T
E
L
K
O
M
NIKA
T
elec
o
mm
un
ica
t
io
n Co
m
pu
t
i
ng
E
lect
ro
nics
a
nd
Co
ntr
o
l
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
,
p
p
.
1
6
0
0
~1
610
I
SS
N:
1
6
9
3
-
6
9
3
0
,
DOI
: 1
0
.
1
2
9
2
8
/
T
E
L
KOM
NI
K
A
.
v
23
i
6
.
26975
1600
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//jo
u
r
n
a
l.u
a
d
.
a
c.
id
/in
d
ex
.
p
h
p
/TELK
OM
N
I
K
A
Auto
m
a
tic
dia
g
no
sis
of rice
plant d
i
sea
ses
using
VGG
-
1
6
and
co
m
pu
ter
v
isio
n
Al
-
B
a
hra
1
,
H
enderi
2
,
Nur
Aziz
a
h
2
,
M
uh
a
m
m
a
d H
ud
za
if
a
h Na
s
rulla
h
3
,
Didi
k Set
iy
a
d
i
4
1
D
e
p
a
r
t
me
n
t
o
f
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
Ed
u
c
a
t
i
o
n
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
y
o
f
R
a
h
a
r
j
a
,
T
a
n
g
e
r
a
n
g
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
I
n
f
o
r
mat
i
c
s
E
n
g
i
n
e
e
r
i
n
g
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
y
o
f
R
a
h
a
r
j
a
,
T
a
n
g
e
r
a
n
g
,
I
n
d
o
n
e
si
a
3
D
e
p
a
r
t
me
n
t
o
f
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
,
Y
a
r
si
P
r
a
t
a
m
a
U
n
i
v
e
r
si
t
y
,
T
a
n
g
e
r
a
n
g
,
I
n
d
o
n
e
si
a
4
D
e
p
a
r
t
me
n
t
o
f
M
a
n
a
g
e
me
n
t
,
S
TI
E
A
r
l
i
n
d
o
,
B
e
k
a
s
i
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
7
,
2025
R
ev
i
s
ed
A
u
g
13
,
2
0
2
5
A
cc
ep
ted
Oct
19
,
2
0
2
5
P
a
t
h
o
g
e
n
s
a
re
o
rg
a
n
ism
s
th
a
t
c
a
u
se
d
ise
a
se
in
p
lan
ts.
In
th
e
c
a
s
e
o
f
rice
,
th
e
se
p
a
th
o
g
e
n
s
c
a
n
in
c
lu
d
e
f
u
n
g
i,
b
a
c
teria
,
n
e
m
a
to
d
e
s,
p
ro
t
o
z
o
a
,
a
n
d
v
iru
se
s.
T
h
is
stu
d
y
a
i
m
s
to
in
v
e
stig
a
te
rice
p
lan
t
d
ise
a
se
s
u
sin
g
a
h
y
b
rid
s
y
ste
m
th
a
t
e
m
p
lo
y
s
th
e
v
isu
a
l
g
e
o
m
e
tr
y
g
ro
u
p
-
1
6
(
V
G
G
-
16
)
a
rc
h
it
e
c
tu
re
a
n
d
c
o
m
p
u
ter
v
isio
n
tec
h
n
iq
u
e
s,
a
lo
n
g
sid
e
v
a
rio
u
s
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
s
a
n
d
h
y
p
e
rp
a
ra
m
e
ter
s.
W
e
u
ti
li
z
e
th
e
c
o
n
v
o
l
u
ti
o
n
a
l
n
e
u
ra
l
n
e
tw
o
rk
(CNN
)
a
rc
h
it
e
c
tu
re
o
f
V
G
G
-
1
6
f
o
r
f
e
a
tu
re
e
x
trac
ti
o
n
,
im
p
le
m
e
n
ti
n
g
a
p
ro
c
e
ss
k
n
o
w
n
a
s
tran
sf
e
r
le
a
rn
in
g
.
A
d
d
it
io
n
a
l
ly
,
th
is
re
se
a
rc
h
c
o
m
p
a
re
s
d
if
f
e
r
e
n
t
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
s
w
it
h
th
e
V
G
G
-
1
6
m
o
d
e
l
to
id
e
n
ti
fy
th
e
m
o
st
e
ffe
c
ti
v
e
o
p
ti
m
i
z
a
ti
o
n
f
o
r
th
e
CNN
a
rc
h
it
e
c
tu
re
a
p
p
li
e
d
t
o
th
e
tes
ted
d
a
tas
e
t.
T
h
e
m
a
in
c
o
n
tri
b
u
ti
o
n
o
f
th
is
s
tu
d
y
is
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
m
o
d
e
l
f
o
r
id
e
n
ti
f
y
in
g
rice
p
lan
t
d
ise
a
se
s
b
a
se
d
o
n
d
a
ta
c
o
ll
e
c
ted
u
sin
g
VG
G
-
1
6
f
o
r
f
e
a
tu
re
e
x
trac
ti
o
n
a
n
d
n
e
u
ra
l
n
e
tw
o
rk
s
f
o
r
c
las
si
f
ica
ti
o
n
w
it
h
sp
e
c
if
ic
p
a
ra
m
e
ters
.
Ou
r
f
in
d
in
g
s
in
d
ica
t
e
th
a
t
th
e
b
e
st
o
p
t
im
iza
ti
o
n
a
lg
o
rit
h
m
is
sto
c
h
a
st
ic
g
ra
d
ien
t
d
e
sc
e
n
t
(S
G
D)
w
it
h
m
o
m
e
n
tu
m
,
a
c
h
iev
in
g
tr
a
in
in
g
a
n
d
v
a
li
d
a
ti
o
n
lo
ss
re
su
lt
s
o
f
0
.
1
7
3
a
n
d
0
.
1
6
8
,
re
sp
e
c
ti
v
e
ly
.
F
u
rth
e
r
m
o
re
,
th
e
train
in
g
a
n
d
v
a
li
d
a
ti
o
n
a
c
c
u
ra
c
ies
we
re
0
.
9
5
a
n
d
0
.
9
5
7
.
T
h
e
m
o
d
e
l
’
s
p
e
rf
o
r
m
a
n
c
e
m
e
tri
c
s
in
c
lu
d
e
a
n
a
c
c
u
ra
c
y
o
f
9
5
.
7
5
,
p
re
c
isio
n
o
f
9
5
.
7
5
,
re
c
a
ll
o
f
9
5
.
7
5
,
a
n
d
a
n
F
1
-
sc
o
re
o
f
9
5
.
7
3
.
K
ey
w
o
r
d
s
:
Au
to
m
a
tic
d
iag
n
o
s
is
C
o
m
p
u
ter
v
is
io
n
Op
ti
m
izatio
n
alg
o
r
it
h
m
R
ice
p
lan
t d
is
ea
s
e
V
is
u
a
l g
eo
m
etr
y
g
r
o
u
p
-
16
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Al
-
B
ah
r
a
Dep
ar
te
m
en
t o
f
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
E
d
u
ca
t
io
n
,
Fac
u
lt
y
o
f
Scien
ce
a
n
d
T
ec
h
n
o
lo
g
y
Un
i
v
er
s
it
y
o
f
R
ah
ar
j
a
St.
J
en
d
er
al
Su
d
ir
m
a
n
No
.
4
0
,
C
ik
o
k
o
l,
T
an
g
er
an
g
Dis
tr
ict
,
T
an
g
er
an
g
C
it
y
,
B
an
ten
1
5
1
1
7
,
I
n
d
o
n
esia
E
m
ail:
alb
ah
r
a
@
r
ah
ar
j
a.
in
f
o
1.
I
NT
RO
D
UCT
I
O
N
I
n
d
o
n
esia
i
s
a
n
ag
r
ic
u
lt
u
r
al
co
u
n
tr
y
w
h
er
e
a
s
i
g
n
if
ican
t
p
o
r
tio
n
o
f
th
e
p
o
p
u
lat
io
n
w
o
r
k
s
i
n
t
h
e
f
ar
m
i
n
g
s
ec
to
r
.
I
n
an
ag
r
ar
ia
n
s
o
ciet
y
,
a
g
r
icu
l
tu
r
e
p
la
y
s
a
cr
u
cial
r
o
le
i
n
v
ar
io
u
s
ar
ea
s
,
i
n
c
lu
d
in
g
b
asic
n
ee
d
s
,
th
e
ec
o
n
o
m
y
,
co
m
m
u
n
it
y
well
-
b
ei
n
g
,
a
n
d
tr
ad
e
[
1
]
.
T
h
e
s
u
cc
es
s
o
f
a
g
r
icu
l
tu
r
e
lar
g
e
l
y
d
ep
en
d
s
o
n
t
h
e
h
ar
v
e
s
t,
w
h
ich
ca
n
b
e
j
eo
p
ar
d
ized
b
y
p
e
s
ts
a
n
d
d
is
ea
s
es
[
2
]
.
Un
d
er
s
tan
d
i
n
g
p
lan
t
d
i
s
ea
s
es
i
s
es
s
e
n
tial
to
p
r
ev
en
t
i
n
f
ec
tio
n
s
th
a
t
co
u
ld
lead
to
cr
o
p
f
ailu
r
e.
T
h
er
ef
o
r
e,
tech
n
o
lo
g
y
is
e
m
p
lo
y
ed
to
id
en
tif
y
t
y
p
es
o
f
d
is
ea
s
es
i
n
p
lan
t
s
,
allo
w
i
n
g
f
o
r
ap
p
r
o
p
r
iate
tr
ea
tm
e
n
t
s
to
b
e
ap
p
lied
b
ased
o
n
s
p
ec
if
ic
is
s
u
e
s
.
On
e
ef
f
ic
ien
t
ap
p
r
o
ac
h
to
d
is
ce
r
n
P
h
y
to
p
ath
o
lo
g
ical
co
n
d
itio
n
s
is
v
ia
s
u
p
er
v
is
ed
m
ac
h
i
n
e
lear
n
i
n
g
t
ec
h
n
iq
u
es,
n
o
tab
l
y
d
ee
p
lear
n
in
g
,
w
h
ich
is
r
eg
ar
d
ed
as
p
r
ee
m
in
e
n
t
o
w
in
g
to
i
ts
ca
p
ac
it
y
to
p
r
o
f
icie
n
tl
y
e
x
t
r
ac
t
s
alie
n
t
f
ea
t
u
r
es
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
u
to
ma
tic
d
ia
g
n
o
s
is
o
f rice
p
la
n
t d
is
ea
s
es u
s
in
g
V
GG
-
1
6
a
n
d
co
mp
u
ter visi
o
n
(
Al
-
B
a
h
r
a
)
1601
f
r
o
m
v
is
u
al
d
ata
[
3
]
-
[
5
]
.
A
p
r
o
m
i
n
en
t
ar
c
h
itect
u
r
e
w
it
h
in
d
ee
p
lear
n
i
n
g
f
o
r
i
m
a
g
e
an
al
y
s
is
i
s
th
e
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
et
w
o
r
k
(
C
NN)
[
6
]
.
I
n
th
e
f
o
r
w
ar
d
p
r
o
p
ag
atio
n
p
r
o
ce
s
s
,
s
e
v
er
al
h
y
p
er
p
ar
am
e
t
er
s
p
la
y
a
cr
u
cial
r
o
le
ac
r
o
s
s
all
la
y
er
s
,
in
cl
u
d
in
g
t
h
e
ac
ti
v
atio
n
f
u
n
cti
o
n
,
lear
n
i
n
g
r
ate,
b
atch
s
ize,
a
n
d
o
p
ti
m
izer
.
T
h
e
o
u
tp
u
t
w
ei
g
h
t
s
ar
e
iter
ati
v
el
y
m
o
d
i
f
ied
d
u
r
i
n
g
t
h
e
b
ac
k
p
r
o
p
ag
atio
n
p
h
ase
f
o
r
i
m
p
r
o
v
ed
o
u
tco
m
e
[
7
]
.
Op
ti
m
izatio
n
m
e
t
h
o
d
s
ar
e
e
m
p
lo
y
ed
to
m
o
d
if
y
th
e
w
ei
g
h
ts
an
d
t
o
ad
d
r
ess
o
v
er
f
itti
n
g
,
w
h
ich
is
a
co
m
m
o
n
ch
al
len
g
e
in
n
eu
r
al
n
et
w
o
r
k
s
[
8
]
.
Nu
m
er
o
u
s
o
p
ti
m
izatio
n
al
g
o
r
i
th
m
s
ca
n
ef
f
ec
ti
v
el
y
ad
j
u
s
t
th
e
w
ei
g
h
ts
,
allo
w
i
n
g
t
h
e
n
eu
r
al
n
et
w
o
r
k
m
o
d
el
to
p
er
f
o
r
m
o
p
ti
m
all
y
[
9
]
.
R
esear
ch
o
n
th
e
clas
s
if
icatio
n
o
f
r
ice
p
la
n
t
d
i
s
ea
s
es
h
a
s
b
e
en
e
x
ten
s
i
v
el
y
co
n
d
u
cted
u
s
i
n
g
v
ar
io
u
s
m
et
h
o
d
s
,
w
it
h
C
NN
b
ein
g
o
n
e
o
f
th
e
m
o
s
t
w
id
el
y
u
s
ed
ap
p
r
o
ac
h
es.
I
n
ad
d
itio
n
to
tr
a
d
itio
n
al
C
NNs,
r
ec
en
t
s
tu
d
ie
s
h
a
v
e
b
ee
n
e
x
p
lo
r
in
g
tr
an
s
f
er
lear
n
i
n
g
,
w
h
ic
h
i
n
v
o
l
v
e
s
ad
o
p
tin
g
p
r
e
-
tr
ai
n
ed
m
o
d
els
an
d
ap
p
ly
in
g
t
h
e
m
to
n
e
w
ca
s
e
s
.
So
m
e
o
f
t
h
e
co
m
m
o
n
l
y
u
s
ed
tr
an
s
f
er
lear
n
in
g
ar
ch
itect
u
r
es
i
n
clu
d
e
v
is
u
al
g
eo
m
etr
y
g
r
o
u
p
-
1
6
(
VGG
-
16
)
,
Alex
Ne
t,
I
n
ce
p
tio
n
,
r
esid
u
a
l
n
et
w
o
r
k
(
R
esNet
)
,
an
d
d
en
s
e
l
y
co
n
n
ec
ted
co
n
v
o
lu
tio
n
al
n
et
w
o
r
k
(
Den
s
eNe
t
)
[
1
0
]
.
Fu
r
th
er
m
o
r
e,
r
esear
ch
in
r
ice
p
lan
t
d
i
s
ea
s
e
clas
s
i
f
icatio
n
i
s
ev
o
l
v
i
n
g
b
y
o
p
ti
m
izi
n
g
p
r
o
ce
s
s
es
to
en
h
an
ce
r
es
u
lts
,
f
o
cu
s
i
n
g
o
n
s
elec
t
in
g
o
p
ti
m
al
m
et
h
o
d
s
f
o
r
s
p
ec
if
ic
d
atasets
.
