id	sid	tid	token	lemma	pos
aiti-14032	1	1	advances	advance	NOUN
aiti-14032	1	2	in	in	ADP
aiti-14032	1	3	technology	technology	NOUN
aiti-14032	1	4	innovation	innovation	NOUN
aiti-14032	1	5	,	,	PUNCT
aiti-14032	1	6	vol	vol	NOUN
aiti-14032	1	7	.	.	PROPN
aiti-14032	2	1	10	10	NUM
aiti-14032	2	2	,	,	PUNCT
aiti-14032	2	3	no	no	INTJ
aiti-14032	2	4	.	.	NOUN
aiti-14032	2	5	4	4	NUM
aiti-14032	2	6	,	,	PUNCT
aiti-14032	2	7	2025	2025	NUM
aiti-14032	2	8	,	,	PUNCT
aiti-14032	2	9	pp	pp	ADJ
aiti-14032	2	10	.	.	PUNCT
aiti-14032	3	1	370	370	NUM
aiti-14032	3	2	-	-	SYM
aiti-14032	3	3	382	382	NUM
aiti-14032	3	4	multiclass	multiclass	ADJ
aiti-14032	3	5	plant	plant	NOUN
aiti-14032	3	6	leaf	leaf	NOUN
aiti-14032	3	7	disease	disease	NOUN
aiti-14032	3	8	prediction	prediction	NOUN
aiti-14032	3	9	using	use	VERB
aiti-14032	3	10	fuzzy	fuzzy	ADJ
aiti-14032	3	11	multimodal	multimodal	NOUN
aiti-14032	3	12	feature	feature	NOUN
aiti-14032	3	13	extraction	extraction	NOUN
aiti-14032	3	14	vijay	vijay	NOUN
aiti-14032	3	15	choudhary1	choudhary1	PROPN
aiti-14032	3	16	,	,	PUNCT
aiti-14032	3	17	*	*	PROPN
aiti-14032	3	18	,	,	PUNCT
aiti-14032	3	19	archana	archana	PROPN
aiti-14032	3	20	thakur2	thakur2	PROPN
aiti-14032	4	1	1institute	1institute	NUM
aiti-14032	4	2	of	of	ADP
aiti-14032	4	3	engineering	engineering	NOUN
aiti-14032	4	4	and	and	CCONJ
aiti-14032	4	5	technology	technology	NOUN
aiti-14032	4	6	,	,	PUNCT
aiti-14032	4	7	davv/	davv/	NUM
aiti-14032	4	8	ips	ips	PROPN
aiti-14032	4	9	academy	academy	PROPN
aiti-14032	4	10	,	,	PUNCT
aiti-14032	4	11	institute	institute	NOUN
aiti-14032	4	12	of	of	ADP
aiti-14032	4	13	engineering	engineering	NOUN
aiti-14032	4	14	and	and	CCONJ
aiti-14032	4	15	science	science	NOUN
aiti-14032	4	16	,	,	PUNCT
aiti-14032	4	17	indore	indore	PROPN
aiti-14032	4	18	,	,	PUNCT
aiti-14032	4	19	india	india	PROPN
aiti-14032	4	20	2school	2school	PROPN
aiti-14032	4	21	of	of	ADP
aiti-14032	4	22	computer	computer	NOUN
aiti-14032	4	23	science	science	NOUN
aiti-14032	4	24	&	&	CCONJ
aiti-14032	4	25	it	it	PRON
aiti-14032	4	26	,	,	PUNCT
aiti-14032	4	27	devi	devi	PROPN
aiti-14032	4	28	ahilya	ahilya	PROPN
aiti-14032	4	29	university	university	PROPN
aiti-14032	4	30	,	,	PUNCT
aiti-14032	4	31	indore	indore	PROPN
aiti-14032	4	32	,	,	PUNCT
aiti-14032	4	33	india	india	PROPN
aiti-14032	4	34	received	receive	VERB
aiti-14032	4	35	20	20	NUM
aiti-14032	4	36	july	july	PROPN
aiti-14032	4	37	2024	2024	NUM
aiti-14032	4	38	;	;	PUNCT
aiti-14032	4	39	received	receive	VERB
aiti-14032	4	40	in	in	ADP
aiti-14032	4	41	revised	revise	VERB
aiti-14032	4	42	form	form	NOUN
aiti-14032	4	43	19	19	NUM
aiti-14032	4	44	december	december	PROPN
aiti-14032	4	45	2024	2024	NUM
aiti-14032	4	46	;	;	PUNCT
aiti-14032	4	47	accepted	accept	VERB
aiti-14032	4	48	23	23	NUM
aiti-14032	4	49	december	december	PROPN
aiti-14032	4	50	2024	2024	NUM
aiti-14032	4	51	doi	doi	NOUN
aiti-14032	4	52	:	:	PUNCT
aiti-14032	4	53	https://doi.org/10.46604/aiti.2025.14032	https://doi.org/10.46604/aiti.2025.14032	PROPN
aiti-14032	4	54	abstract	abstract	ADV
aiti-14032	4	55	delayed	delay	VERB
aiti-14032	4	56	identification	identification	NOUN
aiti-14032	4	57	of	of	ADP
aiti-14032	4	58	crop	crop	NOUN
aiti-14032	4	59	diseases	disease	NOUN
aiti-14032	4	60	,	,	PUNCT
aiti-14032	4	61	which	which	PRON
aiti-14032	4	62	significantly	significantly	ADV
aiti-14032	4	63	impact	impact	VERB
aiti-14032	4	64	agricultural	agricultural	ADJ
aiti-14032	4	65	yields	yield	NOUN
aiti-14032	4	66	,	,	PUNCT
aiti-14032	4	67	remains	remain	VERB
aiti-14032	4	68	a	a	DET
aiti-14032	4	69	critical	critical	ADJ
aiti-14032	4	70	challenge	challenge	NOUN
aiti-14032	4	71	.	.	PUNCT
aiti-14032	5	1	crop	crop	NOUN
aiti-14032	5	2	diseases	disease	NOUN
aiti-14032	5	3	are	be	AUX
aiti-14032	5	4	a	a	DET
aiti-14032	5	5	major	major	ADJ
aiti-14032	5	6	factor	factor	NOUN
aiti-14032	5	7	contributing	contribute	VERB
aiti-14032	5	8	to	to	ADP
aiti-14032	5	9	reducing	reduce	VERB
aiti-14032	5	10	productivity	productivity	NOUN
aiti-14032	5	11	.	.	PUNCT
aiti-14032	6	1	since	since	SCONJ
aiti-14032	6	2	leaves	leave	NOUN
aiti-14032	6	3	are	be	AUX
aiti-14032	6	4	the	the	DET
aiti-14032	6	5	mirrors	mirror	NOUN
aiti-14032	6	6	of	of	ADP
aiti-14032	6	7	crop	crop	NOUN
aiti-14032	6	8	health	health	NOUN
aiti-14032	6	9	,	,	PUNCT
aiti-14032	6	10	by	by	ADP
aiti-14032	6	11	investigating	investigate	VERB
aiti-14032	6	12	the	the	DET
aiti-14032	6	13	leaves	leave	NOUN
aiti-14032	6	14	,	,	PUNCT
aiti-14032	6	15	a	a	DET
aiti-14032	6	16	prediction	prediction	NOUN
aiti-14032	6	17	of	of	ADP
aiti-14032	6	18	crop	crop	NOUN
aiti-14032	6	19	health	health	NOUN
aiti-14032	6	20	can	can	AUX
aiti-14032	6	21	be	be	AUX
aiti-14032	6	22	made	make	VERB
aiti-14032	6	23	.	.	PUNCT
aiti-14032	7	1	this	this	DET
aiti-14032	7	2	study	study	NOUN
aiti-14032	7	3	aims	aim	VERB
aiti-14032	7	4	to	to	PART
aiti-14032	7	5	predict	predict	VERB
aiti-14032	7	6	crop	crop	NOUN
aiti-14032	7	7	disease	disease	NOUN
aiti-14032	7	8	in	in	ADP
aiti-14032	7	9	the	the	DET
aiti-14032	7	10	vegetative	vegetative	ADJ
aiti-14032	7	11	growth	growth	NOUN
aiti-14032	7	12	phase	phase	NOUN
aiti-14032	7	13	with	with	ADP
aiti-14032	7	14	greater	great	ADJ
aiti-14032	7	15	efficiency	efficiency	NOUN
aiti-14032	7	16	.	.	PUNCT
aiti-14032	8	1	the	the	DET
aiti-14032	8	2	two	two	NUM
aiti-14032	8	3	most	most	ADV
aiti-14032	8	4	prominent	prominent	ADJ
aiti-14032	8	5	features	feature	NOUN
aiti-14032	8	6	,	,	PUNCT
aiti-14032	8	7	color	color	NOUN
aiti-14032	8	8	and	and	CCONJ
aiti-14032	8	9	texture	texture	NOUN
aiti-14032	8	10	of	of	ADP
aiti-14032	8	11	the	the	DET
aiti-14032	8	12	leaves	leave	NOUN
aiti-14032	8	13	,	,	PUNCT
aiti-14032	8	14	are	be	AUX
aiti-14032	8	15	extracted	extract	VERB
aiti-14032	8	16	with	with	ADP
aiti-14032	8	17	different	different	ADJ
aiti-14032	8	18	techniques	technique	NOUN
aiti-14032	8	19	,	,	PUNCT
aiti-14032	8	20	followed	follow	VERB
aiti-14032	8	21	by	by	ADP
aiti-14032	8	22	fuzzification	fuzzification	NOUN
aiti-14032	8	23	of	of	ADP
aiti-14032	8	24	these	these	DET
aiti-14032	8	25	features	feature	NOUN
aiti-14032	8	26	.	.	PUNCT
aiti-14032	9	1	two	two	NUM
aiti-14032	9	2	machine	machine	NOUN
aiti-14032	9	3	learning	learning	NOUN
aiti-14032	9	4	models	model	NOUN
aiti-14032	9	5	,	,	PUNCT
aiti-14032	9	6	the	the	DET
aiti-14032	9	7	bootstrap	bootstrap	NOUN
aiti-14032	9	8	model	model	NOUN
aiti-14032	9	9	and	and	CCONJ
aiti-14032	9	10	the	the	DET
aiti-14032	9	11	multi	multi	ADJ
aiti-14032	9	12	-	-	ADJ
aiti-14032	9	13	class	class	ADJ
aiti-14032	9	14	support	support	NOUN
aiti-14032	9	15	vector	vector	NOUN
aiti-14032	9	16	machine	machine	NOUN
aiti-14032	9	17	(	(	PUNCT
aiti-14032	9	18	msvm	msvm	PROPN
aiti-14032	9	19	)	)	PUNCT
aiti-14032	9	20	,	,	PUNCT
aiti-14032	9	21	are	be	AUX
aiti-14032	9	22	employed	employ	VERB
aiti-14032	9	23	for	for	ADP
aiti-14032	9	24	disease	disease	NOUN
aiti-14032	9	25	prediction	prediction	NOUN
aiti-14032	9	26	.	.	PUNCT
aiti-14032	10	1	the	the	DET
aiti-14032	10	2	findings	finding	NOUN
aiti-14032	10	3	show	show	VERB
aiti-14032	10	4	that	that	SCONJ
aiti-14032	10	5	for	for	ADP
aiti-14032	10	6	multi	multi	ADJ
aiti-14032	10	7	-	-	ADJ
aiti-14032	10	8	class	class	ADJ
aiti-14032	10	9	disease	disease	NOUN
aiti-14032	10	10	prediction	prediction	NOUN
aiti-14032	10	11	,	,	PUNCT
aiti-14032	10	12	the	the	DET
aiti-14032	10	13	bootstrap	bootstrap	NOUN
aiti-14032	10	14	model	model	NOUN
aiti-14032	10	15	with	with	ADP
aiti-14032	10	16	histogram	histogram	NOUN
aiti-14032	10	17	and	and	CCONJ
aiti-14032	10	18	modified	modified	ADJ
aiti-14032	10	19	co	co	NOUN
aiti-14032	10	20	-	-	NOUN
aiti-14032	10	21	occurrence	occurrence	ADJ
aiti-14032	10	22	matrix	matrix	NOUN
aiti-14032	10	23	features	feature	NOUN
aiti-14032	10	24	obtains	obtain	VERB
aiti-14032	10	25	a	a	DET
aiti-14032	10	26	superior	superior	ADJ
aiti-14032	10	27	average	average	ADJ
aiti-14032	10	28	accuracy	accuracy	NOUN
aiti-14032	10	29	of	of	ADP
aiti-14032	10	30	98.07	98.07	NUM
aiti-14032	10	31	%	%	NOUN
aiti-14032	10	32	,	,	PUNCT
aiti-14032	10	33	while	while	SCONJ
aiti-14032	10	34	the	the	DET
aiti-14032	10	35	msvm	msvm	NOUN
aiti-14032	10	36	with	with	ADP
aiti-14032	10	37	fuzzy	fuzzy	ADJ
aiti-14032	10	38	features	feature	NOUN
aiti-14032	10	39	delivers	deliver	VERB
aiti-14032	10	40	an	an	DET
aiti-14032	10	41	average	average	ADJ
aiti-14032	10	42	accuracy	accuracy	NOUN
aiti-14032	10	43	of	of	ADP
aiti-14032	10	44	80.11	80.11	NUM
aiti-14032	10	45	%	%	NOUN
aiti-14032	10	46	in	in	ADP
aiti-14032	10	47	the	the	DET
aiti-14032	10	48	potato	potato	NOUN
aiti-14032	10	49	crop	crop	NOUN
aiti-14032	10	50	with	with	ADP
aiti-14032	10	51	early	early	ADJ
aiti-14032	10	52	blight	blight	NOUN
aiti-14032	10	53	disease	disease	NOUN
aiti-14032	10	54	.	.	PUNCT
aiti-14032	11	1	keywords	keyword	NOUN
aiti-14032	11	2	:	:	PUNCT
aiti-14032	11	3	modified	modify	VERB
aiti-14032	11	4	co	co	NOUN
aiti-14032	11	5	-	-	NOUN
aiti-14032	11	6	occurrence	occurrence	ADJ
aiti-14032	11	7	matrix	matrix	NOUN
aiti-14032	11	8	(	(	PUNCT
aiti-14032	11	9	mccm	mccm	NOUN
aiti-14032	11	10	)	)	PUNCT
aiti-14032	11	11	,	,	PUNCT
aiti-14032	11	12	fuzzy	fuzzy	ADJ
aiti-14032	11	13	hue	hue	NOUN
aiti-14032	11	14	saturation	saturation	NOUN
aiti-14032	11	15	value	value	NOUN
aiti-14032	11	16	(	(	PUNCT
aiti-14032	11	17	hsv	hsv	PROPN
aiti-14032	11	18	)	)	PUNCT
aiti-14032	11	19	,	,	PUNCT
aiti-14032	11	20	local	local	ADJ
aiti-14032	11	21	binary	binary	ADJ
aiti-14032	11	22	pattern	pattern	NOUN
aiti-14032	11	23	(	(	PUNCT
aiti-14032	11	24	lbp	lbp	PROPN
aiti-14032	11	25	)	)	PUNCT
aiti-14032	11	26	,	,	PUNCT
aiti-14032	11	27	multi	multi	ADJ
aiti-14032	11	28	-	-	ADJ
aiti-14032	11	29	class	class	ADJ
aiti-14032	11	30	support	support	NOUN
aiti-14032	11	31	vector	vector	NOUN
aiti-14032	11	32	machine	machine	NOUN
aiti-14032	11	33	(	(	PUNCT
aiti-14032	11	34	msvm	msvm	NOUN
aiti-14032	11	35	)	)	PUNCT
aiti-14032	11	36	1	1	NUM
aiti-14032	11	37	.	.	PUNCT
aiti-14032	11	38	introduction	introduction	NOUN
aiti-14032	11	39	crop	crop	NOUN
aiti-14032	11	40	diseases	disease	NOUN
aiti-14032	11	41	and	and	CCONJ
aiti-14032	11	42	pests	pest	NOUN
aiti-14032	11	43	pose	pose	VERB
aiti-14032	11	44	a	a	DET
aiti-14032	11	45	significant	significant	ADJ
aiti-14032	11	46	threat	threat	NOUN
aiti-14032	11	47	to	to	ADP
aiti-14032	11	48	global	global	ADJ
aiti-14032	11	49	agricultural	agricultural	ADJ
aiti-14032	11	50	production	production	NOUN
aiti-14032	11	51	and	and	CCONJ
aiti-14032	11	52	food	food	NOUN
aiti-14032	11	53	security	security	NOUN
aiti-14032	11	54	.	.	PUNCT
aiti-14032	12	1	the	the	DET
aiti-14032	12	2	detrimental	detrimental	ADJ
aiti-14032	12	3	impact	impact	NOUN
aiti-14032	12	4	on	on	ADP
aiti-14032	12	5	crop	crop	NOUN
aiti-14032	12	6	yields	yield	NOUN
aiti-14032	12	7	is	be	AUX
aiti-14032	12	8	increasing	increase	VERB
aiti-14032	12	9	,	,	PUNCT
aiti-14032	12	10	leading	lead	VERB
aiti-14032	12	11	to	to	ADP
aiti-14032	12	12	substantial	substantial	ADJ
aiti-14032	12	13	losses	loss	NOUN
aiti-14032	12	14	.	.	PUNCT
aiti-14032	13	1	alterations	alteration	NOUN
aiti-14032	13	2	in	in	ADP
aiti-14032	13	3	plant	plant	NOUN
aiti-14032	13	4	morphology	morphology	NOUN
aiti-14032	13	5	not	not	PART
aiti-14032	13	6	only	only	ADV
aiti-14032	13	7	detrimentally	detrimentally	ADV
aiti-14032	13	8	impact	impact	VERB
aiti-14032	13	9	crop	crop	NOUN
aiti-14032	13	10	growth	growth	NOUN
aiti-14032	13	11	but	but	CCONJ
aiti-14032	13	12	also	also	ADV
aiti-14032	13	13	result	result	VERB
aiti-14032	13	14	in	in	ADP
aiti-14032	13	15	a	a	DET
aiti-14032	13	16	significant	significant	ADJ
aiti-14032	13	17	decline	decline	NOUN
aiti-14032	13	18	in	in	ADP
aiti-14032	13	19	both	both	CCONJ
aiti-14032	13	20	quality	quality	NOUN
aiti-14032	13	21	and	and	CCONJ
aiti-14032	13	22	yield	yield	NOUN
aiti-14032	13	23	.	.	PUNCT
aiti-14032	14	1	in	in	ADP
aiti-14032	14	2	severe	severe	ADJ
aiti-14032	14	3	instances	instance	NOUN
aiti-14032	14	4	,	,	PUNCT
aiti-14032	14	5	entire	entire	ADJ
aiti-14032	14	6	harvests	harvest	NOUN
aiti-14032	14	7	may	may	AUX
aiti-14032	14	8	be	be	AUX
aiti-14032	14	9	lost	lose	VERB
aiti-14032	14	10	[	[	X
aiti-14032	14	11	1	1	NUM
aiti-14032	14	12	]	]	PUNCT
aiti-14032	14	13	.	.	PUNCT
aiti-14032	15	1	crop	crop	NOUN
aiti-14032	15	2	diseases	disease	NOUN
aiti-14032	15	3	are	be	AUX
aiti-14032	15	4	significant	significant	ADJ
aiti-14032	15	5	biological	biological	ADJ
aiti-14032	15	6	calamities	calamity	NOUN
aiti-14032	15	7	that	that	PRON
aiti-14032	15	8	badly	badly	ADV
aiti-14032	15	9	impact	impact	VERB
aiti-14032	15	10	agricultural	agricultural	ADJ
aiti-14032	15	11	productivity	productivity	NOUN
aiti-14032	15	12	and	and	CCONJ
aiti-14032	15	13	the	the	DET
aiti-14032	15	14	safety	safety	NOUN
aiti-14032	15	15	of	of	ADP
aiti-14032	15	16	the	the	DET
aiti-14032	15	17	ecosystem	ecosystem	NOUN
aiti-14032	15	18	.	.	PUNCT
aiti-14032	16	1	accurate	accurate	ADJ
aiti-14032	16	2	detection	detection	NOUN
aiti-14032	16	3	and	and	CCONJ
aiti-14032	16	4	identification	identification	NOUN
aiti-14032	16	5	of	of	ADP
aiti-14032	16	6	disease	disease	NOUN
aiti-14032	16	7	types	type	NOUN
aiti-14032	16	8	are	be	AUX
aiti-14032	16	9	crucial	crucial	ADJ
aiti-14032	16	10	for	for	ADP
aiti-14032	16	11	minimizing	minimize	VERB
aiti-14032	16	12	damage	damage	NOUN
aiti-14032	16	13	[	[	X
aiti-14032	16	14	2	2	NUM
aiti-14032	16	15	]	]	PUNCT
aiti-14032	16	16	.	.	PUNCT
aiti-14032	17	1	thus	thus	ADV
aiti-14032	17	2	,	,	PUNCT
aiti-14032	17	3	precise	precise	ADJ
aiti-14032	17	4	crop	crop	NOUN
aiti-14032	17	5	disease	disease	NOUN
aiti-14032	17	6	diagnosis	diagnosis	NOUN
aiti-14032	17	7	remains	remain	VERB
aiti-14032	17	8	a	a	DET
aiti-14032	17	9	critical	critical	ADJ
aiti-14032	17	10	challenge	challenge	NOUN
aiti-14032	17	11	.	.	PUNCT
aiti-14032	18	1	leaves	leave	NOUN
aiti-14032	18	2	are	be	AUX
aiti-14032	18	3	the	the	DET
aiti-14032	18	4	most	most	ADV
aiti-14032	18	5	exposed	expose	VERB
aiti-14032	18	6	constituent	constituent	NOUN
aiti-14032	18	7	of	of	ADP
aiti-14032	18	8	a	a	DET
aiti-14032	18	9	plant	plant	NOUN
aiti-14032	18	10	.	.	PUNCT
aiti-14032	19	1	some	some	DET
aiti-14032	19	2	insects	insect	NOUN
aiti-14032	19	3	may	may	AUX
aiti-14032	19	4	attack	attack	VERB
aiti-14032	19	5	leaves	leave	NOUN
aiti-14032	19	6	or	or	CCONJ
aiti-14032	19	7	may	may	AUX
aiti-14032	19	8	face	face	VERB
aiti-14032	19	9	unfavorable	unfavorable	ADJ
aiti-14032	19	10	weather	weather	NOUN
aiti-14032	19	11	conditions	condition	NOUN
aiti-14032	19	12	during	during	ADP
aiti-14032	19	13	the	the	DET
aiti-14032	19	14	plant	plant	NOUN
aiti-14032	19	15	's	's	PART
aiti-14032	19	16	growth	growth	NOUN
aiti-14032	19	17	period	period	NOUN
aiti-14032	19	18	,	,	PUNCT
aiti-14032	19	19	leading	lead	VERB
aiti-14032	19	20	to	to	ADP
aiti-14032	19	21	severe	severe	ADJ
aiti-14032	19	22	disease	disease	NOUN
aiti-14032	19	23	.	.	PUNCT
aiti-14032	20	1	commonly	commonly	ADV
aiti-14032	20	2	,	,	PUNCT
aiti-14032	20	3	the	the	DET
aiti-14032	20	4	hue	hue	NOUN
aiti-14032	20	5	saturation	saturation	NOUN
aiti-14032	20	6	intensity	intensity	NOUN
aiti-14032	20	7	(	(	PUNCT
aiti-14032	20	8	hsi	hsi	PROPN
aiti-14032	20	9	)	)	PUNCT
aiti-14032	20	10	and	and	CCONJ
aiti-14032	20	11	hue	hue	NOUN
aiti-14032	20	12	saturation	saturation	NOUN
aiti-14032	20	13	value	value	NOUN
aiti-14032	20	14	(	(	PUNCT
aiti-14032	20	15	hsv	hsv	PROPN
aiti-14032	20	16	)	)	PUNCT
aiti-14032	20	17	color	color	NOUN
aiti-14032	20	18	models	model	NOUN
aiti-14032	20	19	are	be	AUX
aiti-14032	20	20	widely	widely	ADV
aiti-14032	20	21	employed	employ	VERB
aiti-14032	20	22	for	for	ADP
aiti-14032	20	23	color	color	NOUN
aiti-14032	20	24	feature	feature	NOUN
aiti-14032	20	25	extraction	extraction	NOUN
aiti-14032	20	26	,	,	PUNCT
aiti-14032	20	27	while	while	SCONJ
aiti-14032	20	28	the	the	DET
aiti-14032	20	29	local	local	ADJ
aiti-14032	20	30	binary	binary	ADJ
aiti-14032	20	31	pattern	pattern	NOUN
aiti-14032	20	32	(	(	PUNCT
aiti-14032	20	33	lbp	lbp	PROPN
aiti-14032	20	34	)	)	PUNCT
aiti-14032	20	35	is	be	AUX
aiti-14032	20	36	commonly	commonly	ADV
aiti-14032	20	37	utilized	utilize	VERB
aiti-14032	20	38	for	for	ADP
aiti-14032	20	39	texture	texture	ADJ
aiti-14032	20	40	feature	feature	NOUN
aiti-14032	20	41	extraction	extraction	NOUN
aiti-14032	20	42	.	.	PUNCT
aiti-14032	21	1	in	in	ADP
aiti-14032	21	2	the	the	DET
aiti-14032	21	3	proposed	propose	VERB
aiti-14032	21	4	work	work	NOUN
aiti-14032	21	5	,	,	PUNCT
aiti-14032	21	6	a	a	DET
aiti-14032	21	7	novel	novel	ADJ
aiti-14032	21	8	approach	approach	NOUN
aiti-14032	21	9	is	be	AUX
aiti-14032	21	10	introduced	introduce	VERB
aiti-14032	21	11	that	that	SCONJ
aiti-14032	21	12	leverages	leverage	VERB
aiti-14032	21	13	fuzzy	fuzzy	ADJ
aiti-14032	21	14	hsv	hsv	NOUN
aiti-14032	21	15	for	for	ADP
aiti-14032	21	16	color	color	NOUN
aiti-14032	21	17	feature	feature	NOUN
aiti-14032	21	18	extraction	extraction	NOUN
aiti-14032	21	19	and	and	CCONJ
aiti-14032	21	20	fuzzy	fuzzy	ADJ
aiti-14032	21	21	lbp	lbp	NOUN
aiti-14032	21	22	for	for	ADP
aiti-14032	21	23	texture	texture	ADJ
aiti-14032	21	24	feature	feature	NOUN
aiti-14032	21	25	extraction	extraction	NOUN
aiti-14032	21	26	.	.	PUNCT
aiti-14032	22	1	additionally	additionally	ADV
aiti-14032	22	2	,	,	PUNCT
aiti-14032	22	3	a	a	DET
aiti-14032	22	4	second	second	ADJ
aiti-14032	22	5	feature	feature	NOUN
aiti-14032	22	6	extraction	extraction	NOUN
aiti-14032	22	7	technique	technique	NOUN
aiti-14032	22	8	based	base	VERB
aiti-14032	22	9	on	on	ADP
aiti-14032	22	10	a	a	DET
aiti-14032	22	11	histogram	histogram	NOUN
aiti-14032	22	12	and	and	CCONJ
aiti-14032	22	13	a	a	DET
aiti-14032	22	14	modified	modified	ADJ
aiti-14032	22	15	co	co	NOUN
aiti-14032	22	16	-	-	NOUN
aiti-14032	22	17	occurrence	occurrence	ADJ
aiti-14032	22	18	matrix	matrix	NOUN
aiti-14032	22	19	(	(	PUNCT
aiti-14032	22	20	mccm	mccm	NOUN
aiti-14032	22	21	)	)	PUNCT
aiti-14032	22	22	is	be	AUX
aiti-14032	22	23	utilized	utilize	VERB
aiti-14032	22	24	to	to	PART
aiti-14032	22	25	capture	capture	VERB
aiti-14032	22	26	both	both	CCONJ
aiti-14032	22	27	color	color	NOUN
aiti-14032	22	28	and	and	CCONJ
aiti-14032	22	29	texture	texture	NOUN
aiti-14032	22	30	features	feature	NOUN
aiti-14032	22	31	.	.	PUNCT
aiti-14032	23	1	*	*	PUNCT
aiti-14032	23	2	corresponding	correspond	VERB
aiti-14032	23	3	author	author	NOUN
aiti-14032	23	4	.	.	PUNCT
aiti-14032	24	1	e	e	X
aiti-14032	24	2	-	-	NOUN
aiti-14032	24	3	mail	mail	NOUN
aiti-14032	24	4	address	address	NOUN
aiti-14032	24	5	:	:	PUNCT
aiti-14032	24	6	vij.choudhary@gmail.com	vij.choudhary@gmail.com	PROPN
aiti-14032	24	7	advances	advance	NOUN
aiti-14032	24	8	in	in	ADP
aiti-14032	24	9	technology	technology	NOUN
aiti-14032	24	10	innovation	innovation	NOUN
aiti-14032	24	11	,	,	PUNCT
aiti-14032	24	12	vol	vol	NOUN
aiti-14032	24	13	.	.	PROPN
aiti-14032	25	1	10	10	NUM
aiti-14032	25	2	,	,	PUNCT
aiti-14032	25	3	no	no	INTJ
aiti-14032	25	4	.	.	NOUN
aiti-14032	25	5	4	4	NUM
aiti-14032	25	6	,	,	PUNCT
aiti-14032	25	7	2025	2025	NUM
aiti-14032	25	8	,	,	PUNCT
aiti-14032	25	9	pp	pp	ADJ
aiti-14032	25	10	.	.	PUNCT
aiti-14032	26	1	370	370	NUM
aiti-14032	26	2	-	-	SYM
aiti-14032	26	3	382	382	NUM
aiti-14032	26	4	371	371	NUM
aiti-14032	26	5	the	the	DET
aiti-14032	26	6	extracted	extract	VERB
aiti-14032	26	7	fuzzy	fuzzy	ADJ
aiti-14032	26	8	hsv	hsv	NOUN
aiti-14032	26	9	and	and	CCONJ
aiti-14032	26	10	fuzzy	fuzzy	ADJ
aiti-14032	26	11	lbp	lbp	PROPN
aiti-14032	26	12	features	feature	NOUN
aiti-14032	26	13	are	be	AUX
aiti-14032	26	14	fed	feed	VERB
aiti-14032	26	15	into	into	ADP
aiti-14032	26	16	a	a	DET
aiti-14032	26	17	multi	multi	ADJ
aiti-14032	26	18	-	-	ADJ
aiti-14032	26	19	class	class	ADJ
aiti-14032	26	20	support	support	NOUN
aiti-14032	26	21	vector	vector	NOUN
aiti-14032	26	22	machine	machine	NOUN
aiti-14032	26	23	(	(	PUNCT
aiti-14032	26	24	msvm	msvm	PROPN
aiti-14032	26	25	)	)	PUNCT
aiti-14032	26	26	for	for	ADP
aiti-14032	26	27	classification	classification	NOUN
aiti-14032	26	28	,	,	PUNCT
aiti-14032	26	29	while	while	SCONJ
aiti-14032	26	30	the	the	DET
aiti-14032	26	31	histogram	histogram	NOUN
aiti-14032	26	32	and	and	CCONJ
aiti-14032	26	33	mccm	mccm	NOUN
aiti-14032	26	34	features	feature	NOUN
aiti-14032	26	35	are	be	AUX
aiti-14032	26	36	used	use	VERB
aiti-14032	26	37	to	to	PART
aiti-14032	26	38	train	train	VERB
aiti-14032	26	39	a	a	DET
aiti-14032	26	40	bootstrap	bootstrap	NOUN
aiti-14032	26	41	model	model	NOUN
aiti-14032	26	42	.	.	PUNCT
aiti-14032	27	1	the	the	DET
aiti-14032	27	2	primary	primary	ADJ
aiti-14032	27	3	objective	objective	NOUN
aiti-14032	27	4	of	of	ADP
aiti-14032	27	5	this	this	DET
aiti-14032	27	6	study	study	NOUN
aiti-14032	27	7	is	be	AUX
aiti-14032	27	8	to	to	PART
aiti-14032	27	9	compare	compare	VERB
aiti-14032	27	10	fuzzy	fuzzy	ADJ
aiti-14032	27	11	hsv	hsv	NOUN
aiti-14032	27	12	and	and	CCONJ
aiti-14032	27	13	fuzzy	fuzzy	ADJ
aiti-14032	27	14	lbp	lbp	NOUN
aiti-14032	27	15	with	with	ADP
aiti-14032	27	16	histogram	histogram	NOUN
aiti-14032	27	17	and	and	CCONJ
aiti-14032	27	18	mccm	mccm	NOUN
aiti-14032	27	19	across	across	ADP
aiti-14032	27	20	various	various	ADJ
aiti-14032	27	21	performance	performance	NOUN
aiti-14032	27	22	parameters	parameter	NOUN
aiti-14032	27	23	,	,	PUNCT
aiti-14032	27	24	to	to	PART
aiti-14032	27	25	evaluate	evaluate	VERB
aiti-14032	27	26	their	their	PRON
aiti-14032	27	27	effectiveness	effectiveness	NOUN
aiti-14032	27	28	in	in	ADP
aiti-14032	27	29	classification	classification	NOUN
aiti-14032	27	30	tasks	task	NOUN
aiti-14032	27	31	.	.	PUNCT
aiti-14032	28	1	1.1	1.1	NUM
aiti-14032	28	2	.	.	PUNCT
aiti-14032	28	3	background	background	NOUN
aiti-14032	28	4	the	the	DET
aiti-14032	28	5	primary	primary	ADJ
aiti-14032	28	6	method	method	NOUN
aiti-14032	28	7	for	for	ADP
aiti-14032	28	8	predicting	predict	VERB
aiti-14032	28	9	crop	crop	NOUN
aiti-14032	28	10	diseases	disease	NOUN
aiti-14032	28	11	is	be	AUX
aiti-14032	28	12	manual	manual	ADJ
aiti-14032	28	13	leaf	leaf	NOUN
aiti-14032	28	14	observation	observation	NOUN
aiti-14032	28	15	in	in	ADP
aiti-14032	28	16	the	the	DET
aiti-14032	28	17	field	field	NOUN
aiti-14032	28	18	.	.	PUNCT
aiti-14032	29	1	however	however	ADV
aiti-14032	29	2	,	,	PUNCT
aiti-14032	29	3	this	this	DET
aiti-14032	29	4	manual	manual	ADJ
aiti-14032	29	5	observation	observation	NOUN
aiti-14032	29	6	requires	require	VERB
aiti-14032	29	7	expertise	expertise	NOUN
aiti-14032	29	8	and	and	CCONJ
aiti-14032	29	9	much	much	ADJ
aiti-14032	29	10	experience	experience	NOUN
aiti-14032	29	11	in	in	ADP
aiti-14032	29	12	agriculture	agriculture	NOUN
aiti-14032	29	13	.	.	PUNCT
aiti-14032	30	1	this	this	PRON
aiti-14032	30	2	is	be	AUX
aiti-14032	30	3	a	a	DET
aiti-14032	30	4	time	time	NOUN
aiti-14032	30	5	-	-	PUNCT
aiti-14032	30	6	consuming	consume	VERB
aiti-14032	30	7	process	process	NOUN
aiti-14032	30	8	and	and	CCONJ
aiti-14032	30	9	can	can	AUX
aiti-14032	30	10	cover	cover	VERB
aiti-14032	30	11	only	only	ADV
aiti-14032	30	12	a	a	DET
aiti-14032	30	13	limited	limited	ADJ
aiti-14032	30	14	field	field	NOUN
aiti-14032	30	15	area	area	NOUN
aiti-14032	30	16	.	.	PUNCT
aiti-14032	31	1	the	the	DET
aiti-14032	31	2	solution	solution	NOUN
aiti-14032	31	3	to	to	ADP
aiti-14032	31	4	every	every	DET
aiti-14032	31	5	contemporary	contemporary	ADJ
aiti-14032	31	6	problem	problem	NOUN
aiti-14032	31	7	can	can	AUX
aiti-14032	31	8	be	be	AUX
aiti-14032	31	9	found	find	VERB
aiti-14032	31	10	in	in	ADP
aiti-14032	31	11	the	the	DET
aiti-14032	31	12	technology	technology	NOUN
aiti-14032	31	13	that	that	PRON
aiti-14032	31	14	evolves	evolve	VERB
aiti-14032	31	15	every	every	DET
aiti-14032	31	16	day	day	NOUN
aiti-14032	31	17	.	.	PUNCT
aiti-14032	32	1	hence	hence	ADV
aiti-14032	32	2	,	,	PUNCT
aiti-14032	32	3	the	the	DET
aiti-14032	32	4	above	above	ADJ
aiti-14032	32	5	problem	problem	NOUN
aiti-14032	32	6	can	can	AUX
aiti-14032	32	7	be	be	AUX
aiti-14032	32	8	conquered	conquer	VERB
aiti-14032	32	9	by	by	ADP
aiti-14032	32	10	technology	technology	NOUN
aiti-14032	32	11	.	.	PUNCT
aiti-14032	33	1	advancing	advance	VERB
aiti-14032	33	2	technology	technology	NOUN
aiti-14032	33	3	offers	offer	VERB
aiti-14032	33	4	a	a	DET
aiti-14032	33	5	solution	solution	NOUN
aiti-14032	33	6	to	to	ADP
aiti-14032	33	7	this	this	DET
aiti-14032	33	8	challenge	challenge	NOUN
aiti-14032	33	9	,	,	PUNCT
aiti-14032	33	10	as	as	SCONJ
aiti-14032	33	11	the	the	DET
aiti-14032	33	12	agricultural	agricultural	ADJ
aiti-14032	33	13	industry	industry	NOUN
aiti-14032	33	14	increasingly	increasingly	ADV
aiti-14032	33	15	benefits	benefit	VERB
aiti-14032	33	16	from	from	ADP
aiti-14032	33	17	innovations	innovation	NOUN
aiti-14032	33	18	in	in	ADP
aiti-14032	33	19	disease	disease	NOUN
aiti-14032	33	20	management	management	NOUN
aiti-14032	33	21	and	and	CCONJ
aiti-14032	33	22	control	control	NOUN
aiti-14032	33	23	.	.	PUNCT
aiti-14032	34	1	high	high	ADJ
aiti-14032	34	2	-	-	PUNCT
aiti-14032	34	3	resolution	resolution	NOUN
aiti-14032	34	4	images	image	NOUN
aiti-14032	34	5	of	of	ADP
aiti-14032	34	6	agricultural	agricultural	ADJ
aiti-14032	34	7	fields	field	NOUN
aiti-14032	34	8	are	be	AUX
aiti-14032	34	9	captured	capture	VERB
aiti-14032	34	10	by	by	ADP
aiti-14032	34	11	remote	remote	ADJ
aiti-14032	34	12	sensing	sense	VERB
aiti-14032	34	13	technologies	technology	NOUN
aiti-14032	34	14	,	,	PUNCT
aiti-14032	34	15	including	include	VERB
aiti-14032	34	16	satellites	satellite	NOUN
aiti-14032	34	17	,	,	PUNCT
aiti-14032	34	18	drones	drone	NOUN
aiti-14032	34	19	,	,	PUNCT
aiti-14032	34	20	and	and	CCONJ
aiti-14032	34	21	airborne	airborne	ADJ
aiti-14032	34	22	sensors	sensor	NOUN
aiti-14032	34	23	.	.	PUNCT
aiti-14032	35	1	these	these	DET
aiti-14032	35	2	images	image	NOUN
aiti-14032	35	3	are	be	AUX
aiti-14032	35	4	then	then	ADV
aiti-14032	35	5	analyzed	analyze	VERB
aiti-14032	35	6	using	use	VERB
aiti-14032	35	7	image	image	NOUN
aiti-14032	35	8	processing	processing	NOUN
aiti-14032	35	9	techniques	technique	NOUN
aiti-14032	35	10	to	to	PART
aiti-14032	35	11	identify	identify	VERB
aiti-14032	35	12	and	and	CCONJ
aiti-14032	35	13	monitor	monitor	VERB
aiti-14032	35	14	disease	disease	NOUN
aiti-14032	35	15	patterns	pattern	NOUN
aiti-14032	35	16	across	across	ADP
aiti-14032	35	17	vast	vast	ADJ
aiti-14032	35	18	areas	area	NOUN
aiti-14032	35	19	,	,	PUNCT
aiti-14032	35	20	thereby	thereby	ADV
aiti-14032	35	21	facilitating	facilitate	VERB
aiti-14032	35	22	early	early	ADJ
aiti-14032	35	23	detection	detection	NOUN
aiti-14032	35	24	and	and	CCONJ
aiti-14032	35	25	intervention	intervention	NOUN
aiti-14032	35	26	[	[	X
aiti-14032	35	27	3	3	NUM
aiti-14032	35	28	]	]	PUNCT
aiti-14032	35	29	.	.	PUNCT
aiti-14032	36	1	machine	machine	NOUN
aiti-14032	36	2	learning	learning	NOUN
aiti-14032	36	3	and	and	CCONJ
aiti-14032	36	4	artificial	artificial	ADJ
aiti-14032	36	5	intelligence	intelligence	NOUN
aiti-14032	36	6	(	(	PUNCT
aiti-14032	36	7	ai	ai	AUX
aiti-14032	36	8	)	)	PUNCT
aiti-14032	36	9	play	play	VERB
aiti-14032	36	10	a	a	DET
aiti-14032	36	11	key	key	ADJ
aiti-14032	36	12	role	role	NOUN
aiti-14032	36	13	in	in	ADP
aiti-14032	36	14	analyzing	analyze	VERB
aiti-14032	36	15	large	large	ADJ
aiti-14032	36	16	datasets	dataset	NOUN
aiti-14032	36	17	,	,	PUNCT
aiti-14032	36	18	including	include	VERB
aiti-14032	36	19	images	image	NOUN
aiti-14032	36	20	,	,	PUNCT
aiti-14032	36	21	environmental	environmental	ADJ
aiti-14032	36	22	data	datum	NOUN
aiti-14032	36	23	,	,	PUNCT
aiti-14032	36	24	and	and	CCONJ
aiti-14032	36	25	disease	disease	NOUN
aiti-14032	36	26	records	record	NOUN
aiti-14032	36	27	.	.	PUNCT
aiti-14032	37	1	these	these	DET
aiti-14032	37	2	technologies	technology	NOUN
aiti-14032	37	3	detect	detect	VERB
aiti-14032	37	4	disease	disease	NOUN
aiti-14032	37	5	patterns	pattern	NOUN
aiti-14032	37	6	,	,	PUNCT
aiti-14032	37	7	predict	predict	VERB
aiti-14032	37	8	outbreaks	outbreak	NOUN
aiti-14032	37	9	,	,	PUNCT
aiti-14032	37	10	and	and	CCONJ
aiti-14032	37	11	recommend	recommend	VERB
aiti-14032	37	12	optimal	optimal	ADJ
aiti-14032	37	13	management	management	NOUN
aiti-14032	37	14	strategies	strategy	NOUN
aiti-14032	37	15	[	[	X
aiti-14032	37	16	4	4	NUM
aiti-14032	37	17	]	]	PUNCT
aiti-14032	37	18	.	.	PUNCT
aiti-14032	38	1	through	through	ADP
aiti-14032	38	2	continuous	continuous	ADJ
aiti-14032	38	3	learning	learning	NOUN
aiti-14032	38	4	and	and	CCONJ
aiti-14032	38	5	improvement	improvement	NOUN
aiti-14032	38	6	,	,	PUNCT
aiti-14032	38	7	ai	ai	AUX
aiti-14032	38	8	and	and	CCONJ
aiti-14032	38	9	machine	machine	NOUN
aiti-14032	38	10	learning	learning	NOUN
aiti-14032	38	11	contribute	contribute	VERB
aiti-14032	38	12	to	to	ADP
aiti-14032	38	13	more	more	ADV
aiti-14032	38	14	efficient	efficient	ADJ
aiti-14032	38	15	and	and	CCONJ
aiti-14032	38	16	sustainable	sustainable	ADJ
aiti-14032	38	17	crop	crop	NOUN
aiti-14032	38	18	production	production	NOUN
aiti-14032	38	19	.	.	PUNCT
aiti-14032	39	1	precise	precise	ADJ
aiti-14032	39	2	diagnosis	diagnosis	NOUN
aiti-14032	39	3	in	in	ADP
aiti-14032	39	4	plant	plant	NOUN
aiti-14032	39	5	disease	disease	NOUN
aiti-14032	39	6	identification	identification	NOUN
aiti-14032	39	7	systems	system	NOUN
aiti-14032	39	8	can	can	AUX
aiti-14032	39	9	be	be	AUX
aiti-14032	39	10	problematic	problematic	ADJ
aiti-14032	39	11	,	,	PUNCT
aiti-14032	39	12	as	as	SCONJ
aiti-14032	39	13	disease	disease	NOUN
aiti-14032	39	14	symptoms	symptom	NOUN
aiti-14032	39	15	may	may	AUX
aiti-14032	39	16	appear	appear	VERB
aiti-14032	39	17	visually	visually	ADV
aiti-14032	39	18	identical	identical	ADJ
aiti-14032	39	19	across	across	ADP
aiti-14032	39	20	different	different	ADJ
aiti-14032	39	21	conditions	condition	NOUN
aiti-14032	39	22	.	.	PUNCT
aiti-14032	40	1	therefore	therefore	ADV
aiti-14032	40	2	,	,	PUNCT
aiti-14032	40	3	it	it	PRON
aiti-14032	40	4	is	be	AUX
aiti-14032	40	5	essential	essential	ADJ
aiti-14032	40	6	to	to	PART
aiti-14032	40	7	extract	extract	VERB
aiti-14032	40	8	features	feature	NOUN
aiti-14032	40	9	that	that	PRON
aiti-14032	40	10	can	can	AUX
aiti-14032	40	11	effectively	effectively	ADV
aiti-14032	40	12	capture	capture	VERB
aiti-14032	40	13	the	the	DET
aiti-14032	40	14	visual	visual	ADJ
aiti-14032	40	15	aspects	aspect	NOUN
aiti-14032	40	16	of	of	ADP
aiti-14032	40	17	a	a	DET
aiti-14032	40	18	leaf	leaf	NOUN
aiti-14032	40	19	image	image	NOUN
aiti-14032	40	20	to	to	PART
aiti-14032	40	21	provide	provide	VERB
aiti-14032	40	22	the	the	DET
aiti-14032	40	23	most	most	ADV
aiti-14032	40	24	relevant	relevant	ADJ
aiti-14032	40	25	description	description	NOUN
aiti-14032	40	26	of	of	ADP
aiti-14032	40	27	the	the	DET
aiti-14032	40	28	disease	disease	NOUN
aiti-14032	40	29	class	class	NOUN
aiti-14032	40	30	.	.	PUNCT
aiti-14032	41	1	feature	feature	NOUN
aiti-14032	41	2	extraction	extraction	NOUN
aiti-14032	41	3	reduces	reduce	VERB
aiti-14032	41	4	the	the	DET
aiti-14032	41	5	data	data	NOUN
aiti-14032	41	6	dimensionality	dimensionality	NOUN
aiti-14032	41	7	by	by	ADP
aiti-14032	41	8	grouping	group	VERB
aiti-14032	41	9	relevant	relevant	ADJ
