id	sid	tid	token	lemma	pos
bjmsr-609	1	1	copyright	copyright	NOUN
bjmsr-609	1	2	©	©	PROPN
bjmsr-609	1	3	cc	cc	PROPN
bjmsr-609	1	4	-	-	PUNCT
bjmsr-609	1	5	by	by	ADP
bjmsr-609	1	6	-	-	PUNCT
bjmsr-609	1	7	nc	nc	PROPN
bjmsr-609	1	8	2020	2020	NUM
bjmsr-609	1	9	,	,	PUNCT
bjmsr-609	1	10	cribfb	cribfb	PROPN
bjmsr-609	1	11	|bjmsr	|bjmsr	PROPN
bjmsr-609	1	12	bangladesh	bangladesh	PROPN
bjmsr-609	1	13	journal	journal	PROPN
bjmsr-609	1	14	of	of	ADP
bjmsr-609	1	15	multidisciplinary	multidisciplinary	ADJ
bjmsr-609	1	16	scientific	scientific	ADJ
bjmsr-609	1	17	research	research	NOUN
bjmsr-609	1	18	;	;	PUNCT
bjmsr-609	1	19	vol	vol	NOUN
bjmsr-609	1	20	.	.	NOUN
bjmsr-609	1	21	2	2	NUM
bjmsr-609	1	22	,	,	PUNCT
bjmsr-609	1	23	no	no	INTJ
bjmsr-609	1	24	.	.	NOUN
bjmsr-609	1	25	1	1	NUM
bjmsr-609	1	26	;	;	PUNCT
bjmsr-609	1	27	2020	2020	NUM
bjmsr-609	1	28	issn	issn	VERB
bjmsr-609	1	29	2687	2687	NUM
bjmsr-609	1	30	-	-	PUNCT
bjmsr-609	1	31	850x	850x	NUM
bjmsr-609	1	32	e	e	NOUN
bjmsr-609	1	33	-	-	PROPN
bjmsr-609	1	34	issn	issn	PROPN
bjmsr-609	1	35	2687	2687	NUM
bjmsr-609	1	36	-	-	SYM
bjmsr-609	1	37	8518	8518	NUM
bjmsr-609	1	38	published	publish	VERB
bjmsr-609	1	39	by	by	ADP
bjmsr-609	1	40	centre	centre	NOUN
bjmsr-609	1	41	for	for	ADP
bjmsr-609	1	42	research	research	NOUN
bjmsr-609	1	43	on	on	ADP
bjmsr-609	1	44	islamic	islamic	ADJ
bjmsr-609	1	45	banking	banking	PROPN
bjmsr-609	1	46	&	&	CCONJ
bjmsr-609	1	47	finance	finance	PROPN
bjmsr-609	1	48	and	and	CCONJ
bjmsr-609	1	49	business	business	NOUN
bjmsr-609	1	50	,	,	PUNCT
bjmsr-609	1	51	usa	usa	PROPN
bjmsr-609	1	52	48	48	NUM
bjmsr-609	1	53	40	40	NUM
bjmsr-609	1	54	a	a	DET
bjmsr-609	1	55	review	review	NOUN
bjmsr-609	1	56	on	on	ADP
bjmsr-609	1	57	artificial	artificial	ADJ
bjmsr-609	1	58	neural	neural	ADJ
bjmsr-609	1	59	networks	network	NOUN
bjmsr-609	1	60	and	and	CCONJ
bjmsr-609	1	61	its	its	PRON
bjmsr-609	1	62	’	'	PUNCT
bjmsr-609	1	63	applicability	applicability	NOUN
bjmsr-609	1	64	mustafa	mustafa	PROPN
bjmsr-609	1	65	nizamul	nizamul	PROPN
bjmsr-609	1	66	aziz	aziz	PROPN
bjmsr-609	1	67	senior	senior	ADJ
bjmsr-609	1	68	lecturer	lecturer	PROPN
bjmsr-609	1	69	east	east	PROPN
bjmsr-609	1	70	west	west	PROPN
bjmsr-609	1	71	university	university	PROPN
bjmsr-609	1	72	dhaka	dhaka	PROPN
bjmsr-609	1	73	,	,	PUNCT
bjmsr-609	1	74	bangladesh	bangladesh	NOUN
bjmsr-609	1	75	e	e	NOUN
bjmsr-609	1	76	-	-	NOUN
bjmsr-609	1	77	mail	mail	NOUN
bjmsr-609	1	78	:	:	PUNCT
bjmsr-609	1	79	mustafa.nizamul@gmail.com	mustafa.nizamul@gmail.com	X
bjmsr-609	1	80	abstract	abstract	NOUN
bjmsr-609	1	81	the	the	DET
bjmsr-609	1	82	field	field	NOUN
bjmsr-609	1	83	of	of	ADP
bjmsr-609	1	84	artificial	artificial	ADJ
bjmsr-609	1	85	neural	neural	ADJ
bjmsr-609	1	86	networks	network	NOUN
bjmsr-609	1	87	(	(	PUNCT
bjmsr-609	1	88	ann	ann	PROPN
bjmsr-609	1	89	)	)	PUNCT
bjmsr-609	1	90	started	start	VERB
bjmsr-609	1	91	from	from	ADP
bjmsr-609	1	92	humble	humble	ADJ
bjmsr-609	1	93	beginnings	beginning	NOUN
bjmsr-609	1	94	in	in	ADP
bjmsr-609	1	95	the	the	DET
bjmsr-609	1	96	1950s	1950	NOUN
bjmsr-609	1	97	but	but	CCONJ
bjmsr-609	1	98	got	get	VERB
bjmsr-609	1	99	attention	attention	NOUN
bjmsr-609	1	100	in	in	ADP
bjmsr-609	1	101	the	the	DET
bjmsr-609	1	102	1980s	1980s	NUM
bjmsr-609	1	103	.	.	PUNCT
bjmsr-609	2	1	ann	ann	PROPN
bjmsr-609	2	2	tries	try	VERB
bjmsr-609	2	3	to	to	PART
bjmsr-609	2	4	emulate	emulate	VERB
bjmsr-609	2	5	the	the	DET
bjmsr-609	2	6	neural	neural	ADJ
bjmsr-609	2	7	structure	structure	NOUN
bjmsr-609	2	8	of	of	ADP
bjmsr-609	2	9	the	the	DET
bjmsr-609	2	10	brain	brain	NOUN
bjmsr-609	2	11	,	,	PUNCT
bjmsr-609	2	12	which	which	PRON
bjmsr-609	2	13	consists	consist	VERB
bjmsr-609	2	14	of	of	ADP
bjmsr-609	2	15	several	several	ADJ
bjmsr-609	2	16	thousand	thousand	NUM
bjmsr-609	2	17	cells	cell	NOUN
bjmsr-609	2	18	,	,	PUNCT
bjmsr-609	2	19	neuron	neuron	PROPN
bjmsr-609	2	20	,	,	PUNCT
bjmsr-609	2	21	which	which	PRON
bjmsr-609	2	22	is	be	AUX
bjmsr-609	2	23	interconnected	interconnect	VERB
bjmsr-609	2	24	in	in	ADP
bjmsr-609	2	25	a	a	DET
bjmsr-609	2	26	large	large	ADJ
bjmsr-609	2	27	network	network	NOUN
bjmsr-609	2	28	.	.	PUNCT
bjmsr-609	3	1	this	this	PRON
bjmsr-609	3	2	is	be	AUX
bjmsr-609	3	3	done	do	VERB
bjmsr-609	3	4	through	through	ADP
bjmsr-609	3	5	artificial	artificial	ADJ
bjmsr-609	3	6	neurons	neuron	NOUN
bjmsr-609	3	7	,	,	PUNCT
bjmsr-609	3	8	handling	handle	VERB
bjmsr-609	3	9	the	the	DET
bjmsr-609	3	10	input	input	NOUN
bjmsr-609	3	11	and	and	CCONJ
bjmsr-609	3	12	output	output	NOUN
bjmsr-609	3	13	,	,	PUNCT
bjmsr-609	3	14	and	and	CCONJ
bjmsr-609	3	15	connecting	connect	VERB
bjmsr-609	3	16	to	to	ADP
bjmsr-609	3	17	other	other	ADJ
bjmsr-609	3	18	neurons	neuron	NOUN
bjmsr-609	3	19	,	,	PUNCT
bjmsr-609	3	20	creating	create	VERB
bjmsr-609	3	21	a	a	DET
bjmsr-609	3	22	large	large	ADJ
bjmsr-609	3	23	network	network	NOUN
bjmsr-609	3	24	.	.	PUNCT
bjmsr-609	4	1	the	the	DET
bjmsr-609	4	2	potential	potential	NOUN
bjmsr-609	4	3	for	for	ADP
bjmsr-609	4	4	artificial	artificial	ADJ
bjmsr-609	4	5	neural	neural	ADJ
bjmsr-609	4	6	networks	network	NOUN
bjmsr-609	4	7	is	be	AUX
bjmsr-609	4	8	considered	consider	VERB
bjmsr-609	4	9	to	to	PART
bjmsr-609	4	10	be	be	AUX
bjmsr-609	4	11	huge	huge	ADJ
bjmsr-609	4	12	,	,	PUNCT
bjmsr-609	4	13	today	today	NOUN
bjmsr-609	4	14	there	there	PRON
bjmsr-609	4	15	are	be	VERB
bjmsr-609	4	16	several	several	ADJ
bjmsr-609	4	17	different	different	ADJ
bjmsr-609	4	18	uses	use	NOUN
bjmsr-609	4	19	for	for	ADP
bjmsr-609	4	20	ann	ann	PROPN
bjmsr-609	4	21	,	,	PUNCT
bjmsr-609	4	22	ranging	range	VERB
bjmsr-609	4	23	from	from	ADP
bjmsr-609	4	24	academic	academic	ADJ
bjmsr-609	4	25	research	research	NOUN
bjmsr-609	4	26	in	in	ADP
bjmsr-609	4	27	such	such	ADJ
bjmsr-609	4	28	fields	field	NOUN
bjmsr-609	4	29	as	as	ADP
bjmsr-609	4	30	mathematics	mathematic	NOUN
bjmsr-609	4	31	and	and	CCONJ
bjmsr-609	4	32	medicine	medicine	NOUN
bjmsr-609	4	33	to	to	ADP
bjmsr-609	4	34	business	business	NOUN
bjmsr-609	4	35	-	-	PUNCT
bjmsr-609	4	36	based	base	VERB
bjmsr-609	4	37	purposes	purpose	NOUN
bjmsr-609	4	38	and	and	CCONJ
bjmsr-609	4	39	sports	sport	NOUN
bjmsr-609	4	40	prediction	prediction	NOUN
bjmsr-609	4	41	.	.	PUNCT
bjmsr-609	5	1	the	the	DET
bjmsr-609	5	2	purpose	purpose	NOUN
bjmsr-609	5	3	of	of	ADP
bjmsr-609	5	4	this	this	DET
bjmsr-609	5	5	paper	paper	NOUN
bjmsr-609	5	6	is	be	AUX
bjmsr-609	5	7	to	to	PART
bjmsr-609	5	8	give	give	VERB
bjmsr-609	5	9	words	word	NOUN
bjmsr-609	5	10	to	to	ADP
bjmsr-609	5	11	artificial	artificial	ADJ
bjmsr-609	5	12	neural	neural	ADJ
bjmsr-609	5	13	networks	network	NOUN
bjmsr-609	5	14	and	and	CCONJ
bjmsr-609	5	15	to	to	PART
bjmsr-609	5	16	show	show	VERB
bjmsr-609	5	17	its	its	PRON
bjmsr-609	5	18	applicability	applicability	NOUN
bjmsr-609	5	19	.	.	PUNCT
bjmsr-609	6	1	documents	document	NOUN
bjmsr-609	6	2	analysis	analysis	NOUN
bjmsr-609	6	3	was	be	AUX
bjmsr-609	6	4	used	use	VERB
bjmsr-609	6	5	here	here	ADV
bjmsr-609	6	6	as	as	ADP
bjmsr-609	6	7	the	the	DET
bjmsr-609	6	8	data	data	NOUN
bjmsr-609	6	9	collection	collection	NOUN
bjmsr-609	6	10	method	method	NOUN
bjmsr-609	6	11	.	.	PUNCT
bjmsr-609	7	1	the	the	DET
bjmsr-609	7	2	paper	paper	NOUN
bjmsr-609	7	3	figured	figure	VERB
bjmsr-609	7	4	out	out	ADP
bjmsr-609	7	5	network	network	NOUN
bjmsr-609	7	6	structures	structure	NOUN
bjmsr-609	7	7	,	,	PUNCT
bjmsr-609	7	8	steps	step	NOUN
bjmsr-609	7	9	for	for	ADP
bjmsr-609	7	10	constructing	construct	VERB
bjmsr-609	7	11	an	an	DET
bjmsr-609	7	12	ann	ann	PROPN
bjmsr-609	7	13	,	,	PUNCT
bjmsr-609	7	14	architectures	architecture	NOUN
bjmsr-609	7	15	,	,	PUNCT
bjmsr-609	7	16	and	and	CCONJ
bjmsr-609	7	17	learning	learn	VERB
bjmsr-609	7	18	algorithms	algorithm	NOUN
bjmsr-609	7	19	.	.	PUNCT
bjmsr-609	8	1	keywords	keyword	NOUN
bjmsr-609	8	2	:	:	PUNCT
bjmsr-609	8	3	artificial	artificial	ADJ
bjmsr-609	8	4	neural	neural	ADJ
bjmsr-609	8	5	networks	network	NOUN
bjmsr-609	8	6	,	,	PUNCT
bjmsr-609	8	7	artificial	artificial	ADJ
bjmsr-609	8	8	neural	neural	ADJ
bjmsr-609	8	9	network	network	NOUN
bjmsr-609	8	10	architectures	architecture	NOUN
bjmsr-609	8	11	,	,	PUNCT
bjmsr-609	8	12	artificial	artificial	ADJ
bjmsr-609	8	13	neurons	neuron	NOUN
bjmsr-609	8	14	.	.	PUNCT
bjmsr-609	9	1	1	1	X
bjmsr-609	9	2	.	.	X
bjmsr-609	9	3	introduction	introduction	NOUN
bjmsr-609	9	4	as	as	ADV
bjmsr-609	9	5	early	early	ADV
bjmsr-609	9	6	as	as	ADP
bjmsr-609	9	7	1943	1943	NUM
bjmsr-609	9	8	a	a	DET
bjmsr-609	9	9	model	model	NOUN
bjmsr-609	9	10	was	be	AUX
bjmsr-609	9	11	created	create	VERB
bjmsr-609	9	12	by	by	ADP
bjmsr-609	9	13	warren	warren	PROPN
bjmsr-609	9	14	mcculloch	mcculloch	PROPN
bjmsr-609	9	15	and	and	CCONJ
bjmsr-609	9	16	walter	walter	PROPN
bjmsr-609	9	17	pitts	pitts	PROPN
bjmsr-609	9	18	called	call	VERB
bjmsr-609	9	19	mcculloch	mcculloch	PROPN
bjmsr-609	9	20	-	-	PUNCT
bjmsr-609	9	21	pitts	pitts	PROPN
bjmsr-609	9	22	neuron	neuron	NOUN
bjmsr-609	9	23	which	which	PRON
bjmsr-609	9	24	tried	try	VERB
bjmsr-609	9	25	to	to	PART
bjmsr-609	9	26	mimic	mimic	VERB
bjmsr-609	9	27	the	the	DET
bjmsr-609	9	28	structure	structure	NOUN
bjmsr-609	9	29	of	of	ADP
bjmsr-609	9	30	a	a	DET
bjmsr-609	9	31	biological	biological	ADJ
bjmsr-609	9	32	neural	neural	ADJ
bjmsr-609	9	33	network	network	NOUN
bjmsr-609	9	34	.	.	PUNCT
bjmsr-609	10	1	this	this	DET
bjmsr-609	10	2	model	model	NOUN
bjmsr-609	10	3	was	be	AUX
bjmsr-609	10	4	divided	divide	VERB
bjmsr-609	10	5	into	into	ADP
bjmsr-609	10	6	two	two	NUM
bjmsr-609	10	7	parts	part	NOUN
bjmsr-609	10	8	,	,	PUNCT
bjmsr-609	10	9	one	one	NUM
bjmsr-609	10	10	consisting	consist	VERB
bjmsr-609	10	11	of	of	ADP
bjmsr-609	10	12	a	a	DET
bjmsr-609	10	13	summation	summation	NOUN
bjmsr-609	10	14	of	of	ADP
bjmsr-609	10	15	weighted	weight	VERB
bjmsr-609	10	16	input	input	NOUN
bjmsr-609	10	17	and	and	CCONJ
bjmsr-609	10	18	the	the	DET
bjmsr-609	10	19	other	other	ADJ
bjmsr-609	10	20	consisting	consisting	NOUN
bjmsr-609	10	21	of	of	ADP
bjmsr-609	10	22	an	an	DET
bjmsr-609	10	23	output	output	NOUN
bjmsr-609	10	24	function	function	NOUN
bjmsr-609	10	25	of	of	ADP
bjmsr-609	10	26	the	the	DET
bjmsr-609	10	27	sum	sum	NOUN
bjmsr-609	10	28	.	.	PUNCT
bjmsr-609	11	1	the	the	DET
bjmsr-609	11	2	neural	neural	ADJ
bjmsr-609	11	3	network	network	NOUN
bjmsr-609	11	4	model	model	NOUN
bjmsr-609	11	5	created	create	VERB
bjmsr-609	11	6	consisted	consist	VERB
bjmsr-609	11	7	of	of	ADP
bjmsr-609	11	8	several	several	ADJ
bjmsr-609	11	9	binary	binary	ADJ
bjmsr-609	11	10	neurons	neuron	NOUN
bjmsr-609	11	11	interconnected	interconnect	VERB
bjmsr-609	11	12	in	in	ADP
bjmsr-609	11	13	a	a	DET
bjmsr-609	11	14	large	large	ADJ
bjmsr-609	11	15	network	network	NOUN
bjmsr-609	11	16	(	(	PUNCT
bjmsr-609	11	17	yegnanarayana	yegnanarayana	PROPN
bjmsr-609	11	18	,	,	PUNCT
bjmsr-609	11	19	2009	2009	NUM
bjmsr-609	11	20	)	)	PUNCT
bjmsr-609	11	21	.	.	PUNCT
bjmsr-609	12	1	artificial	artificial	ADJ
bjmsr-609	12	2	neural	neural	ADJ
bjmsr-609	12	3	networks	network	NOUN
bjmsr-609	12	4	became	become	VERB
bjmsr-609	12	5	a	a	DET
bjmsr-609	12	6	popular	popular	ADJ
bjmsr-609	12	7	research	research	NOUN
bjmsr-609	12	8	topic	topic	NOUN
bjmsr-609	12	9	in	in	ADP
bjmsr-609	12	10	the	the	DET
bjmsr-609	12	11	late	late	ADJ
bjmsr-609	12	12	1950s	1950	NOUN
bjmsr-609	12	13	and	and	CCONJ
bjmsr-609	12	14	early	early	ADJ
bjmsr-609	12	15	1960s	1960	NOUN
bjmsr-609	12	16	.	.	PUNCT
bjmsr-609	13	1	the	the	DET
bjmsr-609	13	2	first	first	ADJ
bjmsr-609	13	3	computational	computational	ADJ
bjmsr-609	13	4	trainable	trainable	ADJ
bjmsr-609	13	5	neural	neural	ADJ
bjmsr-609	13	6	networks	network	NOUN
bjmsr-609	13	7	were	be	AUX
bjmsr-609	13	8	created	create	VERB
bjmsr-609	13	9	by	by	ADP
bjmsr-609	13	10	frank	frank	PROPN
bjmsr-609	13	11	rosenblatt	rosenblatt	PROPN
bjmsr-609	13	12	and	and	CCONJ
bjmsr-609	13	13	others	other	NOUN
bjmsr-609	13	14	in	in	ADP
bjmsr-609	13	15	the	the	DET
bjmsr-609	13	16	late	late	ADJ
bjmsr-609	13	17	1950s	1950s	NUM
bjmsr-609	13	18	.	.	PUNCT
bjmsr-609	14	1	called	call	VERB
bjmsr-609	14	2	perceptron	perceptron	PROPN
bjmsr-609	14	3	,	,	PUNCT
bjmsr-609	14	4	this	this	DET
bjmsr-609	14	5	neural	neural	ADJ
bjmsr-609	14	6	network	network	NOUN
bjmsr-609	14	7	consisted	consist	VERB
bjmsr-609	14	8	of	of	ADP
bjmsr-609	14	9	two	two	NUM
bjmsr-609	14	10	computational	computational	ADJ
bjmsr-609	14	11	nodes	node	NOUN
bjmsr-609	14	12	and	and	CCONJ
bjmsr-609	14	13	a	a	DET
bjmsr-609	14	14	single	single	ADJ
bjmsr-609	14	15	layer	layer	NOUN
bjmsr-609	14	16	of	of	ADP
bjmsr-609	14	17	interconnections	interconnection	NOUN
bjmsr-609	14	18	and	and	CCONJ
bjmsr-609	14	19	was	be	AUX
bjmsr-609	14	20	used	use	VERB
bjmsr-609	14	21	solely	solely	ADV
bjmsr-609	14	22	to	to	PART
bjmsr-609	14	23	solve	solve	VERB
bjmsr-609	14	24	linear	linear	ADJ
bjmsr-609	14	25	problems	problem	NOUN
bjmsr-609	14	26	.	.	PUNCT
bjmsr-609	15	1	during	during	ADP
