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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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2956
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r
eg
r
ess
io
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to
f
o
r
ec
ast
C
P
O
p
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o
d
u
ctio
n
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T
h
e
SVR
m
e
th
o
d
s
u
r
p
ass
ed
th
e
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w
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p
r
ed
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n
ac
cu
r
ac
y
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w
it
h
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p
o
s
itiv
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ac
cu
r
ac
y
o
f
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6
9
4
,
m
ea
n
s
q
u
ar
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er
r
o
r
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MSE
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o
f
1
1
4
6
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0
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4
,
m
ea
n
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s
o
lu
te
p
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ce
n
tag
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r
o
r
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o
f
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4
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5
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an
d
m
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n
ab
s
o
lu
te
d
ev
iat
io
n
(
MA
D)
o
f
2
2
.
3
3
3
[
1
]
.
T
h
r
ee
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
el
s
w
er
e
an
al
y
ze
d
u
s
i
n
g
h
is
to
r
ical
d
ata
f
r
o
m
2
0
2
0
to
2
0
2
3
t
o
p
r
ed
ict
C
P
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p
r
ices.
T
h
e
R
F
m
e
th
o
d
s
h
o
wed
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
;
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n
t
h
e
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0
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0
s
ce
n
ar
io
,
R
F
o
u
tp
er
f
o
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m
ed
li
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ea
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tic
r
e
g
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y
ield
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g
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m
aller
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3
9
4
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)
,
MA
E
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0
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an
d
R
MSE
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0
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.
6
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l
y
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n
th
e
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s
ce
n
ar
io
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th
e
RF
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ad
s
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aller
M
SE
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3
7
7
8
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,
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A
E
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d
R
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3
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I
n
th
e
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ce
n
ar
io
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th
e
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F
s
h
o
w
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s
m
a
ll
er
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(
1
0
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MA
E
(
1
0
4
.
1
3
)
,
an
d
R
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(
3
2
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[
2
8
]
.
T
h
e
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
ST
M
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tr
e
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e
g
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ad
ien
t
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tin
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t
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els
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er
e
ev
alu
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ted
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y
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m
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h
y
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e
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ar
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t
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n
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o
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ti
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s
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m
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lti
v
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ata
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th
e
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o
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o
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ti
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al
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o
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el
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n
f
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r
o
d
u
ctio
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it
h
th
e
lo
w
e
s
t
er
r
o
r
r
ate.
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h
e
r
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h
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w
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a
t
th
e
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ST
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m
o
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el
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ce
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ar
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m
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n
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h
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ac
c
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r
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ate
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f
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d
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f
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h
e
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t
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ter
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n
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n
g
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w
it
h
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n
R
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f
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2
.
1
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d
an
ac
cu
r
ac
y
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ate
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f
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8
%
[
2
9
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h
e
m
ac
h
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n
e
lear
n
i
n
g
f
r
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m
e
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k
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m
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ty
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ic
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er
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h
e
p
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p
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el
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n
co
r
p
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ltip
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e
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i
n
cl
u
d
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ANN,
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d
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w
h
ich
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h
ib
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tio
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l
p
r
ec
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n
f
o
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v
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s
u
c
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ch
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m
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t.
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d
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itio
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all
y
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t
h
e
f
r
a
m
e
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k
f
ac
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id
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ig
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ld
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s
tr
e
s
s
-
to
ler
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t
o
il
p
al
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c
u
lti
v
ar
s
f
o
r
s
u
s
ta
in
ab
le
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g
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lt
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r
al
p
r
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d
u
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[
3
0
]
.
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h
is
r
esear
ch
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m
s
to
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h
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cla
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t
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ad
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O
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teg
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ize
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in
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p
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s
ta
n
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ar
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h
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m
et
h
o
d
en
ta
ils
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ep
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u
n
p
r
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s
ed
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ata
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m
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o
r
ef
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al
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is
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h
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iev
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h
r
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u
g
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ata
p
u
r
if
ic
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tr
an
s
f
o
r
m
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,
n
o
r
m
al
izatio
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d
r
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m
p
li
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g
u
s
i
n
g
m
ac
h
i
n
e
lear
n
i
n
g
alg
o
r
ith
m
s
.
2.
M
E
T
H
O
D
T
h
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s
tu
d
y
ai
m
s
to
ca
lcu
late
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alit
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at
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h
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ar
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ir
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h
e
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s
is
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r
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t
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h
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an
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m
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h
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et
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q
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alit
y
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al
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is
d
is
p
la
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ed
in
Fig
u
r
e
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Fig
u
r
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r
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m
eth
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Da
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ata
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les
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ch
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s
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o
w
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2
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2
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1
.
