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in
t
h
e
lite
r
atu
r
e
s
tu
d
ie
s
.
T
h
is
is
s
u
e
is
i
m
p
o
r
tan
t
to
s
o
lv
e
b
ec
au
s
e
t
h
e
d
is
ea
s
e
i
s
v
e
r
y
d
an
g
er
o
u
s
an
d
w
ill
b
e
f
at
ef
u
l
if
n
o
t
tr
ea
ted
i
m
m
ed
iate
l
y
a
s
s
o
o
n
a
s
p
o
s
s
ib
le.
T
h
er
ef
o
r
e,
th
is
r
ese
ar
ch
w
ill
cla
s
s
i
f
y
ST
D
d
is
ea
s
e
s
b
ased
o
n
ex
i
s
ti
n
g
s
y
m
p
to
m
s
w
it
h
f
u
tu
r
e
g
o
als
i
f
t
h
er
e
is
a
n
e
w
s
y
m
p
to
m
ca
n
b
e
d
etec
ted
ea
r
ly
d
is
ea
s
e.
Au
th
o
r
w
i
ll
f
o
cu
s
o
n
test
i
n
g
th
e
ac
c
u
r
ac
y
o
f
t
h
r
ee
d
ata
m
i
n
i
n
g
m
e
th
o
d
s
w
h
ic
h
ar
e
Naïv
e
B
a
y
e
s
,
K
-
Me
a
n
s
a
n
d
K
-
Nea
r
est
Nei
g
h
b
o
r
(K
-
NN)
ag
ai
n
s
t
t
h
e
ST
D
d
is
e
ase
class
i
f
icatio
n
in
ad
d
itio
n
to
test
in
g
r
esear
ch
m
et
h
o
d
s
also
ai
m
ed
at
ea
r
l
y
d
etec
tio
n
o
f
ST
D.
So
,
in
th
e
f
i
n
al
co
n
cl
u
s
io
n
w
e
w
ill
g
et
t
h
e
b
est
m
et
h
o
d
f
o
r
class
i
f
y
in
g
ST
D.
2.
L
I
T
E
R
AT
U
RE
S
T
UDY
Data
m
i
n
i
n
g
is
al
s
o
ca
lled
k
n
o
w
led
g
e
d
i
s
co
v
er
y
in
d
atab
as
e
[
1
0
]
.
T
h
e
p
r
o
b
lem
o
f
clas
s
i
f
icatio
n
o
f
d
is
ea
s
es
l
ik
e
th
i
s
ca
n
b
e
s
aid
i
n
clu
d
ed
i
n
t
h
e
d
ata
m
i
n
i
n
g
ca
s
e
b
ec
au
s
e
it
r
eq
u
ir
es
th
e
ex
i
s
t
en
ce
o
f
k
n
o
w
led
g
e
b
ef
o
r
e
it
ca
n
b
e
class
i
f
ied
.
A
l
o
t
o
f
m
et
h
o
d
s
in
d
ata
m
i
n
in
g
th
at
ca
n
b
e
u
s
ed
,
b
u
t
i
n
t
h
is
r
esear
ch
t
h
e
au
t
h
o
r
s
f
o
cu
s
to
co
m
p
ar
e
t
h
r
ee
m
et
h
o
d
s
n
a
m
e
l
y
Naï
v
e
B
a
y
e
s
,
K
-
M
ea
n
s
a
n
d
K
-
NN
.
T
h
e
Naïv
e
B
ay
es
m
eth
o
d
ca
n
b
e
ap
p
lied
to
s
o
lv
e
class
i
f
icati
o
n
p
r
o
b
lem
s
as
in
p
r
ev
io
u
s
s
t
u
d
ies.
P
atil
et
a
l
.,
[
1
1
]
ap
p
lied
th
e
Naï
v
e
B
a
y
esa
n
d
J
4
.
8
Dec
is
io
n
T
r
ee
m
et
h
o
d
s
i
n
clas
s
i
f
y
i
n
g
d
at
a.
