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m
e
n
t
o
f
1
.
3
%
p
o
p
u
latio
n
g
r
o
w
t
h
in
2
0
1
7
co
m
p
ar
ed
to
2
0
1
6
w
it
h
ap
p
r
o
x
i
m
atel
y
3
2
.
0
Millio
n
p
eo
p
le
ar
e
liv
in
g
in
Ma
la
y
s
ia
in
2
0
1
7
[
2
]
.
Un
d
en
iab
l
y
,
w
a
s
te
g
en
er
atio
n
will
co
n
ti
n
u
a
ll
y
to
in
cr
ea
s
e
w
it
h
t
h
e
g
r
o
w
t
h
o
f
p
o
p
u
latio
n
o
v
er
ti
m
e.
A
s
tat
is
tic
h
as
o
u
tli
n
ed
f
r
o
m
y
ea
r
2
0
1
2
u
n
til
y
ea
r
2
0
1
5
,
th
e
in
cr
ea
s
e
d
am
o
u
n
t
o
f
w
a
s
te
g
en
er
ated
is
in
cr
ea
s
in
g
f
r
o
m
3
2
,
8
0
0
to
n
n
es
p
er
d
a
y
to
3
8
,
5
0
0
to
n
n
es
p
er
d
a
y
[
3
]
.
T
h
is
p
r
o
b
lem
is
m
ain
l
y
r
esu
lted
f
r
o
m
t
h
e
i
n
cr
e
m
e
n
t
o
f
p
o
p
u
latio
n
an
d
v
ar
iatio
n
in
th
e
h
o
u
s
e
h
o
ld
s
ize
[
4
]
.
Sa
m
e
s
it
u
atio
n
also
h
ap
p
en
s
i
n
I
n
d
o
n
es
ia,
w
h
er
e
t
h
e
in
cr
ea
s
ed
i
n
p
o
p
u
latio
n
i
s
g
r
ea
tl
y
tr
ig
g
er
i
n
g
t
h
e
MSW
M
i
s
s
u
es
[
5
]
.
As
t
h
er
e
ar
e
m
o
r
e
p
eo
p
le,
m
o
r
e
r
eso
u
r
ce
s
s
u
c
h
as
f
o
o
d
w
ill
b
e
co
n
s
u
m
ed
.
Sad
l
y
,
s
o
m
e
p
eo
p
le
u
s
e
th
is
r
eso
u
r
ce
y
et
ca
r
eless
l
y
lit
ter
in
g
.
As
an
ex
a
m
p
le,
s
o
m
e
m
i
g
h
t
t
h
r
o
w
t
h
e
g
ar
b
ag
e
a
w
a
y
b
y
th
e
r
o
ad
s
id
e,
th
u
s
ig
n
o
r
in
g
th
e
et
h
ical
is
s
u
es
an
d
th
e
la
w
s
m
ad
e
b
y
th
e
g
o
v
er
n
m
en
t.
T
h
is
w
o
u
ld
b
e
th
e
m
o
s
t
u
n
e
th
ica
l
p
r
ac
tice
to
w
ar
d
s
th
e
n
at
u
r
e.
C
o
m
m
o
n
e
n
v
ir
o
n
m
en
ta
l
is
s
u
e
s
r
elate
d
to
p
o
o
r
m
an
a
g
e
m
en
t
o
f
MSW
M
ca
n
b
e
id
en
ti
f
ied
s
u
c
h
a
s
air
p
o
llu
tio
n
,
w
ater
p
o
llu
tio
n
as
w
ell
a
s
ex
ce
s
s
iv
e
g
e
n
er
atio
n
o
f
m
et
h
an
e
g
a
s
.
T
h
is
c
y
cle
w
il
l
co
n
t
in
u
o
u
s
l
y
to
r
ep
ea
t
i
f
ea
r
l
y
p
r
ev
e
n
tio
n
is
ta
k
e
n
f
o
r
g
r
an
ted
.
