id	sid	tid	token	lemma	pos
cana-5388	1	1	communications	communication	NOUN
cana-5388	1	2	on	on	ADP
cana-5388	1	3	applied	apply	VERB
cana-5388	1	4	nonlinear	nonlinear	ADJ
cana-5388	1	5	analysis	analysis	NOUN
cana-5388	1	6	issn	issn	NOUN
cana-5388	1	7	:	:	PUNCT
cana-5388	1	8	1074	1074	NUM
cana-5388	1	9	-	-	PUNCT
cana-5388	1	10	133x	133x	NUM
cana-5388	1	11	vol	vol	NOUN
cana-5388	1	12	32	32	NUM
cana-5388	1	13	no	no	NOUN
cana-5388	1	14	.	.	PUNCT
cana-5388	2	1	1s	1s	NUM
cana-5388	2	2	(	(	PUNCT
cana-5388	2	3	2025	2025	NUM
cana-5388	2	4	)	)	PUNCT
cana-5388	2	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	2	6	655	655	NUM
cana-5388	2	7	augmenting	augment	VERB
cana-5388	2	8	the	the	DET
cana-5388	2	9	predictive	predictive	ADJ
cana-5388	2	10	precision	precision	NOUN
cana-5388	2	11	of	of	ADP
cana-5388	2	12	air	air	NOUN
cana-5388	2	13	quality	quality	NOUN
cana-5388	2	14	index	index	NOUN
cana-5388	2	15	estimation	estimation	NOUN
cana-5388	2	16	via	via	ADP
cana-5388	2	17	synergistic	synergistic	ADJ
cana-5388	2	18	integration	integration	NOUN
cana-5388	2	19	of	of	ADP
cana-5388	2	20	machine	machine	NOUN
cana-5388	2	21	learning	learning	NOUN
cana-5388	2	22	paradigms	paradigm	NOUN
cana-5388	2	23	and	and	CCONJ
cana-5388	2	24	optimization	optimization	NOUN
cana-5388	2	25	heuristics	heuristics	PROPN
cana-5388	2	26	dr	dr	PROPN
cana-5388	2	27	.	.	PROPN
cana-5388	2	28	n.	n.	PROPN
cana-5388	2	29	ravi1	ravi1	PROPN
cana-5388	2	30	,	,	PUNCT
cana-5388	2	31	dr	dr	PROPN
cana-5388	2	32	.	.	PROPN
cana-5388	2	33	k.	k.	PROPN
cana-5388	2	34	azhahudurai2	azhahudurai2	PROPN
cana-5388	3	1	*	*	PROPN
cana-5388	3	2	1associate	1associate	NUM
cana-5388	3	3	professor	professor	NOUN
cana-5388	3	4	,	,	PUNCT
cana-5388	3	5	pg	pg	PROPN
cana-5388	3	6	department	department	PROPN
cana-5388	3	7	of	of	ADP
cana-5388	3	8	computer	computer	NOUN
cana-5388	3	9	science	science	NOUN
cana-5388	3	10	,	,	PUNCT
cana-5388	3	11	government	government	NOUN
cana-5388	3	12	arts	arts	PROPN
cana-5388	3	13	college	college	PROPN
cana-5388	3	14	,	,	PUNCT
cana-5388	3	15	chidambaram	chidambaram	PROPN
cana-5388	3	16	–	–	PUNCT
cana-5388	3	17	608	608	NUM
cana-5388	3	18	102	102	NUM
cana-5388	3	19	,	,	PUNCT
cana-5388	3	20	tamil	tamil	PROPN
cana-5388	3	21	nadu	nadu	PROPN
cana-5388	3	22	,	,	PUNCT
cana-5388	3	23	india	india	PROPN
cana-5388	3	24	.	.	PUNCT
cana-5388	4	1	2assistant	2assistant	NUM
cana-5388	4	2	professor	professor	NOUN
cana-5388	4	3	,	,	PUNCT
cana-5388	4	4	department	department	NOUN
cana-5388	4	5	of	of	ADP
cana-5388	4	6	computer	computer	NOUN
cana-5388	4	7	science	science	NOUN
cana-5388	4	8	,	,	PUNCT
cana-5388	4	9	government	government	NOUN
cana-5388	4	10	arts	arts	PROPN
cana-5388	4	11	college	college	PROPN
cana-5388	4	12	(	(	PUNCT
cana-5388	4	13	autonomous	autonomous	ADJ
cana-5388	4	14	)	)	PUNCT
cana-5388	4	15	,	,	PUNCT
cana-5388	4	16	kumbakonam	kumbakonam	PROPN
cana-5388	4	17	612	612	NUM
cana-5388	4	18	002	002	NUM
cana-5388	4	19	,	,	PUNCT
cana-5388	4	20	(	(	PUNCT
cana-5388	4	21	deputed	depute	VERB
cana-5388	4	22	from	from	ADP
cana-5388	4	23	annamalai	annamalai	PROPN
cana-5388	4	24	university	university	PROPN
cana-5388	4	25	)	)	PUNCT
cana-5388	4	26	,	,	PUNCT
cana-5388	4	27	tamil	tamil	PROPN
cana-5388	4	28	nadu	nadu	PROPN
cana-5388	4	29	,	,	PUNCT
cana-5388	4	30	india	india	PROPN
cana-5388	4	31	.	.	PUNCT
cana-5388	4	32	email	email	NOUN
cana-5388	4	33	:	:	PUNCT
cana-5388	4	34	1csravi2017@gmail.com	1csravi2017@gmail.com	NUM
cana-5388	4	35	corresponding	correspond	VERB
cana-5388	4	36	email	email	NOUN
cana-5388	4	37	:	:	PUNCT
cana-5388	4	38	*	*	PUNCT
cana-5388	4	39	2jkmaz477@gmail.com	2jkmaz477@gmail.com	NUM
cana-5388	4	40	article	article	NOUN
cana-5388	4	41	history	history	NOUN
cana-5388	4	42	:	:	PUNCT
cana-5388	4	43	received	receive	VERB
cana-5388	4	44	:	:	PUNCT
cana-5388	4	45	21	21	NUM
cana-5388	4	46	-	-	SYM
cana-5388	4	47	12	12	NUM
cana-5388	4	48	-	-	PUNCT
cana-5388	4	49	2024	2024	NUM
cana-5388	4	50	revised	revise	VERB
cana-5388	4	51	:	:	PUNCT
cana-5388	4	52	10	10	NUM
cana-5388	4	53	-	-	SYM
cana-5388	4	54	01	01	NUM
cana-5388	4	55	-	-	PUNCT
cana-5388	4	56	2025	2025	NUM
cana-5388	4	57	accepted	accept	VERB
cana-5388	4	58	:	:	PUNCT
cana-5388	4	59	08	08	NUM
cana-5388	4	60	-	-	PUNCT
cana-5388	4	61	02	02	NUM
cana-5388	4	62	-	-	PUNCT
cana-5388	4	63	2025	2025	NUM
cana-5388	4	64	abstract	abstract	NOUN
cana-5388	4	65	:	:	PUNCT
cana-5388	4	66	air	air	NOUN
cana-5388	4	67	pollution	pollution	NOUN
cana-5388	4	68	has	have	AUX
cana-5388	4	69	become	become	VERB
cana-5388	4	70	a	a	DET
cana-5388	4	71	serious	serious	ADJ
cana-5388	4	72	issue	issue	NOUN
cana-5388	4	73	that	that	PRON
cana-5388	4	74	affects	affect	VERB
cana-5388	4	75	public	public	ADJ
cana-5388	4	76	health	health	NOUN
cana-5388	4	77	and	and	CCONJ
cana-5388	4	78	the	the	DET
cana-5388	4	79	environment	environment	NOUN
cana-5388	4	80	.	.	PUNCT
cana-5388	5	1	to	to	PART
cana-5388	5	2	deal	deal	VERB
cana-5388	5	3	with	with	ADP
cana-5388	5	4	this	this	PRON
cana-5388	5	5	,	,	PUNCT
cana-5388	5	6	it	it	PRON
cana-5388	5	7	is	be	AUX
cana-5388	5	8	important	important	ADJ
cana-5388	5	9	to	to	PART
cana-5388	5	10	predict	predict	VERB
cana-5388	5	11	the	the	DET
cana-5388	5	12	air	air	NOUN
cana-5388	5	13	quality	quality	NOUN
cana-5388	5	14	index	index	NOUN
cana-5388	5	15	(	(	PUNCT
cana-5388	5	16	aqi	aqi	PROPN
cana-5388	5	17	)	)	PUNCT
cana-5388	5	18	accurately	accurately	ADV
cana-5388	5	19	.	.	PUNCT
cana-5388	6	1	this	this	DET
cana-5388	6	2	research	research	NOUN
cana-5388	6	3	presents	present	VERB
cana-5388	6	4	a	a	DET
cana-5388	6	5	method	method	NOUN
cana-5388	6	6	that	that	PRON
cana-5388	6	7	combines	combine	VERB
cana-5388	6	8	machine	machine	NOUN
cana-5388	6	9	learning	learn	VERB
cana-5388	6	10	techniques	technique	NOUN
cana-5388	6	11	with	with	ADP
cana-5388	6	12	optimization	optimization	NOUN
cana-5388	6	13	strategies	strategy	NOUN
cana-5388	6	14	to	to	PART
cana-5388	6	15	improve	improve	VERB
cana-5388	6	16	aqi	aqi	ADJ
cana-5388	6	17	prediction	prediction	NOUN
cana-5388	6	18	.	.	PUNCT
cana-5388	7	1	the	the	DET
cana-5388	7	2	approach	approach	NOUN
cana-5388	7	3	uses	use	VERB
cana-5388	7	4	past	past	ADP
cana-5388	7	5	environmental	environmental	ADJ
cana-5388	7	6	data	datum	NOUN
cana-5388	7	7	and	and	CCONJ
cana-5388	7	8	applies	apply	VERB
cana-5388	7	9	machine	machine	NOUN
cana-5388	7	10	learning	learning	NOUN
cana-5388	7	11	models	model	NOUN
cana-5388	7	12	,	,	PUNCT
cana-5388	7	13	especially	especially	ADV
cana-5388	7	14	ensemble	ensemble	ADJ
cana-5388	7	15	methods	method	NOUN
cana-5388	7	16	to	to	PART
cana-5388	7	17	understand	understand	VERB
cana-5388	7	18	the	the	DET
cana-5388	7	19	complex	complex	ADJ
cana-5388	7	20	patterns	pattern	NOUN
cana-5388	7	21	between	between	ADP
cana-5388	7	22	pollutants	pollutant	NOUN
cana-5388	7	23	and	and	CCONJ
cana-5388	7	24	air	air	NOUN
cana-5388	7	25	quality	quality	NOUN
cana-5388	7	26	.	.	PUNCT
cana-5388	8	1	in	in	ADP
cana-5388	8	2	addition	addition	NOUN
cana-5388	8	3	,	,	PUNCT
cana-5388	8	4	optimization	optimization	NOUN
cana-5388	8	5	algorithms	algorithm	NOUN
cana-5388	8	6	are	be	AUX
cana-5388	8	7	used	use	VERB
cana-5388	8	8	to	to	ADP
cana-5388	8	9	fine	fine	ADJ
cana-5388	8	10	-	-	PUNCT
cana-5388	8	11	tune	tune	NOUN
cana-5388	8	12	the	the	DET
cana-5388	8	13	models	model	NOUN
cana-5388	8	14	,	,	PUNCT
cana-5388	8	15	helping	help	VERB
cana-5388	8	16	them	they	PRON
cana-5388	8	17	perform	perform	VERB
cana-5388	8	18	better	well	ADV
cana-5388	8	19	and	and	CCONJ
cana-5388	8	20	give	give	VERB
cana-5388	8	21	more	more	ADV
cana-5388	8	22	accurate	accurate	ADJ
cana-5388	8	23	results	result	NOUN
cana-5388	8	24	.	.	PUNCT
cana-5388	9	1	tests	test	NOUN
cana-5388	9	2	conducted	conduct	VERB
cana-5388	9	3	on	on	ADP
cana-5388	9	4	standard	standard	ADJ
cana-5388	9	5	datasets	dataset	NOUN
cana-5388	9	6	show	show	VERB
cana-5388	9	7	that	that	SCONJ
cana-5388	9	8	this	this	DET
cana-5388	9	9	combined	combined	ADJ
cana-5388	9	10	method	method	NOUN
cana-5388	9	11	gives	give	VERB
cana-5388	9	12	higher	high	ADJ
cana-5388	9	13	accuracy	accuracy	NOUN
cana-5388	9	14	and	and	CCONJ
cana-5388	9	15	better	well	ADJ
cana-5388	9	16	performance	performance	NOUN
cana-5388	9	17	compared	compare	VERB
cana-5388	9	18	to	to	ADP
cana-5388	9	19	traditional	traditional	ADJ
cana-5388	9	20	approaches	approach	NOUN
cana-5388	9	21	.	.	PUNCT
cana-5388	10	1	overall	overall	ADV
cana-5388	10	2	,	,	PUNCT
cana-5388	10	3	this	this	DET
cana-5388	10	4	study	study	NOUN
cana-5388	10	5	highlights	highlight	VERB
cana-5388	10	6	how	how	SCONJ
cana-5388	10	7	machine	machine	NOUN
cana-5388	10	8	learning	learning	NOUN
cana-5388	10	9	and	and	CCONJ
cana-5388	10	10	optimization	optimization	NOUN
cana-5388	10	11	can	can	AUX
cana-5388	10	12	work	work	VERB
cana-5388	10	13	together	together	ADV
cana-5388	10	14	to	to	PART
cana-5388	10	15	solve	solve	VERB
cana-5388	10	16	real	real	ADJ
cana-5388	10	17	-	-	PUNCT
cana-5388	10	18	world	world	NOUN
cana-5388	10	19	air	air	NOUN
cana-5388	10	20	quality	quality	NOUN
cana-5388	10	21	prediction	prediction	NOUN
cana-5388	10	22	problems	problem	NOUN
cana-5388	10	23	effectively	effectively	ADV
cana-5388	10	24	.	.	PUNCT
cana-5388	11	1	keywords	keyword	NOUN
cana-5388	11	2	:	:	PUNCT
cana-5388	11	3	air	air	NOUN
cana-5388	11	4	quality	quality	PROPN
cana-5388	11	5	index	index	NOUN
cana-5388	11	6	(	(	PUNCT
cana-5388	11	7	aqi	aqi	NOUN
cana-5388	11	8	)	)	PUNCT
cana-5388	11	9	prediction	prediction	NOUN
cana-5388	11	10	,	,	PUNCT
cana-5388	11	11	machine	machine	NOUN
cana-5388	11	12	learning	learning	NOUN
cana-5388	11	13	algorithms	algorithm	NOUN
cana-5388	11	14	,	,	PUNCT
cana-5388	11	15	optimization	optimization	NOUN
cana-5388	11	16	heuristics	heuristic	NOUN
cana-5388	11	17	,	,	PUNCT
cana-5388	11	18	predictive	predictive	ADJ
cana-5388	11	19	modeling	modeling	NOUN
cana-5388	11	20	,	,	PUNCT
cana-5388	11	21	and	and	CCONJ
cana-5388	11	22	environmental	environmental	ADJ
cana-5388	11	23	data	datum	NOUN
cana-5388	11	24	analytics	analytic	NOUN
cana-5388	11	25	1	1	NUM
cana-5388	11	26	.	.	PUNCT
cana-5388	12	1	introduction	introduction	NOUN
cana-5388	12	2	air	air	NOUN
cana-5388	12	3	pollution	pollution	NOUN
cana-5388	12	4	has	have	AUX
cana-5388	12	5	become	become	VERB
cana-5388	12	6	a	a	DET
cana-5388	12	7	major	major	ADJ
cana-5388	12	8	environmental	environmental	ADJ
cana-5388	12	9	issue	issue	NOUN
cana-5388	12	10	in	in	ADP
cana-5388	12	11	recent	recent	ADJ
cana-5388	12	12	years	year	NOUN
cana-5388	12	13	.	.	PUNCT
cana-5388	13	1	it	it	PRON
cana-5388	13	2	affects	affect	VERB
cana-5388	13	3	not	not	PART
cana-5388	13	4	only	only	ADV
cana-5388	13	5	human	human	ADJ
cana-5388	13	6	health	health	NOUN
cana-5388	13	7	but	but	CCONJ
cana-5388	13	8	also	also	ADV
cana-5388	13	9	the	the	DET
cana-5388	13	10	natural	natural	ADJ
cana-5388	13	11	surroundings	surrounding	NOUN
cana-5388	13	12	and	and	CCONJ
cana-5388	13	13	climate	climate	NOUN
cana-5388	13	14	.	.	PUNCT
cana-5388	14	1	the	the	DET
cana-5388	14	2	air	air	NOUN
cana-5388	14	3	quality	quality	PROPN
cana-5388	14	4	index	index	NOUN
cana-5388	14	5	(	(	PUNCT
cana-5388	14	6	aqi	aqi	PROPN
cana-5388	14	7	)	)	PUNCT
cana-5388	14	8	is	be	AUX
cana-5388	14	9	a	a	DET
cana-5388	14	10	standard	standard	ADJ
cana-5388	14	11	measure	measure	NOUN
cana-5388	14	12	used	use	VERB
cana-5388	14	13	to	to	PART
cana-5388	14	14	understand	understand	VERB
cana-5388	14	15	how	how	SCONJ
cana-5388	14	16	polluted	polluted	ADJ
cana-5388	14	17	the	the	DET
cana-5388	14	18	air	air	NOUN
cana-5388	14	19	is	be	AUX
cana-5388	14	20	.	.	PUNCT
cana-5388	15	1	it	it	PRON
cana-5388	15	2	provides	provide	VERB
cana-5388	15	3	important	important	ADJ
cana-5388	15	4	information	information	NOUN
cana-5388	15	5	to	to	ADP
cana-5388	15	6	the	the	DET
cana-5388	15	7	public	public	NOUN
cana-5388	15	8	and	and	CCONJ
cana-5388	15	9	helps	help	VERB
cana-5388	15	10	authorities	authority	NOUN
cana-5388	15	11	take	take	VERB
cana-5388	15	12	necessary	necessary	ADJ
cana-5388	15	13	actions	action	NOUN
cana-5388	15	14	.	.	PUNCT
cana-5388	16	1	predicting	predict	VERB
cana-5388	16	2	aqi	aqi	PROPN
cana-5388	16	3	in	in	ADP
cana-5388	16	4	advance	advance	NOUN
cana-5388	16	5	is	be	AUX
cana-5388	16	6	very	very	ADV
cana-5388	16	7	important	important	ADJ
cana-5388	16	8	to	to	PART
cana-5388	16	9	manage	manage	VERB
cana-5388	16	10	air	air	NOUN
cana-5388	16	11	quality	quality	NOUN
cana-5388	16	12	and	and	CCONJ
cana-5388	16	13	reduce	reduce	VERB
cana-5388	16	14	health	health	NOUN
cana-5388	16	15	problems	problem	NOUN
cana-5388	16	16	caused	cause	VERB
cana-5388	16	17	by	by	ADP
cana-5388	16	18	harmful	harmful	ADJ
cana-5388	16	19	pollutants	pollutant	NOUN
cana-5388	16	20	like	like	ADP
cana-5388	16	21	pm₂.₅	pm₂.₅	ADJ
cana-5388	16	22	,	,	PUNCT
cana-5388	16	23	pm₁₀	pm₁₀	PROPN
cana-5388	16	24	,	,	PUNCT
cana-5388	16	25	no₂	no₂	PROPN
cana-5388	16	26	,	,	PUNCT
cana-5388	16	27	so₂	so₂	PROPN
cana-5388	16	28	,	,	PUNCT
cana-5388	16	29	co	co	NOUN
cana-5388	16	30	,	,	PUNCT
cana-5388	16	31	and	and	CCONJ
cana-5388	16	32	o₃.	o₃.	X
cana-5388	16	33	nowadays	nowadays	ADV
cana-5388	16	34	,	,	PUNCT
cana-5388	16	35	machine	machine	NOUN
cana-5388	16	36	learning	learning	NOUN
cana-5388	16	37	(	(	PUNCT
cana-5388	16	38	ml	ml	NOUN
cana-5388	16	39	)	)	PUNCT
cana-5388	16	40	methods	method	NOUN
cana-5388	16	41	are	be	AUX
cana-5388	16	42	being	be	AUX
cana-5388	16	43	used	use	VERB
cana-5388	16	44	to	to	PART
cana-5388	16	45	understand	understand	VERB
cana-5388	16	46	and	and	CCONJ
cana-5388	16	47	predict	predict	VERB
cana-5388	16	48	aqi	aqi	PROPN
cana-5388	16	49	more	more	ADV
cana-5388	16	50	accurately	accurately	ADV
cana-5388	16	51	.	.	PUNCT
cana-5388	17	1	techniques	technique	NOUN
cana-5388	17	2	such	such	ADJ
cana-5388	17	3	as	as	ADP
cana-5388	17	4	support	support	NOUN
cana-5388	17	5	vector	vector	NOUN
cana-5388	17	6	machines	machine	NOUN
cana-5388	17	7	(	(	PUNCT
cana-5388	17	8	svm	svm	PROPN
cana-5388	17	9	)	)	PUNCT
cana-5388	17	10	[	[	X
cana-5388	17	11	1	1	NUM
cana-5388	17	12	]	]	PUNCT
cana-5388	17	13	,	,	PUNCT
cana-5388	17	14	random	random	ADJ
cana-5388	17	15	forest	forest	NOUN
cana-5388	18	1	[	[	X
cana-5388	18	2	2	2	NUM
cana-5388	18	3	]	]	PUNCT
cana-5388	18	4	,	,	PUNCT
cana-5388	18	5	gradient	gradient	NOUN
cana-5388	18	6	boosting	boost	VERB
cana-5388	18	7	[	[	X
cana-5388	18	8	3	3	NUM
cana-5388	18	9	]	]	PUNCT
cana-5388	18	10	,	,	PUNCT
cana-5388	18	11	and	and	CCONJ
cana-5388	18	12	artificial	artificial	ADJ
cana-5388	18	13	neural	neural	ADJ
cana-5388	18	14	networks	network	NOUN
cana-5388	18	15	(	(	PUNCT
cana-5388	18	16	ann	ann	PROPN
cana-5388	18	17	)	)	PUNCT
cana-5388	19	1	[	[	X
cana-5388	19	2	4	4	X
cana-5388	19	3	]	]	PUNCT
cana-5388	19	4	have	have	AUX
cana-5388	19	5	been	be	AUX
cana-5388	19	6	successfully	successfully	ADV
cana-5388	19	7	applied	apply	VERB
cana-5388	19	8	in	in	ADP
cana-5388	19	9	this	this	DET
cana-5388	19	10	field	field	NOUN
cana-5388	19	11	.	.	PUNCT
cana-5388	20	1	especially	especially	ADV
cana-5388	20	2	,	,	PUNCT
cana-5388	20	3	ensemble	ensemble	ADJ
cana-5388	20	4	learning	learning	NOUN
cana-5388	20	5	methods	method	NOUN
cana-5388	20	6	have	have	AUX
cana-5388	20	7	shown	show	VERB
cana-5388	20	8	better	well	ADJ
cana-5388	20	9	performance	performance	NOUN
cana-5388	20	10	by	by	ADP
cana-5388	20	11	combining	combine	VERB
cana-5388	20	12	results	result	NOUN
cana-5388	20	13	from	from	ADP
cana-5388	20	14	multiple	multiple	ADJ
cana-5388	20	15	models	model	NOUN
cana-5388	20	16	to	to	PART
cana-5388	20	17	give	give	VERB
cana-5388	20	18	more	more	ADV
cana-5388	20	19	accurate	accurate	ADJ
cana-5388	20	20	predictions	prediction	NOUN
cana-5388	20	21	[	[	X
cana-5388	20	22	5	5	NUM
cana-5388	20	23	]	]	PUNCT
cana-5388	20	24	.	.	PUNCT
cana-5388	21	1	however	however	ADV
cana-5388	21	2	,	,	PUNCT
cana-5388	21	3	the	the	DET
cana-5388	21	4	success	success	NOUN
cana-5388	21	5	of	of	ADP
cana-5388	21	6	these	these	DET
cana-5388	21	7	models	model	NOUN
cana-5388	21	8	depends	depend	VERB
cana-5388	21	9	on	on	ADP
cana-5388	21	10	selecting	select	VERB
cana-5388	21	11	the	the	DET
cana-5388	21	12	right	right	ADJ
cana-5388	21	13	parameters	parameter	NOUN
cana-5388	21	14	and	and	CCONJ
cana-5388	21	15	input	input	NOUN
cana-5388	21	16	features	feature	NOUN
cana-5388	21	17	.	.	PUNCT
cana-5388	22	1	to	to	PART
cana-5388	22	2	improve	improve	VERB
cana-5388	22	3	model	model	NOUN
cana-5388	22	4	accuracy	accuracy	NOUN
cana-5388	22	5	,	,	PUNCT
cana-5388	22	6	optimization	optimization	NOUN
cana-5388	22	7	techniques	technique	NOUN
cana-5388	22	8	like	like	ADP
cana-5388	22	9	genetic	genetic	ADJ
cana-5388	22	10	algorithms	algorithm	NOUN
cana-5388	22	11	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-5388	22	12	mailto:jkmaz477@gmail.com	mailto:jkmaz477@gmail.com	PROPN
cana-5388	22	13	communications	communication	NOUN
cana-5388	22	14	on	on	ADP
cana-5388	22	15	applied	apply	VERB
cana-5388	22	16	nonlinear	nonlinear	ADJ
cana-5388	22	17	analysis	analysis	NOUN
cana-5388	22	18	issn	issn	NOUN
cana-5388	22	19	:	:	PUNCT
cana-5388	22	20	1074	1074	NUM
cana-5388	22	21	-	-	PUNCT
cana-5388	22	22	133x	133x	NUM
cana-5388	22	23	vol	vol	NOUN
cana-5388	22	24	32	32	NUM
cana-5388	22	25	no	no	NOUN
cana-5388	22	26	.	.	PUNCT
cana-5388	23	1	1s	1s	NUM
cana-5388	23	2	(	(	PUNCT
cana-5388	23	3	2025	2025	NUM
cana-5388	23	4	)	)	PUNCT
cana-5388	24	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	24	2	656	656	NUM
cana-5388	24	3	(	(	PUNCT
cana-5388	24	4	ga	ga	NOUN
cana-5388	24	5	)	)	PUNCT
cana-5388	24	6	,	,	PUNCT
cana-5388	24	7	particle	particle	NOUN
cana-5388	24	8	swarm	swarm	NOUN
cana-5388	24	9	optimization	optimization	NOUN
cana-5388	24	10	(	(	PUNCT
cana-5388	24	11	pso	pso	NOUN
cana-5388	24	12	)	)	PUNCT
cana-5388	24	13	,	,	PUNCT
cana-5388	24	14	and	and	CCONJ
cana-5388	24	15	ant	ant	ADJ
cana-5388	24	16	colony	colony	NOUN
cana-5388	24	17	optimization	optimization	NOUN
cana-5388	24	18	(	(	PUNCT
cana-5388	24	19	aco	aco	PROPN
cana-5388	24	20	)	)	PUNCT
cana-5388	24	21	are	be	AUX
cana-5388	24	22	used	use	VERB
cana-5388	24	23	.	.	PUNCT
cana-5388	25	1	these	these	DET
cana-5388	25	2	methods	method	NOUN
cana-5388	25	3	help	help	VERB
cana-5388	25	4	find	find	VERB
cana-5388	25	5	the	the	DET
cana-5388	25	6	best	good	ADJ
cana-5388	25	7	values	value	NOUN
cana-5388	25	8	for	for	ADP
cana-5388	25	9	model	model	NOUN
cana-5388	25	10	parameters	parameter	NOUN
cana-5388	25	11	in	in	ADP
cana-5388	25	12	an	an	DET
cana-5388	25	13	efficient	efficient	ADJ
cana-5388	25	14	way	way	NOUN
cana-5388	26	1	[	[	X
cana-5388	26	2	6	6	NUM
cana-5388	26	3	]	]	PUNCT
cana-5388	26	4	,	,	PUNCT
cana-5388	26	5	[	[	X
cana-5388	26	6	7	7	NUM
cana-5388	26	7	]	]	PUNCT
cana-5388	26	8	.	.	PUNCT
cana-5388	27	1	when	when	SCONJ
cana-5388	27	2	machine	machine	NOUN
cana-5388	27	3	learning	learning	NOUN
cana-5388	27	4	is	be	AUX
cana-5388	27	5	combined	combine	VERB
cana-5388	27	6	with	with	ADP
cana-5388	27	7	these	these	DET
cana-5388	27	8	optimization	optimization	NOUN
cana-5388	27	9	methods	method	NOUN
cana-5388	27	10	,	,	PUNCT
cana-5388	27	11	the	the	DET
cana-5388	27	12	results	result	NOUN
cana-5388	27	13	are	be	AUX
cana-5388	27	14	even	even	ADV
cana-5388	27	15	better	well	ADJ
cana-5388	27	16	for	for	ADP
cana-5388	27	17	predicting	predict	VERB
cana-5388	27	18	environmental	environmental	ADJ
cana-5388	27	19	factors	factor	NOUN
cana-5388	27	20	[	[	X
cana-5388	27	21	8	8	NUM
cana-5388	27	22	]	]	PUNCT
cana-5388	27	23	,	,	PUNCT
cana-5388	27	24	[	[	X
cana-5388	27	25	9	9	NUM
cana-5388	27	26	]	]	PUNCT
cana-5388	27	27	.	.	PUNCT
cana-5388	28	1	in	in	ADP
cana-5388	28	2	this	this	DET
cana-5388	28	3	study	study	NOUN
cana-5388	28	4	,	,	PUNCT
cana-5388	28	5	we	we	PRON
cana-5388	28	6	focus	focus	VERB
cana-5388	28	7	on	on	ADP
cana-5388	28	8	improving	improve	VERB
cana-5388	28	9	the	the	DET
cana-5388	28	10	accuracy	accuracy	NOUN
cana-5388	28	11	of	of	ADP
cana-5388	28	12	aqi	aqi	PROPN
cana-5388	28	13	prediction	prediction	NOUN
cana-5388	28	14	by	by	ADP
cana-5388	28	15	combining	combine	VERB
cana-5388	28	16	machine	machine	NOUN
cana-5388	28	17	learning	learning	NOUN
cana-5388	28	18	models	model	NOUN
cana-5388	28	19	with	with	ADP
cana-5388	28	20	optimization	optimization	NOUN
cana-5388	28	21	techniques	technique	NOUN
cana-5388	28	22	.	.	PUNCT
cana-5388	29	1	we	we	PRON
cana-5388	29	2	use	use	VERB
cana-5388	29	3	past	past	ADJ
cana-5388	29	4	environmental	environmental	ADJ
cana-5388	29	5	data	datum	NOUN
cana-5388	29	6	and	and	CCONJ
cana-5388	29	7	apply	apply	VERB
cana-5388	29	8	ensemble	ensemble	ADJ
cana-5388	29	9	learning	learning	NOUN
cana-5388	29	10	methods	method	NOUN
cana-5388	29	11	with	with	ADP
cana-5388	29	12	fine	fine	ADV
cana-5388	29	13	-	-	PUNCT
cana-5388	29	14	tuning	tuning	NOUN
cana-5388	29	15	through	through	ADP
cana-5388	29	16	optimization	optimization	NOUN
cana-5388	29	17	algorithms	algorithm	NOUN
cana-5388	29	18	.	.	PUNCT
cana-5388	30	1	this	this	DET
cana-5388	30	2	approach	approach	NOUN
cana-5388	30	3	helps	help	VERB
cana-5388	30	4	reduce	reduce	VERB
cana-5388	30	5	errors	error	NOUN
cana-5388	30	6	and	and	CCONJ
cana-5388	30	7	avoid	avoid	VERB
cana-5388	30	8	overfitting	overfitte	VERB
cana-5388	30	9	.	.	PUNCT
cana-5388	31	1	the	the	DET
cana-5388	31	2	results	result	NOUN
cana-5388	31	3	show	show	VERB
cana-5388	31	4	that	that	SCONJ
cana-5388	31	5	our	our	PRON
cana-5388	31	6	method	method	NOUN
cana-5388	31	7	performs	perform	VERB
cana-5388	31	8	better	well	ADV
cana-5388	31	9	than	than	ADP
cana-5388	31	10	using	use	VERB
cana-5388	31	11	machine	machine	NOUN
cana-5388	31	12	learning	learning	NOUN
cana-5388	31	13	alone	alone	ADV
cana-5388	31	14	,	,	PUNCT
cana-5388	31	15	making	make	VERB
cana-5388	31	16	it	it	PRON
cana-5388	31	17	more	more	ADV
cana-5388	31	18	suitable	suitable	ADJ
cana-5388	31	19	for	for	ADP
cana-5388	31	20	real	real	ADJ
cana-5388	31	21	-	-	PUNCT
cana-5388	31	22	world	world	NOUN
cana-5388	31	23	air	air	NOUN
cana-5388	31	24	quality	quality	NOUN
cana-5388	31	25	prediction	prediction	NOUN
cana-5388	31	26	tasks	task	NOUN
cana-5388	31	27	.	.	PUNCT
cana-5388	32	1	many	many	ADJ
cana-5388	32	2	researchers	researcher	NOUN
cana-5388	32	3	have	have	AUX
cana-5388	32	4	worked	work	VERB
cana-5388	32	5	on	on	ADP
cana-5388	32	6	using	use	VERB
cana-5388	32	7	machine	machine	NOUN
