I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
8
,
No
.
5
,
Octo
b
e
r
2
0
1
8
,
p
p
.
33
99
~
3
4
0
6
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v8
i
5
.
pp
3
3
9
9
-
3406
3399
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e
.
co
m/
jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
I
JE
C
E
S
m
o
o
th
Suppo
rt
Vect
o
r Ma
chin
e f
o
r Suicide
-
Rela
te
d
Beha
v
io
urs Predi
ction
G
.
I
n
dra
w
a
n,
I
K
.
P
.
Su
dia
r
s
a
,
K
.
Ag
us
t
ini
,
Sa
riy
a
s
a
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter S
c
ien
c
e
,
Un
iv
e
rsitas
P
e
n
d
i
d
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
I
n
d
o
n
e
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ma
y
21
,
2
0
1
7
R
ev
i
s
ed
J
an
2
5
,
2
0
1
8
A
cc
ep
ted
Feb
11
,
2
0
1
8
S
u
icid
e
-
re
late
d
b
e
h
a
v
io
u
rs
n
e
e
d
to
b
e
p
re
v
e
n
ted
o
n
p
sy
c
h
iatri
c
p
a
ti
e
n
ts.
P
re
d
ictio
n
o
f
th
o
se
b
e
h
a
v
io
u
rs
b
a
se
d
o
n
p
a
ti
e
n
t
m
e
d
ica
l
re
c
o
rd
s
w
o
u
ld
b
e
v
e
r
y
u
se
f
u
l
f
o
r
th
e
p
re
v
e
n
ti
o
n
b
y
th
e
p
s
y
c
h
iatric
h
o
sp
it
a
l.
T
h
i
s
re
se
a
r
c
h
f
o
c
u
se
d
o
n
d
e
v
e
lo
p
in
g
t
h
is
p
re
d
i
c
ti
o
n
a
t
th
e
o
n
ly
o
n
e
p
sy
c
h
iatric
h
o
sp
i
tal
o
f
Ba
li
P
ro
v
in
c
e
b
y
u
sin
g
S
m
o
o
th
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
m
e
th
o
d
,
a
s
th
e
f
u
rth
e
r
d
e
v
e
lo
p
m
e
n
t
o
f
S
u
p
p
o
r
t
V
e
c
to
r
M
a
c
h
i
n
e
.
T
h
e
m
e
th
o
d
u
s
e
d
3
0
.
6
6
0
p
a
ti
e
n
t
m
e
d
ica
l
re
c
o
rd
s
f
ro
m
th
e
las
t
f
iv
e
y
e
a
rs.
Da
ta
c
lea
n
in
g
g
a
v
e
2
6
6
5
re
lev
a
n
t
d
a
ta
f
o
r
th
is
re
se
a
rc
h
,
in
c
lu
d
e
s
1
1
1
p
a
ti
e
n
ts
th
a
t
h
a
v
e
su
icid
e
-
re
late
d
b
e
h
a
v
io
u
rs
a
n
d
u
n
d
e
r
a
c
t
iv
e
t
re
a
t
m
e
n
t.
T
h
o
se
c
lea
n
e
d
d
a
ta
th
e
n
w
e
re
tran
sf
o
r
m
e
d
in
to
ten
p
re
d
ict
o
r
v
a
riab
les
a
n
d
a
re
sp
o
n
se
v
a
riab
le.
S
p
li
tt
in
g
train
in
g
a
n
d
tes
ti
n
g
d
a
ta
o
n
t
h
o
s
e
tran
sf
o
r
m
e
d
d
a
ta
w
e
re
d
o
n
e
f
o
r
b
u
i
ld
i
n
g
a
n
d
a
c
c
u
ra
c
y
e
v
a
lu
a
ti
o
n
o
f
th
e
m
e
th
o
d
m
o
d
e
l.
Ba
se
d
o
n
th
e
e
x
p
e
r
im
e
n
t,
th
e
b
e
st
a
v
e
ra
g
e
a
c
c
u
ra
c
y
a
t
6
3
%
c
a
n
b
e
o
b
tai
n
e
d
b
y
u
sin
g
3
0
%
o
f
re
lev
a
n
t
d
a
ta
a
s
d
a
ta
tes
ti
n
g
a
n
d
b
y
u
sin
g
train
in
g
d
a
ta
w
h
ich
h
a
s
o
n
e
-
to
-
o
n
e
ra
ti
o
in
n
u
m
b
e
r
b
e
tw
e
e
n
p
a
ti
e
n
ts
th
a
t
h
a
v
e
su
icid
e
-
re
late
d
b
e
h
a
v
io
u
rs
a
n
d
p
a
ti
e
n
ts
th
a
t
h
a
v
e
n
o
su
c
h
b
e
h
a
v
io
u
rs.
In
th
e
f
u
tu
re
w
o
rk
,
a
c
c
u
ra
c
y
i
m
p
ro
v
e
m
e
n
t
n
e
e
d
to
b
e
c
o
n
f
irme
d
b
y
u
sin
g
R
e
d
u
c
e
d
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
i
n
e
m
e
th
o
d
,
a
s
th
e
f
u
rth
e
r
d
e
v
e
lo
p
m
e
n
t
o
f
S
m
o
o
t
h
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
i
n
e
.
K
ey
w
o
r
d
:
Ma
ch
i
n
e
lear
n
i
n
g
P
atien
t
P
s
y
c
h
iatr
ic
SR
B
s
SS
VM
Co
p
y
rig
h
t
©
201
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
G.
I
n
d
r
a
w
a
n
,
Dep
ar
t
m
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
,
Un
i
v
er
s
ita
s
P
en
d
id
ik
an
Ga
n
e
s
h
a,
J
l.
Ud
ay
an
a
1
1
,
Sin
g
ar
aj
a
8
1
1
1
6
,
B
ali,
I
n
d
o
n
esia
.
E
m
ail:
g
i
n
d
r
a
w
a
n
@
u
n
d
ik
s
h
a.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Su
icid
e
-
r
elate
d
b
eh
a
v
io
u
r
s
(
S
R
B
s
)
n
ee
d
to
b
e
p
r
ev
e
n
ted
o
n
p
s
y
c
h
iatr
ic
p
atie
n
t
s
.
S
R
B
s
in
c
lu
d
es
s
u
icid
e
atte
m
p
t
o
r
in
s
tr
u
m
e
n
ta
l
SR
B
s
[
1
]
.
Su
icid
e
is
th
e
ac
t
o
f
in
te
n
tio
n
a
ll
y
ca
u
s
i
n
g
o
n
e
'
s
o
w
n
d
ea
th
[
2
]
.
R
is
k
f
ac
to
r
s
in
cl
u
d
e
m
e
n
tal
d
is
o
r
d
er
s
s
u
c
h
as
d
ep
r
ess
io
n
,
b
ip
o
l
ar
d
is
o
r
d
e
r
,
s
ch
izo
p
h
r
en
ia,
p
er
s
o
n
alit
y
d
i
s
o
r
d
er
s
,
alco
h
o
lis
m
,
o
r
s
u
b
s
tan
ce
m
is
u
s
e
[
3
]
,
[
4
]
.
P
eo
p
le
h
av
e
SR
B
s
th
at
d
o
n
o
t
r
esu
lt
in
d
ea
th
ar
e
at
h
ig
h
r
is
k
f
o
r
f
u
tu
r
e
s
el
f
-
i
n
j
u
r
y
an
d
co
m
p
let
ed
s
u
icid
e
[
5
]
,
[
6
]
.
P
r
ed
ictio
n
o
f
t
h
o
s
e
S
R
B
s
b
a
s
ed
o
n
p
atien
t
m
ed
ical
r
ec
o
r
d
s
w
o
u
ld
b
e
v
er
y
u
s
e
f
u
l
f
o
r
th
e
p
r
ev
en
tio
n
b
y
t
h
e
p
s
y
ch
iatr
ic
h
o
s
p
ital.
T
h
is
r
esear
c
h
f
o
c
u
s
ed
o
n
d
ev
el
o
p
in
g
t
h
is
p
r
ed
ictio
n
at
t
h
e
o
n
l
y
o
n
e
p
s
y
c
h
iatr
i
c
h
o
s
p
ital
o
f
B
ali
P
r
o
v
in
ce
b
y
u
s
i
n
g
S
m
o
o
t
h
S
u
p
p
o
r
t
V
ec
to
r
Ma
ch
i
n
e
(
S
SV
M)
m
et
h
o
d
,
as
t
h
e
f
u
r
t
h
er
d
ev
elo
p
m
en
t
o
f
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
i
n
e
(
SVM)
[
7
]
-
[
9
]
.
A
cc
o
r
d
in
g
to
[
1
0
]
,
SVM
u
ti
lizes
q
u
ad
r
atic
p
r
o
g
r
am
m
i
n
g
o
p
ti
m
izatio
n
s
o
th
at
it
is
les
s
ef
f
icie
n
t
f
o
r
h
i
g
h
-
d
i
m
e
n
s
io
n
al
an
d
lar
g
e
d
ata.