T
h
en
f
r
o
m
b
o
t
h
m
et
h
o
d
s
co
m
p
ar
ed
to
th
eir
p
e
r
f
o
r
m
an
ce
an
d
b
ased
o
n
th
e
r
esu
lt
s
o
f
th
e
ex
p
er
i
m
en
t
it
ca
n
b
e
co
n
cl
u
d
ed
th
a
t
th
e
Naï
v
e
B
a
y
e
s
Me
th
o
d
is
m
o
r
e
ef
f
ic
ien
t.
D
u
r
g
alak
s
h
m
i
e
t
a
l
.,
[
1
2
]
im
p
le
m
e
n
ted
an
i
m
p
r
o
v
ed
v
er
s
io
n
o
f
t
h
e
Naïv
e
B
a
y
es
m
et
h
o
d
f
o
r
cla
s
s
i
f
y
in
g
b
r
ea
s
t
ca
n
n
ab
i
s
d
is
ea
s
es.
T
h
e
i
m
p
r
o
v
ed
v
er
s
i
o
n
o
f
th
e
Naïv
e
B
ay
e
s
m
e
th
o
d
lie
s
i
n
it
s
p
er
f
o
r
m
an
ce
a
n
d
ac
c
u
r
ac
y
ca
lc
u
lat
io
n
s
.
I
n
co
n
tr
ast,
Gr
if
f
i
s
et
a
l
.,
[
1
3
]
ad
o
p
ted
an
au
to
m
ated
ap
p
r
o
ac
h
to
id
e
n
ti
f
y
s
tr
o
k
e
b
y
u
s
in
g
t
h
e
Naïv
e
B
a
y
es
m
et
h
o
d
.
W
h
ile
L
a
k
o
u
m
en
tas
e
t
a
l
.,
[
1
4
]
o
p
tim
ized
th
e
m
et
h
o
d
o
f
Naïv
e
B
a
y
esi
n
th
e
clas
s
i
f
icatio
n
o
f
B
-
C
h
r
o
n
ic
L
y
m
p
h
o
c
y
tic
L
eu
k
e
m
ia
(
B
-
C
L
L
)
d
is
ea
s
e.
T
h
e
d
if
f
er
en
ce
w
ith
co
n
v
e
n
tio
n
al
Naï
v
e
B
a
y
es
m
et
h
o
d
s
lies
in
attr
ib
u
te
s
w
h
e
n
class
i
f
y
in
g
d
is
cr
e
te
v
alu
e
s
an
d
o
p
ti
m
izin
g
t
h
eir
ac
cu
r
ac
y
v
alu
e
s
.
I
n
ad
d
itio
n
to
th
e
m
eth
o
d
o
f
Naïv
e
B
a
y
es,
ca
n
al
s
o
d
o
t
h
e
class
i
f
icatio
n
b
y
u
s
i
n
g
K
-
Me
a
n
s
m
et
h
o
d
.
C
i
m
en
et
a
l
.,
[
1
5
]
ap
p
lie
d
th
e
K
-
Me
a
n
s
m
et
h
o
d
to
cla
s
s
if
y
A
r
r
h
y
t
h
m
ia
b
ased
o
n
C
o
n
ic
's
o
ly
h
ed
r
al
f
u
n
ctio
n
alg
o
r
ith
m
.
T
h
e
p
er
f
o
r
m
an
ce
test
r
es
u
lts
ar
e
s
h
o
w
n
th
r
o
u
g
h
n
u
m
er
ical
ex
p
er
i
m
e
n
ts
,
w
h
ile
th
e
ac
c
u
r
ac
y
i
s
9
8
%.
Kh
an
m
o
h
a
m
m
ad
i
[
1
6
]
i
m
p
r
o
v
is
ed
t
h
e
K
-
Me
a
n
s
m
et
h
o
d
f
o
r
m
ed
ical
ap
p
licatio
n
s
.
I
m
p
r
o
v
is
atio
n
is
d
o
n
e
b
y
u
s
in
g
o
v
er
lap
p
in
g
te
ch
n
iq
u
e,
w
h
ic
h
is
a
tech
n
iq
u
e
d
er
iv
ed
f
r
o
m
co
n
v
en
t
io
n
al
m
et
h
o
d
K
-
Me
an
s
.
