id	sid	tid	token	lemma	pos
cana-4953	1	1	communications	communication	NOUN
cana-4953	1	2	on	on	ADP
cana-4953	1	3	applied	apply	VERB
cana-4953	1	4	nonlinear	nonlinear	ADJ
cana-4953	1	5	analysis	analysis	NOUN
cana-4953	1	6	issn	issn	NOUN
cana-4953	1	7	:	:	PUNCT
cana-4953	1	8	1074	1074	NUM
cana-4953	1	9	-	-	PUNCT
cana-4953	1	10	133x	133x	NUM
cana-4953	1	11	vol	vol	VERB
cana-4953	1	12	32	32	NUM
cana-4953	1	13	no	no	NOUN
cana-4953	1	14	.	.	PUNCT
cana-4953	2	1	10s	10	NOUN
cana-4953	2	2	(	(	PUNCT
cana-4953	2	3	2025	2025	NUM
cana-4953	2	4	)	)	PUNCT
cana-4953	2	5	1022	1022	NUM
cana-4953	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	2	7	forest	forest	NOUN
cana-4953	2	8	fire	fire	NOUN
cana-4953	2	9	detection	detection	NOUN
cana-4953	2	10	using	use	VERB
cana-4953	2	11	cnn	cnn	PROPN
cana-4953	2	12	minit	minit	PROPN
cana-4953	2	13	arora1	arora1	PROPN
cana-4953	2	14	,	,	PUNCT
cana-4953	2	15	sanjay	sanjay	PROPN
cana-4953	2	16	sharma2	sharma2	PROPN
cana-4953	2	17	*	*	PROPN
cana-4953	2	18	12school	12school	NUM
cana-4953	2	19	of	of	ADP
cana-4953	2	20	engineering	engineering	NOUN
cana-4953	2	21	and	and	CCONJ
cana-4953	2	22	technology	technology	NOUN
cana-4953	2	23	,	,	PUNCT
cana-4953	2	24	shri	shri	PROPN
cana-4953	2	25	guru	guru	PROPN
cana-4953	2	26	ram	ram	PROPN
cana-4953	2	27	rai	rai	PROPN
cana-4953	2	28	university	university	PROPN
cana-4953	2	29	,	,	PUNCT
cana-4953	2	30	dehradun	dehradun	PROPN
cana-4953	2	31	,	,	PUNCT
cana-4953	2	32	uttarakhand	uttarakhand	PROPN
cana-4953	2	33	-248001	-248001	PROPN
cana-4953	2	34	,	,	PUNCT
cana-4953	2	35	india	india	PROPN
cana-4953	2	36	1minitarora@gmail.com	1minitarora@gmail.com	NUM
cana-4953	2	37	.	.	PROPN
cana-4953	2	38	,	,	PUNCT
cana-4953	2	39	orcid0009	orcid0009	PROPN
cana-4953	2	40	-	-	PUNCT
cana-4953	2	41	0002	0002	NUM
cana-4953	2	42	-	-	PUNCT
cana-4953	2	43	5310	5310	NUM
cana-4953	2	44	-	-	SYM
cana-4953	2	45	4025	4025	NUM
cana-4953	2	46	2sanjaypokhriyal@yahoo.com	2sanjaypokhriyal@yahoo.com	NUM
cana-4953	2	47	,	,	PUNCT
cana-4953	2	48	orcid	orcid	NOUN
cana-4953	2	49	-0000	-0000	NOUN
cana-4953	2	50	-	-	PUNCT
cana-4953	2	51	0001	0001	NUM
cana-4953	2	52	-	-	PUNCT
cana-4953	2	53	7625	7625	NUM
cana-4953	2	54	-	-	SYM
cana-4953	2	55	5091	5091	NUM
cana-4953	2	56	corresponding	correspond	VERB
cana-4953	2	57	author	author	NOUN
cana-4953	2	58	–	–	PUNCT
cana-4953	2	59	2*sanjaypokhriyal@yahoo.com	2*sanjaypokhriyal@yahoo.com	NUM
cana-4953	2	60	article	article	NOUN
cana-4953	2	61	history	history	NOUN
cana-4953	2	62	:	:	PUNCT
cana-4953	2	63	received	receive	VERB
cana-4953	2	64	:	:	PUNCT
cana-4953	2	65	12	12	NUM
cana-4953	2	66	-	-	SYM
cana-4953	2	67	01	01	NUM
cana-4953	2	68	-	-	PUNCT
cana-4953	2	69	2025	2025	NUM
cana-4953	2	70	revised	revise	VERB
cana-4953	2	71	:	:	PUNCT
cana-4953	2	72	15	15	NUM
cana-4953	2	73	-	-	NUM
cana-4953	2	74	02	02	NUM
cana-4953	2	75	-	-	PUNCT
cana-4953	2	76	2025	2025	NUM
cana-4953	2	77	accepted	accept	VERB
cana-4953	2	78	:	:	PUNCT
cana-4953	2	79	01	01	NUM
cana-4953	2	80	-	-	SYM
cana-4953	2	81	03	03	NUM
cana-4953	2	82	-	-	PUNCT
cana-4953	2	83	2025	2025	NUM
cana-4953	2	84	abstract	abstract	NOUN
cana-4953	2	85	:	:	PUNCT
cana-4953	2	86	forest	forest	NOUN
cana-4953	2	87	fires	fire	NOUN
cana-4953	2	88	contribute	contribute	VERB
cana-4953	2	89	significantly	significantly	ADV
cana-4953	2	90	to	to	ADP
cana-4953	2	91	air	air	NOUN
cana-4953	2	92	pollution	pollution	NOUN
cana-4953	2	93	by	by	ADP
cana-4953	2	94	emitting	emit	VERB
cana-4953	2	95	large	large	ADJ
cana-4953	2	96	quantities	quantity	NOUN
cana-4953	2	97	of	of	ADP
cana-4953	2	98	carbon	carbon	NOUN
cana-4953	2	99	dioxide	dioxide	NOUN
cana-4953	2	100	and	and	CCONJ
cana-4953	2	101	carbon	carbon	NOUN
cana-4953	2	102	monoxide	monoxide	NOUN
cana-4953	2	103	.	.	PUNCT
cana-4953	3	1	these	these	DET
cana-4953	3	2	fires	fire	NOUN
cana-4953	3	3	also	also	ADV
cana-4953	3	4	release	release	VERB
cana-4953	3	5	fine	fine	ADJ
cana-4953	3	6	particulate	particulate	NOUN
cana-4953	3	7	matter	matter	NOUN
cana-4953	3	8	into	into	ADP
cana-4953	3	9	the	the	DET
cana-4953	3	10	air	air	NOUN
cana-4953	3	11	,	,	PUNCT
cana-4953	3	12	worsening	worsen	VERB
cana-4953	3	13	pollution	pollution	NOUN
cana-4953	3	14	levels	level	NOUN
cana-4953	3	15	and	and	CCONJ
cana-4953	3	16	diminishing	diminish	VERB
cana-4953	3	17	air	air	NOUN
cana-4953	3	18	quality	quality	NOUN
cana-4953	3	19	.	.	PUNCT
cana-4953	4	1	the	the	DET
cana-4953	4	2	impact	impact	NOUN
cana-4953	4	3	on	on	ADP
cana-4953	4	4	vegetation	vegetation	NOUN
cana-4953	4	5	and	and	CCONJ
cana-4953	4	6	wildlife	wildlife	NOUN
cana-4953	4	7	is	be	AUX
cana-4953	4	8	severe	severe	ADJ
cana-4953	4	9	,	,	PUNCT
cana-4953	4	10	resulting	result	VERB
cana-4953	4	11	in	in	ADP
cana-4953	4	12	substantial	substantial	ADJ
cana-4953	4	13	ecological	ecological	ADJ
cana-4953	4	14	damage	damage	NOUN
cana-4953	4	15	and	and	CCONJ
cana-4953	4	16	a	a	DET
cana-4953	4	17	reduction	reduction	NOUN
cana-4953	4	18	in	in	ADP
cana-4953	4	19	biodiversity	biodiversity	NOUN
cana-4953	4	20	.	.	PUNCT
cana-4953	5	1	timely	timely	ADJ
cana-4953	5	2	detection	detection	NOUN
cana-4953	5	3	of	of	ADP
cana-4953	5	4	forest	forest	NOUN
cana-4953	5	5	fires	fire	NOUN
cana-4953	5	6	is	be	AUX
cana-4953	5	7	crucial	crucial	ADJ
cana-4953	5	8	for	for	ADP
cana-4953	5	9	minimizing	minimize	VERB
cana-4953	5	10	their	their	PRON
cana-4953	5	11	destructive	destructive	ADJ
cana-4953	5	12	effects	effect	NOUN
cana-4953	5	13	.	.	PUNCT
cana-4953	6	1	while	while	SCONJ
cana-4953	6	2	iot	iot	ADJ
cana-4953	6	3	sensors	sensor	NOUN
cana-4953	6	4	can	can	AUX
cana-4953	6	5	be	be	AUX
cana-4953	6	6	employed	employ	VERB
cana-4953	6	7	to	to	PART
cana-4953	6	8	identify	identify	VERB
cana-4953	6	9	smoke	smoke	NOUN
cana-4953	6	10	and	and	CCONJ
cana-4953	6	11	fire	fire	NOUN
cana-4953	6	12	,	,	PUNCT
cana-4953	6	13	they	they	PRON
cana-4953	6	14	are	be	AUX
cana-4953	6	15	susceptible	susceptible	ADJ
cana-4953	6	16	to	to	ADP
cana-4953	6	17	damage	damage	NOUN
cana-4953	6	18	during	during	ADP
cana-4953	6	19	the	the	DET
cana-4953	6	20	blaze	blaze	NOUN
cana-4953	6	21	.	.	PUNCT
cana-4953	7	1	a	a	DET
cana-4953	7	2	more	more	ADV
cana-4953	7	3	effective	effective	ADJ
cana-4953	7	4	approach	approach	NOUN
cana-4953	7	5	to	to	ADP
cana-4953	7	6	fire	fire	NOUN
cana-4953	7	7	detection	detection	NOUN
cana-4953	7	8	involves	involve	VERB
cana-4953	7	9	machine	machine	NOUN
cana-4953	7	10	learning	learn	VERB
cana-4953	7	11	techniques	technique	NOUN
cana-4953	7	12	.	.	PUNCT
cana-4953	8	1	various	various	ADJ
cana-4953	8	2	machine	machine	NOUN
cana-4953	8	3	learning	learning	NOUN
cana-4953	8	4	methods	method	NOUN
cana-4953	8	5	,	,	PUNCT
cana-4953	8	6	such	such	ADJ
cana-4953	8	7	as	as	ADP
cana-4953	8	8	artificial	artificial	ADJ
cana-4953	8	9	neural	neural	ADJ
cana-4953	8	10	networks	network	NOUN
cana-4953	8	11	(	(	PUNCT
cana-4953	8	12	ann	ann	PROPN
cana-4953	8	13	)	)	PUNCT
cana-4953	8	14	,	,	PUNCT
cana-4953	8	15	convolutional	convolutional	ADJ
cana-4953	8	16	neural	neural	ADJ
cana-4953	8	17	networks	network	NOUN
cana-4953	8	18	(	(	PUNCT
cana-4953	8	19	cnn	cnn	PROPN
cana-4953	8	20	)	)	PUNCT
cana-4953	8	21	,	,	PUNCT
cana-4953	8	22	long	long	ADV
cana-4953	8	23	shortterm	shortterm	PROPN
cana-4953	8	24	memory	memory	NOUN
cana-4953	8	25	(	(	PUNCT
cana-4953	8	26	lstm	lstm	NOUN
cana-4953	8	27	)	)	PUNCT
cana-4953	8	28	,	,	PUNCT
cana-4953	8	29	regression	regression	NOUN
cana-4953	8	30	,	,	PUNCT
cana-4953	8	31	and	and	CCONJ
cana-4953	8	32	support	support	VERB
cana-4953	8	33	vector	vector	NOUN
cana-4953	8	34	machines	machine	NOUN
cana-4953	8	35	(	(	PUNCT
cana-4953	8	36	svm	svm	PROPN
cana-4953	8	37	)	)	PUNCT
cana-4953	8	38	,	,	PUNCT
cana-4953	8	39	can	can	AUX
cana-4953	8	40	be	be	AUX
cana-4953	8	41	utilized	utilize	VERB
cana-4953	8	42	for	for	ADP
cana-4953	8	43	this	this	DET
cana-4953	8	44	purpose	purpose	NOUN
cana-4953	8	45	.this	.this	PRON
cana-4953	8	46	paper	paper	NOUN
cana-4953	8	47	analyses	analyse	VERB
cana-4953	8	48	some	some	DET
cana-4953	8	49	machine	machine	NOUN
cana-4953	8	50	learning	learn	VERB
cana-4953	8	51	techniques	technique	NOUN
cana-4953	8	52	and	and	CCONJ
cana-4953	8	53	proposes	propose	VERB
cana-4953	8	54	the	the	DET
cana-4953	8	55	use	use	NOUN
cana-4953	8	56	of	of	ADP
cana-4953	8	57	a	a	DET
cana-4953	8	58	cnn	cnn	PROPN
cana-4953	8	59	based	base	VERB
cana-4953	8	60	model	model	NOUN
cana-4953	8	61	to	to	PART
cana-4953	8	62	detect	detect	VERB
cana-4953	8	63	fire	fire	NOUN
cana-4953	8	64	keywords	keyword	NOUN
cana-4953	8	65	:	:	PUNCT
cana-4953	8	66	deep	deep	ADJ
cana-4953	8	67	learning	learning	NOUN
cana-4953	8	68	,	,	PUNCT
cana-4953	8	69	convolution	convolution	NOUN
cana-4953	8	70	neural	neural	ADJ
cana-4953	8	71	network	network	NOUN
cana-4953	8	72	,	,	PUNCT
cana-4953	8	73	cnn	cnn	PROPN
cana-4953	8	74	,	,	PUNCT
cana-4953	8	75	air	air	NOUN
cana-4953	8	76	quality	quality	PROPN
cana-4953	8	77	index	index	PROPN
cana-4953	8	78	,	,	PUNCT
cana-4953	8	79	ann	ann	PROPN
cana-4953	8	80	,	,	PUNCT
cana-4953	8	81	iot	iot	NOUN
cana-4953	8	82	.	.	PROPN
cana-4953	8	83	1	1	NUM
cana-4953	8	84	.	.	X
cana-4953	8	85	introduction	introduction	NOUN
cana-4953	8	86	the	the	DET
cana-4953	8	87	interplay	interplay	NOUN
cana-4953	8	88	between	between	ADP
cana-4953	8	89	climate	climate	NOUN
cana-4953	8	90	change	change	NOUN
cana-4953	8	91	and	and	CCONJ
cana-4953	8	92	wildfires	wildfire	NOUN
cana-4953	8	93	has	have	AUX
cana-4953	8	94	resulted	result	VERB
cana-4953	8	95	in	in	ADP
cana-4953	8	96	many	many	ADJ
cana-4953	8	97	regions	region	NOUN
cana-4953	8	98	worldwide	worldwide	ADV
cana-4953	8	99	experiencing	experience	VERB
cana-4953	8	100	more	more	ADV
cana-4953	8	101	extensive	extensive	ADJ
cana-4953	8	102	,	,	PUNCT
cana-4953	8	103	intense	intense	ADJ
cana-4953	8	104	,	,	PUNCT
cana-4953	8	105	and	and	CCONJ
cana-4953	8	106	prolonged	prolonged	ADJ
cana-4953	8	107	fires	fire	NOUN
cana-4953	8	108	compared	compare	VERB
cana-4953	8	109	to	to	ADP
cana-4953	8	110	previous	previous	ADJ
cana-4953	8	111	years	year	NOUN
cana-4953	8	112	.	.	PUNCT
cana-4953	9	1	if	if	SCONJ
cana-4953	9	2	current	current	ADJ
cana-4953	9	3	trends	trend	NOUN
cana-4953	9	4	persist	persist	VERB
cana-4953	9	5	,	,	PUNCT
cana-4953	9	6	the	the	DET
cana-4953	9	7	long	long	ADJ
cana-4953	9	8	-	-	PUNCT
cana-4953	9	9	term	term	NOUN
cana-4953	9	10	consequences	consequence	NOUN
cana-4953	9	11	for	for	ADP
cana-4953	9	12	humans	human	NOUN
cana-4953	9	13	,	,	PUNCT
cana-4953	9	14	wildlife	wildlife	NOUN
cana-4953	9	15	,	,	PUNCT
cana-4953	9	16	and	and	CCONJ
cana-4953	9	17	the	the	DET
cana-4953	9	18	climate	climate	NOUN
cana-4953	9	19	will	will	AUX
cana-4953	9	20	be	be	AUX
cana-4953	9	21	devastating	devastating	ADJ
cana-4953	9	22	.	.	PUNCT
cana-4953	10	1	a	a	DET
cana-4953	10	2	wildfire	wildfire	NOUN
cana-4953	10	3	is	be	AUX
cana-4953	10	4	defined	define	VERB
cana-4953	10	5	as	as	ADP
cana-4953	10	6	an	an	DET
cana-4953	10	7	uncontrolled	uncontrolled	ADJ
cana-4953	10	8	blaze	blaze	NOUN
cana-4953	10	9	that	that	PRON
cana-4953	10	10	starts	start	VERB
cana-4953	10	11	in	in	ADP
cana-4953	10	12	a	a	DET
cana-4953	10	13	natural	natural	ADJ
cana-4953	10	14	area	area	NOUN
cana-4953	10	15	such	such	ADJ
cana-4953	10	16	as	as	ADP
cana-4953	10	17	a	a	DET
cana-4953	10	18	forest	forest	NOUN
cana-4953	10	19	,	,	PUNCT
cana-4953	10	20	grassland	grassland	NOUN
cana-4953	10	21	,	,	PUNCT
cana-4953	10	22	or	or	CCONJ
cana-4953	10	23	prairie	prairie	NOUN
cana-4953	10	24	.	.	PUNCT
cana-4953	11	1	these	these	DET
cana-4953	11	2	fires	fire	NOUN
cana-4953	11	3	can	can	AUX
cana-4953	11	4	ignite	ignite	VERB
cana-4953	11	5	anywhere	anywhere	ADV
cana-4953	11	6	and	and	CCONJ
cana-4953	11	7	at	at	ADP
cana-4953	11	8	any	any	DET
cana-4953	11	9	time	time	NOUN
cana-4953	11	10	,	,	PUNCT
cana-4953	11	11	often	often	ADV
cana-4953	11	12	stemming	stem	VERB
cana-4953	11	13	from	from	ADP
cana-4953	11	14	human	human	ADJ
cana-4953	11	15	activities	activity	NOUN
cana-4953	11	16	or	or	CCONJ
cana-4953	11	17	natural	natural	ADJ
cana-4953	11	18	phenomena	phenomenon	NOUN
cana-4953	11	19	like	like	ADP
cana-4953	11	20	lightning	lightning	NOUN
cana-4953	11	21	strikes	strike	NOUN
cana-4953	11	22	.	.	PUNCT
cana-4953	12	1	the	the	DET
cana-4953	12	2	origin	origin	NOUN
cana-4953	12	3	of	of	ADP
cana-4953	12	4	50	50	NUM
cana-4953	12	5	%	%	NOUN
cana-4953	12	6	of	of	ADP
cana-4953	12	7	reported	report	VERB
cana-4953	12	8	wildfires	wildfire	NOUN
cana-4953	12	9	remains	remain	VERB
cana-4953	12	10	undetermined	undetermined	ADJ
cana-4953	12	11	.	.	PUNCT
cana-4953	13	1	extremely	extremely	ADV
cana-4953	13	2	arid	arid	ADJ
cana-4953	13	3	conditions	condition	NOUN
cana-4953	13	4	,	,	PUNCT
cana-4953	13	5	including	include	VERB
cana-4953	13	6	droughts	drought	NOUN
cana-4953	13	7	,	,	PUNCT
cana-4953	13	8	and	and	CCONJ
cana-4953	13	9	high	high	ADJ
cana-4953	13	10	winds	wind	NOUN
cana-4953	13	11	contribute	contribute	VERB
cana-4953	13	12	to	to	ADP
cana-4953	13	13	an	an	DET
cana-4953	13	14	increased	increase	VERB
cana-4953	13	15	likelihood	likelihood	NOUN
cana-4953	13	16	of	of	ADP
cana-4953	13	17	wildfires	wildfires	PROPN
cana-4953	13	18	.	.	PUNCT
cana-4953	14	1	these	these	DET
cana-4953	14	2	blazes	blaze	NOUN
cana-4953	14	3	can	can	AUX
cana-4953	14	4	disrupt	disrupt	VERB
cana-4953	14	5	the	the	DET
cana-4953	14	6	availability	availability	NOUN
cana-4953	14	7	of	of	ADP
cana-4953	14	8	water	water	NOUN
cana-4953	14	9	,	,	PUNCT
cana-4953	14	10	electricity	electricity	NOUN
cana-4953	14	11	,	,	PUNCT
cana-4953	14	12	and	and	CCONJ
cana-4953	14	13	gas	gas	NOUN
cana-4953	14	14	services	service	NOUN
cana-4953	14	15	,	,	PUNCT
cana-4953	14	16	as	as	ADV
cana-4953	14	17	well	well	ADV
cana-4953	14	18	as	as	ADP
cana-4953	14	19	communication	communication	NOUN
cana-4953	14	20	and	and	CCONJ
cana-4953	14	21	transportation	transportation	NOUN
cana-4953	14	22	networks	network	NOUN
cana-4953	14	23	.	.	PUNCT
cana-4953	15	1	additionally	additionally	ADV
cana-4953	15	2	,	,	PUNCT