B
ec
au
s
e
o
f
th
at,
a
d
ev
elo
p
ed
s
m
o
o
t
h
in
g
tech
n
iq
u
e
is
u
s
ed
to
r
ep
lace
p
lu
s
f
u
n
ctio
n
o
f
SVM
b
y
u
s
in
g
i
n
teg
r
al
o
f
n
eu
r
al
n
et
w
o
r
k
s
ig
m
o
id
f
u
n
ct
io
n
.
T
h
is
s
m
o
o
t
h
in
g
tec
h
n
iq
u
e
is
k
n
o
w
n
as
SS
VM
.
W
h
en
co
m
p
ar
ed
w
i
th
SVM,
SS
VM
h
as
b
etter
r
u
n
n
i
n
g
ti
m
e
a
n
d
ac
cu
r
ac
y
.
T
h
e
SS
VM
g
en
er
ated
an
d
s
o
lv
e
an
u
n
co
n
s
tr
ain
ed
s
m
o
o
th
r
ef
o
r
m
u
latio
n
o
f
th
e
SVM
f
o
r
p
atter
n
class
i
f
i
ca
tio
n
u
s
in
g
co
m
p
letel
y
ar
b
itra
r
y
k
er
n
el
[
8
]
.
SS
VM
is
s
o
l
v
ed
b
y
a
Ne
w
to
n
-
A
r
m
ij
o
alg
o
r
ith
m
a
n
d
h
a
s
b
ee
n
ex
te
n
d
ed
to
n
o
n
lin
ea
r
s
ep
ar
atio
n
s
u
r
f
ac
e
s
b
y
u
s
i
n
g
n
o
n
lin
ea
r
k
er
n
e
l
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
3
9
9
–
3
4
0
6
3400
tech
n
iq
u
es.
T
h
e
n
u
m
er
ical
r
es
u
lts
s
h
o
w
th
at
SS
VM
is
f
aster
th
a
n
o
th
er
m
eth
o
d
s
a
n
d
h
a
s
b
etter
g
en
er
aliza
tio
n
ab
ilit
y
[
7
]
.
2.
SM
O
O
T
H
SUPP
O
RT
VE
C
T
O
R
M
ACH
I
NE
As
a
b
ase
o
f
SS
VM
,
SVM
[
1
1
]
is
a
m
eth
o
d
to
f
in
d
o
p
ti
m
al
h
y
p
er
p
lan
e
th
at
s
ep
ar
ates
t
w
o
class
es
o
f
in
p
u
t
s
p
ac
e.
Sep
ar
atio
n
o
f
m
o
r
e
th
an
t
w
o
clas
s
es
h
a
v
e
co
n
d
u
cted
p
r
ev
io
u
s
l
y
b
y
au
th
o
r
s
o
n
f
i
n
g
er
p
r
in
t
d
ata
[
1
2
]
-
[
1
5
]
.
Fig
u
r
e
1
s
h
o
w
s
s
ev
er
al
al
ter
n
ati
v
e
h
y
p
er
p
la
n
es
(
d
is
cr
i
m
i
n
atio
n
b
o
u
n
d
ar
ies)
an
d
t
h
e
b
est
h
y
p
er
p
lan
e
o
f
a
d
ata
s
et
co
n
s
i
s
ts
o
f
t
w
o
cla
s
s
es,
i.e
.
cl
ass
{−
1
}
a
n
d
{+
1
}.
T
h
e
b
es
t
h
y
p
er
p
lan
e
i
s
t
h
e
h
y
p
er
p
lan
e
w
h
ic
h
h
as
a
m
a
x
i
m
u
m
m
ar
g
i
n
o
b
tain
ed
f
r
o
m
al
ter
n
ati
v
e
d
iv
id
in
g
li
n
es
(
d
is
cr
i
m
i
n
an
t
b
o
u
n
d
ar
ies).
Ma
r
g
in
is
th
e
d
is
ta
n
ce
b
et
w
e
en
t
h
e
h
y
p
er
p
lan
e
to
th
e
n
ea
r
est
p
o
in
t
o
f
ea
c
h
cla
s
s
.
T
h
is
n
ea
r
est
p
o
in
t
is
s
o
-
ca
lled
s
u
p
p
o
r
t v
ec
to
r
[
1
6
]
.
Fig
u
r
e
1
.
Sev
er
al
alter
n
at
iv
e
h
y
p
er
p
la
n
es (
lef
t)
,
th
e
b
est h
y
p
er
p
lan
e
(
r
ig
h
t)
C
las
s
i
f
icatio
n
p
r
o
b
lem
o
f
m
p
o
in
ts
i
n
n
-
d
i
m
e
n
s
io
n
al
s
p
ac
e
(
R
n
)
is
r
ep
r
ese
n
ted
as
m
×
n
-
s
iz
ed
m
atr
i
x
A.
T
h
e
m
atr
i
x
ele
m
en
t
T
to
th
e
class
{−
1
}
a
n
d
{+
1
}
is
d
e
f
i
n
ed
o
n
m
×
m
-
si
ze
d
d
iag
o
n
al
m
atr
i
x
D
w
i
th
−1
an
d
+1
at
its
d
iag
o
n
al.
L
in
ea
r
SVM
alg
o
r
ith
m
is
s
h
o
w
n
b
y
(1
)
,
w
it
h
co
n
s
tr
ain
s
+
an
d
;
a
p
o
s
itiv
e
v
al
u
e
S
VM
p
ar
a
m
e
ter
v
;
m
×1
-
s
ized
s
lack
v
ar
iab
le
v
ec
to
r
y
t
h
at
m
ea
s
u
r
es
c
las
s
if
ica
tio
n
er
r
o
r
an
d
h
as
n
o
n
-
n
e
g
ati
v
e
v
a
lu
e;
m
-
s
iz
ed
co
lu
m
n
v
ec
to
r
e
an
d
h
as
v
alu
e
o
f
1
;
n
×1
-
s
ized
n
o
r
m
al
v
ec
to
r
w
;
an
d
b
ias
v
alu
e
γ
t
h
at
d
eter
m
i
n
e
h
y
p
er
p
lan
e
r
elativ
e
lo
ca
tio
n
to
th
e
o
r
ig
in
a
l c
lass
.
m
in
,
γ
,
+
1+
T
+
1
T
(
1
)
T
h
e
co
n
s
tr
ain
s
eq
u
atio
n
ab
o
v
e
co
m
p
ar
es
ea
ch
v
ec
to
r
ele
m
en
t.
W
h
e
n
t
w
o
cla
s
s
e
s
ca
n
b
e
s
ep
ar
ated
p
er
f
ec
tl
y
b
y
th
e
d
ef
i
n
ed
h
y
p
e
r
p
lan
e
T
+
γ
,
th
er
e
ar
e
t
w
o
p
ar
allel
h
y
p
er
p
lan
e
w
h
ic
h
ar
e
b
o
u
n
d
ar
ies
o
f
th
o
s
e
t
w
o
clas
s
es,
i.e
.
T
+
γ
-
1
o
f
th
e
class
{−
1
}
an
d
T
+
γ
+1
o
f
th
e
cla
s
s
{+
1
}.
A
n
o
n
-
li
n
ea
r
h
y
p
er
p
lan
e
i
s
o
b
tain
ed
b
y
tr
an
s
f
o
r
m
i
n
g
th
e
s
ta
n
d
ar
d
SVM
f
o
r
m
u
latio
n
(2
)
,
an
d
b
y
u
s
i
n
g
"
k
er
n
el
tr
ic
k
"
th
r
o
u
g
h
a
Ga
u
s
s
ian
k
er
n
e
l f
u
n
ctio
n
(3
)
,
w
h
er
e
μ
is
a
k
er
n
el
p
ar
a
m
eter
an
d
i
,
j
=
1
,
2
,
.
.
.
,
m
.
T
(
2
)
K
,
ex
p
|
|
||
,
μ
(
3
)
B
y
u
s
i
n
g
(2
)
in
to
(1
)
,
n
o
n
-
lin
ea
r
p
r
o
b
lem
f
u
n
c
tio
n
s
is
o
b
tain
ed
,
as
s
h
o
w
n
b
y
(4
)
,
w
it
h
co
n
s
tr
ain
s
T
+
an
d
.
m
in
,
γ
,
+
1+
T
+
1
T
T
(
4
)
T
h
e
s
o
lu
tio
n
f
o
r
th
e
f
u
n
ctio
n
s
(4
)
is
K
T
T
γ
.
B
y
r
ep
lacin
g
T
w
it
h
n
o
n
-
lin
ea
r
k
er
n
e
l
K
T
an
d
v
ar
iab
le
y
is
m
i
n
i
m
ized
b
y
w
ei
g
h
tin
g
,
g
e
n
er
alize
d
n
o
n
-
li
n
ea
r
SVM
is
s
h
o
w
n
b
y
(5
)
w
it
h
co
n
s
tr
ain
s
K
T
-
γ
+
an
d
.
m
in
,
γ
,
+
1+
T
+
1
T
+
γ
(
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
S
mo
o
th
S
u
p
p
o
r
t V
ec
to
r
Ma
ch
in
e
fo
r
S
u
icid
e
-
R
el
a
ted
B
eh
a
vi
o
u
r
s
P
r
ed
ictio
n
(
G.