cana-5388	32	8	learning	learning	NOUN
cana-5388	32	9	methods	method	NOUN
cana-5388	32	10	to	to	PART
cana-5388	32	11	predict	predict	VERB
cana-5388	32	12	the	the	DET
cana-5388	32	13	air	air	NOUN
cana-5388	32	14	quality	quality	NOUN
cana-5388	32	15	index	index	NOUN
cana-5388	32	16	(	(	PUNCT
cana-5388	32	17	aqi	aqi	PROPN
cana-5388	32	18	)	)	PUNCT
cana-5388	32	19	by	by	ADP
cana-5388	32	20	studying	study	VERB
cana-5388	32	21	past	past	ADP
cana-5388	32	22	environmental	environmental	ADJ
cana-5388	32	23	data	datum	NOUN
cana-5388	32	24	.	.	PUNCT
cana-5388	33	1	these	these	DET
cana-5388	33	2	approaches	approach	NOUN
cana-5388	33	3	help	help	VERB
cana-5388	33	4	in	in	ADP
cana-5388	33	5	understanding	understand	VERB
cana-5388	33	6	the	the	DET
cana-5388	33	7	complicated	complicated	ADJ
cana-5388	33	8	and	and	CCONJ
cana-5388	33	9	non	non	ADJ
cana-5388	33	10	-	-	ADJ
cana-5388	33	11	linear	linear	ADJ
cana-5388	33	12	link	link	NOUN
cana-5388	33	13	between	between	ADP
cana-5388	33	14	different	different	ADJ
cana-5388	33	15	pollutants	pollutant	NOUN
cana-5388	33	16	and	and	CCONJ
cana-5388	33	17	the	the	DET
cana-5388	33	18	quality	quality	NOUN
cana-5388	33	19	of	of	ADP
cana-5388	33	20	air	air	NOUN
cana-5388	33	21	.	.	PUNCT
cana-5388	34	1	hybrid	hybrid	ADJ
cana-5388	34	2	system	system	NOUN
cana-5388	34	3	using	use	VERB
cana-5388	34	4	deep	deep	ADJ
cana-5388	34	5	learning	learning	NOUN
cana-5388	34	6	along	along	ADP
cana-5388	34	7	with	with	ADP
cana-5388	34	8	statistical	statistical	ADJ
cana-5388	34	9	feature	feature	NOUN
cana-5388	34	10	selection	selection	NOUN
cana-5388	34	11	to	to	PART
cana-5388	34	12	improve	improve	VERB
cana-5388	34	13	aqi	aqi	ADJ
cana-5388	34	14	prediction	prediction	NOUN
cana-5388	34	15	.	.	PUNCT
cana-5388	35	1	their	their	PRON
cana-5388	35	2	work	work	NOUN
cana-5388	35	3	showed	show	VERB
cana-5388	35	4	that	that	SCONJ
cana-5388	35	5	deep	deep	ADJ
cana-5388	35	6	neural	neural	ADJ
cana-5388	35	7	networks	network	NOUN
cana-5388	35	8	are	be	AUX
cana-5388	35	9	good	good	ADJ
cana-5388	35	10	at	at	ADP
cana-5388	35	11	learning	learn	VERB
cana-5388	35	12	complex	complex	ADJ
cana-5388	35	13	patterns	pattern	NOUN
cana-5388	35	14	from	from	ADP
cana-5388	35	15	environmental	environmental	ADJ
cana-5388	35	16	data	datum	NOUN
cana-5388	35	17	[	[	X
cana-5388	35	18	1	1	NUM
cana-5388	35	19	]	]	PUNCT
cana-5388	35	20	.	.	PUNCT
cana-5388	36	1	in	in	ADP
cana-5388	36	2	a	a	DET
cana-5388	36	3	similar	similar	ADJ
cana-5388	36	4	study	study	NOUN
cana-5388	36	5	,	,	PUNCT
cana-5388	36	6	used	use	VERB
cana-5388	36	7	principal	principal	ADJ
cana-5388	36	8	component	component	NOUN
cana-5388	36	9	analysis	analysis	NOUN
cana-5388	36	10	(	(	PUNCT
cana-5388	36	11	pca	pca	NOUN
cana-5388	36	12	)	)	PUNCT
cana-5388	36	13	with	with	ADP
cana-5388	36	14	the	the	DET
cana-5388	36	15	random	random	ADJ
cana-5388	36	16	forest	forest	NOUN
cana-5388	36	17	model	model	NOUN
cana-5388	36	18	to	to	PART
cana-5388	36	19	reduce	reduce	VERB
cana-5388	36	20	the	the	DET
cana-5388	36	21	number	number	NOUN
cana-5388	36	22	of	of	ADP
cana-5388	36	23	input	input	NOUN
cana-5388	36	24	features	feature	NOUN
cana-5388	36	25	and	and	CCONJ
cana-5388	36	26	improve	improve	VERB
cana-5388	36	27	accuracy	accuracy	NOUN
cana-5388	36	28	,	,	PUNCT
cana-5388	36	29	which	which	PRON
cana-5388	36	30	proved	prove	VERB
cana-5388	36	31	how	how	SCONJ
cana-5388	36	32	effective	effective	ADJ
cana-5388	36	33	ensemble	ensemble	ADJ
cana-5388	36	34	methods	method	NOUN
cana-5388	36	35	can	can	AUX
cana-5388	36	36	be	be	AUX
cana-5388	36	37	used	use	VERB
cana-5388	36	38	[	[	PUNCT
cana-5388	36	39	2	2	NUM
cana-5388	36	40	]	]	PUNCT
cana-5388	36	41	.	.	PUNCT
cana-5388	37	1	gradient	gradient	ADJ
cana-5388	37	2	boosting	boost	VERB
cana-5388	37	3	models	model	NOUN
cana-5388	37	4	like	like	ADP
cana-5388	37	5	xgboost	xgboost	ADV
cana-5388	37	6	have	have	AUX
cana-5388	37	7	also	also	ADV
cana-5388	37	8	become	become	VERB
cana-5388	37	9	popular	popular	ADJ
cana-5388	37	10	.	.	PUNCT
cana-5388	38	1	xgboost	xgboost	X
cana-5388	38	2	to	to	PART
cana-5388	38	3	forecast	forecast	VERB
cana-5388	38	4	pollution	pollution	NOUN
cana-5388	38	5	levels	level	NOUN
cana-5388	38	6	and	and	CCONJ
cana-5388	38	7	got	get	VERB
cana-5388	38	8	good	good	ADJ
cana-5388	38	9	accuracy	accuracy	NOUN
cana-5388	38	10	because	because	SCONJ
cana-5388	38	11	the	the	DET
cana-5388	38	12	model	model	NOUN
cana-5388	38	13	could	could	AUX
cana-5388	38	14	manage	manage	VERB
cana-5388	38	15	uneven	uneven	ADJ
cana-5388	38	16	and	and	CCONJ
cana-5388	38	17	incomplete	incomplete	ADJ
cana-5388	38	18	data	datum	NOUN
cana-5388	39	1	well	well	INTJ
cana-5388	40	1	[	[	X
cana-5388	40	2	3	3	NUM
cana-5388	40	3	]	]	PUNCT
cana-5388	40	4	.	.	PUNCT
cana-5388	41	1	internet	internet	NOUN
cana-5388	41	2	of	of	ADP
cana-5388	41	3	things	thing	NOUN
cana-5388	41	4	(	(	PUNCT
cana-5388	41	5	iot	iot	NOUN
cana-5388	41	6	)	)	PUNCT
cana-5388	41	7	devices	device	NOUN
cana-5388	41	8	with	with	ADP
cana-5388	41	9	deep	deep	ADJ
cana-5388	41	10	learning	learning	NOUN
cana-5388	41	11	for	for	ADP
cana-5388	41	12	real	real	ADJ
cana-5388	41	13	-	-	PUNCT
cana-5388	41	14	time	time	NOUN
cana-5388	41	15	aqi	aqi	NOUN
cana-5388	41	16	prediction	prediction	NOUN
cana-5388	41	17	in	in	ADP
cana-5388	41	18	smart	smart	ADJ
cana-5388	41	19	cities	city	NOUN
cana-5388	41	20	,	,	PUNCT
cana-5388	41	21	showing	show	VERB
cana-5388	41	22	how	how	SCONJ
cana-5388	41	23	practical	practical	ADJ
cana-5388	41	24	and	and	CCONJ
cana-5388	41	25	useful	useful	ADJ
cana-5388	41	26	such	such	ADJ
cana-5388	41	27	systems	system	NOUN
cana-5388	41	28	can	can	AUX
cana-5388	41	29	be	be	AUX
cana-5388	41	30	[	[	X
cana-5388	41	31	4	4	NUM
cana-5388	41	32	]	]	PUNCT
cana-5388	41	33	.	.	PUNCT
cana-5388	42	1	other	other	ADJ
cana-5388	42	2	ensemble	ensemble	ADJ
cana-5388	42	3	learning	learning	NOUN
cana-5388	42	4	methods	method	NOUN
cana-5388	42	5	,	,	PUNCT
cana-5388	42	6	such	such	ADJ
cana-5388	42	7	as	as	ADP
cana-5388	42	8	bagging	bagging	NOUN
cana-5388	42	9	,	,	PUNCT
cana-5388	42	10	boost	boost	NOUN
cana-5388	42	11	,	,	PUNCT
cana-5388	42	12	and	and	CCONJ
cana-5388	42	13	stacking	stacking	NOUN
cana-5388	42	14	,	,	PUNCT
cana-5388	42	15	have	have	AUX
cana-5388	42	16	also	also	ADV
cana-5388	42	17	helped	help	VERB
cana-5388	42	18	make	make	VERB
cana-5388	42	19	aqi	aqi	ADJ
cana-5388	42	20	prediction	prediction	NOUN
cana-5388	42	21	more	more	ADV
cana-5388	42	22	reliable	reliable	ADJ
cana-5388	42	23	.	.	PUNCT
cana-5388	43	1	combining	combine	VERB
cana-5388	43	2	regression	regression	NOUN
cana-5388	43	3	and	and	CCONJ
cana-5388	43	4	classification	classification	NOUN
cana-5388	43	5	models	model	NOUN
cana-5388	43	6	in	in	ADP
cana-5388	43	7	an	an	DET
cana-5388	43	8	ensemble	ensemble	ADJ
cana-5388	43	9	setup	setup	NOUN
cana-5388	43	10	gave	give	VERB
cana-5388	43	11	better	well	ADJ
cana-5388	43	12	results	result	NOUN
cana-5388	43	13	than	than	ADP
cana-5388	43	14	using	use	VERB
cana-5388	43	15	a	a	DET
cana-5388	43	16	single	single	ADJ
cana-5388	43	17	model	model	NOUN
cana-5388	43	18	[	[	X
cana-5388	43	19	5	5	NUM
cana-5388	43	20	]	]	PUNCT
cana-5388	43	21	.	.	PUNCT
cana-5388	44	1	still	still	ADV
cana-5388	44	2	,	,	PUNCT
cana-5388	44	3	the	the	DET
cana-5388	44	4	success	success	NOUN
cana-5388	44	5	of	of	ADP
cana-5388	44	6	these	these	DET
cana-5388	44	7	models	model	NOUN
cana-5388	44	8	depends	depend	VERB
cana-5388	44	9	a	a	DET
cana-5388	44	10	lot	lot	NOUN
cana-5388	44	11	on	on	ADP
cana-5388	44	12	choosing	choose	VERB
cana-5388	44	13	the	the	DET
cana-5388	44	14	right	right	ADJ
cana-5388	44	15	parameters	parameter	NOUN
cana-5388	44	16	and	and	CCONJ
cana-5388	44	17	input	input	NOUN
cana-5388	44	18	features	feature	NOUN
cana-5388	44	19	.	.	PUNCT
cana-5388	45	1	to	to	PART
cana-5388	45	2	solve	solve	VERB
cana-5388	45	3	this	this	PRON
cana-5388	45	4	,	,	PUNCT
cana-5388	45	5	researchers	researcher	NOUN
cana-5388	45	6	started	start	VERB
cana-5388	45	7	using	use	VERB
cana-5388	45	8	optimization	optimization	NOUN
cana-5388	45	9	methods	method	NOUN
cana-5388	45	10	.	.	PUNCT
cana-5388	46	1	wang	wang	PROPN
cana-5388	46	2	et	et	PROPN
cana-5388	46	3	al	al	PROPN
cana-5388	46	4	.	.	PUNCT
cana-5388	47	1	[	[	X
cana-5388	47	2	6	6	NUM
cana-5388	47	3	]	]	PUNCT
cana-5388	47	4	used	use	VERB
cana-5388	47	5	genetic	genetic	ADJ
cana-5388	47	6	algorithms	algorithm	NOUN
cana-5388	47	7	(	(	PUNCT
cana-5388	47	8	ga	ga	NOUN
cana-5388	47	9	)	)	PUNCT
cana-5388	47	10	to	to	PART
cana-5388	47	11	improve	improve	VERB
cana-5388	47	12	the	the	DET
cana-5388	47	13	performance	performance	NOUN
cana-5388	47	14	of	of	ADP
cana-5388	47	15	neural	neural	ADJ
cana-5388	47	16	networks	network	NOUN
cana-5388	47	17	in	in	ADP
cana-5388	47	18	aqi	aqi	PROPN
cana-5388	47	19	forecasting	forecasting	NOUN
cana-5388	47	20	.	.	PUNCT
cana-5388	48	1	xue	xue	PROPN
cana-5388	48	2	and	and	CCONJ
cana-5388	48	3	zhang	zhang	PROPN
cana-5388	49	1	[	[	X
cana-5388	49	2	7	7	NUM
cana-5388	49	3	]	]	PUNCT
cana-5388	49	4	applied	apply	VERB
cana-5388	49	5	particle	particle	NOUN
cana-5388	49	6	swarm	swarm	NOUN
cana-5388	49	7	optimization	optimization	NOUN
cana-5388	49	8	(	(	PUNCT
cana-5388	49	9	pso	pso	NOUN
cana-5388	49	10	)	)	PUNCT
cana-5388	49	11	to	to	ADP
cana-5388	49	12	fine	fine	ADJ
cana-5388	49	13	-	-	PUNCT
cana-5388	49	14	tune	tune	NOUN
cana-5388	49	15	support	support	NOUN
cana-5388	49	16	vector	vector	NOUN
cana-5388	49	17	machine	machine	NOUN
cana-5388	49	18	(	(	PUNCT
cana-5388	49	19	svm	svm	ADJ
cana-5388	49	20	)	)	PUNCT
cana-5388	49	21	models	model	NOUN
cana-5388	49	22	,	,	PUNCT
cana-5388	49	23	which	which	PRON
cana-5388	49	24	helped	help	VERB
cana-5388	49	25	them	they	PRON
cana-5388	49	26	get	get	VERB
cana-5388	49	27	more	more	ADV
cana-5388	49	28	accurate	accurate	ADJ
cana-5388	49	29	results	result	NOUN
cana-5388	49	30	.	.	PUNCT
cana-5388	50	1	some	some	DET
cana-5388	50	2	researchers	researcher	NOUN
cana-5388	50	3	have	have	VERB
cana-5388	50	4	combined	combine	VERB
cana-5388	50	5	machine	machine	NOUN
cana-5388	50	6	learning	learn	VERB
cana-5388	50	7	with	with	ADP
cana-5388	50	8	optimization	optimization	NOUN
cana-5388	50	9	to	to	PART
cana-5388	50	10	make	make	VERB
cana-5388	50	11	hybrid	hybrid	ADJ
cana-5388	50	12	models	model	NOUN
cana-5388	50	13	.	.	PUNCT
cana-5388	51	1	patel	patel	NOUN
cana-5388	51	2	and	and	CCONJ
cana-5388	51	3	shah	shah	PROPN
cana-5388	51	4	[	[	X
cana-5388	51	5	8	8	NUM
cana-5388	51	6	]	]	PUNCT
cana-5388	51	7	used	use	VERB
cana-5388	51	8	a	a	DET
cana-5388	51	9	pso	pso	NOUN
cana-5388	51	10	-	-	PUNCT
cana-5388	51	11	xgboost	xgboost	NOUN
cana-5388	51	12	model	model	NOUN
cana-5388	51	13	,	,	PUNCT
cana-5388	51	14	which	which	PRON
cana-5388	51	15	gave	give	VERB
cana-5388	51	16	better	well	ADJ
cana-5388	51	17	results	result	NOUN
cana-5388	51	18	than	than	ADP
cana-5388	51	19	regular	regular	ADJ
cana-5388	51	20	methods	method	NOUN
cana-5388	51	21	.	.	PUNCT
cana-5388	52	1	likewise	likewise	ADV
cana-5388	52	2	,	,	PUNCT
cana-5388	52	3	khan	khan	PROPN
cana-5388	52	4	et	et	PROPN
cana-5388	52	5	al	al	PROPN
cana-5388	52	6	.	.	PUNCT
cana-5388	53	1	[	[	X
cana-5388	53	2	9	9	NUM
cana-5388	53	3	]	]	PUNCT
cana-5388	53	4	proposed	propose	VERB
cana-5388	53	5	a	a	DET
cana-5388	53	6	hybrid	hybrid	ADJ
cana-5388	53	7	system	system	NOUN
cana-5388	53	8	using	use	VERB
cana-5388	53	9	machine	machine	NOUN
cana-5388	53	10	learning	learning	NOUN
cana-5388	53	11	and	and	CCONJ
cana-5388	53	12	optimization	optimization	NOUN
cana-5388	53	13	for	for	ADP
cana-5388	53	14	real	real	ADJ
cana-5388	53	15	-	-	PUNCT
cana-5388	53	16	time	time	NOUN
cana-5388	53	17	aqi	aqi	PROPN
cana-5388	53	18	prediction	prediction	NOUN
cana-5388	53	19	,	,	PUNCT
cana-5388	53	20	showing	show	VERB
cana-5388	53	21	improved	improved	ADJ
cana-5388	53	22	performance	performance	NOUN
cana-5388	53	23	and	and	CCONJ
cana-5388	53	24	efficiency	efficiency	NOUN
cana-5388	53	25	.	.	PUNCT
cana-5388	54	1	overall	overall	ADV
cana-5388	54	2	,	,	PUNCT
cana-5388	54	3	these	these	DET
cana-5388	54	4	works	work	NOUN
cana-5388	54	5	show	show	VERB
cana-5388	54	6	that	that	SCONJ
cana-5388	54	7	combining	combine	VERB
cana-5388	54	8	machine	machine	NOUN
cana-5388	54	9	learning	learn	VERB
cana-5388	54	10	techniques	technique	NOUN
cana-5388	54	11	with	with	ADP
cana-5388	54	12	optimization	optimization	NOUN
cana-5388	54	13	methods	method	NOUN
cana-5388	54	14	is	be	AUX
cana-5388	54	15	a	a	DET
cana-5388	54	16	good	good	ADJ
cana-5388	54	17	way	way	NOUN
cana-5388	54	18	to	to	PART
cana-5388	54	19	build	build	VERB
cana-5388	54	20	accurate	accurate	ADJ
cana-5388	54	21	and	and	CCONJ
cana-5388	54	22	dependable	dependable	ADJ
cana-5388	54	23	aqi	aqi	NOUN
cana-5388	54	24	prediction	prediction	NOUN
cana-5388	54	25	systems	system	NOUN
cana-5388	54	26	.	.	PUNCT
cana-5388	55	1	the	the	DET
cana-5388	55	2	weather	weather	NOUN
cana-5388	55	3	dataset	dataset	NOUN
cana-5388	55	4	is	be	AUX
cana-5388	55	5	organized	organize	VERB
cana-5388	55	6	such	such	ADJ
cana-5388	55	7	that	that	SCONJ
cana-5388	55	8	each	each	DET
cana-5388	55	9	row	row	NOUN
cana-5388	55	10	represents	represent	VERB
cana-5388	55	11	data	datum	NOUN
cana-5388	55	12	from	from	ADP
cana-5388	55	13	a	a	DET
cana-5388	55	14	specific	specific	ADJ
cana-5388	55	15	day	day	NOUN
cana-5388	55	16	,	,	PUNCT
cana-5388	55	17	and	and	CCONJ
cana-5388	55	18	the	the	DET
cana-5388	55	19	attributes	attribute	NOUN
cana-5388	55	20	indicate	indicate	VERB
cana-5388	55	21	various	various	ADJ
cana-5388	55	22	weather	weather	NOUN
cana-5388	55	23	conditions	condition	NOUN
cana-5388	55	24	for	for	ADP
cana-5388	55	25	that	that	DET
cana-5388	55	26	day	day	NOUN
cana-5388	55	27	.	.	PUNCT
cana-5388	56	1	each	each	DET
cana-5388	56	2	instance	instance	NOUN
cana-5388	56	3	includes	include	VERB
cana-5388	56	4	values	value	NOUN
cana-5388	56	5	for	for	ADP
cana-5388	56	6	attributes	attribute	NOUN
cana-5388	56	7	like	like	ADP
cana-5388	56	8	outlook	outlook	NOUN
cana-5388	56	9	,	,	PUNCT
cana-5388	56	10	temperature	temperature	NOUN
cana-5388	56	11	,	,	PUNCT
cana-5388	56	12	humidity	humidity	NOUN
cana-5388	56	13	,	,	PUNCT
cana-5388	56	14	windy	windy	ADJ
cana-5388	56	15	,	,	PUNCT
cana-5388	56	16	and	and	CCONJ
cana-5388	56	17	a	a	DET
cana-5388	56	18	boolean	boolean	ADJ
cana-5388	56	19	playgolf	playgolf	NOUN
cana-5388	56	20	variable	variable	NOUN
cana-5388	56	21	.	.	PUNCT
cana-5388	57	1	all	all	DET
cana-5388	57	2	the	the	DET
cana-5388	57	3	data	datum	NOUN
cana-5388	57	4	is	be	AUX
cana-5388	57	5	used	use	VERB
cana-5388	57	6	for	for	ADP
cana-5388	57	7	training	training	NOUN
cana-5388	57	8	and	and	CCONJ
cana-5388	57	9	is	be	AUX
cana-5388	57	10	evaluated	evaluate	VERB
cana-5388	57	11	using	use	VERB
cana-5388	57	12	seven	seven	NUM
cana-5388	57	13	different	different	ADJ
cana-5388	57	14	classification	classification	NOUN
cana-5388	57	15	algorithms	algorithm	NOUN
cana-5388	57	16	.	.	PUNCT
cana-5388	58	1	this	this	DET
cana-5388	58	2	study	study	NOUN
cana-5388	58	3	analyses	analyse	VERB
cana-5388	58	4	and	and	CCONJ
cana-5388	58	5	compares	compare	VERB
cana-5388	58	6	the	the	DET
cana-5388	58	7	accuracy	accuracy	NOUN
cana-5388	58	8	of	of	ADP
cana-5388	58	9	decision	decision	NOUN
cana-5388	58	10	tree	tree	NOUN
cana-5388	58	11	-	-	PUNCT
cana-5388	58	12	based	base	VERB
cana-5388	58	13	data	datum	NOUN
cana-5388	58	14	mining	mining	NOUN
cana-5388	58	15	algorithms	algorithm	NOUN
cana-5388	58	16	using	use	VERB
cana-5388	58	17	the	the	DET
cana-5388	58	18	weka	weka	PROPN
cana-5388	58	19	tool	tool	NOUN
cana-5388	58	20	to	to	PART
cana-5388	58	21	identify	identify	VERB
cana-5388	58	22	key	key	ADJ
cana-5388	58	23	factors	factor	NOUN
cana-5388	58	24	influencing	influence	VERB
cana-5388	58	25	tree	tree	NOUN
cana-5388	58	26	structure	structure	NOUN
cana-5388	58	27	.	.	PUNCT
cana-5388	59	1	the	the	DET
cana-5388	59	2	classification	classification	NOUN
cana-5388	59	3	methods	method	NOUN
cana-5388	59	4	used	use	VERB
cana-5388	59	5	include	include	VERB
cana-5388	59	6	j48	j48	PROPN
cana-5388	59	7	,	,	PUNCT
cana-5388	59	8	random	random	ADJ
cana-5388	59	9	tree	tree	NOUN
cana-5388	59	10	(	(	PUNCT
cana-5388	59	11	rt	rt	NOUN
cana-5388	59	12	)	)	PUNCT
cana-5388	59	13	,	,	PUNCT
cana-5388	59	14	decision	decision	NOUN
cana-5388	59	15	stump	stump	NOUN
cana-5388	59	16	(	(	PUNCT
cana-5388	59	17	ds	ds	NOUN
cana-5388	59	18	)	)	PUNCT
cana-5388	59	19	,	,	PUNCT
cana-5388	59	20	logistic	logistic	ADJ
cana-5388	59	21	model	model	NOUN
cana-5388	59	22	tree	tree	NOUN
cana-5388	59	23	(	(	PUNCT
cana-5388	59	24	lmt	lmt	PROPN
cana-5388	59	25	)	)	PUNCT
cana-5388	59	26	,	,	PUNCT
cana-5388	59	27	hoeffding	hoeffde	VERB
cana-5388	59	28	tree	tree	NOUN
cana-5388	59	29	(	(	PUNCT
cana-5388	59	30	ht	ht	PROPN
cana-5388	59	31	)	)	PUNCT
cana-5388	59	32	,	,	PUNCT
cana-5388	59	33	reduced	reduce	VERB
cana-5388	59	34	error	error	NOUN
cana-5388	59	35	pruning	pruning	NOUN
cana-5388	59	36	(	(	PUNCT
cana-5388	59	37	rep	rep	NOUN
cana-5388	59	38	)	)	PUNCT
cana-5388	59	39	,	,	PUNCT
cana-5388	59	40	and	and	CCONJ
cana-5388	59	41	random	random	ADJ
cana-5388	59	42	forest	forest	NOUN
cana-5388	59	43	(	(	PUNCT
cana-5388	59	44	rf	rf	NOUN
cana-5388	59	45	)	)	PUNCT
cana-5388	59	46	,	,	PUNCT
cana-5388	59	47	and	and	CCONJ
cana-5388	59	48	their	their	PRON
cana-5388	59	49	accuracies	accuracy	NOUN
cana-5388	59	50	are	be	AUX
cana-5388	59	51	assessed	assess	VERB
cana-5388	59	52	[	[	X
cana-5388	59	53	10	10	NUM
cana-5388	59	54	]	]	PUNCT
cana-5388	59	55	and	and	CCONJ
cana-5388	59	56	the	the	DET
cana-5388	59	57	similar	similar	ADJ
cana-5388	59	58	paper	paper	NOUN
cana-5388	59	59	analysis	analysis	NOUN
cana-5388	59	60	using	use	VERB
cana-5388	59	61	medical	medical	ADJ
cana-5388	59	62	related	related	ADJ
cana-5388	59	63	research	research	NOUN
cana-5388	59	64	using	use	VERB
cana-5388	59	65	same	same	ADJ
cana-5388	59	66	approaches	approach	NOUN
cana-5388	59	67	using	use	VERB
cana-5388	59	68	data	datum	NOUN
cana-5388	59	69	mining	mining	NOUN
cana-5388	59	70	and	and	CCONJ
cana-5388	59	71	machine	machine	NOUN
cana-5388	59	72	learning	learn	VERB
cana-5388	59	73	algorithms	algorithm	NOUN
cana-5388	59	74	[	[	X
cana-5388	59	75	11	11	NUM
cana-5388	59	76	]	]	PUNCT
cana-5388	59	77	.	.	PUNCT
cana-5388	60	1	predicting	predict	VERB
cana-5388	60	2	air	air	NOUN
cana-5388	60	3	pollution	pollution	NOUN
cana-5388	60	4	has	have	AUX
cana-5388	60	5	become	become	VERB
cana-5388	60	6	an	an	DET
cana-5388	60	7	important	important	ADJ
cana-5388	60	8	research	research	NOUN
cana-5388	60	9	topic	topic	NOUN
cana-5388	60	10	in	in	ADP
cana-5388	60	11	recent	recent	ADJ
cana-5388	60	12	years	year	NOUN
cana-5388	60	13	because	because	SCONJ
cana-5388	60	14	of	of	ADP
cana-5388	60	15	its	its	PRON
cana-5388	60	16	serious	serious	ADJ
cana-5388	60	17	effects	effect	NOUN
cana-5388	60	18	on	on	ADP
cana-5388	60	19	people	people	NOUN
cana-5388	60	20	’s	’s	PART
cana-5388	60	21	health	health	NOUN
cana-5388	60	22	and	and	CCONJ
cana-5388	60	23	the	the	DET
cana-5388	60	24	environment	environment	NOUN
cana-5388	60	25	.	.	PUNCT
cana-5388	61	1	many	many	ADJ
cana-5388	61	2	researchers	researcher	NOUN
cana-5388	61	3	are	be	AUX
cana-5388	61	4	using	use	VERB
cana-5388	61	5	machine	machine	NOUN
cana-5388	61	6	learning	learning	NOUN
cana-5388	61	7	and	and	CCONJ
cana-5388	61	8	optimization	optimization	NOUN
cana-5388	61	9	techniques	technique	NOUN
cana-5388	61	10	to	to	PART
cana-5388	61	11	improve	improve	VERB
cana-5388	61	12	the	the	DET
cana-5388	61	13	prediction	prediction	NOUN
cana-5388	61	14	of	of	ADP
cana-5388	61	15	the	the	DET
cana-5388	61	16	air	air	NOUN
cana-5388	61	17	quality	quality	PROPN
cana-5388	61	18	index	index	NOUN
cana-5388	61	19	(	(	PUNCT
cana-5388	61	20	aqi	aqi	PROPN
cana-5388	61	21	)	)	PUNCT
cana-5388	61	22	by	by	ADP
cana-5388	61	23	studying	study	VERB
cana-5388	61	24	old	old	ADJ
cana-5388	61	25	weather	weather	NOUN
cana-5388	61	26	and	and	CCONJ
cana-5388	61	27	pollution	pollution	NOUN
cana-5388	61	28	data	datum	NOUN
cana-5388	61	29	.	.	PUNCT
cana-5388	62	1	kumar	kumar	PROPN
cana-5388	62	2	and	and	CCONJ
cana-5388	62	3	reddy	reddy	PROPN
cana-5388	62	4	[	[	X
cana-5388	62	5	12	12	NUM
cana-5388	62	6	]	]	PUNCT
cana-5388	62	7	reviewed	review	VERB
cana-5388	62	8	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	62	9	communications	communication	NOUN
cana-5388	62	10	on	on	ADP
cana-5388	62	11	applied	apply	VERB
cana-5388	62	12	nonlinear	nonlinear	ADJ
cana-5388	62	13	analysis	analysis	NOUN
cana-5388	62	14	issn	issn	NOUN
cana-5388	62	15	:	:	PUNCT
cana-5388	62	16	1074	1074	NUM
cana-5388	62	17	-	-	PUNCT
cana-5388	62	18	133x	133x	NUM
cana-5388	62	19	vol	vol	NOUN
cana-5388	62	20	32	32	NUM
cana-5388	62	21	no	no	NOUN
cana-5388	62	22	.	.	PUNCT
cana-5388	63	1	1s	1s	NUM
cana-5388	63	2	(	(	PUNCT
cana-5388	63	3	2025	2025	NUM
cana-5388	63	4	)	)	PUNCT
cana-5388	64	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	64	2	657	657	NUM
cana-5388	64	3	different	different	ADJ
cana-5388	64	4	machine	machine	NOUN
cana-5388	64	5	learning	learn	VERB
cana-5388	64	6	methods	method	NOUN
cana-5388	64	7	for	for	ADP
cana-5388	64	8	forecasting	forecast	VERB
cana-5388	64	9	aqi	aqi	PROPN
cana-5388	64	10	and	and	CCONJ
cana-5388	64	11	found	find	VERB
cana-5388	64	12	that	that	SCONJ
cana-5388	64	13	modern	modern	ADJ
cana-5388	64	14	algorithms	algorithm	NOUN
cana-5388	64	15	give	give	VERB
cana-5388	64	16	more	more	ADV
cana-5388	64	17	accurate	accurate	ADJ
cana-5388	64	18	results	result	NOUN
cana-5388	64	19	than	than	ADP
cana-5388	64	20	old	old	ADJ
cana-5388	64	21	statistical	statistical	ADJ
cana-5388	64	22	models	model	NOUN
cana-5388	64	23	.	.	PUNCT
cana-5388	65	1	chen	chen	PROPN
cana-5388	65	2	et	et	PROPN
cana-5388	65	3	al	al	PROPN
cana-5388	65	4	.	.	PUNCT
cana-5388	66	1	[	[	X
cana-5388	66	2	13	13	NUM
cana-5388	66	3	]	]	PUNCT
cana-5388	66	4	built	build	VERB
cana-5388	66	5	a	a	DET
cana-5388	66	6	deep	deep	ADJ
cana-5388	66	7	learning	learning	NOUN
cana-5388	66	8	model	model	NOUN
cana-5388	66	9	using	use	VERB
cana-5388	66	10	both	both	DET
cana-5388	66	11	time	time	NOUN
cana-5388	66	12	and	and	CCONJ
cana-5388	66	13	location	location	NOUN
cana-5388	66	14	data	datum	NOUN
cana-5388	66	15	and	and	CCONJ
cana-5388	66	16	showed	show	VERB
cana-5388	66	17	that	that	SCONJ
cana-5388	66	18	such	such	ADJ
cana-5388	66	19	models	model	NOUN
