id	sid	tid	token	lemma	pos
cana-2697	1	1	communications	communication	NOUN
cana-2697	1	2	on	on	ADP
cana-2697	1	3	applied	apply	VERB
cana-2697	1	4	nonlinear	nonlinear	ADJ
cana-2697	1	5	analysis	analysis	NOUN
cana-2697	1	6	issn	issn	NOUN
cana-2697	1	7	:	:	PUNCT
cana-2697	1	8	1074	1074	NUM
cana-2697	1	9	-	-	PUNCT
cana-2697	1	10	133x	133x	NUM
cana-2697	1	11	vol	vol	NOUN
cana-2697	1	12	32	32	NUM
cana-2697	1	13	no	no	NOUN
cana-2697	1	14	.	.	PUNCT
cana-2697	2	1	3s	3s	NUM
cana-2697	2	2	(	(	PUNCT
cana-2697	2	3	2025	2025	NUM
cana-2697	2	4	)	)	PUNCT
cana-2697	2	5	578	578	NUM
cana-2697	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	2	7	hybrid	hybrid	ADJ
cana-2697	2	8	model	model	NOUN
cana-2697	2	9	for	for	ADP
cana-2697	2	10	dissolved	dissolve	VERB
cana-2697	2	11	oxygen	oxygen	NOUN
cana-2697	2	12	prediction	prediction	NOUN
cana-2697	2	13	using	use	VERB
cana-2697	2	14	ensemble	ensemble	ADJ
cana-2697	2	15	mine	mine	NOUN
cana-2697	2	16	-	-	PUNCT
cana-2697	2	17	bisru	bisru	NOUN
cana-2697	2	18	-	-	PUNCT
cana-2697	2	19	attention	attention	NOUN
cana-2697	2	20	and	and	CCONJ
cana-2697	2	21	lightgbm	lightgbm	ADJ
cana-2697	2	22	-	-	PUNCT
cana-2697	2	23	bisru	bisru	NOUN
cana-2697	2	24	-	-	PUNCT
cana-2697	2	25	attention	attention	NOUN
cana-2697	2	26	approaches	approach	NOUN
cana-2697	2	27	alla	alla	NOUN
cana-2697	2	28	rajendra1	rajendra1	PROPN
cana-2697	2	29	,	,	PUNCT
cana-2697	2	30	anil	anil	PROPN
cana-2697	2	31	kumar	kumar	PROPN
cana-2697	2	32	muthevi2	muthevi2	PROPN
cana-2697	2	33	1	1	NUM
cana-2697	2	34	department	department	NOUN
cana-2697	2	35	of	of	ADP
cana-2697	2	36	computer	computer	NOUN
cana-2697	2	37	science	science	PROPN
cana-2697	2	38	&	&	CCONJ
cana-2697	2	39	engineering	engineering	PROPN
cana-2697	2	40	,	,	PUNCT
cana-2697	2	41	aditya	aditya	PROPN
cana-2697	2	42	college	college	PROPN
cana-2697	2	43	of	of	ADP
cana-2697	2	44	engineering	engineering	NOUN
cana-2697	2	45	and	and	CCONJ
cana-2697	2	46	technology	technology	NOUN
cana-2697	2	47	,	,	PUNCT
cana-2697	2	48	surampalem	surampalem	NOUN
cana-2697	2	49	,	,	PUNCT
cana-2697	2	50	india	india	PROPN
cana-2697	2	51	,	,	PUNCT
cana-2697	2	52	rajendracivil127@gmail.com	rajendracivil127@gmail.com	X
cana-2697	2	53	2	2	NUM
cana-2697	2	54	professor	professor	NOUN
cana-2697	2	55	,	,	PUNCT
cana-2697	2	56	department	department	NOUN
cana-2697	2	57	of	of	ADP
cana-2697	2	58	computer	computer	NOUN
cana-2697	2	59	science	science	PROPN
cana-2697	2	60	&	&	CCONJ
cana-2697	2	61	engineering	engineering	PROPN
cana-2697	2	62	,	,	PUNCT
cana-2697	2	63	aditya	aditya	PROPN
cana-2697	2	64	college	college	PROPN
cana-2697	2	65	of	of	ADP
cana-2697	2	66	engineering	engineering	NOUN
cana-2697	2	67	and	and	CCONJ
cana-2697	2	68	technology	technology	NOUN
cana-2697	2	69	,	,	PUNCT
cana-2697	2	70	surampalem	surampalem	NOUN
cana-2697	2	71	,	,	PUNCT
cana-2697	2	72	india	india	PROPN
cana-2697	2	73	,	,	PUNCT
cana-2697	2	74	lettertoanil@gmail.com	lettertoanil@gmail.com	X
cana-2697	2	75	article	article	NOUN
cana-2697	2	76	history	history	NOUN
cana-2697	2	77	:	:	PUNCT
cana-2697	2	78	received	receive	VERB
cana-2697	2	79	:	:	PUNCT
cana-2697	2	80	24	24	NUM
cana-2697	2	81	-	-	PUNCT
cana-2697	2	82	09	09	NUM
cana-2697	2	83	-	-	PUNCT
cana-2697	2	84	2024	2024	NUM
cana-2697	2	85	revised	revise	VERB
cana-2697	2	86	:	:	PUNCT
cana-2697	2	87	10	10	NUM
cana-2697	2	88	-	-	SYM
cana-2697	2	89	11	11	NUM
cana-2697	2	90	-	-	PUNCT
cana-2697	2	91	2024	2024	NUM
cana-2697	2	92	accepted	accept	VERB
cana-2697	2	93	:	:	PUNCT
cana-2697	2	94	27	27	NUM
cana-2697	2	95	-	-	SYM
cana-2697	2	96	11	11	NUM
cana-2697	2	97	-	-	PUNCT
cana-2697	2	98	2024	2024	NUM
cana-2697	2	99	abstract	abstract	NOUN
cana-2697	2	100	:	:	PUNCT
cana-2697	2	101	aquaculture	aquaculture	NOUN
cana-2697	2	102	productivity	productivity	NOUN
cana-2697	2	103	in	in	ADP
cana-2697	2	104	most	most	ADJ
cana-2697	2	105	cases	case	NOUN
cana-2697	2	106	depends	depend	VERB
cana-2697	2	107	on	on	ADP
cana-2697	2	108	the	the	DET
cana-2697	2	109	quality	quality	NOUN
cana-2697	2	110	of	of	ADP
cana-2697	2	111	water	water	NOUN
cana-2697	2	112	which	which	PRON
cana-2697	2	113	is	be	AUX
cana-2697	2	114	one	one	NUM
cana-2697	2	115	of	of	ADP
cana-2697	2	116	the	the	DET
cana-2697	2	117	determinants	determinant	NOUN
cana-2697	2	118	of	of	ADP
cana-2697	2	119	water	water	NOUN
cana-2697	2	120	health	health	NOUN
cana-2697	2	121	.	.	PUNCT
cana-2697	3	1	one	one	NUM
cana-2697	3	2	of	of	ADP
cana-2697	3	3	the	the	DET
cana-2697	3	4	most	most	ADV
cana-2697	3	5	important	important	ADJ
cana-2697	3	6	variables	variable	NOUN
cana-2697	3	7	needing	need	VERB
cana-2697	3	8	monitoring	monitoring	NOUN
cana-2697	3	9	and	and	CCONJ
cana-2697	3	10	control	control	NOUN
cana-2697	3	11	is	be	AUX
cana-2697	3	12	the	the	DET
cana-2697	3	13	dissolved	dissolve	VERB
cana-2697	3	14	oxygen	oxygen	NOUN
cana-2697	3	15	(	(	PUNCT
cana-2697	3	16	do	do	NOUN
cana-2697	3	17	)	)	PUNCT
cana-2697	3	18	.	.	PUNCT
cana-2697	4	1	even	even	ADV
cana-2697	4	2	with	with	ADP
cana-2697	4	3	complex	complex	ADJ
cana-2697	4	4	relationships	relationship	NOUN
cana-2697	4	5	typical	typical	ADJ
cana-2697	4	6	in	in	ADP
cana-2697	4	7	aquaculture	aquaculture	NOUN
cana-2697	4	8	processes	process	NOUN
cana-2697	4	9	,	,	PUNCT
cana-2697	4	10	some	some	DET
cana-2697	4	11	traditional	traditional	ADJ
cana-2697	4	12	methods	method	NOUN
cana-2697	4	13	of	of	ADP
cana-2697	4	14	prediction	prediction	NOUN
cana-2697	4	15	have	have	VERB
cana-2697	4	16	more	more	ADV
cana-2697	4	17	often	often	ADV
cana-2697	4	18	than	than	SCONJ
cana-2697	4	19	not	not	PART
cana-2697	4	20	been	be	AUX
cana-2697	4	21	ineffective	ineffective	ADJ
cana-2697	4	22	.	.	PUNCT
cana-2697	5	1	in	in	ADP
cana-2697	5	2	this	this	DET
cana-2697	5	3	work	work	NOUN
cana-2697	5	4	,	,	PUNCT
cana-2697	5	5	the	the	DET
cana-2697	5	6	lightgbm	lightgbm	ADJ
cana-2697	5	7	-	-	PUNCT
cana-2697	5	8	bisru	bisru	NOUN
cana-2697	5	9	-	-	PUNCT
cana-2697	5	10	attention	attention	NOUN
cana-2697	5	11	hybrid	hybrid	NOUN
cana-2697	5	12	approach	approach	NOUN
cana-2697	5	13	is	be	AUX
cana-2697	5	14	proposed	propose	VERB
cana-2697	5	15	which	which	PRON
cana-2697	5	16	uses	use	VERB
cana-2697	5	17	lightgbm	lightgbm	VERB
cana-2697	5	18	for	for	ADP
cana-2697	5	19	feature	feature	NOUN
cana-2697	5	20	selection	selection	NOUN
cana-2697	5	21	,	,	PUNCT
cana-2697	5	22	bisru	bisru	VERB
cana-2697	5	23	for	for	ADP
cana-2697	5	24	sequence	sequence	NOUN
cana-2697	5	25	learning	learning	NOUN
cana-2697	5	26	and	and	CCONJ
cana-2697	5	27	attention	attention	NOUN
cana-2697	5	28	for	for	ADP
cana-2697	5	29	parameter	parameter	NOUN
cana-2697	5	30	tuning	tuning	NOUN
cana-2697	5	31	.	.	PUNCT
cana-2697	6	1	further	far	ADV
cana-2697	6	2	,	,	PUNCT
cana-2697	6	3	an	an	DET
cana-2697	6	4	enhanced	enhanced	ADJ
cana-2697	6	5	model	model	NOUN
cana-2697	6	6	called	call	VERB
cana-2697	6	7	ensemble	ensemble	ADJ
cana-2697	6	8	mine	mine	ADJ
cana-2697	6	9	bisru	bisru	NOUN
cana-2697	6	10	-	-	PUNCT
cana-2697	6	11	attention	attention	NOUN
cana-2697	6	12	is	be	AUX
cana-2697	6	13	proposed	propose	VERB
cana-2697	6	14	which	which	PRON
cana-2697	6	15	further	far	ADV
cana-2697	6	16	explores	explore	VERB
cana-2697	6	17	the	the	DET
cana-2697	6	18	use	use	NOUN
cana-2697	6	19	of	of	ADP
cana-2697	6	20	the	the	DET
cana-2697	6	21	maximal	maximal	ADJ
cana-2697	6	22	information	information	NOUN
cana-2697	6	23	coefficient	coefficient	NOUN
cana-2697	6	24	(	(	PUNCT
cana-2697	6	25	mic	mic	ADJ
cana-2697	6	26	)	)	PUNCT
cana-2697	6	27	for	for	ADP
cana-2697	6	28	more	more	ADV
cana-2697	6	29	advanced	advanced	ADJ
cana-2697	6	30	feature	feature	NOUN
cana-2697	6	31	selection	selection	NOUN
cana-2697	6	32	.	.	PUNCT
cana-2697	7	1	in	in	ADP
cana-2697	7	2	this	this	DET
cana-2697	7	3	study	study	NOUN
cana-2697	7	4	,	,	PUNCT
cana-2697	7	5	these	these	DET
cana-2697	7	6	models	model	NOUN
cana-2697	7	7	were	be	AUX
cana-2697	7	8	evaluated	evaluate	VERB
cana-2697	7	9	against	against	ADP
cana-2697	7	10	the	the	DET
cana-2697	7	11	kaggle	kaggle	ADJ
cana-2697	7	12	water	water	NOUN
cana-2697	7	13	quality	quality	NOUN
cana-2697	7	14	dataset	dataset	NOUN
cana-2697	7	15	and	and	CCONJ
cana-2697	7	16	consistently	consistently	ADV
cana-2697	7	17	with	with	ADP
cana-2697	7	18	good	good	ADJ
cana-2697	7	19	accuracy	accuracy	NOUN
cana-2697	7	20	.	.	PUNCT
cana-2697	8	1	the	the	DET
cana-2697	8	2	models	model	NOUN
cana-2697	8	3	predicted	predict	VERB
cana-2697	8	4	the	the	DET
cana-2697	8	5	average	average	ADJ
cana-2697	8	6	mean	mean	ADJ
cana-2697	8	7	square	square	ADJ
cana-2697	8	8	error	error	NOUN
cana-2697	8	9	with	with	ADP
cana-2697	8	10	the	the	DET
cana-2697	8	11	lightgbm	lightgbm	ADJ
cana-2697	8	12	-	-	PUNCT
cana-2697	8	13	bisru	bisru	NOUN
cana-2697	8	14	-	-	PUNCT
cana-2697	8	15	attention	attention	NOUN
cana-2697	8	16	model	model	NOUN
cana-2697	8	17	to	to	PART
cana-2697	8	18	be	be	AUX
cana-2697	8	19	0.178	0.178	NUM
cana-2697	8	20	while	while	SCONJ
cana-2697	8	21	the	the	DET
cana-2697	8	22	ensemble	ensemble	ADJ
cana-2697	8	23	mine	mine	NOUN
cana-2697	8	24	-	-	PUNCT
cana-2697	8	25	bisru	bisru	NOUN
cana-2697	8	26	-	-	PUNCT
cana-2697	8	27	attention	attention	NOUN
cana-2697	8	28	further	far	ADV
cana-2697	8	29	lowered	lower	VERB
cana-2697	8	30	the	the	DET
cana-2697	8	31	number	number	NOUN
cana-2697	8	32	to	to	ADP
cana-2697	8	33	0.104	0.104	NUM
cana-2697	8	34	.	.	PUNCT
cana-2697	9	1	this	this	PRON
cana-2697	9	2	also	also	ADV
cana-2697	9	3	acts	act	VERB
cana-2697	9	4	as	as	ADP
cana-2697	9	5	a	a	DET
cana-2697	9	6	shift	shift	NOUN
cana-2697	9	7	towards	towards	ADP
cana-2697	9	8	the	the	DET
cana-2697	9	9	intelligent	intelligent	ADJ
cana-2697	9	10	aquaculture	aquaculture	NOUN
cana-2697	9	11	systems	system	NOUN
cana-2697	9	12	which	which	PRON
cana-2697	9	13	provide	provide	VERB
cana-2697	9	14	a	a	DET
cana-2697	9	15	solution	solution	NOUN
cana-2697	9	16	to	to	ADP
cana-2697	9	17	the	the	DET
cana-2697	9	18	gaps	gap	NOUN
cana-2697	9	19	which	which	PRON
cana-2697	9	20	existed	exist	VERB
cana-2697	9	21	in	in	ADP
cana-2697	9	22	water	water	NOUN
cana-2697	9	23	quality	quality	NOUN
cana-2697	9	24	prediction	prediction	NOUN
cana-2697	9	25	models	model	NOUN
cana-2697	9	26	.	.	PUNCT
cana-2697	10	1	keywords	keyword	NOUN
cana-2697	10	2	:	:	PUNCT
cana-2697	10	3	aquaculture	aquaculture	NOUN
cana-2697	10	4	,	,	PUNCT
cana-2697	10	5	attention	attention	NOUN
cana-2697	10	6	mechanism	mechanism	NOUN
cana-2697	10	7	,	,	PUNCT
cana-2697	10	8	ensemble	ensemble	ADJ
cana-2697	10	9	model	model	NOUN
cana-2697	10	10	,	,	PUNCT
cana-2697	10	11	mic	mic	ADJ
cana-2697	10	12	.	.	PUNCT
cana-2697	10	13	i.	i.	PROPN
cana-2697	10	14	introduction	introduction	NOUN
cana-2697	10	15	aquaculture	aquaculture	NOUN
cana-2697	10	16	has	have	AUX
cana-2697	10	17	taken	take	VERB
cana-2697	10	18	a	a	DET
cana-2697	10	19	place	place	NOUN
cana-2697	10	20	in	in	ADP
cana-2697	10	21	the	the	DET
cana-2697	10	22	global	global	ADJ
cana-2697	10	23	food	food	NOUN
cana-2697	10	24	supply	supply	NOUN
cana-2697	10	25	chain	chain	NOUN
cana-2697	10	26	,	,	PUNCT
cana-2697	10	27	addressing	address	VERB
cana-2697	10	28	the	the	DET
cana-2697	10	29	growing	grow	VERB
cana-2697	10	30	appetite	appetite	NOUN
cana-2697	10	31	for	for	ADP
cana-2697	10	32	seafood	seafood	NOUN
cana-2697	10	33	and	and	CCONJ
cana-2697	10	34	enabling	enable	VERB
cana-2697	10	35	increased	increase	VERB
cana-2697	10	36	economic	economic	ADJ
cana-2697	10	37	activity	activity	NOUN
cana-2697	10	38	.	.	PUNCT
cana-2697	11	1	optimal	optimal	ADJ
cana-2697	11	2	water	water	NOUN
cana-2697	11	3	quality	quality	NOUN
cana-2697	11	4	is	be	AUX
cana-2697	11	5	paramount	paramount	ADJ
cana-2697	11	6	in	in	ADP
cana-2697	11	7	the	the	DET
cana-2697	11	8	health	health	NOUN
cana-2697	11	9	,	,	PUNCT
cana-2697	11	10	growth	growth	NOUN
cana-2697	11	11	,	,	PUNCT
cana-2697	11	12	and	and	CCONJ
cana-2697	11	13	productivity	productivity	NOUN
cana-2697	11	14	of	of	ADP
cana-2697	11	15	aquaculture	aquaculture	NOUN
cana-2697	11	16	organisms	organism	NOUN
cana-2697	11	17	.	.	PUNCT
cana-2697	12	1	among	among	ADP
cana-2697	12	2	the	the	DET
cana-2697	12	3	various	various	ADJ
cana-2697	12	4	parameters	parameter	NOUN
cana-2697	12	5	of	of	ADP
cana-2697	12	6	water	water	NOUN
cana-2697	12	7	quality	quality	NOUN
cana-2697	12	8	,	,	PUNCT
cana-2697	12	9	the	the	DET
cana-2697	12	10	most	most	ADV
cana-2697	12	11	important	important	ADJ
cana-2697	12	12	is	be	AUX
cana-2697	12	13	dissolved	dissolve	VERB
cana-2697	12	14	oxygen	oxygen	NOUN
cana-2697	12	15	(	(	PUNCT
cana-2697	12	16	do	do	NOUN
cana-2697	12	17	)	)	PUNCT
cana-2697	12	18	.	.	PUNCT
cana-2697	13	1	low	low	ADJ
cana-2697	13	2	levels	level	NOUN
cana-2697	13	3	of	of	ADP
cana-2697	13	4	do	do	AUX
cana-2697	13	5	can	can	AUX
cana-2697	13	6	result	result	VERB
cana-2697	13	7	in	in	ADP
cana-2697	13	8	stress	stress	NOUN
cana-2697	13	9	,	,	PUNCT
cana-2697	13	10	decreased	decrease	VERB
cana-2697	13	11	growth	growth	NOUN
cana-2697	13	12	rates	rate	NOUN
cana-2697	13	13	,	,	PUNCT
cana-2697	13	14	death	death	NOUN
cana-2697	13	15	,	,	PUNCT
cana-2697	13	16	and	and	CCONJ
cana-2697	13	17	these	these	DET
cana-2697	13	18	result	result	NOUN
cana-2697	13	19	in	in	ADP
cana-2697	13	20	serious	serious	ADJ
cana-2697	13	21	financial	financial	ADJ
cana-2697	13	22	losses	loss	NOUN
cana-2697	13	23	[	[	X
cana-2697	13	24	1][2][3	1][2][3	NUM
cana-2697	13	25	]	]	PUNCT
cana-2697	13	26	.	.	PUNCT
cana-2697	14	1	challenges	challenge	NOUN
cana-2697	14	2	in	in	ADP
cana-2697	14	3	do	do	VERB
cana-2697	14	4	prediction	prediction	NOUN
cana-2697	14	5	do	do	AUX
cana-2697	14	6	levels	level	NOUN
cana-2697	14	7	are	be	AUX
cana-2697	14	8	difficult	difficult	ADJ
cana-2697	14	9	to	to	PART
cana-2697	14	10	predict	predict	VERB
cana-2697	14	11	because	because	SCONJ
cana-2697	14	12	they	they	PRON
cana-2697	14	13	are	be	AUX
cana-2697	14	14	influenced	influence	VERB
cana-2697	14	15	by	by	ADP
cana-2697	14	16	multiple	multiple	ADJ
cana-2697	14	17	environmental	environmental	ADJ
cana-2697	14	18	factors	factor	NOUN
cana-2697	14	19	such	such	ADJ
cana-2697	14	20	as	as	ADP
cana-2697	14	21	ph	ph	ADJ
cana-2697	14	22	,	,	PUNCT
cana-2697	14	23	turbidity	turbidity	NOUN
cana-2697	14	24	,	,	PUNCT
cana-2697	14	25	and	and	CCONJ
cana-2697	14	26	temperature	temperature	NOUN
cana-2697	14	27	.	.	PUNCT
cana-2697	15	1	conventional	conventional	ADJ
cana-2697	15	2	approaches	approach	NOUN
cana-2697	15	3	,	,	PUNCT
cana-2697	15	4	including	include	VERB
cana-2697	15	5	regression	regression	NOUN
cana-2697	15	6	analysis	analysis	NOUN
cana-2697	15	7	models	model	NOUN
cana-2697	15	8	and	and	CCONJ
cana-2697	15	9	two	two	NUM
cana-2697	15	10	-	-	PUNCT
cana-2697	15	11	way	way	NOUN
cana-2697	15	12	anova	anova	PROPN
cana-2697	15	13	,	,	PUNCT
cana-2697	15	14	can	can	AUX
cana-2697	15	15	manage	manage	VERB
cana-2697	15	16	non	non	ADJ
cana-2697	15	17	-	-	ADJ
cana-2697	15	18	linear	linear	ADJ
cana-2697	15	19	and	and	CCONJ
cana-2697	15	20	interdependent	interdependent	ADJ
cana-2697	15	21	relationships	relationship	NOUN
cana-2697	15	22	in	in	ADP
cana-2697	15	23	aquaculture	aquaculture	NOUN
cana-2697	15	24	systems	system	NOUN
cana-2697	15	25	[	[	X
cana-2697	15	26	4][5	4][5	X
cana-2697	15	27	]	]	PUNCT
cana-2697	15	28	.	.	PUNCT
cana-2697	16	1	machine	machine	NOUN
cana-2697	16	2	learning	learn	VERB
cana-2697	16	3	techniques	technique	NOUN
cana-2697	16	4	such	such	ADJ
cana-2697	16	5	as	as	ADP
cana-2697	16	6	random	random	ADJ
cana-2697	16	7	forests	forest	NOUN
cana-2697	16	8	(	(	PUNCT
cana-2697	16	9	rf	rf	NOUN
cana-2697	16	10	)	)	PUNCT
cana-2697	16	11	and	and	CCONJ
cana-2697	16	12	support	support	VERB
cana-2697	16	13	vector	vector	NOUN
cana-2697	16	14	machines	machine	NOUN
cana-2697	16	15	(	(	PUNCT
cana-2697	16	16	svm	svm	PROPN
cana-2697	16	17	)	)	PUNCT
cana-2697	16	18	,	,	PUNCT
cana-2697	16	19	although	although	SCONJ
cana-2697	16	20	achieving	achieve	VERB
cana-2697	16	21	better	well	ADJ
cana-2697	16	22	predictive	predictive	ADJ
cana-2697	16	23	accuracy	accuracy	NOUN
cana-2697	16	24	,	,	PUNCT
cana-2697	16	25	suffer	suffer	VERB
cana-2697	16	26	from	from	ADP
cana-2697	16	27	poor	poor	ADJ
cana-2697	16	28	sequential	sequential	ADJ
cana-2697	16	29	data	datum	NOUN
cana-2697	16	30	handling	handling	NOUN
cana-2697	16	31	capability	capability	NOUN
cana-2697	16	32	[	[	X
cana-2697	16	33	6][7	6][7	NUM
cana-2697	16	34	]	]	PUNCT
cana-2697	16	35	.	.	PUNCT
cana-2697	17	1	mailto:rajendracivil127@gmail.com	mailto:rajendracivil127@gmail.com	PROPN
cana-2697	17	2	mailto:lettertoanil@gmail.com	mailto:lettertoanil@gmail.com	X
cana-2697	17	3	communications	communication	NOUN
cana-2697	17	4	on	on	ADP
cana-2697	17	5	applied	apply	VERB
cana-2697	17	6	nonlinear	nonlinear	ADJ
cana-2697	17	7	analysis	analysis	NOUN
cana-2697	17	8	issn	issn	NOUN
cana-2697	17	9	:	:	PUNCT
cana-2697	17	10	1074	1074	NUM
cana-2697	17	11	-	-	PUNCT
cana-2697	17	12	133x	133x	NUM
cana-2697	17	13	vol	vol	NOUN
cana-2697	17	14	32	32	NUM
cana-2697	17	15	no	no	NOUN
cana-2697	17	16	.	.	PUNCT
cana-2697	18	1	3s	3s	NUM
cana-2697	18	2	(	(	PUNCT
cana-2697	18	3	2025	2025	NUM
cana-2697	18	4	)	)	PUNCT
cana-2697	18	5	579	579	NUM
cana-2697	18	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	18	7	over	over	ADP
cana-2697	18	8	the	the	DET
cana-2697	18	9	years	year	NOUN
cana-2697	18	10	,	,	PUNCT
cana-2697	18	11	there	there	PRON
cana-2697	18	12	has	have	AUX
cana-2697	18	13	been	be	AUX
cana-2697	18	14	a	a	DET
cana-2697	18	15	rise	rise	NOUN
cana-2697	18	16	in	in	ADP
cana-2697	18	17	sequential	sequential	ADJ
cana-2697	18	18	models	model	NOUN
cana-2697	18	19	within	within	ADP
cana-2697	18	20	the	the	DET
cana-2697	18	21	deep	deep	ADJ
cana-2697	18	22	learning	learning	NOUN
cana-2697	18	23	literature	literature	NOUN
cana-2697	18	24	,	,	PUNCT
cana-2697	18	25	such	such	ADJ
cana-2697	18	26	as	as	ADP
cana-2697	18	27	long	long	ADJ
cana-2697	18	28	short	short	ADJ
cana-2697	18	29	-	-	PUNCT
cana-2697	18	30	term	term	NOUN
cana-2697	18	31	memory	memory	NOUN
cana-2697	18	32	(	(	PUNCT
cana-2697	18	33	lstm	lstm	NOUN
cana-2697	18	34	)	)	PUNCT
cana-2697	18	35	and	and	CCONJ
cana-2697	18	36	gated	gate	VERB
cana-2697	18	37	recurrent	recurrent	ADJ
cana-2697	18	38	units	unit	NOUN
cana-2697	18	39	(	(	PUNCT
cana-2697	18	40	gru	gru	NOUN
cana-2697	18	41	)	)	PUNCT
cana-2697	18	42	,	,	PUNCT
cana-2697	18	43	that	that	PRON
cana-2697	18	44	are	be	AUX
cana-2697	18	45	capable	capable	ADJ
cana-2697	18	46	of	of	ADP
cana-2697	18	47	identifying	identify	VERB
cana-2697	18	48	temporal	temporal	ADJ
cana-2697	18	49	dependencies	dependency	NOUN
cana-2697	18	50	in	in	ADP
cana-2697	18	51	the	the	DET
cana-2697	18	52	data	datum	NOUN
cana-2697	18	53	[	[	X
cana-2697	18	54	8][9][10	8][9][10	NUM
cana-2697	18	55	]	]	PUNCT
cana-2697	18	56	.	.	PUNCT
cana-2697	19	1	but	but	CCONJ
cana-2697	19	2	despite	despite	SCONJ
cana-2697	19	3	some	some	DET
cana-2697	19	4	advantages	advantage	NOUN
cana-2697	19	5	,	,	PUNCT
cana-2697	19	6	these	these	DET
cana-2697	19	7	models	model	NOUN
cana-2697	19	8	tend	tend	VERB
cana-2697	19	9	to	to	PART
cana-2697	19	10	require	require	VERB
cana-2697	19	11	large	large	ADJ
cana-2697	19	12	computational	computational	ADJ
cana-2697	19	13	resources	resource	NOUN
cana-2697	19	14	and	and	CCONJ
cana-2697	19	15	overfit	overfit	NOUN
cana-2697	19	16	easily	easily	ADV
cana-2697	19	17	on	on	ADP
cana-2697	19	18	small	small	ADJ
cana-2697	19	19	or	or	CCONJ
cana-2697	19	20	medium	medium	ADJ
cana-2697	19	21	-	-	PUNCT
cana-2697	19	22	sized	sized	ADJ
cana-2697	19	23	datasets	dataset	NOUN
cana-2697	20	1	[	[	X
cana-2697	20	2	11][12	11][12	PROPN
cana-2697	20	3	]	]	X
cana-2697	20	4	.	.	PUNCT
cana-2697	21	1	such	such	ADJ
cana-2697	21	2	limitations	limitation	NOUN
cana-2697	21	3	have	have	AUX
cana-2697	21	4	prompted	prompt	VERB
cana-2697	21	5	the	the	DET
cana-2697	21	6	emergence	emergence	NOUN
cana-2697	21	7	of	of	ADP
cana-2697	21	8	hybrid	hybrid	ADJ
cana-2697	21	9	methods	method	NOUN
cana-2697	21	10	that	that	PRON
cana-2697	21	11	integrate	integrate	VERB
cana-2697	21	12	feature	feature	NOUN
cana-2697	21	13	extraction	extraction	NOUN
cana-2697	21	14	techniques	technique	NOUN
cana-2697	21	15	with	with	ADP
cana-2697	21	16	more	more	ADV
cana-2697	21	17	sophisticated	sophisticated	ADJ
cana-2697	21	18	sequence	sequence	NOUN
cana-2697	21	19	-	-	PUNCT
cana-2697	21	20	to	to	ADP
cana-2697	21	21	-	-	PUNCT
cana-2697	21	22	sequence	sequence	NOUN
cana-2697	21	23	models	model	NOUN
cana-2697	21	24	[	[	X
cana-2697	21	25	13][14	13][14	PROPN
cana-2697	21	26	]	]	PUNCT
cana-2697	21	27	.	.	PUNCT
cana-2697	22	1	ii	ii	PROPN
cana-2697	22	2	.	.	PUNCT
cana-2697	22	3	objectives	objective	NOUN
cana-2697	22	4	in	in	ADP
cana-2697	22	5	this	this	DET
cana-2697	22	6	thesis	thesis	NOUN
cana-2697	22	7	,	,	PUNCT
cana-2697	22	8	we	we	PRON
cana-2697	22	9	present	present	VERB
cana-2697	22	10	two	two	NUM
cana-2697	22	11	advanced	advanced	ADJ
cana-2697	22	12	hybrid	hybrid	NOUN
cana-2697	22	13	models	model	NOUN
cana-2697	22	14	for	for	ADP
cana-2697	22	15	do	do	AUX
