id	sid	tid	token	lemma	pos
cana-4510	1	1	communications	communication	NOUN
cana-4510	1	2	on	on	ADP
cana-4510	1	3	applied	apply	VERB
cana-4510	1	4	nonlinear	nonlinear	ADJ
cana-4510	1	5	analysis	analysis	NOUN
cana-4510	1	6	issn	issn	NOUN
cana-4510	1	7	:	:	PUNCT
cana-4510	1	8	1074	1074	NUM
cana-4510	1	9	-	-	PUNCT
cana-4510	1	10	133x	133x	NUM
cana-4510	1	11	vol	vol	NOUN
cana-4510	1	12	32	32	NUM
cana-4510	1	13	no	no	NOUN
cana-4510	1	14	.	.	PUNCT
cana-4510	2	1	9s	9s	NUM
cana-4510	2	2	(	(	PUNCT
cana-4510	2	3	2025	2025	NUM
cana-4510	2	4	)	)	PUNCT
cana-4510	2	5	2244	2244	NUM
cana-4510	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	2	7	integration	integration	NOUN
cana-4510	2	8	of	of	ADP
cana-4510	2	9	a	a	DET
cana-4510	2	10	dual	dual	ADJ
cana-4510	2	11	hybrid	hybrid	ADJ
cana-4510	2	12	deep	deep	ADJ
cana-4510	2	13	convolutional	convolutional	ADJ
cana-4510	2	14	neural	neural	ADJ
cana-4510	2	15	network	network	NOUN
cana-4510	2	16	framework	framework	NOUN
cana-4510	2	17	for	for	ADP
cana-4510	2	18	insect	insect	NOUN
cana-4510	2	19	taxonomic	taxonomic	ADJ
cana-4510	2	20	classification	classification	NOUN
cana-4510	2	21	m.	m.	NOUN
cana-4510	2	22	santhiya	santhiya	PROPN
cana-4510	2	23	1	1	NUM
cana-4510	2	24	,	,	PUNCT
cana-4510	2	25	dr	dr	PROPN
cana-4510	2	26	.	.	PROPN
cana-4510	2	27	s.	s.	PROPN
cana-4510	2	28	karpagavalli	karpagavalli	PROPN
cana-4510	2	29	2	2	NUM
cana-4510	2	30	1research	1research	NUM
cana-4510	2	31	scholar	scholar	NOUN
cana-4510	2	32	,	,	PUNCT
cana-4510	2	33	department	department	NOUN
cana-4510	2	34	of	of	ADP
cana-4510	2	35	computer	computer	NOUN
cana-4510	2	36	science	science	NOUN
cana-4510	2	37	,	,	PUNCT
cana-4510	2	38	psgr	psgr	PROPN
cana-4510	2	39	krishnammal	krishnammal	PROPN
cana-4510	2	40	college	college	NOUN
cana-4510	2	41	for	for	ADP
cana-4510	2	42	women	woman	NOUN
cana-4510	2	43	,	,	PUNCT
cana-4510	2	44	coimbatore	coimbatore	NOUN
cana-4510	2	45	2associate	2associate	NUM
cana-4510	2	46	professor	professor	NOUN
cana-4510	2	47	and	and	CCONJ
cana-4510	2	48	head	head	NOUN
cana-4510	2	49	,	,	PUNCT
cana-4510	2	50	department	department	NOUN
cana-4510	2	51	of	of	ADP
cana-4510	2	52	computer	computer	NOUN
cana-4510	2	53	science	science	NOUN
cana-4510	2	54	,	,	PUNCT
cana-4510	2	55	psgr	psgr	PROPN
cana-4510	2	56	krishnammal	krishnammal	PROPN
cana-4510	2	57	college	college	NOUN
cana-4510	2	58	for	for	ADP
cana-4510	2	59	women	woman	NOUN
cana-4510	2	60	,	,	PUNCT
cana-4510	2	61	coimbatore	coimbatore	PROPN
cana-4510	2	62	e	e	NOUN
cana-4510	2	63	-	-	NOUN
cana-4510	2	64	mail	mail	NOUN
cana-4510	2	65	:	:	PUNCT
cana-4510	2	66	santhyam310phd@gmail.com	santhyam310phd@gmail.com	X
cana-4510	2	67	article	article	NOUN
cana-4510	2	68	history	history	NOUN
cana-4510	2	69	:	:	PUNCT
cana-4510	2	70	received	receive	VERB
cana-4510	2	71	:	:	PUNCT
cana-4510	2	72	12	12	NUM
cana-4510	2	73	-	-	SYM
cana-4510	2	74	01	01	NUM
cana-4510	2	75	-	-	PUNCT
cana-4510	2	76	2025	2025	NUM
cana-4510	2	77	revised	revise	VERB
cana-4510	2	78	:	:	PUNCT
cana-4510	2	79	15	15	NUM
cana-4510	2	80	-	-	NUM
cana-4510	2	81	02	02	NUM
cana-4510	2	82	-	-	PUNCT
cana-4510	2	83	2025	2025	NUM
cana-4510	2	84	accepted	accept	VERB
cana-4510	2	85	:	:	PUNCT
cana-4510	2	86	01	01	NUM
cana-4510	2	87	-	-	SYM
cana-4510	2	88	03	03	NUM
cana-4510	2	89	-	-	PUNCT
cana-4510	2	90	2025	2025	NUM
cana-4510	2	91	abstract	abstract	NOUN
cana-4510	2	92	:	:	PUNCT
cana-4510	2	93	insects	insect	NOUN
cana-4510	2	94	constitute	constitute	VERB
cana-4510	2	95	a	a	DET
cana-4510	2	96	vital	vital	ADJ
cana-4510	2	97	element	element	NOUN
cana-4510	2	98	within	within	ADP
cana-4510	2	99	numerous	numerous	ADJ
cana-4510	2	100	ecosystems	ecosystem	NOUN
cana-4510	2	101	,	,	PUNCT
cana-4510	2	102	exerting	exert	VERB
cana-4510	2	103	significant	significant	ADJ
cana-4510	2	104	influence	influence	NOUN
cana-4510	2	105	on	on	ADP
cana-4510	2	106	biodiversity	biodiversity	NOUN
cana-4510	2	107	,	,	PUNCT
cana-4510	2	108	ecological	ecological	ADJ
cana-4510	2	109	dynamics	dynamic	NOUN
cana-4510	2	110	,	,	PUNCT
cana-4510	2	111	and	and	CCONJ
cana-4510	2	112	the	the	DET
cana-4510	2	113	well	well	ADV
cana-4510	2	114	-	-	PUNCT
cana-4510	2	115	being	being	NOUN
cana-4510	2	116	of	of	ADP
cana-4510	2	117	human	human	ADJ
cana-4510	2	118	health	health	NOUN
cana-4510	2	119	as	as	ADV
cana-4510	2	120	well	well	ADV
cana-4510	2	121	as	as	ADP
cana-4510	2	122	natural	natural	ADJ
cana-4510	2	123	resources	resource	NOUN
cana-4510	2	124	.	.	PUNCT
cana-4510	3	1	the	the	DET
cana-4510	3	2	taxonomic	taxonomic	ADJ
cana-4510	3	3	group	group	NOUN
cana-4510	3	4	"	"	PUNCT
cana-4510	3	5	insecta	insecta	PROPN
cana-4510	3	6	"	"	PUNCT
cana-4510	3	7	stands	stand	VERB
cana-4510	3	8	out	out	ADP
cana-4510	3	9	as	as	ADP
cana-4510	3	10	one	one	NUM
cana-4510	3	11	of	of	ADP
cana-4510	3	12	the	the	DET
cana-4510	3	13	largest	large	ADJ
cana-4510	3	14	and	and	CCONJ
cana-4510	3	15	most	most	ADV
cana-4510	3	16	extensive	extensive	ADJ
cana-4510	3	17	within	within	ADP
cana-4510	3	18	the	the	DET
cana-4510	3	19	realm	realm	NOUN
cana-4510	3	20	of	of	ADP
cana-4510	3	21	biodiversity	biodiversity	NOUN
cana-4510	3	22	taxonomy	taxonomy	NOUN
cana-4510	3	23	.	.	PUNCT
cana-4510	4	1	given	give	VERB
cana-4510	4	2	their	their	PRON
cana-4510	4	3	importance	importance	NOUN
cana-4510	4	4	,	,	PUNCT
cana-4510	4	5	sustainable	sustainable	ADJ
cana-4510	4	6	management	management	NOUN
cana-4510	4	7	of	of	ADP
cana-4510	4	8	insects	insect	NOUN
cana-4510	4	9	,	,	PUNCT
cana-4510	4	10	ecosystems	ecosystem	NOUN
cana-4510	4	11	,	,	PUNCT
cana-4510	4	12	and	and	CCONJ
cana-4510	4	13	their	their	PRON
cana-4510	4	14	interrelationships	interrelationship	NOUN
cana-4510	4	15	is	be	AUX
cana-4510	4	16	vital	vital	ADJ
cana-4510	4	17	for	for	ADP
cana-4510	4	18	the	the	DET
cana-4510	4	19	survival	survival	NOUN
cana-4510	4	20	of	of	ADP
cana-4510	4	21	all	all	DET
cana-4510	4	22	organisms	organism	NOUN
cana-4510	4	23	.	.	PUNCT
cana-4510	5	1	the	the	DET
cana-4510	5	2	novel	novel	ADJ
cana-4510	5	3	approach	approach	NOUN
cana-4510	5	4	for	for	ADP
cana-4510	5	5	classifying	classify	VERB
cana-4510	5	6	insect	insect	NOUN
cana-4510	5	7	images	image	NOUN
cana-4510	5	8	presented	present	VERB
cana-4510	5	9	in	in	ADP
cana-4510	5	10	this	this	DET
cana-4510	5	11	research	research	NOUN
cana-4510	5	12	is	be	AUX
cana-4510	5	13	based	base	VERB
cana-4510	5	14	on	on	ADP
cana-4510	5	15	systematic	systematic	ADJ
cana-4510	5	16	taxonomic	taxonomic	ADJ
cana-4510	5	17	ranks	rank	NOUN
cana-4510	5	18	at	at	ADP
cana-4510	5	19	the	the	DET
cana-4510	5	20	order	order	NOUN
cana-4510	5	21	,	,	PUNCT
cana-4510	5	22	family	family	NOUN
cana-4510	5	23	,	,	PUNCT
cana-4510	5	24	and	and	CCONJ
cana-4510	5	25	species	species	NOUN
cana-4510	5	26	levels	level	NOUN
cana-4510	5	27	and	and	CCONJ
cana-4510	5	28	combining	combine	VERB
cana-4510	5	29	deep	deep	ADJ
cana-4510	5	30	convolutional	convolutional	ADJ
cana-4510	5	31	neural	neural	ADJ
cana-4510	5	32	network	network	NOUN
cana-4510	5	33	,	,	PUNCT
cana-4510	5	34	termed	term	VERB
cana-4510	5	35	as	as	ADP
cana-4510	5	36	“	"	PUNCT
cana-4510	5	37	dual	dual	ADJ
cana-4510	5	38	hybrid	hybrid	NOUN
cana-4510	5	39	”	"	PUNCT
cana-4510	5	40	.	.	PUNCT
cana-4510	6	1	convolutional	convolutional	ADJ
cana-4510	6	2	neural	neural	ADJ
cana-4510	6	3	network	network	NOUN
cana-4510	6	4	this	this	DET
cana-4510	6	5	model	model	NOUN
cana-4510	6	6	utilized	utilize	VERB
cana-4510	6	7	a	a	DET
cana-4510	6	8	total	total	NOUN
cana-4510	6	9	of	of	ADP
cana-4510	6	10	6060	6060	NUM
cana-4510	6	11	images	image	NOUN
cana-4510	6	12	for	for	ADP
cana-4510	6	13	classification	classification	NOUN
cana-4510	6	14	at	at	ADP
cana-4510	6	15	the	the	DET
cana-4510	6	16	order	order	NOUN
cana-4510	6	17	level	level	NOUN
cana-4510	6	18	,	,	PUNCT
cana-4510	6	19	3740	3740	NUM
cana-4510	6	20	images	image	NOUN
cana-4510	6	21	for	for	ADP
cana-4510	6	22	the	the	DET
cana-4510	6	23	family	family	NOUN
cana-4510	6	24	level	level	NOUN
cana-4510	6	25	,	,	PUNCT
cana-4510	6	26	and	and	CCONJ
cana-4510	6	27	1582	1582	NUM
cana-4510	6	28	images	image	NOUN
cana-4510	6	29	for	for	ADP
cana-4510	6	30	the	the	DET
cana-4510	6	31	species	specie	NOUN
cana-4510	6	32	level	level	NOUN
cana-4510	6	33	.	.	PUNCT
cana-4510	7	1	various	various	ADJ
cana-4510	7	2	fine	fine	ADV
cana-4510	7	3	-	-	PUNCT
cana-4510	7	4	tuned	tune	VERB
cana-4510	7	5	pre	pre	ADJ
cana-4510	7	6	-	-	ADJ
cana-4510	7	7	trained	train	VERB
cana-4510	7	8	dcnn	dcnn	ADJ
cana-4510	7	9	models	model	NOUN
cana-4510	7	10	were	be	AUX
cana-4510	7	11	employed	employ	VERB
cana-4510	7	12	to	to	PART
cana-4510	7	13	create	create	VERB
cana-4510	7	14	the	the	DET
cana-4510	7	15	hybrid	hybrid	ADJ
cana-4510	7	16	model	model	NOUN
cana-4510	7	17	.	.	PUNCT
cana-4510	8	1	this	this	DET
cana-4510	8	2	proposed	propose	VERB
cana-4510	8	3	research	research	NOUN
cana-4510	8	4	work	work	NOUN
cana-4510	8	5	mainly	mainly	ADV
cana-4510	8	6	focuses	focus	VERB
cana-4510	8	7	on	on	ADP
cana-4510	8	8	increasing	increase	VERB
cana-4510	8	9	accuracy	accuracy	NOUN
cana-4510	8	10	and	and	CCONJ
cana-4510	8	11	efficiency	efficiency	NOUN
cana-4510	8	12	of	of	ADP
cana-4510	8	13	taxonomic	taxonomic	ADJ
cana-4510	8	14	level	level	NOUN
cana-4510	8	15	insect	insect	NOUN
cana-4510	8	16	images	image	VERB
cana-4510	8	17	classification	classification	NOUN
cana-4510	8	18	and	and	CCONJ
cana-4510	8	19	identification	identification	NOUN
cana-4510	8	20	.	.	PUNCT
cana-4510	9	1	experimental	experimental	ADJ
cana-4510	9	2	results	result	NOUN
cana-4510	9	3	indicate	indicate	VERB
cana-4510	9	4	promising	promising	ADJ
cana-4510	9	5	outcomes	outcome	NOUN
cana-4510	9	6	.	.	PUNCT
cana-4510	10	1	the	the	DET
cana-4510	10	2	dhdcnet	dhdcnet	ADJ
cana-4510	10	3	model	model	NOUN
cana-4510	10	4	proposed	propose	VERB
cana-4510	10	5	in	in	ADP
cana-4510	10	6	this	this	DET
cana-4510	10	7	research	research	NOUN
cana-4510	10	8	achieved	achieve	VERB
cana-4510	10	9	classification	classification	NOUN
cana-4510	10	10	accuracies	accuracy	NOUN
cana-4510	10	11	of	of	ADP
cana-4510	10	12	98.97	98.97	NUM
cana-4510	10	13	%	%	NOUN
cana-4510	10	14	for	for	ADP
cana-4510	10	15	order	order	NOUN
cana-4510	10	16	classification	classification	NOUN
cana-4510	10	17	,	,	PUNCT
cana-4510	10	18	97.37	97.37	NUM
cana-4510	10	19	%	%	NOUN
cana-4510	10	20	for	for	ADP
cana-4510	10	21	family	family	NOUN
cana-4510	10	22	classification	classification	NOUN
cana-4510	10	23	,	,	PUNCT
cana-4510	10	24	and	and	CCONJ
cana-4510	10	25	89	89	NUM
cana-4510	10	26	%	%	NOUN
cana-4510	10	27	for	for	ADP
cana-4510	10	28	species	specie	NOUN
cana-4510	10	29	classification	classification	NOUN
cana-4510	10	30	across	across	ADP
cana-4510	10	31	five	five	NUM
cana-4510	10	32	distinct	distinct	ADJ
cana-4510	10	33	insect	insect	NOUN
cana-4510	10	34	classes	class	NOUN
cana-4510	10	35	.	.	PUNCT
cana-4510	11	1	a	a	DET
cana-4510	11	2	detailed	detailed	ADJ
cana-4510	11	3	evaluation	evaluation	NOUN
cana-4510	11	4	of	of	ADP
cana-4510	11	5	the	the	DET
cana-4510	11	6	model	model	NOUN
cana-4510	11	7	's	's	PART
cana-4510	11	8	performance	performance	NOUN
cana-4510	11	9	was	be	AUX
cana-4510	11	10	conducted	conduct	VERB
cana-4510	11	11	using	use	VERB
cana-4510	11	12	metrics	metric	NOUN
cana-4510	11	13	such	such	ADJ
cana-4510	11	14	as	as	ADP
cana-4510	11	15	precision	precision	NOUN
cana-4510	11	16	,	,	PUNCT
cana-4510	11	17	recall	recall	NOUN
cana-4510	11	18	,	,	PUNCT
cana-4510	11	19	and	and	CCONJ
cana-4510	11	20	f1	f1	NOUN
cana-4510	11	21	-	-	PUNCT
cana-4510	11	22	score	score	NOUN
cana-4510	11	23	,	,	PUNCT
cana-4510	11	24	which	which	PRON
cana-4510	11	25	provided	provide	VERB
cana-4510	11	26	valuable	valuable	ADJ
cana-4510	11	27	insights	insight	NOUN
cana-4510	11	28	into	into	ADP
cana-4510	11	29	its	its	PRON
cana-4510	11	30	effectiveness	effectiveness	NOUN
cana-4510	11	31	across	across	ADP
cana-4510	11	32	various	various	ADJ
cana-4510	11	33	dimensions	dimension	NOUN
cana-4510	11	34	.	.	PUNCT
cana-4510	12	1	keywords	keyword	NOUN
cana-4510	12	2	:	:	PUNCT
cana-4510	12	3	insect	insect	VERB
cana-4510	12	4	classification	classification	NOUN
cana-4510	12	5	and	and	CCONJ
cana-4510	12	6	identification	identification	NOUN
cana-4510	12	7	,	,	PUNCT
cana-4510	12	8	data	datum	NOUN
cana-4510	12	9	augmentation	augmentation	NOUN
cana-4510	12	10	,	,	PUNCT
cana-4510	12	11	pre	pre	ADJ
cana-4510	12	12	-	-	ADJ
cana-4510	12	13	trained	train	VERB
cana-4510	12	14	models	model	NOUN
cana-4510	12	15	,	,	PUNCT
cana-4510	12	16	deep	deep	ADJ
cana-4510	12	17	convolutional	convolutional	ADJ
cana-4510	12	18	neural	neural	ADJ
cana-4510	12	19	networks	network	NOUN
cana-4510	12	20	(	(	PUNCT
cana-4510	12	21	dcnn	dcnn	PROPN
cana-4510	12	22	)	)	PUNCT
cana-4510	12	23	,	,	PUNCT
cana-4510	12	24	deep	deep	ADJ
cana-4510	12	25	learning	learning	NOUN
cana-4510	12	26	,	,	PUNCT
cana-4510	12	27	hybrid	hybrid	ADJ
cana-4510	12	28	method	method	NOUN
cana-4510	12	29	,	,	PUNCT
cana-4510	12	30	dual	dual	ADJ
cana-4510	12	31	hybrid	hybrid	ADJ
cana-4510	12	32	deep	deep	ADJ
cana-4510	12	33	convolutional	convolutional	ADJ
cana-4510	12	34	neural	neural	ADJ
cana-4510	12	35	network	network	NOUN
cana-4510	12	36	.	.	PUNCT
cana-4510	13	1	1	1	X
cana-4510	13	2	.	.	X
cana-4510	13	3	introduction	introduction	NOUN
cana-4510	13	4	establishing	establish	VERB
cana-4510	13	5	precise	precise	ADJ
cana-4510	13	6	understanding	understanding	NOUN
cana-4510	13	7	of	of	ADP
cana-4510	13	8	the	the	DET
cana-4510	13	9	identity	identity	NOUN
cana-4510	13	10	,	,	PUNCT
cana-4510	13	11	geographic	geographic	ADJ
cana-4510	13	12	prevalence	prevalence	NOUN
cana-4510	13	13	,	,	PUNCT
cana-4510	13	14	and	and	CCONJ
cana-4510	13	15	evolutionary	evolutionary	ADJ
cana-4510	13	16	pathways	pathway	NOUN
cana-4510	13	17	of	of	ADP
cana-4510	13	18	living	live	VERB
cana-4510	13	19	species	specie	NOUN
cana-4510	13	20	is	be	AUX
cana-4510	13	21	crucial	crucial	ADJ
cana-4510	13	22	not	not	PART
cana-4510	13	23	only	only	ADV
cana-4510	13	24	for	for	ADP
cana-4510	13	25	sustainable	sustainable	ADJ
cana-4510	13	26	human	human	ADJ
cana-4510	13	27	development	development	NOUN
cana-4510	13	28	but	but	CCONJ
cana-4510	13	29	also	also	ADV
cana-4510	13	30	for	for	ADP
cana-4510	13	31	the	the	DET
cana-4510	13	32	preservation	preservation	NOUN
cana-4510	13	33	of	of	ADP
cana-4510	13	34	biodiversity	biodiversity	NOUN
cana-4510	13	35	[	[	X
cana-4510	13	36	1	1	NUM
cana-4510	13	37	]	]	PUNCT
cana-4510	13	38	.	.	PUNCT
cana-4510	14	1	insects	insect	NOUN
cana-4510	14	2	constitute	constitute	VERB
cana-4510	14	3	over	over	ADP
cana-4510	14	4	50	50	NUM
cana-4510	14	5	%	%	NOUN
cana-4510	14	6	of	of	ADP
cana-4510	14	7	all	all	DET
cana-4510	14	8	documented	document	VERB
cana-4510	14	9	species	specie	NOUN
cana-4510	14	10	within	within	ADP
cana-4510	14	11	the	the	DET
cana-4510	14	12	“	"	PUNCT
cana-4510	14	13	animalia	animalia	NOUN
cana-4510	14	14	”	"	PUNCT
cana-4510	14	15	kingdom	kingdom	NOUN
cana-4510	14	16	and	and	CCONJ
cana-4510	14	17	“	"	PUNCT
cana-4510	14	18	arthropoda	arthropoda	NOUN
cana-4510	14	19	”	"	PUNCT
cana-4510	14	20	phylum	phylum	NOUN
cana-4510	14	21	,	,	PUNCT
cana-4510	14	22	representing	represent	VERB
cana-4510	14	23	a	a	DET
cana-4510	14	24	substantial	substantial	ADJ
cana-4510	14	25	portion	portion	NOUN
cana-4510	14	26	of	of	ADP
cana-4510	14	27	the	the	DET
cana-4510	14	28	earth	earth	NOUN
cana-4510	14	29	's	's	PART
cana-4510	14	30	overall	overall	ADJ
cana-4510	14	31	biodiversity	biodiversity	NOUN
cana-4510	14	32	[	[	X
cana-4510	14	33	2	2	NUM
cana-4510	14	34	]	]	PUNCT
cana-4510	14	35	.	.	PUNCT
cana-4510	15	1	in	in	ADP
cana-4510	15	2	the	the	DET
cana-4510	15	3	traditional	traditional	ADJ
cana-4510	15	4	approach	approach	NOUN
cana-4510	15	5	,	,	PUNCT
cana-4510	15	6	resulting	result	VERB
cana-4510	15	7	in	in	ADP
cana-4510	15	8	a	a	DET
cana-4510	15	9	labour	labour	NOUN
cana-4510	15	10	-	-	PUNCT
cana-4510	15	11	intensive	intensive	ADJ
cana-4510	15	12	process	process	NOUN
cana-4510	15	13	with	with	ADP
cana-4510	15	14	limited	limited	ADJ
cana-4510	15	15	speed	speed	NOUN
cana-4510	15	16	,	,	PUNCT
cana-4510	15	17	time	time	NOUN
cana-4510	15	18	-	-	PUNCT
cana-4510	15	19	consuming	consume	VERB
cana-4510	15	20	and	and	CCONJ
cana-4510	15	21	need	need	VERB
cana-4510	15	22	experts	expert	NOUN
cana-4510	15	23	’	'	PUNCT
cana-4510	15	24	knowledge	knowledge	NOUN
cana-4510	15	25	.	.	PUNCT
cana-4510	16	1	this	this	DET
cana-4510	16	2	approach	approach	NOUN
cana-4510	16	3	falls	fall	VERB
cana-4510	16	4	short	short	ADJ
cana-4510	16	5	in	in	ADP
cana-4510	16	6	fulfilling	fulfil	VERB
cana-4510	16	7	the	the	DET
cana-4510	16	8	requirements	requirement	NOUN
cana-4510	16	9	for	for	ADP
cana-4510	16	10	effective	effective	ADJ
cana-4510	16	11	insect	insect	NOUN
cana-4510	16	12	identification	identification	NOUN
cana-4510	16	13	and	and	CCONJ
cana-4510	16	14	detection	detection	NOUN
cana-4510	16	15	[	[	X
cana-4510	16	16	3	3	NUM
cana-4510	16	17	]	]	PUNCT
cana-4510	16	18	.	.	PUNCT
cana-4510	17	1	hence	hence	ADV
cana-4510	17	2	,	,	PUNCT
cana-4510	17	3	there	there	PRON
cana-4510	17	4	is	be	VERB
cana-4510	17	5	considerable	considerable	ADJ
cana-4510	17	6	importance	importance	NOUN
cana-4510	17	7	in	in	ADP
cana-4510	17	8	investigating	investigate	VERB
cana-4510	17	9	the	the	DET
cana-4510	17	10	principles	principle	NOUN
cana-4510	17	11	and	and	CCONJ
cana-4510	17	12	techniques	technique	NOUN
cana-4510	17	13	for	for	ADP
cana-4510	17	14	computer	computer	NOUN
cana-4510	17	15	vision	vision	NOUN
cana-4510	17	16	based	base	VERB
cana-4510	17	17	automatic	automatic	ADJ
cana-4510	17	18	insect	insect	NOUN
cana-4510	17	19	classification	classification	NOUN
cana-4510	17	20	.	.	PUNCT
cana-4510	18	1	in	in	ADP
cana-4510	18	2	recent	recent	ADJ
cana-4510	18	3	times	time	NOUN
cana-4510	18	4	,	,	PUNCT
cana-4510	18	5	the	the	DET
cana-4510	18	6	computer	computer	NOUN
cana-4510	18	7	vision	vision	NOUN
cana-4510	18	8	technology	technology	NOUN
cana-4510	18	9	has	have	AUX
cana-4510	18	10	laid	lay	VERB
cana-4510	18	11	the	the	DET
cana-4510	18	12	technical	technical	ADJ
cana-4510	18	13	foundation	foundation	NOUN
cana-4510	18	14	for	for	ADP
cana-4510	18	15	achieving	achieve	VERB
cana-4510	18	16	image	image	NOUN
cana-4510	18	17	classification	classification	NOUN
cana-4510	18	18	.	.	PUNCT
cana-4510	19	1	machine	machine	NOUN
cana-4510	19	2	learning	learning	NOUN
cana-4510	19	3	(	(	PUNCT
cana-4510	19	4	ml	ml	NOUN
cana-4510	19	5	)	)	PUNCT
cana-4510	19	6	has	have	AUX
cana-4510	19	7	demonstrated	demonstrate	VERB
cana-4510	19	8	considerable	considerable	ADJ
cana-4510	19	9	success	success	NOUN
cana-4510	19	10	results	result	NOUN
cana-4510	19	11	in	in	ADP
cana-4510	19	12	image	image	NOUN
cana-4510	19	13	communications	communication	NOUN
cana-4510	19	14	on	on	ADP
cana-4510	19	15	applied	apply	VERB
cana-4510	19	16	nonlinear	nonlinear	ADJ
cana-4510	19	17	analysis	analysis	NOUN
cana-4510	19	18	issn	issn	NOUN
cana-4510	19	19	:	:	PUNCT
cana-4510	19	20	1074	1074	NUM
cana-4510	19	21	-	-	PUNCT
cana-4510	19	22	133x	133x	NUM
cana-4510	19	23	vol	vol	NOUN
cana-4510	19	24	32	32	NUM
cana-4510	19	25	no	no	NOUN
cana-4510	19	26	.	.	PUNCT
cana-4510	20	1	9s	9s	NUM
cana-4510	20	2	(	(	PUNCT
cana-4510	20	3	2025	2025	NUM
cana-4510	20	4	)	)	PUNCT
cana-4510	20	5	2245	2245	NUM
cana-4510	20	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	20	7	classification	classification	NOUN
cana-4510	20	8	tasks	task	NOUN
cana-4510	20	9	.	.	PUNCT
cana-4510	21	1	however	however	ADV
cana-4510	21	2	,	,	PUNCT
cana-4510	21	3	it	it	PRON
cana-4510	21	4	encounters	encounter	VERB
cana-4510	21	5	challenges	challenge	NOUN
cana-4510	21	6	in	in	ADP
cana-4510	21	7	learning	learn	VERB
cana-4510	21	8	complex	complex	ADJ
cana-4510	21	9	patterns	pattern	NOUN
cana-4510	21	10	present	present	ADJ
cana-4510	21	11	in	in	ADP
cana-4510	21	12	images	image	NOUN
cana-4510	21	13	of	of	ADP
cana-4510	21	14	insect	insect	NOUN
cana-4510	21	15	species	specie	NOUN
cana-4510	21	16	,	,	PUNCT
cana-4510	21	17	particularly	particularly	ADV
cana-4510	21	18	when	when	SCONJ
cana-4510	21	19	these	these	DET
cana-4510	21	20	species	specie	NOUN
cana-4510	21	21	have	have	VERB
cana-4510	21	22	morphological	morphological	ADJ
cana-4510	21	23	similarities	similarity	NOUN
cana-4510	21	24	.	.	PUNCT
cana-4510	22	1	deep	deep	ADJ
cana-4510	22	2	learning	learning	NOUN
cana-4510	22	3	(	(	PUNCT
cana-4510	22	4	dl	dl	NOUN
cana-4510	22	5	)	)	PUNCT
cana-4510	22	6	represents	represent	VERB
cana-4510	22	7	advanced	advanced	ADJ
cana-4510	22	8	approach	approach	NOUN
cana-4510	22	9	that	that	PRON
cana-4510	22	10	leverages	leverage	VERB
cana-4510	22	11	neural	neural	ADJ
cana-4510	22	12	networks	network	NOUN
cana-4510	22	13	(	(	PUNCT
cana-4510	22	14	nn	nn	NOUN
cana-4510	22	15	)	)	PUNCT
cana-4510	22	16	designed	design	VERB
cana-4510	22	17	to	to	PART
cana-4510	22	18	function	function	VERB
cana-4510	22	19	in	in	ADP
cana-4510	22	20	a	a	DET
cana-4510	22	21	manner	manner	NOUN
cana-4510	22	22	comparable	comparable	ADJ
cana-4510	22	23	to	to	ADP
cana-4510	22	24	the	the	DET
cana-4510	22	25	functioning	functioning	NOUN
cana-4510	22	26	of	of	ADP
cana-4510	22	27	the	the	DET
cana-4510	22	28	human	human	ADJ
cana-4510	22	29	brain	brain	NOUN
cana-4510	23	1	[	[	X
cana-4510	23	2	4	4	NUM
cana-4510	23	3	]	]	PUNCT
cana-4510	23	4	.	.	PUNCT
cana-4510	24	1	recent	recent	ADJ
cana-4510	24	2	advancements	advancement	NOUN
cana-4510	24	3	in	in	ADP
cana-4510	24	4	dl	dl	PROPN
cana-4510	24	5	include	include	VERB
cana-4510	24	6	the	the	DET
cana-4510	24	7	use	use	NOUN
cana-4510	24	8	of	of	ADP
cana-4510	24	9	deep	deep	ADJ
cana-4510	24	10	convolutional	convolutional	ADJ
cana-4510	24	11	neural	neural	ADJ
cana-4510	24	12	networks	network	NOUN
cana-4510	24	13	(	(	PUNCT
cana-4510	24	14	dcnns	dcnns	ADJ
cana-4510	24	15	)	)	PUNCT
cana-4510	24	16	with	with	ADP
cana-4510	24	17	fine	fine	ADV
cana-4510	24	18	-	-	PUNCT
cana-4510	24	19	tuned	tune	VERB
cana-4510	24	20	pre	pre	ADJ
cana-4510	24	21	-	-	ADJ
cana-4510	24	22	trained	train	VERB
cana-4510	24	23	models	model	NOUN
cana-4510	24	24	and	and	CCONJ
cana-4510	24	25	the	the	DET
cana-4510	24	26	adoption	adoption	NOUN
cana-4510	24	27	of	of	ADP
cana-4510	24	28	hybrid	hybrid	ADJ
cana-4510	24	29	approaches	approach	NOUN
cana-4510	24	30	to	to	PART
cana-4510	24	31	design	design	VERB
cana-4510	24	32	model	model	NOUN
cana-4510	24	33	architectures	architecture	NOUN
cana-4510	24	34	.	.	PUNCT
cana-4510	25	1	these	these	DET
cana-4510	25	2	advancements	advancement	NOUN
cana-4510	25	3	have	have	AUX
cana-4510	25	4	jointly	jointly	ADV
cana-4510	25	5	enhanced	enhance	VERB
cana-4510	25	6	the	the	DET
cana-4510	25	7	precision	precision	NOUN
cana-4510	25	8	and	and	CCONJ
cana-4510	25	9	effectiveness	effectiveness	NOUN
cana-4510	25	10	of	of	ADP
cana-4510	25	11	insect	insect	NOUN
cana-4510	25	12	species	species	NOUN
cana-4510	25	13	identification	identification	NOUN
cana-4510	25	14	and	and	CCONJ
cana-4510	25	15	classification	classification	NOUN
cana-4510	25	16	processes	process	NOUN
cana-4510	25	17	[	[	X
cana-4510	25	18	5	5	NUM
cana-4510	25	19	]	]	PUNCT
cana-4510	25	20	.	.	PUNCT
cana-4510	26	1	in	in	ADP
cana-4510	26	2	dcnn	dcnn	PROPN
cana-4510	26	3	,	,	PUNCT
cana-4510	26	4	fine	fine	ADV
cana-4510	26	5	-	-	PUNCT
cana-4510	26	6	tuning	tuning	NOUN
cana-4510	26	7	is	be	AUX
cana-4510	26	8	a	a	DET
cana-4510	26	9	widely	widely	ADV
cana-4510	26	10	employed	employ	VERB
cana-4510	26	11	and	and	CCONJ
cana-4510	26	12	efficient	efficient	ADJ
cana-4510	26	13	approach	approach	NOUN
cana-4510	26	14	that	that	PRON
cana-4510	26	15	used	use	VERB
cana-4510	26	16	a	a	DET
cana-4510	26	17	pre	pre	ADJ
cana-4510	26	18	-	-	ADJ
cana-4510	26	19	trained	train	VERB
cana-4510	26	20	nn	nn	PROPN
cana-4510	26	21	model	model	NOUN
cana-4510	26	22	,	,	PUNCT
cana-4510	26	23	often	often	ADV
cana-4510	26	24	trained	train	VERB
cana-4510	26	25	on	on	ADP
cana-4510	26	26	large	large	ADJ
cana-4510	26	27	datasets	dataset	NOUN
cana-4510	26	28	like	like	ADP
cana-4510	26	29	imagenet	imagenet	NOUN
cana-4510	26	30	[	[	X
cana-4510	26	31	6	6	NUM
cana-4510	26	32	]	]	PUNCT
cana-4510	26	33	.	.	PUNCT
cana-4510	27	1	choosing	choose	VERB
cana-4510	27	2	the	the	DET
cana-4510	27	3	most	most	ADV
cana-4510	27	4	effective	effective	ADJ
cana-4510	27	5	classification	classification	NOUN
cana-4510	27	6	technique	technique	NOUN
cana-4510	27	7	is	be	AUX
cana-4510	27	8	vital	vital	ADJ
cana-4510	27	9	for	for	ADP
cana-4510	27	10	accurately	accurately	ADV
cana-4510	27	11	distinguishing	distinguish	VERB
cana-4510	27	12	insect	insect	NOUN
cana-4510	27	13	species	specie	NOUN
cana-4510	27	14	through	through	ADP
cana-4510	27	15	general	general	ADJ
cana-4510	27	16	image	image	NOUN
cana-4510	27	17	classification	classification	NOUN
cana-4510	27	18	.	.	PUNCT
cana-4510	28	1	key	key	ADJ
cana-4510	28	2	considerations	consideration	NOUN
cana-4510	28	3	include	include	VERB
cana-4510	28	4	the	the	DET
cana-4510	28	5	specific	specific	ADJ
cana-4510	28	6	insect	insect	NOUN
cana-4510	28	7	groups	group	NOUN
cana-4510	28	8	under	under	ADP
cana-4510	28	9	study	study	NOUN
cana-4510	28	10	,	,	PUNCT
cana-4510	28	11	the	the	DET
cana-4510	28	12	experimental	experimental	ADJ
cana-4510	28	13	setup	setup	NOUN
cana-4510	28	14	(	(	PUNCT
cana-4510	28	15	e.g.	e.g.	ADV
cana-4510	28	16	,	,	PUNCT
cana-4510	28	17	digital	digital	ADJ
cana-4510	28	18	or	or	CCONJ
cana-4510	28	19	manual	manual	NOUN
cana-4510	28	20	,	,	PUNCT
cana-4510	28	21	in	in	ADP
cana-4510	28	22	-	-	PUNCT
cana-4510	28	23	field	field	NOUN
cana-4510	28	24	or	or	CCONJ
cana-4510	28	25	off	off	ADP
cana-4510	28	26	-	-	PUNCT
cana-4510	28	27	field	field	NOUN
cana-4510	28	28	)	)	PUNCT
cana-4510	28	29	,	,	PUNCT
cana-4510	28	30	and	and	CCONJ
cana-4510	28	31	the	the	DET
cana-4510	28	32	evaluation	evaluation	NOUN
cana-4510	28	33	of	of	ADP
cana-4510	28	34	imagebased	imagebase	VERB
cana-4510	28	35	insect	insect	NOUN
cana-4510	28	36	detection	detection	NOUN
cana-4510	28	37	and	and	CCONJ
cana-4510	28	38	classification	classification	NOUN
cana-4510	28	39	methods	method	NOUN
