id	sid	tid	token	lemma	pos
cana-1758	1	1	communications	communication	NOUN
cana-1758	1	2	on	on	ADP
cana-1758	1	3	applied	apply	VERB
cana-1758	1	4	nonlinear	nonlinear	ADJ
cana-1758	1	5	analysis	analysis	NOUN
cana-1758	1	6	issn	issn	NOUN
cana-1758	1	7	:	:	PUNCT
cana-1758	1	8	1074	1074	NUM
cana-1758	1	9	-	-	PUNCT
cana-1758	1	10	133x	133x	NUM
cana-1758	1	11	vol	vol	NOUN
cana-1758	1	12	32	32	NUM
cana-1758	1	13	no	no	NOUN
cana-1758	1	14	.	.	NOUN
cana-1758	1	15	2	2	NUM
cana-1758	1	16	(	(	PUNCT
cana-1758	1	17	2025	2025	NUM
cana-1758	1	18	)	)	PUNCT
cana-1758	2	1	459	459	NUM
cana-1758	2	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	2	3	improving	improve	VERB
cana-1758	2	4	software	software	NOUN
cana-1758	2	5	defect	defect	NOUN
cana-1758	2	6	prediction	prediction	NOUN
cana-1758	2	7	accuracy	accuracy	NOUN
cana-1758	2	8	through	through	ADP
cana-1758	2	9	modified	modify	VERB
cana-1758	2	10	entropy	entropy	NOUN
cana-1758	2	11	calculation	calculation	NOUN
cana-1758	2	12	in	in	ADP
cana-1758	2	13	the	the	DET
cana-1758	2	14	random	random	ADJ
cana-1758	2	15	forest	forest	NOUN
cana-1758	2	16	algorithm	algorithm	NOUN
cana-1758	2	17	*	*	PUNCT
cana-1758	2	18	ranjeetsingh	ranjeetsingh	NOUN
cana-1758	2	19	suryawanshi1	suryawanshi1	PROPN
cana-1758	2	20	,	,	PUNCT
cana-1758	2	21	amol	amol	PROPN
cana-1758	2	22	kadam2	kadam2	PROPN
cana-1758	3	1	*	*	PUNCT
cana-1758	3	2	1ranjeetsinghsuryawanshi@gmail.com	1ranjeetsinghsuryawanshi@gmail.com	NUM
cana-1758	3	3	2akkadam@bvucoep.edu.in	2akkadam@bvucoep.edu.in	NUM
cana-1758	3	4	1,2	1,2	NUM
cana-1758	3	5	bharati	bharati	PROPN
cana-1758	3	6	vidyapeeth	vidyapeeth	PROPN
cana-1758	3	7	deemed	deem	VERB
cana-1758	3	8	to	to	PART
cana-1758	3	9	be	be	AUX
cana-1758	3	10	university	university	NOUN
cana-1758	3	11	,	,	PUNCT
cana-1758	3	12	college	college	NOUN
cana-1758	3	13	of	of	ADP
cana-1758	3	14	engineering	engineering	PROPN
cana-1758	3	15	,	,	PUNCT
cana-1758	3	16	india	india	PROPN
cana-1758	3	17	*	*	PUNCT
cana-1758	3	18	correspondence	correspondence	NOUN
cana-1758	3	19	:	:	PUNCT
cana-1758	4	1	ranjeetsinghsuryawanshi@gmail.com	ranjeetsinghsuryawanshi@gmail.com	X
cana-1758	4	2	article	article	NOUN
cana-1758	4	3	history	history	NOUN
cana-1758	4	4	:	:	PUNCT
cana-1758	4	5	received	receive	VERB
cana-1758	4	6	:	:	PUNCT
cana-1758	4	7	05	05	NUM
cana-1758	4	8	-	-	SYM
cana-1758	4	9	08	08	NUM
cana-1758	4	10	-	-	PUNCT
cana-1758	4	11	2024	2024	NUM
cana-1758	4	12	revised	revise	VERB
cana-1758	4	13	:	:	PUNCT
cana-1758	4	14	12	12	NUM
cana-1758	4	15	-	-	SYM
cana-1758	4	16	09	09	NUM
cana-1758	4	17	-	-	PUNCT
cana-1758	4	18	2024	2024	NUM
cana-1758	4	19	accepted	accept	VERB
cana-1758	4	20	:	:	PUNCT
cana-1758	4	21	21	21	NUM
cana-1758	4	22	-	-	SYM
cana-1758	4	23	09	09	NUM
cana-1758	4	24	-	-	PUNCT
cana-1758	4	25	2024	2024	NUM
cana-1758	4	26	abstract	abstract	NOUN
cana-1758	4	27	:	:	PUNCT
cana-1758	4	28	assume	assume	VERB
cana-1758	4	29	the	the	DET
cana-1758	4	30	scenario	scenario	NOUN
cana-1758	4	31	in	in	ADP
cana-1758	4	32	which	which	PRON
cana-1758	4	33	you	you	PRON
cana-1758	4	34	are	be	AUX
cana-1758	4	35	attempting	attempt	VERB
cana-1758	4	36	to	to	PART
cana-1758	4	37	categorize	categorize	VERB
cana-1758	4	38	software	software	NOUN
cana-1758	4	39	defects	defect	NOUN
cana-1758	4	40	for	for	ADP
cana-1758	4	41	a	a	DET
cana-1758	4	42	broad	broad	ADJ
cana-1758	4	43	dataset	dataset	NOUN
cana-1758	4	44	.	.	PUNCT
cana-1758	5	1	which	which	DET
cana-1758	5	2	algorithm	algorithm	NOUN
cana-1758	5	3	do	do	AUX
cana-1758	5	4	you	you	PRON
cana-1758	5	5	think	think	VERB
cana-1758	5	6	will	will	AUX
cana-1758	5	7	be	be	AUX
cana-1758	5	8	the	the	DET
cana-1758	5	9	most	most	ADV
cana-1758	5	10	effective	effective	ADJ
cana-1758	5	11	for	for	ADP
cana-1758	5	12	accomplishing	accomplish	VERB
cana-1758	5	13	that	that	PRON
cana-1758	5	14	?	?	PUNCT
cana-1758	6	1	random	random	ADJ
cana-1758	6	2	forest	forest	NOUN
cana-1758	6	3	,	,	PUNCT
cana-1758	6	4	support	support	NOUN
cana-1758	6	5	vector	vector	NOUN
cana-1758	6	6	machine	machine	NOUN
cana-1758	6	7	,	,	PUNCT
cana-1758	6	8	neural	neural	ADJ
cana-1758	6	9	networks	network	NOUN
cana-1758	6	10	,	,	PUNCT
cana-1758	6	11	naive	naive	ADJ
cana-1758	6	12	bayes	bayes	NOUN
cana-1758	6	13	,	,	PUNCT
cana-1758	6	14	k	k	NOUN
cana-1758	6	15	-	-	PUNCT
cana-1758	6	16	nearest	near	ADJ
cana-1758	6	17	neighbours	neighbour	NOUN
cana-1758	6	18	,	,	PUNCT
cana-1758	6	19	decision	decision	NOUN
cana-1758	6	20	tree	tree	NOUN
cana-1758	6	21	,	,	PUNCT
cana-1758	6	22	logistic	logistic	ADJ
cana-1758	6	23	regression	regression	NOUN
cana-1758	6	24	,	,	PUNCT
cana-1758	6	25	and	and	CCONJ
cana-1758	6	26	other	other	ADJ
cana-1758	6	27	techniques	technique	NOUN
cana-1758	6	28	are	be	AUX
cana-1758	6	29	among	among	ADP
cana-1758	6	30	those	those	PRON
cana-1758	6	31	that	that	PRON
cana-1758	6	32	can	can	AUX
cana-1758	6	33	be	be	AUX
cana-1758	6	34	utilized	utilize	VERB
cana-1758	6	35	to	to	PART
cana-1758	6	36	solve	solve	VERB
cana-1758	6	37	the	the	DET
cana-1758	6	38	problem	problem	NOUN
cana-1758	6	39	described	describe	VERB
cana-1758	6	40	above	above	ADV
cana-1758	6	41	.	.	PUNCT
cana-1758	7	1	the	the	DET
cana-1758	7	2	random	random	ADJ
cana-1758	7	3	forest	forest	NOUN
cana-1758	7	4	technique	technique	NOUN
cana-1758	7	5	,	,	PUNCT
cana-1758	7	6	which	which	PRON
cana-1758	7	7	allows	allow	VERB
cana-1758	7	8	for	for	ADP
cana-1758	7	9	the	the	DET
cana-1758	7	10	generation	generation	NOUN
cana-1758	7	11	of	of	ADP
cana-1758	7	12	predictions	prediction	NOUN
cana-1758	7	13	through	through	ADP
cana-1758	7	14	the	the	DET
cana-1758	7	15	utilization	utilization	NOUN
cana-1758	7	16	of	of	ADP
cana-1758	7	17	many	many	ADJ
cana-1758	7	18	decision	decision	NOUN
cana-1758	7	19	trees	tree	NOUN
cana-1758	7	20	,	,	PUNCT
cana-1758	7	21	is	be	AUX
cana-1758	7	22	one	one	NUM
cana-1758	7	23	of	of	ADP
cana-1758	7	24	the	the	DET
cana-1758	7	25	most	most	ADV
cana-1758	7	26	often	often	ADV
cana-1758	7	27	utilized	utilize	VERB
cana-1758	7	28	methods	method	NOUN
cana-1758	7	29	.	.	PUNCT
cana-1758	8	1	entropy	entropy	PROPN
cana-1758	8	2	,	,	PUNCT
cana-1758	8	3	a	a	DET
cana-1758	8	4	complicated	complicated	ADJ
cana-1758	8	5	computation	computation	NOUN
cana-1758	8	6	that	that	PRON
cana-1758	8	7	examines	examine	VERB
cana-1758	8	8	the	the	DET
cana-1758	8	9	degree	degree	NOUN
cana-1758	8	10	of	of	ADP
cana-1758	8	11	uncertainty	uncertainty	NOUN
cana-1758	8	12	in	in	ADP
cana-1758	8	13	the	the	DET
cana-1758	8	14	data	datum	NOUN
cana-1758	8	15	,	,	PUNCT
cana-1758	8	16	is	be	AUX
cana-1758	8	17	the	the	DET
cana-1758	8	18	foundation	foundation	NOUN
cana-1758	8	19	upon	upon	SCONJ
cana-1758	8	20	which	which	PRON
cana-1758	8	21	this	this	DET
cana-1758	8	22	algorithm	algorithm	NOUN
cana-1758	8	23	is	be	AUX
cana-1758	8	24	built	build	VERB
cana-1758	8	25	.	.	PUNCT
cana-1758	9	1	it	it	PRON
cana-1758	9	2	is	be	AUX
cana-1758	9	3	possible	possible	ADJ
cana-1758	9	4	that	that	SCONJ
cana-1758	9	5	the	the	DET
cana-1758	9	6	calculation	calculation	NOUN
cana-1758	9	7	of	of	ADP
cana-1758	9	8	entropy	entropy	PROPN
cana-1758	9	9	,	,	PUNCT
cana-1758	9	10	which	which	PRON
cana-1758	9	11	is	be	AUX
cana-1758	9	12	a	a	DET
cana-1758	9	13	function	function	NOUN
cana-1758	9	14	that	that	PRON
cana-1758	9	15	uses	use	VERB
cana-1758	9	16	natural	natural	ADJ
cana-1758	9	17	logarithm	logarithm	NOUN
cana-1758	9	18	,	,	PUNCT
cana-1758	9	19	will	will	AUX
cana-1758	9	20	take	take	VERB
cana-1758	9	21	a	a	DET
cana-1758	9	22	significant	significant	ADJ
cana-1758	9	23	amount	amount	NOUN
cana-1758	9	24	of	of	ADP
cana-1758	9	25	time	time	NOUN
cana-1758	9	26	.	.	PUNCT
cana-1758	10	1	does	do	AUX
cana-1758	10	2	one	one	NUM
cana-1758	10	3	know	know	NOUN
cana-1758	10	4	of	of	ADP
cana-1758	10	5	a	a	DET
cana-1758	10	6	more	more	ADV
cana-1758	10	7	accurate	accurate	ADJ
cana-1758	10	8	method	method	NOUN
cana-1758	10	9	for	for	ADP
cana-1758	10	10	calculating	calculate	VERB
cana-1758	10	11	entropy	entropy	NOUN
cana-1758	10	12	?	?	PUNCT
cana-1758	11	1	the	the	DET
cana-1758	11	2	taylor	taylor	PROPN
cana-1758	11	3	series	series	PROPN
cana-1758	11	4	expression	expression	NOUN
cana-1758	11	5	was	be	AUX
cana-1758	11	6	utilized	utilize	VERB
cana-1758	11	7	in	in	ADP
cana-1758	11	8	this	this	DET
cana-1758	11	9	investigation	investigation	NOUN
cana-1758	11	10	to	to	PART
cana-1758	11	11	investigate	investigate	VERB
cana-1758	11	12	a	a	DET
cana-1758	11	13	different	different	ADJ
cana-1758	11	14	approach	approach	NOUN
cana-1758	11	15	to	to	ADP
cana-1758	11	16	calculating	calculate	VERB
cana-1758	11	17	the	the	DET
cana-1758	11	18	natural	natural	ADJ
cana-1758	11	19	logarithm	logarithm	NOUN
cana-1758	11	20	.	.	PUNCT
cana-1758	12	1	any	any	DET
cana-1758	12	2	function	function	NOUN
cana-1758	12	3	may	may	AUX
cana-1758	12	4	be	be	AUX
cana-1758	12	5	approximated	approximate	VERB
cana-1758	12	6	by	by	ADP
cana-1758	12	7	utilizing	utilize	VERB
cana-1758	12	8	its	its	PRON
cana-1758	12	9	derivatives	derivative	NOUN
cana-1758	12	10	,	,	PUNCT
cana-1758	12	11	and	and	CCONJ
cana-1758	12	12	this	this	DET
cana-1758	12	13	series	series	NOUN
cana-1758	12	14	,	,	PUNCT
cana-1758	12	15	which	which	PRON
cana-1758	12	16	is	be	AUX
cana-1758	12	17	made	make	VERB
cana-1758	12	18	up	up	ADP
cana-1758	12	19	of	of	ADP
cana-1758	12	20	the	the	DET
cana-1758	12	21	sum	sum	NOUN
cana-1758	12	22	of	of	ADP
cana-1758	12	23	infinite	infinite	ADJ
cana-1758	12	24	terms	term	NOUN
cana-1758	12	25	,	,	PUNCT
cana-1758	12	26	is	be	AUX
cana-1758	12	27	what	what	PRON
cana-1758	12	28	it	it	PRON
cana-1758	12	29	is	be	AUX
cana-1758	12	30	.	.	PUNCT
cana-1758	13	1	also	also	ADV
cana-1758	13	2	,	,	PUNCT
cana-1758	13	3	we	we	PRON
cana-1758	13	4	updated	update	VERB
cana-1758	13	5	the	the	DET
cana-1758	13	6	random	random	ADJ
cana-1758	13	7	forest	forest	NOUN
cana-1758	13	8	algorithm	algorithm	NOUN
cana-1758	13	9	by	by	ADP
cana-1758	13	10	substituting	substitute	VERB
cana-1758	13	11	the	the	DET
cana-1758	13	12	natural	natural	ADJ
cana-1758	13	13	logarithm	logarithm	NOUN
cana-1758	13	14	with	with	ADP
cana-1758	13	15	the	the	DET
cana-1758	13	16	taylor	taylor	PROPN
cana-1758	13	17	series	series	PROPN
cana-1758	13	18	equation	equation	NOUN
cana-1758	13	19	in	in	ADP
cana-1758	13	20	the	the	DET
cana-1758	13	21	entropy	entropy	NOUN
cana-1758	13	22	calculation	calculation	NOUN
cana-1758	13	23	.	.	PUNCT
cana-1758	14	1	this	this	PRON
cana-1758	14	2	was	be	AUX
cana-1758	14	3	done	do	VERB
cana-1758	14	4	in	in	ADP
cana-1758	14	5	order	order	NOUN
cana-1758	14	6	to	to	PART
cana-1758	14	7	achieve	achieve	VERB
cana-1758	14	8	these	these	DET
cana-1758	14	9	modifications	modification	NOUN
cana-1758	14	10	.	.	PUNCT
cana-1758	15	1	following	follow	VERB
cana-1758	15	2	the	the	DET
cana-1758	15	3	implementation	implementation	NOUN
cana-1758	15	4	of	of	ADP
cana-1758	15	5	our	our	PRON
cana-1758	15	6	improved	improved	ADJ
cana-1758	15	7	algorithm	algorithm	NOUN
cana-1758	15	8	on	on	ADP
cana-1758	15	9	the	the	DET
cana-1758	15	10	dataset	dataset	NOUN
cana-1758	15	11	,	,	PUNCT
cana-1758	15	12	we	we	PRON
cana-1758	15	13	examined	examine	VERB
cana-1758	15	14	its	its	PRON
cana-1758	15	15	performance	performance	NOUN
cana-1758	15	16	in	in	ADP
cana-1758	15	17	comparison	comparison	NOUN
cana-1758	15	18	to	to	ADP
cana-1758	15	19	the	the	DET
cana-1758	15	20	entropy	entropy	NOUN
cana-1758	15	21	formula	formula	NOUN
cana-1758	15	22	that	that	PRON
cana-1758	15	23	was	be	AUX
cana-1758	15	24	first	first	ADV
cana-1758	15	25	developed	develop	VERB
cana-1758	15	26	.	.	PUNCT
cana-1758	16	1	an	an	DET
cana-1758	16	2	improvement	improvement	NOUN
cana-1758	16	3	in	in	ADP
cana-1758	16	4	the	the	DET
cana-1758	16	5	algorithm	algorithm	NOUN
cana-1758	16	6	's	's	PART
cana-1758	16	7	accuracy	accuracy	NOUN
cana-1758	16	8	in	in	ADP
cana-1758	16	9	predicting	predict	VERB
cana-1758	16	10	software	software	NOUN
cana-1758	16	11	defects	defect	NOUN
cana-1758	16	12	was	be	AUX
cana-1758	16	13	discovered	discover	VERB
cana-1758	16	14	by	by	ADP
cana-1758	16	15	us	we	PRON
cana-1758	16	16	as	as	ADP
cana-1758	16	17	a	a	DET
cana-1758	16	18	result	result	NOUN
cana-1758	16	19	of	of	ADP
cana-1758	16	20	our	our	PRON
cana-1758	16	21	update	update	NOUN
cana-1758	16	22	to	to	ADP
cana-1758	16	23	the	the	DET
cana-1758	16	24	technique	technique	NOUN
cana-1758	16	25	.	.	PUNCT
cana-1758	17	1	keywords	keyword	NOUN
cana-1758	17	2	:	:	PUNCT
cana-1758	17	3	random	random	ADJ
cana-1758	17	4	forest	forest	NOUN
cana-1758	17	5	;	;	PUNCT
cana-1758	17	6	decision	decision	NOUN
cana-1758	17	7	tree	tree	NOUN
cana-1758	17	8	;	;	PUNCT
cana-1758	17	9	classification	classification	NOUN
cana-1758	17	10	;	;	PUNCT
cana-1758	17	11	prediction	prediction	NOUN
cana-1758	17	12	;	;	PUNCT
cana-1758	17	13	entropy	entropy	PROPN
cana-1758	17	14	;	;	PUNCT
cana-1758	17	15	taylor	taylor	PROPN
cana-1758	17	16	series	series	PROPN
cana-1758	17	17	;	;	PUNCT
cana-1758	17	18	1	1	X
cana-1758	17	19	.	.	X
cana-1758	17	20	introduction	introduction	NOUN
cana-1758	17	21	there	there	PRON
cana-1758	17	22	are	be	VERB
cana-1758	17	23	bugs	bug	NOUN
cana-1758	17	24	,	,	PUNCT
cana-1758	17	25	glitches	glitch	NOUN
cana-1758	17	26	,	,	PUNCT
cana-1758	17	27	or	or	CCONJ
cana-1758	17	28	faults	fault	NOUN
cana-1758	17	29	in	in	ADP
cana-1758	17	30	software	software	NOUN
cana-1758	17	31	that	that	PRON
cana-1758	17	32	can	can	AUX
cana-1758	17	33	lead	lead	VERB
cana-1758	17	34	to	to	ADP
cana-1758	17	35	inaccurate	inaccurate	ADJ
cana-1758	17	36	outcomes	outcome	NOUN
cana-1758	17	37	.	.	PUNCT
cana-1758	18	1	these	these	PRON
cana-1758	18	2	are	be	AUX
cana-1758	18	3	referred	refer	VERB
cana-1758	18	4	to	to	ADP
cana-1758	18	5	as	as	ADP
cana-1758	18	6	software	software	NOUN
cana-1758	18	7	defects	defect	NOUN
cana-1758	18	8	.	.	PUNCT
cana-1758	19	1	as	as	ADP
cana-1758	19	2	a	a	DET
cana-1758	19	3	result	result	NOUN
cana-1758	19	4	of	of	ADP
cana-1758	19	5	the	the	DET
cana-1758	19	6	fact	fact	NOUN
cana-1758	19	7	that	that	SCONJ
cana-1758	19	8	people	people	NOUN
cana-1758	19	9	are	be	AUX
cana-1758	19	10	responsible	responsible	ADJ
cana-1758	19	11	for	for	ADP
cana-1758	19	12	a	a	DET
cana-1758	19	13	significant	significant	ADJ
cana-1758	19	14	number	number	NOUN
cana-1758	19	15	of	of	ADP
cana-1758	19	16	tasks	task	NOUN
cana-1758	19	17	during	during	ADP
cana-1758	19	18	the	the	DET
cana-1758	19	19	creation	creation	NOUN
cana-1758	19	20	of	of	ADP
cana-1758	19	21	software	software	NOUN
cana-1758	19	22	,	,	PUNCT
cana-1758	19	23	numerous	numerous	ADJ
cana-1758	19	24	software	software	NOUN
cana-1758	19	25	fault	fault	NOUN
cana-1758	19	26	problems	problem	NOUN
cana-1758	19	27	may	may	AUX
cana-1758	19	28	arise	arise	VERB
cana-1758	19	29	.	.	PUNCT
cana-1758	20	1	some	some	PRON
cana-1758	20	2	of	of	ADP
cana-1758	20	3	the	the	DET
cana-1758	20	4	stages	stage	NOUN
cana-1758	20	5	communications	communication	NOUN
cana-1758	20	6	on	on	ADP
cana-1758	20	7	applied	apply	VERB
cana-1758	20	8	nonlinear	nonlinear	ADJ
cana-1758	20	9	analysis	analysis	NOUN
cana-1758	20	10	issn	issn	NOUN
cana-1758	20	11	:	:	PUNCT
cana-1758	20	12	1074	1074	NUM
cana-1758	20	13	-	-	PUNCT
cana-1758	20	14	133x	133x	NUM
cana-1758	20	15	vol	vol	NOUN
cana-1758	20	16	32	32	NUM
cana-1758	20	17	no	no	NOUN
cana-1758	20	18	.	.	NOUN
cana-1758	20	19	2	2	NUM
cana-1758	20	20	(	(	PUNCT
cana-1758	20	21	2025	2025	NUM
cana-1758	20	22	)	)	PUNCT
cana-1758	21	1	460	460	NUM
cana-1758	21	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	21	3	that	that	PRON
cana-1758	21	4	are	be	AUX
cana-1758	21	5	involved	involve	VERB
cana-1758	21	6	in	in	ADP
cana-1758	21	7	the	the	DET
cana-1758	21	8	process	process	NOUN
cana-1758	21	9	of	of	ADP
cana-1758	21	10	building	building	NOUN
cana-1758	21	11	software	software	NOUN
cana-1758	21	12	,	,	PUNCT
cana-1758	21	13	such	such	ADJ
cana-1758	21	14	as	as	ADP
cana-1758	21	15	requirement	requirement	ADJ
cana-1758	21	16	analysis	analysis	NOUN
cana-1758	21	17	,	,	PUNCT
cana-1758	21	18	design	design	NOUN
cana-1758	21	19	,	,	PUNCT
cana-1758	21	20	coding	coding	NOUN
cana-1758	21	21	,	,	PUNCT
cana-1758	21	22	testing	testing	NOUN
cana-1758	21	23	,	,	PUNCT
cana-1758	21	24	deployment	deployment	NOUN
cana-1758	21	25	,	,	PUNCT
cana-1758	21	26	and	and	CCONJ
cana-1758	21	27	maintenance	maintenance	NOUN
cana-1758	21	28	,	,	PUNCT
cana-1758	21	29	are	be	AUX
cana-1758	21	30	susceptible	susceptible	ADJ
cana-1758	21	31	to	to	ADP
cana-1758	21	32	discovering	discover	VERB
cana-1758	21	33	defects	defect	NOUN
cana-1758	21	34	.	.	PUNCT
cana-1758	22	1	in	in	ADP
cana-1758	22	2	order	order	NOUN
cana-1758	22	3	to	to	PART
cana-1758	22	4	ensure	ensure	VERB
cana-1758	22	5	that	that	SCONJ
cana-1758	22	6	software	software	NOUN
cana-1758	22	7	products	product	NOUN
cana-1758	22	8	continue	continue	VERB
cana-1758	22	9	to	to	PART
cana-1758	22	10	be	be	AUX
cana-1758	22	11	of	of	ADP
cana-1758	22	12	high	high	ADJ
cana-1758	22	13	quality	quality	NOUN
cana-1758	22	14	and	and	CCONJ
cana-1758	22	15	reliability	reliability	NOUN
cana-1758	22	16	,	,	PUNCT
cana-1758	22	17	rigorous	rigorous	ADJ
cana-1758	22	18	software	software	NOUN
cana-1758	22	19	testing	testing	NOUN
cana-1758	22	20	is	be	AUX
cana-1758	22	21	essential	essential	ADJ
cana-1758	22	22	.	.	PUNCT
cana-1758	23	1	in	in	ADP
cana-1758	23	2	order	order	NOUN
cana-1758	23	3	to	to	PART
cana-1758	23	4	keep	keep	VERB
cana-1758	23	5	up	up	ADP
cana-1758	23	6	with	with	ADP
cana-1758	23	7	the	the	DET
cana-1758	23	8	rapid	rapid	ADJ
cana-1758	23	9	growth	growth	NOUN
cana-1758	23	10	of	of	ADP
cana-1758	23	11	software	software	NOUN
cana-1758	23	12	applications	application	NOUN
cana-1758	23	13	,	,	PUNCT
cana-1758	23	14	additional	additional	ADJ
cana-1758	23	15	testing	testing	NOUN
cana-1758	23	16	is	be	AUX
cana-1758	23	17	required	require	VERB
cana-1758	23	18	,	,	PUNCT
cana-1758	23	19	which	which	PRON
cana-1758	23	20	is	be	AUX
cana-1758	23	21	both	both	CCONJ
cana-1758	23	22	costly	costly	ADJ
cana-1758	23	23	and	and	CCONJ
cana-1758	23	24	time	time	NOUN
cana-1758	23	25	intensive	intensive	ADJ
cana-1758	23	26	.	.	PUNCT
cana-1758	24	1	when	when	SCONJ
cana-1758	24	2	compared	compare	VERB
cana-1758	24	3	to	to	ADP
cana-1758	24	4	software	software	NOUN
cana-1758	24	5	testing	testing	NOUN
cana-1758	24	6	and	and	CCONJ
cana-1758	24	7	evaluations	evaluation	NOUN
cana-1758	24	8	,	,	PUNCT
cana-1758	24	9	software	software	NOUN
cana-1758	24	10	defect	defect	NOUN
cana-1758	24	11	prediction	prediction	NOUN
cana-1758	24	12	approaches	approach	NOUN
cana-1758	24	13	are	be	AUX
cana-1758	24	14	significantly	significantly	ADV
cana-1758	24	15	more	more	ADJ
cana-1758	24	16	cost	cost	NOUN
cana-1758	24	17	-	-	PUNCT
cana-1758	24	18	effective	effective	ADJ
cana-1758	24	19	when	when	SCONJ
cana-1758	24	20	it	it	PRON
cana-1758	24	21	comes	come	VERB
cana-1758	24	22	to	to	ADP
cana-1758	24	23	the	the	DET
cana-1758	24	24	detection	detection	NOUN
cana-1758	24	25	of	of	ADP
cana-1758	24	26	software	software	NOUN
cana-1758	24	27	defects	defect	NOUN
cana-1758	24	28	.	.	PUNCT
cana-1758	25	1	machine	machine	NOUN
cana-1758	25	2	learning	learning	NOUN
cana-1758	25	3	is	be	AUX
cana-1758	25	4	a	a	DET
cana-1758	25	5	process	process	NOUN
cana-1758	25	6	that	that	PRON
cana-1758	25	7	uses	use	VERB
cana-1758	25	8	a	a	DET
cana-1758	25	9	number	number	NOUN
cana-1758	25	10	of	of	ADP
cana-1758	25	11	methods	method	NOUN
cana-1758	25	12	to	to	PART
cana-1758	25	13	analyze	analyze	VERB
cana-1758	25	14	input	input	NOUN
cana-1758	25	15	data	datum	NOUN
cana-1758	25	16	and	and	CCONJ
cana-1758	25	17	make	make	VERB
cana-1758	25	18	predictions	prediction	NOUN
cana-1758	25	19	about	about	ADP
cana-1758	25	20	output	output	NOUN
cana-1758	25	21	values	value	NOUN
cana-1758	25	22	.	.	PUNCT
cana-1758	26	1	in	in	ADP
cana-1758	26	2	addition	addition	NOUN
cana-1758	26	3	to	to	ADP
cana-1758	26	4	contributing	contribute	VERB
cana-1758	26	5	to	to	ADP
cana-1758	26	6	the	the	DET
cana-1758	26	7	automation	automation	NOUN
cana-1758	26	8	of	of	ADP
cana-1758	26	9	the	the	DET
cana-1758	26	10	labeling	labeling	NOUN
cana-1758	26	11	process	process	NOUN
cana-1758	26	12	,	,	PUNCT
cana-1758	26	13	machine	machine	NOUN
cana-1758	26	14	learning	learn	VERB
cana-1758	26	15	techniques	technique	NOUN
cana-1758	26	16	contribute	contribute	VERB
cana-1758	26	17	to	to	ADP
cana-1758	26	18	the	the	DET
cana-1758	26	19	learning	learning	NOUN
cana-1758	26	20	and	and	CCONJ
cana-1758	26	21	improvement	improvement	NOUN
cana-1758	26	22	of	of	ADP
cana-1758	26	23	performance	performance	NOUN
cana-1758	26	24	.	.	PUNCT
cana-1758	27	1	the	the	DET
cana-1758	27	2	contributions	contribution	NOUN
cana-1758	27	3	of	of	ADP
cana-1758	27	4	a	a	DET
cana-1758	27	5	large	large	ADJ
cana-1758	27	6	number	number	NOUN
cana-1758	27	7	of	of	ADP
cana-1758	27	8	academics	academic	NOUN
cana-1758	27	9	and	and	CCONJ
cana-1758	27	10	academicians	academician	NOUN
cana-1758	27	11	have	have	AUX
cana-1758	27	12	demonstrated	demonstrate	VERB
cana-1758	27	13	that	that	SCONJ
cana-1758	27	14	the	the	DET
cana-1758	27	15	likelihood	likelihood	NOUN
cana-1758	27	16	of	of	ADP
cana-1758	27	17	identifying	identify	VERB
cana-1758	27	18	software	software	NOUN
cana-1758	27	19	defect	defect	NOUN
cana-1758	27	20	prediction	prediction	NOUN
cana-1758	27	21	models	model	NOUN
cana-1758	27	22	may	may	AUX
cana-1758	27	23	be	be	AUX
cana-1758	27	24	more	more	ADJ
cana-1758	27	25	cost	cost	NOUN
cana-1758	27	26	effective	effective	ADJ
cana-1758	27	27	than	than	ADP
cana-1758	27	28	the	the	DET
cana-1758	27	29	probability	probability	NOUN
cana-1758	27	30	of	of	ADP
cana-1758	27	31	identifying	identify	VERB
cana-1758	27	32	defects	defect	NOUN
cana-1758	27	33	through	through	ADP
cana-1758	27	34	software	software	NOUN
cana-1758	27	35	inspections	inspection	NOUN
cana-1758	27	36	.	.	PUNCT
cana-1758	28	1	a	a	DET
cana-1758	28	2	subject	subject	NOUN
cana-1758	28	3	of	of	ADP
cana-1758	28	4	study	study	NOUN
cana-1758	28	5	that	that	PRON
cana-1758	28	6	is	be	AUX
cana-1758	28	7	still	still	ADV
cana-1758	28	8	in	in	ADP
cana-1758	28	9	progress	progress	NOUN
cana-1758	28	10	,	,	PUNCT
cana-1758	28	11	software	software	NOUN
cana-1758	28	12	defect	defect	NOUN
cana-1758	28	13	prediction	prediction	NOUN
cana-1758	28	14	is	be	AUX
cana-1758	28	15	concerned	concern	VERB
cana-1758	28	16	with	with	ADP
cana-1758	28	17	the	the	DET
cana-1758	28	18	discovery	discovery	NOUN
cana-1758	28	19	of	of	ADP
cana-1758	28	20	software	software	NOUN
cana-1758	28	21	flaws	flaw	NOUN
cana-1758	28	22	through	through	ADP
cana-1758	28	23	the	the	DET
cana-1758	28	24	application	application	NOUN
cana-1758	28	25	of	of	ADP
cana-1758	28	26	algorithmic	algorithmic	ADJ
cana-1758	28	27	and	and	CCONJ
cana-1758	28	28	data	data	NOUN
cana-1758	28	29	-	-	PUNCT
cana-1758	28	30	level	level	NOUN
cana-1758	28	31	methodologies	methodology	NOUN
cana-1758	28	32	.	.	PUNCT
cana-1758	29	1	mahesh	mahesh	PROPN
cana-1758	29	2	kumar	kumar	PROPN
cana-1758	29	3	et	et	PROPN
cana-1758	29	4	al	al	PROPN
cana-1758	29	5	.	.	PROPN
cana-1758	29	6	provide	provide	VERB
cana-1758	29	7	an	an	DET
cana-1758	29	8	explanation	explanation	NOUN
cana-1758	29	9	of	of	ADP
cana-1758	29	10	the	the	DET
cana-1758	29	11	characteristics	characteristic	NOUN
cana-1758	29	12	of	of	ADP
cana-1758	29	13	each	each	DET
cana-1758	29	14	technique	technique	NOUN
cana-1758	29	15	as	as	ADV
cana-1758	29	16	well	well	ADV
cana-1758	29	17	as	as	ADP
cana-1758	29	18	their	their	PRON
cana-1758	29	19	applicability	applicability	NOUN
cana-1758	29	20	to	to	ADP
cana-1758	29	21	the	the	DET
cana-1758	29	22	phenomenon	phenomenon	NOUN
cana-1758	29	23	of	of	ADP
cana-1758	29	24	programming	programming	NOUN
cana-1758	29	25	defect	defect	NOUN
cana-1758	29	26	prediction	prediction	NOUN
cana-1758	29	27	throughout	throughout	ADP
cana-1758	29	28	the	the	DET
