1287	199	13	n	n	CCONJ
cana-1287	200	1	i	i	PRON
cana-1287	200	2	i	i	PRON
cana-1287	201	1	i	i	VERB
cana-1287	201	2	y	y	VERB
cana-1287	201	3	y	y	PROPN
cana-1287	201	4	n	n	NOUN
cana-1287	201	5	=	=	PUNCT
cana-1287	201	6	=	=	NOUN
cana-1287	201	7	−	−	NOUN
cana-1287	201	8	where	where	SCONJ
cana-1287	201	9	yi	yi	PROPN
cana-1287	201	10	true	true	ADJ
cana-1287	201	11	value	value	NOUN
cana-1287	201	12	and	and	CCONJ
cana-1287	201	13	ˆ	ˆ	PROPN
cana-1287	201	14	iy	iy	PROPN
cana-1287	201	15	predicted	predict	VERB
cana-1287	201	16	value	value	NOUN
cana-1287	201	17	for	for	ADP
cana-1287	201	18	the	the	DET
cana-1287	201	19	i	i	PROPN
cana-1287	201	20	-	-	PUNCT
cana-1287	201	21	th	th	X
cana-1287	201	22	sample	sample	NOUN
cana-1287	201	23	.	.	PUNCT
cana-1287	202	1	4	4	X
cana-1287	202	2	.	.	X
cana-1287	202	3	backpropagation	backpropagation	NOUN
cana-1287	202	4	and	and	CCONJ
cana-1287	202	5	optimization	optimization	NOUN
cana-1287	202	6	:	:	PUNCT
cana-1287	202	7	gradient	gradient	ADJ
cana-1287	202	8	descent	descent	NOUN
cana-1287	202	9	or	or	CCONJ
cana-1287	202	10	another	another	DET
cana-1287	202	11	optimization	optimization	NOUN
cana-1287	202	12	technique	technique	NOUN
cana-1287	202	13	is	be	AUX
cana-1287	202	14	used	use	VERB
cana-1287	202	15	to	to	PART
cana-1287	202	16	modify	modify	VERB
cana-1287	202	17	the	the	DET
cana-1287	202	18	weights	weight	NOUN
cana-1287	202	19	(	(	PUNCT
cana-1287	202	20	)	)	PUNCT
cana-1287	202	21	l	l	NOUN
cana-1287	202	22	jw	jw	PROPN
cana-1287	202	23	and	and	CCONJ
cana-1287	202	24	rbf	rbf	PROPN
cana-1287	202	25	unit	unit	NOUN
cana-1287	202	26	parameters	parameter	NOUN
cana-1287	202	27	lowering	lower	VERB
cana-1287	202	28	the	the	DET
cana-1287	202	29	loss	loss	NOUN
cana-1287	202	30	function	function	NOUN
cana-1287	202	31	.	.	PUNCT
cana-1287	203	1	#	#	NOUN
cana-1287	203	2	deep	deep	PROPN
cana-1287	203	3	rbf	rbf	PROPN
cana-1287	203	4	network	network	PROPN
cana-1287	203	5	pseudocode	pseudocode	PROPN
cana-1287	203	6	#	#	PROPN
cana-1287	203	7	initialization	initialization	NOUN
cana-1287	203	8	initialize	initialize	VERB
cana-1287	203	9	the	the	DET
cana-1287	203	10	input	input	NOUN
cana-1287	203	11	layer	layer	NOUN
cana-1287	203	12	with	with	ADP
cana-1287	203	13	n	n	NUM
cana-1287	203	14	features	feature	NOUN
cana-1287	203	15	initialize	initialize	VERB
cana-1287	203	16	l	l	NOUN
cana-1287	203	17	hidden	hide	VERB
cana-1287	203	18	layers	layer	NOUN
cana-1287	203	19	with	with	ADP
cana-1287	203	20	rbf	rbf	PROPN
cana-1287	203	21	units	unit	NOUN
cana-1287	203	22	for	for	ADP
cana-1287	203	23	each	each	DET
cana-1287	203	24	hidden	hide	VERB
cana-1287	203	25	layer	layer	NOUN
cana-1287	203	26	l	l	NOUN
cana-1287	203	27	:	:	PUNCT
cana-1287	203	28	initialize	initialize	VERB
cana-1287	203	29	rbf	rbf	PROPN
cana-1287	203	30	centers	center	NOUN
cana-1287	203	31	μ_i^(l	μ_i^(l	NOUN
cana-1287	203	32	)	)	PUNCT
cana-1287	203	33	using	use	VERB
cana-1287	203	34	clustering	clustering	NOUN
cana-1287	203	35	(	(	PUNCT
cana-1287	203	36	e.g.	e.g.	ADV
cana-1287	203	37	,	,	PUNCT
cana-1287	203	38	k	k	NOUN
cana-1287	203	39	-	-	PUNCT
cana-1287	203	40	means	means	NOUN
cana-1287	203	41	)	)	PUNCT
cana-1287	203	42	initialize	initialize	VERB
cana-1287	203	43	rbf	rbf	PROPN
cana-1287	203	44	widths	width	NOUN
cana-1287	203	45	γ_i^(l	γ_i^(l	PROPN
cana-1287	203	46	)	)	PUNCT
cana-1287	203	47	based	base	VERB
cana-1287	203	48	on	on	ADP
cana-1287	203	49	data	datum	NOUN
cana-1287	203	50	spread	spread	VERB
cana-1287	203	51	initialize	initialize	VERB
cana-1287	203	52	weights	weight	NOUN
cana-1287	203	53	w_j^(l	w_j^(l	PROPN
cana-1287	203	54	)	)	PUNCT
cana-1287	203	55	and	and	CCONJ
cana-1287	203	56	biases	bias	NOUN
cana-1287	203	57	b	b	PROPN
cana-1287	203	58	for	for	ADP
cana-1287	203	59	the	the	DET
cana-1287	203	60	output	output	NOUN
cana-1287	203	61	layer	layer	NOUN
cana-1287	203	62	#	#	NOUN
cana-1287	203	63	training	training	NOUN
cana-1287	203	64	for	for	ADP
cana-1287	203	65	each	each	DET
cana-1287	203	66	epoch	epoch	NOUN
cana-1287	203	67	in	in	ADP
cana-1287	203	68	the	the	DET
cana-1287	203	69	range	range	NOUN
cana-1287	203	70	of	of	ADP
cana-1287	203	71	total_epochs	total_epoch	NOUN
cana-1287	203	72	:	:	PUNCT
cana-1287	203	73	for	for	ADP
cana-1287	203	74	each	each	DET
cana-1287	203	75	training	training	NOUN
cana-1287	203	76	sample	sample	NOUN
cana-1287	203	77	(	(	PUNCT
cana-1287	203	78	x	x	X
cana-1287	203	79	,	,	PUNCT
cana-1287	203	80	y_true	y_true	X
cana-1287	203	81	)	)	PUNCT
cana-1287	203	82	in	in	ADP
cana-1287	203	83	the	the	DET
cana-1287	203	84	training	training	NOUN
cana-1287	203	85	dataset	dataset	NOUN
cana-1287	203	86	:	:	PUNCT
cana-1287	203	87	#	#	NOUN
cana-1287	203	88	forward	forward	NOUN
cana-1287	203	89	propagation	propagation	NOUN
cana-1287	203	90	initialize	initialize	NOUN
cana-1287	203	91	input	input	NOUN
cana-1287	203	92	layer	layer	NOUN
cana-1287	203	93	with	with	ADP
cana-1287	203	94	features	feature	NOUN
cana-1287	203	95	x	x	PUNCT
cana-1287	203	96	for	for	ADP
cana-1287	203	97	each	each	DET
cana-1287	203	98	hidden	hide	VERB
cana-1287	203	99	layer	layer	NOUN
cana-1287	203	100	l	l	NOUN
cana-1287	203	101	in	in	ADP
cana-1287	203	102	the	the	DET
cana-1287	203	103	network	network	NOUN
cana-1287	203	104	:	:	PUNCT
cana-1287	203	105	for	for	ADP
cana-1287	203	106	each	each	DET
cana-1287	203	107	rbf	rbf	PROPN
cana-1287	203	108	unit	unit	NOUN
cana-1287	203	109	i	i	PRON
cana-1287	203	110	in	in	ADP
cana-1287	203	111	layer	layer	NOUN
cana-1287	203	112	l	l	NOUN
cana-1287	203	113	:	:	PUNCT
cana-1287	203	114	compute	compute	VERB
cana-1287	203	115	the	the	DET
cana-1287	203	116	output	output	NOUN
cana-1287	203	117	of	of	ADP
cana-1287	203	118	rbf	rbf	PROPN
cana-1287	203	119	unit	unit	NOUN
cana-1287	203	120	i	i	PRON
cana-1287	203	121	:	:	PUNCT
cana-1287	203	122	h_i^(l	h_i^(l	ADJ
cana-1287	203	123	)	)	PUNCT
cana-1287	203	124	=	=	SYM
cana-1287	203	125	exp(-γ_i^(l	exp(-γ_i^(l	CCONJ
cana-1287	203	126	)	)	PUNCT
cana-1287	203	127	*	*	PUNCT
cana-1287	203	128	||x	||x	PROPN
cana-1287	203	129	μ_i^(l)||^2	μ_i^(l)||^2	PROPN
cana-1287	203	130	)	)	PUNCT
cana-1287	203	131	compute	compute	VERB
cana-1287	203	132	the	the	DET
cana-1287	203	133	output	output	NOUN
cana-1287	203	134	layer	layer	NOUN
cana-1287	203	135	value	value	NOUN
cana-1287	203	136	:	:	PUNCT
cana-1287	203	137	y_pred	y_pred	PROPN
cana-1287	203	138	=	=	SYM
cana-1287	203	139	σ	σ	PROPN
cana-1287	203	140	(	(	PUNCT
cana-1287	203	141	w_j^(l	w_j^(l	PROPN
cana-1287	203	142	)	)	PUNCT
cana-1287	203	143	*	*	PUNCT
cana-1287	203	144	h_j^(l	h_j^(l	PROPN
cana-1287	203	145	)	)	PUNCT
cana-1287	203	146	)	)	PUNCT
cana-1287	204	1	+	+	PUNCT
cana-1287	204	2	b	b	X
cana-1287	204	3	#	#	NOUN
cana-1287	204	4	compute	compute	NOUN
cana-1287	204	5	loss	loss	NOUN
cana-1287	204	6	loss	loss	NOUN
cana-1287	204	7	=	=	SYM
cana-1287	204	8	(	(	PUNCT
cana-1287	204	9	1	1	NUM
cana-1287	204	10	/	/	SYM
cana-1287	204	11	n	n	CCONJ
cana-1287	204	12	)	)	PUNCT
cana-1287	204	13	*	*	PUNCT
cana-1287	205	1	σ	σ	NOUN
cana-1287	205	2	(	(	PUNCT
cana-1287	205	3	y_true	y_true	X
cana-1287	205	4	y_pred)^2	y_pred)^2	PROPN
cana-1287	205	5	#	#	NOUN
cana-1287	205	6	backpropagation	backpropagation	NOUN
cana-1287	205	7	compute	compute	NOUN
cana-1287	205	8	gradients	gradient	NOUN
cana-1287	205	9	for	for	ADP
cana-1287	205	10	output	output	NOUN
cana-1287	205	11	layer	layer	NOUN
cana-1287	205	12	weights	weight	NOUN
cana-1287	205	13	and	and	CCONJ
cana-1287	205	14	biases	bias	NOUN
cana-1287	205	15	for	for	ADP
cana-1287	205	16	each	each	DET
cana-1287	205	17	rbf	rbf	PROPN
cana-1287	205	18	unit	unit	NOUN
cana-1287	205	19	i	i	PRON
cana-1287	205	20	in	in	ADP
cana-1287	205	21	all	all	DET
cana-1287	205	22	hidden	hidden	ADJ
cana-1287	205	23	layers	layer	NOUN
cana-1287	205	24	:	:	PUNCT
cana-1287	205	25	compute	compute	NOUN
cana-1287	205	26	gradients	gradient	NOUN
cana-1287	205	27	for	for	ADP
cana-1287	205	28	rbf	rbf	PROPN
cana-1287	205	29	unit	unit	NOUN
cana-1287	205	30	centers	center	NOUN
cana-1287	205	31	and	and	CCONJ
cana-1287	205	32	widths	width	NOUN
cana-1287	205	33	update	update	VERB
cana-1287	205	34	weights	weight	NOUN
cana-1287	205	35	,	,	PUNCT
cana-1287	205	36	biases	bias	NOUN
cana-1287	205	37	,	,	PUNCT
cana-1287	205	38	and	and	CCONJ
cana-1287	205	39	rbf	rbf	PROPN
cana-1287	205	40	parameters	parameter	NOUN
cana-1287	205	41	:	:	PUNCT
cana-1287	206	1	w_j^(l	w_j^(l	PROPN
cana-1287	206	2	)	)	PUNCT
cana-1287	206	3	=	=	SYM
cana-1287	206	4	w_j^(l	w_j^(l	ADJ
cana-1287	206	5	)	)	PUNCT
cana-1287	206	6	learning_rate	learning_rate	PROPN
cana-1287	206	7	*	*	SYM
cana-1287	206	8	gradient_w_j^(l	gradient_w_j^(l	NOUN
cana-1287	206	9	)	)	PUNCT
cana-1287	206	10	b	b	NOUN
cana-1287	206	11	=	=	SYM
cana-1287	206	12	b	b	PROPN
cana-1287	206	13	learning_rate	learning_rate	ADJ
cana-1287	206	14	*	*	PUNCT
cana-1287	206	15	gradient_b	gradient_b	NOUN
cana-1287	206	16	μ_i^(l	μ_i^(l	X
cana-1287	206	17	)	)	PUNCT
cana-1287	206	18	=	=	SYM
cana-1287	206	19	μ_i^(l	μ_i^(l	X
cana-1287	206	20	)	)	PUNCT
cana-1287	206	21	learning_rate	learning_rate	PROPN
cana-1287	206	22	*	*	SYM
cana-1287	206	23	gradient_μ_i^(l	gradient_μ_i^(l	PROPN
cana-1287	206	24	)	)	PUNCT
cana-1287	206	25	γ_i^(l	γ_i^(l	PROPN
cana-1287	206	26	)	)	PUNCT
cana-1287	206	27	=	=	PUNCT
cana-1287	206	28	γ_i^(l	γ_i^(l	VERB
cana-1287	206	29	)	)	PUNCT
cana-1287	206	30	learning_rate	learning_rate	PROPN
cana-1287	206	31	*	*	PUNCT
cana-1287	206	32	gradient_γ_i^(l	gradient_γ_i^(l	NOUN
cana-1287	206	33	)	)	PUNCT
cana-1287	206	34	#	#	NOUN
cana-1287	206	35	optional	optional	NOUN
cana-1287	206	36	:	:	PUNCT
cana-1287	206	37	print	print	NOUN
cana-1287	206	38	loss	loss	NOUN
cana-1287	206	39	for	for	ADP
cana-1287	206	40	the	the	DET
cana-1287	206	41	current	current	ADJ
cana-1287	206	42	epoch	epoch	NOUN
cana-1287	206	43	#	#	NOUN
cana-1287	206	44	testing	testing	NOUN
cana-1287	206	45	communications	communication	NOUN
cana-1287	206	46	on	on	ADP
cana-1287	206	47	applied	apply	VERB
cana-1287	206	48	nonlinear	nonlinear	ADJ
cana-1287	206	49	analysis	analysis	NOUN
cana-1287	206	50	issn	issn	NOUN
cana-1287	206	51	:	:	PUNCT
cana-1287	206	52	1074	1074	NUM
cana-1287	206	53	-	-	PUNCT
cana-1287	206	54	133x	133x	NUM
cana-1287	206	55	vol	vol	NOUN
cana-1287	206	56	31	31	NUM
cana-1287	206	57	no	no	NOUN
cana-1287	206	58	.	.	PUNCT
cana-1287	207	1	7s	7	NOUN
cana-1287	207	2	(	(	PUNCT
cana-1287	207	3	2024	2024	NUM
cana-1287	207	4	)	)	PUNCT
cana-1287	207	5	103	103	NUM
cana-1287	207	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	207	7	for	for	ADP