I
n
d
r
a
w
a
n
)
3401
T
o
s
o
lv
e
(5
)
,
co
n
s
tr
ain
t f
u
n
cti
o
n
is
d
ef
i
n
ed
b
y
(6
).
(
6
)
B
y
r
ep
lacin
g
(6
)
in
to
(5
)
,
SV
M
p
r
o
b
lem
eq
u
atio
n
i
s
o
b
tain
ed
w
h
ic
h
i
s
eq
u
iv
ale
n
t
to
u
n
c
o
n
s
tr
ain
ed
SVM
o
p
ti
m
izatio
n
,
as s
h
o
w
n
b
y
(7
).
(
7
)
A
t
(
.
)
+
,
n
e
g
ati
v
e
co
m
p
o
n
e
n
ts
ar
e
r
ep
lace
d
b
y
ze
r
o
s
.
E
q
u
atio
n
(7
)
h
a
s
a
u
n
iq
u
e
s
o
lu
t
i
o
n
b
u
t
its
o
b
j
ec
tiv
e
f
u
n
c
tio
n
i
s
n
o
t
t
w
ic
e
d
if
f
er
e
n
tiab
le
w
h
ich
p
r
ec
lu
d
es
th
e
u
s
e
o
f
a
f
ast
Ne
w
to
n
m
et
h
o
d
.
Fo
r
th
at,
a
s
m
o
o
th
in
g
tech
n
iq
u
e
w
as
p
r
o
p
o
s
ed
[
1
0
]
th
at
r
ep
la
ce
s
p
lu
s
f
u
n
ct
io
n
(
.
)
+
b
y
u
s
in
g
i
n
teg
r
al
o
f
s
ig
m
o
id
f
u
n
ctio
n
1
+
ex
p
-
-
1
o
f
n
eu
r
al
n
et
w
o
r
k
.
E
q
u
atio
n
(8
)
s
h
o
w
s
th
e
S
SVM
w
h
er
e
is
th
e
s
m
o
o
th
i
n
g
p
ar
a
m
eter
.
(
8
)
E
q
u
atio
n
(8
)
ca
n
b
e
o
p
tim
ize
d
b
y
u
s
i
n
g
n
u
m
er
ical
ap
p
r
o
ac
h
th
r
o
u
g
h
th
e
Ne
w
to
n
-
A
r
m
ij
o
m
et
h
o
d
.
T
h
e
f
ir
s
t
s
tep
is
to
in
it
iate
,
γ
+1
w
h
er
e
in
d
icate
s
i
t
h
iter
atio
n
o
f
w
.
T
h
e
s
ec
o
n
d
s
tep
is
to
r
ep
ea
t
th
e
iter
atio
n
u
n
til
t
h
e
g
r
ad
ien
t
o
f
t
h
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
at
(8
)
is
eq
u
al
to
ze
r
o
o
r
,
γ
.
T
h
e
th
ir
d
s
tep
is
to
ca
lcu
late
+1
,
γ
+1
as f
o
l
lo
w
s
:
a.
Ne
w
to
n
Dir
ec
tio
n
: d
eter
m
i
n
e
t
h
e
d
ir
ec
tio
n
o
f
+1
,
as sh
o
w
n
b
y
(9
).
(
9
)
b.
A
r
m
ij
o
Step
s
ize:
ch
o
o
s
e
th
e
s
t
ep
s
ize
,
s
u
ch
t
h
at
+
1
,
γ
+
1
,
γ
+
(
1
0
)
w
h
er
e
m
ax
{1
,
1
,
1
,
}
,
s
u
ch
t
h
at
(
1
1
)
w
h
er
e
,
1
W
h
en
,
γ
,
th
e
Ne
w
to
n
-
A
r
m
ij
o
alg
o
r
ith
m
iter
atio
n
s
to
p
p
ed
an
d
co
n
v
er
g
e
n
t
v
al
u
e
o
f
w
an
d
γ
w
er
e
o
b
tain
ed
f
o
r
h
y
p
er
p
lan
e
f
u
n
c
tio
n
,
as s
h
o
w
n
b
y
(
1
2
).
(
1
2
)
3.
RE
S
E
ARCH
M
E
T
H
O
D
Fo
r
SR
B
s
p
r
ed
ictio
n
u
s
i
n
g
S
SVM,
f
i
v
e
s
ta
g
es
o
f
r
esear
ch
m
et
h
o
d
w
er
e
co
n
d
u
cted
,
i.e
.
:
1
)
d
ata
p
r
ep
ar
atio
n
;
2
)
d
ata
tr
an
s
f
o
r
m
atio
n
;
3
)
tr
ain
in
g
a
n
d
test
in
g
d
ata
s
elec
t
io
n
;
4
)
SS
VM
m
o
d
el
d
ev
elo
p
m
e
n
t;
an
d
5
)
SS
VM
m
o
d
el
ev
al
u
atio
n
.
Data
p
r
ep
ar
atio
n
is
r
elate
d
to
th
e
d
ata
co
llectio
n
f
r
o
m
elec
tr
o
n
ic
an
d
n
o
n
-
elec
tr
o
n
ic
m
ed
i
ca
l
r
ec
o
r
d
o
f
th
e
o
n
l
y
o
n
e
p
s
y
c
h
iatr
ic
h
o
s
p
ital
i
n
B
ali
P
r
o
v
in
ce
.
T
h
er
e
ar
e
3
0
.
6
6
0
in
p
atien
t
an
d
o
u
tp
ati
en
t
m
ed
ical
r
ec
o
r
d
f
r
o
m
th
e
la
s
t
f
iv
e
y
ea
r
s
u
p
to
A
p
r
il
2
0
1
6
.
Data
w
er
e
co
llected
th
r
o
u
g
h
d
atab
ase
q
u
er
y
o
n
th
e
h
o
s
p
ital
in
f
o
r
m
atio
n
s
y
s
te
m
an
d
th
e
n
t
h
e
y
w
er
e
e
x
p
o
r
ted
to
C
SV
f
o
r
m
at.
Data
clea
n
in
g
g
a
v
e
2
6
6
5
r
elev
an
t
d
ata
f
o
r
th
is
r
esear
c
h
,
in
cl
u
d
es
1
1
1
p
a
t
ien
t
s
th
at
h
av
e
S
R
B
s
an
d
u
n
d
er
ac
tiv
e
tr
ea
t
m
en
t.
R
e
m
o
v
e
d
d
ata
h
av
e
o
n
e
o
r
m
o
r
e
th
a
n
o
n
e
o
f
t
h
is
th
r
ee
c
o
n
d
itio
n
,
i.e
.
1
)
n
o
t
p
s
y
c
h
iatr
i
c
-
d
is
o
r
d
er
p
atien
t
d
ata
(
d
r
u
g
-
f
r
ee
o
r
p
s
y
c
h
iatr
ic
-
d
is
o
r
d
er
-
f
r
ee
ce
r
tif
icate
ap
p
lican
t,
d
en
tal
p
atien
t,
d
r
u
g
p
atien
t,
n
e
u
r
o
lo
g
y
p
atie
n
t,
o
r
p
h
y
s
io
th
er
ap
y
p
atien
t)
;
2
)
in
co
m
p
lete
d
ata
(
m
a
n
u
al
d
ata
th
at
w
a
s
m
i
g
r
ated
in
to
i
n
f
o
r
m
at
io
n
s
y
s
te
m
an
d
r
elate
d
p
atien
t
h
as
n
o
t
b
ee
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
3
9
9
–
3
4
0
6
3402
in
p
atien
t
o
r
o
u
tp
atien
t
s
in
ce
d
ata
m
ig
r
atio
n
ti
m
e)
;
an
d
3
)
in
ac
tiv
e
p
atien
t
d
ata
(
p
ass
a
w
a
y
p
atie
n
t
o
r
n
o
t
an
o
u
tp
atien
t)
.
Data
tr
an
s
f
o
r
m
at
io
n
is
r
elate
d
to
th
e
tr
an
s
f
o
r
m
atio
n
o
f
p
r
ev
io
u
s
d
ata
in
to
p
r
ed
icto
r
v
ar
iab
les
an
d
r
esp
o
n
s
e
v
ar
iab
le.
T
en
p
r
e
d
icto
r
v
ar
iab
les
w
er
e
o
b
tain
ed
f
r
o
m
d
atab
ase
q
u
er
y
ab
o
v
e,
i.e
.
d
is
ea
s
e
d
iag
n
o
s
i
s
(
x
1
)
,
p
r
o
f
ess
io
n
(
x
2
)
,
ed
u
ca
tio
n
(
x
3
)
,
p
ay
m
e
n
t
t
y
p
e
/
h
ea
l
th
in
s
u
r
an
ce
t
y
p
e
(
x
4
)
,
d
o
m
icile
(
x
5
)
,
ag
e
(
x
6
)
,
ag
e
r
an
g
e
(
x
7
)
,
s
e
x
(
x
8
)
,
m
ar
ital
s
tatu
s
(
x
9
)
,
an
d
f
a
m
il
y
h
is
to
r
y
(
x
10
)
.
