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o
o
m
s
(
HABs
)
,
a
r
ec
u
r
r
in
g
p
h
en
o
m
en
o
n
t
h
at
d
is
r
u
p
ts
m
ar
in
e
ec
o
s
y
s
tem
s
,
f
is
h
er
ies,
an
d
wat
er
q
u
ality
m
an
ag
e
m
en
t.
T
r
ad
it
io
n
al
m
o
n
ito
r
in
g
ap
p
r
o
ac
h
es,
wh
ich
r
ely
h
ea
v
ily
o
n
m
an
u
al
wate
r
q
u
ality
s
am
p
lin
g
,
ar
e
o
f
ten
tim
e
-
c
o
n
s
u
m
i
n
g
an
d
r
ea
ctiv
e.
B
y
co
n
tr
ast,
p
r
ed
ictiv
e
m
o
d
els
o
f
f
er
a
p
r
o
ac
tiv
e
s
tr
ateg
y
f
o
r
ea
r
ly
war
n
i
n
g
an
d
r
esp
o
n
s
e.
Desp
ite
g
r
o
win
g
in
ter
est
in
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
f
o
r
HAB
d
etec
tio
n
,
lim
ited
s
tu
d
ies
h
av
e
ex
p
lo
r
ed
th
e
p
er
f
o
r
m
an
ce
o
f
R
B
FN
s
in
th
is
d
o
m
ain
o
r
b
en
ch
m
ar
k
ed
th
em
a
g
ain
s
t
alter
n
ativ
e
alg
o
r
ith
m
s
s
u
ch
as
r
an
d
o
m
f
o
r
ests
(
R
Fs
)
o
r
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
s
(
SVMs)
[
9
]
,
[
1
0
]
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
h
y
b
r
i
d
m
ac
h
in
e
lear
n
in
g
f
r
am
ew
o
r
k
th
at
co
m
b
i
n
es
R
B
FNs
with
FC
M
clu
s
ter
in
g
to
p
r
ed
ict
HAB
ev
en
ts
b
ased
o
n
k
ey
wate
r
q
u
ality
p
ar
am
eter
s
.
T
h
e
m
o
d
el
lev
er
ag
es
r
ea
l
-
wo
r
l
d
s
en
s
o
r
d
ata,
p
u
b
lic
d
o
m
ain
d
at
asets
,
an
d
s
y
n
th
etic
d
atasets
g
en
er
ated
t
h
r
o
u
g
h
co
m
p
ar
ativ
e
s
tatis
t
ical
m
eth
o
d
s
to
en
s
u
r
e
s
u
f
f
icien
t
v
ar
ia
b
ilit
y
an
d
g
en
er
aliza
b
ilit
y
.
A
co
m
p
ar
ativ
e
an
aly
s
is
is
co
n
d
u
cted
to
ev
alu
ate
m
o
d
el
p
er
f
o
r
m
an
ce
ag
ain
s
t
R
F
class
if
ier
s
,
em
p
h
asizin
g
class
if
icatio
n
ac
c
u
r
ac
y
,
g
e
n
er
aliza
tio
n
to
u
n
s
ee
n
d
ata,
a
n
d
s
en
s
itiv
ity
to
in
co
m
p
lete
o
r
n
o
is
y
in
p
u
ts
.
T
h
e
co
n
tr
i
b
u
tio
n
s
o
f
th
is
p
a
p
er
ar
e
th
r
ee
f
o
ld
.
First,
it
p
r
esen
ts
an
o
p
tim
ized
R
B
FN
ar
ch
itectu
r
e
tailo
r
ed
f
o
r
en
v
ir
o
n
m
en
tal
p
r
e
d
ictio
n
task
s
,
en
h
an
ce
d
with
f
u
zz
y
clu
s
ter
in
g
f
o
r
im
p
r
o
v
ed
ce
n
ter
in
itializatio
n
.
Seco
n
d
,
it
d
em
o
n
s
tr
ates
th
e
c
o
m
p
ar
ativ
e
ad
v
a
n
tag
e
o
f
th
is
h
y
b
r
id
R
B
FN
m
o
d
el
o
v
e
r
co
n
v
en
tio
n
al
m
o
d
els,
in
clu
d
in
g
R
F,
p
ar
ticu
lar
ly
in
r
ea
l
-
tim
e,
lo
w
-
r
eso
u
r
ce
s
e
ttin
g
s
.
T
h
ir
d
,
th
e
s
tu
d
y
p
r
o
v
id
es
in
s
ig
h
ts
in
to
d
ep
lo
y
m
e
n
t
ch
allen
g
es,
s
u
ch
as
d
ata
co
m
p
leten
ess
an
d
o
v
er
f
itti
n
g
r
is
k
s
,
an
d
p
r
o
p
o
s
es
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
to
war
d
im
p
r
o
v
in
g
m
o
d
el
r
o
b
u
s
tn
ess
.
T
h
e
r
em
ai
n
d
er
o
f
th
is
p
ap
er
i
s
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
tio
n
2
r
ev
iews
th
e
f
u
n
d
am
en
tals
o
f
R
B
FN
s
an
d
tr
ain
in
g
m
eth
o
d
o
l
o
g
ies.
Sectio
n
3
d
escr
ib
es
th
e
d
ataset,
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
an
d
m
o
d
el
ar
ch
itectu
r
e.
Sectio
n
4
p
r
esen
ts
ex
p
e
r
im
en
t
al
r
esu
lts
an
d
a
d
etailed
d
is
cu
s
s
io
n
co
m
p
ar
in
g
th
e
p
r
o
p
o
s
ed
m
o
d
el
with
ex
is
tin
g
tech
n
iq
u
es.
Fin
ally
,
s
ec
tio
n
5
co
n
clu
d
es th
e
p
ap
er
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
ab
le
1
s
u
m
m
ar
izes
s
o
m
e
o
f
th
e
k
ey
r
ec
en
t
a
d
v
an
ce
m
e
n
ts
in
R
B
F
N
r
esear
ch
.
