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ANN)
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1
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ANN
ab
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[
1
2
]
.
SVM
u
s
es
k
er
n
el
in
t
h
e
m
o
d
e
l
to
lear
n
t
h
e
f
u
n
ctio
n
s
h
o
wev
er
th
e
r
esu
lts
ar
e
co
m
m
o
n
ly
d
if
f
icu
lt
to
in
ter
p
r
et
an
d
u
n
d
e
r
s
tan
d
b
y
th
e
d
ec
is
io
n
m
ak
e
r
[
1
3
]
.
Me
a
n
wh
ile,
DT
A
is
a
v
e
r
y
s
im
p
le
m
eth
o
d
to
i
n
ter
p
r
et
a
n
d
ea
s
y
to
im
p
lem
en
t.
T
h
e
d
r
awb
ac
k
s
o
f
th
is
m
eth
o
d
ar
e
it
is
n
o
t
b
ein
g
b
ale
to
co
p
e
with
th
e
lo
s
t
d
a
ta
an
d
th
e
tr
ee
m
u
s
t
b
e
r
eb
u
ilt
ev
e
r
y
tim
e
a
n
ew
s
a
m
p
le
is
ad
d
ed
to
f
i
n
d
th
e
s
o
lu
t
io
n
s
.
[
1
4
]
,
[
1
5
]
.
I
n
co
n
s
eq
u
en
ce
,
f
u
zz
y
s
y
s
te
m
is
a
g
o
o
d
m
eth
o
d
th
at
ca
n
d
ea
l w
ith
in
ac
cu
r
ate
an
d
in
co
m
p
lete
is
s
u
es [
3
]
,
[
1
6
]
.
I
n
f
u
z
z
y
s
y
s
t
e
m
,
o
n
e
o
f
t
h
e
p
r
o
c
e
s
s
es
is
t
o
i
d
e
n
t
i
f
y
t
h
e
f
u
z
z
y
p
a
r
a
m
e
t
e
r
n
a
m
e
d
f
u
z
z
y
r
u
l
e
s
a
n
d
m
e
m
b
e
r
s
h
i
p
f
u
n
c
t
i
o
n
s
.
T
h
is
p
r
o
c
e
s
s
is
c
a
ll
e
d
f
u
z
z
y
m
o
d
e
l
l
i
n
g
.
T
h
e
c
o
n
s
t
r
u
c
t
i
o
n
o
f
f
u
z
z
y
s
y
s
t
e
m
b
e
c
o
m
es
c
o
m
p
l
i
c
a
t
e
d
w
h
e
n
it
is
a
p
p
li
e
d
t
o
a
c
o
m
p
le
x
is
s
u
e
h
e
n
c
e
t
h
e
r
e
s
u
l
ts
p
r
o
d
u
c
e
d
b
y
t
h
e
s
y
s
t
em
a
r
e
n
o
t
g
u
a
r
a
n
t
ee
o
p
t
i
m
a
l
i
n
t
e
r
m
s
o
f
t
h
e
s
y
s
t
em
a
c
c
u
r
a
t
e
n
e
s
s
.
T
h
e
r
e
f
o
r
e
,
a
n
o
p
t
i
m
i
z
a
ti
o
n
m
e
t
h
o
d
i
s
n
e
e
d
e
d
t
o
a
u
t
o
m
a
t
e
t
h
e
p
r
o
c
e
s
s
o
f
i
d
e
n
t
i
f
y
i
n
g
t
h
e
f
u
z
zy
p
a
r
a
m
e
t
e
r
i
n
t
h
e
f
u
z
z
y
s
y
s
tem
.
B
as
e
d
o
n
t
h
e
o
b
s
e
r
v
a
t
i
o
n
in
t
h
e
p
r
e
v
i
o
u
s
w
o
r
k
s
d
o
n
e
b
y
o
t
h
e
r
r
e
s
e
a
r
c
h
e
r
s
,
a
p
p
l
y
i
n
g
m
e
t
a
h
e
u
r
is
t
ic
a
l
g
o
r
it
h
m
i
s
a
w
el
l
-
l
i
k
e
d
a
p
p
r
o
a
c
h
t
h
a
t
h
a
s
b
e
e
n
u
s
e
d
s
i
n
c
e
a
g
e
s
f
o
r
m
a
n
y
p
u
r
p
o
s
e
s
[
1
7
]
,
[
1
8
]
.
A
s
i
n
s
t
a
n
c
es
,
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
(
G
A
)
[
1
9
]
,
[
2
0
]
.
d
i
f
f
e
r
e
n
t
i
a
l
e
v
o
l
u
t
i
o
n
a
l
g
o
r
i
t
h
m
(
D
E
)
[
2
1
]
,
[
2
2
]
,
p
a
r
t
i
c
l
e
s
w
a
r
m
o
p
t
i
m
i
za
t
i
o
n
(
P
S
O
)
[
2
3
]
-
[
2
5
]
,
b
u
t
t
e
r
f
l
y
o
p
t
i
m
i
z
a
ti
o
n
a
l
g
o
r
i
t
h
m
(
B
O
A
)
[
2
6
]
,
[
2
7
]
,
t
e
a
c
h
i
n
g
-
l
e
a
r
n
i
n
g
-
b
a
s
e
d
o
p
t
i
m
i
za
t
i
o
n
(
T
L
B
O
)
[
2
8
]
,
[
2
9
]
,
h
a
r
m
o
n
y
s
e
a
r
c
h
a
l
g
o
r
i
th
m
(
H
S
A
)
[
3
0
]
,
[
3
1
]
,
a
n
d
g
r
a
v
i
t
a
t
i
o
n
a
l
s
e
a
r
c
h
a
l
g
o
r
i
t
h
m
(
G
S
A
)
[
3
2
]
-
[
3
4
]
.