cana-1287	207	8	each	each	DET
cana-1287	207	9	test	test	NOUN
cana-1287	207	10	sample	sample	NOUN
cana-1287	207	11	(	(	PUNCT
cana-1287	207	12	x_test	x_test	X
cana-1287	207	13	,	,	PUNCT
cana-1287	207	14	y_test	y_test	X
cana-1287	207	15	)	)	PUNCT
cana-1287	207	16	in	in	ADP
cana-1287	207	17	the	the	DET
cana-1287	207	18	test	test	NOUN
cana-1287	207	19	dataset	dataset	NOUN
cana-1287	207	20	:	:	PUNCT
cana-1287	207	21	initialize	initialize	VERB
cana-1287	207	22	input	input	NOUN
cana-1287	207	23	layer	layer	NOUN
cana-1287	207	24	with	with	ADP
cana-1287	207	25	features	feature	NOUN
cana-1287	207	26	x_test	x_t	ADJ
cana-1287	207	27	for	for	ADP
cana-1287	207	28	each	each	DET
cana-1287	207	29	hidden	hide	VERB
cana-1287	207	30	layer	layer	NOUN
cana-1287	207	31	l	l	NOUN
cana-1287	207	32	in	in	ADP
cana-1287	207	33	the	the	DET
cana-1287	207	34	network	network	NOUN
cana-1287	207	35	:	:	PUNCT
cana-1287	207	36	for	for	ADP
cana-1287	207	37	each	each	DET
cana-1287	207	38	rbf	rbf	PROPN
cana-1287	207	39	unit	unit	NOUN
cana-1287	207	40	i	i	PRON
cana-1287	207	41	in	in	ADP
cana-1287	207	42	layer	layer	NOUN
cana-1287	207	43	l	l	NOUN
cana-1287	207	44	:	:	PUNCT
cana-1287	207	45	compute	compute	VERB
cana-1287	207	46	the	the	DET
cana-1287	207	47	output	output	NOUN
cana-1287	207	48	of	of	ADP
cana-1287	207	49	rbf	rbf	PROPN
cana-1287	207	50	unit	unit	NOUN
cana-1287	207	51	i	i	PRON
cana-1287	207	52	:	:	PUNCT
cana-1287	207	53	h_i^(l	h_i^(l	ADJ
cana-1287	207	54	)	)	PUNCT
cana-1287	207	55	=	=	SYM
cana-1287	207	56	exp(-γ_i^(l	exp(-γ_i^(l	CCONJ
cana-1287	207	57	)	)	PUNCT
cana-1287	207	58	*	*	PUNCT
cana-1287	207	59	||x_test	||x_t	ADJ
cana-1287	207	60	μ_i^(l)||^2	μ_i^(l)||^2	NOUN
cana-1287	207	61	)	)	PUNCT
cana-1287	207	62	compute	compute	VERB
cana-1287	207	63	the	the	DET
cana-1287	207	64	final	final	ADJ
cana-1287	207	65	output	output	NOUN
cana-1287	207	66	:	:	PUNCT
cana-1287	207	67	y_pred_test	y_pred_t	ADJ
cana-1287	207	68	=	=	SYM
cana-1287	207	69	σ	σ	PROPN
cana-1287	207	70	(	(	PUNCT
cana-1287	207	71	w_j^(l	w_j^(l	PROPN
cana-1287	207	72	)	)	PUNCT
cana-1287	207	73	*	*	PUNCT
cana-1287	207	74	h_j^(l	h_j^(l	PROPN
cana-1287	207	75	)	)	PUNCT
cana-1287	207	76	)	)	PUNCT
cana-1287	208	1	+	+	PUNCT
cana-1287	208	2	b	b	X
cana-1287	208	3	#	#	NOUN
cana-1287	208	4	compute	compute	NOUN
cana-1287	208	5	and	and	CCONJ
cana-1287	208	6	store	store	NOUN
cana-1287	208	7	testing	testing	NOUN
cana-1287	208	8	metrics	metric	NOUN
cana-1287	208	9	(	(	PUNCT
cana-1287	208	10	e.g.	e.g.	ADV
cana-1287	208	11	,	,	PUNCT
cana-1287	208	12	mse	mse	NOUN
cana-1287	208	13	,	,	PUNCT
cana-1287	208	14	accuracy	accuracy	NOUN
cana-1287	208	15	)	)	PUNCT
cana-1287	208	16	#	#	NOUN
cana-1287	208	17	validation	validation	NOUN
cana-1287	208	18	(	(	PUNCT
cana-1287	208	19	optional	optional	ADJ
cana-1287	208	20	)	)	PUNCT
cana-1287	208	21	for	for	ADP
cana-1287	208	22	each	each	DET
cana-1287	208	23	validation	validation	NOUN
cana-1287	208	24	sample	sample	NOUN
cana-1287	208	25	(	(	PUNCT
cana-1287	208	26	x_val	x_val	PROPN
cana-1287	208	27	,	,	PUNCT
cana-1287	208	28	y_val	y_val	NOUN
cana-1287	208	29	)	)	PUNCT
cana-1287	208	30	in	in	ADP
cana-1287	208	31	the	the	DET
cana-1287	208	32	validation	validation	NOUN
cana-1287	208	33	dataset	dataset	NOUN
cana-1287	208	34	:	:	PUNCT
cana-1287	208	35	initialize	initialize	VERB
cana-1287	208	36	input	input	NOUN
cana-1287	208	37	layer	layer	NOUN
cana-1287	208	38	with	with	ADP
cana-1287	208	39	features	feature	NOUN
cana-1287	208	40	x_val	x_val	PROPN
cana-1287	208	41	for	for	ADP
cana-1287	208	42	each	each	DET
cana-1287	208	43	hidden	hide	VERB
cana-1287	208	44	layer	layer	NOUN
cana-1287	208	45	l	l	NOUN
cana-1287	208	46	in	in	ADP
cana-1287	208	47	the	the	DET
cana-1287	208	48	network	network	NOUN
cana-1287	208	49	:	:	PUNCT
cana-1287	208	50	for	for	ADP
cana-1287	208	51	each	each	DET
cana-1287	208	52	rbf	rbf	PROPN
cana-1287	208	53	unit	unit	NOUN
cana-1287	208	54	i	i	PRON
cana-1287	208	55	in	in	ADP
cana-1287	208	56	layer	layer	NOUN
cana-1287	208	57	l	l	NOUN
cana-1287	208	58	:	:	PUNCT
cana-1287	208	59	compute	compute	VERB
cana-1287	208	60	the	the	DET
cana-1287	208	61	output	output	NOUN
cana-1287	208	62	of	of	ADP
cana-1287	208	63	rbf	rbf	PROPN
cana-1287	208	64	unit	unit	NOUN
cana-1287	208	65	i	i	PRON
cana-1287	208	66	:	:	PUNCT
cana-1287	208	67	h_i^(l	h_i^(l	ADJ
cana-1287	208	68	)	)	PUNCT
cana-1287	208	69	=	=	SYM
cana-1287	208	70	exp(-γ_i^(l	exp(-γ_i^(l	CCONJ
cana-1287	208	71	)	)	PUNCT
cana-1287	208	72	*	*	PUNCT
cana-1287	209	1	||x_val	||x_val	PROPN
cana-1287	209	2	μ_i^(l)||^2	μ_i^(l)||^2	PROPN
cana-1287	209	3	)	)	PUNCT
cana-1287	209	4	compute	compute	VERB
cana-1287	209	5	the	the	DET
cana-1287	209	6	final	final	ADJ
cana-1287	209	7	output	output	NOUN
cana-1287	209	8	:	:	PUNCT
cana-1287	210	1	y_pred_val	y_pred_val	NOUN
cana-1287	210	2	=	=	SYM
cana-1287	210	3	σ	σ	PROPN
cana-1287	210	4	(	(	PUNCT
cana-1287	210	5	w_j^(l	w_j^(l	PROPN
cana-1287	210	6	)	)	PUNCT
cana-1287	210	7	*	*	PUNCT
cana-1287	210	8	h_j^(l	h_j^(l	PROPN
cana-1287	210	9	)	)	PUNCT
cana-1287	210	10	)	)	PUNCT
cana-1287	211	1	+	+	PUNCT
cana-1287	211	2	b	b	X
cana-1287	211	3	#	#	NOUN
cana-1287	211	4	compute	compute	NOUN
cana-1287	211	5	and	and	CCONJ
cana-1287	211	6	store	store	NOUN
cana-1287	211	7	validation	validation	NOUN
cana-1287	211	8	metrics	metric	NOUN
cana-1287	211	9	(	(	PUNCT
cana-1287	211	10	e.g.	e.g.	ADV
cana-1287	211	11	,	,	PUNCT
cana-1287	211	12	mse	mse	NOUN
cana-1287	211	13	,	,	PUNCT
cana-1287	211	14	accuracy	accuracy	NOUN
cana-1287	211	15	)	)	PUNCT
cana-1287	211	16	#	#	NOUN
cana-1287	211	17	end	end	NOUN
cana-1287	211	18	of	of	ADP
cana-1287	211	19	training	training	NOUN
cana-1287	211	20	return	return	NOUN
cana-1287	211	21	trained	train	VERB
cana-1287	211	22	model	model	NOUN
cana-1287	211	23	parameters	parameter	NOUN
cana-1287	211	24	(	(	PUNCT
cana-1287	211	25	weights	weight	NOUN
cana-1287	211	26	,	,	PUNCT
cana-1287	211	27	biases	bias	NOUN
cana-1287	211	28	,	,	PUNCT
cana-1287	211	29	rbf	rbf	PROPN
cana-1287	211	30	centers	center	NOUN
cana-1287	211	31	,	,	PUNCT
cana-1287	211	32	and	and	CCONJ
cana-1287	211	33	widths	width	NOUN
cana-1287	211	34	)	)	PUNCT
cana-1287	211	35	5	5	NUM
cana-1287	211	36	.	.	NOUN
cana-1287	211	37	results	result	NOUN
cana-1287	211	38	and	and	CCONJ
cana-1287	211	39	discussion	discussion	NOUN
cana-1287	211	40	source	source	NOUN
cana-1287	211	41	of	of	ADP
cana-1287	211	42	remotely	remotely	ADV
cana-1287	211	43	sensed	sense	VERB
cana-1287	211	44	images	image	NOUN
cana-1287	211	45	from	from	ADP
cana-1287	211	46	publicly	publicly	ADV
cana-1287	211	47	available	available	ADJ
cana-1287	211	48	datasets	dataset	NOUN
cana-1287	211	49	is	be	AUX
cana-1287	211	50	kaggle	kaggle	VERB
cana-1287	211	51	[	[	X
cana-1287	211	52	15	15	NUM
cana-1287	211	53	]	]	X
cana-1287	211	54	,	,	PUNCT
cana-1287	211	55	which	which	PRON
cana-1287	211	56	comprised	comprise	VERB
cana-1287	211	57	the	the	DET
cana-1287	211	58	experimental	experimental	ADJ
cana-1287	211	59	setup	setup	NOUN
cana-1287	211	60	as	as	ADP
cana-1287	211	61	in	in	ADP
cana-1287	211	62	table	table	NOUN
cana-1287	211	63	3	3	NUM
cana-1287	211	64	for	for	ADP
cana-1287	211	65	evaluating	evaluate	VERB
cana-1287	211	66	the	the	DET
cana-1287	211	67	proposed	propose	VERB
cana-1287	211	68	deep	deep	ADJ
cana-1287	211	69	radial	radial	ADJ
cana-1287	211	70	basis	basis	NOUN
cana-1287	211	71	function	function	NOUN
cana-1287	211	72	(	(	PUNCT
cana-1287	211	73	rbf	rbf	PROPN
cana-1287	211	74	)	)	PUNCT
cana-1287	211	75	network	network	NOUN
cana-1287	211	76	using	use	VERB
cana-1287	211	77	tensorflow	tensorflow	NOUN
cana-1287	211	78	for	for	ADP
cana-1287	211	79	model	model	NOUN
cana-1287	211	80	construction	construction	NOUN
cana-1287	211	81	and	and	CCONJ
cana-1287	211	82	training	training	NOUN
cana-1287	211	83	and	and	CCONJ
cana-1287	211	84	python	python	NOUN
cana-1287	211	85	for	for	ADP
cana-1287	211	86	a	a	DET
cana-1287	211	87	comprehensive	comprehensive	ADJ
cana-1287	211	88	simulation	simulation	NOUN
cana-1287	211	89	and	and	CCONJ
cana-1287	211	90	noise	noise	NOUN
cana-1287	211	91	reduction	reduction	NOUN
cana-1287	211	92	and	and	CCONJ
cana-1287	211	93	feature	feature	NOUN
cana-1287	211	94	improvement	improvement	NOUN
cana-1287	211	95	preprocessing	preprocessing	NOUN
cana-1287	211	96	.	.	PUNCT
cana-1287	212	1	training	training	NOUN
cana-1287	212	2	and	and	CCONJ
cana-1287	212	3	evaluation	evaluation	NOUN
cana-1287	212	4	were	be	AUX
cana-1287	212	5	conducted	conduct	VERB
cana-1287	212	6	on	on	ADP
cana-1287	212	7	a	a	DET
cana-1287	212	8	cluster	cluster	NOUN
cana-1287	212	9	with	with	ADP
cana-1287	212	10	16	16	NUM
cana-1287	212	11	cpu	cpu	NOUN
cana-1287	212	12	cores	core	NOUN
cana-1287	212	13	,	,	PUNCT
cana-1287	212	14	64	64	NUM
cana-1287	212	15	gb	gb	NOUN
cana-1287	212	16	ram	ram	NOUN
cana-1287	212	17	,	,	PUNCT
cana-1287	212	18	and	and	CCONJ
cana-1287	212	19	8	8	NUM
cana-1287	212	20	gpus	gpu	NOUN
cana-1287	212	21	to	to	PART
cana-1287	212	22	guarantee	guarantee	VERB
cana-1287	212	23	effective	effective	ADJ
cana-1287	212	24	processing	processing	NOUN
cana-1287	212	25	of	of	ADP
cana-1287	212	26	vast	vast	ADJ
cana-1287	212	27	datasets	dataset	NOUN
cana-1287	212	28	and	and	CCONJ
cana-1287	212	29	challenging	challenging	ADJ
cana-1287	212	30	model	model	NOUN
cana-1287	212	31	computations	computation	NOUN
cana-1287	212	32	.	.	PUNCT
cana-1287	213	1	the	the	DET
cana-1287	213	2	deep	deep	PROPN
cana-1287	213	3	rbf	rbf	PROPN
cana-1287	213	4	network	network	NOUN
cana-1287	213	5	was	be	AUX
cana-1287	213	6	built	build	VERB
cana-1287	213	7	utilizing	utilize	VERB
cana-1287	213	8	tensorflow	tensorflow	NOUN
cana-1287	213	9	's	's	PART
cana-1287	213	10	keras	keras	PROPN
cana-1287	213	11	api	api	NOUN
cana-1287	213	12	using	use	VERB
cana-1287	213	13	a	a	DET
cana-1287	213	14	high	high	ADJ
cana-1287	213	15	-	-	PUNCT
cana-1287	213	16	performance	performance	NOUN
cana-1287	213	17	computing	computing	NOUN
cana-1287	213	18	cluster	cluster	NOUN
cana-1287	213	19	loaded	load	VERB
cana-1287	213	20	with	with	ADP
cana-1287	213	21	nvidia	nvidia	PROPN
cana-1287	213	22	rtx	rtx	PROPN
cana-1287	213	23	3090	3090	NUM
cana-1287	213	24	gpus	gpu	NOUN
cana-1287	213	25	handling	handle	VERB
cana-1287	213	26	demanding	demand	VERB
cana-1287	213	27	computations	computation	NOUN
cana-1287	213	28	.	.	PUNCT
cana-1287	214	1	predicting	predict	VERB
cana-1287	214	2	accuracy	accuracy	NOUN
cana-1287	214	3	was	be	AUX
cana-1287	214	4	assessed	assess	VERB
cana-1287	214	5	in	in	ADP
