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21
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f
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m
is
s
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le,
g
en
et
ics,
b
io
lo
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a
n
d
o
th
er
s
[
1
]
.
B
lo
o
d
-
s
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c
k
i
n
g
m
o
s
q
u
it
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ates
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ly
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ir
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s
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f
th
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li
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o
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g
a
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m
s
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o
s
q
u
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p
ar
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to
ler
ates
p
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p
ar
am
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f
ex
p
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o
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ta
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as
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q
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ak
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n
g
a
n
en
h
a
n
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ed
s
y
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te
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atic
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e
m
o
d
el
f
o
r
m
alar
ia
v
ec
to
r
tr
an
s
cr
ip
ts
[
2
-
3
].
A
p
p
r
o
ac
h
ab
le
r
ev
ea
li
n
g
g
e
n
e
tic
in
q
u
ir
ies
h
a
v
e
b
ee
n
m
ad
e
in
R
N
A
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Seq
s
tu
d
y
b
y
u
n
f
o
ld
in
g
a
ca
u
tio
u
s
p
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r
p
o
s
ef
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l
b
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lo
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ical
s
tr
ate
g
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b
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e
n
h
a
n
ce
m
en
t
o
f
s
eq
u
e
n
ci
n
g
s
t
u
d
y
.
R
N
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d
ata
n
ec
e
s
s
itate
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t
h
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r
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m
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l
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th
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h
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g
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-
d
i
m
e
n
s
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alit
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c
u
r
s
e
,
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u
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;
d
is
o
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o
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d
s
,
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u
r
r
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e
v
er
an
ce
,
u
n
s
u
itab
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d
ata,
an
d
o
th
er
s
[
4
]
.
C
u
r
r
en
t
s
k
ill
s
co
n
s
is
t
o
f
e
n
h
a
n
ce
d
m
et
h
o
d
s
in
d
ev
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p
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g
r
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-
b
r
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ak
in
g
m
ed
ical
ca
r
e
m
o
d
el
s
,
f
o
r
ex
a
m
p
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,
k
ee
n
h
u
m
an
w
ell
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b
ei
n
g
tr
ea
t
m
en
t
s
y
s
t
e
m
s
,
en
h
a
n
ce
d
tr
ea
t
m
e
n
t
s
,
a
m
o
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g
o
t
h
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d
etec
ts
o
f
ail
m
e
n
ts
a
n
d
co
m
p
lain
ts
[
5
].
So
m
e
m
ac
h
i
n
e
lear
n
i
n
g
ap
p
r
o
ac
h
es
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ti
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h
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r
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en
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a
w
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th
p
er
s
u
asi
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o
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p
Sci,
Vo
l.
21
,
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,
Feb
r
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0
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s
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b
io
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p
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u
tlin
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[
6
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.
R
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s
h
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x
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s
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y
w
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n
g
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ates
o
f
s
u
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ce
s
s
v
ar
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le
[
7
,
8
].
C
o
m
p
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t
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al
ap
p
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ac
h
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h
av
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b
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t o
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v
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s
ti
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al
p
r
ed
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le
o
p
e
n
in
g
s
[
5
]
.
B
lo
o
d
-
b
ased
g
en
e
ex
p
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ess
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n
d
i
s
ea
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s
a
n
d
m
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,
to
d
etec
t
tr
an
s
cr
ip
tio
n
s
f
o
r
class
i
f
icatio
n
[
9
]
,
w
it
h
R
N
A
d
ata
f
r
o
m
o
m
n
ib
u
s
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en
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ata
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h
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lear
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to
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al
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o
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ith
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s
ar
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p
r
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ted
.
R
NA
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Seq
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ata
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im
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ir
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r
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r
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ith
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ata
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tat
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eq
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i
m
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s
io
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alit
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r
ed
u
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p
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d
u
r
es [
1
0
]
.
A
Ge
n
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alg
o
r
it
h
m
d
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m
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n
s
io
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alit
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r
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r
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o
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n
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al
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g
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d
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m
en
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io
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ex
p
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d
ata,
E
n
s
e
m
b
le
class
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f
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alg
o
r
it
h
m
ap
p
r
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h
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ar
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ca
r
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t
to
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eg
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la
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k
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te
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t
ab
le
f
o
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ec
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n
d
d
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2.
M
E
T
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f
r
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k
f
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th
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d
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tab
u
lated
in
Fi
g
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r
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1
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v
ital
k
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led
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e
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p
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to
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ith
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le
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o
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h
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A
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s
eq
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alar
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v
ec
to
r
d
ataset.
Fig
u
r
e
1
.
P
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r
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ith
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ased
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ex
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d
ataset
in
v
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ti
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[
1
1
]
.
A
s
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p
er
v
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class
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f
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n
R
N
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w
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s
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S
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-
P
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cr
ea
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g
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m
an
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c
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[
1
2
]
.
R
N
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NA
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d
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tin
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m
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P
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H
[
1
3
]
.
C
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s
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f
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o
f
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p
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astro
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test
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lear
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,
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s
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ab
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60,
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3
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s
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ata,
P
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A
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ea
t
m
ap
s
,
an
d
th
e
C
N
N
alg
o
r
ith
m
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p
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s
in
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ti
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d
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s
eq
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e
n
e
ex
p
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ata
in
v
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tio
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d
clas
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r
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9
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%
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d
5
0
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5
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%
w
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h
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[
1
4
]
.