cana-5388	66	20	can	can	AUX
cana-5388	66	21	learn	learn	VERB
cana-5388	66	22	complex	complex	ADJ
cana-5388	66	23	pollution	pollution	NOUN
cana-5388	66	24	patterns	pattern	NOUN
cana-5388	66	25	.	.	PUNCT
cana-5388	67	1	kannimuthu	kannimuthu	PROPN
cana-5388	67	2	and	and	CCONJ
cana-5388	67	3	rajesh	rajesh	PROPN
cana-5388	68	1	[	[	X
cana-5388	68	2	14	14	NUM
cana-5388	68	3	]	]	PUNCT
cana-5388	68	4	used	use	VERB
cana-5388	68	5	lstm	lstm	NOUN
cana-5388	68	6	models	model	NOUN
cana-5388	68	7	with	with	ADP
cana-5388	68	8	parameter	parameter	NOUN
cana-5388	68	9	tuning	tuning	NOUN
cana-5388	68	10	and	and	CCONJ
cana-5388	68	11	reported	report	VERB
cana-5388	68	12	better	well	ADJ
cana-5388	68	13	performance	performance	NOUN
cana-5388	68	14	in	in	ADP
cana-5388	68	15	aqi	aqi	PROPN
cana-5388	68	16	prediction	prediction	NOUN
cana-5388	68	17	.	.	PUNCT
cana-5388	69	1	li	li	PROPN
cana-5388	69	2	et	et	PROPN
cana-5388	69	3	al	al	PROPN
cana-5388	69	4	.	.	PUNCT
cana-5388	70	1	[	[	X
cana-5388	70	2	15	15	NUM
cana-5388	70	3	]	]	PUNCT
cana-5388	70	4	discussed	discuss	VERB
cana-5388	70	5	hybrid	hybrid	NOUN
cana-5388	70	6	models	model	NOUN
cana-5388	70	7	that	that	PRON
cana-5388	70	8	mix	mix	VERB
cana-5388	70	9	machine	machine	NOUN
cana-5388	70	10	learning	learn	VERB
cana-5388	70	11	with	with	ADP
cana-5388	70	12	optimization	optimization	NOUN
cana-5388	70	13	methods	method	NOUN
cana-5388	70	14	and	and	CCONJ
cana-5388	70	15	found	find	VERB
cana-5388	70	16	them	they	PRON
cana-5388	70	17	more	more	ADV
cana-5388	70	18	dependable	dependable	ADJ
cana-5388	70	19	for	for	ADP
cana-5388	70	20	large	large	ADJ
cana-5388	70	21	-	-	PUNCT
cana-5388	70	22	scale	scale	NOUN
cana-5388	70	23	forecasting	forecasting	NOUN
cana-5388	70	24	.	.	PUNCT
cana-5388	71	1	zhang	zhang	PROPN
cana-5388	71	2	and	and	CCONJ
cana-5388	71	3	sun	sun	PROPN
cana-5388	71	4	[	[	X
cana-5388	71	5	16	16	NUM
cana-5388	71	6	]	]	PUNCT
cana-5388	71	7	used	use	VERB
cana-5388	71	8	lstm	lstm	NOUN
cana-5388	71	9	models	model	NOUN
cana-5388	71	10	to	to	PART
cana-5388	71	11	predict	predict	VERB
cana-5388	71	12	aqi	aqi	PROPN
cana-5388	71	13	and	and	CCONJ
cana-5388	71	14	found	find	VERB
cana-5388	71	15	them	they	PRON
cana-5388	71	16	good	good	ADJ
cana-5388	71	17	for	for	ADP
cana-5388	71	18	time	time	NOUN
cana-5388	71	19	-	-	PUNCT
cana-5388	71	20	series	series	NOUN
cana-5388	71	21	data	data	PROPN
cana-5388	71	22	.	.	PUNCT
cana-5388	72	1	sharma	sharma	PROPN
cana-5388	72	2	and	and	CCONJ
cana-5388	72	3	ghosh	ghosh	PROPN
cana-5388	73	1	[	[	X
cana-5388	73	2	17	17	NUM
cana-5388	73	3	]	]	PUNCT
cana-5388	73	4	made	make	VERB
cana-5388	73	5	a	a	DET
cana-5388	73	6	deep	deep	ADJ
cana-5388	73	7	learning	learning	NOUN
cana-5388	73	8	model	model	NOUN
cana-5388	73	9	for	for	ADP
cana-5388	73	10	smart	smart	ADJ
cana-5388	73	11	cities	city	NOUN
cana-5388	73	12	that	that	PRON
cana-5388	73	13	could	could	AUX
cana-5388	73	14	process	process	VERB
cana-5388	73	15	big	big	ADJ
cana-5388	73	16	data	datum	NOUN
cana-5388	73	17	in	in	ADP
cana-5388	73	18	real	real	ADJ
cana-5388	73	19	time	time	NOUN
cana-5388	73	20	.	.	PUNCT
cana-5388	74	1	luo	luo	PROPN
cana-5388	74	2	et	et	PROPN
cana-5388	74	3	al	al	PROPN
cana-5388	74	4	.	.	PUNCT
cana-5388	75	1	[	[	X
cana-5388	75	2	18	18	NUM
cana-5388	75	3	]	]	PUNCT
cana-5388	75	4	suggested	suggest	VERB
cana-5388	75	5	a	a	DET
cana-5388	75	6	hybrid	hybrid	ADJ
cana-5388	75	7	model	model	NOUN
cana-5388	75	8	with	with	ADP
cana-5388	75	9	data	datum	NOUN
cana-5388	75	10	breakdown	breakdown	NOUN
cana-5388	75	11	and	and	CCONJ
cana-5388	75	12	optimization	optimization	NOUN
cana-5388	75	13	to	to	PART
cana-5388	75	14	improve	improve	VERB
cana-5388	75	15	prediction	prediction	NOUN
cana-5388	75	16	results	result	NOUN
cana-5388	75	17	.	.	PUNCT
cana-5388	76	1	pandey	pandey	PROPN
cana-5388	76	2	and	and	CCONJ
cana-5388	76	3	choudhury	choudhury	PROPN
cana-5388	77	1	[	[	X
cana-5388	77	2	19	19	NUM
cana-5388	77	3	]	]	PUNCT
cana-5388	77	4	applied	applied	ADJ
cana-5388	77	5	stacking	stack	VERB
cana-5388	77	6	ensemble	ensemble	ADJ
cana-5388	77	7	methods	method	NOUN
cana-5388	77	8	and	and	CCONJ
cana-5388	77	9	showed	show	VERB
cana-5388	77	10	that	that	SCONJ
cana-5388	77	11	combining	combine	VERB
cana-5388	77	12	different	different	ADJ
cana-5388	77	13	models	model	NOUN
cana-5388	77	14	improved	improve	VERB
cana-5388	77	15	the	the	DET
cana-5388	77	16	accuracy	accuracy	NOUN
cana-5388	77	17	.	.	PUNCT
cana-5388	78	1	hussain	hussain	PROPN
cana-5388	78	2	et	et	PROPN
cana-5388	78	3	al	al	PROPN
cana-5388	78	4	.	.	PUNCT
cana-5388	79	1	[	[	X
cana-5388	79	2	20	20	NUM
cana-5388	79	3	]	]	PUNCT
cana-5388	79	4	compared	compare	VERB
cana-5388	79	5	several	several	ADJ
cana-5388	79	6	ml	ml	NOUN
cana-5388	79	7	algorithms	algorithm	NOUN
cana-5388	79	8	and	and	CCONJ
cana-5388	79	9	found	find	VERB
cana-5388	79	10	that	that	SCONJ
cana-5388	79	11	ensemble	ensemble	ADJ
cana-5388	79	12	models	model	NOUN
cana-5388	79	13	gave	give	VERB
cana-5388	79	14	the	the	DET
cana-5388	79	15	best	good	ADJ
cana-5388	79	16	results	result	NOUN
cana-5388	79	17	.	.	PUNCT
cana-5388	80	1	ravishankar	ravishankar	NOUN
cana-5388	80	2	and	and	CCONJ
cana-5388	80	3	rajesh	rajesh	PROPN
cana-5388	80	4	[	[	X
cana-5388	80	5	21	21	NUM
cana-5388	80	6	]	]	PUNCT
cana-5388	80	7	studied	study	VERB
cana-5388	80	8	the	the	DET
cana-5388	80	9	impact	impact	NOUN
cana-5388	80	10	of	of	ADP
cana-5388	80	11	selecting	select	VERB
cana-5388	80	12	important	important	ADJ
cana-5388	80	13	variables	variable	NOUN
cana-5388	80	14	from	from	ADP
cana-5388	80	15	climate	climate	NOUN
cana-5388	80	16	change	change	NOUN
cana-5388	80	17	datasets	dataset	NOUN
cana-5388	80	18	and	and	CCONJ
cana-5388	80	19	how	how	SCONJ
cana-5388	80	20	this	this	PRON
cana-5388	80	21	affects	affect	VERB
cana-5388	80	22	prediction	prediction	NOUN
cana-5388	80	23	accuracy	accuracy	NOUN
cana-5388	80	24	.	.	PUNCT
cana-5388	81	1	they	they	PRON
cana-5388	81	2	applied	apply	VERB
cana-5388	81	3	different	different	ADJ
cana-5388	81	4	data	datum	NOUN
cana-5388	81	5	mining	mining	NOUN
cana-5388	81	6	techniques	technique	NOUN
cana-5388	81	7	along	along	ADP
cana-5388	81	8	with	with	ADP
cana-5388	81	9	machine	machine	NOUN
cana-5388	81	10	learning	learning	NOUN
cana-5388	81	11	models	model	NOUN
cana-5388	81	12	to	to	PART
cana-5388	81	13	understand	understand	VERB
cana-5388	81	14	climate	climate	NOUN
cana-5388	81	15	patterns	pattern	NOUN
cana-5388	81	16	.	.	PUNCT
cana-5388	82	1	in	in	ADP
cana-5388	82	2	another	another	DET
cana-5388	82	3	related	relate	VERB
cana-5388	82	4	study	study	NOUN
cana-5388	82	5	,	,	PUNCT
cana-5388	82	6	ravishankar	ravishankar	NOUN
cana-5388	82	7	and	and	CCONJ
cana-5388	82	8	rajesh	rajesh	PROPN
cana-5388	82	9	[	[	X
cana-5388	82	10	22	22	NUM
cana-5388	82	11	]	]	PUNCT
cana-5388	82	12	extended	extend	VERB
cana-5388	82	13	their	their	PRON
cana-5388	82	14	research	research	NOUN
cana-5388	82	15	using	use	VERB
cana-5388	82	16	a	a	DET
cana-5388	82	17	global	global	ADJ
cana-5388	82	18	weather	weather	NOUN
cana-5388	82	19	repository	repository	NOUN
cana-5388	82	20	to	to	PART
cana-5388	82	21	predict	predict	VERB
cana-5388	82	22	climate	climate	NOUN
cana-5388	82	23	change	change	NOUN
cana-5388	82	24	indicators	indicator	NOUN
cana-5388	82	25	more	more	ADV
cana-5388	82	26	effectively	effectively	ADV
cana-5388	82	27	.	.	PUNCT
cana-5388	83	1	they	they	PRON
cana-5388	83	2	used	use	VERB
cana-5388	83	3	data	data	NOUN
cana-5388	83	4	mining	mining	NOUN
cana-5388	83	5	tools	tool	NOUN
cana-5388	83	6	combined	combine	VERB
cana-5388	83	7	with	with	ADP
cana-5388	83	8	advanced	advanced	ADJ
cana-5388	83	9	machine	machine	NOUN
cana-5388	83	10	learning	learning	NOUN
cana-5388	83	11	methods	method	NOUN
cana-5388	83	12	to	to	PART
cana-5388	83	13	process	process	VERB
cana-5388	83	14	large	large	ADJ
cana-5388	83	15	-	-	PUNCT
cana-5388	83	16	scale	scale	NOUN
cana-5388	83	17	environmental	environmental	ADJ
cana-5388	83	18	data	datum	NOUN
cana-5388	83	19	.	.	PUNCT
cana-5388	84	1	sahu	sahu	PROPN
cana-5388	84	2	and	and	CCONJ
cana-5388	84	3	tripathy	tripathy	ADJ
cana-5388	84	4	[	[	X
cana-5388	84	5	23	23	NUM
cana-5388	84	6	]	]	PUNCT
cana-5388	84	7	developed	develop	VERB
cana-5388	84	8	bi	bi	ADJ
cana-5388	84	9	-	-	ADJ
cana-5388	84	10	lstm	lstm	ADJ
cana-5388	84	11	models	model	NOUN
cana-5388	84	12	for	for	ADP
cana-5388	84	13	aqi	aqi	NOUN
cana-5388	84	14	forecasting	forecasting	NOUN
cana-5388	84	15	and	and	CCONJ
cana-5388	84	16	proved	prove	VERB
cana-5388	84	17	their	their	PRON
cana-5388	84	18	strength	strength	NOUN
cana-5388	84	19	in	in	ADP
cana-5388	84	20	capturing	capture	VERB
cana-5388	84	21	both	both	PRON
cana-5388	84	22	past	past	ADJ
cana-5388	84	23	and	and	CCONJ
cana-5388	84	24	future	future	ADJ
cana-5388	84	25	data	datum	NOUN
cana-5388	84	26	trends	trend	NOUN
cana-5388	84	27	.	.	PUNCT
cana-5388	85	1	patel	patel	PROPN
cana-5388	85	2	and	and	CCONJ
cana-5388	85	3	joshi	joshi	PROPN
cana-5388	86	1	[	[	X
cana-5388	86	2	24	24	NUM
cana-5388	86	3	]	]	PUNCT
cana-5388	86	4	improved	improved	ADJ
cana-5388	86	5	accuracy	accuracy	NOUN
cana-5388	86	6	using	use	VERB
cana-5388	86	7	pca	pca	NOUN
cana-5388	86	8	with	with	ADP
cana-5388	86	9	hybrid	hybrid	ADJ
cana-5388	86	10	models	model	NOUN
cana-5388	86	11	,	,	PUNCT
cana-5388	86	12	showing	show	VERB
cana-5388	86	13	that	that	SCONJ
cana-5388	86	14	feature	feature	NOUN
cana-5388	86	15	reduction	reduction	NOUN
cana-5388	86	16	helped	helped	AUX
cana-5388	86	17	avoid	avoid	VERB
cana-5388	86	18	overfitting	overfitte	VERB
cana-5388	86	19	.	.	PUNCT
cana-5388	87	1	khan	khan	PROPN
cana-5388	87	2	and	and	CCONJ
cana-5388	87	3	abbas	abbas	PROPN
cana-5388	87	4	[	[	X
cana-5388	87	5	25	25	NUM
cana-5388	87	6	]	]	PUNCT
cana-5388	87	7	used	use	VERB
cana-5388	87	8	ant	ant	ADJ
cana-5388	87	9	colony	colony	NOUN
cana-5388	87	10	optimization	optimization	NOUN
cana-5388	87	11	with	with	ADP
cana-5388	87	12	svm	svm	NOUN
cana-5388	87	13	to	to	PART
cana-5388	87	14	enhance	enhance	VERB
cana-5388	87	15	model	model	NOUN
cana-5388	87	16	tuning	tuning	NOUN
cana-5388	87	17	and	and	CCONJ
cana-5388	87	18	performance	performance	NOUN
cana-5388	87	19	.	.	PUNCT
cana-5388	88	1	ravishankar	ravishankar	NOUN
cana-5388	88	2	and	and	CCONJ
cana-5388	88	3	rajesh	rajesh	PROPN
cana-5388	89	1	[	[	X
cana-5388	89	2	26	26	NUM
cana-5388	89	3	]	]	PUNCT
cana-5388	89	4	carried	carry	VERB
cana-5388	89	5	out	out	ADP
cana-5388	89	6	a	a	DET
cana-5388	89	7	detailed	detailed	ADJ
cana-5388	89	8	study	study	NOUN
cana-5388	89	9	on	on	ADP
cana-5388	89	10	analyzing	analyze	VERB
cana-5388	89	11	climate	climate	NOUN
cana-5388	89	12	change	change	NOUN
cana-5388	89	13	datasets	dataset	NOUN
cana-5388	89	14	in	in	ADP
cana-5388	89	15	relation	relation	NOUN
cana-5388	89	16	to	to	ADP
cana-5388	89	17	the	the	DET
cana-5388	89	18	air	air	NOUN
cana-5388	89	19	quality	quality	PROPN
cana-5388	89	20	index	index	NOUN
cana-5388	89	21	(	(	PUNCT
cana-5388	89	22	aqi	aqi	PROPN
cana-5388	89	23	)	)	PUNCT
cana-5388	89	24	using	use	VERB
cana-5388	89	25	data	datum	NOUN
cana-5388	89	26	mining	mining	NOUN
cana-5388	89	27	and	and	CCONJ
cana-5388	89	28	machine	machine	NOUN
cana-5388	89	29	learning	learn	VERB
cana-5388	89	30	techniques	technique	NOUN
cana-5388	89	31	.	.	PUNCT
cana-5388	90	1	their	their	PRON
cana-5388	90	2	research	research	NOUN
cana-5388	90	3	focused	focus	VERB
cana-5388	90	4	on	on	ADP
cana-5388	90	5	understanding	understand	VERB
cana-5388	90	6	how	how	SCONJ
cana-5388	90	7	different	different	ADJ
cana-5388	90	8	environmental	environmental	ADJ
cana-5388	90	9	parameters	parameter	NOUN
cana-5388	90	10	contribute	contribute	VERB
cana-5388	90	11	to	to	ADP
cana-5388	90	12	aqi	aqi	PROPN
cana-5388	90	13	levels	level	NOUN
cana-5388	90	14	.	.	PUNCT
cana-5388	91	1	by	by	ADP
cana-5388	91	2	applying	apply	VERB
cana-5388	91	3	classification	classification	NOUN
cana-5388	91	4	and	and	CCONJ
cana-5388	91	5	regression	regression	NOUN
cana-5388	91	6	models	model	NOUN
cana-5388	91	7	,	,	PUNCT
cana-5388	91	8	they	they	PRON
cana-5388	91	9	demonstrated	demonstrate	VERB
cana-5388	91	10	how	how	SCONJ
cana-5388	91	11	machine	machine	NOUN
cana-5388	91	12	learning	learning	NOUN
cana-5388	91	13	can	can	AUX
cana-5388	91	14	accurately	accurately	ADV
cana-5388	91	15	predict	predict	VERB
cana-5388	91	16	air	air	NOUN
cana-5388	91	17	quality	quality	NOUN
cana-5388	91	18	trends	trend	NOUN
cana-5388	91	19	.	.	PUNCT
cana-5388	92	1	santhoshkumar	santhoshkumar	NOUN
cana-5388	92	2	and	and	CCONJ
cana-5388	92	3	rajesh	rajesh	PROPN
cana-5388	92	4	[	[	X
cana-5388	92	5	27	27	NUM
cana-5388	92	6	]	]	PUNCT
cana-5388	92	7	explored	explore	VERB
cana-5388	92	8	how	how	SCONJ
cana-5388	92	9	machine	machine	NOUN
cana-5388	92	10	learning	learn	VERB
cana-5388	92	11	techniques	technique	NOUN
cana-5388	92	12	can	can	AUX
cana-5388	92	13	be	be	AUX
cana-5388	92	14	used	use	VERB
cana-5388	92	15	to	to	PART
cana-5388	92	16	analyze	analyze	VERB
cana-5388	92	17	the	the	DET
cana-5388	92	18	connection	connection	NOUN
cana-5388	92	19	between	between	ADP
cana-5388	92	20	various	various	ADJ
cana-5388	92	21	types	type	NOUN
cana-5388	92	22	of	of	ADP
cana-5388	92	23	energy	energy	NOUN
cana-5388	92	24	usage	usage	NOUN
cana-5388	92	25	and	and	CCONJ
cana-5388	92	26	the	the	DET
cana-5388	92	27	sustainable	sustainable	ADJ
cana-5388	92	28	development	development	NOUN
cana-5388	92	29	goals	goal	NOUN
cana-5388	92	30	(	(	PUNCT
cana-5388	92	31	sdgs	sdgs	ADJ
cana-5388	92	32	)	)	PUNCT
cana-5388	92	33	.	.	PUNCT
cana-5388	93	1	their	their	PRON
cana-5388	93	2	study	study	NOUN
cana-5388	93	3	applied	apply	VERB
cana-5388	93	4	predictive	predictive	ADJ
cana-5388	93	5	modeling	modeling	NOUN
cana-5388	93	6	to	to	PART
cana-5388	93	7	identify	identify	VERB
cana-5388	93	8	how	how	SCONJ
cana-5388	93	9	changes	change	NOUN
cana-5388	93	10	in	in	ADP
cana-5388	93	11	energy	energy	NOUN
cana-5388	93	12	consumption	consumption	NOUN
cana-5388	93	13	patterns	pattern	NOUN
cana-5388	93	14	affect	affect	VERB
cana-5388	93	15	progress	progress	NOUN
cana-5388	93	16	toward	toward	ADP
cana-5388	93	17	sdg	sdg	NOUN
cana-5388	93	18	targets	target	NOUN
cana-5388	93	19	.	.	PUNCT
cana-5388	94	1	the	the	DET
cana-5388	94	2	study	study	NOUN
cana-5388	94	3	also	also	ADV
cana-5388	94	4	emphasized	emphasize	VERB
cana-5388	94	5	the	the	DET
cana-5388	94	6	usefulness	usefulness	NOUN
cana-5388	94	7	of	of	ADP
cana-5388	94	8	combining	combine	VERB
cana-5388	94	9	multiple	multiple	ADJ
cana-5388	94	10	algorithms	algorithm	NOUN
cana-5388	94	11	to	to	PART
cana-5388	94	12	improve	improve	VERB
cana-5388	94	13	prediction	prediction	NOUN
cana-5388	94	14	accuracy	accuracy	NOUN
cana-5388	94	15	and	and	CCONJ
cana-5388	94	16	help	help	VERB
cana-5388	94	17	environmental	environmental	ADJ
cana-5388	94	18	monitoring	monitoring	NOUN
cana-5388	94	19	systems	system	NOUN
cana-5388	94	20	become	become	VERB
cana-5388	94	21	more	more	ADV
cana-5388	94	22	efficient	efficient	ADJ
cana-5388	94	23	and	and	CCONJ
cana-5388	94	24	responsive	responsive	ADJ
cana-5388	94	25	.	.	PUNCT
cana-5388	95	1	dataset	dataset	NOUN
cana-5388	95	2	sample	sample	NOUN
cana-5388	95	3	dataset	dataset	VERB
cana-5388	95	4	in	in	ADP
cana-5388	95	5	row	row	NOUN
cana-5388	95	6	and	and	CCONJ
cana-5388	95	7	column	column	NOUN
cana-5388	95	8	format	format	NOUN
cana-5388	95	9	that	that	PRON
cana-5388	95	10	fits	fit	VERB
cana-5388	95	11	the	the	DET
cana-5388	95	12	context	context	NOUN
cana-5388	95	13	of	of	ADP
cana-5388	95	14	your	your	PRON
cana-5388	95	15	research	research	NOUN
cana-5388	95	16	on	on	ADP
cana-5388	95	17	aqi	aqi	ADJ
cana-5388	95	18	prediction	prediction	NOUN
cana-5388	95	19	using	use	VERB
cana-5388	95	20	machine	machine	NOUN
cana-5388	95	21	learning	learning	NOUN
cana-5388	95	22	and	and	CCONJ
cana-5388	95	23	optimization	optimization	NOUN
cana-5388	95	24	techniques	technique	NOUN
cana-5388	95	25	.	.	PUNCT
cana-5388	96	1	this	this	DET
cana-5388	96	2	example	example	NOUN
cana-5388	96	3	includes	include	VERB
cana-5388	96	4	commonly	commonly	ADV
cana-5388	96	5	used	use	VERB
cana-5388	96	6	environmental	environmental	ADJ
cana-5388	96	7	and	and	CCONJ
cana-5388	96	8	meteorological	meteorological	ADJ
cana-5388	96	9	features	feature	NOUN
cana-5388	96	10	relevant	relevant	ADJ
cana-5388	96	11	for	for	ADP
cana-5388	96	12	aqi	aqi	PROPN
cana-5388	96	13	forecasting	forecasting	NOUN
cana-5388	96	14	table	table	NOUN
cana-5388	96	15	1	1	NUM
cana-5388	96	16	.	.	PUNCT
cana-5388	96	17	environmental	environmental	ADJ
cana-5388	96	18	and	and	CCONJ
cana-5388	96	19	meteorological	meteorological	ADJ
cana-5388	96	20	features	feature	NOUN
cana-5388	96	21	relevant	relevant	ADJ
cana-5388	96	22	for	for	ADP
cana-5388	96	23	aqi	aqi	PROPN
cana-5388	96	24	d	d	PROPN
cana-5388	96	25	a	a	X
cana-5388	96	26	te	te	PROPN
cana-5388	96	27	p	p	X
cana-5388	96	28	m	m	PROPN
cana-5388	96	29	₂.	₂.	X
cana-5388	96	30	₅	₅	INTJ
cana-5388	96	31	(	(	PUNCT
cana-5388	96	32	µ	µ	X
cana-5388	96	33	g	g	NOUN
cana-5388	96	34	/m	/m	X
cana-5388	96	35	³	³	X
cana-5388	96	36	)	)	PUNCT
cana-5388	96	37	p	p	NOUN
cana-5388	96	38	m	m	PROPN
cana-5388	96	39	₁₀	₁₀	NUM
cana-5388	96	40	(	(	PUNCT
cana-5388	96	41	µ	µ	PRON
cana-5388	96	42	g	g	NOUN
cana-5388	96	43	/m	/m	PUNCT
cana-5388	96	44	³	³	NOUN
cana-5388	96	45	)	)	PUNCT
cana-5388	96	46	n	n	NOUN
cana-5388	96	47	o	o	NOUN
cana-5388	96	48	₂	₂	X
cana-5388	97	1	(	(	PUNCT
cana-5388	97	2	p	p	X
cana-5388	97	3	p	p	PROPN
cana-5388	97	4	b	b	PROPN
cana-5388	97	5	)	)	PUNCT
cana-5388	97	6	s	s	PART
cana-5388	97	7	o	o	NOUN
cana-5388	97	8	₂	₂	X
cana-5388	97	9	(	(	PUNCT
cana-5388	97	10	p	p	X
cana-5388	97	11	p	p	PROPN
cana-5388	97	12	b	b	PROPN
cana-5388	97	13	)	)	PUNCT
cana-5388	98	1	c	c	NOUN
cana-5388	98	2	o	o	NOUN
cana-5388	98	3	(	(	PUNCT
cana-5388	98	4	p	p	X
cana-5388	98	5	p	p	NOUN
cana-5388	98	6	m	m	NOUN
cana-5388	98	7	)	)	PUNCT
cana-5388	99	1	o	o	NOUN
cana-5388	99	2	₃	₃	X
cana-5388	100	1	(	(	PUNCT
cana-5388	100	2	p	p	X
cana-5388	100	3	p	p	PROPN
cana-5388	100	4	b	b	PROPN
cana-5388	100	5	)	)	PUNCT
cana-5388	100	6	t	t	PROPN
cana-5388	100	7	e	e	NOUN
cana-5388	100	8	m	m	PROPN
cana-5388	100	9	p	p	NOUN
cana-5388	100	10	e	e	X
cana-5388	100	11	r	r	PROPN
cana-5388	100	12	a	a	DET
cana-5388	100	13	tu	tu	PROPN
cana-5388	100	14	re	re	X
cana-5388	100	15	(	(	PUNCT
cana-5388	100	16	°	°	ADP
cana-5388	100	17	c	c	NOUN
cana-5388	100	18	)	)	PUNCT
cana-5388	100	19	h	h	NOUN
cana-5388	101	1	u	u	NOUN
cana-5388	101	2	m	m	VERB
cana-5388	101	3	i	i	VERB
cana-5388	101	4	d	d	NOUN
cana-5388	101	5	it	it	PRON
cana-5388	101	6	y	y	INTJ
cana-5388	101	7	(	(	PUNCT
cana-5388	101	8	%	%	NOUN
cana-5388	101	9	)	)	PUNCT
cana-5388	102	1	w	w	NOUN
cana-5388	102	2	in	in	ADP
cana-5388	102	3	d	d	PROPN
cana-5388	102	4	s	s	X
cana-5388	102	5	p	p	X
cana-5388	102	6	e	e	X
cana-5388	102	7	e	e	X
cana-5388	102	8	d	d	X
cana-5388	102	9	(	(	PUNCT
cana-5388	102	10	k	k	NOUN
cana-5388	102	11	m	m	PROPN
cana-5388	102	12	/h	/h	PUNCT
cana-5388	102	13	)	)	PUNCT
cana-5388	102	14	a	a	DET
cana-5388	102	15	q	q	X
cana-5388	102	16	i	i	PRON
cana-5388	102	17	(	(	PUNCT
cana-5388	102	18	t	t	PROPN
cana-5388	102	19	a	a	DET
cana-5388	102	20	r	r	NOUN
cana-5388	102	21	g	g	PROPN
cana-5388	102	22	e	e	PROPN
cana-5388	102	23	t	t	PROPN
cana-5388	102	24	)	)	PUNCT
cana-5388	102	25	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-5388	102	26	communications	communication	NOUN
cana-5388	102	27	on	on	ADP
cana-5388	102	28	applied	apply	VERB
cana-5388	102	29	nonlinear	nonlinear	ADJ
cana-5388	102	30	analysis	analysis	NOUN
cana-5388	102	31	issn	issn	NOUN
cana-5388	102	32	:	:	PUNCT
cana-5388	102	33	1074	1074	NUM
cana-5388	102	34	-	-	PUNCT
cana-5388	102	35	133x	133x	NUM
cana-5388	102	36	vol	vol	NOUN
cana-5388	102	37	32	32	NUM
cana-5388	102	38	no	no	NOUN
cana-5388	102	39	.	.	PUNCT
cana-5388	103	1	1s	1s	NUM
cana-5388	103	2	(	(	PUNCT
cana-5388	103	3	2025	2025	NUM
cana-5388	103	4	)	)	PUNCT
cana-5388	104	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	104	2	658	658	NUM
cana-5388	104	3	2025	2025	NUM
cana-5388	104	4	-	-	SYM
cana-5388	104	5	05	05	NUM
cana-5388	104	6	-	-	SYM
cana-5388	104	7	01	01	NUM
cana-5388	104	8	35.4	35.4	NUM
cana-5388	104	9	78.2	78.2	NUM
cana-5388	104	10	23.4	23.4	NUM
cana-5388	104	11	15.2	15.2	NUM
cana-5388	104	12	0.85	0.85	NUM
cana-5388	104	13	30.5	30.5	NUM
cana-5388	104	14	25.4	25.4	NUM
cana-5388	104	15	60	60	NUM
cana-5388	104	16	12.4	12.4	NUM
cana-5388	104	17	120	120	NUM
cana-5388	104	18	2025	2025	NUM
cana-5388	104	19	-	-	SYM
cana-5388	104	20	05	05	NUM
cana-5388	104	21	-	-	PUNCT
cana-5388	104	22	02	02	NUM
cana-5388	104	23	42.8	42.8	NUM
cana-5388	104	24	88.1	88.1	NUM
cana-5388	104	25	25.8	25.8	NUM
cana-5388	104	26	18.5	18.5	NUM
cana-5388	104	27	1.02	1.02	NUM
cana-5388	104	28	28.3	28.3	NUM
cana-5388	104	29	27.8	27.8	NUM
cana-5388	104	30	55	55	NUM
cana-5388	104	31	10.6	10.6	NUM
cana-5388	104	32	135	135	NUM
cana-5388	104	33	2025	2025	NUM
cana-5388	104	34	-	-	SYM
cana-5388	104	35	05	05	NUM
cana-5388	104	36	-	-	PUNCT
cana-5388	104	37	03	03	NUM
cana-5388	104	38	29.5	29.5	NUM
cana-5388	104	39	65.7	65.7	NUM
cana-5388	104	40	20.1	20.1	NUM
cana-5388	104	41	14.1	14.1	NUM
cana-5388	104	42	0.78	0.78	NUM
cana-5388	104	43	35.4	35.4	NUM
cana-5388	104	44	22.6	22.6	NUM
cana-5388	104	45	70	70	NUM
cana-5388	104	46	15.3	15.3	NUM
cana-5388	104	47	105	105	NUM
cana-5388	104	48	2025	2025	NUM
cana-5388	104	49	-	-	SYM
cana-5388	104	50	05	05	NUM
cana-5388	104	51	-	-	PUNCT
cana-5388	104	52	04	04	NUM
cana-5388	104	53	50.2	50.2	NUM
cana-5388	104	54	95.3	95.3	NUM
cana-5388	104	55	30.7	30.7	NUM
cana-5388	104	56	20.3	20.3	NUM
cana-5388	104	57	1.15	1.15	NUM
cana-5388	104	58	25.7	25.7	NUM
cana-5388	104	59	28.9	28.9	NUM
cana-5388	104	60	50	50	NUM
cana-5388	104	61	11.8	11.8	NUM
cana-5388	104	62	150	150	NUM
cana-5388	104	63	2025	2025	NUM
cana-5388	104	64	-	-	SYM
cana-5388	104	65	05	05	NUM
cana-5388	104	66	-	-	SYM
cana-5388	104	67	05	05	NUM
cana-5388	104	68	37.9	37.9	NUM
cana-5388	104	69	80.5	80.5	NUM
cana-5388	104	70	27.3	27.3	NUM
cana-5388	104	71	16.7	16.7	NUM
cana-5388	104	72	0.90	0.90	NUM
cana-5388	104	73	32.6	32.6	NUM
cana-5388	104	74	26.2	26.2	NUM
cana-5388	104	75	65	65	NUM
cana-5388	104	76	13.4	13.4	NUM
cana-5388	104	77	125	125	NUM
cana-5388	104	78	➢	➢	NOUN
cana-5388	104	79	date	date	NOUN
cana-5388	104	80	:	:	PUNCT