Ov
er
lap
p
in
g
K
-
Me
a
n
s
(
O
K
M)
is
co
n
s
id
er
ed
to
b
e
ef
f
ic
ien
t
i
n
c
lass
if
y
i
n
g
d
ata
f
o
r
m
ed
ical
ap
p
licatio
n
s
.
An
a
n
d
[
1
7
]
d
etec
ted
p
lan
t
d
is
ea
s
es
i
n
th
e
B
r
i
n
j
al
leav
es
t
h
r
o
u
g
h
i
m
ag
e
p
r
o
ce
s
s
i
n
g
tech
n
i
q
u
es.
I
n
t
h
e
p
r
o
ce
s
s
o
f
d
etec
ti
n
g
th
e
i
m
ag
e,
th
e
r
esear
ch
er
u
s
es
K
-
Me
an
s
m
et
h
o
d
f
o
r
s
e
g
m
e
n
tat
io
n
a
n
d
Ne
u
r
a
l
Net
w
o
r
k
m
et
h
o
d
f
o
r
clas
s
if
icatio
n
.
T
h
er
e
i
s
a
r
en
e
w
al
f
r
a
m
e
w
o
r
k
f
o
r
clas
s
i
f
y
in
g
s
y
m
p
to
m
s
o
f
S
y
n
co
p
e
d
i
s
ea
s
e.
U
s
in
g
th
e
K
-
Me
an
s
m
et
h
o
d
,
Gu
f
tar
[
1
8
]
p
r
ed
icts
th
e
m
aj
o
r
ca
u
s
e
s
t
h
at
ca
n
ca
u
s
e
S
y
n
co
p
e's
d
is
ea
s
e.
T
h
e
r
esu
lts
o
f
h
i
s
ex
p
e
r
i
m
e
n
ts
w
er
e
co
m
p
ar
ed
w
it
h
o
t
h
er
m
et
h
o
d
s
s
u
ch
a
s
K
-
Me
a
n
s
f
ast,
K
-
Me
d
o
id
s
an
d
X
-
Me
an
s
.
W
h
ile
San
t
h
an
a
m
et
a
l
.,
[
1
9
]
co
m
b
i
n
es
th
r
ee
m
et
h
o
d
s
a
t
o
n
ce
n
a
m
el
y
t
h
e
m
et
h
o
d
o
f
K
-
Me
an
s
,
Gen
etic
A
l
g
o
r
ith
m
an
d
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
i
n
es
(
SVM)
to
d
ia
g
n
o
s
e
d
iab
etes.
T
h
e
K
-
Me
a
n
s
m
e
th
o
d
i
s
u
s
ed
to
eli
m
i
n
ate
n
o
is
y
d
ata,
Gen
etic
Alg
o
r
it
h
m
s
ar
e
u
s
ed
to
f
i
n
d
o
p
ti
m
al
f
ea
tu
r
e
s
w
h
er
ea
s
SVM
i
s
u
s
ed
f
o
r
clas
s
if
ica
tio
n
.
Fro
m
t
h
e
r
esu
lt
s
o
f
t
h
e
ex
p
er
i
m
e
n
t o
b
tai
n
ed
an
ac
cu
r
ac
y
o
f
9
6
.
7
1
%.
Ud
o
v
y
c
h
en
k
o
et
a
l
.,
[
2
0
]
class
if
ies
h
ea
r
t
f
ai
lu
r
e
b
y
u
s
i
n
g
K
-
NN
B
in
ar
y
.
Fro
m
t
h
e
ex
p
er
i
m
en
t
r
esu
lts
o
b
tain
ed
8
0
-
8
8
%
ac
cu
r
ac
y
r
an
g
e,
7
0
-
9
5
%
s
en
s
iti
v
it
y
,
7
8
-
9
5
%
s
p
ec
if
ica
tio
n
a
n
d
7
7
-
9
3
%
p
r
ec
is
io
n
.
I
n
an
o
t
h
er
r
esear
ch
,
Ud
o
v
y
c
h
e
n
k
o
et
a
l
.,
[
2
1
]
class
if
ied
th
e
I
s
ch
e
m
ic
h
e
ar
tb
ea
t u
s
i
n
g
th
e
K
-
Me
a
n
s
m
e
th
o
d
.
