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LI)
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t
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n
e
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li
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is
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r
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d
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a
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d
a
su
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m
a
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Pre
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R
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CC B
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li
c
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C
o
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r
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s
p
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ing
A
uth
o
r
:
T
h
an
g
av
el
u
An
an
a
d
ar
aj
R
ajasek
ar
an
Dep
ar
tm
en
t o
f
E
lectr
o
n
ics
a
n
d
C
o
m
m
u
n
icatio
n
E
n
g
in
ee
r
i
n
g
Vels
I
n
s
titu
te
o
f
Scien
ce
,
T
ec
h
n
o
lo
g
y
an
d
Ad
v
a
n
ce
Stu
d
ies
(
VI
STAS)
C
h
en
n
ai,
T
am
il n
ad
u
,
I
n
d
ia
E
m
ail: ta
r
ajasek
ar
an
@
g
m
ail.
in
1.
I
NT
RO
D
UCT
I
O
N
B
en
ef
iciar
ies
o
f
life
in
s
u
r
an
ce
ar
e
s
af
eg
u
ar
d
ed
in
th
e
ca
s
e
o
f
an
u
n
p
lan
n
ed
in
cid
e
n
t
o
r
ac
cid
en
tal
d
ea
th
.
Ho
we
v
er
,
th
e
d
em
an
d
f
o
r
life
in
s
u
r
an
ce
is
o
f
ten
m
o
d
e
s
t
in
n
atio
n
s
with
well
-
estab
lis
h
ed
s
o
cial
s
ec
u
r
ity
s
y
s
tem
s
[
1
]
.
Pu
r
ch
asin
g
a
lif
e
in
s
u
r
an
ce
p
o
lic
y
is
h
ea
v
ily
in
f
lu
en
ce
d
b
y
o
n
e'
s
v
iews
o
n
life
in
s
u
r
an
ce
an
d
p
er
ce
p
tio
n
s
o
f
m
o
r
tality
r
is
k
.
T
h
e
f
in
a
n
cial
r
is
k
ass
o
ciate
d
with
d
ea
th
is
k
n
o
wn
to
m
o
s
t
f
am
ilies
.
Ho
wev
er
,
th
is
d
o
esn
'
t
r
esu
lt
in
th
eir
b
u
y
in
g
life
in
s
u
r
a
n
ce
,
wh
ich
m
i
g
h
t
im
p
ac
t
h
o
w
lo
n
g
th
eir
f
u
n
d
s
ca
n
last
.
Un
ited
States
Po
s
tal
L
if
e
C
o
[
2
]
.
I
n
itiated
in
1
8
8
4
f
o
r
t
h
e
b
en
ef
it
o
f
p
o
s
tal
wo
r
k
er
s
,
p
o
s
tal
life
in
s
u
r
an
ce
(
PLI
)
was
s
u
b
s
eq
u
en
tly
ex
p
a
n
d
ed
to
i
n
clu
d
e
th
e
Dep
ar
tm
en
t
o
f
T
e
leg
r
ap
h
in
1
8
8
8
.
I
t
n
o
w
ex
t
en
d
s
to
lo
ca
l
an
d
au
to
n
o
m
o
u
s
b
o
d
ies,
u
n
iv
e
r
s
ities
,
g
o
v
er
n
m
e
n
t
-
aid
ed
s
ch
o
o
ls
,
n
atio
n
alize
d
b
an
k
s
,
cr
ed
it
co
o
p
er
ativ
e
s
o
cieties,
jo
in
t
v
en
tu
r
es
with
at
least
a
1
0
%
g
o
v
er
n
m
en
t
o
r
p
u
b
lic
s
ec
to
r
u
n
d
er
tak
in
g
s
(
PS
U)
s
tak
e,
an
d
o
th
er
o
r
g
an
izatio
n
s
.
Me
m
b
er
s
o
f
t
h
e
Par
am
ilit
ar
y
f
o
r
ce
s
an
d
th
e
d
ef
en
s
e
s
er
v
ic
es,
as
well
as
t
h
eir
p
er
s
o
n
n
el,
ar
e
f
u
r
th
er
i
n
s
u
r
ed
b
y
PLI
.
W
ith
ef
f
ec
t
f
r
o
m
2
4
.
3
.
1
9
9
5
,
t
h
e
g
o
v
er
n
m
en
t
g
r
an
ted
p
er
m
is
s
io
n
to
PLI
to
ex
p
a
n
d
its
co
v
er
ag
e
to
r
u
r
al
r
eg
io
n
s
f
o
r
l
if
e
in
s
u
r
an
ce
tr
an
s
ac
tio
n
s
.
T
h
i
s
d
ec
is
io
n
was
b
ased
o
n
th
e
e
x
ten
s
iv
e
n
etwo
r
k
o
f
Po
s
t O
f
f
ices in
r
u
r
al
ar
ea
s
an
d
th
e
r
elativ
ely
ch
ea
p
o
p
er
atin
g
co
s
ts
[
3
]
.
E
v
er
y
o
r
g
an
izatio
n
h
as
an
ess
en
tial
task
in
m
ea
s
u
r
in
g
g
r
o
w
th
p
er
f
o
r
m
a
n
ce
p
er
io
d
ically
t
o
ch
ec
k
its
d
em
an
d
a
n
d
to
im
p
r
o
v
e
its
p
e
r
f
o
r
m
a
n
ce
h
ab
itu
ally
t
o
m
ee
t
i
ts
tar
g
et;
in
PLI
s
ec
to
r
t
h
e
g
r
o
wth
o
f
th
e
p
r
o
d
u
ct
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
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lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
S
tu
d
y
o
n
p
o
s
ta
l life
in
s
u
r
a
n
ce
a
ttr
ib
u
tes a
n
d
its
g
r
o
w
th
p
r
ed
ictio
n
…
(
Th
a
n
g
a
ve
lu
A
n
a
n
a
d
a
r
a
j R
a
ja
s
ek
a
r
a
n
)
623
is
m
ea
s
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r
ed
u
s
in
g
th
e
p
o
licies
th
at
h
av
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b
ee
n
en
r
o
lled
p
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io
d
ically
an
d
b
r
an
c
h
-
wis
e
p
er
f
o
r
m
an
ce
wh
ich
ca
n
b
e
p
o
r
tr
ait
with
th
e
h
elp
o
f
tim
e
s
er
ies
alg
o
r
ith
m
,
a
s
av
in
g
s
ch
em
es
p
latf
o
r
m
'
s
l
if
etim
e,
i
n
clu
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in
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its
p
r
o
d
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ct,
g
r
o
wth
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s
tab
ilit
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d
d
ec
lin
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m
ay
b
e
b
etter
u
n
d
er
s
to
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d
with
th
e
u
s
e
o
f
ac
co
u
n
t
o
p
en
i
n
g
p
r
ed
ictio
n
[
4
]
.
I
n
r
ec
en
t
y
ea
r
s
,
m
ac
h
i
n
e
lear
n
in
g
(
ML
)
h
as
b
ee
n
s
u
g
g
ested
as
a
s
u
b
s
titu
te
ap
p
r
o
ac
h
i
n
life
i
n
s
u
r
an
ce
r
esear
ch
,
an
d
in
2
0
2
1
,
it
was
am
o
n
g
th
e
m
o
s
t
p
o
p
u
lar
s
u
b
jects.
Ho
wev
er
,
m
o
s
t
o
f
p
ast
ac
ad
em
i
cs'
ML
r
esear
ch
h
as
b
ee
n
o
n
n
o
n
-
life
an
d
life
in
s
u
r
an
ce
lap
s
es.
R
esear
ch
in
d
icate
s
th
at
b
ig
tr
an
s
ac
tio
n
s
o
f
ten
n
ee
d
m
o
r
e
d
ata
an
d
o
u
tlier
s
th
at
af
f
ec
t
s
ales.
T
o
im
p
r
o
v
e
p
r
ed
ictio
n
ac
cu
r
ac
y
,
tim
e
s
er
ies
an
aly
s
ts
co
m
b
in
e
an
d
cr
e
ate
n
ew
alg
o
r
ith
m
s
[
5
]
.
T
h
is
s
tu
d
y
ch
o
s
e
f
iv
e
s
u
p
er
v
is
ed
alg
o
r
ith
m
s
ex
tr
a
tr
ee
,
au
to
r
eg
r
ess
iv
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in
teg
r
ated
m
o
v
i
n
g
av
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(
AR
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MA
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,
r
an
d
o
m
f
o
r
est
(
RF
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,
L
ass
o
,
an
d
n
eu
r
al
n
etwo
r
k
.
B
ased
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th
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r
esu
lts
o
f
th
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f
o
r
ec
asti
n
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,
er
r
o
r
test
in
g
h
as
le
d
to
m
o
d
e
r
n
b
u
s
in
e
s
s
s
tu
d
y
m
o
d
els
r
o
u
te
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
[
6
]
.