aiti-14032	41	10	information	information	NOUN
aiti-14032	41	11	into	into	ADP
aiti-14032	41	12	manageable	manageable	ADJ
aiti-14032	41	13	subsets	subset	NOUN
aiti-14032	41	14	.	.	PUNCT
aiti-14032	42	1	further	far	ADV
aiti-14032	42	2	,	,	PUNCT
aiti-14032	42	3	data	datum	NOUN
aiti-14032	42	4	reduction	reduction	NOUN
aiti-14032	42	5	accelerates	accelerate	VERB
aiti-14032	42	6	the	the	DET
aiti-14032	42	7	learning	learning	NOUN
aiti-14032	42	8	process	process	NOUN
aiti-14032	42	9	and	and	CCONJ
aiti-14032	42	10	minimizes	minimize	VERB
aiti-14032	42	11	the	the	DET
aiti-14032	42	12	computational	computational	ADJ
aiti-14032	42	13	demands	demand	NOUN
aiti-14032	42	14	placed	place	VERB
aiti-14032	42	15	on	on	ADP
aiti-14032	42	16	the	the	DET
aiti-14032	42	17	machine	machine	NOUN
aiti-14032	42	18	learning	learning	NOUN
aiti-14032	42	19	model	model	NOUN
aiti-14032	42	20	[	[	X
aiti-14032	42	21	5	5	NUM
aiti-14032	42	22	]	]	PUNCT
aiti-14032	42	23	.	.	PUNCT
aiti-14032	43	1	the	the	DET
aiti-14032	43	2	feature	feature	NOUN
aiti-14032	43	3	extraction	extraction	NOUN
aiti-14032	43	4	phase	phase	NOUN
aiti-14032	43	5	plays	play	VERB
aiti-14032	43	6	a	a	DET
aiti-14032	43	7	vital	vital	ADJ
aiti-14032	43	8	role	role	NOUN
aiti-14032	43	9	in	in	ADP
aiti-14032	43	10	precisely	precisely	ADV
aiti-14032	43	11	classifying	classify	VERB
aiti-14032	43	12	different	different	ADJ
aiti-14032	43	13	diseases	disease	NOUN
aiti-14032	43	14	by	by	ADP
aiti-14032	43	15	distinguishing	distinguish	VERB
aiti-14032	43	16	infections	infection	NOUN
aiti-14032	43	17	from	from	ADP
aiti-14032	43	18	similar	similar	ADJ
aiti-14032	43	19	ones	one	NOUN
aiti-14032	43	20	,	,	PUNCT
aiti-14032	43	21	based	base	VERB
aiti-14032	43	22	on	on	ADP
aiti-14032	43	23	specific	specific	ADJ
aiti-14032	43	24	symptoms	symptom	NOUN
aiti-14032	43	25	or	or	CCONJ
aiti-14032	43	26	visible	visible	ADJ
aiti-14032	43	27	lesions	lesion	NOUN
aiti-14032	43	28	.	.	PUNCT
aiti-14032	44	1	however	however	ADV
aiti-14032	44	2	,	,	PUNCT
aiti-14032	44	3	some	some	DET
aiti-14032	44	4	plant	plant	NOUN
aiti-14032	44	5	leaf	leaf	NOUN
aiti-14032	44	6	images	image	NOUN
aiti-14032	44	7	exhibit	exhibit	VERB
aiti-14032	44	8	nearly	nearly	ADV
aiti-14032	44	9	identical	identical	ADJ
aiti-14032	44	10	spots	spot	NOUN
aiti-14032	44	11	,	,	PUNCT
aiti-14032	44	12	posing	pose	VERB
aiti-14032	44	13	classification	classification	NOUN
aiti-14032	44	14	challenges	challenge	NOUN
aiti-14032	44	15	for	for	ADP
aiti-14032	44	16	such	such	ADJ
aiti-14032	44	17	systems	system	NOUN
aiti-14032	44	18	.	.	PUNCT
aiti-14032	45	1	nevertheless	nevertheless	ADV
aiti-14032	45	2	,	,	PUNCT
aiti-14032	45	3	by	by	ADP
aiti-14032	45	4	employing	employ	VERB
aiti-14032	45	5	a	a	DET
aiti-14032	45	6	suitable	suitable	ADJ
aiti-14032	45	7	and	and	CCONJ
aiti-14032	45	8	effective	effective	ADJ
aiti-14032	45	9	feature	feature	NOUN
aiti-14032	45	10	extraction	extraction	NOUN
aiti-14032	45	11	approach	approach	NOUN
aiti-14032	45	12	,	,	PUNCT
aiti-14032	45	13	it	it	PRON
aiti-14032	45	14	is	be	AUX
aiti-14032	45	15	possible	possible	ADJ
aiti-14032	45	16	to	to	PART
aiti-14032	45	17	address	address	VERB
aiti-14032	45	18	the	the	DET
aiti-14032	45	19	issue	issue	NOUN
aiti-14032	45	20	of	of	ADP
aiti-14032	45	21	similar	similar	ADJ
aiti-14032	45	22	lesion	lesion	NOUN
aiti-14032	45	23	visibility	visibility	NOUN
aiti-14032	45	24	and	and	CCONJ
aiti-14032	45	25	achieve	achieve	VERB
aiti-14032	45	26	a	a	DET
aiti-14032	45	27	satisfactory	satisfactory	ADJ
aiti-14032	45	28	resolution	resolution	NOUN
aiti-14032	45	29	.	.	PUNCT
aiti-14032	46	1	notably	notably	ADV
aiti-14032	46	2	,	,	PUNCT
aiti-14032	46	3	color	color	NOUN
aiti-14032	46	4	,	,	PUNCT
aiti-14032	46	5	texture	texture	NOUN
aiti-14032	46	6	,	,	PUNCT
aiti-14032	46	7	and	and	CCONJ
aiti-14032	46	8	shape	shape	NOUN
aiti-14032	46	9	are	be	AUX
aiti-14032	46	10	crucial	crucial	ADJ
aiti-14032	46	11	features	feature	NOUN
aiti-14032	46	12	that	that	PRON
aiti-14032	46	13	play	play	VERB
aiti-14032	46	14	a	a	DET
aiti-14032	46	15	key	key	ADJ
aiti-14032	46	16	role	role	NOUN
aiti-14032	46	17	in	in	ADP
aiti-14032	46	18	the	the	DET
aiti-14032	46	19	prediction	prediction	NOUN
aiti-14032	46	20	of	of	ADP
aiti-14032	46	21	plant	plant	NOUN
aiti-14032	46	22	diseases	disease	NOUN
aiti-14032	46	23	.	.	PUNCT
aiti-14032	47	1	1.2	1.2	NUM
aiti-14032	47	2	.	.	PUNCT
aiti-14032	48	1	existing	exist	VERB
aiti-14032	48	2	models	model	NOUN
aiti-14032	48	3	literature	literature	NOUN
aiti-14032	48	4	survey	survey	PROPN
aiti-14032	48	5	li	li	PROPN
aiti-14032	48	6	et	et	PROPN
aiti-14032	48	7	al	al	PROPN
aiti-14032	48	8	.	.	PUNCT
aiti-14032	49	1	[	[	X
aiti-14032	49	2	6	6	NUM
aiti-14032	49	3	]	]	PUNCT
aiti-14032	49	4	presented	present	VERB
aiti-14032	49	5	an	an	DET
aiti-14032	49	6	innovative	innovative	ADJ
aiti-14032	49	7	approach	approach	NOUN
aiti-14032	49	8	for	for	ADP
aiti-14032	49	9	feature	feature	NOUN
aiti-14032	49	10	extraction	extraction	NOUN
aiti-14032	49	11	in	in	ADP
aiti-14032	49	12	hyperspectral	hyperspectral	ADJ
aiti-14032	49	13	image	image	NOUN
aiti-14032	49	14	analysis	analysis	NOUN
aiti-14032	49	15	called	call	VERB
aiti-14032	49	16	spectral	spectral	ADJ
aiti-14032	49	17	-	-	PUNCT
aiti-14032	49	18	gabor	gabor	PROPN
aiti-14032	49	19	space	space	NOUN
aiti-14032	49	20	discriminant	discriminant	NOUN
aiti-14032	49	21	analysis	analysis	NOUN
aiti-14032	49	22	(	(	PUNCT
aiti-14032	49	23	sgda	sgda	NOUN
aiti-14032	49	24	)	)	PUNCT
aiti-14032	49	25	.	.	PUNCT
aiti-14032	50	1	the	the	DET
aiti-14032	50	2	authors	author	NOUN
aiti-14032	50	3	showed	show	VERB
aiti-14032	50	4	that	that	SCONJ
aiti-14032	50	5	hyperspectral	hyperspectral	ADJ
aiti-14032	50	6	images	image	NOUN
aiti-14032	50	7	are	be	AUX
aiti-14032	50	8	high	high	ADJ
aiti-14032	50	9	-	-	PUNCT
aiti-14032	50	10	dimensional	dimensional	ADJ
aiti-14032	50	11	data	datum	NOUN
aiti-14032	50	12	,	,	PUNCT
aiti-14032	50	13	making	make	VERB
aiti-14032	50	14	preprocessing	preprocesse	VERB
aiti-14032	50	15	essential	essential	ADJ
aiti-14032	50	16	before	before	ADP
aiti-14032	50	17	extracting	extract	VERB
aiti-14032	50	18	spatial	spatial	ADJ
aiti-14032	50	19	features	feature	NOUN
aiti-14032	50	20	.	.	PUNCT
aiti-14032	51	1	in	in	ADP
aiti-14032	51	2	this	this	DET
aiti-14032	51	3	study	study	NOUN
aiti-14032	51	4	,	,	PUNCT
aiti-14032	51	5	principal	principal	ADJ
aiti-14032	51	6	component	component	NOUN
aiti-14032	51	7	analysis	analysis	NOUN
aiti-14032	51	8	(	(	PUNCT
aiti-14032	51	9	pca	pca	NOUN
aiti-14032	51	10	)	)	PUNCT
aiti-14032	51	11	was	be	AUX
aiti-14032	51	12	employed	employ	VERB
aiti-14032	51	13	to	to	PART
aiti-14032	51	14	extract	extract	VERB
aiti-14032	51	15	the	the	DET
aiti-14032	51	16	desired	desire	VERB
aiti-14032	51	17	principal	principal	ADJ
aiti-14032	51	18	components	component	NOUN
aiti-14032	51	19	.	.	PUNCT
aiti-14032	52	1	the	the	DET
aiti-14032	52	2	extracted	extract	VERB
aiti-14032	52	3	principal	principal	ADJ
aiti-14032	52	4	components	component	NOUN
aiti-14032	52	5	are	be	AUX
aiti-14032	52	6	then	then	ADV
aiti-14032	52	7	fed	feed	VERB
aiti-14032	52	8	to	to	PART
aiti-14032	52	9	derive	derive	VERB
aiti-14032	52	10	gabor	gabor	PROPN
aiti-14032	52	11	spatial	spatial	ADJ
aiti-14032	52	12	features	feature	NOUN
aiti-14032	52	13	,	,	PUNCT
aiti-14032	52	14	which	which	PRON
aiti-14032	52	15	effectively	effectively	ADV
aiti-14032	52	16	capture	capture	VERB
aiti-14032	52	17	low	low	ADJ
aiti-14032	52	18	-	-	PUNCT
aiti-14032	52	19	level	level	NOUN
aiti-14032	52	20	spatial	spatial	ADJ
aiti-14032	52	21	structures	structure	NOUN
aiti-14032	52	22	of	of	ADP
aiti-14032	52	23	various	various	ADJ
aiti-14032	52	24	orientations	orientation	NOUN
aiti-14032	52	25	and	and	CCONJ
aiti-14032	52	26	scales	scale	NOUN
aiti-14032	52	27	.	.	PUNCT
aiti-14032	53	1	to	to	PART
aiti-14032	53	2	improve	improve	VERB
aiti-14032	53	3	the	the	DET
aiti-14032	53	4	representation	representation	NOUN
aiti-14032	53	5	of	of	ADP
aiti-14032	53	6	the	the	DET
aiti-14032	53	7	hyperspectral	hyperspectral	ADJ
aiti-14032	53	8	data	datum	NOUN
aiti-14032	53	9	,	,	PUNCT
aiti-14032	53	10	the	the	DET
aiti-14032	53	11	original	original	ADJ
aiti-14032	53	12	spectral	spectral	ADJ
aiti-14032	53	13	features	feature	NOUN
aiti-14032	53	14	are	be	AUX
aiti-14032	53	15	combined	combine	VERB
aiti-14032	53	16	with	with	ADP
aiti-14032	53	17	the	the	DET
aiti-14032	53	18	extracted	extract	VERB
aiti-14032	53	19	gabor	gabor	PROPN
aiti-14032	53	20	spatial	spatial	ADJ
aiti-14032	53	21	features	feature	NOUN
aiti-14032	53	22	,	,	PUNCT
aiti-14032	53	23	resulting	result	VERB
aiti-14032	53	24	in	in	ADP
aiti-14032	53	25	fused	fused	ADJ
aiti-14032	53	26	features	feature	NOUN
aiti-14032	53	27	.	.	PUNCT
aiti-14032	54	1	in	in	ADP
aiti-14032	54	2	the	the	DET
aiti-14032	54	3	suggested	suggest	VERB
aiti-14032	54	4	sgda	sgda	NOUN
aiti-14032	54	5	method	method	NOUN
aiti-14032	54	6	,	,	PUNCT
aiti-14032	54	7	a	a	DET
aiti-14032	54	8	p	p	NOUN
aiti-14032	54	9	-	-	PUNCT
aiti-14032	54	10	factor	factor	NOUN
aiti-14032	54	11	α	α	NOUN
aiti-14032	54	12	was	be	AUX
aiti-14032	54	13	introduced	introduce	VERB
aiti-14032	54	14	to	to	PART
aiti-14032	54	15	regulate	regulate	VERB
aiti-14032	54	16	the	the	DET
aiti-14032	54	17	relative	relative	ADJ
aiti-14032	54	18	contributions	contribution	NOUN
aiti-14032	54	19	of	of	ADP
aiti-14032	54	20	spectral	spectral	ADJ
aiti-14032	54	21	and	and	CCONJ
aiti-14032	54	22	gabor	gabor	PROPN
aiti-14032	54	23	spatial	spatial	ADJ
aiti-14032	54	24	information	information	NOUN
aiti-14032	54	25	.	.	PUNCT
aiti-14032	55	1	hegde	hegde	PROPN
aiti-14032	55	2	et	et	PROPN
aiti-14032	55	3	al	al	PROPN
aiti-14032	55	4	.	.	PUNCT
aiti-14032	56	1	[	[	X
aiti-14032	56	2	7	7	X
aiti-14032	56	3	]	]	PUNCT
aiti-14032	56	4	explored	explore	VERB
aiti-14032	56	5	two	two	NUM
aiti-14032	56	6	approaches	approach	NOUN
aiti-14032	56	7	for	for	ADP
aiti-14032	56	8	feature	feature	NOUN
aiti-14032	56	9	extraction	extraction	NOUN
aiti-14032	56	10	in	in	ADP
aiti-14032	56	11	the	the	DET
aiti-14032	56	12	categorization	categorization	NOUN
aiti-14032	56	13	of	of	ADP
aiti-14032	56	14	white	white	ADJ
aiti-14032	56	15	blood	blood	NOUN
aiti-14032	56	16	cells	cell	NOUN
aiti-14032	56	17	(	(	PUNCT
aiti-14032	56	18	wbcs	wbc	NOUN
aiti-14032	56	19	):	):	PUNCT
aiti-14032	56	20	the	the	DET
aiti-14032	56	21	run	run	VERB
aiti-14032	56	22	-	-	PUNCT
aiti-14032	56	23	of	of	ADP
aiti-14032	56	24	-	-	PUNCT
aiti-14032	56	25	the	the	DET
aiti-14032	56	26	-	-	PUNCT
aiti-14032	56	27	mill	mill	NOUN
aiti-14032	56	28	image	image	NOUN
aiti-14032	56	29	processing	processing	NOUN
aiti-14032	56	30	approach	approach	NOUN
aiti-14032	56	31	and	and	CCONJ
aiti-14032	56	32	the	the	DET
aiti-14032	56	33	use	use	NOUN
aiti-14032	56	34	of	of	ADP
aiti-14032	56	35	a	a	DET
aiti-14032	56	36	convolutional	convolutional	ADJ
aiti-14032	56	37	neural	neural	ADJ
aiti-14032	56	38	network	network	NOUN
aiti-14032	56	39	(	(	PUNCT
aiti-14032	56	40	cnn	cnn	PROPN
aiti-14032	56	41	)	)	PUNCT
aiti-14032	56	42	as	as	ADP
aiti-14032	56	43	a	a	DET
aiti-14032	56	44	feature	feature	NOUN
aiti-14032	56	45	generator	generator	NOUN
aiti-14032	56	46	.	.	PUNCT
aiti-14032	57	1	the	the	DET
aiti-14032	57	2	classification	classification	NOUN
aiti-14032	57	3	of	of	ADP
aiti-14032	57	4	wbcs	wbc	NOUN
aiti-14032	57	5	was	be	AUX
aiti-14032	57	6	conducted	conduct	VERB
aiti-14032	57	7	in	in	ADP
aiti-14032	57	8	two	two	NUM
aiti-14032	57	9	steps	step	NOUN
aiti-14032	57	10	.	.	PUNCT
aiti-14032	58	1	initially	initially	ADV
aiti-14032	58	2	,	,	PUNCT
aiti-14032	58	3	wbcs	wbc	NOUN
aiti-14032	58	4	are	be	AUX
aiti-14032	58	5	categorized	categorize	VERB
aiti-14032	58	6	as	as	ADP
aiti-14032	58	7	normal	normal	ADJ
aiti-14032	58	8	or	or	CCONJ
aiti-14032	58	9	abnormal	abnormal	ADJ
aiti-14032	58	10	,	,	PUNCT
aiti-14032	58	11	followed	follow	VERB
aiti-14032	58	12	by	by	ADP
aiti-14032	58	13	the	the	DET
aiti-14032	58	14	division	division	NOUN
aiti-14032	58	15	of	of	ADP
aiti-14032	58	16	normal	normal	ADJ
aiti-14032	58	17	wbcs	wbc	NOUN
aiti-14032	58	18	into	into	ADP
aiti-14032	58	19	five	five	NUM
aiti-14032	58	20	variants	variant	NOUN
aiti-14032	58	21	:	:	PUNCT
aiti-14032	58	22	lymphocyte	lymphocyte	ADJ
aiti-14032	58	23	,	,	PUNCT
aiti-14032	58	24	monocyte	monocyte	ADJ
aiti-14032	58	25	,	,	PUNCT
aiti-14032	58	26	neutrophil	neutrophil	ADJ
aiti-14032	58	27	,	,	PUNCT
aiti-14032	58	28	eosinophil	eosinophil	ADJ
aiti-14032	58	29	,	,	PUNCT
aiti-14032	58	30	and	and	CCONJ
aiti-14032	58	31	basophil	basophil	NOUN
aiti-14032	58	32	.	.	PUNCT
aiti-14032	59	1	for	for	ADP
aiti-14032	59	2	feature	feature	NOUN
aiti-14032	59	3	extraction	extraction	NOUN
aiti-14032	59	4	,	,	PUNCT
aiti-14032	59	5	advances	advance	NOUN
aiti-14032	59	6	in	in	ADP
aiti-14032	59	7	technology	technology	NOUN
aiti-14032	59	8	innovation	innovation	NOUN
aiti-14032	59	9	,	,	PUNCT
aiti-14032	59	10	vol	vol	NOUN
aiti-14032	59	11	.	.	PROPN
aiti-14032	60	1	10	10	NUM
aiti-14032	60	2	,	,	PUNCT
aiti-14032	60	3	no	no	INTJ
aiti-14032	60	4	.	.	NOUN
aiti-14032	60	5	4	4	NUM
aiti-14032	60	6	,	,	PUNCT
aiti-14032	60	7	2025	2025	NUM
aiti-14032	60	8	,	,	PUNCT
aiti-14032	60	9	pp	pp	ADJ
aiti-14032	60	10	.	.	PUNCT
aiti-14032	61	1	370	370	NUM
aiti-14032	61	2	-	-	SYM
aiti-14032	61	3	382	382	NUM
aiti-14032	61	4	372	372	NUM
aiti-14032	61	5	they	they	PRON
aiti-14032	61	6	employed	employ	VERB
aiti-14032	61	7	state	state	NOUN
aiti-14032	61	8	-	-	PUNCT
aiti-14032	61	9	of	of	ADP
aiti-14032	61	10	-	-	PUNCT
aiti-14032	61	11	the	the	DET
aiti-14032	61	12	-	-	PUNCT
aiti-14032	61	13	art	art	NOUN
aiti-14032	61	14	image	image	NOUN
aiti-14032	61	15	processing	processing	NOUN
aiti-14032	61	16	techniques	technique	NOUN
aiti-14032	61	17	to	to	PART
aiti-14032	61	18	capture	capture	VERB
aiti-14032	61	19	shape	shape	NOUN
aiti-14032	61	20	,	,	PUNCT
aiti-14032	61	21	texture	texture	NOUN
aiti-14032	61	22	,	,	PUNCT
aiti-14032	61	23	and	and	CCONJ
aiti-14032	61	24	color	color	NOUN
aiti-14032	61	25	features	feature	NOUN
aiti-14032	61	26	.	.	PUNCT
aiti-14032	62	1	in	in	ADP
aiti-14032	62	2	particular	particular	ADJ
aiti-14032	62	3	,	,	PUNCT
aiti-14032	62	4	the	the	DET
aiti-14032	62	5	authors	author	NOUN
aiti-14032	62	6	use	use	VERB
aiti-14032	62	7	the	the	DET
aiti-14032	62	8	lbp	lbp	PROPN
aiti-14032	62	9	representation	representation	NOUN
aiti-14032	62	10	of	of	ADP
aiti-14032	62	11	grayscale	grayscale	NOUN
aiti-14032	62	12	images	image	NOUN
aiti-14032	62	13	to	to	PART
aiti-14032	62	14	effectively	effectively	ADV
aiti-14032	62	15	capture	capture	VERB
aiti-14032	62	16	local	local	ADJ
aiti-14032	62	17	textures	texture	NOUN
aiti-14032	62	18	.	.	PUNCT
aiti-14032	63	1	additionally	additionally	ADV
aiti-14032	63	2	,	,	PUNCT
aiti-14032	63	3	the	the	DET
aiti-14032	63	4	authors	author	NOUN
aiti-14032	63	5	explored	explore	VERB
aiti-14032	63	6	the	the	DET
aiti-14032	63	7	suitability	suitability	NOUN
aiti-14032	63	8	of	of	ADP
aiti-14032	63	9	features	feature	NOUN
aiti-14032	63	10	obtained	obtain	VERB
aiti-14032	63	11	from	from	ADP
aiti-14032	63	12	different	different	ADJ
aiti-14032	63	13	layers	layer	NOUN
aiti-14032	63	14	of	of	ADP
aiti-14032	63	15	a	a	DET
aiti-14032	63	16	pre	pre	ADJ
aiti-14032	63	17	-	-	ADJ
aiti-14032	63	18	trained	train	VERB
aiti-14032	63	19	cnn	cnn	PROPN
aiti-14032	63	20	,	,	PUNCT
aiti-14032	63	21	specifically	specifically	ADV
aiti-14032	63	22	alexnet	alexnet	ADJ
aiti-14032	63	23	,	,	PUNCT
aiti-14032	63	24	using	use	VERB
aiti-14032	63	25	the	the	DET
aiti-14032	63	26	“	"	PUNCT
aiti-14032	63	27	cnn	cnn	PROPN
aiti-14032	63	28	as	as	ADP
aiti-14032	63	29	a	a	DET
aiti-14032	63	30	feature	feature	NOUN
aiti-14032	63	31	generator	generator	NOUN
aiti-14032	63	32	”	"	PUNCT
aiti-14032	63	33	method	method	NOUN
aiti-14032	63	34	.	.	PUNCT
aiti-14032	64	1	the	the	DET
aiti-14032	64	2	results	result	NOUN
aiti-14032	64	3	from	from	ADP
aiti-14032	64	4	both	both	DET
aiti-14032	64	5	feature	feature	NOUN
aiti-14032	64	6	extraction	extraction	NOUN
aiti-14032	64	7	methods	method	NOUN
aiti-14032	64	8	are	be	AUX
aiti-14032	64	9	compared	compare	VERB
aiti-14032	64	10	.	.	PUNCT
aiti-14032	65	1	the	the	DET
aiti-14032	65	2	predictor	predictor	NOUN
aiti-14032	65	3	's	's	PART
aiti-14032	65	4	performance	performance	NOUN
aiti-14032	65	5	is	be	AUX
aiti-14032	65	6	evaluated	evaluate	VERB
aiti-14032	65	7	using	use	VERB
aiti-14032	65	8	the	the	DET
aiti-14032	65	9	extracted	extract	VERB
aiti-14032	65	10	features	feature	NOUN
aiti-14032	65	11	.	.	PUNCT
aiti-14032	66	1	remarkably	remarkably	ADV
aiti-14032	66	2	,	,	PUNCT
aiti-14032	66	3	comparable	comparable	ADJ
aiti-14032	66	4	accuracy	accuracy	NOUN
aiti-14032	66	5	using	use	VERB
aiti-14032	66	6	both	both	CCONJ
aiti-14032	66	7	the	the	DET
aiti-14032	66	8	existing	exist	VERB
aiti-14032	66	9	approach	approach	NOUN
aiti-14032	66	10	and	and	CCONJ
aiti-14032	66	11	the	the	DET
aiti-14032	66	12	“	"	PUNCT
aiti-14032	66	13	cnn	cnn	NOUN
aiti-14032	66	14	as	as	ADP
aiti-14032	66	15	a	a	DET
aiti-14032	66	16	feature	feature	NOUN
aiti-14032	66	17	generator	generator	NOUN
aiti-14032	66	18	”	"	PUNCT
aiti-14032	66	19	approach	approach	NOUN
aiti-14032	66	20	was	be	AUX
aiti-14032	66	21	achieved	achieve	VERB
aiti-14032	66	22	.	.	PUNCT
aiti-14032	67	1	however	however	ADV
aiti-14032	67	2	,	,	PUNCT
aiti-14032	67	3	the	the	DET
aiti-14032	67	4	classifier	classifier	NOUN
aiti-14032	67	5	demonstrated	demonstrate	VERB
aiti-14032	67	6	slightly	slightly	ADV
aiti-14032	67	7	better	well	ADJ
aiti-14032	67	8	performance	performance	NOUN
aiti-14032	67	9	when	when	SCONJ
aiti-14032	67	10	utilizing	utilize	VERB
aiti-14032	67	11	the	the	DET
aiti-14032	67	12	features	feature	NOUN
aiti-14032	67	13	from	from	ADP
aiti-14032	67	14	the	the	DET
aiti-14032	67	15	fully	fully	ADV
aiti-14032	67	16	connected	connected	ADJ
aiti-14032	67	17	layer	layer	NOUN
aiti-14032	67	18	8	8	NUM
aiti-14032	67	19	(	(	PUNCT
aiti-14032	67	20	fc8	fc8	NOUN
aiti-14032	67	21	)	)	PUNCT
aiti-14032	67	22	of	of	ADP
aiti-14032	67	23	alexnet	alexnet	NOUN
aiti-14032	67	24	for	for	ADP
aiti-14032	67	25	the	the	DET
aiti-14032	67	26	classification	classification	NOUN
aiti-14032	67	27	of	of	ADP
aiti-14032	67	28	wbcs	wbc	NOUN
aiti-14032	67	29	.	.	PUNCT
aiti-14032	68	1	overall	overall	ADV
aiti-14032	68	2	,	,	PUNCT
aiti-14032	68	3	an	an	DET
aiti-14032	68	4	accuracy	accuracy	NOUN
aiti-14032	68	5	of	of	ADP
aiti-14032	68	6	99.7	99.7	NUM
aiti-14032	68	7	%	%	NOUN
aiti-14032	68	8	in	in	ADP
aiti-14032	68	9	differentiating	differentiate	VERB
aiti-14032	68	10	between	between	ADP
aiti-14032	68	11	usual	usual	ADJ
aiti-14032	68	12	and	and	CCONJ
aiti-14032	68	13	unusual	unusual	ADJ
aiti-14032	68	14	wbcs	wbc	NOUN
aiti-14032	68	15	and	and	CCONJ
aiti-14032	68	16	an	an	DET
aiti-14032	68	17	average	average	ADJ
aiti-14032	68	18	accuracy	accuracy	NOUN
aiti-14032	68	19	of	of	ADP
aiti-14032	68	20	98.9	98.9	NUM
aiti-14032	68	21	%	%	NOUN
aiti-14032	68	22	in	in	ADP
aiti-14032	68	23	classifying	classify	VERB
aiti-14032	68	24	normal	normal	ADJ
aiti-14032	68	25	wbcs	wbc	NOUN
aiti-14032	68	26	into	into	ADP
aiti-14032	68	27	their	their	PRON
aiti-14032	68	28	respective	respective	ADJ
aiti-14032	68	29	types	type	NOUN
aiti-14032	68	30	was	be	AUX
aiti-14032	68	31	achieved	achieve	VERB
aiti-14032	68	32	.	.	PUNCT
aiti-14032	69	1	consequently	consequently	ADV
aiti-14032	69	2	,	,	PUNCT
aiti-14032	69	3	the	the	DET
aiti-14032	69	4	authors	author	NOUN
aiti-14032	69	5	quote	quote	VERB
aiti-14032	69	6	that	that	SCONJ
aiti-14032	69	7	training	training	NOUN
aiti-14032	69	8	at	at	ADP
aiti-14032	69	9	cnn	cnn	PROPN
aiti-14032	69	10	requires	require	VERB
aiti-14032	69	11	a	a	DET
aiti-14032	69	12	large	large	ADJ
aiti-14032	69	13	dataset	dataset	NOUN
aiti-14032	69	14	and	and	CCONJ
aiti-14032	69	15	significant	significant	ADJ
aiti-14032	69	16	computing	compute	VERB
aiti-14032	69	17	resources	resource	NOUN
aiti-14032	69	18	compared	compare	VERB
aiti-14032	69	19	to	to	ADP
aiti-14032	69	20	the	the	DET
aiti-14032	69	21	well	well	ADV
aiti-14032	69	22	-	-	PUNCT
aiti-14032	69	23	known	know	VERB
aiti-14032	69	24	image	image	NOUN
aiti-14032	69	25	processing	processing	NOUN
aiti-14032	69	26	approach	approach	NOUN
aiti-14032	69	27	.	.	PUNCT
aiti-14032	70	1	2	2	X
aiti-14032	70	2	.	.	X
aiti-14032	70	3	literature	literature	PROPN
aiti-14032	70	4	review	review	PROPN
aiti-14032	70	5	ahmad	ahmad	PROPN
aiti-14032	70	6	et	et	PROPN
aiti-14032	70	7	al	al	PROPN
aiti-14032	70	8	.	.	PUNCT
aiti-14032	71	1	[	[	X
aiti-14032	71	2	8	8	NUM
aiti-14032	71	3	]	]	PUNCT
aiti-14032	71	4	proposed	propose	VERB
aiti-14032	71	5	a	a	DET
aiti-14032	71	6	novel	novel	ADJ
aiti-14032	71	7	approach	approach	NOUN
aiti-14032	71	8	aimed	aim	VERB
aiti-14032	71	9	at	at	ADP
aiti-14032	71	10	automating	automate	VERB
aiti-14032	71	11	the	the	DET
aiti-14032	71	12	identification	identification	NOUN
aiti-14032	71	13	of	of	ADP
aiti-14032	71	14	plant	plant	NOUN
aiti-14032	71	15	diseases	disease	NOUN
aiti-14032	71	16	through	through	ADP
aiti-14032	71	17	a	a	DET
aiti-14032	71	18	series	series	NOUN
aiti-14032	71	19	of	of	ADP
aiti-14032	71	20	sequential	sequential	ADJ
aiti-14032	71	21	steps	step	NOUN
aiti-14032	71	22	,	,	PUNCT
aiti-14032	71	23	including	include	VERB
aiti-14032	71	24	pre	pre	ADJ
aiti-14032	71	25	-	-	ADJ
aiti-14032	71	26	processing	processing	ADJ
aiti-14032	71	27	,	,	PUNCT
aiti-14032	71	28	segmentation	segmentation	NOUN
aiti-14032	71	29	of	of	ADP
aiti-14032	71	30	the	the	DET
aiti-14032	71	31	diseased	diseased	ADJ
aiti-14032	71	32	leaf	leaf	NOUN
aiti-14032	71	33	area	area	NOUN
aiti-14032	71	34	,	,	PUNCT
aiti-14032	71	35	feature	feature	NOUN
aiti-14032	71	36	calculation	calculation	NOUN
aiti-14032	71	37	using	use	VERB
aiti-14032	71	38	the	the	DET
aiti-14032	71	39	gray	gray	ADJ
aiti-14032	71	40	-	-	PUNCT
aiti-14032	71	41	level	level	NOUN
aiti-14032	71	42	cooccurrence	cooccurrence	NOUN
aiti-14032	71	43	matrix	matrix	NOUN
aiti-14032	71	44	(	(	PUNCT
aiti-14032	71	45	glcm	glcm	PROPN
aiti-14032	71	46	)	)	PUNCT
aiti-14032	71	47	,	,	PUNCT
aiti-14032	71	48	feature	feature	NOUN
aiti-14032	71	49	filtration	filtration	NOUN
aiti-14032	71	50	,	,	PUNCT
aiti-14032	71	51	and	and	CCONJ
aiti-14032	71	52	categorization	categorization	NOUN
aiti-14032	71	53	.	.	PUNCT
aiti-14032	72	1	in	in	ADP
aiti-14032	72	2	the	the	DET
aiti-14032	72	3	study	study	NOUN
aiti-14032	72	4	,	,	PUNCT
aiti-14032	72	5	the	the	DET
aiti-14032	72	6	authors	author	NOUN
aiti-14032	72	7	computed	compute	VERB
aiti-14032	72	8	six	six	NUM
aiti-14032	72	9	color	color	NOUN
aiti-14032	72	10	features	feature	NOUN
aiti-14032	72	11	and	and	CCONJ
aiti-14032	72	12	twenty	twenty	NUM
aiti-14032	72	13	-	-	PUNCT
aiti-14032	72	14	two	two	NUM
aiti-14032	72	15	texture	texture	NOUN
aiti-14032	72	16	features	feature	NOUN
aiti-14032	72	17	.	.	PUNCT
aiti-14032	73	1	to	to	PART
aiti-14032	73	2	perform	perform	VERB
aiti-14032	73	3	the	the	DET
aiti-14032	73	4	classification	classification	NOUN
aiti-14032	73	5	of	of	ADP
aiti-14032	73	6	plant	plant	NOUN
aiti-14032	73	7	diseases	disease	NOUN
aiti-14032	73	8	,	,	PUNCT
aiti-14032	73	9	support	support	NOUN
aiti-14032	73	10	vector	vector	NOUN
aiti-14032	73	11	machines	machine	NOUN
aiti-14032	73	12	(	(	PUNCT
aiti-14032	73	13	svm	svm	PROPN
aiti-14032	73	14	)	)	PUNCT
aiti-14032	73	15	in	in	ADP
aiti-14032	73	16	a	a	DET
aiti-14032	73	17	one	one	NUM
aiti-14032	73	18	-	-	PUNCT
aiti-14032	73	19	vs	vs	ADP
aiti-14032	73	20	-	-	PUNCT
aiti-14032	73	21	one	one	NUM
aiti-14032	73	22	configuration	configuration	NOUN
aiti-14032	73	23	were	be	AUX
aiti-14032	73	24	utilized	utilize	VERB
aiti-14032	73	25	.	.	PUNCT
aiti-14032	74	1	the	the	DET
aiti-14032	74	2	proposed	propose	VERB
aiti-14032	74	3	model	model	NOUN
aiti-14032	74	4	for	for	ADP
aiti-14032	74	5	disease	disease	NOUN
aiti-14032	74	6	identification	identification	NOUN
aiti-14032	74	7	achieved	achieve	VERB
aiti-14032	74	8	an	an	DET
aiti-14032	74	9	impressive	impressive	ADJ
aiti-14032	74	10	accuracy	accuracy	NOUN
aiti-14032	74	11	of	of	ADP
aiti-14032	74	12	98.79	98.79	NUM
aiti-14032	74	13	%	%	NOUN
aiti-14032	74	14	with	with	ADP
aiti-14032	74	15	a	a	DET
aiti-14032	74	16	standard	standard	ADJ
aiti-14032	74	17	deviation	deviation	NOUN
aiti-14032	74	18	of	of	ADP
aiti-14032	74	19	0.57	0.57	NUM
aiti-14032	74	20	through	through	ADP
aiti-14032	74	21	ten	ten	NUM
aiti-14032	74	22	-	-	ADJ
aiti-14032	74	23	fold	fold	ADJ
aiti-14032	74	24	cross	cross	NOUN
aiti-14032	74	25	-	-	NOUN
aiti-14032	74	26	validation	validation	NOUN
aiti-14032	74	27	.	.	PUNCT
aiti-14032	75	1	when	when	SCONJ
aiti-14032	75	2	tested	test	VERB
aiti-14032	75	3	on	on	ADP
aiti-14032	75	4	a	a	DET
aiti-14032	75	5	self	self	NOUN
aiti-14032	75	6	-	-	PUNCT
aiti-14032	75	7	created	create	VERB
aiti-14032	75	8	dataset	dataset	NOUN
aiti-14032	75	9	,	,	PUNCT
aiti-14032	75	10	the	the	DET
aiti-14032	75	11	accuracy	accuracy	NOUN
aiti-14032	75	12	for	for	ADP
aiti-14032	75	13	disease	disease	NOUN
aiti-14032	75	14	identification	identification	NOUN
aiti-14032	75	15	is	be	AUX
aiti-14032	75	16	82.47	82.47	NUM
aiti-14032	75	17	%	%	NOUN
aiti-14032	75	18	,	,	PUNCT
aiti-14032	75	19	while	while	SCONJ
aiti-14032	75	20	the	the	DET
aiti-14032	75	21	accuracy	accuracy	NOUN
aiti-14032	75	22	for	for	ADP
aiti-14032	75	23	differentiating	differentiate	VERB
aiti-14032	75	24	between	between	ADP
aiti-14032	75	25	healthy	healthy	ADJ
aiti-14032	75	26	and	and	CCONJ
aiti-14032	75	27	diseased	diseased	ADJ
aiti-14032	75	28	samples	sample	NOUN
aiti-14032	75	29	is	be	AUX
aiti-14032	75	30	91.40	91.40	NUM
aiti-14032	75	31	%	%	NOUN
aiti-14032	75	32	.	.	PUNCT
aiti-14032	76	1	these	these	PRON
aiti-14032	76	2	reported	report	VERB
aiti-14032	76	3	performance	performance	NOUN
aiti-14032	76	4	measures	measure	NOUN
aiti-14032	76	5	either	either	CCONJ
aiti-14032	76	6	surpass	surpass	ADJ
aiti-14032	76	7	or	or	CCONJ
aiti-14032	76	8	are	be	AUX
aiti-14032	76	9	on	on	ADP
aiti-14032	76	10	par	par	NOUN
aiti-14032	76	11	with	with	ADP
aiti-14032	76	12	existing	exist	VERB
aiti-14032	76	13	approaches	approach	NOUN
aiti-14032	76	14	and	and	CCONJ
aiti-14032	76	15	are	be	AUX
aiti-14032	76	16	particularly	particularly	ADV
aiti-14032	76	17	superior	superior	ADJ
aiti-14032	76	18	to	to	ADP
aiti-14032	76	19	featurebased	featurebase	VERB
aiti-14032	76	20	methods	method	NOUN
aiti-14032	76	21	.	.	PUNCT
aiti-14032	77	1	consequently	consequently	ADV
aiti-14032	77	2	,	,	PUNCT
aiti-14032	77	3	the	the	DET
aiti-14032	77	4	authors	author	NOUN
aiti-14032	77	5	demonstrated	demonstrate	VERB
aiti-14032	77	6	that	that	SCONJ
aiti-14032	77	7	their	their	PRON
aiti-14032	77	8	method	method	NOUN
aiti-14032	77	9	is	be	AUX
aiti-14032	77	10	the	the	DET
aiti-14032	77	11	most	most	ADV
aiti-14032	77	12	suitable	suitable	ADJ
aiti-14032	77	13	approach	approach	NOUN
aiti-14032	77	14	for	for	ADP
aiti-14032	77	15	automating	automate	VERB
aiti-14032	77	16	leafbased	leafbased	ADJ
aiti-14032	77	17	plant	plant	NOUN
aiti-14032	77	18	disease	disease	NOUN
aiti-14032	77	19	identification	identification	NOUN
aiti-14032	77	20	.	.	PUNCT
aiti-14032	78	1	nagi	nagi	PROPN
aiti-14032	78	2	et	et	PROPN
aiti-14032	78	3	al	al	PROPN
aiti-14032	78	4	.	.	PUNCT
aiti-14032	79	1	[	[	X
aiti-14032	79	2	9	9	NUM
aiti-14032	79	3	]	]	PUNCT
aiti-14032	79	4	developed	develop	VERB
aiti-14032	79	5	a	a	DET
aiti-14032	79	6	model	model	NOUN
aiti-14032	79	7	for	for	ADP
aiti-14032	79	8	identifying	identify	VERB
aiti-14032	79	9	plant	plant	NOUN
aiti-14032	79	10	leaf	leaf	NOUN
aiti-14032	79	11	diseases	disease	NOUN
aiti-14032	79	12	using	use	VERB
aiti-14032	79	13	fuzzy	fuzzy	ADJ
aiti-14032	79	14	feature	feature	NOUN
aiti-14032	79	15	extraction	extraction	NOUN
aiti-14032	79	16	and	and	CCONJ
aiti-14032	79	17	the	the	DET
aiti-14032	79	18	probabilistic	probabilistic	ADJ
aiti-14032	79	19	neural	neural	ADJ
aiti-14032	79	20	network	network	NOUN
aiti-14032	79	21	(	(	PUNCT
aiti-14032	79	22	pnn	pnn	PROPN
aiti-14032	79	23	)	)	PUNCT
aiti-14032	79	24	.	.	PUNCT
aiti-14032	80	1	the	the	DET
aiti-14032	80	2	proposed	propose	VERB
aiti-14032	80	3	method	method	NOUN
aiti-14032	80	4	consisted	consist	VERB
aiti-14032	80	5	of	of	ADP
aiti-14032	80	6	two	two	NUM
aiti-14032	80	7	main	main	ADJ
aiti-14032	80	8	sections	section	NOUN
aiti-14032	80	9	.	.	PUNCT
aiti-14032	81	1	firstly	firstly	ADV
aiti-14032	81	2	,	,	PUNCT
aiti-14032	81	3	the	the	DET
aiti-14032	81	4	features	feature	NOUN
aiti-14032	81	5	(	(	PUNCT
aiti-14032	81	6	color	color	NOUN
aiti-14032	81	7	and	and	CCONJ
aiti-14032	81	8	texture	texture	NOUN
aiti-14032	81	9	)	)	PUNCT
aiti-14032	81	10	were	be	AUX
aiti-14032	81	11	obtained	obtain	VERB
aiti-14032	81	12	from	from	ADP
aiti-14032	81	13	the	the	DET
aiti-14032	81	14	leaf	leaf	NOUN
aiti-14032	81	15	images	image	NOUN
aiti-14032	81	16	using	use	VERB
aiti-14032	81	17	a	a	DET
aiti-14032	81	18	fuzzy	fuzzy	ADJ
aiti-14032	81	19	variant	variant	NOUN
aiti-14032	81	20	of	of	ADP
aiti-14032	81	21	the	the	DET
aiti-14032	81	22	gray	gray	ADJ
aiti-14032	81	23	-	-	PUNCT
aiti-14032	81	24	level	level	NOUN
aiti-14032	81	25	co	co	NOUN
aiti-14032	81	26	-	-	NOUN
aiti-14032	81	27	occurrence	occurrence	ADJ
aiti-14032	81	28	matrix	matrix	NOUN
aiti-14032	81	29	and	and	CCONJ
aiti-14032	81	30	a	a	DET
aiti-14032	81	31	color	color	NOUN
aiti-14032	81	32	histogram	histogram	NOUN
aiti-14032	81	33	.	.	PUNCT
aiti-14032	82	1	secondly	secondly	ADV
aiti-14032	82	2	,	,	PUNCT
aiti-14032	82	3	the	the	DET
aiti-14032	82	4	pnn	pnn	PROPN
aiti-14032	82	5	was	be	AUX
aiti-14032	82	6	employed	employ	VERB
aiti-14032	82	7	for	for	ADP
aiti-14032	82	8	classification	classification	NOUN
aiti-14032	82	9	.	.	PUNCT
aiti-14032	83	1	to	to	PART
aiti-14032	83	2	evaluate	evaluate	VERB
aiti-14032	83	3	the	the	DET
aiti-14032	83	4	effectiveness	effectiveness	NOUN
aiti-14032	83	5	of	of	ADP
aiti-14032	83	6	the	the	DET
aiti-14032	83	7	proposed	propose	VERB
aiti-14032	83	8	method	method	NOUN
aiti-14032	83	9	,	,	PUNCT
aiti-14032	83	10	leaf	leaf	NOUN
aiti-14032	83	11	images	image	NOUN
aiti-14032	83	12	of	of	ADP
aiti-14032	83	13	maize	maize	NOUN
aiti-14032	83	14	,	,	PUNCT
aiti-14032	83	15	grapevine	grapevine	NOUN
aiti-14032	83	16	,	,	PUNCT
aiti-14032	83	17	and	and	CCONJ
aiti-14032	83	18	tomato	tomato	NOUN
aiti-14032	83	19	were	be	AUX
aiti-14032	83	20	obtained	obtain	VERB
aiti-14032	83	21	from	from	ADP
aiti-14032	83	22	the	the	DET
aiti-14032	83	23	plantvillage	plantvillage	NOUN
aiti-14032	83	24	database	database	NOUN
aiti-14032	83	25	.	.	PUNCT
aiti-14032	84	1	the	the	DET
aiti-14032	84	2	model	model	NOUN
aiti-14032	84	3	achieved	achieve	VERB
aiti-14032	84	4	an	an	DET
aiti-14032	84	5	impressive	impressive	ADJ
aiti-14032	84	6	recognition	recognition	NOUN
aiti-14032	84	7	accuracy	accuracy	NOUN
aiti-14032	84	8	of	of	ADP
aiti-14032	84	9	95.68	95.68	NUM
aiti-14032	84	10	%	%	NOUN
aiti-14032	84	11	.	.	PUNCT
aiti-14032	85	1	furthermore	furthermore	ADV
aiti-14032	85	2	,	,	PUNCT