cana-1287	214	6	performance	performance	NOUN
cana-1287	214	7	evaluation	evaluation	NOUN
cana-1287	214	8	using	use	VERB
cana-1287	214	9	mean	mean	NOUN
cana-1287	214	10	squared	square	VERB
cana-1287	214	11	error	error	NOUN
cana-1287	214	12	(	(	PUNCT
cana-1287	214	13	mse	mse	NOUN
cana-1287	214	14	)	)	PUNCT
cana-1287	214	15	and	and	CCONJ
cana-1287	214	16	coefficient	coefficient	NOUN
cana-1287	214	17	of	of	ADP
cana-1287	214	18	determination	determination	NOUN
cana-1287	214	19	(	(	PUNCT
cana-1287	214	20	r²	r²	NOUN
cana-1287	214	21	)	)	PUNCT
cana-1287	214	22	.	.	PUNCT
cana-1287	215	1	among	among	ADP
cana-1287	215	2	the	the	DET
cana-1287	215	3	standard	standard	ADJ
cana-1287	215	4	models	model	NOUN
cana-1287	215	5	against	against	ADP
cana-1287	215	6	which	which	PRON
cana-1287	215	7	the	the	DET
cana-1287	215	8	novel	novel	ADJ
cana-1287	215	9	method	method	NOUN
cana-1287	215	10	was	be	AUX
cana-1287	215	11	tested	test	VERB
cana-1287	215	12	artificial	artificial	ADJ
cana-1287	215	13	neural	neural	ADJ
cana-1287	215	14	networks	network	NOUN
cana-1287	215	15	(	(	PUNCT
cana-1287	215	16	ann	ann	PROPN
cana-1287	215	17	)	)	PUNCT
cana-1287	215	18	,	,	PUNCT
cana-1287	215	19	bi	bi	ADJ
cana-1287	215	20	-	-	ADJ
cana-1287	215	21	directional	directional	ADJ
cana-1287	215	22	gated	gate	VERB
cana-1287	215	23	recurrent	recurrent	ADJ
cana-1287	215	24	unit	unit	NOUN
cana-1287	215	25	with	with	ADP
cana-1287	215	26	dynamic	dynamic	ADJ
cana-1287	215	27	memory	memory	NOUN
cana-1287	215	28	networks	network	NOUN
cana-1287	215	29	(	(	PUNCT
cana-1287	215	30	bi	bi	PROPN
cana-1287	215	31	-	-	PROPN
cana-1287	215	32	gru	gru	NOUN
cana-1287	215	33	-	-	NOUN
cana-1287	215	34	dmn	dmn	NOUN
cana-1287	215	35	)	)	PUNCT
cana-1287	215	36	,	,	PUNCT
cana-1287	215	37	random	random	ADJ
cana-1287	215	38	forest	forest	NOUN
cana-1287	215	39	regression	regression	NOUN
cana-1287	215	40	(	(	PUNCT
cana-1287	215	41	rfr	rfr	PROPN
cana-1287	215	42	)	)	PUNCT
cana-1287	215	43	,	,	PUNCT
cana-1287	215	44	and	and	CCONJ
cana-1287	215	45	recurrent	recurrent	ADJ
cana-1287	215	46	neural	neural	ADJ
cana-1287	215	47	networks	network	NOUN
cana-1287	215	48	(	(	PUNCT
cana-1287	215	49	rnn	rnn	PROPN
cana-1287	215	50	)	)	PUNCT
cana-1287	215	51	.	.	PUNCT
cana-1287	216	1	table	table	NOUN
cana-1287	216	2	3	3	NUM
cana-1287	216	3	:	:	PUNCT
cana-1287	216	4	experimental	experimental	ADJ
cana-1287	216	5	setup	setup	NOUN
cana-1287	216	6	and	and	CCONJ
cana-1287	216	7	parameters	parameter	NOUN
cana-1287	216	8	parameter	parameter	NOUN
cana-1287	216	9	value	value	NOUN
cana-1287	216	10	remote	remote	ADJ
cana-1287	216	11	sensing	sense	VERB
cana-1287	216	12	image	image	NOUN
cana-1287	216	13	source	source	NOUN
cana-1287	216	14	kaggle	kaggle	NOUN
cana-1287	216	15	image	image	NOUN
cana-1287	216	16	resolution	resolution	NOUN
cana-1287	216	17	30	30	NUM
cana-1287	216	18	meters	meter	NOUN
cana-1287	216	19	preprocessing	preprocesse	VERB
cana-1287	216	20	tool	tool	NOUN
cana-1287	216	21	opencv	opencv	NOUN
cana-1287	216	22	communications	communication	NOUN
cana-1287	216	23	on	on	ADP
cana-1287	216	24	applied	apply	VERB
cana-1287	216	25	nonlinear	nonlinear	ADJ
cana-1287	216	26	analysis	analysis	NOUN
cana-1287	216	27	issn	issn	NOUN
cana-1287	216	28	:	:	PUNCT
cana-1287	216	29	1074	1074	NUM
cana-1287	216	30	-	-	PUNCT
cana-1287	216	31	133x	133x	NUM
cana-1287	216	32	vol	vol	NOUN
cana-1287	216	33	31	31	NUM
cana-1287	216	34	no	no	NOUN
cana-1287	216	35	.	.	PUNCT
cana-1287	217	1	7s	7	NOUN
cana-1287	217	2	(	(	PUNCT
cana-1287	217	3	2024	2024	NUM
cana-1287	217	4	)	)	PUNCT
cana-1287	217	5	104	104	NUM
cana-1287	217	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	217	7	feature	feature	NOUN
cana-1287	217	8	extraction	extraction	NOUN
cana-1287	217	9	techniques	technique	NOUN
cana-1287	217	10	edge	edge	NOUN
cana-1287	217	11	detection	detection	NOUN
cana-1287	217	12	,	,	PUNCT
cana-1287	217	13	texture	texture	ADJ
cana-1287	217	14	analysis	analysis	NOUN
cana-1287	217	15	simulation	simulation	NOUN
cana-1287	217	16	tool	tool	PROPN
cana-1287	217	17	python	python	PROPN
cana-1287	217	18	,	,	PUNCT
cana-1287	217	19	tensorflow	tensorflow	NOUN
cana-1287	217	20	gpu	gpu	PROPN
cana-1287	217	21	model	model	NOUN
cana-1287	217	22	nvidia	nvidia	PROPN
cana-1287	217	23	rtx	rtx	PROPN
cana-1287	217	24	3090	3090	NUM
cana-1287	217	25	cpu	cpu	NOUN
cana-1287	217	26	configuration	configuration	NOUN
cana-1287	217	27	16	16	NUM
cana-1287	217	28	cores	core	NOUN
cana-1287	217	29	ram	ram	VERB
cana-1287	217	30	64	64	NUM
cana-1287	217	31	gb	gb	NOUN
cana-1287	217	32	number	number	NOUN
cana-1287	217	33	of	of	ADP
cana-1287	217	34	gpus	gpus	PROPN
cana-1287	217	35	8	8	NUM
cana-1287	217	36	deep	deep	ADJ
cana-1287	217	37	rbf	rbf	PROPN
cana-1287	217	38	network	network	NOUN
cana-1287	217	39	layers	layer	VERB
cana-1287	217	40	3	3	NUM
cana-1287	217	41	hidden	hide	VERB
cana-1287	217	42	layers	layer	NOUN
cana-1287	217	43	radial	radial	ADJ
cana-1287	217	44	basis	basis	NOUN
cana-1287	217	45	function	function	NOUN
cana-1287	217	46	type	type	NOUN
cana-1287	217	47	gaussian	gaussian	ADJ
cana-1287	217	48	training	training	NOUN
cana-1287	217	49	epochs	epoch	NOUN
cana-1287	217	50	1000	1000	NUM
cana-1287	217	51	batch	batch	NOUN
cana-1287	217	52	size	size	NOUN
cana-1287	217	53	32	32	NUM
cana-1287	217	54	learning	learning	NOUN
cana-1287	217	55	rate	rate	NOUN
cana-1287	217	56	0.001	0.001	NUM
cana-1287	217	57	optimization	optimization	NOUN
cana-1287	217	58	algorithm	algorithm	NOUN
cana-1287	217	59	adam	adam	PROPN
cana-1287	217	60	data	data	PROPN
cana-1287	217	61	split	split	VERB
cana-1287	217	62	ratio	ratio	NOUN
cana-1287	217	63	80:10:10	80:10:10	NUM
cana-1287	217	64	regularization	regularization	NOUN
cana-1287	217	65	method	method	NOUN
cana-1287	217	66	l2	l2	NOUN
cana-1287	217	67	regularization	regularization	NOUN
cana-1287	217	68	performance	performance	NOUN
cana-1287	217	69	metrics	metric	NOUN
cana-1287	217	70	•	•	NOUN
cana-1287	217	71	mean	mean	VERB
cana-1287	217	72	squared	square	VERB
cana-1287	217	73	error	error	NOUN
cana-1287	217	74	(	(	PUNCT
cana-1287	217	75	mse	mse	NOUN
cana-1287	217	76	):	):	PUNCT
cana-1287	217	77	it	it	PRON
cana-1287	217	78	gauges	gauge	VERB
cana-1287	217	79	how	how	SCONJ
cana-1287	217	80	much	much	ADV
cana-1287	217	81	the	the	DET
cana-1287	217	82	expected	expect	VERB
cana-1287	217	83	values	value	NOUN
cana-1287	217	84	vary	vary	VERB
cana-1287	217	85	from	from	ADP
cana-1287	217	86	the	the	DET
cana-1287	217	87	actual	actual	ADJ
cana-1287	217	88	values	value	NOUN
cana-1287	217	89	even	even	ADV
cana-1287	217	90	if	if	SCONJ
cana-1287	217	91	lower	low	ADJ
cana-1287	217	92	mse	mse	PROPN
cana-1287	217	93	suggests	suggest	VERB
cana-1287	217	94	better	well	ADJ
cana-1287	217	95	model	model	NOUN
cana-1287	217	96	performance	performance	NOUN
cana-1287	217	97	.	.	PUNCT
cana-1287	218	1	it	it	PRON
cana-1287	218	2	is	be	AUX
cana-1287	218	3	particularly	particularly	ADV
cana-1287	218	4	useful	useful	ADJ
cana-1287	218	5	for	for	ADP
cana-1287	218	6	punishing	punish	VERB
cana-1287	218	7	more	more	ADV
cana-1287	218	8	forcefully	forcefully	ADV
cana-1287	218	9	bigger	big	ADJ
cana-1287	218	10	mistakes	mistake	NOUN
cana-1287	218	11	,	,	PUNCT
cana-1287	218	12	hence	hence	ADV
cana-1287	218	13	assessing	assess	VERB
cana-1287	218	14	the	the	DET
cana-1287	218	15	precision	precision	NOUN
cana-1287	218	16	of	of	ADP
cana-1287	218	17	regression	regression	NOUN
cana-1287	218	18	models	model	NOUN
cana-1287	218	19	.	.	PUNCT
cana-1287	219	1	•	•	NOUN
cana-1287	219	2	coefficient	coefficient	NOUN
cana-1287	219	3	of	of	ADP
cana-1287	219	4	determination	determination	NOUN
cana-1287	219	5	(	(	PUNCT
cana-1287	219	6	r²	r²	NOUN
cana-1287	219	7	):	):	PUNCT
cana-1287	219	8	it	it	PRON
cana-1287	219	9	goes	go	VERB
cana-1287	219	10	from	from	ADP
cana-1287	219	11	0	0	NUM
cana-1287	219	12	to	to	ADP
cana-1287	219	13	1	1	NUM
cana-1287	219	14	;	;	PUNCT
cana-1287	219	15	a	a	DET
cana-1287	219	16	value	value	NOUN
cana-1287	219	17	of	of	ADP
cana-1287	219	18	0	0	NUM
cana-1287	219	19	indicates	indicate	VERB
cana-1287	219	20	no	no	DET
cana-1287	219	21	predictive	predictive	ADJ
cana-1287	219	22	ability	ability	NOUN
cana-1287	219	23	and	and	CCONJ
cana-1287	219	24	a	a	DET
cana-1287	219	25	score	score	NOUN
cana-1287	219	26	of	of	ADP
cana-1287	219	27	1	1	NUM
cana-1287	219	28	indicates	indicate	VERB
cana-1287	219	29	ideal	ideal	ADJ
cana-1287	219	30	prediction	prediction	NOUN
cana-1287	219	31	.	.	PUNCT
cana-1287	220	1	greater	great	ADJ
cana-1287	220	2	r²	r²	NOUN
cana-1287	220	3	values	value	NOUN
cana-1287	220	4	reflect	reflect	VERB
cana-1287	220	5	better	well	ADJ
cana-1287	220	6	fit	fit	NOUN
cana-1287	220	7	of	of	ADP
cana-1287	220	8	the	the	DET
cana-1287	220	9	model	model	NOUN
cana-1287	220	10	to	to	ADP
cana-1287	220	11	the	the	DET
cana-1287	220	12	data	datum	NOUN
cana-1287	220	13	.	.	PUNCT
cana-1287	221	1	•	•	NUM
cana-1287	221	2	root	root	NOUN
cana-1287	221	3	mean	mean	VERB
cana-1287	221	4	squared	square	VERB
cana-1287	221	5	error	error	NOUN
cana-1287	221	6	(	(	PUNCT
cana-1287	221	7	rmse	rmse	NOUN
cana-1287	221	8	):	):	PUNCT
cana-1287	221	9	root	root	NOUN
cana-1287	221	10	mean	mean	VERB
cana-1287	221	11	squared	square	VERB
cana-1287	221	12	error	error	NOUN
cana-1287	221	13	,	,	PUNCT
cana-1287	221	14	or	or	CCONJ
cana-1287	221	15	rmse	rmse	NOUN
cana-1287	221	16	,	,	PUNCT
cana-1287	221	17	has	have	VERB
cana-1287	221	18	an	an	DET
cana-1287	221	19	error	error	NOUN
cana-1287	221	20	measure	measure	NOUN
cana-1287	221	21	in	in	ADP
cana-1287	221	22	the	the	DET
cana-1287	221	23	same	same	ADJ
cana-1287	221	24	units	unit	NOUN
cana-1287	221	25	as	as	ADP
cana-1287	221	26	the	the	DET
cana-1287	221	27	target	target	NOUN
cana-1287	221	28	variable	variable	NOUN
cana-1287	221	29	.	.	PUNCT
cana-1287	222	1	it	it	PRON
cana-1287	222	2	offers	offer	VERB
cana-1287	222	3	a	a	DET
cana-1287	222	4	more	more	ADV
cana-1287	222	5	reasonable	reasonable	ADJ
cana-1287	222	6	estimate	estimate	NOUN
cana-1287	222	7	of	of	ADP
cana-1287	222	8	prediction	prediction	NOUN
cana-1287	222	9	accuracy	accuracy	NOUN
cana-1287	222	10	when	when	SCONJ
cana-1287	222	11	lower	low	ADJ
cana-1287	222	12	rmse	rmse	NOUN
cana-1287	222	13	values	value	NOUN
cana-1287	222	14	indicate	indicate	VERB
cana-1287	222	15	better	well	ADJ
cana-1287	222	16	performance	performance	NOUN
cana-1287	222	17	.	.	PUNCT
cana-1287	223	1	•	•	NUM
cana-1287	223	2	mean	mean	VERB
cana-1287	223	3	absolute	absolute	ADJ
cana-1287	223	4	error	error	NOUN
cana-1287	223	5	(	(	PUNCT