An
R
NA
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Seq
d
i
s
clo
s
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r
e
o
f
co
n
c
ea
l
ed
tr
an
s
cr
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n
m
alar
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ar
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if
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ct
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an
s
cr
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ig
n
s
[
1
5
]
.
C
las
s
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f
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n
w
it
h
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n
s
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m
b
le
m
ac
h
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lear
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p
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r
ca
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ce
r
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s
d
ata
ex
p
r
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w
as
p
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p
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u
s
in
g
C
4
.
5
,
b
ag
g
in
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d
b
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in
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m
b
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p
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ac
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lear
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ce
r
d
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m
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a
b
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er
f
o
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m
a
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ce
ac
c
u
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ac
y
[
1
6
]
.
A
n
in
v
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s
ti
g
ati
v
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en
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m
b
le
clas
s
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An
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Ga
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Data
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(
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Evaluation Warning : The document was created with Spire.PDF for Python.
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N:
2502
-
4752
P
r
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R
N
A
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S
eq
d
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in
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ith
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i
m
p
r
o
v
e
m
e
n
t.
C
a
n
ce
r
o
u
s
g
e
n
e
ex
p
r
ess
io
n
d
ata
c
lass
if
icatio
n
w
a
s
d
o
n
e
u
s
i
n
g
an
e
n
s
e
m
b
le
class
i
f
icat
io
n
m
et
h
o
d
;
th
e
p
er
f
o
r
m
an
ce
a
n
d
o
u
t
co
m
es
o
f
th
e
r
es
u
lt
s
h
o
w
e
d
a
r
e
d
u
ce
d
am
o
u
n
t
o
f
d
ep
en
d
en
t
o
n
o
r
ig
in
al
ities
o
f
a
s
i
n
g
le
tr
ain
i
n
g
d
ata
s
et
[
1
7
]
.
A
m
eta
h
e
u
r
is
tic
s
tech
n
i
q
u
e
f
o
r
f
etc
h
in
g
g
en
e
s
an
d
R
NA/DN
A
d
ata
class
if
icatio
n
b
y
b
r
ief
in
g
ex
is
ti
n
g
ad
v
an
ce
s
o
f
m
etah
e
u
r
is
t
ic
-
b
a
s
ed
m
e
th
o
d
s
in
th
e
e
m
b
ed
d
e
d
tech
n
iq
u
e
o
f
f
ea
t
u
r
e
s
elec
t
io
n
ap
p
r
o
ac
h
w
as
p
r
o
p
o
s
ed
,
em
p
h
asiz
i
n
g
h
e
lp
f
u
l
an
d
i
n
te
g
r
atin
g
p
r
o
b
le
m
-
s
p
ec
i
f
ic
d
ata
r
elev
a
n
ce
i
n
t
o
th
e
ex
a
m
i
n
atio
n
o
p
er
ativ
es
o
f
d
ev
elo
p
m
e
n
t
s
.
A
r
a
n
k
i
n
g
co
ef
f
icie
n
t
o
f
li
n
ea
r
SVM
class
i
f
ier
w
a
s
u
s
ed
in
th
e
lo
ca
l
o
p
er
ativ
e
in
v
e
s
ti
g
atio
n
f
o
r
f
ea
t
u
r
e
s
elec
t
io
n
an
d
class
i
f
icat
io
n
[
1
8
]
.
A
f
au
lt
in
v
est
ig
at
io
n
f
o
r
tr
ain
i
n
g
en
g
i
n
es
u
s
i
n
g
G
A
an
d
class
i
f
icat
io
n
lear
n
er
s
,
th
e
ap
p
r
o
ac
h
less
en
s
t
h
e
co
m
p
u
ta
tio
n
al
co
m
p
licatio
n
an
d
ad
v
a
n
ce
s
th
e
ac
c
u
r
ac
y
to
ab
o
u
t
9
7
%
[
1
9
]
.
T
r
ee
m
o
d
el
en
h
a
n
ce
m
en
t
f
o
r
clas
s
i
f
y
in
g
c
er
ta
in
en
s
e
m
b
led
f
ea
t
u
r
es
w
a
s
p
r
o
p
o
s
ed
u
s
in
g
an
en
s
e
m
b
le
-
b
a
s
ed
f
ea
t
u
r
e
s
elec
t
io
n
,
r
an
d
o
m
tr
ee
s
a
n
d
w
r
ap
p
er
-
b
ased
f
ea
t
u
r
e
s
elec
tio
n
s
y
s
te
m
i
n
d
e
v
elo
p
in
g
a
class
i
f
icatio
n
m
o
d
el,
an
d
t
h
e
en
s
e
m
b
le
d
ata
cla
s
s
i
f
icat
io
n
p
r
o
ce
d
u
r
e
in
itiates
a
s
u
b
clas
s
u
s
in
g
t
h
e
b
a
g
g
in
g
,
w
r
ap
p
er
d
im
e
n
s
io
n
alit
y
r
ed
u
ctio
n
m
et
h
o
d
,
an
d
r
an
d
o
m
t
r
ee
s
.