F
o
r
t
h
a
t
r
e
a
s
o
n
,
a
c
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
s
is
o
f
m
e
t
a
h
e
u
r
i
s
t
i
c
al
g
o
r
i
t
h
m
s
b
as
e
d
o
n
t
h
e
p
e
r
f
o
r
m
a
n
c
e
s
is
c
a
r
r
i
e
d
o
u
t
i
n
t
h
i
s
s
t
u
d
y
.
Se
v
e
n
a
l
g
o
r
i
th
m
s
w
e
r
e
p
r
o
p
o
s
e
d
t
o
d
e
t
e
r
m
i
n
e
t
h
e
b
es
t
al
g
o
r
i
t
h
m
i
n
f
i
n
e
-
t
u
n
i
n
g
t
h
e
p
a
r
a
m
e
t
e
r
i
n
t
h
e
f
u
z
z
y
s
y
s
t
e
m
.
Nex
t
s
ec
tio
n
is
th
e
d
etail
ex
p
l
an
atio
n
o
f
ea
ch
ca
teg
o
r
y
will
b
e
v
iewe
d
in
th
e
n
ex
t
s
ec
tio
n
f
o
llo
wed
b
y
th
e
r
esear
ch
m
et
h
o
d
s
ec
t
io
n
.
I
n
th
at
s
ec
tio
n
,
d
ata
co
llectio
n
,
ex
p
er
im
e
n
tal
d
esig
n
an
d
p
er
f
o
r
m
a
n
c
e
m
ea
s
u
r
em
en
t
ar
e
s
tated
th
o
r
o
u
g
h
ly
.
R
esu
lts
an
d
d
is
cu
s
s
i
o
n
will
b
e
in
th
e
n
ex
t
p
ar
t
b
ef
o
r
e
th
is
p
ap
e
r
is
wr
ap
p
ed
with
a
c
o
n
clu
s
io
n
o
f
o
v
er
all
s
tu
d
y
.
2.
M
E
T
AH
E
URI
ST
I
C
AL
G
O
RIT
H
M
Me
tah
eu
r
is
tic
alg
o
r
ith
m
ca
n
b
e
ca
teg
o
r
ized
in
to
f
o
u
r
ca
te
g
o
r
ies
wh
ich
ar
e
ev
o
lu
tio
n
-
b
ased
m
eth
o
d
,
s
war
m
-
b
ased
m
eth
o
d
,
h
u
m
an
-
b
ased
m
eth
o
d
,
an
d
p
h
y
s
ics
-
b
ased
m
eth
o
d
[
3
5
]
,
[
3
6
]
as
s
h
o
wn
in
th
e
Fig
u
r
e
2
.
E
v
er
y
ex
a
m
p
le
o
f
m
etah
eu
r
is
tic
alg
o
r
ith
m
th
at
f
all
in
ea
c
h
ca
teg
o
r
y
will
b
e
c
o
m
p
ar
e
d
a
n
d
an
aly
ze
d
in
th
e
ex
p
er
im
en
t
p
h
ase.
T
h
e
r
ea
s
o
n
b
eh
in
d
th
e
ch
o
s
en
m
etah
e
u
r
i
s
tic
alg
o
r
ith
m
f
o
r
ea
ch
ca
te
g
o
r
y
s
im
p
le
b
ec
a
u
s
e
th
ey
ar
e
wid
ely
u
s
ed
an
d
h
as sh
o
wn
ef
f
ec
ti
v
e
r
esu
lt in
p
h
is
h
in
g
an
d
f
u
zz
y
m
o
d
ellin
g
[
3
7
]
-
[
3
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
23
,
No
.
2
,
Au
g
u
s
t 2
0
2
1
:
1
1
4
6
-
115
8
1148
Fig
u
r
e
2
.
C
ateg
o
r
ies o
f
m
etah
eu
r
is
tic
alg
o
r
ith
m
2
.
1
.
E
v
o
lutio
n
-
ba
s
ed
m
et
ho
d
2
.
1
.
1
.
G
enet
ic
a
lg
o
rit
hm
GA
was
d
ev
elo
p
ed
th
at
f
o
llo
w
th
e
p
r
in
cip
le
o
f
th
e
b
io
lo
g
i
ca
l
ev
o
lu
tio
n
p
r
o
ce
s
s
an
d
was
d
is
co
v
er
ed
in
1
9
7
5
.
I
t is a
r
o
b
u
s
t sear
ch
ap
p
r
o
ac
h
to
s
o
lv
e
a
wid
e
r
an
g
e
p
r
o
b
lem
d
e
v
elo
p
e
d
b
y
Ho
llan
d
[
4
0
]
.
T
h
e
p
r
o
ce
s
s
in
v
o
lv
es
ar
e
r
e
p
r
o
d
u
ctio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
.
T
h
e
b
es
t
g
en
es
a
r
e
ca
lled
p
a
r
en
t
c
h
r
o
m
o
s
o
m
es
wh
ile
th
e
n
ew
ch
r
o
m
o
s
o
m
es o
b
tain
e
d
ar
e
k
n
o
wn
as c
h
ild
ch
r
o
m
o
s
o
m
e
s
.