T
h
e
r
esear
ch
s
tu
d
y
was
m
ad
e
in
f
o
r
ec
asti
n
g
th
e
s
ales
o
f
W
alm
ar
t
wh
er
e
d
if
f
er
en
t
class
if
icatio
n
alg
o
r
ith
m
was
u
s
ed
,
an
d
o
n
co
m
p
ar
is
o
n
an
d
test
co
n
d
u
cte
d
u
s
in
g
m
ea
n
a
b
s
o
lu
te
er
r
o
r
(
MA
E
)
an
d
R
MSE
p
ar
am
ete
r
s
o
f
ea
ch
m
o
d
el
was
o
b
tain
ed
a
n
d
b
ased
o
n
th
e
s
co
r
e
h
ig
h
ac
c
u
r
ac
y
o
f
th
e
m
o
d
el
s
wer
e
p
ick
ed
[
7
]
.
An
o
th
er
s
tu
d
y
was
m
ad
e
o
n
Am
az
o
n
s
ales
d
ata.
T
wo
m
et
h
o
d
s
wer
e
u
s
ed
in
th
e
an
aly
s
i
s
to
p
r
ed
ict
th
e
u
s
e
o
f
r
eliab
le
an
d
k
n
o
w
n
ap
p
r
o
ac
h
es
t
h
er
eb
y
r
esu
ltin
g
in
b
ett
er
ac
c
u
r
ac
y
;
th
e
s
tu
d
y
was
co
n
d
u
cted
o
n
th
r
ee
alg
o
r
ith
m
s
:
W
in
ter
s
'
ex
p
o
n
en
tial
s
m
o
o
t
h
in
g
,
tim
e
-
s
er
ies
d
ec
o
m
p
o
s
itio
n
,
an
d
AR
I
MA
.
T
h
e
r
esu
lts
wer
e
m
ea
s
u
r
ed
u
s
in
g
MA
E
an
d
R
MSE
[
8
]
.
T
h
is
s
tu
d
y
aim
ed
to
s
o
lv
e
th
e
c
h
allen
g
e
o
f
f
i
n
d
in
g
th
e
r
i
g
h
t
m
o
d
el
u
tili
zin
g
i
n
tellig
en
ce
a
n
d
d
at
a
d
r
iv
e
n
ap
p
r
o
ac
h
es,
co
n
s
id
er
in
g
th
e
b
u
s
in
ess
'
s
k
n
o
wled
g
e
o
f
th
e
s
itu
atio
n
.
Fin
d
in
g
o
u
t
h
o
w
u
s
ef
u
l
a
n
d
s
u
cc
ess
f
u
l
ea
ch
m
o
d
el
is
was
th
e
d
r
iv
in
g
f
o
r
ce
f
o
r
th
is
s
tu
d
y
[
9
]
.
All
o
f
th
is
is
d
o
n
e
to
en
s
u
r
e
t
h
at
th
e
ch
o
s
en
ap
p
r
o
ac
h
is
s
u
itab
le
f
o
r
th
e
c
h
o
s
en
co
m
p
an
y
en
v
ir
o
n
m
e
n
t
[
1
0
]
.
Dec
is
io
n
tr
ee
s
,
n
eu
r
al
n
etwo
r
k
s
,
Naïv
e
B
ay
es
,
RF
,
an
d
s
u
p
p
o
r
t
v
ec
t
o
r
m
a
ch
in
es
(
SVM)
wer
e
th
e
alg
o
r
ith
m
s
u
s
ed
in
th
is
s
tu
d
y
.
T
h
e
r
esu
lts
ar
e
b
ein
g
t
ab
u
lated
a
g
ain
s
t
th
e
a
cc
u
r
ac
y
,
d
ep
e
n
d
in
g
o
n
t
h
e
m
eth
o
d
u
s
ed
.
T
h
e
f
o
l
lo
win
g
alg
o
r
ith
m
s
h
av
e
th
e
lo
west
s
co
r
es:
RF
(
8
5
%),
N
aïv
e
B
ay
es
(
8
3
%),
d
ec
is
io
n
tr
ee
(
7
6
%),
n
eu
r
al
n
etwo
r
k
7
0
%,
an
d
SVM
5
9
%.
B
ec
au
s
e
o
f
its
h
ig
h
ac
c
u
r
ac
y
m
o
d
el
(
8
5
%),
RF
is
th
e
b
est wa
y
to
b
e
p
ick
e
d
[
1
1
]
.
T
h
e
d
o
m
ain
f
o
r
th
is
s
tu
d
y
is
E
-
co
m
m
er
ce
.
Star
tin
g
a
n
ew
ap
p
r
o
ac
h
to
ac
q
u
ir
e
an
d
an
a
ly
ze
d
ata
m
ig
h
t
h
av
e
a
m
ajo
r
in
f
lu
en
ce
o
n
a
b
u
s
in
ess
s
in
ce
th
e
r
esu
lt
ca
n
b
e
f
av
o
r
ab
le
o
r
g
o
th
e
o
th
er
way
.
E
-
co
m
m
er
ce
pl
atf
o
r
m
s
ca
p
t
u
r
e
v
ast
am
o
u
n
ts
o
f
d
ata
an
d
s
to
r
e
it
in
th
eir
d
ata
ce
n
ter
s
.
I
t'
s
r
ea
s
o
n
ab
le
th
at
th
ey
wo
n
'
t
wan
t
o
th
er
f
ir
m
s
to
an
a
ly
ze
th
eir
d
ata
f
o
r
p
r
iv
ac
y
r
e
aso
n
s
,
b
u
t
th
ey
ca
n
also
f
o
r
m
th
eir
o
wn
team
t
o
an
aly
ze
th
e
d
ata,
wh
ich
m
ay
b
e
lu
cr
ativ
e
f
o
r
th
em
[
1
2
]
.
Kn
o
win
g
wh
en
a
f
u
tu
r
e
e
p
i
d
em
ic
m
ay
o
cc
u
r
,
p
r
ev
en
tiv
e
ef
f
o
r
ts
ca
n
b
e
m
a
d
e
to
r
ed
u
ce
its
ef
f
ec
t.
S
u
ch
p
r
ev
en
tativ
e
ac
tio
n
s
in
clu
d
e
v
ec
to
r
m
a
n
ag
em
e
n
t,
p
u
b
lic
h
ea
lth
m
ess
ag
es
to
av
o
id
h
ig
h
-
r
is
k
b
eh
a
v
io
r
s
o
r
r
eg
io
n
s
,
an
d
en
h
an
cin
g
p
h
y
s
ician
k
n
o
wled
g
e
f
o
r
ea
r
l
y
d
iag
n
o
s
is
an
d
tr
ea
tm
en
t.
Fo
r
s
u
ch
p
r
ev
en
tio
n
to
tak
e
p
la
ce
,
ea
r
ly
an
d
p
r
ec
is
e
p
r
e
d
ictio
n
o
f
ep
id
e
m
ics
is
im
p
o
r
tan
t [
1
3
]
.
T
h
ey
n
ee
d
to
lo
o
k
at
th
is
as a
n
ad
v
an
tag
e
f
o
r
th
eir
co
m
p
an
y
'
s
p
o
ten
tial,
s
u
ch
as e
x
am
in
in
g
th
e
d
ata
an
d
its
p
atter
n
t
h
r
o
u
g
h
o
u
t
th
e
y
ea
r
s
.
Fo
r
ex
am
p
le,
a
ll
th
e
clien
t
d
ata
f
r
o
m
r
eg
is
tr
atio
n
,
s
ea
r
ch
h
is
to
r
y
,
p
u
r
ch
ases
,
an
d
co
n
v
er
s
ati
o
n
s
ar
e
s
av
ed
o
n
th
eir
s
er
v
er
.
T
h
ey
will
o
n
ly
b
e
ac
ce
s
s
ed
wh
e
n
th
er
e
is
an
is
s
u
e
with
cu
r
r
en
t
d
ata
[
1
4
]
.
T
r
af
f
ic
p
r
ed
ictio
n
is
in
teg
r
al
to
ad
v
an
ce
d
tr
af
f
ic
m
an
a
g
em
en
t
s
y
s
tem
s
(
AT
MSs)
an
d
ad
v
an
ce
d
tr
a
v
eler
in
f
o
r
m
atio
n
s
y
s
tem
s
(
AT
I
Ss
)
.
T
h
e
f
ed
er
al
h
ig
h
way
ad
m
i
n
is
tr
atio
n
(
FHW
A)
en
co
u
r
ag
es
all
tr
af
f
ic
m
a
n
ag
em
en
t
ce
n
ter
s
(
T
MCs
)
to
p
o
s
t
-
tr
av
el
tim
es
an
d
in
cid
en
t
in
f
o
r
m
atio
n
,
g
i
v
in
g
h
elp
f
u
l
in
f
o
r
m
atio
n
to
m
o
t
o
r
is
ts
an
d
s
u
p
p
o
r
tin
g
t
h
em
in
m
ak
i
n
g
r
o
u
te
ch
o
ice
o
p
tio
n
s
.