T
h
ese
d
ev
elo
p
m
en
ts
s
p
an
en
h
an
ce
d
tr
ain
in
g
alg
o
r
ith
m
s
th
r
o
u
g
h
h
y
b
r
id
izatio
n
with
d
ee
p
lear
n
in
g
m
o
d
els,
h
ig
h
lig
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tin
g
a
tr
e
n
d
to
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d
in
teg
r
atin
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R
B
FNs
wit
h
m
o
r
e
co
m
p
lex
n
e
u
r
al
ar
ch
ite
ctu
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es.
Fu
r
th
er
m
o
r
e,
th
e
r
esear
ch
also
f
o
cu
s
es o
n
p
r
ac
tical
im
p
lem
en
tatio
n
s
,
in
clu
d
in
g
ad
v
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n
ce
m
en
ts
in
h
a
r
d
war
e
ac
ce
ler
atio
n
f
o
r
co
m
p
u
tatio
n
al
ef
f
icien
cy
an
d
ap
p
licatio
n
s
in
em
er
g
i
n
g
a
r
ea
s
s
u
ch
as b
eh
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io
r
r
ec
o
g
n
it
io
n
.
R
B
FNs
d
if
f
er
en
tiate
th
em
s
el
v
es
f
r
o
m
o
th
er
ANN
ar
ch
itectu
r
es
d
u
e
to
th
eir
u
n
iq
u
e
s
tr
u
ctu
r
e
an
d
tr
ain
in
g
m
eth
o
d
o
lo
g
y
.
C
o
m
p
ar
ed
to
m
u
lti
-
lay
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r
p
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ce
p
tr
o
n
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ML
Ps
)
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d
co
n
v
o
lu
tio
n
a
l
n
eu
r
al
n
etwo
r
k
s
(
C
NNs),
R
B
F
Ns
o
f
f
er
s
ev
er
al
ad
v
an
tag
es
b
u
t
also
h
av
e
s
o
m
e
lim
itatio
n
s
[
1
6
]
–
[
2
2
]
.
T
ab
le
2
p
r
o
v
id
es
a
co
m
p
ar
ativ
e
an
al
y
s
is
o
f
d
if
f
er
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t
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alg
o
r
ith
m
s
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s
ed
in
R
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N
tr
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g
,
f
o
cu
s
in
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o
n
th
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m
eth
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ad
v
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an
d
ty
p
ical
ap
p
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s
.
T
ab
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1
.
R
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d
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ts
in
R
B
FN
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R
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D
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ms
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y
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s
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n
t
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w
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p
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n
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b
e
r
-
p
h
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c
a
l
s
y
st
e
m
s.
[
1
1
]
D
e
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p
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r
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n
g
a
n
d
R
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s
H
y
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c
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r
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y
.
[
1
2
]
,
[
1
3
]
H
a
r
d
w
a
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c
c
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l
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r
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mo
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R
B
F
N
s fo
r
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f
f
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sca
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g
.
[
1
4
]
Emerg
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p
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C
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AI
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s
f
o
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b
e
h
a
v
i
o
r
a
n
a
l
y
si
s
.
[
1
5
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
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&
C
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m
p
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n
g
I
SS
N:
2088
-
8
7
0
8
Op
timiz
in
g
r
a
d
ia
l b
a
s
is
fu
n
ctio
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n
etw
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r
ks fo
r
…
(
N
ik
N
o
r
Mu
h
a
mma
d
S
a
ifu
d
in
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ik
Mo
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K
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ma
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)
5649
T
ab
le
2
.
L
ea
r
n
in
g
a
l
g
o
r
ith
m
s
o
f
R
B
FNs
Le
a
r
n
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n
g
a
l
g
o
r
i
t
h
m
D
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scri
p
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A
p
p
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c
a
t
i
o
n
En
se
mb
l
e
c
l
u
st
e
r
i
n
g
[
2
3
]
C
o
m
b
i
n
e
s m
u
l
t
i
p
l
e
c
l
u
st
e
r
i
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g
met
h
o
d
s t
o
d
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mi
n
e
R
B
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c
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t
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s,
e
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i
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g
r
o
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u
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a
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g
RBF
-
A
R
X
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n
t
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g
r
a
t
i
o
n
[
2
4
]
C
o
m
b
i
n
e
s R
B
F
N
s wi
t
h
A
u
t
o
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e
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H
y
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r
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d
R
B
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N
s
[
2
5
]
I
n
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r
a
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s
R
B
F
N
s wi
t
h
o
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v
e
t
i
me
seri
e
s f
o
r
e
c
a
st
i
n
g
.
I
n
c
r
e
a
se
d
f
o
r
e
c
a
s
t
i
n
g
a
c
c
u
r
a
c
y
,
v
e
r
sa
t
i
l
i
t
y
F
i
n
a
n
c
i
a
l
f
o
r
e
c
a
st
i
n
g
,
w
e
a
t
h
e
r
p
r
e
d
i
c
t
i
o
n
K
-
M
e
a
n
s
a
n
d
g
r
a
d
i
e
n
t
d
e
sc
e
n
t
[
7
]
Emp
l
o
y
s
k
-
me
a
n
s
f
o
r
c
e
n
t
e
r
d
e
t
e
r
mi
n
a
t
i
o
n
a
n
d
g
r
a
d
i
e
n
t
d
e
sc
e
n
t
f
o
r
w
e
i
g
h
t
o
p
t
i
m
i
z
a
t
i
o
n
i
n
r
e
s
e
r
v
o
i
r
c
h
a
r
a
c
t
e
r
i
z
a
t
i
o
n
.
Ef
f
e
c
t
i
v
e
m
o
d
e
l
l
i
n
g
o
f
g
e
o
l
o
g
i
c
a
l
d
a
t
a
,
r
o
b
u
s
t
p
r
e
d
i
c
t
i
o
n
s
G
e
o
l
o
g
i
c
a
l
r
e
ser
v
o
i
r
c
h
a
r
a
c
t
e
r
i
z
a
t
i
o
n
3.
