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Science
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l.
3
7
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3
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Ma
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ch
20
2
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~
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I
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1
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1626
J
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h
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cs
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M
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bas
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sea
ves
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detec
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2
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Ill
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a
ll
v
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sig
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ize
d
m
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d
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l
with
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x
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g
m
o
d
e
l
wi
th
d
iffere
n
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e
v
a
lu
a
ti
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n
m
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tri
c
s,
o
u
r
m
o
d
e
l’s
p
e
rfo
rm
a
n
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e
is o
u
tstan
d
i
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g
.
K
ey
w
o
r
d
s
:
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lleg
al
f
is
h
in
g
Ma
ch
in
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lear
n
in
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Sea
v
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els lo
ca
tio
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r
eg
u
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f
is
h
in
g
Un
r
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ted
f
is
h
in
g
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s
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o
p
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c
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ss
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u
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d
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r th
e
CC B
Y
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SA
li
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se
.
C
o
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r
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s
p
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nd
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A
uth
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r
:
Ajay
Ku
m
ar
Dep
ar
tm
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t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
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in
g
,
J
SS
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h
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ca
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C
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r
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2
,
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Pra
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1
,
I
n
d
ia
E
m
ail: a
jay
.
itcs
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Ov
er
f
is
h
in
g
an
d
illeg
al
f
is
h
in
g
ar
e
m
ajo
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d
an
g
er
s
to
m
ar
in
e
ec
o
s
y
s
tem
s
an
d
th
e
g
lo
b
al
s
ec
u
r
ity
o
f
f
o
o
d
.
Fo
r
eig
n
f
leets
ex
p
lo
itin
g
m
ar
in
e
r
eso
u
r
ce
s
,
p
ar
ticu
lar
ly
in
r
eg
io
n
s
s
u
ch
as
W
est
Af
r
ica,
h
av
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a
n
e
g
ativ
e
im
p
ac
t
on
t
h
e
l
o
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n
d
co
m
m
u
n
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ties
.
T
h
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d
is
co
v
er
y
th
at
illeg
al,
u
n
r
e
p
o
r
ted
,
an
d
u
n
r
eg
u
lated
(
I
UU)
f
is
h
in
g
is
a
s
ig
n
if
ican
t
p
r
o
b
le
m
f
o
r
th
e
r
e
g
io
n
h
as
in
cr
ea
s
ed
wo
r
r
ies
ab
o
u
t
th
e
s
u
s
tain
ab
ili
ty
of
t
h
e
f
is
h
er
y
[
1
]
.
B
ec
au
s
e
of
m
eth
o
d
s
f
o
r
r
ec
o
n
s
tr
u
ctin
g
p
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ev
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ca
p
tu
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d
co
r
r
ec
tin
g
m
is
r
ep
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tin
g
,
th
e
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tu
al
q
u
an
tity
o
f
f
is
h
th
at
h
as
b
ee
n
r
em
o
v
ed
c
o
u
ld
b
e
s
ig
n
if
ican
tly
lar
g
er
th
an
r
ec
o
r
d
e
d
[
2
]
.
Fis
h
er
ies
m
an
ag
em
en
t
r
eq
u
ir
es
r
ig
o
r
o
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s
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ito
r
i
n
g
a
n
d
en
f
o
r
ce
m
en
t
of
r
ig
i
d
laws
[
3
]
.
T
h
e
f
is
h
in
g
in
d
u
s
tr
y
s
u
p
p
o
r
ted
t
h
e
Gu
lf
’
s
ec
o
n
o
m
y
f
o
r
g
en
er
atio
n
s
,
y
et
s
tatis
tics
o
n
ca
tch
ar
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s
till
in
ac
cu
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ate
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ay
.
C
alcu
latin
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ese
ca
p
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am
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th
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tain
ab
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y
o
f
ce
r
tain
m
an
ag
em
en
t
d
ec
is
io
n
s
[
4
]
.
T
h
e
p
r
esen
t
s
tate
o
f
g
lo
b
al
f
is
h
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ies
p
r
o
v
id
es
a
n
o
u
tlo
o
k
o
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m
ar
in
e
f
is
h
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ies
g
lo
b
ally
with
a
f
o
cu
s
o
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m
it
ig
atin
g
th
e
lev
el
o
f
s
u
s
tain
ab
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as
well
as
in
d
u
s
tr
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ex
p
lo
itatio
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wh
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r
esu
lt
s
in
u
n
ce
asin
g
ec
o
n
o
m
ic
g
r
o
wth
[
5
]
.
C
o
m
b
atin
g
th
ese
is
s
u
es
r
eq
u
ir
es
cr
ea
tiv
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s
tr
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th
at
m
ak
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u
s
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of
t
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h
n
o
lo
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d
d
ata
-
d
r
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s
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lu
tio
n
s
.
T
h
is
p
ap
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p
r
esen
ts
a
f
r
am
ewo
r
k
o
f
g
l
o
b
al
f
is
h
in
g
watc
h
(
GFW
)
to
id
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tify
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n
lawf
u
l
f
is
h
in
g
i
n
th
e
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ce
an
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s
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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J
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&
C
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m
p
Sci
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N:
2
5
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-
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Ma
ch
in
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lea
r
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in
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b
a
s
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o
p
ti
miz
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s
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ve
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tio
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d
etec
tio
n
to
…
(
A
ja
y
K
u
ma
r
)
1627
au
to
m
atic
id
en
tific
atio
n
s
y
s
tem
(
AI
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d
ata.
I
n
th
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APFIC
r
eg
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,
I
UU
f
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s
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[
6
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.
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h
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ar
m
f
u
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p
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o
f
u
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lawf
u
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t
h
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p
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f
itab
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ali
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ar
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d
em
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s
tr
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in
g
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d
b
r
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a
k
in
g
r
esear
ch
en
titl
ed
“
Secu
r
in
g
So
m
ali
f
is
h
er
ies
”
[
7
]
.
I
n
o
r
d
e
r
to
s
o
lv
e
th
e
ab
o
v
em
en
tio
n
ed
ch
allen
g
e,
B
all
[
8
]
p
r
o
v
i
d
ed
a
u
n
iq
u
e
m
eth
o
d
f
o
r
ass
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s
in
g
I
UU
f
is
h
an
d
b
ir
d
b
y
-
ca
tch
.
Fo
r
p
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v
id
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n
g
em
p
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r
ically
tr
u
s
two
r
th
y
d
ata
an
d
c
o
n
f
id
en
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in
ter
v
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th
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ap
p
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ac
h
u
s
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an
an
im
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m
o
d
el
th
at
in
cu
lcate
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“n
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d
“u
n
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”
tr
ac
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o
f
I
UU
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is
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g
ca
s
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Ag
n
ew
[
9
]
p
r
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ted
a
d
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d
d
escr
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tio
n
o
f
t
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1
9
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f
i
s
h
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g
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g
tr
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-
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elate
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m
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.
T
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m
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d
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lo
ca
tio
n
,
an
d
th
e
tim
e
o
f
ca
p
t
u
r
e,
an
d
co
n
f
ir
m
in
g
th
at
th
e
f
is
h
in
g
ac
tiv
ity
was
lawf
u
l.
B
u
t
it
r
eq
u
i
r
es
co
llab
o
r
atio
n
f
r
o
m
a
lo
t
o
f
d
if
f
er
en
t
p
eo
p
le,
s
u
c
h
as
im
p
o
r
ter
s
,
ex
p
o
r
ter
s
,
f
is
h
er
s
,
a
n
d
p
r
o
ce
s
s
o
r
s
.
M
R
AG
in
2
0
1
6
[
1
0
]
p
r
esen
te
d
a
Mo
n
te
C
ar
lo
s
im
u
latio
n
to
m
ea
s
u
r
e
th
e
ex
t
en
t
o
f
illi
cit
f
is
h
in
g
u
n
d
er
th
e
s
ea
(
I
UU)
lin
k
ed
t
o
elev
en
p
r
im
ar
y
r
is
k
s
in
f
o
u
r
r
is
k
ca
te
g
o
r
ies:
i)
f
is
h
in
g
with
o
u
t
a
licen
s
e
o
r
au
th
o
r
izatio
n
;
ii)
f
alsi
f
y
in
g
in
f
o
r
m
atio
n
;
iii)
v
io
latin
g
o
th
e
r
licen
s
e
r
eq
u
ir
e
m
en
ts
;
an
d
iv
)
r
is
k
s
ar
is
in
g
af
ter
h
ar
v
est.
