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J
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15
,
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3
,
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20
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9
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I
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,
Vo
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15
,
No
.
3
,
Sep
tem
b
er
20
26
:
925
-
9
3
4
926
ac
co
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n
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es
s
m
en
ts
an
d
f
in
an
cial
f
o
r
ec
asti
n
g
[
4
]
,
[
5
]
.
I
n
th
is
co
n
te
x
t,
th
e
Ma
u
r
ita
n
ian
I
n
v
estme
n
t
B
an
k
o
p
er
a
tes
its
d
ig
ital
b
an
k
,
Sed
ad
B
an
k
,
to
im
p
lem
en
t
a
d
v
an
ce
d
tech
n
o
lo
g
ies
th
at
d
e
v
elo
p
s
o
lu
tio
n
s
f
o
r
its
o
p
er
atio
n
al
c
h
allen
g
es.
T
h
e
m
ain
g
o
al
o
f
th
is
in
itiativ
e
is
to
d
ev
elo
p
p
r
ed
i
ctiv
e
m
o
d
els
th
at
p
r
e
d
ict
th
e
d
aily
p
r
o
f
itab
ilit
y
o
f
b
an
k
in
g
o
p
er
atio
n
s
.
T
h
is
r
esear
ch
,
in
clu
d
ed
in
th
is
s
tr
ateg
ic
d
y
n
am
ic,
p
r
esen
ts
a
m
eth
o
d
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
id
en
tify
f
ac
to
r
s
o
f
d
aily
p
r
o
f
itab
ilit
y
an
d
p
r
e
d
ict
th
e
p
r
o
f
itab
ilit
y
f
o
r
b
etter
b
an
k
p
lan
n
i
n
g
an
d
m
an
a
g
em
en
t
ca
p
ab
ilit
ies.
I
n
th
is
s
tu
d
y
,
t
h
e
s
co
p
e
is
to
v
alid
ate
a
n
d
co
n
tr
ast
f
o
u
r
m
ac
h
in
e
lea
r
n
in
g
m
o
d
els
u
s
ed
:
lo
g
is
tic
r
eg
r
ess
io
n
(LR
)
,
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M
)
,
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
K
NN
)
,
an
d
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
(
MLP
)
class
if
ier
to
f
o
r
ec
ast p
r
o
f
itab
ilit
y
.
T
h
e
d
ail
y
p
r
o
f
its
ar
e
in
f
l
u
en
ce
d
b
y
ele
m
en
ts
lik
e
th
e
ty
p
e
o
f
tr
an
s
ac
tio
n
s
,
th
eir
am
o
u
n
ts
,
as
well
a
s
b
an
k
co
m
m
is
s
io
n
an
d
ag
en
c
y
co
m
m
is
s
io
n
,
alo
n
g
with
T
OF
tax
p
ay
m
en
ts
.
T
o
ev
alu
ate
h
o
w
well
th
ese
m
o
d
els
wo
r
k
in
p
r
ac
tice,
we
u
s
ed
th
e
Sed
ad
B
an
k
d
aily
o
p
er
atio
n
d
ata
s
et
an
d
an
aly
ze
d
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
es
s
u
ch
as p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
an
d
th
e
co
n
f
u
s
io
n
m
a
tr
ix
.
Ou
r
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws
:
we
s
tar
t
wi
th
a
r
ev
iew
o
f
th
e
r
elate
d
wo
r
k
in
Sectio
n
2
.
T
h
en
m
o
v
e
o
n
to
o
u
tlin
in
g
th
e
m
et
h
o
d
o
lo
g
y
in
S
ec
tio
n
3
b
ef
o
r
e
p
r
esen
tin
g
th
e
r
esu
lts
an
d
d
is
cu
s
s
in
g
th
e
f
in
d
in
g
s
in
Sectio
n
4
an
d
co
n
clu
d
in
g
th
e
s
tu
d
y
in
S
ec
tio
n
5
.
2.
RE
L
AT
E
D
WO
RK
T
h
e
b
a
n
k
in
g
s
ec
to
r
is
b
ein
g
tr
an
s
f
o
r
m
ed
b
y
ar
tific
ial
in
telli
g
en
ce
(
A
I
)
,
ch
an
g
in
g
h
o
w
o
p
e
r
atio
n
s
ar
e
ca
r
r
ied
o
u
t
an
d
b
o
o
s
tin
g
c
u
s
to
m
er
in
ter
ac
tio
n
s
wh
ile
also
s
tr
en
g
th
en
in
g
r
is
k
m
an
ag
em
en
t
p
r
ac
tices
s
ig
n
if
ican
tly
.
B
y
in
teg
r
atin
g
AI
i
n
to
t
h
eir
s
y
s
tem
s
an
d
p
r
o
ce
s
s
es,
f
in
an
cial
in
s
titu
t
io
n
s
ca
n
im
p
r
o
v
e
ef
f
icien
cy
,
p
r
o
v
i
d
e
p
er
s
o
n
alize
d
cu
s
to
m
er
e
x
p
er
ien
ce
s
an
d
e
n
h
an
ce
s
ec
u
r
ity
m
ea
s
u
r
es.
R
ec
en
t
s
tu
d
ies
h
av
e
s
h
o
wn
th
at
AI
d
r
iv
en
ad
v
an
ce
m
e
n
ts
ar
e
m
ak
in
g
an
im
p
ac
t
ac
r
o
s
s
in
d
u
s
tr
ies
lik
e
f
in
an
ce
,
with
ex
am
p
les in
clu
d
in
g
th
e
u
s
e
o
f
B
lo
ck
ch
ain
f
o
r
s
ec
u
r
e
au
th
en
ticatio
n
in
p
r
o
ce
s
s
m
in
in
g
as
well
a
s
em
p
lo
y
in
g
ad
v
an
ce
d
p
r
e
d
ictiv
e
m
o
d
els
b
ased
o
n
I
o
T
tech
n
o
lo
g
y
an
d
r
e
m
o
te
s
en
s
in
g
f
o
r
p
r
ec
is
io
n
ag
r
icu
ltu
r
e
[
6
]
-
[
8
]
.