M
E
T
H
O
D
T
h
is
s
tu
d
y
ad
o
p
ted
a
h
y
b
r
i
d
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
to
p
r
e
d
ict
Har
m
f
u
l
Alg
al
B
lo
o
m
(
HAB)
ev
en
ts
u
s
in
g
wate
r
q
u
ality
d
ata.
T
h
e
m
eth
o
d
o
lo
g
y
c
o
n
s
is
ted
o
f
f
i
v
e
m
ain
s
tag
es
wh
ich
d
ata
p
r
ep
ar
atio
n
,
s
y
n
th
etic
d
ata
g
en
e
r
atio
n
,
f
ea
t
u
r
e
n
o
r
m
aliza
tio
n
,
m
o
d
el
d
esig
n
,
an
d
p
e
r
f
o
r
m
an
ce
ev
al
u
atio
n
.
An
o
v
er
v
iew
o
f
th
e
f
u
ll
wo
r
k
f
lo
w
is
illu
s
tr
ate
d
in
Fig
u
r
e
1
.
T
wo
ty
p
es
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
wer
e
u
s
ed
,
a
s
tan
d
ar
d
R
F
class
if
ier
an
d
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el
th
at
co
m
b
in
es R
B
FNs
with
F
C
M
clu
s
ter
in
g
.
Mo
d
el
tr
ain
in
g
an
d
ev
alu
atio
n
wer
e
p
e
r
f
o
r
m
ed
u
s
in
g
co
n
s
is
ten
t
d
ata
s
p
lit
f
o
r
all
m
o
d
els,
with
7
0
%
o
f
th
e
d
ata
u
s
ed
f
o
r
t
r
ain
in
g
an
d
3
0
%
r
eser
v
e
d
f
o
r
test
in
g
.
T
h
e
m
o
d
els
wer
e
ass
ess
ed
b
ased
o
n
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
,
with
th
e
latter
p
r
io
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itized
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u
e
to
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b
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ce
d
r
ep
r
esen
tatio
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o
f
s
en
s
itiv
ity
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d
p
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ec
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n
,
esp
ec
ially
u
n
d
er
im
b
alan
ce
d
class
co
n
d
itio
n
s
.
T
h
is
ev
alu
atio
n
a
p
p
r
o
ac
h
e
n
s
u
r
e
s
th
at
th
e
m
o
d
el
is
n
o
t
o
n
ly
ac
cu
r
ate
in
g
en
er
al
c
lass
if
icatio
n
b
u
t
also
ef
f
ec
tiv
e
in
id
en
tify
in
g
m
in
o
r
ity
class
in
s
tan
ce
s
,
wh
ich
in
th
is
ca
s
e
ar
e
th
e
r
ar
e
HAB ev
e
n
ts
.
T
h
e
m
o
d
els
wer
e
d
ev
el
o
p
ed
u
s
in
g
Py
th
o
n
a
n
d
MA
T
L
AB
an
d
ex
ec
u
te
d
o
n
Go
o
g
le
C
o
la
b
p
latf
o
r
m
.
L
ib
r
ar
ies
s
u
ch
as
Scik
it
-
lear
n
,
Nu
m
Py
,
Pan
d
as,
an
d
KE
R
AS
wer
e
u
tili
ze
d
f
o
r
d
ata
m
an
ip
u
latio
n
,
m
o
d
el
tr
ain
in
g
,
an
d
ev
alu
atio
n
.
T
h
i
s
h
y
b
r
i
d
p
r
o
g
r
am
m
in
g
en
v
ir
o
n
m
en
t
en
ab
led
ef
f
icien
t
ex
p
er
im
en
tatio
n
an
d
en
s
u
r
ed
c
o
m
p
atib
ilit
y
ac
r
o
s
s
t
o
o
ls
.
All
co
d
e
a
n
d
d
atasets
u
s
ed
in
th
is
s
tu
d
y
h
a
v
e
b
ee
n
s
tr
u
ctu
r
ed
to
s
u
p
p
o
r
t
r
ep
r
o
d
u
cib
ilit
y
an
d
f
u
tu
r
e
ex
te
n
s
io
n
b
y
o
th
er
r
esear
ch
er
s
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
th
e
HA
B
p
r
ed
ictio
n
f
r
a
m
ewo
r
k
3
.
1
.
T
ra
ini
ng
a
pp
ro
a
ch
T
h
e
tr
ain
in
g
p
r
o
ce
s
s
in
R
B
FNs
f
o
llo
ws
a
two
-
s
tep
a
p
p
r
o
ac
h
,
d
eter
m
in
in
g
ce
n
ter
v
ec
t
o
r
s
wh
er
e
ce
n
ter
s
ar
e
ty
p
ically
s
elec
ted
u
s
in
g
clu
s
ter
in
g
alg
o
r
ith
m
s
s
u
ch
as
k
-
m
ea
n
s
o
r
r
an
d
o
m
s
am
p
lin
g
,
a
n
d
o
p
tim
izin
g
weig
h
ts
o
n
ce
ce
n
te
r
s
ar
e
f
ix
ed
,
weig
h
ts
in
th
e
o
u
tp
u
t
lay
er
ar
e
o
p
tim
ized
u
s
in
g
m
eth
o
d
s
lik
e
leas
t
s
q
u
ar
es
r
eg
r
ess
io
n
o
r
g
r
a
d
ien
t
-
b
ased
tech
n
iq
u
es.
T
h
e
s
ep
a
r
a
tio
n
o
f
ce
n
te
r
s
elec
tio
n
f
r
o
m
weig
h
t
o
p
tim
izatio
n
allo
ws
R
B
FN
s
to
tr
ain
ef
f
ici
en
tly
,
av
o
i
d
in
g
b
ac
k
p
r
o
p
ag
ati
o
n
th
r
o
u
g
h
m
u
ltip
le
lay
e
r
s
as
in
d
ee
p
lear
n
i
n
g
m
o
d
els.
T
h
is
tr
ain
in
g
p
r
o
ce
s
s
is
g
en
er
ally
ca
teg
o
r
ize
d
in
to
s
u
p
er
v
is
ed
a
n
d
u
n
s
u
p
er
v
is
ed
le
ar
n
in
g
a
p
p
r
o
ac
h
es.
