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1
64
~
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I
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N:
2252
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1
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.
v
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.
pp
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164
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Predic
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ing
lon
g
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rt
-
term
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mo
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and ma
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l
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a
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ticle
his
to
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y:
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ed
Sep
6
,
2
0
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4
R
ev
is
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Oct
9
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2024
Acc
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ted
No
v
1
9
,
2
0
2
4
Rice
,
a
sta
p
le
fo
o
d
so
u
rc
e
g
lo
b
a
ll
y
,
is
in
h
i
g
h
d
e
m
a
n
d
a
n
d
p
r
o
d
u
c
ti
o
n
a
c
ro
ss
th
e
wo
rld
.
I
ts
c
o
n
su
m
p
ti
o
n
v
a
ri
e
s
in
d
iffere
n
t
c
o
u
n
tri
e
s,
with
e
a
c
h
n
a
ti
o
n
h
a
v
in
g
it
s
u
n
iq
u
e
wa
y
o
f
i
n
c
o
rp
o
ra
ti
n
g
rice
in
t
o
it
s
d
iet.
Re
c
o
g
n
izin
g
t
h
e
g
lo
b
a
l
n
a
tu
re
o
f
rice
,
it
s
p
r
o
d
u
c
ti
o
n
is
a
c
ru
c
ial
a
sp
e
c
t
o
f
e
n
su
rin
g
it
s
a
v
a
il
a
b
il
it
y
,
a
g
ric
u
lt
u
re
f
o
re
c
a
stin
g
,
e
c
o
n
o
m
ic
sta
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il
it
y
,
a
n
d
fo
o
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se
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u
rit
y
.
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re
d
ictin
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it
s
p
ro
d
u
c
ti
o
n
,
we
c
a
n
d
e
v
e
lo
p
a
g
lo
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a
l
p
lan
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o
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it
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ro
d
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c
ti
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a
n
d
st
o
c
k
,
th
e
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b
y
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re
v
e
n
ti
n
g
issu
e
s
li
k
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fa
m
in
e
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is
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a
p
e
r
p
r
o
p
o
se
s
m
a
c
h
in
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lea
rn
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L)
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n
d
d
e
e
p
lea
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g
(DL)
m
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r
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ss
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g
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n
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d
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sti
n
g
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a
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rica
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g
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n
t
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sti
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g
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Bo
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ra
d
ien
t
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st
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g
,
d
e
c
isio
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tree
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a
n
d
lo
n
g
s
h
o
rt
-
term
m
e
m
o
ry
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TM
)
to
p
re
d
ict
i
n
tern
a
ti
o
n
a
l
r
ice
p
ro
d
u
c
ti
o
n
.
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to
tal
o
f
n
in
e
M
L
a
n
d
o
n
e
DL
m
o
d
e
ls are
train
e
d
a
n
d
tes
ted
o
n
th
e
i
n
tern
a
ti
o
n
a
l
d
a
tas
e
t,
wh
ic
h
c
o
n
tai
n
s
th
e
rice
p
ro
d
u
c
ti
o
n
d
e
tails
o
f
1
9
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c
o
u
n
tri
e
s
o
v
e
r
t
h
e
las
t
6
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y
e
a
rs.
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tab
ly
,
li
n
e
a
r
re
g
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ss
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a
n
d
t
h
e
LS
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a
lg
o
rit
h
m
p
re
d
ict
rice
p
ro
d
u
c
ti
o
n
with
th
e
h
ig
h
e
st
p
e
rc
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n
tag
e
o
f
R
-
s
q
u
a
re
d
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2
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,
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8
.
4
0
%
a
n
d
9
8
.
1
9
%
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re
s
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e
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ti
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e
ly
.
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e
se
p
re
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icti
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n
d
t
h
e
d
e
v
e
l
o
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m
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n
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n
re
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ro
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ti
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n
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i
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th
e
g
l
o
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a
l
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g
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lt
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ra
l
c
o
m
m
u
n
it
y
in
a
c
o
m
m
o
n
c
a
u
se
.
K
ey
w
o
r
d
s
:
Fo
o
d
s
ec
u
r
ity
Ma
ch
in
e
lear
n
in
g
Pre
d
ictio
n
R
eg
r
ess
io
n
R
ice
p
r
o
d
u
ctio
n
T
h
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s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
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e
CC B
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SA
li
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e
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se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
No
r
Azu
an
a
R
am
li
C
en
ter
f
o
r
Ma
th
em
atica
l Scie
n
ce
s
,
Un
iv
er
s
it
i
Ma
lay
s
ia
Pah
an
g
AI
-
Su
ltan
Ab
d
u
llah
2
6
3
0
0
,
Ku
an
ta
n
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Pah
an
g
,
Ma
l
ay
s
ia
E
m
ail: a
zu
an
a@
u
m
p
s
a.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
I
n
late
2
0
2
3
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a
n
o
tab
le
p
r
ice
s
u
r
g
e
an
d
a
g
l
o
b
al
r
ice
s
h
o
r
ta
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s
u
r
p
r
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ed
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y
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n
o
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aly
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ts
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th
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itu
atio
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to
o
cc
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r
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ly
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u
ly
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0
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3
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en
all
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o
n
-
B
asm
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ice
in
I
n
d
ia
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u
l
d
b
e
s
u
b
jecte
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p
o
r
t
r
estrictio
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s
.
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en
th
at
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n
d
ia
p
r
o
d
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ce
s
n
ea
r
ly
4
0
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o
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th
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ld
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m
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e
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f
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d
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l
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ice
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p
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ter
also
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tr
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g
g
led
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ad
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r
ess
th
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s
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o
r
tag
e.
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ef
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e
t
h
e
f
u
ll
im
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ar
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y
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lay
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n
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o
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ice
s
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o
r
ta
g
e,
ev
i
d
e
n
t th
r
o
u
g
h
m
u
ltip
le
p
r
ice
in
c
r
e
ases
.
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t
is
n
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t
ea
s
y
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n
d
er
s
tan
d
r
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ce
p
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an
y
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o
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o
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o
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es
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ar
m
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Acc
o
r
d
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to
th
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r
esear
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y
[
1
]
,
tem
p
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r
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r
ain
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all
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So
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ch
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itu
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wh
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ca
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lead
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if
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ca
n
t
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ter
r
u
p
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o
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ac
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
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f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
P
r
ed
ictio
n
o
f in
tern
a
tio
n
a
l rice
p
r
o
d
u
ctio
n
u
s
in
g
lo
n
g
s
h
o
r
t
-
term me
mo
r
y
…
(
S
u
r
a
j A
r
ya
)
165
to
r
ice
p
r
o
d
u
ctio
n
a
n
d
ex
p
o
r
ts
,
r
esu
ltin
g
in
g
lo
b
al
s
u
p
p
l
y
f
lu
c
tu
atio
n
s
an
d
m
a
r
k
et
p
r
ice
c
h
a
n
g
es,
af
f
ir
m
in
g
th
e
in
d
u
s
tr
y
’
s
s
u
s
ce
p
tib
ilit
y
to
g
e
o
p
o
liti
ca
l f
ac
to
r
s
.
Acc
u
r
a
te
p
o
licy
-
m
ak
in
g
m
a
tter
s
to
th
e
ag
r
icu
ltu
r
e
in
d
u
s
tr
y
,
f
o
r
f
ar
m
e
r
s
,
s
tak
eh
o
l
d
er
s
,
an
d
p
o
licy
m
ak
er
s
.
Hen
ce
,
h
av
in
g
an
ac
cu
r
ate
p
r
e
d
ictio
n
m
o
d
el
will
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elp
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o
r
r
ec
t
f
o
o
d
s
u
p
p
l
y
ch
o
ices,
r
eso
u
r
ce
allo
ca
tio
n
,
an
d
m
ar
k
et
s
tr
ateg
ies.
E
n
s
u
r
in
g
g
lo
b
al
f
o
o
d
s
ec
u
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ity
a
n
d
ac
h
iev
in
g
s
u
s
tain
ab
le
d
ev
elo
p
m
e
n
t
g
o
al
(
SDG)
2
:
ze
r
o
h
u
n
g
er
,
h
ea
v
il
y
r
elies
o
n
ac
c
u
r
ate
r
ice
p
r
o
d
u
ctio
n
f
o
r
ec
asts
b
ec
a
u
s
e
r
ic
e
is
a
s
tap
le
t
o
th
e
m
ajo
r
ity
o
f
p
eo
p
le
ac
r
o
s
s
th
e
g
lo
b
e.
T
h
e
im
p
o
r
ta
n
ce
o
f
p
r
ec
is
e
p
r
ed
ictio
n
s
o
n
g
lo
b
al
f
o
o
d
s
af
ety
ca
n
n
o
t
b
e
o
v
er
em
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h
asized
.
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wev
e
r
,
t
r
ad
itio
n
al
cr
o
p
y
ield
p
r
e
d
ictio
n
m
eth
o
d
s
b
ased
o
n
s
tatis
tical
m
o
d
els
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d
h
is
to
r
ical
d
ata
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ay
n
o
t
co
n
s
id
er
s
o
m
e
o
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th
e
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m
p
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ities
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h
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lex
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ltu
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al
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tem
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ted
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ate
ch
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e,
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o
il c
o
n
d
itio
n
s
,
p
est in
v
asio
n
,
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n
d
tech
n
o
l
o
g
ical
ad
v
a
n
ce
m
en
t.
Ag
r
icu
ltu
r
al
p
r
ed
ictio
n
h
as
r
ec
en
tly
b
ee
n
h
ig
h
ly
im
p
r
o
v
e
d
b
y
m
ac
h
i
n
e
lear
n
i
n
g
(
ML
)
an
d
d
ee
p
lear
n
in
g
(
DL
)
tech
n
i
q
u
es
th
a
t
h
av
e
in
cr
ea
s
ed
th
e
ac
cu
r
ac
y
o
f
t
h
e
p
r
e
d
ictio
n
th
r
o
u
g
h
t
r
ain
in
g
with
lar
g
e
d
atasets
o
n
co
m
p
lex
p
atter
n
s
.
So
m
e
o
f
th
ese
ad
v
an
ce
d
m
eth
o
d
s
in
clu
d
e
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
,
a
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN)
ty
p
e.
T
h
ese
h
av
e
s
u
cc
ess
f
u
lly
p
r
e
d
icted
tim
e
-
s
er
ies
d
ata
b
ec
au
s
e
t
h
ey
ca
n
ca
p
tu
r
e
d
e
p
en
d
e
n
ce
o
v
er
tim
e
an
d
l
o
n
g
-
r
an
g
e
co
r
r
elatio
n
am
o
n
g
th
e
v
a
r
iab
les.
Pre
d
icti
n
g
cr
o
p
y
ield
is
an
ex
am
p
le
o
f
h
o
w
L
STM
m
o
d
e
ls
h
av
e
p
r
o
v
ed
v
er
y
e
f
f
ec
tiv
e
b
ec
au
s
e
th
ey
ar
e
s
o
g
o
o
d
at
c
ap
tu
r
in
g
s
eq
u
en
tial
p
atter
n
s
an
d
tem
p
o
r
al
d
e
p
en
d
e
n
cies in
h
er
en
t in
a
g
r
icu
ltu
r
al
d
ata.