A
r
esp
o
n
s
e
v
ar
iab
le
(
y
)
is
a
v
ar
iab
le
w
it
h
v
alu
e
−1
an
d
+1
th
at
r
ep
r
ese
n
ts
cla
s
s
o
f
p
atien
ts
t
h
at
h
av
e
n
o
SR
B
s
a
n
d
clas
s
o
f
p
atie
n
ts
th
a
t
h
a
v
e
S
R
B
s
,
r
esp
ec
tiv
el
y
.
R
esp
o
n
s
e
v
ar
ia
b
le
d
ata
w
as
o
b
tain
ed
f
r
o
m
n
o
n
-
elec
tr
o
n
ic
m
ed
ical
r
ec
o
r
d
r
elate
d
t
o
d
ata
o
f
s
u
icid
e
atte
m
p
t
o
r
in
s
tr
u
m
en
tal
SR
B
s
[
1
]
.
Fig
u
r
e
2
s
h
o
w
s
r
esear
c
h
s
a
m
p
le
o
f
p
r
ed
icto
r
(
in
s
tan
ce
)
an
d
r
esp
o
n
s
e
(
lab
el)
m
atr
i
x
b
y
u
s
i
n
g
S
SVM
to
o
lb
o
x
lib
r
ar
y
[
1
7
]
.
R
o
w
an
d
co
lu
m
n
o
f
i
n
s
ta
n
ce
m
a
tr
ix
r
ep
r
esen
t
a
p
atien
t d
ata
an
d
h
i
s
/
h
er
r
elate
d
ten
p
r
ed
icto
r
v
ar
iab
les,
r
esp
ec
tiv
el
y
.
Fig
u
r
e
2
.
P
r
ed
ict
o
r
an
d
r
esp
o
n
s
e
m
atr
i
x
T
r
ain
in
g
a
n
d
test
i
n
g
d
ata
s
el
ec
tio
n
is
r
elate
d
to
th
e
n
ex
t
s
tag
e
o
f
S
SVM
m
o
d
el
d
ev
e
lo
p
m
e
n
t
a
n
d
ev
alu
a
tio
n
.
T
w
o
d
ata
s
elec
tio
n
m
ec
h
a
n
i
s
m
s
w
er
e
u
s
ed
to
g
et
th
e
b
est h
y
p
er
p
lan
e
i
n
Fi
g
u
r
e
1
,
i.e
.
b
y
u
s
i
n
g
:
a.
T
en
-
f
o
ld
s
cr
o
s
s
v
al
id
atio
n
(
1
0
-
f
c
v
)
s
elec
t
io
n
[
1
8
]
an
d
d
ata
r
atio
s
elec
tio
n
o
f
2
6
6
5
r
elev
an
t
d
ata.
k
-
f
o
l
d
cr
o
s
s
v
al
id
atio
n
s
p
lits
th
e
d
ata
in
to
k
s
ec
tio
n
s
at
r
a
n
d
o
m
.
E
a
ch
s
ec
tio
n
h
a
s
t
h
e
s
a
m
e
clas
s
p
r
o
p
o
r
tio
n
to
th
e
in
itial
cla
s
s
p
r
o
p
o
r
tio
n
.
E
ac
h
s
ec
tio
n
w
ill
b
e
u
s
ed
as
tr
ain
i
n
g
d
ata
an
d
th
e
r
est
is
u
s
ed
as
test
i
n
g
d
ata,
s
o
th
er
e
w
ill
b
e
k
ac
cu
r
ac
y
.
Fin
a
l
ac
cu
r
ac
y
is
t
h
e
av
er
ag
e
o
f
t
h
o
s
e
k
ac
cu
r
ac
y
.
On
d
ata
r
atio
,
d
ata
o
f
p
atien
ts
th
at
h
a
v
e
S
R
B
s
a
n
d
p
atie
n
ts
t
h
at
h
a
v
e
n
o
S
R
B
s
w
er
e
r
a
n
d
o
m
l
y
s
e
lecte
d
i
n
ce
r
tai
n
r
atio
f
o
r
tr
ain
in
g
a
n
d
test
i
n
g
d
ata,
r
e
s
p
ec
tiv
el
y
.
Fo
r
an
e
x
a
m
p
le,
r
atio
9
0
:1
0
m
ea
n
s
9
0
%
o
f
r
ele
v
an
t
d
ata
as
tr
ain
i
n
g
d
ata
an
d
1
0
%
o
f
r
elev
an
t
d
ata
as
te
s
ti
n
g
d
ata.
T
r
ain
in
g
d
ata
co
n
s
is
t
o
f
9
0
%
o
f
d
ata
o
f
p
atien
t
s
t
h
at
h
av
e
SR
B
s
a
n
d
9
0
%
o
f
d
ata
o
f
p
atien
ts
t
h
at
h
av
e
n
o
S
R
B
s
,
w
h
ile
test
in
g
d
ata
co
n
s
is
t
o
f
1
0
%
o
f
d
ata
o
f
p
atien
t
s
th
at
h
a
v
e
SR
B
s
an
d
1
0
% o
f
d
ata
o
f
p
ati
en
ts
t
h
at
h
av
e
n
o
SR
B
s
;
b.
Data
r
atio
s
elec
tio
n
b
ased
o
n
1
1
1
d
ata
o
f
p
atie
n
ts
th
at
h
av
e
S
R
B
s
f
r
o
m
2
6
6
5
r
elev
a
n
t
d
ata.
Fo
r
a
n
ex
a
m
p
le,
d
ata
r
atio
1
:2
m
ea
n
s
tr
ain
i
n
g
d
ata
(
also
u
s
ed
as test
in
g
d
ata)
co
n
s
is
t o
f
d
ata
o
f
p
atien
ts
t
h
at
h
a
v
e
SR
B
s
a
n
d
2
2
2
d
ata
o
f
p
atien
t
s
t
h
at
h
av
e
n
o
S
R
B
s
.
Sev
er
al
b
est
r
esu
lts
o
f
o
b
tain
ed
SS
V
M
m
o
d
els
th
a
n
w
er
e
test
ed
b
y
u
s
i
n
g
r
an
d
o
m
l
y
s
elec
ted
d
ata
in
n
u
m
b
er
o
f
1
%,
%
an
d
1
0
0
% o
f
2
6
6
5
r
elev
an
t d
ata.
SS
VM
m
o
d
el
d
ev
elo
p
m
en
t
is
r
elate
d
to
p
ar
am
e
ter
w
an
d
γ
(
1
2
)
th
at
w
as
co
m
p
u
ted
b
y
u
s
i
n
g
SS
VM
to
o
lb
o
x
lib
r
ar
y
[
1
7
]
.
Sev
er
al
p
ar
am
eter
w
an
d
γ
w
as
co
m
p
u
ted
b
ased
o
n
v
ar
io
u
s
tr
ai
n
in
g
an
d
te
s
ti
n
g
d
at
a
s
elec
tio
n
ab
o
v
e.
SS
V
M
m
o
d
el
ev
alu
a
tio
n
is
r
elate
d
to
th
e
c
la
s
s
i
f
icatio
n
ac
cu
r
ac
y
t
h
at
ca
n
b
e
d
eter
m
in
ed
b
y
u
s
i
n
g
co
n
ti
n
g
e
n
c
y
tab
le
[
1
9
]
,
as
s
h
o
w
n
b
y
T
ab
le
1
.
B
ased
o
n
th
at
tab
le,
th
e
class
i
f
icati
o
n
ac
cu
r
ac
y
ca
n
b
e
m
ea
s
u
r
ed
b
y
u
s
in
g
(
1
3
)
.
T
ab
le
1
.
C
lass
if
icatio
n
ac
c
u
r
ac
y
co
n
ti
n
g
e
n
c
y
A
c
t
u
a
l
P
r
e
d
i
c
t
i
o
n
I
(
N
e
g
a
t
i
v
e
)
I
I
(
P
o
si
t
i
v
e
)
N
e
g
a
t
i
v
e
T
r
u
e
N
e
g
a
t
i
v
e
(
T
N
)
F
a
l
s
e
P
o
si
t
i
v
e
(
F
P
)
P
o
si
t
i
v
e
F
a
l
s
e
N
e
g
a
t
i
v
e
(
F
N
)
T
r
u
e
P
o
si
t
i
v
e
(
T
P
)
%
(
1
3
)
w
h
er
e
TN
is
t
h
e
n
u
m
b
er
o
f
p
r
ed
ictio
n
o
f
p
atie
n
ts
t
h
at
h
av
e
n
o
SR
B
s
a
n
d
in
f
ac
t
t
h
at
p
atie
n
ts
h
a
v
e
n
o
s
u
c
h
b
eh
a
v
io
u
r
s
;
TP
is
th
e
n
u
m
b
er
o
f
p
r
ed
ictio
n
o
f
p
atie
n
ts
th
at
h
av
e
SR
B
s
a
n
d
in
f
ac
t t
h
a
t p
atien
ts
h
a
v
e
s
u
c
h
b
eh
av
io
u
r
s
;
FP
is
th
e
n
u
m
b
er
o
f
p
r
ed
ictio
n
o
f
p
atie
n
t
s
t
h
at
h
av
e
S
R
B
s
a
n
d
i
n
f
ac
t
t
h
at
p
atien
t
s
h
av
e
n
o
su
c
h
b
eh
av
io
u
r
s
;
an
d
FN
i
s
th
e
n
u
m
b
er
o
f
p
r
ed
ictio
n
o
f
p
atien
t
s
th
at
h
a
v
e
n
o
SR
B
s
a
n
d
in
f
ac
t
th
at
p
atien
ts
h
av
e
s
u
c
h
b
eh
a
v
io
u
r
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
S
mo
o
th
S
u
p
p
o
r
t V
ec
to
r
Ma
ch
in
e
fo
r
S
u
icid
e
-
R
el
a
ted
B
eh
a
vi
o
u
r
s
P
r
ed
ictio
n
(
G.