Su
p
er
v
is
ed
lear
n
in
g
in
v
o
l
v
es
ad
ju
s
tin
g
n
etwo
r
k
p
ar
a
m
ete
r
s
b
ased
o
n
lab
ele
d
tr
ain
i
n
g
d
ata.
T
h
e
p
r
im
ar
y
g
o
al
is
to
m
in
im
ize
th
e
er
r
o
r
b
etwe
en
th
e
p
r
e
d
icted
o
u
tp
u
ts
an
d
th
e
ac
tu
al
tar
g
et
v
alu
es
[
2
6
]
.
Un
s
u
p
er
v
is
ed
lear
n
in
g
u
tili
ze
s
clu
s
ter
in
g
tech
n
iq
u
es,
s
u
ch
a
s
k
-
m
ea
n
s
clu
s
ter
in
g
,
to
d
eter
m
in
e
th
e
ce
n
ter
s
o
f
th
e
r
ad
ial
b
asis
f
u
n
ctio
n
s
with
o
u
t
r
eq
u
ir
in
g
lab
ele
d
d
ata.
T
h
i
s
m
eth
o
d
is
co
m
m
o
n
ly
u
s
ed
i
n
th
e
in
itial
tr
ain
in
g
p
h
ase
to
id
e
n
tify
r
ep
r
esen
tativ
e
d
ata
p
o
in
ts
b
ef
o
r
e
f
in
e
-
tu
n
in
g
th
e
n
etwo
r
k
'
s
weig
h
ts
[
2
7
]
.
Sev
er
al
alg
o
r
ith
m
s
ar
e
f
u
n
d
am
en
tal
to
t
h
e
tr
ain
in
g
o
f
R
B
FNs
ac
co
r
d
in
g
to
T
a
b
le
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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8
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I
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,
Vo
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15
,
No
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6
,
Decem
b
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r
20
25
:
5
6
4
7
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5650
T
ab
le
3
.
T
r
ai
n
in
g
alg
o
r
ith
m
o
f
R
B
F
Ns
A
l
g
o
r
i
t
h
m
F
u
n
c
t
i
o
n
a
l
i
t
y
K
-
M
e
a
n
s
c
l
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r
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A
n
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t
h
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F
s
b
y
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t
i
t
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o
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h
e
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a
t
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l
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c
h
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e
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t
o
r
f
o
r
a
c
o
r
r
e
s
p
o
n
d
i
n
g
R
B
F
n
e
u
r
o
n
.
G
r
a
d
i
e
n
t
d
e
s
c
e
n
t
A
su
p
e
r
v
i
se
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l
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e
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i
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e
t
h
e
w
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i
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h
t
s
o
f
t
h
e
o
u
t
p
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t
l
a
y
e
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y
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t
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a
t
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v
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En
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mb
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C
o
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b
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e
s m
u
l
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c
l
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i
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g
met
h
o
d
s t
o
e
n
h
a
n
c
e
t
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b
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st
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a
n
d
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c
c
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r
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y
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e
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t
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s
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l
e
c
t
i
o
n
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t
h
e
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b
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mp
r
o
v
i
n
g
t
h
e
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e
r
a
l
l
p
e
r
f
o
r
ma
n
c
e
o
f
t
h
e
R
B
F
N
.
3
.
2
.
Da
t
a
prepro
ce
s
s
ing
T
o
p
r
ep
ar
e
th
e
d
ataset
f
o
r
m
o
d
el
tr
ain
in
g
,
m
u
ltip
le
E
x
ce
l
f
iles
co
n
tain
i
n
g
wat
er
q
u
ality
m
ea
s
u
r
em
en
ts
wer
e
co
n
s
o
lid
a
ted
in
to
a
u
n
if
ied
d
ataset.
Stan
d
ar
d
izatio
n
s
tep
s
in
clu
d
ed
r
en
am
in
g
co
lu
m
n
s
f
o
r
co
n
s
is
ten
cy
,
s
elec
tin
g
r
elev
an
t
f
ea
tu
r
es,
an
d
en
c
o
d
in
g
t
h
e
tar
g
et
v
ar
iab
le
as a
b
in
ar
y
class
if
icatio
n
(
1
f
o
r
HAB
o
u
tb
r
ea
k
,
0
f
o
r
n
o
o
u
tb
r
ea
k
)
.
Key
en
v
ir
o
n
m
en
tal
p
ar
a
m
e
ter
s
s
u
ch
as
tem
p
er
atu
r
e
,
to
t
al
d
is
s
o
lv
ed
s
o
lid
s
(
T
DS)
,
s
alin
ity
,
an
d
n
u
tr
ien
t
co
n
ce
n
tr
atio
n
s
wer
e
r
etain
ed
,
wh
ile
n
o
n
-
r
elev
an
t
o
r
r
ed
u
n
d
an
t
f
ea
tu
r
es
wer
e
ex
clu
d
ed
.
Miss
in
g
v
alu
es
wer
e
h
an
d
le
d
th
r
o
u
g
h
im
p
u
tatio
n
wh
er
e
p
o
s
s
ib
le,
wh
ile
r
ec
o
r
d
s
with
ex
ce
s
s
iv
e
m
is
s
in
g
d
ata
wer
e
r
em
o
v
ed
to
en
s
u
r
e
m
o
d
el
r
eliab
ilit
y
.
Fo
llo
win
g
d
ata
clea
n
in
g
an
d
f
ea
tu
r
e
s
elec
tio
n
,
th
e
d
ataset
was
n
o
r
m
alize
d
to
en
s
u
r
e
co
n
s
is
ten
t
v
alu
e
r
an
g
es
ac
r
o
s
s
f
ea
tu
r
es,
wh
ic
h
is
ess
en
tial
f
o
r
o
p
tim
izin
g
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
,
p
ar
ticu
lar
ly
th
o
s
e
s
en
s
itiv
e
to
s
ca
le
d
if
f
er
en
ce
s
.
