I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
39
,
No
.
1
,
J
u
l
y
2
0
2
5
,
p
p
.
1
78
~
1
89
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
39
.i
1
.
p
p
1
78
-
1
89
178
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
Ro
bust
k
-
NN app
ro
a
ch f
o
r
cla
ss
ifyi
ng
Aq
uila
ria
o
il species by
co
mpo
unds
No
o
r
Aida
Sy
a
k
ir
a
Ahm
a
d S
a
bri
1
,
Nur
At
hira
h Sy
a
f
iqa
h No
ra
m
li
1
,
Nik
F
a
s
ha
E
do
ra
Nik
K
a
m
a
ruza
m
a
n
1
,
Nurla
ila
I
s
m
a
il
1
,
Z
a
k
ia
h M
o
hd
Yus
o
f
f
1
,
Ali A
bd
Alm
is
re
b
2
,
Sa
if
ul Niza
m
T
a
j
ud
din
3
,
M
o
hd
Na
s
ir
T
a
i
b
1
1
A
d
v
a
n
c
e
d
S
i
g
n
a
l
P
r
o
c
e
ss
i
n
g
R
e
sea
r
c
h
I
n
t
e
r
e
st
G
r
o
u
p
,
F
a
c
u
l
t
y
o
f
E
l
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
U
n
i
v
e
r
s
i
t
i
T
e
k
n
o
l
o
g
i
M
A
R
A
,
S
h
a
h
A
l
a
m,
M
a
l
a
y
s
i
a
2
F
a
c
u
l
t
y
o
f
C
o
mp
u
t
e
r
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
I
n
t
e
r
n
a
t
i
o
n
a
l
U
n
i
v
e
r
si
t
y
o
f
S
a
r
a
j
e
v
o
,
S
a
r
a
j
e
v
o
,
B
o
s
n
i
a
a
n
d
H
e
r
z
e
g
o
v
i
n
a
3
Bi
o
a
r
o
ma
t
i
c
R
e
se
a
r
c
h
C
e
n
t
r
e
o
f
Ex
c
e
l
l
e
n
c
e
(
B
A
R
C
E)
,
U
n
i
v
e
r
si
t
i
M
a
l
a
y
si
a
P
a
h
a
n
g
A
l
-
S
u
l
t
a
n
A
b
d
u
l
l
a
h
,
G
a
mb
a
n
g
K
u
a
n
t
a
n
,
M
a
l
a
y
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
No
v
1
3
,
2
0
2
4
R
ev
is
ed
Ma
r
1
7
,
2
0
2
5
Acc
ep
ted
Ma
r
2
6
,
2
0
2
5
Ac
c
u
ra
te
c
las
sific
a
ti
o
n
o
f
Aq
u
il
a
ria
o
il
s
p
e
c
ies
is
e
ss
e
n
ti
a
l
fo
r
e
n
su
rin
g
th
e
q
u
a
li
t
y
a
n
d
a
u
t
h
e
n
ti
c
it
y
o
f
a
g
a
rwo
o
d
o
il
s
,
wh
ich
a
re
wid
e
ly
u
se
d
i
n
p
e
rfu
m
e
s
a
n
d
trad
i
ti
o
n
a
l
m
e
d
icin
e
.
T
h
is
st
u
d
y
in
v
e
stig
a
ted
t
h
e
e
ffe
c
ti
v
e
n
e
s
s
o
f
th
e
k
-
n
e
a
re
st
n
e
ig
h
b
o
u
rs
(k
-
NN
)
m
a
c
h
i
n
e
lea
rn
i
n
g
m
o
d
e
l
fo
r
c
las
si
fy
in
g
Aq
u
il
a
ria
o
il
s
p
e
c
ies
b
a
se
d
o
n
f
o
u
r
sig
n
ifi
c
a
n
t
c
h
e
m
ica
l
c
o
m
p
o
u
n
d
s:
d
ih
y
ro
-
β
-
a
g
a
ro
fu
ra
n
,
δ
-
g
u
a
ien
e
,
1
0
-
e
p
i
-
γ
-
e
u
d
e
sm
o
l,
a
n
d
γ
-
e
u
d
e
sm
o
l.
T
h
e
d
a
tas
e
t
c
o
m
p
rise
d
4
8
0
sa
m
p
les
o
f
A
q
u
i
la
ria
o
i
l,
w
h
ich
we
re
a
n
a
ly
z
e
d
u
sin
g
g
a
s
c
h
ro
m
a
to
g
ra
p
h
y
-
m
a
ss
sp
e
c
tr
o
m
e
try
(G
C
-
M
S
)
a
n
d
g
a
s
c
h
ro
m
a
to
g
ra
p
h
y
-
flam
e
io
n
iza
ti
o
n
d
e
tec
to
r
(G
C
-
F
ID).
Th
e
k
-
NN
m
o
d
e
l
,
wit
h
a
n
o
p
ti
m
a
l
k
-
v
a
lu
e
o
f
1
0
a
n
d
u
sin
g
e
u
c
li
d
e
a
n
d
istan
c
e
a
s
t
h
e
d
istan
c
e
m
e
tri
c
,
a
c
h
iev
e
d
1
0
0
%
a
c
c
u
ra
c
y
,
se
n
siti
v
it
y
,
sp
e
c
ifi
c
it
y
,
a
n
d
p
re
c
isio
n
i
n
b
o
t
h
train
in
g
a
n
d
tes
ti
n
g
d
a
tas
e
ts.
T
h
e
se
re
su
lt
s
d
e
m
o
n
stra
te
t
h
e
r
o
b
u
stn
e
ss
o
f
k
-
NN
in
sp
e
c
ies
id
e
n
ti
fica
ti
o
n
,
h
ig
h
li
g
h
t
in
g
th
e
d
isc
rimin
a
ti
v
e
p
o
we
r
o
f
th
e
se
lec
ted
c
o
m
p
o
u
n
d
s.
Th
is
stu
d
y
v
e
rifi
e
s
th
a
t
th
e
in
teg
ra
ti
o
n
o
f
c
h
e
m
ica
l
p
ro
fil
i
n
g
with
m
a
c
h
in
e
lea
rn
i
n
g
o
ffe
rs
a
sc
a
lab
le
so
lu
ti
o
n
f
o
r
a
c
c
u
ra
t
e
sp
e
c
ies
id
e
n
ti
fica
ti
o
n
in
t
h
e
e
ss
e
n
ti
a
l
o
i
l
i
n
d
u
str
y
.
F
u
t
u
re
wo
r
k
c
o
u
ld
e
x
p
l
o
re
h
y
b
r
id
m
o
d
e
ls
a
n
d
d
a
ta
e
x
p
a
n
sio
n
tec
h
n
iq
u
e
s
to
f
u
rth
e
r
e
n
h
a
n
c
e
th
e
c
la
ss
ifi
c
a
ti
o
n
p
e
rfo
rm
a
n
c
e
in
m
o
re
c
o
m
p
lex
e
n
v
iro
n
m
e
n
tal
c
o
n
d
it
io
n
s
.
K
ey
w
o
r
d
s
:
A
q
u
ila
r
ia
o
il sp
ec
ies
k
-
n
ea
r
est n
eig
h
b
o
u
r
s
C
h
em
ical
co
m
p
o
u
n
d
s
E
s
s
en
tial o
ils
Ma
ch
in
e
lear
n
in
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Nu
r
laila
I
s
m
ail
Ad
v
an
ce
d
Sig
n
al
Pro
ce
s
s
in
g
R
esear
ch
I
n
ter
est Gr
o
u
p
,
Facu
lty
o
f
E
lectr
ical
E
n
g
in
ee
r
in
g
Un
iv
er
s
iti T
ek
n
o
lo
g
i M
AR
A
4
0
4
5
0
Sh
ah
Alam
,
Selan
g
o
r
,
Ma
lay
s
ia
E
m
ail: n
u
r
laila0
5
8
3
@
u
itm
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
A
q
u
ila
r
ia
,
a
g
en
u
s
o
f
tr
ee
s
k
n
o
wn
f
o
r
p
r
o
d
u
cin
g
h
ig
h
ly
v
alu
ed
ag
ar
wo
o
d
,
y
ield
s
ess
en
tial
o
ils
wid
ely
u
s
ed
in
p
er
f
u
m
es,
tr
a
d
itio
n
al
m
ed
icin
e,
an
d
ar
o
m
a
th
er
ap
y
.
Ho
wev
er
,
th
e
ac
c
u
r
a
te
class
if
icatio
n
o
f
A
q
u
ila
r
ia
o
il
s
p
ec
ies
p
r
esen
t
s
s
ig
n
if
ican
t
ch
allen
g
es
d
u
e
to
th
eir
co
m
p
lex
c
h
em
ical
c
o
m
p
o
s
itio
n
a
n
d
th
e
o
v
er
lap
o
f
c
o
m
p
o
u
n
d
s
ac
r
o
s
s
d
if
f
er
en
t
s
p
ec
ies.
I
d
en
tify
i
n
g
t
h
e
s
p
ec
if
ic
s
p
ec
ies
o
f
A
q
u
ila
r
ia
o
il
is
im
p
o
r
tan
t
f
o
r
q
u
ality
co
n
tr
o
l
an
d
en
s
u
r
in
g
th
e
au
th
en
ticity
o
f
p
r
o
d
u
ct
s
,
b
u
t
m
an
u
al
an
d
tr
ad
itio
n
al
m
eth
o
d
s
o
f
ten
f
all
s
h
o
r
t in
ac
cu
r
ac
y
an
d
ef
f
icien
c
y
[
1
]
,
[
2
]
.
On
e
o
f
th
e
m
aj
o
r
ch
allen
g
es
in
class
if
y
in
g
A
q
u
ila
r
ia
o
il
s
p
ec
ies
is
th
e
h
ig
h
d
eg
r
ee
o
f
ch
em
ical
s
im
ilar
ity
b
etwe
en
d
if
f
er
en
t
s
p
ec
ies,
p
ar
ticu
lar
ly
in
k
e
y
co
m
p
o
u
n
d
s
s
u
ch
as
d
ih
y
r
o
-
β
-
ag
ar
o
f
u
r
a
n
,
δ
-
g
u
aien
e,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
o
b
u
s
t K
-
N
N
a
p
p
r
o
a
ch
fo
r
cl
a
s
s
ifyin
g
A
q
u
ila
r
ia
o
il sp
ec
ies b
y
…
(
N
o
o
r
A
id
a
S
ya
kira
A
h
ma
d
S
a
b
r
i
)
179
10
-
ep
i
-
γ
-
eu
d
esm
o
l,
a
n
d
γ
-
eu
d
esm
o
l.
T
h
ese
s
im
ilar
ities
co
m
p
licate
s
p
ec
ies
d
if
f
er
e
n
tiatio
n
wh
en
r
el
y
in
g
s
o
lely
o
n
co
n
v
en
tio
n
al
m
eth
o
d
s
lik
e
GC
-
MS
an
d
GC
-
FID
,
o
f
te
n
lead
in
g
to
p
o
ten
tial
m
is
cla
s
s
if
icatio
n
[
3
]
,
[
4
]
.
Ad
d
itio
n
ally
,
e
n
v
ir
o
n
m
en
tal
f
ac
to
r
s
s
u
ch
as
s
o
il
co
n
d
itio
n
s
,
clim
ate,
an
d
ex
t
r
ac
tio
n
m
eth
o
d
s
ca
n
ca
u
s
e
v
ar
iatio
n
s
in
t
h
e
ch
e
m
ical
c
o
m
p
o
u
n
d
s
o
f
o
ils
d
er
iv
ed
f
r
o
m
th
e
s
am
e
s
p
ec
ies,
f
u
r
th
e
r
co
m
p
licatin
g
th
e
class
if
icatio
n
p
r
o
ce
s
s
[
3
]
.
R
ec
en
t
r
ep
o
r
ts
[
5
]
,
[
6
]
i
n
d
ic
ate
th
at
th
ese
c
h
allen
g
es
c
o
m
e
f
r
o
m
t
h
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
tech
n
iq
u
es,
wh
ich
lack
th
e
p
r
ec
is
io
n
r
eq
u
ir
e
d
to
h
an
d
le
s
u
ch
o
v
er
lap
p
in
g
d
ata,
esp
ec
ially
in
in
d
u
s
tr
ial
ap
p
licatio
n
s
wh
er
e
lar
g
e
-
s
ca
le
id
en
tific
atio
n
is
n
ec
ess
ar
y
.
Ad
v
an
ce
s
in
m
ac
h
in
e
lea
r
n
in
g
(
ML
)
h
av
e
b
eg
u
n
to
o
f
f
er
p
r
o
m
is
in
g
s
o
lu
tio
n
s
b
y
lev
er
ag
i
n
g
co
m
p
u
tatio
n
al
m
o
d
els
th
at
ca
n
p
r
o
ce
s
s
an
d
an
al
y
ze
c
o
m
p
lex
ch
em
ical
d
ata
with
g
r
ea
ter
ac
cu
r
ac
y
.
T
h
ese
tech
n
iq
u
es,
in
clu
d
in
g
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
r
an
d
o
m
f
o
r
ests
,
an
d
k
-
n
ea
r
est
n
eig
h
b
o
u
r
(
k
-
NN)
m
o
d
els,
h
a
v
e
b
ee
n
ap
p
lied
to
th
i
s
p
r
o
b
lem
with
v
ar
y
in
g
d
e
g
r
ee
s
o
f
s
u
cc
ess
[
7
]
,
[
8
]
.
I
n
p
ar
ticu
la
r
,
th
e
k
-
NN
m
o
d
el
h
as
g
ain
ed
atten
tio
n
d
u
e
to
its
s
im
p
licity
,
v
er
s
atility
,
an
d
ef
f
e
ctiv
en
es
s
in
h
an
d
lin
g
m
u
lti
-
d
im
e
n
s
io
n
a
l
d
atasets
.
k
-
NN
is
a
n
o
n
-
p
ar
am
etr
ic
ML
alg
o
r
ith
m
th
at
cl
ass
if
ies
d
at
a
p
o
in
ts
b
ased
o
n
th
e
m
ajo
r
ity
lab
el
o
f
th
eir
clo
s
est
n
eig
h
b
o
u
r
s
in
a
m
u
ltid
im
en
s
io
n
al
s
p
ac
e
[
9
]
.
T
h
is
tech
n
iq
u
e
h
as
b
ee
n
s
h
o
wn
to
p
er
f
o
r
m
well
in
s
p
ec
ies
id
en
tific
atio
n
task
s
wh
er
e
ch
em
ical
co
m
p
o
u
n
d
d
ata,
s
u
ch
as
th
o
s
e
o
b
tain
ed
f
r
o
m
A
q
u
ila
r
ia
o
il,
n
ee
d
to
b
e
a
n
aly
ze
d
.