I
n
d
r
a
w
a
n
)
3403
4.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
E
x
p
er
i
m
e
n
t
w
as
b
ased
o
n
p
r
ev
io
u
s
t
w
o
s
e
lectio
n
m
ec
h
a
n
i
s
m
s
o
f
tr
ai
n
i
n
g
a
n
d
test
i
n
g
d
at
a,
r
u
n
n
i
n
g
o
n
I
n
tel
C
o
r
e™
i5
-
4
4
6
0
T
C
PU @
1
.
9
0
GHz
w
it
h
4
GB
R
A
M
an
d
W
in
d
o
w
s
1
0
6
4
-
b
it Op
er
atin
g
S
y
s
te
m
.
4
.
1
.
Da
t
a
s
elec
t
io
n
m
ec
ha
ni
s
m
1
T
ab
le
2
s
h
o
w
s
h
i
g
h
ac
c
u
r
ac
y
o
f
SS
VM
m
o
d
el
o
n
e
v
er
y
d
ata
s
elec
tio
n
t
y
p
e
b
u
t
al
l
o
f
th
e
i
r
TP
w
er
e
ze
r
o
th
at
m
a
k
e
all
o
f
t
h
o
s
e
m
o
d
el
ca
n
n
o
t
b
e
u
s
ed
f
o
r
p
r
ed
i
ctio
n
o
f
p
atie
n
ts
t
h
at
h
av
e
SR
B
s
.
Hig
h
ac
c
u
r
ac
y
ca
m
e
f
r
o
m
h
i
g
h
n
u
m
b
er
o
f
TN
(
1
3
)
s
in
ce
m
a
n
y
p
atien
t
s
th
a
t h
av
e
n
o
SR
B
s
ar
e
o
n
t
h
e
d
ata
in
th
is
r
esear
ch
.
4
.
2
.
Da
t
a
s
elec
t
io
n
m
ec
ha
ni
s
m
2
T
ab
le
3
s
h
o
w
s
n
o
n
-
ze
r
o
TP
s
b
y
s
ix
S
SVM
m
o
d
el
s
th
at
w
er
e
o
b
tain
ed
b
y
u
s
i
n
g
s
ix
d
ata
r
atio
s
elec
tio
n
s
f
r
o
m
1
:0
5
u
p
to
1
:1
.
E
ac
h
o
f
t
h
o
s
e
SS
V
M
m
o
d
el
s
th
e
n
w
er
e
test
ed
b
y
u
s
in
g
te
n
p
o
r
tio
n
s
o
f
2
6
6
5
d
ata.
So
,
ea
ch
SS
VM
m
o
d
el
will g
i
v
e
ten
ac
c
u
r
ac
y
r
es
u
lt
w
h
er
e
its
av
er
ag
e
ac
c
u
r
ac
y
is
s
h
o
w
n
b
y
Fi
g
u
r
e
3
.
T
ab
le
2
.
SS
VM
Mo
d
el
P
er
f
o
r
m
an
ce
b
y
u
s
in
g
Data
Selectio
n
Me
c
h
an
is
m
1
T
ab
le
3
.
SS
VM
Mo
d
el
P
er
f
o
r
m
an
ce
b
y
u
s
in
g
Data
Selectio
n
Me
c
h
an
is
m
2
B
ased
o
n
Fig
u
r
e
3
,
SS
VM
m
o
d
el
g
en
er
ated
b
y
d
ata
r
atio
1
:1
(
tr
ain
in
g
d
ata
co
n
s
i
s
t
o
f
1
1
1
d
ata
o
f
p
atien
ts
th
at
h
a
v
e
S
R
B
s
a
n
d
1
1
1
d
ata
o
f
p
atien
ts
th
a
t
h
a
v
e
n
o
SR
B
s
)
g
a
v
e
t
h
e
b
es
t
av
er
a
g
e
ac
cu
r
ac
y
a
t
ab
o
u
t
6
3
%.
On
r
u
n
n
in
g
ti
m
e,
m
o
s
t
o
f
th
e
ti
m
e
w
as
u
s
ed
f
o
r
SS
V
M
m
o
d
el
d
ev
elo
p
m
e
n
t
r
elate
d
to
p
ar
am
eter
w
an
d
γ
(
1
2
)
.
No
s
ig
n
i
f
ica
n
t in
cr
ea
s
e
o
n
r
u
n
n
i
n
g
t
i
m
e
f
o
r
d
if
f
er
en
t p
o
r
tio
n
o
f
r
elev
an
t d
ata,
as sh
o
w
n
b
y
T
ab
le
4
.
Fig
u
r
e
3
.
Six
SS
VM
m
o
d
els p
er
f
o
r
m
an
ce
b
y
u
s
in
g
s
i
x
d
ata
r
atio
s
elec
tio
n
s
S
e
l
e
c
t
i
o
n
T
e
s
t
i
n
g
D
a
t
a
w
γ
TN
FN
FP
TP
A
c
u
r
a
c
y
T
i
m
e
(
s
)
1
0
-
f
c
v
v
a
r
i
a
b
l
e
0
.
0
1
0
.
0
0
1
7
2554
111
0
0
0
.
9
5
8
3
9
0
7
.
9
5
4
9
0
:
1
0
266
0
.
0
1
0
.
0
0
1
8
255
11
0
0
0
.
9
5
8
6
7
2
2
.
5
9
7
8
0
:
2
0
533
0
.
0
1
0
.
0
0
1
9
511
22
0
0
0
.
9
5
8
7
5
5
5
.
3
9
7
7
0
:
3
0
799
0
.
0
1
0
.
0
0
1
6
766
33
0
0
0
.
9
5
8
7
3
6
1
.
7
2
9
6
0
:
4
0
1065
0
.
0
1
0
.
0
0
2
3
1021
22
0
0
0
.
9
5
8
7
2
5
0
.
6
3
9
5
0
:
5
0
1282
0
.
0
1
0
.
0
0
1
8
1227
55
0
0
0
.
9
5
7
1
1
6
5
.
3
2
1
S
e
l
e
c
t
i
o
n
T
e
s
t
i
n
g
D
a
t
a
w
γ
TN
FN
FP
TP
A
c
u
r
a
c
y
T
i
m
e
(
s
)
1
:
0
.
5
167
0
.
0
1
7
.
8
8
E
-
0
5
8
5
48
106
0
.
6
8
2
6
1
.
3
1
2
1
:
0
.
6
178
0
.
0
1
8
.
8
3
E
-
0
5
10
5
57
106
0
.
6
5
1
7
1
.
3
1
7
1
:
0
.
7
189
1
.
7
8
E
+
0
3
5
.
7
9
E
-
0
4
77
52
1
59
0
.
7
1
9
6
1
.
6
2
7
1
:
0
.
8
200
1
.
7
8
E
+
0
3
5
.
6
0
E
-
0
4
86
53
3
58
0
.
7
2
1
.
9
7
8
1
:
0
.
9
211
1
.
0
0
E
+
0
4
7
.
7
8
E
-
0
5
96
49
4
62
0
.
7
4
8
8
2
.
6
3
4
1
:
1
222
1
.
7
7
8
3
2
.
6
7
E
-
0
5
101
57
10
54
0
.
6
9
8
2
2
.
0
9
4
1
:
2
333
0
.
0
5
6
2
0
.
0
3
2
3
222
111
0
0
0
.
6
6
6
7
4
.
1
5
5
1
:
3
444
0
.
0
5
6
2
0
.
0
2
6
8
333
111
0
0
0
.
7
5
1
0
.
1
1
1
:
4
555
0
.
0
5
6
2
0
.
0
2
4
5
444
111
0
0
0
.
8
1
8
.
3
3
7
1
:
5
666
0
.
0
5
6
2
0
.
0
2
1
6
555
111
0
0
0
.
8
3
3
3
3
2
.
2
7
3
1
:
6
777
0
.
0
1
0
.
0
0
2
666
111
0
0
0
.
8
5
7
1
4
0
.
8
4
5
1
:
7
888
0
.
0
1
0
.
0
0
3
777
111
0
0
0
.
8
7
5
6
9
.
5
3
2
1
:
8
999
0
.
0
1
0
.
0
0
2
2
888
111
0
0
0
.
