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as
p
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f
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m
er
y
an
d
tr
ad
itio
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al
m
ed
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e
[
1
]
–
[
6
]
.
Ho
we
v
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,
a
n
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zin
g
th
e
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p
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x
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[
7
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[
1
0
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.
In
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s
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atasets
with
h
ig
h
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ac
cu
r
ac
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an
d
ef
f
icien
c
y
[
7
]
,
[
1
1
]
,
[
1
2
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.
ML
alg
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r
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m
s
s
u
ch
as
ar
tific
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eu
r
al
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etwo
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k
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ANN)
,
r
an
d
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KNN
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h
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s
tr
ated
t
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p
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ten
tial
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to
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as
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d
ata
[
1
3
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.
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1
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[
1
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.
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tech
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t
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s
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f
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2
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3
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r
o
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iq
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ee
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ap
p
lied
in
ess
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tial
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il
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aly
s
is
.
T
h
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r
ev
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also
ad
d
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ess
es
th
e
ch
allen
g
es
an
d
f
u
t
u
r
e
p
o
ten
tial
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ap
p
l
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AI
to
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h
an
ce
th
e
f
ield
of
ess
en
tial
o
il
ch
em
is
tr
y
[
1
5
]
,
[
1
7
]
–
[
1
9
]
.
2.
M
E
T
H
O
D
ML
an
aly
s
is
of
A
q
u
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r
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es
s
en
tial
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r
elies
on
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d
er
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ch
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p
r
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eq
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ir
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ig
h
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d
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f
f
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f
ea
t
u
r
e
e
x
tr
ac
tio
n
[
1
0
]
,
[
2
0
]
–
[
2
6
]
.
C
lass
ical
clas
s
if
ier
s
(
ANN,
R
F,
SVM
,
an
d
KNN
)
ar
e
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ely
v
alid
ated
f
o
r
ess
en
tial
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il
an
aly
s
is
[
2
0
]
,
[
2
7
]
.
Acc
o
r
d
in
g
l
y
,
a
s
y
s
tem
atic
liter
atu
r
e
r
ev
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of
S
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p
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s
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in
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tu
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0
1
3
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0
2
4
)
was
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u
cted
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s
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i
b
lio
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ic
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o
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a
n
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2
.
1
.
L
it
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t
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a
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cr
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g
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ra
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o
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r
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ed
r
ep
o
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tin
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item
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f
o
r
s
y
s
tem
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r
ev
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d
m
et
a
-
an
aly
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es
(
PR
I
SMA)
r
ep
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tin
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g
u
id
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to
en
s
u
r
e
t
r
an
s
p
ar
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c
y
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d
r
ep
r
o
d
u
cib
ilit
y
of
th
e
liter
atu
r
e
s
cr
ee
n
in
g
p
r
o
ce
s
s
[
2
8
]
,
[
2
9
]
.
L
iter
atu
r
e
r
etr
iev
a
l
was
co
n
d
u
cte
d
u
s
in
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p
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d
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ap
p
lied
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s
tr
ac
ts
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d
k
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3
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4
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itle
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ased
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ile
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al
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an
aly
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s
u
m
m
ar
iz
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in
Fig
u
r
e
1.
Fig
u
r
e
1.
PR
I
SMA
-
s
ty
le
f
lo
w
d
iag
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of
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2
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2
.
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Evaluation Warning : The document was created with Spire.PDF for Python.
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[
3
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[
3
2
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[
3
3
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3
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w
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2
1
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.
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[
3
9
]
.
M
o
s
t
s
t
u
d
i
e
s
em
p
lo
y
ed
p
r
ep
r
o
c
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t
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ch
as
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l
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za
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o
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ed
u
c
ti
o
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e
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tu
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t
io
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p
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p
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p
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t
a
n
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l
y
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i
s
(
P
C
A
)
to
i
m
p
r
o
v
e
d
at
a
q
u
a
l
i
ty
[
1
6
]
,
[
2
6
]
,
w
i
th
m
o
d
e
l
p
ar
a
m
e
te
r
s
o
p
t
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m
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h
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r
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p
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,
a
n
d
F1
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s
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o
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e
[
1
3
]
,
[
4
0
]
.
