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I
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3956
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e
b
e
co
m
e
ess
en
tial.
R
ec
en
t
ad
v
a
n
ce
m
en
ts
in
in
f
o
r
m
atio
n
tech
n
o
lo
g
y
an
d
m
ac
h
i
n
e
lear
n
in
g
h
av
e
d
r
i
v
en
r
esear
ch
e
r
s
to
a
p
p
ly
th
ese
tech
n
iq
u
es
to
t
h
e
f
in
an
cial
s
ec
to
r
to
co
m
b
at
cy
b
er
f
r
au
d
,
wh
ich
in
v
o
lv
es
illeg
al
ac
tiv
ities
co
n
d
u
cted
v
ia
th
e
in
ter
n
et,
elec
tr
o
n
ic
co
m
m
u
n
icatio
n
,
o
r
d
ig
ital
m
ea
n
s
[
5
]
.
Ma
c
h
in
e
lear
n
i
n
g
(
ML
)
s
ig
n
i
f
ican
tly
en
h
an
ce
s
in
f
o
r
m
atio
n
m
an
ag
em
en
t
th
r
o
u
g
h
s
m
ar
t
alg
o
r
ith
m
s
,
d
ata
-
d
r
iv
en
d
ec
is
io
n
-
m
ak
in
g
,
an
d
im
p
r
o
v
ed
d
ata
an
aly
s
is
.
T
h
e
ap
p
licatio
n
o
f
ML
will
im
p
r
o
v
e
h
is
to
r
ical
b
ased
o
u
tc
o
m
e
p
r
ed
ictio
n
s
b
y
lo
w
er
in
g
er
r
o
r
r
ates.
Ov
er
th
e
last
d
ec
ad
e
th
er
e
h
as
b
ee
n
a
g
r
ea
t
d
ea
l
o
f
in
v
esti
g
atio
n
in
th
is
a
r
ea
d
u
e
to
t
h
e
ef
f
ec
tiv
e
n
ess
o
f
ML
tech
n
iq
u
es
in
d
ea
lin
g
with
o
n
lin
e
f
r
a
u
d
p
r
o
b
lem
s
[
6
]
–
[
1
0
]
.
ML
is
f
av
o
r
e
d
f
o
r
its
r
a
p
id
c
o
m
p
u
tatio
n
ca
p
ab
ilit
ies,
allo
win
g
q
u
ick
d
ata
an
aly
s
is
an
d
p
att
er
n
r
ec
o
g
n
itio
n
.
C
o
m
p
ar
e
d
t
o
ML
tech
n
iq
u
es,
r
u
le
-
b
ased
f
r
au
d
p
r
e
v
en
tio
n
s
y
s
tem
s
th
at
u
s
e
lo
g
ic
-
b
ased
ap
p
r
o
ac
h
ar
e
n
o
t
as
ef
f
icien
t.
Sin
ce
f
r
au
d
u
len
t
tr
an
s
ac
tio
n
s
ar
e
r
ar
e,
th
e
b
alan
c
e
class
wh
en
lab
eled
b
ec
o
m
es
a
cr
u
cial
co
n
s
id
er
atio
n
.
T
h
e
Ali
et
a
l.
m
o
d
el
[
1
1
]
attem
p
ts
to
m
itig
ate
f
r
au
d
o
u
t
o
f
d
ata
b
y
e
m
p
lo
y
i
n
g
s
u
p
er
v
i
s
ed
lear
n
in
g
alg
o
r
ith
m
to
d
ete
ct
f
r
au
d
u
len
t
b
eh
a
v
io
r
b
ased
o
n
p
ast
in
s
tan
ce
s
o
f
f
r
au
d
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
to
d
is
co
v
er
n
ew
ty
p
es
o
f
f
r
au
d
.
C
lass
if
ier
s
in
clu
d
in
g
lo
g
is
tic
r
eg
r
ess
io
n
,
wh
ich
f
lag
s
tr
an
s
ac
tio
n
as e
ith
er
'
f
r
au
d
'
o
r
'
n
o
n
-
f
r
a
u
d
'
,
ar
e
im
p
lem
en
ted
u
s
in
g
Py
th
o
n
[
1
1
]
–
[
1
5
]
.
T
h
e
s
tu
d
y
d
ata
a
n
aly
s
is
b
y
T
o
d
o
r
o
v
ić
et
a
l.