cana-1758	29	29	various	various	ADJ
cana-1758	29	30	phases	phase	NOUN
cana-1758	29	31	of	of	ADP
cana-1758	29	32	the	the	DET
cana-1758	29	33	programming	programming	NOUN
cana-1758	29	34	development	development	NOUN
cana-1758	29	35	life	life	NOUN
cana-1758	29	36	cycle	cycle	NOUN
cana-1758	29	37	.	.	PUNCT
cana-1758	30	1	expert	expert	ADJ
cana-1758	30	2	opinions	opinion	NOUN
cana-1758	30	3	are	be	AUX
cana-1758	30	4	the	the	DET
cana-1758	30	5	quickest	quick	ADJ
cana-1758	30	6	and	and	CCONJ
cana-1758	30	7	easiest	easiest	ADJ
cana-1758	30	8	way	way	NOUN
cana-1758	30	9	to	to	PART
cana-1758	30	10	receive	receive	VERB
cana-1758	30	11	software	software	NOUN
cana-1758	30	12	inspection	inspection	NOUN
cana-1758	30	13	;	;	PUNCT
cana-1758	30	14	nevertheless	nevertheless	ADV
cana-1758	30	15	,	,	PUNCT
cana-1758	30	16	there	there	PRON
cana-1758	30	17	is	be	VERB
cana-1758	30	18	a	a	DET
cana-1758	30	19	large	large	ADJ
cana-1758	30	20	degree	degree	NOUN
cana-1758	30	21	of	of	ADP
cana-1758	30	22	prediction	prediction	NOUN
cana-1758	30	23	uncertainty	uncertainty	NOUN
cana-1758	30	24	,	,	PUNCT
cana-1758	30	25	and	and	CCONJ
cana-1758	30	26	the	the	DET
cana-1758	30	27	prediction	prediction	NOUN
cana-1758	30	28	may	may	AUX
cana-1758	30	29	be	be	AUX
cana-1758	30	30	impacted	impact	VERB
cana-1758	30	31	by	by	ADP
cana-1758	30	32	personal	personal	ADJ
cana-1758	30	33	biases	bias	NOUN
cana-1758	30	34	.	.	PUNCT
cana-1758	31	1	despite	despite	SCONJ
cana-1758	31	2	this	this	DET
cana-1758	31	3	factor	factor	NOUN
cana-1758	31	4	,	,	PUNCT
cana-1758	31	5	expert	expert	ADJ
cana-1758	31	6	opinions	opinion	NOUN
cana-1758	31	7	are	be	AUX
cana-1758	31	8	the	the	DET
cana-1758	31	9	most	most	ADV
cana-1758	31	10	straightforward	straightforward	ADJ
cana-1758	31	11	technique	technique	NOUN
cana-1758	31	12	.	.	PUNCT
cana-1758	32	1	however	however	ADV
cana-1758	32	2	,	,	PUNCT
cana-1758	32	3	when	when	SCONJ
cana-1758	32	4	dealing	deal	VERB
cana-1758	32	5	with	with	ADP
cana-1758	32	6	huge	huge	ADJ
cana-1758	32	7	volumes	volume	NOUN
cana-1758	32	8	of	of	ADP
cana-1758	32	9	data	datum	NOUN
cana-1758	32	10	,	,	PUNCT
cana-1758	32	11	models	model	NOUN
cana-1758	32	12	that	that	PRON
cana-1758	32	13	are	be	AUX
cana-1758	32	14	based	base	VERB
cana-1758	32	15	on	on	ADP
cana-1758	32	16	machine	machine	NOUN
cana-1758	32	17	learning	learning	NOUN
cana-1758	32	18	improve	improve	VERB
cana-1758	32	19	the	the	DET
cana-1758	32	20	accuracy	accuracy	NOUN
cana-1758	32	21	of	of	ADP
cana-1758	32	22	their	their	PRON
cana-1758	32	23	predictions	prediction	NOUN
cana-1758	32	24	by	by	ADP
cana-1758	32	25	altering	alter	VERB
cana-1758	32	26	the	the	DET
cana-1758	32	27	values	value	NOUN
cana-1758	32	28	of	of	ADP
cana-1758	32	29	their	their	PRON
cana-1758	32	30	parameters	parameter	NOUN
cana-1758	32	31	[	[	X
cana-1758	32	32	1].through	1].through	NUM
cana-1758	32	33	the	the	DET
cana-1758	32	34	utilization	utilization	NOUN
cana-1758	32	35	of	of	ADP
cana-1758	32	36	ensemble	ensemble	ADJ
cana-1758	32	37	learning	learning	NOUN
cana-1758	32	38	,	,	PUNCT
cana-1758	32	39	it	it	PRON
cana-1758	32	40	is	be	AUX
cana-1758	32	41	possible	possible	ADJ
cana-1758	32	42	to	to	PART
cana-1758	32	43	improve	improve	VERB
cana-1758	32	44	the	the	DET
cana-1758	32	45	performance	performance	NOUN
cana-1758	32	46	of	of	ADP
cana-1758	32	47	the	the	DET
cana-1758	32	48	model	model	NOUN
cana-1758	32	49	or	or	CCONJ
cana-1758	32	50	reduce	reduce	VERB
cana-1758	32	51	the	the	DET
cana-1758	32	52	risk	risk	NOUN
cana-1758	32	53	of	of	ADP
cana-1758	32	54	selecting	select	VERB
cana-1758	32	55	an	an	DET
cana-1758	32	56	inaccurate	inaccurate	ADJ
cana-1758	32	57	model	model	NOUN
cana-1758	32	58	.	.	PUNCT
cana-1758	33	1	when	when	SCONJ
cana-1758	33	2	it	it	PRON
cana-1758	33	3	comes	come	VERB
cana-1758	33	4	to	to	ADP
cana-1758	33	5	ensemble	ensemble	ADJ
cana-1758	33	6	learning	learning	NOUN
cana-1758	33	7	,	,	PUNCT
cana-1758	33	8	there	there	PRON
cana-1758	33	9	are	be	VERB
cana-1758	33	10	three	three	NUM
cana-1758	33	11	fundamental	fundamental	ADJ
cana-1758	33	12	types	type	NOUN
cana-1758	33	13	:	:	PUNCT
cana-1758	33	14	bagging	bagging	NOUN
cana-1758	33	15	,	,	PUNCT
cana-1758	33	16	boosting	boost	VERB
cana-1758	33	17	,	,	PUNCT
cana-1758	33	18	and	and	CCONJ
cana-1758	33	19	stacking	stacking	NOUN
cana-1758	33	20	.	.	PUNCT
cana-1758	34	1	it	it	PRON
cana-1758	34	2	is	be	AUX
cana-1758	34	3	possible	possible	ADJ
cana-1758	34	4	to	to	PART
cana-1758	34	5	generate	generate	VERB
cana-1758	34	6	statistical	statistical	ADJ
cana-1758	34	7	distributions	distribution	NOUN
cana-1758	34	8	and	and	CCONJ
cana-1758	34	9	confidence	confidence	NOUN
cana-1758	34	10	intervals	interval	NOUN
cana-1758	34	11	with	with	ADP
cana-1758	34	12	the	the	DET
cana-1758	34	13	use	use	NOUN
cana-1758	34	14	of	of	ADP
cana-1758	34	15	bagging	bagging	NOUN
cana-1758	34	16	.	.	PUNCT
cana-1758	35	1	by	by	ADP
cana-1758	35	2	use	use	NOUN
cana-1758	35	3	boosting	boost	VERB
cana-1758	35	4	,	,	PUNCT
cana-1758	35	5	bias	bias	NOUN
cana-1758	35	6	can	can	AUX
cana-1758	35	7	be	be	AUX
cana-1758	35	8	reduced	reduce	VERB
cana-1758	35	9	to	to	ADP
cana-1758	35	10	a	a	DET
cana-1758	35	11	minimum	minimum	NOUN
cana-1758	35	12	.	.	PUNCT
cana-1758	36	1	through	through	ADP
cana-1758	36	2	stacking	stacking	NOUN
cana-1758	36	3	,	,	PUNCT
cana-1758	36	4	the	the	DET
cana-1758	36	5	most	most	ADV
cana-1758	36	6	effective	effective	ADJ
cana-1758	36	7	method	method	NOUN
cana-1758	36	8	of	of	ADP
cana-1758	36	9	integrating	integrate	VERB
cana-1758	36	10	machine	machine	NOUN
cana-1758	36	11	learning	learning	NOUN
cana-1758	36	12	models	model	NOUN
cana-1758	36	13	can	can	AUX
cana-1758	36	14	be	be	AUX
cana-1758	36	15	identified	identify	VERB
cana-1758	36	16	[	[	PUNCT
cana-1758	36	17	2	2	NUM
cana-1758	36	18	]	]	PUNCT
cana-1758	36	19	.	.	PUNCT
cana-1758	37	1	2	2	X
cana-1758	37	2	.	.	X
cana-1758	37	3	related	relate	VERB
cana-1758	37	4	work	work	NOUN
cana-1758	37	5	when	when	SCONJ
cana-1758	37	6	it	it	PRON
cana-1758	37	7	comes	come	VERB
cana-1758	37	8	to	to	ADP
cana-1758	37	9	constructing	construct	VERB
cana-1758	37	10	prediction	prediction	NOUN
cana-1758	37	11	models	model	NOUN
cana-1758	37	12	,	,	PUNCT
cana-1758	37	13	there	there	PRON
cana-1758	37	14	are	be	VERB
cana-1758	37	15	a	a	DET
cana-1758	37	16	variety	variety	NOUN
cana-1758	37	17	of	of	ADP
cana-1758	37	18	data	datum	NOUN
cana-1758	37	19	level	level	NOUN
cana-1758	37	20	and	and	CCONJ
cana-1758	37	21	algorithm	algorithm	NOUN
cana-1758	37	22	approaches	approach	NOUN
cana-1758	37	23	that	that	PRON
cana-1758	37	24	may	may	AUX
cana-1758	37	25	be	be	AUX
cana-1758	37	26	utilized	utilize	VERB
cana-1758	37	27	.	.	PUNCT
cana-1758	38	1	some	some	DET
cana-1758	38	2	examples	example	NOUN
cana-1758	38	3	of	of	ADP
cana-1758	38	4	these	these	DET
cana-1758	38	5	approaches	approach	NOUN
cana-1758	38	6	are	be	AUX
cana-1758	38	7	data	datum	NOUN
cana-1758	38	8	balance	balance	NOUN
cana-1758	38	9	,	,	PUNCT
cana-1758	38	10	feature	feature	NOUN
cana-1758	38	11	selection	selection	NOUN
cana-1758	38	12	,	,	PUNCT
cana-1758	38	13	and	and	CCONJ
cana-1758	38	14	machine	machine	NOUN
cana-1758	38	15	learning	learn	VERB
cana-1758	38	16	algorithms	algorithm	NOUN
cana-1758	38	17	.	.	PUNCT
cana-1758	39	1	there	there	PRON
cana-1758	39	2	are	be	VERB
cana-1758	39	3	numerous	numerous	ADJ
cana-1758	39	4	academics	academic	NOUN
cana-1758	39	5	and	and	CCONJ
cana-1758	39	6	academicians	academician	NOUN
cana-1758	39	7	who	who	PRON
cana-1758	39	8	have	have	AUX
cana-1758	39	9	made	make	VERB
cana-1758	39	10	contributions	contribution	NOUN
cana-1758	39	11	to	to	ADP
cana-1758	39	12	the	the	DET
cana-1758	39	13	field	field	NOUN
cana-1758	39	14	of	of	ADP
cana-1758	39	15	defect	defect	ADJ
cana-1758	39	16	prediction	prediction	NOUN
cana-1758	39	17	using	use	VERB
cana-1758	39	18	both	both	CCONJ
cana-1758	39	19	an	an	DET
cana-1758	39	20	empirical	empirical	ADJ
cana-1758	39	21	and	and	CCONJ
cana-1758	39	22	conceptual	conceptual	ADJ
cana-1758	39	23	approach	approach	NOUN
cana-1758	39	24	.	.	PUNCT
cana-1758	40	1	these	these	DET
cana-1758	40	2	contributions	contribution	NOUN
cana-1758	40	3	are	be	AUX
cana-1758	40	4	described	describe	VERB
cana-1758	40	5	in	in	ADP
cana-1758	40	6	the	the	DET
cana-1758	40	7	literature	literature	NOUN
cana-1758	40	8	.	.	PUNCT
cana-1758	41	1	the	the	DET
cana-1758	41	2	contributions	contribution	NOUN
cana-1758	41	3	of	of	ADP
cana-1758	41	4	many	many	ADJ
cana-1758	41	5	researchers	researcher	NOUN
cana-1758	41	6	to	to	ADP
cana-1758	41	7	the	the	DET
cana-1758	41	8	prediction	prediction	NOUN
cana-1758	41	9	of	of	ADP
cana-1758	41	10	software	software	NOUN
cana-1758	41	11	defects	defect	NOUN
cana-1758	41	12	are	be	AUX
cana-1758	41	13	presented	present	VERB
cana-1758	41	14	in	in	ADP
cana-1758	41	15	table	table	NOUN
cana-1758	41	16	1	1	NUM
cana-1758	41	17	.	.	PUNCT
cana-1758	42	1	communications	communication	NOUN
cana-1758	42	2	on	on	ADP
cana-1758	42	3	applied	apply	VERB
cana-1758	42	4	nonlinear	nonlinear	ADJ
cana-1758	42	5	analysis	analysis	NOUN
cana-1758	42	6	issn	issn	NOUN
cana-1758	42	7	:	:	PUNCT
cana-1758	42	8	1074	1074	NUM
cana-1758	42	9	-	-	PUNCT
cana-1758	42	10	133x	133x	NUM
cana-1758	42	11	vol	vol	NOUN
cana-1758	42	12	32	32	NUM
cana-1758	42	13	no	no	NOUN
cana-1758	42	14	.	.	NOUN
cana-1758	42	15	2	2	NUM
cana-1758	42	16	(	(	PUNCT
cana-1758	42	17	2025	2025	NUM
cana-1758	42	18	)	)	PUNCT
cana-1758	42	19	461	461	NUM
cana-1758	42	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	42	21	table	table	NOUN
cana-1758	42	22	1	1	NUM
cana-1758	42	23	.	.	PUNCT
cana-1758	43	1	summary	summary	NOUN
cana-1758	43	2	of	of	ADP
cana-1758	43	3	related	relate	VERB
cana-1758	43	4	works	work	NOUN
cana-1758	43	5	classifier	classifier	NOUN
cana-1758	43	6	used	use	VERB
cana-1758	43	7	dataset	dataset	NOUN
cana-1758	43	8	used	use	VERB
cana-1758	43	9	evaluation	evaluation	NOUN
cana-1758	43	10	measures	measure	NOUN
cana-1758	43	11	future	future	ADJ
cana-1758	43	12	work	work	NOUN
cana-1758	43	13	/	/	SYM
cana-1758	43	14	scope	scope	NOUN
cana-1758	43	15	svm	svm	NOUN
cana-1758	43	16	[	[	X
cana-1758	43	17	3	3	NUM
cana-1758	43	18	]	]	SYM
cana-1758	43	19	cm1	cm1	PROPN
cana-1758	43	20	,	,	PUNCT
cana-1758	43	21	pc1	pc1	PROPN
cana-1758	43	22	,	,	PUNCT
cana-1758	43	23	jm1	jm1	PROPN
cana-1758	43	24	,	,	PUNCT
cana-1758	43	25	pc3	pc3	ADJ
cana-1758	43	26	,	,	PUNCT
cana-1758	43	27	kc1	kc1	NOUN
cana-1758	43	28	,	,	PUNCT
cana-1758	43	29	eq	eq	NOUN
cana-1758	43	30	and	and	CCONJ
cana-1758	43	31	jdt	jdt	PROPN
cana-1758	43	32	precision	precision	PROPN
cana-1758	43	33	,	,	PUNCT
cana-1758	43	34	recall	recall	NOUN
cana-1758	43	35	,	,	PUNCT
cana-1758	43	36	f1	f1	NOUN
cana-1758	43	37	-	-	PUNCT
cana-1758	43	38	score	score	NOUN
cana-1758	43	39	,	,	PUNCT
cana-1758	43	40	and	and	CCONJ
cana-1758	43	41	accuracy	accuracy	NOUN
cana-1758	43	42	model	model	NOUN
cana-1758	43	43	can	can	AUX
cana-1758	43	44	be	be	AUX
cana-1758	43	45	evaluated	evaluate	VERB
cana-1758	43	46	on	on	ADP
cana-1758	43	47	different	different	ADJ
cana-1758	43	48	datasets	dataset	NOUN
cana-1758	43	49	to	to	PART
cana-1758	43	50	ensure	ensure	VERB
cana-1758	43	51	its	its	PRON
cana-1758	43	52	performance	performance	NOUN
cana-1758	43	53	.	.	PUNCT
cana-1758	44	1	nb	nb	INTJ
cana-1758	44	2	,	,	PUNCT
cana-1758	44	3	j48	j48	PROPN
cana-1758	44	4	,	,	PUNCT
cana-1758	44	5	rf	rf	NOUN
cana-1758	44	6	,	,	PUNCT
cana-1758	44	7	svm	svm	NOUN
cana-1758	44	8	,	,	PUNCT
cana-1758	44	9	adaboost	adaboost	ADV
cana-1758	44	10	[	[	X
cana-1758	44	11	4	4	NUM
cana-1758	44	12	]	]	X
cana-1758	44	13	eclipse	eclipse	NOUN
cana-1758	44	14	,	,	PUNCT
cana-1758	44	15	columba	columba	NOUN
cana-1758	44	16	,	,	PUNCT
cana-1758	44	17	and	and	CCONJ
cana-1758	44	18	scarab	scarab	NOUN
cana-1758	44	19	f1	f1	NOUN
cana-1758	44	20	-	-	PUNCT
cana-1758	44	21	measure	measure	NOUN
cana-1758	44	22	and	and	CCONJ
cana-1758	44	23	roc	roc	PROPN
cana-1758	44	24	feature	feature	NOUN
cana-1758	44	25	selection	selection	NOUN
cana-1758	44	26	can	can	AUX
cana-1758	44	27	be	be	AUX
cana-1758	44	28	tested	test	VERB
cana-1758	44	29	with	with	ADP
cana-1758	44	30	deep	deep	ADJ
cana-1758	44	31	learning	learning	NOUN
cana-1758	44	32	adaboost	adaboost	ADV
cana-1758	44	33	,	,	PUNCT
cana-1758	44	34	knn	knn	PROPN
cana-1758	44	35	,	,	PUNCT
cana-1758	44	36	lr	lr	INTJ
cana-1758	44	37	,	,	PUNCT
cana-1758	44	38	nb	nb	PROPN
cana-1758	45	1	and	and	CCONJ
cana-1758	45	2	rf	rf	PRON
cana-1758	46	1	[	[	X
cana-1758	46	2	5	5	NUM
cana-1758	46	3	]	]	PUNCT
cana-1758	46	4	promise	promise	VERB
cana-1758	46	5	repository	repository	NOUN
cana-1758	46	6	accuracy	accuracy	NOUN
cana-1758	46	7	,	,	PUNCT
cana-1758	46	8	fmeasure	fmeasure	NOUN
cana-1758	46	9	,	,	PUNCT
cana-1758	46	10	specificity	specificity	NOUN
cana-1758	46	11	,	,	PUNCT
cana-1758	46	12	g	g	PROPN
cana-1758	46	13	mean	mean	VERB
cana-1758	46	14	can	can	AUX
cana-1758	46	15	be	be	AUX
cana-1758	46	16	extended	extend	VERB
cana-1758	46	17	to	to	PART
cana-1758	46	18	predict	predict	VERB
cana-1758	46	19	cross	cross	ADJ
cana-1758	46	20	-	-	ADJ
cana-1758	46	21	project	project	ADJ
cana-1758	46	22	defects	defect	NOUN
cana-1758	46	23	.	.	PUNCT
cana-1758	47	1	knn	knn	PROPN
cana-1758	47	2	,	,	PUNCT
cana-1758	47	3	nb	nb	INTJ
cana-1758	47	4	,	,	PUNCT
cana-1758	47	5	svm	svm	VERB
cana-1758	47	6	[	[	X
cana-1758	47	7	6	6	NUM
cana-1758	47	8	]	]	PUNCT
cana-1758	47	9	promise	promise	NOUN
cana-1758	47	10	repository	repository	PROPN
cana-1758	47	11	roc	roc	PROPN
cana-1758	47	12	curve	curve	NOUN
cana-1758	47	13	can	can	AUX
cana-1758	47	14	be	be	AUX
cana-1758	47	15	tested	test	VERB
cana-1758	47	16	on	on	ADP
cana-1758	47	17	large	large	ADJ
cana-1758	47	18	datasets	dataset	NOUN
cana-1758	47	19	.	.	PUNCT
cana-1758	48	1	svm	svm	PROPN
cana-1758	48	2	,	,	PUNCT
cana-1758	48	3	nb	nb	INTJ
cana-1758	48	4	,	,	PUNCT
cana-1758	48	5	rf	rf	VERB
cana-1758	48	6	[	[	X
cana-1758	48	7	7	7	NUM
cana-1758	48	8	]	]	SYM
cana-1758	48	9	cm1	cm1	PROPN
cana-1758	48	10	,	,	PUNCT
cana-1758	48	11	kc1	kc1	PROPN
cana-1758	48	12	,	,	PUNCT
cana-1758	48	13	mc1	mc1	PROPN
cana-1758	48	14	,	,	PUNCT
cana-1758	48	15	pc1	pc1	PROPN
cana-1758	48	16	,	,	PUNCT
cana-1758	48	17	jm1	jm1	PROPN
cana-1758	48	18	,	,	PUNCT
cana-1758	48	19	mw1	mw1	NOUN
cana-1758	48	20	,	,	PUNCT
cana-1758	48	21	pc2	pc2	PROPN
cana-1758	48	22	,	,	PUNCT
cana-1758	48	23	mcc	mcc	NOUN
cana-1758	48	24	,	,	PUNCT
cana-1758	48	25	roc	roc	PROPN
cana-1758	48	26	,	,	PUNCT
cana-1758	48	27	prc	prc	PROPN
cana-1758	48	28	,	,	PUNCT
cana-1758	48	29	f1	f1	ADJ
cana-1758	48	30	-	-	PUNCT
cana-1758	48	31	score	score	NOUN
cana-1758	48	32	model	model	NOUN
cana-1758	48	33	can	can	AUX
cana-1758	48	34	be	be	AUX
cana-1758	48	35	build	build	VERB
cana-1758	48	36	using	use	VERB
cana-1758	48	37	deep	deep	ADJ
cana-1758	48	38	learning	learning	NOUN
cana-1758	48	39	when	when	SCONJ
cana-1758	48	40	it	it	PRON
cana-1758	48	41	comes	come	VERB
cana-1758	48	42	to	to	ADP
cana-1758	48	43	improving	improve	VERB
cana-1758	48	44	the	the	DET
cana-1758	48	45	performance	performance	NOUN
cana-1758	48	46	of	of	ADP
cana-1758	48	47	defect	defect	ADJ
cana-1758	48	48	prediction	prediction	NOUN
cana-1758	48	49	models	model	NOUN
cana-1758	48	50	,	,	PUNCT
cana-1758	48	51	feature	feature	NOUN
cana-1758	48	52	selection	selection	NOUN
cana-1758	48	53	and	and	CCONJ
cana-1758	48	54	data	datum	NOUN
cana-1758	48	55	balancing	balancing	NOUN
cana-1758	48	56	are	be	AUX
cana-1758	48	57	two	two	NUM
cana-1758	48	58	crucial	crucial	ADJ
cana-1758	48	59	factors	factor	NOUN
cana-1758	48	60	to	to	PART
cana-1758	48	61	account	account	VERB
cana-1758	48	62	for	for	ADP
cana-1758	48	63	.	.	PUNCT
cana-1758	49	1	in	in	ADP
cana-1758	49	2	the	the	DET
cana-1758	49	3	event	event	NOUN
cana-1758	49	4	where	where	SCONJ
cana-1758	49	5	the	the	DET
cana-1758	49	6	inaccuracy	inaccuracy	NOUN
cana-1758	49	7	increases	increase	VERB
cana-1758	49	8	as	as	ADP
cana-1758	49	9	a	a	DET
cana-1758	49	10	result	result	NOUN
cana-1758	49	11	of	of	ADP
cana-1758	49	12	changing	change	VERB
cana-1758	49	13	feature	feature	NOUN
cana-1758	49	14	values	value	NOUN
cana-1758	49	15	,	,	PUNCT
cana-1758	49	16	the	the	DET
cana-1758	49	17	software	software	NOUN
cana-1758	49	18	feature	feature	NOUN
cana-1758	49	19	turns	turn	VERB
cana-1758	49	20	into	into	ADP
cana-1758	49	21	an	an	DET
cana-1758	49	22	important	important	ADJ
cana-1758	49	23	factor	factor	NOUN
cana-1758	49	24	for	for	ADP
cana-1758	49	25	the	the	DET
cana-1758	49	26	prediction	prediction	NOUN
cana-1758	49	27	.	.	PUNCT
cana-1758	50	1	robust	robust	ADJ
cana-1758	50	2	machine	machine	NOUN
cana-1758	50	3	learning	learning	NOUN
cana-1758	50	4	models	model	NOUN
cana-1758	50	5	are	be	AUX
cana-1758	50	6	built	build	VERB
cana-1758	50	7	by	by	ADP
cana-1758	50	8	means	mean	NOUN
cana-1758	50	9	of	of	ADP
cana-1758	50	10	the	the	DET
cana-1758	50	11	complicated	complicated	ADJ
cana-1758	50	12	interaction	interaction	NOUN
cana-1758	50	13	between	between	ADP
cana-1758	50	14	software	software	NOUN
cana-1758	50	15	attributes	attribute	VERB
cana-1758	50	16	,	,	PUNCT
cana-1758	50	17	and	and	CCONJ
cana-1758	50	18	shapley	shapley	NOUN
cana-1758	50	19	values	value	NOUN
cana-1758	50	20	can	can	AUX
cana-1758	50	21	be	be	AUX
cana-1758	50	22	utilized	utilize	VERB
cana-1758	50	23	in	in	ADP
cana-1758	50	24	order	order	NOUN
cana-1758	50	25	to	to	PART
cana-1758	50	26	compute	compute	VERB
cana-1758	50	27	the	the	DET
cana-1758	50	28	degree	degree	NOUN
cana-1758	50	29	of	of	ADP
cana-1758	50	30	complexity	complexity	NOUN
cana-1758	50	31	that	that	PRON
cana-1758	50	32	exists	exist	VERB
cana-1758	50	33	between	between	ADP
cana-1758	50	34	these	these	DET
cana-1758	50	35	attributes	attribute	NOUN
cana-1758	50	36	[	[	X
cana-1758	50	37	8	8	NUM
cana-1758	50	38	]	]	PUNCT
cana-1758	50	39	.	.	PUNCT
cana-1758	51	1	it	it	PRON
cana-1758	51	2	is	be	AUX
cana-1758	51	3	possible	possible	ADJ
cana-1758	51	4	to	to	PART
cana-1758	51	5	improve	improve	VERB
cana-1758	51	6	the	the	DET
cana-1758	51	7	quality	quality	NOUN
cana-1758	51	8	of	of	ADP
cana-1758	51	9	software	software	NOUN
cana-1758	51	10	defect	defect	NOUN
cana-1758	51	11	prediction	prediction	NOUN
cana-1758	51	12	by	by	ADP
cana-1758	51	13	including	include	VERB
cana-1758	51	14	the	the	DET
cana-1758	51	15	best	well	ADV
cana-1758	51	16	selected	select	VERB
cana-1758	51	17	features	feature	NOUN
cana-1758	51	18	into	into	ADP
cana-1758	51	19	the	the	DET
cana-1758	51	20	random	random	ADJ
cana-1758	51	21	forest	forest	NOUN
cana-1758	51	22	classification	classification	NOUN
cana-1758	51	23	process	process	NOUN
cana-1758	51	24	.	.	PUNCT
cana-1758	52	1	this	this	PRON
cana-1758	52	2	will	will	AUX
cana-1758	52	3	result	result	VERB
cana-1758	52	4	in	in	ADP
cana-1758	52	5	more	more	ADV
cana-1758	52	6	accurate	accurate	ADJ
cana-1758	52	7	results	result	NOUN
cana-1758	52	8	than	than	SCONJ
cana-1758	52	9	would	would	AUX
cana-1758	52	10	otherwise	otherwise	ADV
cana-1758	52	11	be	be	AUX
cana-1758	52	12	possible	possible	ADJ
cana-1758	52	13	[	[	X
cana-1758	52	14	9	9	NUM
cana-1758	52	15	]	]	PUNCT
cana-1758	52	16	.	.	PUNCT
cana-1758	53	1	using	use	VERB
cana-1758	53	2	heterogeneous	heterogeneous	ADJ
cana-1758	53	3	attribute	attribute	NOUN
cana-1758	53	4	selection	selection	NOUN
cana-1758	53	5	and	and	CCONJ
cana-1758	53	6	hierarchical	hierarchical	ADJ
cana-1758	53	7	stacking	stacking	NOUN
cana-1758	53	8	,	,	PUNCT
cana-1758	53	9	the	the	DET
cana-1758	53	10	author	author	NOUN
cana-1758	53	11	presents	present	VERB
cana-1758	53	12	a	a	DET
cana-1758	53	13	paradigm	paradigm	NOUN
cana-1758	53	14	for	for	ADP
cana-1758	53	15	forecasting	forecasting	NOUN
cana-1758	53	16	software	software	NOUN
cana-1758	53	17	faults	fault	NOUN
cana-1758	53	18	.	.	PUNCT
cana-1758	54	1	this	this	DET
cana-1758	54	2	paradigm	paradigm	NOUN
cana-1758	54	3	is	be	AUX
cana-1758	54	4	founded	found	VERB
cana-1758	54	5	on	on	ADP
cana-1758	54	6	the	the	DET
cana-1758	54	7	idea	idea	NOUN
cana-1758	54	8	that	that	SCONJ
cana-1758	54	9	software	software	NOUN
cana-1758	54	10	might	might	AUX
cana-1758	54	11	have	have	AUX
cana-1758	54	12	errors	error	NOUN
cana-1758	54	13	.	.	PUNCT
cana-1758	55	1	in	in	ADP
cana-1758	55	2	order	order	NOUN
cana-1758	55	3	to	to	PART
cana-1758	55	4	improve	improve	VERB
cana-1758	55	5	the	the	DET
cana-1758	55	6	performance	performance	NOUN
cana-1758	55	7	of	of	ADP
cana-1758	55	8	the	the	DET
cana-1758	55	9	model	model	NOUN
cana-1758	55	10	nestedstacking	nestedstacke	VERB
cana-1758	55	11	involves	involve	VERB
cana-1758	55	12	the	the	DET
cana-1758	55	13	utilization	utilization	NOUN
cana-1758	55	14	of	of	ADP
cana-1758	55	15	heterogeneous	heterogeneous	ADJ
cana-1758	55	16	attribute	attribute	NOUN
cana-1758	55	17	selection	selection	NOUN
cana-1758	55	18	methods	method	NOUN
cana-1758	55	19	in	in	ADP
cana-1758	55	20	conjunction	conjunction	NOUN
cana-1758	55	21	with	with	ADP
cana-1758	55	22	normalization	normalization	NOUN
cana-1758	55	23	in	in	ADP
cana-1758	55	24	order	order	NOUN
cana-1758	55	25	to	to	PART
cana-1758	55	26	enhance	enhance	VERB
cana-1758	55	27	the	the	DET
cana-1758	55	28	quality	quality	NOUN
cana-1758	55	29	of	of	ADP
cana-1758	55	30	the	the	DET
cana-1758	55	31	data	datum	NOUN
cana-1758	55	32	[	[	X
cana-1758	55	33	10	10	NUM
cana-1758	55	34	]	]	PUNCT
cana-1758	55	35	.	.	PUNCT
cana-1758	56	1	using	use	VERB
cana-1758	56	2	a	a	DET
cana-1758	56	3	decision	decision	NOUN
cana-1758	56	4	tree	tree	NOUN
cana-1758	56	5	induction	induction	NOUN
cana-1758	56	6	,	,	PUNCT
cana-1758	56	7	gayatri	gayatri	NOUN
cana-1758	56	8	and	and	CCONJ
cana-1758	56	9	colleagues	colleague	NOUN
cana-1758	56	10	are	be	AUX
cana-1758	56	11	able	able	ADJ
cana-1758	56	12	to	to	PART
cana-1758	56	13	determine	determine	VERB
cana-1758	56	14	which	which	PRON
cana-1758	56	15	traits	trait	NOUN
cana-1758	56	16	are	be	AUX
cana-1758	56	17	appropriate	appropriate	ADJ
cana-1758	56	18	.	.	PUNCT
cana-1758	57	1	each	each	PRON
cana-1758	57	2	and	and	CCONJ
cana-1758	57	3	every	every	PRON
cana-1758	57	4	characteristic	characteristic	NOUN
cana-1758	57	5	that	that	PRON
cana-1758	57	6	was	be	AUX
cana-1758	57	7	discovered	discover	VERB
cana-1758	57	8	through	through	ADP
cana-1758	57	9	the	the	DET
cana-1758	57	10	application	application	NOUN
cana-1758	57	11	of	of	ADP
cana-1758	57	12	the	the	DET
cana-1758	57	13	decision	decision	NOUN
cana-1758	57	14	tree	tree	NOUN
cana-1758	57	15	induction	induction	NOUN
cana-1758	57	16	rule	rule	NOUN
cana-1758	57	17	is	be	AUX
cana-1758	57	18	included	include	VERB
cana-1758	57	19	in	in	ADP
cana-1758	57	20	the	the	DET
cana-1758	57	21	subset	subset	NOUN
cana-1758	57	22	of	of	ADP
cana-1758	57	23	attributes	attribute	NOUN
cana-1758	57	24	.	.	PUNCT
cana-1758	58	1	the	the	DET
cana-1758	58	2	performance	performance	NOUN
cana-1758	58	3	of	of	ADP
cana-1758	58	4	the	the	DET
cana-1758	58	5	classifier	classifier	NOUN
cana-1758	58	6	is	be	AUX
cana-1758	58	7	improved	improve	VERB
cana-1758	58	8	when	when	SCONJ
cana-1758	58	9	it	it	PRON
cana-1758	58	10	is	be	AUX
cana-1758	58	11	taught	teach	VERB
cana-1758	58	12	this	this	DET
cana-1758	58	13	new	new	ADJ
cana-1758	58	14	feature	feature	NOUN
cana-1758	58	15	set	set	VERB
cana-1758	58	16	by	by	ADP
cana-1758	58	17	utilizing	utilize	VERB
cana-1758	58	18	the	the	DET
cana-1758	58	19	same	same	ADJ
cana-1758	58	20	models	model	NOUN
cana-1758	58	21	[	[	X
cana-1758	58	22	11	11	NUM
cana-1758	58	23	]	]	PUNCT
cana-1758	58	24	.	.	PUNCT
cana-1758	59	1	in	in	ADP
cana-1758	59	2	their	their	PRON
cana-1758	59	3	work	work	NOUN
cana-1758	59	4	,	,	PUNCT
cana-1758	59	5	pham	pham	PROPN
cana-1758	59	6	et	et	PROPN
cana-1758	59	7	al	al	PROPN
cana-1758	59	8	.	.	PROPN
cana-1758	59	9	make	make	VERB
cana-1758	59	10	use	use	NOUN
cana-1758	59	11	of	of	ADP