T
h
e
p
r
o
ce
d
u
r
e
o
f
GA
is
s
h
o
wn
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
Ps
eu
d
o
co
d
e
o
f
GA
I
n
[
3
7
]
,
th
e
a
u
th
o
r
s
p
r
o
p
o
s
e
a
m
eth
o
d
to
c
o
m
b
in
e
f
u
zz
y
a
n
d
GA,
wh
e
r
e
GA
u
s
ed
as
o
p
tim
izatio
n
m
eth
o
d
in
th
e
f
u
zz
y
s
y
s
tem
.
B
y
u
s
in
g
d
is
s
o
lu
tio
n
an
d
s
in
ter
in
g
p
r
o
ce
s
s
in
th
e
m
an
u
f
ac
tu
r
e
o
f
alu
m
in
u
m
f
o
am
s
,
f
u
zz
y
-
GA
ca
n
d
escr
ib
e
th
e
in
h
er
en
t u
n
ce
r
tain
ties
.
As a
r
esu
lt,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
a
p
r
o
m
is
in
g
to
o
l
to
b
e
u
s
ed
in
m
an
u
f
ac
tu
r
i
n
g
p
r
o
ce
s
s
.
An
o
th
er
wo
r
k
th
at
i
m
p
lem
en
ted
f
u
zz
y
an
d
GA
is
f
r
o
m
[
2
0
]
.
I
n
th
eir
wo
r
k
,
th
e
r
o
u
tin
g
i
n
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
wer
e
o
p
tim
ize
b
y
u
s
in
g
f
u
zz
y
an
d
GA.
Fu
zz
y
lo
g
ic
r
ed
u
ce
th
e
tim
e
co
n
s
u
m
e
to
r
ea
ch
d
esti
n
atio
n
,
m
ea
n
wh
ile
GA
was
u
tili
ze
d
to
t
u
n
e
f
u
zz
y
r
u
les
t
ab
le
to
r
ed
u
ce
th
e
tr
av
elled
d
is
tan
ce
.
2
.
1
.
2
.
Dif
f
er
ent
ia
l
ev
o
lutio
n
Sto
r
n
an
d
Pric
e
[
1
1
]
p
r
o
p
o
s
ed
th
e
DE
alg
o
r
ith
m
wh
ich
b
a
s
ed
o
n
p
o
p
u
latio
n
th
at
s
im
ilar
with
GA.
T
h
e
co
n
ce
p
t
o
f
th
is
alg
o
r
ith
m
is
q
u
ite
s
im
ilar
with
GA
w
h
e
r
e
it
is
in
s
p
ir
e
d
b
y
th
e
s
p
ec
ies
’
ev
o
lu
tio
n
liv
ed
in
th
is
wo
r
ld
.
T
h
er
e
ar
e
th
r
ee
o
p
er
ato
r
s
in
th
is
alg
o
r
ith
m
:
m
u
ta
tio
n
,
cr
o
s
s
o
v
er
,
an
d
s
elec
tio
n
.
I
n
DE
p
r
o
ce
s
s
,
th
e
n
ew
v
ec
to
r
s
(
n
ew
g
en
er
atio
n
o
f
p
o
p
u
latio
n
)
is
g
e
n
er
ate
d
b
y
m
u
tatio
n
an
d
cr
o
s
s
o
v
e
r
p
r
o
ce
s
s
,
th
en
t
h
e
s
elec
tio
n
p
r
o
ce
s
s
tak
e
p
lace
to
d
eter
m
in
e
wh
eth
er
th
e
n
e
w
g
en
er
ated
v
ec
to
r
s
wo
u
ld
s
u
r
v
iv
e
s
in
th
e
n
ex
t
g
en
er
atio
n
o
r
n
o
t
.
Fig
u
r
e
4
p
r
e
s
en
ts
th
e
p
s
eu
d
o
co
d
e
o
f
DE
.
Fig
u
r
e
4
.
Ps
eu
d
o
co
d
e
o
f
DE
C
at
eg
ori
es o
f
m
et
ahe
ur
i
st
i
c
al
g
or
i
t
hm
Ev
ol
ut
i
on
ar
y
al
g
or
i
t
hm
Sw
ar
m
i
nt
el
l
i
g
enc
e
H
um
an behav
i
or
-
based
Phy
si
cs
-
che
m
i
ca
l
sy
st
em
-
bas
ed
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:
2502
-
4
7
5
2
A
co
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
eta
h
eu
r
is
tic
a
lg
o
r
ith
ms in
fu
z
z
y
mo
d
elli
n
g
fo
r
…
(
N
o
o
r
S
y
a
h
ir
a
h
N
o
r
d
in
)
1149
A
p
ap
er
in
[
4
1
]
h
as
p
r
esen
ted
a
n
ew
ad
a
p
tiv
e
DE
b
ased
o
n
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
wh
er
e
f
u
zz
y
was
u
s
ed
to
tu
n
e
th
e
m
u
tatio
n
f
ac
to
r
in
DE
.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
p
r
o
v
ed
to
h
av
e
a
b
etter
r
esu
lt
th
an
th
e
o
th
er
m
eth
o
d
m
en
tio
n
ed
in
th
e
s
tu
d
y
.
Oth
er
th
an
th
at,
[
3
8
]
h
as
p
r
o
p
o
s
e
a
m
eth
o
d
n
am
e
MO
DE
-
FM,
wh
ich
was
a
m
u
lti
-
o
b
jectiv
e
DE
an
d
co
m
b
in
e
with
f
u
zz
y
d
y
n
a
m
ic
m
u
tatio
n
f
ac
to
r
.