cana-1287	223	6	mae	mae	PROPN
cana-1287	223	7	):	):	PUNCT
cana-1287	223	8	mean	mean	ADJ
cana-1287	223	9	absolute	absolute	ADJ
cana-1287	223	10	error	error	NOUN
cana-1287	223	11	,	,	PUNCT
cana-1287	223	12	or	or	CCONJ
cana-1287	223	13	mae	mae	PROPN
cana-1287	223	14	,	,	PUNCT
cana-1287	223	15	measures	measure	VERB
cana-1287	223	16	the	the	DET
cana-1287	223	17	mean	mean	ADJ
cana-1287	223	18	absolute	absolute	ADJ
cana-1287	223	19	difference	difference	NOUN
cana-1287	223	20	between	between	ADP
cana-1287	223	21	the	the	DET
cana-1287	223	22	projected	project	VERB
cana-1287	223	23	and	and	CCONJ
cana-1287	223	24	actual	actual	ADJ
cana-1287	223	25	counts	count	NOUN
cana-1287	223	26	.	.	PUNCT
cana-1287	224	1	since	since	SCONJ
cana-1287	224	2	unlike	unlike	ADP
cana-1287	224	3	mse	mse	NOUN
cana-1287	224	4	it	it	PRON
cana-1287	224	5	does	do	AUX
cana-1287	224	6	not	not	PART
cana-1287	224	7	square	square	VERB
cana-1287	224	8	the	the	DET
cana-1287	224	9	errors	error	NOUN
cana-1287	224	10	,	,	PUNCT
cana-1287	224	11	so	so	SCONJ
cana-1287	224	12	it	it	PRON
cana-1287	224	13	is	be	AUX
cana-1287	224	14	less	less	ADV
cana-1287	224	15	sensitive	sensitive	ADJ
cana-1287	224	16	to	to	ADP
cana-1287	224	17	outliers	outlier	NOUN
cana-1287	224	18	.	.	PUNCT
cana-1287	225	1	mae	mae	PROPN
cana-1287	225	2	provides	provide	VERB
cana-1287	225	3	a	a	DET
cana-1287	225	4	basic	basic	ADJ
cana-1287	225	5	estimate	estimate	NOUN
cana-1287	225	6	of	of	ADP
cana-1287	225	7	prediction	prediction	NOUN
cana-1287	225	8	accuracy	accuracy	NOUN
cana-1287	225	9	.	.	PUNCT
cana-1287	226	1	•	•	NUM
cana-1287	226	2	adjusted	adjust	VERB
cana-1287	226	3	r²	r²	NOUN
cana-1287	226	4	:	:	PUNCT
cana-1287	226	5	it	it	PRON
cana-1287	226	6	analyzes	analyze	VERB
cana-1287	226	7	the	the	DET
cana-1287	226	8	complexity	complexity	NOUN
cana-1287	226	9	of	of	ADP
cana-1287	226	10	the	the	DET
cana-1287	226	11	model	model	NOUN
cana-1287	226	12	,	,	PUNCT
cana-1287	226	13	it	it	PRON
cana-1287	226	14	helps	help	VERB
cana-1287	226	15	one	one	NUM
cana-1287	226	16	to	to	PART
cana-1287	226	17	compare	compare	VERB
cana-1287	226	18	models	model	NOUN
cana-1287	226	19	with	with	ADP
cana-1287	226	20	different	different	ADJ
cana-1287	226	21	amounts	amount	NOUN
cana-1287	226	22	of	of	ADP
cana-1287	226	23	predictors	predictor	NOUN
cana-1287	226	24	.	.	PUNCT
cana-1287	227	1	•	•	NUM
cana-1287	227	2	mean	mean	VERB
cana-1287	227	3	absolute	absolute	ADJ
cana-1287	227	4	percentage	percentage	NOUN
cana-1287	227	5	error	error	NOUN
cana-1287	227	6	(	(	PUNCT
cana-1287	227	7	mape	mape	NOUN
cana-1287	227	8	):	):	PUNCT
cana-1287	227	9	lower	low	ADJ
cana-1287	227	10	mape	mape	NOUN
cana-1287	227	11	values	value	NOUN
cana-1287	227	12	especially	especially	ADV
cana-1287	227	13	when	when	SCONJ
cana-1287	227	14	comparing	compare	VERB
cana-1287	227	15	forecasts	forecast	NOUN
cana-1287	227	16	over	over	ADP
cana-1287	227	17	several	several	ADJ
cana-1287	227	18	scales	scale	NOUN
cana-1287	227	19	or	or	CCONJ
cana-1287	227	20	units	unit	NOUN
cana-1287	227	21	indicate	indicate	VERB
cana-1287	227	22	better	well	ADJ
cana-1287	227	23	model	model	NOUN
cana-1287	227	24	performance	performance	NOUN
cana-1287	227	25	.	.	PUNCT
cana-1287	228	1	table	table	NOUN
cana-1287	228	2	4	4	NUM
cana-1287	228	3	:	:	PUNCT
cana-1287	228	4	performance	performance	NOUN
cana-1287	228	5	comparison	comparison	NOUN
cana-1287	228	6	over	over	ADP
cana-1287	228	7	training	training	NOUN
cana-1287	228	8	,	,	PUNCT
cana-1287	228	9	testing	testing	NOUN
cana-1287	228	10	and	and	CCONJ
cana-1287	228	11	validation	validation	NOUN
cana-1287	228	12	method	method	NOUN
cana-1287	228	13	dataset	dataset	NOUN
cana-1287	228	14	mse	mse	NOUN
cana-1287	228	15	r²	r²	PROPN
cana-1287	228	16	rmse	rmse	PROPN
cana-1287	228	17	mae	mae	PROPN
cana-1287	228	18	adjusted	adjust	VERB
cana-1287	228	19	r²	r²	NOUN
cana-1287	228	20	mape	mape	NOUN
cana-1287	228	21	(	(	PUNCT
cana-1287	228	22	%	%	NOUN
cana-1287	228	23	)	)	PUNCT
cana-1287	228	24	ann	ann	PROPN
cana-1287	228	25	training	training	NOUN
cana-1287	228	26	0.040	0.040	NUM
cana-1287	228	27	0.82	0.82	NUM
cana-1287	228	28	0.200	0.200	NUM
cana-1287	228	29	0.150	0.150	NUM
cana-1287	228	30	0.81	0.81	NUM
cana-1287	228	31	5.2	5.2	NUM
cana-1287	228	32	testing	testing	NOUN
cana-1287	228	33	0.045	0.045	NUM
cana-1287	228	34	0.80	0.80	NUM
cana-1287	228	35	0.213	0.213	NUM
cana-1287	228	36	0.160	0.160	NUM
cana-1287	228	37	0.79	0.79	NUM
cana-1287	228	38	5.8	5.8	NUM
cana-1287	228	39	validation	validation	NOUN
cana-1287	228	40	0.048	0.048	NUM
cana-1287	228	41	0.78	0.78	NUM
cana-1287	228	42	0.219	0.219	NUM
cana-1287	228	43	0.165	0.165	NUM
cana-1287	228	44	0.77	0.77	NUM
cana-1287	228	45	6.0	6.0	NUM
cana-1287	228	46	bi	bi	PROPN
cana-1287	228	47	-	-	NOUN
cana-1287	228	48	gru	gru	NOUN
cana-1287	228	49	-	-	ADJ
cana-1287	228	50	dmn	dmn	NOUN
cana-1287	228	51	training	training	NOUN
cana-1287	228	52	0.035	0.035	NUM
cana-1287	228	53	0.85	0.85	NUM
cana-1287	228	54	0.187	0.187	NUM
cana-1287	228	55	0.140	0.140	NUM
cana-1287	228	56	0.84	0.84	NUM
cana-1287	228	57	4.7	4.7	NUM
cana-1287	228	58	testing	testing	NOUN
cana-1287	228	59	0.038	0.038	NUM
cana-1287	228	60	0.83	0.83	NUM
cana-1287	228	61	0.195	0.195	NUM
cana-1287	228	62	0.148	0.148	NUM
cana-1287	228	63	0.82	0.82	NUM
cana-1287	228	64	5.1	5.1	NUM
cana-1287	228	65	communications	communication	NOUN
cana-1287	228	66	on	on	ADP
cana-1287	228	67	applied	apply	VERB
cana-1287	228	68	nonlinear	nonlinear	ADJ
cana-1287	228	69	analysis	analysis	NOUN
cana-1287	228	70	issn	issn	NOUN
cana-1287	228	71	:	:	PUNCT
cana-1287	228	72	1074	1074	NUM
cana-1287	228	73	-	-	PUNCT
cana-1287	228	74	133x	133x	NUM
cana-1287	228	75	vol	vol	NOUN
cana-1287	228	76	31	31	NUM
cana-1287	228	77	no	no	NOUN
cana-1287	228	78	.	.	PUNCT
cana-1287	229	1	7s	7	NOUN
cana-1287	229	2	(	(	PUNCT
cana-1287	229	3	2024	2024	NUM
cana-1287	229	4	)	)	PUNCT
cana-1287	229	5	105	105	NUM
cana-1287	229	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	229	7	validation	validation	NOUN
cana-1287	229	8	0.041	0.041	NUM
cana-1287	229	9	0.81	0.81	NUM
cana-1287	229	10	0.202	0.202	NUM
cana-1287	229	11	0.153	0.153	NUM
cana-1287	229	12	0.80	0.80	NUM
cana-1287	229	13	5.3	5.3	NUM
cana-1287	229	14	rfr	rfr	PROPN
cana-1287	229	15	training	training	NOUN
cana-1287	229	16	0.042	0.042	NUM
cana-1287	229	17	0.83	0.83	NUM
cana-1287	229	18	0.205	0.205	NUM
cana-1287	229	19	0.155	0.155	NUM
cana-1287	229	20	0.82	0.82	NUM
cana-1287	229	21	5.4	5.4	NUM
cana-1287	229	22	testing	testing	NOUN
cana-1287	229	23	0.046	0.046	NUM
cana-1287	229	24	0.80	0.80	NUM
cana-1287	229	25	0.214	0.214	NUM
cana-1287	229	26	0.163	0.163	NUM
cana-1287	229	27	0.79	0.79	NUM
cana-1287	229	28	5.9	5.9	NUM
cana-1287	229	29	validation	validation	NOUN
cana-1287	229	30	0.049	0.049	NUM
cana-1287	229	31	0.77	0.77	NUM
cana-1287	229	32	0.221	0.221	NUM
cana-1287	229	33	0.168	0.168	NUM
cana-1287	229	34	0.76	0.76	NUM
cana-1287	229	35	6.2	6.2	NUM
cana-1287	229	36	rnn	rnn	NOUN
cana-1287	229	37	training	train	VERB
cana-1287	229	38	0.037	0.037	NUM
cana-1287	229	39	0.84	0.84	NUM
cana-1287	229	40	0.192	0.192	NUM
cana-1287	229	41	0.145	0.145	NUM
cana-1287	229	42	0.83	0.83	NUM
cana-1287	229	43	4.9	4.9	NUM
cana-1287	229	44	testing	testing	NOUN
cana-1287	229	45	0.040	0.040	NUM
cana-1287	229	46	0.82	0.82	NUM
cana-1287	229	47	0.200	0.200	NUM
cana-1287	229	48	0.152	0.152	NUM
cana-1287	229	49	0.81	0.81	NUM
cana-1287	229	50	5.4	5.4	NUM
cana-1287	229	51	validation	validation	NOUN
cana-1287	229	52	0.043	0.043	NUM
cana-1287	229	53	0.80	0.80	NUM
cana-1287	229	54	0.207	0.207	NUM
cana-1287	229	55	0.156	0.156	NUM
cana-1287	229	56	0.79	0.79	NUM
cana-1287	229	57	5.6	5.6	NUM
cana-1287	229	58	proposed	propose	VERB
cana-1287	229	59	rbf	rbf	PROPN
cana-1287	229	60	training	training	NOUN
cana-1287	229	61	0.032	0.032	NUM
cana-1287	229	62	0.87	0.87	NUM
cana-1287	229	63	0.179	0.179	NUM
cana-1287	229	64	0.130	0.130	NUM
cana-1287	229	65	0.86	0.86	NUM
cana-1287	229	66	4.5	4.5	NUM
cana-1287	229	67	testing	test	VERB
cana-1287	229	68	0.035	0.035	NUM
cana-1287	229	69	0.85	0.85	NUM
cana-1287	229	70	0.187	0.187	NUM
cana-1287	229	71	0.138	0.138	NUM
cana-1287	229	72	0.84	0.84	NUM
cana-1287	229	73	4.8	4.8	NUM
cana-1287	229	74	validation	validation	NOUN
cana-1287	229	75	0.038	0.038	NUM
cana-1287	229	76	0.83	0.83	NUM
cana-1287	229	77	0.195	0.195	NUM
cana-1287	229	78	0.144	0.144	NUM
cana-1287	229	79	0.82	0.82	NUM
cana-1287	229	80	5.0	5.0	NUM
cana-1287	229	81	consistently	consistently	ADV
cana-1287	229	82	outperforming	outperform	VERB
cana-1287	229	83	current	current	ADJ
cana-1287	229	84	techniques	technique	NOUN
cana-1287	229	85	over	over	ADP
cana-1287	229	86	all	all	DET
cana-1287	229	87	datasets	dataset	NOUN
cana-1287	229	88	,	,	PUNCT
cana-1287	229	89	the	the	DET
cana-1287	229	90	proposed	propose	VERB
cana-1287	229	91	deep	deep	ADJ
cana-1287	229	92	radial	radial	ADJ
cana-1287	229	93	basis	basis	NOUN
cana-1287	229	94	function	function	NOUN
cana-1287	229	95	(	(	PUNCT
cana-1287	229	96	rbf	rbf	PROPN
cana-1287	229	97	)	)	PUNCT
cana-1287	229	98	network	network	NOUN
cana-1287	229	99	shows	show	VERB
cana-1287	229	100	table	table	NOUN
cana-1287	229	101	4	4	NUM
cana-1287	229	102	indicating	indicate	VERB
cana-1287	229	103	better	well	ADJ
cana-1287	229	104	accuracy	accuracy	NOUN
cana-1287	229	105	and	and	CCONJ
cana-1287	229	106	model	model	NOUN
cana-1287	229	107	fit	fit	PROPN
cana-1287	229	108	,	,	PUNCT
cana-1287	229	109	the	the	DET
cana-1287	229	110	rbf	rbf	PROPN
cana-1287	229	111	network	network	NOUN
cana-1287	229	112	performs	perform	VERB
cana-1287	229	113	the	the	DET
cana-1287	229	114	lowest	low	ADJ
cana-1287	229	115	mean	mean	NOUN
cana-1287	229	116	squared	square	VERB
cana-1287	229	117	error	error	NOUN
cana-1287	229	118	(	(	PUNCT
cana-1287	229	119	mse	mse	NOUN
cana-1287	229	120	)	)	PUNCT
cana-1287	229	121	of	of	ADP
cana-1287	229	122	0.032	0.032	NUM
cana-1287	229	123	and	and	CCONJ
cana-1287	229	124	the	the	DET
cana-1287	229	125	highest	high	ADJ
cana-1287	229	126	coefficient	coefficient	NOUN
cana-1287	229	127	of	of	ADP
cana-1287	229	128	determination	determination	NOUN
cana-1287	229	129	(	(	PUNCT
cana-1287	229	130	r2	r2	PROPN
cana-1287	229	131	)	)	PUNCT
cana-1287	229	132	of	of	ADP
cana-1287	229	133	0.87	0.87	NUM
cana-1287	229	134	for	for	ADP
cana-1287	229	135	training	training	NOUN
cana-1287	229	136	datasets	dataset	NOUN
cana-1287	229	137	.	.	PUNCT
cana-1287	230	1	with	with	ADP
cana-1287	230	2	a	a	DET
cana-1287	230	3	mean	mean	ADJ