T
ec
h
n
iq
u
es
s
u
ch
as
en
s
e
m
b
le
m
eth
o
d
s
ar
e
al
s
o
b
ein
g
u
til
ized
[
1
1
]
.
C
o
m
m
o
n
l
y
co
m
p
ar
ed
o
p
ti
m
izatio
n
alg
o
r
ith
m
s
in
c
lu
d
e
s
to
ch
ast
ic
g
r
ad
ien
t
d
escen
t
(
SGD)
,
SGD
w
it
h
m
o
m
en
t
u
m
,
Nester
o
v
m
o
m
en
t
u
m
,
Ad
aDe
lta,
A
d
aGr
ad
,
ad
ap
tiv
e
m
o
m
e
n
t e
s
ti
m
atio
n
(
A
d
a
m
)
,
a
n
d
R
MSp
r
o
p
.
W
h
ile
p
r
ev
io
u
s
s
tu
d
ie
s
s
h
o
w
p
r
o
m
i
s
e,
a
n
o
tab
le
g
ap
ex
is
t
s
in
ev
a
l
u
ati
n
g
o
p
ti
m
izatio
n
alg
o
r
ith
m
s
o
n
th
e
VGG
-
1
6
ar
ch
itect
u
r
e
f
o
r
r
ice
d
is
ea
s
e
class
i
f
icatio
n
.
Desp
ite
VGG
-
16
’
s
p
o
p
u
lar
it
y
,
co
m
p
r
eh
e
n
s
i
v
e
co
m
p
ar
ati
v
e
a
n
al
y
s
i
s
o
n
o
p
ti
m
izer
e
f
f
ec
ts
r
e
m
ai
n
s
i
n
s
u
f
f
ic
ien
t,
esp
ec
ial
l
y
r
e
g
ar
d
in
g
m
o
d
if
icatio
n
s
f
r
o
m
t
h
i
s
r
esear
ch
.
T
h
is
s
t
u
d
y
ai
m
s
to
a
d
d
r
ess
th
is
g
ap
b
y
ex
a
m
i
n
i
n
g
th
e
e
f
f
icac
y
o
f
v
ar
io
u
s
o
p
ti
m
i
ze
r
s
lik
e
SGD
w
i
t
h
m
o
m
e
n
t
u
m
,
A
d
a
m
,
a
n
d
R
MS
p
r
o
p
o
n
a
VGG
-
1
6
m
o
d
el
s
p
e
cif
ic
to
a
r
ice
leaf
d
is
ea
s
e
d
ataset.
T
h
r
o
u
g
h
d
ir
ec
t
co
m
p
ar
is
o
n
,
t
h
e
r
esear
ch
s
ee
k
s
to
d
eter
m
i
n
e
th
e
o
p
ti
m
al
o
p
ti
m
izer
f
o
r
en
h
an
ce
d
ac
c
u
r
ac
y
an
d
e
f
f
icie
n
c
y
i
n
ag
r
icu
l
tu
r
al
ap
p
licatio
n
s
.
T
h
is
s
t
u
d
y
a
s
s
e
s
s
e
s
a
h
y
b
r
id
s
y
s
te
m
e
m
p
lo
y
i
n
g
th
e
VGG
-
1
6
ar
ch
itectu
r
e
f
o
r
clas
s
i
f
y
i
n
g
r
ice
p
lan
t
d
is
ea
s
es.
I
t
s
ee
k
s
to
co
n
d
u
ct
a
co
m
p
ar
ativ
e
a
n
al
y
s
is
o
f
d
i
v
er
s
e
o
p
tim
izatio
n
al
g
o
r
it
h
m
s
a
n
d
h
y
p
er
p
ar
am
eter
s
,
w
it
h
r
es
u
lts
r
ep
r
esen
ted
th
r
o
u
g
h
li
n
ea
r
d
iag
r
a
m
s
an
d
tab
les
f
o
r
en
h
an
ce
d
p
er
f
o
r
m
a
n
c
e
ev
alu
atio
n
.
T
h
e
r
esear
ch
e
m
p
lo
y
s
P
y
t
h
o
n
i
n
co
n
j
u
n
ctio
n
w
it
h
th
e
T
en
s
o
r
F
lo
w
f
r
a
m
e
w
o
r
k
to
ev
al
u
ate
t
h
e
ef
f
ec
ti
v
e
n
es
s
o
f
VGG
-
1
6
alo
n
g
s
id
e
v
ar
io
u
s
o
p
t
i
m
izat
io
n
al
g
o
r
ith
m
s
.
2.
M
E
T
H
O
D
T
h
e
d
ataset
u
s
ed
in
th
is
s
t
u
d
y
w
a
s
co
m
p
iled
f
r
o
m
f
i
v
e
p
u
b
licl
y
a
v
ailab
le
s
o
u
r
ce
s
o
f
r
ice
p
lan
t
d
is
ea
s
e
i
m
ag
e
s
,
ea
c
h
p
r
o
v
id
in
g
d
if
f
er
e
n
t
cla
s
s
es,
r
e
s
o
lu
tio
n
s
,
an
d
s
a
m
p
le
d
is
tr
ib
u
tio
n
s
[
1
2
]
–
[
1
6
]
.
A
ll
d
ataset
s
w
er
e
m
er
g
ed
in
to
a
u
n
if
ied
co
llectio
n
to
en
s
u
r
e
co
n
s
is
te
n
c
y
d
u
r
i
n
g
m
o
d
el
d
ev
elo
p
m
en
t.
T
h
e
co
m
b
in
ed
d
ataset
w
a
s
th
e
n
r
ef
o
r
m
atted
t
o
ac
h
iev
e
a
u
n
i
f
o
r
m
d
ir
ec
to
r
y
s
tr
u
ct
u
r
e
an
d
lab
elin
g
s
c
h
e
m
e.
Af
ter
m
er
g
in
g
,
a
s
er
ies o
f
p
r
e
-
p
r
o
ce
s
s
in
g
s
ta
g
es
w
as
ap
p
lied
to
s
tan
d
ar
d
ize
i
m
a
g
e
c
h
ar
ac
ter
is
tics
an
d
i
m
p
r
o
v
e
d
ata
q
u
ali
t
y
.
T
h
e
p
r
e
-
p
r
o
ce
s
s
i
n
g
s
tep
s
i
n
cl
u
d
ed
r
esizin
g
,
cr
o
p
p
in
g
,
co
n
tr
a
s
t
en
h
a
n
ce
m
e
n
t,
a
n
d
co
lo
r
n
o
r
m
aliza
t
io
n
to
ac
ce
ler
ate
m
o
d
el
co
n
v
er
g
en
ce
a
n
d
r
ed
u
ce
co
m
p
u
ta
tio
n
al
co
m
p
le
x
it
y
[
1
7
]
–
[
2
3
]
.
T
h
ese
o
p
er
atio
n
s
w
er
e
i
m
p
le
m
e
n
ted
to
en
s
u
r
e
th
a
t
all
i
m
a
g
es
h
ad
co
n
s
is
te
n
t
d
i
m
en
s
io
n
s
a
n
d
w
er
e
s
u
itab
le
f
o
r
tr
ain
i
n
g
a
d
ee
p
lear
n
in
g
m
o
d
el
.
C
NNs
h
a
v
e
b
ee
n
w
id
el
y
e
m
p
lo
y
ed
i
n
i
m
ag
e
cla
s
s
i
f
icat
io
n
task
s
b
ec
au
s
e
o
f
th
eir
ab
ilit
y
to
ex
tr
ac
t
h
ier
ar
ch
ical
f
ea
tu
r
e
r
ep
r
esen
t
atio
n
s
[
2
4
]
–
[
2
7
]
.
P
r
io
r
r
e
s
ea
r
ch
h
as
d
e
m
o
n
s
tr
ated
th
e
ef
f
e
ctiv
e
n
ess
o
f
C
N
N
-
b
ased
tech
n
iq
u
es
ac
r
o
s
s
v
ar
io
u
s
co
m
p
u
ter
v
is
io
n
p
r
o
b
lem
s
,
r
an
g
i
n
g
f
r
o
m
g
en
er
at
iv
e
i
m
ag
e
m
o
d
elin
g
to
m
ed
ical
i
m
a
g
e
an
al
y
s
is
[
2
8
]
–
[
3
1
]
.
T
h
ese
f
in
d
i
n
g
s
p
r
o
v
id
e
s
tr
o
n
g
s
u
p
p
o
r
t
f
o
r
ad
o
p
tin
g
C
NN
ar
ch
itectu
r
e
s
in
a
g
r
icu
l
tu
r
al
d
is
ea
s
e
clas
s
i
f
icat
io
n
.
I
n
t
h
is
s
tu
d
y
,
t
h
e
C
NN
f
r
a
m
e
w
o
r
k
w
as
i
m
p
le
m
e
n
ted
u
s
i
n
g
th
e
VGG
-
16
ar
ch
itect
u
r
e,
w
h
ic
h
h
a
s
b
ee
n
r
ec
o
g
n
ized
f
o
r
its
s
tab
le
s
tr
u
ct
u
r
e
an
d
ca
p
ab
ilit
y
to
ca
p
tu
r
e
f
i
n
e
-
g
r
ai
n
ed
v
is
u
al
p
atter
n
s
th
r
o
u
g
h
its
d
ee
p
,
s
eq
u
en
tial c
o
n
v
o
lu
tio
n
al
la
y
er
s
[
3
2
]
,
[
3
3
]
.
T
h
e
VGG
-
1
6
m
o
d
el
co
n
s
i
s
ts
o
f
1
3
co
n
v
o
l
u
tio
n
a
l
la
y
er
s
a
n
d
th
r
ee
f
u
l
l
y
co
n
n
ec
ted
la
y
er
s
,
o
p
er
atin
g
w
it
h
u
n
i
f
o
r
m
3
×3
f
ilter
s
th
r
o
u
g
h
o
u
t
its
ar
ch
itect
u
r
e.
T
o
a
d
ap
t
VGG
-
1
6
f
o
r
th
e
r
ice
d
is
ea
s
e
d
ataset,
tr
an
s
f
er
lear
n
in
g
w
as
e
m
p
lo
y
ed
b
y
i
n
itializ
in
g
t
h
e
m
o
d
el
w
it
h
p
r
e
-
tr
ain
ed
I
m
ag
eNe
t
w
eig
h
t
s
.
T
h
e
co
n
v
o
lu
tio
n
al
la
y
er
s
w
er
e
u
s
ed
as
f
ix
ed
f
ea
tu
r
e
ex
tr
ac
to
r
s
,
an
d
th
e
o
r
ig
i
n
al
f
u
ll
y
co
n
n
ec
ted
la
y
er
s
wer
e
r
ep
lace
d
w
ith
a
cu
s
to
m
cla
s
s
i
f
ier
tailo
r
ed
to
th
e
n
u
m
b
er
o
f
class
e
s
in
t
h
i
s
s
tu
d
y
.
Var
io
u
s
o
p
ti
m
izatio
n
alg
o
r
ith
m
s
w
er
e
ev
alu
a
ted
to
d
eter
m
i
n
e
t
h
e
m
o
s
t e
f
f
ec
ti
v
e
co
n
f
i
g
u
r
atio
n
f
o
r
t
h
e
m
o
d
i
f
ied
VGG
-
1
6
m
o
d
el
[
3
4
]
–
[
4
6
]
.
T
h
e
d
ataset
w
as
d
iv
id
ed
in
to
tr
ain
in
g
,
v
al
id
atio
n
,
an
d
test
in
g
s
u
b
s
ets
f
o
llo
w
in
g
r
ec
o
m
m
e
n
d
ed
p
r
ac
tices
f
o
r
ac
h
ie
v
i
n
g
r
elia
b
le
m
o
d
el
g
e
n
er
aliza
tio
n
.
T
h
e
tr
ai
n
in
g
s
et
w
a
s
u
s
ed
t
o
o
p
tim
ize
m
o
d
el
p
ar
am
eter
s
,
th
e
v
alid
atio
n
s
et
m
o
n
ito
r
ed
p
er
f
o
r
m
a
n
ce
d
u
r
in
g
tr
ain
i
n
g
,
an
d
t
h
e
test
i
n
g
s
et
ass
es
s
ed
f
i
n
al
m
o
d
el
q
u
alit
y
.
T
h
e
m
o
d
el
w
a
s
tr
ai
n
ed
u
s
i
n
g
ca
te
g
o
r
ical
cr
o
s
s
-
en
tr
o
p
y
a
s
t
h
e
lo
s
s
f
u
n
ct
io
n
a
n
d
ac
cu
r
ac
y
a
s
t
h
e
p
r
im
ar
y
ev
a
lu
at
io
n
m
etr
ic
[
4
7
]
–
[
4
9
]
.
P
er
f
o
r
m
a
n
ce
ass
e
s
s
m
en
ts
al
s
o
u
til
ized
ad
d
itio
n
al
m
etr
ics
ac
c
u
r
ac
y
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
6
0
0
-
1
610
1602
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
w
h
ic
h
p
r
o
v
id
e
a
m
o
r
e
co
m
p
r
e
h
en
s
i
v
e
e
v
alu
a
tio
n
o
f
cla
s
s
i
f
ic
atio
n
e
f
f
ec
tiv
e
n
es
s
[
5
0
]
,
[
5
1
]
.
A
d
e
tailed
d
ep
ictio
n
o
f
t
h
e
r
esear
ch
w
o
r
k
f
lo
w
is
p
r
esen
ted
in
Fi
g
u
r
e
1
.
Fig
u
r
e
1
.
R
esear
ch
p
r
o
ce
s
s
d
ia
g
r
a
m
T
r
u
e
p
o
s
itiv
es
(
T
P)
r
ef
er
to
t
h
e
n
u
m
b
er
o
f
p
o
s
itiv
e
i
m
a
g
es
th
at
ar
e
co
r
r
ec
tly
p
r
ed
icted
,
w
h
ile
f
al
s
e
n
eg
at
iv
e
s
(
FN)
ar
e
th
e
p
o
s
itiv
e
i
m
a
g
es
t
h
at
ar
e
m
is
cla
s
s
i
f
ied
.
Fals
e
p
o
s
itiv
es
(
FP
)
in
d
icate
th
e
n
u
m
b
er
o
f
n
eg
at
iv
e
i
m
a
g
es
th
a
t
ar
e
in
co
r
r
ec
tly
p
r
ed
icted
as
p
o
s
i
tiv
e,
an
d
tr
u
e
n
eg
a
tiv
e
s
(
T
N)
r
ep
r
e
s
en
t
t
h
e
n
u
m
b
er
o
f
n
eg
at
iv
e
i
m
a
g
es t
h
at
ar
e
co
r
r
ec
tl
y
id
en
t
if
ied
[
5
2
]
,
[
5
3
]
.