cana-4510	28	40	.	.	PUNCT
cana-4510	29	1	assessing	assess	VERB
cana-4510	29	2	these	these	DET
cana-4510	29	3	methods	method	NOUN
cana-4510	29	4	requires	require	VERB
cana-4510	29	5	analysing	analyse	VERB
cana-4510	29	6	performance	performance	NOUN
cana-4510	29	7	factors	factor	NOUN
cana-4510	29	8	such	such	ADJ
cana-4510	29	9	as	as	ADP
cana-4510	29	10	time	time	NOUN
cana-4510	29	11	complexity	complexity	NOUN
cana-4510	29	12	,	,	PUNCT
cana-4510	29	13	memory	memory	NOUN
cana-4510	29	14	usage	usage	NOUN
cana-4510	29	15	,	,	PUNCT
cana-4510	29	16	precision	precision	NOUN
cana-4510	29	17	,	,	PUNCT
cana-4510	29	18	and	and	CCONJ
cana-4510	29	19	recall	recall	NOUN
cana-4510	29	20	,	,	PUNCT
cana-4510	29	21	along	along	ADP
cana-4510	29	22	with	with	ADP
cana-4510	29	23	the	the	DET
cana-4510	29	24	image	image	NOUN
cana-4510	29	25	acquisition	acquisition	NOUN
cana-4510	29	26	techniques	technique	NOUN
cana-4510	29	27	employed	employ	VERB
cana-4510	29	28	.	.	PUNCT
cana-4510	30	1	this	this	DET
cana-4510	30	2	comprehensive	comprehensive	ADJ
cana-4510	30	3	evaluation	evaluation	NOUN
cana-4510	30	4	ensures	ensure	VERB
cana-4510	30	5	accurate	accurate	ADJ
cana-4510	30	6	and	and	CCONJ
cana-4510	30	7	reliable	reliable	ADJ
cana-4510	30	8	insect	insect	NOUN
cana-4510	30	9	identification	identification	NOUN
cana-4510	30	10	.	.	PUNCT
cana-4510	31	1	from	from	ADP
cana-4510	31	2	this	this	DET
cana-4510	31	3	perspective	perspective	NOUN
cana-4510	31	4	,	,	PUNCT
cana-4510	31	5	multi	multi	ADJ
cana-4510	31	6	-	-	ADJ
cana-4510	31	7	class	class	ADJ
cana-4510	31	8	classification	classification	NOUN
cana-4510	31	9	of	of	ADP
cana-4510	31	10	insects	insect	NOUN
cana-4510	31	11	using	use	VERB
cana-4510	31	12	cnn	cnn	PROPN
cana-4510	31	13	frameworks	framework	NOUN
cana-4510	31	14	like	like	ADP
cana-4510	31	15	vgg19	vgg19	PROPN
cana-4510	32	1	[	[	X
cana-4510	32	2	7	7	NUM
cana-4510	32	3	]	]	PUNCT
cana-4510	32	4	has	have	AUX
cana-4510	32	5	been	be	AUX
cana-4510	32	6	explored	explore	VERB
cana-4510	32	7	in	in	ADP
cana-4510	32	8	previous	previous	ADJ
cana-4510	32	9	research	research	NOUN
cana-4510	32	10	.	.	PUNCT
cana-4510	33	1	the	the	DET
cana-4510	33	2	taxonomic	taxonomic	ADJ
cana-4510	33	3	classification	classification	NOUN
cana-4510	33	4	focused	focus	VERB
cana-4510	33	5	on	on	ADP
cana-4510	33	6	five	five	NUM
cana-4510	33	7	insect	insect	NOUN
cana-4510	33	8	categories	category	NOUN
cana-4510	33	9	within	within	ADP
cana-4510	33	10	the	the	DET
cana-4510	33	11	class	class	NOUN
cana-4510	33	12	insecta	insecta	NOUN
cana-4510	33	13	:	:	PUNCT
cana-4510	33	14	butterfly	butterfly	NOUN
cana-4510	33	15	,	,	PUNCT
cana-4510	33	16	dragonfly	dragonfly	NOUN
cana-4510	33	17	,	,	PUNCT
cana-4510	33	18	grasshopper	grasshopper	NOUN
cana-4510	33	19	,	,	PUNCT
cana-4510	33	20	ladybird	ladybird	NOUN
cana-4510	33	21	,	,	PUNCT
cana-4510	33	22	and	and	CCONJ
cana-4510	33	23	mosquito	mosquito	NOUN
cana-4510	33	24	.	.	PUNCT
cana-4510	34	1	data	datum	NOUN
cana-4510	34	2	for	for	ADP
cana-4510	34	3	these	these	DET
cana-4510	34	4	insects	insect	NOUN
cana-4510	34	5	was	be	AUX
cana-4510	34	6	collected	collect	VERB
cana-4510	34	7	and	and	CCONJ
cana-4510	34	8	utilized	utilize	VERB
cana-4510	34	9	for	for	ADP
cana-4510	34	10	training	training	NOUN
cana-4510	34	11	,	,	PUNCT
cana-4510	34	12	testing	testing	NOUN
cana-4510	34	13	,	,	PUNCT
cana-4510	34	14	and	and	CCONJ
cana-4510	34	15	validating	validate	VERB
cana-4510	34	16	the	the	DET
cana-4510	34	17	cnn	cnn	PROPN
cana-4510	34	18	model	model	NOUN
cana-4510	34	19	.	.	PUNCT
cana-4510	35	1	in	in	ADP
cana-4510	35	2	addition	addition	NOUN
cana-4510	35	3	,	,	PUNCT
cana-4510	35	4	various	various	ADJ
cana-4510	35	5	data	datum	NOUN
cana-4510	35	6	augmentation	augmentation	NOUN
cana-4510	35	7	techniques	technique	NOUN
cana-4510	35	8	were	be	AUX
cana-4510	35	9	used	use	VERB
cana-4510	35	10	to	to	PART
cana-4510	35	11	ensure	ensure	VERB
cana-4510	35	12	a	a	DET
cana-4510	35	13	balanced	balanced	ADJ
cana-4510	35	14	dataset	dataset	NOUN
cana-4510	35	15	,	,	PUNCT
cana-4510	35	16	reducing	reduce	VERB
cana-4510	35	17	overfitting	overfitting	NOUN
cana-4510	35	18	in	in	ADP
cana-4510	35	19	the	the	DET
cana-4510	35	20	cnn	cnn	PROPN
cana-4510	35	21	model	model	NOUN
cana-4510	35	22	.	.	PUNCT
cana-4510	36	1	the	the	DET
cana-4510	36	2	primary	primary	ADJ
cana-4510	36	3	objective	objective	NOUN
cana-4510	36	4	of	of	ADP
cana-4510	36	5	this	this	DET
cana-4510	36	6	research	research	NOUN
cana-4510	36	7	work	work	NOUN
cana-4510	36	8	is	be	AUX
cana-4510	36	9	to	to	PART
cana-4510	36	10	employ	employ	VERB
cana-4510	36	11	hybrid	hybrid	NOUN
cana-4510	36	12	models	model	NOUN
cana-4510	36	13	based	base	VERB
cana-4510	36	14	on	on	ADP
cana-4510	36	15	dcnn	dcnn	PROPN
cana-4510	36	16	for	for	ADP
cana-4510	36	17	classifying	classify	VERB
cana-4510	36	18	insect	insect	NOUN
cana-4510	36	19	species	specie	NOUN
cana-4510	36	20	within	within	ADP
cana-4510	36	21	six	six	NUM
cana-4510	36	22	different	different	ADJ
cana-4510	36	23	insect	insect	NOUN
cana-4510	36	24	orders	order	NOUN
cana-4510	36	25	,	,	PUNCT
cana-4510	36	26	focusing	focus	VERB
cana-4510	36	27	particularly	particularly	ADV
cana-4510	36	28	on	on	ADP
cana-4510	36	29	the	the	DET
cana-4510	36	30	“	"	PUNCT
cana-4510	36	31	andrena	andrena	NOUN
cana-4510	36	32	”	"	PUNCT
cana-4510	36	33	species	specie	NOUN
cana-4510	36	34	within	within	ADP
cana-4510	36	35	the	the	DET
cana-4510	36	36	“	"	PUNCT
cana-4510	36	37	andrenidae	andrenidae	NOUN
cana-4510	36	38	”	"	PUNCT
cana-4510	36	39	family	family	NOUN
cana-4510	36	40	,	,	PUNCT
cana-4510	36	41	which	which	PRON
cana-4510	36	42	is	be	AUX
cana-4510	36	43	derived	derive	VERB
cana-4510	36	44	from	from	ADP
cana-4510	36	45	the	the	DET
cana-4510	36	46	“	"	PUNCT
cana-4510	36	47	hymenoptera	hymenoptera	NOUN
cana-4510	36	48	”	"	PUNCT
cana-4510	36	49	order	order	NOUN
cana-4510	36	50	in	in	ADP
cana-4510	36	51	systematic	systematic	ADJ
cana-4510	36	52	taxonomic	taxonomic	ADJ
cana-4510	36	53	hierarchy	hierarchy	NOUN
cana-4510	36	54	.	.	PUNCT
cana-4510	37	1	the	the	DET
cana-4510	37	2	proposed	propose	VERB
cana-4510	37	3	model	model	NOUN
cana-4510	37	4	“	"	PUNCT
cana-4510	37	5	dhdcnet	dhdcnet	ADJ
cana-4510	37	6	model	model	NOUN
cana-4510	37	7	”	"	PUNCT
cana-4510	37	8	,	,	PUNCT
cana-4510	37	9	was	be	AUX
cana-4510	37	10	selected	select	VERB
cana-4510	37	11	for	for	ADP
cana-4510	37	12	its	its	PRON
cana-4510	37	13	superior	superior	ADJ
cana-4510	37	14	performance	performance	NOUN
cana-4510	37	15	and	and	CCONJ
cana-4510	37	16	high	high	ADJ
cana-4510	37	17	accuracy	accuracy	NOUN
cana-4510	37	18	in	in	ADP
cana-4510	37	19	achieving	achieve	VERB
cana-4510	37	20	the	the	DET
cana-4510	37	21	expected	expect	VERB
cana-4510	37	22	results	result	NOUN
cana-4510	37	23	across	across	ADP
cana-4510	37	24	all	all	DET
cana-4510	37	25	taxonomic	taxonomic	ADJ
cana-4510	37	26	levels	level	NOUN
cana-4510	37	27	.	.	PUNCT
cana-4510	38	1	this	this	DET
cana-4510	38	2	paper	paper	NOUN
cana-4510	38	3	used	use	VERB
cana-4510	38	4	three	three	NUM
cana-4510	38	5	distinct	distinct	ADJ
cana-4510	38	6	datasets	dataset	NOUN
cana-4510	38	7	—	—	PUNCT
cana-4510	38	8	order	order	NOUN
cana-4510	38	9	level	level	NOUN
cana-4510	38	10	,	,	PUNCT
cana-4510	38	11	family	family	NOUN
cana-4510	38	12	level	level	NOUN
cana-4510	38	13	,	,	PUNCT
cana-4510	38	14	and	and	CCONJ
cana-4510	38	15	species	species	NOUN
cana-4510	38	16	level	level	NOUN
cana-4510	38	17	—	—	PUNCT
cana-4510	38	18	to	to	PART
cana-4510	38	19	classify	classify	VERB
cana-4510	38	20	the	the	DET
cana-4510	38	21	images	image	NOUN
cana-4510	38	22	of	of	ADP
cana-4510	38	23	insects	insect	NOUN
cana-4510	38	24	at	at	ADP
cana-4510	38	25	the	the	DET
cana-4510	38	26	taxonomic	taxonomic	ADJ
cana-4510	38	27	hierarchy	hierarchy	NOUN
cana-4510	38	28	levels	level	NOUN
cana-4510	38	29	.	.	PUNCT
cana-4510	39	1	the	the	DET
cana-4510	39	2	images	image	NOUN
cana-4510	39	3	were	be	AUX
cana-4510	39	4	trained	train	VERB
cana-4510	39	5	utilizing	utilize	VERB
cana-4510	39	6	fine	fine	ADV
cana-4510	39	7	-	-	PUNCT
cana-4510	39	8	tuned	tune	VERB
cana-4510	39	9	pre	pre	ADJ
cana-4510	39	10	-	-	ADJ
cana-4510	39	11	trained	train	VERB
cana-4510	39	12	dcnn	dcnn	ADJ
cana-4510	39	13	models	model	NOUN
cana-4510	39	14	including	include	VERB
cana-4510	39	15	xception	xception	PROPN
cana-4510	39	16	,	,	PUNCT
cana-4510	39	17	resnet	resnet	NOUN
cana-4510	39	18	152	152	NUM
cana-4510	39	19	,	,	PUNCT
cana-4510	39	20	vgg	vgg	NOUN
cana-4510	39	21	19	19	NUM
cana-4510	39	22	,	,	PUNCT
cana-4510	39	23	inception	inception	NOUN
cana-4510	39	24	v3	v3	PROPN
cana-4510	39	25	,	,	PUNCT
cana-4510	39	26	and	and	CCONJ
cana-4510	39	27	mobile	mobile	ADJ
cana-4510	39	28	net	net	ADJ
cana-4510	39	29	v2	v2	PROPN
cana-4510	39	30	.	.	PUNCT
cana-4510	40	1	the	the	DET
cana-4510	40	2	rest	rest	NOUN
cana-4510	40	3	of	of	ADP
cana-4510	40	4	this	this	DET
cana-4510	40	5	research	research	NOUN
cana-4510	40	6	work	work	NOUN
cana-4510	40	7	is	be	AUX
cana-4510	40	8	structured	structure	VERB
cana-4510	40	9	as	as	SCONJ
cana-4510	40	10	follows	follow	VERB
cana-4510	40	11	:	:	PUNCT
cana-4510	40	12	section	section	NOUN
cana-4510	40	13	2	2	NUM
cana-4510	40	14	presents	present	VERB
cana-4510	40	15	a	a	DET
cana-4510	40	16	review	review	NOUN
cana-4510	40	17	of	of	ADP
cana-4510	40	18	relevant	relevant	ADJ
cana-4510	40	19	researches	research	NOUN
cana-4510	40	20	on	on	ADP
cana-4510	40	21	taxonomic	taxonomic	ADJ
cana-4510	40	22	-	-	PUNCT
cana-4510	40	23	level	level	NOUN
cana-4510	40	24	insect	insect	NOUN
cana-4510	40	25	image	image	NOUN
cana-4510	40	26	classification	classification	NOUN
cana-4510	40	27	.	.	PUNCT
cana-4510	41	1	section	section	NOUN
cana-4510	41	2	3	3	NUM
cana-4510	41	3	describes	describe	VERB
cana-4510	41	4	the	the	DET
cana-4510	41	5	materials	material	NOUN
cana-4510	41	6	and	and	CCONJ
cana-4510	41	7	methods	method	NOUN
cana-4510	41	8	,	,	PUNCT
cana-4510	41	9	including	include	VERB
cana-4510	41	10	dcnn	dcnn	PROPN
cana-4510	41	11	and	and	CCONJ
cana-4510	41	12	pretrained	pretraine	VERB
cana-4510	41	13	models	model	NOUN
cana-4510	41	14	,	,	PUNCT
cana-4510	41	15	the	the	DET
cana-4510	41	16	methodology	methodology	NOUN
cana-4510	41	17	of	of	ADP
cana-4510	41	18	the	the	DET
cana-4510	41	19	proposed	propose	VERB
cana-4510	41	20	approach	approach	NOUN
cana-4510	41	21	,	,	PUNCT
cana-4510	41	22	and	and	CCONJ
cana-4510	41	23	a	a	DET
cana-4510	41	24	brief	brief	ADJ
cana-4510	41	25	explanation	explanation	NOUN
cana-4510	41	26	of	of	ADP
cana-4510	41	27	data	datum	NOUN
cana-4510	41	28	collection	collection	NOUN
cana-4510	41	29	and	and	CCONJ
cana-4510	41	30	preprocessing	preprocesse	VERB
cana-4510	41	31	techniques	technique	NOUN
cana-4510	41	32	such	such	ADJ
cana-4510	41	33	as	as	ADP
cana-4510	41	34	augmentation	augmentation	NOUN
cana-4510	41	35	.	.	PUNCT
cana-4510	42	1	the	the	DET
cana-4510	42	2	experimental	experimental	ADJ
cana-4510	42	3	results	result	NOUN
cana-4510	42	4	are	be	AUX
cana-4510	42	5	discussed	discuss	VERB
cana-4510	42	6	in	in	ADP
cana-4510	42	7	section	section	NOUN
cana-4510	42	8	4	4	NUM
cana-4510	42	9	,	,	PUNCT
cana-4510	42	10	and	and	CCONJ
cana-4510	42	11	section	section	NOUN
cana-4510	42	12	5	5	NUM
cana-4510	42	13	concludes	conclude	VERB
cana-4510	42	14	the	the	DET
cana-4510	42	15	paper	paper	NOUN
cana-4510	42	16	with	with	ADP
cana-4510	42	17	a	a	DET
cana-4510	42	18	review	review	NOUN
cana-4510	42	19	of	of	ADP
cana-4510	42	20	the	the	DET
cana-4510	42	21	study	study	NOUN
cana-4510	42	22	and	and	CCONJ
cana-4510	42	23	recommendations	recommendation	NOUN
cana-4510	42	24	for	for	ADP
cana-4510	42	25	future	future	ADJ
cana-4510	42	26	research	research	NOUN
cana-4510	42	27	directions	direction	NOUN
cana-4510	42	28	.	.	PUNCT
cana-4510	43	1	2	2	X
cana-4510	43	2	.	.	NUM
cana-4510	43	3	related	relate	VERB
cana-4510	43	4	works	work	NOUN
cana-4510	43	5	in	in	ADP
cana-4510	43	6	recent	recent	ADJ
cana-4510	43	7	times	time	NOUN
cana-4510	43	8	,	,	PUNCT
cana-4510	43	9	numerous	numerous	ADJ
cana-4510	43	10	studies	study	NOUN
cana-4510	43	11	have	have	AUX
cana-4510	43	12	leveraged	leverage	VERB
cana-4510	43	13	dl	dl	PROPN
cana-4510	43	14	particularly	particularly	ADV
cana-4510	43	15	cnn	cnn	PROPN
cana-4510	43	16	methods	method	NOUN
cana-4510	43	17	for	for	ADP
cana-4510	43	18	many	many	ADJ
cana-4510	43	19	species	specie	NOUN
cana-4510	43	20	image	image	NOUN
cana-4510	43	21	classification	classification	NOUN
cana-4510	43	22	and	and	CCONJ
cana-4510	43	23	identification	identification	NOUN
cana-4510	43	24	like	like	ADP
cana-4510	43	25	plants	plant	NOUN
cana-4510	43	26	,	,	PUNCT
cana-4510	43	27	fish	fish	NOUN
cana-4510	43	28	species	specie	NOUN
cana-4510	43	29	,	,	PUNCT
cana-4510	43	30	birds	bird	NOUN
cana-4510	43	31	and	and	CCONJ
cana-4510	43	32	insects	insect	NOUN
cana-4510	43	33	,	,	PUNCT
cana-4510	43	34	yielding	yield	VERB
cana-4510	43	35	promising	promising	ADJ
cana-4510	43	36	outcomes	outcome	NOUN
cana-4510	43	37	.	.	PUNCT
cana-4510	44	1	the	the	DET
cana-4510	44	2	following	follow	VERB
cana-4510	44	3	presents	present	VERB
cana-4510	44	4	a	a	DET
cana-4510	44	5	concise	concise	ADJ
cana-4510	44	6	summary	summary	NOUN
cana-4510	44	7	of	of	ADP
cana-4510	44	8	notable	notable	ADJ
cana-4510	44	9	accomplishments	accomplishment	NOUN
cana-4510	44	10	within	within	ADP
cana-4510	44	11	this	this	DET
cana-4510	44	12	research	research	NOUN
cana-4510	44	13	field	field	NOUN
cana-4510	44	14	.	.	PUNCT
cana-4510	45	1	in	in	ADP
cana-4510	45	2	[	[	X
cana-4510	45	3	8	8	NUM
cana-4510	45	4	]	]	X
cana-4510	45	5	ong	ong	PROPN
cana-4510	45	6	,	,	PUNCT
cana-4510	45	7	song	song	NOUN
cana-4510	45	8	-	-	PUNCT
cana-4510	45	9	quan	quan	PROPN
cana-4510	45	10	,	,	PUNCT
cana-4510	45	11	and	and	CCONJ
cana-4510	45	12	suhaila	suhaila	PROPN
cana-4510	45	13	ab	ab	PROPN
cana-4510	45	14	.	.	PROPN
cana-4510	45	15	hamid	hamid	PROPN
cana-4510	45	16	proposed	propose	VERB
cana-4510	45	17	a	a	DET
cana-4510	45	18	classification	classification	NOUN
cana-4510	45	19	task	task	NOUN
cana-4510	45	20	based	base	VERB
cana-4510	45	21	on	on	ADP
cana-4510	45	22	the	the	DET
cana-4510	45	23	taxonomic	taxonomic	ADJ
cana-4510	45	24	ranks	rank	NOUN
cana-4510	45	25	of	of	ADP
cana-4510	45	26	insects	insect	NOUN
cana-4510	45	27	,	,	PUNCT
cana-4510	45	28	specifically	specifically	ADV
cana-4510	45	29	orders	order	NOUN
cana-4510	45	30	,	,	PUNCT
cana-4510	45	31	families	family	NOUN
cana-4510	45	32	,	,	PUNCT
cana-4510	45	33	and	and	CCONJ
cana-4510	45	34	genera	genera	NOUN
cana-4510	45	35	.	.	PUNCT
cana-4510	46	1	they	they	PRON
cana-4510	46	2	conducted	conduct	VERB
cana-4510	46	3	a	a	DET
cana-4510	46	4	comparison	comparison	NOUN
cana-4510	46	5	of	of	ADP
cana-4510	46	6	communications	communication	NOUN
cana-4510	46	7	on	on	ADP
cana-4510	46	8	applied	apply	VERB
cana-4510	46	9	nonlinear	nonlinear	ADJ
cana-4510	46	10	analysis	analysis	NOUN
cana-4510	46	11	issn	issn	NOUN
cana-4510	46	12	:	:	PUNCT
cana-4510	46	13	1074	1074	NUM
cana-4510	46	14	-	-	PUNCT
cana-4510	46	15	133x	133x	NUM
cana-4510	46	16	vol	vol	NOUN
cana-4510	46	17	32	32	NUM
cana-4510	46	18	no	no	NOUN
cana-4510	46	19	.	.	PUNCT
cana-4510	47	1	9s	9s	NUM
cana-4510	47	2	(	(	PUNCT
cana-4510	47	3	2025	2025	NUM
cana-4510	47	4	)	)	PUNCT
cana-4510	47	5	2246	2246	NUM
cana-4510	47	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	48	1	the	the	DET
cana-4510	48	2	generalization	generalization	NOUN
cana-4510	48	3	capabilities	capability	NOUN
cana-4510	48	4	of	of	ADP
cana-4510	48	5	four	four	NUM
cana-4510	48	6	advanced	advanced	ADJ
cana-4510	48	7	dcnn	dcnn	ADJ
cana-4510	48	8	architectures	architecture	NOUN
cana-4510	48	9	.	.	PUNCT
cana-4510	49	1	for	for	ADP
cana-4510	49	2	the	the	DET
cana-4510	49	3	order	order	NOUN
cana-4510	49	4	level	level	NOUN
cana-4510	49	5	,	,	PUNCT
cana-4510	49	6	they	they	PRON
cana-4510	49	7	utilized	utilize	VERB
cana-4510	49	8	three	three	NUM
cana-4510	49	9	specimens	specimen	NOUN
cana-4510	49	10	,	,	PUNCT
cana-4510	49	11	consisting	consist	VERB
cana-4510	49	12	of	of	ADP
cana-4510	49	13	7	7	NUM
cana-4510	49	14	classes	class	NOUN
cana-4510	49	15	,	,	PUNCT
cana-4510	49	16	family	family	NOUN
cana-4510	49	17	level	level	NOUN
cana-4510	49	18	,	,	PUNCT
cana-4510	49	19	5	5	NUM
cana-4510	49	20	specimens	specimen	NOUN
cana-4510	49	21	were	be	AUX
cana-4510	49	22	employed	employ	VERB
cana-4510	49	23	,	,	PUNCT
cana-4510	49	24	representing	represent	VERB
cana-4510	49	25	25	25	NUM
cana-4510	49	26	classes	class	NOUN
cana-4510	49	27	.	.	PUNCT
cana-4510	50	1	additionally	additionally	ADV
cana-4510	50	2	,	,	PUNCT
cana-4510	50	3	at	at	ADP
cana-4510	50	4	the	the	DET
cana-4510	50	5	genus	genus	ADJ
cana-4510	50	6	level	level	NOUN
cana-4510	50	7	,	,	PUNCT
cana-4510	50	8	5	5	NUM
cana-4510	50	9	specimens	specimen	NOUN
cana-4510	50	10	were	be	AUX
cana-4510	50	11	used	use	VERB
cana-4510	50	12	,	,	PUNCT
cana-4510	50	13	encompassing	encompass	VERB
cana-4510	50	14	25	25	NUM
cana-4510	50	15	classes	class	NOUN
cana-4510	50	16	.	.	PUNCT
cana-4510	51	1	they	they	PRON
cana-4510	51	2	used	use	VERB
cana-4510	51	3	mobile	mobile	ADJ
cana-4510	51	4	net	net	ADJ
cana-4510	51	5	v2	v2	PROPN
cana-4510	51	6	,	,	PUNCT
cana-4510	51	7	xception	xception	PROPN
cana-4510	51	8	,	,	PUNCT
cana-4510	51	9	vgg	vgg	NOUN
cana-4510	51	10	19	19	NUM
cana-4510	51	11	and	and	CCONJ
cana-4510	51	12	inception	inception	PROPN
cana-4510	51	13	v3	v3	PROPN
cana-4510	51	14	architectures	architecture	NOUN
cana-4510	51	15	for	for	ADP
cana-4510	51	16	classification	classification	NOUN
cana-4510	51	17	task	task	NOUN
cana-4510	51	18	.	.	PUNCT
cana-4510	52	1	but	but	CCONJ
cana-4510	52	2	low	low	ADJ
cana-4510	52	3	performance	performance	NOUN
cana-4510	52	4	in	in	ADP
cana-4510	52	5	classifying	classify	VERB
cana-4510	52	6	genus	genus	ADJ
cana-4510	52	7	level	level	NOUN
cana-4510	52	8	.	.	PUNCT
cana-4510	53	1	in	in	ADP
cana-4510	53	2	[	[	X
cana-4510	53	3	9	9	NUM
cana-4510	53	4	]	]	PUNCT
cana-4510	53	5	,	,	PUNCT
cana-4510	53	6	yang	yang	PROPN
cana-4510	53	7	,	,	PUNCT
cana-4510	53	8	fan	fan	PROPN
cana-4510	53	9	,	,	PUNCT
cana-4510	53	10	and	and	CCONJ
cana-4510	53	11	their	their	PRON
cana-4510	53	12	team	team	NOUN
cana-4510	53	13	developed	develop	VERB
cana-4510	53	14	a	a	DET
cana-4510	53	15	fine	fine	ADV
cana-4510	53	16	-	-	PUNCT
cana-4510	53	17	grained	grain	VERB
cana-4510	53	18	visual	visual	ADJ
cana-4510	53	19	classification	classification	NOUN
cana-4510	53	20	(	(	PUNCT
cana-4510	53	21	fgvc	fgvc	NOUN
cana-4510	53	22	)	)	PUNCT
cana-4510	53	23	approach	approach	NOUN
cana-4510	53	24	using	use	VERB
cana-4510	53	25	deep	deep	ADJ
cana-4510	53	26	learning	learning	NOUN
cana-4510	53	27	to	to	PART
cana-4510	53	28	investigate	investigate	VERB
cana-4510	53	29	insect	insect	NOUN
cana-4510	53	30	recognition	recognition	NOUN
cana-4510	53	31	and	and	CCONJ
cana-4510	53	32	classification	classification	NOUN
cana-4510	53	33	.	.	PUNCT
cana-4510	54	1	they	they	PRON
cana-4510	54	2	utilized	utilize	VERB
cana-4510	54	3	inception	inception	NOUN
cana-4510	54	4	v3	v3	PROPN
cana-4510	54	5	,	,	PUNCT
cana-4510	54	6	vgg16_bn	vgg16_bn	NOUN
cana-4510	54	7	,	,	PUNCT
cana-4510	54	8	and	and	CCONJ
cana-4510	54	9	resnet50	resnet50	NOUN
cana-4510	54	10	models	model	NOUN
cana-4510	54	11	in	in	ADP
cana-4510	54	12	their	their	PRON
cana-4510	54	13	research	research	NOUN
cana-4510	54	14	.	.	PUNCT
cana-4510	55	1	inception	inception	PROPN
cana-4510	55	2	v3	v3	PROPN
cana-4510	55	3	achieved	achieve	VERB
cana-4510	55	4	an	an	DET
cana-4510	55	5	accuracy	accuracy	NOUN
cana-4510	55	6	of	of	ADP
cana-4510	55	7	98.69	98.69	NUM
cana-4510	55	8	%	%	NOUN
cana-4510	55	9	,	,	PUNCT
cana-4510	55	10	while	while	SCONJ
cana-4510	55	11	vgg16_bn	vgg16_bn	NOUN
cana-4510	55	12	and	and	CCONJ
cana-4510	55	13	resnet50	resnet50	NOUN
cana-4510	55	14	achieved	achieve	VERB
cana-4510	55	15	accuracies	accuracy	NOUN
cana-4510	55	16	of	of	ADP
cana-4510	55	17	97.80	97.80	NUM
cana-4510	55	18	%	%	NOUN
cana-4510	55	19	and	and	CCONJ
cana-4510	55	20	97.94	97.94	NUM
cana-4510	55	21	%	%	NOUN
cana-4510	55	22	,	,	PUNCT
cana-4510	55	23	respectively	respectively	ADV
cana-4510	55	24	.	.	PUNCT
cana-4510	56	1	to	to	PART
cana-4510	56	2	enhance	enhance	VERB
cana-4510	56	3	model	model	NOUN
cana-4510	56	4	accuracy	accuracy	NOUN
cana-4510	56	5	further	far	ADV
cana-4510	56	6	,	,	PUNCT
cana-4510	56	7	they	they	PRON
cana-4510	56	8	implemented	implement	VERB
cana-4510	56	9	label	label	NOUN
cana-4510	56	10	smoothing	smooth	VERB
cana-4510	56	11	technology	technology	NOUN
cana-4510	56	12	to	to	PART
cana-4510	56	13	mitigate	mitigate	VERB
cana-4510	56	14	errors	error	NOUN
cana-4510	56	15	originating	originate	VERB
cana-4510	56	16	from	from	ADP
cana-4510	56	17	label	label	NOUN
cana-4510	56	18	inaccuracies	inaccuracy	NOUN
cana-4510	56	19	.	.	PUNCT
cana-4510	57	1	several	several	ADJ
cana-4510	57	2	cnn	cnn	PROPN
cana-4510	57	3	architectures	architecture	NOUN
cana-4510	57	4	,	,	PUNCT
cana-4510	57	5	including	include	VERB
cana-4510	57	6	resnet101	resnet101	PROPN
cana-4510	57	7	,	,	PUNCT
cana-4510	57	8	wide	wide	NOUN
cana-4510	57	9	-	-	PUNCT
cana-4510	57	10	resnet101	resnet101	NOUN
cana-4510	57	11	,	,	PUNCT
cana-4510	57	12	inceptionv3	inceptionv3	NOUN
cana-4510	57	13	,	,	PUNCT
cana-4510	57	14	and	and	CCONJ
cana-4510	57	15	mnasnet	mnasnet	NOUN
cana-4510	57	16	-	-	PUNCT
cana-4510	57	17	a1	a1	NOUN
cana-4510	57	18	[	[	X
cana-4510	57	19	10	10	NUM
cana-4510	57	20	]	]	PUNCT
cana-4510	57	21	,	,	PUNCT
cana-4510	57	22	were	be	AUX
cana-4510	57	23	tested	test	VERB
cana-4510	57	24	for	for	ADP
cana-4510	57	25	identifying	identify	VERB
cana-4510	57	26	bumble	bumble	ADJ
cana-4510	57	27	bee	bee	NOUN
cana-4510	57	28	species	specie	NOUN
cana-4510	57	29	from	from	ADP
cana-4510	57	30	images	image	NOUN
cana-4510	57	31	.	.	PUNCT
cana-4510	58	1	however	however	ADV
cana-4510	58	2	,	,	PUNCT
cana-4510	58	3	these	these	DET
cana-4510	58	4	models	model	NOUN
cana-4510	58	5	did	do	AUX
cana-4510	58	6	not	not	PART
cana-4510	58	7	achieve	achieve	VERB
cana-4510	58	8	high	high	ADJ
cana-4510	58	9	accuracy	accuracy	NOUN
cana-4510	58	10	and	and	CCONJ
cana-4510	58	11	recall	recall	NOUN
cana-4510	58	12	.	.	PUNCT
cana-4510	59	1	kaiming	kaime	VERB
cana-4510	59	2	he	he	PRON
cana-4510	60	1	[	[	X
cana-4510	60	2	11	11	NUM
cana-4510	60	3	]	]	PUNCT
cana-4510	60	4	proposed	propose	VERB
cana-4510	60	5	resnet	resnet	NOUN
cana-4510	60	6	152	152	NUM
cana-4510	60	7	architecture	architecture	NOUN
cana-4510	60	8	for	for	ADP
cana-4510	60	9	image	image	NOUN
cana-4510	60	10	recognition	recognition	NOUN
cana-4510	60	11	task	task	NOUN
cana-4510	60	12	.	.	PUNCT
cana-4510	61	1	but	but	CCONJ
cana-4510	61	2	the	the	DET
cana-4510	61	3	accuracy	accuracy	NOUN
cana-4510	61	4	was	be	AUX
cana-4510	61	5	not	not	PART
cana-4510	61	6	effective	effective	ADJ
cana-4510	61	7	.	.	PUNCT
cana-4510	62	1	in	in	ADP
cana-4510	62	2	order	order	NOUN
cana-4510	62	3	to	to	PART
cana-4510	62	4	resolve	resolve	VERB
cana-4510	62	5	the	the	DET
cana-4510	62	6	classification	classification	NOUN
cana-4510	62	7	problem	problem	NOUN
cana-4510	62	8	for	for	ADP
cana-4510	62	9	plant	plant	NOUN
cana-4510	62	10	species	specie	NOUN
cana-4510	62	11	described	describe	VERB
cana-4510	62	12	in	in	ADP
cana-4510	62	13	[	[	X
cana-4510	62	14	12	12	NUM
cana-4510	62	15	]	]	PUNCT
cana-4510	62	16	,	,	PUNCT
cana-4510	62	17	wei	wei	PROPN
cana-4510	62	18	liu	liu	PROPN
cana-4510	62	19	et	et	PROPN
cana-4510	62	20	al	al	PROPN
cana-4510	62	21	.	.	PROPN
cana-4510	62	22	utilized	utilize	VERB
cana-4510	62	23	the	the	DET
cana-4510	62	24	flavia	flavia	PROPN
cana-4510	62	25	and	and	CCONJ
cana-4510	62	26	hn	hn	PROPN
cana-4510	62	27	plant	plant	NOUN
cana-4510	62	28	datasets	dataset	NOUN
cana-4510	62	29	.	.	PUNCT
cana-4510	63	1	the	the	DET
cana-4510	63	2	flavia	flavia	PROPN
cana-4510	63	3	dataset	dataset	PROPN
cana-4510	63	4	contained	contain	VERB
cana-4510	63	5	32	32	NUM
cana-4510	63	6	plant	plant	NOUN
cana-4510	63	7	species	specie	NOUN
cana-4510	63	8	,	,	PUNCT
cana-4510	63	9	while	while	SCONJ
cana-4510	63	10	the	the	DET
cana-4510	63	11	hn	hn	PROPN
cana-4510	63	12	dataset	dataset	NOUN
cana-4510	63	13	included	include	VERB
cana-4510	63	14	10	10	NUM
cana-4510	63	15	plant	plant	NOUN
cana-4510	63	16	species	specie	NOUN
cana-4510	63	17	.	.	PUNCT
cana-4510	64	1	the	the	DET
cana-4510	64	2	researchers	researcher	NOUN
cana-4510	64	3	proposed	propose	VERB
cana-4510	64	4	a	a	DET
cana-4510	64	5	deep	deep	ADJ
cana-4510	64	6	convolutional	convolutional	ADJ
cana-4510	64	7	neural	neural	ADJ
cana-4510	64	8	network	network	NOUN
cana-4510	64	9	architecture	architecture	NOUN
cana-4510	64	10	called	call	VERB
cana-4510	64	11	resnet	resnet	NOUN
cana-4510	64	12	.	.	PUNCT
cana-4510	65	1	however	however	ADV
cana-4510	65	2	,	,	PUNCT
cana-4510	65	3	these	these	DET
cana-4510	65	4	models	model	NOUN
cana-4510	65	5	did	do	AUX
cana-4510	65	6	not	not	PART
cana-4510	65	7	achieve	achieve	VERB
cana-4510	65	8	high	high	ADJ
cana-4510	65	9	accuracy	accuracy	NOUN
cana-4510	65	10	and	and	CCONJ
cana-4510	65	11	recall	recall	NOUN
cana-4510	65	12	.	.	PUNCT
cana-4510	66	1	in	in	ADP
cana-4510	66	2	[	[	X
cana-4510	66	3	13	13	NUM
cana-4510	66	4	]	]	SYM
cana-4510	66	5	cao	cao	PROPN
cana-4510	66	6	,	,	PUNCT
cana-4510	66	7	xu	xu	PROPN
cana-4510	66	8	,	,	PUNCT
cana-4510	66	9	and	and	CCONJ
cana-4510	66	10	other	other	ADJ
cana-4510	66	11	collaborators	collaborator	NOUN
cana-4510	66	12	introduce	introduce	VERB
cana-4510	66	13	a	a	DET
cana-4510	66	14	novel	novel	ADJ
cana-4510	66	15	approach	approach	NOUN
cana-4510	66	16	in	in	ADP
cana-4510	66	17	their	their	PRON
cana-4510	66	18	research	research	NOUN
cana-4510	66	19	,	,	PUNCT
cana-4510	66	20	focusing	focus	VERB
cana-4510	66	21	on	on	ADP
cana-4510	66	22	the	the	DET
cana-4510	66	23	recognition	recognition	NOUN
cana-4510	66	24	of	of	ADP
cana-4510	66	25	common	common	ADJ
cana-4510	66	26	field	field	NOUN
cana-4510	66	27	insects	insect	NOUN
cana-4510	66	28	through	through	ADP
cana-4510	66	29	tl	tl	PROPN
cana-4510	66	30	.	.	PUNCT
cana-4510	67	1	their	their	PRON