T
h
is
p
r
o
ce
d
u
r
e
r
em
o
v
e
s
th
e
u
n
co
n
n
ec
ted
f
ea
t
u
r
es
an
d
p
ic
k
s
t
h
e
b
est
f
ea
tu
r
es
f
o
r
class
i
f
icat
io
n
w
it
h
a
p
r
o
b
ab
ilit
y
w
eig
h
ti
n
g
v
a
lu
e.
T
h
e
s
tu
d
y
w
a
s
ev
alu
a
ted
an
d
co
m
p
ar
ed
w
i
th
a
class
i
f
icatio
n
ac
cu
r
ac
y
o
f
9
2
%
[
2
0
].
A
n
en
s
e
m
b
l
e
-
f
ea
tu
r
e
s
elec
t
io
n
i
m
p
le
m
en
ta
tio
n
p
r
o
ce
d
u
r
e
u
s
in
g
R
-
p
ac
k
ag
e
to
o
l
w
a
s
p
r
o
p
o
s
ed
,
s
ev
er
al
f
ea
t
u
r
e
s
elec
tio
n
tec
h
n
iq
u
e
s
w
er
e
co
m
b
i
n
ed
w
it
h
r
eg
u
lar
ized
o
u
tp
u
t
s
to
a
q
u
a
n
ti
f
iab
le
en
s
e
m
b
le
r
an
k
i
n
g
,
f
ea
t
u
r
e
s
elec
ti
o
n
p
r
o
ce
d
u
r
es
w
er
e
co
m
b
i
n
ed
,
an
d
u
s
ed
[
2
1
].
3.
RE
S
E
ARCH
M
E
T
H
O
D
Hig
h
d
i
m
en
s
io
n
al
d
ata
in
v
e
s
t
ig
atio
n
s
h
av
e
b
ee
n
d
i
s
cu
s
s
ed
ex
te
n
s
i
v
el
y
,
a
Ge
n
etic
A
l
g
o
r
ith
m
an
d
E
n
s
e
m
b
le
clas
s
if
icatio
n
al
g
o
r
ith
m
is
p
r
o
p
o
s
ed
u
s
i
n
g
a
n
R
NA
-
Seq
d
ata
co
n
s
is
ti
n
g
o
f
2
4
5
7
in
s
tan
ce
s
w
it
h
s
ev
e
n
at
tr
ib
u
tes
o
f
w
ester
n
K
en
y
a,
m
o
s
q
u
ito
’
s
g
e
n
e
d
ata
[
2
2
]
w
it
h
i
ts
p
r
o
f
i
l
e
tr
a
n
s
cr
ip
t
co
n
ten
t
s
,
R
N
A
-
Seq
g
en
e
s
,
tr
an
s
cr
ip
t
v
ar
iatio
n
s
o
f
d
elta
m
et
h
r
in
-
r
esi
s
tan
t
an
d
v
u
ln
er
ab
le
An
o
p
h
ele
s
g
a
m
b
iae
Ken
y
an
m
o
s
q
u
i
to
es
w
h
ic
h
is
an
o
p
en
l
y
ac
ce
s
s
i
b
le
d
ata
o
n
f
ig
s
h
ar
e.
co
m
[
23
-
24
]
,
it
is
tab
u
lated
in
T
ab
le
1
.
MA
T
L
A
B
ex
p
er
i
m
e
n
tal
to
o
l
i
s
u
s
ed
to
ca
r
r
y
o
u
t t
h
e
e
x
p
er
i
m
e
n
t,
G
A
i
s
p
r
o
p
o
s
ed
an
d
u
s
ed
to
f
etc
h
r
el
ev
an
t
f
ea
tu
r
es
.
T
h
e
s
elec
ted
w
er
e
cl
a
s
s
i
f
i
ed
u
s
in
g
th
e
E
n
s
e
m
b
le
al
g
o
r
ith
m
[
2
5
].
T
ab
le
1
.
Data
s
et
s
tr
u
ctu
r
e
s
D
a
t
a
se
t
A
t
t
r
i
b
u
t
e
s
I
n
st
a
n
c
e
s
M
o
sq
u
i
t
o
A
n
o
p
h
e
l
e
s G
a
mb
i
a
e
7
2
4
5
7
3
.
1
.
G
enet
ic
a
lg
o
rit
h
m
GA
i
s
a
p
r
o
f
icien
t
m
e
th
o
d
f
o
r
in
v
e
s
ti
g
ati
ng
s
u
i
tab
le
f
ea
t
u
r
es
f
r
o
m
h
i
g
h
d
i
m
e
n
s
io
n
al
d
atasets
,
an
d
p
r
ed
o
m
in
a
n
t
G
A
ar
e
w
r
ap
p
er
-
b
ased
f
ea
tu
r
e
s
elec
tio
n
m
e
th
o
d
s
.
Qu
ite
a
lo
t
o
f
li
m
itat
i
o
n
p
r
o
ce
d
u
r
es
f
o
r
g
en
et
ic
al
g
o
r
ith
m
e
x
is
ts
,
w
h
er
e
alter
atio
n
a
n
d
cr
o
s
s
o
v
er
o
p
e
r
ativ
es p
er
s
i
s
t
a
n
d
co
m
m
o
n
l
y
co
n
n
ec
ted
to
b
i
n
ar
y
co
n
s
tr
ain
t
v
al
u
es.
