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Hig
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Stu
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In
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C
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[
1
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2
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.
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3
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Desp
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ta
k
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g
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s
,
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e
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v
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to
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k
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m
h
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b
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ap
p
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s
[
4
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.
B
ased
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n
th
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
IJ
-
A
I
Vo
l.
8
,
No
.
4
,
Dec
em
b
er
201
9
:
3
6
7
–
3
7
4
36
8
s
ce
n
ar
io
,
m
o
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t
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h
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ities
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ap
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Fu
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a
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ld
b
e
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s
ed
b
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r
tain
r
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s
[
4
-
8
]
a
n
d
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n
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v
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to
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[
7
,
9
-
1
0
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.
T
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w
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u
itab
le
attr
ib
u
tes
to
p
r
ed
ict
an
ac
ce
p
tan
ce
o
r
r
e
j
ec
tio
n
o
f
a
u
n
i
v
er
s
it
y
’
s
o
f
f
er
ed
p
r
o
g
r
a
m
m
e.
T
h
e
s
ec
o
n
d
o
b
j
ec
tiv
e
is
to
d
e
v
elo
p
a
p
r
e
d
ictio
n
m
o
d
el
o
f
ac
ce
p
tan
ce
o
r
r
e
j
ec
tio
n
o
f
th
e
o
f
f
er
ed
co
u
r
s
e.
T
h
e
f
i
n
al
o
b
j
ec
tiv
e
is
to
d
e
v
elo
p
a
d
ash
b
o
ar
d
to
v
is
u
alize
t
h
e
d
ata
r
elate
d
to
ac
ce
p
tan
ce
o
r
r
ej
ec
tio
n
p
r
ed
ictio
n
m
o
d
el.
I
n
id
en
t
if
y
i
n
g
s
u
itab
le
attr
ib
u
tes
to
p
r
ed
ict
an
ac
ce
p
t
an
ce
o
r
r
ej
ec
tio
n
o
f
th
e
o
f
f
er
e
d
p
r
o
g
r
am
,
s
e
v
er
al
ar
ticl
es
h
ad
b
ee
n
a
n
al
y
ze
d
f
r
o
m
1
9
9
0
s
till
r
ec
en
t
y
ea
r
s
.
T
h
er
e
ar
e
eig
h
t
f
ac
to
r
s
i
n
f
l
u
en
ci
n
g
t
h
e
u
n
i
v
er
s
it
y
c
h
o
ice
a
m
o
n
g
th
e
ap
p
lica
n
ts
,
as
s
h
o
wn
in
T
ab
le
1
.
T
ab
le
1
.
Facto
r
s
af
f
ec
ti
n
g
s
tu
d
en
t to
ac
ce
p
t o
r
r
e
j
ec
t o
f
f
er
L
o
c
a
t
i
o
n
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i
n
a
n
c
i
a
l
F
a
c
i
l
i
t
y
F
r
i
e
n
d
F
a
mi
l
y
C
a
m
p
u
s
v
i
si
t
F
i
n
a
n
c
i
a
l
a
i
d
P
e
r
so
n
a
l
c
h
o
i
c
e
[
4
]
✔
✔
✔
✔
✔
[
5
]
✔
✔
[
6
]
✔
✔
✔
✔
✔
[
7
]
✔
✔
[
8
]
✔
[
9
]
✔
✔
✔
✔
✔
[
1
0
]
✔
✔
✔
✔
✔
✔
[
1
1
]
✔
✔
✔
✔
✔
✔
✔
I
mp
o
r
t
a
n
c
e
T
o
p
r
a
n
k
f
a
c
t
o
r
T
o
p
r
a
n
k
f
a
c
t
o
r
C
o
mm
o
n
f
a
c
t
o
r
C
o
mm
o
n
f
a
c
t
o
r
C
o
mm
o
n
f
a
c
t
o
r
C
o
mm
o
n
f
a
c
t
o
r
L
o
w
r
a
n
k
f
a
c
t
o
r
L
o
w
r
a
n
k
f
a
c
t
o
r
B
ased
o
n
T
ab
le
1
,
th
e
ap
p
li
ca
n
ts
’
lo
ca
tio
n
a
n
d
f
i
n
an
cia
l
ar
e
co
n
s
id
er
ed
as
th
e
m
o
s
t
a
f
f
ec
t
in
g
f
ac
to
r
s
to
w
ar
d
s
t
h
e
o
f
f
er
ed
u
n
i
v
er
s
it
y
p
r
o
g
r
am
.
In
[
1
1
]
s
tated
th
at
t
h
e
ap
p
lican
ts
p
r
ef
er
to
c
h
o
o
s
e
th
e
u
n
i
v
er
s
it
y
t
h
at
i
s
n
ea
r
to
th
eir
h
o
m
e,
w
h
ic
h
is
alig
n
ed
w
i
t
h
[
4
,
6
,
7
,
9
-
1
1
]
’
s
f
i
n
d
in
g
s
.
T
h
is
p
r
ef
er
en
c
e
is
to
en
s
u
r
e
th
a
t
th
e
ap
p
lican
ts
w
o
u
ld
to
b
e
cl
o
s
er
to
th
eir
f
a
m
il
y
a
n
d
f
r
ie
n
d
s
.
I
n
f
in
a
n
cial
is
s
u
e,
s
o
m
e
ap
p
lican
ts
ar
e
f
r
o
m
th
e
ca
teg
o
r
y
o
f
p
o
o
r
f
a
m
il
y
o
r
lo
w
er
in
co
m
e
f
a
m
il
y
[
4
,
6
-
8
,
1
0
-
1
1
]
.