I
n
th
e
last
f
ew
y
ea
r
s
,
a
n
u
m
b
er
o
f
s
tu
d
ies
h
av
e
s
h
o
wn
th
e
g
r
ea
t
p
o
ten
tial
o
f
ML
a
n
d
DL
m
o
d
els
in
ag
r
icu
ltu
r
e
y
ield
p
r
ed
ictio
n
.
A
s
tu
d
y
d
o
n
e
b
y
[
2
]
wh
er
e
i
n
co
r
p
o
r
ated
co
n
v
o
l
u
tio
n
al
L
STM
,
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
,
an
d
h
y
b
r
id
izatio
n
o
f
C
NN
an
d
L
S
T
M
(
C
NN
-
L
STN)
f
o
r
p
r
ed
ictin
g
th
e
an
n
u
al
r
ice
y
ield
at
th
e
co
u
n
t
y
s
ca
le
in
Hu
b
ei
Pro
v
in
ce
,
C
h
in
a
.
T
h
is
r
e
s
ea
r
ch
co
m
b
in
e
d
m
u
ltip
le
s
o
u
r
ce
s
o
f
in
f
o
r
m
atio
n
s
u
ch
as
g
r
o
s
s
p
r
im
ar
y
p
r
o
d
u
c
tiv
ity
(
GPP),
E
R
A5
tem
p
er
atu
r
e
(
AT
)
,
s
o
il
-
ad
a
p
ted
v
e
g
eta
tio
n
in
d
ex
(
SAVI
)
,
an
d
MO
DI
S
r
em
o
te
s
en
s
in
g
,
wh
ich
in
clu
d
es
en
h
an
ce
d
v
eg
etatio
n
i
n
d
ex
(
E
VI
)
,
d
u
m
m
y
s
p
atial
h
eter
o
g
en
eit
y
v
ar
iab
le.
T
h
ese
m
o
d
els
h
av
e
im
p
r
o
v
ed
th
ei
r
p
r
ed
ictio
n
ac
cu
r
ac
y
as
s
o
o
n
as
th
is
d
u
m
m
y
v
ar
iab
le
is
in
tr
o
d
u
ce
d
.
I
t
was
f
o
u
n
d
th
at
in
co
r
p
o
r
atin
g
s
p
atial
h
ete
r
o
g
en
eity
in
to
m
o
d
els
s
ig
n
i
f
ican
tly
im
p
r
o
v
ed
p
r
ed
ictio
n
ac
cu
r
ac
y
co
m
p
ar
ed
to
r
em
o
te
s
en
s
in
g
d
ata
alo
n
e
.
I
n
ad
d
itio
n
,
t
h
e
C
o
n
v
L
STM
an
d
C
NN
m
o
d
els
o
u
tp
er
f
o
r
m
ed
th
e
C
NN
-
L
STM
m
o
d
el.
Ad
v
an
ce
d
m
o
d
els,
s
u
ch
as
C
NN
-
L
STM
-
Atten
tio
n
m
o
d
els,
h
av
e
co
m
b
in
ed
DL
ar
ch
it
ec
tu
r
es
an
d
h
av
e
b
ee
n
s
atis
f
ac
to
r
y
in
h
an
d
lin
g
th
e
n
o
n
lin
ea
r
r
elat
io
n
s
h
ip
s
with
in
ag
r
icu
ltu
r
al
d
ata,
ac
co
r
d
in
g
to
[
3
]
.
T
h
ese
m
o
d
els
ca
n
h
an
d
le
m
ass
iv
e,
c
o
m
p
lex
d
atasets
,
ca
p
tu
r
i
n
g
t
h
e
m
o
s
t
im
p
o
r
tan
t
s
p
atial
an
d
t
em
p
o
r
al
v
ar
iab
ilit
y
an
d
g
i
v
in
g
ac
cu
r
ate
p
r
ed
ictio
n
s
.
T
h
eir
r
esu
lts
s
h
o
wed
th
at
ad
v
an
ce
d
DL
m
o
d
els
co
n
s
id
er
ab
ly
o
u
tp
er
f
o
r
m
tr
ad
itio
n
al
m
o
d
els,
lik
e
r
an
d
o
m
f
o
r
est
(
RF
)
an
d
ex
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
,
wh
ich
im
p
lies
in
teg
r
atin
g
th
ese
m
eth
o
d
s
o
f
e
f
f
ec
tiv
e
m
u
lti
-
s
o
u
r
ce
d
ata
i
n
to
cr
o
p
y
ield
p
r
ed
ictio
n
in
th
e
f
u
tu
r
e.
T
h
e
r
esu
lt
o
f
th
is
s
tu
d
y
ca
n
b
en
ef
it
p
o
licy
m
ak
er
s
an
d
p
r
o
f
ess
io
n
als
wo
r
k
in
g
in
th
e
ag
r
icu
ltu
r
e
s
ec
to
r
b
y
m
ak
in
g
s
cien
tific
ally
b
ased
p
o
licies to
g
u
id
e
a
g
r
icu
ltu
r
al
p
r
o
d
u
ctio
n
f
o
r
a
s
af
e
a
n
d
s
u
s
tain
ab
le
f
o
o
d
s
u
p
p
ly
.
T
h
is
p
ap
er
ex
p
l
o
r
es
th
e
wo
r
k
o
n
r
ice
p
r
o
d
u
ctio
n
at
an
in
t
er
n
atio
n
al
lev
el
u
s
in
g
L
STM
an
d
o
th
er
m
ac
h
in
e
-
lear
n
i
n
g
m
o
d
els.
T
h
e
r
ef
o
r
e,
t
h
e
p
r
in
ci
p
al
f
o
cu
s
o
f
t
h
is
r
esear
ch
will
b
e
to
estab
lis
h
th
e
ef
f
ec
tiv
e
n
ess
o
f
th
ese
m
o
d
els
in
m
ak
in
g
p
r
ed
ictio
n
s
th
at
ar
e
ac
cu
r
ate
an
d
r
eliab
le
f
o
r
u
s
e
in
s
tr
ateg
ic
p
lan
n
in
g
an
d
r
is
k
m
an
ag
em
en
t
in
a
g
r
icu
l
tu
r
e.
I
n
d
ee
d
,
th
ese
a
d
v
an
ce
d
ML
an
d
DL
m
o
d
els
th
at
lev
er
a
g
e
h
ete
r
o
g
en
e
o
u
s
d
atasets
will
lead
to
h
ig
h
ac
cu
r
ac
y
,
o
u
t
p
er
f
o
r
m
in
g
tr
ad
itio
n
al
m
eth
o
d
s
to
p
r
o
v
id
e
n
ew
in
s
ig
h
ts
an
d
to
o
ls
f
o
r
en
h
a
n
ce
d
g
lo
b
al
f
o
o
d
s
ec
u
r
ity
.
T
h
e
co
n
t
en
ts
o
f
th
is
p
ap
er
ar
e
o
u
tlin
ed
as
f
o
llo
ws.
Sectio
n
2
r
ev
iews r
elate
d
liter
atu
r
e
o
n
co
tto
n
cr
o
p
y
ield
p
r
ed
ictio
n
u
s
in
g
ML
a
n
d
DL
tech
n
iq
u
e
s
.
Sectio
n
3
d
escr
ib
es
th
e
m
eth
o
d
o
lo
g
y
w
h
ich
in
v
o
lv
es
d
ata
c
o
llectio
n
,
d
eter
m
in
atio
n
o
f
v
ar
ia
b
les,
d
ata
p
r
ep
o
s
s
ess
in
g
,
m
o
d
el
d
esig
n
,
m
o
d
el
v
alid
atio
n
,
a
nd
v
er
if
icatio
n
.
Sectio
n
4
p
r
esen
ts
th
e
f
in
d
in
g
s
an
d
r
esu
lts
o
f
th
e
ex
p
er
im
en
t
o
u
tp
u
t,
g
iv
in
g
o
u
t
s
o
m
e
an
aly
s
is
with
r
eg
ar
d
to
c
h
ec
k
in
g
th
e
m
o
d
els.
Fin
ally
,
s
ec
tio
n
5
wil
l
d
is
cu
s
s
th
e
f
in
d
in
g
s
an
d
s
u
g
g
esti
o
n
s
f
o
r
f
u
r
th
e
r
r
esear
ch
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
R
ice,
th
e
m
o
s
t
wid
ely
co
n
s
u
m
ed
ce
r
ea
l
g
r
ai
n
g
l
o
b
ally
,
s
er
v
es
as
a
s
tap
le
f
o
o
d
f
o
r
b
illi
o
n
s
,
p
ar
ticu
lar
ly
in
Asi
a
[
4
]
.
I
ts
p
r
o
d
u
ctio
n
is
cr
u
cial
f
o
r
g
lo
b
al
f
o
o
d
s
ec
u
r
ity
an
d
th
e
liv
elih
o
o
d
o
f
m
an
y
f
ar
m
er
s
.
Acc
u
r
ate
p
r
ed
ictio
n
o
f
r
ice
p
r
o
d
u
ctio
n
is
n
o
t
ju
s
t
v
ital
,
b
u
t
a
p
r
ac
tical
n
ec
ess
ity
f
o
r
ef
f
e
ctiv
e
p
lan
n
in
g
a
n
d
d
ec
is
io
n
-
m
ak
in
g
in
th
e
ag
r
icu
l
tu
r
al
s
ec
to
r
[
5
]
.
T
h
e
p
o
ten
tial o
f
ML
an
d
DL
in
p
r
ed
ictin
g
cr
o
p
y
ield
,
in
clu
d
in
g
r
ice,
is
a
p
r
o
m
is
in
g
a
r
ea
o
f
r
esear
ch
th
at
h
as sh
o
wn
s
ig
n
if
ica
n
t r
esu
lts
in
r
ec
en
t y
ea
r
s
[
6]
.
A
s
tu
d
y
co
n
d
u
cted
b
y
[
7
]
h
a
s
s
ig
n
if
ican
tly
co
n
tr
ib
u
te
d
to
th
e
f
ield
b
y
u
s
in
g
C
NNs
to
p
r
ed
ict
r
ice
y
ield
.
T
h
ey
u
tili
ze
d
u
n
m
an
n
e
d
ae
r
ial
v
eh
icle
(
UAV)
m
u
ltis
p
ec
tr
al
im
ag
es
an
d
in
c
o
r
p
o
r
a
ted
wea
th
er
d
ata
at
th
e
h
ea
d
in
g
s
tag
e.
T
h
is
in
n
o
v
ativ
e
ap
p
r
o
ac
h
co
n
s
i
d
er
s
wea
th
er
d
ata
in
its
an
aly
s
is
an
d
ad
d
s
v
alu
ab
le
k
n
o
wled
g
e
to
t
h
e
ag
r
icu
ltu
r
al
tech
n
o
lo
g
y
an
d
r
e
m
o
te
s
en
s
in
g
d
o
m
ain
.
T
h
e
s
tu
d
y
'
s
r
esu
lts
d
em
o
n
s
tr
ate
th
at
a
s
im
p
le
C
NN
f
ea
tu
r
e
ex
tr
ac
to
r
f
o
r
UAV
-
b
ased
m
u
ltis
p
ec
tr
al
im
ag
e
in
p
u
t
d
ata
ca
n
ac
cu
r
ately
p
r
ed
ict
cr
o
p
y
ield
s
.
T
h
e
m
o
d
els
tr
ain
ed
with
wee
k
ly
wea
th
er
d
ata
p
er
f
o
r
m
ed
th
e
b
est.