cana-1758	59	12	probabilistic	probabilistic	ADJ
cana-1758	59	13	categorization	categorization	NOUN
cana-1758	59	14	,	,	PUNCT
cana-1758	59	15	which	which	PRON
cana-1758	59	16	identifies	identify	VERB
cana-1758	59	17	the	the	DET
cana-1758	59	18	class	class	NOUN
cana-1758	59	19	value	value	NOUN
cana-1758	59	20	that	that	PRON
cana-1758	59	21	maximizes	maximize	VERB
cana-1758	59	22	the	the	DET
cana-1758	59	23	posterior	posterior	ADJ
cana-1758	59	24	probability	probability	NOUN
cana-1758	59	25	of	of	ADP
cana-1758	59	26	the	the	DET
cana-1758	59	27	class	class	NOUN
cana-1758	59	28	for	for	ADP
cana-1758	59	29	a	a	DET
cana-1758	59	30	particular	particular	ADJ
cana-1758	59	31	set	set	NOUN
cana-1758	59	32	communications	communication	NOUN
cana-1758	59	33	on	on	ADP
cana-1758	59	34	applied	apply	VERB
cana-1758	59	35	nonlinear	nonlinear	ADJ
cana-1758	59	36	analysis	analysis	NOUN
cana-1758	59	37	issn	issn	NOUN
cana-1758	59	38	:	:	PUNCT
cana-1758	59	39	1074	1074	NUM
cana-1758	59	40	-	-	PUNCT
cana-1758	59	41	133x	133x	NUM
cana-1758	59	42	vol	vol	NOUN
cana-1758	59	43	32	32	NUM
cana-1758	59	44	no	no	NOUN
cana-1758	59	45	.	.	NOUN
cana-1758	59	46	2	2	NUM
cana-1758	59	47	(	(	PUNCT
cana-1758	59	48	2025	2025	NUM
cana-1758	59	49	)	)	PUNCT
cana-1758	59	50	462	462	NUM
cana-1758	59	51	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	59	52	of	of	ADP
cana-1758	59	53	attributes	attribute	NOUN
cana-1758	59	54	[	[	X
cana-1758	59	55	12	12	NUM
cana-1758	59	56	]	]	PUNCT
cana-1758	59	57	.	.	PUNCT
cana-1758	60	1	in	in	ADP
cana-1758	60	2	their	their	PRON
cana-1758	60	3	article	article	NOUN
cana-1758	60	4	,	,	PUNCT
cana-1758	60	5	chennappan	chennappan	PROPN
cana-1758	60	6	et	et	PROPN
cana-1758	60	7	al	al	PROPN
cana-1758	60	8	.	.	PROPN
cana-1758	60	9	discuss	discuss	VERB
cana-1758	60	10	data	datum	NOUN
cana-1758	60	11	balancing	balancing	NOUN
cana-1758	60	12	,	,	PUNCT
cana-1758	60	13	which	which	PRON
cana-1758	60	14	might	might	AUX
cana-1758	60	15	lead	lead	VERB
cana-1758	60	16	to	to	ADP
cana-1758	60	17	biassed	biassed	ADJ
cana-1758	60	18	results	result	NOUN
cana-1758	60	19	for	for	ADP
cana-1758	60	20	a	a	DET
cana-1758	60	21	significant	significant	ADJ
cana-1758	60	22	number	number	NOUN
cana-1758	60	23	of	of	ADP
cana-1758	60	24	classes	class	NOUN
cana-1758	60	25	if	if	SCONJ
cana-1758	60	26	the	the	DET
cana-1758	60	27	data	datum	NOUN
cana-1758	60	28	are	be	AUX
cana-1758	60	29	not	not	PART
cana-1758	60	30	balanced	balanced	ADJ
cana-1758	60	31	or	or	CCONJ
cana-1758	60	32	balanced	balance	VERB
cana-1758	60	33	[	[	X
cana-1758	60	34	13	13	NUM
cana-1758	60	35	]	]	PUNCT
cana-1758	60	36	the	the	DET
cana-1758	60	37	prediction	prediction	NOUN
cana-1758	60	38	of	of	ADP
cana-1758	60	39	defects	defect	NOUN
cana-1758	60	40	appears	appear	VERB
cana-1758	60	41	to	to	PART
cana-1758	60	42	be	be	AUX
cana-1758	60	43	a	a	DET
cana-1758	60	44	significant	significant	ADJ
cana-1758	60	45	difficulty	difficulty	NOUN
cana-1758	60	46	when	when	SCONJ
cana-1758	60	47	dealing	deal	VERB
cana-1758	60	48	with	with	ADP
cana-1758	60	49	vast	vast	ADJ
cana-1758	60	50	volumes	volume	NOUN
cana-1758	60	51	of	of	ADP
cana-1758	60	52	data	datum	NOUN
cana-1758	60	53	;	;	PUNCT
cana-1758	60	54	hence	hence	ADV
cana-1758	60	55	,	,	PUNCT
cana-1758	60	56	numerous	numerous	ADJ
cana-1758	60	57	machine	machine	NOUN
cana-1758	60	58	learning	learning	NOUN
cana-1758	60	59	techniques	technique	NOUN
cana-1758	60	60	have	have	AUX
cana-1758	60	61	been	be	AUX
cana-1758	60	62	utilized	utilize	VERB
cana-1758	60	63	in	in	ADP
cana-1758	60	64	order	order	NOUN
cana-1758	60	65	to	to	PART
cana-1758	60	66	construct	construct	VERB
cana-1758	60	67	prediction	prediction	NOUN
cana-1758	60	68	models	model	NOUN
cana-1758	60	69	.	.	PUNCT
cana-1758	61	1	the	the	DET
cana-1758	61	2	categorization	categorization	NOUN
cana-1758	61	3	process	process	NOUN
cana-1758	61	4	is	be	AUX
cana-1758	61	5	carried	carry	VERB
cana-1758	61	6	out	out	ADP
cana-1758	61	7	by	by	ADP
cana-1758	61	8	random	random	ADJ
cana-1758	61	9	forest	forest	NOUN
cana-1758	61	10	through	through	ADP
cana-1758	61	11	the	the	DET
cana-1758	61	12	utilization	utilization	NOUN
cana-1758	61	13	of	of	ADP
cana-1758	61	14	many	many	ADJ
cana-1758	61	15	decision	decision	NOUN
cana-1758	61	16	trees	tree	NOUN
cana-1758	61	17	that	that	PRON
cana-1758	61	18	collaborate	collaborate	VERB
cana-1758	61	19	to	to	PART
cana-1758	61	20	build	build	VERB
cana-1758	61	21	a	a	DET
cana-1758	61	22	decision	decision	NOUN
cana-1758	61	23	forest	forest	NOUN
cana-1758	61	24	.	.	PUNCT
cana-1758	62	1	with	with	ADP
cana-1758	62	2	a	a	DET
cana-1758	62	3	relatively	relatively	ADV
cana-1758	62	4	high	high	ADJ
cana-1758	62	5	level	level	NOUN
cana-1758	62	6	of	of	ADP
cana-1758	62	7	accuracy	accuracy	NOUN
cana-1758	62	8	,	,	PUNCT
cana-1758	62	9	it	it	PRON
cana-1758	62	10	is	be	AUX
cana-1758	62	11	able	able	ADJ
cana-1758	62	12	to	to	PART
cana-1758	62	13	manage	manage	VERB
cana-1758	62	14	enormous	enormous	ADJ
cana-1758	62	15	datasets	dataset	NOUN
cana-1758	62	16	and	and	CCONJ
cana-1758	62	17	inputs	input	NOUN
cana-1758	62	18	that	that	PRON
cana-1758	62	19	contain	contain	VERB
cana-1758	62	20	multiple	multiple	ADJ
cana-1758	62	21	variables	variable	NOUN
cana-1758	62	22	;	;	PUNCT
cana-1758	62	23	it	it	PRON
cana-1758	62	24	is	be	AUX
cana-1758	62	25	particularly	particularly	ADV
cana-1758	62	26	effective	effective	ADJ
cana-1758	62	27	at	at	ADP
cana-1758	62	28	regulating	regulate	VERB
cana-1758	62	29	imbalanced	imbalanced	ADJ
cana-1758	62	30	datasets	dataset	NOUN
cana-1758	62	31	.	.	PUNCT
cana-1758	63	1	random	random	ADJ
cana-1758	63	2	forest	forest	NOUN
cana-1758	63	3	is	be	AUX
cana-1758	63	4	a	a	DET
cana-1758	63	5	technique	technique	NOUN
cana-1758	63	6	that	that	PRON
cana-1758	63	7	improves	improve	VERB
cana-1758	63	8	the	the	DET
cana-1758	63	9	interaction	interaction	NOUN
cana-1758	63	10	between	between	ADP
cana-1758	63	11	decision	decision	NOUN
cana-1758	63	12	trees	tree	NOUN
cana-1758	63	13	[	[	X
cana-1758	63	14	14	14	NUM
cana-1758	63	15	]	]	PUNCT
cana-1758	63	16	.	.	PUNCT
cana-1758	64	1	multiple	multiple	ADJ
cana-1758	64	2	decision	decision	NOUN
cana-1758	64	3	trees	tree	NOUN
cana-1758	64	4	cast	cast	VERB
cana-1758	64	5	their	their	PRON
cana-1758	64	6	votes	vote	NOUN
cana-1758	64	7	for	for	ADP
cana-1758	64	8	the	the	DET
cana-1758	64	9	outcomes	outcome	NOUN
cana-1758	64	10	that	that	PRON
cana-1758	64	11	better	well	ADJ
cana-1758	64	12	suit	suit	VERB
cana-1758	64	13	their	their	PRON
cana-1758	64	14	preferences	preference	NOUN
cana-1758	64	15	.	.	PUNCT
cana-1758	65	1	through	through	ADP
cana-1758	65	2	the	the	DET
cana-1758	65	3	use	use	NOUN
cana-1758	65	4	of	of	ADP
cana-1758	65	5	the	the	DET
cana-1758	65	6	random	random	ADJ
cana-1758	65	7	forests	forest	NOUN
cana-1758	65	8	methodology	methodology	NOUN
cana-1758	65	9	,	,	PUNCT
cana-1758	65	10	a	a	DET
cana-1758	65	11	limited	limited	ADJ
cana-1758	65	12	number	number	NOUN
cana-1758	65	13	of	of	ADP
cana-1758	65	14	rows	row	NOUN
cana-1758	65	15	are	be	AUX
cana-1758	65	16	chosen	choose	VERB
cana-1758	65	17	at	at	ADP
cana-1758	65	18	random	random	ADJ
cana-1758	65	19	from	from	ADP
cana-1758	65	20	the	the	DET
cana-1758	65	21	overall	overall	ADJ
cana-1758	65	22	quantity	quantity	NOUN
cana-1758	65	23	of	of	ADP
cana-1758	65	24	data	datum	NOUN
cana-1758	65	25	.	.	PUNCT
cana-1758	66	1	subsequently	subsequently	ADV
cana-1758	66	2	,	,	PUNCT
cana-1758	66	3	a	a	DET
cana-1758	66	4	collection	collection	NOUN
cana-1758	66	5	of	of	ADP
cana-1758	66	6	decision	decision	NOUN
cana-1758	66	7	trees	tree	NOUN
cana-1758	66	8	is	be	AUX
cana-1758	66	9	formed	form	VERB
cana-1758	66	10	for	for	ADP
cana-1758	66	11	each	each	DET
cana-1758	66	12	module	module	NOUN
cana-1758	66	13	in	in	ADP
cana-1758	66	14	order	order	NOUN
cana-1758	66	15	to	to	PART
cana-1758	66	16	generate	generate	VERB
cana-1758	66	17	classification	classification	NOUN
cana-1758	66	18	output	output	NOUN
cana-1758	66	19	[	[	X
cana-1758	66	20	15	15	NUM
cana-1758	66	21	]	]	PUNCT
cana-1758	66	22	.	.	PUNCT
cana-1758	67	1	a	a	DET
cana-1758	67	2	novel	novel	ADJ
cana-1758	67	3	categorization	categorization	NOUN
cana-1758	67	4	and	and	CCONJ
cana-1758	67	5	prediction	prediction	NOUN
cana-1758	67	6	strategy	strategy	NOUN
cana-1758	67	7	for	for	ADP
cana-1758	67	8	enhancing	enhance	VERB
cana-1758	67	9	accuracy	accuracy	NOUN
cana-1758	67	10	is	be	AUX
cana-1758	67	11	proposed	propose	VERB
cana-1758	67	12	by	by	ADP
cana-1758	67	13	premalatha	premalatha	PROPN
cana-1758	67	14	et	et	PROPN
cana-1758	67	15	al	al	PROPN
cana-1758	67	16	.	.	PUNCT
cana-1758	68	1	[	[	X
cana-1758	68	2	16	16	NUM
cana-1758	68	3	]	]	PUNCT
cana-1758	68	4	.	.	PUNCT
cana-1758	69	1	this	this	DET
cana-1758	69	2	technique	technique	NOUN
cana-1758	69	3	is	be	AUX
cana-1758	69	4	based	base	VERB
cana-1758	69	5	on	on	ADP
cana-1758	69	6	the	the	DET
cana-1758	69	7	cost	cost	NOUN
cana-1758	69	8	random	random	ADJ
cana-1758	69	9	forest	forest	NOUN
cana-1758	69	10	algorithm	algorithm	NOUN
cana-1758	69	11	,	,	PUNCT
cana-1758	69	12	which	which	PRON
cana-1758	69	13	significantly	significantly	ADV
cana-1758	69	14	reduces	reduce	VERB
cana-1758	69	15	the	the	DET
cana-1758	69	16	impact	impact	NOUN
cana-1758	69	17	of	of	ADP
cana-1758	69	18	errors	error	NOUN
cana-1758	69	19	that	that	PRON
cana-1758	69	20	occur	occur	VERB
cana-1758	69	21	in	in	ADP
cana-1758	69	22	software	software	NOUN
cana-1758	69	23	components	component	NOUN
cana-1758	69	24	that	that	PRON
cana-1758	69	25	are	be	AUX
cana-1758	69	26	not	not	PART
cana-1758	69	27	relevant	relevant	ADJ
cana-1758	69	28	to	to	ADP
cana-1758	69	29	the	the	DET
cana-1758	69	30	problem	problem	NOUN
cana-1758	69	31	at	at	ADP
cana-1758	69	32	hand	hand	NOUN
cana-1758	69	33	.	.	PUNCT
cana-1758	70	1	[	[	X
cana-1758	70	2	17	17	NUM
cana-1758	70	3	]	]	PUNCT
cana-1758	70	4	a	a	DET
cana-1758	70	5	bayesian	bayesian	NOUN
cana-1758	70	6	network	network	NOUN
cana-1758	70	7	is	be	AUX
cana-1758	70	8	a	a	DET
cana-1758	70	9	probabilistic	probabilistic	ADJ
cana-1758	70	10	visual	visual	ADJ
cana-1758	70	11	model	model	NOUN
cana-1758	70	12	that	that	PRON
cana-1758	70	13	displays	display	VERB
cana-1758	70	14	a	a	DET
cana-1758	70	15	joint	joint	ADJ
cana-1758	70	16	distribution	distribution	NOUN
cana-1758	70	17	of	of	ADP
cana-1758	70	18	probabilities	probability	NOUN
cana-1758	70	19	across	across	ADP
cana-1758	70	20	a	a	DET
cana-1758	70	21	set	set	NOUN
cana-1758	70	22	of	of	ADP
cana-1758	70	23	independent	independent	ADJ
cana-1758	70	24	random	random	ADJ
cana-1758	70	25	variables	variable	NOUN
cana-1758	70	26	.	.	PUNCT
cana-1758	71	1	bayesian	bayesian	NOUN
cana-1758	71	2	networks	network	NOUN
cana-1758	71	3	are	be	AUX
cana-1758	71	4	used	use	VERB
cana-1758	71	5	to	to	PART
cana-1758	71	6	analyze	analyze	VERB
cana-1758	71	7	bayesian	bayesian	NOUN
cana-1758	71	8	networks	network	NOUN
cana-1758	71	9	.	.	PUNCT
cana-1758	72	1	it	it	PRON
cana-1758	72	2	is	be	AUX
cana-1758	72	3	possible	possible	ADJ
cana-1758	72	4	for	for	SCONJ
cana-1758	72	5	bayesian	bayesian	NOUN
cana-1758	72	6	networks	network	NOUN
cana-1758	72	7	to	to	PART
cana-1758	72	8	handle	handle	VERB
cana-1758	72	9	uncertainty	uncertainty	NOUN
cana-1758	72	10	in	in	ADP
cana-1758	72	11	an	an	DET
cana-1758	72	12	effective	effective	ADJ
cana-1758	72	13	manner	manner	NOUN
cana-1758	72	14	.	.	PUNCT
cana-1758	73	1	through	through	ADP
cana-1758	73	2	the	the	DET
cana-1758	73	3	utilization	utilization	NOUN
cana-1758	73	4	of	of	ADP
cana-1758	73	5	conditional	conditional	ADJ
cana-1758	73	6	probability	probability	NOUN
cana-1758	73	7	,	,	PUNCT
cana-1758	73	8	a	a	DET
cana-1758	73	9	bayesian	bayesian	NOUN
cana-1758	73	10	network	network	NOUN
cana-1758	73	11	is	be	AUX
cana-1758	73	12	able	able	ADJ
cana-1758	73	13	to	to	PART
cana-1758	73	14	effectively	effectively	ADV
cana-1758	73	15	express	express	VERB
cana-1758	73	16	the	the	DET
cana-1758	73	17	relationship	relationship	NOUN
cana-1758	73	18	between	between	ADP
cana-1758	73	19	information	information	NOUN
cana-1758	73	20	components	component	NOUN
cana-1758	73	21	and	and	CCONJ
cana-1758	73	22	acquire	acquire	VERB
cana-1758	73	23	knowledge	knowledge	NOUN
cana-1758	73	24	from	from	ADP
cana-1758	73	25	data	datum	NOUN
cana-1758	73	26	that	that	PRON
cana-1758	73	27	is	be	AUX
cana-1758	73	28	unclear	unclear	ADJ
cana-1758	74	1	[	[	X
cana-1758	74	2	14].through	14].through	PROPN
cana-1758	74	3	the	the	DET
cana-1758	74	4	utilization	utilization	NOUN
cana-1758	74	5	of	of	ADP
cana-1758	74	6	data	datum	NOUN
cana-1758	74	7	on	on	ADP
cana-1758	74	8	boundary	boundary	ADJ
cana-1758	74	9	points	point	NOUN
cana-1758	74	10	of	of	ADP
cana-1758	74	11	the	the	DET
cana-1758	74	12	hyperplane	hyperplane	NOUN
cana-1758	74	13	,	,	PUNCT
cana-1758	74	14	the	the	DET
cana-1758	74	15	support	support	NOUN
cana-1758	74	16	vector	vector	NOUN
cana-1758	74	17	machine	machine	NOUN
cana-1758	74	18	(	(	PUNCT
cana-1758	74	19	svm	svm	PROPN
cana-1758	74	20	)	)	PUNCT
cana-1758	74	21	is	be	AUX
cana-1758	74	22	a	a	DET
cana-1758	74	23	linear	linear	ADJ
cana-1758	74	24	classifier	classifier	NOUN
cana-1758	74	25	that	that	PRON
cana-1758	74	26	is	be	AUX
cana-1758	74	27	capable	capable	ADJ
cana-1758	74	28	of	of	ADP
cana-1758	74	29	performing	perform	VERB
cana-1758	74	30	binary	binary	ADJ
cana-1758	74	31	classification	classification	NOUN
cana-1758	74	32	.	.	PUNCT
cana-1758	75	1	support	support	NOUN
cana-1758	75	2	vector	vector	NOUN
cana-1758	75	3	machines	machine	NOUN
cana-1758	75	4	are	be	AUX
cana-1758	75	5	able	able	ADJ
cana-1758	75	6	to	to	PART
cana-1758	75	7	conduct	conduct	VERB
cana-1758	75	8	non	non	ADJ
cana-1758	75	9	-	-	ADJ
cana-1758	75	10	linear	linear	ADJ
cana-1758	75	11	classification	classification	NOUN
cana-1758	75	12	in	in	ADP
cana-1758	75	13	addition	addition	NOUN
cana-1758	75	14	to	to	ADP
cana-1758	75	15	linear	linear	VERB
cana-1758	75	16	classification	classification	NOUN
cana-1758	75	17	by	by	ADP
cana-1758	75	18	utilizing	utilize	VERB
cana-1758	75	19	the	the	DET
cana-1758	75	20	kernel	kernel	NOUN
cana-1758	75	21	technique	technique	NOUN
cana-1758	75	22	,	,	PUNCT
cana-1758	75	23	which	which	PRON
cana-1758	75	24	implicitly	implicitly	ADV
cana-1758	75	25	transforms	transform	VERB
cana-1758	75	26	inputs	input	NOUN
cana-1758	75	27	into	into	ADP
cana-1758	75	28	highly	highly	ADV
cana-1758	75	29	dimensional	dimensional	ADJ
cana-1758	75	30	vector	vector	NOUN
cana-1758	75	31	spaces	space	NOUN
cana-1758	75	32	[	[	PUNCT
cana-1758	75	33	14].the	14].the	DET
cana-1758	75	34	support	support	NOUN
cana-1758	75	35	vector	vector	NOUN
cana-1758	75	36	machine	machine	NOUN
cana-1758	75	37	makes	make	VERB
cana-1758	75	38	an	an	DET
cana-1758	75	39	effort	effort	NOUN
cana-1758	75	40	to	to	PART
cana-1758	75	41	pick	pick	VERB
cana-1758	75	42	the	the	DET
cana-1758	75	43	ideal	ideal	ADJ
cana-1758	75	44	hyperplane	hyperplane	NOUN
cana-1758	75	45	that	that	PRON
cana-1758	75	46	has	have	VERB
cana-1758	75	47	the	the	DET
cana-1758	75	48	largest	large	ADJ
cana-1758	75	49	margins	margin	NOUN
cana-1758	75	50	between	between	ADP
cana-1758	75	51	data	datum	NOUN
cana-1758	75	52	instances	instance	NOUN
cana-1758	75	53	[	[	X
cana-1758	75	54	18	18	NUM
cana-1758	75	55	]	]	PUNCT
cana-1758	75	56	.	.	PUNCT
cana-1758	76	1	this	this	PRON
cana-1758	76	2	is	be	AUX
cana-1758	76	3	done	do	VERB
cana-1758	76	4	in	in	ADP
cana-1758	76	5	order	order	NOUN
cana-1758	76	6	to	to	PART
cana-1758	76	7	improve	improve	VERB
cana-1758	76	8	the	the	DET
cana-1758	76	9	accuracy	accuracy	NOUN
cana-1758	76	10	of	of	ADP
cana-1758	76	11	the	the	DET
cana-1758	76	12	classification	classification	NOUN
cana-1758	76	13	process	process	NOUN
cana-1758	76	14	.	.	PUNCT
cana-1758	77	1	due	due	ADP
cana-1758	77	2	to	to	ADP
cana-1758	77	3	the	the	DET
cana-1758	77	4	fact	fact	NOUN
cana-1758	77	5	that	that	SCONJ
cana-1758	77	6	it	it	PRON
cana-1758	77	7	learns	learn	VERB
cana-1758	77	8	during	during	ADP
cana-1758	77	9	the	the	DET
cana-1758	77	10	assessment	assessment	NOUN
cana-1758	77	11	step	step	NOUN
cana-1758	77	12	and	and	CCONJ
cana-1758	77	13	continues	continue	VERB
cana-1758	77	14	to	to	PART
cana-1758	77	15	maintain	maintain	VERB
cana-1758	77	16	data	data	NOUN
cana-1758	77	17	samples	sample	NOUN
cana-1758	77	18	throughout	throughout	ADP
cana-1758	77	19	the	the	DET
cana-1758	77	20	learning	learning	NOUN
cana-1758	77	21	step	step	NOUN
cana-1758	77	22	,	,	PUNCT
cana-1758	77	23	the	the	DET
cana-1758	77	24	knn	knn	PROPN
cana-1758	77	25	classifier	classifier	PROPN
cana-1758	77	26	is	be	AUX
cana-1758	77	27	a	a	DET
cana-1758	77	28	method	method	NOUN
cana-1758	77	29	that	that	PRON
cana-1758	77	30	is	be	AUX
cana-1758	77	31	particularly	particularly	ADV
cana-1758	77	32	sluggish	sluggish	ADJ
cana-1758	77	33	.	.	PUNCT
cana-1758	78	1	it	it	PRON
cana-1758	78	2	is	be	AUX
cana-1758	78	3	[	[	X
cana-1758	78	4	14	14	NUM
cana-1758	78	5	]	]	PUNCT
cana-1758	78	6	through	through	ADP
cana-1758	78	7	the	the	DET
cana-1758	78	8	use	use	NOUN
cana-1758	78	9	of	of	ADP
cana-1758	78	10	the	the	DET
cana-1758	78	11	knn	knn	PROPN
cana-1758	78	12	method	method	PROPN
cana-1758	78	13	,	,	PUNCT
cana-1758	78	14	the	the	DET
cana-1758	78	15	class	class	NOUN
cana-1758	78	16	of	of	ADP
cana-1758	78	17	samples	sample	NOUN
cana-1758	78	18	that	that	PRON
cana-1758	78	19	are	be	AUX
cana-1758	78	20	to	to	PART
cana-1758	78	21	be	be	AUX
cana-1758	78	22	classed	class	VERB
cana-1758	78	23	is	be	AUX
cana-1758	78	24	determined	determine	VERB
cana-1758	78	25	by	by	ADP
cana-1758	78	26	comparing	compare	VERB
cana-1758	78	27	the	the	DET
cana-1758	78	28	samples	sample	NOUN
cana-1758	78	29	that	that	PRON
cana-1758	78	30	are	be	AUX
cana-1758	78	31	most	most	ADV
cana-1758	78	32	similar	similar	ADJ
cana-1758	78	33	to	to	ADP
cana-1758	78	34	one	one	NUM
cana-1758	78	35	another.[15	another.[15	PROPN
cana-1758	78	36	]	]	PUNCT
cana-1758	78	37	given	give	VERB
cana-1758	78	38	that	that	SCONJ
cana-1758	78	39	k	k	PROPN
cana-1758	78	40	continues	continue	VERB
cana-1758	78	41	to	to	PART
cana-1758	78	42	be	be	AUX
cana-1758	78	43	positive	positive	ADJ
cana-1758	78	44	,	,	PUNCT
cana-1758	78	45	the	the	DET
cana-1758	78	46	selection	selection	NOUN
cana-1758	78	47	of	of	ADP
cana-1758	78	48	the	the	DET
cana-1758	78	49	neighbors	neighbor	NOUN
cana-1758	78	50	is	be	AUX
cana-1758	78	51	accomplished	accomplish	VERB
cana-1758	78	52	by	by	ADP
cana-1758	78	53	using	use	VERB
cana-1758	78	54	a	a	DET
cana-1758	78	55	collection	collection	NOUN
cana-1758	78	56	of	of	ADP
cana-1758	78	57	objects	object	NOUN
cana-1758	78	58	that	that	PRON
cana-1758	78	59	have	have	VERB
cana-1758	78	60	label	label	NOUN
cana-1758	78	61	information	information	NOUN
cana-1758	78	62	.	.	PUNCT
cana-1758	79	1	[	[	X
cana-1758	79	2	16	16	NUM
cana-1758	79	3	]	]	PUNCT
cana-1758	79	4	the	the	DET
cana-1758	79	5	adaboost	adaboost	ADJ
cana-1758	79	6	classifiers	classifier	NOUN
cana-1758	79	7	algorithm	algorithm	NOUN
cana-1758	79	8	employs	employ	VERB
cana-1758	79	9	a	a	DET
cana-1758	79	10	weak	weak	ADJ
cana-1758	79	11	learner	learner	NOUN
cana-1758	79	12	to	to	PART
cana-1758	79	13	assist	assist	VERB
cana-1758	79	14	in	in	ADP
cana-1758	79	15	the	the	DET
cana-1758	79	16	training	training	NOUN
cana-1758	79	17	of	of	ADP
cana-1758	79	18	a	a	DET
cana-1758	79	19	collection	collection	NOUN
cana-1758	79	20	of	of	ADP
cana-1758	79	21	classifiers	classifier	NOUN
cana-1758	79	22	in	in	ADP
cana-1758	79	23	order	order	NOUN
cana-1758	79	24	to	to	PART
cana-1758	79	25	provide	provide	VERB
cana-1758	79	26	the	the	DET
cana-1758	79	27	best	good	ADJ
cana-1758	79	28	possible	possible	ADJ
cana-1758	79	29	classifier	classifier	NOUN
cana-1758	79	30	.	.	PUNCT
cana-1758	80	1	[	[	X
cana-1758	80	2	17	17	NUM
cana-1758	80	3	]	]	X
cana-1758	80	4	an	an	DET
cana-1758	80	5	application	application	NOUN
cana-1758	80	6	that	that	SCONJ
cana-1758	80	7	communications	communication	NOUN
cana-1758	80	8	on	on	ADP
cana-1758	80	9	applied	apply	VERB
cana-1758	80	10	nonlinear	nonlinear	ADJ
cana-1758	80	11	analysis	analysis	NOUN
cana-1758	80	12	issn	issn	NOUN
cana-1758	80	13	:	:	PUNCT
cana-1758	80	14	1074	1074	NUM
cana-1758	80	15	-	-	PUNCT
cana-1758	80	16	133x	133x	NUM
cana-1758	80	17	vol	vol	NOUN
cana-1758	80	18	32	32	NUM
cana-1758	80	19	no	no	NOUN
cana-1758	80	20	.	.	NOUN
cana-1758	80	21	2	2	NUM
cana-1758	80	22	(	(	PUNCT
cana-1758	80	23	2025	2025	NUM
cana-1758	80	24	)	)	PUNCT
cana-1758	80	25	463	463	NUM
cana-1758	80	26	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	80	27	combines	combine	VERB
cana-1758	80	28	the	the	DET
cana-1758	80	29	backpropagation	backpropagation	NOUN
cana-1758	80	30	neural	neural	ADJ
cana-1758	80	31	network	network	NOUN
cana-1758	80	32	approach	approach	NOUN
cana-1758	80	33	with	with	ADP
cana-1758	80	34	adaptive	adaptive	ADJ
cana-1758	80	35	boosting	boosting	NOUN
cana-1758	80	36	is	be	AUX
cana-1758	80	37	recommended	recommend	VERB
cana-1758	80	38	by	by	ADP
cana-1758	80	39	yan	yan	PROPN
cana-1758	80	40	gao	gao	PROPN
cana-1758	80	41	et	et	PROPN
cana-1758	80	42	al	al	PROPN
cana-1758	80	43	.	.	PROPN
cana-1758	81	1	for	for	ADP
cana-1758	81	2	the	the	DET
cana-1758	81	3	purpose	purpose	NOUN
cana-1758	81	4	of	of	ADP
cana-1758	81	5	training	train	VERB
cana-1758	81	6	a	a	DET
cana-1758	81	7	software	software	NOUN
cana-1758	81	8	fault	fault	NOUN
cana-1758	81	9	prediction	prediction	NOUN
cana-1758	81	10	model	model	NOUN
cana-1758	81	11	.	.	PUNCT
cana-1758	82	1	the	the	DET
cana-1758	82	2	problem	problem	NOUN
cana-1758	82	3	of	of	ADP
cana-1758	82	4	overfitting	overfitting	NOUN
cana-1758	82	5	is	be	AUX
cana-1758	82	6	avoided	avoid	VERB
cana-1758	82	7	by	by	ADP
cana-1758	82	8	adaboost	adaboost	ADV
cana-1758	82	9	,	,	PUNCT
cana-1758	82	10	which	which	PRON
cana-1758	82	11	also	also	ADV
cana-1758	82	12	functions	function	VERB
cana-1758	82	13	as	as	ADP
cana-1758	82	14	an	an	DET
cana-1758	82	15	efficient	efficient	ADJ
cana-1758	82	16	predictor	predictor	NOUN
cana-1758	82	17	for	for	ADP
cana-1758	82	18	data	datum	NOUN
cana-1758	82	19	that	that	PRON
cana-1758	82	20	is	be	AUX
cana-1758	82	21	not	not	PART
cana-1758	82	22	consistent	consistent	ADJ
cana-1758	82	23	[	[	X
cana-1758	82	24	18	18	NUM
cana-1758	82	25	]	]	PUNCT
cana-1758	82	26	.	.	PUNCT
cana-1758	83	1	combined	combine	VERB
cana-1758	83	2	defect	defect	NOUN
cana-1758	83	3	predictor	predictor	NOUN
cana-1758	83	4	is	be	AUX
cana-1758	83	5	the	the	DET
cana-1758	83	6	name	name	NOUN
cana-1758	83	7	given	give	VERB
cana-1758	83	8	to	to	ADP
cana-1758	83	9	the	the	DET
cana-1758	83	10	integrated	integrate	VERB
cana-1758	83	11	prediction	prediction	NOUN
cana-1758	83	12	model	model	NOUN
cana-1758	83	13	that	that	PRON
cana-1758	83	14	was	be	AUX
cana-1758	83	15	developed	develop	VERB
cana-1758	83	16	by	by	ADP
cana-1758	83	17	yun	yun	PROPN
cana-1758	83	18	zhang	zhang	PROPN
cana-1758	83	19	and	and	CCONJ
cana-1758	83	20	colleagues	colleague	NOUN
cana-1758	83	21	.	.	PUNCT
cana-1758	84	1	this	this	DET
cana-1758	84	2	model	model	NOUN
cana-1758	84	3	was	be	AUX
cana-1758	84	4	created	create	VERB
cana-1758	84	5	by	by	ADP
cana-1758	84	6	combining	combine	VERB
cana-1758	84	7	multiple	multiple	ADJ
cana-1758	84	8	fundamental	fundamental	ADJ
cana-1758	84	9	classifiers	classifier	NOUN
cana-1758	84	10	.	.	PUNCT
cana-1758	85	1	integrated	integrate	VERB
cana-1758	85	2	model	model	NOUN
cana-1758	85	3	was	be	AUX
cana-1758	85	4	constructed	construct	VERB
cana-1758	85	5	by	by	ADP
cana-1758	85	6	the	the	DET
cana-1758	85	7	author	author	NOUN
cana-1758	85	8	using	use	VERB
cana-1758	85	9	two	two	NUM
cana-1758	85	10	different	different	ADJ
cana-1758	85	11	sorts	sort	NOUN
cana-1758	85	12	of	of	ADP
cana-1758	85	13	voting	vote	VERB
cana-1758	85	14	techniques	technique	NOUN
cana-1758	85	15	.	.	PUNCT
cana-1758	86	1	on	on	ADP
