id	sid	tid	token	lemma	pos
ajst-24985	1	1	academic	academic	ADJ
ajst-24985	1	2	journal	journal	NOUN
ajst-24985	1	3	of	of	ADP
ajst-24985	1	4	science	science	NOUN
ajst-24985	1	5	and	and	CCONJ
ajst-24985	1	6	technology	technology	NOUN
ajst-24985	1	7	issn	issn	NOUN
ajst-24985	1	8	:	:	PUNCT
ajst-24985	1	9	2771	2771	NUM
ajst-24985	1	10	-	-	SYM
ajst-24985	1	11	3032	3032	NUM
ajst-24985	1	12	|	|	NOUN
ajst-24985	1	13	vol	vol	NOUN
ajst-24985	1	14	.	.	PROPN
ajst-24985	2	1	12	12	NUM
ajst-24985	2	2	,	,	PUNCT
ajst-24985	2	3	no	no	INTJ
ajst-24985	2	4	.	.	NOUN
ajst-24985	2	5	1	1	NUM
ajst-24985	2	6	,	,	PUNCT
ajst-24985	2	7	2024	2024	NUM
ajst-24985	2	8	199	199	NUM
ajst-24985	2	9	machine	machine	NOUN
ajst-24985	2	10	learning	learning	NOUN
ajst-24985	2	11	based	base	VERB
ajst-24985	2	12	approach	approach	NOUN
ajst-24985	2	13	to	to	PART
ajst-24985	2	14	identify	identify	VERB
ajst-24985	2	15	predictive	predictive	ADJ
ajst-24985	2	16	signal	signal	NOUN
ajst-24985	2	17	models	model	NOUN
ajst-24985	2	18	guona	guona	PROPN
ajst-24985	2	19	chen	chen	PROPN
ajst-24985	2	20	,	,	PUNCT
ajst-24985	2	21	yixuan	yixuan	PROPN
ajst-24985	2	22	guo	guo	PROPN
ajst-24985	2	23	china	china	PROPN
ajst-24985	2	24	university	university	PROPN
ajst-24985	2	25	of	of	ADP
ajst-24985	2	26	petroleum	petroleum	PROPN
ajst-24985	2	27	-	-	PUNCT
ajst-24985	2	28	beijing	beijing	PROPN
ajst-24985	2	29	at	at	ADP
ajst-24985	2	30	karamay	karamay	PROPN
ajst-24985	2	31	,	,	PUNCT
ajst-24985	2	32	karamay	karamay	PROPN
ajst-24985	2	33	,	,	PUNCT
ajst-24985	2	34	xinjiang	xinjiang	PROPN
ajst-24985	2	35	,	,	PUNCT
ajst-24985	2	36	834000	834000	NUM
ajst-24985	2	37	,	,	PUNCT
ajst-24985	2	38	china	china	PROPN
ajst-24985	2	39	abstract	abstract	NOUN
ajst-24985	2	40	:	:	PUNCT
ajst-24985	2	41	in	in	ADP
ajst-24985	2	42	modern	modern	ADJ
ajst-24985	2	43	industrial	industrial	ADJ
ajst-24985	2	44	,	,	PUNCT
ajst-24985	2	45	medical	medical	ADJ
ajst-24985	2	46	and	and	CCONJ
ajst-24985	2	47	communication	communication	NOUN
ajst-24985	2	48	fields	field	NOUN
ajst-24985	2	49	,	,	PUNCT
ajst-24985	2	50	signal	signal	ADJ
ajst-24985	2	51	identification	identification	NOUN
ajst-24985	2	52	and	and	CCONJ
ajst-24985	2	53	prediction	prediction	NOUN
ajst-24985	2	54	are	be	AUX
ajst-24985	2	55	key	key	ADJ
ajst-24985	2	56	technologies	technology	NOUN
ajst-24985	2	57	to	to	PART
ajst-24985	2	58	ensure	ensure	VERB
ajst-24985	2	59	system	system	NOUN
ajst-24985	2	60	stability	stability	NOUN
ajst-24985	2	61	and	and	CCONJ
ajst-24985	2	62	efficiency	efficiency	NOUN
ajst-24985	2	63	.	.	PUNCT
ajst-24985	3	1	traditional	traditional	ADJ
ajst-24985	3	2	signal	signal	NOUN
ajst-24985	3	3	processing	processing	NOUN
ajst-24985	3	4	methods	method	NOUN
ajst-24985	3	5	such	such	ADJ
ajst-24985	3	6	as	as	ADP
ajst-24985	3	7	autoregressive	autoregressive	ADJ
ajst-24985	3	8	modelling	modelling	NOUN
ajst-24985	3	9	(	(	PUNCT
ajst-24985	3	10	ar	ar	NOUN
ajst-24985	3	11	)	)	PUNCT
ajst-24985	3	12	,	,	PUNCT
ajst-24985	3	13	fast	fast	ADJ
ajst-24985	3	14	fourier	fourier	NOUN
ajst-24985	3	15	transform	transform	NOUN
ajst-24985	3	16	(	(	PUNCT
ajst-24985	3	17	fft	fft	PROPN
ajst-24985	3	18	)	)	PUNCT
ajst-24985	3	19	and	and	CCONJ
ajst-24985	3	20	wavelet	wavelet	NOUN
ajst-24985	3	21	transform	transform	NOUN
ajst-24985	3	22	(	(	PUNCT
ajst-24985	3	23	wt	wt	NOUN
ajst-24985	3	24	)	)	PUNCT
ajst-24985	3	25	are	be	AUX
ajst-24985	3	26	able	able	ADJ
ajst-24985	3	27	to	to	PART
ajst-24985	3	28	satisfy	satisfy	VERB
ajst-24985	3	29	the	the	DET
ajst-24985	3	30	demand	demand	NOUN
ajst-24985	3	31	to	to	ADP
ajst-24985	3	32	some	some	DET
ajst-24985	3	33	extent	extent	NOUN
ajst-24985	3	34	,	,	PUNCT
ajst-24985	3	35	but	but	CCONJ
ajst-24985	3	36	their	their	PRON
ajst-24985	3	37	performance	performance	NOUN
ajst-24985	3	38	is	be	AUX
ajst-24985	3	39	limited	limit	VERB
ajst-24985	3	40	when	when	SCONJ
ajst-24985	3	41	dealing	deal	VERB
ajst-24985	3	42	with	with	ADP
ajst-24985	3	43	complex	complex	ADJ
ajst-24985	3	44	and	and	CCONJ
ajst-24985	3	45	nonlinear	nonlinear	ADJ
ajst-24985	3	46	signals	signal	NOUN
ajst-24985	3	47	.	.	PUNCT
ajst-24985	4	1	with	with	ADP
ajst-24985	4	2	the	the	DET
ajst-24985	4	3	increase	increase	NOUN
ajst-24985	4	4	of	of	ADP
ajst-24985	4	5	computational	computational	ADJ
ajst-24985	4	6	power	power	NOUN
ajst-24985	4	7	and	and	CCONJ
ajst-24985	4	8	data	datum	NOUN
ajst-24985	4	9	volume	volume	NOUN
ajst-24985	4	10	,	,	PUNCT
ajst-24985	4	11	machine	machine	NOUN
ajst-24985	4	12	learning	learning	NOUN
ajst-24985	4	13	methods	method	NOUN
ajst-24985	4	14	are	be	AUX
ajst-24985	4	15	gradually	gradually	ADV
ajst-24985	4	16	occupying	occupy	VERB
ajst-24985	4	17	an	an	DET
ajst-24985	4	18	important	important	ADJ
ajst-24985	4	19	position	position	NOUN
ajst-24985	4	20	in	in	ADP
ajst-24985	4	21	the	the	DET
ajst-24985	4	22	field	field	NOUN
ajst-24985	4	23	of	of	ADP
ajst-24985	4	24	signal	signal	NOUN
ajst-24985	4	25	processing	processing	NOUN
ajst-24985	4	26	due	due	ADP
ajst-24985	4	27	to	to	ADP
ajst-24985	4	28	their	their	PRON
ajst-24985	4	29	powerful	powerful	ADJ
ajst-24985	4	30	feature	feature	NOUN
ajst-24985	4	31	extraction	extraction	NOUN
ajst-24985	4	32	and	and	CCONJ
ajst-24985	4	33	pattern	pattern	NOUN
ajst-24985	4	34	recognition	recognition	NOUN
ajst-24985	4	35	capabilities	capability	NOUN
ajst-24985	4	36	.	.	PUNCT
ajst-24985	5	1	the	the	DET
ajst-24985	5	2	aim	aim	NOUN
ajst-24985	5	3	of	of	ADP
ajst-24985	5	4	this	this	DET
ajst-24985	5	5	study	study	NOUN
ajst-24985	5	6	is	be	AUX
ajst-24985	5	7	to	to	PART
ajst-24985	5	8	explore	explore	VERB
ajst-24985	5	9	the	the	DET
ajst-24985	5	10	application	application	NOUN
ajst-24985	5	11	of	of	ADP
ajst-24985	5	12	machine	machine	NOUN
ajst-24985	5	13	learning	learning	NOUN
ajst-24985	5	14	based	base	VERB
ajst-24985	5	15	methods	method	NOUN
ajst-24985	5	16	in	in	ADP
ajst-24985	5	17	signal	signal	ADJ
ajst-24985	5	18	recognition	recognition	NOUN
ajst-24985	5	19	and	and	CCONJ
ajst-24985	5	20	prediction	prediction	NOUN
ajst-24985	5	21	.	.	PUNCT
ajst-24985	6	1	we	we	PRON
ajst-24985	6	2	propose	propose	VERB
ajst-24985	6	3	a	a	DET
ajst-24985	6	4	systematic	systematic	ADJ
ajst-24985	6	5	signal	signal	NOUN
ajst-24985	6	6	processing	processing	NOUN
ajst-24985	6	7	framework	framework	NOUN
ajst-24985	6	8	,	,	PUNCT
ajst-24985	6	9	including	include	VERB
ajst-24985	6	10	steps	step	NOUN
ajst-24985	6	11	of	of	ADP
ajst-24985	6	12	data	datum	NOUN
ajst-24985	6	13	preprocessing	preprocessing	NOUN
ajst-24985	6	14	,	,	PUNCT
ajst-24985	6	15	feature	feature	NOUN
ajst-24985	6	16	extraction	extraction	NOUN
ajst-24985	6	17	,	,	PUNCT
ajst-24985	6	18	model	model	NOUN
ajst-24985	6	19	training	training	NOUN
ajst-24985	6	20	and	and	CCONJ
ajst-24985	6	21	validation	validation	NOUN
ajst-24985	6	22	.	.	PUNCT
ajst-24985	7	1	the	the	DET
ajst-24985	7	2	advantages	advantage	NOUN
ajst-24985	7	3	of	of	ADP
ajst-24985	7	4	deep	deep	ADJ
ajst-24985	7	5	learning	learning	NOUN
ajst-24985	7	6	models	model	NOUN
ajst-24985	7	7	in	in	ADP
ajst-24985	7	8	complex	complex	ADJ
ajst-24985	7	9	signal	signal	NOUN
ajst-24985	7	10	processing	processing	NOUN
ajst-24985	7	11	are	be	AUX
ajst-24985	7	12	verified	verify	VERB
ajst-24985	7	13	by	by	ADP
ajst-24985	7	14	comparing	compare	VERB
ajst-24985	7	15	the	the	DET
ajst-24985	7	16	performance	performance	NOUN
ajst-24985	7	17	of	of	ADP
ajst-24985	7	18	algorithms	algorithm	NOUN
ajst-24985	7	19	such	such	ADJ
ajst-24985	7	20	as	as	ADP
ajst-24985	7	21	support	support	NOUN
ajst-24985	7	22	vector	vector	NOUN
ajst-24985	7	23	machine	machine	NOUN
ajst-24985	7	24	(	(	PUNCT
ajst-24985	7	25	svm	svm	PROPN
ajst-24985	7	26	)	)	PUNCT
ajst-24985	7	27	,	,	PUNCT
ajst-24985	7	28	artificial	artificial	ADJ
ajst-24985	7	29	neural	neural	ADJ
ajst-24985	7	30	network	network	NOUN
ajst-24985	7	31	(	(	PUNCT
ajst-24985	7	32	ann	ann	PROPN
ajst-24985	7	33	)	)	PUNCT
ajst-24985	7	34	,	,	PUNCT
ajst-24985	7	35	random	random	ADJ
ajst-24985	7	36	forest	forest	NOUN
ajst-24985	7	37	(	(	PUNCT
ajst-24985	7	38	rf	rf	NOUN
ajst-24985	7	39	)	)	PUNCT
ajst-24985	7	40	and	and	CCONJ
ajst-24985	7	41	convolutional	convolutional	ADJ
ajst-24985	7	42	neural	neural	ADJ
ajst-24985	7	43	network	network	NOUN
ajst-24985	7	44	(	(	PUNCT
ajst-24985	7	45	cnn	cnn	PROPN
ajst-24985	7	46	)	)	PUNCT
ajst-24985	7	47	.	.	PUNCT
ajst-24985	8	1	the	the	DET
ajst-24985	8	2	experimental	experimental	ADJ
ajst-24985	8	3	results	result	NOUN
ajst-24985	8	4	show	show	VERB
ajst-24985	8	5	that	that	SCONJ
ajst-24985	8	6	cnn	cnn	PROPN
ajst-24985	8	7	performs	perform	VERB
ajst-24985	8	8	best	good	ADJ
ajst-24985	8	9	in	in	ADP
ajst-24985	8	10	signal	signal	ADJ
ajst-24985	8	11	recognition	recognition	NOUN
ajst-24985	8	12	and	and	CCONJ
ajst-24985	8	13	prediction	prediction	NOUN
ajst-24985	8	14	tasks	task	NOUN
ajst-24985	8	15	,	,	PUNCT
ajst-24985	8	16	followed	follow	VERB
ajst-24985	8	17	by	by	ADP
ajst-24985	8	18	ann	ann	PROPN
ajst-24985	8	19	and	and	CCONJ
ajst-24985	8	20	rf	rf	PROPN
ajst-24985	8	21	,	,	PUNCT
ajst-24985	8	22	while	while	SCONJ
ajst-24985	8	23	svm	svm	PROPN
ajst-24985	8	24	has	have	VERB
ajst-24985	8	25	relatively	relatively	ADV
ajst-24985	8	26	low	low	ADJ
ajst-24985	8	27	performance	performance	NOUN
ajst-24985	8	28	.	.	PUNCT
ajst-24985	9	1	deep	deep	ADJ
ajst-24985	9	2	learning	learning	NOUN
ajst-24985	9	3	models	model	NOUN
ajst-24985	9	4	perform	perform	VERB
ajst-24985	9	5	well	well	ADV
ajst-24985	9	6	in	in	ADP
ajst-24985	9	7	processing	process	VERB
ajst-24985	9	8	high	high	ADV
ajst-24985	9	9	-	-	PUNCT
ajst-24985	9	10	dimensional	dimensional	ADJ
ajst-24985	9	11	and	and	CCONJ
ajst-24985	9	12	nonlinear	nonlinear	ADJ
ajst-24985	9	13	signals	signal	NOUN
ajst-24985	9	14	and	and	CCONJ
ajst-24985	9	15	can	can	AUX
ajst-24985	9	16	significantly	significantly	ADV
ajst-24985	9	17	improve	improve	VERB
ajst-24985	9	18	the	the	DET
ajst-24985	9	19	accuracy	accuracy	NOUN
ajst-24985	9	20	and	and	CCONJ
ajst-24985	9	21	robustness	robustness	NOUN
ajst-24985	9	22	of	of	ADP
ajst-24985	9	23	signal	signal	ADJ
ajst-24985	9	24	processing	processing	NOUN
ajst-24985	9	25	.	.	PUNCT
ajst-24985	10	1	however	however	ADV
ajst-24985	10	2	,	,	PUNCT
ajst-24985	10	3	the	the	DET
ajst-24985	10	4	training	training	NOUN
ajst-24985	10	5	time	time	NOUN
ajst-24985	10	6	of	of	ADP
ajst-24985	10	7	deep	deep	ADJ
ajst-24985	10	8	learning	learning	NOUN
ajst-24985	10	9	models	model	NOUN
ajst-24985	10	10	is	be	AUX
ajst-24985	10	11	long	long	ADJ
ajst-24985	10	12	and	and	CCONJ
ajst-24985	10	13	the	the	DET
ajst-24985	10	14	demand	demand	NOUN
ajst-24985	10	15	for	for	ADP
ajst-24985	10	16	computational	computational	ADJ
ajst-24985	10	17	resources	resource	NOUN
ajst-24985	10	18	is	be	AUX
ajst-24985	10	19	high	high	ADJ
ajst-24985	10	20	.	.	PUNCT
ajst-24985	11	1	the	the	DET
ajst-24985	11	2	main	main	ADJ
ajst-24985	11	3	contribution	contribution	NOUN
ajst-24985	11	4	of	of	ADP
ajst-24985	11	5	this	this	DET
ajst-24985	11	6	study	study	NOUN
ajst-24985	11	7	is	be	AUX
ajst-24985	11	8	to	to	PART
ajst-24985	11	9	propose	propose	VERB
ajst-24985	11	10	a	a	DET
ajst-24985	11	11	machine	machine	NOUN
ajst-24985	11	12	learning	learning	NOUN
ajst-24985	11	13	-	-	PUNCT
ajst-24985	11	14	based	base	VERB
ajst-24985	11	15	signal	signal	NOUN
ajst-24985	11	16	processing	processing	NOUN
ajst-24985	11	17	framework	framework	NOUN
ajst-24985	11	18	that	that	PRON
ajst-24985	11	19	systematically	systematically	ADV
ajst-24985	11	20	compares	compare	VERB
ajst-24985	11	21	the	the	DET
ajst-24985	11	22	performance	performance	NOUN
ajst-24985	11	23	of	of	ADP
ajst-24985	11	24	multiple	multiple	ADJ
ajst-24985	11	25	algorithms	algorithm	NOUN
ajst-24985	11	26	and	and	CCONJ
ajst-24985	11	27	provides	provide	VERB
ajst-24985	11	28	suggestions	suggestion	NOUN
ajst-24985	11	29	for	for	ADP
ajst-24985	11	30	selecting	select	VERB
ajst-24985	11	31	and	and	CCONJ
ajst-24985	11	32	optimising	optimise	VERB
ajst-24985	11	33	models	model	NOUN
ajst-24985	11	34	in	in	ADP
ajst-24985	11	35	different	different	ADJ
ajst-24985	11	36	application	application	NOUN
ajst-24985	11	37	scenarios	scenario	NOUN
ajst-24985	11	38	.	.	PUNCT
ajst-24985	12	1	future	future	ADJ
ajst-24985	12	2	research	research	NOUN
ajst-24985	12	3	can	can	AUX
ajst-24985	12	4	further	far	ADV
ajst-24985	12	5	extend	extend	VERB
ajst-24985	12	6	the	the	DET
ajst-24985	12	7	diversity	diversity	NOUN
ajst-24985	12	8	of	of	ADP
ajst-24985	12	9	datasets	dataset	NOUN
ajst-24985	12	10	,	,	PUNCT
ajst-24985	12	11	optimise	optimise	VERB
ajst-24985	12	12	the	the	DET
ajst-24985	12	13	computational	computational	ADJ
ajst-24985	12	14	efficiency	efficiency	NOUN
ajst-24985	12	15	of	of	ADP
ajst-24985	12	16	models	model	NOUN
ajst-24985	12	17	,	,	PUNCT
ajst-24985	12	18	and	and	CCONJ
ajst-24985	12	19	explore	explore	VERB
ajst-24985	12	20	more	more	ADV
ajst-24985	12	21	advanced	advanced	ADJ
ajst-24985	12	22	machine	machine	NOUN
ajst-24985	12	23	learning	learning	NOUN
ajst-24985	12	24	methods	method	NOUN
ajst-24985	12	25	to	to	PART
ajst-24985	12	26	advance	advance	VERB
ajst-24985	12	27	the	the	DET
ajst-24985	12	28	development	development	NOUN
ajst-24985	12	29	of	of	ADP
ajst-24985	12	30	signal	signal	ADJ
ajst-24985	12	31	processing	processing	NOUN
ajst-24985	12	32	techniques	technique	NOUN
ajst-24985	12	33	.	.	PUNCT
ajst-24985	13	1	keywords	keyword	NOUN
ajst-24985	13	2	:	:	PUNCT
ajst-24985	13	3	machine	machine	NOUN
ajst-24985	13	4	learning	learning	NOUN
ajst-24985	13	5	;	;	PUNCT
ajst-24985	13	6	signal	signal	ADJ
ajst-24985	13	7	recognition	recognition	NOUN
ajst-24985	13	8	;	;	PUNCT
ajst-24985	13	9	predictive	predictive	ADJ
ajst-24985	13	10	models	model	NOUN
ajst-24985	13	11	;	;	PUNCT
ajst-24985	13	12	feature	feature	NOUN
ajst-24985	13	13	extraction	extraction	NOUN
ajst-24985	13	14	;	;	PUNCT
ajst-24985	13	15	data	datum	NOUN
ajst-24985	13	16	analysis	analysis	NOUN
ajst-24985	13	17	.	.	PUNCT
ajst-24985	14	1	1	1	X
ajst-24985	14	2	.	.	X
ajst-24985	14	3	introduction	introduction	NOUN
ajst-24985	14	4	with	with	ADP
ajst-24985	14	5	the	the	DET
ajst-24985	14	6	rapid	rapid	ADJ
ajst-24985	14	7	development	development	NOUN
ajst-24985	14	8	of	of	ADP
ajst-24985	14	9	modern	modern	ADJ
ajst-24985	14	10	science	science	NOUN
ajst-24985	14	11	and	and	CCONJ
ajst-24985	14	12	technology	technology	NOUN
ajst-24985	14	13	,	,	PUNCT
ajst-24985	14	14	signal	signal	ADJ
ajst-24985	14	15	processing	processing	NOUN
ajst-24985	14	16	technology	technology	NOUN
ajst-24985	14	17	plays	play	VERB
ajst-24985	14	18	a	a	DET
ajst-24985	14	19	vital	vital	ADJ
ajst-24985	14	20	role	role	NOUN
ajst-24985	14	21	in	in	ADP
ajst-24985	14	22	many	many	ADJ
ajst-24985	14	23	fields	field	NOUN
ajst-24985	14	24	such	such	ADJ
ajst-24985	14	25	as	as	ADP
ajst-24985	14	26	industry	industry	NOUN
ajst-24985	14	27	,	,	PUNCT
ajst-24985	14	28	medical	medical	ADJ
ajst-24985	14	29	treatment	treatment	NOUN
ajst-24985	14	30	,	,	PUNCT
ajst-24985	14	31	communication	communication	NOUN
ajst-24985	14	32	and	and	CCONJ
ajst-24985	14	33	smart	smart	ADJ
ajst-24985	14	34	home	home	NOUN
ajst-24985	14	35	.	.	PUNCT
ajst-24985	15	1	signal	signal	NOUN
ajst-24985	15	2	recognition	recognition	NOUN
ajst-24985	15	3	and	and	CCONJ
ajst-24985	15	4	prediction	prediction	NOUN
ajst-24985	15	5	,	,	PUNCT
ajst-24985	15	6	as	as	ADP
ajst-24985	15	7	an	an	DET
ajst-24985	15	8	important	important	ADJ
ajst-24985	15	9	branch	branch	NOUN
ajst-24985	15	10	of	of	ADP
ajst-24985	15	11	signal	signal	NOUN
ajst-24985	15	12	processing	processing	NOUN
ajst-24985	15	13	,	,	PUNCT
ajst-24985	15	14	can	can	AUX
ajst-24985	15	15	effectively	effectively	ADV
ajst-24985	15	16	extract	extract	VERB
ajst-24985	15	17	useful	useful	ADJ
ajst-24985	15	18	information	information	NOUN
ajst-24985	15	19	,	,	PUNCT
ajst-24985	15	20	identify	identify	VERB
ajst-24985	15	21	anomalies	anomaly	NOUN
ajst-24985	15	22	and	and	CCONJ
ajst-24985	15	23	predict	predict	VERB
ajst-24985	15	24	future	future	ADJ
ajst-24985	15	25	trends	trend	NOUN
ajst-24985	15	26	by	by	ADP
ajst-24985	15	27	analysing	analyse	VERB
ajst-24985	15	28	and	and	CCONJ
ajst-24985	15	29	processing	process	VERB
ajst-24985	15	30	various	various	ADJ
ajst-24985	15	31	signal	signal	NOUN
ajst-24985	15	32	data	datum	NOUN
ajst-24985	15	33	,	,	PUNCT
ajst-24985	15	34	thus	thus	ADV
ajst-24985	15	35	improving	improve	VERB
ajst-24985	15	36	the	the	DET
ajst-24985	15	37	stability	stability	NOUN
ajst-24985	15	38	and	and	CCONJ
ajst-24985	15	39	efficiency	efficiency	NOUN
ajst-24985	15	40	of	of	ADP
ajst-24985	15	41	the	the	DET
ajst-24985	15	42	system	system	NOUN
ajst-24985	15	43	.	.	PUNCT
ajst-24985	16	1	for	for	ADP
ajst-24985	16	2	example	example	NOUN
ajst-24985	16	3	,	,	PUNCT
ajst-24985	16	4	in	in	ADP
ajst-24985	16	5	the	the	DET
ajst-24985	16	6	maintenance	maintenance	NOUN
ajst-24985	16	7	of	of	ADP
ajst-24985	16	8	industrial	industrial	ADJ
ajst-24985	16	9	equipment	equipment	NOUN
ajst-24985	16	10	,	,	PUNCT
ajst-24985	16	11	through	through	ADP
ajst-24985	16	12	real	real	ADJ
ajst-24985	16	13	-	-	PUNCT
ajst-24985	16	14	time	time	NOUN
ajst-24985	16	15	monitoring	monitoring	NOUN
ajst-24985	16	16	and	and	CCONJ
ajst-24985	16	17	prediction	prediction	NOUN
ajst-24985	16	18	of	of	ADP
ajst-24985	16	19	equipment	equipment	NOUN
ajst-24985	16	20	operation	operation	NOUN
ajst-24985	16	21	signals	signal	NOUN
ajst-24985	16	22	,	,	PUNCT
ajst-24985	16	23	potential	potential	ADJ
ajst-24985	16	24	failures	failure	NOUN
ajst-24985	16	25	can	can	AUX
ajst-24985	16	26	be	be	AUX
ajst-24985	16	27	detected	detect	VERB
ajst-24985	16	28	in	in	ADP
ajst-24985	16	29	time	time	NOUN
ajst-24985	16	30	,	,	PUNCT
ajst-24985	16	31	reducing	reduce	VERB
ajst-24985	16	32	downtime	downtime	NOUN
ajst-24985	16	33	and	and	CCONJ
ajst-24985	16	34	maintenance	maintenance	NOUN
ajst-24985	16	35	costs	cost	NOUN
ajst-24985	16	36	;	;	PUNCT
ajst-24985	16	37	in	in	ADP
ajst-24985	16	38	the	the	DET
ajst-24985	16	39	medical	medical	ADJ
ajst-24985	16	40	field	field	NOUN
ajst-24985	16	41	,	,	PUNCT
ajst-24985	16	42	through	through	ADP
ajst-24985	16	43	the	the	DET
ajst-24985	16	44	analysis	analysis	NOUN
ajst-24985	16	45	and	and	CCONJ
ajst-24985	16	46	prediction	prediction	NOUN
ajst-24985	16	47	of	of	ADP
ajst-24985	16	48	biological	biological	ADJ
ajst-24985	16	49	signals	signal	NOUN
ajst-24985	16	50	,	,	PUNCT
ajst-24985	16	51	diseases	disease	NOUN
ajst-24985	16	52	can	can	AUX
ajst-24985	16	53	be	be	AUX
ajst-24985	16	54	detected	detect	VERB
ajst-24985	16	55	at	at	ADP
ajst-24985	16	56	an	an	DET
ajst-24985	16	57	early	early	ADJ