R
esear
ch
er
s
h
av
e
b
ee
n
in
s
p
ir
ed
to
ap
p
ly
th
e
k
-
NN
m
o
d
el
to
th
e
class
if
icatio
n
o
f
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
y
th
e
n
ee
d
f
o
r
m
o
r
e
r
eliab
le
an
d
s
ca
lab
le
id
en
tific
atio
n
tech
n
iq
u
es.
R
ec
en
t
s
tu
d
ies
h
av
e
d
em
o
n
s
tr
ated
th
e
p
o
ten
tial
o
f
k
-
NN
an
d
s
im
ilar
m
o
d
els
to
a
ch
iev
e
h
ig
h
er
ac
cu
r
ac
y
b
y
co
n
s
id
er
in
g
m
u
ltip
le
ch
em
ical
co
m
p
o
u
n
d
s
s
im
u
ltan
eo
u
s
ly
[
1
0
]
.
B
y
e
m
p
lo
y
i
n
g
3
D
p
lo
ttin
g
tech
n
iq
u
es,
r
esear
c
h
er
s
h
av
e
b
ee
n
a
b
le
to
v
is
u
alize
th
e
s
ep
ar
atio
n
o
f
s
p
ec
ies
b
ased
o
n
th
eir
ch
em
ical
c
o
m
p
o
u
n
d
s
,
en
a
b
lin
g
m
o
r
e
p
r
ec
i
s
e
class
if
icatio
n
.
T
h
ese
ad
v
an
ce
s
s
u
g
g
est
th
at
k
-
NN,
wh
en
c
o
m
p
ar
ed
to
o
t
h
er
m
o
d
els
lik
e
SVM
an
d
r
an
d
o
m
f
o
r
ests
,
is
p
ar
ticu
lar
ly
well
-
s
u
ited
f
o
r
th
is
task
,
ac
h
ie
v
in
g
n
o
tab
le
r
esu
lts
in
s
ep
ar
atin
g
A
q
u
ila
r
ia
s
p
ec
ies
with
g
r
ea
ter
th
an
9
0
% a
cc
u
r
ac
y
[
1
1
]
.
On
e
o
f
th
e
m
o
s
t
s
ig
n
if
ican
t
ac
h
iev
em
en
ts
in
th
is
f
ield
h
as
b
ee
n
th
e
d
ev
elo
p
m
en
t
o
f
h
y
b
r
id
m
o
d
els
th
at
co
m
b
in
e
k
-
NN
with
d
im
en
s
io
n
ality
r
ed
u
ctio
n
tech
n
iq
u
es
s
u
ch
as
p
r
in
cip
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
.
T
h
is
co
m
b
in
atio
n
h
elp
s
in
r
ed
u
cin
g
th
e
co
m
p
lex
ity
o
f
t
h
e
d
at
a,
lead
in
g
to
f
aster
p
r
o
ce
s
s
in
g
tim
es
an
d
im
p
r
o
v
e
d
class
if
icatio
n
ac
cu
r
ac
y
[
1
2
]
,
[
1
3
]
.
Ho
wev
e
r
,
wh
ile
k
-
NN
h
as
s
h
o
wn
co
n
s
id
er
ab
le
p
r
o
m
is
e,
s
tu
d
ies
th
at
cr
o
s
s
-
ev
alu
ate
m
ac
h
in
e
lear
n
in
g
m
o
d
els
u
n
d
e
r
v
ar
y
in
g
b
ac
k
g
r
o
u
n
d
co
n
d
itio
n
s
,
s
u
ch
as
d
if
f
er
en
t
ex
tr
ac
ti
o
n
m
eth
o
d
s
o
r
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
,
in
d
icate
th
at
k
-
NN's
p
er
f
o
r
m
an
ce
m
ay
d
ec
r
e
ase
in
h
ig
h
ly
n
o
is
y
d
atasets
,
r
eq
u
ir
in
g
f
u
r
th
er
im
p
r
o
v
em
en
ts
[
1
4
]
.
T
h
e
o
b
jectiv
e
o
f
th
is
s
tu
d
y
w
as
;
i
)
t
o
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
k
-
n
ea
r
est
n
eig
h
b
o
u
r
s
(
k
-
NN)
m
o
d
el
in
ac
cu
r
ately
class
if
y
i
n
g
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
ase
d
o
n
th
e
ch
em
ical
co
m
p
o
u
n
d
s
s
u
ch
as
d
ih
y
r
o
-
β
-
ag
ar
o
f
u
r
an
,
δ
-
g
u
aie
n
e,
1
0
-
ep
i
-
γ
-
eu
d
esm
o
l,
an
d
γ
-
eu
d
esm
o
l.
ii
)
T
o
d
eter
m
in
e
t
h
e
s
ig
n
if
ica
n
ce
o
f
th
e
s
elec
te
d
ch
em
ical
co
m
p
o
u
n
d
s
in
d
i
s
tin
g
u
is
h
in
g
b
etwe
en
d
if
f
er
en
t
A
q
u
ila
r
ia
o
il
s
p
ec
ies
an
d
en
h
an
ci
n
g
th
e
class
if
icatio
n
ac
cu
r
ac
y
u
s
in
g
t
h
e
k
-
NN
m
o
d
el.
2.
M
AT
E
R
I
AL
S AN
D
M
E
T
H
O
D
S
T
h
e
in
p
u
t
d
ata
u
s
ed
in
th
is
s
tu
d
y
co
n
s
is
ted
o
f
th
e
p
ea
k
ar
ea
(
%)
o
f
s
ig
n
if
ica
n
t
ch
em
ical
c
o
m
p
o
u
n
d
s
f
o
u
n
d
in
f
o
u
r
s
p
ec
ies o
f
A
q
u
ila
r
ia
o
il sam
p
les.
T
h
ese
ch
em
ical
co
m
p
o
u
n
d
s
,
in
clu
d
i
n
g
d
ih
y
r
o
-
β
-
ag
a
r
o
f
u
r
an
,
δ
-
g
u
aien
e,
1
0
-
e
p
i
-
γ
-
e
u
d
esm
o
l,
a
n
d
γ
-
eu
d
esm
o
l,
wer
e
e
x
tr
a
cte
d
u
s
in
g
GC
-
MS
an
d
GC
-
FID
an
aly
s
es.
T
h
e
d
ata
was
th
en
s
u
b
jecte
d
to
a
k
-
NN
clas
s
if
icatio
n
m
o
d
el
d
ev
elo
p
ed
with
in
MA
T
L
AB
s
o
f
twar
e.
T
h
e
k
-
NN
alg
o
r
ith
m
o
p
er
ates
b
y
an
aly
z
in
g
th
e
c
h
em
ical
co
m
p
o
u
n
d
s
o
f
ea
c
h
s
am
p
le
an
d
class
if
y
in
g
it
b
ased
o
n
t
h
e
m
ajo
r
ity
class
o
f
its
n
ea
r
est
n
eig
h
b
o
u
r
s
in
a
m
u
lti
-
d
im
en
s
io
n
al
s
p
ac
e,
w
h
er
e
t
h
e
d
e
p
en
d
e
n
t
v
ar
ia
b
le
was
th
e
s
p
ec
ies
lab
el
o
f
th
e
A
q
u
ila
r
ia
o
il.
T
h
e
e
v
alu
atio
n
o
f
th
e
m
o
d
el
was
co
n
d
u
cte
d
b
y
c
o
m
p
ar
in
g
its
p
r
ed
icted
class
if
icatio
n
s
ag
ain
s
t
ac
tu
al
s
p
ec
ies
lab
els,
u
s
in
g
p
er
f
o
r
m
an
ce
m
etr
ics
s
u
ch
as
ac
c
u
r
ac
y
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
p
r
ec
is
io
n
.
C
r
o
s
s
-
v
alid
atio
n
tech
n
iq
u
es
wer
e
ap
p
lied
to
en
s
u
r
e
t
h
e
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
b
ilit
y
o
f
th
e
m
o
d
el
u
n
d
er
v
ar
io
u
s
co
n
d
itio
n
s
,
with
ac
cu
r
ac
y
b
ein
g
th
e
p
r
im
ar
y
cr
iter
io
n
f
o
r
m
o
d
e
l
p
er
f
o
r
m
an
ce
.
2
.
1
.
Da
t
a
c
o
llect
io
n a
nd
ex
perim
ent
a
l set
up
Data
co
llectio
n
as
illu
s
tr
ated
in
T
ab
le
1
ar
e
u
s
ed
t
o
class
if
y
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
ase
d
o
n
th
ei
r
ch
em
ical
co
m
p
o
s
itio
n
.
T
h
e
d
a
taset
co
m
p
r
is
es
4
8
0
s
am
p
les
o
f
ag
ar
wo
o
d
o
il
o
b
tain
ed
f
r
o
m
th
e
B
io
Ar
o
m
at
ic
R
esear
ch
C
en
tr
e
o
f
E
x
ce
llen
ce
(
B
AR
C
E
)
at
Un
iv
er
s
iti
Ma
lay
s
ia
Pah
an
g
Al
-
Su
ltan
Ab
d
u
llah
(
UM
PS
A)
.
T
h
ese
s
am
p
les
wer
e
ch
o
s
en
f
o
r
th
eir
co
m
p
r
eh
en
s
iv
e
r
ep
r
esen
tatio
n
o
f
f
o
u
r
A
q
u
ila
r
ia
o
il
s
p
ec
ies:
A
q
u
ila
r
ia
B
ec
ca
r
ia
n
a
(
AB
)
,
A
q
u
ila
r
ia
Ma
la
cc
en
s
is
(
AM
)
,
A
q
u
ila
r
ia
C
r
a
s
s
n
a
(
AC
)
,
an
d
A
q
u
ila
r
i
a
S
u
b
i
n
teg
r
a
(
AS)
.
E
ac
h
s
am
p
le
was
an
aly
ze
d
to
d
eter
m
in
e
th
e
p
er
ce
n
tag
e
o
f
p
ea
k
ar
ea
f
o
r
f
o
u
r
ch
em
ical
co
m
p
o
u
n
d
s
:
d
ih
y
r
o
-
β
-
ag
ar
o
f
u
r
an
(
a)
,
δ
-
g
u
aien
e
(
b
)
,
1
0
-
ep
i
-
γ
-
e
u
d
esm
o
l
(
c)
,
an
d
γ
-
eu
d
esm
o
l
(
d
)
.
T
h
e
n
ee
d
f
o
r
c
o
llectin
g
d
ata
f
r
o
m
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.
39
,
No
.
1
,
J
u
ly
20
25
:
1
78
-
1
89
180
th
ese
co
m
p
o
u
n
d
s
o
r
i
g
in
ates
f
r
o
m
th
eir
r
elev
an
ce
as
k
ey
b
io
m
ar
k
e
r
s
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
d
if
f
e
r
en
t
A
q
u
ila
r
ia
o
il sp
ec
ies
.
T
ab
le
1
.
L
is
t o
f
c
h
em
ical
co
m
p
o
u
n
d
s
d
ataset
an
d
p
ea
k
ar
ea
(
%)
f
o
r
ea
ch
A
q
u
ila
r
ia
o
il sp
ec
ies
C
o
d
e
C
h
e
mi
c
a
l
c
o
m
p
o
u
n
d
s
I
n
d
e
n
t
.
m
o
d
e
P
e
a
k
a
r
e
a
(
%)
/
O
i
l
s
p
e
c
i
e
s
AB
AM
AC
AS
a
d
i
h
y
r
o
-
β
-
a
g
a
r
o
f
u
r
a
n
F
I
D
,
M
S
1
.
2
5
0
.
5
5
0
.
4
8
0
.
4
4
b
δ
-
g
u
a
i
e
n
e
F
I
D
,
M
S
0
.
7
4
2
.
0
2
0
.
2
1
0
.
3
5
c
10
-
e
p
i
-
γ
-
e
u
d
e
sm
o
l
F
I
D
,
M
S
0
.
3
4
6
.
7
3
2
.
5
4
2
.
1
6
d
γ
-
e
u
d
e
smo
l
F
I
D
,
M
S
0
.
2
6
2
.
1
7
0
.
9
5
1
.
8
5
T
h
e
p
ea
k
ar
ea
(
%)
v
ar
ies
s
ig
n
if
ican
tly
b
etwe
en
th
e
A
q
u
ila
r
ia
s
p
ec
ies.
Fo
r
in
s
tan
ce
,
co
m
p
o
u
n
d
a
s
h
o
ws
its
h
ig
h
est
p
r
esen
ce
in
AB
at
1
.
2
5
%,
wh
ile
AM
r
ec
o
r
d
s
a
lo
wer
v
alu
e
o
f
0
.
5
5
%.
Si
m
ilar
ly
,
co
m
p
o
u
n
d
b
is
s
ig
n
if
ican
tly
h
ig
h
er
in
A
M,
with
a
p
ea
k
ar
ea
o
f
2
.
0
2
%,
co
m
p
ar
e
d
to
ju
s
t
0
.
2
1
%
in
AC
,
em
p
h
asizin
g
th
e
co
m
p
o
u
n
d
'
s
r
o
le
as
a
d
is
tin
g
u
is
h
in
g
f
ac
to
r
f
o
r
s
p
ec
ies
id
e
n
tific
atio
n
.
T
h
e
p
r
esen
ce
o
f
c
o
m
p
o
u
n
d
c
is
also
m
o
s
t
p
r
o
n
o
u
n
ce
d
in
AM
,
wh
e
r
e
it
r
ea
c
h
es
6
.
7
3
%,
u
n
d
er
s
co
r
in
g
its
im
p
o
r
tan
ce
i
n
d
i
f
f
er
en
ti
atin
g
AM
f
r
o
m
th
e
o
th
er
s
p
ec
ies.
I
n
co
n
tr
ast,
co
m
p
o
u
n
d
d
s
h
o
ws
a
m
o
r
e
b
al
an
ce
d
d
is
tr
ib
u
tio
n
ac
r
o
s
s
all
s
p
ec
ies,
a
lth
o
u
g
h
it
p
ea
k
s
in
AM
at
2
.
1
7
%,
s
u
g
g
e
s
tin
g
it
p
lay
s
a
r
o
le
in
s
p
ec
ies
d
if
f
er
e
n
tiatio
n
b
u
t
with
less
d
is
tin
ctiv
en
ess
th
an
o
th
er
co
m
p
o
u
n
d
s
.