[
1
6
]
co
n
clu
d
ed
th
at
p
er
ce
iv
e
d
c
y
b
er
f
r
a
u
d
d
o
es
n
o
t
s
ig
n
if
ican
tly
in
f
lu
en
ce
e
-
c
o
m
m
er
ce
u
s
ag
e
b
eh
a
v
io
r
.
Ho
wev
er
,
p
er
ce
iv
e
d
ea
s
e
o
f
u
s
e
p
o
s
itiv
ely
af
f
ec
ts
e
-
co
m
m
er
ce
ad
o
p
tio
n
,
h
ig
h
lig
h
tin
g
th
e
im
p
o
r
ta
n
ce
o
f
u
s
er
-
f
r
ien
d
ly
s
y
s
tem
s
.
R
is
k
p
er
ce
p
ti
o
n
also
in
f
lu
en
ce
s
e
-
co
m
m
er
ce
b
eh
av
io
r
,
in
d
icat
in
g
th
at
awa
r
en
ess
o
f
tr
an
s
ac
t
io
n
r
is
k
s
in
cr
ea
s
es
th
e
d
esire
to
u
s
e
e
-
co
m
m
er
ce
s
y
s
tem
s
.
T
o
d
o
r
o
v
ić
et
a
l.
m
o
d
el
,
u
s
in
g
th
e
tech
n
o
lo
g
y
a
cc
ep
tan
ce
m
o
d
el
(
T
AM
)
,
p
r
o
v
id
es
in
s
ig
h
ts
f
o
r
d
esig
n
in
g
e
f
f
ec
tiv
e
o
n
lin
e
tr
a
n
s
ac
tio
n
s
y
s
tem
s
,
em
p
h
asizin
g
f
r
a
u
d
p
er
ce
p
tio
n
,
r
is
k
le
v
els,
an
d
tr
a
n
s
ac
tio
n
ea
s
e
to
en
h
an
ce
e
-
co
m
m
e
r
ce
i
n
I
n
d
o
n
esia.
Ko
ib
ich
u
k
et
a
l.
[
1
7
]
ex
p
l
o
r
e
s
th
e
r
is
k
ty
p
o
l
o
g
y
f
o
r
p
ee
r
-
to
-
p
ee
r
len
d
in
g
i
n
a
d
ig
itally
t
r
an
s
f
o
r
m
ed
f
in
an
cial
lan
d
s
ca
p
e.
Key
f
e
atu
r
es
in
clu
d
e
ac
ce
s
s
in
f
r
as
tr
u
ctu
r
e,
tr
an
s
ac
tio
n
in
f
r
astr
u
ctu
r
e,
f
u
lf
illme
n
t
in
f
r
astru
ctu
r
e,
h
u
m
a
n
co
n
d
itio
n
in
d
icato
r
s
,
d
ev
ice
an
d
b
r
o
ad
b
an
d
u
p
tak
e,
d
ig
ital
in
clu
s
io
n
,
d
ig
ital
p
a
y
m
en
t
u
p
tak
e,
in
s
titu
tio
n
al
ef
f
icien
c
y
,
tr
u
s
t
in
d
icato
r
s
,
an
d
th
e
d
i
g
ital
ec
o
s
y
s
tem
.
Un
d
er
s
tan
d
in
g
th
ese
f
ac
to
r
s
is
ess
en
tial
f
o
r
lo
ca
l
b
an
k
s
to
n
a
v
ig
ate
d
ig
italizatio
n
an
d
r
eg
u
l
ato
r
y
en
v
ir
o
n
m
en
ts
.
T
h
e
in
n
o
v
atio
n
an
d
ch
an
g
e
f
ac
to
r
ass
ess
e
s
th
e
s
ta
te
o
f
k
ey
in
n
o
v
atio
n
ec
o
s
y
s
tem
in
p
u
ts
an
d
o
u
tp
u
ts
,
cr
u
cial
f
o
r
ad
v
an
cin
g
d
i
g
ital
p
r
o
d
u
cts
an
d
s
er
v
ices
[
1
8
]
.
C
o
r
p
o
r
ate
g
o
v
er
n
a
n
ce
is
a
cr
itical
elem
en
t
in
p
r
ev
en
tin
g
an
d
d
etec
tin
g
f
r
a
u
d
.