C
o
n
s
eq
u
e
n
tl
y
,
n
e
g
ati
v
e
i
m
p
ac
ts
t
h
at
b
r
in
g
h
ar
m
to
t
h
e
en
v
ir
o
n
m
en
t
w
ill
s
lo
w
l
y
b
e
th
e
alar
m
i
n
g
i
s
s
u
es
to
th
e
s
o
ciet
y
[
6
]
.
On
t
h
e
o
t
h
er
h
a
n
d
,
h
u
m
a
n
o
v
e
r
p
o
p
u
latio
n
is
o
n
e
o
f
th
e
m
o
s
t
u
n
a
v
o
id
ed
ca
u
s
e
s
f
o
r
th
e
en
v
ir
o
n
m
e
n
tal
is
s
u
e.
A
s
t
h
e
p
o
p
u
latio
n
g
r
o
w
t
h
i
s
e
s
ca
lati
n
g
r
ap
id
l
y
,
t
h
e
r
e
w
i
ll
b
e
m
o
r
e
p
eo
p
le
w
h
o
w
il
l
co
n
s
u
m
e
m
o
r
e
r
eso
u
r
ce
s
.
Un
d
o
u
b
ted
l
y
,
t
h
e
ex
ce
s
s
iv
e
n
atu
r
al
r
eso
u
r
ce
co
n
s
u
m
p
tio
n
f
o
r
th
e
d
ev
elo
p
m
e
n
t
o
f
th
e
co
u
n
tr
y
w
ill
co
n
tr
ib
u
te
to
th
e
s
a
m
e
p
r
o
b
le
m
th
a
t
th
i
s
p
r
o
j
ec
t
h
as
d
is
cu
s
s
ed
ea
r
lier
w
h
ich
i
s
th
e
i
n
cr
ea
s
ed
o
f
w
ast
e
g
en
er
atio
n
.
C
u
r
r
e
n
tl
y
,
M
SW
M
in
Ma
la
y
s
ia
d
o
n
o
t
h
av
e
t
h
e
ex
ac
t
s
ta
tis
tic
o
f
h
o
w
m
u
c
h
w
aste
is
g
en
er
ated
an
d
h
o
w
m
an
y
ti
m
e
s
th
e
waste
b
i
n
s
g
et
f
u
ll
p
er
d
a
y
.
W
ith
o
u
t
th
e
s
e
s
tati
s
tics
,
it
i
s
v
er
y
h
ar
d
f
o
r
t
h
e
g
o
v
er
n
m
e
n
t
to
p
r
o
v
id
e
a
m
p
le
s
p
ac
es o
f
t
h
e
co
m
p
o
s
t
s
ite
s
a
n
d
to
p
lan
f
o
r
t
h
e
g
ar
b
ag
e
p
ic
k
-
u
p
s
c
h
ed
u
le
f
o
r
t
h
e
f
u
tu
r
e.
T
h
er
ef
o
r
e,
it
is
v
er
y
i
m
p
o
r
tan
t
to
p
r
ed
ict
t
h
e
a
m
o
u
n
t
o
f
w
a
s
te
g
e
n
er
ated
to
ea
s
e
th
e
p
r
o
ce
s
s
in
m
an
a
g
i
n
g
f
u
tu
r
e
MSW
M.
R
e
ce
n
tl
y
,
t
h
er
e
ar
e
m
a
n
y
r
esear
c
h
es
o
n
f
o
r
ec
asti
n
g
th
e
SW
G
b
ased
o
n
p
r
e
d
ictio
n
m
o
d
el
s
.
P
r
ed
ictio
n
m
o
d
els
c
an
g
i
v
e
in
f
o
r
m
atio
n
ab
o
u
t
th
e
f
u
t
u
r
e
SW
G
b
ased
o
n
m
an
y
p
er
f
o
r
m
a
n
ce
‟
s
cr
iter
i
o
n
s
u
c
h
a
s
Me
an
Sq
u
ar
e
E
r
r
o
r
(
MSE
)
,
Me
an
A
b
s
o
lu
te
P
er
ce
n
tag
e
E
r
r
o
r
(
MA
P
E
)
an
d
R
².