T
a
b
l
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p
r
e
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t
s
a
co
m
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e
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ia
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m
ai
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m
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t
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u
e
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m
a
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d
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m
ak
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h
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l
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m
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t
p
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t
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l
c
h
o
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ce
s
in
c
u
r
r
en
t
s
t
u
d
i
e
s
.
T
ab
le
2.
C
o
m
p
a
r
ativ
e
an
aly
s
is
of
ML
tech
n
iq
u
es
f
o
r
ess
en
tial
o
il
class
if
icatio
n
F
e
a
t
u
r
e
s
ANN
RF
S
V
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K
N
N
A
l
g
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h
m
ANN
En
se
mb
l
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of
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H
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l
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t
marg
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l
a
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a
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on
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N
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t
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p
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x
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l
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e
a
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p
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b
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ms
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l
a
s
si
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c
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t
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(
p
r
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mar
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l
y
)
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i
g
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R
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t
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h
a
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p
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Ef
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t
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d
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s
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,
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h
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e
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s
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to
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h
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g
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d
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me
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si
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a
l
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t
y
i
ssu
e
s
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
h
is
s
tu
d
y
co
n
d
u
cted
an
in
-
d
ep
th
an
aly
s
is
of
ML
ap
p
licatio
n
s
f
o
r
th
e
class
if
icatio
n
of
A
q
u
ila
r
ia
ess
en
tial
o
il
s
.
It
ex
am
in
ed
k
ey
ch
allen
g
es
in
ch
em
ical
a
n
aly
s
is
,
p
ar
ticu
lar
ly
th
e
co
m
p
lex
s
esq
u
iter
p
en
e
co
m
p
o
s
itio
n
of
ag
ar
wo
o
d
o
i
l.
T
h
e
g
r
o
win
g
in
te
g
r
atio
n
of
AI
an
d
ML
h
as
s
ig
n
if
ica
n
tly
im
p
r
o
v
ed
th
e
p
r
ec
is
io
n
,
au
to
m
atio
n
,
a
n
d
ef
f
icien
cy
of
ess
en
tial
o
il
d
etec
tio
n
an
d
class
if
icatio
n
.
3
.
1
.
Yea
rl
y
dis
t
ributio
n
of
ML
a
lg
o
rit
hm
s
in
a
g
a
rwo
o
d
o
il
(
2
0
1
4
-
2
0
2
4
)
T
h
e
tem
p
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is
tr
ib
u
tio
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of
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p
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Fig
u
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2,
illu
s
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ates
th
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lu
tio
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o
r
ith
m
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ag
e
f
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ar
w
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ANN
was
th
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o
r
ith
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ap
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3
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m
ar
k
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th
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in
itial
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p
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of
ML
in
th
is
f
ield
[
4
1
]
,
f
o
llo
wed
by
ANN
an
d
KNN
in
2
0
1
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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By
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,
SVM,
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ted
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wh
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ANN
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NN
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ily
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b
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t.
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0
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1
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ANN
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e
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r
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en
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lo
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r
en
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m
u
lti
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m
o
d
el
co
m
p
a
r
is
o
n
.
Su
b
s
eq
u
en
tly
,
KNN
,
SVM
,
an
d
ANN
b
ec
am
e
m
o
r
e
p
r
o
m
in
e
n
t
in
2
0
2
2
an
d
2
0
2
4
,
wh
er
ea
s
RF
wa
s
no
lo
n
g
er
ap
p
lied
in
2
0
2
4
.
O
v
er
all,
ANN
d
o
m
in
ated
ea
r
ly
s
tu
d
ies,
RF
p
r
ev
ailed
in
m
id
-
y
ea
r
s
,
an
d
KNN
an
d
SVM
g
ain
ed
p
r
o
m
in
en
ce
in
r
ec
en
t
r
esear
ch
,
r
ef
lectin
g
co
n
tin
u
al
m
eth
o
d
o
l
o
g
ical
r
ef
i
n
em
en
t.