cana-4510	67	2	research	research	NOUN
cana-4510	67	3	involves	involve	VERB
cana-4510	67	4	the	the	DET
cana-4510	67	5	collection	collection	NOUN
cana-4510	67	6	and	and	CCONJ
cana-4510	67	7	classification	classification	NOUN
cana-4510	67	8	of	of	ADP
cana-4510	67	9	9	9	NUM
cana-4510	67	10	distinct	distinct	ADJ
cana-4510	67	11	insect	insect	NOUN
cana-4510	67	12	species	specie	NOUN
cana-4510	67	13	,	,	PUNCT
cana-4510	67	14	utilizing	utilize	VERB
cana-4510	67	15	digital	digital	ADJ
cana-4510	67	16	image	image	NOUN
cana-4510	67	17	processing	processing	NOUN
cana-4510	67	18	techniques	technique	NOUN
cana-4510	67	19	and	and	CCONJ
cana-4510	67	20	generative	generative	ADJ
cana-4510	67	21	adversarial	adversarial	ADJ
cana-4510	67	22	networks	network	NOUN
cana-4510	67	23	to	to	PART
cana-4510	67	24	expand	expand	VERB
cana-4510	67	25	the	the	DET
cana-4510	67	26	insect	insect	NOUN
cana-4510	67	27	dataset	dataset	VERB
cana-4510	67	28	.	.	PUNCT
cana-4510	68	1	they	they	PRON
cana-4510	68	2	establish	establish	VERB
cana-4510	68	3	a	a	DET
cana-4510	68	4	model	model	NOUN
cana-4510	68	5	based	base	VERB
cana-4510	68	6	on	on	ADP
cana-4510	68	7	tl	tl	PROPN
cana-4510	68	8	,	,	PUNCT
cana-4510	68	9	harnessing	harness	VERB
cana-4510	68	10	the	the	DET
cana-4510	68	11	learned	learn	VERB
cana-4510	68	12	features	feature	NOUN
cana-4510	68	13	of	of	ADP
cana-4510	68	14	vgg16	vgg16	PROPN
cana-4510	68	15	,	,	PUNCT
cana-4510	68	16	vgg19	vgg19	PROPN
cana-4510	68	17	,	,	PUNCT
cana-4510	68	18	inceptionv3	inceptionv3	NOUN
cana-4510	68	19	,	,	PUNCT
cana-4510	68	20	and	and	CCONJ
cana-4510	68	21	inceptionv4	inceptionv4	PROPN
cana-4510	68	22	from	from	ADP
cana-4510	68	23	the	the	DET
cana-4510	68	24	imagenet	imagenet	NOUN
cana-4510	68	25	dataset	dataset	VERB
cana-4510	68	26	to	to	PART
cana-4510	68	27	classify	classify	VERB
cana-4510	68	28	and	and	CCONJ
cana-4510	68	29	recognize	recognize	VERB
cana-4510	68	30	insects	insect	NOUN
cana-4510	68	31	.	.	PUNCT
cana-4510	69	1	in	in	ADP
cana-4510	69	2	[	[	X
cana-4510	69	3	14	14	NUM
cana-4510	69	4	]	]	PUNCT
cana-4510	69	5	,	,	PUNCT
cana-4510	69	6	valan	valan	PROPN
cana-4510	69	7	,	,	PUNCT
cana-4510	69	8	miroslav	miroslav	NOUN
cana-4510	69	9	,	,	PUNCT
cana-4510	69	10	et	et	PROPN
cana-4510	69	11	al	al	PROPN
cana-4510	69	12	.	.	PROPN
cana-4510	69	13	proposed	propose	VERB
cana-4510	69	14	an	an	DET
cana-4510	69	15	innovative	innovative	ADJ
cana-4510	69	16	approach	approach	NOUN
cana-4510	69	17	aimed	aim	VERB
cana-4510	69	18	at	at	ADP
cana-4510	69	19	developing	develop	VERB
cana-4510	69	20	an	an	DET
cana-4510	69	21	efficient	efficient	ADJ
cana-4510	69	22	method	method	NOUN
cana-4510	69	23	for	for	ADP
cana-4510	69	24	transferring	transfer	VERB
cana-4510	69	25	cnn	cnn	PROPN
cana-4510	69	26	features	feature	NOUN
cana-4510	69	27	.	.	PUNCT
cana-4510	70	1	their	their	PRON
cana-4510	70	2	study	study	NOUN
cana-4510	70	3	involved	involve	VERB
cana-4510	70	4	the	the	DET
cana-4510	70	5	utilization	utilization	NOUN
cana-4510	70	6	of	of	ADP
cana-4510	70	7	four	four	NUM
cana-4510	70	8	distinct	distinct	ADJ
cana-4510	70	9	datasets	dataset	NOUN
cana-4510	70	10	.	.	PUNCT
cana-4510	71	1	vgg16	vgg16	PROPN
cana-4510	71	2	achieved	achieve	VERB
cana-4510	71	3	more	more	ADJ
cana-4510	71	4	than	than	ADP
cana-4510	71	5	90	90	NUM
cana-4510	71	6	%	%	NOUN
cana-4510	71	7	of	of	ADP
cana-4510	71	8	accuracy	accuracy	NOUN
cana-4510	71	9	in	in	ADP
cana-4510	71	10	the	the	DET
cana-4510	71	11	classification	classification	NOUN
cana-4510	71	12	tasks	task	NOUN
cana-4510	71	13	.	.	PUNCT
cana-4510	72	1	in	in	ADP
cana-4510	72	2	[	[	X
cana-4510	72	3	15	15	NUM
cana-4510	72	4	]	]	X
cana-4510	72	5	hansen	hansen	PROPN
cana-4510	72	6	,	,	PUNCT
cana-4510	72	7	oskar	oskar	PROPN
cana-4510	72	8	l.	l.	PROPN
cana-4510	72	9	p.	p.	PROPN
cana-4510	72	10	,	,	PUNCT
cana-4510	72	11	and	and	CCONJ
cana-4510	72	12	their	their	PRON
cana-4510	72	13	team	team	NOUN
cana-4510	72	14	developed	develop	VERB
cana-4510	72	15	a	a	DET
cana-4510	72	16	fine	fine	ADV
cana-4510	72	17	-	-	PUNCT
cana-4510	72	18	tuned	tune	VERB
cana-4510	72	19	cnn	cnn	PROPN
cana-4510	72	20	image	image	NOUN
cana-4510	72	21	classification	classification	NOUN
cana-4510	72	22	approach	approach	NOUN
cana-4510	72	23	in	in	ADP
cana-4510	72	24	their	their	PRON
cana-4510	72	25	research	research	NOUN
cana-4510	72	26	.	.	PUNCT
cana-4510	73	1	the	the	DET
cana-4510	73	2	cnn	cnn	PROPN
cana-4510	73	3	demonstrated	demonstrate	VERB
cana-4510	73	4	an	an	DET
cana-4510	73	5	accuracy	accuracy	NOUN
cana-4510	73	6	of	of	ADP
cana-4510	73	7	51.9	51.9	NUM
cana-4510	73	8	%	%	NOUN
cana-4510	73	9	in	in	ADP
cana-4510	73	10	correctly	correctly	ADV
cana-4510	73	11	classifying	classify	VERB
cana-4510	73	12	19,164	19,164	NUM
cana-4510	73	13	test	test	NOUN
cana-4510	73	14	images	image	NOUN
cana-4510	73	15	at	at	ADP
cana-4510	73	16	the	the	DET
cana-4510	73	17	species	specie	NOUN
cana-4510	73	18	level	level	NOUN
cana-4510	73	19	and	and	CCONJ
cana-4510	73	20	achieved	achieve	VERB
cana-4510	73	21	74.9	74.9	NUM
cana-4510	73	22	%	%	NOUN
cana-4510	73	23	of	of	ADP
cana-4510	73	24	lew	lew	NOUN
cana-4510	73	25	level	level	NOUN
cana-4510	73	26	accuracy	accuracy	NOUN
cana-4510	73	27	at	at	ADP
cana-4510	73	28	the	the	DET
cana-4510	73	29	genus	genus	ADJ
cana-4510	73	30	level	level	NOUN
cana-4510	73	31	.	.	PUNCT
cana-4510	74	1	in	in	ADP
cana-4510	74	2	the	the	DET
cana-4510	74	3	study	study	NOUN
cana-4510	74	4	by	by	ADP
cana-4510	74	5	anwar	anwar	PROPN
cana-4510	74	6	,	,	PUNCT
cana-4510	74	7	zeba	zeba	PROPN
cana-4510	74	8	,	,	PUNCT
cana-4510	74	9	and	and	CCONJ
cana-4510	74	10	sarfaraz	sarfaraz	PROPN
cana-4510	74	11	masood	masood	PROPN
cana-4510	74	12	[	[	X
cana-4510	74	13	16	16	NUM
cana-4510	74	14	]	]	X
cana-4510	74	15	,	,	PUNCT
cana-4510	74	16	an	an	DET
cana-4510	74	17	ensemble	ensemble	ADJ
cana-4510	74	18	-	-	PUNCT
cana-4510	74	19	based	base	VERB
cana-4510	74	20	model	model	NOUN
cana-4510	74	21	leveraging	leverage	VERB
cana-4510	74	22	transfer	transfer	NOUN
cana-4510	74	23	learning	learning	NOUN
cana-4510	74	24	(	(	PUNCT
cana-4510	74	25	tl	tl	PROPN
cana-4510	74	26	)	)	PUNCT
cana-4510	74	27	was	be	AUX
cana-4510	74	28	proposed	propose	VERB
cana-4510	74	29	.	.	PUNCT
cana-4510	75	1	the	the	DET
cana-4510	75	2	experimental	experimental	ADJ
cana-4510	75	3	setup	setup	NOUN
cana-4510	75	4	incorporated	incorporate	VERB
cana-4510	75	5	pre	pre	ADJ
cana-4510	75	6	-	-	ADJ
cana-4510	75	7	trained	train	VERB
cana-4510	75	8	models	model	NOUN
cana-4510	75	9	such	such	ADJ
cana-4510	75	10	as	as	ADP
cana-4510	75	11	vgg16	vgg16	PROPN
cana-4510	75	12	,	,	PUNCT
cana-4510	75	13	vgg19	vgg19	PROPN
cana-4510	75	14	,	,	PUNCT
cana-4510	75	15	and	and	CCONJ
cana-4510	75	16	resnet50	resnet50	NOUN
cana-4510	75	17	,	,	PUNCT
cana-4510	75	18	which	which	PRON
cana-4510	75	19	were	be	AUX
cana-4510	75	20	combined	combine	VERB
cana-4510	75	21	using	use	VERB
cana-4510	75	22	a	a	DET
cana-4510	75	23	voting	voting	NOUN
cana-4510	75	24	classifier	classifier	NOUN
cana-4510	75	25	ensemble	ensemble	ADJ
cana-4510	75	26	technique	technique	NOUN
cana-4510	75	27	.	.	PUNCT
cana-4510	76	1	the	the	DET
cana-4510	76	2	model	model	NOUN
cana-4510	76	3	was	be	AUX
cana-4510	76	4	evaluated	evaluate	VERB
cana-4510	76	5	on	on	ADP
cana-4510	76	6	the	the	DET
cana-4510	76	7	ip102	ip102	PROPN
cana-4510	76	8	dataset	dataset	NOUN
cana-4510	76	9	,	,	PUNCT
cana-4510	76	10	a	a	DET
cana-4510	76	11	benchmark	benchmark	NOUN
cana-4510	76	12	dataset	dataset	NOUN
cana-4510	76	13	comprising	comprising	NOUN
cana-4510	76	14	over	over	ADP
cana-4510	76	15	75,000	75,000	NUM
cana-4510	76	16	samples	sample	NOUN
cana-4510	76	17	across	across	ADP
cana-4510	76	18	102	102	NUM
cana-4510	76	19	classes	class	NOUN
cana-4510	76	20	.	.	PUNCT
cana-4510	77	1	the	the	DET
cana-4510	77	2	automated	automate	VERB
cana-4510	77	3	bee	bee	NOUN
cana-4510	77	4	identification	identification	NOUN
cana-4510	77	5	system	system	NOUN
cana-4510	77	6	(	(	PUNCT
cana-4510	77	7	deepabis	deepabis	ADV
cana-4510	77	8	)	)	PUNCT
cana-4510	78	1	[	[	X
cana-4510	78	2	17	17	NUM
cana-4510	78	3	]	]	X
cana-4510	78	4	,	,	PUNCT
cana-4510	78	5	a	a	DET
cana-4510	78	6	deep	deep	ADJ
cana-4510	78	7	learning	learning	NOUN
cana-4510	78	8	-	-	PUNCT
cana-4510	78	9	based	base	VERB
cana-4510	78	10	system	system	NOUN
cana-4510	78	11	built	build	VERB
cana-4510	78	12	on	on	ADP
cana-4510	78	13	the	the	DET
cana-4510	78	14	mobilenetv2	mobilenetv2	PROPN
cana-4510	78	15	model	model	NOUN
cana-4510	78	16	,	,	PUNCT
cana-4510	78	17	was	be	AUX
cana-4510	78	18	developed	develop	VERB
cana-4510	78	19	for	for	ADP
cana-4510	78	20	bee	bee	NOUN
cana-4510	78	21	identification	identification	NOUN
cana-4510	78	22	.	.	PUNCT
cana-4510	79	1	however	however	ADV
cana-4510	79	2	,	,	PUNCT
cana-4510	79	3	its	its	PRON
cana-4510	79	4	accuracy	accuracy	NOUN
cana-4510	79	5	and	and	CCONJ
cana-4510	79	6	precision	precision	NOUN
cana-4510	79	7	were	be	AUX
cana-4510	79	8	found	find	VERB
cana-4510	79	9	to	to	PART
cana-4510	79	10	be	be	AUX
cana-4510	79	11	unsatisfactory	unsatisfactory	ADJ
cana-4510	79	12	.	.	PUNCT
cana-4510	80	1	in	in	ADP
cana-4510	80	2	[	[	X
cana-4510	80	3	18	18	NUM
cana-4510	80	4	]	]	PUNCT
cana-4510	80	5	,	,	PUNCT
cana-4510	80	6	the	the	DET
cana-4510	80	7	author	author	NOUN
cana-4510	80	8	used	use	VERB
cana-4510	80	9	data	datum	NOUN
cana-4510	80	10	from	from	ADP
cana-4510	80	11	three	three	NUM
cana-4510	80	12	fish	fish	NOUN
cana-4510	80	13	species	specie	NOUN
cana-4510	80	14	captured	capture	VERB
cana-4510	80	15	by	by	ADP
cana-4510	80	16	the	the	DET
cana-4510	80	17	deep	deep	ADJ
cana-4510	80	18	vision	vision	NOUN
cana-4510	80	19	trawl	trawl	NOUN
cana-4510	80	20	camera	camera	NOUN
cana-4510	80	21	system	system	NOUN
cana-4510	80	22	.	.	PUNCT
cana-4510	81	1	the	the	DET
cana-4510	81	2	proposed	propose	VERB
cana-4510	81	3	cnn	cnn	PROPN
cana-4510	81	4	model	model	NOUN
cana-4510	81	5	achieved	achieve	VERB
cana-4510	81	6	an	an	DET
cana-4510	81	7	accuracy	accuracy	NOUN
cana-4510	81	8	of	of	ADP
cana-4510	81	9	94	94	NUM
cana-4510	81	10	%	%	NOUN
cana-4510	81	11	.	.	PUNCT
cana-4510	82	1	in	in	ADP
cana-4510	82	2	[	[	X
cana-4510	82	3	19	19	NUM
cana-4510	82	4	]	]	PUNCT
cana-4510	82	5	,	,	PUNCT
cana-4510	82	6	the	the	DET
cana-4510	82	7	author	author	NOUN
cana-4510	82	8	collected	collect	VERB
cana-4510	82	9	images	image	NOUN
cana-4510	82	10	of	of	ADP
cana-4510	82	11	68	68	NUM
cana-4510	82	12	pantanal	pantanal	ADJ
cana-4510	82	13	fish	fish	NOUN
cana-4510	82	14	species	specie	NOUN
cana-4510	82	15	sourced	source	VERB
cana-4510	82	16	from	from	ADP
cana-4510	82	17	google	google	PROPN
cana-4510	82	18	images	image	NOUN
cana-4510	82	19	,	,	PUNCT
cana-4510	82	20	categorized	categorize	VERB
cana-4510	82	21	by	by	ADP
cana-4510	82	22	taxonomic	taxonomic	ADJ
cana-4510	82	23	levels	level	NOUN
cana-4510	82	24	.	.	PUNCT
cana-4510	83	1	several	several	ADJ
cana-4510	83	2	pre	pre	ADJ
cana-4510	83	3	-	-	ADJ
cana-4510	83	4	trained	train	VERB
cana-4510	83	5	models	model	NOUN
cana-4510	83	6	were	be	AUX
cana-4510	83	7	tested	test	VERB
cana-4510	83	8	,	,	PUNCT
cana-4510	83	9	with	with	ADP
cana-4510	83	10	inception	inception	PROPN
cana-4510	83	11	v3	v3	PROPN
cana-4510	83	12	ultimately	ultimately	ADV
cana-4510	83	13	achieving	achieve	VERB
cana-4510	83	14	87.3	87.3	NUM
cana-4510	83	15	%	%	NOUN
cana-4510	83	16	accuracy	accuracy	NOUN
cana-4510	83	17	at	at	ADP
cana-4510	83	18	the	the	DET
cana-4510	83	19	species	species	NOUN
cana-4510	83	20	level	level	NOUN
cana-4510	83	21	.	.	PUNCT
cana-4510	84	1	however	however	ADV
cana-4510	84	2	,	,	PUNCT
cana-4510	84	3	its	its	PRON
cana-4510	84	4	accuracy	accuracy	NOUN
cana-4510	84	5	and	and	CCONJ
cana-4510	84	6	precision	precision	NOUN
cana-4510	84	7	were	be	AUX
cana-4510	84	8	still	still	ADV
cana-4510	84	9	deemed	deem	VERB
cana-4510	84	10	unsatisfactory	unsatisfactory	ADJ
cana-4510	84	11	.	.	PUNCT
cana-4510	85	1	a	a	DET
cana-4510	85	2	new	new	ADJ
cana-4510	85	3	technique	technique	NOUN
cana-4510	85	4	[	[	X
cana-4510	85	5	20	20	NUM
cana-4510	85	6	]	]	PUNCT
cana-4510	85	7	was	be	AUX
cana-4510	85	8	developed	develop	VERB
cana-4510	85	9	for	for	ADP
cana-4510	85	10	classifying	classify	VERB
cana-4510	85	11	butterfly	butterfly	NOUN
cana-4510	85	12	species	specie	NOUN
cana-4510	85	13	using	use	VERB
cana-4510	85	14	pre	pre	ADJ
cana-4510	85	15	-	-	ADJ
cana-4510	85	16	trained	train	VERB
cana-4510	85	17	communications	communication	NOUN
cana-4510	85	18	on	on	ADP
cana-4510	85	19	applied	apply	VERB
cana-4510	85	20	nonlinear	nonlinear	ADJ
cana-4510	85	21	analysis	analysis	NOUN
cana-4510	85	22	issn	issn	NOUN
cana-4510	85	23	:	:	PUNCT
cana-4510	85	24	1074	1074	NUM
cana-4510	85	25	-	-	PUNCT
cana-4510	85	26	133x	133x	NUM
cana-4510	85	27	vol	vol	NOUN
cana-4510	85	28	32	32	NUM
cana-4510	85	29	no	no	NOUN
cana-4510	85	30	.	.	PUNCT
cana-4510	86	1	9s	9s	NUM
cana-4510	86	2	(	(	PUNCT
cana-4510	86	3	2025	2025	NUM
cana-4510	86	4	)	)	PUNCT
cana-4510	86	5	2247	2247	NUM
cana-4510	86	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	86	7	cnn	cnn	PROPN
cana-4510	86	8	frameworks	framework	NOUN
cana-4510	86	9	,	,	PUNCT
cana-4510	86	10	including	include	VERB
cana-4510	86	11	vgg16	vgg16	PROPN
cana-4510	86	12	,	,	PUNCT
cana-4510	86	13	vgg19	vgg19	PROPN
cana-4510	86	14	,	,	PUNCT
cana-4510	86	15	mobilenet	mobilenet	NOUN
cana-4510	86	16	,	,	PUNCT
cana-4510	86	17	xception	xception	NOUN
cana-4510	86	18	,	,	PUNCT
cana-4510	86	19	resnet50	resnet50	NOUN
cana-4510	86	20	,	,	PUNCT
cana-4510	86	21	and	and	CCONJ
cana-4510	86	22	inceptionv3	inceptionv3	NOUN
cana-4510	86	23	.	.	PUNCT
cana-4510	87	1	however	however	ADV
cana-4510	87	2	,	,	PUNCT
cana-4510	87	3	these	these	DET
cana-4510	87	4	models	model	NOUN
cana-4510	87	5	exhibited	exhibit	VERB
cana-4510	87	6	low	low	ADJ
cana-4510	87	7	recall	recall	NOUN
cana-4510	87	8	and	and	CCONJ
cana-4510	87	9	precision	precision	NOUN
cana-4510	87	10	.	.	PUNCT
cana-4510	88	1	based	base	VERB
cana-4510	88	2	on	on	ADP
cana-4510	88	3	the	the	DET
cana-4510	88	4	insights	insight	NOUN
cana-4510	88	5	from	from	ADP
cana-4510	88	6	the	the	DET
cana-4510	88	7	aforementioned	aforementioned	ADJ
cana-4510	88	8	literature	literature	NOUN
cana-4510	88	9	and	and	CCONJ
cana-4510	88	10	related	related	ADJ
cana-4510	88	11	studies	study	NOUN
cana-4510	88	12	,	,	PUNCT
cana-4510	88	13	it	it	PRON
cana-4510	88	14	would	would	AUX
cana-4510	88	15	be	be	AUX
cana-4510	88	16	beneficial	beneficial	ADJ
cana-4510	88	17	to	to	PART
cana-4510	88	18	explore	explore	VERB
cana-4510	88	19	the	the	DET
cana-4510	88	20	development	development	NOUN
cana-4510	88	21	and	and	CCONJ
cana-4510	88	22	training	training	NOUN
cana-4510	88	23	of	of	ADP
cana-4510	88	24	various	various	ADJ
cana-4510	88	25	pre	pre	ADJ
cana-4510	88	26	-	-	ADJ
cana-4510	88	27	trained	train	VERB
cana-4510	88	28	dcnn	dcnn	ADJ
cana-4510	88	29	models	model	NOUN
cana-4510	88	30	to	to	PART
cana-4510	88	31	achieve	achieve	VERB
cana-4510	88	32	more	more	ADV
cana-4510	88	33	accurate	accurate	ADJ
cana-4510	88	34	classification	classification	NOUN
cana-4510	88	35	of	of	ADP
cana-4510	88	36	insect	insect	NOUN
cana-4510	88	37	species	specie	NOUN
cana-4510	88	38	.	.	PUNCT
cana-4510	89	1	3	3	X
cana-4510	89	2	.	.	NUM
cana-4510	89	3	proposed	propose	VERB
cana-4510	89	4	methodology	methodology	NOUN
cana-4510	89	5	in	in	ADP
cana-4510	89	6	this	this	DET
cana-4510	89	7	section	section	NOUN
cana-4510	89	8	,	,	PUNCT
cana-4510	89	9	the	the	DET
cana-4510	89	10	presented	present	VERB
cana-4510	89	11	dhdcnet	dhdcnet	ADJ
cana-4510	89	12	model	model	NOUN
cana-4510	89	13	for	for	ADP
cana-4510	89	14	classifying	classify	VERB
cana-4510	89	15	and	and	CCONJ
cana-4510	89	16	identifying	identifying	NOUN
cana-4510	89	17	of	of	ADP
cana-4510	89	18	order	order	NOUN
cana-4510	89	19	,	,	PUNCT
cana-4510	89	20	family	family	NOUN
cana-4510	89	21	and	and	CCONJ
cana-4510	89	22	species	specie	NOUN
cana-4510	89	23	level	level	NOUN
cana-4510	89	24	insect	insect	NOUN
cana-4510	89	25	images	image	NOUN
cana-4510	89	26	.	.	PUNCT
cana-4510	90	1	figure	figure	NOUN
cana-4510	90	2	1	1	NUM
cana-4510	90	3	illustrates	illustrate	VERB
cana-4510	90	4	the	the	DET
cana-4510	90	5	procedural	procedural	ADJ
cana-4510	90	6	diagram	diagram	NOUN
cana-4510	90	7	of	of	ADP
cana-4510	90	8	the	the	DET
cana-4510	90	9	proposed	propose	VERB
cana-4510	90	10	methodology	methodology	NOUN
cana-4510	90	11	.	.	PUNCT
cana-4510	91	1	figure	figure	NOUN
cana-4510	91	2	1	1	NUM
cana-4510	91	3	:	:	PUNCT
cana-4510	91	4	procedural	procedural	ADJ
cana-4510	91	5	diagram	diagram	NOUN
cana-4510	91	6	of	of	ADP
cana-4510	91	7	the	the	DET
cana-4510	91	8	suggested	suggest	VERB
cana-4510	91	9	research	research	NOUN
cana-4510	91	10	work	work	NOUN
cana-4510	91	11	3.1	3.1	NUM
cana-4510	91	12	data	datum	NOUN
cana-4510	91	13	collection	collection	NOUN
cana-4510	91	14	in	in	ADP
cana-4510	91	15	the	the	DET
cana-4510	91	16	context	context	NOUN
cana-4510	91	17	of	of	ADP
cana-4510	91	18	a	a	DET
cana-4510	91	19	data	data	NOUN
cana-4510	91	20	collection	collection	NOUN
cana-4510	91	21	focused	focus	VERB
cana-4510	91	22	on	on	ADP
cana-4510	91	23	insects	insect	NOUN
cana-4510	91	24	,	,	PUNCT
cana-4510	91	25	images	image	NOUN
cana-4510	91	26	were	be	AUX
cana-4510	91	27	collected	collect	VERB
cana-4510	91	28	from	from	ADP
cana-4510	91	29	various	various	ADJ
cana-4510	91	30	online	online	ADJ
cana-4510	91	31	sources	source	NOUN
cana-4510	91	32	such	such	ADJ
cana-4510	91	33	as	as	ADP
cana-4510	91	34	kaggle	kaggle	NOUN
cana-4510	91	35	,	,	PUNCT
cana-4510	91	36	gbif	gbif	NOUN
cana-4510	91	37	insecta	insecta	NOUN
cana-4510	91	38	(	(	PUNCT
cana-4510	91	39	gbif.org	gbif.org	X
cana-4510	91	40	)	)	PUNCT
cana-4510	91	41	and	and	CCONJ
cana-4510	91	42	bug	bug	NOUN
cana-4510	91	43	guide	guide	NOUN
cana-4510	91	44	class	class	NOUN
cana-4510	91	45	insecta	insecta	PROPN
cana-4510	91	46	insects	insect	NOUN
cana-4510	91	47	bugguide.net	bugguide.net	X
cana-4510	91	48	.	.	PUNCT
cana-4510	92	1	this	this	DET
cana-4510	92	2	study	study	NOUN
cana-4510	92	3	involved	involve	VERB
cana-4510	92	4	the	the	DET
cana-4510	92	5	use	use	NOUN
cana-4510	92	6	of	of	ADP
cana-4510	92	7	three	three	NUM
cana-4510	92	8	levels	level	NOUN
cana-4510	92	9	of	of	ADP
cana-4510	92	10	datasets	dataset	NOUN
cana-4510	92	11	.	.	PUNCT
cana-4510	93	1	first	first	ADJ
cana-4510	93	2	one	one	NUM
cana-4510	93	3	is	be	AUX
cana-4510	93	4	order	order	NOUN
cana-4510	93	5	level	level	NOUN
cana-4510	93	6	and	and	CCONJ
cana-4510	93	7	included	include	VERB
cana-4510	93	8	six	six	NUM
cana-4510	93	9	different	different	ADJ
cana-4510	93	10	classes	class	NOUN
cana-4510	93	11	:	:	PUNCT
cana-4510	93	12	diptera	diptera	NOUN
cana-4510	93	13	,	,	PUNCT
cana-4510	93	14	coleoptera	coleoptera	PROPN
cana-4510	93	15	,	,	PUNCT
cana-4510	93	16	hemiptera	hemiptera	PROPN
cana-4510	93	17	,	,	PUNCT
cana-4510	93	18	hymenoptera	hymenoptera	PROPN
cana-4510	93	19	,	,	PUNCT
cana-4510	93	20	lepidoptera	lepidoptera	NOUN
cana-4510	93	21	,	,	PUNCT
cana-4510	93	22	and	and	CCONJ
cana-4510	93	23	odonata	odonata	NOUN
cana-4510	93	24	.	.	PUNCT
cana-4510	94	1	in	in	ADP
cana-4510	94	2	this	this	DET
cana-4510	94	3	order	order	NOUN
cana-4510	94	4	level	level	NOUN
cana-4510	94	5	data	data	NOUN
cana-4510	94	6	comprises	comprise	VERB
cana-4510	94	7	100	100	NUM
cana-4510	94	8	images	image	NOUN
cana-4510	94	9	per	per	ADP
cana-4510	94	10	class	class	NOUN
cana-4510	94	11	,	,	PUNCT
cana-4510	94	12	totalling	total	VERB
cana-4510	94	13	600	600	NUM
cana-4510	94	14	images	image	NOUN
cana-4510	94	15	in	in	ADV
cana-4510	94	16	all	all	ADV
cana-4510	94	17	.	.	PUNCT
cana-4510	95	1	on	on	ADP
cana-4510	95	2	the	the	DET
cana-4510	95	3	other	other	ADJ
cana-4510	95	4	hand	hand	NOUN
cana-4510	95	5	,	,	PUNCT
cana-4510	95	6	was	be	AUX
cana-4510	95	7	more	more	ADV
cana-4510	95	8	specific	specific	ADJ
cana-4510	95	9	,	,	PUNCT
cana-4510	95	10	concentrating	concentrate	VERB
cana-4510	95	11	on	on	ADP
cana-4510	95	12	insect	insect	NOUN
cana-4510	95	13	families	family	NOUN
cana-4510	95	14	.	.	PUNCT
cana-4510	96	1	it	it	PRON
cana-4510	96	2	encompassed	encompass	VERB
cana-4510	96	3	five	five	NUM
cana-4510	96	4	different	different	ADJ
cana-4510	96	5	family	family	NOUN
cana-4510	96	6	classes	class	NOUN
cana-4510	96	7	:	:	PUNCT
cana-4510	96	8	andrenidae	andrenidae	VERB
cana-4510	96	9	,	,	PUNCT
cana-4510	96	10	anthophoridae	anthophoridae	NOUN
cana-4510	96	11	,	,	PUNCT
cana-4510	96	12	apidae	apidae	NOUN
cana-4510	96	13	,	,	PUNCT
cana-4510	96	14	colletidae	colletidae	NOUN
cana-4510	96	15	,	,	PUNCT
cana-4510	96	16	and	and	CCONJ
cana-4510	96	17	dasaypodaidae	dasaypodaidae	NOUN
cana-4510	96	18	.	.	PUNCT
cana-4510	97	1	in	in	ADP
cana-4510	97	2	this	this	DET
cana-4510	97	3	level	level	NOUN
cana-4510	97	4	each	each	DET
cana-4510	97	5	class	class	NOUN
cana-4510	97	6	contains	contain	VERB
cana-4510	97	7	between	between	ADP
cana-4510	97	8	68	68	NUM
cana-4510	97	9	and	and	CCONJ
cana-4510	97	10	80	80	NUM
cana-4510	97	11	images	image	NOUN
cana-4510	97	12	,	,	PUNCT
cana-4510	97	13	with	with	ADP
cana-4510	97	14	a	a	DET
cana-4510	97	15	total	total	NOUN
cana-4510	97	16	of	of	ADP
cana-4510	97	17	374	374	NUM
cana-4510	97	18	across	across	ADP
cana-4510	97	19	all	all	DET
cana-4510	97	20	classes	class	NOUN
cana-4510	97	21	.	.	PUNCT
cana-4510	98	1	finally	finally	ADV
cana-4510	98	2	,	,	PUNCT
cana-4510	98	3	species	species	NOUN
cana-4510	98	4	level	level	NOUN
cana-4510	98	5	data	datum	NOUN
cana-4510	98	6	delved	delve	VERB
cana-4510	98	7	even	even	ADV
cana-4510	98	8	deeper	deep	ADJ
cana-4510	98	9	,	,	PUNCT
cana-4510	98	10	focusing	focus	VERB
cana-4510	98	11	on	on	ADP
cana-4510	98	12	insect	insect	NOUN
cana-4510	98	13	species	specie	NOUN
cana-4510	98	14	.	.	PUNCT
cana-4510	99	1	it	it	PRON
cana-4510	99	2	featured	feature	VERB
cana-4510	99	3	five	five	NUM
cana-4510	99	4	distinct	distinct	ADJ
cana-4510	99	5	species	specie	NOUN
cana-4510	99	6	classes	class	NOUN
cana-4510	99	7	:	:	PUNCT
cana-4510	99	8	andrena	andrena	NOUN
cana-4510	99	9	scotica	scotica	PROPN
cana-4510	99	10	,	,	PUNCT
cana-4510	99	11	andrena	andrena	NOUN
cana-4510	99	12	fulva	fulva	NOUN
cana-4510	99	13	,	,	PUNCT
cana-4510	99	14	andrena	andrena	NOUN
cana-4510	99	15	haemorrhoa	haemorrhoa	NOUN
cana-4510	99	16	,	,	PUNCT
cana-4510	99	17	andrena	andrena	NOUN
cana-4510	99	18	cineraria	cineraria	NOUN
cana-4510	99	19	,	,	PUNCT
cana-4510	99	20	and	and	CCONJ
cana-4510	99	21	andrena	andrena	NOUN
cana-4510	99	22	vaga	vaga	NOUN
cana-4510	99	23	.	.	PUNCT
cana-4510	100	1	in	in	ADP
cana-4510	100	2	this	this	DET
cana-4510	100	3	species	species	NOUN
cana-4510	100	4	level	level	NOUN
cana-4510	100	5	dataset	dataset	NOUN
cana-4510	100	6	,	,	PUNCT
cana-4510	100	7	each	each	DET
cana-4510	100	8	class	class	NOUN
cana-4510	100	9	contains	contain	VERB
cana-4510	100	10	between	between	ADP
cana-4510	100	11	33	33	NUM
cana-4510	100	12	and	and	CCONJ
cana-4510	100	13	57	57	NUM
cana-4510	100	14	images	image	NOUN
cana-4510	100	15	,	,	PUNCT
cana-4510	100	16	totalling	total	VERB
cana-4510	100	17	communications	communication	NOUN
cana-4510	100	18	on	on	ADP
cana-4510	100	19	applied	apply	VERB
cana-4510	100	20	nonlinear	nonlinear	ADJ
cana-4510	100	21	analysis	analysis	NOUN
cana-4510	100	22	issn	issn	NOUN
cana-4510	100	23	:	:	PUNCT
cana-4510	100	24	1074	1074	NUM
cana-4510	100	25	-	-	PUNCT
cana-4510	100	26	133x	133x	NUM
cana-4510	100	27	vol	vol	NOUN
cana-4510	100	28	32	32	NUM
cana-4510	100	29	no	no	NOUN
cana-4510	100	30	.	.	PUNCT
cana-4510	101	1	9s	9s	NUM
cana-4510	101	2	(	(	PUNCT
cana-4510	101	3	2025	2025	NUM
cana-4510	101	4	)	)	PUNCT
cana-4510	101	5	2248	2248	NUM
cana-4510	101	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	101	7	230	230	NUM
cana-4510	101	8	images	image	NOUN
cana-4510	101	9	were	be	AUX
cana-4510	101	10	utilized	utilize	VERB
cana-4510	101	11	.	.	PUNCT
cana-4510	102	1	figure	figure	NOUN
cana-4510	102	2	2	2	NUM
cana-4510	102	3	depicts	depict	VERB
cana-4510	102	4	the	the	DET
cana-4510	102	5	sample	sample	NOUN
cana-4510	102	6	images	image	NOUN
cana-4510	102	7	in	in	ADP
cana-4510	102	8	order	order	NOUN
cana-4510	102	9	,	,	PUNCT
cana-4510	102	10	family	family	NOUN
cana-4510	102	11	and	and	CCONJ
cana-4510	102	12	species	specie	NOUN
cana-4510	102	13	levels	level	NOUN
cana-4510	102	14	.	.	PUNCT
cana-4510	103	1	this	this	DET
cana-4510	103	2	comprehensive	comprehensive	ADJ
cana-4510	103	3	approach	approach	NOUN
cana-4510	103	4	was	be	AUX
cana-4510	103	5	collected	collect	VERB
cana-4510	103	6	and	and	CCONJ
cana-4510	103	7	organized	organize	VERB
cana-4510	103	8	insect	insect	NOUN
cana-4510	103	9	image	image	NOUN
cana-4510	103	10	data	datum	NOUN
cana-4510	103	11	according	accord	VERB
cana-4510	103	12	to	to	ADP
cana-4510	103	13	various	various	ADJ
cana-4510	103	14	taxonomic	taxonomic	ADJ
cana-4510	103	15	levels	level	NOUN
cana-4510	103	16	likely	likely	ADV
cana-4510	103	17	enabled	enable	VERB
cana-4510	103	18	a	a	DET
cana-4510	103	19	more	more	ADV
cana-4510	103	20	detailed	detailed	ADJ
cana-4510	103	21	and	and	CCONJ
cana-4510	103	22	precise	precise	ADJ
cana-4510	103	23	analysis	analysis	NOUN
cana-4510	103	24	of	of	ADP
cana-4510	103	25	insect	insect	NOUN
cana-4510	103	26	diversity	diversity	NOUN
cana-4510	103	27	and	and	CCONJ
cana-4510	103	28	classification	classification	NOUN
cana-4510	103	29	.	.	PUNCT
cana-4510	104	1	these	these	DET
cana-4510	104	2	collected	collect	VERB
cana-4510	104	3	data	datum	NOUN
cana-4510	104	4	were	be	AUX
cana-4510	104	5	utilized	utilize	VERB
cana-4510	104	6	to	to	ADP
cana-4510	104	7	classification	classification	NOUN
cana-4510	104	8	and	and	CCONJ
cana-4510	104	9	identification	identification	NOUN
cana-4510	104	10	tasks	task	NOUN
cana-4510	104	11	.	.	PUNCT
cana-4510	105	1	table	table	NOUN
cana-4510	105	2	1	1	NUM
cana-4510	105	3	illustrates	illustrate	VERB
cana-4510	105	4	the	the	DET
cana-4510	105	5	different	different	ADJ
cana-4510	105	6	classes	class	NOUN
cana-4510	105	7	in	in	ADP
cana-4510	105	8	taxonomic	taxonomic	ADJ
cana-4510	105	9	levels	level	NOUN