Sin
ce
th
e
d
ataset
ex
h
ib
ited
class
im
b
alan
ce
with
s
ig
n
if
ican
tly
f
ewe
r
HAB
o
u
tb
r
ea
k
r
ec
o
r
d
s
co
m
p
ar
ed
to
n
o
n
-
o
u
tb
r
ea
k
ca
s
es,
s
y
n
th
etic
m
in
o
r
ity
o
v
er
-
s
am
p
lin
g
tech
n
iq
u
e
(
SMOT
E
)
was
u
s
ed
to
a
d
d
r
e
s
s
th
is
is
s
u
e.
SMOT
E
g
en
er
ates
s
y
n
th
etic
s
am
p
les
o
f
th
e
m
in
o
r
ity
class
b
y
in
ter
p
o
latin
g
b
etwe
en
ex
is
tin
g
m
in
o
r
ity
class
in
s
tan
ce
s
,
ef
f
ec
tiv
ely
b
alan
cin
g
t
h
e
class
d
is
tr
ib
u
tio
n
.
T
h
is
s
tep
was
cr
u
cial
to
p
r
ev
en
t
th
e
m
o
d
els
f
r
o
m
b
ein
g
b
iased
to
war
d
th
e
m
ajo
r
ity
class
an
d
to
en
h
an
ce
th
e
r
eliab
ilit
y
o
f
th
e
class
if
icatio
n
o
u
tco
m
es.
3
.
3
.
E
x
plo
ra
t
o
ry
da
t
a
a
na
l
y
s
is
a
nd
f
ea
t
ure
re
la
t
io
ns
hip
s
A
co
r
r
elatio
n
m
atr
i
x
was
c
o
m
p
u
ted
t
o
e
x
am
in
e
r
elatio
n
s
h
ip
s
b
etwe
en
wate
r
q
u
ality
p
ar
a
m
eter
s
an
d
HAB
o
cc
u
r
r
en
ce
s
.
Fig
u
r
e
2
s
h
o
ws
th
e
C
h
lo
r
o
p
h
y
ll
-
a
ex
h
ib
ited
th
e
s
tr
o
n
g
est
p
o
s
itiv
e
co
r
r
elatio
n
(
0
.
9
4
)
wit
h
HAB
o
u
tb
r
ea
k
s
,
r
ein
f
o
r
cin
g
its
s
ig
n
if
ican
ce
as
a
k
ey
p
r
ed
ictiv
e
f
ea
tu
r
e.
Oth
er
p
a
r
am
eter
s
,
s
u
ch
as
tem
p
er
atu
r
e,
T
DS,
a
n
d
p
H,
r
e
s
u
lted
in
wea
k
er
co
r
r
elatio
n
s
,
in
d
icatin
g
th
at
HAB
o
cc
u
r
r
e
n
ce
is
in
f
lu
en
ce
d
b
y
co
m
p
lex
,
n
o
n
-
lin
ea
r
in
ter
ac
ti
o
n
s
.
Ker
n
el
d
en
s
ity
esti
m
atio
n
(
KDE
)
p
lo
ts
r
ev
ea
led
s
u
b
s
tan
tial
o
v
er
lap
in
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
s
b
etwe
en
o
u
tb
r
ea
k
an
d
n
o
n
-
o
u
tb
r
ea
k
co
n
d
itio
n
s
,
s
u
g
g
esti
n
g
th
at
s
i
m
p
le
lin
ea
r
m
o
d
els
m
ay
b
e
in
s
u
f
f
icien
t f
o
r
ac
cu
r
at
e
class
if
icatio
n
.
Fig
u
r
e
2
.
C
o
r
r
elatio
n
m
atr
ix
b
etwe
en
wate
r
q
u
ality
p
a
r
am
ete
r
s
an
d
tar
g
et
v
ar
iab
les
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Op
timiz
in
g
r
a
d
ia
l b
a
s
is
fu
n
ctio
n
n
etw
o
r
ks fo
r
…
(
N
ik
N
o
r
Mu
h
a
mma
d
S
a
ifu
d
in
N
ik
Mo
h
d
K
a
ma
l
)
5651
Ov
er
lap
p
in
g
d
is
tr
ib
u
tio
n
s
wer
e
o
b
s
er
v
ed
f
o
r
m
o
s
t
f
ea
tu
r
e
s
,
in
clu
d
in
g
tem
p
er
atu
r
e
,
p
H,
an
d
DO,
h
ig
h
lig
h
tin
g
th
e
co
m
p
lex
ity
o
f
s
ep
ar
atin
g
th
e
class
es
u
s
in
g
lin
ea
r
m
eth
o
d
s
.
Fig
u
r
e
3
in
d
icate
s
th
e
C
h
lo
r
o
p
h
y
ll
-
a
s
h
o
wed
th
e
m
o
s
t d
is
tin
ct
s
ep
ar
atio
n
,
alig
n
in
g
with
its
s
tr
o
n
g
co
r
r
elatio
n
t
o
t
h
e
tar
g
et
v
a
r
iab
le.
Fig
u
r
e
3
.
C
o
r
r
elatio
n
p
lo
t
b
et
wee
n
C
h
lo
r
o
p
h
y
ll
-
a
an
d
tar
g
et
v
ar
iab
les
4.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
R
B
FNs
h
av
e
attr
ac
ted
co
n
s
id
er
ab
le
in
ter
est
d
u
e
to
th
eir
ab
ilit
y
to
ef
f
icien
tly
ap
p
r
o
x
im
at
e
co
m
p
lex
f
u
n
ctio
n
s
wh
ile
m
ain
tain
in
g
in
ter
p
r
etab
ilit
y
in
n
e
u
r
al
co
m
p
u
tatio
n
s
.
Ho
wev
er
,
lik
e
an
y
c
o
m
p
u
tatio
n
al
m
o
d
el,
R
B
FNs
co
m
e
wi
th
b
o
th
s
tr
en
g
th
s
an
d
wea
k
n
ess
es.