E
f
f
ec
tiv
e
in
ter
n
al
co
n
tr
o
l
s
y
s
tem
s
,
ac
co
u
n
tab
ilit
y
,
an
d
tr
an
s
p
ar
en
c
y
ar
e
v
ital.
R
e
s
ea
r
ch
s
h
o
ws
th
at
g
o
v
er
n
an
ce
p
o
licies,
b
o
ar
d
ch
ar
ac
ter
is
tics
,
an
d
o
wn
er
s
h
ip
co
n
ce
n
tr
atio
n
r
ed
u
ce
ac
co
u
n
tin
g
f
r
au
d
a
n
d
en
h
a
n
ce
f
in
an
cial
in
f
o
r
m
atio
n
r
eliab
ili
ty
[
1
9
]
–
[
2
1
]
.
A
u
d
ito
r
s
p
lay
a
s
ig
n
if
ican
t
r
o
le
i
n
d
etec
tin
g
f
r
a
u
d
,
an
d
th
eir
d
u
ties
s
h
o
u
ld
b
e
e
x
p
an
d
ed
to
i
n
clu
d
e
u
n
d
er
s
tan
d
in
g
wh
ite
-
c
o
llar
c
r
im
e
p
atter
n
s
an
d
im
p
r
o
v
in
g
a
u
d
it
s
tan
d
ar
d
s
.
T
h
e
s
tu
d
y
in
[
2
2
]
em
p
h
asizes
th
e
im
p
o
r
tan
ce
o
f
u
n
d
er
s
tan
d
in
g
s
elf
-
p
r
o
tectiv
e
an
d
c
r
im
e
p
r
ev
en
tio
n
b
eh
a
v
io
r
s
t
o
d
ev
elo
p
ef
f
ec
tiv
e
cy
b
er
f
r
a
u
d
p
r
ev
e
n
tio
n
p
r
o
g
r
am
s
.
T
h
e
r
esear
ch
ad
v
o
c
ates
f
o
r
a
v
ict
im
-
ce
n
tr
ic
p
o
licin
g
ap
p
r
o
ac
h
an
d
c
y
b
e
r
f
r
a
u
d
cr
im
e
p
r
ev
en
tio
n
ed
u
ca
tio
n
to
ac
h
i
ev
e
p
o
s
itiv
e
o
u
tc
o
m
es.
T
h
e
p
r
o
b
lem
o
f
c
r
ed
it
ca
r
d
f
r
au
d
in
o
n
lin
e
en
v
i
r
o
n
m
e
n
ts
h
as
r
ec
eiv
ed
co
n
s
id
er
a
b
le
atten
tio
n
,
b
u
t
o
th
er
s
ig
n
if
ica
n
t
is
s
u
es
lik
e
in
tellectu
al
p
r
o
p
er
ty
t
h
ef
t,
p
a
g
ejac
k
in
g
,
f
ak
e
m
o
n
e
y
o
r
d
er
s
,
an
d
wir
e
-
tr
a
n
s
f
er
f
r
au
d
n
ee
d
m
o
r
e
f
o
cu
s
.
T
h
e
b
est
f
r
au
d
d
etec
tio
n
r
esu
lts
ar
e
ac
h
iev
ed
b
y
s
u
p
e
r
v
is
ed
lear
n
in
g
tech
n
iq
u
es
lik
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es,
ar
t
if
icial
n
eu
r
al
n
etwo
r
k
s
,
an
d
d
ec
is
io
n
tr
ee
s
.
Fu
tu
r
e
r
esea
r
ch
s
h
o
u
l
d
aim
to
im
p
r
o
v
e
alg
o
r
ith
m
s
to
co
v
er
o
th
er
ty
p
es
o
f
o
n
lin
e
f
r
au
d
with
h
ig
h
ac
cu
r
ac
y
an
d
lo
w
co
s
ts
,
f
o
cu
s
in
g
o
n
h
y
b
r
id
izin
g
th
e
m
o
s
t
ef
f
ec
ti
v
e
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
[
2
3
]
,
[
2
4
]
.