cana-2697	22	16	prediction	prediction	NOUN
cana-2697	22	17	:	:	PUNCT
cana-2697	22	18	1	1	X
cana-2697	22	19	.	.	NUM
cana-2697	22	20	lightgbm	lightgbm	ADJ
cana-2697	22	21	-	-	PUNCT
cana-2697	22	22	bisru	bisru	NOUN
cana-2697	22	23	-	-	PUNCT
cana-2697	22	24	attention	attention	NOUN
cana-2697	22	25	:	:	PUNCT
cana-2697	22	26	utilizes	utilize	NOUN
cana-2697	22	27	lightgbm	lightgbm	VERB
cana-2697	22	28	for	for	ADP
cana-2697	22	29	feature	feature	NOUN
cana-2697	22	30	selection	selection	NOUN
cana-2697	22	31	,	,	PUNCT
cana-2697	22	32	bisru	bisru	NOUN
cana-2697	22	33	for	for	ADP
cana-2697	22	34	bidirectional	bidirectional	ADJ
cana-2697	22	35	sequence	sequence	NOUN
cana-2697	22	36	learning	learning	NOUN
cana-2697	22	37	,	,	PUNCT
cana-2697	22	38	and	and	CCONJ
cana-2697	22	39	attention	attention	NOUN
cana-2697	22	40	for	for	ADP
cana-2697	22	41	parameter	parameter	NOUN
cana-2697	22	42	optimization	optimization	NOUN
cana-2697	22	43	.	.	PUNCT
cana-2697	23	1	2	2	X
cana-2697	23	2	.	.	X
cana-2697	23	3	ensemble	ensemble	ADJ
cana-2697	23	4	mine	mine	NOUN
cana-2697	23	5	-	-	PUNCT
cana-2697	23	6	bisru	bisru	NOUN
cana-2697	23	7	-	-	PUNCT
cana-2697	23	8	attention	attention	NOUN
cana-2697	23	9	:	:	PUNCT
cana-2697	23	10	incorporates	incorporate	VERB
cana-2697	23	11	a	a	DET
cana-2697	23	12	more	more	ADV
cana-2697	23	13	sophisticated	sophisticated	ADJ
cana-2697	23	14	feature	feature	NOUN
cana-2697	23	15	selection	selection	NOUN
cana-2697	23	16	mechanism	mechanism	NOUN
cana-2697	23	17	through	through	ADP
cana-2697	23	18	mine	mine	NOUN
cana-2697	23	19	within	within	ADP
cana-2697	23	20	an	an	DET
cana-2697	23	21	ensemble	ensemble	ADJ
cana-2697	23	22	framework	framework	NOUN
cana-2697	23	23	to	to	PART
cana-2697	23	24	improve	improve	VERB
cana-2697	23	25	prediction	prediction	NOUN
cana-2697	23	26	performance	performance	NOUN
cana-2697	23	27	.	.	PUNCT
cana-2697	24	1	this	this	PRON
cana-2697	24	2	is	be	AUX
cana-2697	24	3	the	the	DET
cana-2697	24	4	first	first	ADJ
cana-2697	24	5	model	model	NOUN
cana-2697	24	6	to	to	PART
cana-2697	24	7	use	use	VERB
cana-2697	24	8	mine	mine	PRON
cana-2697	24	9	for	for	ADP
cana-2697	24	10	advanced	advanced	ADJ
cana-2697	24	11	feature	feature	NOUN
cana-2697	24	12	selection	selection	NOUN
cana-2697	24	13	while	while	SCONJ
cana-2697	24	14	combining	combine	VERB
cana-2697	24	15	algorithms	algorithm	NOUN
cana-2697	24	16	in	in	ADP
cana-2697	24	17	an	an	DET
cana-2697	24	18	ensemble	ensemble	ADJ
cana-2697	24	19	manner	manner	NOUN
cana-2697	24	20	.	.	PUNCT
cana-2697	25	1	the	the	DET
cana-2697	25	2	models	model	NOUN
cana-2697	25	3	are	be	AUX
cana-2697	25	4	tested	test	VERB
cana-2697	25	5	on	on	ADP
cana-2697	25	6	the	the	DET
cana-2697	25	7	kaggle	kaggle	ADJ
cana-2697	25	8	water	water	NOUN
cana-2697	25	9	quality	quality	NOUN
cana-2697	25	10	dataset	dataset	NOUN
cana-2697	25	11	and	and	CCONJ
cana-2697	25	12	evaluated	evaluate	VERB
cana-2697	25	13	using	use	VERB
cana-2697	25	14	metrics	metric	NOUN
cana-2697	25	15	such	such	ADJ
cana-2697	25	16	as	as	ADP
cana-2697	25	17	mean	mean	ADJ
cana-2697	25	18	square	square	ADJ
cana-2697	25	19	error	error	NOUN
cana-2697	25	20	(	(	PUNCT
cana-2697	25	21	mse	mse	NOUN
cana-2697	25	22	)	)	PUNCT
cana-2697	25	23	,	,	PUNCT
cana-2697	25	24	root	root	NOUN
cana-2697	25	25	mean	mean	VERB
cana-2697	25	26	square	square	ADJ
cana-2697	25	27	error	error	NOUN
cana-2697	25	28	(	(	PUNCT
cana-2697	25	29	rmse	rmse	NOUN
cana-2697	25	30	)	)	PUNCT
cana-2697	25	31	and	and	CCONJ
cana-2697	25	32	mean	mean	VERB
cana-2697	25	33	absolute	absolute	ADJ
cana-2697	25	34	error	error	NOUN
cana-2697	25	35	(	(	PUNCT
cana-2697	25	36	mae	mae	PROPN
cana-2697	25	37	)	)	PUNCT
cana-2697	25	38	.	.	PUNCT
cana-2697	26	1	iii	iii	X
cana-2697	26	2	.	.	PUNCT
cana-2697	27	1	literature	literature	PROPN
cana-2697	27	2	review	review	VERB
cana-2697	27	3	these	these	DET
cana-2697	27	4	days	day	NOUN
cana-2697	27	5	,	,	PUNCT
cana-2697	27	6	predicting	predict	VERB
cana-2697	27	7	do	do	AUX
cana-2697	27	8	concentration	concentration	NOUN
cana-2697	27	9	extends	extend	VERB
cana-2697	27	10	beyond	beyond	ADP
cana-2697	27	11	statistical	statistical	ADJ
cana-2697	27	12	approaches	approach	NOUN
cana-2697	27	13	,	,	PUNCT
cana-2697	27	14	with	with	ADP
cana-2697	27	15	the	the	DET
cana-2697	27	16	attention	attention	NOUN
cana-2697	27	17	of	of	ADP
cana-2697	27	18	researchers	researcher	NOUN
cana-2697	27	19	moving	move	VERB
cana-2697	27	20	toward	toward	ADP
cana-2697	27	21	machine	machine	NOUN
cana-2697	27	22	learning	learning	NOUN
cana-2697	27	23	and	and	CCONJ
cana-2697	27	24	deep	deep	ADJ
cana-2697	27	25	learning	learning	NOUN
cana-2697	27	26	methods	method	NOUN
cana-2697	27	27	.	.	PUNCT
cana-2697	28	1	several	several	ADJ
cana-2697	28	2	standard	standard	ADJ
cana-2697	28	3	definitions	definition	NOUN
cana-2697	28	4	in	in	ADP
cana-2697	28	5	this	this	DET
cana-2697	28	6	area	area	NOUN
cana-2697	28	7	are	be	AUX
cana-2697	28	8	listed	list	VERB
cana-2697	28	9	below	below	ADP
cana-2697	28	10	:	:	PUNCT
cana-2697	28	11	1	1	X
cana-2697	28	12	.	.	X
cana-2697	28	13	sana	sana	PROPN
cana-2697	28	14	et	et	PROPN
cana-2697	28	15	al	al	PROPN
cana-2697	28	16	.	.	PUNCT
cana-2697	29	1	[	[	X
cana-2697	29	2	7	7	NUM
cana-2697	29	3	]	]	X
cana-2697	29	4	:	:	PUNCT
cana-2697	29	5	integrated	integrated	ADJ
cana-2697	29	6	svm	svm	PROPN
cana-2697	29	7	with	with	ADP
cana-2697	29	8	ann	ann	PROPN
cana-2697	29	9	as	as	SCONJ
cana-2697	29	10	do	do	VERB
cana-2697	29	11	model	model	NOUN
cana-2697	29	12	for	for	ADP
cana-2697	29	13	aquaculture	aquaculture	NOUN
cana-2697	29	14	systems	system	NOUN
cana-2697	29	15	with	with	ADP
cana-2697	29	16	acceptable	acceptable	ADJ
cana-2697	29	17	accuracies	accuracy	NOUN
cana-2697	29	18	but	but	CCONJ
cana-2697	29	19	limited	limited	ADJ
cana-2697	29	20	scalability	scalability	NOUN
cana-2697	29	21	.	.	PUNCT
cana-2697	30	1	2	2	X
cana-2697	30	2	.	.	X
cana-2697	30	3	cao	cao	PROPN
cana-2697	30	4	et	et	PROPN
cana-2697	30	5	al	al	PROPN
cana-2697	30	6	.	.	PUNCT
cana-2697	31	1	[	[	X
cana-2697	31	2	16	16	NUM
cana-2697	31	3	]	]	PUNCT
cana-2697	31	4	:	:	PUNCT
cana-2697	31	5	formulated	formulate	VERB
cana-2697	31	6	a	a	DET
cana-2697	31	7	hybrid	hybrid	ADJ
cana-2697	31	8	extreme	extreme	ADJ
cana-2697	31	9	learning	learning	NOUN
cana-2697	31	10	machine	machine	NOUN
cana-2697	31	11	(	(	PUNCT
cana-2697	31	12	elm	elm	NOUN
cana-2697	31	13	)	)	PUNCT
cana-2697	31	14	model	model	NOUN
cana-2697	31	15	that	that	PRON
cana-2697	31	16	showed	show	VERB
cana-2697	31	17	elm	elm	NOUN
cana-2697	31	18	was	be	AUX
cana-2697	31	19	effective	effective	ADJ
cana-2697	31	20	in	in	ADP
cana-2697	31	21	aquatic	aquatic	ADJ
cana-2697	31	22	culture	culture	NOUN
cana-2697	31	23	data	datum	NOUN
cana-2697	31	24	.	.	PUNCT
cana-2697	32	1	3	3	X
cana-2697	32	2	.	.	X
cana-2697	32	3	ding	ding	NOUN
cana-2697	32	4	et	et	PROPN
cana-2697	32	5	al	al	PROPN
cana-2697	32	6	.	.	PUNCT
cana-2697	33	1	[	[	X
cana-2697	33	2	15	15	NUM
cana-2697	33	3	]	]	PUNCT
cana-2697	33	4	:	:	PUNCT
cana-2697	33	5	showed	show	VERB
cana-2697	33	6	the	the	DET
cana-2697	33	7	usefulness	usefulness	ADJ
cana-2697	33	8	bisru	bisru	NOUN
cana-2697	33	9	for	for	ADP
cana-2697	33	10	structural	structural	ADJ
cana-2697	33	11	and	and	CCONJ
cana-2697	33	12	sequential	sequential	ADJ
cana-2697	33	13	data	datum	NOUN
cana-2697	33	14	since	since	SCONJ
cana-2697	33	15	it	it	PRON
cana-2697	33	16	suffices	suffice	VERB
cana-2697	33	17	for	for	ADP
cana-2697	33	18	bidirectional	bidirectional	ADJ
cana-2697	33	19	sequence	sequence	NOUN
cana-2697	33	20	learning	learning	NOUN
cana-2697	33	21	.	.	PUNCT
cana-2697	34	1	4	4	X
cana-2697	34	2	.	.	X
cana-2697	34	3	liu	liu	PROPN
cana-2697	34	4	et	et	PROPN
cana-2697	34	5	al	al	PROPN
cana-2697	34	6	.	.	PUNCT
cana-2697	35	1	[	[	X
cana-2697	35	2	17	17	NUM
cana-2697	35	3	]	]	SYM
cana-2697	35	4	:	:	PUNCT
cana-2697	35	5	utilized	utilize	VERB
cana-2697	35	6	enhanced	enhance	VERB
cana-2697	35	7	gru	gru	PROPN
cana-2697	35	8	with	with	ADP
cana-2697	35	9	attention	attention	NOUN
cana-2697	35	10	for	for	ADP
cana-2697	35	11	temporal	temporal	ADJ
cana-2697	35	12	data	datum	NOUN
cana-2697	35	13	modelling	model	VERB
cana-2697	35	14	achieving	achieve	VERB
cana-2697	35	15	substantial	substantial	ADJ
cana-2697	35	16	improvements	improvement	NOUN
cana-2697	35	17	.	.	PUNCT
cana-2697	36	1	5	5	X
cana-2697	36	2	.	.	PUNCT
cana-2697	36	3	ahmed	ahmed	PROPN
cana-2697	36	4	et	et	PROPN
cana-2697	36	5	al	al	PROPN
cana-2697	37	1	[	[	X
cana-2697	37	2	21	21	NUM
cana-2697	37	3	]	]	PUNCT
cana-2697	37	4	:	:	PUNCT
cana-2697	37	5	studied	study	VERB
cana-2697	37	6	anfis	anfis	PROPN
cana-2697	37	7	,	,	PUNCT
cana-2697	37	8	specifically	specifically	ADV
cana-2697	37	9	,	,	PUNCT
cana-2697	37	10	feature	feature	NOUN
cana-2697	37	11	importance	importance	NOUN
cana-2697	37	12	,	,	PUNCT
cana-2697	37	13	on	on	ADP
cana-2697	37	14	small	small	ADJ
cana-2697	37	15	datasets	dataset	NOUN
cana-2697	37	16	.	.	PUNCT
cana-2697	38	1	6	6	X
cana-2697	38	2	.	.	X
cana-2697	38	3	wei	wei	PROPN
cana-2697	38	4	et	et	PROPN
cana-2697	38	5	al	al	PROPN
cana-2697	38	6	.	.	PUNCT
cana-2697	39	1	[	[	X
cana-2697	39	2	22	22	NUM
cana-2697	39	3	]	]	PUNCT
cana-2697	39	4	:	:	PUNCT
cana-2697	39	5	demonstrated	demonstrate	VERB
cana-2697	39	6	the	the	DET
cana-2697	39	7	use	use	NOUN
cana-2697	39	8	of	of	ADP
cana-2697	39	9	empirical	empirical	ADJ
cana-2697	39	10	mode	mode	NOUN
cana-2697	39	11	decomposition	decomposition	NOUN
cana-2697	39	12	(	(	PUNCT
cana-2697	39	13	emd	emd	PROPN
cana-2697	39	14	)	)	PUNCT
cana-2697	39	15	for	for	ADP
cana-2697	39	16	feature	feature	NOUN
cana-2697	39	17	extraction	extraction	NOUN
cana-2697	39	18	resulting	result	VERB
cana-2697	39	19	in	in	ADP
cana-2697	39	20	significant	significant	ADJ
cana-2697	39	21	improvement	improvement	NOUN
cana-2697	39	22	in	in	ADP
cana-2697	39	23	do	do	PROPN
cana-2697	39	24	prediction	prediction	NOUN
cana-2697	39	25	.	.	PUNCT
cana-2697	40	1	7	7	X
cana-2697	40	2	.	.	X
cana-2697	40	3	zhang	zhang	PROPN
cana-2697	40	4	et	et	PROPN
cana-2697	40	5	al	al	PROPN
cana-2697	40	6	.	.	PUNCT
cana-2697	41	1	[	[	X
cana-2697	41	2	20	20	NUM
cana-2697	41	3	]	]	PUNCT
cana-2697	41	4	:	:	PUNCT
cana-2697	41	5	proposed	propose	VERB
cana-2697	41	6	wavelet	wavelet	NOUN
cana-2697	41	7	decomposition	decomposition	NOUN
cana-2697	41	8	and	and	CCONJ
cana-2697	41	9	machine	machine	NOUN
cana-2697	41	10	learning	learning	NOUN
cana-2697	41	11	for	for	ADP
cana-2697	41	12	aquaculture	aquaculture	NOUN
cana-2697	41	13	high	high	ADJ
cana-2697	41	14	frequency	frequency	NOUN
cana-2697	41	15	data	datum	NOUN
cana-2697	41	16	.	.	PUNCT
cana-2697	42	1	8	8	X
cana-2697	42	2	.	.	X
cana-2697	43	1	wenjun	wenjun	PROPN
cana-2697	43	2	liu	liu	PROPN
cana-2697	43	3	et	et	PROPN
cana-2697	43	4	al	al	PROPN
cana-2697	43	5	.	.	PUNCT
cana-2697	44	1	[	[	X
cana-2697	44	2	26	26	NUM
cana-2697	44	3	]	]	X
cana-2697	44	4	:	:	PUNCT
cana-2697	44	5	combined	combined	ADJ
cana-2697	44	6	lightgbm	lightgbm	NOUN
cana-2697	44	7	and	and	CCONJ
cana-2697	44	8	bisru	bisru	VERB
cana-2697	44	9	for	for	ADP
cana-2697	44	10	dissolved	dissolve	VERB
cana-2697	44	11	oxygen	oxygen	NOUN
cana-2697	44	12	prediction	prediction	NOUN
cana-2697	44	13	and	and	CCONJ
cana-2697	44	14	further	far	ADV
cana-2697	44	15	showed	show	VERB
cana-2697	44	16	improvements	improvement	NOUN
cana-2697	44	17	in	in	ADP
cana-2697	44	18	accuracy	accuracy	NOUN
cana-2697	44	19	and	and	CCONJ
cana-2697	44	20	efficiency	efficiency	NOUN
cana-2697	44	21	of	of	ADP
cana-2697	44	22	prediction	prediction	NOUN
cana-2697	44	23	methods	method	NOUN
cana-2697	44	24	.	.	PUNCT
cana-2697	45	1	communications	communication	NOUN
cana-2697	45	2	on	on	ADP
cana-2697	45	3	applied	apply	VERB
cana-2697	45	4	nonlinear	nonlinear	ADJ
cana-2697	45	5	analysis	analysis	NOUN
cana-2697	45	6	issn	issn	NOUN
cana-2697	45	7	:	:	PUNCT
cana-2697	45	8	1074	1074	NUM
cana-2697	45	9	-	-	PUNCT
cana-2697	45	10	133x	133x	NUM
cana-2697	45	11	vol	vol	NOUN
cana-2697	45	12	32	32	NUM
cana-2697	45	13	no	no	NOUN
cana-2697	45	14	.	.	PUNCT
cana-2697	46	1	3s	3s	NUM
cana-2697	46	2	(	(	PUNCT
cana-2697	46	3	2025	2025	NUM
cana-2697	46	4	)	)	PUNCT
cana-2697	46	5	580	580	NUM
cana-2697	46	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	46	7	these	these	DET
cana-2697	46	8	studies	study	NOUN
cana-2697	46	9	allow	allow	VERB
cana-2697	46	10	selecting	select	VERB
cana-2697	46	11	the	the	DET
cana-2697	46	12	appropriate	appropriate	ADJ
cana-2697	46	13	features	feature	NOUN
cana-2697	46	14	and	and	CCONJ
cana-2697	46	15	applicable	applicable	ADJ
cana-2697	46	16	models	model	NOUN
cana-2697	46	17	for	for	ADP
cana-2697	46	18	sequence	sequence	NOUN
cana-2697	46	19	learning	learning	NOUN
cana-2697	46	20	and	and	CCONJ
cana-2697	46	21	prediction.nevertheless	prediction.nevertheless	NOUN
cana-2697	46	22	,	,	PUNCT
cana-2697	46	23	it	it	PRON
cana-2697	46	24	can	can	AUX
cana-2697	46	25	be	be	AUX
cana-2697	46	26	argued	argue	VERB
cana-2697	46	27	that	that	SCONJ
cana-2697	46	28	the	the	DET
cana-2697	46	29	specific	specific	ADJ
cana-2697	46	30	methods	method	NOUN
cana-2697	46	31	previously	previously	ADV
cana-2697	46	32	discussed	discuss	VERB
cana-2697	46	33	are	be	AUX
cana-2697	46	34	not	not	PART
cana-2697	46	35	scalable	scalable	ADJ
cana-2697	46	36	and	and	CCONJ
cana-2697	46	37	do	do	AUX
cana-2697	46	38	not	not	PART
cana-2697	46	39	perform	perform	VERB
cana-2697	46	40	well	well	ADV
cana-2697	46	41	in	in	ADP
cana-2697	46	42	case	case	NOUN
cana-2697	46	43	of	of	ADP
cana-2697	46	44	high	high	ADJ
cana-2697	46	45	dimensional	dimensional	ADJ
cana-2697	46	46	data	datum	NOUN
cana-2697	46	47	,	,	PUNCT
cana-2697	46	48	which	which	PRON
cana-2697	46	49	calls	call	VERB
cana-2697	46	50	for	for	ADP
cana-2697	46	51	the	the	DET
cana-2697	46	52	integration	integration	NOUN
cana-2697	46	53	of	of	ADP
cana-2697	46	54	methods	method	NOUN
cana-2697	46	55	such	such	ADJ
cana-2697	46	56	as	as	ADP
cana-2697	46	57	ensemble	ensemble	ADJ
cana-2697	46	58	mine	mine	NOUN
cana-2697	46	59	-	-	PUNCT
cana-2697	46	60	bisru	bisru	NOUN
cana-2697	46	61	-	-	PUNCT
cana-2697	46	62	attention	attention	NOUN
cana-2697	46	63	.	.	PUNCT
cana-2697	47	1	table	table	NOUN
cana-2697	47	2	1.summary	1.summary	NUM
cana-2697	47	3	table	table	NOUN
cana-2697	47	4	:	:	PUNCT
cana-2697	47	5	author	author	NOUN
cana-2697	47	6	year	year	NOUN
cana-2697	47	7	method	method	NOUN
cana-2697	47	8	methodology	methodology	NOUN
cana-2697	47	9	dataset	dataset	VERB
cana-2697	47	10	positives	positive	NOUN
cana-2697	47	11	negatives	negative	NOUN
cana-2697	47	12	sana	sana	PROPN
cana-2697	47	13	et	et	PROPN
cana-2697	47	14	al	al	PROPN
cana-2697	47	15	.	.	PROPN
cana-2697	47	16	2021	2021	NUM
cana-2697	47	17	svm	svm	PROPN
cana-2697	47	18	-	-	ADJ
cana-2697	47	19	ann	ann	ADJ
cana-2697	47	20	combined	combine	VERB
cana-2697	47	21	machine	machine	NOUN
cana-2697	47	22	learning	learn	VERB
cana-2697	47	23	techniques	technique	NOUN
cana-2697	47	24	custom	custom	NOUN
cana-2697	47	25	dataset	dataset	VERB
cana-2697	47	26	improved	improve	VERB
cana-2697	47	27	accuracy	accuracy	NOUN
cana-2697	47	28	over	over	ADP
cana-2697	47	29	standalone	standalone	ADJ
cana-2697	47	30	methods	method	NOUN
cana-2697	47	31	limited	limit	VERB
cana-2697	47	32	scalability	scalability	NOUN
cana-2697	47	33	cao	cao	PROPN
cana-2697	47	34	et	et	PROPN
cana-2697	47	35	al	al	PROPN
cana-2697	47	36	.	.	PROPN
cana-2697	47	37	2019	2019	NUM
cana-2697	47	38	extreme	extreme	ADJ
cana-2697	47	39	learning	learning	NOUN
cana-2697	47	40	machine	machine	NOUN
cana-2697	47	41	hybrid	hybrid	NOUN
cana-2697	47	42	modelling	modelling	NOUN
cana-2697	47	43	for	for	ADP
cana-2697	47	44	feature	feature	NOUN
cana-2697	47	45	selection	selection	NOUN
cana-2697	47	46	aquaculture	aquaculture	NOUN
cana-2697	47	47	dataset	dataset	NOUN
cana-2697	47	48	precision	precision	NOUN
cana-2697	47	49	in	in	ADP
cana-2697	47	50	feature	feature	NOUN
cana-2697	47	51	handling	handle	VERB
cana-2697	47	52	computational	computational	ADJ
cana-2697	47	53	inefficiencies	inefficiency	NOUN
cana-2697	47	54	ding	de	VERB
cana-2697	47	55	et	et	PROPN
cana-2697	47	56	al	al	PROPN
cana-2697	47	57	.	.	PROPN
cana-2697	47	58	2018	2018	NUM
cana-2697	47	59	bisru	bisru	PROPN
cana-2697	47	60	bidirectional	bidirectional	ADJ
cana-2697	47	61	sequence	sequence	NOUN
cana-2697	47	62	modelling	model	VERB
cana-2697	47	63	simulation	simulation	NOUN
cana-2697	47	64	data	datum	NOUN
cana-2697	47	65	effective	effective	ADJ
cana-2697	47	66	for	for	ADP
cana-2697	47	67	sequential	sequential	ADJ
cana-2697	47	68	data	datum	NOUN
cana-2697	47	69	limited	limit	VERB
cana-2697	47	70	real	real	ADJ
cana-2697	47	71	world	world	NOUN
cana-2697	47	72	validation	validation	PROPN
cana-2697	47	73	liu	liu	PROPN
cana-2697	47	74	et	et	PROPN
cana-2697	47	75	al	al	PROPN
cana-2697	47	76	.	.	PROPN
cana-2697	47	77	2020	2020	NUM
cana-2697	47	78	gru	gru	VERB
cana-2697	47	79	+	+	CCONJ
cana-2697	47	80	attention	attention	NOUN
cana-2697	47	81	temporal	temporal	ADJ
cana-2697	47	82	feature	feature	NOUN
cana-2697	47	83	modelling	model	VERB
cana-2697	47	84	small	small	ADJ
cana-2697	47	85	aquaculture	aquaculture	NOUN
cana-2697	47	86	dataset	dataset	NOUN
cana-2697	47	87	enhanced	enhance	VERB
cana-2697	47	88	temporal	temporal	ADJ
cana-2697	47	89	dependency	dependency	NOUN
cana-2697	47	90	handling	handling	NOUN
cana-2697	47	91	prone	prone	ADJ
cana-2697	47	92	to	to	ADP
cana-2697	47	93	overfitting	overfitte	VERB
cana-2697	47	94	ahmed	ahmed	PROPN
cana-2697	47	95	et	et	PROPN
cana-2697	47	96	al	al	PROPN
cana-2697	47	97	.	.	PROPN
cana-2697	47	98	2017	2017	NUM
cana-2697	47	99	neuro	neuro	NOUN
cana-2697	47	100	-	-	PUNCT
cana-2697	47	101	fuzzy	fuzzy	ADJ
cana-2697	47	102	inference	inference	NOUN
cana-2697	47	103	adaptive	adaptive	ADJ
cana-2697	47	104	model	model	NOUN
cana-2697	47	105	for	for	ADP
cana-2697	47	106	small	small	ADJ
cana-2697	47	107	datasets	dataset	NOUN
cana-2697	47	108	small	small	ADJ
cana-2697	47	109	aquaculture	aquaculture	NOUN
cana-2697	47	110	dataset	dataset	VERB
cana-2697	47	111	flexible	flexible	ADJ
cana-2697	47	112	and	and	CCONJ
cana-2697	47	113	adaptable	adaptable	ADJ
cana-2697	47	114	requires	require	VERB
cana-2697	47	115	extensive	extensive	ADJ
cana-2697	47	116	tuning	tuning	NOUN
cana-2697	47	117	wei	wei	PROPN
cana-2697	47	118	et	et	PROPN
cana-2697	47	119	al	al	PROPN
cana-2697	47	120	.	.	PROPN
cana-2697	47	121	2020	2020	NUM
cana-2697	47	122	emd	emd	PROPN
cana-2697	47	123	feature	feature	NOUN
cana-2697	47	124	extraction	extraction	NOUN
cana-2697	47	125	custom	custom	NOUN
cana-2697	47	126	dataset	dataset	VERB
cana-2697	47	127	improved	improve	VERB
cana-2697	47	128	feature	feature	NOUN
cana-2697	47	129	precision	precision	NOUN
cana-2697	47	130	complex	complex	ADJ
cana-2697	47	131	preprocessing	preprocessing	NOUN
cana-2697	47	132	pipeline	pipeline	NOUN
cana-2697	47	133	zhang	zhang	PROPN
cana-2697	47	134	et	et	PROPN
cana-2697	47	135	al	al	PROPN
cana-2697	47	136	.	.	PROPN
cana-2697	47	137	2021	2021	NUM
cana-2697	47	138	wavelet	wavelet	NOUN
cana-2697	47	139	decomposition	decomposition	NOUN
cana-2697	47	140	high	high	ADJ
cana-2697	47	141	frequency	frequency	NOUN
cana-2697	47	142	data	datum	NOUN
cana-2697	47	143	handling	handle	VERB
cana-2697	47	144	high	high	ADJ
cana-2697	47	145	frequency	frequency	NOUN
cana-2697	47	146	aquaculture	aquaculture	NOUN
cana-2697	47	147	effective	effective	ADJ
cana-2697	47	148	for	for	ADP
cana-2697	47	149	high	high	ADJ
cana-2697	47	150	frequency	frequency	NOUN
cana-2697	47	151	datasets	dataset	NOUN
cana-2697	47	152	high	high	ADJ
cana-2697	47	153	computational	computational	ADJ
cana-2697	47	154	cost	cost	NOUN
cana-2697	47	155	wenjun	wenjun	NOUN
cana-2697	47	156	liu	liu	PROPN
cana-2697	47	157	et	et	PROPN
cana-2697	47	158	al	al	PROPN
cana-2697	47	159	2023	2023	NUM
cana-2697	47	160	lightgbm	lightgbm	VERB
cana-2697	47	161	+	+	CCONJ
cana-2697	47	162	bisru	bisru	NOUN
cana-2697	47	163	hybrid	hybrid	NOUN
cana-2697	47	164	do	do	AUX
cana-2697	47	165	prediction	prediction	NOUN
cana-2697	47	166	model	model	NOUN
cana-2697	47	167	water	water	NOUN
cana-2697	47	168	quality	quality	NOUN
cana-2697	47	169	dataset	dataset	NOUN
cana-2697	47	170	accuracy	accuracy	NOUN
cana-2697	47	171	improvement	improvement	NOUN
cana-2697	47	172	with	with	ADP
cana-2697	47	173	feature	feature	NOUN
cana-2697	47	174	selection	selection	NOUN
cana-2697	47	175	limited	limit	VERB
cana-2697	47	176	exploration	exploration	NOUN
cana-2697	47	177	of	of	ADP
cana-2697	47	178	alternative	alternative	ADJ
cana-2697	47	179	datasets	dataset	NOUN
cana-2697	47	180	iv	iv	NOUN
cana-2697	47	181	.	.	PUNCT
cana-2697	47	182	model	model	PROPN
cana-2697	47	183	diagram	diagram	PROPN
cana-2697	47	184	figure	figure	NOUN
cana-2697	47	185	1	1	NUM
cana-2697	47	186	:	:	PUNCT
cana-2697	47	187	structural	structural	ADJ
cana-2697	47	188	composition	composition	NOUN
cana-2697	47	189	of	of	ADP
cana-2697	47	190	the	the	DET
cana-2697	47	191	ensemble	ensemble	ADJ
cana-2697	47	192	mine	mine	NOUN
cana-2697	47	193	-	-	PUNCT