B
ased
o
n
th
e
r
esu
lt
s
o
f
t
h
e
e
x
p
er
i
m
e
n
t,
t
h
e
o
p
ti
m
al
n
u
m
b
er
o
f
n
ei
g
h
b
o
r
s
in
i
n
cr
ea
s
i
n
g
ac
c
u
r
ac
y
was
2
0
-
2
5
n
ei
g
h
b
o
r
s
.
An
o
th
er
ca
s
e
w
i
th
Sah
a
et
a
l
.,
[
2
2
]
class
if
ied
g
en
e
s
e
lectio
n
u
s
in
g
K
-
NN
a
n
d
o
th
er
h
e
u
r
is
tic
m
eth
o
d
s
.
T
h
e
K
-
NN
m
eth
o
d
i
s
u
s
ed
to
clas
s
if
y
th
e
ex
a
m
p
le.
W
h
ile
t
h
e
h
eu
r
i
s
tic
m
et
h
o
d
ch
o
s
en
is
Si
m
u
lated
An
n
ea
lin
g
(
SA
)
a
n
d
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
(
P
SO)
.
B
ased
o
n
th
e
r
esu
lt
s
o
f
th
e
e
x
p
er
i
m
e
n
t,
r
es
ea
r
ch
er
s
clai
m
t
h
at
th
e
S
A
m
et
h
o
d
is
b
etter
th
an
t
h
e
P
SO.
B
ased
o
n
s
o
m
e
p
r
ev
io
u
s
s
tu
d
i
es
th
at
ap
p
l
y
an
y
m
et
h
o
d
s
w
it
h
th
e
ir
ad
v
an
tag
e
s
.
So
,
th
e
a
u
th
o
r
s
w
ill
p
er
f
o
r
m
ac
cu
r
ac
y
a
n
al
y
s
i
s
o
f
th
r
ee
d
ata
m
i
n
i
n
g
m
et
h
o
d
s
i
n
cla
s
s
i
f
y
in
g
s
e
x
u
al
l
y
tr
an
s
m
itted
d
is
ea
s
e
s
.
T
h
e
th
r
ee
m
et
h
o
d
s
ar
e
Naïv
e
B
a
y
e
s
,
K
-
Me
a
n
s
a
n
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[
2
3
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T
h
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et
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ass
u
m
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all
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ib
u
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[
1
1
]
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T
h
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m
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[
2
4
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T
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[
2
5
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h
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1
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2
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K
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Me
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n
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n
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ed
lear
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o
r
it
h
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s
[
2
6
]
.
K
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Me
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s
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et
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2
5
]
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[
2
4
]
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T
h
e
K
-
Me
a
n
s
s
tep
s
ar
e
[
2
7
]
:
a.
I
n
itialize,
d
eter
m
in
e
th
e
v
al
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f
K
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s
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ter
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ata
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tr
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in
E
q
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2
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(
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√
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)
(
2
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W
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,
d
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b)
:
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o
f
o
b
j
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t b
etw
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j
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t
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an
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d
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ate
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ased
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R
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co
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d
it
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s
ar
e
r
ea
ch
ed
w
h
er
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t
h
e
ch
a
n
g
e
o
f
o
b
j
ec
tiv
e
f
u
n
ctio
n
is
b
elo
w
th
e
t
h
r
es
h
o
ld
o
r
n
o
clu
s
ter
-
s
h
i
f
ti
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d
ata
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th
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id
p
o
s
itio
n
ch
an
g
e
is
b
elo
w
th
e
th
r
es
h
o
ld
.
4
.
3
.
K
-
n
ea
re
s
t
n
eig
hb
o
r
K
-
NN
i
s
a
n
o
n
-
p
ar
a
m
etr
ic
cl
ass
i
f
icatio
n
m
et
h
o
d
[
2
8
]
.
C
o
m
p
u
tat
io
n
all
y
,
it
is
s
i
m
p
ler
th
an
o
t
h
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m
et
h
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s
.