cana-4510	105	10	.	.	PUNCT
cana-4510	106	1	order	order	NOUN
cana-4510	106	2	level	level	NOUN
cana-4510	106	3	images	image	NOUN
cana-4510	106	4	figure	figure	NOUN
cana-4510	106	5	2	2	NUM
cana-4510	106	6	:	:	PUNCT
cana-4510	106	7	sample	sample	NOUN
cana-4510	106	8	images	image	NOUN
cana-4510	106	9	of	of	ADP
cana-4510	106	10	order	order	NOUN
cana-4510	106	11	level	level	NOUN
cana-4510	106	12	,	,	PUNCT
cana-4510	106	13	family	family	NOUN
cana-4510	106	14	level	level	NOUN
cana-4510	106	15	and	and	CCONJ
cana-4510	106	16	species	species	NOUN
cana-4510	106	17	level	level	NOUN
cana-4510	106	18	dataset	dataset	NOUN
cana-4510	106	19	table	table	NOUN
cana-4510	106	20	1	1	NUM
cana-4510	106	21	:	:	PUNCT
cana-4510	106	22	data	data	NOUN
cana-4510	106	23	collection	collection	NOUN
cana-4510	106	24	with	with	ADP
cana-4510	106	25	classes	class	NOUN
cana-4510	106	26	in	in	ADP
cana-4510	106	27	order	order	NOUN
cana-4510	106	28	,	,	PUNCT
cana-4510	106	29	family	family	NOUN
cana-4510	106	30	and	and	CCONJ
cana-4510	106	31	species	specie	NOUN
cana-4510	106	32	taxonomic	taxonomic	ADJ
cana-4510	106	33	levels	level	NOUN
cana-4510	106	34	order	order	NOUN
cana-4510	106	35	level	level	NOUN
cana-4510	106	36	classes	class	NOUN
cana-4510	106	37	family	family	NOUN
cana-4510	106	38	level	level	NOUN
cana-4510	106	39	classes	class	NOUN
cana-4510	106	40	species	species	NOUN
cana-4510	106	41	level	level	NOUN
cana-4510	106	42	classes	class	NOUN
cana-4510	106	43	diptera	diptera	VERB
cana-4510	106	44	andrenidae	andrenidae	VERB
cana-4510	106	45	andrena	andrena	NOUN
cana-4510	106	46	scotica	scotica	PROPN
cana-4510	106	47	coleoptera	coleoptera	PROPN
cana-4510	106	48	anthophoridae	anthophoridae	NOUN
cana-4510	106	49	andrena	andrena	NOUN
cana-4510	106	50	fulva	fulva	PROPN
cana-4510	106	51	hemiptera	hemiptera	PROPN
cana-4510	106	52	apidae	apidae	PROPN
cana-4510	106	53	andrena	andrena	PROPN
cana-4510	106	54	haemorrhoa	haemorrhoa	PROPN
cana-4510	106	55	hymenoptera	hymenoptera	PROPN
cana-4510	106	56	colletidae	colletidae	NOUN
cana-4510	106	57	andrena	andrena	NOUN
cana-4510	106	58	cineraria	cineraria	NOUN
cana-4510	106	59	lepidoptera	lepidoptera	NOUN
cana-4510	106	60	dasypodidae	dasypodidae	NOUN
cana-4510	106	61	andrena	andrena	NOUN
cana-4510	106	62	vaga	vaga	NOUN
cana-4510	106	63	odonata	odonata	NOUN
cana-4510	106	64	3.2	3.2	NUM
cana-4510	106	65	data	datum	NOUN
cana-4510	106	66	preprocessing	preprocessing	NOUN
cana-4510	106	67	in	in	ADP
cana-4510	106	68	the	the	DET
cana-4510	106	69	preprocessing	preprocessing	NOUN
cana-4510	106	70	stage	stage	NOUN
cana-4510	106	71	of	of	ADP
cana-4510	106	72	the	the	DET
cana-4510	106	73	image	image	NOUN
cana-4510	106	74	classification	classification	NOUN
cana-4510	106	75	pipeline	pipeline	NOUN
cana-4510	106	76	,	,	PUNCT
cana-4510	106	77	the	the	DET
cana-4510	106	78	input	input	NOUN
cana-4510	106	79	data	datum	NOUN
cana-4510	106	80	for	for	ADP
cana-4510	106	81	dcnn	dcnn	ADJ
cana-4510	106	82	models	model	NOUN
cana-4510	106	83	was	be	AUX
cana-4510	106	84	prepared	prepare	VERB
cana-4510	106	85	.	.	PUNCT
cana-4510	107	1	initially	initially	ADV
cana-4510	107	2	,	,	PUNCT
cana-4510	107	3	all	all	PRON
cana-4510	107	4	images	image	VERB
cana-4510	107	5	underwent	underwent	NOUN
cana-4510	107	6	resizing	resize	VERB
cana-4510	107	7	to	to	ADP
cana-4510	107	8	a	a	DET
cana-4510	107	9	resolution	resolution	NOUN
cana-4510	107	10	of	of	ADP
cana-4510	107	11	224x224	224x224	NUM
cana-4510	107	12	pixels	pixel	NOUN
cana-4510	107	13	.	.	PUNCT
cana-4510	108	1	this	this	DET
cana-4510	108	2	resizing	resizing	NOUN
cana-4510	108	3	step	step	NOUN
cana-4510	108	4	ensures	ensure	VERB
cana-4510	108	5	consistent	consistent	ADJ
cana-4510	108	6	and	and	CCONJ
cana-4510	108	7	compatibility	compatibility	NOUN
cana-4510	108	8	of	of	ADP
cana-4510	108	9	the	the	DET
cana-4510	108	10	input	input	NOUN
cana-4510	108	11	data	datum	NOUN
cana-4510	108	12	with	with	ADP
cana-4510	108	13	the	the	DET
cana-4510	108	14	network	network	NOUN
cana-4510	108	15	architectures	architecture	NOUN
cana-4510	108	16	being	be	AUX
cana-4510	108	17	utilized	utilize	VERB
cana-4510	108	18	,	,	PUNCT
cana-4510	108	19	thereby	thereby	ADV
cana-4510	108	20	enhancing	enhance	VERB
cana-4510	108	21	the	the	DET
cana-4510	108	22	effectiveness	effectiveness	NOUN
cana-4510	108	23	of	of	ADP
cana-4510	108	24	the	the	DET
cana-4510	108	25	subsequent	subsequent	ADJ
cana-4510	108	26	feature	feature	NOUN
cana-4510	108	27	extraction	extraction	NOUN
cana-4510	108	28	process	process	NOUN
cana-4510	108	29	.	.	PUNCT
cana-4510	109	1	to	to	PART
cana-4510	109	2	improve	improve	VERB
cana-4510	109	3	the	the	DET
cana-4510	109	4	quality	quality	NOUN
cana-4510	109	5	and	and	CCONJ
cana-4510	109	6	clarity	clarity	NOUN
cana-4510	109	7	of	of	ADP
cana-4510	109	8	the	the	DET
cana-4510	109	9	images	image	NOUN
cana-4510	109	10	,	,	PUNCT
cana-4510	109	11	employ	employ	VERB
cana-4510	109	12	various	various	ADJ
cana-4510	109	13	image	image	NOUN
cana-4510	109	14	enhancement	enhancement	NOUN
cana-4510	109	15	techniques	technique	NOUN
cana-4510	109	16	,	,	PUNCT
cana-4510	109	17	such	such	ADJ
cana-4510	109	18	as	as	ADP
cana-4510	109	19	contrast	contrast	NOUN
cana-4510	109	20	adjustment	adjustment	NOUN
cana-4510	109	21	,	,	PUNCT
cana-4510	109	22	brightness	brightness	NOUN
cana-4510	109	23	correction	correction	NOUN
cana-4510	109	24	,	,	PUNCT
cana-4510	109	25	and	and	CCONJ
cana-4510	109	26	noise	noise	NOUN
cana-4510	109	27	reduction	reduction	NOUN
cana-4510	109	28	.	.	PUNCT
cana-4510	110	1	additionally	additionally	ADV
cana-4510	110	2	,	,	PUNCT
cana-4510	110	3	utilize	utilize	VERB
cana-4510	110	4	lambda	lambda	NOUN
cana-4510	110	5	functions	function	NOUN
cana-4510	110	6	to	to	ADP
cana-4510	110	7	family	family	NOUN
cana-4510	110	8	level	level	NOUN
cana-4510	110	9	images	image	NOUN
cana-4510	110	10	species	species	NOUN
cana-4510	110	11	level	level	NOUN
cana-4510	110	12	images	image	NOUN
cana-4510	110	13	communications	communication	NOUN
cana-4510	110	14	on	on	ADP
cana-4510	110	15	applied	apply	VERB
cana-4510	110	16	nonlinear	nonlinear	ADJ
cana-4510	110	17	analysis	analysis	NOUN
cana-4510	110	18	issn	issn	NOUN
cana-4510	110	19	:	:	PUNCT
cana-4510	110	20	1074	1074	NUM
cana-4510	110	21	-	-	PUNCT
cana-4510	110	22	133x	133x	NUM
cana-4510	110	23	vol	vol	NOUN
cana-4510	110	24	32	32	NUM
cana-4510	110	25	no	no	NOUN
cana-4510	110	26	.	.	PUNCT
cana-4510	111	1	9s	9s	NUM
cana-4510	111	2	(	(	PUNCT
cana-4510	111	3	2025	2025	NUM
cana-4510	111	4	)	)	PUNCT
cana-4510	111	5	2249	2249	NUM
cana-4510	112	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	112	2	further	further	ADJ
cana-4510	112	3	process	process	NOUN
cana-4510	112	4	and	and	CCONJ
cana-4510	112	5	standardize	standardize	VERB
cana-4510	112	6	the	the	DET
cana-4510	112	7	data	datum	NOUN
cana-4510	112	8	.	.	PUNCT
cana-4510	113	1	lambda	lambda	ADJ
cana-4510	113	2	functions	function	NOUN
cana-4510	113	3	enable	enable	VERB
cana-4510	113	4	the	the	DET
cana-4510	113	5	application	application	NOUN
cana-4510	113	6	of	of	ADP
cana-4510	113	7	custom	custom	NOUN
cana-4510	113	8	transformations	transformation	NOUN
cana-4510	113	9	to	to	ADP
cana-4510	113	10	the	the	DET
cana-4510	113	11	images	image	NOUN
cana-4510	113	12	,	,	PUNCT
cana-4510	113	13	encompassing	encompass	VERB
cana-4510	113	14	data	datum	NOUN
cana-4510	113	15	augmentation	augmentation	NOUN
cana-4510	113	16	,	,	PUNCT
cana-4510	113	17	normalization	normalization	NOUN
cana-4510	113	18	,	,	PUNCT
cana-4510	113	19	and	and	CCONJ
cana-4510	113	20	colour	colour	ADJ
cana-4510	113	21	channel	channel	NOUN
cana-4510	113	22	adjustments	adjustment	NOUN
cana-4510	113	23	.	.	PUNCT
cana-4510	114	1	several	several	ADJ
cana-4510	114	2	functions	function	NOUN
cana-4510	114	3	were	be	AUX
cana-4510	114	4	applied	apply	VERB
cana-4510	114	5	in	in	ADP
cana-4510	114	6	the	the	DET
cana-4510	114	7	data	data	NOUN
cana-4510	114	8	augmentation	augmentation	NOUN
cana-4510	114	9	process	process	NOUN
cana-4510	114	10	:	:	PUNCT
cana-4510	114	11	the	the	DET
cana-4510	114	12	fill	fill	NOUN
cana-4510	114	13	mode	mode	NOUN
cana-4510	114	14	was	be	AUX
cana-4510	114	15	configured	configure	VERB
cana-4510	114	16	to	to	PART
cana-4510	114	17	nearest	nearest	VERB
cana-4510	114	18	;	;	PUNCT
cana-4510	114	19	both	both	CCONJ
cana-4510	114	20	horizontal	horizontal	ADJ
cana-4510	114	21	and	and	CCONJ
cana-4510	114	22	vertical	vertical	ADJ
cana-4510	114	23	flip	flip	NOUN
cana-4510	114	24	functions	function	NOUN
cana-4510	114	25	were	be	AUX
cana-4510	114	26	activated	activate	VERB
cana-4510	114	27	,	,	PUNCT
cana-4510	114	28	with	with	ADP
cana-4510	114	29	the	the	DET
cana-4510	114	30	rotation	rotation	NOUN
cana-4510	114	31	range	range	NOUN
cana-4510	114	32	of	of	ADP
cana-4510	114	33	2	2	NUM
cana-4510	114	34	degrees	degree	NOUN
cana-4510	114	35	;	;	PUNCT
cana-4510	114	36	and	and	CCONJ
cana-4510	114	37	the	the	DET
cana-4510	114	38	width	width	ADJ
cana-4510	114	39	,	,	PUNCT
cana-4510	114	40	height	height	NOUN
cana-4510	114	41	,	,	PUNCT
cana-4510	114	42	shear	shear	NOUN
cana-4510	114	43	,	,	PUNCT
cana-4510	114	44	and	and	CCONJ
cana-4510	114	45	zoom	zoom	PRON
cana-4510	114	46	ranges	range	NOUN
cana-4510	114	47	were	be	AUX
cana-4510	114	48	all	all	PRON
cana-4510	114	49	set	set	VERB
cana-4510	114	50	to	to	ADP
cana-4510	114	51	0.2	0.2	NUM
cana-4510	114	52	.	.	PUNCT
cana-4510	115	1	these	these	DET
cana-4510	115	2	processes	process	NOUN
cana-4510	115	3	led	lead	VERB
cana-4510	115	4	to	to	ADP
cana-4510	115	5	an	an	DET
cana-4510	115	6	expansion	expansion	NOUN
cana-4510	115	7	in	in	ADP
cana-4510	115	8	the	the	DET
cana-4510	115	9	dataset	dataset	NOUN
cana-4510	115	10	volume	volume	NOUN
cana-4510	115	11	.	.	PUNCT
cana-4510	116	1	at	at	ADP
cana-4510	116	2	the	the	DET
cana-4510	116	3	order	order	NOUN
cana-4510	116	4	level	level	NOUN
cana-4510	116	5	,	,	PUNCT
cana-4510	116	6	the	the	DET
cana-4510	116	7	dataset	dataset	NOUN
cana-4510	116	8	was	be	AUX
cana-4510	116	9	expanded	expand	VERB
cana-4510	116	10	through	through	ADP
cana-4510	116	11	data	datum	NOUN
cana-4510	116	12	augmentation	augmentation	NOUN
cana-4510	116	13	,	,	PUNCT
cana-4510	116	14	resulting	result	VERB
cana-4510	116	15	in	in	ADP
cana-4510	116	16	a	a	DET
cana-4510	116	17	total	total	NOUN
cana-4510	116	18	of	of	ADP
cana-4510	116	19	6060	6060	NUM
cana-4510	116	20	images	image	NOUN
cana-4510	116	21	.	.	PUNCT
cana-4510	117	1	of	of	ADP
cana-4510	117	2	these	these	PRON
cana-4510	117	3	,	,	PUNCT
cana-4510	117	4	4242	4242	NUM
cana-4510	117	5	images	image	NOUN
cana-4510	117	6	were	be	AUX
cana-4510	117	7	allocated	allocate	VERB
cana-4510	117	8	for	for	ADP
cana-4510	117	9	training	training	NOUN
cana-4510	117	10	,	,	PUNCT
cana-4510	117	11	908	908	NUM
cana-4510	117	12	for	for	ADP
cana-4510	117	13	testing	testing	NOUN
cana-4510	117	14	,	,	PUNCT
cana-4510	117	15	and	and	CCONJ
cana-4510	117	16	the	the	DET
cana-4510	117	17	remaining	remain	VERB
cana-4510	117	18	images	image	NOUN
cana-4510	117	19	for	for	ADP
cana-4510	117	20	validation	validation	NOUN
cana-4510	117	21	.	.	PUNCT
cana-4510	118	1	in	in	ADP
cana-4510	118	2	the	the	DET
cana-4510	118	3	family	family	NOUN
cana-4510	118	4	-	-	PUNCT
cana-4510	118	5	level	level	NOUN
cana-4510	118	6	dataset	dataset	NOUN
cana-4510	118	7	,	,	PUNCT
cana-4510	118	8	each	each	DET
cana-4510	118	9	class	class	NOUN
cana-4510	118	10	contained	contain	VERB
cana-4510	118	11	680–800	680–800	NUM
cana-4510	118	12	images	image	NOUN
cana-4510	118	13	,	,	PUNCT
cana-4510	118	14	totalling	total	VERB
cana-4510	118	15	3740	3740	NUM
cana-4510	118	16	images	image	NOUN
cana-4510	118	17	.	.	PUNCT
cana-4510	119	1	among	among	ADP
cana-4510	119	2	these	these	PRON
cana-4510	119	3	,	,	PUNCT
cana-4510	119	4	2616	2616	NUM
cana-4510	119	5	images	image	NOUN
cana-4510	119	6	were	be	AUX
cana-4510	119	7	used	use	VERB
cana-4510	119	8	for	for	ADP
cana-4510	119	9	training	training	NOUN
cana-4510	119	10	,	,	PUNCT
cana-4510	119	11	560	560	NUM
cana-4510	119	12	for	for	ADP
cana-4510	119	13	testing	testing	NOUN
cana-4510	119	14	,	,	PUNCT
cana-4510	119	15	and	and	CCONJ
cana-4510	119	16	564	564	NUM
cana-4510	119	17	for	for	ADP
cana-4510	119	18	validation	validation	NOUN
cana-4510	119	19	.	.	PUNCT
cana-4510	120	1	the	the	DET
cana-4510	120	2	species	species	NOUN
cana-4510	120	3	-	-	PUNCT
cana-4510	120	4	level	level	NOUN
cana-4510	120	5	dataset	dataset	NOUN
cana-4510	120	6	consisted	consist	VERB
cana-4510	120	7	of	of	ADP
cana-4510	120	8	1582	1582	NUM
cana-4510	120	9	images	image	NOUN
cana-4510	120	10	,	,	PUNCT
cana-4510	120	11	with	with	ADP
cana-4510	120	12	1105	1105	NUM
cana-4510	120	13	designated	designate	VERB
cana-4510	120	14	for	for	ADP
cana-4510	120	15	training	training	NOUN
cana-4510	120	16	,	,	PUNCT
cana-4510	120	17	235	235	NUM
cana-4510	120	18	for	for	ADP
cana-4510	120	19	testing	testing	NOUN
cana-4510	120	20	,	,	PUNCT
cana-4510	120	21	and	and	CCONJ
cana-4510	120	22	242	242	NUM
cana-4510	120	23	for	for	ADP
cana-4510	120	24	validation	validation	NOUN
cana-4510	120	25	purposes	purpose	NOUN
cana-4510	120	26	.	.	PUNCT
cana-4510	121	1	subsequently	subsequently	ADV
cana-4510	121	2	,	,	PUNCT
cana-4510	121	3	the	the	DET
cana-4510	121	4	dataset	dataset	NOUN
cana-4510	121	5	was	be	AUX
cana-4510	121	6	partitioned	partition	VERB
cana-4510	121	7	into	into	ADP
cana-4510	121	8	three	three	NUM
cana-4510	121	9	sections	section	NOUN
cana-4510	121	10	:	:	PUNCT
cana-4510	121	11	70	70	NUM
cana-4510	121	12	%	%	NOUN
cana-4510	121	13	used	use	VERB
cana-4510	121	14	for	for	ADP
cana-4510	121	15	training	training	NOUN
cana-4510	121	16	,	,	PUNCT
cana-4510	121	17	15	15	NUM
cana-4510	121	18	%	%	NOUN
cana-4510	121	19	for	for	ADP
cana-4510	121	20	testing	testing	NOUN
cana-4510	121	21	,	,	PUNCT
cana-4510	121	22	and	and	CCONJ
cana-4510	121	23	15	15	NUM
cana-4510	121	24	%	%	NOUN
cana-4510	121	25	for	for	ADP
cana-4510	121	26	validation	validation	NOUN
cana-4510	121	27	.	.	PUNCT
cana-4510	122	1	these	these	DET
cana-4510	122	2	steps	step	NOUN
cana-4510	122	3	ensure	ensure	VERB
cana-4510	122	4	that	that	SCONJ
cana-4510	122	5	the	the	DET
cana-4510	122	6	images	image	NOUN
cana-4510	122	7	are	be	AUX
cana-4510	122	8	prepared	prepare	VERB
cana-4510	122	9	for	for	ADP
cana-4510	122	10	efficient	efficient	ADJ
cana-4510	122	11	training	training	NOUN
cana-4510	122	12	,	,	PUNCT
cana-4510	122	13	enabling	enable	VERB
cana-4510	122	14	the	the	DET
cana-4510	122	15	dcnn	dcnn	ADJ
cana-4510	122	16	models	model	NOUN
cana-4510	122	17	to	to	PART
cana-4510	122	18	effectively	effectively	ADV
cana-4510	122	19	learn	learn	VERB
cana-4510	122	20	features	feature	NOUN
cana-4510	122	21	from	from	ADP
cana-4510	122	22	the	the	DET
cana-4510	122	23	data	datum	NOUN
cana-4510	122	24	and	and	CCONJ
cana-4510	122	25	make	make	VERB
cana-4510	122	26	accurate	accurate	ADJ
cana-4510	122	27	and	and	CCONJ
cana-4510	122	28	reliable	reliable	ADJ
cana-4510	122	29	predictions	prediction	NOUN
cana-4510	122	30	for	for	ADP
cana-4510	122	31	insect	insect	NOUN
cana-4510	122	32	image	image	NOUN
cana-4510	122	33	classification	classification	NOUN
cana-4510	122	34	.	.	PUNCT
cana-4510	123	1	3.3	3.3	NUM
cana-4510	123	2	taxonomic	taxonomic	ADJ
cana-4510	123	3	level	level	NOUN
cana-4510	123	4	insect	insect	NOUN
cana-4510	123	5	image	image	NOUN
cana-4510	123	6	classification	classification	NOUN
cana-4510	123	7	using	use	VERB
cana-4510	123	8	dcnn	dcnn	PROPN
cana-4510	123	9	an	an	DET
cana-4510	123	10	explanation	explanation	NOUN
cana-4510	123	11	of	of	ADP
cana-4510	123	12	the	the	DET
cana-4510	123	13	dcnn	dcnn	PROPN
cana-4510	123	14	-	-	PUNCT
cana-4510	123	15	based	base	VERB
cana-4510	123	16	models	model	NOUN
cana-4510	123	17	is	be	AUX
cana-4510	123	18	given	give	VERB
cana-4510	123	19	in	in	ADP
cana-4510	123	20	this	this	DET
cana-4510	123	21	section	section	NOUN
cana-4510	123	22	.	.	PUNCT
cana-4510	124	1	the	the	DET
cana-4510	124	2	aim	aim	NOUN
cana-4510	124	3	of	of	ADP
cana-4510	124	4	present	present	ADJ
cana-4510	124	5	research	research	NOUN
cana-4510	124	6	work	work	NOUN
cana-4510	124	7	is	be	AUX
cana-4510	124	8	to	to	PART
cana-4510	124	9	create	create	VERB
cana-4510	124	10	an	an	DET
cana-4510	124	11	efficient	efficient	ADJ
cana-4510	124	12	method	method	NOUN
cana-4510	124	13	for	for	ADP
cana-4510	124	14	classifying	classify	VERB
cana-4510	124	15	and	and	CCONJ
cana-4510	124	16	identifying	identify	VERB
cana-4510	124	17	insect	insect	NOUN
cana-4510	124	18	images	image	NOUN
cana-4510	124	19	at	at	ADP
cana-4510	124	20	various	various	ADJ
cana-4510	124	21	taxonomic	taxonomic	ADJ
cana-4510	124	22	levels	level	NOUN
cana-4510	124	23	.	.	PUNCT
cana-4510	125	1	following	follow	VERB
cana-4510	125	2	preprocessing	preprocesse	VERB
cana-4510	125	3	,	,	PUNCT
cana-4510	125	4	the	the	DET
cana-4510	125	5	classification	classification	NOUN
cana-4510	125	6	networks	network	NOUN
cana-4510	125	7	include	include	VERB
cana-4510	125	8	inception	inception	NOUN
cana-4510	125	9	v3[10	v3[10	PROPN
cana-4510	125	10	]	]	X
cana-4510	125	11	,	,	PUNCT
cana-4510	125	12	vgg19[7	vgg19[7	PROPN
cana-4510	125	13	]	]	PUNCT
cana-4510	125	14	,	,	PUNCT
cana-4510	125	15	resnet	resnet	VERB
cana-4510	125	16	152[11	152[11	NUM
cana-4510	125	17	]	]	X
cana-4510	125	18	,	,	PUNCT
cana-4510	125	19	mobile	mobile	ADJ
cana-4510	125	20	net	net	NOUN
cana-4510	125	21	v2[17	v2[17	PROPN
cana-4510	125	22	]	]	PUNCT
cana-4510	125	23	,	,	PUNCT
cana-4510	125	24	and	and	CCONJ
cana-4510	125	25	xception	xception	NOUN
cana-4510	125	26	[	[	X
cana-4510	125	27	20	20	NUM
cana-4510	125	28	]	]	PUNCT
cana-4510	125	29	structure	structure	NOUN
cana-4510	125	30	-	-	PUNCT
cana-4510	125	31	based	base	VERB
cana-4510	125	32	dcnn	dcnn	PROPN
cana-4510	125	33	classifiers	classifier	NOUN
cana-4510	125	34	used	use	VERB
cana-4510	125	35	for	for	ADP
cana-4510	125	36	classification	classification	NOUN
cana-4510	125	37	task	task	NOUN
cana-4510	125	38	in	in	ADP
cana-4510	125	39	order	order	NOUN
cana-4510	125	40	,	,	PUNCT
cana-4510	125	41	family	family	NOUN
cana-4510	125	42	and	and	CCONJ
cana-4510	125	43	species	specie	NOUN
cana-4510	125	44	levels	level	NOUN
cana-4510	125	45	.	.	PUNCT
cana-4510	126	1	adam	adam	PROPN
cana-4510	126	2	optimizer	optimizer	PROPN
cana-4510	126	3	,	,	PUNCT
cana-4510	126	4	configured	configure	VERB
cana-4510	126	5	with	with	ADP
cana-4510	126	6	a	a	DET
cana-4510	126	7	learning	learn	VERB
cana-4510	126	8	rate	rate	NOUN
cana-4510	126	9	of	of	ADP
cana-4510	126	10	0.0001	0.0001	NUM
cana-4510	126	11	,	,	PUNCT
cana-4510	126	12	was	be	AUX
cana-4510	126	13	employed	employ	VERB
cana-4510	126	14	during	during	ADP
cana-4510	126	15	the	the	DET
cana-4510	126	16	training	training	NOUN
cana-4510	126	17	,	,	PUNCT
cana-4510	126	18	which	which	PRON
cana-4510	126	19	lasted	last	VERB
cana-4510	126	20	for	for	ADP
cana-4510	126	21	50	50	NUM
cana-4510	126	22	epochs	epoch	NOUN
cana-4510	126	23	with	with	ADP
cana-4510	126	24	a	a	DET
cana-4510	126	25	batch	batch	NOUN
cana-4510	126	26	size	size	NOUN
cana-4510	126	27	of	of	ADP
cana-4510	126	28	32	32	NUM
cana-4510	126	29	.	.	PUNCT
cana-4510	127	1	categorical	categorical	PROPN
cana-4510	127	2	cross	cross	PROPN
cana-4510	127	3	entropy	entropy	PROPN
cana-4510	127	4	was	be	AUX
cana-4510	127	5	employed	employ	VERB
cana-4510	127	6	as	as	ADP
cana-4510	127	7	the	the	DET
cana-4510	127	8	loss	loss	NOUN
cana-4510	127	9	function	function	NOUN
cana-4510	127	10	,	,	PUNCT
cana-4510	127	11	while	while	SCONJ
cana-4510	127	12	softmax	softmax	NOUN
cana-4510	127	13	and	and	CCONJ
cana-4510	127	14	relu	relu	NOUN
cana-4510	127	15	were	be	AUX
cana-4510	127	16	selected	select	VERB
cana-4510	127	17	as	as	ADP
cana-4510	127	18	activation	activation	NOUN
cana-4510	127	19	functions	function	NOUN
cana-4510	127	20	in	in	ADP
cana-4510	127	21	all	all	DET
cana-4510	127	22	taxonomic	taxonomic	ADJ
cana-4510	127	23	levels	level	NOUN
cana-4510	127	24	including	include	VERB
cana-4510	127	25	order	order	NOUN
cana-4510	127	26	,	,	PUNCT
cana-4510	127	27	family	family	NOUN
cana-4510	127	28	and	and	CCONJ
cana-4510	127	29	species	specie	NOUN
cana-4510	127	30	levels	level	NOUN
cana-4510	127	31	.	.	PUNCT
cana-4510	128	1	table	table	NOUN
cana-4510	128	2	2	2	NUM
cana-4510	128	3	displays	display	VERB
cana-4510	128	4	the	the	DET
cana-4510	128	5	training	training	NOUN
cana-4510	128	6	hyperparameters	hyperparameter	NOUN
cana-4510	128	7	for	for	ADP
cana-4510	128	8	five	five	NUM
cana-4510	128	9	different	different	ADJ
cana-4510	128	10	dcnn	dcnn	ADJ
cana-4510	128	11	models	model	NOUN
cana-4510	128	12	.	.	PUNCT
cana-4510	129	1	table	table	NOUN
cana-4510	129	2	2	2	NUM
cana-4510	129	3	:	:	PUNCT
cana-4510	129	4	hyperparameters	hyperparameter	NOUN
cana-4510	129	5	for	for	ADP
cana-4510	129	6	deep	deep	ADJ
cana-4510	129	7	convolutional	convolutional	ADJ
cana-4510	129	8	neural	neural	ADJ
cana-4510	129	9	network	network	NOUN
cana-4510	129	10	(	(	PUNCT
cana-4510	129	11	dcnn	dcnn	PROPN
cana-4510	129	12	)	)	PUNCT
cana-4510	129	13	models	model	NOUN
cana-4510	129	14	parameters	parameter	NOUN
cana-4510	129	15	range	range	NOUN
cana-4510	129	16	models	model	NOUN
cana-4510	129	17	:	:	PUNCT
cana-4510	129	18	inception	inception	PROPN
cana-4510	129	19	v3	v3	PROPN
cana-4510	130	1	[	[	X
cana-4510	130	2	10	10	NUM
cana-4510	130	3	]	]	PUNCT
cana-4510	130	4	,	,	PUNCT
cana-4510	130	5	resnet	resnet	VERB
cana-4510	130	6	152	152	NUM
cana-4510	131	1	[	[	X
cana-4510	131	2	11	11	NUM
cana-4510	131	3	]	]	PUNCT
cana-4510	131	4	,	,	PUNCT
cana-4510	131	5	vgg	vgg	NOUN
cana-4510	131	6	19	19	NUM
cana-4510	132	1	[	[	X
cana-4510	132	2	7	7	NUM
cana-4510	132	3	]	]	PUNCT
cana-4510	132	4	,	,	PUNCT
cana-4510	132	5	mobile	mobile	ADJ
cana-4510	132	6	net	net	NOUN
cana-4510	132	7	v2	v2	PROPN
cana-4510	133	1	[	[	X
cana-4510	133	2	17	17	NUM
cana-4510	133	3	]	]	PUNCT
cana-4510	133	4	,	,	PUNCT
cana-4510	133	5	xception	xception	NOUN
cana-4510	134	1	[	[	X
cana-4510	134	2	20	20	NUM
cana-4510	134	3	]	]	PUNCT
cana-4510	134	4	optimizer	optimizer	NOUN
cana-4510	134	5	adam	adam	PROPN
cana-4510	134	6	learning	learning	PROPN
cana-4510	134	7	rate	rate	NOUN
cana-4510	134	8	0.0001	0.0001	NUM
cana-4510	134	9	epochs	epoch	NOUN
cana-4510	134	10	50	50	NUM
cana-4510	134	11	batch	batch	NOUN
cana-4510	134	12	size	size	NOUN
cana-4510	134	13	32	32	NUM
cana-4510	134	14	loss	loss	NOUN
cana-4510	134	15	function	function	NOUN
cana-4510	134	16	categorical	categorical	PROPN
cana-4510	134	17	cross	cross	NOUN
cana-4510	134	18	entropy	entropy	PROPN
cana-4510	134	19	activation	activation	PROPN
cana-4510	134	20	function	function	NOUN
cana-4510	134	21	softmax	softmax	NOUN
cana-4510	134	22	,	,	PUNCT
cana-4510	134	23	relu	relu	NOUN
cana-4510	134	24	communications	communication	NOUN
cana-4510	134	25	on	on	ADP
cana-4510	134	26	applied	apply	VERB
cana-4510	134	27	nonlinear	nonlinear	ADJ
cana-4510	134	28	analysis	analysis	NOUN
cana-4510	134	29	issn	issn	NOUN
cana-4510	134	30	:	:	PUNCT
cana-4510	134	31	1074	1074	NUM
cana-4510	134	32	-	-	PUNCT
cana-4510	134	33	133x	133x	NUM
cana-4510	134	34	vol	vol	NOUN
cana-4510	134	35	32	32	NUM
cana-4510	134	36	no	no	NOUN
cana-4510	134	37	.	.	PUNCT
cana-4510	135	1	9s	9s	NUM
cana-4510	135	2	(	(	PUNCT
cana-4510	135	3	2025	2025	NUM
cana-4510	135	4	)	)	PUNCT
cana-4510	135	5	2250	2250	NUM
cana-4510	135	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	135	7	3.3.1	3.3.1	NUM
cana-4510	135	8	inceptionv3	inceptionv3	NOUN
cana-4510	135	9	inception	inception	PROPN
cana-4510	135	10	v3	v3	PROPN
cana-4510	136	1	[	[	X
cana-4510	136	2	10	10	NUM
cana-4510	136	3	]	]	PUNCT
cana-4510	136	4	is	be	AUX
cana-4510	136	5	a	a	DET
cana-4510	136	6	powerful	powerful	ADJ
cana-4510	136	7	and	and	CCONJ
cana-4510	136	8	efficient	efficient	ADJ
cana-4510	136	9	architecture	architecture	NOUN
cana-4510	136	10	for	for	ADP
cana-4510	136	11	image	image	NOUN
cana-4510	136	12	classification	classification	NOUN
cana-4510	136	13	tasks	task	NOUN
cana-4510	136	14	,	,	PUNCT
cana-4510	136	15	offering	offer	VERB
cana-4510	136	16	a	a	DET
cana-4510	136	17	good	good	ADJ
cana-4510	136	18	balance	balance	NOUN
cana-4510	136	19	between	between	ADP
cana-4510	136	20	accuracy	accuracy	NOUN
cana-4510	136	21	of	of	ADP
cana-4510	136	22	all	all	DET
cana-4510	136	23	levels	level	NOUN
cana-4510	136	24	.	.	PUNCT
cana-4510	137	1	a	a	DET
cana-4510	137	2	combination	combination	NOUN
cana-4510	137	3	of	of	ADP
cana-4510	137	4	parallel	parallel	ADJ
cana-4510	137	5	convolutional	convolutional	ADJ
cana-4510	137	6	operations	operation	NOUN
cana-4510	137	7	,	,	PUNCT
cana-4510	137	8	pooling	pooling	NOUN
cana-4510	137	9	,	,	PUNCT
cana-4510	137	10	and	and	CCONJ
cana-4510	137	11	concatenation	concatenation	NOUN
cana-4510	137	12	,	,	PUNCT
cana-4510	137	13	formulated	formulate	VERB
cana-4510	137	14	as	as	ADP
cana-4510	137	15	:	:	PUNCT
cana-4510	137	16	𝑌	𝑌	PROPN
cana-4510	137	17	=	=	SYM
cana-4510	137	18	𝐶𝑜𝑛𝑐𝑎𝑡	𝐶𝑜𝑛𝑐𝑎𝑡	PROPN
cana-4510	137	19	(	(	PUNCT
cana-4510	137	20	𝐶1𝑥1(𝑥	𝐶1𝑥1(𝑥	NOUN
cana-4510	137	21	)	)	PUNCT
cana-4510	137	22	,	,	PUNCT
cana-4510	137	23	𝐶3𝑥3(𝑥	𝐶3𝑥3(𝑥	NOUN
cana-4510	137	24	)	)	PUNCT
cana-4510	137	25	,	,	PUNCT
cana-4510	137	26	𝐶5𝑥5(𝑥	𝐶5𝑥5(𝑥	NOUN
cana-4510	137	27	)	)	PUNCT
cana-4510	137	28	,	,	PUNCT
cana-4510	137	29	𝑃𝑎𝑣𝑔(𝑥	𝑃𝑎𝑣𝑔(𝑥	PROPN
cana-4510	137	30	)	)	PUNCT
cana-4510	137	31	)	)	PUNCT
cana-4510	137	32	(	(	PUNCT
cana-4510	137	33	1	1	X
cana-4510	137	34	)	)	PUNCT
cana-4510	137	35	where	where	SCONJ
cana-4510	137	36	𝑌	𝑌	PROPN
cana-4510	137	37	is	be	AUX
cana-4510	137	38	the	the	DET
cana-4510	137	39	output	output	NOUN
cana-4510	137	40	feature	feature	NOUN
cana-4510	137	41	representation	representation	NOUN
cana-4510	137	42	.	.	PUNCT
cana-4510	138	1	𝐶𝑘𝑥𝑘	𝐶𝑘𝑥𝑘	PROPN
cana-4510	138	2	represents	represent	VERB
cana-4510	138	3	a	a	DET
cana-4510	138	4	convolutional	convolutional	ADJ
cana-4510	138	5	operation	operation	NOUN
cana-4510	138	6	with	with	ADP
cana-4510	138	7	a	a	DET
cana-4510	138	8	kxk	kxk	ADJ
cana-4510	138	9	kernel	kernel	NOUN
cana-4510	138	10	applied	apply	VERB
cana-4510	138	11	to	to	ADP
cana-4510	138	12	(	(	PUNCT