cana-2697	47	194	bisru	bisru	NOUN
cana-2697	47	195	-	-	PUNCT
cana-2697	47	196	attention	attention	NOUN
cana-2697	47	197	network	network	NOUN
cana-2697	47	198	model	model	NOUN
cana-2697	47	199	.	.	PUNCT
cana-2697	48	1	(	(	PUNCT
cana-2697	48	2	include	include	VERB
cana-2697	48	3	a	a	DET
cana-2697	48	4	workflow	workflow	NOUN
cana-2697	48	5	diagram	diagram	NOUN
cana-2697	48	6	showing	show	VERB
cana-2697	48	7	the	the	DET
cana-2697	48	8	synergistic	synergistic	ADJ
cana-2697	48	9	role	role	NOUN
cana-2697	48	10	of	of	ADP
cana-2697	48	11	mic	mic	ADJ
cana-2697	48	12	,	,	PUNCT
cana-2697	48	13	bisru	bisru	NOUN
cana-2697	48	14	,	,	PUNCT
cana-2697	48	15	attention	attention	NOUN
cana-2697	48	16	mechanisms	mechanism	NOUN
cana-2697	48	17	,	,	PUNCT
cana-2697	48	18	and	and	CCONJ
cana-2697	48	19	ensemble	ensemble	ADJ
cana-2697	48	20	modelling	modelling	NOUN
cana-2697	48	21	.	.	PUNCT
cana-2697	48	22	)	)	PUNCT
cana-2697	49	1	communications	communication	NOUN
cana-2697	49	2	on	on	ADP
cana-2697	49	3	applied	apply	VERB
cana-2697	49	4	nonlinear	nonlinear	ADJ
cana-2697	49	5	analysis	analysis	NOUN
cana-2697	49	6	issn	issn	NOUN
cana-2697	49	7	:	:	PUNCT
cana-2697	49	8	1074	1074	NUM
cana-2697	49	9	-	-	PUNCT
cana-2697	49	10	133x	133x	NUM
cana-2697	49	11	vol	vol	NOUN
cana-2697	49	12	32	32	NUM
cana-2697	49	13	no	no	NOUN
cana-2697	49	14	.	.	PUNCT
cana-2697	50	1	3s	3s	NUM
cana-2697	50	2	(	(	PUNCT
cana-2697	50	3	2025	2025	NUM
cana-2697	50	4	)	)	PUNCT
cana-2697	50	5	581	581	NUM
cana-2697	50	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	50	7	figure	figure	NOUN
cana-2697	50	8	.1	.1	PROPN
cana-2697	50	9	ensemble	ensemble	ADJ
cana-2697	50	10	mine	mine	NOUN
cana-2697	50	11	-	-	PUNCT
cana-2697	50	12	bisru	bisru	NOUN
cana-2697	50	13	-	-	PUNCT
cana-2697	50	14	attention	attention	NOUN
cana-2697	50	15	network	network	NOUN
cana-2697	50	16	model	model	NOUN
cana-2697	50	17	the	the	DET
cana-2697	50	18	architecture	architecture	NOUN
cana-2697	50	19	of	of	ADP
cana-2697	50	20	the	the	DET
cana-2697	50	21	ensemble	ensemble	ADJ
cana-2697	50	22	mine	mine	NOUN
cana-2697	50	23	-	-	PUNCT
cana-2697	50	24	bisru	bisru	NOUN
cana-2697	50	25	-	-	PUNCT
cana-2697	50	26	attention	attention	NOUN
cana-2697	50	27	network	network	NOUN
cana-2697	50	28	model	model	NOUN
cana-2697	50	29	is	be	AUX
cana-2697	50	30	depicted	depict	VERB
cana-2697	50	31	in	in	ADP
cana-2697	50	32	figure	figure	NOUN
cana-2697	50	33	1	1	NUM
cana-2697	50	34	for	for	ADP
cana-2697	50	35	the	the	DET
cana-2697	50	36	prediction	prediction	NOUN
cana-2697	50	37	of	of	ADP
cana-2697	50	38	the	the	DET
cana-2697	50	39	concentration	concentration	NOUN
cana-2697	50	40	of	of	ADP
cana-2697	50	41	dissolved	dissolved	ADJ
cana-2697	50	42	oxygen	oxygen	NOUN
cana-2697	50	43	(	(	PUNCT
cana-2697	50	44	do	do	AUX
cana-2697	50	45	)	)	PUNCT
cana-2697	50	46	in	in	ADP
cana-2697	50	47	aquaculture	aquaculture	NOUN
cana-2697	50	48	systems	system	NOUN
cana-2697	50	49	.	.	PUNCT
cana-2697	51	1	the	the	DET
cana-2697	51	2	water	water	NOUN
cana-2697	51	3	quality	quality	NOUN
cana-2697	51	4	dataset	dataset	NOUN
cana-2697	51	5	,	,	PUNCT
cana-2697	51	6	the	the	DET
cana-2697	51	7	first	first	ADJ
cana-2697	51	8	stage	stage	NOUN
cana-2697	51	9	,	,	PUNCT
cana-2697	51	10	is	be	AUX
cana-2697	51	11	normalized	normalize	VERB
cana-2697	51	12	and	and	CCONJ
cana-2697	51	13	then	then	ADV
cana-2697	51	14	passed	pass	VERB
cana-2697	51	15	to	to	PART
cana-2697	51	16	feature	feature	VERB
cana-2697	51	17	selection	selection	NOUN
cana-2697	51	18	processes	process	NOUN
cana-2697	51	19	.	.	PUNCT
cana-2697	52	1	the	the	DET
cana-2697	52	2	selection	selection	NOUN
cana-2697	52	3	of	of	ADP
cana-2697	52	4	features	feature	NOUN
cana-2697	52	5	is	be	AUX
cana-2697	52	6	done	do	VERB
cana-2697	52	7	by	by	ADP
cana-2697	52	8	employing	employ	VERB
cana-2697	52	9	lightgbm	lightgbm	NOUN
cana-2697	52	10	for	for	ADP
cana-2697	52	11	ranking	ranking	NOUN
cana-2697	52	12	and	and	CCONJ
cana-2697	52	13	mic	mic	ADJ
cana-2697	52	14	to	to	PART
cana-2697	52	15	identify	identify	VERB
cana-2697	52	16	features	feature	NOUN
cana-2697	52	17	that	that	PRON
cana-2697	52	18	are	be	AUX
cana-2697	52	19	not	not	PART
cana-2697	52	20	linear	linear	ADJ
cana-2697	52	21	combinations	combination	NOUN
cana-2697	52	22	,	,	PUNCT
cana-2697	52	23	and	and	CCONJ
cana-2697	52	24	thus	thus	ADV
cana-2697	52	25	contain	contain	VERB
cana-2697	52	26	adequate	adequate	ADJ
cana-2697	52	27	redundancy	redundancy	NOUN
cana-2697	52	28	,	,	PUNCT
cana-2697	52	29	ensuring	ensure	VERB
cana-2697	52	30	that	that	SCONJ
cana-2697	52	31	only	only	ADV
cana-2697	52	32	a	a	DET
cana-2697	52	33	few	few	ADJ
cana-2697	52	34	features	feature	NOUN
cana-2697	52	35	predictive	predictive	NOUN
cana-2697	52	36	of	of	ADP
cana-2697	52	37	the	the	DET
cana-2697	52	38	target	target	NOUN
cana-2697	52	39	are	be	AUX
cana-2697	52	40	left	leave	VERB
cana-2697	52	41	[	[	X
cana-2697	52	42	12][13	12][13	PROPN
cana-2697	52	43	]	]	PUNCT
cana-2697	52	44	.	.	PUNCT
cana-2697	53	1	then	then	ADV
cana-2697	53	2	the	the	DET
cana-2697	53	3	dataset	dataset	NOUN
cana-2697	53	4	is	be	AUX
cana-2697	53	5	divided	divide	VERB
cana-2697	53	6	into	into	ADP
cana-2697	53	7	k	k	ADJ
cana-2697	53	8	-	-	ADJ
cana-2697	53	9	fold	fold	ADJ
cana-2697	53	10	training	training	NOUN
cana-2697	53	11	and	and	CCONJ
cana-2697	53	12	test	test	NOUN
cana-2697	53	13	datasets	dataset	NOUN
cana-2697	53	14	increasing	increase	VERB
cana-2697	53	15	the	the	DET
cana-2697	53	16	reliability	reliability	NOUN
cana-2697	53	17	of	of	ADP
cana-2697	53	18	the	the	DET
cana-2697	53	19	model	model	NOUN
cana-2697	53	20	predictions	prediction	NOUN
cana-2697	53	21	and	and	CCONJ
cana-2697	53	22	its	its	PRON
cana-2697	53	23	generalizability	generalizability	NOUN
cana-2697	53	24	.	.	PUNCT
cana-2697	54	1	in	in	ADP
cana-2697	54	2	the	the	DET
cana-2697	54	3	model	model	NOUN
cana-2697	54	4	-	-	PUNCT
cana-2697	54	5	building	building	NOUN
cana-2697	54	6	phase	phase	NOUN
cana-2697	54	7	,	,	PUNCT
cana-2697	54	8	attention	attention	NOUN
cana-2697	54	9	is	be	AUX
cana-2697	54	10	given	give	VERB
cana-2697	54	11	to	to	ADP
cana-2697	54	12	the	the	DET
cana-2697	54	13	ensemble	ensemble	ADJ
cana-2697	54	14	lightgbm	lightgbm	ADJ
cana-2697	54	15	-	-	PUNCT
cana-2697	54	16	bisru	bisru	NOUN
cana-2697	54	17	-	-	PUNCT
cana-2697	54	18	attention	attention	NOUN
cana-2697	54	19	and	and	CCONJ
cana-2697	54	20	ensemble	ensemble	ADJ
cana-2697	54	21	mine	mine	NOUN
cana-2697	54	22	-	-	PUNCT
cana-2697	54	23	bisru	bisru	NOUN
cana-2697	54	24	-	-	PUNCT
cana-2697	54	25	attention	attention	NOUN
cana-2697	54	26	models	model	NOUN
cana-2697	54	27	.	.	PUNCT
cana-2697	55	1	some	some	PRON
cana-2697	55	2	of	of	ADP
cana-2697	55	3	the	the	DET
cana-2697	55	4	aspects	aspect	NOUN
cana-2697	55	5	of	of	ADP
cana-2697	55	6	the	the	DET
cana-2697	55	7	models	model	NOUN
cana-2697	55	8	such	such	ADJ
cana-2697	55	9	as	as	ADP
cana-2697	55	10	bisru	bisru	NOUN
cana-2697	55	11	designed	design	VERB
cana-2697	55	12	to	to	PART
cana-2697	55	13	support	support	VERB
cana-2697	55	14	bidirectional	bidirectional	ADJ
cana-2697	55	15	learning	learning	NOUN
cana-2697	55	16	and	and	CCONJ
cana-2697	55	17	attention	attention	NOUN
cana-2697	55	18	mechanisms	mechanism	NOUN
cana-2697	55	19	that	that	PRON
cana-2697	55	20	control	control	VERB
cana-2697	55	21	parameter	parameter	NOUN
cana-2697	55	22	tuning	tune	VERB
cana-2697	55	23	over	over	ADP
cana-2697	55	24	time	time	NOUN
cana-2697	55	25	are	be	AUX
cana-2697	55	26	retained	retain	VERB
cana-2697	55	27	for	for	ADP
cana-2697	55	28	future	future	ADJ
cana-2697	55	29	validation	validation	NOUN
cana-2697	55	30	[	[	X
cana-2697	55	31	15	15	NUM
cana-2697	55	32	]	]	PUNCT
cana-2697	55	33	.	.	PUNCT
cana-2697	56	1	the	the	DET
cana-2697	56	2	last	last	ADJ
cana-2697	56	3	task	task	NOUN
cana-2697	56	4	is	be	AUX
cana-2697	56	5	the	the	DET
cana-2697	56	6	evaluation	evaluation	NOUN
cana-2697	56	7	of	of	ADP
cana-2697	56	8	the	the	DET
cana-2697	56	9	predictive	predictive	ADJ
cana-2697	56	10	power	power	NOUN
cana-2697	56	11	of	of	ADP
cana-2697	56	12	the	the	DET
cana-2697	56	13	trained	train	VERB
cana-2697	56	14	model	model	NOUN
cana-2697	56	15	,	,	PUNCT
cana-2697	56	16	it	it	PRON
cana-2697	56	17	accuracy	accuracy	VERB
cana-2697	56	18	being	be	AUX
cana-2697	56	19	measured	measure	VERB
cana-2697	56	20	in	in	ADP
cana-2697	56	21	terms	term	NOUN
cana-2697	56	22	of	of	ADP
cana-2697	56	23	mse	mse	NOUN
cana-2697	56	24	,	,	PUNCT
cana-2697	56	25	rmse	rmse	NOUN
cana-2697	56	26	and	and	CCONJ
cana-2697	56	27	mae	mae	PROPN
cana-2697	56	28	among	among	ADP
cana-2697	56	29	others	other	NOUN
cana-2697	56	30	.	.	PUNCT
cana-2697	57	1	the	the	DET
cana-2697	57	2	targets	target	NOUN
cana-2697	57	3	of	of	ADP
cana-2697	57	4	the	the	DET
cana-2697	57	5	predicted	predict	VERB
cana-2697	57	6	outputs	output	NOUN
cana-2697	57	7	are	be	AUX
cana-2697	57	8	the	the	DET
cana-2697	57	9	dissolved	dissolved	ADJ
cana-2697	57	10	oxygen	oxygen	NOUN
cana-2697	57	11	levels	level	NOUN
cana-2697	57	12	,	,	PUNCT
cana-2697	57	13	and	and	CCONJ
cana-2697	57	14	this	this	PRON
cana-2697	57	15	makes	make	VERB
cana-2697	57	16	that	that	PRON
cana-2697	57	17	completes	complete	VERB
cana-2697	57	18	the	the	DET
cana-2697	57	19	cycle	cycle	NOUN
cana-2697	57	20	.	.	PUNCT
cana-2697	58	1	this	this	DET
cana-2697	58	2	structure	structure	NOUN
cana-2697	58	3	on	on	ADP
cana-2697	58	4	the	the	DET
cana-2697	58	5	other	other	ADJ
cana-2697	58	6	hand	hand	NOUN
cana-2697	58	7	allow	allow	VERB
cana-2697	58	8	for	for	ADP
cana-2697	58	9	a	a	DET
cana-2697	58	10	perfect	perfect	ADJ
cana-2697	58	11	blend	blend	NOUN
cana-2697	58	12	of	of	ADP
cana-2697	58	13	a	a	DET
cana-2697	58	14	feature	feature	NOUN
cana-2697	58	15	selection	selection	NOUN
cana-2697	58	16	,	,	PUNCT
cana-2697	58	17	advanced	advanced	ADJ
cana-2697	58	18	learning	learning	NOUN
cana-2697	58	19	and	and	CCONJ
cana-2697	58	20	attention	attention	NOUN
cana-2697	58	21	levels	level	NOUN
cana-2697	58	22	in	in	ADP
cana-2697	58	23	the	the	DET
cana-2697	58	24	quest	quest	NOUN
cana-2697	58	25	for	for	ADP
cana-2697	58	26	improved	improved	ADJ
cana-2697	58	27	prediction	prediction	NOUN
cana-2697	58	28	accuracy	accuracy	NOUN
cana-2697	58	29	and	and	CCONJ
cana-2697	58	30	reliability	reliability	NOUN
cana-2697	58	31	which	which	PRON
cana-2697	58	32	can	can	AUX
cana-2697	58	33	be	be	AUX
cana-2697	58	34	used	use	VERB
cana-2697	58	35	in	in	ADP
cana-2697	58	36	practice	practice	NOUN
cana-2697	58	37	within	within	ADP
cana-2697	58	38	the	the	DET
cana-2697	58	39	aquaculture	aquaculture	NOUN
cana-2697	58	40	settings	setting	NOUN
cana-2697	58	41	.	.	PUNCT
cana-2697	59	1	v.	v.	ADP
cana-2697	59	2	proposed	propose	VERB
cana-2697	59	3	work	work	NOUN
cana-2697	59	4	in	in	ADP
cana-2697	59	5	particular	particular	ADJ
cana-2697	59	6	,	,	PUNCT
cana-2697	59	7	this	this	DET
cana-2697	59	8	work	work	NOUN
cana-2697	59	9	tackles	tackle	VERB
cana-2697	59	10	the	the	DET
cana-2697	59	11	problem	problem	NOUN
cana-2697	59	12	of	of	ADP
cana-2697	59	13	anticipating	anticipate	VERB
cana-2697	59	14	dissolved	dissolve	VERB
cana-2697	59	15	oxygen	oxygen	NOUN
cana-2697	59	16	(	(	PUNCT
cana-2697	59	17	do	do	AUX
cana-2697	59	18	)	)	PUNCT
cana-2697	59	19	in	in	ADP
cana-2697	59	20	highly	highly	ADV
cana-2697	59	21	variable	variable	ADJ
cana-2697	59	22	water	water	NOUN
cana-2697	59	23	systems	system	NOUN
cana-2697	59	24	by	by	ADP
cana-2697	59	25	employing	employ	VERB
cana-2697	59	26	hybrid	hybrid	ADJ
cana-2697	59	27	models	model	NOUN
cana-2697	59	28	,	,	PUNCT
cana-2697	59	29	which	which	PRON
cana-2697	59	30	emphasize	emphasize	VERB
cana-2697	59	31	,	,	PUNCT
cana-2697	59	32	among	among	ADP
cana-2697	59	33	other	other	ADJ
cana-2697	59	34	things	thing	NOUN
cana-2697	59	35	,	,	PUNCT
cana-2697	59	36	feature	feature	NOUN
cana-2697	59	37	selection	selection	NOUN
cana-2697	59	38	,	,	PUNCT
cana-2697	59	39	sequence	sequence	NOUN
cana-2697	59	40	learning	learning	NOUN
cana-2697	59	41	,	,	PUNCT
cana-2697	59	42	and	and	CCONJ
cana-2697	59	43	improved	improved	ADJ
cana-2697	59	44	prediction	prediction	NOUN
cana-2697	59	45	accuracy	accuracy	NOUN
cana-2697	59	46	.	.	PUNCT
cana-2697	60	1	this	this	DET
cana-2697	60	2	proposed	propose	VERB
cana-2697	60	3	integrated	integrate	VERB
cana-2697	60	4	model	model	NOUN
cana-2697	60	5	will	will	AUX
cana-2697	60	6	attempt	attempt	VERB
cana-2697	60	7	to	to	PART
cana-2697	60	8	enhance	enhance	VERB
cana-2697	60	9	existing	exist	VERB
cana-2697	60	10	processes	process	NOUN
cana-2697	60	11	by	by	ADP
cana-2697	60	12	utilizing	utilize	VERB
cana-2697	60	13	new	new	ADJ
cana-2697	60	14	methodologies	methodology	NOUN
cana-2697	60	15	together	together	ADV
cana-2697	60	16	with	with	ADP
cana-2697	60	17	the	the	DET
cana-2697	60	18	advancements	advancement	NOUN
cana-2697	60	19	gained	gain	VERB
cana-2697	60	20	in	in	ADP
cana-2697	60	21	communications	communication	NOUN
cana-2697	60	22	on	on	ADP
cana-2697	60	23	applied	apply	VERB
cana-2697	60	24	nonlinear	nonlinear	ADJ
cana-2697	60	25	analysis	analysis	NOUN
cana-2697	60	26	issn	issn	NOUN
cana-2697	60	27	:	:	PUNCT
cana-2697	60	28	1074	1074	NUM
cana-2697	60	29	-	-	PUNCT
cana-2697	60	30	133x	133x	NUM
cana-2697	60	31	vol	vol	NOUN
cana-2697	60	32	32	32	NUM
cana-2697	60	33	no	no	NOUN
cana-2697	60	34	.	.	PUNCT
cana-2697	61	1	3s	3s	NUM
cana-2697	61	2	(	(	PUNCT
cana-2697	61	3	2025	2025	NUM
cana-2697	61	4	)	)	PUNCT
cana-2697	61	5	582	582	NUM
cana-2697	61	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	61	7	machine	machine	NOUN
cana-2697	61	8	learning	learning	NOUN
cana-2697	61	9	and	and	CCONJ
cana-2697	61	10	deep	deep	ADJ
cana-2697	61	11	learning	learning	NOUN
cana-2697	61	12	.	.	PUNCT
cana-2697	62	1	they	they	PRON
cana-2697	62	2	are	be	AUX
cana-2697	62	3	the	the	DET
cana-2697	62	4	models	model	NOUN
cana-2697	62	5	,	,	PUNCT
cana-2697	62	6	which	which	PRON
cana-2697	62	7	discern	discern	VERB
cana-2697	62	8	the	the	DET
cana-2697	62	9	most	most	ADV
cana-2697	62	10	important	important	ADJ
cana-2697	62	11	elements	element	NOUN
cana-2697	62	12	needed	need	VERB
cana-2697	62	13	for	for	ADP
cana-2697	62	14	do	do	AUX
cana-2697	62	15	forecasting	forecast	VERB
cana-2697	62	16	potentially	potentially	ADV
cana-2697	62	17	eliminating	eliminate	VERB
cana-2697	62	18	noise	noise	NOUN
cana-2697	62	19	and	and	CCONJ
cana-2697	62	20	enhancing	enhance	VERB
cana-2697	62	21	the	the	DET
cana-2697	62	22	robustness	robustness	NOUN
cana-2697	62	23	of	of	ADP
cana-2697	62	24	the	the	DET
cana-2697	62	25	models	model	NOUN
cana-2697	62	26	in	in	ADP
cana-2697	62	27	prediction	prediction	NOUN
cana-2697	62	28	tasks	task	NOUN
cana-2697	62	29	.	.	PUNCT
cana-2697	63	1	contributions	contribution	NOUN
cana-2697	63	2	:	:	PUNCT
cana-2697	63	3	lightgbm	lightgbm	ADJ
cana-2697	63	4	-	-	PUNCT
cana-2697	63	5	bisru	bisru	NOUN
cana-2697	63	6	-	-	PUNCT
cana-2697	63	7	attention	attention	NOUN
cana-2697	63	8	:	:	PUNCT
cana-2697	63	9	this	this	PRON
cana-2697	63	10	applies	apply	VERB
cana-2697	63	11	lightgbm	lightgbm	VERB
cana-2697	63	12	’s	’s	PART
cana-2697	63	13	feature	feature	NOUN
cana-2697	63	14	manipulation	manipulation	NOUN
cana-2697	63	15	to	to	PART
cana-2697	63	16	reduce	reduce	VERB
cana-2697	63	17	noise	noise	NOUN
cana-2697	63	18	by	by	ADP
cana-2697	63	19	focusing	focus	VERB
cana-2697	63	20	on	on	ADP
cana-2697	63	21	a	a	DET
cana-2697	63	22	limited	limited	ADJ
cana-2697	63	23	number	number	NOUN
cana-2697	63	24	of	of	ADP
cana-2697	63	25	key	key	ADJ
cana-2697	63	26	variables	variable	NOUN
cana-2697	63	27	.	.	PUNCT
cana-2697	64	1	bisru	bisru	NOUN
cana-2697	64	2	is	be	AUX
cana-2697	64	3	included	include	VERB
cana-2697	64	4	for	for	ADP
cana-2697	64	5	bidirectional	bidirectional	ADJ
cana-2697	64	6	learning	learning	NOUN
cana-2697	64	7	and	and	CCONJ
cana-2697	64	8	attention	attention	NOUN
cana-2697	64	9	mechanisms	mechanism	NOUN
cana-2697	64	10	for	for	ADP
cana-2697	64	11	dynamic	dynamic	ADJ
cana-2697	64	12	parameter	parameter	NOUN
cana-2697	64	13	optimization	optimization	NOUN
cana-2697	64	14	enabling	enable	VERB
cana-2697	64	15	this	this	DET
cana-2697	64	16	model	model	NOUN
cana-2697	64	17	to	to	PART
cana-2697	64	18	be	be	AUX
cana-2697	64	19	more	more	ADV
cana-2697	64	20	accurate	accurate	ADJ
cana-2697	64	21	and	and	CCONJ
cana-2697	64	22	efficient	efficient	ADJ
cana-2697	64	23	.	.	PUNCT
cana-2697	65	1	ensemble	ensemble	ADJ
cana-2697	65	2	mine	mine	NOUN
cana-2697	65	3	-	-	PUNCT
cana-2697	65	4	bisru	bisru	NOUN
cana-2697	65	5	-	-	PUNCT
cana-2697	65	6	attention	attention	NOUN
cana-2697	65	7	:	:	PUNCT
cana-2697	65	8	this	this	PRON
cana-2697	65	9	makes	make	VERB
cana-2697	65	10	use	use	NOUN
cana-2697	65	11	of	of	ADP
cana-2697	65	12	incorporating	incorporate	VERB
cana-2697	65	13	mic	mic	NOUN
cana-2697	65	14	for	for	ADP
cana-2697	65	15	enhanced	enhanced	ADJ
cana-2697	65	16	feature	feature	NOUN
cana-2697	65	17	selection	selection	NOUN
cana-2697	65	18	that	that	PRON
cana-2697	65	19	captures	capture	VERB
cana-2697	65	20	both	both	CCONJ
cana-2697	65	21	linear	linear	ADJ
cana-2697	65	22	and	and	CCONJ
cana-2697	65	23	not	not	PART
cana-2697	65	24	linear	linear	VERB
cana-2697	65	25	dependencies	dependency	NOUN
cana-2697	65	26	within	within	ADP
cana-2697	65	27	the	the	DET
cana-2697	65	28	dataset	dataset	NOUN
cana-2697	65	29	.	.	PUNCT
cana-2697	66	1	this	this	DET
cana-2697	66	2	ensemble	ensemble	ADJ
cana-2697	66	3	structure	structure	NOUN
cana-2697	66	4	reinforces	reinforce	VERB
cana-2697	66	5	the	the	DET
cana-2697	66	6	strength	strength	NOUN
cana-2697	66	7	of	of	ADP
cana-2697	66	8	bisru	bisru	NOUN
cana-2697	66	9	,	,	PUNCT
cana-2697	66	10	lstm	lstm	ADJ
cana-2697	66	11	,	,	PUNCT
cana-2697	66	12	and	and	CCONJ
cana-2697	66	13	gru	gru	VERB
cana-2697	66	14	by	by	ADP
cana-2697	66	15	combining	combine	VERB
cana-2697	66	16	learning	learn	VERB
cana-2697	66	17	with	with	ADP
cana-2697	66	18	the	the	DET
cana-2697	66	19	potential	potential	NOUN
cana-2697	66	20	to	to	PART
cana-2697	66	21	improve	improve	VERB
cana-2697	66	22	the	the	DET
cana-2697	66	23	robustness	robustness	NOUN
cana-2697	66	24	and	and	CCONJ
cana-2697	66	25	accuracy	accuracy	NOUN
cana-2697	66	26	of	of	ADP
cana-2697	66	27	predictions	prediction	NOUN
cana-2697	66	28	[	[	X
cana-2697	66	29	16	16	NUM
cana-2697	66	30	]	]	PUNCT
cana-2697	66	31	.	.	PUNCT
cana-2697	67	1	these	these	DET
cana-2697	67	2	contributions	contribution	NOUN
cana-2697	67	3	improve	improve	VERB
cana-2697	67	4	oxygen	oxygen	NOUN
cana-2697	67	5	water	water	NOUN
cana-2697	67	6	quality	quality	NOUN
cana-2697	67	7	in	in	ADP
cana-2697	67	8	aquaculture	aquaculture	NOUN
cana-2697	67	9	as	as	SCONJ
cana-2697	67	10	those	those	DET
cana-2697	67	11	traditional	traditional	ADJ
cana-2697	67	12	models	model	NOUN
cana-2697	67	13	have	have	VERB
cana-2697	67	14	their	their	PRON
cana-2697	67	15	limitations	limitation	NOUN
cana-2697	67	16	.	.	PUNCT
cana-2697	68	1	these	these	DET
cana-2697	68	2	works	work	NOUN
cana-2697	68	3	therefore	therefore	ADV
cana-2697	68	4	define	define	VERB
cana-2697	68	5	the	the	DET
cana-2697	68	6	way	way	NOUN
cana-2697	68	7	forward	forward	ADV
cana-2697	68	8	in	in	ADP
cana-2697	68	9	terms	term	NOUN
cana-2697	68	10	of	of	ADP
cana-2697	68	11	more	more	ADV
cana-2697	68	12	accurate	accurate	ADJ
cana-2697	68	13	standards	standard	NOUN
cana-2697	68	14	on	on	ADP
cana-2697	68	15	how	how	SCONJ
cana-2697	68	16	to	to	PART
cana-2697	68	17	manage	manage	VERB
cana-2697	68	18	water	water	NOUN
cana-2697	68	19	in	in	ADP
cana-2697	68	20	aquaculture	aquaculture	NOUN
cana-2697	68	21	ponds	pond	NOUN
cana-2697	68	22	.	.	PUNCT
cana-2697	69	1	vi	vi	X
cana-2697	69	2	.	.	PROPN
cana-2697	69	3	proposed	propose	VERB
cana-2697	69	4	work	work	NOUN
cana-2697	69	5	and	and	CCONJ
cana-2697	69	6	methods	method	NOUN
cana-2697	69	7	[	[	X
cana-2697	69	8	a	a	X
cana-2697	69	9	]	]	X
cana-2697	69	10	dataset	dataset	NOUN
cana-2697	69	11	this	this	DET
cana-2697	69	12	study	study	NOUN
cana-2697	69	13	’s	’s	PART
cana-2697	69	14	dataset	dataset	NOUN
cana-2697	69	15	is	be	AUX
cana-2697	69	16	from	from	ADP
cana-2697	69	17	kaggle	kaggle	PROPN
cana-2697	69	18	(	(	PUNCT
cana-2697	69	19	link	link	NOUN
cana-2697	69	20	)	)	PUNCT
cana-2697	69	21	which	which	PRON
cana-2697	69	22	has	have	VERB
cana-2697	69	23	500	500	NUM
cana-2697	69	24	records	record	NOUN
cana-2697	69	25	and	and	CCONJ
cana-2697	69	26	six	six	NUM
cana-2697	69	27	variables	variable	NOUN
cana-2697	69	28	:	:	PUNCT
cana-2697	69	29	sample	sample	NOUN
cana-2697	69	30	i	i	PROPN
cana-2697	69	31	d	d	PROPN
cana-2697	69	32	,	,	PUNCT
cana-2697	69	33	ph	ph	VERB
cana-2697	69	34	,	,	PUNCT
cana-2697	69	35	temperature	temperature	NOUN
cana-2697	69	36	(	(	PUNCT
cana-2697	69	37	°	°	ADP
cana-2697	69	38	c	c	NOUN
cana-2697	69	39	)	)	PUNCT
cana-2697	69	40	,	,	PUNCT
cana-2697	69	41	turbidity	turbidity	NOUN
cana-2697	69	42	(	(	PUNCT
cana-2697	69	43	ntu	ntu	PROPN
cana-2697	69	44	)	)	PUNCT
cana-2697	69	45	,	,	PUNCT
cana-2697	69	46	dissolved	dissolve	VERB
cana-2697	69	47	oxygen	oxygen	NOUN
cana-2697	69	48	(	(	PUNCT
cana-2697	69	49	mg	mg	PROPN