Ma
n
y
s
t
u
d
ie
s
s
u
g
g
e
s
ted
u
s
in
g
A
N
N
as th
e
p
r
ed
ictio
n
to
o
l [
7
]
,
[
8
]
.
Su
n
&
C
h
u
n
g
p
aib
u
lp
atan
a
u
s
e
d
ML
P
u
n
d
er
A
NN
m
o
d
el
a
n
d
P
ea
r
s
o
n
C
o
r
r
elatio
n
to
p
r
ed
ic
t
SW
G
in
B
an
g
k
o
k
[
9
]
.
A
t
t
h
e
b
eg
i
n
n
i
n
g
o
f
t
h
e
r
esear
ch
,
f
e
w
m
o
d
ell
in
g
tech
n
iq
u
e
s
h
a
v
e
b
ee
n
e
x
p
lo
r
ed
b
ased
o
n
f
e
w
in
f
lu
e
n
ce
s
s
u
c
h
as
p
o
p
u
latio
n
g
r
o
w
t
h
an
d
h
o
u
s
e
h
o
ld
in
co
m
e.
A
ls
o
,
in
ter
p
o
latio
n
tec
h
n
iq
u
e
h
as
b
ee
n
ap
p
lied
d
u
r
in
g
d
ata
co
llectio
n
s
ta
g
e
d
u
e
to
s
o
m
e
m
i
s
s
i
n
g
v
al
u
es.
Ne
u
r
al
f
itti
n
g
to
o
l h
as b
ee
n
u
s
ed
t
o
s
elec
t,
cr
ea
te
an
d
tr
ain
d
ata
o
f
th
e
n
et
w
o
r
k
b
ased
o
n
MSE
an
d
r
eg
r
ess
io
n
an
al
y
s
i
s
.
Fo
r
ML
P
,
o
n
e
n
eu
r
o
n
h
i
d
d
en
lay
er
h
as
b
ee
n
ap
p
lied
th
at
r
esu
l
ts
i
n
t
h
e
ac
ce
p
tab
le
f
itti
n
g
v
al
u
e
R
²
o
f
0
.
9
6
.
Du
r
in
g
t
h
e
e
v
al
u
atio
n
s
tag
e,
t
h
e
p
e
r
f
o
r
m
a
n
ce
f
o
r
b
o
th
tech
n
iq
u
es
h
a
s
b
ee
n
co
m
p
ar
ed
.
T
h
e
r
esu
lts
m
an
a
g
e
d
to
illu
s
tr
ate
th
at
A
NN
m
o
d
el
is
m
u
c
h
m
o
r
e
ac
cu
r
ate
co
m
p
ar
ed
to
P
r
in
cip
al
C
o
m
p
o
n
e
n
t
An
al
y
s
i
s
-
R
e
g
r
ess
io
n
(
P
C
A
-
R
e
g
r
es
s
io
n
)
b
y
1
0
%
b
ased
o
n
R
²
v
alu
e.
Ho
w
ev
er
,
t
h
e
v
al
u
es
o
f
MSE
f
o
r
b
o
th
P
C
A
-
R
e
g
r
ess
io
n
an
d
ANN
m
o
d
el
w
er
e
v
e
r
y
h
ig
h
w
h
ic
h
ar
e
2
2
1
8
0
5
.
2
an
d
6
3
9
2
9
r
esp
ec
tiv
el
y
.
I
n
ad
d
itio
n
,
m
a
n
y
r
esear
c
h
e
r
s
u
s
e
ANN
as
th
eir
clas
s
if
icatio
n
m
o
d
el
to
p
r
ed
ict
m
a
n
y
t
h
i
n
g
s
[
1
0
]
-
[
1
2
]
.
L
itta
et
al.
s
tr
ess
ed
th
at
th
e
f
o
r
ec
asti
n
g
o
f
t
h
u
n
d
er
s
to
r
m
is
o
n
e
o
f
t
h
e
to
u
g
h
e
s
t
p
r
ed
ictio
n
task
s
.