Da
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a
c
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nin
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Du
r
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t
h
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d
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clea
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ta
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th
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P
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v
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lu
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d
in
co
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p
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T
h
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p
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is
cr
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to
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3
p
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an
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x
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m
p
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f
C
P
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q
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d
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p
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s
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h
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h
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in
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l
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2
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2
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2
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Da
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Data
tr
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m
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Da
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Data
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is
a
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m
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s
u
c
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C
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A
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in
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ataset
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1
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5
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o
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o
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ated
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l
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s
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etr
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g
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ith
m
d
e
m
o
n
s
tr
ated
ex
ce
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ti
o
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al
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ce
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n
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er
s
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r
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t
h
e
ef
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icac
y
o
f
l
o
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l
s
i
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ilar
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y
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n
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ti
o
n
.
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lt
h
o
u
g
h
A
NN
attain
ed
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,
r
ec
all
,
ac
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r
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y
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n
d
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s
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r
e,
its
p
er
f
o
r
m
an
ce
w
as
i
n
f
er
io
r
to
th
at
o
f
o
th
er
m
o
d
els.
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d
em
o
n
s
tr
ated
a
7
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1
s
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e,
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4
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n
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h
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er
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it
s
p
er
f
o
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m
a
n
ce
w
a
s
s
u
b
p
ar
.
SVM's
ca
p
ac
it
y
to
m
a
n
ag
e
i
n
tr
icate
d
ata
d
is
tr
ib
u
t
io
n
s
was
ill
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s
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ated
b
y
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s
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n
s
is
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h
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e
m
e
n
t o
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r
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4
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cc
u
r
ac
y
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T
h
e
k
-
f
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ld
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al
u
e
o
f
5
w
a
s
ap
p
lied
t
o
C
4
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5
,
an
d
DT
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ar
e
m
ac
h
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e
lear
n
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g
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m
s
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m
o
t
h
er
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e
s
s
i
n
g
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P
O
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u
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y
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h
e
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ig
n
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ican
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te
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er
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y
th
e
9
9
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5
%
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c
u
r
ac
y
o
f
KNN,
w
h
ic
h
h
ad
a
k
-
v
a
lu
e
o
f
5
.
Desp
ite
a
m
in
o
r
i
m
p
r
o
v
e
m
e
n
t,
A
N
N
co
u
ld
n
o
t
s
u
r
p
as
s
t
h
e
to
p
-
p
er
f
o
r
m
i
n
g
al
g
o
r
ith
m
s
,
in
d
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n
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t
h
at
t
h
e
co
n
s
tr
ai
n
t
s
o
f
ca
p
tu
r
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g
d
elica
te
d
ata
p
atter
n
s
m
a
y
n
o
t
b
e
r
eso
lv
ed
b
y
i
n
cr
ea
s
i
n
g
th
e
a
m
o
u
n
t
o
f
t
r
ai
n
in
g
d
ata.
NB
,
w
h
ich
e
x
h
i
b
ited
a
co
m
p
ar
ab
le
p
er
f
o
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m
a
n
ce
p
atter
n
,
al
s
o
d
e
m
o
n
s
tr
ated
p
o
ten
tial
li
m
itat
io
n
s
i
n
its
ab
ilit
y
to
r
ep
r
es
en
t
co
m
p
lex
d
ata
s
tr
u
ct
u
r
es.
SVM
i
s
a
d
ep
en
d
ab
le
o
p
tio
n
f
o
r
th
is
ap
p
licatio
n
,
a
s
it b
o
asts
a
p
er
f
o
r
m
an
ce
le
v
el
o
f
o
v
er
8
4
%.
T
h
e
k
-
f
o
ld
v
al
u
e
o
f
1
0
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m
p
le
m
en
ted
to
C
4
.
5
an
d
DT
m
o
d
els
d
e
m
o
n
s
tr
ated
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
i
n
id
en
ti
f
y
i
n
g
u
n
d
er
l
y
in
g
p
atter
n
s
i
n
d
ata,
ev
e
n
w
ith
li
m
ited
tr
ain
i
n
g
d
ata.
Ho
w
ev
er
,
u
s
i
n
g
a
lar
g
er
k
-
f
o
ld
v
a
lu
e
led
to
a
s
m
al
l
d
ec
r
ea
s
e
in
KNN
p
er
f
o
r
m
an
ce
,
s
u
g
g
e
s
tin
g
th
at
s
u
r
p
as
s
in
g
a
s
p
ec
if
ic
t
h
r
esh
o
ld
f
o
r
n
eig
h
b
o
r
h
o
o
d
s
ize
co
u
ld
i
m
p
ed
e
its
ab
ilit
y
to
id
en
tify
lo
ca
l
p
atter
n
s
.