T
h
is
s
e
ctio
n
p
r
o
v
i
d
es
th
e
r
esu
lts
o
b
t
ain
ed
f
r
o
m
th
e
d
at
a
tr
ain
in
g
an
d
a
c
o
m
p
r
e
h
en
s
iv
e
d
is
cu
s
s
io
n
in
clu
d
in
g
th
e
ad
v
an
tag
es
an
d
lim
itatio
n
s
o
f
R
B
FN
s
,
s
u
p
p
o
r
ted
b
y
r
elev
an
t liter
atu
r
e.
4
.
1
.
P
re
dict
io
n
o
n
re
a
l
-
wo
rl
d unl
a
bele
d da
t
a
s
et
T
o
ev
alu
ate
th
e
p
r
ac
tical
ap
p
l
icab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el,
th
e
tr
ain
ed
R
B
FN
-
FC
M
m
o
d
el
was
test
ed
o
n
an
u
n
la
b
eled
d
ataset
r
ef
er
r
ed
t
o
as
“Da
tan
HL
7
2
0
2
3
–
2
0
2
4
.
”
T
h
is
d
ataset
co
m
p
r
is
ed
1
1
,
7
0
1
r
ec
o
r
d
s
co
n
tain
in
g
r
ea
l
-
tim
e
wate
r
q
u
ality
r
ea
d
in
g
s
.
On
ly
th
r
ee
in
p
u
t
p
ar
am
eter
s
,
wh
ich
ar
e
te
m
p
er
atu
r
e,
p
H,
an
d
ch
lo
r
o
p
h
y
ll
-
a,
wer
e
d
ir
ec
tly
a
v
ailab
le
f
r
o
m
th
e
d
ata
s
o
u
r
ce
,
wh
ile
o
th
er
f
ea
tu
r
es
u
s
ed
d
u
r
in
g
tr
ain
in
g
wer
e
u
n
av
ailab
le.
T
o
m
ain
tain
c
o
n
s
is
ten
cy
in
th
e
in
p
u
t
s
h
a
p
e,
m
is
s
in
g
v
alu
es
wer
e
f
illed
wi
th
ze
r
o
s
.
T
h
e
m
o
d
el
p
r
ed
icted
all
in
s
tan
ce
s
in
th
e
d
ataset
as “
No
Ou
tb
r
ea
k
.
”
T
ab
l
e
4
s
h
o
ws s
o
m
e
o
f
t
h
e
p
r
e
d
icted
d
ata:
Alth
o
u
g
h
th
is
r
esu
lt
m
ay
in
d
icate
th
at
n
o
HAB
ev
en
ts
o
cc
u
r
r
ed
d
u
r
in
g
th
e
p
er
io
d
o
f
d
ata
co
llectio
n
,
it
also
r
ef
lects
a
p
o
ten
tial
lim
itatio
n
o
f
th
e
m
o
d
el
wh
en
a
p
p
l
ied
to
d
atasets
with
in
co
m
p
lete
f
ea
tu
r
e
s
ets.
T
h
e
r
elian
ce
o
n
m
u
ltip
le
p
ar
am
ete
r
s
f
o
r
class
if
icatio
n
s
u
g
g
ests
th
at
th
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
m
ay
d
ec
lin
e
wh
en
o
n
ly
a
s
u
b
s
et
o
f
in
p
u
ts
is
av
ail
ab
le.
T
h
is
o
u
tco
m
e
em
p
h
asize
s
th
e
n
ee
d
f
o
r
a
m
o
r
e
r
o
b
u
s
t m
o
d
el
th
at
ca
n
a
d
ap
t
to
m
is
s
in
g
o
r
im
b
alan
ce
d
d
ata
with
o
u
t sig
n
if
ican
t p
er
f
o
r
m
a
n
ce
d
eg
r
ad
atio
n
.
T
ab
le
4
.
Sam
p
le
p
r
ed
icted
d
at
a
D
a
t
e
Ti
me
Te
mp
e
r
a
t
u
r
e
pH
C
h
l
o
r
o
p
h
y
l
l
-
a
P
r
e
d
i
c
t
e
d
H
A
B
O
u
t
c
o
m
e
2
0
2
4
-
04
-
0
8
1
7
:
1
0
:
0
0
3
2
.
2
8
.
3
2
4
.
1
1
N
o
O
u
t
b
r
e
a
k
2
0
2
3
-
10
-
0
7
1
7
:
5
0
:
0
0
3
1
.
9
6
8
.
0
9
3
.
1
5
N
o
O
u
t
b
r
e
a
k
2
0
2
3
-
12
-
2
8
2
3
:
0
0
:
0
0
2
9
.
4
8
.
2
5
4
.
6
1
N
o
O
u
t
b
r
e
a
k
2
0
2
4
-
09
-
2
1
0
0
:
0
0
:
0
0
2
9
.
7
7
7
.
8
5
2
.
6
2
N
o
O
u
t
b
r
e
a
k
2
0
2
4
-
03
-
2
5
1
6
:
5
0
:
0
0
3
2
.
4
6
8
.
3
1
4
.
8
7
N
o
O
u
t
b
r
e
a
k
4
.
2
.
M
o
del
perf
o
rma
nce
a
n
d e
v
a
lua
t
io
n
Du
r
in
g
th
e
tr
ain
in
g
p
h
ase,
t
h
e
m
o
d
el
ac
h
iev
ed
p
er
f
ec
t
p
er
f
o
r
m
an
ce
m
et
r
ics,
in
clu
d
i
n
g
1
0
0
%
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
an
d
F1
-
s
co
r
e
o
n
th
e
test
d
ataset.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
co
n
f
ir
m
ed
th
e
ab
s
en
ce
o
f
b
o
th
f
alse
p
o
s
itiv
es
an
d
f
alse
n
eg
ativ
es,
in
d
icatin
g
th
at
t
h
e
m
o
d
el
lear
n
e
d
th
e
p
atter
n
s
in
th
e
tr
ain
in
g
d
ata
ex
ce
p
tio
n
ally
well.
Ho
wev
e
r
,
th
is
lev
el
o
f
p
e
r
f
o
r
m
an
ce
also
r
aises
co
n
ce
r
n
s
r
e
g
ar
d
in
g
p
o
ten
tial
o
v
er
f
itti
n
g
,
p
ar
ticu
lar
ly
g
iv
e
n
th
e
s
y
n
t
h
etic
n
atu
r
e
o
f
p
ar
t
o
f
th
e
tr
ain
in
g
d
ata.