A
p
p
r
o
p
r
iate
f
ea
t
u
r
es
ar
e
r
ec
o
g
n
ized
u
s
i
n
g
a
g
e
n
etic
al
g
o
r
ith
m
[
2
6
]
.
T
h
e
R
N
A
h
as
N
n
u
m
b
er
o
f
f
ea
tu
r
es
r
ep
r
esen
tin
g
f
ea
t
u
r
e
s
w
i
th
v
alu
es
0
a
n
d
1
as
s
e
lecte
d
an
d
u
n
s
e
lecte
d
,
co
r
r
esp
o
n
d
in
g
l
y
.
A
d
d
r
ess
i
n
g
th
e
i
m
p
o
r
tan
ce
o
f
f
ea
tu
r
es,
G
A
is
u
s
ed
i
n
f
i
n
d
in
g
t
h
e
id
ea
l
f
ea
t
u
r
e
s
u
b
s
et
b
y
m
ea
n
s
o
f
t
h
e
n
o
m
i
n
ated
f
i
g
u
r
e
o
f
f
ea
t
u
r
es
f
o
r
co
m
p
le
x
clas
s
i
f
ica
tio
n
p
r
ese
n
tatio
n
.
T
h
e
g
en
er
al
co
n
s
tr
u
ct
io
n
o
f
th
e
G
A
i
s
d
ef
i
n
ed
in
A
l
g
o
r
i
th
m
1
b
elo
w
b
y
ad
o
p
tin
g
[
2
7
]:
A
l
g
o
r
ith
m
1
.
Gen
et
ic
alg
o
r
it
h
m
Require: Initialize the parameters nPop =
m, t
max
, t = 0;
Ensure: Optimal feature subset with the highest fitness value.
1: while (
t<=t
max
) do
2:
Create pop
m, t
max
;
3:
For
k =
1 to
m
do
4:
Parents [
m
1
, m
2
] = system selection (m, nPop)
5:
Child = Xo
r[
m
1
, m
2
]
6:
M u
= mutation [Child}
7:
End for
8:
Replace
m
with Child
1
, Child
2
, …, Child
m
9:
t = t+ 1;
10:
End while
11:
Store the Highest fitness value;
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
1
0
73
-
10
81
1076
m
is
t
h
e
p
o
p
u
latio
n
s
ize,
r
is
a
r
an
d
o
m
n
u
m
b
er
l
y
i
n
g
f
la
n
k
ed
b
y
0
to
1
,
s
i
g
n
i
f
ies
th
e
n
o
m
i
n
ated
ch
r
o
m
e
o
r
u
n
s
elec
ted
f
ea
tu
r
e
w
it
h
a
th
r
es
h
o
ld
δ
s
et
v
a
lu
e
to
b
e
0
.
5
,
an
d
α
is
th
e
th
r
esh
o
ld
n
u
m
b
er
o
f
f
ea
t
u
r
es
n
o
m
i
n
ated
.
T
h
e
s
ig
n
if
ican
t
p
r
o
b
lem
s
o
f
th
e
p
r
ec
is
e
m
e
th
o
d
ar
e
s
elec
tin
g
t
h
e
m
a
x
i
m
u
m
f
i
ttin
g
f
ea
t
u
r
es
f
r
o
m
th
e
p
r
ed
ictab
le
d
atasets
.
3
.
2
.
E
ns
e
m
ble
cla
s
s
if
ier
E
n
s
e
m
b
le
c
lass
if
ier
s
ar
e
tr
ain
ed
u
s
in
g
d
is
ti
n
ct
s
ec
to
r
s
o
f
t
h
e
tr
ain
in
g
d
ata,
d
iv
er
s
e
co
n
s
tr
ain
ts
o
f
th
e
class
i
f
ier
s
,
o
r
v
ar
ied
s
ec
to
r
s
o
f
f
ea
t
u
r
es
as
i
n
a
m
o
d
el
o
f
r
an
d
o
m
s
u
b
s
p
ac
e
[2
8
].
E
n
s
em
b
le
class
i
f
ier
in
cl
u
d
e
s
in
te
g
r
ati
ng
o
u
tco
m
e
s
o
f
n
u
m
e
r
o
u
s
clas
s
i
f
ier
s
to
y
ie
ld
a
f
i
n
a
l
r
esu
lt
;
it
is
r
eg
u
lar
l
y
u
s
ed
f
o
r
th
e
ac
q
u
is
it
io
n
o
f
ex
tr
e
m
e
l
y
ac
c
u
r
ate
r
es
u
lts
.
E
n
s
e
m
b
le
clas
s
i
f
ier
s
ar
e
q
u
ite
m
u
tu
al
in
m
ac
h
i
n
e
lear
n
i
n
g
p
r
o
b
lem
s
an
d
ca
n
b
e
ac
tiv
e
in
th
e
b
io
in
f
o
r
m
at
ics
f
ield
.
T
h
e
cl
a
s
s
i
f
icatio
n
r
es
u
lt
is
attai
n
ed
b
y
t
h
e
i
n
cl
u
s
i
o
n
o
f
a
c
h
o
ice
o
f
in
d
iv
id
u
al
cla
s
s
i
f
ier
[2
9
].
E
n
s
e
m
b
le
ap
p
r
o
ac
h
es
ar
e
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
co
m
b
in
es
d
ec
is
io
n
s
to
ad
v
an
c
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
g
en
er
al
c
lass
if
ic
a
tio
n
.