Aan
es
et
a
l.
[
1
1
]
p
r
o
p
o
s
ed
a
No
r
weg
ian
E
c
o
n
o
m
ic
Z
o
n
e’
s
s
atellite
-
b
ased
m
ar
itime
tr
ac
k
i
n
g
s
y
s
tem
th
at
o
f
f
er
s
co
m
p
r
e
h
en
s
iv
e
d
a
ta
o
n
in
d
iv
id
u
al
v
ess
el
v
o
y
a
g
es.
T
h
e
m
ain
lim
itatio
n
o
f
th
is
s
y
s
tem
is
it
s
co
m
p
lex
s
tr
u
ctu
r
e.
Sev
er
al
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
wer
e
u
s
ed
b
y
B
ellin
et
a
l.
[
1
2
]
to
f
o
r
ec
ast
th
e
q
u
an
tity
an
d
d
is
tr
ib
u
tio
n
o
f
m
ar
in
e
o
r
g
a
n
is
m
s
.
Mo
d
els
lik
e
r
an
d
o
m
f
o
r
ests
(
R
F)
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
an
d
n
eu
r
al
n
etwo
r
k
s
wer
e
u
s
ed
in
th
e
s
tu
d
y
.
T
h
e
in
ter
ac
tio
n
s
b
etwe
en
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
an
d
s
p
ec
ies
d
is
tr
ib
u
tio
n
s
th
at
ar
e
i
n
tr
icate
an
d
n
o
n
-
lin
ea
r
ca
n
b
e
h
an
d
le
d
b
y
th
ese
m
o
d
els.
T
h
e
m
ain
d
r
awb
ac
k
o
f
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
is
th
eir
h
ea
v
y
r
el
ian
ce
o
n
b
ig
,
h
ig
h
-
q
u
ality
tr
ain
in
g
d
atasets
.
Fre
e
et
a
l.
[
1
3
]
p
r
o
p
o
s
ed
a
s
tu
d
y
to
ass
ess
th
e
ex
ten
t
o
f
illi
cit
f
is
h
in
g
an
d
its
n
eg
ativ
e
im
p
ac
ts
o
n
a
p
ar
ticu
lar
en
d
an
g
e
r
ed
f
is
h
s
p
ec
ies
u
s
in
g
a
c
o
m
b
in
atio
n
o
f
q
u
a
n
titativ
e
an
d
q
u
alitativ
e
m
eth
o
d
o
l
o
g
ies,
o
f
f
er
in
g
im
p
o
r
tan
t
in
f
o
r
m
atio
n
f
o
r
co
n
s
er
v
atio
n
ef
f
o
r
ts
.
A
th
o
r
o
u
g
h
ev
al
u
atio
n
o
f
th
e
liter
atu
r
e
was
d
o
n
e
b
y
Li
et
a
l.
[
1
4
]
in
o
r
d
er
t
o
g
at
h
er
an
d
ev
alu
ate
th
e
c
u
r
r
en
t
liter
atu
r
e
o
n
m
a
r
itime
s
u
r
v
eillan
ce
e
m
p
lo
y
in
g
o
p
tical
s
en
s
in
g
m
eth
o
d
s
.
s
en
s
o
r
f
u
s
io
n
,
o
b
ject
id
en
tific
atio
n
an
d
tr
ac
k
i
n
g
,
p
ictu
r
e
p
r
o
ce
s
s
in
g
,
a
n
d
s
u
r
v
eillan
ce
.
A
s
h
ip
d
etec
tio
n
tec
h
n
iq
u
e
th
at
m
a
k
es
u
s
e
o
f
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
was
p
r
e
s
en
ted
b
y
Hu
a
n
g
et
a
l.
[
1
5
]
.
Dee
p
lear
n
in
g
ar
ch
itectu
r
es
ca
lled
C
NNs
ar
e
cr
ea
ted
esp
ec
ially
f
o
r
im
a
g
e
p
r
o
ce
s
s
in
g
ap
p
licatio
n
s
.
I
n
o
r
d
er
to
teac
h
a
C
NN
m
o
d
el
,
th
e
d
is
tin
g
u
is
h
in
g
ch
a
r
ac
ter
is
tics
o
f
s
h
ip
s
,
a
s
izab
le
d
ataset
o
f
tag
g
ed
r
e
m
o
te
s
en
s
in
g
p
h
o
t
o
s
was
u
s
ed
as
tr
ain
in
g
d
ata.
T
h
e
co
m
p
u
tatio
n
al
in
ten
s
ity
o
f
C
NNs
i
s
a
d
r
awb
ac
k
,
p
ar
ticu
lar
l
y
wh
en
h
an
d
lin
g
h
u
g
e
d
atasets
an
d
in
tr
icate
im
ag
e
p
r
o
ce
s
s
in
g
jo
b
s
.
I
t
f
r
eq
u
en
tly
tak
es
a
lo
t
o
f
p
r
o
ce
s
s
in
g
p
o
wer
an
d
m
em
o
r
y
to
tr
ain
C
NNs.
Dee
p
C
NNs
wer
e
u
s
ed
b
y
Gr
ac
e
et
a
l.
[
1
6
]
to
r
e
co
g
n
ize
s
h
ip
s
f
r
o
m
h
ig
h
-
r
eso
lu
tio
n
r
em
o
te
s
en
s
in
g
p
h
o
to
s
.
T
h
e
p
r
o
ce
s
s
en
tailed
u
s
in
g
a
s
izab
le
d
atas
et
o
f
an
n
o
tated
r
em
o
te
s
en
s
in
g
p
h
o
t
o
s
to
tr
ain
a
C
NN
m
o
d
el
in
o
r
d
er
to
id
e
n
tif
y
h
ier
ar
ch
ical
ch
a
r
ac
ter
is
tics
t
y
p
ical
o
f
s
h
ip
s
in
v
ar
io
u
s
en
v
i
r
o
n
m
en
tal
s
ettin
g
s
.
A
d
r
awb
ac
k
o
f
d
ee
p
n
eu
r
al
n
etwo
r
k
(
DNN)
m
o
d
els
is
th
ei
r
h
u
g
e
n
u
m
b
e
r
o
f
lay
er
s
a
n
d
p
ar
am
eter
s
,
wh
ich
r
eq
u
ir
e
a
lo
t
o
f
p
r
o
ce
s
s
in
g
p
o
wer
an
d
m
em
o
r
y
wh
en
tr
ain
in
g
o
n
b
i
g
d
atasets
.
T
h
e
o
v
er
f
e
at
s
y
s
tem
,
wh
ich
is
b
u
ilt
o
n
C
NNs
f
o
r
in
teg
r
ated
r
ec
o
g
n
itio
n
,
l
o
ca
lizatio
n
,
an
d
d
etec
tio
n
task
s
in
co
m
p
u
ter
v
is
i
o
n
,
was
s
u
g
g
ested
b
y
Ser
m
an
et
et
a
l.
[
1
7
]
.
T
h
e
o
v
er
f
ea
t
s
y
s
tem
'
s
u
n
if
ied
ar
ch
itectu
r
e
f
o
r
o
b
ject
co
m
p
r
eh
e
n
s
io
n
is
o
n
e
o
f
its
m
ain
b
en
ef
its
b
u
t
its
co
s
t
an
d
co
m
p
lex
ity
in
tr
icac
y
m
ay
b
e
a
d
r
awb
ac
k
.
A
c
o
m
p
r
e
h
en
s
iv
e
ass
es
s
m
en
t
o
f
th
e
liter
atu
r
e
was
ca
r
r
ied
o
u
t
b
y
Kh
atam
i
et
a
l.
[
1
8
]
to
ass
ess
th
e
s
tate
o
f
th
e
f
ield
r
eg
ar
d
i
n
g
s
h
ip
d
etec
tio
n
in
o
p
tical
r
em
o
te
s
en
s
in
g
p
ictu
r
es.