T
h
e
m
u
tatio
n
f
ac
to
r
was
tu
n
ed
b
y
f
u
z
zy
.
T
h
e
t
u
n
in
g
p
r
o
ce
s
s
was
d
o
n
e
u
s
in
g
co
u
n
t
g
en
e
r
atio
n
an
d
d
iv
er
s
ity
o
f
p
o
p
u
latio
n
.
T
h
is
in
ten
d
ed
to
o
v
er
co
m
e
th
e
lack
o
f
DE
.
As
a
r
esu
lt,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
s
ee
m
s
to
h
av
e
a
p
r
o
m
is
in
g
r
esu
lt
co
m
p
ar
ed
with
p
r
ev
io
u
s
wo
r
k
.
I
n
p
r
o
d
u
ct
lin
e
d
esig
n
(
PLD)
,
f
u
zz
y
an
d
G
A
also
h
as
b
ee
n
ap
p
lied
in
th
i
s
f
ield
.
Fu
zz
y
l
o
g
ic
u
s
ed
to
ca
lcu
late
th
e
p
ar
am
ete
r
au
to
m
atica
lly
lead
t
o
DE
th
a
t settin
g
s
-
f
r
ee
an
d
s
h
o
ws a
p
r
o
m
is
in
g
r
esu
lt [
2
1
]
.
2
.
2
.
Swa
r
m
-
ba
s
ed
m
et
ho
d
2
.
2
.
1
.
P
a
rt
icle
s
wa
r
m
o
ptim
i
za
t
io
n
Ken
n
ed
y
an
d
E
b
e
r
h
ar
t
wer
e
t
h
e
f
ir
s
t
p
er
s
o
n
wh
o
d
e
v
elo
p
ed
th
e
PS
O
alg
o
r
ith
m
in
th
eir
w
o
r
k
[
1
4
]
.
I
t
is
a
p
o
p
u
latio
n
-
b
ased
s
to
ch
ast
ic
o
p
tim
izatio
n
m
eth
o
d
th
at
m
im
ics
th
e
s
o
cial
b
eh
av
io
r
o
f
b
ir
d
s
f
lo
ck
i
n
g
a
n
d
f
is
h
s
ch
o
o
lin
g
.
T
h
e
r
e
ar
e
ce
r
t
ain
p
ar
am
eter
s
i
n
th
e
o
r
ig
in
al
v
e
r
s
io
n
o
f
PS
O
ca
lled
c
o
n
tr
o
l
p
ar
am
eter
s
.
T
h
e
p
ar
am
eter
s
in
v
o
lv
e
d
ar
e
ac
ce
l
er
atio
n
co
ef
f
icie
n
ts
,
v
elo
city
c
lam
p
in
g
-
lim
it,
s
war
m
s
ize
an
d
m
ax
im
u
m
n
u
m
b
e
r
o
f
iter
atio
n
s
.
Ma
n
y
m
o
d
if
icat
io
n
s
h
av
e
b
ee
n
m
a
d
e
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
s
tan
d
ar
d
PS
O.
T
h
e
p
s
eu
d
o
co
d
e
o
f
PS
O
ca
n
b
e
r
e
v
iewe
d
in
Fig
u
r
e
5
.
Fig
u
r
e
5
.
Ps
eu
d
o
co
d
e
o
f
PS
O
PS
O
was
u
s
ed
as
o
p
tim
izatio
n
m
eth
o
d
to
tu
n
e
t
h
e
m
em
b
er
s
h
ip
f
u
n
ctio
n
in
th
e
f
u
zz
y
s
y
s
tem
as
it
is
h
ar
d
to
d
eter
m
in
e
th
e
p
ar
am
et
er
m
an
u
ally
.
T
h
er
ef
o
r
e,
Nu
r
m
ain
i
an
d
Setian
in
g
s
ih
[
4
2
]
h
as
p
r
o
p
o
s
ed
a
m
eth
o
d
u
s
in
g
PS
O
an
d
f
u
zz
y
to
co
n
tr
o
l
th
e
p
o
s
itio
n
o
f
d
if
f
er
en
tial d
r
iv
e
m
o
b
ile
r
o
b
o
t
(
DDM
R
)
a
n
d
r
esu
ltin
g
to
f
aster
tim
e
f
o
r
th
e
r
o
b
o
t
to
r
ea
ch
s
te
ad
y
-
s
tate
co
n
d
itio
n
.
I
n
p
h
is
h
in
g
ar
ea
,
[
3
9
]
u
s
ed
PS
O
to
weig
h
t
v
ar
io
u
s
f
ea
tu
r
es
in
web
s
ite
in
o
r
d
er
to
r
ea
c
h
h
i
g
h
er
ac
cu
r
ac
y
in
th
e
r
esu
lt
p
r
o
d
u
ce
d
.
T
h
e
m
eth
o
d
was
p
r
o
p
o
s
ed
to
en
h
an
ce
th
e
p
h
is
h
in
g
web
s
ite
d
etec
tio
n
p
r
o
ce
s
s
wh
er
e
PS
O
ab
le
to
d
if
f
er
en
tiate
b
etwe
en
th
e
f
ea
tu
r
es.
B
y
u
s
in
g
d
ataset
f
r
o
m
UC
I
m
ac
h
in
e
lear
n
in
g
r
e
p
o
s
ito
r
y
,
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
s
h
o
ws
an
o
u
ts
tan
d
in
g
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
to
th
e
p
r
ev
io
u
s
m
eth
o
d
s
.