8
8
8
9
8
0
.
7
9
6
1
:
9
1110
0
.
0
1
0
.
0
0
2
1
999
111
0
0
0
.
9
1
2
7
.
7
5
7
1
:
1
0
1221
0
.
0
1
0
.
0
0
2
2
1110
111
0
0
0
.
9
0
9
1
1
5
5
.
9
9
8
1
:
1
1
1332
0
.
0
1
0
.
0
0
2
1
1221
111
0
0
0
.
9
1
6
7
1
9
7
.
3
3
9
1
:
1
2
1443
0
.
0
1
0
.
0
0
2
8
1332
111
0
0
0
.
9
2
3
1
2
3
3
.
0
4
6
1
:
1
3
1554
0
.
0
1
0
.
0
0
1
5
1443
111
0
0
0
.
9
2
8
6
2
2
4
.
1
1
2
1
:
1
4
1665
0
.
0
1
0
.
0
0
2
1
1554
111
0
0
0
.
9
3
3
3
2
7
8
.
6
4
4
1
:
1
5
1776
0
.
0
1
0
.
0
0
2
2
1665
111
0
0
0
.
9
3
7
5
3
2
7
.
8
0
2
1
:
1
6
1887
0
.
0
1
0
.
0
0
2
3
1776
111
0
0
0
.
9
4
1
2
3
9
6
.
6
2
2
1
:
1
7
1998
0
.
0
1
0
.
0
0
2
1
1887
111
0
0
0
.
9
4
4
4
4
6
5
.
3
3
5
1
:
1
8
2109
0
.
0
1
0
.
0
0
2
2
1998
111
0
0
0
.
9
4
7
4
5
6
0
.
4
6
6
1
:
1
9
2220
0
.
0
1
0
.
0
0
1
7
2109
111
0
0
0
.
9
5
5
7
6
.
8
6
1
1
:
2
0
2331
0
.
0
1
0
.
0
0
2
3
2220
111
0
0
0
.
9
5
2
4
6
3
7
.
3
9
5
1
:
2
1
2442
0
.
0
1
0
.
0
0
2
4
2331
111
0
0
0
.
9
5
4
5
7
6
8
.
0
9
2
1
:
2
2
2553
0
.
0
1
0
.
0
0
2
3
2442
111
0
0
0
.
9
5
6
5
8
6
0
.
4
2
7
1
:
2
3
2665
0
.
0
1
0
.
0
0
1
7
2554
111
0
0
0
.
9
5
8
3
9
0
7
.
9
5
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
3
9
9
–
3
4
0
6
3404
T
ab
le
4
.
SS
VM
Mo
d
el
P
er
f
o
r
m
an
ce
b
y
u
s
in
g
Data
R
atio
1
:1
R
es
u
lts
ab
o
v
e
w
er
e
b
ased
o
n
t
en
p
r
ed
icto
r
v
ar
iab
les,
a
s
d
esc
r
ib
ed
p
r
ev
io
u
s
l
y
.
T
ab
le
5
g
av
e
th
e
r
esu
lt
ab
o
u
t
th
e
in
f
l
u
e
n
ce
o
f
ea
ch
o
f
t
h
o
s
e
v
ar
iab
les
to
th
e
SS
V
M
m
o
d
el
p
er
f
o
r
m
a
n
ce
b
y
u
s
i
n
g
d
ata
r
atio
1
:1
an
d
test
i
n
g
d
ata
at
3
0
%
o
f
2
6
6
5
r
el
ev
an
t d
ata
as
s
h
o
w
n
in
T
ab
le
4
.
T
ab
le
5
.
SS
VM
Mo
d
el
P
er
f
o
r
m
an
ce
o
n
R
ed
u
ce
d
P
r
ed
icto
r
Var
iab
les
T
ab
le
5
s
h
o
w
s
t
h
at
s
i
x
p
r
ed
icto
r
v
ar
iab
les,
i.e
.
d
is
ea
s
e
d
ia
g
n
o
s
is
(
x
1
)
,
p
r
o
f
es
s
io
n
(
x
2
)
,
ed
u
c
atio
n
(
x
3
)
,
p
ay
m
e
n
t t
y
p
e/h
ea
lth
in
s
u
r
a
n
ce
t
y
p
e
(
x
4
)
,
d
o
m
icile
(
x
5
)
,
an
d
a
g
e
(
x
6
)
,
h
a
v
e
m
u
c
h
i
n
f
l
u
en
ce
t
o
th
e
S
SVM
m
o
d
el
r
esu
lt
b
ec
a
u
s
e
o
f
t
h
e
d
ec
r
ea
s
i
n
g
v
al
u
e
o
f
i
ts
TP
a
n
d
/o
r
th
e
in
cr
ea
s
i
n
g
v
a
lu
e
o
f
its
FP
w
it
h
o
u
t
ea
c
h
o
f
t
h
o
s
e
v
ar
iab
les.
H
y
p
o
th
etica
ll
y
,
ag
e
r
an
g
e
(
x
7
)
,
s
e
x
(
x
8
)
,
m
ar
ital
s
t
atu
s
(
x
9
)
,
an
d
f
a
m
il
y
h
i
s
to
r
y
(
x
10
)
h
a
v
e
i
n
f
l
u
en
c
e
o
n
t
h
e
p
r
ed
ictio
n
.
Ag
e
r
a
n
g
e
b
et
w
ee
n
1
9
−
5
y
ea
r
s
o
ld
th
at
w
a
s
u
s
ed
a
s
r
ef
er
e
n
ce
b
y
t
h
e
p
s
y
c
h
iatr
ic
h
o
s
p
ital
ap
p
ar
en
tl
y
h
as
r
elativ
e
l
y
s
m
a
ll
i
n
f
l
u
e
n
ce
i
n
t
h
i
s
S
SVM
m
o
d
el,
n
eith
er
d
o
s
ex
,
m
ar
ital
s
tat
u
s
,
n
o
r
f
a
m
il
y
h
is
t
o
r
y
,
ev
e
n
t
h
o
u
g
h
f
e
m
ale
o
r
u
n
m
ar
r
ied
s
tat
u
s
o
r
s
o
cial
n
et
w
o
r
k
w
as
co
n
s
id
er
ed
to
b
e
th
e
r
i
s
k
f
ac
to
r
s
o
f
SR
B
s
[
1
]
.
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
WO
RK
Su
icid
e
-
r
elate
d
b
eh
a
v
io
u
r
s
(
S
R
B
s
)
p
r
ed
ictio
n
w
it
h
SS
VM
g
av
e
th
e
b
est
a
v
er
ag
e
ac
c
u
r
a
c
y
at
6
3
%.
T
h
is
ac
cu
r
ac
y
ca
n
b
e
o
b
tain
ed
b
y
u
s
i
n
g
3
0
%
o
f
2
6
6
5
r
elev
a
n
t
d
ata
a
s
d
ata
test
i
n
g
a
n
d
b
y
u
s
i
n
g
tr
ai
n
i
n
g
d
ata
w
h
ic
h
h
av
e
o
n
e
-
to
-
o
n
e
r
at
io
i
n
n
u
m
b
er
b
et
w
ee
n
p
atie
n
t
s
t
h
at
h
a
v
e
SR
B
s
a
n
d
p
atien
t
s
th
a
t
h
a
v
e
n
o
SR
B
s
.
I
n
th
e
f
u
t
u
r
e
w
o
r
k
,
ac
cu
r
ac
y
i
m
p
r
o
v
e
m
en
t
n
ee
d
to
b
e
co
n
f
ir
m
ed
b
y
u
s
i
n
g
R
ed
u
ce
d
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
in
e
(
R
SVM)
m
e
th
o
d
,
as th
e
f
u
r
th
e
r
d
ev
elo
p
m
e
n
t o
f
S
SVM
[
1
0
]
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
lik
e
to
th
a
n
k
to
B
ali
P
r
o
v
in
ce
Go
v
er
n
m
en
t
th
r
o
u
g
h
its
I
n
v
est
m
e
n
t
an
d
L
icen
s
in
g
Ag
e
n
c
y
a
n
d
its
P
s
y
c
h
iatr
ic
Ho
s
p
ital f
o
r
th
e
p
er
m
i
s
s
io
n
to
co
n
d
u
ct
t
h
is
r
esear
c
h
.
D
a
t
a
P
o
r
t
i
o
n
T
e
s
t
i
n
g
D
a
t
a
TN
FN
FP
TP
A
c
u
r
a
c
y
T
i
m
e
(
s
)
10%
266
170
4
85
7
0
.
6
6
5
4
2
.
0
4
2
20%
533
394
12
117
10
0
.
7
5
7
9
7
2
.
0
4
4
30%
799
672
19
94
14
0
.
8
5
8
6
2
.
0
4
2
40%
1065
531
27
490
17
0
.
5
1
4
5
5
2
.
2
0
2
50%
1383
860
28
467
28
0
.
6
4
2
1
2
.
0
4
2
60%
1600
959
30
574
37
0
.
6
2
2
5
2
.
0
3
4
70%
1866
818
38
970
40
0
.
4
5
9
8
2
.
0
2
80%
2243
1207
45
947
44
0
.