Stu
d
ies,
r
esear
ch
p
ap
er
s
,
an
d
tech
n
ical
r
e
p
o
r
ts
th
at
wer
e
p
u
b
lis
h
ed
in
s
ch
o
lar
ly
jo
u
r
n
als
an
d
co
n
f
er
en
ce
p
r
o
ce
e
d
in
g
s
wer
e
in
clu
d
ed
in
th
e
s
tu
d
y
.
T
h
e
in
teg
r
ati
o
n
o
f
d
ee
p
lear
n
i
n
g
ar
ch
itectu
r
es
an
d
s
o
p
h
is
ticated
m
ac
h
in
e
lear
n
in
g
alg
o
r
it
h
m
s
,
am
o
n
g
o
t
h
er
r
ec
en
t
d
e
v
elo
p
m
en
ts
in
s
h
ip
id
en
tific
atio
n
ap
p
r
o
ac
h
es,
was
em
p
h
asized
in
th
e
ar
ticle.
Mitten
d
o
r
f
et
a
l.
[
1
9
]
u
s
ed
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
to
d
eter
m
in
e
s
ea
c
o
n
d
itio
n
s
b
ased
o
n
an
a
n
aly
s
is
o
f
in
-
s
er
v
ice
d
ata
o
b
tain
ed
f
r
o
m
a
ca
r
g
o
v
es
s
el.
Pre
p
r
o
ce
s
s
in
g
th
e
o
p
er
atin
g
d
ata
f
r
o
m
th
e
v
ess
el,
in
clu
d
in
g
m
o
tio
n
,
ac
ce
ler
atio
n
,
an
d
o
t
h
er
p
er
tin
en
t
s
en
s
o
r
r
ea
d
in
g
s
,
was
p
ar
t
o
f
th
e
tech
n
iq
u
e.
T
h
e
p
r
e
p
r
o
ce
s
s
ed
d
ata
is
u
s
ed
to
tr
ain
an
d
ass
ess
a
v
ar
iety
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
in
clu
d
in
g
RF
,
SVM
,
an
d
n
eu
r
al
n
etwo
r
k
s
,
with
th
e
p
u
r
p
o
s
e
o
f
class
if
y
in
g
d
is
tin
ct
s
ea
s
tate
s
.
RF
alg
o
r
ith
m
s
wer
e
u
s
ed
b
y
W
an
g
et
a
l.
[
2
0
]
to
class
if
y
s
h
ip
s
b
ased
o
n
s
tatic
in
f
o
r
m
atio
n
o
b
tain
ed
f
r
o
m
AI
S
d
ata.
Pre
p
r
o
ce
s
s
in
g
AI
S
d
ata
t
o
ex
tr
ac
t
p
er
tin
e
n
t
s
tatic
p
r
o
p
e
r
ties
s
u
ch
as
s
h
ip
class
,
len
g
th
,
b
r
ea
d
th
,
an
d
d
r
a
f
t
was
p
ar
t
o
f
th
e
tec
h
n
iq
u
e
.
T
h
e
ca
p
ac
ity
o
f
RF
to
p
r
o
d
u
ce
h
i
g
h
class
if
icatio
n
ac
cu
r
ac
y
is
a
m
ajo
r
b
en
e
f
it
wh
en
it
co
m
es
to
s
h
ip
class
if
icat
io
n
.
T
h
e
lo
w
in
ter
p
r
etab
ilit
y
o
f
RF
alg
o
r
ith
m
s
in
co
m
p
ar
is
o
n
to
m
o
r
e
s
tr
aig
h
tf
o
r
war
d
m
o
d
els s
u
ch
a
s
lo
g
is
tic
r
eg
r
ess
io
n
o
r
d
ec
is
io
n
tr
ee
s
m
ay
b
e
a
d
r
awb
ac
k
.
Z
h
o
n
g
et
a
l.
[
2
1
]
class
if
ied
v
ess
els
b
ased
o
n
s
p
ac
e
-
b
ased
AI
S
d
ata
b
y
u
tili
zin
g
RF
al
g
o
r
ith
m
s
.
Pre
p
r
o
ce
s
s
in
g
AI
S
d
ata
f
r
o
m
s
atellite
v
iews
was
p
ar
t
o
f
th
e
t
ec
h
n
iq
u
e
t
o
ex
tr
ac
t p
er
tin
en
t
i
n
f
o
r
m
atio
n
s
u
ch
as
v
ess
el
ty
p
e,
s
ize,
an
d
s
p
ee
d
.
C
o
m
p
u
tatio
n
al
co
m
p
lex
ity
is
a
d
r
awb
ac
k
th
at
RF
m
ay
h
a
v
e
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
,
p
ar
ticu
lar
ly
wh
e
n
wo
r
k
in
g
w
ith
b
ig
d
atasets
an
d
h
ig
h
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
es.
C
NN
s
ar
e
u
s
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h
an
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l.
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2
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]
to
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e
co
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n
ize
s
h
ip
s
f
r
o
m
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ig
h
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s
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izab
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f
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h
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to
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o
f
s
h
ip
s
with
b
ac
k
g
r
o
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n
d
clu
tter
as
p
a
r
t
o
f
th
e
p
r
o
ce
s
s
.
Hig
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
626
-
1
6
3
6
1628
d
etec
tio
n
ac
cu
r
ac
y
is
an
ad
v
a
n
tag
e,
b
u
t
s
ig
n
i
f
ican
t
q
u
an
tit
y
o
f
lab
eled
d
ata
is
u
s
u
ally
n
ee
d
ed
to
tr
ain
a
R
-
C
NN
m
o
d
el.
Ou
r
s
tu
d
y
co
m
p
ar
es
v
ar
i
o
u
s
ty
p
es
o
f
v
ess
el
g
ea
r
in
o
r
d
er
to
id
en
tify
th
e
m
o
s
t
r
ea
d
ily
m
o
n
ito
r
ed
an
d
less
l
ik
ely
to
b
e
u
s
ed
f
o
r
illi
cit
p
u
r
p
o
s
es
v
ess
el
co
n
tai
n
er
.
T
h
e
r
esear
ch
we
co
n
d
u
cted
s
u
g
g
ested
an
o
p
tim
izatio
n
tech
n
iq
u
e
th
at
s
elec
ts
h
y
p
er
p
ar
am
eter
s
v
ia
a
g
e
n
etic
alg
o
r
ith
m
r
ath
er
th
a
n
a
g
r
id
s
ea
r
ch
in
o
r
d
er
to
ac
co
m
p
lis
h
t
h
is
.
T
h
is
ap
p
r
o
ac
h
is
s
u
itab
le
with
m
o
r
e
ex
te
n
s
iv
e
d
atasets
an
d
u
n
k
n
o
wn
s
ea
r
ch
s
p
ac
es,
wh
ich
is
n
o
t
ac
h
iev
ab
le
with
th
e
b
a
s
elin
e
tech
n
iq
u
e,
i.e
.
g
r
id
s
ea
r
ch
,
th
a
n
k
s
to
th
e
g
en
etic
al
g
o
r
ith
m
'
s
cr
o
s
s
o
v
er
m
eth
o
d
.
Fu
r
th
er
m
o
r
e,
all
v
ess
el
ty
p
es
ex
p
er
ien
ce
a
co
n
s
id
er
ab
le
in
cr
ea
s
e
in
o
v
e
r
all
ac
cu
r
ac
y
,
r
ec
all,
an
d
F1
s
co
r
e
f
o
llo
win
g
t
h
e
u
s
e
o
f
th
e
g
en
etic
h
y
p
er
p
ar
a
m
eter
o
p
tim
izatio
n
tech
n
iq
u
e.
T
h
e
wo
r
k
is
o
r
g
an
ized
as
f
o
l
lo
ws.
Sectio
n
2
d
elin
ea
tes
th
e
th
eo
r
etica
l
f
u
n
d
am
e
n
tals
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
em
p
lo
y
ed
in
o
u
r
r
esear
ch
wo
r
k
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
m
e
th
o
d
o
lo
g
y
th
at
h
as
b
ee
n
ch
o
s
en
to
s
atis
f
y
th
e
s
ee
v
e
s
s
el
lo
ca
tio
n
d
etec
tio
n
d
ata
r
eq
u
ir
e
m
en
ts
is
d
escr
ib
ed
in
s
ec
tio
n
3
.