2
.
2
.
2
.
B
utt
er
f
ly
o
ptim
iz
a
t
io
n
a
lg
o
rit
hm
T
h
e
r
ec
en
t
n
atu
r
e
in
s
p
ir
e
d
alg
o
r
ith
m
ca
lled
B
OA
was
in
tr
o
d
u
ce
d
b
y
Ar
o
r
a
in
h
is
wo
r
k
in
[
4
3
]
.
I
n
o
r
d
er
to
p
e
r
f
o
r
m
o
p
tim
izatio
n
,
b
u
tter
f
lies
ac
t
as
th
e
s
ea
r
c
h
ag
en
t
in
B
OA.
T
h
er
e
ar
e
th
r
ee
p
h
ases
in
th
e
alg
o
r
ith
m
:
(
i)
in
itializatio
n
p
h
ase,
(
ii)
iter
atio
n
p
h
ase
an
d
(
iii)
f
in
al
p
h
ase.
I
n
ea
ch
iter
atio
n
,
all
b
u
tter
f
lies
will
b
e
ev
alu
ated
b
y
ca
lcu
latin
g
its
f
itn
ess
f
u
n
ctio
n
b
ef
o
r
e
g
en
er
a
tin
g
th
e
f
r
a
g
r
an
ce
u
s
in
g
(
1
)
.
=
(
1
)
wh
er
e
is
th
e
f
itn
ess
f
u
n
ctio
n
wh
er
e
it
s
u
p
p
o
s
ed
to
attr
ac
t
o
t
h
er
b
u
tter
f
lies
with
t
h
eir
f
r
ag
r
an
ce
.
Me
an
w
h
ile
is
th
e
s
en
s
o
r
y
m
o
d
ality
,
is
th
e
v
ar
iatio
n
o
f
b
u
tter
f
ly
a
n
d
a
d
en
o
te
as
p
o
wer
e
x
p
o
n
en
t
p
ar
am
eter
d
ep
en
d
s
o
n
th
e
s
en
s
o
r
y
m
o
d
ality
.
T
h
e
n
,
th
e
iter
atio
n
will
co
n
tin
u
e
u
n
til
th
e
ter
m
in
atio
n
cr
iter
ia
s
atis
f
ied
.
Fig
u
r
e
6
p
r
esen
ts
th
e
s
tep
s
o
f
B
OA
in
p
s
eu
d
o
co
d
e
.
A
wo
r
k
s
b
y
Fan
et
a
l.
[
4
4
]
h
as
in
tr
o
d
u
ce
d
a
n
ew
im
p
r
o
v
e
d
B
OA
to
en
h
an
ce
th
e
s
ea
r
ch
in
g
p
r
o
ce
s
s
an
d
th
e
iter
atio
n
ca
p
ab
ilit
y
in
s
o
lv
in
g
n
u
m
er
ical
o
p
t
im
izati
o
n
p
r
o
b
lem
.
T
h
e
au
th
o
r
s
h
av
e
u
s
ed
s
elf
-
ad
ap
tio
n
m
eth
o
d
i
n
B
OA
n
am
ed
SAB
OA
th
at
ap
p
lied
n
ew
iter
atio
n
,
u
p
d
ati
n
g
s
tr
ateg
y
an
d
n
ew
f
r
ag
r
an
ce
co
ef
f
icien
t
in
th
e
b
asic
B
OA.
As
a
r
esu
lt,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
g
iv
es
ad
v
an
tag
es
in
te
r
m
s
o
f
p
r
ec
is
io
n
v
alu
e,
it
er
ativ
e
s
p
ee
d
an
d
s
im
p
le
s
tr
u
ctu
r
e
co
m
p
ar
ed
with
o
th
er
alg
o
r
ith
m
m
en
tio
n
ed
in
th
e
p
ap
e
r
.
Oth
er
th
an
th
at,
B
OA
was
also
u
s
ed
as
o
p
tim
izatio
n
m
eth
o
d
to
t
u
n
e
th
e
f
u
zz
y
p
ar
am
eter
au
to
m
atica
lly
in
f
u
zz
y
s
y
s
tem
[
4
5
]
.
I
n
ev
alu
atin
g
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
t
h
e
p
h
is
h
i
n
g
web
s
ite
d
atas
et
th
at
o
b
tain
ed
f
r
o
m
r
ep
o
s
ito
r
y
o
f
UC
I
m
ac
h
in
e
lear
n
in
g
was
u
s
ed
.
T
h
e
r
esu
lt
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
s
h
o
w
s
a
p
r
o
m
is
in
g
an
d
co
m
p
etitiv
e
r
esu
lt
co
m
p
ar
ed
to
o
th
er
m
etah
e
u
r
is
tic
alg
o
r
ith
m
m
en
tio
n
ed
i
n
th
e
p
a
p
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
23
,
No
.
2
,
Au
g
u
s
t 2
0
2
1
:
1
1
4
6
-
115
8
1150
Fig
u
r
e
6
.
Ps
eu
d
o
co
d
e
o
f
B
OA
2
.
3
.
H
u
m
a
n
-
ba
s
ed
m
et
ho
d
2
.
3
.
1
.