Qu
alitativ
e
r
esear
ch
in
v
o
l
v
in
g
s
tr
u
ctu
r
ed
q
u
esti
o
n
n
air
es
c
o
m
p
lete
d
b
y
b
a
n
k
s
taf
f
ca
n
p
r
o
m
o
te
cy
b
e
r
f
r
au
d
r
ed
u
ctio
n
,
en
h
an
ce
m
a
n
ag
em
e
n
t
co
n
tr
o
l
s
y
s
tem
s
,
an
d
im
p
r
o
v
e
cu
s
to
m
er
an
d
s
h
ar
eh
o
ld
er
s
atis
f
ac
tio
n
[
2
5
]
.
R
ev
iews
o
f
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
f
o
r
d
etec
tin
g
cr
ed
it
ca
r
d
f
r
au
d
s
h
o
w
th
at
s
u
p
er
v
is
ed
lear
n
in
g
tec
h
n
iq
u
e
s
,
s
u
ch
as
R
an
d
o
m
Fo
r
est,
ef
f
ec
tiv
ely
class
if
y
t
r
an
s
ac
tio
n
s
as
f
r
au
d
u
len
t
o
r
au
th
o
r
ized
b
ased
o
n
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
an
d
s
p
ec
if
icity
m
etr
ics
[
2
6
]
.
T
h
e
i
n
ter
n
et'
s
ab
ilit
y
to
r
ea
ch
v
ast
au
d
ien
ce
s
m
ak
es
it
ea
s
y
f
o
r
f
r
au
d
s
ter
s
to
s
p
r
ea
d
cr
ed
ib
le
-
lo
o
k
in
g
m
ess
ag
es,
m
ak
in
g
it
h
a
r
d
to
d
if
f
er
e
n
tiate
b
etwe
en
f
ac
t
an
d
f
ictio
n
.
Desp
i
te
n
u
m
er
o
u
s
s
tu
d
ies
o
n
f
r
a
u
d
p
atter
n
s
,
a
s
y
s
tem
a
tic
s
o
lu
tio
n
to
u
n
d
er
s
tan
d
an
d
d
etec
t
ab
n
o
r
m
al
cu
s
to
m
er
b
eh
av
io
r
s
r
em
ain
s
elu
s
iv
e.
Mo
s
t
p
r
ev
io
u
s
wo
r
k
h
as
f
o
cu
s
ed
o
n
f
r
a
u
d
d
etec
tio
n
r
ath
e
r
th
an
p
r
ev
e
n
tio
n
.
I
m
p
r
o
v
e
d
ap
p
r
o
ac
h
es
b
ased
o
n
en
s
em
b
le
class
if
ier
s
ar
e
n
ec
ess
ar
y
to
s
wif
tly
an
d
r
eliab
l
y
d
etec
t
an
d
p
r
e
v
en
t
d
i
g
ital
f
r
au
d
.
C
o
m
b
in
in
g
h
is
to
r
ical
d
ata
s
e
ts
en
h
an
ce
s
p
r
ev
en
tio
n
ac
cu
r
ac
y
an
d
aid
s
d
ec
is
io
n
-
m
ak
er
s
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
f
r
au
d
,
s
u
s
p
icio
u
s
,
an
d
g
en
u
i
n
e
ac
tiv
ities
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
yb
er
-
fr
a
u
d
d
etec
tio
n
meth
o
d
o
lo
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b
y
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ma
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ith
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(
A
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med
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a
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3951
I
n
th
is
p
ap
e
r
,
a
n
ew
alg
o
r
ith
m
is
p
r
o
p
o
s
ed
to
d
etec
t
n
o
n
-
t
r
an
s
ac
tio
n
al
f
r
au
d
b
e
h
av
io
r
s
,
en
h
an
cin
g
f
r
au
d
p
r
ev
e
n
tio
n
s
tr
ateg
ies.
Usi
n
g
r
ea
l
d
ata
f
r
o
m
th
e
f
i
n
an
cial
s
ec
to
r
,
th
e
s
tu
d
y
v
alid
ates
th
e
r
esu
lts
an
d
ev
alu
ates
th
e
ef
f
ec
tiv
en
ess
o
f
lo
g
is
tic
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est,
an
d
n
aïv
e
B
ay
es
b
ased
o
n
d
etec
tio
n
acc
u
r
ac
y
.
T
h
e
r
esear
ch
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
ce
o
f
in
cr
em
en
tal
im
p
r
o
v
e
m
en
ts
a
n
d
p
e
r
s
is
ten
ce
in
cy
b
er
s
ec
u
r
ity
a
d
v
an
ce
m
e
n
ts
.