T
h
e
ch
a
n
g
e
is
d
r
iv
en
b
y
th
e
e
x
p
an
s
io
n
o
f
d
ata
v
o
lu
m
e,
th
e
u
r
g
en
t
n
ec
ess
ity
to
cu
t
d
o
wn
o
n
e
x
p
en
s
es
as we
ll a
s
th
e
g
r
o
win
g
d
esire
f
o
r
tailo
r
ed
a
n
d
ea
s
ily
ac
ce
s
s
ib
le
b
an
k
in
g
s
o
lu
tio
n
s
.
I
n
th
is
s
eg
m
en
t
o
f
o
u
r
s
tu
d
y
m
ater
ial,
we
d
elv
e
i
n
to
p
u
b
lis
h
ed
wo
r
k
s
o
n
AI
im
p
lem
e
n
tatio
n
s
in
th
e
b
an
k
in
g
s
ec
to
r
,
h
ig
h
lig
h
tin
g
h
o
w
m
ac
h
i
n
e
lear
n
in
g
p
lay
s
a
r
o
le
in
e
m
p
o
wer
i
n
g
b
an
k
s
to
m
ak
e
u
s
e
o
f
ex
ten
s
iv
e
d
ata
r
eser
v
o
ir
s
to
d
eliv
er
s
wif
ter
an
d
m
o
r
e
ac
c
u
r
ate
s
er
v
ices
th
at
ar
e
h
ig
h
ly
p
e
r
s
o
n
alize
d
.
T
h
r
o
u
g
h
th
e
ap
p
licatio
n
o
f
al
g
o
r
ith
m
s
,
o
r
g
an
izatio
n
s
ca
n
en
h
a
n
ce
t
h
e
ir
o
p
e
r
atio
n
s
a
n
d
f
o
r
esee
cu
s
to
m
er
r
eq
u
ir
e
m
en
ts
ah
ea
d
o
f
tim
e
[
9
]
-
[
1
4
]
.
R
esear
ch
co
n
d
u
cted
b
y
C
atalin
i
et
a
l.
[
1
5
]
s
h
o
ws
th
at
m
ac
h
in
e
lear
n
in
g
m
o
d
els
cr
ea
ted
to
im
itate
ju
d
g
em
en
t
p
er
f
o
r
m
b
etter
t
h
a
n
th
o
s
e
c
r
ea
ted
f
o
r
f
in
an
cial
g
ain
p
u
r
p
o
s
es.
T
h
eir
r
e
s
ea
r
c
h
em
p
h
asizes
two
p
o
in
ts
:
(
i
)
;
m
o
d
els
th
at
im
itate
d
ec
is
io
n
m
ak
in
g
s
k
ills
p
er
f
o
r
m
well
in
n
ew
s
itu
atio
n
s
,
in
d
icatin
g
th
at
in
v
estme
n
t
ch
o
ices
co
u
l
d
b
e
m
ir
r
o
r
ed
.
(
ii
)
;
m
o
d
els
f
o
c
u
s
ed
o
n
m
a
x
im
izin
g
s
u
cc
ess
r
at
es
o
u
ts
h
in
e
m
o
d
els
m
im
ick
in
g
h
u
m
an
s
wh
en
ev
alu
atin
g
ca
n
d
id
ates
n
o
t
in
cl
u
d
ed
in
th
e
d
ata
p
o
o
l,
s
u
g
g
esti
n
g
th
at
h
u
m
an
ev
alu
ato
r
s
m
ig
h
t
m
is
s
o
u
t
o
n
p
r
o
m
is
in
g
o
p
p
o
r
tu
n
ities
.
Dis
cr
ep
an
cies,
in
m
o
d
els
o
f
ten
s
tem
f
r
o
m
in
tu
itio
n
,
ca
n
r
esu
lt
in
o
v
e
r
lo
o
k
in
g
th
e
in
tr
icac
ies
o
f
ap
p
licatio
n
s
at
tim
es.
T
h
is
in
d
icat
es
th
e
f
u
n
ctio
n
o
f
AI
i
n
m
an
ag
in
g
in
f
o
r
m
atio
n
a
n
d
m
a
k
in
g
d
ec
is
io
n
s
in
d
ata
s
ettin
g
s
.
A
s
tu
d
y
b
y
J
ewa
n
d
ah
[
1
6
]
ex
am
in
ed
h
o
w
AI
im
p
ac
ted
lead
in
g
b
an
k
s
in
I
n
d
ia
d
u
r
in
g
2
0
1
8
.
I
t
f
o
u
n
d
th
at,
d
esp
ite
th
e
ch
a
n
g
es
b
r
o
u
g
h
t
ab
o
u
t
b
y
AI
tech
n
o
lo
g
y
,
a
lo
n
g
with
b
lo
c
k
ch
ain
an
d
clo
u
d
co
m
p
u
tin
g
in
th
e
b
an
k
in
g
s
ec
to
r
,
tan
g
i
b
le
ac
c
ep
tan
ce
is
s
till
co
n
s
tr
ain
e
d
,
s
tr
ess
in
g
th
e
o
n
g
o
in
g
im
p
o
r
tan
ce
o
f
h
u
m
a
n
in
ter
ac
tio
n
.
I
n
d
ian
f
in
a
n
cial
in
s
titu
tio
n
s
ar
e
lo
o
k
in
g
in
to
i
n
co
r
p
o
r
atin
g
AI
tech
n
o
lo
g
ies
to
b
o
o
s
t
p
r
o
d
u
ctiv
ity
an
d
im
p
r
o
v
e
c
u
s
to
m
er
s
atis
f
ac
tio
n
.