cana-1758	86	2	the	the	DET
cana-1758	86	3	other	other	ADJ
cana-1758	86	4	hand	hand	NOUN
cana-1758	86	5	,	,	PUNCT
cana-1758	86	6	max	max	PROPN
cana-1758	86	7	voting	voting	NOUN
cana-1758	86	8	generates	generate	VERB
cana-1758	86	9	the	the	DET
cana-1758	86	10	highest	high	ADJ
cana-1758	86	11	confidence	confidence	NOUN
cana-1758	86	12	values	value	NOUN
cana-1758	86	13	from	from	ADP
cana-1758	86	14	a	a	DET
cana-1758	86	15	wide	wide	ADJ
cana-1758	86	16	variety	variety	NOUN
cana-1758	86	17	of	of	ADP
cana-1758	86	18	core	core	NOUN
cana-1758	86	19	classifiers	classifier	NOUN
cana-1758	86	20	,	,	PUNCT
cana-1758	86	21	whereas	whereas	SCONJ
cana-1758	86	22	first	first	ADV
cana-1758	86	23	ave	ave	PROPN
cana-1758	86	24	voting	voting	NOUN
cana-1758	86	25	is	be	AUX
cana-1758	86	26	responsible	responsible	ADJ
cana-1758	86	27	for	for	ADP
cana-1758	86	28	aggregating	aggregate	VERB
cana-1758	86	29	confidence	confidence	NOUN
cana-1758	86	30	ratings	rating	NOUN
cana-1758	86	31	[	[	X
cana-1758	86	32	19	19	NUM
cana-1758	86	33	]	]	PUNCT
cana-1758	86	34	.	.	PUNCT
cana-1758	87	1	with	with	ADP
cana-1758	87	2	the	the	DET
cana-1758	87	3	use	use	NOUN
cana-1758	87	4	of	of	ADP
cana-1758	87	5	the	the	DET
cana-1758	87	6	weka	weka	PROPN
cana-1758	87	7	tool	tool	PROPN
cana-1758	87	8	,	,	PUNCT
cana-1758	87	9	marwa	marwa	PROPN
cana-1758	87	10	assim	assim	PROPN
cana-1758	87	11	and	and	CCONJ
cana-1758	87	12	her	her	PRON
cana-1758	87	13	colleagues	colleague	NOUN
cana-1758	87	14	were	be	AUX
cana-1758	87	15	able	able	ADJ
cana-1758	87	16	to	to	PART
cana-1758	87	17	classify	classify	VERB
cana-1758	87	18	the	the	DET
cana-1758	87	19	data	datum	NOUN
cana-1758	87	20	using	use	VERB
cana-1758	87	21	eight	eight	NUM
cana-1758	87	22	different	different	ADJ
cana-1758	87	23	machine	machine	NOUN
cana-1758	87	24	learning	learn	VERB
cana-1758	87	25	techniques	technique	NOUN
cana-1758	87	26	,	,	PUNCT
cana-1758	87	27	including	include	VERB
cana-1758	87	28	percentage	percentage	NOUN
cana-1758	87	29	split	split	NOUN
cana-1758	87	30	and	and	CCONJ
cana-1758	87	31	k	k	ADJ
cana-1758	87	32	-	-	ADJ
cana-1758	87	33	fold	fold	ADJ
cana-1758	87	34	cross	cross	NOUN
cana-1758	87	35	-	-	NOUN
cana-1758	87	36	validation	validation	ADJ
cana-1758	87	37	[	[	X
cana-1758	87	38	20	20	NUM
cana-1758	87	39	]	]	PUNCT
cana-1758	87	40	having	have	VERB
cana-1758	87	41	a	a	DET
cana-1758	87	42	conversation	conversation	NOUN
cana-1758	87	43	on	on	ADP
cana-1758	87	44	machine	machine	NOUN
cana-1758	87	45	learning	learning	NOUN
cana-1758	87	46	without	without	ADP
cana-1758	87	47	supervision	supervision	NOUN
cana-1758	87	48	a	a	DET
cana-1758	87	49	straightforward	straightforward	ADJ
cana-1758	87	50	explanation	explanation	NOUN
cana-1758	87	51	of	of	ADP
cana-1758	87	52	kmeans	kmean	NOUN
cana-1758	87	53	clustering	clustering	NOUN
cana-1758	87	54	has	have	AUX
cana-1758	87	55	been	be	AUX
cana-1758	87	56	provided	provide	VERB
cana-1758	87	57	by	by	ADP
cana-1758	87	58	xin	xin	PROPN
cana-1758	87	59	dong	dong	PROPN
cana-1758	87	60	and	and	CCONJ
cana-1758	87	61	colleagues	colleague	NOUN
cana-1758	87	62	.	.	PUNCT
cana-1758	88	1	after	after	ADP
cana-1758	88	2	selecting	select	VERB
cana-1758	88	3	k	k	PROPN
cana-1758	88	4	cluster	cluster	NOUN
cana-1758	88	5	centers	center	NOUN
cana-1758	88	6	at	at	ADP
cana-1758	88	7	random	random	ADJ
cana-1758	88	8	,	,	PUNCT
cana-1758	88	9	the	the	DET
cana-1758	88	10	next	next	ADJ
cana-1758	88	11	step	step	NOUN
cana-1758	88	12	is	be	AUX
cana-1758	88	13	to	to	PART
cana-1758	88	14	place	place	VERB
cana-1758	88	15	the	the	DET
cana-1758	88	16	element	element	NOUN
cana-1758	88	17	in	in	ADP
cana-1758	88	18	the	the	DET
cana-1758	88	19	cluster	cluster	NOUN
cana-1758	88	20	that	that	PRON
cana-1758	88	21	is	be	AUX
cana-1758	88	22	the	the	DET
cana-1758	88	23	most	most	ADV
cana-1758	88	24	similar	similar	ADJ
cana-1758	88	25	to	to	ADP
cana-1758	88	26	it	it	PRON
cana-1758	88	27	by	by	ADP
cana-1758	88	28	analyzing	analyze	VERB
cana-1758	88	29	how	how	SCONJ
cana-1758	88	30	it	it	PRON
cana-1758	88	31	compares	compare	VERB
cana-1758	88	32	to	to	ADP
cana-1758	88	33	the	the	DET
cana-1758	88	34	other	other	ADJ
cana-1758	88	35	clusters	cluster	NOUN
cana-1758	88	36	.	.	PUNCT
cana-1758	89	1	utilizing	utilize	VERB
cana-1758	89	2	the	the	DET
cana-1758	89	3	average	average	NOUN
cana-1758	89	4	of	of	ADP
cana-1758	89	5	all	all	DET
cana-1758	89	6	the	the	DET
cana-1758	89	7	objects	object	NOUN
cana-1758	89	8	contained	contain	VERB
cana-1758	89	9	within	within	ADP
cana-1758	89	10	a	a	DET
cana-1758	89	11	particular	particular	ADJ
cana-1758	89	12	cluster	cluster	NOUN
cana-1758	89	13	,	,	PUNCT
cana-1758	89	14	one	one	PRON
cana-1758	89	15	can	can	AUX
cana-1758	89	16	ascertain	ascertain	VERB
cana-1758	89	17	the	the	DET
cana-1758	89	18	cluster	cluster	NOUN
cana-1758	89	19	center	center	NOUN
cana-1758	89	20	for	for	ADP
cana-1758	89	21	each	each	DET
cana-1758	89	22	individual	individual	ADJ
cana-1758	89	23	cluster	cluster	NOUN
cana-1758	89	24	[	[	X
cana-1758	89	25	2	2	NUM
cana-1758	89	26	]	]	PUNCT
cana-1758	89	27	.	.	PUNCT
cana-1758	90	1	there	there	PRON
cana-1758	90	2	are	be	VERB
cana-1758	90	3	researchers	researcher	NOUN
cana-1758	90	4	who	who	PRON
cana-1758	90	5	construct	construct	VERB
cana-1758	90	6	software	software	NOUN
cana-1758	90	7	defect	defect	NOUN
cana-1758	90	8	models	model	NOUN
cana-1758	90	9	by	by	ADP
cana-1758	90	10	employing	employ	VERB
cana-1758	90	11	the	the	DET
cana-1758	90	12	deep	deep	ADJ
cana-1758	90	13	learning	learning	NOUN
cana-1758	90	14	methodology	methodology	NOUN
cana-1758	90	15	.	.	PUNCT
cana-1758	91	1	the	the	DET
cana-1758	91	2	original	original	ADJ
cana-1758	91	3	minimal	minimal	ADJ
cana-1758	91	4	feature	feature	NOUN
cana-1758	91	5	representation	representation	NOUN
cana-1758	91	6	is	be	AUX
cana-1758	91	7	transformed	transform	VERB
cana-1758	91	8	into	into	ADP
cana-1758	91	9	a	a	DET
cana-1758	91	10	high	high	ADJ
cana-1758	91	11	-	-	PUNCT
cana-1758	91	12	level	level	NOUN
cana-1758	91	13	feature	feature	NOUN
cana-1758	91	14	by	by	ADP
cana-1758	91	15	deep	deep	ADJ
cana-1758	91	16	learning	learning	NOUN
cana-1758	91	17	[	[	X
cana-1758	91	18	22][23	22][23	NUM
cana-1758	91	19	]	]	PUNCT
cana-1758	91	20	,	,	PUNCT
cana-1758	91	21	which	which	PRON
cana-1758	91	22	is	be	AUX
cana-1758	91	23	accomplished	accomplish	VERB
cana-1758	91	24	through	through	ADP
cana-1758	91	25	the	the	DET
cana-1758	91	26	use	use	NOUN
cana-1758	91	27	of	of	ADP
cana-1758	91	28	multi	multi	ADJ
cana-1758	91	29	-	-	ADJ
cana-1758	91	30	layer	layer	NOUN
cana-1758	91	31	processing	processing	NOUN
cana-1758	91	32	.	.	PUNCT
cana-1758	92	1	it	it	PRON
cana-1758	92	2	has	have	VERB
cana-1758	92	3	the	the	DET
cana-1758	92	4	ability	ability	NOUN
cana-1758	92	5	to	to	PART
cana-1758	92	6	accomplish	accomplish	VERB
cana-1758	92	7	complex	complex	ADJ
cana-1758	92	8	categorization	categorization	NOUN
cana-1758	92	9	tasks	task	NOUN
cana-1758	92	10	by	by	ADP
cana-1758	92	11	utilizing	utilize	VERB
cana-1758	92	12	fundamental	fundamental	ADJ
cana-1758	92	13	models	model	NOUN
cana-1758	92	14	[	[	X
cana-1758	92	15	14	14	NUM
cana-1758	92	16	]	]	PUNCT
cana-1758	92	17	.	.	PUNCT
cana-1758	93	1	neural	neural	ADJ
cana-1758	93	2	networks	network	NOUN
cana-1758	93	3	are	be	AUX
cana-1758	93	4	formed	form	VERB
cana-1758	93	5	from	from	ADP
cana-1758	93	6	biological	biological	ADJ
cana-1758	93	7	neural	neural	ADJ
cana-1758	93	8	networks	network	NOUN
cana-1758	93	9	,	,	PUNCT
cana-1758	93	10	which	which	PRON
cana-1758	93	11	are	be	AUX
cana-1758	93	12	composed	compose	VERB
cana-1758	93	13	of	of	ADP
cana-1758	93	14	synapses	synapsis	NOUN
cana-1758	93	15	and	and	CCONJ
cana-1758	93	16	neurons	neuron	NOUN
cana-1758	93	17	.	.	PUNCT
cana-1758	94	1	neural	neural	ADJ
cana-1758	94	2	network	network	NOUN
cana-1758	94	3	models	model	NOUN
cana-1758	94	4	are	be	AUX
cana-1758	94	5	created	create	VERB
cana-1758	94	6	from	from	ADP
cana-1758	94	7	these	these	DET
cana-1758	94	8	systems	system	NOUN
cana-1758	94	9	.	.	PUNCT
cana-1758	95	1	when	when	SCONJ
cana-1758	95	2	neurons	neuron	NOUN
cana-1758	95	3	are	be	AUX
cana-1758	95	4	modeled	model	VERB
cana-1758	95	5	,	,	PUNCT
cana-1758	95	6	they	they	PRON
cana-1758	95	7	are	be	AUX
cana-1758	95	8	represented	represent	VERB
cana-1758	95	9	as	as	ADP
cana-1758	95	10	nodes	node	NOUN
cana-1758	95	11	in	in	ADP
cana-1758	95	12	a	a	DET
cana-1758	95	13	graphical	graphical	ADJ
cana-1758	95	14	network	network	NOUN
cana-1758	95	15	,	,	PUNCT
cana-1758	95	16	and	and	CCONJ
cana-1758	95	17	synapses	synapsis	NOUN
cana-1758	95	18	serve	serve	VERB
cana-1758	95	19	as	as	ADP
cana-1758	95	20	weighted	weight	VERB
cana-1758	95	21	edges	edge	NOUN
cana-1758	95	22	that	that	PRON
cana-1758	95	23	connect	connect	VERB
cana-1758	95	24	the	the	DET
cana-1758	95	25	nodes	node	NOUN
cana-1758	95	26	[	[	X
cana-1758	95	27	17	17	NUM
cana-1758	95	28	]	]	PUNCT
cana-1758	95	29	.	.	PUNCT
cana-1758	96	1	in	in	ADP
cana-1758	96	2	the	the	DET
cana-1758	96	3	context	context	NOUN
cana-1758	96	4	of	of	ADP
cana-1758	96	5	machine	machine	NOUN
cana-1758	96	6	learning	learn	VERB
cana-1758	96	7	applications	application	NOUN
cana-1758	96	8	,	,	PUNCT
cana-1758	96	9	artificial	artificial	ADJ
cana-1758	96	10	neurons	neuron	NOUN
cana-1758	96	11	are	be	AUX
cana-1758	96	12	comprised	comprise	VERB
cana-1758	96	13	of	of	ADP
cana-1758	96	14	artificial	artificial	ADJ
cana-1758	96	15	neural	neural	ADJ
cana-1758	96	16	networks	network	NOUN
cana-1758	96	17	,	,	PUNCT
cana-1758	96	18	which	which	PRON
cana-1758	96	19	have	have	VERB
cana-1758	96	20	the	the	DET
cana-1758	96	21	potential	potential	NOUN
cana-1758	96	22	to	to	PART
cana-1758	96	23	function	function	VERB
cana-1758	96	24	as	as	ADP
cana-1758	96	25	a	a	DET
cana-1758	96	26	non	non	ADJ
cana-1758	96	27	-	-	ADJ
cana-1758	96	28	linear	linear	ADJ
cana-1758	96	29	classifier	classifier	NOUN
cana-1758	96	30	.	.	PUNCT
cana-1758	97	1	interconnections	interconnection	NOUN
cana-1758	97	2	between	between	ADP
cana-1758	97	3	the	the	DET
cana-1758	97	4	input	input	NOUN
cana-1758	97	5	neurons	neuron	NOUN
cana-1758	97	6	make	make	VERB
cana-1758	97	7	it	it	PRON
cana-1758	97	8	possible	possible	ADJ
cana-1758	97	9	for	for	SCONJ
cana-1758	97	10	them	they	PRON
cana-1758	97	11	to	to	PART
cana-1758	97	12	work	work	VERB
cana-1758	97	13	simultaneously	simultaneously	ADV
cana-1758	97	14	.	.	PUNCT
cana-1758	98	1	they	they	PRON
cana-1758	98	2	do	do	VERB
cana-1758	98	3	this	this	PRON
cana-1758	98	4	by	by	ADP
cana-1758	98	5	exchanging	exchange	VERB
cana-1758	98	6	signals	signal	NOUN
cana-1758	98	7	with	with	ADP
cana-1758	98	8	one	one	NUM
cana-1758	98	9	another	another	DET
cana-1758	98	10	and	and	CCONJ
cana-1758	98	11	calculating	calculate	VERB
cana-1758	98	12	output	output	NOUN
cana-1758	98	13	using	use	VERB
cana-1758	98	14	a	a	DET
cana-1758	98	15	non	non	ADJ
cana-1758	98	16	-	-	ADJ
cana-1758	98	17	linear	linear	ADJ
cana-1758	98	18	function	function	NOUN
cana-1758	98	19	[	[	X
cana-1758	98	20	21	21	NUM
cana-1758	98	21	]	]	PUNCT
cana-1758	98	22	.	.	PUNCT
cana-1758	99	1	3	3	X
cana-1758	99	2	.	.	X
cana-1758	99	3	materials	material	NOUN
cana-1758	99	4	and	and	CCONJ
cana-1758	99	5	methods	method	NOUN
cana-1758	99	6	the	the	DET
cana-1758	99	7	random	random	ADJ
cana-1758	99	8	forest	forest	NOUN
cana-1758	99	9	algorithm	algorithm	NOUN
cana-1758	99	10	is	be	AUX
cana-1758	99	11	a	a	DET
cana-1758	99	12	machine	machine	NOUN
cana-1758	99	13	learning	learning	NOUN
cana-1758	99	14	technique	technique	NOUN
cana-1758	99	15	that	that	PRON
cana-1758	99	16	use	use	VERB
cana-1758	99	17	numerous	numerous	ADJ
cana-1758	99	18	decision	decision	NOUN
cana-1758	99	19	trees	tree	NOUN
cana-1758	99	20	to	to	PART
cana-1758	99	21	create	create	VERB
cana-1758	99	22	predictions	prediction	NOUN
cana-1758	99	23	and	and	CCONJ
cana-1758	99	24	classify	classify	VERB
cana-1758	99	25	the	the	DET
cana-1758	99	26	data	datum	NOUN
cana-1758	99	27	.	.	PUNCT
cana-1758	100	1	in	in	ADP
cana-1758	100	2	this	this	DET
cana-1758	100	3	project	project	NOUN
cana-1758	100	4	,	,	PUNCT
cana-1758	100	5	we	we	PRON
cana-1758	100	6	have	have	AUX
cana-1758	100	7	implemented	implement	VERB
cana-1758	100	8	a	a	DET
cana-1758	100	9	modified	modify	VERB
cana-1758	100	10	version	version	NOUN
cana-1758	100	11	of	of	ADP
cana-1758	100	12	the	the	DET
cana-1758	100	13	random	random	ADJ
cana-1758	100	14	forest	forest	NOUN
cana-1758	100	15	algorithm	algorithm	NOUN
cana-1758	100	16	.	.	PUNCT
cana-1758	101	1	entropy	entropy	PROPN
cana-1758	101	2	is	be	AUX
cana-1758	101	3	a	a	DET
cana-1758	101	4	measurement	measurement	NOUN
cana-1758	101	5	that	that	PRON
cana-1758	101	6	is	be	AUX
cana-1758	101	7	utilized	utilize	VERB
cana-1758	101	8	to	to	PART
cana-1758	101	9	estimate	estimate	VERB
cana-1758	101	10	the	the	DET
cana-1758	101	11	degree	degree	NOUN
cana-1758	101	12	of	of	ADP
cana-1758	101	13	impurity	impurity	NOUN
cana-1758	101	14	or	or	CCONJ
cana-1758	101	15	randomness	randomness	NOUN
cana-1758	101	16	that	that	PRON
cana-1758	101	17	exists	exist	VERB
cana-1758	101	18	within	within	ADP
cana-1758	101	19	a	a	DET
cana-1758	101	20	collection	collection	NOUN
cana-1758	101	21	of	of	ADP
cana-1758	101	22	samples	sample	NOUN
cana-1758	101	23	.	.	PUNCT
cana-1758	102	1	the	the	DET
cana-1758	102	2	formula	formula	NOUN
cana-1758	102	3	for	for	ADP
cana-1758	102	4	calculating	calculate	VERB
cana-1758	102	5	entropy	entropy	NOUN
cana-1758	102	6	using	use	VERB
cana-1758	102	7	the	the	DET
cana-1758	102	8	natural	natural	ADJ
cana-1758	102	9	logarithm	logarithm	NOUN
cana-1758	102	10	is	be	AUX
cana-1758	102	11	as	as	SCONJ
cana-1758	102	12	follows	follow	VERB
cana-1758	102	13	:	:	PUNCT
cana-1758	102	14	𝐸	𝐸	PROPN
cana-1758	102	15	=	=	SYM
cana-1758	102	16	−𝛴𝑝(𝑦𝑖	−𝛴𝑝(𝑦𝑖	VERB
cana-1758	102	17	)	)	PUNCT
cana-1758	102	18	𝑙𝑜𝑔2(𝑝(𝑦𝑖	𝑙𝑜𝑔2(𝑝(𝑦𝑖	ADV
cana-1758	102	19	)	)	PUNCT
cana-1758	102	20	)	)	PUNCT
cana-1758	103	1	(	(	PUNCT
cana-1758	103	2	1	1	X
cana-1758	103	3	)	)	PUNCT
cana-1758	103	4	communications	communication	NOUN
cana-1758	103	5	on	on	ADP
cana-1758	103	6	applied	apply	VERB
cana-1758	103	7	nonlinear	nonlinear	ADJ
cana-1758	103	8	analysis	analysis	NOUN
cana-1758	103	9	issn	issn	NOUN
cana-1758	103	10	:	:	PUNCT
cana-1758	103	11	1074	1074	NUM
cana-1758	103	12	-	-	PUNCT
cana-1758	103	13	133x	133x	NUM
cana-1758	103	14	vol	vol	NOUN
cana-1758	103	15	32	32	NUM
cana-1758	103	16	no	no	NOUN
cana-1758	103	17	.	.	NOUN
cana-1758	103	18	2	2	NUM
cana-1758	103	19	(	(	PUNCT
cana-1758	103	20	2025	2025	NUM
cana-1758	103	21	)	)	PUNCT
cana-1758	103	22	464	464	NUM
cana-1758	103	23	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	103	24	using	use	VERB
cana-1758	103	25	a	a	DET
cana-1758	103	26	taylor	taylor	PROPN
cana-1758	103	27	series	series	NOUN
cana-1758	103	28	expression	expression	NOUN
cana-1758	103	29	,	,	PUNCT
cana-1758	103	30	we	we	PRON
cana-1758	103	31	have	have	AUX
cana-1758	103	32	updated	update	VERB
cana-1758	103	33	the	the	DET
cana-1758	103	34	algorithm	algorithm	NOUN
cana-1758	103	35	in	in	ADP
cana-1758	103	36	order	order	NOUN
cana-1758	103	37	to	to	PART
cana-1758	103	38	approximate	approximate	VERB
cana-1758	103	39	the	the	DET
cana-1758	103	40	natural	natural	ADJ
cana-1758	103	41	logarithm	logarithm	NOUN
cana-1758	103	42	that	that	PRON
cana-1758	103	43	is	be	AUX
cana-1758	103	44	utilized	utilize	VERB
cana-1758	103	45	in	in	ADP
cana-1758	103	46	the	the	DET
cana-1758	103	47	entropy	entropy	NOUN
cana-1758	103	48	formula	formula	NOUN
cana-1758	103	49	.	.	PUNCT
cana-1758	104	1	this	this	PRON
cana-1758	104	2	is	be	AUX
cana-1758	104	3	done	do	VERB
cana-1758	104	4	in	in	ADP
cana-1758	104	5	order	order	NOUN
cana-1758	104	6	to	to	PART
cana-1758	104	7	measure	measure	VERB
cana-1758	104	8	the	the	DET
cana-1758	104	9	quality	quality	NOUN
cana-1758	104	10	of	of	ADP
cana-1758	104	11	each	each	DET
cana-1758	104	12	node	node	NOUN
cana-1758	104	13	split	split	NOUN
cana-1758	104	14	in	in	ADP
cana-1758	104	15	the	the	DET
cana-1758	104	16	different	different	ADJ
cana-1758	104	17	splits	split	NOUN
cana-1758	104	18	that	that	SCONJ
cana-1758	104	19	the	the	DET
cana-1758	104	20	decision	decision	NOUN
cana-1758	104	21	tree	tree	NOUN
cana-1758	104	22	algorithm	algorithm	NOUN
cana-1758	104	23	uses	use	VERB
cana-1758	104	24	.	.	PUNCT
cana-1758	105	1	the	the	DET
cana-1758	105	2	workflow	workflow	NOUN
cana-1758	105	3	for	for	ADP
cana-1758	105	4	our	our	PRON
cana-1758	105	5	proposed	propose	VERB
cana-1758	105	6	framework	framework	NOUN
cana-1758	105	7	for	for	ADP
cana-1758	105	8	software	software	NOUN
cana-1758	105	9	defect	defect	NOUN
cana-1758	105	10	prediction	prediction	NOUN
cana-1758	105	11	is	be	AUX
cana-1758	105	12	depicted	depict	VERB
cana-1758	105	13	in	in	ADP
cana-1758	105	14	detail	detail	NOUN
cana-1758	105	15	in	in	ADP
cana-1758	105	16	figure	figure	NOUN
cana-1758	105	17	1	1	NUM
cana-1758	105	18	.	.	PUNCT
cana-1758	106	1	figure	figure	NOUN
cana-1758	106	2	1	1	NUM
cana-1758	106	3	.	.	PUNCT
cana-1758	106	4	proposed	propose	VERB
cana-1758	106	5	framework	framework	NOUN
cana-1758	106	6	for	for	ADP
cana-1758	106	7	software	software	NOUN
cana-1758	106	8	defect	defect	NOUN
cana-1758	106	9	prediction	prediction	NOUN
cana-1758	106	10	3.1	3.1	NUM
cana-1758	106	11	main	main	ADJ
cana-1758	106	12	modules	module	NOUN
cana-1758	106	13	of	of	ADP
cana-1758	106	14	prediction	prediction	NOUN
cana-1758	106	15	model	model	NOUN
cana-1758	106	16	class	class	NOUN
cana-1758	106	17	node	node	NOUN
cana-1758	106	18	:	:	PUNCT
cana-1758	106	19	specify	specify	VERB
cana-1758	106	20	the	the	DET
cana-1758	106	21	decision	decision	NOUN
cana-1758	106	22	tree	tree	NOUN
cana-1758	106	23	node	node	NOUN
cana-1758	106	24	's	's	PART
cana-1758	106	25	structure	structure	NOUN
cana-1758	106	26	.	.	PUNCT
cana-1758	107	1	class	class	NOUN
cana-1758	107	2	decision	decision	NOUN
cana-1758	107	3	tree	tree	NOUN
cana-1758	107	4	:	:	PUNCT
cana-1758	107	5	define	define	VERB
cana-1758	107	6	the	the	DET
cana-1758	107	7	decision	decision	NOUN
cana-1758	107	8	tree	tree	NOUN
cana-1758	107	9	's	's	PART
cana-1758	107	10	structure	structure	NOUN
cana-1758	107	11	and	and	CCONJ
cana-1758	107	12	put	put	VERB
cana-1758	107	13	a	a	DET
cana-1758	107	14	few	few	ADJ
cana-1758	107	15	functions	function	NOUN
cana-1758	107	16	in	in	ADP
cana-1758	107	17	place	place	NOUN
cana-1758	107	18	for	for	ADP
cana-1758	107	19	the	the	DET
cana-1758	107	20	dataset	dataset	NOUN
cana-1758	107	21	's	's	PART
cana-1758	107	22	splitting	splitting	NOUN
cana-1758	107	23	,	,	PUNCT
cana-1758	107	24	growth	growth	NOUN
cana-1758	107	25	,	,	PUNCT
cana-1758	107	26	and	and	CCONJ
cana-1758	107	27	information	information	NOUN
cana-1758	107	28	-	-	PUNCT
cana-1758	107	29	gathering	gathering	NOUN
cana-1758	107	30	,	,	PUNCT
cana-1758	107	31	among	among	ADP
cana-1758	107	32	other	other	ADJ
cana-1758	107	33	things	thing	NOUN
cana-1758	107	34	.	.	PUNCT
cana-1758	108	1	class	class	NOUN
cana-1758	108	2	random	random	ADJ
cana-1758	108	3	forest	forest	NOUN
cana-1758	108	4	:	:	PUNCT
cana-1758	108	5	define	define	VERB
cana-1758	108	6	the	the	DET
cana-1758	108	7	random	random	ADJ
cana-1758	108	8	forest	forest	NOUN
cana-1758	108	9	's	's	PART
cana-1758	108	10	structure	structure	NOUN
cana-1758	108	11	and	and	CCONJ
cana-1758	108	12	put	put	VERB
cana-1758	108	13	a	a	DET
cana-1758	108	14	few	few	ADJ
cana-1758	108	15	functions	function	NOUN
cana-1758	108	16	into	into	ADP
cana-1758	108	17	place	place	NOUN
cana-1758	108	18	for	for	ADP
cana-1758	108	19	things	thing	NOUN
cana-1758	108	20	like	like	ADP
cana-1758	108	21	model	model	NOUN
cana-1758	108	22	fitting	fitting	ADJ
cana-1758	108	23	and	and	CCONJ
cana-1758	108	24	outcome	outcome	NOUN
cana-1758	108	25	prediction	prediction	NOUN
cana-1758	108	26	.	.	PUNCT
cana-1758	109	1	main	main	ADJ
cana-1758	109	2	methodology	methodology	NOUN
cana-1758	109	3	:	:	PUNCT
cana-1758	109	4	apply	apply	VERB
cana-1758	109	5	the	the	DET
cana-1758	109	6	original	original	ADJ
cana-1758	109	7	and	and	CCONJ
cana-1758	109	8	changed	change	VERB
cana-1758	109	9	algorithms	algorithm	NOUN
cana-1758	109	10	on	on	ADP
cana-1758	109	11	the	the	DET
cana-1758	109	12	cm1	cm1	PROPN
cana-1758	109	13	dataset	dataset	PROPN
cana-1758	109	14	,	,	PUNCT
cana-1758	109	15	assess	assess	VERB
cana-1758	109	16	and	and	CCONJ
cana-1758	109	17	analyze	analyze	VERB
cana-1758	109	18	the	the	DET
cana-1758	109	19	outcomes	outcome	NOUN
cana-1758	109	20	,	,	PUNCT
cana-1758	109	21	and	and	CCONJ
cana-1758	109	22	contrast	contrast	VERB
cana-1758	109	23	the	the	DET
cana-1758	109	24	updated	update	VERB
cana-1758	109	25	algorithm	algorithm	NOUN
cana-1758	109	26	's	's	PART
cana-1758	109	27	performance	performance	NOUN
cana-1758	109	28	with	with	ADP
cana-1758	109	29	the	the	DET
cana-1758	109	30	original	original	ADJ
cana-1758	109	31	.	.	PUNCT
cana-1758	110	1	3.2	3.2	NUM
cana-1758	110	2	data	datum	NOUN
cana-1758	110	3	source	source	NOUN
cana-1758	110	4	:	:	PUNCT
cana-1758	110	5	we	we	PRON
cana-1758	110	6	have	have	AUX
cana-1758	110	7	tested	test	VERB
cana-1758	110	8	our	our	PRON
cana-1758	110	9	model	model	NOUN
cana-1758	110	10	's	's	PART
cana-1758	110	11	performance	performance	NOUN
cana-1758	110	12	using	use	VERB
cana-1758	110	13	the	the	DET
cana-1758	110	14	cm1	cm1	PROPN
cana-1758	110	15	nasa	nasa	PROPN
cana-1758	110	16	dataset	dataset	NOUN
cana-1758	110	17	.	.	PUNCT
cana-1758	111	1	nasa	nasa	PROPN
cana-1758	111	2	software	software	NOUN
cana-1758	111	3	defect	defect	VERB
cana-1758	111	4	repository	repository	NOUN
cana-1758	111	5	:	:	PUNCT
cana-1758	111	6	this	this	PRON
cana-1758	111	7	is	be	AUX
cana-1758	111	8	a	a	DET
cana-1758	111	9	dataset	dataset	NOUN
cana-1758	111	10	that	that	PRON
cana-1758	111	11	is	be	AUX
cana-1758	111	12	accessible	accessible	ADJ
cana-1758	111	13	to	to	ADP
cana-1758	111	14	the	the	DET
cana-1758	111	15	general	general	ADJ
cana-1758	111	16	public	public	NOUN
cana-1758	111	17	and	and	CCONJ
cana-1758	111	18	was	be	AUX
cana-1758	111	19	obtained	obtain	VERB
cana-1758	111	20	from	from	ADP
cana-1758	111	21	a	a	DET
cana-1758	111	22	software	software	NOUN
cana-1758	111	23	defect	defect	NOUN
cana-1758	111	24	repository	repository	NOUN
cana-1758	111	25	that	that	PRON
cana-1758	111	26	is	be	AUX
cana-1758	111	27	administered	administer	VERB
cana-1758	111	28	by	by	ADP
cana-1758	111	29	nasa	nasa	PROPN
cana-1758	111	30	.	.	PUNCT
cana-1758	112	1	a	a	DET
cana-1758	112	2	wide	wide	ADJ
cana-1758	112	3	variety	variety	NOUN
cana-1758	112	4	of	of	ADP
cana-1758	112	5	nasa	nasa	PROPN
cana-1758	112	6	software	software	NOUN
cana-1758	112	7	defect	defect	VERB
cana-1758	112	8	repository	repository	NOUN
cana-1758	112	9	pre	pre	ADJ
cana-1758	112	10	-	-	NOUN
cana-1758	112	11	process	process	ADJ
cana-1758	112	12	data	datum	NOUN
cana-1758	112	13	for	for	ADP
cana-1758	112	14	model	model	NOUN
cana-1758	112	15	fitting	fit	VERB
cana-1758	112	16	define	define	VERB
cana-1758	112	17	tress	tress	NOUN
cana-1758	112	18	,	,	PUNCT
cana-1758	112	19	nodes	node	NOUN
cana-1758	112	20	,	,	PUNCT
cana-1758	112	21	entropy	entropy	PROPN
cana-1758	112	22	,	,	PUNCT
cana-1758	112	23	best	good	ADJ
cana-1758	112	24	split	split	VERB
cana-1758	112	25	fit	fit	ADJ
cana-1758	112	26	the	the	DET
cana-1758	112	27	training	training	NOUN
cana-1758	112	28	data	datum	NOUN
cana-1758	112	29	on	on	ADP
cana-1758	112	30	the	the	DET
cana-1758	112	31	defined	define	VERB
cana-1758	112	32	model	model	NOUN
cana-1758	112	33	calculate	calculate	NOUN
cana-1758	112	34	entropy	entropy	NOUN
cana-1758	112	35	using	use	VERB
cana-1758	112	36	taylor	taylor	PROPN
cana-1758	112	37	series	series	PROPN
cana-1758	112	38	apply	apply	VERB
cana-1758	112	39	train_test	train_t	ADJ
cana-1758	112	40	_	_	PUNCT
cana-1758	112	41	split	split	NOUN
cana-1758	112	42	(	(	PUNCT