T
ea
ching
-
lea
rning
-
ba
s
ed
o
ptim
iza
t
io
n
T
L
B
O
is
o
n
e
o
f
th
e
m
o
d
er
n
h
eu
r
is
tic
o
p
tim
izatio
n
alg
o
r
ith
m
s
th
at
s
im
u
lates
a
s
ce
n
ar
io
o
f
teac
h
in
g
an
d
lear
n
in
g
b
etwe
en
teac
h
er
an
d
s
tu
d
en
t
in
a
class
r
o
o
m
en
v
ir
o
n
m
e
n
t.
I
t
is
p
r
o
p
o
s
ed
b
y
R
ao
et
a
l.
in
2
0
1
1
an
d
d
em
o
n
s
tr
ates
a
g
o
o
d
p
er
f
o
r
m
an
ce
in
s
o
lv
in
g
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[
1
9
]
.
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as
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d
am
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tal
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ase.
Stu
d
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[
4
6
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.
2
.
3
.
2
.
H
a
r
m
o
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s
ea
rc
h
HS
is
a
p
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ased
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s
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in
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p
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ec
t
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tate
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f
h
ar
m
o
n
y
in
m
u
s
ic
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s
ed
b
y
m
u
s
ician
[
4
7
]
.
T
h
r
ee
m
ain
co
m
p
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n
en
t
s
in
HS
ar
e
u
s
ag
e
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em
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r
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o
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izatio
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.
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ir
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co
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t
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at
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,
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(
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2
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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4
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[
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1
,
1
]
.
T
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e
p
s
eu
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co
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HS
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s
h
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u
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h
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was
ca
teg
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ized
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ailu
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izatio
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lem
s
in
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m
ac
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in
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m
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el.
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h
e
p
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h
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p
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m
ac
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[
3
1
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.
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n
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ju
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p
ar
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w
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led
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in
p
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[
4
8
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.
2
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4
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s
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2
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4
.
1
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ra
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h a
lg
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R
ash
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Nez
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ith
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ith
m
(
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ased
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atu
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[
2
6
]
.
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s
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r
c
h
in
g
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g
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n
t
in
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is
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with
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s
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ass
.
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ts
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co
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s
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as
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g
lo
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al
m
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th
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tiv
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p
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an
d
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ass
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as (
3
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(
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wh
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r
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(
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(
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4
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p
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ca
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eu
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GSA
Z
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t
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l
.
[
4
9
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I
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5
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(
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[
5
0
]
.
T
h
e
f
u
n
ctio
n
o
f
GSA
was
to
m
ax
im
ize
th
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f
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s
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n
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ch
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th
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etec
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,
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f
ea
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s
elec
tio
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to
o
l
th
at
ca
n
elim
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ate
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y
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tu
r
e.
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y
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s
in
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d
ataset
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Ph
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s
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b
s
ets s
elec
tio
n
[
5
1
]
.
3.
M
E
T
H
O
D
Data
s
ets
f
r
o
m
th
e
Un
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s
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C
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UC
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a
ch
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s
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e
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d
atasets
f
r
o
m
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ata.
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ci.
ed
u
/m
l/.
T
h
e
d
a
tasets
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e
well
u
n
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er
s
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o
d
a
n
d
ca
n
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e
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s
ed
f
r
ee
ly
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y
e
v
er
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f
o
r
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esear
ch
p
u
r
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o
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h
er
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y
,
th
e
d
atasets
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s
ed
a
r
e
r
elate
d
to
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h
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h
in
g
web
s
ites
:
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s
ite
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h
is
h
in
g
d
ataset
(
W
PD)
an
d
p
h
is
h
in
g
web
s
ites
d
atase
t
(
P
W
D)
.
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PD
ca
n
b
e
ac
ce
s
s
ed
f
r
o
m
h
ttp
s
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ch
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ci.
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l/
d
atasets
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W
eb
s
ite+Ph
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s
h
in
g
#
wh
ile
PW
D
f
r
o
m
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ttp
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ci.
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u
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l/
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s
h
in
g
+Web
s
ites
#
.
I
n
o
r
d
e
r
to
test
th
e
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
,
ex
p
er
im
en
t
s
wer
e
ex
ec
u
ted
b
y
u
s
in
g
k
-
f
o
ld
cr
o
s
s
v
alid
atio
n
tech
n
iq
u
es
f
o
r
p
r
e
d
ictin
g
th
e
class
if
icatio
n
alg
o
r
ith
m
p
er
f
o
r
m
an
ce
.
T
h
is
m
eth
o
d
is
o
n
e
o
f
th
e
p
o
p
u
lar
m
eth
o
d
s
as
it
is
s
im
p
l
e
an
d
ea
s
y
to
u
n
d
er
s
tan
d
.
Mo
r
eo
v
er
,
s
ev
en
al
g
o
r
ith
m
s
we
r
e
co
m
p
ar
ed
in
ter
m
s
o
f
ac
c
u
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
f
-
m
ea
s
u
r
e.
T
h
ese
f
o
u
r
m
e
asu
r
em
en
ts
ar
e
th
e
m
o
s
t
well
-
k
n
o
wn
m
etr
ics
u
s
ed
in
th
e
ev
al
u
atio
n
p
r
o
ce
s
s
an
d
it
is
also
s
u
itab
le
to
b
e
u
s
e
d
in
th
is
s
tu
d
y
to
m
ak
e
a
co
m
p
ar
ativ
e
a
n
aly
s
is
b
etwe
en
th
e
m
eth
o
d
s
m
en
tio
n
ed
.
I
t is p
o
s
s
ib
le
to
f
o
r
m
u
late
all
th
ese
m
ea
s
u
r
em
en
ts
as (
5
)
,
(
6
)
,
(
7
)
an
d
(
8
)
.