Fig
u
r
e
2.
Nu
m
b
er
of
s
tu
d
ies
a
p
p
ly
in
g
ANN,
R
F,
SVM
,
an
d
KNN
alg
o
r
ith
m
s
f
o
r
a
g
ar
wo
o
d
o
il
class
if
icatio
n
3
.
2
.
ML
a
lg
o
rit
hm
dis
t
ribut
io
n
in
a
g
a
rwo
o
d
o
il
s
t
ud
ies
Fig
u
r
e
3
illu
s
tr
ates
th
e
d
is
tr
ib
u
tio
n
of
ML
alg
o
r
ith
m
s
u
s
ed
in
ag
ar
wo
o
d
o
il
class
if
icatio
n
ac
r
o
s
s
th
e
r
ev
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d
s
tu
d
ies.
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wa
s
th
e
m
o
s
t
f
r
eq
u
en
tly
ap
p
lied
m
et
h
o
d
(
3
9
%),
r
ef
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g
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ata
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tio
n
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o
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KNN
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ANN
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e
each
u
s
ed
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s
ev
en
s
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ies
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o
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s
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atin
g
co
m
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lem
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tar
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tr
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h
s
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o
r
m
e
d
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s
m
aller
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well
-
s
ep
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ated
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atas
ets,
wh
ile
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h
iev
ed
s
tr
o
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g
p
r
e
d
ictiv
e
p
e
r
f
o
r
m
an
ce
,
p
ar
ticu
lar
ly
wh
en
tr
ain
ed
with
s
y
n
th
etic
d
ata.
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ap
p
ea
r
ed
in
th
r
ee
s
tu
d
ies
(
1
1
%),
p
r
o
v
id
in
g
r
eliab
le
m
u
lti
-
d
im
en
s
io
n
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aly
s
is
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d
u
s
ef
u
l
f
ea
tu
r
e
-
im
p
o
r
tan
ce
ev
alu
atio
n
,
c
o
n
f
ir
m
i
n
g
its
co
n
t
in
u
ed
r
elev
a
n
ce
in
a
g
ar
wo
o
d
o
il
r
esear
ch
.
Fig
u
r
e
3.
Dis
tr
ib
u
tio
n
of
ML
a
lg
o
r
ith
m
s
ap
p
lied
in
ag
ar
w
o
o
d
o
il
class
if
icatio
n
s
tu
d
ies
3
.
3
.
Su
mm
a
ry
of
ML
a
pp
lica
t
io
ns
in
ess
ent
ia
l
o
il
cla
s
s
if
i
ca
t
io
n
T
ab
le
3
s
u
m
m
ar
izes
s
u
cc
ess
f
u
l
ML
ap
p
licatio
n
s
in
ag
ar
w
o
o
d
an
d
ess
en
tial
o
il
class
if
icat
io
n
.
ANN,
R
F,
SVM
,
an
d
KNN
ar
e
m
o
s
t
f
r
eq
u
e
n
tly
r
e
p
o
r
ted
due
to
t
h
eir
ab
ilit
y
to
m
o
d
el
co
m
p
lex
,
n
o
n
-
lin
ea
r
GC
-
b
ased
ch
em
ical
d
ata
[
1
5
]
,
[
1
9
]
,
[
2
1
]
.
C
o
llectiv
ely
,
th
ese
ap
p
r
o
ac
h
es
s
u
p
p
o
r
t
ac
cu
r
ate,
r
e
p
r
o
d
u
c
ib
le
,
an
d
o
b
jectiv
e
q
u
ality
ev
alu
atio
n
ac
r
o
s
s
A
q
u
i
la
r
ia
s
p
ec
ies
[
3
4
]
,
[
3
7
]
,
[
4
2
]
,
[
4
3
]
.