A
s
tu
d
y
b
y
An
d
r
ew
[
1
7
]
,
A
n
d
r
ew
d
elv
ed
i
n
to
th
e
in
f
lu
e
n
ce
o
f
AI
in
in
d
u
s
tr
ies,
with
a
s
p
ec
ial
em
p
h
asis
o
n
au
t
o
m
atio
n
tech
n
iq
u
es
lik
e
r
o
b
o
tics
an
d
m
ac
h
in
e
lear
n
in
g
ap
p
licatio
n
s
.
H
e
em
p
h
asized
th
e
im
p
o
r
tan
ce
o
f
s
elec
tin
g
an
d
p
r
o
v
i
d
i
n
g
d
ata
th
o
u
g
h
tf
u
lly
f
o
r
AI
d
ep
lo
y
m
en
t,
to
g
r
asp
c
o
n
n
ec
tio
n
s
b
etwe
en
v
ar
iab
les
th
at
h
av
e
alr
ea
d
y
b
r
o
u
g
h
t
a
b
o
u
t
s
u
b
s
tan
tial
in
d
u
s
tr
y
tr
an
s
f
o
r
m
atio
n
s
an
d
h
o
ld
th
e
p
o
ten
tial
f
o
r
m
o
r
e
g
r
o
u
n
d
b
r
ea
k
in
g
ad
v
a
n
ce
m
en
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Ma
ch
in
e
lea
r
n
in
g
mo
d
els fo
r
p
r
ed
ictin
g
d
a
ily
p
r
o
fita
b
ilit
y
in
M
a
u
r
ita
n
ia
n
…
(
Mo
h
a
med
Lemin
e
S
id
ib
b
a
)
927
T
h
im
an
d
Seah
[
1
8
]
d
elv
e
d
in
to
r
ea
l
wo
r
ld
AI
u
s
es
in
th
e
wo
r
ld
b
y
f
o
c
u
s
in
g
o
n
n
eu
r
al
n
etwo
r
k
s
(
NN)
.
T
h
ey
lo
o
k
ed
at
h
o
w
h
e
u
r
is
tic
n
eu
r
al
n
etwo
r
k
s
co
u
ld
b
e
co
m
b
in
e
d
with
Ma
r
k
o
witz
s
E
f
f
icien
t
Fro
n
tier
in
p
o
r
tf
o
lio
th
eo
r
y
to
im
p
r
o
v
e
p
o
r
tf
o
lio
co
n
s
tr
u
ctio
n
an
d
m
ax
i
m
ize
in
v
estme
n
t
r
etu
r
n
s
.
Kash
iwag
i
[
1
9
]
h
ig
h
lig
h
ted
th
e
r
e
n
ewe
d
in
t
er
est
in
AI
,
d
r
iv
en
b
y
a
d
v
a
n
ce
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x
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elate
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Ma
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ir
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Sed
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d
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ig
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er
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h
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th
at
ca
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esti
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ate
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y
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ak
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s
e
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ata
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ak
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ata
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d
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licity
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ac
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r
ac
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r
eliab
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in
o
u
r
an
aly
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is
,
we
h
av
e
ch
o
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en
f
o
u
r
u
s
ed
m
ac
h
in
e
lear
n
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m
o
d
els
f
o
r
th
is
s
tu
d
y
.
I
n
th
is
ar
ticle,
we
will
co
m
p
a
r
e
f
o
u
r
m
ac
h
in
e
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r
n
in
g
m
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d
els
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s
ed
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o
r
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ast
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aily
p
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o
f
its
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th
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r
ea
lm
o
f
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ig
ital
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an
k
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n
M
au
r
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ia:
LR
,
SVM,
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a
n
d
ML
PC
lass
if
ier
.
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h
e
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esear
ch
will
m
ak
e
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s
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ad
B
an
k
Daily
Op
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Data
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et
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ce
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e
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in
in
g
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v
ar
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les
im
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ac
t
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g
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o
f
itab
ilit
y
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cr
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tin
g
p
r
e
d
ictio
n
m
o
d
els.
3.
M
E
T
H
O
DO
L
O
G
Y
I
n
th
is
p
ar
t
o
f
o
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r
p
ap
e
r
,
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ex
p
lo
r
e
h
o
w
to
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o
r
ec
ast
p
r
o
f
its
b
y
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tili
zin
g
m
ac
h
i
n
e
lear
n
in
g
tech
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i
q
u
es.
W
e
o
p
ted
f
o
r
f
o
u
r
m
o
d
els:
LR
,
SVM,
KNN,
an
d
ML
PC
las
s
if
ier
.
Fo
r
th
is
ta
s
k
,
o
u
r
ev
al
u
atio
n
o
f
t
h
e
m
o
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l
s
w
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s
b
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s
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d
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Se
d
a
d
B
a
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k
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il
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a
t
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m
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r
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5
4
3
s
a
m
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le
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n
d
1
6
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e
a
t
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r
e
s
.
I
n
F
i
g
u
r
e
1
,
we
illu
s
tr
ate
th
e
ap
p
r
o
ac
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we
u
s
ed
o
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tlin
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g
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e
s
tep
s
tak
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d
p
r
o
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id
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g
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p
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s
in
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Fig
u
r
e
1
.
Pro
ce
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s
o
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r
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eth
o
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3
.
1
.
Seda
d ba
nk
da
ily
o
pera
t
io
n da
t
a
s
et
Sed
ad
B
an
k
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a
d
ig
ital
b
an
k
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n
Ma
u
r
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at
p
r
o
v
id
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s
to
m
er
s
with
th
e
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ilit
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ag
e
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eir
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u
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ts
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er
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t
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ay
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ills
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t
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er
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k
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g
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er
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ices.
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ts
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aily
o
p
er
atio
n
d
ataset
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r
ep
r
esen
ted
b
y
a
C
SV
f
ile
co
n
tain
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g
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d
aily
tr
a
n
s
ac
tio
n
s
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clu
d
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etails
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o
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t
t
h
e
ac
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ities
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f
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an
k
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e
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ed
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tu
d
y
wh
at
f
ac
to
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s
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p
ac
t
th
eir
p
r
o
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an
d
cr
ea
te
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o
d
els
to
p
r
ed
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g
tr
an
s
ac
tio
n
s
p
r
o
f
itab
ilit
y
ac
c
u
r
ately
.