Ho
w
e
v
er
,
th
e
s
t
u
d
y
h
as
u
s
ed
A
N
N
as t
h
eir
cla
s
s
i
f
icat
io
n
m
eth
o
d
to
f
o
r
ec
ast t
h
e
i
n
co
m
in
g
th
u
n
d
er
s
to
r
m
b
ased
o
n
th
e
o
b
tain
ed
m
eteo
r
o
lo
g
ic
al
p
ar
am
eter
s
.
Si
x
lear
n
i
n
g
al
g
o
r
ith
m
s
w
er
e
u
s
ed
a
n
d
th
e
p
er
f
o
r
m
a
n
ce
s
h
a
v
e
b
ee
n
co
m
p
ar
ed
.
T
h
e
r
esu
lt
s
o
u
tli
n
ed
th
a
t
t
h
e
L
e
v
en
b
er
g
-
Ma
r
q
u
ar
d
t
h
a
s
o
u
tp
er
f
o
r
m
ed
th
e
r
est
o
f
t
h
e
alg
o
r
ith
m
to
p
r
ed
ict
th
e
t
h
u
n
d
er
s
to
r
m
i
n
ter
m
s
o
f
th
e
s
tatis
tical
m
ea
s
u
r
es.
T
h
e
o
u
tc
o
m
e
o
f
t
h
e
s
t
u
d
y
co
n
clu
d
ed
t
h
at
ANN
i
s
b
est
u
s
ed
to
p
r
ed
ict
an
y
r
ea
l
-
ti
m
e
d
at
a
w
it
h
le
s
s
er
r
o
r
s
.
T
h
er
ef
o
r
e,
t
h
is
p
r
o
j
ec
t
w
ill
u
s
e
A
N
N
as
t
h
e
cla
s
s
i
f
icat
io
n
m
o
d
el
as
it
h
as
b
ee
n
w
id
el
y
k
n
o
w
n
to
p
o
r
tr
ait
th
e
b
es
t
r
es
u
lts
as
co
m
p
ar
ed
to
an
y
o
th
er
m
o
d
els
w
h
ile
R
²
v
al
u
e
will b
e
u
s
ed
to
ev
al
u
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
r
ed
ictio
n
alg
o
r
ith
m
.
T
h
e
m
ai
n
o
b
j
ec
tiv
e
o
f
th
is
r
es
ea
r
ch
is
to
d
esig
n
e
f
f
icien
t
p
r
e
d
ictio
n
alg
o
r
ith
m
f
o
r
w
aste
m
an
ag
e
m
e
n
t
to
p
r
ed
ict
th
e
g
e
n
er
atio
n
o
f
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ased
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p
o
p
u
latio
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r
o
w
t
h
i
n
Ma
la
y
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ia.
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h
e
r
e
m
ai
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in
g
s
ec
tio
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o
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t
h
i
s
p
ap
er
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m
p
r
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o
f
f
e
w
m
a
in
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ar
ts
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w
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e
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lai
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t t
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s
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la
s
t
s
ec
tio
n
w
il
l
co
n
clu
d
e
th
e
p
ap
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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2.
RE
S
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M
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T
H
O
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ill
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p
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ed
ict
th
e
SW
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b
ased
o
n
p
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p
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latio
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g
r
o
w
t
h
.
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r
th
i
s
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ac
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r
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th
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s
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d
y
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as
c
h
o
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e
n
Ma
la
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as
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h
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m
p
le
s
ize.
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h
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s
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tio
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w
il
l
b
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iv
id
ed
in
to
th
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ee
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tag
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s
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d
ata
ac
q
u
is
itio
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p
r
e
-
p
r
o
ce
s
s
i
n
g
an
d
ev
alu
a
tio
n
.
Sectio
n
2
.
1
w
il
l
ex
p
lai
n
o
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th
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m
et
h
o
d
f
o
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co
llectio
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f
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ata
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a
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a
m
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n
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g
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n
er
ated
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d
n
u
m
b
er
o
f
p
o
p
u
latio
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in
Ma
la
y
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ia.
W
h
ils
t,
s
ec
tio
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2
.
2
d
escr
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es
t
h
e
p
r
o
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s
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o
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-
p
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ce
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i
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o
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llected
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ata.