cana-4953	15	3	wildfires	wildfire	VERB
cana-4953	15	4	deplete	deplete	ADJ
cana-4953	15	5	resources	resource	NOUN
cana-4953	15	6	,	,	PUNCT
cana-4953	15	7	destroy	destroy	VERB
cana-4953	15	8	crops	crop	NOUN
cana-4953	15	9	,	,	PUNCT
cana-4953	15	10	cause	cause	VERB
cana-4953	15	11	fatalities	fatality	NOUN
cana-4953	15	12	among	among	ADP
cana-4953	15	13	humans	human	NOUN
cana-4953	15	14	and	and	CCONJ
cana-4953	15	15	animals	animal	NOUN
cana-4953	15	16	,	,	PUNCT
cana-4953	15	17	damage	damage	NOUN
cana-4953	15	18	property	property	NOUN
cana-4953	15	19	,	,	PUNCT
cana-4953	15	20	and	and	CCONJ
cana-4953	15	21	deteriorate	deteriorate	VERB
cana-4953	15	22	air	air	NOUN
cana-4953	15	23	quality	quality	NOUN
cana-4953	15	24	.	.	PUNCT
cana-4953	16	1	wildfires	wildfire	NOUN
cana-4953	16	2	are	be	AUX
cana-4953	16	3	known	know	VERB
cana-4953	16	4	for	for	ADP
cana-4953	16	5	their	their	PRON
cana-4953	16	6	rapid	rapid	ADJ
cana-4953	16	7	expansion	expansion	NOUN
cana-4953	16	8	and	and	CCONJ
cana-4953	16	9	resistance	resistance	NOUN
cana-4953	16	10	to	to	ADP
cana-4953	16	11	extinguishment	extinguishment	NOUN
cana-4953	16	12	.	.	PUNCT
cana-4953	17	1	accurately	accurately	ADV
cana-4953	17	2	pinpointing	pinpoint	VERB
cana-4953	17	3	the	the	DET
cana-4953	17	4	affected	affected	ADJ
cana-4953	17	5	area	area	NOUN
cana-4953	17	6	and	and	CCONJ
cana-4953	17	7	evaluating	evaluate	VERB
cana-4953	17	8	the	the	DET
cana-4953	17	9	intensity	intensity	NOUN
cana-4953	17	10	of	of	ADP
cana-4953	17	11	smoke	smoke	NOUN
cana-4953	17	12	poses	pose	VERB
cana-4953	17	13	a	a	DET
cana-4953	17	14	significant	significant	ADJ
cana-4953	17	15	challenge	challenge	NOUN
cana-4953	17	16	,	,	PUNCT
cana-4953	17	17	yet	yet	CCONJ
cana-4953	17	18	it	it	PRON
cana-4953	17	19	is	be	AUX
cana-4953	17	20	crucial	crucial	ADJ
cana-4953	17	21	for	for	SCONJ
cana-4953	17	22	firefighters	firefighter	NOUN
cana-4953	17	23	to	to	PART
cana-4953	17	24	implement	implement	VERB
cana-4953	17	25	appropriate	appropriate	ADJ
cana-4953	17	26	measures	measure	NOUN
cana-4953	17	27	and	and	CCONJ
cana-4953	17	28	swiftly	swiftly	ADV
cana-4953	17	29	contain	contain	VERB
cana-4953	17	30	the	the	DET
cana-4953	17	31	blaze	blaze	NOUN
cana-4953	17	32	.	.	PUNCT
cana-4953	18	1	moreover	moreover	ADV
cana-4953	18	2	,	,	PUNCT
cana-4953	18	3	it	it	PRON
cana-4953	18	4	is	be	AUX
cana-4953	18	5	crucial	crucial	ADJ
cana-4953	18	6	mailto:1minitarora@gmail.com	mailto:1minitarora@gmail.com	SYM
cana-4953	18	7	mailto:2*sanjaypokhriyal@yahoo.com	mailto:2*sanjaypokhriyal@yahoo.com	PROPN
cana-4953	18	8	communications	communication	NOUN
cana-4953	18	9	on	on	ADP
cana-4953	18	10	applied	apply	VERB
cana-4953	18	11	nonlinear	nonlinear	ADJ
cana-4953	18	12	analysis	analysis	NOUN
cana-4953	18	13	issn	issn	NOUN
cana-4953	18	14	:	:	PUNCT
cana-4953	18	15	1074	1074	NUM
cana-4953	18	16	-	-	PUNCT
cana-4953	18	17	133x	133x	NUM
cana-4953	18	18	vol	vol	VERB
cana-4953	18	19	32	32	NUM
cana-4953	18	20	no	no	NOUN
cana-4953	18	21	.	.	PUNCT
cana-4953	19	1	10s	10	NOUN
cana-4953	19	2	(	(	PUNCT
cana-4953	19	3	2025	2025	NUM
cana-4953	19	4	)	)	PUNCT
cana-4953	19	5	1023	1023	NUM
cana-4953	19	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	19	7	to	to	PART
cana-4953	19	8	know	know	VERB
cana-4953	19	9	critical	critical	ADJ
cana-4953	19	10	information	information	NOUN
cana-4953	19	11	such	such	ADJ
cana-4953	19	12	as	as	ADP
cana-4953	19	13	the	the	DET
cana-4953	19	14	location	location	NOUN
cana-4953	19	15	,	,	PUNCT
cana-4953	19	16	extent	extent	NOUN
cana-4953	19	17	,	,	PUNCT
cana-4953	19	18	and	and	CCONJ
cana-4953	19	19	intensity	intensity	NOUN
cana-4953	19	20	(	(	PUNCT
cana-4953	19	21	i.e.	i.e.	X
cana-4953	19	22	,	,	PUNCT
cana-4953	19	23	risk	risk	NOUN
cana-4953	19	24	level	level	NOUN
cana-4953	19	25	)	)	PUNCT
cana-4953	19	26	of	of	ADP
cana-4953	19	27	the	the	DET
cana-4953	19	28	smoke	smoke	NOUN
cana-4953	19	29	.	.	PUNCT
cana-4953	20	1	consequently	consequently	ADV
cana-4953	20	2	,	,	PUNCT
cana-4953	20	3	it	it	PRON
cana-4953	20	4	is	be	AUX
cana-4953	20	5	vital	vital	ADJ
cana-4953	20	6	to	to	PART
cana-4953	20	7	develop	develop	VERB
cana-4953	20	8	a	a	DET
cana-4953	20	9	fire	fire	NOUN
cana-4953	20	10	monitoring	monitoring	NOUN
cana-4953	20	11	system	system	NOUN
cana-4953	20	12	capable	capable	ADJ
cana-4953	20	13	of	of	ADP
cana-4953	20	14	early	early	ADJ
cana-4953	20	15	detection	detection	NOUN
cana-4953	20	16	of	of	ADP
cana-4953	20	17	flames	flame	NOUN
cana-4953	20	18	or	or	CCONJ
cana-4953	20	19	smoke	smoke	NOUN
cana-4953	20	20	in	in	ADP
cana-4953	20	21	specific	specific	ADJ
cana-4953	20	22	areas	area	NOUN
cana-4953	20	23	and	and	CCONJ
cana-4953	20	24	assessing	assess	VERB
cana-4953	20	25	their	their	PRON
cana-4953	20	26	severity	severity	NOUN
cana-4953	20	27	.	.	PUNCT
cana-4953	21	1	we	we	PRON
cana-4953	21	2	have	have	AUX
cana-4953	21	3	proposed	propose	VERB
cana-4953	21	4	a	a	DET
cana-4953	21	5	model	model	NOUN
cana-4953	21	6	that	that	PRON
cana-4953	21	7	detects	detect	NOUN
cana-4953	21	8	fire	fire	NOUN
cana-4953	21	9	from	from	ADP
cana-4953	21	10	a	a	DET
cana-4953	21	11	surveillance	surveillance	NOUN
cana-4953	21	12	image	image	NOUN
cana-4953	21	13	which	which	PRON
cana-4953	21	14	could	could	AUX
cana-4953	21	15	be	be	AUX
cana-4953	21	16	helpful	helpful	ADJ
cana-4953	21	17	to	to	PART
cana-4953	21	18	detect	detect	VERB
cana-4953	21	19	fire	fire	NOUN
cana-4953	21	20	timely	timely	ADV
cana-4953	21	21	.	.	PUNCT
cana-4953	22	1	2	2	X
cana-4953	22	2	.	.	X
cana-4953	22	3	related	relate	VERB
cana-4953	22	4	work	work	NOUN
cana-4953	22	5	d.	d.	PROPN
cana-4953	22	6	q.	q.	PROPN
cana-4953	22	7	tran	tran	PROPN
cana-4953	22	8	et	et	PROPN
cana-4953	22	9	al	al	PROPN
cana-4953	23	1	[	[	X
cana-4953	23	2	1	1	X
cana-4953	23	3	]	]	PUNCT
cana-4953	23	4	introduced	introduce	VERB
cana-4953	23	5	a	a	DET
cana-4953	23	6	model	model	NOUN
cana-4953	23	7	for	for	ADP
cana-4953	23	8	forest	forest	NOUN
cana-4953	23	9	fire	fire	NOUN
cana-4953	23	10	response	response	NOUN
cana-4953	23	11	that	that	PRON
cana-4953	23	12	focuses	focus	VERB
cana-4953	23	13	on	on	ADP
cana-4953	23	14	implementing	implement	VERB
cana-4953	23	15	a	a	DET
cana-4953	23	16	bush	bush	PROPN
cana-4953	23	17	fire	fire	NOUN
cana-4953	23	18	defense	defense	NOUN
cana-4953	23	19	system	system	NOUN
cana-4953	23	20	with	with	ADP
cana-4953	23	21	two	two	NUM
cana-4953	23	22	primary	primary	ADJ
cana-4953	23	23	elements	element	NOUN
cana-4953	23	24	:	:	PUNCT
cana-4953	23	25	detection	detection	NOUN
cana-4953	23	26	and	and	CCONJ
cana-4953	23	27	damage	damage	NOUN
cana-4953	23	28	assessment	assessment	NOUN
cana-4953	23	29	.	.	PUNCT
cana-4953	24	1	they	they	PRON
cana-4953	24	2	utilized	utilize	VERB
cana-4953	24	3	detnas	detna	NOUN
cana-4953	24	4	,	,	PUNCT
cana-4953	24	5	an	an	DET
cana-4953	24	6	object	object	NOUN
cana-4953	24	7	authentication	authentication	NOUN
cana-4953	24	8	method	method	NOUN
cana-4953	24	9	based	base	VERB
cana-4953	24	10	on	on	ADP
cana-4953	24	11	neural	neural	ADJ
cana-4953	24	12	architecture	architecture	NOUN
cana-4953	24	13	search	search	NOUN
cana-4953	24	14	,	,	PUNCT
cana-4953	24	15	to	to	PART
cana-4953	24	16	identify	identify	VERB
cana-4953	24	17	the	the	DET
cana-4953	24	18	most	most	ADV
cana-4953	24	19	effective	effective	ADJ
cana-4953	24	20	backbone	backbone	NOUN
cana-4953	24	21	.	.	PUNCT
cana-4953	25	1	a	a	DET
cana-4953	25	2	fire	fire	NOUN
cana-4953	25	3	dataset	dataset	NOUN
cana-4953	25	4	comprising	comprise	VERB
cana-4953	25	5	approximately	approximately	ADV
cana-4953	25	6	400,000	400,000	NUM
cana-4953	25	7	images	image	NOUN
cana-4953	25	8	was	be	AUX
cana-4953	25	9	employed	employ	VERB
cana-4953	25	10	for	for	ADP
cana-4953	25	11	training	training	NOUN
cana-4953	25	12	and	and	CCONJ
cana-4953	25	13	testing	testing	NOUN
cana-4953	25	14	.	.	PUNCT
cana-4953	26	1	a	a	DET
cana-4953	26	2	bayesian	bayesian	ADJ
cana-4953	26	3	neural	neural	ADJ
cana-4953	26	4	network	network	NOUN
cana-4953	26	5	(	(	PUNCT
cana-4953	26	6	bnn	bnn	PROPN
cana-4953	26	7	)	)	PUNCT
cana-4953	26	8	was	be	AUX
cana-4953	26	9	used	use	VERB
cana-4953	26	10	to	to	PART
cana-4953	26	11	estimate	estimate	VERB
cana-4953	26	12	the	the	DET
cana-4953	26	13	affected	affected	ADJ
cana-4953	26	14	area	area	NOUN
cana-4953	26	15	.	.	PUNCT
cana-4953	27	1	the	the	DET
cana-4953	27	2	system	system	NOUN
cana-4953	27	3	uses	use	VERB
cana-4953	27	4	ai	ai	VERB
cana-4953	27	5	-	-	PUNCT
cana-4953	27	6	enhanced	enhance	VERB
cana-4953	27	7	cctv	cctv	NOUN
cana-4953	27	8	cameras	camera	NOUN
cana-4953	27	9	to	to	PART
cana-4953	27	10	transmit	transmit	VERB
cana-4953	27	11	real	real	ADJ
cana-4953	27	12	-	-	PUNCT
cana-4953	27	13	time	time	NOUN
cana-4953	27	14	data	datum	NOUN
cana-4953	27	15	to	to	ADP
cana-4953	27	16	a	a	DET
cana-4953	27	17	central	central	ADJ
cana-4953	27	18	server	server	NOUN
cana-4953	27	19	upon	upon	SCONJ
cana-4953	27	20	detecting	detect	VERB
cana-4953	27	21	smoke	smoke	NOUN
cana-4953	27	22	or	or	CCONJ
cana-4953	27	23	flames	flame	NOUN
cana-4953	27	24	in	in	ADP
cana-4953	27	25	forested	forested	ADJ
cana-4953	27	26	areas	area	NOUN
cana-4953	27	27	.	.	PUNCT
cana-4953	28	1	subsequently	subsequently	ADV
cana-4953	28	2	,	,	PUNCT
cana-4953	28	3	the	the	DET
cana-4953	28	4	server	server	NOUN
cana-4953	28	5	dispatches	dispatch	NOUN
cana-4953	28	6	a	a	DET
cana-4953	28	7	uav	uav	PROPN
cana-4953	28	8	to	to	PART
cana-4953	28	9	examine	examine	VERB
cana-4953	28	10	the	the	DET
cana-4953	28	11	fire	fire	NOUN
cana-4953	28	12	site	site	NOUN
cana-4953	28	13	for	for	ADP
cana-4953	28	14	damage	damage	NOUN
cana-4953	28	15	.	.	PUNCT
cana-4953	29	1	a	a	DET
cana-4953	29	2	regression	regression	NOUN
cana-4953	29	3	model	model	NOUN
cana-4953	29	4	gathers	gather	VERB
cana-4953	29	5	data	datum	NOUN
cana-4953	29	6	from	from	ADP
cana-4953	29	7	the	the	DET
cana-4953	29	8	burning	burn	VERB
cana-4953	29	9	area	area	NOUN
cana-4953	29	10	to	to	PART
cana-4953	29	11	enable	enable	VERB
cana-4953	29	12	seamless	seamless	ADJ
cana-4953	29	13	monitoring	monitoring	NOUN
cana-4953	29	14	and	and	CCONJ
cana-4953	29	15	visualization	visualization	NOUN
cana-4953	29	16	of	of	ADP
cana-4953	29	17	the	the	DET
cana-4953	29	18	fire	fire	NOUN
cana-4953	29	19	.	.	PUNCT
cana-4953	30	1	the	the	DET
cana-4953	30	2	total	total	ADJ
cana-4953	30	3	damaged	damage	VERB
cana-4953	30	4	area	area	NOUN
cana-4953	30	5	can	can	AUX
cana-4953	30	6	be	be	AUX
cana-4953	30	7	determined	determine	VERB
cana-4953	30	8	using	use	VERB
cana-4953	30	9	a	a	DET
cana-4953	30	10	uav	uav	PROPN
cana-4953	30	11	and	and	CCONJ
cana-4953	30	12	segmentation	segmentation	NOUN
cana-4953	30	13	techniques	technique	NOUN
cana-4953	30	14	.	.	PUNCT
cana-4953	31	1	the	the	DET
cana-4953	31	2	main	main	ADJ
cana-4953	31	3	drawback	drawback	NOUN
cana-4953	31	4	of	of	ADP
cana-4953	31	5	this	this	DET
cana-4953	31	6	response	response	NOUN
cana-4953	31	7	system	system	NOUN
cana-4953	31	8	is	be	AUX
cana-4953	31	9	the	the	DET
cana-4953	31	10	difficulty	difficulty	NOUN
cana-4953	31	11	in	in	ADP
cana-4953	31	12	maintaining	maintain	VERB
cana-4953	31	13	continuous	continuous	ADJ
cana-4953	31	14	real	real	ADJ
cana-4953	31	15	-	-	PUNCT
cana-4953	31	16	time	time	NOUN
cana-4953	31	17	monitoring	monitoring	NOUN
cana-4953	31	18	.	.	PUNCT
cana-4953	32	1	k	k	PROPN
cana-4953	32	2	gayathri	gayathri	PROPN
cana-4953	32	3	et	et	PROPN
cana-4953	32	4	al	al	PROPN
cana-4953	33	1	[	[	X
cana-4953	33	2	2]introduce	2]introduce	NUM
cana-4953	33	3	a	a	DET
cana-4953	33	4	convolutional	convolutional	ADJ
cana-4953	33	5	neural	neural	ADJ
cana-4953	33	6	network	network	NOUN
cana-4953	33	7	(	(	PUNCT
cana-4953	33	8	cnn	cnn	PROPN
cana-4953	33	9	)	)	PUNCT
cana-4953	33	10	model	model	NOUN
cana-4953	33	11	for	for	ADP
cana-4953	33	12	detecting	detect	VERB
cana-4953	33	13	forest	forest	NOUN
cana-4953	33	14	fires	fire	NOUN
cana-4953	33	15	.	.	PUNCT
cana-4953	34	1	according	accord	VERB
cana-4953	34	2	to	to	ADP
cana-4953	34	3	them	they	PRON
cana-4953	34	4	,	,	PUNCT
cana-4953	34	5	given	give	VERB
cana-4953	34	6	the	the	DET
cana-4953	34	7	substantial	substantial	ADJ
cana-4953	34	8	environmental	environmental	ADJ
cana-4953	34	9	and	and	CCONJ
cana-4953	34	10	economic	economic	ADJ
cana-4953	34	11	impact	impact	NOUN
cana-4953	34	12	of	of	ADP
cana-4953	34	13	such	such	ADJ
cana-4953	34	14	fires	fire	NOUN
cana-4953	34	15	,	,	PUNCT
cana-4953	34	16	early	early	ADJ
cana-4953	34	17	detection	detection	NOUN
cana-4953	34	18	is	be	AUX
cana-4953	34	19	essential	essential	ADJ
cana-4953	34	20	.	.	PUNCT
cana-4953	35	1	the	the	DET
cana-4953	35	2	proposed	propose	VERB
cana-4953	35	3	system	system	NOUN
cana-4953	35	4	categorizes	categorize	VERB
cana-4953	35	5	images	image	NOUN
cana-4953	35	6	into	into	ADP
cana-4953	35	7	two	two	NUM
cana-4953	35	8	groups	group	NOUN
cana-4953	35	9	:	:	PUNCT
cana-4953	35	10	those	those	PRON
cana-4953	35	11	containing	contain	VERB
cana-4953	35	12	fires	fire	NOUN
cana-4953	35	13	and	and	CCONJ
cana-4953	35	14	those	those	PRON
cana-4953	35	15	without	without	ADP
cana-4953	35	16	.	.	PUNCT
cana-4953	36	1	it	it	PRON
cana-4953	36	2	demonstrates	demonstrate	VERB
cana-4953	36	3	superior	superior	ADJ
cana-4953	36	4	accuracy	accuracy	NOUN
cana-4953	36	5	compared	compare	VERB
cana-4953	36	6	to	to	ADP
cana-4953	36	7	conventional	conventional	ADJ
cana-4953	36	8	methods	method	NOUN
cana-4953	36	9	like	like	ADP
cana-4953	36	10	yolo	yolo	NOUN
cana-4953	36	11	and	and	CCONJ
cana-4953	36	12	ssd	ssd	PROPN
cana-4953	36	13	.	.	PUNCT
cana-4953	37	1	the	the	DET
cana-4953	37	2	research	research	NOUN
cana-4953	37	3	utilizes	utilize	VERB
cana-4953	37	4	a	a	DET
cana-4953	37	5	balanced	balanced	ADJ
cana-4953	37	6	dataset	dataset	NOUN
cana-4953	37	7	comprising	comprise	VERB
cana-4953	37	8	1,900	1,900	NUM
cana-4953	37	9	images	image	NOUN
cana-4953	37	10	,	,	PUNCT
cana-4953	37	11	evenly	evenly	ADV
cana-4953	37	12	split	split	VERB
cana-4953	37	13	between	between	ADP
cana-4953	37	14	fire	fire	NOUN