I
n
T
a
b
le
1
a
r
e
lis
ted
all
th
e
c
h
ar
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te
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is
tics
o
f
th
e
d
ataset
alo
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g
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h
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e
x
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lan
atio
n
s
an
d
th
e
ty
p
es o
f
d
ata
th
ey
r
e
p
r
esen
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
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I
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&
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m
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T
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,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
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928
T
ab
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Data
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ef
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el,
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im
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o
r
tan
t
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am
in
e
th
e
d
ataset
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g
r
asp
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lay
o
u
t
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d
f
ea
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r
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o
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o
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ly
.
R
eg
ar
d
in
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m
atter
,
a
co
r
r
ela
tio
n
m
atr
ix
(
d
e
p
icted
in
Fig
u
r
e
2
)
was
cr
ea
ted
to
ev
alu
ate
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e
co
n
n
ec
tio
n
s
b
etwe
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te
s
.
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o
r
r
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n
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r
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e
f
r
o
m
+1
to
-
1
,
r
e
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lectin
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en
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d
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ir
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tio
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r
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ativ
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ig
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ip
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−
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ests
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ip
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Fig
u
r
e
2
.
C
o
r
r
elatio
n
m
atr
ix
3
.
2
.
Da
t
a
pre
-
pro
ce
s
s
ing
T
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tep
s
in
v
o
lv
e
s
ev
er
al
k
ey
tr
an
s
f
o
r
m
ati
o
n
s
to
p
r
ep
ar
e
th
e
d
ataset
f
o
r
an
aly
s
is
.
First,
f
ield
ty
p
es
m
u
s
t
b
e
ad
j
u
s
ted
s
p
ec
if
ically
;
‘
Sen
d
er
A
cc
o
u
n
t
’
a
n
d
‘
B
an
k
Acc
o
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n
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h
o
u
ld
b
e
co
n
v
er
ted
f
r
o
m
th
e
Flo
at6
4
d
ata
ty
p
e
to
th
e
o
bj
ec
t
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ata
ty
p
e.
Similar
ly
,
f
ield
s
lik
e
‘
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r
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e
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Pro
f
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o
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ld
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e
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n
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ateg
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ical
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ata
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t6
4
.
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n
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ess
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l
u
m
n
s
th
at
d
o
n
o
t
c
o
n
tr
ib
u
te
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
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n
f
&
C
o
m
m
u
n
T
ec
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n
o
l
I
SS
N:
2252
-
8
7
7
6
Ma
ch
in
e
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r
n
in
g
mo
d
els fo
r
p
r
ed
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d
a
ily
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r
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fita
b
ilit
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in
M
a
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ia
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…
(
Mo
h
a
med
Lemin
e
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id
ib
b
a
)
929
p
r
ed
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g
p
r
o
f
itab
ilit
y
ar
e
r
e
m
o
v
ed
to
s
tr
ea
m
lin
e
t
h
e
d
ata
s
et.
Han
d
lin
g
m
is
s
in
g
d
ata
is
also
cr
u
cial;
th
is
ty
p
ically
en
tails
r
em
o
v
i
n
g
r
o
ws
with
m
is
s
in
g
v
alu
es
to
u
p
h
o
ld
d
at
a
i
n
teg
r
ity
.
Fin
ally
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th
e
d
ataset
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d
iv
id
ed
in
to
tr
ain
in
g
,
test
in
g
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d
v
alid
atio
n
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ets to
en
s
u
r
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o
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tim
al
m
o
d
el
p
er
f
o
r
m
a
n
ce
an
d
r
eliab
ilit
y
[
2
0
]
.
3
.
3
.
Da
t
a
s
et
s
pli
t
s
W
e
s
p
lit
d
ata
in
to
tr
ain
in
g
s
ets,
v
alid
atio
n
an
d
test
s
ets
to
as
s
ess
h
o
w
well
a
m
o
d
el
p
er
f
o
r
m
s
o
n
d
ata
it
h
asn
’
t
s
ee
n
b
ef
o
r
e.
T
h
is
h
el
p
s
p
r
ev
e
n
t
o
v
er
f
itti
n
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a
n
d
g
u
a
r
an
tees
th
e
m
o
d
el
wo
r
k
s
ef
f
ec
tiv
ely
in
r
ea
l
w
o
r
ld
s
ce
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ar
io
s
.
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n
o
u
r
s
itu
atio
n
,
w
e
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iv
id
ed
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e
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ata
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n
to
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o
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ain
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d
2
0
%
f
o
r
test
i
n
g
wh
en
u
s
in
g
LR
.
Fo
r
th
e
SVM
an
d
KNN
m
o
d
e
ls
,
we
s
p
lit
th
e
d
ata
in
to
8
0
%
f
o
r
tr
ain
in
g
an
d
2
0
%
f
o
r
test
in
g
an
d
u
s
in
g
c
r
o
s
s
v
alid
atio
n
to
a
d
ju
s
t
SVM
h
y
p
er
p
a
r
am
eter
s
an
d
im
p
r
o
v
e
n
eig
h
b
o
r
q
u
ality
f
o
r
KNN
m
o
d
el.
L
astl
y
,
f
o
r
ML
PC
la
s
s
if
ier
we
u
s
ed
th
e
r
atio
o
f
7
0
%/1
5
%/1
5
% to
m
o
n
ito
r
p
er
f
o
r
m
an
ce
an
d
a
v
o
id
o
v
er
f
itti
n
g
is
s
u
es.
3
.
4
.
M
a
chine
lea
rning
m
o
de
ls
3
.
4
.
1
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
LR
is
a
lin
ea
r
m
o
d
el
m
ain
ly
em
p
lo
y
ed
f
o
r
task
s
r
elate
d
to
cl
ass
if
icatio
n
.