cana-1287	230	4	absolute	absolute	ADJ
cana-1287	230	5	error	error	NOUN
cana-1287	230	6	(	(	PUNCT
cana-1287	230	7	mae	mae	PROPN
cana-1287	230	8	)	)	PUNCT
cana-1287	230	9	of	of	ADP
cana-1287	230	10	0.130	0.130	NUM
cana-1287	230	11	and	and	CCONJ
cana-1287	230	12	a	a	DET
cana-1287	230	13	root	root	NOUN
cana-1287	230	14	mean	mean	VERB
cana-1287	230	15	squared	square	VERB
cana-1287	230	16	error	error	NOUN
cana-1287	230	17	(	(	PUNCT
cana-1287	230	18	rmse	rmse	NOUN
cana-1287	230	19	)	)	PUNCT
cana-1287	230	20	of	of	ADP
cana-1287	230	21	0.179	0.179	NUM
cana-1287	230	22	,	,	PUNCT
cana-1287	230	23	the	the	DET
cana-1287	230	24	rbf	rbf	PROPN
cana-1287	230	25	model	model	PROPN
cana-1287	230	26	's	's	PART
cana-1287	230	27	mean	mean	ADJ
cana-1287	230	28	absolute	absolute	ADJ
cana-1287	230	29	error	error	NOUN
cana-1287	230	30	(	(	PUNCT
cana-1287	230	31	mae	mae	PROPN
cana-1287	230	32	)	)	PUNCT
cana-1287	230	33	is	be	AUX
cana-1287	230	34	lower	low	ADJ
cana-1287	230	35	than	than	ADP
cana-1287	230	36	those	those	PRON
cana-1287	230	37	of	of	ADP
cana-1287	230	38	other	other	ADJ
cana-1287	230	39	methods	method	NOUN
cana-1287	230	40	,	,	PUNCT
cana-1287	230	41	therefore	therefore	ADV
cana-1287	230	42	showing	show	VERB
cana-1287	230	43	reduced	reduce	VERB
cana-1287	230	44	prediction	prediction	NOUN
cana-1287	230	45	error	error	NOUN
cana-1287	230	46	.	.	PUNCT
cana-1287	231	1	adjusted	adjust	VERB
cana-1287	231	2	r²	r²	NOUN
cana-1287	231	3	and	and	CCONJ
cana-1287	231	4	mean	mean	VERB
cana-1287	231	5	absolute	absolute	ADJ
cana-1287	231	6	percentage	percentage	NOUN
cana-1287	231	7	error	error	NOUN
cana-1287	231	8	(	(	PUNCT
cana-1287	231	9	mape	mape	NOUN
cana-1287	231	10	)	)	PUNCT
cana-1287	231	11	measurements	measurement	NOUN
cana-1287	231	12	also	also	ADV
cana-1287	231	13	help	help	VERB
cana-1287	231	14	for	for	ADP
cana-1287	231	15	the	the	DET
cana-1287	231	16	rbf	rbf	PROPN
cana-1287	231	17	network	network	PROPN
cana-1287	231	18	reflection	reflection	NOUN
cana-1287	231	19	of	of	ADP
cana-1287	231	20	resilience	resilience	NOUN
cana-1287	231	21	and	and	CCONJ
cana-1287	231	22	accuracy	accuracy	NOUN
cana-1287	231	23	.	.	PUNCT
cana-1287	232	1	for	for	ADP
cana-1287	232	2	testing	testing	NOUN
cana-1287	232	3	and	and	CCONJ
cana-1287	232	4	validation	validation	NOUN
cana-1287	232	5	datasets	dataset	NOUN
cana-1287	232	6	,	,	PUNCT
cana-1287	232	7	the	the	DET
cana-1287	232	8	rbf	rbf	PROPN
cana-1287	232	9	network	network	PROPN
cana-1287	232	10	maintains	maintain	VERB
cana-1287	232	11	leadership	leadership	NOUN
cana-1287	232	12	with	with	ADP
cana-1287	232	13	lower	low	ADJ
cana-1287	232	14	mse	mse	NOUN
cana-1287	232	15	,	,	PUNCT
cana-1287	232	16	rmse	rmse	NOUN
cana-1287	232	17	,	,	PUNCT
cana-1287	232	18	and	and	CCONJ
cana-1287	232	19	mae	mae	PROPN
cana-1287	232	20	values	value	NOUN
cana-1287	232	21	than	than	ADP
cana-1287	232	22	ann	ann	PROPN
cana-1287	232	23	,	,	PUNCT
cana-1287	232	24	bi	bi	PROPN
cana-1287	232	25	-	-	PROPN
cana-1287	232	26	gru	gru	NOUN
cana-1287	232	27	-	-	NOUN
cana-1287	232	28	dmn	dmn	NOUN
cana-1287	232	29	,	,	PUNCT
cana-1287	232	30	rfr	rfr	PROPN
cana-1287	232	31	,	,	PUNCT
cana-1287	232	32	and	and	CCONJ
cana-1287	232	33	rnn	rnn	VERB
cana-1287	232	34	.	.	PUNCT
cana-1287	233	1	the	the	DET
cana-1287	233	2	r²	r²	ADJ
cana-1287	233	3	values	value	NOUN
cana-1287	233	4	still	still	ADV
cana-1287	233	5	show	show	VERB
cana-1287	233	6	the	the	DET
cana-1287	233	7	best	good	ADJ
cana-1287	233	8	since	since	SCONJ
cana-1287	233	9	the	the	DET
cana-1287	233	10	rbf	rbf	PROPN
cana-1287	233	11	model	model	NOUN
cana-1287	233	12	continuously	continuously	ADV
cana-1287	233	13	effectively	effectively	ADV
cana-1287	233	14	caputrees	caputree	VERB
cana-1287	233	15	the	the	DET
cana-1287	233	16	volatility	volatility	NOUN
cana-1287	233	17	in	in	ADP
cana-1287	233	18	soil	soil	NOUN
cana-1287	233	19	properties	property	NOUN
cana-1287	233	20	.	.	PUNCT
cana-1287	234	1	consequently	consequently	ADV
cana-1287	234	2	,	,	PUNCT
cana-1287	234	3	the	the	DET
cana-1287	234	4	results	result	NOUN
cana-1287	234	5	provide	provide	VERB
cana-1287	234	6	more	more	ADV
cana-1287	234	7	consistent	consistent	ADJ
cana-1287	234	8	forecasts	forecast	NOUN
cana-1287	234	9	than	than	ADP
cana-1287	234	10	current	current	ADJ
cana-1287	234	11	techniques	technique	NOUN
cana-1287	234	12	since	since	SCONJ
cana-1287	234	13	the	the	DET
cana-1287	234	14	rbf	rbf	PROPN
cana-1287	234	15	network	network	NOUN
cana-1287	234	16	represents	represent	VERB
cana-1287	234	17	complex	complex	ADJ
cana-1287	234	18	nonlinear	nonlinear	ADJ
cana-1287	234	19	interactions	interaction	NOUN
cana-1287	234	20	with	with	ADP
cana-1287	234	21	great	great	ADJ
cana-1287	234	22	accuracy	accuracy	NOUN
cana-1287	234	23	.	.	PUNCT
cana-1287	235	1	figure	figure	NOUN
cana-1287	235	2	2	2	NUM
cana-1287	235	3	:	:	PUNCT
cana-1287	235	4	mse	mse	NOUN
cana-1287	235	5	figure	figure	NOUN
cana-1287	235	6	3	3	NUM
cana-1287	235	7	:	:	PUNCT
cana-1287	235	8	r²	r²	VERB
cana-1287	235	9	communications	communication	NOUN
cana-1287	235	10	on	on	ADP
cana-1287	235	11	applied	apply	VERB
cana-1287	235	12	nonlinear	nonlinear	ADJ
cana-1287	235	13	analysis	analysis	NOUN
cana-1287	235	14	issn	issn	NOUN
cana-1287	235	15	:	:	PUNCT
cana-1287	235	16	1074	1074	NUM
cana-1287	235	17	-	-	PUNCT
cana-1287	235	18	133x	133x	NUM
cana-1287	235	19	vol	vol	NOUN
cana-1287	235	20	31	31	NUM
cana-1287	235	21	no	no	NOUN
cana-1287	235	22	.	.	PUNCT
cana-1287	236	1	7s	7	NOUN
cana-1287	236	2	(	(	PUNCT
cana-1287	236	3	2024	2024	NUM
cana-1287	236	4	)	)	PUNCT
cana-1287	236	5	106	106	NUM
cana-1287	237	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	237	2	figure	figure	NOUN
cana-1287	237	3	4	4	NUM
cana-1287	237	4	:	:	PUNCT
cana-1287	237	5	rmse	rmse	ADJ
cana-1287	237	6	figure	figure	NOUN
cana-1287	237	7	5	5	NUM
cana-1287	237	8	:	:	PUNCT
cana-1287	237	9	mae	mae	PROPN
cana-1287	237	10	figure	figure	NOUN
cana-1287	237	11	6	6	NUM
cana-1287	237	12	:	:	PUNCT
cana-1287	237	13	adjusted	adjust	VERB
cana-1287	237	14	r²	r²	NOUN
cana-1287	237	15	figure	figure	NOUN
cana-1287	237	16	7	7	NUM
cana-1287	237	17	:	:	PUNCT
cana-1287	237	18	mape	mape	NOUN
cana-1287	237	19	(	(	PUNCT
cana-1287	237	20	%	%	INTJ
cana-1287	237	21	)	)	PUNCT
cana-1287	237	22	the	the	DET
cana-1287	237	23	proposed	propose	VERB
cana-1287	237	24	deep	deep	ADJ
cana-1287	237	25	radial	radial	ADJ
cana-1287	237	26	basis	basis	NOUN
cana-1287	237	27	function	function	NOUN
cana-1287	237	28	(	(	PUNCT
cana-1287	237	29	rbf	rbf	PROPN
cana-1287	237	30	)	)	PUNCT
cana-1287	237	31	network	network	NOUN
cana-1287	237	32	demonstrates	demonstrate	VERB
cana-1287	237	33	appreciable	appreciable	ADJ
cana-1287	237	34	performance	performance	NOUN
cana-1287	237	35	gain	gain	NOUN
cana-1287	237	36	in	in	ADP
cana-1287	237	37	figure	figure	NOUN
cana-1287	237	38	2	2	NUM
cana-1287	237	39	–	–	PUNCT
cana-1287	237	40	7	7	NUM
cana-1287	237	41	over	over	ADP
cana-1287	237	42	training	training	NOUN
cana-1287	237	43	epochs	epoch	NOUN
cana-1287	237	44	,	,	PUNCT
cana-1287	237	45	compared	compare	VERB
cana-1287	237	46	to	to	ADP
cana-1287	237	47	current	current	ADJ
cana-1287	237	48	methods	method	NOUN
cana-1287	237	49	.	.	PUNCT
cana-1287	238	1	first	first	ADV
cana-1287	238	2	at	at	ADP
cana-1287	238	3	250	250	NUM
cana-1287	238	4	epochs	epoch	NOUN
cana-1287	238	5	,	,	PUNCT
cana-1287	238	6	the	the	DET
cana-1287	238	7	rbf	rbf	PROPN
cana-1287	238	8	network	network	PROPN
cana-1287	238	9	finds	finds	AUX
cana-1287	238	10	mean	mean	VERB
cana-1287	238	11	squared	square	VERB
cana-1287	238	12	error	error	NOUN
cana-1287	238	13	(	(	PUNCT
cana-1287	238	14	mse	mse	NOUN
cana-1287	238	15	)	)	PUNCT
cana-1287	238	16	of	of	ADP
cana-1287	238	17	0.039	0.039	NUM
cana-1287	238	18	,	,	PUNCT
cana-1287	238	19	coefficient	coefficient	NOUN
cana-1287	238	20	of	of	ADP
cana-1287	238	21	determination	determination	NOUN
cana-1287	238	22	(	(	PUNCT
cana-1287	238	23	r²	r²	NOUN
cana-1287	238	24	)	)	PUNCT
cana-1287	238	25	of	of	ADP
cana-1287	238	26	0.82	0.82	NUM
cana-1287	238	27	,	,	PUNCT
cana-1287	238	28	and	and	CCONJ
cana-1287	238	29	rmse	rmse	NOUN
cana-1287	238	30	of	of	ADP
cana-1287	238	31	0.197	0.197	NUM
cana-1287	238	32	.	.	PUNCT
cana-1287	239	1	training	training	NOUN
cana-1287	239	2	runs	run	NOUN
cana-1287	239	3	to	to	ADP
cana-1287	239	4	1000	1000	NUM
cana-1287	239	5	epochs	epoch	NOUN
cana-1287	239	6	helps	help	VERB
cana-1287	239	7	these	these	DET
cana-1287	239	8	values	value	NOUN
cana-1287	239	9	to	to	PART
cana-1287	239	10	improve	improve	VERB
cana-1287	239	11	;	;	PUNCT
cana-1287	239	12	the	the	DET
cana-1287	239	13	rbf	rbf	PROPN
cana-1287	239	14	network	network	PROPN
cana-1287	239	15	achieves	achieve	VERB
cana-1287	239	16	mse	mse	NOUN
cana-1287	239	17	of	of	ADP
cana-1287	239	18	0.032	0.032	NUM
cana-1287	239	19	,	,	PUNCT
cana-1287	239	20	r²	r²	NOUN
cana-1287	239	21	of	of	ADP
cana-1287	239	22	0.87	0.87	NUM
cana-1287	239	23	,	,	PUNCT
cana-1287	239	24	and	and	CCONJ
cana-1287	239	25	rmse	rmse	NOUN
cana-1287	239	26	of	of	ADP
cana-1287	239	27	0.179	0.179	NUM
cana-1287	239	28	.	.	PUNCT
cana-1287	240	1	this	this	PRON
cana-1287	240	2	implies	imply	VERB
cana-1287	240	3	that	that	SCONJ
cana-1287	240	4	by	by	ADP
cana-1287	240	5	gradually	gradually	ADV
cana-1287	240	6	caputreing	caputree	VERB
cana-1287	240	7	the	the	DET
cana-1287	240	8	basic	basic	ADJ
cana-1287	240	9	trends	trend	NOUN
cana-1287	240	10	in	in	ADP
cana-1287	240	11	the	the	DET
cana-1287	240	12	data	datum	NOUN
cana-1287	240	13	,	,	PUNCT
cana-1287	240	14	the	the	DET
cana-1287	240	15	rbf	rbf	PROPN
cana-1287	240	16	model	model	NOUN
cana-1287	240	17	reduces	reduce	VERB
cana-1287	240	18	prediction	prediction	NOUN
cana-1287	240	19	errors	error	NOUN
cana-1287	240	20	and	and	CCONJ
cana-1287	240	21	increases	increase	NOUN
cana-1287	240	22	accuracy	accuracy	NOUN
cana-1287	240	23	.	.	PUNCT
cana-1287	241	1	current	current	ADJ
cana-1287	241	2	methods	method	NOUN
cana-1287	241	3	include	include	VERB
cana-1287	241	4	ann	ann	PROPN
cana-1287	241	5	,	,	PUNCT
cana-1287	241	6	bi	bi	PROPN
cana-1287	241	7	-	-	PROPN
cana-1287	241	8	gru	gru	NOUN
cana-1287	241	9	-	-	NOUN
cana-1287	241	10	dmn	dmn	NOUN
cana-1287	241	11	,	,	PUNCT
cana-1287	241	12	rfr	rfr	PROPN
cana-1287	241	13	,	,	PUNCT
cana-1287	241	14	and	and	CCONJ
cana-1287	241	15	rnn	rnn	VERB
cana-1287	241	16	show	show	VERB
cana-1287	241	17	slower	slow	ADJ
cana-1287	241	18	convergence	convergence	NOUN
cana-1287	241	19	and	and	CCONJ
cana-1287	241	20	less	less	ADJ
cana-1287	241	21	performance	performance	NOUN