T
h
e
im
p
lem
en
tatio
n
o
f
a
g
en
etic
alg
o
r
ith
m
to
o
p
tim
ize
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
el’
s
ac
cu
r
ac
y
an
d
g
e
o
g
r
a
p
h
ical
m
a
p
r
ep
r
esen
tatio
n
o
f
ac
tu
al
an
d
p
r
ed
icted
s
ites
to
s
h
o
w
th
at
th
e
illeg
al
ar
ea
s
ar
e
d
escr
ib
ed
in
s
ec
tio
n
4
.
Py
th
o
n
So
f
twar
e
is
u
s
ed
to
o
b
tain
r
esu
lts
.
T
h
e
s
tu
d
y
'
s
co
n
clu
s
io
n
s
ar
e
p
r
esen
ted
in
s
ec
tio
n
5
.
2.
T
H
E
O
R
E
T
I
CA
L
F
UNDA
M
E
NT
AL
S
T
h
is
s
ec
tio
n
d
is
cu
s
s
es
s
o
m
e
o
f
th
e
th
eo
r
etica
l
f
u
n
d
a
m
en
tals
o
f
co
n
d
u
ctin
g
th
e
e
x
p
er
im
e
n
t.
Dif
f
er
en
t
alg
o
r
ith
m
s
ar
e
u
s
ed
to
tr
ain
t
h
e
m
o
d
el
an
d
co
m
p
a
r
ed
b
ase
d
o
n
ev
alu
atio
n
m
etr
ics
lik
e
F1
s
co
r
e,
r
ec
all
an
d
ac
cu
r
ac
y
.
Her
e
is
a
b
r
ief
d
escr
ip
tio
n
o
f
t
h
e
alg
o
r
ith
m
s
u
s
ed
.
2
.
1
.
Ra
nd
o
m
f
o
re
s
t
RF
is
a
cla
s
s
if
ier
th
at
ag
g
r
eg
ates
th
e
r
esu
lts
o
f
s
ev
er
al
d
ec
is
io
n
tr
ee
s
ap
p
lied
to
v
ar
i
o
u
s
d
ataset
s
u
b
s
ets.
I
t
o
p
er
ates
b
y
g
e
n
er
atin
g
m
an
y
“
d
ec
is
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n
tr
ee
s
”
in
th
e
p
r
o
ce
s
s
o
f
tr
ain
in
g
.
W
ith
th
e
h
elp
o
f
th
e
in
s
ig
h
ts
f
r
o
m
s
ev
er
al
tr
ee
s
,
th
i
s
co
o
p
er
ativ
e
a
p
p
r
o
a
ch
to
“
d
e
cisi
o
n
-
m
ak
in
g
”
p
r
o
d
u
ce
s
ac
cu
r
ate
an
d
c
o
n
s
is
ten
t
o
u
tco
m
es
[
2
3
]
-
[
2
5
]
.
I
n
o
u
r
r
e
s
ea
r
ch
af
ter
th
e
s
elec
tio
n
o
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y
p
er
p
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eter
s
,
a
m
o
d
el
is
tr
ain
ed
u
s
in
g
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alg
o
r
ith
m
wh
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h
is
f
u
r
t
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er
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lu
ated
u
s
in
g
d
if
f
er
e
n
t
ev
alu
ati
o
n
m
etr
ics.
T
h
e
ar
ch
itectu
r
e
o
f
th
e
RF
alg
o
r
ith
m
is
g
iv
en
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
R
an
d
o
m
f
o
r
est ar
ch
i
tectu
r
e
2
.
2
.
G
ra
dient
bo
o
s
t
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cla
s
s
if
ier
Gr
ad
ien
t
b
o
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s
tin
g
class
if
ier
(
GB
C
)
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s
a
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l
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h
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eth
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th
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u
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d
ien
t
d
escen
t
to
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ain
ev
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y
f
r
esh
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el
t
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r
e
d
u
ce
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e
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s
s
f
u
n
ctio
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o
f
th
e
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ec
ed
in
g
m
o
d
el,
s
u
ch
as
th
e
m
ea
n
s
q
u
ar
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d
er
r
o
r
[
2
6
]
-
[
3
0
]
.
T
h
is
ap
p
r
o
ac
h
tr
ain
s
a
n
ew
wea
k
m
o
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el
to
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ce
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e
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e
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f
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lo
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f
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n
cti
o
n
with
r
esp
ec
t
to
th
e
p
r
ed
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s
m
ad
e
b
y
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e
c
u
r
r
en
t
en
s
em
b
le
in
ea
ch
iter
atio
n
.
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h
en
,
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e
p
r
e
d
ictio
n
s
o
f
t
h
e
n
ew
m
o
d
el
a
r
e
in
clu
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in
th
e
g
r
o
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p
,
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d
th
e
p
r
o
ce
d
u
r
e
is
r
ep
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ted
u
n
til
a
s
to
p
p
in
g
co
n
d
itio
n
is
s
atis
f
i
ed
.
I
n
o
u
r
r
esear
ch
af
ter
th
e
s
elec
tio
n
o
f
h
y
p
e
r
p
a
r
am
eter
s
,
a
m
o
d
el
is
tr
ain
ed
u
s
in
g
GB
C
alg
o
r
ith
m
wh
ich
is
f
u
r
th
er
ev
alu
ated
u
s
in
g
d
if
f
er
en
t e
v
alu
atio
n
m
et
r
ics [
3
1
]
-
[
3
3
]
.
T
h
e
a
r
ch
itectu
r
e
o
f
th
e
RF
alg
o
r
ith
m
is
g
iv
en
in
Fig
u
r
e
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
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-
4
7
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Ma
ch
in
e
lea
r
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a
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o
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ti
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ea
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el
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(
A
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u
ma
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)
1629
Fig
u
r
e
2
.
Gr
a
d
ien
t
b
o
o
s
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g
cl
ass
if
ier
ar
ch
itectu
r
e
2
.
3
.
G
enet
ic
a
lg
o
rit
h
m
Gen
etic
a
lg
o
r
ith
m
is
an
iter
ati
v
e
ap
p
r
o
ac
h
in
wh
ich
th
e
p
o
p
u
latio
n
u
n
d
e
r
g
o
es
s
ev
er
al
m
o
d
if
icatio
n
s
to
f
in
d
th
e
b
est
p
o
p
u
latio
n
[
3
4
]
-
[
3
8
]
.
T
h
is
alg
o
r
ith
m
m
ain
l
y
f
o
cu
s
es
o
n
f
in
d
i
n
g
th
e
b
est
in
d
iv
id
u
als
s
o
th
at
n
ew
o
f
f
s
p
r
in
g
ca
n
b
e
g
en
er
ated
u
s
in
g
th
em
.
I
t
g
r
ad
u
all
y
ev
o
lv
es
as
th
e
b
est
s
o
lu
ti
o
n
af
ter
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u
o
u
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im
p
r
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e
n
t
[
3
9
]
,
[
4
0
]
.
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n
o
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r
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c
h
h
y
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er
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ar
am
eter
s
ar
e
s
elec
ted
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d
o
p
tim
ized
u
s
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g
an
iter
ativ
e
m
eth
o
d
o
f
g
en
etic
alg
o
r
ith
m
t
o
s
elec
t
th
e
b
est
h
y
p
er
p
a
r
am
e
ter
b
ef
o
r
e
t
r
ain
in
g
o
u
r
m
o
d
el.
T
h
e
f
lo
wch
ar
t
o
f
th
e
g
en
etic
alg
o
r
ith
m
is
g
iv
e
n
in
Fig
u
r
e
3
.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
u
s
ed
in
t
h
e
r
esear
ch
wo
r
k
is
d
escr
ib
ed
in
th
e
n
ex
t sectio
n
.
Fig
u
r
e
3
.
Flo
w
ch
a
r
t
o
f
g
en
eti
c
alg
o
r
ith
m
3.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es th
e
p
r
o
p
o
s
ed
r
esear
ch
m
eth
o
d
o
l
o
g
y
i
n
wh
ich
s
tep
-
wis
e
p
r
o
ce
d
u
r
e
i
s
g
iv
en
.
