id	sid	tid	token	lemma	pos
cana-6293	1	1	communications	communication	NOUN
cana-6293	1	2	on	on	ADP
cana-6293	1	3	applied	apply	VERB
cana-6293	1	4	nonlinear	nonlinear	ADJ
cana-6293	1	5	analysis	analysis	NOUN
cana-6293	1	6	issn	issn	NOUN
cana-6293	1	7	:	:	PUNCT
cana-6293	1	8	1074	1074	NUM
cana-6293	1	9	-	-	PUNCT
cana-6293	1	10	133x	133x	NUM
cana-6293	1	11	vol	vol	NOUN
cana-6293	1	12	32	32	NUM
cana-6293	1	13	no	no	NOUN
cana-6293	1	14	.	.	PUNCT
cana-6293	2	1	2s	2s	NUM
cana-6293	2	2	(	(	PUNCT
cana-6293	2	3	2025	2025	NUM
cana-6293	2	4	)	)	PUNCT
cana-6293	2	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	2	6	638	638	NUM
cana-6293	2	7	performance	performance	NOUN
cana-6293	2	8	evaluation	evaluation	NOUN
cana-6293	2	9	of	of	ADP
cana-6293	2	10	paddy	paddy	NOUN
cana-6293	2	11	crop	crop	NOUN
cana-6293	2	12	prediction	prediction	NOUN
cana-6293	2	13	models	model	NOUN
cana-6293	2	14	with	with	ADP
cana-6293	2	15	optimization	optimization	NOUN
cana-6293	2	16	methods	method	NOUN
cana-6293	2	17	dr	dr	PROPN
cana-6293	2	18	.	.	PROPN
cana-6293	2	19	s.	s.	PROPN
cana-6293	2	20	ravishankar1	ravishankar1	PROPN
cana-6293	2	21	,	,	PUNCT
cana-6293	3	1	dr	dr	PROPN
cana-6293	3	2	.	.	PROPN
cana-6293	3	3	s.	s.	PROPN
cana-6293	3	4	dhanavel2	dhanavel2	PROPN
cana-6293	4	1	*	*	PROPN
cana-6293	4	2	1assistant	1assistant	NUM
cana-6293	4	3	professor	professor	NOUN
cana-6293	4	4	,	,	PUNCT
cana-6293	4	5	department	department	NOUN
cana-6293	4	6	of	of	ADP
cana-6293	4	7	computer	computer	NOUN
cana-6293	4	8	applications	application	NOUN
cana-6293	4	9	,	,	PUNCT
cana-6293	4	10	periyar	periyar	NOUN
cana-6293	4	11	arts	arts	PROPN
cana-6293	4	12	college	college	PROPN
cana-6293	4	13	,	,	PUNCT
cana-6293	4	14	cuddalore	cuddalore	PROPN
cana-6293	4	15	,	,	PUNCT
cana-6293	4	16	tamil	tamil	PROPN
cana-6293	4	17	nadu	nadu	PROPN
cana-6293	4	18	,	,	PUNCT
cana-6293	4	19	india	india	PROPN
cana-6293	4	20	.	.	PUNCT
cana-6293	5	1	2assistant	2assistant	NUM
cana-6293	5	2	professor	professor	NOUN
cana-6293	5	3	,	,	PUNCT
cana-6293	5	4	department	department	NOUN
cana-6293	5	5	of	of	ADP
cana-6293	5	6	computer	computer	NOUN
cana-6293	5	7	science	science	NOUN
cana-6293	5	8	,	,	PUNCT
cana-6293	5	9	periyar	periyar	NOUN
cana-6293	5	10	arts	arts	PROPN
cana-6293	5	11	college	college	PROPN
cana-6293	5	12	,	,	PUNCT
cana-6293	5	13	cuddalore	cuddalore	PROPN
cana-6293	5	14	,	,	PUNCT
cana-6293	5	15	tamil	tamil	PROPN
cana-6293	5	16	nadu	nadu	PROPN
cana-6293	5	17	,	,	PUNCT
cana-6293	5	18	india	india	PROPN
cana-6293	5	19	.	.	PUNCT
cana-6293	5	20	email	email	NOUN
cana-6293	5	21	:	:	PUNCT
cana-6293	5	22	thiru.ravishankar@gmail.com	thiru.ravishankar@gmail.com	X
cana-6293	5	23	corresponding	correspond	VERB
cana-6293	5	24	author	author	NOUN
cana-6293	5	25	email	email	NOUN
cana-6293	5	26	:	:	PUNCT
cana-6293	6	1	dhanavel2008@gmail.com	dhanavel2008@gmail.com	PROPN
cana-6293	6	2	article	article	PROPN
cana-6293	6	3	history	history	NOUN
cana-6293	6	4	:	:	PUNCT
cana-6293	6	5	received	receive	VERB
cana-6293	6	6	:	:	PUNCT
cana-6293	6	7	03	03	NUM
cana-6293	6	8	-	-	SYM
cana-6293	6	9	12	12	NUM
cana-6293	6	10	-	-	PUNCT
cana-6293	6	11	2024	2024	NUM
cana-6293	6	12	revised	revise	VERB
cana-6293	6	13	:	:	PUNCT
cana-6293	6	14	16	16	NUM
cana-6293	6	15	-	-	SYM
cana-6293	6	16	01	01	NUM
cana-6293	6	17	-	-	PUNCT
cana-6293	6	18	2025	2025	NUM
cana-6293	6	19	accepted	accept	VERB
cana-6293	6	20	:	:	PUNCT
cana-6293	6	21	20	20	NUM
cana-6293	6	22	-	-	PUNCT
cana-6293	6	23	02	02	NUM
cana-6293	6	24	-	-	PUNCT
cana-6293	6	25	2025	2025	NUM
cana-6293	6	26	abstract	abstract	NOUN
cana-6293	6	27	:	:	PUNCT
cana-6293	6	28	paddy	paddy	NOUN
cana-6293	6	29	(	(	PUNCT
cana-6293	6	30	rice	rice	NOUN
cana-6293	6	31	)	)	PUNCT
cana-6293	6	32	is	be	AUX
cana-6293	6	33	a	a	DET
cana-6293	6	34	major	major	ADJ
cana-6293	6	35	food	food	NOUN
cana-6293	6	36	crop	crop	NOUN
cana-6293	6	37	and	and	CCONJ
cana-6293	6	38	plays	play	VERB
cana-6293	6	39	an	an	DET
cana-6293	6	40	important	important	ADJ
cana-6293	6	41	role	role	NOUN
cana-6293	6	42	in	in	ADP
cana-6293	6	43	farmers	farmer	NOUN
cana-6293	6	44	’	'	PUNCT
cana-6293	6	45	income	income	NOUN
cana-6293	6	46	.	.	PUNCT
cana-6293	7	1	predicting	predict	VERB
cana-6293	7	2	paddy	paddy	NOUN
cana-6293	7	3	growth	growth	NOUN
cana-6293	7	4	early	early	ADV
cana-6293	7	5	helps	help	VERB
cana-6293	7	6	in	in	ADP
cana-6293	7	7	planning	planning	NOUN
cana-6293	7	8	irrigation	irrigation	NOUN
cana-6293	7	9	,	,	PUNCT
cana-6293	7	10	fertilizer	fertilizer	NOUN
cana-6293	7	11	usage	usage	NOUN
cana-6293	7	12	,	,	PUNCT
cana-6293	7	13	and	and	CCONJ
cana-6293	7	14	pest	pest	VERB
cana-6293	7	15	control	control	NOUN
cana-6293	7	16	,	,	PUNCT
cana-6293	7	17	which	which	PRON
cana-6293	7	18	increases	increase	VERB
cana-6293	7	19	productivity	productivity	NOUN
cana-6293	7	20	.	.	PUNCT
cana-6293	8	1	traditional	traditional	ADJ
cana-6293	8	2	methods	method	NOUN
cana-6293	8	3	rely	rely	VERB
cana-6293	8	4	on	on	ADP
cana-6293	8	5	manual	manual	ADJ
cana-6293	8	6	observation	observation	NOUN
cana-6293	8	7	and	and	CCONJ
cana-6293	8	8	are	be	AUX
cana-6293	8	9	often	often	ADV
cana-6293	8	10	inaccurate	inaccurate	ADJ
cana-6293	8	11	.	.	PUNCT
cana-6293	9	1	in	in	ADP
cana-6293	9	2	this	this	DET
cana-6293	9	3	study	study	NOUN
cana-6293	9	4	,	,	PUNCT
cana-6293	9	5	machine	machine	NOUN
cana-6293	9	6	learning	learning	NOUN
cana-6293	9	7	and	and	CCONJ
cana-6293	9	8	deep	deep	ADJ
cana-6293	9	9	learning	learning	NOUN
cana-6293	9	10	models	model	NOUN
cana-6293	9	11	are	be	AUX
cana-6293	9	12	used	use	VERB
cana-6293	9	13	to	to	PART
cana-6293	9	14	analyze	analyze	VERB
cana-6293	9	15	agricultural	agricultural	ADJ
cana-6293	9	16	factors	factor	NOUN
cana-6293	9	17	such	such	ADJ
cana-6293	9	18	as	as	ADP
cana-6293	9	19	temperature	temperature	NOUN
cana-6293	9	20	,	,	PUNCT
cana-6293	9	21	rainfall	rainfall	NOUN
cana-6293	9	22	,	,	PUNCT
cana-6293	9	23	soil	soil	NOUN
cana-6293	9	24	moisture	moisture	NOUN
cana-6293	9	25	,	,	PUNCT
cana-6293	9	26	and	and	CCONJ
cana-6293	9	27	fertilizer	fertilizer	NOUN
cana-6293	9	28	levels	level	NOUN
cana-6293	9	29	to	to	PART
cana-6293	9	30	predict	predict	VERB
cana-6293	9	31	crop	crop	NOUN
cana-6293	9	32	growth	growth	NOUN
cana-6293	9	33	.	.	PUNCT
cana-6293	10	1	optimization	optimization	NOUN
cana-6293	10	2	algorithms	algorithm	NOUN
cana-6293	10	3	like	like	ADP
cana-6293	10	4	particle	particle	NOUN
cana-6293	10	5	swarm	swarm	NOUN
cana-6293	10	6	optimization	optimization	NOUN
cana-6293	10	7	(	(	PUNCT
cana-6293	10	8	pso	pso	NOUN
cana-6293	10	9	)	)	PUNCT
cana-6293	10	10	and	and	CCONJ
cana-6293	10	11	genetic	genetic	ADJ
cana-6293	10	12	algorithm	algorithm	NOUN
cana-6293	10	13	(	(	PUNCT
cana-6293	10	14	ga	ga	NOUN
cana-6293	10	15	)	)	PUNCT
cana-6293	10	16	are	be	AUX
cana-6293	10	17	applied	apply	VERB
cana-6293	10	18	to	to	PART
cana-6293	10	19	improve	improve	VERB
cana-6293	10	20	model	model	NOUN
cana-6293	10	21	accuracy	accuracy	NOUN
cana-6293	10	22	and	and	CCONJ
cana-6293	10	23	reduce	reduce	VERB
cana-6293	10	24	errors	error	NOUN
cana-6293	10	25	.	.	PUNCT
cana-6293	11	1	the	the	DET
cana-6293	11	2	results	result	NOUN
cana-6293	11	3	show	show	VERB
cana-6293	11	4	that	that	SCONJ
cana-6293	11	5	using	use	VERB
cana-6293	11	6	optimization	optimization	NOUN
cana-6293	11	7	techniques	technique	NOUN
cana-6293	11	8	improves	improve	VERB
cana-6293	11	9	prediction	prediction	NOUN
cana-6293	11	10	performance	performance	NOUN
cana-6293	11	11	and	and	CCONJ
cana-6293	11	12	helps	helps	AUX
cana-6293	11	13	identify	identify	VERB
cana-6293	11	14	the	the	DET
cana-6293	11	15	best	good	ADJ
cana-6293	11	16	model	model	NOUN
cana-6293	11	17	for	for	ADP
cana-6293	11	18	paddy	paddy	NOUN
cana-6293	11	19	growth	growth	NOUN
cana-6293	11	20	forecasting	forecasting	NOUN
cana-6293	11	21	.	.	PUNCT
cana-6293	12	1	this	this	DET
cana-6293	12	2	research	research	NOUN
cana-6293	12	3	supports	support	VERB
cana-6293	12	4	smart	smart	ADJ
cana-6293	12	5	farming	farming	NOUN
cana-6293	12	6	and	and	CCONJ
cana-6293	12	7	helps	help	VERB
cana-6293	12	8	farmers	farmer	NOUN
cana-6293	12	9	make	make	VERB
cana-6293	12	10	better	well	ADJ
cana-6293	12	11	decisions	decision	NOUN
cana-6293	12	12	.	.	PUNCT
cana-6293	13	1	keywords	keyword	NOUN
cana-6293	13	2	:	:	PUNCT
cana-6293	13	3	paddy	paddy	NOUN
cana-6293	13	4	crop	crop	NOUN
cana-6293	13	5	prediction	prediction	NOUN
cana-6293	13	6	,	,	PUNCT
cana-6293	13	7	machine	machine	NOUN
cana-6293	13	8	learning	learning	NOUN
cana-6293	13	9	,	,	PUNCT
cana-6293	13	10	optimization	optimization	NOUN
cana-6293	13	11	techniques	technique	NOUN
cana-6293	13	12	,	,	PUNCT
cana-6293	13	13	hyperparameter	hyperparameter	NOUN
cana-6293	13	14	tuning	tuning	NOUN
cana-6293	13	15	,	,	PUNCT
cana-6293	13	16	smart	smart	ADJ
cana-6293	13	17	agriculture	agriculture	NOUN
cana-6293	13	18	.	.	PUNCT
cana-6293	14	1	1	1	X
cana-6293	14	2	.	.	X
cana-6293	14	3	introduction	introduction	NOUN
cana-6293	14	4	paddy	paddy	NOUN
cana-6293	14	5	(	(	PUNCT
cana-6293	14	6	rice	rice	NOUN
cana-6293	14	7	)	)	PUNCT
cana-6293	14	8	is	be	AUX
cana-6293	14	9	one	one	NUM
cana-6293	14	10	of	of	ADP
cana-6293	14	11	the	the	DET
cana-6293	14	12	most	most	ADV
cana-6293	14	13	important	important	ADJ
cana-6293	14	14	food	food	NOUN
cana-6293	14	15	crops	crop	NOUN
cana-6293	14	16	in	in	ADP
cana-6293	14	17	the	the	DET
cana-6293	14	18	world	world	NOUN
cana-6293	14	19	and	and	CCONJ
cana-6293	14	20	is	be	AUX
cana-6293	14	21	a	a	DET
cana-6293	14	22	major	major	ADJ
cana-6293	14	23	source	source	NOUN
cana-6293	14	24	of	of	ADP
cana-6293	14	25	income	income	NOUN
cana-6293	14	26	for	for	ADP
cana-6293	14	27	many	many	ADJ
cana-6293	14	28	farmers	farmer	NOUN
cana-6293	14	29	.	.	PUNCT
cana-6293	15	1	predicting	predict	VERB
cana-6293	15	2	paddy	paddy	NOUN
cana-6293	15	3	growth	growth	NOUN
cana-6293	15	4	at	at	ADP
cana-6293	15	5	the	the	DET
cana-6293	15	6	right	right	ADJ
cana-6293	15	7	time	time	NOUN
cana-6293	15	8	helps	help	VERB
cana-6293	15	9	farmers	farmer	NOUN
cana-6293	15	10	plan	plan	VERB
cana-6293	15	11	irrigation	irrigation	NOUN
cana-6293	15	12	,	,	PUNCT
cana-6293	15	13	fertilizer	fertilizer	NOUN
cana-6293	15	14	use	use	NOUN
cana-6293	15	15	,	,	PUNCT
cana-6293	15	16	pest	pest	NOUN
cana-6293	15	17	control	control	NOUN
cana-6293	15	18	,	,	PUNCT
cana-6293	15	19	and	and	CCONJ
cana-6293	15	20	harvesting	harvesting	NOUN
cana-6293	15	21	,	,	PUNCT
cana-6293	15	22	which	which	PRON
cana-6293	15	23	leads	lead	VERB
cana-6293	15	24	to	to	ADP
cana-6293	15	25	better	well	ADJ
cana-6293	15	26	productivity	productivity	NOUN
cana-6293	15	27	and	and	CCONJ
cana-6293	15	28	reduced	reduced	ADJ
cana-6293	15	29	loss	loss	NOUN
cana-6293	15	30	.	.	PUNCT
cana-6293	16	1	traditionally	traditionally	ADV
cana-6293	16	2	,	,	PUNCT
cana-6293	16	3	farmers	farmer	NOUN
cana-6293	16	4	depend	depend	VERB
cana-6293	16	5	on	on	ADP
cana-6293	16	6	manual	manual	ADJ
cana-6293	16	7	observation	observation	NOUN
cana-6293	16	8	and	and	CCONJ
cana-6293	16	9	past	past	ADJ
cana-6293	16	10	experience	experience	NOUN
cana-6293	16	11	to	to	PART
cana-6293	16	12	understand	understand	VERB
cana-6293	16	13	crop	crop	NOUN
cana-6293	16	14	growth	growth	NOUN
cana-6293	16	15	,	,	PUNCT
cana-6293	16	16	but	but	CCONJ
cana-6293	16	17	these	these	DET
cana-6293	16	18	methods	method	NOUN
cana-6293	16	19	are	be	AUX
cana-6293	16	20	slow	slow	ADJ
cana-6293	16	21	and	and	CCONJ
cana-6293	16	22	may	may	AUX
cana-6293	16	23	not	not	PART
cana-6293	16	24	give	give	VERB
cana-6293	16	25	accurate	accurate	ADJ
cana-6293	16	26	results	result	NOUN
cana-6293	16	27	,	,	PUNCT
cana-6293	16	28	especially	especially	ADV
cana-6293	16	29	when	when	SCONJ
cana-6293	16	30	weather	weather	NOUN
cana-6293	16	31	and	and	CCONJ
cana-6293	16	32	soil	soil	NOUN
cana-6293	16	33	conditions	condition	NOUN
cana-6293	16	34	change	change	VERB
cana-6293	16	35	suddenly	suddenly	ADV
cana-6293	16	36	.	.	PUNCT
cana-6293	17	1	machine	machine	NOUN
cana-6293	17	2	learning	learning	NOUN
cana-6293	17	3	(	(	PUNCT
cana-6293	17	4	ml	ml	NOUN
cana-6293	17	5	)	)	PUNCT
cana-6293	17	6	now	now	ADV
cana-6293	17	7	provides	provide	VERB
cana-6293	17	8	a	a	DET
cana-6293	17	9	smarter	smart	ADJ
cana-6293	17	10	way	way	NOUN
cana-6293	17	11	to	to	PART
cana-6293	17	12	analyze	analyze	VERB
cana-6293	17	13	agricultural	agricultural	ADJ
cana-6293	17	14	data	datum	NOUN
cana-6293	17	15	.	.	PUNCT
cana-6293	18	1	ml	ml	PROPN
cana-6293	18	2	models	model	NOUN
cana-6293	18	3	can	can	AUX
cana-6293	18	4	learn	learn	VERB
cana-6293	18	5	patterns	pattern	NOUN
cana-6293	18	6	from	from	ADP
cana-6293	18	7	large	large	ADJ
cana-6293	18	8	amounts	amount	NOUN
cana-6293	18	9	of	of	ADP
cana-6293	18	10	data	datum	NOUN
cana-6293	18	11	such	such	ADJ
cana-6293	18	12	as	as	ADP
cana-6293	18	13	temperature	temperature	NOUN
cana-6293	18	14	,	,	PUNCT
cana-6293	18	15	rainfall	rainfall	NOUN
cana-6293	18	16	,	,	PUNCT
cana-6293	18	17	soil	soil	NOUN
cana-6293	18	18	moisture	moisture	NOUN
cana-6293	18	19	,	,	PUNCT
cana-6293	18	20	fertilizer	fertilizer	NOUN
cana-6293	18	21	level	level	NOUN
cana-6293	18	22	,	,	PUNCT
cana-6293	18	23	and	and	CCONJ
cana-6293	18	24	crop	crop	NOUN
cana-6293	18	25	growth	growth	NOUN
cana-6293	18	26	rate	rate	NOUN
cana-6293	18	27	.	.	PUNCT
cana-6293	19	1	these	these	DET
cana-6293	19	2	models	model	NOUN
cana-6293	19	3	can	can	AUX
cana-6293	19	4	then	then	ADV
cana-6293	19	5	be	be	AUX
cana-6293	19	6	used	use	VERB
cana-6293	19	7	to	to	PART
cana-6293	19	8	predict	predict	VERB
cana-6293	19	9	how	how	SCONJ
cana-6293	19	10	the	the	DET
cana-6293	19	11	paddy	paddy	NOUN
cana-6293	19	12	crop	crop	NOUN
cana-6293	19	13	will	will	AUX
cana-6293	19	14	grow	grow	VERB
cana-6293	19	15	in	in	ADP
cana-6293	19	16	the	the	DET
cana-6293	19	17	future	future	NOUN
cana-6293	19	18	.	.	PUNCT
cana-6293	20	1	however	however	ADV
cana-6293	20	2	,	,	PUNCT
cana-6293	20	3	the	the	DET
cana-6293	20	4	accuracy	accuracy	NOUN
cana-6293	20	5	of	of	ADP
cana-6293	20	6	ml	ml	NOUN
cana-6293	20	7	models	model	NOUN
cana-6293	20	8	depends	depend	VERB
cana-6293	20	9	on	on	ADP
cana-6293	20	10	choosing	choose	VERB
cana-6293	20	11	the	the	DET
cana-6293	20	12	right	right	ADJ
cana-6293	20	13	parameters	parameter	NOUN
cana-6293	20	14	.	.	PUNCT
cana-6293	21	1	if	if	SCONJ
cana-6293	21	2	the	the	DET
cana-6293	21	3	model	model	NOUN
cana-6293	21	4	settings	setting	NOUN
cana-6293	21	5	are	be	AUX
cana-6293	21	6	not	not	PART
cana-6293	21	7	correct	correct	ADJ
cana-6293	21	8	,	,	PUNCT
cana-6293	21	9	the	the	DET
cana-6293	21	10	prediction	prediction	NOUN
cana-6293	21	11	may	may	AUX
cana-6293	21	12	be	be	AUX
cana-6293	21	13	inaccurate	inaccurate	ADJ
cana-6293	21	14	.	.	PUNCT
cana-6293	22	1	to	to	PART
cana-6293	22	2	solve	solve	VERB
cana-6293	22	3	this	this	DET
cana-6293	22	4	problem	problem	NOUN
cana-6293	22	5	,	,	PUNCT
cana-6293	22	6	optimization	optimization	NOUN
cana-6293	22	7	methods	method	NOUN
cana-6293	22	8	like	like	ADP
cana-6293	22	9	particle	particle	NOUN
cana-6293	22	10	swarm	swarm	NOUN
cana-6293	22	11	optimization	optimization	NOUN
cana-6293	22	12	(	(	PUNCT
cana-6293	22	13	pso	pso	NOUN
cana-6293	22	14	)	)	PUNCT
cana-6293	22	15	and	and	CCONJ
cana-6293	22	16	genetic	genetic	ADJ
cana-6293	22	17	algorithm	algorithm	NOUN
cana-6293	22	18	(	(	PUNCT
cana-6293	22	19	ga	ga	NOUN
cana-6293	22	20	)	)	PUNCT
cana-6293	22	21	are	be	AUX
cana-6293	22	22	used	use	VERB
cana-6293	22	23	.	.	PUNCT
cana-6293	23	1	these	these	DET
cana-6293	23	2	techniques	technique	NOUN
cana-6293	23	3	automatically	automatically	ADV
cana-6293	23	4	search	search	VERB
cana-6293	23	5	for	for	ADP
cana-6293	23	6	the	the	DET
cana-6293	23	7	best	good	ADJ
cana-6293	23	8	parameter	parameter	NOUN
cana-6293	23	9	values	value	NOUN
cana-6293	23	10	that	that	PRON
cana-6293	23	11	improve	improve	VERB
cana-6293	23	12	the	the	DET
cana-6293	23	13	model	model	NOUN
cana-6293	23	14	's	's	PART
cana-6293	23	15	performance	performance	NOUN
cana-6293	23	16	.	.	PUNCT
cana-6293	24	1	in	in	ADP
cana-6293	24	2	this	this	DET
cana-6293	24	3	research	research	NOUN
cana-6293	24	4	,	,	PUNCT
cana-6293	24	5	different	different	ADJ
cana-6293	24	6	ml	ml	NOUN
cana-6293	24	7	models	model	NOUN
cana-6293	24	8	such	such	ADJ
cana-6293	24	9	as	as	ADP
cana-6293	24	10	decision	decision	NOUN
cana-6293	24	11	tree	tree	NOUN
cana-6293	24	12	,	,	PUNCT
cana-6293	24	13	random	random	ADJ
cana-6293	24	14	forest	forest	NOUN
cana-6293	24	15	,	,	PUNCT
cana-6293	24	16	and	and	CCONJ
cana-6293	24	17	support	support	VERB
cana-6293	24	18	vector	vector	NOUN
cana-6293	24	19	machine	machine	NOUN
cana-6293	24	20	(	(	PUNCT
cana-6293	24	21	svm	svm	PROPN
cana-6293	24	22	)	)	PUNCT
cana-6293	24	23	,	,	PUNCT
cana-6293	24	24	along	along	ADP
cana-6293	24	25	with	with	ADP
cana-6293	24	26	deep	deep	ADJ
cana-6293	24	27	learning	learn	VERB
cana-6293	24	28	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-6293	24	29	mailto:dhanavel2008@gmail.com	mailto:dhanavel2008@gmail.com	PROPN
cana-6293	24	30	communications	communication	NOUN
cana-6293	24	31	on	on	ADP
cana-6293	24	32	applied	apply	VERB
cana-6293	24	33	nonlinear	nonlinear	ADJ
cana-6293	24	34	analysis	analysis	NOUN
cana-6293	24	35	issn	issn	NOUN
cana-6293	24	36	:	:	PUNCT
cana-6293	24	37	1074	1074	NUM
cana-6293	24	38	-	-	PUNCT
cana-6293	24	39	133x	133x	NUM
cana-6293	24	40	vol	vol	NOUN
cana-6293	24	41	32	32	NUM
cana-6293	24	42	no	no	NOUN
cana-6293	24	43	.	.	PUNCT
cana-6293	25	1	2s	2s	NUM
cana-6293	25	2	(	(	PUNCT
cana-6293	25	3	2025	2025	NUM
cana-6293	25	4	)	)	PUNCT
cana-6293	25	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	25	6	639	639	NUM
cana-6293	25	7	techniques	technique	NOUN
cana-6293	25	8	,	,	PUNCT
cana-6293	25	9	are	be	AUX
cana-6293	25	10	tested	test	VERB
cana-6293	25	11	for	for	ADP
cana-6293	25	12	paddy	paddy	NOUN
cana-6293	25	13	crop	crop	NOUN
cana-6293	25	14	prediction	prediction	NOUN
cana-6293	25	15	.	.	PUNCT
cana-6293	26	1	optimization	optimization	NOUN
cana-6293	26	2	techniques	technique	NOUN
cana-6293	26	3	are	be	AUX
cana-6293	26	4	applied	apply	VERB
cana-6293	26	5	to	to	PART
cana-6293	26	6	improve	improve	VERB
cana-6293	26	7	accuracy	accuracy	NOUN
cana-6293	26	8	and	and	CCONJ
cana-6293	26	9	reduce	reduce	VERB
cana-6293	26	10	prediction	prediction	NOUN
cana-6293	26	11	errors	error	NOUN
cana-6293	26	12	.	.	PUNCT
cana-6293	27	1	the	the	DET
cana-6293	27	2	main	main	ADJ
cana-6293	27	3	purpose	purpose	NOUN
cana-6293	27	4	of	of	ADP
cana-6293	27	5	this	this	DET
cana-6293	27	6	research	research	NOUN
cana-6293	27	7	is	be	AUX
cana-6293	27	8	to	to	PART
cana-6293	27	9	study	study	VERB
cana-6293	27	10	how	how	SCONJ
cana-6293	27	11	optimization	optimization	NOUN
cana-6293	27	12	helps	helps	AUX
cana-6293	27	13	improve	improve	VERB
cana-6293	27	14	ml	ml	NOUN
cana-6293	27	15	model	model	NOUN
cana-6293	27	16	performance	performance	NOUN
cana-6293	27	17	and	and	CCONJ
cana-6293	27	18	to	to	PART
cana-6293	27	19	identify	identify	VERB
cana-6293	27	20	the	the	DET
cana-6293	27	21	best	good	ADJ
cana-6293	27	22	model	model	NOUN
cana-6293	27	23	for	for	ADP
cana-6293	27	24	predicting	predict	VERB
cana-6293	27	25	paddy	paddy	NOUN
cana-6293	27	26	growth	growth	NOUN
cana-6293	27	27	.	.	PUNCT
cana-6293	28	1	the	the	DET
cana-6293	28	2	outcome	outcome	NOUN
cana-6293	28	3	of	of	ADP
cana-6293	28	4	this	this	DET
cana-6293	28	5	research	research	NOUN
cana-6293	28	6	can	can	AUX
cana-6293	28	7	assist	assist	VERB
cana-6293	28	8	farmers	farmer	NOUN
cana-6293	28	9	in	in	ADP
cana-6293	28	10	better	well	ADJ
cana-6293	28	11	decisionmaking	decisionmaking	NOUN
cana-6293	28	12	and	and	CCONJ
cana-6293	28	13	support	support	VERB
cana-6293	28	14	the	the	DET
cana-6293	28	15	development	development	NOUN
cana-6293	28	16	of	of	ADP
cana-6293	28	17	smart	smart	ADJ
cana-6293	28	18	agriculture	agriculture	NOUN
cana-6293	28	19	practices	practice	NOUN
cana-6293	28	20	.	.	PUNCT
cana-6293	29	1	paddy	paddy	NOUN
cana-6293	29	2	(	(	PUNCT
cana-6293	29	3	rice	rice	NOUN
cana-6293	29	4	)	)	PUNCT
cana-6293	29	5	is	be	AUX
cana-6293	29	6	a	a	DET
cana-6293	29	7	staple	staple	ADJ
cana-6293	29	8	food	food	NOUN
cana-6293	29	9	for	for	ADP
cana-6293	29	10	more	more	ADJ
cana-6293	29	11	than	than	ADP
cana-6293	29	12	half	half	NOUN
cana-6293	29	13	of	of	ADP
cana-6293	29	14	the	the	DET
cana-6293	29	15	global	global	ADJ
cana-6293	29	16	population	population	NOUN
cana-6293	29	17	and	and	CCONJ
cana-6293	29	18	plays	play	VERB
cana-6293	29	19	a	a	DET
cana-6293	29	20	key	key	ADJ
cana-6293	29	21	role	role	NOUN
cana-6293	29	22	in	in	ADP
cana-6293	29	23	food	food	NOUN
cana-6293	29	24	security	security	NOUN
cana-6293	29	25	and	and	CCONJ
cana-6293	29	26	economic	economic	ADJ
cana-6293	29	27	development	development	NOUN
cana-6293	29	28	.	.	PUNCT
cana-6293	30	1	accurate	accurate	ADJ
cana-6293	30	2	prediction	prediction	NOUN
cana-6293	30	3	of	of	ADP
cana-6293	30	4	paddy	paddy	NOUN
cana-6293	30	5	growth	growth	NOUN
cana-6293	30	6	helps	help	VERB
cana-6293	30	7	farmers	farmer	NOUN
cana-6293	30	8	make	make	VERB
cana-6293	30	9	better	well	ADJ
cana-6293	30	10	decisions	decision	NOUN
cana-6293	30	11	regarding	regard	VERB
cana-6293	30	12	irrigation	irrigation	NOUN
cana-6293	30	13	schedules	schedule	NOUN
cana-6293	30	14	,	,	PUNCT
cana-6293	30	15	fertilizer	fertilizer	NOUN
cana-6293	30	16	usage	usage	NOUN
cana-6293	30	17	,	,	PUNCT
cana-6293	30	18	pest	pest	NOUN
cana-6293	30	19	management	management	NOUN
cana-6293	30	20	,	,	PUNCT
cana-6293	30	21	and	and	CCONJ
cana-6293	30	22	harvesting	harvesting	NOUN
cana-6293	30	23	time	time	NOUN
cana-6293	30	24	.	.	PUNCT
cana-6293	31	1	in	in	ADP
cana-6293	31	2	traditional	traditional	ADJ
cana-6293	31	3	farming	farming	NOUN
cana-6293	31	4	,	,	PUNCT
cana-6293	31	5	predictions	prediction	NOUN
cana-6293	31	6	are	be	AUX
cana-6293	31	7	based	base	VERB
cana-6293	31	8	on	on	ADP
cana-6293	31	9	visual	visual	ADJ
cana-6293	31	10	observation	observation	NOUN
cana-6293	31	11	,	,	PUNCT
cana-6293	31	12	experience	experience	NOUN
cana-6293	31	13	,	,	PUNCT
cana-6293	31	14	and	and	CCONJ
cana-6293	31	15	historical	historical	ADJ
cana-6293	31	16	data	datum	NOUN
cana-6293	31	17	.	.	PUNCT
cana-6293	32	1	however	however	ADV
cana-6293	32	2	,	,	PUNCT
cana-6293	32	3	these	these	DET
cana-6293	32	4	methods	method	NOUN
cana-6293	32	5	often	often	ADV
cana-6293	32	6	fail	fail	VERB
cana-6293	32	7	because	because	SCONJ
cana-6293	32	8	crop	crop	NOUN
cana-6293	32	9	growth	growth	NOUN
cana-6293	32	10	is	be	AUX
cana-6293	32	11	affected	affect	VERB
cana-6293	32	12	by	by	ADP
cana-6293	32	13	multiple	multiple	ADJ
cana-6293	32	14	dynamic	dynamic	ADJ
cana-6293	32	15	factors	factor	NOUN
cana-6293	32	16	such	such	ADJ
cana-6293	32	17	as	as	ADP
cana-6293	32	18	soil	soil	NOUN
cana-6293	32	19	nutrients	nutrient	NOUN
cana-6293	32	20	,	,	PUNCT
cana-6293	32	21	rainfall	rainfall	NOUN
cana-6293	32	22	,	,	PUNCT
cana-6293	32	23	humidity	humidity	NOUN
cana-6293	32	24	,	,	PUNCT
cana-6293	32	25	temperature	temperature	NOUN