I
t
u
s
es
a
lo
g
is
tic
f
u
n
ctio
n
to
p
r
ed
ict
th
e
lik
elih
o
o
d
o
f
a
b
i
n
ar
y
r
esu
lt.
T
h
e
m
o
d
el
u
s
es
a
lin
ea
r
c
o
m
b
in
atio
n
o
f
in
p
u
t
f
ea
tu
r
es
to
c
o
m
p
u
te
p
r
o
b
a
b
ilit
ies,
wh
ich
ar
e
th
en
co
n
v
er
ted
in
t
o
a
r
an
g
e
o
f
0
to
1
with
a
s
ig
m
o
id
f
u
n
ctio
n
.
L
R
is
h
ig
h
ly
r
eg
ar
d
ed
f
o
r
its
s
im
p
licity
an
d
ea
s
e
o
f
u
n
d
er
s
tan
d
in
g
,
wh
ich
m
ak
es
it
s
u
cc
ess
f
u
l
f
o
r
d
ata
th
at
ca
n
b
e
s
ep
ar
ated
lin
ea
r
ly
.
Nev
er
th
eless
,
it
m
ig
h
t
f
ac
e
ch
allen
g
es
with
in
tr
icate
co
n
n
ec
tio
n
s
o
r
e
x
ten
s
iv
e
d
atasets
co
n
tain
in
g
n
u
m
er
o
u
s
ch
ar
ac
ter
is
tics
.
3
.
4
.
2
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
SVM
is
a
f
lex
ib
le
alg
o
r
ith
m
t
h
at
ca
n
b
e
a
p
p
lied
t
o
p
er
f
o
r
m
class
if
icatio
n
as
well
as
r
eg
r
es
s
io
n
task
s
.
I
t
o
p
er
ates
b
y
d
eter
m
in
in
g
t
h
e
h
y
p
e
r
p
lan
e
th
at
m
o
s
t
ef
f
e
ctiv
ely
s
ep
ar
ates
t
h
e
d
ata
in
to
s
ep
ar
ate
class
es.
I
n
lin
ea
r
s
ce
n
ar
io
s
,
th
e
h
y
p
e
r
p
lan
e
is
s
im
p
le,
b
u
t
in
n
o
n
-
lin
ea
r
s
ce
n
ar
io
s
,
SVM
u
s
es
k
er
n
el
f
u
n
ctio
n
s
t
o
tr
an
s
f
o
r
m
th
e
d
ata
in
t
o
a
h
i
g
h
er
-
d
im
en
s
io
n
al
s
p
ac
e
wh
er
e
a
lin
ea
r
d
iv
is
io
n
is
p
o
s
s
ib
le.
T
h
is
m
eth
o
d
e
n
ab
les
SVM
to
ef
f
e
ctiv
ely
m
an
a
g
e
i
n
tr
icate
d
ata
s
tr
u
ct
u
r
es.
SVM
s
h
o
ws
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
i
n
h
ig
h
-
d
im
e
n
s
io
n
al
s
p
ac
es
an
d
is
h
ig
h
ly
ef
f
ec
tiv
e
wh
en
th
e
r
e
is
a
d
is
tin
ct
s
ep
ar
atio
n
b
etwe
en
class
es.
Nev
er
th
eless
,
it
m
ay
en
co
u
n
ter
d
if
f
icu
lties
wh
en
d
ea
lin
g
with
n
o
is
y
d
ata
an
d
m
ig
h
t
n
o
t
f
u
n
ctio
n
ef
f
ec
tiv
ely
wh
en
th
er
e
ar
e
s
ig
n
if
ican
tly
m
o
r
e
f
ea
tu
r
es th
a
n
s
am
p
les.
3
.
4
.
3
.
K
-
nea
re
s
t
neig
hb
o
rs
KNN
is
a
m
o
d
el
th
at
r
elies
o
n
in
s
tan
ce
s
to
p
er
f
o
r
m
ta
s
k
s
in
clas
s
if
icatio
n
an
d
r
eg
r
ess
io
n
.
I
n
class
if
icatio
n
,
KNN
a
s
s
ig
n
s
a
d
ata
p
o
in
t
to
a
class
b
y
an
aly
z
in
g
th
e
m
o
s
t
co
m
m
o
n
class
am
o
n
g
its
‘
k
’
clo
s
est
n
eig
h
b
o
r
s
in
th
e
f
ea
tu
r
e
s
p
ac
e.
I
n
r
eg
r
ess
io
n
,
a
v
alu
e
i
s
p
r
ed
icted
b
y
av
er
ag
i
n
g
th
e
v
alu
e
s
o
f
th
e
‘
k
’
clo
s
est
n
eig
h
b
o
r
s
.
T
h
e
alg
o
r
ith
m
s
is
h
ig
h
ly
ap
p
r
ec
iated
f
o
r
its
s
i
m
p
licity
an
d
ea
s
e
o
f
u
s
e,
as
it
d
o
es
n
o
t
n
ee
d
a
d
ed
icate
d
tr
ain
in
g
p
h
ase
an
d
in
s
tead
f
u
n
ctio
n
s
as
a
“
lazy
lear
n
er
.
”
Nev
e
r
th
eless
,
KNN
m
ay
ex
p
er
ien
ce
s
lo
wn
ess
with
b
ig
d
atasets
an
d
is
af
f
ec
ted
b
y
th
e
s
elec
tio
n
o
f
‘
k
’
an
d
th
e
d
is
tan
ce
m
etr
ic
u
tili
ze
d
.
Mo
r
eo
v
er
,
ir
r
elev
an
t f
ea
tu
r
es c
an
h
a
v
e
a
n
eg
ativ
e
im
p
ac
t
o
n
th
e
p
er
f
o
r
m
an
ce
o
f
KNN.
3
.
4
.
4
.
MLP
c
la
s
s
if
ier
T
h
e
ML
PC
las
s
if
ier
,
also
k
n
o
wn
as
M
LP
C
lass
if
ier
,
is
a
n
e
u
r
al
n
etwo
r
k
m
o
d
el
s
p
ec
if
ically
m
ad
e
f
o
r
class
if
icatio
n
p
u
r
p
o
s
es.
