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52
In
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J
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3
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in
ad
d
r
ess
in
g
p
o
ten
tial
n
etwo
r
k
th
r
ea
ts
b
ef
o
r
e
e
x
h
ib
itin
g
m
alicio
u
s
b
eh
av
io
r
.
I
DS
is
r
esp
o
n
s
ib
le
f
o
r
id
en
tif
y
in
g
m
alicio
u
s
ac
tiv
ities
o
n
a
h
o
s
t
th
at
ca
n
s
u
b
s
e
q
u
en
tly
s
p
r
ea
d
to
o
th
er
h
o
s
ts
with
in
th
e
n
etwo
r
k
.
R
esear
ch
u
tili
zin
g
I
DS
d
at
asets
h
as
b
ee
n
co
n
d
u
cted
.
Ou
r
in
n
o
v
ativ
e
I
DS
m
o
d
el
em
p
lo
y
s
s
tatis
tica
l
p
r
e
-
p
r
o
ce
s
s
in
g
,
Stack
Den
o
is
in
g
Au
to
E
n
co
d
e
r
(
SDAE
)
f
o
r
d
ata
r
ed
u
ctio
n
,
a
n
d
a
tr
an
s
f
o
r
m
er
-
en
h
a
n
ce
d
class
if
icatio
n
ap
p
r
o
ac
h
,
d
em
o
n
s
tr
ated
o
n
t
h
e
NSL
-
KDD
d
ataset
[
6
]
.
I
n
th
e
s
tu
d
y
b
y
s
u
n
et
a
l.
[
7
]
,
th
e
UNSW
-
N
B
1
5
d
ataset
was
em
p
lo
y
ed
f
o
r
a
R
an
d
o
m
Fo
r
est
class
if
icatio
n
m
o
d
el.
T
h
e
en
s
em
b
le
m
o
d
el
ap
p
lied
to
t
h
e
NSL
-
KDD,
Ky
o
to
,
an
d
C
SE
-
C
I
C
-
I
DS
-
2
0
1
8
d
atasets
y
ield
ed
s
atis
f
ac
to
r
y
r
esu
lts
[
8
]
.
E
x
p
er
im
en
ts
wer
e
co
n
d
u
cte
d
o
n
th
e
C
SE
-
C
I
C
-
DS2
0
1
8
d
ataset,
co
m
b
in
in
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
an
d
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN)
m
o
d
e
ls
[
9
]
.
E
x
p
er
im
e
n
tatio
n
o
n
th
e
B
o
t
-
I
o
T
d
ataset
u
s
in
g
th
e
p
r
o
p
o
s
ed
m
eth
o
d
p
r
o
v
ed
ef
f
icien
t
an
d
ac
h
iev
e
d
a
n
av
e
r
ag
e
a
cc
u
r
ac
y
ex
ce
e
d
in
g
9
6
%
[
1
0
]
.
I
n
th
is
s
tu
d
y
,
th
e
au
th
o
r
s
r
ef
er
to
th
e
I
o
T
I
D2
0
d
ataset
[
1
1
]
.
A
s
s
er
ts
th
e
ex
is
ten
ce
o
f
v
ar
io
u
s
ty
p
es
o
f
attac
k
s
o
n
th
e
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
,
in
cl
u
d
in
g
d
ata
e
x
f
iltra
tio
n
,
Do
S
an
d
DDo
S
attac
k
s
,
Key
l
o
g
g
in
g
,
as
well
as
o
p
er
atin
g
s
y
s
tem
(
OS
s
ca
n
)
a
n
d
s
er
v
ice
s
ca
n
n
in
g
(
s
er
v
ice
s
ca
n
).
Ullah
in
[
1
2
]
in
tr
o
d
u
ce
d
a
d
ataset
n
a
m
e
d
I
o
T
I
D2
0
,
wh
ich
c
o
n
tain
s
d
i
v
er
s
e
ty
p
es
o
f
I
o
T
attac
k
s
an
d
f
am
ilies
.
I
o
T
I
D2
0
was
d
ev
elo
p
e
d
f
o
r
th
e
d
etec
tio
n
o
f
ab
n
o
r
m
al
b
e
h
av
io
r
in
I
o
T
,
en
c
o
m
p
ass
in
g
Mir
ai
attac
k
s
,
Do
S,
Scan
,
MI
T
M
A
R
P
s
p
o
o
f
in
g
,
s
ca
n
h
o
s
t
p
o
r
t,
a
n
d
Mir
ai
-
UDP
[
1
3
]
.
Ho
wev
er
,
a
n
is
s
u
e
ar
is
es
in
th
e
I
o
T
I
D2
0
d
ataset,
n
a
m
ely
,
i
m
b
alan
ce
.
I
m
b
alan
ce
is
a
n
o
v
el
co
n
ce
r
n
in
th
e
f
ield
o
f
m
ac
h
in
e
lear
n
in
g
,
wh
er
e
im
b
alan
ce
o
cc
u
r
s
wh
en
th
e
n
u
m
b
e
r
o
f
s
am
p
le
s
in
o
n
e
class
i
s
g
r
ea
ter
t
h
an
t
h
e
o
th
er
in
a
d
a
t
aset
with
two
o
r
m
u
ltip
le
cl
ass
es
[
1
4
]
.
T
h
e
c
o
n
s
eq
u
e
n
ce
is
th
at
th
e
m
o
d
e
l
ten
d
s
to
l
ea
r
n
less
ab
o
u
t
m
i
n
o
r
ity
class
es,
r
esu
ltin
g
in
t
r
ain
in
g
b
ias
to
war
d
s
th
e
m
a
jo
r
ity
class
[
1
5
]
.
T
o
ad
d
r
ess
th
e
im
b
ala
n
ce
is
s
u
e
in
th
e
d
ata,
v
ar
io
u
s
s
am
p
lin
g
tech
n
iq
u
es
h
av
e
b
ee
n
p
r
o
p
o
s
ed
,
s
u
ch
as
o
v
er
s
am
p
lin
g
,
u
n
d
er
s
am
p
lin
g
,
r
an
d
o
m
s
am
p
lin
g
,
an
d
o
th
e
r
s
[
1
4
]
.
Sev
er
al
s
tu
d
ies
h
av
e
in
v
esti
g
ated
th
e
im
b
alan
ce
p
r
o
b
lem
in
m
u
lti
-
cl
ass
s
ce
n
ar
io
s
.
Fo
r
in
s
tan
ce
,
u
ti
lized
a
co
m
b
in
atio
n
o
f
s
y
n
th
et
ic
m
in
o
r
ity
o
v
er
-
s
am
p
lin
g
tech
n
iq
u
e
(
SMOT
E
)
an
d
u
n
d
e
r
s
am
p
lin
g
b
ased
o
n
g
au
s
s
ian
m
ix
tu
r
e
m
o
d
el
(
GM
M)
o
n
th
e
UNSW
-
N
B
1
5
an
d
C
I
C
I
DS2
0
1
7
d
atasets
[
1
6
]
.