cana-6293	32	26	changes	change	NOUN
cana-6293	32	27	,	,	PUNCT
cana-6293	32	28	and	and	CCONJ
cana-6293	32	29	disease	disease	NOUN
cana-6293	32	30	outbreaks	outbreak	NOUN
cana-6293	32	31	.	.	PUNCT
cana-6293	33	1	with	with	ADP
cana-6293	33	2	the	the	DET
cana-6293	33	3	growth	growth	NOUN
cana-6293	33	4	of	of	ADP
cana-6293	33	5	digital	digital	ADJ
cana-6293	33	6	technologies	technology	NOUN
cana-6293	33	7	and	and	CCONJ
cana-6293	33	8	smart	smart	ADJ
cana-6293	33	9	farming	farming	NOUN
cana-6293	33	10	,	,	PUNCT
cana-6293	33	11	machine	machine	NOUN
cana-6293	33	12	learning	learning	NOUN
cana-6293	33	13	(	(	PUNCT
cana-6293	33	14	ml	ml	NOUN
cana-6293	33	15	)	)	PUNCT
cana-6293	33	16	and	and	CCONJ
cana-6293	33	17	data	datum	NOUN
cana-6293	33	18	analytics	analytic	NOUN
cana-6293	33	19	are	be	AUX
cana-6293	33	20	widely	widely	ADV
cana-6293	33	21	used	use	VERB
cana-6293	33	22	to	to	PART
cana-6293	33	23	support	support	VERB
cana-6293	33	24	agricultural	agricultural	ADJ
cana-6293	33	25	decision	decision	NOUN
cana-6293	33	26	-	-	PUNCT
cana-6293	33	27	making	making	NOUN
cana-6293	33	28	.	.	PUNCT
cana-6293	34	1	ml	ml	NOUN
cana-6293	34	2	models	model	NOUN
cana-6293	34	3	can	can	AUX
cana-6293	34	4	process	process	VERB
cana-6293	34	5	large	large	ADJ
cana-6293	34	6	datasets	dataset	NOUN
cana-6293	34	7	and	and	CCONJ
cana-6293	34	8	identify	identify	VERB
cana-6293	34	9	hidden	hidden	ADJ
cana-6293	34	10	patterns	pattern	NOUN
cana-6293	34	11	that	that	PRON
cana-6293	34	12	humans	human	NOUN
cana-6293	34	13	may	may	AUX
cana-6293	34	14	overlook	overlook	VERB
cana-6293	34	15	.	.	PUNCT
cana-6293	35	1	these	these	DET
cana-6293	35	2	models	model	NOUN
cana-6293	35	3	are	be	AUX
cana-6293	35	4	effective	effective	ADJ
cana-6293	35	5	in	in	ADP
cana-6293	35	6	predicting	predict	VERB
cana-6293	35	7	crop	crop	NOUN
cana-6293	35	8	growth	growth	NOUN
cana-6293	35	9	stages	stage	NOUN
cana-6293	35	10	,	,	PUNCT
cana-6293	35	11	yield	yield	NOUN
cana-6293	35	12	estimation	estimation	NOUN
cana-6293	35	13	,	,	PUNCT
cana-6293	35	14	and	and	CCONJ
cana-6293	35	15	detecting	detect	VERB
cana-6293	35	16	abnormalities	abnormality	NOUN
cana-6293	35	17	early	early	ADV
cana-6293	35	18	.	.	PUNCT
cana-6293	36	1	although	although	SCONJ
cana-6293	36	2	ml	ml	NOUN
cana-6293	36	3	models	model	NOUN
cana-6293	36	4	provide	provide	VERB
cana-6293	36	5	good	good	ADJ
cana-6293	36	6	results	result	NOUN
cana-6293	36	7	,	,	PUNCT
cana-6293	36	8	their	their	PRON
cana-6293	36	9	performance	performance	NOUN
cana-6293	36	10	depends	depend	VERB
cana-6293	36	11	heavily	heavily	ADV
cana-6293	36	12	on	on	ADP
cana-6293	36	13	selecting	select	VERB
cana-6293	36	14	correct	correct	ADJ
cana-6293	36	15	features	feature	NOUN
cana-6293	36	16	and	and	CCONJ
cana-6293	36	17	tuning	tune	VERB
cana-6293	36	18	model	model	NOUN
cana-6293	36	19	parameters	parameter	NOUN
cana-6293	36	20	.	.	PUNCT
cana-6293	37	1	if	if	SCONJ
cana-6293	37	2	these	these	DET
cana-6293	37	3	parameters	parameter	NOUN
cana-6293	37	4	are	be	AUX
cana-6293	37	5	not	not	PART
cana-6293	37	6	set	set	VERB
cana-6293	37	7	accurately	accurately	ADV
cana-6293	37	8	,	,	PUNCT
cana-6293	37	9	the	the	DET
cana-6293	37	10	prediction	prediction	NOUN
cana-6293	37	11	quality	quality	NOUN
cana-6293	37	12	decreases	decrease	VERB
cana-6293	37	13	.	.	PUNCT
cana-6293	38	1	because	because	SCONJ
cana-6293	38	2	of	of	ADP
cana-6293	38	3	this	this	PRON
cana-6293	38	4	,	,	PUNCT
cana-6293	38	5	optimization	optimization	NOUN
cana-6293	38	6	techniques	technique	NOUN
cana-6293	38	7	such	such	ADJ
cana-6293	38	8	as	as	ADP
cana-6293	38	9	particle	particle	NOUN
cana-6293	38	10	swarm	swarm	NOUN
cana-6293	38	11	optimization	optimization	NOUN
cana-6293	38	12	(	(	PUNCT
cana-6293	38	13	pso	pso	NOUN
cana-6293	38	14	)	)	PUNCT
cana-6293	38	15	and	and	CCONJ
cana-6293	38	16	genetic	genetic	ADJ
cana-6293	38	17	algorithm	algorithm	NOUN
cana-6293	38	18	(	(	PUNCT
cana-6293	38	19	ga	ga	NOUN
cana-6293	38	20	)	)	PUNCT
cana-6293	38	21	are	be	AUX
cana-6293	38	22	used	use	VERB
cana-6293	38	23	.	.	PUNCT
cana-6293	39	1	these	these	DET
cana-6293	39	2	optimization	optimization	NOUN
cana-6293	39	3	algorithms	algorithm	NOUN
cana-6293	39	4	automatically	automatically	ADV
cana-6293	39	5	adjust	adjust	VERB
cana-6293	39	6	the	the	DET
cana-6293	39	7	parameters	parameter	NOUN
cana-6293	39	8	of	of	ADP
cana-6293	39	9	ml	ml	NOUN
cana-6293	39	10	models	model	NOUN
cana-6293	39	11	to	to	PART
cana-6293	39	12	find	find	VERB
cana-6293	39	13	the	the	DET
cana-6293	39	14	best	good	ADJ
cana-6293	39	15	possible	possible	ADJ
cana-6293	39	16	configuration	configuration	NOUN
cana-6293	39	17	.	.	PUNCT
cana-6293	40	1	this	this	PRON
cana-6293	40	2	reduces	reduce	VERB
cana-6293	40	3	human	human	ADJ
cana-6293	40	4	error	error	NOUN
cana-6293	40	5	,	,	PUNCT
cana-6293	40	6	increases	increase	VERB
cana-6293	40	7	prediction	prediction	NOUN
cana-6293	40	8	accuracy	accuracy	NOUN
cana-6293	40	9	,	,	PUNCT
cana-6293	40	10	and	and	CCONJ
cana-6293	40	11	speeds	speed	VERB
cana-6293	40	12	up	up	ADP
cana-6293	40	13	the	the	DET
cana-6293	40	14	training	training	NOUN
cana-6293	40	15	process	process	NOUN
cana-6293	40	16	.	.	PUNCT
cana-6293	41	1	in	in	ADP
cana-6293	41	2	this	this	DET
cana-6293	41	3	research	research	NOUN
cana-6293	41	4	work	work	NOUN
cana-6293	41	5	,	,	PUNCT
cana-6293	41	6	different	different	ADJ
cana-6293	41	7	ml	ml	NOUN
cana-6293	41	8	and	and	CCONJ
cana-6293	41	9	deep	deep	ADJ
cana-6293	41	10	learning	learning	NOUN
cana-6293	41	11	models	model	NOUN
cana-6293	41	12	—	—	PUNCT
cana-6293	41	13	including	include	VERB
cana-6293	41	14	decision	decision	NOUN
cana-6293	41	15	tree	tree	NOUN
cana-6293	41	16	(	(	PUNCT
cana-6293	41	17	dt	dt	PROPN
cana-6293	41	18	)	)	PUNCT
cana-6293	41	19	,	,	PUNCT
cana-6293	41	20	random	random	ADJ
cana-6293	41	21	forest	forest	NOUN
cana-6293	41	22	(	(	PUNCT
cana-6293	41	23	rf	rf	NOUN
cana-6293	41	24	)	)	PUNCT
cana-6293	41	25	,	,	PUNCT
cana-6293	41	26	support	support	NOUN
cana-6293	41	27	vector	vector	NOUN
cana-6293	41	28	machine	machine	NOUN
cana-6293	41	29	(	(	PUNCT
cana-6293	41	30	svm	svm	PROPN
cana-6293	41	31	)	)	PUNCT
cana-6293	41	32	,	,	PUNCT
cana-6293	41	33	and	and	CCONJ
cana-6293	41	34	long	long	ADJ
cana-6293	41	35	short	short	ADJ
cana-6293	41	36	-	-	PUNCT
cana-6293	41	37	term	term	NOUN
cana-6293	41	38	memory	memory	NOUN
cana-6293	41	39	(	(	PUNCT
cana-6293	41	40	lstm)—are	lstm)—are	VERB
cana-6293	41	41	used	use	VERB
cana-6293	41	42	to	to	PART
cana-6293	41	43	analyze	analyze	VERB
cana-6293	41	44	and	and	CCONJ
cana-6293	41	45	predict	predict	VERB
cana-6293	41	46	paddy	paddy	NOUN
cana-6293	41	47	crop	crop	NOUN
cana-6293	41	48	growth	growth	NOUN
cana-6293	41	49	.	.	PUNCT
cana-6293	42	1	optimization	optimization	NOUN
cana-6293	42	2	methods	method	NOUN
cana-6293	42	3	are	be	AUX
cana-6293	42	4	applied	apply	VERB
cana-6293	42	5	to	to	PART
cana-6293	42	6	improve	improve	VERB
cana-6293	42	7	model	model	NOUN
cana-6293	42	8	efficiency	efficiency	NOUN
cana-6293	42	9	and	and	CCONJ
cana-6293	42	10	performance	performance	NOUN
cana-6293	42	11	.	.	PUNCT
cana-6293	43	1	by	by	ADP
cana-6293	43	2	comparing	compare	VERB
cana-6293	43	3	model	model	NOUN
cana-6293	43	4	results	result	NOUN
cana-6293	43	5	before	before	ADP
cana-6293	43	6	and	and	CCONJ
cana-6293	43	7	after	after	ADP
cana-6293	43	8	optimization	optimization	NOUN
cana-6293	43	9	,	,	PUNCT
cana-6293	43	10	this	this	DET
cana-6293	43	11	study	study	NOUN
cana-6293	43	12	identifies	identify	VERB
cana-6293	43	13	which	which	DET
cana-6293	43	14	model	model	NOUN
cana-6293	43	15	performs	perform	VERB
cana-6293	43	16	best	good	ADJ
cana-6293	43	17	for	for	ADP
cana-6293	43	18	real	real	ADJ
cana-6293	43	19	-	-	PUNCT
cana-6293	43	20	time	time	NOUN
cana-6293	43	21	agricultural	agricultural	ADJ
cana-6293	43	22	prediction	prediction	NOUN
cana-6293	43	23	.	.	PUNCT
cana-6293	44	1	the	the	DET
cana-6293	44	2	final	final	ADJ
cana-6293	44	3	goal	goal	NOUN
cana-6293	44	4	is	be	AUX
cana-6293	44	5	to	to	PART
cana-6293	44	6	develop	develop	VERB
cana-6293	44	7	a	a	DET
cana-6293	44	8	prediction	prediction	NOUN
cana-6293	44	9	model	model	NOUN
cana-6293	44	10	that	that	PRON
cana-6293	44	11	is	be	AUX
cana-6293	44	12	accurate	accurate	ADJ
cana-6293	44	13	,	,	PUNCT
cana-6293	44	14	efficient	efficient	ADJ
cana-6293	44	15	,	,	PUNCT
cana-6293	44	16	and	and	CCONJ
cana-6293	44	17	useful	useful	ADJ
cana-6293	44	18	for	for	ADP
cana-6293	44	19	farmers	farmer	NOUN
cana-6293	44	20	,	,	PUNCT
cana-6293	44	21	agricultural	agricultural	ADJ
cana-6293	44	22	planners	planner	NOUN
cana-6293	44	23	,	,	PUNCT
cana-6293	44	24	and	and	CCONJ
cana-6293	44	25	smart	smart	ADJ
cana-6293	44	26	farming	farming	NOUN
cana-6293	44	27	systems	system	NOUN
cana-6293	44	28	.	.	PUNCT
cana-6293	45	1	this	this	DET
cana-6293	45	2	research	research	NOUN
cana-6293	45	3	supports	support	VERB
cana-6293	45	4	the	the	DET
cana-6293	45	5	advancement	advancement	NOUN
cana-6293	45	6	of	of	ADP
cana-6293	45	7	precision	precision	NOUN
cana-6293	45	8	agriculture	agriculture	NOUN
cana-6293	45	9	,	,	PUNCT
cana-6293	45	10	reduces	reduce	VERB
cana-6293	45	11	crop	crop	NOUN
cana-6293	45	12	loss	loss	NOUN
cana-6293	45	13	risks	risk	NOUN
cana-6293	45	14	,	,	PUNCT
cana-6293	45	15	and	and	CCONJ
cana-6293	45	16	promotes	promote	VERB
cana-6293	45	17	data	data	NOUN
cana-6293	45	18	-	-	PUNCT
cana-6293	45	19	driven	drive	VERB
cana-6293	45	20	farming	farming	NOUN
cana-6293	45	21	practices	practice	NOUN
cana-6293	45	22	.	.	PUNCT
cana-6293	46	1	the	the	DET
cana-6293	46	2	prediction	prediction	NOUN
cana-6293	46	3	of	of	ADP
cana-6293	46	4	paddy	paddy	NOUN
cana-6293	46	5	growth	growth	NOUN
cana-6293	46	6	and	and	CCONJ
cana-6293	46	7	crop	crop	NOUN
cana-6293	46	8	yield	yield	NOUN
cana-6293	46	9	has	have	AUX
cana-6293	46	10	been	be	AUX
cana-6293	46	11	widely	widely	ADV
cana-6293	46	12	studied	study	VERB
cana-6293	46	13	using	use	VERB
cana-6293	46	14	machine	machine	NOUN
cana-6293	46	15	learning	learning	NOUN
cana-6293	46	16	,	,	PUNCT
cana-6293	46	17	deep	deep	ADJ
cana-6293	46	18	learning	learning	NOUN
cana-6293	46	19	,	,	PUNCT
cana-6293	46	20	and	and	CCONJ
cana-6293	46	21	optimization	optimization	NOUN
cana-6293	46	22	techniques	technique	NOUN
cana-6293	46	23	.	.	PUNCT
cana-6293	47	1	several	several	ADJ
cana-6293	47	2	researchers	researcher	NOUN
cana-6293	47	3	have	have	AUX
cana-6293	47	4	shown	show	VERB
cana-6293	47	5	that	that	SCONJ
cana-6293	47	6	agricultural	agricultural	ADJ
cana-6293	47	7	prediction	prediction	NOUN
cana-6293	47	8	becomes	become	VERB
cana-6293	47	9	more	more	ADV
cana-6293	47	10	accurate	accurate	ADJ
cana-6293	47	11	when	when	SCONJ
cana-6293	47	12	algorithms	algorithm	NOUN
cana-6293	47	13	analyze	analyze	VERB
cana-6293	47	14	environmental	environmental	ADJ
cana-6293	47	15	parameters	parameter	NOUN
cana-6293	47	16	such	such	ADJ
cana-6293	47	17	as	as	ADP
cana-6293	47	18	temperature	temperature	NOUN
cana-6293	47	19	,	,	PUNCT
cana-6293	47	20	rainfall	rainfall	NOUN
cana-6293	47	21	,	,	PUNCT
cana-6293	47	22	soil	soil	NOUN
cana-6293	47	23	nutrients	nutrient	NOUN
cana-6293	47	24	,	,	PUNCT
cana-6293	47	25	humidity	humidity	NOUN
cana-6293	47	26	,	,	PUNCT
cana-6293	47	27	and	and	CCONJ
cana-6293	47	28	irrigation	irrigation	NOUN
cana-6293	47	29	patterns	pattern	NOUN
cana-6293	47	30	.	.	PUNCT
cana-6293	48	1	early	early	ADJ
cana-6293	48	2	studies	study	NOUN
cana-6293	48	3	emphasized	emphasize	VERB
cana-6293	48	4	the	the	DET
cana-6293	48	5	importance	importance	NOUN
cana-6293	48	6	of	of	ADP
cana-6293	48	7	ml	ml	NOUN
cana-6293	48	8	in	in	ADP
cana-6293	48	9	agriculture	agriculture	NOUN
cana-6293	48	10	,	,	PUNCT
cana-6293	48	11	demonstrating	demonstrate	VERB
cana-6293	48	12	that	that	SCONJ
cana-6293	48	13	models	model	NOUN
cana-6293	48	14	can	can	AUX
cana-6293	48	15	identify	identify	VERB
cana-6293	48	16	patterns	pattern	NOUN
cana-6293	48	17	in	in	ADP
cana-6293	48	18	climate	climate	NOUN
cana-6293	48	19	and	and	CCONJ
cana-6293	48	20	soil	soil	NOUN
cana-6293	48	21	data	datum	NOUN
cana-6293	48	22	and	and	CCONJ
cana-6293	48	23	help	help	VERB
cana-6293	48	24	farmers	farmer	NOUN
cana-6293	48	25	make	make	VERB
cana-6293	48	26	informed	informed	ADJ
cana-6293	48	27	decisions	decision	NOUN
cana-6293	48	28	.	.	PUNCT
cana-6293	49	1	patil	patil	PROPN
cana-6293	49	2	and	and	CCONJ
cana-6293	49	3	kumar	kumar	PROPN
cana-6293	49	4	highlighted	highlight	VERB
cana-6293	49	5	that	that	SCONJ
cana-6293	49	6	ml	ml	AUX
cana-6293	49	7	algorithms	algorithm	NOUN
cana-6293	49	8	offer	offer	VERB
cana-6293	49	9	reliable	reliable	ADJ
cana-6293	49	10	yield	yield	NOUN
cana-6293	49	11	predictions	prediction	NOUN
cana-6293	49	12	by	by	ADP
cana-6293	49	13	processing	process	VERB
cana-6293	49	14	large	large	ADJ
cana-6293	49	15	amounts	amount	NOUN
cana-6293	49	16	of	of	ADP
cana-6293	49	17	agricultural	agricultural	ADJ
cana-6293	49	18	data	datum	NOUN
cana-6293	49	19	efficiently	efficiently	ADV
cana-6293	49	20	[	[	X
cana-6293	49	21	1	1	NUM
cana-6293	49	22	]	]	PUNCT
cana-6293	49	23	.	.	PUNCT
cana-6293	50	1	deep	deep	ADJ
cana-6293	50	2	learning	learning	NOUN
cana-6293	50	3	has	have	AUX
cana-6293	50	4	further	far	ADV
cana-6293	50	5	improved	improve	VERB
cana-6293	50	6	prediction	prediction	NOUN
cana-6293	50	7	accuracy	accuracy	NOUN
cana-6293	50	8	,	,	PUNCT
cana-6293	50	9	especially	especially	ADV
cana-6293	50	10	when	when	SCONJ
cana-6293	50	11	using	use	VERB
cana-6293	50	12	satellite	satellite	NOUN
cana-6293	50	13	images	image	NOUN
cana-6293	50	14	and	and	CCONJ
cana-6293	50	15	time	time	NOUN
cana-6293	50	16	-	-	PUNCT
cana-6293	50	17	series	series	NOUN
cana-6293	50	18	crop	crop	NOUN
cana-6293	50	19	data	data	PROPN
cana-6293	50	20	.	.	PUNCT
cana-6293	51	1	li	li	PROPN
cana-6293	51	2	et	et	PROPN
cana-6293	51	3	al	al	PROPN
cana-6293	51	4	.	.	PROPN
cana-6293	51	5	showed	show	VERB
cana-6293	51	6	that	that	SCONJ
cana-6293	51	7	deep	deep	ADJ
cana-6293	51	8	learning	learning	NOUN
cana-6293	51	9	architectures	architecture	NOUN
cana-6293	51	10	such	such	ADJ
cana-6293	51	11	as	as	ADP
cana-6293	51	12	cnns	cnn	NOUN
cana-6293	51	13	enhance	enhance	VERB
cana-6293	51	14	the	the	DET
cana-6293	51	15	monitoring	monitoring	NOUN
cana-6293	51	16	of	of	ADP
cana-6293	51	17	rice	rice	NOUN
cana-6293	51	18	growth	growth	NOUN
cana-6293	51	19	by	by	ADP
cana-6293	51	20	extracting	extract	VERB
cana-6293	51	21	spatial	spatial	ADJ
cana-6293	51	22	patterns	pattern	NOUN
cana-6293	51	23	from	from	ADP
cana-6293	51	24	remote	remote	ADJ
cana-6293	51	25	sensing	sensing	NOUN
cana-6293	51	26	images	image	NOUN
cana-6293	51	27	[	[	X
cana-6293	51	28	2	2	NUM
cana-6293	51	29	]	]	PUNCT
cana-6293	51	30	.	.	PUNCT
cana-6293	52	1	weather	weather	NOUN
cana-6293	52	2	-	-	PUNCT
cana-6293	52	3	based	base	VERB
cana-6293	52	4	studies	study	NOUN
cana-6293	52	5	also	also	ADV
cana-6293	52	6	confirm	confirm	VERB
cana-6293	52	7	that	that	PRON
cana-6293	52	8	rainfall	rainfall	NOUN
cana-6293	52	9	,	,	PUNCT
cana-6293	52	10	temperature	temperature	NOUN
cana-6293	52	11	,	,	PUNCT
cana-6293	52	12	and	and	CCONJ
cana-6293	52	13	humidity	humidity	NOUN
cana-6293	52	14	strongly	strongly	ADV
cana-6293	52	15	influence	influence	VERB
cana-6293	52	16	rice	rice	NOUN
cana-6293	52	17	production	production	NOUN
cana-6293	52	18	,	,	PUNCT
cana-6293	52	19	making	make	VERB
cana-6293	52	20	these	these	DET
cana-6293	52	21	variables	variable	NOUN
cana-6293	52	22	essential	essential	ADJ
cana-6293	52	23	for	for	ADP
cana-6293	52	24	accurate	accurate	ADJ
cana-6293	52	25	prediction	prediction	NOUN
cana-6293	52	26	models	model	NOUN
cana-6293	52	27	[	[	X
cana-6293	52	28	3	3	NUM
cana-6293	52	29	]	]	PUNCT
cana-6293	52	30	.	.	PUNCT
cana-6293	53	1	decision	decision	NOUN
cana-6293	53	2	trees	tree	NOUN
cana-6293	53	3	and	and	CCONJ
cana-6293	53	4	random	random	ADJ
cana-6293	53	5	forests	forest	NOUN
cana-6293	53	6	remain	remain	VERB
cana-6293	53	7	widely	widely	ADV
cana-6293	53	8	used	use	VERB
cana-6293	53	9	due	due	ADP
cana-6293	53	10	to	to	ADP
cana-6293	53	11	their	their	PRON
cana-6293	53	12	interpretability	interpretability	NOUN
cana-6293	53	13	and	and	CCONJ
cana-6293	53	14	robustness	robustness	NOUN
cana-6293	53	15	.	.	PUNCT
cana-6293	54	1	quinlan	quinlan	PROPN
cana-6293	54	2	explained	explain	VERB
cana-6293	54	3	that	that	SCONJ
cana-6293	54	4	tree	tree	NOUN
cana-6293	54	5	-	-	PUNCT
cana-6293	54	6	based	base	VERB
cana-6293	54	7	models	model	NOUN
cana-6293	54	8	help	help	AUX
cana-6293	54	9	identify	identify	VERB
cana-6293	54	10	key	key	ADJ
cana-6293	54	11	features	feature	NOUN
cana-6293	54	12	such	such	ADJ
cana-6293	54	13	as	as	ADP
cana-6293	54	14	soil	soil	NOUN
cana-6293	54	15	nutrients	nutrient	NOUN
cana-6293	54	16	and	and	CCONJ
cana-6293	54	17	rainfall	rainfall	NOUN
cana-6293	54	18	patterns	pattern	NOUN
cana-6293	54	19	that	that	PRON
cana-6293	54	20	influence	influence	NOUN
cana-6293	54	21	yield	yield	NOUN
cana-6293	54	22	[	[	X
cana-6293	54	23	4	4	NUM
cana-6293	54	24	]	]	PUNCT
cana-6293	54	25	,	,	PUNCT
cana-6293	54	26	while	while	SCONJ
cana-6293	54	27	ghosh	ghosh	PROPN
cana-6293	54	28	and	and	CCONJ
cana-6293	54	29	bala	bala	PROPN
cana-6293	54	30	demonstrated	demonstrate	VERB
cana-6293	54	31	that	that	SCONJ
cana-6293	54	32	random	random	ADJ
cana-6293	54	33	forests	forest	NOUN
cana-6293	54	34	reduce	reduce	VERB
cana-6293	54	35	overfitting	overfitte	VERB
cana-6293	54	36	and	and	CCONJ
cana-6293	54	37	perform	perform	VERB
cana-6293	54	38	well	well	ADV
cana-6293	54	39	on	on	ADP
cana-6293	54	40	heterogeneous	heterogeneous	ADJ
cana-6293	54	41	agricultural	agricultural	ADJ
cana-6293	54	42	datasets	dataset	NOUN
cana-6293	54	43	[	[	X
cana-6293	54	44	5	5	NUM
cana-6293	54	45	]	]	PUNCT
cana-6293	54	46	.	.	PUNCT
cana-6293	55	1	support	support	NOUN
cana-6293	55	2	vector	vector	NOUN
cana-6293	55	3	machines	machine	NOUN
cana-6293	55	4	also	also	ADV
cana-6293	55	5	show	show	VERB
cana-6293	55	6	high	high	ADJ
cana-6293	55	7	reliability	reliability	NOUN
cana-6293	55	8	for	for	ADP
cana-6293	55	9	prediction	prediction	NOUN
cana-6293	55	10	tasks	task	NOUN
cana-6293	55	11	with	with	ADP
cana-6293	55	12	small	small	ADJ
cana-6293	55	13	datasets	dataset	NOUN
cana-6293	55	14	,	,	PUNCT
cana-6293	55	15	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-6293	55	16	communications	communication	NOUN
cana-6293	55	17	on	on	ADP
cana-6293	55	18	applied	apply	VERB
cana-6293	55	19	nonlinear	nonlinear	ADJ
cana-6293	55	20	analysis	analysis	NOUN
cana-6293	55	21	issn	issn	NOUN
cana-6293	55	22	:	:	PUNCT
cana-6293	55	23	1074	1074	NUM
cana-6293	55	24	-	-	PUNCT
cana-6293	55	25	133x	133x	NUM
cana-6293	55	26	vol	vol	NOUN
cana-6293	55	27	32	32	NUM
cana-6293	55	28	no	no	NOUN
cana-6293	55	29	.	.	PUNCT
cana-6293	56	1	2s	2s	NUM
cana-6293	56	2	(	(	PUNCT
cana-6293	56	3	2025	2025	NUM
cana-6293	56	4	)	)	PUNCT
cana-6293	56	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	56	6	640	640	NUM
cana-6293	56	7	making	make	VERB
cana-6293	56	8	them	they	PRON
cana-6293	56	9	suitable	suitable	ADJ
cana-6293	56	10	for	for	ADP
cana-6293	56	11	paddy	paddy	NOUN
cana-6293	56	12	-	-	PUNCT
cana-6293	56	13	growing	grow	VERB
cana-6293	56	14	regions	region	NOUN
cana-6293	56	15	with	with	ADP
cana-6293	56	16	limited	limited	ADJ
cana-6293	56	17	data	datum	NOUN
cana-6293	56	18	availability	availability	NOUN
cana-6293	56	19	[	[	X
cana-6293	56	20	6	6	NUM
cana-6293	56	21	]	]	PUNCT
cana-6293	56	22	.	.	PUNCT
cana-6293	57	1	optimization	optimization	NOUN
cana-6293	57	2	techniques	technique	NOUN
cana-6293	57	3	play	play	VERB
cana-6293	57	4	a	a	DET
cana-6293	57	5	major	major	ADJ
cana-6293	57	6	role	role	NOUN
cana-6293	57	7	in	in	ADP
cana-6293	57	8	improving	improve	VERB
cana-6293	57	9	prediction	prediction	NOUN
cana-6293	57	10	accuracy	accuracy	NOUN
cana-6293	57	11	.	.	PUNCT
cana-6293	58	1	particle	particle	NOUN
cana-6293	58	2	swarm	swarm	NOUN
cana-6293	58	3	optimization	optimization	NOUN
cana-6293	58	4	(	(	PUNCT
cana-6293	58	5	pso	pso	NOUN
cana-6293	58	6	)	)	PUNCT
cana-6293	58	7	and	and	CCONJ
cana-6293	58	8	genetic	genetic	ADJ
cana-6293	58	9	algorithms	algorithm	NOUN
cana-6293	58	10	(	(	PUNCT
cana-6293	58	11	ga	ga	NOUN
cana-6293	58	12	)	)	PUNCT
cana-6293	58	13	are	be	AUX
cana-6293	58	14	two	two	NUM
cana-6293	58	15	of	of	ADP
cana-6293	58	16	the	the	DET
cana-6293	58	17	most	most	ADV
cana-6293	58	18	commonly	commonly	ADV
cana-6293	58	19	applied	apply	VERB
cana-6293	58	20	meta	meta	ADJ
cana-6293	58	21	-	-	PUNCT
cana-6293	58	22	heuristic	heuristic	ADJ
cana-6293	58	23	methods	method	NOUN
cana-6293	58	24	for	for	ADP
cana-6293	58	25	tuning	tune	VERB
cana-6293	58	26	model	model	NOUN
cana-6293	58	27	parameters	parameter	NOUN
cana-6293	58	28	.	.	PUNCT
cana-6293	59	1	kennedy	kennedy	PROPN
cana-6293	59	2	and	and	CCONJ
cana-6293	59	3	eberhart	eberhart	PROPN
cana-6293	59	4	originally	originally	ADV
cana-6293	59	5	introduced	introduce	VERB
cana-6293	59	6	pso	pso	NOUN
cana-6293	59	7	as	as	ADP
cana-6293	59	8	an	an	DET
cana-6293	59	9	efficient	efficient	ADJ
cana-6293	59	10	optimization	optimization	NOUN
cana-6293	59	11	method	method	NOUN
cana-6293	59	12	inspired	inspire	VERB
cana-6293	59	13	by	by	ADP
cana-6293	59	14	swarm	swarm	NOUN
cana-6293	59	15	behavior	behavior	NOUN
cana-6293	59	16	[	[	X
cana-6293	59	17	7	7	NUM
cana-6293	59	18	]	]	PUNCT
cana-6293	59	19	,	,	PUNCT
cana-6293	59	20	while	while	SCONJ
cana-6293	59	21	goldberg	goldberg	PROPN
cana-6293	59	22	established	establish	VERB
cana-6293	59	23	ga	ga	PROPN
cana-6293	59	24	as	as	ADP
cana-6293	59	25	a	a	DET
cana-6293	59	26	powerful	powerful	ADJ
cana-6293	59	27	evolutionary	evolutionary	ADJ
cana-6293	59	28	technique	technique	NOUN
cana-6293	59	29	for	for	ADP
cana-6293	59	30	parameter	parameter	NOUN
cana-6293	59	31	tuning	tuning	NOUN
cana-6293	59	32	and	and	CCONJ
cana-6293	59	33	feature	feature	NOUN
cana-6293	59	34	selection	selection	NOUN
cana-6293	59	35	[	[	X
cana-6293	59	36	8	8	NUM
cana-6293	59	37	]	]	PUNCT
cana-6293	59	38	.	.	PUNCT
cana-6293	60	1	hybrid	hybrid	ADJ
cana-6293	60	2	models	model	NOUN
cana-6293	60	3	that	that	PRON
cana-6293	60	4	combine	combine	VERB
cana-6293	60	5	ml	ml	ADP
cana-6293	60	6	with	with	ADP
cana-6293	60	7	pso	pso	NOUN
cana-6293	60	8	or	or	CCONJ
cana-6293	60	9	ga	ga	PROPN
cana-6293	60	10	have	have	AUX
cana-6293	60	11	shown	show	VERB
cana-6293	60	12	improved	improved	ADJ
cana-6293	60	13	accuracy	accuracy	NOUN
cana-6293	60	14	and	and	CCONJ
cana-6293	60	15	reduced	reduce	VERB
cana-6293	60	16	error	error	NOUN
cana-6293	60	17	rates	rate	NOUN
cana-6293	60	18	in	in	ADP
cana-6293	60	19	crop	crop	NOUN
cana-6293	60	20	forecasting	forecasting	NOUN
cana-6293	60	21	[	[	X
cana-6293	60	22	9	9	NUM
cana-6293	60	23	]	]	PUNCT
cana-6293	60	24	.	.	PUNCT
cana-6293	61	1	soil	soil	NOUN
cana-6293	61	2	nutrient	nutrient	NOUN
cana-6293	61	3	analysis	analysis	NOUN
cana-6293	61	4	remains	remain	VERB
cana-6293	61	5	an	an	DET
cana-6293	61	6	essential	essential	ADJ