cana-5388	104	81	the	the	DET
cana-5388	104	82	date	date	NOUN
cana-5388	104	83	when	when	SCONJ
cana-5388	104	84	the	the	DET
cana-5388	104	85	data	datum	NOUN
cana-5388	104	86	was	be	AUX
cana-5388	104	87	recorded	record	VERB
cana-5388	104	88	(	(	PUNCT
cana-5388	104	89	yyyy	yyyy	PROPN
cana-5388	104	90	-	-	PUNCT
cana-5388	104	91	mm	mm	PROPN
cana-5388	104	92	-	-	PUNCT
cana-5388	104	93	dd	dd	NOUN
cana-5388	104	94	format	format	NOUN
cana-5388	104	95	)	)	PUNCT
cana-5388	104	96	.	.	PUNCT
cana-5388	105	1	➢	➢	PROPN
cana-5388	105	2	pm₂.₅	pm₂.₅	NUM
cana-5388	105	3	(	(	PUNCT
cana-5388	105	4	µg	µg	ADJ
cana-5388	105	5	/	/	SYM
cana-5388	105	6	m³	m³	ADJ
cana-5388	105	7	):	):	PUNCT
cana-5388	105	8	concentration	concentration	NOUN
cana-5388	105	9	of	of	ADP
cana-5388	105	10	fine	fine	ADJ
cana-5388	105	11	particulate	particulate	NOUN
cana-5388	105	12	matter	matter	NOUN
cana-5388	105	13	(	(	PUNCT
cana-5388	105	14	pm₂.₅	pm₂.₅	X
cana-5388	105	15	)	)	PUNCT
cana-5388	105	16	in	in	ADP
cana-5388	105	17	micrograms	microgram	NOUN
cana-5388	105	18	per	per	ADP
cana-5388	105	19	cubic	cubic	ADJ
cana-5388	105	20	meter	meter	NOUN
cana-5388	105	21	.	.	PUNCT
cana-5388	106	1	➢	➢	VERB
cana-5388	107	1	pm₁₀	pm₁₀	PROPN
cana-5388	107	2	(	(	PUNCT
cana-5388	107	3	µg	µg	ADJ
cana-5388	107	4	/	/	SYM
cana-5388	107	5	m³	m³	ADJ
cana-5388	107	6	):	):	PUNCT
cana-5388	107	7	concentration	concentration	NOUN
cana-5388	107	8	of	of	ADP
cana-5388	107	9	coarse	coarse	ADJ
cana-5388	107	10	particulate	particulate	NOUN
cana-5388	107	11	matter	matter	NOUN
cana-5388	107	12	(	(	PUNCT
cana-5388	107	13	pm₁₀	pm₁₀	PROPN
cana-5388	107	14	)	)	PUNCT
cana-5388	107	15	in	in	ADP
cana-5388	107	16	micrograms	microgram	NOUN
cana-5388	107	17	per	per	ADP
cana-5388	107	18	cubic	cubic	ADJ
cana-5388	107	19	meter	meter	NOUN
cana-5388	107	20	.	.	PUNCT
cana-5388	108	1	➢	➢	VERB
cana-5388	108	2	no₂	no₂	PROPN
cana-5388	108	3	(	(	PUNCT
cana-5388	108	4	ppb	ppb	NOUN
cana-5388	108	5	):	):	PUNCT
cana-5388	108	6	concentration	concentration	NOUN
cana-5388	108	7	of	of	ADP
cana-5388	108	8	nitrogen	nitrogen	NOUN
cana-5388	108	9	dioxide	dioxide	NOUN
cana-5388	108	10	in	in	ADP
cana-5388	108	11	parts	part	NOUN
cana-5388	108	12	per	per	ADP
cana-5388	108	13	billion	billion	NUM
cana-5388	108	14	.	.	PUNCT
cana-5388	109	1	➢	➢	VERB
cana-5388	109	2	so₂	so₂	NOUN
cana-5388	109	3	(	(	PUNCT
cana-5388	109	4	ppb	ppb	NOUN
cana-5388	109	5	):	):	PUNCT
cana-5388	109	6	concentration	concentration	NOUN
cana-5388	109	7	of	of	ADP
cana-5388	109	8	sulfur	sulfur	NOUN
cana-5388	109	9	dioxide	dioxide	NOUN
cana-5388	109	10	in	in	ADP
cana-5388	109	11	parts	part	NOUN
cana-5388	109	12	per	per	ADP
cana-5388	109	13	billion	billion	NUM
cana-5388	109	14	.	.	PUNCT
cana-5388	110	1	➢	➢	PROPN
cana-5388	110	2	co	co	PROPN
cana-5388	110	3	(	(	PUNCT
cana-5388	110	4	ppm	ppm	NOUN
cana-5388	110	5	):	):	PUNCT
cana-5388	110	6	concentration	concentration	NOUN
cana-5388	110	7	of	of	ADP
cana-5388	110	8	carbon	carbon	NOUN
cana-5388	110	9	monoxide	monoxide	NOUN
cana-5388	110	10	in	in	ADP
cana-5388	110	11	parts	part	NOUN
cana-5388	110	12	per	per	ADP
cana-5388	110	13	million	million	NUM
cana-5388	110	14	.	.	PUNCT
cana-5388	111	1	➢	➢	PROPN
cana-5388	111	2	o₃	o₃	PROPN
cana-5388	111	3	(	(	PUNCT
cana-5388	111	4	ppb	ppb	NOUN
cana-5388	111	5	):	):	PUNCT
cana-5388	111	6	concentration	concentration	NOUN
cana-5388	111	7	of	of	ADP
cana-5388	111	8	ozone	ozone	NOUN
cana-5388	111	9	in	in	ADP
cana-5388	111	10	parts	part	NOUN
cana-5388	111	11	per	per	ADP
cana-5388	111	12	billion	billion	NUM
cana-5388	111	13	.	.	PUNCT
cana-5388	112	1	➢	➢	VERB
cana-5388	112	2	temperature	temperature	NOUN
cana-5388	112	3	(	(	PUNCT
cana-5388	112	4	°	°	ADP
cana-5388	112	5	c	c	NOUN
cana-5388	112	6	):	):	PUNCT
cana-5388	112	7	ambient	ambient	ADJ
cana-5388	112	8	temperature	temperature	NOUN
cana-5388	112	9	in	in	ADP
cana-5388	112	10	degrees	degree	NOUN
cana-5388	112	11	celsius	celsius	NOUN
cana-5388	112	12	.	.	PUNCT
cana-5388	113	1	➢	➢	NOUN
cana-5388	113	2	humidity	humidity	NOUN
cana-5388	113	3	(	(	PUNCT
cana-5388	113	4	%	%	INTJ
cana-5388	113	5	):	):	PUNCT
cana-5388	113	6	relative	relative	ADJ
cana-5388	113	7	humidity	humidity	NOUN
cana-5388	113	8	in	in	ADP
cana-5388	113	9	percentage	percentage	NOUN
cana-5388	113	10	.	.	PUNCT
cana-5388	114	1	➢	➢	VERB
cana-5388	114	2	wind	wind	NOUN
cana-5388	114	3	speed	speed	NOUN
cana-5388	114	4	(	(	PUNCT
cana-5388	114	5	km	km	NOUN
cana-5388	114	6	/	/	SYM
cana-5388	114	7	h	h	NOUN
cana-5388	114	8	):	):	PUNCT
cana-5388	114	9	wind	wind	NOUN
cana-5388	114	10	speed	speed	NOUN
cana-5388	114	11	in	in	ADP
cana-5388	114	12	kilometers	kilometer	NOUN
cana-5388	114	13	per	per	ADP
cana-5388	114	14	hour	hour	NOUN
cana-5388	114	15	.	.	PUNCT
cana-5388	115	1	➢	➢	PROPN
cana-5388	115	2	aqi	aqi	PROPN
cana-5388	115	3	(	(	PUNCT
cana-5388	115	4	target	target	NOUN
cana-5388	115	5	):	):	PUNCT
cana-5388	115	6	the	the	DET
cana-5388	115	7	calculated	calculate	VERB
cana-5388	115	8	air	air	NOUN
cana-5388	115	9	quality	quality	NOUN
cana-5388	115	10	index	index	NOUN
cana-5388	115	11	value	value	NOUN
cana-5388	115	12	,	,	PUNCT
cana-5388	115	13	which	which	PRON
cana-5388	115	14	serves	serve	VERB
cana-5388	115	15	as	as	ADP
cana-5388	115	16	the	the	DET
cana-5388	115	17	target	target	NOUN
cana-5388	115	18	variable	variable	NOUN
cana-5388	115	19	for	for	ADP
cana-5388	115	20	prediction	prediction	NOUN
cana-5388	115	21	.	.	PUNCT
cana-5388	116	1	2	2	X
cana-5388	116	2	.	.	X
cana-5388	116	3	background	background	NOUN
cana-5388	116	4	and	and	CCONJ
cana-5388	116	5	methodology	methodology	NOUN
cana-5388	116	6	2.1	2.1	NUM
cana-5388	116	7	background	background	NOUN
cana-5388	116	8	to	to	PART
cana-5388	116	9	systematically	systematically	ADV
cana-5388	116	10	evaluate	evaluate	VERB
cana-5388	116	11	and	and	CCONJ
cana-5388	116	12	communicate	communicate	VERB
cana-5388	116	13	the	the	DET
cana-5388	116	14	level	level	NOUN
cana-5388	116	15	of	of	ADP
cana-5388	116	16	atmospheric	atmospheric	ADJ
cana-5388	116	17	pollution	pollution	NOUN
cana-5388	116	18	,	,	PUNCT
cana-5388	116	19	the	the	DET
cana-5388	116	20	air	air	NOUN
cana-5388	116	21	quality	quality	NOUN
cana-5388	116	22	index	index	NOUN
cana-5388	116	23	(	(	PUNCT
cana-5388	116	24	aqi	aqi	PROPN
cana-5388	116	25	)	)	PUNCT
cana-5388	116	26	has	have	AUX
cana-5388	116	27	been	be	AUX
cana-5388	116	28	established	establish	VERB
cana-5388	116	29	as	as	ADP
cana-5388	116	30	a	a	DET
cana-5388	116	31	standardized	standardized	ADJ
cana-5388	116	32	metric	metric	NOUN
cana-5388	116	33	,	,	PUNCT
cana-5388	116	34	which	which	PRON
cana-5388	116	35	aggregates	aggregate	VERB
cana-5388	116	36	the	the	DET
cana-5388	116	37	concentrations	concentration	NOUN
cana-5388	116	38	of	of	ADP
cana-5388	116	39	major	major	ADJ
cana-5388	116	40	pollutants	pollutant	NOUN
cana-5388	116	41	such	such	ADJ
cana-5388	116	42	as	as	ADP
cana-5388	116	43	pm₂.₅	pm₂.₅	NUM
cana-5388	116	44	,	,	PUNCT
cana-5388	116	45	pm₁₀	pm₁₀	PROPN
cana-5388	116	46	,	,	PUNCT
cana-5388	116	47	no₂	no₂	PROPN
cana-5388	116	48	,	,	PUNCT
cana-5388	116	49	so₂	so₂	PROPN
cana-5388	116	50	,	,	PUNCT
cana-5388	116	51	co	co	NOUN
cana-5388	116	52	,	,	PUNCT
cana-5388	116	53	and	and	CCONJ
cana-5388	116	54	o₃	o₃	VERB
cana-5388	116	55	into	into	ADP
cana-5388	116	56	a	a	DET
cana-5388	116	57	single	single	ADJ
cana-5388	116	58	numerical	numerical	ADJ
cana-5388	116	59	indicator	indicator	NOUN
cana-5388	116	60	.	.	PUNCT
cana-5388	117	1	the	the	DET
cana-5388	117	2	accurate	accurate	ADJ
cana-5388	117	3	forecasting	forecasting	NOUN
cana-5388	117	4	of	of	ADP
cana-5388	117	5	aqi	aqi	PROPN
cana-5388	117	6	is	be	AUX
cana-5388	117	7	crucial	crucial	ADJ
cana-5388	117	8	,	,	PUNCT
cana-5388	117	9	as	as	SCONJ
cana-5388	117	10	it	it	PRON
cana-5388	117	11	facilitates	facilitate	VERB
cana-5388	117	12	proactive	proactive	ADJ
cana-5388	117	13	decision	decision	NOUN
cana-5388	117	14	-	-	PUNCT
cana-5388	117	15	making	making	NOUN
cana-5388	117	16	by	by	ADP
cana-5388	117	17	governmental	governmental	ADJ
cana-5388	117	18	bodies	body	NOUN
cana-5388	117	19	and	and	CCONJ
cana-5388	117	20	raises	raise	VERB
cana-5388	117	21	public	public	ADJ
cana-5388	117	22	awareness	awareness	NOUN
cana-5388	117	23	,	,	PUNCT
cana-5388	117	24	thereby	thereby	ADV
cana-5388	117	25	enabling	enable	VERB
cana-5388	117	26	timely	timely	ADJ
cana-5388	117	27	health	health	NOUN
cana-5388	117	28	and	and	CCONJ
cana-5388	117	29	safety	safety	NOUN
cana-5388	117	30	interventions	intervention	NOUN
cana-5388	117	31	.	.	PUNCT
cana-5388	118	1	to	to	PART
cana-5388	118	2	further	far	ADV
cana-5388	118	3	elevate	elevate	VERB
cana-5388	118	4	predictive	predictive	ADJ
cana-5388	118	5	performance	performance	NOUN
cana-5388	118	6	,	,	PUNCT
cana-5388	118	7	the	the	DET
cana-5388	118	8	integration	integration	NOUN
cana-5388	118	9	of	of	ADP
cana-5388	118	10	optimization	optimization	NOUN
cana-5388	118	11	heuristics	heuristic	NOUN
cana-5388	118	12	such	such	ADJ
cana-5388	118	13	as	as	ADP
cana-5388	118	14	genetic	genetic	ADJ
cana-5388	118	15	algorithms	algorithm	NOUN
cana-5388	118	16	(	(	PUNCT
cana-5388	118	17	ga	ga	NOUN
cana-5388	118	18	)	)	PUNCT
cana-5388	118	19	,	,	PUNCT
cana-5388	118	20	particle	particle	NOUN
cana-5388	118	21	swarm	swarm	NOUN
cana-5388	118	22	optimization	optimization	NOUN
cana-5388	118	23	(	(	PUNCT
cana-5388	118	24	pso	pso	NOUN
cana-5388	118	25	)	)	PUNCT
cana-5388	118	26	,	,	PUNCT
cana-5388	118	27	and	and	CCONJ
cana-5388	118	28	ant	ant	ADJ
cana-5388	118	29	colony	colony	NOUN
cana-5388	118	30	optimization	optimization	NOUN
cana-5388	118	31	(	(	PUNCT
cana-5388	118	32	aco	aco	PROPN
cana-5388	118	33	)	)	PUNCT
cana-5388	118	34	has	have	AUX
cana-5388	118	35	been	be	AUX
cana-5388	118	36	explored	explore	VERB
cana-5388	118	37	.	.	PUNCT
cana-5388	119	1	these	these	DET
cana-5388	119	2	nature	nature	NOUN
cana-5388	119	3	-	-	PUNCT
cana-5388	119	4	inspired	inspire	VERB
cana-5388	119	5	metaheuristic	metaheuristic	ADJ
cana-5388	119	6	techniques	technique	NOUN
cana-5388	119	7	are	be	AUX
cana-5388	119	8	instrumental	instrumental	ADJ
cana-5388	119	9	in	in	ADP
cana-5388	119	10	navigating	navigate	VERB
cana-5388	119	11	the	the	DET
cana-5388	119	12	multidimensional	multidimensional	ADJ
cana-5388	119	13	search	search	NOUN
cana-5388	119	14	space	space	NOUN
cana-5388	119	15	of	of	ADP
cana-5388	119	16	hyperparameters	hyperparameter	NOUN
cana-5388	119	17	,	,	PUNCT
cana-5388	119	18	thereby	thereby	ADV
cana-5388	119	19	facilitating	facilitate	VERB
cana-5388	119	20	the	the	DET
cana-5388	119	21	identification	identification	NOUN
cana-5388	119	22	of	of	ADP
cana-5388	119	23	optimal	optimal	ADJ
cana-5388	119	24	model	model	NOUN
cana-5388	119	25	configurations	configuration	NOUN
cana-5388	119	26	.	.	PUNCT
cana-5388	120	1	when	when	SCONJ
cana-5388	120	2	machine	machine	NOUN
cana-5388	120	3	learning	learn	VERB
cana-5388	120	4	algorithms	algorithm	NOUN
cana-5388	120	5	are	be	AUX
cana-5388	120	6	coupled	couple	VERB
cana-5388	120	7	with	with	ADP
cana-5388	120	8	these	these	DET
cana-5388	120	9	optimization	optimization	NOUN
cana-5388	120	10	strategies	strategy	NOUN
cana-5388	120	11	,	,	PUNCT
cana-5388	120	12	the	the	DET
cana-5388	120	13	resulting	result	VERB
cana-5388	120	14	hybrid	hybrid	ADJ
cana-5388	120	15	models	model	NOUN
cana-5388	120	16	tend	tend	VERB
cana-5388	120	17	to	to	PART
cana-5388	120	18	deliver	deliver	VERB
cana-5388	120	19	more	more	ADV
cana-5388	120	20	accurate	accurate	ADJ
cana-5388	120	21	,	,	PUNCT
cana-5388	120	22	robust	robust	ADJ
cana-5388	120	23	,	,	PUNCT
cana-5388	120	24	and	and	CCONJ
cana-5388	120	25	generalizable	generalizable	ADJ
cana-5388	120	26	results	result	NOUN
cana-5388	120	27	in	in	ADP
cana-5388	120	28	aqi	aqi	ADJ
cana-5388	120	29	prediction	prediction	NOUN
cana-5388	120	30	scenarios	scenario	NOUN
cana-5388	120	31	.	.	PUNCT
cana-5388	121	1	2.2	2.2	NUM
cana-5388	121	2	methodology	methodology	NOUN
cana-5388	121	3	the	the	DET
cana-5388	121	4	present	present	ADJ
cana-5388	121	5	study	study	NOUN
cana-5388	121	6	proposes	propose	VERB
cana-5388	121	7	a	a	DET
cana-5388	121	8	hybrid	hybrid	ADJ
cana-5388	121	9	framework	framework	NOUN
cana-5388	121	10	aimed	aim	VERB
cana-5388	121	11	at	at	ADP
cana-5388	121	12	enhancing	enhance	VERB
cana-5388	121	13	the	the	DET
cana-5388	121	14	precision	precision	NOUN
cana-5388	121	15	of	of	ADP
cana-5388	121	16	aqi	aqi	PROPN
cana-5388	121	17	forecasting	forecasting	NOUN
cana-5388	121	18	by	by	ADP
cana-5388	121	19	synergistically	synergistically	ADV
cana-5388	121	20	integrating	integrate	VERB
cana-5388	121	21	machine	machine	NOUN
cana-5388	121	22	learning	learn	VERB
cana-5388	121	23	algorithms	algorithm	NOUN
cana-5388	121	24	with	with	ADP
cana-5388	121	25	optimization	optimization	NOUN
cana-5388	121	26	heuristics	heuristic	NOUN
cana-5388	121	27	.	.	PUNCT
cana-5388	122	1	the	the	DET
cana-5388	122	2	methodological	methodological	ADJ
cana-5388	122	3	workflow	workflow	NOUN
cana-5388	122	4	comprises	comprise	VERB
cana-5388	122	5	the	the	DET
cana-5388	122	6	following	follow	VERB
cana-5388	122	7	stages	stage	NOUN
cana-5388	122	8	:	:	PUNCT
cana-5388	122	9	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	122	10	communications	communication	NOUN
cana-5388	122	11	on	on	ADP
cana-5388	122	12	applied	apply	VERB
cana-5388	122	13	nonlinear	nonlinear	ADJ
cana-5388	122	14	analysis	analysis	NOUN
cana-5388	122	15	issn	issn	NOUN
cana-5388	122	16	:	:	PUNCT
cana-5388	122	17	1074	1074	NUM
cana-5388	122	18	-	-	PUNCT
cana-5388	122	19	133x	133x	NUM
cana-5388	122	20	vol	vol	NOUN
cana-5388	122	21	32	32	NUM
cana-5388	123	1	no	no	NOUN
cana-5388	123	2	.	.	PUNCT
cana-5388	124	1	1s	1s	NUM
cana-5388	124	2	(	(	PUNCT
cana-5388	124	3	2025	2025	NUM
cana-5388	124	4	)	)	PUNCT
cana-5388	124	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	125	1	659	659	NUM
cana-5388	125	2	step	step	NOUN
cana-5388	125	3	1	1	NUM
cana-5388	125	4	:	:	PUNCT
cana-5388	125	5	data	data	NOUN
cana-5388	125	6	collection	collection	NOUN
cana-5388	125	7	:	:	PUNCT
cana-5388	125	8	environmental	environmental	ADJ
cana-5388	125	9	datasets	dataset	NOUN
cana-5388	125	10	containing	contain	VERB
cana-5388	125	11	historical	historical	ADJ
cana-5388	125	12	records	record	NOUN
cana-5388	125	13	of	of	ADP
cana-5388	125	14	air	air	NOUN
cana-5388	125	15	quality	quality	NOUN
cana-5388	125	16	parameters	parameter	NOUN
cana-5388	125	17	are	be	AUX
cana-5388	125	18	obtained	obtain	VERB
cana-5388	125	19	from	from	ADP
cana-5388	125	20	credible	credible	ADJ
cana-5388	125	21	and	and	CCONJ
cana-5388	125	22	publicly	publicly	ADV
cana-5388	125	23	accessible	accessible	ADJ
cana-5388	125	24	platforms	platform	NOUN
cana-5388	125	25	,	,	PUNCT
cana-5388	125	26	such	such	ADJ
cana-5388	125	27	as	as	ADP
cana-5388	125	28	the	the	DET
cana-5388	125	29	central	central	ADJ
cana-5388	125	30	pollution	pollution	NOUN
cana-5388	125	31	control	control	PROPN
cana-5388	125	32	board	board	PROPN
cana-5388	125	33	(	(	PUNCT
cana-5388	125	34	cpcb	cpcb	PROPN
cana-5388	125	35	)	)	PUNCT
cana-5388	125	36	,	,	PUNCT
cana-5388	125	37	kaggle	kaggle	VERB
cana-5388	125	38	,	,	PUNCT
cana-5388	125	39	or	or	CCONJ
cana-5388	125	40	the	the	DET
cana-5388	125	41	uci	uci	PROPN
cana-5388	125	42	machine	machine	NOUN
cana-5388	125	43	learning	learn	VERB
cana-5388	125	44	repository	repository	NOUN
cana-5388	125	45	.	.	PUNCT
cana-5388	126	1	these	these	DET
cana-5388	126	2	datasets	dataset	NOUN
cana-5388	126	3	typically	typically	ADV
cana-5388	126	4	include	include	VERB
cana-5388	126	5	pollutant	pollutant	ADJ
cana-5388	126	6	concentrations	concentration	NOUN
cana-5388	126	7	(	(	PUNCT
cana-5388	126	8	e.g.	e.g.	ADV
cana-5388	126	9	,	,	PUNCT
cana-5388	126	10	pm₂.₅	pm₂.₅	X
cana-5388	126	11	,	,	PUNCT
cana-5388	126	12	pm₁₀	pm₁₀	PROPN
cana-5388	126	13	,	,	PUNCT
cana-5388	126	14	no₂	no₂	PROPN
cana-5388	126	15	)	)	PUNCT
cana-5388	126	16	,	,	PUNCT
cana-5388	126	17	meteorological	meteorological	ADJ
cana-5388	126	18	variables	variable	NOUN
cana-5388	126	19	(	(	PUNCT
cana-5388	126	20	such	such	ADJ
cana-5388	126	21	as	as	ADP
cana-5388	126	22	temperature	temperature	NOUN
cana-5388	126	23	,	,	PUNCT
cana-5388	126	24	humidity	humidity	NOUN
cana-5388	126	25	,	,	PUNCT
cana-5388	126	26	and	and	CCONJ
cana-5388	126	27	wind	wind	NOUN
cana-5388	126	28	speed	speed	NOUN
cana-5388	126	29	)	)	PUNCT
cana-5388	126	30	,	,	PUNCT
cana-5388	126	31	and	and	CCONJ
cana-5388	126	32	corresponding	corresponding	ADJ
cana-5388	126	33	aqi	aqi	ADJ
cana-5388	126	34	values	value	NOUN
cana-5388	126	35	.	.	PUNCT
cana-5388	127	1	step	step	NOUN
cana-5388	127	2	2	2	NUM
cana-5388	127	3	:	:	PUNCT
cana-5388	127	4	data	datum	NOUN
cana-5388	127	5	preprocessing	preprocesse	VERB
cana-5388	127	6	to	to	PART
cana-5388	127	7	ensure	ensure	VERB
cana-5388	127	8	the	the	DET
cana-5388	127	9	reliability	reliability	NOUN
cana-5388	127	10	and	and	CCONJ
cana-5388	127	11	consistency	consistency	NOUN
cana-5388	127	12	of	of	ADP
cana-5388	127	13	the	the	DET
cana-5388	127	14	input	input	NOUN
cana-5388	127	15	data	datum	NOUN
cana-5388	127	16	,	,	PUNCT
cana-5388	127	17	preprocessing	preprocesse	VERB
cana-5388	127	18	operations	operation	NOUN
cana-5388	127	19	are	be	AUX
cana-5388	127	20	conducted	conduct	VERB
cana-5388	127	21	.	.	PUNCT
cana-5388	128	1	missing	miss	VERB
cana-5388	128	2	values	value	NOUN
cana-5388	128	3	are	be	AUX
cana-5388	128	4	addressed	address	VERB
cana-5388	128	5	through	through	ADP
cana-5388	128	6	interpolation	interpolation	NOUN
cana-5388	128	7	or	or	CCONJ
cana-5388	128	8	imputation	imputation	NOUN
cana-5388	128	9	techniques	technique	NOUN
cana-5388	128	10	,	,	PUNCT
cana-5388	128	11	while	while	SCONJ
cana-5388	128	12	statistical	statistical	ADJ
cana-5388	128	13	methods	method	NOUN
cana-5388	128	14	are	be	AUX
cana-5388	128	15	employed	employ	VERB
cana-5388	128	16	to	to	PART
cana-5388	128	17	detect	detect	VERB
cana-5388	128	18	and	and	CCONJ
cana-5388	128	19	remove	remove	VERB
cana-5388	128	20	outliers	outlier	NOUN
cana-5388	128	21	.	.	PUNCT
cana-5388	129	1	data	datum	NOUN
cana-5388	129	2	normalization	normalization	NOUN
cana-5388	129	3	or	or	CCONJ
cana-5388	129	4	standardization	standardization	NOUN
cana-5388	129	5	is	be	AUX
cana-5388	129	6	applied	apply	VERB
cana-5388	129	7	to	to	PART
cana-5388	129	8	maintain	maintain	VERB
cana-5388	129	9	uniform	uniform	ADJ
cana-5388	129	10	scaling	scaling	NOUN
cana-5388	129	11	,	,	PUNCT
cana-5388	129	12	and	and	CCONJ
cana-5388	129	13	feature	feature	NOUN
cana-5388	129	14	selection	selection	NOUN
cana-5388	129	15	algorithms	algorithm	NOUN
cana-5388	129	16	may	may	AUX
cana-5388	129	17	be	be	AUX
cana-5388	129	18	implemented	implement	VERB
cana-5388	129	19	to	to	PART
cana-5388	129	20	reduce	reduce	VERB
cana-5388	129	21	dimensionality	dimensionality	NOUN
cana-5388	129	22	,	,	PUNCT
cana-5388	129	23	thus	thus	ADV
cana-5388	129	24	enhancing	enhance	VERB
cana-5388	129	25	computational	computational	ADJ
cana-5388	129	26	efficiency	efficiency	NOUN
cana-5388	129	27	and	and	CCONJ
cana-5388	129	28	model	model	NOUN
cana-5388	129	29	performance	performance	NOUN
cana-5388	129	30	.	.	PUNCT
cana-5388	130	1	step	step	NOUN
cana-5388	130	2	3	3	NUM
cana-5388	130	3	:	:	PUNCT
cana-5388	130	4	model	model	NOUN
cana-5388	130	5	selection	selection	NOUN
cana-5388	130	6	a	a	DET
cana-5388	130	7	set	set	NOUN
cana-5388	130	8	of	of	ADP
cana-5388	130	9	machine	machine	NOUN
cana-5388	130	10	learning	learning	NOUN
cana-5388	130	11	models	model	NOUN
cana-5388	130	12	—	—	PUNCT
cana-5388	130	13	including	include	VERB
cana-5388	130	14	random	random	ADJ
cana-5388	130	15	forest	forest	NOUN
cana-5388	130	16	,	,	PUNCT
cana-5388	130	17	xgboost	xgboost	ADV
cana-5388	130	18	,	,	PUNCT
cana-5388	130	19	and	and	CCONJ
cana-5388	130	20	support	support	VERB
cana-5388	130	21	vector	vector	NOUN
cana-5388	130	22	machines	machine	NOUN
cana-5388	130	23	(	(	PUNCT
cana-5388	130	24	svm)—is	svm)—is	ADV
cana-5388	130	25	selected	select	VERB
cana-5388	130	26	based	base	VERB
cana-5388	130	27	on	on	ADP
cana-5388	130	28	their	their	PRON
cana-5388	130	29	suitability	suitability	NOUN
cana-5388	130	30	for	for	ADP
cana-5388	130	31	regression	regression	NOUN
cana-5388	130	32	and	and	CCONJ
cana-5388	130	33	classification	classification	NOUN
cana-5388	130	34	tasks	task	NOUN
cana-5388	130	35	.	.	PUNCT
cana-5388	131	1	special	special	ADJ
cana-5388	131	2	emphasis	emphasis	NOUN
cana-5388	131	3	is	be	AUX
cana-5388	131	4	placed	place	VERB
cana-5388	131	5	on	on	ADP
cana-5388	131	6	ensemble	ensemble	ADJ
cana-5388	131	7	learning	learning	NOUN
cana-5388	131	8	techniques	technique	NOUN
cana-5388	131	9	,	,	PUNCT
cana-5388	131	10	given	give	VERB
cana-5388	131	11	their	their	PRON
cana-5388	131	12	proven	prove	VERB
cana-5388	131	13	advantages	advantage	NOUN
cana-5388	131	14	in	in	ADP
cana-5388	131	15	terms	term	NOUN
cana-5388	131	16	of	of	ADP
cana-5388	131	17	accuracy	accuracy	NOUN
cana-5388	131	18	and	and	CCONJ
cana-5388	131	19	robustness	robustness	NOUN
cana-5388	131	20	.	.	PUNCT
cana-5388	132	1	step	step	NOUN
cana-5388	132	2	4	4	NUM
cana-5388	132	3	:	:	PUNCT
cana-5388	132	4	optimization	optimization	NOUN
cana-5388	132	5	using	use	VERB
cana-5388	132	6	heuristics	heuristic	NOUN
cana-5388	132	7	to	to	ADP
cana-5388	132	8	fine	fine	ADJ
cana-5388	132	9	-	-	PUNCT
cana-5388	132	10	tune	tune	NOUN
cana-5388	132	11	the	the	DET
cana-5388	132	12	hyperparameters	hyperparameter	NOUN
cana-5388	132	13	of	of	ADP
cana-5388	132	14	the	the	DET
cana-5388	132	15	selected	select	VERB
cana-5388	132	16	models	model	NOUN
cana-5388	132	17	,	,	PUNCT
cana-5388	132	18	optimization	optimization	NOUN
cana-5388	132	19	algorithms	algorithm	NOUN
cana-5388	132	20	such	such	ADJ
cana-5388	132	21	as	as	ADP
cana-5388	132	22	ga	ga	PROPN
cana-5388	132	23	and	and	CCONJ
cana-5388	132	24	pso	pso	NOUN
cana-5388	132	25	are	be	AUX
cana-5388	132	26	utilized	utilize	VERB
cana-5388	132	27	.	.	PUNCT
cana-5388	133	1	these	these	DET
cana-5388	133	2	techniques	technique	NOUN
cana-5388	133	3	evaluate	evaluate	VERB
cana-5388	133	4	candidate	candidate	NOUN
cana-5388	133	5	solutions	solution	NOUN
cana-5388	133	6	using	use	VERB
cana-5388	133	7	a	a	DET
cana-5388	133	8	fitness	fitness	NOUN
cana-5388	133	9	function	function	NOUN
cana-5388	133	10	that	that	PRON
cana-5388	133	11	considers	consider	VERB