aiti-14032	85	3	it	it	PRON
aiti-14032	85	4	outperformed	outperform	VERB
aiti-14032	85	5	other	other	ADJ
aiti-14032	85	6	commonly	commonly	ADV
aiti-14032	85	7	used	use	VERB
aiti-14032	85	8	classifiers	classifier	NOUN
aiti-14032	85	9	such	such	ADJ
aiti-14032	85	10	as	as	ADP
aiti-14032	85	11	svm	svm	ADJ
aiti-14032	85	12	,	,	PUNCT
aiti-14032	85	13	decision	decision	NOUN
aiti-14032	85	14	tree	tree	NOUN
aiti-14032	85	15	(	(	PUNCT
aiti-14032	85	16	dt	dt	NOUN
aiti-14032	85	17	)	)	PUNCT
aiti-14032	85	18	,	,	PUNCT
aiti-14032	85	19	and	and	CCONJ
aiti-14032	85	20	random	random	ADJ
aiti-14032	85	21	forest	forest	NOUN
aiti-14032	85	22	(	(	PUNCT
aiti-14032	85	23	rf	rf	NOUN
aiti-14032	85	24	)	)	PUNCT
aiti-14032	85	25	in	in	ADP
aiti-14032	85	26	terms	term	NOUN
aiti-14032	85	27	of	of	ADP
aiti-14032	85	28	accuracy	accuracy	NOUN
aiti-14032	85	29	.	.	PUNCT
aiti-14032	86	1	overall	overall	ADV
aiti-14032	86	2	,	,	PUNCT
aiti-14032	86	3	the	the	DET
aiti-14032	86	4	combination	combination	NOUN
aiti-14032	86	5	of	of	ADP
aiti-14032	86	6	fuzzy	fuzzy	ADJ
aiti-14032	86	7	feature	feature	NOUN
aiti-14032	86	8	extraction	extraction	NOUN
aiti-14032	86	9	and	and	CCONJ
aiti-14032	86	10	the	the	DET
aiti-14032	86	11	pnn	pnn	PROPN
aiti-14032	86	12	classification	classification	NOUN
aiti-14032	86	13	approach	approach	NOUN
aiti-14032	86	14	has	have	AUX
aiti-14032	86	15	been	be	AUX
aiti-14032	86	16	proven	prove	VERB
aiti-14032	86	17	to	to	PART
aiti-14032	86	18	be	be	AUX
aiti-14032	86	19	highly	highly	ADV
aiti-14032	86	20	effective	effective	ADJ
aiti-14032	86	21	for	for	ADP
aiti-14032	86	22	plant	plant	NOUN
aiti-14032	86	23	leaf	leaf	NOUN
aiti-14032	86	24	disease	disease	NOUN
aiti-14032	86	25	recognition	recognition	NOUN
aiti-14032	86	26	.	.	PUNCT
aiti-14032	87	1	finally	finally	ADV
aiti-14032	87	2	,	,	PUNCT
aiti-14032	87	3	the	the	DET
aiti-14032	87	4	authors	author	NOUN
aiti-14032	87	5	concluded	conclude	VERB
aiti-14032	87	6	that	that	SCONJ
aiti-14032	87	7	the	the	DET
aiti-14032	87	8	results	result	NOUN
aiti-14032	87	9	obtained	obtain	VERB
aiti-14032	87	10	surpass	surpass	ADJ
aiti-14032	87	11	those	those	PRON
aiti-14032	87	12	of	of	ADP
aiti-14032	87	13	other	other	ADJ
aiti-14032	87	14	classifiers	classifier	NOUN
aiti-14032	87	15	,	,	PUNCT
aiti-14032	87	16	validating	validate	VERB
aiti-14032	87	17	the	the	DET
aiti-14032	87	18	applicability	applicability	NOUN
aiti-14032	87	19	of	of	ADP
aiti-14032	87	20	this	this	DET
aiti-14032	87	21	method	method	NOUN
aiti-14032	87	22	.	.	PUNCT
aiti-14032	88	1	in	in	ADP
aiti-14032	88	2	a	a	DET
aiti-14032	88	3	study	study	NOUN
aiti-14032	88	4	,	,	PUNCT
aiti-14032	88	5	basavaiah	basavaiah	PROPN
aiti-14032	88	6	et	et	PROPN
aiti-14032	88	7	al	al	PROPN
aiti-14032	88	8	.	.	PUNCT
aiti-14032	89	1	[	[	X
aiti-14032	89	2	10	10	NUM
aiti-14032	89	3	]	]	PUNCT
aiti-14032	89	4	proposed	propose	VERB
aiti-14032	89	5	a	a	DET
aiti-14032	89	6	methodology	methodology	NOUN
aiti-14032	89	7	for	for	ADP
aiti-14032	89	8	detecting	detect	VERB
aiti-14032	89	9	and	and	CCONJ
aiti-14032	89	10	classifying	classify	VERB
aiti-14032	89	11	four	four	NUM
aiti-14032	89	12	major	major	ADJ
aiti-14032	89	13	diseases	disease	NOUN
aiti-14032	89	14	of	of	ADP
aiti-14032	89	15	tomato	tomato	NOUN
aiti-14032	89	16	plants	plant	NOUN
aiti-14032	89	17	:	:	PUNCT
aiti-14032	89	18	septoria	septoria	PROPN
aiti-14032	89	19	spot	spot	PROPN
aiti-14032	89	20	,	,	PUNCT
aiti-14032	89	21	bacterial	bacterial	ADJ
aiti-14032	89	22	spot	spot	NOUN
aiti-14032	89	23	,	,	PUNCT
aiti-14032	89	24	yellow	yellow	ADJ
aiti-14032	89	25	curl	curl	NOUN
aiti-14032	89	26	,	,	PUNCT
aiti-14032	89	27	and	and	CCONJ
aiti-14032	89	28	mosaic	mosaic	ADJ
aiti-14032	89	29	virus	virus	NOUN
aiti-14032	89	30	.	.	PUNCT
aiti-14032	90	1	multiple	multiple	ADJ
aiti-14032	90	2	feature	feature	NOUN
aiti-14032	90	3	extraction	extraction	NOUN
aiti-14032	90	4	methods	method	NOUN
aiti-14032	90	5	were	be	AUX
aiti-14032	90	6	employed	employ	VERB
aiti-14032	90	7	to	to	PART
aiti-14032	90	8	capture	capture	VERB
aiti-14032	90	9	distinctive	distinctive	ADJ
aiti-14032	90	10	characteristics	characteristic	NOUN
aiti-14032	90	11	of	of	ADP
aiti-14032	90	12	these	these	DET
aiti-14032	90	13	diseases	disease	NOUN
aiti-14032	90	14	.	.	PUNCT
aiti-14032	91	1	subsequently	subsequently	ADV
aiti-14032	91	2	,	,	PUNCT
aiti-14032	91	3	the	the	DET
aiti-14032	91	4	dt	dt	X
aiti-14032	91	5	classifier	classifier	NOUN
aiti-14032	91	6	and	and	CCONJ
aiti-14032	91	7	rf	rf	NOUN
aiti-14032	91	8	classifier	classifier	NOUN
aiti-14032	91	9	were	be	AUX
aiti-14032	91	10	utilized	utilize	VERB
aiti-14032	91	11	for	for	ADP
aiti-14032	91	12	disease	disease	NOUN
aiti-14032	91	13	classification	classification	NOUN
aiti-14032	91	14	.	.	PUNCT
aiti-14032	92	1	the	the	DET
aiti-14032	92	2	classification	classification	NOUN
aiti-14032	92	3	results	result	NOUN
aiti-14032	92	4	demonstrated	demonstrate	VERB
aiti-14032	92	5	an	an	DET
aiti-14032	92	6	accuracy	accuracy	NOUN
aiti-14032	92	7	of	of	ADP
aiti-14032	92	8	90	90	NUM
aiti-14032	92	9	%	%	NOUN
aiti-14032	92	10	for	for	ADP
aiti-14032	92	11	the	the	DET
aiti-14032	92	12	dt	dt	PROPN
aiti-14032	92	13	classifier	classifier	NOUN
aiti-14032	92	14	and	and	CCONJ
aiti-14032	92	15	94	94	NUM
aiti-14032	92	16	%	%	NOUN
aiti-14032	92	17	for	for	ADP
aiti-14032	92	18	the	the	DET
aiti-14032	92	19	rf	rf	NOUN
aiti-14032	92	20	classifier	classifier	NOUN
aiti-14032	92	21	.	.	PUNCT
aiti-14032	93	1	the	the	DET
aiti-14032	93	2	authors	author	NOUN
aiti-14032	93	3	noted	note	VERB
aiti-14032	93	4	that	that	SCONJ
aiti-14032	93	5	the	the	DET
aiti-14032	93	6	random	random	ADJ
aiti-14032	93	7	forest	forest	NOUN
aiti-14032	93	8	classifier	classifier	NOUN
aiti-14032	93	9	exhibited	exhibit	VERB
aiti-14032	93	10	higher	high	ADJ
aiti-14032	93	11	accuracy	accuracy	NOUN
aiti-14032	93	12	compared	compare	VERB
aiti-14032	93	13	to	to	ADP
aiti-14032	93	14	the	the	DET
aiti-14032	93	15	decision	decision	NOUN
aiti-14032	93	16	tree	tree	NOUN
aiti-14032	93	17	classifier	classifier	NOUN
aiti-14032	93	18	.	.	PUNCT
aiti-14032	94	1	this	this	DET
aiti-14032	94	2	finding	finding	NOUN
aiti-14032	94	3	highlighted	highlight	VERB
aiti-14032	94	4	the	the	DET
aiti-14032	94	5	superiority	superiority	NOUN
aiti-14032	94	6	of	of	ADP
aiti-14032	94	7	the	the	DET
aiti-14032	94	8	rf	rf	NOUN
aiti-14032	94	9	classifier	classifier	NOUN
aiti-14032	94	10	in	in	ADP
aiti-14032	94	11	this	this	DET
aiti-14032	94	12	context	context	NOUN
aiti-14032	94	13	.	.	PUNCT
aiti-14032	95	1	the	the	DET
aiti-14032	95	2	method	method	NOUN
aiti-14032	95	3	proposed	propose	VERB
aiti-14032	95	4	in	in	ADP
aiti-14032	95	5	this	this	DET
aiti-14032	95	6	study	study	NOUN
aiti-14032	95	7	offered	offer	VERB
aiti-14032	95	8	several	several	ADJ
aiti-14032	95	9	advantages	advantage	NOUN
aiti-14032	95	10	.	.	PUNCT
aiti-14032	96	1	firstly	firstly	ADV
aiti-14032	96	2	,	,	PUNCT
aiti-14032	96	3	it	it	PRON
aiti-14032	96	4	significantly	significantly	ADV
aiti-14032	96	5	reduced	reduce	VERB
aiti-14032	96	6	computational	computational	ADJ
aiti-14032	96	7	time	time	NOUN
aiti-14032	96	8	,	,	PUNCT
aiti-14032	96	9	making	make	VERB
aiti-14032	96	10	it	it	PRON
aiti-14032	96	11	more	more	ADV
aiti-14032	96	12	efficient	efficient	ADJ
aiti-14032	96	13	than	than	ADP
aiti-14032	96	14	other	other	ADJ
aiti-14032	96	15	commonly	commonly	ADV
aiti-14032	96	16	used	use	VERB
aiti-14032	96	17	techniques	technique	NOUN
aiti-14032	96	18	.	.	PUNCT
aiti-14032	97	1	through	through	ADP
aiti-14032	97	2	a	a	DET
aiti-14032	97	3	rigorous	rigorous	ADJ
aiti-14032	97	4	literature	literature	NOUN
aiti-14032	97	5	review	review	NOUN
aiti-14032	97	6	,	,	PUNCT
aiti-14032	97	7	several	several	ADJ
aiti-14032	97	8	areas	area	NOUN
aiti-14032	97	9	for	for	ADP
aiti-14032	97	10	improvement	improvement	NOUN
aiti-14032	97	11	are	be	AUX
aiti-14032	97	12	identified	identify	VERB
aiti-14032	97	13	:	:	PUNCT
aiti-14032	97	14	(	(	PUNCT
aiti-14032	97	15	1	1	X
aiti-14032	97	16	)	)	PUNCT
aiti-14032	97	17	the	the	DET
aiti-14032	97	18	robustness	robustness	NOUN
aiti-14032	97	19	of	of	ADP
aiti-14032	97	20	the	the	DET
aiti-14032	97	21	system	system	NOUN
aiti-14032	97	22	in	in	ADP
aiti-14032	97	23	the	the	DET
aiti-14032	97	24	presence	presence	NOUN
aiti-14032	97	25	of	of	ADP
aiti-14032	97	26	noise	noise	NOUN
aiti-14032	97	27	and	and	CCONJ
aiti-14032	97	28	artifacts	artifact	NOUN
aiti-14032	97	29	commonly	commonly	ADV
aiti-14032	97	30	found	find	VERB
aiti-14032	97	31	in	in	ADP
aiti-14032	97	32	imaging	imaging	NOUN
aiti-14032	97	33	,	,	PUNCT
aiti-14032	97	34	such	such	ADJ
aiti-14032	97	35	as	as	ADP
aiti-14032	97	36	motion	motion	NOUN
aiti-14032	97	37	artifacts	artifact	NOUN
aiti-14032	97	38	or	or	CCONJ
aiti-14032	97	39	variations	variation	NOUN
aiti-14032	97	40	in	in	ADP
aiti-14032	97	41	imaging	imaging	NOUN
aiti-14032	97	42	conditions	condition	NOUN
aiti-14032	97	43	,	,	PUNCT
aiti-14032	97	44	needs	need	VERB
aiti-14032	97	45	further	further	ADJ
aiti-14032	97	46	evaluation	evaluation	NOUN
aiti-14032	97	47	.	.	PUNCT
aiti-14032	98	1	enhancing	enhance	VERB
aiti-14032	98	2	the	the	DET
aiti-14032	98	3	method	method	NOUN
aiti-14032	98	4	’s	’s	PART
aiti-14032	98	5	robustness	robustness	NOUN
aiti-14032	98	6	is	be	AUX
aiti-14032	98	7	crucial	crucial	ADJ
aiti-14032	98	8	for	for	ADP
aiti-14032	98	9	ensuring	ensure	VERB
aiti-14032	98	10	reliable	reliable	ADJ
aiti-14032	98	11	disease	disease	NOUN
aiti-14032	98	12	prediction	prediction	NOUN
aiti-14032	98	13	.	.	PUNCT
aiti-14032	99	1	advances	advance	NOUN
aiti-14032	99	2	in	in	ADP
aiti-14032	99	3	technology	technology	NOUN
aiti-14032	99	4	innovation	innovation	NOUN
aiti-14032	99	5	,	,	PUNCT
aiti-14032	99	6	vol	vol	NOUN
aiti-14032	99	7	.	.	PROPN
aiti-14032	100	1	10	10	NUM
aiti-14032	100	2	,	,	PUNCT
aiti-14032	100	3	no	no	INTJ
aiti-14032	100	4	.	.	NOUN
aiti-14032	100	5	4	4	NUM
aiti-14032	100	6	,	,	PUNCT
aiti-14032	100	7	2025	2025	NUM
aiti-14032	100	8	,	,	PUNCT
aiti-14032	100	9	pp	pp	ADJ
aiti-14032	100	10	.	.	PUNCT
aiti-14032	101	1	370	370	NUM
aiti-14032	101	2	-	-	SYM
aiti-14032	101	3	382	382	NUM
aiti-14032	101	4	373	373	NUM
aiti-14032	101	5	(	(	PUNCT
aiti-14032	101	6	2	2	NUM
aiti-14032	101	7	)	)	PUNCT
aiti-14032	101	8	training	training	NOUN
aiti-14032	101	9	cnn	cnn	PROPN
aiti-14032	101	10	and	and	CCONJ
aiti-14032	101	11	other	other	ADJ
aiti-14032	101	12	deep	deep	ADJ
aiti-14032	101	13	learning	learning	NOUN
aiti-14032	101	14	models	model	NOUN
aiti-14032	101	15	require	require	VERB
aiti-14032	101	16	large	large	ADJ
aiti-14032	101	17	datasets	dataset	NOUN
aiti-14032	101	18	and	and	CCONJ
aiti-14032	101	19	substantial	substantial	ADJ
aiti-14032	101	20	computational	computational	ADJ
aiti-14032	101	21	resources	resource	NOUN
aiti-14032	101	22	,	,	PUNCT
aiti-14032	101	23	which	which	PRON
aiti-14032	101	24	limits	limit	VERB
aiti-14032	101	25	their	their	PRON
aiti-14032	101	26	accessibility	accessibility	NOUN
aiti-14032	101	27	and	and	CCONJ
aiti-14032	101	28	practicality	practicality	NOUN
aiti-14032	101	29	,	,	PUNCT
aiti-14032	101	30	especially	especially	ADV
aiti-14032	101	31	in	in	ADP
aiti-14032	101	32	resource	resource	NOUN
aiti-14032	101	33	-	-	PUNCT
aiti-14032	101	34	constrained	constrain	VERB
aiti-14032	101	35	environments	environment	NOUN
aiti-14032	101	36	.	.	PUNCT
aiti-14032	102	1	(	(	PUNCT
aiti-14032	102	2	3	3	X
aiti-14032	102	3	)	)	PUNCT
aiti-14032	102	4	research	research	NOUN
aiti-14032	102	5	into	into	ADP
aiti-14032	102	6	more	more	ADV
aiti-14032	102	7	efficient	efficient	ADJ
aiti-14032	102	8	algorithms	algorithm	NOUN
aiti-14032	102	9	or	or	CCONJ
aiti-14032	102	10	models	model	NOUN
aiti-14032	102	11	that	that	PRON
aiti-14032	102	12	can	can	AUX
aiti-14032	102	13	achieve	achieve	VERB
aiti-14032	102	14	high	high	ADJ
aiti-14032	102	15	accuracy	accuracy	NOUN
aiti-14032	102	16	with	with	ADP
aiti-14032	102	17	fewer	few	ADJ
aiti-14032	102	18	resources	resource	NOUN
aiti-14032	102	19	and	and	CCONJ
aiti-14032	102	20	smaller	small	ADJ
aiti-14032	102	21	datasets	dataset	NOUN
aiti-14032	102	22	is	be	AUX
aiti-14032	102	23	needed	need	VERB
aiti-14032	102	24	.	.	PUNCT
aiti-14032	103	1	(	(	PUNCT
aiti-14032	103	2	4	4	X
aiti-14032	103	3	)	)	PUNCT
aiti-14032	103	4	further	further	ADJ
aiti-14032	103	5	research	research	NOUN
aiti-14032	103	6	should	should	AUX
aiti-14032	103	7	focus	focus	VERB
aiti-14032	103	8	on	on	ADP
aiti-14032	103	9	how	how	SCONJ
aiti-14032	103	10	to	to	PART
aiti-14032	103	11	effectively	effectively	ADV
aiti-14032	103	12	integrate	integrate	VERB
aiti-14032	103	13	multimodal	multimodal	NOUN
aiti-14032	103	14	features	feature	NOUN
aiti-14032	103	15	to	to	PART
aiti-14032	103	16	improve	improve	VERB
aiti-14032	103	17	the	the	DET
aiti-14032	103	18	accuracy	accuracy	NOUN
aiti-14032	103	19	and	and	CCONJ
aiti-14032	103	20	reliability	reliability	NOUN
aiti-14032	103	21	of	of	ADP
aiti-14032	103	22	disease	disease	NOUN
aiti-14032	103	23	prediction	prediction	NOUN
aiti-14032	103	24	models	model	NOUN
aiti-14032	103	25	.	.	PUNCT
aiti-14032	104	1	(	(	PUNCT
aiti-14032	104	2	5	5	X
aiti-14032	104	3	)	)	PUNCT
aiti-14032	104	4	a	a	DET
aiti-14032	104	5	lack	lack	NOUN
aiti-14032	104	6	of	of	ADP
aiti-14032	104	7	standardized	standardized	ADJ
aiti-14032	104	8	evaluation	evaluation	NOUN
aiti-14032	104	9	metrics	metric	NOUN
aiti-14032	104	10	for	for	ADP
aiti-14032	104	11	comparing	compare	VERB
aiti-14032	104	12	the	the	DET
aiti-14032	104	13	performance	performance	NOUN
aiti-14032	104	14	of	of	ADP
aiti-14032	104	15	different	different	ADJ
aiti-14032	104	16	feature	feature	NOUN
aiti-14032	104	17	extraction	extraction	NOUN
aiti-14032	104	18	and	and	CCONJ
aiti-14032	104	19	classification	classification	NOUN
aiti-14032	104	20	methods	method	NOUN
aiti-14032	104	21	.	.	PUNCT
aiti-14032	105	1	the	the	DET
aiti-14032	105	2	purpose	purpose	NOUN
aiti-14032	105	3	of	of	ADP
aiti-14032	105	4	this	this	DET
aiti-14032	105	5	study	study	NOUN
aiti-14032	105	6	is	be	AUX
aiti-14032	105	7	to	to	PART
aiti-14032	105	8	present	present	VERB
aiti-14032	105	9	multimodal	multimodal	NOUN
aiti-14032	105	10	feature	feature	NOUN
aiti-14032	105	11	extraction	extraction	NOUN
aiti-14032	105	12	techniques	technique	NOUN
aiti-14032	105	13	that	that	PRON
aiti-14032	105	14	effectively	effectively	ADV
aiti-14032	105	15	capture	capture	VERB
aiti-14032	105	16	essential	essential	ADJ
aiti-14032	105	17	features	feature	NOUN
aiti-14032	105	18	and	and	CCONJ
aiti-14032	105	19	introduce	introduce	VERB
aiti-14032	105	20	a	a	DET
aiti-14032	105	21	unique	unique	ADJ
aiti-14032	105	22	integration	integration	NOUN
aiti-14032	105	23	of	of	ADP
aiti-14032	105	24	machine	machine	NOUN
aiti-14032	105	25	learning	learning	NOUN
aiti-14032	105	26	models	model	NOUN
aiti-14032	105	27	with	with	ADP
aiti-14032	105	28	the	the	DET
aiti-14032	105	29	extracted	extract	VERB
aiti-14032	105	30	features	feature	NOUN
aiti-14032	105	31	.	.	PUNCT
aiti-14032	106	1	the	the	DET
aiti-14032	106	2	study	study	NOUN
aiti-14032	106	3	also	also	ADV
aiti-14032	106	4	explores	explore	VERB
aiti-14032	106	5	the	the	DET
aiti-14032	106	6	fusion	fusion	NOUN
aiti-14032	106	7	of	of	ADP
aiti-14032	106	8	fuzzy	fuzzy	ADJ
aiti-14032	106	9	features	feature	NOUN
aiti-14032	106	10	with	with	ADP
aiti-14032	106	11	a	a	DET
aiti-14032	106	12	multiclass	multiclass	ADJ
aiti-14032	106	13	disease	disease	NOUN
aiti-14032	106	14	prediction	prediction	NOUN
aiti-14032	106	15	model	model	NOUN
aiti-14032	106	16	,	,	PUNCT
aiti-14032	106	17	such	such	ADJ
aiti-14032	106	18	as	as	ADP
aiti-14032	106	19	multiclass	multiclass	ADJ
aiti-14032	106	20	svm	svm	NOUN
aiti-14032	106	21	.	.	PROPN
aiti-14032	106	22	3	3	NUM
aiti-14032	106	23	.	.	NUM
aiti-14032	106	24	proposed	propose	VERB
aiti-14032	106	25	methodology	methodology	NOUN
aiti-14032	106	26	the	the	DET
aiti-14032	106	27	present	present	ADJ
aiti-14032	106	28	work	work	NOUN
aiti-14032	106	29	is	be	AUX
aiti-14032	106	30	structured	structure	VERB
aiti-14032	106	31	as	as	ADP
aiti-14032	106	32	a	a	DET
aiti-14032	106	33	dataset	dataset	NOUN
aiti-14032	106	34	collection	collection	NOUN
aiti-14032	106	35	followed	follow	VERB
aiti-14032	106	36	by	by	ADP
aiti-14032	106	37	preprocessing	preprocesse	VERB
aiti-14032	106	38	,	,	PUNCT
aiti-14032	106	39	feature	feature	NOUN
aiti-14032	106	40	extraction	extraction	NOUN
aiti-14032	106	41	,	,	PUNCT
aiti-14032	106	42	training	training	NOUN
aiti-14032	106	43	of	of	ADP
aiti-14032	106	44	the	the	DET
aiti-14032	106	45	machine	machine	NOUN
aiti-14032	106	46	learning	learning	NOUN
aiti-14032	106	47	model	model	NOUN
aiti-14032	106	48	,	,	PUNCT
aiti-14032	106	49	testing	testing	NOUN
aiti-14032	106	50	of	of	ADP
aiti-14032	106	51	the	the	DET
aiti-14032	106	52	trained	train	VERB
aiti-14032	106	53	model	model	NOUN
aiti-14032	106	54	,	,	PUNCT
aiti-14032	106	55	and	and	CCONJ
aiti-14032	106	56	disease	disease	NOUN
aiti-14032	106	57	prediction	prediction	NOUN
aiti-14032	106	58	.	.	PUNCT
aiti-14032	107	1	for	for	ADP
aiti-14032	107	2	data	data	NOUN
aiti-14032	107	3	collection	collection	NOUN
aiti-14032	107	4	,	,	PUNCT
aiti-14032	107	5	a	a	DET
aiti-14032	107	6	real	real	ADJ
aiti-14032	107	7	-	-	PUNCT
aiti-14032	107	8	life	life	NOUN
aiti-14032	107	9	dataset	dataset	NOUN
aiti-14032	107	10	of	of	ADP
aiti-14032	107	11	potato	potato	NOUN
aiti-14032	107	12	crop	crop	NOUN
aiti-14032	107	13	leaves	leave	NOUN
aiti-14032	107	14	is	be	AUX
aiti-14032	107	15	used	use	VERB
aiti-14032	107	16	,	,	PUNCT
aiti-14032	107	17	which	which	PRON
aiti-14032	107	18	is	be	AUX
aiti-14032	107	19	taken	take	VERB
aiti-14032	107	20	from	from	ADP
aiti-14032	107	21	kaggle	kaggle	PROPN
aiti-14032	107	22	.	.	PUNCT
aiti-14032	108	1	3.1	3.1	NUM
aiti-14032	108	2	.	.	PUNCT
aiti-14032	108	3	data	datum	NOUN
aiti-14032	108	4	collection	collection	NOUN
aiti-14032	108	5	the	the	DET
aiti-14032	108	6	dataset	dataset	NOUN
aiti-14032	108	7	used	use	VERB
aiti-14032	108	8	in	in	ADP
aiti-14032	108	9	the	the	DET
aiti-14032	108	10	present	present	ADJ
aiti-14032	108	11	work	work	NOUN
aiti-14032	108	12	is	be	AUX
aiti-14032	108	13	adopted	adopt	VERB
aiti-14032	108	14	from	from	ADP
aiti-14032	108	15	kaggle.com	kaggle.com	X
aiti-14032	109	1	[	[	X
aiti-14032	109	2	11	11	NUM
aiti-14032	109	3	]	]	PUNCT
aiti-14032	109	4	.	.	PUNCT
aiti-14032	110	1	the	the	DET
aiti-14032	110	2	dataset	dataset	NOUN
aiti-14032	110	3	consists	consist	VERB
aiti-14032	110	4	of	of	ADP
aiti-14032	110	5	1500	1500	NUM
aiti-14032	110	6	images	image	NOUN
aiti-14032	110	7	of	of	ADP
aiti-14032	110	8	potato	potato	NOUN
aiti-14032	110	9	plant	plant	NOUN
aiti-14032	110	10	leaves	leave	NOUN
aiti-14032	110	11	.	.	PUNCT
aiti-14032	111	1	the	the	DET
aiti-14032	111	2	data	data	NOUN
aiti-14032	111	3	is	be	AUX
aiti-14032	111	4	distributed	distribute	VERB
aiti-14032	111	5	into	into	ADP
aiti-14032	111	6	three	three	NUM
aiti-14032	111	7	directories	directory	NOUN
aiti-14032	111	8	:	:	PUNCT
aiti-14032	111	9	train	train	NOUN
aiti-14032	111	10	,	,	PUNCT
aiti-14032	111	11	test	test	NOUN
aiti-14032	111	12	,	,	PUNCT
aiti-14032	111	13	and	and	CCONJ
aiti-14032	111	14	validation	validation	NOUN
aiti-14032	111	15	.	.	PUNCT
aiti-14032	112	1	within	within	ADP
aiti-14032	112	2	each	each	DET
aiti-14032	112	3	directory	directory	NOUN
aiti-14032	112	4	,	,	PUNCT
aiti-14032	112	5	data	datum	NOUN
aiti-14032	112	6	is	be	AUX
aiti-14032	112	7	categorized	categorize	VERB
aiti-14032	112	8	into	into	ADP
aiti-14032	112	9	three	three	NUM
aiti-14032	112	10	different	different	ADJ
aiti-14032	112	11	classes	class	NOUN
aiti-14032	112	12	:	:	PUNCT
aiti-14032	112	13	potato	potato	NOUN
aiti-14032	112	14	leaf	leaf	NOUN
aiti-14032	112	15	with	with	ADP
aiti-14032	112	16	early	early	ADJ
aiti-14032	112	17	blight	blight	NOUN
aiti-14032	112	18	,	,	PUNCT
aiti-14032	112	19	potato	potato	NOUN
aiti-14032	112	20	leaf	leaf	NOUN
aiti-14032	112	21	with	with	ADP
aiti-14032	112	22	late	late	ADJ
aiti-14032	112	23	blight	blight	NOUN
aiti-14032	112	24	,	,	PUNCT
aiti-14032	112	25	and	and	CCONJ
aiti-14032	112	26	healthy	healthy	ADJ
aiti-14032	112	27	leaf	leaf	NOUN
aiti-14032	112	28	.	.	PUNCT
aiti-14032	113	1	the	the	DET
aiti-14032	113	2	training	training	NOUN
aiti-14032	113	3	set	set	NOUN
aiti-14032	113	4	contains	contain	VERB
aiti-14032	113	5	300	300	NUM
aiti-14032	113	6	images	image	NOUN
aiti-14032	113	7	per	per	ADP
aiti-14032	113	8	class	class	NOUN
aiti-14032	113	9	,	,	PUNCT
aiti-14032	113	10	while	while	SCONJ
aiti-14032	113	11	the	the	DET
aiti-14032	113	12	test	test	NOUN
aiti-14032	113	13	set	set	VERB
aiti-14032	113	14	and	and	CCONJ
aiti-14032	113	15	validation	validation	NOUN
aiti-14032	113	16	set	set	NOUN
aiti-14032	113	17	contain	contain	VERB
aiti-14032	113	18	100	100	NUM
aiti-14032	113	19	images	image	NOUN
aiti-14032	113	20	corresponding	correspond	VERB
aiti-14032	113	21	to	to	ADP
aiti-14032	113	22	each	each	DET
aiti-14032	113	23	class	class	NOUN
aiti-14032	113	24	of	of	ADP
aiti-14032	113	25	potato	potato	NOUN
aiti-14032	113	26	images	image	NOUN
aiti-14032	113	27	.	.	PUNCT
aiti-14032	114	1	the	the	DET
aiti-14032	114	2	sample	sample	NOUN
aiti-14032	114	3	of	of	ADP
aiti-14032	114	4	the	the	DET
aiti-14032	114	5	early	early	ADJ
aiti-14032	114	6	blight	blight	NOUN
aiti-14032	114	7	is	be	AUX
aiti-14032	114	8	shown	show	VERB
aiti-14032	114	9	in	in	ADP
aiti-14032	114	10	fig	fig	NOUN
aiti-14032	114	11	.	.	PUNCT
aiti-14032	115	1	1	1	X
aiti-14032	115	2	.	.	PUNCT
aiti-14032	116	1	the	the	DET
aiti-14032	116	2	characteristic	characteristic	ADJ
aiti-14032	116	3	symptoms	symptom	NOUN
aiti-14032	116	4	of	of	ADP
aiti-14032	116	5	the	the	DET
aiti-14032	116	6	early	early	ADJ
aiti-14032	116	7	blight	blight	NOUN
aiti-14032	116	8	include	include	VERB
aiti-14032	116	9	small	small	ADJ
aiti-14032	116	10	,	,	PUNCT
aiti-14032	116	11	dry	dry	ADJ
aiti-14032	116	12	,	,	PUNCT
aiti-14032	116	13	papery	papery	ADJ
aiti-14032	116	14	spots	spot	NOUN
aiti-14032	116	15	that	that	PRON
aiti-14032	116	16	turn	turn	VERB
aiti-14032	116	17	dark	dark	ADJ
aiti-14032	116	18	brown	brown	NOUN
aiti-14032	116	19	to	to	AUX
aiti-14032	116	20	black	black	VERB
aiti-14032	116	21	and	and	CCONJ
aiti-14032	116	22	become	become	VERB
aiti-14032	116	23	oval	oval	NOUN
aiti-14032	116	24	or	or	CCONJ
aiti-14032	116	25	angular	angular	ADJ
aiti-14032	116	26	.	.	PUNCT
aiti-14032	117	1	the	the	DET
aiti-14032	117	2	spots	spot	NOUN
aiti-14032	117	3	can	can	AUX
aiti-14032	117	4	grow	grow	VERB
aiti-14032	117	5	up	up	ADP
aiti-14032	117	6	to	to	PART
aiti-14032	117	7	12	12	NUM
aiti-14032	117	8	mm	mm	NOUN
aiti-14032	117	9	in	in	ADP
aiti-14032	117	10	diameter	diameter	NOUN
aiti-14032	117	11	and	and	CCONJ
aiti-14032	117	12	are	be	AUX
aiti-14032	117	13	usually	usually	ADV
aiti-14032	117	14	confined	confine	VERB
aiti-14032	117	15	to	to	ADP
aiti-14032	117	16	the	the	DET
aiti-14032	117	17	main	main	ADJ
aiti-14032	117	18	veins	vein	NOUN
aiti-14032	117	19	of	of	ADP
aiti-14032	117	20	the	the	DET
aiti-14032	117	21	leaflets	leaflet	NOUN
aiti-14032	117	22	.	.	PUNCT
aiti-14032	118	1	fig	fig	NOUN
aiti-14032	118	2	.	.	PUNCT
aiti-14032	119	1	1	1	NUM
aiti-14032	119	2	potato	potato	NOUN
aiti-14032	119	3	leaf	leaf	NOUN
aiti-14032	119	4	with	with	ADP
aiti-14032	119	5	early	early	ADJ
aiti-14032	119	6	blight	blight	NOUN
aiti-14032	119	7	this	this	DET
aiti-14032	119	8	section	section	NOUN
aiti-14032	119	9	considers	consider	VERB
aiti-14032	119	10	samples	sample	NOUN
aiti-14032	119	11	of	of	ADP
aiti-14032	119	12	two	two	NUM
aiti-14032	119	13	well	well	ADV
aiti-14032	119	14	-	-	PUNCT
aiti-14032	119	15	known	know	VERB
aiti-14032	119	16	diseases	disease	NOUN
aiti-14032	119	17	affecting	affect	VERB
aiti-14032	119	18	potato	potato	NOUN
aiti-14032	119	19	crops	crop	NOUN
aiti-14032	119	20	.	.	PUNCT
aiti-14032	120	1	in	in	ADP
aiti-14032	120	2	case	case	NOUN
aiti-14032	120	3	of	of	ADP
aiti-14032	120	4	late	late	ADJ
aiti-14032	120	5	blight	blight	NOUN
aiti-14032	120	6	,	,	PUNCT
aiti-14032	120	7	dark	dark	ADJ
aiti-14032	120	8	,	,	PUNCT
aiti-14032	120	9	watersoaked	watersoaked	ADJ
aiti-14032	120	10	lesions	lesion	NOUN
aiti-14032	120	11	appear	appear	VERB
aiti-14032	120	12	on	on	ADP
aiti-14032	120	13	the	the	DET
aiti-14032	120	14	leaves	leave	NOUN
aiti-14032	120	15	,	,	PUNCT
aiti-14032	120	16	often	often	ADV
aiti-14032	120	17	starting	start	VERB
aiti-14032	120	18	at	at	ADP
aiti-14032	120	19	the	the	DET
aiti-14032	120	20	tips	tip	NOUN
aiti-14032	120	21	or	or	CCONJ
aiti-14032	120	22	margins	margin	NOUN
aiti-14032	120	23	and	and	CCONJ
aiti-14032	120	24	spreading	spread	VERB
aiti-14032	120	25	toward	toward	ADP
aiti-14032	120	26	the	the	DET
aiti-14032	120	27	center	center	NOUN
aiti-14032	120	28	.	.	PUNCT
aiti-14032	121	1	the	the	DET
aiti-14032	121	2	lesions	lesion	NOUN
aiti-14032	121	3	are	be	AUX
aiti-14032	121	4	not	not	PART
aiti-14032	121	5	confined	confine	VERB
aiti-14032	121	6	by	by	ADP
aiti-14032	121	7	the	the	DET
aiti-14032	121	8	leaf	leaf	NOUN
aiti-14032	121	9	veins	vein	NOUN
aiti-14032	121	10	,	,	PUNCT
aiti-14032	121	11	and	and	CCONJ
aiti-14032	121	12	they	they	PRON
aiti-14032	121	13	may	may	AUX
aiti-14032	121	14	have	have	VERB
aiti-14032	121	15	a	a	DET
aiti-14032	121	16	yellow	yellow	ADJ
aiti-14032	121	17	edge	edge	NOUN
aiti-14032	121	18	.	.	PUNCT
aiti-14032	122	1	the	the	DET
aiti-14032	122	2	primary	primary	ADJ
aiti-14032	122	3	difference	difference	NOUN
aiti-14032	122	4	between	between	ADP
aiti-14032	122	5	early	early	ADJ
aiti-14032	122	6	blight	blight	NOUN
aiti-14032	122	7	and	and	CCONJ
aiti-14032	122	8	late	late	ADJ
aiti-14032	122	9	blight	blight	NOUN
aiti-14032	122	10	is	be	AUX
aiti-14032	122	11	that	that	SCONJ
aiti-14032	122	12	early	early	ADJ
aiti-14032	122	13	blight	blight	NOUN
aiti-14032	122	14	first	first	ADV
aiti-14032	122	15	infects	infect	VERB
aiti-14032	122	16	the	the	DET
aiti-14032	122	17	oldest	old	ADJ
aiti-14032	122	18	leaves	leave	NOUN
aiti-14032	122	19	,	,	PUNCT
aiti-14032	122	20	causing	cause	VERB
aiti-14032	122	21	brown	brown	ADJ
aiti-14032	122	22	areas	area	NOUN
aiti-14032	122	23	with	with	ADP
aiti-14032	122	24	concentric	concentric	ADJ
aiti-14032	122	25	rings	ring	NOUN
aiti-14032	122	26	,	,	PUNCT
aiti-14032	122	27	while	while	SCONJ
aiti-14032	122	28	late	late	ADJ
aiti-14032	122	29	blight	blight	NOUN
aiti-14032	122	30	causes	cause	VERB
aiti-14032	122	31	watery	watery	ADJ
aiti-14032	122	32	blisters	blister	NOUN
aiti-14032	122	33	on	on	ADP
aiti-14032	122	34	leaves	leave	NOUN
aiti-14032	122	35	,	,	PUNCT
aiti-14032	122	36	brown	brown	ADJ
aiti-14032	122	37	or	or	CCONJ
aiti-14032	122	38	black	black	ADJ
aiti-14032	122	39	lesions	lesion	NOUN
aiti-14032	122	40	on	on	ADP
aiti-14032	122	41	the	the	DET
aiti-14032	122	42	lower	low	ADJ
aiti-14032	122	43	leaves	leave	NOUN
aiti-14032	122	44	,	,	PUNCT
aiti-14032	122	45	and	and	CCONJ
aiti-14032	122	46	leaf	leaf	NOUN
aiti-14032	122	47	rotting	rot	VERB
aiti-14032	122	48	.	.	PUNCT
aiti-14032	123	1	a	a	DET
aiti-14032	123	2	sample	sample	NOUN
aiti-14032	123	3	of	of	ADP
aiti-14032	123	4	a	a	DET
aiti-14032	123	5	leaf	leaf	NOUN
aiti-14032	123	6	infected	infect	VERB
aiti-14032	123	7	with	with	ADP
aiti-14032	123	8	late	late	ADJ
aiti-14032	123	9	blight	blight	NOUN
aiti-14032	123	10	disease	disease	NOUN
aiti-14032	123	11	and	and	CCONJ
aiti-14032	123	12	a	a	DET
aiti-14032	123	13	sample	sample	NOUN
aiti-14032	123	14	of	of	ADP
aiti-14032	123	15	a	a	DET
aiti-14032	123	16	healthy	healthy	ADJ
aiti-14032	123	17	potato	potato	NOUN
aiti-14032	123	18	leaf	leaf	NOUN
aiti-14032	123	19	are	be	AUX
aiti-14032	123	20	shown	show	VERB
aiti-14032	123	21	in	in	ADP
aiti-14032	123	22	fig	fig	NOUN
aiti-14032	123	23	.	.	PUNCT
aiti-14032	124	1	2	2	NUM
aiti-14032	124	2	and	and	CCONJ
aiti-14032	124	3	fig	fig	NOUN
aiti-14032	124	4	.	.	PUNCT
aiti-14032	125	1	3	3	NUM
aiti-14032	125	2	,	,	PUNCT
aiti-14032	125	3	respectively	respectively	ADV
aiti-14032	125	4	.	.	PUNCT
aiti-14032	126	1	advances	advance	NOUN
aiti-14032	126	2	in	in	ADP
aiti-14032	126	3	technology	technology	NOUN
aiti-14032	126	4	innovation	innovation	NOUN
aiti-14032	126	5	,	,	PUNCT
aiti-14032	126	6	vol	vol	NOUN
aiti-14032	126	7	.	.	PROPN
aiti-14032	127	1	10	10	NUM
aiti-14032	127	2	,	,	PUNCT
aiti-14032	127	3	no	no	INTJ
aiti-14032	127	4	.	.	NOUN
aiti-14032	127	5	4	4	NUM
aiti-14032	127	6	,	,	PUNCT
aiti-14032	127	7	2025	2025	NUM
aiti-14032	127	8	,	,	PUNCT
aiti-14032	127	9	pp	pp	ADJ
aiti-14032	127	10	.	.	PUNCT
aiti-14032	128	1	370	370	NUM
aiti-14032	128	2	-	-	SYM
aiti-14032	128	3	382	382	NUM
aiti-14032	128	4	374	374	NUM
aiti-14032	128	5	fig	fig	NOUN
aiti-14032	128	6	.	.	PUNCT
aiti-14032	129	1	2	2	NUM
aiti-14032	129	2	potato	potato	NOUN
aiti-14032	129	3	leaf	leaf	NOUN
aiti-14032	129	4	with	with	ADP
aiti-14032	129	5	late	late	ADJ
aiti-14032	129	6	blight	blight	NOUN
aiti-14032	129	7	fig	fig	NOUN
aiti-14032	129	8	.	.	PUNCT
aiti-14032	130	1	3	3	NUM
aiti-14032	130	2	healthy	healthy	ADJ
aiti-14032	130	3	potato	potato	NOUN
aiti-14032	130	4	leaf	leaf	NOUN
aiti-14032	130	5	3.2	3.2	NUM
aiti-14032	130	6	.	.	PUNCT
aiti-14032	131	1	pre	pre	ADJ
aiti-14032	131	2	-	-	ADJ
aiti-14032	131	3	processing	processing	NOUN
aiti-14032	131	4	of	of	ADP
aiti-14032	131	5	the	the	DET
aiti-14032	131	6	image	image	NOUN
aiti-14032	131	7	preprocessing	preprocesse	VERB
aiti-14032	131	8	an	an	DET
aiti-14032	131	9	image	image	NOUN
aiti-14032	131	10	involves	involve	VERB
aiti-14032	131	11	making	make	VERB
aiti-14032	131	12	it	it	PRON
aiti-14032	131	13	more	more	ADV
aiti-14032	131	14	suitable	suitable	ADJ
aiti-14032	131	15	for	for	ADP
aiti-14032	131	16	further	further	ADJ
aiti-14032	131	17	analysis	analysis	NOUN
aiti-14032	131	18	.	.	PUNCT
aiti-14032	132	1	this	this	DET
aiti-14032	132	2	process	process	NOUN
aiti-14032	132	3	includes	include	VERB
aiti-14032	132	4	filtering	filtering	NOUN
aiti-14032	132	5	noise	noise	NOUN
aiti-14032	132	6	and	and	CCONJ
aiti-14032	132	7	isolating	isolate	VERB
aiti-14032	132	8	the	the	DET
aiti-14032	132	9	actual	actual	ADJ
aiti-14032	132	10	leaf	leaf	NOUN
aiti-14032	132	11	from	from	ADP
aiti-14032	132	12	the	the	DET
aiti-14032	132	13	background	background	NOUN
aiti-14032	132	14	.	.	PUNCT
aiti-14032	133	1	for	for	ADP
aiti-14032	133	2	image	image	NOUN
aiti-14032	133	3	processing	processing	NOUN
aiti-14032	133	4	,	,	PUNCT
aiti-14032	133	5	a	a	DET
aiti-14032	133	6	modified	modify	VERB
aiti-14032	133	7	grab	grab	NOUN
aiti-14032	133	8	-	-	PUNCT
aiti-14032	133	9	cut	cut	VERB
aiti-14032	133	10	method	method	NOUN
aiti-14032	133	11	has	have	AUX
aiti-14032	133	12	been	be	AUX
aiti-14032	133	13	utilized	utilize	VERB
aiti-14032	133	14	.	.	PUNCT
aiti-14032	134	1	the	the	DET
aiti-14032	134	2	method	method	NOUN
aiti-14032	134	3	is	be	AUX
aiti-14032	134	4	explained	explain	VERB
aiti-14032	134	5	as	as	SCONJ
aiti-14032	134	6	follows	follow	VERB
aiti-14032	134	7	.	.	PUNCT
aiti-14032	135	1	3.2.1	3.2.1	X
aiti-14032	135	2	.	.	PUNCT
aiti-14032	135	3	grab	grab	NOUN