bjmsr-609	15	2	the	the	DET
bjmsr-609	15	3	1970s	1970	NOUN
bjmsr-609	15	4	the	the	DET
bjmsr-609	15	5	interest	interest	NOUN
bjmsr-609	15	6	for	for	ADP
bjmsr-609	15	7	artificial	artificial	ADJ
bjmsr-609	15	8	neural	neural	ADJ
bjmsr-609	15	9	networks	network	NOUN
bjmsr-609	15	10	faded	fade	VERB
bjmsr-609	15	11	,	,	PUNCT
bjmsr-609	15	12	one	one	NUM
bjmsr-609	15	13	reason	reason	NOUN
bjmsr-609	15	14	was	be	AUX
bjmsr-609	15	15	the	the	DET
bjmsr-609	15	16	book	book	NOUN
bjmsr-609	15	17	written	write	VERB
bjmsr-609	15	18	by	by	ADP
bjmsr-609	15	19	minsky	minsky	PROPN
bjmsr-609	15	20	&	&	CCONJ
bjmsr-609	15	21	papert	papert	PROPN
bjmsr-609	15	22	in	in	ADP
bjmsr-609	15	23	1969	1969	NUM
bjmsr-609	15	24	,	,	PUNCT
bjmsr-609	15	25	in	in	ADP
bjmsr-609	15	26	which	which	PRON
bjmsr-609	15	27	a	a	DET
bjmsr-609	15	28	somewhat	somewhat	ADV
bjmsr-609	15	29	pessimistic	pessimistic	ADJ
bjmsr-609	15	30	view	view	NOUN
bjmsr-609	15	31	of	of	ADP
bjmsr-609	15	32	the	the	DET
bjmsr-609	15	33	future	future	NOUN
bjmsr-609	15	34	of	of	ADP
bjmsr-609	15	35	the	the	DET
bjmsr-609	15	36	field	field	NOUN
bjmsr-609	15	37	was	be	AUX
bjmsr-609	15	38	presented	present	VERB
bjmsr-609	15	39	(	(	PUNCT
bjmsr-609	15	40	turban	turban	PROPN
bjmsr-609	15	41	et	et	PROPN
bjmsr-609	15	42	al	al	PROPN
bjmsr-609	15	43	.	.	PROPN
bjmsr-609	15	44	,	,	PUNCT
bjmsr-609	15	45	2011	2011	NUM
bjmsr-609	15	46	)	)	PUNCT
bjmsr-609	15	47	.	.	PUNCT
bjmsr-609	16	1	since	since	SCONJ
bjmsr-609	16	2	the	the	DET
bjmsr-609	16	3	1980s	1980	NOUN
bjmsr-609	16	4	interest	interest	NOUN
bjmsr-609	16	5	for	for	ADP
bjmsr-609	16	6	artificial	artificial	ADJ
bjmsr-609	16	7	neural	neural	ADJ
bjmsr-609	16	8	networks	network	NOUN
bjmsr-609	16	9	increased	increase	VERB
bjmsr-609	16	10	with	with	ADP
bjmsr-609	16	11	more	more	ADJ
bjmsr-609	16	12	funding	funding	NOUN
bjmsr-609	16	13	to	to	PART
bjmsr-609	16	14	research	research	VERB
bjmsr-609	16	15	in	in	ADP
bjmsr-609	16	16	the	the	DET
bjmsr-609	16	17	field	field	NOUN
bjmsr-609	16	18	in	in	ADP
bjmsr-609	16	19	several	several	ADJ
bjmsr-609	16	20	countries	country	NOUN
bjmsr-609	16	21	,	,	PUNCT
bjmsr-609	16	22	especially	especially	ADV
bjmsr-609	16	23	in	in	ADP
bjmsr-609	16	24	japan	japan	PROPN
bjmsr-609	16	25	and	and	CCONJ
bjmsr-609	16	26	the	the	DET
bjmsr-609	16	27	usa	usa	PROPN
bjmsr-609	16	28	after	after	SCONJ
bjmsr-609	16	29	a	a	DET
bjmsr-609	16	30	joint	joint	ADJ
bjmsr-609	16	31	conference	conference	NOUN
bjmsr-609	16	32	being	be	AUX
bjmsr-609	16	33	held	hold	VERB
bjmsr-609	16	34	in	in	ADP
bjmsr-609	16	35	kyoto	kyoto	PROPN
bjmsr-609	16	36	,	,	PUNCT
bjmsr-609	16	37	japan	japan	PROPN
bjmsr-609	16	38	on	on	ADP
bjmsr-609	16	39	neural	neural	ADJ
bjmsr-609	16	40	networks	network	NOUN
bjmsr-609	16	41	.	.	PUNCT
bjmsr-609	17	1	this	this	DET
bjmsr-609	17	2	newfound	newfound	NOUN
bjmsr-609	17	3	optimism	optimism	NOUN
bjmsr-609	17	4	stemmed	stem	VERB
bjmsr-609	17	5	from	from	ADP
bjmsr-609	17	6	discoveries	discovery	NOUN
bjmsr-609	17	7	in	in	ADP
bjmsr-609	17	8	the	the	DET
bjmsr-609	17	9	field	field	NOUN
bjmsr-609	17	10	which	which	PRON
bjmsr-609	17	11	overcame	overcome	VERB
bjmsr-609	17	12	some	some	PRON
bjmsr-609	17	13	of	of	ADP
bjmsr-609	17	14	the	the	DET
bjmsr-609	17	15	obstacles	obstacle	NOUN
bjmsr-609	17	16	earlier	early	ADV
bjmsr-609	17	17	encountered	encounter	VERB
bjmsr-609	17	18	in	in	ADP
bjmsr-609	17	19	creating	create	VERB
bjmsr-609	17	20	artificial	artificial	ADJ
bjmsr-609	17	21	neural	neural	ADJ
bjmsr-609	17	22	network	network	NOUN
bjmsr-609	17	23	and	and	CCONJ
bjmsr-609	17	24	progress	progress	NOUN
bjmsr-609	17	25	in	in	ADP
bjmsr-609	17	26	the	the	DET
bjmsr-609	17	27	fields	field	NOUN
bjmsr-609	17	28	of	of	ADP
bjmsr-609	17	29	neuroscience	neuroscience	NOUN
bjmsr-609	17	30	and	and	CCONJ
bjmsr-609	17	31	cognitive	cognitive	ADJ
bjmsr-609	17	32	science	science	NOUN
bjmsr-609	17	33	(	(	PUNCT
bjmsr-609	17	34	anderson	anderson	PROPN
bjmsr-609	17	35	&	&	CCONJ
bjmsr-609	17	36	mcneill	mcneill	PROPN
bjmsr-609	17	37	,	,	PUNCT
bjmsr-609	17	38	1992	1992	NUM
bjmsr-609	17	39	)	)	PUNCT
bjmsr-609	17	40	.	.	PUNCT
bjmsr-609	18	1	successful	successful	ADJ
bjmsr-609	18	2	implementations	implementation	NOUN
bjmsr-609	18	3	of	of	ADP
bjmsr-609	18	4	artificial	artificial	ADJ
bjmsr-609	18	5	neural	neural	ADJ
bjmsr-609	18	6	networks	network	NOUN
bjmsr-609	18	7	have	have	AUX
bjmsr-609	18	8	in	in	ADP
bjmsr-609	18	9	recent	recent	ADJ
bjmsr-609	18	10	times	time	NOUN
bjmsr-609	18	11	invoked	invoke	VERB
bjmsr-609	18	12	interest	interest	NOUN
bjmsr-609	18	13	from	from	ADP
bjmsr-609	18	14	several	several	ADJ
bjmsr-609	18	15	agents	agent	NOUN
bjmsr-609	18	16	outside	outside	ADP
bjmsr-609	18	17	the	the	DET
bjmsr-609	18	18	academic	academic	ADJ
bjmsr-609	18	19	sphere	sphere	NOUN
bjmsr-609	18	20	,	,	PUNCT
bjmsr-609	18	21	such	such	ADJ
bjmsr-609	18	22	as	as	ADP
bjmsr-609	18	23	industry	industry	NOUN
bjmsr-609	18	24	and	and	CCONJ
bjmsr-609	18	25	business	business	NOUN
bjmsr-609	18	26	(	(	PUNCT
bjmsr-609	18	27	turban	turban	VERB
bjmsr-609	18	28	et	et	PROPN
bjmsr-609	18	29	al	al	PROPN
bjmsr-609	18	30	.	.	PROPN
bjmsr-609	18	31	,	,	PUNCT
bjmsr-609	18	32	2011	2011	NUM
bjmsr-609	18	33	)	)	PUNCT
bjmsr-609	18	34	2	2	NUM
bjmsr-609	18	35	.	.	X
bjmsr-609	18	36	findings	finding	NOUN
bjmsr-609	18	37	from	from	ADP
bjmsr-609	18	38	the	the	DET
bjmsr-609	18	39	literature	literature	NOUN
bjmsr-609	18	40	study	study	NOUN
bjmsr-609	18	41	2.1	2.1	NUM
bjmsr-609	18	42	neural	neural	ADJ
bjmsr-609	18	43	networks	network	NOUN
bjmsr-609	18	44	a	a	DET
bjmsr-609	18	45	neural	neural	ADJ
bjmsr-609	18	46	network	network	NOUN
bjmsr-609	18	47	is	be	AUX
bjmsr-609	18	48	described	describe	VERB
bjmsr-609	18	49	as	as	ADP
bjmsr-609	18	50	a	a	DET
bjmsr-609	18	51	system	system	NOUN
bjmsr-609	18	52	built	build	VERB
bjmsr-609	18	53	of	of	ADP
bjmsr-609	18	54	several	several	ADJ
bjmsr-609	18	55	processing	processing	NOUN
bjmsr-609	18	56	elements	element	NOUN
bjmsr-609	18	57	operating	operate	VERB
bjmsr-609	18	58	in	in	ADP
bjmsr-609	18	59	parallel	parallel	NOUN
bjmsr-609	18	60	whose	whose	DET
bjmsr-609	18	61	function	function	NOUN
bjmsr-609	18	62	is	be	AUX
bjmsr-609	18	63	determined	determine	VERB
bjmsr-609	18	64	by	by	ADP
bjmsr-609	18	65	network	network	NOUN
bjmsr-609	18	66	structure	structure	NOUN
bjmsr-609	18	67	,	,	PUNCT
bjmsr-609	18	68	connection	connection	NOUN
bjmsr-609	18	69	strengths	strength	NOUN
bjmsr-609	18	70	,	,	PUNCT
bjmsr-609	18	71	and	and	CCONJ
bjmsr-609	18	72	the	the	DET
bjmsr-609	18	73	processing	processing	NOUN
bjmsr-609	18	74	performed	perform	VERB
bjmsr-609	18	75	at	at	ADP
bjmsr-609	18	76	computing	compute	VERB
bjmsr-609	18	77	elements	element	NOUN
bjmsr-609	18	78	or	or	CCONJ
bjmsr-609	18	79	nodes	node	NOUN
bjmsr-609	18	80	(	(	PUNCT
bjmsr-609	18	81	nemadi	nemadi	NOUN
bjmsr-609	18	82	,	,	PUNCT
bjmsr-609	18	83	2012	2012	NUM
bjmsr-609	18	84	)	)	PUNCT
bjmsr-609	18	85	.	.	PUNCT
bjmsr-609	19	1	the	the	DET
bjmsr-609	19	2	neural	neural	ADJ
bjmsr-609	19	3	structure	structure	NOUN
bjmsr-609	19	4	of	of	ADP
bjmsr-609	19	5	the	the	DET
bjmsr-609	19	6	brain	brain	NOUN
bjmsr-609	19	7	consists	consist	VERB
bjmsr-609	19	8	of	of	ADP
bjmsr-609	19	9	approximately	approximately	ADV
bjmsr-609	19	10	100	100	NUM
bjmsr-609	19	11	billion	billion	NUM
bjmsr-609	19	12	cells	cell	NOUN
bjmsr-609	19	13	,	,	PUNCT
bjmsr-609	19	14	called	call	VERB
bjmsr-609	19	15	neurons	neuron	NOUN
bjmsr-609	19	16	,	,	PUNCT
bjmsr-609	19	17	which	which	PRON
bjmsr-609	19	18	all	all	PRON
bjmsr-609	19	19	are	be	AUX
bjmsr-609	19	20	interconnected	interconnect	VERB
bjmsr-609	19	21	to	to	ADP
bjmsr-609	19	22	several	several	ADJ
bjmsr-609	19	23	thousand	thousand	NUM
bjmsr-609	19	24	other	other	ADJ
bjmsr-609	19	25	neurons	neuron	NOUN
bjmsr-609	19	26	through	through	ADP
bjmsr-609	19	27	a	a	DET
bjmsr-609	19	28	huge	huge	ADJ
bjmsr-609	19	29	network	network	NOUN
bjmsr-609	19	30	.	.	PUNCT
bjmsr-609	20	1	a	a	DET
bjmsr-609	20	2	biological	biological	ADJ
bjmsr-609	20	3	neuron	neuron	NOUN
bjmsr-609	20	4	consists	consist	VERB
bjmsr-609	20	5	basically	basically	ADV
bjmsr-609	20	6	of	of	ADP
bjmsr-609	20	7	four	four	NUM
bjmsr-609	20	8	components	component	NOUN
bjmsr-609	20	9	;	;	PUNCT
bjmsr-609	20	10	dendrites	dendrite	NOUN
bjmsr-609	20	11	which	which	PRON
bjmsr-609	20	12	are	be	AUX
bjmsr-609	20	13	accepting	accept	VERB
bjmsr-609	20	14	the	the	DET
bjmsr-609	20	15	input	input	NOUN
bjmsr-609	20	16	,	,	PUNCT
bjmsr-609	20	17	soma	soma	NOUN
bjmsr-609	20	18	which	which	PRON
bjmsr-609	20	19	is	be	AUX
bjmsr-609	20	20	processing	process	VERB
bjmsr-609	20	21	the	the	DET
bjmsr-609	20	22	input	input	NOUN
bjmsr-609	20	23	,	,	PUNCT
bjmsr-609	20	24	the	the	DET
bjmsr-609	20	25	axon	axon	NOUN
bjmsr-609	20	26	which	which	PRON
bjmsr-609	20	27	turns	turn	VERB
bjmsr-609	20	28	the	the	DET
bjmsr-609	20	29	processed	process	VERB
bjmsr-609	20	30	input	input	NOUN
bjmsr-609	20	31	into	into	ADP
bjmsr-609	20	32	output	output	NOUN
bjmsr-609	20	33	and	and	CCONJ
bjmsr-609	20	34	synapses	synapsis	NOUN
bjmsr-609	20	35	which	which	PRON
bjmsr-609	20	36	is	be	AUX
bjmsr-609	20	37	connecting	connect	VERB
bjmsr-609	20	38	the	the	DET
bjmsr-609	20	39	neuron	neuron	NOUN
bjmsr-609	20	40	to	to	ADP
bjmsr-609	20	41	other	other	ADJ
bjmsr-609	20	42	neurons	neuron	NOUN
bjmsr-609	20	43	,	,	PUNCT
bjmsr-609	20	44	allowing	allow	VERB
bjmsr-609	20	45	the	the	DET
bjmsr-609	20	46	neurons	neuron	NOUN
bjmsr-609	20	47	to	to	PART
bjmsr-609	20	48	communicate	communicate	VERB
bjmsr-609	20	49	with	with	ADP
bjmsr-609	20	50	each	each	DET
bjmsr-609	20	51	other	other	ADJ
bjmsr-609	20	52	.	.	PUNCT
bjmsr-609	21	1	the	the	DET
bjmsr-609	21	2	communication	communication	NOUN
bjmsr-609	21	3	between	between	ADP
bjmsr-609	21	4	the	the	DET
bjmsr-609	21	5	different	different	ADJ
bjmsr-609	21	6	neurons	neuron	NOUN
bjmsr-609	21	7	takes	take	VERB
bjmsr-609	21	8	place	place	NOUN
bjmsr-609	21	9	in	in	ADP
bjmsr-609	21	10	the	the	DET
bjmsr-609	21	11	axon	axon	NOUN
bjmsr-609	21	12	and	and	CCONJ
bjmsr-609	21	13	dendrites	dendrite	NOUN
bjmsr-609	21	14	of	of	ADP
bjmsr-609	21	15	each	each	DET
bjmsr-609	21	16	neuron	neuron	NOUN
bjmsr-609	21	17	.	.	PUNCT
bjmsr-609	22	1	the	the	DET
bjmsr-609	22	2	sheer	sheer	ADJ
bjmsr-609	22	3	number	number	NOUN
bjmsr-609	22	4	of	of	ADP
bjmsr-609	22	5	cells	cell	NOUN
bjmsr-609	22	6	,	,	PUNCT
bjmsr-609	22	7	and	and	CCONJ
bjmsr-609	22	8	the	the	DET
bjmsr-609	22	9	fact	fact	NOUN
bjmsr-609	22	10	that	that	SCONJ
bjmsr-609	22	11	the	the	DET
bjmsr-609	22	12	neurons	neuron	NOUN
bjmsr-609	22	13	seemingly	seemingly	ADV
bjmsr-609	22	14	are	be	AUX
bjmsr-609	22	15	the	the	DET
bjmsr-609	22	16	only	only	ADJ
bjmsr-609	22	17	cells	cell	NOUN
bjmsr-609	22	18	not	not	PART
bjmsr-609	22	19	regenerating	regenerate	VERB
bjmsr-609	22	20	,	,	PUNCT
bjmsr-609	22	21	thus	thus	ADV
bjmsr-609	22	22	enabling	enable	VERB
bjmsr-609	22	23	humans	human	NOUN
bjmsr-609	22	24	to	to	PART
bjmsr-609	22	25	remember	remember	VERB
bjmsr-609	22	26	and	and	CCONJ
bjmsr-609	22	27	use	use	VERB
bjmsr-609	22	28	the	the	DET
bjmsr-609	22	29	experience	experience	NOUN
bjmsr-609	22	30	to	to	ADP
bjmsr-609	22	31	actions	action	NOUN
bjmsr-609	22	32	,	,	PUNCT
bjmsr-609	22	33	are	be	AUX
bjmsr-609	22	34	what	what	PRON
bjmsr-609	22	35	often	often	ADV
bjmsr-609	22	36	is	be	AUX
bjmsr-609	22	37	regarded	regard	VERB
bjmsr-609	22	38	as	as	ADP
bjmsr-609	22	39	the	the	DET
bjmsr-609	22	40	strength	strength	NOUN
bjmsr-609	22	41	of	of	ADP
bjmsr-609	22	42	the	the	DET
bjmsr-609	22	43	brain	brain	NOUN
bjmsr-609	22	44	(	(	PUNCT
bjmsr-609	22	45	anderson	anderson	PROPN
bjmsr-609	22	46	&	&	CCONJ
bjmsr-609	22	47	mcneill	mcneill	PROPN
bjmsr-609	22	48	,	,	PUNCT
bjmsr-609	22	49	1992	1992	NUM
bjmsr-609	22	50	)	)	PUNCT
bjmsr-609	22	51	.	.	PUNCT
bjmsr-609	23	1	copyright	copyright	NOUN
bjmsr-609	23	2	©	©	PROPN
bjmsr-609	23	3	cc	cc	PROPN
bjmsr-609	23	4	-	-	PUNCT
bjmsr-609	23	5	by	by	ADP
bjmsr-609	23	6	-	-	PUNCT
bjmsr-609	23	7	nc	nc	PROPN
bjmsr-609	23	8	2020	2020	NUM
bjmsr-609	23	9	,	,	PUNCT
bjmsr-609	23	10	cribfb	cribfb	PROPN
bjmsr-609	23	11	|bjmsr	|bjmsr	PROPN
bjmsr-609	23	12	www.cribfb.com/journal/index.php/bjmsr	www.cribfb.com/journal/index.php/bjmsr	X
bjmsr-609	23	13	bangladesh	bangladesh	PROPN
bjmsr-609	23	14	journal	journal	PROPN
bjmsr-609	23	15	of	of	ADP
bjmsr-609	23	16	multidisciplinary	multidisciplinary	ADJ
bjmsr-609	23	17	scientific	scientific	ADJ
bjmsr-609	23	18	research	research	NOUN
bjmsr-609	23	19	vol	vol	NOUN
bjmsr-609	23	20	.	.	PROPN
bjmsr-609	24	1	2	2	NUM
bjmsr-609	24	2	,	,	PUNCT
bjmsr-609	24	3	no	no	INTJ
bjmsr-609	24	4	.	.	NOUN
bjmsr-609	24	5	1	1	NUM
bjmsr-609	24	6	;	;	PUNCT
bjmsr-609	24	7	2020	2020	NUM
bjmsr-609	24	8	49	49	NUM
bjmsr-609	24	9	2.2	2.2	NUM
bjmsr-609	24	10	artificial	artificial	ADJ
bjmsr-609	24	11	neural	neural	ADJ