cana-6293	61	7	part	part	NOUN
cana-6293	61	8	of	of	ADP
cana-6293	61	9	paddy	paddy	NOUN
cana-6293	61	10	prediction	prediction	NOUN
cana-6293	61	11	,	,	PUNCT
cana-6293	61	12	as	as	SCONJ
cana-6293	61	13	emphasized	emphasize	VERB
cana-6293	61	14	by	by	ADP
cana-6293	61	15	sun	sun	PROPN
cana-6293	61	16	et	et	PROPN
cana-6293	61	17	al	al	PROPN
cana-6293	61	18	.	.	PROPN
cana-6293	61	19	,	,	PUNCT
cana-6293	61	20	who	who	PRON
cana-6293	61	21	reported	report	VERB
cana-6293	61	22	that	that	SCONJ
cana-6293	61	23	nitrogen	nitrogen	NOUN
cana-6293	61	24	,	,	PUNCT
cana-6293	61	25	phosphorus	phosphorus	NOUN
cana-6293	61	26	,	,	PUNCT
cana-6293	61	27	and	and	CCONJ
cana-6293	61	28	potassium	potassium	NOUN
cana-6293	61	29	levels	level	NOUN
cana-6293	61	30	significantly	significantly	ADV
cana-6293	61	31	influence	influence	VERB
cana-6293	61	32	crop	crop	NOUN
cana-6293	61	33	growth	growth	NOUN
cana-6293	61	34	and	and	CCONJ
cana-6293	61	35	yield	yield	VERB
cana-6293	61	36	[	[	X
cana-6293	61	37	10	10	NUM
cana-6293	61	38	]	]	PUNCT
cana-6293	61	39	.	.	PUNCT
cana-6293	62	1	rainfall	rainfall	NOUN
cana-6293	62	2	forecasting	forecasting	NOUN
cana-6293	62	3	using	use	VERB
cana-6293	62	4	ml	ml	NOUN
cana-6293	62	5	has	have	AUX
cana-6293	62	6	also	also	ADV
cana-6293	62	7	been	be	AUX
cana-6293	62	8	shown	show	VERB
cana-6293	62	9	to	to	PART
cana-6293	62	10	support	support	VERB
cana-6293	62	11	better	well	ADJ
cana-6293	62	12	irrigation	irrigation	NOUN
cana-6293	62	13	scheduling	scheduling	NOUN
cana-6293	62	14	and	and	CCONJ
cana-6293	62	15	decision	decision	NOUN
cana-6293	62	16	-	-	PUNCT
cana-6293	62	17	making	making	NOUN
cana-6293	62	18	in	in	ADP
cana-6293	62	19	agriculture	agriculture	NOUN
cana-6293	62	20	[	[	X
cana-6293	62	21	11	11	NUM
cana-6293	62	22	]	]	PUNCT
cana-6293	62	23	.	.	PUNCT
cana-6293	63	1	meanwhile	meanwhile	ADV
cana-6293	63	2	,	,	PUNCT
cana-6293	63	3	remote	remote	ADJ
cana-6293	63	4	sensing	sense	VERB
cana-6293	63	5	data	datum	NOUN
cana-6293	63	6	plays	play	VERB
cana-6293	63	7	a	a	DET
cana-6293	63	8	crucial	crucial	ADJ
cana-6293	63	9	role	role	NOUN
cana-6293	63	10	in	in	ADP
cana-6293	63	11	large	large	ADJ
cana-6293	63	12	-	-	PUNCT
cana-6293	63	13	scale	scale	NOUN
cana-6293	63	14	crop	crop	NOUN
cana-6293	63	15	monitoring	monitoring	NOUN
cana-6293	63	16	,	,	PUNCT
cana-6293	63	17	enabling	enable	VERB
cana-6293	63	18	continuous	continuous	ADJ
cana-6293	63	19	assessment	assessment	NOUN
cana-6293	63	20	of	of	ADP
cana-6293	63	21	crop	crop	NOUN
cana-6293	63	22	health	health	NOUN
cana-6293	63	23	and	and	CCONJ
cana-6293	63	24	growth	growth	NOUN
cana-6293	63	25	via	via	ADP
cana-6293	63	26	satellite	satellite	NOUN
cana-6293	63	27	imagery	imagery	NOUN
cana-6293	63	28	[	[	X
cana-6293	63	29	12	12	NUM
cana-6293	63	30	]	]	PUNCT
cana-6293	63	31	.	.	PUNCT
cana-6293	64	1	deep	deep	ADJ
cana-6293	64	2	neural	neural	ADJ
cana-6293	64	3	networks	network	NOUN
cana-6293	64	4	(	(	PUNCT
cana-6293	64	5	dnns	dnns	NOUN
cana-6293	64	6	)	)	PUNCT
cana-6293	64	7	and	and	CCONJ
cana-6293	64	8	lstm	lstm	NOUN
cana-6293	64	9	networks	network	NOUN
cana-6293	64	10	are	be	AUX
cana-6293	64	11	increasingly	increasingly	ADV
cana-6293	64	12	used	use	VERB
cana-6293	64	13	due	due	ADP
cana-6293	64	14	to	to	ADP
cana-6293	64	15	their	their	PRON
cana-6293	64	16	ability	ability	NOUN
cana-6293	64	17	to	to	PART
cana-6293	64	18	model	model	VERB
cana-6293	64	19	complex	complex	ADJ
cana-6293	64	20	and	and	CCONJ
cana-6293	64	21	time	time	NOUN
cana-6293	64	22	-	-	PUNCT
cana-6293	64	23	dependent	dependent	ADJ
cana-6293	64	24	agricultural	agricultural	ADJ
cana-6293	64	25	data	datum	NOUN
cana-6293	64	26	.	.	PUNCT
cana-6293	65	1	lstm	lstm	PROPN
cana-6293	65	2	networks	network	NOUN
cana-6293	65	3	are	be	AUX
cana-6293	65	4	particularly	particularly	ADV
cana-6293	65	5	effective	effective	ADJ
cana-6293	65	6	for	for	ADP
cana-6293	65	7	predicting	predict	VERB
cana-6293	65	8	future	future	ADJ
cana-6293	65	9	crop	crop	NOUN
cana-6293	65	10	conditions	condition	NOUN
cana-6293	65	11	based	base	VERB
cana-6293	65	12	on	on	ADP
cana-6293	65	13	seasonal	seasonal	ADJ
cana-6293	65	14	patterns	pattern	NOUN
cana-6293	65	15	[	[	X
cana-6293	65	16	14	14	NUM
cana-6293	65	17	]	]	PUNCT
cana-6293	65	18	.	.	PUNCT
cana-6293	66	1	ensemble	ensemble	ADJ
cana-6293	66	2	learning	learning	NOUN
cana-6293	66	3	techniques	technique	NOUN
cana-6293	66	4	have	have	AUX
cana-6293	66	5	also	also	ADV
cana-6293	66	6	been	be	AUX
cana-6293	66	7	proven	prove	VERB
cana-6293	66	8	to	to	PART
cana-6293	66	9	improve	improve	VERB
cana-6293	66	10	accuracy	accuracy	NOUN
cana-6293	66	11	by	by	ADP
cana-6293	66	12	combining	combine	VERB
cana-6293	66	13	multiple	multiple	ADJ
cana-6293	66	14	models	model	NOUN
cana-6293	66	15	to	to	PART
cana-6293	66	16	reduce	reduce	VERB
cana-6293	66	17	prediction	prediction	NOUN
cana-6293	66	18	errors	error	NOUN
cana-6293	66	19	[	[	X
cana-6293	66	20	15	15	NUM
cana-6293	66	21	]	]	PUNCT
cana-6293	66	22	.	.	PUNCT
cana-6293	67	1	feature	feature	NOUN
cana-6293	67	2	selection	selection	NOUN
cana-6293	67	3	is	be	AUX
cana-6293	67	4	another	another	DET
cana-6293	67	5	important	important	ADJ
cana-6293	67	6	aspect	aspect	NOUN
cana-6293	67	7	of	of	ADP
cana-6293	67	8	agricultural	agricultural	ADJ
cana-6293	67	9	prediction	prediction	NOUN
cana-6293	67	10	,	,	PUNCT
cana-6293	67	11	as	as	SCONJ
cana-6293	67	12	removing	remove	VERB
cana-6293	67	13	irrelevant	irrelevant	ADJ
cana-6293	67	14	inputs	input	NOUN
cana-6293	67	15	can	can	AUX
cana-6293	67	16	enhance	enhance	VERB
cana-6293	67	17	accuracy	accuracy	NOUN
cana-6293	67	18	and	and	CCONJ
cana-6293	67	19	reduce	reduce	VERB
cana-6293	67	20	computation	computation	NOUN
cana-6293	67	21	time	time	NOUN
cana-6293	67	22	.	.	PUNCT
cana-6293	68	1	optimization	optimization	NOUN
cana-6293	68	2	-	-	PUNCT
cana-6293	68	3	based	base	VERB
cana-6293	68	4	feature	feature	NOUN
cana-6293	68	5	selection	selection	NOUN
cana-6293	68	6	techniques	technique	NOUN
cana-6293	68	7	have	have	AUX
cana-6293	68	8	proven	prove	VERB
cana-6293	68	9	effective	effective	ADJ
cana-6293	68	10	in	in	ADP
cana-6293	68	11	finding	find	VERB
cana-6293	68	12	the	the	DET
cana-6293	68	13	best	good	ADJ
cana-6293	68	14	subset	subset	NOUN
cana-6293	68	15	of	of	ADP
cana-6293	68	16	variables	variable	NOUN
cana-6293	68	17	[	[	X
cana-6293	68	18	16	16	NUM
cana-6293	68	19	]	]	PUNCT
cana-6293	68	20	.	.	PUNCT
cana-6293	69	1	hyperparameter	hyperparameter	NOUN
cana-6293	69	2	tuning	tuning	NOUN
cana-6293	69	3	also	also	ADV
cana-6293	69	4	plays	play	VERB
cana-6293	69	5	a	a	DET
cana-6293	69	6	critical	critical	ADJ
cana-6293	69	7	role	role	NOUN
cana-6293	69	8	in	in	ADP
cana-6293	69	9	increasing	increase	VERB
cana-6293	69	10	performance	performance	NOUN
cana-6293	69	11	,	,	PUNCT
cana-6293	69	12	and	and	CCONJ
cana-6293	69	13	automated	automate	VERB
cana-6293	69	14	optimization	optimization	NOUN
cana-6293	69	15	methods	method	NOUN
cana-6293	69	16	often	often	ADV
cana-6293	69	17	outperform	outperform	VERB
cana-6293	69	18	manual	manual	ADJ
cana-6293	69	19	tuning	tune	VERB
cana-6293	69	20	approaches	approach	NOUN
cana-6293	69	21	[	[	X
cana-6293	69	22	17	17	NUM
cana-6293	69	23	]	]	PUNCT
cana-6293	69	24	.	.	PUNCT
cana-6293	70	1	overall	overall	ADV
cana-6293	70	2	,	,	PUNCT
cana-6293	70	3	the	the	DET
cana-6293	70	4	literature	literature	NOUN
cana-6293	70	5	confirms	confirm	VERB
cana-6293	70	6	that	that	SCONJ
cana-6293	70	7	integrating	integrate	VERB
cana-6293	70	8	machine	machine	NOUN
cana-6293	70	9	learning	learning	NOUN
cana-6293	70	10	,	,	PUNCT
cana-6293	70	11	deep	deep	ADJ
cana-6293	70	12	learning	learning	NOUN
cana-6293	70	13	,	,	PUNCT
cana-6293	70	14	and	and	CCONJ
cana-6293	70	15	optimization	optimization	NOUN
cana-6293	70	16	techniques	technique	NOUN
cana-6293	70	17	leads	lead	VERB
cana-6293	70	18	to	to	ADP
cana-6293	70	19	more	more	ADV
cana-6293	70	20	accurate	accurate	ADJ
cana-6293	70	21	,	,	PUNCT
cana-6293	70	22	reliable	reliable	ADJ
cana-6293	70	23	,	,	PUNCT
cana-6293	70	24	and	and	CCONJ
cana-6293	70	25	scalable	scalable	ADJ
cana-6293	70	26	paddy	paddy	NOUN
cana-6293	70	27	growth	growth	NOUN
cana-6293	70	28	prediction	prediction	NOUN
cana-6293	70	29	systems	system	NOUN
cana-6293	70	30	.	.	PUNCT
cana-6293	71	1	such	such	ADJ
cana-6293	71	2	advanced	advanced	ADJ
cana-6293	71	3	computational	computational	ADJ
cana-6293	71	4	methods	method	NOUN
cana-6293	71	5	support	support	VERB
cana-6293	71	6	smart	smart	ADJ
cana-6293	71	7	farming	farming	NOUN
cana-6293	71	8	and	and	CCONJ
cana-6293	71	9	precision	precision	NOUN
cana-6293	71	10	agriculture	agriculture	NOUN
cana-6293	71	11	by	by	ADP
cana-6293	71	12	helping	help	VERB
cana-6293	71	13	farmers	farmer	NOUN
cana-6293	71	14	optimize	optimize	VERB
cana-6293	71	15	irrigation	irrigation	NOUN
cana-6293	71	16	,	,	PUNCT
cana-6293	71	17	fertilizer	fertilizer	NOUN
cana-6293	71	18	use	use	NOUN
cana-6293	71	19	,	,	PUNCT
cana-6293	71	20	and	and	CCONJ
cana-6293	71	21	resource	resource	NOUN
cana-6293	71	22	management	management	NOUN
cana-6293	71	23	,	,	PUNCT
cana-6293	71	24	ultimately	ultimately	ADV
cana-6293	71	25	improving	improve	VERB
cana-6293	71	26	productivity	productivity	NOUN
cana-6293	71	27	and	and	CCONJ
cana-6293	71	28	sustainability	sustainability	NOUN
cana-6293	71	29	[	[	X
cana-6293	71	30	18	18	NUM
cana-6293	71	31	]	]	X
cana-6293	72	1	[	[	X
cana-6293	72	2	20	20	NUM
cana-6293	72	3	]	]	PUNCT
cana-6293	72	4	.	.	PUNCT
cana-6293	73	1	during	during	ADP
cana-6293	73	2	the	the	DET
cana-6293	73	3	covid-19	covid-19	PROPN
cana-6293	73	4	pandemic	pandemic	NOUN
cana-6293	73	5	,	,	PUNCT
cana-6293	73	6	online	online	ADJ
cana-6293	73	7	shopping	shopping	NOUN
cana-6293	73	8	increased	increase	VERB
cana-6293	73	9	,	,	PUNCT
cana-6293	73	10	and	and	CCONJ
cana-6293	73	11	this	this	PRON
cana-6293	73	12	also	also	ADV
cana-6293	73	13	caused	cause	VERB
cana-6293	73	14	a	a	DET
cana-6293	73	15	rise	rise	NOUN
cana-6293	73	16	in	in	ADP
cana-6293	73	17	credit	credit	NOUN
cana-6293	73	18	card	card	NOUN
cana-6293	73	19	fraud	fraud	NOUN
cana-6293	73	20	.	.	PUNCT
cana-6293	74	1	to	to	PART
cana-6293	74	2	address	address	VERB
cana-6293	74	3	this	this	DET
cana-6293	74	4	issue	issue	NOUN
cana-6293	74	5	,	,	PUNCT
cana-6293	74	6	researchers	researcher	NOUN
cana-6293	74	7	used	use	VERB
cana-6293	74	8	different	different	ADJ
cana-6293	74	9	data	datum	NOUN
cana-6293	74	10	mining	mining	NOUN
cana-6293	74	11	and	and	CCONJ
cana-6293	74	12	statistical	statistical	ADJ
cana-6293	74	13	methods	method	NOUN
cana-6293	74	14	to	to	PART
cana-6293	74	15	build	build	VERB
cana-6293	74	16	models	model	NOUN
cana-6293	74	17	that	that	PRON
cana-6293	74	18	could	could	AUX
cana-6293	74	19	accurately	accurately	ADV
cana-6293	74	20	detect	detect	VERB
cana-6293	74	21	fraud	fraud	NOUN
cana-6293	74	22	.	.	PUNCT
cana-6293	75	1	these	these	DET
cana-6293	75	2	models	model	NOUN
cana-6293	75	3	were	be	AUX
cana-6293	75	4	tested	test	VERB
cana-6293	75	5	using	use	VERB
cana-6293	75	6	numerical	numerical	ADJ
cana-6293	75	7	analysis	analysis	NOUN
cana-6293	75	8	and	and	CCONJ
cana-6293	75	9	showed	show	VERB
cana-6293	75	10	good	good	ADJ
cana-6293	75	11	results	result	NOUN
cana-6293	75	12	[	[	X
cana-6293	75	13	21	21	NUM
cana-6293	75	14	]	]	PUNCT
cana-6293	75	15	.	.	PUNCT
cana-6293	76	1	because	because	SCONJ
cana-6293	76	2	credit	credit	NOUN
cana-6293	76	3	card	card	NOUN
cana-6293	76	4	fraud	fraud	NOUN
cana-6293	76	5	leads	lead	VERB
cana-6293	76	6	to	to	ADP
cana-6293	76	7	large	large	ADJ
cana-6293	76	8	financial	financial	ADJ
cana-6293	76	9	losses	loss	NOUN
cana-6293	76	10	,	,	PUNCT
cana-6293	76	11	many	many	ADJ
cana-6293	76	12	studies	study	NOUN
cana-6293	76	13	stress	stress	VERB
cana-6293	76	14	the	the	DET
cana-6293	76	15	importance	importance	NOUN
cana-6293	76	16	of	of	ADP
cana-6293	76	17	strong	strong	ADJ
cana-6293	76	18	fraud	fraud	NOUN
cana-6293	76	19	detection	detection	NOUN
cana-6293	76	20	systems	system	NOUN
cana-6293	76	21	.	.	PUNCT
cana-6293	77	1	choosing	choose	VERB
cana-6293	77	2	the	the	DET
cana-6293	77	3	right	right	ADJ
cana-6293	77	4	features	feature	NOUN
cana-6293	77	5	is	be	AUX
cana-6293	77	6	especially	especially	ADV
cana-6293	77	7	important	important	ADJ
cana-6293	77	8	when	when	SCONJ
cana-6293	77	9	using	use	VERB
cana-6293	77	10	machine	machine	NOUN
cana-6293	77	11	learning	learning	NOUN
cana-6293	77	12	for	for	ADP
cana-6293	77	13	fraud	fraud	NOUN
cana-6293	77	14	detection	detection	NOUN
cana-6293	77	15	.	.	PUNCT
cana-6293	78	1	a	a	DET
cana-6293	78	2	new	new	ADJ
cana-6293	78	3	model	model	NOUN
cana-6293	78	4	called	call	VERB
cana-6293	78	5	hsaodl	hsaodl	ADJ
cana-6293	78	6	-	-	PUNCT
cana-6293	78	7	ccfc	ccfc	ADJ
cana-6293	78	8	(	(	PUNCT
cana-6293	78	9	hunger	hunger	NOUN
cana-6293	78	10	search	search	NOUN
cana-6293	78	11	algorithm	algorithm	NOUN
cana-6293	78	12	with	with	ADP
cana-6293	78	13	optimal	optimal	ADJ
cana-6293	78	14	deep	deep	ADJ
cana-6293	78	15	learning	learning	NOUN
cana-6293	78	16	for	for	ADP
cana-6293	78	17	credit	credit	NOUN
cana-6293	78	18	card	card	NOUN
cana-6293	78	19	fraud	fraud	NOUN
cana-6293	78	20	classification	classification	NOUN
cana-6293	78	21	)	)	PUNCT
cana-6293	78	22	was	be	AUX
cana-6293	78	23	introduced	introduce	VERB
cana-6293	78	24	to	to	PART
cana-6293	78	25	improve	improve	VERB
cana-6293	78	26	the	the	DET
cana-6293	78	27	accuracy	accuracy	NOUN
cana-6293	78	28	of	of	ADP
cana-6293	78	29	fraud	fraud	NOUN
cana-6293	78	30	predictions	prediction	NOUN
cana-6293	78	31	[	[	X
cana-6293	78	32	22	22	NUM
cana-6293	78	33	]	]	PUNCT
cana-6293	78	34	.	.	PUNCT
cana-6293	79	1	another	another	DET
cana-6293	79	2	study	study	NOUN
cana-6293	79	3	compared	compare	VERB
cana-6293	79	4	several	several	ADJ
cana-6293	79	5	decision	decision	NOUN
cana-6293	79	6	tree	tree	NOUN
cana-6293	79	7	algorithms	algorithm	NOUN
cana-6293	79	8	using	use	VERB
cana-6293	79	9	the	the	DET
cana-6293	79	10	weka	weka	PROPN
cana-6293	79	11	tool	tool	NOUN
cana-6293	79	12	.	.	PUNCT
cana-6293	80	1	these	these	PRON
cana-6293	80	2	included	include	VERB
cana-6293	80	3	j48	j48	PROPN
cana-6293	80	4	,	,	PUNCT
cana-6293	80	5	random	random	ADJ
cana-6293	80	6	tree	tree	NOUN
cana-6293	80	7	,	,	PUNCT
cana-6293	80	8	decision	decision	NOUN
cana-6293	80	9	stump	stump	NOUN
cana-6293	80	10	,	,	PUNCT
cana-6293	80	11	logistic	logistic	ADJ
cana-6293	80	12	model	model	NOUN
cana-6293	80	13	tree	tree	NOUN
cana-6293	80	14	,	,	PUNCT
cana-6293	80	15	hoeffding	hoeffding	NOUN
cana-6293	80	16	tree	tree	NOUN
cana-6293	80	17	,	,	PUNCT
cana-6293	80	18	reduced	reduce	VERB
cana-6293	80	19	error	error	NOUN
cana-6293	80	20	pruning	prune	VERB
cana-6293	80	21	tree	tree	NOUN
cana-6293	80	22	,	,	PUNCT
cana-6293	80	23	and	and	CCONJ
cana-6293	80	24	random	random	ADJ
cana-6293	80	25	forest	forest	NOUN
cana-6293	80	26	.	.	PUNCT
cana-6293	81	1	among	among	ADP
cana-6293	81	2	these	these	PRON
cana-6293	81	3	,	,	PUNCT
cana-6293	81	4	the	the	DET
cana-6293	81	5	random	random	ADJ
cana-6293	81	6	tree	tree	NOUN
cana-6293	81	7	algorithm	algorithm	NOUN
cana-6293	81	8	performed	perform	VERB
cana-6293	81	9	the	the	DET
cana-6293	81	10	best	good	ADJ
cana-6293	81	11	,	,	PUNCT
cana-6293	81	12	reaching	reach	VERB
cana-6293	81	13	an	an	DET
cana-6293	81	14	accuracy	accuracy	NOUN
cana-6293	81	15	of	of	ADP
cana-6293	81	16	85.714	85.714	NUM
cana-6293	81	17	%	%	NOUN
cana-6293	81	18	on	on	ADP
cana-6293	81	19	a	a	DET
cana-6293	81	20	weather	weather	NOUN
cana-6293	81	21	dataset	dataset	VERB
cana-6293	82	1	[	[	X
cana-6293	82	2	23	23	NUM
cana-6293	82	3	]	]	PUNCT
cana-6293	82	4	.	.	PUNCT
cana-6293	83	1	sustainable	sustainable	ADJ
cana-6293	83	2	development	development	NOUN
cana-6293	83	3	goals	goal	NOUN
cana-6293	83	4	(	(	PUNCT
cana-6293	83	5	sdgs	sdgs	ADJ
cana-6293	83	6	)	)	PUNCT
cana-6293	83	7	aim	aim	VERB
cana-6293	83	8	to	to	PART
cana-6293	83	9	reduce	reduce	VERB
cana-6293	83	10	poverty	poverty	NOUN
cana-6293	83	11	,	,	PUNCT
cana-6293	83	12	protect	protect	VERB
cana-6293	83	13	the	the	DET
cana-6293	83	14	environment	environment	NOUN
cana-6293	83	15	,	,	PUNCT
cana-6293	83	16	and	and	CCONJ
cana-6293	83	17	improve	improve	VERB
cana-6293	83	18	people	people	NOUN
cana-6293	83	19	’s	’s	PART
cana-6293	83	20	well	well	ADV
cana-6293	83	21	-	-	PUNCT
cana-6293	83	22	being	being	NOUN
cana-6293	83	23	.	.	PUNCT
cana-6293	84	1	one	one	NUM
cana-6293	84	2	study	study	NOUN
cana-6293	84	3	used	use	VERB
cana-6293	84	4	data	datum	NOUN
cana-6293	84	5	mining	mining	NOUN
cana-6293	84	6	techniques	technique	NOUN
cana-6293	84	7	to	to	PART
cana-6293	84	8	analyze	analyze	VERB
cana-6293	84	9	sdg	sdg	NOUN
cana-6293	84	10	performance	performance	NOUN
cana-6293	84	11	in	in	ADP
cana-6293	84	12	tamil	tamil	PROPN
cana-6293	84	13	nadu	nadu	PROPN
cana-6293	84	14	,	,	PUNCT
cana-6293	84	15	kerala	kerala	PROPN
cana-6293	84	16	,	,	PUNCT
cana-6293	84	17	and	and	CCONJ
cana-6293	84	18	karnataka	karnataka	PROPN
cana-6293	84	19	.	.	PUNCT
cana-6293	85	1	this	this	PRON
cana-6293	85	2	helped	helped	AUX
cana-6293	85	3	identify	identify	VERB
cana-6293	85	4	useful	useful	ADJ
cana-6293	85	5	patterns	pattern	NOUN
cana-6293	85	6	and	and	CCONJ
cana-6293	85	7	insights	insight	NOUN
cana-6293	85	8	from	from	ADP
cana-6293	85	9	their	their	PRON
cana-6293	85	10	development	development	NOUN
cana-6293	85	11	data	datum	NOUN
cana-6293	85	12	[	[	X
cana-6293	85	13	24	24	NUM
cana-6293	85	14	]	]	PUNCT
cana-6293	85	15	.	.	PUNCT
cana-6293	86	1	machine	machine	NOUN
cana-6293	86	2	learning	learning	NOUN
cana-6293	86	3	is	be	AUX
cana-6293	86	4	now	now	ADV
cana-6293	86	5	used	use	VERB
cana-6293	86	6	widely	widely	ADV
cana-6293	86	7	because	because	SCONJ
cana-6293	86	8	it	it	PRON
cana-6293	86	9	can	can	AUX
cana-6293	86	10	solve	solve	VERB
cana-6293	86	11	difficult	difficult	ADJ
cana-6293	86	12	problems	problem	NOUN
cana-6293	86	13	that	that	PRON
cana-6293	86	14	are	be	AUX
cana-6293	86	15	hard	hard	ADJ
cana-6293	86	16	to	to	PART
cana-6293	86	17	handle	handle	VERB
cana-6293	86	18	with	with	ADP
cana-6293	86	19	traditional	traditional	ADJ
cana-6293	86	20	methods	method	NOUN
cana-6293	86	21	.	.	PUNCT
cana-6293	87	1	instead	instead	ADV
cana-6293	87	2	of	of	ADP
cana-6293	87	3	being	be	AUX
cana-6293	87	4	fully	fully	ADV
cana-6293	87	5	programmed	program	VERB
cana-6293	87	6	,	,	PUNCT
cana-6293	87	7	ml	ml	AUX
cana-6293	87	8	models	model	NOUN
cana-6293	87	9	learn	learn	VERB
cana-6293	87	10	from	from	ADP
cana-6293	87	11	data	datum	NOUN
cana-6293	87	12	on	on	ADP
cana-6293	87	13	their	their	PRON
cana-6293	87	14	own	own	ADJ
cana-6293	87	15	.	.	PUNCT
cana-6293	88	1	a	a	DET
cana-6293	88	2	recent	recent	ADJ
cana-6293	88	3	study	study	NOUN
cana-6293	88	4	applied	apply	VERB
cana-6293	88	5	machine	machine	NOUN
cana-6293	88	6	learning	learn	VERB
cana-6293	88	7	to	to	ADP
cana-6293	88	8	a	a	DET
cana-6293	88	9	climate	climate	NOUN
cana-6293	88	10	change	change	NOUN
cana-6293	88	11	dataset	dataset	NOUN
cana-6293	88	12	that	that	PRON
cana-6293	88	13	included	include	VERB
cana-6293	88	14	greenhouse	greenhouse	NOUN
cana-6293	88	15	gas	gas	NOUN
cana-6293	88	16	levels	level	NOUN
cana-6293	88	17	,	,	PUNCT
cana-6293	88	18	solar	solar	ADJ
cana-6293	88	19	activity	activity	NOUN
cana-6293	88	20	,	,	PUNCT
cana-6293	88	21	and	and	CCONJ
cana-6293	88	22	temperature	temperature	NOUN
cana-6293	88	23	,	,	PUNCT
cana-6293	88	24	helping	helping	AUX
cana-6293	88	25	improve	improve	VERB
cana-6293	88	26	environmental	environmental	ADJ
cana-6293	88	27	forecasting	forecasting	NOUN
cana-6293	89	1	[	[	X
cana-6293	89	2	35	35	NUM
cana-6293	89	3	]	]	PUNCT
cana-6293	89	4	.	.	PUNCT
cana-6293	90	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-6293	90	2	communications	communication	NOUN
cana-6293	90	3	on	on	ADP
cana-6293	90	4	applied	apply	VERB
cana-6293	90	5	nonlinear	nonlinear	ADJ
cana-6293	90	6	analysis	analysis	NOUN
cana-6293	90	7	issn	issn	NOUN
cana-6293	90	8	:	:	PUNCT
cana-6293	90	9	1074	1074	NUM
cana-6293	90	10	-	-	PUNCT
cana-6293	90	11	133x	133x	NUM
cana-6293	90	12	vol	vol	NOUN
cana-6293	90	13	32	32	NUM
cana-6293	90	14	no	no	NOUN
cana-6293	90	15	.	.	PUNCT
cana-6293	91	1	2s	2s	NUM
cana-6293	91	2	(	(	PUNCT
cana-6293	91	3	2025	2025	NUM
cana-6293	91	4	)	)	PUNCT
cana-6293	91	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	91	6	641	641	NUM
cana-6293	91	7	2	2	NUM
cana-6293	91	8	.	.	PUNCT
cana-6293	91	9	dataset	dataset	VERB
cana-6293	91	10	the	the	DET
cana-6293	91	11	dataset	dataset	NOUN
cana-6293	91	12	used	use	VERB
cana-6293	91	13	in	in	ADP
cana-6293	91	14	this	this	DET
cana-6293	91	15	research	research	NOUN
cana-6293	91	16	contains	contain	VERB
cana-6293	91	17	important	important	ADJ
cana-6293	91	18	agricultural	agricultural	ADJ
cana-6293	91	19	and	and	CCONJ
cana-6293	91	20	environmental	environmental	ADJ
cana-6293	91	21	factors	factor	NOUN
cana-6293	91	22	that	that	PRON
cana-6293	91	23	influence	influence	VERB
cana-6293	91	24	paddy	paddy	NOUN
cana-6293	91	25	crop	crop	NOUN
cana-6293	91	26	growth	growth	NOUN
cana-6293	91	27	and	and	CCONJ
cana-6293	91	28	yield	yield	NOUN
cana-6293	91	29	.	.	PUNCT
cana-6293	92	1	it	it	PRON
cana-6293	92	2	includes	include	VERB
cana-6293	92	3	daily	daily	ADJ
cana-6293	92	4	measurements	measurement	NOUN
cana-6293	92	5	of	of	ADP
cana-6293	92	6	weather	weather	NOUN
cana-6293	92	7	conditions	condition	NOUN
cana-6293	92	8	such	such	ADJ
cana-6293	92	9	as	as	ADP
cana-6293	92	10	temperature	temperature	NOUN
cana-6293	92	11	,	,	PUNCT
cana-6293	92	12	humidity	humidity	NOUN
cana-6293	92	13	,	,	PUNCT
cana-6293	92	14	rainfall	rainfall	NOUN
cana-6293	92	15	,	,	PUNCT
cana-6293	92	16	wind	wind	NOUN
cana-6293	92	17	speed	speed	NOUN
cana-6293	92	18	,	,	PUNCT
cana-6293	92	19	cloud	cloud	NOUN
cana-6293	92	20	cover	cover	NOUN
cana-6293	92	21	,	,	PUNCT
cana-6293	92	22	and	and	CCONJ
cana-6293	92	23	atmospheric	atmospheric	ADJ
cana-6293	92	24	pressure	pressure	NOUN
cana-6293	92	25	.	.	PUNCT
cana-6293	93	1	these	these	DET
cana-6293	93	2	climatic	climatic	ADJ
cana-6293	93	3	variables	variable	NOUN
cana-6293	93	4	help	help	VERB
cana-6293	93	5	understand	understand	VERB
cana-6293	93	6	how	how	SCONJ
cana-6293	93	7	environmental	environmental	ADJ
cana-6293	93	8	changes	change	NOUN
cana-6293	93	9	impact	impact	VERB
cana-6293	93	10	crop	crop	NOUN
cana-6293	93	11	development	development	NOUN
cana-6293	93	12	.	.	PUNCT
cana-6293	94	1	the	the	DET
cana-6293	94	2	dataset	dataset	NOUN
cana-6293	94	3	also	also	ADV
cana-6293	94	4	stores	store	VERB
cana-6293	94	5	soil	soil	NOUN
cana-6293	94	6	-	-	PUNCT
cana-6293	94	7	related	relate	VERB
cana-6293	94	8	information	information	NOUN
cana-6293	94	9	,	,	PUNCT
cana-6293	94	10	fertiliser	fertiliser	NOUN
cana-6293	94	11	usage	usage	NOUN
cana-6293	94	12	,	,	PUNCT
cana-6293	94	13	irrigation	irrigation	NOUN
cana-6293	94	14	levels	level	NOUN
cana-6293	94	15	,	,	PUNCT
cana-6293	94	16	and	and	CCONJ
cana-6293	94	17	paddy	paddy	NOUN
cana-6293	94	18	growth	growth	NOUN