T
o
ass
ess
th
e
m
o
d
el’
s
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
,
it
was
test
ed
o
n
an
o
u
tlier
d
ataset
with
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
s
th
at
d
if
f
er
ed
s
ig
n
i
f
ican
tly
f
r
o
m
th
o
s
e
in
th
e
tr
ain
in
g
s
et.
T
h
e
r
esu
lts
s
h
o
wed
a
co
m
p
lete
f
ailu
r
e
to
class
if
y
HA
B
o
u
tb
r
ea
k
ca
s
es,
r
esu
ltin
g
in
a
r
ec
all
s
co
r
e
o
f
0
.
0
0
f
o
r
th
e
p
o
s
itiv
e
class
.
T
h
is
o
u
tco
m
e
u
n
d
er
s
co
r
es
th
e
ch
allen
g
e
o
f
g
en
er
alizin
g
f
r
o
m
s
y
n
th
etic
o
r
u
n
if
o
r
m
d
atasets
to
r
ea
l
-
wo
r
l
d
en
v
ir
o
n
m
e
n
ts
,
wh
er
e
th
e
d
ata
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6
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1
7
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I
n
tern
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsity
M
a
lay
sia
(IIUM
),
sp
e
c
ializin
g
in
b
i
o
se
n
so
rs,
i
n
tern
e
t
o
f
th
in
g
s
(I
o
T),
c
o
m
p
u
ter v
isio
n
,
e
m
b
e
d
d
e
d
s
y
ste
m
s,
a
n
d
b
l
o
c
k
c
h
a
i
n
tec
h
n
o
l
o
g
ies
.
He
h
o
ld
s
a
b
a
c
h
e
l
o
r’s
d
e
g
re
e
in
c
o
m
m
u
n
ica
ti
o
n
e
n
g
in
e
e
rin
g
(2
0
1
1
),
m
a
ste
r’s
in
e
lec
tro
n
ic
e
n
g
i
n
e
e
rin
g
(
2
0
1
4
)
,
a
n
d
P
h
.
D
in
E
n
g
i
n
e
e
rin
g
(
2
0
1
9
)
,
a
ll
fro
m
IIUM
.
Be
y
o
n
d
a
c
a
d
e
m
ia,
h
e
c
o
n
tri
b
u
tes
to
p
r
o
fe
ss
io
n
a
l
c
o
m
m
u
n
it
ies
,
fu
r
th
e
rin
g
a
d
v
a
n
c
e
m
e
n
ts i
n
tec
h
n
o
lo
g
y
a
n
d
e
n
g
in
e
e
rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
a
n
wa
rz
a
in
@iiu
m
.
e
d
u
.
m
y
.
Am
ir
‘Aa
tieff
Am
ir
H
u
ss
in
is
a
n
As
sista
n
t
P
ro
fe
ss
o
r
a
n
d
He
a
d
o
f
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
t
th
e
In
te
rn
a
ti
o
n
a
l
Isla
m
ic
Un
iv
e
rsity
M
a
l
a
y
sia
(IIUM
),
sp
e
c
ializin
g
i
n
Artifi
c
ial
In
telli
g
e
n
c
e
with
a
fo
c
u
s
o
n
in
tell
ig
e
n
t
a
u
to
n
o
m
o
u
s
a
g
e
n
t
s
a
n
d
m
u
lt
i
-
a
g
e
n
t
sy
ste
m
s.
He
e
a
rn
e
d
h
is
Do
c
to
r
o
f
P
h
i
lo
so
p
h
y
in
Co
m
p
u
ter
S
c
ien
c
e
fro
m
L
o
u
g
h
b
o
r
o
u
g
h
Un
i
v
e
rsity
a
n
d
a
m
a
ste
r’s
in
so
ftwa
re
e
n
g
in
e
e
rin
g
fro
m
t
h
e
Op
e
n
Un
iv
e
rsity
M
a
la
y
sia
.
His
sc
h
o
larly
wo
r
k
h
a
s
b
e
e
n
c
it
e
d
1
4
7
ti
m
e
s,
re
flec
ti
n
g
h
is
c
o
n
tri
b
u
ti
o
n
s
t
o
t
h
e
field
.
He
h
a
s
b
e
e
n
re
c
o
g
n
ize
d
a
s
a
n
AWS
Ac
a
d
e
m
y
Ac
c
re
d
it
e
d
Ed
u
c
a
to
r
b
y
Am
a
z
o
n
Web
S
e
r
v
ice
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
m
iraa
ti
e
ff@ii
u
m
.
e
d
u
.
m
y
.
Am
m
a
r
H
a
z
iq
An
n
a
s
is
a
c
o
m
p
u
ter
sc
ien
c
e
d
e
v
e
lo
p
e
r
sp
e
c
ializin
g
i
n
c
y
b
e
rse
c
u
rit
y
,
I
o
T,
a
n
d
AI d
e
v
e
l
o
p
m
e
n
t.
He
h
a
s wo
rk
e
d
a
s a
n
I
o
T
a
n
d
AI De
v
e
lo
p
e
r,
f
o
c
u
si
n
g
o
n
m
a
c
h
in
e
lea
rn
in
g
fo
r
p
re
d
icti
v
e
m
o
d
e
li
n
g
,
se
n
s
o
r
-
b
a
se
d
e
n
v
ir
o
n
m
e
n
tal
m
o
n
i
to
ri
n
g
,
a
n
d
AI
a
u
to
m
a
ti
o
n
.
His
e
x
p
e
rti
se
e
x
ten
d
s
to
d
a
ta
v
isu
a
li
z
a
ti
o
n
,
n
e
two
r
k
s
e
c
u
rit
y
,
a
n
d
c
l
o
u
d
c
o
m
p
u
ti
n
g
,
wh
e
re
h
e
h
a
s
c
o
n
tr
ib
u
ted
t
o
re
a
l
-
t
ime
a
n
a
ly
ti
c
s
a
n
d
i
n
telli
g
e
n
t
sy
ste
m
d
e
v
e
lo
p
m
e
n
t
.