Attack
ca
teg
o
r
ies
in
clu
d
e
co
m
m
o
n
ty
p
es
s
u
ch
as
Do
S,
DDo
S,
B
o
tn
et,
Po
r
tScan
,
w
eb
a
ttack
s
,
an
d
s
o
o
n
.
An
o
t
h
er
s
tu
d
y
f
r
o
m
M
q
ad
i
et
a
l
.
[
1
7
]
em
p
lo
y
ed
u
n
d
er
s
am
p
lin
g
b
ased
o
n
th
e
n
ea
r
-
m
is
s
alg
o
r
ith
m
with
r
an
d
o
m
f
o
r
est
.
T
h
e
r
ef
o
r
e
,
a
m
o
d
el
is
r
eq
u
ir
ed
to
p
r
o
d
u
ce
m
o
r
e
o
p
tim
al
r
esu
lts
,
wh
ich
ca
n
b
e
ac
h
iev
ed
b
y
u
t
ilizin
g
a
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
t
o
ad
d
r
ess
th
e
im
b
ala
n
ce
in
th
e
I
o
T
I
D2
0
d
ataset.
I
n
t
h
is
r
esear
ch
,
t
h
e
im
b
alan
ce
is
s
u
e
in
m
u
ltic
lass
is
tack
led
b
y
em
p
lo
y
in
g
an
im
b
alan
ce
r
atio
,
r
ef
er
r
ed
t
o
as
im
b
alan
ce
r
atio
f
o
r
m
u
la
(
I
R
F)
[
1
8
]
,
wh
e
r
e
ea
ch
m
in
o
r
ity
class
is
g
iv
en
weig
h
ted
em
p
h
asis
to
en
s
u
r
e
th
e
m
o
d
el
p
a
y
s
m
o
r
e
a
tten
tio
n
to
th
e
m
i
n
o
r
ity
class
es.
Ma
ch
in
e
l
ea
r
n
in
g
is
a
s
cien
tific
ex
p
lo
r
atio
n
o
f
alg
o
r
ith
m
s
an
d
s
tatis
t
ical
m
o
d
els
a
p
p
lied
b
y
co
m
p
u
ter
s
y
s
tem
s
to
p
er
f
o
r
m
s
p
ec
if
i
c
task
s
with
o
u
t
r
eq
u
ir
in
g
d
ir
ec
t
p
r
o
g
r
am
m
in
g
[
1
9
]
.
C
u
r
r
en
tly
,
a
n
o
m
aly
d
etec
tio
n
tech
n
iq
u
es
in
n
etwo
r
k
s
g
en
er
ally
r
ely
o
n
m
ac
h
i
n
e
lear
n
in
g
ap
p
r
o
ac
h
es,
s
u
ch
as
KNN
an
d
SVM
[
2
0
]
.
Acc
o
r
d
in
g
t
o
r
esear
c
h
,
s
o
m
e
I
DS
u
s
e
class
if
icatio
n
a
lg
o
r
ith
m
s
lik
e
de
cisi
o
n
tr
ee
s
,
SVM,
K
-
n
ea
r
est,
an
d
o
t
h
er
s
u
s
e
f
ea
t
u
r
e
s
elec
tio
n
[2
1]
.
I
n
th
is
s
tu
d
y
,
th
e
a
u
th
o
r
s
r
e
f
er
to
th
e
L
ig
h
tGB
M
ap
p
r
o
ac
h
.
lig
h
t
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
in
e
(
L
ig
h
tGB
M)
is
o
n
e
o
f
th
e
latest
r
esear
ch
f
in
d
in
g
s
in
th
e
g
r
ad
ien
t
b
o
o
s
tin
g
f
r
am
ewo
r
k
th
at
u
tili
ze
s
tr
ee
-
b
ased
lear
n
in
g
alg
o
r
ith
m
s
[
2
2
]
.
L
ig
h
tGB
M,
as
a
d
o
m
in
a
n
t
e
n
s
em
b
le
m
eth
o
d
,
u
tili
ze
s
th
e
d
ec
is
io
n
tr
ee
alg
o
r
ith
m
an
d
is
o
f
ten
ap
p
lied
to
class
if
icatio
n
task
s
d
u
e
to
its
s
u
p
er
io
r
ity
[
7
]
.
Fo
r
th
is
r
esear
ch
,
th
e
m
o
d
el
u
s
ed
is
XG
B
o
o
s
t
to
p
r
o
v
id
e
b
etter
p
er
f
o
r
m
an
ce
.
T
h
e
p
r
im
ar
y
c
o
n
tr
ib
u
tio
n
s
o
f
th
is
s
tu
d
y
in
clu
d
e:
−
I
n
tr
o
d
u
cin
g
a
n
o
v
el
f
o
r
m
u
la
t
er
m
ed
I
R
F
to
a
p
p
ly
weig
h
ted
em
p
h
asis
o
n
m
in
o
r
ity
class
es,
en
s
u
r
in
g
th
e
m
o
d
el
f
o
c
u
s
es o
n
th
e
m
i
n
o
r
ity
class
es,
p
ar
ticu
lar
l
y
in
b
in
ar
y
an
d
m
u
lticlas
s
s
ce
n
ar
io
s
.
−
I
n
teg
r
atin
g
I
R
F
in
to
XGBo
o
s
t
to
en
h
an
ce
p
er
f
o
r
m
an
ce
i
n
th
e
d
etec
tio
n
o
f
attac
k
s
wi
th
in
th
e
I
o
T
en
v
ir
o
n
m
en
t,
th
e
r
eb
y
ac
h
iev
in
g
im
p
r
o
v
ed
ac
c
u
r
ac
y
a
n
d
ef
f
ic
ien
cy
.
2.
M
E
T
H
O
D
I
n
r
esp
o
n
s
e
to
th
e
p
r
ev
ale
n
t
s
ec
u
r
ity
ch
allen
g
es
in
th
e
in
t
er
n
et
o
f
th
in
g
s
(
I
o
T
)
en
v
ir
o
n
m
en
t,
we
p
r
o
p
o
s
e
a
s
p
ec
if
ically
tailo
r
ed
in
tr
u
s
io
n
d
etec
tio
n
m
eth
o
d
o
lo
g
y
.
T
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
in
clu
d
es
th
e
im
p
lem
en
tatio
n
o
f
an
I
DS
d
es
ig
n
ed
t
o
ad
d
r
ess
th
e
u
n
iq
u
e
ch
a
r
ac
ter
is
tics
o
f
I
o
T
.
T
h
is
s
tr
ate
g
ic
m
eth
o
d
o
lo
g
y
is
cr
af
ted
to
p
r
o
v
id
e
r
o
b
u
s
t
p
r
o
tectio
n
ag
ai
n
s
t
ev
o
lv
in
g
s
ec
u
r
ity
th
r
ea
ts
in
th
e
d
y
n
am
ic
I
o
T
ec
o
s
y
s
tem
.