A
s
a
r
esu
lt,
th
e
y
ar
e
h
ar
d
l
y
to
p
u
r
s
u
e
o
r
co
n
tin
u
e
th
eir
s
t
u
d
ies.
A
l
th
o
u
g
h
f
i
n
an
cia
l
aid
,
f
o
r
ex
a
m
p
le
P
erb
a
d
a
n
a
n
P
en
d
id
ik
a
n
Tin
g
g
i
N
a
s
io
n
a
l
,
(
PT
PT
N
)
an
d
P
in
ja
ma
n
P
ela
ja
r
a
n
Ma
jlis
A
ma
n
a
h
R
a
y
a
(
MA
R
A
)
s
c
h
o
lar
s
h
ip
ar
e
o
f
f
er
ed
b
y
t
h
e
g
o
v
er
n
m
en
t
to
h
e
lp
m
an
y
u
n
f
o
r
tu
n
ate
s
t
u
d
en
ts
to
s
tu
d
y
at
h
ig
h
er
ed
u
ca
tio
n
lev
el,
y
et
th
e
s
e
ap
p
lican
t
s
n
ee
d
to
en
r
o
ll
th
e
s
t
u
d
ies
f
ir
s
t.
B
esid
es
th
e
m
en
tio
n
ed
f
ac
to
r
s
,
ca
m
p
u
s
v
i
s
it,
f
ac
il
it
y
a
v
ailab
ilit
y
i
n
u
n
i
v
er
s
it
y
[
4
,
6
,
9
-
1
0
]
,
f
r
ien
d
an
d
f
a
m
il
y
in
f
l
u
en
ce
s
[
5
,
6
,
9
-
1
1
]
m
a
y
a
ls
o
af
f
ec
t
t
h
e
ap
p
lican
ts
’
ch
o
ices.
C
a
m
p
u
s
v
is
it,
w
h
ich
i
s
o
n
e
o
f
th
e
ap
p
r
o
ac
h
es
t
h
at
h
av
e
b
ee
n
co
n
d
u
cted
b
y
m
a
n
y
Ma
la
y
s
ia
n
u
n
i
v
er
s
it
ies
i
n
g
ai
n
i
n
g
m
o
r
e
p
o
ten
tial
s
tu
d
e
n
ts
th
r
o
u
g
h
r
o
ad
s
h
o
w
an
d
m
ee
ti
n
g
[
4
,
9
-
1
1
]
.
Me
an
w
h
ile
f
ac
ilit
y
av
ailab
ilit
y
is
o
n
e
o
f
t
h
e
ele
m
en
ts
o
f
d
r
e
a
m
p
lace
o
f
s
t
u
d
y
;
in
c
lu
d
i
n
g
ac
ad
e
m
ic
f
asc
ilit
ie
s
s
u
c
h
as
co
m
p
u
ter
o
r
s
cien
ce
lab
a
n
d
,
also
s
p
o
r
ts
an
d
r
ec
r
ea
tio
n
al
f
ascil
ities
.
Frie
n
d
an
d
f
a
m
il
y
i
n
f
lu
e
n
ce
s
i
s
also
a
s
ig
n
i
f
ica
n
t
f
ac
to
r
w
it
h
h
ig
h
p
o
s
s
ib
ilit
y
t
h
at
th
e
ap
p
lican
t
s
m
a
y
c
h
o
o
s
e
th
e
s
a
m
e
u
n
iv
er
s
it
y
as
t
h
eir
f
r
ien
d
s
an
d
f
a
m
ilies
.
I
n
ad
d
itio
n
,
p
er
s
o
n
el
c
h
o
ice
f
ac
to
r
ca
n
also
in
f
lu
e
n
ce
th
e
ap
p
lic
an
t
to
ch
o
o
s
e
th
eir
p
r
ef
er
r
ed
co
u
r
s
e
s
u
b
j
ec
t
to
th
eir
d
esire
d
j
o
b
[
5
,
1
1
-
13]
.
I
n
Ma
la
y
s
ia,
th
e
ap
p
lican
t
ca
n
s
elec
t
t
h
eir
p
r
ef
er
r
ed
co
u
r
s
es
in
a
n
y
p
u
b
lic
u
n
i
v
er
s
it
y
v
ia
Un
it
P
en
g
u
r
u
s
a
n
Un
ivers
ity
,
(
UP
U)
p
o
r
tal.
T
h
r
o
u
g
h
th
i
s
p
o
r
tal,
th
e
ap
p
lican
t
s
w
ill
b
e
allo
w
ed
to
r
an
k
t
h
ei
r
p
r
ef
er
r
ed
c
o
u
r
s
es
f
r
o
m
a
n
y
u
n
i
v
er
s
it
y
t
h
at
th
e
y
w
a
n
t
to
s
t
u
d
y
t
h
e
m
o
s
t.
T
h
at
i
s
w
h
y
,
a
s
t
u
d
y
is
n
ee
d
ed
to
d
eter
m
i
n
e
o
th
er
i
n
f
l
u
en
c
e
fa
cto
r
s
th
a
t sp
ec
if
icall
y
i
n
lo
ca
l p
er
s
p
ec
tiv
e.