As
an
illu
s
tr
atio
n
o
f
th
e
th
o
r
o
u
g
h
ap
p
r
o
ac
h
u
s
ed
in
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e,
Fig
u
r
e
4
s
h
o
ws
th
is
p
r
o
ce
s
s
in
g
r
ap
h
ic
f
o
r
m
,
with
ea
ch
s
tag
e
s
h
o
wn
in
a
s
eq
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en
tial
f
lo
w
.
T
h
e
m
eth
o
d
ical
p
r
o
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d
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e
f
o
r
m
ax
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izin
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s
ea
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o
s
itio
n
d
etec
tio
n
to
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etec
t
illi
cit
f
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h
in
g
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ex
p
lain
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in
th
is
f
lo
w
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iag
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am
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wh
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s
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as
a
r
ef
er
en
ce
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r
im
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lem
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tatio
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m
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eh
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n
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io
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.
Fig
u
r
e
4
.
Pro
p
o
s
ed
r
esear
c
h
m
eth
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d
o
lo
g
y
f
l
o
w
d
iag
r
a
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
626
-
1
6
3
6
1630
3
.
1
.
Da
t
a
c
o
llect
io
n
Data
s
ets ar
e
co
llected
f
r
o
m
GFW
an
d
AI
S.
GF
W
u
s
es
AI
S
d
ata
to
tr
ac
k
an
d
e
x
am
in
e
f
is
h
in
g
ac
tiv
ity
th
r
o
u
g
h
o
u
t
th
e
g
lo
b
e.
T
h
e
d
at
aset
co
n
s
is
ts
o
f
v
ess
el
id
en
tity
u
s
in
g
a
u
n
i
q
u
e
m
ar
itime
m
o
b
ile
s
er
v
ice
id
en
tity
(
MM
SI
)
n
u
m
b
er
,
id
e
n
tity
,
c
o
u
r
s
e
,
s
p
ee
d
,
ty
p
e,
p
o
s
itio
n
(
latit
u
d
e,
l
o
n
g
itu
d
e)
,
an
d
n
a
v
ig
atio
n
al
s
tatu
s
.
Fu
r
th
er
,
th
e
co
llected
d
atasets
ar
e
s
en
t f
o
r
d
ata
p
r
ep
r
o
ce
s
s
in
g
.
3
.
2
.
Da
t
a
p
re
pro
ce
s
s
ing
Data
s
ets
wer
e
alr
ea
d
y
lab
eled
,
clea
n
ed
an
d
n
o
r
m
alize
d
.
So
m
e
d
ata
with
n
u
ll
v
alu
e
was
p
r
esen
t
th
at
was
r
em
o
v
ed
f
o
r
f
u
r
th
er
p
r
o
ce
s
s
es.
I
t
wa
s
d
o
n
e
b
y
t
h
e
‘
p
a
n
d
as’
m
o
d
u
le.
Af
ter
d
ata
p
r
e
p
r
o
ce
s
s
in
g
all
th
e
d
ata
with
n
u
ll v
alu
es is
elim
in
ated
.
T
h
en
,
th
e
p
r
ep
r
o
ce
s
s
ed
d
atasets
ar
e
s
en
t f
o
r
d
ata
f
ea
tu
r
e
e
x
tr
ac
tio
n
.
3
.
3
.
F
e
a
t
ures
e
x
t
ra
c
t
io
n
I
n
th
e
d
ata
f
ea
tu
r
e
ex
tr
a
ctio
n
p
h
ase,
s
ev
er
al
k
ey
f
ea
tu
r
es
w
er
e
ex
tr
ac
ted
f
r
o
m
th
e
d
atasets
to
aid
in
th
e
an
aly
s
is
o
f
s
ea
v
ess
el
ac
tiv
ities
.
T
h
ese
f
ea
tu
r
es
in
clu
d
e
th
e
MM
SI
n
u
m
b
er
,
tim
estam
p
,
latitu
d
e
(
lat)
,
lo
n
g
itu
d
e
(
lo
n
)
,
s
p
ee
d
,
an
in
d
icato
r
o
f
wh
eth
er
th
e
v
es
s
el
is
f
is
h
in
g
,
d
is
tan
ce
f
r
o
m
s
h
o
r
e
,
an
d
d
is
tan
ce
f
r
o
m
p
o
r
t.
T
h
ese
f
ea
tu
r
es p
r
o
v
id
e
cr
itical
in
f
o
r
m
atio
n
f
o
r
ass
ess
in
g
v
ess
el
b
eh
av
io
r
an
d
id
en
tif
y
in
g
p
o
ten
tial illeg
al
f
is
h
in
g
ac
tiv
ities
.
W
ith
th
ese
f
ea
tu
r
es
in
h
an
d
,
th
e
n
ex
t
s
tep
is
to
o
p
tim
ize
th
e
p
er
f
o
r
m
an
ce
o
f
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els
b
y
s
elec
tin
g
th
e
m
o
s
t
ef
f
ec
ti
v
e
h
y
p
er
p
ar
a
m
eter
s
.
T
h
is
o
p
tim
izatio
n
is
ac
h
iev
ed
u
s
in
g
two
d
is
tin
ct
m
eth
o
d
s
:
g
r
id
s
ea
r
ch
an
d
g
en
etic
alg
o
r
it
h
m
.
Gr
id
Sear
ch
in
v
o
lv
es
a
s
y
s
tem
atic,
ex
h
au
s
tiv
e
s
ea
r
ch
o
v
er
a
p
r
ed
ef
in
e
d
s
et
o
f
h
y
p
er
p
ar
am
eter
s
to
f
in
d
th
e
o
p
tim
al
co
m
b
in
atio
n
,
w
h
ile
th
e
g
en
etic
alg
o
r
ith
m
em
p
lo
y
s
ev
o
l
u
tio
n
ar
y
tech
n
i
q
u
es
to
iter
ativ
ely
im
p
r
o
v
e
h
y
p
er
p
ar
am
eter
s
b
ased
o
n
s
im
u
lated
n
atu
r
al
s
elec
tio
n
.
B
o
th
m
eth
o
d
s
aim
to
en
h
an
ce
th
e
m
o
d
el'
s
ab
ilit
y
to
ac
cu
r
ately
d
etec
t
ill
eg
al
f
is
h
in
g
ac
tiv
ities
b
y
r
ef
in
in
g
t
h
e
h
y
p
er
p
a
r
am
eter
s
to
b
est f
it th
e
ex
tr
ac
te
d
f
ea
tu
r
e
s
.
3.
4.
G
rid
s
ea
rc
h
Op
tim
al
h
y
p
er
p
a
r
am
eter
s
ar
e
s
elec
ted
u
s
in
g
th
e
g
r
id
s
ea
r
ch
m
eth
o
d
.
I
t
u
s
es
m
u
ltip
le
co
m
b
in
atio
n
tech
n
iq
u
es
to
f
in
d
t
h
e
b
est
h
y
p
er
p
ar
am
eter
.
I
t
wa
s
d
o
n
e
b
y
th
e
s
k
lear
n
m
o
d
u
le.
Hy
p
er
p
ar
am
eter
s
with
r
a
n
g
e
wer
e
d
ef
in
ed
f
o
r
b
o
th
th
e
R
F
an
d
GB
C
m
o
d
els.
Fo
r
R
F
m
o
d
el:
Par
am
-
g
r
id
=
{'
n
-
esti
m
ato
r
s
'
:
[
1
0
0
,
2
0
0
,
3
0
0
]
,
'
m
ax
-
d
ep
th
'
:
[
1
0
,
2
0
,
3
0
]
,
'
m
in
-
s
am
p
les
-
s
p
lit'
:
[
2
,
5
,
1
0
]
}
.
Fo
r
GB
C
m
o
d
el:
Par
am
-
g
r
i
d
=
{'
le
ar
n
in
g
-
r
ate’
:
[
0
.
0
5
,
0
.
1
,
0
.
2
]
,
'
m
ax
-
d
ep
t
h
'
:
[
2
,
3
,
5
]
,
'
m
in
-
s
am
p
les
-
le
af
'
:
[
3
,
1
0
,
2
0
]
}
.