cana-4510	138	13	𝑥	𝑥	NOUN
cana-4510	138	14	)	)	PUNCT
cana-4510	138	15	.	.	PUNCT
cana-4510	139	1	𝑃𝑎𝑣𝑔(𝑥	𝑃𝑎𝑣𝑔(𝑥	PROPN
cana-4510	139	2	)	)	PUNCT
cana-4510	139	3	denotes	denote	VERB
cana-4510	139	4	the	the	DET
cana-4510	139	5	global	global	ADJ
cana-4510	139	6	average	average	ADJ
cana-4510	139	7	pooling	pool	VERB
cana-4510	139	8	operation	operation	NOUN
cana-4510	139	9	.	.	PUNCT
cana-4510	140	1	additionally	additionally	ADV
cana-4510	140	2	,	,	PUNCT
cana-4510	140	3	in	in	ADP
cana-4510	140	4	this	this	DET
cana-4510	140	5	architecture	architecture	NOUN
cana-4510	140	6	have	have	VERB
cana-4510	140	7	batch	batch	NOUN
cana-4510	140	8	normalization	normalization	NOUN
cana-4510	140	9	and	and	CCONJ
cana-4510	140	10	softmax	softmax	NOUN
cana-4510	140	11	as	as	SCONJ
cana-4510	140	12	activation	activation	NOUN
cana-4510	140	13	function	function	NOUN
cana-4510	140	14	to	to	PART
cana-4510	140	15	classify	classify	VERB
cana-4510	140	16	the	the	DET
cana-4510	140	17	images	image	NOUN
cana-4510	140	18	.	.	PUNCT
cana-4510	141	1	eq	eq	X
cana-4510	141	2	.	.	PUNCT
cana-4510	142	1	(	(	PUNCT
cana-4510	142	2	1	1	X
cana-4510	142	3	)	)	PUNCT
cana-4510	142	4	encapsulates	encapsulate	VERB
cana-4510	142	5	the	the	DET
cana-4510	142	6	architecture	architecture	NOUN
cana-4510	142	7	of	of	ADP
cana-4510	142	8	inception	inception	PROPN
cana-4510	142	9	v3	v3	PROPN
cana-4510	142	10	model	model	NOUN
cana-4510	142	11	.	.	PUNCT
cana-4510	143	1	particularly	particularly	ADV
cana-4510	143	2	in	in	ADP
cana-4510	143	3	this	this	DET
cana-4510	143	4	study	study	NOUN
cana-4510	143	5	inception	inception	PROPN
cana-4510	143	6	v3	v3	PROPN
cana-4510	143	7	has	have	VERB
cana-4510	143	8	an	an	DET
cana-4510	143	9	accuracy	accuracy	NOUN
cana-4510	143	10	of	of	ADP
cana-4510	143	11	96.15	96.15	NUM
cana-4510	143	12	%	%	NOUN
cana-4510	143	13	at	at	ADP
cana-4510	143	14	the	the	DET
cana-4510	143	15	order	order	NOUN
cana-4510	143	16	level	level	NOUN
cana-4510	143	17	,	,	PUNCT
cana-4510	143	18	86.21	86.21	NUM
cana-4510	143	19	%	%	NOUN
cana-4510	143	20	at	at	ADP
cana-4510	143	21	the	the	DET
cana-4510	143	22	family	family	NOUN
cana-4510	143	23	level	level	NOUN
cana-4510	143	24	,	,	PUNCT
cana-4510	143	25	and	and	CCONJ
cana-4510	143	26	79.24	79.24	NUM
cana-4510	143	27	%	%	NOUN
cana-4510	143	28	at	at	ADP
cana-4510	143	29	the	the	DET
cana-4510	143	30	species	species	NOUN
cana-4510	143	31	level	level	NOUN
cana-4510	143	32	.	.	PUNCT
cana-4510	144	1	3.3.2	3.3.2	NUM
cana-4510	144	2	resnet-152	resnet-152	NOUN
cana-4510	144	3	resnet	resnet	NOUN
cana-4510	144	4	152	152	NUM
cana-4510	144	5	[	[	X
cana-4510	144	6	11	11	NUM
cana-4510	144	7	]	]	PUNCT
cana-4510	144	8	model	model	NOUN
cana-4510	144	9	architecture	architecture	NOUN
cana-4510	144	10	involves	involve	VERB
cana-4510	144	11	the	the	DET
cana-4510	144	12	concept	concept	NOUN
cana-4510	144	13	of	of	ADP
cana-4510	144	14	multiple	multiple	ADJ
cana-4510	144	15	residual	residual	ADJ
cana-4510	144	16	connections	connection	NOUN
cana-4510	144	17	.	.	PUNCT
cana-4510	145	1	in	in	ADP
cana-4510	145	2	this	this	DET
cana-4510	145	3	study	study	NOUN
cana-4510	145	4	resnet-152	resnet-152	NOUN
cana-4510	145	5	architecture	architecture	NOUN
cana-4510	145	6	utilized	utilize	VERB
cana-4510	145	7	to	to	PART
cana-4510	145	8	classify	classify	VERB
cana-4510	145	9	the	the	DET
cana-4510	145	10	insect	insect	NOUN
cana-4510	145	11	images	image	NOUN
cana-4510	145	12	.	.	PUNCT
cana-4510	146	1	resnet-152	resnet-152	ADJ
cana-4510	146	2	architecture	architecture	NOUN
cana-4510	146	3	expressed	express	VERB
cana-4510	146	4	as	as	ADP
cana-4510	146	5	eq	eq	ADP
cana-4510	146	6	.	.	PUNCT
cana-4510	147	1	(	(	PUNCT
cana-4510	147	2	2	2	NUM
cana-4510	147	3	)	)	PUNCT
cana-4510	147	4	.	.	PUNCT
cana-4510	148	1	𝑅𝑒𝑠𝑁𝑒𝑡	𝑅𝑒𝑠𝑁𝑒𝑡	ADJ
cana-4510	148	2	−	−	PROPN
cana-4510	148	3	152(𝑥	152(𝑥	PROPN
cana-4510	148	4	)	)	PUNCT
cana-4510	148	5	=	=	SYM
cana-4510	148	6	𝐶𝑜𝑛𝑐𝑎𝑡	𝐶𝑜𝑛𝑐𝑎𝑡	PROPN
cana-4510	148	7	(	(	PUNCT
cana-4510	148	8	𝐶1𝑥1(𝑥	𝐶1𝑥1(𝑥	NOUN
cana-4510	148	9	)	)	PUNCT
cana-4510	148	10	,	,	PUNCT
cana-4510	148	11	𝐶3𝑥3(𝑥	𝐶3𝑥3(𝑥	PROPN
cana-4510	148	12	)	)	PUNCT
cana-4510	148	13	,	,	PUNCT
cana-4510	148	14	𝐶3𝑥3(𝑥	𝐶3𝑥3(𝑥	PROPN
cana-4510	148	15	)	)	PUNCT
cana-4510	148	16	,	,	PUNCT
cana-4510	148	17	𝐴𝑣𝑔𝑝𝑜𝑜𝑙(𝑥	𝐴𝑣𝑔𝑝𝑜𝑜𝑙(𝑥	NOUN
cana-4510	148	18	)	)	PUNCT
cana-4510	149	1	+	+	SYM
cana-4510	150	1	𝑥	𝑥	X
cana-4510	150	2	)	)	PUNCT
cana-4510	150	3	(	(	PUNCT
cana-4510	150	4	2	2	X
cana-4510	150	5	)	)	PUNCT
cana-4510	150	6	where	where	SCONJ
cana-4510	150	7	𝑋	𝑋	PROPN
cana-4510	150	8	represents	represent	VERB
cana-4510	150	9	the	the	DET
cana-4510	150	10	input	input	NOUN
cana-4510	150	11	images	image	NOUN
cana-4510	150	12	,	,	PUNCT
cana-4510	150	13	𝐶1𝑥1(𝑥	𝐶1𝑥1(𝑥	NOUN
cana-4510	150	14	)	)	PUNCT
cana-4510	150	15	denotes	denote	VERB
cana-4510	150	16	a	a	DET
cana-4510	150	17	convolutional	convolutional	ADJ
cana-4510	150	18	operation	operation	NOUN
cana-4510	150	19	with	with	ADP
cana-4510	150	20	a	a	DET
cana-4510	150	21	1𝑥1	1𝑥1	NUM
cana-4510	150	22	filter	filter	NOUN
cana-4510	150	23	size	size	NOUN
cana-4510	150	24	.	.	PUNCT
cana-4510	151	1	𝐶3𝑥3(𝑥	𝐶3𝑥3(𝑥	NUM
cana-4510	151	2	)	)	PUNCT
cana-4510	151	3	represents	represent	VERB
cana-4510	151	4	a	a	DET
cana-4510	151	5	convolutional	convolutional	ADJ
cana-4510	151	6	operation	operation	NOUN
cana-4510	151	7	with	with	ADP
cana-4510	151	8	a	a	DET
cana-4510	151	9	3x3	3x3	NUM
cana-4510	151	10	filter	filter	NOUN
cana-4510	151	11	size	size	NOUN
cana-4510	151	12	.	.	PUNCT
cana-4510	152	1	𝐴𝑣𝑔𝑝𝑜𝑜𝑙(𝑥	𝐴𝑣𝑔𝑝𝑜𝑜𝑙(𝑥	PROPN
cana-4510	152	2	)	)	PUNCT
cana-4510	152	3	is	be	AUX
cana-4510	152	4	the	the	DET
cana-4510	152	5	result	result	NOUN
cana-4510	152	6	of	of	ADP
cana-4510	152	7	global	global	ADJ
cana-4510	152	8	average	average	ADJ
cana-4510	152	9	pooling	pooling	NOUN
cana-4510	152	10	operation	operation	NOUN
cana-4510	152	11	applied	apply	VERB
cana-4510	152	12	to	to	ADP
cana-4510	152	13	𝑥	𝑥	PROPN
cana-4510	152	14	.	.	PUNCT
cana-4510	153	1	the	the	DET
cana-4510	153	2	addition	addition	NOUN
cana-4510	153	3	operation	operation	NOUN
cana-4510	154	1	+	+	CCONJ
cana-4510	154	2	indicates	indicate	VERB
cana-4510	154	3	the	the	DET
cana-4510	154	4	element	element	NOUN
cana-4510	154	5	-	-	PUNCT
cana-4510	154	6	wise	wise	ADJ
cana-4510	154	7	addition	addition	NOUN
cana-4510	154	8	of	of	ADP
cana-4510	154	9	the	the	DET
cana-4510	154	10	input	input	NOUN
cana-4510	154	11	tensor	tensor	NOUN
cana-4510	154	12	𝑥	𝑥	NOUN
cana-4510	154	13	to	to	ADP
cana-4510	154	14	the	the	DET
cana-4510	154	15	output	output	NOUN
cana-4510	154	16	of	of	ADP
cana-4510	154	17	pooling	pool	VERB
cana-4510	154	18	operation	operation	NOUN
cana-4510	154	19	,	,	PUNCT
cana-4510	154	20	known	know	VERB
cana-4510	154	21	as	as	ADP
cana-4510	154	22	the	the	DET
cana-4510	154	23	residual	residual	ADJ
cana-4510	154	24	connection	connection	NOUN
cana-4510	154	25	.	.	PUNCT
cana-4510	155	1	in	in	ADP
cana-4510	155	2	this	this	DET
cana-4510	155	3	study	study	NOUN
cana-4510	155	4	,	,	PUNCT
cana-4510	155	5	resnet-152	resnet-152	NOUN
cana-4510	155	6	model	model	NOUN
cana-4510	155	7	demonstrates	demonstrate	VERB
cana-4510	155	8	the	the	DET
cana-4510	155	9	40.23	40.23	NUM
cana-4510	155	10	%	%	NOUN
cana-4510	155	11	of	of	ADP
cana-4510	155	12	accuracy	accuracy	NOUN
cana-4510	155	13	in	in	ADP
cana-4510	155	14	order	order	NOUN
cana-4510	155	15	level	level	NOUN
cana-4510	155	16	data	datum	NOUN
cana-4510	155	17	26.60	26.60	NUM
cana-4510	155	18	%	%	NOUN
cana-4510	155	19	of	of	ADP
cana-4510	155	20	accuracy	accuracy	NOUN
cana-4510	155	21	in	in	ADP
cana-4510	155	22	family	family	NOUN
cana-4510	155	23	level	level	NOUN
cana-4510	155	24	data	datum	NOUN
cana-4510	155	25	and	and	CCONJ
cana-4510	155	26	24.38	24.38	NUM
cana-4510	155	27	%	%	NOUN
cana-4510	155	28	of	of	ADP
cana-4510	155	29	accuracy	accuracy	NOUN
cana-4510	155	30	results	result	NOUN
cana-4510	155	31	in	in	ADP
cana-4510	155	32	species	species	NOUN
cana-4510	155	33	level	level	NOUN
cana-4510	155	34	data	datum	NOUN
cana-4510	155	35	.	.	PUNCT
cana-4510	156	1	3.3.3	3.3.3	NUM
cana-4510	156	2	vgg	vgg	NOUN
cana-4510	156	3	19	19	NUM
cana-4510	156	4	vgg	vgg	NOUN
cana-4510	156	5	19	19	NUM
cana-4510	156	6	[	[	X
cana-4510	156	7	7	7	NUM
cana-4510	156	8	]	]	PUNCT
cana-4510	156	9	features	feature	VERB
cana-4510	156	10	a	a	DET
cana-4510	156	11	series	series	NOUN
cana-4510	156	12	of	of	ADP
cana-4510	156	13	convolutional	convolutional	ADJ
cana-4510	156	14	layers	layer	NOUN
cana-4510	156	15	combined	combine	VERB
cana-4510	156	16	with	with	ADP
cana-4510	156	17	max	max	PROPN
cana-4510	156	18	-	-	PUNCT
cana-4510	156	19	pooling	pool	VERB
cana-4510	156	20	layers	layer	NOUN
cana-4510	156	21	,	,	PUNCT
cana-4510	156	22	succeeded	succeed	VERB
cana-4510	156	23	by	by	ADP
cana-4510	156	24	a	a	DET
cana-4510	156	25	fully	fully	ADV
cana-4510	156	26	connected	connect	VERB
cana-4510	156	27	layer	layer	NOUN
cana-4510	156	28	to	to	PART
cana-4510	156	29	perform	perform	VERB
cana-4510	156	30	classification	classification	NOUN
cana-4510	156	31	.	.	PUNCT
cana-4510	157	1	it	it	PRON
cana-4510	157	2	is	be	AUX
cana-4510	157	3	characterized	characterize	VERB
cana-4510	157	4	by	by	ADP
cana-4510	157	5	deeper	deep	ADJ
cana-4510	157	6	layer	layer	NOUN
cana-4510	157	7	structure	structure	NOUN
cana-4510	157	8	which	which	PRON
cana-4510	157	9	enables	enable	VERB
cana-4510	157	10	it	it	PRON
cana-4510	157	11	to	to	PART
cana-4510	157	12	learn	learn	VERB
cana-4510	157	13	complex	complex	ADJ
cana-4510	157	14	features	feature	NOUN
cana-4510	157	15	in	in	ADP
cana-4510	157	16	insect	insect	NOUN
cana-4510	157	17	images	image	NOUN
cana-4510	157	18	.	.	PUNCT
cana-4510	158	1	the	the	DET
cana-4510	158	2	structure	structure	NOUN
cana-4510	158	3	of	of	ADP
cana-4510	158	4	vgg	vgg	PROPN
cana-4510	158	5	19	19	NUM
cana-4510	158	6	can	can	AUX
cana-4510	158	7	be	be	AUX
cana-4510	158	8	expresses	express	NOUN
cana-4510	158	9	as	as	SCONJ
cana-4510	158	10	follows	follow	VERB
cana-4510	158	11	in	in	ADP
cana-4510	158	12	eq	eq	ADP
cana-4510	158	13	.	.	PUNCT
cana-4510	159	1	(	(	PUNCT
cana-4510	159	2	3	3	X
cana-4510	159	3	)	)	PUNCT
cana-4510	159	4	𝑉𝐺𝐺19(𝑥	𝑉𝐺𝐺19(𝑥	NOUN
cana-4510	159	5	)	)	PUNCT
cana-4510	159	6	=	=	SYM
cana-4510	159	7	𝐶𝑜𝑛𝑣2𝐷(𝑥	𝐶𝑜𝑛𝑣2𝐷(𝑥	ADJ
cana-4510	159	8	)	)	PUNCT
cana-4510	159	9	→	→	SYM
cana-4510	159	10	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NOUN
cana-4510	159	11	)	)	PUNCT
cana-4510	159	12	→	→	SYM
cana-4510	159	13	𝑐𝑜𝑛𝑣2𝐷(𝑥	𝑐𝑜𝑛𝑣2𝐷(𝑥	NOUN
cana-4510	159	14	)	)	PUNCT
cana-4510	159	15	→	→	SYM
cana-4510	159	16	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NOUN
cana-4510	159	17	)	)	PUNCT
cana-4510	159	18	→	→	SYM
cana-4510	159	19	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	20	)	)	PUNCT
cana-4510	159	21	→	→	PUNCT
cana-4510	159	22	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	23	)	)	PUNCT
cana-4510	159	24	→	→	SYM
cana-4510	159	25	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NOUN
cana-4510	159	26	)	)	PUNCT
cana-4510	159	27	→	→	SYM
cana-4510	159	28	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	29	)	)	PUNCT
cana-4510	159	30	→	→	PUNCT
cana-4510	159	31	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	32	)	)	PUNCT
cana-4510	159	33	→	→	SYM
cana-4510	159	34	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NOUN
cana-4510	159	35	)	)	PUNCT
cana-4510	159	36	→	→	SYM
cana-4510	159	37	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	38	)	)	PUNCT
cana-4510	159	39	→	→	PUNCT
cana-4510	159	40	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	159	41	)	)	PUNCT
cana-4510	159	42	→	→	SYM
cana-4510	159	43	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NOUN
cana-4510	159	44	)	)	PUNCT
cana-4510	159	45	→	→	SYM
cana-4510	159	46	𝐹𝑙𝑎𝑡𝑡𝑒𝑛(𝑥	𝐹𝑙𝑎𝑡𝑡𝑒𝑛(𝑥	NUM
cana-4510	159	47	)	)	PUNCT
cana-4510	159	48	→	→	SYM
cana-4510	159	49	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	159	50	)	)	PUNCT
cana-4510	159	51	→	→	SYM
cana-4510	159	52	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	159	53	)	)	PUNCT
cana-4510	159	54	→	→	SYM
cana-4510	159	55	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	159	56	)	)	PUNCT
cana-4510	159	57	(	(	PUNCT
cana-4510	159	58	3	3	X
cana-4510	159	59	)	)	PUNCT
cana-4510	159	60	where	where	SCONJ
cana-4510	159	61	𝑋	𝑋	PROPN
cana-4510	159	62	represents	represent	VERB
cana-4510	159	63	the	the	DET
cana-4510	159	64	input	input	NOUN
cana-4510	159	65	images	image	NOUN
cana-4510	159	66	,	,	PUNCT
cana-4510	159	67	𝐶𝑜𝑛𝑣2𝐷(𝑥	𝐶𝑜𝑛𝑣2𝐷(𝑥	ADJ
cana-4510	159	68	)	)	PUNCT
cana-4510	159	69	and	and	CCONJ
cana-4510	159	70	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝑀𝑎𝑥𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	NUM
cana-4510	159	71	)	)	PUNCT
cana-4510	159	72	denotes	denote	VERB
cana-4510	159	73	2𝐷	2𝐷	ADJ
cana-4510	159	74	convolutional	convolutional	ADJ
cana-4510	159	75	layer	layer	NOUN
cana-4510	159	76	and	and	CCONJ
cana-4510	159	77	2𝐷	2𝐷	ADJ
cana-4510	159	78	max	max	PROPN
cana-4510	159	79	-	-	PUNCT
cana-4510	159	80	pooling	pool	VERB
cana-4510	159	81	layer	layer	NOUN
cana-4510	159	82	respectively	respectively	ADV
cana-4510	159	83	.	.	PUNCT
cana-4510	160	1	𝐶𝑜𝑛𝑣3𝑥3(𝑥	𝐶𝑜𝑛𝑣3𝑥3(𝑥	NOUN
cana-4510	160	2	)	)	PUNCT
cana-4510	160	3	represents	represent	VERB
cana-4510	160	4	a	a	DET
cana-4510	160	5	3𝑥3	3𝑥3	NUM
cana-4510	160	6	convolution	convolution	NOUN
cana-4510	160	7	function	function	NOUN
cana-4510	160	8	.	.	PUNCT
cana-4510	161	1	𝐹𝑙𝑎𝑡𝑡𝑒𝑛(𝑥	𝐹𝑙𝑎𝑡𝑡𝑒𝑛(𝑥	NOUN
cana-4510	161	2	)	)	PUNCT
cana-4510	161	3	flattens	flatten	VERB
cana-4510	161	4	the	the	DET
cana-4510	161	5	input	input	NOUN
cana-4510	161	6	images	image	NOUN
cana-4510	161	7	into	into	ADP
cana-4510	161	8	a	a	DET
cana-4510	161	9	1𝐷	1𝐷	PROPN
cana-4510	161	10	vector	vector	NOUN
cana-4510	161	11	.	.	PUNCT
cana-4510	162	1	and	and	CCONJ
cana-4510	162	2	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	162	3	)	)	PUNCT
cana-4510	162	4	indicates	indicate	VERB
cana-4510	162	5	a	a	DET
cana-4510	162	6	fully	fully	ADV
cana-4510	162	7	connected	connect	VERB
cana-4510	162	8	layer	layer	NOUN
cana-4510	162	9	.	.	PUNCT
cana-4510	163	1	in	in	ADP
cana-4510	163	2	this	this	DET
cana-4510	163	3	study	study	NOUN
cana-4510	163	4	,	,	PUNCT
cana-4510	163	5	vgg	vgg	PROPN
cana-4510	163	6	19	19	NUM
cana-4510	163	7	model	model	NOUN
cana-4510	163	8	has	have	VERB
cana-4510	163	9	an	an	DET
cana-4510	163	10	accuracy	accuracy	NOUN
cana-4510	163	11	of	of	ADP
cana-4510	163	12	90	90	NUM
cana-4510	163	13	%	%	NOUN
cana-4510	163	14	in	in	ADP
cana-4510	163	15	order	order	NOUN
cana-4510	163	16	level	level	NOUN
cana-4510	163	17	87.23	87.23	NUM
cana-4510	163	18	%	%	NOUN
cana-4510	163	19	of	of	ADP
cana-4510	163	20	accuracy	accuracy	NOUN
cana-4510	163	21	in	in	ADP
cana-4510	163	22	family	family	NOUN
cana-4510	163	23	level	level	NOUN
cana-4510	163	24	and	and	CCONJ
cana-4510	163	25	82.64	82.64	NUM
cana-4510	163	26	%	%	NOUN
cana-4510	163	27	of	of	ADP
cana-4510	163	28	accuracy	accuracy	NOUN
cana-4510	163	29	in	in	ADP
cana-4510	163	30	species	specie	NOUN
cana-4510	163	31	level	level	NOUN
cana-4510	163	32	.	.	PUNCT
cana-4510	164	1	figure	figure	NOUN
cana-4510	164	2	3	3	NUM
cana-4510	164	3	shown	show	VERB
cana-4510	164	4	the	the	DET
cana-4510	164	5	vgg	vgg	ADJ
cana-4510	164	6	19	19	NUM
cana-4510	164	7	model	model	NOUN
cana-4510	164	8	architecture	architecture	NOUN
cana-4510	164	9	.	.	PUNCT
cana-4510	165	1	communications	communication	NOUN
cana-4510	165	2	on	on	ADP
cana-4510	165	3	applied	apply	VERB
cana-4510	165	4	nonlinear	nonlinear	ADJ
cana-4510	165	5	analysis	analysis	NOUN
cana-4510	165	6	issn	issn	NOUN
cana-4510	165	7	:	:	PUNCT
cana-4510	165	8	1074	1074	NUM
cana-4510	165	9	-	-	PUNCT
cana-4510	165	10	133x	133x	NUM
cana-4510	165	11	vol	vol	NOUN
cana-4510	165	12	32	32	NUM
cana-4510	165	13	no	no	NOUN
cana-4510	165	14	.	.	PUNCT
cana-4510	166	1	9s	9s	NUM
cana-4510	166	2	(	(	PUNCT
cana-4510	166	3	2025	2025	NUM
cana-4510	166	4	)	)	PUNCT
cana-4510	166	5	2251	2251	NUM
cana-4510	166	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	166	7	figure	figure	NOUN
cana-4510	166	8	3	3	NUM
cana-4510	166	9	:	:	PUNCT
cana-4510	166	10	architecture	architecture	NOUN
cana-4510	166	11	of	of	ADP
cana-4510	166	12	vgg	vgg	PROPN
cana-4510	166	13	19	19	NUM
cana-4510	166	14	model	model	NOUN
cana-4510	166	15	3.3.4	3.3.4	NUM
cana-4510	166	16	mobile	mobile	NOUN
cana-4510	166	17	net	net	NOUN
cana-4510	166	18	v2	v2	PROPN
cana-4510	166	19	the	the	DET
cana-4510	166	20	mobile	mobile	ADJ
cana-4510	166	21	net	net	NOUN
cana-4510	166	22	v2	v2	PROPN
cana-4510	167	1	[	[	X
cana-4510	167	2	17	17	NUM
cana-4510	167	3	]	]	PUNCT
cana-4510	167	4	architecture	architecture	NOUN
cana-4510	167	5	can	can	AUX
cana-4510	167	6	be	be	AUX
cana-4510	167	7	summarized	summarize	VERB
cana-4510	167	8	as	as	SCONJ
cana-4510	167	9	follows	follow	VERB
cana-4510	167	10	in	in	ADP
cana-4510	167	11	eq	eq	ADP
cana-4510	167	12	.	.	PUNCT
cana-4510	168	1	(	(	PUNCT
cana-4510	168	2	4	4	NUM
cana-4510	168	3	)	)	PUNCT
cana-4510	168	4	.	.	PUNCT
cana-4510	169	1	𝑀𝑜𝑏𝑖𝑙𝑒𝑁𝑒𝑡	𝑀𝑜𝑏𝑖𝑙𝑒𝑁𝑒𝑡	PROPN
cana-4510	169	2	𝑉2(𝑥	𝑉2(𝑥	NOUN
cana-4510	169	3	)	)	PUNCT
cana-4510	169	4	=	=	SYM
cana-4510	169	5	𝐶𝑜𝑛𝑣2𝐷(𝑥	𝐶𝑜𝑛𝑣2𝐷(𝑥	ADJ
cana-4510	169	6	)	)	PUNCT
cana-4510	169	7	→	→	SYM
cana-4510	169	8	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	PROPN
cana-4510	169	9	)	)	PUNCT
cana-4510	169	10	→	→	SYM
cana-4510	169	11	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	PROPN
cana-4510	169	12	)	)	PUNCT
cana-4510	169	13	…	…	PUNCT
cana-4510	169	14	→	→	SYM
cana-4510	169	15	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	PROPN
cana-4510	169	16	)	)	PUNCT
cana-4510	169	17	→	→	PUNCT
cana-4510	169	18	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	PROPN
cana-4510	169	19	)	)	PUNCT
cana-4510	169	20	→	→	SYM
cana-4510	169	21	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	169	22	)	)	PUNCT
cana-4510	169	23	→	→	SYM
cana-4510	169	24	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	169	25	)	)	PUNCT
cana-4510	169	26	(	(	PUNCT
cana-4510	169	27	4	4	X
cana-4510	169	28	)	)	PUNCT
cana-4510	169	29	here	here	ADV
cana-4510	169	30	,	,	PUNCT
cana-4510	169	31	𝑥	𝑥	PROPN
cana-4510	169	32	represents	represent	VERB
cana-4510	169	33	the	the	DET
cana-4510	169	34	input	input	NOUN
cana-4510	169	35	images	image	NOUN
cana-4510	169	36	.	.	PUNCT
cana-4510	170	1	𝐶𝑜𝑛𝑣2𝐷(𝑥	𝐶𝑜𝑛𝑣2𝐷(𝑥	ADJ
cana-4510	170	2	)	)	PUNCT
cana-4510	170	3	denotes	denote	VERB
cana-4510	170	4	the	the	DET
cana-4510	170	5	initial	initial	ADJ
cana-4510	170	6	convolutional	convolutional	ADJ
cana-4510	170	7	layer	layer	NOUN
cana-4510	170	8	with	with	ADP
cana-4510	170	9	224	224	NUM
cana-4510	170	10	𝑥	𝑥	PROPN
cana-4510	170	11	224	224	NUM
cana-4510	170	12	pixels	pixel	NOUN
cana-4510	170	13	.	.	PUNCT
cana-4510	171	1	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	𝐵𝑜𝑡𝑡𝑙𝑒𝑛𝑒𝑐𝑘𝐵𝑙𝑜𝑐𝑘(𝑥	PROPN
cana-4510	171	2	)	)	PUNCT
cana-4510	171	3	represents	represent	VERB
cana-4510	171	4	a	a	DET
cana-4510	171	5	series	series	NOUN
cana-4510	171	6	of	of	ADP
cana-4510	171	7	bottle	bottle	NOUN
cana-4510	171	8	neck	neck	NOUN
cana-4510	171	9	blocks	block	NOUN
cana-4510	171	10	,	,	PUNCT
cana-4510	171	11	each	each	PRON
cana-4510	171	12	including	include	VERB
cana-4510	171	13	depth	depth	NOUN
cana-4510	171	14	wise	wise	ADJ
cana-4510	171	15	separable	separable	ADJ
cana-4510	171	16	convolution	convolution	NOUN
cana-4510	171	17	layers	layer	NOUN
cana-4510	171	18	,	,	PUNCT
cana-4510	171	19	followed	follow	VERB
cana-4510	171	20	by	by	ADP
cana-4510	171	21	pointwise	pointwise	NOUN
cana-4510	171	22	convolution	convolution	NOUN
cana-4510	171	23	layers	layer	NOUN
cana-4510	171	24	.	.	PUNCT
cana-4510	172	1	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	ADJ
cana-4510	172	2	)	)	PUNCT
cana-4510	172	3	calculates	calculate	VERB
cana-4510	172	4	the	the	DET
cana-4510	172	5	mean	mean	ADJ
cana-4510	172	6	value	value	NOUN
cana-4510	172	7	for	for	ADP
cana-4510	172	8	each	each	DET
cana-4510	172	9	feature	feature	NOUN
cana-4510	172	10	representation	representation	NOUN
cana-4510	172	11	over	over	ADP
cana-4510	172	12	its	its	PRON
cana-4510	172	13	spatial	spatial	ADJ
cana-4510	172	14	extent	extent	NOUN
cana-4510	172	15	,	,	PUNCT
cana-4510	172	16	producing	produce	VERB
cana-4510	172	17	a	a	DET
cana-4510	172	18	single	single	ADJ
cana-4510	172	19	feature	feature	NOUN
cana-4510	172	20	vector	vector	NOUN
cana-4510	172	21	per	per	ADP
cana-4510	172	22	channel	channel	NOUN
cana-4510	172	23	.	.	PUNCT
cana-4510	173	1	and	and	CCONJ
cana-4510	173	2	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	173	3	)	)	PUNCT
cana-4510	173	4	denotes	denote	NOUN
cana-4510	173	5	fully	fully	ADV
cana-4510	173	6	connected	connect	VERB
cana-4510	173	7	layers	layer	NOUN
cana-4510	173	8	for	for	ADP
cana-4510	173	9	classification	classification	NOUN
cana-4510	173	10	.	.	PUNCT
cana-4510	174	1	the	the	DET
cana-4510	174	2	bottleneck	bottleneck	NOUN
cana-4510	174	3	blocks	block	NOUN
cana-4510	174	4	help	help	VERB
cana-4510	174	5	to	to	PART
cana-4510	174	6	capture	capture	VERB
cana-4510	174	7	complex	complex	ADJ
cana-4510	174	8	features	feature	NOUN
cana-4510	174	9	in	in	ADP
cana-4510	174	10	the	the	DET
cana-4510	174	11	input	input	NOUN
cana-4510	174	12	data	datum	NOUN
cana-4510	174	13	,	,	PUNCT
cana-4510	174	14	making	make	VERB
cana-4510	174	15	mobile	mobile	ADJ
cana-4510	174	16	net	net	ADJ
cana-4510	174	17	v2	v2	PROPN
cana-4510	174	18	suitable	suitable	ADJ
cana-4510	174	19	for	for	ADP
cana-4510	174	20	taxonomic	taxonomic	ADJ
cana-4510	174	21	level	level	NOUN
cana-4510	174	22	classification	classification	NOUN
cana-4510	174	23	task	task	NOUN
cana-4510	174	24	.	.	PUNCT
cana-4510	175	1	in	in	ADP
cana-4510	175	2	this	this	DET
cana-4510	175	3	study	study	NOUN
cana-4510	175	4	,	,	PUNCT
cana-4510	175	5	the	the	DET
cana-4510	175	6	mobile	mobile	ADJ
cana-4510	175	7	net	net	ADJ
cana-4510	175	8	v2	v2	PROPN
cana-4510	175	9	model	model	NOUN
cana-4510	175	10	has	have	VERB
cana-4510	175	11	an	an	DET
cana-4510	175	12	accuracy	accuracy	NOUN
cana-4510	175	13	of	of	ADP
cana-4510	175	14	98.36	98.36	NUM
cana-4510	175	15	%	%	NOUN
cana-4510	175	16	in	in	ADP
cana-4510	175	17	order	order	NOUN
cana-4510	175	18	level	level	NOUN
cana-4510	175	19	,	,	PUNCT
cana-4510	175	20	93.26	93.26	NUM
cana-4510	175	21	%	%	NOUN
cana-4510	175	22	of	of	ADP
cana-4510	175	23	accuracy	accuracy	NOUN
cana-4510	175	24	in	in	ADP
cana-4510	175	25	family	family	NOUN
cana-4510	175	26	level	level	NOUN
cana-4510	175	27	and	and	CCONJ
cana-4510	175	28	88.33	88.33	NUM
cana-4510	175	29	%	%	NOUN
cana-4510	175	30	of	of	ADP
cana-4510	175	31	accuracy	accuracy	NOUN
cana-4510	175	32	in	in	ADP
cana-4510	175	33	species	specie	NOUN
cana-4510	175	34	level	level	NOUN
cana-4510	175	35	classification	classification	NOUN
cana-4510	175	36	.	.	PUNCT
cana-4510	176	1	3.3.5	3.3.5	NUM
cana-4510	176	2	xception	xception	NOUN
cana-4510	176	3	xception	xception	NOUN
cana-4510	177	1	[	[	X
cana-4510	177	2	20	20	NUM
cana-4510	177	3	]	]	PUNCT
cana-4510	177	4	architecture	architecture	NOUN
cana-4510	177	5	is	be	AUX
cana-4510	177	6	characterized	characterize	VERB
cana-4510	177	7	by	by	ADP
cana-4510	177	8	its	its	PRON
cana-4510	177	9	extreme	extreme	ADJ
cana-4510	177	10	depth	depth	NOUN
cana-4510	177	11	and	and	CCONJ
cana-4510	177	12	use	use	NOUN
cana-4510	177	13	of	of	ADP
cana-4510	177	14	depthwise	depthwise	NOUN
cana-4510	177	15	separable	separable	ADJ
cana-4510	177	16	convolutions	convolution	NOUN
cana-4510	177	17	,	,	PUNCT
cana-4510	177	18	which	which	PRON
cana-4510	177	19	allows	allow	VERB
cana-4510	177	20	it	it	PRON
cana-4510	177	21	to	to	PART
cana-4510	177	22	capture	capture	VERB
cana-4510	177	23	complex	complex	ADJ
cana-4510	177	24	patterns	pattern	NOUN
cana-4510	177	25	and	and	CCONJ
cana-4510	177	26	achieve	achieve	VERB
cana-4510	177	27	best	good	ADJ
cana-4510	177	28	accuracy	accuracy	NOUN
cana-4510	177	29	in	in	ADP
cana-4510	177	30	species	species	NOUN
cana-4510	177	31	level	level	NOUN
cana-4510	177	32	image	image	NOUN
cana-4510	177	33	classification	classification	NOUN
cana-4510	177	34	.	.	PUNCT
cana-4510	178	1	the	the	DET
cana-4510	178	2	xception	xception	NOUN
cana-4510	178	3	architecture	architecture	NOUN
cana-4510	178	4	can	can	AUX
cana-4510	178	5	be	be	AUX
cana-4510	178	6	represented	represent	VERB
cana-4510	178	7	as	as	SCONJ
cana-4510	178	8	follows	follow	VERB
cana-4510	178	9	in	in	ADP
cana-4510	178	10	eq	eq	ADP
cana-4510	178	11	.	.	PUNCT
cana-4510	179	1	(	(	PUNCT
cana-4510	179	2	5	5	NUM
cana-4510	179	3	)	)	PUNCT
cana-4510	179	4	.	.	PUNCT
cana-4510	180	1	𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛(𝑥	𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛(𝑥	NOUN
cana-4510	180	2	)	)	PUNCT
cana-4510	181	1	=	=	SYM
cana-4510	182	1	𝐸𝑛𝑡𝑟𝑦𝐹𝑙𝑜𝑤	𝐸𝑛𝑡𝑟𝑦𝐹𝑙𝑜𝑤	PROPN
cana-4510	182	2	(	(	PUNCT
cana-4510	182	3	𝑥	𝑥	NOUN
cana-4510	182	4	)	)	PUNCT
cana-4510	182	5	→	→	SYM
cana-4510	182	6	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	PROPN
cana-4510	182	7	(	(	PUNCT
cana-4510	182	8	𝑥	𝑥	NOUN
cana-4510	182	9	)	)	PUNCT
cana-4510	182	10	→	→	SYM
cana-4510	182	11	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	PROPN
cana-4510	182	12	(	(	PUNCT
cana-4510	182	13	𝑥	𝑥	NOUN
cana-4510	182	14	)	)	PUNCT
cana-4510	182	15	→	→	SYM
cana-4510	182	16	⋯	⋯	PROPN
cana-4510	182	17	→	→	SYM
cana-4510	182	18	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	PROPN
cana-4510	182	19	(	(	PUNCT
cana-4510	182	20	𝑥	𝑥	NOUN
cana-4510	182	21	)	)	PUNCT
cana-4510	182	22	→	→	SYM
cana-4510	182	23	𝐸𝑥𝑖𝑡𝐹𝑙𝑜𝑤(𝑥	𝐸𝑥𝑖𝑡𝐹𝑙𝑜𝑤(𝑥	NUM
cana-4510	182	24	)	)	PUNCT
cana-4510	182	25	→	→	SYM
cana-4510	182	26	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	PROPN
cana-4510	182	27	)	)	PUNCT
cana-4510	182	28	→	→	SYM
cana-4510	182	29	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	182	30	)	)	PUNCT
cana-4510	182	31	→	→	SYM
cana-4510	182	32	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	NOUN
cana-4510	182	33	)	)	PUNCT
cana-4510	182	34	(	(	PUNCT
cana-4510	182	35	5	5	X
cana-4510	182	36	)	)	PUNCT
cana-4510	182	37	here	here	ADV
cana-4510	182	38	,	,	PUNCT
cana-4510	182	39	𝑥	𝑥	PROPN
cana-4510	182	40	represents	represent	VERB