cana-1287	241	22	measure	measure	NOUN
cana-1287	241	23	improvement	improvement	NOUN
cana-1287	241	24	with	with	ADP
cana-1287	241	25	additional	additional	ADJ
cana-1287	241	26	epochs	epoch	NOUN
cana-1287	241	27	.	.	PUNCT
cana-1287	242	1	for	for	ADP
cana-1287	242	2	1000	1000	NUM
cana-1287	242	3	epochs	epoch	NOUN
cana-1287	242	4	,	,	PUNCT
cana-1287	242	5	bi	bi	PROPN
cana-1287	242	6	-	-	NOUN
cana-1287	242	7	gru	gru	NOUN
cana-1287	242	8	-	-	NOUN
cana-1287	242	9	dmn	dmn	NOUN
cana-1287	242	10	for	for	ADP
cana-1287	242	11	example	example	NOUN
cana-1287	242	12	gets	get	VERB
cana-1287	242	13	a	a	DET
cana-1287	242	14	r²	r²	NOUN
cana-1287	242	15	of	of	ADP
cana-1287	242	16	0.86	0.86	NUM
cana-1287	242	17	,	,	PUNCT
cana-1287	242	18	still	still	ADV
cana-1287	242	19	less	less	ADJ
cana-1287	242	20	than	than	ADP
cana-1287	242	21	the	the	DET
cana-1287	242	22	r²	r²	NOUN
cana-1287	242	23	of	of	ADP
cana-1287	242	24	the	the	DET
cana-1287	242	25	rbf	rbf	PROPN
cana-1287	242	26	network	network	NOUN
cana-1287	242	27	.	.	PUNCT
cana-1287	243	1	likewise	likewise	ADV
cana-1287	243	2	,	,	PUNCT
cana-1287	243	3	rnn	rnn	PROPN
cana-1287	243	4	and	and	CCONJ
cana-1287	243	5	ann	ann	PROPN
cana-1287	243	6	models	model	NOUN
cana-1287	243	7	do	do	AUX
cana-1287	243	8	not	not	PART
cana-1287	243	9	show	show	VERB
cana-1287	243	10	as	as	ADP
cana-1287	243	11	substantial	substantial	ADJ
cana-1287	243	12	declines	decline	NOUN
cana-1287	243	13	in	in	ADP
cana-1287	243	14	mse	mse	NOUN
cana-1287	243	15	and	and	CCONJ
cana-1287	243	16	rmse	rmse	NOUN
cana-1287	243	17	,	,	PUNCT
cana-1287	243	18	therefore	therefore	ADV
cana-1287	243	19	highlighting	highlight	VERB
cana-1287	243	20	the	the	DET
cana-1287	243	21	improved	improved	ADJ
cana-1287	243	22	capacity	capacity	NOUN
cana-1287	243	23	of	of	ADP
cana-1287	243	24	the	the	DET
cana-1287	243	25	rbf	rbf	PROPN
cana-1287	243	26	network	network	NOUN
cana-1287	243	27	to	to	PART
cana-1287	243	28	explain	explain	VERB
cana-1287	243	29	complex	complex	ADJ
cana-1287	243	30	,	,	PUNCT
cana-1287	243	31	nonlinear	nonlinear	ADJ
cana-1287	243	32	interactions	interaction	NOUN
cana-1287	243	33	efficiently	efficiently	ADV
cana-1287	243	34	with	with	ADP
cana-1287	243	35	continuous	continuous	ADJ
cana-1287	243	36	training	training	NOUN
cana-1287	243	37	.	.	PUNCT
cana-1287	244	1	5	5	X
cana-1287	244	2	.	.	X
cana-1287	244	3	conclusion	conclusion	NOUN
cana-1287	244	4	in	in	ADP
cana-1287	244	5	this	this	DET
cana-1287	244	6	work	work	NOUN
cana-1287	244	7	,	,	PUNCT
cana-1287	244	8	we	we	PRON
cana-1287	244	9	studied	study	VERB
cana-1287	244	10	for	for	ADP
cana-1287	244	11	soil	soil	NOUN
cana-1287	244	12	nutrient	nutrient	NOUN
cana-1287	244	13	data	data	NOUN
cana-1287	244	14	classification	classification	NOUN
cana-1287	244	15	using	use	VERB
cana-1287	244	16	nonlinear	nonlinear	ADJ
cana-1287	244	17	regression	regression	NOUN
cana-1287	244	18	and	and	CCONJ
cana-1287	244	19	deep	deep	ADJ
cana-1287	244	20	radial	radial	ADJ
cana-1287	244	21	basis	basis	NOUN
cana-1287	244	22	function	function	NOUN
cana-1287	244	23	(	(	PUNCT
cana-1287	244	24	rbf	rbf	PROPN
cana-1287	244	25	)	)	PUNCT
cana-1287	244	26	networks	network	NOUN
cana-1287	244	27	.	.	PUNCT
cana-1287	245	1	by	by	ADP
cana-1287	245	2	means	mean	NOUN
cana-1287	245	3	of	of	ADP
cana-1287	245	4	nonlinear	nonlinear	ADJ
cana-1287	245	5	regression	regression	NOUN
cana-1287	245	6	techniques	technique	NOUN
cana-1287	245	7	,	,	PUNCT
cana-1287	245	8	the	the	DET
cana-1287	245	9	method	method	NOUN
cana-1287	245	10	captures	capture	VERB
cana-1287	245	11	intricate	intricate	ADJ
cana-1287	245	12	relationships	relationship	NOUN
cana-1287	245	13	between	between	ADP
cana-1287	245	14	soil	soil	NOUN
cana-1287	245	15	features	feature	NOUN
cana-1287	245	16	acquired	acquire	VERB
cana-1287	245	17	from	from	ADP
cana-1287	245	18	high	high	ADJ
cana-1287	245	19	-	-	PUNCT
cana-1287	245	20	resolution	resolution	NOUN
cana-1287	245	21	remote	remote	ADJ
cana-1287	245	22	sensing	sensing	NOUN
cana-1287	245	23	images	image	NOUN
cana-1287	245	24	,	,	PUNCT
cana-1287	245	25	hence	hence	ADV
cana-1287	245	26	improving	improve	VERB
cana-1287	245	27	the	the	DET
cana-1287	245	28	quality	quality	NOUN
cana-1287	245	29	and	and	CCONJ
cana-1287	245	30	relevance	relevance	NOUN
cana-1287	245	31	of	of	ADP
cana-1287	245	32	the	the	DET
cana-1287	245	33	input	input	NOUN
cana-1287	245	34	characteristics	characteristic	NOUN
cana-1287	245	35	.	.	PUNCT
cana-1287	246	1	these	these	DET
cana-1287	246	2	features	feature	NOUN
cana-1287	246	3	then	then	ADV
cana-1287	246	4	are	be	AUX
cana-1287	246	5	sent	send	VERB
cana-1287	246	6	communications	communication	NOUN
cana-1287	246	7	on	on	ADP
cana-1287	246	8	applied	apply	VERB
cana-1287	246	9	nonlinear	nonlinear	ADJ
cana-1287	246	10	analysis	analysis	NOUN
cana-1287	246	11	issn	issn	NOUN
cana-1287	246	12	:	:	PUNCT
cana-1287	246	13	1074	1074	NUM
cana-1287	246	14	-	-	PUNCT
cana-1287	246	15	133x	133x	NUM
cana-1287	246	16	vol	vol	NOUN
cana-1287	246	17	31	31	NUM
cana-1287	246	18	no	no	NOUN
cana-1287	246	19	.	.	PUNCT
cana-1287	247	1	7s	7	NOUN
cana-1287	247	2	(	(	PUNCT
cana-1287	247	3	2024	2024	NUM
cana-1287	247	4	)	)	PUNCT
cana-1287	247	5	107	107	NUM
cana-1287	247	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1287	247	7	into	into	ADP
cana-1287	247	8	a	a	DET
cana-1287	247	9	deep	deep	ADJ
cana-1287	247	10	rbf	rbf	PROPN
cana-1287	247	11	network	network	NOUN
cana-1287	247	12	with	with	ADP
cana-1287	247	13	radial	radial	ADJ
cana-1287	247	14	basis	basis	NOUN
cana-1287	247	15	functions	function	NOUN
cana-1287	247	16	and	and	CCONJ
cana-1287	247	17	many	many	ADJ
cana-1287	247	18	hidden	hidden	ADJ
cana-1287	247	19	layers	layer	NOUN
cana-1287	247	20	to	to	PART
cana-1287	247	21	mimic	mimic	VERB
cana-1287	247	22	complex	complex	ADJ
cana-1287	247	23	,	,	PUNCT
cana-1287	247	24	nonlinear	nonlinear	ADJ
cana-1287	247	25	patterns	pattern	NOUN
cana-1287	247	26	.	.	PUNCT
cana-1287	248	1	this	this	DET
cana-1287	248	2	combined	combine	VERB
cana-1287	248	3	approach	approach	NOUN
cana-1287	248	4	far	far	ADV
cana-1287	248	5	outperforms	outperform	VERB
cana-1287	248	6	standard	standard	ADJ
cana-1287	248	7	methods	method	NOUN
cana-1287	248	8	including	include	VERB
cana-1287	248	9	artificial	artificial	ADJ
cana-1287	248	10	neural	neural	ADJ
cana-1287	248	11	networks	network	NOUN
cana-1287	248	12	(	(	PUNCT
cana-1287	248	13	ann	ann	PROPN
cana-1287	248	14	)	)	PUNCT
cana-1287	248	15	,	,	PUNCT
cana-1287	248	16	bi	bi	ADJ
cana-1287	248	17	-	-	ADJ
cana-1287	248	18	directional	directional	ADJ
cana-1287	248	19	gated	gate	VERB
cana-1287	248	20	recurrent	recurrent	ADJ
cana-1287	248	21	unit	unit	NOUN
cana-1287	248	22	with	with	ADP
cana-1287	248	23	dynamic	dynamic	ADJ
cana-1287	248	24	memory	memory	NOUN
cana-1287	248	25	networks	network	NOUN
cana-1287	248	26	(	(	PUNCT
cana-1287	248	27	bigru	bigru	NOUN
cana-1287	248	28	-	-	PUNCT
cana-1287	248	29	dmn	dmn	NOUN
cana-1287	248	30	)	)	PUNCT
cana-1287	248	31	,	,	PUNCT
cana-1287	248	32	random	random	ADJ
cana-1287	248	33	forest	forest	NOUN
cana-1287	248	34	regression	regression	NOUN
cana-1287	248	35	(	(	PUNCT
cana-1287	248	36	rfr	rfr	PROPN
cana-1287	248	37	)	)	PUNCT
cana-1287	248	38	,	,	PUNCT
cana-1287	248	39	and	and	CCONJ
cana-1287	248	40	recurrent	recurrent	ADJ
cana-1287	248	41	neural	neural	ADJ
cana-1287	248	42	networks	network	NOUN
cana-1287	248	43	(	(	PUNCT
cana-1287	248	44	rnn	rnn	PROPN
cana-1287	248	45	)	)	PUNCT
cana-1287	248	46	,	,	PUNCT
cana-1287	248	47	our	our	PRON
cana-1287	248	48	study	study	NOUN
cana-1287	248	49	found	find	VERB
cana-1287	248	50	.	.	PUNCT
cana-1287	249	1	therefore	therefore	ADV
cana-1287	249	2	,	,	PUNCT
cana-1287	249	3	the	the	DET
cana-1287	249	4	proposed	propose	VERB
cana-1287	249	5	method	method	NOUN
cana-1287	249	6	offers	offer	VERB
cana-1287	249	7	a	a	DET
cana-1287	249	8	robust	robust	ADJ
cana-1287	249	9	and	and	CCONJ
cana-1287	249	10	precise	precise	ADJ
cana-1287	249	11	tool	tool	NOUN
cana-1287	249	12	for	for	ADP
cana-1287	249	13	the	the	DET
cana-1287	249	14	classification	classification	NOUN
cana-1287	249	15	of	of	ADP
cana-1287	249	16	soil	soil	NOUN
cana-1287	249	17	nutrients	nutrient	NOUN
cana-1287	249	18	,	,	PUNCT
cana-1287	249	19	thereby	thereby	ADV
cana-1287	249	20	boosting	boost	VERB
cana-1287	249	21	agricultural	agricultural	ADJ
cana-1287	249	22	control	control	NOUN
cana-1287	249	23	and	and	CCONJ
cana-1287	249	24	environmental	environmental	ADJ
cana-1287	249	25	planning	planning	NOUN
cana-1287	249	26	.	.	PUNCT
cana-1287	250	1	references	reference	NOUN
cana-1287	250	2	[	[	X
cana-1287	250	3	1	1	NUM
cana-1287	250	4	]	]	X
cana-1287	250	5	zhang	zhang	PROPN
cana-1287	250	6	,	,	PUNCT
cana-1287	250	7	y.	y.	PROPN
cana-1287	250	8	,	,	PUNCT
cana-1287	250	9	wei	wei	PROPN
cana-1287	250	10	,	,	PUNCT
cana-1287	250	11	l.	l.	PROPN
cana-1287	250	12	,	,	PUNCT
cana-1287	250	13	lu	lu	PROPN
cana-1287	250	14	,	,	PUNCT
cana-1287	250	15	q.	q.	PROPN
cana-1287	250	16	,	,	PUNCT
cana-1287	250	17	zhong	zhong	PROPN
cana-1287	250	18	,	,	PUNCT
cana-1287	250	19	y.	y.	PROPN
cana-1287	250	20	,	,	PUNCT
cana-1287	250	21	yuan	yuan	PROPN
cana-1287	250	22	,	,	PUNCT
cana-1287	250	23	z.	z.	PROPN
cana-1287	250	24	,	,	PUNCT
cana-1287	250	25	wang	wang	PROPN
cana-1287	250	26	,	,	PUNCT
cana-1287	250	27	z.	z.	PROPN
cana-1287	250	28	,	,	PUNCT
cana-1287	250	29	...	...	PUNCT
cana-1287	250	30	&	&	CCONJ
cana-1287	250	31	yang	yang	PROPN
cana-1287	250	32	,	,	PUNCT
cana-1287	250	33	y.	y.	PROPN
cana-1287	250	34	(	(	PUNCT
cana-1287	250	35	2023	2023	NUM
cana-1287	250	36	)	)	PUNCT
cana-1287	250	37	.	.	PUNCT
cana-1287	251	1	mapping	mapping	NOUN
cana-1287	251	2	soil	soil	NOUN
cana-1287	251	3	available	available	ADJ
cana-1287	251	4	copper	copper	NOUN
cana-1287	251	5	content	content	NOUN
cana-1287	251	6	in	in	ADP
cana-1287	251	7	the	the	DET
cana-1287	251	8	mine	mine	NOUN
cana-1287	251	9	tailings	tailing	NOUN
cana-1287	251	10	pond	pond	NOUN
cana-1287	251	11	with	with	ADP
cana-1287	251	12	combined	combine	VERB
cana-1287	251	13	simulated	simulated	ADJ
cana-1287	251	14	annealing	anneal	VERB
cana-1287	251	15	deep	deep	ADJ
cana-1287	251	16	neural	neural	ADJ
cana-1287	251	17	network	network	NOUN
cana-1287	251	18	and	and	CCONJ
cana-1287	251	19	uav	uav	PROPN
cana-1287	251	20	hyperspectral	hyperspectral	ADJ
cana-1287	251	21	images	image	NOUN
cana-1287	251	22	.	.	PUNCT
cana-1287	252	1	environmental	environmental	ADJ
cana-1287	252	2	pollution	pollution	NOUN
cana-1287	252	3	,	,	PUNCT
cana-1287	252	4	320	320	NUM