I
t
is
o
r
g
an
ized
with
n
u
m
er
o
u
s
lay
er
s
o
f
n
o
d
es,
o
r
n
eu
r
o
n
s
,
wit
h
ea
ch
lay
er
b
ein
g
co
n
n
ec
ted
to
th
e
n
ex
t
la
y
er
in
its
en
tire
ty
.
Activ
atio
n
f
u
n
ctio
n
s
ar
e
u
tili
ze
d
b
y
t
h
e
n
et
wo
r
k
t
o
ad
d
n
o
n
-
lin
ea
r
ity
to
th
e
m
o
d
el,
wh
il
e
b
ac
k
p
r
o
p
ag
atio
n
is
u
s
ed
t
o
m
o
d
if
y
th
e
weig
h
ts
th
r
o
u
g
h
o
u
t
tr
ain
in
g
.
T
h
is
s
tr
u
ctu
r
e
allo
ws
th
e
ML
PC
las
s
if
ier
to
ca
p
tu
r
e
in
tr
icate
,
n
o
n
l
in
ea
r
co
n
n
ec
tio
n
s
an
d
a
d
ju
s
t
to
d
iv
er
s
e
d
ata
s
ets.
Nev
er
th
eless
,
it
f
r
eq
u
en
tly
r
e
q
u
ir
es
ex
ten
s
iv
e
d
atasets
an
d
s
u
b
s
tan
tial
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
,
m
ay
b
e
l
ess
u
n
d
er
s
tan
d
a
b
le
th
an
alter
n
ativ
e
m
o
d
els,
an
d
is
in
f
lu
en
ce
d
b
y
h
y
p
er
p
ar
am
eter
t
u
n
in
g
.
3
.
5
.
P
er
f
o
r
m
a
nce
m
e
a
s
ures
T
h
e
p
er
f
o
r
m
an
ce
o
f
o
u
r
m
o
d
el
s
is
ev
alu
ated
b
y
th
e
co
n
f
u
s
io
n
m
atr
ix
.
I
t
o
u
tlin
es
th
e
p
r
e
d
ictio
n
s
o
f
th
e
m
o
d
el
u
s
in
g
a
s
am
p
le
o
f
d
ata.
T
r
u
e
p
o
s
itiv
es
(
T
P),
f
alse
p
o
s
itiv
es
(
FP
)
,
tr
u
e
n
eg
ativ
es
(
T
N)
,
an
d
f
alse
ne
g
ativ
es
(
FN)
r
ep
r
esen
t
th
e
p
r
im
ar
y
r
esu
lts
o
f
th
e
co
n
f
u
s
i
o
n
m
atr
ix
.
T
h
e
r
esu
lts
ar
e
u
tili
ze
d
in
d
eter
m
in
in
g
d
if
f
er
en
t
m
etr
ics
s
u
ch
as
F1
-
s
co
r
e,
r
ec
all,
an
d
p
r
ec
is
io
n
,
g
iv
in
g
a
th
o
r
o
u
g
h
e
v
alu
ati
o
n
o
f
th
e
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
[
2
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2
3
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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T
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Vo
l.
15
,
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3
,
Sep
tem
b
er
20
26
:
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=
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+
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(
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2
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(
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4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
r
esear
ch
f
o
cu
s
es
o
n
a
to
p
ic
th
at
h
asn
’
t
b
ee
n
s
tu
d
ed
in
Ma
u
r
itan
ia
b
ef
o
r
e;
th
er
ef
o
r
e,
we
ca
n
’
t
d
ir
ec
tly
co
m
p
ar
e
o
u
r
f
in
d
in
g
s
to
ex
is
tin
g
s
tu
d
ies
in
th
e
f
iel
d
.
I
n
th
is
s
ec
tio
n
,
we
will
d
e
s
cr
ib
e
an
d
talk
ab
o
u
t
o
u
r
r
esu
lts
in
d
etail.
W
e
te
s
te
d
th
e
m
o
d
els
o
n
a
co
m
p
u
ter
r
u
n
n
in
g
W
in
d
o
ws
1
0
with
a
p
r
o
ce
s
s
o
r
clo
ck
ed
at
2
.
4
0
GHz
a
n
d
8
GB
o
f
R
AM
.
T
h
e
ex
p
er
im
en
ts
wer
e
co
n
d
u
cted
u
s
in
g
th
e
A
n
ac
o
n
d
a
s
o
f
t
war
e,
ac
ce
s
s
ib
le
f
o
r
ev
er
y
o
n
e
to
u
s
e.
T
h
e
m
o
d
el
was
cr
ea
ted
in
Py
th
o
n
,
with
to
o
ls
f
r
o
m
Scik
it
L
ea
r
n
lib
r
a
r
y
b
ein
g
u
s
ed
f
o
r
im
p
lem
en
tatio
n
p
u
r
p
o
s
es.
T
h
e
m
etr
ics u
s
ed
to
ev
alu
ate
t
h
e
m
o
d
el
s
ar
e
o
u
tlin
ed
in
T
a
b
le
2
.
T
ab
le
2
.
R
esu
lts
o
f
d
if
f
e
r
en
t m
o
d
els o
n
Sed
ad
B
an
k
d
aily
o
p
er
atio
n
s
d
ataset
M
e
a
su
r
e
s/
M
o
d
e
l
s
LR
S
V
M
K
N
N
M
LP
c
l
a
ssi
f
i
e
r
P
r
e
c
i
s
i
o
n
(
%)
99
77
94
99
R
e
c
a
l
l
(
%)
99
53
96
94
F1
-
sc
o
r
e
(
%)
99
54
95
97
T
h
e
r
esu
lts
ac
q
u
ir
e
d
f
r
o
m
th
i
s
s
tu
d
y
will
b
e
ex
am
i
n
ed
in
th
is
s
ec
tio
n
.