Ho
wev
er
,
alth
o
u
g
h
th
e
p
r
e
d
ictio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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:
2
2
5
2
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7
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6
I
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t J I
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&
C
o
m
m
u
n
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ec
h
n
o
l
,
Vo
l.
1
4
,
No
.
1
,
A
p
r
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20
2
5
:
164
-
1
7
3
166
ac
cu
r
ac
y
was
n
ea
r
ly
th
e
s
am
e,
th
e
s
p
atial
p
atter
n
s
o
f
th
e
p
r
e
d
icted
y
ield
m
ap
s
v
ar
ie
d
ac
r
o
s
s
d
if
f
er
en
t
m
o
d
els.
T
h
e
s
tu
d
y
s
u
g
g
ests
th
at
th
e
r
o
b
u
s
tn
ess
o
f
with
in
-
f
ield
p
r
ed
ictio
n
s
s
h
o
u
ld
b
e
ev
alu
ated
al
o
n
g
s
id
e
p
r
ed
ictio
n
ac
cu
r
ac
y
.
An
o
th
er
s
tu
d
y
i
n
tr
o
d
u
ce
d
a
h
y
b
r
id
m
o
d
el,
R
aNN
,
wh
ich
c
o
m
b
in
es
f
ea
tu
r
e
s
am
p
lin
g
an
d
m
ajo
r
ity
v
o
tin
g
tech
n
iq
u
es
f
r
o
m
RF
an
d
m
u
ltil
ay
er
Feed
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
s
to
p
r
ed
ict
c
r
o
p
y
ield
[
8
]
.
T
h
e
s
tu
d
y
was
co
n
d
u
cted
in
Pu
n
ja
b
,
I
n
d
ia,
th
e
lar
g
est
r
ice
p
r
o
d
u
ce
r
in
th
e
co
u
n
tr
y
.
T
h
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
is
r
o
b
u
s
t,
in
co
r
p
o
r
atin
g
ag
r
icu
lt
u
r
e
an
d
wea
th
er
d
atasets
o
b
tain
ed
f
r
o
m
t
h
e
I
n
d
ia
n
Me
teo
r
o
lo
g
ical
Dep
ar
tm
en
t
Pu
n
e
an
d
Pu
n
jab
E
n
v
ir
o
n
m
e
n
t
I
n
f
o
r
m
atio
n
Sy
s
tem
(
E
NVI
S)
C
en
ter
,
Go
v
er
n
m
en
t
o
f
I
n
d
i
a.
R
esu
lts
f
r
o
m
th
is
s
tu
d
y
r
ev
ea
led
th
at
R
aNN
p
r
o
d
u
ce
d
a
n
ac
cu
r
ate
m
o
d
el
with
m
in
im
al
er
r
o
r
,
s
u
r
p
ass
in
g
RF
,
m
u
ltip
le
lin
ea
r
r
eg
r
ess
io
n
,
s
u
p
p
o
r
t
v
ec
to
r
m
a
ch
in
e
r
e
g
r
ess
io
n
,
d
ec
is
io
n
tr
e
e,
ar
tifi
cial
n
eu
r
al
n
etwo
r
k
,
b
o
o
s
tin
g
r
e
g
r
ess
io
n
,
an
d
en
s
em
b
le
lear
n
er
.
Sev
er
al
s
tu
d
ies
in
a
g
r
icu
ltu
r
a
l
r
esear
ch
f
o
cu
s
n
o
t
o
n
ly
o
n
m
eth
o
d
o
l
o
g
y
b
u
t
also
o
n
th
e
v
ar
iab
les
in
f
lu
en
cin
g
m
o
d
el
p
r
ed
ictio
n
ac
cu
r
ac
y
[9
]
-
[
1
2
]
.
Fo
r
in
s
tan
ce
,
[
1
0
]
h
ig
h
lig
h
ted
th
e
s
ig
n
i
f
ican
ce
o
f
wea
th
er
d
ata
an
d
v
eg
etatio
n
co
v
e
r
in
f
o
r
m
atio
n
in
ev
alu
atin
g
in
-
s
ea
s
o
n
r
ice
y
ield
esti
m
atio
n
.
T
h
ey
u
tili
ze
d
th
e
m
o
b
ile
ap
p
C
an
o
p
eo
an
d
th
e
co
n
v
e
n
tio
n
al
Gr
ee
n
Seek
er
h
an
d
h
el
d
d
ev
ice
to
m
ea
s
u
r
e
th
e
n
o
r
m
alize
d
d
if
f
er
en
c
e
v
eg
etatio
n
i
n
d
ex
(
NDVI
)
d
u
r
in
g
o
n
-
fa
r
m
f
ield
ex
p
er
im
e
n
ts
in
r
ice
-
g
r
o
win
g
r
eg
i
o
n
s
in
2
0
1
8
a
n
d
2
0
1
9
.
Ad
d
itio
n
ally
,
th
ey
d
ev
elo
p
e
d
a
g
en
er
alize
d
ad
d
itiv
e
m
o
d
el
(
GAM
)
u
s
in
g
lo
g
-
tr
an
s
f
o
r
m
ed
d
ata
f
o
r
g
r
ai
n
y
ield
,
in
clu
d
in
g
ca
n
o
p
y
co
v
er
an
d
wea
th
er
d
ata
d
u
r
i
n
g
s
p
ec
if
ic
g
r
o
wth
s
tag
es.
H
o
wev
er
,
th
e
s
tu
d
y
’
s
r
esu
lts
wer
e
n
o
t
as
p
r
o
m
is
in
g
as
an
ticip
ate
d
,
p
r
o
m
p
tin
g
th
e
au
t
h
o
r
s
to
e
m
p
h
asize
th
e
n
ee
d
f
o
r
m
o
r
e
f
ield
ex
p
er
im
e
n
ts
to
en
h
an
ce
th
e
m
o
d
el
’
s
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
.
I
n
a
d
if
f
er
e
n
t stu
d
y
,
[
9
]
co
llected
r
ea
l
-
tim
e
m
eteo
r
o
lo
g
ical
d
ata
an
d
an
aly
s
ed
th
e
d
ay
-
to
-
d
ay
im
p
ac
t
o
f
wea
th
er
p
ar
am
eter
s
o
n
p
ad
d
y
cu
ltiv
atio
n
.
T
h
ey
p
r
o
p
o
s
ed
a
r
o
b
u
s
t
o
p
tim
ized
ar
tific
ial
n
e
u
r
al
n
e
two
r
k
(
R
OANN
)
alg
o
r
ith
m
with
g
en
etic
alg
o
r
ith
m
(
GA)
an
d
m
u
lti
-
o
b
jectiv
e
p
ar
ticle
s
war
m
o
p
tim
izatio
n
alg
o
r
it
h
m
(
MO
PS
O)
to
p
r
ed
i
ct
f
ac
to
r
s
th
at
co
u
ld
im
p
r
o
v
e
p
ad
d
y
y
ield
.
B
y
o
p
tim
izin
g
in
p
u
t
v
ar
ia
b
les
u
s
in
g
GA
an
d
f
in
e
-
tu
n
in
g
th
e
n
e
u
r
al
n
etwo
r
k
p
ar
am
eter
s
,
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ac
h
iev
ed
m
a
x
im
u
m
ac
c
u
r
ac
y
an
d
m
in
im
u
m
er
r
o
r
r
ate.
I
s
lam
et
a
l.
[
1
1
]
tack
led
th
e
ch
allen
g
es o
f
d
ata
q
u
ality
,
p
r
o
ce
s
s
in
g
,
an
d
s
elec
tin
g
s
u
itab
le
ML
m
o
d
els
with
lim
ited
tim
e
-
s
er
ies
d
at
a
in
a
n
o
v
el
way
.
T
h
eir
ap
p
licatio
n
o
f
d
ata
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
an
d
a
cu
s
to
m
ized
ML
m
o
d
el
s
ig
n
if
i
ca
n
tly
im
p
r
o
v
ed
cr
o
p
y
ield
e
s
tim
atio
n
ac
cu
r
ac
y
at
th
e
d
is
tr
ict
lev
el
in
Nep
al.
T
h
eir
f
in
d
in
g
th
at
u
s
in
g
r
e
m
o
te
s
en
s
in
g
-
de
r
iv
ed
NDVI
alo
n
e
was
in
s
u
f
f
icien
t
f
o
r
ac
cu
r
ate
cr
o
p
y
ield
esti
m
atio
n
,
an
d
t
h
at
s
tack
in
g
m
u
ltip
le
tr
ee
-
b
ased
r
eg
r
ess
io
n
m
o
d
els
to
g
eth
er
y
ield
ed
b
ette
r
r
esu
lts
,
r
ep
r
esen
ts
a
s
ig
n
if
ican
t
ad
v
a
n
ce
m
en
t
i
n
t
h
e
f
ield
.
Fin
ally
,
B
o
wd
en
et
a
l.
[
1
2
]
id
en
tifie
d
a
r
elat
io
n
s
h
i
p
b
etwe
en
m
o
n
s
o
o
n
v
ar
iab
ilit
y
an
d
r
ice
p
r
o
d
u
ctio
n
in
I
n
d
ia,
d
em
o
n
s
tr
atin
g
th
e
p
o
ten
tial
o
f
RF
m
o
d
ellin
g
t
o
r
e
v
ea
l
co
m
p
le
x
n
o
n
-
lin
ea
r
ities
an
d
in
ter
ac
tio
n
s
b
et
wee
n
clim
ate
an
d
r
ice
p
r
o
d
u
ct
io
n
v
ar
iab
ilit
y
.
W
h
ile
m
o
s
t
p
r
ev
io
u
s
s
tu
d
ies
h
av
e
u
s
ed
ad
v
an
ce
d
tech
n
iq
u
es
to
p
r
ed
ict
r
ice
p
r
o
d
u
ctio
n
,
o
u
r
s
tu
d
y
tak
es
a
d
if
f
e
r
en
t
a
p
p
r
o
ac
h
.
W
e
f
o
cu
s
o
n
m
o
r
e
s
tr
aig
h
tf
o
r
w
ar
d
ML
a
n
d
DL
tech
n
i
q
u
es,
n
o
t
o
n
ly
to
p
r
e
v
en
t
o
v
er
f
itti
n
g
b
u
t
also
to
m
ak
e
it
ea
s
ier
f
o
r
d
ec
is
io
n
-
m
ak
er
s
to
in
co
r
p
o
r
ate
th
ese
m
o
d
els
in
to
th
eir
s
y
s
te
m
s
.
Ou
r
g
lo
b
al
p
er
s
p
ec
tiv
e,
as
o
p
p
o
s
ed
to
a
f
o
cu
s
o
n
a
p
ar
ticu
lar
c
o
u
n
tr
y
,
is
a
d
elib
er
ate
ch
o
ice.
W
e
aim
to
p
r
o
v
id
e
n
ew
in
s
ig
h
ts
an
d
to
o
ls
th
at
ca
n
b
e
ap
p
lied
o
n
a
g
lo
b
al
s
ca
le
,
with
th
e
p
o
ten
tial
to
s
ig
n
if
ican
tly
en
h
an
ce
f
o
o
d
s
ec
u
r
ity
wo
r
ld
wid
e
.
T
h
e
co
n
tr
ib
u
tio
n
s
o
f
th
is
wo
r
k
in
clu
d
e:
a
)
Glo
b
al
d
ata
s
et:
d
ata
u
tili
ze
d
to
f
o
r
ec
ast
th
e
r
ice
p
r
o
d
u
ctio
n
is
r
elate
d
to
a
s
p
e
cif
ic
co
u
n
tr
y
I
n
d
ia,
Nep
al.