cana-4510	182	41	the	the	DET
cana-4510	182	42	input	input	NOUN
cana-4510	182	43	images	image	NOUN
cana-4510	182	44	.	.	PUNCT
cana-4510	183	1	𝐸𝑛𝑡𝑟𝑦𝐹𝑙𝑜𝑤	𝐸𝑛𝑡𝑟𝑦𝐹𝑙𝑜𝑤	PROPN
cana-4510	183	2	(	(	PUNCT
cana-4510	183	3	𝑥	𝑥	NOUN
cana-4510	183	4	)	)	PUNCT
cana-4510	183	5	denotes	denote	VERB
cana-4510	183	6	the	the	DET
cana-4510	183	7	entry	entry	NOUN
cana-4510	183	8	flow	flow	NOUN
cana-4510	183	9	of	of	ADP
cana-4510	183	10	the	the	DET
cana-4510	183	11	xception	xception	PROPN
cana-4510	183	12	model	model	NOUN
cana-4510	183	13	,	,	PUNCT
cana-4510	183	14	which	which	PRON
cana-4510	183	15	consists	consist	VERB
cana-4510	183	16	of	of	ADP
cana-4510	183	17	several	several	ADJ
cana-4510	183	18	convolutional	convolutional	ADJ
cana-4510	183	19	and	and	CCONJ
cana-4510	183	20	depth	depth	ADJ
cana-4510	183	21	wise	wise	ADJ
cana-4510	183	22	separable	separable	ADJ
cana-4510	183	23	convolutional	convolutional	ADJ
cana-4510	183	24	layers	layer	NOUN
cana-4510	183	25	.	.	PUNCT
cana-4510	184	1	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	𝑀𝑖𝑑𝑑𝑙𝑒𝐹𝑙𝑜𝑤	PROPN
cana-4510	184	2	(	(	PUNCT
cana-4510	184	3	𝑥	𝑥	NOUN
cana-4510	184	4	)	)	PUNCT
cana-4510	184	5	represents	represent	VERB
cana-4510	184	6	the	the	DET
cana-4510	184	7	middle	middle	ADJ
cana-4510	184	8	flow	flow	NOUN
cana-4510	184	9	,	,	PUNCT
cana-4510	184	10	which	which	PRON
cana-4510	184	11	repeats	repeat	VERB
cana-4510	184	12	a	a	DET
cana-4510	184	13	series	series	NOUN
cana-4510	184	14	of	of	ADP
cana-4510	184	15	depthwise	depthwise	NOUN
cana-4510	184	16	separable	separable	ADJ
cana-4510	184	17	convolutional	convolutional	ADJ
cana-4510	184	18	layers	layer	NOUN
cana-4510	184	19	multiple	multiple	ADJ
cana-4510	184	20	times	time	NOUN
cana-4510	184	21	to	to	PART
cana-4510	184	22	capture	capture	VERB
cana-4510	184	23	complex	complex	ADJ
cana-4510	184	24	features	feature	NOUN
cana-4510	184	25	.	.	PUNCT
cana-4510	185	1	𝐸𝑥𝑖𝑡𝐹𝑙𝑜𝑤(𝑥	𝐸𝑥𝑖𝑡𝐹𝑙𝑜𝑤(𝑥	NOUN
cana-4510	185	2	)	)	PUNCT
cana-4510	185	3	denotes	denote	VERB
cana-4510	185	4	the	the	DET
cana-4510	185	5	exit	exit	NOUN
cana-4510	185	6	flow	flow	NOUN
cana-4510	185	7	,	,	PUNCT
cana-4510	185	8	which	which	PRON
cana-4510	185	9	further	far	ADV
cana-4510	185	10	processes	process	VERB
cana-4510	185	11	the	the	DET
cana-4510	185	12	features	feature	NOUN
cana-4510	185	13	extracted	extract	VERB
cana-4510	185	14	by	by	ADP
cana-4510	185	15	the	the	DET
cana-4510	185	16	middle	middle	ADJ
cana-4510	185	17	flow	flow	NOUN
cana-4510	185	18	before	before	ADP
cana-4510	185	19	global	global	ADJ
cana-4510	185	20	pooling	pooling	NOUN
cana-4510	185	21	and	and	CCONJ
cana-4510	185	22	classification	classification	NOUN
cana-4510	185	23	.	.	PUNCT
cana-4510	186	1	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	𝐺𝑙𝑜𝑏𝑎𝑙𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝑃𝑜𝑜𝑙𝑖𝑛𝑔2𝐷(𝑥	ADJ
cana-4510	186	2	)	)	PUNCT
cana-4510	186	3	averages	average	VERB
cana-4510	186	4	each	each	DET
cana-4510	186	5	feature	feature	NOUN
cana-4510	186	6	map	map	NOUN
cana-4510	186	7	over	over	ADP
cana-4510	186	8	its	its	PRON
cana-4510	186	9	spatial	spatial	ADJ
cana-4510	186	10	dimensions	dimension	NOUN
cana-4510	186	11	,	,	PUNCT
cana-4510	186	12	communications	communication	NOUN
cana-4510	186	13	on	on	ADP
cana-4510	186	14	applied	apply	VERB
cana-4510	186	15	nonlinear	nonlinear	ADJ
cana-4510	186	16	analysis	analysis	NOUN
cana-4510	186	17	issn	issn	NOUN
cana-4510	186	18	:	:	PUNCT
cana-4510	186	19	1074	1074	NUM
cana-4510	186	20	-	-	PUNCT
cana-4510	186	21	133x	133x	NUM
cana-4510	186	22	vol	vol	NOUN
cana-4510	186	23	32	32	NUM
cana-4510	187	1	no	no	NOUN
cana-4510	187	2	.	.	PUNCT
cana-4510	188	1	9s	9s	NUM
cana-4510	188	2	(	(	PUNCT
cana-4510	188	3	2025	2025	NUM
cana-4510	188	4	)	)	PUNCT
cana-4510	188	5	2252	2252	NUM
cana-4510	189	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	189	2	producing	produce	VERB
cana-4510	189	3	a	a	DET
cana-4510	189	4	single	single	ADJ
cana-4510	189	5	feature	feature	NOUN
cana-4510	189	6	vector	vector	NOUN
cana-4510	189	7	for	for	ADP
cana-4510	189	8	every	every	DET
cana-4510	189	9	channel	channel	NOUN
cana-4510	189	10	.	.	PUNCT
cana-4510	190	1	𝐷𝑒𝑛𝑠𝑒(𝑥	𝐷𝑒𝑛𝑠𝑒(𝑥	PROPN
cana-4510	190	2	)	)	PUNCT
cana-4510	191	1	denotes	denote	NOUN
cana-4510	191	2	fully	fully	ADV
cana-4510	191	3	connected	connect	VERB
cana-4510	191	4	layers	layer	NOUN
cana-4510	191	5	for	for	ADP
cana-4510	191	6	classification	classification	NOUN
cana-4510	191	7	.	.	PUNCT
cana-4510	192	1	in	in	ADP
cana-4510	192	2	this	this	DET
cana-4510	192	3	study	study	NOUN
cana-4510	192	4	,	,	PUNCT
cana-4510	192	5	xception	xception	PROPN
cana-4510	192	6	model	model	NOUN
cana-4510	192	7	has	have	AUX
cana-4510	192	8	demonstrates	demonstrate	VERB
cana-4510	192	9	the	the	DET
cana-4510	192	10	accuracy	accuracy	NOUN
cana-4510	192	11	98.02	98.02	NUM
cana-4510	192	12	%	%	NOUN
cana-4510	192	13	in	in	ADP
cana-4510	192	14	order	order	NOUN
cana-4510	192	15	level	level	NOUN
cana-4510	192	16	,	,	PUNCT
cana-4510	192	17	94.57	94.57	NUM
cana-4510	192	18	%	%	NOUN
cana-4510	192	19	in	in	ADP
cana-4510	192	20	family	family	NOUN
cana-4510	192	21	level	level	NOUN
cana-4510	192	22	and	and	CCONJ
cana-4510	192	23	80.58	80.58	NUM
cana-4510	192	24	%	%	NOUN
cana-4510	192	25	in	in	ADP
cana-4510	192	26	species	species	NOUN
cana-4510	192	27	level	level	NOUN
cana-4510	192	28	insect	insect	NOUN
cana-4510	192	29	image	image	NOUN
cana-4510	192	30	classification	classification	NOUN
cana-4510	192	31	.	.	PUNCT
cana-4510	193	1	figure	figure	NOUN
cana-4510	193	2	4	4	NUM
cana-4510	193	3	shown	show	VERB
cana-4510	193	4	the	the	DET
cana-4510	193	5	xception	xception	PROPN
cana-4510	193	6	model	model	NOUN
cana-4510	193	7	architecture	architecture	NOUN
cana-4510	193	8	.	.	PUNCT
cana-4510	194	1	figure	figure	VERB
cana-4510	194	2	4	4	NUM
cana-4510	194	3	:	:	PUNCT
cana-4510	194	4	architecture	architecture	NOUN
cana-4510	194	5	of	of	ADP
cana-4510	194	6	xception	xception	PROPN
cana-4510	194	7	model	model	PROPN
cana-4510	194	8	3.4	3.4	NUM
cana-4510	194	9	dual	dual	ADJ
cana-4510	194	10	hybrid	hybrid	ADJ
cana-4510	194	11	deep	deep	ADJ
cana-4510	194	12	convolutional	convolutional	ADJ
cana-4510	194	13	neural	neural	ADJ
cana-4510	194	14	network	network	NOUN
cana-4510	194	15	model	model	NOUN
cana-4510	194	16	in	in	ADP
cana-4510	194	17	the	the	DET
cana-4510	194	18	realm	realm	NOUN
cana-4510	194	19	of	of	ADP
cana-4510	194	20	insect	insect	NOUN
cana-4510	194	21	species	species	NOUN
cana-4510	194	22	classification	classification	NOUN
cana-4510	194	23	and	and	CCONJ
cana-4510	194	24	identification	identification	NOUN
cana-4510	194	25	,	,	PUNCT
cana-4510	194	26	dl	dl	NOUN
cana-4510	194	27	-	-	PUNCT
cana-4510	194	28	based	base	VERB
cana-4510	194	29	models	model	NOUN
cana-4510	194	30	through	through	ADP
cana-4510	194	31	ensemble	ensemble	ADJ
cana-4510	194	32	techniques	technique	NOUN
cana-4510	194	33	holds	hold	VERB
cana-4510	194	34	significant	significant	ADJ
cana-4510	194	35	promise	promise	NOUN
cana-4510	194	36	for	for	ADP
cana-4510	194	37	achieving	achieve	VERB
cana-4510	194	38	higher	high	ADJ
cana-4510	194	39	accuracy	accuracy	NOUN
cana-4510	194	40	.	.	PUNCT
cana-4510	195	1	this	this	DET
cana-4510	195	2	research	research	NOUN
cana-4510	195	3	explores	explore	VERB
cana-4510	195	4	the	the	DET
cana-4510	195	5	efficacy	efficacy	NOUN
cana-4510	195	6	of	of	ADP
cana-4510	195	7	a	a	DET
cana-4510	195	8	hybrid	hybrid	ADJ
cana-4510	195	9	approach	approach	NOUN
cana-4510	195	10	combining	combine	VERB
cana-4510	195	11	the	the	DET
cana-4510	195	12	strengths	strength	NOUN
cana-4510	195	13	of	of	ADP
cana-4510	195	14	vgg19	vgg19	PROPN
cana-4510	195	15	and	and	CCONJ
cana-4510	195	16	xception	xception	NOUN
cana-4510	195	17	models	model	NOUN
cana-4510	195	18	for	for	ADP
cana-4510	195	19	accurate	accurate	ADJ
cana-4510	195	20	insect	insect	NOUN
cana-4510	195	21	species	species	NOUN
cana-4510	195	22	classification	classification	NOUN
cana-4510	195	23	across	across	ADP
cana-4510	195	24	multiple	multiple	ADJ
cana-4510	195	25	taxonomic	taxonomic	ADJ
cana-4510	195	26	levels	level	NOUN
cana-4510	195	27	.	.	PUNCT
cana-4510	196	1	after	after	ADP
cana-4510	196	2	dcnn	dcnn	PROPN
cana-4510	196	3	based	base	VERB
cana-4510	196	4	pre	pre	ADJ
cana-4510	196	5	-	-	ADJ
cana-4510	196	6	trained	train	VERB
cana-4510	196	7	models	model	NOUN
cana-4510	196	8	were	be	AUX
cana-4510	196	9	trained	train	VERB
cana-4510	196	10	on	on	ADP
cana-4510	196	11	three	three	NUM
cana-4510	196	12	distinct	distinct	ADJ
cana-4510	196	13	taxonomic	taxonomic	ADJ
cana-4510	196	14	insect	insect	NOUN
cana-4510	196	15	image	image	NOUN
cana-4510	196	16	datasets	dataset	NOUN
cana-4510	196	17	.	.	PUNCT
cana-4510	197	1	the	the	DET
cana-4510	197	2	accuracy	accuracy	NOUN
cana-4510	197	3	was	be	AUX
cana-4510	197	4	evaluated	evaluate	VERB
cana-4510	197	5	and	and	CCONJ
cana-4510	197	6	training	training	NOUN
cana-4510	197	7	images	image	NOUN
cana-4510	197	8	were	be	AUX
cana-4510	197	9	fed	feed	VERB
cana-4510	197	10	into	into	ADP
cana-4510	197	11	the	the	DET
cana-4510	197	12	dhdcnet	dhdcnet	ADJ
cana-4510	197	13	based	base	VERB
cana-4510	197	14	model	model	NOUN
cana-4510	197	15	for	for	ADP
cana-4510	197	16	efficient	efficient	ADJ
cana-4510	197	17	classification	classification	NOUN
cana-4510	197	18	.	.	PUNCT
cana-4510	198	1	dual	dual	ADJ
cana-4510	198	2	hybrid	hybrid	ADJ
cana-4510	198	3	framework	framework	NOUN
cana-4510	198	4	based	base	VERB
cana-4510	198	5	proposed	propose	VERB
cana-4510	198	6	model	model	NOUN
cana-4510	198	7	termed	term	VERB
cana-4510	198	8	as	as	ADP
cana-4510	198	9	“	"	PUNCT
cana-4510	198	10	dhdcnet	dhdcnet	NOUN
cana-4510	198	11	”	"	PUNCT
cana-4510	198	12	,	,	PUNCT
cana-4510	198	13	combines	combine	VERB
cana-4510	198	14	the	the	DET
cana-4510	198	15	predictions	prediction	NOUN
cana-4510	198	16	of	of	ADP
cana-4510	198	17	vgg19	vgg19	PROPN
cana-4510	198	18	and	and	CCONJ
cana-4510	198	19	xception	xception	NOUN
cana-4510	198	20	models	model	NOUN
cana-4510	198	21	by	by	ADP
cana-4510	198	22	averaging	average	VERB
cana-4510	198	23	their	their	PRON
cana-4510	198	24	softmax	softmax	NOUN
cana-4510	198	25	outputs	output	NOUN
cana-4510	198	26	for	for	ADP
cana-4510	198	27	classification	classification	NOUN
cana-4510	198	28	.	.	PUNCT
cana-4510	199	1	mathematically	mathematically	ADV
cana-4510	199	2	,	,	PUNCT
cana-4510	199	3	this	this	PRON
cana-4510	199	4	can	can	AUX
cana-4510	199	5	be	be	AUX
cana-4510	199	6	represented	represent	VERB
cana-4510	199	7	in	in	ADP
cana-4510	199	8	eq	eq	ADP
cana-4510	199	9	.	.	PUNCT
cana-4510	200	1	(	(	PUNCT
cana-4510	200	2	6	6	NUM
cana-4510	200	3	):	):	PUNCT
cana-4510	200	4	𝐷𝐻𝐷𝐶𝑁𝑒𝑡	𝐷𝐻𝐷𝐶𝑁𝑒𝑡	ADJ
cana-4510	200	5	𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛	𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛	NOUN
cana-4510	200	6	=	=	SYM
cana-4510	200	7	½(𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑉𝐺𝐺19	½(𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑉𝐺𝐺19	PROPN
cana-4510	200	8	)	)	PUNCT
cana-4510	200	9	+	+	CCONJ
cana-4510	200	10	𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛	𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛	NOUN
cana-4510	200	11	)	)	PUNCT
cana-4510	200	12	)	)	PUNCT
cana-4510	200	13	(	(	PUNCT
cana-4510	200	14	6	6	NUM
cana-4510	200	15	)	)	PUNCT
cana-4510	200	16	here	here	ADV
cana-4510	200	17	,	,	PUNCT
cana-4510	200	18	𝑠𝑜𝑓𝑡𝑚𝑎𝑥(𝑉𝐺𝐺19	𝑠𝑜𝑓𝑡𝑚𝑎𝑥(𝑉𝐺𝐺19	NUM
cana-4510	200	19	)	)	PUNCT
cana-4510	200	20	represents	represent	VERB
cana-4510	200	21	the	the	DET
cana-4510	200	22	softmax	softmax	ADJ
cana-4510	200	23	output	output	NOUN
cana-4510	200	24	probabilities	probability	NOUN
cana-4510	200	25	predicted	predict	VERB
cana-4510	200	26	by	by	ADP
cana-4510	200	27	the	the	DET
cana-4510	200	28	vgg	vgg	ADJ
cana-4510	200	29	19	19	NUM
cana-4510	200	30	model	model	NOUN
cana-4510	200	31	,	,	PUNCT
cana-4510	200	32	and	and	CCONJ
cana-4510	200	33	𝑠𝑜𝑓𝑡𝑚𝑎𝑥(𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛	𝑠𝑜𝑓𝑡𝑚𝑎𝑥(𝑋𝑐𝑒𝑝𝑡𝑖𝑜𝑛	NOUN
cana-4510	200	34	)	)	PUNCT
cana-4510	200	35	represents	represent	VERB
cana-4510	200	36	the	the	DET
cana-4510	200	37	softmax	softmax	ADJ
cana-4510	200	38	output	output	NOUN
cana-4510	200	39	probabilities	probability	NOUN
cana-4510	200	40	predicted	predict	VERB
cana-4510	200	41	by	by	ADP
cana-4510	200	42	the	the	DET
cana-4510	200	43	xception	xception	PROPN
cana-4510	200	44	model	model	NOUN
cana-4510	200	45	.	.	PUNCT
cana-4510	201	1	the	the	DET
cana-4510	201	2	division	division	NOUN
cana-4510	201	3	by	by	ADP
cana-4510	201	4	2	2	NUM
cana-4510	201	5	used	use	VERB
cana-4510	201	6	to	to	PART
cana-4510	201	7	normalize	normalize	VERB
cana-4510	201	8	the	the	DET
cana-4510	201	9	prediction	prediction	NOUN
cana-4510	201	10	.	.	PUNCT
cana-4510	202	1	table	table	NOUN
cana-4510	202	2	3	3	NUM
cana-4510	202	3	shown	show	VERB
cana-4510	202	4	the	the	DET
cana-4510	202	5	layers	layer	NOUN
cana-4510	202	6	and	and	CCONJ
cana-4510	202	7	parameters	parameter	NOUN
cana-4510	202	8	and	and	CCONJ
cana-4510	202	9	input	input	NOUN
cana-4510	202	10	and	and	CCONJ
cana-4510	202	11	output	output	NOUN
cana-4510	202	12	shapes	shape	NOUN
cana-4510	202	13	of	of	ADP
cana-4510	202	14	dhdcnet	dhdcnet	ADJ
cana-4510	202	15	model	model	NOUN
cana-4510	202	16	.	.	PUNCT
cana-4510	203	1	compared	compare	VERB
cana-4510	203	2	with	with	ADP
cana-4510	203	3	other	other	ADJ
cana-4510	203	4	dcnn	dcnn	PROPN
cana-4510	203	5	models	model	NOUN
cana-4510	203	6	the	the	DET
cana-4510	203	7	proposed	propose	VERB
cana-4510	203	8	dhdcnet	dhdcnet	ADJ
cana-4510	203	9	model	model	NOUN
cana-4510	203	10	has	have	AUX
cana-4510	203	11	achieved	achieve	VERB
cana-4510	203	12	high	high	ADJ
cana-4510	203	13	accuracy	accuracy	NOUN
cana-4510	203	14	as	as	ADP
cana-4510	203	15	98.97	98.97	NUM
cana-4510	203	16	%	%	NOUN
cana-4510	203	17	in	in	ADP
cana-4510	203	18	order	order	NOUN
cana-4510	203	19	level	level	NOUN
cana-4510	203	20	classification	classification	NOUN
cana-4510	203	21	,	,	PUNCT
cana-4510	203	22	97.37	97.37	NUM
cana-4510	203	23	%	%	NOUN
cana-4510	203	24	of	of	ADP
cana-4510	203	25	accuracy	accuracy	NOUN
cana-4510	203	26	in	in	ADP
cana-4510	203	27	family	family	NOUN
cana-4510	203	28	level	level	NOUN
cana-4510	203	29	and	and	CCONJ
cana-4510	203	30	89.79	89.79	NUM
cana-4510	203	31	%	%	NOUN
cana-4510	203	32	of	of	ADP
cana-4510	203	33	accuracy	accuracy	NOUN
cana-4510	203	34	in	in	ADP
cana-4510	203	35	species	specie	NOUN
cana-4510	203	36	level	level	NOUN
cana-4510	203	37	classification	classification	NOUN
cana-4510	203	38	.	.	PUNCT
cana-4510	204	1	through	through	ADP
cana-4510	204	2	rigorous	rigorous	ADJ
cana-4510	204	3	experimentation	experimentation	NOUN
cana-4510	204	4	and	and	CCONJ
cana-4510	204	5	evaluation	evaluation	NOUN
cana-4510	204	6	,	,	PUNCT
cana-4510	204	7	the	the	DET
cana-4510	204	8	effectiveness	effectiveness	NOUN
cana-4510	204	9	of	of	ADP
cana-4510	204	10	this	this	DET
cana-4510	204	11	dual	dual	ADJ
cana-4510	204	12	hybrid	hybrid	NOUN
cana-4510	204	13	-	-	PUNCT
cana-4510	204	14	based	base	VERB
cana-4510	204	15	approach	approach	NOUN
cana-4510	204	16	in	in	ADP
cana-4510	204	17	achieved	achieve	VERB
cana-4510	204	18	superior	superior	ADJ
cana-4510	204	19	classification	classification	NOUN
cana-4510	204	20	performance	performance	NOUN
cana-4510	204	21	was	be	AUX
cana-4510	204	22	thoroughly	thoroughly	ADV
cana-4510	204	23	examined	examine	VERB
cana-4510	204	24	,	,	PUNCT
cana-4510	204	25	offering	offer	VERB
cana-4510	204	26	valuable	valuable	ADJ
cana-4510	204	27	insights	insight	NOUN
cana-4510	204	28	into	into	ADP
cana-4510	204	29	the	the	DET
cana-4510	204	30	potential	potential	NOUN
cana-4510	204	31	of	of	ADP
cana-4510	204	32	automated	automate	VERB
cana-4510	204	33	insect	insect	NOUN
cana-4510	204	34	taxonomy	taxonomy	NOUN
cana-4510	204	35	and	and	CCONJ
cana-4510	204	36	identification	identification	NOUN
cana-4510	204	37	.	.	PUNCT
cana-4510	205	1	figure	figure	NOUN
cana-4510	205	2	5	5	NUM
cana-4510	205	3	displays	display	VERB
cana-4510	205	4	the	the	DET
cana-4510	205	5	dual	dual	ADJ
cana-4510	205	6	hybrid	hybrid	ADJ
cana-4510	205	7	deep	deep	ADJ
cana-4510	205	8	cnn	cnn	PROPN
cana-4510	205	9	dhdcnet	dhdcnet	ADJ
cana-4510	205	10	model	model	NOUN
cana-4510	205	11	architecture	architecture	NOUN
cana-4510	205	12	.	.	PUNCT
cana-4510	206	1	communications	communication	NOUN
cana-4510	206	2	on	on	ADP
cana-4510	206	3	applied	apply	VERB
cana-4510	206	4	nonlinear	nonlinear	ADJ
cana-4510	206	5	analysis	analysis	NOUN
cana-4510	206	6	issn	issn	NOUN
cana-4510	206	7	:	:	PUNCT
cana-4510	206	8	1074	1074	NUM
cana-4510	206	9	-	-	PUNCT
cana-4510	206	10	133x	133x	NUM
cana-4510	206	11	vol	vol	NOUN
cana-4510	206	12	32	32	NUM
cana-4510	206	13	no	no	NOUN
cana-4510	206	14	.	.	PUNCT
cana-4510	207	1	9s	9s	NUM
cana-4510	207	2	(	(	PUNCT
cana-4510	207	3	2025	2025	NUM
cana-4510	207	4	)	)	PUNCT
cana-4510	207	5	2253	2253	NUM
cana-4510	207	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	207	7	figure	figure	NOUN
cana-4510	207	8	5	5	NUM
cana-4510	207	9	:	:	PUNCT
cana-4510	207	10	dual	dual	ADJ
cana-4510	207	11	hybrid	hybrid	ADJ
cana-4510	207	12	deep	deep	ADJ
cana-4510	207	13	convolutional	convolutional	ADJ
cana-4510	207	14	neural	neural	ADJ
cana-4510	207	15	network	network	NOUN
cana-4510	207	16	architecture	architecture	NOUN
cana-4510	207	17	table	table	NOUN
cana-4510	207	18	3	3	NUM
cana-4510	207	19	:	:	PUNCT
cana-4510	207	20	layers	layer	NOUN
cana-4510	207	21	and	and	CCONJ
cana-4510	207	22	parameters	parameter	NOUN
cana-4510	207	23	of	of	ADP
cana-4510	207	24	dhdcnet	dhdcnet	ADJ
cana-4510	207	25	model	model	NOUN
cana-4510	207	26	layer	layer	NOUN
cana-4510	207	27	(	(	PUNCT
cana-4510	207	28	type	type	NOUN
cana-4510	207	29	)	)	PUNCT
cana-4510	207	30	output	output	NOUN
cana-4510	207	31	shape	shape	NOUN
cana-4510	207	32	parameters	parameter	NOUN
cana-4510	207	33	connected	connect	VERB
cana-4510	207	34	to	to	ADP
cana-4510	207	35	input_5	input_5	PROPN
cana-4510	207	36	(	(	PUNCT
cana-4510	207	37	inputlayer	inputlayer	NOUN
cana-4510	207	38	)	)	PUNCT
cana-4510	208	1	[	[	X
cana-4510	208	2	(	(	PUNCT
cana-4510	208	3	none	none	NOUN
cana-4510	208	4	,	,	PUNCT
cana-4510	208	5	224	224	NUM
cana-4510	208	6	,	,	PUNCT
cana-4510	208	7	224	224	NUM
cana-4510	208	8	,	,	PUNCT
cana-4510	208	9	3	3	NUM
cana-4510	208	10	)	)	PUNCT
cana-4510	208	11	]	]	PUNCT
cana-4510	208	12	0	0	PUNCT
cana-4510	209	1	[	[	X
cana-4510	209	2	]	]	X
cana-4510	209	3	model1	model1	X
cana-4510	209	4	(	(	PUNCT
cana-4510	209	5	functional	functional	ADJ
cana-4510	209	6	)	)	PUNCT
cana-4510	209	7	(	(	PUNCT
cana-4510	209	8	none	none	NOUN
cana-4510	209	9	,	,	PUNCT
cana-4510	209	10	5	5	NUM
cana-4510	209	11	)	)	PUNCT
cana-4510	209	12	22964781	22964781	NUM
cana-4510	210	1	[	[	X
cana-4510	210	2	'	'	X
cana-4510	210	3	input_5[0][0	input_5[0][0	NOUN
cana-4510	210	4	]	]	X
cana-4510	210	5	'	'	PUNCT
cana-4510	210	6	]	]	X
cana-4510	211	1	model2	model2	NOUN
cana-4510	211	2	(	(	PUNCT
cana-4510	211	3	functional	functional	ADJ
cana-4510	211	4	)	)	PUNCT
cana-4510	211	5	(	(	PUNCT
cana-4510	211	6	none	none	NOUN
cana-4510	211	7	,	,	PUNCT
cana-4510	211	8	5	5	NUM
cana-4510	211	9	)	)	PUNCT
cana-4510	211	10	20554821	20554821	NUM
cana-4510	212	1	[	[	X
cana-4510	212	2	'	'	X
cana-4510	212	3	input_5[0][0	input_5[0][0	NOUN
cana-4510	212	4	]	]	X
cana-4510	212	5	'	'	PUNCT
cana-4510	212	6	]	]	PUNCT
cana-4510	212	7	average_1	average_1	X
cana-4510	212	8	(	(	PUNCT
cana-4510	212	9	average	average	ADJ
cana-4510	212	10	)	)	PUNCT
cana-4510	212	11	(	(	PUNCT
cana-4510	212	12	none	none	NOUN
cana-4510	212	13	,	,	PUNCT
cana-4510	212	14	5	5	NUM
cana-4510	212	15	)	)	PUNCT
cana-4510	212	16	0	0	PUNCT
cana-4510	213	1	[	[	X
cana-4510	213	2	'	'	PUNCT
cana-4510	213	3	model1[0][0	model1[0][0	NOUN
cana-4510	213	4	]	]	PUNCT
cana-4510	213	5	'	'	PUNCT
cana-4510	213	6	'	'	PUNCT
cana-4510	213	7	model2[0][0	model2[0][0	NOUN
cana-4510	213	8	]	]	PUNCT
cana-4510	213	9	'	'	PUNCT
cana-4510	213	10	]	]	PUNCT
cana-4510	213	11	algorithm	algorithm	NOUN
cana-4510	213	12	for	for	ADP
cana-4510	213	13	dhdcnet	dhdcnet	ADJ
cana-4510	213	14	model	model	NOUN
cana-4510	213	15	for	for	ADP
cana-4510	213	16	taxonomic	taxonomic	ADJ
cana-4510	213	17	level	level	NOUN
cana-4510	213	18	insect	insect	NOUN
cana-4510	213	19	image	image	NOUN
cana-4510	213	20	classification	classification	NOUN
cana-4510	213	21	and	and	CCONJ
cana-4510	213	22	identification	identification	NOUN
cana-4510	213	23	:	:	PUNCT
cana-4510	213	24	input	input	NOUN
cana-4510	213	25	:	:	PUNCT
cana-4510	213	26	order	order	NOUN
cana-4510	213	27	level	level	NOUN
cana-4510	213	28	,	,	PUNCT
cana-4510	213	29	family	family	NOUN
cana-4510	213	30	level	level	NOUN
cana-4510	213	31	and	and	CCONJ
cana-4510	213	32	species	species	NOUN
cana-4510	213	33	level	level	NOUN
cana-4510	213	34	insect	insect	NOUN
cana-4510	213	35	images	image	NOUN
cana-4510	213	36	output	output	NOUN
cana-4510	213	37	:	:	PUNCT
cana-4510	213	38	classified	classify	VERB
cana-4510	213	39	insect	insect	NOUN
cana-4510	213	40	images	image	NOUN
cana-4510	213	41	begin	begin	VERB
cana-4510	213	42	𝑓𝑜𝑟(𝑒𝑣𝑒𝑟𝑦	𝑓𝑜𝑟(𝑒𝑣𝑒𝑟𝑦	NOUN
cana-4510	213	43	𝑖𝑛𝑠𝑒𝑐𝑡	𝑖𝑛𝑠𝑒𝑐𝑡	NOUN
cana-4510	213	44	𝑖𝑚𝑎𝑔𝑒	𝑖𝑚𝑎𝑔𝑒	NOUN
cana-4510	213	45	)	)	PUNCT
cana-4510	213	46	perform	perform	VERB
cana-4510	213	47	pre	pre	ADJ
cana-4510	213	48	-	-	ADJ
cana-4510	213	49	processing	processing	ADJ
cana-4510	213	50	;	;	PUNCT
cana-4510	213	51	dataset	dataset	NOUN
cana-4510	213	52	segmented	segment	VERB
cana-4510	213	53	into	into	ADP
cana-4510	213	54	training	training	NOUN
cana-4510	213	55	,	,	PUNCT
cana-4510	213	56	testing	testing	NOUN
cana-4510	213	57	and	and	CCONJ
cana-4510	213	58	validation	validation	NOUN
cana-4510	213	59	;	;	PUNCT
cana-4510	213	60	end	end	VERB
cana-4510	213	61	for	for	ADP
cana-4510	213	62	training	training	NOUN
cana-4510	213	63	images	image	NOUN
cana-4510	213	64	fed	feed	VERB
cana-4510	213	65	into	into	ADP
cana-4510	213	66	the	the	DET
cana-4510	213	67	dcnn	dcnn	PROPN
cana-4510	213	68	based	base	VERB
cana-4510	213	69	pre	pre	ADJ
cana-4510	213	70	-	-	ADJ
cana-4510	213	71	trained	train	VERB
cana-4510	213	72	models	model	NOUN
cana-4510	213	73	;	;	PUNCT
cana-4510	213	74	training	train	VERB
cana-4510	213	75	images	image	NOUN
cana-4510	213	76	into	into	ADP
cana-4510	213	77	ensemble	ensemble	ADJ
cana-4510	213	78	model	model	NOUN
cana-4510	213	79	“	"	PUNCT
cana-4510	213	80	dual	dual	ADJ
cana-4510	213	81	hybrid	hybrid	ADJ
cana-4510	213	82	deep	deep	ADJ
cana-4510	213	83	convolutional	convolutional	ADJ
cana-4510	213	84	neural	neural	ADJ
cana-4510	213	85	network	network	NOUN
cana-4510	213	86	–	–	PUNCT
cana-4510	213	87	dhdcnet	dhdcnet	ADJ
cana-4510	213	88	model	model	NOUN
cana-4510	213	89	”	"	PUNCT
cana-4510	213	90	;	;	PUNCT
cana-4510	213	91	communications	communication	NOUN
cana-4510	213	92	on	on	ADP
cana-4510	213	93	applied	apply	VERB
cana-4510	213	94	nonlinear	nonlinear	ADJ
cana-4510	213	95	analysis	analysis	NOUN
cana-4510	213	96	issn	issn	NOUN
cana-4510	213	97	:	:	PUNCT
cana-4510	213	98	1074	1074	NUM
cana-4510	213	99	-	-	PUNCT
cana-4510	213	100	133x	133x	NUM
cana-4510	213	101	vol	vol	NOUN
cana-4510	213	102	32	32	NUM
cana-4510	213	103	no	no	NOUN
cana-4510	213	104	.	.	PUNCT
cana-4510	214	1	9s	9s	NUM
cana-4510	214	2	(	(	PUNCT
cana-4510	214	3	2025	2025	NUM
cana-4510	214	4	)	)	PUNCT
cana-4510	214	5	2254	2254	NUM
cana-4510	214	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	214	7	validate	validate	VERB
cana-4510	214	8	the	the	DET
cana-4510	214	9	classifiers	classifier	NOUN
cana-4510	214	10	using	use	VERB
cana-4510	214	11	test	test	NOUN
cana-4510	214	12	dataset	dataset	VERB
cana-4510	214	13	to	to	PART
cana-4510	214	14	classify	classify	VERB
cana-4510	214	15	the	the	DET
cana-4510	214	16	insect	insect	NOUN
cana-4510	214	17	images	image	NOUN
cana-4510	214	18	at	at	ADP
cana-4510	214	19	order	order	NOUN
cana-4510	214	20	,	,	PUNCT
cana-4510	214	21	family	family	NOUN
cana-4510	214	22	and	and	CCONJ
cana-4510	214	23	species	specie	VERB
cana-4510	214	24	taxonomic	taxonomic	ADJ
cana-4510	214	25	levels	level	NOUN
cana-4510	214	26	;	;	PUNCT
cana-4510	214	27	end	end	NOUN
cana-4510	214	28	4	4	NUM
cana-4510	214	29	.	.	PUNCT
cana-4510	214	30	experimental	experimental	ADJ
cana-4510	214	31	results	result	NOUN
cana-4510	214	32	and	and	CCONJ
cana-4510	214	33	insights	insight	NOUN
cana-4510	214	34	the	the	DET
cana-4510	214	35	following	follow	VERB
cana-4510	214	36	section	section	NOUN
cana-4510	214	37	provides	provide	VERB
cana-4510	214	38	experimental	experimental	ADJ
cana-4510	214	39	results	result	NOUN
cana-4510	214	40	and	and	CCONJ
cana-4510	214	41	outcomes	outcome	NOUN
cana-4510	214	42	of	of	ADP
cana-4510	214	43	insect	insect	NOUN
cana-4510	214	44	image	image	NOUN
cana-4510	214	45	classification	classification	NOUN
cana-4510	214	46	utilize	utilize	VERB
cana-4510	214	47	hybrid	hybrid	NOUN
cana-4510	214	48	-	-	PUNCT
cana-4510	214	49	based	base	VERB
cana-4510	214	50	approach	approach	NOUN
cana-4510	214	51	framework	framework	NOUN
cana-4510	214	52	.	.	PUNCT
cana-4510	215	1	the	the	DET
cana-4510	215	2	comparison	comparison	NOUN
cana-4510	215	3	of	of	ADP
cana-4510	215	4	dcnn	dcnn	ADJ
cana-4510	215	5	models	model	NOUN
cana-4510	215	6	and	and	CCONJ
cana-4510	215	7	the	the	DET
cana-4510	215	8	proposed	propose	VERB
cana-4510	215	9	dhdcnet	dhdcnet	ADJ
cana-4510	215	10	model	model	NOUN
cana-4510	215	11	across	across	ADP
cana-4510	215	12	three	three	NUM
cana-4510	215	13	taxonomic	taxonomic	ADJ
cana-4510	215	14	levels	level	NOUN
cana-4510	215	15	order	order	NOUN
cana-4510	215	16	,	,	PUNCT
cana-4510	215	17	family	family	NOUN
cana-4510	215	18	,	,	PUNCT
cana-4510	215	19	and	and	CCONJ
cana-4510	215	20	species	specie	NOUN
cana-4510	215	21	is	be	AUX
cana-4510	215	22	presented	present	VERB
cana-4510	215	23	in	in	ADP
cana-4510	215	24	table	table	NOUN
cana-4510	215	25	4	4	NUM
cana-4510	215	26	,	,	PUNCT
cana-4510	215	27	with	with	ADP
cana-4510	215	28	evaluation	evaluation	NOUN
cana-4510	215	29	metrics	metric	NOUN
cana-4510	215	30	like	like	ADP
cana-4510	215	31	precision	precision	NOUN
