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[
3
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[
1
2
]
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6
.
2.
RE
L
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.
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1
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lied
in
th
e
th
ir
d
p
h
ase
to
m
ea
s
u
r
e
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
el
u
s
in
g
th
e
co
n
f
u
s
io
n
m
atr
i
x
in
th
e
W
E
KA
to
o
l
wh
ich
ar
e
r
ec
all,
p
r
ec
is
io
n
,
s
p
ec
if
icity
,
F
-
m
ea
s
u
r
e,
R
OC
,
P
R
C
a
r
ea
,
an
d
a
ccu
r
ac
y
.
Fin
ally
,
th
e
f
in
al
o
u
tp
u
t
r
esu
lts
ar
e
p
r
esen
ted
b
ased
o
n
t
h
e
av
er
a
g
e
r
esu
lts
f
o
r
ea
ch
m
etr
ic
.
Fig
u
r
e
2
s
h
o
ws th
e
f
r
am
ewo
r
k
o
f
t
h
e
p
r
o
p
o
s
ed
s
y
s
tem
.
Fig
u
r
e
2
.
T
h
e
f
r
a
m
ewo
r
k
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
s
tr
u
ctu
r
e
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.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
7
0
8
-
1
7
1
5
1712
T
h
e
co
n
f
u
s
io
n
m
atr
i
x
co
n
s
is
t
s
o
f
T
P,
T
N,
FP
,
an
d
FN,
wh
er
e
T
P
is
tr
u
e
p
o
s
itiv
es,
T
N
is
tr
u
e
n
eg
ativ
es,
FP
is
f
alse p
o
s
itiv
e
s
,
an
d
FN is f
alse n
eg
ativ
es.
Se
v
en
m
etr
ics ar
e
u
s
ed
to
ev
al
u
a
te
th
e
ef
f
icien
cy
o
f
th
e
s
elec
ted
class
if
ier
:
R
ec
a
ll,
p
r
ec
is
io
n
,
s
p
ec
if
icity
,
F
-
m
ea
s
u
r
e,
R
OC
,
PR
C
ar
ea
,
an
d
ac
c
u
r
ac
y
[
4
]
,
[
1
5
]
.
T
h
e
r
ec
all
d
escr
ib
ed
tr
u
e
p
o
s
itiv
es st
ates d
iv
id
ed
b
y
p
o
s
itiv
e
s
tates e
x
p
r
ess
ed
as:
R
ec
all
=
TP
TP
+
FN
(
1
)
T
h
e
Pre
cisi
o
n
id
en
tifie
s
tr
u
e
p
o
s
itiv
es st
ate
s
d
iv
id
ed
b
y
ex
p
e
cted
p
o
s
itiv
e
s
tates e
x
p
r
ess
ed
a
s
:
Pr
e
c
isio
n
=
TP
TP
+
FP
(
2
)
Sp
ec
if
icity
d
escr
ib
ed
tr
u
e
n
eg
ativ
es st
ates d
iv
id
ed
b
y
th
e
to
t
al
n
u
m
b
er
o
f
n
eg
ativ
e
s
tates e
x
p
r
ess
ed
as:
Sp
ec
if
icity
=
TN
TN
+
FP
(
3
)
F
-
m
ea
s
u
r
e
is
a
m
ix
tu
r
e
o
f
p
r
ec
is
io
n
an
d
r
ec
all
m
ea
s
u
r
e
m
en
t
ex
p
r
ess
ed
as:
F
-
m
ea
s
u
r
e
=
2
∗
R
e
c
a
ll
∗
P
r
e
c
is
ion
R
e
c
a
ll
+
P
r
e
c
is
ion
(
4
)
PR
C
is
th
e
p
r
ec
is
io
n
-
r
ec
all
c
u
r
v
e
u
s
ed
to
ev
alu
ate
th
e
cla
s
s
if
ier
p
er
f
o
r
m
a
n
ce
f
o
r
im
b
al
an
ce
d
an
d
n
o
is
y
d
atasets
.
T
h
e
r
ec
eiv
er
o
p
er
atin
g
ch
ar
ac
ter
is
tics
(
R
OC
)
cu
r
v
e
is
u
s
ed
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
class
if
ier
in
clu
d
es
two
-
ax
es:
f
alse
p
o
s
itiv
e
r
ate
o
n
th
e
x
-
a
x
is
an
d
tr
u
e
p
o
s
itiv
e
r
ate
(
R
ec
all)
o
n
th
e
y
-
a
x
is
.
T
h
er
ef
o
r
e,
R
OC
an
d
PR
C
cu
r
v
es
ar
e
u
s
ed
to
co
m
p
ar
e
th
e
p
er
f
o
r
m
a
n
ce
m
o
d
el
as
a
s
in
g
le
m
etr
ic.
T
h
e
R
OC
is
u
s
ed
to
r
ea
lize
th
e
p
er
f
o
r
m
an
ce
o
f
a
class
if
ier
o
n
a
b
alan
ce
d
d
ataset
at
ea
ch
class
wh
ile
t
h
e
PR
C
r
ep
r
esen
ts
th
e
ch
an
g
e
o
f
th
e
p
r
ec
is
io
n
with
th
e
r
ec
all
f
o
r
d
if
f
er
en
t
t
h
r
esh
o
ld
s
o
f
th
e
im
b
alan
ce
d
d
ataset
[
1
6
]
,
[
1
7
]
.