cana-4953	37	15	and	and	CCONJ
cana-4953	37	16	non	non	ADJ
cana-4953	37	17	-	-	ADJ
cana-4953	37	18	fire	fire	ADJ
cana-4953	37	19	scenarios	scenario	NOUN
cana-4953	37	20	.	.	PUNCT
cana-4953	38	1	the	the	DET
cana-4953	38	2	model	model	NOUN
cana-4953	38	3	is	be	AUX
cana-4953	38	4	developed	develop	VERB
cana-4953	38	5	using	use	VERB
cana-4953	38	6	deep	deep	ADJ
cana-4953	38	7	learning	learning	NOUN
cana-4953	38	8	techniques	technique	NOUN
cana-4953	38	9	,	,	PUNCT
cana-4953	38	10	implemented	implement	VERB
cana-4953	38	11	through	through	ADP
cana-4953	38	12	keras	keras	PROPN
cana-4953	38	13	and	and	CCONJ
cana-4953	38	14	tensorflow	tensorflow	NOUN
cana-4953	38	15	frameworks	framework	NOUN
cana-4953	38	16	.	.	PUNCT
cana-4953	39	1	its	its	PRON
cana-4953	39	2	architecture	architecture	NOUN
cana-4953	39	3	incorporates	incorporate	VERB
cana-4953	39	4	convolutional	convolutional	ADJ
cana-4953	39	5	layers	layer	NOUN
cana-4953	39	6	,	,	PUNCT
cana-4953	39	7	global	global	ADJ
cana-4953	39	8	average	average	ADJ
cana-4953	39	9	pooling	pooling	NOUN
cana-4953	39	10	,	,	PUNCT
cana-4953	39	11	dense	dense	ADJ
cana-4953	39	12	layers	layer	NOUN
cana-4953	39	13	,	,	PUNCT
cana-4953	39	14	and	and	CCONJ
cana-4953	39	15	dropout	dropout	NOUN
cana-4953	39	16	mechanisms	mechanism	NOUN
cana-4953	39	17	to	to	PART
cana-4953	39	18	enhance	enhance	VERB
cana-4953	39	19	performance	performance	NOUN
cana-4953	39	20	.	.	PUNCT
cana-4953	40	1	the	the	DET
cana-4953	40	2	cnn	cnn	PROPN
cana-4953	40	3	-	-	PUNCT
cana-4953	40	4	based	base	VERB
cana-4953	40	5	approach	approach	NOUN
cana-4953	40	6	outperforms	outperform	VERB
cana-4953	40	7	alternative	alternative	ADJ
cana-4953	40	8	methods	method	NOUN
cana-4953	40	9	,	,	PUNCT
cana-4953	40	10	exhibiting	exhibit	VERB
cana-4953	40	11	high	high	ADJ
cana-4953	40	12	precision	precision	NOUN
cana-4953	40	13	in	in	ADP
cana-4953	40	14	fire	fire	NOUN
cana-4953	40	15	detection	detection	NOUN
cana-4953	40	16	.	.	PUNCT
cana-4953	41	1	the	the	DET
cana-4953	41	2	researchers	researcher	NOUN
cana-4953	41	3	conclude	conclude	VERB
cana-4953	41	4	that	that	SCONJ
cana-4953	41	5	cnn	cnn	PROPN
cana-4953	41	6	models	model	NOUN
cana-4953	41	7	effectively	effectively	ADV
cana-4953	41	8	identify	identify	VERB
cana-4953	41	9	fires	fire	NOUN
cana-4953	41	10	in	in	ADP
cana-4953	41	11	images	image	NOUN
cana-4953	41	12	and	and	CCONJ
cana-4953	41	13	suggest	suggest	VERB
cana-4953	41	14	potential	potential	ADJ
cana-4953	41	15	future	future	ADJ
cana-4953	41	16	enhancements	enhancement	NOUN
cana-4953	41	17	for	for	ADP
cana-4953	41	18	smoke	smoke	NOUN
cana-4953	41	19	detection	detection	NOUN
cana-4953	41	20	.	.	PUNCT
cana-4953	42	1	this	this	DET
cana-4953	42	2	technology	technology	NOUN
cana-4953	42	3	could	could	AUX
cana-4953	42	4	be	be	AUX
cana-4953	42	5	incorporated	incorporate	VERB
cana-4953	42	6	into	into	ADP
cana-4953	42	7	existing	exist	VERB
cana-4953	42	8	surveillance	surveillance	NOUN
cana-4953	42	9	systems	system	NOUN
cana-4953	42	10	for	for	ADP
cana-4953	42	11	continuous	continuous	ADJ
cana-4953	42	12	monitoring	monitoring	NOUN
cana-4953	42	13	.	.	PUNCT
cana-4953	43	1	m.	m.	NOUN
cana-4953	43	2	rahul	rahul	PROPN
cana-4953	43	3	et	et	PROPN
cana-4953	44	1	al	al	PROPN
cana-4953	45	1	[	[	X
cana-4953	45	2	3]proposed	3]proposed	NUM
cana-4953	45	3	a	a	DET
cana-4953	45	4	model	model	NOUN
cana-4953	45	5	utilizing	utilize	VERB
cana-4953	45	6	deep	deep	ADJ
cana-4953	45	7	learning	learning	NOUN
cana-4953	45	8	techniques	technique	NOUN
cana-4953	45	9	for	for	ADP
cana-4953	45	10	early	early	ADJ
cana-4953	45	11	forest	forest	NOUN
cana-4953	45	12	fire	fire	NOUN
cana-4953	45	13	detection	detection	NOUN
cana-4953	45	14	.	.	PUNCT
cana-4953	46	1	the	the	DET
cana-4953	46	2	study	study	NOUN
cana-4953	46	3	proposed	propose	VERB
cana-4953	46	4	a	a	DET
cana-4953	46	5	convolutional	convolutional	ADJ
cana-4953	46	6	neural	neural	ADJ
cana-4953	46	7	network	network	NOUN
cana-4953	46	8	to	to	PART
cana-4953	46	9	swiftly	swiftly	ADV
cana-4953	46	10	identify	identify	VERB
cana-4953	46	11	forest	forest	NOUN
cana-4953	46	12	fires	fire	NOUN
cana-4953	46	13	through	through	ADP
cana-4953	46	14	image	image	NOUN
cana-4953	46	15	recognition	recognition	NOUN
cana-4953	46	16	.	.	PUNCT
cana-4953	47	1	to	to	PART
cana-4953	47	2	address	address	VERB
cana-4953	47	3	this	this	DET
cana-4953	47	4	challenge	challenge	NOUN
cana-4953	47	5	,	,	PUNCT
cana-4953	47	6	the	the	DET
cana-4953	47	7	researchers	researcher	NOUN
cana-4953	47	8	employed	employ	VERB
cana-4953	47	9	a	a	DET
cana-4953	47	10	transfer	transfer	NOUN
cana-4953	47	11	learning	learning	NOUN
cana-4953	47	12	approach	approach	NOUN
cana-4953	47	13	.	.	PUNCT
cana-4953	48	1	the	the	DET
cana-4953	48	2	system	system	NOUN
cana-4953	48	3	processes	process	VERB
cana-4953	48	4	image	image	NOUN
cana-4953	48	5	data	datum	NOUN
cana-4953	48	6	and	and	CCONJ
cana-4953	48	7	applies	apply	VERB
cana-4953	48	8	augmentation	augmentation	NOUN
cana-4953	48	9	methods	method	NOUN
cana-4953	48	10	such	such	ADJ
cana-4953	48	11	as	as	ADP
cana-4953	48	12	shearing	shear	VERB
cana-4953	48	13	to	to	PART
cana-4953	48	14	generate	generate	VERB
cana-4953	48	15	additional	additional	ADJ
cana-4953	48	16	images	image	NOUN
cana-4953	48	17	.	.	PUNCT
cana-4953	49	1	the	the	DET
cana-4953	49	2	study	study	NOUN
cana-4953	49	3	utilizes	utilize	VERB
cana-4953	49	4	models	model	NOUN
cana-4953	49	5	like	like	ADP
cana-4953	49	6	resnet50	resnet50	NOUN
cana-4953	49	7	and	and	CCONJ
cana-4953	49	8	vgc16	vgc16	NOUN
cana-4953	49	9	for	for	ADP
cana-4953	49	10	image	image	NOUN
cana-4953	49	11	classification	classification	NOUN
cana-4953	49	12	.	.	PUNCT
cana-4953	50	1	initially	initially	ADV
cana-4953	50	2	,	,	PUNCT
cana-4953	50	3	the	the	DET
cana-4953	50	4	model	model	NOUN
cana-4953	50	5	divides	divide	VERB
cana-4953	50	6	the	the	DET
cana-4953	50	7	data	datum	NOUN
cana-4953	50	8	into	into	ADP
cana-4953	50	9	80	80	NUM
cana-4953	50	10	%	%	NOUN
cana-4953	50	11	training	training	NOUN
cana-4953	50	12	and	and	CCONJ
cana-4953	50	13	20	20	NUM
cana-4953	50	14	%	%	NOUN
cana-4953	50	15	testing	testing	NOUN
cana-4953	50	16	sets	set	NOUN
cana-4953	50	17	.	.	PUNCT
cana-4953	51	1	the	the	DET
cana-4953	51	2	model	model	NOUN
cana-4953	51	3	is	be	AUX
cana-4953	51	4	then	then	ADV
cana-4953	51	5	trained	train	VERB
cana-4953	51	6	to	to	PART
cana-4953	51	7	categorize	categorize	VERB
cana-4953	51	8	data	datum	NOUN
cana-4953	51	9	into	into	ADP
cana-4953	51	10	fire	fire	NOUN
cana-4953	51	11	and	and	CCONJ
cana-4953	51	12	non	non	ADJ
cana-4953	51	13	-	-	ADJ
cana-4953	51	14	fire	fire	ADJ
cana-4953	51	15	classes	class	NOUN
cana-4953	51	16	.	.	PUNCT
cana-4953	52	1	the	the	DET
cana-4953	52	2	proposed	propose	VERB
cana-4953	52	3	model	model	NOUN
cana-4953	52	4	employs	employ	VERB
cana-4953	52	5	resnet50	resnet50	NOUN
cana-4953	52	6	and	and	CCONJ
cana-4953	52	7	transfer	transfer	VERB
cana-4953	52	8	learning	learning	NOUN
cana-4953	52	9	for	for	ADP
cana-4953	52	10	training	training	NOUN
cana-4953	52	11	.	.	PUNCT
cana-4953	53	1	a	a	DET
cana-4953	53	2	key	key	ADJ
cana-4953	53	3	limitation	limitation	NOUN
cana-4953	53	4	of	of	ADP
cana-4953	53	5	this	this	DET
cana-4953	53	6	research	research	NOUN
cana-4953	53	7	is	be	AUX
cana-4953	53	8	that	that	SCONJ
cana-4953	53	9	not	not	PART
cana-4953	53	10	all	all	DET
cana-4953	53	11	images	image	NOUN
cana-4953	53	12	are	be	AUX
cana-4953	53	13	successfully	successfully	ADV
cana-4953	53	14	classified	classify	VERB
cana-4953	53	15	as	as	ADP
cana-4953	53	16	either	either	DET
cana-4953	53	17	fire	fire	NOUN
cana-4953	53	18	or	or	CCONJ
cana-4953	53	19	non	non	ADJ
cana-4953	53	20	-	-	NOUN
cana-4953	53	21	fire	fire	NOUN
cana-4953	53	22	.	.	PUNCT
cana-4953	54	1	mohnish	mohnish	PROPN
cana-4953	54	2	et	et	PROPN
cana-4953	54	3	al	al	PROPN
cana-4953	55	1	[	[	X
cana-4953	55	2	4	4	X
cana-4953	55	3	]	]	PUNCT
cana-4953	55	4	in	in	ADP
cana-4953	55	5	their	their	PRON
cana-4953	55	6	paper	paper	NOUN
cana-4953	55	7	introduce	introduce	VERB
cana-4953	55	8	a	a	DET
cana-4953	55	9	convolutional	convolutional	ADJ
cana-4953	55	10	neural	neural	ADJ
cana-4953	55	11	network	network	NOUN
cana-4953	55	12	(	(	PUNCT
cana-4953	55	13	cnn	cnn	PROPN
cana-4953	55	14	)	)	PUNCT
cana-4953	55	15	model	model	NOUN
cana-4953	55	16	based	base	VERB
cana-4953	55	17	on	on	ADP
cana-4953	55	18	deep	deep	ADJ
cana-4953	55	19	learning	learning	NOUN
cana-4953	55	20	for	for	ADP
cana-4953	55	21	forest	forest	NOUN
cana-4953	55	22	fire	fire	NOUN
cana-4953	55	23	detection	detection	NOUN
cana-4953	55	24	.	.	PUNCT
cana-4953	56	1	the	the	DET
cana-4953	56	2	methodology	methodology	NOUN
cana-4953	56	3	employs	employ	VERB
cana-4953	56	4	three	three	NUM
cana-4953	56	5	key	key	ADJ
cana-4953	56	6	techniques	technique	NOUN
cana-4953	56	7	:	:	PUNCT
cana-4953	56	8	image	image	NOUN
cana-4953	56	9	communications	communication	NOUN
cana-4953	56	10	on	on	ADP
cana-4953	56	11	applied	apply	VERB
cana-4953	56	12	nonlinear	nonlinear	ADJ
cana-4953	56	13	analysis	analysis	NOUN
cana-4953	56	14	issn	issn	NOUN
cana-4953	56	15	:	:	PUNCT
cana-4953	56	16	1074	1074	NUM
cana-4953	56	17	-	-	PUNCT
cana-4953	56	18	133x	133x	NUM
cana-4953	56	19	vol	vol	VERB
cana-4953	56	20	32	32	NUM
cana-4953	56	21	no	no	NOUN
cana-4953	56	22	.	.	PUNCT
cana-4953	57	1	10s	10	NOUN
cana-4953	57	2	(	(	PUNCT
cana-4953	57	3	2025	2025	NUM
cana-4953	57	4	)	)	PUNCT
cana-4953	57	5	1024	1024	NUM
cana-4953	57	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	57	7	collection	collection	NOUN
cana-4953	57	8	,	,	PUNCT
cana-4953	57	9	pre	pre	ADJ
cana-4953	57	10	-	-	ADJ
cana-4953	57	11	processing	processing	ADJ
cana-4953	57	12	,	,	PUNCT
cana-4953	57	13	and	and	CCONJ
cana-4953	57	14	image	image	NOUN
cana-4953	57	15	classification	classification	NOUN
cana-4953	57	16	.	.	PUNCT
cana-4953	58	1	the	the	DET
cana-4953	58	2	process	process	NOUN
cana-4953	58	3	begins	begin	VERB
cana-4953	58	4	with	with	ADP
cana-4953	58	5	pre	pre	ADJ
cana-4953	58	6	-	-	ADJ
cana-4953	58	7	processing	process	VERB
cana-4953	58	8	the	the	DET
cana-4953	58	9	dataset	dataset	NOUN
cana-4953	58	10	images	image	NOUN
cana-4953	58	11	before	before	ADP
cana-4953	58	12	inputting	inputte	VERB
cana-4953	58	13	them	they	PRON
cana-4953	58	14	into	into	ADP
cana-4953	58	15	the	the	DET
cana-4953	58	16	cnn	cnn	PROPN
cana-4953	58	17	for	for	ADP
cana-4953	58	18	feature	feature	NOUN
cana-4953	58	19	extraction	extraction	NOUN
cana-4953	58	20	and	and	CCONJ
cana-4953	58	21	detection	detection	NOUN
cana-4953	58	22	.	.	PUNCT
cana-4953	59	1	additionally	additionally	ADV
cana-4953	59	2	,	,	PUNCT
cana-4953	59	3	the	the	DET
cana-4953	59	4	research	research	NOUN
cana-4953	59	5	implements	implement	VERB
cana-4953	59	6	a	a	DET
cana-4953	59	7	hardware	hardware	NOUN
cana-4953	59	8	setup	setup	NOUN
cana-4953	59	9	utilizing	utilize	VERB
cana-4953	59	10	raspberry	raspberry	NOUN
cana-4953	59	11	pi	pi	NOUN
cana-4953	59	12	,	,	PUNCT
cana-4953	59	13	which	which	PRON
cana-4953	59	14	sends	send	VERB
cana-4953	59	15	fire	fire	NOUN
cana-4953	59	16	detection	detection	NOUN
cana-4953	59	17	alerts	alert	NOUN
cana-4953	59	18	via	via	ADP
cana-4953	59	19	email	email	NOUN
cana-4953	59	20	,	,	PUNCT
cana-4953	59	21	buzzer	buzzer	NOUN
cana-4953	59	22	,	,	PUNCT
cana-4953	59	23	and	and	CCONJ
cana-4953	59	24	lcd	lcd	NOUN
cana-4953	59	25	display	display	NOUN
cana-4953	59	26	to	to	ADP
cana-4953	59	27	relevant	relevant	ADJ
cana-4953	59	28	authorities	authority	NOUN
cana-4953	59	29	.	.	PUNCT
cana-4953	60	1	the	the	DET
cana-4953	60	2	model	model	NOUN
cana-4953	60	3	achieves	achieve	VERB
cana-4953	60	4	detection	detection	NOUN
cana-4953	60	5	accuracies	accuracy	NOUN
cana-4953	60	6	of	of	ADP
cana-4953	60	7	93	93	NUM
cana-4953	60	8	%	%	NOUN
cana-4953	60	9	and	and	CCONJ
cana-4953	60	10	92	92	NUM
cana-4953	60	11	%	%	NOUN
cana-4953	60	12	on	on	ADP
cana-4953	60	13	the	the	DET
cana-4953	60	14	training	training	NOUN
cana-4953	60	15	and	and	CCONJ
cana-4953	60	16	testing	testing	NOUN
cana-4953	60	17	datasets	dataset	NOUN
cana-4953	60	18	,	,	PUNCT
cana-4953	60	19	respectively	respectively	ADV
cana-4953	60	20	.	.	PUNCT
cana-4953	61	1	d.	d.	PROPN
cana-4953	61	2	georgiev	georgiev	PROPN
cana-4953	61	3	et	et	PROPN
cana-4953	61	4	al.[5	al.[5	PROPN
cana-4953	61	5	]	]	PUNCT
cana-4953	61	6	proposed	propose	VERB
cana-4953	61	7	a	a	DET
cana-4953	61	8	system	system	NOUN
cana-4953	61	9	for	for	ADP
cana-4953	61	10	early	early	ADJ
cana-4953	61	11	forest	forest	NOUN
cana-4953	61	12	fire	fire	NOUN
cana-4953	61	13	detection	detection	NOUN
cana-4953	61	14	utilizing	utilize	VERB
cana-4953	61	15	convolution	convolution	NOUN
cana-4953	61	16	neural	neural	ADJ
cana-4953	61	17	network	network	NOUN
cana-4953	61	18	and	and	CCONJ
cana-4953	61	19	uav	uav	PROPN
cana-4953	61	20	imagery	imagery	NOUN
cana-4953	61	21	.	.	PUNCT
cana-4953	62	1	this	this	DET
cana-4953	62	2	research	research	NOUN
cana-4953	62	3	presented	present	VERB
cana-4953	62	4	a	a	DET
cana-4953	62	5	method	method	NOUN
cana-4953	62	6	for	for	ADP
cana-4953	62	7	an	an	DET
cana-4953	62	8	autonomous	autonomous	ADJ
cana-4953	62	9	,	,	PUNCT
cana-4953	62	10	highly	highly	ADV
cana-4953	62	11	reliable	reliable	ADJ
cana-4953	62	12	early	early	ADJ
cana-4953	62	13	fire	fire	NOUN
cana-4953	62	14	detection	detection	NOUN
cana-4953	62	15	system	system	NOUN
cana-4953	62	16	that	that	PRON
cana-4953	62	17	eliminates	eliminate	VERB
cana-4953	62	18	the	the	DET
cana-4953	62	19	need	need	NOUN
cana-4953	62	20	for	for	ADP
cana-4953	62	21	maintenance	maintenance	NOUN
cana-4953	62	22	or	or	CCONJ
cana-4953	62	23	human	human	ADJ
cana-4953	62	24	intervention	intervention	NOUN
cana-4953	62	25	.	.	PUNCT
cana-4953	63	1	rather	rather	ADV
cana-4953	63	2	than	than	ADP
cana-4953	63	3	relying	rely	VERB
cana-4953	63	4	on	on	ADP
cana-4953	63	5	traditional	traditional	ADJ
cana-4953	63	6	methods	method	NOUN
cana-4953	63	7	,	,	PUNCT
cana-4953	63	8	the	the	DET
cana-4953	63	9	system	system	NOUN