=
(
+
)
(
5
)
=
(
+
)
(
6
)
=
(
+
)
(
7
)
−
=
(
2
×
×
(
+
)
(
8
)
wh
er
e
TP
is
tr
u
e
p
o
s
itiv
e,
TN
is
tr
u
e
n
eg
ativ
e,
F
N
is
f
alse
n
eg
ativ
e,
FP
is
f
alse
p
o
s
itiv
e
an
d
n
u
m
b
er
o
f
d
ata
is
th
e
n
u
m
b
e
r
o
f
d
ata
th
at
h
as
b
e
en
test
ed
.
TP
is
wh
en
th
e
ca
s
es
ar
e
p
r
ed
icted
y
es
an
d
th
e
r
esu
lt
is
y
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an
d
TN
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en
th
e
ca
s
es
ar
e
p
r
ed
icted
n
o
an
d
th
e
r
esu
lt
is
n
o
.
Me
an
wh
ile,
F
N
is
wh
en
t
h
e
ca
s
es
a
r
e
p
r
e
d
icted
n
o
a
n
d
th
e
r
esu
lt is
y
es a
n
d
it is
o
th
er
wis
e
f
o
r
FP.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ex
p
er
im
e
n
tal
r
esu
lts
wer
e
co
llected
an
d
p
r
esen
ted
in
th
is
s
ec
tio
n
.
T
h
e
co
llected
r
esu
lts
wer
e
s
h
o
wed
d
if
f
er
e
n
t
r
ea
d
in
g
in
t
er
m
o
f
ac
c
u
r
ac
y
,
r
ec
all,
p
r
ec
i
s
io
n
,
an
d
f
-
m
ea
s
u
r
e.
I
n
ad
d
iti
o
n
,
all
r
esu
lts
wer
e
co
m
p
ar
ed
b
y
u
s
in
g
co
n
v
er
g
e
n
ce
g
r
ap
h
wh
e
r
e
it
m
ea
s
u
r
es
th
e
co
n
v
er
g
en
ce
r
ate
o
f
ea
ch
m
eth
o
d
.
Mo
r
e
o
v
er
,
r
ad
ar
ch
ar
t
was
u
s
ed
in
co
m
p
ar
in
g
ac
cu
r
ac
y
,
r
ec
all,
p
r
ec
is
io
n
,
an
d
f
-
m
ea
s
u
r
e.
T
h
e
r
esu
lt
s
o
f
s
tatis
t
ical
tes
t
also
r
ec
o
r
d
ed
t
o
s
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o
w
th
e
s
ig
n
if
ican
ce
d
if
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er
e
n
ce
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etwe
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ch
m
eth
o
d
.
Fig
u
r
e
1
0
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d
1
1
p
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e
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r
a
p
h
o
f
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itn
ess
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et
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.
T
h
e
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tted
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e
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lts
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o
r
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OA
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it st
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with
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v
alu
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9
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g
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f
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ased
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u
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u
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,
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e
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in
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ate
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[
4
3
]
.
Oth
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th
an
th
at,
f
o
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th
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t
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lt
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8
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ith
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s
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e
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u
m
m
ar
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o
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r
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at
T
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r
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I
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wh
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B
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s
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th
e
h
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h
est
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o
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r
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all
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if
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Oth
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th
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t
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s
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allo
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th
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r
it
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to
s
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r
ch
th
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lu
tio
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tly
[
5
2
]
.
T
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2
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ts
th
e
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e
o
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Fig
u
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ates
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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:
2502
-
4
7
5
2
A
co
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
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h
eu
r
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tic
a
lg
o
r
ith
ms in
fu
z
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y
mo
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fo
r
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(
N
o
o
r
S
y
a
h
ir
a
h
N
o
r
d
in
)
1155
Fro
m
th
e
o
b
s
er
v
atio
n
,
all
p
-
v
a
lu
es
f
o
r
p
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ed
t
-
test
an
d
W
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x
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n
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r
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k
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s
m
aller
th
an
th
e
v
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o
f
α
in
b
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atasets
.
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h
er
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r
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e
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ed
th
at
all
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if
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if
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with
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th
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5.
CO
NCLU
SI
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Me
tah
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r
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ith
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m
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t
m
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alg
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,
d
if
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(
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)
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tter
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ly
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ith
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ased
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im
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m
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r
ith
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m
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p
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e,
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r
ec
all,
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d
,
f
-
m
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s
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r
e
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e
in
th
e
r
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d
ar
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h
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t
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n
d
th
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p
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v
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e
in
th
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t
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r
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it
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ix
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etah
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r
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tic
alg
o
r
ith
m
s
in
b
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th
d
atasets
.
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NO
WL
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DG
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NT
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p
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ate
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(
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wit
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.
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Hig
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r
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tal
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an
t
Sch
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8
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)
.
RE
F
E
R
E
NC
ES
[1
]
APW
G
,
“
P
h
ish
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Ac
ti
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y
Tren
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s
Re
p
o
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rd
Q
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0
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0
,
”
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0
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.
[2
]
P
.
Ba
rra
c
lo
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.
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x
to
n
,
“
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We
b
site
De
tec
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m
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c
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e
a
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d
In
fo
rm
a
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(S
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,
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.
[3
]
H.
Ch
a
p
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R.
K
o
tak
,
a
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d
M
.
Jo
i
se
r,
“
A
M
a
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Lea
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Ap
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Web
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ic
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Clas
sifier,”
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c
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u
rt
h
In
t.