0
1
2
3
4
5
6
7
8
2
0
1
3
2
0
1
4
2
0
1
7
2
0
1
8
2
0
1
9
2
0
2
1
2
0
2
2
2
0
2
3
2
0
2
4
D
o
c
u
m
e
n
t
s
Y
e
a
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o
r
a
g
a
r
w
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o
d
o
i
l
q
u
a
l
i
t
y
.
[
3
6
]
,
[
5
3
]
K
N
N
To
c
l
a
ss
i
f
y
C
y
m
b
o
p
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l
a
a
n
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l
.
A
c
c
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r
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n
t
i
l
k
=
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a
i
n
i
n
g
t
e
st
:
r
a
n
g
e
b
e
t
w
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e
n
8
8
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1
0
0
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‒
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st
i
n
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e
s
t
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l
a
a
n
d
l
e
mo
n
g
r
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ss
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i
l
s.
[
5
4
]
,
[
5
5
]
K
N
N
To
c
l
a
ssi
f
y
a
g
a
r
w
o
o
d
o
i
l
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mp
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on
t
h
e
i
r
o
r
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l
a
b
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o
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a
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u
f
a
c
t
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r
e
r
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m
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r
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)
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si
n
g
a
K
N
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l
.
A
c
c
u
r
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c
y
(k
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n
d
k
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2
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:
‒
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g
t
e
st
:
9
8
.
6
3
%
‒
Te
st
i
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g
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e
s
t
:
1
0
0
%
K
N
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e
f
f
e
c
t
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l
y
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l
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ss
i
f
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d
a
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l
sam
p
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e
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n
t
o
t
h
r
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e
so
u
r
c
e
s.
[
3
7
]
K
N
N
To
d
e
v
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l
o
p
e
d
a
n
e
w
met
h
o
d
f
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.
‒
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c
c
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r
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v
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0
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Th
e
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N
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o
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l
a
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o
r
r
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c
t
l
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a
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e
d
on
t
h
e
i
r
q
u
a
l
i
t
y
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r
a
d
e
.
Fig
u
r
e
4
co
m
p
a
r
es
th
e
p
er
f
o
r
m
an
ce
of
f
o
u
r
ML
alg
o
r
ith
m
s
u
s
in
g
m
ea
n
±
s
tan
d
ar
d
d
e
v
ia
tio
n
(
SD)
ac
cu
r
ac
y
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n
d
F1
-
s
co
r
e
d
is
tr
ib
u
tio
n
s
.
Fig
u
r
e
4
(
a)
s
h
o
ws
th
e
m
ea
n
±
SD
ac
cu
r
ac
y
ac
r
o
s
s
s
tu
d
ies,
wh
er
e
ANN
an
d
KNN
ac
h
iev
ed
th
e
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ig
h
est
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ea
n
ac
cu
r
ac
ies
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7
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.
7
a
n
d
9
9
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3
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1
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1
,
r
esp
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tiv
ely
)
,
wh
ile
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ex
h
ib
ited
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r
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ter
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ar
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in
d
icatin
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s
en
s
itiv
ity
to
d
ataset
co
m
p
o
s
itio
n
.
Fig
u
r
e
4
(
b
)
p
r
esen
ts
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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:
2
2
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F1
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s
co
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d
is
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tio
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SVM
d
em
o
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atin
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le
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s
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f
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ce
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o
m
p
ar
ab
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to
KNN
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n
f
ir
m
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eir
r
o
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s
tn
ess
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cr
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s
s
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iv
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e
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en
tial
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at
asets
[
4
7
]
,
[
5
4
]
.