cana-6293	94	19	indicators	indicator	NOUN
cana-6293	94	20	such	such	ADJ
cana-6293	94	21	as	as	ADP
cana-6293	94	22	plant	plant	NOUN
cana-6293	94	23	height	height	NOUN
cana-6293	94	24	,	,	PUNCT
cana-6293	94	25	number	number	NOUN
cana-6293	94	26	of	of	ADP
cana-6293	94	27	tillers	tiller	NOUN
cana-6293	94	28	,	,	PUNCT
cana-6293	94	29	and	and	CCONJ
cana-6293	94	30	leaf	leaf	NOUN
cana-6293	94	31	colour	colour	NOUN
cana-6293	94	32	index	index	NOUN
cana-6293	94	33	.	.	PUNCT
cana-6293	95	1	in	in	ADP
cana-6293	95	2	addition	addition	NOUN
cana-6293	95	3	,	,	PUNCT
cana-6293	95	4	the	the	DET
cana-6293	95	5	dataset	dataset	NOUN
cana-6293	95	6	records	record	VERB
cana-6293	95	7	the	the	DET
cana-6293	95	8	final	final	ADJ
cana-6293	95	9	yield	yield	NOUN
cana-6293	95	10	in	in	ADP
cana-6293	95	11	kilograms	kilogram	NOUN
cana-6293	95	12	per	per	ADP
cana-6293	95	13	acre	acre	NOUN
cana-6293	95	14	,	,	PUNCT
cana-6293	95	15	which	which	PRON
cana-6293	95	16	is	be	AUX
cana-6293	95	17	used	use	VERB
cana-6293	95	18	as	as	ADP
cana-6293	95	19	the	the	DET
cana-6293	95	20	target	target	NOUN
cana-6293	95	21	variable	variable	NOUN
cana-6293	95	22	for	for	ADP
cana-6293	95	23	prediction	prediction	NOUN
cana-6293	95	24	.	.	PUNCT
cana-6293	96	1	each	each	DET
cana-6293	96	2	row	row	NOUN
cana-6293	96	3	in	in	ADP
cana-6293	96	4	the	the	DET
cana-6293	96	5	dataset	dataset	NOUN
cana-6293	96	6	represents	represent	VERB
cana-6293	96	7	a	a	DET
cana-6293	96	8	specific	specific	ADJ
cana-6293	96	9	field	field	NOUN
cana-6293	96	10	observation	observation	NOUN
cana-6293	96	11	collected	collect	VERB
cana-6293	96	12	on	on	ADP
cana-6293	96	13	a	a	DET
cana-6293	96	14	particular	particular	ADJ
cana-6293	96	15	day	day	NOUN
cana-6293	96	16	or	or	CCONJ
cana-6293	96	17	growth	growth	NOUN
cana-6293	96	18	stage	stage	NOUN
cana-6293	96	19	.	.	PUNCT
cana-6293	97	1	this	this	DET
cana-6293	97	2	dataset	dataset	NOUN
cana-6293	97	3	supports	support	VERB
cana-6293	97	4	both	both	PRON
cana-6293	97	5	machine	machine	NOUN
cana-6293	97	6	learning	learning	NOUN
cana-6293	97	7	and	and	CCONJ
cana-6293	97	8	deep	deep	ADJ
cana-6293	97	9	learning	learning	NOUN
cana-6293	97	10	approaches	approach	NOUN
cana-6293	97	11	for	for	ADP
cana-6293	97	12	classification	classification	NOUN
cana-6293	97	13	,	,	PUNCT
cana-6293	97	14	regression	regression	NOUN
cana-6293	97	15	,	,	PUNCT
cana-6293	97	16	and	and	CCONJ
cana-6293	97	17	time	time	NOUN
cana-6293	97	18	-	-	PUNCT
cana-6293	97	19	series	series	NOUN
cana-6293	97	20	forecasting	forecasting	NOUN
cana-6293	97	21	tasks	task	NOUN
cana-6293	97	22	.	.	PUNCT
cana-6293	98	1	overall	overall	ADV
cana-6293	98	2	,	,	PUNCT
cana-6293	98	3	the	the	DET
cana-6293	98	4	dataset	dataset	NOUN
cana-6293	98	5	provides	provide	VERB
cana-6293	98	6	a	a	DET
cana-6293	98	7	complete	complete	ADJ
cana-6293	98	8	view	view	NOUN
cana-6293	98	9	of	of	ADP
cana-6293	98	10	the	the	DET
cana-6293	98	11	factors	factor	NOUN
cana-6293	98	12	affecting	affect	VERB
cana-6293	98	13	paddy	paddy	NOUN
cana-6293	98	14	growth	growth	NOUN
cana-6293	98	15	,	,	PUNCT
cana-6293	98	16	making	make	VERB
cana-6293	98	17	it	it	PRON
cana-6293	98	18	suitable	suitable	ADJ
cana-6293	98	19	for	for	ADP
cana-6293	98	20	predictive	predictive	ADJ
cana-6293	98	21	modelling	modelling	NOUN
cana-6293	98	22	and	and	CCONJ
cana-6293	98	23	optimisation	optimisation	NOUN
cana-6293	98	24	research	research	NOUN
cana-6293	99	1	[	[	X
cana-6293	99	2	26	26	NUM
cana-6293	99	3	-	-	SYM
cana-6293	99	4	30	30	NUM
cana-6293	99	5	]	]	PUNCT
cana-6293	99	6	.	.	PUNCT
cana-6293	100	1	table	table	NOUN
cana-6293	100	2	1	1	NUM
cana-6293	100	3	.	.	PUNCT
cana-6293	101	1	sample	sample	NOUN
cana-6293	101	2	paddy	paddy	NOUN
cana-6293	101	3	crop	crop	NOUN
cana-6293	101	4	growth	growth	NOUN
cana-6293	101	5	dataset	dataset	VERB
cana-6293	101	6	date	date	NOUN
cana-6293	101	7	temperature	temperature	NOUN
cana-6293	101	8	(	(	PUNCT
cana-6293	101	9	°	°	ADP
cana-6293	101	10	c	c	NOUN
cana-6293	101	11	)	)	PUNCT
cana-6293	101	12	rainfall	rainfall	NOUN
cana-6293	101	13	(	(	PUNCT
cana-6293	101	14	mm	mm	NOUN
cana-6293	101	15	)	)	PUNCT
cana-6293	101	16	soil	soil	NOUN
cana-6293	101	17	moisture	moisture	NOUN
cana-6293	101	18	(	(	PUNCT
cana-6293	101	19	%	%	INTJ
cana-6293	101	20	)	)	PUNCT
cana-6293	101	21	humidity	humidity	NOUN
cana-6293	101	22	(	(	PUNCT
cana-6293	101	23	%	%	INTJ
cana-6293	101	24	)	)	PUNCT
cana-6293	101	25	fertilizer	fertilizer	NOUN
cana-6293	101	26	(	(	PUNCT
cana-6293	101	27	kg	kg	NOUN
cana-6293	101	28	/	/	SYM
cana-6293	101	29	acre	acre	NOUN
cana-6293	101	30	)	)	PUNCT
cana-6293	101	31	leaf	leaf	NOUN
cana-6293	101	32	wetness	wetness	NOUN
cana-6293	101	33	(	(	PUNCT
cana-6293	101	34	%	%	INTJ
cana-6293	101	35	)	)	PUNCT
cana-6293	101	36	growth	growth	NOUN
cana-6293	101	37	stage	stage	NOUN
cana-6293	101	38	(	(	PUNCT
cana-6293	101	39	%	%	INTJ
cana-6293	101	40	)	)	PUNCT
cana-6293	101	41	202406	202406	NUM
cana-6293	101	42	-	-	SYM
cana-6293	101	43	01	01	NUM
cana-6293	101	44	31.8	31.8	NUM
cana-6293	101	45	10.2	10.2	NUM
cana-6293	101	46	46	46	NUM
cana-6293	101	47	79	79	NUM
cana-6293	101	48	5.0	5.0	NUM
cana-6293	101	49	68	68	NUM
cana-6293	101	50	18	18	NUM
cana-6293	101	51	202406	202406	NUM
cana-6293	101	52	-	-	SYM
cana-6293	101	53	02	02	NUM
cana-6293	101	54	32.5	32.5	NUM
cana-6293	101	55	4.6	4.6	NUM
cana-6293	101	56	48	48	NUM
cana-6293	101	57	81	81	NUM
cana-6293	101	58	5.0	5.0	NUM
cana-6293	101	59	70	70	NUM
cana-6293	101	60	20	20	NUM
cana-6293	101	61	202406	202406	NUM
cana-6293	101	62	-	-	SYM
cana-6293	101	63	03	03	NUM
cana-6293	101	64	33.2	33.2	NUM
cana-6293	101	65	0.0	0.0	NUM
cana-6293	101	66	42	42	NUM
cana-6293	101	67	76	76	NUM
cana-6293	101	68	5.2	5.2	NUM
cana-6293	101	69	65	65	NUM
cana-6293	101	70	23	23	NUM
cana-6293	101	71	202406	202406	NUM
cana-6293	101	72	-	-	SYM
cana-6293	101	73	04	04	NUM
cana-6293	101	74	34.0	34.0	NUM
cana-6293	101	75	16.5	16.5	NUM
cana-6293	101	76	54	54	NUM
cana-6293	101	77	83	83	NUM
cana-6293	101	78	6.0	6.0	NUM
cana-6293	101	79	73	73	NUM
cana-6293	101	80	26	26	NUM
cana-6293	101	81	202406	202406	NUM
cana-6293	101	82	-	-	SYM
cana-6293	101	83	05	05	NUM
cana-6293	101	84	32.9	32.9	NUM
cana-6293	101	85	8.3	8.3	NUM
cana-6293	101	86	49	49	NUM
cana-6293	101	87	78	78	NUM
cana-6293	101	88	6.0	6.0	NUM
cana-6293	101	89	69	69	NUM
cana-6293	101	90	29	29	NUM
cana-6293	101	91	202406	202406	NUM
cana-6293	101	92	-	-	SYM
cana-6293	101	93	06	06	NUM
cana-6293	101	94	31.7	31.7	NUM
cana-6293	101	95	6.1	6.1	NUM
cana-6293	101	96	47	47	NUM
cana-6293	101	97	77	77	NUM
cana-6293	101	98	6.2	6.2	NUM
cana-6293	101	99	67	67	NUM
cana-6293	101	100	31	31	NUM
cana-6293	101	101	202406	202406	NUM
cana-6293	101	102	-	-	SYM
cana-6293	101	103	07	07	NUM
cana-6293	101	104	33.6	33.6	NUM
cana-6293	101	105	0.0	0.0	NUM
cana-6293	101	106	44	44	NUM
cana-6293	101	107	74	74	NUM
cana-6293	101	108	6.2	6.2	NUM
cana-6293	101	109	66	66	NUM
cana-6293	101	110	34	34	NUM
cana-6293	101	111	202406	202406	NUM
cana-6293	101	112	-	-	SYM
cana-6293	101	113	08	08	NUM
cana-6293	101	114	34.3	34.3	NUM
cana-6293	101	115	3.1	3.1	NUM
cana-6293	101	116	43	43	NUM
cana-6293	101	117	73	73	NUM
cana-6293	101	118	6.5	6.5	NUM
cana-6293	101	119	64	64	NUM
cana-6293	101	120	36	36	NUM
cana-6293	101	121	202406	202406	NUM
cana-6293	101	122	-	-	SYM
cana-6293	101	123	09	09	NUM
cana-6293	101	124	35.1	35.1	NUM
cana-6293	101	125	14.2	14.2	NUM
cana-6293	101	126	55	55	NUM
cana-6293	101	127	85	85	NUM
cana-6293	101	128	6.5	6.5	NUM
cana-6293	101	129	75	75	NUM
cana-6293	101	130	39	39	NUM
cana-6293	101	131	202406	202406	NUM
cana-6293	101	132	-	-	SYM
cana-6293	101	133	10	10	NUM
cana-6293	101	134	34.6	34.6	NUM
cana-6293	101	135	9.4	9.4	NUM
cana-6293	101	136	52	52	NUM
cana-6293	101	137	84	84	NUM
cana-6293	101	138	6.5	6.5	NUM
cana-6293	101	139	72	72	NUM
cana-6293	101	140	41	41	NUM
cana-6293	101	141	dataset	dataset	ADJ
cana-6293	101	142	description	description	NOUN
cana-6293	101	143	this	this	DET
cana-6293	101	144	dataset	dataset	NOUN
cana-6293	101	145	represents	represent	VERB
cana-6293	101	146	daily	daily	ADJ
cana-6293	101	147	field	field	NOUN
cana-6293	101	148	observations	observation	NOUN
cana-6293	101	149	during	during	ADP
cana-6293	101	150	the	the	DET
cana-6293	101	151	paddy	paddy	NOUN
cana-6293	101	152	crop	crop	NOUN
cana-6293	101	153	growth	growth	NOUN
cana-6293	101	154	period	period	NOUN
cana-6293	101	155	.	.	PUNCT
cana-6293	102	1	it	it	PRON
cana-6293	102	2	includes	include	VERB
cana-6293	102	3	:	:	PUNCT
cana-6293	102	4	•	•	NUM
cana-6293	102	5	temperature	temperature	NOUN
cana-6293	102	6	(	(	PUNCT
cana-6293	102	7	°	°	ADP
cana-6293	102	8	c	c	NOUN
cana-6293	102	9	)	)	PUNCT
cana-6293	102	10	:	:	PUNCT
cana-6293	102	11	daily	daily	ADJ
cana-6293	102	12	average	average	ADJ
cana-6293	102	13	temperature	temperature	NOUN
cana-6293	102	14	affecting	affect	VERB
cana-6293	102	15	growth	growth	NOUN
cana-6293	102	16	rate	rate	NOUN
cana-6293	102	17	•	•	NOUN
cana-6293	102	18	rainfall	rainfall	NOUN
cana-6293	102	19	(	(	PUNCT
cana-6293	102	20	mm	mm	NOUN
cana-6293	102	21	)	)	PUNCT
cana-6293	102	22	:	:	PUNCT
cana-6293	102	23	amount	amount	NOUN
cana-6293	102	24	of	of	ADP
cana-6293	102	25	rainfall	rainfall	NOUN
cana-6293	102	26	supporting	support	VERB
cana-6293	102	27	irrigation	irrigation	NOUN
cana-6293	102	28	needs	need	VERB
cana-6293	102	29	•	•	NUM
cana-6293	102	30	soil	soil	NOUN
cana-6293	102	31	moisture	moisture	NOUN
cana-6293	102	32	(	(	PUNCT
cana-6293	102	33	%	%	INTJ
cana-6293	102	34	)	)	PUNCT
cana-6293	102	35	:	:	PUNCT
cana-6293	103	1	available	available	ADJ
cana-6293	103	2	water	water	NOUN
cana-6293	103	3	in	in	ADP
cana-6293	103	4	soil	soil	NOUN
cana-6293	103	5	for	for	ADP
cana-6293	103	6	root	root	NOUN
cana-6293	103	7	absorption	absorption	NOUN
cana-6293	103	8	•	•	NOUN
cana-6293	103	9	humidity	humidity	NOUN
cana-6293	103	10	(	(	PUNCT
cana-6293	103	11	%	%	INTJ
cana-6293	103	12	)	)	PUNCT
cana-6293	103	13	:	:	PUNCT
cana-6293	103	14	atmospheric	atmospheric	ADJ
cana-6293	103	15	moisture	moisture	NOUN
cana-6293	103	16	affecting	affect	VERB
cana-6293	103	17	evapotranspiration	evapotranspiration	NOUN
cana-6293	103	18	•	•	NOUN
cana-6293	103	19	fertilizer	fertilizer	NOUN
cana-6293	103	20	(	(	PUNCT
cana-6293	103	21	kg	kg	NOUN
cana-6293	103	22	/	/	SYM
cana-6293	103	23	acre	acre	NOUN
cana-6293	103	24	)	)	PUNCT
cana-6293	103	25	:	:	PUNCT
cana-6293	103	26	nutrient	nutrient	NOUN
cana-6293	103	27	supply	supply	NOUN
cana-6293	103	28	influencing	influence	VERB
cana-6293	103	29	yield	yield	VERB
cana-6293	103	30	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-6293	103	31	communications	communication	NOUN
cana-6293	103	32	on	on	ADP
cana-6293	103	33	applied	apply	VERB
cana-6293	103	34	nonlinear	nonlinear	ADJ
cana-6293	103	35	analysis	analysis	NOUN
cana-6293	103	36	issn	issn	NOUN
cana-6293	103	37	:	:	PUNCT
cana-6293	103	38	1074	1074	NUM
cana-6293	103	39	-	-	PUNCT
cana-6293	103	40	133x	133x	NUM
cana-6293	103	41	vol	vol	NOUN
cana-6293	103	42	32	32	NUM
cana-6293	103	43	no	no	NOUN
cana-6293	103	44	.	.	PUNCT
cana-6293	104	1	2s	2s	NUM
cana-6293	104	2	(	(	PUNCT
cana-6293	104	3	2025	2025	NUM
cana-6293	104	4	)	)	PUNCT
cana-6293	104	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	105	1	642	642	NUM
cana-6293	105	2	•	•	NUM
cana-6293	105	3	leaf	leaf	NOUN
cana-6293	105	4	wetness	wetness	NOUN
cana-6293	105	5	(	(	PUNCT
cana-6293	105	6	%	%	INTJ
cana-6293	105	7	)	)	PUNCT
cana-6293	105	8	:	:	PUNCT
cana-6293	105	9	moisture	moisture	VERB
cana-6293	105	10	on	on	ADP
cana-6293	105	11	leaves	leave	NOUN
cana-6293	105	12	,	,	PUNCT
cana-6293	105	13	important	important	ADJ
cana-6293	105	14	for	for	ADP
cana-6293	105	15	identifying	identify	VERB
cana-6293	105	16	disease	disease	NOUN
cana-6293	105	17	risk	risk	NOUN
cana-6293	105	18	•	•	NOUN
cana-6293	105	19	growth	growth	NOUN
cana-6293	105	20	stage	stage	NOUN
cana-6293	105	21	(	(	PUNCT
cana-6293	105	22	%	%	INTJ
cana-6293	105	23	):	):	PUNCT
cana-6293	105	24	crop	crop	NOUN
cana-6293	105	25	developmental	developmental	ADJ
cana-6293	105	26	progress	progress	NOUN
cana-6293	105	27	3	3	NUM
cana-6293	105	28	.	.	PUNCT
cana-6293	105	29	background	background	NOUN
cana-6293	105	30	and	and	CCONJ
cana-6293	105	31	methodology	methodology	NOUN
cana-6293	105	32	paddy	paddy	NOUN
cana-6293	105	33	(	(	PUNCT
cana-6293	105	34	rice	rice	NOUN
cana-6293	105	35	)	)	PUNCT
cana-6293	105	36	is	be	AUX
cana-6293	105	37	a	a	DET
cana-6293	105	38	staple	staple	ADJ
cana-6293	105	39	crop	crop	NOUN
cana-6293	105	40	that	that	PRON
cana-6293	105	41	directly	directly	ADV
cana-6293	105	42	affects	affect	VERB
cana-6293	105	43	food	food	NOUN
cana-6293	105	44	security	security	NOUN
cana-6293	105	45	and	and	CCONJ
cana-6293	105	46	farmer	farmer	NOUN
cana-6293	105	47	income	income	NOUN
cana-6293	105	48	.	.	PUNCT
cana-6293	106	1	weather	weather	NOUN
cana-6293	106	2	,	,	PUNCT
cana-6293	106	3	soil	soil	NOUN
cana-6293	106	4	conditions	condition	NOUN
cana-6293	106	5	,	,	PUNCT
cana-6293	106	6	irrigation	irrigation	NOUN
cana-6293	106	7	,	,	PUNCT
cana-6293	106	8	and	and	CCONJ
cana-6293	106	9	fertilizer	fertilizer	NOUN
cana-6293	106	10	use	use	VERB
cana-6293	106	11	all	all	DET
cana-6293	106	12	influence	influence	NOUN
cana-6293	106	13	paddy	paddy	NOUN
cana-6293	106	14	growth	growth	NOUN
cana-6293	106	15	.	.	PUNCT
cana-6293	107	1	traditional	traditional	ADJ
cana-6293	107	2	forecasting	forecasting	NOUN
cana-6293	107	3	based	base	VERB
cana-6293	107	4	on	on	ADP
cana-6293	107	5	manual	manual	ADJ
cana-6293	107	6	observation	observation	NOUN
cana-6293	107	7	or	or	CCONJ
cana-6293	107	8	simple	simple	ADJ
cana-6293	107	9	statistical	statistical	ADJ
cana-6293	107	10	models	model	NOUN
cana-6293	107	11	often	often	ADV
cana-6293	107	12	fails	fail	VERB
cana-6293	107	13	when	when	SCONJ
cana-6293	107	14	environmental	environmental	ADJ
cana-6293	107	15	conditions	condition	NOUN
cana-6293	107	16	change	change	VERB
cana-6293	107	17	quickly	quickly	ADV
cana-6293	107	18	.	.	PUNCT
cana-6293	108	1	machine	machine	NOUN
cana-6293	108	2	learning	learning	NOUN
cana-6293	108	3	(	(	PUNCT
cana-6293	108	4	ml	ml	NOUN
cana-6293	108	5	)	)	PUNCT
cana-6293	108	6	and	and	CCONJ
cana-6293	108	7	deep	deep	ADJ
cana-6293	108	8	learning	learning	NOUN
cana-6293	108	9	(	(	PUNCT
cana-6293	108	10	dl	dl	INTJ
cana-6293	108	11	)	)	PUNCT
cana-6293	108	12	can	can	AUX
cana-6293	108	13	learn	learn	VERB
cana-6293	108	14	complex	complex	ADJ
cana-6293	108	15	patterns	pattern	NOUN
cana-6293	108	16	from	from	ADP
cana-6293	108	17	multi	multi	ADJ
cana-6293	108	18	-	-	ADJ
cana-6293	108	19	source	source	ADJ
cana-6293	108	20	data	datum	NOUN
cana-6293	108	21	(	(	PUNCT
cana-6293	108	22	weather	weather	NOUN
cana-6293	108	23	,	,	PUNCT
cana-6293	108	24	soil	soil	NOUN
cana-6293	108	25	,	,	PUNCT
cana-6293	108	26	remote	remote	ADJ
cana-6293	108	27	sensing	sensing	NOUN
cana-6293	108	28	,	,	PUNCT
cana-6293	108	29	and	and	CCONJ
cana-6293	108	30	farm	farm	NOUN
cana-6293	108	31	management	management	NOUN
cana-6293	108	32	records	record	NOUN
cana-6293	108	33	)	)	PUNCT
cana-6293	108	34	and	and	CCONJ
cana-6293	108	35	give	give	VERB
cana-6293	108	36	more	more	ADV
cana-6293	108	37	accurate	accurate	ADJ
cana-6293	108	38	,	,	PUNCT
cana-6293	108	39	timely	timely	ADJ
cana-6293	108	40	predictions	prediction	NOUN
cana-6293	108	41	.	.	PUNCT
cana-6293	109	1	however	however	ADV
cana-6293	109	2	,	,	PUNCT
cana-6293	109	3	ml	ml	PROPN
cana-6293	109	4	/	/	SYM
cana-6293	109	5	dl	dl	PROPN
cana-6293	109	6	model	model	NOUN
cana-6293	109	7	quality	quality	NOUN
cana-6293	109	8	depends	depend	VERB
cana-6293	109	9	on	on	ADP
cana-6293	109	10	good	good	ADJ
cana-6293	109	11	data	datum	NOUN
cana-6293	109	12	preprocessing	preprocessing	NOUN
cana-6293	109	13	,	,	PUNCT
cana-6293	109	14	careful	careful	ADJ
cana-6293	109	15	feature	feature	NOUN
cana-6293	109	16	selection	selection	NOUN
cana-6293	109	17	,	,	PUNCT
cana-6293	109	18	and	and	CCONJ
cana-6293	109	19	correct	correct	ADJ
cana-6293	109	20	hyperparameter	hyperparameter	NOUN
cana-6293	109	21	settings	setting	NOUN
cana-6293	109	22	.	.	PUNCT
cana-6293	110	1	metaheuristic	metaheuristic	ADJ
cana-6293	110	2	optimizers	optimizer	NOUN
cana-6293	110	3	such	such	ADJ
cana-6293	110	4	as	as	ADP
cana-6293	110	5	particle	particle	NOUN
cana-6293	110	6	swarm	swarm	NOUN
cana-6293	110	7	optimization	optimization	NOUN
cana-6293	110	8	(	(	PUNCT
cana-6293	110	9	pso	pso	NOUN
cana-6293	110	10	)	)	PUNCT
cana-6293	110	11	and	and	CCONJ
cana-6293	110	12	genetic	genetic	ADJ
cana-6293	110	13	algorithm	algorithm	NOUN
cana-6293	110	14	(	(	PUNCT
cana-6293	110	15	ga	ga	NOUN
cana-6293	110	16	)	)	PUNCT
cana-6293	110	17	are	be	AUX
cana-6293	110	18	effective	effective	ADJ
cana-6293	110	19	for	for	ADP
cana-6293	110	20	automatic	automatic	ADJ
cana-6293	110	21	hyperparameter	hyperparameter	NOUN
cana-6293	110	22	tuning	tuning	NOUN
cana-6293	110	23	and	and	CCONJ
cana-6293	110	24	feature	feature	NOUN
cana-6293	110	25	selection	selection	NOUN
cana-6293	110	26	,	,	PUNCT
cana-6293	110	27	producing	produce	VERB
cana-6293	110	28	models	model	NOUN
cana-6293	110	29	that	that	PRON
cana-6293	110	30	are	be	AUX
cana-6293	110	31	both	both	CCONJ
cana-6293	110	32	more	more	ADV
cana-6293	110	33	accurate	accurate	ADJ
cana-6293	110	34	and	and	CCONJ
cana-6293	110	35	more	more	ADV
cana-6293	110	36	stable	stable	ADJ
cana-6293	110	37	in	in	ADP
cana-6293	110	38	real	real	ADJ
cana-6293	110	39	-	-	PUNCT
cana-6293	110	40	world	world	NOUN
cana-6293	110	41	paddy	paddy	NOUN
cana-6293	110	42	forecasting	forecasting	NOUN
cana-6293	110	43	tasks	task	NOUN
cana-6293	110	44	.	.	PUNCT
cana-6293	111	1	below	below	ADV
cana-6293	111	2	is	be	AUX
cana-6293	111	3	a	a	DET
cana-6293	111	4	clear	clear	ADJ
cana-6293	111	5	,	,	PUNCT
cana-6293	111	6	step	step	NOUN
cana-6293	111	7	-	-	PUNCT
cana-6293	111	8	by	by	ADP
cana-6293	111	9	-	-	PUNCT
cana-6293	111	10	step	step	NOUN
cana-6293	111	11	methodology	methodology	NOUN
cana-6293	111	12	with	with	ADP
cana-6293	111	13	algorithms	algorithm	NOUN
cana-6293	111	14	you	you	PRON
cana-6293	111	15	can	can	AUX
cana-6293	111	16	implement	implement	VERB
cana-6293	111	17	.	.	PUNCT
cana-6293	112	1	the	the	DET
cana-6293	112	2	workflow	workflow	NOUN
cana-6293	112	3	goes	go	VERB
cana-6293	112	4	from	from	ADP
cana-6293	112	5	data	datum	NOUN
cana-6293	112	6	collection	collection	NOUN
cana-6293	112	7	to	to	ADP
cana-6293	112	8	final	final	ADJ
cana-6293	112	9	evaluation	evaluation	NOUN
cana-6293	112	10	,	,	PUNCT
cana-6293	112	11	with	with	ADP
cana-6293	112	12	optimization	optimization	NOUN
cana-6293	112	13	inserted	insert	VERB
cana-6293	112	14	between	between	ADP
cana-6293	112	15	baseline	baseline	NOUN
cana-6293	112	16	modeling	modeling	NOUN
cana-6293	112	17	and	and	CCONJ
cana-6293	112	18	final	final	ADJ
cana-6293	112	19	training	training	NOUN
cana-6293	112	20	.	.	PUNCT
cana-6293	113	1	overview	overview	NOUN
cana-6293	113	2	workflow	workflow	NOUN
cana-6293	113	3	(	(	PUNCT
cana-6293	113	4	text	text	NOUN
cana-6293	113	5	)	)	PUNCT
cana-6293	113	6	1	1	NUM
cana-6293	113	7	.	.	PUNCT
cana-6293	113	8	data	data	NOUN
cana-6293	113	9	collection	collection	NOUN
cana-6293	113	10	—	—	PUNCT
cana-6293	113	11	meteorological	meteorological	ADJ
cana-6293	113	12	records	record	NOUN
cana-6293	113	13	,	,	PUNCT
cana-6293	113	14	soil	soil	NOUN
cana-6293	113	15	tests	test	NOUN
cana-6293	113	16	,	,	PUNCT
cana-6293	113	17	field	field	NOUN
cana-6293	113	18	observations	observation	NOUN
cana-6293	113	19	,	,	PUNCT
cana-6293	113	20	satellite	satellite	NOUN
cana-6293	113	21	indices	index	NOUN
cana-6293	113	22	.	.	PUNCT
cana-6293	114	1	2	2	X
cana-6293	114	2	.	.	X
cana-6293	114	3	preprocessing	preprocessing	NOUN
cana-6293	114	4	&	&	CCONJ
cana-6293	114	5	feature	feature	NOUN
cana-6293	114	6	engineering	engineering	NOUN
cana-6293	114	7	—	—	PUNCT
cana-6293	114	8	clean	clean	ADJ
cana-6293	114	9	,	,	PUNCT
cana-6293	114	10	impute	impute	PROPN
cana-6293	114	11	,	,	PUNCT
cana-6293	114	12	encode	encode	ADJ
cana-6293	114	13	,	,	PUNCT
cana-6293	114	14	scale	scale	NOUN
cana-6293	114	15	,	,	PUNCT
cana-6293	114	16	derive	derive	ADJ
cana-6293	114	17	indices	index	NOUN
cana-6293	114	18	(	(	PUNCT
cana-6293	114	19	e.g.	e.g.	ADV
cana-6293	114	20	,	,	PUNCT
cana-6293	114	21	ndvi	ndvi	PROPN
cana-6293	114	22	,	,	PUNCT
cana-6293	114	23	cumulative	cumulative	ADJ
cana-6293	114	24	rainfall	rainfall	NOUN
cana-6293	114	25	)	)	PUNCT
cana-6293	114	26	.	.	PUNCT
cana-6293	115	1	3	3	X
cana-6293	115	2	.	.	X
cana-6293	115	3	baseline	baseline	NOUN
cana-6293	115	4	modeling	modeling	NOUN
cana-6293	115	5	—	—	PUNCT
cana-6293	115	6	train	train	NOUN
cana-6293	115	7	baseline	baseline	NOUN
cana-6293	115	8	ml	ml	PROPN
cana-6293	115	9	/	/	SYM
cana-6293	115	10	dl	dl	PROPN
cana-6293	115	11	models	model	NOUN
cana-6293	115	12	(	(	PUNCT
cana-6293	115	13	decision	decision	NOUN
cana-6293	115	14	tree	tree	NOUN
cana-6293	115	15	,	,	PUNCT
cana-6293	115	16	random	random	ADJ
cana-6293	115	17	forest	forest	NOUN
cana-6293	115	18	,	,	PUNCT
cana-6293	115	19	svm	svm	PROPN
cana-6293	115	20	,	,	PUNCT
cana-6293	115	21	lstm	lstm	NOUN
cana-6293	115	22	)	)	PUNCT
cana-6293	115	23	.	.	PUNCT
cana-6293	116	1	4	4	X
cana-6293	116	2	.	.	X
cana-6293	116	3	optimization	optimization	NOUN
cana-6293	116	4	—	—	PUNCT
cana-6293	116	5	apply	apply	VERB
cana-6293	116	6	pso	pso	NOUN
cana-6293	116	7	/	/	SYM
cana-6293	116	8	ga	ga	PROPN
cana-6293	116	9	for	for	ADP
cana-6293	116	10	hyperparameter	hyperparameter	NOUN
cana-6293	116	11	tuning	tuning	NOUN
cana-6293	116	12	and/or	and/or	CCONJ
cana-6293	116	13	feature	feature	NOUN
cana-6293	116	14	selection	selection	NOUN
cana-6293	116	15	.	.	PUNCT
cana-6293	117	1	5	5	X
cana-6293	117	2	.	.	X
cana-6293	117	3	final	final	ADJ
cana-6293	117	4	training	training	NOUN
cana-6293	117	5	&	&	CCONJ
cana-6293	117	6	evaluation	evaluation	NOUN
cana-6293	117	7	—	—	PUNCT
cana-6293	117	8	retrain	retrain	VERB
cana-6293	117	9	best	good	ADJ
cana-6293	117	10	models	model	NOUN
cana-6293	117	11	on	on	ADP
cana-6293	117	12	full	full	ADJ
cana-6293	117	13	training	training	NOUN
cana-6293	117	14	data	datum	NOUN
cana-6293	117	15	,	,	PUNCT
cana-6293	117	16	test	test	VERB
cana-6293	117	17	on	on	ADP
cana-6293	117	18	hold	hold	VERB
cana-6293	117	19	-	-	PUNCT
cana-6293	117	20	out	out	ADP
cana-6293	117	21	set	set	NOUN
cana-6293	117	22	,	,	PUNCT
cana-6293	117	23	plot	plot	NOUN
cana-6293	117	24	/	/	SYM
cana-6293	117	25	compare	compare	NOUN
cana-6293	117	26	metrics	metric	NOUN
cana-6293	117	27	.	.	PUNCT
cana-6293	118	1	6	6	X
cana-6293	118	2	.	.	X
cana-6293	118	3	deployment	deployment	NOUN
cana-6293	118	4	—	—	PUNCT
cana-6293	118	5	export	export	NOUN
cana-6293	118	6	model	model	NOUN
cana-6293	118	7	and	and	CCONJ
cana-6293	118	8	monitor	monitor	NOUN
cana-6293	118	9	.	.	PUNCT
cana-6293	119	1	unified	unified	ADJ
cana-6293	119	2	algorithm	algorithm	NOUN
cana-6293	119	3	for	for	ADP
cana-6293	119	4	optimized	optimize	VERB
cana-6293	119	5	paddy	paddy	NOUN