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w
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r
k
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b
y
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l
cu
lati
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p
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m
it
y
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et
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a
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s
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a
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d
an
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s
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b
ased
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m
a
tch
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w
ei
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f
a
n
u
m
b
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o
f
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x
is
t
in
g
f
ea
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r
es
[
2
5
]
.
I
d
en
tify
w
i
th
th
is
m
et
h
o
d
b
ased
o
n
th
e
s
i
m
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it
y
w
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th
t
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p
r
ev
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u
s
ca
s
e.
Her
e
to
ca
lc
u
late
th
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s
i
m
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lar
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b
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w
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n
n
e
w
ca
s
e
s
an
d
o
ld
ca
s
es
w
it
h
th
e
f
o
llo
w
in
g
E
q
u
atio
n
(
3
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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m
p
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I
SS
N:
2088
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8708
P
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A
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3937
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h
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ar
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m
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th
e
p
ar
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m
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ter
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(
th
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u
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n
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ta
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j
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t
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m
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c.
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CO
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[1
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T
.
Y.
A
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,
“
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He
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Org
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Is)
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On
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[3
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Din
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[5
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[6
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.
Da
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,
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sif
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In
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.
Co
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D.
Isa
k
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“
A
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[8
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P
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J.
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J.
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5
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.
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6
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.
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7
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D.
M
.
N.
F
a
jri
,
e
t
a
l
.,
“
Op
p
ti
m
ize
d
F
u
z
z
y
Ne
u
ra
l
Ne
t
w
o
rk
f
o
r
Ja
tro
p
a
Cu
rc
a
s
P
lan
t
Dise
a
se
Id
e
n
ti
f
ica
ti
o
n
”
,
i
n
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
S
u
st
a
in
a
b
le I
n
fo
rm
a
ti
o
n
E
n
g
i
n
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
(
S
IET
)
,
2
0
1
7
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
G
u
sti
E
k
a
Y
u
li
a
stu
ti.
S
h
e
is
c
u
rre
n
tl
y
a
s
a
g
ra
d
u
a
te
stu
d
e
n
t
in
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
Br
a
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
.
S
h
e
o
b
tain
e
d
Ba
c
h
e
l
o
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
i
n
2
0
1
6
.
No
w
sh
e
is
c
u
rre
n
tl
y
f
in
a
li
z
in
g
h
e
r
M
a
ste
r
D
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
r
sity
,
M
a
lan
g
,
In
d
o
n
e
sia
.
He
r
re
se
a
rc
h
f
ield
s
a
re
in
M
a
c
h
in
e
Lea
rn
in
g
,
A
rti
f
icia
l
In
telli
g
e
n
c
e
,
Da
ta M
in
i
n
g
.
Ad
y
a
n
Nur
Alfiy
a
tin
.
S
h
e
is
c
u
rre
n
tl
y
a
s
a
g
r
a
d
u
a
te
stu
d
e
n
t
in
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
.
S
h
e
o
b
tain
e
d
Ba
c
h
e
lo
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
UIN
M
a
u
lan
a
M
a
li
k
Ib
ra
h
im
M
a
lan
g
,
In
d
o
n
e
sia
in
2
0
1
6
.
No
w
sh
e
is
c
u
rre
n
tl
y
f
i
n
a
li
z
in
g
h
e
r
M
a
ste
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
r
sity
,
M
a
l
a
n
g
,
In
d
o
n
e
sia
.
He
r
re
se
a
rc
h
f
ield
s
a
re
in
De
c
isio
n
S
u
p
p
o
rt
S
y
st
e
m
,
Op
ti
m
iza
ti
o
n
M
e
th
o
d
,
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
Ag
u
n
g
M
u
sti
k
a
Riz
k
i
.
He
is
c
u
rre
n
tl
y
a
s
a
g
r
a
d
u
a
te
stu
d
e
n
t
in
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
.
He
o
b
tain
e
d
Ba
c
h
e
lo
r
De
g
re
e
i
n
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
in
2
0
1
6
.