T
h
e
v
ar
iatio
n
s
in
c
h
em
ical
co
m
p
o
s
itio
n
ac
r
o
s
s
th
e
s
p
ec
ies
d
em
o
n
s
tr
ate
th
at
AM
ten
d
s
t
o
h
av
e
th
e
h
ig
h
est
co
n
ce
n
t
r
atio
n
o
f
all
f
o
u
r
co
m
p
o
u
n
d
s
,
p
ar
ticu
lar
ly
co
m
p
o
u
n
d
s
b
a
n
d
c,
in
d
icatin
g
a
r
ich
er
ch
em
ical
co
m
p
o
u
n
d
.
On
th
e
o
th
er
h
a
n
d
,
AC
ex
h
ib
its
co
n
s
is
ten
tly
lo
w
er
p
ea
k
ar
ea
s
,
esp
ec
ially
f
o
r
co
m
p
o
u
n
d
b
,
m
a
k
in
g
it
ch
em
ically
d
is
tin
g
u
is
h
ab
le
f
r
o
m
t
h
e
o
t
h
er
s
p
ec
ies.
T
h
e
m
o
r
e
e
v
en
ly
d
is
tr
ib
u
ted
o
f
c
o
m
p
o
u
n
d
d
ac
r
o
s
s
s
p
ec
ies
in
d
icate
s
th
at
th
is
co
m
p
o
u
n
d
m
ig
h
t
h
av
e
lim
ited
v
alu
e
in
d
is
tin
g
u
is
h
in
g
b
etwe
e
n
d
if
f
er
e
n
t
A
q
u
ila
r
ia
s
p
ec
ies co
m
p
ar
ed
to
t
h
e
o
th
e
r
ch
em
ical
m
ar
k
er
s
.
E
x
p
er
im
en
tal
an
aly
s
is
was
co
n
d
u
cted
u
s
in
g
MA
T
L
AB
s
o
f
twar
e,
wh
ich
was
u
tili
ze
d
to
i
m
p
lem
en
t
th
e
k
-
NN
class
if
icatio
n
m
o
d
el
.
MA
T
L
AB
was
ch
o
s
en
f
o
r
its
p
o
wer
f
u
l
co
m
p
u
tatio
n
al
a
n
d
d
ata
v
is
u
aliza
tio
n
ca
p
ab
ilit
ies,
as
well
as
i
ts
s
p
ec
ialized
m
ac
h
in
e
lear
n
in
g
to
o
lb
o
x
es
th
at
ar
e
id
ea
l
f
o
r
h
an
d
lin
g
c
o
m
p
lex
m
u
ltid
i
m
en
s
io
n
al
d
atasets
lik
e
th
o
s
e
g
en
er
ated
b
y
g
as
c
h
r
o
m
ato
g
r
ap
h
y
-
m
ass
s
p
ec
tr
o
m
etr
y
(
GC
-
MS)
an
d
g
as
ch
r
o
m
ato
g
r
ap
h
y
-
f
lam
e
i
o
n
izat
io
n
d
etec
to
r
(
GC
-
FID
)
an
aly
s
es.
MA
T
L
AB
’
s
f
lex
ib
ilit
y
an
d
p
r
ec
is
io
n
in
m
o
d
el
d
ev
elo
p
m
e
n
t a
n
d
ev
alu
atio
n
m
ak
e
it we
ll
-
s
u
ited
f
o
r
t
h
is
ty
p
e
o
f
c
h
em
ical
co
m
p
o
u
n
d
-
b
as
ed
class
if
icatio
n
task
.
2
.
2
.
Sa
m
ple pre
pa
ra
t
io
n a
n
d G
C
-
M
S/GC
-
F
I
D
a
na
ly
s
i
s
T
h
e
ex
tr
ac
tio
n
p
r
o
ce
s
s
was
co
n
d
u
cte
d
b
y
B
AR
C
E
at
UM
P
SA,
wh
er
e
g
r
o
u
n
d
ag
a
r
wo
o
d
ch
ip
s
wer
e
s
o
ak
ed
in
wate
r
f
o
r
s
ev
er
al
d
ay
s
to
f
ac
ilit
ate
th
e
b
r
ea
k
d
o
wn
o
f
o
il
g
lan
d
s
.
Hy
d
r
o
d
i
s
till
atio
n
wa
s
th
en
p
er
f
o
r
m
ed
o
v
e
r
3
to
5
d
a
y
s
to
ex
tr
ac
t
th
e
ag
ar
wo
o
d
o
il.
O
n
ce
ex
tr
ac
ted
,
th
e
o
il
s
am
p
les
wer
e
p
r
ep
ar
ed
f
o
r
an
aly
s
is
b
y
d
ilu
tin
g
t
h
em
in
a
n
aly
tical
-
g
r
ad
e
d
ich
lo
r
o
m
eth
a
n
e
(
DC
M)
[
1
5
]
.
GC
-
MS
an
aly
s
i
s
wa
s
p
er
f
o
r
m
ed
u
s
i
n
g
an
Ag
ilen
t 7
8
9
0
B
GC
Sy
s
tem
co
u
p
led
with
an
Ag
i
len
t 5
9
7
7
A
m
ass
s
p
ec
tr
o
m
eter
d
etec
to
r
(
MSD)
.
T
h
e
s
y
s
tem
u
tili
ze
d
a
DB
-
1
m
s
co
lu
m
n
(
3
0
m
×2
5
0
μ
m
×0
.
2
5
μ
m
)
with
h
eliu
m
as
th
e
ca
r
r
ier
g
as
at
a
f
lo
w
r
ate
o
f
1
.
0
m
L
/m
in
.
T
h
e
o
v
en
tem
p
er
atu
r
e
was
p
r
o
g
r
a
m
m
ed
to
s
tar
t
at
8
0
°C
an
d
in
cr
ea
s
e
at
a
r
ate
o
f
3
°
C
p
er
m
in
u
te
u
n
til
r
ea
ch
in
g
2
5
0
°C
,
wh
er
e
it
was
h
eld
f
o
r
3
m
in
u
tes.
T
h
e
m
ass
s
p
ec
tr
o
m
eter
o
p
er
ated
i
n
elec
t
r
o
n
im
p
ac
t
(
E
I
)
m
o
d
e
with
7
0
eV
en
er
g
y
.
Ma
s
s
s
p
ec
tr
a
wer
e
id
en
tifie
d
u
s
in
g
th
e
Natio
n
al
I
n
s
titu
te
o
f
Stan
d
ar
d
s
an
d
T
ec
h
n
o
lo
g
y
(
NI
ST
)
lib
r
ar
y
,
r
eq
u
ir
i
n
g
a
m
in
im
u
m
s
im
ilar
ity
o
f
8
0
%
f
o
r
co
n
f
ir
m
atio
n
.
Simu
ltan
eo
u
s
ly
,
GC
-
FID
an
al
y
s
is
was
co
n
d
u
cted
u
s
in
g
a
s
im
ilar
s
y
s
tem
,
b
u
t
with
an
FI
D
d
etec
to
r
o
p
er
atin
g
at
2
5
0
°C
.
Peak
ar
ea
s
o
f
th
e
f
o
u
r
tar
g
et
ch
em
ical
c
o
m
p
o
u
n
d
s
w
er
e
m
ea
s
u
r
ed
,
an
d
th
ese
v
al
u
es
wer
e
u
s
ed
as in
p
u
t f
o
r
th
e
k
-
NN
m
o
d
el.
2
.
3
.
D
a
t
a
inte
g
r
a
t
io
n a
nd
k
-
NN
m
o
del dev
elo
pm
ent
T
h
e
p
ea
k
a
r
ea
s
o
f
th
e
f
o
u
r
c
h
em
ical
co
m
p
o
u
n
d
s
wer
e
in
teg
r
ated
in
to
th
e
k
-
NN
class
if
icat
io
n
m
o
d
el
as
in
p
u
t
d
ata.
E
ac
h
s
am
p
le’
s
p
ea
k
ar
ea
s
f
o
r
c
o
m
p
o
u
n
d
s
a
,
b
,
c,
an
d
d
we
r
e
p
r
o
ce
s
s
ed
to
cla
s
s
if
y
th
e
s
am
p
le
o
f
th
e
f
o
u
r
A
q
u
ila
r
ia
o
il
s
p
ec
ies:
AB
,
A
M,
AC
,
o
r
AS
.
T
h
e
k
-
NN
m
o
d
el
wo
r
k
s
b
y
an
al
y
zin
g
th
e
d
is
tan
ce
s
b
etwe
en
th
e
ch
em
ical
co
m
p
o
u
n
d
s
o
f
ea
ch
s
am
p
le
an
d
th
o
s
e
o
f
its
n
ea
r
est n
eig
h
b
o
u
r
s
,
class
if
y
in
g
ea
ch
s
am
p
le
b
ased
o
n
th
e
m
ajo
r
ity
s
p
ec
ies
o
f
its
n
eig
h
b
o
u
r
s
in
th
e
f
ea
tu
r
e
s
p
ac
e.
As d
ep
icted
in
Fig
u
r
e
1
,
th
e
ex
p
er
im
en
tal
p
r
o
ce
s
s
b
eg
in
s
wi
th
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
wh
ich
is
es
s
en
tial
to
en
s
u
r
e
th
e
ac
cu
r
ac
y
a
n
d
r
e
liab
ilit
y
o
f
th
e
k
-
NN
clas
s
if
ic
atio
n
m
o
d
el.
Data
p
r
e
-
p
r
o
ce
s
s
in
g
in
v
o
lv
es
th
r
ee
cr
itical
s
tep
s
:
n
o
r
m
aliza
tio
n
,
r
an
d
o
m
izatio
n
,
an
d
d
ata
d
iv
i
s
io
n
.
Data
n
o
r
m
a
lizatio
n
is
ap
p
lied
to
s
ca
le
th
e
in
p
u
t
f
ea
tu
r
es,
wh
ich
ar
e
th
e
p
ea
k
ar
ea
p
er
ce
n
ta
g
es
o
f
th
e
f
o
u
r
c
h
em
ical
co
m
p
o
u
n
d
s
in
A
q
u
ila
r
ia
o
il,
in
to
a
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
R
o
b
u
s
t K
-
N
N
a
p
p
r
o
a
ch
fo
r
cl
a
s
s
ifyin
g
A
q
u
ila
r
ia
o
il sp
ec
ies b
y
…
(
N
o
o
r
A
id
a
S
ya
kira
A
h
ma
d
S
a
b
r
i
)
181
u
n
if
o
r
m
r
an
g
e
to
im
p
r
o
v
e
th
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
.
T
h
e
co
n
tin
u
o
u
s
in
p
u
t
f
ea
tu
r
es
ar
e
n
o
r
m
alize
d
in
to
a
r
an
g
e
th
at
co
r
r
elate
s
with
th
e
s
p
ec
ies cla
s
s
e
s
f
r
o
m
1
to
4
,
as
p
r
esen
ted
in
T
ab
le
2
,
co
r
r
esp
o
n
d
in
g
to
AB
(
C
lass
1
)
,
AM
(
C
lass
2
)
,
AC
(
C
lass
3
)
,
an
d
AS
(
C
lass
4
)
.
T
h
is
en
s
u
r
es
th
at
n
o
s
in
g
le
f
ea
t
u
r
e
d
o
m
in
ates
th
e
m
o
d
el
d
u
e
to
v
ar
y
in
g
in
p
u
t
d
ata
s
ca
les.
Fo
llo
win
g
n
o
r
m
ali
za
tio
n
,
th
e
k
-
NN
m
o
d
el’
s
p
ar
a
m
eter
s
wer
e
d
ef
in
ed
as
f
o
llo
ws
:
a)
T
r
ain
in
g
an
d
test
in
g
r
atio
:
8
0
:
2
0
–
8
0
%
o
f
th
e
d
ataset
is
u
s
ed
f
o
r
tr
ain
in
g
,
an
d
2
0
%
f
o
r
test
in
g
.
T
h
is
r
atio
h
elp
s
p
r
ev
e
n
t o
v
er
f
itti
n
g
an
d
e
n
s
u
r
es th
e
m
o
d
el
g
en
er
alize
s
well
to
n
ew,
u
n
s
ee
n
d
ata.
b)
k
-
Valu
e:
1
0
–
T
h
e
m
o
d
el
co
n
s
id
er
s
th
e
1
0
n
ea
r
est
n
eig
h
b
o
u
r
s
f
o
r
class
if
icatio
n
,
a
v
al
u
e
th
at
b
alan
ce
s
m
o
d
el
co
m
p
lex
ity
an
d
ac
cu
r
ac
y
.
c)
Dis
tan
ce
m
etr
ic:
eu
clid
ea
n
d
is
tan
ce
–
t
h
is
m
etr
ic
ca
lcu
lates
th
e
s
tr
aig
h
t
-
lin
e
d
is
tan
ce
b
etwe
en
two
p
o
in
ts
in
m
u
lti
-
d
im
en
s
io
n
al
s
p
ac
e
,
d
e
ter
m
in
in
g
s
im
ilar
ity
b
etwe
en
s
am
p
les.
Fig
u
r
e
1
.
Pro
ce
s
s
f
r
am
ew
o
r
k
o
f
k
-
NN
m
o
d
el
T
h
e
d
a
t
a
s
et
w
as
d
i
v
i
d
e
d
i
n
t
o
t
r
a
i
n
i
n
g
a
n
d
t
e
s
t
i
n
g
s
et
s
,
w
i
t
h
8
0
%
u
s
e
d
f
o
r
t
r
ai
n
i
n
g
a
n
d
2
0
%
r
es
e
r
v
e
d
f
o
r
t
e
s
ti
n
g
.
T
h
e
8
0
%
t
r
a
i
n
i
n
g
d
at
a
a
l
l
o
ws
t
h
e
m
o
d
e
l
t
o
l
e
a
r
n
p
a
t
t
e
r
n
s
a
n
d
c
o
r
r
e
l
at
i
o
n
s
wit
h
i
n
t
h
e
c
h
e
m
i
c
al
c
o
m
p
o
u
n
d
d
a
t
a
,
w
h
i
l
e
t
h
e
r
em
a
i
n
i
n
g
2
0
%
is
u
s
e
d
t
o
e
v
alu
a
t
e
t
h
e
m
o
d
e
l
’
s
p
e
r
f
o
r
m
a
n
c
e.
T
h
i
s
d
at
a
s
p
l
i
t
i
s
c
o
m
m
o
n
l
y
e
m
p
l
o
y
e
d
t
o
p
r
e
v
e
n
t
o
v
e
r
f
i
t
t
i
n
g
,
e
n
s
u
r
i
n
g
t
h
e
m
o
d
e
l
p
e
r
f
o
r
m
s
w
e
ll
o
n
b
o
t
h
k
n
o
w
n
a
n
d
u
n
s
e
e
n
d
a
t
a
.