cana-5388	133	12	key	key	ADJ
cana-5388	133	13	performance	performance	NOUN
cana-5388	133	14	metrics	metric	NOUN
cana-5388	133	15	,	,	PUNCT
cana-5388	133	16	including	include	VERB
cana-5388	133	17	mean	mean	ADJ
cana-5388	133	18	absolute	absolute	ADJ
cana-5388	133	19	error	error	NOUN
cana-5388	133	20	(	(	PUNCT
cana-5388	133	21	mae	mae	PROPN
cana-5388	133	22	)	)	PUNCT
cana-5388	133	23	,	,	PUNCT
cana-5388	133	24	root	root	NOUN
cana-5388	133	25	mean	mean	VERB
cana-5388	133	26	squared	square	VERB
cana-5388	133	27	error	error	NOUN
cana-5388	133	28	(	(	PUNCT
cana-5388	133	29	rmse	rmse	NOUN
cana-5388	133	30	)	)	PUNCT
cana-5388	133	31	,	,	PUNCT
cana-5388	133	32	and	and	CCONJ
cana-5388	133	33	the	the	DET
cana-5388	133	34	coefficient	coefficient	NOUN
cana-5388	133	35	of	of	ADP
cana-5388	133	36	determination	determination	NOUN
cana-5388	133	37	(	(	PUNCT
cana-5388	133	38	r²	r²	VERB
cana-5388	133	39	score	score	NOUN
cana-5388	133	40	)	)	PUNCT
cana-5388	133	41	.	.	PUNCT
cana-5388	134	1	step	step	NOUN
cana-5388	134	2	5	5	NUM
cana-5388	134	3	:	:	PUNCT
cana-5388	134	4	model	model	NOUN
cana-5388	134	5	training	training	NOUN
cana-5388	134	6	and	and	CCONJ
cana-5388	134	7	testing	test	VERB
cana-5388	134	8	the	the	DET
cana-5388	134	9	preprocessed	preprocesse	VERB
cana-5388	134	10	dataset	dataset	NOUN
cana-5388	134	11	is	be	AUX
cana-5388	134	12	divided	divide	VERB
cana-5388	134	13	into	into	ADP
cana-5388	134	14	training	training	NOUN
cana-5388	134	15	and	and	CCONJ
cana-5388	134	16	testing	testing	NOUN
cana-5388	134	17	subsets	subset	NOUN
cana-5388	134	18	,	,	PUNCT
cana-5388	134	19	typically	typically	ADV
cana-5388	134	20	using	use	VERB
cana-5388	134	21	an	an	DET
cana-5388	134	22	80:20	80:20	NUM
cana-5388	134	23	split	split	NOUN
cana-5388	134	24	ratio	ratio	NOUN
cana-5388	134	25	.	.	PUNCT
cana-5388	135	1	the	the	DET
cana-5388	135	2	models	model	NOUN
cana-5388	135	3	are	be	AUX
cana-5388	135	4	trained	train	VERB
cana-5388	135	5	on	on	ADP
cana-5388	135	6	the	the	DET
cana-5388	135	7	training	training	NOUN
cana-5388	135	8	data	datum	NOUN
cana-5388	135	9	and	and	CCONJ
cana-5388	135	10	evaluated	evaluate	VERB
cana-5388	135	11	on	on	ADP
cana-5388	135	12	the	the	DET
cana-5388	135	13	testing	testing	NOUN
cana-5388	135	14	set	set	VERB
cana-5388	135	15	to	to	PART
cana-5388	135	16	assess	assess	VERB
cana-5388	135	17	their	their	PRON
cana-5388	135	18	generalization	generalization	NOUN
cana-5388	135	19	capabilities	capability	NOUN
cana-5388	135	20	.	.	PUNCT
cana-5388	136	1	to	to	PART
cana-5388	136	2	further	far	ADV
cana-5388	136	3	reduce	reduce	VERB
cana-5388	136	4	the	the	DET
cana-5388	136	5	risk	risk	NOUN
cana-5388	136	6	of	of	ADP
cana-5388	136	7	overfitting	overfitte	VERB
cana-5388	136	8	and	and	CCONJ
cana-5388	136	9	ensure	ensure	VERB
cana-5388	136	10	stability	stability	NOUN
cana-5388	136	11	,	,	PUNCT
cana-5388	136	12	k	k	ADJ
cana-5388	136	13	-	-	ADJ
cana-5388	136	14	fold	fold	ADJ
cana-5388	136	15	cross	cross	NOUN
cana-5388	136	16	validation	validation	NOUN
cana-5388	136	17	is	be	AUX
cana-5388	136	18	employed	employ	VERB
cana-5388	136	19	during	during	ADP
cana-5388	136	20	the	the	DET
cana-5388	136	21	training	training	NOUN
cana-5388	136	22	process	process	NOUN
cana-5388	136	23	.	.	PUNCT
cana-5388	137	1	step	step	NOUN
cana-5388	137	2	6	6	NUM
cana-5388	137	3	:	:	PUNCT
cana-5388	137	4	evaluation	evaluation	NOUN
cana-5388	137	5	and	and	CCONJ
cana-5388	137	6	comparison	comparison	NOUN
cana-5388	137	7	after	after	ADP
cana-5388	137	8	training	training	NOUN
cana-5388	137	9	,	,	PUNCT
cana-5388	137	10	the	the	DET
cana-5388	137	11	models	model	NOUN
cana-5388	137	12	are	be	AUX
cana-5388	137	13	evaluated	evaluate	VERB
cana-5388	137	14	using	use	VERB
cana-5388	137	15	performance	performance	NOUN
cana-5388	137	16	indicators	indicator	NOUN
cana-5388	137	17	such	such	ADJ
cana-5388	137	18	as	as	ADP
cana-5388	137	19	mae	mae	PROPN
cana-5388	137	20	,	,	PUNCT
cana-5388	137	21	rmse	rmse	NOUN
cana-5388	137	22	,	,	PUNCT
cana-5388	137	23	r²	r²	NOUN
cana-5388	137	24	,	,	PUNCT
cana-5388	137	25	and	and	CCONJ
cana-5388	137	26	overall	overall	ADJ
cana-5388	137	27	prediction	prediction	NOUN
cana-5388	137	28	accuracy	accuracy	NOUN
cana-5388	137	29	.	.	PUNCT
cana-5388	138	1	the	the	DET
cana-5388	138	2	results	result	NOUN
cana-5388	138	3	obtained	obtain	VERB
cana-5388	138	4	from	from	ADP
cana-5388	138	5	the	the	DET
cana-5388	138	6	hybrid	hybrid	ADJ
cana-5388	138	7	model	model	NOUN
cana-5388	138	8	are	be	AUX
cana-5388	138	9	then	then	ADV
cana-5388	138	10	compared	compare	VERB
cana-5388	138	11	against	against	ADP
cana-5388	138	12	those	those	PRON
cana-5388	138	13	from	from	ADP
cana-5388	138	14	conventional	conventional	ADJ
cana-5388	138	15	machine	machine	NOUN
cana-5388	138	16	learning	learning	NOUN
cana-5388	138	17	models	model	NOUN
cana-5388	138	18	without	without	ADP
cana-5388	138	19	heuristic	heuristic	ADJ
cana-5388	138	20	optimization	optimization	NOUN
cana-5388	138	21	,	,	PUNCT
cana-5388	138	22	to	to	PART
cana-5388	138	23	quantify	quantify	VERB
cana-5388	138	24	the	the	DET
cana-5388	138	25	improvement	improvement	NOUN
cana-5388	138	26	in	in	ADP
cana-5388	138	27	predictive	predictive	ADJ
cana-5388	138	28	performance	performance	NOUN
cana-5388	138	29	.	.	PUNCT
cana-5388	139	1	step	step	NOUN
cana-5388	139	2	7	7	NUM
cana-5388	139	3	:	:	PUNCT
cana-5388	139	4	result	result	VERB
cana-5388	139	5	analysis	analysis	NOUN
cana-5388	139	6	and	and	CCONJ
cana-5388	139	7	interpretation	interpretation	NOUN
cana-5388	139	8	the	the	DET
cana-5388	139	9	final	final	ADJ
cana-5388	139	10	step	step	NOUN
cana-5388	139	11	involves	involve	VERB
cana-5388	139	12	a	a	DET
cana-5388	139	13	detailed	detailed	ADJ
cana-5388	139	14	analysis	analysis	NOUN
cana-5388	139	15	of	of	ADP
cana-5388	139	16	the	the	DET
cana-5388	139	17	results	result	NOUN
cana-5388	139	18	to	to	PART
cana-5388	139	19	examine	examine	VERB
cana-5388	139	20	the	the	DET
cana-5388	139	21	impact	impact	NOUN
cana-5388	139	22	of	of	ADP
cana-5388	139	23	optimization	optimization	NOUN
cana-5388	139	24	on	on	ADP
cana-5388	139	25	model	model	NOUN
cana-5388	139	26	accuracy	accuracy	NOUN
cana-5388	139	27	.	.	PUNCT
cana-5388	140	1	visual	visual	ADJ
cana-5388	140	2	tools	tool	NOUN
cana-5388	140	3	such	such	ADJ
cana-5388	140	4	as	as	ADP
cana-5388	140	5	graphs	graph	NOUN
cana-5388	140	6	and	and	CCONJ
cana-5388	140	7	comparative	comparative	ADJ
cana-5388	140	8	plots	plot	NOUN
cana-5388	140	9	are	be	AUX
cana-5388	140	10	employed	employ	VERB
cana-5388	140	11	to	to	PART
cana-5388	140	12	illustrate	illustrate	VERB
cana-5388	140	13	the	the	DET
cana-5388	140	14	correlation	correlation	NOUN
cana-5388	140	15	between	between	ADP
cana-5388	140	16	actual	actual	ADJ
cana-5388	140	17	and	and	CCONJ
cana-5388	140	18	predicted	predict	VERB
cana-5388	140	19	aqi	aqi	PROPN
cana-5388	140	20	values	value	NOUN
cana-5388	140	21	,	,	PUNCT
cana-5388	140	22	thereby	thereby	ADV
cana-5388	140	23	offering	offer	VERB
cana-5388	140	24	meaningful	meaningful	ADJ
cana-5388	140	25	insights	insight	NOUN
cana-5388	140	26	into	into	ADP
cana-5388	140	27	the	the	DET
cana-5388	140	28	model	model	NOUN
cana-5388	140	29	’s	’s	PART
cana-5388	140	30	practical	practical	ADJ
cana-5388	140	31	applicability	applicability	NOUN
cana-5388	140	32	.	.	PUNCT
cana-5388	141	1	2.3	2.3	NUM
cana-5388	141	2	methodology	methodology	NOUN
cana-5388	141	3	here	here	ADV
cana-5388	141	4	is	be	AUX
cana-5388	141	5	a	a	DET
cana-5388	141	6	step	step	NOUN
cana-5388	141	7	-	-	PUNCT
cana-5388	141	8	by	by	ADP
cana-5388	141	9	-	-	PUNCT
cana-5388	141	10	step	step	NOUN
cana-5388	141	11	explanation	explanation	NOUN
cana-5388	141	12	of	of	ADP
cana-5388	141	13	how	how	SCONJ
cana-5388	141	14	the	the	DET
cana-5388	141	15	random	random	ADJ
cana-5388	141	16	forest	forest	NOUN
cana-5388	141	17	,	,	PUNCT
cana-5388	141	18	xgboost	xgboost	ADV
cana-5388	141	19	,	,	PUNCT
cana-5388	141	20	and	and	CCONJ
cana-5388	141	21	support	support	VERB
cana-5388	141	22	vector	vector	NOUN
cana-5388	141	23	machine	machine	NOUN
cana-5388	141	24	(	(	PUNCT
cana-5388	141	25	svm	svm	ADJ
cana-5388	141	26	)	)	PUNCT
cana-5388	141	27	algorithms	algorithm	NOUN
cana-5388	141	28	work	work	NOUN
cana-5388	141	29	,	,	PUNCT
cana-5388	141	30	followed	follow	VERB
cana-5388	141	31	by	by	ADP
cana-5388	141	32	how	how	SCONJ
cana-5388	141	33	particle	particle	NOUN
cana-5388	141	34	swarm	swarm	NOUN
cana-5388	141	35	optimization	optimization	NOUN
cana-5388	141	36	(	(	PUNCT
cana-5388	141	37	pso	pso	NOUN
cana-5388	141	38	)	)	PUNCT
cana-5388	141	39	is	be	AUX
cana-5388	141	40	used	use	VERB
cana-5388	141	41	to	to	PART
cana-5388	141	42	tune	tune	VERB
cana-5388	141	43	their	their	PRON
cana-5388	141	44	hyperparameters	hyperparameter	NOUN
cana-5388	141	45	all	all	PRON
cana-5388	141	46	in	in	ADP
cana-5388	141	47	simple	simple	ADJ
cana-5388	141	48	and	and	CCONJ
cana-5388	141	49	clear	clear	ADJ
cana-5388	141	50	language	language	NOUN
cana-5388	141	51	:	:	PUNCT
cana-5388	141	52	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	141	53	communications	communication	NOUN
cana-5388	141	54	on	on	ADP
cana-5388	141	55	applied	apply	VERB
cana-5388	141	56	nonlinear	nonlinear	ADJ
cana-5388	141	57	analysis	analysis	NOUN
cana-5388	141	58	issn	issn	NOUN
cana-5388	141	59	:	:	PUNCT
cana-5388	141	60	1074	1074	NUM
cana-5388	141	61	-	-	PUNCT
cana-5388	141	62	133x	133x	NUM
cana-5388	141	63	vol	vol	NOUN
cana-5388	141	64	32	32	NUM
cana-5388	141	65	no	no	NOUN
cana-5388	141	66	.	.	PUNCT
cana-5388	142	1	1s	1s	NUM
cana-5388	142	2	(	(	PUNCT
cana-5388	142	3	2025	2025	NUM
cana-5388	142	4	)	)	PUNCT
cana-5388	143	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	143	2	660	660	NUM
cana-5388	143	3	3.3.1	3.3.1	NUM
cana-5388	143	4	.	.	PUNCT
cana-5388	143	5	random	random	ADJ
cana-5388	143	6	forest	forest	NOUN
cana-5388	143	7	–	–	PUNCT
cana-5388	143	8	step	step	NOUN
cana-5388	143	9	-	-	PUNCT
cana-5388	143	10	by	by	ADP
cana-5388	143	11	-	-	PUNCT
cana-5388	143	12	step	step	NOUN
cana-5388	143	13	step	step	NOUN
cana-5388	143	14	1	1	NUM
cana-5388	143	15	.	.	PUNCT
cana-5388	143	16	start	start	VERB
cana-5388	143	17	with	with	ADP
cana-5388	143	18	the	the	DET
cana-5388	143	19	dataset	dataset	NOUN
cana-5388	143	20	–	–	PUNCT
cana-5388	143	21	containing	contain	VERB
cana-5388	143	22	features	feature	NOUN
cana-5388	143	23	(	(	PUNCT
cana-5388	143	24	like	like	INTJ
cana-5388	143	25	pm2.5	pm2.5	ADJ
cana-5388	143	26	,	,	PUNCT
cana-5388	143	27	temperature	temperature	NOUN
cana-5388	143	28	,	,	PUNCT
cana-5388	143	29	etc	etc	X
cana-5388	143	30	.	.	X
cana-5388	143	31	)	)	PUNCT
cana-5388	144	1	and	and	CCONJ
cana-5388	144	2	the	the	DET
cana-5388	144	3	target	target	NOUN
cana-5388	144	4	variable	variable	NOUN
cana-5388	144	5	(	(	PUNCT
cana-5388	144	6	aqi	aqi	PROPN
cana-5388	144	7	)	)	PUNCT
cana-5388	144	8	.	.	PUNCT
cana-5388	145	1	step	step	NOUN
cana-5388	145	2	2	2	NUM
cana-5388	145	3	.	.	PUNCT
cana-5388	145	4	create	create	VERB
cana-5388	145	5	multiple	multiple	ADJ
cana-5388	145	6	decision	decision	NOUN
cana-5388	145	7	trees	tree	NOUN
cana-5388	145	8	–	–	PUNCT
cana-5388	145	9	using	use	VERB
cana-5388	145	10	different	different	ADJ
cana-5388	145	11	random	random	ADJ
cana-5388	145	12	samples	sample	NOUN
cana-5388	145	13	of	of	ADP
cana-5388	145	14	the	the	DET
cana-5388	145	15	dataset	dataset	NOUN
cana-5388	145	16	(	(	PUNCT
cana-5388	145	17	this	this	PRON
cana-5388	145	18	is	be	AUX
cana-5388	145	19	called	call	VERB
cana-5388	145	20	bootstrapping	bootstrappe	VERB
cana-5388	145	21	)	)	PUNCT
cana-5388	145	22	.	.	PUNCT
cana-5388	146	1	step	step	NOUN
cana-5388	146	2	3	3	NUM
cana-5388	146	3	.	.	PUNCT
cana-5388	147	1	at	at	ADP
cana-5388	147	2	each	each	DET
cana-5388	147	3	tree	tree	NOUN
cana-5388	147	4	node	node	NOUN
cana-5388	147	5	,	,	PUNCT
cana-5388	147	6	randomly	randomly	ADV
cana-5388	147	7	choose	choose	VERB
cana-5388	147	8	a	a	DET
cana-5388	147	9	subset	subset	NOUN
cana-5388	147	10	of	of	ADP
cana-5388	147	11	features	feature	NOUN
cana-5388	147	12	instead	instead	ADV
cana-5388	147	13	of	of	ADP
cana-5388	147	14	using	use	VERB
cana-5388	147	15	all	all	PRON
cana-5388	147	16	.	.	PUNCT
cana-5388	148	1	step	step	NOUN
cana-5388	148	2	4	4	NUM
cana-5388	148	3	.	.	PUNCT
cana-5388	148	4	split	split	VERB
cana-5388	148	5	the	the	DET
cana-5388	148	6	data	datum	NOUN
cana-5388	148	7	at	at	ADP
cana-5388	148	8	the	the	DET
cana-5388	148	9	node	node	NOUN
cana-5388	148	10	using	use	VERB
cana-5388	148	11	the	the	DET
cana-5388	148	12	best	good	ADJ
cana-5388	148	13	feature	feature	NOUN
cana-5388	148	14	from	from	ADP
cana-5388	148	15	the	the	DET
cana-5388	148	16	selected	select	VERB
cana-5388	148	17	subset	subset	NOUN
cana-5388	148	18	.	.	PUNCT
cana-5388	149	1	step	step	NOUN
cana-5388	149	2	5	5	NUM
cana-5388	149	3	.	.	PUNCT
cana-5388	150	1	grow	grow	VERB
cana-5388	150	2	each	each	DET
cana-5388	150	3	tree	tree	NOUN
cana-5388	150	4	fully	fully	ADV
cana-5388	150	5	or	or	CCONJ
cana-5388	150	6	up	up	ADP
cana-5388	150	7	to	to	ADP
cana-5388	150	8	a	a	DET
cana-5388	150	9	defined	define	VERB
cana-5388	150	10	depth	depth	NOUN
cana-5388	150	11	.	.	PUNCT
cana-5388	151	1	step	step	NOUN
cana-5388	151	2	6	6	NUM
cana-5388	151	3	.	.	PUNCT
cana-5388	152	1	for	for	ADP
cana-5388	152	2	prediction	prediction	NOUN
cana-5388	152	3	,	,	PUNCT
cana-5388	152	4	each	each	DET
cana-5388	152	5	tree	tree	NOUN
cana-5388	152	6	gives	give	VERB
cana-5388	152	7	its	its	PRON
cana-5388	152	8	result	result	NOUN
cana-5388	152	9	(	(	PUNCT
cana-5388	152	10	aqi	aqi	NOUN
cana-5388	152	11	value	value	NOUN
cana-5388	152	12	)	)	PUNCT
cana-5388	152	13	.	.	PUNCT
cana-5388	153	1	step	step	NOUN
cana-5388	153	2	7	7	NUM
cana-5388	153	3	.	.	PUNCT
cana-5388	153	4	combine	combine	VERB
cana-5388	153	5	all	all	DET
cana-5388	153	6	the	the	DET
cana-5388	153	7	results	result	NOUN
cana-5388	153	8	–	–	PUNCT
cana-5388	153	9	for	for	ADP
cana-5388	153	10	regression	regression	NOUN
cana-5388	153	11	,	,	PUNCT
cana-5388	153	12	take	take	VERB
cana-5388	153	13	the	the	DET
cana-5388	153	14	average	average	NOUN
cana-5388	153	15	of	of	ADP
cana-5388	153	16	all	all	DET
cana-5388	153	17	trees	tree	NOUN
cana-5388	153	18	'	'	PART
cana-5388	153	19	predictions	prediction	NOUN
cana-5388	153	20	.	.	PUNCT
cana-5388	154	1	3.3.2	3.3.2	NUM
cana-5388	154	2	.	.	PUNCT
cana-5388	154	3	xgboost	xgboost	ADJ
cana-5388	154	4	–	–	PUNCT
cana-5388	154	5	step	step	NOUN
cana-5388	154	6	-	-	PUNCT
cana-5388	154	7	by	by	ADP
cana-5388	154	8	-	-	PUNCT
cana-5388	154	9	step	step	NOUN
cana-5388	154	10	step	step	NOUN
cana-5388	154	11	1	1	NUM
cana-5388	154	12	.	.	PUNCT
cana-5388	155	1	start	start	VERB
cana-5388	155	2	with	with	ADP
cana-5388	155	3	the	the	DET
cana-5388	155	4	dataset	dataset	NOUN
cana-5388	155	5	–	–	PUNCT
cana-5388	155	6	with	with	ADP
cana-5388	155	7	input	input	NOUN
cana-5388	155	8	features	feature	NOUN
cana-5388	155	9	and	and	CCONJ
cana-5388	155	10	target	target	VERB
cana-5388	155	11	aqi	aqi	ADJ
cana-5388	155	12	values	value	NOUN
cana-5388	155	13	.	.	PUNCT
cana-5388	156	1	step	step	NOUN
cana-5388	156	2	2	2	NUM
cana-5388	156	3	.	.	PUNCT
cana-5388	156	4	begin	begin	VERB
cana-5388	156	5	with	with	ADP
cana-5388	156	6	a	a	DET
cana-5388	156	7	weak	weak	ADJ
cana-5388	156	8	model	model	NOUN
cana-5388	156	9	–	–	PUNCT
cana-5388	156	10	usually	usually	ADV
cana-5388	156	11	a	a	DET
cana-5388	156	12	single	single	ADJ
cana-5388	156	13	small	small	ADJ
cana-5388	156	14	decision	decision	NOUN
cana-5388	156	15	tree	tree	NOUN
cana-5388	156	16	.	.	PUNCT
cana-5388	157	1	step	step	NOUN
cana-5388	157	2	3	3	NUM
cana-5388	157	3	.	.	PUNCT
cana-5388	157	4	calculate	calculate	VERB
cana-5388	157	5	the	the	DET
cana-5388	157	6	error	error	NOUN
cana-5388	157	7	between	between	ADP
cana-5388	157	8	actual	actual	ADJ
cana-5388	157	9	and	and	CCONJ
cana-5388	157	10	predicted	predict	VERB
cana-5388	157	11	values	value	NOUN
cana-5388	157	12	.	.	PUNCT
cana-5388	158	1	step	step	NOUN
cana-5388	158	2	4	4	NUM
cana-5388	158	3	.	.	PUNCT
cana-5388	159	1	build	build	VERB
cana-5388	159	2	the	the	DET
cana-5388	159	3	next	next	ADJ
cana-5388	159	4	tree	tree	NOUN
cana-5388	159	5	to	to	PART
cana-5388	159	6	reduce	reduce	VERB
cana-5388	159	7	this	this	DET
cana-5388	159	8	error	error	NOUN
cana-5388	159	9	(	(	PUNCT
cana-5388	159	10	focus	focus	VERB
cana-5388	159	11	on	on	ADP
cana-5388	159	12	correcting	correct	VERB
cana-5388	159	13	previous	previous	ADJ
cana-5388	159	14	mistakes	mistake	NOUN
cana-5388	159	15	)	)	PUNCT
cana-5388	159	16	.	.	PUNCT
cana-5388	160	1	step	step	NOUN
cana-5388	160	2	5	5	NUM
cana-5388	160	3	.	.	PUNCT
cana-5388	160	4	repeat	repeat	VERB
cana-5388	160	5	the	the	DET
cana-5388	160	6	process	process	NOUN
cana-5388	160	7	by	by	ADP
cana-5388	160	8	adding	add	VERB
cana-5388	160	9	new	new	ADJ
cana-5388	160	10	trees	tree	NOUN
cana-5388	160	11	that	that	PRON
cana-5388	160	12	fix	fix	VERB
cana-5388	160	13	the	the	DET
cana-5388	160	14	errors	error	NOUN
cana-5388	160	15	of	of	ADP
cana-5388	160	16	the	the	DET
cana-5388	160	17	previous	previous	ADJ
cana-5388	160	18	ones	one	NOUN
cana-5388	160	19	.	.	PUNCT
cana-5388	161	1	step	step	NOUN
cana-5388	161	2	6	6	NUM
cana-5388	161	3	.	.	PUNCT
cana-5388	162	1	stop	stop	VERB
cana-5388	162	2	when	when	SCONJ
cana-5388	162	3	the	the	DET
cana-5388	162	4	number	number	NOUN
cana-5388	162	5	of	of	ADP
cana-5388	162	6	trees	tree	NOUN
cana-5388	162	7	reaches	reach	VERB
cana-5388	162	8	the	the	DET
cana-5388	162	9	set	set	NOUN
cana-5388	162	10	limit	limit	NOUN
cana-5388	162	11	or	or	CCONJ
cana-5388	162	12	error	error	NOUN
cana-5388	162	13	becomes	become	VERB
cana-5388	162	14	small	small	ADJ
cana-5388	162	15	.	.	PUNCT
cana-5388	163	1	step	step	NOUN
cana-5388	163	2	7	7	NUM
cana-5388	163	3	.	.	PUNCT
cana-5388	163	4	final	final	ADJ
cana-5388	163	5	prediction	prediction	NOUN
cana-5388	163	6	is	be	AUX
cana-5388	163	7	made	make	VERB
cana-5388	163	8	by	by	ADP
cana-5388	163	9	combining	combine	VERB
cana-5388	163	10	the	the	DET
cana-5388	163	11	outputs	output	NOUN
cana-5388	163	12	of	of	ADP
cana-5388	163	13	all	all	DET
cana-5388	163	14	trees	tree	NOUN
cana-5388	163	15	.	.	PUNCT
cana-5388	164	1	3.3.3	3.3.3	NUM
cana-5388	164	2	.	.	PUNCT
cana-5388	165	1	support	support	NOUN
cana-5388	165	2	vector	vector	NOUN
cana-5388	165	3	machine	machine	NOUN
cana-5388	165	4	(	(	PUNCT
cana-5388	165	5	svm	svm	PROPN
cana-5388	165	6	)	)	PUNCT
cana-5388	165	7	–	–	PUNCT
cana-5388	165	8	step	step	NOUN
cana-5388	165	9	-	-	PUNCT
cana-5388	165	10	by	by	ADP
cana-5388	165	11	-	-	PUNCT
cana-5388	165	12	step	step	NOUN
cana-5388	165	13	step	step	NOUN
cana-5388	165	14	1	1	NUM
cana-5388	165	15	.	.	PUNCT
cana-5388	165	16	load	load	VERB
cana-5388	165	17	the	the	DET
cana-5388	165	18	dataset	dataset	NOUN
cana-5388	165	19	with	with	ADP
cana-5388	165	20	input	input	NOUN
cana-5388	165	21	features	feature	NOUN
cana-5388	165	22	and	and	CCONJ
cana-5388	165	23	aqi	aqi	ADJ
cana-5388	165	24	values	value	NOUN
cana-5388	165	25	.	.	PUNCT
cana-5388	166	1	step	step	NOUN
cana-5388	166	2	2	2	NUM
cana-5388	166	3	.	.	PUNCT
cana-5388	166	4	convert	convert	VERB
cana-5388	166	5	the	the	DET
cana-5388	166	6	problem	problem	NOUN
cana-5388	166	7	into	into	ADP
cana-5388	166	8	a	a	DET
cana-5388	166	9	format	format	NOUN
cana-5388	166	10	suitable	suitable	ADJ
cana-5388	166	11	for	for	ADP
cana-5388	166	12	svm	svm	PROPN
cana-5388	166	13	(	(	PUNCT
cana-5388	166	14	regression	regression	NOUN
cana-5388	166	15	or	or	CCONJ
cana-5388	166	16	classification	classification	NOUN
cana-5388	166	17	)	)	PUNCT
cana-5388	166	18	.	.	PUNCT
cana-5388	167	1	step	step	NOUN
cana-5388	167	2	3	3	NUM
cana-5388	167	3	.	.	PUNCT
cana-5388	167	4	use	use	VERB
cana-5388	167	5	a	a	DET
cana-5388	167	6	kernel	kernel	NOUN
cana-5388	167	7	function	function	NOUN
cana-5388	167	8	(	(	PUNCT
cana-5388	167	9	linear	linear	PROPN
cana-5388	167	10	,	,	PUNCT
cana-5388	167	11	rbf	rbf	PROPN
cana-5388	167	12	,	,	PUNCT
cana-5388	167	13	etc	etc	X
cana-5388	167	14	.	.	X
cana-5388	167	15	)	)	PUNCT
cana-5388	167	16	to	to	PART
cana-5388	167	17	map	map	VERB
cana-5388	167	18	input	input	NOUN
cana-5388	167	19	data	datum	NOUN
cana-5388	167	20	to	to	ADP
cana-5388	167	21	a	a	DET
cana-5388	167	22	higher	high	ADJ
cana-5388	167	23	dimension	dimension	NOUN
cana-5388	167	24	.	.	PUNCT
cana-5388	168	1	step	step	NOUN
cana-5388	168	2	4	4	NUM
cana-5388	168	3	.	.	PUNCT
cana-5388	168	4	find	find	VERB
cana-5388	168	5	the	the	DET
cana-5388	168	6	best	good	ADJ
cana-5388	168	7	hyperplane	hyperplane	NOUN
cana-5388	168	8	that	that	PRON
cana-5388	168	9	separates	separate	VERB
cana-5388	168	10	data	data	NOUN
cana-5388	168	11	points	point	NOUN
cana-5388	168	12	with	with	ADP
cana-5388	168	13	the	the	DET
cana-5388	168	14	maximum	maximum	ADJ
cana-5388	168	15	margin	margin	NOUN
cana-5388	168	16	.	.	PUNCT
cana-5388	169	1	step	step	NOUN
cana-5388	169	2	5	5	NUM
cana-5388	169	3	.	.	PUNCT
cana-5388	170	1	use	use	VERB
cana-5388	170	2	this	this	DET
cana-5388	170	3	hyperplane	hyperplane	NOUN
cana-5388	170	4	to	to	PART
cana-5388	170	5	make	make	VERB
cana-5388	170	6	predictions	prediction	NOUN
cana-5388	170	7	for	for	ADP
cana-5388	170	8	new	new	ADJ
cana-5388	170	9	data	data	NOUN
cana-5388	170	10	points	point	NOUN
cana-5388	170	11	.	.	PUNCT
cana-5388	171	1	3.3.4	3.3.4	X
cana-5388	171	2	.	.	PUNCT
cana-5388	171	3	particle	particle	NOUN
cana-5388	171	4	swarm	swarm	NOUN
cana-5388	171	5	optimization	optimization	NOUN
cana-5388	171	6	(	(	PUNCT
cana-5388	171	7	pso	pso	NOUN
cana-5388	171	8	)	)	PUNCT
cana-5388	171	9	–	–	PUNCT
cana-5388	171	10	for	for	ADP
cana-5388	171	11	tuning	tune	VERB
cana-5388	171	12	hyperparameters	hyperparameter	NOUN
cana-5388	171	13	step	step	VERB
cana-5388	171	14	1	1	NUM
cana-5388	171	15	.	.	PUNCT
cana-5388	172	1	define	define	VERB
cana-5388	172	2	the	the	DET
cana-5388	172	3	search	search	NOUN
cana-5388	172	4	space	space	NOUN
cana-5388	172	5	–	–	PUNCT
cana-5388	172	6	decide	decide	VERB
cana-5388	172	7	which	which	DET
cana-5388	172	8	hyperparameters	hyperparameter	NOUN
cana-5388	172	9	of	of	ADP
cana-5388	172	10	the	the	DET
cana-5388	172	11	model	model	NOUN
cana-5388	172	12	you	you	PRON
cana-5388	172	13	want	want	VERB
cana-5388	172	14	to	to	PART
cana-5388	172	15	tune	tune	NOUN
cana-5388	172	16	(	(	PUNCT
cana-5388	172	17	e.g.	e.g.	ADV
cana-5388	172	18	,	,	PUNCT