cana-1758	112	43	80:20	80:20	NUM
cana-1758	112	44	)	)	PUNCT
cana-1758	112	45	ratio	ratio	NOUN
cana-1758	112	46	evaluate	evaluate	VERB
cana-1758	112	47	model	model	NOUN
cana-1758	112	48	on	on	ADP
cana-1758	112	49	testing	test	VERB
cana-1758	112	50	data	datum	NOUN
cana-1758	112	51	display	display	NOUN
cana-1758	112	52	results	result	NOUN
cana-1758	112	53	of	of	ADP
cana-1758	112	54	accuracy	accuracy	NOUN
cana-1758	112	55	,	,	PUNCT
cana-1758	112	56	precision	precision	NOUN
cana-1758	112	57	,	,	PUNCT
cana-1758	112	58	recall	recall	NOUN
cana-1758	112	59	and	and	CCONJ
cana-1758	112	60	rocauc	rocauc	NOUN
cana-1758	112	61	curve	curve	NOUN
cana-1758	112	62	communications	communication	NOUN
cana-1758	112	63	on	on	ADP
cana-1758	112	64	applied	apply	VERB
cana-1758	112	65	nonlinear	nonlinear	ADJ
cana-1758	112	66	analysis	analysis	NOUN
cana-1758	112	67	issn	issn	NOUN
cana-1758	112	68	:	:	PUNCT
cana-1758	112	69	1074	1074	NUM
cana-1758	112	70	-	-	PUNCT
cana-1758	112	71	133x	133x	NUM
cana-1758	112	72	vol	vol	NOUN
cana-1758	112	73	32	32	NUM
cana-1758	112	74	no	no	NOUN
cana-1758	112	75	.	.	NOUN
cana-1758	112	76	2	2	NUM
cana-1758	112	77	(	(	PUNCT
cana-1758	112	78	2025	2025	NUM
cana-1758	112	79	)	)	PUNCT
cana-1758	112	80	465	465	NUM
cana-1758	112	81	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	112	82	systems	system	NOUN
cana-1758	112	83	are	be	AUX
cana-1758	112	84	included	include	VERB
cana-1758	112	85	into	into	ADP
cana-1758	112	86	the	the	DET
cana-1758	112	87	several	several	ADJ
cana-1758	112	88	programs	program	NOUN
cana-1758	112	89	that	that	PRON
cana-1758	112	90	are	be	AUX
cana-1758	112	91	run	run	VERB
cana-1758	112	92	by	by	ADP
cana-1758	112	93	nasa	nasa	PROPN
cana-1758	112	94	.	.	PUNCT
cana-1758	113	1	as	as	ADP
cana-1758	113	2	an	an	DET
cana-1758	113	3	illustration	illustration	NOUN
cana-1758	113	4	,	,	PUNCT
cana-1758	113	5	the	the	DET
cana-1758	113	6	acronym	acronym	PROPN
cana-1758	113	7	cm1	cm1	PROPN
cana-1758	113	8	refers	refer	VERB
cana-1758	113	9	to	to	ADP
cana-1758	113	10	the	the	DET
cana-1758	113	11	instruments	instrument	NOUN
cana-1758	113	12	of	of	ADP
cana-1758	113	13	spacecraft	spacecraft	NOUN
cana-1758	113	14	,	,	PUNCT
cana-1758	113	15	but	but	CCONJ
cana-1758	113	16	the	the	DET
cana-1758	113	17	acronyms	acronym	NOUN
cana-1758	113	18	kc1	kc1	PROPN
cana-1758	113	19	,	,	PUNCT
cana-1758	113	20	kc3	kc3	PROPN
cana-1758	113	21	,	,	PUNCT
cana-1758	113	22	and	and	CCONJ
cana-1758	113	23	mc2	mc2	PROPN
cana-1758	113	24	refer	refer	VERB
cana-1758	113	25	to	to	ADP
cana-1758	113	26	the	the	DET
cana-1758	113	27	administration	administration	NOUN
cana-1758	113	28	of	of	ADP
cana-1758	113	29	ground	ground	NOUN
cana-1758	113	30	data	data	NOUN
cana-1758	113	31	storage	storage	NOUN
cana-1758	113	32	.	.	PUNCT
cana-1758	114	1	mw1	mw1	NOUN
cana-1758	114	2	is	be	AUX
cana-1758	114	3	in	in	ADP
cana-1758	114	4	charge	charge	NOUN
cana-1758	114	5	of	of	ADP
cana-1758	114	6	managing	manage	VERB
cana-1758	114	7	the	the	DET
cana-1758	114	8	data	data	NOUN
cana-1758	114	9	exchanges	exchange	NOUN
cana-1758	114	10	,	,	PUNCT
cana-1758	114	11	and	and	CCONJ
cana-1758	114	12	pc1	pc1	PROPN
cana-1758	114	13	,	,	PUNCT
cana-1758	114	14	pc2	pc2	PROPN
cana-1758	114	15	,	,	PUNCT
cana-1758	114	16	pc3	pc3	ADJ
cana-1758	114	17	,	,	PUNCT
cana-1758	114	18	and	and	CCONJ
cana-1758	114	19	pc4	pc4	PRON
cana-1758	114	20	are	be	AUX
cana-1758	114	21	the	the	DET
cana-1758	114	22	programs	program	NOUN
cana-1758	114	23	that	that	PRON
cana-1758	114	24	are	be	AUX
cana-1758	114	25	responsible	responsible	ADJ
cana-1758	114	26	for	for	ADP
cana-1758	114	27	the	the	DET
cana-1758	114	28	software	software	NOUN
cana-1758	114	29	that	that	PRON
cana-1758	114	30	is	be	AUX
cana-1758	114	31	used	use	VERB
cana-1758	114	32	by	by	ADP
cana-1758	114	33	earth	earth	NOUN
cana-1758	114	34	-	-	PUNCT
cana-1758	114	35	orbiting	orbit	VERB
cana-1758	114	36	satellites	satellite	NOUN
cana-1758	114	37	.	.	PUNCT
cana-1758	115	1	a	a	DET
cana-1758	115	2	number	number	NOUN
cana-1758	115	3	of	of	ADP
cana-1758	115	4	characteristics	characteristic	NOUN
cana-1758	115	5	are	be	AUX
cana-1758	115	6	included	include	VERB
cana-1758	115	7	in	in	ADP
cana-1758	115	8	it	it	PRON
cana-1758	115	9	,	,	PUNCT
cana-1758	115	10	and	and	CCONJ
cana-1758	115	11	these	these	DET
cana-1758	115	12	characteristics	characteristic	NOUN
cana-1758	115	13	are	be	AUX
cana-1758	115	14	used	use	VERB
cana-1758	115	15	as	as	ADP
cana-1758	115	16	independent	independent	ADJ
cana-1758	115	17	variables	variable	NOUN
cana-1758	115	18	to	to	PART
cana-1758	115	19	decide	decide	VERB
cana-1758	115	20	whether	whether	SCONJ
cana-1758	115	21	or	or	CCONJ
cana-1758	115	22	not	not	PART
cana-1758	115	23	a	a	DET
cana-1758	115	24	particular	particular	ADJ
cana-1758	115	25	piece	piece	NOUN
cana-1758	115	26	of	of	ADP
cana-1758	115	27	software	software	NOUN
cana-1758	115	28	may	may	AUX
cana-1758	115	29	have	have	VERB
cana-1758	115	30	some	some	DET
cana-1758	115	31	sort	sort	NOUN
cana-1758	115	32	of	of	ADP
cana-1758	115	33	defect	defect	NOUN
cana-1758	115	34	.	.	PUNCT
cana-1758	116	1	some	some	PRON
cana-1758	116	2	of	of	ADP
cana-1758	116	3	these	these	DET
cana-1758	116	4	qualities	quality	NOUN
cana-1758	116	5	include	include	VERB
cana-1758	116	6	,	,	PUNCT
cana-1758	116	7	but	but	CCONJ
cana-1758	116	8	are	be	AUX
cana-1758	116	9	not	not	PART
cana-1758	116	10	limited	limit	VERB
cana-1758	116	11	to	to	ADP
cana-1758	116	12	,	,	PUNCT
cana-1758	116	13	the	the	DET
cana-1758	116	14	complexity	complexity	NOUN
cana-1758	116	15	of	of	ADP
cana-1758	116	16	the	the	DET
cana-1758	116	17	program	program	NOUN
cana-1758	116	18	,	,	PUNCT
cana-1758	116	19	the	the	DET
cana-1758	116	20	amount	amount	NOUN
cana-1758	116	21	of	of	ADP
cana-1758	116	22	control	control	NOUN
cana-1758	116	23	flow	flow	NOUN
cana-1758	116	24	statements	statement	NOUN
cana-1758	116	25	,	,	PUNCT
cana-1758	116	26	the	the	DET
cana-1758	116	27	number	number	NOUN
cana-1758	116	28	of	of	ADP
cana-1758	116	29	lines	line	NOUN
cana-1758	116	30	of	of	ADP
cana-1758	116	31	code	code	NOUN
cana-1758	116	32	,	,	PUNCT
cana-1758	116	33	the	the	DET
cana-1758	116	34	number	number	NOUN
cana-1758	116	35	of	of	ADP
cana-1758	116	36	comments	comment	NOUN
cana-1758	116	37	contained	contain	VERB
cana-1758	116	38	within	within	ADP
cana-1758	116	39	the	the	DET
cana-1758	116	40	code	code	NOUN
cana-1758	116	41	,	,	PUNCT
cana-1758	116	42	and	and	CCONJ
cana-1758	116	43	a	a	DET
cana-1758	116	44	great	great	ADJ
cana-1758	116	45	deal	deal	NOUN
cana-1758	116	46	of	of	ADP
cana-1758	116	47	other	other	ADJ
cana-1758	116	48	properties	property	NOUN
cana-1758	116	49	.	.	PUNCT
cana-1758	117	1	the	the	DET
cana-1758	117	2	likelihood	likelihood	NOUN
cana-1758	117	3	that	that	SCONJ
cana-1758	117	4	a	a	DET
cana-1758	117	5	program	program	NOUN
cana-1758	117	6	is	be	AUX
cana-1758	117	7	flawed	flawed	ADJ
cana-1758	117	8	is	be	AUX
cana-1758	117	9	represented	represent	VERB
cana-1758	117	10	by	by	ADP
cana-1758	117	11	the	the	DET
cana-1758	117	12	dependent	dependent	ADJ
cana-1758	117	13	variable	variable	NOUN
cana-1758	117	14	known	know	VERB
cana-1758	117	15	as	as	ADP
cana-1758	117	16	"	"	PUNCT
cana-1758	117	17	defective	defective	ADJ
cana-1758	117	18	.	.	PUNCT
cana-1758	117	19	"	"	PUNCT
cana-1758	118	1	3.3	3.3	NUM
cana-1758	118	2	algorithm	algorithm	NOUN
cana-1758	118	3	selection	selection	NOUN
cana-1758	118	4	and	and	CCONJ
cana-1758	118	5	modification	modification	NOUN
cana-1758	118	6	:	:	PUNCT
cana-1758	118	7	we	we	PRON
cana-1758	118	8	decided	decide	VERB
cana-1758	118	9	to	to	PART
cana-1758	118	10	use	use	VERB
cana-1758	118	11	the	the	DET
cana-1758	118	12	random	random	ADJ
cana-1758	118	13	forest	forest	NOUN
cana-1758	118	14	method	method	NOUN
cana-1758	118	15	as	as	ADP
cana-1758	118	16	our	our	PRON
cana-1758	118	17	foundational	foundational	ADJ
cana-1758	118	18	model	model	NOUN
cana-1758	118	19	because	because	SCONJ
cana-1758	118	20	it	it	PRON
cana-1758	118	21	offers	offer	VERB
cana-1758	118	22	a	a	DET
cana-1758	118	23	number	number	NOUN
cana-1758	118	24	of	of	ADP
cana-1758	118	25	benefits	benefit	NOUN
cana-1758	118	26	,	,	PUNCT
cana-1758	118	27	including	include	VERB
cana-1758	118	28	the	the	DET
cana-1758	118	29	control	control	NOUN
cana-1758	118	30	of	of	ADP
cana-1758	118	31	overfitting	overfitting	NOUN
cana-1758	118	32	,	,	PUNCT
cana-1758	118	33	the	the	DET
cana-1758	118	34	management	management	NOUN
cana-1758	118	35	of	of	ADP
cana-1758	118	36	missing	miss	VERB
cana-1758	118	37	values	value	NOUN
cana-1758	118	38	,	,	PUNCT
cana-1758	118	39	and	and	CCONJ
cana-1758	118	40	the	the	DET
cana-1758	118	41	provision	provision	NOUN
cana-1758	118	42	of	of	ADP
cana-1758	118	43	feature	feature	NOUN
cana-1758	118	44	importance	importance	NOUN
cana-1758	118	45	scores	score	NOUN
cana-1758	118	46	.	.	PUNCT
cana-1758	119	1	an	an	DET
cana-1758	119	2	approximation	approximation	NOUN
cana-1758	119	3	of	of	ADP
cana-1758	119	4	the	the	DET
cana-1758	119	5	natural	natural	ADJ
cana-1758	119	6	logarithm	logarithm	NOUN
cana-1758	119	7	that	that	PRON
cana-1758	119	8	is	be	AUX
cana-1758	119	9	utilized	utilize	VERB
cana-1758	119	10	in	in	ADP
cana-1758	119	11	the	the	DET
cana-1758	119	12	entropy	entropy	NOUN
cana-1758	119	13	formula	formula	NOUN
cana-1758	119	14	has	have	AUX
cana-1758	119	15	been	be	AUX
cana-1758	119	16	incorporated	incorporate	VERB
cana-1758	119	17	into	into	ADP
cana-1758	119	18	the	the	DET
cana-1758	119	19	random	random	ADJ
cana-1758	119	20	forest	forest	NOUN
cana-1758	119	21	method	method	NOUN
cana-1758	119	22	through	through	ADP
cana-1758	119	23	the	the	DET
cana-1758	119	24	utilization	utilization	NOUN
cana-1758	119	25	of	of	ADP
cana-1758	119	26	the	the	DET
cana-1758	119	27	taylor	taylor	PROPN
cana-1758	119	28	series	series	PROPN
cana-1758	119	29	expression	expression	NOUN
cana-1758	119	30	.	.	PUNCT
cana-1758	120	1	in	in	ADP
cana-1758	120	2	the	the	DET
cana-1758	120	3	case	case	NOUN
cana-1758	120	4	of	of	ADP
cana-1758	120	5	natural	natural	ADJ
cana-1758	120	6	logarithm	logarithm	NOUN
cana-1758	120	7	,	,	PUNCT
cana-1758	120	8	the	the	DET
cana-1758	120	9	taylor	taylor	PROPN
cana-1758	120	10	series	series	PROPN
cana-1758	120	11	formulation	formulation	NOUN
cana-1758	120	12	is	be	AUX
cana-1758	120	13	as	as	SCONJ
cana-1758	120	14	follows	follow	VERB
cana-1758	120	15	:	:	PUNCT
cana-1758	120	16	entropy	entropy	PROPN
cana-1758	120	17	=	=	SYM
cana-1758	120	18	∑	∑	PUNCT
cana-1758	120	19	(	(	PUNCT
cana-1758	120	20	−1)𝑛+1	−1)𝑛+1	PROPN
cana-1758	120	21	𝑛	𝑛	X
cana-1758	120	22	(	(	PUNCT
cana-1758	120	23	𝑥	𝑥	NOUN
cana-1758	120	24	−	−	X
cana-1758	120	25	1)𝑛	1)𝑛	NUM
cana-1758	120	26	∞	∞	PROPN
cana-1758	120	27	𝑛=1	𝑛=1	NOUN
cana-1758	120	28	from	from	ADP
cana-1758	120	29	n	n	NOUN
cana-1758	120	30	=	=	SYM
cana-1758	120	31	0	0	NUM
cana-1758	120	32	to	to	ADP
cana-1758	120	33	∞	∞	PROPN
cana-1758	120	34	(	(	PUNCT
cana-1758	120	35	2	2	NUM
cana-1758	120	36	)	)	PUNCT
cana-1758	120	37	here	here	ADV
cana-1758	120	38	,	,	PUNCT
cana-1758	120	39	the	the	DET
cana-1758	120	40	variable	variable	NOUN
cana-1758	120	41	x	x	PRON
cana-1758	120	42	can	can	AUX
cana-1758	120	43	be	be	AUX
cana-1758	120	44	any	any	DET
cana-1758	120	45	positive	positive	ADJ
cana-1758	120	46	number	number	NOUN
cana-1758	120	47	between	between	ADP
cana-1758	120	48	0	0	NUM
cana-1758	120	49	and	and	CCONJ
cana-1758	120	50	1	1	NUM
cana-1758	120	51	,	,	PUNCT
cana-1758	120	52	inclusive	inclusive	ADJ
cana-1758	120	53	.	.	PUNCT
cana-1758	121	1	through	through	ADP
cana-1758	121	2	the	the	DET
cana-1758	121	3	utilization	utilization	NOUN
cana-1758	121	4	of	of	ADP
cana-1758	121	5	this	this	DET
cana-1758	121	6	approximation	approximation	NOUN
cana-1758	121	7	,	,	PUNCT
cana-1758	121	8	we	we	PRON
cana-1758	121	9	are	be	AUX
cana-1758	121	10	able	able	ADJ
cana-1758	121	11	to	to	PART
cana-1758	121	12	compute	compute	VERB
cana-1758	121	13	the	the	DET
cana-1758	121	14	natural	natural	ADJ
cana-1758	121	15	logarithm	logarithm	NOUN
cana-1758	121	16	for	for	ADP
cana-1758	121	17	a	a	DET
cana-1758	121	18	certain	certain	ADJ
cana-1758	121	19	value	value	NOUN
cana-1758	121	20	of	of	ADP
cana-1758	121	21	x	x	PUNCT
cana-1758	121	22	by	by	ADP
cana-1758	121	23	employing	employ	VERB
cana-1758	121	24	a	a	DET
cana-1758	121	25	limited	limited	ADJ
cana-1758	121	26	number	number	NOUN
cana-1758	121	27	of	of	ADP
cana-1758	121	28	constituent	constituent	NOUN
cana-1758	121	29	terms	term	NOUN
cana-1758	121	30	.	.	PUNCT
cana-1758	122	1	when	when	SCONJ
cana-1758	122	2	compared	compare	VERB
cana-1758	122	3	to	to	ADP
cana-1758	122	4	the	the	DET
cana-1758	122	5	natural	natural	ADJ
cana-1758	122	6	logarithm	logarithm	NOUN
cana-1758	122	7	,	,	PUNCT
cana-1758	122	8	the	the	DET
cana-1758	122	9	usage	usage	NOUN
cana-1758	122	10	of	of	ADP
cana-1758	122	11	taylor	taylor	PROPN
cana-1758	122	12	series	series	PROPN
cana-1758	122	13	results	result	VERB
cana-1758	122	14	in	in	ADP
cana-1758	122	15	a	a	DET
cana-1758	122	16	significantly	significantly	ADV
cana-1758	122	17	higher	high	ADJ
cana-1758	122	18	degree	degree	NOUN
cana-1758	122	19	of	of	ADP
cana-1758	122	20	precision	precision	NOUN
cana-1758	122	21	throughout	throughout	ADP
cana-1758	122	22	the	the	DET
cana-1758	122	23	entropy	entropy	NOUN
cana-1758	122	24	computation	computation	NOUN
cana-1758	122	25	process	process	NOUN
cana-1758	122	26	.	.	PUNCT
cana-1758	123	1	in	in	ADP
cana-1758	123	2	contrast	contrast	NOUN
cana-1758	123	3	,	,	PUNCT
cana-1758	123	4	the	the	DET
cana-1758	123	5	natural	natural	ADJ
cana-1758	123	6	logarithm	logarithm	NOUN
cana-1758	123	7	function	function	NOUN
cana-1758	123	8	necessitates	necessitate	VERB
cana-1758	123	9	the	the	DET
cana-1758	123	10	utilization	utilization	NOUN
cana-1758	123	11	of	of	ADP
cana-1758	123	12	a	a	DET
cana-1758	123	13	complicated	complicated	ADJ
cana-1758	123	14	algorithm	algorithm	NOUN
cana-1758	123	15	that	that	PRON
cana-1758	123	16	incorporates	incorporate	VERB
cana-1758	123	17	iterative	iterative	ADJ
cana-1758	123	18	approaches	approach	NOUN
cana-1758	123	19	,	,	PUNCT
cana-1758	123	20	floating	float	VERB
cana-1758	123	21	-	-	PUNCT
cana-1758	123	22	point	point	NOUN
cana-1758	123	23	arithmetic	arithmetic	ADJ
cana-1758	123	24	,	,	PUNCT
cana-1758	123	25	and	and	CCONJ
cana-1758	123	26	error	error	NOUN
cana-1758	123	27	management	management	NOUN
cana-1758	123	28	.	.	PUNCT
cana-1758	124	1	in	in	ADP
cana-1758	124	2	addition	addition	NOUN
cana-1758	124	3	,	,	PUNCT
cana-1758	124	4	the	the	DET
cana-1758	124	5	taylor	taylor	PROPN
cana-1758	124	6	series	series	PROPN
cana-1758	124	7	gives	give	VERB
cana-1758	124	8	the	the	DET
cana-1758	124	9	user	user	NOUN
cana-1758	124	10	the	the	DET
cana-1758	124	11	ability	ability	NOUN
cana-1758	124	12	to	to	PART
cana-1758	124	13	personalize	personalize	VERB
cana-1758	124	14	the	the	DET
cana-1758	124	15	approximation	approximation	NOUN
cana-1758	124	16	by	by	ADP
cana-1758	124	17	selecting	select	VERB
cana-1758	124	18	the	the	DET
cana-1758	124	19	degree	degree	NOUN
cana-1758	124	20	of	of	ADP
cana-1758	124	21	the	the	DET
cana-1758	124	22	polynomial	polynomial	NOUN
cana-1758	124	23	for	for	ADP
cana-1758	124	24	the	the	DET
cana-1758	124	25	model	model	NOUN
cana-1758	124	26	.	.	PUNCT
cana-1758	125	1	an	an	DET
cana-1758	125	2	array	array	NOUN
cana-1758	125	3	of	of	ADP
cana-1758	125	4	probabilities	probability	NOUN
cana-1758	125	5	was	be	AUX
cana-1758	125	6	used	use	VERB
cana-1758	125	7	as	as	ADP
cana-1758	125	8	the	the	DET
cana-1758	125	9	input	input	NOUN
cana-1758	125	10	for	for	ADP
cana-1758	125	11	this	this	DET
cana-1758	125	12	adjustment	adjustment	NOUN
cana-1758	125	13	,	,	PUNCT
cana-1758	125	14	and	and	CCONJ
cana-1758	125	15	an	an	DET
cana-1758	125	16	array	array	NOUN
cana-1758	125	17	of	of	ADP
cana-1758	125	18	approximated	approximate	VERB
cana-1758	125	19	logarithms	logarithm	NOUN
cana-1758	125	20	was	be	AUX
cana-1758	125	21	returned	return	VERB
cana-1758	125	22	as	as	ADP
cana-1758	125	23	the	the	DET
cana-1758	125	24	output	output	NOUN
cana-1758	125	25	.	.	PUNCT
cana-1758	126	1	this	this	DET
cana-1758	126	2	modification	modification	NOUN
cana-1758	126	3	was	be	AUX
cana-1758	126	4	implemented	implement	VERB
cana-1758	126	5	by	by	ADP
cana-1758	126	6	constructing	construct	VERB
cana-1758	126	7	a	a	DET
cana-1758	126	8	custom	custom	NOUN
cana-1758	126	9	function	function	NOUN
cana-1758	126	10	that	that	PRON
cana-1758	126	11	follows	follow	VERB
cana-1758	126	12	this	this	DET
cana-1758	126	13	pattern	pattern	NOUN
cana-1758	126	14	.	.	PUNCT
cana-1758	127	1	after	after	ADP
cana-1758	127	2	that	that	PRON
cana-1758	127	3	,	,	PUNCT
cana-1758	127	4	we	we	PRON
cana-1758	127	5	have	have	AUX
cana-1758	127	6	incorporated	incorporate	VERB
cana-1758	127	7	the	the	DET
cana-1758	127	8	function	function	NOUN
cana-1758	127	9	described	describe	VERB
cana-1758	127	10	above	above	ADV
cana-1758	127	11	into	into	ADP
cana-1758	127	12	our	our	PRON
cana-1758	127	13	entropy	entropy	NOUN
cana-1758	127	14	formula	formula	NOUN
cana-1758	127	15	in	in	ADP
cana-1758	127	16	order	order	NOUN
cana-1758	127	17	to	to	PART
cana-1758	127	18	compute	compute	VERB
cana-1758	127	19	the	the	DET
cana-1758	127	20	split	split	NOUN
cana-1758	127	21	of	of	ADP
cana-1758	127	22	each	each	DET
cana-1758	127	23	node	node	NOUN
cana-1758	127	24	.	.	PUNCT
cana-1758	128	1	3.4	3.4	NUM
cana-1758	128	2	training	training	NOUN
cana-1758	128	3	and	and	CCONJ
cana-1758	128	4	testing	testing	NOUN
cana-1758	128	5	model	model	NOUN
cana-1758	128	6	:	:	PUNCT
cana-1758	128	7	through	through	ADP
cana-1758	128	8	the	the	DET
cana-1758	128	9	utilization	utilization	NOUN
cana-1758	128	10	of	of	ADP
cana-1758	128	11	a	a	DET
cana-1758	128	12	wide	wide	ADJ
cana-1758	128	13	range	range	NOUN
cana-1758	128	14	of	of	ADP
cana-1758	128	15	hyperparameters	hyperparameter	NOUN
cana-1758	128	16	,	,	PUNCT
cana-1758	128	17	we	we	PRON
cana-1758	128	18	have	have	AUX
cana-1758	128	19	trained	train	VERB
cana-1758	128	20	our	our	PRON
cana-1758	128	21	modified	modify	VERB
cana-1758	128	22	random	random	ADJ
cana-1758	128	23	forest	forest	NOUN
cana-1758	128	24	method	method	NOUN
cana-1758	128	25	on	on	ADP
cana-1758	128	26	training	training	NOUN
cana-1758	128	27	data	datum	NOUN
cana-1758	128	28	.	.	PUNCT
cana-1758	129	1	these	these	DET
cana-1758	129	2	hyperparameters	hyperparameter	NOUN
cana-1758	129	3	include	include	VERB
cana-1758	129	4	the	the	DET
cana-1758	129	5	number	number	NOUN
cana-1758	129	6	of	of	ADP
cana-1758	129	7	trees	tree	NOUN
cana-1758	129	8	(	(	PUNCT
cana-1758	129	9	n_trees	n_tree	NOUN
cana-1758	129	10	)	)	PUNCT
cana-1758	129	11	,	,	PUNCT
cana-1758	129	12	the	the	DET
cana-1758	129	13	maximum	maximum	ADJ
cana-1758	129	14	depth	depth	NOUN
cana-1758	129	15	(	(	PUNCT
cana-1758	129	16	max_depth	max_depth	NOUN
cana-1758	129	17	)	)	PUNCT
cana-1758	129	18	,	,	PUNCT
cana-1758	129	19	and	and	CCONJ
cana-1758	129	20	the	the	DET
cana-1758	129	21	minimum	minimum	NOUN
cana-1758	129	22	samples	sample	NOUN
cana-1758	129	23	split	split	NOUN
cana-1758	129	24	(	(	PUNCT
cana-1758	129	25	min_samples_split	min_samples_split	ADJ
cana-1758	129	26	)	)	PUNCT
cana-1758	129	27	.	.	PUNCT
cana-1758	130	1	after	after	ADP
cana-1758	130	2	that	that	PRON
cana-1758	130	3	,	,	PUNCT
cana-1758	130	4	we	we	PRON
cana-1758	130	5	communications	communication	VERB
cana-1758	130	6	on	on	ADP
cana-1758	130	7	applied	apply	VERB
cana-1758	130	8	nonlinear	nonlinear	ADJ
cana-1758	130	9	analysis	analysis	NOUN
cana-1758	130	10	issn	issn	NOUN
cana-1758	130	11	:	:	PUNCT
cana-1758	130	12	1074	1074	NUM
cana-1758	130	13	-	-	PUNCT
cana-1758	130	14	133x	133x	NUM
cana-1758	130	15	vol	vol	NOUN
cana-1758	130	16	32	32	NUM
cana-1758	130	17	no	no	NOUN
cana-1758	130	18	.	.	NOUN
cana-1758	130	19	2	2	NUM
cana-1758	130	20	(	(	PUNCT
cana-1758	130	21	2025	2025	NUM
cana-1758	130	22	)	)	PUNCT
cana-1758	130	23	466	466	NUM
cana-1758	130	24	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	130	25	used	use	VERB
cana-1758	130	26	a	a	DET
cana-1758	130	27	grid	grid	NOUN
cana-1758	130	28	search	search	NOUN
cana-1758	130	29	approach	approach	NOUN
cana-1758	130	30	to	to	PART
cana-1758	130	31	find	find	VERB
cana-1758	130	32	the	the	DET
cana-1758	130	33	most	most	ADV
cana-1758	130	34	effective	effective	ADJ
cana-1758	130	35	combination	combination	NOUN
cana-1758	130	36	of	of	ADP
cana-1758	130	37	hyperparameters	hyperparameter	NOUN
cana-1758	130	38	for	for	ADP
cana-1758	130	39	each	each	DET
cana-1758	130	40	dataset	dataset	NOUN
cana-1758	130	41	individually	individually	ADV
cana-1758	130	42	.	.	PUNCT
cana-1758	131	1	this	this	PRON
cana-1758	131	2	was	be	AUX
cana-1758	131	3	then	then	ADV
cana-1758	131	4	done	do	VERB
cana-1758	131	5	in	in	ADP
cana-1758	131	6	order	order	NOUN
cana-1758	131	7	to	to	PART
cana-1758	131	8	find	find	VERB
cana-1758	131	9	the	the	DET
cana-1758	131	10	optimal	optimal	ADJ
cana-1758	131	11	combination	combination	NOUN
cana-1758	131	12	.	.	PUNCT
cana-1758	132	1	using	use	VERB
cana-1758	132	2	the	the	DET
cana-1758	132	3	cm1	cm1	PROPN
cana-1758	132	4	training	training	NOUN
cana-1758	132	5	dataset	dataset	NOUN
cana-1758	132	6	,	,	PUNCT
cana-1758	132	7	our	our	PRON
cana-1758	132	8	revised	revise	VERB
cana-1758	132	9	random	random	ADJ
cana-1758	132	10	forest	forest	NOUN
cana-1758	132	11	method	method	NOUN
cana-1758	132	12	was	be	AUX
cana-1758	132	13	put	put	VERB
cana-1758	132	14	through	through	ADP
cana-1758	132	15	its	its	PRON
cana-1758	132	16	paces	pace	NOUN
cana-1758	132	17	,	,	PUNCT
cana-1758	132	18	and	and	CCONJ
cana-1758	132	19	its	its	PRON
cana-1758	132	20	effectiveness	effectiveness	NOUN
cana-1758	132	21	was	be	AUX
cana-1758	132	22	evaluated	evaluate	VERB
cana-1758	132	23	using	use	VERB
cana-1758	132	24	a	a	DET
cana-1758	132	25	range	range	NOUN
cana-1758	132	26	of	of	ADP
cana-1758	132	27	different	different	ADJ
cana-1758	132	28	measures	measure	NOUN
cana-1758	132	29	.	.	PUNCT
cana-1758	133	1	4	4	X
cana-1758	133	2	.	.	X
cana-1758	133	3	theory	theory	NOUN
cana-1758	133	4	and	and	CCONJ
cana-1758	133	5	calculation	calculation	NOUN
cana-1758	133	6	out	out	ADP
cana-1758	133	7	of	of	ADP
cana-1758	133	8	the	the	DET
cana-1758	133	9	cyclomatic_density	cyclomatic_density	NOUN
cana-1758	133	10	feature	feature	NOUN
cana-1758	133	11	that	that	PRON
cana-1758	133	12	is	be	AUX
cana-1758	133	13	included	include	VERB
cana-1758	133	14	in	in	ADP
cana-1758	133	15	the	the	DET
cana-1758	133	16	cm1	cm1	PROPN
cana-1758	133	17	dataset	dataset	PROPN
cana-1758	133	18	,	,	PUNCT
cana-1758	133	19	we	we	PRON
cana-1758	133	20	have	have	AUX
cana-1758	133	21	chosen	choose	VERB
cana-1758	133	22	ten	ten	NUM
cana-1758	133	23	sample	sample	NOUN
cana-1758	133	24	values	value	NOUN
cana-1758	133	25	to	to	PART
cana-1758	133	26	represent	represent	VERB
cana-1758	133	27	it	it	PRON
cana-1758	133	28	.	.	PUNCT
cana-1758	134	1	our	our	PRON
cana-1758	134	2	next	next	ADJ
cana-1758	134	3	step	step	NOUN
cana-1758	134	4	is	be	AUX
cana-1758	134	5	to	to	PART
cana-1758	134	6	determine	determine	VERB
cana-1758	134	7	the	the	DET
cana-1758	134	8	absolute	absolute	ADJ
cana-1758	134	9	value	value	NOUN
cana-1758	134	10	for	for	ADP
cana-1758	134	11	each	each	DET
cana-1758	134	12	individual	individual	ADJ
cana-1758	134	13	observation	observation	NOUN
cana-1758	134	14	.	.	PUNCT
cana-1758	135	1	following	follow	VERB
cana-1758	135	2	is	be	AUX
cana-1758	135	3	a	a	DET
cana-1758	135	4	list	list	NOUN
cana-1758	135	5	of	of	ADP
cana-1758	135	6	the	the	DET
cana-1758	135	7	values	value	NOUN
cana-1758	135	8	:	:	PUNCT
cana-1758	136	1	[	[	X
cana-1758	136	2	0.2	0.2	NUM
cana-1758	136	3	,	,	PUNCT
cana-1758	136	4	0.13	0.13	NUM
cana-1758	136	5	,	,	PUNCT
cana-1758	136	6	0.15	0.15	NUM
cana-1758	136	7	,	,	PUNCT