Acc
u
r
ac
y
is
u
s
ed
to
m
ea
s
u
r
e
th
e
p
e
r
f
o
r
m
an
ce
o
f
t
h
e
class
if
ier
an
d
d
escr
ib
ed
as
t
r
u
e
class
if
ic
atio
n
s
tates
d
iv
id
ed
b
y
th
e
t
o
tal
n
u
m
b
er
o
f
s
tates e
x
p
r
ess
ed
as:
Acc
u
r
ac
y
=
TP
+
TN
TP
+
TN
+
FP
+
FN
(
5
)
C
r
o
s
s
-
v
alid
atio
n
aim
s
to
ass
ess
lear
n
in
g
alg
o
r
ith
m
s
b
y
d
iv
i
d
in
g
th
e
d
ata
in
to
two
s
ets
wh
ich
ar
e
th
e
tr
ain
in
g
s
et
an
d
test
in
g
s
et.
Als
o
,
it
is
u
s
ed
to
co
m
p
ar
e
t
h
e
r
esu
lts
o
f
d
if
f
e
r
en
t
class
if
ier
s
.
k
-
f
o
ld
s
cr
o
s
s
-
v
alid
atio
n
(
C
V)
is
u
s
ed
to
e
v
alu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
a
n
y
class
if
ier
in
m
ac
h
i
n
e
lear
n
in
g
.
T
h
e
d
ata
a
r
e
r
an
d
o
m
l
y
s
ep
ar
ated
i
n
to
k
-
f
o
l
d
s
,
s
in
ce
th
e
d
ataset
is
s
p
lit
in
to
k
eq
u
ally
f
o
ld
s
,
th
er
ea
f
te
r
k
iter
atio
n
s
o
f
th
e
tr
ain
in
g
an
d
test
in
g
ar
e
ca
r
r
ie
d
o
u
t
s
o
th
at
at
ev
er
y
iter
atio
n
a
v
ar
io
u
s
f
o
l
d
o
f
th
e
d
ataset
is
k
ep
t
f
o
r
test
in
g
wh
er
ea
s
th
e
r
e
m
ain
in
g
(
k
-
1
)
f
o
ld
s
ar
e
ap
p
lied
f
o
r
th
e
tr
ai
n
in
g
s
et.
A
p
ar
ticu
lar
s
itu
atio
n
o
f
k
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
is
th
e
leav
e
o
n
e
o
u
t
cr
o
s
s
-
v
alid
atio
n
(
L
OOCV)
wh
er
e
th
e
n
u
m
b
er
o
f
f
o
ld
s
is
p
r
o
p
o
r
tio
n
al
to
th
e
to
tal
n
u
m
b
er
o
f
in
s
tan
ce
s
.
L
e
av
e
o
n
e
o
u
t
cr
o
s
s
-
v
alid
atio
n
h
as
b
ee
n
u
s
ed
to
ev
alu
ate
th
e
ef
f
icien
cy
o
f
a
n
y
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
w
h
en
th
e
n
u
m
b
e
r
o
f
in
s
tan
ce
s
is
lim
ited
[
1
8
]
,
[
1
9
]
.
4.
E
XP
E
R
I
M
E
N
T
D
E
SI
G
N
W
E
KA
is
im
p
lem
en
ted
to
class
if
y
th
e
d
ataset
s
in
ce
it’s
an
o
p
en
-
s
o
u
r
ce
m
ac
h
i
n
e
lear
n
in
g
s
o
f
twar
e
u
s
ed
f
o
r
d
ata
m
in
in
g
task
s
[
3
]
,
[
1
2
]
,
[
1
4
]
.
W
E
KA
in
clu
d
es
m
an
y
to
o
ls
f
o
r
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
class
if
icatio
n
,
an
d
clu
s
ter
in
g
.
T
h
r
ee
class
if
icatio
n
alg
o
r
ith
m
s
ar
e
s
elec
ted
f
r
o
m
W
E
KA:
NB
C
,
ML
P,
an
d
SVM
to
p
r
ed
ict
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
s
tu
d
en
t.
Naïv
e
B
ay
es
clas
s
if
ier
(
NB
C
)
is
a
s
im
p
le
s
u
p
er
v
is
ed
class
i
f
icatio
n
m
eth
o
d
th
at
d
ep
en
d
s
o
n
a
p
r
esu
m
p
tio
n
o
f
t
h
e
class
co
n
d
itio
n
al
in
d
ep
en
d
en
ce
.
NB
C
is
ass
u
m
ed
th
at
all
attr
ib
u
tes
p
r
o
v
id
e
d
in
a
d
ataset
ar
e
in
d
e
p
en
d
e
n
t b
ased
o
n
th
e
B
ay
es r
u
le
o
f
co
n
d
itio
n
al
p
r
o
b
a
b
ilit
y
[
2
0
]
.
Mu
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P)
is
a
s
u
p
er
v
is
ed
class
if
ier
im
p
lem
en
ted
f
o
r
n
eu
r
al
n
etwo
r
k
t
r
ain
in
g
an
d
to
class
if
y
in
s
tan
ce
s
b
y
a
b
ac
k
p
r
o
p
a
g
atio
n
alg
o
r
ith
m
wh
i
ch
u
s
es
a
g
r
ad
ien
t
d
escen
t
tech
n
iq
u
e
f
o
r
m
in
im
izi
n
g
m
ea
n
s
q
u
ar
e
e
r
r
o
r
i
n
th
e
in
p
u
t
v
ec
to
r
th
r
o
u
g
h
th
e
d
esire
d
an
d
ac
tu
al
o
u
tp
u
ts
.
ML
P c
o
m
p
r
is
es sev
er
al
lay
er
s
o
f
n
eu
r
o
n
s
an
d
ea
ch
n
eu
r
o
n
ex
cl
u
d
in
g
th
e
i
n
p
u
t
n
e
u
r
o
n
s
h
as
ac
tiv
atio
n
f
u
n
ctio
n
s
wh
er
e
ev
e
r
y
lay
er
is
attac
h
ed
to
th
e
n
ex
t
lay
er
[
2
1
]
.