cana-6293	119	6	growth	growth	NOUN
cana-6293	119	7	prediction	prediction	NOUN
cana-6293	119	8	input	input	NOUN
cana-6293	119	9	:	:	PUNCT
cana-6293	119	10	raw	raw	ADJ
cana-6293	119	11	multi	multi	ADJ
cana-6293	119	12	-	-	ADJ
cana-6293	119	13	source	source	ADJ
cana-6293	119	14	dataset	dataset	NOUN
cana-6293	119	15	(	(	PUNCT
cana-6293	119	16	weather	weather	NOUN
cana-6293	119	17	,	,	PUNCT
cana-6293	119	18	soil	soil	NOUN
cana-6293	119	19	,	,	PUNCT
cana-6293	119	20	management	management	NOUN
cana-6293	119	21	,	,	PUNCT
cana-6293	119	22	crop	crop	NOUN
cana-6293	119	23	growth	growth	NOUN
cana-6293	119	24	)	)	PUNCT
cana-6293	119	25	output	output	NOUN
cana-6293	119	26	:	:	PUNCT
cana-6293	119	27	final	final	ADJ
cana-6293	119	28	optimized	optimize	VERB
cana-6293	119	29	prediction	prediction	NOUN
cana-6293	119	30	model	model	NOUN
cana-6293	119	31	,	,	PUNCT
cana-6293	119	32	performance	performance	NOUN
cana-6293	119	33	metrics	metric	NOUN
cana-6293	119	34	,	,	PUNCT
cana-6293	119	35	and	and	CCONJ
cana-6293	119	36	evaluation	evaluation	NOUN
cana-6293	119	37	results	result	NOUN
cana-6293	119	38	step	step	VERB
cana-6293	119	39	1	1	NUM
cana-6293	119	40	:	:	PUNCT
cana-6293	119	41	data	data	NOUN
cana-6293	119	42	collection	collection	NOUN
cana-6293	119	43	1.1	1.1	NUM
cana-6293	119	44	gather	gather	NOUN
cana-6293	119	45	weather	weather	NOUN
cana-6293	119	46	data	datum	NOUN
cana-6293	119	47	(	(	PUNCT
cana-6293	119	48	temperature	temperature	NOUN
cana-6293	119	49	,	,	PUNCT
cana-6293	119	50	rainfall	rainfall	NOUN
cana-6293	119	51	,	,	PUNCT
cana-6293	119	52	humidity	humidity	NOUN
cana-6293	119	53	,	,	PUNCT
cana-6293	119	54	wind	wind	NOUN
cana-6293	119	55	,	,	PUNCT
cana-6293	119	56	etc	etc	X
cana-6293	119	57	.	.	X
cana-6293	119	58	)	)	PUNCT
cana-6293	119	59	1.2	1.2	NUM
cana-6293	119	60	collect	collect	VERB
cana-6293	119	61	soil	soil	NOUN
cana-6293	119	62	data	datum	NOUN
cana-6293	119	63	(	(	PUNCT
cana-6293	119	64	ph	ph	ADJ
cana-6293	119	65	,	,	PUNCT
cana-6293	119	66	moisture	moisture	NOUN
cana-6293	119	67	,	,	PUNCT
cana-6293	119	68	nitrogen	nitrogen	NOUN
cana-6293	119	69	,	,	PUNCT
cana-6293	119	70	etc	etc	X
cana-6293	119	71	.	.	X
cana-6293	119	72	)	)	PUNCT
cana-6293	119	73	1.3	1.3	NUM
cana-6293	119	74	record	record	NOUN
cana-6293	119	75	farm	farm	NOUN
cana-6293	119	76	management	management	NOUN
cana-6293	119	77	data	datum	NOUN
cana-6293	119	78	(	(	PUNCT
cana-6293	119	79	fertilizer	fertilizer	NOUN
cana-6293	119	80	usage	usage	NOUN
cana-6293	119	81	,	,	PUNCT
cana-6293	119	82	irrigation	irrigation	NOUN
cana-6293	119	83	levels	level	NOUN
cana-6293	119	84	)	)	PUNCT
cana-6293	119	85	1.4	1.4	NUM
cana-6293	119	86	collect	collect	VERB
cana-6293	119	87	crop	crop	NOUN
cana-6293	119	88	growth	growth	NOUN
cana-6293	119	89	and	and	CCONJ
cana-6293	119	90	yield	yield	VERB
cana-6293	119	91	information	information	NOUN
cana-6293	119	92	step	step	NOUN
cana-6293	119	93	2	2	NUM
cana-6293	119	94	:	:	PUNCT
cana-6293	119	95	data	datum	NOUN
cana-6293	119	96	preprocessing	preprocesse	VERB
cana-6293	119	97	2.1	2.1	NUM
cana-6293	119	98	convert	convert	NOUN
cana-6293	119	99	date	date	NOUN
cana-6293	119	100	column	column	NOUN
cana-6293	119	101	to	to	PART
cana-6293	119	102	datetime	datetime	VERB
cana-6293	119	103	and	and	CCONJ
cana-6293	119	104	sort	sort	ADV
cana-6293	119	105	chronologically	chronologically	ADV
cana-6293	119	106	.	.	PUNCT
cana-6293	120	1	2.2	2.2	NUM
cana-6293	120	2	handle	handle	VERB
cana-6293	120	3	missing	miss	VERB
cana-6293	120	4	values	value	NOUN
cana-6293	120	5	:	:	PUNCT
cana-6293	120	6	numeric	numeric	ADJ
cana-6293	120	7	→	→	SYM
cana-6293	120	8	mean	mean	ADJ
cana-6293	120	9	/	/	SYM
cana-6293	120	10	median	median	NOUN
cana-6293	120	11	or	or	CCONJ
cana-6293	120	12	interpolation	interpolation	NOUN
cana-6293	120	13	categorical	categorical	ADJ
cana-6293	120	14	→	→	SYM
cana-6293	120	15	mode	mode	NOUN
cana-6293	120	16	or	or	CCONJ
cana-6293	120	17	“	"	PUNCT
cana-6293	120	18	unknown	unknown	ADJ
cana-6293	120	19	”	"	PUNCT
cana-6293	120	20	2.3	2.3	NUM
cana-6293	120	21	remove	remove	NOUN
cana-6293	120	22	outliers	outlier	NOUN
cana-6293	120	23	using	use	VERB
cana-6293	120	24	z	z	NOUN
cana-6293	120	25	-	-	PUNCT
cana-6293	120	26	score	score	NOUN
cana-6293	120	27	or	or	CCONJ
cana-6293	120	28	domain	domain	NOUN
cana-6293	120	29	thresholds	threshold	NOUN
cana-6293	120	30	.	.	PUNCT
cana-6293	121	1	2.4	2.4	NUM
cana-6293	121	2	encode	encode	ADJ
cana-6293	121	3	categorical	categorical	ADJ
cana-6293	121	4	features	feature	NOUN
cana-6293	121	5	using	use	VERB
cana-6293	121	6	label	label	NOUN
cana-6293	121	7	encoding	encoding	NOUN
cana-6293	121	8	or	or	CCONJ
cana-6293	121	9	one	one	NUM
cana-6293	121	10	-	-	PUNCT
cana-6293	121	11	hot	hot	ADJ
cana-6293	121	12	encoding	encoding	NOUN
cana-6293	121	13	.	.	PUNCT
cana-6293	122	1	2.5	2.5	NUM
cana-6293	122	2	scale	scale	NOUN
cana-6293	122	3	numerical	numerical	ADJ
cana-6293	122	4	features	feature	NOUN
cana-6293	122	5	using	use	VERB
cana-6293	122	6	standardscaler	standardscaler	NOUN
cana-6293	122	7	or	or	CCONJ
cana-6293	122	8	minmaxscaler	minmaxscaler	NOUN
cana-6293	122	9	.	.	PUNCT
cana-6293	123	1	2.6	2.6	NUM
cana-6293	123	2	create	create	VERB
cana-6293	123	3	new	new	ADJ
cana-6293	123	4	features	feature	NOUN
cana-6293	123	5	:	:	PUNCT
cana-6293	123	6	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-6293	123	7	communications	communication	NOUN
cana-6293	123	8	on	on	ADP
cana-6293	123	9	applied	apply	VERB
cana-6293	123	10	nonlinear	nonlinear	ADJ
cana-6293	123	11	analysis	analysis	NOUN
cana-6293	123	12	issn	issn	NOUN
cana-6293	123	13	:	:	PUNCT
cana-6293	123	14	1074	1074	NUM
cana-6293	123	15	-	-	PUNCT
cana-6293	123	16	133x	133x	NUM
cana-6293	123	17	vol	vol	NOUN
cana-6293	123	18	32	32	NUM
cana-6293	123	19	no	no	NOUN
cana-6293	123	20	.	.	PUNCT
cana-6293	124	1	2s	2s	NUM
cana-6293	124	2	(	(	PUNCT
cana-6293	124	3	2025	2025	NUM
cana-6293	124	4	)	)	PUNCT
cana-6293	124	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	124	6	643	643	NUM
cana-6293	124	7	ndvi	ndvi	NOUN
cana-6293	124	8	/	/	SYM
cana-6293	124	9	evi	evi	NOUN
cana-6293	124	10	(	(	PUNCT
cana-6293	124	11	if	if	SCONJ
cana-6293	124	12	remote	remote	ADJ
cana-6293	124	13	sensing	sense	VERB
cana-6293	124	14	data	datum	NOUN
cana-6293	124	15	exists	exist	VERB
cana-6293	124	16	)	)	PUNCT
cana-6293	124	17	cumulative	cumulative	ADJ
cana-6293	124	18	rainfall	rainfall	NOUN
cana-6293	124	19	lag	lag	NOUN
cana-6293	124	20	features	feature	NOUN
cana-6293	124	21	(	(	PUNCT
cana-6293	124	22	t-1	t-1	PROPN
cana-6293	124	23	,	,	PUNCT
cana-6293	124	24	t-7	t-7	PROPN
cana-6293	124	25	,	,	PUNCT
cana-6293	124	26	t-14	t-14	NOUN
cana-6293	124	27	)	)	PUNCT
cana-6293	124	28	growing	grow	VERB
cana-6293	124	29	degree	degree	NOUN
cana-6293	124	30	days	day	NOUN
cana-6293	124	31	(	(	PUNCT
cana-6293	124	32	gdd	gdd	PROPN
cana-6293	124	33	)	)	PUNCT
cana-6293	124	34	2.7	2.7	NUM
cana-6293	124	35	split	split	NOUN
cana-6293	124	36	dataset	dataset	VERB
cana-6293	124	37	into	into	ADP
cana-6293	124	38	train	train	NOUN
cana-6293	124	39	,	,	PUNCT
cana-6293	124	40	validation	validation	NOUN
cana-6293	124	41	,	,	PUNCT
cana-6293	124	42	and	and	CCONJ
cana-6293	124	43	test	test	NOUN
cana-6293	124	44	sets	set	NOUN
cana-6293	124	45	(	(	PUNCT
cana-6293	124	46	time	time	NOUN
cana-6293	124	47	-	-	PUNCT
cana-6293	124	48	based	base	VERB
cana-6293	124	49	splitting	splitting	NOUN
cana-6293	124	50	)	)	PUNCT
cana-6293	124	51	.	.	PUNCT
cana-6293	125	1	step	step	NOUN
cana-6293	125	2	3	3	NUM
cana-6293	125	3	:	:	PUNCT
cana-6293	125	4	baseline	baseline	NOUN
cana-6293	125	5	model	model	NOUN
cana-6293	125	6	training	train	VERB
cana-6293	125	7	3.1	3.1	NUM
cana-6293	125	8	define	define	NOUN
cana-6293	125	9	model	model	NOUN
cana-6293	125	10	list	list	NOUN
cana-6293	125	11	=	=	PUNCT
cana-6293	125	12	{	{	PUNCT
cana-6293	125	13	decision	decision	NOUN
cana-6293	125	14	tree	tree	NOUN
cana-6293	125	15	,	,	PUNCT
cana-6293	125	16	random	random	ADJ
cana-6293	125	17	forest	forest	NOUN
cana-6293	125	18	,	,	PUNCT
cana-6293	125	19	svm	svm	PROPN
cana-6293	125	20	,	,	PUNCT
cana-6293	125	21	xgboost	xgboost	ADV
cana-6293	125	22	,	,	PUNCT
cana-6293	125	23	lstm	lstm	ADJ
cana-6293	125	24	}	}	PUNCT
cana-6293	125	25	3.2	3.2	NUM
cana-6293	125	26	for	for	ADP
cana-6293	125	27	each	each	DET
cana-6293	125	28	model	model	NOUN
cana-6293	125	29	in	in	ADP
cana-6293	125	30	model	model	NOUN
cana-6293	125	31	list	list	NOUN
cana-6293	125	32	:	:	PUNCT
cana-6293	125	33	train	train	NOUN
cana-6293	125	34	model	model	NOUN
cana-6293	125	35	on	on	ADP
cana-6293	125	36	training	training	NOUN
cana-6293	125	37	data	datum	NOUN
cana-6293	125	38	predict	predict	VERB
cana-6293	125	39	on	on	ADP
cana-6293	125	40	validation	validation	NOUN
cana-6293	125	41	data	datum	NOUN
cana-6293	125	42	compute	compute	NOUN
cana-6293	125	43	metrics	metric	NOUN
cana-6293	125	44	(	(	PUNCT
cana-6293	125	45	rmse	rmse	PROPN
cana-6293	125	46	,	,	PUNCT
cana-6293	125	47	mae	mae	PROPN
cana-6293	125	48	,	,	PUNCT
cana-6293	125	49	r²	r²	NOUN
cana-6293	125	50	,	,	PUNCT
cana-6293	125	51	accuracy	accuracy	NOUN
cana-6293	125	52	,	,	PUNCT
cana-6293	125	53	precision	precision	NOUN
cana-6293	125	54	,	,	PUNCT
cana-6293	125	55	recall	recall	NOUN
cana-6293	125	56	,	,	PUNCT
cana-6293	125	57	f1	f1	NOUN
cana-6293	125	58	)	)	PUNCT
cana-6293	125	59	store	store	NOUN
cana-6293	125	60	baseline	baseline	NOUN
cana-6293	125	61	performance	performance	NOUN
cana-6293	125	62	results	result	VERB
cana-6293	125	63	3.3	3.3	NUM
cana-6293	125	64	select	select	ADJ
cana-6293	125	65	top	top	ADV
cana-6293	125	66	-	-	PUNCT
cana-6293	125	67	performing	perform	VERB
cana-6293	125	68	models	model	NOUN
cana-6293	125	69	for	for	ADP
cana-6293	125	70	optimization	optimization	NOUN
cana-6293	125	71	.	.	PUNCT
cana-6293	126	1	step	step	NOUN
cana-6293	126	2	4	4	NUM
cana-6293	126	3	:	:	PUNCT
cana-6293	126	4	optimization	optimization	NOUN
cana-6293	126	5	using	use	VERB
cana-6293	126	6	pso	pso	NOUN
cana-6293	126	7	or	or	CCONJ
cana-6293	126	8	ga	ga	PROPN
cana-6293	126	9	4.1	4.1	NUM
cana-6293	126	10	define	define	VERB
cana-6293	126	11	hyperparameter	hyperparameter	NOUN
cana-6293	126	12	search	search	NOUN
cana-6293	126	13	space	space	NOUN
cana-6293	126	14	for	for	ADP
cana-6293	126	15	selected	select	VERB
cana-6293	126	16	models	model	NOUN
cana-6293	126	17	.	.	PUNCT
cana-6293	127	1	4.2	4.2	NUM
cana-6293	127	2	choose	choose	VERB
cana-6293	127	3	optimization	optimization	NOUN
cana-6293	127	4	method	method	NOUN
cana-6293	127	5	:	:	PUNCT
cana-6293	127	6	particle	particle	NOUN
cana-6293	127	7	swarm	swarm	NOUN
cana-6293	127	8	optimization	optimization	NOUN
cana-6293	127	9	(	(	PUNCT
cana-6293	127	10	pso	pso	NOUN
cana-6293	127	11	)	)	PUNCT
cana-6293	127	12	or	or	CCONJ
cana-6293	127	13	genetic	genetic	ADJ
cana-6293	127	14	algorithm	algorithm	NOUN
cana-6293	127	15	(	(	PUNCT
cana-6293	127	16	ga	ga	PROPN
cana-6293	127	17	)	)	PUNCT
cana-6293	127	18	4.3	4.3	NUM
cana-6293	127	19	initialize	initialize	NOUN
cana-6293	127	20	population	population	NOUN
cana-6293	127	21	/	/	SYM
cana-6293	127	22	particles	particle	NOUN
cana-6293	127	23	with	with	ADP
cana-6293	127	24	random	random	ADJ
cana-6293	127	25	hyperparameter	hyperparameter	NOUN
cana-6293	127	26	values	value	NOUN
cana-6293	127	27	.	.	PUNCT
cana-6293	128	1	4.4	4.4	NUM
cana-6293	128	2	repeat	repeat	NOUN
cana-6293	128	3	for	for	ADP
cana-6293	128	4	max	max	PROPN
cana-6293	128	5	iterations	iteration	NOUN
cana-6293	128	6	/	/	SYM
cana-6293	128	7	generations	generation	NOUN
cana-6293	128	8	:	:	PUNCT
cana-6293	128	9	train	train	NOUN
cana-6293	128	10	model	model	NOUN
cana-6293	128	11	with	with	ADP
cana-6293	128	12	candidate	candidate	NOUN
cana-6293	128	13	hyperparameters	hyperparameter	NOUN
cana-6293	128	14	evaluate	evaluate	VERB
cana-6293	128	15	fitness	fitness	NOUN
cana-6293	128	16	=	=	SYM
cana-6293	128	17	negative	negative	ADJ
cana-6293	128	18	rmse	rmse	NOUN
cana-6293	128	19	(	(	PUNCT
cana-6293	128	20	or	or	CCONJ
cana-6293	128	21	chosen	choose	VERB
cana-6293	128	22	metric	metric	ADJ
cana-6293	128	23	)	)	PUNCT
cana-6293	128	24	update	update	NOUN
cana-6293	128	25	pbest	pb	ADJ
cana-6293	128	26	/	/	SYM
cana-6293	128	27	gbest	gb	ADJ
cana-6293	128	28	(	(	PUNCT
cana-6293	128	29	pso	pso	NOUN
cana-6293	128	30	)	)	PUNCT
cana-6293	128	31	or	or	CCONJ
cana-6293	128	32	apply	apply	VERB
cana-6293	128	33	selection	selection	NOUN
cana-6293	128	34	→	→	SYM
cana-6293	128	35	crossover	crossover	NOUN
cana-6293	128	36	→	→	SYM
cana-6293	128	37	mutation	mutation	NOUN
cana-6293	128	38	(	(	PUNCT
cana-6293	128	39	ga	ga	NOUN
cana-6293	128	40	)	)	PUNCT
cana-6293	128	41	4.5	4.5	NUM
cana-6293	128	42	return	return	VERB
cana-6293	128	43	best	good	ADJ
cana-6293	128	44	hyperparameters	hyperparameter	NOUN
cana-6293	128	45	found	find	VERB
cana-6293	128	46	by	by	ADP
cana-6293	128	47	optimizer	optimizer	NOUN
cana-6293	128	48	.	.	PUNCT
cana-6293	129	1	step	step	NOUN
cana-6293	129	2	5	5	NUM
cana-6293	129	3	:	:	PUNCT
cana-6293	129	4	optional	optional	ADJ
cana-6293	129	5	feature	feature	NOUN
cana-6293	129	6	selection	selection	NOUN
cana-6293	129	7	using	use	VERB
cana-6293	129	8	metaheuristics	metaheuristic	NOUN
cana-6293	129	9	5.1	5.1	NUM
cana-6293	129	10	represent	represent	VERB
cana-6293	129	11	each	each	DET
cana-6293	129	12	candidate	candidate	NOUN
cana-6293	129	13	solution	solution	NOUN
cana-6293	129	14	as	as	ADP
cana-6293	129	15	a	a	DET
cana-6293	129	16	binary	binary	ADJ
cana-6293	129	17	mask	mask	NOUN
cana-6293	129	18	for	for	ADP
cana-6293	129	19	feature	feature	NOUN
cana-6293	129	20	selection	selection	NOUN
cana-6293	129	21	.	.	PUNCT
cana-6293	130	1	5.2	5.2	NUM
cana-6293	130	2	use	use	VERB
cana-6293	130	3	pso	pso	NOUN
cana-6293	130	4	or	or	CCONJ
cana-6293	130	5	ga	ga	PROPN
cana-6293	130	6	to	to	PART
cana-6293	130	7	evaluate	evaluate	VERB
cana-6293	130	8	subsets	subset	NOUN
cana-6293	130	9	:	:	PUNCT
cana-6293	130	10	train	train	NOUN
cana-6293	130	11	model	model	NOUN
cana-6293	130	12	using	use	VERB
cana-6293	130	13	selected	select	VERB
cana-6293	130	14	features	feature	NOUN
cana-6293	130	15	score	score	NOUN
cana-6293	130	16	on	on	ADP
cana-6293	130	17	validation	validation	NOUN
cana-6293	130	18	data	datum	NOUN
cana-6293	130	19	apply	apply	VERB
cana-6293	130	20	penalty	penalty	NOUN
cana-6293	130	21	for	for	ADP
cana-6293	130	22	large	large	ADJ
cana-6293	130	23	feature	feature	NOUN
cana-6293	130	24	sets	set	VERB
cana-6293	130	25	5.3	5.3	NUM
cana-6293	130	26	select	select	ADJ
cana-6293	130	27	best	good	ADJ
cana-6293	130	28	feature	feature	NOUN
cana-6293	130	29	subset	subset	NOUN
cana-6293	130	30	.	.	PUNCT
cana-6293	131	1	step	step	NOUN
cana-6293	131	2	6	6	NUM
cana-6293	131	3	:	:	PUNCT
cana-6293	131	4	final	final	ADJ
cana-6293	131	5	model	model	NOUN
cana-6293	131	6	training	training	NOUN
cana-6293	131	7	with	with	ADP
cana-6293	131	8	optimized	optimize	VERB
cana-6293	131	9	settings	setting	NOUN
cana-6293	131	10	6.1	6.1	NUM
cana-6293	131	11	merge	merge	NOUN
cana-6293	131	12	train	train	NOUN
cana-6293	131	13	+	+	CCONJ
cana-6293	131	14	validation	validation	NOUN
cana-6293	131	15	→	→	SYM
cana-6293	131	16	full	full	ADJ
cana-6293	131	17	training	training	NOUN
cana-6293	131	18	dataset	dataset	NOUN
cana-6293	131	19	.	.	PUNCT
cana-6293	132	1	6.2	6.2	NUM
cana-6293	132	2	train	train	NOUN
cana-6293	132	3	final	final	ADJ
cana-6293	132	4	model	model	NOUN
cana-6293	132	5	using	use	VERB
cana-6293	132	6	:	:	PUNCT
cana-6293	132	7	best	good	ADJ
cana-6293	132	8	hyperparameters	hyperparameter	NOUN
cana-6293	132	9	optimal	optimal	ADJ
cana-6293	132	10	feature	feature	NOUN
cana-6293	132	11	subset	subset	VERB
cana-6293	132	12	6.3	6.3	NUM
cana-6293	132	13	evaluate	evaluate	VERB
cana-6293	132	14	final	final	ADJ
cana-6293	132	15	model	model	NOUN
cana-6293	132	16	on	on	ADP
cana-6293	132	17	test	test	NOUN
cana-6293	132	18	data	datum	NOUN
cana-6293	132	19	using	use	VERB
cana-6293	132	20	:	:	PUNCT
cana-6293	133	1	rmse	rmse	PROPN
cana-6293	133	2	,	,	PUNCT
cana-6293	133	3	mae	mae	PROPN
cana-6293	133	4	,	,	PUNCT
cana-6293	133	5	r²	r²	VERB
cana-6293	133	6	for	for	ADP
cana-6293	133	7	regression	regression	NOUN
cana-6293	133	8	accuracy	accuracy	NOUN
cana-6293	133	9	,	,	PUNCT
cana-6293	133	10	precision	precision	NOUN
cana-6293	133	11	,	,	PUNCT
cana-6293	133	12	recall	recall	NOUN
cana-6293	133	13	,	,	PUNCT
cana-6293	133	14	f1	f1	NOUN
cana-6293	133	15	for	for	ADP
cana-6293	133	16	classification	classification	NOUN
cana-6293	133	17	step	step	NOUN
cana-6293	133	18	7	7	NUM
cana-6293	133	19	:	:	PUNCT
cana-6293	133	20	performance	performance	NOUN
cana-6293	133	21	evaluation	evaluation	NOUN
cana-6293	133	22	and	and	CCONJ
cana-6293	133	23	visualization	visualization	NOUN
cana-6293	133	24	7.1	7.1	NUM
cana-6293	133	25	generate	generate	VERB
cana-6293	133	26	comparison	comparison	NOUN
cana-6293	133	27	graphs	graph	NOUN
cana-6293	133	28	:	:	PUNCT
cana-6293	133	29	baseline	baseline	VERB
cana-6293	133	30	vs	vs	ADP
cana-6293	133	31	optimized	optimize	VERB
cana-6293	133	32	(	(	PUNCT
cana-6293	133	33	accuracy	accuracy	NOUN
cana-6293	133	34	,	,	PUNCT
cana-6293	133	35	precision	precision	NOUN
cana-6293	133	36	,	,	PUNCT
cana-6293	133	37	recall	recall	NOUN
cana-6293	133	38	,	,	PUNCT
cana-6293	133	39	f1	f1	NOUN
cana-6293	133	40	)	)	PUNCT
cana-6293	133	41	baseline	baseline	NOUN
cana-6293	133	42	vs	vs	ADP
cana-6293	133	43	optimized	optimize	VERB
cana-6293	133	44	rmse	rmse	NOUN
cana-6293	133	45	actual	actual	ADJ
cana-6293	133	46	vs	vs	ADP
cana-6293	133	47	predicted	predict	VERB
cana-6293	133	48	yield	yield	NOUN
cana-6293	133	49	/	/	SYM
cana-6293	133	50	growth	growth	NOUN
cana-6293	133	51	7.2	7.2	NUM
cana-6293	133	52	plot	plot	NOUN
cana-6293	133	53	feature	feature	NOUN
cana-6293	133	54	importance	importance	NOUN
cana-6293	133	55	(	(	PUNCT
cana-6293	133	56	for	for	ADP
cana-6293	133	57	tree	tree	NOUN
cana-6293	133	58	-	-	PUNCT
cana-6293	133	59	based	base	VERB
cana-6293	133	60	models	model	NOUN
cana-6293	133	61	)	)	PUNCT
cana-6293	133	62	7.3	7.3	NUM
cana-6293	133	63	plot	plot	NOUN
cana-6293	133	64	residual	residual	ADJ
cana-6293	133	65	errors	error	NOUN
cana-6293	133	66	and	and	CCONJ
cana-6293	133	67	error	error	NOUN
cana-6293	133	68	distribution	distribution	NOUN
cana-6293	133	69	.	.	PUNCT
cana-6293	134	1	step	step	NOUN
cana-6293	134	2	8	8	NUM
cana-6293	134	3	:	:	PUNCT
cana-6293	134	4	model	model	NOUN
cana-6293	134	5	saving	saving	NOUN
cana-6293	134	6	and	and	CCONJ
cana-6293	134	7	deployment	deployment	NOUN
cana-6293	134	8	8.1	8.1	NUM
cana-6293	134	9	export	export	NOUN
cana-6293	134	10	trained	train	VERB
cana-6293	134	11	model	model	NOUN
cana-6293	134	12	using	use	VERB
cana-6293	134	13	pickle	pickle	NOUN
cana-6293	134	14	/	/	SYM
cana-6293	134	15	joblib	joblib	NOUN
cana-6293	134	16	.	.	PUNCT
cana-6293	135	1	8.2	8.2	NUM
cana-6293	135	2	save	save	NOUN
cana-6293	135	3	preprocessing	preprocesse	VERB
cana-6293	135	4	pipeline	pipeline	NOUN
cana-6293	135	5	.	.	PUNCT
cana-6293	136	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-6293	136	2	communications	communication	NOUN
cana-6293	136	3	on	on	ADP
cana-6293	136	4	applied	apply	VERB
cana-6293	136	5	nonlinear	nonlinear	ADJ
cana-6293	136	6	analysis	analysis	NOUN
cana-6293	136	7	issn	issn	NOUN
cana-6293	136	8	:	:	PUNCT
cana-6293	136	9	1074	1074	NUM
cana-6293	136	10	-	-	PUNCT
cana-6293	136	11	133x	133x	NUM
cana-6293	136	12	vol	vol	NOUN
cana-6293	136	13	32	32	NUM
cana-6293	136	14	no	no	NOUN
cana-6293	136	15	.	.	PUNCT
cana-6293	137	1	2s	2s	NUM
cana-6293	137	2	(	(	PUNCT
cana-6293	137	3	2025	2025	NUM
cana-6293	137	4	)	)	PUNCT
cana-6293	137	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	137	6	644	644	NUM
cana-6293	137	7	8.3	8.3	NUM
cana-6293	137	8	prepare	prepare	NOUN
cana-6293	137	9	model	model	NOUN
cana-6293	137	10	for	for	ADP
cana-6293	137	11	real	real	ADJ
cana-6293	137	12	-	-	PUNCT
cana-6293	137	13	time	time	NOUN
cana-6293	137	14	or	or	CCONJ
cana-6293	137	15	batch	batch	NOUN
cana-6293	137	16	prediction	prediction	NOUN
cana-6293	137	17	.	.	PUNCT
cana-6293	138	1	4	4	X
cana-6293	138	2	.	.	X
cana-6293	138	3	experimental	experimental	ADJ
cana-6293	138	4	results	result	NOUN
cana-6293	138	5	this	this	DET
cana-6293	138	6	section	section	NOUN
cana-6293	138	7	presents	present	VERB
cana-6293	138	8	the	the	DET
cana-6293	138	9	experimental	experimental	ADJ
cana-6293	138	10	outcomes	outcome	NOUN
cana-6293	138	11	obtained	obtain	VERB
cana-6293	138	12	during	during	ADP
cana-6293	138	13	the	the	DET
cana-6293	138	14	evaluation	evaluation	NOUN
cana-6293	138	15	of	of	ADP
cana-6293	138	16	machine	machine	NOUN
cana-6293	138	17	learning	learning	NOUN
cana-6293	138	18	and	and	CCONJ
cana-6293	138	19	deep	deep	ADJ
cana-6293	138	20	learning	learning	NOUN
cana-6293	138	21	models	model	NOUN
cana-6293	138	22	for	for	ADP
cana-6293	138	23	paddy	paddy	NOUN
cana-6293	138	24	growth	growth	NOUN
cana-6293	138	25	prediction	prediction	NOUN
cana-6293	138	26	.	.	PUNCT
cana-6293	139	1	the	the	DET
cana-6293	139	2	experiments	experiment	NOUN
cana-6293	139	3	were	be	AUX
cana-6293	139	4	conducted	conduct	VERB
cana-6293	139	5	using	use	VERB
cana-6293	139	6	baseline	baseline	NOUN
cana-6293	139	7	models	model	NOUN
cana-6293	139	8	and	and	CCONJ
cana-6293	139	9	their	their	PRON
cana-6293	139	10	optimized	optimize	VERB
cana-6293	139	11	versions	version	NOUN
cana-6293	139	12	using	use	VERB
cana-6293	139	13	particle	particle	NOUN
cana-6293	139	14	swarm	swarm	NOUN
cana-6293	139	15	optimization	optimization	NOUN
cana-6293	139	16	(	(	PUNCT
cana-6293	139	17	pso	pso	NOUN
cana-6293	139	18	)	)	PUNCT
cana-6293	139	19	and	and	CCONJ
cana-6293	139	20	genetic	genetic	ADJ
cana-6293	139	21	algorithm	algorithm	NOUN
cana-6293	139	22	(	(	PUNCT
cana-6293	139	23	ga	ga	PROPN
cana-6293	139	24	)	)	PUNCT
cana-6293	139	25	.	.	PUNCT
cana-6293	140	1	the	the	DET
cana-6293	140	2	results	result	NOUN
cana-6293	140	3	are	be	AUX
cana-6293	140	4	analyzed	analyze	VERB
cana-6293	140	5	based	base	VERB
cana-6293	140	6	on	on	ADP
cana-6293	140	7	multiple	multiple	ADJ
cana-6293	140	8	performance	performance	NOUN
cana-6293	140	9	metrics	metric	NOUN
cana-6293	140	10	such	such	ADJ
cana-6293	140	11	as	as	ADP
cana-6293	140	12	accuracy	accuracy	NOUN
cana-6293	140	13	,	,	PUNCT
cana-6293	140	14	precision	precision	NOUN
cana-6293	140	15	,	,	PUNCT
cana-6293	140	16	recall	recall	NOUN
cana-6293	140	17	,	,	PUNCT
cana-6293	140	18	f1	f1	NOUN
cana-6293	140	19	-	-	PUNCT
cana-6293	140	20	score	score	NOUN
cana-6293	140	21	,	,	PUNCT
cana-6293	140	22	rmse	rmse	NOUN
cana-6293	140	23	,	,	PUNCT
cana-6293	140	24	mae	mae	PROPN
cana-6293	140	25	,	,	PUNCT
cana-6293	140	26	and	and	CCONJ
cana-6293	140	27	r²	r²	NOUN
cana-6293	140	28	.	.	PUNCT
cana-6293	141	1	initially	initially	ADV
cana-6293	141	2	,	,	PUNCT
cana-6293	141	3	standard	standard	ADJ
cana-6293	141	4	ml	ml	NOUN
cana-6293	141	5	models	model	NOUN
cana-6293	141	6	—	—	PUNCT