2.
M
E
T
H
O
D
Fra
u
d
p
r
o
tectio
n
is
a
h
ig
h
-
lo
ad
s
y
s
tem
d
esig
n
ed
to
d
etec
t
,
p
r
ev
e
n
t,
an
d
co
m
b
at
f
r
au
d
ac
r
o
s
s
all
d
ig
ital
ch
an
n
els
(
web
a
n
d
m
o
b
ile
ap
p
licatio
n
s
)
in
r
ea
l
tim
e
.
T
h
is
s
o
lu
tio
n
d
ef
e
n
d
s
ag
ain
s
t
o
n
lin
e
f
r
au
d
an
d
s
o
cial
en
g
in
ee
r
in
g
attac
k
s
,
r
e
p
r
esen
tin
g
th
e
n
ex
t
g
en
er
atio
n
o
f
en
g
in
ee
r
s
an
d
cy
b
er
s
ec
u
r
it
y
p
r
o
f
ess
io
n
als
wh
o
in
tr
o
d
u
ce
b
o
ld
an
d
in
n
o
v
ativ
e
id
ea
s
to
id
en
tify
cy
b
er
attac
k
s
b
ef
o
r
e
th
ey
b
e
g
in
.
T
h
ese
s
o
lu
tio
n
s
ar
e
b
ased
o
n
ex
h
au
s
tiv
e
th
r
ea
t
-
h
u
n
tin
g
o
p
e
r
atio
n
s
an
d
m
o
n
ito
r
in
g
th
e
tacti
cs,
to
o
ls
,
an
d
in
f
r
astru
ctu
r
e
u
s
ed
b
y
attac
k
e
r
s
.
A
ty
p
ical
o
r
g
an
izatio
n
lo
s
es
an
esti
m
ated
5
%
o
f
its
y
ea
r
ly
r
e
v
en
u
e
t
o
f
r
a
u
d
.
T
h
is
co
u
r
s
e
teac
h
es
h
o
w
to
f
ig
h
t
f
r
au
d
u
s
in
g
d
ata,
ap
p
l
y
in
g
s
u
p
e
r
v
is
ed
lear
n
i
n
g
alg
o
r
ith
m
s
to
d
etec
t
f
r
a
u
d
u
len
t
b
eh
av
io
r
b
ased
o
n
p
ast
f
r
au
d
an
d
u
s
in
g
u
n
s
u
p
e
r
v
is
ed
lear
n
in
g
m
eth
o
d
s
to
d
is
co
v
e
r
n
ew
ty
p
es
o
f
f
r
au
d
u
le
n
t
ac
tiv
ities
.
Giv
en
th
at
f
r
au
d
u
le
n
t
tr
an
s
ac
tio
n
s
a
r
e
r
a
r
e
co
m
p
ar
e
d
t
o
n
o
r
m
al
o
n
es,
it
is
ess
en
tial
to
lear
n
h
o
w
t
o
p
r
o
p
er
ly
class
if
y
im
b
alan
ce
d
d
atasets
.
Py
th
o
n
is
th
e
p
r
o
g
r
am
m
i
n
g
lan
g
u
ag
e
u
s
ed
to
im
p
lem
e
n
t
class
if
ier
s
.
T
h
is
s
ec
tio
n
will
d
is
cu
s
s
alg
o
r
ith
m
s
f
o
r
d
etec
tio
n
an
d
p
r
ev
en
tio
n
o
f
f
r
au
d
u
s
in
g
m
ac
h
in
e
lear
n
in
g
,
wh
ich
in
clu
d
es
l
o
g
is
tic
r
eg
r
e
s
s
io
n
,
a
s
u
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
f
o
r
m
ak
in
g
ca
teg
o
r
ic
al
d
ec
is
io
n
s
s
u
ch
as
class
if
y
in
g
tr
an
s
ac
tio
n
s
as
eit
h
er
‘
f
r
a
u
d
’
o
r
‘
n
o
n
-
f
r
au
d
’
.
Als
o
u
s
ed
is
th
e
r
an
d
o
m
f
o
r
es
t,
wh
ich
im
p
r
o
v
es
r
esu
lts
b
y
ch
ec
k
i
n
g
s
ev
er
al
d
i
f
f
er
en
t
c
o
n
d
itio
n
s
u
s
in
g
a
c
o
m
b
in
atio
n
o
f
d
ec
is
io
n
tr
ee
s
.