E
v
er
y
y
ea
r
,
t
h
e
ap
p
lican
t
s
an
d
en
r
o
l
m
en
t
n
u
m
b
er
s
h
as
d
ir
ec
tl
y
es
tab
lis
h
ed
v
a
s
t
d
a
tab
ases
o
f
u
n
i
v
er
s
i
ties
.
T
h
ese
d
atab
ases
w
o
u
ld
b
e
ab
le
to
p
r
o
v
id
e
an
aly
tics
a
n
d
i
n
s
i
g
h
ts
w
h
y
t
h
ese
s
u
cc
es
s
f
u
l
ap
p
lican
t
s
r
ej
ec
ted
th
eir
o
f
f
er
s
.
A
n
al
y
tic
s
w
o
u
ld
h
elp
in
id
e
n
ti
f
y
i
n
g
th
e
m
aj
o
r
attr
ib
u
tes
(
f
ac
to
r
s
)
th
at
m
a
y
co
r
r
elate
to
th
e
r
ej
ec
tio
n
an
d
th
is
an
al
y
tic
s
p
r
o
ce
s
s
ca
n
b
e
d
o
n
e
th
r
o
u
g
h
d
ata
m
i
n
i
n
g
m
o
d
el
[
1
,
9
-
1
2
]
.
Pre
d
ictiv
e
an
al
y
tics
ca
n
b
e
d
ev
elo
p
ed
u
s
in
g
s
u
p
er
v
is
e
d
m
et
h
o
d
s
o
r
u
n
s
u
p
er
v
i
s
ed
m
eth
o
d
s
.
T
h
e
s
u
p
er
v
is
ed
m
et
h
o
d
s
g
ain
ad
v
a
n
ta
g
e
in
lear
n
in
g
th
e
p
atter
n
s
th
r
o
u
g
h
h
is
to
r
ical
d
ata;
m
ea
n
w
h
ile
u
n
s
u
p
er
v
i
s
ed
m
et
h
o
d
s
w
o
u
ld
b
e
m
o
r
e
ap
p
r
o
p
r
iate
w
h
e
n
th
e
p
r
io
r
k
n
o
w
led
g
e
o
f
t
h
e
d
o
m
ai
n
is
u
n
ce
r
tai
n
.
T
h
er
ef
o
r
e,
w
it
h
th
e
estab
li
s
h
ed
d
atab
ases
a
n
d
in
f
o
r
m
at
io
n
o
f
en
r
o
lled
o
r
r
ej
ec
te
d
o
f
f
er
b
y
t
h
e
ap
p
lican
t
s
,
s
u
p
er
v
i
s
ed
m
eth
o
d
s
ca
n
b
e
f
u
r
t
h
er
ex
p
lo
r
ed
.
Fu
r
th
er
m
o
r
e,
th
e
s
u
p
er
v
i
s
ed
m
e
th
o
d
s
ca
n
b
e
e
v
alu
ated
b
y
e
x
p
er
ien
ce
d
s
ta
f
f
a
n
d
ex
i
s
ti
n
g
ca
s
e
s
[
1
4
-
1
5
]
.
Ho
w
e
v
er
,
th
e
u
n
i
v
er
s
it
y
s
elec
tio
n
f
ac
to
r
s
co
u
ld
b
e
d
is
s
i
m
ilar
w
i
th
r
eg
ar
d
s
o
f
lo
ca
li
t
y
[
1
8
,
1
9
,
2
1
-
2
2
]
.
T
h
e
s
tu
d
y
o
n
p
r
ed
ictio
n
o
f
ac
ce
p
tan
ce
o
f
f
er
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
(
ML
)
alg
o
r
ith
m
s
o
r
s
u
p
er
v
i
s
ed
m
et
h
o
d
s
ar
e
h
ar
d
ly
f
o
u
n
d
in
Ma
la
y
s
i
a
v
ie
w
p
o
in
t.
T
o
ac
h
ie
v
e
s
ec
o
n
d
o
b
j
ec
tiv
e
w
h
ic
h
is
to
d
e
v
elo
p
a
p
r
ed
ictio
n
m
o
d
el
o
f
ac
ce
p
tan
ce
o
r
r
ej
ec
tio
n
o
f
th
e
o
f
f
er
ed
co
u
r
s
es,
li
ter
atu
r
e
r
ev
ie
w
also
h
ad
b
ee
n
m
ad
e.
I
t
is
to
en
s
u
r
e
t
h
e
s
elec
t
io
n
o
f
p
r
ed
ictio
n
m
o
d
el
is
Evaluation Warning : The document was created with Spire.PDF for Python.
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r
ed
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a
n
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lytics o
f
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n
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s
tu
d
en
t in
ta
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.
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h
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d
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n
u
s
I
q
b
a
l B
a
s
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r
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a
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est
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o
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ith
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ab
le
2
tab
u
lates
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m
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el
s
ap
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lied
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r
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t
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s
u
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h
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k
Nea
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g
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r
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k
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D
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is
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Net
w
o
r
k
(
NN)
,
clu
s
ter
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d
Gen
etic
A
l
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ith
m
(
G
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u
b
j
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t
to
th
e
f
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d
in
g
s
,
DT
an
d
k
NN
ar
e
th
e
m
o
s
t
p
r
ef
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ab
le
an
d
p
o
p
u
lar
m
eth
o
d
s
u
s
ed
b
y
p
r
ev
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u
s
r
esear
ch
er
s
.
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h
is
m
a
y
h
ap
p
en
b
ec
au
s
e
o
f
DT
is
ea
s
y
to
u
n
d
e
r
s
tan
d
[
1
4
]
.