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
is
a
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
th
at
was
ap
p
lied
b
y
Vap
n
ik
,
u
s
in
g
f
o
r
b
o
th
class
if
icatio
n
,
an
d
r
eg
r
es
s
io
n
.
T
h
is
class
if
ier
h
as
th
e
p
o
ten
tial
to
m
in
im
ize
er
r
o
r
s
o
f
t
h
e
class
if
ier
a
n
d
to
m
ax
im
ize
th
e
g
r
a
p
h
ical
m
ar
g
in
[
2
2
]
.
I
n
t
h
is
s
tu
d
y
,
we
h
av
e
ap
p
lied
lin
ea
r
SVM
to
th
e
d
ataset.
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
P
r
ed
ictio
n
o
f stu
d
en
t’
s
p
erfo
r
ma
n
ce
th
r
o
u
g
h
ed
u
ca
tio
n
a
l d
a
ta
min
in
g
tech
n
iq
u
es
(
N
ib
r
a
s
Z.
S
a
lih
)
1713
Fo
r
th
e
im
b
alan
ce
d
an
d
n
o
is
y
d
ataset,
an
o
v
er
f
itti
n
g
p
r
o
b
le
m
co
u
ld
ap
p
ea
r
wh
ich
ca
n
b
e
ex
clu
d
ed
b
y
f
o
llo
win
g
s
o
m
e
s
tatis
tical
tech
n
iq
u
e
[
2
3
]
.
I
n
th
is
p
ap
er
,
an
im
b
alan
ce
d
d
ataset
is
p
r
esen
ted
b
ec
au
s
e
th
e
in
s
tan
ce
s
n
u
m
b
er
o
f
o
n
e
class
is
s
m
aller
th
an
th
e
o
th
er
o
n
e.
T
h
e
lo
wer
class
es
h
av
e
1
7
in
s
tan
ce
s
b
u
t
th
e
h
ig
h
er
class
es
h
av
e
2
7
in
s
tan
c
es.
T
h
e
s
m
aller
class
is
ca
lled
th
e
m
in
o
r
ity
class
wh
ile
th
e
b
ig
g
er
class
is
ca
lled
th
e
m
ajo
r
ity
class
.
Sy
n
th
etic
m
in
o
r
ity
o
v
er
s
am
p
li
n
g
tech
n
iq
u
e
(
SMOT
E
)
is
th
e
o
v
er
s
am
p
lin
g
m
eth
o
d
u
s
ed
to
s
o
lv
e
th
e
im
b
alan
ce
d
d
ataset
p
r
o
b
lem
.
SMOT
E
tr
an
s
f
o
r
m
s
an
im
b
ala
n
ce
d
d
ataset
an
d
p
r
o
d
u
ce
s
b
al
an
ce
d
d
atasets
.
T
h
e
m
ajo
r
ity
an
d
m
in
o
r
ity
clas
s
es
ar
e
d
is
tr
ib
u
ted
u
s
in
g
SMOT
E
b
y
g
en
er
atin
g
s
y
n
th
etic
in
s
tan
ce
s
in
th
e
m
in
o
r
ity
class
,
th
is
tech
n
iq
u
e
is
u
s
ed
t
o
en
h
an
ce
p
r
e
d
ictio
n
p
er
f
o
r
m
an
ce
in
th
e
m
in
o
r
ity
class
.
I
n
th
e
m
in
o
r
ity
class
,
th
e
s
am
p
le
is
p
o
s
itio
n
ed
ac
r
o
s
s
th
e
lin
e
s
eg
m
en
ts
th
at
in
clu
d
e
o
n
e
o
r
m
o
r
e
o
f
t
h
e
k
-
n
ea
r
est
n
eig
h
b
o
r
s
.
T
h
e
SMOT
E
is
u
s
u
ally
u
s
ed
b
y
f
iv
e
clo
s
est n
eig
h
b
o
r
s
[
2
4
]
,
[
2
5
]
.
Ov
er
s
am
p
lin
g
in
cr
ea
s
es
th
e
n
u
m
b
er
o
f
o
cc
u
r
r
e
n
ce
s
to
r
etain
in
g
b
o
th
o
cc
u
r
r
en
ce
s
an
d
non
-
o
cc
u
r
r
e
n
ce
s
b
y
u
s
in
g
s
a
m
p
lin
g
with
r
e
p
lace
m
en
t
[
2
6
]
.
So
,
th
e
two
class
es
b
ec
am
e
s
im
ilar
wh
en
th
is
tech
n
iq
u
e
was a
p
p
lied
.
SMOT
E
s
u
p
er
v
is
ed
f
ilter
h
as b
ee
n
i
m
p
lem
en
ted
u
s
in
g
two
m
ai
n
p
ar
am
eter
s
wh
ich
ar
e
p
er
ce
n
tag
e
a
n
d
n
ea
r
est
n
eig
h
b
o
r
s
in
W
E
KA.
T
h
e
lo
wer
cl
ass
es
h
av
e
in
cr
ea
s
ed
b
y
5
0
%
(
b
ased
o
n
o
p
tio
n
-
P
5
0
.
0
in
W
E
KA)
an
d
ad
ju
s
tin
g
n
ea
r
est n
eig
h
b
o
r
s
to
o
b
tain
th
e
b
est r
esu
lts
(
b
ased
o
n
o
p
tio
n
-
K
in
W
E
KA)
.
5.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
T
h
e
r
elev
an
t
r
esu
lts
ar
e
illu
s
tr
ated
in
T
ab
les
2
an
d
3
f
o
r
th
e
av
er
ag
e
o
f
t
h
e
f
o
llo
win
g
s
ev
e
n
m
etr
ics:
R
ec
all,
p
r
ec
is
io
n
,
s
p
ec
if
icity
,
F
-
m
ea
s
u
r
e,
R
OC
,
P
R
C
ar
ea
,
an
d
ac
cu
r
ac
y
.