L
ast
l
y
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Sectio
n
2
.
3
e
x
p
lain
s
o
n
th
e
s
tep
s
to
ev
alu
a
te
th
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d
at
a.
2
.
1
.
Da
t
a
Acquis
it
io
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E
ar
lier
,
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is
p
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t
h
as
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lan
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ata
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M
SW
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co
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s
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n
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w
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er
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u
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to
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n
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id
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tial
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es,
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h
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n
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ld
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at
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t
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ir
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h
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m
o
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n
t
o
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w
aste
g
en
er
ated
v
ia
a
u
t
h
o
r
is
ed
w
e
b
s
ites
[
2
]
,
[
3
]
.
T
h
en
,
t
h
ese
d
ata
w
il
l
u
n
d
er
g
o
t
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tag
e
in
Sectio
n
2
.
2
.
2
.
2
.
P
re
-
pro
ce
s
s
ing
As
m
e
n
tio
n
ed
in
t
h
e
p
r
ev
io
u
s
Sectio
n
2
.
1
,
th
i
s
p
r
o
j
ec
t
w
i
ll
u
s
e
ANN
as
th
e
cla
s
s
i
f
icat
io
n
m
o
d
el.
Firstl
y
,
th
e
d
ata
o
f
n
u
m
b
er
o
f
p
o
p
u
latio
n
an
d
SW
G
m
u
s
t
b
e
p
r
e
-
p
r
o
ce
s
s
ed
b
ef
o
r
e
p
r
o
ce
ed
ed
to
n
eu
r
al
n
et
w
o
r
k
tr
ai
n
in
g
,
d
u
e
to
n
o
i
s
e
r
ed
u
ctio
n
an
d
t
h
e
u
n
d
esire
d
A
NN
lear
n
i
n
g
r
ate
[
4
]
.
Sain
i
et
al.
m
e
n
tio
n
ed
t
h
at
t
h
e
f
ir
s
t
s
tep
i
n
t
h
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
s
ta
g
e
is
to
o
b
tain
a
t
r
en
d
lin
e.
T
h
is
co
n
ce
p
t
i
s
ca
l
led
Stat
io
n
ar
y
C
h
ai
n
C
o
n
ce
p
t
[
4
]
.
T
o
m
e
et
th
is
co
n
ce
p
t,
s
tatis
t
ical
m
ea
s
u
r
es
s
u
c
h
as
m
ea
n
,
n
ee
d
to
b
e
co
n
s
tan
t
f
o
r
s
o
m
e
t
i
m
e
an
d
it
ca
n
b
e
ac
h
ie
v
ed
b
y
o
b
s
er
v
in
g
th
e
tr
en
d
li
n
e.
T
h
e
r
ea
s
o
n
o
f
ac
h
ie
v
i
n
g
th
e
Sta
tio
n
ar
y
C
h
ai
n
C
o
n
ce
p
t
is
to
m
ak
e
s
u
r
e
t
h
at
th
e
tr
ain
e
d
m
o
d
el
w
ill
b
e
i
n
t
h
e
r
an
g
e
o
f
t
h
e
o
b
s
er
v
ed
d
ata.
I
n
th
is
s
tu
d
y
,
th
e
tr
en
d
li
n
e
i
s
o
b
tain
ed
v
ia
M
A
T
L
A
B
.
W
it
h
t
h
e
cu
r
v
e
f
itti
n
g
to
o
l
ap
p
licatio
n
i
n
M
A
T
L
A
B
,
f
e
w
s
e
ts
o
f
tr
en
d
li
n
e
w
ill
b
e
d
is
p
la
y
ed
.
T
h
en
,
th
e
R
²
v
a
lu
e
o
f
t
h
e
tr
e
n
d
lin
e
s
w
ill
b
e
co
m
p
ar
ed
an
d
t
h
e
tr
en
d
lin
e
w
h
ich
h
a
s
t
h
e
h
i
g
h
e
s
t
v
al
u
e
o
f
R
²
w
ill
b
e
c
h
o
s
en
.