Mo
r
eo
v
er
,
it
ca
n
also
p
r
o
ce
s
s
th
e
er
r
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eo
u
s
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at
asets
[
1
5
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.
B
esid
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etic
Alg
o
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it
h
m
also
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s
u
all
y
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s
es
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p
ti
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izat
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[
1
6
]
,
w
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ile
Ne
u
r
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Net
w
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k
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o
d
el
i
s
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tl
y
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s
ed
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h
ea
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r
e
d
o
m
ai
n
[
1
7
]
.
On
th
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th
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d
,
k
N
N
is
w
id
el
y
u
s
ed
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ce
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ct
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elate
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els
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ataset.
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ates
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[
1
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[
2
0
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[
2
1
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[
1
4
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[
2
2
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[
1
9
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[
2
3
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[
1
8
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[
2
4
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[
2
0
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[
2
4
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[
1
4
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[
2
0
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[
1
4
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[
1
9
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M
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t
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In
[
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cted
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ased
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m
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k
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(
C
4
.
5
)
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d
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o
r
s
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u
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k
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h
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iq
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n
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s
ed
to
p
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d
ict
s
tu
d
en
t
p
er
f
o
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m
an
ce
[
1
9
-
2
1
]
.
D
T
p
r
ev
io
u
s
l
y
h
ad
b
ee
n
ap
p
lied
in
f
i
n
d
in
g
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d
p
r
ed
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g
s
t
u
d
e
n
t
p
r
o
f
iles
(
w
it
h
clu
s
ter
in
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m
et
h
o
d
)
[
1
4
]
,
s
tu
d
en
t
g
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ad
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atin
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g
r
ad
e
[
2
2
]
,
an
d
s
t
u
d
en
t
i
n
ta
k
e
[
2
3
]
.
Nex
t,
in
[
2
4
]
u
s
ed
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an
d
ass
o
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n
an
al
y
s
i
s
to
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tu
d
e
n
t
en
r
o
l
m
e
n
t.
A
s
DT
an
d
k
N
N
ar
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co
n
s
id
er
ed
a
m
o
n
g
th
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est
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i
n
p
r
o
d
u
c
in
g
h
ig
h
ac
c
u
r
ac
y
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ate
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n
p
r
ed
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n
.
T
h
is
p
ap
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p
r
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x
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lo
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atio
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s
t
u
d
y
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r
p
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an
al
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tic
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g
s
u
p
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ed
m
et
h
o
d
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NN
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d
DT
f
o
r
u
n
iv
er
s
it
y
s
tu
d
e
n
t
in
ta
k
e.
Af
ter
all,
th
i
s
r
esear
ch
is
p
r
esen
ti
n
g
d
ata
an
al
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tics
a
n
d
it
is
v
er
y
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m
p
o
r
t
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t
f
o
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I
n
f
o
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m
atio
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T
ec
h
n
o
lo
g
y
(
I
T
)
b
u
s
in
e
s
s
ar
o
u
n
d
t
h
e
w
o
r
ld
[
2
5
]
.
Nev
er
th
el
ess
,
a
n
al
y
t
ics
i
s
n
o
t
b
ein
g
w
id
el
y
i
m
p
le
m
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i
n
u
n
i
v
er
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i
ties
,
e
s
p
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la
y
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ia
an
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t
h
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i
m
p
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m
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n
tatio
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x
p
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ted
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cr
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s
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f
f
i
cien
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y
i
n
e
n
r
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m
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t
p
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s
an
d
g
ain
k
n
o
w
led
g
e
f
r
o
m
h
i
s
to
r
ical
d
ata
[
2
6
]
.
Hen
ce
,
th
e
an
al
y
tics
i
s
b
eliev
ed
ca
n
h
elp
th
e
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a
p
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c
h
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f
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tial a
p
p
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ts
t
h
at
is
b
elie
v
ed
to
ac
ce
p
t th
e
o
f
f
er
.
2.
RE
S
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ARCH
M
E
T
H
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er
all,
t
h
e
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Fig
u
r
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1
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3.
RE
SU
L
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A
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D
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SCU
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tio
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ataset
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to
t
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3
.
1
.
M
o
delin
g
us
ing
J
4
8
T
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tal
am
o
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t
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f
d
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v
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J
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Kate
g
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U,
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Co
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d
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f
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r
t
h
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p
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,
it
i
s
r
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o
m
m
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n
d
ed
th
at
u
s
ed
J
4
8
.
T
h
is
i
s
b
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e
J
4
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p
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f
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m
an
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in
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s
t
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d
en
t
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an
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tio
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v
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f
f
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h
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KNN.
T
ab
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7
.
C
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m
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ar
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f
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lg
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r
ith
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P
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ce
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K
N
N
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st
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f
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s
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%
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%
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%
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%
3.
4
.
Da
s
hb
o
a
rd
Dev
elo
p
m
en
t
Dash
b
o
ar
d
r
ep
r
esen
ts
in
ter
f
ac
e
as th
e
in
ter
f
ac
e
p
la
y
s
a
n
i
m
p
o
r
t
an
t r
o
le
to
co
n
n
ec
t b
et
w
ee
n
p
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ed
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e
en
g
i
n
e
a
n
d
th
e
u
s
er
.
I
t
is
w
h
er
e
th
e
co
m
m
u
n
icatio
n
b
et
w
ee
n
th
e
e
n
d
u
s
er
an
d
t
h
e
m
o
d
el
i
n
itiate
s
.