Su
ch
in
f
o
r
m
atio
n
m
ay
aid
v
e
h
icles
in
ch
o
o
s
in
g
to
d
iv
er
t
f
r
o
m
cr
o
w
d
ed
r
o
ad
way
s
,
th
er
e
b
y
g
iv
in
g
ess
en
tial
ex
tr
a
ca
p
ac
ity
an
d
co
n
tr
ib
u
tin
g
to
th
e
m
an
ag
em
en
t
o
f
co
n
g
esti
o
n
[
1
5
]
.
All
th
e
p
o
s
t
o
f
f
ice
in
v
estme
n
t
p
lan
s
g
u
ar
an
tee
r
etu
r
n
s
s
in
ce
th
e
g
o
v
er
n
m
en
t
o
f
I
n
d
ia
b
a
ck
s
th
em
.
Mo
r
eo
v
er
,
th
e
p
o
s
t
o
f
f
ice
i
n
v
estm
en
t
p
la
n
s
g
iv
e
tax
s
av
in
g
s
o
f
u
p
t
o
R
s
.
1
.
5
lak
h
s
u
p
o
n
in
v
estme
n
t.
C
u
s
to
m
er
s
m
ay
m
ak
e
u
s
e
o
f
th
e
p
o
s
t
o
f
f
ice'
s
m
an
y
b
an
k
in
g
o
p
tio
n
s
.
B
u
ild
in
g
th
e
m
o
s
t
ac
ce
s
s
ib
le,
in
ex
p
en
s
iv
e,
an
d
tr
u
s
two
r
th
y
b
an
k
f
o
r
th
e
o
r
d
in
ar
y
m
an
is
t
h
e
p
r
im
a
r
y
g
o
al,
alo
n
g
with
lead
in
g
t
h
e
ch
a
r
g
e
to
r
ed
u
ce
c
o
s
ts
an
d
r
em
o
v
e
o
b
s
tacle
s
to
f
in
an
cial
in
clu
s
io
n
[
1
6
]
.
W
ith
th
e
d
ec
lin
e
o
f
s
n
ail
m
ail
an
d
th
e
r
is
e
o
f
m
o
r
e
c
o
n
v
e
n
ien
t
elec
tr
o
n
ic
m
eth
o
d
s
,
p
o
s
tal
o
p
er
at
o
r
s
ar
e
ex
p
lo
r
in
g
n
ew
av
e
n
u
es
f
o
r
g
r
o
wth
,
s
u
ch
as
f
in
an
cial
s
er
v
ices,
in
s
u
r
an
ce
,
a
n
d
h
ig
h
-
v
alu
e
r
etailin
g
,
b
y
e
x
ten
d
in
g
th
eir
n
etwo
r
k
o
f
p
o
s
t
o
f
f
ices
in
cr
ea
tiv
e
way
s
[
1
7
]
.
A
ce
n
tr
alize
d
m
ilit
ar
y
f
o
r
ce
was c
o
n
s
id
er
ed
ess
en
tial f
o
r
m
an
y
r
ea
s
o
n
s
,
in
clu
d
in
g
q
u
ellin
g
in
ter
n
al
r
esis
tan
ce
to
th
e
n
ew
g
o
v
er
n
m
en
t,
r
ec
laim
in
g
co
m
p
lete
s
o
v
er
eig
n
ty
f
r
o
m
W
ester
n
p
o
wer
s
,
an
d
s
af
eg
u
ar
d
in
g
an
d
ad
v
a
n
cin
g
J
ap
a
n
'
s
g
eo
p
o
liti
ca
l
in
ter
ests
in
th
e
ar
ea
.
Alth
o
u
g
h
t
h
er
e
was
u
n
iv
e
r
s
al
ag
r
e
em
en
t
o
n
th
e
n
ee
d
f
o
r
a
n
atio
n
al
m
ilit
ar
y
,
t
h
er
e
was
m
u
ch
d
eb
ate
o
v
e
r
h
o
w
t
o
s
taf
f
th
e
f
o
r
ce
s
.
An
ess
en
tial
p
r
o
b
lem
with
an
y
m
an
d
ato
r
y
m
ilit
ar
y
s
er
v
ice
s
y
s
tem
is
th
e
u
n
f
air
d
is
tr
ib
u
ti
o
n
o
f
t
h
e
f
i
n
an
cial
a
n
d
em
o
t
io
n
al
co
s
ts
am
o
n
g
d
r
af
tees
an
d
th
eir
f
am
ilies
[
1
8
]
.
An
in
s
u
r
an
ce
co
m
p
an
y
is
a
f
in
a
n
cial
en
tity
t
h
at
o
f
f
er
s
p
r
o
tectio
n
ag
ai
n
s
t
f
in
an
cial
lo
s
s
es
ca
u
s
ed
b
y
f
u
t
u
r
e
r
is
k
s
.
I
f
t
h
e
in
s
u
r
e
d
in
cu
r
s
d
am
ag
es,
th
e
in
s
u
r
er
h
as
ag
r
ee
d
to
p
ay
a
ce
r
tai
n
s
u
m
[
1
9
]
.
A
co
m
b
in
atio
n
o
f
f
ac
to
r
s
,
in
clu
d
in
g
th
e
i
d
en
tific
atio
n
o
f
cr
itical
ag
in
g
b
io
m
a
r
k
er
s
a
n
d
th
e
r
is
in
g
p
r
ev
alen
ce
o
f
im
p
air
m
e
n
t
ac
r
o
s
s
all
ag
e
g
r
o
u
p
s
,
h
as
b
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tieth
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
5
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52
In
d
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n
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J
E
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E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
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1
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Ap
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20
2
5
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62
2
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63
1
624
ce
n
tu
r
y
.
T
h
e
ass
o
ciatio
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b
etw
ee
n
r
is
k
f
ac
to
r
s
(
s
u
ch
as
s
m
o
k
in
g
,
alco
h
o
l
u
s
e,
lack
o
f
ex
e
r
ci
s
e,
f
o
o
d
,
o
r
k
in
d
o
f
jo
b
)
an
d
th
e
r
esu
lt
o
f
m
o
r
tality
h
as
b
ee
n
ex
am
in
e
d
i
n
s
ev
er
al
ep
i
d
em
io
lo
g
ical
r
esear
ch
,
esp
ec
ially
lo
n
g
itu
d
in
al
in
v
esti
g
atio
n
s
.
C
o
n
tin
u
o
u
s
o
r
r
ec
u
r
r
en
t
m
o
n
ito
r
in
g
o
f
r
is
k
v
a
r
iab
les,
h
ea
lth
o
u
tco
m
es,
o
r
b
o
th
is
d
o
n
e
in
a
lo
n
g
itu
d
in
al
s
tu
d
y
o
v
er
an
e
x
ten
d
e
d
p
er
i
o
d
[
2
0
]
.
W
ith
1
5
5
,
0
0
0
lo
ca
tio
n
s
th
r
o
u
g
h
o
u
t
th
e
co
u
n
tr
y
,
th
e
I
n
d
ian
p
o
s
tal
s
av
in
g
s
s
y
s
tem
is
th
e
lar
g
est
s
av
in
g
s
b
an
k
in
I
n
d
ia.
E
v
e
n
th
o
u
g
h
I
n
d
ia'
s
ec
o
n
o
m
y
h
as
d
ev
elo
p
e
d
an
d
n
o
w
h
as
o
th
e
r
in
v
estme
n
t
o
p
tio
n
s
,
th
e
I
n
d
ian
g
o
v
e
r
n
m
en
t
co
n
ti
n
u
es
to
s
u
p
p
o
r
t
th
is
tr
ied
-
an
d
-
tr
u
e
in
v
estme
n
t
ch
o
ice.
Dir
ec
t
an
d
in
d
ir
ec
t
in
v
estme
n
ts
ar
e
th
e
two
m
ain
o
p
tio
n
s
f
o
r
th
o
s
e
wh
o
s
av
e
at
p
o
s
t
o
f
f
ices.
T
h
e
r
e
ar
e
n
u
m
er
o
u
s
in
s
titu
tio
n
s
th
at
in
v
esto
r
s
h
a
v
e
f
aith
in
,
b
u
t o
n
ly
s
o
m
e
ca
n
m
at
ch
th
e
p
o
s
t
o
f
f
ice'
s
s
tel
lar
r
ep
u
tatio
n
f
o
r
d
ep
e
n
d
ab
ilit
y
[
2
1
]
.
A
wea
lth
o
f
in
n
o
v
ativ
e
s
er
v
ices,
in
clu
d
i
n
g
a
tr
ac
k
in
g
s
y
s
tem
,
e
-
p
ay
m
en
t,
e
-
p
o
s
t,
b
o
o
k
n
o
w
p
ay
later
(
B
NPL)
,
an
d
m
a
n
y
m
o
r
e,
h
a
v
e
b
ee
n
i
n
tr
o
d
u
ce
d
b
y
th
e
d
ep
a
r
tm
en
t
o
f
I
n
d
ia
-
p
o
s
t
d
u
r
in
g
th
e
last
d
ec
ad
e
to
m
ee
t
th
e
d
em
an
d
s
o
f
c
lien
ts
.