R
²
i
s
o
n
e
o
f
t
h
e
w
id
el
y
u
s
ed
s
tatis
tical
m
ea
s
u
r
es.
R
²
ca
lcu
latio
n
i
s
s
h
o
w
n
in
E
q
u
atio
n
1
[
1
3
]
.
T
h
e
clo
s
er
th
e
v
al
u
e
o
f
R
²
to
1
,
th
e
m
o
r
e
th
e
v
ar
iab
ilit
y
o
f
r
esp
o
n
s
e
s
u
r
r
o
u
n
d
i
n
g
t
h
e
m
ea
n
an
d
th
e
ac
c
u
r
ate
t
h
e
r
esu
lt i
s
.
(
1
)
T
h
e
n
ex
t
s
tep
i
n
t
h
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
s
ta
g
e
i
s
to
tr
ai
n
a
n
d
to
p
r
ed
ict
th
e
d
ata
u
s
i
n
g
A
NN
cla
s
s
i
f
icatio
n
m
o
d
el.
T
h
is
s
tep
w
il
l
b
e
d
o
n
e
v
ia
Vis
u
al
Ge
n
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Dev
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p
er
,
o
n
e
o
f
th
e
s
o
f
t
w
ar
es
t
h
at
ca
n
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e
u
s
ed
to
tr
ain
an
d
p
r
ed
ict
an
y
d
ata.
T
h
e
d
ef
au
lt
alg
o
r
ith
m
o
f
f
ee
d
f
o
r
w
ar
d
n
eu
r
al
n
et
w
o
r
k
w
it
h
b
ac
k
p
r
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p
ag
at
io
n
lear
n
i
n
g
w
h
ic
h
w
il
l p
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f
ec
tl
y
tr
ain
t
h
e
n
et
w
o
r
k
.
T
h
e
eq
u
atio
n
f
o
r
th
is
al
g
o
r
ith
m
i
s
s
h
o
w
n
i
n
E
q
u
atio
n
2
[
1
4
]
.
∑
(
)
(
2
)
w
h
er
e
an
d
w
ill
b
e
th
e
in
p
u
t
an
d
o
u
tp
u
t
v
ar
iab
le
r
esp
ec
tiv
el
y
.
w
i
ll
b
e
t
h
e
tr
an
s
f
er
f
u
n
c
tio
n
,
w
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l b
e
th
e
w
ei
g
h
t
f
ac
to
r
b
et
w
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n
t
w
o
n
o
d
es a
n
d
w
ill b
e
th
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in
ter
n
a
l th
r
e
s
h
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ld
.
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h
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p
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d
u
r
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f
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N
tr
ain
i
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g
i
n
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n
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De
v
e
lo
p
er
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laid
o
u
t
as
i
n
F
ig
u
r
e
1
.
Af
ter
s
etti
n
g
u
p
t
h
e
ar
c
h
i
te
ctu
r
e,
all
d
ata
m
u
s
t
u
n
d
er
g
o
a
p
r
o
ce
s
s
ca
lled
n
o
r
m
aliza
tio
n
.
Fig
u
r
e
1
.
A
NN
tr
ai
n
i
n
g
p
r
o
ce
d
u
r
e
[
1
2
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
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4752
N
eu
r
a
l Net
w
o
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k
P
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fo
r
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W
a
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n
a
g
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Ma
la
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(
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ata
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f
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ll
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ata
m
u
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b
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i
n
t
h
i
s
r
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g
e
to
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er
f
o
r
m
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NN
tr
ain
i
n
g
.
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h
e
n
o
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m
a
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izatio
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i
s
d
o
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e
v
ia
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e
f
o
llo
w
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f
o
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la
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q
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a
tio
n
3
.
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3
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w
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e
is
t
h
e
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ar
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le,
w
ill
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e
th
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n
o
r
m
al
ized
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ar
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w
h
il
e
an
d
ar
e
th
e
m
a
x
i
m
u
m
an
d
m
i
n
i
m
u
m
o
f
th
e
i
n
p
u
t
v
ar
i
ab
les r
esp
ec
tiv
el
y
.