cana-1287	252	5	,	,	PUNCT
cana-1287	252	6	120962	120962	NUM
cana-1287	252	7	.	.	PUNCT
cana-1287	253	1	[	[	X
cana-1287	253	2	2	2	NUM
cana-1287	253	3	]	]	PUNCT
cana-1287	253	4	choudhry	choudhry	NOUN
cana-1287	253	5	,	,	PUNCT
cana-1287	253	6	m.	m.	NOUN
cana-1287	253	7	d.	d.	PROPN
cana-1287	253	8	,	,	PUNCT
cana-1287	253	9	sivaraj	sivaraj	PROPN
cana-1287	253	10	,	,	PUNCT
cana-1287	253	11	j.	j.	PROPN
cana-1287	253	12	,	,	PUNCT
cana-1287	253	13	munusamy	munusamy	PROPN
cana-1287	253	14	,	,	PUNCT
cana-1287	253	15	s.	s.	PROPN
cana-1287	253	16	,	,	PUNCT
cana-1287	253	17	muthusamy	muthusamy	PROPN
cana-1287	253	18	,	,	PUNCT
cana-1287	253	19	p.	p.	PROPN
cana-1287	253	20	d.	d.	PROPN
cana-1287	253	21	,	,	PUNCT
cana-1287	253	22	&	&	CCONJ
cana-1287	253	23	saravanan	saravanan	PROPN
cana-1287	253	24	,	,	PUNCT
cana-1287	253	25	v.	v.	PROPN
cana-1287	253	26	(	(	PUNCT
cana-1287	253	27	2024	2024	NUM
cana-1287	253	28	)	)	PUNCT
cana-1287	253	29	.	.	PUNCT
cana-1287	254	1	industry	industry	NOUN
cana-1287	254	2	4.0	4.0	NUM
cana-1287	254	3	in	in	ADP
cana-1287	254	4	manufacturing	manufacturing	NOUN
cana-1287	254	5	,	,	PUNCT
cana-1287	254	6	communication	communication	NOUN
cana-1287	254	7	,	,	PUNCT
cana-1287	254	8	transportation	transportation	NOUN
cana-1287	254	9	,	,	PUNCT
cana-1287	254	10	and	and	CCONJ
cana-1287	254	11	health	health	NOUN
cana-1287	254	12	care	care	NOUN
cana-1287	254	13	.	.	PUNCT
cana-1287	255	1	topics	topic	NOUN
cana-1287	255	2	in	in	ADP
cana-1287	255	3	artificial	artificial	ADJ
cana-1287	255	4	intelligence	intelligence	NOUN
cana-1287	255	5	applied	apply	VERB
cana-1287	255	6	to	to	ADP
cana-1287	255	7	industry	industry	NOUN
cana-1287	255	8	4.0	4.0	NUM
cana-1287	255	9	,	,	PUNCT
cana-1287	255	10	149	149	NUM
cana-1287	255	11	-	-	SYM
cana-1287	255	12	165	165	NUM
cana-1287	255	13	.	.	PUNCT
cana-1287	256	1	[	[	X
cana-1287	256	2	3	3	NUM
cana-1287	256	3	]	]	X
cana-1287	256	4	liu	liu	PROPN
cana-1287	256	5	,	,	PUNCT
cana-1287	256	6	m.	m.	NOUN
cana-1287	256	7	,	,	PUNCT
cana-1287	256	8	su	su	PROPN
cana-1287	256	9	,	,	PUNCT
cana-1287	256	10	w.	w.	PROPN
cana-1287	256	11	h.	h.	PROPN
cana-1287	256	12	,	,	PUNCT
cana-1287	256	13	&	&	CCONJ
cana-1287	256	14	wang	wang	PROPN
cana-1287	256	15	,	,	PUNCT
cana-1287	256	16	x.	x.	PROPN
cana-1287	256	17	q.	q.	PROPN
cana-1287	256	18	(	(	PUNCT
cana-1287	256	19	2023	2023	NUM
cana-1287	256	20	)	)	PUNCT
cana-1287	256	21	.	.	PUNCT
cana-1287	257	1	quantitative	quantitative	ADJ
cana-1287	257	2	evaluation	evaluation	NOUN
cana-1287	257	3	of	of	ADP
cana-1287	257	4	maize	maize	NOUN
cana-1287	257	5	emergence	emergence	NOUN
cana-1287	257	6	using	use	VERB
cana-1287	257	7	uav	uav	PROPN
cana-1287	257	8	imagery	imagery	NOUN
cana-1287	257	9	and	and	CCONJ
cana-1287	257	10	deep	deep	ADJ
cana-1287	257	11	learning	learning	NOUN
cana-1287	257	12	.	.	PUNCT
cana-1287	258	1	remote	remote	ADJ
cana-1287	258	2	sensing	sensing	NOUN
cana-1287	258	3	,	,	PUNCT
cana-1287	258	4	15(8	15(8	NOUN
cana-1287	258	5	)	)	PUNCT
cana-1287	258	6	,	,	PUNCT
cana-1287	258	7	1979	1979	NUM
cana-1287	258	8	.	.	PUNCT
cana-1287	259	1	[	[	X
cana-1287	259	2	4	4	NUM
cana-1287	259	3	]	]	X
cana-1287	259	4	praghash	praghash	NOUN
cana-1287	259	5	,	,	PUNCT
cana-1287	259	6	k.	k.	PROPN
cana-1287	259	7	,	,	PUNCT
cana-1287	259	8	yuvaraj	yuvaraj	PROPN
cana-1287	259	9	,	,	PUNCT
cana-1287	259	10	n.	n.	PROPN
cana-1287	259	11	,	,	PUNCT
cana-1287	259	12	peter	peter	PROPN
cana-1287	259	13	,	,	PUNCT
cana-1287	259	14	g.	g.	PROPN
cana-1287	259	15	,	,	PUNCT
cana-1287	259	16	stonier	stonier	NOUN
cana-1287	259	17	,	,	PUNCT
cana-1287	259	18	a.	a.	NOUN
cana-1287	259	19	a.	a.	PROPN
cana-1287	259	20	,	,	PUNCT
cana-1287	259	21	&	&	CCONJ
cana-1287	259	22	priya	priya	PROPN
cana-1287	259	23	,	,	PUNCT
cana-1287	259	24	r.	r.	PROPN
cana-1287	259	25	d.	d.	PROPN
cana-1287	259	26	(	(	PUNCT
cana-1287	259	27	2022	2022	NUM
cana-1287	259	28	,	,	PUNCT
cana-1287	259	29	december	december	PROPN
cana-1287	259	30	)	)	PUNCT
cana-1287	259	31	.	.	PUNCT
cana-1287	260	1	financial	financial	ADJ
cana-1287	260	2	big	big	ADJ
cana-1287	260	3	data	datum	NOUN
cana-1287	260	4	analysis	analysis	NOUN
cana-1287	260	5	using	use	VERB
cana-1287	260	6	anti	anti	ADJ
cana-1287	260	7	-	-	ADJ
cana-1287	260	8	tampering	tamper	VERB
cana-1287	260	9	blockchain	blockchain	NOUN
cana-1287	260	10	-	-	PUNCT
cana-1287	260	11	based	base	VERB
cana-1287	260	12	deep	deep	ADJ
cana-1287	260	13	learning	learning	NOUN
cana-1287	260	14	.	.	PUNCT
cana-1287	261	1	in	in	ADP
cana-1287	261	2	international	international	ADJ
cana-1287	261	3	conference	conference	NOUN
cana-1287	261	4	on	on	ADP
cana-1287	261	5	hybrid	hybrid	ADJ
cana-1287	261	6	intelligent	intelligent	ADJ
cana-1287	261	7	systems	system	NOUN
cana-1287	261	8	(	(	PUNCT
cana-1287	261	9	pp	pp	ADJ
cana-1287	261	10	.	.	PUNCT
cana-1287	261	11	1031	1031	NUM
cana-1287	261	12	-	-	SYM
cana-1287	261	13	1040	1040	NUM
cana-1287	261	14	)	)	PUNCT
cana-1287	261	15	.	.	PUNCT
cana-1287	262	1	cham	cham	PROPN
cana-1287	262	2	:	:	PUNCT
cana-1287	262	3	springer	springer	NOUN
cana-1287	262	4	nature	nature	PROPN
cana-1287	262	5	switzerland	switzerland	PROPN
cana-1287	262	6	.	.	PUNCT
cana-1287	263	1	[	[	X
cana-1287	263	2	5	5	NUM
cana-1287	263	3	]	]	X
cana-1287	263	4	singh	singh	PROPN
cana-1287	263	5	,	,	PUNCT
cana-1287	263	6	a.	a.	PROPN
cana-1287	263	7	,	,	PUNCT
cana-1287	263	8	gaurav	gaurav	PROPN
cana-1287	263	9	,	,	PUNCT
cana-1287	263	10	k.	k.	PROPN
cana-1287	263	11	,	,	PUNCT
cana-1287	263	12	sonkar	sonkar	PROPN
cana-1287	263	13	,	,	PUNCT
cana-1287	263	14	g.	g.	PROPN
cana-1287	263	15	k.	k.	PROPN
cana-1287	263	16	,	,	PUNCT
cana-1287	263	17	&	&	CCONJ
cana-1287	263	18	lee	lee	PROPN
cana-1287	263	19	,	,	PUNCT
cana-1287	263	20	c.	c.	PROPN
cana-1287	263	21	c.	c.	PROPN
cana-1287	263	22	(	(	PUNCT
cana-1287	263	23	2023	2023	NUM
cana-1287	263	24	)	)	PUNCT
cana-1287	263	25	.	.	PUNCT
cana-1287	264	1	strategies	strategy	NOUN
cana-1287	264	2	to	to	PART
cana-1287	264	3	measure	measure	VERB
cana-1287	264	4	soil	soil	NOUN
cana-1287	264	5	moisture	moisture	NOUN
cana-1287	264	6	using	use	VERB
cana-1287	264	7	traditional	traditional	ADJ
cana-1287	264	8	methods	method	NOUN
cana-1287	264	9	,	,	PUNCT
cana-1287	264	10	automated	automate	VERB
cana-1287	264	11	sensors	sensor	NOUN
cana-1287	264	12	,	,	PUNCT
cana-1287	264	13	remote	remote	ADJ
cana-1287	264	14	sensing	sensing	NOUN
cana-1287	264	15	,	,	PUNCT
cana-1287	264	16	and	and	CCONJ
cana-1287	264	17	machine	machine	NOUN
cana-1287	264	18	learning	learn	VERB
cana-1287	264	19	techniques	technique	NOUN
cana-1287	264	20	:	:	PUNCT
cana-1287	264	21	review	review	NOUN
cana-1287	264	22	,	,	PUNCT
cana-1287	264	23	bibliometric	bibliometric	ADJ
cana-1287	264	24	analysis	analysis	NOUN
cana-1287	264	25	,	,	PUNCT
cana-1287	264	26	applications	application	NOUN
cana-1287	264	27	,	,	PUNCT
cana-1287	264	28	research	research	NOUN
cana-1287	264	29	findings	finding	NOUN
cana-1287	264	30	,	,	PUNCT
cana-1287	264	31	and	and	CCONJ
cana-1287	264	32	future	future	ADJ
cana-1287	264	33	directions	direction	NOUN
cana-1287	264	34	.	.	PUNCT
cana-1287	265	1	ieee	ieee	NOUN
cana-1287	265	2	access	access	NOUN
cana-1287	265	3	,	,	PUNCT
cana-1287	265	4	11	11	NUM
cana-1287	265	5	,	,	PUNCT
cana-1287	265	6	13605	13605	NUM
cana-1287	265	7	-	-	SYM
cana-1287	265	8	13635	13635	NUM
cana-1287	265	9	.	.	PUNCT
cana-1287	266	1	[	[	X
cana-1287	266	2	6	6	NUM
cana-1287	266	3	]	]	X
cana-1287	266	4	ramkumar	ramkumar	PROPN
cana-1287	266	5	,	,	PUNCT
cana-1287	266	6	m.	m.	NOUN
cana-1287	266	7	,	,	PUNCT
cana-1287	266	8	logeshwaran	logeshwaran	NOUN
cana-1287	266	9	,	,	PUNCT
cana-1287	266	10	j.	j.	PROPN
cana-1287	266	11	,	,	PUNCT
cana-1287	266	12	&	&	CCONJ
cana-1287	266	13	husna	husna	ADJ
cana-1287	266	14	,	,	PUNCT
cana-1287	266	15	t.	t.	PROPN
cana-1287	266	16	(	(	PUNCT
cana-1287	266	17	2022	2022	NUM
cana-1287	266	18	)	)	PUNCT
cana-1287	266	19	.	.	PUNCT
cana-1287	267	1	cea	cea	PROPN
cana-1287	267	2	:	:	PUNCT
cana-1287	267	3	certification	certification	NOUN
cana-1287	267	4	based	base	VERB
cana-1287	267	5	encryption	encryption	NOUN
cana-1287	267	6	algorithm	algorithm	NOUN
cana-1287	267	7	for	for	ADP
cana-1287	267	8	enhanced	enhanced	ADJ
cana-1287	267	9	data	datum	NOUN
cana-1287	267	10	protection	protection	NOUN
cana-1287	267	11	in	in	ADP
cana-1287	267	12	social	social	ADJ
cana-1287	267	13	networks	network	NOUN
cana-1287	267	14	.	.	PUNCT
cana-1287	268	1	fundamentals	fundamental	NOUN
cana-1287	268	2	of	of	ADP
cana-1287	268	3	applied	apply	VERB
cana-1287	268	4	mathematics	mathematic	NOUN
cana-1287	268	5	and	and	CCONJ
cana-1287	268	6	soft	soft	ADJ
cana-1287	268	7	computing	computing	NOUN
cana-1287	268	8	,	,	PUNCT
cana-1287	268	9	1	1	NUM
cana-1287	268	10	,	,	PUNCT
cana-1287	268	11	161	161	NUM
cana-1287	268	12	-	-	SYM
cana-1287	268	13	170	170	NUM
cana-1287	268	14	.	.	PUNCT
cana-1287	269	1	[	[	X
cana-1287	269	2	7	7	X
cana-1287	269	3	]	]	X
cana-1287	269	4	khatti	khatti	PROPN
cana-1287	269	5	,	,	PUNCT
cana-1287	269	6	j.	j.	PROPN
cana-1287	269	7	,	,	PUNCT
cana-1287	269	8	&	&	CCONJ
cana-1287	269	9	grover	grover	PROPN
cana-1287	269	10	,	,	PUNCT
cana-1287	269	11	k.	k.	PROPN
cana-1287	269	12	s.	s.	PROPN
cana-1287	269	13	(	(	PUNCT
cana-1287	269	14	2023	2023	NUM
cana-1287	269	15	)	)	PUNCT
cana-1287	269	16	.	.	PUNCT
cana-1287	270	1	prediction	prediction	NOUN
cana-1287	270	2	of	of	ADP
cana-1287	270	3	compaction	compaction	NOUN
cana-1287	270	4	parameters	parameter	NOUN
cana-1287	270	5	for	for	ADP
cana-1287	270	6	fine	fine	ADV
cana-1287	270	7	-	-	PUNCT
cana-1287	270	8	grained	grain	VERB
cana-1287	270	9	soil	soil	NOUN
cana-1287	270	10	:	:	PUNCT
cana-1287	270	11	critical	critical	ADJ
cana-1287	270	12	comparison	comparison	NOUN
cana-1287	270	13	of	of	ADP
cana-1287	270	14	the	the	DET
cana-1287	270	15	deep	deep	ADJ
cana-1287	270	16	learning	learning	NOUN
cana-1287	270	17	and	and	CCONJ
cana-1287	270	18	standalone	standalone	NOUN
cana-1287	270	19	models	model	NOUN
cana-1287	270	20	.	.	PUNCT
cana-1287	271	1	journal	journal	NOUN
cana-1287	271	2	of	of	ADP
cana-1287	271	3	rock	rock	NOUN
cana-1287	271	4	mechanics	mechanic	NOUN
cana-1287	271	5	and	and	CCONJ
cana-1287	271	6	geotechnical	geotechnical	ADJ
cana-1287	271	7	engineering	engineering	NOUN
cana-1287	271	8	,	,	PUNCT
cana-1287	271	9	15(11	15(11	NUM
cana-1287	271	10	)	)	PUNCT
cana-1287	271	11	,	,	PUNCT
cana-1287	271	12	30103038	30103038	NUM
cana-1287	271	13	.	.	PUNCT
cana-1287	272	1	[	[	X
cana-1287	272	2	8	8	NUM
cana-1287	272	3	]	]	PUNCT