B
y
ad
d
r
ess
in
g
s
p
ec
if
ic
ch
allen
g
e
s
in
th
e
I
o
T
d
o
m
ain
,
o
u
r
m
et
h
o
d
o
lo
g
y
aim
s
to
en
h
a
n
ce
th
e
s
ec
u
r
ity
p
o
s
tu
r
e
an
d
r
esil
ien
ce
o
f
I
o
T
d
ev
ices
an
d
s
y
s
tem
s
.
Fig
u
r
e
1
illu
s
tr
at
es
th
e
im
p
lem
en
tatio
n
o
f
th
e
an
aly
s
is
u
s
in
g
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
h
a
n
cin
g
a
tta
ck
d
etec
tio
n
in
I
o
T th
r
o
u
g
h
in
teg
r
a
tio
n
o
f weig
h
ted
…
(
J
a
n
u
a
r
A
l A
mien
)
643
I
o
T
I
D2
0
d
ataset,
in
v
o
lv
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g
a
s
er
ies
o
f
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
s
u
ch
as
lab
el
en
c
o
d
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g
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n
u
m
er
ica
l
tr
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s
f
o
r
m
atio
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d
n
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alan
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tech
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iq
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m
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ity
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el.
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p
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ith
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is
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n
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th
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I
o
T
I
D2
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ata
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et.
Fig
u
r
e
1
.
Diag
r
a
m
o
f
m
o
d
el
c
o
n
s
tr
u
ctio
n
2
.
1
.
Da
t
a
s
et
I
o
T
I
D2
0
T
h
e
o
r
i
g
in
al
I
o
T
I
D2
0
d
ataset
co
n
s
is
ts
o
f
6
2
5
,
7
8
3
e
n
tr
ies
with
8
6
f
ea
tu
r
es
p
er
en
tr
y
[
2
3
]
.
Af
ter
elim
in
atin
g
d
u
p
licate
d
ata,
th
e
n
u
m
b
er
o
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en
tr
ies
is
r
ed
u
ce
d
t
o
4
6
1
,
6
9
6
with
8
6
f
ea
tu
r
es.
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h
e
p
u
r
p
o
s
e
o
f
th
e
d
u
p
licate
elim
in
atio
n
p
r
o
ce
s
s
is
to
clea
n
th
e
d
ataset
f
r
o
m
p
o
ten
tial
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ed
u
n
d
an
t
d
ata,
e
n
s
u
r
in
g
th
e
ac
c
u
r
ac
y
o
f
th
e
an
aly
s
is
o
n
th
e
d
ataset.
T
ab
le
1
s
h
o
ws
th
e
class
d
is
tr
ib
u
tio
n
f
o
r
b
o
th
b
i
n
ar
y
an
d
m
u
lticlas
s
class
if
icatio
n
:
b
in
ar
y
ca
teg
o
r
izes
d
ata
in
to
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o
m
aly
an
d
n
o
r
m
al
,
wh
ile
m
u
lticlas
s
p
r
o
v
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f
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er
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teg
o
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ies
s
u
ch
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Mir
ai,
s
ca
n
,
Do
S,
an
d
o
th
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s
,
with
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co
n
s
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t
to
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o
f
4
6
1
,
6
9
6
en
tr
ies
ac
r
o
s
s
class
if
icatio
n
s
.
T
h
e
class
if
icatio
n
o
f
class
attr
ib
u
tes in
v
o
lv
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e
u
tili
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o
f
th
e
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L
ab
el
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an
d
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C
at
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attr
ib
u
tes.
T
ab
le
1
.
B
in
ar
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a
n
d
m
u
lticlas
s
class
d
is
tr
ib
u
tio
n
I
o
T
I
D2
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at
aset
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8
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mal
3
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5
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8
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o
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a
l
4
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1
6
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C
a
t
C
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mber
M
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n
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o
S
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mal
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I
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o
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l
4
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6
9
6
I
n
th
e
p
r
ep
r
o
ce
s
s
in
g
p
h
ase,
th
e
co
d
e
u
n
d
er
g
o
es
s
ev
er
al
cr
u
cial
s
tep
s
to
en
h
an
ce
th
e
d
ataset
'
s
s
u
itab
ilit
y
f
o
r
m
ac
h
in
e
lear
n
in
g
task
s
.
I
n
itially
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it u
s
es
L
ab
el
E
n
co
d
er
to
co
n
v
er
t
ca
teg
o
r
ical
f
ea
tu
r
es,
n
am
ely
`
Src
_
I
P`,
an
d
`
Dst
_
I
P`,
in
to
n
u
m
er
ical
r
ep
r
esen
tat
io
n
s
.
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h
e
'
Flo
w_
I
D
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an
d
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T
im
estam
p
'
f
ea
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r
e
will
b
e
r
em
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v
ed
.
T
h
e
co
d
e
th
e
n
ad
d
r
e
s
s
es
p
o
ten
tial
is
s
u
es
r
elate
d
to
in
f
in
ite
v
alu
es
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ce
r
tain
c
o
lu
m
n
s
b
y
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ep
lacin
g
th
em
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lar
g
e
f
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ite
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alu
es.
Fu
r
th
er
m
o
r
e
,
to
en
s
u
r
e
th
e
r
o
b
u
s
tn
ess
o
f
th
e
d
ataset,
th
e
d
a
ta
i
s
s
ca
led
u
s
in
g
R
o
b
u
s
tScaler
,
a
tech
n
iq
u
e
d
esig
n
ed
to
r
ed
u
ce
s
en
s
itiv
ity
to
o
u
tlier
s
.
T
h
ese
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
co
llectiv
ely
co
n
tr
ib
u
te
to
o
p
tim
izin
g
th
e
d
ataset
f
o
r
s
u
b
s
eq
u
en
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
en
h
an
cin
g
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
.
2
.
2
.
I
m
ba
l
a
nce
ra
t
io
f
o
r
m
ula
T
o
en
h
a
n
ce
an
d
ad
v
a
n
ce
th
e
ea
r
lier
m
eth
o
d
in
tr
o
d
u
ce
d
i
n
b
in
ar
y
class
if
icatio
n
[
1
2
]
,
t
h
is
r
esear
ch
pr
esen
ts
a
m
u
lti
-
class
class
if
icatio
n
s
tr
ateg
y
in
c
o
r
p
o
r
atin
g
a
n
o
v
el
im
b
alan
ce
r
ati
o
ap
p
r
o
ac
h
.
T
h
e
an
tece
d
en
t
in
v
esti
g
atio
n
f
o
c
u
s
ed
o
n
b
in
ar
y
class
if
icatio
n
ch
allen
g
es,
p
r
im
ar
ily
ad
d
r
e
s
s
in
g
d
is
tin
ctio
n
s
b
etwe
en
two
class
es.
I
n
th
e
cu
r
r
en
t
s
tu
d
y
,
we
b
r
o
a
d
en
t
h
e
s
co
p
e
to
in
v
esti
g
ate
b
in
ar
y
an
d
m
u
lticlas
s
class
if
icatio
n
co
n
ce
r
n
s
.