aiti-14032	135	4	-	-	PUNCT
aiti-14032	135	5	cut	cut	VERB
aiti-14032	135	6	method	method	NOUN
aiti-14032	135	7	grab	grab	NOUN
aiti-14032	135	8	-	-	PUNCT
aiti-14032	135	9	cut	cut	VERB
aiti-14032	135	10	utilizes	utilizes	ADJ
aiti-14032	135	11	graph	graph	NOUN
aiti-14032	135	12	cuts	cut	NOUN
aiti-14032	135	13	as	as	ADP
aiti-14032	135	14	the	the	DET
aiti-14032	135	15	foundation	foundation	NOUN
aiti-14032	135	16	for	for	ADP
aiti-14032	135	17	its	its	PRON
aiti-14032	135	18	image	image	NOUN
aiti-14032	135	19	segmentation	segmentation	NOUN
aiti-14032	135	20	approach	approach	NOUN
aiti-14032	135	21	.	.	PUNCT
aiti-14032	136	1	by	by	ADP
aiti-14032	136	2	employing	employ	VERB
aiti-14032	136	3	a	a	DET
aiti-14032	136	4	gaussian	gaussian	ADJ
aiti-14032	136	5	mixture	mixture	NOUN
aiti-14032	136	6	model	model	NOUN
aiti-14032	136	7	(	(	PUNCT
aiti-14032	136	8	gmm	gmm	PROPN
aiti-14032	136	9	)	)	PUNCT
aiti-14032	136	10	,	,	PUNCT
aiti-14032	136	11	the	the	DET
aiti-14032	136	12	algorithm	algorithm	NOUN
aiti-14032	136	13	approximates	approximate	VERB
aiti-14032	136	14	the	the	DET
aiti-14032	136	15	color	color	NOUN
aiti-14032	136	16	distribution	distribution	NOUN
aiti-14032	136	17	for	for	ADP
aiti-14032	136	18	both	both	CCONJ
aiti-14032	136	19	the	the	DET
aiti-14032	136	20	desired	desire	VERB
aiti-14032	136	21	object	object	NOUN
aiti-14032	136	22	and	and	CCONJ
aiti-14032	136	23	the	the	DET
aiti-14032	136	24	background	background	NOUN
aiti-14032	137	1	[	[	X
aiti-14032	137	2	12	12	NUM
aiti-14032	137	3	]	]	PUNCT
aiti-14032	137	4	.	.	PUNCT
aiti-14032	138	1	in	in	ADP
aiti-14032	138	2	the	the	DET
aiti-14032	138	3	grabcut	grabcut	NOUN
aiti-14032	138	4	,	,	PUNCT
aiti-14032	138	5	an	an	DET
aiti-14032	138	6	initial	initial	ADJ
aiti-14032	138	7	rectangle	rectangle	NOUN
aiti-14032	138	8	is	be	AUX
aiti-14032	138	9	provided	provide	VERB
aiti-14032	138	10	,	,	PUNCT
aiti-14032	138	11	where	where	SCONJ
aiti-14032	138	12	everything	everything	PRON
aiti-14032	138	13	outside	outside	ADP
aiti-14032	138	14	the	the	DET
aiti-14032	138	15	rectangle	rectangle	NOUN
aiti-14032	138	16	is	be	AUX
aiti-14032	138	17	designated	designate	VERB
aiti-14032	138	18	as	as	ADP
aiti-14032	138	19	a	a	DET
aiti-14032	138	20	definite	definite	ADJ
aiti-14032	138	21	background	background	NOUN
aiti-14032	138	22	.	.	PUNCT
aiti-14032	139	1	conversely	conversely	ADV
aiti-14032	139	2	,	,	PUNCT
aiti-14032	139	3	the	the	DET
aiti-14032	139	4	region	region	NOUN
aiti-14032	139	5	inside	inside	ADP
aiti-14032	139	6	the	the	DET
aiti-14032	139	7	rectangle	rectangle	NOUN
aiti-14032	139	8	is	be	AUX
aiti-14032	139	9	considered	consider	VERB
aiti-14032	139	10	unknown	unknown	ADJ
aiti-14032	139	11	.	.	PUNCT
aiti-14032	140	1	any	any	DET
aiti-14032	140	2	additional	additional	ADJ
aiti-14032	140	3	user	user	NOUN
aiti-14032	140	4	input	input	NOUN
aiti-14032	140	5	indicating	indicate	VERB
aiti-14032	140	6	foreground	foreground	NOUN
aiti-14032	140	7	and	and	CCONJ
aiti-14032	140	8	background	background	NOUN
aiti-14032	140	9	is	be	AUX
aiti-14032	140	10	regarded	regard	VERB
aiti-14032	140	11	as	as	ADP
aiti-14032	140	12	hard	hard	ADJ
aiti-14032	140	13	labeling	labeling	NOUN
aiti-14032	140	14	,	,	PUNCT
aiti-14032	140	15	meaning	mean	VERB
aiti-14032	140	16	these	these	DET
aiti-14032	140	17	designations	designation	NOUN
aiti-14032	140	18	remain	remain	VERB
aiti-14032	140	19	unchanged	unchanged	ADJ
aiti-14032	140	20	throughout	throughout	ADP
aiti-14032	140	21	the	the	DET
aiti-14032	140	22	process	process	NOUN
aiti-14032	140	23	.	.	PUNCT
aiti-14032	141	1	after	after	ADP
aiti-14032	141	2	receiving	receive	VERB
aiti-14032	141	3	user	user	NOUN
aiti-14032	141	4	input	input	NOUN
aiti-14032	141	5	,	,	PUNCT
aiti-14032	141	6	the	the	DET
aiti-14032	141	7	system	system	NOUN
aiti-14032	141	8	performs	perform	VERB
aiti-14032	141	9	an	an	DET
aiti-14032	141	10	initial	initial	ADJ
aiti-14032	141	11	labeling	labeling	NOUN
aiti-14032	141	12	process	process	NOUN
aiti-14032	141	13	based	base	VERB
aiti-14032	141	14	on	on	ADP
aiti-14032	141	15	the	the	DET
aiti-14032	141	16	provided	provide	VERB
aiti-14032	141	17	data	datum	NOUN
aiti-14032	141	18	,	,	PUNCT
aiti-14032	141	19	assigning	assign	VERB
aiti-14032	141	20	pixels	pixel	NOUN
aiti-14032	141	21	as	as	ADP
aiti-14032	141	22	either	either	CCONJ
aiti-14032	141	23	foreground	foreground	NOUN
aiti-14032	141	24	or	or	CCONJ
aiti-14032	141	25	background	background	NOUN
aiti-14032	141	26	(	(	PUNCT
aiti-14032	141	27	hard	hard	ADJ
aiti-14032	141	28	labeling	labeling	NOUN
aiti-14032	141	29	)	)	PUNCT
aiti-14032	141	30	.	.	PUNCT
aiti-14032	142	1	3.2.2	3.2.2	X
aiti-14032	142	2	.	.	PUNCT
aiti-14032	142	3	application	application	NOUN
aiti-14032	142	4	of	of	ADP
aiti-14032	142	5	gaussian	gaussian	ADJ
aiti-14032	142	6	mixture	mixture	NOUN
aiti-14032	142	7	model	model	NOUN
aiti-14032	142	8	a	a	DET
aiti-14032	142	9	gaussian	gaussian	ADJ
aiti-14032	142	10	mixture	mixture	NOUN
aiti-14032	142	11	model	model	NOUN
aiti-14032	142	12	(	(	PUNCT
aiti-14032	142	13	gmm	gmm	NOUN
aiti-14032	142	14	)	)	PUNCT
aiti-14032	142	15	is	be	AUX
aiti-14032	142	16	employed	employ	VERB
aiti-14032	142	17	to	to	PART
aiti-14032	142	18	create	create	VERB
aiti-14032	142	19	models	model	NOUN
aiti-14032	142	20	for	for	ADP
aiti-14032	142	21	the	the	DET
aiti-14032	142	22	foreground	foreground	NOUN
aiti-14032	142	23	and	and	CCONJ
aiti-14032	142	24	background	background	NOUN
aiti-14032	142	25	components	component	NOUN
aiti-14032	143	1	[	[	X
aiti-14032	143	2	13	13	NUM
aiti-14032	143	3	]	]	PUNCT
aiti-14032	143	4	.	.	PUNCT
aiti-14032	144	1	by	by	ADP
aiti-14032	144	2	leveraging	leverage	VERB
aiti-14032	144	3	the	the	DET
aiti-14032	144	4	provided	provide	VERB
aiti-14032	144	5	data	datum	NOUN
aiti-14032	144	6	,	,	PUNCT
aiti-14032	144	7	the	the	DET
aiti-14032	144	8	gmm	gmm	NOUN
aiti-14032	144	9	learns	learn	VERB
aiti-14032	144	10	and	and	CCONJ
aiti-14032	144	11	generates	generate	VERB
aiti-14032	144	12	new	new	ADJ
aiti-14032	144	13	pixel	pixel	NOUN
aiti-14032	144	14	distributions	distribution	NOUN
aiti-14032	144	15	.	.	PUNCT
aiti-14032	145	1	this	this	DET
aiti-14032	145	2	process	process	NOUN
aiti-14032	145	3	involves	involve	VERB
aiti-14032	145	4	assigning	assign	VERB
aiti-14032	145	5	labels	label	NOUN
aiti-14032	145	6	to	to	ADP
aiti-14032	145	7	the	the	DET
aiti-14032	145	8	unknown	unknown	ADJ
aiti-14032	145	9	pixels	pixel	NOUN
aiti-14032	145	10	,	,	PUNCT
aiti-14032	145	11	classifying	classify	VERB
aiti-14032	145	12	them	they	PRON
aiti-14032	145	13	as	as	ADP
aiti-14032	145	14	either	either	CCONJ
aiti-14032	145	15	probable	probable	ADJ
aiti-14032	145	16	foreground	foreground	NOUN
aiti-14032	145	17	or	or	CCONJ
aiti-14032	145	18	probable	probable	ADJ
aiti-14032	145	19	background	background	NOUN
aiti-14032	145	20	based	base	VERB
aiti-14032	145	21	on	on	ADP
aiti-14032	145	22	their	their	PRON
aiti-14032	145	23	color	color	NOUN
aiti-14032	145	24	statistics	statistic	NOUN
aiti-14032	145	25	and	and	CCONJ
aiti-14032	145	26	their	their	PRON
aiti-14032	145	27	relationship	relationship	NOUN
aiti-14032	145	28	with	with	ADP
aiti-14032	145	29	other	other	ADJ
aiti-14032	145	30	hard	hard	ADV
aiti-14032	145	31	-	-	PUNCT
aiti-14032	145	32	labeled	label	VERB
aiti-14032	145	33	pixels	pixel	NOUN
aiti-14032	145	34	[	[	X
aiti-14032	145	35	14	14	NUM
aiti-14032	145	36	]	]	PUNCT
aiti-14032	145	37	.	.	PUNCT
aiti-14032	146	1	subsequently	subsequently	ADV
aiti-14032	146	2	,	,	PUNCT
aiti-14032	146	3	a	a	DET
aiti-14032	146	4	graph	graph	NOUN
aiti-14032	146	5	is	be	AUX
aiti-14032	146	6	constructed	construct	VERB
aiti-14032	146	7	using	use	VERB
aiti-14032	146	8	this	this	DET
aiti-14032	146	9	pixel	pixel	ADJ
aiti-14032	146	10	distribution	distribution	NOUN
aiti-14032	146	11	.	.	PUNCT
aiti-14032	147	1	each	each	DET
aiti-14032	147	2	pixel	pixel	PROPN
aiti-14032	147	3	serves	serve	VERB
aiti-14032	147	4	as	as	ADP
aiti-14032	147	5	a	a	DET
aiti-14032	147	6	node	node	NOUN
aiti-14032	147	7	in	in	ADP
aiti-14032	147	8	the	the	DET
aiti-14032	147	9	graph	graph	NOUN
aiti-14032	147	10	,	,	PUNCT
aiti-14032	147	11	along	along	ADP
aiti-14032	147	12	with	with	ADP
aiti-14032	147	13	two	two	NUM
aiti-14032	147	14	additional	additional	ADJ
aiti-14032	147	15	nodes	node	NOUN
aiti-14032	147	16	:	:	PUNCT
aiti-14032	147	17	the	the	DET
aiti-14032	147	18	source	source	NOUN
aiti-14032	147	19	node	node	NOUN
aiti-14032	147	20	and	and	CCONJ
aiti-14032	147	21	the	the	DET
aiti-14032	147	22	sink	sink	NOUN
aiti-14032	147	23	node	node	PROPN
aiti-14032	147	24	.	.	PUNCT
aiti-14032	148	1	the	the	DET
aiti-14032	148	2	foreground	foreground	NOUN
aiti-14032	148	3	pixels	pixel	NOUN
aiti-14032	148	4	are	be	AUX
aiti-14032	148	5	connected	connect	VERB
aiti-14032	148	6	to	to	ADP
aiti-14032	148	7	the	the	DET
aiti-14032	148	8	source	source	NOUN
aiti-14032	148	9	node	node	NOUN
aiti-14032	148	10	,	,	PUNCT
aiti-14032	148	11	while	while	SCONJ
aiti-14032	148	12	the	the	DET
aiti-14032	148	13	background	background	NOUN
aiti-14032	148	14	pixels	pixel	NOUN
aiti-14032	148	15	are	be	AUX
aiti-14032	148	16	connected	connect	VERB
aiti-14032	148	17	to	to	ADP
aiti-14032	148	18	the	the	DET
aiti-14032	148	19	sink	sink	NOUN
aiti-14032	148	20	node	node	NOUN
aiti-14032	148	21	[	[	X
aiti-14032	148	22	15	15	NUM
aiti-14032	148	23	]	]	PUNCT
aiti-14032	148	24	.	.	PUNCT
aiti-14032	149	1	once	once	SCONJ
aiti-14032	149	2	the	the	DET
aiti-14032	149	3	graph	graph	NOUN
aiti-14032	149	4	is	be	AUX
aiti-14032	149	5	constructed	construct	VERB
aiti-14032	149	6	,	,	PUNCT
aiti-14032	149	7	a	a	DET
aiti-14032	149	8	min	min	NOUN
aiti-14032	149	9	-	-	ADJ
aiti-14032	149	10	cut	cut	ADJ
aiti-14032	149	11	algorithm	algorithm	NOUN
aiti-14032	149	12	is	be	AUX
aiti-14032	149	13	applied	apply	VERB
aiti-14032	149	14	to	to	PART
aiti-14032	149	15	divide	divide	VERB
aiti-14032	149	16	it	it	PRON
aiti-14032	149	17	into	into	ADP
aiti-14032	149	18	two	two	NUM
aiti-14032	149	19	distinct	distinct	ADJ
aiti-14032	149	20	components	component	NOUN
aiti-14032	149	21	:	:	PUNCT
aiti-14032	149	22	the	the	DET
aiti-14032	149	23	source	source	NOUN
aiti-14032	149	24	nodes	nod	VERB
aiti-14032	149	25	and	and	CCONJ
aiti-14032	149	26	the	the	DET
aiti-14032	149	27	sink	sink	NOUN
aiti-14032	149	28	nodes	nod	VERB
aiti-14032	149	29	.	.	PUNCT
aiti-14032	150	1	this	this	DET
aiti-14032	150	2	separation	separation	NOUN
aiti-14032	150	3	is	be	AUX
aiti-14032	150	4	achieved	achieve	VERB
aiti-14032	150	5	by	by	ADP
aiti-14032	150	6	minimizing	minimize	VERB
aiti-14032	150	7	a	a	DET
aiti-14032	150	8	cost	cost	NOUN
aiti-14032	150	9	function	function	NOUN
aiti-14032	150	10	,	,	PUNCT
aiti-14032	150	11	which	which	PRON
aiti-14032	150	12	is	be	AUX
aiti-14032	150	13	determined	determine	VERB
aiti-14032	150	14	by	by	ADP
aiti-14032	150	15	the	the	DET
aiti-14032	150	16	sum	sum	NOUN
aiti-14032	150	17	of	of	ADP
aiti-14032	150	18	edge	edge	NOUN
aiti-14032	150	19	weights	weight	NOUN
aiti-14032	150	20	that	that	PRON
aiti-14032	150	21	are	be	AUX
aiti-14032	150	22	cut	cut	VERB
aiti-14032	150	23	.	.	PUNCT
aiti-14032	151	1	subsequently	subsequently	ADV
aiti-14032	151	2	,	,	PUNCT
aiti-14032	151	3	the	the	DET
aiti-14032	151	4	pixels	pixel	NOUN
aiti-14032	151	5	connected	connect	VERB
aiti-14032	151	6	to	to	ADP
aiti-14032	151	7	the	the	DET
aiti-14032	151	8	source	source	NOUN
aiti-14032	151	9	node	node	NOUN
aiti-14032	151	10	are	be	AUX
aiti-14032	151	11	classified	classify	VERB
aiti-14032	151	12	as	as	ADP
aiti-14032	151	13	foreground	foreground	NOUN
aiti-14032	151	14	,	,	PUNCT
aiti-14032	151	15	while	while	SCONJ
aiti-14032	151	16	those	those	PRON
aiti-14032	151	17	connected	connect	VERB
aiti-14032	151	18	to	to	ADP
aiti-14032	151	19	the	the	DET
aiti-14032	151	20	sink	sink	NOUN
aiti-14032	151	21	node	node	NOUN
aiti-14032	151	22	are	be	AUX
aiti-14032	151	23	classified	classify	VERB
aiti-14032	151	24	as	as	ADP
aiti-14032	151	25	background	background	NOUN
aiti-14032	151	26	.	.	PUNCT
aiti-14032	152	1	this	this	DET
aiti-14032	152	2	iterative	iterative	NOUN
aiti-14032	152	3	process	process	NOUN
aiti-14032	152	4	continues	continue	VERB
aiti-14032	152	5	until	until	SCONJ
aiti-14032	152	6	the	the	DET
aiti-14032	152	7	classification	classification	NOUN
aiti-14032	152	8	reaches	reach	VERB
aiti-14032	152	9	a	a	DET
aiti-14032	152	10	state	state	NOUN
aiti-14032	152	11	of	of	ADP
aiti-14032	152	12	convergence	convergence	NOUN
aiti-14032	152	13	,	,	PUNCT
aiti-14032	152	14	ensuring	ensure	VERB
aiti-14032	152	15	refined	refined	ADJ
aiti-14032	152	16	segmentation	segmentation	NOUN
aiti-14032	152	17	results	result	NOUN
aiti-14032	152	18	[	[	X
aiti-14032	152	19	16	16	NUM
aiti-14032	152	20	]	]	PUNCT
aiti-14032	152	21	.	.	PUNCT
aiti-14032	153	1	3.3	3.3	NUM
aiti-14032	153	2	.	.	PUNCT
aiti-14032	154	1	feature	feature	NOUN
aiti-14032	154	2	extraction	extraction	NOUN
aiti-14032	154	3	feature	feature	NOUN
aiti-14032	154	4	extraction	extraction	NOUN
aiti-14032	154	5	in	in	ADP
aiti-14032	154	6	image	image	NOUN
aiti-14032	154	7	processing	processing	NOUN
aiti-14032	154	8	refers	refer	VERB
aiti-14032	154	9	to	to	ADP
aiti-14032	154	10	the	the	DET
aiti-14032	154	11	process	process	NOUN
aiti-14032	154	12	of	of	ADP
aiti-14032	154	13	identifying	identify	VERB
aiti-14032	154	14	and	and	CCONJ
aiti-14032	154	15	capturing	capture	VERB
aiti-14032	154	16	distinctive	distinctive	ADJ
aiti-14032	154	17	and	and	CCONJ
aiti-14032	154	18	meaningful	meaningful	ADJ
aiti-14032	154	19	characteristics	characteristic	NOUN
aiti-14032	154	20	or	or	CCONJ
aiti-14032	154	21	patterns	pattern	NOUN
aiti-14032	154	22	from	from	ADP
aiti-14032	154	23	an	an	DET
aiti-14032	154	24	image	image	NOUN
aiti-14032	154	25	.	.	PUNCT
aiti-14032	155	1	it	it	PRON
aiti-14032	155	2	involves	involve	VERB
aiti-14032	155	3	transforming	transform	VERB
aiti-14032	155	4	raw	raw	ADJ
aiti-14032	155	5	image	image	NOUN
aiti-14032	155	6	data	datum	NOUN
aiti-14032	155	7	into	into	ADP
aiti-14032	155	8	a	a	DET
aiti-14032	155	9	compact	compact	ADJ
aiti-14032	155	10	representation	representation	NOUN
aiti-14032	155	11	that	that	PRON
aiti-14032	155	12	retains	retain	VERB
aiti-14032	155	13	relevant	relevant	ADJ
aiti-14032	155	14	information	information	NOUN
aiti-14032	155	15	for	for	ADP
aiti-14032	155	16	further	further	ADJ
aiti-14032	155	17	analysis	analysis	NOUN
aiti-14032	155	18	or	or	CCONJ
aiti-14032	155	19	classification	classification	NOUN
aiti-14032	155	20	tasks	task	NOUN
aiti-14032	155	21	.	.	PUNCT
aiti-14032	156	1	in	in	ADP
aiti-14032	156	2	the	the	DET
aiti-14032	156	3	process	process	NOUN
aiti-14032	156	4	of	of	ADP
aiti-14032	156	5	feature	feature	NOUN
aiti-14032	156	6	extraction	extraction	NOUN
aiti-14032	156	7	,	,	PUNCT
aiti-14032	156	8	specific	specific	ADJ
aiti-14032	156	9	algorithms	algorithm	NOUN
aiti-14032	156	10	or	or	CCONJ
aiti-14032	156	11	techniques	technique	NOUN
aiti-14032	156	12	are	be	AUX
aiti-14032	156	13	applied	apply	VERB
aiti-14032	156	14	to	to	PART
aiti-14032	156	15	extract	extract	VERB
aiti-14032	156	16	relevant	relevant	ADJ
aiti-14032	156	17	visual	visual	ADJ
aiti-14032	156	18	cues	cue	NOUN
aiti-14032	156	19	or	or	CCONJ
aiti-14032	156	20	attributes	attribute	NOUN
aiti-14032	156	21	from	from	ADP
aiti-14032	156	22	the	the	DET
aiti-14032	156	23	image	image	NOUN
aiti-14032	156	24	.	.	PUNCT
aiti-14032	157	1	these	these	DET
aiti-14032	157	2	cues	cue	NOUN
aiti-14032	157	3	can	can	AUX
aiti-14032	157	4	be	be	AUX
aiti-14032	157	5	derived	derive	VERB
aiti-14032	157	6	from	from	ADP
aiti-14032	157	7	various	various	ADJ
aiti-14032	157	8	levels	level	NOUN
aiti-14032	157	9	of	of	ADP
aiti-14032	157	10	abstraction	abstraction	NOUN
aiti-14032	157	11	,	,	PUNCT
aiti-14032	157	12	ranging	range	VERB
aiti-14032	157	13	from	from	ADP
aiti-14032	157	14	low	low	ADJ
aiti-14032	157	15	-	-	PUNCT
aiti-14032	157	16	level	level	NOUN
aiti-14032	157	17	features	feature	NOUN
aiti-14032	157	18	like	like	ADP
aiti-14032	157	19	advances	advance	NOUN
aiti-14032	157	20	in	in	ADP
aiti-14032	157	21	technology	technology	NOUN
aiti-14032	157	22	innovation	innovation	NOUN
aiti-14032	157	23	,	,	PUNCT
aiti-14032	157	24	vol	vol	NOUN
aiti-14032	157	25	.	.	PROPN
aiti-14032	157	26	10	10	NUM
aiti-14032	157	27	,	,	PUNCT
aiti-14032	157	28	no	no	INTJ
aiti-14032	157	29	.	.	NOUN
aiti-14032	157	30	4	4	NUM
aiti-14032	157	31	,	,	PUNCT
aiti-14032	157	32	2025	2025	NUM
aiti-14032	157	33	,	,	PUNCT
aiti-14032	157	34	pp	pp	ADJ
aiti-14032	157	35	.	.	PUNCT
aiti-14032	158	1	370	370	NUM
aiti-14032	158	2	-	-	SYM
aiti-14032	158	3	382	382	NUM
aiti-14032	158	4	375	375	NUM
aiti-14032	158	5	color	color	NOUN
aiti-14032	158	6	,	,	PUNCT
aiti-14032	158	7	texture	texture	NOUN
aiti-14032	158	8	,	,	PUNCT
aiti-14032	158	9	and	and	CCONJ
aiti-14032	158	10	shape	shape	NOUN
aiti-14032	158	11	,	,	PUNCT
aiti-14032	158	12	to	to	ADP
aiti-14032	158	13	higher	high	ADJ
aiti-14032	158	14	-	-	PUNCT
aiti-14032	158	15	level	level	NOUN
aiti-14032	158	16	features	feature	NOUN
aiti-14032	158	17	such	such	ADJ
aiti-14032	158	18	as	as	ADP
aiti-14032	158	19	edges	edge	NOUN
aiti-14032	158	20	,	,	PUNCT
aiti-14032	158	21	corners	corner	NOUN
aiti-14032	158	22	,	,	PUNCT
aiti-14032	158	23	or	or	CCONJ
aiti-14032	158	24	even	even	ADV
aiti-14032	158	25	semantic	semantic	ADJ
aiti-14032	158	26	concepts	concept	NOUN
aiti-14032	158	27	[	[	X
aiti-14032	158	28	17	17	NUM
aiti-14032	158	29	]	]	PUNCT
aiti-14032	158	30	.	.	PUNCT
aiti-14032	159	1	in	in	ADP
aiti-14032	159	2	this	this	DET
aiti-14032	159	3	study	study	NOUN
aiti-14032	159	4	,	,	PUNCT
aiti-14032	159	5	feature	feature	NOUN
aiti-14032	159	6	extraction	extraction	NOUN
aiti-14032	159	7	is	be	AUX
aiti-14032	159	8	performed	perform	VERB
aiti-14032	159	9	on	on	ADP
aiti-14032	159	10	the	the	DET
aiti-14032	159	11	color	color	NOUN
aiti-14032	159	12	and	and	CCONJ
aiti-14032	159	13	texture	texture	ADJ
aiti-14032	159	14	features	feature	NOUN
aiti-14032	159	15	of	of	ADP
aiti-14032	159	16	the	the	DET
aiti-14032	159	17	potato	potato	NOUN
aiti-14032	159	18	plant	plant	NOUN
aiti-14032	159	19	leaves	leave	NOUN
aiti-14032	159	20	.	.	PUNCT
aiti-14032	160	1	the	the	DET
aiti-14032	160	2	mccm	mccm	NOUN
aiti-14032	160	3	,	,	PUNCT
aiti-14032	160	4	histogram	histogram	NOUN
aiti-14032	160	5	,	,	PUNCT
aiti-14032	160	6	fuzzy	fuzzy	ADJ
aiti-14032	160	7	hsv	hsv	PROPN
aiti-14032	160	8	,	,	PUNCT
aiti-14032	160	9	and	and	CCONJ
aiti-14032	160	10	fuzzy	fuzzy	ADJ
aiti-14032	160	11	lbp	lbp	NOUN
aiti-14032	160	12	methods	method	NOUN
aiti-14032	160	13	are	be	AUX
aiti-14032	160	14	used	use	VERB
aiti-14032	160	15	for	for	ADP
aiti-14032	160	16	feature	feature	NOUN
aiti-14032	160	17	extraction	extraction	NOUN
aiti-14032	160	18	.	.	PUNCT
aiti-14032	161	1	3.3.1	3.3.1	NUM
aiti-14032	161	2	.	.	NOUN
aiti-14032	161	3	histogram	histogram	NOUN
aiti-14032	161	4	histogram	histogram	PROPN
aiti-14032	161	5	feature	feature	NOUN
aiti-14032	161	6	extraction	extraction	NOUN
aiti-14032	161	7	is	be	AUX
aiti-14032	161	8	a	a	DET
aiti-14032	161	9	fundamental	fundamental	ADJ
aiti-14032	161	10	technique	technique	NOUN
aiti-14032	161	11	in	in	ADP
aiti-14032	161	12	image	image	NOUN
aiti-14032	161	13	processing	processing	NOUN
aiti-14032	161	14	that	that	PRON
aiti-14032	161	15	provides	provide	VERB
aiti-14032	161	16	a	a	DET
aiti-14032	161	17	compact	compact	ADJ
aiti-14032	161	18	yet	yet	CCONJ
aiti-14032	161	19	informative	informative	ADJ
aiti-14032	161	20	representation	representation	NOUN
aiti-14032	161	21	of	of	ADP
aiti-14032	161	22	image	image	NOUN
aiti-14032	161	23	content	content	NOUN
aiti-14032	161	24	,	,	PUNCT
aiti-14032	161	25	making	make	VERB
aiti-14032	161	26	it	it	PRON
aiti-14032	161	27	useful	useful	ADJ
aiti-14032	161	28	for	for	ADP
aiti-14032	161	29	a	a	DET
aiti-14032	161	30	wide	wide	ADJ
aiti-14032	161	31	range	range	NOUN
aiti-14032	161	32	of	of	ADP
aiti-14032	161	33	applications	application	NOUN
aiti-14032	161	34	[	[	X
aiti-14032	161	35	18	18	NUM
aiti-14032	161	36	]	]	PUNCT
aiti-14032	161	37	.	.	PUNCT
aiti-14032	162	1	histogram	histogram	NOUN
aiti-14032	162	2	feature	feature	NOUN
aiti-14032	162	3	extraction	extraction	NOUN
aiti-14032	162	4	from	from	ADP
aiti-14032	162	5	a	a	DET
aiti-14032	162	6	leaf	leaf	NOUN
aiti-14032	162	7	image	image	NOUN
aiti-14032	162	8	involves	involve	VERB
aiti-14032	162	9	quantifying	quantify	VERB
aiti-14032	162	10	the	the	DET
aiti-14032	162	11	distribution	distribution	NOUN
aiti-14032	162	12	of	of	ADP
aiti-14032	162	13	pixel	pixel	PROPN
aiti-14032	162	14	intensities	intensity	NOUN
aiti-14032	162	15	within	within	ADP
aiti-14032	162	16	the	the	DET
aiti-14032	162	17	image	image	NOUN
aiti-14032	162	18	.	.	PUNCT
aiti-14032	163	1	this	this	DET
aiti-14032	163	2	process	process	NOUN
aiti-14032	163	3	involves	involve	VERB
aiti-14032	163	4	grayscale	grayscale	PROPN
aiti-14032	163	5	conversion	conversion	PROPN
aiti-14032	163	6	,	,	PUNCT
aiti-14032	163	7	histogram	histogram	NOUN
aiti-14032	163	8	calculation	calculation	NOUN
aiti-14032	163	9	,	,	PUNCT
aiti-14032	163	10	normalization	normalization	NOUN
aiti-14032	163	11	,	,	PUNCT
aiti-14032	163	12	and	and	CCONJ
aiti-14032	163	13	feature	feature	NOUN
aiti-14032	163	14	representation	representation	NOUN
aiti-14032	163	15	.	.	PUNCT
aiti-14032	164	1	3.3.2	3.3.2	X
aiti-14032	164	2	.	.	X
aiti-14032	164	3	modified	modify	VERB
aiti-14032	164	4	co	co	NOUN
aiti-14032	164	5	-	-	NOUN
aiti-14032	164	6	occurrence	occurrence	ADJ
aiti-14032	164	7	matrix	matrix	NOUN
aiti-14032	164	8	(	(	PUNCT
aiti-14032	164	9	mccm	mccm	NOUN
aiti-14032	164	10	)	)	PUNCT
aiti-14032	164	11	features	feature	VERB
aiti-14032	164	12	to	to	PART
aiti-14032	164	13	enhance	enhance	VERB
aiti-14032	164	14	the	the	DET
aiti-14032	164	15	effectiveness	effectiveness	NOUN
aiti-14032	164	16	of	of	ADP
aiti-14032	164	17	feature	feature	NOUN
aiti-14032	164	18	extraction	extraction	NOUN
aiti-14032	164	19	from	from	ADP
aiti-14032	164	20	images	image	NOUN
aiti-14032	164	21	,	,	PUNCT
aiti-14032	164	22	a	a	DET
aiti-14032	164	23	novel	novel	ADJ
aiti-14032	164	24	method	method	NOUN
aiti-14032	164	25	called	call	VERB
aiti-14032	164	26	mccm	mccm	NOUN
aiti-14032	164	27	is	be	AUX
aiti-14032	164	28	introduced	introduce	VERB
aiti-14032	164	29	.	.	PUNCT
aiti-14032	165	1	the	the	DET
aiti-14032	165	2	color	color	NOUN
aiti-14032	165	3	co	co	NOUN
aiti-14032	165	4	-	-	NOUN
aiti-14032	165	5	occurrence	occurrence	ADJ
aiti-14032	165	6	matrix	matrix	NOUN
aiti-14032	165	7	(	(	PUNCT
aiti-14032	165	8	ccm	ccm	NOUN
aiti-14032	165	9	)	)	PUNCT
aiti-14032	165	10	utilizes	utilize	VERB
aiti-14032	165	11	features	feature	NOUN
aiti-14032	165	12	like	like	ADP
aiti-14032	165	13	energy	energy	NOUN
aiti-14032	165	14	,	,	PUNCT
aiti-14032	165	15	entropy	entropy	PROPN
aiti-14032	165	16	,	,	PUNCT
aiti-14032	165	17	inverse	inverse	ADJ
aiti-14032	165	18	difference	difference	NOUN
aiti-14032	165	19	,	,	PUNCT
aiti-14032	165	20	and	and	CCONJ
aiti-14032	165	21	contrast	contrast	NOUN
aiti-14032	165	22	[	[	X
aiti-14032	165	23	19	19	NUM
aiti-14032	165	24	]	]	PUNCT
aiti-14032	165	25	.	.	PUNCT
aiti-14032	166	1	instead	instead	ADV
aiti-14032	166	2	of	of	ADP
aiti-14032	166	3	employing	employ	VERB
aiti-14032	166	4	all	all	DET
aiti-14032	166	5	the	the	DET
aiti-14032	166	6	traditional	traditional	ADJ
aiti-14032	166	7	ccm	ccm	NOUN
aiti-14032	166	8	features	feature	NOUN
aiti-14032	166	9	,	,	PUNCT
aiti-14032	166	10	some	some	DET
aiti-14032	166	11	negative	negative	ADJ
aiti-14032	166	12	and	and	CCONJ
aiti-14032	166	13	low	low	ADJ
aiti-14032	166	14	-	-	PUNCT
aiti-14032	166	15	value	value	NOUN
aiti-14032	166	16	features	feature	NOUN
aiti-14032	166	17	have	have	AUX
aiti-14032	166	18	been	be	AUX
aiti-14032	166	19	omitted	omit	VERB
aiti-14032	166	20	.	.	PUNCT
aiti-14032	167	1	employing	employ	VERB
aiti-14032	167	2	mccm	mccm	NOUN
aiti-14032	167	3	improves	improve	VERB
aiti-14032	167	4	the	the	DET
aiti-14032	167	5	model	model	NOUN
aiti-14032	167	6	’s	’s	PART
aiti-14032	167	7	learning	learn	VERB
aiti-14032	167	8	outcomes	outcome	NOUN
aiti-14032	167	9	compared	compare	VERB
aiti-14032	167	10	to	to	ADP
aiti-14032	167	11	utilizing	utilize	VERB
aiti-14032	167	12	the	the	DET
aiti-14032	167	13	complete	complete	ADJ
aiti-14032	167	14	set	set	NOUN
aiti-14032	167	15	of	of	ADP
aiti-14032	167	16	ccm	ccm	PROPN
aiti-14032	167	17	features	feature	VERB
aiti-14032	167	18	[	[	X
aiti-14032	167	19	20	20	NUM
aiti-14032	167	20	]	]	PUNCT
aiti-14032	167	21	.	.	PUNCT
aiti-14032	168	1	in	in	ADP
aiti-14032	168	2	the	the	DET
aiti-14032	168	3	equations	equation	NOUN
aiti-14032	168	4	below	below	ADV
aiti-14032	168	5	,	,	PUNCT
aiti-14032	168	6	idm	idm	PROPN
aiti-14032	168	7	represents	represent	VERB
aiti-14032	168	8	the	the	DET
aiti-14032	168	9	inverse	inverse	ADJ
aiti-14032	168	10	difference	difference	NOUN
aiti-14032	168	11	moment	moment	NOUN
aiti-14032	168	12	,	,	PUNCT
aiti-14032	168	13	ir(i	ir(i	X
aiti-14032	168	14	,	,	PUNCT
aiti-14032	168	15	j	j	NOUN
aiti-14032	168	16	)	)	PUNCT
aiti-14032	168	17	denotes	denote	VERB
aiti-14032	168	18	the	the	DET
aiti-14032	168	19	selected	select	VERB
aiti-14032	168	20	image	image	NOUN
aiti-14032	168	21	region	region	NOUN
aiti-14032	168	22	co	co	NOUN
aiti-14032	168	23	-	-	NOUN
aiti-14032	168	24	occurrence	occurrence	ADJ
aiti-14032	168	25	matrix	matrix	NOUN
aiti-14032	168	26	,	,	PUNCT
aiti-14032	168	27	and	and	CCONJ
aiti-14032	168	28	i	i	PRON
aiti-14032	168	29	and	and	CCONJ
aiti-14032	168	30	j	j	PROPN
aiti-14032	168	31	represent	represent	VERB
aiti-14032	168	32	the	the	DET
aiti-14032	168	33	intensity	intensity	NOUN
aiti-14032	168	34	of	of	ADP
aiti-14032	168	35	pixels	pixel	NOUN
aiti-14032	168	36	in	in	ADP
aiti-14032	168	37	the	the	DET
aiti-14032	168	38	image	image	NOUN
aiti-14032	168	39	.	.	PUNCT
aiti-14032	169	1	3.3.3	3.3.3	X
aiti-14032	169	2	.	.	X
aiti-14032	169	3	fuzzy	fuzzy	ADJ
aiti-14032	169	4	hsv	hsv	PROPN
aiti-14032	169	5	feature	feature	NOUN
aiti-14032	169	6	extraction	extraction	NOUN
aiti-14032	169	7	the	the	DET
aiti-14032	169	8	fuzzy	fuzzy	ADJ
aiti-14032	169	9	hsv	hsv	NOUN
aiti-14032	169	10	method	method	NOUN
aiti-14032	169	11	for	for	ADP
aiti-14032	169	12	leaf	leaf	NOUN
aiti-14032	169	13	image	image	NOUN
aiti-14032	169	14	feature	feature	NOUN
aiti-14032	169	15	extraction	extraction	NOUN
aiti-14032	169	16	incorporates	incorporate	VERB
aiti-14032	169	17	fuzzy	fuzzy	ADJ
aiti-14032	169	18	logic	logic	NOUN
aiti-14032	169	19	principles	principle	NOUN
aiti-14032	169	20	into	into	ADP
aiti-14032	169	21	the	the	DET
aiti-14032	169	22	hsv	hsv	NOUN
aiti-14032	169	23	color	color	NOUN
aiti-14032	169	24	space	space	NOUN
aiti-14032	169	25	to	to	PART
aiti-14032	169	26	extract	extract	VERB
aiti-14032	169	27	features	feature	NOUN
aiti-14032	169	28	from	from	ADP
aiti-14032	169	29	leaf	leaf	NOUN
aiti-14032	169	30	images	image	NOUN
aiti-14032	169	31	[	[	X
aiti-14032	169	32	21	21	NUM
aiti-14032	169	33	]	]	PUNCT
aiti-14032	169	34	.	.	PUNCT
aiti-14032	170	1	incorporating	incorporate	VERB
aiti-14032	170	2	“	"	PUNCT
aiti-14032	170	3	fuzzy	fuzzy	ADJ
aiti-14032	170	4	”	"	PUNCT
aiti-14032	170	5	into	into	ADP
aiti-14032	170	6	the	the	DET
aiti-14032	170	7	task	task	NOUN
aiti-14032	170	8	implies	imply	VERB
aiti-14032	170	9	a	a	DET
aiti-14032	170	10	representation	representation	NOUN
aiti-14032	170	11	wherein	wherein	ADJ
aiti-14032	170	12	color	color	NOUN
aiti-14032	170	13	categories	category	NOUN
aiti-14032	170	14	lack	lack	VERB
aiti-14032	170	15	precise	precise	ADJ
aiti-14032	170	16	delineation	delineation	NOUN
aiti-14032	170	17	,	,	PUNCT
aiti-14032	170	18	exhibiting	exhibit	VERB
aiti-14032	170	19	a	a	DET
aiti-14032	170	20	degree	degree	NOUN
aiti-14032	170	21	of	of	ADP
aiti-14032	170	22	ambiguity	ambiguity	NOUN
aiti-14032	170	23	or	or	CCONJ
aiti-14032	170	24	uncertainty	uncertainty	NOUN
aiti-14032	170	25	[	[	X
aiti-14032	170	26	22	22	NUM
aiti-14032	170	27	]	]	PUNCT
aiti-14032	170	28	.	.	PUNCT
aiti-14032	171	1	fuzzy	fuzzy	ADJ
aiti-14032	171	2	logic	logic	NOUN
aiti-14032	171	3	facilitates	facilitate	VERB
aiti-14032	171	4	the	the	DET
aiti-14032	171	5	modeling	modeling	NOUN
aiti-14032	171	6	of	of	ADP
aiti-14032	171	7	imprecision	imprecision	NOUN
aiti-14032	171	8	and	and	CCONJ
aiti-14032	171	9	uncertainty	uncertainty	NOUN
aiti-14032	171	10	,	,	PUNCT
aiti-14032	171	11	offering	offer	VERB
aiti-14032	171	12	a	a	DET
aiti-14032	171	13	valuable	valuable	ADJ
aiti-14032	171	14	framework	framework	NOUN
aiti-14032	171	15	for	for	ADP
aiti-14032	171	16	tasks	task	NOUN
aiti-14032	171	17	involving	involve	VERB
aiti-14032	171	18	color	color	NOUN
aiti-14032	171	19	perception	perception	NOUN
aiti-14032	171	20	and	and	CCONJ
aiti-14032	171	21	classification	classification	NOUN
aiti-14032	171	22	,	,	PUNCT
aiti-14032	171	23	especially	especially	ADV
aiti-14032	171	24	in	in	ADP
aiti-14032	171	25	scenarios	scenario	NOUN
aiti-14032	171	26	where	where	SCONJ
aiti-14032	171	27	colors	color	NOUN
aiti-14032	171	28	exhibit	exhibit	VERB
aiti-14032	171	29	nuanced	nuanced	ADJ
aiti-14032	171	30	variations	variation	NOUN
aiti-14032	171	31	in	in	ADP
aiti-14032	171	32	shades	shade	NOUN
aiti-14032	171	33	or	or	CCONJ
aiti-14032	171	34	tones	tone	NOUN
aiti-14032	171	35	.	.	PUNCT
aiti-14032	172	1	2	2	NUM
aiti-14032	172	2	(	(	PUNCT
aiti-14032	172	3	,	,	PUNCT
aiti-14032	172	4	)	)	PUNCT
aiti-14032	172	5	,	,	PUNCT
aiti-14032	173	1	energy	energy	NOUN
aiti-14032	173	2	ir	ir	INTJ
aiti-14032	173	3	i	i	PRON
aiti-14032	173	4	ji	ji	PROPN
aiti-14032	173	5	j=	j=	PROPN
aiti-14032	173	6	(	(	PUNCT
aiti-14032	173	7	1	1	NUM
aiti-14032	173	8	)	)	PUNCT
aiti-14032	173	9	(	(	PUNCT
aiti-14032	173	10	,	,	PUNCT
aiti-14032	173	11	)	)	PUNCT
aiti-14032	173	12	log	log	NOUN
aiti-14032	173	13	(	(	PUNCT
aiti-14032	173	14	(	(	PUNCT
aiti-14032	173	15	,	,	PUNCT
aiti-14032	173	16	)	)	PUNCT
aiti-14032	173	17	)	)	PUNCT
aiti-14032	173	18	,	,	PUNCT
aiti-14032	173	19	entropy	entropy	VERB
aiti-14032	173	20	ir	ir	PROPN
aiti-14032	174	1	i	i	INTJ
aiti-14032	174	2	j	j	PROPN
aiti-14032	175	1	ir	ir	INTJ
aiti-14032	175	2	i	i	PRON
aiti-14032	175	3	ji	ji	PROPN
aiti-14032	176	1	j=	j=	PROPN
aiti-14032	176	2	−	−	PROPN
aiti-14032	177	1	(	(	PUNCT
aiti-14032	177	2	2	2	NUM
aiti-14032	177	3	)	)	PUNCT
aiti-14032	177	4	(	(	PUNCT
aiti-14032	177	5	1	1	NUM
aiti-14032	177	6	/	/	SYM
aiti-14032	177	7	(	(	PUNCT
aiti-14032	177	8	1	1	NUM
aiti-14032	177	9	|	|	ADV
aiti-14032	177	10	|	|	ADV
aiti-14032	177	11	)	)	PUNCT
aiti-14032	177	12	)	)	PUNCT
aiti-14032	178	1	(	(	PUNCT
aiti-14032	178	2	,	,	PUNCT
aiti-14032	178	3	)	)	PUNCT
aiti-14032	178	4	,	,	PUNCT
aiti-14032	178	5	idm	idm	NOUN
aiti-14032	179	1	i	i	PRON
aiti-14032	179	2	j	j	PROPN
aiti-14032	180	1	ir	ir	INTJ
aiti-14032	180	2	i	i	INTJ
aiti-14032	180	3	ji	ji	PROPN
aiti-14032	180	4	j=	j=	PROPN
aiti-14032	181	1	+	+	CCONJ
aiti-14032	181	2	−	−	PROPN
aiti-14032	181	3	(	(	PUNCT
aiti-14032	181	4	3	3	NUM
aiti-14032	181	5	)	)	SYM
aiti-14032	181	6	2	2	NUM