Fig
u
r
e
3
s
h
o
ws
t
h
at
LR
an
d
ML
PC
la
s
s
if
ier
h
ad
a
p
r
ec
is
io
n
r
ate
o
f
9
9
%,
SVM
h
ad
7
7
%,
an
d
KNN
h
ad
9
4
%.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
p
r
ec
is
io
n
o
f
th
e
LR
m
o
d
el
an
d
ML
PC
las
s
if
ier
o
u
tp
er
f
o
r
m
e
d
th
e
o
th
er
m
o
d
els.
T
h
e
r
ec
all
,
also
k
n
o
wn
as
th
e
tr
u
e
p
o
s
itiv
e
r
ate,
is
a
wid
ely
u
s
ed
m
ea
s
u
r
e
in
m
ac
h
in
e
lear
n
in
g
f
o
r
m
o
d
el
ass
ess
m
en
t.
R
em
em
b
er
th
e
r
ec
all
p
er
ce
n
tag
es
f
o
r
LR
,
SVM
,
K
-
N
K
,
an
d
M
LP
C
lass
if
ier
s
h
o
wn
in
Fig
u
r
e
4
:
9
9
%,
5
3
%,
9
6
%,
an
d
9
4
%
r
esp
ec
tiv
ely
.
T
h
e
R
ec
all
v
alu
es
o
b
tain
ed
s
h
o
w
th
at
th
e
L
o
g
R
eg
r
ess
io
n
m
o
d
el
is
b
etter
th
an
o
th
e
r
m
o
d
els.
T
h
e
F1
-
s
co
r
e
is
a
u
s
ef
u
l
m
etr
i
c
f
o
r
ev
al
u
atin
g
a
m
o
d
el
’
s
p
er
f
o
r
m
an
ce
as
it
ta
k
es
in
to
ac
co
u
n
t
b
o
th
p
r
ec
is
io
n
an
d
r
ec
all.
T
h
e
F1
-
s
co
r
e
p
er
c
en
tag
es
f
o
r
LR
,
SVM,
KNN,
an
d
ML
PC
lass
if
ier
in
Fig
u
r
e
5
ar
e
9
9
%,
5
4
%,
9
5
%,
an
d
9
7
%,
r
esp
ec
tiv
ely
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
ate
th
at
th
e
LR
m
o
d
el
s
u
r
p
ass
e
s
o
th
er
m
o
d
els
in
ter
m
s
o
f
F1
-
s
co
r
e
v
alu
es.
B
ased
o
n
th
e
an
aly
s
is
d
is
cu
s
s
ed
ea
r
lier
,
th
e
f
o
l
lo
win
g
c
o
n
clu
s
io
n
s
ca
n
b
e
m
ad
e:
−
W
h
en
it c
o
m
es to
p
r
e
d
ictin
g
d
aily
p
r
o
f
itab
ilit
y
,
LR
o
u
tp
er
f
o
r
m
s
SVM,
KNN,
an
d
ML
PC
las
s
if
ier
.
−
SVM
is
th
e
wo
r
s
t m
o
d
el
to
p
r
ed
ict
th
e
d
aily
p
r
o
f
itab
ilit
y
.
T
h
e
u
p
co
m
i
n
g
r
esear
ch
will
ex
p
lo
r
e
m
ac
h
i
n
e
lear
n
in
g
an
d
d
ee
p
lear
n
in
g
al
g
o
r
ith
m
s
m
en
tio
n
ed
in
s
tu
d
ies
o
n
d
ec
is
io
n
m
ak
in
g
m
eth
o
d
s
an
d
p
r
e
d
ictiv
e
an
aly
s
is
[
2
4
]
.
B
y
in
teg
r
atin
g
a
m
eth
o
d
o
lo
g
y
t
h
at
in
clu
d
es
lear
n
in
g
m
o
d
els
lik
e
C
NN
L
STM
n
etwo
r
k
s
,
as
d
em
o
n
s
tr
ated
in
r
ec
en
t
r
esear
ch
o
n
s
k
etch
r
ec
o
g
n
itio
n
[
2
5
]
,
we
m
ay
im
p
r
o
v
e
th
e
p
r
e
d
ictiv
e
ac
cu
r
ac
y
f
o
r
in
tr
icate
f
i
n
a
n
cial
d
atasets
.
Mo
r
eo
v
er
,
u
tili
zin
g
alg
o
r
ith
m
s
th
at
p
r
io
r
itize
d
ata
f
o
r
an
aly
s
is
co
u
ld
en
h
an
ce
th
e
ac
cu
r
ac
y
o
f
p
r
ed
ictio
n
s
[
2
6
]
.
Fin
ally
,
we
ai
m
to
co
m
b
in
e
t
h
ese
f
o
u
r
m
o
d
els to
en
h
an
ce
th
e
ca
p
ab
ilit
ies.
Fig
u
r
e
3
.
Pre
cisi
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Ma
ch
in
e
lea
r
n
in
g
mo
d
els fo
r
p
r
ed
ictin
g
d
a
ily
p
r
o
fita
b
ilit
y
in
M
a
u
r
ita
n
ia
n
…
(
Mo
h
a
med
Lemin
e
S
id
ib
b
a
)
931
Fig
u
r
e
4
.
R
ec
all
Fig
u
r
e
5
.
F1
-
s
co
r
e
5.
CO
NCLU
SI
O
N
Pre
d
ictio
n
is
a
well
-
estab
lis
h
ed
r
esear
ch
ar
ea
with
in
t
h
e
b
an
k
in
g
in
d
u
s
tr
y
.
T
o
th
is
,
we
u
s
ed
p
r
ed
ictiv
e
m
o
d
els
s
u
ch
as
LR
,
SVM,
KNN,
an
d
ML
PC
la
s
s
if
ier
to
id
en
tify
p
r
o
f
itab
le
o
p
er
a
tio
n
s
an
d
en
h
a
n
ce
b
an
k
in
g
s
tr
ateg
ies.