T
h
e
p
r
o
ce
d
u
r
al
s
tep
s
f
o
r
co
m
p
u
tin
g
t
h
e
I
R
F f
o
r
a
g
i
v
en
d
ataset
ar
e
as f
o
llo
ws
[
2
4
]
:
-
Fin
d
th
e
n
u
m
b
er
o
f
s
am
p
les in
ea
ch
class
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
64
1
-
64
8
644
-
Fo
r
ea
ch
class
,
ca
lcu
late
th
e
n
u
m
b
er
o
f
s
am
p
les in
th
e
m
ajo
r
ity
class
(
)
an
d
th
e
n
u
m
b
e
r
o
f
s
am
p
les
in
th
e
m
in
o
r
ity
class
(
).
-
C
alcu
late
th
e
im
b
alan
ce
r
atio
(
)
f
o
r
ea
c
h
class
i
as f
o
llo
ws:
=
/
(
1
)
-
C
alcu
late
th
e
I
R
F v
alu
e
f
o
r
th
e
d
ataset
as th
e
m
ax
im
u
m
im
b
alan
ce
r
atio
ac
r
o
s
s
all
class
es:
=
(
1
,
2
,
…
,
)
(
2
)
-
C
alcu
late
th
e
av
er
ag
e
o
f
th
e
v
alu
es o
b
tain
ed
in
s
tep
2
.
-
R
etu
r
n
th
e
r
esu
lt.
W
h
er
e
is
th
e
to
tal
n
u
m
b
e
r
o
f
class
es in
th
e
d
ataset.
2
.
3
.
XG
B
o
o
s
t
Acc
o
r
d
in
g
to
C
h
en
an
d
Gu
est
r
in
[
2
5
]
,
T
h
ese
f
o
r
m
u
las
r
ep
r
esen
t
k
ey
c
o
m
p
o
n
en
ts
o
f
th
e
d
ec
is
io
n
tr
ee
m
o
d
el
with
in
th
e
f
r
a
m
ewo
r
k
o
f
g
r
ad
ie
n
t
b
o
o
s
tin
g
.
I
n
t
h
e
co
n
tex
t
o
f
th
e
g
r
ad
ien
t
b
o
o
s
tin
g
alg
o
r
ith
m
,
th
e
I
R
Fs
t
f
o
r
m
u
la
ℒ
(
)
d
en
o
tes
th
e
lo
s
s
f
u
n
ctio
n
at
iter
atio
n
(
)
,
en
co
m
p
ass
in
g
ter
m
s
r
elate
d
to
p
r
ed
ictio
n
er
r
o
r
s
,
th
e
cu
r
r
e
n
t
m
o
d
el'
s
p
r
ed
ictio
n
s
,
an
d
a
r
eg
u
lar
izatio
n
co
m
p
o
n
en
t.
T
h
e
s
ec
o
n
d
f
o
r
m
u
la
∗
ca
lcu
lates
th
e
o
p
tim
al
weig
h
t
f
o
r
a
s
p
ec
if
ic
n
o
d
e
in
th
e
d
ec
is
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n
tr
ee
,
co
n
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id
er
in
g
t
h
e
g
r
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d
ien
ts
an
d
Hess
ian
s
o
f
th
e
lo
s
s
f
u
n
ctio
n
,
with
an
ad
d
e
d
r
e
g
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lar
izatio
n
ter
m
.
T
h
e
th
ir
d
f
o
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m
u
la
ℒ
(
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(
)
d
ef
in
es
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r
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atin
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ter
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elate
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m
o
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ad
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,
Hess
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s
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a
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eg
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ar
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m
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d
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r
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astl
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e
f
o
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r
th
f
o
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m
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la
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ep
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id
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s
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lit
at
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tr
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n
o
d
e
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v
o
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m
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o
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ig
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eg
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lar
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,
an
d
a
p
r
u
n
in
g
ter
m
.
T
h
ese
f
o
r
m
u
latio
n
s
co
llectiv
ely
c
o
n
tr
ib
u
te
to
th
e
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f
ec
tiv
e
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ai
n
in
g
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d
o
p
t
im
izatio
n
o
f
th
e
g
r
ad
ien
t
b
o
o
s
tin
g
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o
r
ith
m
.
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h
ese
f
o
r
m
u
las
ar
e
p
ar
t
o
f
th
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ec
is
io
n
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ee
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s
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t
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g
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eth
o
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.
Her
e'
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a
b
r
ief
e
x
p
la
n
atio
n
f
o
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ea
ch
f
o
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m
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la:
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)
=
∑
(
ŷᵢ
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(
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)
=
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[
(
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,
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(
−
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)
+
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(
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)
+
1
2
=
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ℎ
2
(
ᵢ
)
]
+
(
)
,
(
4
)
ℒ
(
)
=
∑
(
ᵢ
(
ᵢ
)
+
=
1
1
2
ℎᵢ
2
(
ᵢ
)
]
+
(
)
,
(
5
)
∗
=
−
(
∑
∈
ᵢ
)
2
∑
∈
ℎ
+
,
(
6
)
ℒ
(
)
(
)
=
−
1
2
∑
(
∑
∈
ᵢ
)
2
∑
∈
ℎ
+
+
=
1
,
(
7
)
ℒ
=
1
2
[
(
∑
∈
ᵢ
)
2
∑
∈
ℎ
+
+
(
∑
∈
ᵢ
)
2
∑
∈
ℎ
+
−
(
∑
∈
ᵢ
)
2
∑
∈
ℎ
+
]
−
,
(
)
2
.
4
.
E
v
a
lua
t
i
o
n
I
n
th
e
co
n
tex
t
o
f
b
in
ar
y
a
n
d
m
u
lticlas
s
clas
s
if
icatio
n
,
s
p
ec
if
ically
f
o
r
th
e
L
a
b
el
An
d
a
C
at
task
,
ev
alu
atio
n
m
etr
ics
ar
e
g
en
er
a
ted
s
im
ilar
ly
to
th
e
p
r
o
ce
s
s
f
o
llo
wed
in
b
in
ar
y
a
n
d
m
u
lticl
ass
cla
s
s
if
icatio
n
.
T
h
ese
m
etr
ics
p
r
o
v
id
e
a
q
u
a
n
titativ
e
ass
es
s
m
en
t
o
f
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
in
h
an
d
lin
g
m
u
ltip
le
class
es,
o
f
f
er
in
g
in
s
ig
h
ts
in
to
asp
ec
ts
s
u
ch
as
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
an
d
ac
c
u
r
ac
y
f
o
r
ea
ch
class
wi
th
in
th
e
b
in
ar
y
an
d
m
u
lticlas
s
class
i
f
icatio
n
p
r
o
b
lem
.