T
h
e
p
r
im
ar
y
g
o
al
was
to
clo
s
e
th
e
d
ig
ital
g
ap
b
etwe
en
I
n
d
ia'
s
u
r
b
an
an
d
r
u
r
al
a
r
ea
s
,
p
ar
ticu
lar
l
y
v
ia
n
ew
tech
n
o
lo
g
ies.
I
n
d
ia
p
o
s
t
is
lik
ely
o
n
e
o
f
th
e
f
ew
g
o
v
er
n
m
en
t a
g
en
cies in
I
n
d
ia
th
at
o
f
f
er
s
th
ese
lo
w
-
co
s
t
an
d
ea
s
ily
ac
ce
s
s
ib
le
s
er
v
ice
s
to
r
u
r
al
a
r
ea
s
[
2
2
]
.
Var
io
u
s
in
s
titu
tio
n
s
allo
w
s
o
cieties
to
s
af
eg
u
ar
d
th
em
s
elv
es
ec
o
n
o
m
ically
.
T
h
e
y
e
n
co
u
r
a
g
e
in
d
iv
id
u
al
s
av
in
g
s
o
r
p
r
o
p
er
ty
o
wn
er
s
h
ip
,
s
h
if
t
r
is
k
s
o
n
to
p
u
b
lic
welf
ar
e
o
r
g
an
izatio
n
s
,
o
r
b
o
th
.
T
h
is
s
u
g
g
ests
a
s
ec
u
r
ity
s
y
s
tem
th
at
e
x
p
o
s
es
m
o
r
e
in
d
iv
id
u
als
to
s
h
o
r
t
-
ter
m
v
o
latilit
y
an
d
r
is
k
[
2
3
]
.
A
p
er
s
o
n
s
h
o
u
l
d
in
itiate
leg
al
ac
tio
n
ag
ain
s
t
th
ei
r
co
u
n
tr
y
i
n
a
n
atio
n
al
co
u
r
t
if
th
e
y
b
eliev
e
a
C
o
u
n
cil
o
f
E
u
r
o
p
e
m
em
b
e
r
s
tate
h
as
in
f
r
in
g
e
d
th
eir
h
u
m
an
r
ig
h
ts
.
T
h
ey
m
ay
tak
e
th
eir
ca
s
e
to
th
e
E
C
t
H
R
af
ter
tr
y
in
g
ev
er
y
th
in
g
else
in
th
eir
co
u
n
tr
y
'
s
co
u
r
ts
[
2
4
]
.
Fu
r
th
er
,
m
o
s
t
s
tu
d
ies
o
n
wh
at
f
ac
to
r
s
in
f
lu
en
ce
cu
s
to
m
er
s
'
tr
u
s
t
an
d
p
leasu
r
e
h
av
e
u
s
e
d
eith
er
q
u
an
titativ
e
o
r
q
u
alitativ
e
ap
p
r
o
ac
h
es,
n
eith
er
o
f
wh
i
ch
m
ay
d
o
ju
s
tice
to
th
e
p
h
en
o
m
en
a'
s
d
ep
th
an
d
co
m
p
lex
ity
[
2
5
]
.
C
o
n
ce
r
n
s
ab
o
u
t
p
ai
n
m
an
a
g
em
en
t
a
n
d
o
th
er
p
h
y
s
ical
s
y
m
p
to
m
s
(
s
u
ch
as
s
h
o
r
tn
ess
o
f
b
r
ea
th
,
a
g
itatio
n
,
an
d
s
ec
r
etio
n
s
)
th
at
c
o
m
e
with
ap
p
r
o
ac
h
i
n
g
d
ea
th
ar
e
co
m
m
o
n
am
o
n
g
p
atien
ts
an
d
th
eir
f
am
ily
ca
r
eg
iv
er
s
in
en
d
-
of
-
li
f
e
ca
r
e.
An
o
th
er
co
m
m
o
n
is
s
u
e
is
av
o
id
in
g
n
ee
d
less
tr
an
s
f
er
s
to
ac
u
te
ca
r
e
in
s
titu
tio
n
s
.
Staf
f
m
em
b
er
s
at
lo
n
g
-
ter
m
ca
r
e
f
ac
ilit
ies
ar
e
r
esp
o
n
s
ib
le
f
o
r
ev
alu
atin
g
r
esid
en
ts
f
o
r
s
y
m
p
to
m
s
o
f
p
ain
a
n
d
d
is
co
m
f
o
r
t
a
n
d
a
d
m
in
is
ter
in
g
th
e
p
r
o
p
e
r
p
r
esc
r
ip
tio
n
s
as
p
ar
t
o
f
a
m
u
ltid
is
cip
lin
ar
y
team
th
at
in
clu
d
es
p
h
y
s
ician
s
(
an
d
,
in
ce
r
tain
ca
s
es,
n
u
r
s
e
p
r
ac
titi
o
n
er
s
)
wh
o
m
ay
p
r
escr
ib
e
en
d
-
of
-
life
m
ed
icatio
n
s
[
2
6
]
,
[
2
7
]
.
I
n
s
u
r
an
ce
r
is
k
e
v
alu
atio
n
,
d
ef
i
n
itio
n
,
class
if
icatio
n
,
an
d
p
r
icin
g
is
u
n
d
er
wr
i
tin
g
.
B
ec
au
s
e
r
is
k
m
an
ag
em
en
t is a
p
a
r
t o
f
it,
it is
cr
u
cial
to
th
e
f
u
n
ctio
n
in
g
o
f
an
y
in
s
u
r
an
ce
p
r
o
g
r
am
[
2
8
]
.
B
ac
k
g
r
o
u
n
d
:
tim
e
s
er
ies m
o
d
e
ls
m
ay
b
e
v
er
y
u
s
ef
u
l
wh
en
p
r
ed
ictin
g
th
e
ef
f
icac
y
o
f
ac
c
o
u
n
t o
p
en
in
g
f
o
r
d
i
f
f
er
en
t
m
o
d
est
s
av
in
g
p
r
o
d
u
cts
o
f
th
e
d
i
r
ec
to
r
o
f
p
h
o
t
o
g
r
ap
h
y
(
DOP)
.
Pre
d
ictin
g
f
u
t
u
r
e
o
cc
u
r
r
en
ce
s
is
p
o
s
s
ib
le
with
th
e
u
s
e
o
f
in
ci
d
en
ce
s
tatis
tics
[
2
9
]
.
I
t
is
n
o
w
p
o
s
s
ib
le
to
ev
al
u
ate
th
e
p
r
ed
ictio
n
a
b
ilit
y
o
f
s
ev
er
al
tim
e
s
er
ies
m
o
d
els,
t
h
an
k
s
to
ad
v
a
n
ce
m
en
ts
in
m
o
d
elin
g
tec
h
n
iq
u
es,
f
o
r
ex
am
p
le,
to
f
o
r
ec
ast
th
e
o
cc
u
r
r
e
n
ce
o
f
H5
N1
o
u
tb
r
ea
k
s
in
E
g
y
p
t.
I
n
s
u
m
m
ar
y
,
R
F
tim
e
s
er
ies
m
o
d
elin
g
is
s
u
p
er
i
o
r
in
p
r
ed
ictin
g
th
e
s
p
r
ea
d
o
f
i
n
f
ec
tio
u
s
d
is
ea
s
es
co
m
p
ar
ed
to
o
th
er
tim
e
s
er
ies
m
o
d
els.
T
h
is
f
in
d
in
g
a
n
d
o
t
h
er
s
d
em
o
n
s
tr
ate
th
e
s
im
ilar
ity
b
etwe
en
b
ir
d
an
d
h
u
m
an
e
p
id
em
ics.
I
t
o
f
f
er
s
a
f
r
esh
m
eth
o
d
f
o
r
f
o
r
ec
asti
n
g
th
ese
p
o
ten
tially
d
ev
astatin
g
o
u
tb
r
ea
k
s
in
b
ir
d
p
o
p
u
latio
n
s
u
s
in
g
alr
ea
d
y
-
e
x
is
tin
g
,
p
u
b
licly
av
ailab
le
d
a
ta.
T
h
e
s
ev
er
ity
o
f
h
ig
h
ly
p
ath
o
g
en
ic
a
v
ian
in
f
l
u
en
za
(
H
5
N1
)
o
u
tb
r
ea
k
s
in
E
g
y
p
t
f
o
llo
ws
a
tim
e
-
s
er
ies
p
atter
n
.
R
esu
lts
:
we
f
o
u
n
d
th
a
t
th
e
RF
an
d
lo
g
is
tic
r
eg
r
ess
io
n
ar
e
t
h
e
b
est
m
et
h
o
d
s
f
o
r
p
r
e
d
ictin
g
th
e
v
alu
e
s
f
o
r
PLI
d
ata
s
ets.