ajst-24985	16	58	stage	stage	NOUN
ajst-24985	16	59	and	and	CCONJ
ajst-24985	16	60	treatment	treatment	NOUN
ajst-24985	16	61	effects	effect	NOUN
ajst-24985	16	62	can	can	AUX
ajst-24985	16	63	be	be	AUX
ajst-24985	16	64	improved	improve	VERB
ajst-24985	16	65	.	.	PUNCT
ajst-24985	17	1	therefore	therefore	ADV
ajst-24985	17	2	,	,	PUNCT
ajst-24985	17	3	the	the	DET
ajst-24985	17	4	study	study	NOUN
ajst-24985	17	5	of	of	ADP
ajst-24985	17	6	effective	effective	ADJ
ajst-24985	17	7	signal	signal	NOUN
ajst-24985	17	8	recognition	recognition	NOUN
ajst-24985	17	9	and	and	CCONJ
ajst-24985	17	10	prediction	prediction	NOUN
ajst-24985	17	11	methods	method	NOUN
ajst-24985	17	12	has	have	VERB
ajst-24985	17	13	important	important	ADJ
ajst-24985	17	14	theoretical	theoretical	ADJ
ajst-24985	17	15	significance	significance	NOUN
ajst-24985	17	16	and	and	CCONJ
ajst-24985	17	17	practical	practical	ADJ
ajst-24985	17	18	application	application	NOUN
ajst-24985	17	19	value	value	NOUN
ajst-24985	17	20	.	.	PUNCT
ajst-24985	18	1	traditional	traditional	ADJ
ajst-24985	18	2	signal	signal	NOUN
ajst-24985	18	3	recognition	recognition	NOUN
ajst-24985	18	4	and	and	CCONJ
ajst-24985	18	5	prediction	prediction	NOUN
ajst-24985	18	6	methods	method	NOUN
ajst-24985	18	7	mainly	mainly	ADV
ajst-24985	18	8	include	include	VERB
ajst-24985	18	9	statistic	statistic	NOUN
ajst-24985	18	10	-	-	PUNCT
ajst-24985	18	11	based	base	VERB
ajst-24985	18	12	methods	method	NOUN
ajst-24985	18	13	,	,	PUNCT
ajst-24985	18	14	frequency	frequency	NOUN
ajst-24985	18	15	domain	domain	NOUN
ajst-24985	18	16	analysis	analysis	NOUN
ajst-24985	18	17	methods	method	NOUN
ajst-24985	18	18	and	and	CCONJ
ajst-24985	18	19	time	time	NOUN
ajst-24985	18	20	-	-	PUNCT
ajst-24985	18	21	frequency	frequency	NOUN
ajst-24985	18	22	analysis	analysis	NOUN
ajst-24985	18	23	methods	method	NOUN
ajst-24985	18	24	.	.	PUNCT
ajst-24985	19	1	statistical	statistical	ADJ
ajst-24985	19	2	methods	method	NOUN
ajst-24985	19	3	such	such	ADJ
ajst-24985	19	4	as	as	ADP
ajst-24985	19	5	autoregressive	autoregressive	ADJ
ajst-24985	19	6	model	model	NOUN
ajst-24985	19	7	(	(	PUNCT
ajst-24985	19	8	ar	ar	NOUN
ajst-24985	19	9	)	)	PUNCT
ajst-24985	19	10	,	,	PUNCT
ajst-24985	19	11	moving	move	VERB
ajst-24985	19	12	average	average	ADJ
ajst-24985	19	13	model	model	NOUN
ajst-24985	19	14	(	(	PUNCT
ajst-24985	19	15	ma	ma	PROPN
ajst-24985	19	16	)	)	PUNCT
ajst-24985	19	17	and	and	CCONJ
ajst-24985	19	18	their	their	PRON
ajst-24985	19	19	combinations	combination	NOUN
ajst-24985	19	20	(	(	PUNCT
ajst-24985	19	21	arma	arma	PROPN
ajst-24985	19	22	and	and	CCONJ
ajst-24985	19	23	arima	arima	PROPN
ajst-24985	19	24	)	)	PUNCT
ajst-24985	19	25	are	be	AUX
ajst-24985	19	26	used	use	VERB
ajst-24985	19	27	to	to	PART
ajst-24985	19	28	analyse	analyse	VERB
ajst-24985	19	29	and	and	CCONJ
ajst-24985	19	30	predict	predict	VERB
ajst-24985	19	31	signals	signal	NOUN
ajst-24985	19	32	by	by	ADP
ajst-24985	19	33	establishing	establish	VERB
ajst-24985	19	34	mathematical	mathematical	ADJ
ajst-24985	19	35	models	model	NOUN
ajst-24985	19	36	of	of	ADP
ajst-24985	19	37	the	the	DET
ajst-24985	19	38	signals	signal	NOUN
ajst-24985	19	39	;	;	PUNCT
ajst-24985	19	40	frequencydomain	frequencydomain	ADJ
ajst-24985	19	41	analysis	analysis	NOUN
ajst-24985	19	42	methods	method	NOUN
ajst-24985	19	43	such	such	ADJ
ajst-24985	19	44	as	as	ADP
ajst-24985	19	45	fast	fast	ADJ
ajst-24985	19	46	fourier	fourier	NOUN
ajst-24985	19	47	transform	transform	NOUN
ajst-24985	19	48	(	(	PUNCT
ajst-24985	19	49	fft	fft	PROPN
ajst-24985	19	50	)	)	PUNCT
ajst-24985	19	51	are	be	AUX
ajst-24985	19	52	used	use	VERB
ajst-24985	19	53	to	to	PART
ajst-24985	19	54	analyse	analyse	VERB
ajst-24985	19	55	the	the	DET
ajst-24985	19	56	frequency	frequency	NOUN
ajst-24985	19	57	components	component	NOUN
ajst-24985	19	58	of	of	ADP
ajst-24985	19	59	the	the	DET
ajst-24985	19	60	signals	signal	NOUN
ajst-24985	19	61	by	by	ADP
ajst-24985	19	62	transforming	transform	VERB
ajst-24985	19	63	the	the	DET
ajst-24985	19	64	signals	signal	NOUN
ajst-24985	19	65	from	from	ADP
ajst-24985	19	66	the	the	DET
ajst-24985	19	67	time	time	NOUN
ajst-24985	19	68	domain	domain	NOUN
ajst-24985	19	69	to	to	ADP
ajst-24985	19	70	the	the	DET
ajst-24985	19	71	frequency	frequency	NOUN
ajst-24985	19	72	domain	domain	NOUN
ajst-24985	19	73	;	;	PUNCT
ajst-24985	19	74	time	time	NOUN
ajst-24985	19	75	-	-	PUNCT
ajst-24985	19	76	frequency	frequency	NOUN
ajst-24985	19	77	analysis	analysis	NOUN
ajst-24985	19	78	methods	method	NOUN
ajst-24985	19	79	such	such	ADJ
ajst-24985	19	80	as	as	ADP
ajst-24985	19	81	wavelet	wavelet	NOUN
ajst-24985	19	82	transform	transform	NOUN
ajst-24985	19	83	(	(	PUNCT
ajst-24985	19	84	wt	wt	NOUN
ajst-24985	19	85	)	)	PUNCT
ajst-24985	19	86	are	be	AUX
ajst-24985	19	87	used	use	VERB
ajst-24985	19	88	to	to	PART
ajst-24985	19	89	analyse	analyse	VERB
ajst-24985	19	90	the	the	DET
ajst-24985	19	91	frequency	frequency	NOUN
ajst-24985	19	92	components	component	NOUN
ajst-24985	19	93	of	of	ADP
ajst-24985	19	94	the	the	DET
ajst-24985	19	95	signals	signal	NOUN
ajst-24985	19	96	by	by	ADP
ajst-24985	19	97	simultaneously	simultaneously	ADV
ajst-24985	19	98	considering	consider	VERB
ajst-24985	19	99	the	the	DET
ajst-24985	19	100	time	time	NOUN
ajst-24985	19	101	and	and	CCONJ
ajst-24985	19	102	frequency	frequency	NOUN
ajst-24985	19	103	characteristics	characteristic	NOUN
ajst-24985	19	104	of	of	ADP
ajst-24985	19	105	the	the	DET
ajst-24985	19	106	signal	signal	NOUN
ajst-24985	19	107	to	to	PART
ajst-24985	19	108	provide	provide	VERB
ajst-24985	19	109	a	a	DET
ajst-24985	19	110	more	more	ADV
ajst-24985	19	111	refined	refined	ADJ
ajst-24985	19	112	signal	signal	NOUN
ajst-24985	19	113	analysis	analysis	NOUN
ajst-24985	19	114	.	.	PUNCT
ajst-24985	20	1	however	however	ADV
ajst-24985	20	2	,	,	PUNCT
ajst-24985	20	3	these	these	DET
ajst-24985	20	4	traditional	traditional	ADJ
ajst-24985	20	5	methods	method	NOUN
ajst-24985	20	6	often	often	ADV
ajst-24985	20	7	face	face	VERB
ajst-24985	20	8	problems	problem	NOUN
ajst-24985	20	9	such	such	ADJ
ajst-24985	20	10	as	as	ADP
ajst-24985	20	11	strict	strict	ADJ
ajst-24985	20	12	model	model	NOUN
ajst-24985	20	13	assumptions	assumption	NOUN
ajst-24985	20	14	,	,	PUNCT
ajst-24985	20	15	difficulty	difficulty	NOUN
ajst-24985	20	16	in	in	ADP
ajst-24985	20	17	feature	feature	NOUN
ajst-24985	20	18	extraction	extraction	NOUN
ajst-24985	20	19	,	,	PUNCT
ajst-24985	20	20	and	and	CCONJ
ajst-24985	20	21	insufficient	insufficient	ADJ
ajst-24985	20	22	generalisation	generalisation	NOUN
ajst-24985	20	23	capability	capability	NOUN
ajst-24985	20	24	when	when	SCONJ
ajst-24985	20	25	dealing	deal	VERB
ajst-24985	20	26	with	with	ADP
ajst-24985	20	27	complex	complex	ADJ
ajst-24985	20	28	and	and	CCONJ
ajst-24985	20	29	non	non	ADJ
ajst-24985	20	30	-	-	ADJ
ajst-24985	20	31	linear	linear	ADJ
ajst-24985	20	32	signals	signal	NOUN
ajst-24985	20	33	.	.	PUNCT
ajst-24985	21	1	in	in	ADP
ajst-24985	21	2	recent	recent	ADJ
ajst-24985	21	3	years	year	NOUN
ajst-24985	21	4	,	,	PUNCT
ajst-24985	21	5	with	with	ADP
ajst-24985	21	6	the	the	DET
ajst-24985	21	7	improvement	improvement	NOUN
ajst-24985	21	8	of	of	ADP
ajst-24985	21	9	computing	compute	VERB
ajst-24985	21	10	power	power	NOUN
ajst-24985	21	11	and	and	CCONJ
ajst-24985	21	12	the	the	DET
ajst-24985	21	13	development	development	NOUN
ajst-24985	21	14	of	of	ADP
ajst-24985	21	15	big	big	ADJ
ajst-24985	21	16	data	datum	NOUN
ajst-24985	21	17	technology	technology	NOUN
ajst-24985	21	18	,	,	PUNCT
ajst-24985	21	19	machine	machine	NOUN
ajst-24985	21	20	learning	learning	NOUN
ajst-24985	21	21	methods	method	NOUN
ajst-24985	21	22	have	have	AUX
ajst-24985	21	23	been	be	AUX
ajst-24985	21	24	more	more	ADV
ajst-24985	21	25	and	and	CCONJ
ajst-24985	21	26	more	more	ADV
ajst-24985	21	27	widely	widely	ADV
ajst-24985	21	28	used	use	VERB
ajst-24985	21	29	in	in	ADP
ajst-24985	21	30	signal	signal	ADJ
ajst-24985	21	31	recognition	recognition	NOUN
ajst-24985	21	32	and	and	CCONJ
ajst-24985	21	33	prediction	prediction	NOUN
ajst-24985	21	34	.	.	PUNCT
ajst-24985	22	1	machine	machine	NOUN
ajst-24985	22	2	learning	learning	NOUN
ajst-24985	22	3	has	have	VERB
ajst-24985	22	4	the	the	DET
ajst-24985	22	5	advantage	advantage	NOUN
ajst-24985	22	6	of	of	ADP
ajst-24985	22	7	dealing	deal	VERB
ajst-24985	22	8	with	with	ADP
ajst-24985	22	9	complex	complex	ADJ
ajst-24985	22	10	and	and	CCONJ
ajst-24985	22	11	nonlinear	nonlinear	ADJ
ajst-24985	22	12	signals	signal	NOUN
ajst-24985	22	13	through	through	ADP
ajst-24985	22	14	a	a	DET
ajst-24985	22	15	data	data	NOUN
ajst-24985	22	16	-	-	PUNCT
ajst-24985	22	17	driven	drive	VERB
ajst-24985	22	18	approach	approach	NOUN
ajst-24985	22	19	that	that	PRON
ajst-24985	22	20	does	do	AUX
ajst-24985	22	21	not	not	PART
ajst-24985	22	22	rely	rely	VERB
ajst-24985	22	23	on	on	ADP
ajst-24985	22	24	strict	strict	ADJ
ajst-24985	22	25	model	model	NOUN
ajst-24985	22	26	assumptions	assumption	NOUN
ajst-24985	22	27	,	,	PUNCT
ajst-24985	22	28	and	and	CCONJ
ajst-24985	22	29	is	be	AUX
ajst-24985	22	30	able	able	ADJ
ajst-24985	22	31	to	to	PART
ajst-24985	22	32	automatically	automatically	ADV
ajst-24985	22	33	learn	learn	VERB
ajst-24985	22	34	features	feature	NOUN
ajst-24985	22	35	and	and	CCONJ
ajst-24985	22	36	laws	law	NOUN
ajst-24985	22	37	from	from	ADP
ajst-24985	22	38	a	a	DET
ajst-24985	22	39	large	large	ADJ
ajst-24985	22	40	amount	amount	NOUN
ajst-24985	22	41	of	of	ADP
ajst-24985	22	42	data	datum	NOUN
ajst-24985	22	43	.	.	PUNCT
ajst-24985	23	1	in	in	ADP
ajst-24985	23	2	particular	particular	ADJ
ajst-24985	23	3	,	,	PUNCT
ajst-24985	23	4	deep	deep	ADJ
ajst-24985	23	5	learning	learning	NOUN
ajst-24985	23	6	methods	method	NOUN
ajst-24985	23	7	,	,	PUNCT
ajst-24985	23	8	such	such	ADJ
ajst-24985	23	9	as	as	ADP
ajst-24985	23	10	convolutional	convolutional	ADJ
ajst-24985	23	11	neural	neural	ADJ
ajst-24985	23	12	networks	network	NOUN
ajst-24985	23	13	(	(	PUNCT
ajst-24985	23	14	cnns	cnns	PROPN
ajst-24985	23	15	)	)	PUNCT
ajst-24985	23	16	and	and	CCONJ
ajst-24985	23	17	recurrent	recurrent	ADJ
ajst-24985	23	18	neural	neural	ADJ
ajst-24985	23	19	networks	network	NOUN
ajst-24985	23	20	(	(	PUNCT
ajst-24985	23	21	rnns	rnns	PROPN
ajst-24985	23	22	)	)	PUNCT
ajst-24985	23	23	,	,	PUNCT
ajst-24985	23	24	have	have	AUX
ajst-24985	23	25	achieved	achieve	VERB
ajst-24985	23	26	remarkable	remarkable	ADJ
ajst-24985	23	27	results	result	NOUN
ajst-24985	23	28	in	in	ADP
ajst-24985	23	29	the	the	DET
ajst-24985	23	30	fields	field	NOUN
ajst-24985	23	31	of	of	ADP
ajst-24985	23	32	image	image	NOUN
ajst-24985	23	33	recognition	recognition	NOUN
ajst-24985	23	34	,	,	PUNCT
ajst-24985	23	35	natural	natural	ADJ
ajst-24985	23	36	language	language	NOUN
ajst-24985	23	37	processing	processing	NOUN
ajst-24985	23	38	,	,	PUNCT
ajst-24985	23	39	etc	etc	X
ajst-24985	23	40	.	.	X
ajst-24985	23	41	,	,	PUNCT
ajst-24985	23	42	and	and	CCONJ
ajst-24985	23	43	have	have	AUX
ajst-24985	23	44	been	be	AUX
ajst-24985	23	45	gradually	gradually	ADV
ajst-24985	23	46	applied	apply	VERB
ajst-24985	23	47	to	to	ADP
ajst-24985	23	48	the	the	DET
ajst-24985	23	49	field	field	NOUN
ajst-24985	23	50	of	of	ADP
ajst-24985	23	51	signal	signal	ADJ
ajst-24985	23	52	processing	processing	NOUN
ajst-24985	23	53	.	.	PUNCT
ajst-24985	24	1	compared	compare	VERB
ajst-24985	24	2	with	with	ADP
ajst-24985	24	3	traditional	traditional	ADJ
ajst-24985	24	4	methods	method	NOUN
ajst-24985	24	5	,	,	PUNCT
ajst-24985	24	6	machine	machine	NOUN
ajst-24985	24	7	learning	learning	NOUN
ajst-24985	24	8	methods	method	NOUN
ajst-24985	24	9	have	have	VERB
ajst-24985	24	10	higher	high	ADJ
ajst-24985	24	11	accuracy	accuracy	NOUN
ajst-24985	24	12	and	and	CCONJ
ajst-24985	24	13	generalisation	generalisation	NOUN
ajst-24985	24	14	ability	ability	NOUN
ajst-24985	24	15	,	,	PUNCT
ajst-24985	24	16	and	and	CCONJ
ajst-24985	24	17	can	can	AUX
ajst-24985	24	18	adapt	adapt	VERB
ajst-24985	24	19	to	to	ADP
ajst-24985	24	20	more	more	ADV
ajst-24985	24	21	diverse	diverse	ADJ
ajst-24985	24	22	and	and	CCONJ
ajst-24985	24	23	complex	complex	ADJ
ajst-24985	24	24	signal	signal	ADJ
ajst-24985	24	25	data	datum	NOUN
ajst-24985	24	26	.	.	PUNCT
ajst-24985	25	1	the	the	DET
ajst-24985	25	2	objectives	objective	NOUN
ajst-24985	25	3	of	of	ADP
ajst-24985	25	4	this	this	DET
ajst-24985	25	5	study	study	NOUN
ajst-24985	25	6	are	be	AUX
ajst-24985	25	7	to	to	PART
ajst-24985	25	8	explore	explore	VERB
ajst-24985	25	9	the	the	DET
ajst-24985	25	10	application	application	NOUN
ajst-24985	25	11	of	of	ADP
ajst-24985	25	12	machine	machine	NOUN
ajst-24985	25	13	learning	learning	NOUN
ajst-24985	25	14	-	-	PUNCT
ajst-24985	25	15	based	base	VERB
ajst-24985	25	16	methods	method	NOUN
ajst-24985	25	17	in	in	ADP
ajst-24985	25	18	signal	signal	ADJ
ajst-24985	25	19	recognition	recognition	NOUN
ajst-24985	25	20	and	and	CCONJ
ajst-24985	25	21	prediction	prediction	NOUN
ajst-24985	25	22	,	,	PUNCT
ajst-24985	25	23	to	to	PART
ajst-24985	25	24	compare	compare	VERB
ajst-24985	25	25	the	the	DET
ajst-24985	25	26	performance	performance	NOUN
ajst-24985	25	27	of	of	ADP
ajst-24985	25	28	several	several	ADJ
ajst-24985	25	29	mainstream	mainstream	NOUN
ajst-24985	25	30	machine	machine	NOUN
ajst-24985	25	31	learning	learn	VERB
ajst-24985	25	32	algorithms	algorithm	NOUN
ajst-24985	25	33	,	,	PUNCT
ajst-24985	25	34	and	and	CCONJ
ajst-24985	25	35	to	to	PART
ajst-24985	25	36	propose	propose	VERB
ajst-24985	25	37	an	an	DET
ajst-24985	25	38	efficient	efficient	ADJ
ajst-24985	25	39	signal	signal	NOUN
ajst-24985	25	40	processing	processing	NOUN
ajst-24985	25	41	framework	framework	NOUN
ajst-24985	25	42	.	.	PUNCT
ajst-24985	26	1	specifically	specifically	ADV
ajst-24985	26	2	,	,	PUNCT
ajst-24985	26	3	the	the	DET
ajst-24985	26	4	main	main	ADJ
ajst-24985	26	5	contributions	contribution	NOUN
ajst-24985	26	6	of	of	ADP
ajst-24985	26	7	this	this	DET
ajst-24985	26	8	study	study	NOUN
ajst-24985	26	9	include	include	VERB
ajst-24985	26	10	:	:	PUNCT
ajst-24985	26	11	proposing	propose	VERB
ajst-24985	26	12	a	a	DET
ajst-24985	26	13	machine	machine	NOUN
ajst-24985	26	14	learning	learning	NOUN
ajst-24985	26	15	-	-	PUNCT
ajst-24985	26	16	based	base	VERB
ajst-24985	26	17	framework	framework	NOUN
ajst-24985	26	18	for	for	ADP
ajst-24985	26	19	signal	signal	ADJ
ajst-24985	26	20	recognition	recognition	NOUN
ajst-24985	26	21	and	and	CCONJ
ajst-24985	26	22	prediction	prediction	NOUN
ajst-24985	26	23	,	,	PUNCT
ajst-24985	26	24	including	include	VERB
ajst-24985	26	25	the	the	DET
ajst-24985	26	26	steps	step	NOUN
ajst-24985	26	27	of	of	ADP
ajst-24985	26	28	data	datum	NOUN
ajst-24985	26	29	preprocessing	preprocessing	NOUN
ajst-24985	26	30	,	,	PUNCT
ajst-24985	26	31	feature	feature	VERB
ajst-24985	26	32	200	200	NUM
ajst-24985	26	33	extraction	extraction	NOUN
ajst-24985	26	34	,	,	PUNCT
ajst-24985	26	35	model	model	NOUN
ajst-24985	26	36	training	training	NOUN
ajst-24985	26	37	and	and	CCONJ
ajst-24985	26	38	validation	validation	NOUN
ajst-24985	26	39	,	,	PUNCT
ajst-24985	26	40	and	and	CCONJ
ajst-24985	26	41	systematically	systematically	ADV
ajst-24985	26	42	analysing	analyse	VERB
ajst-24985	26	43	the	the	DET
ajst-24985	26	44	impact	impact	NOUN
ajst-24985	26	45	of	of	ADP
ajst-24985	26	46	each	each	DET
ajst-24985	26	47	step	step	NOUN
ajst-24985	26	48	on	on	ADP
ajst-24985	26	49	model	model	NOUN
ajst-24985	26	50	performance	performance	NOUN
ajst-24985	26	51	.	.	PUNCT
ajst-24985	27	1	the	the	DET
ajst-24985	27	2	performance	performance	NOUN
ajst-24985	27	3	of	of	ADP
ajst-24985	27	4	several	several	ADJ
ajst-24985	27	5	mainstream	mainstream	NOUN
ajst-24985	27	6	machine	machine	NOUN
ajst-24985	27	7	learning	learn	VERB
ajst-24985	27	8	algorithms	algorithm	NOUN
ajst-24985	27	9	in	in	ADP
ajst-24985	27	10	signal	signal	ADJ
ajst-24985	27	11	recognition	recognition	NOUN
ajst-24985	27	12	and	and	CCONJ
ajst-24985	27	13	prediction	prediction	NOUN
ajst-24985	27	14	,	,	PUNCT
ajst-24985	27	15	including	include	VERB
ajst-24985	27	16	support	support	NOUN
ajst-24985	27	17	vector	vector	NOUN
ajst-24985	27	18	machine	machine	NOUN
ajst-24985	27	19	(	(	PUNCT
ajst-24985	27	20	svm	svm	PROPN
ajst-24985	27	21	)	)	PUNCT
ajst-24985	27	22	,	,	PUNCT
ajst-24985	27	23	artificial	artificial	ADJ
ajst-24985	27	24	neural	neural	ADJ
ajst-24985	27	25	network	network	NOUN
ajst-24985	27	26	(	(	PUNCT
ajst-24985	27	27	ann	ann	PROPN
ajst-24985	27	28	)	)	PUNCT
ajst-24985	27	29	,	,	PUNCT
ajst-24985	27	30	random	random	ADJ
ajst-24985	27	31	forest	forest	NOUN
ajst-24985	27	32	(	(	PUNCT
ajst-24985	27	33	rf	rf	NOUN
ajst-24985	27	34	)	)	PUNCT
ajst-24985	27	35	and	and	CCONJ
ajst-24985	27	36	convolutional	convolutional	ADJ
ajst-24985	27	37	neural	neural	ADJ
ajst-24985	27	38	network	network	NOUN
ajst-24985	27	39	(	(	PUNCT
ajst-24985	27	40	cnn	cnn	PROPN
ajst-24985	27	41	)	)	PUNCT
ajst-24985	27	42	,	,	PUNCT
ajst-24985	27	43	is	be	AUX
ajst-24985	27	44	compared	compare	VERB
ajst-24985	27	45	,	,	PUNCT
ajst-24985	27	46	and	and	CCONJ
ajst-24985	27	47	the	the	DET
ajst-24985	27	48	advantages	advantage	NOUN
ajst-24985	27	49	of	of	ADP
ajst-24985	27	50	deep	deep	ADJ
ajst-24985	27	51	learning	learning	NOUN
ajst-24985	27	52	models	model	NOUN
ajst-24985	27	53	in	in	ADP
ajst-24985	27	54	complex	complex	ADJ
ajst-24985	27	55	signal	signal	NOUN
ajst-24985	27	56	processing	processing	NOUN
ajst-24985	27	57	are	be	AUX
ajst-24985	27	58	verified	verify	VERB
ajst-24985	27	59	through	through	ADP
ajst-24985	27	60	experiments	experiment	NOUN
ajst-24985	27	61	.	.	PUNCT
ajst-24985	28	1	the	the	DET
ajst-24985	28	2	advantages	advantage	NOUN
ajst-24985	28	3	and	and	CCONJ
ajst-24985	28	4	disadvantages	disadvantage	NOUN
ajst-24985	28	5	of	of	ADP
ajst-24985	28	6	different	different	ADJ
ajst-24985	28	7	algorithms	algorithm	NOUN
ajst-24985	28	8	are	be	AUX
ajst-24985	28	9	analysed	analyse	VERB
ajst-24985	28	10	in	in	ADP
ajst-24985	28	11	depth	depth	NOUN
ajst-24985	28	12	,	,	PUNCT
ajst-24985	28	13	and	and	CCONJ
ajst-24985	28	14	suggestions	suggestion	NOUN
ajst-24985	28	15	for	for	ADP
ajst-24985	28	16	selecting	select	VERB
ajst-24985	28	17	and	and	CCONJ
ajst-24985	28	18	optimising	optimise	VERB
ajst-24985	28	19	machine	machine	NOUN
ajst-24985	28	20	learning	learn	VERB
ajst-24985	28	21	algorithms	algorithm	NOUN
ajst-24985	28	22	in	in	ADP
ajst-24985	28	23	practical	practical	ADJ
ajst-24985	28	24	applications	application	NOUN
ajst-24985	28	25	are	be	AUX
ajst-24985	28	26	presented	present	VERB
ajst-24985	28	27	.	.	PUNCT