cana-5388	172	19	number	number	NOUN
cana-5388	172	20	of	of	ADP
cana-5388	172	21	trees	tree	NOUN
cana-5388	172	22	,	,	PUNCT
cana-5388	172	23	learning	learn	VERB
cana-5388	172	24	rate	rate	NOUN
cana-5388	172	25	,	,	PUNCT
cana-5388	172	26	kernel	kernel	PROPN
cana-5388	172	27	type	type	NOUN
cana-5388	172	28	)	)	PUNCT
cana-5388	172	29	.	.	PUNCT
cana-5388	173	1	step	step	NOUN
cana-5388	173	2	2	2	NUM
cana-5388	173	3	.	.	PUNCT
cana-5388	173	4	initialize	initialize	VERB
cana-5388	173	5	particles	particle	NOUN
cana-5388	173	6	–	–	PUNCT
cana-5388	173	7	each	each	DET
cana-5388	173	8	particle	particle	NOUN
cana-5388	173	9	represents	represent	VERB
cana-5388	173	10	a	a	DET
cana-5388	173	11	possible	possible	ADJ
cana-5388	173	12	set	set	NOUN
cana-5388	173	13	of	of	ADP
cana-5388	173	14	hyperparameter	hyperparameter	NOUN
cana-5388	173	15	values	value	NOUN
cana-5388	173	16	.	.	PUNCT
cana-5388	174	1	step	step	NOUN
cana-5388	174	2	3	3	NUM
cana-5388	174	3	.	.	PUNCT
cana-5388	174	4	assign	assign	VERB
cana-5388	174	5	random	random	ADJ
cana-5388	174	6	positions	position	NOUN
cana-5388	174	7	and	and	CCONJ
cana-5388	174	8	velocities	velocity	NOUN
cana-5388	174	9	to	to	ADP
cana-5388	174	10	the	the	DET
cana-5388	174	11	particles	particle	NOUN
cana-5388	174	12	.	.	PUNCT
cana-5388	175	1	step	step	NOUN
cana-5388	175	2	4	4	NUM
cana-5388	175	3	.	.	PUNCT
cana-5388	175	4	evaluate	evaluate	VERB
cana-5388	175	5	each	each	DET
cana-5388	175	6	particle	particle	NOUN
cana-5388	175	7	’s	’s	PART
cana-5388	175	8	performance	performance	NOUN
cana-5388	175	9	using	use	VERB
cana-5388	175	10	a	a	DET
cana-5388	175	11	fitness	fitness	NOUN
cana-5388	175	12	function	function	NOUN
cana-5388	175	13	(	(	PUNCT
cana-5388	175	14	e.g.	e.g.	ADV
cana-5388	175	15	,	,	PUNCT
cana-5388	175	16	rmse	rmse	ADJ
cana-5388	175	17	from	from	ADP
cana-5388	175	18	the	the	DET
cana-5388	175	19	model	model	NOUN
cana-5388	175	20	using	use	VERB
cana-5388	175	21	those	those	DET
cana-5388	175	22	hyperparameters	hyperparameter	NOUN
cana-5388	175	23	)	)	PUNCT
cana-5388	175	24	.	.	PUNCT
cana-5388	176	1	step	step	NOUN
cana-5388	176	2	5	5	NUM
cana-5388	176	3	.	.	PUNCT
cana-5388	176	4	update	update	VERB
cana-5388	176	5	each	each	DET
cana-5388	176	6	particle	particle	NOUN
cana-5388	176	7	’s	’s	PART
cana-5388	176	8	best	well	ADV
cana-5388	176	9	-	-	PUNCT
cana-5388	176	10	known	know	VERB
cana-5388	176	11	position	position	NOUN
cana-5388	176	12	based	base	VERB
cana-5388	176	13	on	on	ADP
cana-5388	176	14	its	its	PRON
cana-5388	176	15	performance	performance	NOUN
cana-5388	176	16	.	.	PUNCT
cana-5388	177	1	step	step	NOUN
cana-5388	177	2	6	6	NUM
cana-5388	177	3	.	.	PUNCT
cana-5388	178	1	update	update	VERB
cana-5388	178	2	the	the	DET
cana-5388	178	3	global	global	ADJ
cana-5388	178	4	best	good	ADJ
cana-5388	178	5	–	–	PUNCT
cana-5388	178	6	the	the	DET
cana-5388	178	7	best	well	ADV
cana-5388	178	8	-	-	PUNCT
cana-5388	178	9	performing	perform	VERB
cana-5388	178	10	particle	particle	NOUN
cana-5388	178	11	among	among	ADP
cana-5388	178	12	all	all	PRON
cana-5388	178	13	.	.	PUNCT
cana-5388	179	1	step	step	NOUN
cana-5388	179	2	7	7	NUM
cana-5388	179	3	.	.	PUNCT
cana-5388	180	1	change	change	VERB
cana-5388	180	2	the	the	DET
cana-5388	180	3	velocity	velocity	NOUN
cana-5388	180	4	and	and	CCONJ
cana-5388	180	5	position	position	NOUN
cana-5388	180	6	of	of	ADP
cana-5388	180	7	each	each	DET
cana-5388	180	8	particle	particle	NOUN
cana-5388	180	9	toward	toward	ADP
cana-5388	180	10	its	its	PRON
cana-5388	180	11	own	own	ADJ
cana-5388	180	12	best	good	ADJ
cana-5388	180	13	and	and	CCONJ
cana-5388	180	14	global	global	ADJ
cana-5388	180	15	best	good	ADJ
cana-5388	180	16	.	.	PUNCT
cana-5388	181	1	step	step	NOUN
cana-5388	181	2	8	8	NUM
cana-5388	181	3	.	.	PUNCT
cana-5388	182	1	repeat	repeat	NOUN
cana-5388	182	2	steps	step	NOUN
cana-5388	182	3	4–7	4–7	PROPN
cana-5388	182	4	for	for	ADP
cana-5388	182	5	a	a	DET
cana-5388	182	6	set	set	ADJ
cana-5388	182	7	number	number	NOUN
cana-5388	182	8	of	of	ADP
cana-5388	182	9	iterations	iteration	NOUN
cana-5388	182	10	or	or	CCONJ
cana-5388	182	11	until	until	SCONJ
cana-5388	182	12	the	the	DET
cana-5388	182	13	performance	performance	NOUN
cana-5388	182	14	is	be	AUX
cana-5388	182	15	good	good	ADJ
cana-5388	182	16	enough	enough	ADV
cana-5388	182	17	.	.	PUNCT
cana-5388	183	1	step	step	NOUN
cana-5388	183	2	9	9	NUM
cana-5388	183	3	.	.	PUNCT
cana-5388	184	1	select	select	VERB
cana-5388	184	2	the	the	DET
cana-5388	184	3	best	good	ADJ
cana-5388	184	4	hyperparameters	hyperparameter	NOUN
cana-5388	184	5	(	(	PUNCT
cana-5388	184	6	from	from	ADP
cana-5388	184	7	the	the	DET
cana-5388	184	8	global	global	ADJ
cana-5388	184	9	best	good	ADJ
cana-5388	184	10	particle	particle	NOUN
cana-5388	184	11	)	)	PUNCT
cana-5388	184	12	to	to	PART
cana-5388	184	13	train	train	VERB
cana-5388	184	14	the	the	DET
cana-5388	184	15	final	final	ADJ
cana-5388	184	16	model	model	NOUN
cana-5388	184	17	.	.	PUNCT
cana-5388	185	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	185	2	communications	communication	NOUN
cana-5388	185	3	on	on	ADP
cana-5388	185	4	applied	apply	VERB
cana-5388	185	5	nonlinear	nonlinear	ADJ
cana-5388	185	6	analysis	analysis	NOUN
cana-5388	185	7	issn	issn	NOUN
cana-5388	185	8	:	:	PUNCT
cana-5388	185	9	1074	1074	NUM
cana-5388	185	10	-	-	PUNCT
cana-5388	185	11	133x	133x	NUM
cana-5388	185	12	vol	vol	NOUN
cana-5388	185	13	32	32	NUM
cana-5388	185	14	no	no	NOUN
cana-5388	185	15	.	.	PUNCT
cana-5388	186	1	1s	1s	NUM
cana-5388	186	2	(	(	PUNCT
cana-5388	186	3	2025	2025	NUM
cana-5388	186	4	)	)	PUNCT
cana-5388	187	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	187	2	661	661	NUM
cana-5388	187	3	3	3	NUM
cana-5388	187	4	.	.	PUNCT
cana-5388	187	5	experimental	experimental	ADJ
cana-5388	187	6	results	result	NOUN
cana-5388	187	7	table	table	NOUN
cana-5388	187	8	2	2	NUM
cana-5388	187	9	.	.	PUNCT
cana-5388	187	10	experimental	experimental	ADJ
cana-5388	187	11	results	result	NOUN
cana-5388	187	12	of	of	ADP
cana-5388	187	13	aqi	aqi	ADJ
cana-5388	187	14	prediction	prediction	NOUN
cana-5388	187	15	models	model	NOUN
cana-5388	187	16	model	model	VERB
cana-5388	187	17	mae	mae	PROPN
cana-5388	187	18	rmse	rmse	PROPN
cana-5388	187	19	r²	r²	NOUN
cana-5388	187	20	score	score	NOUN
cana-5388	187	21	accuracy	accuracy	NOUN
cana-5388	187	22	(	(	PUNCT
cana-5388	187	23	%	%	INTJ
cana-5388	187	24	)	)	PUNCT
cana-5388	187	25	support	support	NOUN
cana-5388	187	26	vector	vector	NOUN
cana-5388	187	27	machine	machine	NOUN
cana-5388	187	28	(	(	PUNCT
cana-5388	187	29	svm	svm	PROPN
cana-5388	187	30	)	)	PUNCT
cana-5388	187	31	18.5	18.5	NUM
cana-5388	187	32	24.2	24.2	NUM
cana-5388	187	33	0.84	0.84	NUM
cana-5388	187	34	85.6	85.6	NUM
cana-5388	187	35	random	random	ADJ
cana-5388	187	36	forest	forest	NOUN
cana-5388	187	37	(	(	PUNCT
cana-5388	187	38	rf	rf	NOUN
cana-5388	187	39	)	)	PUNCT
cana-5388	187	40	15.3	15.3	NUM
cana-5388	187	41	21.1	21.1	NUM
cana-5388	187	42	0.89	0.89	NUM
cana-5388	187	43	88.7	88.7	NUM
cana-5388	187	44	xgboost	xgboost	ADV
cana-5388	187	45	13.8	13.8	NUM
cana-5388	187	46	19.4	19.4	NUM
cana-5388	187	47	0.91	0.91	NUM
cana-5388	187	48	90.2	90.2	NUM
cana-5388	187	49	pso	pso	NOUN
cana-5388	187	50	-	-	PUNCT
cana-5388	187	51	tuned	tune	VERB
cana-5388	187	52	svm	svm	PROPN
cana-5388	187	53	14.2	14.2	NUM
cana-5388	187	54	19.7	19.7	NUM
cana-5388	187	55	0.90	0.90	NUM
cana-5388	187	56	89.6	89.6	NUM
cana-5388	187	57	pso	pso	NOUN
cana-5388	187	58	-	-	PUNCT
cana-5388	187	59	tuned	tune	VERB
cana-5388	187	60	random	random	ADJ
cana-5388	187	61	forest	forest	NOUN
cana-5388	188	1	12.1	12.1	NUM
cana-5388	188	2	17.5	17.5	NUM
cana-5388	188	3	0.93	0.93	NUM
cana-5388	188	4	92.1	92.1	NUM
cana-5388	188	5	pso	pso	NOUN
cana-5388	188	6	-	-	PUNCT
cana-5388	188	7	tuned	tune	VERB
cana-5388	188	8	xgboost	xgboost	ADV
cana-5388	188	9	(	(	PUNCT
cana-5388	188	10	hybrid	hybrid	NOUN
cana-5388	188	11	)	)	PUNCT
cana-5388	188	12	10.3	10.3	NUM
cana-5388	188	13	15.9	15.9	NUM
cana-5388	188	14	0.95	0.95	NUM
cana-5388	188	15	94.3	94.3	NUM
cana-5388	188	16	fig	fig	NOUN
cana-5388	188	17	.	.	PUNCT
cana-5388	189	1	1	1	X
cana-5388	189	2	.	.	X
cana-5388	189	3	model	model	PROPN
cana-5388	189	4	comparison	comparison	NOUN
cana-5388	189	5	based	base	VERB
cana-5388	189	6	on	on	ADP
cana-5388	189	7	r2	r2	PROPN
cana-5388	189	8	score	score	NOUN
cana-5388	189	9	fig	fig	NOUN
cana-5388	189	10	.	.	PUNCT
cana-5388	190	1	2	2	X
cana-5388	190	2	.	.	X
cana-5388	190	3	model	model	PROPN
cana-5388	190	4	comparison	comparison	NOUN
cana-5388	190	5	based	base	VERB
cana-5388	190	6	on	on	ADP
cana-5388	190	7	mae	mae	PROPN
cana-5388	190	8	and	and	CCONJ
cana-5388	190	9	rmse	rmse	ADJ
cana-5388	190	10	fig	fig	NOUN
cana-5388	190	11	.	.	PUNCT
cana-5388	191	1	3	3	X
cana-5388	191	2	.	.	PUNCT
cana-5388	191	3	model	model	NOUN
cana-5388	191	4	accuracy	accuracy	PROPN
cana-5388	191	5	comparison	comparison	NOUN
cana-5388	191	6	for	for	ADP
cana-5388	191	7	aqi	aqi	ADJ
cana-5388	191	8	prediction	prediction	NOUN
cana-5388	191	9	https://internationalpubls.com/	https://internationalpubls.com/	PROPN
cana-5388	191	10	communications	communication	NOUN
cana-5388	191	11	on	on	ADP
cana-5388	191	12	applied	apply	VERB
cana-5388	191	13	nonlinear	nonlinear	ADJ
cana-5388	191	14	analysis	analysis	NOUN
cana-5388	191	15	issn	issn	NOUN
cana-5388	191	16	:	:	PUNCT
cana-5388	191	17	1074	1074	NUM
cana-5388	191	18	-	-	PUNCT
cana-5388	191	19	133x	133x	NUM
cana-5388	191	20	vol	vol	NOUN
cana-5388	191	21	32	32	NUM
cana-5388	191	22	no	no	NOUN
cana-5388	191	23	.	.	PUNCT
cana-5388	192	1	1s	1s	NUM
cana-5388	192	2	(	(	PUNCT
cana-5388	192	3	2025	2025	NUM
cana-5388	192	4	)	)	PUNCT
cana-5388	192	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	192	6	662	662	NUM
cana-5388	192	7	4	4	NUM
cana-5388	192	8	.	.	PUNCT
cana-5388	192	9	results	result	NOUN
cana-5388	192	10	and	and	CCONJ
cana-5388	192	11	discussion	discussion	NOUN
cana-5388	192	12	this	this	DET
cana-5388	192	13	research	research	NOUN
cana-5388	192	14	compares	compare	VERB
cana-5388	192	15	the	the	DET
cana-5388	192	16	performance	performance	NOUN
cana-5388	192	17	of	of	ADP
cana-5388	192	18	regular	regular	ADJ
cana-5388	192	19	machine	machine	NOUN
cana-5388	192	20	learning	learning	NOUN
cana-5388	192	21	models	model	NOUN
cana-5388	192	22	and	and	CCONJ
cana-5388	192	23	their	their	PRON
cana-5388	192	24	improved	improved	ADJ
cana-5388	192	25	versions	version	NOUN
cana-5388	192	26	that	that	PRON
cana-5388	192	27	use	use	VERB
cana-5388	192	28	optimization	optimization	NOUN
cana-5388	192	29	methods	method	NOUN
cana-5388	192	30	for	for	ADP
cana-5388	192	31	predicting	predict	VERB
cana-5388	192	32	the	the	DET
cana-5388	192	33	air	air	NOUN
cana-5388	192	34	quality	quality	NOUN
cana-5388	192	35	index	index	NOUN
cana-5388	192	36	(	(	PUNCT
cana-5388	192	37	aqi	aqi	PROPN
cana-5388	192	38	)	)	PUNCT
cana-5388	192	39	.	.	PUNCT
cana-5388	193	1	the	the	DET
cana-5388	193	2	models	model	NOUN
cana-5388	193	3	used	use	VERB
cana-5388	193	4	are	be	AUX
cana-5388	193	5	support	support	NOUN
cana-5388	193	6	vector	vector	NOUN
cana-5388	193	7	machine	machine	NOUN
cana-5388	193	8	(	(	PUNCT
cana-5388	193	9	svm	svm	PROPN
cana-5388	193	10	)	)	PUNCT
cana-5388	193	11	,	,	PUNCT
cana-5388	193	12	random	random	ADJ
cana-5388	193	13	forest	forest	NOUN
cana-5388	193	14	(	(	PUNCT
cana-5388	193	15	rf	rf	NOUN
cana-5388	193	16	)	)	PUNCT
cana-5388	193	17	,	,	PUNCT
cana-5388	193	18	and	and	CCONJ
cana-5388	193	19	xgboost	xgboost	X
cana-5388	193	20	,	,	PUNCT
cana-5388	193	21	along	along	ADP
cana-5388	193	22	with	with	ADP
cana-5388	193	23	versions	version	NOUN
cana-5388	193	24	of	of	ADP
cana-5388	193	25	these	these	DET
cana-5388	193	26	models	model	NOUN
cana-5388	193	27	tuned	tune	VERB
cana-5388	193	28	using	use	VERB
cana-5388	193	29	particle	particle	NOUN
cana-5388	193	30	swarm	swarm	NOUN
cana-5388	193	31	optimization	optimization	NOUN
cana-5388	193	32	(	(	PUNCT
cana-5388	193	33	pso	pso	NOUN
cana-5388	193	34	)	)	PUNCT
cana-5388	193	35	.	.	PUNCT
cana-5388	194	1	the	the	DET
cana-5388	194	2	models	model	NOUN
cana-5388	194	3	were	be	AUX
cana-5388	194	4	evaluated	evaluate	VERB
cana-5388	194	5	using	use	VERB
cana-5388	194	6	common	common	ADJ
cana-5388	194	7	performance	performance	NOUN
cana-5388	194	8	measures	measure	NOUN
cana-5388	194	9	like	like	ADP
cana-5388	194	10	mean	mean	VERB
cana-5388	194	11	absolute	absolute	ADJ
cana-5388	194	12	error	error	NOUN
cana-5388	194	13	(	(	PUNCT
cana-5388	194	14	mae	mae	PROPN
cana-5388	194	15	)	)	PUNCT
cana-5388	194	16	,	,	PUNCT
cana-5388	194	17	root	root	NOUN
cana-5388	194	18	mean	mean	VERB
cana-5388	194	19	squared	square	VERB
cana-5388	194	20	error	error	NOUN
cana-5388	194	21	(	(	PUNCT
cana-5388	194	22	rmse	rmse	NOUN
cana-5388	194	23	)	)	PUNCT
cana-5388	194	24	,	,	PUNCT
cana-5388	194	25	r²	r²	NOUN
cana-5388	194	26	score	score	NOUN
cana-5388	194	27	,	,	PUNCT
cana-5388	194	28	and	and	CCONJ
cana-5388	194	29	accuracy	accuracy	NOUN
cana-5388	194	30	.	.	PUNCT
cana-5388	195	1	the	the	DET
cana-5388	195	2	experimental	experimental	ADJ
cana-5388	195	3	results	result	NOUN
cana-5388	195	4	are	be	AUX
cana-5388	195	5	shown	show	VERB
cana-5388	195	6	in	in	ADP
cana-5388	195	7	table	table	NOUN
cana-5388	195	8	1	1	NUM
cana-5388	195	9	.	.	PUNCT
cana-5388	196	1	it	it	PRON
cana-5388	196	2	is	be	AUX
cana-5388	196	3	clearly	clearly	ADV
cana-5388	196	4	seen	see	VERB
cana-5388	196	5	that	that	SCONJ
cana-5388	196	6	the	the	DET
cana-5388	196	7	models	model	NOUN
cana-5388	196	8	tuned	tune	VERB
cana-5388	196	9	with	with	ADP
cana-5388	196	10	pso	pso	NOUN
cana-5388	196	11	gave	give	VERB
cana-5388	196	12	better	well	ADJ
cana-5388	196	13	results	result	NOUN
cana-5388	196	14	than	than	ADP
cana-5388	196	15	the	the	DET
cana-5388	196	16	original	original	ADJ
cana-5388	196	17	ones	one	NOUN
cana-5388	196	18	.	.	PUNCT
cana-5388	197	1	among	among	ADP
cana-5388	197	2	all	all	DET
cana-5388	197	3	the	the	DET
cana-5388	197	4	models	model	NOUN
cana-5388	197	5	,	,	PUNCT
cana-5388	197	6	pso	pso	NOUN
cana-5388	197	7	-	-	PUNCT
cana-5388	197	8	tuned	tune	VERB
cana-5388	197	9	xgboost	xgboost	ADV
cana-5388	197	10	performed	perform	VERB
cana-5388	197	11	the	the	DET
cana-5388	197	12	best	good	ADJ
cana-5388	197	13	with	with	ADP
cana-5388	197	14	a	a	DET
cana-5388	197	15	mae	mae	PROPN
cana-5388	197	16	of	of	ADP
cana-5388	197	17	10.3	10.3	NUM
cana-5388	197	18	,	,	PUNCT
cana-5388	197	19	rmse	rmse	NOUN
cana-5388	197	20	of	of	ADP
cana-5388	197	21	15.9	15.9	NUM
cana-5388	197	22	,	,	PUNCT
cana-5388	197	23	r²	r²	VERB
cana-5388	197	24	score	score	NOUN
cana-5388	197	25	of	of	ADP
cana-5388	197	26	0.95	0.95	NUM
cana-5388	197	27	,	,	PUNCT
cana-5388	197	28	and	and	CCONJ
cana-5388	197	29	accuracy	accuracy	NOUN
cana-5388	197	30	of	of	ADP
cana-5388	197	31	94.3	94.3	NUM
cana-5388	197	32	%	%	NOUN
cana-5388	197	33	.	.	PUNCT
cana-5388	198	1	figure	figure	NOUN
cana-5388	198	2	1	1	NUM
cana-5388	198	3	shows	show	VERB
cana-5388	198	4	a	a	DET
cana-5388	198	5	bar	bar	NOUN
cana-5388	198	6	graph	graph	NOUN
cana-5388	198	7	comparing	compare	VERB
cana-5388	198	8	the	the	DET
cana-5388	198	9	accuracy	accuracy	NOUN
cana-5388	198	10	of	of	ADP
cana-5388	198	11	all	all	DET
cana-5388	198	12	models	model	NOUN
cana-5388	198	13	.	.	PUNCT
cana-5388	199	1	the	the	DET
cana-5388	199	2	normal	normal	ADJ
cana-5388	199	3	svm	svm	NOUN
cana-5388	199	4	and	and	CCONJ
cana-5388	199	5	random	random	ADJ
cana-5388	199	6	forest	forest	NOUN
cana-5388	199	7	models	model	NOUN
cana-5388	199	8	gave	give	VERB
cana-5388	199	9	accuracy	accuracy	NOUN
cana-5388	199	10	values	value	NOUN
cana-5388	199	11	of	of	ADP
cana-5388	199	12	85.6	85.6	NUM
cana-5388	199	13	%	%	NOUN
cana-5388	199	14	and	and	CCONJ
cana-5388	199	15	88.7	88.7	NUM
cana-5388	199	16	%	%	NOUN
cana-5388	199	17	,	,	PUNCT
cana-5388	199	18	but	but	CCONJ
cana-5388	199	19	when	when	SCONJ
cana-5388	199	20	tuned	tune	VERB
cana-5388	199	21	with	with	ADP
cana-5388	199	22	pso	pso	NOUN
cana-5388	199	23	,	,	PUNCT
cana-5388	199	24	their	their	PRON
cana-5388	199	25	accuracy	accuracy	NOUN
cana-5388	199	26	improved	improve	VERB
cana-5388	199	27	.	.	PUNCT
cana-5388	200	1	pso	pso	NOUN
cana-5388	200	2	-	-	PUNCT
cana-5388	200	3	tuned	tune	VERB
cana-5388	200	4	random	random	ADJ
cana-5388	200	5	forest	forest	NOUN
cana-5388	200	6	gave	give	VERB
cana-5388	200	7	92.1	92.1	NUM
cana-5388	200	8	%	%	NOUN
cana-5388	200	9	accuracy	accuracy	NOUN
cana-5388	200	10	,	,	PUNCT
cana-5388	200	11	and	and	CCONJ
cana-5388	200	12	pso	pso	NOUN
cana-5388	200	13	-	-	PUNCT
cana-5388	200	14	tuned	tune	VERB
cana-5388	200	15	xgboost	xgboost	ADV
cana-5388	200	16	reached	reach	VERB
cana-5388	200	17	94.3	94.3	NUM
cana-5388	200	18	%	%	NOUN
cana-5388	200	19	,	,	PUNCT
cana-5388	200	20	showing	show	VERB
cana-5388	200	21	that	that	SCONJ
cana-5388	200	22	tuning	tune	VERB
cana-5388	200	23	the	the	DET
cana-5388	200	24	model	model	NOUN
cana-5388	200	25	’s	’s	PART
cana-5388	200	26	parameters	parameter	NOUN
cana-5388	200	27	gives	give	VERB
cana-5388	200	28	better	well	ADJ
cana-5388	200	29	results	result	NOUN
cana-5388	200	30	.	.	PUNCT
cana-5388	201	1	figures	figure	NOUN
cana-5388	201	2	2	2	NUM
cana-5388	201	3	and	and	CCONJ
cana-5388	201	4	3	3	NUM
cana-5388	201	5	show	show	VERB
cana-5388	201	6	how	how	SCONJ
cana-5388	201	7	the	the	DET
cana-5388	201	8	models	model	NOUN
cana-5388	201	9	performed	perform	VERB
cana-5388	201	10	based	base	VERB
cana-5388	201	11	on	on	ADP
cana-5388	201	12	mae	mae	PROPN
cana-5388	201	13	and	and	CCONJ
cana-5388	201	14	rmse	rmse	NOUN
cana-5388	201	15	.	.	PUNCT
cana-5388	202	1	these	these	DET
cana-5388	202	2	metrics	metric	NOUN
cana-5388	202	3	help	help	VERB
cana-5388	202	4	to	to	PART
cana-5388	202	5	understand	understand	VERB
cana-5388	202	6	how	how	SCONJ
cana-5388	202	7	close	close	ADJ
cana-5388	202	8	the	the	DET
cana-5388	202	9	predicted	predict	VERB
cana-5388	202	10	aqi	aqi	PROPN
cana-5388	202	11	values	value	NOUN
cana-5388	202	12	are	be	AUX
cana-5388	202	13	to	to	ADP
cana-5388	202	14	the	the	DET
cana-5388	202	15	actual	actual	ADJ
cana-5388	202	16	values	value	NOUN
cana-5388	202	17	.	.	PUNCT
cana-5388	203	1	the	the	DET
cana-5388	203	2	following	follow	VERB
cana-5388	203	3	results	result	NOUN
cana-5388	203	4	were	be	AUX
cana-5388	203	5	observed	observe	VERB
cana-5388	203	6	.	.	PUNCT
cana-5388	204	1	mae	mae	PROPN
cana-5388	204	2	reduced	reduce	VERB
cana-5388	204	3	from	from	ADP
cana-5388	204	4	18.5	18.5	NUM
cana-5388	204	5	in	in	ADP
cana-5388	204	6	svm	svm	PROPN
cana-5388	204	7	to	to	ADP
cana-5388	204	8	10.3	10.3	NUM
cana-5388	204	9	in	in	ADP
cana-5388	204	10	pso	pso	NOUN
cana-5388	204	11	-	-	PUNCT
cana-5388	204	12	tuned	tune	VERB
cana-5388	204	13	xgboost	xgboost	X
cana-5388	204	14	.	.	PUNCT
cana-5388	205	1	rmse	rmse	PROPN
cana-5388	205	2	reduced	reduce	VERB
cana-5388	205	3	from	from	ADP
cana-5388	205	4	24.2	24.2	NUM
cana-5388	205	5	in	in	ADP
cana-5388	205	6	svm	svm	NOUN
cana-5388	205	7	to	to	ADP
cana-5388	205	8	15.9	15.9	NUM
cana-5388	205	9	in	in	ADP
cana-5388	205	10	pso	pso	NOUN
cana-5388	205	11	-	-	PUNCT
cana-5388	205	12	tuned	tune	VERB
cana-5388	205	13	xgboost	xgboost	ADV
cana-5388	205	14	.	.	PUNCT
cana-5388	206	1	this	this	PRON
cana-5388	206	2	clearly	clearly	ADV
cana-5388	206	3	shows	show	VERB
cana-5388	206	4	that	that	SCONJ
cana-5388	206	5	using	use	VERB
cana-5388	206	6	optimization	optimization	NOUN
cana-5388	206	7	helps	help	VERB
cana-5388	206	8	in	in	ADP
cana-5388	206	9	reducing	reduce	VERB
cana-5388	206	10	the	the	DET
cana-5388	206	11	prediction	prediction	NOUN
cana-5388	206	12	errors	error	NOUN
cana-5388	206	13	.	.	PUNCT
cana-5388	207	1	the	the	DET
cana-5388	207	2	better	well	ADJ
cana-5388	207	3	performance	performance	NOUN
cana-5388	207	4	of	of	ADP
cana-5388	207	5	the	the	DET
cana-5388	207	6	hybrid	hybrid	NOUN
cana-5388	207	7	models	model	NOUN
cana-5388	207	8	is	be	AUX
cana-5388	207	9	mainly	mainly	ADV
cana-5388	207	10	because	because	SCONJ
cana-5388	207	11	pso	pso	NOUN
cana-5388	207	12	helps	help	VERB
cana-5388	207	13	in	in	ADP
cana-5388	207	14	selecting	select	VERB
cana-5388	207	15	the	the	DET
cana-5388	207	16	best	good	ADJ
cana-5388	207	17	parameters	parameter	NOUN
cana-5388	207	18	for	for	ADP
cana-5388	207	19	the	the	DET
cana-5388	207	20	models	model	NOUN
cana-5388	207	21	automatically	automatically	ADV
cana-5388	207	22	.	.	PUNCT
cana-5388	208	1	this	this	PRON
cana-5388	208	2	removes	remove	VERB
cana-5388	208	3	the	the	DET
cana-5388	208	4	need	need	NOUN
cana-5388	208	5	for	for	ADP
cana-5388	208	6	manual	manual	ADJ
cana-5388	208	7	tuning	tuning	NOUN
cana-5388	208	8	and	and	CCONJ
cana-5388	208	9	helps	help	VERB
cana-5388	208	10	in	in	ADP
cana-5388	208	11	reducing	reduce	VERB
cana-5388	208	12	both	both	DET
cana-5388	208	13	error	error	NOUN
cana-5388	208	14	and	and	CCONJ
cana-5388	208	15	overfitting	overfitting	NOUN
cana-5388	208	16	.	.	PUNCT
cana-5388	209	1	also	also	ADV
cana-5388	209	2	,	,	PUNCT
cana-5388	209	3	models	model	NOUN
cana-5388	209	4	like	like	ADP
cana-5388	209	5	xgboost	xgboost	ADV
cana-5388	209	6	are	be	AUX
cana-5388	209	7	already	already	ADV
cana-5388	209	8	good	good	ADJ
cana-5388	209	9	at	at	ADP
cana-5388	209	10	handling	handle	VERB
cana-5388	209	11	complex	complex	ADJ
cana-5388	209	12	data	datum	NOUN
cana-5388	209	13	patterns	pattern	NOUN
cana-5388	209	14	,	,	PUNCT
cana-5388	209	15	and	and	CCONJ
cana-5388	209	16	when	when	SCONJ
cana-5388	209	17	they	they	PRON
cana-5388	209	18	are	be	AUX
cana-5388	209	19	optimized	optimize	VERB
cana-5388	209	20	with	with	ADP
cana-5388	209	21	pso	pso	NOUN
cana-5388	209	22	,	,	PUNCT
cana-5388	209	23	their	their	PRON
cana-5388	209	24	predictions	prediction	NOUN
cana-5388	209	25	become	become	VERB
cana-5388	209	26	even	even	ADV
cana-5388	209	27	more	more	ADV
cana-5388	209	28	accurate	accurate	ADJ
cana-5388	209	29	.	.	PUNCT
cana-5388	210	1	therefore	therefore	ADV
cana-5388	210	2	,	,	PUNCT
cana-5388	210	3	this	this	DET
cana-5388	210	4	combined	combine	VERB
cana-5388	210	5	method	method	NOUN
cana-5388	210	6	improves	improve	VERB
cana-5388	210	7	the	the	DET
cana-5388	210	8	prediction	prediction	NOUN
cana-5388	210	9	quality	quality	NOUN
cana-5388	210	10	and	and	CCONJ
cana-5388	210	11	is	be	AUX
cana-5388	210	12	a	a	DET
cana-5388	210	13	reliable	reliable	ADJ
cana-5388	210	14	option	option	NOUN