W
h
er
ea
s
th
e
o
th
er
m
o
d
els,
AR
I
MA
an
d
SVC
,
h
av
e
h
u
g
e
o
b
s
er
v
atio
n
v
alu
es
o
f
e
r
r
o
r
w.
r
.
t
MA
E
an
d
R
MSE
v
alu
es
,
th
e
b
est f
it wa
s
lik
ely
t
o
b
e
o
b
s
er
v
ed
in
RF
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
tu
d
y
u
s
ed
an
ML
tech
n
i
q
u
e
to
class
if
y
clien
ts
b
ased
o
n
th
eir
f
ea
tu
r
es
to
p
r
ed
ict
th
e
class
lab
el
f
o
r
p
o
ten
tial
c
o
n
s
u
m
er
s
,
r
eg
ar
d
less
o
f
wh
eth
e
r
th
e
y
p
u
r
ch
as
e
a
life
in
s
u
r
an
ce
p
o
lic
y
.
I
t
m
ay
h
elp
in
s
u
r
a
n
ce
f
ir
m
s
ch
o
o
s
e
p
r
o
s
p
ec
tiv
e
cu
s
to
m
er
s
m
o
r
e
ca
r
e
f
u
lly
th
r
o
u
g
h
o
u
t
th
e
u
n
d
er
wr
itin
g
p
r
o
ce
s
s
.
I
n
ad
d
itio
n
,
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
u
n
d
e
r
s
tan
d
in
g
o
f
th
e
I
n
d
ia
n
tar
g
et
m
ar
k
et
m
ay
b
e
o
b
tain
e
d
b
y
lo
o
k
in
g
at
th
e
d
escr
ip
tiv
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an
aly
s
is
o
f
th
e
r
esp
o
n
d
en
ts
'
s
o
cio
d
em
o
g
r
ap
h
ic
d
ata,
wh
ich
m
i
g
h
t
r
aise
th
e
n
atio
n
'
s
life
in
s
u
r
an
ce
p
e
n
etr
atio
n
r
ate.
B
ec
au
s
e
th
e
d
ataset
is
u
n
b
alan
ce
d
o
n
a
g
iv
en
class
la
b
el,
th
is
s
tu
d
y
o
f
f
er
s
in
s
ig
h
t
in
to
f
o
r
ec
asti
n
g
b
y
u
tili
zin
g
v
ar
io
u
s
s
am
p
lin
g
a
n
d
en
s
em
b
le
ap
p
r
o
ac
h
es
th
r
o
u
g
h
o
u
t
th
e
class
if
icatio
n
p
r
o
ce
s
s
u
s
in
g
M
L
alg
o
r
ith
m
s
.
T
h
e
p
r
im
a
r
y
g
o
al
is
to
co
m
p
ar
e
th
e
f
o
u
r
m
o
s
t
co
m
m
o
n
k
in
d
s
o
f
tim
e
s
er
ies
f
o
r
ec
asti
n
g
ML
alg
o
r
ith
m
s
to
g
et
th
e
m
o
s
t
a
cc
u
r
ate
p
r
ed
ictio
n
m
o
d
el
f
o
r
th
e
p
r
o
v
id
ed
d
ata.
Fo
u
r
cla
s
s
if
icatio
n
m
o
d
els
AR
I
MA
,
lin
ea
r
r
eg
r
ess
io
n
,
SVC
,
an
d
RF
wil
l
b
e
u
s
ed
in
th
is
wo
r
k
.
Fig
u
r
e
1
illu
s
t
r
ates
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
.
T
h
is
f
ig
u
r
e
ex
p
lain
s
h
o
w
th
e
d
ataset
is
co
n
n
ec
ted
to
a
s
u
p
er
v
is
ed
ML
f
o
r
ca
lcu
l
atin
g
an
d
tr
an
s
f
o
r
m
in
g
th
e
tim
e
s
er
ies.
I
t
is
co
n
n
ec
ted
to
two
m
o
d
els:
test
m
o
d
el:
f
r
o
m
th
e
tim
e
s
er
i
es
tr
an
s
f
o
r
m
atio
n
,
th
e
test
m
o
d
el
is
s
en
t f
o
r
d
ata
v
alid
atio
n
.
T
r
ain
m
o
d
el:
th
e
tr
ain
in
g
m
o
d
el
is
co
n
n
ec
ted
to
f
it th
e
ty
p
e
o
f
m
o
d
el
lear
n
in
g
.
T
h
is
is
co
n
n
ec
ted
to
th
e
b
alan
ce
d
d
ataset
f
o
r
p
r
ed
ic
tin
g
th
e
m
o
d
el
an
d
v
alid
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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esian
J
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Pre
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e
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.
2
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2
.
P
re
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pro
ce
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ing
a
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Acc
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m
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ata
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a
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m
m
o
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ep
ar
ate
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alu
e
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o
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m
at
(
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,
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d
th
e
d
ata
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im
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ted
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Py
th
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o
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m
3
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ies
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ata
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to
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ata
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et.
T
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d
ata
s
ets
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lit
in
t
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s
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p
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o
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els.
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ep
r
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ce
s
s
in
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o
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ata
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et
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Fig
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r
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2
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ata
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I
SS
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52
In
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3
.
M
a
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a
rning
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lg
o
ri
t
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s
RF
m
o
d
el
:
a
R
F
co
m
m
o
n
ly
k
n
o
wn
as
a
b
a
g
g
in
g
alg
o
r
ith
m
in
ML
,
wh
e
r
e
th
e
'
n
'
n
u
m
b
er
o
f
tr
ee
s
a
r
e
g
r
o
wn
f
o
r
th
e
s
am
p
les.
I
n
th
is
m
o
d
el,
th
e
en
s
em
b
les
ar
e
f
ix
ed
u
p
to
1
,
0
0
0
tr
ee
s
,
an
d
th
e
o
u
tp
u
t
d
ata
ar
e
ev
alu
ated
u
s
in
g
th
e
R
F
m
o
d
el,
m
ak
in
g
o
n
e
-
s
tep
f
o
r
ec
asts
f
o
r
th
e
g
iv
en
s
am
p
les.
T
h
e
RF
i
s
a
s
u
p
er
v
is
ed
lear
n
in
g
m
eth
o
d
th
at
ca
n
d
e
al
with
is
s
u
es
ab
o
u
t
r
eg
r
ess
io
n
an
d
class
if
icatio
n
.
I
t
ac
ts
as
a
co
llectiv
e
b
y
f
o
r
m
in
g
a
f
o
r
est,
o
r
n
u
m
e
r
o
u
s
tr
ee
s
,
to
m
ak
e
ch
o
ices.
I
t
o
f
f
er
s
p
r
ec
is
e
f
o
r
ec
asts
f
o
r
s
ev
er
al
u
s
es.
I
t
ca
n
q
u
an
tify
t
h
e
s
ig
n
if
ican
ce
o
f
ev
er
y
f
ea
tu
r
e
o
f
t
h
e
tr
ain
in
g
d
ata
s
et.
AR
I
MA
m
o
d
el:
AR
I
MA
u
s
u
a
lly
cr
ea
tes
lin
ea
r
e
q
u
atio
n
s
t
o
s
o
lv
e
tim
e
s
er
ies
f
o
r
ec
asti
n
g
p
r
o
b
lem
s
.
I
t
is
g
o
v
er
n
ed
b
y
th
r
ee
m
aj
o
r
p
ar
a
m
eter
s
:
au
to
-
r
e
g
r
ess
iv
e
(
P),
m
o
v
in
g
av
er
ag
e
(
q
)
,
an
d
in
teg
r
atio
n
o
r
d
eter
m
in
in
g
th
e
o
r
d
e
r
o
f
d
i
f
f
e
r
en
cin
g
.
An
AR
I
MA
(
5
,
1
,
0
)
m
o
d
el
was
f
itted
in
itially
.
T
h
e
au
to
-
r
eg
r
ess
io
n
lag
is
s
et
to
5
,
th
e
tim
e
s
er
ies
is
s
tatio
n
ar
y
with
a
d
if
f
er
e
n
ce
o
r
d
er
1
,
a
n
d
t
h
e
m
o
v
in
g
av
e
r
ag
e
m
o
d
el
is
s
et
to
0
.
Fu
tu
r
e
tim
e
s
tep
s
m
ay
b
e
p
r
ed
icted
u
s
in
g
th
e
AR
I
MA
m
o
d
el.
T
h
e
AR
I
MA
r
esu
lts
o
b
je
ct
u
s
es
a
p
r
ed
ict
(
)
m
eth
o
d
to
g
en
e
r
ate
f
o
r
ec
asts
.
I
t ta
k
es th
e
tim
e
s
tep
in
d
ex
as
an
in
p
u
t a
n
d
u
s
es it to
p
r
o
d
u
ce
p
r
ed
ictio
n
s
.
T
h
ese
in
d
ices
ar
e
r
elev
a
n
t
to
t
h
e
b
e
g
in
n
in
g
o
f
th
e
tr
ain
i
n
g
d
ataset
u
s
ed
f
o
r
p
r
ed
ictio
n
p
u
r
p
o
s
es.
E
ac
h
d
etail
is
in
a
h
is
to
r
y
d
atab
ase,
in
itially
f
ille
d
with
tr
ain
in
g
d
ata
an
d
u
p
d
a
ted
with
f
r
esh
d
et
ails
with
ea
ch
cy
cle.