Se
v
er
al
ter
m
s
h
a
v
e
b
ee
n
d
i
s
co
v
er
ed
in
t
h
e
liter
at
u
r
e
to
s
ig
n
i
f
y
co
m
p
ar
ab
le
co
n
n
o
ta
tio
n
s
s
u
c
h
as;
m
u
l
ti
-
s
tr
ate
g
y
lear
n
i
n
g
,
ag
g
r
e
g
atio
n
,
m
u
ltip
le
i
n
teg
r
atio
n
class
i
f
ier
s
,
clas
s
i
f
ier
s
y
n
t
h
e
s
is
,
g
r
o
u
p
in
g
,
co
m
m
ittee,
an
d
s
o
o
n
.
E
n
s
e
m
b
le
clas
s
if
ier
tak
es
co
m
p
lete
i
m
p
r
o
v
ed
p
r
esen
tatio
n
t
h
a
n
d
is
cr
ete
b
ase
d
class
if
ier
s
.
T
h
e
ef
f
icie
n
c
y
o
f
en
s
e
m
b
le
ap
p
r
o
ac
h
es is
ex
tr
em
el
y
d
ep
en
d
en
t o
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th
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n
co
n
v
en
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io
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o
f
f
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lt
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n
s
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m
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r
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v
ar
iet
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h
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ase
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er
s
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d
e
n
s
e
m
b
le
class
if
ica
tio
n
h
as
co
m
m
o
n
te
ch
n
iq
u
es;
b
ag
g
i
n
g
an
d
b
o
o
s
tin
g
.
B
ag
g
i
n
g
(
b
oo
t
s
tr
ap
ag
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ati
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e
m
p
lo
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s
t
h
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ain
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ata
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y
ar
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itra
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n
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h
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n
iq
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e
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tr
ain
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n
g
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ata
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y
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ite
m
s
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h
e
ad
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al
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ain
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et
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ar
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c
alled
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ap
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p
licates
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o
m
e
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r
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ap
p
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v
e
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th
o
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g
h
a
p
p
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r
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co
n
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ec
u
ti
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el
y
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T
h
e
cla
s
s
i
f
ier
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*(
x
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is
b
u
il
t
b
y
co
m
b
in
in
g
Ci
(
x
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w
h
er
e
ea
ch
Ci
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x
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h
a
s
an
eq
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i
v
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n
t
v
o
te.
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d
aB
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o
s
t
(
A
d
ap
tiv
e
B
o
o
s
ti
n
g
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tech
n
iq
u
e
a
f
f
ec
ts
t
h
e
tr
ai
n
in
g
d
ata.
Or
ig
i
n
all
y
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t
h
e
p
r
o
ce
d
u
r
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allo
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s
all
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ce
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h
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al
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t.
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n
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ep
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ate
iter
atio
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i
,
th
e
k
n
o
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led
g
e
p
r
o
ce
d
u
r
e
r
ed
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ce
s
th
e
w
ei
g
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ted
er
r
o
r
o
n
th
e
tr
ai
n
i
n
g
s
et
a
n
d
y
ield
s
a
class
i
f
ier
Ci
(
x
)
.
T
h
e
w
eig
h
t
ed
er
r
o
r
o
f
Ci
(
x
)
is
ca
lc
u
lated
w
it
h
u
s
e
to
i
n
f
o
r
m
th
e
w
ei
g
h
ts
o
n
th
e
tr
ai
n
i
n
g
in
s
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ce
s
xi
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T
h
e
w
ei
g
h
t o
f
xi
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is
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g
i
v
i
n
g
to
it
s
e
f
f
ec
t
s
o
n
t
h
e
c
lass
i
f
ier
’
s
o
u
tco
m
e
th
at
a
llo
w
s
a
h
ig
h
w
eig
h
t
f
o
r
a
m
is
cla
s
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i
f
ied
xi
an
d
a
s
m
all
w
ei
g
h
t
f
o
r
a
n
ac
ce
p
tab
ly
cla
s
s
i
f
ied
xi
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h
e
co
n
clu
d
i
n
g
cla
s
s
i
f
ier
C
*(
x
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is
b
u
ilt
b
y
a
w
e
ig
h
ted
v
o
te
o
f
t
h
e
d
is
cr
ete
Ci
(
x
)
r
en
d
er
in
g
to
its
ac
cu
r
ac
y
b
u
i
lt
o
n
th
e
w
ei
g
h
ted
tr
ai
n
i
n
g
s
e
t
[
3
0
-
33]
.
I
m
p
le
m
e
n
ti
n
g
Ka
m
r
a
n
et
al.
[
2
4
]
,
th
ey
s
h
o
w
ed
h
o
w
a
b
o
o
s
tin
g
alg
o
r
it
h
m
w
o
r
k
s
f
o
r
d
atasets
,
t
h
e
n
tr
ain
ed
b
y
m
u
lt
i
-
m
o
d
el
d
esi
g
n
s
(
en
s
e
m
b
le
lear
n
i
n
g
)
.
T
h
ese
ad
v
an
ce
s
r
e
s
u
lted
i
n
t
h
e
Ad
aB
o
o
s
t
(
A
d
ap
tiv
e
B
o
o
s
tin
g
)
.