First
l
y
,
t
h
e
th
e
ch
ar
ts
ar
e
g
en
er
ated
f
o
r
d
escr
ip
tiv
e
an
al
y
s
i
s
w
i
th
s
i
n
g
le
attr
ib
u
te,
s
u
c
h
d
is
ta
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ce
.
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t
is
f
o
u
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d
th
at
th
e
ap
p
lican
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ac
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p
tan
ce
is
m
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s
t
l
y
af
f
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th
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is
ta
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f
th
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ca
m
p
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h
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m
eto
w
n
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an
d
th
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s
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n
d
attr
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t
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s
tr
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t
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also
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n
d
t
h
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ity
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f
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lican
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h
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t
th
e
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f
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w
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f
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m
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s
tr
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m
,
co
m
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ar
ed
to
th
e
ar
t
s
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m
ap
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lican
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s
.
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t
co
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ld
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e
an
im
p
o
r
tan
t
i
n
f
o
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m
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f
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d
ec
is
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n
m
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k
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g
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f
f
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m
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to
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ts
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tr
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m
ap
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t
s
o
r
o
th
er
w
is
e.
D
escr
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tiv
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a
n
al
y
tics
h
elp
s
d
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s
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n
m
ak
er
w
h
at
i
s
h
ap
p
en
in
g
i
n
s
p
ec
if
ic
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ea
r
o
f
s
tu
d
en
t
i
n
ta
k
e,
an
d
also
w
h
o
to
g
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e
o
f
f
er
in
t
h
e
f
u
t
u
r
e.
T
h
e
s
am
e
g
o
e
s
to
p
r
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e
an
al
y
tics
w
h
ic
h
w
il
l a
s
s
is
t d
ec
is
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n
m
a
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k
n
o
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w
h
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ap
p
lican
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w
il
l a
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ep
t o
r
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j
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t u
n
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er
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it
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p
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m
e
o
f
f
er
t
h
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g
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s
e
t o
f
s
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ted
attr
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tes.
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h
e
p
r
ed
i
ctio
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o
u
tp
u
t o
f
t
h
e
m
o
d
el
is
t
h
en
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b
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v
is
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ali
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d
in
th
e
ch
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t
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,
as
s
h
o
w
n
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n
Fi
g
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.
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h
e
b
ar
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f
o
r
ex
a
m
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.
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IJ
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esear
ch
.
RE
F
E
R
E
NC
E
S
[1
]
S
.
L
.
V
a
ll
e
,
E.
L
e
ss
e
r,
R.
S
h
o
c
k
le
y
,
M
.
S
.
.
H
o
p
k
in
s
a
n
d
N.
Kru
s
c
h
w
it
z
,
“
Big
Da
ta,
A
n
a
l
y
ti
c
s
a
n
d
th
e
P
a
t
h
f
ro
m
In
sig
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ts t
o
V
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e
,
”
W
in
ter
2
0
1
1
M
IT
S
lo
a
n
M
a
n
a
g
e
me
n
t
Rev
iew,
v
o
l.
5
2
,
n
o
.
2
,
p
p
.
2
1
-
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1
,
2
0
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1
.
[2
]
A
.
K.
T
i
w
a
ri,
G
.
Ra
m
a
k
rish
n
a
,
L
.
K
S
h
a
rm
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a
n
d
S
.
K.
Ka
sh
y
a
p
“
A
c
a
d
e
m
i
c
p
e
rf
o
r
m
a
n
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e
p
re
d
ictio
n
a
lg
o
rit
h
m
b
a
se
d
o
n
f
u
z
z
y
,
”
IAE
S
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Arti
fi
c
i
a
l
I
n
telli
g
e
n
c
e
(
IJ
-
AI)
,
v
o
l.
8
,
n
o
.
1
,
p
p
.
2
6
-
32
,
2
0
1
9
.
[3
]
M
in
istry
o
f
Ed
u
c
a
ti
o
n
M
a
lay
sia
,
“
Ex
e
c
u
ti
v
e
S
u
m
m
a
r
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M
a
la
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E
d
u
c
a
ti
o
n
Bl
u
e
p
rin
t
2
0
1
5
-
2
0
2
5
”
,
2
0
1
3
.
[4
]
J.
S
.
K.
M
i
n
g
,
“
In
sti
tu
ti
o
n
a
l
F
a
c
to
rs
In
f
lu
e
n
c
i
n
g
S
t
u
d
e
n
ts’
Co
ll
e
g
e
Ch
o
ice
De
c
isio
n
in
M
a
lay
si
a
:
A
Co
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p
tu
a
l
F
ra
m
e
w
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rk
,
”
In
ter
n
a
ti
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n
a
l
J
o
u
rn
a
l
o
f
Bu
s
i
n
e
ss
a
n
d
S
o
c
i
a
l
S
c
ien
c
e
,
v
o
l.
1
,
n
o
.
3
,
p
p
.
5
3
-
5
8
,
2
0
1
0
.
[5
]
K.
Kh
o
o
,
T
.
K.
Ba
n
,
C
.
Y.
Ne
n
g
,
B
.
K.
Ho
o
i
a
n
d
C
.
Y.
J
o
a
n
,
“
S
tu
d
e
n
t
c
h
o
ice
s
o
f
c
h
o
o
sin
g
c
o
ll
e
g
e
s
a
n
d
c
o
u
rse
o
f
stu
d
y
in
P
e
n
a
n
g
,
”
A
b
c
Res
e
a
rc
h
Al
e
rt,
v
o
l.