Her
e
is
a
Py
th
o
n
e
x
am
p
le
o
f
a
r
o
llin
g
f
o
r
ec
ast
u
s
in
g
th
e
AR
I
MA
m
o
d
el
t
o
p
u
t
it
all
to
g
eth
e
r
.
Fin
ally
,
we
m
ay
d
eter
m
in
e
th
e
f
o
r
ec
asts
'
R
M
S
E
.
L
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el:
a
s
s
ig
n
in
g
d
ata
to
a
d
is
cr
ete
s
et
o
f
class
e
s
is
th
e
jo
b
o
f
lo
g
is
tic
r
eg
r
ess
io
n
,
a
class
if
icat
io
n
p
r
o
ce
d
u
r
e.
E
m
ail
s
p
am
v
s
.
n
o
n
-
s
p
am
an
d
o
n
lin
e
tr
an
s
ac
tio
n
s
ar
e
two
in
s
tan
ce
s
o
f
ca
teg
o
r
izatio
n
is
s
u
es.
W
o
u
ld
y
o
u
r
ath
er
h
av
e
a
b
e
n
ig
n
tu
m
o
r
o
r
a
m
alig
n
an
t
o
n
e?
L
o
g
is
tic
r
eg
r
ess
io
n
u
s
es
th
e
lo
g
is
tic
s
ig
m
o
id
f
u
n
ctio
n
t
o
co
n
v
er
t i
ts
o
u
tp
u
t to
g
et
a
p
r
o
b
a
b
ilit
y
v
alu
e.
Or
d
in
al
lo
g
is
tic
r
eg
r
ess
io
n
allo
ws f
o
r
d
ep
en
d
e
n
t
v
ar
ia
b
les
to
b
e
o
f
t
h
r
ee
o
r
m
o
r
e
p
o
ten
tially
o
r
d
er
ed
s
o
r
ts
,
s
u
ch
as
"lo
w,
"
"m
ed
i
u
m
,
"
o
r
"h
ig
h
.
".
W
e
ac
h
iev
ed
o
u
r
aim
o
f
v
is
u
ally
r
ep
r
esen
tin
g
th
e
lo
g
is
tic
r
eg
r
ess
io
n
tr
a
in
in
g
s
et
r
esu
lts
.
No
w,
we
ca
n
g
o
o
n
to
t
h
e
n
ex
t
class
if
icatio
n
:
d
iv
id
in
g
d
ata
s
ets
an
d
tr
ain
in
g
th
em
to
p
r
ed
ict
th
e
v
al
u
es
o
f
th
e
f
o
llo
win
g
twelv
e
co
n
s
ec
u
tiv
e
s
er
ies ac
cu
r
ately
.
Su
p
p
o
r
t
v
ec
to
r
class
if
icatio
n
(
SVC
)
m
o
d
el:
t
h
e
SVM
is
a
lin
ea
r
m
o
d
el
t
h
at
m
ay
b
e
u
s
e
d
to
s
o
lv
e
r
eg
r
ess
io
n
an
d
class
if
icatio
n
is
s
u
es.
I
t
p
r
o
v
id
es
s
atis
f
ac
t
o
r
y
s
o
lu
tio
n
s
,
wh
eth
er
lin
ea
r
o
r
n
o
n
-
lin
ea
r
,
f
o
r
v
ar
io
u
s
r
ea
l
-
wo
r
l
d
is
s
u
es.
A
b
asic p
r
em
is
e
o
f
SVM
is
th
at
to
ca
teg
o
r
ize
th
e
d
ata,
th
e
al
g
o
r
i
th
m
d
r
aws a
lin
e
o
r
h
y
p
er
p
lan
e.
All
th
e
tr
ain
in
g
d
ata
will
g
o
in
to
m
ak
in
g
p
r
ed
ic
tio
n
s
wh
en
we
t
r
a
i
n
t
h
e
cl
a
s
s
i
f
i
e
r
w
it
h
m
a
n
y
d
a
ta
s
e
ts
.
U
s
e
t
h
e
p
r
e
d
i
c
t
i
o
n
t
e
c
h
n
i
q
u
e
t
o
f
o
r
e
c
a
s
t
t
h
e
s
a
m
p
l
e
l
a
b
e
l
.
F
i
g
u
r
e
3
ex
p
lain
s
th
e
SVM
class
if
ica
tio
n
m
o
d
el.
T
h
e
m
ix
e
d
d
ata
is
s
eg
r
eg
ated
u
s
in
g
th
e
SVM
alg
o
r
ith
m
in
to
class
1
,
2
,
3
,
4
.
So
m
e
s
am
p
les
m
ay
b
e
p
r
o
v
id
e
d
,
an
d
th
ese
d
etails ar
e
g
iv
e
n
b
el
o
w
:
C
las
s
1
:
b
asic lin
ea
r
SVM
−
Fo
cu
s
o
n
b
in
a
r
y
class
if
icatio
n
with
lin
ea
r
s
ep
ar
ab
ilit
y
.
−
Har
d
m
ar
g
i
n
an
d
s
o
f
t m
ar
g
i
n
ap
p
r
o
ac
h
es.
C
las
s
2
: K
er
n
el
-
b
ased
n
o
n
-
lin
ea
r
SVM
−
E
x
ten
d
s
SVM
to
h
an
d
le
n
o
n
-
lin
ea
r
s
ep
ar
ab
le
d
ata
u
s
in
g
K
er
n
el
tr
ick
s
.
−
I
n
co
r
p
o
r
ates v
ar
io
u
s
K
er
n
el
f
u
n
ctio
n
s
f
o
r
t
r
an
s
f
o
r
m
atio
n
.
C
las
s
3
: SVM
f
o
r
r
eg
r
ess
io
n
−
Ad
ap
ts
SVM
m
eth
o
d
o
lo
g
y
to
r
eg
r
ess
io
n
task
s
.
−
Han
d
les b
o
th
lin
ea
r
an
d
n
o
n
-
lin
ea
r
r
eg
r
ess
io
n
th
r
o
u
g
h
a
p
p
r
o
p
r
iate
K
er
n
el
s
elec
tio
n
.
C
las
s
4
:
an
o
m
aly
d
etec
tio
n
SVM
(
o
n
e
-
class
SVM)
−
Sp
ec
ializes in
d
etec
tin
g
an
o
m
alies with
in
a
d
ataset.
−
Usef
u
l f
o
r
ap
p
licatio
n
s
lik
e
f
r
a
u
d
d
etec
tio
n
,
an
d
n
etwo
r
k
s
ec
u
r
ity
.
Fig
u
r
e
3
.
SVM
class
if
icatio
n
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
S
tu
d
y
o
n
p
o
s
ta
l life
in
s
u
r
a
n
ce
a
ttr
ib
u
tes a
n
d
its
g
r
o
w
th
p
r
ed
ictio
n
…
(
Th
a
n
g
a
ve
lu
A
n
a
n
a
d
a
r
a
j R
a
ja
s
ek
a
r
a
n
)
627
3.
RE
SU
L
T
S
Dif
f
er
en
t
p
e
r
f
o
r
m
an
ce
c
o
m
p
ar
is
o
n
s
wer
e
o
f
f
er
ed
u
s
in
g
th
e
ML
a
p
p
r
o
ac
h
.
T
h
e
M
L
m
o
d
els'
p
er
f
o
r
m
an
ce
m
u
c
h
r
aises
th
at
o
f
th
e
m
o
d
els.
Sev
er
al
co
m
p
ar
is
o
n
s
wer
e
u
tili
ze
d
to
ev
alu
ate
th
e
alg
o
r
ith
m
s
'
ab
ilit
y
to
p
r
ed
ict
f
u
tu
r
e
o
u
tco
m
es.
T
h
e
R
MSE
an
d
MA
E
ar
e
ca
lcu
lated
;
th
e
MA
E
q
u
an
ti
f
ies
th
e
ty
p
ical
s
ize
o
f
f
o
r
e
ca
s
tin
g
m
is
tak
es,
ig
n
o
r
i
n
g
th
e
d
ir
ec
tio
n
o
f
th
ese
er
r
o
r
s
.
I
n
th
e
ca
s
e
o
f
co
n
tin
u
o
u
s
v
ar
iab
les,
it
ass
es
s
es
p
r
ec
is
io
n
.
T
h
e
MA
E
a
n
d
R
MSE
m
ay
tak
e
v
alu
es
b
etwe
en
0
an
d
in
f
in
ity
,
b
u
t
th
e
wid
er
t
h
e
g
ap
b
etwe
en
t
h
em
,
th
e
m
o
r
e
v
ar
iatio
n
th
er
e
is
in
t
h
e
in
d
iv
i
d
u
al
m
is
tak
es.
C
o
n
s
e
q
u
en
tly
,
th
e
R
MSE
will
alwa
y
s
b
e
m
o
r
e
o
r
eq
u
al
to
th
e
MA
E
.
L
ess
is
m
o
r
e
in
th
is
ca
s
e.