cana-2697	69	50	/	/	SYM
cana-2697	69	51	l	l	NOUN
cana-2697	69	52	)	)	PUNCT
cana-2697	69	53	,	,	PUNCT
cana-2697	69	54	and	and	CCONJ
cana-2697	69	55	conductivity	conductivity	NOUN
cana-2697	69	56	(	(	PUNCT
cana-2697	69	57	μmho	μmho	NOUN
cana-2697	69	58	/	/	SYM
cana-2697	69	59	cm	cm	NOUN
cana-2697	69	60	)	)	PUNCT
cana-2697	69	61	.	.	PUNCT
cana-2697	70	1	each	each	DET
cana-2697	70	2	record	record	NOUN
cana-2697	70	3	pertains	pertain	VERB
cana-2697	70	4	to	to	ADP
cana-2697	70	5	a	a	DET
cana-2697	70	6	specific	specific	ADJ
cana-2697	70	7	sample	sample	NOUN
cana-2697	70	8	unique	unique	ADJ
cana-2697	70	9	to	to	ADP
cana-2697	70	10	a	a	DET
cana-2697	70	11	water	water	NOUN
cana-2697	70	12	body	body	NOUN
cana-2697	70	13	capturing	capture	VERB
cana-2697	70	14	important	important	ADJ
cana-2697	70	15	parameters	parameter	NOUN
cana-2697	70	16	of	of	ADP
cana-2697	70	17	the	the	DET
cana-2697	70	18	water	water	NOUN
cana-2697	70	19	sample	sample	NOUN
cana-2697	70	20	which	which	PRON
cana-2697	70	21	are	be	AUX
cana-2697	70	22	pertinent	pertinent	ADJ
cana-2697	70	23	in	in	ADP
cana-2697	70	24	predicting	predict	VERB
cana-2697	70	25	dissolved	dissolved	ADJ
cana-2697	70	26	oxygen	oxygen	NOUN
cana-2697	70	27	within	within	ADP
cana-2697	70	28	aquaculture	aquaculture	NOUN
cana-2697	70	29	systems	system	NOUN
cana-2697	70	30	.	.	PUNCT
cana-2697	71	1	in	in	ADP
cana-2697	71	2	order	order	NOUN
cana-2697	71	3	to	to	PART
cana-2697	71	4	prepare	prepare	VERB
cana-2697	71	5	the	the	DET
cana-2697	71	6	dataset	dataset	NOUN
cana-2697	71	7	for	for	ADP
cana-2697	71	8	analysis	analysis	NOUN
cana-2697	71	9	,	,	PUNCT
cana-2697	71	10	any	any	DET
cana-2697	71	11	missing	missing	ADJ
cana-2697	71	12	values	value	NOUN
cana-2697	71	13	were	be	AUX
cana-2697	71	14	replaced	replace	VERB
cana-2697	71	15	with	with	ADP
cana-2697	71	16	zeros	zero	NOUN
cana-2697	71	17	so	so	SCONJ
cana-2697	71	18	that	that	SCONJ
cana-2697	71	19	the	the	DET
cana-2697	71	20	datasets	dataset	NOUN
cana-2697	71	21	can	can	AUX
cana-2697	71	22	be	be	AUX
cana-2697	71	23	complete	complete	ADJ
cana-2697	71	24	.	.	PUNCT
cana-2697	72	1	other	other	ADJ
cana-2697	72	2	preprocessing	preprocessing	NOUN
cana-2697	72	3	steps	step	NOUN
cana-2697	72	4	included	include	VERB
cana-2697	72	5	normalization	normalization	NOUN
cana-2697	72	6	of	of	ADP
cana-2697	72	7	the	the	DET
cana-2697	72	8	data	datum	NOUN
cana-2697	72	9	in	in	ADP
cana-2697	72	10	order	order	NOUN
cana-2697	72	11	to	to	PART
cana-2697	72	12	bring	bring	VERB
cana-2697	72	13	all	all	DET
cana-2697	72	14	values	value	NOUN
cana-2697	72	15	to	to	ADP
cana-2697	72	16	one	one	NUM
cana-2697	72	17	scale	scale	NOUN
cana-2697	72	18	and	and	CCONJ
cana-2697	72	19	avoid	avoid	VERB
cana-2697	72	20	bias	bias	NOUN
cana-2697	72	21	during	during	ADP
cana-2697	72	22	training	training	NOUN
cana-2697	72	23	and	and	CCONJ
cana-2697	72	24	also	also	ADV
cana-2697	72	25	to	to	PART
cana-2697	72	26	have	have	VERB
cana-2697	72	27	the	the	DET
cana-2697	72	28	input	input	NOUN
cana-2697	72	29	parameters	parameter	NOUN
cana-2697	72	30	for	for	ADP
cana-2697	72	31	the	the	DET
cana-2697	72	32	models	model	NOUN
cana-2697	72	33	consistent	consistent	ADJ
cana-2697	72	34	.	.	PUNCT
cana-2697	73	1	following	follow	VERB
cana-2697	73	2	the	the	DET
cana-2697	73	3	preprocessing	preprocessing	NOUN
cana-2697	73	4	of	of	ADP
cana-2697	73	5	the	the	DET
cana-2697	73	6	data	datum	NOUN
cana-2697	73	7	,	,	PUNCT
cana-2697	73	8	a	a	DET
cana-2697	73	9	dataset	dataset	NOUN
cana-2697	73	10	consisting	consist	VERB
cana-2697	73	11	of	of	ADP
cana-2697	73	12	500	500	NUM
cana-2697	73	13	rows	row	NOUN
cana-2697	73	14	was	be	AUX
cana-2697	73	15	divided	divide	VERB
cana-2697	73	16	into	into	ADP
cana-2697	73	17	training	training	NOUN
cana-2697	73	18	and	and	CCONJ
cana-2697	73	19	testing	testing	NOUN
cana-2697	73	20	samples	sample	NOUN
cana-2697	73	21	.	.	PUNCT
cana-2697	74	1	for	for	ADP
cana-2697	74	2	the	the	DET
cana-2697	74	3	training	training	NOUN
cana-2697	74	4	sample	sample	NOUN
cana-2697	74	5	,	,	PUNCT
cana-2697	74	6	80	80	NUM
cana-2697	74	7	%	%	NOUN
cana-2697	74	8	of	of	ADP
cana-2697	74	9	the	the	DET
cana-2697	74	10	data	datum	NOUN
cana-2697	74	11	or	or	CCONJ
cana-2697	74	12	400	400	NUM
cana-2697	74	13	rows	row	NOUN
cana-2697	74	14	were	be	AUX
cana-2697	74	15	used	use	VERB
cana-2697	74	16	,	,	PUNCT
cana-2697	74	17	while	while	SCONJ
cana-2697	74	18	for	for	ADP
cana-2697	74	19	testing	testing	NOUN
cana-2697	74	20	,	,	PUNCT
cana-2697	74	21	20	20	NUM
cana-2697	74	22	%	%	NOUN
cana-2697	74	23	or	or	CCONJ
cana-2697	74	24	100	100	NUM
cana-2697	74	25	rows	row	NOUN
cana-2697	74	26	were	be	AUX
cana-2697	74	27	used	use	VERB
cana-2697	74	28	.	.	PUNCT
cana-2697	75	1	the	the	DET
cana-2697	75	2	testing	testing	NOUN
cana-2697	75	3	samples	sample	NOUN
cana-2697	75	4	used	use	VERB
cana-2697	75	5	,	,	PUNCT
cana-2697	75	6	and	and	CCONJ
cana-2697	75	7	training	training	NOUN
cana-2697	75	8	samples	sample	NOUN
cana-2697	75	9	(	(	PUNCT
cana-2697	75	10	80	80	NUM
cana-2697	75	11	%	%	NOUN
cana-2697	75	12	of	of	ADP
cana-2697	75	13	the	the	DET
cana-2697	75	14	500	500	NUM
cana-2697	75	15	dataset	dataset	NOUN
cana-2697	75	16	)	)	PUNCT
cana-2697	75	17	represented	represent	VERB
cana-2697	75	18	did	do	AUX
cana-2697	75	19	not	not	PART
cana-2697	75	20	vary	vary	VERB
cana-2697	75	21	from	from	ADP
cana-2697	75	22	each	each	DET
cana-2697	75	23	other	other	ADJ
cana-2697	75	24	.	.	PUNCT
cana-2697	76	1	the	the	DET
cana-2697	76	2	stratified	stratified	ADJ
cana-2697	76	3	data	datum	NOUN
cana-2697	76	4	split	split	NOUN
cana-2697	76	5	kept	keep	VERB
cana-2697	76	6	the	the	DET
cana-2697	76	7	target	target	NOUN
cana-2697	76	8	variable	variable	NOUN
cana-2697	76	9	(	(	PUNCT
cana-2697	76	10	dissolved	dissolve	VERB
cana-2697	76	11	oxygen	oxygen	NOUN
cana-2697	76	12	)	)	PUNCT
cana-2697	76	13	proportion	proportion	NOUN
cana-2697	76	14	intact	intact	ADJ
cana-2697	76	15	so	so	SCONJ
cana-2697	76	16	that	that	SCONJ
cana-2697	76	17	both	both	DET
cana-2697	76	18	subsets	subset	NOUN
cana-2697	76	19	were	be	AUX
cana-2697	76	20	valid	valid	ADJ
cana-2697	76	21	samples	sample	NOUN
cana-2697	76	22	of	of	ADP
cana-2697	76	23	the	the	DET
cana-2697	76	24	original	original	ADJ
cana-2697	76	25	picture	picture	NOUN
cana-2697	76	26	.	.	PUNCT
cana-2697	77	1	this	this	DET
cana-2697	77	2	integrated	integrate	VERB
cana-2697	77	3	preprocessing	preprocessing	NOUN
cana-2697	77	4	of	of	ADP
cana-2697	77	5	the	the	DET
cana-2697	77	6	data	datum	NOUN
cana-2697	77	7	helped	help	VERB
cana-2697	77	8	in	in	ADP
cana-2697	77	9	reducing	reduce	VERB
cana-2697	77	10	noise	noise	NOUN
cana-2697	77	11	and	and	CCONJ
cana-2697	77	12	improving	improve	VERB
cana-2697	77	13	the	the	DET
cana-2697	77	14	quality	quality	NOUN
cana-2697	77	15	of	of	ADP
cana-2697	77	16	the	the	DET
cana-2697	77	17	data	datum	NOUN
cana-2697	77	18	so	so	SCONJ
cana-2697	77	19	that	that	SCONJ
cana-2697	77	20	it	it	PRON
cana-2697	77	21	was	be	AUX
cana-2697	77	22	suitable	suitable	ADJ
cana-2697	77	23	for	for	ADP
cana-2697	77	24	predictive	predictive	ADJ
cana-2697	77	25	modeling	modeling	NOUN
cana-2697	77	26	[	[	X
cana-2697	77	27	17	17	NUM
cana-2697	77	28	]	]	PUNCT
cana-2697	77	29	.	.	PUNCT
cana-2697	78	1	https://www.kaggle.com/datasets/shreyanshverma27/water-quality-testing	https://www.kaggle.com/datasets/shreyanshverma27/water-quality-teste	VERB
cana-2697	78	2	communications	communication	NOUN
cana-2697	78	3	on	on	ADP
cana-2697	78	4	applied	apply	VERB
cana-2697	78	5	nonlinear	nonlinear	ADJ
cana-2697	78	6	analysis	analysis	NOUN
cana-2697	78	7	issn	issn	NOUN
cana-2697	78	8	:	:	PUNCT
cana-2697	78	9	1074	1074	NUM
cana-2697	78	10	-	-	PUNCT
cana-2697	78	11	133x	133x	NUM
cana-2697	78	12	vol	vol	NOUN
cana-2697	78	13	32	32	NUM
cana-2697	78	14	no	no	NOUN
cana-2697	78	15	.	.	PUNCT
cana-2697	79	1	3s	3s	NUM
cana-2697	79	2	(	(	PUNCT
cana-2697	79	3	2025	2025	NUM
cana-2697	79	4	)	)	PUNCT
cana-2697	79	5	583	583	NUM
cana-2697	79	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	79	7	figure	figure	NOUN
cana-2697	79	8	.2	.2	NUM
cana-2697	79	9	ph	ph	PROPN
cana-2697	79	10	,	,	PUNCT
cana-2697	79	11	turbidity	turbidity	NOUN
cana-2697	79	12	,	,	PUNCT
cana-2697	79	13	conductivity	conductivity	NOUN
cana-2697	79	14	,	,	PUNCT
cana-2697	79	15	temperature	temperature	NOUN
cana-2697	79	16	values	value	NOUN
cana-2697	79	17	observations	observation	NOUN
cana-2697	79	18	:	:	PUNCT
cana-2697	79	19			X
cana-2697	79	20	the	the	DET
cana-2697	79	21	lowest	low	ADJ
cana-2697	79	22	and	and	CCONJ
cana-2697	79	23	the	the	DET
cana-2697	79	24	highest	high	ADJ
cana-2697	79	25	ph	ph	ADJ
cana-2697	79	26	values	value	NOUN
cana-2697	79	27	were	be	AUX
cana-2697	79	28	7.01	7.01	NUM
cana-2697	79	29	and	and	CCONJ
cana-2697	79	30	7.45	7.45	NUM
cana-2697	79	31	,	,	PUNCT
cana-2697	79	32	so	so	ADV
cana-2697	79	33	water	water	NOUN
cana-2697	79	34	quality	quality	NOUN
cana-2697	79	35	can	can	AUX
cana-2697	79	36	be	be	AUX
cana-2697	79	37	considered	consider	VERB
cana-2697	79	38	to	to	PART
cana-2697	79	39	be	be	AUX
cana-2697	79	40	neutral	neutral	ADJ
cana-2697	79	41	level	level	NOUN
cana-2697	79	42	quality	quality	NOUN
cana-2697	79	43	:	:	PUNCT
cana-2697	79	44	7.00	7.00	NUM
cana-2697	79	45	–	–	PUNCT
cana-2697	79	46	7.89	7.89	NUM
cana-2697	79	47	.	.	PUNCT
cana-2697	80	1			PUNCT
cana-2697	81	1	the	the	DET
cana-2697	81	2	turbidity	turbidity	NOUN
cana-2697	81	3	values	value	NOUN
cana-2697	81	4	(	(	PUNCT
cana-2697	81	5	crucial	crucial	ADJ
cana-2697	81	6	determination	determination	NOUN
cana-2697	81	7	of	of	ADP
cana-2697	81	8	water	water	NOUN
cana-2697	81	9	clarity	clarity	NOUN
cana-2697	81	10	)	)	PUNCT
cana-2697	81	11	developed	develop	VERB
cana-2697	81	12	by	by	ADP
cana-2697	81	13	the	the	DET
cana-2697	81	14	ntu	ntu	PROPN
cana-2697	81	15	account	account	NOUN
cana-2697	81	16	for	for	ADP
cana-2697	81	17	values	value	NOUN
cana-2697	81	18	in	in	ADP
cana-2697	81	19	the	the	DET
cana-2697	81	20	range	range	NOUN
cana-2697	81	21	of	of	ADP
cana-2697	81	22	3.2	3.2	NUM
cana-2697	81	23	to	to	ADP
cana-2697	81	24	5.1	5.1	NUM
cana-2697	81	25	,	,	PUNCT
cana-2697	81	26	indicating	indicate	VERB
cana-2697	81	27	the	the	DET
cana-2697	81	28	clarity	clarity	NOUN
cana-2697	81	29	of	of	ADP
cana-2697	81	30	the	the	DET
cana-2697	81	31	water	water	NOUN
cana-2697	81	32	samples	sample	NOUN
cana-2697	81	33	.	.	PUNCT
cana-2697	82	1			X
cana-2697	82	2	the	the	DET
cana-2697	82	3	range	range	NOUN
cana-2697	82	4	of	of	ADP
cana-2697	82	5	conductivity	conductivity	NOUN
cana-2697	82	6	value	value	NOUN
cana-2697	82	7	(	(	PUNCT
cana-2697	82	8	μs	μs	NOUN
cana-2697	82	9	/	/	SYM
cana-2697	82	10	cm	cm	NOUN
cana-2697	82	11	)	)	PUNCT
cana-2697	82	12	accounts	account	NOUN
cana-2697	82	13	to	to	ADP
cana-2697	82	14	around	around	ADP
cana-2697	82	15	327	327	NUM
cana-2697	82	16	to	to	PART
cana-2697	82	17	361	361	NUM
cana-2697	82	18	,	,	PUNCT
cana-2697	82	19	indicating	indicate	VERB
cana-2697	82	20	ionic	ionic	ADJ
cana-2697	82	21	content	content	NOUN
cana-2697	82	22	in	in	ADP
cana-2697	82	23	the	the	DET
cana-2697	82	24	water	water	NOUN
cana-2697	82	25	.	.	PUNCT
cana-2697	83	1			X
cana-2697	83	2	temperature	temperature	NOUN
cana-2697	83	3	(	(	PUNCT
cana-2697	83	4	°	°	ADP
cana-2697	83	5	c	c	NOUN
cana-2697	83	6	)	)	PUNCT
cana-2697	83	7	values	value	NOUN
cana-2697	83	8	account	account	VERB
cana-2697	83	9	to	to	ADP
cana-2697	83	10	around	around	ADV
cana-2697	83	11	20.7	20.7	NUM
cana-2697	83	12	–	–	PUNCT
cana-2697	83	13	23.1	23.1	NUM
cana-2697	83	14	,	,	PUNCT
cana-2697	83	15	which	which	PRON
cana-2697	83	16	may	may	AUX
cana-2697	83	17	have	have	AUX
cana-2697	83	18	later	later	ADV
cana-2697	83	19	been	be	AUX
cana-2697	83	20	analyzed	analyze	VERB
cana-2697	83	21	but	but	CCONJ
cana-2697	83	22	was	be	AUX
cana-2697	83	23	eventually	eventually	ADV
cana-2697	83	24	set	set	VERB
cana-2697	83	25	aside	aside	ADV
cana-2697	83	26	on	on	ADP
cana-2697	83	27	the	the	DET
cana-2697	83	28	argument	argument	NOUN
cana-2697	83	29	of	of	ADP
cana-2697	83	30	having	have	VERB
cana-2697	83	31	almost	almost	ADV
cana-2697	83	32	no	no	DET
cana-2697	83	33	predictive	predictive	ADJ
cana-2697	83	34	strength	strength	NOUN
cana-2697	83	35	[	[	X
cana-2697	83	36	19	19	NUM
cana-2697	83	37	]	]	PUNCT
cana-2697	83	38	.	.	PUNCT
cana-2697	84	1	in	in	ADP
cana-2697	84	2	this	this	DET
cana-2697	84	3	regard	regard	NOUN
cana-2697	84	4	,	,	PUNCT
cana-2697	84	5	this	this	DET
cana-2697	84	6	stochastic	stochastic	ADJ
cana-2697	84	7	approach	approach	NOUN
cana-2697	84	8	to	to	ADP
cana-2697	84	9	dataset	dataset	ADJ
cana-2697	84	10	preparation	preparation	NOUN
cana-2697	84	11	also	also	ADV
cana-2697	84	12	meant	mean	VERB
cana-2697	84	13	that	that	SCONJ
cana-2697	84	14	the	the	DET
cana-2697	84	15	models	model	NOUN
cana-2697	84	16	were	be	AUX
cana-2697	84	17	trained	train	VERB
cana-2697	84	18	on	on	ADP
cana-2697	84	19	relevant	relevant	ADJ
cana-2697	84	20	and	and	CCONJ
cana-2697	84	21	high	high	ADJ
cana-2697	84	22	-	-	PUNCT
cana-2697	84	23	quality	quality	NOUN
cana-2697	84	24	data	datum	NOUN
cana-2697	84	25	that	that	PRON
cana-2697	84	26	would	would	AUX
cana-2697	84	27	lead	lead	VERB
cana-2697	84	28	to	to	ADP
cana-2697	84	29	accurate	accurate	ADJ
cana-2697	84	30	predictions	prediction	NOUN
cana-2697	84	31	of	of	ADP
cana-2697	84	32	dissolved	dissolve	VERB
cana-2697	84	33	oxygen	oxygen	NOUN
cana-2697	84	34	levels	level	NOUN
cana-2697	84	35	.	.	PUNCT
cana-2697	85	1	[	[	X
cana-2697	85	2	b	b	X
cana-2697	85	3	]	]	X
cana-2697	85	4	feature	feature	NOUN
cana-2697	85	5	selection	selection	NOUN
cana-2697	85	6	the	the	DET
cana-2697	85	7	process	process	NOUN
cana-2697	85	8	of	of	ADP
cana-2697	85	9	feature	feature	NOUN
cana-2697	85	10	selection	selection	NOUN
cana-2697	85	11	is	be	AUX
cana-2697	85	12	of	of	ADP
cana-2697	85	13	paramount	paramount	ADJ
cana-2697	85	14	importance	importance	NOUN
cana-2697	85	15	since	since	SCONJ
cana-2697	85	16	it	it	PRON
cana-2697	85	17	capitalizes	capitalize	VERB
cana-2697	85	18	on	on	ADP
cana-2697	85	19	the	the	DET
cana-2697	85	20	key	key	ADJ
cana-2697	85	21	variables	variable	NOUN
cana-2697	85	22	that	that	PRON
cana-2697	85	23	drive	drive	VERB
cana-2697	85	24	the	the	DET
cana-2697	85	25	model	model	NOUN
cana-2697	85	26	while	while	SCONJ
cana-2697	85	27	also	also	ADV
cana-2697	85	28	removing	remove	VERB
cana-2697	85	29	irrelevant	irrelevant	ADJ
cana-2697	85	30	and	and	CCONJ
cana-2697	85	31	excessive	excessive	ADJ
cana-2697	85	32	factors	factor	NOUN
cana-2697	85	33	.	.	PUNCT
cana-2697	86	1	the	the	DET
cana-2697	86	2	present	present	ADJ
cana-2697	86	3	study	study	NOUN
cana-2697	86	4	utilized	utilize	VERB
cana-2697	86	5	two	two	NUM
cana-2697	86	6	advanced	advanced	ADJ
cana-2697	86	7	methods	method	NOUN
cana-2697	86	8	:	:	PUNCT
cana-2697	86	9	1	1	X
cana-2697	86	10	.	.	NUM
cana-2697	86	11	lightgbm	lightgbm	VERB
cana-2697	86	12	(	(	PUNCT
cana-2697	86	13	light	light	ADJ
cana-2697	86	14	gradient	gradient	NOUN
cana-2697	86	15	boosting	boost	VERB
cana-2697	86	16	machine	machine	NOUN
cana-2697	86	17	):	):	PUNCT
cana-2697	86	18	lightgbm	lightgbm	PROPN
cana-2697	86	19	is	be	AUX
cana-2697	86	20	a	a	DET
cana-2697	86	21	very	very	ADV
cana-2697	86	22	effective	effective	ADJ
cana-2697	86	23	gradient	gradient	NOUN
cana-2697	86	24	-	-	PUNCT
cana-2697	86	25	boosting	boost	VERB
cana-2697	86	26	algorithm	algorithm	NOUN
cana-2697	86	27	,	,	PUNCT
cana-2697	86	28	which	which	PRON
cana-2697	86	29	during	during	ADP
cana-2697	86	30	training	training	NOUN
cana-2697	86	31	involves	involve	VERB
cana-2697	86	32	ranking	rank	VERB
cana-2697	86	33	features	feature	NOUN
cana-2697	86	34	and	and	CCONJ
cana-2697	86	35	their	their	PRON
cana-2697	86	36	importance	importance	NOUN
cana-2697	86	37	scores	score	NOUN
cana-2697	86	38	.	.	PUNCT
cana-2697	87	1	in	in	ADP
cana-2697	87	2	this	this	DET
cana-2697	87	3	case	case	NOUN
cana-2697	87	4	study	study	NOUN
cana-2697	87	5	,	,	PUNCT
cana-2697	87	6	lightgbm	lightgbm	NOUN
cana-2697	87	7	has	have	AUX
cana-2697	87	8	also	also	ADV
cana-2697	87	9	been	be	AUX
cana-2697	87	10	used	use	VERB
cana-2697	87	11	for	for	ADP
cana-2697	87	12	feature	feature	NOUN
cana-2697	87	13	engineering	engineering	NOUN
cana-2697	87	14	purposes	purpose	NOUN
cana-2697	87	15	over	over	ADP
cana-2697	87	16	the	the	DET
cana-2697	87	17	water	water	NOUN
cana-2697	87	18	quality	quality	NOUN
cana-2697	87	19	dataset	dataset	NOUN
cana-2697	87	20	.	.	PUNCT
cana-2697	88	1	the	the	DET
cana-2697	88	2	analysis	analysis	NOUN
cana-2697	88	3	showed	show	VERB
cana-2697	88	4	that	that	SCONJ
cana-2697	88	5	in	in	ADP
cana-2697	88	6	predicting	predict	VERB
cana-2697	88	7	the	the	DET
cana-2697	88	8	most	most	ADJ
cana-2697	88	9	communications	communication	NOUN
cana-2697	88	10	on	on	ADP
cana-2697	88	11	applied	apply	VERB
cana-2697	88	12	nonlinear	nonlinear	ADJ
cana-2697	88	13	analysis	analysis	NOUN
cana-2697	88	14	issn	issn	NOUN
cana-2697	88	15	:	:	PUNCT
cana-2697	88	16	1074	1074	NUM
cana-2697	88	17	-	-	PUNCT
cana-2697	88	18	133x	133x	NUM
cana-2697	88	19	vol	vol	NOUN
cana-2697	88	20	32	32	NUM
cana-2697	88	21	no	no	NOUN
cana-2697	88	22	.	.	PUNCT
cana-2697	89	1	3s	3s	NUM
cana-2697	89	2	(	(	PUNCT
cana-2697	89	3	2025	2025	NUM
cana-2697	89	4	)	)	PUNCT
cana-2697	89	5	584	584	NUM
cana-2697	89	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	89	7	importances	importance	VERB
cana-2697	89	8	conductivity	conductivity	NOUN
cana-2697	89	9	(	(	PUNCT
cana-2697	89	10	µs	µs	NOUN
cana-2697	89	11	/	/	SYM
cana-2697	89	12	cm	cm	NOUN
cana-2697	89	13	)	)	PUNCT
cana-2697	89	14	29.347826	29.347826	NUM
cana-2697	89	15	(	(	PUNCT
cana-2697	89	16	ntu	ntu	PROPN
cana-2697	89	17	)	)	PUNCT
cana-2697	89	18	28.26087	28.26087	NUM
cana-2697	89	19	23.913043	23.913043	NUM
cana-2697	89	20	18.478261	18.478261	NUM
cana-2697	89	21	0	0	NUM
cana-2697	89	22	5	5	NUM
cana-2697	89	23	10	10	NUM
cana-2697	89	24	15	15	NUM
cana-2697	89	25	20	20	NUM
cana-2697	89	26	25	25	NUM
cana-2697	89	27	30	30	NUM
cana-2697	89	28	35	35	NUM
cana-2697	89	29	importances	importance	VERB
cana-2697	89	30	significant	significant	ADJ
cana-2697	89	31	contributors	contributor	NOUN
cana-2697	89	32	to	to	PART
cana-2697	89	33	dissolved	dissolve	VERB
cana-2697	89	34	oxygen	oxygen	NOUN
cana-2697	89	35	expectation	expectation	NOUN
cana-2697	89	36	,	,	PUNCT
cana-2697	89	37	ph	ph	NOUN
cana-2697	89	38	,	,	PUNCT
cana-2697	89	39	turbidity	turbidity	NOUN
cana-2697	89	40	,	,	PUNCT
cana-2697	89	41	and	and	CCONJ
cana-2697	89	42	conductivity	conductivity	NOUN
cana-2697	89	43	had	have	VERB
cana-2697	89	44	importance	importance	NOUN
cana-2697	89	45	scores	score	NOUN
cana-2697	89	46	of	of	ADP
cana-2697	89	47	23.91	23.91	NUM
cana-2697	89	48	%	%	NOUN
cana-2697	89	49	,	,	PUNCT
cana-2697	89	50	28.26	28.26	NUM
cana-2697	89	51	%	%	NOUN
cana-2697	89	52	,	,	PUNCT
cana-2697	89	53	and	and	CCONJ
cana-2697	89	54	29.35	29.35	NUM
cana-2697	89	55	%	%	NOUN
cana-2697	89	56	,	,	PUNCT
cana-2697	89	57	respectively	respectively	ADV
cana-2697	89	58	[	[	X
cana-2697	89	59	18][28	18][28	NUM
cana-2697	89	60	]	]	PUNCT
cana-2697	89	61	.	.	PUNCT
cana-2697	90	1	the	the	DET
cana-2697	90	2	temperature	temperature	NOUN
cana-2697	90	3	feature	feature	NOUN
cana-2697	90	4	was	be	AUX
cana-2697	90	5	found	find	VERB
cana-2697	90	6	to	to	PART
cana-2697	90	7	have	have	VERB
cana-2697	90	8	the	the	DET
cana-2697	90	9	least	least	ADJ
cana-2697	90	10	importance	importance	NOUN
cana-2697	90	11	with	with	ADP
cana-2697	90	12	an	an	DET
cana-2697	90	13	importance	importance	NOUN
cana-2697	90	14	score	score	NOUN
cana-2697	90	15	of	of	ADP
cana-2697	90	16	18.48	18.48	NUM
cana-2697	90	17	%	%	NOUN
cana-2697	90	18	and	and	CCONJ
cana-2697	90	19	thus	thus	ADV
cana-2697	90	20	was	be	AUX
cana-2697	90	21	removed	remove	VERB
cana-2697	90	22	from	from	ADP
cana-2697	90	23	further	further	ADJ
cana-2697	90	24	analysis	analysis	NOUN
cana-2697	90	25	.	.	PUNCT
cana-2697	91	1	given	give	VERB
cana-2697	91	2	that	that	SCONJ
cana-2697	91	3	the	the	DET
cana-2697	91	4	feature	feature	NOUN
cana-2697	91	5	selection	selection	NOUN
cana-2697	91	6	procedure	procedure	NOUN
cana-2697	91	7	contributed	contribute	VERB
cana-2697	91	8	very	very	ADV
cana-2697	91	9	little	little	ADJ
cana-2697	91	10	to	to	ADP
cana-2697	91	11	the	the	DET
cana-2697	91	12	computational	computational	ADJ
cana-2697	91	13	complexity	complexity	NOUN
cana-2697	91	14	of	of	ADP
cana-2697	91	15	the	the	DET
cana-2697	91	16	later	later	ADJ
cana-2697	91	17	stage	stage	NOUN
cana-2697	91	18	,	,	PUNCT
cana-2697	91	19	it	it	PRON
cana-2697	91	20	was	be	AUX
cana-2697	91	21	deemed	deem	VERB
cana-2697	91	22	relevant	relevant	ADJ
cana-2697	91	23	.	.	PUNCT
cana-2697	92	1	temperature	temperature	NOUN
cana-2697	92	2	(	(	PUNCT
cana-2697	92	3	°	°	ADP
cana-2697	92	4	c	c	NOUN
cana-2697	92	5	)	)	PUNCT
cana-2697	92	6	ph	ph	NOUN
cana-2697	92	7	turbidity	turbidity	NOUN
cana-2697	92	8	(	(	PUNCT
cana-2697	92	9	ntu	ntu	NOUN
cana-2697	92	10	)	)	PUNCT
cana-2697	92	11	conductivity	conductivity	NOUN
cana-2697	92	12	(	(	PUNCT