cana-4953	63	10	employs	employ	VERB
cana-4953	63	11	a	a	DET
cana-4953	63	12	real	real	ADJ
cana-4953	63	13	-	-	PUNCT
cana-4953	63	14	time	time	NOUN
cana-4953	63	15	video	video	NOUN
cana-4953	63	16	feed	feed	NOUN
cana-4953	63	17	from	from	ADP
cana-4953	63	18	an	an	DET
cana-4953	63	19	unmanned	unmanned	ADJ
cana-4953	63	20	aerial	aerial	ADJ
cana-4953	63	21	vehicle	vehicle	NOUN
cana-4953	63	22	(	(	PUNCT
cana-4953	63	23	uav	uav	PROPN
cana-4953	63	24	)	)	PUNCT
cana-4953	63	25	that	that	PRON
cana-4953	63	26	surveys	survey	VERB
cana-4953	63	27	the	the	DET
cana-4953	63	28	high	high	ADJ
cana-4953	63	29	-	-	PUNCT
cana-4953	63	30	risk	risk	NOUN
cana-4953	63	31	area	area	NOUN
cana-4953	63	32	,	,	PUNCT
cana-4953	63	33	complementing	complement	VERB
cana-4953	63	34	lookout	lookout	ADJ
cana-4953	63	35	towers	tower	NOUN
cana-4953	63	36	and	and	CCONJ
cana-4953	63	37	satellite	satellite	NOUN
cana-4953	63	38	monitoring	monitoring	NOUN
cana-4953	63	39	by	by	ADP
cana-4953	63	40	providing	provide	VERB
cana-4953	63	41	enhanced	enhanced	ADJ
cana-4953	63	42	visual	visual	ADJ
cana-4953	63	43	coverage	coverage	NOUN
cana-4953	63	44	of	of	ADP
cana-4953	63	45	the	the	DET
cana-4953	63	46	monitored	monitored	ADJ
cana-4953	63	47	region	region	NOUN
cana-4953	63	48	.	.	PUNCT
cana-4953	64	1	both	both	DET
cana-4953	64	2	optical	optical	ADJ
cana-4953	64	3	and	and	CCONJ
cana-4953	64	4	thermal	thermal	ADJ
cana-4953	64	5	cameras	camera	NOUN
cana-4953	64	6	on	on	ADP
cana-4953	64	7	the	the	DET
cana-4953	64	8	uav	uav	PROPN
cana-4953	64	9	are	be	AUX
cana-4953	64	10	utilized	utilize	VERB
cana-4953	64	11	to	to	PART
cana-4953	64	12	enhance	enhance	VERB
cana-4953	64	13	predictions	prediction	NOUN
cana-4953	64	14	of	of	ADP
cana-4953	64	15	potential	potential	ADJ
cana-4953	64	16	fire	fire	NOUN
cana-4953	64	17	occurrences	occurrence	NOUN
cana-4953	64	18	.	.	PUNCT
cana-4953	65	1	a	a	DET
cana-4953	65	2	web	web	NOUN
cana-4953	65	3	-	-	PUNCT
cana-4953	65	4	based	base	VERB
cana-4953	65	5	interface	interface	NOUN
cana-4953	65	6	was	be	AUX
cana-4953	65	7	developed	develop	VERB
cana-4953	65	8	using	use	VERB
cana-4953	65	9	node	node	NOUN
cana-4953	65	10	-	-	PUNCT
cana-4953	65	11	red	red	ADJ
cana-4953	65	12	technology	technology	NOUN
cana-4953	65	13	to	to	PART
cana-4953	65	14	display	display	VERB
cana-4953	65	15	the	the	DET
cana-4953	65	16	collected	collect	VERB
cana-4953	65	17	data	datum	NOUN
cana-4953	65	18	in	in	ADP
cana-4953	65	19	real	real	ADJ
cana-4953	65	20	-	-	PUNCT
cana-4953	65	21	time	time	NOUN
cana-4953	65	22	and	and	CCONJ
cana-4953	65	23	notify	notify	VERB
cana-4953	65	24	relevant	relevant	ADJ
cana-4953	65	25	parties	party	NOUN
cana-4953	65	26	.	.	PUNCT
cana-4953	66	1	the	the	DET
cana-4953	66	2	primary	primary	ADJ
cana-4953	66	3	drawback	drawback	NOUN
cana-4953	66	4	of	of	ADP
cana-4953	66	5	this	this	DET
cana-4953	66	6	project	project	NOUN
cana-4953	66	7	is	be	AUX
cana-4953	66	8	its	its	PRON
cana-4953	66	9	reliance	reliance	NOUN
cana-4953	66	10	on	on	ADP
cana-4953	66	11	two	two	NUM
cana-4953	66	12	cameras	camera	NOUN
cana-4953	66	13	for	for	ADP
cana-4953	66	14	the	the	DET
cana-4953	66	15	final	final	ADJ
cana-4953	66	16	fire	fire	NOUN
cana-4953	66	17	determination	determination	NOUN
cana-4953	66	18	.	.	PUNCT
cana-4953	67	1	s.	s.	PROPN
cana-4953	67	2	wu	wu	PROPN
cana-4953	67	3	et	et	PROPN
cana-4953	67	4	al	al	PROPN
cana-4953	67	5	.	.	PUNCT
cana-4953	68	1	[	[	X
cana-4953	68	2	6]in	6]in	NUM
cana-4953	68	3	their	their	PRON
cana-4953	68	4	paper	paper	NOUN
cana-4953	68	5	explore	explore	VERB
cana-4953	68	6	a	a	DET
cana-4953	68	7	real	real	ADJ
cana-4953	68	8	-	-	PUNCT
cana-4953	68	9	time	time	NOUN
cana-4953	68	10	forest	forest	NOUN
cana-4953	68	11	fire	fire	NOUN
cana-4953	68	12	detection	detection	NOUN
cana-4953	68	13	system	system	NOUN
cana-4953	68	14	utilizing	utilize	VERB
cana-4953	68	15	object	object	NOUN
cana-4953	68	16	detection	detection	NOUN
cana-4953	68	17	methods	method	NOUN
cana-4953	68	18	.	.	PUNCT
cana-4953	69	1	the	the	DET
cana-4953	69	2	research	research	NOUN
cana-4953	69	3	addresses	address	VERB
cana-4953	69	4	three	three	NUM
cana-4953	69	5	primary	primary	ADJ
cana-4953	69	6	issues	issue	NOUN
cana-4953	69	7	:	:	PUNCT
cana-4953	69	8	inaccurate	inaccurate	ADJ
cana-4953	69	9	identification	identification	NOUN
cana-4953	69	10	,	,	PUNCT
cana-4953	69	11	early	early	ADJ
cana-4953	69	12	warning	warning	NOUN
cana-4953	69	13	systems	system	NOUN
cana-4953	69	14	,	,	PUNCT
cana-4953	69	15	and	and	CCONJ
cana-4953	69	16	forest	forest	NOUN
cana-4953	69	17	fire	fire	NOUN
cana-4953	69	18	detection	detection	NOUN
cana-4953	69	19	.	.	PUNCT
cana-4953	70	1	to	to	PART
cana-4953	70	2	identify	identify	VERB
cana-4953	70	3	forest	forest	NOUN
cana-4953	70	4	fires	fire	NOUN
cana-4953	70	5	,	,	PUNCT
cana-4953	70	6	the	the	DET
cana-4953	70	7	study	study	NOUN
cana-4953	70	8	employs	employ	VERB
cana-4953	70	9	established	establish	VERB
cana-4953	70	10	object	object	NOUN
cana-4953	70	11	detection	detection	NOUN
cana-4953	70	12	techniques	technique	NOUN
cana-4953	70	13	,	,	PUNCT
cana-4953	70	14	including	include	VERB
cana-4953	70	15	faster	fast	ADJ
cana-4953	70	16	r	r	NOUN
cana-4953	70	17	-	-	PUNCT
cana-4953	70	18	cnn	cnn	PROPN
cana-4953	70	19	,	,	PUNCT
cana-4953	70	20	yolo	yolo	PROPN
cana-4953	70	21	(	(	PUNCT
cana-4953	70	22	with	with	ADP
cana-4953	70	23	variants	variant	NOUN
cana-4953	70	24	tiny	tiny	ADJ
cana-4953	70	25	-	-	PUNCT
cana-4953	70	26	yolo	yolo	NOUN
cana-4953	70	27	-	-	PUNCT
cana-4953	70	28	voc	voc	NOUN
cana-4953	70	29	,	,	PUNCT
cana-4953	70	30	tiny	tiny	ADJ
cana-4953	70	31	-	-	PUNCT
cana-4953	70	32	yolo	yolo	NOUN
cana-4953	70	33	-	-	PUNCT
cana-4953	70	34	voc1	voc1	PROPN
cana-4953	70	35	,	,	PUNCT
cana-4953	70	36	yolo	yolo	PROPN
cana-4953	70	37	-	-	PUNCT
cana-4953	70	38	voc.2.0	voc.2.0	PROPN
cana-4953	70	39	,	,	PUNCT
cana-4953	70	40	and	and	CCONJ
cana-4953	70	41	yolov3	yolov3	PROPN
cana-4953	70	42	)	)	PUNCT
cana-4953	70	43	,	,	PUNCT
cana-4953	70	44	and	and	CCONJ
cana-4953	70	45	ssd	ssd	NOUN
cana-4953	70	46	.	.	PUNCT
cana-4953	71	1	the	the	DET
cana-4953	71	2	ssd	ssd	NOUN
cana-4953	71	3	demonstrates	demonstrate	VERB
cana-4953	71	4	superior	superior	ADJ
cana-4953	71	5	real	real	ADJ
cana-4953	71	6	-	-	PUNCT
cana-4953	71	7	time	time	NOUN
cana-4953	71	8	performance	performance	NOUN
cana-4953	71	9	,	,	PUNCT
cana-4953	71	10	detection	detection	NOUN
cana-4953	71	11	accuracy	accuracy	NOUN
cana-4953	71	12	,	,	PUNCT
cana-4953	71	13	and	and	CCONJ
cana-4953	71	14	advanced	advanced	ADJ
cana-4953	71	15	fire	fire	NOUN
cana-4953	71	16	detection	detection	NOUN
cana-4953	71	17	capabilities	capability	NOUN
cana-4953	71	18	.	.	PUNCT
cana-4953	72	1	the	the	DET
cana-4953	72	2	researchers	researcher	NOUN
cana-4953	72	3	modify	modify	VERB
cana-4953	72	4	the	the	DET
cana-4953	72	5	tiny	tiny	ADJ
cana-4953	72	6	-	-	PUNCT
cana-4953	72	7	yolo	yolo	ADJ
cana-4953	72	8	-	-	PUNCT
cana-4953	72	9	voc	voc	NOUN
cana-4953	72	10	structure	structure	NOUN
cana-4953	72	11	used	use	VERB
cana-4953	72	12	in	in	ADP
cana-4953	72	13	yolo	yolo	NOUN
cana-4953	72	14	and	and	CCONJ
cana-4953	72	15	propose	propose	VERB
cana-4953	72	16	a	a	DET
cana-4953	72	17	novel	novel	ADJ
cana-4953	72	18	approach	approach	NOUN
cana-4953	72	19	.	.	PUNCT
cana-4953	73	1	based	base	VERB
cana-4953	73	2	on	on	ADP
cana-4953	73	3	tinyyolo	tinyyolo	ADJ
cana-4953	73	4	-	-	PUNCT
cana-4953	73	5	voc1	voc1	NOUN
cana-4953	73	6	experiments	experiment	NOUN
cana-4953	73	7	,	,	PUNCT
cana-4953	73	8	this	this	DET
cana-4953	73	9	modification	modification	NOUN
cana-4953	73	10	enhances	enhance	VERB
cana-4953	73	11	the	the	DET
cana-4953	73	12	accuracy	accuracy	NOUN
cana-4953	73	13	of	of	ADP
cana-4953	73	14	fire	fire	NOUN
cana-4953	73	15	detection	detection	NOUN
cana-4953	73	16	.	.	PUNCT
cana-4953	74	1	this	this	DET
cana-4953	74	2	study	study	NOUN
cana-4953	74	3	significantly	significantly	ADV
cana-4953	74	4	contributes	contribute	VERB
cana-4953	74	5	to	to	ADP
cana-4953	74	6	ongoing	ongoing	ADJ
cana-4953	74	7	forest	forest	NOUN
cana-4953	74	8	surveillance	surveillance	NOUN
cana-4953	74	9	and	and	CCONJ
cana-4953	74	10	safety	safety	NOUN
cana-4953	74	11	measures	measure	NOUN
cana-4953	74	12	.	.	PUNCT
cana-4953	75	1	however	however	ADV
cana-4953	75	2	,	,	PUNCT
cana-4953	75	3	the	the	DET
cana-4953	75	4	main	main	ADJ
cana-4953	75	5	limitation	limitation	NOUN
cana-4953	75	6	of	of	ADP
cana-4953	75	7	this	this	DET
cana-4953	75	8	research	research	NOUN
cana-4953	75	9	lies	lie	VERB
cana-4953	75	10	in	in	ADP
cana-4953	75	11	the	the	DET
cana-4953	75	12	challenges	challenge	NOUN
cana-4953	75	13	associated	associate	VERB
cana-4953	75	14	with	with	ADP
cana-4953	75	15	real	real	ADJ
cana-4953	75	16	-	-	PUNCT
cana-4953	75	17	time	time	NOUN
cana-4953	75	18	monitoring	monitoring	NOUN
cana-4953	75	19	.	.	PUNCT
cana-4953	76	1	qingjie	qingjie	PROPN
cana-4953	76	2	zhang	zhang	PROPN
cana-4953	76	3	et	et	PROPN
cana-4953	76	4	al.[7	al.[7	PROPN
cana-4953	76	5	]	]	PUNCT
cana-4953	76	6	introduce	introduce	VERB
cana-4953	76	7	a	a	DET
cana-4953	76	8	novel	novel	ADJ
cana-4953	76	9	forest	forest	NOUN
cana-4953	76	10	fire	fire	NOUN
cana-4953	76	11	detection	detection	NOUN
cana-4953	76	12	technique	technique	NOUN
cana-4953	76	13	utilizing	utilize	VERB
cana-4953	76	14	deep	deep	ADJ
cana-4953	76	15	learning	learning	NOUN
cana-4953	76	16	,	,	PUNCT
cana-4953	76	17	specifically	specifically	ADV
cana-4953	76	18	a	a	DET
cana-4953	76	19	cascaded	cascade	VERB
cana-4953	76	20	convolutional	convolutional	ADJ
cana-4953	76	21	neural	neural	ADJ
cana-4953	76	22	network	network	NOUN
cana-4953	76	23	(	(	PUNCT
cana-4953	76	24	cnn	cnn	PROPN
cana-4953	76	25	)	)	PUNCT
cana-4953	76	26	.	.	PUNCT
cana-4953	77	1	our	our	PRON
cana-4953	77	2	system	system	NOUN
cana-4953	77	3	incorporates	incorporate	VERB
cana-4953	77	4	two	two	NUM
cana-4953	77	5	components	component	NOUN
cana-4953	77	6	:	:	PUNCT
cana-4953	77	7	a	a	DET
cana-4953	77	8	global	global	ADJ
cana-4953	77	9	image	image	NOUN
cana-4953	77	10	classifier	classifier	NOUN
cana-4953	77	11	and	and	CCONJ
cana-4953	77	12	a	a	DET
cana-4953	77	13	detailed	detailed	ADJ
cana-4953	77	14	patch	patch	NOUN
cana-4953	77	15	classifier	classifier	NOUN
cana-4953	77	16	.	.	PUNCT
cana-4953	78	1	the	the	DET
cana-4953	78	2	global	global	ADJ
cana-4953	78	3	classifier	classifier	NOUN
cana-4953	78	4	first	first	ADV
cana-4953	78	5	assesses	assess	VERB
cana-4953	78	6	whether	whether	SCONJ
cana-4953	78	7	an	an	DET
cana-4953	78	8	image	image	NOUN
cana-4953	78	9	contains	contain	VERB
cana-4953	78	10	fire	fire	NOUN
cana-4953	78	11	.	.	PUNCT
cana-4953	79	1	if	if	SCONJ
cana-4953	79	2	fire	fire	NOUN
cana-4953	79	3	is	be	AUX
cana-4953	79	4	detected	detect	VERB
cana-4953	79	5	,	,	PUNCT
cana-4953	79	6	the	the	DET
cana-4953	79	7	patch	patch	NOUN
cana-4953	79	8	classifier	classifier	NOUN
cana-4953	79	9	then	then	ADV
cana-4953	79	10	pinpoints	pinpoint	VERB
cana-4953	79	11	the	the	DET
cana-4953	79	12	exact	exact	ADJ
cana-4953	79	13	fire	fire	NOUN
cana-4953	79	14	locations	location	NOUN
cana-4953	79	15	.	.	PUNCT
cana-4953	80	1	our	our	PRON
cana-4953	80	2	fire	fire	NOUN
cana-4953	80	3	patch	patch	NOUN
cana-4953	80	4	detector	detector	NOUN
cana-4953	80	5	demonstrates	demonstrate	VERB
cana-4953	80	6	97	97	NUM
cana-4953	80	7	%	%	NOUN
cana-4953	80	8	accuracy	accuracy	NOUN
cana-4953	80	9	on	on	ADP
cana-4953	80	10	the	the	DET
cana-4953	80	11	training	training	NOUN
cana-4953	80	12	set	set	NOUN
cana-4953	80	13	and	and	CCONJ
cana-4953	80	14	90	90	NUM
cana-4953	80	15	%	%	NOUN
cana-4953	80	16	on	on	ADP
cana-4953	80	17	the	the	DET
cana-4953	80	18	test	test	NOUN
cana-4953	80	19	set	set	VERB
cana-4953	80	20	.	.	PUNCT
cana-4953	81	1	to	to	PART
cana-4953	81	2	advance	advance	VERB
cana-4953	81	3	fire	fire	NOUN
cana-4953	81	4	detection	detection	NOUN
cana-4953	81	5	research	research	NOUN
cana-4953	81	6	,	,	PUNCT
cana-4953	81	7	they	they	PRON
cana-4953	81	8	present	present	VERB
cana-4953	81	9	a	a	DET
cana-4953	81	10	new	new	ADJ
cana-4953	81	11	benchmark	benchmark	NOUN
cana-4953	81	12	dataset	dataset	NOUN
cana-4953	81	13	featuring	feature	VERB
cana-4953	81	14	patch	patch	ADJ
cana-4953	81	15	-	-	PUNCT
cana-4953	81	16	level	level	NOUN
cana-4953	81	17	annotations	annotation	NOUN
cana-4953	81	18	,	,	PUNCT
cana-4953	81	19	which	which	PRON
cana-4953	81	20	we	we	PRON
cana-4953	81	21	believe	believe	VERB
cana-4953	81	22	is	be	AUX
cana-4953	81	23	the	the	DET
cana-4953	81	24	first	first	ADJ
cana-4953	81	25	of	of	ADP
cana-4953	81	26	its	its	PRON
cana-4953	81	27	kind	kind	NOUN
cana-4953	81	28	.	.	PUNCT
cana-4953	82	1	this	this	DET
cana-4953	82	2	benchmark	benchmark	NOUN
cana-4953	82	3	is	be	AUX
cana-4953	82	4	designed	design	VERB
cana-4953	82	5	to	to	PART
cana-4953	82	6	enable	enable	VERB
cana-4953	82	7	researchers	researcher	NOUN
cana-4953	82	8	to	to	PART
cana-4953	82	9	evaluate	evaluate	VERB
cana-4953	82	10	and	and	CCONJ
cana-4953	82	11	compare	compare	VERB
cana-4953	82	12	various	various	ADJ
cana-4953	82	13	fire	fire	NOUN
cana-4953	82	14	detection	detection	NOUN
cana-4953	82	15	models	model	NOUN
cana-4953	82	16	.	.	PUNCT
cana-4953	83	1	their	their	PRON
cana-4953	83	2	method	method	NOUN
cana-4953	83	3	improves	improve	VERB
cana-4953	83	4	both	both	CCONJ
cana-4953	83	5	the	the	DET
cana-4953	83	6	accuracy	accuracy	NOUN
cana-4953	83	7	of	of	ADP
cana-4953	83	8	fire	fire	NOUN
cana-4953	83	9	detection	detection	NOUN
cana-4953	83	10	and	and	CCONJ
cana-4953	83	11	localization	localization	NOUN
cana-4953	83	12	,	,	PUNCT
cana-4953	83	13	making	make	VERB
cana-4953	83	14	it	it	PRON
cana-4953	83	15	suitable	suitable	ADJ
cana-4953	83	16	for	for	ADP
cana-4953	83	17	practical	practical	ADJ
cana-4953	83	18	applications	application	NOUN
cana-4953	83	19	such	such	ADJ
cana-4953	83	20	as	as	ADP
cana-4953	83	21	early	early	ADJ
cana-4953	83	22	wildfire	wildfire	NOUN
cana-4953	83	23	alert	alert	NOUN
cana-4953	83	24	systems	system	NOUN