T
h
r
ee
class
if
ier
s
ar
e
ap
p
lied
wh
ic
h
ar
e
NB
C
,
ML
P,
an
d
SVM
t
o
p
r
ed
ict
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
s
tu
d
en
t.
I
n
th
e
f
ir
s
t
ca
s
e,
we
p
er
f
o
r
m
t
h
is
ex
p
er
im
en
t
u
s
in
g
th
e
tr
ai
n
in
g
s
et
tech
n
iq
u
e
to
ass
ess
th
e
cla
s
s
if
ier
o
n
h
o
w
well
th
e
class
o
f
ca
s
es
is
tr
ain
ed
to
p
r
ed
ict
an
d
th
en
a
p
p
lied
cr
o
s
s
-
v
alid
atio
n
tech
n
iq
u
es
(
L
OOCV
an
d
5
-
C
V)
.
T
h
er
ef
o
r
e,
to
u
n
d
er
s
tan
d
th
e
p
er
f
o
r
m
an
ce
o
f
ea
c
h
class
if
ier
,
th
e
r
esu
lts
o
f
th
e
ab
o
v
e
-
m
e
n
tio
n
ed
m
etr
ics
f
o
r
th
e
tr
ai
n
in
g
s
et,
L
OOCV,
an
d
f
iv
e
-
tim
es,
5
-
C
V
ar
e
s
h
o
wn
i
n
T
ab
le
2
.
W
e
co
m
p
ar
e
th
e
r
esu
lt
o
f
t
h
e
p
r
ed
ictiv
e
m
o
d
el
f
o
r
t
h
e
tr
ai
n
in
g
s
et,
L
OOCV,
an
d
5
-
C
V.
W
e
f
o
u
n
d
th
at
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
cl
ass
if
ier
o
n
th
e
tr
ai
n
in
g
s
et
is
b
etter
th
an
L
OOCV
an
d
5
-
C
V,
wh
ich
m
ea
n
s
th
at
th
e
p
r
ed
ictiv
e
m
o
d
el
s
u
f
f
er
s
f
r
o
m
o
v
er
f
i
ttin
g
,
wh
ic
h
led
to
a
lar
g
e
d
if
f
er
e
n
ce
b
etwe
en
th
e
r
esu
lts
o
f
th
e
tr
ain
in
g
s
et
an
d
cr
o
s
s
-
v
alid
ati
o
n
tech
n
iq
u
es.
So
SMOT
E
s
u
p
er
v
is
ed
f
ilter
is
u
s
ed
to
o
v
er
c
o
m
e
th
e
o
v
e
r
f
itti
n
g
p
r
o
b
lem
a
n
d
to
en
h
a
n
ce
th
e
p
r
ed
ictio
n
p
er
f
o
r
m
a
n
ce
.
T
ab
le
3
s
h
o
ws
th
e
r
esu
lts
f
o
r
t
h
r
ee
class
if
ier
s
(
NB
C
,
ML
P,
an
d
SVM)
u
s
in
g
L
OOCV
an
d
f
iv
e
tim
es,
5
-
C
V
af
ter
a
s
u
p
er
v
is
ed
SMOT
E
f
ilter
is
ap
p
lied
.
T
h
e
b
est
r
esu
lts
f
o
r
th
is
ex
p
er
im
en
t
h
a
v
e
b
ee
n
u
n
d
er
lin
e
d
.
W
e
h
av
e
n
o
ticed
th
e
SVM
o
u
tp
er
f
o
r
m
i
n
g
i
n
ter
m
s
o
f
s
en
s
itiv
ity
,
p
r
ec
is
io
n
,
F
-
m
ea
s
u
r
e,
PR
C
ar
ea
,
R
OC
,
an
d
ac
cu
r
ac
y
f
o
r
L
OOCV,
an
d
NB
C
o
u
tp
er
f
o
r
m
s
in
ter
m
s
o
f
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
F
-
m
ea
s
u
r
e,
an
d
ac
cu
r
ac
y
f
o
r
5
-
C
V.
T
h
e
lo
w
s
p
ec
if
icity
v
a
lu
es
ar
e
u
s
u
ally
a
r
esu
lt
o
f
h
i
g
h
s
en
s
itiv
ity
v
alu
es,
s
o
t
h
e
s
p
ec
if
icity
p
lay
s
an
im
p
o
r
tan
t r
o
le
b
e
ca
u
s
e
it id
e
n
tifie
s
th
e
s
tu
d
en
t'
s
f
ailu
r
e
in
th
e
ac
ad
em
ic
co
u
r
s
e.
T
ab
le
2
.
T
h
e
class
if
icatio
n
r
esu
lts
: u
s
in
g
th
e
tr
ain
in
g
s
et,
L
O
OC
V,
an
d
5
-
CV
U
si
n
g
Tr
a
i
n
i
n
g
-
s
e
t
LO
O
C
V
5
-
C
V
(
M
e
a
n
±
s
t
d
)
M
e
t
r
i
c
s
N
B
C
M
LP
S
V
M
N
B
C
M
LP
S
V
M
N
B
C
M
LP
S
V
M
S
e
n
s
i
t
i
v
i
t
y
0
.
7
7
3
0
.
9
7
7
0
.
8
6
4
0
.
6
5
9
0
.
6
3
6
0
.
6
5
9
0
.
6
3
6
±
0
.
0
2
2
0
.
6
7
2
±
0
.
0
1
2
0
.
6
3
6
±
0
.
0
3
2
S
p
e
c
i
f
i
c
i
t
y
0
.
7
4
0
0
.
9
6
2
0
.
8
8
8
0
.
7
4
0
0
.
7
0
3
0
.