ajst-24985	29	1	the	the	DET
ajst-24985	29	2	experimental	experimental	ADJ
ajst-24985	29	3	results	result	NOUN
ajst-24985	29	4	demonstrate	demonstrate	VERB
ajst-24985	29	5	the	the	DET
ajst-24985	29	6	potential	potential	NOUN
ajst-24985	29	7	of	of	ADP
ajst-24985	29	8	machine	machine	NOUN
ajst-24985	29	9	learning	learning	NOUN
ajst-24985	29	10	methods	method	NOUN
ajst-24985	29	11	in	in	ADP
ajst-24985	29	12	signal	signal	ADJ
ajst-24985	29	13	recognition	recognition	NOUN
ajst-24985	29	14	and	and	CCONJ
ajst-24985	29	15	prediction	prediction	NOUN
ajst-24985	29	16	,	,	PUNCT
ajst-24985	29	17	providing	provide	VERB
ajst-24985	29	18	valuable	valuable	ADJ
ajst-24985	29	19	references	reference	NOUN
ajst-24985	29	20	for	for	ADP
ajst-24985	29	21	further	further	ADJ
ajst-24985	29	22	research	research	NOUN
ajst-24985	29	23	.	.	PUNCT
ajst-24985	30	1	in	in	ADP
ajst-24985	30	2	summary	summary	NOUN
ajst-24985	30	3	,	,	PUNCT
ajst-24985	30	4	this	this	DET
ajst-24985	30	5	study	study	NOUN
ajst-24985	30	6	aims	aim	VERB
ajst-24985	30	7	to	to	PART
ajst-24985	30	8	promote	promote	VERB
ajst-24985	30	9	the	the	DET
ajst-24985	30	10	application	application	NOUN
ajst-24985	30	11	and	and	CCONJ
ajst-24985	30	12	development	development	NOUN
ajst-24985	30	13	of	of	ADP
ajst-24985	30	14	machine	machine	NOUN
ajst-24985	30	15	learning	learn	VERB
ajst-24985	30	16	techniques	technique	NOUN
ajst-24985	30	17	in	in	ADP
ajst-24985	30	18	the	the	DET
ajst-24985	30	19	field	field	NOUN
ajst-24985	30	20	of	of	ADP
ajst-24985	30	21	signal	signal	NOUN
ajst-24985	30	22	processing	processing	NOUN
ajst-24985	30	23	,	,	PUNCT
ajst-24985	30	24	to	to	PART
ajst-24985	30	25	improve	improve	VERB
ajst-24985	30	26	the	the	DET
ajst-24985	30	27	accuracy	accuracy	NOUN
ajst-24985	30	28	and	and	CCONJ
ajst-24985	30	29	reliability	reliability	NOUN
ajst-24985	30	30	of	of	ADP
ajst-24985	30	31	signal	signal	ADJ
ajst-24985	30	32	recognition	recognition	NOUN
ajst-24985	30	33	and	and	CCONJ
ajst-24985	30	34	prediction	prediction	NOUN
ajst-24985	30	35	,	,	PUNCT
ajst-24985	30	36	and	and	CCONJ
ajst-24985	30	37	to	to	PART
ajst-24985	30	38	provide	provide	VERB
ajst-24985	30	39	theoretical	theoretical	ADJ
ajst-24985	30	40	support	support	NOUN
ajst-24985	30	41	and	and	CCONJ
ajst-24985	30	42	technical	technical	ADJ
ajst-24985	30	43	references	reference	NOUN
ajst-24985	30	44	for	for	ADP
ajst-24985	30	45	research	research	NOUN
ajst-24985	30	46	and	and	CCONJ
ajst-24985	30	47	practice	practice	NOUN
ajst-24985	30	48	in	in	ADP
ajst-24985	30	49	related	related	ADJ
ajst-24985	30	50	fields	field	NOUN
ajst-24985	30	51	.	.	PUNCT
ajst-24985	31	1	2	2	X
ajst-24985	31	2	.	.	X
ajst-24985	31	3	related	relate	VERB
ajst-24985	31	4	work	work	NOUN
ajst-24985	31	5	in	in	ADP
ajst-24985	31	6	the	the	DET
ajst-24985	31	7	early	early	ADJ
ajst-24985	31	8	research	research	NOUN
ajst-24985	31	9	of	of	ADP
ajst-24985	31	10	signal	signal	NOUN
ajst-24985	31	11	processing	processing	NOUN
ajst-24985	31	12	,	,	PUNCT
ajst-24985	31	13	traditional	traditional	ADJ
ajst-24985	31	14	methods	method	NOUN
ajst-24985	31	15	such	such	ADJ
ajst-24985	31	16	as	as	ADP
ajst-24985	31	17	autoregressive	autoregressive	ADJ
ajst-24985	31	18	(	(	PUNCT
ajst-24985	31	19	ar	ar	NOUN
ajst-24985	31	20	)	)	PUNCT
ajst-24985	31	21	model	model	NOUN
ajst-24985	31	22	,	,	PUNCT
ajst-24985	31	23	moving	move	VERB
ajst-24985	31	24	average	average	ADJ
ajst-24985	31	25	(	(	PUNCT
ajst-24985	31	26	ma	ma	PROPN
ajst-24985	31	27	)	)	PUNCT
ajst-24985	31	28	model	model	NOUN
ajst-24985	31	29	,	,	PUNCT
ajst-24985	31	30	and	and	CCONJ
ajst-24985	31	31	their	their	PRON
ajst-24985	31	32	combination	combination	NOUN
ajst-24985	31	33	models	model	NOUN
ajst-24985	31	34	(	(	PUNCT
ajst-24985	31	35	arma	arma	PROPN
ajst-24985	31	36	and	and	CCONJ
ajst-24985	31	37	arima	arima	PROPN
ajst-24985	31	38	)	)	PUNCT
ajst-24985	31	39	were	be	AUX
ajst-24985	31	40	widely	widely	ADV
ajst-24985	31	41	used	use	VERB
ajst-24985	31	42	.	.	PUNCT
ajst-24985	32	1	these	these	DET
ajst-24985	32	2	methods	method	NOUN
ajst-24985	32	3	describe	describe	VERB
ajst-24985	32	4	the	the	DET
ajst-24985	32	5	dynamic	dynamic	ADJ
ajst-24985	32	6	process	process	NOUN
ajst-24985	32	7	of	of	ADP
ajst-24985	32	8	a	a	DET
ajst-24985	32	9	signal	signal	NOUN
ajst-24985	32	10	by	by	ADP
ajst-24985	32	11	constructing	construct	VERB
ajst-24985	32	12	a	a	DET
ajst-24985	32	13	linear	linear	ADJ
ajst-24985	32	14	mathematical	mathematical	ADJ
ajst-24985	32	15	model	model	NOUN
ajst-24985	32	16	.	.	PUNCT
ajst-24985	33	1	for	for	ADP
ajst-24985	33	2	example	example	NOUN
ajst-24985	33	3	,	,	PUNCT
ajst-24985	33	4	the	the	DET
ajst-24985	33	5	time	time	NOUN
ajst-24985	33	6	series	series	PROPN
ajst-24985	33	7	analysis	analysis	NOUN
ajst-24985	33	8	method	method	NOUN
ajst-24985	33	9	proposed	propose	VERB
ajst-24985	33	10	by	by	ADP
ajst-24985	33	11	box	box	PROPN
ajst-24985	33	12	and	and	CCONJ
ajst-24985	33	13	jenkins	jenkins	PROPN
ajst-24985	33	14	(	(	PUNCT
ajst-24985	33	15	1970	1970	NUM
ajst-24985	33	16	)	)	PUNCT
ajst-24985	33	17	became	become	VERB
ajst-24985	33	18	a	a	DET
ajst-24985	33	19	classical	classical	ADJ
ajst-24985	33	20	method	method	NOUN
ajst-24985	33	21	for	for	ADP
ajst-24985	33	22	signal	signal	ADJ
ajst-24985	33	23	forecasting	forecasting	NOUN
ajst-24985	33	24	[	[	X
ajst-24985	33	25	1	1	NUM
ajst-24985	33	26	]	]	PUNCT
ajst-24985	33	27	.	.	PUNCT
ajst-24985	34	1	however	however	ADV
ajst-24985	34	2	,	,	PUNCT
ajst-24985	34	3	these	these	DET
ajst-24985	34	4	methods	method	NOUN
ajst-24985	34	5	have	have	VERB
ajst-24985	34	6	limited	limit	VERB
ajst-24985	34	7	performance	performance	NOUN
ajst-24985	34	8	in	in	ADP
ajst-24985	34	9	dealing	deal	VERB
ajst-24985	34	10	with	with	ADP
ajst-24985	34	11	non	non	ADJ
ajst-24985	34	12	-	-	ADJ
ajst-24985	34	13	linear	linear	ADJ
ajst-24985	34	14	and	and	CCONJ
ajst-24985	34	15	non	non	ADJ
ajst-24985	34	16	-	-	ADJ
ajst-24985	34	17	stationary	stationary	ADJ
ajst-24985	34	18	signals	signal	NOUN
ajst-24985	34	19	,	,	PUNCT
ajst-24985	34	20	requiring	require	VERB
ajst-24985	34	21	complex	complex	ADJ
ajst-24985	34	22	pre	pre	ADJ
ajst-24985	34	23	-	-	ADJ
ajst-24985	34	24	processing	process	VERB
ajst-24985	34	25	and	and	CCONJ
ajst-24985	34	26	modelling	model	VERB
ajst-24985	34	27	assumptions	assumption	NOUN
ajst-24985	34	28	on	on	ADP
ajst-24985	34	29	the	the	DET
ajst-24985	34	30	data	data	PROPN
ajst-24985	34	31	.	.	PUNCT
ajst-24985	35	1	frequency	frequency	NOUN
ajst-24985	35	2	domain	domain	NOUN
ajst-24985	35	3	analysis	analysis	NOUN
ajst-24985	35	4	methods	method	NOUN
ajst-24985	35	5	such	such	ADJ
ajst-24985	35	6	as	as	ADP
ajst-24985	35	7	the	the	DET
ajst-24985	35	8	fast	fast	ADJ
ajst-24985	35	9	fourier	fourier	NOUN
ajst-24985	35	10	transform	transform	NOUN
ajst-24985	35	11	(	(	PUNCT
ajst-24985	35	12	fft	fft	NOUN
ajst-24985	35	13	)	)	PUNCT
ajst-24985	35	14	also	also	ADV
ajst-24985	35	15	occupy	occupy	VERB
ajst-24985	35	16	an	an	DET
ajst-24985	35	17	important	important	ADJ
ajst-24985	35	18	position	position	NOUN
ajst-24985	35	19	in	in	ADP
ajst-24985	35	20	the	the	DET
ajst-24985	35	21	field	field	NOUN
ajst-24985	35	22	of	of	ADP
ajst-24985	35	23	signal	signal	ADJ
ajst-24985	35	24	processing	processing	NOUN
ajst-24985	35	25	.	.	PUNCT
ajst-24985	36	1	the	the	DET
ajst-24985	36	2	fft	fft	PROPN
ajst-24985	36	3	algorithm	algorithm	NOUN
ajst-24985	36	4	proposed	propose	VERB
ajst-24985	36	5	by	by	ADP
ajst-24985	36	6	cooley	cooley	PROPN
ajst-24985	36	7	and	and	CCONJ
ajst-24985	36	8	tukey	tukey	PROPN
ajst-24985	36	9	(	(	PUNCT
ajst-24985	36	10	1965	1965	NUM
ajst-24985	36	11	)	)	PUNCT
ajst-24985	36	12	significantly	significantly	ADV
ajst-24985	36	13	improves	improve	VERB
ajst-24985	36	14	the	the	DET
ajst-24985	36	15	computational	computational	ADJ
ajst-24985	36	16	efficiency	efficiency	NOUN
ajst-24985	36	17	and	and	CCONJ
ajst-24985	36	18	makes	make	VERB
ajst-24985	36	19	frequency	frequency	NOUN
ajst-24985	36	20	domain	domain	NOUN
ajst-24985	36	21	analysis	analysis	NOUN
ajst-24985	36	22	feasible	feasible	ADJ
ajst-24985	36	23	for	for	ADP
ajst-24985	36	24	practical	practical	ADJ
ajst-24985	36	25	applications	application	NOUN
ajst-24985	36	26	[	[	X
ajst-24985	36	27	2	2	NUM
ajst-24985	36	28	]	]	PUNCT
ajst-24985	36	29	.	.	PUNCT
ajst-24985	37	1	the	the	DET
ajst-24985	37	2	fft	fft	PROPN
ajst-24985	37	3	is	be	AUX
ajst-24985	37	4	commonly	commonly	ADV
ajst-24985	37	5	used	use	VERB
ajst-24985	37	6	in	in	ADP
ajst-24985	37	7	the	the	DET
ajst-24985	37	8	fields	field	NOUN
ajst-24985	37	9	of	of	ADP
ajst-24985	37	10	vibration	vibration	NOUN
ajst-24985	37	11	analysis	analysis	NOUN
ajst-24985	37	12	,	,	PUNCT
ajst-24985	37	13	speech	speech	NOUN
ajst-24985	37	14	recognition	recognition	NOUN
ajst-24985	37	15	,	,	PUNCT
ajst-24985	37	16	etc	etc	X
ajst-24985	37	17	.	.	X
ajst-24985	37	18	,	,	PUNCT
ajst-24985	37	19	by	by	ADP
ajst-24985	37	20	converting	convert	VERB
ajst-24985	37	21	a	a	DET
ajst-24985	37	22	signal	signal	NOUN
ajst-24985	37	23	from	from	ADP
ajst-24985	37	24	the	the	DET
ajst-24985	37	25	time	time	NOUN
ajst-24985	37	26	domain	domain	NOUN
ajst-24985	37	27	to	to	ADP
ajst-24985	37	28	the	the	DET
ajst-24985	37	29	frequency	frequency	NOUN
ajst-24985	37	30	domain	domain	NOUN
ajst-24985	37	31	and	and	CCONJ
ajst-24985	37	32	analysing	analyse	VERB
ajst-24985	37	33	its	its	PRON
ajst-24985	37	34	frequency	frequency	NOUN
ajst-24985	37	35	components	component	NOUN
ajst-24985	37	36	.	.	PUNCT
ajst-24985	38	1	however	however	ADV
ajst-24985	38	2	,	,	PUNCT
ajst-24985	38	3	fft	fft	PROPN
ajst-24985	38	4	can	can	AUX
ajst-24985	38	5	only	only	ADV
ajst-24985	38	6	provide	provide	VERB
ajst-24985	38	7	the	the	DET
ajst-24985	38	8	frequency	frequency	NOUN
ajst-24985	38	9	information	information	NOUN
ajst-24985	38	10	of	of	ADP
ajst-24985	38	11	the	the	DET
ajst-24985	38	12	signal	signal	NOUN
ajst-24985	38	13	and	and	CCONJ
ajst-24985	38	14	can	can	AUX
ajst-24985	38	15	not	not	PART
ajst-24985	38	16	consider	consider	VERB
ajst-24985	38	17	the	the	DET
ajst-24985	38	18	changes	change	NOUN
ajst-24985	38	19	in	in	ADP
ajst-24985	38	20	time	time	NOUN
ajst-24985	38	21	and	and	CCONJ
ajst-24985	38	22	frequency	frequency	NOUN
ajst-24985	38	23	at	at	ADP
ajst-24985	38	24	the	the	DET
ajst-24985	38	25	same	same	ADJ
ajst-24985	38	26	time	time	NOUN
ajst-24985	38	27	.	.	PUNCT
ajst-24985	39	1	in	in	ADP
ajst-24985	39	2	order	order	NOUN
ajst-24985	39	3	to	to	PART
ajst-24985	39	4	overcome	overcome	VERB
ajst-24985	39	5	the	the	DET
ajst-24985	39	6	limitations	limitation	NOUN
ajst-24985	39	7	of	of	ADP
ajst-24985	39	8	the	the	DET
ajst-24985	39	9	above	above	ADJ
ajst-24985	39	10	methods	method	NOUN
ajst-24985	39	11	,	,	PUNCT
ajst-24985	39	12	time	time	NOUN
ajst-24985	39	13	-	-	PUNCT
ajst-24985	39	14	frequency	frequency	NOUN
ajst-24985	39	15	analysis	analysis	NOUN
ajst-24985	39	16	methods	method	NOUN
ajst-24985	39	17	such	such	ADJ
ajst-24985	39	18	as	as	ADP
ajst-24985	39	19	the	the	DET
ajst-24985	39	20	wavelet	wavelet	NOUN
ajst-24985	39	21	transform	transform	NOUN
ajst-24985	39	22	(	(	PUNCT
ajst-24985	39	23	wt	wt	NOUN
ajst-24985	39	24	)	)	PUNCT
ajst-24985	39	25	have	have	AUX
ajst-24985	39	26	been	be	AUX
ajst-24985	39	27	introduced	introduce	VERB
ajst-24985	39	28	into	into	ADP
ajst-24985	39	29	the	the	DET
ajst-24985	39	30	field	field	NOUN
ajst-24985	39	31	of	of	ADP
ajst-24985	39	32	signal	signal	ADJ
ajst-24985	39	33	processing	processing	NOUN
ajst-24985	39	34	.	.	PUNCT
ajst-24985	40	1	the	the	DET
ajst-24985	40	2	wavelet	wavelet	NOUN
ajst-24985	40	3	transform	transform	NOUN
ajst-24985	40	4	proposed	propose	VERB
ajst-24985	40	5	by	by	ADP
ajst-24985	40	6	daubechies	daubechie	NOUN
ajst-24985	40	7	(	(	PUNCT
ajst-24985	40	8	1992	1992	NUM
ajst-24985	40	9	)	)	PUNCT
ajst-24985	40	10	can	can	AUX
ajst-24985	40	11	provide	provide	VERB
ajst-24985	40	12	both	both	DET
ajst-24985	40	13	time	time	NOUN
ajst-24985	40	14	and	and	CCONJ
ajst-24985	40	15	frequency	frequency	NOUN
ajst-24985	40	16	information	information	NOUN
ajst-24985	40	17	of	of	ADP
ajst-24985	40	18	signals	signal	NOUN
ajst-24985	40	19	,	,	PUNCT
ajst-24985	40	20	and	and	CCONJ
ajst-24985	40	21	has	have	AUX
ajst-24985	40	22	been	be	AUX
ajst-24985	40	23	widely	widely	ADV
ajst-24985	40	24	used	use	VERB
ajst-24985	40	25	in	in	ADP
ajst-24985	40	26	the	the	DET
ajst-24985	40	27	fields	field	NOUN
ajst-24985	40	28	of	of	ADP
ajst-24985	40	29	image	image	NOUN
ajst-24985	40	30	processing	processing	NOUN
ajst-24985	40	31	,	,	PUNCT
ajst-24985	40	32	seismic	seismic	ADJ
ajst-24985	40	33	signal	signal	NOUN
ajst-24985	40	34	analysis	analysis	NOUN
ajst-24985	40	35	and	and	CCONJ
ajst-24985	40	36	so	so	ADV
ajst-24985	40	37	on	on	ADP
ajst-24985	40	38	[	[	X
ajst-24985	40	39	3	3	NUM
ajst-24985	40	40	]	]	PUNCT
ajst-24985	40	41	.	.	PUNCT
ajst-24985	41	1	although	although	SCONJ
ajst-24985	41	2	the	the	DET
ajst-24985	41	3	wavelet	wavelet	NOUN
ajst-24985	41	4	transform	transform	NOUN
ajst-24985	41	5	performs	perform	VERB
ajst-24985	41	6	well	well	ADV
ajst-24985	41	7	in	in	ADP
ajst-24985	41	8	multi	multi	ADJ
ajst-24985	41	9	-	-	ADJ
ajst-24985	41	10	scale	scale	ADJ
ajst-24985	41	11	analysis	analysis	NOUN
ajst-24985	41	12	,	,	PUNCT
ajst-24985	41	13	its	its	PRON
ajst-24985	41	14	computational	computational	ADJ
ajst-24985	41	15	complexity	complexity	NOUN
ajst-24985	41	16	is	be	AUX
ajst-24985	41	17	high	high	ADJ
ajst-24985	41	18	when	when	SCONJ
ajst-24985	41	19	dealing	deal	VERB
ajst-24985	41	20	with	with	ADP
ajst-24985	41	21	highdimensional	highdimensional	ADJ
ajst-24985	41	22	data	datum	NOUN
ajst-24985	41	23	.	.	PUNCT
ajst-24985	42	1	with	with	ADP
ajst-24985	42	2	the	the	DET
ajst-24985	42	3	development	development	NOUN
ajst-24985	42	4	of	of	ADP
ajst-24985	42	5	machine	machine	NOUN
ajst-24985	42	6	learning	learn	VERB
ajst-24985	42	7	technology	technology	NOUN
ajst-24985	42	8	,	,	PUNCT
ajst-24985	42	9	researchers	researcher	NOUN
ajst-24985	42	10	have	have	AUX
ajst-24985	42	11	begun	begin	VERB
ajst-24985	42	12	to	to	PART
ajst-24985	42	13	explore	explore	VERB
ajst-24985	42	14	its	its	PRON
ajst-24985	42	15	application	application	NOUN
ajst-24985	42	16	in	in	ADP
ajst-24985	42	17	signal	signal	ADJ
ajst-24985	42	18	recognition	recognition	NOUN
ajst-24985	42	19	and	and	CCONJ
ajst-24985	42	20	prediction	prediction	NOUN
ajst-24985	42	21	.	.	PUNCT
ajst-24985	43	1	support	support	NOUN
ajst-24985	43	2	vector	vector	NOUN
ajst-24985	43	3	machines	machine	NOUN
ajst-24985	43	4	(	(	PUNCT
ajst-24985	43	5	svms	svms	NOUN
ajst-24985	43	6	)	)	PUNCT
ajst-24985	43	7	are	be	AUX
ajst-24985	43	8	widely	widely	ADV
ajst-24985	43	9	used	use	VERB
ajst-24985	43	10	in	in	ADP
ajst-24985	43	11	signal	signal	ADJ
ajst-24985	43	12	classification	classification	NOUN
ajst-24985	43	13	and	and	CCONJ
ajst-24985	43	14	regression	regression	NOUN
ajst-24985	43	15	tasks	task	NOUN
ajst-24985	43	16	due	due	ADP
ajst-24985	43	17	to	to	ADP
ajst-24985	43	18	their	their	PRON
ajst-24985	43	19	superior	superior	ADJ
ajst-24985	43	20	performance	performance	NOUN
ajst-24985	43	21	in	in	ADP
ajst-24985	43	22	small	small	ADJ
ajst-24985	43	23	-	-	PUNCT
ajst-24985	43	24	sample	sample	NOUN
ajst-24985	43	25	learning	learning	NOUN
ajst-24985	43	26	.	.	PUNCT
ajst-24985	44	1	svms	svms	NOUN
ajst-24985	44	2	proposed	propose	VERB
ajst-24985	44	3	by	by	ADP
ajst-24985	44	4	vapnik	vapnik	X
ajst-24985	44	5	(	(	PUNCT
ajst-24985	44	6	1995	1995	NUM
ajst-24985	44	7	)	)	PUNCT
ajst-24985	44	8	achieve	achieve	VERB
ajst-24985	44	9	effective	effective	ADJ
ajst-24985	44	10	classification	classification	NOUN
ajst-24985	44	11	of	of	ADP
ajst-24985	44	12	data	datum	NOUN
ajst-24985	44	13	points	point	NOUN
ajst-24985	44	14	in	in	ADP
ajst-24985	44	15	high	high	ADJ
ajst-24985	44	16	-	-	PUNCT
ajst-24985	44	17	dimensional	dimensional	ADJ
ajst-24985	44	18	spaces	space	NOUN
ajst-24985	44	19	by	by	ADP
ajst-24985	44	20	constructing	construct	VERB
ajst-24985	44	21	optimal	optimal	ADJ
ajst-24985	44	22	hyperplanes	hyperplane	NOUN
ajst-24985	44	23	[	[	X
ajst-24985	44	24	4	4	NUM
ajst-24985	44	25	]	]	PUNCT
ajst-24985	44	26	.	.	PUNCT
ajst-24985	45	1	for	for	ADP
ajst-24985	45	2	example	example	NOUN
ajst-24985	45	3	,	,	PUNCT
ajst-24985	45	4	schölkopf	schölkopf	VERB
ajst-24985	45	5	et	et	PROPN
ajst-24985	45	6	al	al	PROPN
ajst-24985	45	7	.	.	PUNCT
ajst-24985	46	1	(	(	PUNCT
ajst-24985	46	2	1999	1999	NUM
ajst-24985	46	3	)	)	PUNCT
ajst-24985	46	4	applied	apply	VERB
ajst-24985	46	5	svm	svm	NOUN
ajst-24985	46	6	to	to	PART
ajst-24985	46	7	fault	fault	VERB
ajst-24985	46	8	detection	detection	NOUN
ajst-24985	46	9	and	and	CCONJ
ajst-24985	46	10	diagnosis	diagnosis	NOUN
ajst-24985	46	11	with	with	ADP
ajst-24985	46	12	remarkable	remarkable	ADJ
ajst-24985	46	13	results	result	NOUN
ajst-24985	46	14	.	.	PUNCT
ajst-24985	47	1	artificial	artificial	ADJ
ajst-24985	47	2	neural	neural	ADJ
ajst-24985	47	3	networks	network	NOUN
ajst-24985	47	4	(	(	PUNCT
ajst-24985	47	5	ann	ann	PROPN
ajst-24985	47	6	)	)	PUNCT
ajst-24985	47	7	have	have	VERB
ajst-24985	47	8	the	the	DET
ajst-24985	47	9	ability	ability	NOUN
ajst-24985	47	10	to	to	PART
ajst-24985	47	11	deal	deal	VERB
ajst-24985	47	12	with	with	ADP
ajst-24985	47	13	nonlinear	nonlinear	ADJ
ajst-24985	47	14	problems	problem	NOUN
ajst-24985	47	15	and	and	CCONJ
ajst-24985	47	16	are	be	AUX
ajst-24985	47	17	widely	widely	ADV
ajst-24985	47	18	used	use	VERB
ajst-24985	47	19	in	in	ADP
ajst-24985	47	20	signal	signal	ADJ
ajst-24985	47	21	prediction	prediction	NOUN
ajst-24985	47	22	and	and	CCONJ
ajst-24985	47	23	pattern	pattern	NOUN
ajst-24985	47	24	recognition	recognition	NOUN
ajst-24985	48	1	[	[	X
ajst-24985	48	2	5	5	NUM
ajst-24985	48	3	]	]	PUNCT
ajst-24985	48	4	.	.	PUNCT
ajst-24985	49	1	the	the	DET
ajst-24985	49	2	backpropagation	backpropagation	NOUN
ajst-24985	49	3	algorithm	algorithm	NOUN
ajst-24985	49	4	(	(	PUNCT
ajst-24985	49	5	bp	bp	PROPN
ajst-24985	49	6	)	)	PUNCT
ajst-24985	49	7	proposed	propose	VERB
ajst-24985	49	8	by	by	ADP
ajst-24985	49	9	rumelhart	rumelhart	PROPN
ajst-24985	49	10	et	et	PROPN
ajst-24985	49	11	al	al	PROPN
ajst-24985	49	12	.	.	PROPN
ajst-24985	49	13	(	(	PUNCT
ajst-24985	49	14	1986	1986	NUM
ajst-24985	49	15	)	)	PUNCT
ajst-24985	49	16	greatly	greatly	ADV
ajst-24985	49	17	contributed	contribute	VERB
ajst-24985	49	18	to	to	ADP
ajst-24985	49	19	the	the	DET
ajst-24985	49	20	development	development	NOUN
ajst-24985	49	21	of	of	ADP
ajst-24985	49	22	anns	ann	NOUN
ajst-24985	49	23	.	.	PUNCT
ajst-24985	50	1	anns	anns	PROPN
ajst-24985	50	2	are	be	AUX