Pr
o
p
o
s
ed
m
o
d
el
h
as
th
e
d
etails
o
f
1
9
2
co
u
n
tr
ies
.
T
h
u
s
,
its
r
esu
lts
h
av
e
th
e
in
ter
n
atio
n
al
r
elev
an
ce
.
b
)
UAV
a
n
d
m
u
l
t
i
s
p
e
c
t
r
a
l
i
m
a
g
e
s
:
t
o
p
r
e
d
i
c
t
t
h
e
r
i
c
e
y
i
e
l
d
p
r
e
v
i
o
u
s
s
t
u
d
i
e
s
c
o
m
b
i
n
i
n
g
t
h
e
w
e
a
t
h
e
r
d
a
t
a
w
i
t
h
m
u
l
t
i
s
p
e
c
t
r
a
l
i
m
a
g
e
s
c
a
p
t
u
r
e
d
t
h
r
o
u
g
h
U
A
V
.
S
o
m
e
p
a
p
e
r
s
a
r
e
b
a
s
e
d
o
n
s
p
a
t
i
a
l
p
a
t
t
e
r
n
s
a
n
d
y
i
e
l
d
m
a
p
s
.
c)
Data
s
et
d
u
r
atio
n
:
d
ataset
u
s
ed
f
o
r
p
r
ed
ictio
n
s
c
o
n
tain
s
th
e
d
etails
o
f
6
5
y
ea
r
s
o
ld
r
ice
p
r
o
d
u
ctio
n
.
T
h
is
m
ak
es
in
n
o
v
ativ
e
u
s
e
o
f
h
is
to
r
ical
d
ataset
.
d
)
Alg
o
r
ith
m
s
:
b
ac
k
g
r
o
u
n
d
Stu
d
ies
ar
e
u
s
in
g
th
e
C
NN,
h
y
b
r
id
m
o
d
el,
GA
,
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
an
d
R
aNN
m
o
d
els
to
p
r
ed
ict
th
e
r
ice
p
r
o
d
u
ctio
n
.
Pro
p
o
s
ed
m
o
d
els
ar
e
th
e
b
ein
g
an
o
r
ig
i
n
al
co
n
tr
ib
u
tio
n
u
s
in
g
tim
e
s
er
ies
m
o
d
el
AR
I
MA
an
d
L
STM
with
th
e
h
ig
h
er
ac
cu
r
ate
r
esu
lts
.
e
)
I
n
ter
n
atio
n
al
ap
p
licab
ilit
y
:
p
r
o
p
o
s
ed
s
tu
d
y
en
s
u
r
es
av
ailab
ilit
y
o
f
r
ice
at
th
e
i
n
ter
n
at
io
n
al
le
v
e
l.
3.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
elv
es
in
to
th
e
r
ice
p
r
o
d
u
ctio
n
d
ataset
an
d
th
e
m
eth
o
d
s
u
s
ed
f
o
r
p
r
ed
i
ctin
g
r
ice
p
r
o
d
u
ctio
n
.
Py
th
o
n
,
a
v
er
s
atile
an
d
p
o
wer
f
u
l
p
r
o
g
r
am
m
in
g
to
o
l,
is
th
e
co
r
n
e
r
s
to
n
e
o
f
o
u
r
m
o
d
el
d
ev
el
o
p
m
en
t.
T
h
e
f
o
llo
win
g
p
r
o
ce
d
u
r
es
a
r
e
ad
o
p
ted
t
o
p
r
ed
ict
in
ter
n
atio
n
al
r
ice
p
r
o
d
u
ctio
n
:
i)
d
ata
co
llectio
n
,
ii)
I
d
en
tific
atio
n
o
f
v
ar
iab
les,
ii
i)
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
iv
)
f
e
atu
r
e
s
elec
tio
n
,
v
)
d
ata
p
ar
ti
tio
n
in
g
,
v
i)
m
o
d
el
tr
ain
in
g
,
an
d
v
ii)
m
o
d
el
p
er
f
o
r
m
an
ce
ev
alu
atio
n
.
Var
iab
le
id
en
tific
atio
n
,
s
u
ch
as
wea
th
er
co
n
d
itio
n
s
,
s
o
il
q
u
ality
,
an
d
p
r
ev
i
o
u
s
y
ea
r
’
s
p
r
o
d
u
ctio
n
,
d
ata
c
o
llectio
n
,
a
n
d
p
r
e
-
p
r
o
ce
s
s
in
g
ar
e
s
o
m
e
o
f
th
e
m
o
s
t
ess
en
tial
s
tep
s
in
tr
ain
in
g
ML
m
o
d
els.
T
h
e
m
o
d
el’
s
e
f
f
ec
tiv
en
ess
d
ep
en
d
s
o
n
t
h
e
d
ata’
s
q
u
alit
y
,
co
n
s
is
ten
cy
,
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
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&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
P
r
ed
ictio
n
o
f in
tern
a
tio
n
a
l rice
p
r
o
d
u
ctio
n
u
s
in
g
lo
n
g
s
h
o
r
t
-
term me
mo
r
y
…
(
S
u
r
a
j A
r
ya
)
167
co
r
r
ec
tn
ess
.
Fig
u
r
e
1
d
ep
icts
th
e
g
en
er
al
f
lo
w
o
f
th
e
p
r
o
ce
s
s
.
T
h
e
p
r
o
ce
s
s
b
eg
an
with
d
at
a
co
llectio
n
an
d
s
o
f
o
r
th
.
T
h
e
s
u
b
s
eq
u
e
n
t su
b
-
s
ec
t
io
n
s
will p
r
o
v
id
e
a
d
etailed
ex
p
lan
atio
n
o
f
th
e
p
r
o
ce
s
s
.
3
.
1
.
Da
t
a
c
o
llect
io
n
T
h
is
s
tu
d
y
u
s
ed
th
e
in
ter
n
atio
n
al
d
ata
s
et,
wh
ich
co
n
tain
s
th
e
d
etails
o
f
r
ice
p
r
o
d
u
cti
o
n
in
1
9
2
co
u
n
tr
ies.
T
h
is
d
ataset
is
av
a
ilab
le
o
n
th
e
o
p
en
-
ac
ce
s
s
d
ata
r
ep
o
s
ito
r
y
o
u
r
wo
r
ld
in
d
ata.
o
r
g
[
1
3
]
.
I
t
co
n
tain
s
10
,
1
2
8
r
o
ws an
d
4
c
o
lu
m
n
s
.
3
.
2
.
I
dentif
ica
t
io
n o
f
v
a
ri
a
bles
Ou
r
r
esear
ch
h
as
m
eticu
lo
u
s
l
y
id
en
tifie
d
t
h
e
v
ar
ia
b
les
cr
u
cial
f
o
r
p
r
ed
ictin
g
r
ice
p
r
o
d
u
ctio
n
.
T
h
e
en
tity
u
n
d
er
d
is
cu
s
s
io
n
,
a
s
ig
n
if
ican
t
c
o
n
tr
ib
u
tin
g
f
ac
to
r
af
f
ec
tin
g
r
ice
p
r
o
d
u
ctio
n
,
h
as
b
ee
n
ca
r
ef
u
lly
co
n
s
id
er
ed
.
W
e
h
av
e
also
id
e
n
tifie
d
th
e
r
eg
i
o
n
s
th
at
p
lay
a
k
ey
r
o
le
in
th
is
an
aly
s
is
.
Ou
r
ap
p
r
o
ac
h
,
wh
ic
h
in
clu
d
es c
o
n
s
id
er
in
g
g
lo
b
al
en
titi
es,
in
s
til
s
co
n
f
id
en
ce
in
th
e
ac
cu
r
ac
y
o
f
o
u
r
p
r
ed
ictio
n
s
.
3
.
3
.
Da
t
a
pre
-
pro
ce
s
s
ing
Data
p
r
e
-
p
r
o
ce
s
s
in
g
in
v
o
l
v
es
p
r
ep
ar
in
g
th
e
d
ata
f
o
r
th
e
ML
m
o
d
el.
T
h
is
in
clu
d
es
tak
in
g
n
ec
ess
ar
y
ac
tio
n
s
to
im
p
r
o
v
e
its
u
s
ab
ilit
y
an
d
e
n
s
u
r
e
its
p
r
o
p
er
f
o
r
m
a
t
an
d
s
tr
u
ctu
r
e,
s
u
ch
as
h
an
d
li
n
g
m
is
s
in
g
v
alu
es,
d
ata
in
co
n
s
is
ten
cies,
an
d
co
n
f
licts
.
T
h
e
r
ice
p
r
o
d
u
ctio
n
d
ataset
in
itially
co
n
tain
s
a
to
tal
o
f
1
0
,
1
2
8
r
o
ws
an
d
4
co
lu
m
n
s
.
Af
ter
r
em
o
v
in
g
th
e
co
u
n
tr
ies
with
d
is
co
n
tin
u
o
u
s
v
alu
es
f
r
o
m
1
9
6
1
to
2
0
2
2
,
t
h
e
d
ataset
co
n
tain
s
9
,
3
0
0
r
o
ws an
d
4
co
l
u
m
n
s
.
T
h
e
to
p
f
iv
e
r
o
ws o
f
th
e
r
ice
p
r
o
d
u
ctio
n
d
ataset
ar
e
d
is
p
lay
ed
i
n
T
ab
le
1
.
3
.
4
.
F
e
a
t
ure
s
elec
t
io
n
Featu
r
e
s
elec
tio
n
,
th
e
n
ex
t
cr
u
cial
s
tep
in
th
e
p
ip
elin
e,
in
v
o
lv
es
id
en
tify
in
g
an
d
r
e
d
u
cin
g
th
e
d
ataset
to
th
e
m
o
s
t
s
ig
n
if
ican
t
f
ea
tu
r
es.
T
h
is
p
r
o
ce
s
s
also
in
v
o
lv
es
r
em
o
v
in
g
f
ea
t
u
r
es
th
at
d
o
n
o
t
af
f
ec
t
th
e
o
u
t
p
u
t
v
ar
iab
le.
I
n
o
u
r
ca
s
e,
th
e
‘
c
o
d
e’
f
ea
tu
r
e
i
s
th
e
s
elec
ted
f
ea
tu
r
e.
3
.
5
.
Da
t
a
pa
rt
i
t
io
nin
g
I
n
th
is
p
h
ase,
th
e
d
ataset
was d
iv
id
ed
i
n
to
two
p
ar
ts
:
tr
ain
in
g
an
d
test
in
g
f
o
r
th
e
ML
an
d
DL
m
o
d
els.
T
h
is
s
tu
d
y
s
elec
ted
a
co
m
m
o
n
ly
u
s
ed
r
atio
f
o
r
b
alan
cin
g
tr
ai
n
in
g
an
d
test
in
g
,
8
0
:2
0
.
3
.
6
.
M
o
del t
ra
ini
n
g
T
r
ain
in
g
d
ata
is
p
r
o
v
id
ed
as
in
p
u
t
to
all
o
f
th
e
ML
an
d
DL
m
o
d
els.