bjmsr-609	24	12	networks	network	NOUN
bjmsr-609	24	13	while	while	SCONJ
bjmsr-609	24	14	computers	computer	NOUN
bjmsr-609	24	15	can	can	AUX
bjmsr-609	24	16	keep	keep	VERB
bjmsr-609	24	17	ledgers	ledger	NOUN
bjmsr-609	24	18	and	and	CCONJ
bjmsr-609	24	19	perform	perform	VERB
bjmsr-609	24	20	complex	complex	ADJ
bjmsr-609	24	21	mathematical	mathematical	ADJ
bjmsr-609	24	22	calculations	calculation	NOUN
bjmsr-609	24	23	,	,	PUNCT
bjmsr-609	24	24	they	they	PRON
bjmsr-609	24	25	often	often	ADV
bjmsr-609	24	26	fail	fail	VERB
bjmsr-609	24	27	when	when	SCONJ
bjmsr-609	24	28	faced	face	VERB
bjmsr-609	24	29	with	with	ADP
bjmsr-609	24	30	tasks	task	NOUN
bjmsr-609	24	31	involving	involve	VERB
bjmsr-609	24	32	recognizing	recognize	VERB
bjmsr-609	24	33	patterns	pattern	NOUN
bjmsr-609	24	34	and	and	CCONJ
bjmsr-609	24	35	learning	learn	VERB
bjmsr-609	24	36	by	by	ADP
bjmsr-609	24	37	experience	experience	NOUN
bjmsr-609	24	38	,	,	PUNCT
bjmsr-609	24	39	tasks	task	VERB
bjmsr-609	24	40	the	the	DET
bjmsr-609	24	41	brain	brain	NOUN
bjmsr-609	24	42	manages	manage	VERB
bjmsr-609	24	43	far	far	ADV
bjmsr-609	24	44	superior	superior	ADJ
bjmsr-609	24	45	(	(	PUNCT
bjmsr-609	24	46	anderson	anderson	PROPN
bjmsr-609	24	47	&	&	CCONJ
bjmsr-609	24	48	mcneill	mcneill	PROPN
bjmsr-609	24	49	,	,	PUNCT
bjmsr-609	24	50	1992	1992	NUM
bjmsr-609	24	51	)	)	PUNCT
bjmsr-609	24	52	.	.	PUNCT
bjmsr-609	25	1	artificial	artificial	ADJ
bjmsr-609	25	2	neural	neural	ADJ
bjmsr-609	25	3	networks	network	NOUN
bjmsr-609	25	4	are	be	AUX
bjmsr-609	25	5	computational	computational	ADJ
bjmsr-609	25	6	methodologies	methodology	NOUN
bjmsr-609	25	7	or	or	CCONJ
bjmsr-609	25	8	models	model	NOUN
bjmsr-609	25	9	inspired	inspire	VERB
bjmsr-609	25	10	by	by	ADP
bjmsr-609	25	11	the	the	DET
bjmsr-609	25	12	networks	network	NOUN
bjmsr-609	25	13	of	of	ADP
bjmsr-609	25	14	biological	biological	ADJ
bjmsr-609	25	15	neural	neural	ADJ
bjmsr-609	25	16	structures	structure	NOUN
bjmsr-609	25	17	of	of	ADP
bjmsr-609	25	18	the	the	DET
bjmsr-609	25	19	brain	brain	NOUN
bjmsr-609	25	20	.	.	PUNCT
bjmsr-609	26	1	however	however	ADV
bjmsr-609	26	2	,	,	PUNCT
bjmsr-609	26	3	these	these	DET
bjmsr-609	26	4	networks	network	NOUN
bjmsr-609	26	5	use	use	VERB
bjmsr-609	26	6	mostly	mostly	ADV
bjmsr-609	26	7	the	the	DET
bjmsr-609	26	8	main	main	ADJ
bjmsr-609	26	9	idea	idea	NOUN
bjmsr-609	26	10	of	of	ADP
bjmsr-609	26	11	a	a	DET
bjmsr-609	26	12	biological	biological	ADJ
bjmsr-609	26	13	neural	neural	ADJ
bjmsr-609	26	14	network	network	NOUN
bjmsr-609	26	15	,	,	PUNCT
bjmsr-609	26	16	and	and	CCONJ
bjmsr-609	26	17	should	should	AUX
bjmsr-609	26	18	not	not	PART
bjmsr-609	26	19	be	be	AUX
bjmsr-609	26	20	regarded	regard	VERB
bjmsr-609	26	21	as	as	ADP
bjmsr-609	26	22	a	a	DET
bjmsr-609	26	23	correct	correct	ADJ
bjmsr-609	26	24	model	model	NOUN
bjmsr-609	26	25	of	of	ADP
bjmsr-609	26	26	the	the	DET
bjmsr-609	26	27	actual	actual	ADJ
bjmsr-609	26	28	brain	brain	NOUN
bjmsr-609	26	29	’s	’s	PART
bjmsr-609	26	30	neural	neural	ADJ
bjmsr-609	26	31	structure	structure	NOUN
bjmsr-609	26	32	,	,	PUNCT
bjmsr-609	26	33	since	since	SCONJ
bjmsr-609	26	34	this	this	DET
bjmsr-609	26	35	neural	neural	ADJ
bjmsr-609	26	36	structure	structure	NOUN
bjmsr-609	26	37	is	be	AUX
bjmsr-609	26	38	considered	consider	VERB
bjmsr-609	26	39	vastly	vastly	ADV
bjmsr-609	26	40	more	more	ADV
bjmsr-609	26	41	complicated	complicated	ADJ
bjmsr-609	26	42	.	.	PUNCT
bjmsr-609	27	1	like	like	ADP
bjmsr-609	27	2	their	their	PRON
bjmsr-609	27	3	biological	biological	ADJ
bjmsr-609	27	4	counterpart	counterpart	NOUN
bjmsr-609	27	5	,	,	PUNCT
bjmsr-609	27	6	artificial	artificial	ADJ
bjmsr-609	27	7	neural	neural	ADJ
bjmsr-609	27	8	networks	network	NOUN
bjmsr-609	27	9	consist	consist	VERB
bjmsr-609	27	10	of	of	ADP
bjmsr-609	27	11	several	several	ADJ
bjmsr-609	27	12	small	small	ADJ
bjmsr-609	27	13	elements	element	NOUN
bjmsr-609	27	14	called	call	VERB
bjmsr-609	27	15	artificial	artificial	ADJ
bjmsr-609	27	16	neurons	neuron	NOUN
bjmsr-609	27	17	which	which	PRON
bjmsr-609	27	18	are	be	AUX
bjmsr-609	27	19	responsible	responsible	ADJ
bjmsr-609	27	20	for	for	ADP
bjmsr-609	27	21	processing	process	VERB
bjmsr-609	27	22	information	information	NOUN
bjmsr-609	27	23	.	.	PUNCT
bjmsr-609	28	1	the	the	DET
bjmsr-609	28	2	neurons	neuron	NOUN
bjmsr-609	28	3	of	of	ADP
bjmsr-609	28	4	an	an	DET
bjmsr-609	28	5	artificial	artificial	ADJ
bjmsr-609	28	6	neural	neural	ADJ
bjmsr-609	28	7	network	network	NOUN
bjmsr-609	28	8	work	work	NOUN
bjmsr-609	28	9	parallel	parallel	NOUN
bjmsr-609	28	10	and	and	CCONJ
bjmsr-609	28	11	together	together	ADV
bjmsr-609	28	12	while	while	SCONJ
bjmsr-609	28	13	using	use	VERB
bjmsr-609	28	14	some	some	PRON
bjmsr-609	28	15	of	of	ADP
bjmsr-609	28	16	the	the	DET
bjmsr-609	28	17	abilities	ability	NOUN
bjmsr-609	28	18	of	of	ADP
bjmsr-609	28	19	biological	biological	ADJ
bjmsr-609	28	20	neural	neural	ADJ
bjmsr-609	28	21	networks	network	NOUN
bjmsr-609	28	22	,	,	PUNCT
bjmsr-609	28	23	such	such	ADJ
bjmsr-609	28	24	as	as	ADP
bjmsr-609	28	25	self	self	NOUN
bjmsr-609	28	26	-	-	PUNCT
bjmsr-609	28	27	organizing	organize	VERB
bjmsr-609	28	28	and	and	CCONJ
bjmsr-609	28	29	learning	learning	NOUN
bjmsr-609	28	30	.	.	PUNCT
bjmsr-609	29	1	just	just	ADV
bjmsr-609	29	2	as	as	SCONJ
bjmsr-609	29	3	the	the	DET
bjmsr-609	29	4	dendrite	dendrite	NOUN
bjmsr-609	29	5	of	of	ADP
bjmsr-609	29	6	the	the	DET
bjmsr-609	29	7	biological	biological	ADJ
bjmsr-609	29	8	neuron	neuron	NOUN
bjmsr-609	29	9	receives	receive	VERB
bjmsr-609	29	10	input	input	NOUN
bjmsr-609	29	11	from	from	ADP
bjmsr-609	29	12	other	other	ADJ
bjmsr-609	29	13	neurons	neuron	NOUN
bjmsr-609	29	14	by	by	ADP
bjmsr-609	29	15	electrochemical	electrochemical	ADJ
bjmsr-609	29	16	impulse	impulse	PROPN
bjmsr-609	29	17	,	,	PUNCT
bjmsr-609	29	18	the	the	DET
bjmsr-609	29	19	artificial	artificial	ADJ
bjmsr-609	29	20	neuron	neuron	NOUN
bjmsr-609	29	21	receives	receive	VERB
bjmsr-609	29	22	input	input	NOUN
bjmsr-609	29	23	from	from	ADP
bjmsr-609	29	24	other	other	ADJ
bjmsr-609	29	25	neurons	neuron	NOUN
bjmsr-609	29	26	by	by	ADP
bjmsr-609	29	27	analog	analog	NOUN
bjmsr-609	29	28	signals	signal	NOUN
bjmsr-609	29	29	.	.	PUNCT
bjmsr-609	30	1	2.2.1	2.2.1	NUM
bjmsr-609	30	2	network	network	NOUN
bjmsr-609	30	3	structure	structure	NOUN
bjmsr-609	30	4	in	in	ADP
bjmsr-609	30	5	translating	translate	VERB
bjmsr-609	30	6	the	the	DET
bjmsr-609	30	7	basic	basic	ADJ
bjmsr-609	30	8	structure	structure	NOUN
bjmsr-609	30	9	of	of	ADP
bjmsr-609	30	10	the	the	DET
bjmsr-609	30	11	biological	biological	ADJ
bjmsr-609	30	12	neuron	neuron	NOUN
bjmsr-609	30	13	to	to	ADP
bjmsr-609	30	14	an	an	DET
bjmsr-609	30	15	artificial	artificial	ADJ
bjmsr-609	30	16	equivalent	equivalent	NOUN
bjmsr-609	30	17	the	the	DET
bjmsr-609	30	18	names	name	NOUN
bjmsr-609	30	19	of	of	ADP
bjmsr-609	30	20	the	the	DET
bjmsr-609	30	21	neurons	neuron	NOUN
bjmsr-609	30	22	different	different	ADJ
bjmsr-609	30	23	parts	part	NOUN
bjmsr-609	30	24	are	be	AUX
bjmsr-609	30	25	translated	translate	VERB
bjmsr-609	30	26	to	to	ADP
bjmsr-609	30	27	names	name	NOUN
bjmsr-609	30	28	more	more	ADV
bjmsr-609	30	29	associated	associate	VERB
bjmsr-609	30	30	with	with	ADP
bjmsr-609	30	31	computer	computer	NOUN
bjmsr-609	30	32	science	science	NOUN
bjmsr-609	30	33	.	.	PUNCT
bjmsr-609	31	1	the	the	DET
bjmsr-609	31	2	dendrites	dendrite	NOUN
bjmsr-609	31	3	and	and	CCONJ
bjmsr-609	31	4	axons	axon	NOUN
bjmsr-609	31	5	are	be	AUX
bjmsr-609	31	6	known	know	VERB
bjmsr-609	31	7	as	as	ADP
bjmsr-609	31	8	the	the	DET
bjmsr-609	31	9	input	input	NOUN
bjmsr-609	31	10	and	and	CCONJ
bjmsr-609	31	11	output	output	NOUN
bjmsr-609	31	12	,	,	PUNCT
bjmsr-609	31	13	the	the	DET
bjmsr-609	31	14	soma	soma	NOUN
bjmsr-609	31	15	known	know	VERB
bjmsr-609	31	16	as	as	ADP
bjmsr-609	31	17	a	a	DET
bjmsr-609	31	18	node	node	NOUN
bjmsr-609	31	19	and	and	CCONJ
bjmsr-609	31	20	the	the	DET
bjmsr-609	31	21	synapse	synapse	NOUN
bjmsr-609	31	22	translates	translate	VERB
bjmsr-609	31	23	to	to	ADP
bjmsr-609	31	24	connection	connection	NOUN
bjmsr-609	31	25	weights	weight	NOUN
bjmsr-609	31	26	(	(	PUNCT
bjmsr-609	31	27	turban	turban	NOUN
bjmsr-609	31	28	et	et	PROPN
bjmsr-609	31	29	al	al	PROPN
bjmsr-609	31	30	.	.	PROPN
bjmsr-609	31	31	,	,	PUNCT
bjmsr-609	31	32	2011	2011	NUM
bjmsr-609	31	33	)	)	PUNCT
bjmsr-609	31	34	.	.	PUNCT
bjmsr-609	32	1	the	the	DET
bjmsr-609	32	2	basic	basic	ADJ
bjmsr-609	32	3	processing	processing	NOUN
bjmsr-609	32	4	element	element	NOUN
bjmsr-609	32	5	of	of	ADP
bjmsr-609	32	6	the	the	DET
bjmsr-609	32	7	neural	neural	ADJ
bjmsr-609	32	8	network	network	NOUN
bjmsr-609	32	9	,	,	PUNCT
bjmsr-609	32	10	which	which	PRON
bjmsr-609	32	11	forms	form	VERB
bjmsr-609	32	12	the	the	DET
bjmsr-609	32	13	neural	neural	ADJ
bjmsr-609	32	14	network	network	NOUN
bjmsr-609	32	15	’s	’s	PART
bjmsr-609	32	16	structure	structure	NOUN
bjmsr-609	32	17	by	by	ADP
bjmsr-609	32	18	being	be	AUX
bjmsr-609	32	19	set	set	VERB
bjmsr-609	32	20	up	up	ADP
bjmsr-609	32	21	in	in	ADP
bjmsr-609	32	22	different	different	ADJ
bjmsr-609	32	23	formations	formation	NOUN
bjmsr-609	32	24	,	,	PUNCT
bjmsr-609	32	25	is	be	AUX
bjmsr-609	32	26	the	the	DET
bjmsr-609	32	27	neuron	neuron	NOUN
bjmsr-609	32	28	.	.	PUNCT
bjmsr-609	33	1	in	in	ADP
bjmsr-609	33	2	a	a	DET
bjmsr-609	33	3	network	network	NOUN
bjmsr-609	33	4	structure	structure	NOUN
bjmsr-609	33	5	the	the	DET
bjmsr-609	33	6	processing	processing	NOUN
bjmsr-609	33	7	element	element	NOUN
bjmsr-609	33	8	,	,	PUNCT
bjmsr-609	33	9	the	the	DET
bjmsr-609	33	10	neuron	neuron	NOUN
bjmsr-609	33	11	,	,	PUNCT
bjmsr-609	33	12	receives	receive	VERB
bjmsr-609	33	13	input	input	NOUN
bjmsr-609	33	14	either	either	CCONJ
bjmsr-609	33	15	consisting	consist	VERB
bjmsr-609	33	16	of	of	ADP
bjmsr-609	33	17	raw	raw	ADJ
bjmsr-609	33	18	data	datum	NOUN
bjmsr-609	33	19	or	or	CCONJ
bjmsr-609	33	20	data	datum	NOUN
bjmsr-609	33	21	processed	process	VERB
bjmsr-609	33	22	in	in	ADP
bjmsr-609	33	23	another	another	DET
bjmsr-609	33	24	neuron	neuron	NOUN
bjmsr-609	33	25	,	,	PUNCT
bjmsr-609	33	26	processing	process	VERB
bjmsr-609	33	27	the	the	DET
bjmsr-609	33	28	input	input	NOUN
bjmsr-609	33	29	,	,	PUNCT
bjmsr-609	33	30	and	and	CCONJ
bjmsr-609	33	31	delivers	deliver	VERB
bjmsr-609	33	32	the	the	DET
bjmsr-609	33	33	output	output	NOUN
bjmsr-609	33	34	,	,	PUNCT
bjmsr-609	33	35	which	which	PRON
bjmsr-609	33	36	could	could	AUX
bjmsr-609	33	37	be	be	AUX
bjmsr-609	33	38	a	a	DET
bjmsr-609	33	39	final	final	ADJ
bjmsr-609	33	40	result	result	NOUN
bjmsr-609	33	41	or	or	CCONJ
bjmsr-609	33	42	serve	serve	VERB
bjmsr-609	33	43	as	as	ADP
bjmsr-609	33	44	input	input	NOUN
bjmsr-609	33	45	to	to	ADP
bjmsr-609	33	46	other	other	ADJ
bjmsr-609	33	47	processing	processing	NOUN
bjmsr-609	33	48	elements	element	NOUN
bjmsr-609	33	49	.	.	PUNCT
bjmsr-609	34	1	generally	generally	ADV
bjmsr-609	34	2	,	,	PUNCT
bjmsr-609	34	3	an	an	DET
bjmsr-609	34	4	artificial	artificial	ADJ
bjmsr-609	34	5	neural	neural	ADJ
bjmsr-609	34	6	network	network	NOUN
bjmsr-609	34	7	consists	consist	VERB
bjmsr-609	34	8	of	of	ADP
bjmsr-609	34	9	groups	group	NOUN
bjmsr-609	34	10	of	of	ADP
bjmsr-609	34	11	neurons	neuron	NOUN
bjmsr-609	34	12	,	,	PUNCT
bjmsr-609	34	13	clustered	cluster	VERB
bjmsr-609	34	14	together	together	ADV
bjmsr-609	34	15	to	to	ADP
bjmsr-609	34	16	different	different	ADJ
bjmsr-609	34	17	layers	layer	NOUN
bjmsr-609	34	18	.	.	PUNCT
bjmsr-609	35	1	typically	typically	ADV
bjmsr-609	35	2	,	,	PUNCT
bjmsr-609	35	3	these	these	DET
bjmsr-609	35	4	layers	layer	NOUN
bjmsr-609	35	5	are	be	AUX
bjmsr-609	35	6	the	the	DET
bjmsr-609	35	7	input	input	NOUN
bjmsr-609	35	8	layer	layer	NOUN
bjmsr-609	35	9	,	,	PUNCT
bjmsr-609	35	10	the	the	DET
bjmsr-609	35	11	hidden	hidden	ADJ
bjmsr-609	35	12	layer	layer	NOUN
bjmsr-609	35	13	,	,	PUNCT
bjmsr-609	35	14	and	and	CCONJ
bjmsr-609	35	15	the	the	DET
bjmsr-609	35	16	output	output	NOUN
bjmsr-609	35	17	layer	layer	NOUN
bjmsr-609	35	18	.	.	PUNCT
bjmsr-609	36	1	the	the	DET
bjmsr-609	36	2	hidden	hide	VERB
bjmsr-609	36	3	layer	layer	NOUN
bjmsr-609	36	4	is	be	AUX
bjmsr-609	36	5	the	the	DET
bjmsr-609	36	6	layer	layer	NOUN
bjmsr-609	36	7	of	of	ADP
bjmsr-609	36	8	neurons	neuron	NOUN
bjmsr-609	36	9	responsible	responsible	ADJ
bjmsr-609	36	10	for	for	ADP
bjmsr-609	36	11	transforming	transform	VERB
bjmsr-609	36	12	the	the	DET
bjmsr-609	36	13	input	input	NOUN
bjmsr-609	36	14	data	datum	NOUN
bjmsr-609	36	15	from	from	ADP
bjmsr-609	36	16	the	the	DET
bjmsr-609	36	17	input	input	NOUN
bjmsr-609	36	18	layer	layer	NOUN
bjmsr-609	36	19	to	to	ADP
bjmsr-609	36	20	suitable	suitable	ADJ
bjmsr-609	36	21	data	datum	NOUN
bjmsr-609	36	22	for	for	ADP
bjmsr-609	36	23	the	the	DET
bjmsr-609	36	24	output	output	NOUN
bjmsr-609	36	25	layer	layer	NOUN
bjmsr-609	36	26	.	.	PUNCT
bjmsr-609	37	1	depending	depend	VERB