cana-1758	136	8	0.17	0.17	NUM
cana-1758	136	9	,	,	PUNCT
cana-1758	136	10	0.12	0.12	NUM
cana-1758	136	11	,	,	PUNCT
cana-1758	136	12	0.2	0.2	NUM
cana-1758	136	13	,	,	PUNCT
cana-1758	136	14	0.14	0.14	NUM
cana-1758	136	15	,	,	PUNCT
cana-1758	136	16	0.28	0.28	NUM
cana-1758	136	17	,	,	PUNCT
cana-1758	136	18	0.11	0.11	NUM
cana-1758	136	19	,	,	PUNCT
cana-1758	136	20	0.17	0.17	NUM
cana-1758	136	21	]	]	PUNCT
cana-1758	136	22	.	.	PUNCT
cana-1758	137	1	we	we	PRON
cana-1758	137	2	can	can	AUX
cana-1758	137	3	calculate	calculate	VERB
cana-1758	137	4	entropy	entropy	NOUN
cana-1758	137	5	for	for	ADP
cana-1758	137	6	value	value	NOUN
cana-1758	137	7	(	(	PUNCT
cana-1758	137	8	0.2	0.2	NUM
cana-1758	137	9	)	)	PUNCT
cana-1758	137	10	as	as	SCONJ
cana-1758	137	11	follow	follow	VERB
cana-1758	137	12	,	,	PUNCT
cana-1758	137	13	e	e	X
cana-1758	137	14	(	(	PUNCT
cana-1758	137	15	0.2	0.2	NUM
cana-1758	137	16	)	)	PUNCT
cana-1758	137	17	=	=	SYM
cana-1758	137	18	(	(	PUNCT
cana-1758	137	19	−1)1	−1)1	NUM
cana-1758	137	20	+	+	NOUN
cana-1758	137	21	1	1	NUM
cana-1758	137	22	1	1	NUM
cana-1758	137	23	(	(	PUNCT
cana-1758	137	24	0.2	0.2	NUM
cana-1758	137	25	−	−	NUM
cana-1758	137	26	1)1	1)1	NUM
cana-1758	137	27	+	+	CCONJ
cana-1758	137	28	(	(	PUNCT
cana-1758	137	29	−1)2	−1)2	X
cana-1758	137	30	+	+	NOUN
cana-1758	137	31	1	1	NUM
cana-1758	137	32	2	2	NUM
cana-1758	137	33	(	(	PUNCT
cana-1758	137	34	0.2	0.2	NUM
cana-1758	137	35	−	−	NUM
cana-1758	137	36	1)2	1)2	NUM
cana-1758	137	37	+	+	CCONJ
cana-1758	137	38	(	(	PUNCT
cana-1758	137	39	−1)3	−1)3	ADV
cana-1758	137	40	+	+	NOUN
cana-1758	137	41	1	1	NUM
cana-1758	137	42	3	3	NUM
cana-1758	137	43	(	(	PUNCT
cana-1758	137	44	0.2	0.2	NUM
cana-1758	137	45	−	−	NUM
cana-1758	137	46	1)3	1)3	PROPN
cana-1758	138	1	+	+	CCONJ
cana-1758	138	2	(	(	PUNCT
cana-1758	138	3	−1)4	−1)4	PUNCT
cana-1758	138	4	+	+	SYM
cana-1758	138	5	1	1	NUM
cana-1758	138	6	4	4	NUM
cana-1758	138	7	(	(	PUNCT
cana-1758	138	8	0.2	0.2	NUM
cana-1758	138	9	−	−	NUM
cana-1758	138	10	1)4	1)4	PROPN
cana-1758	138	11	+	+	CCONJ
cana-1758	138	12	(	(	PUNCT
cana-1758	138	13	−1)5	−1)5	X
cana-1758	138	14	+	+	ADJ
cana-1758	138	15	1	1	NUM
cana-1758	138	16	5	5	NUM
cana-1758	138	17	(	(	PUNCT
cana-1758	138	18	0.2	0.2	NUM
cana-1758	138	19	−	−	PROPN
cana-1758	138	20	1)5	1)5	NUM
cana-1758	138	21	+	+	CCONJ
cana-1758	138	22	(	(	PUNCT
cana-1758	138	23	−1)6	−1)6	PUNCT
cana-1758	138	24	+	+	NOUN
cana-1758	138	25	1	1	NUM
cana-1758	138	26	6	6	NUM
cana-1758	138	27	(	(	PUNCT
cana-1758	138	28	0.2	0.2	NUM
cana-1758	138	29	−	−	PROPN
cana-1758	138	30	1)6	1)6	NUM
cana-1758	138	31	+	+	CCONJ
cana-1758	138	32	(	(	PUNCT
cana-1758	138	33	−1)7	−1)7	X
cana-1758	138	34	+	+	NOUN
cana-1758	138	35	1	1	NUM
cana-1758	138	36	7	7	NUM
cana-1758	138	37	(	(	PUNCT
cana-1758	138	38	0.2	0.2	NUM
cana-1758	138	39	−	−	NUM
cana-1758	138	40	1)7	1)7	NUM
cana-1758	138	41	+	+	CCONJ
cana-1758	138	42	(	(	PUNCT
cana-1758	138	43	−1)8	−1)8	PROPN
cana-1758	138	44	+	+	NOUN
cana-1758	138	45	1	1	NUM
cana-1758	138	46	8	8	NUM
cana-1758	138	47	(	(	PUNCT
cana-1758	138	48	0.2	0.2	NUM
cana-1758	138	49	−	−	PROPN
cana-1758	138	50	1)8	1)8	NUM
cana-1758	138	51	+	+	CCONJ
cana-1758	138	52	(	(	PUNCT
cana-1758	138	53	−1)9	−1)9	NUM
cana-1758	138	54	+	+	NOUN
cana-1758	138	55	1	1	NUM
cana-1758	138	56	9	9	NUM
cana-1758	138	57	(	(	PUNCT
cana-1758	138	58	0.2	0.2	NUM
cana-1758	138	59	−	−	NUM
cana-1758	138	60	1)9	1)9	NUM
cana-1758	138	61	+	+	CCONJ
cana-1758	138	62	(	(	PUNCT
cana-1758	138	63	−1)10	−1)10	ADJ
cana-1758	138	64	+	+	PROPN
cana-1758	138	65	1	1	NUM
cana-1758	138	66	10	10	NUM
cana-1758	138	67	(	(	PUNCT
cana-1758	138	68	0.2	0.2	NUM
cana-1758	138	69	−	−	NOUN
cana-1758	138	70	1)10	1)10	NOUN
cana-1758	138	71	=	=	SYM
cana-1758	138	72	1.60	1.60	NUM
cana-1758	138	73	.	.	PUNCT
cana-1758	139	1	similarly	similarly	ADV
cana-1758	139	2	,	,	PUNCT
cana-1758	139	3	we	we	PRON
cana-1758	139	4	can	can	AUX
cana-1758	139	5	calculate	calculate	VERB
cana-1758	139	6	for	for	ADP
cana-1758	139	7	other	other	ADJ
cana-1758	139	8	points	point	NOUN
cana-1758	139	9	e	e	X
cana-1758	139	10	(	(	PUNCT
cana-1758	139	11	0.13	0.13	NUM
cana-1758	139	12	)	)	PUNCT
cana-1758	139	13	=	=	SYM
cana-1758	139	14	(	(	PUNCT
cana-1758	139	15	−1)1	−1)1	NUM
cana-1758	139	16	+	+	NOUN
cana-1758	139	17	1	1	NUM
cana-1758	139	18	1	1	NUM
cana-1758	139	19	(	(	PUNCT
cana-1758	139	20	0.13	0.13	NUM
cana-1758	139	21	−	−	PROPN
cana-1758	139	22	1)1	1)1	NUM
cana-1758	139	23	+	+	CCONJ
cana-1758	139	24	…	…	PUNCT
cana-1758	139	25	+	+	CCONJ
cana-1758	139	26	(	(	PUNCT
cana-1758	139	27	−1)10	−1)10	ADJ
cana-1758	139	28	+	+	PROPN
cana-1758	139	29	1	1	NUM
cana-1758	139	30	10	10	NUM
cana-1758	139	31	(	(	PUNCT
cana-1758	139	32	0.13	0.13	NUM
cana-1758	139	33	−	−	NOUN
cana-1758	139	34	1)10	1)10	NOUN
cana-1758	139	35	=	=	SYM
cana-1758	139	36	2.04	2.04	NUM
cana-1758	139	37	,	,	PUNCT
cana-1758	139	38	e	e	X
cana-1758	139	39	(	(	PUNCT
cana-1758	139	40	0.15	0.15	NUM
cana-1758	139	41	)	)	PUNCT
cana-1758	139	42	=	=	SYM
cana-1758	139	43	(	(	PUNCT
cana-1758	139	44	−1)1	−1)1	NUM
cana-1758	139	45	+	+	NOUN
cana-1758	139	46	1	1	NUM
cana-1758	139	47	1	1	NUM
cana-1758	139	48	(	(	PUNCT
cana-1758	139	49	0.15	0.15	NUM
cana-1758	139	50	−	−	PROPN
cana-1758	139	51	1)1	1)1	NUM
cana-1758	139	52	+	+	CCONJ
cana-1758	139	53	…	…	PUNCT
cana-1758	139	54	+	+	CCONJ
cana-1758	139	55	(	(	PUNCT
cana-1758	139	56	−1)10	−1)10	ADJ
cana-1758	139	57	+	+	PROPN
cana-1758	139	58	1	1	NUM
cana-1758	139	59	10	10	NUM
cana-1758	139	60	(	(	PUNCT
cana-1758	139	61	0.15	0.15	NUM
cana-1758	139	62	−	−	NOUN
cana-1758	139	63	1)10	1)10	NOUN
cana-1758	139	64	=	=	SYM
cana-1758	139	65	1.89	1.89	NUM
cana-1758	139	66	e	e	NOUN
cana-1758	139	67	(	(	PUNCT
cana-1758	139	68	0.17	0.17	NUM
cana-1758	139	69	)	)	PUNCT
cana-1758	139	70	=	=	SYM
cana-1758	139	71	(	(	PUNCT
cana-1758	139	72	−1)1	−1)1	NUM
cana-1758	139	73	+	+	NOUN
cana-1758	139	74	1	1	NUM
cana-1758	139	75	1	1	NUM
cana-1758	139	76	(	(	PUNCT
cana-1758	139	77	0.17	0.17	NUM
cana-1758	139	78	−	−	PROPN
cana-1758	139	79	1)1	1)1	NUM
cana-1758	139	80	+	+	CCONJ
cana-1758	139	81	…	…	PUNCT
cana-1758	139	82	+	+	CCONJ
cana-1758	139	83	(	(	PUNCT
cana-1758	139	84	−1)10	−1)10	ADJ
cana-1758	139	85	+	+	PROPN
cana-1758	139	86	1	1	NUM
cana-1758	139	87	10	10	NUM
cana-1758	139	88	(	(	PUNCT
cana-1758	139	89	0.17	0.17	NUM
cana-1758	139	90	−	−	NOUN
cana-1758	139	91	1)10	1)10	NOUN
cana-1758	139	92	=	=	SYM
cana-1758	139	93	1.77	1.77	NUM
cana-1758	139	94	,	,	PUNCT
cana-1758	139	95	e	e	X
cana-1758	139	96	(	(	PUNCT
cana-1758	139	97	0.12	0.12	NUM
cana-1758	139	98	)	)	PUNCT
cana-1758	139	99	=	=	SYM
cana-1758	139	100	(	(	PUNCT
cana-1758	139	101	−1)1	−1)1	NUM
cana-1758	139	102	+	+	NOUN
cana-1758	139	103	1	1	NUM
cana-1758	139	104	1	1	NUM
cana-1758	139	105	(	(	PUNCT
cana-1758	139	106	0.12	0.12	NUM
cana-1758	139	107	−	−	NUM
cana-1758	139	108	1)1	1)1	NUM
cana-1758	139	109	+	+	CCONJ
cana-1758	139	110	…	…	PUNCT
cana-1758	139	111	+	+	CCONJ
cana-1758	139	112	(	(	PUNCT
cana-1758	139	113	−1)10	−1)10	ADJ
cana-1758	139	114	+	+	PROPN
cana-1758	139	115	1	1	NUM
cana-1758	139	116	10	10	NUM
cana-1758	139	117	(	(	PUNCT
cana-1758	139	118	0.12	0.12	NUM
cana-1758	139	119	−	−	NOUN
cana-1758	139	120	1)10	1)10	NOUN
cana-1758	139	121	=	=	SYM
cana-1758	139	122	2.12	2.12	NUM
cana-1758	139	123	e	e	NOUN
cana-1758	139	124	(	(	PUNCT
cana-1758	139	125	0.2	0.2	NUM
cana-1758	139	126	)	)	PUNCT
cana-1758	139	127	=	=	SYM
cana-1758	139	128	(	(	PUNCT
cana-1758	139	129	−1)1	−1)1	NUM
cana-1758	139	130	+	+	NOUN
cana-1758	139	131	1	1	NUM
cana-1758	139	132	1	1	NUM
cana-1758	139	133	(	(	PUNCT
cana-1758	139	134	0.2	0.2	NUM
cana-1758	139	135	−	−	NUM
cana-1758	139	136	1)1	1)1	NUM
cana-1758	139	137	+	+	CCONJ
cana-1758	139	138	…	…	PUNCT
cana-1758	139	139	+	+	CCONJ
cana-1758	139	140	(	(	PUNCT
cana-1758	139	141	−1)10	−1)10	ADJ
cana-1758	139	142	+	+	PROPN
cana-1758	139	143	1	1	NUM
cana-1758	139	144	10	10	NUM
cana-1758	139	145	(	(	PUNCT
cana-1758	139	146	0.2	0.2	NUM
cana-1758	139	147	−	−	NOUN
cana-1758	139	148	1)10	1)10	NOUN
cana-1758	139	149	=	=	SYM
cana-1758	139	150	1.60	1.60	NUM
cana-1758	139	151	,	,	PUNCT
cana-1758	139	152	e	e	X
cana-1758	139	153	(	(	PUNCT
cana-1758	139	154	0.14	0.14	NUM
cana-1758	139	155	)	)	PUNCT
cana-1758	139	156	=	=	SYM
cana-1758	139	157	(	(	PUNCT
cana-1758	139	158	−1)1	−1)1	NUM
cana-1758	139	159	+	+	NOUN
cana-1758	139	160	1	1	NUM
cana-1758	139	161	1	1	NUM
cana-1758	139	162	(	(	PUNCT
cana-1758	139	163	0.14	0.14	NUM
cana-1758	139	164	−	−	PROPN
cana-1758	139	165	1)1	1)1	NUM
cana-1758	139	166	+	+	CCONJ
cana-1758	139	167	…	…	PUNCT
cana-1758	139	168	+	+	CCONJ
cana-1758	139	169	(	(	PUNCT
cana-1758	139	170	−1)10	−1)10	ADJ
cana-1758	139	171	+	+	PROPN
cana-1758	139	172	1	1	NUM
cana-1758	139	173	10	10	NUM
cana-1758	139	174	(	(	PUNCT
cana-1758	139	175	0.14	0.14	NUM
cana-1758	139	176	−	−	NOUN
cana-1758	139	177	1)10	1)10	NOUN
cana-1758	139	178	=	=	SYM
cana-1758	139	179	1.96	1.96	NUM
cana-1758	139	180	e	e	NOUN
cana-1758	139	181	(	(	PUNCT
cana-1758	139	182	0.28	0.28	NUM
cana-1758	139	183	)	)	PUNCT
cana-1758	139	184	=	=	NOUN
cana-1758	139	185	(	(	PUNCT
cana-1758	139	186	−1)1	−1)1	NUM
cana-1758	139	187	+	+	NOUN
cana-1758	139	188	1	1	NUM
cana-1758	139	189	1	1	NUM
cana-1758	139	190	(	(	PUNCT
cana-1758	139	191	0.28	0.28	NUM
cana-1758	139	192	−	−	PROPN
cana-1758	139	193	1)1	1)1	NUM
cana-1758	139	194	+	+	CCONJ
cana-1758	139	195	…	…	PUNCT
cana-1758	139	196	+	+	CCONJ
cana-1758	139	197	(	(	PUNCT
cana-1758	139	198	−1)10	−1)10	ADJ
cana-1758	139	199	+	+	PROPN
cana-1758	139	200	1	1	NUM
cana-1758	139	201	10	10	NUM
cana-1758	139	202	(	(	PUNCT
cana-1758	139	203	0.28	0.28	NUM
cana-1758	139	204	−	−	NOUN
cana-1758	139	205	1)10	1)10	NOUN
cana-1758	139	206	=	=	SYM
cana-1758	139	207	1.27	1.27	NUM
cana-1758	139	208	,	,	PUNCT
cana-1758	139	209	e	e	X
cana-1758	139	210	(	(	PUNCT
cana-1758	139	211	0.11	0.11	NUM
cana-1758	139	212	)	)	PUNCT
cana-1758	139	213	=	=	SYM
cana-1758	139	214	(	(	PUNCT
cana-1758	139	215	−1)1	−1)1	NUM
cana-1758	139	216	+	+	NOUN
cana-1758	139	217	1	1	NUM
cana-1758	139	218	1	1	NUM
cana-1758	139	219	(	(	PUNCT
cana-1758	139	220	0.11	0.11	NUM
cana-1758	139	221	−	−	PROPN
cana-1758	139	222	1)1	1)1	NUM
cana-1758	139	223	+	+	CCONJ
cana-1758	139	224	…	…	PUNCT
cana-1758	139	225	+	+	CCONJ
cana-1758	139	226	(	(	PUNCT
cana-1758	139	227	−1)10	−1)10	ADJ
cana-1758	139	228	+	+	PROPN
cana-1758	139	229	1	1	NUM
cana-1758	139	230	10	10	NUM
cana-1758	139	231	(	(	PUNCT
cana-1758	139	232	0.11	0.11	NUM
cana-1758	139	233	−	−	NOUN
cana-1758	139	234	1)10	1)10	NOUN
cana-1758	139	235	=	=	SYM
cana-1758	139	236	2.20	2.20	NUM
cana-1758	139	237	e	e	X
cana-1758	139	238	(	(	PUNCT
cana-1758	139	239	0.17	0.17	NUM
cana-1758	139	240	)	)	PUNCT
cana-1758	139	241	=	=	SYM
cana-1758	139	242	(	(	PUNCT
cana-1758	139	243	−1)1	−1)1	NUM
cana-1758	139	244	+	+	NOUN
cana-1758	139	245	1	1	NUM
cana-1758	139	246	1	1	NUM
cana-1758	139	247	(	(	PUNCT
cana-1758	139	248	0.17	0.17	NUM
cana-1758	139	249	−	−	PROPN
cana-1758	139	250	1)1	1)1	NUM
cana-1758	139	251	+	+	CCONJ
cana-1758	139	252	…	…	PUNCT
cana-1758	139	253	+	+	CCONJ
cana-1758	139	254	(	(	PUNCT
cana-1758	139	255	−1)10	−1)10	ADJ
cana-1758	139	256	+	+	PROPN
cana-1758	139	257	1	1	NUM
cana-1758	139	258	10	10	NUM
cana-1758	139	259	(	(	PUNCT
cana-1758	139	260	0.17	0.17	NUM
cana-1758	139	261	−	−	NOUN
cana-1758	139	262	1)10	1)10	NOUN
cana-1758	139	263	=	=	SYM
cana-1758	139	264	1.77	1.77	NUM
cana-1758	139	265	communications	communication	NOUN
cana-1758	139	266	on	on	ADP
cana-1758	139	267	applied	apply	VERB
cana-1758	139	268	nonlinear	nonlinear	ADJ
cana-1758	139	269	analysis	analysis	NOUN
cana-1758	139	270	issn	issn	NOUN
cana-1758	139	271	:	:	PUNCT
cana-1758	139	272	1074	1074	NUM
cana-1758	139	273	-	-	PUNCT
cana-1758	139	274	133x	133x	NUM
cana-1758	139	275	vol	vol	NOUN
cana-1758	139	276	32	32	NUM
cana-1758	139	277	no	no	NOUN
cana-1758	139	278	.	.	NOUN
cana-1758	139	279	2	2	NUM
cana-1758	139	280	(	(	PUNCT
cana-1758	139	281	2025	2025	NUM
cana-1758	139	282	)	)	PUNCT
cana-1758	139	283	467	467	NUM
cana-1758	139	284	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	139	285	table	table	NOUN
cana-1758	139	286	2	2	NUM
cana-1758	139	287	.	.	X
cana-1758	139	288	entropy	entropy	PROPN
cana-1758	139	289	calculation	calculation	NOUN
cana-1758	139	290	values	value	NOUN
cana-1758	139	291	by	by	ADP
cana-1758	139	292	taylor	taylor	PROPN
cana-1758	139	293	series	series	PROPN
cana-1758	139	294	value	value	VERB
cana-1758	139	295	0.2	0.2	NUM
cana-1758	139	296	0.13	0.13	NUM
cana-1758	139	297	0.15	0.15	NUM
cana-1758	139	298	0.17	0.17	NUM
cana-1758	139	299	0.12	0.12	NUM
cana-1758	139	300	0.2	0.2	NUM
cana-1758	139	301	0.14	0.14	NUM
cana-1758	139	302	0.28	0.28	NUM
cana-1758	139	303	0.11	0.11	NUM
cana-1758	139	304	entropy	entropy	NOUN
cana-1758	139	305	1.6094	1.6094	NUM
cana-1758	139	306	2.0402	2.0402	NUM
cana-1758	139	307	1.8971	1.8971	NUM
cana-1758	139	308	1.7719	1.7719	NUM
cana-1758	139	309	2.1202	2.1202	NUM
cana-1758	139	310	1.6094	1.6094	NUM
cana-1758	139	311	1.9661	1.9661	NUM
cana-1758	139	312	1.2729	1.2729	NUM
cana-1758	139	313	2.2072	2.2072	NUM
cana-1758	139	314	5	5	NUM
cana-1758	139	315	.	.	PUNCT
cana-1758	139	316	results	result	NOUN
cana-1758	139	317	in	in	ADP
cana-1758	139	318	the	the	DET
cana-1758	139	319	current	current	ADJ
cana-1758	139	320	method	method	NOUN
cana-1758	139	321	,	,	PUNCT
cana-1758	139	322	the	the	DET
cana-1758	139	323	quality	quality	NOUN
cana-1758	139	324	of	of	ADP
cana-1758	139	325	each	each	DET
cana-1758	139	326	node	node	NOUN
cana-1758	139	327	split	split	NOUN
cana-1758	139	328	in	in	ADP
cana-1758	139	329	the	the	DET
cana-1758	139	330	decision	decision	NOUN
cana-1758	139	331	tree	tree	NOUN
cana-1758	139	332	is	be	AUX
cana-1758	139	333	determined	determine	VERB
cana-1758	139	334	by	by	ADP
cana-1758	139	335	combining	combine	VERB
cana-1758	139	336	entropy	entropy	NOUN
cana-1758	139	337	with	with	ADP
cana-1758	139	338	the	the	DET
cana-1758	139	339	natural	natural	ADJ
cana-1758	139	340	logarithm	logarithm	NOUN
cana-1758	139	341	.	.	PUNCT
cana-1758	140	1	as	as	ADP
cana-1758	140	2	a	a	DET
cana-1758	140	3	result	result	NOUN
cana-1758	140	4	of	of	ADP
cana-1758	140	5	our	our	PRON
cana-1758	140	6	desire	desire	NOUN
cana-1758	140	7	to	to	PART
cana-1758	140	8	investigate	investigate	VERB
cana-1758	140	9	an	an	DET
cana-1758	140	10	alternative	alternative	ADJ
cana-1758	140	11	approach	approach	NOUN
cana-1758	140	12	to	to	ADP
cana-1758	140	13	calculating	calculate	VERB
cana-1758	140	14	the	the	DET
cana-1758	140	15	natural	natural	ADJ
cana-1758	140	16	logarithm	logarithm	NOUN
cana-1758	140	17	that	that	PRON
cana-1758	140	18	was	be	AUX
cana-1758	140	19	utilized	utilize	VERB
cana-1758	140	20	in	in	ADP
cana-1758	140	21	the	the	DET
cana-1758	140	22	initial	initial	ADJ
cana-1758	140	23	entropy	entropy	NOUN
cana-1758	140	24	formula	formula	NOUN
cana-1758	140	25	,	,	PUNCT
cana-1758	140	26	we	we	PRON
cana-1758	140	27	are	be	AUX
cana-1758	140	28	currently	currently	ADV
cana-1758	140	29	employing	employ	VERB
cana-1758	140	30	the	the	DET
cana-1758	140	31	modified	modify	VERB
cana-1758	140	32	entropy	entropy	NOUN
cana-1758	140	33	formula	formula	NOUN
cana-1758	140	34	that	that	PRON
cana-1758	140	35	makes	make	VERB
cana-1758	140	36	use	use	NOUN
cana-1758	140	37	of	of	ADP
cana-1758	140	38	the	the	DET
cana-1758	140	39	taylor	taylor	PROPN
cana-1758	140	40	series	series	PROPN
cana-1758	140	41	.	.	PUNCT
cana-1758	141	1	when	when	SCONJ
cana-1758	141	2	applied	apply	VERB
cana-1758	141	3	to	to	ADP
cana-1758	141	4	the	the	DET
cana-1758	141	5	cm1	cm1	PROPN
cana-1758	141	6	dataset	dataset	PROPN
cana-1758	141	7	,	,	PUNCT
cana-1758	141	8	the	the	DET
cana-1758	141	9	results	result	NOUN
cana-1758	141	10	that	that	PRON
cana-1758	141	11	were	be	AUX
cana-1758	141	12	obtained	obtain	VERB
cana-1758	141	13	by	by	ADP
cana-1758	141	14	using	use	VERB
cana-1758	141	15	both	both	CCONJ
cana-1758	141	16	the	the	DET
cana-1758	141	17	original	original	ADJ
cana-1758	141	18	entropy	entropy	NOUN
cana-1758	141	19	formula	formula	NOUN
cana-1758	141	20	and	and	CCONJ
cana-1758	141	21	the	the	DET
cana-1758	141	22	modified	modify	VERB
cana-1758	141	23	entropy	entropy	NOUN
cana-1758	141	24	formula	formula	NOUN
cana-1758	141	25	utilizing	utilize	VERB
cana-1758	141	26	taylor	taylor	PROPN
cana-1758	141	27	's	's	PART
cana-1758	141	28	series	series	NOUN
cana-1758	141	29	are	be	AUX
cana-1758	141	30	presented	present	VERB
cana-1758	141	31	in	in	ADP
cana-1758	141	32	table	table	NOUN
cana-1758	141	33	3	3	NUM
cana-1758	141	34	.	.	PUNCT
cana-1758	142	1	we	we	PRON
cana-1758	142	2	are	be	AUX
cana-1758	142	3	able	able	ADJ
cana-1758	142	4	to	to	PART
cana-1758	142	5	clearly	clearly	ADV
cana-1758	142	6	notice	notice	VERB
cana-1758	142	7	the	the	DET
cana-1758	142	8	change	change	NOUN
cana-1758	142	9	in	in	ADP
cana-1758	142	10	accuracy	accuracy	NOUN
cana-1758	142	11	,	,	PUNCT
cana-1758	142	12	precision	precision	NOUN
cana-1758	142	13	,	,	PUNCT
cana-1758	142	14	and	and	CCONJ
cana-1758	142	15	recall	recall	VERB
cana-1758	142	16	in	in	ADP
cana-1758	142	17	both	both	CCONJ
cana-1758	142	18	the	the	DET
cana-1758	142	19	original	original	ADJ
cana-1758	142	20	random	random	ADJ
cana-1758	142	21	forest	forest	NOUN
cana-1758	142	22	method	method	NOUN
cana-1758	142	23	and	and	CCONJ
cana-1758	142	24	the	the	DET
cana-1758	142	25	updated	update	VERB
cana-1758	142	26	random	random	ADJ
cana-1758	142	27	forest	forest	NOUN
cana-1758	142	28	algorithm	algorithm	NOUN
cana-1758	142	29	by	by	ADP
cana-1758	142	30	analyzing	analyze	VERB
cana-1758	142	31	the	the	DET
cana-1758	142	32	results	result	NOUN
cana-1758	142	33	that	that	PRON
cana-1758	142	34	were	be	AUX
cana-1758	142	35	acquired	acquire	VERB
cana-1758	142	36	by	by	ADP
cana-1758	142	37	the	the	DET
cana-1758	142	38	random	random	ADJ
cana-1758	142	39	datasets	dataset	NOUN
cana-1758	142	40	they	they	PRON
cana-1758	142	41	contained	contain	VERB
cana-1758	142	42	.	.	PUNCT
cana-1758	143	1	table	table	NOUN
cana-1758	143	2	3	3	NUM
cana-1758	143	3	.	.	PUNCT
cana-1758	143	4	software	software	NOUN
cana-1758	143	5	defect	defect	NOUN
cana-1758	143	6	prediction	prediction	NOUN
cana-1758	143	7	model	model	NOUN
cana-1758	143	8	result	result	NOUN
cana-1758	143	9	accuracy	accuracy	NOUN
cana-1758	143	10	,	,	PUNCT
cana-1758	143	11	precision	precision	NOUN
cana-1758	143	12	,	,	PUNCT
cana-1758	143	13	and	and	CCONJ
cana-1758	143	14	recall	recall	NOUN
cana-1758	143	15	parameters	parameter	NOUN
cana-1758	143	16	precision	precision	NOUN
cana-1758	143	17	(	(	PUNCT
cana-1758	143	18	0	0	NUM
cana-1758	143	19	)	)	PUNCT
cana-1758	143	20	precision	precision	NOUN
cana-1758	143	21	(	(	PUNCT
cana-1758	143	22	1	1	X
cana-1758	143	23	)	)	PUNCT
cana-1758	143	24	recall	recall	NOUN
cana-1758	143	25	(	(	PUNCT
cana-1758	143	26	0	0	NUM
cana-1758	143	27	)	)	PUNCT
cana-1758	143	28	recall	recall	NOUN
cana-1758	143	29	(	(	PUNCT
cana-1758	143	30	1	1	NUM
cana-1758	143	31	)	)	PUNCT
cana-1758	143	32	accuracy	accuracy	NOUN
cana-1758	143	33	original	original	ADJ
cana-1758	143	34	formula	formula	NOUN
cana-1758	143	35	0.88	0.88	NUM
cana-1758	143	36	0.5	0.5	NUM
cana-1758	143	37	0.98	0.98	NUM
cana-1758	143	38	0.11	0.11	NUM
cana-1758	143	39	0.8695	0.8695	NUM
cana-1758	143	40	modified	modify	VERB
cana-1758	143	41	formula	formula	NOUN
cana-1758	143	42	0.88	0.88	NUM
cana-1758	143	43	1	1	NUM
cana-1758	143	44	1	1	NUM
cana-1758	143	45	0.11	0.11	NUM
cana-1758	143	46	0.884	0.884	NUM
cana-1758	143	47	with	with	ADP
cana-1758	143	48	the	the	DET
cana-1758	143	49	help	help	NOUN
cana-1758	143	50	of	of	ADP
cana-1758	143	51	this	this	DET
cana-1758	143	52	table	table	NOUN
cana-1758	143	53	,	,	PUNCT
cana-1758	143	54	we	we	PRON
cana-1758	143	55	are	be	AUX
cana-1758	143	56	able	able	ADJ
cana-1758	143	57	to	to	PART
cana-1758	143	58	quickly	quickly	ADV
cana-1758	143	59	draw	draw	VERB
cana-1758	143	60	the	the	DET
cana-1758	143	61	conclusion	conclusion	NOUN
cana-1758	143	62	that	that	SCONJ
cana-1758	143	63	we	we	PRON
cana-1758	143	64	can	can	AUX
cana-1758	143	65	increase	increase	VERB
cana-1758	143	66	the	the	DET
cana-1758	143	67	accuracy	accuracy	NOUN
cana-1758	143	68	of	of	ADP
cana-1758	143	69	our	our	PRON
cana-1758	143	70	calculations	calculation	NOUN
cana-1758	143	71	by	by	ADP
cana-1758	143	72	utilizing	utilize	VERB
cana-1758	143	73	the	the	DET
cana-1758	143	74	taylor	taylor	PROPN
cana-1758	143	75	series	series	PROPN
cana-1758	143	76	to	to	PART
cana-1758	143	77	calculate	calculate	VERB
cana-1758	143	78	entropy	entropy	NOUN
cana-1758	143	79	.	.	PUNCT
cana-1758	144	1	the	the	DET
cana-1758	144	2	roc	roc	PROPN
cana-1758	144	3	curve	curve	NOUN
cana-1758	144	4	for	for	ADP
cana-1758	144	5	the	the	DET
cana-1758	144	6	training	training	NOUN
cana-1758	144	7	dataset	dataset	VERB
cana-1758	144	8	as	as	ADV
cana-1758	144	9	well	well	ADV
cana-1758	144	10	as	as	ADP
cana-1758	144	11	the	the	DET
cana-1758	144	12	testing	testing	NOUN
cana-1758	144	13	dataset	dataset	NOUN
cana-1758	144	14	is	be	AUX
cana-1758	144	15	displayed	display	VERB
cana-1758	144	16	in	in	ADP
cana-1758	144	17	the	the	DET
cana-1758	144	18	figure	figure	NOUN
cana-1758	144	19	.	.	PUNCT
cana-1758	145	1	this	this	DET
cana-1758	145	2	curve	curve	NOUN
cana-1758	145	3	is	be	AUX
cana-1758	145	4	shown	show	VERB
cana-1758	145	5	for	for	ADP
cana-1758	145	6	both	both	CCONJ
cana-1758	145	7	the	the	DET
cana-1758	145	8	original	original	ADJ
cana-1758	145	9	random	random	ADJ
cana-1758	145	10	forest	forest	NOUN
cana-1758	145	11	algorithm	algorithm	NOUN
cana-1758	145	12	and	and	CCONJ
cana-1758	145	13	the	the	DET
cana-1758	145	14	modified	modify	VERB
cana-1758	145	15	random	random	ADJ
cana-1758	145	16	forest	forest	NOUN
cana-1758	145	17	algorithm	algorithm	NOUN
cana-1758	145	18	.	.	PUNCT
cana-1758	146	1	figure	figure	NOUN
cana-1758	146	2	2	2	NUM
cana-1758	146	3	.	.	PUNCT
cana-1758	147	1	(	(	PUNCT
cana-1758	147	2	a	a	X
cana-1758	147	3	)	)	PUNCT
cana-1758	147	4	roc	roc	PROPN
cana-1758	147	5	curve	curve	NOUN
cana-1758	147	6	for	for	ADP
cana-1758	147	7	original	original	ADJ
cana-1758	147	8	random	random	ADJ
cana-1758	147	9	forest	forest	NOUN
cana-1758	147	10	algorithm	algorithm	NOUN
cana-1758	147	11	;	;	PUNCT
cana-1758	147	12	(	(	PUNCT
cana-1758	147	13	b	b	X
cana-1758	147	14	)	)	PUNCT
cana-1758	147	15	roc	roc	PROPN
cana-1758	147	16	curve	curve	NOUN
cana-1758	147	17	for	for	ADP
cana-1758	147	18	modified	modify	VERB
cana-1758	147	19	random	random	ADJ
cana-1758	147	20	forest	forest	NOUN
cana-1758	147	21	algorithm	algorithm	NOUN
cana-1758	147	22	communications	communication	NOUN
cana-1758	147	23	on	on	ADP
cana-1758	147	24	applied	apply	VERB
cana-1758	147	25	nonlinear	nonlinear	ADJ
cana-1758	147	26	analysis	analysis	NOUN
cana-1758	147	27	issn	issn	NOUN
cana-1758	147	28	:	:	PUNCT
cana-1758	147	29	1074	1074	NUM
cana-1758	147	30	-	-	PUNCT
cana-1758	147	31	133x	133x	NUM
cana-1758	147	32	vol	vol	NOUN
cana-1758	147	33	32	32	NUM
cana-1758	147	34	no	no	NOUN
cana-1758	147	35	.	.	NOUN
cana-1758	147	36	2	2	NUM
cana-1758	147	37	(	(	PUNCT
cana-1758	147	38	2025	2025	NUM
cana-1758	147	39	)	)	PUNCT
cana-1758	147	40	468	468	NUM
cana-1758	147	41	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	147	42	the	the	DET
cana-1758	147	43	roc	roc	PROPN
cana-1758	147	44	curves	curve	NOUN
cana-1758	147	45	that	that	PRON
cana-1758	147	46	were	be	AUX
cana-1758	147	47	presented	present	VERB
cana-1758	147	48	earlier	early	ADV
cana-1758	147	49	allow	allow	VERB
cana-1758	147	50	us	we	PRON
cana-1758	147	51	to	to	PART
cana-1758	147	52	draw	draw	VERB
cana-1758	147	53	the	the	DET
cana-1758	147	54	conclusion	conclusion	NOUN
cana-1758	147	55	that	that	SCONJ
cana-1758	147	56	the	the	DET
cana-1758	147	57	area	area	NOUN
cana-1758	147	58	under	under	ADP
cana-1758	147	59	the	the	DET
cana-1758	147	60	training	training	NOUN