aiti-14032	181	7	(	(	PUNCT
aiti-14032	181	8	)	)	PUNCT
aiti-14032	181	9	(	(	PUNCT
aiti-14032	181	10	(	(	PUNCT
aiti-14032	181	11	,	,	PUNCT
aiti-14032	181	12	)	)	PUNCT
aiti-14032	181	13	)	)	PUNCT
aiti-14032	181	14	,	,	PUNCT
aiti-14032	181	15	contrast	contrast	VERB
aiti-14032	181	16	i	i	PRON
aiti-14032	181	17	j	j	PROPN
aiti-14032	182	1	ir	ir	INTJ
aiti-14032	182	2	i	i	PRON
aiti-14032	182	3	ji	ji	PROPN
aiti-14032	183	1	j=	j=	PROPN
aiti-14032	183	2	−	−	PROPN
aiti-14032	183	3	(	(	PUNCT
aiti-14032	183	4	4	4	NUM
aiti-14032	183	5	)	)	PUNCT
aiti-14032	183	6	3.3.4	3.3.4	NUM
aiti-14032	183	7	.	.	PUNCT
aiti-14032	183	8	fuzzy	fuzzy	ADJ
aiti-14032	183	9	lbp	lbp	PROPN
aiti-14032	183	10	feature	feature	NOUN
aiti-14032	183	11	extraction	extraction	NOUN
aiti-14032	183	12	the	the	DET
aiti-14032	183	13	lbp	lbp	NOUN
aiti-14032	183	14	technique	technique	NOUN
aiti-14032	183	15	is	be	AUX
aiti-14032	183	16	a	a	DET
aiti-14032	183	17	statistical	statistical	ADJ
aiti-14032	183	18	method	method	NOUN
aiti-14032	183	19	in	in	ADP
aiti-14032	183	20	image	image	NOUN
aiti-14032	183	21	processing	processing	NOUN
aiti-14032	183	22	,	,	PUNCT
aiti-14032	183	23	offering	offer	VERB
aiti-14032	183	24	a	a	DET
aiti-14032	183	25	means	means	NOUN
aiti-14032	183	26	to	to	PART
aiti-14032	183	27	extract	extract	VERB
aiti-14032	183	28	potent	potent	ADJ
aiti-14032	183	29	features	feature	NOUN
aiti-14032	183	30	from	from	ADP
aiti-14032	183	31	images	image	NOUN
aiti-14032	183	32	.	.	PUNCT
aiti-14032	184	1	its	its	PRON
aiti-14032	184	2	widespread	widespread	ADJ
aiti-14032	184	3	adoption	adoption	NOUN
aiti-14032	184	4	in	in	ADP
aiti-14032	184	5	computer	computer	NOUN
aiti-14032	184	6	vision	vision	NOUN
aiti-14032	184	7	applications	application	NOUN
aiti-14032	184	8	underscores	underscore	VERB
aiti-14032	184	9	its	its	PRON
aiti-14032	184	10	efficacy	efficacy	NOUN
aiti-14032	184	11	,	,	PUNCT
aiti-14032	184	12	making	make	VERB
aiti-14032	184	13	it	it	PRON
aiti-14032	184	14	a	a	DET
aiti-14032	184	15	cornerstone	cornerstone	NOUN
aiti-14032	184	16	in	in	ADP
aiti-14032	184	17	visual	visual	ADJ
aiti-14032	184	18	computing	computing	NOUN
aiti-14032	185	1	[	[	X
aiti-14032	185	2	23	23	NUM
aiti-14032	185	3	]	]	PUNCT
aiti-14032	185	4	.	.	PUNCT
aiti-14032	186	1	fuzzy	fuzzy	ADJ
aiti-14032	186	2	lbp	lbp	PROPN
aiti-14032	186	3	incorporates	incorporate	VERB
aiti-14032	186	4	fuzzy	fuzzy	ADJ
aiti-14032	186	5	set	set	NOUN
aiti-14032	186	6	theory	theory	NOUN
aiti-14032	186	7	to	to	PART
aiti-14032	186	8	handle	handle	VERB
aiti-14032	186	9	imprecise	imprecise	ADV
aiti-14032	186	10	or	or	CCONJ
aiti-14032	186	11	uncertain	uncertain	ADJ
aiti-14032	186	12	pixel	pixel	NOUN
aiti-14032	186	13	intensity	intensity	NOUN
aiti-14032	186	14	values	value	NOUN
aiti-14032	186	15	.	.	PUNCT
aiti-14032	187	1	instead	instead	ADV
aiti-14032	187	2	of	of	ADP
aiti-14032	187	3	strictly	strictly	ADV
aiti-14032	187	4	binary	binary	ADJ
aiti-14032	187	5	decisions	decision	NOUN
aiti-14032	187	6	,	,	PUNCT
aiti-14032	187	7	fuzzy	fuzzy	ADJ
aiti-14032	187	8	logic	logic	NOUN
aiti-14032	187	9	allows	allow	VERB
aiti-14032	187	10	for	for	ADP
aiti-14032	187	11	gradual	gradual	ADJ
aiti-14032	187	12	transitions	transition	NOUN
aiti-14032	187	13	between	between	ADP
aiti-14032	187	14	foreground	foreground	NOUN
aiti-14032	187	15	and	and	CCONJ
aiti-14032	187	16	background	background	NOUN
aiti-14032	187	17	intensities	intensity	NOUN
aiti-14032	187	18	.	.	PUNCT
aiti-14032	188	1	the	the	DET
aiti-14032	188	2	computation	computation	NOUN
aiti-14032	188	3	of	of	ADP
aiti-14032	188	4	membership	membership	NOUN
aiti-14032	188	5	function	function	NOUN
aiti-14032	188	6	values	value	NOUN
aiti-14032	188	7	of	of	ADP
aiti-14032	188	8	neighboring	neighboring	NOUN
aiti-14032	188	9	pixels	pixel	NOUN
aiti-14032	188	10	concerning	concern	VERB
aiti-14032	188	11	a	a	DET
aiti-14032	188	12	center	center	NOUN
aiti-14032	188	13	pixel	pixel	NOUN
aiti-14032	188	14	,	,	PUNCT
aiti-14032	188	15	considering	consider	VERB
aiti-14032	188	16	their	their	PRON
aiti-14032	188	17	intensity	intensity	NOUN
aiti-14032	188	18	differences	difference	NOUN
aiti-14032	188	19	and	and	CCONJ
aiti-14032	188	20	fuzziness	fuzziness	NOUN
aiti-14032	188	21	,	,	PUNCT
aiti-14032	188	22	is	be	AUX
aiti-14032	188	23	given	give	VERB
aiti-14032	188	24	in	in	ADP
aiti-14032	188	25	eq	eq	ADP
aiti-14032	188	26	.	.	PUNCT
aiti-14032	189	1	(	(	PUNCT
aiti-14032	189	2	5	5	NUM
aiti-14032	189	3	)	)	PUNCT
aiti-14032	189	4	.	.	PUNCT
aiti-14032	190	1	if	if	SCONJ
aiti-14032	190	2	ic	ic	PRON
aiti-14032	190	3	represents	represent	VERB
aiti-14032	190	4	the	the	DET
aiti-14032	190	5	intensity	intensity	NOUN
aiti-14032	190	6	value	value	NOUN
aiti-14032	190	7	of	of	ADP
aiti-14032	190	8	the	the	DET
aiti-14032	190	9	center	center	NOUN
aiti-14032	190	10	pixel	pixel	NOUN
aiti-14032	190	11	and	and	CCONJ
aiti-14032	190	12	in	in	ADP
aiti-14032	190	13	denotes	denote	NOUN
aiti-14032	190	14	the	the	DET
aiti-14032	190	15	intensity	intensity	NOUN
aiti-14032	190	16	values	value	NOUN
aiti-14032	190	17	of	of	ADP
aiti-14032	190	18	its	its	PRON
aiti-14032	190	19	neighbors	neighbor	NOUN
aiti-14032	190	20	,	,	PUNCT
aiti-14032	190	21	then	then	ADV
aiti-14032	190	22	the	the	DET
aiti-14032	190	23	fuzzy	fuzzy	ADJ
aiti-14032	190	24	lbp	lbp	NOUN
aiti-14032	190	25	formula	formula	NOUN
aiti-14032	190	26	for	for	ADP
aiti-14032	190	27	a	a	DET
aiti-14032	190	28	center	center	NOUN
aiti-14032	190	29	pixel	pixel	NOUN
aiti-14032	190	30	with	with	ADP
aiti-14032	190	31	n	n	PRON
aiti-14032	190	32	neighbors	neighbor	NOUN
aiti-14032	190	33	is	be	AUX
aiti-14032	190	34	expressed	express	VERB
aiti-14032	190	35	as	as	ADP
aiti-14032	190	36	:	:	PUNCT
aiti-14032	190	37	1	1	NUM
aiti-14032	190	38	(	(	PUNCT
aiti-14032	190	39	)	)	PUNCT
aiti-14032	190	40	(	(	PUNCT
aiti-14032	190	41	)	)	SYM
aiti-14032	190	42	20	20	NUM
aiti-14032	190	43	fuz	fuz	NOUN
aiti-14032	190	44	pr	pr	NOUN
aiti-14032	190	45	nn	nn	PROPN
aiti-14032	190	46	lbp	lbp	PROPN
aiti-14032	191	1	i	i	PROPN
aiti-14032	191	2	i	i	PROPN
aiti-14032	191	3	inc	inc	PROPN
aiti-14032	191	4	n	n	PROPN
aiti-14032	191	5	c−=	c−=	PROPN
aiti-14032	191	6	−=	−=	VERB
aiti-14032	191	7	(	(	PUNCT
aiti-14032	191	8	5	5	NUM
aiti-14032	191	9	)	)	PUNCT
aiti-14032	191	10	advances	advance	NOUN
aiti-14032	191	11	in	in	ADP
aiti-14032	191	12	technology	technology	NOUN
aiti-14032	191	13	innovation	innovation	NOUN
aiti-14032	191	14	,	,	PUNCT
aiti-14032	191	15	vol	vol	NOUN
aiti-14032	191	16	.	.	PROPN
aiti-14032	192	1	10	10	NUM
aiti-14032	192	2	,	,	PUNCT
aiti-14032	192	3	no	no	INTJ
aiti-14032	192	4	.	.	NOUN
aiti-14032	192	5	4	4	NUM
aiti-14032	192	6	,	,	PUNCT
aiti-14032	192	7	2025	2025	NUM
aiti-14032	192	8	,	,	PUNCT
aiti-14032	192	9	pp	pp	ADJ
aiti-14032	192	10	.	.	PUNCT
aiti-14032	193	1	370	370	NUM
aiti-14032	193	2	-	-	SYM
aiti-14032	193	3	382	382	NUM
aiti-14032	193	4	376	376	NUM
aiti-14032	193	5	where	where	SCONJ
aiti-14032	193	6	p	p	NOUN
aiti-14032	193	7	represents	represent	VERB
aiti-14032	193	8	the	the	DET
aiti-14032	193	9	number	number	NOUN
aiti-14032	193	10	of	of	ADP
aiti-14032	193	11	sampling	sample	VERB
aiti-14032	193	12	points	point	NOUN
aiti-14032	193	13	around	around	ADP
aiti-14032	193	14	the	the	DET
aiti-14032	193	15	center	center	NOUN
aiti-14032	193	16	.	.	PUNCT
aiti-14032	194	1	r	r	NOUN
aiti-14032	194	2	denotes	denote	VERB
aiti-14032	194	3	the	the	DET
aiti-14032	194	4	radius	radius	NOUN
aiti-14032	194	5	of	of	ADP
aiti-14032	194	6	the	the	DET
aiti-14032	194	7	circular	circular	ADJ
aiti-14032	194	8	sampling	sample	VERB
aiti-14032	194	9	region	region	NOUN
aiti-14032	194	10	.	.	PUNCT
aiti-14032	195	1	µ(inic	µ(inic	ADJ
aiti-14032	195	2	)	)	PUNCT
aiti-14032	195	3	is	be	AUX
aiti-14032	195	4	the	the	DET
aiti-14032	195	5	membership	membership	NOUN
aiti-14032	195	6	function	function	NOUN
aiti-14032	195	7	indicating	indicate	VERB
aiti-14032	195	8	the	the	DET
aiti-14032	195	9	degree	degree	NOUN
aiti-14032	195	10	of	of	ADP
aiti-14032	195	11	membership	membership	NOUN
aiti-14032	195	12	of	of	ADP
aiti-14032	195	13	the	the	DET
aiti-14032	195	14	neighboring	neighboring	NOUN
aiti-14032	195	15	pixel	pixel	NOUN
aiti-14032	195	16	intensity	intensity	NOUN
aiti-14032	195	17	to	to	ADP
aiti-14032	195	18	the	the	DET
aiti-14032	195	19	foreground	foreground	NOUN
aiti-14032	195	20	or	or	CCONJ
aiti-14032	195	21	background	background	NOUN
aiti-14032	195	22	.	.	PUNCT
aiti-14032	196	1	3.4	3.4	NUM
aiti-14032	196	2	.	.	PUNCT
aiti-14032	196	3	model	model	NOUN
aiti-14032	196	4	learning	learn	VERB
aiti-14032	196	5	this	this	DET
aiti-14032	196	6	phase	phase	NOUN
aiti-14032	196	7	of	of	ADP
aiti-14032	196	8	machine	machine	NOUN
aiti-14032	196	9	learning	learning	NOUN
aiti-14032	196	10	involves	involve	VERB
aiti-14032	196	11	the	the	DET
aiti-14032	196	12	training	training	NOUN
aiti-14032	196	13	of	of	ADP
aiti-14032	196	14	two	two	NUM
aiti-14032	196	15	multiclass	multiclass	ADJ
aiti-14032	196	16	models	model	NOUN
aiti-14032	196	17	.	.	PUNCT
aiti-14032	197	1	the	the	DET
aiti-14032	197	2	first	first	ADJ
aiti-14032	197	3	model	model	NOUN
aiti-14032	197	4	,	,	PUNCT
aiti-14032	197	5	which	which	PRON
aiti-14032	197	6	uses	use	VERB
aiti-14032	197	7	mccm	mccm	NOUN
aiti-14032	197	8	and	and	CCONJ
aiti-14032	197	9	histogram	histogram	NOUN
aiti-14032	197	10	features	feature	NOUN
aiti-14032	197	11	as	as	ADP
aiti-14032	197	12	the	the	DET
aiti-14032	197	13	training	training	NOUN
aiti-14032	197	14	input	input	NOUN
aiti-14032	197	15	,	,	PUNCT
aiti-14032	197	16	is	be	AUX
aiti-14032	197	17	a	a	DET
aiti-14032	197	18	bootstrap	bootstrap	NOUN
aiti-14032	197	19	model	model	NOUN
aiti-14032	197	20	,	,	PUNCT
aiti-14032	197	21	while	while	SCONJ
aiti-14032	197	22	the	the	DET
aiti-14032	197	23	second	second	ADJ
aiti-14032	197	24	model	model	NOUN
aiti-14032	197	25	,	,	PUNCT
aiti-14032	197	26	which	which	PRON
aiti-14032	197	27	uses	use	VERB
aiti-14032	197	28	fuzzy	fuzzy	ADJ
aiti-14032	197	29	lbp	lbp	NOUN
aiti-14032	197	30	and	and	CCONJ
aiti-14032	197	31	fuzzy	fuzzy	ADJ
aiti-14032	197	32	hsv	hsv	NOUN
aiti-14032	197	33	as	as	ADP
aiti-14032	197	34	the	the	DET
aiti-14032	197	35	training	training	NOUN
aiti-14032	197	36	features	feature	NOUN
aiti-14032	197	37	,	,	PUNCT
aiti-14032	197	38	is	be	AUX
aiti-14032	197	39	a	a	DET
aiti-14032	197	40	multiclass	multiclass	ADJ
aiti-14032	197	41	svm	svm	ADJ
aiti-14032	197	42	model	model	NOUN
aiti-14032	197	43	.	.	PUNCT
aiti-14032	198	1	3.4.1	3.4.1	X
aiti-14032	198	2	.	.	X
aiti-14032	198	3	bootstrap	bootstrap	NOUN
aiti-14032	198	4	sampling	sample	VERB
aiti-14032	198	5	bootstrap	bootstrap	NOUN
aiti-14032	198	6	learning	learning	NOUN
aiti-14032	198	7	,	,	PUNCT
aiti-14032	198	8	commonly	commonly	ADV
aiti-14032	198	9	referred	refer	VERB
aiti-14032	198	10	to	to	ADP
aiti-14032	198	11	as	as	ADP
aiti-14032	198	12	bootstrap	bootstrap	NOUN
aiti-14032	198	13	aggregating	aggregating	NOUN
aiti-14032	198	14	or	or	CCONJ
aiti-14032	198	15	bagging	bagging	NOUN
aiti-14032	198	16	,	,	PUNCT
aiti-14032	198	17	represents	represent	VERB
aiti-14032	198	18	a	a	DET
aiti-14032	198	19	machine	machine	NOUN
aiti-14032	198	20	learning	learn	VERB
aiti-14032	198	21	ensemble	ensemble	ADJ
aiti-14032	198	22	technique	technique	NOUN
aiti-14032	198	23	designed	design	VERB
aiti-14032	198	24	to	to	PART
aiti-14032	198	25	enhance	enhance	VERB
aiti-14032	198	26	model	model	NOUN
aiti-14032	198	27	stability	stability	NOUN
aiti-14032	198	28	and	and	CCONJ
aiti-14032	198	29	accuracy	accuracy	NOUN
aiti-14032	198	30	by	by	ADP
aiti-14032	198	31	mitigating	mitigate	VERB
aiti-14032	198	32	variance	variance	NOUN
aiti-14032	198	33	and	and	CCONJ
aiti-14032	198	34	overfitting	overfitting	NOUN
aiti-14032	198	35	.	.	PUNCT
aiti-14032	199	1	it	it	PRON
aiti-14032	199	2	involves	involve	VERB
aiti-14032	199	3	training	train	VERB
aiti-14032	199	4	multiple	multiple	ADJ
aiti-14032	199	5	models	model	NOUN
aiti-14032	199	6	utilizing	utilize	VERB
aiti-14032	199	7	subsets	subset	NOUN
aiti-14032	199	8	of	of	ADP
aiti-14032	199	9	the	the	DET
aiti-14032	199	10	initial	initial	ADJ
aiti-14032	199	11	dataset	dataset	NOUN
aiti-14032	199	12	and	and	CCONJ
aiti-14032	199	13	combining	combine	VERB
aiti-14032	199	14	their	their	PRON
aiti-14032	199	15	predictions	prediction	NOUN
aiti-14032	199	16	to	to	PART
aiti-14032	199	17	yield	yield	VERB
aiti-14032	199	18	a	a	DET
aiti-14032	199	19	conclusive	conclusive	ADJ
aiti-14032	199	20	decision	decision	NOUN
aiti-14032	199	21	[	[	X
aiti-14032	199	22	24	24	NUM
aiti-14032	199	23	]	]	PUNCT
aiti-14032	199	24	.	.	PUNCT
aiti-14032	200	1	below	below	ADV
aiti-14032	200	2	is	be	AUX
aiti-14032	200	3	an	an	DET
aiti-14032	200	4	outline	outline	NOUN
aiti-14032	200	5	of	of	ADP
aiti-14032	200	6	how	how	SCONJ
aiti-14032	200	7	the	the	DET
aiti-14032	200	8	bootstrap	bootstrap	NOUN
aiti-14032	200	9	learning	learn	VERB
aiti-14032	200	10	model	model	NOUN
aiti-14032	200	11	operates	operate	VERB
aiti-14032	200	12	:	:	PUNCT
aiti-14032	200	13	bootstrap	bootstrap	NOUN
aiti-14032	200	14	sampling	sampling	NOUN
aiti-14032	200	15	:	:	PUNCT
aiti-14032	200	16	given	give	VERB
aiti-14032	200	17	an	an	DET
aiti-14032	200	18	original	original	ADJ
aiti-14032	200	19	dataset	dataset	NOUN
aiti-14032	200	20	d	d	NOUN
aiti-14032	200	21	of	of	ADP
aiti-14032	200	22	size	size	NOUN
aiti-14032	200	23	n	n	CCONJ
aiti-14032	200	24	,	,	PUNCT
aiti-14032	200	25	bootstrap	bootstrap	NOUN
aiti-14032	200	26	sampling	sample	VERB
aiti-14032	200	27	involves	involve	NOUN
aiti-14032	200	28	selecting	select	VERB
aiti-14032	200	29	n	n	PRON
aiti-14032	200	30	samples	sample	NOUN
aiti-14032	200	31	randomly	randomly	ADV
aiti-14032	200	32	with	with	ADP
aiti-14032	200	33	replacement	replacement	NOUN
aiti-14032	200	34	from	from	ADP
aiti-14032	200	35	d	d	PROPN
aiti-14032	200	36	to	to	PART
aiti-14032	200	37	create	create	VERB
aiti-14032	200	38	a	a	DET
aiti-14032	200	39	sample	sample	NOUN
aiti-14032	200	40	di	di	NOUN
aiti-14032	200	41	.	.	PUNCT
aiti-14032	201	1	this	this	DET
aiti-14032	201	2	process	process	NOUN
aiti-14032	201	3	is	be	AUX
aiti-14032	201	4	repeated	repeat	VERB
aiti-14032	201	5	to	to	PART
aiti-14032	201	6	create	create	VERB
aiti-14032	201	7	multiple	multiple	ADJ
aiti-14032	201	8	bootstrap	bootstrap	NOUN
aiti-14032	201	9	samples	sample	NOUN
aiti-14032	201	10	.	.	PUNCT
aiti-14032	202	1	(	(	PUNCT
aiti-14032	202	2	)	)	PUNCT
aiti-14032	202	3	d	d	X
aiti-14032	202	4	bootstrapsample	bootstrapsample	NOUN
aiti-14032	202	5	di	di	X
aiti-14032	202	6	=	=	PUNCT
aiti-14032	202	7	(	(	PUNCT
aiti-14032	202	8	6	6	NUM
aiti-14032	202	9	)	)	PUNCT
aiti-14032	202	10	furthermore	furthermore	ADV
aiti-14032	202	11	,	,	PUNCT
aiti-14032	202	12	the	the	DET
aiti-14032	202	13	process	process	NOUN
aiti-14032	202	14	includes	include	VERB
aiti-14032	202	15	model	model	NOUN
aiti-14032	202	16	training	training	NOUN
aiti-14032	202	17	and	and	CCONJ
aiti-14032	202	18	model	model	NOUN
aiti-14032	202	19	aggregation	aggregation	NOUN
aiti-14032	202	20	.	.	PUNCT
aiti-14032	203	1	in	in	ADP
aiti-14032	203	2	model	model	NOUN
aiti-14032	203	3	training	training	NOUN
aiti-14032	203	4	,	,	PUNCT
aiti-14032	203	5	corresponding	correspond	VERB
aiti-14032	203	6	to	to	ADP
aiti-14032	203	7	each	each	DET
aiti-14032	203	8	bootstrap	bootstrap	NOUN
aiti-14032	203	9	sample	sample	PROPN
aiti-14032	203	10	di	di	PROPN
aiti-14032	203	11	,	,	PUNCT
aiti-14032	203	12	a	a	DET
aiti-14032	203	13	base	base	NOUN
aiti-14032	203	14	learning	learn	VERB
aiti-14032	203	15	algorithm	algorithm	NOUN
aiti-14032	203	16	,	,	PUNCT
aiti-14032	203	17	such	such	ADJ
aiti-14032	203	18	as	as	ADP
aiti-14032	203	19	a	a	DET
aiti-14032	203	20	decision	decision	NOUN
aiti-14032	203	21	tree	tree	NOUN
aiti-14032	203	22	or	or	CCONJ
aiti-14032	203	23	neural	neural	ADJ
aiti-14032	203	24	network	network	NOUN
aiti-14032	203	25	,	,	PUNCT
aiti-14032	203	26	is	be	AUX
aiti-14032	203	27	trained	train	VERB
aiti-14032	203	28	independently	independently	ADV
aiti-14032	203	29	to	to	PART
aiti-14032	203	30	create	create	VERB
aiti-14032	203	31	a	a	DET
aiti-14032	203	32	base	base	NOUN
aiti-14032	203	33	model	model	NOUN
aiti-14032	203	34	mi	mi	PROPN
aiti-14032	203	35	.	.	PROPN
aiti-14032	203	36	in	in	ADP
aiti-14032	203	37	the	the	DET
aiti-14032	203	38	model	model	NOUN
aiti-14032	203	39	aggregation	aggregation	NOUN
aiti-14032	203	40	phase	phase	NOUN
aiti-14032	203	41	,	,	PUNCT
aiti-14032	203	42	the	the	DET
aiti-14032	203	43	predictions	prediction	NOUN
aiti-14032	203	44	of	of	ADP
aiti-14032	203	45	all	all	DET
aiti-14032	203	46	base	base	NOUN
aiti-14032	203	47	models	model	NOUN
aiti-14032	203	48	are	be	AUX
aiti-14032	203	49	combined	combine	VERB
aiti-14032	203	50	to	to	PART
aiti-14032	203	51	make	make	VERB
aiti-14032	203	52	a	a	DET
aiti-14032	203	53	final	final	ADJ
aiti-14032	203	54	prediction	prediction	NOUN
aiti-14032	203	55	.	.	PUNCT
aiti-14032	204	1	the	the	DET
aiti-14032	204	2	aggregation	aggregation	NOUN
aiti-14032	204	3	method	method	NOUN
aiti-14032	204	4	varies	vary	VERB
aiti-14032	204	5	depending	depend	VERB
aiti-14032	204	6	on	on	ADP
aiti-14032	204	7	the	the	DET
aiti-14032	204	8	problem	problem	NOUN
aiti-14032	204	9	,	,	PUNCT
aiti-14032	204	10	with	with	ADP
aiti-14032	204	11	averaging	average	VERB
aiti-14032	204	12	for	for	ADP
aiti-14032	204	13	regression	regression	NOUN
aiti-14032	204	14	and	and	CCONJ
aiti-14032	204	15	voting	voting	NOUN
aiti-14032	204	16	for	for	ADP
aiti-14032	204	17	classification	classification	NOUN
aiti-14032	204	18	.	.	PUNCT
aiti-14032	205	1	if	if	SCONJ
aiti-14032	205	2	y1	y1	PROPN
aiti-14032	205	3	,	,	PUNCT
aiti-14032	205	4	y2	y2	PROPN
aiti-14032	205	5	,	,	PUNCT
aiti-14032	205	6	y3	y3	PROPN
aiti-14032	205	7	,	,	PUNCT
aiti-14032	205	8	y4	y4	PROPN
aiti-14032	205	9	,	,	PUNCT
aiti-14032	205	10	…	…	PUNCT
aiti-14032	205	11	.	.	PROPN
aiti-14032	205	12	,	,	PUNCT
aiti-14032	205	13	yk	yk	PROPN
aiti-14032	205	14	represent	represent	VERB
aiti-14032	205	15	different	different	ADJ
aiti-14032	205	16	bootstrap	bootstrap	NOUN
aiti-14032	205	17	samples	sample	NOUN
aiti-14032	205	18	,	,	PUNCT
aiti-14032	205	19	then	then	ADV
aiti-14032	205	20	the	the	DET
aiti-14032	205	21	equations	equation	NOUN
aiti-14032	205	22	are	be	AUX
aiti-14032	205	23	(	(	PUNCT
aiti-14032	205	24	)	)	PUNCT
aiti-14032	205	25	m	m	VERB
aiti-14032	205	26	trainmodel	trainmodel	PROPN
aiti-14032	205	27	di	di	PROPN
aiti-14032	205	28	i=	i=	PROPN
aiti-14032	205	29	(	(	PUNCT
aiti-14032	205	30	7	7	NUM
aiti-14032	205	31	)	)	PUNCT
aiti-14032	205	32	(	(	PUNCT
aiti-14032	205	33	,	,	PUNCT
aiti-14032	205	34	,	,	PUNCT
aiti-14032	205	35	,	,	PUNCT
aiti-14032	205	36	...	...	PUNCT
aiti-14032	205	37	,	,	PUNCT
aiti-14032	205	38	)	)	PUNCT
aiti-14032	205	39	1	1	NUM
aiti-14032	205	40	2	2	NUM
aiti-14032	205	41	3	3	NUM
aiti-14032	205	42	y	y	NOUN
aiti-14032	205	43	aggrepredicn	aggrepredicn	VERB
aiti-14032	205	44	y	y	PROPN
aiti-14032	205	45	y	y	PROPN
aiti-14032	205	46	y	y	PROPN
aiti-14032	205	47	y	y	PROPN
aiti-14032	205	48	k	k	PROPN
aiti-14032	206	1	=	=	PUNCT
aiti-14032	207	1	(	(	PUNCT
aiti-14032	207	2	8)	8)	NUM
aiti-14032	207	3	fig	fig	NOUN
aiti-14032	207	4	.	.	PUNCT
aiti-14032	208	1	4	4	NUM
aiti-14032	208	2	architectural	architectural	ADJ
aiti-14032	208	3	design	design	NOUN
aiti-14032	208	4	of	of	ADP
aiti-14032	208	5	the	the	DET
aiti-14032	208	6	disease	disease	NOUN
aiti-14032	208	7	prediction	prediction	NOUN
aiti-14032	208	8	model	model	NOUN
aiti-14032	208	9	fig	fig	NOUN
aiti-14032	208	10	.	.	PUNCT
aiti-14032	209	1	4	4	NUM
aiti-14032	209	2	depicts	depict	VERB
aiti-14032	209	3	the	the	DET
aiti-14032	209	4	complete	complete	ADJ
aiti-14032	209	5	architecture	architecture	NOUN
aiti-14032	209	6	of	of	ADP
aiti-14032	209	7	the	the	DET
aiti-14032	209	8	proposed	propose	VERB
aiti-14032	209	9	work	work	NOUN
aiti-14032	209	10	.	.	PUNCT
aiti-14032	210	1	the	the	DET
aiti-14032	210	2	block	block	NOUN
aiti-14032	210	3	diagram	diagram	NOUN
aiti-14032	210	4	illustrates	illustrate	VERB
aiti-14032	210	5	the	the	DET
aiti-14032	210	6	overall	overall	ADJ
aiti-14032	210	7	workflow	workflow	NOUN
aiti-14032	210	8	,	,	PUNCT
aiti-14032	210	9	including	include	VERB
aiti-14032	210	10	the	the	DET
aiti-14032	210	11	distinct	distinct	ADJ
aiti-14032	210	12	phases	phase	NOUN
aiti-14032	210	13	of	of	ADP
aiti-14032	210	14	the	the	DET
aiti-14032	210	15	multi	multi	ADJ
aiti-14032	210	16	-	-	ADJ
aiti-14032	210	17	class	class	ADJ
aiti-14032	210	18	disease	disease	NOUN
aiti-14032	210	19	prediction	prediction	NOUN
aiti-14032	210	20	process	process	NOUN
aiti-14032	210	21	.	.	PUNCT
aiti-14032	211	1	the	the	DET
aiti-14032	211	2	first	first	ADJ
aiti-14032	211	3	phase	phase	NOUN
aiti-14032	211	4	involves	involve	VERB
aiti-14032	211	5	pre	pre	ADJ
aiti-14032	211	6	-	-	ADJ
aiti-14032	211	7	processing	process	VERB
aiti-14032	211	8	the	the	DET
aiti-14032	211	9	dataset	dataset	NOUN
aiti-14032	211	10	using	use	VERB
aiti-14032	211	11	the	the	DET
aiti-14032	211	12	three	three	NUM
aiti-14032	211	13	algorithms	algorithm	NOUN
aiti-14032	211	14	shown	show	VERB
aiti-14032	211	15	in	in	ADP
aiti-14032	211	16	the	the	DET
aiti-14032	211	17	block	block	NOUN
aiti-14032	211	18	diagram	diagram	NOUN
aiti-14032	211	19	.	.	PUNCT
aiti-14032	212	1	the	the	DET
aiti-14032	212	2	second	second	ADJ
aiti-14032	212	3	phase	phase	NOUN
aiti-14032	212	4	covers	cover	VERB
aiti-14032	212	5	feature	feature	NOUN
aiti-14032	212	6	extraction	extraction	NOUN
aiti-14032	212	7	using	use	VERB
aiti-14032	212	8	the	the	DET
aiti-14032	212	9	techniques	technique	NOUN
aiti-14032	212	10	advances	advance	NOUN
aiti-14032	212	11	in	in	ADP
aiti-14032	212	12	technology	technology	NOUN
aiti-14032	212	13	innovation	innovation	NOUN
aiti-14032	212	14	,	,	PUNCT
aiti-14032	212	15	vol	vol	NOUN
aiti-14032	212	16	.	.	PROPN
aiti-14032	213	1	10	10	NUM
aiti-14032	213	2	,	,	PUNCT
aiti-14032	213	3	no	no	INTJ
aiti-14032	213	4	.	.	NOUN
aiti-14032	213	5	4	4	NUM
aiti-14032	213	6	,	,	PUNCT
aiti-14032	213	7	2025	2025	NUM
aiti-14032	213	8	,	,	PUNCT
aiti-14032	213	9	pp	pp	ADJ
aiti-14032	213	10	.	.	PUNCT
aiti-14032	214	1	370	370	NUM
aiti-14032	214	2	-	-	SYM
aiti-14032	214	3	382	382	NUM
aiti-14032	214	4	377	377	NUM
aiti-14032	214	5	highlighted	highlight	VERB
aiti-14032	214	6	in	in	ADP
aiti-14032	214	7	the	the	DET
aiti-14032	214	8	diagram	diagram	NOUN
aiti-14032	214	9	.	.	PUNCT
aiti-14032	215	1	phase	phase	NOUN
aiti-14032	215	2	three	three	NUM
aiti-14032	215	3	incorporates	incorporate	VERB
aiti-14032	215	4	the	the	DET
aiti-14032	215	5	training	training	NOUN
aiti-14032	215	6	of	of	ADP
aiti-14032	215	7	the	the	DET
aiti-14032	215	8	bootstrap	bootstrap	NOUN
aiti-14032	215	9	and	and	CCONJ
aiti-14032	215	10	msvm	msvm	NOUN
aiti-14032	215	11	models	model	NOUN
aiti-14032	215	12	for	for	ADP
aiti-14032	215	13	disease	disease	NOUN
aiti-14032	215	14	prediction	prediction	NOUN
aiti-14032	215	15	,	,	PUNCT
aiti-14032	215	16	and	and	CCONJ
aiti-14032	215	17	finally	finally	ADV
aiti-14032	215	18	,	,	PUNCT
aiti-14032	215	19	the	the	DET
aiti-14032	215	20	two	two	NUM
aiti-14032	215	21	models	model	NOUN
aiti-14032	215	22	are	be	AUX
aiti-14032	215	23	compared	compare	VERB
aiti-14032	215	24	based	base	VERB
aiti-14032	215	25	on	on	ADP
aiti-14032	215	26	the	the	DET
aiti-14032	215	27	four	four	NUM
aiti-14032	215	28	parameters	parameter	NOUN
aiti-14032	215	29	shown	show	VERB
aiti-14032	215	30	at	at	ADP
aiti-14032	215	31	the	the	DET
aiti-14032	215	32	leaves	leave	NOUN
aiti-14032	215	33	of	of	ADP
aiti-14032	215	34	the	the	DET
aiti-14032	215	35	architectural	architectural	ADJ
aiti-14032	215	36	diagram	diagram	NOUN
aiti-14032	215	37	.	.	PUNCT
aiti-14032	216	1	algorithm	algorithm	PROPN
aiti-14032	216	2	1	1	NUM
aiti-14032	216	3	health	health	NOUN
aiti-14032	216	4	prediction	prediction	NOUN
aiti-14032	216	5	using	use	VERB
aiti-14032	216	6	bootstrap	bootstrap	NOUN
aiti-14032	216	7	learning	learn	VERB
aiti-14032	216	8	input	input	NOUN
aiti-14032	216	9	:	:	PUNCT
aiti-14032	216	10	potato	potato	NOUN
aiti-14032	216	11	image	image	NOUN
aiti-14032	216	12	dataset	dataset	NOUN
aiti-14032	216	13	,	,	PUNCT
aiti-14032	216	14	which	which	PRON
aiti-14032	216	15	contains	contain	VERB
aiti-14032	216	16	a	a	DET
aiti-14032	216	17	set	set	NOUN
aiti-14032	216	18	of	of	ADP
aiti-14032	216	19	healthy	healthy	ADJ
aiti-14032	216	20	and	and	CCONJ
aiti-14032	216	21	diseased	diseased	ADJ
aiti-14032	216	22	leaf	leaf	NOUN
aiti-14032	216	23	images	image	NOUN
aiti-14032	216	24	.	.	PUNCT
aiti-14032	217	1	output	output	NOUN
aiti-14032	217	2	:	:	PUNCT
aiti-14032	217	3	prediction	prediction	NOUN
aiti-14032	217	4	accuracy	accuracy	NOUN
aiti-14032	217	5	using	use	VERB
aiti-14032	217	6	the	the	DET
aiti-14032	217	7	bootstrap	bootstrap	NOUN
aiti-14032	217	8	model	model	NOUN
aiti-14032	217	9	.	.	PUNCT
aiti-14032	218	1	method	method	NOUN
aiti-14032	218	2	:	:	PUNCT
aiti-14032	218	3	step	step	NOUN
aiti-14032	218	4	1	1	NUM
aiti-14032	218	5	.	.	PUNCT
aiti-14032	218	6	apply	apply	VERB
aiti-14032	218	7	the	the	DET
aiti-14032	218	8	bat	bat	NOUN
aiti-14032	218	9	algorithm	algorithm	NOUN
aiti-14032	218	10	and	and	CCONJ
aiti-14032	218	11	gmm	gmm	NOUN
aiti-14032	218	12	for	for	ADP
aiti-14032	218	13	preprocessing	preprocessing	NOUN
aiti-14032	218	14	and	and	CCONJ
aiti-14032	218	15	leaf	leaf	NOUN
aiti-14032	218	16	region	region	NOUN
aiti-14032	218	17	selection	selection	NOUN
aiti-14032	218	18	of	of	ADP
aiti-14032	218	19	the	the	DET
aiti-14032	218	20	input	input	NOUN
aiti-14032	218	21	image	image	NOUN
aiti-14032	218	22	.	.	PUNCT
aiti-14032	219	1	step	step	NOUN
aiti-14032	219	2	2	2	NUM
aiti-14032	219	3	.	.	PUNCT
aiti-14032	219	4	apply	apply	VERB
aiti-14032	219	5	the	the	DET
aiti-14032	219	6	histogram	histogram	NOUN
aiti-14032	219	7	method	method	NOUN
aiti-14032	219	8	and	and	CCONJ
aiti-14032	219	9	mccm	mccm	NOUN
aiti-14032	219	10	method	method	NOUN
aiti-14032	219	11	for	for	ADP
aiti-14032	219	12	color	color	NOUN
aiti-14032	219	13	and	and	CCONJ
aiti-14032	219	14	texture	texture	NOUN
aiti-14032	219	15	features	feature	NOUN
aiti-14032	219	16	,	,	PUNCT
aiti-14032	219	17	respectively	respectively	ADV
aiti-14032	219	18	.	.	PUNCT
aiti-14032	220	1	step	step	NOUN
aiti-14032	220	2	3	3	NUM
aiti-14032	220	3	.	.	PUNCT
aiti-14032	220	4	normalize	normalize	VERB
aiti-14032	220	5	the	the	DET
aiti-14032	220	6	features	feature	NOUN
aiti-14032	220	7	obtained	obtain	VERB
aiti-14032	220	8	from	from	ADP
aiti-14032	220	9	the	the	DET
aiti-14032	220	10	above	above	ADJ
aiti-14032	220	11	methods	method	NOUN
aiti-14032	220	12	.	.	PUNCT
aiti-14032	221	1	step	step	NOUN
aiti-14032	221	2	4	4	NUM
aiti-14032	221	3	.	.	PUNCT
aiti-14032	221	4	apply	apply	VERB
aiti-14032	221	5	the	the	DET
aiti-14032	221	6	bootstrap	bootstrap	NOUN
aiti-14032	221	7	learning	learn	VERB
aiti-14032	221	8	model	model	NOUN
aiti-14032	221	9	to	to	PART
aiti-14032	221	10	get	get	VERB
aiti-14032	221	11	prediction	prediction	NOUN
aiti-14032	221	12	accuracy	accuracy	NOUN
aiti-14032	221	13	.	.	PUNCT
aiti-14032	222	1	3.4.2	3.4.2	X
aiti-14032	222	2	.	.	PUNCT
aiti-14032	223	1	msvm	msvm	PROPN
aiti-14032	223	2	learning	learn	VERB
aiti-14032	223	3	the	the	DET
aiti-14032	223	4	msvm	msvm	ADJ
aiti-14032	223	5	formulation	formulation	NOUN
aiti-14032	223	6	aims	aim	VERB
aiti-14032	223	7	to	to	PART
aiti-14032	223	8	find	find	VERB
aiti-14032	223	9	optimal	optimal	ADJ
aiti-14032	223	10	hyperplanes	hyperplane	NOUN
aiti-14032	223	11	that	that	SCONJ
aiti-14032	223	12	best	good	ADJ
aiti-14032	223	13	separate	separate	ADJ
aiti-14032	223	14	data	datum	NOUN
aiti-14032	223	15	points	point	NOUN
aiti-14032	223	16	into	into	ADP
aiti-14032	223	17	k	k	PROPN
aiti-14032	223	18	classes	class	NOUN
aiti-14032	223	19	using	use	VERB
aiti-14032	223	20	the	the	DET
aiti-14032	223	21	one	one	NUM
aiti-14032	223	22	-	-	PUNCT
aiti-14032	223	23	vsall	vsall	NOUN
aiti-14032	223	24	strategy	strategy	NOUN
aiti-14032	223	25	[	[	X
aiti-14032	223	26	25	25	NUM
aiti-14032	223	27	]	]	PUNCT
aiti-14032	223	28	.	.	PUNCT
aiti-14032	224	1	a	a	DET
aiti-14032	224	2	training	training	NOUN
aiti-14032	224	3	dataset	dataset	NOUN
aiti-14032	224	4	{	{	PUNCT
aiti-14032	224	5	(	(	PUNCT
aiti-14032	224	6	x1	x1	PROPN
aiti-14032	224	7	,	,	PUNCT
aiti-14032	224	8	y1	y1	PROPN
aiti-14032	224	9	)	)	PUNCT
aiti-14032	224	10	,	,	PUNCT
aiti-14032	224	11	(	(	PUNCT
aiti-14032	224	12	x2	x2	PROPN
aiti-14032	224	13	,	,	PUNCT
aiti-14032	224	14	y2	y2	PROPN
aiti-14032	224	15	)	)	PUNCT
aiti-14032	224	16	,	,	PUNCT
aiti-14032	224	17	..	..	PUNCT
aiti-14032	224	18	,	,	PUNCT
aiti-14032	224	19	(	(	PUNCT
aiti-14032	224	20	xn	xn	PROPN
aiti-14032	224	21	,	,	PUNCT
aiti-14032	224	22	yn	yn	PROPN
aiti-14032	224	23	)	)	PUNCT
aiti-14032	224	24	}	}	PUNCT
aiti-14032	224	25	where	where	SCONJ
aiti-14032	224	26	xi	xi	PROPN
aiti-14032	224	27	is	be	AUX
aiti-14032	224	28	the	the	DET
aiti-14032	224	29	feature	feature	NOUN
aiti-14032	224	30	vector	vector	NOUN
aiti-14032	224	31	and	and	CCONJ
aiti-14032	224	32	yi	yi	NOUN
aiti-14032	224	33	is	be	AUX
aiti-14032	224	34	the	the	DET
aiti-14032	224	35	class	class	NOUN
aiti-14032	224	36	label	label	NOUN
aiti-14032	224	37	with	with	ADP
aiti-14032	224	38	yi	yi	PROPN
aiti-14032	224	39	ꜫ	ꜫ	PROPN
aiti-14032	224	40	{	{	PUNCT
aiti-14032	224	41	1,2	1,2	NUM
aiti-14032	224	42	,	,	PUNCT
aiti-14032	224	43	..	..	PUNCT
aiti-14032	224	44	,	,	PUNCT
aiti-14032	224	45	k	k	X
aiti-14032	224	46	}	}	PUNCT
aiti-14032	224	47	for	for	ADP
aiti-14032	224	48	k	k	PROPN
aiti-14032	224	49	classes	class	NOUN
aiti-14032	224	50	.	.	PUNCT
aiti-14032	225	1	the	the	DET
aiti-14032	225	2	optimization	optimization	NOUN
aiti-14032	225	3	function	function	NOUN
aiti-14032	225	4	for	for	ADP
aiti-14032	225	5	the	the	DET
aiti-14032	225	6	problem	problem	NOUN
aiti-14032	225	7	is	be	AUX
aiti-14032	225	8	1	1	NUM
aiti-14032	225	9	2	2	NUM
aiti-14032	225	10	min	min	NOUN
aiti-14032	225	11	max(0,1	max(0,1	PROPN
aiti-14032	225	12	(	(	PUNCT
aiti-14032	225	13	)	)	PUNCT
aiti-14032	225	14	)	)	PUNCT
aiti-14032	225	15	,	,	PUNCT
aiti-14032	226	1	12	12	NUM
aiti-14032	226	2	n	n	NOUN
aiti-14032	226	3	w	w	NOUN
aiti-14032	226	4	c	c	PROPN
aiti-14032	226	5	y	y	PROPN
aiti-14032	226	6	w	w	PROPN
aiti-14032	226	7	x	x	VERB
aiti-14032	226	8	bj	bj	VERB
aiti-14032	226	9	ij	ij	INTJ
aiti-14032	226	10	j	j	PROPN
aiti-14032	227	1	i	i	PRON
aiti-14032	227	2	j	j	PROPN
aiti-14032	227	3	w	w	PROPN
aiti-14032	227	4	b	b	PROPN
aiti-14032	228	1	ij	ij	INTJ
aiti-14032	228	2	j	j	PROPN
aiti-14032	228	3	+	+	NUM
aiti-14032	228	4	−	−	PROPN
aiti-14032	229	1	+	+	CCONJ
aiti-14032	229	2	=	=	SYM
aiti-14032	229	3			VERB
aiti-14032	229	4	(	(	PUNCT
aiti-14032	229	5	9	9	NUM
aiti-14032	229	6	)	)	PUNCT
aiti-14032	229	7	algorithm	algorithm	NOUN
aiti-14032	229	8	2	2	NUM
aiti-14032	229	9	health	health	NOUN
aiti-14032	229	10	prediction	prediction	NOUN
aiti-14032	229	11	using	use	VERB
aiti-14032	229	12	msvm	msvm	ADJ
aiti-14032	229	13	input	input	NOUN
aiti-14032	229	14	:	:	PUNCT
aiti-14032	229	15	potato	potato	NOUN