5
5
7
7
2
.
0
3
8
90%
2399
1320
53
979
47
0
.
5
6
9
8
2
.
0
6
4
100%
2665
1490
57
1064
54
0
.
5
7
9
3
6
2
.
0
5
8
No
R
e
d
u
c
e
d
P
r
e
d
i
c
t
o
r
V
a
r
i
a
b
l
e
s
TN
FN
FP
TP
A
c
u
r
a
c
y
1
x
1
,
x
2
,
x
3
,
x
4
,
x
5
,
x
6
,
x
7
,
x
8
,
x
9
629
18
137
15
0
.
8
0
6
0
1
2
x
1
,
x
2
,
x
3
,
x
4
,
x
5
,
x
6
,
x
7
,
x
8
,
x
10
629
18
137
15
0
.
8
0
6
0
1
3
x
1
,
x
2
,
x
3
,
x
4
,
x
5
,
x
6
,
x
7
,
x
9
,
x
10
629
18
137
15
0
.
8
0
6
0
1
4
x
1
,
x
2
,
x
3
,
x
4
,
x
5
,
x
6
,
x
8
,
x
9
,
x
10
629
18
137
15
0
.
8
0
6
0
1
5
x
1
,
x
2
,
x
3
,
x
4
,
x
5
,
x
7
,
x
8
,
x
9
,
x
10
619
18
147
15
0
.
7
9
3
4
9
6
x
1
,
x
2
,
x
3
,
x
4
,
x
6
,
x
7
,
x
8
,
x
9
,
x
10
667
30
99
3
0
.
8
3
8
5
5
7
x
1
,
x
2
,
x
3
,
x
5
,
x
6
,
x
7
,
x
8
,
x
9
,
x
10
759
32
7
1
0
.
9
5
1
2
9
8
x
1
,
x
2
,
x
4
,
x
5
,
x
6
,
x
7
,
x
8
,
x
9
,
x
10
722
30
44
3
0
.
9
0
7
4
9
x
1
,
x
3
,
x
4
,
x
5
,
x
6
,
x
7
,
x
8
,
x
9
,
x
10
737
33
29
0
0
.
9
2
2
4
10
x
2
,
x
3
,
x
4
,
x
5
,
x
6
,
x
7
,
x
8
,
x
9
,
x
10
716
32
50
1
0
.
8
9
7
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
S
mo
o
th
S
u
p
p
o
r
t V
ec
to
r
Ma
ch
in
e
fo
r
S
u
icid
e
-
R
el
a
ted
B
eh
a
vi
o
u
r
s
P
r
ed
ictio
n
(
G.
I
n
d
r
a
w
a
n
)
3405
RE
F
E
R
E
NC
E
S
[1
]
J.
L
.
S
k
e
e
m
,
E.
S
il
v
e
r,
P
.
S
.
A
ip
p
e
lb
a
u
m
,
a
n
d
J.
T
ie
m
a
n
n
,
“
S
u
i
c
id
e
-
Re
late
d
Be
h
a
v
io
r
a
f
ter
P
sy
c
h
iatric
Ho
sp
it
a
l
Disc
h
a
rg
e
:
I
m
p
li
c
a
ti
o
n
s
f
o
r
Ris
k
A
s
se
ss
m
e
n
t
a
n
d
M
a
n
a
g
e
m
e
n
t
”
,
Beh
a
v
i
o
ra
l
S
c
ien
c
e
s
a
n
d
th
e
L
a
w
,
v
o
l.
2
4
,
p
p
.
7
3
1
-
7
4
6
,
2
0
0
6
.
[2
]
T
.
L
.
S
ted
m
a
n
,
S
ted
m
a
n
’s M
e
d
ica
l
Dic
ti
o
n
a
ry
,
2
8
t
h
e
d
.
P
h
i
lad
e
lp
h
ia:
L
ip
p
in
c
o
tt
W
il
li
a
m
s &
W
il
k
in
s,
2
0
0
6
.
[3
]
K.
Ha
w
to
n
a
n
d
K.
v
a
n
He
e
rin
g
e
n
,
“
S
u
ici
d
e
”
,
L
a
n
c
e
t
,
v
o
l.
3
7
3
,
n
o
.
9
6
7
2
,
p
p
.
1
3
7
2
-
1
3
8
1
,
2
0
0
9
.
[4
]
W
HO
,
“
S
u
icid
e
F
a
c
t
S
h
e
e
t,
”
1
6
.
[5
]
G
.
K.
Bro
w
n
,
A
.
T
.
Be
c
k
,
.
S
t
e
e
r,
a
n
d
J.
.
G
rish
a
m
,
“
is
k
F
a
c
to
rs
f
o
r
S
u
icid
e
in
P
sy
c
h
iatric
Ou
tp
a
ti
e
n
ts:
A
20
-
y
e
a
r
P
ro
sp
e
c
ti
v
e
S
tu
d
y
”
,
J
o
u
rn
a
l
o
f
C
o
n
s
u
lt
i
n
g
a
n
d
Cli
n
ica
l
Ps
y
c
h
o
lo
g
y
, v
o
l.
6
8
,
n
o
.
5
,
p
p
.
3
7
1
-
3
7
7
,
2
0
0
0
.
[6
]
J.
Co
o
p
e
r
,
e
t
a
l
.
,
“
S
u
ici
d
e
a
f
ter
De
li
b
e
ra
te
S
e
lf
-
Ha
r
m
:
A
4
-
y
e
a
r
Co
h
o
r
t
S
tu
d
y
”
,
Ame
ric
a
n
J
o
u
rn
a
l
o
f
Psy
c
h
ia
try
,
v
o
l.
1
6
2
,
n
o
.
2
,
p
p
.
2
9
7
-
3
0
3
,
2
0
0
5
.
[7
]
Y.
J.
L
e
e
a
n
d
O.
L
.
M
a
n
g
a
sa
rian
,
“
A
S
m
o
o
th
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
f
o
r
Ca
ss
if
ic
a
ti
o
n
”
,
J
o
u
r
n
a
l
o
f
Co
mp
u
t
a
ti
o
n
a
l
Op
ti
miza
ti
o
n
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
p
p
.
5
-
2
2
,
2
0
0
1
.
[8
]
S
.
W
.
P
u
rn
a
m
i
a
n
d
A
.
E
m
b
o
n
g
,
“
S
m
o
o
th
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
f
o
r
Bre
a
st
Ca
n
c
e
r
Clas
si
f
ic
a
ti
o
n
”
,
in
T
h
e
4
t
h
IM
T
-
GT
2
0
0
8
C
o
n
fer
e
n
c
e
o
f
M
a
t
h
e
ma
ti
c
s,
S
ta
ti
stics
a
n
d
Its
A
p
p
li
c
a
ti
o
n
(
ICM
S
A
2
0
0
8
)
,
2
0
0
8
.
[9
]
M
.
F
u
r
q
a
n
,
A
.
Em
b
o
n
g
,
A
.
S
u
ry
a
n
ti
,
S
.
W
.
P
u
rn
a
m
i,
a
n
d
S
.
S
a
jad
in
,
“
S
m
o
o
t
h
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
f
o
r
F
a
c
e
Re
c
o
g
n
it
io
n
u
s
in
g
P
rin
c
i
p
a
l
Co
m
p
o
n
e
n
A
n
a
l
y
sis
”
,
in
2
n
d
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
On
Gr
e
e
n
T
e
c
h
n
o
l
o
g
y
a
n
d
En
g
i
n
e
e
rin
g
(
ICGTE
)
,
2
0
0
9
.
[1
0
]
Y.
J.
L
e
e
a
n
d
O.
L
.
M
a
n
g
a
sa
rian
,
“
S
V
M
:Red
u
c
e
d
S
u
p
p
o
r
t
V
e
c
to
r
M
a
c
h
i
n
e
”
,
in
T
h
e
F
irst
S
IA
M
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Da
t
a
M
in
i
n
g
,
2
0
0
1
.
[1
1
]
J.
Ha
n
a
n
d
M
.
Ka
m
b
e
r,
Da
ta
M
in
i
n
g
:
Co
n
c
e
p
ts
a
n
d
T
e
c
h
n
iq
u
e
s
,
2
n
d
e
d
.
S
a
n
F
ra
n
c
isc
o
:
M
o
r
g
a
n
Ka
u
fm
a
n
n
P
u
b
l
ish
e
rs,
2
0
0
6
.
[1
2
]
G
.
In
d
ra
w
a
n
,
S
.
A
k
b
a
r,
a
n
d
B.
S
it
o
h
a
n
g
,
“
e
v
ie
w
o
f
S
e
q
u
e
n
ti
a
l
A
c
c
e
ss
M
e
th
o
d
f
o
r
F
in
g
e
rp
rin
t
Id
e
n
ti
f
ica
ti
o
n
”
,
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
t
io
n
,
Co
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l)
,
v
o
l
.
1
0
,
n
o
.
2
,
p
p
.
1
9
9
-
2
0
6
,
J
u
n
.
2
0
1
2
.
[1
3
]
G
.
In
d
ra
w
a
n
,
A
.