cana-1758	147	61	roc	roc	PROPN
cana-1758	147	62	curve	curve	NOUN
cana-1758	147	63	and	and	CCONJ
cana-1758	147	64	the	the	DET
cana-1758	147	65	area	area	NOUN
cana-1758	147	66	under	under	ADP
cana-1758	147	67	the	the	DET
cana-1758	147	68	testing	testing	NOUN
cana-1758	147	69	roc	roc	PROPN
cana-1758	147	70	curve	curve	NOUN
cana-1758	147	71	both	both	PRON
cana-1758	147	72	exhibit	exhibit	VERB
cana-1758	147	73	considerable	considerable	ADJ
cana-1758	147	74	variations	variation	NOUN
cana-1758	147	75	in	in	ADP
cana-1758	147	76	their	their	PRON
cana-1758	147	77	respective	respective	ADJ
cana-1758	147	78	results	result	NOUN
cana-1758	147	79	.	.	PUNCT
cana-1758	148	1	6	6	X
cana-1758	148	2	.	.	X
cana-1758	148	3	conclusions	conclusion	NOUN
cana-1758	148	4	the	the	DET
cana-1758	148	5	taylor	taylor	PROPN
cana-1758	148	6	series	series	PROPN
cana-1758	148	7	expression	expression	NOUN
cana-1758	148	8	was	be	AUX
cana-1758	148	9	utilized	utilize	VERB
cana-1758	148	10	in	in	ADP
cana-1758	148	11	this	this	DET
cana-1758	148	12	study	study	NOUN
cana-1758	148	13	to	to	PART
cana-1758	148	14	approximate	approximate	VERB
cana-1758	148	15	the	the	DET
cana-1758	148	16	natural	natural	ADJ
cana-1758	148	17	logarithm	logarithm	NOUN
cana-1758	148	18	that	that	PRON
cana-1758	148	19	is	be	AUX
cana-1758	148	20	utilized	utilize	VERB
cana-1758	148	21	in	in	ADP
cana-1758	148	22	the	the	DET
cana-1758	148	23	entropy	entropy	NOUN
cana-1758	148	24	formula	formula	NOUN
cana-1758	148	25	to	to	PART
cana-1758	148	26	quantify	quantify	VERB
cana-1758	148	27	the	the	DET
cana-1758	148	28	quality	quality	NOUN
cana-1758	148	29	of	of	ADP
cana-1758	148	30	each	each	DET
cana-1758	148	31	node	node	NOUN
cana-1758	148	32	split	split	NOUN
cana-1758	148	33	.	.	PUNCT
cana-1758	149	1	this	this	PRON
cana-1758	149	2	was	be	AUX
cana-1758	149	3	done	do	VERB
cana-1758	149	4	in	in	ADP
cana-1758	149	5	order	order	NOUN
cana-1758	149	6	to	to	PART
cana-1758	149	7	investigate	investigate	VERB
cana-1758	149	8	the	the	DET
cana-1758	149	9	computation	computation	NOUN
cana-1758	149	10	of	of	ADP
cana-1758	149	11	entropy	entropy	PROPN
cana-1758	149	12	.	.	PUNCT
cana-1758	150	1	a	a	DET
cana-1758	150	2	cyclomatic_density	cyclomatic_density	NOUN
cana-1758	150	3	feature	feature	NOUN
cana-1758	150	4	of	of	ADP
cana-1758	150	5	the	the	DET
cana-1758	150	6	cm1	cm1	PROPN
cana-1758	150	7	dataset	dataset	NOUN
cana-1758	150	8	was	be	AUX
cana-1758	150	9	used	use	VERB
cana-1758	150	10	to	to	PART
cana-1758	150	11	choose	choose	VERB
cana-1758	150	12	ten	ten	NUM
cana-1758	150	13	sample	sample	NOUN
cana-1758	150	14	values	value	NOUN
cana-1758	150	15	,	,	PUNCT
cana-1758	150	16	and	and	CCONJ
cana-1758	150	17	we	we	PRON
cana-1758	150	18	manually	manually	ADV
cana-1758	150	19	calculated	calculate	VERB
cana-1758	150	20	the	the	DET
cana-1758	150	21	entropy	entropy	NOUN
cana-1758	150	22	for	for	ADP
cana-1758	150	23	each	each	PRON
cana-1758	150	24	of	of	ADP
cana-1758	150	25	those	those	DET
cana-1758	150	26	values	value	NOUN
cana-1758	150	27	.	.	PUNCT
cana-1758	151	1	this	this	DET
cana-1758	151	2	entropy	entropy	PROPN
cana-1758	151	3	computation	computation	NOUN
cana-1758	151	4	has	have	AUX
cana-1758	151	5	been	be	AUX
cana-1758	151	6	implemented	implement	VERB
cana-1758	151	7	in	in	ADP
cana-1758	151	8	the	the	DET
cana-1758	151	9	random	random	ADJ
cana-1758	151	10	forest	forest	NOUN
cana-1758	151	11	algorithm	algorithm	NOUN
cana-1758	151	12	,	,	PUNCT
cana-1758	151	13	and	and	CCONJ
cana-1758	151	14	evaluations	evaluation	NOUN
cana-1758	151	15	have	have	AUX
cana-1758	151	16	been	be	AUX
cana-1758	151	17	performed	perform	VERB
cana-1758	151	18	on	on	ADP
cana-1758	151	19	the	the	DET
cana-1758	151	20	cm1	cm1	PROPN
cana-1758	151	21	dataset	dataset	PROPN
cana-1758	151	22	.	.	PUNCT
cana-1758	152	1	the	the	DET
cana-1758	152	2	results	result	NOUN
cana-1758	152	3	suggest	suggest	VERB
cana-1758	152	4	that	that	SCONJ
cana-1758	152	5	it	it	PRON
cana-1758	152	6	has	have	VERB
cana-1758	152	7	the	the	DET
cana-1758	152	8	potential	potential	NOUN
cana-1758	152	9	to	to	PART
cana-1758	152	10	enhance	enhance	VERB
cana-1758	152	11	accuracy	accuracy	NOUN
cana-1758	152	12	,	,	PUNCT
cana-1758	152	13	and	and	CCONJ
cana-1758	152	14	the	the	DET
cana-1758	152	15	roc	roc	PROPN
cana-1758	152	16	curve	curve	NOUN
cana-1758	152	17	demonstrates	demonstrate	VERB
cana-1758	152	18	that	that	SCONJ
cana-1758	152	19	there	there	PRON
cana-1758	152	20	is	be	VERB
cana-1758	152	21	a	a	DET
cana-1758	152	22	shift	shift	NOUN
cana-1758	152	23	in	in	ADP
cana-1758	152	24	the	the	DET
cana-1758	152	25	true	true	ADJ
cana-1758	152	26	positive	positive	ADJ
cana-1758	152	27	rate	rate	NOUN
cana-1758	152	28	,	,	PUNCT
cana-1758	152	29	which	which	PRON
cana-1758	152	30	is	be	AUX
cana-1758	152	31	beneficial	beneficial	ADJ
cana-1758	152	32	for	for	ADP
cana-1758	152	33	testing	test	VERB
cana-1758	152	34	datasets	dataset	NOUN
cana-1758	152	35	.	.	PUNCT
cana-1758	153	1	future	future	ADJ
cana-1758	153	2	work	work	NOUN
cana-1758	153	3	could	could	AUX
cana-1758	153	4	be	be	AUX
cana-1758	153	5	expanded	expand	VERB
cana-1758	153	6	to	to	PART
cana-1758	153	7	include	include	VERB
cana-1758	153	8	the	the	DET
cana-1758	153	9	prediction	prediction	NOUN
cana-1758	153	10	of	of	ADP
cana-1758	153	11	software	software	NOUN
cana-1758	153	12	defects	defect	NOUN
cana-1758	153	13	in	in	ADP
cana-1758	153	14	commercial	commercial	ADJ
cana-1758	153	15	datasets	dataset	NOUN
cana-1758	153	16	and	and	CCONJ
cana-1758	153	17	the	the	DET
cana-1758	153	18	construction	construction	NOUN
cana-1758	153	19	of	of	ADP
cana-1758	153	20	a	a	DET
cana-1758	153	21	generalized	generalized	ADJ
cana-1758	153	22	model	model	NOUN
cana-1758	153	23	through	through	ADP
cana-1758	153	24	the	the	DET
cana-1758	153	25	testing	testing	NOUN
cana-1758	153	26	of	of	ADP
cana-1758	153	27	software	software	NOUN
cana-1758	153	28	defect	defect	NOUN
cana-1758	153	29	prediction	prediction	NOUN
cana-1758	153	30	on	on	ADP
cana-1758	153	31	cross	cross	ADJ
cana-1758	153	32	-	-	ADJ
cana-1758	153	33	project	project	ADJ
cana-1758	153	34	instances	instance	NOUN
cana-1758	153	35	.	.	PUNCT
cana-1758	154	1	author	author	NOUN
cana-1758	154	2	contributions	contribution	NOUN
cana-1758	154	3	:	:	PUNCT
cana-1758	154	4	ranjeetsingh	ranjeetsingh	NOUN
cana-1758	154	5	suryawanshi	suryawanshi	NOUN
cana-1758	154	6	:	:	PUNCT
cana-1758	154	7	data	datum	NOUN
cana-1758	154	8	collection	collection	NOUN
cana-1758	154	9	and	and	CCONJ
cana-1758	154	10	analysis	analysis	NOUN
cana-1758	154	11	,	,	PUNCT
cana-1758	154	12	developing	develop	VERB
cana-1758	154	13	methodology	methodology	NOUN
cana-1758	154	14	,	,	PUNCT
cana-1758	154	15	design	design	NOUN
cana-1758	154	16	and	and	CCONJ
cana-1758	154	17	development	development	NOUN
cana-1758	154	18	of	of	ADP
cana-1758	154	19	an	an	DET
cana-1758	154	20	application	application	NOUN
cana-1758	154	21	.	.	PUNCT
cana-1758	155	1	amol	amol	PROPN
cana-1758	155	2	kadam	kadam	PROPN
cana-1758	155	3	:	:	PUNCT
cana-1758	155	4	reviewing	review	VERB
cana-1758	155	5	and	and	CCONJ
cana-1758	155	6	editing	editing	NOUN
cana-1758	155	7	of	of	ADP
cana-1758	155	8	the	the	DET
cana-1758	155	9	article	article	NOUN
cana-1758	155	10	.	.	PUNCT
cana-1758	156	1	conflicts	conflict	NOUN
cana-1758	156	2	of	of	ADP
cana-1758	156	3	interest	interest	NOUN
cana-1758	156	4	:	:	PUNCT
cana-1758	156	5	the	the	DET
cana-1758	156	6	authors	author	NOUN
cana-1758	156	7	declare	declare	VERB
cana-1758	156	8	no	no	DET
cana-1758	156	9	conflict	conflict	NOUN
cana-1758	156	10	of	of	ADP
cana-1758	156	11	interest	interest	NOUN
cana-1758	156	12	.	.	PUNCT
cana-1758	157	1	references	reference	NOUN
cana-1758	157	2	[	[	X
cana-1758	157	3	1	1	NUM
cana-1758	157	4	]	]	PUNCT
cana-1758	157	5	m.	m.	NOUN
cana-1758	157	6	k.	k.	PROPN
cana-1758	157	7	thota	thota	PROPN
cana-1758	157	8	,	,	PUNCT
cana-1758	157	9	f.	f.	PROPN
cana-1758	157	10	h.	h.	PROPN
cana-1758	157	11	shajin	shajin	PROPN
cana-1758	157	12	,	,	PUNCT
cana-1758	157	13	and	and	CCONJ
cana-1758	157	14	p.	p.	PROPN
cana-1758	157	15	rajesh	rajesh	PROPN
cana-1758	157	16	,	,	PUNCT
cana-1758	157	17	“	"	PUNCT
cana-1758	157	18	survey	survey	NOUN
cana-1758	157	19	on	on	ADP
cana-1758	157	20	software	software	NOUN
cana-1758	157	21	defect	defect	NOUN
cana-1758	157	22	prediction	prediction	NOUN
cana-1758	157	23	techniques	technique	NOUN
cana-1758	157	24	,	,	PUNCT
cana-1758	157	25	”	"	PUNCT
cana-1758	157	26	int	int	NOUN
cana-1758	157	27	.	.	PUNCT
cana-1758	158	1	j.	j.	PROPN
cana-1758	158	2	appl	appl	PROPN
cana-1758	158	3	.	.	PUNCT
cana-1758	159	1	sci	sci	PROPN
cana-1758	159	2	.	.	PUNCT
cana-1758	160	1	eng	eng	PROPN
cana-1758	160	2	.	.	PROPN
cana-1758	160	3	,	,	PUNCT
cana-1758	160	4	vol	vol	NOUN
cana-1758	160	5	.	.	PROPN
cana-1758	161	1	17	17	NUM
cana-1758	161	2	,	,	PUNCT
cana-1758	161	3	no	no	INTJ
cana-1758	161	4	.	.	NOUN
cana-1758	161	5	4	4	NUM
cana-1758	161	6	,	,	PUNCT
cana-1758	161	7	pp	pp	ADJ
cana-1758	161	8	.	.	PUNCT
cana-1758	162	1	331–344	331–344	NUM
cana-1758	162	2	,	,	PUNCT
cana-1758	162	3	2020	2020	NUM
cana-1758	162	4	,	,	PUNCT
cana-1758	162	5	doi	doi	NOUN
cana-1758	162	6	:	:	PUNCT
cana-1758	162	7	10.6703	10.6703	NUM
cana-1758	162	8	/	/	SYM
cana-1758	162	9	ijase.202012_17(4).331	ijase.202012_17(4).331	NOUN
cana-1758	162	10	.	.	PUNCT
cana-1758	163	1	[	[	X
cana-1758	163	2	2	2	NUM
cana-1758	163	3	]	]	PUNCT
cana-1758	163	4	x.	x.	NOUN
cana-1758	163	5	dong	dong	PROPN
cana-1758	163	6	,	,	PUNCT
cana-1758	163	7	y.	y.	PROPN
cana-1758	163	8	liang	liang	PROPN
cana-1758	163	9	,	,	PUNCT
cana-1758	163	10	s.	s.	PROPN
cana-1758	163	11	miyamoto	miyamoto	PROPN
cana-1758	163	12	,	,	PUNCT
cana-1758	163	13	and	and	CCONJ
cana-1758	163	14	s.	s.	PROPN
cana-1758	163	15	yamaguchi	yamaguchi	PROPN
cana-1758	163	16	,	,	PUNCT
cana-1758	163	17	“	"	PUNCT
cana-1758	163	18	ensemble	ensemble	ADJ
cana-1758	163	19	learning	learning	NOUN
cana-1758	163	20	based	base	VERB
cana-1758	163	21	software	software	NOUN
cana-1758	163	22	defect	defect	NOUN
cana-1758	163	23	prediction	prediction	NOUN
cana-1758	163	24	,	,	PUNCT
cana-1758	163	25	”	"	PUNCT
cana-1758	163	26	j.	j.	PROPN
cana-1758	163	27	eng	eng	PROPN
cana-1758	163	28	.	.	PUNCT
cana-1758	164	1	res	res	PROPN
cana-1758	164	2	.	.	PROPN
cana-1758	164	3	,	,	PUNCT
cana-1758	164	4	no	no	INTJ
cana-1758	164	5	.	.	PUNCT
cana-1758	165	1	november	november	PROPN
cana-1758	165	2	,	,	PUNCT
cana-1758	165	3	2023	2023	NUM
cana-1758	165	4	,	,	PUNCT
cana-1758	165	5	doi	doi	NOUN
cana-1758	165	6	:	:	PUNCT
cana-1758	165	7	10.1016	10.1016	NUM
cana-1758	165	8	/	/	SYM
cana-1758	165	9	j.jer.2023.10.038	j.jer.2023.10.038	PROPN
cana-1758	165	10	.	.	PUNCT
cana-1758	166	1	[	[	X
cana-1758	166	2	3	3	X
cana-1758	166	3	]	]	X
cana-1758	166	4	m.	m.	NOUN
cana-1758	166	5	mustaqeem	mustaqeem	PROPN
cana-1758	166	6	and	and	CCONJ
cana-1758	166	7	t.	t.	NOUN
cana-1758	166	8	siddiqui	siddiqui	NOUN
cana-1758	166	9	,	,	PUNCT
cana-1758	166	10	“	"	PUNCT
cana-1758	166	11	a	a	DET
cana-1758	166	12	hybrid	hybrid	ADJ
cana-1758	166	13	software	software	NOUN
cana-1758	166	14	defects	defect	VERB
cana-1758	166	15	prediction	prediction	NOUN
cana-1758	166	16	model	model	NOUN
cana-1758	166	17	for	for	ADP
cana-1758	166	18	imbalance	imbalance	NOUN
cana-1758	166	19	datasets	dataset	NOUN
cana-1758	166	20	using	use	VERB
cana-1758	166	21	machine	machine	NOUN
cana-1758	166	22	learning	learn	VERB
cana-1758	166	23	techniques	technique	NOUN
cana-1758	166	24	:	:	PUNCT
cana-1758	166	25	(	(	PUNCT
cana-1758	166	26	s	s	NOUN
cana-1758	166	27	-	-	PUNCT
cana-1758	166	28	svm	svm	ADJ
cana-1758	166	29	model	model	NOUN
cana-1758	166	30	)	)	PUNCT
cana-1758	166	31	,	,	PUNCT
cana-1758	166	32	”	"	PUNCT
cana-1758	166	33	j.	j.	PROPN
cana-1758	166	34	auton	auton	PROPN
cana-1758	166	35	.	.	PUNCT
cana-1758	167	1	intell	intell	PROPN
cana-1758	167	2	.	.	PUNCT
cana-1758	167	3	,	,	PUNCT
cana-1758	168	1	vol	vol	NOUN
cana-1758	168	2	.	.	PROPN
cana-1758	169	1	6	6	NUM
cana-1758	169	2	,	,	PUNCT
cana-1758	169	3	no	no	INTJ
cana-1758	169	4	.	.	NOUN
cana-1758	169	5	1	1	NUM
cana-1758	169	6	,	,	PUNCT
cana-1758	169	7	pp	pp	ADJ
cana-1758	169	8	.	.	PUNCT
cana-1758	169	9	1–19	1–19	NOUN
cana-1758	169	10	,	,	PUNCT
cana-1758	169	11	2023	2023	NUM
cana-1758	169	12	,	,	PUNCT
cana-1758	169	13	doi	doi	NOUN
cana-1758	169	14	:	:	PUNCT
cana-1758	169	15	10.32629	10.32629	NUM
cana-1758	169	16	/	/	SYM
cana-1758	169	17	jai.v6i1.559	jai.v6i1.559	NOUN
cana-1758	169	18	.	.	PUNCT
cana-1758	170	1	[	[	X
cana-1758	170	2	4	4	X
cana-1758	170	3	]	]	PUNCT
cana-1758	170	4	s.	s.	PROPN
cana-1758	170	5	k.	k.	PROPN
cana-1758	170	6	pandey	pandey	PROPN
cana-1758	170	7	and	and	CCONJ
cana-1758	170	8	a.	a.	PROPN
cana-1758	170	9	k.	k.	PROPN
cana-1758	170	10	tripathi	tripathi	PROPN
cana-1758	170	11	,	,	PUNCT
cana-1758	170	12	“	"	PUNCT
cana-1758	170	13	an	an	DET
cana-1758	170	14	empirical	empirical	ADJ
cana-1758	170	15	study	study	NOUN
cana-1758	170	16	toward	toward	ADP
cana-1758	170	17	dealing	deal	VERB
cana-1758	170	18	with	with	ADP
cana-1758	170	19	noise	noise	NOUN
cana-1758	170	20	and	and	CCONJ
cana-1758	170	21	class	class	NOUN
cana-1758	170	22	imbalance	imbalance	NOUN
cana-1758	170	23	issues	issue	NOUN
cana-1758	170	24	in	in	ADP
cana-1758	170	25	software	software	NOUN
cana-1758	170	26	defect	defect	NOUN
cana-1758	170	27	prediction	prediction	NOUN
cana-1758	170	28	,	,	PUNCT
cana-1758	170	29	”	"	PUNCT
cana-1758	170	30	soft	soft	ADJ
cana-1758	170	31	comput	comput	NOUN
cana-1758	170	32	.	.	PUNCT
cana-1758	170	33	,	,	PUNCT
cana-1758	170	34	vol	vol	NOUN
cana-1758	170	35	.	.	PROPN
cana-1758	171	1	25	25	NUM
cana-1758	171	2	,	,	PUNCT
cana-1758	171	3	no	no	INTJ
cana-1758	171	4	.	.	NOUN
cana-1758	171	5	21	21	NUM
cana-1758	171	6	,	,	PUNCT
cana-1758	171	7	pp	pp	ADJ
cana-1758	171	8	.	.	PUNCT
cana-1758	171	9	13465–13492	13465–13492	NUM
cana-1758	171	10	,	,	PUNCT
cana-1758	171	11	2021	2021	NUM
cana-1758	171	12	,	,	PUNCT
cana-1758	171	13	doi	doi	NOUN
cana-1758	171	14	:	:	PUNCT
cana-1758	171	15	10.1007	10.1007	NUM
cana-1758	171	16	/	/	SYM
cana-1758	171	17	s00500	s00500	PROPN
cana-1758	171	18	-	-	PUNCT
cana-1758	171	19	021	021	NUM
cana-1758	171	20	-	-	PUNCT
cana-1758	171	21	06096	06096	NUM
cana-1758	171	22	-	-	SYM
cana-1758	171	23	3	3	NUM
cana-1758	171	24	.	.	PUNCT
cana-1758	172	1	[	[	X
cana-1758	172	2	5	5	X
cana-1758	172	3	]	]	PUNCT
cana-1758	172	4	k.	k.	PROPN
cana-1758	172	5	k.	k.	PROPN
cana-1758	172	6	bejjanki	bejjanki	PROPN
cana-1758	172	7	,	,	PUNCT
cana-1758	172	8	j.	j.	PROPN
cana-1758	172	9	gyani	gyani	PROPN
cana-1758	172	10	,	,	PUNCT
cana-1758	172	11	and	and	CCONJ
cana-1758	172	12	n.	n.	PROPN
cana-1758	172	13	gugulothu	gugulothu	NOUN
cana-1758	172	14	,	,	PUNCT
cana-1758	172	15	“	"	PUNCT
cana-1758	172	16	class	class	NOUN
cana-1758	172	17	imbalance	imbalance	NOUN
cana-1758	172	18	reduction	reduction	NOUN
cana-1758	172	19	(	(	PUNCT
cana-1758	172	20	cir	cir	NOUN
cana-1758	172	21	):	):	PUNCT
cana-1758	172	22	a	a	DET
cana-1758	172	23	novel	novel	ADJ
cana-1758	172	24	approach	approach	NOUN
cana-1758	172	25	to	to	ADP
cana-1758	172	26	software	software	NOUN
cana-1758	172	27	defect	defect	NOUN
cana-1758	172	28	prediction	prediction	NOUN
cana-1758	172	29	in	in	ADP
cana-1758	172	30	the	the	DET
cana-1758	172	31	presence	presence	NOUN
cana-1758	172	32	of	of	ADP
cana-1758	172	33	class	class	NOUN
cana-1758	172	34	imbalance	imbalance	NOUN
cana-1758	172	35	,	,	PUNCT
cana-1758	172	36	”	"	PUNCT
cana-1758	172	37	symmetry	symmetry	NOUN
cana-1758	172	38	(	(	PUNCT
cana-1758	172	39	basel	basel	PROPN
cana-1758	172	40	)	)	PUNCT
cana-1758	172	41	.	.	PUNCT
cana-1758	173	1	,	,	PUNCT
cana-1758	173	2	vol	vol	NOUN
cana-1758	173	3	.	.	PROPN
cana-1758	174	1	12	12	NUM
cana-1758	174	2	,	,	PUNCT
cana-1758	174	3	no	no	INTJ
cana-1758	174	4	.	.	NOUN
cana-1758	174	5	3	3	NUM
cana-1758	174	6	,	,	PUNCT
cana-1758	174	7	2020	2020	NUM
cana-1758	174	8	,	,	PUNCT
cana-1758	174	9	doi	doi	NOUN
cana-1758	174	10	:	:	PUNCT
cana-1758	174	11	10.3390	10.3390	NUM
cana-1758	174	12	/	/	SYM
cana-1758	174	13	sym12030407	sym12030407	NOUN
cana-1758	174	14	.	.	PUNCT
cana-1758	175	1	[	[	X
cana-1758	175	2	6	6	NUM
cana-1758	175	3	]	]	PUNCT
cana-1758	175	4	k.	k.	PROPN
cana-1758	175	5	j.	j.	PROPN
cana-1758	175	6	eldho	eldho	PROPN
cana-1758	175	7	,	,	PUNCT
cana-1758	175	8	“	"	PUNCT
cana-1758	175	9	impact	impact	NOUN
cana-1758	175	10	of	of	ADP
cana-1758	175	11	unbalanced	unbalanced	ADJ
cana-1758	175	12	classification	classification	NOUN
cana-1758	175	13	on	on	ADP
cana-1758	175	14	the	the	DET
cana-1758	175	15	performance	performance	NOUN
cana-1758	175	16	of	of	ADP
cana-1758	175	17	software	software	NOUN
cana-1758	175	18	defect	defect	NOUN
cana-1758	175	19	prediction	prediction	NOUN
cana-1758	175	20	models	model	NOUN
cana-1758	175	21	,	,	PUNCT
cana-1758	175	22	”	"	PUNCT
cana-1758	175	23	indian	indian	PROPN
cana-1758	175	24	j.	j.	PROPN
cana-1758	175	25	sci	sci	PROPN
cana-1758	175	26	.	.	PROPN
cana-1758	175	27	technol	technol	PROPN
cana-1758	175	28	.	.	PROPN
cana-1758	175	29	,	,	PUNCT
cana-1758	175	30	vol	vol	NOUN
cana-1758	175	31	.	.	PROPN
cana-1758	175	32	15	15	NUM
cana-1758	175	33	,	,	PUNCT
cana-1758	175	34	no	no	INTJ
cana-1758	175	35	.	.	NOUN
cana-1758	175	36	6	6	NUM
cana-1758	175	37	,	,	PUNCT
cana-1758	175	38	pp	pp	ADJ
cana-1758	175	39	.	.	PUNCT
cana-1758	176	1	237–242	237–242	NUM
cana-1758	176	2	,	,	PUNCT
cana-1758	176	3	2022	2022	NUM
cana-1758	176	4	,	,	PUNCT
cana-1758	176	5	doi	doi	NOUN
cana-1758	176	6	:	:	PUNCT
cana-1758	176	7	10.17485	10.17485	NUM
cana-1758	176	8	/	/	SYM
cana-1758	176	9	ijst	ijst	ADJ
cana-1758	176	10	/	/	SYM
cana-1758	176	11	v15i6.2193	v15i6.2193	PROPN
cana-1758	176	12	.	.	PUNCT
cana-1758	177	1	[	[	X
cana-1758	177	2	7	7	X
cana-1758	177	3	]	]	PUNCT
cana-1758	177	4	s.	s.	PROPN
cana-1758	177	5	k.	k.	PROPN
cana-1758	177	6	pandey	pandey	PROPN
cana-1758	177	7	,	,	PUNCT
cana-1758	177	8	r.	r.	PROPN
cana-1758	177	9	b.	b.	PROPN
cana-1758	177	10	mishra	mishra	PROPN
cana-1758	177	11	,	,	PUNCT
cana-1758	177	12	and	and	CCONJ
cana-1758	177	13	a.	a.	PROPN
cana-1758	177	14	k.	k.	PROPN
cana-1758	177	15	tripathi	tripathi	PROPN
cana-1758	177	16	,	,	PUNCT
cana-1758	177	17	“	"	PUNCT
cana-1758	177	18	bpdet	bpdet	NOUN
cana-1758	177	19	:	:	PUNCT
cana-1758	177	20	an	an	DET
cana-1758	177	21	effective	effective	ADJ
cana-1758	177	22	software	software	NOUN
cana-1758	177	23	bug	bug	NOUN
cana-1758	177	24	prediction	prediction	NOUN
cana-1758	177	25	model	model	NOUN
cana-1758	177	26	using	use	VERB
cana-1758	177	27	deep	deep	ADJ
cana-1758	177	28	communications	communication	NOUN
cana-1758	177	29	on	on	ADP
cana-1758	177	30	applied	apply	VERB
cana-1758	177	31	nonlinear	nonlinear	ADJ
cana-1758	177	32	analysis	analysis	NOUN
cana-1758	177	33	issn	issn	NOUN
cana-1758	177	34	:	:	PUNCT
cana-1758	177	35	1074	1074	NUM
cana-1758	177	36	-	-	PUNCT
cana-1758	177	37	133x	133x	NUM
cana-1758	177	38	vol	vol	NOUN
cana-1758	177	39	32	32	NUM
cana-1758	177	40	no	no	NOUN
cana-1758	177	41	.	.	NOUN
cana-1758	177	42	2	2	NUM
cana-1758	177	43	(	(	PUNCT
cana-1758	177	44	2025	2025	NUM
cana-1758	177	45	)	)	PUNCT
cana-1758	177	46	469	469	NUM
cana-1758	177	47	https://internationalpubls.com	https://internationalpubls.com	X
cana-1758	177	48	representation	representation	NOUN
cana-1758	177	49	and	and	CCONJ
cana-1758	177	50	ensemble	ensemble	ADJ
cana-1758	177	51	learning	learning	NOUN
cana-1758	177	52	techniques	technique	NOUN
cana-1758	177	53	,	,	PUNCT
cana-1758	177	54	”	"	PUNCT
cana-1758	177	55	expert	expert	NOUN
cana-1758	177	56	syst	syst	NOUN
cana-1758	177	57	.	.	PUNCT
cana-1758	178	1	appl	appl	PROPN
cana-1758	178	2	.	.	PROPN
cana-1758	178	3	,	,	PUNCT
cana-1758	178	4	vol	vol	NOUN
cana-1758	178	5	.	.	PUNCT
cana-1758	179	1	144	144	NUM
cana-1758	179	2	,	,	PUNCT
cana-1758	179	3	p.	p.	NOUN
cana-1758	179	4	113085	113085	NUM
cana-1758	179	5	,	,	PUNCT
cana-1758	179	6	2020	2020	NUM
cana-1758	179	7	,	,	PUNCT
cana-1758	179	8	doi	doi	NOUN
cana-1758	179	9	:	:	PUNCT
cana-1758	179	10	10.1016	10.1016	NUM
cana-1758	179	11	/	/	SYM
cana-1758	179	12	j.eswa.2019.113085	j.eswa.2019.113085	NOUN
cana-1758	179	13	.	.	PUNCT
cana-1758	180	1	[	[	X
cana-1758	180	2	8	8	NUM
cana-1758	180	3	]	]	PUNCT
cana-1758	180	4	l.	l.	PROPN
cana-1758	180	5	s.	s.	PROPN
cana-1758	180	6	shapley	shapley	PROPN
cana-1758	180	7	,	,	PUNCT
cana-1758	180	8	“	"	PUNCT
cana-1758	180	9	a	a	DET
cana-1758	180	10	value	value	NOUN
cana-1758	180	11	for	for	ADP
cana-1758	180	12	n	n	CCONJ
cana-1758	180	13	-	-	PUNCT
cana-1758	180	14	person	person	NOUN
cana-1758	180	15	games	game	NOUN
cana-1758	180	16	contributions	contribution	NOUN
cana-1758	180	17	to	to	ADP
cana-1758	180	18	the	the	DET
cana-1758	180	19	theory	theory	NOUN
cana-1758	180	20	of	of	ADP
cana-1758	180	21	games	game	NOUN
cana-1758	180	22	,	,	PUNCT
cana-1758	180	23	”	"	PUNCT
cana-1758	180	24	in	in	ADP
cana-1758	180	25	annals	annal	NOUN
cana-1758	180	26	of	of	ADP
cana-1758	180	27	mathematical	mathematical	ADJ
cana-1758	180	28	studies	study	NOUN
cana-1758	180	29	,	,	PUNCT
cana-1758	180	30	edited	edit	VERB
cana-1758	180	31	by	by	ADP
cana-1758	180	32	harold	harold	PROPN
cana-1758	180	33	william	william	PROPN
cana-1758	180	34	kuhn	kuhn	PROPN
cana-1758	180	35	and	and	CCONJ
cana-1758	180	36	albert	albert	PROPN
cana-1758	180	37	william	william	PROPN
cana-1758	180	38	tucker	tucker	PROPN
cana-1758	180	39	,	,	PUNCT
cana-1758	180	40	princeton	princeton	PROPN
cana-1758	180	41	university	university	PROPN
cana-1758	180	42	press	press	NOUN
cana-1758	180	43	,	,	PUNCT
cana-1758	180	44	vol	vol	NOUN
cana-1758	180	45	.	.	PROPN
cana-1758	180	46	2	2	NUM
cana-1758	180	47	,	,	PUNCT
cana-1758	180	48	no	no	INTJ
cana-1758	180	49	.	.	NOUN
cana-1758	181	1	4	4	X
cana-1758	181	2	.	.	X
cana-1758	182	1	pp	pp	ADJ
cana-1758	182	2	.	.	PUNCT
cana-1758	183	1	307	307	NUM
cana-1758	183	2	–	–	PUNCT
cana-1758	183	3	318	318	NUM
cana-1758	183	4	,	,	PUNCT
cana-1758	183	5	1953	1953	NUM
cana-1758	183	6	.	.	PUNCT
cana-1758	184	1	doi	doi	NOUN
cana-1758	184	2	:	:	PUNCT
cana-1758	184	3	10.1515/9781400881970	10.1515/9781400881970	NUM
cana-1758	184	4	-	-	SYM
cana-1758	184	5	018	018	NUM
cana-1758	184	6	.	.	PUNCT
cana-1758	185	1	[	[	X
cana-1758	185	2	9	9	NUM
cana-1758	185	3	]	]	PUNCT
cana-1758	185	4	k.	k.	NOUN
cana-1758	185	5	magal.r	magal.r	PROPN
cana-1758	185	6	and	and	CCONJ
cana-1758	185	7	s.	s.	PROPN
cana-1758	185	8	gracia	gracia	PROPN
cana-1758	185	9	jacob	jacob	PROPN
cana-1758	185	10	,	,	PUNCT
cana-1758	185	11	“	"	PUNCT
cana-1758	185	12	improved	improve	VERB
cana-1758	185	13	random	random	ADJ
cana-1758	185	14	forest	forest	NOUN
cana-1758	185	15	algorithm	algorithm	NOUN
cana-1758	185	16	for	for	ADP
cana-1758	185	17	software	software	NOUN
cana-1758	185	18	defect	defect	NOUN
cana-1758	185	19	prediction	prediction	NOUN
cana-1758	185	20	through	through	ADP
cana-1758	185	21	data	datum	NOUN
cana-1758	185	22	mining	mining	NOUN
cana-1758	185	23	techniques	technique	NOUN
cana-1758	185	24	,	,	PUNCT
cana-1758	185	25	”	"	PUNCT
cana-1758	185	26	int	int	NOUN
cana-1758	185	27	.	.	PUNCT
cana-1758	186	1	j.	j.	PROPN
cana-1758	186	2	comput	comput	PROPN
cana-1758	186	3	.	.	PUNCT
cana-1758	187	1	appl	appl	PROPN
cana-1758	187	2	.	.	PROPN
cana-1758	187	3	,	,	PUNCT
cana-1758	187	4	vol	vol	NOUN
cana-1758	187	5	.	.	PROPN
cana-1758	187	6	117	117	NUM
cana-1758	187	7	,	,	PUNCT
cana-1758	187	8	no	no	INTJ
cana-1758	187	9	.	.	NOUN
cana-1758	187	10	23	23	NUM
cana-1758	187	11	,	,	PUNCT
cana-1758	187	12	pp	pp	ADJ
cana-1758	187	13	.	.	PUNCT
cana-1758	187	14	18–22	18–22	NUM
cana-1758	187	15	,	,	PUNCT
cana-1758	187	16	2015	2015	NUM
cana-1758	187	17	,	,	PUNCT
cana-1758	187	18	doi	doi	NOUN
cana-1758	187	19	:	:	PUNCT
cana-1758	187	20	10.5120/20693	10.5120/20693	NUM
cana-1758	187	21	-	-	SYM
cana-1758	187	22	3582	3582	NUM
cana-1758	187	23	.	.	PUNCT