Acc
u
r
ac
y
m
ea
s
u
r
em
en
t
in
p
r
ed
ictio
n
s
estab
lis
h
in
g
m
etr
ics
th
at
e
n
ab
l
e
th
e
co
m
p
ar
is
o
n
o
f
th
e
v
ar
i
o
u
s
m
eth
o
d
o
lo
g
ies
is
v
ital
f
o
r
ass
ess
in
g
th
e
ac
c
u
r
ac
y
o
f
th
e
f
o
r
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asts
.
T
h
e
f
o
r
ec
ast
o
u
tco
m
es
m
u
s
t
b
e
co
m
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ar
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to
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e
p
r
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icted
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cu
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at
th
e
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es
ig
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ated
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ate,
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a
to
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n
u
m
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er
o
f
ac
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n
ts
m
u
s
t b
e
estab
lis
h
ed
a
s
p
ar
t o
f
th
is
ass
es
s
m
en
t.
Fig
u
r
e
4
s
h
o
ws th
e
R
F
m
o
d
el
o
u
tp
u
t.
Fig
u
r
e
4
.
R
F
m
o
d
el
o
u
tp
u
t
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h
e
g
r
ap
h
s
h
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ws
th
e
ex
p
ec
ted
r
an
g
e
o
f
(
0
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,
th
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v
alu
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6
5
,
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e
r
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r
ed
icted
r
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e
o
f
(
0
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is
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0
0
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e
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p
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im
at
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9
0
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;
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n
th
e
r
an
g
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(
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,
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alu
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o
x
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ately
6
0
,
0
0
0
;
in
th
e
r
an
g
e
o
f
(
6
-
8
)
th
e
v
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e
will
b
e
ap
p
r
o
x
im
ately
5
5
,
0
0
0
,
in
th
e
r
a
n
g
e
o
f
(
8
-
1
0
)
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h
e
v
alu
e
will
b
e
ap
p
r
o
x
im
ately
8
0
,
0
0
0
.
Fig
u
r
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5
s
h
o
ws th
e
AR
I
MA
m
o
d
el
o
u
t
p
u
t.
Fig
u
r
e
5
.
AR
I
M
A
m
o
d
el
o
u
tp
u
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
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g
&
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m
p
Sci
,
Vo
l.
3
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r
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icted
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e
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n
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e
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g
e
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,
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e
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e
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an
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f
(
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e
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ately
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in
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e
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an
g
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o
f
(
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1
0
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h
e
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e
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e
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atel
y
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6
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h
o
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e
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icted
r
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g
e
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ately
6
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0
0
;
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e
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a
n
g
e
o
f
(
6
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e
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e
a
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x
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ately
1
,
6
5
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0
0
0
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t
h
e
r
a
n
g
e
o
f
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e
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e
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e
ap
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o
x
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ately
2
,
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0
,
0
0
0
.
Fig
u
r
e
7
s
h
o
ws th
e
SVC
m
o
d
el
o
u
tp
u
t.
Fig
u
r
e
6
.
L
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
o
u
tp
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t
Fig
u
r
e
7
.
SVC
m
o
d
el
o
u
tp
u
t
T
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MA
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r
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n
d
s
[
3
0
]
.
R
MSE
in
d
icate
s
th
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s
q
u
ar
e
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(
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s
h
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Pre
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MA
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s
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AR
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d
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f
t
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R
F,
lo
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d
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m
o
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els
f
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M
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l
/
p
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c
t
M
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M
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57
,
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046
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45
,
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CO
NCLU
SI
O
N
An
ef
f
icien
t
way
to
d
ea
l
wit
h
d
is
tr
ib
u
tio
n
c
h
an
g
es
in
th
e
f
u
tu
r
e
e
n
v
ir
o
n
m
en
t
was
to
d
o
f
o
r
ec
asts
.
Ass
u
m
in
g
th
e
d
is
tr
ib
u
tio
n
v
a
r
ies
s
lo
wly
,
we
m
ay
tr
ain
th
e
m
o
d
els
to
f
ilter
o
u
t
th
e
n
o
is
e.
T
h
e
c
o
n
clu
s
io
n
s
o
f
th
is
ess
ay
will
b
e
ad
v
an
tag
eo
u
s
to
s
o
ciety
as
a
w
h
o
le
s
in
ce
in
s
u
r
an
ce
p
r
o
tectio
n
is
ess
en
tial
to
th
e
lo
n
g
-
ter
m
v
iab
ilit
y
an
d
f
in
a
n
cial
s
tab
ilit
y
o
f
f
am
ilies
.
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m
p
r
o
v
in
g
t
h
e
u
n
d
e
r
wr
itin
g
p
r
o
ce
s
s
ca
n
aid
th
e
in
s
u
r
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ce
co
m
p
an
y
in
c
h
o
o
s
in
g
p
o
s
s
ib
l
e
cu
s
to
m
er
s
m
o
r
e
ef
f
icien
tly
.
T
h
is
r
esear
ch
will
also
clar
if
y
t
h
e
p
r
ed
ictio
n
b
y
u
tili
zin
g
d
if
f
er
e
n
t
s
am
p
lin
g
s
t
h
r
o
u
g
h
o
u
t
th
e
class
if
icatio
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p
r
o
ce
s
s
u
s
in
g
ML
alg
o
r
ith
m
s
with
an
u
n
b
alan
ce
d
d
ataset.
Simu
latio
n
r
esu
lts
s
h
o
w
th
at
ex
p
ec
ted
is
o
n
e
o
f
th
e
m
o
s
t
im
p
o
r
tan
t
v
ar
iab
les
to
p
r
ed
ict
an
d
th
at
b
o
th
R
F
an
d
lo
g
is
tic
r
eg
r
e
s
s
io
n
o
u
tp
er
f
o
r
m
e
d
th
e
o
th
er
two
m
o
d
els.
T
h
e
R
F
m
o
d
el,
o
n
th
e
o
th
er
h
a
n
d
,
d
o
esn
'
t
h
av
e
a
m
atch
i
n
g
ter
m
as
it
u
s
es
lin
ea
r
ass
u
m
p
tio
n
s
ab
o
u
t
t
h
e
co
n
n
ec
tio
n
b
etwe
en
an
ticip
ated
an
d
p
r
ed
icted
v
alu
es.
B
y
co
m
p
ilin
g
im
p
o
r
t
an
t
in
f
o
r
m
atio
n
f
r
o
m
r
elev
a
n
t
s
tu
d
ies
an
d
o
f
f
er
in
g
p
r
ac
tical
s
u
g
g
esti
o
n
s
f
o
r
ac
ad
em
ics
an
d
f
in
a
n
cial
an
aly
s
ts
,
th
is
s
tu
d
y
ad
d
s
to
th
e
b
o
d
y
o
f
k
n
o
wled
g
e
in
th
e
f
ield
.
T
h
e
R
F
m
o
d
el
is
th
e
m
o
s
t
ef
f
ec
tiv
e
a
n
d
f
astes
t
in
p
r
ed
ictin
g
th
e
s
y
s
tem
'
s
f
u
tu
r
e
s
tate,
an
d
it
s
h
o
ws
th
e
h
ig
h
e
s
t
v
alu
e
f
o
r
th
e
PLI
p
r
o
d
u
ct
ad
.
I
n
th
e
f
u
tu
r
e,
we
wan
t
to
em
p
lo
y
p
r
e
d
ictiv
e
alg
o
r
ith
m
s
th
at
ar
e
well
-
s
u
ited
to
s
ec
to
r
s
tu
d
ies
to
ex
tr
ac
t
d
ata
f
r
o
m
th
e
p
o
s
tal
d
ep
ar
tm
en
t'
s
lo
g
is
tics
s
er
v
ice
s
an
d
b
en
c
h
m
ar
k
an
d
c
o
m
p
a
r
e
th
ese
alg
o
r
ith
m
s
with
th
o
s
e
o
f
o
th
er
co
m
p
etin
g
o
r
g
an
izatio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
K
.
K
a
r
t
h
i
k
a
,
S
.
D
h
a
n
a
l
a
k
s
h
m
i
,
S
.
M
.
M
u
r
t
h
y
,
N
.
M
i
s
h
r
a
,
S
.
S
a
s
i
k
a
l
a
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
R
a
s
p
b
e
r
r
y
p
i
-
e
n
a
b
l
e
d
w
e
a
r
a
b
l
e
se
n
so
r
s
f
o
r
p
e
r
so
n
a
l
h
e
a
l
t
h
t
r
a
c
k
i
n
g
a
n
d
a
n
a
l
y
si
s,”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
e
l
f
S
u
s
t
a
i
n
a
b
l
e
A
rt
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
S
y
s
t
e
m
s,
I
C
S
S
AS
2
0
2
3
-
Pr
o
c
e
e
d
i
n
g
s
,
O
c
t
.
2
0
2
3
,
p
p
.
1
2
5
4
–
1
2
5
9
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
S
A
S
5
7
9
1
8
.
2
0
2
3
.
1
0
3
3
1
9
0
9
.
[
2
]
R
.