T
h
e
en
tire
r
esear
ch
p
r
o
ce
s
s
is
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
Fig
u
r
e
1
ill
u
s
tr
ates
t
h
e
r
e
s
ea
r
ch
w
o
r
k
f
lo
w
,
i
n
itiat
in
g
w
i
th
d
ata
co
llectio
n
f
r
o
m
v
ar
io
u
s
s
o
u
r
c
es,
f
o
llo
w
ed
b
y
p
r
e
-
p
r
o
ce
s
s
in
g
f
o
r
co
n
s
is
te
n
c
y
a
n
d
q
u
ali
t
y
.
T
h
e
d
ata
is
d
iv
id
ed
in
to
tr
ai
n
in
g
,
v
alid
atio
n
,
a
n
d
test
s
ets
to
f
ac
ilit
a
te
ef
f
ec
t
iv
e
m
o
d
el
d
ev
elo
p
m
en
t
an
d
e
v
al
u
ati
o
n
.
I
n
t
h
e
m
o
d
eli
n
g
p
h
a
s
e,
a
m
o
d
if
ied
V
GG
-
16
ar
ch
itect
u
r
e
is
e
m
p
lo
y
ed
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
v
ia
tr
a
n
s
f
er
l
ea
r
n
in
g
.
T
h
e
ex
tr
ac
ted
f
ea
t
u
r
e
s
ar
e
th
e
n
f
ed
i
n
to
a
n
e
w
l
y
d
ev
elo
p
ed
FC
la
y
er
f
o
r
class
i
f
icatio
n
,
u
til
izin
g
m
u
l
ti
p
le
o
p
tim
izat
io
n
al
g
o
r
ith
m
s
f
o
r
tr
ain
in
g
.
Fin
al
l
y
,
th
e
e
f
f
ec
ti
v
en
e
s
s
o
f
t
h
e
tr
ai
n
ed
m
o
d
el
is
r
i
g
o
r
o
u
s
l
y
e
v
al
u
ated
u
s
i
n
g
s
ta
n
d
ar
d
m
etr
ic
s
s
u
c
h
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F
1
-
s
co
r
e
to
d
eter
m
i
n
e
th
e
o
p
ti
m
al
co
n
f
ig
u
r
at
io
n
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
T
he
pro
po
s
e
d
m
o
del
T
h
e
m
o
d
el
e
m
p
lo
y
ed
in
th
is
s
tu
d
y
is
f
in
e
-
t
u
n
i
n
g
,
as
ill
u
s
tr
a
ted
in
Fig
u
r
e
1
.
I
t
c
o
n
s
is
ts
o
f
a
to
t
al
o
f
1
4
,
7
1
4
,
6
8
8
p
a
r
am
eter
s
,
i
n
cl
u
d
in
g
b
o
th
tr
ai
n
ab
le
an
d
n
o
n
-
tr
ain
ab
le
p
ar
a
m
e
ter
s
.
T
h
is
ap
p
r
o
ac
h
in
v
o
lv
e
s
f
r
ee
zin
g
s
o
m
e
p
ar
a
m
eter
s
w
h
i
le
ad
j
u
s
tin
g
o
th
er
s
to
f
it
t
h
e
d
ata,
a
p
r
o
ce
s
s
r
ef
er
r
ed
to
as
f
in
e
-
tu
n
i
n
g
t
h
e
VGG
-
1
6
m
o
d
el
[
3
2
]
,
[
3
3
]
.
Fin
e
-
t
u
n
in
g
th
e
VGG
-
1
6
m
o
d
el
in
th
e
co
d
e
ab
o
v
e
u
s
es
a
lo
o
p
to
ex
p
licitl
y
f
r
ee
ze
th
e
f
ir
s
t 1
5
la
y
er
s
a
n
d
allo
w
t
h
e
r
e
s
t to
b
e
tr
ain
ed
is
s
h
o
w
n
i
n
T
a
b
le
1
.
T
ab
le
1
.
C
o
m
p
ar
is
o
n
o
f
VGG
-
1
6
,
f
in
e
-
t
u
n
i
n
g
V
GG
-
1
6
an
d
FC
la
y
er
P
a
r
a
me
t
e
r
s
VGG
-
16
F
i
n
e
-
t
u
n
i
n
g
V
G
G
-
16
FC
l
a
y
e
r
T
o
t
a
l
p
a
r
a
ms
1
4
.
7
1
4
.
6
8
8
1
4
.
7
1
4
.
6
8
8
1
4
.
8
4
3
.
5
9
4
T
r
a
i
n
a
b
l
e
p
a
r
a
ms
1
4
.
7
1
4
.
6
8
8
0
1
2
8
.
6
5
0
N
o
n
-
t
r
a
i
n
a
b
l
e
p
a
r
a
ms
0
1
4
.
7
1
4
.
6
8
8
1
4
.
7
1
4
.
9
9
4
FC
la
y
er
[
2
8
]
,
[
4
7
]
in
th
is
s
tu
d
y
is
d
if
f
er
en
t
f
r
o
m
th
e
VGG
-
1
6
m
o
d
el,
d
u
e
to
th
e
d
if
f
er
en
ce
in
th
e
a
m
o
u
n
t
o
f
d
ata
an
d
class
i
f
ica
tio
n
o
b
j
ec
ts
,
th
e
co
m
p
ar
is
o
n
o
f
p
ar
am
eter
s
u
s
ed
i
n
th
i
s
s
t
u
d
y
w
i
th
VGG1
6
is
s
h
o
w
n
i
n
T
ab
le
2
.
T
h
is
r
ese
ar
ch
m
o
d
el
m
o
d
if
ie
s
t
h
e
VG
G
-
1
6
ar
ch
itect
u
r
e,
s
p
ec
i
f
icall
y
it
s
FC
la
y
er
.
Ou
r
m
o
d
el
f
ea
tu
r
e
s
t
w
o
la
y
er
s
in
s
t
ea
d
o
f
th
r
ee
,
w
i
th
f
e
w
er
n
eu
r
o
n
s
(
1
2
8
an
d
6
4
)
as
d
etailed
in
T
ab
le
2
.
Mo
r
e
o
v
er
,
it
e
m
p
lo
y
s
d
r
o
p
o
u
t
lay
er
s
,
b
a
tch
n
o
r
m
aliza
tio
n
,
a
n
d
L
2
r
eg
u
lar
iza
tio
n
to
m
i
tig
a
te
o
v
er
f
itti
n
g
an
d
i
m
p
r
o
v
e
s
tab
i
lit
y
,
u
n
li
k
e
th
e
s
tan
d
ar
d
VGG
-
1
6
ar
ch
itect
u
r
e.
T
ab
le
2
.
C
o
m
p
ar
is
o
n
o
f
VGG
-
16
m
o
d
el
an
d
r
esear
ch
m
o
d
el
A
sp
e
c
t
VGG
-
16
m
o
d
e
l
R
e
se
a
r
c
h
m
o
d
e
l
N
u
mb
e
r
o
f
F
C
l
a
y
e
r
s
3
2
N
u
mb
e
r
o
f
f
i
r
st
F
C
l
a
y
e
r
n
e
u
r
o
n
s
4
0
9
6
1
2
8
D
r
o
p
o
u
t
T
h
e
r
e
i
s n
o
n
e
T
h
e
r
e
i
s,
w
i
t
h
a
r
a
t
e
o
f
0
.
6
B
a
t
c
h
n
o
r
mal
i
z
a
t
i
o
n
T
h
e
r
e
i
sn
’
t
a
n
y
Y
e
s
R
e
g
u
l
a
r
i
z
a
t
i
o
n
T
h
e
r
e
i
sn
’
t
a
n
y
Y
e
s,
w
i
t
h
L
2
r
e
g
u
l
a
r
i
z
a
t
i
o
n
a
n
d
a
f
a
c
t
o
r
o
f
0
.
0
1
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
u
to
ma
tic
d
ia
g
n
o
s
is
o
f rice
p
la
n
t d
is
ea
s
es u
s
in
g
V
GG
-
1
6
a
n
d
co
mp
u
ter visi
o
n
(
Al
-
B
a
h
r
a
)
1603
T
h
e
m
o
d
if
icatio
n
s
to
VGG
-
1
6
b
alan
ce
co
m
p
le
x
it
y
a
n
d
p
er
f
o
r
m
an
ce
.
T
h
e
r
ed
u
ctio
n
o
f
la
y
er
s
an
d
n
eu
r
o
n
s
d
ec
r
ea
s
ed
tr
ai
n
ab
le
p
ar
am
eter
s
s
i
g
n
i
f
ican
tl
y
.
T
h
is
r
ed
u
ctio
n
i
m
p
r
o
v
e
s
co
m
p
u
tatio
n
al
e
f
f
icie
n
c
y
,
s
p
ee
d
s
u
p
tr
ain
i
n
g
,
an
d
r
ed
u
c
es
o
v
er
f
itt
in
g
.
Desp
ite
it
s
s
i
m
p
lif
icatio
n
,
th
e
m
o
d
el
m
a
in
ta
i
n
s
s
tr
o
n
g
ac
c
u
r
ac
y
an
d
p
er
f
o
r
m
an
ce
,
d
e
m
o
n
s
tr
ati
n
g
t
h
at
le
s
s
co
m
p
le
x
it
y
ca
n
e
f
f
ec
tiv
el
y
ad
d
r
ess
ce
r
tain
clas
s
i
f
icatio
n
tas
k
s
.
3
.
2
.
T
ra
ini
ng
in
m
o
del
T
h
e
m
o
d
el
to
b
e
tr
ain
ed
in
th
is
s
t
u
d
y
u
s
es
s
e
v
er
al
p
ar
am
eter
s
i
n
m
o
d
el
co
m
p
i
latio
n
,
in
clu
d
i
n
g
o
p
tim
izatio
n
,
lear
n
in
g
r
ate,
lo
s
s
f
u
n
c
tio
n
a
n
d
ev
alu
a
tio
n
m
etr
ic
s
.
T
h
e
m
o
d
el
tr
ain
in
g
u
s
e
s
d
if
f
er
en
t
p
ar
am
eter
s
f
r
o
m
t
h
e
o
r
ig
i
n
al
VGG
-
1
6
m
o
d
el
w
it
h
t
h
e
g
o
al
o
f
ad
ap
tin
g
to
g
r
ap
h
ics
p
r
o
ce
s
s
i
n
g
u
n
it
(
GP
U
)
m
e
m
o
r
y
,
ac
ce
ler
atin
g
m
o
d
el
tr
ain
in
g
,
m
ai
n
tai
n
in
g
tr
ai
n
in
g
s
tab
ilit
y
a
n
d
i
m
p
r
o
v
i
n
g
m
o
d
el.
T
h
e
tr
ain
in
g
p
r
o
ce
s
s
is
g
i
v
en
a
li
m
itat
io
n
,
if
t
h
e
v
ali
d
atio
n
lo
s
s
d
o
es
n
o
t
in
cr
ea
s
e
f
o
r
5
ep
o
ch
s
,
th
e
n
t
h
e
tr
ai
n
in
g
w
ill
b
e
s
to
p
p
ed
w
it
h
t
h
e
g
o
al
o
f
ef
f
icien
t
tr
ai
n
in
g
ti
m
e.
A
co
m
p
ar
is
o
n
b
et
w
ee
n
t
h
e
tr
ain
i
n
g
p
ar
am
eter
s
o
n
th
e
r
esear
ch
m
o
d
el
an
d
t
h
e
VGG
-
1
6
m
o
d
el
ca
n
b
e
s
ee
n
i
n
T
ab
le
3
.
T
ab
le
3
.
C
o
m
p
ar
is
o
n
o
f
VGG
-
16
tr
ain
in
g
a
n
d
r
esear
ch
m
o
d
e
l
F
e
a
t
u
r
e
M
o
d
e
l
V
G
G
-
16
R
e
se
a
r
c
h
mo
d
e
l
Ea
r
l
y
st
o
p
p
i
n
g
No
Y
e
s
O
p
t
i
mi
z
e
r
S
G
D
A
d
a
m
L
e
a
r
n
i
n
g
r
a
t
e
0
.
0
0
1
0
.
0
0
1
L
o
ss fu
n
c
t
i
o
n
C
a
t
e
g
o
r
i
c
a
l
c
r
o
sse
n
t
r
o
p
y
C
a
t
e
g
o
r
i
c
a
l
c
r
o
sse
n
t
r
o
p
y
M
e
t
r
i
c
A
c
c
u
r
a
c
y
,
t
o
p
5
a
c
c
u
r
a
c
y
A
c
c
u
r
a
c
y
B
a
t
c
h
si
z
e
64
8
Ep
o
c
h
s
1
0
0
30
3
.
3
.
M
o
del
e
v
a
lua
t
io
n
3
.
3
.
1
.
Ada
m
a
nd
A
da
m
a
x
T
h
e
m
o
d
el
w
it
h
A
d
a
m
’
s
o
p
ti
m
izatio
n
al
g
o
r
ith
m
[
3
4
]
s
ee
s
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
m
o
d
el
t
h
r
o
u
g
h
th
e
class
i
f
icatio
n
r
ep
o
r
t
w
it
h
a
class
i
f
icatio
n
r
esu
lt
ac
c
u
r
ac
y
o
f
0
.
9
5
g
en
er
all
y
i
n
d
icatin
g
g
o
o
d
p
er
f
o
r
m
an
ce
.
Fu
r
t
h
er
m
o
r
e,
ev
alu
at
in
g
th
e
o
r
ig
in
a
l
class
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
it
h
co
n
f
u
s
io
n
m
atr
i
x
o
n
1
0
class
es
w
it
h
th
e
co
m
p
ar
is
o
n
r
esu
lt
s
o
f
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
m
etr
ic
s
s
h
o
w
n
in
Fi
g
u
r
e
2
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el
i
s
d
o
n
e
w
it
h
an
o
th
er
d
ataset
o
f
th
i
s
m
o
d
el
th
at
h
a
s
b
ee
n
s
av
ed
w
it
h
th
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
ad
a
m
_
lr
.
h
5
”
w
it
h
t
u
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
it
h
a
r
es
u
lt o
f
9
9
.
9
%.
T
h
e
m
o
d
el
w
ith
A
d
a
m
ax
’
s
o
p
ti
m
izatio
n
alg
o
r
ith
m
s
ee
s
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
m
o
d
el
t
h
r
o
u
g
h
t
h
e
class
i
f
icatio
n
r
ep
o
r
t
w
it
h
a
cla
s
s
i
f
icatio
n
r
esu
l
t
ac
cu
r
ac
y
o
f
0
.
9
5
g
en
er
all
y
i
n
d
icatin
g
g
o
o
d
p
er
f
o
r
m
a
n
ce
[
3
4
]
.