B
ased
o
n
th
ese
r
an
g
es
b
est
h
y
p
er
p
ar
am
eter
s
ar
e
s
elec
ted
f
o
r
r
esp
ec
tiv
e
m
o
d
els b
y
g
r
id
s
ea
r
ch
f
o
r
f
u
r
th
er
m
o
d
el
tr
ain
in
g
.
3.
5.
G
enet
ic
cr
o
s
s
o
v
er
a
nd
m
uta
t
io
n
Op
tim
al
h
y
p
e
r
p
ar
am
eter
s
ar
e
s
elec
ted
u
s
in
g
a
g
en
etic
al
g
o
r
i
th
m
.
I
t
u
s
es
o
p
e
r
atio
n
s
s
u
ch
as
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
tatio
n
to
r
ep
ea
ted
ly
d
ev
elo
p
a
p
o
p
u
latio
n
o
f
p
o
ten
tial
s
o
lu
tio
n
s
.
Dif
f
er
en
t
co
m
b
in
atio
n
s
o
f
p
ar
am
eter
s
ar
e
s
elec
ted
an
d
th
en
cr
o
s
s
o
v
er
is
p
er
f
o
r
m
ed
.
C
r
o
s
s
o
v
er
is
n
o
th
in
g
b
u
t
th
e
m
ix
in
g
o
f
b
est
g
u
ess
es
f
r
o
m
th
e
p
r
ev
io
u
s
r
o
u
n
d
s
.
A
f
ter
cr
o
s
s
o
v
er
ce
r
tai
n
m
em
b
er
s
o
f
th
e
p
o
p
u
latio
n
ar
e
s
u
b
jecte
d
to
r
an
d
o
m
m
o
d
if
icatio
n
s
to
k
ee
p
d
iv
er
s
e
s
ea
r
ch
s
p
ac
e
th
is
p
r
o
ce
s
s
is
ca
lled
m
u
tatio
n
.
I
n
o
u
r
ca
s
e,
th
e
p
o
p
u
latio
n
s
ize
is
s
et
as 1
0
an
d
th
e
m
u
tatio
n
r
ate
as 0
.
1
.
3.
6.
M
o
del
t
ra
ini
ng
Dif
f
er
en
t
h
y
p
er
p
a
r
am
eter
o
p
tim
izatio
n
s
tr
ateg
ies
ar
e
u
s
ed
to
tr
ain
th
e
RF
an
d
GB
C
m
o
d
els
in
o
r
d
er
to
m
ax
im
ize
s
ea
v
ess
el
lo
ca
tio
n
d
etec
tio
n
f
o
r
id
e
n
tify
in
g
illi
cit
f
is
h
in
g
.
G
r
id
s
ea
r
ch
is
u
s
ed
to
o
p
tim
ize
th
e
RF
m
o
d
el
b
y
m
et
h
o
d
ically
ass
es
s
in
g
ev
er
y
co
n
ce
iv
ab
le
c
o
m
b
in
atio
n
o
f
h
y
p
er
p
ar
am
eter
s
a
n
d
id
e
n
tify
in
g
th
e
m
o
d
el
th
at
p
e
r
f
o
r
m
s
th
e
b
est.
3.
7.
M
o
del
e
v
a
lua
t
i
o
n
E
v
alu
atio
n
m
etr
ics
lik
e
ac
c
u
r
ac
y
,
r
ec
all
,
an
d
F1
s
co
r
e
ar
e
u
s
ed
to
e
v
alu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
ea
ch
m
o
d
el.
R
F
an
d
GB
C
m
o
d
el
s
tr
ain
ed
u
s
in
g
t
h
e
g
r
id
s
ea
r
ch
m
eth
o
d
a
n
d
g
e
n
etic
m
et
h
o
d
a
r
e
ev
alu
ate
d
s
ep
ar
ately
b
y
ev
alu
atio
n
m
et
r
ics.
I
n
t
h
e
n
e
x
t
s
tep
,
a
co
m
p
ar
i
s
o
n
o
f
all
th
e
m
o
d
els
is
d
o
n
e
u
s
in
g
th
ese
m
o
d
el
ev
alu
atio
n
d
ata
.
3.
8.
M
o
del
c
o
m
pa
riso
n
Ma
tp
lo
tlib
is
u
s
ed
to
p
lo
t
a
g
r
ap
h
b
ased
o
n
th
e
F1
s
co
r
e
o
f
ea
ch
m
o
d
el.
T
h
e
b
est
m
o
d
e
l
an
d
b
est
h
y
p
er
p
ar
am
eter
s
elec
tio
n
tech
n
iq
u
e
ar
e
ea
s
ily
id
en
tifie
d
b
y
r
ea
d
in
g
t
h
e
g
r
ap
h
.
Af
ter
t
h
e
d
etailed
d
escr
ip
tio
n
o
f
th
e
p
r
o
p
o
s
ed
r
esear
ch
m
eth
o
d
o
lo
g
y
g
iv
e
n
ab
o
v
e
,
th
e
im
p
lem
en
tatio
n
r
esu
lts
with
d
is
cu
s
s
io
n
ar
e
p
r
o
v
id
ed
in
th
e
n
ex
t sectio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
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2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
o
p
ti
miz
ed
s
ea
ve
s
s
el
lo
ca
tio
n
d
etec
tio
n
to
…
(
A
ja
y
K
u
ma
r
)
1631
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
th
e
im
p
lem
en
tatio
n
r
esu
lts
ar
e
ex
p
lain
ed
with
th
e
h
elp
o
f
s
o
m
e
p
er
f
o
r
m
an
ce
p
ar
am
eter
s
an
d
g
eo
g
r
ap
h
ical
m
o
d
els.
Usi
n
g
g
r
ap
h
ical
r
ep
r
esen
tatio
n
s
o
f
f
is
h
in
g
tr
ac
k
s
,
s
u
ch
as
tr
ac
k
s
o
f
f
is
h
in
g
v
ess
els
an
d
th
e
esti
m
ated
f
is
h
in
g
lo
ca
tio
n
s
an
d
ar
ea
s
,
v
ess
el
m
o
v
em
en
ts
,
an
d
f
is
h
in
g
p
atter
n
s
ca
n
b
e
ex
p
lain
ed
in
a
s
tr
aig
h
ter
f
o
r
w
ar
d
an
d
ea
s
y
to
f
o
llo
w
m
an
n
e
r
.
Fo
r
a
g
iv
en
MM
SI
in
o
u
r
r
e
s
ea
r
ch
,
two
im
a
g
es
o
f
th
e
tr
ac
k
s
ar
e
g
en
e
r
ated
,
o
n
e
in
d
icatin
g
th
e
ac
tu
al
f
is
h
in
g
s
ites
(
in
r
ed
)
a
n
d
a
n
o
th
er
im
ag
e
with
th
e
p
r
ed
icted
f
is
h
in
g
s
ites
(
in
g
r
ee
n
)
as sh
o
wn
in
Fig
u
r
es 5
an
d
6
.
Fig
u
r
e
5
.
Geo
g
r
ap
h
ical
r
ep
r
es
en
tatio
n
o
f
ac
t
u
al
an
d
p
r
ed
icte
d
s
ites
Fig
u
r
e
6
.
Z
o
o
m
e
d
g
eo
g
r
ap
h
ical
r
ep
r
esen
tatio
n
o
f
ac
tu
al
an
d
p
r
ed
icted
s
ites
Ou
r
r
esear
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p
r
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p
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ased
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p
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ter
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etic
h
y
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e
r
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ar
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tim
izatio
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h
r
o
u
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h
a
d
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led
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is
o
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ix
m
o
d
el
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air
s
’
F1
s
co
r
es
an
d
o
th
er
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e
r
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en
t
m
etr
ics,
as
s
h
o
wn
in
T
ab
le
1
,
o
u
r
r
es
ea
r
ch
p
r
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wh
ich
alg
o
r
ith
m
s
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r
k
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est
f
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r
class
if
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g
th
e
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ar
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tem
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est
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ith
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r
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R
F is
b
es
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o
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ce
r
n
in
g
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is
tab
le,
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e
b
est m
o
d
e
l c
an
b
e
im
p
lem
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ted
o
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lar
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e
s
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le
to
im
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r
ac
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d
ef
f
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h
e
p
er
f
o
r
m
an
ce
m
atr
ix
o
f
s
ea
v
e
s
s
el
m
o
d
els
af
ter
g
e
n
etic
o
p
ti
m
izati
o
n
is
g
iv
e
n
in
T
ab
le
1
.