Oth
er
alg
o
r
ith
m
s
th
at
will
b
e
d
is
cu
s
s
ed
in
clu
d
e
n
aïv
e
B
ay
es,
a
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
u
s
ed
f
o
r
class
if
icatio
n
p
r
o
b
lem
s
,
m
o
s
t
n
o
tab
ly
f
o
r
te
x
t
ca
teg
o
r
izatio
n
.
He
is
a
m
e
m
b
er
o
f
a
class
o
f
alg
o
r
ith
m
s
k
n
o
wn
as
g
en
er
ativ
e
lear
n
in
g
alg
o
r
ith
m
s
th
at
m
o
d
e
l
a
g
i
v
en
class
o
r
ca
teg
o
r
y
b
y
ca
p
tu
r
in
g
th
e
in
p
u
t
d
is
tr
ib
u
tio
n
.
T
h
e
s
tep
s
o
f
th
e
p
r
o
p
o
s
al
alg
o
r
ith
m
a
r
e
d
em
o
n
s
tr
ated
in
Fig
u
r
e
1
.
Fig
u
r
e
1.
Ov
e
r
all
m
eth
o
d
o
lo
g
y
T
o
d
etec
t
f
r
au
d
u
len
t
b
eh
av
io
r
,
s
u
p
er
v
is
ed
lea
r
n
in
g
alg
o
r
ith
m
s
ar
e
ap
p
lied
b
ased
o
n
p
ast
f
r
a
u
d
,
wh
ile
u
n
s
u
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
s
d
is
co
v
er
n
ew
f
r
a
u
d
ac
tiv
i
ties
.
Sin
ce
f
r
au
d
u
le
n
t
tr
an
s
ac
tio
n
s
ar
e
r
ar
e
,
it’s
cr
u
cial
to
p
r
o
p
er
ly
class
if
y
im
b
alan
ce
d
d
atasets
.
Py
th
o
n
is
u
s
ed
to
im
p
lem
en
t th
e
class
if
ier
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
9
4
9
-
3956
3952
L
o
g
is
tic
r
eg
r
ess
io
n
,
a
s
u
p
er
v
i
s
ed
lear
n
in
g
tec
h
n
iq
u
e
,
ca
teg
o
r
izes
tr
an
s
ac
tio
n
s
as
'
f
r
au
d
,
'
's
u
s
p
icio
u
s
,
'
o
r
'
g
en
u
in
e.
'
R
an
d
o
m
f
o
r
est im
p
r
o
v
es r
esu
lts
b
y
co
m
b
in
in
g
d
ec
is
io
n
tr
ee
s
,
ea
ch
ch
ec
k
in
g
d
if
f
er
en
t c
o
n
d
itio
n
s
.
n
aïv
e
B
ay
es
f
u
r
t
h
er
en
h
a
n
ce
s
f
r
au
d
d
etec
tio
n
ac
c
u
r
ac
y
.
T
h
e
m
eth
o
d
o
lo
g
y
tr
ain
s
r
an
d
o
m
d
atasets
,
with
ea
ch
tr
ee
ass
ig
n
in
g
p
r
o
b
ab
ilit
ies
to
b
eh
av
io
r
s
as
'
f
r
au
d
,
'
'
s
u
s
p
icio
u
s
,
'
o
r
'
g
en
u
in
e,
'
an
d
th
e
m
o
d
el
p
r
ed
icts
o
u
tco
m
es
ac
co
r
d
in
g
l
y
.
T
r
ain
i
n
g
d
ata
,
a
s
u
b
s
et
o
f
o
r
ig
in
al
d
ata
,
t
r
ain
s
th
e
m
o
d
el,
wh
ile
test
in
g
d
ata
ch
ec
k
s
its
ac
cu
r
ac
y
.
Usi
n
g
a
r
ea
l
d
ataset
f
r
o
m
th
e
f
in
an
cial
s
ec
to
r
,
th
e
s
y
s
tem
ce
n
tr
alize
s
ac
co
u
n
t
life
cy
cle
ev
en
ts
f
o
r
co
n
tin
u
o
u
s
m
o
n
ito
r
i
n
g
an
d
p
r
o
tectio
n
.