T
h
is
p
ap
er
ap
p
lied
th
e
f
o
llo
win
g
m
o
d
els:
lin
ea
r
r
eg
r
ess
io
n
(
L
R
)
,
RF
r
eg
r
ess
o
r
(
R
FR
)
,
XGBo
o
s
t
r
eg
r
ess
o
r
,
d
ec
is
io
n
tr
e
e
r
eg
r
ess
o
r
(
DT
R
)
,
Ad
aBo
o
s
t
r
eg
r
ess
o
r
(
AB
R
)
,
g
r
ad
ien
t
b
o
o
s
tin
g
r
e
g
r
ess
o
r
(
GB
R
)
,
C
a
tB
o
o
s
t
r
eg
r
ess
o
r
,
r
id
g
e
r
eg
r
ess
o
r
(
R
R
)
,
an
d
L
STM
.
T
h
e
ab
o
v
e
-
m
en
ti
o
n
ed
ML
an
d
DL
m
o
d
els
wer
e
s
elec
ted
b
ec
au
s
e
th
ese
m
o
d
els
p
r
o
v
id
e
th
e
b
est
R
-
s
q
u
ar
ed
(R
2
)
v
alu
es
b
ased
o
n
p
r
ev
io
u
s
s
tu
d
ies.
W
e
h
a
v
e
u
tili
ze
d
v
ar
i
o
u
s
r
eg
r
ess
io
n
m
o
d
els
f
o
r
m
ak
in
g
p
r
e
d
ictio
n
s
s
in
ce
o
u
r
d
ataset
d
id
n
o
t e
x
h
ib
it a
n
y
tim
e
s
er
ies p
r
o
p
er
ties
.
3.
6
.
1
.
L
inea
r
re
g
re
s
s
io
n
LR
,
a
ty
p
e
o
f
s
u
p
er
v
is
ed
ML
r
eg
r
ess
o
r
alg
o
r
ith
m
,
is
ch
ar
ac
ter
ized
b
y
its
in
ter
p
r
etab
ilit
y
.
I
t
co
m
es
in
two
ty
p
es:
s
im
p
le
lin
ea
r
r
e
g
r
ess
io
n
with
ju
s
t
o
n
e
in
d
e
p
en
d
en
t
v
a
r
iab
le
a
n
d
m
u
ltip
le
li
n
ea
r
r
e
g
r
ess
io
n
with
m
o
r
e
th
an
o
n
e
i
n
d
ep
e
n
d
en
t
v
a
r
iab
le
[
1
4
]
.
T
h
e
eq
u
atio
n
f
o
r
s
im
p
le
lin
ea
r
r
e
g
r
ess
io
n
is
as
(
1
)
:
=
0
+
1
(
1
)
wh
er
e
y
is
d
e
p
en
d
e
n
t
v
a
r
iab
l
e,
x
is
in
d
e
p
en
d
e
n
t
v
a
r
iab
le,
0
is
in
ter
ce
p
t,
an
d
1
is
s
lo
p
e.
M
ea
n
wh
ile,
th
e
eq
u
atio
n
f
o
r
m
u
ltip
le
r
eg
r
ess
io
n
:
=
0
+
1
1
+
2
2
+
.
.
+
+
(
2
)
wh
er
e,
f
o
r
i
=
n
o
b
s
er
v
atio
n
s
:
y
i
is
d
ep
e
n
d
en
t
v
ar
iab
le,
x
1
,
x
2
,..,
x
n
a
r
e
th
e
in
d
e
p
en
d
e
n
t
v
ar
ia
b
les,
0
is
y
-
in
ter
ce
p
t/co
n
s
tan
t,
a
n
d
is
s
l
o
p
e
co
ef
f
icien
ts
f
o
r
ea
c
h
in
d
e
p
en
d
en
t
v
ar
iab
le
[
1
5
]
.
3
.
6
.
2
.
Ra
nd
o
m
f
o
re
s
t
re
g
re
s
s
o
r
RF
R
is
a
ty
p
e
o
f
en
s
em
b
le
lea
r
n
in
g
th
at
im
p
r
o
v
es
ac
cu
r
ac
y
b
y
co
m
b
in
in
g
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
as
s
h
o
wn
in
Fig
u
r
e
2
.
I
t
is
u
s
e
d
f
o
r
class
if
icatio
n
an
d
r
eg
r
e
s
s
io
n
p
r
o
b
lem
s
.
I
t
u
s
es
th
e
e
n
s
em
b
le’
s
b
ag
g
in
g
,
b
o
o
s
tin
g
,
an
d
s
tack
in
g
m
eth
o
d
s
f
o
r
r
an
d
o
m
f
ea
tu
r
e
s
elec
tio
n
[
1
6
]
,
[
17]
.
Dif
f
er
en
t
p
ar
a
m
eter
s
wer
e
u
s
ed
to
tu
n
e
th
is
alg
o
r
ith
m
.
So
m
e
o
f
t
h
e
m
o
s
t
wid
ely
u
s
ed
a
r
e:
m
ax
_
d
e
p
th
:
i
t in
d
icate
s
th
e
m
a
x
im
u
m
d
e
p
th
o
f
ea
ch
d
ec
is
io
n
tr
ee
u
s
ed
in
th
is
m
o
d
el.
n
_
esti
m
ato
r
s
:
t
h
is
p
ar
am
eter
i
n
d
icate
s
th
e
n
u
m
b
er
o
f
d
ec
is
io
n
tr
ee
s
th
is
m
o
d
el
will u
s
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
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t J I
n
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&
C
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m
u
n
T
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h
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o
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,
Vo
l.
1
4
,
No
.
1
,
A
p
r
il
20
2
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:
164
-
1
7
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168
T
ab
le
1
.
Sam
p
le
o
f
in
ter
n
atio
n
al
r
ice
p
r
o
d
u
ctio
n
d
ataset
I
n
d
e
x
En
t
i
t
y
C
o
d
e
Y
e
a
r
R
i
c
e
|
0
0
0
0
0
0
2
7
|
|
p
r
o
d
u
c
t
i
o
n
|
0
0
5
5
1
0
|
|
t
o
n
n
e
s
0
A
f
g
h
a
n
i
st
a
n
A
F
G
1
9
6
1
3
1
9
0
0
0
.
0
1
A
f
g
h
a
n
i
st
a
n
A
F
G
1
9
6
2
3
1
9
0
0
0
.
0
2
A
f
g
h
a
n
i
st
a
n
A
F
G
1
9
6
3
3
1
9
0
0
0
.
0
3
A
f
g
h
a
n
i
st
a
n
A
F
G
1
9
6
4
3
8
0
0
0
0
.
0
4
A
f
g
h
a
n
i
st
a
n
A
F
G
1
9
6
5
3
8
0
0
0
0
.
0
Fig
u
r
e
1
.
Flo
w
o
f
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
Fig
u
r
e
2
.
R
an
d
o
m
f
o
r
est
3
.
6
.
3
.
XG
B
o
o
s
t
r
eg
re
s
s
o
r
T
h
is
r
eg
r
ess
o
r
is
th
e
ex
ten
s
io
n
o
f
th
e
GB
R
.
I
t
is
u
s
ed
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
ML
m
o
d
els
with
th
e
h
elp
o
f
o
b
jectiv
e
f
u
n
ctio
n
s
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
ad
o
p
ted
b
y
th
e
XGB
r
eg
r
ess
o
r
is
a
m
ea
n
s
q
u
a
r
ed
er
r
o
r
(
MSE
)
,
b
u
t
it
ca
n
also
b
e
o
th
er
f
u
n
ctio
n
s
lik
e
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
o
r
Hu
b
er
l
o
s
s
.
T
h
is
r
eg
r
ess
o
r
u
tili
ze
s
r
eg
u
lar
izatio
n
tech
n
iq
u
es,
s
u
ch
as
L
1
(
l
ass
o
r
eg
u
lar
izatio
n
)
an
d
L
2
(
r
id
g
e
r
eg
u
lar
izatio
n
)
,
to
p
r
ev
en
t
o
v
er
f
itti
n
g
[
1
8
]
,
[
1
9
]
.
3
.
6
.
4
.
Ada
B
o
o
s
t
r
eg
re
s
s
o
r
Ad
aBo
o
s
t
is
an
en
s
em
b
le
m
eth
o
d
th
at
u
tili
z
es
th
e
b
o
o
s
tin
g
tech
n
iq
u
e
an
d
a
d
ec
is
io
n
tr
ee
as
a
b
as
e
m
o
d
el.
I
t
is
k
n
o
wn
as
a
d
ap
tiv
e
b
o
o
s
tin
g
b
ec
au
s
e
weig
h
ts
ar
e
r
ea
s
s
ig
n
ed
to
ea
ch
in
s
tan
ce
,
an
d
h
ig
h
er
weig
h
ts
ar
e
r
ea
s
s
ig
n
ed
to
in
co
r
r
ec
tly
c
lass
if
ied
in
s
tan
ce
s
[
2
0
]
.
T
h
is
m
o
d
el
u
s
es
th
e
lear
n
in
g
r
ate
a
n
d
n
u
m
b
er
o
f
b
ase
m
o
d
els
as
p
ar
am
eter
s
.
Ad
aBo
o
s
t
h
as
less
o
f
a
p
o
s
s
ib
ilit
y
o
f
o
v
er
f
itti
n
g
th
an
t
h
e
o
th
e
r
m
o
d
els,
an
d
it
ca
n
b
e
u
s
ed
to
in
teg
r
ate
o
th
er
m
o
d
els
to
im
p
r
o
v
e
p
e
r
f
o
r
m
an
ce
[
2
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
P
r
ed
ictio
n
o
f in
tern
a
tio
n
a
l rice
p
r
o
d
u
ctio
n
u
s
in
g
lo
n
g
s
h
o
r
t
-
term me
mo
r
y
…
(
S
u
r
a
j A
r
ya
)
169
3
.
6
.
5
.
L
o
ng
s
ho
rt
-
t
er
m m
e
mo
ry
T
h
e
L
STM
is
a
well
-
k
n
o
wn
al
g
o
r
ith
m
f
o
r
esti
m
atin
g
tim
e
-
s
er
ies
an
d
s
eq
u
en
tial
d
atasets
.
L
STM
ca
n
h
an
d
le
l
o
n
g
-
ter
m
d
ep
e
n
d
en
ci
es
to
p
r
ed
icate
th
e
tar
g
et
v
a
r
iab
le.
Oth
er
v
a
r
ian
ts
o
f
L
S
T
M
in
clu
d
e
class
ic
L
STM
,
s
tack
ed
L
STM
,
an
d
b
id
ir
ec
tio
n
al
L
STM
.
L
STM
u
s
es
a
m
em
o
r
y
ce
ll
to
s
to
r
e
i
n
f
o
r
m
atio
n
f
o
r
lo
n
g
p
er
io
d
s
.
I
t
h
as
th
r
ee
g
ates:
in
p
u
t,
f
o
r
g
et,
a
n
d
o
u
tp
u
t.
T
h
e
i
n
p
u
t
g
ate
d
eter
m
in
es
wh
at
in
f
o
r
m
atio
n
is
ad
d
ed
in
th
e
ce
ll
s
tate,
th
e
f
o
r
g
et
g
ate
tells
u
s
wh
ich
ty
p
e
o
f
in
f
o
r
m
atio
n
is
r
em
o
v
ed
,
an
d
th
e
o
u
t
p
u
t
g
ate
d
eter
m
in
es
th
e
o
u
tp
u
t f
r
o
m
th
e
m
em
o
r
y
c
ell
[
2
2
]
.
3
.
7
.