cana-4510	215	32	,	,	PUNCT
cana-4510	215	33	recall	recall	NOUN
cana-4510	215	34	,	,	PUNCT
cana-4510	215	35	f1	f1	NOUN
cana-4510	215	36	-	-	PUNCT
cana-4510	215	37	score	score	NOUN
cana-4510	215	38	,	,	PUNCT
cana-4510	215	39	and	and	CCONJ
cana-4510	215	40	accuracy	accuracy	NOUN
cana-4510	215	41	.	.	PUNCT
cana-4510	216	1	figure	figure	NOUN
cana-4510	216	2	6	6	NUM
cana-4510	216	3	illustrates	illustrate	VERB
cana-4510	216	4	the	the	DET
cana-4510	216	5	accuracy	accuracy	NOUN
cana-4510	216	6	comparison	comparison	NOUN
cana-4510	216	7	between	between	ADP
cana-4510	216	8	all	all	DET
cana-4510	216	9	dcnn	dcnn	ADJ
cana-4510	216	10	models	model	NOUN
cana-4510	216	11	and	and	CCONJ
cana-4510	216	12	proposed	propose	VERB
cana-4510	216	13	model	model	NOUN
cana-4510	216	14	.	.	PUNCT
cana-4510	217	1	table	table	NOUN
cana-4510	217	2	4	4	NUM
cana-4510	217	3	:	:	PUNCT
cana-4510	217	4	comparison	comparison	NOUN
cana-4510	217	5	results	result	NOUN
cana-4510	217	6	of	of	ADP
cana-4510	217	7	dcnn	dcnn	ADJ
cana-4510	217	8	models	model	NOUN
cana-4510	217	9	with	with	ADP
cana-4510	217	10	proposed	propose	VERB
cana-4510	217	11	dhdcnet	dhdcnet	ADJ
cana-4510	217	12	model	model	NOUN
cana-4510	217	13	taxonomic	taxonomic	ADJ
cana-4510	217	14	level	level	NOUN
cana-4510	217	15	of	of	ADP
cana-4510	217	16	insects	insect	NOUN
cana-4510	217	17	dcnn	dcnn	PROPN
cana-4510	217	18	models	model	NOUN
cana-4510	217	19	precision	precision	NOUN
cana-4510	217	20	recall	recall	NOUN
cana-4510	217	21	f1	f1	NOUN
cana-4510	217	22	-	-	PUNCT
cana-4510	217	23	score	score	NOUN
cana-4510	217	24	accuracy	accuracy	NOUN
cana-4510	217	25	order	order	NOUN
cana-4510	217	26	level	level	NOUN
cana-4510	217	27	inception	inception	PROPN
cana-4510	217	28	v3	v3	PROPN
cana-4510	217	29	0.96	0.96	NUM
cana-4510	218	1	0.95	0.95	NUM
cana-4510	218	2	0.96	0.96	NUM
cana-4510	218	3	96.15	96.15	NUM
cana-4510	218	4	%	%	NOUN
cana-4510	218	5	resnet	resnet	NOUN
cana-4510	218	6	152	152	NUM
cana-4510	218	7	0.39	0.39	NUM
cana-4510	218	8	0.40	0.40	NUM
cana-4510	218	9	0.39	0.39	NUM
cana-4510	218	10	40.23	40.23	NUM
cana-4510	218	11	%	%	NOUN
cana-4510	218	12	vgg	vgg	NOUN
cana-4510	218	13	19	19	NUM
cana-4510	218	14	0.88	0.88	NUM
cana-4510	218	15	0.87	0.87	NUM
cana-4510	218	16	0.88	0.88	NUM
cana-4510	218	17	88.66	88.66	NUM
cana-4510	218	18	%	%	NOUN
cana-4510	218	19	mobile	mobile	ADJ
cana-4510	218	20	net	net	NOUN
cana-4510	218	21	v2	v2	PROPN
cana-4510	218	22	0.98	0.98	NUM
cana-4510	218	23	0.98	0.98	NUM
cana-4510	218	24	0.98	0.98	NUM
cana-4510	218	25	98.36	98.36	NUM
cana-4510	218	26	%	%	NOUN
cana-4510	218	27	xception	xception	NOUN
cana-4510	218	28	0.98	0.98	NUM
cana-4510	218	29	0.98	0.98	NUM
cana-4510	218	30	0.98	0.98	NUM
cana-4510	218	31	98.02	98.02	NUM
cana-4510	218	32	%	%	NOUN
cana-4510	218	33	proposed	propose	VERB
cana-4510	218	34	dhdcnet	dhdcnet	NOUN
cana-4510	218	35	0.98	0.98	NUM
cana-4510	218	36	0.99	0.99	NUM
cana-4510	218	37	0.99	0.99	NUM
cana-4510	218	38	98.97	98.97	NUM
cana-4510	218	39	%	%	NOUN
cana-4510	218	40	family	family	NOUN
cana-4510	218	41	level	level	NOUN
cana-4510	218	42	inception	inception	NOUN
cana-4510	218	43	v3	v3	PROPN
cana-4510	218	44	0.87	0.87	NUM
cana-4510	218	45	0.86	0.86	NUM
cana-4510	218	46	0.86	0.86	NUM
cana-4510	218	47	86.21	86.21	NUM
cana-4510	218	48	%	%	NOUN
cana-4510	218	49	resnet	resnet	NOUN
cana-4510	218	50	152	152	NUM
cana-4510	218	51	0.09	0.09	NUM
cana-4510	218	52	0.24	0.24	NUM
cana-4510	218	53	0.12	0.12	NUM
cana-4510	218	54	26.60	26.60	NUM
cana-4510	218	55	%	%	NOUN
cana-4510	218	56	vgg	vgg	NOUN
cana-4510	218	57	19	19	NUM
cana-4510	218	58	0.87	0.87	NUM
cana-4510	218	59	0.86	0.86	NUM
cana-4510	218	60	0.86	0.86	NUM
cana-4510	218	61	87.23	87.23	NUM
cana-4510	218	62	%	%	NOUN
cana-4510	218	63	mobile	mobile	ADJ
cana-4510	218	64	net	net	NOUN
cana-4510	218	65	v2	v2	PROPN
cana-4510	218	66	0.92	0.92	NUM
cana-4510	218	67	0.93	0.93	NUM
cana-4510	218	68	0.93	0.93	NUM
cana-4510	218	69	93.26	93.26	NUM
cana-4510	218	70	%	%	NOUN
cana-4510	218	71	xception	xception	NOUN
cana-4510	218	72	0.94	0.94	NUM
cana-4510	218	73	0.91	0.91	NUM
cana-4510	218	74	0.91	0.91	NUM
cana-4510	218	75	94.57	94.57	NUM
cana-4510	218	76	%	%	NOUN
cana-4510	218	77	proposed	propose	VERB
cana-4510	218	78	dhdcnet	dhdcnet	NOUN
cana-4510	218	79	0.96	0.96	NUM
cana-4510	218	80	0.97	0.97	NUM
cana-4510	218	81	0.97	0.97	NUM
cana-4510	218	82	97.37	97.37	NUM
cana-4510	218	83	%	%	NOUN
cana-4510	218	84	species	specie	NOUN
cana-4510	218	85	level	level	NOUN
cana-4510	218	86	inception	inception	NOUN
cana-4510	218	87	v3	v3	PROPN
cana-4510	218	88	0.80	0.80	NUM
cana-4510	218	89	0.79	0.79	NUM
cana-4510	218	90	0.79	0.79	NUM
cana-4510	218	91	79.24	79.24	NUM
cana-4510	218	92	%	%	NOUN
cana-4510	218	93	resnet	resnet	NOUN
cana-4510	218	94	152	152	NUM
cana-4510	218	95	0.09	0.09	NUM
cana-4510	218	96	0.24	0.24	NUM
cana-4510	218	97	0.12	0.12	NUM
cana-4510	218	98	24.38	24.38	NUM
cana-4510	218	99	%	%	NOUN
cana-4510	218	100	vgg	vgg	NOUN
cana-4510	218	101	19	19	NUM
cana-4510	218	102	0.83	0.83	NUM
cana-4510	218	103	0.83	0.83	NUM
cana-4510	218	104	0.83	0.83	NUM
cana-4510	218	105	82.64	82.64	NUM
cana-4510	218	106	%	%	NOUN
cana-4510	218	107	mobile	mobile	ADJ
cana-4510	218	108	net	net	NOUN
cana-4510	218	109	v2	v2	PROPN
cana-4510	219	1	0.87	0.87	NUM
cana-4510	219	2	0.88	0.88	NUM
cana-4510	219	3	0.88	0.88	NUM
cana-4510	219	4	88.33	88.33	NUM
cana-4510	219	5	%	%	NOUN
cana-4510	219	6	xception	xception	NOUN
cana-4510	219	7	0.85	0.85	NUM
cana-4510	219	8	0.81	0.81	NUM
cana-4510	219	9	0.81	0.81	NUM
cana-4510	219	10	80.58	80.58	NUM
cana-4510	219	11	%	%	NOUN
cana-4510	219	12	proposed	propose	VERB
cana-4510	219	13	dhdcnet	dhdcnet	NOUN
cana-4510	219	14	0.88	0.88	NUM
cana-4510	219	15	0.89	0.89	NUM
cana-4510	219	16	0.88	0.88	NUM
cana-4510	219	17	89.79	89.79	NUM
cana-4510	219	18	%	%	NOUN
cana-4510	219	19	communications	communication	NOUN
cana-4510	219	20	on	on	ADP
cana-4510	219	21	applied	apply	VERB
cana-4510	219	22	nonlinear	nonlinear	ADJ
cana-4510	219	23	analysis	analysis	NOUN
cana-4510	219	24	issn	issn	NOUN
cana-4510	219	25	:	:	PUNCT
cana-4510	219	26	1074	1074	NUM
cana-4510	219	27	-	-	PUNCT
cana-4510	219	28	133x	133x	NUM
cana-4510	219	29	vol	vol	NOUN
cana-4510	219	30	32	32	NUM
cana-4510	219	31	no	no	NOUN
cana-4510	219	32	.	.	PUNCT
cana-4510	220	1	9s	9s	NUM
cana-4510	220	2	(	(	PUNCT
cana-4510	220	3	2025	2025	NUM
cana-4510	220	4	)	)	PUNCT
cana-4510	220	5	2255	2255	NUM
cana-4510	221	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	221	2	figure	figure	NOUN
cana-4510	221	3	6	6	NUM
cana-4510	221	4	:	:	PUNCT
cana-4510	221	5	accuracy	accuracy	NOUN
cana-4510	221	6	comparison	comparison	NOUN
cana-4510	221	7	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-4510	221	8	=	=	SYM
cana-4510	222	1	𝑇𝑟𝑢𝑒	𝑇𝑟𝑢𝑒	PROPN
cana-4510	222	2	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-4510	222	3	(	(	PUNCT
cana-4510	222	4	𝑖𝑛𝑠𝑒𝑐𝑡	𝑖𝑛𝑠𝑒𝑐𝑡	NOUN
cana-4510	222	5	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	PROPN
cana-4510	222	6	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	PROPN
cana-4510	222	7	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	PROPN
cana-4510	222	8	)	)	PUNCT
cana-4510	222	9	𝑇𝑟𝑢𝑒	𝑇𝑟𝑢𝑒	PROPN
cana-4510	222	10	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-4510	222	11	(	(	PUNCT
cana-4510	222	12	𝑖𝑛𝑠𝑒𝑐𝑡	𝑖𝑛𝑠𝑒𝑐𝑡	NOUN
cana-4510	222	13	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	PROPN
cana-4510	222	14	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	PROPN
cana-4510	222	15	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑)+	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑)+	ADP
cana-4510	222	16	𝐹𝑎𝑙𝑠𝑒	𝐹𝑎𝑙𝑠𝑒	PROPN
cana-4510	222	17	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑖𝑒𝑠(𝑛𝑜𝑛−𝑖𝑛𝑠𝑒𝑐𝑡	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑖𝑒𝑠(𝑛𝑜𝑛−𝑖𝑛𝑠𝑒𝑐𝑡	PROPN
cana-4510	222	18	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	NOUN
cana-4510	222	19	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	PROPN
cana-4510	222	20	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	VERB
cana-4510	222	21	𝑎𝑠	𝑎𝑠	PROPN
cana-4510	222	22	𝑖𝑛𝑠𝑒𝑐𝑡𝑠	𝑖𝑛𝑠𝑒𝑐𝑡𝑠	PROPN
cana-4510	222	23	)	)	PUNCT
cana-4510	222	24	(	(	PUNCT
cana-4510	222	25	7	7	X
cana-4510	222	26	)	)	PUNCT
cana-4510	222	27	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-4510	222	28	=	=	SYM
cana-4510	222	29	𝑇𝑟𝑢𝑒	𝑇𝑟𝑢𝑒	PROPN
cana-4510	222	30	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠	PROPN
cana-4510	222	31	(	(	PUNCT
cana-4510	222	32	𝑖𝑛𝑠𝑒𝑐𝑡	𝑖𝑛𝑠𝑒𝑐𝑡	NOUN
cana-4510	222	33	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	PROPN
cana-4510	222	34	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	PROPN
cana-4510	222	35	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	NOUN
cana-4510	222	36	)	)	PUNCT
cana-4510	222	37	𝐹𝑎𝑙𝑠𝑒	𝐹𝑎𝑙𝑠𝑒	PROPN
cana-4510	222	38	𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠	PROPN
cana-4510	222	39	(	(	PUNCT
cana-4510	222	40	𝑖𝑛𝑠𝑒𝑐𝑡	𝑖𝑛𝑠𝑒𝑐𝑡	PROPN
cana-4510	222	41	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	NOUN
cana-4510	222	42	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	PROPN
cana-4510	222	43	𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑)+	𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑)+	X
cana-4510	223	1	𝑇𝑟𝑢𝑒	𝑇𝑟𝑢𝑒	PROPN
cana-4510	223	2	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑖𝑒𝑠(𝑖𝑛𝑠𝑒𝑐𝑡	𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑖𝑒𝑠(𝑖𝑛𝑠𝑒𝑐𝑡	PROPN
cana-4510	223	3	𝑖𝑚𝑎𝑔𝑒𝑠	𝑖𝑚𝑎𝑔𝑒𝑠	NOUN
cana-4510	223	4	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦	AUX
cana-4510	223	5	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑	NOUN
cana-4510	223	6	)	)	PUNCT
cana-4510	224	1	(	(	PUNCT
cana-4510	224	2	8)	8)	NUM
cana-4510	224	3	𝐹1	𝐹1	NOUN
cana-4510	224	4	−	−	PROPN
cana-4510	224	5	𝑆𝑐𝑜𝑟𝑒	𝑆𝑐𝑜𝑟𝑒	PROPN
cana-4510	224	6	=	=	NOUN
cana-4510	224	7	2	2	NUM
cana-4510	224	8	𝑥	𝑥	NOUN
cana-4510	224	9	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	NOUN
cana-4510	224	10	𝑥	𝑥	PRON
cana-4510	224	11	𝑟𝑒𝑐𝑎𝑙𝑙	𝑟𝑒𝑐𝑎𝑙𝑙	ADJ
cana-4510	224	12	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙	PROPN
cana-4510	224	13	(	(	PUNCT
cana-4510	224	14	9	9	NUM
cana-4510	224	15	)	)	PUNCT
cana-4510	225	1	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	PROPN
cana-4510	225	2	=	=	SYM
cana-4510	225	3	𝑇𝑃+𝑇𝑟𝑢𝑒	𝑇𝑃+𝑇𝑟𝑢𝑒	PROPN
cana-4510	225	4	𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒	𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒	PROPN
cana-4510	225	5	(	(	PUNCT
cana-4510	225	6	𝑇𝑁	𝑇𝑁	PROPN
cana-4510	225	7	)	)	PUNCT
cana-4510	225	8	𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁	𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁	PUNCT
cana-4510	225	9	(	(	PUNCT
cana-4510	225	10	10	10	NUM
cana-4510	225	11	)	)	PUNCT
cana-4510	225	12	eq	eq	NOUN
cana-4510	225	13	.	.	PUNCT
cana-4510	226	1	(	(	PUNCT
cana-4510	226	2	7	7	NUM
cana-4510	226	3	)	)	PUNCT
cana-4510	226	4	,	,	PUNCT
cana-4510	226	5	(	(	PUNCT
cana-4510	226	6	8)	8)	NUM
cana-4510	226	7	,	,	PUNCT
cana-4510	226	8	(	(	PUNCT
cana-4510	226	9	9	9	NUM
cana-4510	226	10	)	)	PUNCT
cana-4510	226	11	,	,	PUNCT
cana-4510	226	12	and	and	CCONJ
cana-4510	226	13	(	(	PUNCT
cana-4510	226	14	10	10	NUM
cana-4510	226	15	)	)	PUNCT
cana-4510	226	16	represents	represent	VERB
cana-4510	226	17	the	the	DET
cana-4510	226	18	measures	measure	NOUN
cana-4510	226	19	and	and	CCONJ
cana-4510	226	20	calculation	calculation	NOUN
cana-4510	226	21	of	of	ADP
cana-4510	226	22	classification	classification	NOUN
cana-4510	226	23	performance	performance	NOUN
cana-4510	226	24	metrics	metric	NOUN
cana-4510	226	25	including	include	VERB
cana-4510	226	26	precision	precision	NOUN
cana-4510	226	27	,	,	PUNCT
cana-4510	226	28	recall	recall	NOUN
cana-4510	226	29	,	,	PUNCT
cana-4510	226	30	f1	f1	NOUN
cana-4510	226	31	-	-	PUNCT
cana-4510	226	32	score	score	NOUN
cana-4510	226	33	and	and	CCONJ
cana-4510	226	34	accuracy	accuracy	NOUN
cana-4510	226	35	.	.	PUNCT
cana-4510	227	1	this	this	DET
cana-4510	227	2	comparative	comparative	ADJ
cana-4510	227	3	result	result	NOUN
cana-4510	227	4	ensures	ensure	VERB
cana-4510	227	5	the	the	DET
cana-4510	227	6	proposed	propose	VERB
cana-4510	227	7	integrative	integrative	ADJ
cana-4510	227	8	hybrid	hybrid	ADJ
cana-4510	227	9	framework	framework	NOUN
cana-4510	227	10	model	model	NOUN
cana-4510	227	11	“	"	PUNCT
cana-4510	227	12	dhdcnet	dhdcnet	ADJ
cana-4510	227	13	model	model	NOUN
cana-4510	227	14	”	"	PUNCT
cana-4510	227	15	was	be	AUX
cana-4510	227	16	classified	classify	VERB
cana-4510	227	17	and	and	CCONJ
cana-4510	227	18	achieved	achieve	VERB
cana-4510	227	19	high	high	ADJ
cana-4510	227	20	accuracy	accuracy	NOUN
cana-4510	227	21	in	in	ADP
cana-4510	227	22	various	various	ADJ
cana-4510	227	23	taxonomic	taxonomic	ADJ
cana-4510	227	24	level	level	NOUN
cana-4510	227	25	of	of	ADP
cana-4510	227	26	insect	insect	NOUN
cana-4510	227	27	images	image	NOUN
cana-4510	227	28	in	in	ADP
cana-4510	227	29	efficient	efficient	ADJ
cana-4510	227	30	manner	manner	NOUN
cana-4510	227	31	.	.	PUNCT
cana-4510	228	1	5	5	NUM
cana-4510	228	2	.	.	X
cana-4510	228	3	conclusion	conclusion	NOUN
cana-4510	228	4	and	and	CCONJ
cana-4510	228	5	future	future	ADJ
cana-4510	228	6	challenges	challenge	NOUN
cana-4510	228	7	in	in	ADP
cana-4510	228	8	summary	summary	NOUN
cana-4510	228	9	,	,	PUNCT
cana-4510	228	10	this	this	DET
cana-4510	228	11	work	work	NOUN
cana-4510	228	12	demonstrates	demonstrate	VERB
cana-4510	228	13	the	the	DET
cana-4510	228	14	effectiveness	effectiveness	NOUN
cana-4510	228	15	of	of	ADP
cana-4510	228	16	deep	deep	ADJ
cana-4510	228	17	learning	learning	NOUN
cana-4510	228	18	in	in	ADP
cana-4510	228	19	insect	insect	NOUN
cana-4510	228	20	image	image	NOUN
cana-4510	228	21	classification	classification	NOUN
cana-4510	228	22	across	across	ADP
cana-4510	228	23	different	different	ADJ
cana-4510	228	24	taxonomic	taxonomic	ADJ
cana-4510	228	25	levels	level	NOUN
cana-4510	228	26	.	.	PUNCT
cana-4510	229	1	various	various	ADJ
cana-4510	229	2	dcnn	dcnn	ADJ
cana-4510	229	3	models	model	NOUN
cana-4510	229	4	,	,	PUNCT
cana-4510	229	5	including	include	VERB
cana-4510	229	6	inception	inception	PROPN
cana-4510	229	7	v3	v3	PROPN
cana-4510	229	8	,	,	PUNCT
cana-4510	229	9	resnet	resnet	VERB
cana-4510	229	10	152	152	NUM
cana-4510	229	11	,	,	PUNCT
cana-4510	229	12	vgg	vgg	NOUN
cana-4510	229	13	19	19	NUM
cana-4510	229	14	,	,	PUNCT
cana-4510	229	15	mobile	mobile	ADJ
cana-4510	229	16	net	net	NOUN
cana-4510	229	17	v2	v2	PROPN
cana-4510	229	18	,	,	PUNCT
cana-4510	229	19	and	and	CCONJ
cana-4510	229	20	xception	xception	NOUN
cana-4510	229	21	,	,	PUNCT
cana-4510	229	22	and	and	CCONJ
cana-4510	229	23	introducing	introduce	VERB
cana-4510	229	24	the	the	DET
cana-4510	229	25	innovative	innovative	ADJ
cana-4510	229	26	"	"	PUNCT
cana-4510	229	27	dual	dual	ADJ
cana-4510	229	28	hybrid	hybrid	ADJ
cana-4510	229	29	deep	deep	ADJ
cana-4510	229	30	cnn	cnn	PROPN
cana-4510	229	31	dhdcnet	dhdcnet	NOUN
cana-4510	229	32	"	"	PUNCT
cana-4510	229	33	framework	framework	NOUN
cana-4510	229	34	,	,	PUNCT
cana-4510	229	35	our	our	PRON
cana-4510	229	36	study	study	NOUN
cana-4510	229	37	achieves	achieve	VERB
cana-4510	229	38	outstanding	outstanding	ADJ
cana-4510	229	39	results	result	NOUN
cana-4510	229	40	.	.	PUNCT
cana-4510	230	1	notably	notably	ADV
cana-4510	230	2	,	,	PUNCT
cana-4510	230	3	it	it	PRON
cana-4510	230	4	attains	attain	VERB
cana-4510	230	5	an	an	DET
cana-4510	230	6	accuracy	accuracy	NOUN
cana-4510	230	7	of	of	ADP
cana-4510	230	8	98.97	98.97	NUM
cana-4510	230	9	%	%	NOUN
cana-4510	230	10	at	at	ADP
cana-4510	230	11	the	the	DET
cana-4510	230	12	order	order	NOUN
cana-4510	230	13	level	level	NOUN
cana-4510	230	14	and	and	CCONJ
cana-4510	230	15	97.37	97.37	NUM
cana-4510	230	16	%	%	NOUN
cana-4510	230	17	at	at	ADP
cana-4510	230	18	the	the	DET
cana-4510	230	19	family	family	NOUN
cana-4510	230	20	and	and	CCONJ
cana-4510	230	21	89.79	89.79	NUM
cana-4510	230	22	%	%	NOUN
cana-4510	230	23	at	at	ADP
cana-4510	230	24	the	the	DET
cana-4510	230	25	species	species	NOUN
cana-4510	230	26	level	level	NOUN
cana-4510	230	27	,	,	PUNCT
cana-4510	230	28	all	all	PRON
cana-4510	230	29	with	with	ADP
cana-4510	230	30	significantly	significantly	ADV
cana-4510	230	31	reduced	reduce	VERB
cana-4510	230	32	training	training	NOUN
cana-4510	230	33	durations	duration	NOUN
cana-4510	230	34	compared	compare	VERB
cana-4510	230	35	to	to	ADP
cana-4510	230	36	individual	individual	ADJ
cana-4510	230	37	pre	pre	ADJ
cana-4510	230	38	-	-	ADJ
cana-4510	230	39	trained	train	VERB
cana-4510	230	40	models	model	NOUN
cana-4510	230	41	.	.	PUNCT
cana-4510	231	1	these	these	DET
cana-4510	231	2	remarkable	remarkable	ADJ
cana-4510	231	3	outcomes	outcome	NOUN
cana-4510	231	4	hold	hold	VERB
cana-4510	231	5	great	great	ADJ
cana-4510	231	6	promise	promise	NOUN
cana-4510	231	7	for	for	ADP
cana-4510	231	8	entomology	entomology	NOUN
cana-4510	231	9	,	,	PUNCT
cana-4510	231	10	ecology	ecology	NOUN
cana-4510	231	11	,	,	PUNCT
cana-4510	231	12	and	and	CCONJ
cana-4510	231	13	conservation	conservation	NOUN
cana-4510	231	14	efforts	effort	NOUN
cana-4510	231	15	,	,	PUNCT
cana-4510	231	16	paving	pave	VERB
cana-4510	231	17	the	the	DET
cana-4510	231	18	way	way	NOUN
cana-4510	231	19	for	for	ADP
cana-4510	231	20	more	more	ADV
cana-4510	231	21	efficient	efficient	ADJ
cana-4510	231	22	and	and	CCONJ
cana-4510	231	23	accurate	accurate	ADJ
cana-4510	231	24	insect	insect	NOUN
cana-4510	231	25	classification	classification	NOUN
cana-4510	231	26	processes	process	NOUN
cana-4510	231	27	.	.	PUNCT
cana-4510	232	1	beyond	beyond	ADP
cana-4510	232	2	its	its	PRON
cana-4510	232	3	academic	academic	ADJ
cana-4510	232	4	significance	significance	NOUN
cana-4510	232	5	,	,	PUNCT
cana-4510	232	6	this	this	DET
cana-4510	232	7	23	23	NUM
cana-4510	232	8	33	33	NUM
cana-4510	232	9	43	43	NUM
cana-4510	232	10	53	53	NUM
cana-4510	232	11	63	63	NUM
cana-4510	232	12	73	73	NUM
cana-4510	232	13	83	83	NUM
cana-4510	232	14	93	93	NUM
cana-4510	232	15	103	103	NUM
cana-4510	232	16	113	113	NUM
cana-4510	232	17	species	specie	NOUN
cana-4510	232	18	level	level	NOUN
cana-4510	232	19	family	family	NOUN
cana-4510	232	20	level	level	NOUN
cana-4510	232	21	order	order	NOUN
cana-4510	232	22	level	level	NOUN
cana-4510	232	23	a	a	DET
cana-4510	232	24	c	c	NOUN
cana-4510	232	25	c	c	NOUN
cana-4510	232	26	u	u	NOUN
cana-4510	232	27	r	r	NOUN
cana-4510	232	28	a	a	PROPN
cana-4510	232	29	c	c	NOUN
cana-4510	232	30	y	y	PROPN
cana-4510	232	31	(	(	PUNCT
cana-4510	232	32	%	%	NOUN
cana-4510	232	33	)	)	PUNCT
cana-4510	232	34	classification	classification	NOUN
cana-4510	232	35	levels	level	NOUN
cana-4510	232	36	inceptionv3	inceptionv3	NOUN
cana-4510	232	37	resnet	resnet	VERB
cana-4510	232	38	152	152	NUM
cana-4510	232	39	vgg	vgg	ADJ
cana-4510	232	40	19	19	NUM
cana-4510	232	41	mobilenet	mobilenet	NOUN
cana-4510	232	42	xception	xception	PROPN
cana-4510	232	43	proposed	propose	VERB
cana-4510	232	44	dhdcnet	dhdcnet	ADJ
cana-4510	232	45	communications	communication	NOUN
cana-4510	232	46	on	on	ADP
cana-4510	232	47	applied	apply	VERB
cana-4510	232	48	nonlinear	nonlinear	ADJ
cana-4510	232	49	analysis	analysis	NOUN
cana-4510	232	50	issn	issn	NOUN
cana-4510	232	51	:	:	PUNCT
cana-4510	232	52	1074	1074	NUM
cana-4510	232	53	-	-	PUNCT
cana-4510	232	54	133x	133x	NUM
cana-4510	232	55	vol	vol	NOUN
cana-4510	232	56	32	32	NUM
cana-4510	232	57	no	no	NOUN
cana-4510	232	58	.	.	PUNCT
cana-4510	233	1	9s	9s	NUM
cana-4510	233	2	(	(	PUNCT
cana-4510	233	3	2025	2025	NUM
cana-4510	233	4	)	)	PUNCT
cana-4510	233	5	2256	2256	NUM
cana-4510	234	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4510	234	2	research	research	NOUN
cana-4510	234	3	offers	offer	VERB
cana-4510	234	4	practical	practical	ADJ
cana-4510	234	5	applications	application	NOUN
cana-4510	234	6	in	in	ADP
cana-4510	234	7	the	the	DET
cana-4510	234	8	development	development	NOUN
cana-4510	234	9	of	of	ADP
cana-4510	234	10	automated	automate	VERB
cana-4510	234	11	systems	system	NOUN
cana-4510	234	12	for	for	ADP
cana-4510	234	13	rapid	rapid	ADJ
cana-4510	234	14	and	and	CCONJ
cana-4510	234	15	reliable	reliable	ADJ
cana-4510	234	16	insect	insect	NOUN
cana-4510	234	17	identification	identification	NOUN
cana-4510	234	18	,	,	PUNCT
cana-4510	234	19	benefitting	benefit	VERB
cana-4510	234	20	researchers	researcher	NOUN
cana-4510	234	21	and	and	CCONJ
cana-4510	234	22	environmental	environmental	ADJ
cana-4510	234	23	preservation	preservation	NOUN
cana-4510	234	24	endeavours	endeavour	VERB
cana-4510	234	25	.	.	PUNCT
cana-4510	235	1	ultimately	ultimately	ADV
cana-4510	235	2	,	,	PUNCT
cana-4510	235	3	this	this	DET
cana-4510	235	4	study	study	NOUN
cana-4510	235	5	significantly	significantly	ADV
cana-4510	235	6	enhances	enhance	VERB
cana-4510	235	7	our	our	PRON
cana-4510	235	8	understanding	understanding	NOUN
cana-4510	235	9	of	of	ADP
cana-4510	235	10	insect	insect	NOUN
cana-4510	235	11	diversity	diversity	NOUN
cana-4510	235	12	and	and	CCONJ
cana-4510	235	13	ecological	ecological	ADJ
cana-4510	235	14	roles	role	NOUN
cana-4510	235	15	,	,	PUNCT
cana-4510	235	16	highlighting	highlight	VERB
cana-4510	235	17	the	the	DET
cana-4510	235	18	vast	vast	ADJ
cana-4510	235	19	capabilities	capability	NOUN
cana-4510	235	20	of	of	ADP
cana-4510	235	21	dl	dl	PROPN
cana-4510	235	22	in	in	ADP
cana-4510	235	23	the	the	DET
cana-4510	235	24	field	field	NOUN
cana-4510	235	25	of	of	ADP
cana-4510	235	26	insect	insect	NOUN
cana-4510	235	27	taxonomy	taxonomy	NOUN
cana-4510	235	28	and	and	CCONJ
cana-4510	235	29	image	image	NOUN
cana-4510	235	30	classification	classification	NOUN
cana-4510	235	31	.	.	PUNCT
cana-4510	236	1	future	future	ADJ
cana-4510	236	2	investigations	investigation	NOUN
cana-4510	236	3	will	will	AUX
cana-4510	236	4	aim	aim	VERB
cana-4510	236	5	to	to	PART
cana-4510	236	6	broaden	broaden	VERB
cana-4510	236	7	the	the	DET
cana-4510	236	8	research	research	NOUN
cana-4510	236	9	's	's	PART
cana-4510	236	10	scope	scope	NOUN
cana-4510	236	11	to	to	PART
cana-4510	236	12	encompass	encompass	VERB
cana-4510	236	13	a	a	DET
cana-4510	236	14	wider	wide	ADJ
cana-4510	236	15	variety	variety	NOUN
cana-4510	236	16	of	of	ADP
cana-4510	236	17	insect	insect	NOUN
cana-4510	236	18	species	specie	NOUN
cana-4510	236	19	across	across	ADP
cana-4510	236	20	various	various	ADJ
cana-4510	236	21	taxonomic	taxonomic	ADJ
cana-4510	236	22	levels	level	NOUN
cana-4510	236	23	,	,	PUNCT
cana-4510	236	24	further	far	ADV
cana-4510	236	25	improving	improve	VERB
cana-4510	236	26	the	the	DET
cana-4510	236	27	capabilities	capability	NOUN
cana-4510	236	28	of	of	ADP
cana-4510	236	29	the	the	DET
cana-4510	236	30	classification	classification	NOUN
cana-4510	236	31	system	system	NOUN
cana-4510	236	32	.	.	PUNCT
cana-4510	237	1	references	reference	NOUN
cana-4510	237	2	[	[	X
cana-4510	237	3	1	1	NUM
cana-4510	237	4	]	]	PUNCT
cana-4510	237	5	c.	c.	PROPN
cana-4510	237	6	martineau	martineau	PROPN
cana-4510	237	7	,	,	PUNCT
cana-4510	237	8	d.	d.	PROPN
cana-4510	237	9	chloé	chloé	PROPN
cana-4510	237	10	,	,	PUNCT
cana-4510	237	11	et	et	PROPN
cana-4510	237	12	al	al	PROPN
cana-4510	237	13	.	.	PROPN
cana-4510	237	14	,	,	PUNCT
cana-4510	237	15	“	"	PUNCT
cana-4510	237	16	a	a	DET
cana-4510	237	17	survey	survey	NOUN
cana-4510	237	18	on	on	ADP
cana-4510	237	19	image	image	NOUN
cana-4510	237	20	-	-	PUNCT
cana-4510	237	21	based	base	VERB
cana-4510	237	22	insect	insect	NOUN
cana-4510	237	23	classification	classification	NOUN
cana-4510	237	24	,	,	PUNCT
cana-4510	237	25	”	"	PUNCT
cana-4510	237	26	pattern	pattern	NOUN
cana-4510	237	27	recognition	recognition	NOUN
cana-4510	237	28	,	,	PUNCT
cana-4510	237	29	vol	vol	NOUN
cana-4510	237	30	.	.	PROPN
cana-4510	237	31	65	65	NUM
cana-4510	237	32	,	,	PUNCT
cana-4510	237	33	pp	pp	ADJ
cana-4510	237	34	.	.	PUNCT
cana-4510	238	1	273–84	273–84	NOUN
cana-4510	238	2	,	,	PUNCT
cana-4510	238	3	2017	2017	NUM
cana-4510	238	4	,	,	PUNCT
cana-4510	238	5	doi	doi	NOUN
cana-4510	238	6	:	:	PUNCT
cana-4510	238	7	https://doi.org/10.1016/j.patcog.2016.12.020	https://doi.org/10.1016/j.patcog.2016.12.020	NOUN
cana-4510	238	8	.	.	PUNCT
cana-4510	239	1	[	[	X
cana-4510	239	2	2	2	X
cana-4510	239	3	]	]	PUNCT
cana-4510	239	4	e.	e.	PROPN
cana-4510	239	5	tihelka	tihelka	PROPN
cana-4510	239	6	,	,	PUNCT
cana-4510	239	7	c.	c.	PROPN
cana-4510	239	8	cai	cai	PROPN
cana-4510	239	9	,	,	PUNCT
cana-4510	239	10	m.	m.	NOUN
cana-4510	239	11	giacomelli	giacomelli	PROPN
cana-4510	239	12	,	,	PUNCT
cana-4510	239	13	j.	j.	PROPN
cana-4510	239	14	lozano	lozano	PROPN
cana-4510	239	15	-	-	PUNCT
cana-4510	239	16	fernandez	fernandez	PROPN
cana-4510	239	17	,	,	PUNCT
cana-4510	239	18	o.	o.	NOUN
cana-4510	239	19	rota	rota	PROPN
cana-4510	239	20	-	-	PUNCT
cana-4510	239	21	stabelli	stabelli	PROPN
cana-4510	239	22	,	,	PUNCT
cana-4510	239	23	d.	d.	PROPN
cana-4510	239	24	huang	huang	PROPN
cana-4510	239	25	,	,	PUNCT
cana-4510	239	26	m.s	m.s	PROPN
cana-4510	239	27	.	.	PROPN
cana-4510	239	28	engel	engel	PROPN
cana-4510	239	29	,	,	PUNCT
cana-4510	239	30	p.c.j	p.c.j	PROPN
cana-4510	239	31	.	.	PUNCT
cana-4510	239	32	donoghue	donoghue	PROPN
cana-4510	239	33	,	,	PUNCT
cana-4510	239	34	d.	d.	PROPN
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cana-4510	239	36	,	,	PUNCT
cana-4510	239	37	“	"	PUNCT
cana-4510	239	38	the	the	DET
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cana-4510	239	40	of	of	ADP
cana-4510	239	41	insect	insect	NOUN
cana-4510	239	42	biodiversity	biodiversity	NOUN
cana-4510	239	43	,	,	PUNCT
cana-4510	239	44	”	"	PUNCT
cana-4510	239	45	curr	curr	PROPN
cana-4510	239	46	.	.	PUNCT
cana-4510	240	1	biol	biol	PROPN
cana-4510	240	2	.	.	PUNCT
cana-4510	241	1	2021	2021	NUM
cana-4510	241	2	,	,	PUNCT
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cana-4510	241	4	,	,	PUNCT
cana-4510	241	5	no.19	no.19	PROPN
cana-4510	241	6	,	,	PUNCT
cana-4510	241	7	pp	pp	ADV
cana-4510	241	8	.	.	PUNCT
cana-4510	242	1	r1299	r1299	NOUN
cana-4510	242	2	–	–	PUNCT
cana-4510	242	3	r1311	r1311	PROPN
cana-4510	242	4	,	,	PUNCT
cana-4510	242	5	2021	2021	NUM
cana-4510	242	6	,	,	PUNCT