On
ce
p
r
e
-
p
r
o
ce
s
s
in
g
is
co
m
p
lete,
th
e
k
-
NN
m
o
d
el
is
tr
ain
ed
an
d
ap
p
lied
to
class
if
y
th
e
s
am
p
les
b
ased
o
n
th
e
e
u
clid
ea
n
d
is
tan
ce
.
T
h
e
e
u
clid
ea
n
d
is
tan
ce
m
ea
s
u
r
es
th
e
s
tr
aig
h
t
-
lin
e
d
is
tan
ce
b
etwe
en
two
p
o
in
ts
,
r
ep
r
esen
te
d
b
y
th
eir
ch
e
m
ical
co
m
p
o
u
n
d
v
alu
es,
an
d
is
ca
lcu
lated
as sh
o
wn
in
(
1
)
:
(
,
)
=
√
(
1
−
1
)
2
+
(
2
−
2
)
2
+
⋯
+
(
−
)
2
(
1
)
I
n
th
is
f
o
r
m
u
la
,
an
d
r
ep
r
esen
t
two
d
ata
p
o
in
ts
in
th
e
f
ea
tu
r
e
s
p
ac
e,
with
d
im
en
s
io
n
s
co
r
r
esp
o
n
d
in
g
to
th
e
f
o
u
r
c
h
em
ical
co
m
p
o
u
n
d
s
.
T
h
e
k
-
NN
alg
o
r
ith
m
th
en
s
elec
ts
t
h
e
k
clo
s
est
p
o
in
ts
(
n
eig
h
b
o
u
r
s
)
a
n
d
class
if
ies
a
s
am
p
le
b
ased
o
n
m
ajo
r
ity
v
o
tin
g
am
o
n
g
th
ese
n
ei
g
h
b
o
u
r
s
.
T
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
is
ev
alu
ated
u
s
in
g
a
co
n
f
u
s
io
n
m
atr
ix
,
as illu
s
tr
ated
in
T
ab
le
2
.
T
ab
le
2
p
r
o
v
i
d
es a
n
ex
am
p
le
o
f
a
4
×
4
m
u
lticlas
s
co
n
f
u
s
io
n
m
atr
i
x
f
o
r
C
lass
1
.
T
h
e
m
atr
i
x
h
ig
h
lig
h
ts
f
o
u
r
k
ey
p
er
f
o
r
m
an
ce
m
etr
ics
:
a)
T
r
u
e
p
o
s
itiv
e
(
T
P):
t
h
e
m
o
d
el
co
r
r
ec
tly
class
if
ies
a
s
am
p
le
in
to
th
e
co
r
r
ec
t
A
q
u
ila
r
ia
o
il
s
p
ec
ies
(
e.
g
.
,
co
r
r
ec
tly
id
e
n
tify
in
g
AM
).
b)
T
r
u
e
n
e
g
ativ
e
(
T
N)
:
t
h
e
m
o
d
e
l
co
r
r
ec
tly
id
en
tifie
s
th
at
a
s
am
p
le
d
o
es
n
o
t
b
elo
n
g
to
a
p
a
r
ticu
lar
s
p
ec
ies
(
e.
g
.
,
c
o
r
r
ec
tly
r
ejec
tin
g
a
s
am
p
le
th
at
is
n
o
t
AB
).
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.
39
,
No
.
1
,
J
u
ly
20
25
:
1
78
-
1
89
182
c)
Fals
e
p
o
s
itiv
e
(
FP
)
:
t
h
e
m
o
d
el
in
co
r
r
ec
tl
y
p
r
ed
icts
a
s
am
p
le
as
b
elo
n
g
in
g
to
a
s
p
ec
ies
wh
en
it
d
o
es
n
o
t
(
e.
g
.
,
p
r
ed
ictin
g
AC
as
AS
)
.
d)
Fals
e
n
eg
ativ
e
(
FN)
:
t
h
e
m
o
d
el
f
ails
to
co
r
r
ec
tly
clas
s
if
y
a
s
am
p
le,
in
s
tead
m
is
cla
s
s
if
y
in
g
it
as
an
o
th
er
s
p
ec
ies (
e.
g
.
,
class
if
y
in
g
AB
as
AC
)
.
T
ab
le
2
.
Mu
lticlas
s
co
n
f
u
s
io
n
m
atr
ix
f
o
r
class
1
(
4
×
4)
A
c
t
u
a
l
/p
r
e
d
i
c
t
e
d
C
l
a
s
s
1
C
l
a
s
s
2
C
l
a
s
s
3
C
l
a
s
s
4
C
l
a
s
s
1
TP
FN
1
FN
2
FN
3
C
l
a
s
s
2
FP
1
TN
1
TN
2
TN
3
C
l
a
s
s
3
FP
2
TN
4
TN
5
TN
6
C
l
a
s
s
4
FP
3
TN
7
TN
8
TN
9
*
TN
T
=
T
N
(
1
+
2
+
…
+
9
)
FP
T
=
FP
(
1
+
2
+
3
)
FN
T
=
F
N
(
1
+
2
+
3
)
I
n
a
m
u
lticlas
s
co
n
f
u
s
io
n
m
atr
ix
,
th
ese
m
etr
ics
ar
e
c
alcu
lated
f
o
r
ea
ch
class
,
p
r
o
v
id
in
g
a
co
m
p
r
eh
e
n
s
iv
e
o
v
er
v
iew
o
f
t
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
in
h
an
d
lin
g
m
u
ltip
le
s
p
ec
ies.
T
h
e
d
iag
o
n
al
elem
en
ts
r
ep
r
esen
t
th
e
co
r
r
ec
tly
class
if
ied
s
am
p
les
f
o
r
ea
ch
s
p
ec
ies,
wh
ile
o
ff
-
d
iag
o
n
al
elem
en
ts
h
ig
h
lig
h
t
th
e
m
is
class
if
ied
s
am
p
les
[1
6
]
-
[1
8
]
.
B
y
an
aly
zin
g
th
e
b
alan
ce
b
etwe
en
tr
u
e
p
o
s
itiv
es
an
d
f
alse
p
o
s
itiv
es/n
eg
ativ
es,
th
e
m
o
d
el’
s
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
th
e
f
o
u
r
A
q
u
ila
r
ia
o
i
l
s
p
ec
ies ca
n
b
e
ass
ess
ed
.
T
h
e
o
v
e
r
all
p
er
f
o
r
m
a
n
ce
o
f
th
e
k
-
NN
m
o
d
el
is
ev
alu
ate
d
u
s
in
g
k
ey
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
s
en
s
itiv
ity
(
r
ec
all)
,
s
p
ec
if
icity
,
an
d
p
r
ec
is
io
n
[
19
]
.
Acc
u
r
ac
y
is
ca
lcu
lated
as
+
+
+
+
,
in
d
icatin
g
h
o
w
o
f
ten
th
e
m
o
d
el
m
a
k
es
co
r
r
ec
t
p
r
ed
ict
io
n
s
.
Sen
s
itiv
ity
ev
alu
ates
th
e
m
o
d
el’
s
ab
ilit
y
to
id
en
tify
tr
u
e
p
o
s
itiv
es,
wh
ile
s
p
ec
if
icity
m
ea
s
u
r
es
h
o
w
well
th
e
m
o
d
el
av
o
id
s
f
alse
p
o
s
itiv
es.
Pre
cisi
o
n
r
ep
r
esen
ts
th
e
r
atio
o
f
t
r
u
e
p
o
s
itiv
es
to
th
e
s
u
m
o
f
t
r
u
e
p
o
s
itiv
es
an
d
f
alse
p
o
s
itiv
es,
il
lu
s
tr
atin
g
th
e
m
o
d
el’
s
ex
ac
tn
e
s
s
in
class
if
icatio
n
[
19
]
,
[2
0
]
.
T
h
e
k
-
N
N
m
o
d
e
l
’
s
p
e
r
f
o
r
m
a
n
c
e
i
s
o
p
t
i
m
i
z
e
d
b
y
a
d
j
u
s
ti
n
g
t
h
e
k
-
v
a
l
u
e
a
n
d
u
s
i
n
g
t
h
e
E
u
c
l
i
d
e
a
n
d
i
s
t
a
n
ce
m
e
t
r
i
c
.
B
y
f
i
n
e
-
t
u
n
i
n
g
t
h
es
e
p
a
r
a
m
e
t
e
r
s
,
t
h
e
m
o
d
e
l
m
i
n
i
m
iz
e
s
m
is
c
la
s
s
i
f
ic
a
t
i
o
n
e
r
r
o
r
s
,
p
a
r
t
i
c
u
l
a
r
l
y
r
e
d
u
ci
n
g
f
a
l
s
e
p
o
s
i
t
i
v
es
a
n
d
f
a
ls
e
n
e
g
a
tiv
e
s
[2
1
]
.
T
h
e
f
o
r
m
u
l
a
s
f
o
r
e
a
ch
e
v
a
l
u
a
t
i
o
n
m
e
t
r
i
c
a
r
e
p
r
o
v
i
d
e
d
i
n
(
2
)
-
(
5
)
.
=
+
T
+
+
+
100
(
2
)
=
+
x
100
(
3
)
=
+
x
100
(
4
)
=
T
+
x
100
(
5
)
Fin
ally
,
th
e
k
-
NN
m
o
d
el
was
v
alid
ated
u
s
in
g
in
d
ep
en
d
en
t
s
am
p
les
th
at
wer
e
n
o
t
in
clu
d
ed
in
th
e
o
r
ig
in
al
tr
ain
in
g
d
ataset.
T
h
e
s
e
test
s
co
n
f
ir
m
ed
th
at
th
e
m
o
d
el
m
et
all
p
er
f
o
r
m
an
ce
cr
it
er
ia,
d
em
o
n
s
tr
atin
g
h
ig
h
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
p
r
ec
is
io
n
.
T
h
e
r
e
s
u
lts
af
f
ir
m
th
e
r
o
b
u
s
tn
ess
o
f
th
e
k
-
NN
m
o
d
el
in
class
if
y
in
g
A
q
u
ila
r
ia
o
il
s
p
ec
ies,
m
ak
in
g
it
a
r
eliab
le
to
o
l
f
o
r
s
p
ec
ies
id
en
tific
atio
n
b
ased
o
n
ch
em
ical
co
m
p
o
u
n
d
s
.
3.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
S
I
n
th
is
s
ec
tio
n
,
it
i
s
ex
p
l
ain
ed
th
e
r
esu
lts
o
f
r
esear
ch
an
d
at
th
e
s
am
e
tim
e
is
g
iv
en
th
e
co
m
p
r
e
h
en
s
iv
e
d
is
cu
s
s
io
n
.
R
esu
lts
ca
n
b
e
p
r
esen
ted
in
f
ig
u
r
es,
g
r
ap
h
s
,
tab
les
an
d
o
th
er
s
th
at
m
a
k
e
th
e
r
ea
d
er
u
n
d
e
r
s
tan
d
ea
s
ily
[2
2
]
,
[
2
3
]
.
T
h
e
d
is
cu
s
s
io
n
ca
n
b
e
m
ad
e
in
s
ev
er
al
s
u
b
-
s
ec
tio
n
s
.
3
.
1
.
B
o
x
plo
t
a
na
ly
s
is
T
h
e
b
o
x
p
lo
t
an
aly
s
is
was
co
n
d
u
cted
to
id
e
n
tify
s
ig
n
if
ican
t
ch
em
ical
co
m
p
o
u
n
d
s
in
A
q
u
ila
r
ia
o
il
s
am
p
les.
T
h
e
b
o
x
p
lo
t
m
eth
o
d
h
elp
ed
in
v
is
u
alizin
g
th
e
d
is
t
r
ib
u
tio
n
an
d
v
ar
iab
ilit
y
o
f
t
h
e
p
ea
k
ar
ea
s
o
f
th
e
co
m
p
o
u
n
d
s
ac
r
o
s
s
f
o
u
r
A
q
u
ila
r
ia
o
il sp
ec
ies:
AB
,
AM
,
A
C
,
an
d
AS.
I
n
th
is
ca
s
e,
th
e
b
o
x
p
lo
t
i
d
en
t
if
ied
f
o
u
r
s
ig
n
if
ican
t
c
o
m
p
o
u
n
d
s
b
ased
o
n
th
eir
p
ea
k
ar
ea
(
%)
v
alu
es,
f
o
cu
s
in
g
o
n
th
e
h
ig
h
est
m
e
d
ia
n
s
.
B
y
v
is
u
ally
i
n
s
p
ec
tin
g
t
h
e
b
o
x
p
lo
ts
f
o
r
ea
ch
s
p
ec
ies,
th
e
s
e
f
o
u
r
co
m
p
o
u
n
d
s
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
R
o
b
u
s
t K
-
N
N
a
p
p
r
o
a
ch
fo
r
cl
a
s
s
ifyin
g
A
q
u
ila
r
ia
o
il sp
ec
ies b
y
…
(
N
o
o
r
A
id
a
S
ya
kira
A
h
ma
d
S
a
b
r
i
)
183
em
er
g
ed
as
th
e
m
o
s
t
f
r
e
q
u
e
n
tl
y
s
elec
ted
co
m
p
o
u
n
d
s
with
t
h
e
h
ig
h
est
p
ea
k
a
r
ea
s
.
T
h
is
s
el
ec
tio
n
p
r
o
ce
s
s
was
p
iv
o
tal
b
ec
au
s
e
th
e
c
o
m
p
o
u
n
d
s
with
th
e
h
ig
h
est
m
ed
ian
p
e
ak
ar
ea
s
ac
r
o
s
s
s
p
ec
ies
in
d
icate
th
eir
im
p
o
r
ta
n
ce
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
th
e
o
il sp
ec
ies
[
24
]
.
T
h
ese
co
m
p
o
u
n
d
s
wer
e
t
h
en
s
elec
ted
f
o
r
f
u
r
th
er
an
aly
s
is
.
3.
2
.
3
D
g
ra
ph
s
a
nd
co
nfusi
o
n m
a
t
rix
T
h
e
3
D
v
is
u
aliza
tio
n
r
esu
lts
(
Fig
u
r
e
2
)
o
f
f
er
cr
itical
in
s
ig
h
ts
in
to
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
k
-
NN
m
o
d
el
in
class
if
y
in
g
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
ased
o
n
th
e
s
e
lecte
d
ch
em
ical
co
m
p
o
u
n
d
s
,
a,
b
,
c,
an
d
d
.