cana-4953	83	25	and	and	CCONJ
cana-4953	83	26	automated	automated	ADJ
cana-4953	83	27	surveillance	surveillance	NOUN
cana-4953	83	28	.	.	PUNCT
cana-4953	84	1	b.	b.	PROPN
cana-4953	84	2	arteaga	arteaga	PROPN
cana-4953	84	3	,	,	PUNCT
cana-4953	84	4	et.al	et.al	VERB
cana-4953	84	5	[	[	X
cana-4953	84	6	8]examines	8]examines	ADP
cana-4953	84	7	the	the	DET
cana-4953	84	8	effectiveness	effectiveness	NOUN
cana-4953	84	9	of	of	ADP
cana-4953	84	10	various	various	ADJ
cana-4953	84	11	convolutional	convolutional	ADJ
cana-4953	84	12	neural	neural	ADJ
cana-4953	84	13	network	network	NOUN
cana-4953	84	14	(	(	PUNCT
cana-4953	84	15	cnn	cnn	PROPN
cana-4953	84	16	)	)	PUNCT
cana-4953	84	17	models	model	NOUN
cana-4953	84	18	in	in	ADP
cana-4953	84	19	detecting	detect	VERB
cana-4953	84	20	forest	forest	NOUN
cana-4953	84	21	fires	fire	NOUN
cana-4953	84	22	using	use	VERB
cana-4953	84	23	pre	pre	ADJ
cana-4953	84	24	-	-	ADJ
cana-4953	84	25	trained	train	VERB
cana-4953	84	26	images	image	NOUN
cana-4953	84	27	on	on	ADP
cana-4953	84	28	cost	cost	NOUN
cana-4953	84	29	-	-	PUNCT
cana-4953	84	30	effective	effective	ADJ
cana-4953	84	31	hardware	hardware	NOUN
cana-4953	84	32	like	like	ADP
cana-4953	84	33	the	the	DET
cana-4953	84	34	raspberry	raspberry	NOUN
cana-4953	84	35	pi	pi	NOUN
cana-4953	84	36	.	.	PUNCT
cana-4953	85	1	the	the	DET
cana-4953	85	2	research	research	NOUN
cana-4953	85	3	explores	explore	VERB
cana-4953	85	4	potential	potential	ADJ
cana-4953	85	5	methods	method	NOUN
cana-4953	85	6	for	for	ADP
cana-4953	85	7	predicting	predict	VERB
cana-4953	85	8	forest	forest	NOUN
cana-4953	85	9	fires	fire	NOUN
cana-4953	85	10	and	and	CCONJ
cana-4953	85	11	presents	present	VERB
cana-4953	85	12	two	two	NUM
cana-4953	85	13	pre	pre	ADJ
cana-4953	85	14	-	-	ADJ
cana-4953	85	15	trained	train	VERB
cana-4953	85	16	cnn	cnn	PROPN
cana-4953	85	17	models	model	NOUN
cana-4953	85	18	to	to	PART
cana-4953	85	19	accomplish	accomplish	VERB
cana-4953	85	20	this	this	DET
cana-4953	85	21	task	task	NOUN
cana-4953	85	22	and	and	CCONJ
cana-4953	85	23	evaluate	evaluate	VERB
cana-4953	85	24	their	their	PRON
cana-4953	85	25	performance	performance	NOUN
cana-4953	85	26	.	.	PUNCT
cana-4953	86	1	the	the	DET
cana-4953	86	2	proposed	propose	VERB
cana-4953	86	3	models	model	NOUN
cana-4953	86	4	are	be	AUX
cana-4953	86	5	derived	derive	VERB
cana-4953	86	6	from	from	ADP
cana-4953	86	7	resnet	resnet	NOUN
cana-4953	86	8	and	and	CCONJ
cana-4953	86	9	two	two	NUM
cana-4953	86	10	families	family	NOUN
cana-4953	86	11	of	of	ADP
cana-4953	86	12	pre	pre	ADJ
cana-4953	86	13	-	-	ADJ
cana-4953	86	14	trained	train	VERB
cana-4953	86	15	cnns	cnn	NOUN
cana-4953	86	16	,	,	PUNCT
cana-4953	86	17	encompassing	encompass	VERB
cana-4953	86	18	8	8	NUM
cana-4953	86	19	models	model	NOUN
cana-4953	86	20	and	and	CCONJ
cana-4953	86	21	5	5	NUM
cana-4953	86	22	types	type	NOUN
cana-4953	86	23	.	.	PUNCT
cana-4953	87	1	a	a	DET
cana-4953	87	2	key	key	ADJ
cana-4953	87	3	limitation	limitation	NOUN
cana-4953	87	4	of	of	ADP
cana-4953	87	5	this	this	DET
cana-4953	87	6	study	study	NOUN
cana-4953	87	7	is	be	AUX
cana-4953	87	8	that	that	SCONJ
cana-4953	87	9	while	while	SCONJ
cana-4953	87	10	the	the	DET
cana-4953	87	11	resnet152	resnet152	PROPN
cana-4953	87	12	model	model	NOUN
cana-4953	87	13	can	can	AUX
cana-4953	87	14	be	be	AUX
cana-4953	87	15	utilized	utilize	VERB
cana-4953	87	16	on	on	ADP
cana-4953	87	17	a	a	DET
cana-4953	87	18	raspberry	raspberry	NOUN
cana-4953	87	19	pi	pi	NOUN
cana-4953	87	20	3	3	NUM
cana-4953	87	21	model	model	NOUN
cana-4953	87	22	communications	communication	NOUN
cana-4953	87	23	on	on	ADP
cana-4953	87	24	applied	apply	VERB
cana-4953	87	25	nonlinear	nonlinear	ADJ
cana-4953	87	26	analysis	analysis	NOUN
cana-4953	87	27	issn	issn	NOUN
cana-4953	87	28	:	:	PUNCT
cana-4953	87	29	1074	1074	NUM
cana-4953	87	30	-	-	PUNCT
cana-4953	87	31	133x	133x	NUM
cana-4953	87	32	vol	vol	VERB
cana-4953	87	33	32	32	NUM
cana-4953	87	34	no	no	NOUN
cana-4953	87	35	.	.	PUNCT
cana-4953	88	1	10s	10	NOUN
cana-4953	88	2	(	(	PUNCT
cana-4953	88	3	2025	2025	NUM
cana-4953	88	4	)	)	PUNCT
cana-4953	88	5	1025	1025	NUM
cana-4953	89	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	89	2	b	b	X
cana-4953	89	3	,	,	PUNCT
cana-4953	89	4	it	it	PRON
cana-4953	89	5	can	can	AUX
cana-4953	89	6	not	not	PART
cana-4953	89	7	continuously	continuously	ADV
cana-4953	89	8	process	process	VERB
cana-4953	89	9	images	image	NOUN
cana-4953	89	10	.	.	PUNCT
cana-4953	90	1	the	the	DET
cana-4953	90	2	research	research	NOUN
cana-4953	90	3	employed	employ	VERB
cana-4953	90	4	a	a	DET
cana-4953	90	5	small	small	ADJ
cana-4953	90	6	dataset	dataset	NOUN
cana-4953	90	7	consisting	consist	VERB
cana-4953	90	8	of	of	ADP
cana-4953	90	9	1800	1800	NUM
cana-4953	90	10	images	image	NOUN
cana-4953	90	11	for	for	ADP
cana-4953	90	12	analysis	analysis	NOUN
cana-4953	90	13	.	.	PUNCT
cana-4953	91	1	3	3	X
cana-4953	91	2	.	.	NUM
cana-4953	91	3	proposed	propose	VERB
cana-4953	91	4	model	model	NOUN
cana-4953	91	5	the	the	DET
cana-4953	91	6	proposed	propose	VERB
cana-4953	91	7	model	model	NOUN
cana-4953	91	8	aims	aim	VERB
cana-4953	91	9	to	to	PART
cana-4953	91	10	accomplish	accomplish	VERB
cana-4953	91	11	two	two	NUM
cana-4953	91	12	main	main	ADJ
cana-4953	91	13	goals	goal	NOUN
cana-4953	91	14	:	:	PUNCT
cana-4953	91	15	1	1	X
cana-4953	91	16	)	)	PUNCT
cana-4953	91	17	identify	identify	VERB
cana-4953	91	18	whether	whether	SCONJ
cana-4953	91	19	an	an	DET
cana-4953	91	20	image	image	NOUN
cana-4953	91	21	contains	contain	VERB
cana-4953	91	22	fire	fire	NOUN
cana-4953	91	23	.	.	PUNCT
cana-4953	92	1	2	2	X
cana-4953	92	2	)	)	PUNCT
cana-4953	92	3	display	display	VERB
cana-4953	92	4	the	the	DET
cana-4953	92	5	result	result	NOUN
cana-4953	92	6	.	.	PUNCT
cana-4953	93	1	the	the	DET
cana-4953	93	2	output	output	NOUN
cana-4953	93	3	will	will	AUX
cana-4953	93	4	read	read	VERB
cana-4953	93	5	"	"	PUNCT
cana-4953	93	6	fire	fire	NOUN
cana-4953	93	7	"	"	PUNCT
cana-4953	93	8	if	if	SCONJ
cana-4953	93	9	flames	flame	NOUN
cana-4953	93	10	are	be	AUX
cana-4953	93	11	present	present	ADJ
cana-4953	93	12	,	,	PUNCT
cana-4953	93	13	or	or	CCONJ
cana-4953	93	14	"	"	PUNCT
cana-4953	93	15	no	no	DET
cana-4953	93	16	fire	fire	NOUN
cana-4953	93	17	"	"	PUNCT
cana-4953	93	18	if	if	SCONJ
cana-4953	93	19	no	no	DET
cana-4953	93	20	fire	fire	NOUN
cana-4953	93	21	is	be	AUX
cana-4953	93	22	observed	observe	VERB
cana-4953	93	23	.	.	PUNCT
cana-4953	94	1	3.1	3.1	NUM
cana-4953	94	2	dataset	dataset	NOUN
cana-4953	94	3	used	use	VERB
cana-4953	94	4	data	data	NOUN
cana-4953	94	5	sets	set	NOUN
cana-4953	94	6	has	have	AUX
cana-4953	94	7	been	be	AUX
cana-4953	94	8	extracted	extract	VERB
cana-4953	94	9	from	from	ADP
cana-4953	94	10	forest	forest	NOUN
cana-4953	94	11	fire	fire	NOUN
cana-4953	94	12	dataset	dataset	VERB
cana-4953	94	13	available	available	ADJ
cana-4953	94	14	on	on	ADP
cana-4953	94	15	kaggle	kaggle	PROPN
cana-4953	94	16	[	[	X
cana-4953	94	17	9]this	9]this	NUM
cana-4953	94	18	collection	collection	NOUN
cana-4953	94	19	of	of	ADP
cana-4953	94	20	images	image	NOUN
cana-4953	94	21	has	have	AUX
cana-4953	94	22	been	be	AUX
cana-4953	94	23	assembled	assemble	VERB
cana-4953	94	24	to	to	PART
cana-4953	94	25	tackle	tackle	VERB
cana-4953	94	26	the	the	DET
cana-4953	94	27	challenge	challenge	NOUN
cana-4953	94	28	of	of	ADP
cana-4953	94	29	detecting	detect	VERB
cana-4953	94	30	forest	forest	NOUN
cana-4953	94	31	fires	fire	NOUN
cana-4953	94	32	.	.	PUNCT
cana-4953	95	1	the	the	DET
cana-4953	95	2	dataset	dataset	NOUN
cana-4953	95	3	comprises	comprise	VERB
cana-4953	95	4	1900	1900	NUM
cana-4953	95	5	images	image	NOUN
cana-4953	95	6	in	in	ADP
cana-4953	95	7	total	total	ADJ
cana-4953	95	8	,	,	PUNCT
cana-4953	95	9	evenly	evenly	ADV
cana-4953	95	10	distributed	distribute	VERB
cana-4953	95	11	between	between	ADP
cana-4953	95	12	classes	class	NOUN
cana-4953	95	13	.	.	PUNCT
cana-4953	96	1	each	each	DET
cana-4953	96	2	image	image	NOUN
cana-4953	96	3	in	in	ADP
cana-4953	96	4	the	the	DET
cana-4953	96	5	set	set	NOUN
cana-4953	96	6	is	be	AUX
cana-4953	96	7	composed	compose	VERB
cana-4953	96	8	of	of	ADP
cana-4953	96	9	three	three	NUM
cana-4953	96	10	channels	channel	NOUN
cana-4953	96	11	and	and	CCONJ
cana-4953	96	12	has	have	VERB
cana-4953	96	13	dimensions	dimension	NOUN
cana-4953	96	14	of	of	ADP
cana-4953	96	15	250	250	NUM
cana-4953	96	16	×	×	NOUN
cana-4953	96	17	250	250	NUM
cana-4953	96	18	pixels	pixel	NOUN
cana-4953	96	19	.	.	PUNCT
cana-4953	97	1	to	to	PART
cana-4953	97	2	create	create	VERB
cana-4953	97	3	this	this	DET
cana-4953	97	4	dataset	dataset	NOUN
cana-4953	97	5	,	,	PUNCT
cana-4953	97	6	various	various	ADJ
cana-4953	97	7	search	search	NOUN
cana-4953	97	8	terms	term	NOUN
cana-4953	97	9	were	be	AUX
cana-4953	97	10	used	use	VERB
cana-4953	97	11	across	across	ADP
cana-4953	97	12	multiple	multiple	ADJ
cana-4953	97	13	search	search	NOUN
cana-4953	97	14	engines	engine	NOUN
cana-4953	97	15	to	to	PART
cana-4953	97	16	gather	gather	VERB
cana-4953	97	17	images	image	NOUN
cana-4953	97	18	.	.	PUNCT
cana-4953	98	1	subsequently	subsequently	ADV
cana-4953	98	2	,	,	PUNCT
cana-4953	98	3	the	the	DET
cana-4953	98	4	collected	collect	VERB
cana-4953	98	5	images	image	NOUN
cana-4953	98	6	underwent	undergo	VERB
cana-4953	98	7	careful	careful	ADJ
cana-4953	98	8	examination	examination	NOUN
cana-4953	98	9	to	to	PART
cana-4953	98	10	remove	remove	VERB
cana-4953	98	11	any	any	DET
cana-4953	98	12	irrelevant	irrelevant	ADJ
cana-4953	98	13	elements	element	NOUN
cana-4953	98	14	such	such	ADJ
cana-4953	98	15	as	as	ADP
cana-4953	98	16	individuals	individual	NOUN
cana-4953	98	17	or	or	CCONJ
cana-4953	98	18	firefighting	firefighting	NOUN
cana-4953	98	19	equipment	equipment	NOUN
cana-4953	98	20	.	.	PUNCT
cana-4953	99	1	this	this	DET
cana-4953	99	2	process	process	NOUN
cana-4953	99	3	ensured	ensure	VERB
cana-4953	99	4	that	that	SCONJ
cana-4953	99	5	each	each	DET
cana-4953	99	6	image	image	NOUN
cana-4953	99	7	focuses	focus	VERB
cana-4953	99	8	solely	solely	ADV
cana-4953	99	9	on	on	ADP
cana-4953	99	10	the	the	DET
cana-4953	99	11	pertinent	pertinent	ADJ
cana-4953	99	12	fire	fire	NOUN
cana-4953	99	13	area	area	NOUN
cana-4953	99	14	.	.	PUNCT
cana-4953	100	1	the	the	DET
cana-4953	100	2	resulting	result	VERB
cana-4953	100	3	dataset	dataset	NOUN
cana-4953	100	4	is	be	AUX
cana-4953	100	5	wellbalanced	wellbalance	VERB
cana-4953	100	6	,	,	PUNCT
cana-4953	100	7	containing	contain	VERB
cana-4953	100	8	an	an	DET
cana-4953	100	9	equal	equal	ADJ
cana-4953	100	10	number	number	NOUN
cana-4953	100	11	of	of	ADP
cana-4953	100	12	images	image	NOUN
cana-4953	100	13	for	for	ADP
cana-4953	100	14	each	each	DET
cana-4953	100	15	category	category	NOUN
cana-4953	100	16	.	.	PUNCT
cana-4953	101	1	the	the	DET
cana-4953	101	2	dataset	dataset	NOUN
cana-4953	101	3	consists	consist	VERB
cana-4953	101	4	of	of	ADP
cana-4953	101	5	two	two	NUM
cana-4953	101	6	sets	set	NOUN
cana-4953	101	7	of	of	ADP
cana-4953	101	8	images	image	NOUN
cana-4953	101	9	1	1	NUM
cana-4953	101	10	.	.	PUNCT
cana-4953	102	1	training	train	VERB
cana-4953	102	2	2	2	NUM
cana-4953	102	3	.	.	X
cana-4953	102	4	testing	testing	NOUN
cana-4953	102	5	in	in	ADP
cana-4953	102	6	both	both	DET
cana-4953	102	7	categories	category	NOUN
cana-4953	102	8	of	of	ADP
cana-4953	102	9	folders	folder	NOUN
cana-4953	102	10	there	there	PRON
cana-4953	102	11	are	be	VERB
cana-4953	102	12	two	two	NUM
cana-4953	102	13	sets	set	NOUN
cana-4953	102	14	of	of	ADP
cana-4953	102	15	images	image	NOUN
cana-4953	102	16	labelled	label	VERB
cana-4953	102	17	as	as	ADP
cana-4953	102	18	fire	fire	NOUN
cana-4953	102	19	and	and	CCONJ
cana-4953	102	20	no	no	DET
cana-4953	102	21	fire	fire	NOUN
cana-4953	102	22	dataset	dataset	NOUN
cana-4953	102	23	contains	contain	VERB
cana-4953	102	24	1520	1520	NUM
cana-4953	102	25	images	image	NOUN
cana-4953	102	26	for	for	ADP
cana-4953	102	27	training	train	VERB
cana-4953	102	28	the	the	DET
cana-4953	102	29	model	model	NOUN
cana-4953	102	30	and	and	CCONJ
cana-4953	102	31	380	380	NUM
cana-4953	102	32	images	image	NOUN
cana-4953	102	33	for	for	ADP
cana-4953	102	34	testing	testing	NOUN
cana-4953	102	35	for	for	ADP
cana-4953	102	36	the	the	DET
cana-4953	102	37	proposed	propose	VERB
cana-4953	102	38	study	study	NOUN
cana-4953	102	39	training	training	NOUN
cana-4953	102	40	and	and	CCONJ
cana-4953	102	41	testing	testing	NOUN
cana-4953	102	42	purposes	purpose	NOUN
cana-4953	102	43	,	,	PUNCT
cana-4953	102	44	the	the	DET
cana-4953	102	45	dataset	dataset	NOUN
cana-4953	102	46	is	be	AUX
cana-4953	102	47	split	split	VERB
cana-4953	102	48	80:20	80:20	NUM
cana-4953	102	49	.	.	PUNCT
cana-4953	103	1	3.2	3.2	NUM
cana-4953	103	2	model	model	NOUN
cana-4953	103	3	development	development	NOUN
cana-4953	103	4	the	the	DET
cana-4953	103	5	suggested	suggest	VERB
cana-4953	103	6	framework	framework	NOUN
cana-4953	103	7	will	will	AUX
cana-4953	103	8	process	process	VERB
cana-4953	103	9	images	image	NOUN
cana-4953	103	10	containing	contain	VERB
cana-4953	103	11	both	both	CCONJ
cana-4953	103	12	fire	fire	NOUN
cana-4953	103	13	and	and	CCONJ
cana-4953	103	14	non	non	ADJ
cana-4953	103	15	-	-	ADJ
cana-4953	103	16	fire	fire	ADJ
cana-4953	103	17	elements	element	NOUN
cana-4953	103	18	.	.	PUNCT
cana-4953	104	1	these	these	DET
cana-4953	104	2	images	image	NOUN
cana-4953	104	3	are	be	AUX
cana-4953	104	4	fed	feed	VERB
cana-4953	104	5	through	through	ADP
cana-4953	104	6	various	various	ADJ
cana-4953	104	7	layers	layer	NOUN
cana-4953	104	8	,	,	PUNCT
cana-4953	104	9	including	include	VERB
cana-4953	104	10	convolution	convolution	NOUN
cana-4953	104	11	,	,	PUNCT
cana-4953	104	12	maxpooling	maxpooling	NOUN
cana-4953	104	13	,	,	PUNCT
cana-4953	104	14	and	and	CCONJ
cana-4953	104	15	dense	dense	ADJ
cana-4953	104	16	layers	layer	NOUN
cana-4953	104	17	.	.	PUNCT
cana-4953	105	1	additionally	additionally	ADV
cana-4953	105	2	,	,	PUNCT
cana-4953	105	3	data	datum	NOUN