ajst-24985	50	3	capable	capable	ADJ
ajst-24985	50	4	of	of	ADP
ajst-24985	50	5	automatically	automatically	ADV
ajst-24985	50	6	learning	learn	VERB
ajst-24985	50	7	and	and	CCONJ
ajst-24985	50	8	extracting	extract	VERB
ajst-24985	50	9	the	the	DET
ajst-24985	50	10	complex	complex	ADJ
ajst-24985	50	11	features	feature	NOUN
ajst-24985	50	12	of	of	ADP
ajst-24985	50	13	signals	signal	NOUN
ajst-24985	50	14	by	by	ADP
ajst-24985	50	15	mimicking	mimic	VERB
ajst-24985	50	16	the	the	DET
ajst-24985	50	17	workings	working	NOUN
ajst-24985	50	18	of	of	ADP
ajst-24985	50	19	neurons	neuron	NOUN
ajst-24985	50	20	in	in	ADP
ajst-24985	50	21	the	the	DET
ajst-24985	50	22	human	human	ADJ
ajst-24985	50	23	brain	brain	NOUN
ajst-24985	51	1	[	[	X
ajst-24985	51	2	6	6	NUM
ajst-24985	51	3	]	]	PUNCT
ajst-24985	51	4	.	.	PUNCT
ajst-24985	52	1	anns	anns	PROPN
ajst-24985	52	2	have	have	AUX
ajst-24985	52	3	demonstrated	demonstrate	VERB
ajst-24985	52	4	their	their	PRON
ajst-24985	52	5	strong	strong	ADJ
ajst-24985	52	6	application	application	NOUN
ajst-24985	52	7	potential	potential	NOUN
ajst-24985	52	8	in	in	ADP
ajst-24985	52	9	areas	area	NOUN
ajst-24985	52	10	such	such	ADJ
ajst-24985	52	11	as	as	ADP
ajst-24985	52	12	power	power	NOUN
ajst-24985	52	13	load	load	NOUN
ajst-24985	52	14	prediction	prediction	NOUN
ajst-24985	52	15	and	and	CCONJ
ajst-24985	52	16	speech	speech	NOUN
ajst-24985	52	17	recognition	recognition	NOUN
ajst-24985	52	18	.	.	PUNCT
ajst-24985	53	1	random	random	ADJ
ajst-24985	53	2	forest	forest	NOUN
ajst-24985	53	3	(	(	PUNCT
ajst-24985	53	4	rf	rf	NOUN
ajst-24985	53	5	)	)	PUNCT
ajst-24985	53	6	,	,	PUNCT
ajst-24985	53	7	as	as	ADP
ajst-24985	53	8	an	an	DET
ajst-24985	53	9	integrated	integrated	ADJ
ajst-24985	53	10	learning	learning	NOUN
ajst-24985	53	11	method	method	NOUN
ajst-24985	53	12	,	,	PUNCT
ajst-24985	53	13	improves	improve	VERB
ajst-24985	53	14	the	the	DET
ajst-24985	53	15	accuracy	accuracy	NOUN
ajst-24985	53	16	and	and	CCONJ
ajst-24985	53	17	stability	stability	NOUN
ajst-24985	53	18	of	of	ADP
ajst-24985	53	19	the	the	DET
ajst-24985	53	20	model	model	NOUN
ajst-24985	53	21	by	by	ADP
ajst-24985	53	22	constructing	construct	VERB
ajst-24985	53	23	multiple	multiple	ADJ
ajst-24985	53	24	decision	decision	NOUN
ajst-24985	53	25	trees	tree	NOUN
ajst-24985	53	26	.	.	PUNCT
ajst-24985	54	1	the	the	DET
ajst-24985	54	2	rf	rf	ADJ
ajst-24985	54	3	algorithm	algorithm	NOUN
ajst-24985	54	4	proposed	propose	VERB
ajst-24985	54	5	by	by	ADP
ajst-24985	54	6	breiman	breiman	NOUN
ajst-24985	54	7	(	(	PUNCT
ajst-24985	54	8	2001	2001	NUM
ajst-24985	54	9	)	)	PUNCT
ajst-24985	54	10	performs	perform	VERB
ajst-24985	54	11	well	well	ADV
ajst-24985	54	12	when	when	SCONJ
ajst-24985	54	13	dealing	deal	VERB
ajst-24985	54	14	with	with	ADP
ajst-24985	54	15	high	high	ADJ
ajst-24985	54	16	-	-	PUNCT
ajst-24985	54	17	dimensional	dimensional	ADJ
ajst-24985	54	18	data	datum	NOUN
ajst-24985	54	19	and	and	CCONJ
ajst-24985	54	20	complex	complex	ADJ
ajst-24985	54	21	features	feature	NOUN
ajst-24985	54	22	[	[	X
ajst-24985	54	23	7	7	NUM
ajst-24985	54	24	]	]	PUNCT
ajst-24985	54	25	.	.	PUNCT
ajst-24985	55	1	for	for	ADP
ajst-24985	55	2	example	example	NOUN
ajst-24985	55	3	,	,	PUNCT
ajst-24985	55	4	in	in	ADP
ajst-24985	55	5	the	the	DET
ajst-24985	55	6	field	field	NOUN
ajst-24985	55	7	of	of	ADP
ajst-24985	55	8	medical	medical	ADJ
ajst-24985	55	9	signal	signal	NOUN
ajst-24985	55	10	processing	processing	NOUN
ajst-24985	55	11	,	,	PUNCT
ajst-24985	55	12	rf	rf	PRON
ajst-24985	55	13	is	be	AUX
ajst-24985	55	14	used	use	VERB
ajst-24985	55	15	for	for	ADP
ajst-24985	55	16	classification	classification	NOUN
ajst-24985	55	17	and	and	CCONJ
ajst-24985	55	18	abnormality	abnormality	NOUN
ajst-24985	55	19	detection	detection	NOUN
ajst-24985	55	20	of	of	ADP
ajst-24985	55	21	electrocardiogram	electrocardiogram	NOUN
ajst-24985	55	22	(	(	PUNCT
ajst-24985	55	23	ecg	ecg	PROPN
ajst-24985	55	24	)	)	PUNCT
ajst-24985	55	25	signals	signal	NOUN
ajst-24985	55	26	,	,	PUNCT
ajst-24985	55	27	demonstrating	demonstrate	VERB
ajst-24985	55	28	its	its	PRON
ajst-24985	55	29	excellent	excellent	ADJ
ajst-24985	55	30	performance	performance	NOUN
ajst-24985	55	31	.	.	PUNCT
ajst-24985	56	1	convolutional	convolutional	ADJ
ajst-24985	56	2	neural	neural	ADJ
ajst-24985	56	3	network	network	NOUN
ajst-24985	56	4	(	(	PUNCT
ajst-24985	56	5	cnn	cnn	PROPN
ajst-24985	56	6	)	)	PUNCT
ajst-24985	56	7	,	,	PUNCT
ajst-24985	56	8	as	as	ADP
ajst-24985	56	9	a	a	DET
ajst-24985	56	10	representative	representative	ADJ
ajst-24985	56	11	model	model	NOUN
ajst-24985	56	12	for	for	ADP
ajst-24985	56	13	deep	deep	ADJ
ajst-24985	56	14	learning	learning	NOUN
ajst-24985	56	15	,	,	PUNCT
ajst-24985	56	16	is	be	AUX
ajst-24985	56	17	particularly	particularly	ADV
ajst-24985	56	18	suitable	suitable	ADJ
ajst-24985	56	19	for	for	ADP
ajst-24985	56	20	processing	processing	NOUN
ajst-24985	56	21	images	image	NOUN
ajst-24985	56	22	and	and	CCONJ
ajst-24985	56	23	2d	2d	NUM
ajst-24985	56	24	signal	signal	PROPN
ajst-24985	56	25	data	datum	NOUN
ajst-24985	56	26	.	.	PUNCT
ajst-24985	57	1	the	the	DET
ajst-24985	57	2	cnn	cnn	PROPN
ajst-24985	57	3	proposed	propose	VERB
ajst-24985	57	4	by	by	ADP
ajst-24985	57	5	lecun	lecun	PROPN
ajst-24985	57	6	et	et	PROPN
ajst-24985	57	7	al	al	PROPN
ajst-24985	57	8	.	.	PROPN
ajst-24985	57	9	(	(	PUNCT
ajst-24985	57	10	1998	1998	NUM
ajst-24985	57	11	)	)	PUNCT
ajst-24985	57	12	achieves	achieve	VERB
ajst-24985	57	13	effective	effective	ADJ
ajst-24985	57	14	extraction	extraction	NOUN
ajst-24985	57	15	and	and	CCONJ
ajst-24985	57	16	learning	learning	NOUN
ajst-24985	57	17	of	of	ADP
ajst-24985	57	18	local	local	ADJ
ajst-24985	57	19	features	feature	NOUN
ajst-24985	57	20	of	of	ADP
ajst-24985	57	21	signals	signal	NOUN
ajst-24985	57	22	through	through	ADP
ajst-24985	57	23	the	the	DET
ajst-24985	57	24	design	design	NOUN
ajst-24985	57	25	of	of	ADP
ajst-24985	57	26	convolutional	convolutional	ADJ
ajst-24985	57	27	and	and	CCONJ
ajst-24985	57	28	pooling	pool	VERB
ajst-24985	57	29	layers	layer	NOUN
ajst-24985	57	30	[	[	X
ajst-24985	57	31	8	8	NUM
ajst-24985	57	32	]	]	PUNCT
ajst-24985	57	33	.	.	PUNCT
ajst-24985	58	1	in	in	ADP
ajst-24985	58	2	recent	recent	ADJ
ajst-24985	58	3	years	year	NOUN
ajst-24985	58	4	,	,	PUNCT
ajst-24985	58	5	cnns	cnn	NOUN
ajst-24985	58	6	have	have	AUX
ajst-24985	58	7	been	be	AUX
ajst-24985	58	8	widely	widely	ADV
ajst-24985	58	9	used	use	VERB
ajst-24985	58	10	in	in	ADP
ajst-24985	58	11	image	image	NOUN
ajst-24985	58	12	recognition	recognition	NOUN
ajst-24985	58	13	,	,	PUNCT
ajst-24985	58	14	video	video	NOUN
ajst-24985	58	15	analysis	analysis	NOUN
ajst-24985	58	16	,	,	PUNCT
ajst-24985	58	17	speech	speech	NOUN
ajst-24985	58	18	recognition	recognition	NOUN
ajst-24985	58	19	and	and	CCONJ
ajst-24985	58	20	other	other	ADJ
ajst-24985	58	21	fields	field	NOUN
ajst-24985	58	22	,	,	PUNCT
ajst-24985	58	23	and	and	CCONJ
ajst-24985	58	24	breakthroughs	breakthrough	NOUN
ajst-24985	58	25	have	have	AUX
ajst-24985	58	26	been	be	AUX
ajst-24985	58	27	achieved	achieve	VERB
ajst-24985	58	28	.	.	PUNCT
ajst-24985	59	1	for	for	ADP
ajst-24985	59	2	example	example	NOUN
ajst-24985	59	3	,	,	PUNCT
ajst-24985	59	4	the	the	DET
ajst-24985	59	5	application	application	NOUN
ajst-24985	59	6	of	of	ADP
ajst-24985	59	7	deep	deep	ADJ
ajst-24985	59	8	convolutional	convolutional	ADJ
ajst-24985	59	9	networks	network	NOUN
ajst-24985	59	10	in	in	ADP
ajst-24985	59	11	speech	speech	NOUN
ajst-24985	59	12	recognition	recognition	NOUN
ajst-24985	59	13	proposed	propose	VERB
ajst-24985	59	14	by	by	ADP
ajst-24985	59	15	hinton	hinton	PROPN
ajst-24985	59	16	et	et	PROPN
ajst-24985	59	17	al	al	PROPN
ajst-24985	59	18	.	.	PROPN
ajst-24985	60	1	(	(	PUNCT
ajst-24985	60	2	2012	2012	NUM
ajst-24985	60	3	)	)	PUNCT
ajst-24985	60	4	has	have	AUX
ajst-24985	60	5	significantly	significantly	ADV
ajst-24985	60	6	improved	improve	VERB
ajst-24985	60	7	the	the	DET
ajst-24985	60	8	recognition	recognition	NOUN
ajst-24985	60	9	accuracy	accuracy	NOUN
ajst-24985	60	10	of	of	ADP
ajst-24985	60	11	the	the	DET
ajst-24985	60	12	system	system	NOUN
ajst-24985	60	13	[	[	X
ajst-24985	60	14	9	9	NUM
ajst-24985	60	15	]	]	PUNCT
ajst-24985	60	16	.	.	PUNCT
ajst-24985	61	1	although	although	SCONJ
ajst-24985	61	2	the	the	DET
ajst-24985	61	3	above	above	ADJ
ajst-24985	61	4	methods	method	NOUN
ajst-24985	61	5	have	have	AUX
ajst-24985	61	6	achieved	achieve	VERB
ajst-24985	61	7	remarkable	remarkable	ADJ
ajst-24985	61	8	results	result	NOUN
ajst-24985	61	9	in	in	ADP
ajst-24985	61	10	signal	signal	ADJ
ajst-24985	61	11	recognition	recognition	NOUN
ajst-24985	61	12	and	and	CCONJ
ajst-24985	61	13	prediction	prediction	NOUN
ajst-24985	61	14	,	,	PUNCT
ajst-24985	61	15	there	there	PRON
ajst-24985	61	16	are	be	VERB
ajst-24985	61	17	still	still	ADV
ajst-24985	61	18	some	some	DET
ajst-24985	61	19	challenges	challenge	NOUN
ajst-24985	61	20	and	and	CCONJ
ajst-24985	61	21	limitations	limitation	NOUN
ajst-24985	61	22	.	.	PUNCT
ajst-24985	62	1	firstly	firstly	ADV
ajst-24985	62	2	,	,	PUNCT
ajst-24985	62	3	traditional	traditional	ADJ
ajst-24985	62	4	methods	method	NOUN
ajst-24985	62	5	have	have	VERB
ajst-24985	62	6	limited	limit	VERB
ajst-24985	62	7	performance	performance	NOUN
ajst-24985	62	8	in	in	ADP
ajst-24985	62	9	dealing	deal	VERB
ajst-24985	62	10	with	with	ADP
ajst-24985	62	11	complex	complex	ADJ
ajst-24985	62	12	nonlinear	nonlinear	ADJ
ajst-24985	62	13	signals	signal	NOUN
ajst-24985	62	14	,	,	PUNCT
ajst-24985	62	15	which	which	PRON
ajst-24985	62	16	makes	make	VERB
ajst-24985	62	17	it	it	PRON
ajst-24985	62	18	difficult	difficult	ADJ
ajst-24985	62	19	to	to	PART
ajst-24985	62	20	adapt	adapt	VERB
ajst-24985	62	21	to	to	PART
ajst-24985	62	22	diverse	diverse	VERB
ajst-24985	62	23	signals	signal	NOUN
ajst-24985	62	24	in	in	ADP
ajst-24985	62	25	practical	practical	ADJ
ajst-24985	62	26	applications	application	NOUN
ajst-24985	62	27	.	.	PUNCT
ajst-24985	63	1	second	second	ADJ
ajst-24985	63	2	,	,	PUNCT
ajst-24985	63	3	although	although	SCONJ
ajst-24985	63	4	machine	machine	NOUN
ajst-24985	63	5	learning	learning	NOUN
ajst-24985	63	6	methods	method	NOUN
ajst-24985	63	7	perform	perform	VERB
ajst-24985	63	8	well	well	ADV
ajst-24985	63	9	in	in	ADP
ajst-24985	63	10	feature	feature	NOUN
ajst-24985	63	11	extraction	extraction	NOUN
ajst-24985	63	12	and	and	CCONJ
ajst-24985	63	13	pattern	pattern	NOUN
ajst-24985	63	14	recognition	recognition	NOUN
ajst-24985	63	15	,	,	PUNCT
ajst-24985	63	16	they	they	PRON
ajst-24985	63	17	have	have	VERB
ajst-24985	63	18	a	a	DET
ajst-24985	63	19	high	high	ADJ
ajst-24985	63	20	computational	computational	ADJ
ajst-24985	63	21	demand	demand	NOUN
ajst-24985	63	22	for	for	ADP
ajst-24985	63	23	large	large	ADJ
ajst-24985	63	24	-	-	PUNCT
ajst-24985	63	25	scale	scale	NOUN
ajst-24985	63	26	data	datum	NOUN
ajst-24985	63	27	and	and	CCONJ
ajst-24985	63	28	a	a	DET
ajst-24985	63	29	time	time	NOUN
ajst-24985	63	30	-	-	PUNCT
ajst-24985	63	31	consuming	consume	VERB
ajst-24985	63	32	training	training	NOUN
ajst-24985	63	33	process	process	NOUN
ajst-24985	63	34	.	.	PUNCT
ajst-24985	64	1	in	in	ADP
ajst-24985	64	2	addition	addition	NOUN
ajst-24985	64	3	,	,	PUNCT
ajst-24985	64	4	the	the	DET
ajst-24985	64	5	"	"	PUNCT
ajst-24985	64	6	black	black	ADJ
ajst-24985	64	7	-	-	PUNCT
ajst-24985	64	8	box	box	NOUN
ajst-24985	64	9	"	"	PUNCT
ajst-24985	64	10	nature	nature	NOUN
ajst-24985	64	11	of	of	ADP
ajst-24985	64	12	deep	deep	ADJ
ajst-24985	64	13	learning	learning	NOUN
ajst-24985	64	14	models	model	NOUN
ajst-24985	64	15	makes	make	VERB
ajst-24985	64	16	the	the	DET
ajst-24985	64	17	results	result	NOUN
ajst-24985	64	18	less	less	ADV
ajst-24985	64	19	interpretable	interpretable	ADJ
ajst-24985	64	20	,	,	PUNCT
ajst-24985	64	21	which	which	PRON
ajst-24985	64	22	limits	limit	VERB
ajst-24985	64	23	their	their	PRON
ajst-24985	64	24	popularity	popularity	NOUN
ajst-24985	64	25	in	in	ADP
ajst-24985	64	26	some	some	DET
ajst-24985	64	27	application	application	NOUN
ajst-24985	64	28	scenarios	scenario	NOUN
ajst-24985	64	29	.	.	PUNCT
ajst-24985	65	1	3	3	X
ajst-24985	65	2	.	.	X
ajst-24985	65	3	methodology	methodology	NOUN
ajst-24985	65	4	3.1	3.1	NUM
ajst-24985	65	5	.	.	PUNCT
ajst-24985	66	1	data	datum	NOUN
ajst-24985	66	2	preprocessing	preprocessing	NOUN
ajst-24985	66	3	in	in	ADP
ajst-24985	66	4	this	this	DET
ajst-24985	66	5	study	study	NOUN
ajst-24985	66	6	,	,	PUNCT
ajst-24985	66	7	data	datum	NOUN
ajst-24985	66	8	preprocessing	preprocessing	NOUN
ajst-24985	66	9	is	be	AUX
ajst-24985	66	10	a	a	DET
ajst-24985	66	11	crucial	crucial	ADJ
ajst-24985	66	12	step	step	NOUN
ajst-24985	66	13	,	,	PUNCT
ajst-24985	66	14	which	which	PRON
ajst-24985	66	15	includes	include	VERB
ajst-24985	66	16	data	datum	NOUN
ajst-24985	66	17	acquisition	acquisition	NOUN
ajst-24985	66	18	,	,	PUNCT
ajst-24985	66	19	data	datum	NOUN
ajst-24985	66	20	cleaning	cleaning	NOUN
ajst-24985	66	21	and	and	CCONJ
ajst-24985	66	22	data	datum	NOUN
ajst-24985	66	23	normalisation	normalisation	NOUN
ajst-24985	66	24	.	.	PUNCT
ajst-24985	67	1	firstly	firstly	ADV
ajst-24985	67	2	,	,	PUNCT
ajst-24985	67	3	data	datum	NOUN
ajst-24985	67	4	acquisition	acquisition	NOUN
ajst-24985	67	5	mainly	mainly	ADV
ajst-24985	67	6	comes	come	VERB
ajst-24985	67	7	from	from	ADP
ajst-24985	67	8	multiple	multiple	ADJ
ajst-24985	67	9	signal	signal	NOUN
ajst-24985	67	10	sources	source	NOUN
ajst-24985	67	11	,	,	PUNCT
ajst-24985	67	12	including	include	VERB
ajst-24985	67	13	sensors	sensor	NOUN
ajst-24985	67	14	,	,	PUNCT
ajst-24985	67	15	laboratory	laboratory	NOUN
ajst-24985	67	16	experimental	experimental	ADJ
ajst-24985	67	17	data	datum	NOUN
ajst-24985	67	18	and	and	CCONJ
ajst-24985	67	19	public	public	ADJ
ajst-24985	67	20	datasets	dataset	NOUN
ajst-24985	67	21	.	.	PUNCT
ajst-24985	68	1	these	these	DET
ajst-24985	68	2	data	datum	NOUN
ajst-24985	68	3	need	need	VERB
ajst-24985	68	4	to	to	PART
ajst-24985	68	5	be	be	AUX
ajst-24985	68	6	cleaned	clean	VERB
ajst-24985	68	7	to	to	PART
ajst-24985	68	8	remove	remove	VERB
ajst-24985	68	9	noise	noise	NOUN
ajst-24985	68	10	and	and	CCONJ
ajst-24985	68	11	incomplete	incomplete	ADJ
ajst-24985	68	12	data	datum	NOUN
ajst-24985	69	1	[	[	X
ajst-24985	69	2	10	10	NUM
ajst-24985	69	3	]	]	PUNCT
ajst-24985	69	4	.	.	PUNCT
ajst-24985	70	1	for	for	ADP
ajst-24985	70	2	example	example	NOUN
ajst-24985	70	3	,	,	PUNCT
ajst-24985	70	4	for	for	ADP
ajst-24985	70	5	time	time	NOUN
ajst-24985	70	6	series	series	PROPN
ajst-24985	70	7	data	data	PROPN
ajst-24985	70	8	,	,	PUNCT
ajst-24985	70	9	missing	miss	VERB
ajst-24985	70	10	values	value	NOUN
ajst-24985	70	11	are	be	AUX
ajst-24985	70	12	filled	fill	VERB
ajst-24985	70	13	in	in	ADP
ajst-24985	70	14	using	use	VERB
ajst-24985	70	15	linear	linear	ADJ
ajst-24985	70	16	interpolation	interpolation	NOUN
ajst-24985	70	17	and	and	CCONJ
ajst-24985	70	18	a	a	DET
ajst-24985	70	19	median	median	ADJ
ajst-24985	70	20	filter	filter	NOUN
ajst-24985	70	21	is	be	AUX
ajst-24985	70	22	applied	apply	VERB
ajst-24985	70	23	to	to	PART
ajst-24985	70	24	remove	remove	VERB
ajst-24985	70	25	noise	noise	NOUN
ajst-24985	70	26	.	.	PUNCT
ajst-24985	71	1	the	the	DET
ajst-24985	71	2	data	data	NOUN
ajst-24985	71	3	normalisation	normalisation	NOUN
ajst-24985	71	4	step	step	NOUN
ajst-24985	71	5	scales	scale	VERB
ajst-24985	71	6	all	all	PRON
ajst-24985	71	7	eigenvalues	eigenvalue	VERB
ajst-24985	71	8	to	to	ADP
ajst-24985	71	9	a	a	DET
ajst-24985	71	10	standard	standard	ADJ
ajst-24985	71	11	range	range	NOUN
ajst-24985	71	12	such	such	ADJ
ajst-24985	71	13	as	as	ADP
ajst-24985	71	14	[	[	X
ajst-24985	71	15	0	0	NUM
ajst-24985	71	16	,	,	PUNCT
ajst-24985	71	17	1	1	NUM
ajst-24985	71	18	]	]	PUNCT
ajst-24985	71	19	or	or	CCONJ
ajst-24985	71	20	[	[	X
ajst-24985	71	21	-1	-1	X
ajst-24985	71	22	,	,	PUNCT
ajst-24985	71	23	1	1	NUM
ajst-24985	71	24	]	]	PUNCT
ajst-24985	71	25	to	to	PART
ajst-24985	71	26	improve	improve	VERB
ajst-24985	71	27	model	model	NOUN
ajst-24985	71	28	training	training	NOUN
ajst-24985	71	29	efficiency	efficiency	NOUN
ajst-24985	71	30	and	and	CCONJ
ajst-24985	71	31	prediction	prediction	NOUN
ajst-24985	71	32	accuracy	accuracy	NOUN
ajst-24985	71	33	.	.	PUNCT
ajst-24985	72	1	min	min	PROPN
ajst-24985	72	2	max	max	PROPN
ajst-24985	72	3	min	min	PROPN
ajst-24985	72	4	1	1	NUM
ajst-24985	72	5	3.2	3.2	NUM
ajst-24985	72	6	.	.	PUNCT
ajst-24985	73	1	feature	feature	NOUN
ajst-24985	73	2	extraction	extraction	NOUN
ajst-24985	73	3	feature	feature	NOUN
ajst-24985	73	4	extraction	extraction	NOUN
ajst-24985	73	5	is	be	AUX
ajst-24985	73	6	a	a	DET
ajst-24985	73	7	core	core	ADJ
ajst-24985	73	8	step	step	NOUN
ajst-24985	73	9	in	in	ADP
ajst-24985	73	10	signal	signal	ADJ
ajst-24985	73	11	processing	processing	NOUN
ajst-24985	73	12	,	,	PUNCT
ajst-24985	73	13	which	which	PRON
ajst-24985	73	14	improves	improve	VERB
ajst-24985	73	15	the	the	DET
ajst-24985	73	16	prediction	prediction	NOUN
ajst-24985	73	17	ability	ability	NOUN
ajst-24985	73	18	of	of	ADP
ajst-24985	73	19	the	the	DET
ajst-24985	73	20	model	model	NOUN
ajst-24985	73	21	by	by	ADP
ajst-24985	73	22	extracting	extract	VERB
ajst-24985	73	23	201	201	NUM
ajst-24985	73	24	useful	useful	ADJ
ajst-24985	73	25	features	feature	NOUN
ajst-24985	73	26	from	from	ADP
ajst-24985	73	27	the	the	DET
ajst-24985	73	28	original	original	ADJ
ajst-24985	73	29	data	datum	NOUN
ajst-24985	73	30	.	.	PUNCT
ajst-24985	74	1	we	we	PRON
ajst-24985	74	2	adopt	adopt	VERB
ajst-24985	74	3	three	three	NUM
ajst-24985	74	4	methods	method	NOUN
ajst-24985	74	5	:	:	PUNCT
ajst-24985	74	6	time	time	NOUN
ajst-24985	74	7	domain	domain	NOUN
ajst-24985	74	8	features	feature	NOUN
ajst-24985	74	9	,	,	PUNCT
ajst-24985	74	10	frequency	frequency	NOUN
ajst-24985	74	11	domain	domain	NOUN
ajst-24985	74	12	features	feature	NOUN
ajst-24985	74	13	and	and	CCONJ
ajst-24985	74	14	timefrequency	timefrequency	NOUN
ajst-24985	74	15	features	feature	NOUN
ajst-24985	74	16	.	.	PUNCT
ajst-24985	75	1	time	time	NOUN
ajst-24985	75	2	-	-	PUNCT
ajst-24985	75	3	domain	domain	NOUN
ajst-24985	75	4	features	feature	NOUN
ajst-24985	75	5	:	:	PUNCT
ajst-24985	75	6	time	time	NOUN
ajst-24985	75	7	-	-	PUNCT
ajst-24985	75	8	domain	domain	NOUN
ajst-24985	75	9	features	feature	NOUN
ajst-24985	75	10	include	include	VERB
ajst-24985	75	11	the	the	DET
ajst-24985	75	12	basic	basic	ADJ
ajst-24985	75	13	statistical	statistical	ADJ
ajst-24985	75	14	characteristics	characteristic	NOUN
ajst-24985	75	15	of	of	ADP
ajst-24985	75	16	the	the	DET
ajst-24985	75	17	signal	signal	NOUN