aiti-14032	229	16	image	image	NOUN
aiti-14032	229	17	dataset	dataset	NOUN
aiti-14032	229	18	,	,	PUNCT
aiti-14032	229	19	which	which	PRON
aiti-14032	229	20	contains	contain	VERB
aiti-14032	229	21	a	a	DET
aiti-14032	229	22	set	set	NOUN
aiti-14032	229	23	of	of	ADP
aiti-14032	229	24	healthy	healthy	ADJ
aiti-14032	229	25	and	and	CCONJ
aiti-14032	229	26	diseased	diseased	ADJ
aiti-14032	229	27	leaf	leaf	NOUN
aiti-14032	229	28	images	image	NOUN
aiti-14032	229	29	.	.	PUNCT
aiti-14032	230	1	output	output	NOUN
aiti-14032	230	2	:	:	PUNCT
aiti-14032	230	3	prediction	prediction	NOUN
aiti-14032	230	4	accuracy	accuracy	NOUN
aiti-14032	230	5	using	use	VERB
aiti-14032	230	6	the	the	DET
aiti-14032	230	7	msvm	msvm	ADJ
aiti-14032	230	8	model	model	PROPN
aiti-14032	230	9	.	.	PUNCT
aiti-14032	231	1	method	method	NOUN
aiti-14032	231	2	:	:	PUNCT
aiti-14032	231	3	step	step	NOUN
aiti-14032	231	4	1	1	NUM
aiti-14032	231	5	.	.	PUNCT
aiti-14032	231	6	extract	extract	VERB
aiti-14032	231	7	fuzzy	fuzzy	ADJ
aiti-14032	231	8	hsv	hsv	PROPN
aiti-14032	231	9	features	feature	NOUN
aiti-14032	231	10	from	from	ADP
aiti-14032	231	11	the	the	DET
aiti-14032	231	12	input	input	NOUN
aiti-14032	231	13	image	image	NOUN
aiti-14032	231	14	.	.	PUNCT
aiti-14032	232	1	step	step	NOUN
aiti-14032	232	2	2	2	NUM
aiti-14032	232	3	.	.	PUNCT
aiti-14032	232	4	extract	extract	VERB
aiti-14032	232	5	fuzzy	fuzzy	ADJ
aiti-14032	232	6	lbp	lbp	PROPN
aiti-14032	232	7	features	feature	NOUN
aiti-14032	232	8	from	from	ADP
aiti-14032	232	9	the	the	DET
aiti-14032	232	10	input	input	NOUN
aiti-14032	232	11	image	image	NOUN
aiti-14032	232	12	.	.	PUNCT
aiti-14032	233	1	step	step	NOUN
aiti-14032	233	2	3	3	NUM
aiti-14032	233	3	.	.	PUNCT
aiti-14032	233	4	normalize	normalize	VERB
aiti-14032	233	5	the	the	DET
aiti-14032	233	6	features	feature	NOUN
aiti-14032	233	7	obtained	obtain	VERB
aiti-14032	233	8	from	from	ADP
aiti-14032	233	9	the	the	DET
aiti-14032	233	10	above	above	ADJ
aiti-14032	233	11	methods	method	NOUN
aiti-14032	233	12	.	.	PUNCT
aiti-14032	234	1	step	step	NOUN
aiti-14032	234	2	4	4	NUM
aiti-14032	234	3	.	.	PUNCT
aiti-14032	234	4	apply	apply	VERB
aiti-14032	234	5	the	the	DET
aiti-14032	234	6	msvm	msvm	NOUN
aiti-14032	234	7	learning	learn	VERB
aiti-14032	234	8	model	model	NOUN
aiti-14032	234	9	to	to	PART
aiti-14032	234	10	obtain	obtain	VERB
aiti-14032	234	11	prediction	prediction	NOUN
aiti-14032	234	12	accuracy	accuracy	NOUN
aiti-14032	234	13	.	.	PUNCT
aiti-14032	235	1	4	4	X
aiti-14032	235	2	.	.	NOUN
aiti-14032	235	3	result	result	NOUN
aiti-14032	235	4	and	and	CCONJ
aiti-14032	235	5	discussion	discussion	VERB
aiti-14032	235	6	the	the	DET
aiti-14032	235	7	disease	disease	NOUN
aiti-14032	235	8	prediction	prediction	NOUN
aiti-14032	235	9	results	result	NOUN
aiti-14032	235	10	of	of	ADP
aiti-14032	235	11	the	the	DET
aiti-14032	235	12	bootstrap	bootstrap	NOUN
aiti-14032	235	13	model	model	NOUN
aiti-14032	235	14	and	and	CCONJ
aiti-14032	235	15	multiclass	multiclass	ADJ
aiti-14032	235	16	svm	svm	PROPN
aiti-14032	235	17	learning	learning	NOUN
aiti-14032	235	18	model	model	NOUN
aiti-14032	235	19	have	have	AUX
aiti-14032	235	20	been	be	AUX
aiti-14032	235	21	presented	present	VERB
aiti-14032	235	22	in	in	ADP
aiti-14032	235	23	this	this	DET
aiti-14032	235	24	section	section	NOUN
aiti-14032	235	25	.	.	PUNCT
aiti-14032	236	1	the	the	DET
aiti-14032	236	2	bat	bat	NOUN
aiti-14032	236	3	-	-	PUNCT
aiti-14032	236	4	based	base	VERB
aiti-14032	236	5	crop	crop	NOUN
aiti-14032	236	6	leaf	leaf	NOUN
aiti-14032	236	7	disease	disease	NOUN
aiti-14032	236	8	prediction	prediction	NOUN
aiti-14032	236	9	bootstrap	bootstrap	NOUN
aiti-14032	236	10	model	model	NOUN
aiti-14032	236	11	(	(	PUNCT
aiti-14032	236	12	bcdpbm	bcdpbm	NOUN
aiti-14032	236	13	)	)	PUNCT
aiti-14032	237	1	[	[	X
aiti-14032	237	2	24	24	NUM
aiti-14032	237	3	]	]	PUNCT
aiti-14032	237	4	uses	use	VERB
aiti-14032	237	5	a	a	DET
aiti-14032	237	6	novel	novel	ADJ
aiti-14032	237	7	approach	approach	NOUN
aiti-14032	237	8	of	of	ADP
aiti-14032	237	9	the	the	DET
aiti-14032	237	10	bat	bat	NOUN
aiti-14032	237	11	algorithm	algorithm	NOUN
aiti-14032	237	12	for	for	ADP
aiti-14032	237	13	preprocessing	preprocessing	NOUN
aiti-14032	237	14	of	of	ADP
aiti-14032	237	15	the	the	DET
aiti-14032	237	16	image	image	NOUN
aiti-14032	237	17	,	,	PUNCT
aiti-14032	237	18	and	and	CCONJ
aiti-14032	237	19	the	the	DET
aiti-14032	237	20	feature	feature	NOUN
aiti-14032	237	21	extraction	extraction	NOUN
aiti-14032	237	22	is	be	AUX
aiti-14032	237	23	done	do	VERB
aiti-14032	237	24	using	use	VERB
aiti-14032	237	25	a	a	DET
aiti-14032	237	26	unique	unique	ADJ
aiti-14032	237	27	combination	combination	NOUN
aiti-14032	237	28	of	of	ADP
aiti-14032	237	29	histogram	histogram	NOUN
aiti-14032	237	30	feature	feature	NOUN
aiti-14032	237	31	and	and	CCONJ
aiti-14032	237	32	mccm	mccm	NOUN
aiti-14032	237	33	feature	feature	NOUN
aiti-14032	237	34	.	.	PUNCT
aiti-14032	238	1	the	the	DET
aiti-14032	238	2	prediction	prediction	NOUN
aiti-14032	238	3	of	of	ADP
aiti-14032	238	4	multi	multi	ADJ
aiti-14032	238	5	-	-	ADJ
aiti-14032	238	6	class	class	ADJ
aiti-14032	238	7	leaf	leaf	NOUN
aiti-14032	238	8	disease	disease	NOUN
aiti-14032	238	9	is	be	AUX
aiti-14032	238	10	also	also	ADV
aiti-14032	238	11	performed	perform	VERB
aiti-14032	238	12	using	use	VERB
aiti-14032	238	13	multi	multi	ADJ
aiti-14032	238	14	-	-	ADJ
aiti-14032	238	15	class	class	ADJ
aiti-14032	238	16	svm	svm	NOUN
aiti-14032	238	17	with	with	ADP
aiti-14032	238	18	fuzzy	fuzzy	ADJ
aiti-14032	238	19	hsv	hsv	NOUN
aiti-14032	238	20	and	and	CCONJ
aiti-14032	238	21	fuzzy	fuzzy	ADJ
aiti-14032	238	22	lbp	lbp	NOUN
aiti-14032	238	23	feature	feature	NOUN
aiti-14032	238	24	extraction	extraction	NOUN
aiti-14032	238	25	techniques	technique	NOUN
aiti-14032	238	26	.	.	PUNCT
aiti-14032	239	1	it	it	PRON
aiti-14032	239	2	is	be	AUX
aiti-14032	239	3	found	find	VERB
aiti-14032	239	4	that	that	SCONJ
aiti-14032	239	5	the	the	DET
aiti-14032	239	6	bootstrap	bootstrap	NOUN
aiti-14032	239	7	model	model	NOUN
aiti-14032	239	8	,	,	PUNCT
aiti-14032	239	9	along	along	ADP
aiti-14032	239	10	with	with	ADP
aiti-14032	239	11	the	the	DET
aiti-14032	239	12	mccm	mccm	NOUN
aiti-14032	239	13	features	feature	NOUN
aiti-14032	239	14	,	,	PUNCT
aiti-14032	239	15	outperformed	outperform	VERB
aiti-14032	239	16	the	the	DET
aiti-14032	239	17	msvm	msvm	ADJ
aiti-14032	239	18	model	model	NOUN
aiti-14032	239	19	with	with	ADP
aiti-14032	239	20	fuzzy	fuzzy	ADJ
aiti-14032	239	21	features	feature	NOUN
aiti-14032	239	22	.	.	PUNCT
aiti-14032	240	1	the	the	DET
aiti-14032	240	2	bootstrap	bootstrap	NOUN
aiti-14032	240	3	model	model	NOUN
aiti-14032	240	4	gives	give	VERB
aiti-14032	240	5	an	an	DET
aiti-14032	240	6	average	average	ADJ
aiti-14032	240	7	accuracy	accuracy	NOUN
aiti-14032	240	8	of	of	ADP
aiti-14032	240	9	98.07	98.07	NUM
aiti-14032	240	10	%	%	NOUN
aiti-14032	240	11	as	as	ADP
aiti-14032	240	12	compared	compare	VERB
aiti-14032	240	13	to	to	ADP
aiti-14032	240	14	the	the	DET
aiti-14032	240	15	average	average	ADJ
aiti-14032	240	16	accuracy	accuracy	NOUN
aiti-14032	240	17	of	of	ADP
aiti-14032	240	18	80.11	80.11	NUM
aiti-14032	240	19	%	%	NOUN
aiti-14032	240	20	exhibited	exhibit	VERB
aiti-14032	240	21	by	by	ADP
aiti-14032	240	22	msvm	msvm	NOUN
aiti-14032	240	23	in	in	ADP
aiti-14032	240	24	multi	multi	ADJ
aiti-14032	240	25	-	-	ADJ
aiti-14032	240	26	class	class	ADJ
aiti-14032	240	27	disease	disease	NOUN
aiti-14032	240	28	prediction	prediction	NOUN
aiti-14032	240	29	.	.	PUNCT
aiti-14032	241	1	table	table	NOUN
aiti-14032	241	2	1	1	NUM
aiti-14032	241	3	shows	show	VERB
aiti-14032	241	4	the	the	DET
aiti-14032	241	5	accuracy	accuracy	NOUN
aiti-14032	241	6	of	of	ADP
aiti-14032	241	7	the	the	DET
aiti-14032	241	8	bcdpbm	bcdpbm	NOUN
aiti-14032	241	9	model	model	NOUN
aiti-14032	241	10	along	along	ADP
aiti-14032	241	11	with	with	ADP
aiti-14032	241	12	multi	multi	ADJ
aiti-14032	241	13	-	-	ADJ
aiti-14032	241	14	class	class	ADJ
aiti-14032	241	15	svm	svm	NOUN
aiti-14032	241	16	.	.	PUNCT
aiti-14032	242	1	the	the	DET
aiti-14032	242	2	table	table	NOUN
aiti-14032	242	3	highlights	highlight	VERB
aiti-14032	242	4	significant	significant	ADJ
aiti-14032	242	5	differences	difference	NOUN
aiti-14032	242	6	in	in	ADP
aiti-14032	242	7	the	the	DET
aiti-14032	242	8	performance	performance	NOUN
aiti-14032	242	9	of	of	ADP
aiti-14032	242	10	the	the	DET
aiti-14032	242	11	bcdpbm	bcdpbm	NOUN
aiti-14032	242	12	model	model	NOUN
aiti-14032	242	13	and	and	CCONJ
aiti-14032	242	14	msvm	msvm	ADJ
aiti-14032	242	15	model	model	NOUN
aiti-14032	242	16	across	across	ADP
aiti-14032	242	17	varying	vary	VERB
aiti-14032	242	18	numbers	number	NOUN
aiti-14032	242	19	of	of	ADP
aiti-14032	242	20	test	test	NOUN
aiti-14032	242	21	images	image	NOUN
aiti-14032	242	22	,	,	PUNCT
aiti-14032	242	23	the	the	DET
aiti-14032	242	24	result	result	NOUN
aiti-14032	242	25	shows	show	VERB
aiti-14032	242	26	that	that	SCONJ
aiti-14032	242	27	bcdpbm	bcdpbm	NOUN
aiti-14032	242	28	encounters	encounter	VERB
aiti-14032	242	29	a	a	DET
aiti-14032	242	30	very	very	ADV
aiti-14032	242	31	low	low	ADJ
aiti-14032	242	32	error	error	NOUN
aiti-14032	242	33	rate	rate	NOUN
aiti-14032	242	34	of	of	ADP
aiti-14032	242	35	0.51	0.51	NUM
aiti-14032	242	36	%	%	NOUN
aiti-14032	242	37	as	as	ADP
aiti-14032	242	38	compared	compare	VERB
aiti-14032	242	39	to	to	ADP
aiti-14032	242	40	the	the	DET
aiti-14032	242	41	21.63	21.63	NUM
aiti-14032	242	42	%	%	NOUN
aiti-14032	242	43	error	error	NOUN
aiti-14032	242	44	rate	rate	NOUN
aiti-14032	242	45	of	of	ADP
aiti-14032	242	46	msvm	msvm	NOUN
aiti-14032	242	47	.	.	PUNCT
aiti-14032	243	1	fig	fig	NOUN
aiti-14032	243	2	.	.	PUNCT
aiti-14032	244	1	5	5	NUM
aiti-14032	244	2	graphs	graph	VERB
aiti-14032	244	3	the	the	DET
aiti-14032	244	4	accuracy	accuracy	NOUN
aiti-14032	244	5	comparison	comparison	NOUN
aiti-14032	244	6	of	of	ADP
aiti-14032	244	7	the	the	DET
aiti-14032	244	8	two	two	NUM
aiti-14032	244	9	models	model	NOUN
aiti-14032	244	10	along	along	ADP
aiti-14032	244	11	with	with	ADP
aiti-14032	244	12	the	the	DET
aiti-14032	244	13	error	error	NOUN
aiti-14032	244	14	rate	rate	NOUN
aiti-14032	244	15	.	.	PUNCT
aiti-14032	245	1	advances	advance	NOUN
aiti-14032	245	2	in	in	ADP
aiti-14032	245	3	technology	technology	NOUN
aiti-14032	245	4	innovation	innovation	NOUN
aiti-14032	245	5	,	,	PUNCT
aiti-14032	245	6	vol	vol	NOUN
aiti-14032	245	7	.	.	PROPN
aiti-14032	246	1	10	10	NUM
aiti-14032	246	2	,	,	PUNCT
aiti-14032	246	3	no	no	INTJ
aiti-14032	246	4	.	.	NOUN
aiti-14032	246	5	4	4	NUM
aiti-14032	246	6	,	,	PUNCT
aiti-14032	246	7	2025	2025	NUM
aiti-14032	246	8	,	,	PUNCT
aiti-14032	246	9	pp	pp	ADJ
aiti-14032	246	10	.	.	PUNCT
aiti-14032	247	1	370	370	NUM
aiti-14032	247	2	-	-	SYM
aiti-14032	247	3	382	382	NUM
aiti-14032	247	4	378	378	NUM
aiti-14032	247	5	table	table	NOUN
aiti-14032	247	6	1	1	NUM
aiti-14032	247	7	accuracy	accuracy	NOUN
aiti-14032	247	8	and	and	CCONJ
aiti-14032	247	9	error	error	NOUN
aiti-14032	247	10	rate	rate	NOUN
aiti-14032	247	11	of	of	ADP
aiti-14032	247	12	multiclass	multiclass	ADJ
aiti-14032	247	13	plant	plant	NOUN
aiti-14032	247	14	leaf	leaf	NOUN
aiti-14032	247	15	disease	disease	NOUN
aiti-14032	247	16	prediction	prediction	NOUN
aiti-14032	247	17	in	in	ADP
aiti-14032	247	18	the	the	DET
aiti-14032	247	19	bcdpbm	bcdpbm	NOUN
aiti-14032	247	20	and	and	CCONJ
aiti-14032	247	21	msvm	msvm	NOUN
aiti-14032	247	22	models	model	NOUN
aiti-14032	247	23	testing	test	VERB
aiti-14032	247	24	images	image	NOUN
aiti-14032	247	25	bcdpbm	bcdpbm	NOUN
aiti-14032	247	26	msvm	msvm	ADJ
aiti-14032	247	27	accuracy	accuracy	NOUN
aiti-14032	247	28	%	%	NOUN
aiti-14032	247	29	error	error	NOUN
aiti-14032	247	30	%	%	NOUN
aiti-14032	247	31	accuracy	accuracy	NOUN
aiti-14032	247	32	%	%	NOUN
aiti-14032	247	33	error	error	NOUN
aiti-14032	247	34	%	%	NOUN
aiti-14032	247	35	75	75	NUM
aiti-14032	247	36	97.3	97.3	NUM
aiti-14032	247	37	2.7	2.7	NUM
aiti-14032	247	38	82.67	82.67	NUM
aiti-14032	247	39	17.33	17.33	NUM
aiti-14032	247	40	150	150	NUM
aiti-14032	247	41	95.12	95.12	NUM
aiti-14032	247	42	4.88	4.88	NUM
aiti-14032	247	43	80.92	80.92	NUM
aiti-14032	247	44	19.08	19.08	NUM
aiti-14032	247	45	225	225	NUM
aiti-14032	247	46	99.12	99.12	NUM
aiti-14032	247	47	0.88	0.88	NUM
aiti-14032	247	48	79.82	79.82	NUM
aiti-14032	247	49	20.18	20.18	NUM
aiti-14032	247	50	300	300	NUM
aiti-14032	247	51	99.34	99.34	NUM
aiti-14032	247	52	0.66	0.66	NUM
aiti-14032	247	53	78.81	78.81	NUM
aiti-14032	247	54	21.19	21.19	NUM
aiti-14032	247	55	400	400	NUM
aiti-14032	247	56	99.49	99.49	NUM
aiti-14032	247	57	0.51	0.51	NUM
aiti-14032	247	58	78.37	78.37	NUM
aiti-14032	247	59	21.63	21.63	NUM
aiti-14032	247	60	fig	fig	NOUN
aiti-14032	247	61	.	.	PUNCT
aiti-14032	248	1	5	5	NUM
aiti-14032	248	2	accuracy	accuracy	NOUN
aiti-14032	248	3	and	and	CCONJ
aiti-14032	248	4	error	error	NOUN
aiti-14032	248	5	rate	rate	NOUN
aiti-14032	248	6	of	of	ADP
aiti-14032	248	7	multiclass	multiclass	ADJ
aiti-14032	248	8	plant	plant	NOUN
aiti-14032	248	9	leaf	leaf	NOUN
aiti-14032	248	10	disease	disease	NOUN
aiti-14032	248	11	prediction	prediction	NOUN
aiti-14032	248	12	in	in	ADP
aiti-14032	248	13	the	the	DET
aiti-14032	248	14	bcdpbm	bcdpbm	NOUN
aiti-14032	248	15	and	and	CCONJ
aiti-14032	248	16	msvm	msvm	NOUN
aiti-14032	248	17	models	model	NOUN
aiti-14032	248	18	table	table	NOUN
aiti-14032	248	19	2	2	NUM
aiti-14032	248	20	shows	show	VERB
aiti-14032	248	21	the	the	DET
aiti-14032	248	22	precision	precision	NOUN
aiti-14032	248	23	value	value	NOUN
aiti-14032	248	24	comparison	comparison	NOUN
aiti-14032	248	25	between	between	ADP
aiti-14032	248	26	the	the	DET
aiti-14032	248	27	bcdpbm	bcdpbm	NOUN
aiti-14032	248	28	model	model	NOUN
aiti-14032	248	29	and	and	CCONJ
aiti-14032	248	30	the	the	DET
aiti-14032	248	31	multi	multi	ADJ
aiti-14032	248	32	-	-	ADJ
aiti-14032	248	33	class	class	ADJ
aiti-14032	248	34	svm	svm	ADJ
aiti-14032	248	35	model	model	NOUN
aiti-14032	248	36	for	for	ADP
aiti-14032	248	37	different	different	ADJ
aiti-14032	248	38	datasets	dataset	NOUN
aiti-14032	248	39	.	.	PUNCT
aiti-14032	249	1	the	the	DET
aiti-14032	249	2	results	result	NOUN
aiti-14032	249	3	indicate	indicate	VERB
aiti-14032	249	4	that	that	SCONJ
aiti-14032	249	5	the	the	DET
aiti-14032	249	6	bcdpbm	bcdpbm	NOUN
aiti-14032	249	7	model	model	NOUN
aiti-14032	249	8	achieves	achieve	VERB
aiti-14032	249	9	a	a	DET
aiti-14032	249	10	precision	precision	NOUN
aiti-14032	249	11	value	value	NOUN
aiti-14032	249	12	of	of	ADP
aiti-14032	249	13	“	"	PUNCT
aiti-14032	249	14	1	1	NUM
aiti-14032	249	15	”	"	PUNCT
aiti-14032	249	16	for	for	ADP
aiti-14032	249	17	all	all	DET
aiti-14032	249	18	datasets	dataset	NOUN
aiti-14032	249	19	,	,	PUNCT
aiti-14032	249	20	surpassing	surpass	VERB
aiti-14032	249	21	the	the	DET
aiti-14032	249	22	multiclass	multiclass	ADJ
aiti-14032	249	23	svm	svm	PROPN
aiti-14032	249	24	model	model	NOUN
aiti-14032	249	25	,	,	PUNCT
aiti-14032	249	26	which	which	PRON
aiti-14032	249	27	displays	display	VERB
aiti-14032	249	28	precision	precision	NOUN
aiti-14032	249	29	values	value	NOUN
aiti-14032	249	30	of	of	ADP
aiti-14032	249	31	0.84	0.84	NUM
aiti-14032	249	32	,	,	PUNCT
aiti-14032	249	33	0.80	0.80	NUM
aiti-14032	249	34	,	,	PUNCT
aiti-14032	249	35	and	and	CCONJ
aiti-14032	249	36	0.83	0.83	NUM
aiti-14032	249	37	for	for	ADP
aiti-14032	249	38	datasets	dataset	NOUN
aiti-14032	249	39	of	of	ADP
aiti-14032	249	40	sizes	size	NOUN
aiti-14032	249	41	225	225	NUM
aiti-14032	249	42	,	,	PUNCT
aiti-14032	249	43	300	300	NUM
aiti-14032	249	44	,	,	PUNCT
aiti-14032	249	45	and	and	CCONJ
aiti-14032	249	46	400	400	NUM
aiti-14032	249	47	,	,	PUNCT
aiti-14032	249	48	respectively	respectively	ADV
aiti-14032	249	49	.	.	PUNCT
aiti-14032	249	50	table	table	NOUN
aiti-14032	249	51	2	2	NUM
aiti-14032	249	52	precision	precision	NOUN
aiti-14032	249	53	values	value	NOUN
aiti-14032	249	54	for	for	ADP
aiti-14032	249	55	plant	plant	NOUN
aiti-14032	249	56	leaf	leaf	NOUN
aiti-14032	249	57	disease	disease	NOUN
aiti-14032	249	58	prediction	prediction	NOUN
aiti-14032	249	59	using	use	VERB
aiti-14032	249	60	bcdpbm	bcdpbm	NOUN
aiti-14032	249	61	and	and	CCONJ
aiti-14032	249	62	multiclass	multiclass	ADJ
aiti-14032	249	63	svm	svm	NOUN
aiti-14032	249	64	models	model	NOUN
aiti-14032	249	65	for	for	ADP
aiti-14032	249	66	distinct	distinct	ADJ
aiti-14032	249	67	size	size	NOUN
aiti-14032	249	68	datasets	dataset	NOUN
aiti-14032	249	69	testing	testing	NOUN
aiti-14032	249	70	images	image	NOUN
aiti-14032	249	71	bcdpbm	bcdpbm	NOUN
aiti-14032	249	72	msvm	msvm	NOUN
aiti-14032	249	73	75	75	NUM
aiti-14032	249	74	1	1	NUM
aiti-14032	249	75	1	1	NUM
aiti-14032	249	76	150	150	NUM
aiti-14032	249	77	1	1	NUM
aiti-14032	249	78	1	1	NUM
aiti-14032	249	79	225	225	NUM
aiti-14032	249	80	1	1	NUM
aiti-14032	249	81	0.8491	0.8491	NUM
aiti-14032	249	82	300	300	NUM
aiti-14032	249	83	1	1	NUM
aiti-14032	249	84	0.8052	0.8052	NUM
aiti-14032	249	85	400	400	NUM
aiti-14032	249	86	1	1	NUM
aiti-14032	249	87	0.8300	0.8300	NUM
aiti-14032	249	88	table	table	NOUN
aiti-14032	249	89	3	3	NUM
aiti-14032	249	90	presents	present	VERB
aiti-14032	249	91	the	the	DET
aiti-14032	249	92	recall	recall	NOUN
aiti-14032	249	93	values	value	NOUN
aiti-14032	249	94	in	in	ADP
aiti-14032	249	95	the	the	DET
aiti-14032	249	96	case	case	NOUN
aiti-14032	249	97	of	of	ADP
aiti-14032	249	98	different	different	ADJ
aiti-14032	249	99	numbers	number	NOUN
aiti-14032	249	100	of	of	ADP
aiti-14032	249	101	images	image	NOUN
aiti-14032	249	102	shown	show	VERB
aiti-14032	249	103	by	by	ADP
aiti-14032	249	104	the	the	DET
aiti-14032	249	105	two	two	NUM
aiti-14032	249	106	models	model	NOUN
aiti-14032	249	107	discussed	discuss	VERB
aiti-14032	249	108	above	above	ADV
aiti-14032	249	109	.	.	PUNCT
aiti-14032	250	1	table	table	NOUN
aiti-14032	250	2	4	4	NUM
aiti-14032	250	3	shows	show	VERB
aiti-14032	250	4	the	the	DET
aiti-14032	250	5	f	f	NUM
aiti-14032	250	6	-	-	PUNCT
aiti-14032	250	7	measure	measure	NOUN
aiti-14032	250	8	values	value	NOUN
aiti-14032	250	9	exhibited	exhibit	VERB
aiti-14032	250	10	by	by	ADP
aiti-14032	250	11	the	the	DET
aiti-14032	250	12	bcdpbm	bcdpbm	NOUN
aiti-14032	250	13	and	and	CCONJ
aiti-14032	250	14	multiclass	multiclass	ADJ
aiti-14032	250	15	svm	svm	ADJ
aiti-14032	250	16	models	model	NOUN
aiti-14032	250	17	.	.	PUNCT
aiti-14032	251	1	the	the	DET
aiti-14032	251	2	results	result	NOUN
aiti-14032	251	3	show	show	VERB
aiti-14032	251	4	that	that	SCONJ
aiti-14032	251	5	the	the	DET
aiti-14032	251	6	bcdpbm	bcdpbm	NOUN
aiti-14032	251	7	model	model	NOUN
aiti-14032	251	8	gives	give	VERB
aiti-14032	251	9	an	an	DET
aiti-14032	251	10	average	average	ADJ
aiti-14032	251	11	f	f	NUM
aiti-14032	251	12	-	-	PUNCT
aiti-14032	251	13	measure	measure	NOUN
aiti-14032	251	14	value	value	NOUN
aiti-14032	251	15	of	of	ADP
aiti-14032	251	16	0.99	0.99	NUM
aiti-14032	251	17	as	as	SCONJ
aiti-14032	251	18	compared	compare	VERB
aiti-14032	251	19	to	to	ADP
aiti-14032	251	20	the	the	DET
aiti-14032	251	21	f	f	ADJ
aiti-14032	251	22	-	-	PUNCT
aiti-14032	251	23	measure	measure	NOUN
aiti-14032	251	24	value	value	NOUN
aiti-14032	251	25	of	of	ADP
aiti-14032	251	26	0.84	0.84	NUM
aiti-14032	251	27	given	give	VERB
aiti-14032	251	28	by	by	ADP
aiti-14032	251	29	multi	multi	ADJ
aiti-14032	251	30	-	-	ADJ
aiti-14032	251	31	class	class	ADJ
aiti-14032	251	32	svm	svm	PROPN
aiti-14032	251	33	.	.	PROPN
aiti-14032	251	34	table	table	NOUN
aiti-14032	251	35	3	3	NUM
aiti-14032	251	36	recall	recall	VERB
aiti-14032	251	37	the	the	DET
aiti-14032	251	38	value	value	NOUN
aiti-14032	251	39	of	of	ADP
aiti-14032	251	40	plant	plant	NOUN
aiti-14032	251	41	leaf	leaf	NOUN
aiti-14032	251	42	disease	disease	NOUN
aiti-14032	251	43	prediction	prediction	NOUN
aiti-14032	251	44	in	in	ADP
aiti-14032	251	45	bcdpbm	bcdpbm	NOUN
aiti-14032	251	46	and	and	CCONJ
aiti-14032	251	47	a	a	DET
aiti-14032	251	48	multiclass	multiclass	ADJ
aiti-14032	251	49	svm	svm	ADJ
aiti-14032	251	50	model	model	NOUN
aiti-14032	251	51	for	for	ADP
aiti-14032	251	52	distinct	distinct	ADJ
aiti-14032	251	53	-	-	PUNCT
aiti-14032	251	54	sized	sized	ADJ
aiti-14032	251	55	datasets	dataset	NOUN
aiti-14032	251	56	testing	testing	NOUN
aiti-14032	251	57	images	image	NOUN
aiti-14032	251	58	bcdpbm	bcdpbm	NOUN
aiti-14032	251	59	msvm	msvm	NOUN
aiti-14032	251	60	75	75	NUM
aiti-14032	252	1	0.9600	0.9600	NUM
aiti-14032	252	2	0.8000	0.8000	NUM
aiti-14032	252	3	150	150	NUM
aiti-14032	252	4	0.9804	0.9804	NUM
aiti-14032	252	5	0.7632	0.7632	NUM
aiti-14032	252	6	225	225	NUM
aiti-14032	252	7	0.9867	0.9867	NUM
aiti-14032	252	8	0.7895	0.7895	NUM
aiti-14032	252	9	300	300	NUM
aiti-14032	252	10	0.9901	0.9901	NUM
aiti-14032	252	11	0.8158	0.8158	NUM
aiti-14032	252	12	400	400	NUM
aiti-14032	252	13	0.9924	0.9924	NUM
aiti-14032	252	14	0.8300	0.8300	NUM
aiti-14032	252	15	advances	advance	NOUN
aiti-14032	252	16	in	in	ADP
aiti-14032	252	17	technology	technology	NOUN
aiti-14032	252	18	innovation	innovation	NOUN
aiti-14032	252	19	,	,	PUNCT
aiti-14032	252	20	vol	vol	NOUN
aiti-14032	252	21	.	.	PROPN
aiti-14032	253	1	10	10	NUM
aiti-14032	253	2	,	,	PUNCT
aiti-14032	253	3	no	no	INTJ
aiti-14032	253	4	.	.	NOUN
aiti-14032	253	5	4	4	NUM
aiti-14032	253	6	,	,	PUNCT
aiti-14032	253	7	2025	2025	NUM
aiti-14032	253	8	,	,	PUNCT
aiti-14032	253	9	pp	pp	ADJ
aiti-14032	253	10	.	.	PUNCT
aiti-14032	254	1	370	370	NUM
aiti-14032	254	2	-	-	SYM
aiti-14032	254	3	382	382	NUM
aiti-14032	254	4	379	379	NUM
aiti-14032	254	5	table	table	NOUN
aiti-14032	254	6	4	4	NUM
aiti-14032	254	7	f	f	NOUN
aiti-14032	254	8	-	-	PUNCT
aiti-14032	254	9	measure	measure	NOUN
aiti-14032	254	10	of	of	ADP
aiti-14032	254	11	plant	plant	NOUN
aiti-14032	254	12	leaf	leaf	NOUN
aiti-14032	254	13	disease	disease	NOUN
aiti-14032	254	14	prediction	prediction	NOUN
aiti-14032	254	15	in	in	ADP
aiti-14032	254	16	bcdpbm	bcdpbm	NOUN
aiti-14032	254	17	and	and	CCONJ
aiti-14032	254	18	a	a	DET
aiti-14032	254	19	multiclass	multiclass	ADJ
aiti-14032	254	20	svm	svm	ADJ
aiti-14032	254	21	model	model	NOUN
aiti-14032	254	22	for	for	ADP
aiti-14032	254	23	distinct	distinct	ADJ
aiti-14032	254	24	-	-	PUNCT
aiti-14032	254	25	sized	sized	ADJ
aiti-14032	254	26	datasets	dataset	NOUN
aiti-14032	254	27	testing	testing	NOUN
aiti-14032	254	28	images	image	NOUN
aiti-14032	254	29	bcdpbm	bcdpbm	NOUN
aiti-14032	254	30	msvm	msvm	NOUN
aiti-14032	254	31	75	75	NUM
aiti-14032	254	32	0.9796	0.9796	NUM
aiti-14032	254	33	0.8889	0.8889	NUM
aiti-14032	254	34	150	150	NUM
aiti-14032	254	35	0.9901	0.9901	NUM
aiti-14032	254	36	0.8529	0.8529	NUM
aiti-14032	254	37	225	225	NUM
aiti-14032	254	38	0.9933	0.9933	NUM
aiti-14032	254	39	0.8182	0.8182	NUM
aiti-14032	254	40	300	300	NUM
aiti-14032	254	41	0.9950	0.9950	NUM
aiti-14032	255	1	0.8105	0.8105	NUM
aiti-14032	255	2	400	400	NUM
aiti-14032	255	3	0.9962	0.9962	NUM
aiti-14032	255	4	0.8300	0.8300	NUM
aiti-14032	255	5	the	the	DET
aiti-14032	255	6	accuracy	accuracy	NOUN
aiti-14032	255	7	of	of	ADP
aiti-14032	255	8	binary	binary	ADJ
aiti-14032	255	9	class	class	NOUN
aiti-14032	255	10	disease	disease	NOUN
aiti-14032	255	11	prediction	prediction	NOUN
aiti-14032	255	12	is	be	AUX
aiti-14032	255	13	shown	show	VERB
aiti-14032	255	14	in	in	ADP
aiti-14032	255	15	table	table	NOUN
aiti-14032	255	16	5	5	NUM
aiti-14032	255	17	.	.	PUNCT
aiti-14032	256	1	if	if	SCONJ
aiti-14032	256	2	the	the	DET
aiti-14032	256	3	number	number	NOUN
aiti-14032	256	4	of	of	ADP
aiti-14032	256	5	images	image	NOUN
aiti-14032	256	6	is	be	AUX
aiti-14032	256	7	increased	increase	VERB
aiti-14032	256	8	for	for	ADP
aiti-14032	256	9	the	the	DET
aiti-14032	256	10	testing	testing	NOUN
aiti-14032	256	11	,	,	PUNCT
aiti-14032	256	12	the	the	DET
aiti-14032	256	13	accuracy	accuracy	NOUN
aiti-14032	256	14	is	be	AUX
aiti-14032	256	15	also	also	ADV
aiti-14032	256	16	proportionally	proportionally	ADV
aiti-14032	256	17	increased	increase	VERB
aiti-14032	256	18	in	in	ADP
aiti-14032	256	19	the	the	DET
aiti-14032	256	20	case	case	NOUN
aiti-14032	256	21	of	of	ADP
aiti-14032	256	22	bcdpbm	bcdpbm	NOUN
aiti-14032	256	23	,	,	PUNCT
aiti-14032	256	24	while	while	SCONJ
aiti-14032	256	25	the	the	DET
aiti-14032	256	26	multiclass	multiclass	ADJ
aiti-14032	256	27	svm	svm	NOUN
aiti-14032	256	28	shows	show	VERB
aiti-14032	256	29	a	a	DET
aiti-14032	256	30	random	random	ADJ
aiti-14032	256	31	accuracy	accuracy	NOUN
aiti-14032	256	32	value	value	NOUN
aiti-14032	256	33	.	.	PUNCT
aiti-14032	257	1	the	the	DET
aiti-14032	257	2	first	first	ADJ
aiti-14032	257	3	model	model	NOUN
aiti-14032	257	4	shows	show	VERB
aiti-14032	257	5	the	the	DET
aiti-14032	257	6	highest	high	ADJ
aiti-14032	257	7	accuracy	accuracy	NOUN
aiti-14032	257	8	of	of	ADP
aiti-14032	257	9	99.74	99.74	NUM
aiti-14032	257	10	%	%	NOUN
aiti-14032	257	11	,	,	PUNCT
aiti-14032	257	12	whereas	whereas	SCONJ
aiti-14032	257	13	the	the	DET
aiti-14032	257	14	second	second	ADJ
aiti-14032	257	15	model	model	NOUN
aiti-14032	257	16	depicts	depict	VERB
aiti-14032	257	17	the	the	DET
aiti-14032	257	18	highest	high	ADJ
aiti-14032	257	19	accuracy	accuracy	NOUN
aiti-14032	257	20	of	of	ADP
aiti-14032	257	21	94.67	94.67	NUM
aiti-14032	257	22	%	%	NOUN
aiti-14032	257	23	.	.	PUNCT
aiti-14032	258	1	the	the	DET
aiti-14032	258	2	graphical	graphical	ADJ
aiti-14032	258	3	representation	representation	NOUN
aiti-14032	258	4	of	of	ADP
aiti-14032	258	5	the	the	DET
aiti-14032	258	6	accuracy	accuracy	NOUN
aiti-14032	258	7	comparison	comparison	NOUN
aiti-14032	258	8	of	of	ADP
aiti-14032	258	9	the	the	DET
aiti-14032	258	10	two	two	NUM
aiti-14032	258	11	models	model	NOUN
aiti-14032	258	12	mentioned	mention	VERB
aiti-14032	258	13	in	in	ADP
aiti-14032	258	14	table	table	NOUN
aiti-14032	258	15	5	5	NUM
aiti-14032	258	16	is	be	AUX
aiti-14032	258	17	presented	present	VERB
aiti-14032	258	18	in	in	ADP
aiti-14032	258	19	fig	fig	NOUN
aiti-14032	258	20	.	.	PUNCT
aiti-14032	259	1	6	6	NUM
aiti-14032	259	2	.	.	X
aiti-14032	259	3	a	a	DET
aiti-14032	259	4	comprehensive	comprehensive	ADJ
aiti-14032	259	5	comparison	comparison	NOUN
aiti-14032	259	6	of	of	ADP
aiti-14032	259	7	the	the	DET
aiti-14032	259	8	bcdpbm	bcdpbm	NOUN
aiti-14032	259	9	and	and	CCONJ
aiti-14032	259	10	msvm	msvm	ADJ
aiti-14032	259	11	models	model	NOUN
aiti-14032	259	12	with	with	ADP
aiti-14032	259	13	the	the	DET
aiti-14032	259	14	existing	exist	VERB
aiti-14032	259	15	models	model	NOUN
aiti-14032	259	16	is	be	AUX
aiti-14032	259	17	presented	present	VERB
aiti-14032	259	18	in	in	ADP
aiti-14032	259	19	table	table	NOUN
aiti-14032	259	20	6	6	NUM
aiti-14032	259	21	.	.	PUNCT
aiti-14032	260	1	models	model	NOUN
aiti-14032	260	2	like	like	ADP
aiti-14032	260	3	fine	fine	ADV
aiti-14032	260	4	-	-	PUNCT
aiti-14032	260	5	tuned	tune	VERB
aiti-14032	260	6	densenet	densenet	NOUN
aiti-14032	260	7	,	,	PUNCT
aiti-14032	260	8	fine	fine	ADV
aiti-14032	260	9	-	-	PUNCT
aiti-14032	260	10	tuned	tune	VERB
aiti-14032	260	11	mobilenet	mobilenet	NOUN
aiti-14032	260	12	based	base	VERB
aiti-14032	260	13	on	on	ADP
aiti-14032	260	14	optimal	optimal	ADJ
aiti-14032	260	15	mobile	mobile	ADJ
aiti-14032	260	16	network	network	NOUN
aiti-14032	260	17	-	-	PUNCT
aiti-14032	260	18	based	base	VERB
aiti-14032	260	19	convolutional	convolutional	ADJ
aiti-14032	260	20	neural	neural	ADJ
aiti-14032	260	21	network	network	NOUN
aiti-14032	260	22	(	(	PUNCT
aiti-14032	260	23	omncnn	omncnn	PROPN
aiti-14032	260	24	)	)	PUNCT
aiti-14032	260	25	,	,	PUNCT
aiti-14032	260	26	and	and	CCONJ
aiti-14032	260	27	crop	crop	NOUN
aiti-14032	260	28	leaf	leaf	NOUN
aiti-14032	260	29	health	health	NOUN
aiti-14032	260	30	prediction	prediction	NOUN
aiti-14032	260	31	model	model	NOUN
aiti-14032	260	32	(	(	PUNCT
aiti-14032	260	33	clhpm	clhpm	PROPN
aiti-14032	260	34	)	)	PUNCT
aiti-14032	260	35	have	have	AUX
aiti-14032	260	36	been	be	AUX
aiti-14032	260	37	added	add	VERB
aiti-14032	260	38	to	to	ADP
aiti-14032	260	39	the	the	DET
aiti-14032	260	40	comparison	comparison	NOUN
aiti-14032	260	41	table	table	NOUN
aiti-14032	260	42	.	.	PUNCT
aiti-14032	261	1	the	the	DET
aiti-14032	261	2	bcdpbm	bcdpbm	NOUN
aiti-14032	261	3	and	and	CCONJ
aiti-14032	261	4	msvm	msvm	ADJ
aiti-14032	261	5	models	model	NOUN
aiti-14032	261	6	with	with	ADP
aiti-14032	261	7	proposed	propose	VERB
aiti-14032	261	8	features	feature	NOUN
aiti-14032	261	9	show	show	VERB
aiti-14032	261	10	noteworthy	noteworthy	ADJ
aiti-14032	261	11	contributions	contribution	NOUN
aiti-14032	261	12	.	.	PUNCT
aiti-14032	262	1	table	table	NOUN
aiti-14032	262	2	5	5	NUM
aiti-14032	262	3	accuracy	accuracy	NOUN
aiti-14032	262	4	of	of	ADP
aiti-14032	262	5	binary	binary	ADJ
aiti-14032	262	6	class	class	NOUN
aiti-14032	262	7	plant	plant	NOUN
aiti-14032	262	8	leaf	leaf	NOUN
aiti-14032	262	9	disease	disease	NOUN
aiti-14032	262	10	prediction	prediction	NOUN
aiti-14032	262	11	in	in	ADP
aiti-14032	262	12	bcdpbm	bcdpbm	NOUN
aiti-14032	262	13	and	and	CCONJ
aiti-14032	262	14	the	the	DET
aiti-14032	262	15	msvm	msvm	ADJ
aiti-14032	262	16	model	model	NOUN
aiti-14032	262	17	across	across	ADP
aiti-14032	262	18	different	different	ADJ
aiti-14032	262	19	dataset	dataset	NOUN
aiti-14032	262	20	sizes	size	NOUN
aiti-14032	262	21	testing	test	VERB
aiti-14032	262	22	images	image	NOUN
aiti-14032	262	23	bcdpbm	bcdpbm	NOUN
aiti-14032	262	24	msvm	msvm	NOUN
aiti-14032	262	25	75	75	NUM
aiti-14032	262	26	98.65	98.65	NUM
aiti-14032	262	27	94.67	94.67	NUM
aiti-14032	262	28	150	150	NUM
aiti-14032	262	29	99.34	99.34	NUM
aiti-14032	262	30	93.42	93.42	NUM
aiti-14032	262	31	225	225	NUM
aiti-14032	262	32	99.56	99.56	NUM
aiti-14032	262	33	91.23	91.23	NUM
aiti-14032	262	34	300	300	NUM
aiti-14032	262	35	99.67	99.67	NUM
aiti-14032	262	36	90.40	90.40	NUM
aiti-14032	262	37	400	400	NUM
aiti-14032	262	38	99.74	99.74	NUM
aiti-14032	262	39	91.35	91.35	NUM
aiti-14032	262	40	fig	fig	NOUN
aiti-14032	262	41	.	.	PUNCT
aiti-14032	263	1	6	6	NUM
aiti-14032	263	2	accuracy	accuracy	NOUN
aiti-14032	263	3	of	of	ADP
aiti-14032	263	4	binary	binary	ADJ
aiti-14032	263	5	class	class	NOUN
aiti-14032	263	6	plant	plant	NOUN
aiti-14032	263	7	leaf	leaf	NOUN
aiti-14032	263	8	disease	disease	NOUN
aiti-14032	263	9	prediction	prediction	NOUN
aiti-14032	263	10	in	in	ADP
aiti-14032	263	11	the	the	DET
aiti-14032	263	12	bcdpbm	bcdpbm	NOUN
aiti-14032	263	13	and	and	CCONJ
aiti-14032	263	14	msvm	msvm	NOUN
aiti-14032	263	15	models	model	NOUN
aiti-14032	263	16	table	table	VERB
aiti-14032	263	17	6	6	NUM
aiti-14032	263	18	comparison	comparison	NOUN
aiti-14032	263	19	of	of	ADP
aiti-14032	263	20	msvm	msvm	NOUN
aiti-14032	263	21	and	and	CCONJ
aiti-14032	263	22	bcdpbm	bcdpbm	NOUN
aiti-14032	263	23	models	model	NOUN
aiti-14032	263	24	with	with	ADP
aiti-14032	263	25	the	the	DET
aiti-14032	263	26	existing	exist	VERB
aiti-14032	263	27	models	model	NOUN
aiti-14032	263	28	dataset	dataset	VERB
aiti-14032	263	29	model	model	NOUN
aiti-14032	263	30	accuracy	accuracy	NOUN
aiti-14032	263	31	in	in	ADP
aiti-14032	263	32	%	%	NOUN