3
,
n
o
.
1
,
p
p
.
1
-
7
,
2
0
1
5
.
[6
]
S
.
M
d
.
S
i
d
i
n
,
S
.
R.
Hu
ss
i
n
a
n
d
T
.
H.
S
o
o
n
,
“
A
n
Ex
p
lo
ra
t
o
ry
S
tu
d
y
o
f
F
a
c
to
rs
In
f
lu
e
n
c
i
n
g
t
h
e
Co
ll
e
g
e
Ch
o
ice
De
c
isio
n
o
f
Un
d
e
rg
ra
d
u
a
te S
tu
d
e
n
ts i
n
M
a
l
a
y
sia
,
”
Asia
Pa
c
if
ic M
a
n
a
g
e
me
n
t
Rev
iew,
v
o
l.
8
,
n
o
3
,
p
p
.
2
5
9
-
2
8
0
,
2
0
0
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
IJ
-
A
I
Vo
l.
8
,
No
.
4
,
Dec
em
b
er
201
9
:
3
6
7
–
3
7
4
374
[7
]
R.
E.
Ka
ll
i
o
,
“
F
a
c
to
rs
I
n
f
lu
e
n
c
in
g
T
h
e
Co
ll
e
g
e
Ch
o
ice
De
c
isi
o
n
s
Of
G
ra
d
u
a
te
S
tu
d
e
n
ts,”
Res
e
a
rc
h
in
Hig
h
e
r
Ed
u
c
a
ti
o
n
,
v
o
l
.
3
6
,
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o
.
1
,
p
p
.
1
0
9
-
1
1
0
,
1
9
9
5
.
[8
]
F
.
A
n
g
u
lo
-
Ru
iz,
A
.
P
e
rg
e
lo
v
a
a
n
d
R.
Jo
se
p
,
“
A
M
a
rk
e
t
S
e
g
m
e
n
t
Ap
p
r
o
a
c
h
f
o
r
Hig
h
e
r
Ed
u
c
a
ti
o
n
Ba
se
d
On
Ra
ti
o
n
a
l
a
n
d
Em
o
ti
o
n
a
l
F
a
c
to
rs,”
J
o
u
rn
a
l
o
f
M
a
rk
e
ti
n
g
fo
r Hi
g
h
e
r
E
d
u
c
a
ti
o
n
,
v
o
l.
2
0
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n
o
.
1
,
p
p
.
1
-
1
7
,
2
0
1
0
.
[9
]
J.B.
F
o
rd
,
M
.
Jo
se
p
h
a
n
d
B
J
o
se
p
h
,
“
Im
p
o
rtan
c
e
-
P
e
rf
o
rm
a
n
c
e
A
n
a
ly
sis
A
s
A
S
trate
g
ic
T
o
o
l
F
o
r
S
e
r
v
ice
M
a
rk
e
ters
:
T
h
e
Ca
se
O
f
S
e
rv
ice
Qu
a
li
ty
P
e
rc
e
p
ti
o
n
s
Of
Bu
sin
e
ss
S
tu
d
e
n
ts
In
Ne
w
Zea
lan
d
A
n
d
T
h
e
US
A
,
”
T
h
e
J
o
u
rn
a
l
Of
S
e
rv
ice
s M
a
rk
e
ti
n
g
,
v
o
l.
1
3
,
n
o
.
2
,
p
p
.
1
7
1
-
1
8
6
,
1
9
9
9
.
[1
0
]
B.
J.
M
a
th
e
w
Jo
se
p
h
,
“
In
d
o
n
e
sia
n
st
u
d
e
n
ts'
p
e
rc
e
p
ti
o
n
s
o
f
c
h
o
ice
c
rit
e
ria
in
t
h
e
se
lec
ti
o
n
o
f
a
tert
iar
y
in
stit
u
ti
o
n
:
stra
teg
ic i
m
p
li
c
a
ti
o
n
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
d
u
c
a
ti
o
n
a
l
M
a
n
a
g
e
me
n
t,
v
o
l.
1
4
,
n
o
1
,
p
p
.
4
0
-
4
4
,
2
0
0
0
.
[1
1
]
J.
B.
Do
n
Ho
ss
ler,
“
Un
d
e
rsta
n
d
in
g
S
tu
d
e
n
t
Co
ll
e
g
e
Ch
o
ice
,
”
Hig
h
e
r
Ed
u
c
a
ti
o
n
:
Ha
n
d
b
o
o
k
o
f
T
h
e
o
ry
a
n
d
Res
e
a
rc
h
,
v
o
l.
5
,
p
p
.
2
3
1
-
2
8
8
,
1
9
8
9
.
[1
2
]
S
.
Bh
a
r
d
w
a
,
“
W
h
y
d
o
stu
d
e
n
ts
g
o
to
u
n
iv
e
rsity
a
n
d
h
o
w
d
o
th
e
y
c
h
o
o
se
w
h
ich
o
n
e
?
,
”
T
ime
s
Hi
g
h
e
r
E
d
u
c
a
t
io
n
(
T
HE),
p
p
.
1
-
1
7
,
2
0
1
7
.
[1
3
]
J.
M
.
Be
g
g
s,
J.H.