P
r
esu
m
e
to
co
n
s
tr
u
ct
D
t
s
u
c
h
th
a
t D
1
(
i)
=
g
iv
e
n
D
t
an
d
h
t
:
{
}
{
(
3
)
=
(
4
)
W
h
er
e
s
tates to
th
e
n
o
r
m
aliza
tio
n
f
ac
to
r
an
d
is
as f
o
llo
w
s
;
(
5
)
B
asic
e
n
s
e
m
b
le
clas
s
if
icatio
n
t
ec
h
n
iq
u
es
:
W
eig
h
ted
Av
er
ag
i
n
g
(
W
A
)
;
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er
ag
i
n
g
an
d
Ma
x
Vo
ti
n
g
(
MV
)
.
Ma
x
Vo
tin
g
(
MV
)
ex
i
s
ts
[
31
]
E
n
s
e
m
b
le
lear
n
i
n
g
h
a
s
th
r
ee
co
m
b
in
a
tio
n
ad
v
a
n
ce
d
tech
n
iq
u
es
;
Stack
i
n
g
(
ST
K)
;
B
len
d
in
g
(
B
L
D)
;
B
ag
g
in
g
(
B
A
G)
,
an
d
B
o
o
s
tin
g
(
B
OT
)
[
32
-
37
]
.
3
.
3
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
P
er
f
o
r
m
a
n
ce
e
v
al
u
atio
n
o
f
m
a
ch
in
e
lear
n
in
g
tec
h
n
iq
u
e
en
ta
i
ls
v
alid
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n
m
e
tr
ics
s
u
c
h
a
s
a
c
o
n
f
u
s
io
n
m
atr
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x
,
u
s
ed
f
o
r
an
a
l
y
zi
n
g
cl
ass
i
f
icatio
n
m
o
d
els
f
ea
tu
r
es,
d
is
co
v
er
in
g
t
h
e
cla
s
s
i
f
ied
ill
u
s
tr
atio
n
s
f
r
o
m
t
h
e
g
iv
e
n
m
o
d
el
o
f
test
ed
d
ataset
m
o
d
el
s
a
m
p
le
s
[
5
]
u
s
in
g
t
h
e
p
er
f
o
r
m
an
ce
m
etr
ic
s
f
o
r
m
u
la
s
[2
2,
2
7
]
.
3
.
4
Appl
ica
t
io
ns
Gen
e
a
n
al
y
s
i
s
e
x
p
r
ess
io
n
p
r
o
jects
a
n
i
m
p
r
o
v
ed
ap
p
r
o
ac
h
in
id
en
ti
f
y
in
g
R
N
A
-
Seq
d
ata
,
f
e
tch
i
n
g
f
o
r
r
elev
an
t
ess
e
n
tia
l
g
en
e
s
f
o
r
d
ev
elo
p
in
g
ap
p
licatio
n
s
li
k
e
tr
ea
t
m
e
n
t
s
,
g
e
n
es
a
n
d
d
r
u
g
s
d
i
s
co
v
er
ies
,
d
iag
n
o
s
i
s
,
class
i
f
icatio
n
o
f
ca
n
ce
r
o
u
s
d
is
ea
s
es,
m
alar
ia,
f
e
v
er
,
an
d
s
o
o
n
.
F
in
d
in
g
t
h
e
m
ac
h
i
n
e
lea
r
n
in
g
d
ata
d
esig
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
P
r
ed
ictin
g
R
N
A
-
S
eq
d
a
ta
u
s
in
g
g
en
etic
a
l
g
o
r
ith
m
a
n
d
…
(
M
ich
ea
l O
la
o
lu
A
r
o
w
o
lo
)
1077
r
eq
u
ir
es
a
g
r
ea
t
al
g
o
r
ith
m
a
n
d
to
o
ls
u
s
ed
b
y
s
ev
er
al
e
x
p
er
i
m
e
n
ts
.
M
A
T
L
A
B
to
o
l
is
u
s
e
d
to
ca
r
r
y
o
u
t
th
e
ex
p
er
i
m
e
n
t
[
3
8
-
39]
.
P
r
ed
ictin
g
R
N
A
-
Seq
tec
h
n
o
lo
g
y
u
s
in
g
M
A
T
L
A
B
to
o
l
,
m
alar
ia
v
ec
to
r
d
ata
,
a
n
d
co
m
p
u
ter
r
eso
lu
tio
n
co
n
f
o
r
m
a
tio
n
u
s
es i
C
o
r
e2
p
r
o
ce
s
s
o
r
,
8
GB
R
A
M
s
ize,
6
4
-
b
it
S
y
s
te
m
an
d
M
A
T
L
A
B
2
0
1
5
to
o
l.