cana-4510	242	7	doi	doi	NOUN
cana-4510	242	8	:	:	PUNCT
cana-4510	242	9	10.1016	10.1016	NUM
cana-4510	242	10	/	/	SYM
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cana-4510	242	12	.	.	PUNCT
cana-4510	243	1	[	[	X
cana-4510	243	2	3	3	X
cana-4510	243	3	]	]	PUNCT
cana-4510	243	4	x.	x.	PROPN
cana-4510	243	5	cao	cao	PROPN
cana-4510	243	6	,	,	PUNCT
cana-4510	243	7	z.	z.	PROPN
cana-4510	243	8	wei	wei	PROPN
cana-4510	243	9	,	,	PUNCT
cana-4510	243	10	y.	y.	PROPN
cana-4510	243	11	gao	gao	PROPN
cana-4510	243	12	,	,	PUNCT
cana-4510	243	13	and	and	CCONJ
cana-4510	243	14	y.	y.	PROPN
cana-4510	243	15	huo	huo	PROPN
cana-4510	243	16	,	,	PUNCT
cana-4510	243	17	“	"	PUNCT
cana-4510	243	18	recognition	recognition	NOUN
cana-4510	243	19	of	of	ADP
cana-4510	243	20	common	common	ADJ
cana-4510	243	21	insect	insect	NOUN
cana-4510	243	22	in	in	ADP
cana-4510	243	23	field	field	NOUN
cana-4510	243	24	based	base	VERB
cana-4510	243	25	on	on	ADP
cana-4510	243	26	deep	deep	ADJ
cana-4510	243	27	learning	learning	NOUN
cana-4510	243	28	,	,	PUNCT
cana-4510	243	29	”	"	PUNCT
cana-4510	243	30	journal	journal	NOUN
cana-4510	243	31	of	of	ADP
cana-4510	243	32	physics	physics	PROPN
cana-4510	243	33	:	:	PUNCT
cana-4510	243	34	conference	conference	NOUN
cana-4510	243	35	series	series	NOUN
cana-4510	243	36	,	,	PUNCT
cana-4510	243	37	vol	vol	NOUN
cana-4510	243	38	.	.	PROPN
cana-4510	243	39	1634	1634	NUM
cana-4510	243	40	,	,	PUNCT
cana-4510	243	41	no	no	INTJ
cana-4510	243	42	.	.	NOUN
cana-4510	243	43	1	1	NUM
cana-4510	243	44	,	,	PUNCT
cana-4510	243	45	p.	p.	NOUN
cana-4510	243	46	012034	012034	NUM
cana-4510	243	47	,	,	PUNCT
cana-4510	243	48	2020	2020	NUM
cana-4510	243	49	,	,	PUNCT
cana-4510	243	50	doi	doi	NOUN
cana-4510	243	51	:	:	PUNCT
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cana-4510	243	53	.	.	PUNCT
cana-4510	244	1	[	[	X
cana-4510	244	2	4	4	X
cana-4510	244	3	]	]	X
cana-4510	244	4	b.	b.	PROPN
cana-4510	244	5	benuwa	benuwa	PROPN
cana-4510	244	6	,	,	PUNCT
cana-4510	244	7	y.	y.	PROPN
cana-4510	244	8	zhao	zhao	PROPN
cana-4510	244	9	zhan	zhan	PROPN
cana-4510	244	10	,	,	PUNCT
cana-4510	244	11	b.	b.	PROPN
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cana-4510	244	13	,	,	PUNCT
cana-4510	244	14	d.	d.	PROPN
cana-4510	244	15	wornyo	wornyo	PROPN
cana-4510	244	16	,	,	PUNCT
cana-4510	244	17	and	and	CCONJ
cana-4510	244	18	f.	f.	PROPN
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cana-4510	244	20	,	,	PUNCT
cana-4510	244	21	“	"	PUNCT
cana-4510	244	22	a	a	DET
cana-4510	244	23	review	review	NOUN
cana-4510	244	24	of	of	ADP
cana-4510	244	25	deep	deep	ADJ
cana-4510	244	26	machine	machine	NOUN
cana-4510	244	27	learning	learning	NOUN
cana-4510	244	28	,	,	PUNCT
cana-4510	244	29	”	"	PUNCT
cana-4510	244	30	in	in	ADP
cana-4510	244	31	international	international	ADJ
cana-4510	244	32	journal	journal	NOUN
cana-4510	244	33	of	of	ADP
cana-4510	244	34	engineering	engineering	NOUN
cana-4510	244	35	research	research	NOUN
cana-4510	244	36	,	,	PUNCT
cana-4510	244	37	vol	vol	NOUN
cana-4510	244	38	.	.	PROPN
cana-4510	244	39	24	24	NUM
cana-4510	244	40	,	,	PUNCT
cana-4510	244	41	pp.124	pp.124	NOUN
cana-4510	244	42	-	-	PUNCT
cana-4510	244	43	136	136	NUM
cana-4510	244	44	,	,	PUNCT
cana-4510	244	45	2016	2016	NUM
cana-4510	244	46	,	,	PUNCT
cana-4510	244	47	doi	doi	NOUN
cana-4510	244	48	:	:	PUNCT
cana-4510	244	49	https://doi.org/10.4028/www.scientific.net/jera.24.124	https://doi.org/10.4028/www.scientific.net/jera.24.124	ADJ
cana-4510	244	50	.	.	PUNCT
cana-4510	245	1	[	[	X
cana-4510	245	2	5	5	X
cana-4510	245	3	]	]	PUNCT
cana-4510	245	4	s.	s.	PROPN
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cana-4510	245	6	,	,	PUNCT
cana-4510	245	7	m.	m.	NOUN
cana-4510	245	8	sadaati	sadaati	NOUN
cana-4510	245	9	,	,	PUNCT
cana-4510	245	10	z.k	z.k	PROPN
cana-4510	245	11	.	.	PROPN
cana-4510	245	12	deng	deng	PROPN
cana-4510	245	13	,	,	PUNCT
cana-4510	245	14	j.	j.	PROPN
cana-4510	245	15	koushik	koushik	PROPN
cana-4510	245	16	,	,	PUNCT
cana-4510	245	17	t.z	t.z	PROPN
cana-4510	245	18	.	.	PROPN
cana-4510	245	19	jubery	jubery	PROPN
cana-4510	245	20	,	,	PUNCT
cana-4510	245	21	d.	d.	PROPN
cana-4510	245	22	mueller	mueller	PROPN
cana-4510	245	23	and	and	CCONJ
cana-4510	245	24	b.	b.	PROPN
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cana-4510	245	26	,	,	PUNCT
cana-4510	245	27	“	"	PUNCT
cana-4510	245	28	deep	deep	ADJ
cana-4510	245	29	learning	learning	NOUN
cana-4510	245	30	powered	power	VERB
cana-4510	245	31	real	real	ADJ
cana-4510	245	32	-	-	PUNCT
cana-4510	245	33	time	time	NOUN
cana-4510	245	34	identification	identification	NOUN
cana-4510	245	35	of	of	ADP
cana-4510	245	36	insects	insect	NOUN
cana-4510	245	37	using	use	VERB
cana-4510	245	38	citizen	citizen	PROPN
cana-4510	245	39	science	science	NOUN
cana-4510	245	40	data	datum	NOUN
cana-4510	245	41	,	,	PUNCT
cana-4510	245	42	”	"	PUNCT
cana-4510	245	43	arxiv	arxiv	PROPN
cana-4510	245	44	preprint	preprint	NOUN
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cana-4510	245	46	,	,	PUNCT
cana-4510	245	47	2023	2023	NUM
cana-4510	245	48	,	,	PUNCT
cana-4510	245	49	doi	doi	NOUN
cana-4510	245	50	:	:	PUNCT
cana-4510	245	51	http://arxiv.org/abs/2306.02507	http://arxiv.org/abs/2306.02507	NOUN
cana-4510	245	52	.	.	PUNCT
cana-4510	246	1	[	[	X
cana-4510	246	2	6	6	NUM
cana-4510	246	3	]	]	X
cana-4510	246	4	g.	g.	PROPN
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cana-4510	246	6	,	,	PUNCT
cana-4510	246	7	and	and	CCONJ
cana-4510	246	8	v.	v.	PROPN
cana-4510	246	9	podgorelec	podgorelec	PROPN
cana-4510	246	10	,	,	PUNCT
cana-4510	246	11	“	"	PUNCT
cana-4510	246	12	transfer	transfer	NOUN
cana-4510	246	13	learning	learning	NOUN
cana-4510	246	14	with	with	ADP
cana-4510	246	15	adaptive	adaptive	ADJ
cana-4510	246	16	fine	fine	ADJ
cana-4510	246	17	-	-	PUNCT
cana-4510	246	18	tuning	tuning	NOUN
cana-4510	246	19	.	.	PUNCT
cana-4510	246	20	”	"	PUNCT
cana-4510	247	1	ieee	ieee	NOUN
cana-4510	247	2	access	access	NOUN
cana-4510	247	3	,	,	PUNCT
cana-4510	247	4	vol	vol	NOUN
cana-4510	247	5	.	.	PROPN
cana-4510	247	6	8	8	NUM
cana-4510	247	7	,	,	PUNCT
cana-4510	247	8	pp	pp	ADJ
cana-4510	247	9	.	.	PUNCT
cana-4510	247	10	196197–211	196197–211	NOUN
cana-4510	247	11	,	,	PUNCT
cana-4510	247	12	2020	2020	NUM
cana-4510	247	13	,	,	PUNCT
cana-4510	247	14	doi	doi	INTJ
cana-4510	247	15	:	:	PUNCT
cana-4510	247	16	https://doi.org/10.1109/access.2020.3034343	https://doi.org/10.1109/access.2020.3034343	PROPN
cana-4510	247	17	.	.	PUNCT
cana-4510	248	1	[	[	X
cana-4510	248	2	7	7	X
cana-4510	248	3	]	]	X
cana-4510	248	4	m.	m.	NOUN
cana-4510	248	5	santhiya	santhiya	PROPN
cana-4510	248	6	,	,	PUNCT
cana-4510	248	7	s.	s.	PROPN
cana-4510	248	8	karpagavalli	karpagavalli	PROPN
cana-4510	248	9	,	,	PUNCT
cana-4510	248	10	“	"	PUNCT
cana-4510	248	11	multi	multi	ADJ
cana-4510	248	12	-	-	ADJ
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cana-4510	248	18	deep	deep	ADJ
cana-4510	248	19	neural	neural	ADJ
cana-4510	248	20	networks	network	NOUN
cana-4510	248	21	,	,	PUNCT
cana-4510	248	22	”	"	PUNCT
cana-4510	248	23	in	in	ADP
cana-4510	248	24	ieee	ieee	PROPN
cana-4510	248	25	international	international	ADJ
cana-4510	248	26	conference	conference	NOUN
cana-4510	248	27	on	on	ADP
cana-4510	248	28	computer	computer	NOUN
cana-4510	248	29	communication	communication	NOUN
cana-4510	248	30	and	and	CCONJ
cana-4510	248	31	informatics	informatic	NOUN
cana-4510	248	32	,	,	PUNCT
cana-4510	248	33	pp	pp	ADJ
cana-4510	248	34	.	.	PUNCT
cana-4510	248	35	1	1	NUM
cana-4510	248	36	-	-	SYM
cana-4510	248	37	5	5	NUM
cana-4510	248	38	,	,	PUNCT
cana-4510	248	39	2023	2023	NUM
cana-4510	248	40	,	,	PUNCT
cana-4510	248	41	doi	doi	NOUN
cana-4510	248	42	:	:	PUNCT
cana-4510	248	43	10.1109	10.1109	NUM
cana-4510	248	44	/	/	SYM
cana-4510	248	45	iccci56745.2023.10128549	iccci56745.2023.10128549	NOUN
cana-4510	248	46	.	.	PUNCT
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cana-4510	249	2	8	8	NUM
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cana-4510	249	5	.	.	PROPN
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cana-4510	249	10	.	.	PROPN
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cana-4510	249	17	taxonomic	taxonomic	ADJ
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cana-4510	249	19	by	by	ADP
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cana-4510	249	22	deep	deep	ADJ
cana-4510	249	23	learning	learning	NOUN
cana-4510	249	24	algorithms	algorithm	NOUN
cana-4510	249	25	,	,	PUNCT
cana-4510	249	26	”	"	PUNCT
cana-4510	249	27	plos	plos	PROPN
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cana-4510	249	29	,	,	PUNCT
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cana-4510	249	32	17	17	NUM
cana-4510	249	33	,	,	PUNCT
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cana-4510	249	36	12	12	NUM
cana-4510	249	37	,	,	PUNCT
cana-4510	249	38	p.	p.	NOUN
cana-4510	249	39	e0279094	e0279094	PROPN
cana-4510	249	40	,	,	PUNCT
cana-4510	249	41	2022	2022	NUM
cana-4510	249	42	,	,	PUNCT
cana-4510	249	43	doi	doi	NOUN
cana-4510	249	44	;	;	PUNCT
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cana-4510	249	46	.	.	PUNCT
cana-4510	250	1	[	[	X
cana-4510	250	2	9	9	NUM
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cana-4510	250	10	j.	j.	PROPN
cana-4510	250	11	xu	xu	PROPN
cana-4510	250	12	,	,	PUNCT
cana-4510	250	13	g.	g.	PROPN
cana-4510	250	14	su	su	PROPN
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cana-4510	250	16	j.	j.	PROPN
cana-4510	250	17	li	li	PROPN
cana-4510	250	18	,	,	PUNCT
cana-4510	250	19	m.	m.	PROPN
cana-4510	250	20	ji	ji	PROPN
cana-4510	250	21	,	,	PUNCT
cana-4510	250	22	and	and	CCONJ
cana-4510	250	23	b.	b.	PROPN
cana-4510	250	24	zhao	zhao	PROPN
cana-4510	250	25	,	,	PUNCT
cana-4510	250	26	“	"	PUNCT
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cana-4510	250	30	based	base	VERB
cana-4510	250	31	on	on	ADP
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cana-4510	250	33	neural	neural	ADJ
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cana-4510	250	35	models	model	NOUN
cana-4510	250	36	in	in	ADP
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cana-4510	250	40	”	"	PUNCT
cana-4510	250	41	in	in	ADP
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cana-4510	250	44	the	the	DET
cana-4510	250	45	5th	5th	ADJ
cana-4510	250	46	international	international	ADJ
cana-4510	250	47	conference	conference	NOUN
cana-4510	250	48	on	on	ADP
cana-4510	250	49	high	high	ADJ
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cana-4510	250	51	compilation	compilation	NOUN
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cana-4510	250	53	computing	computing	NOUN
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cana-4510	250	58	.	.	PUNCT
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cana-4510	250	60	-	-	SYM
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cana-4510	250	64	,	,	PUNCT
cana-4510	250	65	doi	doi	NOUN
cana-4510	250	66	:	:	PUNCT
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cana-4510	250	68	.	.	PUNCT
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cana-4510	251	2	10	10	NUM
cana-4510	251	3	]	]	X
cana-4510	251	4	b.j	b.j	PROPN
cana-4510	251	5	.	.	PROPN
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cana-4510	251	7	,	,	PUNCT
cana-4510	251	8	c.	c.	PROPN
cana-4510	251	9	gratton	gratton	PROPN
cana-4510	251	10	,	,	PUNCT
cana-4510	251	11	r.g	r.g	PROPN
cana-4510	251	12	.	.	PROPN
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cana-4510	251	14	,	,	PUNCT
cana-4510	251	15	w.h	w.h	PROPN
cana-4510	251	16	.	.	PROPN
cana-4510	251	17	hsu	hsu	PROPN
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cana-4510	251	19	s.	s.	PROPN
cana-4510	251	20	jepsen	jepsen	PROPN
cana-4510	251	21	,	,	PUNCT
cana-4510	251	22	b.	b.	PROPN
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cana-4510	251	24	,	,	PUNCT
cana-4510	251	25	and	and	CCONJ
cana-4510	251	26	g.	g.	PROPN
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cana-4510	251	29	“	"	PUNCT
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cana-4510	251	35	learning	learning	NOUN
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cana-4510	251	37	computer	computer	NOUN
cana-4510	251	38	vision	vision	NOUN
cana-4510	251	39	to	to	PART
cana-4510	251	40	identify	identify	VERB
cana-4510	251	41	bumble	bumble	ADJ
cana-4510	251	42	bee	bee	NOUN
cana-4510	251	43	species	specie	NOUN
cana-4510	251	44	from	from	ADP
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cana-4510	251	47	”	"	PUNCT
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cana-4510	251	50	,	,	PUNCT
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cana-4510	251	54	,	,	PUNCT
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cana-4510	251	56	,	,	PUNCT
cana-4510	251	57	2021	2021	NUM
cana-4510	251	58	,	,	PUNCT
cana-4510	251	59	doi	doi	NOUN
cana-4510	251	60	:	:	PUNCT
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cana-4510	251	62	.	.	PUNCT
cana-4510	252	1	[	[	X
cana-4510	252	2	11	11	NUM
cana-4510	252	3	]	]	PUNCT
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cana-4510	252	7	x.	x.	PROPN
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cana-4510	252	10	s.	s.	PROPN
cana-4510	252	11	ren	ren	PROPN
cana-4510	252	12	,	,	PUNCT
cana-4510	252	13	and	and	CCONJ
cana-4510	252	14	j.	j.	PROPN
cana-4510	252	15	sun	sun	PROPN
cana-4510	252	16	,	,	PUNCT
cana-4510	252	17	“	"	PUNCT
cana-4510	252	18	deep	deep	ADJ
cana-4510	252	19	residual	residual	ADJ
cana-4510	252	20	learning	learning	NOUN
cana-4510	252	21	for	for	ADP
cana-4510	252	22	image	image	NOUN
cana-4510	252	23	recognition	recognition	NOUN
cana-4510	252	24	,	,	PUNCT
cana-4510	252	25	”	"	PUNCT
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cana-4510	252	30	ieee	ieee	NOUN
cana-4510	252	31	conference	conference	NOUN
cana-4510	252	32	on	on	ADP
cana-4510	252	33	computer	computer	NOUN
cana-4510	252	34	vision	vision	NOUN
cana-4510	252	35	and	and	CCONJ
cana-4510	252	36	pattern	pattern	NOUN
cana-4510	252	37	recognition	recognition	NOUN
cana-4510	252	38	,	,	PUNCT
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cana-4510	252	40	.	.	PUNCT
cana-4510	253	1	770	770	NUM
cana-4510	253	2	-	-	SYM
cana-4510	253	3	778	778	NUM
cana-4510	253	4	,	,	PUNCT
cana-4510	253	5	2016	2016	NUM
cana-4510	253	6	,	,	PUNCT
cana-4510	253	7	doi	doi	NOUN
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cana-4510	255	1	https://doi.org/10.48550/arxiv.1512.03385	https://doi.org/10.48550/arxiv.1512.03385	PROPN
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cana-4510	255	8	:	:	PUNCT
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cana-4510	255	10	-	-	PUNCT
cana-4510	255	11	133x	133x	NUM
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cana-4510	257	16	and	and	CCONJ
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cana-4510	257	20	“	"	PUNCT
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cana-4510	257	29	neural	neural	ADJ
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cana-4510	257	50	and	and	CCONJ
cana-4510	257	51	communications	communication	NOUN
cana-4510	257	52	(	(	PUNCT
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cana-4510	257	54	)	)	PUNCT
cana-4510	257	55	,	,	PUNCT
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cana-4510	257	57	.	.	PUNCT
cana-4510	257	58	1461	1461	NUM
cana-4510	257	59	-	-	SYM
cana-4510	257	60	1467	1467	NUM
cana-4510	257	61	,	,	PUNCT
cana-4510	257	62	2020	2020	NUM
cana-4510	257	63	,	,	PUNCT
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cana-4510	257	65	:	:	PUNCT
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cana-4510	257	67	.	.	PUNCT
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cana-4510	258	2	13	13	NUM
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cana-4510	258	29	”	"	PUNCT
cana-4510	258	30	in	in	ADP
cana-4510	258	31	journal	journal	NOUN
cana-4510	258	32	of	of	ADP
cana-4510	258	33	physics	physics	PROPN
cana-4510	258	34	:	:	PUNCT
cana-4510	258	35	conference	conference	NOUN
cana-4510	258	36	series	series	NOUN
cana-4510	258	37	,	,	PUNCT
cana-4510	258	38	vol	vol	NOUN
cana-4510	258	39	.	.	PROPN
cana-4510	258	40	1634	1634	NUM
cana-4510	258	41	,	,	PUNCT
cana-4510	258	42	no	no	INTJ
cana-4510	258	43	.	.	NOUN
cana-4510	258	44	1	1	NUM
cana-4510	258	45	,	,	PUNCT
cana-4510	258	46	p.	p.	NOUN
cana-4510	258	47	012034	012034	NUM
cana-4510	258	48	,	,	PUNCT
cana-4510	258	49	2020	2020	NUM
cana-4510	258	50	,	,	PUNCT
cana-4510	258	51	doi	doi	NOUN
cana-4510	258	52	:	:	PUNCT
cana-4510	258	53	https://doi.org/10.1088/1742-6596/1634/1/012034	https://doi.org/10.1088/1742-6596/1634/1/012034	X
cana-4510	258	54	.	.	PUNCT
cana-4510	259	1	[	[	X
cana-4510	259	2	14	14	NUM
cana-4510	259	3	]	]	PUNCT
cana-4510	259	4	m.	m.	NOUN
cana-4510	259	5	valan	valan	PROPN
cana-4510	259	6	,	,	PUNCT
cana-4510	259	7	k.	k.	PROPN
cana-4510	259	8	makonyi	makonyi	PROPN
cana-4510	259	9	,	,	PUNCT
cana-4510	259	10	a.	a.	NOUN
cana-4510	259	11	maki	maki	PROPN
cana-4510	259	12	,	,	PUNCT
cana-4510	259	13	d.	d.	PROPN
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cana-4510	259	17	f.	f.	PROPN
cana-4510	259	18	ronquist	ronquist	PROPN
cana-4510	259	19	,	,	PUNCT
cana-4510	259	20	“	"	PUNCT
cana-4510	259	21	automated	automate	VERB
cana-4510	259	22	taxonomic	taxonomic	ADJ
cana-4510	259	23	identification	identification	NOUN
cana-4510	259	24	of	of	ADP
cana-4510	259	25	insects	insect	NOUN
cana-4510	259	26	with	with	ADP
cana-4510	259	27	expert	expert	NOUN
cana-4510	259	28	-	-	PUNCT
cana-4510	259	29	level	level	NOUN
cana-4510	259	30	accuracy	accuracy	NOUN
cana-4510	259	31	using	use	VERB
cana-4510	259	32	effective	effective	ADJ
cana-4510	259	33	feature	feature	NOUN
cana-4510	259	34	transfer	transfer	NOUN
cana-4510	259	35	from	from	ADP
cana-4510	259	36	convolutional	convolutional	ADJ
cana-4510	259	37	networks	network	NOUN
cana-4510	259	38	,	,	PUNCT
cana-4510	259	39	”	"	PUNCT
cana-4510	259	40	systematic	systematic	ADJ
cana-4510	259	41	biology	biology	NOUN
cana-4510	259	42	,	,	PUNCT
cana-4510	259	43	vol	vol	NOUN
cana-4510	259	44	.	.	PROPN
cana-4510	259	45	68	68	NUM
cana-4510	259	46	,	,	PUNCT
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cana-4510	259	48	.	.	NOUN
cana-4510	259	49	6	6	NUM
cana-4510	259	50	,	,	PUNCT
cana-4510	259	51	pp	pp	ADJ
cana-4510	259	52	.	.	PUNCT
cana-4510	260	1	876–895	876–895	NUM
cana-4510	260	2	,	,	PUNCT
cana-4510	260	3	2019	2019	NUM
cana-4510	260	4	,	,	PUNCT
cana-4510	260	5	doi	doi	INTJ
cana-4510	260	6	:	:	PUNCT
cana-4510	260	7	https://doi.org/10.1093/sysbio/syz014	https://doi.org/10.1093/sysbio/syz014	PROPN
cana-4510	260	8	.	.	PUNCT
cana-4510	261	1	[	[	X
cana-4510	261	2	15	15	NUM
cana-4510	261	3	]	]	X
cana-4510	261	4	o.l	o.l	PROPN
cana-4510	261	5	.	.	PROPN
cana-4510	261	6	hansen	hansen	PROPN
cana-4510	261	7	,	,	PUNCT
cana-4510	261	8	j.c	j.c	PROPN
cana-4510	261	9	.	.	PROPN
cana-4510	261	10	svenning	svenning	PROPN
cana-4510	261	11	,	,	PUNCT
cana-4510	261	12	k.	k.	PROPN
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cana-4510	261	14	,	,	PUNCT
cana-4510	261	15	s.	s.	PROPN
cana-4510	261	16	dupont	dupont	PROPN
cana-4510	261	17	,	,	PUNCT
cana-4510	261	18	b.h	b.h	PROPN
cana-4510	261	19	.	.	PROPN
cana-4510	261	20	garner	garner	PROPN
cana-4510	261	21	,	,	PUNCT
cana-4510	261	22	a.	a.	NOUN
cana-4510	261	23	iosifidis	iosifidis	PROPN
cana-4510	261	24	,	,	PUNCT
cana-4510	261	25	and	and	CCONJ
cana-4510	261	26	t.t	t.t	PROPN
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cana-4510	261	30	“	"	PUNCT
cana-4510	261	31	species‐level	species‐level	ADJ
cana-4510	261	32	image	image	NOUN
cana-4510	261	33	classification	classification	NOUN
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cana-4510	261	41	from	from	ADP
cana-4510	261	42	habitus	habitus	NOUN
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cana-4510	261	44	,	,	PUNCT
cana-4510	261	45	”	"	PUNCT
cana-4510	261	46	ecology	ecology	NOUN
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cana-4510	261	49	,	,	PUNCT
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cana-4510	261	51	.	.	PROPN
cana-4510	262	1	10	10	NUM
cana-4510	262	2	,	,	PUNCT
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cana-4510	262	5	2	2	NUM
cana-4510	262	6	,	,	PUNCT
cana-4510	262	7	pp	pp	ADJ
cana-4510	262	8	.	.	PUNCT
cana-4510	263	1	737	737	NUM
cana-4510	263	2	-	-	SYM
cana-4510	263	3	747	747	NUM
cana-4510	263	4	,	,	PUNCT
cana-4510	263	5	2020	2020	NUM
cana-4510	263	6	,	,	PUNCT
cana-4510	263	7	doi	doi	INTJ
cana-4510	263	8	:	:	PUNCT
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cana-4510	263	10	.	.	PUNCT
cana-4510	264	1	[	[	X
cana-4510	264	2	16	16	NUM
cana-4510	264	3	]	]	PUNCT
cana-4510	264	4	z.	z.	PROPN
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cana-4510	264	6	,	,	PUNCT
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cana-4510	264	10	,	,	PUNCT
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cana-4510	264	12	exploring	explore	VERB
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cana-4510	264	15	model	model	NOUN
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cana-4510	264	17	insect	insect	NOUN
cana-4510	264	18	and	and	CCONJ
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cana-4510	264	20	detection	detection	NOUN
cana-4510	264	21	from	from	ADP
cana-4510	264	22	images	image	NOUN
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cana-4510	264	24	”	"	PUNCT
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cana-4510	264	28	,	,	PUNCT
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cana-4510	264	30	.	.	PROPN
cana-4510	264	31	218	218	NUM
cana-4510	264	32	,	,	PUNCT
cana-4510	264	33	pp	pp	ADJ
cana-4510	264	34	.	.	PUNCT
cana-4510	265	1	2328–37	2328–37	NUM
cana-4510	265	2	,	,	PUNCT
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cana-4510	265	4	,	,	PUNCT
cana-4510	265	5	doi	doi	INTJ
cana-4510	265	6	:	:	PUNCT
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cana-4510	266	2	.	.	PUNCT
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cana-4510	267	2	17	17	NUM
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cana-4510	267	17	“	"	PUNCT
cana-4510	267	18	image	image	NOUN
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cana-4510	267	23	of	of	ADP
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cana-4510	267	27	convolutional	convolutional	ADJ
cana-4510	267	28	neural	neural	ADJ
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cana-4510	267	30	,	,	PUNCT
cana-4510	267	31	”	"	PUNCT
cana-4510	267	32	ecological	ecological	ADJ
cana-4510	267	33	informatics	informatic	NOUN
cana-4510	267	34	,	,	PUNCT
cana-4510	267	35	vol	vol	NOUN
cana-4510	267	36	.	.	PROPN
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cana-4510	267	38	,	,	PUNCT
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cana-4510	267	40	,	,	PUNCT
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cana-4510	267	42	,	,	PUNCT
cana-4510	267	43	doi	doi	NOUN
cana-4510	267	44	:	:	PUNCT
cana-4510	267	45	https://doi.org/10.1016/j.ecoinf.2019.101017	https://doi.org/10.1016/j.ecoinf.2019.101017	ADV
cana-4510	267	46	.	.	PUNCT
cana-4510	268	1	[	[	X
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cana-4510	268	21	k.	k.	PROPN
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cana-4510	268	24	“	"	PUNCT
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cana-4510	268	31	neural	neural	ADJ
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cana-4510	268	34	on	on	ADP
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cana-4510	268	38	”	"	PUNCT
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cana-4510	268	44	,	,	PUNCT
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cana-4510	268	54	,	,	PUNCT
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cana-4510	269	37	,	,	PUNCT
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cana-4510	269	39	.	.	PUNCT
cana-4510	270	1	doi	doi	NOUN
cana-4510	270	2	:	:	PUNCT
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cana-4510	270	4	.	.	PUNCT
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cana-4510	271	2	20	20	NUM
cana-4510	271	3	]	]	X
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cana-4510	271	5	.	.	PUNCT
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cana-4510	271	10	,	,	PUNCT
cana-4510	271	11	k.s	k.s	PROPN
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cana-4510	271	25	and	and	CCONJ
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cana-4510	271	31	a	a	DET
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cana-4510	271	39	species	specie	NOUN
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cana-4510	271	42	-	-	ADJ
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cana-4510	271	47	”	"	PUNCT
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cana-4510	271	51	,	,	PUNCT
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cana-4510	271	56	2022	2022	NUM
cana-4510	271	57	,	,	PUNCT
cana-4510	271	58	doi	doi	NOUN
cana-4510	271	59	:	:	PUNCT
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cana-4510	271	61	.	.	PUNCT
cana-4510	272	1	https://doi.org/10.1109/trustcom50675.2020.00197	https://doi.org/10.1109/trustcom50675.2020.00197	PROPN
cana-4510	272	2	https://doi.org/10.1088/1742-6596/1634/1/012034	https://doi.org/10.1088/1742-6596/1634/1/012034	PUNCT
cana-4510	273	1	https://doi.org/10.1093/sysbio/syz014	https://doi.org/10.1093/sysbio/syz014	PROPN
cana-4510	273	2	https://doi.org/10.1002/ece3.5921	https://doi.org/10.1002/ece3.5921	X
cana-4510	273	3	https://doi.org/10.1016/j.procs.2023.01.208	https://doi.org/10.1016/j.procs.2023.01.208	VERB
cana-4510	273	4	https://doi.org/10.1016/j.ecoinf.2019.101017	https://doi.org/10.1016/j.ecoinf.2019.101017	ADJ
cana-4510	273	5	https://doi.org/10.1093/icesjms/fsy147	https://doi.org/10.1093/icesjms/fsy147	ADJ
cana-4510	273	6	https://doi.org/10.1016/j.ecoinf.2019.100977	https://doi.org/10.1016/j.ecoinf.2019.100977	ADJ
cana-4510	273	7	https://doi.org/10.3390/electronics11132016	https://doi.org/10.3390/electronics11132016	NOUN