aiti-14032	263	33	references	reference	NOUN
aiti-14032	263	34	kaggle	kaggle	VERB
aiti-14032	263	35	dataset	dataset	PROPN
aiti-14032	263	36	clhpm	clhpm	NOUN
aiti-14032	263	37	99.23	99.23	NUM
aiti-14032	263	38	[	[	X
aiti-14032	263	39	20	20	NUM
aiti-14032	263	40	]	]	PUNCT
aiti-14032	263	41	plantvillage	plantvillage	NOUN
aiti-14032	263	42	dataset	dataset	VERB
aiti-14032	263	43	fine	fine	ADV
aiti-14032	263	44	-	-	PUNCT
aiti-14032	263	45	tuned	tune	VERB
aiti-14032	263	46	densenet	densenet	NOUN
aiti-14032	263	47	98.17	98.17	NUM
aiti-14032	264	1	[	[	X
aiti-14032	264	2	26	26	NUM
aiti-14032	264	3	]	]	X
aiti-14032	264	4	plantvillage	plantvillage	NOUN
aiti-14032	264	5	dataset	dataset	VERB
aiti-14032	264	6	fine	fine	ADV
aiti-14032	264	7	-	-	PUNCT
aiti-14032	264	8	tuned	tune	VERB
aiti-14032	264	9	mobilenet	mobilenet	NOUN
aiti-14032	264	10	based	base	VERB
aiti-14032	264	11	with	with	ADP
aiti-14032	264	12	omncnn	omncnn	PROPN
aiti-14032	264	13	98.7	98.7	NUM
aiti-14032	264	14	[	[	SYM
aiti-14032	264	15	27	27	NUM
aiti-14032	264	16	]	]	PUNCT
aiti-14032	264	17	kaggle	kaggle	NOUN
aiti-14032	264	18	dataset	dataset	ADJ
aiti-14032	264	19	msvm	msvm	PROPN
aiti-14032	264	20	92.21	92.21	NUM
aiti-14032	264	21	proposed	propose	VERB
aiti-14032	264	22	kaggle	kaggle	NOUN
aiti-14032	264	23	dataset	dataset	NOUN
aiti-14032	264	24	bcdpbm	bcdpbm	NOUN
aiti-14032	264	25	99.39	99.39	NUM
aiti-14032	264	26	proposed	propose	VERB
aiti-14032	264	27	advances	advance	NOUN
aiti-14032	264	28	in	in	ADP
aiti-14032	264	29	technology	technology	NOUN
aiti-14032	264	30	innovation	innovation	NOUN
aiti-14032	264	31	,	,	PUNCT
aiti-14032	264	32	vol	vol	NOUN
aiti-14032	264	33	.	.	PROPN
aiti-14032	265	1	10	10	NUM
aiti-14032	265	2	,	,	PUNCT
aiti-14032	265	3	no	no	INTJ
aiti-14032	265	4	.	.	NOUN
aiti-14032	265	5	4	4	NUM
aiti-14032	265	6	,	,	PUNCT
aiti-14032	265	7	2025	2025	NUM
aiti-14032	265	8	,	,	PUNCT
aiti-14032	265	9	pp	pp	ADJ
aiti-14032	265	10	.	.	PUNCT
aiti-14032	266	1	370	370	NUM
aiti-14032	266	2	-	-	SYM
aiti-14032	266	3	382	382	NUM
aiti-14032	266	4	380	380	NUM
aiti-14032	266	5	table	table	NOUN
aiti-14032	266	6	7	7	NUM
aiti-14032	266	7	additionally	additionally	ADV
aiti-14032	266	8	compares	compare	VERB
aiti-14032	266	9	the	the	DET
aiti-14032	266	10	accuracy	accuracy	NOUN
aiti-14032	266	11	performance	performance	NOUN
aiti-14032	266	12	of	of	ADP
aiti-14032	266	13	msvm	msvm	NOUN
aiti-14032	266	14	and	and	CCONJ
aiti-14032	266	15	bcdpbm	bcdpbm	NOUN
aiti-14032	266	16	across	across	ADP
aiti-14032	266	17	different	different	ADJ
aiti-14032	266	18	dataset	dataset	NOUN
aiti-14032	266	19	sizes	size	NOUN
aiti-14032	266	20	for	for	ADP
aiti-14032	266	21	three	three	NUM
aiti-14032	266	22	types	type	NOUN
aiti-14032	266	23	of	of	ADP
aiti-14032	266	24	conditions	condition	NOUN
aiti-14032	266	25	:	:	PUNCT
aiti-14032	266	26	healthy	healthy	ADJ
aiti-14032	266	27	leaf	leaf	NOUN
aiti-14032	266	28	,	,	PUNCT
aiti-14032	266	29	leaf	leaf	NOUN
aiti-14032	266	30	with	with	ADP
aiti-14032	266	31	early	early	ADJ
aiti-14032	266	32	blight	blight	NOUN
aiti-14032	266	33	,	,	PUNCT
aiti-14032	266	34	and	and	CCONJ
aiti-14032	266	35	leaf	leaf	VERB
aiti-14032	266	36	with	with	ADP
aiti-14032	266	37	late	late	ADJ
aiti-14032	266	38	blight	blight	NOUN
aiti-14032	266	39	.	.	PUNCT
aiti-14032	267	1	the	the	DET
aiti-14032	267	2	table	table	NOUN
aiti-14032	267	3	shows	show	VERB
aiti-14032	267	4	accuracy	accuracy	NOUN
aiti-14032	267	5	in	in	ADP
aiti-14032	267	6	each	each	DET
aiti-14032	267	7	disease	disease	NOUN
aiti-14032	267	8	category	category	NOUN
aiti-14032	267	9	.	.	PUNCT
aiti-14032	268	1	the	the	DET
aiti-14032	268	2	msvm	msvm	PROPN
aiti-14032	268	3	model	model	NOUN
aiti-14032	268	4	shows	show	VERB
aiti-14032	268	5	variability	variability	NOUN
aiti-14032	268	6	in	in	ADP
aiti-14032	268	7	performance	performance	NOUN
aiti-14032	268	8	,	,	PUNCT
aiti-14032	268	9	particularly	particularly	ADV
aiti-14032	268	10	for	for	ADP
aiti-14032	268	11	early	early	ADJ
aiti-14032	268	12	blight	blight	NOUN
aiti-14032	268	13	,	,	PUNCT
aiti-14032	268	14	whereas	whereas	SCONJ
aiti-14032	268	15	bcdpbm	bcdpbm	NOUN
aiti-14032	268	16	demonstrates	demonstrate	VERB
aiti-14032	268	17	consistent	consistent	ADJ
aiti-14032	268	18	and	and	CCONJ
aiti-14032	268	19	superior	superior	ADJ
aiti-14032	268	20	accuracy	accuracy	NOUN
aiti-14032	268	21	across	across	ADP
aiti-14032	268	22	all	all	DET
aiti-14032	268	23	classes	class	NOUN
aiti-14032	268	24	and	and	CCONJ
aiti-14032	268	25	dataset	dataset	NOUN
aiti-14032	268	26	sizes	size	NOUN
aiti-14032	268	27	.	.	PUNCT
aiti-14032	269	1	table	table	NOUN
aiti-14032	269	2	7	7	NUM
aiti-14032	269	3	accuracy	accuracy	NOUN
aiti-14032	269	4	in	in	ADP
aiti-14032	269	5	each	each	DET
aiti-14032	269	6	disease	disease	NOUN
aiti-14032	269	7	category	category	NOUN
aiti-14032	269	8	by	by	ADP
aiti-14032	269	9	bcdpbm	bcdpbm	NOUN
aiti-14032	269	10	and	and	CCONJ
aiti-14032	269	11	msvm	msvm	ADJ
aiti-14032	269	12	model	model	NOUN
aiti-14032	269	13	testing	testing	NOUN
aiti-14032	269	14	images	image	NOUN
aiti-14032	269	15	bcdpbm	bcdpbm	NOUN
aiti-14032	269	16	msvm	msvm	ADJ
aiti-14032	269	17	healthy	healthy	ADJ
aiti-14032	269	18	leaf	leaf	NOUN
aiti-14032	269	19	early	early	ADJ
aiti-14032	269	20	blight	blight	NOUN
aiti-14032	269	21	late	late	ADV
aiti-14032	269	22	blight	blight	NOUN
aiti-14032	269	23	healthy	healthy	ADJ
aiti-14032	269	24	leaf	leaf	NOUN
aiti-14032	269	25	early	early	ADJ
aiti-14032	269	26	blight	blight	NOUN
aiti-14032	269	27	late	late	ADJ
aiti-14032	269	28	blight	blight	NOUN
aiti-14032	269	29	75	75	NUM
aiti-14032	269	30	88	88	NUM
aiti-14032	269	31	96	96	NUM
aiti-14032	269	32	64	64	NUM
aiti-14032	269	33	96	96	NUM
aiti-14032	269	34	96.5	96.5	NUM
aiti-14032	269	35	99.90	99.90	NUM
aiti-14032	269	36	150	150	NUM
aiti-14032	269	37	91.83	91.83	NUM
aiti-14032	269	38	93.87	93.87	NUM
aiti-14032	269	39	57.14	57.14	NUM
aiti-14032	269	40	98	98	NUM
aiti-14032	269	41	98.40	98.40	NUM
aiti-14032	269	42	99.96	99.96	NUM
aiti-14032	269	43	225	225	NUM
aiti-14032	269	44	86.48	86.48	NUM
aiti-14032	269	45	94.66	94.66	NUM
aiti-14032	269	46	59.21	59.21	NUM
aiti-14032	269	47	98.64	98.64	NUM
aiti-14032	269	48	98.64	98.64	NUM
aiti-14032	269	49	99.50	99.50	NUM
aiti-14032	269	50	300	300	NUM
aiti-14032	269	51	96	96	NUM
aiti-14032	269	52	79	79	NUM
aiti-14032	269	53	61.38	61.38	NUM
aiti-14032	269	54	99	99	NUM
aiti-14032	269	55	99.20	99.20	NUM
aiti-14032	269	56	99.98	99.98	NUM
aiti-14032	269	57	400	400	NUM
aiti-14032	269	58	88.40	88.40	NUM
aiti-14032	269	59	81.67	81.67	NUM
aiti-14032	269	60	62.04	62.04	NUM
aiti-14032	269	61	99.25	99.25	NUM
aiti-14032	269	62	99.23	99.23	NUM
aiti-14032	269	63	99.92	99.92	NUM
aiti-14032	269	64	5	5	NUM
aiti-14032	269	65	.	.	PUNCT
aiti-14032	270	1	conclusions	conclusion	NOUN
aiti-14032	270	2	and	and	CCONJ
aiti-14032	270	3	future	future	ADJ
aiti-14032	270	4	scope	scope	NOUN
aiti-14032	270	5	this	this	DET
aiti-14032	270	6	research	research	NOUN
aiti-14032	270	7	presents	present	VERB
aiti-14032	270	8	a	a	DET
aiti-14032	270	9	novel	novel	ADJ
aiti-14032	270	10	approach	approach	NOUN
aiti-14032	270	11	to	to	ADP
aiti-14032	270	12	crop	crop	NOUN
aiti-14032	270	13	disease	disease	NOUN
aiti-14032	270	14	prediction	prediction	NOUN
aiti-14032	270	15	by	by	ADP
aiti-14032	270	16	leveraging	leverage	VERB
aiti-14032	270	17	leaf	leaf	NOUN
aiti-14032	270	18	features	feature	NOUN
aiti-14032	270	19	and	and	CCONJ
aiti-14032	270	20	machine	machine	NOUN
aiti-14032	270	21	learning	learn	VERB
aiti-14032	270	22	techniques	technique	NOUN
aiti-14032	270	23	.	.	PUNCT
aiti-14032	271	1	the	the	DET
aiti-14032	271	2	present	present	ADJ
aiti-14032	271	3	work	work	NOUN
aiti-14032	271	4	contributes	contribute	VERB
aiti-14032	271	5	to	to	ADP
aiti-14032	271	6	advancing	advance	VERB
aiti-14032	271	7	the	the	DET
aiti-14032	271	8	field	field	NOUN
aiti-14032	271	9	of	of	ADP
aiti-14032	271	10	agricultural	agricultural	ADJ
aiti-14032	271	11	disease	disease	NOUN
aiti-14032	271	12	prediction	prediction	NOUN
aiti-14032	271	13	by	by	ADP
aiti-14032	271	14	introducing	introduce	VERB
aiti-14032	271	15	fuzzy	fuzzy	ADJ
aiti-14032	271	16	hsv	hsv	NOUN
aiti-14032	271	17	and	and	CCONJ
aiti-14032	271	18	fuzzy	fuzzy	ADJ
aiti-14032	271	19	lbp	lbp	NOUN
aiti-14032	271	20	methods	method	NOUN
aiti-14032	271	21	for	for	ADP
aiti-14032	271	22	color	color	NOUN
aiti-14032	271	23	and	and	CCONJ
aiti-14032	271	24	texture	texture	ADJ
aiti-14032	271	25	feature	feature	NOUN
aiti-14032	271	26	extraction	extraction	NOUN
aiti-14032	271	27	,	,	PUNCT
aiti-14032	271	28	respectively	respectively	ADV
aiti-14032	271	29	,	,	PUNCT
aiti-14032	271	30	along	along	ADP
aiti-14032	271	31	with	with	ADP
aiti-14032	271	32	instance	instance	NOUN
aiti-14032	271	33	histogram	histogram	NOUN
aiti-14032	271	34	and	and	CCONJ
aiti-14032	271	35	modified	modified	ADJ
aiti-14032	271	36	cooccurrence	cooccurrence	NOUN
aiti-14032	271	37	matrix	matrix	NOUN
aiti-14032	271	38	(	(	PUNCT
aiti-14032	271	39	mccm	mccm	NOUN
aiti-14032	271	40	)	)	PUNCT
aiti-14032	271	41	techniques	technique	NOUN
aiti-14032	271	42	.	.	PUNCT
aiti-14032	272	1	through	through	ADP
aiti-14032	272	2	comparative	comparative	ADJ
aiti-14032	272	3	analysis	analysis	NOUN
aiti-14032	272	4	,	,	PUNCT
aiti-14032	272	5	the	the	DET
aiti-14032	272	6	effectiveness	effectiveness	NOUN
aiti-14032	272	7	of	of	ADP
aiti-14032	272	8	the	the	DET
aiti-14032	272	9	bootstrap	bootstrap	NOUN
aiti-14032	272	10	model	model	NOUN
aiti-14032	272	11	utilizing	utilize	VERB
aiti-14032	272	12	mccm	mccm	NOUN
aiti-14032	272	13	features	feature	NOUN
aiti-14032	272	14	is	be	AUX
aiti-14032	272	15	demonstrated	demonstrate	VERB
aiti-14032	272	16	.	.	PUNCT
aiti-14032	273	1	the	the	DET
aiti-14032	273	2	work	work	NOUN
aiti-14032	273	3	is	be	AUX
aiti-14032	273	4	concluded	conclude	VERB
aiti-14032	273	5	as	as	SCONJ
aiti-14032	273	6	follows	follow	VERB
aiti-14032	273	7	(	(	PUNCT
aiti-14032	273	8	1	1	X
aiti-14032	273	9	)	)	PUNCT
aiti-14032	273	10	an	an	DET
aiti-14032	273	11	impressive	impressive	ADJ
aiti-14032	273	12	average	average	ADJ
aiti-14032	273	13	accuracy	accuracy	NOUN
aiti-14032	273	14	of	of	ADP
aiti-14032	273	15	98.07	98.07	NUM
aiti-14032	273	16	%	%	NOUN
aiti-14032	273	17	for	for	ADP
aiti-14032	273	18	multiclass	multiclass	ADJ
aiti-14032	273	19	classification	classification	NOUN
aiti-14032	273	20	and	and	CCONJ
aiti-14032	273	21	an	an	DET
aiti-14032	273	22	average	average	ADJ
aiti-14032	273	23	accuracy	accuracy	NOUN
aiti-14032	273	24	of	of	ADP
aiti-14032	273	25	99.39	99.39	NUM
aiti-14032	273	26	%	%	NOUN
aiti-14032	273	27	for	for	ADP
aiti-14032	273	28	binary	binary	ADJ
aiti-14032	273	29	classification	classification	NOUN
aiti-14032	273	30	is	be	AUX
aiti-14032	273	31	achieved	achieve	VERB
aiti-14032	273	32	.	.	PUNCT
aiti-14032	274	1	(	(	PUNCT
aiti-14032	274	2	2	2	X
aiti-14032	274	3	)	)	PUNCT
aiti-14032	274	4	significant	significant	ADJ
aiti-14032	274	5	differences	difference	NOUN
aiti-14032	274	6	in	in	ADP
aiti-14032	274	7	precision	precision	NOUN
aiti-14032	274	8	,	,	PUNCT
aiti-14032	274	9	recall	recall	NOUN
aiti-14032	274	10	,	,	PUNCT
aiti-14032	274	11	and	and	CCONJ
aiti-14032	274	12	f	f	X
aiti-14032	274	13	-	-	PUNCT
aiti-14032	274	14	measure	measure	NOUN
aiti-14032	274	15	are	be	AUX
aiti-14032	274	16	also	also	ADV
aiti-14032	274	17	found	find	VERB
aiti-14032	274	18	in	in	ADP
aiti-14032	274	19	the	the	DET
aiti-14032	274	20	experiments	experiment	NOUN
aiti-14032	274	21	.	.	PUNCT
aiti-14032	275	1	(	(	PUNCT
aiti-14032	275	2	3	3	X
aiti-14032	275	3	)	)	PUNCT
aiti-14032	275	4	overall	overall	ADV
aiti-14032	275	5	,	,	PUNCT
aiti-14032	275	6	the	the	DET
aiti-14032	275	7	bootstrap	bootstrap	NOUN
aiti-14032	275	8	model	model	NOUN
aiti-14032	275	9	with	with	ADP
aiti-14032	275	10	mccm	mccm	NOUN
aiti-14032	275	11	and	and	CCONJ
aiti-14032	275	12	histogram	histogram	NOUN
aiti-14032	275	13	feature	feature	NOUN
aiti-14032	275	14	extraction	extraction	NOUN
aiti-14032	275	15	methods	method	NOUN
aiti-14032	275	16	give	give	VERB
aiti-14032	275	17	better	well	ADJ
aiti-14032	275	18	results	result	NOUN
aiti-14032	275	19	as	as	ADP
aiti-14032	275	20	compared	compare	VERB
aiti-14032	275	21	to	to	ADP
aiti-14032	275	22	the	the	DET
aiti-14032	275	23	msvm	msvm	ADJ
aiti-14032	275	24	model	model	NOUN
aiti-14032	275	25	with	with	ADP
aiti-14032	275	26	fuzzy	fuzzy	ADJ
aiti-14032	275	27	hsv	hsv	NOUN
aiti-14032	275	28	and	and	CCONJ
aiti-14032	275	29	fuzzy	fuzzy	ADJ
aiti-14032	275	30	lbp	lbp	NOUN
aiti-14032	275	31	feature	feature	NOUN
aiti-14032	275	32	extraction	extraction	NOUN
aiti-14032	275	33	methods	method	NOUN
aiti-14032	275	34	.	.	PUNCT
aiti-14032	276	1	future	future	ADJ
aiti-14032	276	2	research	research	NOUN
aiti-14032	276	3	could	could	AUX
aiti-14032	276	4	focus	focus	VERB
aiti-14032	276	5	on	on	ADP
aiti-14032	276	6	enhancing	enhance	VERB
aiti-14032	276	7	the	the	DET
aiti-14032	276	8	robustness	robustness	NOUN
aiti-14032	276	9	and	and	CCONJ
aiti-14032	276	10	scalability	scalability	NOUN
aiti-14032	276	11	of	of	ADP
aiti-14032	276	12	the	the	DET
aiti-14032	276	13	bootstrap	bootstrap	NOUN
aiti-14032	276	14	model	model	NOUN
aiti-14032	276	15	,	,	PUNCT
aiti-14032	276	16	potentially	potentially	ADV
aiti-14032	276	17	incorporating	incorporate	VERB
aiti-14032	276	18	additional	additional	ADJ
aiti-14032	276	19	features	feature	NOUN
aiti-14032	276	20	or	or	CCONJ
aiti-14032	276	21	refining	refine	VERB
aiti-14032	276	22	existing	exist	VERB
aiti-14032	276	23	ones	one	NOUN
aiti-14032	276	24	to	to	PART
aiti-14032	276	25	achieve	achieve	VERB
aiti-14032	276	26	even	even	ADV
aiti-14032	276	27	higher	high	ADJ
aiti-14032	276	28	accuracies	accuracy	NOUN
aiti-14032	276	29	.	.	PUNCT
aiti-14032	277	1	real	real	ADJ
aiti-14032	277	2	-	-	PUNCT
aiti-14032	277	3	time	time	NOUN
aiti-14032	277	4	on	on	ADP
aiti-14032	277	5	-	-	PUNCT
aiti-14032	277	6	field	field	NOUN
aiti-14032	277	7	images	image	NOUN
aiti-14032	277	8	can	can	AUX
aiti-14032	277	9	also	also	ADV
aiti-14032	277	10	be	be	AUX
aiti-14032	277	11	considered	consider	VERB
aiti-14032	277	12	for	for	SCONJ
aiti-14032	277	13	experimentation	experimentation	NOUN
aiti-14032	277	14	to	to	PART
aiti-14032	277	15	get	get	VERB
aiti-14032	277	16	area	area	NOUN
aiti-14032	277	17	-	-	PUNCT
aiti-14032	277	18	specific	specific	ADJ
aiti-14032	277	19	disease	disease	NOUN
aiti-14032	277	20	predictions	prediction	NOUN
aiti-14032	277	21	in	in	ADP
aiti-14032	277	22	crops	crop	NOUN
aiti-14032	277	23	.	.	PUNCT
aiti-14032	278	1	conflicts	conflict	NOUN
aiti-14032	278	2	of	of	ADP
aiti-14032	278	3	interest	interest	NOUN
aiti-14032	278	4	the	the	DET
aiti-14032	278	5	authors	author	NOUN
aiti-14032	278	6	declare	declare	VERB
aiti-14032	278	7	no	no	DET
aiti-14032	278	8	conflict	conflict	NOUN
aiti-14032	278	9	of	of	ADP
aiti-14032	278	10	interest	interest	NOUN
aiti-14032	278	11	.	.	PUNCT
aiti-14032	279	1	references	reference	NOUN
aiti-14032	279	2	[	[	X
aiti-14032	279	3	1	1	NUM
aiti-14032	279	4	]	]	PUNCT
aiti-14032	279	5	h.	h.	PROPN
aiti-14032	279	6	zhao	zhao	PROPN
aiti-14032	279	7	,	,	PUNCT
aiti-14032	279	8	y.	y.	PROPN
aiti-14032	279	9	yang	yang	PROPN
aiti-14032	279	10	,	,	PUNCT
aiti-14032	279	11	c.	c.	PROPN
aiti-14032	279	12	yang	yang	PROPN
aiti-14032	279	13	,	,	PUNCT
aiti-14032	279	14	r.	r.	PROPN
aiti-14032	279	15	song	song	PROPN
aiti-14032	279	16	,	,	PUNCT
aiti-14032	279	17	and	and	CCONJ
aiti-14032	279	18	w.	w.	PROPN
aiti-14032	279	19	guo	guo	PROPN
aiti-14032	279	20	,	,	PUNCT
aiti-14032	279	21	“	"	PUNCT
aiti-14032	279	22	evaluation	evaluation	NOUN
aiti-14032	279	23	of	of	ADP
aiti-14032	279	24	spatial	spatial	ADJ
aiti-14032	279	25	resolution	resolution	NOUN
aiti-14032	279	26	on	on	ADP
aiti-14032	279	27	crop	crop	NOUN
aiti-14032	279	28	disease	disease	NOUN
aiti-14032	279	29	detection	detection	NOUN
aiti-14032	279	30	based	base	VERB
aiti-14032	279	31	on	on	ADP
aiti-14032	279	32	multiscale	multiscale	ADJ
aiti-14032	279	33	images	image	NOUN
aiti-14032	279	34	and	and	CCONJ
aiti-14032	279	35	category	category	NOUN
aiti-14032	279	36	variance	variance	NOUN
aiti-14032	279	37	ratio	ratio	NOUN
aiti-14032	279	38	,	,	PUNCT
aiti-14032	279	39	”	"	PUNCT
aiti-14032	279	40	computers	computer	NOUN
aiti-14032	279	41	and	and	CCONJ
aiti-14032	279	42	electronics	electronic	NOUN
aiti-14032	279	43	in	in	ADP
aiti-14032	279	44	agriculture	agriculture	NOUN
aiti-14032	279	45	,	,	PUNCT
aiti-14032	279	46	vol	vol	NOUN
aiti-14032	279	47	.	.	PROPN
aiti-14032	279	48	207	207	NUM
aiti-14032	279	49	,	,	PUNCT
aiti-14032	279	50	article	article	NOUN
aiti-14032	279	51	no	no	NOUN
aiti-14032	279	52	.	.	PROPN
aiti-14032	279	53	107743	107743	NUM
aiti-14032	279	54	,	,	PUNCT
aiti-14032	279	55	2023	2023	NUM
aiti-14032	279	56	.	.	PUNCT
aiti-14032	280	1	[	[	X
aiti-14032	280	2	2	2	NUM
aiti-14032	280	3	]	]	PUNCT
aiti-14032	280	4	x.	x.	NOUN
aiti-14032	280	5	gu	gu	PROPN
aiti-14032	280	6	,	,	PUNCT
aiti-14032	280	7	m.	m.	PROPN
aiti-14032	280	8	wang	wang	PROPN
aiti-14032	280	9	,	,	PUNCT
aiti-14032	280	10	y.	y.	PROPN
aiti-14032	280	11	wang	wang	PROPN
aiti-14032	280	12	,	,	PUNCT
aiti-14032	280	13	g.	g.	PROPN
aiti-14032	280	14	zhou	zhou	PROPN
aiti-14032	280	15	,	,	PUNCT
aiti-14032	280	16	and	and	CCONJ
aiti-14032	280	17	t.	t.	PROPN
aiti-14032	280	18	ni	ni	PROPN
aiti-14032	280	19	,	,	PUNCT
aiti-14032	280	20	“	"	PUNCT
aiti-14032	280	21	discriminative	discriminative	NOUN
aiti-14032	280	22	semisupervised	semisupervise	VERB
aiti-14032	280	23	dictionary	dictionary	ADJ
aiti-14032	280	24	learning	learning	NOUN
aiti-14032	280	25	method	method	NOUN
aiti-14032	280	26	with	with	ADP
aiti-14032	280	27	graph	graph	NOUN
aiti-14032	280	28	embedding	embed	VERB
aiti-14032	280	29	and	and	CCONJ
aiti-14032	280	30	pairwise	pairwise	NOUN
aiti-14032	280	31	constraints	constraint	NOUN
aiti-14032	280	32	for	for	ADP
aiti-14032	280	33	crop	crop	NOUN
aiti-14032	280	34	disease	disease	NOUN
aiti-14032	280	35	image	image	NOUN
aiti-14032	280	36	recognition	recognition	NOUN
aiti-14032	280	37	,	,	PUNCT
aiti-14032	280	38	”	"	PUNCT
aiti-14032	280	39	crop	crop	NOUN
aiti-14032	280	40	protection	protection	NOUN
aiti-14032	280	41	,	,	PUNCT
aiti-14032	280	42	vol	vol	NOUN
aiti-14032	280	43	.	.	PROPN
aiti-14032	280	44	176	176	NUM
aiti-14032	280	45	,	,	PUNCT
aiti-14032	280	46	article	article	NOUN
aiti-14032	280	47	no	no	NOUN
aiti-14032	280	48	.	.	PROPN
aiti-14032	280	49	106489	106489	NUM
aiti-14032	280	50	,	,	PUNCT
aiti-14032	280	51	2024	2024	NUM
aiti-14032	280	52	.	.	PUNCT
aiti-14032	281	1	[	[	X
aiti-14032	281	2	3	3	X
aiti-14032	281	3	]	]	X
aiti-14032	281	4	u.	u.	PROPN
aiti-14032	281	5	mokhtar	mokhtar	PROPN
aiti-14032	281	6	,	,	PUNCT
aiti-14032	281	7	n.	n.	PROPN
aiti-14032	281	8	e.	e.	PROPN
aiti-14032	281	9	bendary	bendary	PROPN
aiti-14032	281	10	,	,	PUNCT
aiti-14032	281	11	a.	a.	PROPN
aiti-14032	281	12	e.	e.	PROPN
aiti-14032	281	13	hassenian	hassenian	PROPN
aiti-14032	281	14	,	,	PUNCT
aiti-14032	281	15	e.	e.	PROPN
aiti-14032	281	16	emary	emary	PROPN
aiti-14032	281	17	,	,	PUNCT
aiti-14032	281	18	m.	m.	NOUN
aiti-14032	281	19	a.	a.	PROPN
aiti-14032	281	20	mahmoud	mahmoud	PROPN
aiti-14032	281	21	,	,	PUNCT
aiti-14032	281	22	h.	h.	PROPN
aiti-14032	281	23	hefny	hefny	PROPN
aiti-14032	281	24	,	,	PUNCT
aiti-14032	281	25	et	et	PROPN
aiti-14032	281	26	al	al	PROPN
aiti-14032	281	27	.	.	PUNCT
aiti-14032	282	1	“	"	PUNCT
aiti-14032	282	2	svm	svm	ADJ
aiti-14032	282	3	-	-	PUNCT
aiti-14032	282	4	based	base	VERB
aiti-14032	282	5	detection	detection	NOUN
aiti-14032	282	6	of	of	ADP
aiti-14032	282	7	tomato	tomato	NOUN
aiti-14032	282	8	leaves	leave	NOUN
aiti-14032	282	9	diseases	disease	NOUN
aiti-14032	282	10	,	,	PUNCT
aiti-14032	282	11	”	"	PUNCT
aiti-14032	282	12	advances	advance	NOUN
aiti-14032	282	13	in	in	ADP
aiti-14032	282	14	intelligent	intelligent	ADJ
aiti-14032	282	15	systems	system	NOUN
aiti-14032	282	16	and	and	CCONJ
aiti-14032	282	17	computing	computing	NOUN
aiti-14032	282	18	,	,	PUNCT
aiti-14032	282	19	vol	vol	NOUN
aiti-14032	282	20	.	.	PROPN
aiti-14032	282	21	323	323	NUM
aiti-14032	282	22	,	,	PUNCT
aiti-14032	282	23	pp	pp	ADJ
aiti-14032	282	24	.	.	PUNCT
aiti-14032	283	1	641	641	NUM
aiti-14032	283	2	-	-	SYM
aiti-14032	283	3	652	652	NUM
aiti-14032	283	4	,	,	PUNCT
aiti-14032	283	5	2015	2015	NUM
aiti-14032	283	6	.	.	PUNCT
aiti-14032	284	1	[	[	X
aiti-14032	284	2	4	4	X
aiti-14032	284	3	]	]	X
aiti-14032	284	4	v.	v.	CCONJ
aiti-14032	284	5	choudhary	choudhary	PROPN
aiti-14032	284	6	and	and	CCONJ
aiti-14032	284	7	a.	a.	NOUN
aiti-14032	284	8	thakur	thakur	PROPN
aiti-14032	284	9	,	,	PUNCT
aiti-14032	284	10	“	"	PUNCT
aiti-14032	284	11	comparative	comparative	ADJ
aiti-14032	284	12	analysis	analysis	NOUN
aiti-14032	284	13	of	of	ADP
aiti-14032	284	14	machine	machine	NOUN
aiti-14032	284	15	learning	learn	VERB
aiti-14032	284	16	techniques	technique	NOUN
aiti-14032	284	17	for	for	ADP
aiti-14032	284	18	disease	disease	NOUN
aiti-14032	284	19	prediction	prediction	NOUN
aiti-14032	284	20	in	in	ADP
aiti-14032	284	21	crops	crop	NOUN
aiti-14032	284	22	,	,	PUNCT
aiti-14032	284	23	”	"	PUNCT
aiti-14032	284	24	proceedings	proceeding	NOUN
aiti-14032	284	25	of	of	ADP
aiti-14032	284	26	international	international	ADJ
aiti-14032	284	27	conference	conference	NOUN
aiti-14032	284	28	on	on	ADP
aiti-14032	284	29	communication	communication	NOUN
aiti-14032	284	30	systems	system	NOUN
aiti-14032	284	31	and	and	CCONJ
aiti-14032	284	32	network	network	NOUN
aiti-14032	284	33	technologies	technology	NOUN
aiti-14032	284	34	,	,	PUNCT
aiti-14032	284	35	pp	pp	PROPN
aiti-14032	284	36	.	.	PUNCT
aiti-14032	284	37	190	190	NUM
aiti-14032	284	38	-	-	SYM
aiti-14032	284	39	195	195	NUM
aiti-14032	284	40	,	,	PUNCT
aiti-14032	284	41	2022	2022	NUM
aiti-14032	284	42	.	.	PUNCT
aiti-14032	285	1	[	[	X
aiti-14032	285	2	5	5	X
aiti-14032	285	3	]	]	PUNCT
aiti-14032	285	4	v.	v.	PROPN
aiti-14032	285	5	k.	k.	PROPN
aiti-14032	285	6	vishnoi	vishnoi	PROPN
aiti-14032	285	7	,	,	PUNCT
aiti-14032	285	8	k.	k.	PROPN
aiti-14032	285	9	kumar	kumar	PROPN
aiti-14032	285	10	,	,	PUNCT
aiti-14032	285	11	and	and	CCONJ
aiti-14032	285	12	b.	b.	PROPN
aiti-14032	285	13	kumar	kumar	PROPN
aiti-14032	285	14	,	,	PUNCT
aiti-14032	285	15	“	"	PUNCT
aiti-14032	285	16	a	a	DET
aiti-14032	285	17	comprehensive	comprehensive	ADJ
aiti-14032	285	18	study	study	NOUN
aiti-14032	285	19	of	of	ADP
aiti-14032	285	20	feature	feature	NOUN
aiti-14032	285	21	extraction	extraction	NOUN
aiti-14032	285	22	techniques	technique	NOUN
aiti-14032	285	23	for	for	ADP
aiti-14032	285	24	plant	plant	NOUN
aiti-14032	285	25	leaf	leaf	NOUN
aiti-14032	285	26	disease	disease	NOUN
aiti-14032	285	27	detection	detection	NOUN
aiti-14032	285	28	,	,	PUNCT
aiti-14032	285	29	”	"	PUNCT
aiti-14032	285	30	multimedia	multimedia	NOUN
aiti-14032	285	31	tools	tool	NOUN
aiti-14032	285	32	and	and	CCONJ
aiti-14032	285	33	applications	application	NOUN
aiti-14032	285	34	,	,	PUNCT
aiti-14032	285	35	vol	vol	NOUN
aiti-14032	285	36	.	.	PROPN
aiti-14032	285	37	81	81	NUM
aiti-14032	285	38	,	,	PUNCT
aiti-14032	285	39	pp	pp	ADJ
aiti-14032	285	40	.	.	PUNCT
aiti-14032	285	41	367	367	NUM
aiti-14032	285	42	-	-	SYM
aiti-14032	285	43	419	419	NUM
aiti-14032	285	44	,	,	PUNCT
aiti-14032	285	45	2022	2022	NUM
aiti-14032	285	46	.	.	PUNCT
aiti-14032	286	1	advances	advance	NOUN
aiti-14032	286	2	in	in	ADP
aiti-14032	286	3	technology	technology	NOUN
aiti-14032	286	4	innovation	innovation	NOUN
aiti-14032	286	5	,	,	PUNCT
aiti-14032	286	6	vol	vol	NOUN
aiti-14032	286	7	.	.	PROPN
aiti-14032	287	1	10	10	NUM
aiti-14032	287	2	,	,	PUNCT
aiti-14032	287	3	no	no	INTJ
aiti-14032	287	4	.	.	NOUN
aiti-14032	287	5	4	4	NUM
aiti-14032	287	6	,	,	PUNCT
aiti-14032	287	7	2025	2025	NUM
aiti-14032	287	8	,	,	PUNCT
aiti-14032	287	9	pp	pp	ADJ
aiti-14032	287	10	.	.	PUNCT
aiti-14032	288	1	370	370	NUM
aiti-14032	288	2	-	-	SYM
aiti-14032	288	3	382	382	NUM
aiti-14032	288	4	381	381	NUM
aiti-14032	288	5	[	[	X
aiti-14032	288	6	6	6	NUM
aiti-14032	288	7	]	]	PUNCT
aiti-14032	288	8	l.	l.	PROPN
aiti-14032	288	9	li	li	PROPN
aiti-14032	288	10	,	,	PUNCT
aiti-14032	288	11	j.	j.	PROPN
aiti-14032	288	12	gao	gao	PROPN
aiti-14032	288	13	,	,	PUNCT
aiti-14032	288	14	h.	h.	PROPN
aiti-14032	288	15	ge	ge	PROPN
aiti-14032	288	16	,	,	PUNCT
aiti-14032	288	17	y.	y.	PROPN
aiti-14032	288	18	zhang	zhang	PROPN
aiti-14032	288	19	,	,	PUNCT
aiti-14032	288	20	and	and	CCONJ
aiti-14032	288	21	j.	j.	PROPN
aiti-14032	288	22	yang	yang	PROPN
aiti-14032	288	23	,	,	PUNCT
aiti-14032	288	24	“	"	PUNCT
aiti-14032	288	25	an	an	DET
aiti-14032	288	26	effective	effective	ADJ
aiti-14032	288	27	feature	feature	NOUN
aiti-14032	288	28	extraction	extraction	NOUN
aiti-14032	288	29	approach	approach	NOUN
aiti-14032	288	30	based	base	VERB
aiti-14032	288	31	on	on	ADP
aiti-14032	288	32	spectral	spectral	ADJ
aiti-14032	288	33	-	-	PUNCT
aiti-14032	288	34	gabor	gabor	NOUN
aiti-14032	288	35	space	space	NOUN
aiti-14032	288	36	discriminant	discriminant	NOUN
aiti-14032	288	37	analysis	analysis	NOUN
aiti-14032	288	38	for	for	ADP
aiti-14032	288	39	hyperspectral	hyperspectral	ADJ
aiti-14032	288	40	image	image	NOUN
aiti-14032	288	41	,	,	PUNCT
aiti-14032	288	42	”	"	PUNCT
aiti-14032	288	43	neural	neural	ADJ
aiti-14032	288	44	processing	processing	NOUN
aiti-14032	288	45	letters	letter	NOUN
aiti-14032	288	46	,	,	PUNCT
aiti-14032	288	47	vol	vol	NOUN
aiti-14032	288	48	.	.	PROPN
aiti-14032	289	1	54	54	NUM
aiti-14032	289	2	,	,	PUNCT
aiti-14032	289	3	no	no	INTJ
aiti-14032	289	4	.	.	NOUN
aiti-14032	289	5	2	2	NUM
aiti-14032	289	6	,	,	PUNCT
aiti-14032	289	7	pp	pp	ADJ
aiti-14032	289	8	.	.	PUNCT
aiti-14032	290	1	909	909	NUM
aiti-14032	290	2	-	-	SYM
aiti-14032	290	3	959	959	NUM
aiti-14032	290	4	,	,	PUNCT
aiti-14032	290	5	2022	2022	NUM
aiti-14032	290	6	.	.	PUNCT
aiti-14032	291	1	[	[	X
aiti-14032	291	2	7	7	X
aiti-14032	291	3	]	]	X
aiti-14032	291	4	r.	r.	PROPN
aiti-14032	291	5	b.	b.	PROPN
aiti-14032	291	6	hegde	hegde	PROPN
aiti-14032	291	7	,	,	PUNCT
aiti-14032	291	8	k.	k.	PROPN
aiti-14032	291	9	prasad	prasad	PROPN
aiti-14032	291	10	,	,	PUNCT
aiti-14032	291	11	h.	h.	PROPN
aiti-14032	291	12	hebbar	hebbar	PROPN
aiti-14032	291	13	,	,	PUNCT
aiti-14032	291	14	and	and	CCONJ
aiti-14032	291	15	b.	b.	PROPN
aiti-14032	291	16	m.	m.	PROPN
aiti-14032	291	17	k.	k.	PROPN
aiti-14032	291	18	singh	singh	PROPN
aiti-14032	291	19	,	,	PUNCT
aiti-14032	291	20	“	"	PUNCT
aiti-14032	291	21	feature	feature	NOUN
aiti-14032	291	22	extraction	extraction	NOUN
aiti-14032	291	23	using	use	VERB
aiti-14032	291	24	traditional	traditional	ADJ
aiti-14032	291	25	image	image	NOUN
aiti-14032	291	26	processing	processing	NOUN
aiti-14032	291	27	and	and	CCONJ
aiti-14032	291	28	convolutional	convolutional	ADJ
aiti-14032	291	29	neural	neural	ADJ
aiti-14032	291	30	network	network	NOUN
aiti-14032	291	31	methods	method	NOUN
aiti-14032	291	32	to	to	PART
aiti-14032	291	33	classify	classify	VERB
aiti-14032	291	34	white	white	ADJ
aiti-14032	291	35	blood	blood	NOUN
aiti-14032	291	36	cells	cell	NOUN
aiti-14032	291	37	:	:	PUNCT
aiti-14032	291	38	a	a	DET
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aiti-14032	291	40	,	,	PUNCT
aiti-14032	291	41	”	"	PUNCT
aiti-14032	291	42	physical	physical	ADJ
aiti-14032	291	43	and	and	CCONJ
aiti-14032	291	44	engineering	engineering	NOUN
aiti-14032	291	45	sciences	science	NOUN
aiti-14032	291	46	in	in	ADP
aiti-14032	291	47	medicine	medicine	NOUN
aiti-14032	291	48	,	,	PUNCT
aiti-14032	291	49	vol	vol	NOUN
aiti-14032	291	50	.	.	PROPN
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aiti-14032	291	52	,	,	PUNCT
aiti-14032	291	53	pp.627	pp.627	PROPN
aiti-14032	291	54	-	-	PUNCT
aiti-14032	291	55	638	638	NUM
aiti-14032	291	56	,	,	PUNCT
aiti-14032	291	57	2019	2019	NUM
aiti-14032	291	58	.	.	PUNCT
aiti-14032	292	1	[	[	X
aiti-14032	292	2	8	8	NUM
aiti-14032	292	3	]	]	X
aiti-14032	292	4	n.	n.	PROPN
aiti-14032	292	5	ahmad	ahmad	PROPN
aiti-14032	292	6	,	,	PUNCT
aiti-14032	292	7	h.	h.	PROPN
aiti-14032	292	8	m.	m.	PROPN
aiti-14032	292	9	s.	s.	PROPN
aiti-14032	292	10	asif	asif	PROPN
aiti-14032	292	11	,	,	PUNCT
aiti-14032	292	12	g.	g.	PROPN
aiti-14032	292	13	saleem	saleem	PROPN
aiti-14032	292	14	,	,	PUNCT
aiti-14032	292	15	m.	m.	PROPN
aiti-14032	292	16	u.	u.	PROPN
aiti-14032	292	17	younus	younus	PROPN
aiti-14032	292	18	,	,	PUNCT
aiti-14032	292	19	s.	s.	PROPN
aiti-14032	292	20	anwar	anwar	PROPN
aiti-14032	292	21	,	,	PUNCT
aiti-14032	292	22	and	and	CCONJ
aiti-14032	292	23	m.	m.	PROPN
aiti-14032	292	24	r.	r.	PROPN
aiti-14032	292	25	anjum	anjum	PROPN
aiti-14032	292	26	“	"	PUNCT
aiti-14032	292	27	leaf	leaf	NOUN
aiti-14032	292	28	image	image	NOUN
aiti-14032	292	29	-	-	PUNCT
aiti-14032	292	30	based	base	VERB
aiti-14032	292	31	plant	plant	NOUN
aiti-14032	292	32	disease	disease	NOUN
aiti-14032	292	33	identification	identification	NOUN
aiti-14032	292	34	using	use	VERB
aiti-14032	292	35	color	color	NOUN
aiti-14032	292	36	and	and	CCONJ
aiti-14032	292	37	texture	texture	NOUN
aiti-14032	292	38	features	feature	NOUN
aiti-14032	292	39	,	,	PUNCT
aiti-14032	292	40	”	"	PUNCT
aiti-14032	292	41	wireless	wireless	ADJ
aiti-14032	292	42	personal	personal	ADJ
aiti-14032	292	43	communications	communication	NOUN
aiti-14032	292	44	,	,	PUNCT
aiti-14032	292	45	vol	vol	NOUN
aiti-14032	292	46	.	.	PROPN
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aiti-14032	292	48	,	,	PUNCT
aiti-14032	292	49	pp	pp	ADJ
aiti-14032	292	50	.	.	PUNCT
aiti-14032	293	1	1139	1139	NUM
aiti-14032	293	2	-	-	SYM
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aiti-14032	293	4	,	,	PUNCT
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aiti-14032	294	4	r.	r.	PROPN
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aiti-14032	294	6	and	and	CCONJ
aiti-14032	294	7	s.	s.	PROPN
aiti-14032	294	8	s.	s.	PROPN
aiti-14032	294	9	tripathy	tripathy	PROPN
aiti-14032	294	10	,	,	PUNCT
aiti-14032	294	11	“	"	PUNCT
aiti-14032	294	12	plant	plant	NOUN
aiti-14032	294	13	disease	disease	NOUN
aiti-14032	294	14	identification	identification	NOUN
aiti-14032	294	15	using	use	VERB
aiti-14032	294	16	fuzzy	fuzzy	ADJ
aiti-14032	294	17	feature	feature	NOUN
aiti-14032	294	18	extraction	extraction	NOUN
aiti-14032	294	19	and	and	CCONJ
aiti-14032	294	20	pnn	pnn	NOUN
aiti-14032	294	21	,	,	PUNCT
aiti-14032	294	22	”	"	PUNCT
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aiti-14032	294	24	,	,	PUNCT
aiti-14032	294	25	image	image	NOUN
aiti-14032	294	26	and	and	CCONJ
aiti-14032	294	27	video	video	NOUN
aiti-14032	294	28	processing	processing	NOUN
aiti-14032	294	29	,	,	PUNCT
aiti-14032	294	30	vol	vol	NOUN
aiti-14032	294	31	.	.	PROPN
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aiti-14032	294	33	,	,	PUNCT
aiti-14032	294	34	pp	pp	ADJ
aiti-14032	294	35	.	.	PUNCT
aiti-14032	294	36	2809	2809	NUM
aiti-14032	294	37	-	-	SYM
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aiti-14032	294	39	,	,	PUNCT
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aiti-14032	294	41	.	.	PUNCT