Fu
r
t
h
er
m
o
r
e,
ev
alu
at
in
g
th
e
o
r
ig
in
a
l
class
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
it
h
co
n
f
u
s
io
n
m
atr
i
x
o
n
1
0
class
es
w
it
h
th
e
co
m
p
ar
is
o
n
r
esu
lt
s
o
f
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
m
etr
ic
s
s
h
o
w
n
in
Fi
g
u
r
e
3
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el
i
s
d
o
n
e
w
it
h
an
o
th
e
r
d
ataset
o
f
th
i
s
m
o
d
el
th
at
h
a
s
b
ee
n
s
av
ed
w
it
h
th
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
ad
a
m
ax
_
lr
.
h
5
”
w
it
h
tu
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
it
h
a
r
esu
l
t o
f
9
9
.
9
%
.
Fig
u
r
e
2
.
A
d
a
m
m
o
d
el
tr
ain
i
n
g
Fig
u
r
e
3
.
A
d
a
m
ax
m
o
d
el
tr
ai
n
in
g
3
.
3
.
2
.
Ada
G
ra
d a
nd
Ada
D
elt
a
T
h
e
m
o
d
el
w
it
h
A
d
a
G
r
ad
’
s
o
p
ti
m
izatio
n
alg
o
r
it
h
m
[
3
5
]
s
ee
s
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el
th
r
o
u
g
h
th
e
clas
s
i
f
icatio
n
r
ep
o
r
t
w
it
h
a
class
i
f
icatio
n
r
es
u
lt
ac
c
u
r
ac
y
o
f
0
.
9
5
g
en
er
all
y
i
n
d
icati
n
g
g
o
o
d
p
er
f
o
r
m
an
ce
.
Fu
r
t
h
er
m
o
r
e,
ev
alu
at
in
g
th
e
o
r
ig
in
a
l
class
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
it
h
co
n
f
u
s
io
n
m
atr
i
x
o
n
1
0
class
es
w
it
h
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
6
0
0
-
1
610
1604
co
m
p
ar
is
o
n
r
esu
lt
s
o
f
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
m
etr
ic
s
s
h
o
w
n
in
Fi
g
u
r
e
4
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el
i
s
d
o
n
e
w
it
h
an
o
th
er
d
ataset
o
f
th
i
s
m
o
d
el
th
at
h
a
s
b
ee
n
s
av
ed
w
it
h
th
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
ad
ag
r
ad
_
lr
.
h
5
”
w
it
h
tu
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
it
h
a
r
esu
l
t o
f
9
9
.
8
7
8
%.
T
h
e
m
o
d
el
w
ith
A
d
ad
elta
’
s
o
p
tim
izatio
n
a
lg
o
r
it
h
m
[
3
5
]
s
h
o
w
s
a
clas
s
i
f
icatio
n
ac
cu
r
a
c
y
o
f
0
.
7
3
w
h
ic
h
g
e
n
er
all
y
i
n
d
icate
s
t
h
a
t
th
e
m
o
d
el
h
as
p
o
o
r
p
er
f
o
r
m
an
ce
i
n
ea
ch
cl
as
s
ca
u
s
ed
b
y
t
h
e
o
p
ti
m
izatio
n
alg
o
r
ith
m
u
s
ed
,
e
s
p
ec
iall
y
i
n
c
lass
3
w
i
th
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
clas
s
i
f
icatio
n
s
co
r
e
s
o
f
0
.
4
3
,
0
.
4
4
an
d
0
.
4
3
w
h
ich
i
n
d
icate
th
at
t
h
e
m
o
d
el
h
as
d
if
f
icu
lt
y
tr
ai
n
in
g
an
d
class
if
y
i
n
g
cla
s
s
3
.
Fu
r
t
h
er
m
o
r
e,
ev
a
lu
at
in
g
th
e
o
r
ig
in
al
clas
s
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
it
h
co
n
f
u
s
io
n
m
atr
i
x
o
n
1
0
class
es
w
it
h
th
e
co
m
p
ar
is
o
n
r
esu
lts
o
f
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
m
etr
ics
s
h
o
w
n
i
n
Fi
g
u
r
e
5
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
t
h
e
m
o
d
el
i
s
d
o
n
e
w
it
h
an
o
th
er
d
ataset
o
f
t
h
is
m
o
d
el
th
at
h
a
s
b
ee
n
s
a
v
ed
w
i
th
t
h
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
ad
ad
elta
_
lr
.
h
5
”
w
i
th
tu
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
ith
a
r
esu
lt o
f
8
7
.
1
1
%.
Fig
u
r
e
4
.
A
d
a
G
r
ad
m
o
d
el
tr
ain
in
g
Fig
u
r
e
5
.
A
d
a
D
elta
m
o
d
el
tr
ai
n
in
g
3
.
3
.
3
.
R
M
Sp
ro
p a
nd
s
t
o
cha
s
t
ic
g
ra
dient
des
ce
nt
A
m
o
d
el
w
it
h
t
h
e
SGD
w
it
h
Mo
m
en
t
u
m
o
p
ti
m
izat
io
n
alg
o
r
ith
m
t
h
at
s
h
o
w
s
a
clas
s
i
f
icat
io
n
r
esu
lt
ac
cu
r
ac
y
o
f
0
.
9
6
w
h
ich
g
e
n
er
a
ll
y
in
d
icate
s
th
at
th
e
m
o
d
el
h
a
s
g
o
o
d
p
e
r
f
o
r
m
a
n
ce
.
Fu
r
t
h
er
m
o
r
e,
ev
alu
atin
g
th
e
o
r
ig
in
al
clas
s
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
it
h
co
n
f
u
s
io
n
m
at
r
i
x
o
n
1
0
class
es
w
it
h
th
e
co
m
p
ar
is
o
n
r
esu
lts
o
f
P
r
ec
is
io
n
,
R
ec
all
a
n
d
F1
-
s
co
r
e
m
etr
ics
s
h
o
w
n
i
n
F
ig
u
r
e
6
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el
is
d
o
n
e
w
i
t
h
an
o
th
er
d
ataset
o
f
t
h
is
m
o
d
el
th
at
h
a
s
b
ee
n
s
a
v
ed
w
it
h
t
h
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
SGD_
Mo
m
e
n
tu
m
_l
r
.
h
5
”
w
it
h
tu
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
ith
a
r
esu
lt o
f
9
9
.
9
9
9
9
7
%
.
A
m
o
d
el
w
ith
t
h
e
SGD
o
p
tim
izatio
n
alg
o
r
ith
m
th
at
s
h
o
ws
a
class
if
icatio
n
r
esu
lt
ac
cu
r
ac
y
o
f
0
.
9
5
w
h
ic
h
g
e
n
er
all
y
in
d
icate
s
t
h
at
th
e
m
o
d
el
h
as
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
F
u
r
th
er
m
o
r
e,
ev
al
u
atin
g
th
e
o
r
i
g
i
n
a
l
class
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
with
co
n
f
u
s
io
n
m
atr
i
x
o
n
1
0
class
es
w
it
h
t
h
e
co
m
p
ar
i
s
o
n
r
esu
lt
s
o
f
P
r
ec
is
io
n
,
R
ec
all
an
d
F1
-
s
co
r
e
m
etr
ic
s
s
h
o
w
n
in
F
ig
u
r
e
7
.
T
esti
n
g
t
h
e
r
eliab
ilit
y
o
f
th
e
m
o
d
el
is
d
o
n
e
w
it
h
an
o
t
h
er
d
ataset
o
f
th
i
s
m
o
d
el
th
a
t
h
as
b
ee
n
s
av
ed
w
it
h
th
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
SGD
_
lr
.
h
5
”
w
it
h
t
u
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
it
h
a
r
esu
lt
o
f
9
9
.
9
8
8
%
.
Fig
u
r
e
6
.
SGD
w
ith
m
o
m
en
t
u
m
m
o
d
el
tr
ai
n
in
g
Fig
u
r
e
7
.
SDG
m
o
d
el
tr
ain
i
n
g
3
.
3
.
4
.
SG
D
w
it
h
m
o
m
e
ntu
m
T
h
e
m
o
d
el
w
it
h
t
h
e
R
M
Sp
r
o
p
o
p
tim
izat
io
n
al
g
o
r
ith
m
h
as
a
class
i
f
icatio
n
r
esu
lt
ac
cu
r
a
c
y
o
f
0
.
9
3
w
h
ic
h
g
e
n
er
all
y
in
d
icate
s
t
h
at
th
e
m
o
d
el
h
as
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
F
u
r
th
er
m
o
r
e,
ev
al
u
atin
g
th
e
o
r
i
g
in
a
l
class
i
f
icatio
n
r
ep
o
r
t
m
o
d
el
w
ith
co
n
f
u
s
io
n
m
atr
ix
o
n
1
0
class
es
w
i
th
t
h
e
co
m
p
ar
is
o
n
r
esu
lt
s
o
f
p
r
ec
is
io
n
,
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
u
to
ma
tic
d
ia
g
n
o
s
is
o
f rice
p
la
n
t d
is
ea
s
es u
s
in
g
V
GG
-
1
6
a
n
d
co
mp
u
ter visi
o
n
(
Al
-
B
a
h
r
a
)
1605
r
ec
all
an
d
F1
-
s
co
r
e
m
etr
ic
s
s
h
o
w
n
in
Fi
g
u
r
e
8
.
T
esti
n
g
th
e
r
eliab
ilit
y
o
f
t
h
e
m
o
d
el
is
d
o
n
e
w
it
h
an
o
th
er
d
ataset
o
f
th
is
m
o
d
el
t
h
at
h
as
b
ee
n
s
av
ed
w
it
h
t
h
e
n
a
m
e
“
m
o
d
el_
r
ice_
d
is
ea
s
e_
R
MSP
r
o
p
_
lr
.
h
5
”
w
ith
tu
n
g
r
o
d
is
ea
s
e
d
ata
[
1
6
]
w
it
h
a
r
esu
l
t
o
f
9
9
.
8
7
8
%
.
Fig
u
r
e
8
.
R
MSP
r
o
p
m
o
d
el
tr
ain
in
g
3
.
4
.
Co
m
pa
riso
n o
f
o
pti
m
iza
t
io
n
a
lg
o
rit
h
m
re
s
u
lt
s
T
h
is
f
ir
s
t
co
m
p
ar
is
o
n
ev
al
u
at
io
n
co
m
p
ar
es
th
e
lo
s
s
a
n
d
ac
cu
r
ac
y
o
f
all
o
p
ti
m
iza
tio
n
al
g
o
r
ith
m
s
at
th
e
3
0
th
ep
o
ch
an
d
th
e
cu
r
v
e
r
esu
lt
s
o
f
ea
ch
o
p
ti
m
izatio
n
al
g
o
r
ith
m
.
T
h
e
co
m
p
ar
is
o
n
o
f
lo
s
s
an
d
ac
cu
r
ac
y
o
f
o
p
tim
izatio
n
al
g
o
r
ith
m
s
at
th
e
3
0
th
ep
o
ch
s
ar
e
s
h
o
w
n
in
T
ab
le
4
.
T
ab
le
4
s
h
o
w
s
th
at
t
h
e
b
est
o
p
tim
izatio
n
alg
o
r
ith
m
i
s
S
GD
w
it
h
m
o
m
e
n
tu
m
w
it
h
tr
ai
n
in
g
a
n
d
v
a
lid
atio
n
lo
s
s
v
a
lu
e
s
o
f
0
.
1
7
3
an
d
0
.
1
6
8
,
tr
ain
in
g
a
n
d
v
alid
atio
n
ac
c
u
r
ac
y
o
f
0
.
9
5
a
n
d
0
.
9
5
7
.
I
n
ad
d
itio
n
,
SGD
w
it
h
m
o
m
e
n
t
u
m
h
as
t
h
e
b
est
lo
s
s
an
d
ac
c
u
r
ac
y
cu
r
v
e
s
w
it
h
s
tab
le
cu
r
v
es
a
n
d
m
in
i
m
a
l
r
ip
p
les
th
at
s
h
o
w
s
m
all
m
o
d
el
o
v
er
f
itt
in
g
as
s
h
o
w
n
i
n
Fi
g
u
r
e
9
.
T
h
e
w
o
r
s
t
o
p
ti
m
izatio
n
al
g
o
r
ith
m
is
A
da
D
elta
w
it
h
tr
ain
in
g
an
d
v
alid
atio
n
lo
s
s
v
al
u
e
s
o
f
2
.
7
an
d
2
.
3
5
,
tr
ain
in
g
an
d
v
alid
atio
n
ac
c
u
r
ac
y
o
f
0
.
6
an
d
0
.
7
3
.
T
ab
le
4
.
C
o
m
p
ar
is
o
n
o
f
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
M
o
d
e
l
L
o
ss t
r
a
i
n
i
n
g
L
o
ss v
a
l
i
d
a
t
i
o
n
A
c
c
u
r
a
c
y
t
r
a
i
n
i
n
g
A
c
c
u
r
a
c
y
v
a
l
i
d
a
t
i
o
n
Ep
o
c
h
s
t
o
A
d
a
m
0
.
1
7
5
0
.
2
0
.
9
5
3
0
.
9
5
30
A
d
a
max
0
.
1
5
5
0
.
2
3
0
.
9
5
8
0
.
9
4
7
30
A
d
a
G
r
a
d
0
.
7
0
.
6
8
0
.
9
4
1
0
.
9
5
2
30
A
d
a
D
e
l
t
a
2
.
7
2
.
3
5
0
.
6
0
.
7
3
30
R
M
S
p
r
o
p
0
.
2
9
4
0
.
2
9
4
0
.
9
1
6
0
.
9
3
30
S
G
D
0
.
5
5
0
.
5
7
0
.
9
5
1
0
.
9
4
7
30
S
G
D
w
i
t
h
mo
me
n
t
u
m
0
.
1
7
3
0
.
1
6
8
0
.
9
5
0
.
9
5
7
30
Fig
u
r
e
9
.
SGD
m
o
m
en
t
u
m
lo
s
s
an
d
ac
cu
r
ac
y
c
u
r
v
e
Ov
er
co
m
i
n
g
o
v
er
f
itti
n
g
is
e
s
s
en
tial
i
n
d
ee
p
lear
n
i
n
g
m
o
d
el
d
ev
elo
p
m
en
t.
T
h
is
r
esear
ch
e
m
p
lo
y
ed
r
eg
u
lar
izatio
n
tec
h
n
iq
u
es,
i
n
clu
d
in
g
d
r
o
p
o
u
t,
b
atch
n
o
r
m
aliza
tio
n
,
a
n
d
L
2
r
e
g
u
lar
izat
io
n
.
T
h
e
ap
p
r
o
ac
h
d
em
o
n
s
tr
ated
e
f
f
icac
y
,
ev
id
en
ce
d
b
y
s
tab
le
tr
ai
n
i
n
g
an
d
v
al
id
atio
n
cu
r
v
es,
p
ar
tic
u
lar
l
y
w
i
th
t
h
e
m
o
m
en
t
u
m
-
en
ab
l
ed
SGD
o
p
ti
m
izer
.