bjmsr-609	37	2	on	on	ADP
bjmsr-609	37	3	the	the	DET
bjmsr-609	37	4	complexity	complexity	NOUN
bjmsr-609	37	5	of	of	ADP
bjmsr-609	37	6	the	the	DET
bjmsr-609	37	7	application	application	NOUN
bjmsr-609	37	8	the	the	DET
bjmsr-609	37	9	number	number	NOUN
bjmsr-609	37	10	of	of	ADP
bjmsr-609	37	11	the	the	DET
bjmsr-609	37	12	hidden	hide	VERB
bjmsr-609	37	13	layer	layer	NOUN
bjmsr-609	37	14	varies	vary	VERB
bjmsr-609	37	15	,	,	PUNCT
bjmsr-609	37	16	usually	usually	ADV
bjmsr-609	37	17	in	in	ADP
bjmsr-609	37	18	the	the	DET
bjmsr-609	37	19	commercial	commercial	ADJ
bjmsr-609	37	20	system	system	NOUN
bjmsr-609	37	21	from	from	ADP
bjmsr-609	37	22	one	one	NUM
bjmsr-609	37	23	to	to	ADP
bjmsr-609	37	24	three	three	NUM
bjmsr-609	37	25	hidden	hidden	ADJ
bjmsr-609	37	26	layers	layer	NOUN
bjmsr-609	37	27	,	,	PUNCT
bjmsr-609	37	28	with	with	ADP
bjmsr-609	37	29	each	each	PRON
bjmsr-609	37	30	potentially	potentially	ADV
bjmsr-609	37	31	containing	contain	VERB
bjmsr-609	37	32	thousands	thousand	NOUN
bjmsr-609	37	33	of	of	ADP
bjmsr-609	37	34	processing	processing	NOUN
bjmsr-609	37	35	elements	element	NOUN
bjmsr-609	37	36	(	(	PUNCT
bjmsr-609	37	37	turban	turban	VERB
bjmsr-609	37	38	et	et	PROPN
bjmsr-609	37	39	al	al	PROPN
bjmsr-609	37	40	.	.	PROPN
bjmsr-609	37	41	,	,	PUNCT
bjmsr-609	37	42	2011	2011	NUM
bjmsr-609	37	43	)	)	PUNCT
bjmsr-609	37	44	in	in	ADP
bjmsr-609	37	45	a	a	DET
bjmsr-609	37	46	similar	similar	ADJ
bjmsr-609	37	47	process	process	NOUN
bjmsr-609	37	48	of	of	ADP
bjmsr-609	37	49	the	the	DET
bjmsr-609	37	50	brain	brain	NOUN
bjmsr-609	37	51	,	,	PUNCT
bjmsr-609	37	52	the	the	DET
bjmsr-609	37	53	different	different	ADJ
bjmsr-609	37	54	processing	processing	NOUN
bjmsr-609	37	55	elements	element	NOUN
bjmsr-609	37	56	of	of	ADP
bjmsr-609	37	57	the	the	DET
bjmsr-609	37	58	artificial	artificial	ADJ
bjmsr-609	37	59	neural	neural	ADJ
bjmsr-609	37	60	network	network	NOUN
bjmsr-609	37	61	perform	perform	VERB
bjmsr-609	37	62	their	their	PRON
bjmsr-609	37	63	computations	computation	NOUN
bjmsr-609	37	64	simultaneously	simultaneously	ADV
bjmsr-609	37	65	,	,	PUNCT
bjmsr-609	37	66	known	know	VERB
bjmsr-609	37	67	as	as	ADP
bjmsr-609	37	68	parallel	parallel	ADJ
bjmsr-609	37	69	processing	processing	NOUN
bjmsr-609	37	70	,	,	PUNCT
bjmsr-609	37	71	a	a	DET
bjmsr-609	37	72	process	process	NOUN
bjmsr-609	37	73	that	that	PRON
bjmsr-609	37	74	differs	differ	VERB
bjmsr-609	37	75	a	a	DET
bjmsr-609	37	76	lot	lot	NOUN
bjmsr-609	37	77	from	from	ADP
bjmsr-609	37	78	the	the	DET
bjmsr-609	37	79	traditional	traditional	ADJ
bjmsr-609	37	80	serial	serial	ADJ
bjmsr-609	37	81	programming	programming	NOUN
bjmsr-609	37	82	.	.	PUNCT
bjmsr-609	38	1	the	the	DET
bjmsr-609	38	2	main	main	ADJ
bjmsr-609	38	3	practice	practice	NOUN
bjmsr-609	38	4	of	of	ADP
bjmsr-609	38	5	network	network	NOUN
bjmsr-609	38	6	information	information	NOUN
bjmsr-609	38	7	processing	processing	NOUN
bjmsr-609	38	8	in	in	ADP
bjmsr-609	38	9	artificial	artificial	ADJ
bjmsr-609	38	10	neural	neural	ADJ
bjmsr-609	38	11	networks	network	NOUN
bjmsr-609	38	12	consists	consist	VERB
bjmsr-609	38	13	of	of	ADP
bjmsr-609	38	14	some	some	DET
bjmsr-609	38	15	important	important	ADJ
bjmsr-609	38	16	concepts	concept	NOUN
bjmsr-609	38	17	such	such	ADJ
bjmsr-609	38	18	as	as	ADP
bjmsr-609	38	19	input	input	NOUN
bjmsr-609	38	20	,	,	PUNCT
bjmsr-609	38	21	output	output	NOUN
bjmsr-609	38	22	,	,	PUNCT
bjmsr-609	38	23	and	and	CCONJ
bjmsr-609	38	24	connection	connection	NOUN
bjmsr-609	38	25	weight	weight	NOUN
bjmsr-609	38	26	.	.	PUNCT
bjmsr-609	39	1	often	often	ADV
bjmsr-609	39	2	each	each	DET
bjmsr-609	39	3	input	input	NOUN
bjmsr-609	39	4	relates	relate	VERB
bjmsr-609	39	5	to	to	ADP
bjmsr-609	39	6	a	a	DET
bjmsr-609	39	7	specific	specific	ADJ
bjmsr-609	39	8	attribute	attribute	NOUN
bjmsr-609	39	9	or	or	CCONJ
bjmsr-609	39	10	variable	variable	NOUN
bjmsr-609	39	11	,	,	PUNCT
bjmsr-609	39	12	describing	describe	VERB
bjmsr-609	39	13	a	a	DET
bjmsr-609	39	14	specific	specific	ADJ
bjmsr-609	39	15	condition	condition	NOUN
bjmsr-609	39	16	.	.	PUNCT
bjmsr-609	40	1	the	the	DET
bjmsr-609	40	2	output	output	NOUN
bjmsr-609	40	3	of	of	ADP
bjmsr-609	40	4	artificial	artificial	ADJ
bjmsr-609	40	5	neural	neural	ADJ
bjmsr-609	40	6	networks	network	NOUN
bjmsr-609	40	7	is	be	AUX
bjmsr-609	40	8	what	what	PRON
bjmsr-609	40	9	’s	’s	AUX
bjmsr-609	40	10	considered	consider	VERB
bjmsr-609	40	11	the	the	DET
bjmsr-609	40	12	solution	solution	NOUN
bjmsr-609	40	13	to	to	ADP
bjmsr-609	40	14	the	the	DET
bjmsr-609	40	15	problem	problem	NOUN
bjmsr-609	40	16	.	.	PUNCT
bjmsr-609	41	1	for	for	ADP
bjmsr-609	41	2	instance	instance	NOUN
bjmsr-609	41	3	,	,	PUNCT
bjmsr-609	41	4	if	if	SCONJ
bjmsr-609	41	5	the	the	DET
bjmsr-609	41	6	problem	problem	NOUN
bjmsr-609	41	7	at	at	ADP
bjmsr-609	41	8	hand	hand	NOUN
bjmsr-609	41	9	is	be	AUX
bjmsr-609	41	10	a	a	DET
bjmsr-609	41	11	decision	decision	NOUN
bjmsr-609	41	12	-	-	PUNCT
bjmsr-609	41	13	making	make	VERB
bjmsr-609	41	14	system	system	NOUN
bjmsr-609	41	15	the	the	DET
bjmsr-609	41	16	solution	solution	NOUN
bjmsr-609	41	17	often	often	ADV
bjmsr-609	41	18	can	can	AUX
bjmsr-609	41	19	consist	consist	VERB
bjmsr-609	41	20	of	of	ADP
bjmsr-609	41	21	a	a	DET
bjmsr-609	41	22	simple	simple	ADJ
bjmsr-609	41	23	yes	yes	INTJ
bjmsr-609	41	24	or	or	CCONJ
bjmsr-609	41	25	no	no	DET
bjmsr-609	41	26	answer	answer	NOUN
bjmsr-609	41	27	.	.	PUNCT
bjmsr-609	42	1	connection	connection	NOUN
bjmsr-609	42	2	weights	weight	NOUN
bjmsr-609	42	3	describe	describe	VERB
bjmsr-609	42	4	the	the	DET
bjmsr-609	42	5	amount	amount	NOUN
bjmsr-609	42	6	a	a	DET
bjmsr-609	42	7	specific	specific	ADJ
bjmsr-609	42	8	input	input	NOUN
bjmsr-609	42	9	affects	affect	VERB
bjmsr-609	42	10	another	another	DET
bjmsr-609	42	11	processing	processing	NOUN
bjmsr-609	42	12	element	element	NOUN
bjmsr-609	42	13	and	and	CCONJ
bjmsr-609	42	14	,	,	PUNCT
bjmsr-609	42	15	in	in	ADP
bjmsr-609	42	16	the	the	DET
bjmsr-609	42	17	end	end	NOUN
bjmsr-609	42	18	,	,	PUNCT
bjmsr-609	42	19	the	the	DET
bjmsr-609	42	20	output	output	NOUN
bjmsr-609	42	21	.	.	PUNCT
bjmsr-609	43	1	by	by	ADP
bjmsr-609	43	2	adjusting	adjust	VERB
bjmsr-609	43	3	the	the	DET
bjmsr-609	43	4	values	value	NOUN
bjmsr-609	43	5	of	of	ADP
bjmsr-609	43	6	the	the	DET
bjmsr-609	43	7	weights	weight	NOUN
bjmsr-609	43	8	,	,	PUNCT
bjmsr-609	43	9	the	the	DET
bjmsr-609	43	10	network	network	NOUN
bjmsr-609	43	11	can	can	AUX
bjmsr-609	43	12	learn	learn	VERB
bjmsr-609	43	13	patterns	pattern	NOUN
bjmsr-609	43	14	of	of	ADP
bjmsr-609	43	15	information	information	NOUN
bjmsr-609	43	16	and	and	CCONJ
bjmsr-609	43	17	store	store	VERB
bjmsr-609	43	18	these	these	PRON
bjmsr-609	43	19	(	(	PUNCT
bjmsr-609	43	20	turban	turban	PROPN
bjmsr-609	43	21	et	et	PROPN
bjmsr-609	43	22	al	al	PROPN
bjmsr-609	43	23	.	.	PROPN
bjmsr-609	43	24	,	,	PUNCT
bjmsr-609	43	25	2011	2011	NUM
bjmsr-609	43	26	)	)	PUNCT
bjmsr-609	43	27	.	.	PUNCT
bjmsr-609	44	1	each	each	DET
bjmsr-609	44	2	input	input	NOUN
bjmsr-609	44	3	value	value	NOUN
bjmsr-609	44	4	is	be	AUX
bjmsr-609	44	5	multiplied	multiply	VERB
bjmsr-609	44	6	with	with	ADP
bjmsr-609	44	7	its	its	PRON
bjmsr-609	44	8	weight	weight	NOUN
bjmsr-609	44	9	to	to	PART
bjmsr-609	44	10	calculate	calculate	VERB
bjmsr-609	44	11	a	a	DET
bjmsr-609	44	12	total	total	ADJ
bjmsr-609	44	13	weighted	weight	VERB
bjmsr-609	44	14	sum	sum	NOUN
bjmsr-609	44	15	in	in	ADP
bjmsr-609	44	16	a	a	DET
bjmsr-609	44	17	summation	summation	NOUN
bjmsr-609	44	18	function	function	NOUN
bjmsr-609	44	19	.	.	PUNCT
bjmsr-609	45	1	this	this	DET
bjmsr-609	45	2	calculation	calculation	NOUN
bjmsr-609	45	3	serves	serve	VERB
bjmsr-609	45	4	later	later	ADV
bjmsr-609	45	5	as	as	ADP
bjmsr-609	45	6	an	an	DET
bjmsr-609	45	7	activation	activation	NOUN
bjmsr-609	45	8	level	level	NOUN
bjmsr-609	45	9	for	for	ADP
bjmsr-609	45	10	the	the	DET
bjmsr-609	45	11	input	input	NOUN
bjmsr-609	45	12	,	,	PUNCT
bjmsr-609	45	13	which	which	PRON
bjmsr-609	45	14	determinates	determinate	VERB
bjmsr-609	45	15	whether	whether	SCONJ
bjmsr-609	45	16	a	a	DET
bjmsr-609	45	17	neuron	neuron	NOUN
bjmsr-609	45	18	should	should	AUX
bjmsr-609	45	19	produce	produce	VERB
bjmsr-609	45	20	an	an	DET
bjmsr-609	45	21	output	output	NOUN
bjmsr-609	45	22	or	or	CCONJ
bjmsr-609	45	23	not	not	PART
bjmsr-609	45	24	.	.	PUNCT
bjmsr-609	46	1	2.2.2	2.2.2	NUM
bjmsr-609	46	2	constructing	construct	VERB
bjmsr-609	46	3	an	an	DET
bjmsr-609	46	4	artificial	artificial	ADJ
bjmsr-609	46	5	neural	neural	ADJ
bjmsr-609	46	6	network	network	NOUN
bjmsr-609	46	7	when	when	SCONJ
bjmsr-609	46	8	constructing	construct	VERB
bjmsr-609	46	9	an	an	DET
bjmsr-609	46	10	artificial	artificial	ADJ
bjmsr-609	46	11	neural	neural	ADJ
bjmsr-609	46	12	network	network	NOUN
bjmsr-609	46	13	nine	nine	NUM
bjmsr-609	46	14	steps	step	NOUN
bjmsr-609	46	15	are	be	AUX
bjmsr-609	46	16	usually	usually	ADV
bjmsr-609	46	17	followed	follow	VERB
bjmsr-609	46	18	and	and	CCONJ
bjmsr-609	46	19	repeated	repeat	VERB
bjmsr-609	46	20	.	.	PUNCT
bjmsr-609	47	1	these	these	PRON
bjmsr-609	47	2	are	be	AUX
bjmsr-609	47	3	:	:	PUNCT
bjmsr-609	47	4	▪	▪	X
bjmsr-609	47	5	collecting	collect	VERB
bjmsr-609	47	6	data	datum	NOUN
bjmsr-609	47	7	.	.	PUNCT
bjmsr-609	48	1	▪	▪	NOUN
bjmsr-609	48	2	separating	separate	VERB
bjmsr-609	48	3	the	the	DET
bjmsr-609	48	4	data	datum	NOUN
bjmsr-609	48	5	into	into	ADP
bjmsr-609	48	6	subgroups	subgroup	NOUN
bjmsr-609	48	7	;	;	PUNCT
bjmsr-609	48	8	training	training	NOUN
bjmsr-609	48	9	,	,	PUNCT
bjmsr-609	48	10	validation	validation	NOUN
bjmsr-609	48	11	,	,	PUNCT
bjmsr-609	48	12	and	and	CCONJ
bjmsr-609	48	13	testing	testing	NOUN
bjmsr-609	48	14	sets	set	NOUN
bjmsr-609	48	15	.	.	PUNCT
bjmsr-609	49	1	▪	▪	NOUN
bjmsr-609	49	2	deciding	decide	VERB
bjmsr-609	49	3	on	on	ADP
bjmsr-609	49	4	suitable	suitable	ADJ
bjmsr-609	49	5	architecture	architecture	NOUN
bjmsr-609	49	6	and	and	CCONJ
bjmsr-609	49	7	structure	structure	NOUN
bjmsr-609	49	8	of	of	ADP
bjmsr-609	49	9	the	the	DET
bjmsr-609	49	10	proposed	propose	VERB
bjmsr-609	49	11	network	network	NOUN
bjmsr-609	49	12	.	.	PUNCT
bjmsr-609	50	1	▪	▪	NOUN
bjmsr-609	50	2	selecting	select	VERB
bjmsr-609	50	3	a	a	DET
bjmsr-609	50	4	learning	learning	NOUN
bjmsr-609	50	5	algorithm	algorithm	NOUN
bjmsr-609	50	6	.	.	PUNCT
bjmsr-609	51	1	▪	▪	X
bjmsr-609	51	2	setting	set	VERB
bjmsr-609	51	3	network	network	NOUN
bjmsr-609	51	4	parameters	parameter	NOUN
bjmsr-609	51	5	and	and	CCONJ
bjmsr-609	51	6	their	their	PRON
bjmsr-609	51	7	initial	initial	ADJ
bjmsr-609	51	8	values	value	NOUN
bjmsr-609	51	9	.	.	PUNCT
bjmsr-609	52	1	▪	▪	NOUN
bjmsr-609	52	2	setting	set	VERB
bjmsr-609	52	3	the	the	DET
bjmsr-609	52	4	initial	initial	ADJ
bjmsr-609	52	5	values	value	NOUN
bjmsr-609	52	6	of	of	ADP
bjmsr-609	52	7	the	the	DET
bjmsr-609	52	8	weights	weight	NOUN
bjmsr-609	52	9	,	,	PUNCT
bjmsr-609	52	10	and	and	CCONJ
bjmsr-609	52	11	start	start	VERB
bjmsr-609	52	12	committing	commit	VERB
bjmsr-609	52	13	training	training	NOUN
bjmsr-609	52	14	.	.	PUNCT
bjmsr-609	53	1	▪	▪	ADJ
bjmsr-609	53	2	training	training	NOUN
bjmsr-609	53	3	is	be	AUX
bjmsr-609	53	4	committed	commit	VERB
bjmsr-609	53	5	,	,	PUNCT
bjmsr-609	53	6	check	check	VERB
bjmsr-609	53	7	▪	▪	ADJ
bjmsr-609	53	8	training	training	NOUN
bjmsr-609	53	9	stops	stop	NOUN
bjmsr-609	53	10	,	,	PUNCT
bjmsr-609	53	11	weights	weight	NOUN
bjmsr-609	53	12	are	be	AUX
bjmsr-609	53	13	adjusted	adjust	VERB
bjmsr-609	53	14	and	and	CCONJ
bjmsr-609	53	15	the	the	DET
bjmsr-609	53	16	training	training	NOUN
bjmsr-609	53	17	process	process	NOUN
bjmsr-609	53	18	is	be	AUX
bjmsr-609	53	19	iterated	iterate	VERB
bjmsr-609	53	20	▪	▪	ADV
bjmsr-609	53	21	a	a	DET
bjmsr-609	53	22	stable	stable	ADJ
bjmsr-609	53	23	set	set	NOUN
bjmsr-609	53	24	of	of	ADP
bjmsr-609	53	25	weights	weight	NOUN
bjmsr-609	53	26	have	have	AUX
bjmsr-609	53	27	been	be	AUX
bjmsr-609	53	28	found	find	VERB
bjmsr-609	53	29	;	;	PUNCT
bjmsr-609	53	30	the	the	DET
bjmsr-609	53	31	network	network	NOUN
bjmsr-609	53	32	can	can	AUX
bjmsr-609	53	33	be	be	AUX
bjmsr-609	53	34	used	use	VERB
bjmsr-609	53	35	on	on	ADP
bjmsr-609	53	36	new	new	ADJ
bjmsr-609	53	37	non	non	ADJ
bjmsr-609	53	38	-	-	ADJ
bjmsr-609	53	39	training	training	NOUN
bjmsr-609	53	40	-	-	PUNCT
bjmsr-609	53	41	based	base	VERB
bjmsr-609	53	42	cases	case	NOUN
bjmsr-609	53	43	.	.	PUNCT
bjmsr-609	54	1	much	much	ADJ
bjmsr-609	54	2	of	of	ADP
bjmsr-609	54	3	the	the	DET
bjmsr-609	54	4	process	process	NOUN
bjmsr-609	54	5	of	of	ADP
bjmsr-609	54	6	constructing	construct	VERB
bjmsr-609	54	7	an	an	DET
bjmsr-609	54	8	artificial	artificial	ADJ
bjmsr-609	54	9	neural	neural	ADJ
bjmsr-609	54	10	network	network	NOUN
bjmsr-609	54	11	consists	consist	VERB
bjmsr-609	54	12	of	of	ADP
bjmsr-609	54	13	collecting	collect	VERB
bjmsr-609	54	14	data	datum	NOUN
bjmsr-609	54	15	that	that	PRON
bjmsr-609	54	16	are	be	AUX