cana-5388	210	15	for	for	ADP
cana-5388	210	16	real	real	ADJ
cana-5388	210	17	-	-	PUNCT
cana-5388	210	18	world	world	NOUN
cana-5388	210	19	aqi	aqi	ADJ
cana-5388	210	20	monitoring	monitoring	NOUN
cana-5388	210	21	and	and	CCONJ
cana-5388	210	22	forecasting	forecasting	NOUN
cana-5388	210	23	systems	system	NOUN
cana-5388	210	24	.	.	PUNCT
cana-5388	211	1	5	5	X
cana-5388	211	2	.	.	X
cana-5388	211	3	conclusions	conclusion	NOUN
cana-5388	211	4	in	in	ADP
cana-5388	211	5	this	this	DET
cana-5388	211	6	research	research	NOUN
cana-5388	211	7	,	,	PUNCT
cana-5388	211	8	a	a	DET
cana-5388	211	9	mixed	mixed	ADJ
cana-5388	211	10	approach	approach	NOUN
cana-5388	211	11	was	be	AUX
cana-5388	211	12	used	use	VERB
cana-5388	211	13	to	to	PART
cana-5388	211	14	predict	predict	VERB
cana-5388	211	15	the	the	DET
cana-5388	211	16	air	air	NOUN
cana-5388	211	17	quality	quality	NOUN
cana-5388	211	18	index	index	NOUN
cana-5388	211	19	(	(	PUNCT
cana-5388	211	20	aqi	aqi	PROPN
cana-5388	211	21	)	)	PUNCT
cana-5388	211	22	by	by	ADP
cana-5388	211	23	combining	combine	VERB
cana-5388	211	24	machine	machine	NOUN
cana-5388	211	25	learning	learning	NOUN
cana-5388	211	26	models	model	NOUN
cana-5388	211	27	with	with	ADP
cana-5388	211	28	optimization	optimization	NOUN
cana-5388	211	29	techniques	technique	NOUN
cana-5388	211	30	.	.	PUNCT
cana-5388	212	1	machine	machine	NOUN
cana-5388	212	2	learning	learning	NOUN
cana-5388	212	3	models	model	NOUN
cana-5388	212	4	like	like	ADP
cana-5388	212	5	support	support	NOUN
cana-5388	212	6	vector	vector	NOUN
cana-5388	212	7	machine	machine	NOUN
cana-5388	212	8	(	(	PUNCT
cana-5388	212	9	svm	svm	PROPN
cana-5388	212	10	)	)	PUNCT
cana-5388	212	11	,	,	PUNCT
cana-5388	212	12	random	random	ADJ
cana-5388	212	13	forest	forest	NOUN
cana-5388	212	14	(	(	PUNCT
cana-5388	212	15	rf	rf	NOUN
cana-5388	212	16	)	)	PUNCT
cana-5388	212	17	,	,	PUNCT
cana-5388	212	18	and	and	CCONJ
cana-5388	212	19	xgboost	xgboost	PRON
cana-5388	212	20	were	be	AUX
cana-5388	212	21	tested	test	VERB
cana-5388	212	22	,	,	PUNCT
cana-5388	212	23	and	and	CCONJ
cana-5388	212	24	their	their	PRON
cana-5388	212	25	performance	performance	NOUN
cana-5388	212	26	improved	improve	VERB
cana-5388	212	27	a	a	DET
cana-5388	212	28	lot	lot	NOUN
cana-5388	212	29	after	after	ADP
cana-5388	212	30	using	use	VERB
cana-5388	212	31	particle	particle	NOUN
cana-5388	212	32	swarm	swarm	NOUN
cana-5388	212	33	optimization	optimization	NOUN
cana-5388	212	34	(	(	PUNCT
cana-5388	212	35	pso	pso	NOUN
cana-5388	212	36	)	)	PUNCT
cana-5388	212	37	to	to	PART
cana-5388	212	38	choose	choose	VERB
cana-5388	212	39	the	the	DET
cana-5388	212	40	best	good	ADJ
cana-5388	212	41	parameters	parameter	NOUN
cana-5388	212	42	.	.	PUNCT
cana-5388	213	1	among	among	ADP
cana-5388	213	2	all	all	DET
cana-5388	213	3	the	the	DET
cana-5388	213	4	models	model	NOUN
cana-5388	213	5	,	,	PUNCT
cana-5388	213	6	the	the	DET
cana-5388	213	7	pso	pso	NOUN
cana-5388	213	8	-	-	PUNCT
cana-5388	213	9	tuned	tune	VERB
cana-5388	213	10	xgboost	xgboost	ADV
cana-5388	213	11	gave	give	VERB
cana-5388	213	12	the	the	DET
cana-5388	213	13	best	good	ADJ
cana-5388	213	14	results	result	NOUN
cana-5388	213	15	with	with	ADP
cana-5388	213	16	94.3	94.3	NUM
cana-5388	213	17	%	%	NOUN
cana-5388	213	18	accuracy	accuracy	NOUN
cana-5388	213	19	,	,	PUNCT
cana-5388	213	20	10.3	10.3	NUM
cana-5388	213	21	mae	mae	PROPN
cana-5388	213	22	,	,	PUNCT
cana-5388	213	23	and	and	CCONJ
cana-5388	213	24	15.9	15.9	NUM
cana-5388	213	25	rmse	rmse	NOUN
cana-5388	213	26	.	.	PUNCT
cana-5388	214	1	the	the	DET
cana-5388	214	2	results	result	NOUN
cana-5388	214	3	clearly	clearly	ADV
cana-5388	214	4	show	show	VERB
cana-5388	214	5	that	that	SCONJ
cana-5388	214	6	using	use	VERB
cana-5388	214	7	optimization	optimization	NOUN
cana-5388	214	8	helps	helps	AUX
cana-5388	214	9	improve	improve	VERB
cana-5388	214	10	the	the	DET
cana-5388	214	11	performance	performance	NOUN
cana-5388	214	12	of	of	ADP
cana-5388	214	13	machine	machine	NOUN
cana-5388	214	14	learning	learning	NOUN
cana-5388	214	15	models	model	NOUN
cana-5388	214	16	.	.	PUNCT
cana-5388	215	1	by	by	ADP
cana-5388	215	2	automatically	automatically	ADV
cana-5388	215	3	selecting	select	VERB
cana-5388	215	4	the	the	DET
cana-5388	215	5	most	most	ADV
cana-5388	215	6	suitable	suitable	ADJ
cana-5388	215	7	parameters	parameter	NOUN
cana-5388	215	8	,	,	PUNCT
cana-5388	215	9	the	the	DET
cana-5388	215	10	hybrid	hybrid	NOUN
cana-5388	215	11	models	model	NOUN
cana-5388	215	12	made	make	VERB
cana-5388	215	13	better	well	ADJ
cana-5388	215	14	predictions	prediction	NOUN
cana-5388	215	15	with	with	ADP
cana-5388	215	16	fewer	few	ADJ
cana-5388	215	17	errors	error	NOUN
cana-5388	215	18	.	.	PUNCT
cana-5388	216	1	this	this	PRON
cana-5388	216	2	makes	make	VERB
cana-5388	216	3	the	the	DET
cana-5388	216	4	proposed	propose	VERB
cana-5388	216	5	method	method	NOUN
cana-5388	216	6	suitable	suitable	ADJ
cana-5388	216	7	for	for	ADP
cana-5388	216	8	real	real	ADJ
cana-5388	216	9	-	-	PUNCT
cana-5388	216	10	time	time	NOUN
cana-5388	216	11	air	air	NOUN
cana-5388	216	12	quality	quality	NOUN
cana-5388	216	13	prediction	prediction	NOUN
cana-5388	216	14	,	,	PUNCT
cana-5388	216	15	especially	especially	ADV
cana-5388	216	16	in	in	ADP
cana-5388	216	17	cities	city	NOUN
cana-5388	216	18	where	where	SCONJ
cana-5388	216	19	pollution	pollution	NOUN
cana-5388	216	20	levels	level	NOUN
cana-5388	216	21	change	change	VERB
cana-5388	216	22	quickly	quickly	ADV
cana-5388	216	23	.	.	PUNCT
cana-5388	217	1	6	6	X
cana-5388	217	2	.	.	X
cana-5388	217	3	future	future	ADJ
cana-5388	217	4	research	research	NOUN
cana-5388	217	5	though	though	SCONJ
cana-5388	217	6	the	the	DET
cana-5388	217	7	current	current	ADJ
cana-5388	217	8	method	method	NOUN
cana-5388	217	9	gives	give	VERB
cana-5388	217	10	good	good	ADJ
cana-5388	217	11	results	result	NOUN
cana-5388	217	12	,	,	PUNCT
cana-5388	217	13	there	there	PRON
cana-5388	217	14	are	be	VERB
cana-5388	217	15	still	still	ADV
cana-5388	217	16	some	some	DET
cana-5388	217	17	areas	area	NOUN
cana-5388	217	18	where	where	SCONJ
cana-5388	217	19	it	it	PRON
cana-5388	217	20	can	can	AUX
cana-5388	217	21	be	be	AUX
cana-5388	217	22	improved	improve	VERB
cana-5388	217	23	.	.	PUNCT
cana-5388	218	1	real	real	ADJ
cana-5388	218	2	-	-	PUNCT
cana-5388	218	3	time	time	NOUN
cana-5388	218	4	use	use	NOUN
cana-5388	218	5	with	with	ADP
cana-5388	218	6	future	future	ADJ
cana-5388	218	7	work	work	NOUN
cana-5388	218	8	can	can	AUX
cana-5388	218	9	try	try	VERB
cana-5388	218	10	to	to	PART
cana-5388	218	11	use	use	VERB
cana-5388	218	12	this	this	DET
cana-5388	218	13	hybrid	hybrid	ADJ
cana-5388	218	14	model	model	NOUN
cana-5388	218	15	in	in	ADP
cana-5388	218	16	iot	iot	PROPN
cana-5388	218	17	-	-	PUNCT
cana-5388	218	18	based	base	VERB
cana-5388	218	19	smart	smart	ADJ
cana-5388	218	20	systems	system	NOUN
cana-5388	218	21	to	to	PART
cana-5388	218	22	give	give	VERB
cana-5388	218	23	live	live	ADJ
cana-5388	218	24	aqi	aqi	NOUN
cana-5388	218	25	forecasts	forecast	NOUN
cana-5388	218	26	.	.	PUNCT
cana-5388	219	1	the	the	DET
cana-5388	219	2	use	use	NOUN
cana-5388	219	3	of	of	ADP
cana-5388	219	4	deep	deep	ADJ
cana-5388	219	5	learning	learning	NOUN
cana-5388	219	6	with	with	ADP
cana-5388	219	7	advanced	advanced	ADJ
cana-5388	219	8	models	model	NOUN
cana-5388	219	9	like	like	ADP
cana-5388	219	10	lstm	lstm	NOUN
cana-5388	219	11	and	and	CCONJ
cana-5388	219	12	cnn	cnn	PROPN
cana-5388	219	13	-	-	PUNCT
cana-5388	219	14	lstm	lstm	PROPN
cana-5388	219	15	can	can	AUX
cana-5388	219	16	be	be	AUX
cana-5388	219	17	used	use	VERB
cana-5388	219	18	to	to	PART
cana-5388	219	19	learn	learn	VERB
cana-5388	219	20	from	from	ADP
cana-5388	219	21	time	time	NOUN
cana-5388	219	22	-	-	PUNCT
cana-5388	219	23	based	base	VERB
cana-5388	219	24	air	air	NOUN
cana-5388	219	25	quality	quality	NOUN
cana-5388	219	26	data	datum	NOUN
cana-5388	219	27	.	.	PUNCT
cana-5388	220	1	the	the	DET
cana-5388	220	2	multiple	multiple	ADJ
cana-5388	220	3	objectives	objective	NOUN
cana-5388	220	4	of	of	ADP
cana-5388	220	5	future	future	ADJ
cana-5388	220	6	studies	study	NOUN
cana-5388	220	7	can	can	AUX
cana-5388	220	8	focus	focus	VERB
cana-5388	220	9	not	not	PART
cana-5388	220	10	only	only	ADV
cana-5388	220	11	on	on	ADP
cana-5388	220	12	accuracy	accuracy	NOUN
cana-5388	220	13	but	but	CCONJ
cana-5388	220	14	also	also	ADV
cana-5388	220	15	on	on	ADP
cana-5388	220	16	reducing	reduce	VERB
cana-5388	220	17	time	time	NOUN
cana-5388	220	18	and	and	CCONJ
cana-5388	220	19	cost	cost	NOUN
cana-5388	220	20	by	by	ADP
cana-5388	220	21	using	use	VERB
cana-5388	220	22	multi	multi	ADJ
cana-5388	220	23	-	-	ADJ
cana-5388	220	24	objective	objective	ADJ
cana-5388	220	25	optimization	optimization	NOUN
cana-5388	220	26	.	.	PUNCT
cana-5388	221	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	221	2	communications	communication	NOUN
cana-5388	221	3	on	on	ADP
cana-5388	221	4	applied	apply	VERB
cana-5388	221	5	nonlinear	nonlinear	ADJ
cana-5388	221	6	analysis	analysis	NOUN
cana-5388	221	7	issn	issn	NOUN
cana-5388	221	8	:	:	PUNCT
cana-5388	221	9	1074	1074	NUM
cana-5388	221	10	-	-	PUNCT
cana-5388	221	11	133x	133x	NUM
cana-5388	221	12	vol	vol	NOUN
cana-5388	221	13	32	32	NUM
cana-5388	221	14	no	no	NOUN
cana-5388	221	15	.	.	PUNCT
cana-5388	222	1	1s	1s	NUM
cana-5388	222	2	(	(	PUNCT
cana-5388	222	3	2025	2025	NUM
cana-5388	222	4	)	)	PUNCT
cana-5388	223	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	223	2	663	663	NUM
cana-5388	223	3	the	the	DET
cana-5388	223	4	transfer	transfer	NOUN
cana-5388	223	5	learning	learn	VERB
cana-5388	223	6	for	for	ADP
cana-5388	223	7	this	this	DET
cana-5388	223	8	method	method	NOUN
cana-5388	223	9	can	can	AUX
cana-5388	223	10	be	be	AUX
cana-5388	223	11	trained	train	VERB
cana-5388	223	12	once	once	ADV
cana-5388	223	13	and	and	CCONJ
cana-5388	223	14	then	then	ADV
cana-5388	223	15	used	use	VERB
cana-5388	223	16	for	for	ADP
cana-5388	223	17	different	different	ADJ
cana-5388	223	18	cities	city	NOUN
cana-5388	223	19	or	or	CCONJ
cana-5388	223	20	areas	area	NOUN
cana-5388	223	21	that	that	PRON
cana-5388	223	22	have	have	VERB
cana-5388	223	23	less	less	ADJ
cana-5388	223	24	data	datum	NOUN
cana-5388	223	25	.	.	PUNCT
cana-5388	224	1	more	more	ADJ
cana-5388	224	2	data	data	NOUN
cana-5388	224	3	features	feature	NOUN
cana-5388	224	4	,	,	PUNCT
cana-5388	224	5	with	with	ADP
cana-5388	224	6	adding	add	VERB
cana-5388	224	7	more	more	ADJ
cana-5388	224	8	details	detail	NOUN
cana-5388	224	9	like	like	ADP
cana-5388	224	10	traffic	traffic	NOUN
cana-5388	224	11	data	datum	NOUN
cana-5388	224	12	,	,	PUNCT
cana-5388	224	13	factory	factory	NOUN
cana-5388	224	14	emissions	emission	NOUN
cana-5388	224	15	,	,	PUNCT
cana-5388	224	16	and	and	CCONJ
cana-5388	224	17	satellite	satellite	NOUN
cana-5388	224	18	data	datum	NOUN
cana-5388	224	19	,	,	PUNCT
cana-5388	224	20	may	may	AUX
cana-5388	224	21	improve	improve	VERB
cana-5388	224	22	the	the	DET
cana-5388	224	23	model	model	NOUN
cana-5388	224	24	’s	’s	PART
cana-5388	224	25	accuracy	accuracy	NOUN
cana-5388	224	26	.	.	PUNCT
cana-5388	225	1	this	this	DET
cana-5388	225	2	study	study	NOUN
cana-5388	225	3	gives	give	VERB
cana-5388	225	4	a	a	DET
cana-5388	225	5	good	good	ADJ
cana-5388	225	6	starting	starting	NOUN
cana-5388	225	7	point	point	NOUN
cana-5388	225	8	for	for	ADP
cana-5388	225	9	creating	create	VERB
cana-5388	225	10	smart	smart	ADJ
cana-5388	225	11	and	and	CCONJ
cana-5388	225	12	accurate	accurate	ADJ
cana-5388	225	13	aqi	aqi	NOUN
cana-5388	225	14	prediction	prediction	NOUN
cana-5388	225	15	systems	system	NOUN
cana-5388	225	16	using	use	VERB
cana-5388	225	17	a	a	DET
cana-5388	225	18	mix	mix	NOUN
cana-5388	225	19	of	of	ADP
cana-5388	225	20	machine	machine	NOUN
cana-5388	225	21	learning	learning	NOUN
cana-5388	225	22	and	and	CCONJ
cana-5388	225	23	optimization	optimization	NOUN
cana-5388	225	24	methods	method	NOUN
cana-5388	225	25	.	.	PUNCT
cana-5388	226	1	reference	reference	NOUN
cana-5388	226	2	[	[	X
cana-5388	226	3	1	1	NUM
cana-5388	226	4	]	]	PUNCT
cana-5388	226	5	a.	a.	PROPN
cana-5388	226	6	b.	b.	PROPN
cana-5388	226	7	patel	patel	PROPN
cana-5388	226	8	and	and	CCONJ
cana-5388	226	9	h.	h.	PROPN
cana-5388	226	10	shah	shah	PROPN
cana-5388	226	11	,	,	PUNCT
cana-5388	226	12	"	"	PUNCT
cana-5388	226	13	air	air	NOUN
cana-5388	226	14	quality	quality	NOUN
cana-5388	226	15	index	index	NOUN
cana-5388	226	16	prediction	prediction	NOUN
cana-5388	226	17	using	use	VERB
cana-5388	226	18	hybrid	hybrid	ADJ
cana-5388	226	19	pso	pso	NOUN
cana-5388	226	20	–	–	PUNCT
cana-5388	226	21	xgboost	xgboost	VERB
cana-5388	226	22	model	model	NOUN
cana-5388	226	23	,	,	PUNCT
cana-5388	226	24	"	"	PUNCT
cana-5388	226	25	journal	journal	NOUN
cana-5388	226	26	of	of	ADP
cana-5388	226	27	ambient	ambient	ADJ
cana-5388	226	28	intelligence	intelligence	NOUN
cana-5388	226	29	and	and	CCONJ
cana-5388	226	30	humanized	humanize	VERB
cana-5388	226	31	computing	computing	NOUN
cana-5388	226	32	,	,	PUNCT
cana-5388	226	33	vol	vol	NOUN
cana-5388	226	34	.	.	PROPN
cana-5388	226	35	12	12	NUM
cana-5388	226	36	,	,	PUNCT
cana-5388	226	37	pp	pp	ADJ
cana-5388	226	38	.	.	PUNCT
cana-5388	226	39	4789	4789	NUM
cana-5388	226	40	–	–	PUNCT
cana-5388	226	41	4798	4798	NUM
cana-5388	226	42	,	,	PUNCT
cana-5388	226	43	2021	2021	NUM
cana-5388	226	44	.	.	PUNCT
cana-5388	227	1	[	[	X
cana-5388	227	2	2	2	NUM
cana-5388	227	3	]	]	PUNCT
cana-5388	227	4	a.	a.	NOUN
cana-5388	227	5	bellinger	bellinger	NOUN
cana-5388	227	6	,	,	PUNCT
cana-5388	227	7	d.	d.	PROPN
cana-5388	227	8	drikvandi	drikvandi	PROPN
cana-5388	227	9	,	,	PUNCT
cana-5388	227	10	and	and	CCONJ
cana-5388	227	11	m.	m.	NOUN
cana-5388	227	12	uddin	uddin	PROPN
cana-5388	227	13	,	,	PUNCT
cana-5388	227	14	"	"	PUNCT
cana-5388	227	15	predicting	predict	VERB
cana-5388	227	16	air	air	NOUN
cana-5388	227	17	pollution	pollution	NOUN
cana-5388	227	18	levels	level	NOUN
cana-5388	227	19	using	use	VERB
cana-5388	227	20	extreme	extreme	ADJ
cana-5388	227	21	gradient	gradient	NOUN
cana-5388	227	22	boosting	boost	VERB
cana-5388	227	23	(	(	PUNCT
cana-5388	227	24	xgboost	xgboost	ADV
cana-5388	227	25	)	)	PUNCT
cana-5388	227	26	,	,	PUNCT
cana-5388	227	27	"	"	PUNCT
cana-5388	227	28	environmental	environmental	ADJ
cana-5388	227	29	modelling	modelling	NOUN
cana-5388	227	30	&	&	CCONJ
cana-5388	227	31	software	software	NOUN
cana-5388	227	32	,	,	PUNCT
cana-5388	227	33	vol	vol	NOUN
cana-5388	227	34	.	.	PROPN
cana-5388	227	35	134	134	NUM
cana-5388	227	36	,	,	PUNCT
cana-5388	227	37	104867	104867	NUM
cana-5388	227	38	,	,	PUNCT
cana-5388	227	39	2020	2020	NUM
cana-5388	227	40	.	.	PUNCT
cana-5388	228	1	[	[	X
cana-5388	228	2	3	3	X
cana-5388	228	3	]	]	X
cana-5388	228	4	h.	h.	PROPN
cana-5388	228	5	y.	y.	PROPN
cana-5388	228	6	wang	wang	PROPN
cana-5388	228	7	,	,	PUNCT
cana-5388	228	8	j.	j.	PROPN
cana-5388	228	9	liu	liu	PROPN
cana-5388	228	10	,	,	PUNCT
cana-5388	228	11	and	and	CCONJ
cana-5388	228	12	j.	j.	PROPN
cana-5388	228	13	li	li	PROPN
cana-5388	228	14	,	,	PUNCT
cana-5388	228	15	"	"	PUNCT
cana-5388	228	16	hybrid	hybrid	ADJ
cana-5388	228	17	modeling	modeling	NOUN
cana-5388	228	18	for	for	ADP
cana-5388	228	19	air	air	NOUN
cana-5388	228	20	quality	quality	NOUN
cana-5388	228	21	forecasting	forecasting	NOUN
cana-5388	228	22	using	use	VERB
cana-5388	228	23	genetic	genetic	ADJ
cana-5388	228	24	algorithm	algorithm	NOUN
cana-5388	228	25	and	and	CCONJ
cana-5388	228	26	neural	neural	ADJ
cana-5388	228	27	networks	network	NOUN
cana-5388	228	28	,	,	PUNCT
cana-5388	228	29	"	"	PUNCT
cana-5388	228	30	applied	apply	VERB
cana-5388	228	31	soft	soft	ADJ
cana-5388	228	32	computing	computing	NOUN
cana-5388	228	33	,	,	PUNCT
cana-5388	228	34	vol	vol	NOUN
cana-5388	228	35	.	.	PROPN
cana-5388	228	36	80	80	NUM
cana-5388	228	37	,	,	PUNCT
cana-5388	228	38	pp	pp	ADJ
cana-5388	228	39	.	.	PUNCT
cana-5388	229	1	480–490	480–490	NUM
cana-5388	229	2	,	,	PUNCT
cana-5388	229	3	2019	2019	NUM
cana-5388	229	4	.	.	PUNCT
cana-5388	230	1	[	[	X
cana-5388	230	2	4	4	X
cana-5388	230	3	]	]	PUNCT
cana-5388	230	4	j.	j.	PROPN
cana-5388	230	5	jiang	jiang	PROPN
cana-5388	230	6	,	,	PUNCT
cana-5388	230	7	j.	j.	PROPN
cana-5388	230	8	ma	ma	PROPN
cana-5388	230	9	,	,	PUNCT
cana-5388	230	10	z.	z.	PROPN
cana-5388	230	11	du	du	PROPN
cana-5388	230	12	,	,	PUNCT
cana-5388	230	13	and	and	CCONJ
cana-5388	230	14	j.	j.	PROPN
cana-5388	230	15	wang	wang	PROPN
cana-5388	230	16	,	,	PUNCT
cana-5388	230	17	"	"	PUNCT
cana-5388	230	18	air	air	NOUN
cana-5388	230	19	quality	quality	NOUN
cana-5388	230	20	prediction	prediction	NOUN
cana-5388	230	21	using	use	VERB
cana-5388	230	22	a	a	DET
cana-5388	230	23	hybrid	hybrid	ADJ
cana-5388	230	24	model	model	NOUN
cana-5388	230	25	based	base	VERB
cana-5388	230	26	on	on	ADP
cana-5388	230	27	deep	deep	ADJ
cana-5388	230	28	learning	learning	NOUN
cana-5388	230	29	and	and	CCONJ
cana-5388	230	30	statistical	statistical	ADJ
cana-5388	230	31	feature	feature	NOUN
cana-5388	230	32	selection	selection	NOUN
cana-5388	230	33	,	,	PUNCT
cana-5388	230	34	"	"	PUNCT
cana-5388	230	35	ieee	ieee	NOUN
cana-5388	230	36	access	access	NOUN
cana-5388	230	37	,	,	PUNCT
cana-5388	230	38	vol	vol	NOUN
cana-5388	230	39	.	.	PROPN
cana-5388	230	40	8	8	NUM
cana-5388	230	41	,	,	PUNCT
cana-5388	230	42	pp	pp	ADJ
cana-5388	230	43	.	.	PUNCT
cana-5388	231	1	67245–67256	67245–67256	NUM
cana-5388	231	2	,	,	PUNCT
cana-5388	231	3	2020	2020	NUM
cana-5388	231	4	.	.	PUNCT
cana-5388	232	1	[	[	X
cana-5388	232	2	5	5	NUM
cana-5388	232	3	]	]	PUNCT
cana-5388	232	4	m.	m.	NOUN
cana-5388	232	5	khan	khan	PROPN
cana-5388	232	6	,	,	PUNCT
cana-5388	232	7	a.	a.	PROPN
cana-5388	232	8	zafar	zafar	PROPN
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cana-5388	232	10	and	and	CCONJ
cana-5388	232	11	i.	i.	PROPN
cana-5388	232	12	hussain	hussain	PROPN
cana-5388	232	13	,	,	PUNCT
cana-5388	232	14	"	"	PUNCT
cana-5388	232	15	a	a	DET
cana-5388	232	16	hybrid	hybrid	ADJ
cana-5388	232	17	machine	machine	NOUN
cana-5388	232	18	learning	learning	NOUN
cana-5388	232	19	and	and	CCONJ
cana-5388	232	20	optimization	optimization	NOUN
cana-5388	232	21	approach	approach	NOUN
cana-5388	232	22	for	for	ADP
cana-5388	232	23	real	real	ADJ
cana-5388	232	24	-	-	PUNCT
cana-5388	232	25	time	time	NOUN
cana-5388	232	26	aqi	aqi	PROPN
cana-5388	232	27	prediction	prediction	NOUN
cana-5388	232	28	,	,	PUNCT
cana-5388	232	29	"	"	PUNCT
cana-5388	232	30	ieee	ieee	NOUN
cana-5388	232	31	access	access	NOUN
cana-5388	232	32	,	,	PUNCT
cana-5388	232	33	vol	vol	NOUN
cana-5388	232	34	.	.	NOUN
cana-5388	232	35	9	9	NUM
cana-5388	232	36	,	,	PUNCT
cana-5388	232	37	pp	pp	ADJ
cana-5388	232	38	.	.	PUNCT
cana-5388	232	39	145217–145230	145217–145230	NUM
cana-5388	232	40	,	,	PUNCT
cana-5388	232	41	2021	2021	NUM
cana-5388	232	42	.	.	PUNCT
cana-5388	233	1	[	[	X
cana-5388	233	2	6	6	NUM
cana-5388	233	3	]	]	PUNCT
cana-5388	233	4	r.	r.	PROPN
cana-5388	233	5	zhang	zhang	PROPN
cana-5388	233	6	and	and	CCONJ
cana-5388	233	7	m.	m.	PROPN
cana-5388	233	8	ding	ding	PROPN
cana-5388	233	9	,	,	PUNCT
cana-5388	233	10	"	"	PUNCT
cana-5388	233	11	a	a	DET
cana-5388	233	12	random	random	ADJ
cana-5388	233	13	forest	forest	NOUN
cana-5388	233	14	model	model	NOUN
cana-5388	233	15	based	base	VERB
cana-5388	233	16	on	on	ADP
cana-5388	233	17	pca	pca	PROPN
cana-5388	233	18	for	for	ADP
cana-5388	233	19	air	air	NOUN
cana-5388	233	20	quality	quality	NOUN
cana-5388	233	21	prediction	prediction	NOUN
cana-5388	233	22	,	,	PUNCT
cana-5388	233	23	"	"	PUNCT
cana-5388	233	24	procedia	procedia	NOUN
cana-5388	233	25	computer	computer	NOUN
cana-5388	233	26	science	science	NOUN
cana-5388	233	27	,	,	PUNCT
cana-5388	233	28	vol	vol	NOUN
cana-5388	233	29	.	.	PROPN
cana-5388	233	30	174	174	NUM
cana-5388	233	31	,	,	PUNCT
cana-5388	233	32	pp	pp	ADJ
cana-5388	233	33	.	.	PUNCT
cana-5388	234	1	188–195	188–195	NUM
cana-5388	234	2	,	,	PUNCT
cana-5388	234	3	2020	2020	NUM
cana-5388	234	4	.	.	PUNCT
cana-5388	235	1	[	[	X
cana-5388	235	2	7	7	X
cana-5388	235	3	]	]	PUNCT
cana-5388	235	4	s.	s.	PROPN
cana-5388	235	5	y.	y.	PROPN
cana-5388	235	6	lee	lee	PROPN
cana-5388	235	7	,	,	PUNCT
cana-5388	235	8	y.	y.	PROPN
cana-5388	235	9	c.	c.	PROPN
cana-5388	235	10	lin	lin	PROPN
cana-5388	235	11	,	,	PUNCT
cana-5388	235	12	and	and	CCONJ
cana-5388	235	13	c.	c.	PROPN
cana-5388	235	14	h.	h.	PROPN
cana-5388	235	15	lo	lo	PROPN
cana-5388	235	16	,	,	PUNCT
cana-5388	235	17	"	"	PUNCT
cana-5388	235	18	forecasting	forecast	VERB
cana-5388	235	19	air	air	NOUN
cana-5388	235	20	quality	quality	NOUN
cana-5388	235	21	in	in	ADP
cana-5388	235	22	smart	smart	ADJ
cana-5388	235	23	cities	city	NOUN
cana-5388	235	24	using	use	VERB
cana-5388	235	25	deep	deep	ADJ
cana-5388	235	26	learning	learning	NOUN
cana-5388	235	27	and	and	CCONJ
cana-5388	235	28	iot	iot	PROPN
cana-5388	235	29	technologies	technology	NOUN
cana-5388	235	30	,	,	PUNCT
cana-5388	235	31	"	"	PUNCT
cana-5388	235	32	ieee	ieee	NOUN
cana-5388	235	33	internet	internet	NOUN
cana-5388	235	34	of	of	ADP
cana-5388	235	35	things	thing	NOUN
cana-5388	235	36	journal	journal	NOUN
cana-5388	235	37	,	,	PUNCT
cana-5388	235	38	vol	vol	NOUN
cana-5388	235	39	.	.	PROPN
cana-5388	235	40	8	8	NUM
cana-5388	235	41	,	,	PUNCT
cana-5388	235	42	no	no	INTJ
cana-5388	235	43	.	.	NOUN
cana-5388	235	44	7	7	NUM
cana-5388	235	45	,	,	PUNCT
cana-5388	235	46	pp	pp	ADJ
cana-5388	235	47	.	.	PUNCT
cana-5388	236	1	5644	5644	NUM
cana-5388	236	2	–	–	PUNCT
cana-5388	236	3	5651	5651	NUM
cana-5388	236	4	,	,	PUNCT
cana-5388	236	5	apr	apr	PROPN
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cana-5388	237	1	[	[	X
cana-5388	237	2	8	8	NUM
cana-5388	237	3	]	]	X
cana-5388	237	4	w.	w.	PROPN
cana-5388	237	5	zhang	zhang	PROPN
cana-5388	237	6	,	,	PUNCT
cana-5388	237	7	y.	y.	PROPN
cana-5388	237	8	chen	chen	PROPN
cana-5388	237	9	,	,	PUNCT
cana-5388	237	10	and	and	CCONJ
cana-5388	237	11	s.	s.	PROPN
cana-5388	237	12	liu	liu	PROPN
cana-5388	237	13	,	,	PUNCT
cana-5388	237	14	"	"	PUNCT
cana-5388	237	15	air	air	NOUN
cana-5388	237	16	quality	quality	NOUN
cana-5388	237	17	prediction	prediction	NOUN
cana-5388	237	18	based	base	VERB
cana-5388	237	19	on	on	ADP
cana-5388	237	20	ensemble	ensemble	ADJ
cana-5388	237	21	learning	learning	NOUN
cana-5388	237	22	using	use	VERB
cana-5388	237	23	multiple	multiple	ADJ
cana-5388	237	24	regression	regression	NOUN
cana-5388	237	25	and	and	CCONJ