T
h
e
m
i
n
i
m
al
d
iv
er
g
e
n
ce
i
n
ac
cu
r
a
c
y
cu
r
v
e
s
s
u
g
g
es
ts
t
h
at
th
e
m
o
d
el
p
r
eser
v
es
s
tr
o
n
g
g
en
er
aliza
tio
n
ca
p
ab
ilit
ies.
T
h
is
s
ec
o
n
d
co
m
p
ar
is
o
n
e
v
alu
at
i
o
n
co
m
p
ar
es
t
h
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
F1
-
s
co
r
e
o
f
t
h
e
test
ed
o
p
ti
m
iz
atio
n
alg
o
r
it
h
m
s
as i
n
T
ab
le
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
6
0
0
-
1
610
1606
T
ab
le
5
.
C
o
m
p
ar
is
o
n
o
f
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
M
o
d
e
l
A
c
c
u
r
a
c
y
(
%)
P
r
e
c
i
si
o
n
(
%)
R
e
c
a
l
l
(
%)
F1
-
s
c
o
r
e
(
%)
A
d
a
m
95
9
5
.
3
2
95
9
5
.
0
9
A
d
a
max
9
4
.
7
1
9
4
.
8
3
9
4
.
7
1
9
4
.
5
6
A
d
a
G
r
a
d
9
5
.
2
9
9
5
.
3
9
5
.
2
9
9
5
.
2
7
A
d
a
D
e
l
t
a
7
1
.
5
7
7
1
.
9
5
7
1
.
5
7
7
1
.
5
6
R
M
S
p
r
o
p
9
3
.
1
8
9
3
.
2
5
9
3
.
1
8
9
2
.
9
8
S
G
D
9
4
.
7
9
9
4
.
8
5
9
4
.
7
9
9
4
.
7
1
S
G
D
w
i
t
h
m
o
me
n
t
u
m
9
5
.
7
5
9
5
.
7
5
9
5
.
7
5
9
5
.
7
3
I
n
T
a
b
le
5
th
e
m
o
d
el
w
it
h
th
e
h
ig
h
est
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
[
5
1
]
n
a
m
el
y
9
5
.
7
5
,
9
5
.
7
5
,
9
5
.
7
5
an
d
9
5
.
7
3
is
SGD
w
ith
m
o
m
en
tu
m
w
i
th
m
o
m
en
tu
m
o
f
0
.
8
an
d
Nester
o
v
=
f
alse
a
n
d
t
h
e
m
o
d
el
w
it
h
t
h
e
w
o
r
s
t
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
an
d
F
1
-
s
co
r
e
n
am
el
y
7
1
.
5
7
,
7
1
.
9
5
,
7
1
.
5
7
an
d
7
1
.
5
6
is
ad
a
d
elta,
th
ese
r
esu
lts
s
h
o
w
t
h
at
th
e
lat
est
m
o
d
els
s
u
c
h
as
A
d
a
m
a
n
d
A
d
a
m
ax
ar
e
n
o
t
n
ec
ess
ar
il
y
th
e
b
est
f
o
r
ev
er
y
class
i
f
icatio
n
ca
s
e.
3
.
5
.
M
o
del
t
esting
us
ing
s
m
a
rt
ph
o
ne
T
h
e
m
o
d
el
w
as
test
ed
o
n
an
An
d
r
o
id
s
m
ar
tp
h
o
n
e
to
v
alid
ate
its
r
ea
l
-
w
o
r
ld
p
r
ed
ictio
n
ac
cu
r
ac
y
.
It
ac
h
iev
ed
1
0
0
%
ac
cu
r
ac
y
in
d
etec
tin
g
r
ice
b
last
d
is
ea
s
e,
co
n
f
ir
m
i
n
g
it
s
p
r
ac
tical
r
elia
b
ilit
y
.
T
h
e
ap
p
also
s
u
p
p
o
r
ts
r
ea
l
-
ti
m
e
i
m
ag
e
ca
p
t
u
r
e
v
ia
t
h
e
p
h
o
n
e
’
s
ca
m
er
a
n
o
t
j
u
s
t
p
r
e
-
lo
ad
ed
im
a
g
es
as
s
h
o
w
n
i
n
Fi
g
u
r
e
1
0
,
m
ak
in
g
it
m
o
r
e
u
s
er
-
f
r
ie
n
d
l
y
f
o
r
f
ield
d
ep
lo
y
m
e
n
t.
Fig
u
r
e
1
0
.
Mo
d
el
test
in
g
w
it
h
s
m
ar
tp
h
o
n
e
4.
CO
NCLU
SI
O
N
T
h
is
s
t
u
d
y
d
e
v
elo
p
ed
a
d
ee
p
lear
n
in
g
m
o
d
el
f
o
r
r
ice
p
la
n
t
d
is
ea
s
e
id
e
n
ti
f
icatio
n
u
s
i
n
g
th
e
VGG
-
16
ar
ch
itect
u
r
e
f
o
r
f
ea
tu
r
e
ex
tr
ac
t
io
n
an
d
a
cu
s
to
m
ized
n
eu
r
al
n
et
w
o
r
k
f
o
r
class
if
icatio
n
.
T
h
e
r
esear
ch
id
en
ti
f
ied
SGD
w
it
h
m
o
m
en
t
u
m
a
s
t
h
e
m
o
s
t
e
f
f
ec
ti
v
e
o
p
ti
m
izatio
n
a
l
g
o
r
ith
m
,
ac
h
iev
in
g
tr
ai
n
i
n
g
a
n
d
v
al
id
atio
n
lo
s
s
es
o
f
0
.
1
7
3
an
d
0
.
1
6
8
,
an
d
ac
cu
r
ac
y
o
f
9
5
%
an
d
9
5
.
7
%,
r
es
p
ec
tiv
el
y
.
T
h
e
m
o
d
el
d
e
m
o
n
s
tr
ated
h
ig
h
o
v
er
all
p
er
f
o
r
m
a
n
ce
,
w
it
h
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all
,
an
d
F1
-
s
co
r
e
all
ap
p
r
o
x
i
m
ati
n
g
9
5
.
7
5
%.
T
h
e
u
tili
za
tio
n
o
f
VGG
-
1
6
as
a
f
ea
t
u
r
e
ex
tr
ac
to
r
w
as
s
ig
n
i
f
ica
n
tl
y
e
f
f
ec
ti
v
e,
al
lo
w
i
n
g
th
e
m
o
d
el
to
lev
er
a
g
e
p
r
e
-
tr
ain
ed
w
e
ig
h
t
s
th
at
w
er
e
f
i
n
e
-
tu
n
ed
f
o
r
o
p
ti
m
al
r
esu
lts
o
n
t
h
e
d
ataset.
T
h
e
o
p
tim
a
l
in
te
g
r
atio
n
o
f
t
h
e
m
o
d
if
ied
FC
la
y
er
an
d
th
e
SGD
w
it
h
m
o
m
e
n
tu
m
alg
o
r
ith
m
y
ie
ld
ed
a
r
em
ar
k
ab
le
test
ac
cu
r
ac
y
o
f
9
9
.
9
9
%
o
n
n
e
w
d
ata,
o
u
tp
er
f
o
r
m
i
n
g
o
t
h
er
o
p
ti
m
izer
s
s
u
c
h
as
A
d
a
m
an
d
A
d
a
m
a
x
.
B
u
ild
in
g
u
p
o
n
th
ese
f
in
d
i
n
g
s
,
f
u
tu
r
e
r
esear
ch
av
e
n
u
e
s
m
er
it
ex
p
lo
r
atio
n
.
I
n
v
est
ig
at
in
g
m
o
d
if
icatio
n
s
to
th
e
n
eu
r
al
n
et
w
o
r
k
ar
ch
ite
ctu
r
e,
in
c
lu
d
i
n
g
ad
d
itio
n
al
FC
la
y
er
s
o
r
in
cr
ea
s
ed
n
e
u
r
o
n
co
u
n
t
s
,
m
a
y
r
e
v
ea
l
m
o
r
e
co
m
p
le
x
p
atter
n
s
.
A
s
y
s
te
m
atic
s
t
u
d
y
o
n
h
y
p
er
p
ar
a
m
eter
tu
n
in
g
,
e
m
p
h
a
s
izi
n
g
th
e
o
p
ti
m
izatio
n
o
f
lear
n
in
g
r
ate,
d
r
o
p
o
u
t
r
ates,
a
n
d
r
eg
u
lar
izatio
n
v
al
u
es,
is
e
s
s
e
n
tial
f
o
r
en
h
a
n
ce
d
p
er
f
o
r
m
an
ce
.
C
o
m
p
ar
ati
v
e
an
al
y
s
is
o
f
o
u
r
VG
G
-
16
-
b
ase
d
m
o
d
el
w
it
h
o
t
h
er
lead
in
g
tr
an
s
f
er
lear
n
i
n
g
ar
ch
itect
u
r
es,
s
u
c
h
as
R
esNet
o
r
Den
s
eNe
t,
is
r
ec
o
m
m
en
d
ed
to
id
en
ti
f
y
t
h
e
m
o
s
t e
f
f
ec
ti
v
e
cl
a
s
s
i
f
icatio
n
ap
p
r
o
ac
h
.
L
a
s
tl
y
,
e
x
p
lo
r
in
g
lar
g
er
a
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
u
to
ma
tic
d
ia
g
n
o
s
is
o
f rice
p
la
n
t d
is
ea
s
es u
s
in
g
V
GG
-
1
6
a
n
d
co
mp
u
ter visi
o
n
(
Al
-
B
a
h
r
a
)
1607
m
o
r
e
d
iv
er
s
e
d
ataset
s
co
u
ld
im
p
r
o
v
e
th
e
m
o
d
el
’
s
g
e
n
er
aliz
atio
n
ca
p
ab
ilit
ies,
en
s
u
r
in
g
r
eliab
ilit
y
in
p
r
ac
tical
ag
r
icu
l
tu
r
al
ap
p
licatio
n
s
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
ex
p
r
ess
es
g
r
atit
u
d
e
to
th
e
Un
iv
er
s
it
y
o
f
R
a
h
ar
j
a
a
n
d
th
e
Natio
n
a
l
I
n
s
ti
tu
te
o
f
Sc
ien
ce
an
d
T
ec
h
n
o
lo
g
y
f
o
r
th
eir
s
u
p
p
o
r
t a
n
d
f
u
n
d
i
n
g
o
f
th
is
r
esear
ch
.
F
UNDIN
G
I
NF
O
RM
AT
I
O
N
Au
t
h
o
r
s
s
tate
n
o
f
u
n
d
i
n
g
i
n
v
o
l
v
ed
.
AUTHO
R
CO
NT
RIB
UT
I
O
NS ST
A
T
E
M
E
NT
T
h
is
j
o
u
r
n
al
u
s
e
s
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
to
r
ec
o
g
n
ize
in
d
i
v
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
t
h
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
lla
b
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Al
-
B
ah
r
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Hen
d
er
i
✓
✓
✓
✓
✓
✓
✓
✓
Nu
r
A
ziza
h
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mu
h
a
m
m
ad
H
u
d
za
if
a
h
Nasr
u
lla
h
✓
✓
✓
✓
✓
✓
✓
✓
Did
ik
Seti
y
ad
i
A
ziza
h
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
si
s
I
:
I
n
v
e
st
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
si
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
t
h
o
r
s
s
tate
n
o
co
n
f
lic
t o
f
i
n
t
er
est.
I
NF
O
RM
E
D
CO
NSE
N
T
W
e
h
av
e
o
b
tain
ed
in
f
o
r
m
ed
c
o
n
s
en
t f
r
o
m
al
l in
d
i
v
id
u
al
s
in
c
lu
d
ed
in
th
is
s
t
u
d
y
.
E
T
H
I
CAL AP
P
RO
V
AL
T
h
e
r
esear
ch
r
elate
d
to
h
u
m
a
n
u
s
e
h
as
b
ee
n
co
m
p
lied
w
it
h
all
th
e
r
ele
v
an
t
n
at
io
n
al
r
e
g
u
l
atio
n
s
a
n
d
in
s
t
itu
tio
n
al
p
o
licies
i
n
ac
co
r
d
an
ce
w
ith
t
h
e
ten
e
ts
o
f
th
e
He
ls
in
k
i
Dec
lar
atio
n
an
d
h
as
b
ee
n
ap
p
r
o
v
ed
b
y
th
e
au
th
o
r
s
’
i
n
s
ti
tu
t
io
n
al
r
ev
ie
w
b
o
ar
d
o
r
eq
u
iv
alen
t c
o
m
m
i
ttee
.
DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
i
s
n
o
t
ap
p
licab
le
to
th
is
p
ap
er
as
n
o
n
e
w
d
ata
w
er
e
cr
ea
ted
o
r
an
al
y
ze
d
in
th
i
s
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
J.
W
u
,
M
.
Z
h
a
n
g
,
X
.
Y
a
n
g
,
a
n
d
B
.
W
u
,
“
Ef
f
e
c
t
s
o
f
l
a
n
d
a
n
d
l
a
b
o
r
c
o
s
t
s
g
r
o
w
t
h
o
n
a
g
r
i
c
u
l
t
u
r
a
l
p
r
o
d
u
c
t
p
r
i
c
e
s
a
n
d
f
a
r
me
r
s’
i
n
c
o
m
e
,
”
L
a
n
d
,
v
o
l
.
1
3
,
n
o
.
1
1
,
O
c
t
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
l
a
n
d
1
3
1
1
1
7
5
4
.
[
2
]
Z
.
Z
h
a
i
,
J.
-
F
.
M
.
O
r
t
e
g
a
,
N
.
L
.
M
a
r
t
í
n
e
z
,
a
n
d
H
.
X
u
,
“
A
n
e
f
f
i
c
i
e
n
t
c
a
se
r
e
t
r
i
e
v
a
l
a
l
g
o
r
i
t
h
m
f
o
r
a
g
r
i
c
u
l
t
u
r
a
l
c
a
se
-
b
a
se
d
r
e
a
so
n
i
n
g
sy
st
e
ms,
w
i
t
h
c
o
n
s
i
d
e
r
a
t
i
o
n
o
f
c
a
se
b
a
se
mai
n
t
e
n
a
n
c
e
,
”
A
g
ri
c
u
l
t
u
r
e
,
v
o
l
.
1
0
,
n
o
.
9
,
S
e
p
t
.
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
i
c
u
l
t
u
r
e
1
0
0
9
0
3
8
7
.
[
3
]
I
.
H
.
S
a
r
k
e
r
,
A
.
S
.
M
.
K
a
y
e
s,
a
n
d
P
.
W
a
t
t
e
r
s,
“
Ef
f
e
c
t
i
v
e
n
e
ss
a
n
a
l
y
si
s
o
f
mac
h
i
n
e
l
e
a
r
n
i
n
g
c
l
a
ss
i
f
i
c
a
t
i
o
n
mo
d
e
l
s
f
o
r
p
r
e
d
i
c
t
i
n
g
p
e
r
so
n
a
l
i
z
e
d
c
o
n
t
e
x
t
-
a
w
a
r
e
smar
t
p
h
o
n
e
u
s
a
g
e
,
”
J
o
u
r
n
a
l
o
f
B
i
g
D
a
t
a
,
v
o
l
.