cana-2697	92	13	µs	µs	NOUN
cana-2697	92	14	/	/	SYM
cana-2697	92	15	cm	cm	NOUN
cana-2697	92	16	)	)	PUNCT
cana-2697	92	17	importances	importance	VERB
cana-2697	92	18	18.478261	18.478261	NUM
cana-2697	92	19	23.913043	23.913043	NUM
cana-2697	92	20	28.26087	28.26087	NUM
cana-2697	92	21	29.347826	29.347826	NUM
cana-2697	92	22	figure	figure	NOUN
cana-2697	92	23	.3	.3	NUM
cana-2697	92	24	lightgbm	lightgbm	VERB
cana-2697	92	25	features	feature	NOUN
cana-2697	92	26	selection	selection	NOUN
cana-2697	92	27	2	2	NUM
cana-2697	92	28	.	.	PUNCT
cana-2697	92	29	mic	mic	ADJ
cana-2697	92	30	(	(	PUNCT
cana-2697	92	31	maximal	maximal	ADJ
cana-2697	92	32	information	information	NOUN
cana-2697	92	33	coefficient	coefficient	NOUN
cana-2697	92	34	)	)	PUNCT
cana-2697	92	35	:	:	PUNCT
cana-2697	92	36	the	the	DET
cana-2697	92	37	maximal	maximal	ADJ
cana-2697	92	38	information	information	NOUN
cana-2697	92	39	coefficient	coefficient	NOUN
cana-2697	92	40	(	(	PUNCT
cana-2697	92	41	mic	mic	ADJ
cana-2697	92	42	)	)	PUNCT
cana-2697	92	43	is	be	AUX
cana-2697	92	44	a	a	DET
cana-2697	92	45	robust	robust	ADJ
cana-2697	92	46	statistical	statistical	ADJ
cana-2697	92	47	technique	technique	NOUN
cana-2697	92	48	which	which	PRON
cana-2697	92	49	helps	help	VERB
cana-2697	92	50	in	in	ADP
cana-2697	92	51	shedding	shed	VERB
cana-2697	92	52	light	light	NOUN
cana-2697	92	53	about	about	ADP
cana-2697	92	54	the	the	DET
cana-2697	92	55	strength	strength	NOUN
cana-2697	92	56	and	and	CCONJ
cana-2697	92	57	existence	existence	NOUN
cana-2697	92	58	of	of	ADP
cana-2697	92	59	relationship	relationship	NOUN
cana-2697	92	60	between	between	ADP
cana-2697	92	61	two	two	NUM
cana-2697	92	62	or	or	CCONJ
cana-2697	92	63	more	more	ADJ
cana-2697	92	64	variables	variable	NOUN
cana-2697	92	65	,	,	PUNCT
cana-2697	92	66	whether	whether	SCONJ
cana-2697	92	67	linear	linear	ADJ
cana-2697	92	68	or	or	CCONJ
cana-2697	92	69	not	not	PART
cana-2697	92	70	.	.	PUNCT
cana-2697	93	1	it	it	PRON
cana-2697	93	2	assesses	assess	VERB
cana-2697	93	3	the	the	DET
cana-2697	93	4	significance	significance	NOUN
cana-2697	93	5	of	of	ADP
cana-2697	93	6	such	such	ADJ
cana-2697	93	7	features	feature	NOUN
cana-2697	93	8	by	by	ADP
cana-2697	93	9	scoring	score	VERB
cana-2697	93	10	them	they	PRON
cana-2697	93	11	first	first	ADV
cana-2697	93	12	and	and	CCONJ
cana-2697	93	13	later	later	ADV
cana-2697	93	14	on	on	ADP
cana-2697	93	15	establishing	establish	VERB
cana-2697	93	16	the	the	DET
cana-2697	93	17	relevance	relevance	NOUN
cana-2697	93	18	of	of	ADP
cana-2697	93	19	each	each	PRON
cana-2697	93	20	of	of	ADP
cana-2697	93	21	the	the	DET
cana-2697	93	22	scored	score	VERB
cana-2697	93	23	features	feature	NOUN
cana-2697	93	24	with	with	ADP
cana-2697	93	25	regards	regard	NOUN
cana-2697	93	26	to	to	ADP
cana-2697	93	27	the	the	DET
cana-2697	93	28	target	target	NOUN
cana-2697	93	29	variable	variable	NOUN
cana-2697	93	30	.	.	PUNCT
cana-2697	94	1	from	from	ADP
cana-2697	94	2	the	the	DET
cana-2697	94	3	study	study	NOUN
cana-2697	94	4	,	,	PUNCT
cana-2697	94	5	mic	mic	ADJ
cana-2697	94	6	not	not	PART
cana-2697	94	7	only	only	ADV
cana-2697	94	8	pointed	point	VERB
cana-2697	94	9	out	out	ADP
cana-2697	94	10	the	the	DET
cana-2697	94	11	important	important	ADJ
cana-2697	94	12	features	feature	NOUN
cana-2697	94	13	of	of	ADP
cana-2697	94	14	ph	ph	NOUN
cana-2697	94	15	,	,	PUNCT
cana-2697	94	16	turbidity	turbidity	NOUN
cana-2697	94	17	(	(	PUNCT
cana-2697	94	18	ntu	ntu	PROPN
cana-2697	94	19	)	)	PUNCT
cana-2697	94	20	and	and	CCONJ
cana-2697	94	21	temperature	temperature	NOUN
cana-2697	94	22	(	(	PUNCT
cana-2697	94	23	°	°	ADP
cana-2697	94	24	c	c	NOUN
cana-2697	94	25	)	)	PUNCT
cana-2697	94	26	as	as	ADP
cana-2697	94	27	the	the	DET
cana-2697	94	28	most	most	ADV
cana-2697	94	29	prominent	prominent	ADJ
cana-2697	94	30	features	feature	NOUN
cana-2697	94	31	but	but	CCONJ
cana-2697	94	32	was	be	AUX
cana-2697	94	33	also	also	ADV
cana-2697	94	34	able	able	ADJ
cana-2697	94	35	to	to	PART
cana-2697	94	36	reduce	reduce	VERB
cana-2697	94	37	the	the	DET
cana-2697	94	38	number	number	NOUN
cana-2697	94	39	of	of	ADP
cana-2697	94	40	variables	variable	NOUN
cana-2697	94	41	in	in	ADP
cana-2697	94	42	the	the	DET
cana-2697	94	43	data	datum	NOUN
cana-2697	94	44	set	set	VERB
cana-2697	94	45	from	from	ADP
cana-2697	94	46	four	four	NUM
cana-2697	94	47	to	to	PART
cana-2697	94	48	three	three	NUM
cana-2697	94	49	.	.	PUNCT
cana-2697	95	1	this	this	DET
cana-2697	95	2	method	method	NOUN
cana-2697	95	3	of	of	ADP
cana-2697	95	4	feature	feature	NOUN
cana-2697	95	5	selection	selection	NOUN
cana-2697	95	6	is	be	AUX
cana-2697	95	7	strategic	strategic	ADJ
cana-2697	95	8	in	in	ADP
cana-2697	95	9	a	a	DET
cana-2697	95	10	manner	manner	NOUN
cana-2697	95	11	that	that	PRON
cana-2697	95	12	only	only	ADV
cana-2697	95	13	the	the	DET
cana-2697	95	14	best	good	ADJ
cana-2697	95	15	predictors	predictor	NOUN
cana-2697	95	16	are	be	AUX
cana-2697	95	17	chosen	choose	VERB
cana-2697	95	18	and	and	CCONJ
cana-2697	95	19	thus	thus	ADV
cana-2697	95	20	makes	make	VERB
cana-2697	95	21	the	the	DET
cana-2697	95	22	model	model	NOUN
cana-2697	95	23	easier	easy	ADJ
cana-2697	95	24	and	and	CCONJ
cana-2697	95	25	more	more	ADV
cana-2697	95	26	accurate	accurate	ADJ
cana-2697	95	27	.	.	PUNCT
cana-2697	96	1	the	the	DET
cana-2697	96	2	performance	performance	NOUN
cana-2697	96	3	metrics	metric	NOUN
cana-2697	96	4	for	for	ADP
cana-2697	96	5	the	the	DET
cana-2697	96	6	ensemble	ensemble	ADJ
cana-2697	96	7	mine	mine	NOUN
cana-2697	96	8	-	-	PUNCT
cana-2697	96	9	bisru	bisru	NOUN
cana-2697	96	10	-	-	PUNCT
cana-2697	96	11	attention	attention	NOUN
cana-2697	96	12	model	model	NOUN
cana-2697	96	13	improved	improve	VERB
cana-2697	96	14	substantially	substantially	ADV
cana-2697	96	15	because	because	SCONJ
cana-2697	96	16	mic	mic	NOUN
cana-2697	96	17	demonstrated	demonstrate	VERB
cana-2697	96	18	the	the	DET
cana-2697	96	19	ability	ability	NOUN
cana-2697	96	20	to	to	PART
cana-2697	96	21	identify	identify	VERB
cana-2697	96	22	very	very	ADV
cana-2697	96	23	small	small	ADJ
cana-2697	96	24	relationships	relationship	NOUN
cana-2697	96	25	that	that	PRON
cana-2697	96	26	other	other	ADJ
cana-2697	96	27	conventional	conventional	ADJ
cana-2697	96	28	approaches	approach	NOUN
cana-2697	96	29	may	may	AUX
cana-2697	96	30	have	have	AUX
cana-2697	96	31	overlooked	overlook	VERB
cana-2697	96	32	[	[	PUNCT
cana-2697	96	33	20	20	NUM
cana-2697	96	34	]	]	PUNCT
cana-2697	96	35	.	.	PUNCT
cana-2697	97	1	[	[	X
cana-2697	97	2	c	c	X
cana-2697	97	3	]	]	X
cana-2697	97	4	evaluation	evaluation	NOUN
cana-2697	97	5	in	in	ADP
cana-2697	97	6	order	order	NOUN
cana-2697	97	7	to	to	PART
cana-2697	97	8	conduct	conduct	VERB
cana-2697	97	9	the	the	DET
cana-2697	97	10	evaluation	evaluation	NOUN
cana-2697	97	11	of	of	ADP
cana-2697	97	12	the	the	DET
cana-2697	97	13	outlined	outline	VERB
cana-2697	97	14	models	model	NOUN
cana-2697	97	15	in	in	ADP
cana-2697	97	16	the	the	DET
cana-2697	97	17	previous	previous	ADJ
cana-2697	97	18	sub	sub	NOUN
cana-2697	97	19	sections	section	NOUN
cana-2697	97	20	,	,	PUNCT
cana-2697	97	21	three	three	NUM
cana-2697	97	22	metrics	metric	NOUN
cana-2697	97	23	were	be	AUX
cana-2697	97	24	chosen	choose	VERB
cana-2697	97	25	and	and	CCONJ
cana-2697	97	26	these	these	PRON
cana-2697	97	27	are	be	AUX
cana-2697	97	28	mean	mean	ADJ
cana-2697	97	29	square	square	ADJ
cana-2697	97	30	error	error	NOUN
cana-2697	97	31	(	(	PUNCT
cana-2697	97	32	mse	mse	NOUN
cana-2697	97	33	)	)	PUNCT
cana-2697	97	34	,	,	PUNCT
cana-2697	97	35	root	root	NOUN
cana-2697	97	36	mean	mean	VERB
cana-2697	97	37	square	square	ADJ
cana-2697	97	38	error	error	NOUN
cana-2697	97	39	(	(	PUNCT
cana-2697	97	40	rmse	rmse	NOUN
cana-2697	97	41	)	)	PUNCT
cana-2697	97	42	and	and	CCONJ
cana-2697	97	43	mean	mean	VERB
cana-2697	97	44	absolute	absolute	ADJ
cana-2697	97	45	error	error	NOUN
cana-2697	97	46	(	(	PUNCT
cana-2697	97	47	mae	mae	PROPN
cana-2697	97	48	)	)	PUNCT
cana-2697	97	49	.	.	PUNCT
cana-2697	98	1	all	all	PRON
cana-2697	98	2	of	of	ADP
cana-2697	98	3	these	these	PRON
cana-2697	98	4	are	be	AUX
cana-2697	98	5	closely	closely	ADV
cana-2697	98	6	related	relate	VERB
cana-2697	98	7	in	in	ADP
cana-2697	98	8	that	that	SCONJ
cana-2697	98	9	they	they	PRON
cana-2697	98	10	quantitatively	quantitatively	ADV
cana-2697	98	11	measure	measure	VERB
cana-2697	98	12	the	the	DET
cana-2697	98	13	error	error	NOUN
cana-2697	98	14	of	of	ADP
cana-2697	98	15	prediction	prediction	NOUN
cana-2697	98	16	and	and	CCONJ
cana-2697	98	17	present	present	VERB
cana-2697	98	18	a	a	DET
cana-2697	98	19	comprehensive	comprehensive	ADJ
cana-2697	98	20	performance	performance	NOUN
cana-2697	98	21	of	of	ADP
cana-2697	98	22	the	the	DET
cana-2697	98	23	model	model	NOUN
cana-2697	98	24	communications	communication	NOUN
cana-2697	98	25	on	on	ADP
cana-2697	98	26	applied	apply	VERB
cana-2697	98	27	nonlinear	nonlinear	ADJ
cana-2697	98	28	analysis	analysis	NOUN
cana-2697	98	29	issn	issn	NOUN
cana-2697	98	30	:	:	PUNCT
cana-2697	98	31	1074	1074	NUM
cana-2697	98	32	-	-	PUNCT
cana-2697	98	33	133x	133x	NUM
cana-2697	98	34	vol	vol	NOUN
cana-2697	98	35	32	32	NUM
cana-2697	99	1	no	no	NOUN
cana-2697	99	2	.	.	PUNCT
cana-2697	100	1	3s	3s	NUM
cana-2697	100	2	(	(	PUNCT
cana-2697	100	3	2025	2025	NUM
cana-2697	100	4	)	)	PUNCT
cana-2697	100	5	585	585	NUM
cana-2697	100	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	100	7	1	1	NUM
cana-2697	100	8	.	.	PUNCT
cana-2697	100	9	mse	mse	NOUN
cana-2697	100	10	:	:	PUNCT
cana-2697	100	11	mse=	mse=	PROPN
cana-2697	100	12	1	1	NUM
cana-2697	100	13	∑	∑	PUNCT
cana-2697	100	14	(	(	PUNCT
cana-2697	100	15	𝑦	𝑦	NOUN
cana-2697	100	16	−	−	PROPN
cana-2697	100	17	𝑦	𝑦	NOUN
cana-2697	100	18	)	)	PUNCT
cana-2697	100	19	2	2	NUM
cana-2697	100	20	𝑛	𝑛	PROPN
cana-2697	100	21	𝑖=1	𝑖=1	PUNCT
cana-2697	100	22	𝑖	𝑖	SYM
cana-2697	100	23	𝑖	𝑖	PRON
cana-2697	101	1	it	it	PRON
cana-2697	101	2	is	be	AUX
cana-2697	101	3	also	also	ADV
cana-2697	101	4	a	a	DET
cana-2697	101	5	robust	robust	ADJ
cana-2697	101	6	statistic	statistic	NOUN
cana-2697	101	7	that	that	PRON
cana-2697	101	8	measures	measure	VERB
cana-2697	101	9	the	the	DET
cana-2697	101	10	average	average	NOUN
cana-2697	101	11	of	of	ADP
cana-2697	101	12	squares	square	NOUN
cana-2697	101	13	of	of	ADP
cana-2697	101	14	the	the	DET
cana-2697	101	15	errors	error	NOUN
cana-2697	101	16	between	between	ADP
cana-2697	101	17	the	the	DET
cana-2697	101	18	actual	actual	ADJ
cana-2697	101	19	values	value	NOUN
cana-2697	101	20	(	(	PUNCT
cana-2697	101	21	y^i	y^i	NOUN
cana-2697	101	22	)	)	PUNCT
cana-2697	101	23	and	and	CCONJ
cana-2697	101	24	the	the	DET
cana-2697	101	25	predicted	predict	VERB
cana-2697	101	26	values	value	NOUN
cana-2697	101	27	(	(	PUNCT
cana-2697	101	28	yi	yi	NOUN
cana-2697	101	29	)	)	PUNCT
cana-2697	101	30	.	.	PUNCT
cana-2697	102	1	the	the	DET
cana-2697	102	2	score	score	NOUN
cana-2697	102	3	drops	drop	VERB
cana-2697	102	4	as	as	SCONJ
cana-2697	102	5	mse	mse	NOUN
cana-2697	102	6	improves	improve	VERB
cana-2697	102	7	.	.	PUNCT
cana-2697	103	1	2	2	X
cana-2697	103	2	.	.	X
cana-2697	103	3	rmse	rmse	NOUN
cana-2697	103	4	:	:	PUNCT
cana-2697	103	5	rmse=	rmse=	ADJ
cana-2697	103	6	√𝑀𝑆𝐸	√𝑀𝑆𝐸	X
cana-2697	103	7	it	it	PRON
cana-2697	103	8	gives	give	VERB
cana-2697	103	9	an	an	DET
cana-2697	103	10	error	error	NOUN
cana-2697	103	11	measure	measure	NOUN
cana-2697	103	12	that	that	PRON
cana-2697	103	13	is	be	AUX
cana-2697	103	14	in	in	ADP
cana-2697	103	15	the	the	DET
cana-2697	103	16	unit	unit	NOUN
cana-2697	103	17	of	of	ADP
cana-2697	103	18	the	the	DET
cana-2697	103	19	predicted	predict	VERB
cana-2697	103	20	values	value	NOUN
cana-2697	103	21	.	.	PUNCT
cana-2697	104	1	3	3	X
cana-2697	104	2	.	.	X
cana-2697	104	3	mae	mae	PROPN
cana-2697	104	4	:	:	PUNCT
cana-2697	104	5	mae	mae	PROPN
cana-2697	104	6	=	=	PROPN
cana-2697	104	7	1	1	NUM
cana-2697	104	8	∑𝑛	∑𝑛	PROPN
cana-2697	104	9	|𝑦	|𝑦	X
cana-2697	104	10	−	−	PROPN
cana-2697	104	11	𝑦	𝑦	PRON
cana-2697	104	12	|	|	NOUN
cana-2697	104	13	𝑛	𝑛	PRON
cana-2697	104	14	𝑖=1	𝑖=1	PUNCT
cana-2697	104	15	𝑖	𝑖	SYM
cana-2697	104	16	𝑖	𝑖	PROPN
cana-2697	104	17	calculates	calculate	VERB
cana-2697	104	18	the	the	DET
cana-2697	104	19	generalized	generalized	ADJ
cana-2697	104	20	average	average	ADJ
cana-2697	104	21	absolute	absolute	ADJ
cana-2697	104	22	error	error	NOUN
cana-2697	104	23	as	as	ADP
cana-2697	104	24	the	the	DET
cana-2697	104	25	one	one	NOUN
cana-2697	104	26	that	that	PRON
cana-2697	104	27	depicts	depict	VERB
cana-2697	104	28	the	the	DET
cana-2697	104	29	average	average	ADJ
cana-2697	104	30	bias	bias	NOUN
cana-2697	104	31	of	of	ADP
cana-2697	104	32	the	the	DET
cana-2697	104	33	predictions	prediction	NOUN
cana-2697	104	34	.	.	PUNCT
cana-2697	105	1	how	how	SCONJ
cana-2697	105	2	much	much	ADJ
cana-2697	105	3	do	do	AUX
cana-2697	105	4	the	the	DET
cana-2697	105	5	predictions	prediction	NOUN
cana-2697	105	6	differ	differ	VERB
cana-2697	105	7	from	from	ADP
cana-2697	105	8	the	the	DET
cana-2697	105	9	actual	actual	ADJ
cana-2697	105	10	values	value	NOUN
cana-2697	105	11	.	.	PUNCT
cana-2697	106	1	[	[	X
cana-2697	106	2	d	d	X
cana-2697	106	3	]	]	X
cana-2697	106	4	methods	method	NOUN
cana-2697	106	5	1	1	NUM
cana-2697	106	6	.	.	PUNCT
cana-2697	106	7	lightgbm	lightgbm	ADJ
cana-2697	106	8	-	-	PUNCT
cana-2697	106	9	lstm	lstm	NOUN
cana-2697	106	10	lightgbm	lightgbm	VERB
cana-2697	106	11	selects	select	NOUN
cana-2697	106	12	the	the	DET
cana-2697	106	13	strongest	strong	ADJ
cana-2697	106	14	features	feature	NOUN
cana-2697	106	15	which	which	PRON
cana-2697	106	16	lstm	lstm	NOUN
cana-2697	106	17	processes	process	NOUN
cana-2697	106	18	to	to	PART
cana-2697	106	19	build	build	VERB
cana-2697	106	20	temporal	temporal	ADJ
cana-2697	106	21	relations	relation	NOUN
cana-2697	106	22	effectively	effectively	ADV
cana-2697	106	23	.	.	PUNCT
cana-2697	107	1	lstm	lstm	PROPN
cana-2697	107	2	model	model	PROPN
cana-2697	107	3	captures	capture	VERB
cana-2697	107	4	order	order	NOUN
cana-2697	107	5	dependencies	dependency	NOUN
cana-2697	107	6	present	present	ADJ
cana-2697	107	7	in	in	ADP
cana-2697	107	8	the	the	DET
cana-2697	107	9	data	datum	NOUN
cana-2697	107	10	and	and	CCONJ
cana-2697	107	11	increases	increase	VERB
cana-2697	107	12	the	the	DET
cana-2697	107	13	accuracy	accuracy	NOUN
cana-2697	107	14	of	of	ADP
cana-2697	107	15	the	the	DET
cana-2697	107	16	predictions	prediction	NOUN
cana-2697	107	17	of	of	ADP
cana-2697	107	18	the	the	DET
cana-2697	107	19	dissolved	dissolve	VERB
cana-2697	107	20	oxygen	oxygen	NOUN
cana-2697	107	21	values	value	NOUN
cana-2697	107	22	.	.	PUNCT
cana-2697	108	1	this	this	DET
cana-2697	108	2	combination	combination	NOUN
cana-2697	108	3	guarantees	guarantee	VERB
cana-2697	108	4	good	good	ADJ
cana-2697	108	5	but	but	CCONJ
cana-2697	108	6	still	still	ADV
cana-2697	108	7	performance	performance	NOUN
cana-2697	108	8	that	that	PRON
cana-2697	108	9	can	can	AUX
cana-2697	108	10	be	be	AUX
cana-2697	108	11	enhanced	enhance	VERB
cana-2697	108	12	.	.	PUNCT
cana-2697	109	1	figure	figure	VERB
cana-2697	109	2	.4	.4	NUM
cana-2697	109	3	lightgbm	lightgbm	ADJ
cana-2697	109	4	-	-	PUNCT
cana-2697	109	5	lstm	lstm	NOUN
cana-2697	109	6	performance	performance	NOUN
cana-2697	109	7	:	:	PUNCT
cana-2697	109	8	mse	mse	NOUN
cana-2697	109	9	=	=	SYM
cana-2697	109	10	0.186240	0.186240	NUM
cana-2697	109	11	,	,	PUNCT
cana-2697	109	12	rmse	rmse	NOUN
cana-2697	109	13	=	=	PROPN
cana-2697	109	14	0.431555	0.431555	NUM
cana-2697	109	15	,	,	PUNCT
cana-2697	109	16	mae	mae	PROPN
cana-2697	109	17	=	=	PROPN
cana-2697	109	18	0.322099	0.322099	NUM
cana-2697	109	19	2	2	NUM
cana-2697	109	20	.	.	NUM
cana-2697	109	21	lightgbm	lightgbm	PROPN
cana-2697	109	22	-	-	PUNCT
cana-2697	109	23	gru	gru	NOUN
cana-2697	109	24	the	the	DET
cana-2697	109	25	gru	gru	NOUN
cana-2697	109	26	part	part	NOUN
cana-2697	109	27	has	have	VERB
cana-2697	109	28	a	a	DET
cana-2697	109	29	great	great	ADJ
cana-2697	109	30	advantage	advantage	NOUN
cana-2697	109	31	since	since	SCONJ
cana-2697	109	32	it	it	PRON
cana-2697	109	33	has	have	VERB
cana-2697	109	34	to	to	PART
cana-2697	109	35	deal	deal	VERB
cana-2697	109	36	only	only	ADV
cana-2697	109	37	with	with	ADP
cana-2697	109	38	lightgbm	lightgbm	ADJ
cana-2697	109	39	-	-	PUNCT
cana-2697	109	40	selected	select	VERB
cana-2697	109	41	features	feature	NOUN
cana-2697	109	42	.	.	PUNCT
cana-2697	110	1	this	this	PRON
cana-2697	110	2	speeds	speed	VERB
cana-2697	110	3	up	up	ADP
cana-2697	110	4	the	the	DET
cana-2697	110	5	training	training	NOUN
cana-2697	110	6	process	process	NOUN
cana-2697	110	7	and	and	CCONJ
cana-2697	110	8	incomparably	incomparably	ADV
cana-2697	110	9	reduces	reduce	VERB
cana-2697	110	10	the	the	DET
cana-2697	110	11	time	time	NOUN
cana-2697	110	12	.	.	PUNCT
cana-2697	111	1	its	its	PRON
cana-2697	111	2	architecture	architecture	NOUN
cana-2697	111	3	is	be	AUX
cana-2697	111	4	not	not	PART
cana-2697	111	5	as	as	ADV
cana-2697	111	6	complex	complex	ADJ
cana-2697	111	7	as	as	ADP
cana-2697	111	8	lstm	lstm	NOUN
cana-2697	111	9	,	,	PUNCT
cana-2697	111	10	allowing	allow	VERB
cana-2697	111	11	it	it	PRON
cana-2697	111	12	to	to	PART
cana-2697	111	13	cover	cover	VERB
cana-2697	111	14	temporal	temporal	ADJ
cana-2697	111	15	data	datum	NOUN
cana-2697	111	16	while	while	SCONJ
cana-2697	111	17	also	also	ADV
cana-2697	111	18	being	be	AUX
cana-2697	111	19	cost	cost	NOUN
cana-2697	111	20	-	-	PUNCT
cana-2697	111	21	effective	effective	ADJ
cana-2697	111	22	.	.	PUNCT
cana-2697	112	1	although	although	SCONJ
cana-2697	112	2	it	it	PRON
cana-2697	112	3	has	have	VERB
cana-2697	112	4	these	these	DET
cana-2697	112	5	benefits	benefit	NOUN
cana-2697	112	6	,	,	PUNCT
cana-2697	112	7	it	it	PRON
cana-2697	112	8	has	have	VERB
cana-2697	112	9	worse	bad	ADJ
cana-2697	112	10	efficiency	efficiency	NOUN
cana-2697	112	11	than	than	ADP
cana-2697	112	12	lstm	lstm	ADJ
cana-2697	112	13	communication	communication	NOUN
cana-2697	112	14	of	of	ADP
cana-2697	112	15	8.4	8.4	NUM
cana-2697	112	16	%	%	NOUN
cana-2697	112	17	.	.	PUNCT
cana-2697	113	1	communications	communication	NOUN
cana-2697	113	2	on	on	ADP
cana-2697	113	3	applied	apply	VERB
cana-2697	113	4	nonlinear	nonlinear	ADJ
cana-2697	113	5	analysis	analysis	NOUN
cana-2697	113	6	issn	issn	NOUN
cana-2697	113	7	:	:	PUNCT
cana-2697	113	8	1074	1074	NUM
cana-2697	113	9	-	-	PUNCT
cana-2697	113	10	133x	133x	NUM
cana-2697	113	11	vol	vol	NOUN
cana-2697	113	12	32	32	NUM
cana-2697	113	13	no	no	NOUN
cana-2697	113	14	.	.	PUNCT
cana-2697	114	1	3s	3s	NUM
cana-2697	114	2	(	(	PUNCT
cana-2697	114	3	2025	2025	NUM
cana-2697	114	4	)	)	PUNCT
cana-2697	114	5	586	586	NUM
cana-2697	114	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-2697	114	7	figure	figure	NOUN
cana-2697	114	8	.5	.5	NUM
cana-2697	114	9	lightgbm	lightgbm	ADJ
cana-2697	114	10	-	-	PUNCT
cana-2697	114	11	gru	gru	NOUN
cana-2697	114	12	performance	performance	NOUN
cana-2697	114	13	:	:	PUNCT
cana-2697	114	14	mse	mse	NOUN
cana-2697	114	15	=	=	SYM
cana-2697	114	16	0.190407	0.190407	PROPN
cana-2697	114	17	,	,	PUNCT
cana-2697	114	18	rmse	rmse	NOUN
cana-2697	114	19	=	=	PROPN
cana-2697	114	20	0.436357	0.436357	NUM
cana-2697	114	21	,	,	PUNCT
cana-2697	114	22	mae	mae	PROPN
cana-2697	114	23	=	=	PROPN
cana-2697	114	24	0.320164	0.320164	NUM
cana-2697	114	25	.	.	PUNCT
cana-2697	115	1	3	3	X
cana-2697	115	2	.	.	NUM
cana-2697	115	3	lightgbm	lightgbm	ADJ
cana-2697	115	4	-	-	PUNCT
cana-2697	115	5	bisru	bisru	NOUN
cana-2697	115	6	-	-	PUNCT
cana-2697	115	7	attention	attention	NOUN
cana-2697	115	8	bisru	bisru	NOUN
cana-2697	115	9	effectively	effectively	ADV
cana-2697	115	10	captures	capture	VERB
cana-2697	115	11	bidirectional	bidirectional	ADJ
cana-2697	115	12	temporal	temporal	ADJ
cana-2697	115	13	dependencies	dependency	NOUN
cana-2697	115	14	while	while	SCONJ
cana-2697	115	15	attention	attention	NOUN
cana-2697	115	16	mechanisms	mechanism	NOUN
cana-2697	115	17	can	can	AUX
cana-2697	115	18	focus	focus	VERB
cana-2697	115	19	on	on	ADP
cana-2697	115	20	the	the	DET
cana-2697	115	21	parameters	parameter	NOUN
cana-2697	115	22	and	and	CCONJ
cana-2697	115	23	dynamically	dynamically	ADV
cana-2697	115	24	change	change	VERB
cana-2697	115	25	its	its	PRON
cana-2697	115	26	weights	weight	NOUN
cana-2697	115	27	,	,	PUNCT
cana-2697	115	28	thus	thus	ADV
cana-2697	115	29	improving	improve	VERB
cana-2697	115	30	the	the	DET
cana-2697	115	31	accuracy	accuracy	NOUN
cana-2697	115	32	of	of	ADP
cana-2697	115	33	the	the	DET
cana-2697	115	34	model	model	NOUN
cana-2697	115	35	.	.	PUNCT
cana-2697	116	1	this	this	DET
cana-2697	116	2	method	method	NOUN
cana-2697	116	3	integrates	integrate	VERB
cana-2697	116	4	lightgbm	lightgbm	ADJ
cana-2697	116	5	-	-	PUNCT
cana-2697	116	6	selected	select	VERB
cana-2697	116	7	features	feature	NOUN
cana-2697	116	8	and	and	CCONJ
cana-2697	116	9	allows	allow	VERB
cana-2697	116	10	the	the	DET
cana-2697	116	11	model	model	NOUN
cana-2697	116	12	to	to	PART
cana-2697	116	13	deal	deal	VERB
cana-2697	116	14	effectively	effectively	ADV
cana-2697	116	15	with	with	ADP