cana-6293	141	7	decision	decision	NOUN
cana-6293	141	8	tree	tree	NOUN
cana-6293	141	9	(	(	PUNCT
cana-6293	141	10	dt	dt	PROPN
cana-6293	141	11	)	)	PUNCT
cana-6293	141	12	,	,	PUNCT
cana-6293	141	13	random	random	ADJ
cana-6293	141	14	forest	forest	NOUN
cana-6293	141	15	(	(	PUNCT
cana-6293	141	16	rf	rf	NOUN
cana-6293	141	17	)	)	PUNCT
cana-6293	141	18	,	,	PUNCT
cana-6293	141	19	and	and	CCONJ
cana-6293	141	20	support	support	VERB
cana-6293	141	21	vector	vector	NOUN
cana-6293	141	22	machine	machine	NOUN
cana-6293	141	23	(	(	PUNCT
cana-6293	141	24	svm)—were	svm)—were	X
cana-6293	141	25	trained	train	VERB
cana-6293	141	26	using	use	VERB
cana-6293	141	27	default	default	NOUN
cana-6293	141	28	parameters	parameter	NOUN
cana-6293	141	29	.	.	PUNCT
cana-6293	142	1	their	their	PRON
cana-6293	142	2	performance	performance	NOUN
cana-6293	142	3	on	on	ADP
cana-6293	142	4	the	the	DET
cana-6293	142	5	test	test	NOUN
cana-6293	142	6	dataset	dataset	NOUN
cana-6293	142	7	is	be	AUX
cana-6293	142	8	summarized	summarize	VERB
cana-6293	142	9	in	in	ADP
cana-6293	142	10	table	table	NOUN
cana-6293	142	11	1	1	NUM
cana-6293	142	12	.	.	PUNCT
cana-6293	142	13	table	table	NOUN
cana-6293	142	14	1	1	NUM
cana-6293	142	15	.	.	PUNCT
cana-6293	142	16	baseline	baseline	PROPN
cana-6293	142	17	performance	performance	NOUN
cana-6293	142	18	model	model	NOUN
cana-6293	142	19	accuracy	accuracy	NOUN
cana-6293	142	20	precision	precision	NOUN
cana-6293	142	21	recall	recall	VERB
cana-6293	142	22	f1	f1	NOUN
cana-6293	142	23	-	-	PUNCT
cana-6293	142	24	score	score	NOUN
cana-6293	142	25	rmse	rmse	NOUN
cana-6293	142	26	mae	mae	PROPN
cana-6293	142	27	r²	r²	NOUN
cana-6293	142	28	decision	decision	NOUN
cana-6293	142	29	tree	tree	NOUN
cana-6293	142	30	0.7837	0.7837	NUM
cana-6293	143	1	0.7637	0.7637	NUM
cana-6293	143	2	0.7437	0.7437	NUM
cana-6293	143	3	0.7537	0.7537	NUM
cana-6293	143	4	6.8837	6.8837	NUM
cana-6293	143	5	5.3937	5.3937	NUM
cana-6293	143	6	0.8137	0.8137	NUM
cana-6293	143	7	random	random	ADJ
cana-6293	143	8	forest	forest	NOUN
cana-6293	143	9	0.8337	0.8337	NUM
cana-6293	143	10	0.8137	0.8137	NUM
cana-6293	143	11	0.8037	0.8037	NUM
cana-6293	143	12	0.8137	0.8137	NUM
cana-6293	143	13	5.6937	5.6937	NUM
cana-6293	143	14	4.3537	4.3537	NUM
cana-6293	143	15	0.8637	0.8637	NUM
cana-6293	143	16	svm	svm	PROPN
cana-6293	143	17	0.8037	0.8037	NUM
cana-6293	143	18	0.7937	0.7937	NUM
cana-6293	143	19	0.7737	0.7737	NUM
cana-6293	143	20	0.7837	0.7837	NUM
cana-6293	143	21	6.1137	6.1137	NUM
cana-6293	143	22	4.8837	4.8837	NUM
cana-6293	143	23	0.8437	0.8437	NUM
cana-6293	143	24	fig	fig	NOUN
cana-6293	143	25	.	.	PUNCT
cana-6293	144	1	1	1	X
cana-6293	144	2	.	.	X
cana-6293	144	3	baseline	baseline	ADJ
cana-6293	144	4	performance	performance	NOUN
cana-6293	144	5	table	table	NOUN
cana-6293	144	6	2	2	NUM
cana-6293	144	7	.	.	PUNCT
cana-6293	145	1	after	after	SCONJ
cana-6293	145	2	optimization	optimization	NOUN
cana-6293	145	3	model	model	NOUN
cana-6293	145	4	accuracy	accuracy	NOUN
cana-6293	145	5	precision	precision	NOUN
cana-6293	145	6	recall	recall	VERB
cana-6293	145	7	f1	f1	NOUN
cana-6293	145	8	-	-	PUNCT
cana-6293	145	9	score	score	NOUN
cana-6293	145	10	rmse	rmse	NOUN
cana-6293	145	11	mae	mae	PROPN
cana-6293	145	12	r²	r²	PROPN
cana-6293	145	13	dt	dt	NOUN
cana-6293	146	1	+	+	CCONJ
cana-6293	146	2	optimization	optimization	NOUN
cana-6293	146	3	0.8537	0.8537	NUM
cana-6293	146	4	0.8437	0.8437	NUM
cana-6293	146	5	0.8237	0.8237	NUM
cana-6293	146	6	0.8337	0.8337	NUM
cana-6293	146	7	5.0937	5.0937	NUM
cana-6293	146	8	3.9837	3.9837	NUM
cana-6293	146	9	0.8837	0.8837	NUM
cana-6293	146	10	rf	rf	NUM
cana-6293	146	11	+	+	NUM
cana-6293	146	12	optimization	optimization	NOUN
cana-6293	146	13	0.9037	0.9037	NUM
cana-6293	146	14	0.8937	0.8937	NUM
cana-6293	146	15	0.8837	0.8837	NUM
cana-6293	146	16	0.8937	0.8937	NUM
cana-6293	146	17	4.3537	4.3537	NUM
cana-6293	146	18	3.4937	3.4937	NUM
cana-6293	146	19	0.9237	0.9237	NUM
cana-6293	146	20	svm	svm	PROPN
cana-6293	146	21	+	+	CCONJ
cana-6293	146	22	optimization	optimization	NOUN
cana-6293	146	23	0.8637	0.8637	NUM
cana-6293	146	24	0.8537	0.8537	NUM
cana-6293	146	25	0.8437	0.8437	NUM
cana-6293	146	26	0.8437	0.8437	NUM
cana-6293	146	27	4.9237	4.9237	NUM
cana-6293	146	28	3.9537	3.9537	NUM
cana-6293	146	29	0.8937	0.8937	NUM
cana-6293	146	30	https://internationalpubls.com/	https://internationalpubls.com/	NOUN
cana-6293	146	31	communications	communication	NOUN
cana-6293	146	32	on	on	ADP
cana-6293	146	33	applied	apply	VERB
cana-6293	146	34	nonlinear	nonlinear	ADJ
cana-6293	146	35	analysis	analysis	NOUN
cana-6293	146	36	issn	issn	NOUN
cana-6293	146	37	:	:	PUNCT
cana-6293	146	38	1074	1074	NUM
cana-6293	146	39	-	-	PUNCT
cana-6293	146	40	133x	133x	NUM
cana-6293	146	41	vol	vol	NOUN
cana-6293	146	42	32	32	NUM
cana-6293	146	43	no	no	NOUN
cana-6293	146	44	.	.	PUNCT
cana-6293	147	1	2s	2s	NUM
cana-6293	147	2	(	(	PUNCT
cana-6293	147	3	2025	2025	NUM
cana-6293	147	4	)	)	PUNCT
cana-6293	147	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	147	6	645	645	NUM
cana-6293	147	7	fig	fig	NOUN
cana-6293	147	8	.	.	PUNCT
cana-6293	148	1	2	2	NUM
cana-6293	148	2	.	.	X
cana-6293	148	3	after	after	ADP
cana-6293	148	4	optimization	optimization	NOUN
cana-6293	148	5	table	table	NOUN
cana-6293	148	6	3	3	NUM
cana-6293	148	7	.	.	PUNCT
cana-6293	148	8	improvement	improvement	NOUN
cana-6293	148	9	summary	summary	NOUN
cana-6293	148	10	metric	metric	ADJ
cana-6293	148	11	before	before	ADP
cana-6293	148	12	optimization	optimization	NOUN
cana-6293	148	13	after	after	ADP
cana-6293	148	14	optimization	optimization	NOUN
cana-6293	148	15	accuracy	accuracy	NOUN
cana-6293	148	16	0.8337	0.8337	NUM
cana-6293	148	17	0.9037	0.9037	NUM
cana-6293	148	18	precision	precision	NOUN
cana-6293	148	19	0.8137	0.8137	NUM
cana-6293	148	20	0.8937	0.8937	NUM
cana-6293	148	21	recall	recall	VERB
cana-6293	148	22	0.8037	0.8037	NUM
cana-6293	148	23	0.8837	0.8837	NUM
cana-6293	148	24	f1	f1	NOUN
cana-6293	148	25	-	-	PUNCT
cana-6293	148	26	score	score	NOUN
cana-6293	148	27	0.8137	0.8137	NUM
cana-6293	148	28	0.8937	0.8937	NUM
cana-6293	148	29	rmse	rmse	NOUN
cana-6293	148	30	5.6937	5.6937	NUM
cana-6293	148	31	4.3537	4.3537	NUM
cana-6293	148	32	mae	mae	PROPN
cana-6293	148	33	4.3537	4.3537	NUM
cana-6293	148	34	3.4937	3.4937	NUM
cana-6293	148	35	fig	fig	NOUN
cana-6293	148	36	.	.	PUNCT
cana-6293	149	1	3	3	X
cana-6293	149	2	.	.	X
cana-6293	149	3	improvement	improvement	NOUN
cana-6293	149	4	summary	summary	NOUN
cana-6293	149	5	4	4	NUM
cana-6293	149	6	.	.	NOUN
cana-6293	149	7	results	result	NOUN
cana-6293	149	8	and	and	CCONJ
cana-6293	149	9	discussion	discussion	VERB
cana-6293	149	10	the	the	DET
cana-6293	149	11	performance	performance	NOUN
cana-6293	149	12	of	of	ADP
cana-6293	149	13	the	the	DET
cana-6293	149	14	baseline	baseline	ADJ
cana-6293	149	15	machine	machine	NOUN
cana-6293	149	16	learning	learning	NOUN
cana-6293	149	17	models	model	NOUN
cana-6293	149	18	is	be	AUX
cana-6293	149	19	presented	present	VERB
cana-6293	149	20	in	in	ADP
cana-6293	149	21	table	table	NOUN
cana-6293	149	22	1	1	NUM
cana-6293	149	23	,	,	PUNCT
cana-6293	149	24	and	and	CCONJ
cana-6293	149	25	the	the	DET
cana-6293	149	26	trends	trend	NOUN
cana-6293	149	27	are	be	AUX
cana-6293	149	28	illustrated	illustrate	VERB
cana-6293	149	29	in	in	ADP
cana-6293	149	30	figure	figure	NOUN
cana-6293	149	31	1	1	NUM
cana-6293	149	32	.	.	PUNCT
cana-6293	150	1	the	the	DET
cana-6293	150	2	results	result	NOUN
cana-6293	150	3	show	show	VERB
cana-6293	150	4	that	that	SCONJ
cana-6293	150	5	the	the	DET
cana-6293	150	6	random	random	ADJ
cana-6293	150	7	forest	forest	NOUN
cana-6293	150	8	model	model	NOUN
cana-6293	150	9	achieved	achieve	VERB
cana-6293	150	10	the	the	DET
cana-6293	150	11	highest	high	ADJ
cana-6293	150	12	baseline	baseline	ADJ
cana-6293	150	13	accuracy	accuracy	NOUN
cana-6293	150	14	(	(	PUNCT
cana-6293	150	15	0.8337	0.8337	NUM
cana-6293	150	16	)	)	PUNCT
cana-6293	150	17	,	,	PUNCT
cana-6293	150	18	followed	follow	VERB
cana-6293	150	19	by	by	ADP
cana-6293	150	20	svm	svm	PROPN
cana-6293	150	21	(	(	PUNCT
cana-6293	150	22	0.8037	0.8037	NUM
cana-6293	150	23	)	)	PUNCT
cana-6293	150	24	and	and	CCONJ
cana-6293	150	25	decision	decision	NOUN
cana-6293	150	26	tree	tree	NOUN
cana-6293	150	27	(	(	PUNCT
cana-6293	150	28	0.7837	0.7837	NUM
cana-6293	150	29	)	)	PUNCT
cana-6293	150	30	.	.	PUNCT
cana-6293	151	1	precision	precision	NOUN
cana-6293	151	2	,	,	PUNCT
cana-6293	151	3	recall	recall	NOUN
cana-6293	151	4	,	,	PUNCT
cana-6293	151	5	and	and	CCONJ
cana-6293	151	6	f1	f1	NOUN
cana-6293	151	7	-	-	PUNCT
cana-6293	151	8	score	score	NOUN
cana-6293	151	9	followed	follow	VERB
cana-6293	151	10	a	a	DET
cana-6293	151	11	similar	similar	ADJ
cana-6293	151	12	pattern	pattern	NOUN
cana-6293	151	13	,	,	PUNCT
cana-6293	151	14	confirming	confirm	VERB
cana-6293	151	15	that	that	SCONJ
cana-6293	151	16	random	random	ADJ
cana-6293	151	17	forest	forest	NOUN
cana-6293	151	18	initially	initially	ADV
cana-6293	151	19	performed	perform	VERB
cana-6293	151	20	better	well	ADV
cana-6293	151	21	than	than	ADP
cana-6293	151	22	the	the	DET
cana-6293	151	23	other	other	ADJ
cana-6293	151	24	models	model	NOUN
cana-6293	151	25	.	.	PUNCT
cana-6293	152	1	the	the	DET
cana-6293	152	2	error	error	NOUN
cana-6293	152	3	metrics	metric	NOUN
cana-6293	152	4	(	(	PUNCT
cana-6293	152	5	rmse	rmse	PROPN
cana-6293	152	6	and	and	CCONJ
cana-6293	152	7	mae	mae	PROPN
cana-6293	152	8	)	)	PUNCT
cana-6293	152	9	in	in	ADP
cana-6293	152	10	table	table	NOUN
cana-6293	152	11	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-6293	152	12	communications	communication	NOUN
cana-6293	152	13	on	on	ADP
cana-6293	152	14	applied	apply	VERB
cana-6293	152	15	nonlinear	nonlinear	ADJ
cana-6293	152	16	analysis	analysis	NOUN
cana-6293	152	17	issn	issn	NOUN
cana-6293	152	18	:	:	PUNCT
cana-6293	152	19	1074	1074	NUM
cana-6293	152	20	-	-	PUNCT
cana-6293	152	21	133x	133x	NUM
cana-6293	152	22	vol	vol	NOUN
cana-6293	152	23	32	32	NUM
cana-6293	152	24	no	no	NOUN
cana-6293	152	25	.	.	PUNCT
cana-6293	153	1	2s	2s	NUM
cana-6293	153	2	(	(	PUNCT
cana-6293	153	3	2025	2025	NUM
cana-6293	153	4	)	)	PUNCT
cana-6293	153	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	153	6	646	646	NUM
cana-6293	153	7	1	1	NUM
cana-6293	153	8	reveal	reveal	VERB
cana-6293	153	9	that	that	SCONJ
cana-6293	153	10	decision	decision	NOUN
cana-6293	153	11	tree	tree	NOUN
cana-6293	153	12	had	have	VERB
cana-6293	153	13	the	the	DET
cana-6293	153	14	highest	high	ADJ
cana-6293	153	15	prediction	prediction	NOUN
cana-6293	153	16	error	error	NOUN
cana-6293	153	17	,	,	PUNCT
cana-6293	153	18	while	while	SCONJ
cana-6293	153	19	random	random	ADJ
cana-6293	153	20	forest	forest	NOUN
cana-6293	153	21	had	have	VERB
cana-6293	153	22	the	the	DET
cana-6293	153	23	lowest	low	ADJ
cana-6293	153	24	,	,	PUNCT
cana-6293	153	25	indicating	indicate	VERB
cana-6293	153	26	more	more	ADV
cana-6293	153	27	stable	stable	ADJ
cana-6293	153	28	learning	learning	NOUN
cana-6293	153	29	behavior	behavior	NOUN
cana-6293	153	30	.	.	PUNCT
cana-6293	154	1	these	these	DET
cana-6293	154	2	observations	observation	NOUN
cana-6293	154	3	are	be	AUX
cana-6293	154	4	clearly	clearly	ADV
cana-6293	154	5	reflected	reflect	VERB
cana-6293	154	6	in	in	ADP
cana-6293	154	7	figure	figure	NOUN
cana-6293	154	8	1	1	NUM
cana-6293	154	9	,	,	PUNCT
cana-6293	154	10	where	where	SCONJ
cana-6293	154	11	the	the	DET
cana-6293	154	12	random	random	ADJ
cana-6293	154	13	forest	forest	NOUN
cana-6293	154	14	curve	curve	NOUN
cana-6293	154	15	remains	remain	VERB
cana-6293	154	16	consistently	consistently	ADV
cana-6293	154	17	higher	high	ADJ
cana-6293	154	18	for	for	ADP
cana-6293	154	19	accuracy	accuracy	NOUN
cana-6293	154	20	-	-	PUNCT
cana-6293	154	21	based	base	VERB
cana-6293	154	22	metrics	metric	NOUN
cana-6293	154	23	and	and	CCONJ
cana-6293	154	24	lower	low	ADJ
cana-6293	154	25	for	for	ADP
cana-6293	154	26	error	error	NOUN
cana-6293	154	27	-	-	PUNCT
cana-6293	154	28	based	base	VERB
cana-6293	154	29	metrics	metric	NOUN
cana-6293	154	30	.	.	PUNCT
cana-6293	155	1	after	after	ADP
cana-6293	155	2	optimization	optimization	NOUN
cana-6293	155	3	,	,	PUNCT
cana-6293	155	4	the	the	DET
cana-6293	155	5	performance	performance	NOUN
cana-6293	155	6	of	of	ADP
cana-6293	155	7	all	all	DET
cana-6293	155	8	three	three	NUM
cana-6293	155	9	models	model	NOUN
cana-6293	155	10	improved	improve	VERB
cana-6293	155	11	significantly	significantly	ADV
cana-6293	155	12	,	,	PUNCT
cana-6293	155	13	as	as	SCONJ
cana-6293	155	14	shown	show	VERB
cana-6293	155	15	in	in	ADP
cana-6293	155	16	table	table	NOUN
cana-6293	155	17	2	2	NUM
cana-6293	155	18	.	.	PUNCT
cana-6293	156	1	the	the	DET
cana-6293	156	2	optimized	optimize	VERB
cana-6293	156	3	random	random	ADJ
cana-6293	156	4	forest	forest	NOUN
cana-6293	156	5	model	model	NOUN
cana-6293	156	6	recorded	record	VERB
cana-6293	156	7	the	the	DET
cana-6293	156	8	highest	high	ADJ
cana-6293	156	9	accuracy	accuracy	NOUN
cana-6293	156	10	of	of	ADP
cana-6293	156	11	0.9037	0.9037	NUM
cana-6293	156	12	,	,	PUNCT
cana-6293	156	13	while	while	SCONJ
cana-6293	156	14	the	the	DET
cana-6293	156	15	decision	decision	NOUN
cana-6293	156	16	tree	tree	NOUN
cana-6293	156	17	and	and	CCONJ
cana-6293	156	18	svm	svm	ADJ
cana-6293	156	19	models	model	NOUN
cana-6293	156	20	also	also	ADV
cana-6293	156	21	showed	show	VERB
cana-6293	156	22	noticeable	noticeable	ADJ
cana-6293	156	23	improvements	improvement	NOUN
cana-6293	156	24	across	across	ADP
cana-6293	156	25	all	all	DET
cana-6293	156	26	metrics	metric	NOUN
cana-6293	156	27	.	.	PUNCT
cana-6293	157	1	these	these	DET
cana-6293	157	2	gains	gain	NOUN
cana-6293	157	3	can	can	AUX
cana-6293	157	4	be	be	AUX
cana-6293	157	5	observed	observe	VERB
cana-6293	157	6	visually	visually	ADV
cana-6293	157	7	in	in	ADP
cana-6293	157	8	figure	figure	NOUN
cana-6293	157	9	2	2	NUM
cana-6293	157	10	,	,	PUNCT
cana-6293	157	11	where	where	SCONJ
cana-6293	157	12	the	the	DET
cana-6293	157	13	optimized	optimize	VERB
cana-6293	157	14	curves	curve	NOUN
cana-6293	157	15	appear	appear	VERB
cana-6293	157	16	distinctly	distinctly	ADV
cana-6293	157	17	higher	high	ADJ
cana-6293	157	18	than	than	ADP
cana-6293	157	19	the	the	DET
cana-6293	157	20	baseline	baseline	NOUN
cana-6293	157	21	curves	curve	NOUN
cana-6293	157	22	for	for	ADP
cana-6293	157	23	accuracy	accuracy	NOUN
cana-6293	157	24	,	,	PUNCT
cana-6293	157	25	precision	precision	NOUN
cana-6293	157	26	,	,	PUNCT
cana-6293	157	27	recall	recall	NOUN
cana-6293	157	28	,	,	PUNCT
cana-6293	157	29	and	and	CCONJ
cana-6293	157	30	f1	f1	NOUN
cana-6293	157	31	-	-	PUNCT
cana-6293	157	32	score	score	NOUN
cana-6293	157	33	.	.	PUNCT
cana-6293	158	1	the	the	DET
cana-6293	158	2	reduction	reduction	NOUN
cana-6293	158	3	in	in	ADP
cana-6293	158	4	rmse	rmse	NOUN
cana-6293	158	5	and	and	CCONJ
cana-6293	158	6	mae	mae	PROPN
cana-6293	158	7	values	value	NOUN
cana-6293	158	8	also	also	ADV
cana-6293	158	9	confirms	confirm	VERB
cana-6293	158	10	the	the	DET
cana-6293	158	11	effectiveness	effectiveness	NOUN
cana-6293	158	12	of	of	ADP
cana-6293	158	13	the	the	DET
cana-6293	158	14	optimization	optimization	NOUN
cana-6293	158	15	algorithms	algorithm	NOUN
cana-6293	158	16	in	in	ADP
cana-6293	158	17	minimizing	minimize	VERB
cana-6293	158	18	prediction	prediction	NOUN
cana-6293	158	19	errors	error	NOUN
cana-6293	158	20	.	.	PUNCT
cana-6293	159	1	a	a	DET
cana-6293	159	2	comparison	comparison	NOUN
cana-6293	159	3	of	of	ADP
cana-6293	159	4	the	the	DET
cana-6293	159	5	overall	overall	ADJ
cana-6293	159	6	improvement	improvement	NOUN
cana-6293	159	7	before	before	ADV
cana-6293	159	8	and	and	CCONJ
cana-6293	159	9	after	after	ADP
cana-6293	159	10	optimization	optimization	NOUN
cana-6293	159	11	is	be	AUX
cana-6293	159	12	summarized	summarize	VERB
cana-6293	159	13	in	in	ADP
cana-6293	159	14	table	table	NOUN
cana-6293	159	15	3	3	NUM
cana-6293	159	16	,	,	PUNCT
cana-6293	159	17	and	and	CCONJ
cana-6293	159	18	the	the	DET
cana-6293	159	19	progression	progression	NOUN
cana-6293	159	20	is	be	AUX
cana-6293	159	21	further	far	ADV
cana-6293	159	22	visualized	visualize	VERB
cana-6293	159	23	in	in	ADP
cana-6293	159	24	figure	figure	NOUN
cana-6293	159	25	3	3	NUM
cana-6293	159	26	.	.	PUNCT
cana-6293	160	1	the	the	DET
cana-6293	160	2	accuracy	accuracy	NOUN
cana-6293	160	3	increased	increase	VERB
cana-6293	160	4	from	from	ADP
cana-6293	160	5	0.8337	0.8337	NUM
cana-6293	160	6	to	to	ADP
cana-6293	160	7	0.9037	0.9037	NUM
cana-6293	160	8	,	,	PUNCT
cana-6293	160	9	while	while	SCONJ
cana-6293	160	10	precision	precision	NOUN
cana-6293	160	11	,	,	PUNCT
cana-6293	160	12	recall	recall	NOUN
cana-6293	160	13	,	,	PUNCT
cana-6293	160	14	and	and	CCONJ
cana-6293	160	15	f1	f1	NOUN
cana-6293	160	16	-	-	PUNCT
cana-6293	160	17	score	score	NOUN
cana-6293	160	18	also	also	ADV
cana-6293	160	19	experienced	experience	VERB
cana-6293	160	20	significant	significant	ADJ
cana-6293	160	21	enhancements	enhancement	NOUN
cana-6293	160	22	.	.	PUNCT
cana-6293	161	1	the	the	DET
cana-6293	161	2	reduction	reduction	NOUN
cana-6293	161	3	in	in	ADP
cana-6293	161	4	rmse	rmse	NOUN
cana-6293	161	5	from	from	ADP
cana-6293	161	6	5.6937	5.6937	NUM
cana-6293	161	7	to	to	ADP
cana-6293	161	8	4.3537	4.3537	NUM
cana-6293	161	9	highlights	highlight	NOUN
cana-6293	161	10	the	the	DET
cana-6293	161	11	positive	positive	ADJ
cana-6293	161	12	impact	impact	NOUN
cana-6293	161	13	of	of	ADP
cana-6293	161	14	optimization	optimization	NOUN
cana-6293	161	15	techniques	technique	NOUN
cana-6293	161	16	on	on	ADP
cana-6293	161	17	reducing	reduce	VERB
cana-6293	161	18	error	error	NOUN
cana-6293	161	19	levels	level	NOUN
cana-6293	161	20	.	.	PUNCT
cana-6293	162	1	the	the	DET
cana-6293	162	2	line	line	NOUN
cana-6293	162	3	graph	graph	NOUN
cana-6293	162	4	in	in	ADP
cana-6293	162	5	figure	figure	NOUN
cana-6293	162	6	3	3	NUM
cana-6293	162	7	clearly	clearly	ADV
cana-6293	162	8	shows	show	VERB
cana-6293	162	9	upward	upward	ADJ
cana-6293	162	10	improvement	improvement	NOUN
cana-6293	162	11	trends	trend	NOUN
cana-6293	162	12	for	for	ADP
cana-6293	162	13	evaluation	evaluation	NOUN
cana-6293	162	14	metrics	metric	NOUN
cana-6293	162	15	and	and	CCONJ
cana-6293	162	16	downward	downward	ADJ
cana-6293	162	17	trends	trend	NOUN
cana-6293	162	18	for	for	ADP
cana-6293	162	19	error	error	NOUN
cana-6293	162	20	values	value	NOUN
cana-6293	162	21	,	,	PUNCT
cana-6293	162	22	demonstrating	demonstrate	VERB
cana-6293	162	23	the	the	DET
cana-6293	162	24	increased	increase	VERB
cana-6293	162	25	predictive	predictive	ADJ
cana-6293	162	26	strength	strength	NOUN
cana-6293	162	27	of	of	ADP
cana-6293	162	28	the	the	DET
cana-6293	162	29	optimized	optimize	VERB
cana-6293	162	30	models	model	NOUN
cana-6293	162	31	.	.	PUNCT
cana-6293	163	1	overall	overall	ADV
cana-6293	163	2	,	,	PUNCT
cana-6293	163	3	the	the	DET
cana-6293	163	4	results	result	NOUN
cana-6293	163	5	from	from	ADP
cana-6293	163	6	tables	table	NOUN
cana-6293	163	7	1	1	NUM
cana-6293	163	8	,	,	PUNCT
cana-6293	163	9	2	2	NUM
cana-6293	163	10	,	,	PUNCT
cana-6293	163	11	and	and	CCONJ
cana-6293	163	12	3	3	NUM
cana-6293	163	13	and	and	CCONJ
cana-6293	163	14	the	the	DET
cana-6293	163	15	corresponding	corresponding	ADJ
cana-6293	163	16	visual	visual	ADJ
cana-6293	163	17	trends	trend	NOUN
cana-6293	163	18	in	in	ADP
cana-6293	163	19	figures	figure	NOUN
cana-6293	163	20	1	1	NUM
cana-6293	163	21	,	,	PUNCT
cana-6293	163	22	2	2	NUM
cana-6293	163	23	,	,	PUNCT
cana-6293	163	24	and	and	CCONJ
cana-6293	163	25	3	3	NUM
cana-6293	163	26	confirm	confirm	VERB
cana-6293	163	27	that	that	SCONJ
cana-6293	163	28	optimization	optimization	NOUN
cana-6293	163	29	techniques	technique	NOUN
cana-6293	163	30	considerably	considerably	ADV
cana-6293	163	31	enhance	enhance	VERB
cana-6293	163	32	the	the	DET
cana-6293	163	33	accuracy	accuracy	NOUN
cana-6293	163	34	,	,	PUNCT
cana-6293	163	35	consistency	consistency	NOUN
cana-6293	163	36	,	,	PUNCT
cana-6293	163	37	and	and	CCONJ
cana-6293	163	38	reliability	reliability	NOUN
cana-6293	163	39	of	of	ADP
cana-6293	163	40	machine	machine	NOUN
cana-6293	163	41	learning	learning	NOUN
cana-6293	163	42	models	model	NOUN
cana-6293	163	43	used	use	VERB
cana-6293	163	44	for	for	ADP
cana-6293	163	45	paddy	paddy	NOUN
cana-6293	163	46	crop	crop	NOUN
cana-6293	163	47	prediction	prediction	NOUN
cana-6293	163	48	.	.	PUNCT
cana-6293	164	1	among	among	ADP
cana-6293	164	2	the	the	DET
cana-6293	164	3	models	model	NOUN
cana-6293	164	4	tested	test	VERB
cana-6293	164	5	,	,	PUNCT
cana-6293	164	6	the	the	DET
cana-6293	164	7	optimized	optimize	VERB
cana-6293	164	8	random	random	ADJ
cana-6293	164	9	forest	forest	NOUN
cana-6293	164	10	model	model	NOUN
cana-6293	164	11	consistently	consistently	ADV
cana-6293	164	12	outperformed	outperform	VERB
cana-6293	164	13	the	the	DET
cana-6293	164	14	others	other	NOUN
cana-6293	164	15	,	,	PUNCT
cana-6293	164	16	making	make	VERB
cana-6293	164	17	it	it	PRON
cana-6293	164	18	the	the	DET
cana-6293	164	19	most	most	ADV
cana-6293	164	20	effective	effective	ADJ
cana-6293	164	21	model	model	NOUN
cana-6293	164	22	for	for	ADP
cana-6293	164	23	this	this	DET
cana-6293	164	24	research	research	NOUN
cana-6293	164	25	.	.	PUNCT
cana-6293	165	1	5	5	X
cana-6293	165	2	.	.	X
cana-6293	165	3	conclusions	conclusion	NOUN
cana-6293	165	4	this	this	DET
cana-6293	165	5	research	research	NOUN
cana-6293	165	6	concludes	conclude	VERB
cana-6293	165	7	that	that	SCONJ
cana-6293	165	8	optimization	optimization	NOUN
cana-6293	165	9	techniques	technique	NOUN
cana-6293	165	10	play	play	VERB
cana-6293	165	11	a	a	DET
cana-6293	165	12	crucial	crucial	ADJ
cana-6293	165	13	role	role	NOUN
cana-6293	165	14	in	in	ADP
cana-6293	165	15	improving	improve	VERB
cana-6293	165	16	the	the	DET
cana-6293	165	17	accuracy	accuracy	NOUN
cana-6293	165	18	and	and	CCONJ
cana-6293	165	19	reliability	reliability	NOUN
cana-6293	165	20	of	of	ADP
cana-6293	165	21	machine	machine	NOUN
cana-6293	165	22	learning	learning	NOUN
cana-6293	165	23	models	model	NOUN
cana-6293	165	24	used	use	VERB
cana-6293	165	25	for	for	ADP
cana-6293	165	26	paddy	paddy	NOUN
cana-6293	165	27	crop	crop	NOUN
cana-6293	165	28	prediction	prediction	NOUN
cana-6293	165	29	.	.	PUNCT
cana-6293	166	1	while	while	SCONJ
cana-6293	166	2	the	the	DET
cana-6293	166	3	baseline	baseline	NOUN
cana-6293	166	4	models	model	NOUN
cana-6293	166	5	showed	show	VERB
cana-6293	166	6	moderate	moderate	ADJ
cana-6293	166	7	levels	level	NOUN
cana-6293	166	8	of	of	ADP
cana-6293	166	9	accuracy	accuracy	NOUN
cana-6293	166	10	,	,	PUNCT
cana-6293	166	11	the	the	DET
cana-6293	166	12	application	application	NOUN
cana-6293	166	13	of	of	ADP
cana-6293	166	14	optimization	optimization	NOUN
cana-6293	166	15	algorithms	algorithm	NOUN
cana-6293	166	16	significantly	significantly	ADV
cana-6293	166	17	enhanced	enhance	VERB
cana-6293	166	18	their	their	PRON
cana-6293	166	19	overall	overall	ADJ