6
,
n
o
.
57
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
8
6
/
s
4
0
5
3
7
-
0
1
9
-
0
2
1
9
-
y.
[
4
]
R
.
V
.
K
.
R
e
d
d
y
,
B
.
S
r
i
n
i
v
a
s
a
R
a
o
,
a
n
d
K
.
P
.
R
a
j
u
,
“
H
a
n
d
w
r
i
t
t
e
n
H
i
n
d
i
d
i
g
i
t
s
r
e
c
o
g
n
i
t
i
o
n
u
s
i
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
w
i
t
h
R
M
S
p
r
o
p
o
p
t
i
mi
z
a
t
i
o
n
,
”
2
0
1
8
S
e
c
o
n
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
t
e
l
l
i
g
e
n
t
C
o
m
p
u
t
i
n
g
a
n
d
C
o
n
t
ro
l
S
y
s
t
e
m
s
(
I
C
I
C
C
S
)
.
I
EEE,
p
p
.
4
5
-
5
1
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
i
c
c
o
n
s.
2
0
1
8
.
8
6
6
2
9
6
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
6
0
0
-
1
610
1608
[
5
]
Ò
.
L
o
r
e
n
t
e
,
I
.
R
i
e
r
a
,
a
n
d
A
.
R
a
n
a
,
“
I
mag
e
c
l
a
ssi
f
i
c
a
t
i
o
n
w
i
t
h
c
l
a
ssi
c
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
,
”
C
o
m
p
u
t
e
r
V
i
si
o
n
a
n
d
P
a
t
t
e
rn
Re
c
o
g
n
i
t
i
o
n
,
a
rX
i
v
,
2
0
2
1
,
d
o
i
:
1
0
.
4
8
5
5
0
/
a
r
X
i
v
.
2
1
0
5
.
0
4
8
9
5
[
6
]
I
.
K
a
n
d
e
l
,
M
.
C
a
st
e
l
l
i
,
a
n
d
A
.
P
o
p
o
v
i
č
,
“
C
o
mp
a
r
a
t
i
v
e
st
u
d
y
o
f
f
i
r
st
o
r
d
e
r
o
p
t
i
mi
z
e
r
s
f
o
r
i
mag
e
c
l
a
ssi
f
i
c
a
t
i
o
n
u
si
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s o
n
h
i
st
o
p
a
t
h
o
l
o
g
y
i
mag
e
s,”
J
o
u
r
n
a
l
o
f
I
m
a
g
i
n
g
,
v
o
l
.
6
,
n
o
.
9
,
p
.
9
2
,
S
e
p
t
.
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
j
i
m
a
g
i
n
g
6
0
9
0
0
9
2
.
[
7
]
F
.
L
i
a
n
g
,
C
.
S
h
e
n
,
a
n
d
F
.
W
u
,
“
A
n
i
t
e
r
a
t
i
v
e
BP
-
C
N
N
a
r
c
h
i
t
e
c
t
u
r
e
f
o
r
c
h
a
n
n
e
l
d
e
c
o
d
i
n
g
,
”
i
n
I
EE
E
J
o
u
r
n
a
l
o
f
S
e
l
e
c
t
e
d
T
o
p
i
c
s
i
n
S
i
g
n
a
l
Pr
o
c
e
ssi
n
g
,
v
o
l
.
1
2
,
n
o
.
1
,
p
p
.
1
4
4
-
1
5
9
,
F
e
b
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
T
S
P
.
2
0
1
8
.
2
7
9
4
0
6
2
.
[
8
]
S
.
L
e
e
,
J.
K
i
m,
H
.
K
a
n
g
,
D
.
-
Y
.
K
a
n
g
,
a
n
d
J.
P
a
r
k
,
“
G
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
b
a
se
d
d
e
e
p
l
e
a
r
n
i
n
g
n
e
u
r
a
l
n
e
t
w
o
r
k
st
r
u
c
t
u
r
e
a
n
d
h
y
p
e
r
p
a
r
a
me
t
e
r
o
p
t
i
m
i
z
a
t
i
o
n
,
”
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
1
1
,
n
o
.
2
,
p
.
7
4
4
,
Ja
n
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
a
p
p
1
1
0
2
0
7
4
4
.
[
9
]
J.
Z
h
a
o
e
t
a
l
.,
“
T
r
u
c
k
t
r
a
f
f
i
c
sp
e
e
d
p
r
e
d
i
c
t
i
o
n
u
n
d
e
r
n
o
n
-
r
e
c
u
r
r
e
n
t
c
o
n
g
e
st
i
o
n
:
B
a
se
d
o
n
o
p
t
i
mi
z
e
d
d
e
e
p
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
a
n
d
G
P
S
d
a
t
a
,
”
i
n
I
EEE
Ac
c
e
ss
,
v
o
l
.
7
,
p
p
.
9
1
1
6
-
9
1
2
7
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
1
8
.
2
8
9
0
4
1
4
.
[
1
0
]
S
.
M
.
H
a
ssa
n
a
n
d
A
.
K
.
M
a
j
i
,
“
P
l
a
n
t
d
i
se
a
se
i
d
e
n
t
i
f
i
c
a
t
i
o
n
u
s
i
n
g
a
n
o
v
e
l
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
i
n
I
E
E
E
Ac
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
5
3
9
0
-
5
4
0
1
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
2
.
3
1
4
1
3
7
1
.
[
1
1
]
S
.
S
l
a
d
o
j
e
v
i
c
,
M
.
A
r
se
n
o
v
i
c
,
A
.
A
n
d
e
r
l
a
,
D
.
C
u
l
i
b
r
k
,
a
n
d
D
.
S
t
e
f
a
n
o
v
i
c
,
“
D
e
e
p
n
e
u
r
a
l
n
e
t
w
o
r
k
s
-
b
a
se
d
r
e
c
o
g
n
i
t
i
o
n
o
f
p
l
a
n
t
d
i
se
a
se
s
b
y
l
e
a
f
i
mag
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
”
C
o
m
p
u
t
a
t
i
o
n
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
N
e
u
r
o
sc
i
e
n
c
e
,
v
o
l
.
2
0
1
6
,
n
o
.
1
,
p
p
.
1
–
1
1
,
2
0
1
6
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
1
6
/
3
2
8
9
8
0
1
.
[
1
2
]
J.
C
h
e
n
,
W
.
C
h
e
n
,
A
.
Z
e
b
,
S
.
Y
a
n
g
,
a
n
d
D
.
Z
h
a
n
g
,
“
L
i
g
h
t
w
e
i
g
h
t
i
n
c
e
p
t
i
o
n
n
e
t
w
o
r
k
s fo
r
t
h
e
r
e
c
o
g
n
i
t
i
o
n
a
n
d
d
e
t
e
c
t
i
o
n
o
f
r
i
c
e
p
l
a
n
t
d
i
se
a
se
s,
”
i
n
I
EE
E
S
e
n
so
rs J
o
u
rn
a
l
,
v
o
l
.
2
2
,
n
o
.
1
4
,
p
p
.
1
4
6
2
8
-
1
4
6
3
8
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
2
.
3
1
8
2
3
0
4
.
[
1
3
]
M
.
F
.
H
o
ssai
n
,
“
D
h
a
n
-
S
h
o
mad
h
a
n
:
A
d
a
t
a
se
t
o
f
r
i
c
e
l
e
a
f
d
i
se
a
se
c
l
a
ss
i
f
i
c
a
t
i
o
n
f
o
r
B
a
n
g
l
a
d
e
sh
i
l
o
c
a
l
r
i
c
e
,
”
C
o
m
p
u
t
e
r
Vi
s
i
o
n
a
n
d
Pa
t
t
e
r
n
Re
c
o
g
n
i
t
i
o
n
,
2
0
2
3
,
a
rX
i
v
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
2
3
0
9
.
0
7
5
1
5
.
[
1
4
]
E.
S
.
K
u
m
a
r
,
A
.
K
u
mar,
H
.
V
a
r
d
h
a
n
,
R
.
P
r
a
sa
n
t
h
,
a
n
d
P
.
P
.
S
a
i
,
“
C
l
a
s
si
f
i
c
a
t
i
o
n
o
f
h
e
a
l
t
h
y
a
n
d
u
n
h
e
a
l
t
h
y
l
e
a
f
d
i
se
a
se
u
s
i
n
g
mo
d
i
f
i
e
d
M
o
b
i
l
e
N
e
t
V
2
,
”
2
0
2
4
5
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
D
a
t
a
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
C
o
g
n
i
t
i
v
e
I
n
f
o
r
m
a
t
i
c
s
(
I
C
D
I
C
I
)
,
T
i
r
u
n
e
l
v
e
l
i
,
I
n
d
i
a
,
2
0
2
4
,
p
p
.
4
8
2
-
4
8
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
D
I
C
I
6
2
9
9
3
.
2
0
2
4
.
1
0
8
1
0
7
6
3
.
[
1
5
]
M
.
M
.
R
a
h
m
a
n
e
t
a
l
.
,
“
Ev
a
l
u
a
t
i
o
n
o
f
S
e
n
n
a
t
o
r
a
(
L
.
)
R
o
x
b
.
l
e
a
v
e
s
a
s
so
u
r
c
e
o
f
b
i
o
a
c
t
i
v
e
mo
l
e
c
u
l
e
s
w
i
t
h
a
n
t
i
o
x
i
d
a
n
t
,
a
n
t
i
-
i
n
f
l
a
mm
a
t
o
r
y
a
n
d
a
n
t
i
b
a
c
t
e
r
i
a
l
p
o
t
e
n
t
i
a
l
,
”
H
e
l
i
y
o
n
,
v
o
l
.
9
,
n
o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
h
e
l
i
y
o
n
.
2
0
2
3
.
e
1
2
8
5
5
.
[
1
6
]
A
.
K
.
S
i
n
g
h
,
A
.
R
a
o
,
P
.
C
h
a
t
t
o
p
a
d
h
y
a
y
,
R
.
M
a
u
r
y
a
,
a
n
d
L
.
S
i
n
g
h
,
“
Ef
f
e
c
t
i
v
e
p
l
a
n
t
d
i
se
a
se
d
i
a
g
n
o
si
s
u
si
n
g
v
i
si
o
n
t
r
a
n
sf
o
r
me
r
t
r
a
i
n
e
d
w
i
t
h
l
e
a
f
y
-
g
e
n
e
r
a
t
i
v
e
a
d
v
e
r
sari
a
l
n
e
t
w
o
r
k
-
g
e
n
e
r
a
t
e
d
i
mag
e
s,
”
E
x
p
e
r
t
S
y
st
e
m
s
w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
2
5
4
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
2
4
.
1
2
4
3
8
7
.
[
1
7
]
A
.
P
.
T
h
e
n
a
t
a
,
“
T
e
x
t
mi
n
i
n
g
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
o
n
I
n
d
o
n
e
si
a
n
so
c
i
a
l
me
d
i
a
,
”
J
o
u
r
n
a
l
o
f
I
n
f
o
rm
a
t
i
c
s
E
d
u
c
a
t
i
o
n
a
n
d
Re
s
e
a
r
c
h
(
J
EPIN),
v
o
l
.
7
,
n
o
.
2
,
2
0
2
1
,
d
o
i
:
1
0
.
2
6
4
1
8
/
j
p
.
v
7
i
2
.
4
7
9
7
5
.
[
1
8
]
M
.
M
u
n
s
a
r
i
f
,
E
.
N
o
e
r
saso
n
g
k
o
,
P
.
N
.
A
n
d
o
n
o
,
A
.
S
o
e
l
e
man
,
P
u
j
i
o
n
o
,
a
n
d
M
u
l
j
o
n
o
,
“
T
h
e
h
a
n
d
w
r
i
t
i
n
g
o
f
i
m
a
g
e
seg
me
n
t
a
t
i
o
n
u
si
n
g
t
h
e
k
-
me
a
n
s
c
l
u
st
e
r
i
n
g
a
l
g
o
r
i
t
h
m
w
i
t
h
c
o
n
t
r
a
s
t
s
t
r
e
t
c
h
i
n
g
a
n
d
h
i
s
t
o
g
r
a
m
e
q
u
a
l
i
z
a
t
i
o
n
,
”
2
0
2
1
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
S
e
m
i
n
a
r
o
n
Re
se
a
rc
h
o
f
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
a
n
d
I
n
t
e
l
l
i
g
e
n
t
S
y
s
t
e
m
s
(
I
S
RI
T
I
)
,
Y
o
g
y
a
k
a
r
t
a
,
I
n
d
o
n
e
si
a
,
2
0
2
1
,
p
p
.
1
2
4
-
1
3
1
,
d
o
i
:
1
0
.
1
1
0
9
/
I
S
R
I
T
I
5
4
0
4
3
.
2
0
2
1
.
9
7
0
2
8
0
0
.
[
1
9
]
P
.
S
a
n
e
a
n
d
R
.
A
g
r
a
w
a
l
,
“
P
i
x
e
l
n
o
r
mal
i
z
a
t
i
o
n
f
r
o
m
n
u
me
r
i
c
d
a
t
a
a
s
i
n
p
u
t
t
o
n
e
u
r
a
l
n
e
t
w
o
r
k
s:
F
o
r
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
i
m
a
g
e
p
r
o
c
e
ssi
n
g
,
”
2
0
1
7
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
W
i
re
l
e
ss
C
o
m
m
u
n
i
c
a
t
i
o
n
s,
S
i
g
n
a
l
Pr
o
c
e
ssi
n
g
a
n
d
N
e
t
w
o
r
k
i
n
g
(
W
i
S
PN
ET)
,
C
h
e
n
n
a
i
,
I
n
d
i
a
,
2
0
1
7
,
p
p
.
2
2
2
1
-
2
2
2
5
,
d
o
i
:
1
0
.
1
1
0
9
/
W
i
S
P
N
ET
.
2
0
1
7
.
8
3
0
0
1
5
4
.
[
2
0
]
A
.
I
.
J.
I
.
B
.
W
.
S
a
m
u
e
l
s
,
“
O
n
e
-
h
o
t
e
n
c
o
d
i
n
g
a
n
d
t
w
o
-
h
o
t
e
n
c
o
d
i
n
g
:
a
n
i
n
t
r
o
d
u
c
t
i
o
n
,
”
Ag
r
i
c
u
l
t
u
ra
l
P
h
i
l
o
s
o
p
h
y
,
2
0
2
4
,
d
o
i
:
1
0
.
1
3
1
4
0
/
R
G
.
2
.
2
.
2
1
4
5
9
.
7
6
3
2
7
.
[
2
1
]
P
.