S
.
Nu
g
ro
h
o
,
S
.
Ak
b
a
r,
a
n
d
B.
S
it
o
h
a
n
g
,
“
A
M
u
l
ti
-
T
h
re
a
d
e
d
F
in
g
e
rp
ri
n
t
Dire
c
t
-
A
c
c
e
ss
S
trate
g
y
Us
in
g
L
o
c
a
l
-
S
tar
-
S
tru
c
tu
re
-
b
a
se
d
Disc
rim
in
a
to
r
F
e
a
tu
re
s
”
,
T
E
L
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
,
C
o
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l)
,
v
o
l.
1
2
,
n
o
.
5
,
p
p
.
4
0
79
-
4
0
9
0
,
2
0
1
4
.
[1
4
]
G
.
In
d
ra
w
a
n
,
S
.
Ak
b
a
r,
a
n
d
B.
S
it
o
h
a
n
g
,
“
F
in
g
e
rp
rin
t
Dire
c
t
-
Ac
c
e
ss
S
trate
g
y
Us
in
g
L
o
c
a
l
-
S
tar
-
S
tru
c
tu
re
-
b
a
se
d
Disc
ri
m
in
a
t
o
r
F
e
a
tu
re
s:
A
Co
m
p
a
riso
n
S
t
u
d
y
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
t
e
r
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
4
,
n
o
.
5
,
p
p
.
8
1
7
-
8
2
9
,
2
0
1
4
.
[1
5
]
G
.
In
d
ra
w
a
n
,
S
.
A
k
b
a
r,
a
n
d
B.
S
i
to
h
a
n
g
,
“
On
A
n
a
ly
z
in
g
o
f
F
in
g
e
rp
rin
t
Dire
c
t
-
A
c
c
e
ss
S
trate
g
ies
”
,
in
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Da
t
a
a
n
d
S
o
ft
w
a
re
En
g
i
n
e
e
rin
g
(
ICo
DS
E)
,
2
0
1
4
.
[1
6
]
A
.
S
.
Nu
g
ro
h
o
,
A
.
B.
W
it
a
rto
,
a
n
d
D.
Ha
n
d
o
k
o
,
“
A
p
p
li
c
a
ti
o
n
o
f
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
i
n
e
in
Bio
in
f
o
rm
a
ti
c
s
”
,
in
In
d
o
n
e
sia
n
S
c
ie
n
ti
fi
c
M
e
e
ti
n
g
in
Ce
n
tra
l
J
a
p
a
n
,
2
0
0
3
.
[1
7
]
DSM
I,
“
S
m
o
o
th
S
u
p
p
o
r
t
V
e
c
to
r
M
a
c
h
in
e
T
o
o
lb
o
x
|
Da
t
a
S
c
ien
c
e
a
n
d
M
a
c
h
in
e
In
telli
g
e
n
c
e
Lab
”
,
2
0
1
4
.
[
On
li
n
e
]
.
Av
a
il
a
b
le:
h
tt
p
:/
/d
m
lab
8
.
c
sie
.
n
t
u
s
t.
e
d
u
.
tw
/#
to
o
l
b
o
x
.
[
A
c
c
e
ss
e
d
:
0
6
-
A
p
r
-
2
0
1
7
].
[1
8
]
P
.
Re
f
a
e
il
z
a
d
e
h
,
L
.
Tan
g
,
a
n
d
H.
L
iu
,
“
Cro
ss
V
a
li
d
a
ti
o
n
”
,
E
n
c
y
c
lo
p
e
d
ia
o
f
Da
t
a
b
a
se
S
y
ste
ms
.
S
p
ri
n
g
e
r,
2
0
0
9
.
[1
9
]
S
.
W
.
P
u
rn
a
m
i,
J.
M
.
Zai
n
,
a
n
d
T
.
He
ria
w
a
n
,
“
A
n
A
lt
e
rn
a
ti
v
e
A
l
g
o
rit
h
m
f
o
r
Clas
si
f
ic
a
t
io
n
L
a
rg
e
Ca
te
g
o
rica
l
Da
tas
e
t:
k
-
m
o
d
e
Clu
ste
rin
g
Re
d
u
c
e
d
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
D
a
ta
b
a
se
T
h
e
o
ry
a
n
d
Ap
p
li
c
a
ti
o
n
,
v
o
l.
4
,
n
o
.
1
,
2
0
1
1
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Dr
.
G
.
I
n
d
r
a
w
a
n
.
He
a
d
o
f
C
o
m
p
u
ter
S
c
ien
c
e
De
p
a
rt
m
e
n
t,
G
ra
d
u
a
te
P
ro
g
ra
m
,
Un
iv
e
rsitas
P
e
n
d
i
d
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
In
d
o
n
e
sia
.
He
re
c
e
iv
e
d
h
is
d
o
c
to
ra
l
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
In
f
o
rm
a
ti
c
s
f
ro
m
Ba
n
d
u
n
g
In
stit
u
te
o
f
T
e
c
h
n
o
l
o
g
y
,
In
d
o
n
e
si
a
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
b
io
m
e
tri
c
s,
p
a
tt
e
rn
re
c
o
g
n
it
i
o
n
,
a
n
d
r
o
b
o
ti
c
s.
He
c
a
n
b
e
re
a
c
h
e
d
a
t
g
in
d
ra
w
a
n
@u
n
d
ik
sh
a
.
a
c
.
id
.
I
K
.
P.
S
u
d
ia
r
s
a
,
M
.
K
o
m
.
I
T
A
d
m
in
istrato
r
o
f
P
sy
c
h
iatric
Ho
sp
it
a
l
o
f
Ba
li
,
In
d
o
n
e
sia
.
He
re
c
e
iv
e
d
h
is
m
a
ste
r
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Un
iv
e
r
sitas
P
e
n
d
i
d
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
In
d
o
n
e
sia
.
His
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
d
a
ta
m
in
in
g
.
He
c
a
n
b
e
re
a
c
h
e
d
a
t
k
o
m
p
a
s
su
d
iars
a
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
3
9
9
–
3
4
0
6
3406
Dr
.
K
.
Ag
u
stin
i
.
V
ice
He
a
d
o
f
In
stru
c
ti
o
n
a
l
T
e
c
h
n
o
lo
g
y
D
e
p
a
rtme
n
t,
G
r
a
d
u
a
te
P
ro
g
ra
m
,
Un
iv
e
rsitas
P
e
n
d
id
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
In
d
o
n
e
sia
.
S
h
e
is
a
lso
lec
tu
re
r
a
t
Co
m
p
u
ter
S
c
ien
c
e
De
p
a
rtme
n
t,
G
ra
d
u
a
te
P
ro
g
ra
m
,
Un
iv
e
rsitas
P
e
n
d
i
d
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
In
d
o
n
e
sia
.
S
h
e
re
c
e
iv
e
d
h
e
r
d
o
c
to
ra
l
d
e
g
re
e
in
Ed
u
c
a
ti
o
n
T
e
c
h
n
o
lo
g
y
f
ro
m
Ja
k
a
rta
S
tate
U
n
iv
e
rsity
a
n
d
h
e
r
m
a
st
e
r
d
e
g
r
e
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bo
g
o
r
A
g
ricu
lt
u
ra
l
In
stit
u
te.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
a
tt
e
rn
re
c
o
g
n
it
io
n
,
a
n
d
lea
rn
i
n
g
m
e
d
ia.
S
h
e
c
a
n
b
e
re
a
c
h
e
d
a
t
k
e
tu
tag
u
stin
i@u
n
d
ik
sh
a
.
a
c
.
id
.
Pro
f.
Dr
.
S
a
r
iy
a
sa
.
H
e
a
d
o
f
M
a
th
e
m
a
ti
c
s
Ed
u
c
a
ti
o
n
De
p
a
rtm
e
n
t,
G
ra
d
u
a
te
P
ro
g
ra
m
,
Un
iv
e
rsita
s
P
e
n
d
i
d
ik
a
n
G
a
n
e
sh
a
,
B
a
li
,
In
d
o
n
e
sia
.
He
is
a
lso
lec
tu
re
r
a
t
C
o
m
p
u
ter
S
c
ien
c
e
De
p
a
rt
m
e
n
t,
G
ra
d
u
a
te
P
ro
g
ra
m
,
Un
iv
e
rsitas
P
e
n
d
id
ik
a
n
G
a
n
e
sh
a
,
Ba
li
,
In
d
o
n
e
sia
.
He
re
c
e
iv
e
d
h
is
m
a
ste
r
a
n
d
d
o
c
to
ra
l
d
e
g
re
e
in
M
a
th
e
m
a
ti
c
s
fro
m
F
li
n
d
e
rs
Un
iv
e
rsit
y
,
A
u
stra
li
a
.
His
re
s
e
a
rc
h
in
tere
sts
in
c
lu
d
e
c
o
m
p
lex
a
n
a
l
y
sis,
a
n
d
n
u
m
e
rica
l
m
e
th
o
d
.
He
c
a
n
b
e
re
a
c
h
e
d
a
t
sa
ri
y
a
sa
@u
n
d
ik
sh
a
.
a
c
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.