bjmsr-609	54	17	appropriate	appropriate	ADJ
bjmsr-609	54	18	and	and	CCONJ
bjmsr-609	54	19	have	have	VERB
bjmsr-609	54	20	enough	enough	ADJ
bjmsr-609	54	21	information	information	NOUN
bjmsr-609	54	22	for	for	ADP
bjmsr-609	54	23	creating	create	VERB
bjmsr-609	54	24	test	test	NOUN
bjmsr-609	54	25	sets	set	NOUN
bjmsr-609	54	26	for	for	ADP
bjmsr-609	54	27	the	the	DET
bjmsr-609	54	28	system	system	NOUN
bjmsr-609	54	29	to	to	PART
bjmsr-609	54	30	train	train	VERB
bjmsr-609	54	31	on	on	ADP
bjmsr-609	54	32	.	.	PUNCT
bjmsr-609	55	1	the	the	DET
bjmsr-609	55	2	choice	choice	NOUN
bjmsr-609	55	3	of	of	ADP
bjmsr-609	55	4	structure	structure	NOUN
bjmsr-609	55	5	and	and	CCONJ
bjmsr-609	55	6	architecture	architecture	NOUN
bjmsr-609	55	7	consists	consist	VERB
bjmsr-609	55	8	of	of	ADP
bjmsr-609	55	9	copyright	copyright	NOUN
bjmsr-609	55	10	©	©	PROPN
bjmsr-609	55	11	cc	cc	PROPN
bjmsr-609	55	12	-	-	PUNCT
bjmsr-609	55	13	by	by	ADP
bjmsr-609	55	14	-	-	PUNCT
bjmsr-609	55	15	nc	nc	PROPN
bjmsr-609	55	16	2020	2020	NUM
bjmsr-609	55	17	,	,	PUNCT
bjmsr-609	55	18	cribfb	cribfb	PROPN
bjmsr-609	55	19	|bjmsr	|bjmsr	PROPN
bjmsr-609	55	20	www.cribfb.com/journal/index.php/bjmsr	www.cribfb.com/journal/index.php/bjmsr	X
bjmsr-609	56	1	bangladesh	bangladesh	PROPN
bjmsr-609	56	2	journal	journal	PROPN
bjmsr-609	56	3	of	of	ADP
bjmsr-609	56	4	multidisciplinary	multidisciplinary	ADJ
bjmsr-609	56	5	scientific	scientific	ADJ
bjmsr-609	56	6	research	research	NOUN
bjmsr-609	56	7	vol	vol	NOUN
bjmsr-609	56	8	.	.	PROPN
bjmsr-609	57	1	2	2	NUM
bjmsr-609	57	2	,	,	PUNCT
bjmsr-609	57	3	no	no	INTJ
bjmsr-609	57	4	.	.	NOUN
bjmsr-609	57	5	1	1	NUM
bjmsr-609	57	6	;	;	PUNCT
bjmsr-609	57	7	2020	2020	NUM
bjmsr-609	57	8	50	50	NUM
bjmsr-609	57	9	establishing	establish	VERB
bjmsr-609	57	10	input	input	NOUN
bjmsr-609	57	11	nodes	node	NOUN
bjmsr-609	57	12	,	,	PUNCT
bjmsr-609	57	13	output	output	NOUN
bjmsr-609	57	14	nodes	node	NOUN
bjmsr-609	57	15	,	,	PUNCT
bjmsr-609	57	16	number	number	NOUN
bjmsr-609	57	17	of	of	ADP
bjmsr-609	57	18	hidden	hidden	ADJ
bjmsr-609	57	19	layers	layer	NOUN
bjmsr-609	57	20	,	,	PUNCT
bjmsr-609	57	21	and	and	CCONJ
bjmsr-609	57	22	hidden	hidden	ADJ
bjmsr-609	57	23	nodes	node	NOUN
bjmsr-609	57	24	.	.	PUNCT
bjmsr-609	58	1	choosing	choose	VERB
bjmsr-609	58	2	a	a	DET
bjmsr-609	58	3	learning	learning	NOUN
bjmsr-609	58	4	algorithm	algorithm	NOUN
bjmsr-609	58	5	is	be	AUX
bjmsr-609	58	6	done	do	VERB
bjmsr-609	58	7	to	to	PART
bjmsr-609	58	8	find	find	VERB
bjmsr-609	58	9	sets	set	NOUN
bjmsr-609	58	10	of	of	ADP
bjmsr-609	58	11	connection	connection	NOUN
bjmsr-609	58	12	weights	weight	NOUN
bjmsr-609	58	13	that	that	PRON
bjmsr-609	58	14	are	be	AUX
bjmsr-609	58	15	deemed	deem	VERB
bjmsr-609	58	16	to	to	PART
bjmsr-609	58	17	have	have	VERB
bjmsr-609	58	18	the	the	DET
bjmsr-609	58	19	best	good	ADJ
bjmsr-609	58	20	predictive	predictive	ADJ
bjmsr-609	58	21	accuracy	accuracy	NOUN
bjmsr-609	58	22	and	and	CCONJ
bjmsr-609	58	23	best	well	ADV
bjmsr-609	58	24	suited	suit	VERB
bjmsr-609	58	25	for	for	ADP
bjmsr-609	58	26	the	the	DET
bjmsr-609	58	27	training	training	NOUN
bjmsr-609	58	28	data	datum	NOUN
bjmsr-609	58	29	.	.	PUNCT
bjmsr-609	59	1	testing	testing	NOUN
bjmsr-609	59	2	is	be	AUX
bjmsr-609	59	3	done	do	VERB
bjmsr-609	59	4	in	in	ADP
bjmsr-609	59	5	the	the	DET
bjmsr-609	59	6	8th	8th	ADJ
bjmsr-609	59	7	step	step	NOUN
bjmsr-609	59	8	by	by	ADP
bjmsr-609	59	9	using	use	VERB
bjmsr-609	59	10	the	the	DET
bjmsr-609	59	11	testing	testing	NOUN
bjmsr-609	59	12	data	datum	NOUN
bjmsr-609	59	13	set	set	VERB
bjmsr-609	59	14	to	to	PART
bjmsr-609	59	15	verify	verify	VERB
bjmsr-609	59	16	that	that	SCONJ
bjmsr-609	59	17	the	the	DET
bjmsr-609	59	18	input	input	NOUN
bjmsr-609	59	19	produces	produce	VERB
bjmsr-609	59	20	suitable	suitable	ADJ
bjmsr-609	59	21	output	output	NOUN
bjmsr-609	59	22	(	(	PUNCT
bjmsr-609	59	23	turban	turban	NOUN
bjmsr-609	59	24	et	et	PROPN
bjmsr-609	59	25	al	al	PROPN
bjmsr-609	59	26	.	.	PROPN
bjmsr-609	59	27	,	,	PUNCT
bjmsr-609	59	28	2011	2011	NUM
bjmsr-609	59	29	)	)	PUNCT
bjmsr-609	59	30	.	.	PUNCT
bjmsr-609	60	1	2.2.3	2.2.3	NUM
bjmsr-609	60	2	artificial	artificial	ADJ
bjmsr-609	60	3	neural	neural	ADJ
bjmsr-609	60	4	network	network	NOUN
bjmsr-609	60	5	architectures	architecture	NOUN
bjmsr-609	60	6	there	there	PRON
bjmsr-609	60	7	are	be	VERB
bjmsr-609	60	8	several	several	ADJ
bjmsr-609	60	9	different	different	ADJ
bjmsr-609	60	10	types	type	NOUN
bjmsr-609	60	11	of	of	ADP
bjmsr-609	60	12	neural	neural	ADJ
bjmsr-609	60	13	network	network	NOUN
bjmsr-609	60	14	architectures	architecture	NOUN
bjmsr-609	60	15	being	be	AUX
bjmsr-609	60	16	used	use	VERB
bjmsr-609	60	17	in	in	ADP
bjmsr-609	60	18	a	a	DET
bjmsr-609	60	19	different	different	ADJ
bjmsr-609	60	20	context	context	NOUN
bjmsr-609	60	21	,	,	PUNCT
bjmsr-609	60	22	mainly	mainly	ADV
bjmsr-609	60	23	depending	depend	VERB
bjmsr-609	60	24	on	on	ADP
bjmsr-609	60	25	the	the	DET
bjmsr-609	60	26	actual	actual	ADJ
bjmsr-609	60	27	task	task	NOUN
bjmsr-609	60	28	at	at	ADP
bjmsr-609	60	29	hand	hand	NOUN
bjmsr-609	60	30	.	.	PUNCT
bjmsr-609	61	1	another	another	DET
bjmsr-609	61	2	important	important	ADJ
bjmsr-609	61	3	aspect	aspect	NOUN
bjmsr-609	61	4	of	of	ADP
bjmsr-609	61	5	the	the	DET
bjmsr-609	61	6	architecture	architecture	NOUN
bjmsr-609	61	7	of	of	ADP
bjmsr-609	61	8	the	the	DET
bjmsr-609	61	9	networks	network	NOUN
bjmsr-609	61	10	is	be	AUX
bjmsr-609	61	11	the	the	DET
bjmsr-609	61	12	learning	learning	NOUN
bjmsr-609	61	13	process	process	NOUN
bjmsr-609	61	14	.	.	PUNCT
bjmsr-609	62	1	supervised	supervised	ADJ
bjmsr-609	62	2	learning	learn	VERB
bjmsr-609	62	3	in	in	ADP
bjmsr-609	62	4	artificial	artificial	ADJ
bjmsr-609	62	5	neural	neural	ADJ
bjmsr-609	62	6	networks	network	NOUN
bjmsr-609	62	7	is	be	AUX
bjmsr-609	62	8	a	a	DET
bjmsr-609	62	9	concept	concept	NOUN
bjmsr-609	62	10	where	where	SCONJ
bjmsr-609	62	11	a	a	DET
bjmsr-609	62	12	training	training	NOUN
bjmsr-609	62	13	set	set	NOUN
bjmsr-609	62	14	is	be	AUX
bjmsr-609	62	15	used	use	VERB
bjmsr-609	62	16	to	to	PART
bjmsr-609	62	17	teach	teach	VERB
bjmsr-609	62	18	the	the	DET
bjmsr-609	62	19	network	network	NOUN
bjmsr-609	62	20	of	of	ADP
bjmsr-609	62	21	the	the	DET
bjmsr-609	62	22	problem	problem	NOUN
bjmsr-609	62	23	and	and	CCONJ
bjmsr-609	62	24	its	its	PRON
bjmsr-609	62	25	domain	domain	NOUN
bjmsr-609	62	26	,	,	PUNCT
bjmsr-609	62	27	unlike	unlike	ADP
bjmsr-609	62	28	unsupervised	unsupervised	ADJ
bjmsr-609	62	29	learning	learning	NOUN
bjmsr-609	62	30	where	where	SCONJ
bjmsr-609	62	31	the	the	DET
bjmsr-609	62	32	neural	neural	ADJ
bjmsr-609	62	33	network	network	NOUN
bjmsr-609	62	34	is	be	AUX
bjmsr-609	62	35	working	work	VERB
bjmsr-609	62	36	with	with	ADP
bjmsr-609	62	37	a	a	DET
bjmsr-609	62	38	more	more	ADV
bjmsr-609	62	39	self	self	NOUN
bjmsr-609	62	40	-	-	PUNCT
bjmsr-609	62	41	organizing	organize	VERB
bjmsr-609	62	42	approach	approach	NOUN
bjmsr-609	62	43	by	by	ADP
bjmsr-609	62	44	learning	learn	VERB
bjmsr-609	62	45	pattern	pattern	NOUN
bjmsr-609	62	46	through	through	ADP
bjmsr-609	62	47	repeated	repeat	VERB
bjmsr-609	62	48	exposure	exposure	NOUN
bjmsr-609	62	49	(	(	PUNCT
bjmsr-609	62	50	turban	turban	NOUN
bjmsr-609	62	51	et	et	PROPN
bjmsr-609	62	52	al	al	PROPN
bjmsr-609	62	53	.	.	PROPN
bjmsr-609	62	54	,	,	PUNCT
bjmsr-609	62	55	2011	2011	NUM
bjmsr-609	62	56	)	)	PUNCT
bjmsr-609	62	57	.	.	PUNCT
bjmsr-609	63	1	one	one	NUM
bjmsr-609	63	2	of	of	ADP
bjmsr-609	63	3	the	the	DET
bjmsr-609	63	4	most	most	ADV
bjmsr-609	63	5	popular	popular	ADJ
bjmsr-609	63	6	artificial	artificial	ADJ
bjmsr-609	63	7	neural	neural	ADJ
bjmsr-609	63	8	network	network	NOUN
bjmsr-609	63	9	architectures	architecture	NOUN
bjmsr-609	63	10	is	be	AUX
bjmsr-609	63	11	the	the	DET
bjmsr-609	63	12	multilayer	multilayer	ADJ
bjmsr-609	63	13	architecture	architecture	NOUN
bjmsr-609	63	14	,	,	PUNCT
bjmsr-609	63	15	which	which	PRON
bjmsr-609	63	16	consists	consist	VERB
bjmsr-609	63	17	of	of	ADP
bjmsr-609	63	18	several	several	ADJ
bjmsr-609	63	19	layers	layer	NOUN
bjmsr-609	63	20	of	of	ADP
bjmsr-609	63	21	neurons	neuron	NOUN
bjmsr-609	63	22	.	.	PUNCT
bjmsr-609	64	1	often	often	ADV
bjmsr-609	64	2	the	the	DET
bjmsr-609	64	3	feedforward	feedforward	NOUN
bjmsr-609	64	4	approach	approach	NOUN
bjmsr-609	64	5	,	,	PUNCT
bjmsr-609	64	6	where	where	SCONJ
bjmsr-609	64	7	the	the	DET
bjmsr-609	64	8	output	output	NOUN
bjmsr-609	64	9	of	of	ADP
bjmsr-609	64	10	a	a	DET
bjmsr-609	64	11	node	node	NOUN
bjmsr-609	64	12	in	in	ADP
bjmsr-609	64	13	one	one	NUM
bjmsr-609	64	14	layer	layer	NOUN
bjmsr-609	64	15	is	be	AUX
bjmsr-609	64	16	not	not	PART
bjmsr-609	64	17	connected	connect	VERB
bjmsr-609	64	18	to	to	ADP
bjmsr-609	64	19	the	the	DET
bjmsr-609	64	20	input	input	NOUN
bjmsr-609	64	21	of	of	ADP
bjmsr-609	64	22	a	a	DET
bjmsr-609	64	23	node	node	NOUN
bjmsr-609	64	24	in	in	ADP
bjmsr-609	64	25	a	a	DET
bjmsr-609	64	26	previous	previous	ADJ
bjmsr-609	64	27	layer	layer	NOUN
bjmsr-609	64	28	nor	nor	CCONJ
bjmsr-609	64	29	the	the	DET
bjmsr-609	64	30	same	same	ADJ
bjmsr-609	64	31	layer	layer	NOUN
bjmsr-609	64	32	,	,	PUNCT
bjmsr-609	64	33	but	but	CCONJ
bjmsr-609	64	34	only	only	ADV
bjmsr-609	64	35	to	to	ADP
bjmsr-609	64	36	nodes	node	NOUN
bjmsr-609	64	37	in	in	ADP
bjmsr-609	64	38	subsequent	subsequent	ADJ
bjmsr-609	64	39	layers	layer	NOUN
bjmsr-609	64	40	,	,	PUNCT
bjmsr-609	64	41	is	be	AUX
bjmsr-609	64	42	used	use	VERB
bjmsr-609	64	43	on	on	ADP
bjmsr-609	64	44	this	this	DET
bjmsr-609	64	45	type	type	NOUN
bjmsr-609	64	46	of	of	ADP
bjmsr-609	64	47	architecture	architecture	NOUN
bjmsr-609	64	48	.	.	PUNCT
bjmsr-609	65	1	other	other	ADJ
bjmsr-609	65	2	popular	popular	ADJ
bjmsr-609	65	3	architectures	architecture	NOUN
bjmsr-609	65	4	are	be	AUX
bjmsr-609	65	5	kohonen	kohonen	PROPN
bjmsr-609	65	6	’s	’s	PART
bjmsr-609	65	7	self	self	NOUN
bjmsr-609	65	8	-	-	PUNCT
bjmsr-609	65	9	organizing	organize	VERB
bjmsr-609	65	10	feature	feature	NOUN
bjmsr-609	65	11	maps	map	NOUN
bjmsr-609	65	12	and	and	CCONJ
bjmsr-609	65	13	hopfield	hopfield	PROPN
bjmsr-609	65	14	networks	network	NOUN
bjmsr-609	65	15	.	.	PUNCT
bjmsr-609	66	1	kohonen	kohonen	PROPN
bjmsr-609	66	2	’s	’s	PART
bjmsr-609	66	3	selforganizing	selforganize	VERB
bjmsr-609	66	4	feature	feature	NOUN
bjmsr-609	66	5	maps	map	NOUN
bjmsr-609	66	6	,	,	PUNCT
bjmsr-609	66	7	also	also	ADV
bjmsr-609	66	8	known	know	VERB
bjmsr-609	66	9	as	as	ADP
bjmsr-609	66	10	som	som	NOUN
bjmsr-609	66	11	,	,	PUNCT
bjmsr-609	66	12	is	be	AUX
bjmsr-609	66	13	one	one	NUM
bjmsr-609	66	14	of	of	ADP
bjmsr-609	66	15	the	the	DET
bjmsr-609	66	16	most	most	ADV
bjmsr-609	66	17	popular	popular	ADJ
bjmsr-609	66	18	neural	neural	ADJ
bjmsr-609	66	19	networks	network	NOUN
bjmsr-609	66	20	for	for	ADP
bjmsr-609	66	21	use	use	NOUN
bjmsr-609	66	22	in	in	ADP
bjmsr-609	66	23	data	datum	NOUN
bjmsr-609	66	24	mining	mining	NOUN
bjmsr-609	66	25	.	.	PUNCT
bjmsr-609	67	1	in	in	ADP
bjmsr-609	67	2	the	the	DET
bjmsr-609	67	3	som	som	NOUN
bjmsr-609	67	4	architecture	architecture	NOUN
bjmsr-609	67	5	,	,	PUNCT
bjmsr-609	67	6	the	the	DET
bjmsr-609	67	7	network	network	NOUN
bjmsr-609	67	8	uses	use	VERB
bjmsr-609	67	9	a	a	DET
bjmsr-609	67	10	type	type	NOUN
bjmsr-609	67	11	of	of	ADP
bjmsr-609	67	12	unsupervised	unsupervised	ADJ
bjmsr-609	67	13	learning	learning	NOUN
bjmsr-609	67	14	to	to	PART
bjmsr-609	67	15	produce	produce	VERB
bjmsr-609	67	16	a	a	DET
bjmsr-609	67	17	low	low	ADJ
bjmsr-609	67	18	-	-	PUNCT
bjmsr-609	67	19	dimensional	dimensional	ADJ
bjmsr-609	67	20	representation	representation	NOUN
bjmsr-609	67	21	of	of	ADP
bjmsr-609	67	22	the	the	DET
bjmsr-609	67	23	input	input	NOUN
bjmsr-609	67	24	which	which	PRON
bjmsr-609	67	25	often	often	ADV
bjmsr-609	67	26	is	be	AUX
bjmsr-609	67	27	consisting	consist	VERB
bjmsr-609	67	28	of	of	ADP
bjmsr-609	67	29	high	high	ADJ
bjmsr-609	67	30	dimensional	dimensional	ADJ
bjmsr-609	67	31	data	datum	NOUN
bjmsr-609	67	32	sets	set	NOUN
bjmsr-609	67	33	.	.	PUNCT
bjmsr-609	68	1	hopfield	hopfield	PROPN
bjmsr-609	68	2	networks	network	NOUN
bjmsr-609	68	3	are	be	AUX
bjmsr-609	68	4	known	know	VERB
bjmsr-609	68	5	as	as	ADP
bjmsr-609	68	6	recurrent	recurrent	ADJ
bjmsr-609	68	7	neural	neural	ADJ
bjmsr-609	68	8	networks	network	NOUN
bjmsr-609	68	9	,	,	PUNCT
bjmsr-609	68	10	which	which	PRON
bjmsr-609	68	11	mean	mean	VERB
bjmsr-609	68	12	that	that	SCONJ
bjmsr-609	68	13	it	it	PRON
bjmsr-609	68	14	consists	consist	VERB
bjmsr-609	68	15	basically	basically	ADV
bjmsr-609	68	16	of	of	ADP
bjmsr-609	68	17	a	a	DET
bjmsr-609	68	18	single	single	ADJ
bjmsr-609	68	19	layer	layer	NOUN
bjmsr-609	68	20	of	of	ADP
bjmsr-609	68	21	a	a	DET
bjmsr-609	68	22	neutron	neutron	NOUN
bjmsr-609	68	23	in	in	ADP
bjmsr-609	68	24	which	which	PRON
bjmsr-609	68	25	every	every	DET
bjmsr-609	68	26	neuron	neuron	NOUN
bjmsr-609	68	27	is	be	AUX