As
s
h
o
wn
in
Fig
u
r
e
s
2
(
a
)
-
2
(
c)
,
t
h
ese
r
esu
lts
p
r
esen
t
th
e
ch
em
ical
co
m
p
o
u
n
d
s
o
f
t
h
e
A
q
u
ila
r
ia
o
ils
in
a
th
r
ee
-
d
im
en
s
io
n
al
s
p
ac
e,
allo
win
g
f
o
r
a
clea
r
v
is
u
al
d
is
tin
ctio
n
b
etwe
en
th
e
d
if
f
e
r
en
t
A
q
u
ila
r
ia
s
p
ec
ies.
T
h
e
3
D
p
lo
t a
x
es r
ep
r
esen
t t
h
e
p
ea
k
ar
ea
(
%)
o
f
th
e
co
m
p
o
u
n
d
s
,
h
ig
h
lig
h
tin
g
th
eir
im
p
o
r
tan
ce
in
s
ep
ar
atin
g
s
p
ec
ies.
(
a)
(
b
)
(
c)
Fig
u
r
e
2
.
T
h
e
3
-
Dim
e
n
s
io
n
g
r
ap
h
s
o
f
f
o
u
r
A
q
u
ila
r
ia
o
il sp
ec
ies
b
ased
o
n
(
a)
a,
b
,
c
co
m
p
o
u
n
d
s
,
(
b
)
a,
c,
d
,
an
d
(
c)
b
,
c,
d
T
h
e
3
D
p
lo
ts
d
em
o
n
s
tr
ate
d
is
tin
ct
clu
s
ter
s
f
o
r
ea
ch
A
q
u
ila
r
ia
s
p
ec
ies,
co
n
f
ir
m
i
n
g
th
e
d
is
cr
im
in
ativ
e
p
o
wer
o
f
th
e
s
elec
ted
co
m
p
o
u
n
d
s
.
E
ac
h
clu
s
ter
co
r
r
esp
o
n
d
s
to
a
s
p
ec
if
ic
s
p
ec
ies,
a
n
d
th
e
s
ep
ar
atio
n
b
etwe
en
clu
s
ter
s
in
d
icate
s
th
at
th
e
ch
em
ical
co
m
p
o
s
itio
n
s
o
f
th
e
s
p
ec
ies
ar
e
s
u
f
f
icien
tly
d
is
tin
ct.
T
h
is
s
ep
ar
atio
n
v
is
u
ally
s
u
p
p
o
r
ts
th
e
h
ig
h
cla
s
s
if
icatio
n
ac
cu
r
ac
y
ac
h
iev
e
d
b
y
th
e
k
-
NN
m
o
d
el,
as
th
e
well
-
d
ef
in
ed
clu
s
ter
s
s
u
g
g
est
th
at
th
e
m
o
d
el
ca
n
ea
s
ily
d
is
tin
g
u
is
h
b
etwe
en
th
e
s
p
ec
ies.
T
h
e
s
tr
o
n
g
s
ep
ar
atio
n
b
etwe
en
th
e
clu
s
ter
s
f
o
r
s
p
ec
ies
s
u
c
h
as
AC
an
d
AM
u
n
d
er
s
co
r
es
t
h
e
r
ele
v
a
n
ce
o
f
th
e
s
elec
ted
co
m
p
o
u
n
d
s
in
en
h
a
n
cin
g
class
if
icatio
n
p
er
f
o
r
m
an
ce
.
Ad
d
itio
n
ally
,
t
h
e
3
D
r
esu
lts
e
m
p
h
asize
th
e
r
o
le
o
f
s
p
ec
if
ic
co
m
p
o
u
n
d
s
i
n
th
e
class
if
icatio
n
p
r
o
ce
s
s
.
Ax
es
co
r
r
esp
o
n
d
in
g
to
co
m
p
o
u
n
d
s
b
an
d
c
d
is
p
lay
m
o
r
e
s
ig
n
if
ican
t
s
ep
ar
atio
n
s
b
etwe
en
s
p
ec
ies,
in
d
icatin
g
th
eir
h
ig
h
e
r
r
elev
an
ce
in
d
is
tin
g
u
is
h
in
g
th
e
o
il
p
r
o
f
iles
.
T
h
i
s
o
b
s
er
v
atio
n
f
u
r
th
e
r
v
alid
ates
th
e
ch
o
ice
o
f
th
ese
co
m
p
o
u
n
d
s
f
o
r
in
clu
s
io
n
in
th
e
class
if
icatio
n
m
o
d
el.
I
n
co
n
t
r
ast,
an
y
o
v
e
r
lap
in
th
e
3
D
s
p
ac
e
co
u
ld
s
u
g
g
est
a
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.
39
,
No
.
1
,
J
u
ly
20
25
:
1
78
-
1
89
184
n
ee
d
f
o
r
f
u
r
th
er
r
ef
in
em
e
n
t
o
f
th
e
m
o
d
el
o
r
th
e
a
d
d
itio
n
o
f
m
o
r
e
d
is
cr
im
in
ativ
e
f
e
atu
r
es
to
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
in
ca
s
e
s
wh
er
e
s
p
ec
ies s
h
o
w
s
im
ilar
ch
em
ical
co
m
p
o
u
n
d
s
.
Fig
u
r
e
3
p
r
esen
ts
th
e
c
o
n
f
u
s
io
n
m
atr
ices
u
s
ed
t
o
ev
al
u
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
k
-
NN
cl
ass
if
icatio
n
m
o
d
el
in
d
is
tin
g
u
is
h
in
g
f
o
u
r
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
ased
o
n
s
elec
ted
ch
em
ical
co
m
p
o
u
n
d
s
.
As
s
h
o
wn
in
Fig
u
r
e
s
3
(
a)
an
d
3
(
b
)
,
t
h
e
m
atr
ices
co
r
r
esp
o
n
d
to
th
e
tr
ai
n
in
g
a
n
d
test
in
g
d
atasets
,
r
esp
ec
tiv
ely
,
o
f
f
er
in
g
in
s
ig
h
ts
in
to
th
e
m
o
d
el’
s
class
if
icatio
n
ac
cu
r
ac
y
.
(
a)
(
b
)
Fig
u
r
e
3
.
C
o
n
f
u
s
io
n
m
atr
i
x
o
f
f
o
u
r
A
q
u
ila
r
ia
o
il sp
ec
ies f
o
r
t
r
ain
in
g
(
a
)
test
in
g
an
d
(
b
)
d
ata
Fo
r
th
e
tr
ain
in
g
d
ata
(
3
.
1
)
,
w
h
ich
u
tili
ze
d
8
0
%
o
f
th
e
to
tal
d
ataset,
th
e
co
n
f
u
s
io
n
m
atr
i
x
r
ev
ea
led
ac
cu
r
ate
class
if
icatio
n
r
esu
lts
.
T
h
e
f
o
u
r
s
p
ec
ies
(
AB
,
AM
,
AC
,
an
d
AS)
wer
e
co
r
r
ec
tly
p
r
ed
icted
f
o
r
ev
er
y
s
am
p
le.
Sp
ec
if
ically
,
th
er
e
w
er
e
2
4
T
P
f
o
r
AB
,
2
7
f
o
r
A
M,
2
4
f
o
r
AC
,
an
d
2
1
f
o
r
AS.
No
FP
o
r
FN
wer
e
o
b
s
er
v
ed
in
th
e
tr
ain
in
g
d
ata
,
r
esu
ltin
g
in
1
0
0
%
ac
cu
r
ac
y
.
T
h
is
s
h
o
ws
th
at
th
e
k
-
NN
m
o
d
el
s
u
cc
ess
f
u
lly
lear
n
ed
th
e
p
atter
n
s
f
r
o
m
th
e
tr
ain
in
g
d
ata,
ac
c
u
r
ately
d
is
tin
g
u
is
h
in
g
t
h
e
s
p
ec
ies
b
ased
o
n
th
eir
ch
em
ical
co
m
p
o
u
n
d
s
.
W
h
en
ev
alu
ated
o
n
t
h
e
test
in
g
d
ata
(
3
.
2
)
,
wh
ich
c
o
m
p
r
is
ed
2
0
%
o
f
th
e
d
ataset,
th
e
m
o
d
el
o
n
ce
ag
ain
ac
h
iev
ed
ac
cu
r
ate
class
if
icati
o
n
.
T
h
e
co
n
f
u
s
io
n
m
atr
i
x
f
o
r
th
e
test
in
g
d
ata
in
d
icate
d
s
ix
co
r
r
ec
t
p
r
ed
ictio
n
s
f
o
r
AB
,
th
r
ee
f
o
r
AM
,
s
ix
f
o
r
AC
,
an
d
n
in
e
f
o
r
AS.
Similar
to
th
e
tr
ain
in
g
s
et,
n
o
FP
o
r
FN
wer
e
r
ep
o
r
ted
,
an
d
th
e
m
o
d
el
d
em
o
n
s
tr
at
ed
1
0
0
%
ac
cu
r
ac
y
.
T
h
e
f
ac
t
th
at
th
e
k
-
NN
m
o
d
el
m
ain
tain
ed
ac
cu
r
ate
ac
cu
r
ac
y
o
n
u
n
s
ee
n
test
in
g
d
ata
h
i
g
h
lig
h
ts
its
ab
ilit
y
to
g
en
er
alize
well
b
ey
o
n
d
t
h
e
tr
ain
in
g
p
h
ase
with
o
u
t o
v
e
r
f
itti
n
g
.
As
d
ep
icted
in
T
ab
le
3
,
f
u
r
th
er
v
alid
atio
n
o
f
th
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
c
e
is
d
em
o
n
s
tr
ated
th
r
o
u
g
h
k
ey
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
s
en
s
itiv
ity
,
s
p
ec
if
icity
,
an
d
p
r
ec
is
io
n
,
all
o
f
wh
ich
r
ea
ch
ed
1
0
0
%.
T
h
is
o
u
tco
m
e
is
b
ased
o
n
th
e
co
n
f
u
s
io
n
m
atr
i
x
,
wh
ich
r
ev
ea
ls
th
at
ev
er
y
s
am
p
le
was
co
r
r
ec
tly
class
if
ie
d
,
with
n
o
er
r
o
r
s
in
p
r
ed
ictin
g
th
e
s
p
ec
ies.
E
ac
h
c
lass
,
r
ep
r
esen
tin
g
d
if
f
er
e
n
t
s
p
ec
ies
o
f
A
q
u
ila
r
ia
o
ils
,
was
a
cc
u
r
ately
id
e
n
tifie
d
,
with
n
o
FP
o
r
FN
ac
r
o
s
s
b
o
th
th
e
tr
ain
in
g
a
n
d
test
in
g
d
atasets
.
T
h
is
in
d
icate
s
th
at
th
e
ch
em
ical
co
m
p
o
u
n
d
s
o
f
ea
ch
s
p
ec
ies we
r
e
d
is
tin
ctiv
e
f
o
r
th
e
m
o
d
e
l to
class
if
y
th
em
with
o
u
t c
o
n
f
u
s
io
n
.
T
ab
le
3
.
Stan
d
a
r
d
p
e
r
f
o
r
m
an
c
e
ev
alu
atio
n
o
f
f
o
u
r
d
if
f
er
e
n
t
A
q
u
ila
r
ia
o
il sp
ec
ies
P
a
r
a
me
t
e
r
s
P
e
r
c
e
n
t
a
g
e
s (%)
A
c
c
u
r
a
c
y
1
0
0
S
e
n
s
i
t
i
v
i
t
y
1
0
0
S
p
e
c
i
f
i
c
i
t
y
1
0
0
P
r
e
c
i
s
i
o
n
1
0
0
Star
tin
g
with
ac
cu
r
ac
y
,
t
h
e
m
e
tr
ic
is
ca
lcu
lated
as
th
e
r
atio
o
f
co
r
r
ec
t
p
r
e
d
ictio
n
s
(
T
P
a
n
d
T
N)
to
th
e
to
tal
n
u
m
b
er
o
f
p
r
e
d
ictio
n
s
.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
s
h
o
ws
th
a
t
all
s
am
p
les
wer
e
p
lace
d
in
th
e
co
r
r
ec
t
ca
teg
o
r
y
,
lead
in
g
to
n
o
m
is
class
if
icati
o
n
s
.
As
a
r
es
u
lt,
th
e
ac
c
u
r
a
cy
r
ea
ch
e
d
1
0
0
%,
as
th
e
m
o
d
el
m
ad
e
co
r
r
ec
t
p
r
ed
ictio
n
s
f
o
r
all
s
p
ec
ies.
Fu
r
th
er
m
o
r
e
,
s
en
s
itiv
ity
(
r
ec
all)
ev
alu
ates
th
e
m
o
d
el'
s
ab
il
ity
t
o
co
r
r
ec
tly
id
en
tify
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
R
o
b
u
s
t K
-
N
N
a
p
p
r
o
a
ch
fo
r
cl
a
s
s
ifyin
g
A
q
u
ila
r
ia
o
il sp
ec
ies b
y
…
(
N
o
o
r
A
id
a
S
ya
kira
A
h
ma
d
S
a
b
r
i
)
185
T
P
f
o
r
ea
ch
s
p
ec
ies.
Giv
en
t
h
at
n
o
s
am
p
les
wer
e
m
is
class
if
ied
as
an
o
th
er
s
p
ec
ies,
t
h
e
m
o
d
el'
s
s
en
s
itiv
ity
f
o
r
ea
ch
s
p
ec
ies wa
s
ac
cu
r
ate,
in
d
icatin
g
th
at
ev
er
y
s
p
ec
ies'
tr
u
e
s
am
p
les we
r
e
co
r
r
ec
tly
id
e
n
tifie
d
with
o
u
t e
r
r
o
r
.
Ad
d
itio
n
ally
,
th
e
m
o
d
el
ac
h
ie
v
ed
1
0
0
%
in
s
p
ec
if
icity
,
wh
ic
h
m
ea
s
u
r
es
th
e
a
b
ilit
y
to
co
r
r
ec
tly
r
ejec
t
s
am
p
les
th
at
d
o
n
o
t
b
elo
n
g
to
a
p
ar
ticu
lar
s
p
ec
ies.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
co
n
f
ir
m
s
th
at
n
o
s
p
ec
ies
wa
s
f
alsely
class
if
ied
as
an
o
th
er
.
Fo
r
ex
am
p
le,
th
e
m
o
d
el
n
ev
er
in
c
o
r
r
e
ctly
id
en
tifie
d
AB
a
s
A
C
,
s
h
o
win
g
th
at
it
av
o
id
ed
f
alse
p
o
s
itiv
es
en
tire
ly
.