T
h
is
p
r
o
v
id
es
a
s
o
lid
f
o
u
n
d
atio
n
f
o
r
ev
alu
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
el
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
d
i
f
f
er
en
t
class
es
in
th
e
b
in
a
r
y
an
d
m
u
lticlas
s
class
if
icatio
n
d
ataset
[
2
6
]
.
=
+
(
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
E
n
h
a
n
cin
g
a
tta
ck
d
etec
tio
n
in
I
o
T th
r
o
u
g
h
in
teg
r
a
tio
n
o
f weig
h
ted
…
(
J
a
n
u
a
r
A
l A
mien
)
645
=
+
(
1
0
)
1
=
2
∗
∗
+
(
1
1
)
=
+
(
1
2
)
=
+
(
1
3
)
=
+
+
+
+
(
1
4
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
I
m
ba
l
a
nce
ra
t
io
f
o
r
m
ula
T
h
e
p
u
r
p
o
s
e
o
f
th
e
r
esu
lts
f
r
o
m
T
ab
le
2
is
to
d
eter
m
in
e
th
e
class
weig
h
ts
ap
p
lied
to
ea
ch
class
with
in
th
e
d
ataset.
T
h
ese
ca
lcu
latio
n
s
y
ield
th
e
class
weig
h
ts
f
o
r
ea
c
h
class
,
ex
p
r
ess
ed
a
s
t
h
e
I
R
F.
I
n
b
in
ar
y
class
es,
th
e
a
n
o
m
aly
class
h
a
s
an
I
R
F
o
f
0
.
5
4
5
6
9
8
4
6
,
wh
il
e
th
e
n
o
r
m
al
class
h
as
an
I
R
F
o
f
5
.
9
7
0
6
4
4
3
4
.
T
h
e
to
tal
I
R
F
f
o
r
b
in
ar
y
class
es
is
1
0
.
9
4
1
2
8
8
6
0
.
Ad
d
itio
n
ally
,
f
o
r
m
u
lticlas
s
clas
s
es,
th
e
Mir
ai
class
h
as
an
I
R
F
o
f
1
.
5
5
4
7
9
3
7
3
6
,
th
e
s
ca
n
class
h
as
an
I
R
F
o
f
3
.
5
7
0
4
5
8
5
8
8
,
t
h
e
Do
S
class
h
as
an
I
R
F
o
f
0
.
3
2
8
4
9
0
0
1
4
,
th
e
n
o
r
m
al
class
h
as
an
I
R
F
o
f
2
.
3
9
2
3
3
1
2
0
9
,
an
d
th
e
MI
T
M
AR
P
Sp
o
o
f
in
g
class
h
as
an
I
R
F
o
f
1
.
6
2
7
2
9
4
5
1
6
.
T
h
ese
r
esu
lts
d
e
m
o
n
s
tr
ate
th
e
weig
h
ts
ass
ig
n
ed
to
ea
ch
class
to
ad
d
r
ess
t
h
e
im
b
alan
ce
with
in
th
e
d
ataset.
T
ab
le
2
.
C
lass
weig
h
ts
b
ased
o
n
I
R
F
ca
lcu
latio
n
La
b
e
l
C
l
a
s
s
n
u
m
b
e
r
C
l
a
s
s
o
f
n
u
m
b
e
r
I
R
F
B
i
n
a
r
y
A
n
o
m
a
l
y
4
2
3
0
9
8
0
.
5
4
5
6
9
8
4
6
N
o
r
mal
3
8
5
9
8
5
.
9
7
0
6
4
4
3
4
T
o
t
a
l
4
6
1
6
9
6
C
a
t
C
l
a
ss
n
u
mber
C
l
a
ss
o
f
n
u
mber
IR
F
M
u
l
t
i
c
l
a
ss
M
i
r
a
i
2
8
1
1
0
2
1
.
5
5
4
7
9
3
7
3
6
S
c
a
n
5
9
3
9
0
3
.
5
7
0
4
5
8
5
8
8
D
o
S
5
6
7
4
4
0
.
3
2
8
4
9
0
0
1
4
N
o
r
mal
3
8
5
9
8
2
.
3
9
2
3
3
1
2
0
9
M
I
TM
A
R
P
S
p
o
o
f
i
n
g
2
5
8
6
2
1
.
6
2
7
2
9
4
5
1
6
T
o
t
a
l
4
6
1
6
9
6
3
.
2
.
XG
B
o
o
s
t
m
o
del a
nd
ev
a
lua
t
io
n
T
ab
le
3
p
r
esen
ts
a
co
m
p
r
eh
e
n
s
iv
e
ev
alu
atio
n
o
f
t
h
e
I
R
F
m
o
d
el'
s
p
er
f
o
r
m
an
ce
in
b
o
th
b
i
n
ar
y
an
d
m
u
lticlas
s
s
ce
n
ar
io
s
.
I
n
th
e
b
in
ar
y
an
aly
s
is
,
m
etr
ics
in
clu
d
i
n
g
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
ar
e
d
etailed
f
o
r
th
e
"
a
n
o
m
aly
"
an
d
"
n
o
r
m
al"
class
es.
No
tab
ly
,
th
e
"
a
n
o
m
aly
"
class
ac
h
iev
e
s
ex
ce
p
tio
n
al
p
er
f
o
r
m
an
ce
with
an
ac
cu
r
ac
y
o
f
0
.
9
9
9
9
8
4
,
p
r
ec
is
io
n
o
f
0
.
9
9
9
7
5
,
r
ec
all
o
f
1
.
0
0
0
0
0
,
an
d
an
F1
-
s
co
r
e
o
f
0
.
9
9
9
8
8
.
Similar
ly
,
th
e
"
n
o
r
m
al"
class
ac
h
iev
es
p
er
f
ec
t
ac
c
u
r
ac
y
(
1
.
0
0
0
0
0
)
with
h
ig
h
p
r
ec
is
io
n
(
0
.
9
9
9
9
8
)
an
d
r
ec
all
(
0
.
9
9
9
9
9
)
.
Fig
u
r
e
2
p
r
o
v
id
es
a
v
is
u
al
d
ep
ictio
n
o
f
th
ese
m
etr
ics
f
o
r
cla
r
ity
an
d
en
h
an
ce
d
in
ter
p
r
etatio
n
.
I
n
th
e
m
u
lticlas
s
as
s
ess
m
en
t,
class
es
s
u
ch
as
"Do
S,"
"M
I
T
M
AR
P
Sp
o
o
f
in
g
,
"
"M
ir
ai,
"
"
n
o
r
m
al,
"
an
d
"
s
ca
n
"
ar
e
ev
alu
ated
.
Key
h
ig
h
lig
h
ts
in
clu
d
e
th
e
"Do
S"
class
with
an
ac
cu
r
ac
y
o
f
0
.
9
9
9
9
1
2
,
p
r
ec
is
io
n
o
f
1
.
0
0
0
0
0
,
r
ec
all
o
f
0
.
9
9
9
4
1
,
an
d
a
n
im
p
r
ess
iv
e
F1
-
s
co
r
e
o
f
0
.