ajst-24985	75	18	,	,	PUNCT
ajst-24985	75	19	such	such	ADJ
ajst-24985	75	20	as	as	ADP
ajst-24985	75	21	mean	mean	ADJ
ajst-24985	75	22	,	,	PUNCT
ajst-24985	75	23	standard	standard	ADJ
ajst-24985	75	24	deviation	deviation	NOUN
ajst-24985	75	25	,	,	PUNCT
ajst-24985	75	26	skewness	skewness	NOUN
ajst-24985	75	27	and	and	CCONJ
ajst-24985	75	28	kurtosis	kurtosis	NOUN
ajst-24985	75	29	.	.	PUNCT
ajst-24985	76	1	these	these	DET
ajst-24985	76	2	features	feature	NOUN
ajst-24985	76	3	can	can	AUX
ajst-24985	76	4	be	be	AUX
ajst-24985	76	5	calculated	calculate	VERB
ajst-24985	76	6	by	by	ADP
ajst-24985	76	7	the	the	DET
ajst-24985	76	8	following	follow	VERB
ajst-24985	76	9	formula	formula	NOUN
ajst-24985	76	10	:	:	PUNCT
ajst-24985	76	11	average	average	ADJ
ajst-24985	76	12	1	1	NUM
ajst-24985	76	13	  	  	SPACE
ajst-24985	76	14	2	2	NUM
ajst-24985	76	15	standard	standard	ADJ
ajst-24985	76	16	deviation	deviation	NOUN
ajst-24985	76	17	1	1	NUM
ajst-24985	76	18	  	  	SPACE
ajst-24985	76	19	3	3	NUM
ajst-24985	76	20	frequency	frequency	NOUN
ajst-24985	76	21	domain	domain	NOUN
ajst-24985	76	22	features	feature	VERB
ajst-24985	76	23	:	:	PUNCT
ajst-24985	76	24	the	the	DET
ajst-24985	76	25	time	time	NOUN
ajst-24985	76	26	domain	domain	NOUN
ajst-24985	76	27	signal	signal	NOUN
ajst-24985	76	28	is	be	AUX
ajst-24985	76	29	converted	convert	VERB
ajst-24985	76	30	to	to	ADP
ajst-24985	76	31	the	the	DET
ajst-24985	76	32	frequency	frequency	NOUN
ajst-24985	76	33	domain	domain	NOUN
ajst-24985	76	34	by	by	ADP
ajst-24985	76	35	fast	fast	ADJ
ajst-24985	76	36	fourier	fourier	NOUN
ajst-24985	76	37	transform	transform	NOUN
ajst-24985	76	38	(	(	PUNCT
ajst-24985	76	39	fft	fft	PROPN
ajst-24985	76	40	)	)	PUNCT
ajst-24985	76	41	to	to	PART
ajst-24985	76	42	extract	extract	VERB
ajst-24985	76	43	the	the	DET
ajst-24985	76	44	frequency	frequency	NOUN
ajst-24985	76	45	domain	domain	NOUN
ajst-24985	76	46	features	feature	NOUN
ajst-24985	76	47	.	.	PUNCT
ajst-24985	77	1	common	common	ADJ
ajst-24985	77	2	frequency	frequency	NOUN
ajst-24985	77	3	domain	domain	NOUN
ajst-24985	77	4	features	feature	NOUN
ajst-24985	77	5	include	include	VERB
ajst-24985	77	6	the	the	DET
ajst-24985	77	7	dominant	dominant	ADJ
ajst-24985	77	8	frequency	frequency	NOUN
ajst-24985	77	9	,	,	PUNCT
ajst-24985	77	10	spectral	spectral	ADJ
ajst-24985	77	11	entropy	entropy	NOUN
ajst-24985	77	12	and	and	CCONJ
ajst-24985	77	13	so	so	ADV
ajst-24985	77	14	on	on	ADV
ajst-24985	77	15	.	.	PUNCT
ajst-24985	78	1	the	the	DET
ajst-24985	78	2	spectral	spectral	ADJ
ajst-24985	78	3	entropy	entropy	PROPN
ajst-24985	78	4	is	be	AUX
ajst-24985	78	5	calculated	calculate	VERB
ajst-24985	78	6	as	as	SCONJ
ajst-24985	78	7	follows	follow	VERB
ajst-24985	78	8	:	:	PUNCT
ajst-24985	78	9	  	  	SPACE
ajst-24985	78	10	4	4	NUM
ajst-24985	78	11	|	|	ADV
ajst-24985	79	1	|	|	ADV
ajst-24985	79	2	∑	∑	ADP
ajst-24985	79	3	  	  	SPACE
ajst-24985	79	4	|	|	ADV
ajst-24985	79	5	|	|	ADV
ajst-24985	79	6	5	5	NUM
ajst-24985	79	7	  	  	SPACE
ajst-24985	79	8	log	log	VERB
ajst-24985	79	9	6	6	NUM
ajst-24985	79	10	time	time	NOUN
ajst-24985	79	11	-	-	PUNCT
ajst-24985	79	12	frequency	frequency	NOUN
ajst-24985	79	13	features	feature	NOUN
ajst-24985	79	14	:	:	PUNCT
ajst-24985	79	15	the	the	DET
ajst-24985	79	16	wavelet	wavelet	NOUN
ajst-24985	79	17	transform	transform	NOUN
ajst-24985	79	18	is	be	AUX
ajst-24985	79	19	used	use	VERB
ajst-24985	79	20	to	to	PART
ajst-24985	79	21	extract	extract	VERB
ajst-24985	79	22	the	the	DET
ajst-24985	79	23	time	time	NOUN
ajst-24985	79	24	-	-	PUNCT
ajst-24985	79	25	frequency	frequency	NOUN
ajst-24985	79	26	features	feature	NOUN
ajst-24985	79	27	of	of	ADP
ajst-24985	79	28	the	the	DET
ajst-24985	79	29	signal	signal	NOUN
ajst-24985	79	30	.	.	PUNCT
ajst-24985	80	1	the	the	DET
ajst-24985	80	2	wavelet	wavelet	NOUN
ajst-24985	80	3	transform	transform	NOUN
ajst-24985	80	4	is	be	AUX
ajst-24985	80	5	effective	effective	ADJ
ajst-24985	80	6	in	in	ADP
ajst-24985	80	7	capturing	capture	VERB
ajst-24985	80	8	the	the	DET
ajst-24985	80	9	variations	variation	NOUN
ajst-24985	80	10	of	of	ADP
ajst-24985	80	11	a	a	DET
ajst-24985	80	12	signal	signal	NOUN
ajst-24985	80	13	over	over	ADP
ajst-24985	80	14	time	time	NOUN
ajst-24985	80	15	and	and	CCONJ
ajst-24985	80	16	frequency	frequency	NOUN
ajst-24985	80	17	.	.	PUNCT
ajst-24985	81	1	,	,	PUNCT
ajst-24985	81	2	1	1	NUM
ajst-24985	81	3	√	√	VERB
ajst-24985	81	4	  	  	SPACE
ajst-24985	81	5	7	7	NUM
ajst-24985	81	6	3.3	3.3	NUM
ajst-24985	81	7	.	.	PUNCT
ajst-24985	82	1	machine	machine	NOUN
ajst-24985	82	2	learning	learn	VERB
ajst-24985	82	3	algorithms	algorithm	NOUN
ajst-24985	82	4	choosing	choose	VERB
ajst-24985	82	5	appropriate	appropriate	ADJ
ajst-24985	82	6	machine	machine	NOUN
ajst-24985	82	7	learning	learn	VERB
ajst-24985	82	8	algorithms	algorithm	NOUN
ajst-24985	82	9	is	be	AUX
ajst-24985	82	10	the	the	DET
ajst-24985	82	11	key	key	NOUN
ajst-24985	82	12	to	to	ADP
ajst-24985	82	13	model	model	NOUN
ajst-24985	82	14	performance	performance	NOUN
ajst-24985	82	15	.	.	PUNCT
ajst-24985	83	1	we	we	PRON
ajst-24985	83	2	chose	choose	VERB
ajst-24985	83	3	four	four	NUM
ajst-24985	83	4	algorithms	algorithm	NOUN
ajst-24985	83	5	,	,	PUNCT
ajst-24985	83	6	support	support	NOUN
ajst-24985	83	7	vector	vector	NOUN
ajst-24985	83	8	machine	machine	NOUN
ajst-24985	83	9	(	(	PUNCT
ajst-24985	83	10	svm	svm	PROPN
ajst-24985	83	11	)	)	PUNCT
ajst-24985	83	12	,	,	PUNCT
ajst-24985	83	13	artificial	artificial	ADJ
ajst-24985	83	14	neural	neural	ADJ
ajst-24985	83	15	network	network	NOUN
ajst-24985	83	16	(	(	PUNCT
ajst-24985	83	17	ann	ann	PROPN
ajst-24985	83	18	)	)	PUNCT
ajst-24985	83	19	,	,	PUNCT
ajst-24985	83	20	random	random	ADJ
ajst-24985	83	21	forest	forest	NOUN
ajst-24985	83	22	(	(	PUNCT
ajst-24985	83	23	rf	rf	NOUN
ajst-24985	83	24	)	)	PUNCT
ajst-24985	83	25	and	and	CCONJ
ajst-24985	83	26	convolutional	convolutional	ADJ
ajst-24985	83	27	neural	neural	ADJ
ajst-24985	83	28	network	network	NOUN
ajst-24985	83	29	(	(	PUNCT
ajst-24985	83	30	cnn	cnn	PROPN
ajst-24985	83	31	)	)	PUNCT
ajst-24985	83	32	,	,	PUNCT
ajst-24985	83	33	based	base	VERB
ajst-24985	83	34	on	on	ADP
ajst-24985	83	35	the	the	DET
ajst-24985	83	36	characteristics	characteristic	NOUN
ajst-24985	83	37	of	of	ADP
ajst-24985	83	38	the	the	DET
ajst-24985	83	39	data	datum	NOUN
ajst-24985	83	40	and	and	CCONJ
ajst-24985	83	41	the	the	DET
ajst-24985	83	42	needs	need	NOUN
ajst-24985	83	43	of	of	ADP
ajst-24985	83	44	the	the	DET
ajst-24985	83	45	problem	problem	NOUN
ajst-24985	83	46	[	[	X
ajst-24985	83	47	11	11	NUM
ajst-24985	83	48	]	]	PUNCT
ajst-24985	83	49	.	.	PUNCT
ajst-24985	84	1	support	support	NOUN
ajst-24985	84	2	vector	vector	NOUN
ajst-24985	84	3	machine	machine	NOUN
ajst-24985	84	4	(	(	PUNCT
ajst-24985	84	5	svm	svm	PROPN
ajst-24985	84	6	):	):	PUNCT
ajst-24985	84	7	svm	svm	PROPN
ajst-24985	84	8	performs	perform	VERB
ajst-24985	84	9	classification	classification	NOUN
ajst-24985	84	10	by	by	ADP
ajst-24985	84	11	finding	find	VERB
ajst-24985	84	12	the	the	DET
ajst-24985	84	13	hyperplane	hyperplane	NOUN
ajst-24985	84	14	that	that	PRON
ajst-24985	84	15	maximises	maximise	VERB
ajst-24985	84	16	the	the	DET
ajst-24985	84	17	classification	classification	NOUN
ajst-24985	84	18	boundary	boundary	NOUN
ajst-24985	84	19	,	,	PUNCT
ajst-24985	84	20	and	and	CCONJ
ajst-24985	84	21	is	be	AUX
ajst-24985	84	22	suitable	suitable	ADJ
ajst-24985	84	23	for	for	ADP
ajst-24985	84	24	high	high	ADJ
ajst-24985	84	25	-	-	PUNCT
ajst-24985	84	26	dimensional	dimensional	ADJ
ajst-24985	84	27	data	datum	NOUN
ajst-24985	84	28	.	.	PUNCT
ajst-24985	85	1	its	its	PRON
ajst-24985	85	2	optimisation	optimisation	NOUN
ajst-24985	85	3	objective	objective	NOUN
ajst-24985	85	4	is	be	AUX
ajst-24985	85	5	:	:	PUNCT
ajst-24985	85	6	1	1	NUM
ajst-24985	85	7	2	2	NUM
ajst-24985	85	8	||	||	NOUN
ajst-24985	85	9	||	||	NOUN
ajst-24985	85	10	subject	subject	NOUN
ajst-24985	85	11	to	to	ADP
ajst-24985	85	12	⋅	⋅	PROPN
ajst-24985	85	13	1	1	NUM
ajst-24985	85	14	8	8	NUM
ajst-24985	85	15	artificial	artificial	ADJ
ajst-24985	85	16	neural	neural	ADJ
ajst-24985	85	17	network	network	NOUN
ajst-24985	85	18	(	(	PUNCT
ajst-24985	85	19	ann	ann	PROPN
ajst-24985	85	20	):	):	PUNCT
ajst-24985	85	21	ann	ann	PROPN
ajst-24985	85	22	simulates	simulate	VERB
ajst-24985	85	23	the	the	DET
ajst-24985	85	24	learning	learning	NOUN
ajst-24985	85	25	process	process	NOUN
ajst-24985	85	26	of	of	ADP
ajst-24985	85	27	the	the	DET
ajst-24985	85	28	human	human	ADJ
ajst-24985	85	29	brain	brain	NOUN
ajst-24985	85	30	through	through	ADP
ajst-24985	85	31	the	the	DET
ajst-24985	85	32	connection	connection	NOUN
ajst-24985	85	33	of	of	ADP
ajst-24985	85	34	multiple	multiple	ADJ
ajst-24985	85	35	layers	layer	NOUN
ajst-24985	85	36	of	of	ADP
ajst-24985	85	37	neurons	neuron	NOUN
ajst-24985	85	38	and	and	CCONJ
ajst-24985	85	39	is	be	AUX
ajst-24985	85	40	suitable	suitable	ADJ
ajst-24985	85	41	for	for	ADP
ajst-24985	85	42	dealing	deal	VERB
ajst-24985	85	43	with	with	ADP
ajst-24985	85	44	complex	complex	ADJ
ajst-24985	85	45	non	non	ADJ
ajst-24985	85	46	-	-	ADJ
ajst-24985	85	47	linear	linear	ADJ
ajst-24985	85	48	relationships	relationship	NOUN
ajst-24985	85	49	.	.	PUNCT
ajst-24985	86	1	its	its	PRON
ajst-24985	86	2	basic	basic	ADJ
ajst-24985	86	3	unit	unit	NOUN
ajst-24985	86	4	is	be	AUX
ajst-24985	86	5	the	the	DET
ajst-24985	86	6	neuron	neuron	NOUN
ajst-24985	86	7	,	,	PUNCT
ajst-24985	86	8	using	use	VERB
ajst-24985	86	9	an	an	DET
ajst-24985	86	10	activation	activation	NOUN
ajst-24985	86	11	function	function	NOUN
ajst-24985	86	12	:	:	PUNCT
ajst-24985	86	13	  	  	SPACE
ajst-24985	86	14	9	9	NUM
ajst-24985	86	15	random	random	ADJ
ajst-24985	86	16	forest	forest	NOUN
ajst-24985	86	17	(	(	PUNCT
ajst-24985	86	18	rf	rf	NOUN
ajst-24985	86	19	):	):	PUNCT
ajst-24985	86	20	the	the	DET
ajst-24985	86	21	rf	rf	NOUN
ajst-24985	86	22	is	be	AUX
ajst-24985	86	23	an	an	DET
ajst-24985	86	24	integrated	integrated	ADJ
ajst-24985	86	25	learning	learning	NOUN
ajst-24985	86	26	method	method	NOUN
ajst-24985	86	27	consisting	consist	VERB
ajst-24985	86	28	of	of	ADP
ajst-24985	86	29	multiple	multiple	ADJ
ajst-24985	86	30	decision	decision	NOUN
ajst-24985	86	31	trees	tree	NOUN
ajst-24985	86	32	that	that	PRON
ajst-24985	86	33	decide	decide	VERB
ajst-24985	86	34	the	the	DET
ajst-24985	86	35	final	final	ADJ
ajst-24985	86	36	output	output	NOUN
ajst-24985	86	37	by	by	ADP
ajst-24985	86	38	voting	vote	VERB
ajst-24985	86	39	,	,	PUNCT
ajst-24985	86	40	with	with	ADP
ajst-24985	86	41	good	good	ADJ
ajst-24985	86	42	generalisation	generalisation	NOUN
ajst-24985	86	43	ability	ability	NOUN
ajst-24985	86	44	.	.	PUNCT
ajst-24985	87	1	rf	rf	PRON
ajst-24985	87	2	1	1	NUM
ajst-24985	87	3	  	  	SPACE
ajst-24985	87	4	10	10	NUM
ajst-24985	87	5	convolutional	convolutional	ADJ
ajst-24985	87	6	neural	neural	ADJ
ajst-24985	87	7	network	network	NOUN
ajst-24985	87	8	(	(	PUNCT
ajst-24985	87	9	cnn	cnn	PROPN
ajst-24985	87	10	):	):	PUNCT
ajst-24985	87	11	the	the	DET
ajst-24985	87	12	cnn	cnn	PROPN
ajst-24985	87	13	is	be	AUX
ajst-24985	87	14	suitable	suitable	ADJ
ajst-24985	87	15	for	for	ADP
ajst-24985	87	16	processing	process	VERB
ajst-24985	87	17	2d	2d	NUM
ajst-24985	87	18	image	image	NOUN
ajst-24985	87	19	data	datum	NOUN
ajst-24985	87	20	and	and	CCONJ
ajst-24985	87	21	is	be	AUX
ajst-24985	87	22	particularly	particularly	ADV
ajst-24985	87	23	suited	suit	VERB
ajst-24985	87	24	for	for	ADP
ajst-24985	87	25	extracting	extract	VERB
ajst-24985	87	26	spatial	spatial	ADJ
ajst-24985	87	27	features	feature	NOUN
ajst-24985	87	28	.	.	PUNCT
ajst-24985	88	1	its	its	PRON
ajst-24985	88	2	convolutional	convolutional	ADJ
ajst-24985	88	3	layer	layer	NOUN
ajst-24985	88	4	performs	perform	VERB
ajst-24985	88	5	feature	feature	NOUN
ajst-24985	88	6	extraction	extraction	NOUN
ajst-24985	88	7	through	through	ADP
ajst-24985	88	8	convolutional	convolutional	ADJ
ajst-24985	88	9	kernels	kernel	NOUN
ajst-24985	88	10	:	:	PUNCT
ajst-24985	88	11	∗	∗	NOUN
ajst-24985	88	12	,	,	PUNCT
ajst-24985	88	13	    	    	SPACE
ajst-24985	88	14	,	,	PUNCT
ajst-24985	88	15	,	,	PUNCT
ajst-24985	88	16	11	11	NUM
ajst-24985	88	17	4	4	NUM
ajst-24985	88	18	.	.	PUNCT
ajst-24985	88	19	experiments	experiment	NOUN
ajst-24985	88	20	and	and	CCONJ
ajst-24985	88	21	results	result	NOUN
ajst-24985	88	22	in	in	ADP
ajst-24985	88	23	this	this	DET
ajst-24985	88	24	study	study	NOUN
ajst-24985	88	25	,	,	PUNCT
ajst-24985	88	26	a	a	DET
ajst-24985	88	27	series	series	NOUN
ajst-24985	88	28	of	of	ADP
ajst-24985	88	29	experiments	experiment	NOUN
ajst-24985	88	30	were	be	AUX
ajst-24985	88	31	conducted	conduct	VERB
ajst-24985	88	32	to	to	PART
ajst-24985	88	33	validate	validate	VERB
ajst-24985	88	34	the	the	DET
ajst-24985	88	35	effectiveness	effectiveness	NOUN
ajst-24985	88	36	of	of	ADP
ajst-24985	88	37	the	the	DET
ajst-24985	88	38	machine	machine	NOUN
ajst-24985	88	39	learning	learning	NOUN
ajst-24985	88	40	based	base	VERB
ajst-24985	88	41	approach	approach	NOUN
ajst-24985	88	42	in	in	ADP
ajst-24985	88	43	signal	signal	ADJ
ajst-24985	88	44	recognition	recognition	NOUN
ajst-24985	88	45	and	and	CCONJ
ajst-24985	88	46	prediction	prediction	NOUN
ajst-24985	88	47	.	.	PUNCT
ajst-24985	89	1	the	the	DET
ajst-24985	89	2	setup	setup	NOUN
ajst-24985	89	3	and	and	CCONJ
ajst-24985	89	4	execution	execution	NOUN
ajst-24985	89	5	steps	step	NOUN
ajst-24985	89	6	of	of	ADP
ajst-24985	89	7	the	the	DET
ajst-24985	89	8	experiments	experiment	NOUN
ajst-24985	89	9	are	be	AUX
ajst-24985	89	10	detailed	detail	VERB
ajst-24985	89	11	below	below	ADV
ajst-24985	89	12	.	.	PUNCT
ajst-24985	90	1	the	the	DET
ajst-24985	90	2	experiments	experiment	NOUN
ajst-24985	90	3	were	be	AUX
ajst-24985	90	4	conducted	conduct	VERB
ajst-24985	90	5	on	on	ADP
ajst-24985	90	6	a	a	DET
ajst-24985	90	7	computer	computer	NOUN
ajst-24985	90	8	equipped	equip	VERB
ajst-24985	90	9	with	with	ADP
ajst-24985	90	10	high	high	ADJ
ajst-24985	90	11	-	-	PUNCT
ajst-24985	90	12	performance	performance	NOUN
ajst-24985	90	13	computing	computing	NOUN
ajst-24985	90	14	hardware	hardware	NOUN
ajst-24985	90	15	with	with	ADP
ajst-24985	90	16	the	the	DET
ajst-24985	90	17	following	follow	VERB
ajst-24985	90	18	configurations	configuration	NOUN
ajst-24985	90	19	:	:	PUNCT
ajst-24985	90	20	an	an	DET
ajst-24985	90	21	intel	intel	PROPN
ajst-24985	90	22	core	core	NOUN
ajst-24985	90	23	i9	i9	ADJ
ajst-24985	90	24	-	-	PUNCT
ajst-24985	90	25	13900kf	13900kf	NOUN
ajst-24985	90	26	cpu	cpu	NOUN
ajst-24985	90	27	,	,	PUNCT
ajst-24985	90	28	32	32	NUM
ajst-24985	90	29	gb	gb	NOUN
ajst-24985	90	30	of	of	ADP
ajst-24985	90	31	ram	ram	NOUN
ajst-24985	90	32	,	,	PUNCT
ajst-24985	90	33	an	an	DET
ajst-24985	90	34	nvidia	nvidia	PROPN
ajst-24985	90	35	rtx	rtx	PROPN
ajst-24985	90	36	4080	4080	NUM
ajst-24985	90	37	ti	ti	PROPN
ajst-24985	90	38	gpu	gpu	PROPN
ajst-24985	90	39	,	,	PUNCT
ajst-24985	90	40	and	and	CCONJ
ajst-24985	90	41	a	a	DET
ajst-24985	90	42	3	3	NUM
ajst-24985	90	43	tb	tb	NOUN
ajst-24985	90	44	ssd	ssd	NOUN
ajst-24985	90	45	for	for	ADP
ajst-24985	90	46	storage	storage	NOUN
ajst-24985	90	47	.	.	PUNCT
ajst-24985	91	1	the	the	DET
ajst-24985	91	2	software	software	NOUN
ajst-24985	91	3	environment	environment	NOUN
ajst-24985	91	4	uses	use	VERB
ajst-24985	91	5	python	python	PROPN
ajst-24985	91	6	3.9	3.9	NUM
ajst-24985	91	7	,	,	PUNCT
ajst-24985	91	8	and	and	CCONJ
ajst-24985	91	9	the	the	DET
ajst-24985	91	10	main	main	ADJ
ajst-24985	91	11	toolkits	toolkit	NOUN
ajst-24985	91	12	include	include	VERB
ajst-24985	91	13	numpy	numpy	NOUN
ajst-24985	91	14	,	,	PUNCT
ajst-24985	91	15	pandas	panda	NOUN
ajst-24985	91	16	for	for	ADP
ajst-24985	91	17	data	data	NOUN
ajst-24985	91	18	processing	processing	NOUN
ajst-24985	91	19	,	,	PUNCT
ajst-24985	91	20	scikit	scikit	NOUN
ajst-24985	91	21	-	-	PUNCT
ajst-24985	91	22	learn	learn	VERB
ajst-24985	91	23	for	for	ADP
ajst-24985	91	24	machine	machine	NOUN
ajst-24985	91	25	learning	learning	NOUN
ajst-24985	91	26	models	model	NOUN
ajst-24985	91	27	,	,	PUNCT
ajst-24985	91	28	tensorflow	tensorflow	NOUN
ajst-24985	91	29	and	and	CCONJ
ajst-24985	91	30	keras	keras	PROPN
ajst-24985	91	31	for	for	ADP
ajst-24985	91	32	deep	deep	ADJ
ajst-24985	91	33	learning	learning	NOUN
ajst-24985	91	34	models	model	NOUN
ajst-24985	91	35	,	,	PUNCT
ajst-24985	91	36	and	and	CCONJ
ajst-24985	91	37	matplotlib	matplotlib	PROPN
ajst-24985	91	38	and	and	CCONJ
ajst-24985	91	39	seaborn	seaborn	PROPN
ajst-24985	91	40	for	for	ADP
ajst-24985	91	41	result	result	NOUN
ajst-24985	91	42	visualisation	visualisation	NOUN
ajst-24985	91	43	.	.	PUNCT
ajst-24985	92	1	the	the	DET
ajst-24985	92	2	dataset	dataset	NOUN
ajst-24985	92	3	comes	come	VERB
ajst-24985	92	4	from	from	ADP
ajst-24985	92	5	a	a	DET
ajst-24985	92	6	publicly	publicly	ADV
ajst-24985	92	7	available	available	ADJ
ajst-24985	92	8	signal	signal	NOUN
ajst-24985	92	9	processing	processing	NOUN
ajst-24985	92	10	dataset	dataset	NOUN
ajst-24985	92	11	that	that	PRON
ajst-24985	92	12	contains	contain	VERB
ajst-24985	92	13	multiple	multiple	ADJ
ajst-24985	92	14	time	time	NOUN
ajst-24985	92	15	series	series	NOUN
ajst-24985	92	16	,	,	PUNCT
ajst-24985	92	17	each	each	PRON
ajst-24985	92	18	consisting	consist	VERB
ajst-24985	92	19	of	of	ADP
ajst-24985	92	20	thousands	thousand	NOUN
ajst-24985	92	21	of	of	ADP
ajst-24985	92	22	data	datum	NOUN
ajst-24985	92	23	points	point	NOUN
ajst-24985	92	24	representing	represent	VERB
ajst-24985	92	25	signal	signal	NOUN
ajst-24985	92	26	strength	strength	NOUN
ajst-24985	92	27	under	under	ADP
ajst-24985	92	28	different	different	ADJ
ajst-24985	92	29	conditions	condition	NOUN
ajst-24985	92	30	.	.	PUNCT
ajst-24985	93	1	we	we	PRON
ajst-24985	93	2	divided	divide	VERB
ajst-24985	93	3	the	the	DET
ajst-24985	93	4	dataset	dataset	NOUN
ajst-24985	93	5	into	into	ADP
ajst-24985	93	6	a	a	DET
ajst-24985	93	7	training	training	NOUN
ajst-24985	93	8	set	set	NOUN
ajst-24985	93	9	and	and	CCONJ
ajst-24985	93	10	a	a	DET
ajst-24985	93	11	test	test	NOUN
ajst-24985	93	12	set	set	VERB
ajst-24985	93	13	in	in	ADP
ajst-24985	93	14	the	the	DET
ajst-24985	93	15	ratio	ratio	NOUN
ajst-24985	93	16	of	of	ADP
ajst-24985	93	17	70:30	70:30	NUM
ajst-24985	93	18	.	.	PUNCT
ajst-24985	94	1	the	the	DET
ajst-24985	94	2	data	datum	NOUN
ajst-24985	94	3	preprocessing	preprocessing	NOUN
ajst-24985	94	4	steps	step	NOUN
ajst-24985	94	5	include	include	VERB
ajst-24985	94	6	data	datum	NOUN
ajst-24985	94	7	cleaning	cleaning	NOUN
ajst-24985	94	8	,	,	PUNCT
ajst-24985	94	9	normalisation	normalisation	NOUN
ajst-24985	94	10	and	and	CCONJ