7
7
7
0
.
6
9
5
±
0
.
0
3
0
.
7
6
9
±
0
.
0
1
6
0
.
7
4
±
0
.
0
7
4
P
r
e
c
i
s
i
o
n
0
.
7
9
1
0
.
9
7
9
0
.
8
6
4
0
.
6
5
6
0
.
6
3
6
0
.
6
5
0
0
.
6
3
8
±
0
.
0
2
0
.
6
6
6
±
0
.
0
1
5
0
.
6
3
1
2
±
0
.
0
2
F
-
M
e
a
s
u
r
e
0
.
7
7
6
0
.
9
7
7
0
.
8
6
4
0
.
6
5
7
0
.
6
3
6
0
.
6
5
2
0
.
6
3
7
±
0
.
0
2
1
0
.
6
6
8
±
0
.
0
1
5
0
.
6
2
9
±
0
.
0
3
1
R
O
C
0
.
8
5
2
0
.
9
6
9
0
.
8
5
6
0
.
5
9
0
0
.
6
4
3
0
.
6
2
4
0
.
6
0
3
±
0
.
0
2
3
0
.
6
7
7
±
0
.
0
1
2
0
.
6
0
6
±
0
.
0
3
P
R
C
A
r
e
a
0
.
8
7
4
0
.
9
6
0
0
.
8
1
5
0
.
6
1
0
0
.
6
7
9
0
.
6
0
1
0
.
6
1
7
±
0
.
0
1
5
0
.
7
0
5
±
0
.
0
1
7
0
.
5
8
7
±
0
.
0
2
A
c
c
u
r
a
c
y
7
7
.
2
7
%
9
7
.
7
2
%
8
6
.
3
6
%
6
5
.
9
0
%
6
3
.
6
3
%
6
5
.
9
0
%
6
3
.
6
3
6
±
2
.
2
7
6
7
.
2
7
±
1
.
2
4
6
3
.
6
3
6
±
3
.
2
1
T
ab
le
3
.
T
h
e
class
if
icatio
n
r
esu
lts
u
s
in
g
SMOT
E
f
ilter
LO
O
C
V
5
-
C
V
(
M
e
a
n
±
s
t
d
)
M
e
t
r
i
c
s
N
B
C
M
LP
S
V
M
N
B
C
M
LP
S
V
M
S
e
n
s
i
t
i
v
i
t
y
0
.
7
1
2
0
.
7
6
9
0
.
7
8
8
0
.
7
3
8
±
0
.
0
2
9
0
.
7
2
3
±
0
.
0
2
2
0
.
7
3
8
±
0
.
0
4
S
p
e
c
i
f
i
c
i
t
y
0
.
6
6
6
0
.
7
7
7
0
.
7
4
0
0
.
7
0
3
±
0
.
0
2
6
0
.
6
3
6
±
0
.
0
1
7
0
.
6
7
3
±
0
.
0
3
P
r
e
c
i
s
i
o
n
0
.
7
1
6
0
.
7
6
9
0
.
7
9
3
0
.
7
4
2
±
0
.
0
3
0
.
7
3
4
±
0
.
0
2
4
0
.
7
4
8
±
0
.
0
5
F
-
M
e
a
s
u
r
e
0
.
7
1
1
0
.
7
6
9
0
.
7
8
8
0
.
7
3
8
±
0
.
0
2
9
0
.
7
2
1
±
0
.
0
2
2
0
.
7
3
7
±
0
.
0
4
R
O
C
0
.
7
3
6
0
.
7
5
4
0
.
7
9
0
0
.
7
4
6
±
0
.
0
2
3
0
.
7
7
4
±
0
.
0
1
4
0
.
7
4
1
±
0
.
0
4
P
R
C
A
r
e
a
0
.
7
0
3
0
.
7
1
7
0
.
7
3
0
0
.
7
2
0
±
0
.
0
1
5
0
.
7
8
0
±
0
.
0
1
8
0
.
6
9
±
0
.
0
5
8
A
c
c
u
r
a
c
y
7
1
.
1
5
3
%
7
6
.
9
2
3
%
7
8
.
8
4
6
%
7
3
.
8
4
6
±
2
.
9
1
7
2
.
3
0
±
2
.
1
9
7
3
.
8
4
±
4
.
4
2
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ig
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ad
em
y
.
Pre
d
ictin
g
s
tu
d
en
ts
'
p
er
f
o
r
m
an
ce
d
e
p
en
d
in
g
o
n
m
ar
k
s
an
d
co
u
r
s
e
a
tten
d
an
ce
with
o
u
t
a
n
y
s
o
cio
e
co
n
o
m
ic
d
ata.
T
h
e
d
atasets
wer
e
co
llected
f
r
o
m
th
r
ee
s
tu
d
ies
y
ea
r
s
o
f
ac
a
d
em
ic
s
tag
es
o
f
Mu
s
tan
s
ir
iy
ah
Un
iv
er
s
ity
in
I
r
a
q
,
it
co
n
s
is
ts
o
f
4
4
s
tu
d
e
n
ts
an
d
1
3
attr
ib
u
tes
th
at
in
clu
d
ed
f
i
v
e
c
o
u
r
s
es.
W
e
h
av
e
p
r
o
p
o
s
ed
a
m
o
d
el
th
at
e
x
p
lain
s
th
e
co
r
r
elatio
n
b
etwe
en
two
b
asic
s
u
b
jects
wh
ich
ar
e
m
ath
e
m
atics
o
f
th
e
f
ir
s
t
an
d
s
ec
o
n
d
y
ea
r
s
an
d
c
o
n
tr
o
l
s
y
s
tem
s
o
f
th
e
th
ir
d
y
ea
r
.