9
9
9
7
0
.
"M
I
T
M
AR
P
Sp
o
o
f
in
g
"
also
d
em
o
n
s
tr
ates
ex
ce
p
tio
n
al
ac
c
u
r
ac
y
an
d
r
o
b
u
s
t
p
r
ec
is
io
n
a
n
d
r
ec
all
m
etr
ics.
Similar
ly
,
cl
ass
es
lik
e
"M
ir
ai,
"
"No
r
m
al,
"
an
d
"Sca
n
"
co
n
s
is
t
e
n
tly
ex
h
ib
it
s
tr
o
n
g
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
all
ev
alu
ated
m
etr
ics,
u
n
d
er
s
co
r
in
g
th
e
I
R
F
m
o
d
el'
s
ef
f
icac
y
i
n
ac
cu
r
ately
class
if
y
in
g
in
s
tan
ce
s
ac
r
o
s
s
d
iv
er
s
e
class
es.
A
g
r
ap
h
ical
r
ep
r
esen
tatio
n
o
f
th
ese
m
u
lticlas
s
ev
alu
atio
n
m
etr
ics
in
Fig
u
r
e
2
co
m
p
lem
en
ts
th
e
te
x
tu
al
f
in
d
in
g
s
.
Ov
er
all
,
I
R
F
p
r
o
v
es
ef
f
ec
tiv
e
in
im
p
r
o
v
in
g
th
e
d
etec
tio
n
o
f
m
i
n
o
r
i
ty
class
es
ac
r
o
s
s
v
ar
io
u
s
s
ce
n
ar
io
s
.
Alth
o
u
g
h
it
r
eq
u
ir
es
m
o
r
e
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
in
m
u
lticlas
s
m
o
d
el
s
,
th
is
in
teg
r
atio
n
d
em
o
n
s
tr
ates
I
R
F
'
s
p
o
ten
tial
f
o
r
ap
p
licatio
n
i
n
attac
k
d
etec
tio
n
s
y
s
tem
s
with
in
I
o
T
en
v
ir
o
n
m
en
ts
.
I
t
s
ig
n
if
ican
tly
en
h
a
n
ce
s
p
er
f
o
r
m
a
n
ce
in
th
r
ea
t d
etec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
64
1
-
64
8
646
T
ab
le
3
.
E
v
alu
atio
n
r
esu
lts
o
f
I
R
F
m
o
d
el
p
er
f
o
r
m
an
ce
i
n
b
in
ar
y
an
d
m
u
lticlas
s
s
ce
n
ar
io
s
La
b
e
l
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
Ti
me
(
S
e
c
)
A
n
o
m
a
l
y
0
.
9
9
9
9
8
4
0
.
9
9
9
7
5
1
.
0
0
0
0
0
0
.
9
9
9
8
8
2
3
.
5
0
1
8
9
8
N
o
r
mal
1
.
0
0
0
0
0
0
.
9
9
9
9
8
0
.
9
9
9
9
9
A
v
e
r
a
g
e
0
.
9
9
9
9
8
4
0
.
9
9
9
9
8
4
0
.
9
9
9
9
8
4
C
a
t
A
c
c
u
r
a
c
y
Pr
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
D
o
S
0
.
9
9
9
9
1
2
1
.
0
0
0
0
0
0
.
9
9
9
4
1
0
.
9
9
9
7
0
5
0
.
8
3
2
9
4
6
M
I
TM
A
R
P
S
p
o
o
f
i
n
g
0
.
9
9
9
5
7
1
.
0
0
0
0
0
0
.
9
9
9
7
8
M
i
r
a
i
0
.
9
9
9
9
5
0
.
9
9
9
9
6
0
.
9
9
9
9
6
N
o
r
mal
0
.
9
9
9
7
5
1
.
0
0
0
0
0
0
.
9
9
9
8
8
S
c
a
n
0
.
9
9
9
8
7
0
.
9
9
9
9
3
0
.
9
9
9
9
0
We
i
g
h
t
e
d
a
v
e
r
a
g
e
0
.
9
9
9
9
1
2
0
.
9
9
9
9
1
2
0
.
9
9
9
9
1
2
Fig
u
r
e
2
.
Per
f
o
r
m
an
c
e
m
etr
ics f
o
r
d
i
f
f
er
en
t
XGBo
o
s
t
lab
el
s
tr
ateg
ies
T
ab
le
4
s
u
m
m
ar
izes
th
e
p
er
f
o
r
m
an
ce
m
etr
ics
o
f
v
ar
i
o
u
s
alg
o
r
ith
m
s
f
o
r
b
in
a
r
y
an
d
m
u
lticlas
s
class
if
icatio
n
.
W
h
ile
m
eth
o
d
s
lik
e
s
h
allo
w
n
eu
r
al
n
etwo
r
k
s
(
1
0
0
%
ac
cu
r
ac
y
)
an
d
r
an
d
o
m
f
o
r
est
(
9
9
.
9
6
%)
d
em
o
n
s
tr
ate
s
tr
o
n
g
r
esu
lts
,
th
e
p
r
o
p
o
s
ed
XGBo
o
s
t M
u
lticlas
s
I
R
F
ac
h
iev
es th
e
h
ig
h
est ac
c
u
r
ac
y
o
f
9
9
.
9
9
%,
o
u
tp
er
f
o
r
m
in
g
p
r
io
r
ap
p
r
o
ac
h
es.
T
h
is
h
ig
h
lig
h
ts
th
e
ef
f
e
ctiv
en
ess
o
f
XGBo
o
s
t
in
h
a
n
d
lin
g
m
u
lticlas
s
class
if
icatio
n
task
s
wi
th
ex
ce
p
tio
n
al
p
r
ec
is
io
n
.
T
ab
le
4
.
Per
f
o
r
m
an
ce
m
etr
ics f
o
r
d
if
f
er
en
t
XGBo
o
s
t
b
in
ar
y
an
d
m
u
lticlas
s
s
tr
ateg
ies
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
U
l
l
a
h
,
S
a
f
i
e
t
a
l
.
[
2
7
]
D
C
N
N
9
8
%
Y
.
S
o
n
g
[
2
8
]
D
e
e
p
Le
a
r
n
i
n
g
-
M
C
C
9
4
%
R
.
Q
a
d
d
o
u
r
a
[
2
9
]
S
i
n
g
l
e
H
i
d
d
e
n
L
a
y
e
r
F
e
e
d
-
F
o
r
w
a
r
d
N
e
u
r
a
l
N
e
t
w
o
r
k
(
S
LFN
)
9
8
%
A.
A
.
A
l
s
u
l
a
m
i
[
3
0
]
S
h
a
l
l
o
w
N
e
u
r
a
l
N
e
t
w
o
r
k
s
(
S
N
N
s)
1
0
0
%
P
.
M
a
n
i
r
i
h
o
[
3
1
]
R
a
n
d
o
m F
o
r
e
s
t
(
D
o
S
,
M
I
TM
,
S
c
a
n
)
9
9
,
9
6
%
K
.