A
N
N
s
h
o
w
ed
s
li
g
h
t
i
m
p
r
o
v
e
m
e
n
t
s
b
u
t
s
till
la
g
g
ed
b
eh
i
n
d
to
p
-
p
er
f
o
r
m
i
n
g
alg
o
r
it
h
m
s
,
s
u
g
g
e
s
ti
n
g
i
n
tr
in
s
ic
co
n
s
tr
ai
n
ts
in
th
e
s
y
s
t
e
m
'
s
ar
c
h
itect
u
r
e
o
r
lear
n
in
g
d
y
n
a
m
ic
s
.
NB
p
er
s
i
s
ten
t
u
n
d
er
p
er
f
o
r
m
an
ce
u
n
d
e
r
s
co
r
es
th
e
n
ee
d
to
u
n
d
er
s
ta
n
d
its
u
n
d
er
l
y
i
n
g
ass
u
m
p
tio
n
s
a
n
d
p
o
ten
tial
li
m
it
s
w
h
e
n
u
s
i
n
g
it
o
n
d
atase
ts
w
it
h
f
ea
t
u
r
e
d
ep
en
d
en
cie
s
.
SVM
d
e
m
o
n
s
tr
ated
co
n
s
is
ten
t
p
er
f
o
r
m
a
n
ce
,
s
u
r
p
a
s
s
i
n
g
8
4
%
ac
r
o
s
s
all
cr
iter
ia,
s
o
lid
if
y
i
n
g
its
r
ep
u
tatio
n
as
a
r
eliab
le
class
i
f
ier
f
o
r
ev
alu
a
tin
g
C
P
O
q
u
alit
y
.
T
h
is
s
t
u
d
y
h
as
id
en
ti
f
ied
DT
an
d
C
4
.
5
as
th
e
m
o
s
t
e
f
f
ec
tiv
e
m
ac
h
i
n
e
lear
n
i
n
g
al
g
o
r
ith
m
s
f
o
r
ass
es
s
i
n
g
t
h
e
q
u
alit
y
o
f
C
P
Os.
T
h
ese
alg
o
r
ith
m
s
co
n
s
is
te
n
tl
y
o
u
tp
er
f
o
r
m
o
t
h
er
o
p
tio
n
s
,
s
u
c
h
as
KNN
an
d
NB
,
d
u
e
to
th
eir
ca
p
ac
it
y
to
ef
f
ec
tiv
e
l
y
r
ep
r
ese
n
t
co
m
p
le
x
d
ata.
SVM
co
n
ti
n
u
e
s
to
b
e
a
v
iab
le
alter
n
at
iv
e.
T
h
e
s
tu
d
y
r
es
u
lts
e
m
p
h
a
s
ize
t
h
e
i
m
p
o
r
tan
ce
o
f
m
ac
h
i
n
e
lea
r
n
in
g
i
n
i
m
p
r
o
v
i
n
g
q
u
alit
y
ev
alu
atio
n
i
n
t
h
e
p
al
m
o
il
in
d
u
s
tr
y
.
Fu
tu
r
e
r
esear
c
h
s
h
o
u
ld
in
v
e
s
ti
g
ate
e
n
s
e
m
b
le
m
et
h
o
d
s
,
ex
p
a
n
d
d
atasets
,
a
n
d
u
n
d
er
s
ta
n
d
th
e
f
ac
to
r
s
co
n
tr
ib
u
t
in
g
to
q
u
alit
y
ch
an
g
es to
i
m
p
r
o
v
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
4.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
in
v
esti
g
ates
t
h
e
ap
p
licatio
n
o
f
m
ac
h
i
n
e
lear
n
i
n
g
in
ev
al
u
ati
n
g
C
P
O
q
u
alit
y
.
T
h
e
f
in
d
i
n
g
s
in
d
icate
t
h
at
C
4
.
5
an
d
DT
alg
o
r
ith
m
s
ar
e
m
o
r
e
ef
f
ec
ti
v
e
in
as
s
es
s
in
g
C
P
O
q
u
alit
y
.
T
h
ese
alg
o
r
ith
m
s
d
em
o
n
s
tr
ate
e
x
ce
p
tio
n
al
p
er
f
o
r
m
a
n
ce
in
r
ep
r
esen
tin
g
i
n
tr
i
ca
te
r
elatio
n
s
h
ip
s
b
et
w
ee
n
d
a
tasets
,
es
s
en
t
ial
f
o
r
p
r
ec
is
el
y
p
r
ed
ictin
g
C
P
O
q
u
al
it
y
.