Acr
o
s
s
th
e
r
ev
iewe
d
liter
atu
r
e,
ANN
p
er
f
o
r
m
e
d
well
on
lar
g
er
or
s
y
n
th
etica
lly
au
g
m
en
ted
d
atasets
[
3
8
]
,
[
3
9
]
,
[
4
4
]
,
RF
d
eliv
er
ed
m
o
d
er
at
e
but
r
eliab
le
r
esu
lts
[
1
9
]
,
[
2
1
]
,
[
4
3
]
,
[
4
7
]
,
SVM
with
RBF
k
er
n
els
s
h
o
wed
s
tr
o
n
g
ac
cu
r
ac
y
an
d
g
e
n
er
aliza
tio
n
on
c
o
m
p
lex
GC
-
MS
d
ata
[
4
2
]
,
[
4
9
]
,
an
d
KNN
ac
h
ie
v
ed
n
ea
r
p
er
f
ec
t
class
if
icatio
n
on
s
m
a
ller
,
well
s
ep
ar
ated
d
atasets
[
3
7
]
,
[
5
2
]
,
[
5
4
]
.
T
h
ese
f
in
d
in
g
s
i
n
d
icate
th
at
alg
o
r
ith
m
ic
r
o
b
u
s
tn
ess
im
p
r
o
v
es
with
d
ataset
r
ep
r
esen
tativ
en
ess
,
wh
ile
cla
s
s
ical
ML
ap
p
r
o
ac
h
es
r
em
ai
n
th
e
m
o
s
t
p
r
ac
tical
ch
o
ice
due
to
lim
ited
d
ata
av
ailab
ilit
y
an
d
s
tan
d
ar
d
izatio
n
.
(
a)
(
b
)
Fig
u
r
e
4.
C
o
m
p
a
r
ativ
e
p
e
r
f
o
r
m
an
ce
of
ML
alg
o
r
ith
m
s
f
o
r
A
q
u
ila
r
ia
ess
en
tial
o
il
class
if
ica
tio
n
of
(
a)
m
ea
n
±
SD
ac
cu
r
ac
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a
n
d
(
b
)
F1
-
s
co
r
e
d
is
tr
ib
u
tio
n
ac
r
o
s
s
alg
o
r
ith
m
s
3
.
4
.
Cha
lleng
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a
nd
lim
it
a
t
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o
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Desp
ite
p
r
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g
p
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o
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m
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n
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,
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ev
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allen
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it
th
e
b
r
o
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ap
p
licatio
n
of
ML
f
o
r
ag
ar
wo
o
d
ess
en
tial
o
il
class
if
icatio
n
.
Mo
s
t
s
tu
d
ies
r
ely
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m
all
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d
im
b
alan
ce
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d
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o
f
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r
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ata
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o
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ce
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ias
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ed
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ci
n
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g
e
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ilit
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.
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s
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ce
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ize
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GC
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ata
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r
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ats,
ac
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is
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n
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eter
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m
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n
d
r
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ti
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d
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in
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r
th
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m
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m
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r
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o
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r
ep
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Var
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lin
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co
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to
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,
m
ea
n
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ate
d
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o
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m
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ce
m
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s
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o
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ld
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in
ter
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e
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ig
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ized
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atasets
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n
s
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t
an
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tical
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k
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ws,
ex
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licit
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o
r
tin
g
of
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c
o
n
f
ig
u
r
atio
n
s
,
lar
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er
m
u
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-
s
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ies
s
am
p
les
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d
cr
o
s
s
-
lab
o
r
ato
r
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to
s
u
p
p
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r
t
r
eliab
le
r
ea
l
-
wo
r
ld
d
ep
lo
y
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en
t.
4.
CO
NCLU
SI
O
N
T
h
is
r
ev
iew
d
em
o
n
s
tr
ates
th
e
ef
f
ec
tiv
en
ess
of
AI
an
d
ML
te
ch
n
iq
u
es,
p
ar
ticu
lar
ly
ANN,
R
F,
SVM
,
an
d
KNN
,
in
ad
v
a
n
cin
g
t
h
e
c
lass
if
icatio
n
an
d
q
u
ality
ass
es
s
m
en
t
of
A
q
u
ila
r
ia
ess
en
tial
o
ils
u
s
in
g
GC
–
MS
-
d
er
iv
ed
ch
em
ical
d
ata.