A
l
b
u
l
a
y
h
i
[
3
2
]
I
n
t
e
r
sec
t
i
o
n
M
a
t
h
e
m
a
t
i
c
a
l
(
I
M
F
)
a
n
d
U
n
i
o
n
M
a
t
h
e
m
a
t
i
c
a
l
(
U
M
F
)
9
9
.
7
%
a
n
d
9
9
,
7
%
I
.
U
l
l
a
h
[
1
2
]
D
e
c
i
s
i
o
n
Tr
e
e
(
S
u
b
-
C
a
t
e
g
o
r
y
)
8
8
%
P
r
o
p
o
se
d
m
e
t
h
o
d
X
G
B
o
o
st
M
u
l
t
i
c
l
a
ss I
R
F
9
9
,
9
9
%
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
s
u
cc
ess
f
u
lly
in
tr
o
d
u
ce
s
an
d
in
teg
r
ates
th
e
I
R
F
in
to
th
e
XGBo
o
s
t
alg
o
r
ith
m
to
en
h
an
ce
attac
k
d
etec
tio
n
p
e
r
f
o
r
m
an
ce
in
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
e
n
v
ir
o
n
m
en
ts
.
E
x
p
e
r
im
en
tal
r
e
s
u
lts
d
em
o
n
s
tr
ate
th
at
ap
p
ly
in
g
I
R
F
ef
f
ec
tiv
ely
ad
d
r
ess
es
class
im
b
alan
ce
wit
h
in
d
atasets
.
I
n
b
in
ar
y
s
ce
n
ar
i
o
s
,
I
R
F
in
cr
ea
s
ed
th
e
r
ec
all
m
etr
ic
f
r
o
m
0
.
9
8
8
9
1
4
to
0
.
9
9
8
6
3
5
,
with
a
n
eg
lig
ib
le
d
ec
r
ea
s
e
in
ac
cu
r
ac
y
f
r
o
m
0
.
9
9
8
9
7
1
t
o
0
.
9
9
8
6
3
5
.
Similar
ly
,
in
m
u
lti
class
s
ce
n
ar
io
s
,
I
R
F
s
h
o
wed
b
alan
ce
d
p
er
f
o
r
m
an
ce
with
a
s
lig
h
t
d
ec
r
ea
s
e
in
ac
cu
r
ac
y
f
r
o
m
0
.
9
9
3
2
5
3
to
0
.
9
9
2
7
3
3
,
th
o
u
g
h
p
r
o
ce
s
s
in
g
tim
e
in
cr
ea
s
ed
f
r
o
m
3
7
.
6
3
s
ec
o
n
d
s
to
4
9
.
4
0
s
ec
o
n
d
s
.
T
h
e
im
p
lem
en
tatio
n
o
f
I
R
F
n
o
t
o
n
ly
im
p
r
o
v
e
s
th
e
d
etec
tio
n
o
f
m
in
o
r
ity
class
es
b
u
t
also
d
em
o
n
s
tr
ates
s
ig
n
if
ican
t
p
o
te
n
tial
f
o
r
ap
p
licatio
n
in
in
t
r
u
s
io
n
d
etec
tio
n
s
y
s
tem
s
with
in
I
o
T
en
v
ir
o
n
m
e
n
ts
.
Alth
o
u
g
h
I
R
F
r
eq
u
ir
es
g
r
ea
te
r
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
,
th
e
en
h
an
ce
d
p
e
r
f
o
r
m
an
ce
i
n
d
etec
tin
g
m
alicio
u
s
ac
tiv
ities
s
u
b
s
tan
tiates
i
ts
ef
f
ec
tiv
en
ess
an
d
r
eliab
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as
a
p
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m
is
in
g
s
o
lu
tio
n
to
b
o
ls
ter
cy
b
er
s
ec
u
r
ity
m
ea
s
u
r
es
in
I
o
T
s
ettin
g
s
.
Fu
tu
r
e
wo
r
k
s
h
o
u
ld
ex
p
lo
r
e
o
p
tim
izin
g
th
e
I
R
F
al
g
o
r
ith
m
f
o
r
co
m
p
u
tatio
n
al
ef
f
icien
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an
d
test
its
ap
p
licatio
n
o
n
v
ar
i
o
u
s
I
o
T
d
atas
ets
to
as
s
ess
g
en
er
aliza
tio
n
an
d
r
o
b
u
s
tn
ess
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
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n
g
&
C
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m
p
Sci
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2
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4
7
52
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tta
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d
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tio
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r
o
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h
in
teg
r
a
tio
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f weig
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…
(
J
a
n
u
a
r
A
l A
mien
)
647
Ad
d
itio
n
ally
,
th
e
d
e
v
elo
p
m
e
n
t
o
f
ad
ap
tiv
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d
etec
tio
n
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tem
s
with
co
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tin
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s
lear
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g
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p
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s
h
o
u
ld
b
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in
v
esti
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to
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p
r
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p
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to
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i
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th
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in
d
y
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am
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en
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ass
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p
r
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v
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b
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Un
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Mu
h
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R
iau
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I
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d
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esia,
an
d
Un
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Ma
lay
s
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Kela
n
tan
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Ma
l
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s
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wh
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ab
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e
co
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p
letio
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o
f
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is
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ticle.
Ad
d
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n
ally
,
th
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au
t
h
o
r
s
ex
ten
d
th
eir
g
r
atitu
d
e
to
f
e
llo
w
r
esear
ch
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s
wh
o
co
n
tr
i
b
u
ted
,
b
o
th
f
o
r
m
all
y
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d
i
n
f
o
r
m
ally
,
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th
e
p
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ep
a
r
atio
n
o
f
th
is
p
a
p
er
.
RE
F
E
R
E
NC
E
S
[
1
]
K
.
Ji
a
n
g
,
W
.
W
a
n
g
,
A
.
W
a
n
g
,
a
n
d
H
.
W
u
,
“
N
e
t
w
o
r
k
i
n
t
r
u
s
i
o
n
d
e
t
e
c
t
i
o
n
c
o
m
b
i
n
e
d
h
y
b
r
i
d
sam
p
l
i
n
g
w
i
t
h
d
e
e
p
h
i
e
r
a
r
c
h
i
c
a
l
n
e
t
w
o
r
k
,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
8
,
p
p
.
3
2
4
6
4
–
3
2
4
7
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
a
c
c
e
s
s.2
0
2
0
.
2
9
7
3
7
3
0
.
[
2
]
N
.
K
o
r
o
n
i
o
t
i
s,
N
.
M
o
u
s
t
a
f
a
,
E.
S
i
t
n
i
k
o
v
a
,
a
n
d
B
.