cana-2697	116	16	complex	complex	ADJ
cana-2697	116	17	relationships	relationship	NOUN
cana-2697	116	18	between	between	ADP
cana-2697	116	19	features	feature	NOUN
cana-2697	116	20	and	and	CCONJ
cana-2697	116	21	target	target	VERB
cana-2697	116	22	variables	variable	NOUN
cana-2697	116	23	and	and	CCONJ
cana-2697	116	24	achieve	achieve	VERB
cana-2697	116	25	great	great	ADJ
cana-2697	116	26	success	success	NOUN
cana-2697	117	1	[	[	X
cana-2697	117	2	20][21	20][21	X
cana-2697	117	3	]	]	PUNCT
cana-2697	117	4	.	.	PUNCT
cana-2697	118	1	figure	figure	NOUN
cana-2697	118	2	.6	.6	NUM
cana-2697	118	3	lightgbm	lightgbm	ADJ
cana-2697	118	4	-	-	PUNCT
cana-2697	118	5	bisru	bisru	NOUN
cana-2697	118	6	-	-	PUNCT
cana-2697	118	7	attention	attention	NOUN
cana-2697	118	8	performance	performance	NOUN
cana-2697	118	9	:	:	PUNCT
cana-2697	118	10	mse	mse	PROPN
cana-2697	118	11	=	=	SYM
cana-2697	118	12	0.178316	0.178316	NUM
cana-2697	118	13	,	,	PUNCT
cana-2697	118	14	rmse	rmse	NOUN
cana-2697	118	15	=	=	SYM
cana-2697	118	16	0.422275	0.422275	NUM
cana-2697	118	17	,	,	PUNCT
cana-2697	118	18	mae	mae	PROPN
cana-2697	118	19	=	=	PROPN
cana-2697	118	20	0.296403	0.296403	NUM
cana-2697	118	21	.	.	PUNCT
cana-2697	119	1	4	4	X
cana-2697	119	2	.	.	X
cana-2697	119	3	ensemble	ensemble	ADJ
cana-2697	119	4	lightgbm	lightgbm	ADJ
cana-2697	119	5	-	-	PUNCT
cana-2697	119	6	bisru	bisru	NOUN
cana-2697	119	7	-	-	PUNCT
cana-2697	119	8	attention	attention	NOUN
cana-2697	119	9	this	this	PRON
cana-2697	119	10	is	be	AUX
cana-2697	119	11	an	an	DET
cana-2697	119	12	ensemble	ensemble	ADJ
cana-2697	119	13	model	model	NOUN
cana-2697	119	14	which	which	PRON
cana-2697	119	15	consists	consist	VERB
cana-2697	119	16	of	of	ADP
cana-2697	119	17	bisru	bisru	NOUN
cana-2697	119	18	,	,	PUNCT
cana-2697	119	19	lstm	lstm	PROPN
cana-2697	119	20	,	,	PUNCT
cana-2697	119	21	gru	gru	PROPN
cana-2697	119	22	,	,	PUNCT
cana-2697	119	23	and	and	CCONJ
cana-2697	119	24	attention	attention	NOUN
cana-2697	119	25	individually	individually	ADV
cana-2697	119	26	serving	serve	VERB
cana-2697	119	27	different	different	ADJ
cana-2697	119	28	functions	function	NOUN
cana-2697	119	29	.	.	PUNCT
cana-2697	120	1	the	the	DET
cana-2697	120	2	model	model	NOUN
cana-2697	120	3	combines	combine	VERB
cana-2697	120	4	these	these	DET
cana-2697	120	5	algorithms	algorithm	NOUN
cana-2697	120	6	and	and	CCONJ
cana-2697	120	7	produces	produce	VERB
cana-2697	120	8	a	a	DET
cana-2697	120	9	robust	robust	ADJ
cana-2697	120	10	prediction	prediction	NOUN
cana-2697	120	11	due	due	ADP
cana-2697	120	12	to	to	ADP
cana-2697	120	13	the	the	DET
cana-2697	120	14	synergetic	synergetic	ADJ
cana-2697	120	15	effect	effect	NOUN
cana-2697	120	16	of	of	ADP
cana-2697	120	17	different	different	ADJ
cana-2697	120	18	models	model	NOUN
cana-2697	120	19	that	that	PRON
cana-2697	120	20	serve	serve	VERB
cana-2697	120	21	different	different	ADJ
cana-2697	120	22	types	type	NOUN
cana-2697	120	23	of	of	ADP
cana-2697	120	24	learning	learn	VERB
cana-2697	120	25	.	.	PUNCT
cana-2697	121	1	figure	figure	VERB
cana-2697	121	2	.7	.7	PROPN
cana-2697	121	3	ensemble	ensemble	ADJ
cana-2697	121	4	lightgbm	lightgbm	ADJ
cana-2697	121	5	-	-	PUNCT
cana-2697	121	6	bisru	bisru	NOUN
cana-2697	121	7	-	-	PUNCT
cana-2697	121	8	attention	attention	NOUN
cana-2697	121	9	performance	performance	NOUN
cana-2697	121	10	:	:	PUNCT
cana-2697	121	11	mse	mse	NOUN
cana-2697	121	12	=	=	SYM
cana-2697	121	13	0.168309	0.168309	NUM
cana-2697	121	14	,	,	PUNCT
cana-2697	121	15	rmse	rmse	NOUN
cana-2697	121	16	=	=	SYM
cana-2697	121	17	0.410254	0.410254	NUM
cana-2697	121	18	,	,	PUNCT
cana-2697	121	19	mae	mae	PROPN
cana-2697	121	20	=	=	PROPN
cana-2697	121	21	0.286148	0.286148	NUM
cana-2697	121	22	.	.	PUNCT
cana-2697	122	1	communications	communication	NOUN
cana-2697	122	2	on	on	ADP
cana-2697	122	3	applied	apply	VERB
cana-2697	122	4	nonlinear	nonlinear	ADJ
cana-2697	122	5	analysis	analysis	NOUN
cana-2697	122	6	issn	issn	NOUN
cana-2697	122	7	:	:	PUNCT
cana-2697	122	8	1074	1074	NUM
cana-2697	122	9	-	-	PUNCT
cana-2697	122	10	133x	133x	NUM
cana-2697	122	11	vol	vol	NOUN
cana-2697	122	12	32	32	NUM
cana-2697	122	13	no	no	NOUN
cana-2697	122	14	.	.	PUNCT
cana-2697	123	1	3s	3s	NUM
cana-2697	123	2	(	(	PUNCT
cana-2697	123	3	2025	2025	NUM
cana-2697	123	4	)	)	PUNCT
cana-2697	123	5	587	587	NUM
cana-2697	123	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	123	7	5	5	X
cana-2697	123	8	.	.	PUNCT
cana-2697	123	9	ensemble	ensemble	ADJ
cana-2697	123	10	mine	mine	NOUN
cana-2697	123	11	-	-	PUNCT
cana-2697	123	12	bisru	bisru	NOUN
cana-2697	123	13	-	-	PUNCT
cana-2697	123	14	attention	attention	NOUN
cana-2697	123	15	it	it	PRON
cana-2697	123	16	integrates	integrate	VERB
cana-2697	123	17	mic	mic	ADV
cana-2697	123	18	-	-	PUNCT
cana-2697	123	19	selected	select	VERB
cana-2697	123	20	features	feature	NOUN
cana-2697	123	21	with	with	ADP
cana-2697	123	22	bisru	bisru	PROPN
cana-2697	123	23	,	,	PUNCT
cana-2697	123	24	gru	gru	PROPN
cana-2697	123	25	,	,	PUNCT
cana-2697	123	26	and	and	CCONJ
cana-2697	123	27	lstm	lstm	NOUN
cana-2697	123	28	integrated	integrate	VERB
cana-2697	123	29	in	in	ADP
cana-2697	123	30	an	an	DET
cana-2697	123	31	ensemble	ensemble	ADJ
cana-2697	123	32	model	model	NOUN
cana-2697	123	33	.	.	PUNCT
cana-2697	124	1	the	the	DET
cana-2697	124	2	model	model	NOUN
cana-2697	124	3	improves	improve	VERB
cana-2697	124	4	non	non	ADJ
cana-2697	124	5	-	-	ADJ
cana-2697	124	6	linear	linear	ADJ
cana-2697	124	7	feature	feature	NOUN
cana-2697	124	8	handling	handling	NOUN
cana-2697	124	9	and	and	CCONJ
cana-2697	124	10	achieves	achieve	VERB
cana-2697	124	11	the	the	DET
cana-2697	124	12	best	good	ADJ
cana-2697	124	13	accuracy	accuracy	NOUN
cana-2697	124	14	as	as	SCONJ
cana-2697	124	15	such	such	ADJ
cana-2697	124	16	is	be	AUX
cana-2697	124	17	well	well	ADV
cana-2697	124	18	suited	suited	ADJ
cana-2697	124	19	for	for	ADP
cana-2697	124	20	prediction	prediction	NOUN
cana-2697	124	21	of	of	ADP
cana-2697	124	22	dissolved	dissolve	VERB
cana-2697	124	23	oxygen	oxygen	NOUN
cana-2697	124	24	concentration	concentration	NOUN
cana-2697	124	25	in	in	ADP
cana-2697	124	26	aquaculture	aquaculture	NOUN
cana-2697	124	27	[	[	X
cana-2697	124	28	23	23	NUM
cana-2697	124	29	]	]	PUNCT
cana-2697	124	30	.	.	PUNCT
cana-2697	125	1	figure	figure	NOUN
cana-2697	125	2	.8	.8	PROPN
cana-2697	125	3	ensemble	ensemble	ADJ
cana-2697	125	4	mine	mine	NOUN
cana-2697	125	5	-	-	PUNCT
cana-2697	125	6	bisru	bisru	NOUN
cana-2697	125	7	-	-	PUNCT
cana-2697	125	8	attention	attention	NOUN
cana-2697	125	9	performance	performance	NOUN
cana-2697	125	10	:	:	PUNCT
cana-2697	125	11	mse	mse	NOUN
cana-2697	125	12	=	=	SYM
cana-2697	125	13	0.104238	0.104238	NUM
cana-2697	125	14	,	,	PUNCT
cana-2697	125	15	rmse	rmse	NOUN
cana-2697	125	16	=	=	SYM
cana-2697	125	17	0.322859	0.322859	PROPN
cana-2697	125	18	,	,	PUNCT
cana-2697	125	19	mae	mae	PROPN
cana-2697	125	20	=	=	PROPN
cana-2697	125	21	0.232395	0.232395	PROPN
cana-2697	125	22	.	.	NOUN
cana-2697	126	1	6	6	NUM
cana-2697	126	2	.	.	X
cana-2697	126	3	comparison	comparison	NOUN
cana-2697	126	4	graph	graph	NOUN
cana-2697	126	5	for	for	ADP
cana-2697	126	6	all	all	DET
cana-2697	126	7	models	model	NOUN
cana-2697	126	8	figure	figure	VERB
cana-2697	126	9	.9	.9	PROPN
cana-2697	126	10	mse	mse	PROPN
cana-2697	126	11	,	,	PUNCT
cana-2697	126	12	mae	mae	PROPN
cana-2697	126	13	and	and	CCONJ
cana-2697	126	14	rmse	rmse	ADJ
cana-2697	126	15	comparison	comparison	NOUN
cana-2697	126	16	graph	graph	NOUN
cana-2697	126	17	for	for	ADP
cana-2697	126	18	all	all	DET
cana-2697	126	19	models	model	NOUN
cana-2697	126	20	figure	figure	VERB
cana-2697	126	21	.9	.9	NUM
cana-2697	126	22	graph	graph	NOUN
cana-2697	126	23	x	x	NOUN
cana-2697	126	24	-	-	NOUN
cana-2697	126	25	axis	axis	NOUN
cana-2697	126	26	represents	represent	VERB
cana-2697	126	27	algorithm	algorithm	NOUN
cana-2697	126	28	names	name	NOUN
cana-2697	126	29	and	and	CCONJ
cana-2697	126	30	y	y	NOUN
cana-2697	126	31	-	-	PUNCT
cana-2697	126	32	axis	axis	NOUN
cana-2697	126	33	represents	represent	VERB
cana-2697	126	34	mse	mse	PROPN
cana-2697	126	35	,	,	PUNCT
cana-2697	126	36	mae	mae	PROPN
cana-2697	126	37	,	,	PUNCT
cana-2697	126	38	and	and	CCONJ
cana-2697	126	39	rmse	rmse	ADJ
cana-2697	126	40	values	value	NOUN
cana-2697	126	41	in	in	ADP
cana-2697	126	42	different	different	ADJ
cana-2697	126	43	color	color	NOUN
cana-2697	126	44	bars	bar	NOUN
cana-2697	126	45	,	,	PUNCT
cana-2697	126	46	and	and	CCONJ
cana-2697	126	47	in	in	ADP
cana-2697	126	48	all	all	DET
cana-2697	126	49	algorithms	algorithm	NOUN
cana-2697	126	50	,	,	PUNCT
cana-2697	126	51	mic	mic	NOUN
cana-2697	126	52	has	have	AUX
cana-2697	126	53	got	get	VERB
cana-2697	126	54	less	less	ADJ
cana-2697	126	55	mse	mse	NOUN
cana-2697	126	56	error	error	NOUN
cana-2697	126	57	[	[	X
cana-2697	126	58	25	25	NUM
cana-2697	126	59	]	]	PUNCT
cana-2697	126	60	.	.	PUNCT
cana-2697	127	1	table	table	NOUN
cana-2697	127	2	2.performance	2.performance	NUM
cana-2697	127	3	comparison	comparison	NOUN
cana-2697	127	4	table	table	NOUN
cana-2697	127	5	for	for	ADP
cana-2697	127	6	all	all	DET
cana-2697	127	7	models	model	NOUN
cana-2697	127	8	model	model	NOUN
cana-2697	127	9	feature	feature	NOUN
cana-2697	127	10	selection	selection	PROPN
cana-2697	127	11	mse	mse	PROPN
cana-2697	127	12	rmse	rmse	PROPN
cana-2697	127	13	mae	mae	PROPN
cana-2697	127	14	lightgbm	lightgbm	ADJ
cana-2697	127	15	-	-	PUNCT
cana-2697	127	16	lstm	lstm	NOUN
cana-2697	127	17	lightgbm	lightgbm	VERB
cana-2697	127	18	0.186240	0.186240	NUM
cana-2697	127	19	0.431555	0.431555	NUM
cana-2697	127	20	0.322099	0.322099	NUM
cana-2697	127	21	lightgbm	lightgbm	ADJ
cana-2697	127	22	-	-	PUNCT
cana-2697	127	23	gru	gru	NOUN
cana-2697	127	24	lightgbm	lightgbm	VERB
cana-2697	127	25	0.190407	0.190407	NUM
cana-2697	127	26	0.436357	0.436357	NUM
cana-2697	127	27	0.320164	0.320164	NUM
cana-2697	127	28	lightgbm	lightgbm	ADJ
cana-2697	127	29	-	-	PUNCT
cana-2697	127	30	bisru	bisru	NOUN
cana-2697	127	31	-	-	PUNCT
cana-2697	127	32	attention	attention	NOUN
cana-2697	127	33	lightgbm	lightgbm	VERB
cana-2697	127	34	0.178316	0.178316	NUM
cana-2697	127	35	0.422275	0.422275	NUM
cana-2697	127	36	0.296403	0.296403	NUM
cana-2697	127	37	ensemble	ensemble	ADJ
cana-2697	127	38	lightgbm	lightgbm	ADJ
cana-2697	127	39	-	-	PUNCT
cana-2697	127	40	bisru	bisru	NOUN
cana-2697	127	41	-	-	PUNCT
cana-2697	127	42	attention	attention	NOUN
cana-2697	127	43	lightgbm	lightgbm	VERB
cana-2697	127	44	0.168309	0.168309	NUM
cana-2697	127	45	0.410254	0.410254	NUM
cana-2697	127	46	0.286148	0.286148	NUM
cana-2697	127	47	ensemble	ensemble	ADJ
cana-2697	127	48	mine	mine	NOUN
cana-2697	127	49	-	-	PUNCT
cana-2697	127	50	bisru	bisru	NOUN
cana-2697	127	51	-	-	PUNCT
cana-2697	127	52	attention	attention	NOUN
cana-2697	127	53	mic	mic	NOUN
cana-2697	127	54	0.104238	0.104238	NUM
cana-2697	127	55	0.322859	0.322859	NUM
cana-2697	127	56	0.232395	0.232395	NUM
cana-2697	127	57	communications	communication	NOUN
cana-2697	127	58	on	on	ADP
cana-2697	127	59	applied	apply	VERB
cana-2697	127	60	nonlinear	nonlinear	ADJ
cana-2697	127	61	analysis	analysis	NOUN
cana-2697	127	62	issn	issn	NOUN
cana-2697	127	63	:	:	PUNCT
cana-2697	127	64	1074	1074	NUM
cana-2697	127	65	-	-	PUNCT
cana-2697	127	66	133x	133x	NUM
cana-2697	127	67	vol	vol	NOUN
cana-2697	127	68	32	32	NUM
cana-2697	127	69	no	no	NOUN
cana-2697	127	70	.	.	PUNCT
cana-2697	128	1	3s	3s	NUM
cana-2697	128	2	(	(	PUNCT
cana-2697	128	3	2025	2025	NUM
cana-2697	128	4	)	)	PUNCT
cana-2697	128	5	588	588	NUM
cana-2697	128	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	128	7	vii	vii	PROPN
cana-2697	128	8	.	.	PROPN
cana-2697	128	9	results	result	VERB
cana-2697	128	10	the	the	DET
cana-2697	128	11	ensemble	ensemble	ADJ
cana-2697	128	12	mine	mine	ADJ
cana-2697	128	13	-	-	PUNCT
cana-2697	128	14	bisru	bisru	NOUN
cana-2697	128	15	-	-	PUNCT
cana-2697	128	16	attention	attention	NOUN
cana-2697	128	17	model	model	NOUN
cana-2697	128	18	was	be	AUX
cana-2697	128	19	shown	show	VERB
cana-2697	128	20	to	to	PART
cana-2697	128	21	be	be	AUX
cana-2697	128	22	superior	superior	ADJ
cana-2697	128	23	as	as	SCONJ
cana-2697	128	24	compared	compare	VERB
cana-2697	128	25	to	to	ADP
cana-2697	128	26	all	all	PRON
cana-2697	128	27	of	of	ADP
cana-2697	128	28	the	the	DET
cana-2697	128	29	models	model	NOUN
cana-2697	128	30	in	in	ADP
cana-2697	128	31	terms	term	NOUN
cana-2697	128	32	of	of	ADP
cana-2697	128	33	dissolved	dissolve	VERB
cana-2697	128	34	oxygen	oxygen	NOUN
cana-2697	128	35	level	level	NOUN
cana-2697	128	36	(	(	PUNCT
cana-2697	128	37	do	do	NOUN
cana-2697	128	38	)	)	PUNCT
cana-2697	128	39	prediction	prediction	NOUN
cana-2697	128	40	exhibiting	exhibit	VERB
cana-2697	128	41	great	great	ADJ
cana-2697	128	42	strength	strength	NOUN
cana-2697	128	43	.	.	PUNCT
cana-2697	129	1	while	while	SCONJ
cana-2697	129	2	utilizing	utilize	VERB
cana-2697	129	3	mic	mic	NOUN
cana-2697	129	4	-	-	PUNCT
cana-2697	129	5	selected	select	VERB
cana-2697	129	6	features	feature	NOUN
cana-2697	129	7	and	and	CCONJ
cana-2697	129	8	bisru	bisru	NOUN
cana-2697	129	9	-	-	PUNCT
cana-2697	129	10	gru	gru	NOUN
cana-2697	129	11	-	-	NOUN
cana-2697	129	12	lstm	lstm	NOUN
cana-2697	129	13	in	in	ADP
cana-2697	129	14	combination	combination	NOUN
cana-2697	129	15	,	,	PUNCT
cana-2697	129	16	the	the	DET
cana-2697	129	17	model	model	NOUN
cana-2697	129	18	performed	perform	VERB
cana-2697	129	19	well	well	ADV
cana-2697	129	20	in	in	ADP
cana-2697	129	21	locating	locate	VERB
cana-2697	129	22	both	both	CCONJ
cana-2697	129	23	linear	linear	ADJ
cana-2697	129	24	and	and	CCONJ
cana-2697	129	25	non	non	ADJ
cana-2697	129	26	-	-	ADJ
cana-2697	129	27	linear	linear	ADJ
cana-2697	129	28	dependencies	dependency	NOUN
cana-2697	129	29	in	in	ADP
cana-2697	129	30	the	the	DET
cana-2697	129	31	dataset	dataset	NOUN
cana-2697	129	32	.	.	PUNCT
cana-2697	130	1	for	for	ADP
cana-2697	130	2	instance	instance	NOUN
cana-2697	130	3	,	,	PUNCT
cana-2697	130	4	considering	consider	VERB
cana-2697	130	5	input	input	NOUN
cana-2697	130	6	values	value	NOUN
cana-2697	130	7	[	[	X
cana-2697	130	8	7.12	7.12	NUM
cana-2697	130	9	,	,	PUNCT
cana-2697	130	10	4.1	4.1	NUM
cana-2697	130	11	,	,	PUNCT
cana-2697	130	12	343	343	NUM
cana-2697	130	13	]	]	PUNCT
cana-2697	130	14	which	which	PRON
cana-2697	130	15	refer	refer	VERB
cana-2697	130	16	to	to	ADP
cana-2697	130	17	ph	ph	ADJ
cana-2697	130	18	,	,	PUNCT
cana-2697	130	19	turbidity	turbidity	NOUN
cana-2697	130	20	,	,	PUNCT
cana-2697	130	21	and	and	CCONJ
cana-2697	130	22	electrical	electrical	ADJ
cana-2697	130	23	conductivity	conductivity	NOUN
cana-2697	130	24	respectively	respectively	ADV
cana-2697	130	25	,	,	PUNCT
cana-2697	130	26	the	the	DET
cana-2697	130	27	predicted	predict	VERB
cana-2697	130	28	value	value	NOUN
cana-2697	130	29	for	for	ADP
cana-2697	130	30	do	do	PROPN
cana-2697	130	31	was	be	AUX
cana-2697	130	32	8.78	8.78	NUM
cana-2697	130	33	which	which	PRON
cana-2697	130	34	is	be	AUX
cana-2697	130	35	close	close	ADJ
cana-2697	130	36	to	to	ADP
cana-2697	130	37	the	the	DET
cana-2697	130	38	expected	expect	VERB
cana-2697	130	39	value	value	NOUN
cana-2697	130	40	.	.	PUNCT
cana-2697	131	1	also	also	ADV
cana-2697	131	2	,	,	PUNCT
cana-2697	131	3	for	for	ADP
cana-2697	131	4	the	the	DET
cana-2697	131	5	input	input	NOUN
cana-2697	131	6	values	value	NOUN
cana-2697	131	7	[	[	X
cana-2697	131	8	7.35	7.35	NUM
cana-2697	131	9	,	,	PUNCT
cana-2697	131	10	3.7	3.7	NUM
cana-2697	131	11	,	,	PUNCT
cana-2697	131	12	369	369	NUM
cana-2697	131	13	]	]	PUNCT
cana-2697	131	14	predicted	predict	VERB
cana-2697	131	15	value	value	NOUN
cana-2697	131	16	for	for	ADP
cana-2697	131	17	do	do	PROPN
cana-2697	131	18	was	be	AUX
cana-2697	131	19	9.39	9.39	NUM
cana-2697	131	20	.	.	PUNCT
cana-2697	132	1	thus	thus	ADV
cana-2697	132	2	,	,	PUNCT
cana-2697	132	3	these	these	DET
cana-2697	132	4	findings	finding	NOUN
cana-2697	132	5	indicate	indicate	VERB
cana-2697	132	6	the	the	DET
cana-2697	132	7	model	model	NOUN
cana-2697	132	8	's	's	PART
cana-2697	132	9	need	need	NOUN
cana-2697	132	10	in	in	ADP
cana-2697	132	11	most	most	ADJ
cana-2697	132	12	aquaculture	aquaculture	NOUN
cana-2697	132	13	applications	application	NOUN
cana-2697	132	14	since	since	SCONJ
cana-2697	132	15	predictions	prediction	NOUN
cana-2697	132	16	are	be	AUX
cana-2697	132	17	accurate	accurate	ADJ
cana-2697	132	18	and	and	CCONJ
cana-2697	132	19	dependable	dependable	ADJ
cana-2697	132	20	.	.	PUNCT
cana-2697	133	1	viii	viii	PROPN
cana-2697	133	2	.	.	PUNCT
cana-2697	134	1	discussion	discussion	NOUN
cana-2697	134	2	the	the	DET
cana-2697	134	3	results	result	NOUN
cana-2697	134	4	obtained	obtain	VERB
cana-2697	134	5	from	from	ADP
cana-2697	134	6	the	the	DET
cana-2697	134	7	previously	previously	ADV
cana-2697	134	8	proposed	propose	VERB
cana-2697	134	9	hybrid	hybrid	NOUN
cana-2697	134	10	models	model	NOUN
cana-2697	134	11	showed	show	VERB
cana-2697	134	12	a	a	DET
cana-2697	134	13	large	large	ADJ
cana-2697	134	14	increase	increase	NOUN
cana-2697	134	15	when	when	SCONJ
cana-2697	134	16	compared	compare	VERB
cana-2697	134	17	to	to	ADP
cana-2697	134	18	the	the	DET
cana-2697	134	19	base	base	NOUN
cana-2697	134	20	models	model	NOUN
cana-2697	134	21	and	and	CCONJ
cana-2697	134	22	the	the	DET
cana-2697	134	23	results	result	NOUN
cana-2697	134	24	obtained	obtain	VERB
cana-2697	134	25	from	from	ADP
cana-2697	134	26	the	the	DET
cana-2697	134	27	stated	state	VERB
cana-2697	134	28	base	base	NOUN
cana-2697	134	29	paper	paper	NOUN
cana-2697	134	30	.	.	PUNCT
cana-2697	135	1	for	for	ADP
cana-2697	135	2	instance	instance	NOUN
cana-2697	135	3	,	,	PUNCT
cana-2697	135	4	the	the	DET
cana-2697	135	5	existing	exist	VERB
cana-2697	135	6	lstm	lstm	NOUN
cana-2697	135	7	model	model	NOUN
cana-2697	135	8	registered	register	VERB
cana-2697	135	9	mse	mse	NOUN
cana-2697	135	10	at	at	ADP
cana-2697	135	11	the	the	DET
cana-2697	135	12	rate	rate	NOUN
cana-2697	135	13	of	of	ADP
cana-2697	135	14	0.186240	0.186240	NUM
cana-2697	135	15	,	,	PUNCT
cana-2697	135	16	while	while	SCONJ
cana-2697	135	17	the	the	DET
cana-2697	135	18	existing	exist	VERB
cana-2697	135	19	gru	gru	NOUN
cana-2697	135	20	model	model	NOUN
cana-2697	135	21	recorded	record	VERB
cana-2697	135	22	a	a	DET
cana-2697	135	23	relatively	relatively	ADV
cana-2697	135	24	moderate	moderate	ADJ
cana-2697	135	25	rating	rating	NOUN
cana-2697	135	26	of	of	ADP
cana-2697	135	27	0.190407	0.190407	NUM
cana-2697	135	28	mse	mse	NOUN
cana-2697	135	29	as	as	ADV
cana-2697	135	30	well	well	ADV
cana-2697	135	31	.	.	PUNCT
cana-2697	136	1	the	the	DET
cana-2697	136	2	proposed	propose	VERB
cana-2697	136	3	lightgbm	lightgbm	ADJ
cana-2697	136	4	-	-	PUNCT
cana-2697	136	5	bisru	bisru	NOUN
cana-2697	136	6	attention	attention	NOUN
cana-2697	136	7	model	model	NOUN
cana-2697	136	8	has	have	AUX
cana-2697	136	9	registered	register	VERB
cana-2697	136	10	appreciable	appreciable	ADJ
cana-2697	136	11	mse	mse	NOUN
cana-2697	136	12	performance	performance	NOUN
cana-2697	136	13	of	of	ADP
cana-2697	136	14	0.178316	0.178316	NUM
cana-2697	136	15	.	.	PUNCT
cana-2697	137	1	however	however	ADV
cana-2697	137	2	,	,	PUNCT
cana-2697	137	3	this	this	DET
cana-2697	137	4	improvement	improvement	NOUN
cana-2697	137	5	in	in	ADP
cana-2697	137	6	performance	performance	NOUN
cana-2697	137	7	has	have	AUX
cana-2697	137	8	been	be	AUX
cana-2697	137	9	made	make	VERB
cana-2697	137	10	possible	possible	ADJ
cana-2697	137	11	mainly	mainly	ADV
cana-2697	137	12	because	because	SCONJ
cana-2697	137	13	of	of	ADP
cana-2697	137	14	the	the	DET
cana-2697	137	15	incorporation	incorporation	NOUN
cana-2697	137	16	of	of	ADP
cana-2697	137	17	advanced	advanced	ADJ
cana-2697	137	18	sequence	sequence	NOUN
cana-2697	137	19	modeling	model	VERB
cana-2697	137	20	techniques	technique	NOUN
cana-2697	137	21	in	in	ADP
cana-2697	137	22	the	the	DET
cana-2697	137	23	various	various	ADJ
cana-2697	137	24	tasks	task	NOUN
cana-2697	137	25	of	of	ADP
cana-2697	137	26	do	do	AUX
cana-2697	137	27	modeling	modeling	NOUN
cana-2697	137	28	.	.	PUNCT
cana-2697	138	1	moreover	moreover	ADV
cana-2697	138	2	,	,	PUNCT
cana-2697	138	3	the	the	DET
cana-2697	138	4	ensemble	ensemble	ADJ
cana-2697	138	5	lightgbm	lightgbm	ADJ
cana-2697	138	6	-	-	PUNCT
cana-2697	138	7	bisru	bisru	NOUN
cana-2697	138	8	-	-	PUNCT
cana-2697	138	9	attention	attention	NOUN
cana-2697	138	10	model	model	NOUN
cana-2697	138	11	lowered	lower	VERB
cana-2697	138	12	mse	mse	NOUN
cana-2697	138	13	to	to	ADP
cana-2697	138	14	0.168309	0.168309	NUM
cana-2697	138	15	,	,	PUNCT
cana-2697	138	16	which	which	PRON
cana-2697	138	17	was	be	AUX
cana-2697	138	18	made	make	VERB
cana-2697	138	19	possible	possible	ADJ
cana-2697	138	20	by	by	ADP
cana-2697	138	21	the	the	DET
cana-2697	138	22	incorporation	incorporation	NOUN
cana-2697	138	23	of	of	ADP
cana-2697	138	24	more	more	ADJ
cana-2697	138	25	than	than	ADP
cana-2697	138	26	one	one	NUM
cana-2697	138	27	algorithm	algorithm	NOUN
cana-2697	138	28	,	,	PUNCT
cana-2697	138	29	including	include	VERB
cana-2697	138	30	bisru	bisru	NOUN