T
h
e
s
tu
d
y
aim
s
to
im
p
r
o
v
e
s
tu
d
e
n
t
p
er
f
o
r
m
an
ce
b
y
an
aly
zin
g
ac
ad
em
ic
f
ea
tu
r
es
o
f
m
ath
em
atics
co
u
r
s
es
to
av
o
i
d
s
tu
d
en
t
f
ailu
r
e
o
f
t
h
e
co
n
t
r
o
l
s
y
s
tem
s
co
u
r
s
e.
T
h
u
s
,
th
is
p
r
ed
ictio
n
lea
d
s
to
g
u
id
e
t
h
e
s
tu
d
e
n
t
f
o
r
im
p
r
o
v
i
n
g
th
ei
r
ac
a
d
em
ic
f
ea
tu
r
es
o
f
m
ath
em
atics
co
u
r
s
es
in
th
e
f
ir
s
t
an
d
s
ec
o
n
d
y
ea
r
s
o
f
th
eir
s
tu
d
ies.
T
h
e
r
esu
lts
s
h
o
w
th
at
is
th
e
p
o
ten
tial
to
p
r
e
d
ict
th
e
s
tu
d
en
t
’
s
r
esu
lt
o
f
o
n
e
s
u
b
ject
in
t
h
e
u
n
iv
er
s
ity
p
r
o
g
r
a
m
to
o
b
tain
g
o
o
d
u
n
d
er
g
r
ad
u
ate
m
ar
k
s
with
p
lau
s
ib
le
ac
cu
r
ac
y
.
W
ith
th
e
ass
i
s
tan
ce
o
f
NB
C
,
ML
P,
an
d
SVM
alg
o
r
ith
m
s
,
th
r
ee
tech
n
i
q
u
es
ar
e
ap
p
lied
to
th
e
d
atase
t
in
clu
d
in
g
th
e
tr
ain
in
g
s
et,
L
OOCV,
an
d
5
-
C
V
u
s
in
g
th
e
W
E
KA
to
o
l.
W
e
f
o
u
n
d
th
e
p
r
ed
ictiv
e
m
o
d
el
s
u
f
f
e
r
s
f
r
o
m
o
v
er
f
itti
n
g
b
ec
au
s
e
an
i
m
b
alan
ce
d
d
ataset
was
u
tili
ze
d
,
th
er
ef
o
r
e
a
s
u
p
er
v
is
ed
SMOT
E
ap
p
r
o
ac
h
is
im
p
lem
en
ted
to
o
v
er
c
o
m
e
t
h
is
p
r
o
b
lem
a
n
d
to
en
h
an
ce
th
e
p
r
ed
ictio
n
o
f
th
e
s
tu
d
en
ts
’
p
er
f
o
r
m
an
ce
.
W
e
co
n
clu
d
e
th
at
th
e
b
est
class
if
ier
r
esu
lt
h
as
ap
p
ea
r
ed
af
ter
ap
p
lied
SMOT
E
tech
n
iq
u
e
wh
ich
is
th
e
SVM
f
o
r
L
O
OC
V.
T
h
e
f
u
tu
r
e
wo
r
k
is
to
e
n
lar
g
e
th
e
d
ataset
to
s
tr
en
g
th
en
t
h
e
g
e
n
er
aliza
b
ilit
y
o
f
th
e
p
r
e
d
ictio
n
.
Als
o
,
we
will
s
tu
d
y
th
e
c
o
r
r
elatio
n
b
et
wee
n
o
th
er
s
u
b
jects
s
u
ch
as c
o
m
m
u
n
icatio
n
s
y
s
tem
an
d
d
ig
ital sig
n
al
p
r
o
ce
s
s
in
g
,
an
d
t
h
eir
im
p
ac
t
o
n
s
tu
d
e
n
t’
s
p
er
f
o
r
m
a
n
ce
.
ACK
NO
WL
E
DG
M
E
N
T
S
My
s
in
ce
r
e
ap
p
r
ec
iatio
n
an
d
t
h
an
k
s
to
th
e
Un
iv
er
s
ity
o
f
Mu
s
ta
n
s
ir
iy
ah
f
o
r
th
e
g
u
id
a
n
ce
a
n
d
s
u
p
p
o
r
t.
Als
o
,
all
th
an
k
s
an
d
a
p
p
r
ec
iati
o
n
to
th
o
s
e
wh
o
h
el
p
ed
m
e
a
n
d
g
av
e
m
e
s
cien
tific
ad
v
ice
in
th
is
r
esear
ch
.
RE
F
E
R
E
NC
E
S
[1
]
E.
B.
C
o
sta
,
B.
F
o
n
se
c
a
,
M
.
A.
S
a
n
tan
a
,
F
.
F
.
d
e
Ara
ú
j
o
,
a
n
d
J.
R
e
g
o
,
“
E
v
a
lu
a
ti
n
g
th
e
e
ffe
c
ti
v
e
n
e
s
s
o
f
e
d
u
c
a
ti
o
n
a
l
d
a
ta
m
in
in
g
tec
h
n
i
q
u
e
s
f
o
r
e
a
rly
p
re
d
icti
o
n
o
f
stu
d
e
n
ts’
a
c
a
d
e
m
i
c
fa
il
u
re
i
n
i
n
tro
d
u
c
t
o
ry
p
r
o
g
ra
m
m
in
g
c
o
u
rse
s,”
Co
mp
u
ter
s i
n
Hu
m
a
n
Beh
a
v
i
o
r
,
v
o
l.
7
3
,
p
p
.
2
4
7
-
2
5
6
,
Au
g
u
st
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
c
h
b
.
2
0
1
7
.
0
1
.
0
4
7
.
[2
]
A.
I.
A
d
e
k
it
a
n
a
n
d
O.