4.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
R
N
A
-
Seq
in
n
o
v
a
tio
n
w
i
th
Mo
s
q
u
ito
es
An
o
p
h
eles
Ga
m
b
iae
d
ata
h
av
i
n
g
2
4
5
7
s
u
s
c
ep
tib
le
an
d
r
esis
ta
n
t
g
e
n
es
a
s
s
h
o
w
n
i
n
F
i
g
u
r
e
2
b
elo
w
i
s
i
m
p
le
m
e
n
ted
b
y
u
s
in
g
Ge
n
etic
al
g
o
r
ith
m
o
n
th
e
d
ata
to
r
ed
u
ce
th
e
cu
r
s
e
o
f
d
i
m
en
s
io
n
al
it
y
a
n
d
f
etc
h
t
h
e
o
p
ti
m
al
s
u
b
s
e
t
o
f
d
ata
,
r
em
o
v
e
u
n
co
r
r
elate
d
a
ttrib
u
tes,
an
d
c
h
o
o
s
e
d
eter
m
in
ed
v
ar
ia
n
ce
w
it
h
a
r
ed
u
ce
d
n
u
m
b
er
o
f
s
u
b
s
et
f
ea
t
u
r
es
in
th
e
v
ar
iab
le.
T
h
e
GA
g
i
v
es
i
m
p
o
r
ta
n
t
g
e
n
e
d
ata
f
o
r
a
s
u
itab
le
s
tu
d
y
.
T
h
e
en
s
e
m
b
le
clas
s
i
f
icatio
n
alg
o
r
ith
m
is
u
s
ed
.
Usi
n
g
G
A
as
a
f
ea
tu
r
e
s
elec
tio
n
m
et
h
o
d
,
w
it
h
a
th
r
es
h
o
ld
o
f
0
.
5
,
7
0
8
o
p
tim
al
s
u
b
s
et
f
ea
tu
r
e
s
o
f
g
e
n
es
w
er
e
s
i
g
n
if
ican
t.
T
h
e
class
i
f
ier
u
s
es
a
n
e
n
s
e
m
b
l
e
class
i
f
icatio
n
lear
n
in
g
e
v
al
u
atio
n
p
r
o
ce
d
u
r
e,
th
e
tr
ain
in
g
a
n
d
test
i
n
g
s
eg
m
e
n
ts
u
s
e
10
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
f
o
r
el
i
m
in
at
i
n
g
s
e
lectio
n
p
ar
tialit
ies
u
s
in
g
M
A
T
L
A
B
.
E
v
a
lu
at
io
n
o
u
tco
m
e
is
co
n
s
tr
u
cted
u
s
i
n
g
t
h
e
co
m
p
u
tatio
n
al
ti
m
e
an
d
p
er
f
o
r
m
an
ce
m
etr
ics
[2
7
]
—
c
lass
if
icat
io
n
p
er
f
o
r
m
a
n
ce
w
it
h
A
d
a
-
B
o
o
s
t
an
d
B
ag
g
i
n
g
E
n
s
e
m
b
le
cla
s
s
i
f
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tio
n
m
o
d
els
,
w
ith
9
3
.
3
%
an
d
95
%
ac
cu
r
ac
y
r
esp
ec
tiv
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y
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T
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e
r
es
u
lt
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ce
d
u
r
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ar
e
s
h
o
w
n
in
F
ig
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NC
E
S
[1
]
S
S
h
a
n
w
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,
W
Ch
u
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,
D
Hu
i,
Z
Qu
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n
.
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a
c
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lea
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p
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[2
]
F
R
Da
v
id
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C
Ka
te,
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L
Ya
n
k
,
G
Ka
rin
e
,
a
n
d
L
Ro
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h
.
―
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re
d
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G
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Ex
p
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sio
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site
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las
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Us
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]
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―
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su
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NT
Jo
h
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so
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A
Dh
ro
so
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KJ
Hu
g
h
e
s,
D
Ko
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in
,
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lo
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[7
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M
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W
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No
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7
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DH
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h
a
d
h
e
r
M
.
S
c
a
n
d
P
h
D
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Co
v
e
n
a
n
t
Un
iv
e
rsit
y
,
Nig
e
ria
re
sp
e
c
ti
v
e
l
y
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
Bio
in
f
o
rm
a
ti
c
s
o
f
In
f
e
c
ti
o
u
s
(Af
ric
a
n
)
Dise
a
s
e
s/
P
o
p
u
latio
n
,
Org
a
n
is
m
’s
In
ter
-
p
a
th
w
a
y
a
n
a
ly
sis,
Hig
h
th
ro
u
g
h
p
u
t
d
a
ta
a
n
a
ly
ti
c
s,
Ho
m
o
lo
g
y
m
o
d
e
ll
in
g
a
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
S
h
e
h
a
s
p
u
b
li
s
h
e
d
w
id
e
ly
in
lo
c
a
l
a
n
d
in
ter
n
a
ti
o
n
a
l
re
p
u
tab
le
jo
u
rn
a
ls.
S
h
e
is
a
m
e
m
b
e
r
o
f
th
e
Nig
e
rian
Co
m
p
u
ter
S
o
c
iety
(NCS),
th
e
Co
m
p
u
ter Reg
istratio
n
Co
u
n
c
il
o
f
Nig
e
ria (CP
N) an
d
IEE
E
m
e
m
b
e
r.
Pro
fe
ss
o
r
A
d
e
b
iy
i
,
Ay
o
d
e
le
A
ri
y
o
,
is
a
f
a
c
u
lt
y
a
n
d
f
o
r
m
e
r
He
a
d
o
f
De
p
a
rtm
e
n
t
o
f
Co
m
p
u
ter
a
n
d
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
s,
Co
v
e
n
a
n
t
Un
iv
e
rsity
,
Ota
Nig
e
ri
a
.