cana-4953	105	4	augmentation	augmentation	NOUN
cana-4953	105	5	techniques	technique	NOUN
cana-4953	105	6	such	such	ADJ
cana-4953	105	7	as	as	ADP
cana-4953	105	8	rescaling	rescaling	NOUN
cana-4953	105	9	are	be	AUX
cana-4953	105	10	employed	employ	VERB
cana-4953	105	11	to	to	PART
cana-4953	105	12	extract	extract	VERB
cana-4953	105	13	crucial	crucial	ADJ
cana-4953	105	14	features	feature	NOUN
cana-4953	105	15	from	from	ADP
cana-4953	105	16	the	the	DET
cana-4953	105	17	input	input	NOUN
cana-4953	105	18	image	image	NOUN
cana-4953	105	19	[	[	X
cana-4953	105	20	10	10	NUM
cana-4953	105	21	]	]	X
cana-4953	105	22	the	the	DET
cana-4953	105	23	final	final	ADJ
cana-4953	105	24	classification	classification	NOUN
cana-4953	105	25	is	be	AUX
cana-4953	105	26	performed	perform	VERB
cana-4953	105	27	by	by	ADP
cana-4953	105	28	the	the	DET
cana-4953	105	29	output	output	NOUN
cana-4953	105	30	layer	layer	NOUN
cana-4953	105	31	,	,	PUNCT
cana-4953	105	32	which	which	PRON
cana-4953	105	33	displays	display	VERB
cana-4953	105	34	"	"	PUNCT
cana-4953	105	35	fire	fire	NOUN
cana-4953	105	36	"	"	PUNCT
cana-4953	105	37	if	if	SCONJ
cana-4953	105	38	fire	fire	NOUN
cana-4953	105	39	is	be	AUX
cana-4953	105	40	detected	detect	VERB
cana-4953	105	41	in	in	ADP
cana-4953	105	42	the	the	DET
cana-4953	105	43	image	image	NOUN
cana-4953	105	44	,	,	PUNCT
cana-4953	105	45	or	or	CCONJ
cana-4953	105	46	"	"	PUNCT
cana-4953	105	47	no	no	DET
cana-4953	105	48	fire	fire	NOUN
cana-4953	105	49	"	"	PUNCT
cana-4953	105	50	if	if	SCONJ
cana-4953	105	51	no	no	DET
cana-4953	105	52	fire	fire	NOUN
cana-4953	105	53	is	be	AUX
cana-4953	105	54	present	present	ADJ
cana-4953	105	55	.	.	PUNCT
cana-4953	106	1	the	the	DET
cana-4953	106	2	suggested	suggest	VERB
cana-4953	106	3	framework	framework	NOUN
cana-4953	106	4	incorporates	incorporate	VERB
cana-4953	106	5	three	three	NUM
cana-4953	106	6	key	key	ADJ
cana-4953	106	7	components	component	NOUN
cana-4953	106	8	,	,	PUNCT
cana-4953	106	9	as	as	SCONJ
cana-4953	106	10	illustrated	illustrate	VERB
cana-4953	106	11	in	in	ADP
cana-4953	106	12	:	:	PUNCT
cana-4953	106	13	•	•	NUM
cana-4953	106	14	gathering	gathering	NOUN
cana-4953	106	15	of	of	ADP
cana-4953	106	16	dataset	dataset	ADJ
cana-4953	106	17	•	•	NOUN
cana-4953	106	18	augmentation	augmentation	NOUN
cana-4953	106	19	of	of	ADP
cana-4953	106	20	data	datum	NOUN
cana-4953	106	21	and	and	CCONJ
cana-4953	106	22	extraction	extraction	NOUN
cana-4953	106	23	of	of	ADP
cana-4953	106	24	features	feature	NOUN
cana-4953	106	25	•	•	ADP
cana-4953	106	26	development	development	NOUN
cana-4953	106	27	and	and	CCONJ
cana-4953	106	28	training	training	NOUN
cana-4953	106	29	of	of	ADP
cana-4953	106	30	model	model	NOUN
cana-4953	106	31	3.3	3.3	NUM
cana-4953	106	32	detailed	detailed	ADJ
cana-4953	106	33	model	model	NOUN
cana-4953	106	34	model	model	NOUN
cana-4953	106	35	uses	use	VERB
cana-4953	106	36	cnn	cnn	PROPN
cana-4953	106	37	(	(	PUNCT
cana-4953	106	38	convolusion	convolusion	NOUN
cana-4953	106	39	neural	neural	ADJ
cana-4953	106	40	network	network	NOUN
cana-4953	106	41	)	)	PUNCT
cana-4953	106	42	as	as	SCONJ
cana-4953	106	43	the	the	DET
cana-4953	106	44	base	base	NOUN
cana-4953	106	45	technique	technique	NOUN
cana-4953	106	46	will	will	AUX
cana-4953	106	47	take	take	VERB
cana-4953	106	48	images	image	NOUN
cana-4953	106	49	as	as	ADP
cana-4953	106	50	input	input	NOUN
cana-4953	106	51	followed	follow	VERB
cana-4953	106	52	by	by	ADP
cana-4953	106	53	4	4	NUM
cana-4953	106	54	sets	set	NOUN
cana-4953	106	55	of	of	ADP
cana-4953	106	56	layers	layer	NOUN
cana-4953	106	57	of	of	ADP
cana-4953	106	58	convolusion	convolusion	NOUN
cana-4953	106	59	layer	layer	NOUN
cana-4953	106	60	with	with	ADP
cana-4953	106	61	relu	relu	NOUN
cana-4953	106	62	(	(	PUNCT
cana-4953	106	63	rectified	rectify	VERB
cana-4953	106	64	linear	linear	NOUN
cana-4953	106	65	unit	unit	NOUN
cana-4953	106	66	)	)	PUNCT
cana-4953	106	67	and	and	CCONJ
cana-4953	106	68	sigmoid	sigmoid	NOUN
cana-4953	106	69	communications	communication	NOUN
cana-4953	106	70	on	on	ADP
cana-4953	106	71	applied	apply	VERB
cana-4953	106	72	nonlinear	nonlinear	ADJ
cana-4953	106	73	analysis	analysis	NOUN
cana-4953	106	74	issn	issn	NOUN
cana-4953	106	75	:	:	PUNCT
cana-4953	106	76	1074	1074	NUM
cana-4953	106	77	-	-	PUNCT
cana-4953	106	78	133x	133x	NUM
cana-4953	106	79	vol	vol	VERB
cana-4953	106	80	32	32	NUM
cana-4953	106	81	no	no	NOUN
cana-4953	106	82	.	.	PUNCT
cana-4953	107	1	10s	10	NOUN
cana-4953	107	2	(	(	PUNCT
cana-4953	107	3	2025	2025	NUM
cana-4953	107	4	)	)	PUNCT
cana-4953	107	5	1026	1026	NUM
cana-4953	107	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	107	7	activation	activation	NOUN
cana-4953	107	8	function	function	NOUN
cana-4953	107	9	for	for	ADP
cana-4953	107	10	feature	feature	NOUN
cana-4953	107	11	extraction	extraction	NOUN
cana-4953	107	12	and	and	CCONJ
cana-4953	107	13	maxpooling	maxpooling	NOUN
cana-4953	107	14	layer	layer	NOUN
cana-4953	107	15	for	for	ADP
cana-4953	107	16	reducing	reduce	VERB
cana-4953	107	17	the	the	DET
cana-4953	107	18	dimensionality	dimensionality	NOUN
cana-4953	107	19	of	of	ADP
cana-4953	107	20	the	the	DET
cana-4953	107	21	features	feature	NOUN
cana-4953	107	22	.	.	PUNCT
cana-4953	108	1	this	this	PRON
cana-4953	108	2	is	be	AUX
cana-4953	108	3	followed	follow	VERB
cana-4953	108	4	by	by	ADP
cana-4953	108	5	flatten	flatten	ADJ
cana-4953	108	6	function	function	NOUN
cana-4953	108	7	to	to	PART
cana-4953	108	8	minimize	minimize	VERB
cana-4953	108	9	the	the	DET
cana-4953	108	10	loss	loss	NOUN
cana-4953	108	11	following	follow	VERB
cana-4953	108	12	extraction	extraction	NOUN
cana-4953	108	13	,	,	PUNCT
cana-4953	108	14	the	the	DET
cana-4953	108	15	model	model	NOUN
cana-4953	108	16	incorporates	incorporate	VERB
cana-4953	108	17	an	an	DET
cana-4953	108	18	optimizer	optimizer	NOUN
cana-4953	108	19	and	and	CCONJ
cana-4953	108	20	employs	employ	VERB
cana-4953	108	21	binary	binary	PROPN
cana-4953	108	22	cross	cross	NOUN
cana-4953	108	23	-	-	NOUN
cana-4953	108	24	entropy	entropy	NOUN
cana-4953	108	25	as	as	ADP
cana-4953	108	26	the	the	DET
cana-4953	108	27	loss	loss	NOUN
cana-4953	108	28	function	function	NOUN
cana-4953	108	29	,	,	PUNCT
cana-4953	108	30	and	and	CCONJ
cana-4953	108	31	result	result	NOUN
cana-4953	108	32	will	will	AUX
cana-4953	108	33	be	be	AUX
cana-4953	108	34	input	input	ADJ
cana-4953	108	35	to	to	ADP
cana-4953	108	36	fully	fully	ADV
cana-4953	108	37	connected	connected	ADJ
cana-4953	108	38	layers	layer	NOUN
cana-4953	108	39	to	to	PART
cana-4953	108	40	classify	classify	VERB
cana-4953	108	41	the	the	DET
cana-4953	108	42	images	image	NOUN
cana-4953	108	43	into	into	ADP
cana-4953	108	44	fire	fire	NOUN
cana-4953	108	45	and	and	CCONJ
cana-4953	108	46	no	no	DET
cana-4953	108	47	fire	fire	NOUN
cana-4953	108	48	.	.	PUNCT
cana-4953	109	1	algorithm	algorithm	NOUN
cana-4953	109	2	1	1	NUM
cana-4953	109	3	:	:	PUNCT
cana-4953	109	4	developing	develop	VERB
cana-4953	109	5	the	the	DET
cana-4953	109	6	model	model	NOUN
cana-4953	109	7	step1	step1	PROPN
cana-4953	109	8	:	:	PUNCT
cana-4953	109	9	input	input	VERB
cana-4953	109	10	the	the	DET
cana-4953	109	11	dataset	dataset	NOUN
cana-4953	109	12	step2	step2	PROPN
cana-4953	109	13	:	:	PUNCT
cana-4953	109	14	split	split	VERB
cana-4953	109	15	the	the	DET
cana-4953	109	16	dataset	dataset	NOUN
cana-4953	109	17	into	into	ADP
cana-4953	109	18	training	training	NOUN
cana-4953	109	19	and	and	CCONJ
cana-4953	109	20	testing	testing	NOUN
cana-4953	109	21	subsets	subset	NOUN
cana-4953	109	22	and	and	CCONJ
cana-4953	109	23	generate	generate	VERB
cana-4953	109	24	training	training	NOUN
cana-4953	109	25	and	and	CCONJ
cana-4953	109	26	testing	testing	NOUN
cana-4953	109	27	data	datum	NOUN
cana-4953	109	28	generators	generator	NOUN
cana-4953	109	29	step3	step3	PROPN
cana-4953	109	30	:	:	PUNCT
cana-4953	109	31	construct	construct	VERB
cana-4953	109	32	the	the	DET
cana-4953	109	33	preparation	preparation	NOUN
cana-4953	109	34	,	,	PUNCT
cana-4953	109	35	analysis	analysis	NOUN
cana-4953	109	36	,	,	PUNCT
cana-4953	109	37	and	and	CCONJ
cana-4953	109	38	detection	detection	NOUN
cana-4953	109	39	networks	network	NOUN
cana-4953	109	40	step4	step4	PROPN
cana-4953	109	41	:	:	PUNCT
cana-4953	109	42	incorporate	incorporate	VERB
cana-4953	109	43	pooling	pooling	NOUN
cana-4953	109	44	,	,	PUNCT
cana-4953	109	45	dense	dense	ADJ
cana-4953	109	46	,	,	PUNCT
cana-4953	109	47	and	and	CCONJ
cana-4953	109	48	conv2d(n	conv2d(n	NOUN
cana-4953	109	49	,	,	PUNCT
cana-4953	109	50	w	w	NOUN
cana-4953	109	51	,	,	PUNCT
cana-4953	109	52	h	h	NOUN
cana-4953	109	53	)	)	PUNCT
cana-4953	109	54	layers	layer	NOUN
cana-4953	109	55	,	,	PUNCT
cana-4953	109	56	where	where	SCONJ
cana-4953	109	57	n	n	PRON
cana-4953	109	58	represents	represent	VERB
cana-4953	109	59	the	the	DET
cana-4953	109	60	number	number	NOUN
cana-4953	109	61	of	of	ADP
cana-4953	109	62	layers	layer	NOUN
cana-4953	109	63	,	,	PUNCT
cana-4953	109	64	w	w	PROPN
cana-4953	109	65	indicates	indicate	VERB
cana-4953	109	66	the	the	DET
cana-4953	109	67	width	width	NOUN
cana-4953	109	68	of	of	ADP
cana-4953	109	69	each	each	DET
cana-4953	109	70	layer	layer	NOUN
cana-4953	109	71	,	,	PUNCT
cana-4953	109	72	and	and	CCONJ
cana-4953	109	73	h	h	NOUN
cana-4953	109	74	denotes	denote	VERB
cana-4953	109	75	the	the	DET
cana-4953	109	76	layer	layer	NOUN
cana-4953	109	77	height	height	NOUN
cana-4953	109	78	.	.	PUNCT
cana-4953	110	1	include	include	VERB
cana-4953	110	2	pooling	pool	VERB
cana-4953	110	3	and	and	CCONJ
cana-4953	110	4	implement	implement	VERB
cana-4953	110	5	relu	relu	NOUN
cana-4953	110	6	(	(	PUNCT
cana-4953	110	7	)	)	PUNCT
cana-4953	110	8	and	and	CCONJ
cana-4953	110	9	sigmoid	sigmoid	NOUN
cana-4953	110	10	activation	activation	NOUN
cana-4953	110	11	functions	function	NOUN
cana-4953	110	12	step5	step5	PROPN
cana-4953	110	13	:	:	PUNCT
cana-4953	110	14	compile	compile	VERB
cana-4953	110	15	the	the	DET
cana-4953	110	16	final	final	ADJ
cana-4953	110	17	model	model	NOUN
cana-4953	110	18	3.3	3.3	NUM
cana-4953	110	19	training	training	NOUN
cana-4953	110	20	and	and	CCONJ
cana-4953	110	21	testing	testing	NOUN
cana-4953	110	22	of	of	ADP
cana-4953	110	23	model	model	NOUN
cana-4953	110	24	algorithm	algorithm	NOUN
cana-4953	110	25	:	:	PUNCT
cana-4953	110	26	step	step	NOUN
cana-4953	110	27	1	1	NUM
cana-4953	110	28	:	:	PUNCT
cana-4953	110	29	the	the	DET
cana-4953	110	30	model	model	NOUN
cana-4953	110	31	undergoes	undergo	VERB
cana-4953	110	32	training	training	NOUN
cana-4953	110	33	using	use	VERB
cana-4953	110	34	a	a	DET
cana-4953	110	35	prepared	prepared	ADJ
cana-4953	110	36	dataset	dataset	NOUN
cana-4953	110	37	.	.	PUNCT
cana-4953	111	1	step	step	NOUN
cana-4953	111	2	2	2	NUM
cana-4953	111	3	:	:	PUNCT
cana-4953	111	4	model	model	NOUN
cana-4953	111	5	evaluation	evaluation	NOUN
cana-4953	111	6	is	be	AUX
cana-4953	111	7	conducted	conduct	VERB
cana-4953	111	8	to	to	PART
cana-4953	111	9	determine	determine	VERB
cana-4953	111	10	how	how	SCONJ
cana-4953	111	11	well	well	ADV
cana-4953	111	12	it	it	PRON
cana-4953	111	13	generalizes	generalize	VERB
cana-4953	111	14	to	to	ADP
cana-4953	111	15	similar	similar	ADJ
cana-4953	111	16	data	datum	NOUN
cana-4953	111	17	used	use	VERB
cana-4953	111	18	in	in	ADP
cana-4953	111	19	its	its	PRON
cana-4953	111	20	training	training	NOUN
cana-4953	111	21	.	.	PUNCT
cana-4953	112	1	step	step	NOUN
cana-4953	112	2	3	3	NUM
cana-4953	112	3	:	:	PUNCT
cana-4953	112	4	the	the	DET
cana-4953	112	5	matplotlib	matplotlib	PROPN
cana-4953	112	6	library	library	NOUN
cana-4953	112	7	is	be	AUX
cana-4953	112	8	utilized	utilize	VERB
cana-4953	112	9	to	to	PART
cana-4953	112	10	visualize	visualize	VERB
cana-4953	112	11	the	the	DET
cana-4953	112	12	model	model	NOUN
cana-4953	112	13	's	's	PART
cana-4953	112	14	training	training	NOUN
cana-4953	112	15	accuracy	accuracy	NOUN
cana-4953	112	16	.	.	PUNCT
cana-4953	113	1	step	step	NOUN
cana-4953	113	2	4	4	NUM
cana-4953	113	3	:	:	PUNCT
cana-4953	113	4	the	the	DET
cana-4953	113	5	trained	train	VERB
cana-4953	113	6	model	model	NOUN
cana-4953	113	7	is	be	AUX
cana-4953	113	8	then	then	ADV
cana-4953	113	9	tested	test	VERB
cana-4953	113	10	by	by	ADP
cana-4953	113	11	inputting	inputte	VERB
cana-4953	113	12	a	a	DET
cana-4953	113	13	random	random	ADJ
cana-4953	113	14	image	image	NOUN
cana-4953	113	15	.	.	PUNCT
cana-4953	114	1	step	step	NOUN
cana-4953	114	2	5	5	NUM
cana-4953	114	3	:	:	PUNCT
cana-4953	114	4	the	the	DET
cana-4953	114	5	final	final	ADJ
cana-4953	114	6	outcome	outcome	NOUN
cana-4953	114	7	is	be	AUX
cana-4953	114	8	displayed	display	VERB
cana-4953	114	9	as	as	ADP
cana-4953	114	10	text	text	NOUN
cana-4953	114	11	on	on	ADP
cana-4953	114	12	the	the	DET
cana-4953	114	13	given	give	VERB
cana-4953	114	14	image	image	NOUN
cana-4953	114	15	,	,	PUNCT
cana-4953	114	16	indicating	indicate	VERB
cana-4953	114	17	whether	whether	SCONJ
cana-4953	114	18	a	a	DET
cana-4953	114	19	fire	fire	NOUN
cana-4953	114	20	is	be	AUX
cana-4953	114	21	detected	detect	VERB
cana-4953	114	22	or	or	CCONJ
cana-4953	114	23	not	not	PART
cana-4953	114	24	.	.	PUNCT
cana-4953	115	1	step	step	VERB
cana-4953	115	2	6	6	NUM
cana-4953	115	3	:	:	PUNCT
cana-4953	115	4	if	if	SCONJ
cana-4953	115	5	a	a	DET
cana-4953	115	6	fire	fire	NOUN
cana-4953	115	7	is	be	AUX
cana-4953	115	8	identified	identify	VERB
cana-4953	115	9	,	,	PUNCT
cana-4953	115	10	the	the	DET
cana-4953	115	11	output	output	NOUN
cana-4953	115	12	will	will	AUX
cana-4953	115	13	read	read	VERB
cana-4953	115	14	"	"	PUNCT
cana-4953	115	15	fire	fire	NOUN
cana-4953	115	16	"	"	PUNCT
cana-4953	115	17	;	;	PUNCT
cana-4953	115	18	otherwise	otherwise	ADV
cana-4953	115	19	,	,	PUNCT
cana-4953	115	20	it	it	PRON
cana-4953	115	21	will	will	AUX
cana-4953	115	22	display	display	VERB
cana-4953	115	23	"	"	PUNCT
cana-4953	115	24	no	no	DET
cana-4953	115	25	fire	fire	NOUN
cana-4953	115	26	"	"	PUNCT
cana-4953	115	27	.	.	PUNCT
cana-4953	116	1	3.4	3.4	NUM
cana-4953	116	2	steps	step	NOUN