M
o
d
el
r
o
b
u
s
tn
ess
an
d
class
if
icatio
n
ac
c
u
r
ac
y
ar
e
s
tr
o
n
g
l
y
in
f
lu
en
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d
by
d
ataset
q
u
ality
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co
m
p
o
u
n
d
d
iv
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s
ity
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d
p
ar
am
eter
o
p
tim
izatio
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.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
ex
p
l
o
r
e
tr
an
s
f
er
lear
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in
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,
d
ee
p
c
o
n
v
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lu
tio
n
al
n
eu
r
al
n
e
two
r
k
s
an
d
m
u
ltimo
d
al
d
ata
f
u
s
io
n
in
te
g
r
atin
g
GC
-
MS
with
co
m
p
lem
en
tar
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s
p
ec
tr
o
s
co
p
ic
tech
n
iq
u
es
s
u
ch
as
Fo
u
r
ier
tr
an
s
f
o
r
m
in
f
r
ar
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d
(
FTI
R
)
.
T
h
e
estab
lis
h
m
en
t
of
o
p
en
,
s
tan
d
ar
d
ized
b
en
ch
m
ar
k
d
atasets
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ld
f
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r
th
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ce
r
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r
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d
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f
air
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el
co
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Alth
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to
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o
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th
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co
n
s
tr
ain
ts
r
ef
lect
t
h
e
cu
r
r
en
t
s
tate
of
th
e
liter
atu
r
e.
Ov
er
all,
AI
-
d
r
iv
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n
f
r
am
ewo
r
k
s
o
f
f
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o
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p
r
ac
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ten
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to
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ated
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ticatio
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p
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d
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Hig
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Ma
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Fu
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en
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R
esear
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Gr
an
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Sch
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(
FR
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M/0
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)
.
Ap
p
r
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n
is
ex
ten
d
ed
to
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2252
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8
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3
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ig
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a
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atic
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can
be
c
o
n
tac
ted
at
e
m
a
il
:
m
u
h
a
m
m
a
d
ik
h
sa
n
r
o
sla
n
@g
m
a
il
.
c
o
m
.
Nurla
il
a
Is
m
a
il
re
c
e
iv
e
d
th
e
M
.
S
c
.
a
n
d
P
h
.
D.
d
e
g
re
e
s
in
El
e
c
t
rica
l
En
g
i
n
e
e
rin
g
fro
m
Un
iv
e
rsit
i
Tek
n
o
l
o
g
i
M
AR
A
(UiTM
),
M
a
lay
sia
.
S
h
e
is
c
u
rr
e
n
tl
y
an
a
ss
o
c
iate
p
ro
fe
ss
o
r
at
F
a
c
u
lt
y
of
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Un
i
v
e
rsiti
Tek
n
o
l
o
g
i
M
ARA
(UiTM
)
S
h
a
h
Ala
m
,
M
a
lay
sia
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
a
d
v
a
n
c
e
d
sig
n
a
l
p
r
o
c
e
ss
in
g
,
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
.
S
h
e
can
be
c
o
n
tac
ted
at
e
m
a
il
:
n
u
rlaila0
5
8
3
@u
it
m
.
e
d
u
.
m
y
.
Za
k
ia
h
Mo
h
d
Yuso
ff
re
c
e
iv
e
d
h
e
r
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
El
e
c
t
rica
l
En
g
i
n
e
e
rin
g
a
n
d
Ph
.
D
.
in
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
fr
o
m
Un
iv
e
rsiti
Te
k
n
o
lo
g
i
M
ARA
,
S
h
a
h
Ala
m
,
in
2
0
0
9
a
n
d
2
0
1
4
,
re
sp
e
c
ti
v
e
l
y
.
S
h
e
is
a
se
n
io
r
lec
tu
re
r
wh
o
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
at
F
a
c
u
lt
y
of
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
,
Un
iv
e
rsiti
Tek
n
o
l
o
g
i
M
ARA
,
S
h
a
h
Ala
m
,
M
a
lay
sia
.
In
M
ay
2
0
1
4
,
sh
e
jo
in
e
d
Un
iv
e
rsiti
Tek
n
o
lo
g
i
M
A
RA
as
a
tea
c
h
in
g
sta
ff.