cana-2697	138	31	,	,	PUNCT
cana-2697	138	32	gru	gru	PROPN
cana-2697	138	33	,	,	PUNCT
cana-2697	138	34	and	and	CCONJ
cana-2697	138	35	lstm	lstm	NOUN
cana-2697	138	36	in	in	ADP
cana-2697	138	37	an	an	DET
cana-2697	138	38	ensemble	ensemble	NOUN
cana-2697	138	39	.	.	PUNCT
cana-2697	139	1	the	the	DET
cana-2697	139	2	optimum	optimum	ADJ
cana-2697	139	3	performance	performance	NOUN
cana-2697	139	4	was	be	AUX
cana-2697	139	5	reported	report	VERB
cana-2697	139	6	with	with	ADP
cana-2697	139	7	the	the	DET
cana-2697	139	8	ensemble	ensemble	ADJ
cana-2697	139	9	mine	mine	NOUN
cana-2697	139	10	-	-	PUNCT
cana-2697	139	11	bisru	bisru	NOUN
cana-2697	139	12	-	-	PUNCT
cana-2697	139	13	attention	attention	NOUN
cana-2697	139	14	model	model	NOUN
cana-2697	139	15	,	,	PUNCT
cana-2697	139	16	achieving	achieve	VERB
cana-2697	139	17	an	an	DET
cana-2697	139	18	mse	mse	NOUN
cana-2697	139	19	of	of	ADP
cana-2697	139	20	0.104238	0.104238	NUM
cana-2697	139	21	.	.	PUNCT
cana-2697	140	1	error	error	NOUN
cana-2697	140	2	prediction	prediction	NOUN
cana-2697	140	3	was	be	AUX
cana-2697	140	4	further	far	ADV
cana-2697	140	5	improved	improve	VERB
cana-2697	140	6	as	as	SCONJ
cana-2697	140	7	this	this	DET
cana-2697	140	8	model	model	NOUN
cana-2697	140	9	used	use	VERB
cana-2697	140	10	mic	mic	NOUN
cana-2697	140	11	in	in	ADP
cana-2697	140	12	feature	feature	NOUN
cana-2697	140	13	selection	selection	NOUN
cana-2697	140	14	.	.	PUNCT
cana-2697	141	1	it	it	PRON
cana-2697	141	2	is	be	AUX
cana-2697	141	3	also	also	ADV
cana-2697	141	4	noteworthy	noteworthy	ADJ
cana-2697	141	5	that	that	SCONJ
cana-2697	141	6	this	this	DET
cana-2697	141	7	model	model	NOUN
cana-2697	141	8	was	be	AUX
cana-2697	141	9	able	able	ADJ
cana-2697	141	10	to	to	PART
cana-2697	141	11	reduce	reduce	VERB
cana-2697	141	12	the	the	DET
cana-2697	141	13	rmse	rmse	NOUN
cana-2697	141	14	to	to	ADP
cana-2697	141	15	0.322859	0.322859	NUM
cana-2697	141	16	and	and	CCONJ
cana-2697	141	17	mae	mae	PROPN
cana-2697	141	18	to	to	ADP
cana-2697	141	19	0.232395	0.232395	NUM
cana-2697	141	20	,	,	PUNCT
cana-2697	141	21	which	which	PRON
cana-2697	141	22	indeed	indeed	ADV
cana-2697	141	23	shows	show	VERB
cana-2697	141	24	the	the	DET
cana-2697	141	25	reliability	reliability	NOUN
cana-2697	141	26	and	and	CCONJ
cana-2697	141	27	strength	strength	NOUN
cana-2697	141	28	of	of	ADP
cana-2697	141	29	this	this	DET
cana-2697	141	30	ensemble	ensemble	ADJ
cana-2697	141	31	technique	technique	NOUN
cana-2697	141	32	and	and	CCONJ
cana-2697	141	33	,	,	PUNCT
cana-2697	141	34	in	in	ADP
cana-2697	141	35	particular	particular	ADJ
cana-2697	141	36	,	,	PUNCT
cana-2697	141	37	the	the	DET
cana-2697	141	38	models	model	NOUN
cana-2697	141	39	developed	develop	VERB
cana-2697	141	40	for	for	ADP
cana-2697	141	41	use	use	NOUN
cana-2697	141	42	in	in	ADP
cana-2697	141	43	aquaculture	aquaculture	NOUN
cana-2697	141	44	[	[	X
cana-2697	141	45	25	25	NUM
cana-2697	141	46	]	]	PUNCT
cana-2697	141	47	.	.	PUNCT
cana-2697	142	1	these	these	DET
cana-2697	142	2	results	result	NOUN
cana-2697	142	3	confirm	confirm	VERB
cana-2697	142	4	that	that	SCONJ
cana-2697	142	5	the	the	DET
cana-2697	142	6	prediction	prediction	NOUN
cana-2697	142	7	accuracy	accuracy	NOUN
cana-2697	142	8	,	,	PUNCT
cana-2697	142	9	along	along	ADP
cana-2697	142	10	with	with	ADP
cana-2697	142	11	computing	computing	NOUN
cana-2697	142	12	efficiency	efficiency	NOUN
cana-2697	142	13	and	and	CCONJ
cana-2697	142	14	overall	overall	ADJ
cana-2697	142	15	scalability	scalability	NOUN
cana-2697	142	16	,	,	PUNCT
cana-2697	142	17	are	be	AUX
cana-2697	142	18	all	all	ADV
cana-2697	142	19	enhanced	enhance	VERB
cana-2697	142	20	thanks	thank	NOUN
cana-2697	142	21	to	to	ADP
cana-2697	142	22	the	the	DET
cana-2697	142	23	integration	integration	NOUN
cana-2697	142	24	of	of	ADP
cana-2697	142	25	advanced	advanced	ADJ
cana-2697	142	26	feature	feature	NOUN
cana-2697	142	27	selection	selection	NOUN
cana-2697	142	28	and	and	CCONJ
cana-2697	142	29	ensemble	ensemble	ADJ
cana-2697	142	30	learning	learning	NOUN
cana-2697	142	31	.	.	PUNCT
cana-2697	143	1	they	they	PRON
cana-2697	143	2	have	have	AUX
cana-2697	143	3	indeed	indeed	ADV
cana-2697	143	4	set	set	VERB
cana-2697	143	5	a	a	DET
cana-2697	143	6	new	new	ADJ
cana-2697	143	7	benchmark	benchmark	NOUN
cana-2697	143	8	for	for	ADP
cana-2697	143	9	determining	determine	VERB
cana-2697	143	10	dissolved	dissolve	VERB
cana-2697	143	11	oxygen	oxygen	NOUN
cana-2697	143	12	levels	level	NOUN
cana-2697	143	13	in	in	ADP
cana-2697	143	14	aquaculture	aquaculture	NOUN
cana-2697	143	15	systems	system	NOUN
cana-2697	143	16	[	[	X
cana-2697	143	17	27	27	NUM
cana-2697	143	18	]	]	PUNCT
cana-2697	143	19	.	.	PUNCT
cana-2697	144	1	ix	ix	PROPN
cana-2697	144	2	.	.	PUNCT
cana-2697	145	1	conclusion	conclusion	NOUN
cana-2697	145	2	the	the	DET
cana-2697	145	3	current	current	ADJ
cana-2697	145	4	study	study	NOUN
cana-2697	145	5	offers	offer	VERB
cana-2697	145	6	some	some	DET
cana-2697	145	7	assistance	assistance	NOUN
cana-2697	145	8	with	with	ADP
cana-2697	145	9	the	the	DET
cana-2697	145	10	suggestions	suggestion	NOUN
cana-2697	145	11	of	of	ADP
cana-2697	145	12	two	two	NUM
cana-2697	145	13	models	model	NOUN
cana-2697	145	14	of	of	ADP
cana-2697	145	15	the	the	DET
cana-2697	145	16	architecture	architecture	NOUN
cana-2697	145	17	for	for	ADP
cana-2697	145	18	predicting	predict	VERB
cana-2697	145	19	do	do	AUX
cana-2697	145	20	levels	level	NOUN
cana-2697	145	21	,	,	PUNCT
cana-2697	145	22	which	which	PRON
cana-2697	145	23	are	be	AUX
cana-2697	145	24	lightgbm	lightgbm	ADJ
cana-2697	145	25	-	-	PUNCT
cana-2697	145	26	bisru	bisru	NOUN
cana-2697	145	27	-	-	PUNCT
cana-2697	145	28	attention	attention	NOUN
cana-2697	145	29	and	and	CCONJ
cana-2697	145	30	ensemble	ensemble	ADJ
cana-2697	145	31	mine	mine	NOUN
cana-2697	145	32	-	-	PUNCT
cana-2697	145	33	bisru	bisru	NOUN
cana-2697	145	34	attention	attention	NOUN
cana-2697	145	35	,	,	PUNCT
cana-2697	145	36	which	which	PRON
cana-2697	145	37	offer	offer	VERB
cana-2697	145	38	more	more	ADJ
cana-2697	145	39	accuracy	accuracy	NOUN
cana-2697	145	40	and	and	CCONJ
cana-2697	145	41	reliability	reliability	NOUN
cana-2697	145	42	when	when	SCONJ
cana-2697	145	43	predicting	predict	VERB
cana-2697	145	44	the	the	DET
cana-2697	145	45	levels	level	NOUN
cana-2697	145	46	of	of	ADP
cana-2697	145	47	do	do	AUX
cana-2697	145	48	in	in	ADP
cana-2697	145	49	comparison	comparison	NOUN
cana-2697	145	50	with	with	ADP
cana-2697	145	51	existing	exist	VERB
cana-2697	145	52	techniques	technique	NOUN
cana-2697	145	53	.	.	PUNCT
cana-2697	146	1	combining	combine	VERB
cana-2697	146	2	the	the	DET
cana-2697	146	3	proven	prove	VERB
cana-2697	146	4	working	work	VERB
cana-2697	146	5	strengths	strength	NOUN
cana-2697	146	6	of	of	ADP
cana-2697	146	7	lightgbm	lightgbm	ADJ
cana-2697	146	8	and	and	CCONJ
cana-2697	146	9	mic	mic	ADJ
cana-2697	146	10	,	,	PUNCT
cana-2697	146	11	which	which	PRON
cana-2697	146	12	are	be	AUX
cana-2697	146	13	both	both	CCONJ
cana-2697	146	14	effective	effective	ADJ
cana-2697	146	15	in	in	ADP
cana-2697	146	16	performing	perform	VERB
cana-2697	146	17	advanced	advanced	ADJ
cana-2697	146	18	feature	feature	NOUN
cana-2697	146	19	selection	selection	NOUN
cana-2697	146	20	,	,	PUNCT
cana-2697	146	21	as	as	ADV
cana-2697	146	22	well	well	ADV
cana-2697	146	23	as	as	ADP
cana-2697	146	24	the	the	DET
cana-2697	146	25	effective	effective	ADJ
cana-2697	146	26	sequence	sequence	NOUN
cana-2697	146	27	learning	learning	NOUN
cana-2697	146	28	models	model	NOUN
cana-2697	146	29	such	such	ADJ
cana-2697	146	30	as	as	ADP
cana-2697	146	31	bisru	bisru	NOUN
cana-2697	146	32	,	,	PUNCT
cana-2697	146	33	gru	gru	PROPN
cana-2697	146	34	,	,	PUNCT
cana-2697	146	35	and	and	CCONJ
cana-2697	146	36	lstm	lstm	NOUN
cana-2697	146	37	networks	network	NOUN
cana-2697	146	38	,	,	PUNCT
cana-2697	146	39	these	these	DET
cana-2697	146	40	hybrid	hybrid	NOUN
cana-2697	146	41	models	model	NOUN
cana-2697	146	42	were	be	AUX
cana-2697	146	43	able	able	ADJ
cana-2697	146	44	to	to	PART
cana-2697	146	45	account	account	VERB
cana-2697	146	46	for	for	ADP
cana-2697	146	47	both	both	DET
cana-2697	146	48	non	non	ADJ
cana-2697	146	49	-	-	ADJ
cana-2697	146	50	linear	linear	ADJ
cana-2697	146	51	and	and	CCONJ
cana-2697	146	52	linear	linear	ADJ
cana-2697	146	53	relationships	relationship	NOUN
cana-2697	146	54	within	within	ADP
cana-2697	146	55	the	the	DET
cana-2697	146	56	datasets	dataset	NOUN
cana-2697	146	57	.	.	PUNCT
cana-2697	147	1	communications	communication	NOUN
cana-2697	147	2	on	on	ADP
cana-2697	147	3	applied	apply	VERB
cana-2697	147	4	nonlinear	nonlinear	ADJ
cana-2697	147	5	analysis	analysis	NOUN
cana-2697	147	6	issn	issn	NOUN
cana-2697	147	7	:	:	PUNCT
cana-2697	147	8	1074	1074	NUM
cana-2697	147	9	-	-	PUNCT
cana-2697	147	10	133x	133x	NUM
cana-2697	147	11	vol	vol	NOUN
cana-2697	147	12	32	32	NUM
cana-2697	147	13	no	no	NOUN
cana-2697	147	14	.	.	PUNCT
cana-2697	148	1	3s	3s	NUM
cana-2697	148	2	(	(	PUNCT
cana-2697	148	3	2025	2025	NUM
cana-2697	148	4	)	)	PUNCT
cana-2697	148	5	589	589	NUM
cana-2697	148	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	148	7	out	out	ADP
cana-2697	148	8	of	of	ADP
cana-2697	148	9	the	the	DET
cana-2697	148	10	models	model	NOUN
cana-2697	148	11	tested	test	VERB
cana-2697	148	12	,	,	PUNCT
cana-2697	148	13	the	the	DET
cana-2697	148	14	one	one	NOUN
cana-2697	148	15	with	with	ADP
cana-2697	148	16	ensemble	ensemble	ADJ
cana-2697	148	17	mechanisms	mechanism	NOUN
cana-2697	148	18	,	,	PUNCT
cana-2697	148	19	like	like	ADP
cana-2697	148	20	the	the	DET
cana-2697	148	21	mine	mine	NOUN
cana-2697	148	22	-	-	PUNCT
cana-2697	148	23	bisru	bisru	NOUN
cana-2697	148	24	-	-	PUNCT
cana-2697	148	25	attention	attention	NOUN
cana-2697	148	26	model	model	NOUN
cana-2697	148	27	had	have	VERB
cana-2697	148	28	the	the	DET
cana-2697	148	29	least	least	ADJ
cana-2697	148	30	mean	mean	ADJ
cana-2697	148	31	square	square	ADJ
cana-2697	148	32	error	error	NOUN
cana-2697	148	33	(	(	PUNCT
cana-2697	148	34	mse	mse	NOUN
cana-2697	148	35	)	)	PUNCT
cana-2697	148	36	of	of	ADP
cana-2697	148	37	0.104238	0.104238	NUM
cana-2697	148	38	and	and	CCONJ
cana-2697	148	39	performed	perform	VERB
cana-2697	148	40	better	well	ADV
cana-2697	148	41	than	than	ADP
cana-2697	148	42	the	the	DET
cana-2697	148	43	rest	rest	NOUN
cana-2697	148	44	,	,	PUNCT
cana-2697	148	45	which	which	PRON
cana-2697	148	46	is	be	AUX
cana-2697	148	47	more	more	ADJ
cana-2697	148	48	than	than	ADP
cana-2697	148	49	what	what	PRON
cana-2697	148	50	was	be	AUX
cana-2697	148	51	achieved	achieve	VERB
cana-2697	148	52	in	in	ADP
cana-2697	148	53	the	the	DET
cana-2697	148	54	base	base	NOUN
cana-2697	148	55	paper	paper	NOUN
cana-2697	148	56	and	and	CCONJ
cana-2697	148	57	confirms	confirm	VERB
cana-2697	148	58	its	its	PRON
cana-2697	148	59	use	use	NOUN
cana-2697	148	60	in	in	ADP
cana-2697	148	61	aquaculture	aquaculture	NOUN
cana-2697	148	62	environments	environment	NOUN
cana-2697	148	63	.	.	PUNCT
cana-2697	149	1	this	this	DET
cana-2697	149	2	model	model	NOUN
cana-2697	149	3	also	also	ADV
cana-2697	149	4	has	have	VERB
cana-2697	149	5	much	much	ADV
cana-2697	149	6	more	more	ADJ
cana-2697	149	7	to	to	PART
cana-2697	149	8	offer	offer	VERB
cana-2697	149	9	in	in	ADP
cana-2697	149	10	terms	term	NOUN
cana-2697	149	11	of	of	ADP
cana-2697	149	12	it	it	PRON
cana-2697	149	13	being	be	AUX
cana-2697	149	14	able	able	ADJ
cana-2697	149	15	to	to	PART
cana-2697	149	16	incorporate	incorporate	VERB
cana-2697	149	17	various	various	ADJ
cana-2697	149	18	functions	function	NOUN
cana-2697	149	19	and	and	CCONJ
cana-2697	149	20	still	still	ADV
cana-2697	149	21	maintain	maintain	VERB
cana-2697	149	22	reasonable	reasonable	ADJ
cana-2697	149	23	computational	computational	ADJ
cana-2697	149	24	cost	cost	NOUN
cana-2697	149	25	.	.	PUNCT
cana-2697	150	1	clearly	clearly	ADV
cana-2697	150	2	making	make	VERB
cana-2697	150	3	this	this	DET
cana-2697	150	4	model	model	NOUN
cana-2697	150	5	a	a	DET
cana-2697	150	6	benchmark	benchmark	NOUN
cana-2697	150	7	for	for	ADP
cana-2697	150	8	the	the	DET
cana-2697	150	9	next	next	ADJ
cana-2697	150	10	generation	generation	NOUN
cana-2697	150	11	of	of	ADP
cana-2697	150	12	intelligent	intelligent	ADJ
cana-2697	150	13	systems	system	NOUN
cana-2697	150	14	designed	design	VERB
cana-2697	150	15	to	to	PART
cana-2697	150	16	manage	manage	VERB
cana-2697	150	17	water	water	NOUN
cana-2697	150	18	quality	quality	NOUN
cana-2697	150	19	[	[	X
cana-2697	150	20	24	24	NUM
cana-2697	150	21	]	]	PUNCT
cana-2697	150	22	.	.	PUNCT
cana-2697	151	1	references	reference	NOUN
cana-2697	151	2	[	[	X
cana-2697	151	3	1	1	NUM
cana-2697	151	4	]	]	X
cana-2697	151	5	sana	sana	PROPN
cana-2697	151	6	,	,	PUNCT
cana-2697	151	7	m.	m.	NOUN
cana-2697	151	8	,	,	PUNCT
cana-2697	151	9	et	et	PROPN
cana-2697	151	10	al	al	PROPN
cana-2697	151	11	.	.	PROPN
cana-2697	151	12	,	,	PUNCT
cana-2697	151	13	“	"	PUNCT
cana-2697	151	14	a	a	DET
cana-2697	151	15	hybrid	hybrid	ADJ
cana-2697	151	16	svm	svm	ADJ
cana-2697	151	17	-	-	ADJ
cana-2697	151	18	ann	ann	ADJ
cana-2697	151	19	model	model	NOUN
cana-2697	151	20	for	for	ADP
cana-2697	151	21	predicting	predict	VERB
cana-2697	151	22	dissolved	dissolved	ADJ
cana-2697	151	23	oxygen	oxygen	NOUN
cana-2697	151	24	in	in	ADP
cana-2697	151	25	aquaculture	aquaculture	NOUN
cana-2697	151	26	systems	system	NOUN
cana-2697	151	27	,	,	PUNCT
cana-2697	151	28	”	"	PUNCT
cana-2697	151	29	journal	journal	NOUN
cana-2697	151	30	of	of	ADP
cana-2697	151	31	aquaculture	aquaculture	NOUN
cana-2697	151	32	technology	technology	NOUN
cana-2697	151	33	,	,	PUNCT
cana-2697	151	34	2021	2021	NUM
cana-2697	151	35	.	.	PUNCT
cana-2697	152	1	[	[	X
cana-2697	152	2	2	2	NUM
cana-2697	152	3	]	]	PUNCT
cana-2697	152	4	cao	cao	PROPN
cana-2697	152	5	,	,	PUNCT
cana-2697	152	6	j.	j.	PROPN
cana-2697	152	7	,	,	PUNCT
cana-2697	152	8	et	et	PROPN
cana-2697	152	9	al	al	PROPN
cana-2697	152	10	.	.	PROPN
cana-2697	152	11	,	,	PUNCT
cana-2697	152	12	“	"	PUNCT
cana-2697	152	13	improving	improve	VERB
cana-2697	152	14	dissolved	dissolve	VERB
cana-2697	152	15	oxygen	oxygen	NOUN
cana-2697	152	16	prediction	prediction	NOUN
cana-2697	152	17	using	use	VERB
cana-2697	152	18	extreme	extreme	ADJ
cana-2697	152	19	learning	learning	NOUN
cana-2697	152	20	machines	machine	NOUN
cana-2697	152	21	in	in	ADP
cana-2697	152	22	aquaculture	aquaculture	NOUN
cana-2697	152	23	,	,	PUNCT
cana-2697	152	24	”	"	PUNCT
cana-2697	152	25	environmental	environmental	ADJ
cana-2697	152	26	modelling	modelling	NOUN
cana-2697	152	27	&	&	CCONJ
cana-2697	152	28	software	software	NOUN
cana-2697	152	29	,	,	PUNCT
cana-2697	152	30	2019	2019	NUM
cana-2697	152	31	.	.	PUNCT
cana-2697	153	1	[	[	X
cana-2697	153	2	3	3	X
cana-2697	153	3	]	]	X
cana-2697	153	4	ding	ding	NOUN
cana-2697	153	5	,	,	PUNCT
cana-2697	153	6	y.	y.	PROPN
cana-2697	153	7	,	,	PUNCT
cana-2697	153	8	et	et	PROPN
cana-2697	153	9	al	al	PROPN
cana-2697	153	10	.	.	PROPN
cana-2697	153	11	,	,	PUNCT
cana-2697	153	12	“	"	PUNCT
cana-2697	153	13	bisru	bisru	NOUN
cana-2697	153	14	:	:	PUNCT
cana-2697	153	15	a	a	DET
cana-2697	153	16	bidirectional	bidirectional	ADJ
cana-2697	153	17	simple	simple	ADJ
cana-2697	153	18	recurrent	recurrent	ADJ
cana-2697	153	19	unit	unit	NOUN
cana-2697	153	20	for	for	ADP
cana-2697	153	21	sequential	sequential	ADJ
cana-2697	153	22	data	datum	NOUN
cana-2697	153	23	processing	processing	NOUN
cana-2697	153	24	,	,	PUNCT
cana-2697	153	25	”	"	PUNCT
cana-2697	153	26	ieee	ieee	NOUN
cana-2697	153	27	transactions	transaction	NOUN
cana-2697	153	28	on	on	ADP
cana-2697	153	29	neural	neural	ADJ
cana-2697	153	30	networks	network	NOUN
cana-2697	153	31	,	,	PUNCT
cana-2697	153	32	2018	2018	NUM
cana-2697	153	33	.	.	PUNCT
cana-2697	154	1	[	[	X
cana-2697	154	2	4	4	NUM
cana-2697	154	3	]	]	X
cana-2697	154	4	liu	liu	PROPN
cana-2697	154	5	,	,	PUNCT
cana-2697	154	6	f.	f.	PROPN
cana-2697	154	7	,	,	PUNCT
cana-2697	154	8	et	et	PROPN
cana-2697	154	9	al	al	PROPN
cana-2697	154	10	.	.	PROPN
cana-2697	154	11	,	,	PUNCT
cana-2697	154	12	“	"	PUNCT
cana-2697	154	13	enhancing	enhance	VERB
cana-2697	154	14	gru	gru	NOUN
cana-2697	154	15	with	with	ADP
cana-2697	154	16	attention	attention	NOUN
cana-2697	154	17	mechanisms	mechanism	NOUN
cana-2697	154	18	for	for	ADP
cana-2697	154	19	temporal	temporal	ADJ
cana-2697	154	20	data	datum	NOUN
cana-2697	154	21	analysis	analysis	NOUN
cana-2697	154	22	in	in	ADP
cana-2697	154	23	aquaculture	aquaculture	NOUN
cana-2697	154	24	,	,	PUNCT
cana-2697	154	25	”	"	PUNCT
cana-2697	154	26	international	international	ADJ
cana-2697	154	27	journal	journal	NOUN
cana-2697	154	28	of	of	ADP
cana-2697	154	29	computational	computational	ADJ
cana-2697	154	30	intelligence	intelligence	NOUN
cana-2697	154	31	systems	system	NOUN
cana-2697	154	32	,	,	PUNCT
cana-2697	154	33	2020	2020	NUM
cana-2697	154	34	.	.	PUNCT
cana-2697	155	1	[	[	X
cana-2697	155	2	5	5	NUM
cana-2697	155	3	]	]	PUNCT
cana-2697	155	4	ahmed	ahmed	PROPN
cana-2697	155	5	,	,	PUNCT
cana-2697	155	6	s.	s.	PROPN
cana-2697	155	7	,	,	PUNCT
cana-2697	155	8	et	et	PROPN
cana-2697	155	9	al	al	PROPN
cana-2697	155	10	.	.	PROPN
cana-2697	155	11	,	,	PUNCT
cana-2697	155	12	“	"	PUNCT
cana-2697	155	13	adaptive	adaptive	ADJ
cana-2697	155	14	neuro	neuro	NOUN
cana-2697	155	15	-	-	PUNCT
cana-2697	155	16	fuzzy	fuzzy	ADJ
cana-2697	155	17	inference	inference	NOUN
cana-2697	155	18	systems	system	NOUN
cana-2697	155	19	for	for	ADP
cana-2697	155	20	water	water	NOUN
cana-2697	155	21	quality	quality	NOUN
cana-2697	155	22	prediction	prediction	NOUN
cana-2697	155	23	,	,	PUNCT
cana-2697	155	24	”	"	PUNCT
cana-2697	155	25	applied	apply	VERB
cana-2697	155	26	soft	soft	ADJ
cana-2697	155	27	computing	computing	NOUN
cana-2697	155	28	,	,	PUNCT
cana-2697	155	29	2017	2017	NUM
cana-2697	155	30	.	.	PUNCT
cana-2697	156	1	[	[	X
cana-2697	156	2	6	6	NUM
cana-2697	156	3	]	]	X
cana-2697	156	4	wei	wei	PROPN
cana-2697	156	5	,	,	PUNCT
cana-2697	156	6	c.	c.	PROPN
cana-2697	156	7	,	,	PUNCT
cana-2697	156	8	et	et	PROPN
cana-2697	156	9	al	al	PROPN
cana-2697	156	10	.	.	PROPN
cana-2697	156	11	,	,	PUNCT
cana-2697	156	12	“	"	PUNCT
cana-2697	156	13	empirical	empirical	ADJ
cana-2697	156	14	mode	mode	NOUN
cana-2697	156	15	decomposition	decomposition	NOUN
cana-2697	156	16	for	for	ADP
cana-2697	156	17	feature	feature	NOUN
cana-2697	156	18	extraction	extraction	NOUN
cana-2697	156	19	in	in	ADP
cana-2697	156	20	do	do	PROPN
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cana-2697	156	22	,	,	PUNCT
cana-2697	156	23	”	"	PUNCT
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cana-2697	156	25	of	of	ADP
cana-2697	156	26	environmental	environmental	ADJ
cana-2697	156	27	informatics	informatic	NOUN
cana-2697	156	28	,	,	PUNCT
cana-2697	156	29	2020	2020	NUM
cana-2697	156	30	.	.	PUNCT
cana-2697	157	1	[	[	X
cana-2697	157	2	7	7	NUM
cana-2697	157	3	]	]	X
cana-2697	157	4	zhang	zhang	PROPN
cana-2697	157	5	,	,	PUNCT
cana-2697	157	6	w.	w.	PROPN
cana-2697	157	7	,	,	PUNCT
cana-2697	157	8	et	et	PROPN
cana-2697	157	9	al	al	PROPN
cana-2697	157	10	.	.	PROPN
cana-2697	157	11	,	,	PUNCT
cana-2697	157	12	“	"	PUNCT
cana-2697	157	13	wavelet	wavelet	NOUN
cana-2697	157	14	decomposition	decomposition	NOUN
cana-2697	157	15	combined	combine	VERB
cana-2697	157	16	with	with	ADP
cana-2697	157	17	machine	machine	NOUN
cana-2697	157	18	learning	learning	NOUN
cana-2697	157	19	for	for	ADP
cana-2697	157	20	high	high	ADJ
cana-2697	157	21	-	-	PUNCT
cana-2697	157	22	frequency	frequency	NOUN
cana-2697	157	23	data	datum	NOUN
cana-2697	157	24	in	in	ADP
cana-2697	157	25	aquaculture	aquaculture	NOUN
cana-2697	157	26	,	,	PUNCT
cana-2697	157	27	”	"	PUNCT
cana-2697	157	28	ocean	ocean	NOUN
cana-2697	157	29	engineering	engineering	NOUN
cana-2697	157	30	,	,	PUNCT
cana-2697	157	31	2021	2021	NUM
cana-2697	157	32	.	.	PUNCT
cana-2697	158	1	[	[	X
cana-2697	158	2	8	8	NUM
cana-2697	158	3	]	]	X
cana-2697	158	4	pal	pal	NOUN
cana-2697	158	5	,	,	PUNCT
cana-2697	158	6	r.	r.	PROPN
cana-2697	158	7	,	,	PUNCT
cana-2697	158	8	et	et	PROPN
cana-2697	158	9	al	al	PROPN
cana-2697	158	10	.	.	PROPN
cana-2697	158	11	,	,	PUNCT
cana-2697	158	12	“	"	PUNCT
cana-2697	158	13	iot	iot	ADJ
cana-2697	158	14	-	-	PUNCT
cana-2697	158	15	based	base	VERB
cana-2697	158	16	real	real	ADJ
cana-2697	158	17	-	-	PUNCT
cana-2697	158	18	time	time	NOUN
cana-2697	158	19	monitoring	monitoring	NOUN
cana-2697	158	20	of	of	ADP
cana-2697	158	21	dissolved	dissolve	VERB
cana-2697	158	22	oxygen	oxygen	NOUN
cana-2697	158	23	levels	level	NOUN
cana-2697	158	24	in	in	ADP
cana-2697	158	25	aquaculture	aquaculture	NOUN
cana-2697	158	26	,	,	PUNCT
cana-2697	158	27	”	"	PUNCT
cana-2697	158	28	ieee	ieee	NOUN
cana-2697	158	29	iot	iot	PROPN
cana-2697	158	30	journal	journal	PROPN
cana-2697	158	31	,	,	PUNCT
cana-2697	158	32	2019	2019	NUM
cana-2697	158	33	.	.	PUNCT
cana-2697	159	1	[	[	X
cana-2697	159	2	9	9	NUM
cana-2697	159	3	]	]	X
cana-2697	159	4	huan	huan	NOUN
cana-2697	159	5	,	,	PUNCT
cana-2697	159	6	t.	t.	PROPN
cana-2697	159	7	,	,	PUNCT
cana-2697	159	8	et	et	PROPN
cana-2697	159	9	al	al	PROPN