cana-1287	272	4	gobinathan	gobinathan	NOUN
cana-1287	272	5	,	,	PUNCT
cana-1287	272	6	b.	b.	PROPN
cana-1287	272	7	,	,	PUNCT
cana-1287	272	8	mukunthan	mukunthan	PROPN
cana-1287	272	9	,	,	PUNCT
cana-1287	272	10	m.	m.	NOUN
cana-1287	272	11	a.	a.	PROPN
cana-1287	272	12	,	,	PUNCT
cana-1287	272	13	surendran	surendran	NOUN
cana-1287	272	14	,	,	PUNCT
cana-1287	272	15	s.	s.	PROPN
cana-1287	272	16	,	,	PUNCT
cana-1287	272	17	somasundaram	somasundaram	PROPN
cana-1287	272	18	,	,	PUNCT
cana-1287	272	19	k.	k.	PROPN
cana-1287	272	20	,	,	PUNCT
cana-1287	272	21	moeed	moeed	PROPN
cana-1287	272	22	,	,	PUNCT
cana-1287	272	23	s.	s.	PROPN
cana-1287	272	24	a.	a.	PROPN
cana-1287	272	25	,	,	PUNCT
cana-1287	272	26	niranjan	niranjan	PROPN
cana-1287	272	27	,	,	PUNCT
cana-1287	272	28	p.	p.	PROPN
cana-1287	272	29	,	,	PUNCT
cana-1287	272	30	...	...	PUNCT
cana-1287	272	31	&	&	CCONJ
cana-1287	272	32	sundramurthy	sundramurthy	PROPN
cana-1287	272	33	,	,	PUNCT
cana-1287	272	34	v.	v.	PROPN
cana-1287	272	35	p.	p.	NOUN
cana-1287	272	36	(	(	PUNCT
cana-1287	272	37	2021	2021	NUM
cana-1287	272	38	)	)	PUNCT
cana-1287	272	39	.	.	PUNCT
cana-1287	273	1	a	a	DET
cana-1287	273	2	novel	novel	ADJ
cana-1287	273	3	method	method	NOUN
cana-1287	273	4	to	to	PART
cana-1287	273	5	solve	solve	VERB
cana-1287	273	6	real	real	ADJ
cana-1287	273	7	time	time	NOUN
cana-1287	273	8	security	security	NOUN
cana-1287	273	9	issues	issue	NOUN
cana-1287	273	10	in	in	ADP
cana-1287	273	11	software	software	NOUN
cana-1287	273	12	industry	industry	NOUN
cana-1287	273	13	using	use	VERB
cana-1287	273	14	advanced	advanced	ADJ
cana-1287	273	15	cryptographic	cryptographic	ADJ
cana-1287	273	16	techniques	technique	NOUN
cana-1287	273	17	.	.	PUNCT
cana-1287	274	1	scientific	scientific	ADJ
cana-1287	274	2	programming	programming	NOUN
cana-1287	274	3	,	,	PUNCT
cana-1287	274	4	2021(1	2021(1	NUM
cana-1287	274	5	)	)	PUNCT
cana-1287	274	6	,	,	PUNCT
cana-1287	274	7	3611182	3611182	NUM
cana-1287	274	8	.	.	PUNCT
cana-1287	275	1	[	[	X
cana-1287	275	2	9	9	NUM
cana-1287	275	3	]	]	SYM
cana-1287	275	4	bawa	bawa	PROPN
cana-1287	275	5	,	,	PUNCT
cana-1287	275	6	a.	a.	PROPN
cana-1287	275	7	,	,	PUNCT
cana-1287	275	8	samanta	samanta	PROPN
cana-1287	275	9	,	,	PUNCT
cana-1287	275	10	s.	s.	PROPN
cana-1287	275	11	,	,	PUNCT
cana-1287	275	12	himanshu	himanshu	PROPN
cana-1287	275	13	,	,	PUNCT
cana-1287	275	14	s.	s.	PROPN
cana-1287	275	15	k.	k.	PROPN
cana-1287	275	16	,	,	PUNCT
cana-1287	275	17	singh	singh	PROPN
cana-1287	275	18	,	,	PUNCT
cana-1287	275	19	j.	j.	PROPN
cana-1287	275	20	,	,	PUNCT
cana-1287	275	21	kim	kim	PROPN
cana-1287	275	22	,	,	PUNCT
cana-1287	275	23	j.	j.	PROPN
cana-1287	275	24	,	,	PUNCT
cana-1287	275	25	zhang	zhang	PROPN
cana-1287	275	26	,	,	PUNCT
cana-1287	275	27	t.	t.	PROPN
cana-1287	275	28	,	,	PUNCT
cana-1287	275	29	...	...	PUNCT
cana-1287	275	30	&	&	CCONJ
cana-1287	275	31	ale	ale	PROPN
cana-1287	275	32	,	,	PUNCT
cana-1287	275	33	s.	s.	PROPN
cana-1287	275	34	(	(	PUNCT
cana-1287	275	35	2023	2023	NUM
cana-1287	275	36	)	)	PUNCT
cana-1287	275	37	.	.	PUNCT
cana-1287	276	1	a	a	DET
cana-1287	276	2	support	support	NOUN
cana-1287	276	3	vector	vector	NOUN
cana-1287	276	4	machine	machine	NOUN
cana-1287	276	5	and	and	CCONJ
cana-1287	276	6	image	image	NOUN
cana-1287	276	7	processing	processing	NOUN
cana-1287	276	8	based	base	VERB
cana-1287	276	9	approach	approach	NOUN
cana-1287	276	10	for	for	ADP
cana-1287	276	11	counting	count	VERB
cana-1287	276	12	open	open	ADJ
cana-1287	276	13	cotton	cotton	NOUN
cana-1287	276	14	bolls	boll	NOUN
cana-1287	276	15	and	and	CCONJ
cana-1287	276	16	estimating	estimate	VERB
cana-1287	276	17	lint	lint	NOUN
cana-1287	276	18	yield	yield	NOUN
cana-1287	276	19	from	from	ADP
cana-1287	276	20	uav	uav	PROPN
cana-1287	276	21	imagery	imagery	NOUN
cana-1287	276	22	.	.	PUNCT
cana-1287	277	1	smart	smart	ADJ
cana-1287	277	2	agricultural	agricultural	ADJ
cana-1287	277	3	technology	technology	NOUN
cana-1287	277	4	,	,	PUNCT
cana-1287	277	5	3	3	NUM
cana-1287	277	6	,	,	PUNCT
cana-1287	277	7	100140	100140	NUM
cana-1287	277	8	.	.	PUNCT
cana-1287	278	1	[	[	X
cana-1287	278	2	10	10	NUM
cana-1287	278	3	]	]	X
cana-1287	278	4	singh	singh	PROPN
cana-1287	278	5	,	,	PUNCT
cana-1287	278	6	a.	a.	PROPN
cana-1287	278	7	,	,	PUNCT
cana-1287	278	8	&	&	CCONJ
cana-1287	278	9	gaurav	gaurav	PROPN
cana-1287	278	10	,	,	PUNCT
cana-1287	278	11	k.	k.	PROPN
cana-1287	278	12	(	(	PUNCT
cana-1287	278	13	2023	2023	NUM
cana-1287	278	14	)	)	PUNCT
cana-1287	278	15	.	.	PUNCT
cana-1287	279	1	deep	deep	ADJ
cana-1287	279	2	learning	learning	NOUN
cana-1287	279	3	and	and	CCONJ
cana-1287	279	4	data	datum	NOUN
cana-1287	279	5	fusion	fusion	NOUN
cana-1287	279	6	to	to	PART
cana-1287	279	7	estimate	estimate	VERB
cana-1287	279	8	surface	surface	NOUN
cana-1287	279	9	soil	soil	NOUN
cana-1287	279	10	moisture	moisture	NOUN
cana-1287	279	11	from	from	ADP
cana-1287	279	12	multi	multi	ADJ
cana-1287	279	13	-	-	ADJ
cana-1287	279	14	sensor	sensor	ADJ
cana-1287	279	15	satellite	satellite	NOUN
cana-1287	279	16	images	image	NOUN
cana-1287	279	17	.	.	PUNCT
cana-1287	280	1	scientific	scientific	ADJ
cana-1287	280	2	reports	report	NOUN
cana-1287	280	3	,	,	PUNCT
cana-1287	280	4	13(1	13(1	NUM
cana-1287	280	5	)	)	PUNCT
cana-1287	280	6	,	,	PUNCT
cana-1287	280	7	2251	2251	NUM
cana-1287	280	8	.	.	PUNCT
cana-1287	281	1	[	[	X
cana-1287	281	2	11	11	NUM
cana-1287	281	3	]	]	SYM
cana-1287	281	4	nijaguna	nijaguna	PROPN
cana-1287	281	5	,	,	PUNCT
cana-1287	281	6	g.	g.	PROPN
cana-1287	281	7	s.	s.	PROPN
cana-1287	281	8	,	,	PUNCT
cana-1287	281	9	manjunath	manjunath	PROPN
cana-1287	281	10	,	,	PUNCT
cana-1287	281	11	d.	d.	PROPN
cana-1287	281	12	r.	r.	PROPN
cana-1287	281	13	,	,	PUNCT
cana-1287	281	14	abouhawwash	abouhawwash	NOUN
cana-1287	281	15	,	,	PUNCT
cana-1287	281	16	m.	m.	NOUN
cana-1287	281	17	,	,	PUNCT
cana-1287	281	18	askar	askar	PROPN
cana-1287	281	19	,	,	PUNCT
cana-1287	281	20	s.	s.	PROPN
cana-1287	281	21	s.	s.	PROPN
cana-1287	281	22	,	,	PUNCT
cana-1287	281	23	basha	basha	PROPN
cana-1287	281	24	,	,	PUNCT
cana-1287	281	25	d.	d.	PROPN
cana-1287	281	26	k.	k.	PROPN
cana-1287	281	27	,	,	PUNCT
cana-1287	281	28	&	&	CCONJ
cana-1287	281	29	sengupta	sengupta	PROPN
cana-1287	281	30	,	,	PUNCT
cana-1287	281	31	j.	j.	PROPN
cana-1287	281	32	(	(	PUNCT
cana-1287	281	33	2023	2023	NUM
cana-1287	281	34	)	)	PUNCT
cana-1287	281	35	.	.	PUNCT
cana-1287	282	1	deep	deep	ADJ
cana-1287	282	2	learning	learning	NOUN
cana-1287	282	3	-	-	PUNCT
cana-1287	282	4	based	base	VERB
cana-1287	282	5	improved	improved	ADJ
cana-1287	282	6	wcm	wcm	NOUN
cana-1287	282	7	technique	technique	NOUN
cana-1287	282	8	for	for	ADP
cana-1287	282	9	soil	soil	NOUN
cana-1287	282	10	moisture	moisture	NOUN
cana-1287	282	11	retrieval	retrieval	NOUN
cana-1287	282	12	with	with	ADP
cana-1287	282	13	satellite	satellite	NOUN
cana-1287	282	14	images	image	NOUN
cana-1287	282	15	.	.	PUNCT
cana-1287	283	1	remote	remote	ADJ
cana-1287	283	2	sensing	sensing	NOUN
cana-1287	283	3	,	,	PUNCT
cana-1287	283	4	15(8	15(8	NOUN
cana-1287	283	5	)	)	PUNCT
cana-1287	283	6	,	,	PUNCT
cana-1287	283	7	2005	2005	NUM
cana-1287	283	8	.	.	PUNCT
cana-1287	284	1	[	[	X
cana-1287	284	2	12	12	NUM
cana-1287	284	3	]	]	X
cana-1287	284	4	zhang	zhang	PROPN
cana-1287	284	5	,	,	PUNCT
cana-1287	284	6	y.	y.	PROPN
cana-1287	284	7	,	,	PUNCT
cana-1287	284	8	han	han	PROPN
cana-1287	284	9	,	,	PUNCT
cana-1287	284	10	w.	w.	PROPN
cana-1287	284	11	,	,	PUNCT
cana-1287	284	12	zhang	zhang	PROPN
cana-1287	284	13	,	,	PUNCT
cana-1287	284	14	h.	h.	PROPN
cana-1287	284	15	,	,	PUNCT
cana-1287	284	16	niu	niu	PROPN
cana-1287	284	17	,	,	PUNCT
cana-1287	284	18	x.	x.	NOUN
cana-1287	284	19	,	,	PUNCT
cana-1287	284	20	&	&	CCONJ
cana-1287	284	21	shao	shao	PROPN
cana-1287	284	22	,	,	PUNCT
cana-1287	284	23	g.	g.	PROPN
cana-1287	284	24	(	(	PUNCT
cana-1287	284	25	2023	2023	NUM
cana-1287	284	26	)	)	PUNCT
cana-1287	284	27	.	.	PUNCT
cana-1287	285	1	evaluating	evaluate	VERB
cana-1287	285	2	soil	soil	NOUN
cana-1287	285	3	moisture	moisture	NOUN
cana-1287	285	4	content	content	NOUN
cana-1287	285	5	under	under	ADP
cana-1287	285	6	maize	maize	NOUN
cana-1287	285	7	coverage	coverage	NOUN
cana-1287	285	8	using	use	VERB
cana-1287	285	9	uav	uav	PROPN
cana-1287	285	10	multimodal	multimodal	NOUN
cana-1287	285	11	data	datum	NOUN
cana-1287	285	12	by	by	ADP
cana-1287	285	13	machine	machine	NOUN
cana-1287	285	14	learning	learn	VERB
cana-1287	285	15	algorithms	algorithm	NOUN
cana-1287	285	16	.	.	PUNCT
cana-1287	286	1	journal	journal	NOUN
cana-1287	286	2	of	of	ADP
cana-1287	286	3	hydrology	hydrology	NOUN
cana-1287	286	4	,	,	PUNCT
cana-1287	286	5	617	617	NUM
cana-1287	286	6	,	,	PUNCT
cana-1287	286	7	129086	129086	NUM
cana-1287	286	8	.	.	PUNCT
cana-1287	287	1	[	[	X
cana-1287	287	2	13	13	NUM
cana-1287	287	3	]	]	X
cana-1287	287	4	khosravi	khosravi	NOUN
cana-1287	287	5	,	,	PUNCT
cana-1287	287	6	k.	k.	PROPN
cana-1287	287	7	,	,	PUNCT
cana-1287	287	8	rezaie	rezaie	PROPN
cana-1287	287	9	,	,	PUNCT
cana-1287	287	10	f.	f.	PROPN
cana-1287	287	11	,	,	PUNCT
cana-1287	287	12	cooper	cooper	PROPN
cana-1287	287	13	,	,	PUNCT
cana-1287	287	14	j.	j.	PROPN
cana-1287	287	15	r.	r.	PROPN
cana-1287	287	16	,	,	PUNCT
cana-1287	287	17	kalantari	kalantari	PROPN
cana-1287	287	18	,	,	PUNCT
cana-1287	287	19	z.	z.	PROPN
cana-1287	287	20	,	,	PUNCT
cana-1287	287	21	abolfathi	abolfathi	PROPN
cana-1287	287	22	,	,	PUNCT
cana-1287	287	23	s.	s.	PROPN
cana-1287	287	24	,	,	PUNCT
cana-1287	287	25	&	&	CCONJ
cana-1287	287	26	hatamiafkoueieh	hatamiafkoueieh	PROPN
cana-1287	287	27	,	,	PUNCT
cana-1287	287	28	j.	j.	PROPN
cana-1287	287	29	(	(	PUNCT
cana-1287	287	30	2023	2023	NUM
cana-1287	287	31	)	)	PUNCT
cana-1287	287	32	.	.	PUNCT
cana-1287	288	1	soil	soil	NOUN
cana-1287	288	2	water	water	NOUN
cana-1287	288	3	erosion	erosion	NOUN
cana-1287	288	4	susceptibility	susceptibility	NOUN
cana-1287	288	5	assessment	assessment	NOUN
cana-1287	288	6	using	use	VERB
cana-1287	288	7	deep	deep	ADJ
cana-1287	288	8	learning	learning	NOUN
cana-1287	288	9	algorithms	algorithm	NOUN
cana-1287	288	10	.	.	PUNCT
cana-1287	289	1	journal	journal	NOUN
cana-1287	289	2	of	of	ADP
cana-1287	289	3	hydrology	hydrology	NOUN
cana-1287	289	4	,	,	PUNCT
cana-1287	289	5	618	618	NUM
cana-1287	289	6	,	,	PUNCT
cana-1287	289	7	129229	129229	NUM
cana-1287	289	8	.	.	PUNCT
cana-1287	290	1	[	[	X
cana-1287	290	2	14	14	NUM
cana-1287	290	3	]	]	PUNCT
cana-1287	290	4	https://www.kaggle.com/datasets/jayaprakashpondy/soil-image-dataset	https://www.kaggle.com/datasets/jayaprakashpondy/soil-image-dataset	VERB