cana-6293	166	20	performance	performance	NOUN
cana-6293	166	21	.	.	PUNCT
cana-6293	167	1	the	the	DET
cana-6293	167	2	optimized	optimize	VERB
cana-6293	167	3	versions	version	NOUN
cana-6293	167	4	of	of	ADP
cana-6293	167	5	decision	decision	NOUN
cana-6293	167	6	tree	tree	NOUN
cana-6293	167	7	,	,	PUNCT
cana-6293	167	8	random	random	ADJ
cana-6293	167	9	forest	forest	NOUN
cana-6293	167	10	,	,	PUNCT
cana-6293	167	11	and	and	CCONJ
cana-6293	167	12	svm	svm	PROPN
cana-6293	167	13	recorded	record	VERB
cana-6293	167	14	higher	high	ADJ
cana-6293	167	15	accuracy	accuracy	NOUN
cana-6293	167	16	,	,	PUNCT
cana-6293	167	17	precision	precision	NOUN
cana-6293	167	18	,	,	PUNCT
cana-6293	167	19	recall	recall	NOUN
cana-6293	167	20	,	,	PUNCT
cana-6293	167	21	and	and	CCONJ
cana-6293	167	22	f1scores	f1score	NOUN
cana-6293	167	23	,	,	PUNCT
cana-6293	167	24	alongside	alongside	ADP
cana-6293	167	25	considerable	considerable	ADJ
cana-6293	167	26	reductions	reduction	NOUN
cana-6293	167	27	in	in	ADP
cana-6293	167	28	rmse	rmse	NOUN
cana-6293	167	29	and	and	CCONJ
cana-6293	167	30	mae	mae	PROPN
cana-6293	167	31	values	value	NOUN
cana-6293	167	32	.	.	PUNCT
cana-6293	168	1	among	among	ADP
cana-6293	168	2	the	the	DET
cana-6293	168	3	tested	test	VERB
cana-6293	168	4	models	model	NOUN
cana-6293	168	5	,	,	PUNCT
cana-6293	168	6	the	the	DET
cana-6293	168	7	optimized	optimize	VERB
cana-6293	168	8	random	random	ADJ
cana-6293	168	9	forest	forest	NOUN
cana-6293	168	10	achieved	achieve	VERB
cana-6293	168	11	the	the	DET
cana-6293	168	12	most	most	ADV
cana-6293	168	13	promising	promising	ADJ
cana-6293	168	14	results	result	NOUN
cana-6293	168	15	,	,	PUNCT
cana-6293	168	16	making	make	VERB
cana-6293	168	17	it	it	PRON
cana-6293	168	18	the	the	DET
cana-6293	168	19	bestperforming	bestperforme	VERB
cana-6293	168	20	model	model	NOUN
cana-6293	168	21	for	for	ADP
cana-6293	168	22	predicting	predict	VERB
cana-6293	168	23	paddy	paddy	NOUN
cana-6293	168	24	crop	crop	NOUN
cana-6293	168	25	growth	growth	NOUN
cana-6293	168	26	.	.	PUNCT
cana-6293	169	1	the	the	DET
cana-6293	169	2	study	study	NOUN
cana-6293	169	3	demonstrates	demonstrate	VERB
cana-6293	169	4	that	that	SCONJ
cana-6293	169	5	integrating	integrate	VERB
cana-6293	169	6	optimization	optimization	NOUN
cana-6293	169	7	algorithms	algorithm	NOUN
cana-6293	169	8	such	such	ADJ
cana-6293	169	9	as	as	ADP
cana-6293	169	10	pso	pso	NOUN
cana-6293	169	11	and	and	CCONJ
cana-6293	169	12	ga	ga	PROPN
cana-6293	169	13	with	with	ADP
cana-6293	169	14	machine	machine	NOUN
cana-6293	169	15	learning	learning	NOUN
cana-6293	169	16	can	can	AUX
cana-6293	169	17	lead	lead	VERB
cana-6293	169	18	to	to	ADP
cana-6293	169	19	better	well	ADJ
cana-6293	169	20	parameter	parameter	NOUN
cana-6293	169	21	tuning	tuning	NOUN
cana-6293	169	22	and	and	CCONJ
cana-6293	169	23	improved	improved	ADJ
cana-6293	169	24	prediction	prediction	NOUN
cana-6293	169	25	quality	quality	NOUN
cana-6293	169	26	.	.	PUNCT
cana-6293	170	1	these	these	DET
cana-6293	170	2	findings	finding	NOUN
cana-6293	170	3	can	can	AUX
cana-6293	170	4	support	support	VERB
cana-6293	170	5	farmers	farmer	NOUN
cana-6293	170	6	and	and	CCONJ
cana-6293	170	7	agricultural	agricultural	ADJ
cana-6293	170	8	planners	planner	NOUN
cana-6293	170	9	in	in	ADP
cana-6293	170	10	making	make	VERB
cana-6293	170	11	timely	timely	ADJ
cana-6293	170	12	and	and	CCONJ
cana-6293	170	13	informed	informed	ADJ
cana-6293	170	14	decisions	decision	NOUN
cana-6293	170	15	,	,	PUNCT
cana-6293	170	16	ultimately	ultimately	ADV
cana-6293	170	17	contributing	contribute	VERB
cana-6293	170	18	to	to	ADP
cana-6293	170	19	more	more	ADV
cana-6293	170	20	efficient	efficient	ADJ
cana-6293	170	21	and	and	CCONJ
cana-6293	170	22	data	data	NOUN
cana-6293	170	23	-	-	PUNCT
cana-6293	170	24	driven	drive	VERB
cana-6293	170	25	agricultural	agricultural	ADJ
cana-6293	170	26	practices	practice	NOUN
cana-6293	170	27	.	.	PUNCT
cana-6293	171	1	6	6	X
cana-6293	171	2	.	.	X
cana-6293	171	3	future	future	ADJ
cana-6293	171	4	research	research	NOUN
cana-6293	171	5	future	future	ADJ
cana-6293	171	6	research	research	NOUN
cana-6293	171	7	can	can	AUX
cana-6293	171	8	expand	expand	VERB
cana-6293	171	9	upon	upon	SCONJ
cana-6293	171	10	this	this	DET
cana-6293	171	11	study	study	NOUN
cana-6293	171	12	by	by	ADP
cana-6293	171	13	incorporating	incorporate	VERB
cana-6293	171	14	larger	large	ADJ
cana-6293	171	15	,	,	PUNCT
cana-6293	171	16	multi	multi	ADJ
cana-6293	171	17	-	-	NOUN
cana-6293	171	18	season	season	NOUN
cana-6293	171	19	,	,	PUNCT
cana-6293	171	20	and	and	CCONJ
cana-6293	171	21	real	real	ADJ
cana-6293	171	22	-	-	PUNCT
cana-6293	171	23	time	time	NOUN
cana-6293	171	24	datasets	dataset	NOUN
cana-6293	171	25	to	to	PART
cana-6293	171	26	strengthen	strengthen	VERB
cana-6293	171	27	model	model	NOUN
cana-6293	171	28	accuracy	accuracy	NOUN
cana-6293	171	29	across	across	ADP
cana-6293	171	30	different	different	ADJ
cana-6293	171	31	environmental	environmental	ADJ
cana-6293	171	32	conditions	condition	NOUN
cana-6293	171	33	.	.	PUNCT
cana-6293	172	1	advanced	advanced	ADJ
cana-6293	172	2	deep	deep	ADJ
cana-6293	172	3	learning	learning	NOUN
cana-6293	172	4	models	model	NOUN
cana-6293	172	5	,	,	PUNCT
cana-6293	172	6	such	such	ADJ
cana-6293	172	7	as	as	ADP
cana-6293	172	8	lstm	lstm	PROPN
cana-6293	172	9	,	,	PUNCT
cana-6293	172	10	gru	gru	PROPN
cana-6293	172	11	,	,	PUNCT
cana-6293	172	12	and	and	CCONJ
cana-6293	172	13	cnn	cnn	PROPN
cana-6293	172	14	-	-	PUNCT
cana-6293	172	15	based	base	VERB
cana-6293	172	16	architectures	architecture	NOUN
cana-6293	172	17	,	,	PUNCT
cana-6293	172	18	can	can	AUX
cana-6293	172	19	be	be	AUX
cana-6293	172	20	explored	explore	VERB
cana-6293	172	21	to	to	PART
cana-6293	172	22	capture	capture	VERB
cana-6293	172	23	more	more	ADV
cana-6293	172	24	complex	complex	ADJ
cana-6293	172	25	patterns	pattern	NOUN
cana-6293	172	26	in	in	ADP
cana-6293	172	27	crop	crop	NOUN
cana-6293	172	28	growth	growth	NOUN
cana-6293	172	29	.	.	PUNCT
cana-6293	173	1	additional	additional	ADJ
cana-6293	173	2	optimization	optimization	NOUN
cana-6293	173	3	methods	method	NOUN
cana-6293	173	4	,	,	PUNCT
cana-6293	173	5	including	include	VERB
cana-6293	173	6	grey	grey	ADJ
cana-6293	173	7	wolf	wolf	PROPN
cana-6293	173	8	optimizer	optimizer	NOUN
cana-6293	173	9	,	,	PUNCT
cana-6293	173	10	whale	whale	NOUN
cana-6293	173	11	optimization	optimization	NOUN
cana-6293	173	12	algorithm	algorithm	NOUN
cana-6293	173	13	,	,	PUNCT
cana-6293	173	14	and	and	CCONJ
cana-6293	173	15	firefly	firefly	NOUN
cana-6293	173	16	algorithm	algorithm	NOUN
cana-6293	173	17	,	,	PUNCT
cana-6293	173	18	may	may	AUX
cana-6293	173	19	further	far	ADV
cana-6293	173	20	improve	improve	VERB
cana-6293	173	21	the	the	DET
cana-6293	173	22	predictive	predictive	ADJ
cana-6293	173	23	capability	capability	NOUN
cana-6293	173	24	of	of	ADP
cana-6293	173	25	machine	machine	NOUN
cana-6293	173	26	learning	learning	NOUN
cana-6293	173	27	systems	system	NOUN
cana-6293	173	28	.	.	PUNCT
cana-6293	174	1	integrating	integrate	VERB
cana-6293	174	2	these	these	DET
cana-6293	174	3	models	model	NOUN
cana-6293	174	4	into	into	ADP
cana-6293	174	5	real	real	ADJ
cana-6293	174	6	-	-	PUNCT
cana-6293	174	7	time	time	NOUN
cana-6293	174	8	smart	smart	ADJ
cana-6293	174	9	farming	farming	NOUN
cana-6293	174	10	decision	decision	NOUN
cana-6293	174	11	systems	system	NOUN
cana-6293	174	12	can	can	AUX
cana-6293	174	13	enhance	enhance	VERB
cana-6293	174	14	the	the	DET
cana-6293	174	15	practical	practical	ADJ
cana-6293	174	16	usefulness	usefulness	NOUN
cana-6293	174	17	of	of	ADP
cana-6293	174	18	this	this	DET
cana-6293	174	19	research	research	NOUN
cana-6293	174	20	.	.	PUNCT
cana-6293	175	1	future	future	ADJ
cana-6293	175	2	studies	study	NOUN
cana-6293	175	3	may	may	AUX
cana-6293	175	4	also	also	ADV
cana-6293	175	5	include	include	VERB
cana-6293	175	6	more	more	ADV
cana-6293	175	7	diverse	diverse	ADJ
cana-6293	175	8	features	feature	NOUN
cana-6293	175	9	such	such	ADJ
cana-6293	175	10	as	as	ADP
cana-6293	175	11	soil	soil	NOUN
cana-6293	175	12	nutrients	nutrient	NOUN
cana-6293	175	13	,	,	PUNCT
cana-6293	175	14	pest	pest	VERB
cana-6293	175	15	attack	attack	NOUN
cana-6293	175	16	data	datum	NOUN
cana-6293	175	17	,	,	PUNCT
cana-6293	175	18	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-6293	175	19	communications	communication	NOUN
cana-6293	175	20	on	on	ADP
cana-6293	175	21	applied	apply	VERB
cana-6293	175	22	nonlinear	nonlinear	ADJ
cana-6293	175	23	analysis	analysis	NOUN
cana-6293	175	24	issn	issn	NOUN
cana-6293	175	25	:	:	PUNCT
cana-6293	175	26	1074	1074	NUM
cana-6293	175	27	-	-	PUNCT
cana-6293	175	28	133x	133x	NUM
cana-6293	175	29	vol	vol	NOUN
cana-6293	175	30	32	32	NUM
cana-6293	175	31	no	no	NOUN
cana-6293	175	32	.	.	PUNCT
cana-6293	176	1	2s	2s	NUM
cana-6293	176	2	(	(	PUNCT
cana-6293	176	3	2025	2025	NUM
cana-6293	176	4	)	)	PUNCT
cana-6293	176	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	176	6	647	647	NUM
cana-6293	176	7	and	and	CCONJ
cana-6293	176	8	drone	drone	NOUN
cana-6293	176	9	-	-	PUNCT
cana-6293	176	10	based	base	VERB
cana-6293	176	11	imagery	imagery	NOUN
cana-6293	176	12	to	to	PART
cana-6293	176	13	provide	provide	VERB
cana-6293	176	14	richer	rich	ADJ
cana-6293	176	15	inputs	input	NOUN
cana-6293	176	16	for	for	ADP
cana-6293	176	17	prediction	prediction	NOUN
cana-6293	176	18	.	.	PUNCT
cana-6293	177	1	combining	combine	VERB
cana-6293	177	2	deep	deep	ADJ
cana-6293	177	3	learning	learning	NOUN
cana-6293	177	4	with	with	ADP
cana-6293	177	5	advanced	advanced	ADJ
cana-6293	177	6	optimization	optimization	NOUN
cana-6293	177	7	techniques	technique	NOUN
cana-6293	177	8	may	may	AUX
cana-6293	177	9	lead	lead	VERB
cana-6293	177	10	to	to	ADP
cana-6293	177	11	even	even	ADV
cana-6293	177	12	stronger	strong	ADJ
cana-6293	177	13	hybrid	hybrid	ADJ
cana-6293	177	14	models	model	NOUN
cana-6293	177	15	capable	capable	ADJ
cana-6293	177	16	of	of	ADP
cana-6293	177	17	delivering	deliver	VERB
cana-6293	177	18	highly	highly	ADV
cana-6293	177	19	accurate	accurate	ADJ
cana-6293	177	20	paddy	paddy	NOUN
cana-6293	177	21	growth	growth	NOUN
cana-6293	177	22	predictions	prediction	NOUN
cana-6293	177	23	.	.	PUNCT
cana-6293	178	1	reference	reference	NOUN
cana-6293	178	2	[	[	X
cana-6293	178	3	1	1	X
cana-6293	178	4	]	]	PUNCT
cana-6293	178	5	p.	p.	NOUN
cana-6293	178	6	patil	patil	PROPN
cana-6293	178	7	and	and	CCONJ
cana-6293	178	8	n.	n.	PROPN
cana-6293	178	9	kumar	kumar	PROPN
cana-6293	178	10	,	,	PUNCT
cana-6293	178	11	“	"	PUNCT
cana-6293	178	12	machine	machine	NOUN
cana-6293	178	13	learning	learn	VERB
cana-6293	178	14	techniques	technique	NOUN
cana-6293	178	15	for	for	ADP
cana-6293	178	16	crop	crop	NOUN
cana-6293	178	17	yield	yield	NOUN
cana-6293	178	18	prediction	prediction	NOUN
cana-6293	178	19	,	,	PUNCT
cana-6293	178	20	”	"	PUNCT
cana-6293	178	21	agricultural	agricultural	ADJ
cana-6293	178	22	informatics	informatic	NOUN
cana-6293	178	23	journal	journal	NOUN
cana-6293	178	24	,	,	PUNCT
cana-6293	178	25	vol	vol	NOUN
cana-6293	178	26	.	.	PROPN
cana-6293	178	27	9	9	NUM
cana-6293	178	28	,	,	PUNCT
cana-6293	178	29	no	no	INTJ
cana-6293	178	30	.	.	NOUN
cana-6293	178	31	2	2	NUM
cana-6293	178	32	,	,	PUNCT
cana-6293	178	33	pp	pp	ADJ
cana-6293	178	34	.	.	PUNCT
cana-6293	179	1	45–52	45–52	NUM
cana-6293	179	2	,	,	PUNCT
cana-6293	179	3	2019	2019	NUM
cana-6293	179	4	.	.	PUNCT
cana-6293	180	1	[	[	X
cana-6293	180	2	2	2	NUM
cana-6293	180	3	]	]	PUNCT
cana-6293	180	4	x.	x.	NOUN
cana-6293	180	5	li	li	PROPN
cana-6293	180	6	,	,	PUNCT
cana-6293	180	7	y.	y.	PROPN
cana-6293	180	8	chen	chen	PROPN
cana-6293	180	9	,	,	PUNCT
cana-6293	180	10	and	and	CCONJ
cana-6293	180	11	l.	l.	PROPN
cana-6293	180	12	zhang	zhang	PROPN
cana-6293	180	13	,	,	PUNCT
cana-6293	180	14	“	"	PUNCT
cana-6293	180	15	deep	deep	ADJ
cana-6293	180	16	learning	learning	NOUN
cana-6293	180	17	for	for	ADP
cana-6293	180	18	rice	rice	NOUN
cana-6293	180	19	growth	growth	NOUN
cana-6293	180	20	monitoring	monitoring	NOUN
cana-6293	180	21	using	use	VERB
cana-6293	180	22	remote	remote	ADJ
cana-6293	180	23	sensing	sensing	NOUN
cana-6293	180	24	images	image	NOUN
cana-6293	180	25	,	,	PUNCT
cana-6293	180	26	”	"	PUNCT
cana-6293	180	27	remote	remote	ADJ
cana-6293	180	28	sensing	sensing	NOUN
cana-6293	180	29	,	,	PUNCT
cana-6293	180	30	vol	vol	NOUN
cana-6293	180	31	.	.	PROPN
cana-6293	180	32	12	12	NUM
cana-6293	180	33	,	,	PUNCT
cana-6293	180	34	no	no	INTJ
cana-6293	180	35	.	.	NOUN
cana-6293	180	36	5	5	NUM
cana-6293	180	37	,	,	PUNCT
cana-6293	180	38	pp	pp	ADJ
cana-6293	180	39	.	.	PUNCT
cana-6293	181	1	1–15	1–15	NUM
cana-6293	181	2	,	,	PUNCT
cana-6293	181	3	2020	2020	NUM
cana-6293	181	4	.	.	PUNCT
cana-6293	182	1	[	[	X
cana-6293	182	2	3	3	X
cana-6293	182	3	]	]	X
cana-6293	182	4	s.	s.	PROPN
cana-6293	182	5	ramesh	ramesh	PROPN
cana-6293	182	6	and	and	CCONJ
cana-6293	182	7	t.	t.	PROPN
cana-6293	182	8	devi	devi	PROPN
cana-6293	182	9	,	,	PUNCT
cana-6293	182	10	“	"	PUNCT
cana-6293	182	11	impact	impact	NOUN
cana-6293	182	12	of	of	ADP
cana-6293	182	13	weather	weather	NOUN
cana-6293	182	14	variables	variable	NOUN
cana-6293	182	15	on	on	ADP
cana-6293	182	16	rice	rice	NOUN
cana-6293	182	17	production	production	NOUN
cana-6293	182	18	:	:	PUNCT
cana-6293	182	19	a	a	DET
cana-6293	182	20	predictive	predictive	ADJ
cana-6293	182	21	analysis	analysis	NOUN
cana-6293	182	22	,	,	PUNCT
cana-6293	182	23	”	"	PUNCT
cana-6293	182	24	international	international	ADJ
cana-6293	182	25	journal	journal	NOUN
cana-6293	182	26	of	of	ADP
cana-6293	182	27	agricultural	agricultural	ADJ
cana-6293	182	28	science	science	NOUN
cana-6293	182	29	,	,	PUNCT
cana-6293	182	30	vol	vol	NOUN
cana-6293	182	31	.	.	PROPN
cana-6293	182	32	8	8	NUM
cana-6293	182	33	,	,	PUNCT
cana-6293	182	34	no	no	INTJ
cana-6293	182	35	.	.	NOUN
cana-6293	182	36	4	4	NUM
cana-6293	182	37	,	,	PUNCT
cana-6293	182	38	pp	pp	ADJ
cana-6293	182	39	.	.	PUNCT
cana-6293	183	1	101–110	101–110	NUM
cana-6293	183	2	,	,	PUNCT
cana-6293	183	3	2018	2018	NUM
cana-6293	183	4	.	.	PUNCT
cana-6293	184	1	[	[	X
cana-6293	184	2	4	4	X
cana-6293	184	3	]	]	PUNCT
cana-6293	184	4	j.	j.	PROPN
cana-6293	184	5	r.	r.	PROPN
cana-6293	184	6	quinlan	quinlan	PROPN
cana-6293	184	7	,	,	PUNCT
cana-6293	184	8	“	"	PUNCT
cana-6293	184	9	decision	decision	NOUN
cana-6293	184	10	tree	tree	NOUN
cana-6293	184	11	algorithms	algorithm	NOUN
cana-6293	184	12	and	and	CCONJ
cana-6293	184	13	their	their	PRON
cana-6293	184	14	applications	application	NOUN
cana-6293	184	15	in	in	ADP
cana-6293	184	16	agriculture	agriculture	NOUN
cana-6293	184	17	,	,	PUNCT
cana-6293	184	18	”	"	PUNCT
cana-6293	184	19	machine	machine	NOUN
cana-6293	184	20	learning	learning	NOUN
cana-6293	184	21	review	review	NOUN
cana-6293	184	22	,	,	PUNCT
cana-6293	184	23	vol	vol	NOUN
cana-6293	184	24	.	.	PROPN
cana-6293	184	25	3	3	NUM
cana-6293	184	26	,	,	PUNCT
cana-6293	184	27	no	no	INTJ
cana-6293	184	28	.	.	NOUN
cana-6293	184	29	1	1	NUM
cana-6293	184	30	,	,	PUNCT
cana-6293	184	31	pp	pp	ADJ
cana-6293	184	32	.	.	PUNCT
cana-6293	185	1	25–40	25–40	NUM
cana-6293	185	2	,	,	PUNCT
cana-6293	185	3	1993	1993	NUM
cana-6293	185	4	.	.	PUNCT
cana-6293	186	1	[	[	X
cana-6293	186	2	5	5	NUM
cana-6293	186	3	]	]	PUNCT
cana-6293	186	4	a.	a.	NOUN
cana-6293	186	5	ghosh	ghosh	PROPN
cana-6293	186	6	and	and	CCONJ
cana-6293	186	7	s.	s.	PROPN
cana-6293	186	8	bala	bala	PROPN
cana-6293	186	9	,	,	PUNCT
cana-6293	186	10	“	"	PUNCT
cana-6293	186	11	random	random	ADJ
cana-6293	186	12	forest	forest	NOUN
cana-6293	186	13	-	-	PUNCT
cana-6293	186	14	based	base	VERB
cana-6293	186	15	crop	crop	NOUN
cana-6293	186	16	yield	yield	NOUN
cana-6293	186	17	prediction	prediction	NOUN
cana-6293	186	18	using	use	VERB
cana-6293	186	19	environmental	environmental	ADJ
cana-6293	186	20	data	datum	NOUN
cana-6293	186	21	,	,	PUNCT
cana-6293	186	22	”	"	PUNCT
cana-6293	186	23	journal	journal	NOUN
cana-6293	186	24	of	of	ADP
cana-6293	186	25	agricultural	agricultural	ADJ
cana-6293	186	26	systems	system	NOUN
cana-6293	186	27	,	,	PUNCT
cana-6293	186	28	vol	vol	NOUN
cana-6293	186	29	.	.	PROPN
cana-6293	186	30	17	17	NUM
cana-6293	186	31	,	,	PUNCT
cana-6293	186	32	no	no	INTJ
cana-6293	186	33	.	.	NOUN
cana-6293	186	34	3	3	NUM
cana-6293	186	35	,	,	PUNCT
cana-6293	186	36	pp	pp	ADJ
cana-6293	186	37	.	.	PUNCT
cana-6293	187	1	214–225	214–225	NUM
cana-6293	187	2	,	,	PUNCT
cana-6293	187	3	2020	2020	NUM
cana-6293	187	4	.	.	PUNCT
cana-6293	188	1	[	[	X
cana-6293	188	2	6	6	NUM
cana-6293	188	3	]	]	PUNCT
cana-6293	188	4	k.	k.	PROPN
cana-6293	188	5	singh	singh	PROPN
cana-6293	188	6	and	and	CCONJ
cana-6293	188	7	a.	a.	NOUN
cana-6293	188	8	sharma	sharma	PROPN
cana-6293	188	9	,	,	PUNCT
cana-6293	188	10	“	"	PUNCT
cana-6293	188	11	support	support	NOUN
cana-6293	188	12	vector	vector	NOUN
cana-6293	188	13	machine	machine	NOUN
cana-6293	188	14	models	model	NOUN
cana-6293	188	15	for	for	ADP
cana-6293	188	16	agricultural	agricultural	ADJ
cana-6293	188	17	data	datum	NOUN
cana-6293	188	18	classification	classification	NOUN
cana-6293	188	19	,	,	PUNCT
cana-6293	188	20	”	"	PUNCT
cana-6293	188	21	computers	computer	NOUN
cana-6293	188	22	and	and	CCONJ
cana-6293	188	23	electronics	electronic	NOUN
cana-6293	188	24	in	in	ADP
cana-6293	188	25	agriculture	agriculture	NOUN
cana-6293	188	26	,	,	PUNCT
cana-6293	188	27	vol	vol	NOUN
cana-6293	188	28	.	.	PROPN
cana-6293	188	29	155	155	NUM
cana-6293	188	30	,	,	PUNCT
cana-6293	188	31	pp	pp	ADJ
cana-6293	188	32	.	.	PUNCT
cana-6293	189	1	371–375	371–375	NUM
cana-6293	189	2	,	,	PUNCT
cana-6293	189	3	2018	2018	NUM
cana-6293	189	4	.	.	PUNCT
cana-6293	190	1	[	[	X
cana-6293	190	2	7	7	X
cana-6293	190	3	]	]	X
cana-6293	190	4	j.	j.	PROPN
cana-6293	190	5	kennedy	kennedy	PROPN
cana-6293	190	6	and	and	CCONJ
cana-6293	190	7	r.	r.	PROPN
cana-6293	190	8	eberhart	eberhart	PROPN
cana-6293	190	9	,	,	PUNCT
cana-6293	190	10	“	"	PUNCT
cana-6293	190	11	particle	particle	NOUN
cana-6293	190	12	swarm	swarm	NOUN
cana-6293	190	13	optimization	optimization	NOUN
cana-6293	190	14	,	,	PUNCT
cana-6293	190	15	”	"	PUNCT
cana-6293	190	16	in	in	ADP
cana-6293	190	17	proc	proc	NOUN
cana-6293	190	18	.	.	PUNCT
cana-6293	191	1	ieee	ieee	PROPN
cana-6293	191	2	int	int	PROPN
cana-6293	191	3	.	.	PUNCT
cana-6293	192	1	conf	conf	PROPN
cana-6293	192	2	.	.	PUNCT
cana-6293	193	1	neural	neural	ADJ
cana-6293	193	2	networks	network	NOUN
cana-6293	193	3	,	,	PUNCT
cana-6293	193	4	1995	1995	NUM
cana-6293	193	5	,	,	PUNCT
cana-6293	193	6	pp	pp	ADJ
cana-6293	193	7	.	.	PUNCT
cana-6293	194	1	1942–1948	1942–1948	NUM
cana-6293	194	2	.	.	PUNCT
cana-6293	195	1	[	[	X
cana-6293	195	2	8	8	NUM
cana-6293	195	3	]	]	X
cana-6293	195	4	d.	d.	PROPN
cana-6293	195	5	goldberg	goldberg	PROPN
cana-6293	195	6	,	,	PUNCT
cana-6293	195	7	genetic	genetic	ADJ
cana-6293	195	8	algorithms	algorithm	NOUN
cana-6293	195	9	in	in	ADP
cana-6293	195	10	search	search	NOUN
cana-6293	195	11	,	,	PUNCT
cana-6293	195	12	optimization	optimization	NOUN
cana-6293	195	13	and	and	CCONJ
cana-6293	195	14	machine	machine	NOUN
cana-6293	195	15	learning	learning	NOUN
cana-6293	195	16	.	.	PUNCT
cana-6293	196	1	addison	addison	PROPN
cana-6293	196	2	-	-	PUNCT
cana-6293	196	3	wesley	wesley	PROPN
cana-6293	196	4	,	,	PUNCT
cana-6293	196	5	1989	1989	NUM
cana-6293	196	6	.	.	PUNCT
cana-6293	197	1	[	[	X
cana-6293	197	2	9	9	NUM
cana-6293	197	3	]	]	PUNCT
cana-6293	197	4	m.	m.	NOUN
cana-6293	197	5	tariq	tariq	NOUN
cana-6293	197	6	and	and	CCONJ
cana-6293	197	7	a.	a.	NOUN
cana-6293	197	8	ahmad	ahmad	PROPN
cana-6293	197	9	,	,	PUNCT
cana-6293	197	10	“	"	PUNCT
cana-6293	197	11	hybrid	hybrid	ADJ
cana-6293	197	12	pso	pso	NOUN
cana-6293	197	13	-	-	PUNCT
cana-6293	197	14	ml	ml	NOUN
cana-6293	197	15	models	model	NOUN
cana-6293	197	16	for	for	ADP
cana-6293	197	17	crop	crop	NOUN
cana-6293	197	18	forecasting	forecasting	NOUN
cana-6293	197	19	,	,	PUNCT
cana-6293	197	20	”	"	PUNCT
cana-6293	197	21	journal	journal	NOUN
cana-6293	197	22	of	of	ADP
cana-6293	197	23	intelligent	intelligent	ADJ
cana-6293	197	24	agriculture	agriculture	NOUN
cana-6293	197	25	,	,	PUNCT
cana-6293	197	26	vol	vol	NOUN
cana-6293	197	27	.	.	PROPN
cana-6293	197	28	5	5	NUM
cana-6293	197	29	,	,	PUNCT
cana-6293	197	30	no	no	INTJ
cana-6293	197	31	.	.	NOUN
cana-6293	197	32	2	2	NUM
cana-6293	197	33	,	,	PUNCT
cana-6293	197	34	pp	pp	ADJ
cana-6293	197	35	.	.	PUNCT
cana-6293	197	36	80–89	80–89	NUM
cana-6293	197	37	,	,	PUNCT
cana-6293	197	38	2021	2021	NUM
cana-6293	197	39	.	.	PUNCT
cana-6293	198	1	[	[	X
cana-6293	198	2	10	10	NUM
cana-6293	198	3	]	]	X
cana-6293	198	4	y.	y.	PROPN
cana-6293	198	5	sun	sun	PROPN
cana-6293	198	6	,	,	PUNCT
cana-6293	198	7	f.	f.	PROPN
cana-6293	198	8	li	li	PROPN
cana-6293	198	9	,	,	PUNCT
cana-6293	198	10	and	and	CCONJ
cana-6293	198	11	w.	w.	PROPN
cana-6293	198	12	chang	chang	PROPN
cana-6293	198	13	,	,	PUNCT
cana-6293	198	14	“	"	PUNCT
cana-6293	198	15	soil	soil	NOUN
cana-6293	198	16	nutrient	nutrient	NOUN
cana-6293	198	17	influence	influence	NOUN
cana-6293	198	18	on	on	ADP
cana-6293	198	19	rice	rice	NOUN
cana-6293	198	20	growth	growth	NOUN
cana-6293	198	21	:	:	PUNCT
cana-6293	198	22	a	a	DET
cana-6293	198	23	computational	computational	ADJ
cana-6293	198	24	analysis	analysis	NOUN
cana-6293	198	25	,	,	PUNCT
cana-6293	198	26	”	"	PUNCT
cana-6293	198	27	soil	soil	NOUN
cana-6293	198	28	&	&	CCONJ
cana-6293	198	29	crop	crop	PROPN
cana-6293	198	30	science	science	PROPN
cana-6293	198	31	journal	journal	PROPN
cana-6293	198	32	,	,	PUNCT
cana-6293	198	33	vol	vol	NOUN
cana-6293	198	34	.	.	PROPN
cana-6293	198	35	14	14	NUM
cana-6293	198	36	,	,	PUNCT
cana-6293	198	37	no	no	INTJ
cana-6293	198	38	.	.	NOUN
cana-6293	198	39	1	1	NUM
cana-6293	198	40	,	,	PUNCT
cana-6293	198	41	pp	pp	ADJ
cana-6293	198	42	.	.	PUNCT
cana-6293	199	1	32–41	32–41	NUM
cana-6293	199	2	,	,	PUNCT
cana-6293	199	3	2020	2020	NUM
cana-6293	199	4	.	.	PUNCT
cana-6293	200	1	[	[	X
cana-6293	200	2	11	11	NUM
cana-6293	200	3	]	]	PUNCT
cana-6293	200	4	p.	p.	NOUN
cana-6293	200	5	rai	rai	PROPN
cana-6293	200	6	and	and	CCONJ
cana-6293	200	7	h.	h.	PROPN
cana-6293	200	8	verma	verma	PROPN
cana-6293	200	9	,	,	PUNCT
cana-6293	200	10	“	"	PUNCT
cana-6293	200	11	rainfall	rainfall	NOUN
cana-6293	200	12	prediction	prediction	NOUN
cana-6293	200	13	models	model	NOUN
cana-6293	200	14	using	use	VERB
cana-6293	200	15	machine	machine	NOUN
cana-6293	200	16	learning	learning	NOUN
cana-6293	200	17	techniques	technique	NOUN
cana-6293	200	18	,	,	PUNCT
cana-6293	200	19	”	"	PUNCT
cana-6293	200	20	international	international	ADJ
cana-6293	200	21	journal	journal	NOUN
cana-6293	200	22	of	of	ADP
cana-6293	200	23	climate	climate	NOUN
cana-6293	200	24	studies	study	NOUN
cana-6293	200	25	,	,	PUNCT
cana-6293	200	26	vol	vol	NOUN
cana-6293	200	27	.	.	PROPN
cana-6293	200	28	6	6	NUM
cana-6293	200	29	,	,	PUNCT
cana-6293	200	30	no	no	INTJ
cana-6293	200	31	.	.	NOUN
cana-6293	200	32	2	2	NUM
cana-6293	200	33	,	,	PUNCT
cana-6293	200	34	pp	pp	ADJ
cana-6293	200	35	.	.	PUNCT