ajst-24985	94	11	feature	feature	NOUN
ajst-24985	94	12	extraction	extraction	NOUN
ajst-24985	94	13	.	.	PUNCT
ajst-24985	95	1	we	we	PRON
ajst-24985	95	2	extracted	extract	VERB
ajst-24985	95	3	time	time	NOUN
ajst-24985	95	4	-	-	PUNCT
ajst-24985	95	5	domain	domain	NOUN
ajst-24985	95	6	features	feature	NOUN
ajst-24985	95	7	(	(	PUNCT
ajst-24985	95	8	e.g.	e.g.	ADV
ajst-24985	95	9	,	,	PUNCT
ajst-24985	95	10	mean	mean	ADJ
ajst-24985	95	11	,	,	PUNCT
ajst-24985	95	12	standard	standard	ADJ
ajst-24985	95	13	deviation	deviation	NOUN
ajst-24985	95	14	)	)	PUNCT
ajst-24985	95	15	,	,	PUNCT
ajst-24985	95	16	frequency	frequency	NOUN
ajst-24985	95	17	-	-	PUNCT
ajst-24985	95	18	domain	domain	NOUN
ajst-24985	95	19	features	feature	NOUN
ajst-24985	95	20	(	(	PUNCT
ajst-24985	95	21	e.g.	e.g.	ADV
ajst-24985	95	22	,	,	PUNCT
ajst-24985	95	23	principal	principal	ADJ
ajst-24985	95	24	frequency	frequency	NOUN
ajst-24985	95	25	,	,	PUNCT
ajst-24985	95	26	spectral	spectral	ADJ
ajst-24985	95	27	entropy	entropy	PROPN
ajst-24985	95	28	)	)	PUNCT
ajst-24985	95	29	,	,	PUNCT
ajst-24985	95	30	and	and	CCONJ
ajst-24985	95	31	time	time	NOUN
ajst-24985	95	32	-	-	PUNCT
ajst-24985	95	33	frequency	frequency	NOUN
ajst-24985	95	34	features	feature	NOUN
ajst-24985	95	35	(	(	PUNCT
ajst-24985	95	36	e.g.	e.g.	ADV
ajst-24985	95	37	,	,	PUNCT
ajst-24985	95	38	wavelet	wavelet	NOUN
ajst-24985	95	39	coefficients	coefficient	NOUN
ajst-24985	95	40	)	)	PUNCT
ajst-24985	95	41	.	.	PUNCT
ajst-24985	96	1	the	the	DET
ajst-24985	96	2	training	training	NOUN
ajst-24985	96	3	process	process	NOUN
ajst-24985	96	4	uses	use	VERB
ajst-24985	96	5	a	a	DET
ajst-24985	96	6	cross	cross	ADJ
ajst-24985	96	7	-	-	ADJ
ajst-24985	96	8	validation	validation	ADJ
ajst-24985	96	9	method	method	NOUN
ajst-24985	96	10	,	,	PUNCT
ajst-24985	96	11	where	where	SCONJ
ajst-24985	96	12	k	k	X
ajst-24985	96	13	-	-	ADJ
ajst-24985	96	14	fold	fold	ADJ
ajst-24985	96	15	cross	cross	NOUN
ajst-24985	96	16	-	-	NOUN
ajst-24985	96	17	validation	validation	ADJ
ajst-24985	96	18	(	(	PUNCT
ajst-24985	96	19	k=10	k=10	PROPN
ajst-24985	96	20	)	)	PUNCT
ajst-24985	96	21	is	be	AUX
ajst-24985	96	22	used	use	VERB
ajst-24985	96	23	to	to	PART
ajst-24985	96	24	assess	assess	VERB
ajst-24985	96	25	the	the	DET
ajst-24985	96	26	stability	stability	NOUN
ajst-24985	96	27	and	and	CCONJ
ajst-24985	96	28	generalisation	generalisation	NOUN
ajst-24985	96	29	ability	ability	NOUN
ajst-24985	96	30	of	of	ADP
ajst-24985	96	31	the	the	DET
ajst-24985	96	32	model	model	NOUN
ajst-24985	96	33	.	.	PUNCT
ajst-24985	97	1	for	for	ADP
ajst-24985	97	2	each	each	DET
ajst-24985	97	3	machine	machine	NOUN
ajst-24985	97	4	learning	learn	VERB
ajst-24985	97	5	algorithm	algorithm	NOUN
ajst-24985	97	6	,	,	PUNCT
ajst-24985	97	7	including	include	VERB
ajst-24985	97	8	support	support	NOUN
ajst-24985	97	9	vector	vector	NOUN
ajst-24985	97	10	machines	machine	NOUN
ajst-24985	97	11	(	(	PUNCT
ajst-24985	97	12	svm	svm	PROPN
ajst-24985	97	13	)	)	PUNCT
ajst-24985	97	14	,	,	PUNCT
ajst-24985	97	15	artificial	artificial	ADJ
ajst-24985	97	16	neural	neural	ADJ
ajst-24985	97	17	networks	network	NOUN
ajst-24985	97	18	(	(	PUNCT
ajst-24985	97	19	ann	ann	PROPN
ajst-24985	97	20	)	)	PUNCT
ajst-24985	97	21	,	,	PUNCT
ajst-24985	97	22	random	random	ADJ
ajst-24985	97	23	forests	forest	NOUN
ajst-24985	97	24	(	(	PUNCT
ajst-24985	97	25	rf	rf	NOUN
ajst-24985	97	26	)	)	PUNCT
ajst-24985	97	27	,	,	PUNCT
ajst-24985	97	28	and	and	CCONJ
ajst-24985	97	29	convolutional	convolutional	ADJ
ajst-24985	97	30	neural	neural	ADJ
ajst-24985	97	31	networks	network	NOUN
ajst-24985	97	32	(	(	PUNCT
ajst-24985	97	33	cnn	cnn	PROPN
ajst-24985	97	34	)	)	PUNCT
ajst-24985	97	35	,	,	PUNCT
ajst-24985	97	36	we	we	PRON
ajst-24985	97	37	train	train	VERB
ajst-24985	97	38	the	the	DET
ajst-24985	97	39	models	model	NOUN
ajst-24985	97	40	separately	separately	ADV
ajst-24985	97	41	and	and	CCONJ
ajst-24985	97	42	record	record	VERB
ajst-24985	97	43	their	their	PRON
ajst-24985	97	44	performance	performance	NOUN
ajst-24985	97	45	metrics	metric	NOUN
ajst-24985	97	46	.	.	PUNCT
ajst-24985	98	1	the	the	DET
ajst-24985	98	2	prediction	prediction	NOUN
ajst-24985	98	3	process	process	NOUN
ajst-24985	98	4	involves	involve	VERB
ajst-24985	98	5	running	run	VERB
ajst-24985	98	6	the	the	DET
ajst-24985	98	7	trained	train	VERB
ajst-24985	98	8	models	model	NOUN
ajst-24985	98	9	on	on	ADP
ajst-24985	98	10	the	the	DET
ajst-24985	98	11	test	test	NOUN
ajst-24985	98	12	set	set	VERB
ajst-24985	98	13	and	and	CCONJ
ajst-24985	98	14	calculating	calculate	VERB
ajst-24985	98	15	their	their	PRON
ajst-24985	98	16	metrics	metric	NOUN
ajst-24985	98	17	such	such	ADJ
ajst-24985	98	18	as	as	ADP
ajst-24985	98	19	prediction	prediction	NOUN
ajst-24985	98	20	accuracy	accuracy	NOUN
ajst-24985	98	21	,	,	PUNCT
ajst-24985	98	22	recall	recall	NOUN
ajst-24985	98	23	and	and	CCONJ
ajst-24985	98	24	f1	f1	NOUN
ajst-24985	98	25	value	value	NOUN
ajst-24985	98	26	.	.	PUNCT
ajst-24985	99	1	we	we	PRON
ajst-24985	99	2	divided	divide	VERB
ajst-24985	99	3	the	the	DET
ajst-24985	99	4	dataset	dataset	NOUN
ajst-24985	99	5	into	into	ADP
ajst-24985	99	6	training	training	NOUN
ajst-24985	99	7	and	and	CCONJ
ajst-24985	99	8	test	test	NOUN
ajst-24985	99	9	sets	set	NOUN
ajst-24985	99	10	in	in	ADP
ajst-24985	99	11	the	the	DET
ajst-24985	99	12	ratio	ratio	NOUN
ajst-24985	99	13	of	of	ADP
ajst-24985	99	14	70:30	70:30	NUM
ajst-24985	99	15	.	.	PUNCT
ajst-24985	100	1	cross	cross	ADJ
ajst-24985	100	2	-	-	ADJ
ajst-24985	100	3	validation	validation	ADJ
ajst-24985	100	4	uses	use	VERB
ajst-24985	100	5	k	k	ADJ
ajst-24985	100	6	-	-	ADJ
ajst-24985	100	7	fold	fold	ADJ
ajst-24985	100	8	cross	cross	NOUN
ajst-24985	100	9	-	-	NOUN
ajst-24985	100	10	validation	validation	ADJ
ajst-24985	100	11	(	(	PUNCT
ajst-24985	100	12	typically	typically	ADV
ajst-24985	100	13	k=10	k=10	PROPN
ajst-24985	100	14	)	)	PUNCT
ajst-24985	100	15	to	to	PART
ajst-24985	100	16	assess	assess	VERB
ajst-24985	100	17	the	the	DET
ajst-24985	100	18	stability	stability	NOUN
ajst-24985	100	19	and	and	CCONJ
ajst-24985	100	20	generalisation	generalisation	NOUN
ajst-24985	100	21	ability	ability	NOUN
ajst-24985	100	22	of	of	ADP
ajst-24985	100	23	the	the	DET
ajst-24985	100	24	model	model	NOUN
ajst-24985	100	25	.	.	PUNCT
ajst-24985	101	1	accuracy	accuracy	NOUN
ajst-24985	101	2	12	12	NUM
ajst-24985	101	3	recall	recall	NOUN
ajst-24985	101	4	rate	rate	NOUN
ajst-24985	101	5	13	13	NUM
ajst-24985	101	6	1	1	NUM
ajst-24985	101	7	2	2	NUM
ajst-24985	101	8	⋅	⋅	NOUN
ajst-24985	101	9	accuracy	accuracy	NOUN
ajst-24985	101	10	⋅	⋅	PROPN
ajst-24985	101	11	recall	recall	NOUN
ajst-24985	101	12	rate	rate	NOUN
ajst-24985	101	13	accuracy	accuracy	NOUN
ajst-24985	101	14	recall	recall	NOUN
ajst-24985	101	15	rate	rate	NOUN
ajst-24985	101	16	14	14	NUM
ajst-24985	101	17	table	table	NOUN
ajst-24985	101	18	1	1	NUM
ajst-24985	101	19	.	.	PUNCT
ajst-24985	101	20	shows	show	VERB
ajst-24985	101	21	the	the	DET
ajst-24985	101	22	performance	performance	NOUN
ajst-24985	101	23	metrics	metric	NOUN
ajst-24985	101	24	of	of	ADP
ajst-24985	101	25	different	different	ADJ
ajst-24985	101	26	algorithms	algorithm	NOUN
ajst-24985	101	27	on	on	ADP
ajst-24985	101	28	the	the	DET
ajst-24985	101	29	validation	validation	NOUN
ajst-24985	101	30	set	set	NOUN
ajst-24985	101	31	.	.	PUNCT
ajst-24985	102	1	algorithms	algorithms	PROPN
ajst-24985	102	2	accuracy	accuracy	NOUN
ajst-24985	102	3	recall	recall	NOUN
ajst-24985	102	4	rate	rate	NOUN
ajst-24985	102	5	f1	f1	PROPN
ajst-24985	102	6	value	value	NOUN
ajst-24985	102	7	support	support	NOUN
ajst-24985	102	8	vector	vector	NOUN
ajst-24985	102	9	machines	machine	NOUN
ajst-24985	102	10	0.92	0.92	NUM
ajst-24985	102	11	0.91	0.91	NUM
ajst-24985	102	12	0.915	0.915	NUM
ajst-24985	102	13	artificial	artificial	ADJ
ajst-24985	102	14	neural	neural	ADJ
ajst-24985	102	15	networks	network	NOUN
ajst-24985	102	16	0.94	0.94	NUM
ajst-24985	102	17	0.93	0.93	NUM
ajst-24985	102	18	0.935	0.935	NUM
ajst-24985	102	19	random	random	ADJ
ajst-24985	102	20	forest	forest	NOUN
ajst-24985	102	21	0.93	0.93	NUM
ajst-24985	102	22	0.92	0.92	NUM
ajst-24985	102	23	0.925	0.925	NUM
ajst-24985	102	24	convolutional	convolutional	ADJ
ajst-24985	102	25	neural	neural	ADJ
ajst-24985	102	26	networks	network	NOUN
ajst-24985	102	27	0.95	0.95	NUM
ajst-24985	102	28	0.94	0.94	NUM
ajst-24985	102	29	0.945	0.945	NUM
ajst-24985	102	30	from	from	ADP
ajst-24985	102	31	the	the	DET
ajst-24985	102	32	results	result	NOUN
ajst-24985	102	33	,	,	PUNCT
ajst-24985	102	34	it	it	PRON
ajst-24985	102	35	can	can	AUX
ajst-24985	102	36	be	be	AUX
ajst-24985	102	37	seen	see	VERB
ajst-24985	102	38	that	that	SCONJ
ajst-24985	102	39	convolutional	convolutional	ADJ
ajst-24985	102	40	neural	neural	ADJ
ajst-24985	102	41	network	network	NOUN
ajst-24985	102	42	(	(	PUNCT
ajst-24985	102	43	cnn	cnn	PROPN
ajst-24985	102	44	)	)	PUNCT
ajst-24985	102	45	performs	perform	VERB
ajst-24985	102	46	the	the	DET
ajst-24985	102	47	best	good	ADJ
ajst-24985	102	48	in	in	ADP
ajst-24985	102	49	all	all	DET
ajst-24985	102	50	the	the	DET
ajst-24985	102	51	metrics	metric	NOUN
ajst-24985	102	52	,	,	PUNCT
ajst-24985	102	53	followed	follow	VERB
ajst-24985	102	54	by	by	ADP
ajst-24985	102	55	artificial	artificial	ADJ
ajst-24985	102	56	neural	neural	ADJ
ajst-24985	102	57	network	network	NOUN
ajst-24985	102	58	(	(	PUNCT
ajst-24985	102	59	ann	ann	PROPN
ajst-24985	102	60	)	)	PUNCT
ajst-24985	102	61	,	,	PUNCT
ajst-24985	102	62	then	then	ADV
ajst-24985	102	63	random	random	ADJ
ajst-24985	102	64	202	202	NUM
ajst-24985	102	65	forest	forest	NOUN
ajst-24985	102	66	(	(	PUNCT
ajst-24985	102	67	rf	rf	NOUN
ajst-24985	102	68	)	)	PUNCT
ajst-24985	102	69	,	,	PUNCT
ajst-24985	102	70	and	and	CCONJ
ajst-24985	102	71	lastly	lastly	ADV
ajst-24985	102	72	support	support	VERB
ajst-24985	102	73	vector	vector	NOUN
ajst-24985	102	74	machine	machine	NOUN
ajst-24985	102	75	(	(	PUNCT
ajst-24985	102	76	svm	svm	PROPN
ajst-24985	102	77	)	)	PUNCT
ajst-24985	102	78	.	.	PUNCT
ajst-24985	103	1	these	these	DET
ajst-24985	103	2	results	result	NOUN
ajst-24985	103	3	indicate	indicate	VERB
ajst-24985	103	4	that	that	SCONJ
ajst-24985	103	5	deep	deep	ADJ
ajst-24985	103	6	learning	learning	NOUN
ajst-24985	103	7	models	model	NOUN
ajst-24985	103	8	(	(	PUNCT
ajst-24985	103	9	e.g.	e.g.	ADV
ajst-24985	103	10	,	,	PUNCT
ajst-24985	103	11	cnn	cnn	PROPN
ajst-24985	103	12	and	and	CCONJ
ajst-24985	103	13	ann	ann	PROPN
ajst-24985	103	14	)	)	PUNCT
ajst-24985	103	15	have	have	VERB
ajst-24985	103	16	higher	high	ADJ
ajst-24985	103	17	accuracy	accuracy	NOUN
ajst-24985	103	18	and	and	CCONJ
ajst-24985	103	19	generalisation	generalisation	NOUN
ajst-24985	103	20	capabilities	capability	NOUN
ajst-24985	103	21	when	when	SCONJ
ajst-24985	103	22	dealing	deal	VERB
ajst-24985	103	23	with	with	ADP
ajst-24985	103	24	complex	complex	ADJ
ajst-24985	103	25	signal	signal	ADJ
ajst-24985	103	26	data	datum	NOUN
ajst-24985	103	27	.	.	PUNCT
ajst-24985	104	1	5	5	X
ajst-24985	104	2	.	.	X
ajst-24985	104	3	discussion	discussion	NOUN
ajst-24985	104	4	the	the	DET
ajst-24985	104	5	experimental	experimental	ADJ
ajst-24985	104	6	results	result	NOUN
ajst-24985	104	7	show	show	VERB
ajst-24985	104	8	that	that	SCONJ
ajst-24985	104	9	machine	machine	NOUN
ajst-24985	104	10	learning	learn	VERB
ajst-24985	104	11	based	base	VERB
ajst-24985	104	12	approaches	approach	NOUN
ajst-24985	104	13	have	have	VERB
ajst-24985	104	14	significant	significant	ADJ
ajst-24985	104	15	advantages	advantage	NOUN
ajst-24985	104	16	in	in	ADP
ajst-24985	104	17	signal	signal	ADJ
ajst-24985	104	18	recognition	recognition	NOUN
ajst-24985	104	19	and	and	CCONJ
ajst-24985	104	20	prediction	prediction	NOUN
ajst-24985	104	21	.	.	PUNCT
ajst-24985	105	1	specifically	specifically	ADV
ajst-24985	105	2	,	,	PUNCT
ajst-24985	105	3	convolutional	convolutional	ADJ
ajst-24985	105	4	neural	neural	ADJ
ajst-24985	105	5	networks	network	NOUN
ajst-24985	105	6	exhibit	exhibit	VERB
ajst-24985	105	7	the	the	DET
ajst-24985	105	8	highest	high	ADJ
ajst-24985	105	9	accuracy	accuracy	NOUN
ajst-24985	105	10	and	and	CCONJ
ajst-24985	105	11	f1	f1	NOUN
ajst-24985	105	12	values	value	NOUN
ajst-24985	105	13	due	due	ADP
ajst-24985	105	14	to	to	ADP
ajst-24985	105	15	their	their	PRON
ajst-24985	105	16	superior	superior	ADJ
ajst-24985	105	17	ability	ability	NOUN
ajst-24985	105	18	in	in	ADP
ajst-24985	105	19	feature	feature	NOUN
ajst-24985	105	20	extraction	extraction	NOUN
ajst-24985	105	21	and	and	CCONJ
ajst-24985	105	22	pattern	pattern	NOUN
ajst-24985	105	23	recognition	recognition	NOUN
ajst-24985	105	24	.	.	PUNCT
ajst-24985	106	1	however	however	ADV
ajst-24985	106	2	,	,	PUNCT
ajst-24985	106	3	the	the	DET
ajst-24985	106	4	long	long	ADJ
ajst-24985	106	5	training	training	NOUN
ajst-24985	106	6	time	time	NOUN
ajst-24985	106	7	and	and	CCONJ
ajst-24985	106	8	high	high	ADJ
ajst-24985	106	9	hardware	hardware	NOUN
ajst-24985	106	10	requirements	requirement	NOUN
ajst-24985	106	11	of	of	ADP
ajst-24985	106	12	deep	deep	ADJ
ajst-24985	106	13	learning	learning	NOUN
ajst-24985	106	14	models	model	NOUN
ajst-24985	106	15	may	may	AUX
ajst-24985	106	16	be	be	AUX
ajst-24985	106	17	a	a	DET
ajst-24985	106	18	limitation	limitation	NOUN
ajst-24985	106	19	in	in	ADP
ajst-24985	106	20	their	their	PRON
ajst-24985	106	21	application	application	NOUN
ajst-24985	106	22	.	.	PUNCT
ajst-24985	107	1	in	in	ADP
ajst-24985	107	2	contrast	contrast	NOUN
ajst-24985	107	3	,	,	PUNCT
ajst-24985	107	4	support	support	VERB
ajst-24985	107	5	vector	vector	NOUN
ajst-24985	107	6	machines	machine	NOUN
ajst-24985	107	7	and	and	CCONJ
ajst-24985	107	8	random	random	ADJ
ajst-24985	107	9	forests	forest	NOUN
ajst-24985	107	10	,	,	PUNCT
ajst-24985	107	11	while	while	SCONJ
ajst-24985	107	12	performing	perform	VERB
ajst-24985	107	13	slightly	slightly	ADV
ajst-24985	107	14	less	less	ADV
ajst-24985	107	15	well	well	ADV
ajst-24985	107	16	,	,	PUNCT
ajst-24985	107	17	have	have	VERB
ajst-24985	107	18	faster	fast	ADJ
ajst-24985	107	19	training	training	NOUN
ajst-24985	107	20	speeds	speed	NOUN
ajst-24985	107	21	and	and	CCONJ
ajst-24985	107	22	lower	low	ADJ
ajst-24985	107	23	computational	computational	ADJ
ajst-24985	107	24	resource	resource	NOUN
ajst-24985	107	25	requirements	requirement	NOUN
ajst-24985	107	26	,	,	PUNCT
ajst-24985	107	27	making	make	VERB
ajst-24985	107	28	them	they	PRON
ajst-24985	107	29	suitable	suitable	ADJ
ajst-24985	107	30	for	for	ADP
ajst-24985	107	31	resource	resource	NOUN
ajst-24985	107	32	-	-	PUNCT
ajst-24985	107	33	limited	limit	VERB
ajst-24985	107	34	scenarios	scenario	NOUN
ajst-24985	107	35	.	.	PUNCT
ajst-24985	108	1	the	the	DET
ajst-24985	108	2	strength	strength	NOUN
ajst-24985	108	3	of	of	ADP
ajst-24985	108	4	the	the	DET
ajst-24985	108	5	models	model	NOUN
ajst-24985	108	6	lies	lie	VERB
ajst-24985	108	7	in	in	ADP
ajst-24985	108	8	their	their	PRON
ajst-24985	108	9	high	high	ADJ
ajst-24985	108	10	prediction	prediction	NOUN
ajst-24985	108	11	accuracy	accuracy	NOUN
ajst-24985	108	12	and	and	CCONJ
ajst-24985	108	13	good	good	ADJ
ajst-24985	108	14	generalisation	generalisation	NOUN
ajst-24985	108	15	ability	ability	NOUN
ajst-24985	108	16	,	,	PUNCT
ajst-24985	108	17	especially	especially	ADV
ajst-24985	108	18	when	when	SCONJ
ajst-24985	108	19	dealing	deal	VERB
ajst-24985	108	20	with	with	ADP
ajst-24985	108	21	data	datum	NOUN
ajst-24985	108	22	with	with	ADP
ajst-24985	108	23	high	high	ADJ
ajst-24985	108	24	-	-	PUNCT
ajst-24985	108	25	dimensional	dimensional	ADJ
ajst-24985	108	26	features	feature	NOUN
ajst-24985	108	27	and	and	CCONJ
ajst-24985	108	28	complex	complex	ADJ
ajst-24985	108	29	patterns	pattern	NOUN
ajst-24985	108	30	.	.	PUNCT
ajst-24985	109	1	deep	deep	ADJ
ajst-24985	109	2	learning	learning	NOUN
ajst-24985	109	3	models	model	NOUN
ajst-24985	109	4	(	(	PUNCT
ajst-24985	109	5	e.g.	e.g.	ADV
ajst-24985	109	6	,	,	PUNCT
ajst-24985	109	7	cnn	cnn	PROPN
ajst-24985	109	8	and	and	CCONJ
ajst-24985	109	9	ann	ann	PROPN
ajst-24985	109	10	)	)	PUNCT
ajst-24985	109	11	are	be	AUX
ajst-24985	109	12	able	able	ADJ
ajst-24985	109	13	to	to	PART
ajst-24985	109	14	automatically	automatically	ADV
ajst-24985	109	15	extract	extract	VERB
ajst-24985	109	16	high	high	ADJ
ajst-24985	109	17	-	-	PUNCT
ajst-24985	109	18	level	level	NOUN
ajst-24985	109	19	features	feature	NOUN
ajst-24985	109	20	,	,	PUNCT
ajst-24985	109	21	reducing	reduce	VERB
ajst-24985	109	22	the	the	DET
ajst-24985	109	23	reliance	reliance	NOUN
ajst-24985	109	24	on	on	ADP
ajst-24985	109	25	manual	manual	ADJ
ajst-24985	109	26	feature	feature	NOUN
ajst-24985	109	27	engineering	engineering	NOUN
ajst-24985	109	28	.	.	PUNCT
ajst-24985	110	1	however	however	ADV
ajst-24985	110	2	,	,	PUNCT
ajst-24985	110	3	these	these	DET
ajst-24985	110	4	models	model	NOUN
ajst-24985	110	5	also	also	ADV
ajst-24985	110	6	have	have	VERB
ajst-24985	110	7	certain	certain	ADJ
ajst-24985	110	8	limitations	limitation	NOUN
ajst-24985	110	9	,	,	PUNCT
ajst-24985	110	10	such	such	ADJ
ajst-24985	110	11	as	as	ADP
ajst-24985	110	12	the	the	DET
ajst-24985	110	13	need	need	NOUN
ajst-24985	110	14	for	for	ADP
ajst-24985	110	15	a	a	DET
ajst-24985	110	16	large	large	ADJ
ajst-24985	110	17	amount	amount	NOUN
ajst-24985	110	18	of	of	ADP
ajst-24985	110	19	labelled	label	VERB
ajst-24985	110	20	data	datum	NOUN
ajst-24985	110	21	for	for	ADP
ajst-24985	110	22	training	training	NOUN
ajst-24985	110	23	and	and	CCONJ
ajst-24985	110	24	the	the	DET
ajst-24985	110	25	high	high	ADJ
ajst-24985	110	26	consumption	consumption	NOUN
ajst-24985	110	27	of	of	ADP
ajst-24985	110	28	computational	computational	ADJ
ajst-24985	110	29	resources	resource	NOUN
ajst-24985	110	30	during	during	ADP
ajst-24985	110	31	the	the	DET
ajst-24985	110	32	training	training	NOUN
ajst-24985	110	33	process	process	NOUN
ajst-24985	110	34	.	.	PUNCT
ajst-24985	111	1	the	the	DET
ajst-24985	111	2	limitations	limitation	NOUN
ajst-24985	111	3	of	of	ADP
ajst-24985	111	4	this	this	DET
ajst-24985	111	5	experiment	experiment	NOUN
ajst-24985	111	6	are	be	AUX
ajst-24985	111	7	mainly	mainly	ADV
ajst-24985	111	8	in	in	ADP
ajst-24985	111	9	the	the	DET
ajst-24985	111	10	diversity	diversity	NOUN
ajst-24985	111	11	and	and	CCONJ
ajst-24985	111	12	size	size	NOUN
ajst-24985	111	13	of	of	ADP
ajst-24985	111	14	the	the	DET
ajst-24985	111	15	dataset	dataset	NOUN
ajst-24985	111	16	.	.	PUNCT
ajst-24985	112	1	although	although	SCONJ
ajst-24985	112	2	we	we	PRON
ajst-24985	112	3	used	use	VERB
ajst-24985	112	4	a	a	DET