I
n
th
is
c
o
n
tex
t,
th
is
p
ap
e
r
p
r
esen
ts
a
p
r
o
p
o
s
ed
m
eth
o
d
e
u
tili
zin
g
th
ese
m
o
d
els.
Key
m
etr
ics
s
u
ch
as
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
wer
e
u
s
ed
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el
th
r
o
u
g
h
a
co
n
f
u
s
io
n
m
atr
i
x
.
T
h
e
f
in
d
in
g
s
s
h
o
w
th
at
LR
i
s
th
e
to
p
-
p
er
f
o
r
m
in
g
m
o
d
el
in
p
r
ed
ictin
g
d
aily
p
r
o
f
itab
il
ity
,
with
p
r
ec
is
io
n
,
r
ec
all,
an
d
an
F1
-
s
co
r
e
all
at
9
9
%,
d
em
o
n
s
tr
atin
g
th
e
ef
f
icac
y
o
f
LR
in
p
r
o
f
itab
ilit
y
p
r
ed
ictio
n
task
s
.
W
e
a
im
to
ex
p
an
d
o
u
r
r
esear
ch
s
co
p
e
b
y
ass
ess
in
g
tr
a
n
s
ac
tio
n
s
o
v
er
d
u
r
atio
n
s
s
u
ch
as we
ek
ly
,
m
o
n
th
ly
an
d
y
ea
r
l
y
d
atasets
to
g
au
g
e
h
o
w
ef
f
ec
tiv
e
th
e
al
g
o
r
ith
m
s
ar
e
in
s
ettin
g
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
wo
r
k
d
id
n
o
t r
ec
eiv
e
an
y
f
u
n
d
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
925
-
9
3
4
932
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
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R
D
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Vi
Su
P
Fu
Mo
h
am
ed
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e
m
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ib
b
a
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Mo
h
am
ed
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u
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h
eik
h
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r
ad
✓
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m
ad
Ou
tf
ar
o
u
in
✓
✓
✓
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a
Sid
i
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h
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e
d
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wlo
u
d
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Mo
h
am
ed
ad
e
Far
o
u
k
Nan
n
e
✓
✓
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✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
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a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
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o
n
R
:
R
e
so
u
r
c
e
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:
D
a
t
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r
a
t
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:
W
r
i
t
i
n
g
-
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r
i
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i
n
a
l
D
r
a
f
t
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:
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r
i
t
i
n
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-
R
e
v
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w
&
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d
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t
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n
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Vi
:
Vi
su
a
l
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z
a
t
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:
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p
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v
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s
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P
:
P
r
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c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
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R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
i
n
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
a
u
th
o
r
,
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
R
.
K
a
r
t
h
i
g
a
,
S
.
A
n
a
n
t
h
i
,
R
.
K
a
u
r
,
D
.
K
.
D
a
s
,
S
.
N
a
t
a
r
a
j
a
n
,
a
n
d
D
.
P
.
D
h
i
n
a
k
a
r
a
n
,
“
I
mp
a
c
t
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
i
n
t
h
e
b
a
n
k
i
n
g
sec
t
o
r
,
”
Y
u
g
a
t
o
,
v
o
l
.
7
6
,
n
o
.
1
,
p
.
2
0
2
4
.
[
2
]
S
.
U
.
K
h
a
n
a
n
d
B
.
R
e
h
m
a
n
,
“
E
v
a
l
u
a
t
i
o
n
o
f
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
l
s
f
o
r
b
a
n
k
s
’
p
r
o
f
i
t
a
b
i
l
i
t
y
p
r
e
d
i
c
t
i
o
n
i
n
P
a
k
i
st
a
n
,
”
2
0
2
3
,
d
o
i
:
1
0
.
2
1
3
9
/
s
sr
n
.
4
5
0
2
4
7
6
.
[
3
]
A
.
Eg
h
i
a
n
,
“
C
o
mp
a
r
i
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
p
r
e
d
i
c
t
i
n
g
b
a
n
k
f
a
i
l
u
r
e
,
”
Em
p
i
ri
c
a
l
E
c
o
n
o
m
i
c
B
u
l
l
e
t
i
n
,
An
U
n
d
e
rg
r
a
d
u
a
t
e
J
o
u
r
n
a
l
,
v
o
l
.
1
4
,
n
o
.
1
,
p
.
1
1
,
2
0
2
1
,
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s
:
/
/
d
i
g
i
t
a
l
c
o
mm
o
n
s.
b
r
y
a
n
t
.
e
d
u
/
c
g
i
/
v
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e
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c
o
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e
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t
.
c
g
i
?
a
r
t
i
c
l
e
=
1
1
9
4
&c
o
n
t
e
x
t
=
e
e
b
.
[
4
]
S
.
C
.
T
.
K
o
u
mé
t
i
o
a
n
d
H
.
To
u
l
n
i
,
“
I
mp
r
o
v
i
n
g
K
N
N
m
o
d
e
l
f
o
r
d
i
r
e
c
t
mar
k
e
t
i
n
g
p
r
e
d
i
c
t
i
o
n
i
n
sm
a
r
t
c
i
t
i
e
s,
”
i
n
M
a
c
h
i
n
e
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
D
a
t
a
A
n
a
l
y
t
i
c
s
f
o
r
S
u
st
a
i
n
a
b
l
e
Fu
t
u
re
S
m
a
rt
C
i
t
i
e
s
,
S
p
r
i
n
g
e
r
I
n
t
e
r
n
a
t
i
o
n
a
l
P
u
b
l
i
s
h
i
n
g
,
2
0
2
1
,
p
p
.
1
0
7
–
1
1
8
.
[
5
]
S
.
C
.
K
.
Té
k
o
u
a
b
o
u
,
Ș
.
C
.
G
h
e
r
g
h
i
n
a
,
H
.
T
o
u
l
n
i
,
P
.
N
.
M
a
t
a
,
a
n
d
J.
M
.
M
a
r
t
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