M
o
del per
f
o
rma
nce
ev
a
lua
t
io
n
All
ML
m
o
d
els
an
d
L
STM
p
e
r
f
o
r
m
a
n
ce
wer
e
ev
al
u
ated
b
as
ed
o
n
t
h
e
f
o
llo
win
g
m
etr
ics.
T
h
e
m
etr
ics
ar
e
g
iv
en
b
elo
w:
3
.
7
.
1
.
M
ea
n sq
ua
re
d e
rr
o
r
MSE
is
th
e
av
er
ag
e
o
f
s
q
u
a
r
ed
d
if
f
e
r
en
ce
s
b
etwe
en
ac
t
u
a
l
an
d
p
r
ed
icted
v
alu
es.
I
t
m
e
asu
r
es
th
e
av
er
ag
e
s
q
u
a
r
ed
m
a
g
n
itu
d
e
o
f
er
r
o
r
s
[
2
3
]
.
T
h
e
f
o
r
m
u
la
f
o
r
M
SE
is
g
iv
en
as:
M
SE
=
∑
(
−
)
2
=
1
(
3
)
3
.
7
.
2
.
M
ea
n a
bs
o
lute
er
ro
r
MA
E
is
th
e
av
er
ag
e
o
f
d
if
f
er
en
ce
s
b
etwe
en
ac
tu
al
an
d
p
r
e
d
icted
v
alu
es.
I
t
m
ea
s
u
r
es
th
e
av
er
ag
e
m
ag
n
itu
d
e
o
f
er
r
o
r
s
[
2
4
]
.
T
h
e
f
o
r
m
u
la
f
o
r
MA
E
is
g
iv
e
n
as:
MAE
=
∑
|
−
|
=
1
(
4
)
3
.
7
.
3
.
Ro
o
t
m
ea
n sq
ua
re
d e
rr
o
r
R
o
o
t
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
R
MSE
)
is
th
e
s
q
u
ar
e
r
o
o
t
o
f
M
SE.
I
t
m
ea
s
u
r
es
th
e
s
tan
d
ar
d
d
ev
iatio
n
o
f
r
esid
u
als
[
2
5
]
.
T
h
e
f
o
r
m
u
la
f
o
r
R
MSE
is
g
iv
en
as:
R
M
SE
=
√
∑
(
−
)
2
=
1
(
5
)
3
.
7
.
4
.
R
-
s
qu
a
re
d
R
2
,
also
k
n
o
wn
as
a
co
e
f
f
ic
ien
t
o
f
d
eter
m
in
atio
n
,
is
a
s
tatis
tical
tech
n
iq
u
e
th
at
m
ea
s
u
r
es
th
e
g
o
o
d
n
ess
o
f
f
it o
f
a
r
eg
r
ess
io
n
m
o
d
el.
T
h
e
v
alu
e
lies
b
etwe
e
n
0
an
d
1
[
2
6
]
.
R
−
s
q
ua
r
e
d
=
1
−
∑
(
−
̂
)
2
∑
(
−
̅
)
2
(
6
)
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
B
ef
o
r
e
p
r
e
-
p
r
o
ce
s
s
in
g
d
ata,
i
t
is
b
est
to
an
aly
s
e
its
s
tatis
tical
p
r
o
p
er
ties
to
g
ain
in
s
ig
h
ts
.
T
h
is
in
clu
d
es
ex
am
i
n
in
g
s
tatis
tical
p
r
o
p
er
ties
o
f
th
e
r
ice
p
r
o
d
u
cti
o
n
d
ataset
as
tab
u
lated
in
T
ab
le
2
.
T
h
e
m
ea
n
an
d
s
tan
d
ar
d
d
ev
iatio
n
o
f
th
e
r
ice
p
r
o
d
u
ctio
n
d
ataset
ar
e
2
2
8
0
0
6
4
8
.
6
1
6
4
2
6
8
8
an
d
8
1
5
9
1
8
0
8
.
4
9
7
7
9
6
1
6
,
r
esp
ec
tiv
ely
.
T
h
e
m
ea
n
o
f
th
e
d
ataset,
wh
ich
r
ep
r
esen
ts
t
h
e
av
er
ag
e
r
ice
p
r
o
d
u
ctio
n
,
i
s
a
k
ey
s
tatis
tica
l
p
r
o
p
er
t
y
to
c
o
n
s
id
er
.
On
e
DL
an
d
eig
h
t
ML
m
o
d
el
s
wer
e
d
ev
elo
p
ed
to
p
r
e
d
ict
th
e
r
ice
p
r
o
d
u
ctio
n
o
f
1
9
2
co
u
n
tr
ies.
Of
th
ese,
4
2
co
u
n
tr
ies
h
av
e
d
is
c
o
n
tin
u
o
u
s
an
d
ze
r
o
r
ice
p
r
o
d
u
ctio
n
.
So
,
we
h
av
e
r
em
o
v
ed
th
ese
co
u
n
tr
ies
an
d
d
id
n
o
t
co
n
s
id
er
th
em
f
o
r
an
aly
s
is
.
T
h
is
d
atase
t
h
as
f
o
llo
win
g
f
ea
tu
r
es
as
t
ab
u
lated
in
T
ab
le
3
.
‘
E
n
tity
’
,
‘
C
o
d
e’
,
‘
Yea
r
’
,
‘
r
ice
|
0
0
0
0
0
0
2
7
|
|
p
r
o
d
u
ctio
n
|
0
0
5
5
1
0
|
|
to
n
n
es’
.
T
ab
le
2
.
Statis
tical
p
r
o
p
er
ties
o
f
r
ice
d
ataset
I
n
d
e
x
Y
e
a
r
R
i
c
e
|
0
0
0
0
0
0
2
7
|
|
p
r
o
d
u
c
t
i
o
n
|
0
0
5
5
1
0
|
|
t
o
n
n
e
s
C
o
u
n
t
9
3
0
0
.
0
9
3
0
0
.
0
M
e
a
n
1
9
9
1
.
5
2
2
8
0
0
6
4
8
.
6
1
6
4
2
6
8
8
S
t
d
1
7
.
8
9
6
4
9
2
3
7
1
0
4
8
8
4
8
1
5
9
1
8
0
8
.
4
9
7
7
9
6
1
6
M
i
n
1
9
6
1
.
0
0
.
0
2
5
%
1
9
7
6
.
0
3
7
9
7
0
.
2
5
5
0
%
1
9
9
1
.
5
4
0
3
1
9
6
.
0
7
5
%
2
0
0
7
.
0
4
1
6
8
6
7
4
.
0
M
a
x
2
0
2
2
.
0
7
8
9
0
4
5
3
0
0
.
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
4
,
No
.
1
,
A
p
r
il
20
2
5
:
164
-
1
7
3
170
T
ab
le
3
.
Sam
p
le
o
f
in
ter
n
atio
n
al
r
ice
p
r
o
d
u
ctio
n
d
ataset
af
ter
p
r
e
-
p
r
o
ce
s
s
in
g
I
n
d
e
x
En
t
i
t
y
C
o
d
e
Y
e
a
r
R
i
c
e
|
0
0
0
0
0
0
2
7
|
|
P
r
o
d
u
c
t
i
o
n
|
0
0
5
5
1
0
|
|
t
o
n
n
e
s
9
2
9
5
Zi
m
b
a
b
w
e
ZWE
2
0
1
8
1
3
6
3
.
3
2
9
2
9
6
Zi
m
b
a
b
w
e
ZWE
2
0
1
9
1
1
3
4
.
0
9
2
9
7
Zi
m
b
a
b
w
e
ZWE
2
0
2
0
7
5
0
.
0
9
2
9
8
Zi
m
b
a
b
w
e
ZWE
2
0
2
1
2
9
0
8
.
0
9
2
9
9
Zi
m
b
a
b
w
e
ZWE
2
0
2
2
1
9
2
3
.
3
2
Fig
u
r
e
3
d
is
p
lay
s
a
b
u
b
b
le
p
lo
t
o
f
t
h
e
to
p
ten
en
titi
es’
av
er
a
g
e
p
r
o
d
u
ctio
n
(
m
ea
s
u
r
ed
i
n
1
0
0
0
to
n
n
es)
f
r
o
m
2
0
1
7
t
o
2
0
2
2
.
T
h
e
ch
ar
t
in
clu
d
es
th
e
wo
r
ld
,
Asi
a,
lo
wer
-
m
id
d
le
-
i
n
co
m
e
co
u
n
tr
ies,
u
p
p
er
-
m
id
d
le
-
in
co
m
e
co
u
n
t
r
ies,
So
u
th
er
n
A
s
ia
(
FAO)
,
E
as
ter
n
Asi
a
(
FA
O)
,
C
h
in
a
(
FAO)
,
I
n
d
ia
(
FAO)
,
an
d
So
u
th
ea
s
ter
n
Asi
a
(
FAO)
.
E
ac
h
en
tity
is
r
ep
r
esen
ted
b
y
a
b
u
b
b
le,
wit
h
th
e
s
ize
co
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9
4
4
.
7
8
4
4
5
9
7
.
8
8
5
1
2
5
0
.
9
8
5
7
9
0
4
8
6
4
5
5
7
.
1
I
n
2
0
2
5
,
Asi
a
will
p
r
o
d
u
ce
7
5
0
,
087
,
1
0
0
to
n
n
es
o
f
r
ice.
As
d
if
f
er
en
t
m
o
d
els
h
a
v
e
d
if
f
er
en
t
p
r
ed
icted
v
alu
es,
th
e
av
er
ag
e
r
ice
p
r
o
d
u
ctio
n
f
o
r
th
e
y
ea
r
2
0
2
5
is
6
7
6
,
959
,
7
5
0
o
f
th
e
to
p
f
iv
e
en
titi
es.
T
h
e
au
th
o
r
s
o
f
th
is
p
ap
er
p
r
o
p
o
s
e
all
th
ese
as
s
u
m
p
tio
n
s
.
Similar
ly
,
all
p
r
e
d
icted
v
alu
es
o
f
t
h
e
to
p
f
iv
e
en
ti
ties
ar
e
d
ep
icted
in
T
ab
le
6
.
Fig
u
r
e
4
d
e
m
o
n
s
tr
ate
s
th
e
d
if
f
er
en
ce
i
n
r
ice
p
r
o
d
u
ctio
n
f
r
o
m
1
9
6
1
to
2
0
2
2
.
All f
iv
e
co
u
n
tr
ies h
ad
th
e
m
ax
im
u
m
d
if
f
er
en
ce
in
r
ice
p
r
o
d
u
ctio
n
f
r
o
m
th
e
s
tar
tin
g
y
ea
r
(
1
9
6
1
)
to
th
e
e
n
d
in
g
y
ea
r
(
2
0
2
2
)
.
J
ap
a
n
s
h
o
wed
th
e
m
o
s
t sig
n
if
ican
t c
h
an
g
e.
T
h
e
two
co
u
n
t
r
ies
(
i.e
.
,
6
9
,
2
1
0
.
0
0
)
,
in
W
ester
n
E
u
r
o
p
e
an
d
Fra
n
ce
,
h
a
v
e
th
e
lo
west
m
ax
im
u
m
ch
an
g
e
in
r
i
ce
p
r
o
d
u
ctio
n
.
T
h
e
p
er
ce
n
tag
e
ch
an
g
e
f
r
o
m
th
e
p
r
ed
icted
v
alu
e
in
2
0
2
3
to
th
e
p
r
ed
icted
v
alu
e
i
n
2
0
3
0
f
o
r
th
e
u
p
p
er
-
m
id
d
le
-
in
co
m
e
co
u
n
tr
y
is
0
.