ajst-24985	112	5	publicly	publicly	ADV
ajst-24985	112	6	available	available	ADJ
ajst-24985	112	7	dataset	dataset	NOUN
ajst-24985	112	8	,	,	PUNCT
ajst-24985	112	9	the	the	DET
ajst-24985	112	10	scenarios	scenario	NOUN
ajst-24985	112	11	and	and	CCONJ
ajst-24985	112	12	conditions	condition	NOUN
ajst-24985	112	13	of	of	ADP
ajst-24985	112	14	this	this	DET
ajst-24985	112	15	dataset	dataset	NOUN
ajst-24985	112	16	may	may	AUX
ajst-24985	112	17	be	be	AUX
ajst-24985	112	18	limited	limit	VERB
ajst-24985	112	19	and	and	CCONJ
ajst-24985	112	20	can	can	AUX
ajst-24985	112	21	not	not	PART
ajst-24985	112	22	fully	fully	ADV
ajst-24985	112	23	represent	represent	VERB
ajst-24985	112	24	all	all	DET
ajst-24985	112	25	signal	signal	ADJ
ajst-24985	112	26	types	type	NOUN
ajst-24985	112	27	in	in	ADP
ajst-24985	112	28	practical	practical	ADJ
ajst-24985	112	29	applications	application	NOUN
ajst-24985	112	30	.	.	PUNCT
ajst-24985	113	1	in	in	ADP
ajst-24985	113	2	addition	addition	NOUN
ajst-24985	113	3	,	,	PUNCT
ajst-24985	113	4	the	the	DET
ajst-24985	113	5	experiments	experiment	NOUN
ajst-24985	113	6	were	be	AUX
ajst-24985	113	7	conducted	conduct	VERB
ajst-24985	113	8	only	only	ADV
ajst-24985	113	9	in	in	ADP
ajst-24985	113	10	a	a	DET
ajst-24985	113	11	single	single	ADJ
ajst-24985	113	12	hardware	hardware	NOUN
ajst-24985	113	13	environment	environment	NOUN
ajst-24985	113	14	,	,	PUNCT
ajst-24985	113	15	and	and	CCONJ
ajst-24985	113	16	the	the	DET
ajst-24985	113	17	effects	effect	NOUN
ajst-24985	113	18	of	of	ADP
ajst-24985	113	19	different	different	ADJ
ajst-24985	113	20	hardware	hardware	NOUN
ajst-24985	113	21	configurations	configuration	NOUN
ajst-24985	113	22	on	on	ADP
ajst-24985	113	23	model	model	NOUN
ajst-24985	113	24	performance	performance	NOUN
ajst-24985	113	25	were	be	AUX
ajst-24985	113	26	not	not	PART
ajst-24985	113	27	considered	consider	VERB
ajst-24985	113	28	.	.	PUNCT
ajst-24985	114	1	overall	overall	ADV
ajst-24985	114	2	,	,	PUNCT
ajst-24985	114	3	this	this	DET
ajst-24985	114	4	study	study	NOUN
ajst-24985	114	5	validates	validate	VERB
ajst-24985	114	6	the	the	DET
ajst-24985	114	7	effectiveness	effectiveness	NOUN
ajst-24985	114	8	of	of	ADP
ajst-24985	114	9	machine	machine	NOUN
ajst-24985	114	10	learning	learning	NOUN
ajst-24985	114	11	-	-	PUNCT
ajst-24985	114	12	based	base	VERB
ajst-24985	114	13	approaches	approach	NOUN
ajst-24985	114	14	in	in	ADP
ajst-24985	114	15	signal	signal	ADJ
ajst-24985	114	16	recognition	recognition	NOUN
ajst-24985	114	17	and	and	CCONJ
ajst-24985	114	18	prediction	prediction	NOUN
ajst-24985	114	19	,	,	PUNCT
ajst-24985	114	20	and	and	CCONJ
ajst-24985	114	21	provides	provide	VERB
ajst-24985	114	22	a	a	DET
ajst-24985	114	23	valuable	valuable	ADJ
ajst-24985	114	24	reference	reference	NOUN
ajst-24985	114	25	for	for	ADP
ajst-24985	114	26	further	further	ADJ
ajst-24985	114	27	research	research	NOUN
ajst-24985	114	28	.	.	PUNCT
ajst-24985	115	1	future	future	ADJ
ajst-24985	115	2	work	work	NOUN
ajst-24985	115	3	could	could	AUX
ajst-24985	115	4	consider	consider	VERB
ajst-24985	115	5	expanding	expand	VERB
ajst-24985	115	6	the	the	DET
ajst-24985	115	7	diversity	diversity	NOUN
ajst-24985	115	8	of	of	ADP
ajst-24985	115	9	the	the	DET
ajst-24985	115	10	dataset	dataset	NOUN
ajst-24985	115	11	,	,	PUNCT
ajst-24985	115	12	optimising	optimise	VERB
ajst-24985	115	13	the	the	DET
ajst-24985	115	14	computational	computational	ADJ
ajst-24985	115	15	efficiency	efficiency	NOUN
ajst-24985	115	16	of	of	ADP
ajst-24985	115	17	the	the	DET
ajst-24985	115	18	model	model	NOUN
ajst-24985	115	19	,	,	PUNCT
ajst-24985	115	20	and	and	CCONJ
ajst-24985	115	21	exploring	explore	VERB
ajst-24985	115	22	more	more	ADV
ajst-24985	115	23	advanced	advanced	ADJ
ajst-24985	115	24	machine	machine	NOUN
ajst-24985	115	25	learning	learning	NOUN
ajst-24985	115	26	and	and	CCONJ
ajst-24985	115	27	deep	deep	ADJ
ajst-24985	115	28	learning	learning	NOUN
ajst-24985	115	29	methods	method	NOUN
ajst-24985	115	30	.	.	PUNCT
ajst-24985	116	1	6	6	X
ajst-24985	116	2	.	.	X
ajst-24985	116	3	conclusion	conclusion	NOUN
ajst-24985	116	4	the	the	DET
ajst-24985	116	5	aim	aim	NOUN
ajst-24985	116	6	of	of	ADP
ajst-24985	116	7	this	this	DET
ajst-24985	116	8	study	study	NOUN
ajst-24985	116	9	is	be	AUX
ajst-24985	116	10	to	to	PART
ajst-24985	116	11	explore	explore	VERB
ajst-24985	116	12	the	the	DET
ajst-24985	116	13	application	application	NOUN
ajst-24985	116	14	of	of	ADP
ajst-24985	116	15	machine	machine	NOUN
ajst-24985	116	16	learning	learning	NOUN
ajst-24985	116	17	based	base	VERB
ajst-24985	116	18	methods	method	NOUN
ajst-24985	116	19	in	in	ADP
ajst-24985	116	20	signal	signal	ADJ
ajst-24985	116	21	recognition	recognition	NOUN
ajst-24985	116	22	and	and	CCONJ
ajst-24985	116	23	prediction	prediction	NOUN
ajst-24985	116	24	.	.	PUNCT
ajst-24985	117	1	through	through	ADP
ajst-24985	117	2	detailed	detailed	ADJ
ajst-24985	117	3	experiments	experiment	NOUN
ajst-24985	117	4	,	,	PUNCT
ajst-24985	117	5	we	we	PRON
ajst-24985	117	6	verified	verify	VERB
ajst-24985	117	7	the	the	DET
ajst-24985	117	8	effectiveness	effectiveness	NOUN
ajst-24985	117	9	of	of	ADP
ajst-24985	117	10	several	several	ADJ
ajst-24985	117	11	mainstream	mainstream	NOUN
ajst-24985	117	12	machine	machine	NOUN
ajst-24985	117	13	learning	learn	VERB
ajst-24985	117	14	algorithms	algorithm	NOUN
ajst-24985	117	15	,	,	PUNCT
ajst-24985	117	16	including	include	VERB
ajst-24985	117	17	support	support	NOUN
ajst-24985	117	18	vector	vector	NOUN
ajst-24985	117	19	machines	machine	NOUN
ajst-24985	117	20	(	(	PUNCT
ajst-24985	117	21	svms	svms	NOUN
ajst-24985	117	22	)	)	PUNCT
ajst-24985	117	23	,	,	PUNCT
ajst-24985	117	24	artificial	artificial	ADJ
ajst-24985	117	25	neural	neural	ADJ
ajst-24985	117	26	networks	network	NOUN
ajst-24985	117	27	(	(	PUNCT
ajst-24985	117	28	anns	anns	PROPN
ajst-24985	117	29	)	)	PUNCT
ajst-24985	117	30	,	,	PUNCT
ajst-24985	117	31	random	random	ADJ
ajst-24985	117	32	forests	forest	NOUN
ajst-24985	117	33	(	(	PUNCT
ajst-24985	117	34	rfs	rfs	PROPN
ajst-24985	117	35	)	)	PUNCT
ajst-24985	117	36	,	,	PUNCT
ajst-24985	117	37	and	and	CCONJ
ajst-24985	117	38	convolutional	convolutional	ADJ
ajst-24985	117	39	neural	neural	ADJ
ajst-24985	117	40	networks	network	NOUN
ajst-24985	117	41	(	(	PUNCT
ajst-24985	117	42	cnns	cnns	PROPN
ajst-24985	117	43	)	)	PUNCT
ajst-24985	117	44	,	,	PUNCT
ajst-24985	117	45	in	in	ADP
ajst-24985	117	46	processing	process	VERB
ajst-24985	117	47	complex	complex	ADJ
ajst-24985	117	48	signal	signal	NOUN
ajst-24985	117	49	data	datum	NOUN
ajst-24985	117	50	.	.	PUNCT
ajst-24985	118	1	the	the	DET
ajst-24985	118	2	experimental	experimental	ADJ
ajst-24985	118	3	results	result	NOUN
ajst-24985	118	4	show	show	VERB
ajst-24985	118	5	that	that	SCONJ
ajst-24985	118	6	these	these	DET
ajst-24985	118	7	methods	method	NOUN
ajst-24985	118	8	can	can	AUX
ajst-24985	118	9	effectively	effectively	ADV
ajst-24985	118	10	extract	extract	VERB
ajst-24985	118	11	signal	signal	NOUN
ajst-24985	118	12	features	feature	NOUN
ajst-24985	118	13	and	and	CCONJ
ajst-24985	118	14	achieve	achieve	VERB
ajst-24985	118	15	high	high	ADJ
ajst-24985	118	16	accuracy	accuracy	NOUN
ajst-24985	118	17	in	in	ADP
ajst-24985	118	18	recognition	recognition	NOUN
ajst-24985	118	19	and	and	CCONJ
ajst-24985	118	20	prediction	prediction	NOUN
ajst-24985	118	21	on	on	ADP
ajst-24985	118	22	a	a	DET
ajst-24985	118	23	wide	wide	ADJ
ajst-24985	118	24	range	range	NOUN
ajst-24985	118	25	of	of	ADP
ajst-24985	118	26	signal	signal	ADJ
ajst-24985	118	27	types	type	NOUN
ajst-24985	118	28	.	.	PUNCT
ajst-24985	119	1	the	the	DET
ajst-24985	119	2	experimental	experimental	ADJ
ajst-24985	119	3	results	result	NOUN
ajst-24985	119	4	show	show	VERB
ajst-24985	119	5	that	that	SCONJ
ajst-24985	119	6	convolutional	convolutional	ADJ
ajst-24985	119	7	neural	neural	ADJ
ajst-24985	119	8	networks	network	NOUN
ajst-24985	119	9	(	(	PUNCT
ajst-24985	119	10	cnns	cnns	PROPN
ajst-24985	119	11	)	)	PUNCT
ajst-24985	119	12	perform	perform	VERB
ajst-24985	119	13	best	well	ADV
ajst-24985	119	14	in	in	ADP
ajst-24985	119	15	signal	signal	ADJ
ajst-24985	119	16	recognition	recognition	NOUN
ajst-24985	119	17	and	and	CCONJ
ajst-24985	119	18	prediction	prediction	NOUN
ajst-24985	119	19	,	,	PUNCT
ajst-24985	119	20	followed	follow	VERB
ajst-24985	119	21	by	by	ADP
ajst-24985	119	22	artificial	artificial	ADJ
ajst-24985	119	23	neural	neural	ADJ
ajst-24985	119	24	networks	network	NOUN
ajst-24985	119	25	(	(	PUNCT
ajst-24985	119	26	anns	anns	PROPN
ajst-24985	119	27	)	)	PUNCT
ajst-24985	119	28	,	,	PUNCT
ajst-24985	119	29	then	then	ADV
ajst-24985	119	30	random	random	ADJ
ajst-24985	119	31	forests	forest	NOUN
ajst-24985	119	32	(	(	PUNCT
ajst-24985	119	33	rfs	rfs	PROPN
ajst-24985	119	34	)	)	PUNCT
ajst-24985	119	35	,	,	PUNCT
ajst-24985	119	36	and	and	CCONJ
ajst-24985	119	37	finally	finally	ADV
ajst-24985	119	38	support	support	VERB
ajst-24985	119	39	vector	vector	NOUN
ajst-24985	119	40	machines	machine	NOUN
ajst-24985	119	41	(	(	PUNCT
ajst-24985	119	42	svms	svms	NOUN
ajst-24985	119	43	)	)	PUNCT
ajst-24985	119	44	.	.	PUNCT
ajst-24985	120	1	this	this	PRON
ajst-24985	120	2	indicates	indicate	VERB
ajst-24985	120	3	that	that	SCONJ
ajst-24985	120	4	deep	deep	ADJ
ajst-24985	120	5	learning	learning	NOUN
ajst-24985	120	6	models	model	NOUN
ajst-24985	120	7	have	have	VERB
ajst-24985	120	8	significant	significant	ADJ
ajst-24985	120	9	advantages	advantage	NOUN
ajst-24985	120	10	in	in	ADP
ajst-24985	120	11	processing	processing	NOUN
ajst-24985	120	12	complex	complex	ADJ
ajst-24985	120	13	signal	signal	NOUN
ajst-24985	120	14	data	datum	NOUN
ajst-24985	120	15	.	.	PUNCT
ajst-24985	121	1	we	we	PRON
ajst-24985	121	2	demonstrate	demonstrate	VERB
ajst-24985	121	3	the	the	DET
ajst-24985	121	4	importance	importance	NOUN
ajst-24985	121	5	of	of	ADP
ajst-24985	121	6	time	time	NOUN
ajst-24985	121	7	domain	domain	NOUN
ajst-24985	121	8	features	feature	NOUN
ajst-24985	121	9	,	,	PUNCT
ajst-24985	121	10	frequency	frequency	NOUN
ajst-24985	121	11	domain	domain	NOUN
ajst-24985	121	12	features	feature	NOUN
ajst-24985	121	13	and	and	CCONJ
ajst-24985	121	14	time	time	NOUN
ajst-24985	121	15	-	-	PUNCT
ajst-24985	121	16	frequency	frequency	NOUN
ajst-24985	121	17	features	feature	NOUN
ajst-24985	121	18	in	in	ADP
ajst-24985	121	19	signal	signal	ADJ
ajst-24985	121	20	processing	processing	NOUN
ajst-24985	121	21	.	.	PUNCT
ajst-24985	122	1	by	by	ADP
ajst-24985	122	2	combining	combine	VERB
ajst-24985	122	3	these	these	DET
ajst-24985	122	4	features	feature	NOUN
ajst-24985	122	5	,	,	PUNCT
ajst-24985	122	6	the	the	DET
ajst-24985	122	7	model	model	NOUN
ajst-24985	122	8	is	be	AUX
ajst-24985	122	9	able	able	ADJ
ajst-24985	122	10	to	to	PART
ajst-24985	122	11	capture	capture	VERB
ajst-24985	122	12	the	the	DET
ajst-24985	122	13	intrinsic	intrinsic	ADJ
ajst-24985	122	14	patterns	pattern	NOUN
ajst-24985	122	15	and	and	CCONJ
ajst-24985	122	16	properties	property	NOUN
ajst-24985	122	17	of	of	ADP
ajst-24985	122	18	the	the	DET
ajst-24985	122	19	signal	signal	NOUN
ajst-24985	122	20	more	more	ADV
ajst-24985	122	21	accurately	accurately	ADV
ajst-24985	122	22	.	.	PUNCT
ajst-24985	123	1	through	through	ADP
ajst-24985	123	2	cross	cross	ADJ
ajst-24985	123	3	-	-	ADJ
ajst-24985	123	4	validation	validation	ADJ
ajst-24985	123	5	methods	method	NOUN
ajst-24985	123	6	,	,	PUNCT
ajst-24985	123	7	we	we	PRON
ajst-24985	123	8	verify	verify	VERB
ajst-24985	123	9	the	the	DET
ajst-24985	123	10	good	good	ADJ
ajst-24985	123	11	generalisation	generalisation	NOUN
ajst-24985	123	12	ability	ability	NOUN
ajst-24985	123	13	of	of	ADP
ajst-24985	123	14	the	the	DET
ajst-24985	123	15	trained	train	VERB
ajst-24985	123	16	models	model	NOUN
ajst-24985	123	17	on	on	ADP
ajst-24985	123	18	unseen	unseen	ADJ
ajst-24985	123	19	data	datum	NOUN
ajst-24985	123	20	,	,	PUNCT
ajst-24985	123	21	indicating	indicate	VERB
ajst-24985	123	22	that	that	SCONJ
ajst-24985	123	23	these	these	DET
ajst-24985	123	24	machine	machine	NOUN
ajst-24985	123	25	learning	learning	NOUN
ajst-24985	123	26	models	model	NOUN
ajst-24985	123	27	are	be	AUX
ajst-24985	123	28	robust	robust	ADJ
ajst-24985	123	29	and	and	CCONJ
ajst-24985	123	30	adaptable	adaptable	ADJ
ajst-24985	123	31	.	.	PUNCT
ajst-24985	124	1	data	datum	NOUN
ajst-24985	124	2	cleaning	cleaning	NOUN
ajst-24985	124	3	and	and	CCONJ
ajst-24985	124	4	normalisation	normalisation	NOUN
ajst-24985	124	5	steps	step	NOUN
ajst-24985	124	6	play	play	VERB
ajst-24985	124	7	a	a	DET
ajst-24985	124	8	key	key	ADJ
ajst-24985	124	9	role	role	NOUN
ajst-24985	124	10	in	in	ADP
ajst-24985	124	11	improving	improve	VERB
ajst-24985	124	12	model	model	NOUN
ajst-24985	124	13	performance	performance	NOUN
ajst-24985	124	14	.	.	PUNCT
ajst-24985	125	1	clean	clean	ADJ
ajst-24985	125	2	,	,	PUNCT
ajst-24985	125	3	normalised	normalise	VERB
ajst-24985	125	4	data	datum	NOUN
ajst-24985	125	5	can	can	AUX
ajst-24985	125	6	significantly	significantly	ADV
ajst-24985	125	7	improve	improve	VERB
ajst-24985	125	8	model	model	NOUN
ajst-24985	125	9	training	training	NOUN
ajst-24985	125	10	and	and	CCONJ
ajst-24985	125	11	prediction	prediction	NOUN
ajst-24985	125	12	accuracy	accuracy	NOUN
ajst-24985	125	13	.	.	PUNCT
ajst-24985	126	1	future	future	ADJ
ajst-24985	126	2	research	research	NOUN
ajst-24985	126	3	should	should	AUX
ajst-24985	126	4	consider	consider	VERB
ajst-24985	126	5	using	use	VERB
ajst-24985	126	6	larger	large	ADJ
ajst-24985	126	7	and	and	CCONJ
ajst-24985	126	8	diverse	diverse	ADJ
ajst-24985	126	9	datasets	dataset	NOUN
ajst-24985	126	10	to	to	PART
ajst-24985	126	11	validate	validate	VERB
ajst-24985	126	12	the	the	DET
ajst-24985	126	13	applicability	applicability	NOUN
ajst-24985	126	14	of	of	ADP
ajst-24985	126	15	the	the	DET
ajst-24985	126	16	model	model	NOUN
ajst-24985	126	17	in	in	ADP
ajst-24985	126	18	different	different	ADJ
ajst-24985	126	19	scenarios	scenario	NOUN
ajst-24985	126	20	and	and	CCONJ
ajst-24985	126	21	conditions	condition	NOUN
ajst-24985	126	22	.	.	PUNCT
ajst-24985	127	1	this	this	PRON
ajst-24985	127	2	will	will	AUX
ajst-24985	127	3	improve	improve	VERB
ajst-24985	127	4	the	the	DET
ajst-24985	127	5	reliability	reliability	NOUN
ajst-24985	127	6	and	and	CCONJ
ajst-24985	127	7	generalisability	generalisability	NOUN
ajst-24985	127	8	of	of	ADP
ajst-24985	127	9	the	the	DET
ajst-24985	127	10	models	model	NOUN
ajst-24985	127	11	in	in	ADP
ajst-24985	127	12	practical	practical	ADJ
ajst-24985	127	13	applications	application	NOUN
ajst-24985	127	14	.	.	PUNCT
ajst-24985	128	1	although	although	SCONJ
ajst-24985	128	2	deep	deep	ADJ
ajst-24985	128	3	learning	learning	NOUN
ajst-24985	128	4	models	model	NOUN
ajst-24985	128	5	excel	excel	VERB
ajst-24985	128	6	in	in	ADP
ajst-24985	128	7	performance	performance	NOUN
ajst-24985	128	8	,	,	PUNCT
ajst-24985	128	9	they	they	PRON
ajst-24985	128	10	are	be	AUX
ajst-24985	128	11	computationally	computationally	ADV
ajst-24985	128	12	expensive	expensive	ADJ
ajst-24985	128	13	.	.	PUNCT
ajst-24985	129	1	future	future	ADJ
ajst-24985	129	2	work	work	NOUN
ajst-24985	129	3	should	should	AUX
ajst-24985	129	4	be	be	AUX
ajst-24985	129	5	devoted	devote	VERB
ajst-24985	129	6	to	to	ADP
ajst-24985	129	7	optimising	optimise	VERB
ajst-24985	129	8	algorithms	algorithm	NOUN
ajst-24985	129	9	and	and	CCONJ
ajst-24985	129	10	model	model	NOUN
ajst-24985	129	11	structures	structure	NOUN
ajst-24985	129	12	to	to	PART
ajst-24985	129	13	reduce	reduce	VERB
ajst-24985	129	14	the	the	DET
ajst-24985	129	15	consumption	consumption	NOUN
ajst-24985	129	16	of	of	ADP
ajst-24985	129	17	computational	computational	ADJ
ajst-24985	129	18	resources	resource	NOUN
ajst-24985	129	19	and	and	CCONJ
ajst-24985	129	20	improve	improve	VERB
ajst-24985	129	21	the	the	DET
ajst-24985	129	22	efficiency	efficiency	NOUN
ajst-24985	129	23	of	of	ADP
ajst-24985	129	24	training	training	NOUN
ajst-24985	129	25	and	and	CCONJ
ajst-24985	129	26	prediction	prediction	NOUN
ajst-24985	129	27	.	.	PUNCT
ajst-24985	130	1	in	in	ADP
ajst-24985	130	2	addition	addition	NOUN
ajst-24985	130	3	to	to	ADP
ajst-24985	130	4	traditional	traditional	ADJ
ajst-24985	130	5	machine	machine	NOUN
ajst-24985	130	6	learning	learning	NOUN
ajst-24985	130	7	and	and	CCONJ
ajst-24985	130	8	existing	exist	VERB
ajst-24985	130	9	deep	deep	ADJ
ajst-24985	130	10	learning	learning	NOUN
ajst-24985	130	11	models	model	NOUN
ajst-24985	130	12	,	,	PUNCT
ajst-24985	130	13	more	more	ADV
ajst-24985	130	14	emerging	emerge	VERB
ajst-24985	130	15	machine	machine	NOUN
ajst-24985	130	16	learning	learning	NOUN
ajst-24985	130	17	methods	method	NOUN
ajst-24985	130	18	,	,	PUNCT
ajst-24985	130	19	such	such	ADJ
ajst-24985	130	20	as	as	ADP
ajst-24985	130	21	reinforcement	reinforcement	NOUN
ajst-24985	130	22	learning	learning	NOUN
ajst-24985	130	23	and	and	CCONJ
ajst-24985	130	24	generative	generative	ADJ
ajst-24985	130	25	adversarial	adversarial	ADJ
ajst-24985	130	26	networks	network	NOUN
ajst-24985	130	27	(	(	PUNCT
ajst-24985	130	28	gan	gan	PROPN
ajst-24985	130	29	)	)	PUNCT
ajst-24985	130	30	,	,	PUNCT
ajst-24985	130	31	should	should	AUX
ajst-24985	130	32	be	be	AUX
ajst-24985	130	33	explored	explore	VERB
ajst-24985	130	34	to	to	PART
ajst-24985	130	35	further	far	ADV
ajst-24985	130	36	improve	improve	VERB
ajst-24985	130	37	the	the	DET
ajst-24985	130	38	performance	performance	NOUN
ajst-24985	130	39	of	of	ADP
ajst-24985	130	40	signal	signal	ADJ
ajst-24985	130	41	recognition	recognition	NOUN
ajst-24985	130	42	and	and	CCONJ
ajst-24985	130	43	prediction	prediction	NOUN
ajst-24985	130	44	.	.	PUNCT
ajst-24985	131	1	the	the	DET
ajst-24985	131	2	research	research	NOUN
ajst-24985	131	3	can	can	AUX
ajst-24985	131	4	be	be	AUX
ajst-24985	131	5	further	far	ADV
ajst-24985	131	6	extended	extend	VERB
ajst-24985	131	7	to	to	ADP
ajst-24985	131	8	more	more	ADV
ajst-24985	131	9	practical	practical	ADJ
ajst-24985	131	10	application	application	NOUN
ajst-24985	131	11	scenarios	scenario	NOUN
ajst-24985	131	12	,	,	PUNCT
ajst-24985	131	13	such	such	ADJ
ajst-24985	131	14	as	as	ADP
ajst-24985	131	15	industrial	industrial	ADJ
ajst-24985	131	16	monitoring	monitoring	NOUN
ajst-24985	131	17	,	,	PUNCT
ajst-24985	131	18	medical	medical	ADJ
ajst-24985	131	19	signal	signal	NOUN
ajst-24985	131	20	processing	processing	NOUN
ajst-24985	131	21	,	,	PUNCT
ajst-24985	131	22	smart	smart	ADJ
ajst-24985	131	23	home	home	NOUN
ajst-24985	131	24	systems	system	NOUN
ajst-24985	131	25	,	,	PUNCT
ajst-24985	131	26	etc	etc	X
ajst-24985	131	27	.	.	X