cana-4953	116	3	in	in	ADP
cana-4953	116	4	the	the	DET
cana-4953	116	5	model	model	NOUN
cana-4953	116	6	the	the	DET
cana-4953	116	7	model	model	NOUN
cana-4953	116	8	consists	consist	VERB
cana-4953	116	9	of	of	ADP
cana-4953	116	10	the	the	DET
cana-4953	116	11	following	follow	VERB
cana-4953	116	12	steps	step	NOUN
cana-4953	116	13	1	1	NUM
cana-4953	116	14	.	.	PUNCT
cana-4953	116	15	to	to	PART
cana-4953	116	16	begin	begin	VERB
cana-4953	116	17	,	,	PUNCT
cana-4953	116	18	we	we	PRON
cana-4953	116	19	'll	will	AUX
cana-4953	116	20	utilize	utilize	VERB
cana-4953	116	21	the	the	DET
cana-4953	116	22	training	training	NOUN
cana-4953	116	23	dataset	dataset	NOUN
cana-4953	116	24	.	.	PUNCT
cana-4953	117	1	2	2	X
cana-4953	117	2	.	.	X
cana-4953	117	3	the	the	DET
cana-4953	117	4	data	datum	NOUN
cana-4953	117	5	has	have	AUX
cana-4953	117	6	been	be	AUX
cana-4953	117	7	divided	divide	VERB
cana-4953	117	8	into	into	ADP
cana-4953	117	9	two	two	NUM
cana-4953	117	10	segments	segment	NOUN
cana-4953	117	11	:	:	PUNCT
cana-4953	117	12	one	one	NUM
cana-4953	117	13	for	for	ADP
cana-4953	117	14	training	training	NOUN
cana-4953	117	15	and	and	CCONJ
cana-4953	117	16	another	another	PRON
cana-4953	117	17	for	for	ADP
cana-4953	117	18	testing	testing	NOUN
cana-4953	117	19	.	.	PUNCT
cana-4953	118	1	3	3	X
cana-4953	118	2	.	.	X
cana-4953	118	3	we	we	PRON
cana-4953	118	4	'll	will	AUX
cana-4953	118	5	construct	construct	VERB
cana-4953	118	6	and	and	CCONJ
cana-4953	118	7	train	train	VERB
cana-4953	118	8	the	the	DET
cana-4953	118	9	cnn	cnn	PROPN
cana-4953	118	10	model	model	NOUN
cana-4953	118	11	using	use	VERB
cana-4953	118	12	the	the	DET
cana-4953	118	13	training	training	NOUN
cana-4953	118	14	portion	portion	NOUN
cana-4953	118	15	.	.	PUNCT
cana-4953	119	1	4	4	X
cana-4953	119	2	.	.	X
cana-4953	119	3	during	during	ADP
cana-4953	119	4	the	the	DET
cana-4953	119	5	training	training	NOUN
cana-4953	119	6	process	process	NOUN
cana-4953	119	7	,	,	PUNCT
cana-4953	119	8	we	we	PRON
cana-4953	119	9	'll	will	AUX
cana-4953	119	10	extract	extract	VERB
cana-4953	119	11	the	the	DET
cana-4953	119	12	necessary	necessary	ADJ
cana-4953	119	13	features	feature	NOUN
cana-4953	119	14	.	.	PUNCT
cana-4953	120	1	5	5	X
cana-4953	120	2	.	.	X
cana-4953	120	3	the	the	DET
cana-4953	120	4	output	output	NOUN
cana-4953	120	5	layer	layer	NOUN
cana-4953	120	6	will	will	AUX
cana-4953	120	7	serve	serve	VERB
cana-4953	120	8	as	as	ADP
cana-4953	120	9	the	the	DET
cana-4953	120	10	classification	classification	NOUN
cana-4953	120	11	layer	layer	NOUN
cana-4953	120	12	,	,	PUNCT
cana-4953	120	13	categorizing	categorize	VERB
cana-4953	120	14	into	into	ADP
cana-4953	120	15	two	two	NUM
cana-4953	120	16	classes	class	NOUN
cana-4953	120	17	:	:	PUNCT
cana-4953	120	18	fire	fire	NOUN
cana-4953	120	19	and	and	CCONJ
cana-4953	120	20	no	no	DET
cana-4953	120	21	fire	fire	NOUN
cana-4953	120	22	.	.	PUNCT
cana-4953	121	1	6	6	X
cana-4953	121	2	.	.	X
cana-4953	122	1	we	we	PRON
cana-4953	122	2	'll	will	AUX
cana-4953	122	3	then	then	ADV
cana-4953	122	4	apply	apply	VERB
cana-4953	122	5	our	our	PRON
cana-4953	122	6	trained	train	VERB
cana-4953	122	7	model	model	NOUN
cana-4953	122	8	to	to	ADP
cana-4953	122	9	the	the	DET
cana-4953	122	10	test	test	NOUN
cana-4953	122	11	data	datum	NOUN
cana-4953	122	12	.	.	PUNCT
cana-4953	123	1	7	7	X
cana-4953	123	2	.	.	X
cana-4953	123	3	the	the	DET
cana-4953	123	4	output	output	NOUN
cana-4953	123	5	will	will	AUX
cana-4953	123	6	be	be	AUX
cana-4953	123	7	"	"	PUNCT
cana-4953	123	8	fire	fire	NOUN
cana-4953	123	9	"	"	PUNCT
cana-4953	123	10	if	if	SCONJ
cana-4953	123	11	a	a	DET
cana-4953	123	12	fire	fire	NOUN
cana-4953	123	13	is	be	AUX
cana-4953	123	14	detected	detect	VERB
cana-4953	123	15	;	;	PUNCT
cana-4953	123	16	otherwise	otherwise	ADV
cana-4953	123	17	,	,	PUNCT
cana-4953	123	18	it	it	PRON
cana-4953	123	19	will	will	AUX
cana-4953	123	20	be	be	AUX
cana-4953	123	21	"	"	PUNCT
cana-4953	123	22	no	no	DET
cana-4953	123	23	fire	fire	NOUN
cana-4953	123	24	.	.	PUNCT
cana-4953	123	25	"	"	PUNCT
cana-4953	124	1	communications	communication	NOUN
cana-4953	124	2	on	on	ADP
cana-4953	124	3	applied	apply	VERB
cana-4953	124	4	nonlinear	nonlinear	ADJ
cana-4953	124	5	analysis	analysis	NOUN
cana-4953	124	6	issn	issn	NOUN
cana-4953	124	7	:	:	PUNCT
cana-4953	124	8	1074	1074	NUM
cana-4953	124	9	-	-	PUNCT
cana-4953	124	10	133x	133x	NUM
cana-4953	124	11	vol	vol	VERB
cana-4953	124	12	32	32	NUM
cana-4953	124	13	no	no	NOUN
cana-4953	124	14	.	.	PUNCT
cana-4953	125	1	10s	10	NOUN
cana-4953	125	2	(	(	PUNCT
cana-4953	125	3	2025	2025	NUM
cana-4953	125	4	)	)	PUNCT
cana-4953	125	5	1027	1027	NUM
cana-4953	125	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	125	7	fig1	fig1	PROPN
cana-4953	125	8	.	.	PUNCT
cana-4953	126	1	below	below	ADV
cana-4953	126	2	shows	show	VERB
cana-4953	126	3	a	a	DET
cana-4953	126	4	flowchart	flowchart	NOUN
cana-4953	126	5	of	of	ADP
cana-4953	126	6	the	the	DET
cana-4953	126	7	model	model	NOUN
cana-4953	126	8	.	.	PUNCT
cana-4953	127	1	fig1	fig1	PROPN
cana-4953	127	2	.	.	PUNCT
cana-4953	128	1	flowchart	flowchart	NOUN
cana-4953	128	2	of	of	ADP
cana-4953	128	3	the	the	DET
cana-4953	128	4	model	model	NOUN
cana-4953	128	5	4	4	NUM
cana-4953	128	6	.	.	PUNCT
cana-4953	129	1	results	result	NOUN
cana-4953	129	2	and	and	CCONJ
cana-4953	129	3	discussions	discussion	NOUN
cana-4953	129	4	the	the	DET
cana-4953	129	5	model	model	NOUN
cana-4953	129	6	predicts	predict	VERB
cana-4953	129	7	the	the	DET
cana-4953	129	8	correct	correct	ADJ
cana-4953	129	9	result	result	NOUN
cana-4953	129	10	with	with	ADP
cana-4953	129	11	an	an	DET
cana-4953	129	12	accuracy	accuracy	NOUN
cana-4953	129	13	of	of	ADP
cana-4953	129	14	99	99	NUM
cana-4953	129	15	%	%	NOUN
cana-4953	129	16	on	on	ADP
cana-4953	129	17	the	the	DET
cana-4953	129	18	training	training	NOUN
cana-4953	129	19	dataset	dataset	NOUN
cana-4953	129	20	and	and	CCONJ
cana-4953	129	21	94	94	NUM
cana-4953	129	22	%	%	NOUN
cana-4953	129	23	on	on	ADP
cana-4953	129	24	the	the	DET
cana-4953	129	25	testing	testing	NOUN
cana-4953	129	26	dataset	dataset	VERB
cana-4953	129	27	.	.	PUNCT
cana-4953	130	1	the	the	DET
cana-4953	130	2	graph	graph	NOUN
cana-4953	130	3	below	below	ADV
cana-4953	130	4	(	(	PUNCT
cana-4953	130	5	fig	fig	NOUN
cana-4953	130	6	2	2	NUM
cana-4953	130	7	.	.	PUNCT
cana-4953	130	8	)	)	PUNCT
cana-4953	130	9	shows	show	VERB
cana-4953	130	10	the	the	DET
cana-4953	130	11	performance	performance	NOUN
cana-4953	130	12	of	of	ADP
cana-4953	130	13	the	the	DET
cana-4953	130	14	model	model	NOUN
cana-4953	130	15	.	.	PUNCT
cana-4953	131	1	our	our	PRON
cana-4953	131	2	model	model	NOUN
cana-4953	131	3	predicts	predict	VERB
cana-4953	131	4	the	the	DET
cana-4953	131	5	output	output	NOUN
cana-4953	131	6	correctly	correctly	ADV
cana-4953	131	7	[	[	X
cana-4953	131	8	11	11	NUM
cana-4953	131	9	]	]	X
cana-4953	131	10	fig2	fig2	PROPN
cana-4953	131	11	.	.	PUNCT
cana-4953	132	1	accuracy	accuracy	NOUN
cana-4953	132	2	graph	graph	NOUN
cana-4953	132	3	of	of	ADP
cana-4953	132	4	the	the	DET
cana-4953	132	5	model	model	NOUN
cana-4953	132	6	communications	communication	NOUN
cana-4953	132	7	on	on	ADP
cana-4953	132	8	applied	apply	VERB
cana-4953	132	9	nonlinear	nonlinear	ADJ
cana-4953	132	10	analysis	analysis	NOUN
cana-4953	132	11	issn	issn	NOUN
cana-4953	132	12	:	:	PUNCT
cana-4953	132	13	1074	1074	NUM
cana-4953	132	14	-	-	PUNCT
cana-4953	132	15	133x	133x	NUM
cana-4953	132	16	vol	vol	VERB
cana-4953	132	17	32	32	NUM
cana-4953	132	18	no	no	NOUN
cana-4953	132	19	.	.	PUNCT
cana-4953	133	1	10s	10	NOUN
cana-4953	133	2	(	(	PUNCT
cana-4953	133	3	2025	2025	NUM
cana-4953	133	4	)	)	PUNCT
cana-4953	133	5	1028	1028	NUM
cana-4953	133	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	134	1	the	the	DET
cana-4953	134	2	graph	graph	NOUN
cana-4953	134	3	below	below	ADV
cana-4953	134	4	(	(	PUNCT
cana-4953	134	5	fig	fig	NOUN
cana-4953	134	6	2	2	NUM
cana-4953	134	7	)	)	PUNCT
cana-4953	134	8	shows	show	VERB
cana-4953	134	9	the	the	DET
cana-4953	134	10	loss	loss	NOUN
cana-4953	134	11	in	in	ADP
cana-4953	134	12	the	the	DET
cana-4953	134	13	graph	graph	NOUN
cana-4953	134	14	fig	fig	NOUN
cana-4953	134	15	3	3	NUM
cana-4953	134	16	.	.	PUNCT
cana-4953	135	1	loss	loss	NOUN
cana-4953	135	2	values	value	NOUN
cana-4953	135	3	graph	graph	VERB
cana-4953	135	4	the	the	DET
cana-4953	135	5	system	system	NOUN
cana-4953	135	6	responds	respond	VERB
cana-4953	135	7	differently	differently	ADV
cana-4953	135	8	based	base	VERB
cana-4953	135	9	on	on	ADP
cana-4953	135	10	the	the	DET
cana-4953	135	11	type	type	NOUN
cana-4953	135	12	of	of	ADP
cana-4953	135	13	image	image	NOUN
cana-4953	135	14	provided	provide	VERB
cana-4953	135	15	.	.	PUNCT
cana-4953	136	1	when	when	SCONJ
cana-4953	136	2	an	an	DET
cana-4953	136	3	image	image	NOUN
cana-4953	136	4	containing	contain	VERB
cana-4953	136	5	fire	fire	NOUN
cana-4953	136	6	is	be	AUX
cana-4953	136	7	submitted	submit	VERB
cana-4953	136	8	,	,	PUNCT
cana-4953	136	9	the	the	DET
cana-4953	136	10	output	output	NOUN
cana-4953	136	11	indicates	indicate	VERB
cana-4953	136	12	"	"	PUNCT
cana-4953	136	13	fire	fire	NOUN
cana-4953	136	14	.	.	PUNCT
cana-4953	136	15	"	"	PUNCT
cana-4953	137	1	fig	fig	NOUN
cana-4953	137	2	4	4	NUM
cana-4953	137	3	:	:	PUNCT
cana-4953	137	4	fire	fire	NOUN
cana-4953	137	5	detection	detection	NOUN
cana-4953	137	6	in	in	ADP
cana-4953	137	7	the	the	DET
cana-4953	137	8	image	image	NOUN
cana-4953	137	9	conversely	conversely	ADV
cana-4953	137	10	,	,	PUNCT
cana-4953	137	11	if	if	SCONJ
cana-4953	137	12	an	an	DET
cana-4953	137	13	image	image	NOUN
cana-4953	137	14	without	without	ADP
cana-4953	137	15	fire	fire	NOUN
cana-4953	137	16	is	be	AUX
cana-4953	137	17	input	input	NOUN
cana-4953	137	18	,	,	PUNCT
cana-4953	137	19	the	the	DET
cana-4953	137	20	system	system	NOUN
cana-4953	137	21	produces	produce	VERB
cana-4953	137	22	the	the	DET
cana-4953	137	23	result	result	NOUN
cana-4953	137	24	"	"	PUNCT
cana-4953	137	25	no	no	DET
cana-4953	137	26	fire	fire	NOUN
cana-4953	137	27	.	.	PUNCT
cana-4953	137	28	"	"	PUNCT
cana-4953	138	1	fig	fig	NOUN
cana-4953	138	2	5	5	NUM
cana-4953	138	3	.	.	PUNCT
cana-4953	139	1	no	no	DET
cana-4953	139	2	fire	fire	NOUN
cana-4953	139	3	detected	detect	VERB
cana-4953	139	4	in	in	ADP
cana-4953	139	5	the	the	DET
cana-4953	139	6	image	image	NOUN
cana-4953	139	7	communications	communication	NOUN
cana-4953	139	8	on	on	ADP
cana-4953	139	9	applied	apply	VERB
cana-4953	139	10	nonlinear	nonlinear	ADJ
cana-4953	139	11	analysis	analysis	NOUN
cana-4953	139	12	issn	issn	NOUN
cana-4953	139	13	:	:	PUNCT
cana-4953	139	14	1074	1074	NUM
cana-4953	139	15	-	-	PUNCT
cana-4953	139	16	133x	133x	NUM
cana-4953	139	17	vol	vol	VERB
cana-4953	139	18	32	32	NUM
cana-4953	139	19	no	no	NOUN
cana-4953	139	20	.	.	PUNCT
cana-4953	140	1	10s	10	NOUN
cana-4953	140	2	(	(	PUNCT
cana-4953	140	3	2025	2025	NUM
cana-4953	140	4	)	)	PUNCT
cana-4953	140	5	1029	1029	NUM
cana-4953	140	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4953	140	7	5	5	NUM
cana-4953	140	8	.	.	PUNCT
cana-4953	140	9	conclusion	conclusion	NOUN
cana-4953	140	10	the	the	DET
cana-4953	140	11	research	research	NOUN
cana-4953	140	12	work	work	NOUN
cana-4953	140	13	is	be	AUX
cana-4953	140	14	suggestive	suggestive	ADJ
cana-4953	140	15	of	of	ADP
cana-4953	140	16	the	the	DET
cana-4953	140	17	fact	fact	NOUN
cana-4953	140	18	that	that	SCONJ
cana-4953	140	19	the	the	DET
cana-4953	140	20	proposed	propose	VERB
cana-4953	140	21	model	model	NOUN
cana-4953	140	22	is	be	AUX
cana-4953	140	23	based	base	VERB
cana-4953	140	24	on	on	ADP
cana-4953	140	25	cnn	cnn	PROPN
cana-4953	140	26	for	for	ADP
cana-4953	140	27	any	any	DET
cana-4953	140	28	input	input	NOUN
cana-4953	140	29	image	image	NOUN
cana-4953	140	30	we	we	PRON
cana-4953	140	31	are	be	AUX
cana-4953	140	32	getting	get	VERB
cana-4953	140	33	the	the	DET
cana-4953	140	34	results	result	NOUN
cana-4953	140	35	according	accord	VERB
cana-4953	140	36	to	to	ADP
cana-4953	140	37	whether	whether	SCONJ
cana-4953	140	38	there	there	PRON
cana-4953	140	39	is	be	VERB
cana-4953	140	40	fire	fire	NOUN
cana-4953	140	41	or	or	CCONJ
cana-4953	140	42	not	not	PART
cana-4953	140	43	in	in	ADP
cana-4953	140	44	the	the	DET
cana-4953	140	45	image	image	NOUN
cana-4953	140	46	.	.	PUNCT
cana-4953	141	1	for	for	ADP
cana-4953	141	2	fire	fire	NOUN
cana-4953	141	3	we	we	PRON
cana-4953	141	4	get	get	VERB
cana-4953	141	5	the	the	DET
cana-4953	141	6	output	output	NOUN
cana-4953	141	7	“	"	PUNCT
cana-4953	141	8	fire	fire	NOUN
cana-4953	141	9	”	"	PUNCT
cana-4953	141	10	and	and	CCONJ
cana-4953	141	11	we	we	PRON
cana-4953	141	12	get	get	VERB
cana-4953	141	13	the	the	DET
cana-4953	141	14	output	output	NOUN
cana-4953	141	15	“	"	PUNCT
cana-4953	141	16	no	no	DET
cana-4953	141	17	fire	fire	NOUN
cana-4953	141	18	”	"	PUNCT
cana-4953	141	19	when	when	SCONJ
cana-4953	141	20	fire	fire	NOUN
cana-4953	141	21	is	be	AUX
cana-4953	141	22	not	not	PART
cana-4953	141	23	detected	detect	VERB
cana-4953	141	24	in	in	ADP
cana-4953	141	25	the	the	DET
cana-4953	141	26	image	image	NOUN
cana-4953	141	27	.	.	PUNCT
cana-4953	142	1	the	the	DET
cana-4953	142	2	research	research	NOUN
cana-4953	142	3	also	also	ADV
cana-4953	142	4	shows	show	VERB
cana-4953	142	5	that	that	SCONJ
cana-4953	142	6	model	model	NOUN
cana-4953	142	7	has	have	VERB
cana-4953	142	8	a	a	DET
cana-4953	142	9	good	good	ADJ
cana-4953	142	10	accuracy	accuracy	NOUN
cana-4953	142	11	of	of	ADP
cana-4953	142	12	99	99	NUM
cana-4953	142	13	%	%	NOUN
cana-4953	142	14	on	on	ADP
cana-4953	142	15	training	training	NOUN
cana-4953	142	16	data	datum	NOUN
cana-4953	142	17	and	and	CCONJ
cana-4953	142	18	94	94	NUM
cana-4953	142	19	%	%	NOUN
cana-4953	142	20	on	on	ADP
cana-4953	142	21	the	the	DET
cana-4953	142	22	test	test	NOUN
cana-4953	142	23	data	datum	NOUN
cana-4953	142	24	which	which	PRON
cana-4953	142	25	can	can	AUX
cana-4953	142	26	be	be	AUX
cana-4953	142	27	considered	consider	VERB
cana-4953	142	28	to	to	PART