T
h
e
o
v
er
v
ie
w
o
f
th
e
V
is
u
al
G
en
e
De
v
elo
p
er
is
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
T
h
e
n
e
x
t
s
tep
af
ter
n
o
r
m
aliza
tio
n
is
to
tr
ain
th
e
d
ata
a
n
d
ch
a
n
g
in
g
t
h
e
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ar
a
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ter
at
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h
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etti
n
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o
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at
t
h
e
to
tal
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tat
u
s
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ill
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e
t
h
e
m
a
x
i
m
u
m
in
p
u
t
o
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h
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ain
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c
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cle.
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ter
th
e
m
ax
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ated
.
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h
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s
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d
o
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d
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f
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p
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d
u
r
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laid
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t a
s
f
o
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w
:
Fu
n
ctio
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(
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all
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p
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e(
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le
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et
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k
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Neu
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t.I
n
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2
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f
f
icien
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d
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o
s
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lan
d
ea
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ly
w
it
h
r
eg
ar
d
s
to
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h
e
ex
ce
s
s
i
v
e
a
m
o
u
n
t o
f
w
a
s
te
th
a
t
w
ill b
e
g
e
n
er
ated
.
4.
CO
NCLU
SI
O
N
P
o
o
r
MSW
M
w
il
l
lead
to
m
an
y
e
n
v
ir
o
n
m
en
ta
l
an
d
h
ea
lth
i
s
s
u
es
s
u
c
h
a
s
ex
ce
s
s
i
v
e
a
m
o
u
n
t
o
f
m
et
h
an
e
g
as
p
r
o
d
u
ctio
n
an
d
m
alar
ia.
T
h
er
ef
o
r
e,
in
t
h
is
p
r
o
j
ec
t,
p
r
e
d
ictio
n
alg
o
r
ith
m
s
ar
e
p
r
o
p
o
s
ed
to
p
r
o
v
id
e
th
e
f
o
r
ec
asted
SW
G
b
ased
o
n
p
o
p
u
latio
n
g
r
o
w
t
h
f
ac
to
r
.
P
r
ed
ictio
n
al
g
o
r
ith
m
p
la
y
s
a
v
er
y
i
m
p
o
r
tan
t
r
o
le
n
o
t
o
n
l
y
i
n
MSW
M
i
n
Ma
la
y
s
ia
b
u
t
also
i
n
h
a
n
d
li
n
g
t
h
e
w
a
s
t
e.
T
h
is
alg
o
r
ith
m
w
il
l
p
r
o
v
id
e
th
e
m
an
a
g
e
m
en
t
p
er
s
o
n
n
el
to
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av
e
th
e
e
s
ti
m
at
io
n
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d
h
o
w
to
h
a
n
d
le
t
h
e
S
W
G
in
t
h
e
f
u
t
u
r
e.
B
ased
o
n
t
h
e
ex
p
er
i
m
en
tatio
n
r
esu
lt
s
,
it
s
h
o
w
s
t
h
at
t
h
e
o
b
j
ec
tiv
es
o
f
t
h
i
s
p
r
o
j
ec
t
h
av
e
b
e
en
ac
h
iev
ed
.
I
n
ad
d
itio
n
,
t
h
e
r
esu
lt
in
Sectio
n
3
in
d
icate
d
th
at
th
e
p
r
ed
ictio
n
o
f
SW
G
b
ased
o
n
p
o
p
u
latio
n
g
r
o
w
t
h
f
ac
to
r
is
b
est
s
u
it
ed
w
h
e
n
A
N
N
is
u
s
e
d
w
it
h
t
w
o
h
id
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en
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er
s
w
h
er
e
th
e
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u
m
b
er
o
f
n
o
d
es
f
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th
e
f
ir
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t
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er
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n
d
th
e
s
e
co
n
d
la
y
er
is
f
i
v
e.
T
h
e
r
esu
lt
also
s
h
o
w
s
t
h
at
t
h
e
p
r
ed
ictio
n
alg
o
r
it
h
m
h
as
p
r
ed
icted
th
e
r
ate
o
f
i
n
cr
e
m
en
t
o
f
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is
2
9
.