T
h
e
k
e
y
to
t
h
e
e
f
f
ec
tiv
e
n
e
s
s
o
f
KN
N
is
t
h
e
ca
r
ef
u
l
s
elec
tio
n
o
f
t
h
e
o
p
ti
m
a
l
k
-
v
al
u
e,
w
h
ic
h
h
ig
h
li
g
h
ts
t
h
e
n
ee
d
f
o
r
p
r
ec
is
e
p
ar
am
eter
tu
n
i
n
g
.
No
t
w
it
h
s
ta
n
d
in
g
t
h
eir
w
id
esp
r
ea
d
u
s
e,
t
h
e
A
N
N
an
d
NB
al
g
o
r
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AUTHO
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DATA A
V
AI
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AB
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L
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h
e
d
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[
NP
]
,
u
p
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r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
A
.
S
o
l
i
c
h
i
n
,
U
.
H
a
sa
n
a
h
,
a
n
d
Jay
a
n
t
a
,
“
D
e
v
e
l
o
p
me
n
t
o
f
p
r
e
d
i
c
t
i
o
n
sy
st
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m
f
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p
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(
C
P
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)
p
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me
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r
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s
d
a
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a
mi
n
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a
p
p
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h
,
”
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n
Pr
o
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d
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n
g
s
-
2
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d
I
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t
e
rn
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t
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M
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5
1
5
6
7
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2
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0
.
9
3
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3
2
1
.
[
2
]
B
P
S
,
S
t
a
t
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st
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k
k
e
l
a
p
a
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w
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0
2
0
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J
a
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a
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n
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:
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P
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s
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k
,
2
0
2
1
.
[
3
]
M
.
W
a
l
e
e
d
,
T
.
W
.
U
m,
T
.
K
a
mal
,
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d
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.
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.
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sm
a
n
,
“
C
l
a
ssi
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3
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[
4
]
K
.
X
u
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l
.
,
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n
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mac
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[
5
]
H
a
md
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i
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.
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2
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1
6
2
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t
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h
2
0
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6
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n
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p
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C
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c
h
.
2
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1
7
.
[
6
]
H
.
Ji
,
X
.
L
i
u
,
L
.
W
a
n
g
,
L
.
F
a
n
,
a
n
d
S
.
L
i
u
,
“
I
mag
e
r
e
c
o
g
n
i
t
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o
n
o
f
C
h
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n
e
se
h
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b
a
l
me
d
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c
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e
u
si
n
g
a
d
a
p
t
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v
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a
mm
a
c
o
r
r
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c
t
i
o
n
b
a
se
d
o
n
c
o
n
v
o
l
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t
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l
n
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r
a
l
n
e
t
w
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r
k
,
”
i
n
2
0
2
4
I
EE
E
1
3
t
h
D
a
t
a
D
r
i
v
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n
C
o
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a
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d
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(
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2
4
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4
2
8
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3
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0
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1
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/
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D
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6
1
6
2
2
.
2
0
2
4
.
1
0
6
0
6
7
9
8
.
[
7
]
N
.
P
u
s
p
i
t
a
s
a
r
i
,
A
.
S
e
p
t
i
a
r
i
n
i
,
U
.
H
a
i
r
a
h
,
A
.
T
e
j
a
w
a
t
i
,
a
n
d
H
.
S
u
l
a
st
r
i
,
“
B
e
t
e
l
l
e
a
f
c
l
a
ssi
f
i
c
a
t
i
o
n
u
s
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n
g
c
o
l
o
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-
t
e
x
t
u
r
e
f
e
a
t
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r
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s
a
n
d
mac
h
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n
e
l
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a
r
n
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n
g
a
p
p
r
o
a
c
h
,
”
Bu
l
l
e
t
i
n
o
f
E
l
e
c
t
r
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c
a
l
En
g
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n
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g
a
n
d
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o
rm
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t
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s
,
v
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l
.
1
2
,
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o
.
5
,
p
p
.
2
9
3
9
–
2
9
4
7
,
O
c
t
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
e
e
i
.
v
1
2
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5
.
5
1
0
1
.
[
8
]
U
.
H
a
i
r
a
h
,
A
.
S
e
p
t
i
a
r
i
n
i
,
N
.
P
u
s
p
i
t
a
s
a
r
i
,
A
.
T
e
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[
2
2
]
E.
I
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3
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5
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[
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6
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7
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[
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8
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[
2
9
]
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.
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R
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A
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S
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P
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[
3
1
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M
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F
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sad
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Un
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n
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T
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r
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c
a
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c
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tac
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m
a
il
:
a
m
i
n
p
a
d
m
o
@u
n
m
u
l.
a
c
.
id
.
Evaluation Warning : The document was created with Spire.PDF for Python.