cana-1758	188	1	[	[	X
cana-1758	188	2	10	10	NUM
cana-1758	188	3	]	]	X
cana-1758	188	4	l.	l.	PROPN
cana-1758	188	5	qiong	qiong	PROPN
cana-1758	188	6	chen	chen	PROPN
cana-1758	188	7	,	,	PUNCT
cana-1758	188	8	c.	c.	PROPN
cana-1758	188	9	wang	wang	PROPN
cana-1758	188	10	,	,	PUNCT
cana-1758	188	11	and	and	CCONJ
cana-1758	188	12	s.	s.	PROPN
cana-1758	188	13	long	long	ADJ
cana-1758	188	14	song	song	NOUN
cana-1758	188	15	,	,	PUNCT
cana-1758	188	16	“	"	PUNCT
cana-1758	188	17	software	software	NOUN
cana-1758	188	18	defect	defect	NOUN
cana-1758	188	19	prediction	prediction	NOUN
cana-1758	188	20	based	base	VERB
cana-1758	188	21	on	on	ADP
cana-1758	188	22	nested	nest	VERB
cana-1758	188	23	-	-	PUNCT
cana-1758	188	24	stacking	stack	VERB
cana-1758	188	25	and	and	CCONJ
cana-1758	188	26	heterogeneous	heterogeneous	ADJ
cana-1758	188	27	feature	feature	NOUN
cana-1758	188	28	selection	selection	NOUN
cana-1758	188	29	,	,	PUNCT
cana-1758	188	30	”	"	PUNCT
cana-1758	188	31	complex	complex	ADJ
cana-1758	188	32	intell	intell	NOUN
cana-1758	188	33	.	.	PUNCT
cana-1758	189	1	syst	syst	PROPN
cana-1758	189	2	.	.	PUNCT
cana-1758	189	3	,	,	PUNCT
cana-1758	189	4	vol	vol	NOUN
cana-1758	189	5	.	.	PROPN
cana-1758	189	6	8	8	NUM
cana-1758	189	7	,	,	PUNCT
cana-1758	189	8	no	no	INTJ
cana-1758	189	9	.	.	NOUN
cana-1758	189	10	4	4	NUM
cana-1758	189	11	,	,	PUNCT
cana-1758	189	12	pp	pp	ADJ
cana-1758	189	13	.	.	PUNCT
cana-1758	189	14	3333–3348	3333–3348	NUM
cana-1758	189	15	,	,	PUNCT
cana-1758	189	16	2022	2022	NUM
cana-1758	189	17	,	,	PUNCT
cana-1758	189	18	doi	doi	NOUN
cana-1758	189	19	:	:	PUNCT
cana-1758	189	20	10.1007	10.1007	NUM
cana-1758	189	21	/	/	SYM
cana-1758	189	22	s40747	s40747	PROPN
cana-1758	189	23	-	-	PUNCT
cana-1758	189	24	022	022	NUM
cana-1758	189	25	-	-	PUNCT
cana-1758	189	26	00676	00676	NUM
cana-1758	189	27	-	-	PUNCT
cana-1758	189	28	y.	y.	NOUN
cana-1758	190	1	[	[	X
cana-1758	190	2	11	11	NUM
cana-1758	190	3	]	]	X
cana-1758	190	4	n.	n.	PROPN
cana-1758	190	5	gayatri	gayatri	PROPN
cana-1758	190	6	,	,	PUNCT
cana-1758	190	7	s.	s.	PROPN
cana-1758	190	8	nickolas	nickolas	PROPN
cana-1758	190	9	,	,	PUNCT
cana-1758	190	10	and	and	CCONJ
cana-1758	190	11	a.	a.	NOUN
cana-1758	190	12	v	v	PROPN
cana-1758	190	13	reddy	reddy	PROPN
cana-1758	190	14	,	,	PUNCT
cana-1758	190	15	“	"	PUNCT
cana-1758	190	16	feature	feature	NOUN
cana-1758	190	17	selection	selection	NOUN
cana-1758	190	18	using	use	VERB
cana-1758	190	19	decision	decision	NOUN
cana-1758	190	20	tree	tree	NOUN
cana-1758	190	21	induction	induction	NOUN
cana-1758	190	22	in	in	ADP
cana-1758	190	23	class	class	NOUN
cana-1758	190	24	level	level	NOUN
cana-1758	190	25	metrics	metric	NOUN
cana-1758	190	26	dataset	dataset	VERB
cana-1758	190	27	for	for	ADP
cana-1758	190	28	software	software	NOUN
cana-1758	190	29	defect	defect	NOUN
cana-1758	190	30	predictions	prediction	NOUN
cana-1758	190	31	,	,	PUNCT
cana-1758	190	32	”	"	PUNCT
cana-1758	190	33	world	world	NOUN
cana-1758	190	34	congr	congr	NOUN
cana-1758	190	35	.	.	PUNCT
cana-1758	191	1	eng	eng	PROPN
cana-1758	191	2	.	.	PUNCT
cana-1758	192	1	comput	comput	PROPN
cana-1758	192	2	.	.	PUNCT
cana-1758	193	1	sci	sci	PROPN
cana-1758	193	2	.	.	PUNCT
cana-1758	194	1	vols	vol	NOUN
cana-1758	194	2	1	1	NUM
cana-1758	194	3	2	2	NUM
cana-1758	194	4	,	,	PUNCT
cana-1758	194	5	vol	vol	NOUN
cana-1758	194	6	.	.	PUNCT
cana-1758	195	1	i	i	PRON
cana-1758	195	2	,	,	PUNCT
cana-1758	195	3	pp	pp	ADJ
cana-1758	195	4	.	.	PUNCT
cana-1758	196	1	124–129	124–129	NUM
cana-1758	196	2	,	,	PUNCT
cana-1758	196	3	2010	2010	NUM
cana-1758	196	4	,	,	PUNCT
cana-1758	197	1	[	[	X
cana-1758	197	2	online	online	X
cana-1758	197	3	]	]	X
cana-1758	197	4	.	.	PUNCT
cana-1758	198	1	available	available	ADJ
cana-1758	198	2	:	:	PUNCT
cana-1758	198	3	http://www.iaeng.org/publication/wcecs2010/wcecs2010_pp124-129.pdf	http://www.iaeng.org/publication/wcecs2010/wcecs2010_pp124-129.pdf	VERB
cana-1758	198	4	[	[	X
cana-1758	198	5	12	12	NUM
cana-1758	198	6	]	]	X
cana-1758	198	7	d.	d.	PROPN
cana-1758	198	8	t.	t.	PROPN
cana-1758	198	9	pham	pham	PROPN
cana-1758	198	10	and	and	CCONJ
cana-1758	198	11	g.	g.	PROPN
cana-1758	198	12	a.	a.	PROPN
cana-1758	198	13	ruz	ruz	PROPN
cana-1758	198	14	,	,	PUNCT
cana-1758	198	15	“	"	PUNCT
cana-1758	198	16	unsupervised	unsupervised	ADJ
cana-1758	198	17	training	training	NOUN
cana-1758	198	18	of	of	ADP
cana-1758	198	19	bayesian	bayesian	NOUN
cana-1758	198	20	networks	network	NOUN
cana-1758	198	21	for	for	ADP
cana-1758	198	22	data	datum	NOUN
cana-1758	198	23	clustering	cluster	VERB
cana-1758	198	24	,	,	PUNCT
cana-1758	198	25	”	"	PUNCT
cana-1758	198	26	proc	proc	NOUN
cana-1758	198	27	.	.	PUNCT
cana-1758	199	1	r.	r.	PROPN
cana-1758	199	2	soc	soc	PROPN
cana-1758	199	3	.	.	PUNCT
cana-1758	200	1	a	a	DET
cana-1758	200	2	math	math	NOUN
cana-1758	200	3	.	.	PUNCT
cana-1758	201	1	phys	phy	NOUN
cana-1758	201	2	.	.	PUNCT
cana-1758	202	1	eng	eng	PROPN
cana-1758	202	2	.	.	PUNCT
cana-1758	203	1	sci	sci	PROPN
cana-1758	203	2	.	.	PROPN
cana-1758	203	3	,	,	PUNCT
cana-1758	203	4	vol	vol	NOUN
cana-1758	203	5	.	.	PROPN
cana-1758	203	6	465	465	NUM
cana-1758	203	7	,	,	PUNCT
cana-1758	203	8	no	no	INTJ
cana-1758	203	9	.	.	PUNCT
cana-1758	204	1	2109	2109	NUM
cana-1758	204	2	,	,	PUNCT
cana-1758	204	3	pp	pp	ADJ
cana-1758	204	4	.	.	PUNCT
cana-1758	205	1	2927–2948	2927–2948	NUM
cana-1758	205	2	,	,	PUNCT
cana-1758	205	3	2009	2009	NUM
cana-1758	205	4	,	,	PUNCT
cana-1758	205	5	doi	doi	NOUN
cana-1758	205	6	:	:	PUNCT
cana-1758	205	7	10.1098	10.1098	NUM
cana-1758	205	8	/	/	SYM
cana-1758	205	9	rspa.2009.0065	rspa.2009.0065	PROPN
cana-1758	205	10	.	.	PUNCT
cana-1758	206	1	[	[	X
cana-1758	206	2	13	13	NUM
cana-1758	206	3	]	]	X
cana-1758	206	4	r.	r.	PROPN
cana-1758	206	5	chennappan	chennappan	PROPN
cana-1758	206	6	and	and	CCONJ
cana-1758	206	7	vidyaathulasiraman	vidyaathulasiraman	NOUN
cana-1758	206	8	,	,	PUNCT
cana-1758	206	9	“	"	PUNCT
cana-1758	206	10	an	an	DET
cana-1758	206	11	automated	automate	VERB
cana-1758	206	12	software	software	NOUN
cana-1758	206	13	failure	failure	NOUN
cana-1758	206	14	prediction	prediction	NOUN
cana-1758	206	15	technique	technique	NOUN
cana-1758	206	16	using	use	VERB
cana-1758	206	17	hybrid	hybrid	ADJ
cana-1758	206	18	machine	machine	NOUN
cana-1758	206	19	learning	learning	NOUN
cana-1758	206	20	algorithms	algorithm	NOUN
cana-1758	206	21	,	,	PUNCT
cana-1758	206	22	”	"	PUNCT
cana-1758	206	23	j.	j.	PROPN
cana-1758	206	24	eng	eng	PROPN
cana-1758	206	25	.	.	PUNCT
cana-1758	207	1	res	res	PROPN
cana-1758	207	2	.	.	PROPN
cana-1758	207	3	,	,	PUNCT
cana-1758	207	4	vol	vol	NOUN
cana-1758	207	5	.	.	PROPN
cana-1758	207	6	11	11	NUM
cana-1758	207	7	,	,	PUNCT
cana-1758	207	8	no	no	INTJ
cana-1758	207	9	.	.	NOUN
cana-1758	207	10	1	1	NUM
cana-1758	207	11	,	,	PUNCT
cana-1758	207	12	p.	p.	NOUN
cana-1758	207	13	100002	100002	NUM
cana-1758	207	14	,	,	PUNCT
cana-1758	207	15	2023	2023	NUM
cana-1758	207	16	,	,	PUNCT
cana-1758	207	17	doi	doi	NOUN
cana-1758	207	18	:	:	PUNCT
cana-1758	207	19	10.1016	10.1016	NUM
cana-1758	207	20	/	/	SYM
cana-1758	207	21	j.jer.2023.100002	j.jer.2023.100002	NOUN
cana-1758	207	22	.	.	PUNCT
cana-1758	208	1	[	[	X
cana-1758	208	2	14	14	NUM
cana-1758	208	3	]	]	PUNCT
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cana-1758	208	6	,	,	PUNCT
cana-1758	208	7	“	"	PUNCT
cana-1758	208	8	a	a	DET
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cana-1758	208	11	for	for	ADP
cana-1758	208	12	learning	learning	NOUN
cana-1758	208	13	-	-	PUNCT
cana-1758	208	14	based	base	VERB
cana-1758	208	15	software	software	NOUN
cana-1758	208	16	defect	defect	NOUN
cana-1758	208	17	prediction	prediction	NOUN
cana-1758	208	18	,	,	PUNCT
cana-1758	208	19	”	"	PUNCT
cana-1758	208	20	j.	j.	PROPN
cana-1758	208	21	phys	phys	PROPN
cana-1758	208	22	.	.	PUNCT
cana-1758	208	23	conf	conf	PROPN
cana-1758	208	24	.	.	PUNCT
cana-1758	209	1	ser	ser	PROPN
cana-1758	209	2	.	.	PROPN
cana-1758	209	3	,	,	PUNCT
cana-1758	209	4	vol	vol	NOUN
cana-1758	209	5	.	.	NOUN
cana-1758	209	6	1487	1487	NUM
cana-1758	209	7	,	,	PUNCT
cana-1758	209	8	no	no	INTJ
cana-1758	209	9	.	.	NOUN
cana-1758	209	10	1	1	NUM
cana-1758	209	11	,	,	PUNCT
cana-1758	209	12	2020	2020	NUM
cana-1758	209	13	,	,	PUNCT
cana-1758	209	14	doi	doi	NOUN
cana-1758	209	15	:	:	PUNCT
cana-1758	209	16	10.1088/1742	10.1088/1742	NUM
cana-1758	209	17	-	-	SYM
cana-1758	209	18	6596/1487/1/012017	6596/1487/1/012017	NUM
cana-1758	209	19	.	.	PUNCT
cana-1758	210	1	[	[	X
cana-1758	210	2	15	15	NUM
cana-1758	210	3	]	]	PUNCT
cana-1758	210	4	t.	t.	PROPN
cana-1758	210	5	d.	d.	PROPN
cana-1758	210	6	buskirk	buskirk	PROPN
cana-1758	210	7	,	,	PUNCT
cana-1758	210	8	“	"	PUNCT
cana-1758	210	9	surveying	survey	VERB
cana-1758	210	10	the	the	DET
cana-1758	210	11	forests	forest	NOUN
cana-1758	210	12	and	and	CCONJ
cana-1758	210	13	sampling	sample	VERB
cana-1758	210	14	the	the	DET
cana-1758	210	15	trees	tree	NOUN
cana-1758	210	16	:	:	PUNCT
cana-1758	210	17	an	an	DET
cana-1758	210	18	overview	overview	NOUN
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cana-1758	210	20	classification	classification	NOUN
cana-1758	210	21	and	and	CCONJ
cana-1758	210	22	regression	regression	NOUN
cana-1758	210	23	trees	tree	NOUN
cana-1758	210	24	and	and	CCONJ
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cana-1758	210	26	forests	forest	NOUN
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cana-1758	210	28	applications	application	NOUN
cana-1758	210	29	in	in	ADP
cana-1758	210	30	survey	survey	NOUN
cana-1758	210	31	research	research	NOUN
cana-1758	210	32	,	,	PUNCT
cana-1758	210	33	”	"	PUNCT
cana-1758	210	34	surv	surv	NOUN
cana-1758	210	35	.	.	PUNCT
cana-1758	211	1	pract	pract	PROPN
cana-1758	211	2	.	.	PUNCT
cana-1758	212	1	,	,	PUNCT
cana-1758	212	2	vol	vol	NOUN
cana-1758	212	3	.	.	PROPN
cana-1758	213	1	11	11	NUM
cana-1758	213	2	,	,	PUNCT
cana-1758	213	3	no	no	INTJ
cana-1758	213	4	.	.	NOUN
cana-1758	213	5	1	1	NUM
cana-1758	213	6	,	,	PUNCT
cana-1758	213	7	pp	pp	ADJ
cana-1758	213	8	.	.	PUNCT
cana-1758	214	1	1–13	1–13	NOUN
cana-1758	214	2	,	,	PUNCT
cana-1758	214	3	2018	2018	NUM
cana-1758	214	4	,	,	PUNCT
cana-1758	214	5	doi	doi	NOUN
cana-1758	214	6	:	:	PUNCT
cana-1758	214	7	10.29115	10.29115	NUM
cana-1758	214	8	/	/	SYM
cana-1758	214	9	sp-2018	sp-2018	NOUN
cana-1758	214	10	-	-	PUNCT
cana-1758	214	11	0003	0003	NUM
cana-1758	214	12	.	.	PUNCT
cana-1758	215	1	[	[	X
cana-1758	215	2	16	16	NUM
cana-1758	215	3	]	]	X
cana-1758	215	4	h.	h.	PROPN
cana-1758	215	5	m.	m.	PROPN
cana-1758	215	6	premalatha	premalatha	PROPN
cana-1758	215	7	and	and	CCONJ
cana-1758	215	8	c.	c.	PROPN
cana-1758	215	9	v.	v.	PROPN
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cana-1758	215	11	,	,	PUNCT
cana-1758	215	12	“	"	PUNCT
cana-1758	215	13	software	software	NOUN
cana-1758	215	14	fault	fault	NOUN
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cana-1758	215	19	cost	cost	NOUN
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cana-1758	215	24	spiral	spiral	ADJ
cana-1758	215	25	life	life	NOUN
cana-1758	215	26	cycle	cycle	NOUN
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cana-1758	215	28	,	,	PUNCT
cana-1758	215	29	”	"	PUNCT
cana-1758	215	30	int	int	NOUN
cana-1758	215	31	.	.	PUNCT
cana-1758	216	1	j.	j.	PROPN
cana-1758	216	2	intell	intell	PROPN
cana-1758	216	3	.	.	PUNCT
cana-1758	217	1	eng	eng	PROPN
cana-1758	217	2	.	.	PUNCT
cana-1758	217	3	syst	syst	PROPN
cana-1758	217	4	.	.	PROPN
cana-1758	217	5	,	,	PUNCT
cana-1758	217	6	vol	vol	NOUN
cana-1758	217	7	.	.	PROPN
cana-1758	218	1	11	11	NUM
cana-1758	218	2	,	,	PUNCT
cana-1758	218	3	no	no	INTJ
cana-1758	218	4	.	.	NOUN
cana-1758	218	5	2	2	NUM
cana-1758	218	6	,	,	PUNCT
cana-1758	218	7	pp	pp	ADJ
cana-1758	218	8	.	.	PUNCT
cana-1758	219	1	10–17	10–17	NUM
cana-1758	219	2	,	,	PUNCT
cana-1758	219	3	2018	2018	NUM
cana-1758	219	4	,	,	PUNCT
cana-1758	219	5	doi	doi	NOUN
cana-1758	219	6	:	:	PUNCT
cana-1758	219	7	10.22266	10.22266	NUM
cana-1758	219	8	/	/	SYM
cana-1758	219	9	ijies2018.0430.02	ijies2018.0430.02	NOUN
cana-1758	219	10	.	.	PUNCT
cana-1758	220	1	[	[	X
cana-1758	220	2	17	17	NUM
cana-1758	220	3	]	]	X
cana-1758	220	4	l.	l.	PROPN
cana-1758	220	5	perreault	perreault	PROPN
cana-1758	220	6	,	,	PUNCT
cana-1758	220	7	s.	s.	PROPN
cana-1758	220	8	berardinelli	berardinelli	PROPN
cana-1758	220	9	,	,	PUNCT
cana-1758	220	10	c.	c.	PROPN
cana-1758	220	11	izurieta	izurieta	PROPN
cana-1758	220	12	,	,	PUNCT
cana-1758	220	13	and	and	CCONJ
cana-1758	220	14	j.	j.	PROPN
cana-1758	220	15	sheppard	sheppard	PROPN
cana-1758	220	16	,	,	PUNCT
cana-1758	220	17	“	"	PUNCT
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cana-1758	220	20	for	for	ADP
cana-1758	220	21	software	software	NOUN
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cana-1758	220	25	”	"	PUNCT
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cana-1758	220	27	int	int	NOUN
cana-1758	220	28	.	.	PUNCT
cana-1758	221	1	conf	conf	NOUN
cana-1758	221	2	.	.	PUNCT
cana-1758	222	1	softw	softw	PROPN
cana-1758	222	2	.	.	PUNCT
cana-1758	223	1	eng	eng	PROPN
cana-1758	223	2	.	.	PROPN
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cana-1758	223	4	eng	eng	PROPN
cana-1758	223	5	.	.	PROPN
cana-1758	223	6	sede	sede	PROPN
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cana-1758	223	8	,	,	PUNCT
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cana-1758	223	10	.	.	PUNCT
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cana-1758	224	2	,	,	PUNCT
cana-1758	224	3	2017	2017	NUM
cana-1758	224	4	.	.	PUNCT
cana-1758	225	1	[	[	X
cana-1758	225	2	18	18	NUM
cana-1758	225	3	]	]	X
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cana-1758	225	8	d.	d.	PROPN
cana-1758	225	9	m.	m.	PROPN
cana-1758	225	10	schnyer	schnyer	PROPN
cana-1758	225	11	,	,	PUNCT
cana-1758	225	12	support	support	NOUN
cana-1758	225	13	vector	vector	NOUN
cana-1758	225	14	machine	machine	NOUN
cana-1758	225	15	.	.	PUNCT
cana-1758	226	1	elsevier	elsevier	PROPN
cana-1758	226	2	inc	inc	PROPN
cana-1758	226	3	.	.	PROPN
cana-1758	226	4	,	,	PUNCT
cana-1758	226	5	2019	2019	NUM
cana-1758	226	6	.	.	PUNCT
cana-1758	227	1	doi	doi	NOUN
cana-1758	227	2	:	:	PUNCT
cana-1758	227	3	10.1016	10.1016	NUM
cana-1758	227	4	/	/	SYM
cana-1758	227	5	b978	b978	NUM
cana-1758	227	6	-	-	PUNCT
cana-1758	227	7	0	0	NUM
cana-1758	227	8	-	-	PUNCT
cana-1758	227	9	12	12	NUM
cana-1758	227	10	-	-	PUNCT
cana-1758	227	11	8157398.00006	8157398.00006	NUM
cana-1758	227	12	-	-	PUNCT
cana-1758	227	13	7	7	NUM
cana-1758	227	14	.	.	PUNCT
cana-1758	228	1	[	[	X
cana-1758	228	2	19	19	NUM
cana-1758	228	3	]	]	X
cana-1758	228	4	y.	y.	PROPN
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cana-1758	228	6	,	,	PUNCT
cana-1758	228	7	d.	d.	PROPN
cana-1758	228	8	lo	lo	PROPN
cana-1758	228	9	,	,	PUNCT
cana-1758	228	10	x.	x.	PROPN
cana-1758	228	11	xia	xia	PROPN
cana-1758	228	12	,	,	PUNCT
cana-1758	228	13	and	and	CCONJ
cana-1758	228	14	j.	j.	PROPN
cana-1758	228	15	sun	sun	PROPN
cana-1758	228	16	,	,	PUNCT
cana-1758	228	17	“	"	PUNCT
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cana-1758	228	19	classifier	classifier	NOUN
cana-1758	228	20	for	for	ADP
cana-1758	228	21	cross	cross	ADJ
cana-1758	228	22	-	-	ADJ
cana-1758	228	23	project	project	ADJ
cana-1758	228	24	defect	defect	NOUN
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cana-1758	228	26	:	:	PUNCT
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cana-1758	228	30	study	study	NOUN
cana-1758	228	31	,	,	PUNCT
cana-1758	228	32	”	"	PUNCT
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cana-1758	228	34	.	.	PUNCT
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cana-1758	230	1	sci	sci	PROPN
cana-1758	230	2	.	.	PROPN
cana-1758	230	3	,	,	PUNCT
cana-1758	230	4	vol	vol	NOUN
cana-1758	230	5	.	.	PROPN
cana-1758	230	6	12	12	NUM
cana-1758	230	7	,	,	PUNCT
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cana-1758	230	9	.	.	NOUN
cana-1758	230	10	2	2	NUM
cana-1758	230	11	,	,	PUNCT
cana-1758	230	12	pp	pp	ADJ
cana-1758	230	13	.	.	PUNCT
cana-1758	231	1	280–296	280–296	NUM
cana-1758	231	2	,	,	PUNCT
cana-1758	231	3	2018	2018	NUM
cana-1758	231	4	,	,	PUNCT
cana-1758	231	5	doi	doi	NOUN
cana-1758	231	6	:	:	PUNCT
cana-1758	231	7	10.1007	10.1007	NUM
cana-1758	231	8	/	/	SYM
cana-1758	231	9	s11704	s11704	NOUN
cana-1758	231	10	-	-	PUNCT
cana-1758	231	11	017	017	NUM
cana-1758	231	12	-	-	PUNCT
cana-1758	231	13	6015	6015	NUM
cana-1758	231	14	-	-	PUNCT
cana-1758	231	15	y.	y.	NOUN
cana-1758	232	1	[	[	X
cana-1758	232	2	20	20	NUM
cana-1758	232	3	]	]	PUNCT
cana-1758	232	4	q.	q.	PROPN
cana-1758	232	5	o.	o.	PROPN
cana-1758	232	6	and	and	CCONJ
cana-1758	232	7	m.	m.	PROPN
cana-1758	232	8	h.	h.	PROPN
cana-1758	232	9	m.	m.	PROPN
cana-1758	232	10	assim	assim	PROPN
cana-1758	232	11	,	,	PUNCT
cana-1758	232	12	“	"	PUNCT
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cana-1758	232	14	defects	defect	NOUN
cana-1758	232	15	prediction	prediction	NOUN
cana-1758	232	16	using	use	VERB
cana-1758	232	17	machine	machine	NOUN
cana-1758	232	18	learning	learning	NOUN
cana-1758	232	19	algorithms	algorithm	NOUN
cana-1758	232	20	,	,	PUNCT
cana-1758	232	21	”	"	PUNCT
cana-1758	232	22	int	int	NOUN
cana-1758	232	23	.	.	PUNCT
cana-1758	232	24	conf	conf	PROPN
cana-1758	232	25	.	.	PUNCT
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cana-1758	233	2	anal	anal	PROPN
cana-1758	233	3	.	.	PUNCT
cana-1758	234	1	bus	bus	NOUN
cana-1758	234	2	.	.	PUNCT
cana-1758	235	1	ind	ind	PROPN
cana-1758	235	2	.	.	PUNCT
cana-1758	236	1	w.	w.	PROPN
cana-1758	236	2	towar	towar	PROPN
cana-1758	236	3	.	.	PUNCT
cana-1758	237	1	a	a	DET
cana-1758	237	2	sustain	sustain	NOUN
cana-1758	237	3	.	.	PUNCT
cana-1758	238	1	econ	econ	PROPN
cana-1758	238	2	.	.	PUNCT
cana-1758	238	3	,	,	PUNCT
cana-1758	239	1	pp	pp	PROPN
cana-1758	239	2	.	.	PUNCT
cana-1758	240	1	1–6	1–6	NUM
cana-1758	240	2	,	,	PUNCT
cana-1758	240	3	2020	2020	NUM
cana-1758	240	4	,	,	PUNCT
cana-1758	240	5	doi	doi	NOUN
cana-1758	240	6	:	:	PUNCT
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cana-1758	240	8	/	/	SYM
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cana-1758	241	3	]	]	PUNCT
cana-1758	241	4	s.	s.	PROPN
cana-1758	241	5	p.	p.	PROPN
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cana-1758	241	8	“	"	PUNCT
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cana-1758	241	14	algorithms	algorithm	NOUN
cana-1758	241	15	in	in	ADP
cana-1758	241	16	qsar	qsar	NOUN
cana-1758	241	17	,	,	PUNCT
cana-1758	241	18	”	"	PUNCT
cana-1758	241	19	j.	j.	PROPN
cana-1758	241	20	mol	mol	PROPN
cana-1758	241	21	.	.	PROPN
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cana-1758	241	23	.	.	PUNCT
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cana-1758	242	2	,	,	PUNCT
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cana-1758	242	4	.	.	PROPN
cana-1758	243	1	622	622	NUM
cana-1758	243	2	,	,	PUNCT
cana-1758	243	3	no	no	INTJ
cana-1758	243	4	.	.	PUNCT
cana-1758	244	1	1–2	1–2	NUM
cana-1758	244	2	,	,	PUNCT
cana-1758	244	3	pp	pp	ADJ
cana-1758	244	4	.	.	PUNCT
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cana-1758	245	2	,	,	PUNCT
cana-1758	245	3	2003	2003	NUM
cana-1758	245	4	,	,	PUNCT
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cana-1758	245	8	/	/	SYM
cana-1758	245	9	s0166	s0166	PROPN
cana-1758	245	10	-	-	PUNCT
cana-1758	245	11	1280(02)00619	1280(02)00619	NUM
cana-1758	245	12	-	-	PUNCT
cana-1758	245	13	x.	x.	NOUN
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cana-1758	245	15	on	on	ADP
cana-1758	245	16	applied	apply	VERB
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cana-1758	245	19	issn	issn	NOUN
cana-1758	245	20	:	:	PUNCT
cana-1758	245	21	1074	1074	NUM
cana-1758	245	22	-	-	PUNCT
cana-1758	245	23	133x	133x	NUM
cana-1758	245	24	vol	vol	NOUN
cana-1758	245	25	32	32	NUM
cana-1758	245	26	no	no	NOUN
cana-1758	245	27	.	.	NOUN
cana-1758	245	28	2	2	NUM
cana-1758	245	29	(	(	PUNCT
cana-1758	245	30	2025	2025	NUM
cana-1758	245	31	)	)	PUNCT
cana-1758	245	32	470	470	NUM
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cana-1758	246	1	[	[	X
cana-1758	246	2	22	22	NUM
cana-1758	246	3	]	]	X
cana-1758	246	4	mane	mane	PROPN
cana-1758	246	5	,	,	PUNCT
cana-1758	246	6	d.	d.	PROPN
cana-1758	246	7	,	,	PUNCT
cana-1758	246	8	ashtagi	ashtagi	PROPN
cana-1758	246	9	,	,	PUNCT
cana-1758	246	10	r.	r.	PROPN
cana-1758	246	11	,	,	PUNCT
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cana-1758	246	13	,	,	PUNCT
cana-1758	246	14	p.	p.	PROPN
cana-1758	246	15	,	,	PUNCT
cana-1758	246	16	kadam	kadam	PROPN
cana-1758	246	17	,	,	PUNCT
cana-1758	246	18	s.	s.	PROPN
cana-1758	246	19	,	,	PUNCT
cana-1758	246	20	salunkhe	salunkhe	PROPN
cana-1758	246	21	,	,	PUNCT
cana-1758	246	22	d.	d.	PROPN
cana-1758	246	23	,	,	PUNCT
cana-1758	246	24	upadhye	upadhye	NOUN
cana-1758	246	25	,	,	PUNCT
cana-1758	246	26	g.	g.	PROPN
cana-1758	246	27	(	(	PUNCT
cana-1758	246	28	2022	2022	NUM
cana-1758	246	29	)	)	PUNCT
cana-1758	246	30	.	.	PUNCT
cana-1758	247	1	an	an	DET
cana-1758	247	2	improved	improved	ADJ
cana-1758	247	3	transfer	transfer	NOUN
cana-1758	247	4	learning	learn	VERB
cana-1758	247	5	approach	approach	NOUN
cana-1758	247	6	for	for	ADP
cana-1758	247	7	classification	classification	NOUN
cana-1758	247	8	of	of	ADP
cana-1758	247	9	types	type	NOUN
cana-1758	247	10	of	of	ADP
cana-1758	247	11	cancer	cancer	NOUN
cana-1758	247	12	.	.	PUNCT
cana-1758	248	1	traitement	traitement	ADJ
cana-1758	248	2	du	du	PROPN
cana-1758	248	3	signal	signal	NOUN
cana-1758	248	4	,	,	PUNCT
cana-1758	248	5	vol	vol	NOUN
cana-1758	248	6	.	.	PROPN
cana-1758	248	7	39	39	NUM
cana-1758	248	8	,	,	PUNCT
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cana-1758	248	10	.	.	NOUN
cana-1758	248	11	6	6	NUM
cana-1758	248	12	,	,	PUNCT
cana-1758	248	13	pp	pp	ADJ
cana-1758	248	14	.	.	PUNCT
cana-1758	248	15	2095	2095	NUM
cana-1758	248	16	-	-	SYM
cana-1758	248	17	2101	2101	NUM
cana-1758	248	18	.	.	PUNCT
cana-1758	249	1	https://doi.org/10.18280/ts.390622	https://doi.org/10.18280/ts.390622	X
cana-1758	250	1	[	[	X
cana-1758	250	2	23	23	NUM
cana-1758	250	3	]	]	PUNCT
cana-1758	250	4	salunke	salunke	NOUN
cana-1758	250	5	,	,	PUNCT
cana-1758	250	6	dipmala	dipmala	PROPN
cana-1758	250	7	&	&	CCONJ
cana-1758	250	8	mane	mane	PROPN
cana-1758	250	9	,	,	PUNCT
cana-1758	250	10	deepak	deepak	PROPN
cana-1758	250	11	&	&	CCONJ
cana-1758	250	12	joshi	joshi	PROPN
cana-1758	250	13	,	,	PUNCT
cana-1758	250	14	ram	ram	PROPN
cana-1758	250	15	&	&	CCONJ
cana-1758	250	16	peddi	peddi	ADJ
cana-1758	250	17	,	,	PUNCT
cana-1758	250	18	prasadu	prasadu	NOUN
cana-1758	250	19	.	.	PUNCT
cana-1758	251	1	(	(	PUNCT
cana-1758	251	2	2022	2022	NUM
cana-1758	251	3	)	)	PUNCT
cana-1758	251	4	.	.	PUNCT
cana-1758	252	1	customized	customize	VERB
cana-1758	252	2	convolutional	convolutional	ADJ
cana-1758	252	3	neural	neural	ADJ
cana-1758	252	4	network	network	NOUN
cana-1758	252	5	to	to	PART
cana-1758	252	6	detect	detect	VERB
cana-1758	252	7	dental	dental	ADJ
cana-1758	252	8	caries	carie	NOUN
cana-1758	252	9	from	from	ADP
cana-1758	252	10	radiovisiography(rvg	radiovisiography(rvg	NOUN
cana-1758	252	11	)	)	PUNCT
cana-1758	252	12	images	image	NOUN
cana-1758	252	13	.	.	PUNCT
cana-1758	253	1	international	international	ADJ
cana-1758	253	2	journal	journal	NOUN
cana-1758	253	3	of	of	ADP
cana-1758	253	4	advanced	advanced	ADJ
cana-1758	253	5	technology	technology	NOUN
cana-1758	253	6	and	and	CCONJ
cana-1758	253	7	engineering	engineering	NOUN
cana-1758	253	8	exploration	exploration	NOUN
cana-1758	253	9	.	.	PUNCT
cana-1758	254	1	9	9	NUM
cana-1758	254	2	.	.	X
cana-1758	254	3	827	827	NUM
cana-1758	254	4	-	-	SYM
cana-1758	254	5	838	838	NUM
cana-1758	254	6	.	.	PUNCT
cana-1758	254	7	10.19101	10.19101	NUM
cana-1758	254	8	/	/	SYM
cana-1758	254	9	ijatee.2021.874862	ijatee.2021.874862	NOUN
cana-1758	254	10	.	.	PUNCT