S
a
lau
,
“
T
h
e
imp
a
c
t
o
f
e
n
g
in
e
e
rin
g
st
u
d
e
n
t
s’
p
e
rfo
rm
a
n
c
e
in
th
e
first
t
h
re
e
y
e
a
rs
o
n
t
h
e
ir
g
ra
d
u
a
ti
o
n
re
su
lt
u
si
n
g
e
d
u
c
a
t
io
n
a
l
d
a
ta
m
in
in
g
,
”
He
li
y
o
n
,
v
o
l.
5
,
n
o
.
2
,
p
p
.
e
0
1
2
5
0
,
F
e
b
ru
a
ry
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/j
.
h
e
li
y
o
n
.
2
0
1
9
.
e
0
1
2
5
0
.
[3
]
A.
M
.
Ah
m
e
d
,
A
.
Riza
n
e
r,
a
n
d
A.
H.
Ulu
so
y
,
“
Us
in
g
d
a
ta
m
in
i
n
g
t
o
p
re
d
ict
in
str
u
c
to
r
p
e
rfo
rm
a
n
c
e
,
”
Pro
c
e
d
ia
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
0
2
,
p
p
.
1
3
7
-
1
4
2
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
1
6
/
j.
p
r
o
c
s.2
0
1
6
.
0
9
.
3
8
0
.
[4
]
S
.
Hu
ss
a
in
,
N.
A.
Da
h
a
n
,
F
.
M
.
Ba
-
Alwib
,
a
n
d
N.
Rib
a
ta,
“
Ed
u
c
a
ti
o
n
a
l
d
a
ta
m
in
i
n
g
a
n
d
a
n
a
ly
sis
o
f
stu
d
e
n
ts
'
a
c
a
d
e
m
ic
p
e
rfo
rm
a
n
c
e
u
sin
g
WE
KA
,
”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
t
ric
a
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l
.
9
,
n
o
.
2
,
p
p
.
4
4
7
-
4
5
9
,
F
e
b
ru
a
r
y
2
0
1
8
,
d
o
i:
1
0
.
1
1
5
9
1
/
ij
e
e
c
s.v
9
.
i
2
.
p
p
4
4
7
-
4
5
9
.
[5
]
N.
Ke
tu
i,
W.
Wi
s
o
m
k
a
,
a
n
d
K.
Ho
m
ju
n
,
“
Us
in
g
Clas
sifica
ti
o
n
D
a
ta
M
in
in
g
Tec
h
n
i
q
u
e
s
fo
r
S
tu
d
e
n
ts
P
e
rfo
rm
a
n
c
e
P
re
d
ictio
n
,
”
2
0
1
9
J
o
in
t
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Dig
it
a
l
Art
s,
M
e
d
ia
a
n
d
T
e
c
h
n
o
lo
g
y
wit
h
ECT
I
No
rth
e
rn
S
e
c
ti
o
n
C
o
n
fer
e
n
c
e
o
n
E
lec
trica
l,
El
e
c
tro
n
ics
,
Co
mp
u
ter
a
n
d
T
e
lec
o
mm
u
n
ica
ti
o
n
s
E
n
g
in
e
e
rin
g
(ECT
I
DAM
T
-
NCON
)
,
2
0
1
9
,
p
p
.
3
5
9
-
3
6
3
,
d
o
i:
1
0
.
1
1
0
9
/
ECT
I
-
NCO
N.2
0
1
9
.
8
6
9
2
2
2
7
.
[6
]
N.
Ku
m
a
r
a
n
d
S
.
Kh
a
tri
,
“
Im
p
lem
e
n
ti
n
g
WE
KA
fo
r
m
e
d
ica
l
d
a
ta
c
las
sifica
ti
o
n
a
n
d
e
a
rly
d
ise
a
se
p
re
d
ictio
n
,
”
2
0
1
7
3
rd
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mp
u
t
a
ti
o
n
a
l
In
tell
ig
e
n
c
e
&
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
(CICT
)
,
2
0
1
7
,
p
p
.
1
-
6
,
d
o
i:
1
0
.
1
1
0
9
/CIACT.
2
0
1
7
.
7
9
7
7
2
7
7
.
[7
]
Ch
in
g
-
Ch
ieh
Ki
u
,
“
Da
ta
M
in
i
n
g
An
a
ly
sis
o
n
S
tu
d
e
n
t’s
Ac
a
d
e
m
ic
P
e
rfo
rm
a
n
c
e
th
r
o
u
g
h
E
x
p
l
o
ra
ti
o
n
o
f
S
t
u
d
e
n
t’s
Ba
c
k
g
ro
u
n
d
a
n
d
S
o
c
ial
Ac
ti
v
i
ti
e
s,”
2
0
1
8
Fo
u
rth
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
s
in
Co
mp
u
ti
n
g
,
Co
mm
u
n
ica
ti
o
n
&
A
u
to
m
a
ti
o
n
(I
CACCA
)
,
2
0
1
8
,
p
p
.
1
-
5
,
d
o
i
:
1
0
.
1
1
0
9
/ICACCAF.
2
0
1
8
.
8
7
7
6
8
0
9
.
[8
]
L.
C.
Yu
,
e
t
a
l
.
,
“
Im
p
ro
v
in
g
e
a
rly
p
re
d
icti
o
n
o
f
a
c
a
d
e
m
ic
fa
il
u
re
u
sin
g
se
n
ti
m
e
n
t
a
n
a
l
y
sis
o
n
se
lf
-
e
v
a
lu
a
ted
c
o
m
m
e
n
ts,”
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
Assiste
d
L
e
a
rn
in
g
,
v
o
l.
3
4
,
n
o
.
4
,
p
p
.
3
5
8
-
3
6
5
,
2
0
1
8
,
d
o
i:
1
0
.
1
1
1
1
/
jca
l.
1
2
2
4
7
.
[9
]
R.