T
ab
le
1
.
T
h
e
p
er
f
o
r
m
a
n
ce
m
at
r
ix
M
o
d
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l
A
c
c
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r
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y
R
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c
a
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Lo
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9
2
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9
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9
1
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B
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8
8
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8
9
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F
Tr
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9
9
1
4
.
1
.
Co
m
pa
riso
n wit
h ba
s
eline
a
lg
o
rit
hm
s
Pre
v
io
u
s
r
esear
ch
h
as
u
tili
ze
d
v
ar
io
u
s
m
ac
h
in
e
lear
n
in
g
m
e
th
o
d
s
,
in
clu
d
in
g
g
r
ad
ien
t
b
o
o
s
tin
g
an
d
RF
,
to
tack
le
th
e
ch
allen
g
e
o
f
d
etec
tin
g
illeg
al
f
is
h
in
g
ac
t
iv
ities
.
T
h
ese
m
o
d
els
h
av
e
p
r
o
v
en
ef
f
ec
tiv
e
b
u
t
o
f
ten
f
ac
e
lim
itatio
n
s
in
ter
m
s
o
f
ac
c
u
r
ac
y
an
d
p
r
e
d
ictiv
e
ca
p
ab
ilit
y
.
I
n
o
u
r
s
tu
d
y
,
we
en
h
a
n
ce
th
ese
tr
ad
itio
n
al
ap
p
r
o
ac
h
es
b
y
r
ef
i
n
in
g
th
o
s
e
u
s
in
g
g
en
etic
alg
o
r
ith
m
s
,
wh
ich
in
tr
o
d
u
ce
ev
o
lu
t
io
n
ar
y
o
p
tim
izatio
n
tech
n
iq
u
es
to
f
in
e
-
t
u
n
e
h
y
p
er
p
ar
am
eter
s
an
d
im
p
r
o
v
e
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
is
m
eth
o
d
o
lo
g
ical
ad
v
an
ce
m
e
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
626
-
1
6
3
6
1632
aim
s
to
ac
h
iev
e
m
o
r
e
p
r
ec
is
e
d
etec
tio
n
o
f
f
is
h
in
g
ac
tiv
ities
an
d
b
etter
p
r
e
d
ictio
n
o
f
v
ess
el
m
o
v
em
en
ts
.
I
n
th
is
s
ec
tio
n
,
we
co
n
d
u
ct
a
co
m
p
ar
ativ
e
an
al
y
s
is
b
etwe
en
th
e
p
er
f
o
r
m
an
ce
o
f
ex
is
tin
g
a
lg
o
r
ith
m
s
an
d
o
u
r
o
p
tim
ized
m
o
d
els.
T
h
is
co
m
p
ar
is
o
n
h
ig
h
lig
h
ts
th
e
im
p
r
o
v
e
m
en
ts
b
r
o
u
g
h
t
b
y
th
e
g
en
etic
alg
o
r
ith
m
-
e
n
h
an
ce
d
m
o
d
els,
d
em
o
n
s
tr
atin
g
th
eir
s
u
p
er
i
o
r
ab
ilit
y
to
ac
cu
r
ately
i
d
en
tify
illi
cit
f
is
h
in
g
ac
tiv
itie
s
an
d
p
r
ed
ict
v
ess
el
b
eh
av
io
r
m
o
r
e
ef
f
ec
tiv
ely
t
h
a
n
co
n
v
en
tio
n
al
m
et
h
o
d
s
.
4
.
1
.
1
.
RF
v
s
RF
o
pti
m
ized
wit
h g
enet
ic
a
lg
o
rit
hm
B
ec
au
s
e
th
e
h
y
p
e
r
p
ar
am
ete
r
s
ar
e
tailo
r
e
d
ac
co
r
d
in
g
to
th
e
u
n
iq
u
e
d
ata
an
d
jo
b
,
o
p
tim
ized
R
F
p
er
f
o
r
m
s
b
etter
(
f
o
r
e
x
am
p
le,
h
ig
h
er
ac
cu
r
ac
y
,
r
ec
all,
an
d
F1
-
s
co
r
e)
.
B
aselin
e
RF
i
s
q
u
ick
er
to
tr
ain
s
in
ce
it
d
o
e
s
n
o
t
r
e
q
u
i
r
e
a
n
y
h
y
p
e
r
p
a
r
a
m
e
t
e
r
s
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r
c
h
e
s
.
S
i
m
p
l
y
t
r
ai
n
t
h
e
m
o
d
e
l
w
i
t
h
t
h
e
d
e
f
a
u
l
t
v
a
lu
e
s
.
F
i
g
u
r
e
s
7
a
n
d
8
d
em
o
n
s
tr
ates
th
e
m
etr
ics
o
b
ta
in
ed
b
e
f
o
r
e
an
d
af
te
r
g
en
etic
alg
o
r
ith
m
o
p
tim
izatio
n
in
m
o
d
el
-
1
p
u
r
s
e
s
ein
es.
B
aselin
e
R
F
ac
cu
r
ac
y
:
0
.
8
2
6
0
0
,
o
p
tim
ized
R
F
ac
cu
r
ac
y
:
0
.
8
8
7
4
0
,
b
aselin
e
R
F
r
ec
all:
0
.
7
5
5
3
2
,
o
p
tim
ize
d
R
F
r
ec
all: 0
.
8
2
1
4
3
5
,
b
aselin
e
R
F F1
-
s
co
r
e:
0
.
4
9
4
7
7
,
o
p
tim
ized
R
F F1
-
s
co
r
e:
0
.
7
4
9
5
7
.
Fig
u
r
e
7
.
E
v
alu
atio
n
m
etr
ics o
f
R
F m
o
d
el
Fig
u
r
e
8
.
E
v
alu
atio
n
m
etr
ics o
f
R
F m
o
d
el
with
GA
o
p
tim
izatio
n
4
.
1
.
2
.
G
B
C
v
s
G
B
C
o
ptim
ized
wit
h
g
enet
ic
a
lg
o
rit
hm
T
h
e
en
h
an
ce
d
GB
C
with
a
g
en
etic
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
s
th
e
b
aselin
e
m
o
d
el,
alb
eit
at
t
h
e
co
s
t
o
f
ad
d
itio
n
al
c
o
m
p
u
ti
n
g
tim
e
an
d
co
m
p
lex
ity
.
T
h
is
tr
a
d
e
-
o
f
f
is
f
r
eq
u
e
n
tly
ju
s
tifia
b
le
i
n
s
itu
atio
n
s
wh
er
e
th
e
m
ax
im
u
m
p
o
te
n
tial
p
er
f
o
r
m
a
n
ce
is
r
e
q
u
ir
ed
.
Fig
u
r
e
s
9
an
d
1
0
d
em
o
n
s
tr
ates
th
e
m
etr
ics
o
b
tain
ed
b
ef
o
r
e
an
d
af
ter
g
en
etic
alg
o
r
ith
m
o
p
tim
izatio
n
in
m
o
d
el
-
1
p
u
r
s
e
s
ein
es.
B
ase
lin
e
GB
C
a
cc
u
r
ac
y
:
0
.
8
2
8
6
0
,
o
p
tim
ized
GB
C
a
cc
u
r
ac
y
:
0
.
8
8
7
2
0
,
b
aselin
e
GB
C
r
ec
all:
0
.
7
8
1
9
1
,
o
p
t
im
ized
GB
C
r
ec
all:
0
.
9
8
1
2
0
,
b
aselin
e
GB
C
F1
-
s
co
r
e:0
.
5
0
7
1
9
,
o
p
tim
ized
GB
C
F1
-
s
co
r
e:
0
.
9
7
6
1
2
.
Fig
u
r
e
9
.