Ba
n
t
h
a
m
a
n
d
S
T
a
y
lo
r,
“
Distin
g
u
ish
i
n
g
T
h
e
F
a
c
to
rs
In
f
lu
e
n
c
in
g
Co
l
leg
e
S
tu
d
e
n
ts'
Ch
o
ice
Of
M
a
jo
r,
”
Co
ll
e
g
e
S
tu
d
e
n
t
J
o
u
rn
a
l,
v
o
l.
4
2
,
n
o
.
2
,
p
p
.
3
8
1
-
3
9
4
,
2
0
0
8
.
[1
4
]
D.L
.
Re
d
M
a
rti
n
e
z
a
n
d
C.
E.
P
.
Go
m
e
z
,
“
Da
t
a
M
in
i
n
g
T
o
F
i
n
d
P
r
o
f
il
e
s
O
f
S
tu
d
e
n
ts,”
E
u
ro
p
e
a
n
S
c
i
e
n
ti
fi
c
J
o
u
rn
a
l,
v
o
l.
1
0
,
n
o
.
3
0
,
p
p
.
2
3
-
4
3
,
2
0
1
4
.
[1
5
]
N.
B.
G
irj
a
S
h
a
rm
a
,
R.
Bh
a
rg
a
v
a
a
n
d
M
.
M
a
t
h
u
ria,
“
De
c
isio
n
T
re
e
A
n
a
l
y
sis
o
n
J4
8
A
lg
o
rit
h
m
f
o
r
Da
ta
M
in
in
g
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
i
n
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
S
o
ft
w
a
re
En
g
in
e
e
rin
g
,
v
o
l
.
3
,
n
o
6
,
p
p
.
1
1
1
4
-
1
1
1
9
,
2
0
1
3
.
[1
6
]
S
.
M
a
f
o
u
d
a
n
d
G
.
M
a
n
i,
“
F
in
a
n
c
i
a
l
F
o
re
c
a
stin
g
Us
in
g
G
e
n
e
ti
c
A
l
g
o
rit
h
m
s,”
Ap
p
l
ied
Art
if
icia
l
In
tell
i
g
e
n
c
e
,
v
o
l.
1
0
,
n
o
.
6
,
p
p
.
5
4
3
-
5
6
6
,
1
9
9
6
.
[1
7
]
H.
Ka
rim
,
S
.
R.
Nia
k
a
n
a
n
d
R.
S
a
f
d
a
ri,
“
Co
m
p
a
riso
n
o
f
Ne
u
ra
l
Ne
tw
o
rk
T
ra
in
in
g
A
lg
o
rit
h
m
s
f
o
r,
”
IAE
S
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Art
if
icia
l
In
telli
g
e
n
c
e
(
IJ
-
AI)
,
v
o
l.
7
,
n
o
4
,
p
p
.
1
8
5
-
1
8
9
,
2
0
1
8
.
[1
8
]
C.
V
iala
rd
i,
J.
C
h
u
e
,
J.
P
.
P
e
c
h
e
,
G
.
A
lv
a
ra
d
o
,
B.
V
in
a
tea
,
J.
Estre
ll
a
a
n
d
Á
.
Ortig
o
sa
,
“
A
d
a
ta
m
in
i
n
g
a
p
p
r
o
a
c
h
t
o
g
u
id
e
stu
d
e
n
ts
th
r
o
u
g
h
th
e
e
n
ro
ll
m
e
n
t
p
r
o
c
e
ss
b
a
se
d
o
n
a
c
a
d
e
m
ic
p
e
rf
o
r
m
a
n
c
e
,
”
S
p
rin
g
e
r
S
c
ien
c
e
Bu
sin
e
ss
M
e
d
ia
B.V,
v
o
l
.
2
1
,
p
p
.
2
1
7
-
2
1
8
,
2
0
1
1
.
[1
9
]
B.
M
i
n
a
e
i
-
Bid
g
o
li
,
D.
A
.
Ka
sh
y
,
G
.
Ko
rte
m
e
y
e
r
a
n
d
W
.
F
.
P
u
n
c
h
,
“
P
re
d
icti
n
g
S
t
u
d
e
n
t
P
e
rf
o
rm
a
n
c
e
:
A
n
A
p
p
li
c
a
ti
o
n
o
f
Da
ta
M
in
in
g
M
e
th
o
d
s
w
it
h
a
n
Ed
u
c
a
ti
o
n
a
l
W
e
b
-
Ba
se
d
S
y
st
e
m
”
,
3
3
rd
An
n
u
a
l
Fro
n
ti
e
rs
in
E
d
u
c
a
t
io
n
,
2
0
0
3
.
FIE
2
0
0
3
.
2
0
0
3
.
[2
0
]
A
.
M
.
S
h
a
h
iri
,
W
.
Hu
sa
i
n
a
a
n
d
N
.
A
.
Ra
sh
id
,
“
A
Re
v
ie
w
o
n
P
re
d
i
c
ti
n
g
S
tu
d
e
n
t’s
P
e
rf
o
rm
a
n
c
e
u
sin
g
Da
ta
M
in
in
g
T
e
c
h
n
iq
u
e
s,”
Pro
c
e
d
i
a
Co
m
p
u
ter
S
c
ien
c
e
,
v
o
l.
7
2
,
p
p
.
4
1
4
-
4
2
2
,
2
0
1
5
.
[2
1
]
S
A
l
f
e
re
a
n
d
A
.
Y.
A
.