M
u
r
u
g
a
n
,
“
I
mp
l
e
me
n
t
a
t
i
o
n
o
f
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
i
n
m
u
l
t
i
-
c
l
a
ss
c
a
t
e
g
o
r
i
c
a
l
i
mag
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
”
C
o
m
p
u
t
e
r
Vi
si
o
n
a
n
d
Pa
t
t
e
rn
Re
c
o
g
n
i
t
i
o
n
,
2
0
1
8
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
1
8
0
1
.
0
1
3
9
7
.
[
2
2
]
C
.
S
h
o
r
t
e
n
a
n
d
T
.
M
.
K
h
o
sh
g
o
f
t
a
a
r
,
“
A
su
r
v
e
y
o
n
i
mag
e
d
a
t
a
a
u
g
me
n
t
a
t
i
o
n
f
o
r
d
e
e
p
l
e
a
r
n
i
n
g
,
”
J
o
u
r
n
a
l
o
f
B
i
g
D
a
t
a
,
v
o
l
.
6
,
n
o
.
6
0
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
8
6
/
s4
0
5
3
7
-
0
1
9
-
0
1
9
7
-
0.
[
2
3
]
A
.
P
r
i
y
a
n
g
k
a
a
n
d
I
.
M
.
S
.
K
u
mara
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
r
i
c
e
p
l
a
n
t
d
i
se
a
se
s
u
si
n
g
t
h
e
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
me
t
h
o
d
,
”
L
o
n
t
a
r
K
o
m
p
u
t
e
r:
S
c
i
e
n
t
i
f
i
c
J
o
u
r
n
a
l
of
I
n
f
o
r
m
a
t
i
on
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
1
2
,
n
o
.
2
,
2
0
2
1
,
d
o
i
:
1
0
.
2
4
8
4
3
/
L
K
J
I
T
I
.
2
0
2
1
.
v
1
2
.
i
0
2
.
p
0
6
.
[
2
4
]
R
.
Y
a
mas
h
i
t
a
,
M
.
N
i
s
h
i
o
,
R
.
K
.
G
.
D
o
,
a
n
d
K
.
T
o
g
a
sh
i
,
“
C
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s:
A
n
o
v
e
r
v
i
e
w
a
n
d
a
p
p
l
i
c
a
t
i
o
n
i
n
r
a
d
i
o
l
o
g
y
,
”
I
n
s
i
g
h
t
s
i
n
t
o
I
m
a
g
i
n
g
,
v
o
l
.
9
,
n
o
.
4
,
p
p
.
6
1
1
-
6
2
9
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
3
2
4
4
-
0
1
8
-
0
6
3
9
-
9.
[
2
5
]
K
.
T
h
r
o
n
g
n
u
mc
h
a
i
,
P
.
L
o
mv
i
sai
,
C
.
T
a
n
t
a
s
i
r
i
n
,
a
n
d
P
.
P
h
a
s
u
k
k
i
t
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
w
h
i
t
e
b
l
o
o
d
c
e
l
l
u
si
n
g
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
2
0
1
9
1
2
t
h
B
i
o
m
e
d
i
c
a
l
En
g
i
n
e
e
ri
n
g
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
(
BM
Ei
C
O
N
)
,
U
b
o
n
R
a
t
c
h
a
t
h
a
n
i
,
T
h
a
i
l
a
n
d
,
2
0
1
9
,
p
p
.
1
-
4
,
d
o
i
:
1
0
.
1
1
0
9
/
B
M
Ei
C
O
N
4
7
5
1
5
.
2
0
1
9
.
8
9
9
0
3
0
1
.
[
2
6
]
S
.
S
h
r
i
v
a
s
t
a
v
,
V
.
Ji
n
d
a
l
,
R
.
Esw
a
r
a
w
a
k
a
,
a
n
d
D
.
R
a
y
,
“
A
c
o
mp
r
e
h
e
n
si
v
e
r
e
v
i
e
w
a
n
d
d
i
sc
u
ssi
o
n
o
f
se
g
me
n
t
a
t
i
o
n
-
b
a
se
d
t
o
ma
t
o
p
l
a
n
t
d
i
se
a
se
d
e
t
e
c
t
i
o
n
a
p
p
r
o
a
c
h
e
s,
”
2
0
2
3
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
Re
se
a
rc
h
Me
t
h
o
d
o
l
o
g
i
e
s
i
n
K
n
o
w
l
e
d
g
e
Ma
n
a
g
e
m
e
n
t
,
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
T
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
(
RM
K
MA
T
E)
,
C
h
e
n
n
a
i
,
I
n
d
i
a
,
2
0
2
3
,
p
p
.
1
-
7
,
d
o
i
:
1
0
.
1
1
0
9
/
R
M
K
M
A
T
E5
9
2
4
3
.
2
0
2
3
.
1
0
3
6
9
9
1
1
.
[
2
7
]
L
.
F
e
i
-
F
e
i
,
R
.
F
e
r
g
u
s,
a
n
d
P
.
P
e
r
o
n
a
,
“
L
e
a
r
n
i
n
g
g
e
n
e
r
a
t
i
v
e
v
i
su
a
l
mo
d
e
l
s
f
r
o
m
f
e
w
t
r
a
i
n
i
n
g
e
x
a
mp
l
e
s:
A
n
i
n
c
r
e
me
n
t
a
l
B
a
y
e
si
a
n
a
p
p
r
o
a
c
h
t
e
st
e
d
o
n
1
0
1
o
b
j
e
c
t
c
a
t
e
g
o
r
i
e
s,”
C
o
m
p
u
t
e
r V
i
si
o
n
a
n
d
I
m
a
g
e
U
n
d
e
rs
t
a
n
d
i
n
g
,
v
o
l
.
1
0
6
,
n
o
.
1
,
p
p
.
5
9
–
7
0
,
A
p
r
.
2
0
0
7
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
v
i
u
.
2
0
0
5
.
0
9
.
0
1
2
.
[
2
8
]
W
.
N
j
i
ma,
I
.
A
h
r
i
z
,
R
.
Z
a
y
a
n
i
,
M
.
T
e
r
r
e
,
a
n
d
R
.
B
o
u
a
l
l
e
g
u
e
,
“
D
e
e
p
C
N
N
f
o
r
i
n
d
o
o
r
l
o
c
a
l
i
z
a
t
i
o
n
i
n
I
o
T
-
s
e
n
so
r
sy
st
e
ms
,
”
S
e
n
so
rs
,
v
o
l
.
1
9
,
n
o
.
1
4
,
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/
s
1
9
1
4
3
1
2
7
.
[
2
9
]
S
.
G
o
e
l
,
S
.
S
h
a
r
ma,
a
n
d
R
.
T
r
i
p
a
t
h
i
,
“
P
r
e
d
i
c
t
i
n
g
d
i
a
b
e
t
e
s u
s
i
n
g
C
N
N
f
o
r
v
a
r
i
o
u
s a
c
t
i
v
a
t
i
o
n
f
u
n
c
t
i
o
n
s:
A
c
o
mp
a
r
a
t
i
v
e
st
u
d
y
,
”
2
0
2
1
1
0
t
h
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
y
s
t
e
m
Mo
d
e
l
i
n
g
&
Ad
v
a
n
c
e
m
e
n
t
i
n
R
e
s
e
a
r
c
h
T
re
n
d
s
(
S
MA
RT)
,
M
O
R
A
D
A
B
A
D
,
I
n
d
i
a
,
2
0
2
1
,
p
p
.
6
6
5
-
6
6
9
,
d
o
i
:
1
0
.
1
1
0
9
/
S
M
A
R
T
5
2
5
6
3
.
2
0
2
1
.
9
6
7
6
2
8
0
.
[
3
0
]
H
.
H
.
S
u
l
t
a
n
,
N
.
M
.
S
a
l
e
m,
a
n
d
W
.
A
l
-
A
t
a
b
a
n
y
,
“
M
u
l
t
i
-
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
b
r
a
i
n
t
u
mo
r
i
mag
e
s
u
si
n
g
d
e
e
p
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
i
n
I
EEE
Ac
c
e
ss
,
v
o
l
.
7
,
p
p
.
6
9
2
1
5
-
6
9
2
2
5
,
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
1
9
.
2
9
1
9
1
2
2
.
[
3
1
]
C
.
N
w
a
n
k
p
a
,
W
.
I
j
o
mah
,
A
.
G
a
c
h
a
g
a
n
,
a
n
d
S
.
M
a
r
s
h
a
l
l
,
“
A
c
t
i
v
a
t
i
o
n
f
u
n
c
t
i
o
n
s:
c
o
m
p
a
r
i
so
n
o
f
t
r
e
n
d
s
i
n
p
r
a
c
t
i
c
e
a
n
d
r
e
se
a
r
c
h
f
o
r
d
e
e
p
l
e
a
r
n
i
n
g
,
”
Ma
c
h
i
n
e
L
e
a
r
n
i
n
g
,
2
0
1
8
,
a
rXi
v
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
1
8
1
1
.
0
3
3
7
8
.
[
3
2
]
K
.
T
,
S
.
S
a
n
d
R
.
V
u
p
p
a
l
a
,
“
H
y
b
r
i
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
mo
d
e
l
f
o
r
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
p
l
a
n
t
s
p
e
c
i
e
s,
”
2
0
2
2
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
I
n
n
o
v
a
t
i
v
e
T
re
n
d
s
i
n
I
n
f
o
r
m
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
(
I
C
I
T
I
I
T
)
,
K
o
t
t
a
y
a
m,
I
n
d
i
a
,
2
0
2
2
,
p
p
.
1
-
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
I
T
I
I
T
5
4
3
4
6
.
2
0
2
2
.
9
7
4
4
2
2
2
.
[
3
3
]
M
.
F
e
r
g
u
so
n
,
R
.
A
k
,
Y
.
-
T
.
T
.
L
e
e
,
a
n
d
K
.
H
.
L
a
w
,
“
A
u
t
o
mat
i
c
l
o
c
a
l
i
z
a
t
i
o
n
o
f
c
a
s
t
i
n
g
d
e
f
e
c
t
s
w
i
t
h
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s,
”
2
0
1
7
I
EEE
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
B
i
g
D
a
t
a
(
Bi
g
D
a
t
a
)
,
B
o
st
o
n
,
M
A
,
U
S
A
,
2
0
1
7
,
d
o
i
:
1
0
.
1
1
0
9
/
b
i
g
d
a
t
a
.
2
0
1
7
.
8
2
5
8
1
1
5
.
[
3
4
]
D
.
P
.
K
i
n
g
ma
a
n
d
J.
B
a
,
“
A
d
a
m:
A
me
t
h
o
d
f
o
r
s
t
o
c
h
a
st
i
c
o
p
t
i
mi
z
a
t
i
o
n
,
”
Ma
c
h
i
n
e
L
e
a
r
n
i
n
g
,
2
0
1
4
,
a
rX
i
v
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
1
4
1
2
.
6
9
8
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
A
u
to
ma
tic
d
ia
g
n
o
s
is
o
f rice
p
la
n
t d
is
ea
s
es u
s
in
g
V
GG
-
1
6
a
n
d
co
mp
u
ter visi
o
n
(
Al
-
B
a
h
r
a
)
1609
[
3
5
]
K
.
M
i
s
h
c
h
e
n
k
o
a
n
d
A
.
D
e
f
a
z
i
o
,
“
P
r
o
d
i
g
y
:
A
n
e
x
p
e
d
i
t
i
o
u
sl
y
a
d
a
p
t
i
v
e
p
a
r
a
me
t
e
r
-
f
r
e
e
l
e
a
r
n
e
r
,
”
M
a
c
h
i
n
e
L
e
a
r
n
i
n
g
,
2
0
2
3
,
a
rX
i
v
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
2
3
0
6
.
0
6
1
0
1
.
[
3
6
]
M
.
M
.
H
a
sa
n
e
t
a
l
.
,
“
E
n
h
a
n
c
i
n
g
r
i
c
e
c
r
o
p
ma
n
a
g
e
me
n
t
:
d
i
se
a
se
c
l
a
ssi
f
i
c
a
t
i
o
n
u
si
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
a
n
d
mo
b
i
l
e
a
p
p
l
i
c
a
t
i
o
n
i
n
t
e
g
r
a
t
i
o
n
,
”
Ag
r
i
c
u
l
t
u
re
,
v
o
l
.
1
3
,
n
o
.
8
.
M
D
P
I
A
G
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
i
c
u
l
t
u
r
e
1
3
0
8
1
5
4
9
.
[
3
7
]
R
.
D
e
n
g
e
t
a
l
.
,
“
A
u
t
o
ma
t
i
c
d
i
a
g
n
o
si
s
o
f
r
i
c
e
d
i
se
a
se
s
u
si
n
g
d
e
e
p
l
e
a
r
n
i
n
g
,
”
Fro
n
t
i
e
r
s
i
n
Pl
a
n
t
S
c
i
e
n
c
e
,
v
o
l
.
1
2
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
l
s.
2
0
2
1
.
7
0
1
0
3
8
.
[
3
8
]
D
.
C
h
o
i
,
C
.
J.
S
h
a
l
l
u
e
,
Z
.
N
a
d
o
,
J
.
L
e
e
,
C
.
J.
M
a
d
d
i
so
n
,
a
n
d
G
.
E
.
D
a
h
l
,
“
O
n
e
mp
i
r
i
c
a
l
c
o
m
p
a
r
i
so
n
s
o
f
o
p
t
i
mi
z
e
r
s
f
o
r
d
e
e
p
l
e
a
r
n
i
n
g
,
”
M
a
c
h
i
n
e
L
e
a
rn
i
n
g
,
2
0
1
9
,
a
rXi
v
,
d
o
i
:
1
0
.
4
8
5
5
0
/
A
R
X
I
V
.
1
9
1
0
.
0
5
4
4
6
.
[
3
9
]
R
.
S
w
a
t
h
i
k
a
,
S
.
S
r
i
n
i
d
h
i
.
,
N
.
R
a
d
h
a
,
a
n
d
K
.
S
o
w
m
y
a
.
,
“
D
i
se
a
se
i
d
e
n
t
i
f
i
c
a
t
i
o
n
i
n
p
a
d
d
y
l
e
a
v
e
s
u
si
n
g
C
N
N
b
a
se
d
d
e
e
p
l
e
a
r
n
i
n
g
,
”
2
0
2
1
T
h
i
r
d
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
I
n
t
e
l
l
i
g
e
n
t
C
o
m
m
u
n
i
c
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
a
n
d
V
i
rt
u
a
l
Mo
b
i
l
e
N
e
t
w
o
rks
(
I
C
I
C
V)
,
T
i
r
u
n
e
l
v
e
l
i
,
I
n
d
i
a
,
2
0
2
1
,
p
p
.
1
0
0
4
-
1
0
0
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
I
C
V
5
0
8
7
6
.
2
0
2
1
.
9
3
8
8
5
5
7
.
[
4
0
]
J.
C
h
e
n
,
D
.
Z
h
a
n
g
,
Y
.
A
.
N
a
n
e
h
k
a
r
a
n
,
a
n
d
D
.