His
wo
r
k
h
a
s
b
e
e
n
p
u
b
li
sh
e
d
in
se
v
e
ra
l
a
c
a
d
e
m
ic
jo
u
r
n
a
ls,
i
n
c
lu
d
in
g
M
J
S
AT,
IJPCC,
a
n
d
IEE
E
Xp
l
o
re
.
Hi
s
re
se
a
rc
h
h
a
s
a
lso
b
e
e
n
a
c
c
e
p
ted
i
n
to
se
v
e
ra
l
c
o
n
fe
re
n
c
e
s,
i
n
c
lu
d
in
g
t
h
e
IC
o
n
M
AS),
IEE
E
S
tu
d
e
n
t
Co
n
fe
re
n
c
e
o
n
Re
se
a
rc
h
a
n
d
De
v
e
lo
p
m
e
n
t
(S
CORe
D),
a
n
d
th
e
I
n
tern
a
ti
o
n
a
l
G
ra
n
d
In
v
e
n
ti
o
n
,
In
n
o
v
a
ti
o
n
,
a
n
d
De
sig
n
E
x
p
o
(IG
IIDe
a
ti
o
n
).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
m
r.
a
n
wa
rz
a
in
@g
m
a
il
.
c
o
m
.
No
r
m
a
wa
t
y
M
o
h
a
m
m
a
d
-
No
o
r
is
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
a
n
d
He
a
d
o
f
th
e
In
stit
u
te
o
f
Oc
e
a
n
o
g
ra
p
h
y
a
n
d
M
a
rit
ime
S
tu
d
ies
a
t
t
h
e
I
n
tern
a
ti
o
n
a
l
Isla
m
ic
Un
i
v
e
rsity
M
a
lay
sia
(
IIUM
),
sp
e
c
ializin
g
i
n
Eco
lo
g
y
a
n
d
Tax
o
n
o
m
y
o
f
Al
g
a
e
with
a
fo
c
u
s
o
n
h
a
rm
fu
l
a
l
g
a
l
b
lo
o
m
s
(HA
B).
He
e
a
rn
e
d
h
e
r
Do
c
to
r
o
f
P
h
il
o
so
p
h
y
i
n
Bio
lo
g
y
fro
m
Co
p
e
n
h
a
g
e
n
Un
iv
e
rsit
y
,
De
n
m
a
rk
a
n
d
a
m
a
ste
r’s
in
m
a
rin
e
sc
ien
c
e
fro
m
t
h
e
Un
i
v
e
rsiti
P
u
tra
M
a
lay
sia
.
He
r
sc
h
o
larly
wo
r
k
h
a
s
b
e
e
n
c
it
e
d
a
ll
o
v
e
r
t
h
e
wo
rld
a
n
d
s
h
e
h
a
s
p
u
b
li
s
h
e
d
3
re
se
a
rc
h
/refe
re
n
c
e
b
o
o
k
o
n
ta
x
o
n
o
m
y
o
f
a
lg
a
e
a
n
d
m
a
rin
e
sc
ien
c
e
a
n
d
Isla
m
iz
a
ti
o
n
.
S
h
e
a
lso
h
a
s
b
e
e
n
a
p
p
o
i
n
ted
a
s
e
v
a
lu
a
to
r
fo
r
re
se
a
rc
h
g
ra
n
t
p
ro
p
o
sa
l
b
y
th
e
M
in
istry
o
f
H
ig
h
e
r
Ed
u
c
a
ti
o
n
M
a
lay
sia
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
o
rm
a
wa
ty
@iiu
m
.
e
d
u
.
m
y
.
Ro
z
ia
wa
ti
M
o
h
d
Ra
z
a
li
is
a
S
e
n
i
o
r
Re
se
a
rc
h
Offic
e
r
a
t
t
h
e
F
ish
e
ries
Re
se
a
rc
h
In
stit
u
te,
De
p
a
rtme
n
t
o
f
F
ish
e
rie
s
M
a
lay
sia
.
S
h
e
h
o
l
d
s
a
b
a
c
h
e
lo
r
’s
d
e
g
re
e
i
n
Ba
c
h
e
lo
r
S
c
ien
c
e
(Ho
n
s)
Bio
c
h
e
m
istry
(2
0
0
3
)
fro
m
Un
iv
e
rsity
P
u
tra
M
a
lay
sia
a
n
d
a
m
a
ste
r’s
in
a
q
u
a
ti
c
sc
ien
c
e
(2
0
1
6
)
fr
o
m
Un
iv
e
rsity
M
a
lay
s
ia
S
a
ra
wa
k
.
He
r
re
se
a
rc
h
fo
c
u
s
e
s
o
n
t
h
e
st
u
d
ies
o
f
m
a
rin
e
m
icro
a
lg
a
e
,
h
a
rm
fu
l
m
icro
a
lg
a
e
b
lo
o
m
s
(HA
Bs),
th
e
e
c
o
lo
g
ica
l
o
f
m
icro
a
lg
a
e
,
m
it
ig
a
ti
o
n
o
f
HA
Bs
a
n
d
d
e
v
e
lo
p
m
e
n
t
o
f
e
a
rly
wa
rn
in
g
sy
ste
m
fo
r
HA
Bs.
S
h
e
h
a
s
p
u
b
li
s
h
e
d
se
v
e
ra
l
sc
ien
ti
fic
p
a
p
e
rs
o
n
m
a
rin
e
m
icro
a
lg
a
e
,
H
ABs
a
n
d
m
it
ig
a
ti
o
n
o
f
HA
Bs
in
lo
c
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
m
a
g
a
z
in
e
s.
S
h
e
c
o
-
a
u
th
o
re
d
t
h
e
b
o
o
k
Tr
o
p
ica
l
M
a
rin
e
Dia
to
m
s
a
n
d
Din
o
flag
e
ll
a
tes
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
r
o
z
iaw
a
ti
@d
o
f.
g
o
v
.
m
y
.
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