cana-4953	142	29	be	be	AUX
cana-4953	142	30	a	a	DET
cana-4953	142	31	good	good	ADJ
cana-4953	142	32	accuracy	accuracy	NOUN
cana-4953	142	33	.	.	PUNCT
cana-4953	143	1	references	reference	NOUN
cana-4953	143	2	[	[	X
cana-4953	143	3	1	1	X
cana-4953	143	4	]	]	PUNCT
cana-4953	143	5	d.	d.	PROPN
cana-4953	143	6	q.	q.	PROPN
cana-4953	143	7	tran	tran	PROPN
cana-4953	143	8	,	,	PUNCT
cana-4953	143	9	m.	m.	NOUN
cana-4953	143	10	park	park	NOUN
cana-4953	143	11	,	,	PUNCT
cana-4953	143	12	y.	y.	PROPN
cana-4953	143	13	jeon	jeon	PROPN
cana-4953	143	14	,	,	PUNCT
cana-4953	143	15	j.	j.	PROPN
cana-4953	143	16	bak	bak	PROPN
cana-4953	143	17	,	,	PUNCT
cana-4953	143	18	and	and	CCONJ
cana-4953	143	19	s.	s.	PROPN
cana-4953	143	20	park	park	PROPN
cana-4953	143	21	,	,	PUNCT
cana-4953	143	22	“	"	PUNCT
cana-4953	143	23	forest	forest	NOUN
cana-4953	143	24	-	-	PUNCT
cana-4953	143	25	fire	fire	NOUN
cana-4953	143	26	response	response	NOUN
cana-4953	143	27	system	system	NOUN
cana-4953	143	28	using	use	VERB
cana-4953	143	29	deeplearning	deeplearning	NOUN
cana-4953	143	30	-	-	PUNCT
cana-4953	143	31	based	base	VERB
cana-4953	143	32	approaches	approach	NOUN
cana-4953	143	33	with	with	ADP
cana-4953	143	34	cctv	cctv	NOUN
cana-4953	143	35	images	image	NOUN
cana-4953	143	36	and	and	CCONJ
cana-4953	143	37	weather	weather	NOUN
cana-4953	143	38	data	datum	NOUN
cana-4953	143	39	,	,	PUNCT
cana-4953	143	40	”	"	PUNCT
cana-4953	143	41	ieee	ieee	NOUN
cana-4953	143	42	access	access	NOUN
cana-4953	143	43	,	,	PUNCT
cana-4953	143	44	vol	vol	NOUN
cana-4953	143	45	.	.	PROPN
cana-4953	144	1	10	10	NUM
cana-4953	144	2	,	,	PUNCT
cana-4953	144	3	pp	pp	ADJ
cana-4953	144	4	.	.	PUNCT
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cana-4953	145	2	,	,	PUNCT
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cana-4953	145	4	,	,	PUNCT
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cana-4953	145	8	/	/	SYM
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cana-4953	145	10	.	.	PUNCT
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cana-4953	146	2	2	2	NUM
cana-4953	146	3	]	]	PUNCT
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cana-4953	147	1	doi	doi	NOUN
cana-4953	147	2	:	:	PUNCT
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cana-4953	147	4	/	/	SYM
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cana-4953	147	6	.	.	PUNCT
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cana-4953	148	2	3	3	NUM
cana-4953	148	3	]	]	PUNCT
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cana-4953	148	54	,	,	PUNCT
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cana-4953	148	56	.	.	PUNCT
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cana-4953	149	2	.	.	PUNCT
cana-4953	150	1	doi	doi	NOUN
cana-4953	150	2	:	:	PUNCT
cana-4953	150	3	10.1109	10.1109	NUM
cana-4953	150	4	/	/	SYM
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cana-4953	150	6	.	.	PUNCT
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cana-4953	151	3	]	]	PUNCT
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cana-4953	151	6	,	,	PUNCT
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cana-4953	151	14	,	,	PUNCT
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cana-4953	151	65	.	.	PUNCT
cana-4953	152	1	doi	doi	NOUN
cana-4953	152	2	:	:	PUNCT
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cana-4953	152	4	/	/	SYM
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cana-4953	152	6	.	.	PUNCT
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cana-4953	153	3	]	]	PUNCT
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cana-4953	153	13	,	,	PUNCT
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cana-4953	153	62	.	.	PUNCT
cana-4953	154	1	57–60	57–60	X
cana-4953	154	2	.	.	PUNCT
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cana-4953	154	4	:	:	PUNCT
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cana-4953	154	6	/	/	SYM
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cana-4953	154	8	.	.	PUNCT
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cana-4953	155	2	6	6	NUM
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cana-4953	156	2	.	.	PUNCT
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cana-4953	157	2	:	:	PUNCT
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cana-4953	159	51	.	.	PUNCT
cana-4953	160	1	doi	doi	NOUN
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cana-4953	161	1	communications	communication	NOUN
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cana-4953	161	7	:	:	PUNCT
cana-4953	161	8	1074	1074	NUM
cana-4953	161	9	-	-	PUNCT
cana-4953	161	10	133x	133x	NUM
cana-4953	161	11	vol	vol	VERB
cana-4953	161	12	32	32	NUM
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cana-4953	161	14	.	.	PUNCT
cana-4953	162	1	10s	10	NOUN
cana-4953	162	2	(	(	PUNCT
cana-4953	162	3	2025	2025	NUM
cana-4953	162	4	)	)	PUNCT
cana-4953	162	5	1030	1030	NUM
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cana-4953	163	1	[	[	X
cana-4953	163	2	9	9	NUM
cana-4953	163	3	]	]	PUNCT
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cana-4953	163	5	khan	khan	PROPN
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cana-4953	163	7	b.	b.	PROPN
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cana-4953	163	11	khan	khan	PROPN
cana-4953	163	12	,	,	PUNCT
cana-4953	163	13	r.	r.	PROPN
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cana-4953	163	15	,	,	PUNCT
cana-4953	163	16	and	and	CCONJ
cana-4953	163	17	a.	a.	NOUN
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cana-4953	163	20	“	"	PUNCT
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cana-4953	163	22	:	:	PUNCT
cana-4953	163	23	a	a	DET
cana-4953	163	24	novel	novel	NOUN
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cana-4953	163	26	and	and	CCONJ
cana-4953	163	27	deep	deep	ADJ
cana-4953	163	28	transfer	transfer	NOUN
cana-4953	163	29	learning	learn	VERB
cana-4953	163	30	benchmark	benchmark	NOUN
cana-4953	163	31	for	for	ADP
cana-4953	163	32	forest	forest	NOUN
cana-4953	163	33	fire	fire	NOUN
cana-4953	163	34	detection	detection	NOUN
cana-4953	163	35	,	,	PUNCT
cana-4953	163	36	”	"	PUNCT
cana-4953	163	37	mobile	mobile	ADJ
cana-4953	163	38	information	information	NOUN
cana-4953	163	39	systems	system	NOUN
cana-4953	163	40	,	,	PUNCT
cana-4953	163	41	vol	vol	NOUN
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cana-4953	163	44	,	,	PUNCT
cana-4953	163	45	2022	2022	NUM
cana-4953	163	46	,	,	PUNCT
cana-4953	163	47	doi	doi	NOUN
cana-4953	163	48	:	:	PUNCT
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cana-4953	163	50	.	.	PUNCT
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cana-4953	164	4	r.	r.	PROPN
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cana-4953	164	6	priya	priya	PROPN
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cana-4953	164	8	k.	k.	PROPN
cana-4953	164	9	vani	vani	PROPN
cana-4953	164	10	,	,	PUNCT
cana-4953	164	11	“	"	PUNCT
cana-4953	164	12	deep	deep	ADJ
cana-4953	164	13	learning	learning	NOUN
cana-4953	164	14	based	base	VERB
cana-4953	164	15	forest	forest	NOUN
cana-4953	164	16	fire	fire	NOUN
cana-4953	164	17	classification	classification	NOUN
cana-4953	164	18	and	and	CCONJ
cana-4953	164	19	detection	detection	NOUN
cana-4953	164	20	in	in	ADP
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cana-4953	164	22	images	image	NOUN
cana-4953	164	23	,	,	PUNCT
cana-4953	164	24	”	"	PUNCT
cana-4953	164	25	in	in	ADP
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cana-4953	164	28	the	the	DET
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cana-4953	164	31	conference	conference	NOUN
cana-4953	164	32	on	on	ADP
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cana-4953	164	34	computing	computing	NOUN
cana-4953	164	35	,	,	PUNCT
cana-4953	164	36	icoac	icoac	NOUN
cana-4953	164	37	2019	2019	NUM
cana-4953	164	38	,	,	PUNCT
cana-4953	164	39	institute	institute	NOUN
cana-4953	164	40	of	of	ADP
cana-4953	164	41	electrical	electrical	ADJ
cana-4953	164	42	and	and	CCONJ
cana-4953	164	43	electronics	electronics	PROPN
cana-4953	164	44	engineers	engineers	PROPN
cana-4953	164	45	inc	inc	PROPN
cana-4953	164	46	.	.	PROPN
cana-4953	164	47	,	,	PUNCT
cana-4953	164	48	dec	dec	PROPN
cana-4953	164	49	.	.	PROPN
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cana-4953	164	51	,	,	PUNCT
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cana-4953	165	1	61–65	61–65	NUM
cana-4953	165	2	.	.	PUNCT
cana-4953	166	1	doi	doi	NOUN
cana-4953	166	2	:	:	PUNCT
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cana-4953	166	4	/	/	SYM
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cana-4953	167	3	]	]	PUNCT
cana-4953	167	4	x.	x.	PROPN
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cana-4953	167	6	,	,	PUNCT
cana-4953	167	7	l.	l.	PROPN
cana-4953	167	8	sun	sun	PROPN
cana-4953	167	9	,	,	PUNCT
cana-4953	167	10	and	and	CCONJ
cana-4953	167	11	y.	y.	PROPN
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cana-4953	167	20	on	on	ADP
cana-4953	167	21	convolutional	convolutional	ADJ
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cana-4953	167	24	,	,	PUNCT
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cana-4953	167	29	(	(	PUNCT
cana-4953	167	30	harbin	harbin	PROPN
cana-4953	167	31	)	)	PUNCT
cana-4953	167	32	,	,	PUNCT
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cana-4953	167	34	.	.	PROPN
cana-4953	167	35	32	32	NUM
cana-4953	167	36	,	,	PUNCT
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cana-4953	167	38	.	.	NOUN
cana-4953	167	39	5	5	NUM
cana-4953	167	40	,	,	PUNCT
cana-4953	167	41	pp	pp	ADJ
cana-4953	167	42	.	.	PUNCT
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cana-4953	168	2	,	,	PUNCT
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cana-4953	168	6	,	,	PUNCT
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cana-4953	168	10	/	/	SYM
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cana-4953	168	12	-	-	PUNCT
cana-4953	168	13	01230	01230	NUM
cana-4953	168	14	-	-	SYM
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cana-4953	168	16	.	.	PUNCT
cana-4953	169	1	[	[	X
cana-4953	169	2	12	12	NUM
cana-4953	169	3	]	]	PUNCT
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cana-4953	169	5	p.	p.	PROPN
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cana-4953	169	13	,	,	PUNCT
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cana-4953	169	18	image	image	NOUN
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cana-4953	169	20	techniques	technique	NOUN
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cana-4953	169	25	,	,	PUNCT
cana-4953	169	26	"	"	PUNCT
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cana-4953	169	30	on	on	ADP
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cana-4953	169	33	information	information	NOUN
cana-4953	169	34	technology	technology	NOUN
cana-4953	169	35	and	and	CCONJ
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cana-4953	169	37	systems	system	NOUN
cana-4953	169	38	(	(	PUNCT
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cana-4953	169	41	,	,	PUNCT
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cana-4953	169	45	.	.	PUNCT
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cana-4953	169	47	-	-	SYM
cana-4953	169	48	295	295	NUM
cana-4953	169	49	,	,	PUNCT
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cana-4953	170	30	fire	fire	NOUN
cana-4953	170	31	and	and	CCONJ
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cana-4953	170	34	,	,	PUNCT
cana-4953	170	35	"	"	PUNCT
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cana-4953	170	41	of	of	ADP
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cana-4953	170	44	industrial	industrial	PROPN
cana-4953	170	45	electronics	electronic	NOUN
cana-4953	170	46	society	society	NOUN
cana-4953	170	47	,	,	PUNCT
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cana-4953	170	49	,	,	PUNCT
cana-4953	170	50	pp	pp	ADJ
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cana-4953	170	52	877882	877882	NUM
cana-4953	170	53	,	,	PUNCT
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cana-4953	171	34	"	"	PUNCT
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cana-4953	171	37	conference	conference	NOUN
cana-4953	171	38	on	on	ADP
cana-4953	171	39	communication	communication	NOUN
cana-4953	171	40	systems	system	NOUN
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cana-4953	171	42	networks	network	NOUN
cana-4953	171	43	(	(	PUNCT
cana-4953	171	44	comsnets	comsnet	NOUN
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cana-4953	171	46	,	,	PUNCT
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cana-4953	171	48	,	,	PUNCT
cana-4953	171	49	09	09	NUM
cana-4953	171	50	/	/	SYM
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cana-4953	171	54	]	]	X
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cana-4953	171	61	t.	t.	PROPN
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cana-4953	171	64	s.	s.	PROPN
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cana-4953	171	70	"	"	PUNCT
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cana-4953	171	73	of	of	ADP
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cana-4953	171	83	(	(	PUNCT
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cana-4953	171	92	on	on	ADP
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cana-4953	171	94	systems	system	NOUN
cana-4953	171	95	and	and	CCONJ
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cana-4953	171	97	technology	technology	NOUN
cana-4953	171	98	(	(	PUNCT
cana-4953	171	99	icssit	icssit	NOUN
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cana-4953	172	1	1759	1759	NUM
cana-4953	172	2	-	-	SYM
cana-4953	172	3	1765	1765	NUM
cana-4953	172	4	,	,	PUNCT
cana-4953	172	5	doi	doi	NOUN
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cana-4953	172	7	10.1109	10.1109	NUM
cana-4953	172	8	/	/	SYM
cana-4953	172	9	icssit53264.2022.9716284	icssit53264.2022.9716284	NOUN
cana-4953	172	10	.	.	PUNCT