bjmsr-609	68	28	connected	connect	VERB
bjmsr-609	68	29	.	.	PUNCT
bjmsr-609	69	1	this	this	PRON
bjmsr-609	69	2	differs	differ	VERB
bjmsr-609	69	3	from	from	ADP
bjmsr-609	69	4	the	the	DET
bjmsr-609	69	5	feedforward	feedforward	ADJ
bjmsr-609	69	6	type	type	NOUN
bjmsr-609	69	7	of	of	ADP
bjmsr-609	69	8	networks	network	NOUN
bjmsr-609	69	9	where	where	SCONJ
bjmsr-609	69	10	neurons	neuron	NOUN
bjmsr-609	69	11	are	be	AUX
bjmsr-609	69	12	not	not	PART
bjmsr-609	69	13	connected	connect	VERB
bjmsr-609	69	14	in	in	ADP
bjmsr-609	69	15	the	the	DET
bjmsr-609	69	16	same	same	ADJ
bjmsr-609	69	17	layer	layer	NOUN
bjmsr-609	69	18	.	.	PUNCT
bjmsr-609	70	1	the	the	DET
bjmsr-609	70	2	neutrons	neutron	NOUN
bjmsr-609	70	3	in	in	ADP
bjmsr-609	70	4	a	a	DET
bjmsr-609	70	5	hopfield	hopfield	ADJ
bjmsr-609	70	6	network	network	NOUN
bjmsr-609	70	7	are	be	AUX
bjmsr-609	70	8	all	all	PRON
bjmsr-609	70	9	binary	binary	ADJ
bjmsr-609	70	10	units	unit	NOUN
bjmsr-609	70	11	,	,	PUNCT
bjmsr-609	70	12	which	which	PRON
bjmsr-609	70	13	are	be	AUX
bjmsr-609	70	14	either	either	CCONJ
bjmsr-609	70	15	active	active	ADJ
bjmsr-609	70	16	or	or	CCONJ
bjmsr-609	70	17	inactive	inactive	ADJ
bjmsr-609	70	18	.	.	PUNCT
bjmsr-609	71	1	initially	initially	ADV
bjmsr-609	71	2	,	,	PUNCT
bjmsr-609	71	3	all	all	DET
bjmsr-609	71	4	the	the	DET
bjmsr-609	71	5	neutrons	neutron	NOUN
bjmsr-609	71	6	have	have	VERB
bjmsr-609	71	7	random	random	ADJ
bjmsr-609	71	8	values	value	NOUN
bjmsr-609	71	9	but	but	CCONJ
bjmsr-609	71	10	change	change	VERB
bjmsr-609	71	11	through	through	ADP
bjmsr-609	71	12	iteration	iteration	NOUN
bjmsr-609	71	13	by	by	ADP
bjmsr-609	71	14	checking	check	VERB
bjmsr-609	71	15	the	the	DET
bjmsr-609	71	16	connection	connection	NOUN
bjmsr-609	71	17	weight	weight	NOUN
bjmsr-609	71	18	between	between	ADP
bjmsr-609	71	19	neurons	neuron	NOUN
bjmsr-609	71	20	.	.	PUNCT
bjmsr-609	72	1	this	this	PRON
bjmsr-609	72	2	goes	go	VERB
bjmsr-609	72	3	on	on	ADP
bjmsr-609	72	4	until	until	SCONJ
bjmsr-609	72	5	the	the	DET
bjmsr-609	72	6	neurons	neuron	NOUN
bjmsr-609	72	7	reach	reach	VERB
bjmsr-609	72	8	a	a	DET
bjmsr-609	72	9	stable	stable	ADJ
bjmsr-609	72	10	state	state	NOUN
bjmsr-609	72	11	,	,	PUNCT
bjmsr-609	72	12	which	which	PRON
bjmsr-609	72	13	is	be	AUX
bjmsr-609	72	14	deemed	deem	VERB
bjmsr-609	72	15	to	to	PART
bjmsr-609	72	16	be	be	AUX
bjmsr-609	72	17	the	the	DET
bjmsr-609	72	18	final	final	ADJ
bjmsr-609	72	19	state	state	NOUN
bjmsr-609	72	20	(	(	PUNCT
bjmsr-609	72	21	turban	turban	NOUN
bjmsr-609	72	22	et	et	PROPN
bjmsr-609	72	23	al	al	PROPN
bjmsr-609	72	24	.	.	PROPN
bjmsr-609	72	25	,	,	PUNCT
bjmsr-609	72	26	2011	2011	NUM
bjmsr-609	72	27	)	)	PUNCT
bjmsr-609	72	28	.	.	PUNCT
bjmsr-609	73	1	2.2.4	2.2.4	NUM
bjmsr-609	73	2	learning	learn	VERB
bjmsr-609	73	3	algorithms	algorithm	NOUN
bjmsr-609	73	4	a	a	DET
bjmsr-609	73	5	learning	learn	VERB
bjmsr-609	73	6	algorithm	algorithm	NOUN
bjmsr-609	73	7	is	be	AUX
bjmsr-609	73	8	used	use	VERB
bjmsr-609	73	9	to	to	PART
bjmsr-609	73	10	help	help	VERB
bjmsr-609	73	11	the	the	DET
bjmsr-609	73	12	neural	neural	ADJ
bjmsr-609	73	13	network	network	NOUN
bjmsr-609	73	14	specify	specify	VERB
bjmsr-609	73	15	how	how	SCONJ
bjmsr-609	73	16	it	it	PRON
bjmsr-609	73	17	learns	learn	VERB
bjmsr-609	73	18	the	the	DET
bjmsr-609	73	19	relationship	relationship	NOUN
bjmsr-609	73	20	between	between	ADP
bjmsr-609	73	21	inputs	input	NOUN
bjmsr-609	73	22	,	,	PUNCT
bjmsr-609	73	23	or	or	CCONJ
bjmsr-609	73	24	between	between	ADP
bjmsr-609	73	25	inputs	input	NOUN
bjmsr-609	73	26	and	and	CCONJ
bjmsr-609	73	27	outputs	output	NOUN
bjmsr-609	73	28	.	.	PUNCT
bjmsr-609	74	1	one	one	NUM
bjmsr-609	74	2	of	of	ADP
bjmsr-609	74	3	the	the	DET
bjmsr-609	74	4	most	most	ADV
bjmsr-609	74	5	used	used	ADJ
bjmsr-609	74	6	learning	learning	NOUN
bjmsr-609	74	7	algorithms	algorithm	NOUN
bjmsr-609	74	8	is	be	AUX
bjmsr-609	74	9	the	the	DET
bjmsr-609	74	10	back	back	ADJ
bjmsr-609	74	11	-	-	PUNCT
bjmsr-609	74	12	error	error	NOUN
bjmsr-609	74	13	propagation	propagation	NOUN
bjmsr-609	74	14	,	,	PUNCT
bjmsr-609	74	15	often	often	ADV
bjmsr-609	74	16	called	call	VERB
bjmsr-609	74	17	backpropagation	backpropagation	NOUN
bjmsr-609	74	18	.	.	PUNCT
bjmsr-609	75	1	this	this	DET
bjmsr-609	75	2	algorithm	algorithm	NOUN
bjmsr-609	75	3	is	be	AUX
bjmsr-609	75	4	often	often	ADV
bjmsr-609	75	5	used	use	VERB
bjmsr-609	75	6	on	on	ADP
bjmsr-609	75	7	neural	neural	ADJ
bjmsr-609	75	8	networks	network	NOUN
bjmsr-609	75	9	with	with	ADP
bjmsr-609	75	10	a	a	DET
bjmsr-609	75	11	feedforward	feedforward	NOUN
bjmsr-609	75	12	approach	approach	NOUN
bjmsr-609	75	13	.	.	PUNCT
bjmsr-609	76	1	a	a	DET
bjmsr-609	76	2	supervised	supervised	ADJ
bjmsr-609	76	3	learning	learning	NOUN
bjmsr-609	76	4	algorithm	algorithm	NOUN
bjmsr-609	76	5	,	,	PUNCT
bjmsr-609	76	6	the	the	DET
bjmsr-609	76	7	backpropagation	backpropagation	NOUN
bjmsr-609	76	8	algorithm	algorithm	NOUN
bjmsr-609	76	9	is	be	AUX
bjmsr-609	76	10	trained	train	VERB
bjmsr-609	76	11	with	with	ADP
bjmsr-609	76	12	correct	correct	ADJ
bjmsr-609	76	13	patterns	pattern	NOUN
bjmsr-609	76	14	being	be	AUX
bjmsr-609	76	15	provided	provide	VERB
bjmsr-609	76	16	,	,	PUNCT
bjmsr-609	76	17	with	with	ADP
bjmsr-609	76	18	the	the	DET
bjmsr-609	76	19	weights	weight	NOUN
bjmsr-609	76	20	of	of	ADP
bjmsr-609	76	21	inputs	input	NOUN
bjmsr-609	76	22	being	be	AUX
bjmsr-609	76	23	adjusted	adjust	VERB
bjmsr-609	76	24	to	to	PART
bjmsr-609	76	25	match	match	VERB
bjmsr-609	76	26	the	the	DET
bjmsr-609	76	27	patterns	pattern	NOUN
bjmsr-609	76	28	.	.	PUNCT
bjmsr-609	77	1	the	the	DET
bjmsr-609	77	2	algorithm	algorithm	NOUN
bjmsr-609	77	3	follows	follow	VERB
bjmsr-609	77	4	a	a	DET
bjmsr-609	77	5	few	few	ADJ
bjmsr-609	77	6	steps	step	NOUN
bjmsr-609	77	7	;	;	PUNCT
bjmsr-609	77	8	firstly	firstly	ADV
bjmsr-609	77	9	,	,	PUNCT
bjmsr-609	77	10	the	the	DET
bjmsr-609	77	11	weights	weight	NOUN
bjmsr-609	77	12	are	be	AUX
bjmsr-609	77	13	given	give	VERB
bjmsr-609	77	14	random	random	ADJ
bjmsr-609	77	15	values	value	NOUN
bjmsr-609	77	16	,	,	PUNCT
bjmsr-609	77	17	the	the	DET
bjmsr-609	77	18	input	input	NOUN
bjmsr-609	77	19	and	and	CCONJ
bjmsr-609	77	20	the	the	DET
bjmsr-609	77	21	desired	desire	VERB
bjmsr-609	77	22	output	output	NOUN
bjmsr-609	77	23	are	be	AUX
bjmsr-609	77	24	read	read	VERB
bjmsr-609	77	25	,	,	PUNCT
bjmsr-609	77	26	calculate	calculate	VERB
bjmsr-609	77	27	the	the	DET
bjmsr-609	77	28	actual	actual	ADJ
bjmsr-609	77	29	output	output	NOUN
bjmsr-609	77	30	,	,	PUNCT
bjmsr-609	77	31	compare	compare	VERB
bjmsr-609	77	32	the	the	DET
bjmsr-609	77	33	actual	actual	ADJ
bjmsr-609	77	34	output	output	NOUN
bjmsr-609	77	35	to	to	ADP
bjmsr-609	77	36	the	the	DET
bjmsr-609	77	37	desired	desire	VERB
bjmsr-609	77	38	output	output	NOUN
bjmsr-609	77	39	,	,	PUNCT
bjmsr-609	77	40	and	and	CCONJ
bjmsr-609	77	41	finally	finally	ADV
bjmsr-609	77	42	changing	change	VERB
bjmsr-609	77	43	the	the	DET
bjmsr-609	77	44	weights	weight	NOUN
bjmsr-609	77	45	.	.	PUNCT
bjmsr-609	78	1	these	these	DET
bjmsr-609	78	2	steps	step	NOUN
bjmsr-609	78	3	are	be	AUX
bjmsr-609	78	4	then	then	ADV
bjmsr-609	78	5	repeated	repeat	VERB
bjmsr-609	78	6	until	until	ADP
bjmsr-609	78	7	the	the	DET
bjmsr-609	78	8	desired	desire	VERB
bjmsr-609	78	9	output	output	NOUN
bjmsr-609	78	10	and	and	CCONJ
bjmsr-609	78	11	the	the	DET
bjmsr-609	78	12	actual	actual	ADJ
bjmsr-609	78	13	output	output	NOUN
bjmsr-609	78	14	is	be	AUX
bjmsr-609	78	15	consistent	consistent	ADJ
bjmsr-609	78	16	to	to	ADP
bjmsr-609	78	17	a	a	DET
bjmsr-609	78	18	predetermined	predetermine	VERB
bjmsr-609	78	19	degree	degree	NOUN
bjmsr-609	78	20	(	(	PUNCT
bjmsr-609	78	21	turban	turban	NOUN
bjmsr-609	78	22	et	et	PROPN
bjmsr-609	78	23	al	al	PROPN
bjmsr-609	78	24	.	.	PROPN
bjmsr-609	78	25	,	,	PUNCT
bjmsr-609	78	26	2011	2011	NUM
bjmsr-609	78	27	)	)	PUNCT
bjmsr-609	78	28	.	.	PUNCT
bjmsr-609	79	1	to	to	PART
bjmsr-609	79	2	help	help	VERB
bjmsr-609	79	3	with	with	ADP
bjmsr-609	79	4	the	the	DET
bjmsr-609	79	5	process	process	NOUN
bjmsr-609	79	6	of	of	ADP
bjmsr-609	79	7	the	the	DET
bjmsr-609	79	8	learning	learning	NOUN
bjmsr-609	79	9	some	some	DET
bjmsr-609	79	10	learning	learn	VERB
bjmsr-609	79	11	laws	law	NOUN
bjmsr-609	79	12	or	or	CCONJ
bjmsr-609	79	13	rules	rule	NOUN
bjmsr-609	79	14	have	have	AUX
bjmsr-609	79	15	been	be	AUX
bjmsr-609	79	16	stated	state	VERB
bjmsr-609	79	17	.	.	PUNCT
bjmsr-609	80	1	one	one	NUM
bjmsr-609	80	2	of	of	ADP
bjmsr-609	80	3	the	the	DET
bjmsr-609	80	4	best	well	ADV
bjmsr-609	80	5	known	know	VERB
bjmsr-609	80	6	is	be	AUX
bjmsr-609	80	7	hebb	hebb	NOUN
bjmsr-609	80	8	’s	’s	PART
bjmsr-609	80	9	rule	rule	NOUN
bjmsr-609	80	10	.	.	PUNCT
bjmsr-609	81	1	this	this	PRON
bjmsr-609	81	2	says	say	VERB
bjmsr-609	81	3	that	that	SCONJ
bjmsr-609	81	4	if	if	SCONJ
bjmsr-609	81	5	a	a	DET
bjmsr-609	81	6	neuron	neuron	NOUN
bjmsr-609	81	7	receives	receive	VERB
bjmsr-609	81	8	input	input	NOUN
bjmsr-609	81	9	from	from	ADP
bjmsr-609	81	10	another	another	DET
bjmsr-609	81	11	neuron	neuron	NOUN
bjmsr-609	81	12	,	,	PUNCT
bjmsr-609	81	13	which	which	DET
bjmsr-609	81	14	approximately	approximately	ADV
bjmsr-609	81	15	the	the	DET
bjmsr-609	81	16	same	same	ADJ
bjmsr-609	81	17	value	value	NOUN
bjmsr-609	81	18	,	,	PUNCT
bjmsr-609	81	19	the	the	DET
bjmsr-609	81	20	weight	weight	NOUN
bjmsr-609	81	21	between	between	ADP
bjmsr-609	81	22	the	the	DET
bjmsr-609	81	23	neurons	neuron	NOUN
bjmsr-609	81	24	should	should	AUX
bjmsr-609	81	25	be	be	AUX
bjmsr-609	81	26	increased	increase	VERB
bjmsr-609	81	27	.	.	PUNCT
bjmsr-609	82	1	hopfield	hopfield	PROPN
bjmsr-609	82	2	law	law	NOUN
bjmsr-609	82	3	is	be	AUX
bjmsr-609	82	4	in	in	ADP
bjmsr-609	82	5	many	many	ADJ
bjmsr-609	82	6	ways	way	NOUN
bjmsr-609	82	7	similar	similar	ADJ
bjmsr-609	82	8	to	to	ADP
bjmsr-609	82	9	hebb	hebb	PROPN
bjmsr-609	82	10	’s	’s	PART
bjmsr-609	82	11	rule	rule	NOUN
bjmsr-609	82	12	but	but	CCONJ
bjmsr-609	82	13	states	state	VERB
bjmsr-609	82	14	that	that	SCONJ
bjmsr-609	82	15	if	if	SCONJ
bjmsr-609	82	16	the	the	DET
bjmsr-609	82	17	desired	desire	VERB
bjmsr-609	82	18	output	output	NOUN
bjmsr-609	82	19	and	and	CCONJ
bjmsr-609	82	20	the	the	DET
bjmsr-609	82	21	input	input	NOUN
bjmsr-609	82	22	have	have	VERB
bjmsr-609	82	23	the	the	DET
bjmsr-609	82	24	same	same	ADJ
bjmsr-609	82	25	value	value	NOUN
bjmsr-609	82	26	,	,	PUNCT
bjmsr-609	82	27	the	the	DET
bjmsr-609	82	28	weights	weight	NOUN
bjmsr-609	82	29	should	should	AUX
bjmsr-609	82	30	be	be	AUX
bjmsr-609	82	31	increased	increase	VERB
bjmsr-609	82	32	by	by	ADP
bjmsr-609	82	33	the	the	DET
bjmsr-609	82	34	learning	learning	NOUN
bjmsr-609	82	35	rate	rate	NOUN
bjmsr-609	82	36	of	of	ADP
bjmsr-609	82	37	the	the	DET
bjmsr-609	82	38	network	network	NOUN
bjmsr-609	82	39	.	.	PUNCT
bjmsr-609	83	1	if	if	SCONJ
bjmsr-609	83	2	not	not	PART
bjmsr-609	83	3	the	the	DET
bjmsr-609	83	4	case	case	NOUN
bjmsr-609	83	5	,	,	PUNCT
bjmsr-609	83	6	the	the	DET
bjmsr-609	83	7	weights	weight	NOUN
bjmsr-609	83	8	should	should	AUX
bjmsr-609	83	9	be	be	AUX
bjmsr-609	83	10	decreased	decrease	VERB
bjmsr-609	83	11	by	by	ADP
bjmsr-609	83	12	the	the	DET
bjmsr-609	83	13	learning	learning	NOUN
bjmsr-609	83	14	rate	rate	NOUN
bjmsr-609	83	15	.	.	PUNCT
bjmsr-609	84	1	one	one	NUM
bjmsr-609	84	2	of	of	ADP
bjmsr-609	84	3	the	the	DET
bjmsr-609	84	4	most	most	ADV
bjmsr-609	84	5	used	used	ADJ
bjmsr-609	84	6	learning	learning	NOUN
bjmsr-609	84	7	laws	law	NOUN
bjmsr-609	84	8	is	be	AUX
bjmsr-609	84	9	delta	delta	NOUN
bjmsr-609	84	10	rule	rule	NOUN
bjmsr-609	84	11	.	.	PUNCT
bjmsr-609	85	1	this	this	DET
bjmsr-609	85	2	rule	rule	NOUN
bjmsr-609	85	3	is	be	AUX
bjmsr-609	85	4	based	base	VERB
bjmsr-609	85	5	on	on	ADP
bjmsr-609	85	6	continuously	continuously	ADV
bjmsr-609	85	7	modifying	modify	VERB
bjmsr-609	85	8	the	the	DET
bjmsr-609	85	9	connection	connection	NOUN
bjmsr-609	85	10	weights	weight	NOUN
bjmsr-609	85	11	to	to	PART
bjmsr-609	85	12	reduce	reduce	VERB
bjmsr-609	85	13	the	the	DET
bjmsr-609	85	14	difference	difference	NOUN
bjmsr-609	85	15	,	,	PUNCT
bjmsr-609	85	16	or	or	CCONJ
bjmsr-609	85	17	delta	delta	NOUN
bjmsr-609	85	18	,	,	PUNCT
bjmsr-609	85	19	between	between	ADP
bjmsr-609	85	20	the	the	DET
bjmsr-609	85	21	desired	desire	VERB
bjmsr-609	85	22	output	output	NOUN
bjmsr-609	85	23	and	and	CCONJ
bjmsr-609	85	24	input	input	NOUN
bjmsr-609	85	25	(	(	PUNCT
bjmsr-609	85	26	anderson	anderson	PROPN
bjmsr-609	85	27	&	&	CCONJ
bjmsr-609	85	28	mcneill	mcneill	PROPN
bjmsr-609	85	29	,	,	PUNCT
bjmsr-609	85	30	1992	1992	NUM
bjmsr-609	85	31	)	)	PUNCT
bjmsr-609	85	32	.	.	PUNCT
bjmsr-609	86	1	3	3	X
bjmsr-609	86	2	.	.	X
bjmsr-609	86	3	discussion	discussion	NOUN
bjmsr-609	86	4	and	and	CCONJ
bjmsr-609	86	5	conclusion	conclusion	NOUN
bjmsr-609	86	6	today	today	NOUN
bjmsr-609	86	7	there	there	PRON
bjmsr-609	86	8	are	be	VERB