C
o
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f
.
Co
mm
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.
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e
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tro
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.
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y
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(ICCES
2
0
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9
)
,
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o
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2
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p
p
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.
[4
]
K.
N.
M
.
Ku
m
a
r
a
n
d
K.
Ale
k
h
y
a
,
“
De
tec
ti
n
g
P
h
ish
i
n
g
Web
sites
u
s
in
g
F
u
z
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ic,”
In
t
.
J
.
Ad
v
.
Res
.
Co
mp
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E
n
g
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v
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p
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.
[5
]
E.
Zh
u
,
C.
Ye
,
D.
Li
u
,
F
.
Li
u
,
F
.
Wan
g
,
a
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d
X.
Li
,
“
An
Eff
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c
ti
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ra
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two
rk
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h
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De
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f
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ra
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l
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Pro
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wit
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.
,
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.
[6
]
E.
Zh
u
,
Y.
Ju
,
Z.
C
h
e
n
,
F
.
Li
u
,
a
n
d
X.
F
a
n
g
,
“
DTOF
-
AN
N :
An
Art
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c
ial
Ne
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ra
l
Ne
two
rk
p
h
ish
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n
g
d
e
tec
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o
n
m
o
d
e
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b
a
se
d
o
n
De
c
isio
n
Tree
a
n
d
Op
ti
m
a
l
F
e
a
tu
re
s,”
A
p
p
l
ied
S
o
ft
Co
m
p
u
ti
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g
,
v
o
l
.
9
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[7
]
R.
Ka
r
n
ik
,
“
S
u
p
p
o
rt
Ve
c
to
r
M
a
c
h
in
e
Ba
se
d
M
a
lwa
re
a
n
d
P
h
ish
in
g
Web
site
De
tec
ti
o
n
,
”
In
t
.
J
.
Co
mp
u
t.
T
e
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h
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o
l
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,
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l.
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5
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p
p
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9
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0
1
6
.
[8
]
A.
A.
Oru
n
so
l
u
,
A.
S
.
S
o
d
iy
a
,
a
n
d
A.
T.
Ak
i
n
wa
le,
“
A p
re
d
ictiv
e
m
o
d
e
l
fo
r
p
h
ish
i
n
g
d
e
tec
ti
o
n
,
”
J
.
Kin
g
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[9
]
W.
Ni
u
,
X
.
Z
h
a
n
g
,
G
.
Ya
n
g
,
Z.
M
a
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n
d
Z
.
Zh
u
o
,
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P
h
ish
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e
m
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il
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d
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tec
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si
n
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CS
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S
VM
,
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n
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ter
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ra
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d
Distrib
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1
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t
h
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ter
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0
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R.
Ba
wm
,
“
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h
ish
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n
g
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k
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tec
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a
c
h
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rn
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a
ss
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a
ti
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2
0
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t
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telli
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1
]
X.
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,
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Ya
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a
n
d
Y.
Li
,
“
P
h
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h
in
g
Web
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te
De
tec
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n
Us
in
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5
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t.
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f.
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[1
2
]
R.
P
.
F
e
rre
ira
,
e
t
a
l.
,
“
Artifi
c
ial
Ne
u
ra
l
Ne
two
rk
fo
r
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sites
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sifica
ti
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n
with
P
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ish
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g
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ra
c
teristics
,
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7
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8
.
[1
3
]
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.
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ra
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h
,
S
.
M
.
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u
l
lah
,
M
.
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li
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i,
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h
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n
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a
n
d
M
.
J.
Ra
jab
i,
“
Ad
v
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n
ta
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d
d
ra
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su
p
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rt
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c
h
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fu
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t
io
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li
ty
,
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I
4
CT
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t.
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o
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p
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.
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4
]
A.
S
o
o
f
i
a
n
d
A.
Aw
a
n
,
“
Clas
sifi
c
a
ti
o
n
Tec
h
n
i
q
u
e
s
i
n
M
a
c
h
i
n
e
Lea
rn
in
g
:
A
p
p
li
c
a
ti
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n
s
a
n
d
Iss
u
e
s,
”
J
.
Ba
sic
Ap
p
l
.
S
c
i.
,
v
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l
.
1
3
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o
.
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tem
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r,
p
p
.
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1
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-
5
1
2
9
.
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0
1
7
.
1
3
.
7
6
.
[1
5
]
A.
Ya
sin
a
n
d
A.
Ab
u
h
a
sa
n
,
“
An
I
n
telli
g
e
n
t
Clas
sifica
ti
o
n
M
o
d
e
l
f
o
r
P
h
ish
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n
g
Ema
il
De
tec
ti
o
n
,
”
In
t.
J
.
Ne
tw.
S
e
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u
r.
Its
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l.
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.
[1
6
]
A.
A.
Zu
ra
iq
a
n
d
M
.
Al
k
a
sa
ss
b
e
h
,
“
Re
v
iew
:
P
h
is
h
in
g
De
tec
ti
o
n
A
p
p
ro
a
c
h
e
s,
”
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0
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2
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d
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n
ter
n
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t
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l
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fer
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s
in
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o
mp
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ti
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s (ICT
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,
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[1
7
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M
.
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Ab
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fa
tah
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a
n
d
A.
K.
S
a
n
g
a
iah
,
“
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e
tah
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risti
c
Alg
o
rit
h
m
s:
A
Co
m
p
re
h
e
n
siv
e
Re
v
iew
,”
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mp
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t
a
ti
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l
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n
telli
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e
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ta
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.
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