Fin
ally
,
p
r
ec
is
io
n
,
w
h
ic
h
is
th
e
r
a
tio
o
f
T
P
to
th
e
s
u
m
o
f
T
P
an
d
FP
,
was
also
ac
cu
r
ate.
T
h
is
m
ea
n
s
th
at
all
p
r
ed
ictio
n
s
m
ad
e
f
o
r
ea
ch
s
p
ec
ies
wer
e
r
eliab
le,
with
n
o
er
r
o
n
eo
u
s
class
if
icatio
n
s
.
T
h
e
co
m
b
in
atio
n
o
f
th
ese
f
ac
t
o
r
s
wh
ich
a
r
e
d
is
tin
ct
c
h
em
ic
al
co
m
p
o
u
n
d
s
,
an
ap
p
r
o
p
r
iate
k
-
v
alu
e
o
f
1
0
,
an
d
th
e
E
u
clid
ea
n
d
is
tan
ce
m
etr
ic
en
ab
led
th
e
k
-
NN
m
o
d
el
to
ex
ce
l
in
id
en
tif
y
in
g
th
e
s
p
ec
ies.
T
h
e
m
o
d
el'
s
ab
ilit
y
to
m
ap
th
e
ch
e
m
ical
co
m
p
o
u
n
d
d
ata
i
n
to
a
d
i
s
tin
ct
f
ea
tu
r
e
s
p
ac
e
en
s
u
r
ed
th
at
ea
ch
s
p
ec
ies wa
s
s
ep
ar
ated
,
r
e
d
u
cin
g
th
e
li
k
elih
o
o
d
o
f
a
n
y
o
v
er
la
p
.
T
h
er
ef
o
r
e
,
th
e
ac
c
u
r
ate
class
if
icatio
n
d
e
m
o
n
s
tr
ated
b
y
th
e
co
n
f
u
s
io
n
m
atr
ix
r
ef
lects
th
e
k
-
NN
m
o
d
el'
s
r
o
b
u
s
tn
ess
an
d
ac
cu
r
ac
y
in
h
a
n
d
lin
g
th
is
d
ataset,
r
esu
ltin
g
in
r
eliab
le
p
er
f
o
r
m
an
ce
ac
r
o
s
s
all
ev
alu
atio
n
m
etr
ics.
4.
DIS
CU
SS
I
O
N
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
d
e
m
o
n
s
tr
ated
th
at
th
e
k
-
NN
m
o
d
el,
b
ased
o
n
f
o
u
r
s
ig
n
if
ica
n
t
ch
em
ical
co
m
p
o
u
n
d
s
,
a
,
b
,
c,
an
d
d
ac
h
iev
ed
ac
c
u
r
ate
ac
c
u
r
ac
y
in
c
lass
if
y
in
g
A
q
u
ila
r
ia
o
il
s
p
ec
i
es.
T
h
is
alig
n
s
with
r
ec
en
t
r
esear
ch
em
p
h
asizin
g
th
e
im
p
o
r
tan
ce
o
f
ch
e
m
ical
m
ar
k
er
s
in
ess
en
tial
o
il
class
if
icatio
n
.
Fo
r
ex
am
p
le,
[
2
5
]
id
en
tifie
d
th
ese
s
esq
u
iter
p
en
es
an
d
o
x
y
g
en
ated
co
m
p
o
u
n
d
s
as
k
e
y
in
d
icato
r
s
o
f
ag
a
r
wo
o
d
o
il
q
u
ality
an
d
o
r
ig
in
.
T
h
e
u
s
e
o
f
E
u
clid
ea
n
d
is
tan
ce
in
th
e
k
-
NN
m
o
d
el
f
u
r
th
er
s
tr
en
g
th
en
ed
its
ab
ilit
y
to
ac
cu
r
a
tely
class
if
y
s
p
ec
ies
b
ased
o
n
ch
em
ical
co
m
p
o
u
n
d
s
.
Stu
d
ies
h
av
e
s
im
ilar
ly
s
h
o
wn
th
at
co
m
b
in
in
g
ch
em
ical
co
m
p
o
u
n
d
s
with
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
y
ield
s
h
ig
h
ac
c
u
r
ac
y
i
n
ess
en
tial o
il sp
ec
ies id
en
tific
atio
n
[
2
6
]
.
T
h
e
3
D
v
is
u
aliza
tio
n
r
esu
lts
s
tr
o
n
g
ly
c
o
n
f
ir
m
th
e
k
-
NN
m
o
d
el'
s
r
o
b
u
s
t
class
if
icatio
n
ca
p
ab
ilit
ies.
T
h
e
d
is
tin
ct
clu
s
ter
in
g
o
f
A
q
u
ila
r
ia
o
il
s
p
ec
ies
in
th
e
3
D
s
p
ac
e
h
ig
h
lig
h
ts
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
e
s
elec
ted
ch
em
ical
co
m
p
o
u
n
d
s
in
s
p
ec
i
es
d
if
f
er
en
tiatio
n
a
n
d
f
u
r
t
h
er
u
n
d
er
s
co
r
es
th
e
m
o
d
el'
s
ab
ilit
y
to
ac
h
iev
e
h
i
g
h
ac
cu
r
ac
y
.
T
h
is
v
is
u
aliza
tio
n
r
ein
f
o
r
ce
s
th
e
u
tili
ty
o
f
ch
em
i
ca
l
co
m
p
o
u
n
d
s
co
m
b
in
e
d
with
m
ac
h
in
e
lear
n
in
g
f
o
r
lar
g
e
-
s
ca
le
class
if
icatio
n
o
f
ess
en
tial o
ils
,
o
f
f
er
in
g
a
c
o
m
p
ellin
g
ap
p
r
o
ac
h
f
o
r
i
n
d
u
s
tr
ial
ap
p
licatio
n
s
.
Mo
r
eo
v
er
,
th
e
h
ig
h
ac
cu
r
ac
y
ac
h
iev
ed
b
y
th
e
k
-
NN
m
o
d
el
r
ef
lects
th
e
s
tr
o
n
g
d
is
cr
im
in
at
iv
e
p
o
wer
o
f
th
e
s
elec
ted
ch
em
ical
co
m
p
o
u
n
d
s
,
wh
ich
h
av
e
b
ee
n
c
o
n
s
is
ten
tly
r
ep
o
r
ted
in
th
e
lite
r
atu
r
e
as
im
p
o
r
tan
t
m
ar
k
er
s
f
o
r
A
q
u
ila
r
ia
s
p
ec
ies
id
en
tif
icatio
n
.
I
n
[
2
7
]
co
n
f
ir
m
ed
th
at
co
m
p
o
u
n
d
s
a,
b
,
c,
an
d
d
ar
e
p
r
ev
alen
t
i
n
h
ig
h
-
q
u
ality
a
g
ar
wo
o
d
o
ils
,
p
r
o
v
id
in
g
c
o
n
s
is
ten
t
ch
em
ical
s
ig
n
atu
r
es
ac
r
o
s
s
s
p
ec
ies.
T
h
e
s
elec
tio
n
o
f
th
ese
co
m
p
o
u
n
d
s
in
th
is
s
tu
d
y
,
v
alid
ated
b
y
th
e
co
n
f
u
s
io
n
m
atr
i
x
an
d
p
e
r
f
o
r
m
an
ce
m
etr
ics,
a
lig
n
s
with
f
in
d
in
g
s
f
r
o
m
p
r
ev
i
o
u
s
s
tu
d
ies,
f
u
r
th
er
s
u
p
p
o
r
tin
g
th
eir
r
eliab
ilit
y
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
d
if
f
er
e
n
t
A
q
u
ila
r
ia
s
p
ec
ies wh
en
in
teg
r
ated
in
to
m
ac
h
in
e
lear
n
in
g
m
o
d
els.
Ad
d
itio
n
ally
,
th
e
r
esu
lts
s
u
g
g
est
b
r
o
ad
er
ap
p
licab
ilit
y
o
f
th
e
k
-
NN
m
o
d
el
f
o
r
o
th
e
r
ess
en
tial
o
il
class
if
icatio
n
task
s
.
T
h
e
h
ig
h
p
er
f
o
r
m
an
ce
ac
h
ie
v
ed
h
e
r
e
d
em
o
n
s
tr
ates
th
e
p
o
ten
tial
f
o
r
ap
p
ly
in
g
s
im
ilar
ap
p
r
o
ac
h
es
in
in
d
u
s
tr
ial
s
ettin
g
s
wh
er
e
lar
g
e
-
s
ca
le
class
if
ic
atio
n
o
f
o
ils
is
r
eq
u
ir
ed
.
Sin
c
e
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
lik
e
k
-
NN
ca
n
h
an
d
le
co
m
p
lex
ch
em
ical
d
ata
with
m
in
im
al
p
r
e
-
p
r
o
ce
s
s
in
g
,
t
h
is
m
o
d
el
co
u
ld
b
e
p
ar
ticu
lar
ly
u
s
ef
u
l
in
c
o
m
m
er
cial
au
th
en
ticatio
n
p
r
o
ce
s
s
es.
Fu
tu
r
e
r
esear
ch
co
u
ld
ex
p
l
o
r
e
h
y
b
r
id
ap
p
r
o
ac
h
es,
co
m
b
in
in
g
k
-
NN
with
ad
v
a
n
ce
d
m
o
d
els
s
u
ch
as
r
a
n
d
o
m
f
o
r
ests
o
r
s
u
p
p
o
r
t
v
ec
to
r
m
a
ch
in
es,
to
e
n
h
an
ce
class
if
icatio
n
ac
cu
r
ac
y
,
p
ar
tic
u
lar
ly
in
n
o
is
y
o
r
m
o
r
e
v
ar
ied
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
[
2
8
]
.
5.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
in
v
esti
g
ated
th
e
ap
p
licatio
n
o
f
th
e
k
-
NN
m
o
d
el
f
o
r
class
if
y
in
g
A
q
u
ila
r
ia
o
il
s
p
ec
ies
b
ased
o
n
f
o
u
r
s
ig
n
if
ican
t
ch
e
m
ical
co
m
p
o
u
n
d
s
:
d
ih
y
r
o
-
β
-
a
g
ar
o
f
u
r
an
(
a)
,
δ
-
g
u
aien
e
(
b
)
,
1
0
-
ep
i
-
γ
-
eu
d
esm
o
l
(
c)
,
a
n
d
γ
-
eu
d
esm
o
l
(
d
)
.
T
h
e
an
aly
s
is
r
ev
ea
led
th
at
th
e
k
-
NN
m
o
d
el,
u
tili
zin
g
e
u
clid
e
an
d
is
tan
ce
a
n
d
an
o
p
tim
al
k
-
v
alu
e
o
f
1
0
,
ac
h
ie
v
ed
a
class
if
icatio
n
ac
cu
r
ac
y
o
f
1
0
0
%
ac
r
o
s
s
b
o
th
tr
ain
in
g
a
n
d
test
in
g
d
atasets
,
d
em
o
n
s
tr
atin
g
th
e
m
o
d
el’
s
r
o
b
u
s
tn
ess
an
d
e
f
f
icien
cy
in
s
p
ec
ies
id
en
tific
atio
n
.
T
h
e
u
s
e
o
f
t
h
ese
s
elec
ted
co
m
p
o
u
n
d
s
r
esu
lted
in
h
ig
h
p
er
f
o
r
m
a
n
ce
m
etr
ics,
in
clu
d
in
g
p
r
ec
is
io
n
,
s
en
s
itiv
ity
,
an
d
s
p
e
c
if
icity
,
s
u
r
p
ass
in
g
ex
p
ec
tatio
n
s
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
A
q
u
ila
r
ia
s
p
ec
ies.
T
h
ese
f
in
d
in
g
s
s
u
g
g
est
th
at
th
e
in
teg
r
atio
n
o
f
s
p
ec
if
ic
ch
em
ical
m
ar
k
er
s
s
ig
n
if
ican
tly
en
h
a
n
ce
s
th
e
p
er
f
o
r
m
an
ce
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
s
p
ec
ies
id
en
tific
atio
n
in
ess
en
tial
o
ils
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
th
at
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
co
r
p
o
r
atin
g
th
ese
k
e
y
ch
em
ical
m
ar
k
er
s
ca
n
p
r
o
v
i
d
e
s
ca
lab
le
s
o
lu
tio
n
s
f
o
r
lar
g
e
-
s
ca
le
class
if
icatio
n
task
s
,
p
ar
ticu
lar
ly
in
th
e
ess
en
tial
o
il
in
d
u
s
tr
y
.
Ad
d
itio
n
ally
,
f
u
t
u
r
e
r
esear
ch
c
o
u
ld
ex
p
lo
r
e
th
e
c
o
m
b
in
ati
o
n
o
f
m
u
ltip
le
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
es
an
d
d
ata
au
g
m
e
n
tatio
n
tech
n
iq
u
es
to
f
u
r
th
er
en
h
an
ce
class
if
icatio
n
p
e
r
f
o
r
m
a
n
ce
in
m
o
r
e
co
m
p
lex
o
r
v
a
r
ied
ex
tr
ac
tio
n
e
n
v
ir
o
n
m
en
ts
,
en
s
u
r
in
g
b
r
o
ad
er
ap
p
licab
ilit
y
an
d
g
r
ea
ter
g
en
e
r
aliza
tio
n
.
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.
39
,
No
.
1
,
J
u
ly
20
25
:
1
78
-
1
89
186
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
th
o
r
s
g
r
ate
f
u
lly
ac
k
n
o
wled
g
e
th
at
th
is
r
esear
c
h
ar
tic
le
was
f
in
an
cially
s
u
p
p
o
r
ted
b
y
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
Ma
lay
s
ia
th
r
o
u
g
h
Un
iv
e
r
s
iti
T
ek
n
o
lo
g
i
MA
R
A
an
d
I
n
s
titu
te
o
f
P
o
s
tg
r
ad
u
ate
Stu
d
ies
UiT
M
(
I
PS
is
)
,
J
o
u
r
n
al
Su
p
p
o
r
t
Fu
n
d
(
J
SF
)
.
T
h
e
in
s
ig
h
tf
u
l
f
ee
d
b
ac
k
an
d
co
n
tr
ib
u
tio
n
s
f
r
o
m
m
em
b
er
s
o
f
th
e
Ad
v
an
ce
Sig
n
a
l
Pro
ce
s
s
in
g
R
esear
ch
I
n
ter
est
Gr
o
u
p
ar
e
d
e
ep
ly
ap
p
r
ec
iated
.