T
h
is
d
ataset
tr
ain
s
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
to
m
ak
e
p
r
e
d
ictio
n
s
wh
ile
co
m
p
ly
in
g
with
d
ata
p
r
o
tectio
n
r
eg
u
latio
n
s
.
T
h
e
f
r
a
u
d
d
etec
tio
n
s
y
s
tem
d
esig
n
co
n
s
is
ts
o
f
th
r
ee
m
ain
s
tep
s
:
−
Def
in
in
g
tr
ain
in
g
an
d
test
s
ets:
th
e
tr
ain
in
g
s
et
in
clu
d
es h
is
to
r
ical
d
ata
f
o
r
m
o
d
el
tr
ai
n
in
g
,
wh
ile
th
e
test
s
et
ass
e
s
s
e
s
m
o
d
el
ac
cu
r
ac
y
.
−
T
r
ain
in
g
th
e
p
r
ed
ictio
n
m
o
d
el:
th
e
tr
ain
in
g
s
et
is
u
s
ed
to
d
ev
elo
p
a
m
o
d
el
th
at
p
r
e
d
icts
wh
eth
er
cu
s
to
m
er
b
eh
av
io
r
is
g
en
u
in
e
o
r
f
r
au
d
u
l
en
t.
Py
th
o
n
'
s
s
k
lear
n
lib
r
ar
y
f
a
cilitates th
is
ta
s
k
.
−
Ass
es
s
in
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
: th
e
test
s
et,
co
n
s
is
tin
g
o
f
n
e
w
d
ata,
ev
alu
ates th
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
.
I
n
a
f
r
au
d
d
etec
tio
n
c
o
n
tex
t,
test
s
et
tr
an
s
ac
tio
n
s
o
cc
u
r
af
ter
th
o
s
e
u
s
ed
f
o
r
tr
ain
i
n
g
.
T
h
is
ap
p
r
o
ac
h
e
n
s
u
r
es
r
eliab
le
o
u
tco
m
es
b
y
u
tili
zin
g
r
ea
l
f
in
an
cial
d
ata
wh
ile
r
esp
ec
tin
g
d
ata
p
r
o
tectio
n
re
g
u
latio
n
s
.
Fig
u
r
e
1
an
d
T
ab
l
e
1
h
ig
h
lig
h
t th
e
d
etailed
s
tep
s
an
d
f
ea
tu
r
es u
s
ed
in
p
r
o
p
o
s
ed
m
o
d
el
.
T
ab
le
1
.
Me
asu
r
es u
s
ed
f
o
r
m
ac
h
in
e
lear
n
in
g
F
e
a
t
u
r
e
s
D
e
scri
p
t
i
o
n
I
d
e
n
t
i
t
y
U
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.
T
h
is
ap
p
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o
ac
h
aim
s
to
m
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r
i
s
k
s
,
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ed
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ce
wasted
tim
e
an
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t,
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n
d
p
r
ev
en
t
s
u
b
s
tan
ti
al
f
in
an
cial
lo
s
s
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
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g
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
25
:
3
9
4
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-
3956
3954
C
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s
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ln
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to
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as b
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u
r
e
6
.
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atio
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o
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h
ig
h
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with
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at
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p
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ticu
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h
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g
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ev
elo
p
s
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ith
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eg
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ay
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ata
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o
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ith
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th
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ld
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r
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f
r
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,
im
p
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tech
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am
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t p
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.
ACK
NO
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Al
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p
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F
UNDING
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Au
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Sah
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Al
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Ali M
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Ali
✓
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Mu
ath
J
ar
r
ah
✓
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
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C
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DATA AV
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Der
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d
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RE
F
E
R
E
NC
E
S
[
1
]
A
.
B
e
q
u
a
i
,
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B
a
l
a
n
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i
n
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.
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[
2
]
J.
S
h
i
r
e
s,
“
C
a
r
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c
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:
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[
3
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M
.
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N
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.
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d
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iv
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m
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c
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li
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m
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jo
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th
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2
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,
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ly
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in
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stria
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telli
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m
s.
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u
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th
h
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s
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n
w
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rk
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g
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t
d
if
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re
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t
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n
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t
d
iffere
n
t
c
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n
tr
ies
.
He
c
a
n
b
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c
o
n
tac
ted
a
t
e
m
a
il
:
a
lj
a
rra
h
m
u
a
th
@g
m
a
il
.
c
o
m
.
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