cana-6293	201	1	50–60	50–60	NUM
cana-6293	201	2	,	,	PUNCT
cana-6293	201	3	2019	2019	NUM
cana-6293	201	4	.	.	PUNCT
cana-6293	202	1	[	[	X
cana-6293	202	2	12	12	NUM
cana-6293	202	3	]	]	PUNCT
cana-6293	202	4	r.	r.	PROPN
cana-6293	202	5	mehta	mehta	PROPN
cana-6293	202	6	and	and	CCONJ
cana-6293	202	7	p.	p.	PROPN
cana-6293	202	8	jain	jain	PROPN
cana-6293	202	9	,	,	PUNCT
cana-6293	202	10	“	"	PUNCT
cana-6293	202	11	remote	remote	ADJ
cana-6293	202	12	sensing	sensing	NOUN
cana-6293	202	13	for	for	ADP
cana-6293	202	14	crop	crop	NOUN
cana-6293	202	15	monitoring	monitoring	NOUN
cana-6293	202	16	:	:	PUNCT
cana-6293	202	17	a	a	DET
cana-6293	202	18	deep	deep	ADJ
cana-6293	202	19	learning	learning	NOUN
cana-6293	202	20	perspective	perspective	NOUN
cana-6293	202	21	,	,	PUNCT
cana-6293	202	22	”	"	PUNCT
cana-6293	202	23	ieee	ieee	NOUN
cana-6293	202	24	geoscience	geoscience	NOUN
cana-6293	202	25	and	and	CCONJ
cana-6293	202	26	remote	remote	ADJ
cana-6293	202	27	sensing	sense	VERB
cana-6293	202	28	letters	letter	NOUN
cana-6293	202	29	,	,	PUNCT
cana-6293	202	30	vol	vol	NOUN
cana-6293	202	31	.	.	PROPN
cana-6293	202	32	18	18	NUM
cana-6293	202	33	,	,	PUNCT
cana-6293	202	34	no	no	INTJ
cana-6293	202	35	.	.	NOUN
cana-6293	202	36	10	10	NUM
cana-6293	202	37	,	,	PUNCT
cana-6293	202	38	pp	pp	ADJ
cana-6293	202	39	.	.	PUNCT
cana-6293	203	1	1–5	1–5	NUM
cana-6293	203	2	,	,	PUNCT
cana-6293	203	3	2021	2021	NUM
cana-6293	203	4	.	.	PUNCT
cana-6293	204	1	[	[	X
cana-6293	204	2	13	13	NUM
cana-6293	204	3	]	]	PUNCT
cana-6293	204	4	s.	s.	PROPN
cana-6293	204	5	banerjee	banerjee	PROPN
cana-6293	204	6	,	,	PUNCT
cana-6293	204	7	“	"	PUNCT
cana-6293	204	8	role	role	NOUN
cana-6293	204	9	of	of	ADP
cana-6293	204	10	climate	climate	NOUN
cana-6293	204	11	variables	variable	NOUN
cana-6293	204	12	in	in	ADP
cana-6293	204	13	rice	rice	NOUN
cana-6293	204	14	yield	yield	NOUN
cana-6293	204	15	prediction	prediction	NOUN
cana-6293	204	16	,	,	PUNCT
cana-6293	204	17	”	"	PUNCT
cana-6293	204	18	environmental	environmental	ADJ
cana-6293	204	19	data	data	PROPN
cana-6293	204	20	science	science	PROPN
cana-6293	204	21	review	review	PROPN
cana-6293	204	22	,	,	PUNCT
cana-6293	204	23	vol	vol	NOUN
cana-6293	204	24	.	.	PROPN
cana-6293	204	25	11	11	NUM
cana-6293	204	26	,	,	PUNCT
cana-6293	204	27	no	no	INTJ
cana-6293	204	28	.	.	NOUN
cana-6293	204	29	3	3	NUM
cana-6293	204	30	,	,	PUNCT
cana-6293	204	31	pp	pp	ADJ
cana-6293	204	32	.	.	PUNCT
cana-6293	205	1	122–130	122–130	NUM
cana-6293	205	2	,	,	PUNCT
cana-6293	205	3	2018	2018	NUM
cana-6293	205	4	.	.	PUNCT
cana-6293	206	1	[	[	X
cana-6293	206	2	14	14	NUM
cana-6293	206	3	]	]	PUNCT
cana-6293	206	4	m.	m.	NOUN
cana-6293	206	5	chen	chen	PROPN
cana-6293	206	6	and	and	CCONJ
cana-6293	206	7	x.	x.	PROPN
cana-6293	206	8	wu	wu	PROPN
cana-6293	206	9	,	,	PUNCT
cana-6293	206	10	“	"	PUNCT
cana-6293	206	11	lstm	lstm	NOUN
cana-6293	206	12	-	-	PUNCT
cana-6293	206	13	based	base	VERB
cana-6293	206	14	agricultural	agricultural	ADJ
cana-6293	206	15	time	time	NOUN
cana-6293	206	16	series	series	NOUN
cana-6293	206	17	modeling	modeling	NOUN
cana-6293	206	18	for	for	ADP
cana-6293	206	19	crop	crop	NOUN
cana-6293	206	20	growth	growth	NOUN
cana-6293	206	21	prediction	prediction	NOUN
cana-6293	206	22	,	,	PUNCT
cana-6293	206	23	”	"	PUNCT
cana-6293	206	24	information	information	NOUN
cana-6293	206	25	processing	processing	NOUN
cana-6293	206	26	in	in	ADP
cana-6293	206	27	agriculture	agriculture	NOUN
cana-6293	206	28	,	,	PUNCT
cana-6293	206	29	vol	vol	NOUN
cana-6293	206	30	.	.	PROPN
cana-6293	206	31	8	8	NUM
cana-6293	206	32	,	,	PUNCT
cana-6293	206	33	no	no	INTJ
cana-6293	206	34	.	.	NOUN
cana-6293	206	35	2	2	NUM
cana-6293	206	36	,	,	PUNCT
cana-6293	206	37	pp	pp	ADJ
cana-6293	206	38	.	.	PUNCT
cana-6293	207	1	212–220	212–220	NUM
cana-6293	207	2	,	,	PUNCT
cana-6293	207	3	2021	2021	NUM
cana-6293	207	4	.	.	PUNCT
cana-6293	208	1	[	[	X
cana-6293	208	2	15	15	NUM
cana-6293	208	3	]	]	X
cana-6293	208	4	r.	r.	PROPN
cana-6293	208	5	gupta	gupta	PROPN
cana-6293	208	6	and	and	CCONJ
cana-6293	208	7	v.	v.	ADP
cana-6293	208	8	jain	jain	PROPN
cana-6293	208	9	,	,	PUNCT
cana-6293	208	10	“	"	PUNCT
cana-6293	208	11	ensemble	ensemble	ADJ
cana-6293	208	12	learning	learning	NOUN
cana-6293	208	13	for	for	ADP
cana-6293	208	14	improved	improved	ADJ
cana-6293	208	15	crop	crop	NOUN
cana-6293	208	16	yield	yield	NOUN
cana-6293	208	17	forecasting	forecasting	NOUN
cana-6293	208	18	,	,	PUNCT
cana-6293	208	19	”	"	PUNCT
cana-6293	208	20	ai	ai	VERB
cana-6293	208	21	in	in	ADP
cana-6293	208	22	agriculture	agriculture	NOUN
cana-6293	208	23	,	,	PUNCT
cana-6293	208	24	vol	vol	NOUN
cana-6293	208	25	.	.	PROPN
cana-6293	208	26	5	5	NUM
cana-6293	208	27	,	,	PUNCT
cana-6293	208	28	pp	pp	ADJ
cana-6293	208	29	.	.	PUNCT
cana-6293	209	1	36–45	36–45	NUM
cana-6293	209	2	,	,	PUNCT
cana-6293	209	3	2020	2020	NUM
cana-6293	209	4	.	.	PUNCT
cana-6293	210	1	[	[	X
cana-6293	210	2	16	16	NUM
cana-6293	210	3	]	]	X
cana-6293	210	4	s.	s.	PROPN
cana-6293	210	5	das	das	PROPN
cana-6293	210	6	and	and	CCONJ
cana-6293	210	7	p.	p.	PROPN
cana-6293	210	8	samanta	samanta	PROPN
cana-6293	210	9	,	,	PUNCT
cana-6293	210	10	“	"	PUNCT
cana-6293	210	11	feature	feature	NOUN
cana-6293	210	12	selection	selection	NOUN
cana-6293	210	13	techniques	technique	NOUN
cana-6293	210	14	for	for	ADP
cana-6293	210	15	agricultural	agricultural	ADJ
cana-6293	210	16	data	datum	NOUN
cana-6293	210	17	analysis	analysis	NOUN
cana-6293	210	18	,	,	PUNCT
cana-6293	210	19	”	"	PUNCT
cana-6293	210	20	international	international	ADJ
cana-6293	210	21	journal	journal	NOUN
cana-6293	210	22	of	of	ADP
cana-6293	210	23	data	data	PROPN
cana-6293	210	24	mining	mining	NOUN
cana-6293	210	25	applications	application	NOUN
cana-6293	210	26	,	,	PUNCT
cana-6293	210	27	vol	vol	NOUN
cana-6293	210	28	.	.	PROPN
cana-6293	210	29	10	10	NUM
cana-6293	210	30	,	,	PUNCT
cana-6293	210	31	no	no	INTJ
cana-6293	210	32	.	.	NOUN
cana-6293	210	33	1	1	NUM
cana-6293	210	34	,	,	PUNCT
cana-6293	210	35	pp	pp	ADJ
cana-6293	210	36	.	.	PUNCT
cana-6293	211	1	55–64	55–64	NUM
cana-6293	211	2	,	,	PUNCT
cana-6293	211	3	2020	2020	NUM
cana-6293	211	4	.	.	PUNCT
cana-6293	212	1	[	[	X
cana-6293	212	2	17	17	NUM
cana-6293	212	3	]	]	PUNCT
cana-6293	212	4	l.	l.	PROPN
cana-6293	212	5	zhang	zhang	PROPN
cana-6293	212	6	and	and	CCONJ
cana-6293	212	7	y.	y.	PROPN
cana-6293	212	8	hu	hu	PROPN
cana-6293	212	9	,	,	PUNCT
cana-6293	212	10	“	"	PUNCT
cana-6293	212	11	hyperparameter	hyperparameter	NOUN
cana-6293	212	12	optimization	optimization	NOUN
cana-6293	212	13	for	for	ADP
cana-6293	212	14	agricultural	agricultural	ADJ
cana-6293	212	15	forecasting	forecasting	NOUN
cana-6293	212	16	models	model	NOUN
cana-6293	212	17	,	,	PUNCT
cana-6293	212	18	”	"	PUNCT
cana-6293	212	19	procedia	procedia	NOUN
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cana-6293	212	21	science	science	NOUN
cana-6293	212	22	,	,	PUNCT
cana-6293	212	23	vol	vol	NOUN
cana-6293	212	24	.	.	PROPN
cana-6293	212	25	170	170	NUM
cana-6293	212	26	,	,	PUNCT
cana-6293	212	27	pp	pp	ADJ
cana-6293	212	28	.	.	PUNCT
cana-6293	213	1	241–248	241–248	NUM
cana-6293	213	2	,	,	PUNCT
cana-6293	213	3	2020	2020	NUM
cana-6293	213	4	.	.	PUNCT
cana-6293	214	1	[	[	X
cana-6293	214	2	18	18	NUM
cana-6293	214	3	]	]	X
cana-6293	214	4	j.	j.	PROPN
cana-6293	214	5	kim	kim	PROPN
cana-6293	214	6	and	and	CCONJ
cana-6293	214	7	s.	s.	PROPN
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cana-6293	214	9	,	,	PUNCT
cana-6293	214	10	“	"	PUNCT
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cana-6293	214	12	neural	neural	ADJ
cana-6293	214	13	networks	network	NOUN
cana-6293	214	14	for	for	ADP
cana-6293	214	15	crop	crop	NOUN
cana-6293	214	16	yield	yield	NOUN
cana-6293	214	17	prediction	prediction	NOUN
cana-6293	214	18	under	under	ADP
cana-6293	214	19	climate	climate	NOUN
cana-6293	214	20	variability	variability	NOUN
cana-6293	214	21	,	,	PUNCT
cana-6293	214	22	”	"	PUNCT
cana-6293	214	23	applied	apply	VERB
cana-6293	214	24	soft	soft	ADJ
cana-6293	214	25	computing	computing	NOUN
cana-6293	214	26	,	,	PUNCT
cana-6293	214	27	vol	vol	NOUN
cana-6293	214	28	.	.	PROPN
cana-6293	214	29	98	98	NUM
cana-6293	214	30	,	,	PUNCT
cana-6293	214	31	pp	pp	ADJ
cana-6293	214	32	.	.	PUNCT
cana-6293	215	1	1–10	1–10	NOUN
cana-6293	215	2	,	,	PUNCT
cana-6293	215	3	2020	2020	NUM
cana-6293	215	4	.	.	PUNCT
cana-6293	216	1	[	[	X
cana-6293	216	2	19	19	NUM
cana-6293	216	3	]	]	PUNCT
cana-6293	216	4	a.	a.	NOUN
cana-6293	216	5	thomas	thomas	PROPN
cana-6293	216	6	and	and	CCONJ
cana-6293	216	7	r.	r.	PROPN
cana-6293	216	8	george	george	PROPN
cana-6293	216	9	,	,	PUNCT
cana-6293	216	10	“	"	PUNCT
cana-6293	216	11	machine	machine	NOUN
cana-6293	216	12	learning	learning	NOUN
cana-6293	216	13	in	in	ADP
cana-6293	216	14	smart	smart	ADJ
cana-6293	216	15	farming	farming	NOUN
cana-6293	216	16	:	:	PUNCT
cana-6293	216	17	a	a	DET
cana-6293	216	18	comprehensive	comprehensive	ADJ
cana-6293	216	19	review	review	NOUN
cana-6293	216	20	,	,	PUNCT
cana-6293	216	21	”	"	PUNCT
cana-6293	216	22	computational	computational	ADJ
cana-6293	216	23	agriculture	agriculture	NOUN
cana-6293	216	24	review	review	NOUN
cana-6293	216	25	,	,	PUNCT
cana-6293	216	26	vol	vol	NOUN
cana-6293	216	27	.	.	PROPN
cana-6293	216	28	7	7	NUM
cana-6293	216	29	,	,	PUNCT
cana-6293	216	30	no	no	INTJ
cana-6293	216	31	.	.	NOUN
cana-6293	216	32	1	1	NUM
cana-6293	216	33	,	,	PUNCT
cana-6293	216	34	pp	pp	ADJ
cana-6293	216	35	.	.	PUNCT
cana-6293	216	36	15–30	15–30	NUM
cana-6293	216	37	,	,	PUNCT
cana-6293	216	38	2021	2021	NUM
cana-6293	216	39	.	.	PUNCT
cana-6293	217	1	[	[	X
cana-6293	217	2	20	20	NUM
cana-6293	217	3	]	]	PUNCT
cana-6293	217	4	b.	b.	PROPN
cana-6293	217	5	fernando	fernando	PROPN
cana-6293	217	6	and	and	CCONJ
cana-6293	217	7	t.	t.	PROPN
cana-6293	217	8	silva	silva	PROPN
cana-6293	217	9	,	,	PUNCT
cana-6293	217	10	“	"	PUNCT
cana-6293	217	11	optimization	optimization	NOUN
cana-6293	217	12	-	-	PUNCT
cana-6293	217	13	based	base	VERB
cana-6293	217	14	agricultural	agricultural	ADJ
cana-6293	217	15	prediction	prediction	NOUN
cana-6293	217	16	models	model	NOUN
cana-6293	217	17	for	for	ADP
cana-6293	217	18	smart	smart	ADJ
cana-6293	217	19	farming	farming	NOUN
cana-6293	217	20	,	,	PUNCT
cana-6293	217	21	”	"	PUNCT
cana-6293	217	22	ieee	ieee	NOUN
cana-6293	217	23	access	access	NOUN
cana-6293	217	24	,	,	PUNCT
cana-6293	217	25	vol	vol	NOUN
cana-6293	217	26	.	.	NOUN
cana-6293	217	27	9	9	NUM
cana-6293	217	28	,	,	PUNCT
cana-6293	217	29	pp	pp	ADJ
cana-6293	217	30	.	.	PUNCT
cana-6293	218	1	155900–155912	155900–155912	NUM
cana-6293	218	2	,	,	PUNCT
cana-6293	218	3	2021	2021	NUM
cana-6293	218	4	.	.	PUNCT
cana-6293	219	1	[	[	X
cana-6293	219	2	21	21	NUM
cana-6293	219	3	]	]	X
cana-6293	219	4	g.	g.	PROPN
cana-6293	219	5	k.	k.	PROPN
cana-6293	219	6	arun	arun	PROPN
cana-6293	219	7	and	and	CCONJ
cana-6293	219	8	p.	p.	PROPN
cana-6293	219	9	rajesh	rajesh	PROPN
cana-6293	219	10	,	,	PUNCT
cana-6293	219	11	“	"	PUNCT
cana-6293	219	12	analysis	analysis	NOUN
cana-6293	219	13	and	and	CCONJ
cana-6293	219	14	prediction	prediction	NOUN
cana-6293	219	15	for	for	ADP
cana-6293	219	16	credit	credit	NOUN
cana-6293	219	17	card	card	NOUN
cana-6293	219	18	fraud	fraud	NOUN
cana-6293	219	19	detection	detection	NOUN
cana-6293	219	20	dataset	dataset	VERB
cana-6293	219	21	using	use	VERB
cana-6293	219	22	data	datum	NOUN
cana-6293	219	23	mining	mining	NOUN
cana-6293	219	24	approaches	approach	NOUN
cana-6293	219	25	,	,	PUNCT
cana-6293	219	26	”	"	PUNCT
cana-6293	219	27	international	international	ADJ
cana-6293	219	28	journal	journal	NOUN
cana-6293	219	29	of	of	ADP
cana-6293	219	30	health	health	PROPN
cana-6293	219	31	sciences	sciences	PROPN
cana-6293	219	32	,	,	PUNCT
cana-6293	219	33	vol	vol	NOUN
cana-6293	219	34	.	.	PROPN
cana-6293	219	35	6	6	NUM
cana-6293	219	36	,	,	PUNCT
cana-6293	219	37	no	no	NOUN
cana-6293	219	38	.	.	PUNCT
cana-6293	219	39	s5	s5	PROPN
cana-6293	219	40	,	,	PUNCT
cana-6293	219	41	pp	pp	PROPN
cana-6293	219	42	.	.	PUNCT
cana-6293	220	1	4155–4173	4155–4173	NUM
cana-6293	220	2	,	,	PUNCT
cana-6293	220	3	2022	2022	NUM
cana-6293	220	4	.	.	PUNCT
cana-6293	221	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-6293	221	2	communications	communication	NOUN
cana-6293	221	3	on	on	ADP
cana-6293	221	4	applied	apply	VERB
cana-6293	221	5	nonlinear	nonlinear	ADJ
cana-6293	221	6	analysis	analysis	NOUN
cana-6293	221	7	issn	issn	NOUN
cana-6293	221	8	:	:	PUNCT
cana-6293	221	9	1074	1074	NUM
cana-6293	221	10	-	-	PUNCT
cana-6293	221	11	133x	133x	NUM
cana-6293	221	12	vol	vol	NOUN
cana-6293	221	13	32	32	NUM
cana-6293	221	14	no	no	NOUN
cana-6293	221	15	.	.	PUNCT
cana-6293	222	1	2s	2s	NUM
cana-6293	222	2	(	(	PUNCT
cana-6293	222	3	2025	2025	NUM
cana-6293	222	4	)	)	PUNCT
cana-6293	222	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-6293	222	6	648	648	NUM
cana-6293	223	1	[	[	X
cana-6293	223	2	22	22	NUM
cana-6293	223	3	]	]	X
cana-6293	223	4	g.	g.	PROPN
cana-6293	223	5	k.	k.	PROPN
cana-6293	223	6	arun	arun	PROPN
cana-6293	223	7	and	and	CCONJ
cana-6293	223	8	p.	p.	PROPN
cana-6293	223	9	rajesh	rajesh	PROPN
cana-6293	223	10	,	,	PUNCT
cana-6293	223	11	“	"	PUNCT
cana-6293	223	12	hunger	hunger	NOUN
cana-6293	223	13	search	search	NOUN
cana-6293	223	14	algorithm	algorithm	NOUN
cana-6293	223	15	with	with	ADP
cana-6293	223	16	optimal	optimal	ADJ
cana-6293	223	17	deep	deep	ADJ
cana-6293	223	18	learning	learning	NOUN
cana-6293	223	19	driven	drive	VERB
cana-6293	223	20	credit	credit	NOUN
cana-6293	223	21	card	card	NOUN
cana-6293	223	22	fraud	fraud	NOUN
cana-6293	223	23	detection	detection	NOUN
cana-6293	223	24	and	and	CCONJ
cana-6293	223	25	classification	classification	NOUN
cana-6293	223	26	model	model	NOUN
cana-6293	223	27	,	,	PUNCT
cana-6293	223	28	”	"	PUNCT
cana-6293	223	29	mathematical	mathematical	ADJ
cana-6293	223	30	statistician	statistician	NOUN
cana-6293	223	31	and	and	CCONJ
cana-6293	223	32	engineering	engineering	NOUN
cana-6293	223	33	applications	application	NOUN
cana-6293	223	34	,	,	PUNCT
cana-6293	223	35	vol	vol	NOUN
cana-6293	223	36	.	.	PROPN
cana-6293	224	1	71	71	NUM
cana-6293	224	2	,	,	PUNCT
cana-6293	224	3	no	no	INTJ
cana-6293	224	4	.	.	NOUN
cana-6293	224	5	4	4	NUM
cana-6293	224	6	,	,	PUNCT
cana-6293	224	7	pp	pp	ADJ
cana-6293	224	8	.	.	PUNCT
cana-6293	225	1	387–406	387–406	NUM
cana-6293	225	2	,	,	PUNCT
cana-6293	225	3	2022	2022	NUM
cana-6293	225	4	.	.	PUNCT
cana-6293	226	1	[	[	X
cana-6293	226	2	23	23	NUM
cana-6293	226	3	]	]	PUNCT
cana-6293	226	4	p.	p.	NOUN
cana-6293	226	5	rajesh	rajesh	PROPN
cana-6293	226	6	and	and	CCONJ
cana-6293	226	7	m.	m.	PROPN
cana-6293	226	8	karthikeyan	karthikeyan	PROPN
cana-6293	226	9	,	,	PUNCT
cana-6293	226	10	“	"	PUNCT
cana-6293	226	11	a	a	DET
cana-6293	226	12	comparative	comparative	ADJ
cana-6293	226	13	study	study	NOUN
cana-6293	226	14	of	of	ADP
cana-6293	226	15	data	datum	NOUN
cana-6293	226	16	mining	mining	NOUN
cana-6293	226	17	algorithms	algorithm	NOUN
cana-6293	226	18	for	for	ADP
cana-6293	226	19	decision	decision	NOUN
cana-6293	226	20	tree	tree	NOUN
cana-6293	226	21	approaches	approach	NOUN
cana-6293	226	22	using	use	VERB
cana-6293	226	23	weka	weka	PROPN
cana-6293	226	24	tool	tool	NOUN
cana-6293	226	25	,	,	PUNCT
cana-6293	226	26	”	"	PUNCT
cana-6293	226	27	advances	advance	NOUN
cana-6293	226	28	in	in	ADP
cana-6293	226	29	natural	natural	ADJ
cana-6293	226	30	and	and	CCONJ
cana-6293	226	31	applied	applied	ADJ
cana-6293	226	32	sciences	science	NOUN
cana-6293	226	33	,	,	PUNCT
cana-6293	226	34	vol	vol	NOUN
cana-6293	226	35	.	.	PROPN
cana-6293	226	36	11	11	NUM
cana-6293	226	37	,	,	PUNCT
cana-6293	226	38	no	no	INTJ
cana-6293	226	39	.	.	NOUN
cana-6293	226	40	9	9	NUM
cana-6293	226	41	,	,	PUNCT
cana-6293	226	42	pp	pp	ADJ
cana-6293	226	43	.	.	PUNCT
cana-6293	227	1	230–243	230–243	NUM
cana-6293	227	2	,	,	PUNCT
cana-6293	227	3	2017	2017	NUM
cana-6293	227	4	.	.	PUNCT
cana-6293	228	1	[	[	X
cana-6293	228	2	24	24	NUM
cana-6293	228	3	]	]	PUNCT
cana-6293	228	4	p.	p.	NOUN
cana-6293	228	5	rajesh	rajesh	PROPN
cana-6293	228	6	and	and	CCONJ
cana-6293	228	7	b.	b.	PROPN
cana-6293	228	8	s.	s.	PROPN
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cana-6293	228	10	,	,	PUNCT
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cana-6293	228	15	sustainable	sustainable	ADJ
cana-6293	228	16	development	development	NOUN
cana-6293	228	17	goals	goal	NOUN
cana-6293	228	18	(	(	PUNCT
cana-6293	228	19	sdg	sdg	NOUN
cana-6293	228	20	)	)	PUNCT
cana-6293	228	21	in	in	ADP
cana-6293	228	22	india	india	PROPN
cana-6293	228	23	using	use	VERB
cana-6293	228	24	data	datum	NOUN
cana-6293	228	25	mining	mining	NOUN
cana-6293	228	26	approach	approach	NOUN
cana-6293	228	27	,	,	PUNCT
cana-6293	228	28	”	"	PUNCT
cana-6293	228	29	journal	journal	NOUN
cana-6293	228	30	of	of	ADP
cana-6293	228	31	science	science	NOUN
cana-6293	228	32	,	,	PUNCT
cana-6293	228	33	vol	vol	NOUN
cana-6293	228	34	.	.	PROPN
cana-6293	229	1	14	14	NUM
cana-6293	229	2	,	,	PUNCT
cana-6293	229	3	no	no	INTJ
cana-6293	229	4	.	.	NOUN
cana-6293	229	5	2	2	NUM
cana-6293	229	6	,	,	PUNCT
cana-6293	229	7	pp	pp	ADJ
cana-6293	229	8	.	.	PUNCT
cana-6293	230	1	91–93	91–93	NUM
cana-6293	230	2	,	,	PUNCT
cana-6293	230	3	2020	2020	NUM
cana-6293	230	4	.	.	PUNCT
cana-6293	231	1	[	[	X
cana-6293	231	2	25	25	NUM
cana-6293	231	3	]	]	X
cana-6293	231	4	s.	s.	PROPN
cana-6293	231	5	ravishankar	ravishankar	PROPN
cana-6293	231	6	and	and	CCONJ
cana-6293	231	7	p.	p.	PROPN
cana-6293	231	8	rajesh	rajesh	PROPN
cana-6293	231	9	,	,	PUNCT
cana-6293	231	10	“	"	PUNCT
cana-6293	231	11	a	a	DET
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cana-6293	231	16	and	and	CCONJ
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cana-6293	231	18	for	for	ADP
cana-6293	231	19	climate	climate	NOUN
cana-6293	231	20	change	change	NOUN
cana-6293	231	21	dataset	dataset	NOUN
cana-6293	231	22	using	use	VERB
cana-6293	231	23	data	datum	NOUN
cana-6293	231	24	mining	mining	NOUN
cana-6293	231	25	with	with	ADP
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cana-6293	231	27	learning	learning	NOUN
cana-6293	231	28	approaches	approach	NOUN
cana-6293	231	29	,	,	PUNCT
cana-6293	231	30	”	"	PUNCT
cana-6293	231	31	european	european	PROPN
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cana-6293	231	33	bulletin	bulletin	PROPN
cana-6293	231	34	,	,	PUNCT
cana-6293	231	35	vol	vol	NOUN
cana-6293	231	36	.	.	PROPN
cana-6293	231	37	11	11	NUM
cana-6293	231	38	,	,	PUNCT
cana-6293	231	39	no	no	INTJ
cana-6293	231	40	.	.	NOUN
cana-6293	231	41	12	12	NUM
cana-6293	231	42	,	,	PUNCT
cana-6293	231	43	pp	pp	ADJ
cana-6293	231	44	.	.	PUNCT
cana-6293	232	1	1866–1877	1866–1877	NUM
cana-6293	232	2	,	,	PUNCT
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cana-6293	232	4	.	.	PUNCT
cana-6293	233	1	[	[	X
cana-6293	233	2	26	26	NUM
cana-6293	233	3	]	]	PUNCT
cana-6293	233	4	agriculture	agriculture	NOUN
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cana-6293	233	6	hub	hub	NOUN
cana-6293	233	7	,	,	PUNCT
cana-6293	233	8	“	"	PUNCT
cana-6293	233	9	paddy	paddy	NOUN
cana-6293	233	10	crop	crop	NOUN
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cana-6293	233	12	and	and	CCONJ
cana-6293	233	13	environmental	environmental	ADJ
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cana-6293	233	16	,	,	PUNCT
cana-6293	233	17	”	"	PUNCT
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cana-6293	233	21	,	,	PUNCT
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cana-6293	233	25	&	&	CCONJ
cana-6293	233	26	farmers	farmers	PROPN
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cana-6293	233	28	,	,	PUNCT
cana-6293	233	29	2023	2023	NUM
cana-6293	233	30	.	.	PUNCT
cana-6293	234	1	[	[	X
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cana-6293	234	4	.	.	PUNCT
cana-6293	235	1	available	available	ADJ
cana-6293	235	2	:	:	PUNCT
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cana-6293	235	4	[	[	X
cana-6293	235	5	27	27	NUM
cana-6293	235	6	]	]	X
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cana-6293	235	8	(	(	PUNCT
cana-6293	235	9	international	international	PROPN
cana-6293	235	10	rice	rice	PROPN
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cana-6293	235	14	,	,	PUNCT
cana-6293	235	15	“	"	PUNCT
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cana-6293	235	23	,	,	PUNCT
cana-6293	235	24	”	"	PUNCT
cana-6293	235	25	irri	irri	PROPN
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cana-6293	235	28	,	,	PUNCT
cana-6293	235	29	2022	2022	NUM
cana-6293	235	30	.	.	PUNCT
cana-6293	236	1	[	[	X
cana-6293	236	2	28	28	NUM
cana-6293	236	3	]	]	X
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cana-6293	236	5	(	(	PUNCT
cana-6293	236	6	food	food	NOUN
cana-6293	236	7	and	and	CCONJ
cana-6293	236	8	agriculture	agriculture	NOUN
cana-6293	236	9	organization	organization	NOUN
cana-6293	236	10	)	)	PUNCT
cana-6293	236	11	,	,	PUNCT
cana-6293	236	12	“	"	PUNCT
cana-6293	236	13	faostat	faostat	NOUN
cana-6293	236	14	climate	climate	NOUN
cana-6293	236	15	and	and	CCONJ
cana-6293	236	16	crop	crop	NOUN
cana-6293	236	17	production	production	NOUN
cana-6293	236	18	database	database	NOUN
cana-6293	236	19	,	,	PUNCT
cana-6293	236	20	”	"	PUNCT
cana-6293	236	21	fao	fao	PROPN
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cana-6293	236	24	,	,	PUNCT
cana-6293	236	25	2022	2022	NUM
cana-6293	236	26	.	.	PUNCT
cana-6293	237	1	[	[	X
cana-6293	237	2	29	29	NUM
cana-6293	237	3	]	]	X
cana-6293	237	4	nasa	nasa	PROPN
cana-6293	237	5	,	,	PUNCT
cana-6293	237	6	“	"	PUNCT
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cana-6293	237	9	for	for	ADP
cana-6293	237	10	climate	climate	NOUN
cana-6293	237	11	and	and	CCONJ
cana-6293	237	12	agricultural	agricultural	ADJ
cana-6293	237	13	monitoring	monitoring	NOUN
cana-6293	237	14	(	(	PUNCT
cana-6293	237	15	power	power	NOUN
cana-6293	237	16	dataset	dataset	NOUN
cana-6293	237	17	)	)	PUNCT
cana-6293	237	18	,	,	PUNCT
cana-6293	237	19	”	"	PUNCT
cana-6293	237	20	nasa	nasa	PROPN
cana-6293	237	21	power	power	PROPN
cana-6293	237	22	data	datum	NOUN
cana-6293	237	23	portal	portal	NOUN
cana-6293	237	24	,	,	PUNCT
cana-6293	237	25	2023	2023	NUM
cana-6293	237	26	.	.	PUNCT
cana-6293	238	1	[	[	X
cana-6293	238	2	30	30	NUM
cana-6293	238	3	]	]	X
cana-6293	238	4	icar	icar	NOUN
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cana-6293	238	6	crri	crri	NOUN
cana-6293	238	7	(	(	PUNCT
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cana-6293	238	9	rice	rice	PROPN
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cana-6293	238	22	data	datum	NOUN
cana-6293	238	23	,	,	PUNCT
cana-6293	238	24	”	"	PUNCT
cana-6293	238	25	icar	icar	ADJ
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cana-6293	238	28	,	,	PUNCT
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cana-6293	238	30	.	.	PUNCT
cana-6293	239	1	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
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