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aiti-14032	295	4	j.	j.	PROPN
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aiti-14032	295	7	a.	a.	PROPN
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aiti-14032	295	10	,	,	PUNCT
aiti-14032	295	11	“	"	PUNCT
aiti-14032	295	12	tomato	tomato	NOUN
aiti-14032	295	13	leaf	leaf	NOUN
aiti-14032	295	14	disease	disease	NOUN
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aiti-14032	295	16	using	use	VERB
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aiti-14032	295	19	extraction	extraction	NOUN
aiti-14032	295	20	techniques	technique	NOUN
aiti-14032	295	21	,	,	PUNCT
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aiti-14032	295	23	wireless	wireless	ADJ
aiti-14032	295	24	personal	personal	ADJ
aiti-14032	295	25	communications	communication	NOUN
aiti-14032	295	26	,	,	PUNCT
aiti-14032	295	27	vol	vol	NOUN
aiti-14032	295	28	.	.	PROPN
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aiti-14032	295	30	,	,	PUNCT
aiti-14032	295	31	pp	pp	ADJ
aiti-14032	295	32	.	.	PUNCT
aiti-14032	296	1	633	633	NUM
aiti-14032	296	2	-	-	SYM
aiti-14032	296	3	651	651	NUM
aiti-14032	296	4	,	,	PUNCT
aiti-14032	296	5	2020	2020	NUM
aiti-14032	296	6	.	.	PUNCT
aiti-14032	297	1	[	[	X
aiti-14032	297	2	11	11	NUM
aiti-14032	297	3	]	]	PUNCT
aiti-14032	297	4	m.	m.	NOUN
aiti-14032	297	5	a.	a.	PROPN
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aiti-14032	297	7	,	,	PUNCT
aiti-14032	297	8	“	"	PUNCT
aiti-14032	297	9	potato	potato	NOUN
aiti-14032	297	10	leaf	leaf	NOUN
aiti-14032	297	11	disease	disease	NOUN
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aiti-14032	297	13	,	,	PUNCT
aiti-14032	297	14	”	"	PUNCT
aiti-14032	297	15	https://www.kaggle.com/datasets/muhammadardiputra/potato-leaf-diseasedataset	https://www.kaggle.com/datasets/muhammadardiputra/potato-leaf-diseasedataset	NOUN
aiti-14032	297	16	,	,	PUNCT
aiti-14032	297	17	accessed	access	VERB
aiti-14032	297	18	on	on	ADP
aiti-14032	297	19	2023	2023	NUM
aiti-14032	297	20	.	.	PUNCT
aiti-14032	298	1	[	[	X
aiti-14032	298	2	12	12	NUM
aiti-14032	298	3	]	]	X
aiti-14032	298	4	y.	y.	PROPN
aiti-14032	298	5	li	li	PROPN
aiti-14032	298	6	,	,	PUNCT
aiti-14032	298	7	j.	j.	PROPN
aiti-14032	298	8	zhang	zhang	PROPN
aiti-14032	298	9	,	,	PUNCT
aiti-14032	298	10	p.	p.	PROPN
aiti-14032	298	11	gao	gao	PROPN
aiti-14032	298	12	,	,	PUNCT
aiti-14032	298	13	l.	l.	PROPN
aiti-14032	298	14	jiang	jiang	PROPN
aiti-14032	298	15	,	,	PUNCT
aiti-14032	298	16	and	and	CCONJ
aiti-14032	298	17	m.	m.	PROPN
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aiti-14032	298	19	,	,	PUNCT
aiti-14032	298	20	“	"	PUNCT
aiti-14032	298	21	grab	grab	VERB
aiti-14032	298	22	cut	cut	VERB
aiti-14032	298	23	image	image	NOUN
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aiti-14032	298	25	based	base	VERB
aiti-14032	298	26	on	on	ADP
aiti-14032	298	27	image	image	NOUN
aiti-14032	298	28	region	region	NOUN
aiti-14032	298	29	,	,	PUNCT
aiti-14032	298	30	”	"	PUNCT
aiti-14032	298	31	proceedings	proceeding	NOUN
aiti-14032	298	32	of	of	ADP
aiti-14032	298	33	ieee	ieee	PROPN
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aiti-14032	298	35	international	international	ADJ
aiti-14032	298	36	conference	conference	NOUN
aiti-14032	298	37	on	on	ADP
aiti-14032	298	38	image	image	NOUN
aiti-14032	298	39	,	,	PUNCT
aiti-14032	298	40	vision	vision	NOUN
aiti-14032	298	41	,	,	PUNCT
aiti-14032	298	42	and	and	CCONJ
aiti-14032	298	43	computing	computing	NOUN
aiti-14032	298	44	,	,	PUNCT
aiti-14032	298	45	pp	pp	ADJ
aiti-14032	298	46	.	.	PUNCT
aiti-14032	299	1	311	311	NUM
aiti-14032	299	2	-	-	SYM
aiti-14032	299	3	315	315	NUM
aiti-14032	299	4	,	,	PUNCT
aiti-14032	299	5	2018	2018	NUM
aiti-14032	299	6	.	.	PUNCT
aiti-14032	300	1	[	[	X
aiti-14032	300	2	13	13	NUM
aiti-14032	300	3	]	]	PUNCT
aiti-14032	300	4	s.	s.	PROPN
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aiti-14032	300	6	,	,	PUNCT
aiti-14032	300	7	a.	a.	NOUN
aiti-14032	300	8	arsalane	arsalane	NOUN
aiti-14032	300	9	,	,	PUNCT
aiti-14032	300	10	a.	a.	NOUN
aiti-14032	300	11	klilou	klilou	PROPN
aiti-14032	300	12	,	,	PUNCT
aiti-14032	300	13	and	and	CCONJ
aiti-14032	300	14	a.	a.	NOUN
aiti-14032	300	15	abounada	abounada	PROPN
aiti-14032	300	16	,	,	PUNCT
aiti-14032	300	17	“	"	PUNCT
aiti-14032	300	18	efficient	efficient	ADJ
aiti-14032	300	19	parallel	parallel	ADJ
aiti-14032	300	20	implementation	implementation	NOUN
aiti-14032	300	21	of	of	ADP
aiti-14032	300	22	gaussian	gaussian	ADJ
aiti-14032	300	23	mixture	mixture	NOUN
aiti-14032	300	24	model	model	NOUN
aiti-14032	300	25	background	background	NOUN
aiti-14032	300	26	subtraction	subtraction	NOUN
aiti-14032	300	27	algorithm	algorithm	NOUN
aiti-14032	300	28	on	on	ADP
aiti-14032	300	29	an	an	DET
aiti-14032	300	30	embedded	embed	VERB
aiti-14032	300	31	multi	multi	ADJ
aiti-14032	300	32	-	-	ADJ
aiti-14032	300	33	core	core	ADJ
aiti-14032	300	34	digital	digital	ADJ
aiti-14032	300	35	signal	signal	NOUN
aiti-14032	300	36	processor	processor	NOUN
aiti-14032	300	37	,	,	PUNCT
aiti-14032	300	38	”	"	PUNCT
aiti-14032	300	39	computers	computer	NOUN
aiti-14032	300	40	and	and	CCONJ
aiti-14032	300	41	electrical	electrical	ADJ
aiti-14032	300	42	engineering	engineering	NOUN
aiti-14032	300	43	,	,	PUNCT
aiti-14032	300	44	vol	vol	NOUN
aiti-14032	300	45	.	.	PROPN
aiti-14032	300	46	110	110	NUM
aiti-14032	300	47	,	,	PUNCT
aiti-14032	300	48	article	article	NOUN
aiti-14032	300	49	no	no	NOUN
aiti-14032	300	50	.	.	PROPN
aiti-14032	300	51	108827	108827	NUM
aiti-14032	300	52	,	,	PUNCT
aiti-14032	300	53	2023	2023	NUM
aiti-14032	300	54	.	.	PUNCT
aiti-14032	301	1	[	[	X
aiti-14032	301	2	14	14	NUM
aiti-14032	301	3	]	]	PUNCT
aiti-14032	301	4	p.	p.	PROPN
aiti-14032	301	5	e.	e.	PROPN
aiti-14032	301	6	jebarani	jebarani	PROPN
aiti-14032	301	7	,	,	PUNCT
aiti-14032	301	8	n.	n.	PROPN
aiti-14032	301	9	umadevi	umadevi	PROPN
aiti-14032	301	10	,	,	PUNCT
aiti-14032	301	11	h.	h.	PROPN
aiti-14032	301	12	dang	dang	PROPN
aiti-14032	301	13	,	,	PUNCT
aiti-14032	301	14	and	and	CCONJ
aiti-14032	301	15	m.	m.	NOUN
aiti-14032	301	16	pomplun	pomplun	PROPN
aiti-14032	301	17	,	,	PUNCT
aiti-14032	301	18	“	"	PUNCT
aiti-14032	301	19	a	a	DET
aiti-14032	301	20	novel	novel	ADJ
aiti-14032	301	21	hybrid	hybrid	ADJ
aiti-14032	301	22	k	k	NOUN
aiti-14032	301	23	-	-	PUNCT
aiti-14032	301	24	means	means	PROPN
aiti-14032	301	25	and	and	CCONJ
aiti-14032	301	26	gmm	gmm	PROPN
aiti-14032	301	27	machine	machine	NOUN
aiti-14032	301	28	learning	learn	VERB
aiti-14032	301	29	model	model	NOUN
aiti-14032	301	30	for	for	ADP
aiti-14032	301	31	breast	breast	NOUN
aiti-14032	301	32	cancer	cancer	NOUN
aiti-14032	301	33	detection	detection	NOUN
aiti-14032	301	34	,	,	PUNCT
aiti-14032	301	35	”	"	PUNCT
aiti-14032	301	36	ieee	ieee	NOUN
aiti-14032	301	37	access	access	NOUN
aiti-14032	301	38	,	,	PUNCT
aiti-14032	301	39	vol	vol	NOUN
aiti-14032	301	40	.	.	NOUN
aiti-14032	301	41	9	9	NUM
aiti-14032	301	42	,	,	PUNCT
aiti-14032	301	43	pp	pp	ADJ
aiti-14032	301	44	.	.	PUNCT
aiti-14032	302	1	146153	146153	NUM
aiti-14032	302	2	-	-	SYM
aiti-14032	302	3	146162	146162	NUM
aiti-14032	302	4	,	,	PUNCT
aiti-14032	302	5	2021	2021	NUM
aiti-14032	302	6	.	.	PUNCT
aiti-14032	303	1	[	[	X
aiti-14032	303	2	15	15	NUM
aiti-14032	303	3	]	]	X
aiti-14032	303	4	p.	p.	NOUN
aiti-14032	303	5	s.	s.	PROPN
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aiti-14032	303	7	and	and	CCONJ
aiti-14032	303	8	a.	a.	PROPN
aiti-14032	303	9	s.	s.	PROPN
aiti-14032	303	10	rajan	rajan	PROPN
aiti-14032	303	11	,	,	PUNCT
aiti-14032	303	12	“	"	PUNCT
aiti-14032	303	13	an	an	DET
aiti-14032	303	14	inquiry	inquiry	NOUN
aiti-14032	303	15	of	of	ADP
aiti-14032	303	16	image	image	NOUN
aiti-14032	303	17	processing	processing	NOUN
aiti-14032	303	18	in	in	ADP
aiti-14032	303	19	agriculture	agriculture	NOUN
aiti-14032	303	20	to	to	PART
aiti-14032	303	21	perceive	perceive	VERB
aiti-14032	303	22	the	the	DET
aiti-14032	303	23	infirmity	infirmity	NOUN
aiti-14032	303	24	of	of	ADP
aiti-14032	303	25	plants	plant	NOUN
aiti-14032	303	26	using	use	VERB
aiti-14032	303	27	machine	machine	NOUN
aiti-14032	303	28	learning	learning	NOUN
aiti-14032	303	29	,	,	PUNCT
aiti-14032	303	30	”	"	PUNCT
aiti-14032	303	31	multimedia	multimedia	NOUN
aiti-14032	303	32	tools	tool	NOUN
aiti-14032	303	33	and	and	CCONJ
aiti-14032	303	34	applications	application	NOUN
aiti-14032	303	35	,	,	PUNCT
aiti-14032	303	36	vol	vol	NOUN
aiti-14032	303	37	.	.	PROPN
aiti-14032	303	38	83	83	NUM
aiti-14032	303	39	,	,	PUNCT
aiti-14032	303	40	pp	pp	ADJ
aiti-14032	303	41	.	.	PUNCT
aiti-14032	304	1	80631	80631	NUM
aiti-14032	304	2	-	-	SYM
aiti-14032	304	3	80640	80640	NUM
aiti-14032	304	4	,	,	PUNCT
aiti-14032	304	5	2024	2024	NUM
aiti-14032	304	6	.	.	PUNCT
aiti-14032	305	1	[	[	X
aiti-14032	305	2	16	16	NUM
aiti-14032	305	3	]	]	PUNCT
aiti-14032	305	4	m.	m.	NOUN
aiti-14032	305	5	a.	a.	PROPN
aiti-14032	305	6	iqbal	iqbal	PROPN
aiti-14032	305	7	and	and	CCONJ
aiti-14032	305	8	k.	k.	PROPN
aiti-14032	305	9	h.	h.	PROPN
aiti-14032	305	10	talukder	talukder	PROPN
aiti-14032	305	11	,	,	PUNCT
aiti-14032	305	12	“	"	PUNCT
aiti-14032	305	13	detection	detection	NOUN
aiti-14032	305	14	of	of	ADP
aiti-14032	305	15	potato	potato	NOUN
aiti-14032	305	16	disease	disease	NOUN
aiti-14032	305	17	using	use	VERB
aiti-14032	305	18	image	image	NOUN
aiti-14032	305	19	segmentation	segmentation	NOUN
aiti-14032	305	20	and	and	CCONJ
aiti-14032	305	21	machine	machine	NOUN
aiti-14032	305	22	learning	learning	NOUN
aiti-14032	305	23	,	,	PUNCT
aiti-14032	305	24	”	"	PUNCT
aiti-14032	305	25	proceedings	proceeding	NOUN
aiti-14032	305	26	of	of	ADP
aiti-14032	305	27	international	international	ADJ
aiti-14032	305	28	conference	conference	NOUN
aiti-14032	305	29	on	on	ADP
aiti-14032	305	30	wireless	wireless	ADJ
aiti-14032	305	31	communications	communication	NOUN
aiti-14032	305	32	signal	signal	NOUN
aiti-14032	305	33	processing	processing	NOUN
aiti-14032	305	34	and	and	CCONJ
aiti-14032	305	35	networking	networking	NOUN
aiti-14032	305	36	,	,	PUNCT
aiti-14032	305	37	pp.43	pp.43	NOUN
aiti-14032	305	38	-	-	PUNCT
aiti-14032	305	39	47	47	NUM
aiti-14032	305	40	,	,	PUNCT
aiti-14032	305	41	2020	2020	NUM
aiti-14032	305	42	.	.	PUNCT
aiti-14032	306	1	[	[	X
aiti-14032	306	2	17	17	NUM
aiti-14032	306	3	]	]	X
aiti-14032	306	4	r.	r.	PROPN
aiti-14032	306	5	bhagwat	bhagwat	PROPN
aiti-14032	306	6	and	and	CCONJ
aiti-14032	306	7	y.	y.	PROPN
aiti-14032	306	8	dandawate	dandawate	PROPN
aiti-14032	306	9	,	,	PUNCT
aiti-14032	306	10	“	"	PUNCT
aiti-14032	306	11	a	a	DET
aiti-14032	306	12	framework	framework	NOUN
aiti-14032	306	13	for	for	ADP
aiti-14032	306	14	crop	crop	NOUN
aiti-14032	306	15	disease	disease	NOUN
aiti-14032	306	16	detection	detection	NOUN
aiti-14032	306	17	using	use	VERB
aiti-14032	306	18	feature	feature	NOUN
aiti-14032	306	19	fusion	fusion	NOUN
aiti-14032	306	20	method	method	NOUN
aiti-14032	306	21	,	,	PUNCT
aiti-14032	306	22	”	"	PUNCT
aiti-14032	306	23	international	international	ADJ
aiti-14032	306	24	journal	journal	NOUN
aiti-14032	306	25	of	of	ADP
aiti-14032	306	26	engineering	engineering	NOUN
aiti-14032	306	27	and	and	CCONJ
aiti-14032	306	28	technology	technology	NOUN
aiti-14032	306	29	innovation	innovation	NOUN
aiti-14032	306	30	,	,	PUNCT
aiti-14032	306	31	vol	vol	NOUN
aiti-14032	306	32	.	.	PROPN
aiti-14032	306	33	11	11	NUM
aiti-14032	306	34	,	,	PUNCT
aiti-14032	306	35	no	no	INTJ
aiti-14032	306	36	.	.	NOUN
aiti-14032	306	37	3	3	NUM
aiti-14032	306	38	,	,	PUNCT
aiti-14032	306	39	pp	pp	ADJ
aiti-14032	306	40	.	.	PUNCT
aiti-14032	307	1	216	216	NUM
aiti-14032	307	2	-	-	SYM
aiti-14032	307	3	228	228	NUM
aiti-14032	307	4	,	,	PUNCT
aiti-14032	307	5	2021	2021	NUM
aiti-14032	307	6	.	.	PUNCT
aiti-14032	308	1	[	[	X
aiti-14032	308	2	18	18	NUM
aiti-14032	308	3	]	]	X
aiti-14032	308	4	s.	s.	PROPN
aiti-14032	308	5	jain	jain	PROPN
aiti-14032	308	6	and	and	CCONJ
aiti-14032	308	7	s.	s.	PROPN
aiti-14032	308	8	malviya	malviya	PROPN
aiti-14032	308	9	,	,	PUNCT
aiti-14032	308	10	“	"	PUNCT
aiti-14032	308	11	digital	digital	ADJ
aiti-14032	308	12	image	image	NOUN
aiti-14032	308	13	retrieval	retrieval	NOUN
aiti-14032	308	14	using	use	VERB
aiti-14032	308	15	annotation	annotation	NOUN
aiti-14032	308	16	,	,	PUNCT
aiti-14032	308	17	ccm	ccm	NOUN
aiti-14032	308	18	and	and	CCONJ
aiti-14032	308	19	histogram	histogram	NOUN
aiti-14032	308	20	features	feature	NOUN
aiti-14032	308	21	,	,	PUNCT
aiti-14032	308	22	”	"	PUNCT
aiti-14032	308	23	international	international	ADJ
aiti-14032	308	24	journal	journal	NOUN
aiti-14032	308	25	of	of	ADP
aiti-14032	308	26	scientific	scientific	ADJ
aiti-14032	308	27	research	research	NOUN
aiti-14032	308	28	&	&	CCONJ
aiti-14032	308	29	engineering	engineering	NOUN
aiti-14032	308	30	trends	trend	NOUN
aiti-14032	308	31	,	,	PUNCT
aiti-14032	308	32	vol	vol	NOUN
aiti-14032	308	33	.	.	PROPN
aiti-14032	308	34	4	4	NUM
aiti-14032	308	35	,	,	PUNCT
aiti-14032	308	36	no	no	INTJ
aiti-14032	308	37	.	.	NOUN
aiti-14032	308	38	5	5	NUM
aiti-14032	308	39	,	,	PUNCT
aiti-14032	308	40	pp	pp	ADJ
aiti-14032	308	41	.	.	PUNCT
aiti-14032	309	1	859	859	NUM
aiti-14032	309	2	-	-	NUM
aiti-14032	309	3	864	864	NUM
aiti-14032	309	4	,	,	PUNCT
aiti-14032	309	5	2018	2018	NUM
aiti-14032	309	6	.	.	PUNCT
aiti-14032	310	1	[	[	X
aiti-14032	310	2	19	19	NUM
aiti-14032	310	3	]	]	PUNCT
aiti-14032	310	4	k.	k.	PROPN
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aiti-14032	310	7	d.	d.	PROPN
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aiti-14032	310	9	,	,	PUNCT
aiti-14032	310	10	g.	g.	PROPN
aiti-14032	310	11	hariharan	hariharan	PROPN
aiti-14032	310	12	,	,	PUNCT
aiti-14032	310	13	m.	m.	NOUN
aiti-14032	310	14	s.	s.	PROPN
aiti-14032	310	15	sanaj	sanaj	PROPN
aiti-14032	310	16	,	,	PUNCT
aiti-14032	310	17	s.	s.	PROPN
aiti-14032	310	18	kumer	kumer	PROPN
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aiti-14032	310	20	m.	m.	PROPN
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aiti-14032	310	29	feature	feature	NOUN
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aiti-14032	310	36	using	use	VERB
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aiti-14032	310	40	-	-	NOUN
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aiti-14032	310	45	of	of	ADP
aiti-14032	310	46	cervical	cervical	ADJ
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aiti-14032	310	48	,	,	PUNCT
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aiti-14032	310	52	sciences	science	NOUN
aiti-14032	310	53	,	,	PUNCT
aiti-14032	310	54	vol	vol	NOUN
aiti-14032	310	55	.	.	PROPN
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aiti-14032	310	61	,	,	PUNCT
aiti-14032	310	62	article	article	NOUN
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aiti-14032	310	66	,	,	PUNCT
aiti-14032	310	67	2023	2023	NUM
aiti-14032	310	68	.	.	PUNCT
aiti-14032	311	1	[	[	X
aiti-14032	311	2	20	20	NUM
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aiti-14032	311	4	v.	v.	CCONJ
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aiti-14032	311	7	a.	a.	NOUN
aiti-14032	311	8	thakur	thakur	PROPN
aiti-14032	311	9	,	,	PUNCT
aiti-14032	311	10	“	"	PUNCT
aiti-14032	311	11	prediction	prediction	NOUN
aiti-14032	311	12	of	of	ADP
aiti-14032	311	13	crop	crop	NOUN
aiti-14032	311	14	leaf	leaf	NOUN
aiti-14032	311	15	health	health	NOUN
aiti-14032	311	16	by	by	ADP
aiti-14032	311	17	mccm	mccm	NOUN
aiti-14032	311	18	and	and	CCONJ
aiti-14032	311	19	histogram	histogram	NOUN
aiti-14032	311	20	learning	learning	NOUN
aiti-14032	311	21	model	model	NOUN
aiti-14032	311	22	using	use	VERB
aiti-14032	311	23	leaf	leaf	NOUN
aiti-14032	311	24	region	region	NOUN
aiti-14032	311	25	,	,	PUNCT
aiti-14032	311	26	”	"	PUNCT
aiti-14032	311	27	proceedings	proceeding	NOUN
aiti-14032	311	28	of	of	ADP
aiti-14032	311	29	engineering	engineering	NOUN
aiti-14032	311	30	and	and	CCONJ
aiti-14032	311	31	technology	technology	NOUN
aiti-14032	311	32	innovation	innovation	NOUN
aiti-14032	311	33	,	,	PUNCT
aiti-14032	311	34	vol	vol	NOUN
aiti-14032	311	35	.	.	PROPN
aiti-14032	311	36	27	27	NUM
aiti-14032	311	37	,	,	PUNCT
aiti-14032	311	38	pp	pp	ADJ
aiti-14032	311	39	.	.	PUNCT
aiti-14032	312	1	110	110	NUM
aiti-14032	312	2	-	-	SYM
aiti-14032	312	3	121	121	NUM
aiti-14032	312	4	,	,	PUNCT
aiti-14032	312	5	2024	2024	NUM
aiti-14032	312	6	.	.	PUNCT
aiti-14032	313	1	[	[	X
aiti-14032	313	2	21	21	NUM
aiti-14032	313	3	]	]	X
aiti-14032	313	4	b.	b.	PROPN
aiti-14032	313	5	kumari	kumari	PROPN
aiti-14032	313	6	,	,	PUNCT
aiti-14032	313	7	r.	r.	PROPN
aiti-14032	313	8	kumar	kumar	PROPN
aiti-14032	313	9	,	,	PUNCT
aiti-14032	313	10	v.	v.	PROPN
aiti-14032	313	11	k.	k.	PROPN
aiti-14032	313	12	singh	singh	PROPN
aiti-14032	313	13	,	,	PUNCT
aiti-14032	313	14	l.	l.	PROPN
aiti-14032	313	15	pawar	pawar	PROPN
aiti-14032	313	16	,	,	PUNCT
aiti-14032	313	17	p.	p.	PROPN
aiti-14032	313	18	pandey	pandey	PROPN
aiti-14032	313	19	,	,	PUNCT
aiti-14032	313	20	and	and	CCONJ
aiti-14032	313	21	m.	m.	NOUN
aiti-14032	313	22	sharma	sharma	PROPN
aiti-14032	313	23	,	,	PUNCT
aiti-14032	313	24	“	"	PUNCT
aiti-14032	313	25	an	an	DET
aiti-14032	313	26	efficient	efficient	ADJ
aiti-14032	313	27	system	system	NOUN
aiti-14032	313	28	for	for	ADP
aiti-14032	313	29	color	color	NOUN
aiti-14032	313	30	image	image	NOUN
aiti-14032	313	31	retrieval	retrieval	NOUN
aiti-14032	313	32	representing	represent	VERB
aiti-14032	313	33	semantic	semantic	ADJ
aiti-14032	313	34	information	information	NOUN
aiti-14032	313	35	to	to	PART
aiti-14032	313	36	enhance	enhance	VERB
aiti-14032	313	37	performance	performance	NOUN
aiti-14032	313	38	by	by	ADP
aiti-14032	313	39	optimizing	optimize	VERB
aiti-14032	313	40	feature	feature	NOUN
aiti-14032	313	41	extraction	extraction	NOUN
aiti-14032	313	42	,	,	PUNCT
aiti-14032	313	43	”	"	PUNCT
aiti-14032	313	44	procedia	procedia	NOUN
aiti-14032	313	45	computer	computer	NOUN
aiti-14032	313	46	science	science	NOUN
aiti-14032	313	47	,	,	PUNCT
aiti-14032	313	48	vol	vol	NOUN
aiti-14032	313	49	.	.	PROPN
aiti-14032	313	50	152	152	NUM
aiti-14032	313	51	,	,	PUNCT
aiti-14032	313	52	pp	pp	ADJ
aiti-14032	313	53	.	.	PUNCT
aiti-14032	314	1	102	102	NUM
aiti-14032	314	2	-	-	SYM
aiti-14032	314	3	110	110	NUM
aiti-14032	314	4	,	,	PUNCT
aiti-14032	314	5	2019	2019	NUM
aiti-14032	314	6	.	.	PUNCT
aiti-14032	315	1	[	[	X
aiti-14032	315	2	22	22	NUM
aiti-14032	315	3	]	]	PUNCT
aiti-14032	315	4	s.	s.	PROPN
aiti-14032	315	5	l.	l.	PROPN
aiti-14032	315	6	chen	chen	PROPN
aiti-14032	315	7	,	,	PUNCT
aiti-14032	315	8	h.	h.	PROPN
aiti-14032	315	9	s.	s.	PROPN
aiti-14032	315	10	zhou	zhou	PROPN
aiti-14032	315	11	,	,	PUNCT
aiti-14032	315	12	t.	t.	PROPN
aiti-14032	315	13	y.	y.	PROPN
aiti-14032	315	14	chen	chen	PROPN
aiti-14032	315	15	,	,	PUNCT
aiti-14032	315	16	t.	t.	PROPN
aiti-14032	315	17	h.	h.	PROPN
aiti-14032	315	18	lee	lee	PROPN
aiti-14032	315	19	,	,	PUNCT
aiti-14032	315	20	c.	c.	PROPN
aiti-14032	315	21	a.	a.	PROPN
aiti-14032	315	22	chen	chen	PROPN
aiti-14032	315	23	,	,	PUNCT
aiti-14032	315	24	t.	t.	PROPN
aiti-14032	315	25	l.	l.	PROPN
aiti-14032	315	26	lin	lin	PROPN
aiti-14032	315	27	,	,	PUNCT
aiti-14032	315	28	et	et	PROPN
aiti-14032	315	29	al	al	PROPN
aiti-14032	315	30	.	.	PROPN
aiti-14032	315	31	,	,	PUNCT
aiti-14032	315	32	“	"	PUNCT
aiti-14032	315	33	dental	dental	ADJ
aiti-14032	315	34	shade	shade	NOUN
aiti-14032	315	35	matching	matching	NOUN
aiti-14032	315	36	method	method	NOUN
aiti-14032	315	37	based	base	VERB
aiti-14032	315	38	on	on	ADP
aiti-14032	315	39	hue	hue	NOUN
aiti-14032	315	40	,	,	PUNCT
aiti-14032	315	41	saturation	saturation	NOUN
aiti-14032	315	42	,	,	PUNCT
aiti-14032	315	43	value	value	NOUN
aiti-14032	315	44	color	color	NOUN
aiti-14032	315	45	model	model	NOUN
aiti-14032	315	46	with	with	ADP
aiti-14032	315	47	machine	machine	NOUN
aiti-14032	315	48	learning	learning	NOUN
aiti-14032	315	49	and	and	CCONJ
aiti-14032	315	50	fuzzy	fuzzy	ADJ
aiti-14032	315	51	decision	decision	NOUN
aiti-14032	315	52	,	,	PUNCT
aiti-14032	315	53	”	"	PUNCT
aiti-14032	315	54	sensors	sensor	NOUN
aiti-14032	315	55	and	and	CCONJ
aiti-14032	315	56	materials	material	NOUN
aiti-14032	315	57	,	,	PUNCT
aiti-14032	315	58	vol	vol	NOUN
aiti-14032	315	59	.	.	PROPN
aiti-14032	315	60	32	32	NUM
aiti-14032	315	61	,	,	PUNCT
aiti-14032	315	62	no	no	INTJ
aiti-14032	315	63	.	.	NOUN
aiti-14032	315	64	10	10	NUM
aiti-14032	315	65	,	,	PUNCT
aiti-14032	315	66	pp	pp	ADJ
aiti-14032	315	67	.	.	PUNCT
aiti-14032	315	68	3185	3185	NUM
aiti-14032	315	69	-	-	SYM
aiti-14032	315	70	3207	3207	NUM
aiti-14032	315	71	,	,	PUNCT
aiti-14032	315	72	2020	2020	NUM
aiti-14032	315	73	.	.	PUNCT
aiti-14032	316	1	[	[	X
aiti-14032	316	2	23	23	NUM
aiti-14032	316	3	]	]	PUNCT
aiti-14032	316	4	k.	k.	PROPN
aiti-14032	316	5	kaplan	kaplan	PROPN
aiti-14032	316	6	,	,	PUNCT
aiti-14032	316	7	y.	y.	PROPN
aiti-14032	316	8	kaya	kaya	PROPN
aiti-14032	316	9	,	,	PUNCT
aiti-14032	316	10	m.	m.	NOUN
aiti-14032	316	11	kuncan	kuncan	PROPN
aiti-14032	316	12	,	,	PUNCT
aiti-14032	316	13	and	and	CCONJ
aiti-14032	316	14	h.	h.	PROPN
aiti-14032	316	15	m.	m.	PROPN
aiti-14032	316	16	ertunç	ertunç	PROPN
aiti-14032	316	17	,	,	PUNCT
aiti-14032	316	18	“	"	PUNCT
aiti-14032	316	19	brain	brain	NOUN
aiti-14032	316	20	tumor	tumor	NOUN
aiti-14032	316	21	classification	classification	NOUN
aiti-14032	316	22	using	use	VERB
aiti-14032	316	23	modified	modify	VERB
aiti-14032	316	24	local	local	ADJ
aiti-14032	316	25	binary	binary	ADJ
aiti-14032	316	26	patterns	pattern	NOUN
aiti-14032	316	27	(	(	PUNCT
aiti-14032	316	28	lbp	lbp	NOUN
aiti-14032	316	29	)	)	PUNCT
aiti-14032	316	30	feature	feature	NOUN
aiti-14032	316	31	extraction	extraction	NOUN
aiti-14032	316	32	methods	method	NOUN
aiti-14032	316	33	,	,	PUNCT
aiti-14032	316	34	”	"	PUNCT
aiti-14032	316	35	medical	medical	ADJ
aiti-14032	316	36	hypotheses	hypothesis	NOUN
aiti-14032	316	37	,	,	PUNCT
aiti-14032	316	38	vol	vol	NOUN
aiti-14032	316	39	.	.	NOUN
aiti-14032	316	40	139	139	NUM
aiti-14032	316	41	,	,	PUNCT
aiti-14032	316	42	article	article	NOUN
aiti-14032	316	43	no	no	NOUN
aiti-14032	316	44	.	.	PROPN
aiti-14032	316	45	109696	109696	NUM
aiti-14032	316	46	,	,	PUNCT
aiti-14032	316	47	2020	2020	NUM
aiti-14032	316	48	.	.	PUNCT
aiti-14032	317	1	[	[	X
aiti-14032	317	2	24	24	NUM
aiti-14032	317	3	]	]	X
aiti-14032	317	4	v.	v.	CCONJ
aiti-14032	317	5	choudhary	choudhary	PROPN
aiti-14032	317	6	and	and	CCONJ
aiti-14032	317	7	a.	a.	NOUN
aiti-14032	317	8	thakur	thakur	PROPN
aiti-14032	317	9	,	,	PUNCT
aiti-14032	317	10	“	"	PUNCT
aiti-14032	317	11	bat	bat	NOUN
aiti-14032	317	12	algorithm	algorithm	NOUN
aiti-14032	317	13	-	-	PUNCT
aiti-14032	317	14	based	base	VERB
aiti-14032	317	15	multi	multi	ADJ
aiti-14032	317	16	-	-	ADJ
aiti-14032	317	17	class	class	ADJ
aiti-14032	317	18	crop	crop	NOUN
aiti-14032	317	19	leaf	leaf	NOUN
aiti-14032	317	20	disease	disease	NOUN
aiti-14032	317	21	prediction	prediction	NOUN
aiti-14032	317	22	bootstrap	bootstrap	NOUN
aiti-14032	317	23	model	model	NOUN
aiti-14032	317	24	,	,	PUNCT
aiti-14032	317	25	”	"	PUNCT
aiti-14032	317	26	proceedings	proceeding	NOUN
aiti-14032	317	27	of	of	ADP
aiti-14032	317	28	engineering	engineering	NOUN
aiti-14032	317	29	and	and	CCONJ
aiti-14032	317	30	technology	technology	NOUN
aiti-14032	317	31	innovation	innovation	NOUN
aiti-14032	317	32	,	,	PUNCT
aiti-14032	317	33	vol	vol	NOUN
aiti-14032	317	34	.	.	PROPN
aiti-14032	317	35	26	26	NUM
aiti-14032	317	36	,	,	PUNCT
aiti-14032	317	37	pp	pp	ADJ
aiti-14032	317	38	.	.	PUNCT
aiti-14032	317	39	72	72	NUM
aiti-14032	317	40	-	-	SYM
aiti-14032	317	41	82	82	NUM
aiti-14032	317	42	,	,	PUNCT
aiti-14032	317	43	2024	2024	NUM
aiti-14032	317	44	.	.	PUNCT
aiti-14032	318	1	advances	advance	NOUN
aiti-14032	318	2	in	in	ADP
aiti-14032	318	3	technology	technology	NOUN
aiti-14032	318	4	innovation	innovation	NOUN
aiti-14032	318	5	,	,	PUNCT
aiti-14032	318	6	vol	vol	NOUN
aiti-14032	318	7	.	.	PROPN
aiti-14032	319	1	10	10	NUM
aiti-14032	319	2	,	,	PUNCT
aiti-14032	319	3	no	no	INTJ
aiti-14032	319	4	.	.	NOUN
aiti-14032	319	5	4	4	NUM
aiti-14032	319	6	,	,	PUNCT
aiti-14032	319	7	2025	2025	NUM
aiti-14032	319	8	,	,	PUNCT
aiti-14032	319	9	pp	pp	ADJ
aiti-14032	319	10	.	.	PUNCT
aiti-14032	320	1	370	370	NUM
aiti-14032	320	2	-	-	SYM
aiti-14032	320	3	382	382	NUM
aiti-14032	320	4	382	382	NUM
aiti-14032	320	5	[	[	X
aiti-14032	320	6	25	25	NUM
aiti-14032	320	7	]	]	PUNCT
aiti-14032	320	8	s.	s.	PROPN
aiti-14032	320	9	abdelfattah	abdelfattah	PROPN
aiti-14032	320	10	,	,	PUNCT
aiti-14032	320	11	m.	m.	NOUN
aiti-14032	320	12	baza	baza	PROPN
aiti-14032	320	13	,	,	PUNCT
aiti-14032	320	14	m.	m.	NOUN
aiti-14032	320	15	mahmoud	mahmoud	PROPN
aiti-14032	320	16	,	,	PUNCT
aiti-14032	320	17	m.	m.	NOUN
aiti-14032	320	18	m.	m.	PROPN
aiti-14032	320	19	fouda	fouda	PROPN
aiti-14032	320	20	,	,	PUNCT
aiti-14032	320	21	k.	k.	PROPN
aiti-14032	320	22	abualsaud	abualsaud	PROPN
aiti-14032	320	23	,	,	PUNCT
aiti-14032	320	24	e.	e.	PROPN
aiti-14032	320	25	yaacoub	yaacoub	PROPN
aiti-14032	320	26	,	,	PUNCT
aiti-14032	320	27	et	et	PROPN
aiti-14032	320	28	al	al	PROPN
aiti-14032	320	29	.	.	PROPN
aiti-14032	320	30	,	,	PUNCT
aiti-14032	320	31	“	"	PUNCT
aiti-14032	320	32	lightweight	lightweight	ADJ
aiti-14032	320	33	multi	multi	ADJ
aiti-14032	320	34	-	-	ADJ
aiti-14032	320	35	class	class	ADJ
aiti-14032	320	36	support	support	NOUN
aiti-14032	320	37	vector	vector	NOUN
aiti-14032	320	38	machine	machine	NOUN
aiti-14032	320	39	-	-	PUNCT
aiti-14032	320	40	based	base	VERB
aiti-14032	320	41	medical	medical	ADJ
aiti-14032	320	42	diagnosis	diagnosis	NOUN
aiti-14032	320	43	system	system	NOUN
aiti-14032	320	44	with	with	ADP
aiti-14032	320	45	privacy	privacy	NOUN
aiti-14032	320	46	preservation	preservation	NOUN
aiti-14032	320	47	,	,	PUNCT
aiti-14032	320	48	”	"	PUNCT
aiti-14032	320	49	sensors	sensor	NOUN
aiti-14032	320	50	,	,	PUNCT
aiti-14032	320	51	vol	vol	NOUN
aiti-14032	320	52	.	.	PROPN
aiti-14032	320	53	23	23	NUM
aiti-14032	320	54	,	,	PUNCT
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aiti-14032	320	57	22	22	NUM
aiti-14032	320	58	,	,	PUNCT
aiti-14032	320	59	article	article	NOUN
aiti-14032	320	60	no	no	NOUN
aiti-14032	320	61	.	.	PROPN
aiti-14032	320	62	9033	9033	NUM
aiti-14032	320	63	,	,	PUNCT
aiti-14032	320	64	2023	2023	NUM
aiti-14032	320	65	.	.	PUNCT
aiti-14032	321	1	[	[	X
aiti-14032	321	2	26	26	NUM
aiti-14032	321	3	]	]	X
aiti-14032	321	4	y.	y.	PROPN
aiti-14032	321	5	kaya	kaya	PROPN
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aiti-14032	321	7	e.	e.	PROPN
aiti-14032	321	8	gürsoy	gürsoy	PROPN
aiti-14032	321	9	,	,	PUNCT
aiti-14032	321	10	“	"	PUNCT
aiti-14032	321	11	a	a	DET
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aiti-14032	321	13	multi	multi	ADJ
aiti-14032	321	14	-	-	ADJ
aiti-14032	321	15	head	head	ADJ
aiti-14032	321	16	cnn	cnn	PROPN
aiti-14032	321	17	design	design	NOUN
aiti-14032	321	18	to	to	PART
aiti-14032	321	19	identify	identify	VERB
aiti-14032	321	20	plant	plant	NOUN
aiti-14032	321	21	diseases	disease	NOUN
aiti-14032	321	22	using	use	VERB
aiti-14032	321	23	the	the	DET
aiti-14032	321	24	fusion	fusion	NOUN
aiti-14032	321	25	of	of	ADP
aiti-14032	321	26	rgb	rgb	PROPN
aiti-14032	321	27	images	image	NOUN
aiti-14032	321	28	,	,	PUNCT
aiti-14032	321	29	”	"	PUNCT
aiti-14032	321	30	ecological	ecological	ADJ
aiti-14032	321	31	informatics	informatic	NOUN
aiti-14032	321	32	,	,	PUNCT
aiti-14032	321	33	vol	vol	NOUN
aiti-14032	321	34	.	.	PROPN
aiti-14032	321	35	75	75	NUM
aiti-14032	321	36	,	,	PUNCT
aiti-14032	321	37	article	article	NOUN
aiti-14032	321	38	no	no	NOUN
aiti-14032	321	39	.	.	PROPN
aiti-14032	321	40	101998	101998	NUM
aiti-14032	321	41	,	,	PUNCT
aiti-14032	321	42	2023	2023	NUM
aiti-14032	321	43	.	.	PUNCT
aiti-14032	322	1	[	[	X
aiti-14032	322	2	27	27	NUM
aiti-14032	322	3	]	]	X
aiti-14032	322	4	s.	s.	PROPN
aiti-14032	322	5	ashwinkumar	ashwinkumar	PROPN
aiti-14032	322	6	,	,	PUNCT
aiti-14032	322	7	s.	s.	PROPN
aiti-14032	322	8	rajagopal	rajagopal	PROPN
aiti-14032	322	9	,	,	PUNCT
aiti-14032	322	10	v.	v.	CCONJ
aiti-14032	322	11	manimaran	manimaran	NOUN
aiti-14032	322	12	,	,	PUNCT
aiti-14032	322	13	and	and	CCONJ
aiti-14032	322	14	b.	b.	PROPN
aiti-14032	322	15	jegajothi	jegajothi	PROPN
aiti-14032	322	16	,	,	PUNCT
aiti-14032	322	17	“	"	PUNCT
aiti-14032	322	18	automated	automate	VERB
aiti-14032	322	19	plant	plant	NOUN
aiti-14032	322	20	leaf	leaf	NOUN
aiti-14032	322	21	disease	disease	NOUN
aiti-14032	322	22	detection	detection	NOUN
aiti-14032	322	23	and	and	CCONJ
aiti-14032	322	24	classification	classification	NOUN
aiti-14032	322	25	using	use	VERB
aiti-14032	322	26	optimal	optimal	ADJ
aiti-14032	322	27	mobilenet	mobilenet	NOUN
aiti-14032	322	28	based	base	VERB
aiti-14032	322	29	convolutional	convolutional	ADJ
aiti-14032	322	30	neural	neural	ADJ
aiti-14032	322	31	networks	network	NOUN
aiti-14032	322	32	,	,	PUNCT
aiti-14032	322	33	”	"	PUNCT
aiti-14032	322	34	materials	material	NOUN
aiti-14032	322	35	today	today	NOUN
aiti-14032	322	36	:	:	PUNCT
aiti-14032	322	37	proceedings	proceeding	NOUN
aiti-14032	322	38	,	,	PUNCT
aiti-14032	322	39	vol	vol	NOUN
aiti-14032	322	40	.	.	PROPN
aiti-14032	323	1	51	51	NUM
aiti-14032	323	2	,	,	PUNCT
aiti-14032	323	3	no	no	INTJ
aiti-14032	323	4	.	.	NOUN
aiti-14032	323	5	1	1	NUM
aiti-14032	323	6	,	,	PUNCT
aiti-14032	323	7	pp	pp	ADJ
aiti-14032	323	8	.	.	PUNCT
aiti-14032	324	1	480	480	NUM
aiti-14032	324	2	-	-	SYM
aiti-14032	324	3	487	487	NUM
aiti-14032	324	4	,	,	PUNCT
aiti-14032	324	5	2022	2022	NUM
aiti-14032	324	6	.	.	PUNCT
aiti-14032	325	1	copyright	copyright	NOUN
aiti-14032	325	2	©	©	PROPN
aiti-14032	325	3	by	by	ADP
aiti-14032	325	4	the	the	DET
aiti-14032	325	5	authors	author	NOUN
aiti-14032	325	6	.	.	PUNCT
aiti-14032	326	1	licensee	licensee	PROPN
aiti-14032	326	2	taeti	taeti	PROPN
aiti-14032	326	3	,	,	PUNCT
aiti-14032	326	4	taiwan	taiwan	PROPN
aiti-14032	326	5	.	.	PUNCT
aiti-14032	327	1	this	this	DET
aiti-14032	327	2	article	article	NOUN
aiti-14032	327	3	is	be	AUX
aiti-14032	327	4	an	an	DET
aiti-14032	327	5	open	open	ADJ
aiti-14032	327	6	access	access	NOUN
aiti-14032	327	7	article	article	NOUN
aiti-14032	327	8	distributed	distribute	VERB
aiti-14032	327	9	under	under	ADP
aiti-14032	327	10	the	the	DET
aiti-14032	327	11	terms	term	NOUN
aiti-14032	327	12	and	and	CCONJ
aiti-14032	327	13	conditions	condition	NOUN
aiti-14032	327	14	of	of	ADP
aiti-14032	327	15	the	the	DET
aiti-14032	327	16	creative	creative	ADJ
aiti-14032	327	17	commons	common	NOUN
aiti-14032	327	18	attribution	attribution	NOUN
aiti-14032	327	19	(	(	PUNCT
aiti-14032	327	20	cc	cc	NOUN
aiti-14032	327	21	by	by	ADP
aiti-14032	327	22	-	-	PUNCT
aiti-14032	327	23	nc	nc	NOUN
aiti-14032	327	24	)	)	PUNCT
aiti-14032	327	25	license	license	NOUN
aiti-14032	327	26	(	(	PUNCT
aiti-14032	327	27	https://creativecommons.org/licenses/by-nc/4.0/	https://creativecommons.org/licenses/by-nc/4.0/	NOUN
aiti-14032	327	28	)	)	PUNCT
aiti-14032	327	29	.	.	PUNCT