1
4
%.
Similar
ly
,
we
ca
n
ca
lcu
late
th
e
p
e
r
ce
n
tag
e
d
if
f
er
en
c
e
f
o
r
an
y
o
th
e
r
co
u
n
tr
y
.
Fig
u
r
e
4
.
R
ice
p
r
o
d
u
ctio
n
m
ax
ch
an
g
es c
o
u
n
tr
ies
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
2
5
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t J I
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f
&
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1
4
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1
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p
r
il
20
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:
164
-
1
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172
5.
CO
NCLU
SI
O
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T
h
is
p
ap
er
p
r
ed
icts
th
e
in
ter
n
atio
n
al
r
ice
p
r
o
d
u
ctio
n
o
f
1
5
0
co
u
n
tr
ies
with
th
e
h
elp
o
f
ML
an
d
DL
m
o
d
els,
s
h
o
wca
s
in
g
th
e
p
o
te
n
tial
o
f
th
ese
in
n
o
v
ativ
e
tech
n
o
lo
g
ies
in
th
e
f
ield
o
f
ag
r
ic
u
ltu
r
e.
Nin
e
m
o
d
els
wer
e
d
ev
elo
p
ed
,
e
ig
h
t
o
f
wh
i
ch
ar
e
ML
,
an
d
o
n
ly
o
n
e
is
DL
.
T
h
e
s
tu
d
y
co
n
clu
d
es
th
a
t
th
e
in
ter
n
atio
n
al
av
er
ag
e
r
ice
p
r
o
d
u
ctio
n
will
b
e
3
4
,
527
,
7
5
5
.
2
7
to
n
n
es
in
2
0
2
5
.
I
n
th
e
Asi
a
co
n
tin
en
t,
t
h
e
p
r
o
d
u
ctio
n
o
f
r
ice
will
b
e
7
5
0
,
0
8
7
,
1
0
0
in
2
0
2
5
.
T
r
ain
ed
ML
an
d
DL
m
o
d
els
c
an
also
p
r
ed
ict
t
h
e
v
alu
es
f
o
r
Asi
a
(
FAO)
,
u
p
p
e
r
-
m
id
d
le
-
in
co
m
e
,
an
d
l
o
wer
-
m
i
d
d
le
-
in
co
m
e
co
u
n
t
r
ies.
C
o
m
p
ar
ed
to
th
e
cu
r
r
en
t
r
ice
p
r
o
d
u
c
tio
n
p
r
ed
icted
v
alu
e
o
f
u
p
p
e
r
-
m
id
d
le
-
in
co
m
e
co
u
n
tr
ies
in
th
e
f
u
tu
r
e,
0
.
1
4
%
will
in
cr
ea
s
e
in
ter
n
atio
n
ally
f
r
o
m
2
0
2
3
to
2
0
3
0
.
T
h
u
s
,
u
s
in
g
th
ese
ML
an
d
DL
m
o
d
els,
we
ca
n
p
r
ed
ict
th
e
f
u
tu
r
e
v
alu
e
o
f
r
ice
p
r
o
d
u
cti
o
n
in
1
5
0
c
o
u
n
tr
ies.
T
h
e
ac
cu
r
ac
y
le
v
el
o
f
ML
an
d
DL
m
o
d
els
was
m
ea
s
u
r
e
d
u
s
in
g
R
2
.
T
h
e
lin
ea
r
r
eg
r
ess
io
n
m
o
d
el
p
r
o
v
id
es
th
e
b
est
-
p
r
ed
icted
v
alu
e
o
f
r
ice
p
r
o
d
u
ctio
n
co
m
p
ar
ed
t
o
th
e
o
th
er
m
o
d
els.
T
h
e
R
2
v
alu
e
o
f
th
i
s
m
o
d
el
is
s
lig
h
tly
th
e
h
ig
h
est,
s
h
o
win
g
g
o
o
d
n
ess
o
f
f
it.
T
h
u
s
,
th
is
p
ap
er
ca
n
co
n
tr
ib
u
te
to
d
ev
elo
p
in
g
in
ter
n
atio
n
al
ag
r
icu
ltu
r
e
s
tr
ateg
ies
b
ased
o
n
th
e
o
u
tco
m
e
o
f
t
h
is
s
tu
dy.
No
n
eth
eless
,
a
f
ew
lim
itatio
n
s
co
u
ld
b
e
ad
d
r
ess
ed
in
f
u
tu
r
e
r
esear
ch
.
T
h
e
f
ir
s
t
lim
itatio
n
is
th
e
m
eth
o
d
o
lo
g
y
,
wh
er
e
t
h
is
s
tu
d
y
em
p
lo
y
s
n
in
e
m
o
d
els,
b
u
t
o
n
l
y
o
n
e
DL
m
o
d
el
is
em
p
lo
y
ed
.
A
b
r
o
ad
er
co
m
p
ar
is
o
n
in
v
o
lv
in
g
d
iv
er
s
e
DL
ar
ch
itectu
r
es
co
u
ld
o
f
f
er
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
p
er
f
o
r
m
a
n
ce
e
v
alu
atio
n
.
A
n
o
th
er
lim
itatio
n
is
th
e
ass
u
m
p
tio
n
m
a
d
e
d
u
r
in
g
th
e
d
ev
elo
p
m
en
t
o
f
th
e
m
o
d
els.
T
h
e
s
tu
d
y
ass
u
m
es
th
at
cu
r
r
e
n
t
s
o
cio
-
ec
o
n
o
m
ic
an
d
p
o
licy
c
o
n
d
itio
n
s
will
r
em
ain
co
n
s
tan
t,
wh
ich
m
ay
n
o
t
b
e
r
ea
lis
tic
in
th
e
r
ea
l
wo
r
ld
.
C
h
an
g
es
in
ag
r
icu
ltu
r
al
p
o
licies,
tr
ad
e
ag
r
ee
m
en
ts
,
o
r
ec
o
n
o
m
ic
s
h
o
ck
s
co
u
ld
s
ig
n
if
ican
tly
i
m
p
ac
t
p
r
o
d
u
ctio
n
tr
en
d
s
.
Fu
tu
r
e
s
tu
d
ies
s
h
o
u
ld
co
n
s
id
er
th
ese
v
ar
iab
les
to
d
ev
elo
p
m
o
d
els b
etter
s
u
ited
t
o
r
ea
l
-
wo
r
ld
ch
an
g
es.
ACK
NO
WL
E
DG
E
M
E
NT
S
Au
th
o
r
t
h
an
k
s
R
esear
ch
an
d
I
n
n
o
v
atio
n
De
p
ar
tm
en
t,
Un
iv
er
s
iti
Ma
lay
s
ia
Pah
an
g
Al
-
Su
ltan
Ab
d
u
llah
f
o
r
f
u
n
d
in
g
th
is
p
ap
er
u
n
d
e
r
I
n
te
r
n
atio
n
al
Ma
tch
in
g
Gr
an
t w
ith
r
eg
is
tr
atio
n
n
u
m
b
er
UI
C
2
3
1
5
2
4
.
RE
F
E
R
E
NC
E
S
[
1
]
B
.
T.
Ta
n
,
P
.
S
.
F
a
m
,
R
.
B
.
R
.
F
i
r
d
a
u
s,
M
.
L.
T
a
n
,
a
n
d
M
.
S
.
G
u
n
a
r
a
t
n
e
,
“
I
mp
a
c
t
o
f
c
l
i
m
a
t
e
c
h
a
n
g
e
o
n
r
i
c
e
y
i
e
l
d
i
n
M
a
l
a
y
si
a
:
a
p
a
n
e
l
d
a
t
a
a
n
a
l
y
si
s
,
”
Ag
ri
c
u
l
t
u
re
,
v
o
l
.
1
1
,
n
o
.
6
,
p
.
5
6
9
,
Ju
n
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
i
c
u
l
t
u
r
e
1
1
0
6
0
5
6
9
.
[
2
]
S
.
Zh
o
u
,
L
.
X
u
,
a
n
d
N
.
C
h
e
n
,
“
R
i
c
e
y
i
e
l
d
p
r
e
d
i
c
t
i
o
n
i
n
H
u
b
e
i
P
r
o
v
i
n
c
e
b
a
se
d
o
n
d
e
e
p
l
e
a
r
n
i
n
g
a
n
d
t
h
e
e
f
f
e
c
t
o
f
s
p
a
t
i
a
l
h
e
t
e
r
o
g
e
n
e
i
t
y
,
”
Re
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
1
5
,
n
o
.
5
,
p
.
1
3
6
1
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
r
s
1
5
0
5
1
3
6
1
.
[
3
]
J.
L
u
e
t
a
l
.
,
“
D
e
e
p
l
e
a
r
n
i
n
g
f
o
r
m
u
l
t
i
-
so
u
r
c
e
d
a
t
a
-
d
r
i
v
e
n
c
r
o
p
y
i
e
l
d
p
r
e
d
i
c
t
i
o
n
i
n
N
o
r
t
h
e
a
st
C
h
i
n
a
,
”
A
g
ri
c
u
l
t
u
re
,
v
o
l
.
1
4
,
n
o
.
6
,
p
.
7
9
4
,
M
a
y
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
i
c
u
l
t
u
r
e
1
4
0
6
0
7
9
4
.
[
4
]
N
.
A
n
n
a
ma
l
a
i
a
n
d
A
.
Jo
h
n
so
n
,
“
A
n
a
l
y
s
i
s
a
n
d
f
o
r
e
c
a
s
t
i
n
g
o
f
a
r
e
a
u
n
d
e
r
c
u
l
t
i
v
a
t
i
o
n
o
f
r
i
c
e
i
n
i
n
d
i
a
:
u
n
i
v
a
r
i
a
t
e
t
i
me
seri
e
s
a
p
p
r
o
a
c
h
,
”
S
N
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
4
,
n
o
.
2
,
p
.
1
9
3
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
2
9
7
9
-
022
-
0
1
6
0
4
-
0.
[
5
]
N
.
G
a
n
d
h
i
,
L.
J.
A
r
mstr
o
n
g
,
O
.
P
e
t
k
a
r
,
a
n
d
A
.
K
.
Tr
i
p
a
t
h
y
,
“
R
i
c
e
c
r
o
p
y
i
e
l
d
p
r
e
d
i
c
t
i
o
n
i
n
I
n
d
i
a
u
s
i
n
g
su
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
s
,
”
i
n
2
0
1
6
1
3
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
i
n
t
C
o
n
f
e
re
n
c
e
o
n
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
S
o
f
t
w
a
re
E
n
g
i
n
e
e
r
i
n
g
(
J
C
S
S
E)
,
Ju
l
.
2
0
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t
i
o
n
a
l
g
o
r
i
t
h
m:
X
G
B
o
o
s
t
,
”
An
a
l
y
t
i
c
s
,
v
o
l
.
3
,
n
o
.
1
,
p
p
.
3
0
–
4
5
,
Ja
n
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
a
n
a
l
y
t
i
c
s
3
0
1
0
0
0
3
.
[
2
0
]
Y
.
X
v
,
Y
.
S
u
n
,
a
n
d
Y
.
Zh
a
n
g
,
“
P
r
e
d
i
c
t
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o
n
m
e
t
h
o
d
f
o
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s
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l
a
d
d
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c
o
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t
i
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l
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y
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se
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d
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t
a
n
d
A
d
a
B
o
o
st
r
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g
r
e
ssi
o
n
a
n
a
l
y
si
s
,
”
Ma
t
e
r
i
a
l
s
,
v
o
l
.