cana-2697	159	10	.	.	PROPN
cana-2697	159	11	,	,	PUNCT
cana-2697	159	12	“	"	PUNCT
cana-2697	159	13	grey	grey	ADJ
cana-2697	159	14	relational	relational	ADJ
cana-2697	159	15	analysis	analysis	NOUN
cana-2697	159	16	for	for	ADP
cana-2697	159	17	do	do	AUX
cana-2697	159	18	prediction	prediction	NOUN
cana-2697	159	19	in	in	ADP
cana-2697	159	20	aquaculture	aquaculture	NOUN
cana-2697	159	21	systems	system	NOUN
cana-2697	159	22	,	,	PUNCT
cana-2697	159	23	”	"	PUNCT
cana-2697	159	24	journal	journal	NOUN
cana-2697	159	25	of	of	ADP
cana-2697	159	26	aquaculture	aquaculture	NOUN
cana-2697	159	27	research	research	NOUN
cana-2697	159	28	,	,	PUNCT
cana-2697	159	29	2021	2021	NUM
cana-2697	159	30	.	.	PUNCT
cana-2697	160	1	[	[	X
cana-2697	160	2	10	10	NUM
cana-2697	160	3	]	]	X
cana-2697	160	4	sunney	sunney	PROPN
cana-2697	160	5	,	,	PUNCT
cana-2697	160	6	g.	g.	PROPN
cana-2697	160	7	,	,	PUNCT
cana-2697	160	8	et	et	PROPN
cana-2697	160	9	al	al	PROPN
cana-2697	160	10	.	.	PROPN
cana-2697	160	11	,	,	PUNCT
cana-2697	160	12	“	"	PUNCT
cana-2697	160	13	deep	deep	ADJ
cana-2697	160	14	belief	belief	NOUN
cana-2697	160	15	networks	network	NOUN
cana-2697	160	16	for	for	ADP
cana-2697	160	17	predicting	predict	VERB
cana-2697	160	18	water	water	NOUN
cana-2697	160	19	quality	quality	NOUN
cana-2697	160	20	in	in	ADP
cana-2697	160	21	multivariate	multivariate	NOUN
cana-2697	160	22	aquaculture	aquaculture	NOUN
cana-2697	160	23	datasets	dataset	NOUN
cana-2697	160	24	,	,	PUNCT
cana-2697	160	25	”	"	PUNCT
cana-2697	160	26	neural	neural	ADJ
cana-2697	160	27	computing	computing	NOUN
cana-2697	160	28	and	and	CCONJ
cana-2697	160	29	applications	application	NOUN
cana-2697	160	30	,	,	PUNCT
cana-2697	160	31	2020	2020	NUM
cana-2697	160	32	.	.	PUNCT
cana-2697	161	1	[	[	X
cana-2697	161	2	11	11	NUM
cana-2697	161	3	]	]	PUNCT
cana-2697	161	4	ashfauk	ashfauk	NOUN
cana-2697	161	5	ahamed	ahamed	PROPN
cana-2697	161	6	,	,	PUNCT
cana-2697	161	7	a.	a.	PROPN
cana-2697	161	8	k.	k.	PROPN
cana-2697	161	9	,	,	PUNCT
cana-2697	161	10	et	et	PROPN
cana-2697	161	11	al	al	PROPN
cana-2697	161	12	.	.	PUNCT
cana-2697	162	1	"	"	PUNCT
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cana-2697	162	3	of	of	ADP
cana-2697	162	4	the	the	DET
cana-2697	162	5	growing	grow	VERB
cana-2697	162	6	stock	stock	NOUN
cana-2697	162	7	in	in	ADP
cana-2697	162	8	stock	stock	NOUN
cana-2697	162	9	market	market	NOUN
cana-2697	162	10	on	on	ADP
cana-2697	162	11	analysis	analysis	NOUN
cana-2697	162	12	of	of	ADP
cana-2697	162	13	the	the	DET
cana-2697	162	14	opinions	opinion	NOUN
cana-2697	162	15	using	use	VERB
cana-2697	162	16	sentiment	sentiment	NOUN
cana-2697	162	17	lexicon	lexicon	NOUN
cana-2697	162	18	extraction	extraction	NOUN
cana-2697	162	19	and	and	CCONJ
cana-2697	162	20	deep	deep	ADJ
cana-2697	162	21	learning	learning	NOUN
cana-2697	162	22	architectures	architecture	NOUN
cana-2697	162	23	.	.	PUNCT
cana-2697	162	24	"	"	PUNCT
cana-2697	163	1	frontiers	frontier	NOUN
cana-2697	163	2	in	in	ADP
cana-2697	163	3	health	health	NOUN
cana-2697	163	4	informatics	informatic	NOUN
cana-2697	163	5	13.3	13.3	NUM
cana-2697	163	6	(	(	PUNCT
cana-2697	163	7	2024	2024	NUM
cana-2697	163	8	):	):	PUNCT
cana-2697	163	9	1382	1382	NUM
cana-2697	163	10	-	-	SYM
cana-2697	163	11	1392	1392	NUM
cana-2697	163	12	.	.	PUNCT
cana-2697	164	1	[	[	X
cana-2697	164	2	12	12	NUM
cana-2697	164	3	]	]	PUNCT
cana-2697	164	4	reshef	reshef	NOUN
cana-2697	164	5	,	,	PUNCT
cana-2697	164	6	d.	d.	PROPN
cana-2697	164	7	n.	n.	PROPN
cana-2697	164	8	,	,	PUNCT
cana-2697	164	9	et	et	PROPN
cana-2697	164	10	al	al	PROPN
cana-2697	164	11	.	.	PROPN
cana-2697	164	12	,	,	PUNCT
cana-2697	164	13	“	"	PUNCT
cana-2697	164	14	detecting	detect	VERB
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cana-2697	164	16	associations	association	NOUN
cana-2697	164	17	in	in	ADP
cana-2697	164	18	large	large	ADJ
cana-2697	164	19	datasets	dataset	NOUN
cana-2697	164	20	using	use	VERB
cana-2697	164	21	maximal	maximal	ADJ
cana-2697	164	22	information	information	NOUN
cana-2697	164	23	coefficient	coefficient	NOUN
cana-2697	164	24	(	(	PUNCT
cana-2697	164	25	mic	mic	ADJ
cana-2697	164	26	)	)	PUNCT
cana-2697	164	27	,	,	PUNCT
cana-2697	164	28	”	"	PUNCT
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cana-2697	164	30	,	,	PUNCT
cana-2697	164	31	2011	2011	NUM
cana-2697	164	32	.	.	PUNCT
cana-2697	165	1	[	[	X
cana-2697	165	2	13	13	NUM
cana-2697	165	3	]	]	SYM
cana-2697	165	4	fang	fang	X
cana-2697	165	5	,	,	PUNCT
cana-2697	165	6	z.	z.	PROPN
cana-2697	165	7	,	,	PUNCT
cana-2697	165	8	et	et	PROPN
cana-2697	165	9	al	al	PROPN
cana-2697	165	10	.	.	PROPN
cana-2697	165	11	,	,	PUNCT
cana-2697	165	12	“	"	PUNCT
cana-2697	165	13	ensemble	ensemble	ADJ
cana-2697	165	14	learning	learning	NOUN
cana-2697	165	15	techniques	technique	NOUN
cana-2697	165	16	for	for	ADP
cana-2697	165	17	do	do	AUX
cana-2697	165	18	prediction	prediction	NOUN
cana-2697	165	19	,	,	PUNCT
cana-2697	165	20	”	"	PUNCT
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cana-2697	165	22	journal	journal	NOUN
cana-2697	165	23	of	of	ADP
cana-2697	165	24	environmental	environmental	ADJ
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cana-2697	165	26	,	,	PUNCT
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cana-2697	165	28	.	.	PUNCT
cana-2697	166	1	[	[	X
cana-2697	166	2	14	14	NUM
cana-2697	166	3	]	]	X
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cana-2697	166	6	m.	m.	NOUN
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cana-2697	166	8	et	et	PROPN
cana-2697	166	9	al	al	PROPN
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cana-2697	166	11	,	,	PUNCT
cana-2697	166	12	“	"	PUNCT
cana-2697	166	13	a	a	DET
cana-2697	166	14	comparison	comparison	NOUN
cana-2697	166	15	of	of	ADP
cana-2697	166	16	lstm	lstm	NOUN
cana-2697	166	17	and	and	CCONJ
cana-2697	166	18	gru	gru	VERB
cana-2697	166	19	for	for	ADP
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cana-2697	166	24	”	"	PUNCT
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cana-2697	166	30	.	.	PUNCT
cana-2697	167	1	[	[	X
cana-2697	167	2	15	15	NUM
cana-2697	167	3	]	]	X
cana-2697	167	4	guo	guo	PROPN
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cana-2697	167	6	x.	x.	PROPN
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cana-2697	167	8	et	et	PROPN
cana-2697	167	9	al	al	PROPN
cana-2697	167	10	.	.	PROPN
cana-2697	167	11	,	,	PUNCT
cana-2697	167	12	“	"	PUNCT
cana-2697	167	13	attention	attention	NOUN
cana-2697	167	14	mechanisms	mechanism	NOUN
cana-2697	167	15	in	in	ADP
cana-2697	167	16	deep	deep	ADJ
cana-2697	167	17	learning	learning	NOUN
cana-2697	167	18	:	:	PUNCT
cana-2697	167	19	applications	application	NOUN
cana-2697	167	20	in	in	ADP
cana-2697	167	21	aquaculture	aquaculture	NOUN
cana-2697	167	22	,	,	PUNCT
cana-2697	167	23	”	"	PUNCT
cana-2697	167	24	journal	journal	NOUN
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cana-2697	167	26	artificial	artificial	ADJ
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cana-2697	167	28	research	research	NOUN
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cana-2697	167	30	2020	2020	NUM
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cana-2697	168	1	[	[	X
cana-2697	168	2	16	16	NUM
cana-2697	168	3	]	]	X
cana-2697	168	4	singh	singh	PROPN
cana-2697	168	5	,	,	PUNCT
cana-2697	168	6	r.	r.	PROPN
cana-2697	168	7	,	,	PUNCT
cana-2697	168	8	et	et	PROPN
cana-2697	168	9	al	al	PROPN
cana-2697	168	10	.	.	PROPN
cana-2697	168	11	,	,	PUNCT
cana-2697	168	12	“	"	PUNCT
cana-2697	168	13	feature	feature	NOUN
cana-2697	168	14	selection	selection	NOUN
cana-2697	168	15	techniques	technique	NOUN
cana-2697	168	16	for	for	ADP
cana-2697	168	17	improving	improve	VERB
cana-2697	168	18	prediction	prediction	NOUN
cana-2697	168	19	models	model	NOUN
cana-2697	168	20	in	in	ADP
cana-2697	168	21	aquaculture	aquaculture	NOUN
cana-2697	168	22	,	,	PUNCT
cana-2697	168	23	”	"	PUNCT
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cana-2697	168	28	agriculture	agriculture	NOUN
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cana-2697	168	30	2020	2020	NUM
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cana-2697	169	1	[	[	X
cana-2697	169	2	17	17	NUM
cana-2697	169	3	]	]	X
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cana-2697	169	5	,	,	PUNCT
cana-2697	169	6	m.	m.	NOUN
cana-2697	169	7	,	,	PUNCT
cana-2697	169	8	et	et	PROPN
cana-2697	169	9	al	al	PROPN
cana-2697	169	10	.	.	PROPN
cana-2697	169	11	,	,	PUNCT
cana-2697	169	12	“	"	PUNCT
cana-2697	169	13	bidirectional	bidirectional	ADJ
cana-2697	169	14	rnns	rnn	NOUN
cana-2697	169	15	for	for	ADP
cana-2697	169	16	temporal	temporal	ADJ
cana-2697	169	17	data	datum	NOUN
cana-2697	169	18	:	:	PUNCT
cana-2697	169	19	a	a	DET
cana-2697	169	20	review	review	NOUN
cana-2697	169	21	,	,	PUNCT
cana-2697	169	22	”	"	PUNCT
cana-2697	169	23	ieee	ieee	NOUN
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cana-2697	169	25	,	,	PUNCT
cana-2697	169	26	2019	2019	NUM
cana-2697	169	27	.	.	PUNCT
cana-2697	170	1	[	[	X
cana-2697	170	2	18	18	NUM
cana-2697	170	3	]	]	SYM
cana-2697	170	4	verma	verma	PROPN
cana-2697	170	5	,	,	PUNCT
cana-2697	170	6	s.	s.	PROPN
cana-2697	170	7	,	,	PUNCT
cana-2697	170	8	et	et	PROPN
cana-2697	170	9	al	al	PROPN
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cana-2697	170	11	,	,	PUNCT
cana-2697	170	12	“	"	PUNCT
cana-2697	170	13	kaggle	kaggle	VERB
cana-2697	170	14	water	water	NOUN
cana-2697	170	15	quality	quality	NOUN
cana-2697	170	16	dataset	dataset	NOUN
cana-2697	170	17	documentation	documentation	NOUN
cana-2697	170	18	,	,	PUNCT
cana-2697	170	19	”	"	PUNCT
cana-2697	170	20	kaggle	kaggle	NOUN
cana-2697	170	21	,	,	PUNCT
cana-2697	170	22	2021	2021	NUM
cana-2697	170	23	.	.	PUNCT
cana-2697	171	1	[	[	X
cana-2697	171	2	19	19	NUM
cana-2697	171	3	]	]	X
cana-2697	171	4	das	das	PROPN
cana-2697	171	5	,	,	PUNCT
cana-2697	171	6	p.	p.	PROPN
cana-2697	171	7	,	,	PUNCT
cana-2697	171	8	et	et	PROPN
cana-2697	171	9	al	al	PROPN
cana-2697	171	10	.	.	PROPN
cana-2697	171	11	,	,	PUNCT
cana-2697	171	12	“	"	PUNCT
cana-2697	171	13	preprocessing	preprocesse	VERB
cana-2697	171	14	techniques	technique	NOUN
cana-2697	171	15	for	for	ADP
cana-2697	171	16	improving	improve	VERB
cana-2697	171	17	machine	machine	NOUN
cana-2697	171	18	learning	learning	NOUN
cana-2697	171	19	models	model	NOUN
cana-2697	171	20	in	in	ADP
cana-2697	171	21	water	water	NOUN
cana-2697	171	22	quality	quality	NOUN
cana-2697	171	23	monitoring	monitoring	NOUN
cana-2697	171	24	,	,	PUNCT
cana-2697	171	25	”	"	PUNCT
cana-2697	171	26	water	water	NOUN
cana-2697	171	27	resources	resource	NOUN
cana-2697	171	28	research	research	NOUN
cana-2697	171	29	,	,	PUNCT
cana-2697	171	30	2021	2021	NUM
cana-2697	171	31	.	.	PUNCT
cana-2697	172	1	[	[	X
cana-2697	172	2	20	20	NUM
cana-2697	172	3	]	]	X
cana-2697	172	4	zhou	zhou	PROPN
cana-2697	172	5	,	,	PUNCT
cana-2697	172	6	w.	w.	PROPN
cana-2697	172	7	,	,	PUNCT
cana-2697	172	8	et	et	PROPN
cana-2697	172	9	al	al	PROPN
cana-2697	172	10	.	.	PROPN
cana-2697	172	11	,	,	PUNCT
cana-2697	172	12	“	"	PUNCT
cana-2697	172	13	hybrid	hybrid	ADJ
cana-2697	172	14	neural	neural	ADJ
cana-2697	172	15	network	network	NOUN
cana-2697	172	16	models	model	NOUN
cana-2697	172	17	for	for	ADP
cana-2697	172	18	dissolved	dissolve	VERB
cana-2697	172	19	oxygen	oxygen	NOUN
cana-2697	172	20	prediction	prediction	NOUN
cana-2697	172	21	,	,	PUNCT
cana-2697	172	22	”	"	PUNCT
cana-2697	172	23	neural	neural	ADJ
cana-2697	172	24	processing	processing	NOUN
cana-2697	172	25	letters	letter	NOUN
cana-2697	172	26	,	,	PUNCT
cana-2697	172	27	2019	2019	NUM
cana-2697	172	28	.	.	PUNCT
cana-2697	173	1	[	[	X
cana-2697	173	2	21	21	NUM
cana-2697	173	3	]	]	X
cana-2697	173	4	brown	brown	PROPN
cana-2697	173	5	,	,	PUNCT
cana-2697	173	6	e.	e.	PROPN
cana-2697	173	7	,	,	PUNCT
cana-2697	173	8	et	et	PROPN
cana-2697	173	9	al	al	PROPN
cana-2697	173	10	.	.	PROPN
cana-2697	173	11	,	,	PUNCT
cana-2697	173	12	“	"	PUNCT
cana-2697	173	13	understanding	understand	VERB
cana-2697	173	14	ensemble	ensemble	ADJ
cana-2697	173	15	learning	learning	NOUN
cana-2697	173	16	:	:	PUNCT
cana-2697	173	17	a	a	DET
cana-2697	173	18	focus	focus	NOUN
cana-2697	173	19	on	on	ADP
cana-2697	173	20	bagging	bagging	NOUN
cana-2697	173	21	and	and	CCONJ
cana-2697	173	22	boosting	boost	VERB
cana-2697	173	23	,	,	PUNCT
cana-2697	173	24	”	"	PUNCT
cana-2697	173	25	acm	acm	PROPN
cana-2697	173	26	computing	computing	NOUN
cana-2697	173	27	surveys	survey	NOUN
cana-2697	173	28	,	,	PUNCT
cana-2697	173	29	2019	2019	NUM
cana-2697	173	30	.	.	PUNCT
cana-2697	174	1	communications	communication	NOUN
cana-2697	174	2	on	on	ADP
cana-2697	174	3	applied	apply	VERB
cana-2697	174	4	nonlinear	nonlinear	ADJ
cana-2697	174	5	analysis	analysis	NOUN
cana-2697	174	6	issn	issn	NOUN
cana-2697	174	7	:	:	PUNCT
cana-2697	174	8	1074	1074	NUM
cana-2697	174	9	-	-	PUNCT
cana-2697	174	10	133x	133x	NUM
cana-2697	174	11	vol	vol	NOUN
cana-2697	174	12	32	32	NUM
cana-2697	174	13	no	no	NOUN
cana-2697	174	14	.	.	PUNCT
cana-2697	175	1	3s	3s	NUM
cana-2697	175	2	(	(	PUNCT
cana-2697	175	3	2025	2025	NUM
cana-2697	175	4	)	)	PUNCT
cana-2697	175	5	590	590	NUM
cana-2697	175	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2697	176	1	[	[	X
cana-2697	176	2	22	22	NUM
cana-2697	176	3	]	]	SYM
cana-2697	176	4	li	li	PROPN
cana-2697	176	5	,	,	PUNCT
cana-2697	176	6	t.	t.	PROPN
cana-2697	176	7	,	,	PUNCT
cana-2697	176	8	et	et	PROPN
cana-2697	176	9	al	al	PROPN
cana-2697	176	10	.	.	PROPN
cana-2697	176	11	,	,	PUNCT
cana-2697	176	12	“	"	PUNCT
cana-2697	176	13	time	time	NOUN
cana-2697	176	14	-	-	PUNCT
cana-2697	176	15	series	series	NOUN
cana-2697	176	16	analysis	analysis	NOUN
cana-2697	176	17	in	in	ADP
cana-2697	176	18	aquaculture	aquaculture	NOUN
cana-2697	176	19	systems	system	NOUN
cana-2697	176	20	using	use	VERB
cana-2697	176	21	advanced	advanced	ADJ
cana-2697	176	22	machine	machine	NOUN
cana-2697	176	23	learning	learn	VERB
cana-2697	176	24	techniques	technique	NOUN
cana-2697	176	25	,	,	PUNCT
cana-2697	176	26	”	"	PUNCT
cana-2697	176	27	aquaculture	aquaculture	NOUN
cana-2697	176	28	engineering	engineering	NOUN
cana-2697	176	29	,	,	PUNCT
cana-2697	176	30	2021	2021	NUM
cana-2697	176	31	.	.	PUNCT
cana-2697	177	1	[	[	X
cana-2697	177	2	23	23	NUM
cana-2697	177	3	]	]	X
cana-2697	177	4	wu	wu	PROPN
cana-2697	177	5	,	,	PUNCT
cana-2697	177	6	c.	c.	PROPN
cana-2697	177	7	,	,	PUNCT
cana-2697	177	8	et	et	PROPN
cana-2697	177	9	al	al	PROPN
cana-2697	177	10	.	.	PROPN
cana-2697	177	11	,	,	PUNCT
cana-2697	177	12	“	"	PUNCT
cana-2697	177	13	combining	combine	VERB
cana-2697	177	14	feature	feature	NOUN
cana-2697	177	15	selection	selection	NOUN
cana-2697	177	16	and	and	CCONJ
cana-2697	177	17	deep	deep	ADJ
cana-2697	177	18	learning	learning	NOUN
cana-2697	177	19	for	for	ADP
cana-2697	177	20	do	do	AUX
cana-2697	177	21	prediction	prediction	NOUN
cana-2697	177	22	,	,	PUNCT
cana-2697	177	23	”	"	PUNCT
cana-2697	177	24	neural	neural	ADJ
cana-2697	177	25	networks	network	NOUN
cana-2697	177	26	journal	journal	NOUN
cana-2697	177	27	,	,	PUNCT
cana-2697	177	28	2020	2020	NUM
cana-2697	177	29	.	.	PUNCT
cana-2697	178	1	[	[	X
cana-2697	178	2	24	24	NUM
cana-2697	178	3	]	]	SYM
cana-2697	178	4	pandey	pandey	PROPN
cana-2697	178	5	,	,	PUNCT
cana-2697	178	6	r.	r.	PROPN
cana-2697	178	7	,	,	PUNCT
cana-2697	178	8	et	et	PROPN
cana-2697	178	9	al	al	PROPN
cana-2697	178	10	.	.	PROPN
cana-2697	178	11	,	,	PUNCT
cana-2697	178	12	“	"	PUNCT
cana-2697	178	13	applications	application	NOUN
cana-2697	178	14	of	of	ADP
cana-2697	178	15	maximal	maximal	ADJ
cana-2697	178	16	information	information	NOUN
cana-2697	178	17	coefficient	coefficient	NOUN
cana-2697	178	18	in	in	ADP
cana-2697	178	19	environmental	environmental	ADJ
cana-2697	178	20	data	datum	NOUN
cana-2697	178	21	analysis	analysis	NOUN
cana-2697	178	22	,	,	PUNCT
cana-2697	178	23	”	"	PUNCT
cana-2697	178	24	journal	journal	NOUN
cana-2697	178	25	of	of	ADP
cana-2697	178	26	statistical	statistical	ADJ
cana-2697	178	27	science	science	NOUN
cana-2697	178	28	,	,	PUNCT
cana-2697	178	29	2020	2020	NUM
cana-2697	178	30	.	.	PUNCT
cana-2697	179	1	[	[	X
cana-2697	179	2	25	25	NUM
cana-2697	179	3	]	]	X
cana-2697	179	4	liao	liao	PROPN
cana-2697	179	5	,	,	PUNCT
cana-2697	179	6	x.	x.	PROPN
cana-2697	179	7	,	,	PUNCT
cana-2697	179	8	et	et	PROPN
cana-2697	179	9	al	al	PROPN
cana-2697	179	10	.	.	PROPN
cana-2697	179	11	,	,	PUNCT
cana-2697	179	12	“	"	PUNCT
cana-2697	179	13	comprehensive	comprehensive	ADJ
cana-2697	179	14	review	review	NOUN
cana-2697	179	15	of	of	ADP
cana-2697	179	16	do	do	AUX
cana-2697	179	17	prediction	prediction	NOUN
cana-2697	179	18	models	model	NOUN
cana-2697	179	19	:	:	PUNCT
cana-2697	179	20	traditional	traditional	ADJ
cana-2697	179	21	and	and	CCONJ
cana-2697	179	22	modern	modern	ADJ
cana-2697	179	23	approaches	approach	NOUN
cana-2697	179	24	,	,	PUNCT
cana-2697	179	25	”	"	PUNCT
cana-2697	179	26	journal	journal	NOUN
cana-2697	179	27	of	of	ADP
cana-2697	179	28	water	water	NOUN
cana-2697	179	29	research	research	NOUN
cana-2697	179	30	,	,	PUNCT
cana-2697	179	31	2021	2021	NUM
cana-2697	179	32	.	.	PUNCT
cana-2697	180	1	[	[	X
cana-2697	180	2	26	26	NUM
cana-2697	180	3	]	]	X
cana-2697	180	4	wenjun	wenjun	PROPN
cana-2697	180	5	liu	liu	PROPN
cana-2697	180	6	et	et	PROPN
cana-2697	180	7	al	al	PROPN
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cana-2697	180	9	,	,	PUNCT
cana-2697	180	10	“	"	PUNCT
cana-2697	180	11	a	a	DET
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cana-2697	180	13	hybrid	hybrid	NOUN
cana-2697	180	14	model	model	NOUN
cana-2697	180	15	to	to	PART
cana-2697	180	16	predict	predict	VERB
cana-2697	180	17	dissolved	dissolve	VERB
cana-2697	180	18	oxygen	oxygen	NOUN
cana-2697	180	19	for	for	ADP
cana-2697	180	20	efficient	efficient	ADJ
cana-2697	180	21	water	water	NOUN
cana-2697	180	22	quality	quality	NOUN
cana-2697	180	23	in	in	ADP
cana-2697	180	24	intensive	intensive	ADJ
cana-2697	180	25	aquaculture	aquaculture	NOUN
cana-2697	180	26	,	,	PUNCT
cana-2697	180	27	ieee	ieee	NOUN
cana-2697	180	28	access	access	NOUN
cana-2697	180	29	2023	2023	NUM
cana-2697	180	30	[	[	X
cana-2697	180	31	27	27	NUM
cana-2697	180	32	]	]	X
cana-2697	180	33	anilkumar	anilkumar	PROPN
cana-2697	180	34	muthevi	muthevi	PROPN
cana-2697	180	35	,	,	PUNCT
cana-2697	180	36	et	et	PROPN
cana-2697	180	37	al	al	PROPN
cana-2697	180	38	.	.	PROPN
cana-2697	180	39	,	,	PUNCT
cana-2697	180	40	“	"	PUNCT
cana-2697	180	41	novel	novel	ADJ
cana-2697	180	42	nature	nature	NOUN
cana-2697	180	43	-	-	PUNCT
cana-2697	180	44	inspired	inspire	VERB
cana-2697	180	45	optimization	optimization	NOUN
cana-2697	180	46	approach	approach	NOUN
cana-2697	180	47	-	-	PUNCT
cana-2697	180	48	based	base	VERB
cana-2697	180	49	svm	svm	NOUN
cana-2697	180	50	for	for	ADP
cana-2697	180	51	identifying	identify	VERB
cana-2697	180	52	the	the	DET
cana-2697	180	53	android	android	PROPN
cana-2697	180	54	malicious	malicious	ADJ
cana-2697	180	55	data	datum	NOUN
cana-2697	180	56	.	.	PUNCT
cana-2697	180	57	”	"	PUNCT
cana-2697	180	58	multimedia	multimedia	NOUN
cana-2697	180	59	tools	tool	NOUN
cana-2697	180	60	and	and	CCONJ
cana-2697	180	61	applications	application	NOUN
cana-2697	180	62	,	,	PUNCT
cana-2697	180	63	springer	springer	NOUN
cana-2697	180	64	publications	publication	NOUN
cana-2697	180	65	.	.	PUNCT
cana-2697	181	1	doi	doi	NOUN
cana-2697	181	2	:	:	PUNCT
cana-2697	181	3	https://doi.org/10.1007/s11042	https://doi.org/10.1007/s11042	NUM
cana-2697	181	4	023	023	NUM
cana-2697	181	5	-	-	SYM
cana-2697	181	6	18097	18097	NUM
cana-2697	181	7	-	-	SYM
cana-2697	181	8	5	5	NUM
cana-2697	181	9	.	.	PUNCT
cana-2697	182	1	[	[	X
cana-2697	182	2	28	28	NUM
cana-2697	182	3	]	]	X
cana-2697	182	4	anilkumar	anilkumar	PROPN
cana-2697	182	5	muthevi	muthevi	PROPN
cana-2697	182	6	et	et	PROPN
cana-2697	182	7	al	al	PROPN
cana-2697	182	8	“	"	PUNCT
cana-2697	182	9	using	use	VERB
cana-2697	182	10	an	an	DET
cana-2697	182	11	enhanced	enhance	VERB
cana-2697	182	12	lightgbm	lightgbm	ADJ
cana-2697	182	13	model	model	NOUN
cana-2697	182	14	to	to	PART
cana-2697	182	15	predict	predict	VERB
cana-2697	182	16	coronary	coronary	ADJ
cana-2697	182	17	heart	heart	NOUN
cana-2697	182	18	disease	disease	NOUN
cana-2697	182	19	:	:	PUNCT
cana-2697	182	20	performance	performance	NOUN
cana-2697	182	21	evaluation	evaluation	NOUN
cana-2697	182	22	and	and	CCONJ
cana-2697	182	23	comparison	comparison	NOUN
cana-2697	182	24	”	"	PUNCT
cana-2697	182	25	nanotechnology	nanotechnology	NOUN
cana-2697	182	26	perceptions	perception	NOUN
cana-2697	182	27	vol	vol	VERB
cana-2697	182	28	20	20	NUM
cana-2697	182	29	no	no	NOUN
cana-2697	182	30	.	.	PUNCT
cana-2697	182	31	s14	s14	NOUN
cana-2697	182	32	(	(	PUNCT
cana-2697	182	33	2024	2024	NUM
cana-2697	182	34	)	)	PUNCT
cana-2697	183	1	2446–2457	2446–2457	NUM
cana-2697	183	2	https://doi.org/10.1007/s11042-023-18097-5	https://doi.org/10.1007/s11042-023-18097-5	NUM
cana-2697	183	3	https://doi.org/10.1007/s11042-023-18097-5	https://doi.org/10.1007/s11042-023-18097-5	NUM