M
a
g
h
a
ri,
“
P
re
d
ictio
n
o
f
S
tu
d
e
n
t'
s
P
e
rf
o
rm
a
n
c
e
Us
in
g
M
o
d
if
ied
KN
N
Clas
sif
ier
s,”
1
st
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
E
n
g
i
n
e
e
rin
g
&
Fu
tu
re
T
e
c
h
n
o
l
o
g
y
(
ICEF
T
2
0
1
8
),
p
p
.
1
4
3
-
1
5
0
,
2
0
1
8
.
[2
2
]
A
.
O.
O
g
u
n
d
e
a
n
d
D.
A
.
A
ji
b
a
d
e
,
“
A
Da
ta
M
in
in
g
S
y
ste
m
f
o
r
P
re
d
ictin
g
Un
iv
e
rsity
S
tu
d
e
n
ts'
G
r
a
d
u
a
ti
o
n
G
ra
d
e
s
Us
in
g
ID3
De
c
isio
n
T
re
e
A
l
g
o
rit
h
m
,
”
J
o
u
rn
a
l
o
f
Co
mp
u
ter
S
c
ien
c
e
a
n
d
I
n
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
2
,
n
o
.
1
,
p
p
.
21
-
4
6
,
2
0
1
4
.
[2
3
]
M
.
R.
A
.
Ra
z
m
a
n
.
P
re
d
ictiv
e
An
a
ly
ti
c
s
o
f
Un
iv
e
rsit
y
S
tu
d
e
n
t
In
tak
e
Us
in
g
D
a
ta
M
in
in
g
,
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
A
R
A
,
2
0
1
8
.
[2
4
]
H.
S
a
b
n
a
n
i,
M
.
M
o
re
,
P
.
Ku
d
a
le,
P
.
S
.
Ja
n
ra
o
,
T
.
E.
Co
ll
e
g
e
a
n
d
N.
M
u
m
b
a
i,
“
P
re
d
icti
o
n
o
f
S
tu
d
e
n
t
E
n
ro
lm
e
n
t
Us
in
g
Da
ta
M
in
in
g
T
e
c
h
n
iq
u
e
s”
,
In
ter
n
a
ti
o
n
a
l
Res
e
a
rc
h
J
o
u
rn
a
l
o
f
E
n
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
5
(4
)
,
p
p
.
4
–
7
.
,
2
0
1
8
.
[2
5
]
B.
M
a
n
o
j,
K.
V
.
K.
S
a
sik
a
n
t
h
,
M
.
V
.
S
u
b
b
a
ra
o
a
n
d
V
.
J.
P
ra
k
a
sh
,
"
An
a
ly
sis
o
f
D
a
ta
S
c
ien
c
e
w
it
h
th
e
u
se
o
f
Bi
g
Da
t
a
,
"
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
T
re
n
d
s in
Co
m
p
u
ter S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
v
o
l.
7
,
n
o
.
6
,
p
p
.
8
7
-
9
0
,
2
0
1
8
.
[2
6
]
M
.
A
.
A
l
-
Ha
g
e
r
y
,
"
Ex
trac
ti
n
g
H
id
d
e
n
P
a
tt
e
rn
s
f
ro
m
Da
tes
'
P
ro
d
u
c
t
Da
ta
Us
in
g
a
M
a
c
h
in
e
,
"
IAE
S
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Arti
fi
c
i
a
l
I
n
telli
g
e
n
c
e
(
IJ
-
AI)
,
v
o
l.
8
,
n
o
.
3
,
p
p
.
3
1
-
4
1
,
2
0
1
9
.
[2
7
]
S
.
A
b
d
u
l
-
Ra
h
m
a
n
,
S
.
M
u
talib
a
n
d
N.
A
.
Kh
a
n
a
f
i;
A
.
M
o
h
d
A
li
"
Ex
p
lo
rin
g
F
e
a
t
u
re
S
e
lec
ti
o
n
a
n
d
S
u
p
p
o
rt
V
e
c
to
r
,
"
2
0
1
3
IE
EE
1
6
t
h
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mp
u
ta
t
io
n
a
l
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
2
0
1
3
.
[2
8
]
A
.
E.
Ka
rra
r
a
n
d
M
.
F
.
I.
F
a
d
l,
"
S
e
c
u
rit
y
P
ro
to
c
o
l
f
o
r
Da
ta
T
ra
n
s
m
iss
io
n
in
C
lo
u
d
C
o
m
p
u
ti
n
g
,
"
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
T
re
n
d
s i
n
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
,
v
o
l.
7
,
n
o
.
1
,
p
p
.
1
-
5
,
2
0
1
8
.
[2
9
]
E.
F
ra
n
k
,
M
.
A
.
Ha
ll
a
n
d
I.
A
.
W
it
ten
,
“
T
h
e
W
EK
A
W
o
rk
b
e
n
c
h
.
O
n
li
n
e
A
p
p
e
n
d
ix
f
o
r
"
Da
ta
M
in
i
n
g
:
P
ra
c
ti
c
a
l
M
a
c
h
in
e
L
e
a
rn
in
g
T
o
o
ls an
d
T
e
c
h
n
i
q
u
e
s"
,
M
o
rg
a
n
K
a
u
fm
a
n
n
,
F
o
u
rth
Ed
it
io
n
,
2
0
1
6
.
[3
0
]
A
.
Ko
n
d
a
s.
“
P
H
P
-
ML
-
M
a
c
h
in
e
L
e
a
rn
in
g
li
b
ra
ry
f
o
r
P
H
P
”
.
Re
tri
e
v
e
d
f
ro
m
h
tt
p
s://
p
h
p
-
m
l.
re
a
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