He
is
c
u
rre
n
tl
y
th
e
He
a
d
o
f
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ie
n
c
e
a
t
L
a
n
d
m
a
rk
Un
iv
e
rsit
y
,
O
m
u
-
A
ra
n
,
Ni
g
e
ria,
a
siste
r
Un
iv
e
rsit
y
to
Co
v
e
n
a
n
t
Un
iv
e
rsit
y
.
He
h
o
ld
s
a
B.
S
c
d
e
g
r
e
e
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
a
n
M
BA
d
e
g
re
e
f
ro
m
Un
iv
e
rsit
y
o
f
Ilo
rin
,
Il
o
rin
Nig
e
ria.
He
h
a
d
h
is
M
.
S
c
a
n
d
P
h
D
d
e
g
re
e
in
M
a
n
a
g
e
m
e
n
t
In
f
o
r
m
a
ti
o
n
S
y
ste
m
(M
IS
)
f
ro
m
Co
v
e
n
a
n
t
Un
iv
e
rsity
,
Ni
g
e
ria,
re
sp
e
c
ti
v
e
l
y
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
th
e
a
p
p
li
c
a
ti
o
n
o
f
so
f
t
c
o
m
p
u
ti
n
g
tec
h
n
iq
u
e
s
in
so
lv
in
g
re
a
l
-
li
f
e
p
ro
b
lem
s,
so
f
t
w
a
r
e
e
n
g
in
e
e
rin
g
a
n
d
in
f
o
rm
a
ti
o
n
s
y
ste
m
re
s
e
a
rc
h
.
He
h
a
s
su
c
c
e
ss
f
u
ll
y
m
e
n
to
re
d
a
n
d
su
p
e
rv
ise
d
se
v
e
ra
l
p
o
stg
ra
d
u
a
te
stu
d
e
n
ts
a
t
M
a
ste
rs
a
n
d
P
h
D
l
e
v
e
l.
He
h
a
s
p
u
b
l
ish
e
d
w
id
e
ly
in
l
o
c
a
l
a
n
d
in
tern
a
ti
o
n
a
l
re
p
u
ta
b
le
jo
u
rn
a
ls.
He
is
a
m
e
m
b
e
r
o
f
Nig
e
rian
Co
m
p
u
ter
S
o
c
iety
(NCS),
th
e
C
o
m
p
u
ter Reg
istratio
n
Co
u
n
c
il
o
f
Nig
e
ria (CP
N) an
d
I
EE
E
m
e
m
b
e
r.
O
la
tu
n
ji
J
u
li
u
s
O
k
e
so
l
a
is
a
P
r
o
f
e
ss
o
r
o
f
C
y
b
e
rse
c
u
rit
y
a
t
t
h
e
F
irst
T
e
c
h
n
ica
l
Un
iv
e
rsit
y
,
Ib
a
d
a
n
Nig
e
ria.
He
is
a
Ce
rti
fied
In
f
o
rm
a
ti
o
n
S
e
c
u
rit
y
M
a
n
a
g
e
r
(CIS
M
)
a
n
d
a
Ce
rti
f
ied
In
f
o
rm
a
ti
o
n
S
y
ste
m
s
A
u
d
it
o
r
(CI
S
A
)
w
it
h
a
P
h
D
in
Co
m
p
u
ter
S
c
i
e
n
c
e
s.
He
is
a
m
e
m
b
e
r
o
f
th
e
In
f
o
rm
a
ti
o
n
S
y
ste
m
A
u
d
it
a
n
d
C
o
n
tr
o
l
A
ss
o
c
iatio
n
(IS
A
CA
),
Co
m
p
u
ter
P
r
o
f
e
ss
io
n
a
ls
o
f
Nig
e
ria
(CP
N),
a
n
d
a
f
e
ll
o
w
o
f
Nig
e
rian
Co
m
p
u
ter
S
o
c
iety
(NCS).
Ok
e
so
la
is
a
sc
h
o
lar,
a
n
In
f
o
rm
a
ti
o
n
S
e
c
u
rit
y
e
x
p
e
rt
a
n
d
a
se
a
so
n
e
d
b
a
n
k
e
r.
Un
ti
l
N
o
v
e
m
b
e
r
2
0
1
6
,
h
e
w
a
s
th
e
G
ro
u
p
He
a
d
,
f
o
r
In
f
o
rm
a
ti
o
n
S
y
ste
m
s
Co
n
tro
l
a
n
d
Re
v
e
n
u
e
A
ss
u
ra
n
c
e
a
t
K
e
y
sto
n
e
Ba
n
k
(Nig
.
)
L
td
,
L
a
g
o
s.
A
n
a
lu
m
n
u
s
o
f
th
e
Un
iv
e
rsit
y
o
f
S
o
u
t
h
A
f
ric
a
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
Cy
b
e
rse
c
u
rit
y
,
b
io
m
e
tri
c
s,
a
n
d
S
o
f
tw
a
re
e
n
g
in
e
e
rin
g
.
He
h
a
s
se
v
e
r
a
l
p
u
b
li
c
a
ti
o
n
s
in
sc
h
o
larly
jo
u
rn
a
ls
a
n
d
c
o
n
f
e
re
n
c
e
p
ro
c
e
e
d
in
g
s b
o
t
h
l
o
c
a
l
a
n
d
in
tern
a
ti
o
n
a
l
.
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