L
i
,
“
D
e
t
e
c
t
i
o
n
o
f
r
i
c
e
p
l
a
n
t
d
i
se
a
se
s b
a
se
d
o
n
d
e
e
p
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
,
”
J
o
u
r
n
a
l
o
f
t
h
e
S
c
i
e
n
c
e
o
f
Fo
o
d
a
n
d
A
g
r
i
c
u
l
t
u
r
e
,
v
o
l
.
1
0
0
,
n
o
.
7
,
p
p
.
3
2
4
6
–
3
2
5
6
,
M
a
r
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
2
/
j
sf
a
.
1
0
3
6
5
.
[
4
1
]
V
.
K
.
S
h
r
i
v
a
st
a
v
a
,
M
.
K
.
P
r
a
d
h
a
n
a
n
d
M
.
P
.
T
h
a
k
u
r
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
p
r
e
-
t
r
a
i
n
e
d
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s fo
r
r
i
c
e
p
l
a
n
t
d
i
se
a
se
c
l
a
ssi
f
i
c
a
t
i
o
n
,
”
2
0
2
1
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
S
m
a
r
t
S
y
st
e
m
s
(
I
C
AI
S
)
,
C
o
i
mb
a
t
o
r
e
,
I
n
d
i
a
,
2
0
2
1
,
p
p
.
1
0
2
3
-
1
0
3
0
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
I
S
5
0
9
3
0
.
2
0
2
1
.
9
3
9
5
8
1
3
.
[
4
2
]
P
.
K
.
S
e
t
h
y
,
N
.
K
.
B
a
r
p
a
n
d
a
,
A
.
K
.
R
a
t
h
,
a
n
d
S
.
K
.
B
e
h
e
r
a
,
“
I
mag
e
p
r
o
c
e
ssi
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
d
i
a
g
n
o
si
n
g
r
i
c
e
p
l
a
n
t
d
i
se
a
se
:
A
s
u
r
v
e
y
,
”
Pro
c
e
d
i
a
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
1
6
7
,
p
p
.
5
1
6
-
5
3
0
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
r
o
c
s.
2
0
2
0
.
0
3
.
3
0
8
.
[
4
3
]
A
.
A
.
J.
V
.
P
r
i
y
a
n
g
k
a
a
n
d
I
.
M
.
S
.
K
u
mara,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
r
i
c
e
p
l
a
n
t
d
i
s
e
a
se
s
u
si
n
g
t
h
e
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
me
t
h
o
d
,
”
L
o
n
t
a
r
K
o
m
p
u
t
e
r
:
S
c
i
e
n
t
i
f
i
c
J
o
u
r
n
a
l
o
f
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
1
2
,
n
o
.
2
,
2
0
2
1
,
d
o
i
:
1
0
.
2
4
8
4
3
/
L
K
J
I
T
I
.
2
0
2
1
.
v
1
2
.
i
0
2
.
p
0
6
.
[
4
4
]
G
.
K
a
t
h
i
r
e
san
,
M
.
A
n
i
r
u
d
h
,
M
.
N
a
g
h
a
r
j
u
n
,
a
n
d
R
.
K
a
r
t
h
i
k
,
“
D
i
se
a
se
d
e
t
e
c
t
i
o
n
i
n
r
i
c
e
l
e
a
v
e
s
u
si
n
g
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s,
”
J
o
u
rn
a
l
o
f
Ph
y
si
c
s:
C
o
n
f
e
re
n
c
e
S
e
ri
e
s
,
v
o
l
.
1
9
1
1
,
n
o
.
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
1
9
1
1
/
1
/
0
1
2
0
0
4
.
[
4
5
]
C
.
G
.
S
i
mh
a
d
r
i
a
n
d
H
.
K
.
K
o
n
d
a
v
e
e
t
i
,
“
A
u
t
o
mat
i
c
r
e
c
o
g
n
i
t
i
o
n
o
f
r
i
c
e
l
e
a
f
d
i
se
a
se
s
u
si
n
g
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
,
”
A
g
ro
n
o
m
y
,
v
o
l
.
1
3
,
n
o
.
4
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
o
n
o
my
1
3
0
4
0
9
6
1
.
[
4
6
]
S
.
L
a
f
r
a
x
o
,
M
.
E
.
A
n
sari
,
a
n
d
S
.
C
h
a
r
f
i
,
“
M
e
l
a
N
e
t
:
A
n
e
f
f
e
c
t
i
v
e
d
e
e
p
l
e
a
r
n
i
n
g
f
r
a
me
w
o
r
k
f
o
r
me
l
a
n
o
m
a
d
e
t
e
c
t
i
o
n
u
s
i
n
g
d
e
r
mo
sco
p
i
c
i
m
a
g
e
s,
”
M
u
l
t
i
m
e
d
i
a
T
o
o
l
s
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
8
1
,
p
p
.
1
6
0
2
1
-
1
6
0
4
5
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
0
4
2
-
0
2
2
-
1
2
5
2
1
-
y.
[
4
7
]
E.
O
v
a
l
l
e
-
M
a
g
a
l
l
a
n
e
s,
J
.
G
.
A
v
i
n
a
-
C
e
r
v
a
n
t
e
s,
I
.
C
r
u
z
-
A
c
e
v
e
s,
a
n
d
J.
R
u
i
z
-
P
i
n
a
l
e
s,
“
T
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
f
o
r
s
t
e
n
o
si
s
d
e
t
e
c
t
i
o
n
i
n
X
-
r
a
y
c
o
r
o
n
a
r
y
a
n
g
i
o
g
r
a
p
h
y
,
”
M
a
t
h
e
m
a
t
i
c
s
,
v
o
l
.
8
,
n
o
.
9
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
mat
h
8
0
9
1
5
1
0
.
[
4
8
]
V
.
R
.
Jo
se
p
h
,
“
O
p
t
i
mal
r
a
t
i
o
f
o
r
d
a
t
a
sp
l
i
t
t
i
n
g
,
”
S
t
a
t
i
st
i
c
a
l
A
n
a
l
y
s
i
s a
n
d
D
a
t
a
M
i
n
i
n
g
:
T
h
e
A
S
A
D
a
t
a
S
c
i
e
n
c
e
J
o
u
rn
a
l
,
v
o
l
.
1
5
,
n
o
.
4
,
p
p
.
5
3
1
-
5
3
8
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
2
/
sa
m.1
1
5
8
3
.
[
4
9
]
A
.
J.
C
.
W
i
t
si
l
a
n
d
J.
B
.
Jo
h
n
so
n
,
“
V
o
l
c
a
n
o
v
i
d
e
o
d
a
t
a
c
h
a
r
a
c
t
e
r
i
z
e
d
a
n
d
c
l
a
ssi
f
i
e
d
u
si
n
g
c
o
mp
u
t
e
r
v
i
si
o
n
a
n
d
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms,
”
Ge
o
sc
i
e
n
c
e
Fr
o
n
t
i
e
rs
,
v
o
l
.
1
1
,
n
o
.
5
,
p
p
.
1
7
8
9
-
1
8
0
3
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
g
sf
.
2
0
2
0
.
0
1
.
0
1
6
.
[
5
0
]
A
.
P
o
e
r
n
o
mo
a
n
d
D
.
-
K
.
K
a
n
g
,
“
B
i
a
se
d
d
r
o
p
o
u
t
a
n
d
c
r
o
ssm
a
p
d
r
o
p
o
u
t
:
l
e
a
r
n
i
n
g
t
o
w
a
r
d
s
e
f
f
e
c
t
i
v
e
d
r
o
p
o
u
t
r
e
g
u
l
a
r
i
z
a
t
i
o
n
i
n
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
N
e
u
ra
l
N
e
t
w
o
r
k
s
,
v
o
l
.
1
0
4
,
p
p
.
6
0
-
6
7
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
n
e
u
n
e
t
.
2
0
1
8
.
0
3
.
0
1
6
.
[
5
1
]
D
.
K
.
A
.
A
l
-
sae
d
i
a
n
d
S
.
S
a
v
a
ş
,
“
C
l
a
s
si
f
i
c
a
t
i
o
n
o
f
sk
i
n
c
a
n
c
e
r
w
i
t
h
d
e
e
p
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
me
t
h
o
d
,
”
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
.
An
a
t
o
l
i
a
n
S
c
i
e
n
c
e
-
B
i
l
g
i
s
a
y
a
r
Bi
l
i
m
l
e
ri
D
e
r
g
i
s
i
,
v
o
l
.
I
D
A
P
-
2
0
2
2
,
p
p
.
2
0
2
-
2
1
0
,
2
0
2
2
,
d
o
i
:
1
0
.
5
3
0
7
0
/
b
b
d
.
1
1
7
2
7
8
2
.
[
5
2
]
Y
.
L
i
u
,
Y
.
Z
h
o
u
,
S
.
W
e
n
,
a
n
d
C
.
T
a
n
g
,
“
A
st
r
a
t
e
g
y
o
n
se
l
e
c
t
i
n
g
p
e
r
f
o
r
man
c
e
me
t
r
i
c
s
f
o
r
c
l
a
ssi
f
i
e
r
e
v
a
l
u
a
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
M
o
b
i
l
e
C
o
m
p
u
t
i
n
g
a
n
d
Mu
l
t
i
m
e
d
i
a
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
6
,
n
o
.
4
,
p
p
.
2
0
–
3
5
,
2
0
1
4
,
d
o
i
:
1
0
.
4
0
1
8
/
i
j
m
c
mc.
2
0
1
4
1
0
0
1
0
2
.
[
5
3
]
L
.
M
o
a
t
a
z
,
G
.
I
.
S
a
l
a
ma,
a
n
d
M
.
H
.
A
b
d
El
a
z
e
e
m,
“
S
k
i
n
c
a
n
c
e
r
d
i
se
a
se
s
c
l
a
ssi
f
i
c
a
t
i
o
n
u
s
i
n
g
d
e
e
p
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
w
i
t
h
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
mo
d
e
l
,
”
J
o
u
rn
a
l
o
f
P
h
y
s
i
c
s:
C
o
n
f
e
r
e
n
c
e
S
e
r
i
e
s
,
v
o
l
.
2
1
2
8
,
n
o
.
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
2
1
2
8
/
1
/
0
1
2
0
1
3
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Al
-
B
a
h
r
a
is
a
n
A
ss
o
c
iate
P
r
o
f
e
ss
o
r
a
n
d
T
e
a
c
h
e
r
Ed
u
c
a
to
r
a
t
th
e
I
n
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
Ed
u
c
a
ti
o
n
S
tu
d
y
P
ro
g
ra
m
-
Ra
h
a
rja
Un
iv
e
rsit
y
.
Je
n
d
e
ra
l
S
u
d
irm
a
n
No
.
4
0
Cik
o
k
o
l,
T
a
n
g
e
r
a
n
g
,
Telp
:
0
2
1
-
5
5
2
9
6
9
2
.
He
h
a
s
b
e
e
n
a
lec
tu
re
r
sin
c
e
1
9
9
4
.
He
c
o
m
p
lete
d
h
is
Do
c
to
ra
te
in
E
d
u
c
a
ti
o
n
a
l
T
e
c
h
n
o
lo
g
y
f
ro
m
Ja
k
a
rta
S
tate
Un
iv
e
r
sity
.
H
e
is
p
a
ss
io
n
a
te
a
b
o
u
t
im
p
ro
v
in
g
th
e
q
u
a
li
ty
o
f
tea
c
h
in
g
a
n
d
stu
d
e
n
t
lea
rn
i
n
g
a
n
d
t
h
e
ir
d
e
v
e
lo
p
m
e
n
t
in
sc
h
o
o
ls an
d
in
h
ig
h
e
r
e
d
u
c
a
ti
o
n
se
tt
in
g
s.
His
re
se
a
rc
h
in
tere
sts
a
r
e
e
d
u
c
a
ti
o
n
a
l
tec
h
n
o
lo
g
y
,
e
d
u
c
a
ti
o
n
a
l
m
a
n
a
g
e
m
e
n
t,
h
e
a
lt
h
e
d
u
c
a
ti
o
n
,
i
n
f
o
rm
a
ti
o
n
tec
h
n
o
lo
g
y
e
d
u
c
a
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
lb
a
h
ra
@ra
h
a
rja.i
n
f
o
.
H
e
n
d
e
r
i
re
c
e
iv
e
d
th
e
Do
c
to
r
d
e
g
re
e
(Do
c
to
r
in
Co
m
p
u
ter
S
c
ien
c
e
)
f
ro
m
th
e
Un
iv
e
rsitas
G
a
d
jah
M
a
d
a
,
Y
o
g
y
a
k
a
rta,
In
d
o
n
e
sia
,
w
it
h
t
h
e
Diss
e
rtatio
n
“
M
o
d
e
l
o
f
P
e
rf
o
rm
a
n
c
e
E
v
a
lu
a
ti
o
n
S
y
ste
m
fo
r
S
tu
d
y
P
ro
g
ra
m
s
B
a
se
d
o
n
L
ED
K
-
m
e
a
n
s
Clu
ste
rin
g
”
.
H
e
is
a
P
r
o
f
e
ss
o
r
o
f
Co
m
p
u
ter
S
c
ien
c
e
in
t
h
e
In
f
o
rm
a
ti
c
s
Di
v
isio
n
U
n
iv
e
rsity
o
f
Ra
h
a
rja,
T
a
n
g
e
r
a
n
g
,
In
d
o
n
e
sia
.
In
a
d
d
it
i
o
n
,
He
is
se
rv
in
g
a
s
V
ice
Re
c
to
r
i
n
A
c
a
d
e
m
ic
Affa
ir
a
n
d
He
a
d
o
f
th
e
re
se
a
rc
h
g
ro
u
p
o
n
a
rti
f
icia
l
in
telli
g
e
n
c
e
.
His
re
se
a
rc
h
in
tere
sts
a
re
in
d
e
c
isio
n
s
u
p
p
o
rt
s
y
ste
m
,
d
a
ta
m
in
in
g
,
b
u
sin
e
ss
i
n
telli
g
e
n
c
e
th
a
t
a
re
in
i
n
f
o
rm
a
ti
o
n
sy
ste
m
s.
He
is
a
s
De
p
u
ty
Ch
a
irm
a
n
o
f
A
ss
o
c
iatio
n
A
u
d
it
o
r
in
th
e
Ce
n
tral
M
a
n
a
g
e
m
e
n
t
o
f
th
e
In
d
o
n
e
sia
n
In
f
o
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
lo
g
y
(P
A
S
T
IKIN
D
O),
a
n
d
a
s
Ce
n
tral
Bo
a
rd
o
f
In
d
o
n
e
sia
n
Co
m
p
u
ter,
El
e
c
tro
n
ics
a
n
d
I
n
st
ru
m
e
n
tatio
n
S
u
p
p
o
rt
S
o
c
iety
(I
ND
OCEIS
S
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
e
n
d
e
ri@rah
a
rja.i
n
f
o
.
Evaluation Warning : The document was created with Spire.PDF for Python.