T
h
e
au
t
h
o
r
s
also
wis
h
to
e
x
ten
d
th
eir
g
r
atitu
d
e
to
th
e
B
io
-
Ar
o
m
atic
R
esear
ch
C
en
tr
e
o
f
E
x
c
ellen
ce
(
B
AR
C
E
)
at
Un
iv
er
s
iti
Ma
lay
s
ia
Pah
an
g
Al
-
Su
ltan
Ab
d
u
llah
(
UM
PS
A)
f
o
r
th
eir
in
v
alu
a
b
le
ass
is
tan
ce
with
d
ata
ex
tr
ac
tio
n
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
g
r
atef
u
lly
ac
k
n
o
wled
g
e
th
at
th
is
r
esear
ch
ar
ticle
was
f
in
an
cially
s
u
p
p
o
r
ted
b
y
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
Ma
lay
s
ia
th
r
o
u
g
h
Un
iv
e
r
s
iti
T
ek
n
o
lo
g
i
MA
R
A
an
d
I
n
s
titu
te
o
f
P
o
s
tg
r
ad
u
ate
Stu
d
ies
UiT
M
(
I
PS
is
)
,
J
o
u
r
n
al
Su
p
p
o
r
t Fu
n
d
(
J
SF
)
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
No
o
r
Aid
a
Sy
ak
ir
a
Ah
m
ad
Sab
r
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Nu
r
Ath
ir
ah
Sy
af
i
q
ah
No
r
am
li
✓
✓
✓
✓
✓
✓
✓
Nik
Fas
h
a
E
d
o
r
a
Nik
Kam
ar
u
za
m
an
✓
✓
✓
✓
✓
✓
Nu
r
laila
I
s
m
ail
✓
✓
✓
✓
✓
✓
✓
Z
ak
iah
Mo
h
d
Yu
s
o
f
f
✓
✓
✓
✓
✓
✓
✓
Ali A
b
d
Alm
is
r
eb
✓
✓
✓
✓
✓
Saif
u
l N
izam
T
aju
d
d
in
✓
✓
✓
✓
✓
Mo
h
d
Nasir
T
aib
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
tu
a
li
z
a
ti
o
n
M
:
M
e
th
o
d
o
l
o
g
y
So
:
So
ftwa
re
Va
:
Va
li
d
a
ti
o
n
Fo
:
Fo
rm
a
l
a
n
a
ly
sis
I
:
I
n
v
e
stig
a
ti
o
n
R
:
R
e
so
u
rc
e
s
D
:
D
a
ta Cu
ra
ti
o
n
O
:
Wr
it
in
g
-
O
ri
g
in
a
l
Dra
ft
E
:
Wr
it
in
g
-
Re
v
iew
&
E
d
it
i
n
g
Vi
:
Vi
su
a
li
z
a
ti
o
n
Su
:
Su
p
e
rv
isi
o
n
P
:
P
ro
jec
t
a
d
m
in
istrati
o
n
Fu
:
Fu
n
d
in
g
a
c
q
u
isi
ti
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
I
NF
O
RM
E
D
CO
NS
E
N
T
W
e
h
av
e
o
b
tain
ed
in
f
o
r
m
ed
c
o
n
s
en
t f
r
o
m
all
in
d
iv
id
u
als in
c
lu
d
ed
in
t
h
is
s
tu
d
y
.
DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
is
n
o
t
ap
p
li
ca
b
le
to
th
is
p
ap
er
as
n
o
n
e
w
d
ata
wer
e
cr
ea
ted
o
r
an
aly
ze
d
in
th
is
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
F
.
H
a
s
h
e
m
p
o
u
r
-
b
a
l
t
o
r
k
e
t
a
l
.
,
“
R
e
c
e
n
t
me
t
h
o
d
s
i
n
d
e
t
e
c
t
i
o
n
o
f
o
l
i
v
e
o
i
l
a
d
u
l
t
e
r
a
t
i
o
n
:
S
t
a
t
e
-
of
-
t
h
e
-
A
r
t
,
”
J
o
u
r
n
a
l
o
f
A
g
r
i
c
u
l
t
u
r
e
a
n
d
Fo
o
d
R
e
se
a
rc
h
,
v
o
l
.
1
6
,
p
.
1
0
1
1
2
3
,
J
u
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
a
f
r
.
2
0
2
4
.
1
0
1
1
2
3
.
[
2
]
M
.
R
a
s
e
k
h
,
H
.
K
a
r
a
m
i
,
A
.
D
.
W
i
l
s
o
n
,
a
n
d
M
.
G
a
n
c
a
r
z
,
“
C
l
a
s
s
i
f
i
c
a
t
i
o
n
a
n
d
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
e
s
s
e
n
t
i
a
l
o
i
l
s
f
r
o
m
h
e
r
b
s
a
n
d
f
r
u
i
t
s
b
a
s
e
d
o
n
a
M
O
S
e
l
e
c
t
r
o
n
i
c
-
n
o
s
e
t
e
c
h
n
o
l
o
g
y
,
”
C
h
e
m
o
s
e
n
s
o
r
s
,
v
o
l
.
9
,
n
o
.
6
,
p
.
1
4
2
,
J
u
n
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
c
h
e
m
o
s
e
n
s
o
r
s
9
0
6
0
1
4
2
.
[
3
]
A
.
Y
u
l
i
a
n
t
i
,
B
.
A
b
d
u
l
l
a
h
,
S
.
L
e
o
n
g
,
B
.
W
o
n
g
,
S
.
S
a
ml
i
n
g
,
a
n
d
S
.
F
o
n
g
,
“
G
r
a
d
i
n
g
o
f
a
g
a
r
w
o
o
d
b
a
se
d
o
n
t
h
e
i
r
c
h
e
mi
c
a
l
p
r
o
f
i
l
e
s
u
si
n
g
G
C
-
M
S
i
n
c
o
r
p
o
r
a
t
i
n
g
c
h
e
mo
m
e
t
r
i
c
a
p
p
r
o
a
c
h
e
s,
”
2
0
2
4
.
[
4
]
Y
.
W
a
n
g
e
t
a
l
.
,
“
A
q
u
i
l
a
r
i
a
s
p
e
c
i
e
s
(
t
h
y
m
e
l
a
e
a
c
e
a
e
)
d
i
s
t
r
i
b
u
t
i
o
n
,
v
o
l
a
t
i
l
e
a
n
d
n
o
n
-
v
o
l
a
t
i
l
e
p
h
y
t
o
c
h
e
m
i
c
a
l
s
,
p
h
a
r
m
a
c
o
l
o
g
i
c
a
l
u
s
e
s
,
a
g
a
r
w
o
o
d
g
r
a
d
i
n
g
s
y
s
t
e
m
,
a
n
d
i
n
d
u
c
t
i
o
n
m
e
t
h
o
d
s
,
”
M
o
l
e
c
u
l
e
s
,
v
o
l
.
2
6
,
n
o
.
2
4
,
p
.
7
7
0
8
,
D
e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
m
o
l
e
c
u
l
e
s
2
6
2
4
7
7
0
8
.
[
5
]
M
.
A
q
e
e
l
,
A
.
S
o
h
a
i
b
,
M
.
I
q
b
a
l
,
H
.
U
.
R
e
h
m
a
n
,
a
n
d
F
.
R
u
s
t
a
m
,
“
H
y
p
e
r
s
p
e
c
t
r
a
l
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
o
i
l
a
d
u
l
t
e
r
a
t
i
o
n
u
si
n
g
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s,
”
C
u
rr
e
n
t
R
e
se
a
r
c
h
i
n
F
o
o
d
S
c
i
e
n
c
e
,
v
o
l
.
8
,
p
.
1
0
0
7
7
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
r
f
s.
2
0
2
4
.
1
0
0
7
7
3
.
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
R
o
b
u
s
t K
-
N
N
a
p
p
r
o
a
ch
fo
r
cl
a
s
s
ifyin
g
A
q
u
ila
r
ia
o
il sp
ec
ies b
y
…
(
N
o
o
r
A
id
a
S
ya
kira
A
h
ma
d
S
a
b
r
i
)
187
[
6
]
H
.
S
a
p
u
t
r
a
,
B
.
S
a
t
r
i
a
,
N
.
N
a
z
i
r
,
a
n
d
T.
A
n
g
g
r
a
i
n
i
,
“
D
e
v
e
l
o
p
m
e
n
t
o
f
a
g
a
r
w
o
o
d
o
i
l
r
e
sea
r
c
h
a
n
d
b
e
n
e
f
i
t
:
b
i
b
l
i
o
met
r
i
c
a
n
a
l
y
s
i
s,
”
AJ
AR
C
D
E
(
As
i
a
n
J
o
u
r
n
a
l
o
f
Ap
p
l
i
e
d
R
e
se
a
rc
h
f
o
r
C
o
m
m
u
n
i
t
y
D
e
v
e
l
o
p
m
e
n
t
a
n
d
Em
p
o
w
e
rm
e
n
t
)
,
p
p
.
5
5
–
6
0
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
2
9
1
6
5
/
a
j
a
r
c
d
e
.
v
8
i
1
.
3
7
4
.
[
7
]
S
.
H
.
S
h
e
t
t
y
,
S
.
S
h
e
t
t
y
,
C
.
S
i
n
g
h
,
a
n
d
A
.
R
a
o
,
“
S
u
p
e
r
v
i
s
e
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
:
a
l
g
o
r
i
t
h
ms
a
n
d
a
p
p
l
i
c
a
t
i
o
n
s,
”
F
u
n
d
a
m
e
n
t
a
l
s
a
n
d
Me
t
h
o
d
s
o
f
Ma
c
h
i
n
e
a
n
d
D
e
e
p
L
e
a
r
n
i
n
g
.
W
i
l
e
y
,
p
p
.
1
–
1
6
,
J
a
n
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
2
/
9
7
8
1
1
1
9
8
2
1
9
0
8
.
c
h
1
.
[
8
]
M
.
A
.
A
b
a
s
e
t
a
l.
,
“
A
g
a
r
w
o
o
d
o
i
l
q
u
a
l
i
t
y
c
l
a
ssi
f
i
e
r
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
J
o
u
r
n
a
l
o
f
F
u
n
d
a
m
e
n
t
a
l
a
n
d
Ap
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
9
,
n
o
.
4
S
,
p
.
6
2
,
Ja
n
.
2
0
1
8
,
d
o
i
:
1
0
.
4
3
1
4
/
j
f
a
s.
v
9
i
4
s
.
4
.
[
9
]
R
.
K
.
H
a
l
d
e
r
,
M
.
N
.
U
d
d
i
n
,
M
.
A
.
U
d
d
i
n
,
S
.
A
r
y
a
l
,
a
n
d
A
.
K
h
r
a
i
sa
t
,
“
En
h
a
n
c
i
n
g
K
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
a
l
g
o
r
i
t
h
m
:
a
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
a
n
d
p
e
r
f
o
r
ma
n
c
e
a
n
a
l
y
s
i
s
o
f
mo
d
i
f
i
c
a
t
i
o
n
s
,
”
J
o
u
r
n
a
l
o
f
Bi
g
D
a
t
a
,
v
o
l
.
1
1
,
n
o
.
1
,
A
u
g
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
8
6
/
s4
0
5
3
7
-
024
-
0
0
9
7
3
-
y.
[1
0
]
A
.
H
.
Za
i
d
i
e
t
a
l
.
,
“
S
t
a
t
i
st
i
c
a
l
a
n
a
l
y
s
i
s
o
f
a
g
a
r
w
o
o
d
o
i
l
c
h
e
m
i
c
a
l
c
o
m
p
o
u
n
d
e
x
i
st
s
i
n
f
o
u
r
sp
e
c
i
e
s
o
f
A
q
u
i
l
a
r
i
a
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
s
i
n
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
1
3
,
n
o
.
3
,
p
.
7
2
7
,
S
e
p
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
a
a
s.
v
1
3
.
i
3
.
p
p
7
2
7
-
7
3
2
.
[1
1
]
F
.
A
.
M
u
f
a
r
r
o
h
a
,
A
.
Z.
N
u
r
,
M
.
R
.
R
a
h
a
b
i
l
l
a
h
,
A
.
Ja
u
h
a
r
i
,
D
.
R
.
A
n
a
m
i
sa,
a
n
d
M
u
l
a
a
b
,
“
S
p
i
c
e
s
i
d
e
n
t
i
f
i
c
a
t
i
o
n
i
n
e
s
se
n
t
i
a
l
o
i
l
p
r
o
d
u
c
e
r
s
u
s
i
n
g
c
o
m
p
a
r
a
s
i
o
n
o
f
K
N
N
a
n
d
N
a
ï
v
e
B
a
y
e
s
c
l
a
ss
i
f
i
e
r
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
f
o
rm
a
t
i
c
s,
T
e
c
h
n
o
l
o
g
y
a
n
d
E
n
g
i
n
e
e
ri
n
g
2
0
2
3
(
I
n
C
I
T
E
2
0
2
3
)
,
A
t
l
a
n
t
i
s
P
r
e
ss I
n
t
e
r
n
a
t
i
o
n
a
l
B
V
,
2
0
2
3
,
p
p
.
6
1
8
–
6
2
7
.
[1
2
]
M
.
O
.
A
r
o
w
o
l
o
,
M
.
O
.
A
d
e
b
i
y
i
,
A
.
A
.
A
d
e
b
i
y
i
,
a
n
d
O
.
O
l
u
g
b
a
r
a
,
“
O
p
t
i
m
i
z
e
d
h
y
b
r
i
d
i
n
v
e
s
t
i
g
a
t
i
v
e
b
a
s
e
d
d
i
m
e
n
s
i
o
n
a
l
i
t
y
r
e
d
u
c
t
i
o
n
m
e
t
h
o
d
s
f
o
r
m
a
l
a
r
i
a
v
e
c
t
o
r
u
s
i
n
g
K
N
N
c
l
a
s
s
i
f
i
e
r
,
”
J
o
u
r
n
a
l
o
f
B
i
g
D
a
t
a
,
v
o
l
.
8
,
n
o
.
1
,
F
e
b
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
8
6
/
s
4
0
5
3
7
-
021
-
0
0
4
1
5
-
z.
[1
3
]
P
.
M
a
v
a
i
e
,
L.
H
o
l
d
e
r
,
a
n
d
M
.
K
.
S
k
i
n
n
e
r
,
“
H
y
b
r
i
d
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
t
o
i
m
p
r
o
v
e
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
l
o
w
-
v
o
l
u
me
h
i
g
h
-
d
i
m
e
n
s
i
o
n
a
l
d
a
t
a
,
”
BM
C
B
i
o
i
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
2
4
,
n
o
.