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3
p
er
ce
n
t f
o
r
th
e
n
e
x
t t
w
e
n
t
y
y
e
ar
s
.
Ho
w
e
v
er
,
th
e
li
m
itatio
n
in
t
h
i
s
s
tu
d
y
is
t
h
at
d
ata
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o
r
p
o
p
u
latio
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g
r
o
w
th
f
ac
to
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n
o
n
l
y
b
e
o
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tain
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v
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a
u
t
h
o
r
ized
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eb
s
ites
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u
e
t
o
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o
m
e
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estric
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tio
n
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y
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n
e
o
f
t
h
e
a
u
t
h
o
r
ities
h
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n
d
lin
g
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SW
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i
n
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la
y
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ia.
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t
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a
n
d
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m
f
o
r
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m
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t
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n
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th
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u
g
g
e
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tio
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ca
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e
co
n
s
id
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lts
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h
er
e
ar
e
tw
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r
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o
m
m
e
n
d
atio
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s
th
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t
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n
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n
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ith
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te
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r
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r
Au
to
r
eg
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s
i
v
e
Net
w
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r
k
w
it
h
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x
o
g
e
n
o
u
s
I
n
p
u
ts
(
N
AR
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an
d
to
co
n
s
id
e
r
m
o
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G
f
ac
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u
c
h
as
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s
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ize
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n
d
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e.
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M
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R
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14
15
16
17
18
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33
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ra
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t
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p
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0
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4
;
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[9
]
E
J,
P
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,
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A
.
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f
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e
a
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ter:
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0
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0
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3
8
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0
7
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1
2
.
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1
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e
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p
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(
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m
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p
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o
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b
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u
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lo
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f
o
r
m
a
ti
c
s.
2
0
1
6
;
3
6
:1
7
2
-
1
8
0
.
[1
2
]
Yin
C,
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se
n
d
a
h
l
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u
o
Z.
M
e
th
o
d
s
t
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p
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e
p
re
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n
p
e
rf
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r
m
a
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m
o
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ls.
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im
u
lat
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M
o
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ra
c
ti
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a
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h
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.
2
0
0
3
;1
1
(3
-
4
):2
1
1
-
2
2
2
.
[1
3
]
L
it
ta
A
,
M
a
r
y
Id
icu
la
S
,
M
o
h
a
n
t
y
U.
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icia
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ra
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t
w
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o
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in
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re
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f
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e
teo
ro
lo
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ters
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g
P
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m
o
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so
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d
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rsto
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s.
In
tern
a
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rn
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t
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o
sp
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ric S
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2
0
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3
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0
1
3
:
1
-
14.
[1
4
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Ow
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s
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c
1
0
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3
Co
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f
f
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rd
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o
r
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th
e
Esti
m
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te.
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re
se
n
tati
o
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p
re
se
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ted
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t
;
2
0
1
6
.
[1
5
]
A
rti
f
icia
l
n
e
u
ra
l
n
e
t
w
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rk
[
In
tern
e
t].
V
is
u
a
lg
e
n
e
d
e
v
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lo
p
e
r.
n
e
t.
2
0
1
8
[
c
it
e
d
6
Ju
ly
2
0
1
8
].
Av
a
il
a
b
le
f
ro
m
:
h
tt
p
:
//
ww
w
.
v
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a
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_
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NN
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h
tm
l
[1
6
]
W
a
g
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r
M
,
A
n
se
ll
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n
t
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ri
ff
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m
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r
C
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t
a
l.
P
re
d
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g
M
o
rtalit
y
in
In
c
id
e
n
t
Dia
ly
sis
P
a
ti
e
n
ts:
A
n
A
n
a
l
y
sis
o
f
th
e
Un
it
e
d
Kin
g
d
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m
Re
n
a
l
Re
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str
y
.
Am
e
rica
n
Jo
u
rn
a
l
o
f
Kid
n
e
y
Dise
a
se
s.
2
0
1
1
;
5
7
(
6
):8
9
4
-
9
0
2
.
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