As
if,
A.
M
e
rc
e
ro
n
,
S
.
Al
i,
a
n
d
N.
Ha
id
e
r,
“
An
a
l
y
z
in
g
u
n
d
e
rg
r
a
d
u
a
te
stu
d
e
n
ts'
p
e
rfo
rm
a
n
c
e
u
si
n
g
e
d
u
c
a
ti
o
n
a
l
d
a
ta m
in
in
g
,
”
Co
m
p
u
ter
s
a
n
d
Ed
u
c
a
ti
o
n
,
v
o
l.
1
1
3
,
p
p
.
1
7
7
-
1
9
4
,
Oc
t.
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
c
o
m
p
e
d
u
.
2
0
1
7
.
0
5
.
0
0
7
.
[1
0
]
J.
Ja
c
o
b
,
K.
J
h
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[1
2
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P
.
Ka
u
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M
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h
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.
S
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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1715
[1
3
]
A.
A.
S
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a
,
“
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u
c
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ti
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l
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ta
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&
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ter
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4
]
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,
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5
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A.
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“
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m
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Ap
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.
[1
6
]
J.
Va
n
Hu
lse
,
T
.
M
.
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o
sh
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o
ft
a
a
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a
n
d
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p
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li
ta
n
o
,
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ter
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[1
7
]
G
.
H
.
F
u
,
L.
Z.
Yi,
a
n
d
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P
a
n
,
“
T
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8
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.
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ter
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9
]
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-
Tsu
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,
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.
[2
0
]
S
.
S
.
Ath
a
n
i
,
S
.
A.
Ko
d
li
,
M
.
N
.
Ba
n
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n
d
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.
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.
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.
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m
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th
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t
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d
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ta
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tec
h
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iq
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e
s,”
2
0
1
7
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n
ter
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ti
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fer
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7
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7
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2
0
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2
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9
7
9
4
.
[2
1
]
N.
B.
G
a
ik
wa
d
,
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Ti
w
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ri,
A
.
Ke
sk
a
r,
a
n
d
N
.
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S
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iv
a
p
ra
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sh
,
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icie
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G
A
Im
p
lem
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tatio
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o
f
M
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p
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Re
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Ti
m
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m
a
n
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ti
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it
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ti
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n
,
”
I
EE
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Acc
e
ss
,
v
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l.
7
,
p
p
.
2
6
6
9
6
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6
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0
1
9
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1
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1
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9
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S
.
2
0
1
9
.
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9
0
0
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8
4
.
[2
2
]
M
.
G
a
u
d
io
s
o
,
W.
K
h
a
laf,
a
n
d
C
.
P
a
c
e
,
“
On
th
e
Us
e
o
f
th
e
S
VM
Ap
p
ro
a
c
h
in
An
a
l
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n
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c
tr
o
n
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N
o
se
,
”
7
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
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n
c
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o
n
Hy
b
rid
In
tell
ig
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t
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ms
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2
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7
)
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2
0
0
7
,
p
p
.
4
2
-
4
6
,
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o
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1
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.
1
1
0
9
/
HIS.
2
0
0
7
.
1
6
.
[2
3
]
S
.
E.
Ro
sh
a
n
a
n
d
S
.
As
a
d
i
,
“
Im
p
ro
v
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m
e
n
t
o
f
Ba
g
g
in
g
p
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rfo
rm
a
n
c
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fo
r
c
las
sif
ica
ti
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n
o
f
imb
a
lan
c
e
d
d
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tas
e
ts
u
sin
g
e
v
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lu
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ry
m
u
lt
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o
b
jec
ti
v
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o
p
ti
m
iza
ti
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n
,
”
En
g
in
e
e
rin
g
A
p
p
li
c
a
ti
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s
o
f
Arti
f
icia
l
In
telli
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e
n
c
e
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v
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l.
8
7
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p
.
1
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3
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9
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u
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ry
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6
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a
p
p
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i.
2
0
1
9
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1
0
3
3
1
9
.
[2
4
]
S
.
T
.
Jish
a
n
,
R
.
I.
Ra
sh
u
,
N.
Ha
q
u
e
,
a
n
d
R.
M
.
Ra
h
m
a
n
,
“
Im
p
ro
v
in
g
a
c
c
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ra
c
y
o
f
st
u
d
e
n
ts’
fi
n
a
l
g
ra
d
e
p
re
d
ictio
n
m
o
d
e
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sin
g
o
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m
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rit
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sa
m
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tec
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e
,
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De
c
isio
n
An
a
lytics
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v
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l.
2
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o
.
1
,
p
p
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5
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5
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6
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2.
[2
5
]
P
.
Ka
u
r
,
A.
G
o
sa
i
n
,
“
Co
m
p
a
rin
g
th
e
b
e
h
a
v
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v
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s
imb
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lan
c
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p
ro
b
lem
with
n
o
ise
,
”
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p
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g
e
r
,
S
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n
g
a
p
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re
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p
p
.
2
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3
0
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2
0
1
8
,
d
o
i:
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0
.
1
0
0
7
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7
8
-
9
8
1
-
10
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6
6
0
2
-
3
_
3
.
[2
6
]
Y.
Zh
a
n
g
a
n
d
P
.
Tr
u
b
e
y
,
“
M
a
c
h
in
e
Lea
rn
i
n
g
a
n
d
S
a
m
p
li
n
g
S
c
h
e
m
e
:
An
Emp
ir
ica
l
S
t
u
d
y
o
f
M
o
n
e
y
Lau
n
d
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ri
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g
De
tec
ti
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n
,
”
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m
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t
a
ti
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n
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l
Eco
n
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s
,
v
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l.
5
4
,
n
o
.
3
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p
.
1
0
4
3
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0
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3
,
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to
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e
r
2
0
1
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