He
r
m
a
jo
r
in
tere
sts
in
c
lu
d
e
p
ro
c
e
ss
c
o
n
tro
l,
sy
ste
m
id
e
n
ti
fica
ti
o
n
a
n
d
e
ss
e
n
ti
a
l
o
il
e
x
trac
ti
o
n
sy
ste
m
s.
S
h
e
can
be
c
o
n
tac
ted
at
e
m
a
il
:
z
a
k
iah
9
0
1
8
@
u
it
m
.
e
d
u
.
m
y
.
Ali
Abd
Al
m
isr
e
b
is
c
u
rre
n
tl
y
an
a
ss
o
c
iate
p
r
o
fe
ss
o
r
at
th
e
F
a
c
u
lt
y
of
Co
m
p
u
ter
S
c
ien
c
e
s
a
n
d
E
n
g
in
e
e
rin
g
,
Di
re
c
to
r
of
G
ra
d
u
a
te
Co
u
n
c
il
a
n
d
E
d
it
o
r
in
C
h
ief
at
I
n
tern
a
ti
o
n
a
l
Un
iv
e
rsity
of
S
a
ra
jev
o
.
He
re
c
e
iv
e
d
a
M
.
S
c
.
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
P
h
.
D.
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
/Co
m
p
u
ter
En
g
in
e
e
rin
g
fr
o
m
Un
i
v
e
rsiti
Tek
n
o
lo
g
i
M
ARA
,
M
a
lay
sia
.
His
m
a
jo
r
in
tere
sts
in
c
lu
d
e
d
e
e
p
lea
rn
in
g
,
m
a
c
h
in
e
lea
rn
in
g
,
c
o
m
p
u
ter
v
isi
o
n
v
o
ice
re
c
o
g
n
it
i
o
n
,
a
n
d
q
u
a
n
t
u
m
c
o
m
p
u
t
in
g
.
He
can
be
c
o
n
tac
ted
at
e
m
a
il
:
a
li
m
e
s9
6
@y
a
h
o
o
.
c
o
m
.
Mo
h
d
Na
sir
Ta
ib
re
c
e
iv
e
d
th
e
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
fro
m
t
h
e
Un
iv
e
rsity
of
Tas
m
a
n
ia,
Ho
b
a
rt
,
Au
stra
li
a
,
th
e
M
.
S
c
.
d
e
g
re
e
in
Co
n
tr
o
l
En
g
i
n
e
e
rin
g
fr
o
m
S
h
e
ffield
Un
i
v
e
rsity
,
UK,
a
n
d
t
h
e
P
h
.
D.
d
e
g
re
e
in
In
stru
m
e
n
tat
io
n
fro
m
t
h
e
Un
iv
e
rsity
of
M
a
n
c
h
e
ste
r
In
stit
u
te
of
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
,
UK.
He
is
c
u
rr
e
n
tl
y
a
se
n
io
r
p
r
o
fe
ss
o
r
at
Un
iv
e
rsiti
Te
k
n
o
lo
g
i
M
ARA
(Ui
TM
),
M
a
lay
sia
.
He
h
e
a
d
s
t
h
e
A
d
v
a
n
c
e
d
S
ig
n
a
l
P
ro
c
e
ss
in
g
Re
se
a
rc
h
In
tere
st
G
ro
u
p
at
t
h
e
F
a
c
u
lt
y
of
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
UiTM
.
He
h
a
s
b
e
e
n
a
v
e
r
y
a
c
ti
v
e
re
se
a
rc
h
e
r
a
n
d
o
v
e
r
th
e
y
e
a
rs
h
a
d
a
u
th
o
r
a
n
d
/
o
r
co
-
a
u
th
o
r
m
a
n
y
p
a
p
e
rs
p
u
b
li
sh
e
d
in
re
fe
re
e
d
jo
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s.
He
can
be
c
o
n
tac
ted
at
e
m
a
il
:
d
r.
n
a
sir@u
it
m
.
e
d
u
.
m
y
.
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