No
w
h
e
is
c
u
rre
n
tl
y
f
in
a
li
z
in
g
h
is
M
a
ste
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
r
sity
,
M
a
lan
g
,
In
d
o
n
e
sia
.
His
re
se
a
rc
h
f
ield
s
a
re
in
De
c
isio
n
S
u
p
p
o
r
t
S
y
st
e
m
,
Op
ti
m
iza
ti
o
n
M
e
th
o
d
,
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
And
i
H
a
m
d
i
a
n
a
h
.
S
h
e
is
c
u
rre
n
tl
y
a
s
a
g
ra
d
u
a
te
stu
d
e
n
t
i
n
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
Br
a
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
.
S
h
e
o
b
tain
e
d
Ba
c
h
e
lo
r
De
g
re
e
in
Co
m
p
u
ter
S
c
i
e
n
c
e
f
ro
m
UIN
S
u
n
a
n
Ka
li
jag
a
,
Yo
g
y
a
k
a
rta,
In
d
o
n
e
sia
in
2
0
1
5
.
No
w
sh
e
is
c
u
rre
n
tl
y
f
i
n
a
li
z
in
g
h
e
r
M
a
ste
r
De
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
r
sity
,
M
a
l
a
n
g
,
In
d
o
n
e
sia
.
He
r
re
se
a
rc
h
f
ield
s
a
r
e
in
Co
m
p
u
ter
V
isio
n
,
Da
ta
M
in
i
n
g
,
De
c
isio
n
S
u
p
p
o
rt
S
y
ste
m
,
Op
ti
m
iza
ti
o
n
M
e
t
h
o
d
.
H
il
m
a
n
T
a
u
fi
q
.
He
is
c
u
rre
n
tl
y
a
s
a
g
ra
d
u
a
te
stu
d
e
n
t
in
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
In
d
o
n
e
sia
.
He
o
b
tain
e
d
Ba
c
h
e
lo
r
De
g
re
e
in
M
icro
b
i
o
lo
g
y
f
ro
m
In
stit
u
t
T
e
k
n
o
lo
g
i
Ba
n
d
u
n
g
,
In
d
o
n
e
sia
in
2
0
1
3
.
N
o
w
h
e
is
c
u
rre
n
tl
y
f
in
a
li
z
in
g
h
is
M
a
ste
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ie
n
c
e
f
ro
m
Bra
w
ij
a
y
a
Un
iv
e
rsit
y
,
M
a
lan
g
,
I
n
d
o
n
e
sia
.
His
re
se
a
rc
h
f
ield
s
a
r
e
in
Op
ti
m
iza
ti
o
n
M
e
th
o
d
a
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
Wa
y
a
n
Fi
r
d
a
u
s
M
a
h
m
u
d
y
.
He
c
o
m
p
lete
d
h
is
Ba
c
h
e
lo
r
o
f
M
a
th
e
m
a
ti
c
s
e
d
u
c
a
ti
o
n
i
n
Br
a
w
ij
a
y
a
Un
iv
e
rsit
y
M
a
lan
g
,
In
d
o
n
e
sia
,
c
o
n
ti
n
u
e
d
h
is
M
a
ste
r
o
f
In
f
o
rm
a
t
ics
En
g
in
e
e
rin
g
a
t
T
e
c
h
n
o
l
o
g
y
I
n
stit
u
t
e
No
v
e
m
b
e
r
1
0
t
h
S
u
ra
b
a
y
a
,
In
d
o
n
e
sia
a
n
d
o
b
tain
e
d
h
is
Do
c
to
r
o
f
P
h
i
lo
so
p
h
y
d
e
g
re
e
f
ro
m
Un
iv
e
r
sity
o
f
S
o
u
t
h
A
u
stra
li
a
.
He
h
a
s
sp
e
c
ial
in
tere
st
in
Da
ta
M
in
in
g
,
G
e
n
e
ti
c
A
l
g
o
rit
h
m
s,
M
a
c
h
in
e
Lea
rn
in
g
,
a
n
d
Op
ti
m
iza
ti
o
n
T
e
c
h
n
iq
u
e
s f
o
r
M
a
n
u
f
a
c
tu
rin
g
S
y
st
e
m
.
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