cana-5388	237	26	classification	classification	NOUN
cana-5388	237	27	models	model	NOUN
cana-5388	237	28	,	,	PUNCT
cana-5388	237	29	"	"	PUNCT
cana-5388	237	30	atmosphere	atmosphere	NOUN
cana-5388	237	31	,	,	PUNCT
cana-5388	237	32	vol	vol	NOUN
cana-5388	237	33	.	.	PROPN
cana-5388	237	34	11	11	NUM
cana-5388	237	35	,	,	PUNCT
cana-5388	237	36	no	no	INTJ
cana-5388	237	37	.	.	NOUN
cana-5388	237	38	3	3	NUM
cana-5388	237	39	,	,	PUNCT
cana-5388	237	40	2020	2020	NUM
cana-5388	237	41	.	.	PUNCT
cana-5388	238	1	[	[	X
cana-5388	238	2	9	9	X
cana-5388	238	3	]	]	X
cana-5388	238	4	y.	y.	PROPN
cana-5388	238	5	xue	xue	PROPN
cana-5388	238	6	and	and	CCONJ
cana-5388	238	7	l.	l.	PROPN
cana-5388	238	8	zhang	zhang	PROPN
cana-5388	238	9	,	,	PUNCT
cana-5388	238	10	"	"	PUNCT
cana-5388	238	11	optimizing	optimize	VERB
cana-5388	238	12	svm	svm	NOUN
cana-5388	238	13	for	for	ADP
cana-5388	238	14	air	air	NOUN
cana-5388	238	15	quality	quality	NOUN
cana-5388	238	16	prediction	prediction	NOUN
cana-5388	238	17	with	with	ADP
cana-5388	238	18	particle	particle	NOUN
cana-5388	238	19	swarm	swarm	NOUN
cana-5388	238	20	optimization	optimization	NOUN
cana-5388	238	21	,	,	PUNCT
cana-5388	238	22	"	"	PUNCT
cana-5388	238	23	neurocomputing	neurocomputing	NOUN
cana-5388	238	24	,	,	PUNCT
cana-5388	238	25	vol	vol	NOUN
cana-5388	238	26	.	.	PROPN
cana-5388	238	27	362	362	NUM
cana-5388	238	28	,	,	PUNCT
cana-5388	238	29	pp	pp	ADJ
cana-5388	238	30	.	.	PUNCT
cana-5388	239	1	297–304	297–304	NUM
cana-5388	239	2	,	,	PUNCT
cana-5388	239	3	2019	2019	NUM
cana-5388	239	4	.	.	PUNCT
cana-5388	240	1	[	[	X
cana-5388	240	2	10	10	NUM
cana-5388	240	3	]	]	PUNCT
cana-5388	240	4	p.	p.	NOUN
cana-5388	240	5	rajesh	rajesh	PROPN
cana-5388	240	6	and	and	CCONJ
cana-5388	240	7	m.	m.	PROPN
cana-5388	240	8	karthikeyan	karthikeyan	PROPN
cana-5388	240	9	,	,	PUNCT
cana-5388	240	10	"	"	PUNCT
cana-5388	240	11	a	a	DET
cana-5388	240	12	comparative	comparative	ADJ
cana-5388	240	13	study	study	NOUN
cana-5388	240	14	of	of	ADP
cana-5388	240	15	data	datum	NOUN
cana-5388	240	16	mining	mining	NOUN
cana-5388	240	17	algorithms	algorithm	NOUN
cana-5388	240	18	for	for	ADP
cana-5388	240	19	decision	decision	NOUN
cana-5388	240	20	tree	tree	NOUN
cana-5388	240	21	approaches	approach	NOUN
cana-5388	240	22	using	use	VERB
cana-5388	240	23	weka	weka	PROPN
cana-5388	240	24	tool	tool	NOUN
cana-5388	240	25	,	,	PUNCT
cana-5388	240	26	"	"	PUNCT
cana-5388	240	27	advances	advance	NOUN
cana-5388	240	28	in	in	ADP
cana-5388	240	29	natural	natural	ADJ
cana-5388	240	30	and	and	CCONJ
cana-5388	240	31	applied	applied	ADJ
cana-5388	240	32	sciences	science	NOUN
cana-5388	240	33	,	,	PUNCT
cana-5388	240	34	vol	vol	NOUN
cana-5388	240	35	.	.	PROPN
cana-5388	241	1	11	11	NUM
cana-5388	241	2	,	,	PUNCT
cana-5388	241	3	no	no	INTJ
cana-5388	241	4	.	.	NOUN
cana-5388	241	5	9	9	NUM
cana-5388	241	6	,	,	PUNCT
cana-5388	241	7	pp	pp	ADJ
cana-5388	241	8	.	.	PUNCT
cana-5388	242	1	230–243	230–243	NUM
cana-5388	242	2	,	,	PUNCT
cana-5388	242	3	2017	2017	NUM
cana-5388	242	4	.	.	PUNCT
cana-5388	243	1	[	[	X
cana-5388	243	2	11	11	NUM
cana-5388	243	3	]	]	PUNCT
cana-5388	243	4	p.	p.	PROPN
cana-5388	243	5	rajesh	rajesh	PROPN
cana-5388	243	6	,	,	PUNCT
cana-5388	243	7	m.	m.	NOUN
cana-5388	243	8	karthikeyan	karthikeyan	PROPN
cana-5388	243	9	,	,	PUNCT
cana-5388	243	10	b.	b.	PROPN
cana-5388	243	11	santhosh	santhosh	PROPN
cana-5388	243	12	kumar	kumar	PROPN
cana-5388	243	13	,	,	PUNCT
cana-5388	243	14	and	and	CCONJ
cana-5388	243	15	m.	m.	PROPN
cana-5388	243	16	y.	y.	PROPN
cana-5388	243	17	mohamed	mohamed	PROPN
cana-5388	243	18	parvees	parvees	PROPN
cana-5388	243	19	,	,	PUNCT
cana-5388	243	20	"	"	PUNCT
cana-5388	243	21	comparative	comparative	ADJ
cana-5388	243	22	study	study	NOUN
cana-5388	243	23	of	of	ADP
cana-5388	243	24	decision	decision	NOUN
cana-5388	243	25	tree	tree	NOUN
cana-5388	243	26	approaches	approach	VERB
cana-5388	243	27	in	in	ADP
cana-5388	243	28	data	datum	NOUN
cana-5388	243	29	mining	mining	NOUN
cana-5388	243	30	using	use	VERB
cana-5388	243	31	chronic	chronic	ADJ
cana-5388	243	32	disease	disease	NOUN
cana-5388	243	33	indicators	indicator	NOUN
cana-5388	243	34	(	(	PUNCT
cana-5388	243	35	cdi	cdi	PROPN
cana-5388	243	36	)	)	PUNCT
cana-5388	243	37	data	datum	NOUN
cana-5388	243	38	,	,	PUNCT
cana-5388	243	39	"	"	PUNCT
cana-5388	243	40	journal	journal	NOUN
cana-5388	243	41	of	of	ADP
cana-5388	243	42	computational	computational	ADJ
cana-5388	243	43	and	and	CCONJ
cana-5388	243	44	theoretical	theoretical	ADJ
cana-5388	243	45	nanoscience	nanoscience	NOUN
cana-5388	243	46	,	,	PUNCT
cana-5388	243	47	vol	vol	NOUN
cana-5388	243	48	.	.	PROPN
cana-5388	243	49	16	16	NUM
cana-5388	243	50	,	,	PUNCT
cana-5388	243	51	no	no	INTJ
cana-5388	243	52	.	.	NOUN
cana-5388	243	53	4	4	NUM
cana-5388	243	54	,	,	PUNCT
cana-5388	243	55	pp	pp	ADJ
cana-5388	243	56	.	.	PUNCT
cana-5388	244	1	1472–1477	1472–1477	NUM
cana-5388	244	2	,	,	PUNCT
cana-5388	244	3	2019	2019	NUM
cana-5388	244	4	.	.	PUNCT
cana-5388	245	1	[	[	X
cana-5388	245	2	12	12	NUM
cana-5388	245	3	]	]	X
cana-5388	245	4	s.	s.	PROPN
cana-5388	245	5	v.	v.	PROPN
cana-5388	245	6	kumar	kumar	PROPN
cana-5388	245	7	and	and	CCONJ
cana-5388	245	8	r.	r.	PROPN
cana-5388	245	9	r.	r.	PROPN
cana-5388	245	10	reddy	reddy	PROPN
cana-5388	245	11	,	,	PUNCT
cana-5388	245	12	“	"	PUNCT
cana-5388	245	13	machine	machine	NOUN
cana-5388	245	14	learning	learn	VERB
cana-5388	245	15	techniques	technique	NOUN
cana-5388	245	16	for	for	ADP
cana-5388	245	17	air	air	NOUN
cana-5388	245	18	quality	quality	NOUN
cana-5388	245	19	prediction	prediction	NOUN
cana-5388	245	20	:	:	PUNCT
cana-5388	245	21	a	a	DET
cana-5388	245	22	review	review	NOUN
cana-5388	245	23	,	,	PUNCT
cana-5388	245	24	”	"	PUNCT
cana-5388	245	25	ecological	ecological	ADJ
cana-5388	245	26	informatics	informatic	NOUN
cana-5388	245	27	,	,	PUNCT
cana-5388	245	28	vol	vol	NOUN
cana-5388	245	29	.	.	PROPN
cana-5388	245	30	63	63	NUM
cana-5388	245	31	,	,	PUNCT
cana-5388	245	32	p.	p.	NOUN
cana-5388	245	33	101297	101297	NUM
cana-5388	245	34	,	,	PUNCT
cana-5388	245	35	2021	2021	NUM
cana-5388	245	36	.	.	PUNCT
cana-5388	246	1	[	[	X
cana-5388	246	2	13	13	NUM
cana-5388	246	3	]	]	X
cana-5388	246	4	h.	h.	PROPN
cana-5388	246	5	chen	chen	PROPN
cana-5388	246	6	,	,	PUNCT
cana-5388	246	7	z.	z.	PROPN
cana-5388	246	8	li	li	PROPN
cana-5388	246	9	,	,	PUNCT
cana-5388	246	10	and	and	CCONJ
cana-5388	246	11	y.	y.	PROPN
cana-5388	246	12	yang	yang	PROPN
cana-5388	246	13	,	,	PUNCT
cana-5388	246	14	“	"	PUNCT
cana-5388	246	15	a	a	DET
cana-5388	246	16	deep	deep	ADJ
cana-5388	246	17	learning	learning	NOUN
cana-5388	246	18	model	model	NOUN
cana-5388	246	19	for	for	ADP
cana-5388	246	20	predicting	predict	VERB
cana-5388	246	21	air	air	NOUN
cana-5388	246	22	quality	quality	PROPN
cana-5388	246	23	index	index	NOUN
cana-5388	246	24	based	base	VERB
cana-5388	246	25	on	on	ADP
cana-5388	246	26	spatiotemporal	spatiotemporal	ADJ
cana-5388	246	27	data	datum	NOUN
cana-5388	246	28	,	,	PUNCT
cana-5388	246	29	”	"	PUNCT
cana-5388	246	30	science	science	NOUN
cana-5388	246	31	of	of	ADP
cana-5388	246	32	the	the	DET
cana-5388	246	33	total	total	ADJ
cana-5388	246	34	environment	environment	NOUN
cana-5388	246	35	,	,	PUNCT
cana-5388	246	36	vol	vol	NOUN
cana-5388	246	37	.	.	PROPN
cana-5388	246	38	744	744	NUM
cana-5388	246	39	,	,	PUNCT
cana-5388	246	40	p.	p.	NOUN
cana-5388	246	41	140837	140837	NUM
cana-5388	246	42	,	,	PUNCT
cana-5388	246	43	2020	2020	NUM
cana-5388	246	44	.	.	PUNCT
cana-5388	247	1	[	[	X
cana-5388	247	2	14	14	NUM
cana-5388	247	3	]	]	PUNCT
cana-5388	247	4	k.	k.	PROPN
cana-5388	247	5	n.	n.	PROPN
cana-5388	247	6	kannimuthu	kannimuthu	PROPN
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cana-5388	247	8	s.	s.	PROPN
cana-5388	247	9	rajesh	rajesh	PROPN
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cana-5388	247	11	“	"	PUNCT
cana-5388	247	12	air	air	NOUN
cana-5388	247	13	quality	quality	NOUN
cana-5388	247	14	forecasting	forecasting	NOUN
cana-5388	247	15	using	use	VERB
cana-5388	247	16	lstm	lstm	NOUN
cana-5388	247	17	with	with	ADP
cana-5388	247	18	optimization	optimization	NOUN
cana-5388	247	19	-	-	PUNCT
cana-5388	247	20	based	base	VERB
cana-5388	247	21	hyperparameter	hyperparameter	NOUN
cana-5388	247	22	tuning	tuning	NOUN
cana-5388	247	23	,	,	PUNCT
cana-5388	247	24	”	"	PUNCT
cana-5388	247	25	neural	neural	ADJ
cana-5388	247	26	computing	computing	NOUN
cana-5388	247	27	and	and	CCONJ
cana-5388	247	28	applications	application	NOUN
cana-5388	247	29	,	,	PUNCT
cana-5388	247	30	vol	vol	NOUN
cana-5388	247	31	.	.	PROPN
cana-5388	248	1	33	33	NUM
cana-5388	248	2	,	,	PUNCT
cana-5388	248	3	pp	pp	ADJ
cana-5388	248	4	.	.	PUNCT
cana-5388	249	1	14281–14294	14281–14294	NUM
cana-5388	249	2	,	,	PUNCT
cana-5388	249	3	2021	2021	NUM
cana-5388	249	4	.	.	PUNCT
cana-5388	250	1	[	[	X
cana-5388	250	2	15	15	NUM
cana-5388	250	3	]	]	X
cana-5388	250	4	b.	b.	PROPN
cana-5388	250	5	li	li	PROPN
cana-5388	250	6	,	,	PUNCT
cana-5388	250	7	d.	d.	PROPN
cana-5388	250	8	zhang	zhang	PROPN
cana-5388	250	9	,	,	PUNCT
cana-5388	250	10	and	and	CCONJ
cana-5388	250	11	g.	g.	PROPN
cana-5388	250	12	wei	wei	PROPN
cana-5388	250	13	,	,	PUNCT
cana-5388	250	14	“	"	PUNCT
cana-5388	250	15	air	air	NOUN
cana-5388	250	16	pollution	pollution	NOUN
cana-5388	250	17	forecasting	forecasting	NOUN
cana-5388	250	18	using	use	VERB
cana-5388	250	19	hybrid	hybrid	ADJ
cana-5388	250	20	machine	machine	NOUN
cana-5388	250	21	learning	learning	NOUN
cana-5388	250	22	models	model	NOUN
cana-5388	250	23	:	:	PUNCT
cana-5388	250	24	a	a	DET
cana-5388	250	25	review	review	NOUN
cana-5388	250	26	,	,	PUNCT
cana-5388	250	27	”	"	PUNCT
cana-5388	250	28	atmospheric	atmospheric	ADJ
cana-5388	250	29	environment	environment	NOUN
cana-5388	250	30	,	,	PUNCT
cana-5388	250	31	vol	vol	NOUN
cana-5388	250	32	.	.	PROPN
cana-5388	250	33	246	246	NUM
cana-5388	250	34	,	,	PUNCT
cana-5388	250	35	p.	p.	NOUN
cana-5388	250	36	118101	118101	NUM
cana-5388	250	37	,	,	PUNCT
cana-5388	250	38	2021	2021	NUM
cana-5388	250	39	.	.	PUNCT
cana-5388	251	1	[	[	X
cana-5388	251	2	16	16	NUM
cana-5388	251	3	]	]	PUNCT
cana-5388	251	4	j.	j.	PROPN
cana-5388	251	5	zhang	zhang	PROPN
cana-5388	251	6	and	and	CCONJ
cana-5388	251	7	j.	j.	PROPN
cana-5388	251	8	sun	sun	PROPN
cana-5388	251	9	,	,	PUNCT
cana-5388	251	10	“	"	PUNCT
cana-5388	251	11	long	long	ADJ
cana-5388	251	12	short	short	ADJ
cana-5388	251	13	-	-	PUNCT
cana-5388	251	14	term	term	NOUN
cana-5388	251	15	memory	memory	NOUN
cana-5388	251	16	networks	network	NOUN
cana-5388	251	17	for	for	ADP
cana-5388	251	18	air	air	NOUN
cana-5388	251	19	quality	quality	NOUN
cana-5388	251	20	prediction	prediction	NOUN
cana-5388	251	21	,	,	PUNCT
cana-5388	251	22	”	"	PUNCT
cana-5388	251	23	ieee	ieee	NOUN
cana-5388	251	24	access	access	NOUN
cana-5388	251	25	,	,	PUNCT
cana-5388	251	26	vol	vol	NOUN
cana-5388	251	27	.	.	PROPN
cana-5388	252	1	7	7	NUM
cana-5388	252	2	,	,	PUNCT
cana-5388	252	3	pp	pp	ADJ
cana-5388	252	4	.	.	PUNCT
cana-5388	253	1	28402–28410	28402–28410	NUM
cana-5388	253	2	,	,	PUNCT
cana-5388	253	3	2019	2019	NUM
cana-5388	253	4	.	.	PUNCT
cana-5388	254	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ
cana-5388	254	2	communications	communication	NOUN
cana-5388	254	3	on	on	ADP
cana-5388	254	4	applied	apply	VERB
cana-5388	254	5	nonlinear	nonlinear	ADJ
cana-5388	254	6	analysis	analysis	NOUN
cana-5388	254	7	issn	issn	NOUN
cana-5388	254	8	:	:	PUNCT
cana-5388	254	9	1074	1074	NUM
cana-5388	254	10	-	-	PUNCT
cana-5388	254	11	133x	133x	NUM
cana-5388	254	12	vol	vol	NOUN
cana-5388	254	13	32	32	NUM
cana-5388	254	14	no	no	NOUN
cana-5388	254	15	.	.	PUNCT
cana-5388	255	1	1s	1s	NUM
cana-5388	255	2	(	(	PUNCT
cana-5388	255	3	2025	2025	NUM
cana-5388	255	4	)	)	PUNCT
cana-5388	255	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-5388	255	6	664	664	NUM
cana-5388	255	7	[	[	X
cana-5388	255	8	17	17	NUM
cana-5388	255	9	]	]	PUNCT
cana-5388	255	10	a.	a.	NOUN
cana-5388	255	11	sharma	sharma	PROPN
cana-5388	255	12	and	and	CCONJ
cana-5388	255	13	a.	a.	PROPN
cana-5388	255	14	s.	s.	PROPN
cana-5388	255	15	ghosh	ghosh	PROPN
cana-5388	255	16	,	,	PUNCT
cana-5388	255	17	“	"	PUNCT
cana-5388	255	18	hybrid	hybrid	ADJ
cana-5388	255	19	deep	deep	ADJ
cana-5388	255	20	learning	learning	NOUN
cana-5388	255	21	models	model	NOUN
cana-5388	255	22	for	for	ADP
cana-5388	255	23	air	air	NOUN
cana-5388	255	24	quality	quality	NOUN
cana-5388	255	25	prediction	prediction	NOUN
cana-5388	255	26	in	in	ADP
cana-5388	255	27	smart	smart	ADJ
cana-5388	255	28	cities	city	NOUN
cana-5388	255	29	,	,	PUNCT
cana-5388	255	30	”	"	PUNCT
cana-5388	255	31	sustainable	sustainable	ADJ
cana-5388	255	32	cities	city	NOUN
cana-5388	255	33	and	and	CCONJ
cana-5388	255	34	society	society	NOUN
cana-5388	255	35	,	,	PUNCT
cana-5388	255	36	vol	vol	NOUN
cana-5388	255	37	.	.	PROPN
cana-5388	255	38	62	62	NUM
cana-5388	255	39	,	,	PUNCT
cana-5388	255	40	p.	p.	NOUN
cana-5388	255	41	102420	102420	NUM
cana-5388	255	42	,	,	PUNCT
cana-5388	255	43	2020	2020	NUM
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cana-5388	256	1	[	[	X
cana-5388	256	2	18	18	NUM
cana-5388	256	3	]	]	X
cana-5388	256	4	y.	y.	PROPN
cana-5388	256	5	luo	luo	PROPN
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cana-5388	256	14	“	"	PUNCT
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cana-5388	256	16	quality	quality	NOUN
cana-5388	256	17	forecasting	forecasting	NOUN
cana-5388	256	18	using	use	VERB
cana-5388	256	19	hybrid	hybrid	ADJ
cana-5388	256	20	models	model	NOUN
cana-5388	256	21	with	with	ADP
cana-5388	256	22	decomposition	decomposition	NOUN
cana-5388	256	23	and	and	CCONJ
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cana-5388	256	25	:	:	PUNCT
cana-5388	256	26	a	a	DET
cana-5388	256	27	case	case	NOUN
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cana-5388	256	29	,	,	PUNCT
cana-5388	256	30	”	"	PUNCT
cana-5388	256	31	environmental	environmental	ADJ
cana-5388	256	32	modelling	modelling	NOUN
cana-5388	256	33	&	&	CCONJ
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cana-5388	256	35	,	,	PUNCT
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cana-5388	256	42	,	,	PUNCT
cana-5388	256	43	2020	2020	NUM
cana-5388	256	44	.	.	PUNCT
cana-5388	257	1	[	[	X
cana-5388	257	2	19	19	NUM
cana-5388	257	3	]	]	PUNCT
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cana-5388	257	5	c.	c.	PROPN
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cana-5388	257	14	using	use	VERB
cana-5388	257	15	stacking	stack	VERB
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cana-5388	257	17	learning	learning	NOUN
cana-5388	257	18	with	with	ADP
cana-5388	257	19	hyperparameter	hyperparameter	NOUN
cana-5388	257	20	tuning	tuning	NOUN
cana-5388	257	21	,	,	PUNCT
cana-5388	257	22	”	"	PUNCT
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cana-5388	257	25	science	science	NOUN
cana-5388	257	26	,	,	PUNCT
cana-5388	257	27	vol	vol	NOUN
cana-5388	257	28	.	.	PROPN
cana-5388	257	29	173	173	NUM
cana-5388	257	30	,	,	PUNCT
cana-5388	257	31	pp	pp	ADJ
cana-5388	257	32	.	.	PUNCT
cana-5388	258	1	448–456	448–456	NUM
cana-5388	258	2	,	,	PUNCT
cana-5388	258	3	2020	2020	NUM
cana-5388	258	4	.	.	PUNCT
cana-5388	259	1	[	[	X
cana-5388	259	2	20	20	NUM
cana-5388	259	3	]	]	PUNCT
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cana-5388	259	5	hussain	hussain	PROPN
cana-5388	259	6	,	,	PUNCT
cana-5388	259	7	f.	f.	PROPN
cana-5388	259	8	a.	a.	PROPN
cana-5388	259	9	khan	khan	PROPN
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cana-5388	259	11	and	and	CCONJ
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cana-5388	259	15	“	"	PUNCT
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cana-5388	259	18	of	of	ADP
cana-5388	259	19	machine	machine	NOUN
cana-5388	259	20	learning	learn	VERB
cana-5388	259	21	algorithms	algorithm	NOUN
cana-5388	259	22	for	for	ADP
cana-5388	259	23	air	air	NOUN
cana-5388	259	24	pollution	pollution	NOUN
cana-5388	259	25	forecasting	forecasting	NOUN
cana-5388	259	26	,	,	PUNCT
cana-5388	259	27	”	"	PUNCT
cana-5388	259	28	journal	journal	NOUN
cana-5388	259	29	of	of	ADP
cana-5388	259	30	ambient	ambient	ADJ
cana-5388	259	31	intelligence	intelligence	NOUN
cana-5388	259	32	and	and	CCONJ
cana-5388	259	33	humanized	humanize	VERB
cana-5388	259	34	computing	computing	NOUN
cana-5388	259	35	,	,	PUNCT
cana-5388	259	36	vol	vol	NOUN
cana-5388	259	37	.	.	PROPN
cana-5388	259	38	12	12	NUM
cana-5388	259	39	,	,	PUNCT
cana-5388	259	40	pp	pp	ADJ
cana-5388	259	41	.	.	PUNCT
cana-5388	260	1	5725–5736	5725–5736	NUM
cana-5388	260	2	,	,	PUNCT
cana-5388	260	3	2021	2021	NUM
cana-5388	260	4	.	.	PUNCT
cana-5388	261	1	[	[	X
cana-5388	261	2	21	21	NUM
cana-5388	261	3	]	]	X
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cana-5388	261	10	"	"	PUNCT
cana-5388	261	11	a	a	DET
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cana-5388	261	18	for	for	ADP
cana-5388	261	19	climate	climate	NOUN
cana-5388	261	20	change	change	NOUN
cana-5388	261	21	dataset	dataset	NOUN
cana-5388	261	22	using	use	VERB
cana-5388	261	23	data	datum	NOUN
cana-5388	261	24	mining	mining	NOUN
cana-5388	261	25	with	with	ADP
cana-5388	261	26	machine	machine	NOUN
cana-5388	261	27	learning	learning	NOUN
cana-5388	261	28	approaches	approach	NOUN
cana-5388	261	29	,	,	PUNCT
cana-5388	261	30	"	"	PUNCT
cana-5388	261	31	european	european	ADJ
cana-5388	261	32	chemical	chemical	PROPN
cana-5388	261	33	bulletin	bulletin	PROPN
cana-5388	261	34	,	,	PUNCT
cana-5388	261	35	vol	vol	NOUN
cana-5388	261	36	.	.	PROPN
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cana-5388	261	38	,	,	PUNCT
cana-5388	261	39	no	no	INTJ
cana-5388	261	40	.	.	NOUN
cana-5388	261	41	12	12	NUM
cana-5388	261	42	,	,	PUNCT
cana-5388	261	43	pp	pp	ADJ
cana-5388	261	44	.	.	PUNCT
cana-5388	262	1	1866–1877	1866–1877	NUM
cana-5388	262	2	,	,	PUNCT
cana-5388	262	3	2022	2022	NUM
cana-5388	262	4	.	.	PUNCT
cana-5388	263	1	[	[	X
cana-5388	263	2	22	22	NUM
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cana-5388	263	9	,	,	PUNCT
cana-5388	263	10	"	"	PUNCT
cana-5388	263	11	a	a	DET
cana-5388	263	12	study	study	NOUN
cana-5388	263	13	on	on	ADP
cana-5388	263	14	variable	variable	ADJ
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cana-5388	263	18	for	for	ADP
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cana-5388	263	20	change	change	NOUN
cana-5388	263	21	with	with	ADP
cana-5388	263	22	global	global	ADJ
cana-5388	263	23	weather	weather	NOUN
cana-5388	263	24	repository	repository	NOUN
cana-5388	263	25	using	use	VERB
cana-5388	263	26	data	datum	NOUN
cana-5388	263	27	mining	mining	NOUN
cana-5388	263	28	with	with	ADP
cana-5388	263	29	machine	machine	NOUN
cana-5388	263	30	learning	learning	NOUN
cana-5388	263	31	approaches	approach	NOUN
cana-5388	263	32	,	,	PUNCT
cana-5388	263	33	"	"	PUNCT
cana-5388	263	34	journal	journal	NOUN
cana-5388	263	35	of	of	ADP
cana-5388	263	36	propulsion	propulsion	NOUN
cana-5388	263	37	technology	technology	NOUN
cana-5388	263	38	,	,	PUNCT
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cana-5388	263	40	.	.	PROPN
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cana-5388	263	42	,	,	PUNCT
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cana-5388	263	44	.	.	NOUN
cana-5388	263	45	2	2	NUM
cana-5388	263	46	,	,	PUNCT
cana-5388	263	47	pp	pp	ADJ
cana-5388	263	48	.	.	PUNCT
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cana-5388	264	2	.	.	PUNCT
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cana-5388	265	8	r.	r.	PROPN
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cana-5388	265	25	”	"	PUNCT
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cana-5388	265	31	and	and	CCONJ
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cana-5388	265	36	18	18	NUM
cana-5388	265	37	,	,	PUNCT
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cana-5388	265	45	,	,	PUNCT
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cana-5388	265	47	.	.	PUNCT
cana-5388	266	1	[	[	X
cana-5388	266	2	24	24	NUM
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cana-5388	266	14	using	use	VERB
cana-5388	266	15	pca	pca	PROPN
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cana-5388	266	21	”	"	PUNCT
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cana-5388	267	2	,	,	PUNCT
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cana-5388	268	34	.	.	PROPN
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cana-5388	268	40	,	,	PUNCT
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cana-5388	269	2	,	,	PUNCT
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cana-5388	269	4	.	.	PUNCT
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cana-5388	270	28	approaches	approach	NOUN
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cana-5388	270	30	"	"	PUNCT
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cana-5388	270	33	data	datum	NOUN
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cana-5388	270	35	and	and	CCONJ
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cana-5388	273	2	27	27	NUM
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cana-5388	273	10	"	"	PUNCT
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cana-5388	273	14	approach	approach	NOUN
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cana-5388	273	17	and	and	CCONJ
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cana-5388	273	20	relationship	relationship	NOUN
cana-5388	273	21	between	between	ADP
cana-5388	273	22	sustainable	sustainable	ADJ
cana-5388	273	23	development	development	NOUN
cana-5388	273	24	goals	goal	NOUN
cana-5388	273	25	with	with	ADP
cana-5388	273	26	various	various	ADJ
cana-5388	273	27	energy	energy	NOUN
cana-5388	273	28	,	,	PUNCT
cana-5388	273	29	"	"	PUNCT
cana-5388	273	30	journal	journal	NOUN
cana-5388	273	31	of	of	ADP
cana-5388	273	32	propulsion	propulsion	NOUN
cana-5388	273	33	technology	technology	NOUN
cana-5388	273	34	,	,	PUNCT
cana-5388	273	35	vol	vol	NOUN
cana-5388	273	36	.	.	PROPN
cana-5388	273	37	44	44	NUM
cana-5388	273	38	,	,	PUNCT
cana-5388	273	39	no	no	INTJ
cana-5388	273	40	.	.	NOUN
cana-5388	273	41	2	2	NUM
cana-5388	273	42	,	,	PUNCT
cana-5388	273	43	pp	pp	ADJ
cana-5388	273	44	.	.	PUNCT
cana-5388	274	1	956–968	956–968	NUM
cana-5388	274	2	.	.	PUNCT
cana-5388	275	1	https://internationalpubls.com/	https://internationalpubls.com/	ADJ