R
a
m
a
n
,
V
.
S
u
j
a
t
h
a
,
C
.
B
h
u
p
e
s
h
b
h
a
i
T
h
a
c
k
e
r
,
K
.
B
i
k
r
a
m,
M
.
B
S
a
h
a
a
i
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
I
n
t
e
l
l
i
g
e
n
t
p
a
r
k
i
n
g
ma
n
a
g
e
me
n
t
sy
st
e
ms
u
si
n
g
I
o
T
a
n
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
r
e
a
l
-
t
i
m
e
s
p
a
c
e
a
v
a
i
l
a
b
i
l
i
t
y
e
st
i
ma
t
i
o
n
,
”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
u
s
t
a
i
n
a
b
l
e
C
o
m
m
u
n
i
c
a
t
i
o
n
N
e
t
w
o
rks
a
n
d
A
p
p
l
i
c
a
t
i
o
n
,
I
C
S
C
N
A
2
0
2
3
-
Pr
o
c
e
e
d
i
n
g
s
,
N
o
v
.
2
0
2
3
,
p
p
.
2
8
6
–
2
9
1
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
C
N
A
5
8
4
8
9
.
2
0
2
3
.
1
0
3
7
0
6
3
6
.
[
3
]
A
.
D
e
e
p
a
,
R
.
La
t
h
a
,
T.
S
.
K
u
mar
,
N
.
K
.
M
a
n
i
k
a
n
d
a
n
,
J.
P
r
e
e
t
h
a
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
I
o
T
-
b
a
se
d
w
e
a
r
a
b
l
e
d
e
v
i
c
e
s f
o
r
p
e
r
so
n
a
l
saf
e
t
y
a
n
d
a
c
c
i
d
e
n
t
p
r
e
v
e
n
t
i
o
n
s
y
st
e
ms,
”
i
n
2
0
2
3
2
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
m
a
r
t
T
e
c
h
n
o
l
o
g
i
e
s f
o
r S
m
a
r
t
N
a
t
i
o
n
,
S
m
a
rt
T
e
c
h
C
o
n
2
0
2
3
,
A
u
g
.
2
0
2
3
,
p
p
.
1
5
1
0
–
1
5
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/
S
m
a
r
t
T
e
c
h
C
o
n
5
7
5
2
6
.
2
0
2
3
.
1
0
3
9
1
6
9
1
.
[
4
]
B
.
M
e
e
n
a
k
s
h
i
,
B
.
G
o
p
i
,
L.
R
a
ma
l
i
n
g
a
m
,
A
.
V
a
n
a
t
h
i
,
S
.
S
a
n
g
e
e
t
h
a
,
a
n
d
S
.
M
u
r
u
g
a
n
,
“
W
i
r
e
l
e
ss
s
e
n
s
o
r
n
e
t
w
o
r
k
s
f
o
r
d
i
sas
t
e
r
man
a
g
e
me
n
t
a
n
d
e
mer
g
e
n
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AUTH
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Mr.
Th
a
n
g
a
v
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lu
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n
a
d
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r
a
j
R
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ja
se
k
a
r
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tl
y
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p
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te
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s
a
tec
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ica
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a
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ly
st
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t
th
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d
ia
n
P
o
sta
l
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y
m
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t
Ba
n
k
,
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n
a
i.
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h
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c
a
p
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c
it
y
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h
e
h
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n
d
les
th
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re
p
o
rt
fra
m
e
wo
rk
in
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c
le
1
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g
,
c
o
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tri
b
u
ti
n
g
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o
t
h
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t,
a
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n
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iza
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lso
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rv
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s
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n
LS
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sta
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sista
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p
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rtme
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n
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ti
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p
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rts
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rio
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s
tec
h
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ica
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n
d
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p
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ra
ti
o
n
a
l
a
sp
e
c
ts.
In
a
d
d
i
ti
o
n
t
o
h
is
P
h
.
D
.
p
u
rsu
i
ts,
h
e
h
o
l
d
s
a
p
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stg
ra
d
u
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te
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e
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re
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in
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c
tr
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ics
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n
d
Co
m
m
u
n
ica
ti
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n
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n
g
in
e
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rin
g
(EC
E)
fro
m
An
n
a
Un
i
v
e
rsity
,
G
u
in
d
y
,
a
p
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sti
g
io
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stit
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ti
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k
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rl
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c
las
s
e
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g
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rs
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n
d
tec
h
n
o
l
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ists.
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i
s
d
e
e
p
ly
p
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ss
io
n
a
te
a
b
o
u
t
a
rti
ficia
l
in
tell
ig
e
n
c
e
(AI),
m
a
c
h
in
e
lea
rn
i
n
g
(M
L)
,
d
a
ta
sc
ien
c
e
,
a
n
d
b
ig
d
a
ta
h
a
n
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li
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sly
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g
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re
se
a
rc
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fie
ld
s
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d
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o
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tr
ib
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ti
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g
t
o
in
n
o
v
a
ti
v
e
so
lu
t
io
n
s
in
t
h
e
se
field
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
tara
jas
e
k
a
ra
n
@in
d
iap
o
st
.
g
o
v
.
i
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
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n
g
&
C
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m
p
Sci
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N:
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0
2
-
4
7
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S
tu
d
y
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p
o
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ta
l life
in
s
u
r
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ce
a
ttr
ib
u
tes a
n
d
its
g
r
o
w
th
p
r
ed
ictio
n
…
(
Th
a
n
g
a
ve
lu
A
n
a
n
a
d
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r
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j R
a
ja
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ek
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r
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631
Dr
.
Pi
c
h
a
m
u
t
h
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tl
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g
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ro
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th
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p
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rtme
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t
o
f
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tro
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ics
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d
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m
m
u
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g
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ri
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g
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ls
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stit
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te o
f
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ien
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ies
(VIS
TAS
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h
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h
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c
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m
p
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stru
m
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ro
m
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ra
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stit
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o
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y
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i
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.
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p
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e
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tro
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ics
fro
m
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n
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h
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h
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y
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h
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ll
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w
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with
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c
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n
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c
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tac
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m
a
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se
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lsu
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a
c
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in
.
Dr
.
Ve
la
y
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m
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je
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d
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p
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h
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.
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d
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re
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n
d
ian
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stit
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te
o
f
S
c
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c
e
,
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n
g
a
lo
re
,
In
d
ia,
i
n
1
9
7
9
.
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n
1
9
9
3
,
h
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re
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iv
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d
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h
.
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d
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re
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lec
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l
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c
tro
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n
g
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rin
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fr
o
m
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b
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U
n
iv
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JA
P
AN
.
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h
a
s
m
o
re
t
h
a
n
3
5
y
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a
rs’
e
x
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Ac
a
d
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m
ic
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n
d
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se
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p
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e
.
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h
a
s
b
e
e
n
with
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ti
o
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l
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o
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n
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h
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o
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n
n
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n
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a
s
a
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jec
t
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c
to
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an
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a
d
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rin
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stru
m
e
n
tati
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d
Oc
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u
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a
n
d
Oc
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a
n
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se
rv
a
ti
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S
y
ste
m
s).
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is
c
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tl
y
w
o
rk
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a
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to
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n
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p
a
rtme
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t
o
f
EC
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is
re
se
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rc
h
in
tere
sts
in
c
l
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d
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n
d
e
rwa
ter
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les
s
se
n
so
r
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k
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c
o
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it
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d
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fi
n
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ra
d
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su
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a
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m
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ter
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m
e
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re
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sy
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m
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is
a
l
ife
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ll
o
w
o
f
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c
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f
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d
ia,
I
n
d
ia
(USI)
,
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n
u
a
ry
2
0
0
1
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As
so
c
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M
e
m
b
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f
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o
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stica
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c
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f
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e
rica
,
USA,
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n
u
a
r
y
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m
b
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f
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n
u
a
ry
2
0
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0
,
Li
fe
fe
ll
o
w
o
f
In
stit
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ti
o
n
o
f
El
e
c
tro
n
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a
n
d
Tele
c
o
m
m
u
n
ica
ti
o
n
En
g
i
n
e
e
rin
g
(
IET
E),
I
n
d
ia,
Ja
n
u
a
ry
2
0
1
2
.
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wa
s
e
lec
ted
twice
a
s
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C
h
a
irma
n
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ia
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c
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ti
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n
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o
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rd
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ta
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y
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p
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ra
ti
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n
P
a
n
e
l
(DBC
P
)
o
f
In
ter
g
o
v
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n
m
e
n
tal
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e
a
n
o
g
ra
p
h
ic
Co
m
m
issio
n
(I
OC)
/
Wo
rl
d
M
e
teo
ro
l
o
g
ica
l
Org
a
n
iza
ti
o
n
(W
M
O)
o
f
UN
S
CO,
in
Oc
to
b
e
r
2
0
0
8
.
a
n
d
S
e
p
tem
b
e
r
2
0
0
9
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
irec
to
r.
e
c
e
@v
e
lsu
n
i
v
.
a
c
.
in
.
Evaluation Warning : The document was created with Spire.PDF for Python.