bjmsr-609	86	9	several	several	ADJ
bjmsr-609	86	10	different	different	ADJ
bjmsr-609	86	11	areas	area	NOUN
bjmsr-609	86	12	of	of	ADP
bjmsr-609	86	13	use	use	NOUN
bjmsr-609	86	14	for	for	ADP
bjmsr-609	86	15	artificial	artificial	ADJ
bjmsr-609	86	16	neural	neural	ADJ
bjmsr-609	86	17	networks	network	NOUN
bjmsr-609	86	18	,	,	PUNCT
bjmsr-609	86	19	in	in	ADP
bjmsr-609	86	20	both	both	CCONJ
bjmsr-609	86	21	research	research	NOUN
bjmsr-609	86	22	and	and	CCONJ
bjmsr-609	86	23	business	business	NOUN
bjmsr-609	86	24	settings	setting	NOUN
bjmsr-609	86	25	.	.	PUNCT
bjmsr-609	87	1	neural	neural	ADJ
bjmsr-609	87	2	networks	network	NOUN
bjmsr-609	87	3	are	be	AUX
bjmsr-609	87	4	suitable	suitable	ADJ
bjmsr-609	87	5	for	for	ADP
bjmsr-609	87	6	data	datum	NOUN
bjmsr-609	87	7	mining	mining	NOUN
bjmsr-609	87	8	problems	problem	NOUN
bjmsr-609	87	9	with	with	ADP
bjmsr-609	87	10	categorical	categorical	ADJ
bjmsr-609	87	11	and	and	CCONJ
bjmsr-609	87	12	numerical	numerical	ADJ
bjmsr-609	87	13	data	datum	NOUN
bjmsr-609	87	14	where	where	SCONJ
bjmsr-609	87	15	the	the	DET
bjmsr-609	87	16	relationship	relationship	NOUN
bjmsr-609	87	17	between	between	ADP
bjmsr-609	87	18	output	output	NOUN
bjmsr-609	87	19	and	and	CCONJ
bjmsr-609	87	20	input	input	NOUN
bjmsr-609	87	21	is	be	AUX
bjmsr-609	87	22	nonlinear	nonlinear	ADJ
bjmsr-609	87	23	,	,	PUNCT
bjmsr-609	87	24	problems	problem	NOUN
bjmsr-609	87	25	in	in	ADP
bjmsr-609	87	26	which	which	PRON
bjmsr-609	87	27	traditional	traditional	ADJ
bjmsr-609	87	28	statistical	statistical	ADJ
bjmsr-609	87	29	tools	tool	NOUN
bjmsr-609	87	30	often	often	ADV
bjmsr-609	87	31	return	return	VERB
bjmsr-609	87	32	unreliable	unreliable	ADJ
bjmsr-609	87	33	results	result	NOUN
bjmsr-609	87	34	.	.	PUNCT
bjmsr-609	88	1	usually	usually	ADV
bjmsr-609	88	2	,	,	PUNCT
bjmsr-609	88	3	the	the	DET
bjmsr-609	88	4	areas	area	NOUN
bjmsr-609	88	5	of	of	ADP
bjmsr-609	88	6	use	use	NOUN
bjmsr-609	88	7	for	for	ADP
bjmsr-609	88	8	neural	neural	ADJ
bjmsr-609	88	9	networks	network	NOUN
bjmsr-609	88	10	fall	fall	VERB
bjmsr-609	88	11	into	into	ADP
bjmsr-609	88	12	one	one	NUM
bjmsr-609	88	13	or	or	CCONJ
bjmsr-609	88	14	more	more	ADJ
bjmsr-609	88	15	of	of	ADP
bjmsr-609	88	16	five	five	NUM
bjmsr-609	88	17	categories	category	NOUN
bjmsr-609	88	18	of	of	ADP
bjmsr-609	88	19	tasks	task	NOUN
bjmsr-609	88	20	:	:	PUNCT
bjmsr-609	88	21	classification	classification	NOUN
bjmsr-609	88	22	,	,	PUNCT
bjmsr-609	88	23	regression	regression	NOUN
bjmsr-609	88	24	,	,	PUNCT
bjmsr-609	88	25	clustering	clustering	NOUN
bjmsr-609	88	26	,	,	PUNCT
bjmsr-609	88	27	association	association	NOUN
bjmsr-609	88	28	,	,	PUNCT
bjmsr-609	88	29	and	and	CCONJ
bjmsr-609	88	30	prediction	prediction	NOUN
bjmsr-609	88	31	.	.	PUNCT
bjmsr-609	89	1	classification	classification	NOUN
bjmsr-609	89	2	is	be	AUX
bjmsr-609	89	3	an	an	DET
bjmsr-609	89	4	action	action	NOUN
bjmsr-609	89	5	in	in	ADP
bjmsr-609	89	6	which	which	PRON
bjmsr-609	89	7	patterns	pattern	NOUN
bjmsr-609	89	8	are	be	AUX
bjmsr-609	89	9	recognized	recognize	VERB
bjmsr-609	89	10	that	that	PRON
bjmsr-609	89	11	describes	describe	VERB
bjmsr-609	89	12	the	the	DET
bjmsr-609	89	13	group	group	NOUN
bjmsr-609	89	14	of	of	ADP
bjmsr-609	89	15	that	that	PRON
bjmsr-609	89	16	a	a	DET
bjmsr-609	89	17	certain	certain	ADJ
bjmsr-609	89	18	item	item	NOUN
bjmsr-609	89	19	belongs	belong	VERB
bjmsr-609	89	20	to	to	ADP
bjmsr-609	89	21	.	.	PUNCT
bjmsr-609	90	1	this	this	PRON
bjmsr-609	90	2	is	be	AUX
bjmsr-609	90	3	done	do	VERB
bjmsr-609	90	4	by	by	ADP
bjmsr-609	90	5	setting	set	VERB
bjmsr-609	90	6	a	a	DET
bjmsr-609	90	7	set	set	NOUN
bjmsr-609	90	8	of	of	ADP
bjmsr-609	90	9	rules	rule	NOUN
bjmsr-609	90	10	based	base	VERB
bjmsr-609	90	11	on	on	ADP
bjmsr-609	90	12	already	already	ADV
bjmsr-609	90	13	classified	classify	VERB
bjmsr-609	90	14	items	item	NOUN
bjmsr-609	90	15	.	.	PUNCT
bjmsr-609	91	1	with	with	ADP
bjmsr-609	91	2	clustering	clustering	NOUN
bjmsr-609	91	3	,	,	PUNCT
bjmsr-609	91	4	groups	group	NOUN
bjmsr-609	91	5	consisting	consist	VERB
bjmsr-609	91	6	of	of	ADP
bjmsr-609	91	7	elements	element	NOUN
bjmsr-609	91	8	that	that	PRON
bjmsr-609	91	9	are	be	AUX
bjmsr-609	91	10	deemed	deem	VERB
bjmsr-609	91	11	to	to	PART
bjmsr-609	91	12	have	have	VERB
bjmsr-609	91	13	attributes	attribute	NOUN
bjmsr-609	91	14	similar	similar	ADJ
bjmsr-609	91	15	to	to	ADP
bjmsr-609	91	16	each	each	DET
bjmsr-609	91	17	other	other	ADJ
bjmsr-609	91	18	are	be	AUX
bjmsr-609	91	19	created	create	VERB
bjmsr-609	91	20	.	.	PUNCT
bjmsr-609	92	1	when	when	SCONJ
bjmsr-609	92	2	using	use	VERB
bjmsr-609	92	3	prediction	prediction	NOUN
bjmsr-609	92	4	,	,	PUNCT
bjmsr-609	92	5	the	the	DET
bjmsr-609	92	6	value	value	NOUN
bjmsr-609	92	7	of	of	ADP
bjmsr-609	92	8	an	an	DET
bjmsr-609	92	9	item	item	NOUN
bjmsr-609	92	10	is	be	AUX
bjmsr-609	92	11	set	set	VERB
bjmsr-609	92	12	depending	depend	VERB
bjmsr-609	92	13	on	on	ADP
bjmsr-609	92	14	previous	previous	ADJ
bjmsr-609	92	15	set	set	ADJ
bjmsr-609	92	16	values	value	NOUN
bjmsr-609	92	17	(	(	PUNCT
bjmsr-609	92	18	singh	singh	PROPN
bjmsr-609	92	19	&	&	CCONJ
bjmsr-609	92	20	chauhan	chauhan	PROPN
bjmsr-609	92	21	,	,	PUNCT
bjmsr-609	92	22	2005	2005	NUM
bjmsr-609	92	23	)	)	PUNCT
bjmsr-609	92	24	.	.	PUNCT
bjmsr-609	93	1	attempts	attempt	NOUN
bjmsr-609	93	2	have	have	AUX
bjmsr-609	93	3	been	be	AUX
bjmsr-609	93	4	made	make	VERB
bjmsr-609	93	5	to	to	PART
bjmsr-609	93	6	use	use	VERB
bjmsr-609	93	7	artificial	artificial	ADJ
bjmsr-609	93	8	neural	neural	ADJ
bjmsr-609	93	9	networks	network	NOUN
bjmsr-609	93	10	in	in	ADP
bjmsr-609	93	11	the	the	DET
bjmsr-609	93	12	financial	financial	ADJ
bjmsr-609	93	13	sphere	sphere	NOUN
bjmsr-609	93	14	,	,	PUNCT
bjmsr-609	93	15	especially	especially	ADV
bjmsr-609	93	16	in	in	ADP
bjmsr-609	93	17	trying	try	VERB
bjmsr-609	93	18	to	to	PART
bjmsr-609	93	19	predict	predict	VERB
bjmsr-609	93	20	the	the	DET
bjmsr-609	93	21	stock	stock	NOUN
bjmsr-609	93	22	market	market	NOUN
bjmsr-609	93	23	,	,	PUNCT
bjmsr-609	93	24	with	with	SCONJ
bjmsr-609	93	25	some	some	DET
bjmsr-609	93	26	results	result	NOUN
bjmsr-609	93	27	are	be	AUX
bjmsr-609	93	28	successful	successful	ADJ
bjmsr-609	93	29	in	in	ADP
bjmsr-609	93	30	developing	develop	VERB
bjmsr-609	93	31	trading	trading	NOUN
bjmsr-609	93	32	strategies	strategy	NOUN
bjmsr-609	93	33	.	.	PUNCT
bjmsr-609	94	1	banks	bank	NOUN
bjmsr-609	94	2	have	have	AUX
bjmsr-609	94	3	benefited	benefit	VERB
bjmsr-609	94	4	from	from	ADP
bjmsr-609	94	5	artificial	artificial	ADJ
bjmsr-609	94	6	neural	neural	ADJ
bjmsr-609	94	7	networks	network	NOUN
bjmsr-609	94	8	by	by	ADP
bjmsr-609	94	9	using	use	VERB
bjmsr-609	94	10	systems	system	NOUN
bjmsr-609	94	11	that	that	PRON
bjmsr-609	94	12	determine	determine	VERB
bjmsr-609	94	13	if	if	SCONJ
bjmsr-609	94	14	loan	loan	NOUN
bjmsr-609	94	15	applications	application	NOUN
bjmsr-609	94	16	should	should	AUX
bjmsr-609	94	17	be	be	AUX
bjmsr-609	94	18	approved	approve	VERB
bjmsr-609	94	19	,	,	PUNCT
bjmsr-609	94	20	predicting	predict	VERB
bjmsr-609	94	21	solvency	solvency	NOUN
bjmsr-609	94	22	of	of	ADP
bjmsr-609	94	23	mortgage	mortgage	NOUN
bjmsr-609	94	24	applications	application	NOUN
bjmsr-609	94	25	and	and	CCONJ
bjmsr-609	94	26	credit	credit	NOUN
bjmsr-609	94	27	card	card	NOUN
bjmsr-609	94	28	fraud	fraud	NOUN
bjmsr-609	94	29	detection	detection	NOUN
bjmsr-609	94	30	.	.	PUNCT
bjmsr-609	95	1	neural	neural	ADJ
bjmsr-609	95	2	networks	network	NOUN
bjmsr-609	95	3	have	have	AUX
bjmsr-609	95	4	also	also	ADV
bjmsr-609	95	5	been	be	AUX
bjmsr-609	95	6	used	use	VERB
bjmsr-609	95	7	in	in	ADP
bjmsr-609	95	8	predicting	predict	VERB
bjmsr-609	95	9	bankruptcy	bankruptcy	NOUN
bjmsr-609	95	10	by	by	ADP
bjmsr-609	95	11	being	be	AUX
bjmsr-609	95	12	trained	train	VERB
bjmsr-609	95	13	with	with	ADP
bjmsr-609	95	14	several	several	ADJ
bjmsr-609	95	15	examples	example	NOUN
bjmsr-609	95	16	of	of	ADP
bjmsr-609	95	17	failed	fail	VERB
bjmsr-609	95	18	banks	bank	NOUN
bjmsr-609	95	19	(	(	PUNCT
bjmsr-609	95	20	nemadi	nemadi	NOUN
bjmsr-609	95	21	,	,	PUNCT
bjmsr-609	95	22	2012	2012	NUM
bjmsr-609	95	23	)	)	PUNCT
bjmsr-609	95	24	.	.	PUNCT
bjmsr-609	96	1	copyright	copyright	NOUN
bjmsr-609	96	2	©	©	PROPN
bjmsr-609	96	3	cc	cc	PROPN
bjmsr-609	96	4	-	-	PUNCT
bjmsr-609	96	5	by	by	ADP
bjmsr-609	96	6	-	-	PUNCT
bjmsr-609	96	7	nc	nc	PROPN
bjmsr-609	96	8	2020	2020	NUM
bjmsr-609	96	9	,	,	PUNCT
bjmsr-609	96	10	cribfb	cribfb	PROPN
bjmsr-609	96	11	|bjmsr	|bjmsr	PROPN
bjmsr-609	96	12	www.cribfb.com/journal/index.php/bjmsr	www.cribfb.com/journal/index.php/bjmsr	X
bjmsr-609	96	13	bangladesh	bangladesh	PROPN
bjmsr-609	96	14	journal	journal	PROPN
bjmsr-609	96	15	of	of	ADP
bjmsr-609	96	16	multidisciplinary	multidisciplinary	ADJ
bjmsr-609	96	17	scientific	scientific	ADJ
bjmsr-609	96	18	research	research	NOUN
bjmsr-609	96	19	vol	vol	NOUN
bjmsr-609	96	20	.	.	PROPN
bjmsr-609	97	1	2	2	NUM
bjmsr-609	97	2	,	,	PUNCT
bjmsr-609	97	3	no	no	INTJ
bjmsr-609	97	4	.	.	NOUN
bjmsr-609	97	5	1	1	NUM
bjmsr-609	97	6	;	;	PUNCT
bjmsr-609	97	7	2020	2020	NUM
bjmsr-609	97	8	51	51	NUM
bjmsr-609	97	9	neural	neural	ADJ
bjmsr-609	97	10	networks	network	NOUN
bjmsr-609	97	11	have	have	AUX
bjmsr-609	97	12	in	in	ADP
bjmsr-609	97	13	some	some	DET
bjmsr-609	97	14	cases	case	NOUN
bjmsr-609	97	15	been	be	AUX
bjmsr-609	97	16	shown	show	VERB
bjmsr-609	97	17	to	to	PART
bjmsr-609	97	18	be	be	AUX
bjmsr-609	97	19	able	able	ADJ
bjmsr-609	97	20	to	to	PART
bjmsr-609	97	21	predict	predict	VERB
bjmsr-609	97	22	sports	sport	NOUN
bjmsr-609	97	23	results	result	NOUN
bjmsr-609	97	24	.	.	PUNCT
bjmsr-609	98	1	in	in	ADP
bjmsr-609	98	2	a	a	DET
bjmsr-609	98	3	study	study	NOUN
bjmsr-609	98	4	,	,	PUNCT
bjmsr-609	98	5	a	a	DET
bjmsr-609	98	6	neural	neural	ADJ
bjmsr-609	98	7	network	network	NOUN
bjmsr-609	98	8	application	application	NOUN
bjmsr-609	98	9	was	be	AUX
bjmsr-609	98	10	given	give	VERB
bjmsr-609	98	11	data	datum	NOUN
bjmsr-609	98	12	from	from	ADP
bjmsr-609	98	13	the	the	DET
bjmsr-609	98	14	national	national	PROPN
bjmsr-609	98	15	football	football	NOUN
bjmsr-609	98	16	league	league	NOUN
bjmsr-609	98	17	,	,	PUNCT
bjmsr-609	98	18	to	to	PART
bjmsr-609	98	19	predict	predict	VERB
bjmsr-609	98	20	match	match	NOUN
bjmsr-609	98	21	results	result	NOUN
bjmsr-609	98	22	in	in	ADP
bjmsr-609	98	23	american	american	ADJ
bjmsr-609	98	24	football	football	NOUN
bjmsr-609	98	25	.	.	PUNCT
bjmsr-609	99	1	the	the	DET
bjmsr-609	99	2	result	result	NOUN
bjmsr-609	99	3	of	of	ADP
bjmsr-609	99	4	the	the	DET
bjmsr-609	99	5	study	study	NOUN
bjmsr-609	99	6	showed	show	VERB
bjmsr-609	99	7	that	that	SCONJ
bjmsr-609	99	8	the	the	DET
bjmsr-609	99	9	system	system	NOUN
bjmsr-609	99	10	could	could	AUX
bjmsr-609	99	11	predict	predict	VERB
bjmsr-609	99	12	the	the	DET
bjmsr-609	99	13	correct	correct	ADJ
bjmsr-609	99	14	result	result	NOUN
bjmsr-609	99	15	in	in	ADP
bjmsr-609	99	16	75	75	NUM
bjmsr-609	99	17	%	%	NOUN
bjmsr-609	99	18	of	of	ADP
bjmsr-609	99	19	the	the	DET
bjmsr-609	99	20	games	game	NOUN
bjmsr-609	99	21	,	,	PUNCT
bjmsr-609	99	22	compared	compare	VERB
bjmsr-609	99	23	to	to	ADP
bjmsr-609	99	24	experts	expert	NOUN
bjmsr-609	99	25	who	who	PRON
bjmsr-609	99	26	predicted	predict	VERB
bjmsr-609	99	27	the	the	DET
bjmsr-609	99	28	correct	correct	ADJ
bjmsr-609	99	29	result	result	NOUN
bjmsr-609	99	30	in	in	ADP
bjmsr-609	99	31	63	63	NUM
bjmsr-609	99	32	%	%	NOUN
bjmsr-609	99	33	of	of	ADP
bjmsr-609	99	34	the	the	DET
bjmsr-609	99	35	games	game	NOUN
bjmsr-609	99	36	(	(	PUNCT
bjmsr-609	99	37	kahn	kahn	PROPN
bjmsr-609	99	38	,	,	PUNCT
bjmsr-609	99	39	2003	2003	NUM
bjmsr-609	99	40	)	)	PUNCT
bjmsr-609	99	41	.	.	PUNCT
bjmsr-609	100	1	the	the	DET
bjmsr-609	100	2	process	process	NOUN
bjmsr-609	100	3	of	of	ADP
bjmsr-609	100	4	forecasting	forecasting	NOUN
bjmsr-609	100	5	is	be	AUX
bjmsr-609	100	6	another	another	DET
bjmsr-609	100	7	field	field	NOUN
bjmsr-609	100	8	where	where	SCONJ
bjmsr-609	100	9	artificial	artificial	ADJ
bjmsr-609	100	10	neural	neural	ADJ
bjmsr-609	100	11	network	network	NOUN
bjmsr-609	100	12	are	be	AUX
bjmsr-609	100	13	considered	consider	VERB
bjmsr-609	100	14	to	to	PART
bjmsr-609	100	15	apply	apply	VERB
bjmsr-609	100	16	to	to	ADP
bjmsr-609	100	17	.	.	PUNCT
bjmsr-609	101	1	traditionally	traditionally	ADV
bjmsr-609	101	2	statistical	statistical	ADJ
bjmsr-609	101	3	tools	tool	NOUN
bjmsr-609	101	4	have	have	AUX
bjmsr-609	101	5	been	be	AUX
bjmsr-609	101	6	used	use	VERB
bjmsr-609	101	7	when	when	SCONJ
bjmsr-609	101	8	creating	create	VERB
bjmsr-609	101	9	forecasting	forecasting	NOUN
bjmsr-609	101	10	models	model	NOUN
bjmsr-609	101	11	.	.	PUNCT
bjmsr-609	102	1	however	however	ADV
bjmsr-609	102	2	,	,	PUNCT
bjmsr-609	102	3	these	these	DET
bjmsr-609	102	4	tools	tool	NOUN
bjmsr-609	102	5	often	often	ADV
bjmsr-609	102	6	have	have	VERB
bjmsr-609	102	7	limitations	limitation	NOUN
bjmsr-609	102	8	in	in	ADP