ajst-24985	131	28	,	,	PUNCT
ajst-24985	131	29	in	in	ADP
ajst-24985	131	30	order	order	NOUN
ajst-24985	131	31	to	to	PART
ajst-24985	131	32	verify	verify	VERB
ajst-24985	131	33	the	the	DET
ajst-24985	131	34	applicability	applicability	NOUN
ajst-24985	131	35	and	and	CCONJ
ajst-24985	131	36	effectiveness	effectiveness	NOUN
ajst-24985	131	37	of	of	ADP
ajst-24985	131	38	the	the	DET
ajst-24985	131	39	models	model	NOUN
ajst-24985	131	40	in	in	ADP
ajst-24985	131	41	different	different	ADJ
ajst-24985	131	42	fields	field	NOUN
ajst-24985	131	43	.	.	PUNCT
ajst-24985	132	1	in	in	ADP
ajst-24985	132	2	the	the	DET
ajst-24985	132	3	future	future	NOUN
ajst-24985	132	4	,	,	PUNCT
ajst-24985	132	5	we	we	PRON
ajst-24985	132	6	can	can	AUX
ajst-24985	132	7	try	try	VERB
ajst-24985	132	8	to	to	PART
ajst-24985	132	9	fuse	fuse	VERB
ajst-24985	132	10	different	different	ADJ
ajst-24985	132	11	types	type	NOUN
ajst-24985	132	12	of	of	ADP
ajst-24985	132	13	signal	signal	ADJ
ajst-24985	132	14	data	datum	NOUN
ajst-24985	132	15	(	(	PUNCT
ajst-24985	132	16	e.g.	e.g.	ADV
ajst-24985	132	17	,	,	PUNCT
ajst-24985	132	18	audio	audio	ADJ
ajst-24985	132	19	signals	signal	NOUN
ajst-24985	132	20	,	,	PUNCT
ajst-24985	132	21	visual	visual	ADJ
ajst-24985	132	22	signals	signal	NOUN
ajst-24985	132	23	)	)	PUNCT
ajst-24985	132	24	to	to	PART
ajst-24985	132	25	construct	construct	VERB
ajst-24985	132	26	a	a	DET
ajst-24985	132	27	multimodal	multimodal	ADJ
ajst-24985	132	28	machine	machine	NOUN
ajst-24985	132	29	learning	learning	NOUN
ajst-24985	132	30	model	model	NOUN
ajst-24985	132	31	,	,	PUNCT
ajst-24985	132	32	so	so	SCONJ
ajst-24985	132	33	as	as	SCONJ
ajst-24985	132	34	to	to	PART
ajst-24985	132	35	improve	improve	VERB
ajst-24985	132	36	the	the	DET
ajst-24985	132	37	comprehensiveness	comprehensiveness	NOUN
ajst-24985	132	38	and	and	CCONJ
ajst-24985	132	39	accuracy	accuracy	NOUN
ajst-24985	132	40	of	of	ADP
ajst-24985	132	41	signal	signal	ADJ
ajst-24985	132	42	recognition	recognition	NOUN
ajst-24985	132	43	and	and	CCONJ
ajst-24985	132	44	prediction	prediction	NOUN
ajst-24985	132	45	.	.	PUNCT
ajst-24985	133	1	through	through	ADP
ajst-24985	133	2	this	this	DET
ajst-24985	133	3	study	study	NOUN
ajst-24985	133	4	,	,	PUNCT
ajst-24985	133	5	we	we	PRON
ajst-24985	133	6	have	have	AUX
ajst-24985	133	7	demonstrated	demonstrate	VERB
ajst-24985	133	8	the	the	DET
ajst-24985	133	9	great	great	ADJ
ajst-24985	133	10	potential	potential	NOUN
ajst-24985	133	11	of	of	ADP
ajst-24985	133	12	machine	machine	NOUN
ajst-24985	133	13	learning	learning	NOUN
ajst-24985	133	14	methods	method	NOUN
ajst-24985	133	15	in	in	ADP
ajst-24985	133	16	signal	signal	ADJ
ajst-24985	133	17	recognition	recognition	NOUN
ajst-24985	133	18	and	and	CCONJ
ajst-24985	133	19	prediction	prediction	NOUN
ajst-24985	133	20	,	,	PUNCT
ajst-24985	133	21	and	and	CCONJ
ajst-24985	133	22	provided	provide	VERB
ajst-24985	133	23	a	a	DET
ajst-24985	133	24	strong	strong	ADJ
ajst-24985	133	25	theoretical	theoretical	ADJ
ajst-24985	133	26	and	and	CCONJ
ajst-24985	133	27	experimental	experimental	ADJ
ajst-24985	133	28	basis	basis	NOUN
ajst-24985	133	29	for	for	ADP
ajst-24985	133	30	further	further	ADJ
ajst-24985	133	31	research	research	NOUN
ajst-24985	133	32	.	.	PUNCT
ajst-24985	134	1	future	future	ADJ
ajst-24985	134	2	work	work	NOUN
ajst-24985	134	3	will	will	AUX
ajst-24985	134	4	continue	continue	VERB
ajst-24985	134	5	to	to	PART
ajst-24985	134	6	explore	explore	VERB
ajst-24985	134	7	and	and	CCONJ
ajst-24985	134	8	optimise	optimise	VERB
ajst-24985	134	9	these	these	DET
ajst-24985	134	10	methods	method	NOUN
ajst-24985	134	11	in	in	ADP
ajst-24985	134	12	a	a	DET
ajst-24985	134	13	wider	wide	ADJ
ajst-24985	134	14	range	range	NOUN
ajst-24985	134	15	of	of	ADP
ajst-24985	134	16	application	application	NOUN
ajst-24985	134	17	areas	area	NOUN
ajst-24985	134	18	to	to	PART
ajst-24985	134	19	advance	advance	VERB
ajst-24985	134	20	the	the	DET
ajst-24985	134	21	development	development	NOUN
ajst-24985	134	22	and	and	CCONJ
ajst-24985	134	23	application	application	NOUN
ajst-24985	134	24	of	of	ADP
ajst-24985	134	25	signal	signal	NOUN
ajst-24985	134	26	processing	processing	NOUN
ajst-24985	134	27	techniques	technique	NOUN
ajst-24985	134	28	.	.	PUNCT
ajst-24985	135	1	references	reference	NOUN
ajst-24985	135	2	[	[	X
ajst-24985	135	3	1	1	NUM
ajst-24985	135	4	]	]	X
ajst-24985	135	5	box	box	NOUN
ajst-24985	135	6	,	,	PUNCT
ajst-24985	135	7	g.	g.	PROPN
ajst-24985	135	8	e.	e.	PROPN
ajst-24985	135	9	p.	p.	PROPN
ajst-24985	135	10	,	,	PUNCT
ajst-24985	135	11	&	&	CCONJ
ajst-24985	135	12	jenkins	jenkins	PROPN
ajst-24985	135	13	,	,	PUNCT
ajst-24985	135	14	g.	g.	PROPN
ajst-24985	135	15	m.	m.	PROPN
ajst-24985	135	16	(	(	PUNCT
ajst-24985	135	17	1970	1970	NUM
ajst-24985	135	18	)	)	PUNCT
ajst-24985	135	19	.	.	PUNCT
ajst-24985	136	1	time	time	PROPN
ajst-24985	136	2	series	series	PROPN
ajst-24985	136	3	analysis	analysis	NOUN
ajst-24985	136	4	:	:	PUNCT
ajst-24985	136	5	forecasting	forecasting	NOUN
ajst-24985	136	6	and	and	CCONJ
ajst-24985	136	7	control	control	NOUN
ajst-24985	136	8	.	.	PUNCT
ajst-24985	137	1	holden	holden	PROPN
ajst-24985	137	2	-	-	PUNCT
ajst-24985	137	3	day	day	NOUN
ajst-24985	137	4	.	.	PUNCT
ajst-24985	138	1	[	[	X
ajst-24985	138	2	2	2	NUM
ajst-24985	138	3	]	]	X
ajst-24985	138	4	cooley	cooley	PROPN
ajst-24985	138	5	,	,	PUNCT
ajst-24985	138	6	j.	j.	PROPN
ajst-24985	138	7	w.	w.	PROPN
ajst-24985	138	8	,	,	PUNCT
ajst-24985	138	9	&	&	CCONJ
ajst-24985	138	10	tukey	tukey	PROPN
ajst-24985	138	11	,	,	PUNCT
ajst-24985	138	12	j.	j.	PROPN
ajst-24985	138	13	w.	w.	PROPN
ajst-24985	138	14	(	(	PUNCT
ajst-24985	138	15	1965	1965	NUM
ajst-24985	138	16	)	)	PUNCT
ajst-24985	138	17	.	.	PUNCT
ajst-24985	139	1	an	an	DET
ajst-24985	139	2	algorithm	algorithm	NOUN
ajst-24985	139	3	for	for	ADP
ajst-24985	139	4	the	the	DET
ajst-24985	139	5	machine	machine	NOUN
ajst-24985	139	6	calculation	calculation	NOUN
ajst-24985	139	7	of	of	ADP
ajst-24985	139	8	complex	complex	ADJ
ajst-24985	139	9	fourier	fourier	NOUN
ajst-24985	139	10	series	series	NOUN
ajst-24985	139	11	.	.	PUNCT
ajst-24985	140	1	mathematics	mathematic	NOUN
ajst-24985	140	2	of	of	ADP
ajst-24985	140	3	computation	computation	NOUN
ajst-24985	140	4	,	,	PUNCT
ajst-24985	140	5	19(90	19(90	NUM
ajst-24985	140	6	)	)	PUNCT
ajst-24985	140	7	,	,	PUNCT
ajst-24985	140	8	297	297	NUM
ajst-24985	140	9	-	-	SYM
ajst-24985	140	10	301	301	NUM
ajst-24985	140	11	.	.	PUNCT
ajst-24985	141	1	[	[	X
ajst-24985	141	2	3	3	NUM
ajst-24985	141	3	]	]	PUNCT
ajst-24985	141	4	daubechies	daubechie	NOUN
ajst-24985	141	5	,	,	PUNCT
ajst-24985	141	6	i.	i.	PROPN
ajst-24985	141	7	(	(	PUNCT
ajst-24985	141	8	1992	1992	NUM
ajst-24985	141	9	)	)	PUNCT
ajst-24985	141	10	.	.	PUNCT
ajst-24985	142	1	ten	ten	NUM
ajst-24985	142	2	lectures	lecture	NOUN
ajst-24985	142	3	on	on	ADP
ajst-24985	142	4	wavelets	wavelet	NOUN
ajst-24985	142	5	.	.	PUNCT
ajst-24985	143	1	society	society	NOUN
ajst-24985	143	2	for	for	ADP
ajst-24985	143	3	industrial	industrial	ADJ
ajst-24985	143	4	and	and	CCONJ
ajst-24985	143	5	applied	applied	ADJ
ajst-24985	143	6	mathematics	mathematic	NOUN
ajst-24985	143	7	.	.	PUNCT
ajst-24985	144	1	[	[	X
ajst-24985	144	2	4	4	X
ajst-24985	144	3	]	]	X
ajst-24985	144	4	vapnik	vapnik	X
ajst-24985	144	5	,	,	PUNCT
ajst-24985	144	6	v.	v.	PROPN
ajst-24985	144	7	(	(	PUNCT
ajst-24985	144	8	1995	1995	NUM
ajst-24985	144	9	)	)	PUNCT
ajst-24985	144	10	.	.	PUNCT
ajst-24985	145	1	the	the	DET
ajst-24985	145	2	nature	nature	NOUN
ajst-24985	145	3	of	of	ADP
ajst-24985	145	4	statistical	statistical	ADJ
ajst-24985	145	5	learning	learning	NOUN
ajst-24985	145	6	theory	theory	NOUN
ajst-24985	145	7	.	.	PUNCT
ajst-24985	146	1	springer	springer	NOUN
ajst-24985	146	2	.	.	PUNCT
ajst-24985	147	1	[	[	X
ajst-24985	147	2	5	5	NUM
ajst-24985	147	3	]	]	PUNCT
ajst-24985	147	4	schölkopf	schölkopf	NOUN
ajst-24985	147	5	,	,	PUNCT
ajst-24985	147	6	b.	b.	PROPN
ajst-24985	147	7	,	,	PUNCT
ajst-24985	147	8	smola	smola	PROPN
ajst-24985	147	9	,	,	PUNCT
ajst-24985	147	10	a.	a.	PROPN
ajst-24985	147	11	,	,	PUNCT
ajst-24985	147	12	&	&	CCONJ
ajst-24985	147	13	müller	müller	PROPN
ajst-24985	147	14	,	,	PUNCT
ajst-24985	147	15	k.	k.	PROPN
ajst-24985	147	16	r.	r.	PROPN
ajst-24985	147	17	(	(	PUNCT
ajst-24985	147	18	1997	1997	NUM
ajst-24985	147	19	,	,	PUNCT
ajst-24985	147	20	october	october	PROPN
ajst-24985	147	21	)	)	PUNCT
ajst-24985	147	22	.	.	PUNCT
ajst-24985	148	1	kernel	kernel	PROPN
ajst-24985	148	2	principal	principal	PROPN
ajst-24985	148	3	component	component	NOUN
ajst-24985	148	4	analysis	analysis	NOUN
ajst-24985	148	5	.	.	PUNCT
ajst-24985	149	1	in	in	ADP
ajst-24985	149	2	international	international	ADJ
ajst-24985	149	3	conference	conference	NOUN
ajst-24985	149	4	on	on	ADP
ajst-24985	149	5	artificial	artificial	ADJ
ajst-24985	149	6	neural	neural	ADJ
ajst-24985	149	7	networks	network	NOUN
ajst-24985	149	8	(	(	PUNCT
ajst-24985	149	9	pp	pp	ADJ
ajst-24985	149	10	.	.	PUNCT
ajst-24985	150	1	583	583	NUM
ajst-24985	150	2	-	-	SYM
ajst-24985	150	3	588	588	NUM
ajst-24985	150	4	)	)	PUNCT
ajst-24985	150	5	.	.	PUNCT
ajst-24985	151	1	berlin	berlin	PROPN
ajst-24985	151	2	,	,	PUNCT
ajst-24985	151	3	heidelberg	heidelberg	PROPN
ajst-24985	151	4	:	:	PUNCT
ajst-24985	151	5	springer	springer	PROPN
ajst-24985	151	6	berlin	berlin	PROPN
ajst-24985	151	7	heidelberg	heidelberg	PROPN
ajst-24985	151	8	.	.	PUNCT
ajst-24985	152	1	[	[	X
ajst-24985	152	2	6	6	NUM
ajst-24985	152	3	]	]	X
ajst-24985	152	4	rumelhart	rumelhart	NOUN
ajst-24985	152	5	,	,	PUNCT
ajst-24985	152	6	d.	d.	PROPN
ajst-24985	152	7	e.	e.	PROPN
ajst-24985	152	8	,	,	PUNCT
ajst-24985	152	9	hinton	hinton	PROPN
ajst-24985	152	10	,	,	PUNCT
ajst-24985	152	11	g.	g.	PROPN
ajst-24985	152	12	e.	e.	PROPN
ajst-24985	152	13	,	,	PUNCT
ajst-24985	152	14	&	&	CCONJ
ajst-24985	152	15	williams	williams	PROPN
ajst-24985	152	16	,	,	PUNCT
ajst-24985	152	17	r.	r.	PROPN
ajst-24985	152	18	j.	j.	PROPN
ajst-24985	152	19	(	(	PUNCT
ajst-24985	152	20	1986	1986	NUM
ajst-24985	152	21	)	)	PUNCT
ajst-24985	152	22	.	.	PUNCT
ajst-24985	153	1	learning	learn	VERB
ajst-24985	153	2	representations	representation	NOUN
ajst-24985	153	3	by	by	ADP
ajst-24985	153	4	back	back	ADV
ajst-24985	153	5	-	-	PUNCT
ajst-24985	153	6	propagating	propagate	VERB
ajst-24985	153	7	errors	error	NOUN
ajst-24985	153	8	.	.	PUNCT
ajst-24985	154	1	nature	nature	NOUN
ajst-24985	154	2	,	,	PUNCT
ajst-24985	154	3	323	323	NUM
ajst-24985	154	4	(	(	PUNCT
ajst-24985	154	5	6088	6088	NUM
ajst-24985	154	6	)	)	PUNCT
ajst-24985	154	7	,	,	PUNCT
ajst-24985	154	8	533	533	NUM
ajst-24985	154	9	-	-	SYM
ajst-24985	154	10	536	536	NUM
ajst-24985	154	11	.	.	PUNCT
ajst-24985	155	1	[	[	X
ajst-24985	155	2	7	7	NUM
ajst-24985	155	3	]	]	PUNCT
ajst-24985	155	4	breiman	breiman	NOUN
ajst-24985	155	5	,	,	PUNCT
ajst-24985	155	6	l.	l.	PROPN
ajst-24985	155	7	(	(	PUNCT
ajst-24985	155	8	2001	2001	NUM
ajst-24985	155	9	)	)	PUNCT
ajst-24985	155	10	.	.	PUNCT
ajst-24985	156	1	random	random	ADJ
ajst-24985	156	2	forests	forest	NOUN
ajst-24985	156	3	.	.	PUNCT
ajst-24985	157	1	machine	machine	NOUN
ajst-24985	157	2	learning	learning	PROPN
ajst-24985	157	3	,	,	PUNCT
ajst-24985	157	4	45(1	45(1	NOUN
ajst-24985	157	5	)	)	PUNCT
ajst-24985	157	6	,	,	PUNCT
ajst-24985	157	7	5	5	NUM
ajst-24985	157	8	-	-	SYM
ajst-24985	157	9	32	32	NUM
ajst-24985	157	10	.	.	PUNCT
ajst-24985	158	1	[	[	X
ajst-24985	158	2	8	8	NUM
ajst-24985	158	3	]	]	SYM
ajst-24985	158	4	lecun	lecun	ADJ
ajst-24985	158	5	,	,	PUNCT
ajst-24985	158	6	y.	y.	PROPN
ajst-24985	158	7	,	,	PUNCT
ajst-24985	158	8	bottou	bottou	PROPN
ajst-24985	158	9	,	,	PUNCT
ajst-24985	158	10	l.	l.	PROPN
ajst-24985	158	11	,	,	PUNCT
ajst-24985	158	12	bengio	bengio	PROPN
ajst-24985	158	13	,	,	PUNCT
ajst-24985	158	14	y.	y.	PROPN
ajst-24985	158	15	,	,	PUNCT
ajst-24985	158	16	&	&	CCONJ
ajst-24985	158	17	haffner	haffner	PROPN
ajst-24985	158	18	,	,	PUNCT
ajst-24985	158	19	p.	p.	NOUN
ajst-24985	158	20	(	(	PUNCT
ajst-24985	158	21	1998	1998	NUM
ajst-24985	158	22	)	)	PUNCT
ajst-24985	158	23	.	.	PUNCT
ajst-24985	159	1	gradient	gradient	NOUN
ajst-24985	159	2	-	-	PUNCT
ajst-24985	159	3	based	base	VERB
ajst-24985	159	4	learning	learning	NOUN
ajst-24985	159	5	applied	apply	VERB
ajst-24985	159	6	to	to	ADP
ajst-24985	159	7	document	document	NOUN
ajst-24985	159	8	recognition	recognition	NOUN
ajst-24985	159	9	.	.	PUNCT
ajst-24985	160	1	proceedings	proceeding	NOUN
ajst-24985	160	2	of	of	ADP
ajst-24985	160	3	the	the	DET
ajst-24985	160	4	ieee	ieee	NOUN
ajst-24985	160	5	,	,	PUNCT
ajst-24985	160	6	86(11	86(11	NUM
ajst-24985	160	7	)	)	PUNCT
ajst-24985	160	8	,	,	PUNCT
ajst-24985	160	9	2278	2278	NUM
ajst-24985	160	10	-	-	SYM
ajst-24985	160	11	2324	2324	NUM
ajst-24985	160	12	.	.	PUNCT
ajst-24985	161	1	203	203	NUM
ajst-24985	162	1	[	[	SYM
ajst-24985	162	2	9	9	NUM
ajst-24985	162	3	]	]	SYM
ajst-24985	162	4	hinton	hinton	PROPN
ajst-24985	162	5	,	,	PUNCT
ajst-24985	162	6	g.	g.	PROPN
ajst-24985	162	7	,	,	PUNCT
ajst-24985	162	8	deng	deng	PROPN
ajst-24985	162	9	,	,	PUNCT
ajst-24985	162	10	l.	l.	PROPN
ajst-24985	162	11	,	,	PUNCT
ajst-24985	162	12	yu	yu	PROPN
ajst-24985	162	13	,	,	PUNCT
ajst-24985	162	14	d.	d.	PROPN
ajst-24985	162	15	,	,	PUNCT
ajst-24985	162	16	dahl	dahl	PROPN
ajst-24985	162	17	,	,	PUNCT
ajst-24985	162	18	g.	g.	PROPN
ajst-24985	162	19	e.	e.	PROPN
ajst-24985	162	20	,	,	PUNCT
ajst-24985	162	21	mohamed	mohamed	PROPN
ajst-24985	162	22	,	,	PUNCT
ajst-24985	162	23	a.	a.	NOUN
ajst-24985	162	24	,	,	PUNCT
ajst-24985	162	25	jaitly	jaitly	ADV
ajst-24985	162	26	,	,	PUNCT
ajst-24985	162	27	n.	n.	PROPN
ajst-24985	162	28	,	,	PUNCT
ajst-24985	162	29	...	...	PUNCT
ajst-24985	162	30	&	&	CCONJ
ajst-24985	162	31	kingsbury	kingsbury	PROPN
ajst-24985	162	32	,	,	PUNCT
ajst-24985	162	33	b.	b.	PROPN
ajst-24985	162	34	(	(	PUNCT
ajst-24985	162	35	2012	2012	NUM
ajst-24985	162	36	)	)	PUNCT
ajst-24985	162	37	.	.	PUNCT
ajst-24985	163	1	deep	deep	ADJ
ajst-24985	163	2	neural	neural	ADJ
ajst-24985	163	3	networks	network	NOUN
ajst-24985	163	4	for	for	ADP
ajst-24985	163	5	acoustic	acoustic	ADJ
ajst-24985	163	6	modeling	modeling	NOUN
ajst-24985	163	7	in	in	ADP
ajst-24985	163	8	speech	speech	NOUN
ajst-24985	163	9	recognition	recognition	NOUN
ajst-24985	163	10	:	:	PUNCT
ajst-24985	163	11	the	the	DET
ajst-24985	163	12	shared	share	VERB
ajst-24985	163	13	views	view	NOUN
ajst-24985	163	14	of	of	ADP
ajst-24985	163	15	four	four	NUM
ajst-24985	163	16	research	research	NOUN
ajst-24985	163	17	groups	group	NOUN
ajst-24985	163	18	.	.	PUNCT
ajst-24985	164	1	ieee	ieee	NOUN
ajst-24985	164	2	signal	signal	PROPN
ajst-24985	164	3	processing	processing	NOUN
ajst-24985	164	4	magazine	magazine	NOUN
ajst-24985	164	5	,	,	PUNCT
ajst-24985	164	6	29(6	29(6	NUM
ajst-24985	164	7	)	)	PUNCT
ajst-24985	164	8	,	,	PUNCT
ajst-24985	164	9	82	82	NUM
ajst-24985	164	10	-	-	SYM
ajst-24985	164	11	97	97	NUM
ajst-24985	164	12	.	.	PUNCT
ajst-24985	165	1	[	[	X
ajst-24985	165	2	10	10	NUM
ajst-24985	165	3	]	]	X
ajst-24985	165	4	ogunpola	ogunpola	PROPN
ajst-24985	165	5	,	,	PUNCT
ajst-24985	165	6	a.	a.	PROPN
ajst-24985	165	7	,	,	PUNCT
ajst-24985	165	8	saeed	saeed	PROPN
ajst-24985	165	9	,	,	PUNCT
ajst-24985	165	10	f.	f.	PROPN
ajst-24985	165	11	,	,	PUNCT
ajst-24985	165	12	basurra	basurra	PROPN
ajst-24985	165	13	,	,	PUNCT
ajst-24985	165	14	s.	s.	PROPN
ajst-24985	165	15	,	,	PUNCT
ajst-24985	165	16	albarrak	albarrak	PROPN
ajst-24985	165	17	,	,	PUNCT
ajst-24985	165	18	a.	a.	NOUN
ajst-24985	165	19	m.	m.	NOUN
ajst-24985	165	20	,	,	PUNCT
ajst-24985	165	21	&	&	CCONJ
ajst-24985	165	22	qasem	qasem	PROPN
ajst-24985	165	23	,	,	PUNCT
ajst-24985	165	24	s.	s.	PROPN
ajst-24985	165	25	n.	n.	PROPN
ajst-24985	165	26	(	(	PUNCT
ajst-24985	165	27	2024	2024	NUM
ajst-24985	165	28	)	)	PUNCT
ajst-24985	165	29	.	.	PUNCT
ajst-24985	166	1	machine	machine	NOUN
ajst-24985	166	2	learning	learning	NOUN
ajst-24985	166	3	-	-	PUNCT
ajst-24985	166	4	based	base	VERB
ajst-24985	166	5	predictive	predictive	ADJ
ajst-24985	166	6	models	model	NOUN
ajst-24985	166	7	for	for	ADP
ajst-24985	166	8	detection	detection	NOUN
ajst-24985	166	9	of	of	ADP
ajst-24985	166	10	cardiovascular	cardiovascular	ADJ
ajst-24985	166	11	diseases	disease	NOUN
ajst-24985	166	12	.	.	PUNCT
ajst-24985	167	1	diagnostics	diagnostic	NOUN
ajst-24985	167	2	,	,	PUNCT
ajst-24985	167	3	14	14	NUM
ajst-24985	167	4	(	(	PUNCT
ajst-24985	167	5	2	2	NUM
ajst-24985	167	6	)	)	PUNCT
ajst-24985	167	7	,	,	PUNCT
ajst-24985	167	8	144	144	NUM
ajst-24985	167	9	.	.	PUNCT
ajst-24985	168	1	[	[	X
ajst-24985	168	2	11	11	NUM
ajst-24985	168	3	]	]	PUNCT
ajst-24985	168	4	pachiyannan	pachiyannan	NOUN
ajst-24985	168	5	,	,	PUNCT
ajst-24985	168	6	p.	p.	NOUN
ajst-24985	168	7	,	,	PUNCT
ajst-24985	168	8	alsulami	alsulami	NOUN
ajst-24985	168	9	,	,	PUNCT
ajst-24985	168	10	m.	m.	NOUN
ajst-24985	168	11	,	,	PUNCT
ajst-24985	168	12	alsadie	alsadie	NOUN
ajst-24985	168	13	,	,	PUNCT
ajst-24985	168	14	d.	d.	PROPN
ajst-24985	168	15	,	,	PUNCT
ajst-24985	168	16	saudagar	saudagar	PROPN
ajst-24985	168	17	,	,	PUNCT
ajst-24985	168	18	a.	a.	PROPN
ajst-24985	168	19	k.	k.	PROPN
ajst-24985	168	20	j.	j.	PROPN
ajst-24985	168	21	,	,	PUNCT
ajst-24985	168	22	alkhathami	alkhathami	NOUN
ajst-24985	168	23	,	,	PUNCT
ajst-24985	168	24	m.	m.	NOUN
ajst-24985	168	25	,	,	PUNCT
ajst-24985	168	26	&	&	CCONJ
ajst-24985	168	27	poonia	poonia	PROPN
ajst-24985	168	28	,	,	PUNCT
ajst-24985	168	29	r.	r.	PROPN
ajst-24985	168	30	c.	c.	PROPN
ajst-24985	168	31	(	(	PUNCT
ajst-24985	168	32	2024	2024	NUM
ajst-24985	168	33	)	)	PUNCT
ajst-24985	168	34	.	.	PUNCT
ajst-24985	169	1	a	a	DET
ajst-24985	169	2	novel	novel	ADJ
ajst-24985	169	3	machine	machine	NOUN
ajst-24985	169	4	learning	learning	NOUN
ajst-24985	169	5	-	-	PUNCT
ajst-24985	169	6	based	base	VERB
ajst-24985	169	7	prediction	prediction	NOUN
ajst-24985	169	8	method	method	NOUN
ajst-24985	169	9	for	for	ADP
ajst-24985	169	10	early	early	ADJ
ajst-24985	169	11	detection	detection	NOUN
ajst-24985	169	12	and	and	CCONJ
ajst-24985	169	13	diagnosis	diagnosis	NOUN
ajst-24985	169	14	of	of	ADP
ajst-24985	169	15	congenital	congenital	ADJ
ajst-24985	169	16	heart	heart	NOUN
ajst-24985	169	17	disease	disease	NOUN
ajst-24985	169	18	using	use	VERB
ajst-24985	169	19	ecg	ecg	PROPN
ajst-24985	169	20	signal	signal	ADJ
ajst-24985	169	21	processing	processing	NOUN
ajst-24985	169	22	.	.	PUNCT
ajst-24985	170	1	technologies	technology	NOUN
ajst-24985	170	2	,	,	PUNCT
ajst-24985	170	3	12(1	12(1	NUM
ajst-24985	170	4	)	)	PUNCT
ajst-24985	170	5	,	,	PUNCT
ajst-24985	170	6	4	4	X
ajst-24985	170	7	.	.	PUNCT