T
u
r
n
b
u
l
l
,
“
To
w
a
r
d
s
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
r
e
a
l
i
st
i
c
b
o
t
n
e
t
d
a
t
a
s
e
t
i
n
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
f
o
r
n
e
t
w
o
r
k
f
o
r
e
n
si
c
a
n
a
l
y
t
i
c
s:
B
o
t
-
I
o
T
d
a
t
a
se
t
,
”
F
u
t
u
r
e
G
e
n
e
ra
t
i
o
n
C
o
m
p
u
t
e
r
S
y
s
t
e
m
s
,
v
o
l
.
1
0
0
,
p
p
.
7
7
9
-
7
9
6
,
2
0
1
9
,
do
i
:
1
0
.
1
0
1
6
/
j
.
f
u
t
u
r
e
.
2
0
1
9
.
0
5
.
0
4
1
.
[
3
]
H
.
A
.
A
b
d
u
l
-
G
h
a
n
i
,
D
.
K
o
n
st
a
n
t
a
s
,
a
n
d
M
.
M
a
h
y
o
u
b
,
“
A
c
o
mp
r
e
h
e
n
s
i
v
e
I
o
T
a
t
t
a
c
k
s
su
r
v
e
y
b
a
se
d
o
n
a
b
u
i
l
d
i
n
g
-
b
l
o
c
k
e
d
r
e
f
e
r
e
n
c
e
m
o
d
e
l
,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Ad
v
a
n
c
e
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s(
I
J
A
C
S
A)
,
v
o
l
.
9
,
n
o
.
3
,
2
0
1
8
,
d
o
i
:
1
0
.
1
4
5
6
9
/
I
JA
C
S
A
.
2
0
1
8
.
0
9
0
3
4
9
.
[
4
]
N
.
K
o
r
o
n
i
o
t
i
s
,
N
.
M
o
u
s
t
a
f
a
,
a
n
d
E.
S
i
t
n
i
k
o
v
a
,
“
A
n
e
w
n
e
t
w
o
r
k
f
o
r
e
n
si
c
f
r
a
mew
o
r
k
b
a
s
e
d
o
n
d
e
e
p
l
e
a
r
n
i
n
g
f
o
r
I
n
t
e
r
n
e
t
o
f
Th
i
n
g
s
n
e
t
w
o
r
k
s
:
A
p
a
r
t
i
c
l
e
d
e
e
p
f
r
a
mew
o
r
k
,
”
Fu
t
u
r
e
G
e
n
e
r
a
t
i
o
n
C
o
m
p
u
t
e
r
S
y
s
t
e
m
s
,
v
o
l
.
1
1
0
,
p
p
.
9
1
–
1
0
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
f
u
t
u
r
e
.
2
0
2
0
.
0
3
.
0
4
2
.
[
5
]
L.
V
i
g
o
y
a
,
D
.
F
e
r
n
a
n
d
e
z
,
V
.
C
a
r
n
e
i
r
o
,
a
n
d
F
.
J.
N
ó
v
o
a
,
“
I
o
T
d
a
t
a
se
t
v
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rtme
n
t,
S
TM
IK
-
AMIK
Riau
.
An
d
m
a
ste
r'
s
d
e
g
re
e
in
M
a
ste
r
o
f
In
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
a
t
P
u
tra
In
d
o
n
e
sia
Un
i
v
e
rsity
P
a
d
a
n
g
.
No
w
wo
rk
i
n
g
a
s
a
lec
tu
re
r
i
n
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Un
iv
e
rsity
M
u
h
a
m
m
a
d
iy
a
h
o
f
Riau
.
Wi
t
h
re
se
a
rc
h
in
tere
sts
in
th
e
fiel
d
o
f
M
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
s
a
n
d
AI.
He
c
a
n
be
c
o
n
tac
ted
at
e
m
a
il
:
jan
u
a
ra
lam
ien
@u
m
ri.
a
c
.
id
.
H
a
d
h
r
a
m
i
Ab
G
h
a
n
i
re
c
e
iv
e
d
h
is
b
a
c
h
e
lo
r
d
e
g
re
e
in
e
lec
tro
n
ics
e
n
g
in
e
e
rin
g
fro
m
M
u
lt
ime
d
ia
Un
i
v
e
rsity
M
a
l
a
y
sia
(M
M
U)
in
2
0
0
2
.
In
2
0
0
4
,
h
e
c
o
m
p
lete
d
h
is
m
a
ste
r'
s
d
e
g
re
e
in
Tele
c
o
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
a
t
T
h
e
Un
i
v
e
rsity
o
f
M
e
lb
o
u
r
n
e
.
He
th
e
n
p
u
rsu
e
d
h
is
P
h
.
D.
a
t
Im
p
e
rial
Co
ll
e
g
e
L
o
n
d
o
n
i
n
i
n
telli
g
e
n
t
n
e
two
r
k
s
y
ste
m
s
a
n
d
c
o
m
p
lete
d
h
is
P
h
.
D.
in
2
0
1
1
.
He
can
be
c
o
n
tac
ted
at
e
m
a
il
:
h
a
d
h
ra
m
i.
a
g
@
u
m
k
.
e
d
u
.
m
y
.
Nurul
Iz
r
in
Md
S
a
leh
o
b
tain
e
d
a
b
a
c
h
e
lo
r'
s
d
e
g
re
e
in
in
f
o
rm
a
ti
o
n
tec
h
n
o
lo
g
y
fro
m
M
u
lt
ime
d
ia
Un
iv
e
rsit
y
M
a
lay
sia
(M
M
U).
He
c
o
m
p
lete
d
h
is
m
a
ste
r'
s
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
a
t
Th
e
U
n
iv
e
rsity
o
f
P
u
tra
M
a
lay
sia
.
T
h
e
n
c
o
m
p
lete
a
P
h
.
D.
a
t
Th
e
Un
iv
e
rsity
o
f
Br
u
n
e
l
Lo
n
d
o
n
i
n
th
e
sa
m
e
field
o
f
st
u
d
y
.
S
h
e
can
be
c
o
n
tac
ted
at
e
m
a
il
:
izrin
@u
tem
.
e
d
u
.
m
y
.
S
o
n
i
re
c
e
iv
e
d
th
e
b
a
c
h
e
lo
r'
s
d
e
g
re
e
in
In
fo
rm
a
ti
c
s
En
g
in
e
e
r
in
g
De
p
a
rtme
n
t
fro
m
S
TM
IK
AMIK
Ria
u
,
I
n
d
o
n
e
sia
a
n
d
th
e
m
a
ste
r’s
d
e
g
re
e
i
n
c
o
m
p
u
ter
sc
ien
c
e
Isla
m
ic
Un
iv
e
rsity
o
f
In
d
o
n
e
sia
.
n
o
w
w
o
rk
s
a
s
a
lec
t
u
re
r
a
t
t
h
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Un
iv
e
rsity
o
f
M
u
h
a
m
m
a
d
iy
a
h
Ri
a
u
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
M
a
c
h
i
n
e
lea
rn
i
n
g
a
lg
o
rit
h
m
s a
n
d
AI
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
so
n
i@
u
m
ri.
a
c
.
id
.
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