bjmsr-609	102	9	estimating	estimate	VERB
bjmsr-609	102	10	the	the	DET
bjmsr-609	102	11	underlying	underlie	VERB
bjmsr-609	102	12	relationship	relationship	NOUN
bjmsr-609	102	13	that	that	PRON
bjmsr-609	102	14	exists	exist	VERB
bjmsr-609	102	15	between	between	ADP
bjmsr-609	102	16	an	an	DET
bjmsr-609	102	17	input	input	NOUN
bjmsr-609	102	18	,	,	PUNCT
bjmsr-609	102	19	consisting	consist	VERB
bjmsr-609	102	20	of	of	ADP
bjmsr-609	102	21	past	past	ADJ
bjmsr-609	102	22	values	value	NOUN
bjmsr-609	102	23	,	,	PUNCT
bjmsr-609	102	24	and	and	CCONJ
bjmsr-609	102	25	an	an	DET
bjmsr-609	102	26	output	output	NOUN
bjmsr-609	102	27	,	,	PUNCT
bjmsr-609	102	28	consisting	consist	VERB
bjmsr-609	102	29	of	of	ADP
bjmsr-609	102	30	future	future	ADJ
bjmsr-609	102	31	values	value	NOUN
bjmsr-609	102	32	.	.	PUNCT
bjmsr-609	103	1	statistical	statistical	ADJ
bjmsr-609	103	2	methods	method	NOUN
bjmsr-609	103	3	often	often	ADV
bjmsr-609	103	4	make	make	VERB
bjmsr-609	103	5	assumptions	assumption	NOUN
bjmsr-609	103	6	of	of	ADP
bjmsr-609	103	7	data	datum	NOUN
bjmsr-609	103	8	distribution	distribution	NOUN
bjmsr-609	103	9	,	,	PUNCT
bjmsr-609	103	10	which	which	PRON
bjmsr-609	103	11	could	could	AUX
bjmsr-609	103	12	make	make	VERB
bjmsr-609	103	13	them	they	PRON
bjmsr-609	103	14	unreliable	unreliable	ADJ
bjmsr-609	103	15	in	in	ADP
bjmsr-609	103	16	cases	case	NOUN
bjmsr-609	103	17	where	where	SCONJ
bjmsr-609	103	18	the	the	DET
bjmsr-609	103	19	input	input	NOUN
bjmsr-609	103	20	is	be	AUX
bjmsr-609	103	21	not	not	PART
bjmsr-609	103	22	normally	normally	ADV
bjmsr-609	103	23	distributed	distribute	VERB
bjmsr-609	103	24	.	.	PUNCT
bjmsr-609	104	1	furthermore	furthermore	ADV
bjmsr-609	104	2	,	,	PUNCT
bjmsr-609	104	3	the	the	DET
bjmsr-609	104	4	non	non	ADJ
bjmsr-609	104	5	-	-	ADJ
bjmsr-609	104	6	linear	linear	ADJ
bjmsr-609	104	7	nature	nature	NOUN
bjmsr-609	104	8	of	of	ADP
bjmsr-609	104	9	many	many	ADJ
bjmsr-609	104	10	artificial	artificial	ADJ
bjmsr-609	104	11	neural	neural	ADJ
bjmsr-609	104	12	networks	network	NOUN
bjmsr-609	104	13	has	have	VERB
bjmsr-609	104	14	in	in	ADP
bjmsr-609	104	15	many	many	ADJ
bjmsr-609	104	16	instances	instance	NOUN
bjmsr-609	104	17	being	be	AUX
bjmsr-609	104	18	deemed	deem	VERB
bjmsr-609	104	19	to	to	PART
bjmsr-609	104	20	better	well	ADV
bjmsr-609	104	21	represent	represent	VERB
bjmsr-609	104	22	the	the	DET
bjmsr-609	104	23	often	often	ADV
bjmsr-609	104	24	-	-	PUNCT
bjmsr-609	104	25	non	non	ADJ
bjmsr-609	104	26	-	-	ADJ
bjmsr-609	104	27	linear	linear	ADJ
bjmsr-609	104	28	nature	nature	NOUN
bjmsr-609	104	29	of	of	ADP
bjmsr-609	104	30	the	the	DET
bjmsr-609	104	31	problem	problem	NOUN
bjmsr-609	104	32	at	at	ADP
bjmsr-609	104	33	hand	hand	NOUN
bjmsr-609	104	34	(	(	PUNCT
bjmsr-609	104	35	zhang	zhang	PROPN
bjmsr-609	104	36	et	et	PROPN
bjmsr-609	104	37	al	al	PROPN
bjmsr-609	104	38	.	.	PROPN
bjmsr-609	104	39	,	,	PUNCT
bjmsr-609	104	40	1998	1998	NUM
bjmsr-609	104	41	)	)	PUNCT
bjmsr-609	104	42	.	.	PUNCT
bjmsr-609	105	1	diagnosis	diagnosis	NOUN
bjmsr-609	105	2	and	and	CCONJ
bjmsr-609	105	3	pattern	pattern	NOUN
bjmsr-609	105	4	recognition	recognition	NOUN
bjmsr-609	105	5	in	in	ADP
bjmsr-609	105	6	the	the	DET
bjmsr-609	105	7	fields	field	NOUN
bjmsr-609	105	8	of	of	ADP
bjmsr-609	105	9	health	health	NOUN
bjmsr-609	105	10	care	care	NOUN
bjmsr-609	105	11	and	and	CCONJ
bjmsr-609	105	12	medicine	medicine	NOUN
bjmsr-609	105	13	are	be	AUX
bjmsr-609	105	14	areas	area	NOUN
bjmsr-609	105	15	where	where	SCONJ
bjmsr-609	105	16	artificial	artificial	ADJ
bjmsr-609	105	17	neural	neural	ADJ
bjmsr-609	105	18	networks	network	NOUN
bjmsr-609	105	19	are	be	AUX
bjmsr-609	105	20	successful	successful	ADJ
bjmsr-609	105	21	.	.	PUNCT
bjmsr-609	106	1	several	several	ADJ
bjmsr-609	106	2	studies	study	NOUN
bjmsr-609	106	3	have	have	AUX
bjmsr-609	106	4	shown	show	VERB
bjmsr-609	106	5	how	how	SCONJ
bjmsr-609	106	6	neural	neural	ADJ
bjmsr-609	106	7	networks	network	NOUN
bjmsr-609	106	8	can	can	AUX
bjmsr-609	106	9	improve	improve	VERB
bjmsr-609	106	10	diagnostics	diagnostic	NOUN
bjmsr-609	106	11	and	and	CCONJ
bjmsr-609	106	12	lead	lead	VERB
bjmsr-609	106	13	to	to	ADP
bjmsr-609	106	14	more	more	ADV
bjmsr-609	106	15	rapid	rapid	ADJ
bjmsr-609	106	16	decision	decision	NOUN
bjmsr-609	106	17	making	making	NOUN
bjmsr-609	106	18	,	,	PUNCT
bjmsr-609	106	19	which	which	PRON
bjmsr-609	106	20	could	could	AUX
bjmsr-609	106	21	potentially	potentially	ADV
bjmsr-609	106	22	help	help	VERB
bjmsr-609	106	23	save	save	VERB
bjmsr-609	106	24	lives	life	NOUN
bjmsr-609	106	25	(	(	PUNCT
bjmsr-609	106	26	turban	turban	VERB
bjmsr-609	106	27	et	et	PROPN
bjmsr-609	106	28	al	al	PROPN
bjmsr-609	106	29	.	.	PROPN
bjmsr-609	106	30	,	,	PUNCT
bjmsr-609	106	31	2011	2011	NUM
bjmsr-609	106	32	)	)	PUNCT
bjmsr-609	106	33	.	.	PUNCT
bjmsr-609	107	1	references	reference	NOUN
bjmsr-609	107	2	anderson	anderson	PROPN
bjmsr-609	107	3	,	,	PUNCT
bjmsr-609	107	4	d.	d.	PROPN
bjmsr-609	107	5	,	,	PUNCT
bjmsr-609	107	6	&	&	CCONJ
bjmsr-609	107	7	mcneill	mcneill	PROPN
bjmsr-609	107	8	,	,	PUNCT
bjmsr-609	107	9	g.	g.	PROPN
bjmsr-609	107	10	(	(	PUNCT
bjmsr-609	107	11	1992	1992	NUM
bjmsr-609	107	12	)	)	PUNCT
bjmsr-609	107	13	.	.	PUNCT
bjmsr-609	108	1	artificial	artificial	ADJ
bjmsr-609	108	2	neural	neural	ADJ
bjmsr-609	108	3	networks	network	NOUN
bjmsr-609	108	4	technology	technology	NOUN
bjmsr-609	108	5	,	,	PUNCT
bjmsr-609	108	6	rome	rome	PROPN
bjmsr-609	108	7	laboratory	laboratory	PROPN
bjmsr-609	108	8	,	,	PUNCT
bjmsr-609	108	9	new	new	PROPN
bjmsr-609	108	10	york	york	PROPN
bjmsr-609	108	11	.	.	PUNCT
bjmsr-609	109	1	kahn	kahn	PROPN
bjmsr-609	109	2	,	,	PUNCT
bjmsr-609	109	3	j.	j.	PROPN
bjmsr-609	109	4	(	(	PUNCT
bjmsr-609	109	5	2003	2003	NUM
bjmsr-609	109	6	)	)	PUNCT
bjmsr-609	109	7	.	.	PUNCT
bjmsr-609	110	1	neural	neural	ADJ
bjmsr-609	110	2	network	network	NOUN
bjmsr-609	110	3	prediction	prediction	NOUN
bjmsr-609	110	4	of	of	ADP
bjmsr-609	110	5	nfl	nfl	PROPN
bjmsr-609	110	6	football	football	PROPN
bjmsr-609	110	7	games	games	PROPN
bjmsr-609	110	8	,	,	PUNCT
bjmsr-609	110	9	university	university	PROPN
bjmsr-609	110	10	of	of	ADP
bjmsr-609	110	11	wisconsin	wisconsin	PROPN
bjmsr-609	110	12	-	-	PUNCT
bjmsr-609	110	13	madison	madison	PROPN
bjmsr-609	110	14	.	.	PUNCT
bjmsr-609	110	15	retrieved	retrieve	VERB
bjmsr-609	110	16	from	from	ADP
bjmsr-609	110	17	http://homepages.cae.wisc.edu/~ece539/project/f03/kahn.pdf	http://homepages.cae.wisc.edu/~ece539/project/f03/kahn.pdf	PROPN
bjmsr-609	110	18	nemadi	nemadi	NOUN
bjmsr-609	110	19	,	,	PUNCT
bjmsr-609	110	20	h.	h.	PROPN
bjmsr-609	110	21	(	(	PUNCT
bjmsr-609	110	22	2012	2012	NUM
bjmsr-609	110	23	)	)	PUNCT
bjmsr-609	110	24	.	.	PUNCT
bjmsr-609	111	1	introduction	introduction	NOUN
bjmsr-609	111	2	to	to	ADP
bjmsr-609	111	3	data	data	NOUN
bjmsr-609	111	4	mining	mining	NOUN
bjmsr-609	111	5	using	use	VERB
bjmsr-609	111	6	artificial	artificial	ADJ
bjmsr-609	111	7	neural	neural	ADJ
bjmsr-609	111	8	networks	network	NOUN
bjmsr-609	111	9	.	.	PUNCT
bjmsr-609	112	1	retrieved	retrieve	VERB
bjmsr-609	112	2	from	from	ADP
bjmsr-609	112	3	http://www.uncg.edu/ism/ism611/neuralnet.pdf	http://www.uncg.edu/ism/ism611/neuralnet.pdf	PROPN
bjmsr-609	112	4	singh	singh	PROPN
bjmsr-609	112	5	,	,	PUNCT
bjmsr-609	112	6	y.	y.	PROPN
bjmsr-609	112	7	,	,	PUNCT
bjmsr-609	112	8	&	&	CCONJ
bjmsr-609	112	9	chauhan	chauhan	PROPN
bjmsr-609	112	10	,	,	PUNCT
bjmsr-609	112	11	a.	a.	PROPN
bjmsr-609	112	12	s.	s.	PROPN
bjmsr-609	112	13	(	(	PUNCT
bjmsr-609	112	14	2005	2005	NUM
bjmsr-609	112	15	)	)	PUNCT
bjmsr-609	112	16	.	.	PUNCT
bjmsr-609	113	1	neural	neural	ADJ
bjmsr-609	113	2	networks	network	NOUN
bjmsr-609	113	3	in	in	ADP
bjmsr-609	113	4	data	datum	NOUN
bjmsr-609	113	5	mining	mining	NOUN
bjmsr-609	113	6	.	.	PUNCT
bjmsr-609	114	1	journal	journal	PROPN
bjmsr-609	114	2	of	of	ADP
bjmsr-609	114	3	theoretical	theoretical	PROPN
bjmsr-609	114	4	&	&	CCONJ
bjmsr-609	114	5	applied	applied	ADJ
bjmsr-609	114	6	information	information	NOUN
bjmsr-609	114	7	technology	technology	NOUN
bjmsr-609	114	8	,	,	PUNCT
bjmsr-609	114	9	5(1	5(1	NUM
bjmsr-609	114	10	)	)	PUNCT
bjmsr-609	114	11	.	.	PUNCT
bjmsr-609	115	1	turban	turban	PROPN
bjmsr-609	115	2	,	,	PUNCT
bjmsr-609	115	3	e.	e.	PROPN
bjmsr-609	115	4	,	,	PUNCT
bjmsr-609	115	5	sharda	sharda	PROPN
bjmsr-609	115	6	,	,	PUNCT
bjmsr-609	115	7	r.	r.	PROPN
bjmsr-609	115	8	,	,	PUNCT
bjmsr-609	115	9	&	&	CCONJ
bjmsr-609	115	10	delen	delen	PROPN
bjmsr-609	115	11	,	,	PUNCT
bjmsr-609	115	12	d.	d.	PROPN
bjmsr-609	115	13	(	(	PUNCT
bjmsr-609	115	14	2011	2011	NUM
bjmsr-609	115	15	)	)	PUNCT
bjmsr-609	115	16	.	.	PUNCT
bjmsr-609	116	1	decision	decision	NOUN
bjmsr-609	116	2	support	support	NOUN
bjmsr-609	116	3	and	and	CCONJ
bjmsr-609	116	4	business	business	NOUN
bjmsr-609	116	5	intelligence	intelligence	NOUN
bjmsr-609	116	6	systems	system	NOUN
bjmsr-609	116	7	,	,	PUNCT
bjmsr-609	116	8	prentice	prentice	PROPN
bjmsr-609	116	9	hall	hall	PROPN
bjmsr-609	116	10	press	press	PROPN
bjmsr-609	116	11	,	,	PUNCT
bjmsr-609	116	12	nj	nj	PROPN
bjmsr-609	116	13	,	,	PUNCT
bjmsr-609	116	14	united	united	PROPN
bjmsr-609	116	15	states	states	PROPN
bjmsr-609	116	16	.	.	PUNCT
bjmsr-609	117	1	yegnanarayana	yegnanarayana	PROPN
bjmsr-609	117	2	,	,	PUNCT
bjmsr-609	117	3	b.	b.	PROPN
bjmsr-609	117	4	(	(	PUNCT
bjmsr-609	117	5	2009	2009	NUM
bjmsr-609	117	6	)	)	PUNCT
bjmsr-609	117	7	.	.	PUNCT
bjmsr-609	118	1	artificial	artificial	ADJ
bjmsr-609	118	2	neural	neural	ADJ
bjmsr-609	118	3	networks	network	NOUN
bjmsr-609	118	4	,	,	PUNCT
bjmsr-609	118	5	phi	phi	NOUN
bjmsr-609	118	6	learning	learning	PROPN
bjmsr-609	118	7	pvt	pvt	PROPN
bjmsr-609	118	8	.	.	PROPN
bjmsr-609	118	9	ltd	ltd	PROPN
bjmsr-609	118	10	.	.	PROPN
bjmsr-609	119	1	zhang	zhang	PROPN
bjmsr-609	119	2	,	,	PUNCT
bjmsr-609	119	3	g.	g.	PROPN
bjmsr-609	119	4	,	,	PUNCT
bjmsr-609	119	5	patuwo	patuwo	PROPN
bjmsr-609	119	6	,	,	PUNCT
bjmsr-609	119	7	b.	b.	PROPN
bjmsr-609	119	8	e.	e.	PROPN
bjmsr-609	119	9	,	,	PUNCT
bjmsr-609	119	10	&	&	CCONJ
bjmsr-609	119	11	hu	hu	PROPN
bjmsr-609	119	12	,	,	PUNCT
bjmsr-609	119	13	m.	m.	NOUN
bjmsr-609	119	14	y.	y.	PROPN
bjmsr-609	119	15	(	(	PUNCT
bjmsr-609	119	16	1998	1998	NUM
bjmsr-609	119	17	)	)	PUNCT
bjmsr-609	119	18	.	.	PUNCT
bjmsr-609	120	1	forecasting	forecast	VERB
bjmsr-609	120	2	with	with	ADP
bjmsr-609	120	3	artificial	artificial	ADJ
bjmsr-609	120	4	neural	neural	ADJ
bjmsr-609	120	5	networks	network	NOUN
bjmsr-609	120	6	:	:	PUNCT
bjmsr-609	120	7	the	the	DET
bjmsr-609	120	8	state	state	NOUN
bjmsr-609	120	9	of	of	ADP
bjmsr-609	120	10	the	the	DET
bjmsr-609	120	11	art	art	NOUN
bjmsr-609	120	12	.	.	PUNCT
bjmsr-609	121	1	international	international	ADJ
bjmsr-609	121	2	journal	journal	PROPN
bjmsr-609	121	3	of	of	ADP
bjmsr-609	121	4	forecasting	forecasting	NOUN
bjmsr-609	121	5	,	,	PUNCT
bjmsr-609	121	6	14(1	14(1	NUM
bjmsr-609	121	7	)	)	PUNCT
bjmsr-609	121	8	,	,	PUNCT
bjmsr-609	121	9	35	35	NUM
bjmsr-609	121	10	-	-	SYM
bjmsr-609	121	11	62	62	NUM
bjmsr-609	121	12	.	.	PUNCT
bjmsr-609	122	1	copyrights	copyright	VERB
bjmsr-609	122	2	copyright	copyright	NOUN
bjmsr-609	122	3	for	for	ADP
bjmsr-609	122	4	this	this	DET
bjmsr-609	122	5	article	article	NOUN
bjmsr-609	122	6	is	be	AUX
bjmsr-609	122	7	retained	retain	VERB
bjmsr-609	122	8	by	by	ADP
bjmsr-609	122	9	the	the	DET
bjmsr-609	122	10	author(s	author(s	PROPN
bjmsr-609	122	11	)	)	PUNCT
bjmsr-609	122	12	,	,	PUNCT
bjmsr-609	122	13	with	with	ADP
bjmsr-609	122	14	first	first	ADJ
bjmsr-609	122	15	publication	publication	NOUN
bjmsr-609	122	16	rights	right	NOUN
bjmsr-609	122	17	granted	grant	VERB
bjmsr-609	122	18	to	to	ADP
bjmsr-609	122	19	the	the	DET
bjmsr-609	122	20	journal	journal	NOUN
bjmsr-609	122	21	.	.	PUNCT
bjmsr-609	123	1	this	this	PRON
bjmsr-609	123	2	is	be	AUX
bjmsr-609	123	3	an	an	DET
bjmsr-609	123	4	open	open	ADJ
bjmsr-609	123	5	-	-	PUNCT
bjmsr-609	123	6	access	access	NOUN
bjmsr-609	123	7	article	article	NOUN
bjmsr-609	123	8	distributed	distribute	VERB
bjmsr-609	123	9	under	under	ADP
bjmsr-609	123	10	the	the	DET
bjmsr-609	123	11	terms	term	NOUN
bjmsr-609	123	12	and	and	CCONJ
bjmsr-609	123	13	conditions	condition	NOUN
bjmsr-609	123	14	of	of	ADP
bjmsr-609	123	15	the	the	DET
bjmsr-609	123	16	creative	creative	ADJ
bjmsr-609	123	17	commons	common	NOUN
bjmsr-609	123	18	attribution	attribution	NOUN
bjmsr-609	123	19	license	license	NOUN
bjmsr-609	123	20	(	(	PUNCT
bjmsr-609	123	21	http://creativecommons.org/licenses/by/4.0/	http://creativecommons.org/licenses/by/4.0/	PROPN
bjmsr-609	123	22	)	)	PUNCT
bjmsr-609	123	23	.	.	PUNCT
bjmsr-609	124	1	http://homepages.cae.wisc.edu/~ece539/project/f03/kahn.pdf	http://homepages.cae.wisc.edu/~ece539/project/f03/kahn.pdf	PROPN
bjmsr-609	124	2	http://www.uncg.edu/ism/ism611/neuralnet.pdf	http://www.uncg.edu/ism/ism611/neuralnet.pdf	PROPN