1
7
,
n
o
.
6
,
p
.
1
2
6
6
,
M
a
r
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2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
ma
1
7
0
6
1
2
6
6
.
[
2
1
]
S
.
S
.
H
u
ssa
i
n
a
n
d
S
.
S
.
H
.
Z
a
i
d
i
,
“
A
d
a
B
o
o
st
e
n
sem
b
l
e
a
p
p
r
o
a
c
h
w
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t
h
w
e
a
k
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l
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ss
i
f
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r
s fo
r
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e
a
r
f
a
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d
i
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g
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o
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s
a
n
d
p
r
o
g
n
o
s
i
s
i
n
D
C
mo
t
o
r
s,
”
A
p
p
l
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e
d
S
c
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e
s
,
v
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l
.
1
4
,
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o
.
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p
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0
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,
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o
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:
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0
.
3
3
9
0
/
a
p
p
1
4
0
7
3
1
0
5
.
[
2
2
]
J.
W
a
n
g
,
S
.
H
o
n
g
,
Y
.
D
o
n
g
,
Z.
L
i
,
a
n
d
J.
H
u
,
“
P
r
e
d
i
c
t
i
n
g
s
t
o
c
k
m
a
r
k
e
t
t
r
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n
d
s
u
s
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n
g
LST
M
n
e
t
w
o
r
k
s
:
o
v
e
r
c
o
mi
n
g
R
N
N
l
i
m
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t
a
t
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n
s
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m
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d
f
i
n
a
n
c
i
a
l
f
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r
e
c
a
s
t
i
n
g
,
”
J
o
u
r
n
a
l
o
f
C
o
m
p
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t
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r
S
c
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e
a
n
d
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f
t
w
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re
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p
l
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c
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t
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n
s
,
v
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l
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4
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o
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,
p
p
.
1
–
7
,
2
0
2
4
,
d
o
i
:
1
0
.
5
2
8
1
/
z
e
n
o
d
o
.
1
2
2
0
0
7
0
8
.
[
2
3
]
V
.
A
.
N
g
u
y
e
n
,
S
.
S
h
a
f
i
e
e
z
a
d
e
h
-
A
b
a
d
e
h
,
D
.
K
u
h
n
,
a
n
d
P
.
M
.
E
sf
a
h
a
n
i
,
“
B
r
i
d
g
i
n
g
B
a
y
e
si
a
n
a
n
d
m
i
n
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ma
x
m
e
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n
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u
a
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e
r
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o
r
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st
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mat
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n
v
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ssers
t
e
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n
d
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b
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t
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l
l
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b
u
st
o
p
t
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m
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z
a
t
i
o
n
,
”
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a
t
h
e
m
a
t
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c
s
o
f
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p
e
r
a
t
i
o
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s
e
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r
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h
,
v
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l
.
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8
,
n
o
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1
,
p
p
.
1
–
3
7
,
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e
b
.
2
0
2
3
,
d
o
i
:
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0
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1
2
8
7
/
mo
o
r
.
2
0
2
1
.
1
1
7
6
.
[
2
4
]
T.
O
.
H
o
d
s
o
n
,
“
R
o
o
t
-
mea
n
-
s
q
u
a
r
e
e
r
r
o
r
(
R
M
S
E)
o
r
m
e
a
n
a
b
s
o
l
u
t
e
e
r
r
o
r
(
M
A
E)
:
w
h
e
n
t
o
u
se
t
h
e
m
o
r
n
o
t
,
”
G
e
o
sc
i
e
n
t
i
f
i
c
M
o
d
e
l
D
e
v
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l
o
p
m
e
n
t
,
v
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l
.
1
5
,
n
o
.
1
4
,
p
p
.
5
4
8
1
–
5
4
8
7
,
Ju
l
.
2
0
2
2
,
d
o
i
:
1
0
.
5
1
9
4
/
g
m
d
-
15
-
5
481
-
2
0
2
2
.
[
2
5
]
D
.
C
h
i
c
c
o
,
M
.
J
.
W
a
r
r
e
n
s,
a
n
d
G
.
Ju
r
man
,
“
Th
e
c
o
e
f
f
i
c
i
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n
t
o
f
d
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t
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r
mi
n
a
t
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o
n
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-
sq
u
a
r
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d
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s
m
o
r
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n
f
o
r
ma
t
i
v
e
t
h
a
n
S
M
A
P
E
,
M
A
E,
M
A
P
E,
M
S
E
a
n
d
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M
S
E
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n
r
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g
r
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o
n
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n
a
l
y
si
s
e
v
a
l
u
a
t
i
o
n
,
”
Pe
e
rJ
C
o
m
p
u
t
e
r
S
c
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n
c
e
,
v
o
l
.
7
,
p
.
e
6
2
3
,
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u
l
.
2
0
2
1
,
d
o
i
:
1
0
.
7
7
1
7
/
p
e
e
r
j
-
c
s
.
6
2
3
.
[
2
6
]
C
.
O
n
y
u
t
h
a
,
“
F
r
o
m
R
-
sq
u
a
r
e
d
t
o
c
o
e
f
f
i
c
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e
n
t
o
f
m
o
d
e
l
a
c
c
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r
a
c
y
f
o
r
a
ssessi
n
g
‘
g
o
o
d
n
e
ss
-
of
-
f
i
t
s,’
”
G
e
o
sci
e
n
t
i
f
i
c
Mo
d
e
l
D
e
v
e
l
o
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m
e
n
t
D
i
sc
u
ss
i
o
n
s
,
p
p
.
1
–
2
5
,
2
0
2
0
.
[
2
7
]
F
.
R
e
u
ß
,
I
.
G
r
e
i
me
i
st
e
r
-
P
f
e
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l
,
M
.
V
r
e
u
g
d
e
n
h
i
l
,
a
n
d
W
.
W
a
g
n
e
r
,
“
C
o
m
p
a
r
i
so
n
o
f
l
o
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g
s
h
o
r
t
-
t
e
r
m
mem
o
r
y
n
e
t
w
o
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k
s
a
n
d
r
a
n
d
o
m
f
o
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me
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e
s
b
a
se
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l
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a
l
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c
r
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p
c
l
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ss
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f
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c
a
t
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o
n
,
”
R
e
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
1
3
,
n
o
.
2
4
,
p
.
5
0
0
0
,
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e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
r
s
1
3
2
4
5
0
0
0
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Dr
.
S
u
r
a
j
Ar
y
a
is
c
u
rre
n
tl
y
wo
rk
i
n
g
a
s
a
ss
istan
t
p
ro
fe
ss
o
r
i
n
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
I
n
fo
r
m
a
ti
o
n
Tec
h
n
o
lo
g
y
a
n
d
De
p
u
ty
Dire
c
to
r
(Tr
a
in
i
n
g
a
n
d
P
lac
e
m
e
n
t)
in
Ce
n
tral
Un
iv
e
rsit
y
o
f
Ha
ry
a
n
a
,
I
n
d
ia.
His
a
c
a
d
e
m
ic
q
u
a
li
fica
ti
o
n
s
a
re
P
h
.
D
.
(Co
m
p
u
ter
S
c
ien
c
e
),
M
.
P
h
i
l
.
(
Co
m
p
u
ter
S
c
ien
c
e
)
a
n
d
M
.
Tec
h
(Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
)
.
His
re
se
a
rc
h
in
tere
sts
fo
c
u
s
o
n
m
a
c
h
in
e
lea
rn
in
g
(M
L),
in
tern
e
t
o
f
t
h
i
n
g
s
(Io
T)
,
d
a
ta
wa
re
h
o
u
sin
g
a
n
d
m
in
i
n
g
,
s
y
ste
m
a
u
to
m
a
ti
o
n
a
n
d
p
a
ten
ts
w
rit
in
g
s
.
He
h
a
s
g
ra
n
ted
a
n
d
fil
e
s
m
a
n
y
p
a
ten
ts.
He
h
a
s
a
lso
p
u
b
li
sh
e
d
m
a
n
y
re
se
a
rc
h
a
rti
c
l
e
s
in
in
ter
n
a
ti
o
n
a
l
jo
u
rn
a
ls,
b
o
o
k
c
h
a
p
ters
a
n
d
c
o
n
fe
re
n
c
e
s.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
ra
j
a
ry
a
@c
u
h
.
a
c
.
in
.
Anju
is
a
re
se
a
rc
h
sc
h
o
lar
o
f
Ce
n
tral
Un
i
v
e
rsity
o
f
Ha
ry
a
n
a
,
I
n
d
ia.
S
h
e
re
c
e
iv
e
d
h
e
r
B.
Tec
h
.
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
fro
m
M
a
h
a
rsh
i
Da
y
a
n
a
n
d
Un
iv
e
rsity
Ro
h
tak
a
n
d
M
.
Sc
.
i
n
C
o
m
p
u
ter S
c
ien
c
e
fro
m
Ch
a
u
d
h
a
ry
Ba
n
si L
a
l
Un
iv
e
rsit
y
Bh
iwa
n
i
.
S
h
e
is
c
u
rre
n
tl
y
d
o
i
n
g
h
e
r
P
h
.
D
.
(Co
m
p
u
ter
S
c
ien
c
e
)
fro
m
Ce
n
tral
Un
iv
e
rsity
o
f
Ha
ry
a
n
a
.
He
r
re
se
a
rc
h
in
tere
sts: M
L,
a
n
d
Io
T.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
n
j
u
2
4
s
a
n
g
a
@g
m
a
il
.
c
o
m
.
No
r
Az
u
a
n
a
Ra
m
li
is
a
se
n
io
r
lec
tu
re
r
in
th
e
Ce
n
tre
fo
r
M
a
t
h
e
m
a
ti
c
a
l
S
c
ien
c
e
s,
Un
iv
e
rsiti
M
a
lay
sia
P
a
h
a
n
g
Al
-
S
u
lt
a
n
Ab
d
u
ll
a
h
.
S
h
e
re
c
e
iv
e
d
h
e
r
P
h
.
D
.
fro
m
U
n
iv
e
rsiti
S
a
i
n
s
M
a
lay
sia
,
M
a
ste
r
i
n
I
n
n
o
v
a
ti
o
n
a
n
d
E
n
g
in
e
e
rin
g
De
sig
n
fr
o
m
U
n
iv
e
rsiti
P
u
tra
M
a
lay
sia
a
n
d
B
.
Sc
.
d
e
g
re
e
i
n
I
n
d
u
strial
M
a
th
e
m
a
ti
c
s
fro
m
Un
iv
e
rsiti
Te
k
n
o
l
o
g
i
M
a
lay
sia
.
He
r
c
u
rre
n
t
re
se
a
rc
h
in
v
o
l
v
e
s
b
ig
d
a
ta
a
n
a
ly
t
ics
,
m
a
c
h
in
e
lea
rn
in
g
,
d
e
e
p
lea
rn
in
g
,
c
o
m
p
u
ter
v
isio
n
,
d
a
ta
m
in
in
g
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
.
S
h
e
h
a
s
p
u
b
li
sh
e
d
5
7
re
se
a
rc
h
a
rti
c
les
in
re
p
u
ted
S
CI
a
n
d
S
COPUS
in
d
e
x
e
d
jo
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
z
u
a
n
a
@u
m
p
sa
.
e
d
u
.
m
y
.
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