1
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
2
8
5
9
-
0
2
3
-
0
5
5
5
7
-
w.
[1
4
]
E.
O
z
t
u
r
k
K
i
y
a
k
,
B
.
G
h
a
se
mk
h
a
n
i
,
a
n
d
D
.
B
i
r
a
n
t
,
“
H
i
g
h
-
l
e
v
e
l
K
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
s
(
H
LK
N
N
)
:
a
s
u
p
e
r
v
i
se
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
mo
d
e
l
f
o
r
c
l
a
ssi
f
i
c
a
t
i
o
n
a
n
a
l
y
si
s,
”
E
l
e
c
t
r
o
n
i
c
s
,
v
o
l
.
1
2
,
n
o
.
1
8
,
p
.
3
8
2
8
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
2
1
8
3
8
2
8
.
[1
5
]
Z.
M
.
Y
u
s
o
f
f
a
n
d
N
.
I
smai
l
,
“
D
a
t
a
s
e
t
s
o
f
c
h
e
mi
c
a
l
c
o
m
p
o
u
n
d
s
i
n
t
h
r
e
e
d
i
f
f
e
r
e
n
t
sp
e
c
i
e
s
o
f
a
q
u
i
l
a
r
i
a
u
si
n
g
G
C
-
M
S
c
o
u
p
l
e
d
w
i
t
h
GC
-
F
I
D
a
n
a
l
y
si
s
,
”
D
a
t
a
i
n
Br
i
e
f
,
v
o
l
.
5
3
,
p
.
1
1
0
2
0
9
,
A
p
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
d
i
b
.
2
0
2
4
.
1
1
0
2
0
9
.
[
1
6
]
M
.
Z
.
N
a
s
e
r
a
n
d
A
.
H
.
A
l
a
v
i
,
“
I
n
s
i
g
h
t
s
i
n
t
o
p
e
r
f
o
r
ma
n
c
e
f
i
t
n
e
ss
a
n
d
e
r
r
o
r
me
t
r
i
c
s
f
o
r
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
a
rX
i
v
p
r
e
p
r
i
n
t
a
rXi
v
:
2
0
0
6
.
0
0
8
8
7
,
d
o
i
:
1
0
.
4
8
5
5
0
/
a
r
X
i
v
.
2
0
0
6
.
0
0
8
8
7
.
[1
7
]
K
.
K
.
B
i
l
i
a
m
i
n
u
,
S
.
A
.
B
u
s
a
r
i
,
J.
R
o
d
r
i
g
u
e
z
,
a
n
d
F
.
G
i
l
-
C
a
st
i
ñ
e
i
r
a
,
“
B
e
a
m
p
r
e
d
i
c
t
i
o
n
f
o
r
mm
W
a
v
e
V
2
I
c
o
mm
u
n
i
c
a
t
i
o
n
u
si
n
g
M
L
-
b
a
s
e
d
mu
l
t
i
c
l
a
ss
c
l
a
ssi
f
i
c
a
t
i
o
n
a
l
g
o
r
i
t
h
ms,
”
E
l
e
c
t
r
o
n
i
c
s
,
v
o
l
.
1
3
,
n
o
.
1
3
,
p
.
2
6
5
6
,
J
u
l
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
3
1
3
2
6
5
6
.
[1
8
]
R
.
A
.
I
.
A
l
mas
h
h
a
d
a
n
i
,
G
.
C
.
H
o
c
k
,
F
.
H
.
B
t
N
o
r
d
i
n
,
a
n
d
H
.
N
.
A
b
d
u
l
r
a
z
z
a
k
,
“
E
l
e
c
t
r
o
l
u
mi
n
e
sce
n
c
e
i
m
a
g
e
s
f
o
r
so
l
a
r
c
e
l
l
f
a
u
l
t
d
e
t
e
c
t
i
o
n
u
s
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
f
o
r
b
i
n
a
r
y
a
n
d
m
u
l
t
i
c
l
a
s
s
c
l
a
ssi
f
i
c
a
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
E
l
e
c
t
r
o
n
i
c
s
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
1
,
n
o
.
5
,
p
p
.
1
5
0
–
1
6
0
,
M
a
y
2
0
2
4
,
d
o
i
:
1
0
.
1
4
4
4
5
/
2
3
4
8
8
3
7
9
/
i
j
e
e
e
-
v1
1
i
5
p
1
1
4
.
[
19
]
N
.
H
.
M
.
A
r
i
f
f
i
n
,
M
.
I
.
M
.
I
q
b
a
l
,
M
.
Y
u
so
f
f
,
a
n
d
N
.
A
.
M
.
Z
u
l
k
e
f
l
i
,
“
A
st
u
d
y
o
n
t
h
e
b
e
st
c
l
a
ssi
f
i
c
a
t
i
o
n
me
t
h
o
d
f
o
r
a
n
i
n
t
e
l
l
i
g
e
n
t
p
h
i
s
h
i
n
g
w
e
b
s
i
t
e
d
e
t
e
c
t
i
o
n
s
y
st
e
m,”
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
R
e
se
a
rc
h
i
n
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
a
n
d
E
n
g
i
n
e
e
r
i
n
g
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
4
8
,
n
o
.
2
,
p
p
.
1
9
7
–
2
1
0
,
Ju
l
.
2
0
2
4
,
d
o
i
:
1
0
.
3
7
9
3
4
/
a
r
a
s
e
t
.
4
8
.
2
.
1
9
7
2
1
0
.
[2
0
]
K
.
A
.
A
t
h
i
r
a
h
,
N
.
I
smai
l
,
M
.
N
.
Ta
i
b
,
N
.
A
.
M
.
A
l
i
,
M
.
J
a
mi
l
,
a
n
d
S
.
L
i
a
s
,
“
M
o
d
e
l
l
i
n
g
o
f
c
y
mb
o
p
o
g
o
n
o
i
l
s s
p
e
c
i
e
s u
si
n
g
k
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
u
r
s
(
k
-
N
N
)
,
”
i
n
2
0
1
9
I
EE
E
7
t
h
C
o
n
f
e
re
n
c
e
o
n
S
y
st
e
m
s,
Pr
o
c
e
ss
a
n
d
C
o
n
t
r
o
l
(
I
C
S
P
C
)
,
D
e
c
.
2
0
1
9
,
p
p
.
5
–
9
,
d
o
i
:
1
0
.
1
1
0
9
/
i
c
sp
c
4
7
1
3
7
.
2
0
1
9
.
9
0
6
8
0
8
6
.
[2
1
]
K
.
S
t
a
p
o
r
,
P
.
K
s
i
e
n
i
e
w
i
c
z
,
S
.
G
a
r
c
í
a
,
a
n
d
M
.
W
o
ź
n
i
a
k
,
“
H
o
w
t
o
d
e
si
g
n
t
h
e
f
a
i
r
e
x
p
e
r
i
m
e
n
t
a
l
c
l
a
ssi
f
i
e
r
e
v
a
l
u
a
t
i
o
n
,
”
A
p
p
l
i
e
d
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
4
,
p
.
1
0
7
2
1
9
,
J
u
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
s
o
c
.
2
0
2
1
.
1
0
7
2
1
9
.
[2
2
]
J.
S
a
d
o
w
sk
i
,
“
W
h
e
n
d
a
t
a
i
s
c
a
p
i
t
a
l
:
d
a
t
a
f
i
c
a
t
i
o
n
,
a
c
c
u
m
u
l
a
t
i
o
n
,
a
n
d
e
x
t
r
a
c
t
i
o
n
,
”
Bi
g
D
a
t
a
a
n
d
S
o
c
i
e
t
y
,
v
o
l
.
6
,
n
o
.
1
,
p
.
2
0
5
3
9
5
1
7
1
8
8
2
0
5
4
,
Ja
n
.
2
0
1
9
,
d
o
i
:
1
0
.
1
1
7
7
/
2
0
5
3
9
5
1
7
1
8
8
2
0
5
4
9
.
[2
3
]
J.
R
.
S
a
u
r
a
,
B
.
R
.
H
e
r
r
a
e
z
,
a
n
d
A
.
R
e
y
e
s
-
M
e
n
e
n
d
e
z
,
“
C
o
m
p
a
r
i
n
g
a
t
r
a
d
i
t
i
o
n
a
l
a
p
p
r
o
a
c
h
f
o
r
f
i
n
a
n
c
i
a
l
b
r
a
n
d
c
o
mm
u
n
i
c
a
t
i
o
n
a
n
a
l
y
si
s
w
i
t
h
a
b
i
g
d
a
t
a
a
n
a
l
y
t
i
c
s t
e
c
h
n
i
q
u
e
,
”
I
EE
E
A
c
c
e
s
s
,
v
o
l
.
7
,
p
p
.
3
7
1
0
0
–
3
7
1
0
8
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ES
S
.
2
0
1
9
.
2
9
0
5
3
0
1
.
[2
4
]
N
.
A
.
S
.
A
h
ma
d
S
a
b
r
i
e
t
a
l
.
,
“
S
t
a
t
i
s
t
i
c
a
l
a
n
a
l
y
s
i
s
f
o
r
c
h
e
m
i
c
a
l
c
o
m
p
o
u
n
d
b
a
se
d
o
n
se
v
e
r
a
l
sp
e
c
i
e
s
o
f
a
q
u
i
l
a
r
i
a
e
ss
e
n
t
i
a
l
o
i
l
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
(
I
J
E
C
E)
,
v
o
l
.
1
4
,
n
o
.
4
,
p
.
3
6
6
3
,
A
u
g
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
4
i
4
.
p
p
3
6
6
3
-
3
6
7
3
.
[2
5
]
N
.
A
.
S
y
a
m
e
e
r
a
e
t
a
l
.
,
“
Ef
f
e
c
t
s o
f
h
e
a
t
t
r
e
a
t
me
n
t
o
n
t
h
e
c
h
e
mi
c
a
l
c
o
m
p
o
s
i
t
i
o
n
,
a
n
t
i
o
x
i
d
a
n
t
a
c
t
i
v
i
t
y
,
a
n
d
t
o
x
i
c
i
t
y
o
f
a
g
a
r
w
o
o
d
o
i
l
,
”
J
o
u
rn
a
l
o
f
K
i
n
g
S
a
u
d
U
n
i
v
e
rsi
t
y
-
S
c
i
e
n
c
e
,
v
o
l
.
3
6
,
n
o
.
4
,
p
.
1
0
3
1
4
1
,
A
p
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
k
s
u
s.
2
0
2
4
.
1
0
3
1
4
1
.
[2
6
]
M
.
S
a
b
a
t
i
n
o
e
t
a
l
.
,
“
E
x
p
e
r
i
m
e
n
t
a
l
d
a
t
a
b
a
se
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
c
l
a
ssi
f
i
c
a
t
i
o
n
mo
d
e
l
s
w
i
t
h
p
r
e
d
i
c
t
i
v
e
a
b
i
l
i
t
y
t
o
se
l
e
c
t
i
n
v
i
t
r
o
a
c
t
i
v
e
a
n
t
i
v
i
r
a
l
a
n
d
n
o
n
-
t
o
x
i
c
e
sse
n
t
i
a
l
o
i
l
s
,
”
Mo
l
e
c
u
l
e
s
,
v
o
l
.
2
5
,
n
o
.
1
0
,
p
.
2
4
5
2
,
M
a
y
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
m
o
l
e
c
u
l
e
s2
5
1
0
2
4
5
2
.
[2
7
]
T.
Y
a
n
,
S
.
Y
a
n
g
,
Y
.
C
h
e
n
,
Q
.
W
a
n
g
,
a
n
d
G
.
L
i
,
“
C
h
e
m
i
c
a
l
p
r
o
f
i
l
e
s
o
f
c
u
l
t
i
v
a
t
e
d
a
g
a
r
w
o
o
d
i
n
d
u
c
e
d
b
y
d
i
f
f
e
r
e
n
t
t
e
c
h
n
i
q
u
e
s
,
”
Mo
l
e
c
u
l
e
s
,
v
o
l
.
2
4
,
n
o
.
1
0
,
p
.
1
9
9
0
,
M
a
y
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/
m
o
l
e
c
u
l
e
s2
4
1
0
1
9
9
0
.
[
28
]
L.
A
.
D
e
m
i
d
o
v
a
,
“
Tw
o
-
st
a
g
e
h
y
b
r
i
d
d
a
t
a
c
l
a
ss
i
f
i
e
r
s
b
a
se
d
o
n
S
V
M
a
n
d
k
N
N
a
l
g
o
r
i
t
h
m
s,”
S
y
m
m
e
t
ry
,
v
o
l
.
1
3
,
n
o
.
4
,
p
.
6
1
5
,
A
p
r
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
sy
m1
3
0
4
0
6
1
5
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
No
o
r
Aid
a
S
y
a
k
ir
a
Ah
m
a
d
S
a
b
r
i
o
b
tai
n
e
d
h
e
r
b
a
c
h
e
lo
r
o
f
e
n
g
in
e
e
rin
g
(Ho
n
s)
in
e
lec
tro
n
ic
e
n
g
i
n
e
e
rin
g
fr
o
m
U
n
iv
e
rsiti
Te
k
n
o
lo
g
i
M
ARA
(UiT
M
),
S
h
a
h
Ala
m
,
M
a
lay
sia
,
i
n
2
0
2
2
.
C
u
rre
n
tl
y
,
s
h
e
is
a
g
ra
d
u
a
t
e
re
se
a
rc
h
a
ss
i
sta
n
t
a
t
th
e
F
a
c
u
lt
y
o
f
E
lec
tri
c
a
l
En
g
i
n
e
e
rin
g
,
UiTM
S
h
a
h
Ala
m
,
wh
e
re
sh
e
is
p
u
rsu
i
n
g
p
o
stg
ra
d
u
a
te
stu
d
ies
.
H
e
r
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
a
d
v
a
n
c
e
d
si
g
n
a
l
p
r
o
c
e
ss
in
g
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
,
p
a
rti
c
u
l
a
rly
i
n
t
h
e
a
n
a
l
y
sis
a
n
d
c
las
sifica
ti
o
n
o
f
a
g
a
rw
o
o
d
o
il
,
le
v
e
ra
g
in
g
c
o
m
p
u
tati
o
n
a
l
m
e
th
o
d
s
to
imp
ro
v
e
t
h
e
a
c
c
u
ra
c
y
a
n
d
e
fficie
n
c
y
o
f
c
h
e
m
ica
l
c
o
m
p
o
siti
o
n
a
n
a
ly
sis
.
Sh
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
id
a
sy
a
k
iraa
a
0
1
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.