E
v
alu
atio
n
m
etr
ics o
f
GB
C
m
o
d
el
Fig
u
r
e
1
0
.
E
v
alu
atio
n
m
etr
ics
o
f
th
e
GB
C
m
o
d
el
with
GA
o
p
tim
izatio
n
4
.
2
.
O
v
er
a
ll
m
o
del c
o
m
pa
riso
n
Ou
r
r
esear
ch
p
r
o
p
o
s
ed
an
o
v
er
all
m
o
d
el
co
m
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ar
is
o
n
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etw
ee
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e
ex
is
tin
g
m
eth
o
d
s
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.
g
r
id
s
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r
ch
h
y
p
er
p
ar
am
eter
s
elec
tio
n
an
d
th
e
o
p
tim
ized
m
eth
o
d
i.e
.
g
en
etic
h
y
p
er
p
ar
am
eter
s
elec
tio
n
.
C
o
n
ce
r
n
i
n
g
th
e
g
r
ap
h
,
as
s
h
o
wn
i
n
Fig
u
r
e
s
1
1
an
d
1
2
,
o
f
F1
s
co
r
e
it
ca
n
b
e
ea
s
ily
d
eter
m
in
ed
th
at
af
te
r
ap
p
ly
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en
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alg
o
r
ith
m
th
e
o
v
er
all
F1
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e
is
in
cr
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s
ed
f
o
r
b
o
th
GB
C
an
d
R
F
m
eth
o
d
s
s
ig
n
if
ican
tly
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
o
p
ti
miz
ed
s
ea
ve
s
s
el
lo
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tio
n
d
etec
tio
n
to
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(
A
ja
y
K
u
ma
r
)
1633
Fig
u
r
e
1
1
.
F1
s
co
r
e
o
f
d
if
f
e
r
en
t m
o
d
e
Fig
u
r
e
1
2
.
F1
s
co
r
e
o
f
d
if
f
e
r
en
t m
o
d
els af
ter
g
e
n
etic
o
p
tim
izatio
n
5.
CO
NCLU
SI
O
N
T
h
e
r
esear
ch
co
n
cl
u
d
es
th
at
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h
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is
s
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with
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n
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n
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tain
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f
is
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est
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o
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d
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er
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t
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g
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r
ty
p
es
o
f
s
ea
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el
s
u
s
in
g
m
ac
h
in
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n
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alg
o
r
ith
m
s
.
T
h
e
m
o
d
ellin
g
o
f
g
eo
g
r
ap
h
i
ca
l
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s
is
u
s
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to
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r
ed
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co
r
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t
f
is
h
in
g
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ites
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elp
s
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m
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atin
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UU
f
is
h
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g
.
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h
e
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p
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eth
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s
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two
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o
r
ith
m
s
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.
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an
d
g
r
ad
ien
t
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g
to
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t
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est
v
ess
el
co
n
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er
.
Als
o
,
with
g
en
etic
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tim
izatio
n
o
f
h
y
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er
p
a
r
am
et
er
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tio
n
o
n
th
e
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m
o
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iv
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etter
r
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lts
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ter
m
s
o
f
ac
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r
ac
y
,
F1
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co
r
e
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d
r
ec
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as
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m
p
ar
ed
to
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er
p
ar
am
eter
s
elec
tio
n
tech
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iq
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es.
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h
e
d
ataset
u
s
ed
to
d
o
th
is
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is
GFW
AI
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wh
ich
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e
lar
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er
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atasets
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d
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m
p
atib
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ith
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r
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g
h
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r
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UU
f
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if
ican
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p
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ily
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f
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e
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ata,
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in
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ts
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tem
(
VM
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ata
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lete
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iew
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im
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o
r
ith
m
’
s
ac
cu
r
a
cy
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
C
.
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k
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.
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[
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[
3
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D
.
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[
4
]
K
.
L
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.
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Re
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[
5
]
D
.
P
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:
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D
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C
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:
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sl
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0
.
[
6
]
F
.
S
mi
t
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a
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d
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,
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C
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b
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,
2
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5
.
[
O
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l
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n
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]
.
A
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[
7
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S
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M
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,
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2
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.
[
8
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.
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a
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,
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C
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.
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p
p
.
1
4
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.
[
9
]
D
.
J.
A
g
n
e
w
,
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T
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me,
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ti
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l
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so
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iatio
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E
n
g
i
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e
rs
(IAENG
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
jay
k
rv
e
rm
a
@jss
a
ten
.
a
c
.
in
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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in
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las
s
.
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c
a
n
b
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c
o
n
tac
ted
a
t
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m
a
il
:
p
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p
8
9
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ten
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c
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in
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a
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m
VTU,
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rn
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tak
a
,
In
d
ia,
i
n
2
0
1
7
.
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is
c
u
rre
n
tl
y
wo
rk
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n
g
a
s
a
n
As
sista
n
t
P
ro
fe
ss
o
r
with
th
e
JSS
a
c
a
d
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m
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f
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ica
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e
d
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c
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ti
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n
,
No
i
d
a
,
U.
P
,
In
d
ia.
AK
TU.
His
re
se
a
rc
h
i
n
tere
sts
in
c
lu
d
e
b
lo
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k
c
h
a
in
se
c
u
rit
y
,
c
l
o
u
d
se
c
u
rit
y
,
I
o
T
a
n
d
c
y
b
e
r
se
c
u
rit
y
.
He
is
a
m
e
m
b
e
r
o
f
IAENG
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
h
a
rsh
a
k
g
.
o
fficia
l@g
m
a
il
.
c
o
m
.
Vin
o
o
t
h
P.
re
c
e
iv
e
d
th
e
B.
E.
d
e
g
re
e
i
n
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
En
g
i
n
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e
rin
g
fro
m
VV
IET
,
a
n
d
M
.
Tec
h
.
i
n
CS
E
f
ro
m
VTU
-
RC,
Ka
rn
a
tak
a
.
He
is
Wo
rk
i
n
g
a
s
a
n
As
sista
n
t
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ro
fe
ss
o
r
in
t
h
e
D
e
p
a
rtme
n
t
o
f
CS
E,
JSS
Un
i
v
e
rsity
,
No
i
d
a
fro
m
p
a
st
2
.
5
y
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p
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P
h
.
D
.
i
n
Cy
b
e
r
se
c
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rit
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a
t
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S
TU,
M
y
su
r
u
.
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c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
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in
o
o
t
h
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a
ten
.
a
c
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in
.
Pa
n
k
a
j
K
u
m
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n
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ra
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te
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r
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h
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ter
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c
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n
d
En
g
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n
e
e
rin
g
)
stu
d
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n
t
in
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S
Ac
a
d
e
m
y
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f
Tec
h
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E
d
u
c
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ti
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,
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.
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v
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h
is
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ra
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st
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ter
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e
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in
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ro
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ra
m
m
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a
g
e
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d
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ta
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c
tu
re
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n
d
a
l
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rit
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s.
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ird
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t
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se
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h
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e
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h
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ll
a
b
o
ra
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p
r
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ss
o
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t
o
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m
a
c
h
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rn
i
n
g
tec
h
n
iq
u
e
s.
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is
a
p
a
ss
io
n
a
te
so
ftwa
re
d
e
v
e
lo
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e
r
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o
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rsu
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s
h
i
s
re
se
a
r
c
h
e
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p
a
ra
ll
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l
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th
e
m
.
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t
h
th
e
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se
o
f
se
v
e
r
a
l
f
ra
m
e
wo
rk
s
a
n
d
fu
ll
-
sta
c
k
tec
h
n
o
l
o
g
ies
,
h
e
h
a
s
c
o
n
tri
b
u
ted
to
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v
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riet
y
o
f
p
r
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jec
ts.
As
a
S
y
st
e
m
En
g
in
e
e
r
a
t
Tata
C
o
n
s
u
lt
a
n
c
y
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e
rv
ice
s
(TCS
),
h
e
is
a
b
o
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t
to
sta
rt
a
n
e
w
c
h
a
p
ter
i
n
h
is
c
a
re
e
r
in
u
p
c
o
m
in
g
m
o
n
t
h
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
p
a
n
k
a
j
v
e
rm
a
4
9
5
0
@
g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.